diff --git a/Colab_notebooks/Beta notebooks/1D_UNet_for_FCS_ZeroCostDL4Mic.ipynb b/Colab_notebooks/Beta notebooks/1D_UNet_for_FCS_ZeroCostDL4Mic.ipynb new file mode 100644 index 00000000..38bbbe14 --- /dev/null +++ b/Colab_notebooks/Beta notebooks/1D_UNet_for_FCS_ZeroCostDL4Mic.ipynb @@ -0,0 +1,18661 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Av1qDcfthk1a" + }, + "source": [ + "# **1D U-Net for FCS**\n", + "\n", + "---\n", + "\n", + " This 1-dimensional U-Net is capable of segmenting Fluorescence Correlation Spectroscopy (FCS) data and was first published in 2023 by [Seltmann *et al.*](https://www.biorxiv.org/content/10.1101/2023.08.24.554627v1.full). The U-Net architecture was first published by [Ronneberger *et al.*](https://arxiv.org/abs/1505.04597). The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images.\n", + "\n", + " This notebook provides a joined interface for:\n", + "\n", + "- loading published or simulating new FCS datasets with different FCS time-series artifacts ([Section 3](#scrollTo=jKaeBnSuifZn))\n", + "- (re-) training published or new 1D U-Nets for artifact segmentation ([Section 4](#scrollTo=GyRjBdClimfK))\n", + "- quality control of newly trained or published 1D U-Nets ([Section 5](#scrollTo=1Tm3aimXjZ1B))\n", + "- Segmenting FCS time-series, applying correction methods, and saving improved FCS time-series ([Section 6](#scrollTo=fB8QNLekkCyZ))(currently supported for *.csv* and *.ptu* files)\n", + "\n", + "---\n", + "\n", + "*Disclaimer*:\n", + "\n", + "This notebook is inspired from the *Zero-Cost Deep-Learning to Enhance Microscopy* project (ZeroCostDL4Mic) (https://github.com/HenriquesLab/DeepLearning_Collab/wiki) and was created by [Alex Seltmann](https://www.github.com/aseltmann) at the Eggeling Lab Jena (https://www.biophysical-imaging.com)\n", + "\n", + "This notebook is based on the following paper:\n", + "\n", + "**Neural network informed photon filtering reduces artifacts in Fluorescence Correlation Spectroscopy data**, biorxiv, 2023 by *Alex Seltmann, Pablo Carravilla, Katharina Reglinski, Christian Eggeling, Dominic Waithe* [link to paper](https://www.biorxiv.org/content/10.1101/2023.08.24.554627v1.full) [link to source code](https://github.com/aseltmann/fluotracify/)\n", + "\n", + "**Connected datasets** (see [Section 2.3](#scrollTo=6zv2yWb5QM4I) for a download helper):\n", + "\n", + "\n", + "- Fluorescence correlation spectroscopy time-series data with and without peak artifacts - simulated data - [download from Zenodo](https://zenodo.org/records/8074408)\n", + "- Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - AlexaFluor 488 applied experiment - [download from Zenodo](https://zenodo.org/records/8082558)\n", + "- Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - PEX5 applied experiment - [download from Zenodo](https://zenodo.org/records/8109282)\n", + "- Neural network informed photon filtering reduces artifacts in fluorescence correlation spectropscopy data - mlflow records - [download from Zenodo](https://zenodo.org/records/8137129)\n", + "- Fluotracify - doctoral research project done in a reproducible way - [download from Zenodo](https://zenodo.org/records/8137220)\n", + "\n", + "**Please also cite this original paper when using or developing this notebook.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TKktwSaWhq9e" + }, + "source": [ + "# **How to use this notebook?**\n", + "\n", + "---\n", + "\n", + "Video describing how to use ZeroCostDL4Mic notebooks are available on youtube:\n", + " - [**Video 1**](https://www.youtube.com/watch?v=GzD2gamVNHI&feature=youtu.be): Full run through of the workflow to obtain the notebooks and the provided test datasets as well as a common use of the notebook\n", + " - [**Video 2**](https://www.youtube.com/watch?v=PUuQfP5SsqM&feature=youtu.be): Detailed description of the different sections of the notebook\n", + "\n", + "\n", + "---\n", + "###**Structure of a notebook**\n", + "\n", + "The notebook contains two types of cell:\n", + "\n", + "**Text cells** provide information and can be modified by douple-clicking the cell. You are currently reading the text cell. You can create a new text by clicking `+ Text`.\n", + "\n", + "**Code cells** contain code and the code can be modfied by selecting the cell. To execute the cell, move your cursor on the `[ ]`-mark on the left side of the cell (play button appears). Click to execute the cell. After execution is done the animation of play button stops. You can create a new coding cell by clicking `+ Code`.\n", + "\n", + "---\n", + "###**Table of contents, Code snippets** and **Files**\n", + "\n", + "On the top left side of the notebook you find three tabs which contain from top to bottom:\n", + "\n", + "*Table of contents* = contains structure of the notebook. Click the content to move quickly between sections.\n", + "\n", + "*Code snippets* = contain examples how to code certain tasks. You can ignore this when using this notebook.\n", + "\n", + "*Files* = contain all available files. After mounting your google drive (see section 1.) you will find your files and folders here.\n", + "\n", + "**Remember that all uploaded files are purged after changing the runtime.** All files saved in Google Drive will remain. You do not need to use the Mount Drive-button; your Google Drive is connected in section 1.2.\n", + "\n", + "**Note:** The \"sample data\" in \"Files\" contains default files. Do not upload anything in here!\n", + "\n", + "---\n", + "###**Making changes to the notebook**\n", + "\n", + "**You can make a copy** of the notebook and save it to your Google Drive. To do this click file -> save a copy in drive.\n", + "\n", + "To **edit a cell**, double click on the text. This will show you either the source code (in code cells) or the source text (in text cells).\n", + "You can use the `#`-mark in code cells to comment out parts of the code. This allows you to keep the original code piece in the cell as a comment." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_v_Jl2QZhvLh" + }, + "source": [ + "#**0. Before getting started**\n", + "---\n", + " Before you run the notebook, please ensure that you are logged into your Google account and have the training data, quality control data, and - if needed - prior published U-Net models in your Google Drive.\n", + "\n", + " For a U-Net to train, it needs to have access to a **paired training dataset of fluorescence traces (source) and corresponding masks (target)**. This notebook enables simulating such paired datasets in [Section 3](#scrollTo=jKaeBnSuifZn). Furthermore, the authors published a compatible dataset to train U-Nets for detecting peak artifacts in FCS time-series:\n", + "\n", + "- Fluorescence correlation spectroscopy time-series data with and without peak artifacts - simulated data - [download from Zenodo](https://zenodo.org/records/8074408)\n", + "\n", + " **We strongly recommend that you generate (or use published) extra paired FCS time-series. These can be used to assess the quality of your trained model (Quality control dataset)**. The quality control assessment can be done directly in [Section 5](#scrollTo=1Tm3aimXjZ1B).\n", + "\n", + " Here is a common data structure that can work:\n", + "* Experiment A\n", + " - **Model training**\n", + " - train_source_and_target\n", + " - .csv, .csv, ...\n", + " - validation_source_and_target\n", + " - .csv, .csv, ...\n", + " - **Quality control dataset**\n", + " - qc_source_and_target\n", + " - .csv, .csv, ...\n", + " - **Data to be predicted**\n", + " - **Results**\n", + "\n", + " Note that in this notebook, both source and target are provided in a single .csv file. **If you want to simulate your own training data, please only use [Section 3](#scrollTo=jKaeBnSuifZn) of this notebook.** This ensures that the created .csv files are compatible with the training and evaluation functions later on (e.g. the correct header with some metadata, the column delimiter being `,` and the decimal character being `.`).\n", + "\n", + "---\n", + "**Important note**\n", + "\n", + "\n", + "- If you wish to **Train a network from scratch** using your own dataset (and we encourage everyone to do that), you will need to run [Sections 1 to 4](#scrollTo=NvJvtQQgiVDF) then use [Section 5](#scrollTo=1Tm3aimXjZ1B) to assess the quality of your model and [Section 6](#scrollTo=fB8QNLekkCyZ) to run predictions using the model that you trained.\n", + "- If you wish to **Evaluate your model** using a model previously generated and saved on your Google Drive, you will only need to run [Sections 1 and 2](#scrollTo=NvJvtQQgiVDF) to set up the notebook, then use [Section 5](#scrollTo=1Tm3aimXjZ1B) to assess the quality of your model. **Note: this notebook only supports models logged with mlflow, which helps capturing metadata**.\n", + "- If you only wish to **run predictions** using a model previously generated and saved on your Google Drive, you will only need to run [Sections 1 and 2](#scrollTo=NvJvtQQgiVDF) to set up the notebook, then use [Section 6](#scrollTo=fB8QNLekkCyZ) to run the predictions on the desired model.\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NvJvtQQgiVDF" + }, + "source": [ + "# **1. Install 1D U-Net for FCS and dependencies**\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XMi71QrxiZbS", + "outputId": "66336da3-f1be-498c-ded7-24b9b7014571" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: fpdf2 in /usr/local/lib/python3.10/dist-packages (2.7.6)\n", + "Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from fpdf2) (0.7.1)\n", + "Requirement already satisfied: Pillow!=9.2.*,>=6.2.2 in /usr/local/lib/python3.10/dist-packages (from fpdf2) (9.4.0)\n", + "Requirement already satisfied: fonttools>=4.34.0 in /usr/local/lib/python3.10/dist-packages (from fpdf2) (4.44.0)\n", + "Requirement already satisfied: mlflow in /usr/local/lib/python3.10/dist-packages (2.8.0)\n", + "Requirement already satisfied: click<9,>=7.0 in /usr/local/lib/python3.10/dist-packages (from 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mlflow\n", + "!pip install multipletau\n", + "!pip install prettyprinter\n", + "\n", + "import copy\n", + "import fpdf\n", + "import functools\n", + "import glob # because pathlib.Path.rglob does not suffice\n", + "import importlib # for workaround loading zenodo mlflow models\n", + "import io # only .ptu import\n", + "import itertools\n", + "import logging\n", + "import mlflow\n", + "import multipletau # for time-series correlation (Section 6 onwards)\n", + "import os\n", + "import requests\n", + "import scipy # using scipy.stats.norm.rvs in simulation and scipy.ndimage.label in TCSPC 'averaging' correction\n", + "import shutil\n", + "import struct # only .ptu import\n", + "import subprocess\n", + "import sys\n", + "import time # only .ptu import\n", + "# import tqdm\n", + "import uuid # only simulations\n", + "\n", + "import ipywidgets as widgets # for plots by pressing a button\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import sklearn.preprocessing as skp\n", + "import tensorflow as tf\n", + "import tensorflow.experimental.numpy as tnp\n", + "\n", + "from builtins import any as b_any\n", + "from dataclasses import dataclass, replace, field, astuple\n", + "from datetime import datetime\n", + "from IPython.display import clear_output\n", + "from multiprocessing import Pool\n", + "from pathlib import Path\n", + "# substituted freeze by function freeze_to_path(), because\n", + "# this import was not compatible with cython in Section 6.2.\n", + "# from pip._internal.operations.freeze import freeze\n", + "from prettyprinter import pprint, install_extras\n", + "from tqdm.notebook import tqdm\n", + "from typing import Union, Optional, List, Literal, Dict, Tuple, Any\n", + "\n", + "install_extras(['dataclasses', 'numpy'])\n", + "\n", + "logging.basicConfig(format='%(asctime)s - build model - %(message)s')\n", + "log = logging.getLogger(__name__)\n", + "log.setLevel(logging.DEBUG)\n", + "# fix a problem with tf.experimental.numpy\n", + "tnp.experimental_enable_numpy_behavior(prefer_float32=False)\n", + "# there was the following error during model training: Tensorflow ValueError:\n", + "# Unexpected result of `train_function` (Empty logs). Please use\n", + "# `Model.compile(..., run_eagerly=True). If it returns, uncomment the following:\n", + "# tf.config.run_functions_eagerly(True)\n", + "\n", + "# Fix seeds for reproducible random state\n", + "np.random.seed(0)\n", + "# TODO: https://stackoverflow.com/questions/16016959/scipy-stats-seed\n", + "# either replace scipy.stats.norm.rvs with numpy version\n", + "# or use numpy generator for both numpy and scipy seeds\n", + "# also: correct_TCSPC uses own seed for random weights currently\n", + "\n", + "\n", + "# Contact the ZeroCostDL4Mic team to find out about the version number\n", + "NOTEBOOK_VERSION = ['2.1.2']\n", + "\n", + "# Build requirements file for local run\n", + "# -- the developers should leave this below all the other installations\n", + "\n", + "def freeze_to_path():\n", + " \"\"\"see https://stackoverflow.com/a/74022456\"\"\"\n", + " args = [sys.executable, \"-m\", \"pip\", \"freeze\"]\n", + " p = subprocess.run(args, check=True, capture_output=True)\n", + " return p.stdout.decode().split('\\n')\n", + "\n", + "def get_requirements_path():\n", + " # Store requirements file in 'contents' directory\n", + " current_dir = os.getcwd()\n", + " dir_count = current_dir.count('/') - 1\n", + " path = '../' * (dir_count) + 'requirements.txt'\n", + " return path\n", + "\n", + "def filter_files(file_list, filter_list):\n", + " filtered_list = []\n", + " for fname in file_list:\n", + " if b_any(fname.split('==')[0] in s for s in filter_list):\n", + " filtered_list.append(fname)\n", + " return filtered_list\n", + "\n", + "def build_requirements_file(before, after):\n", + " path = get_requirements_path()\n", + "\n", + " # Exporting requirements.txt for local run\n", + " req_list = freeze_to_path()\n", + "\n", + " # Get minimum requirements file\n", + " mod_list = [m.split('.')[0] for m in after if not m in before]\n", + "\n", + " # Replace with package name and handle cases where import name is different\n", + " # to module name\n", + " mod_name_list = [['sklearn', 'scikit-learn'], ['skimage', 'scikit-image']]\n", + " mod_replace_list = [[x[1] for x in mod_name_list]\n", + " if s in [x[0] for x in mod_name_list]\n", + " else s for s in mod_list]\n", + " filtered_list = filter_files(req_list, mod_replace_list)\n", + "\n", + " with open(path, 'w') as f:\n", + " f.write(f'# Requirements for U-Net_for_FCS_1D_ZeroCostDL4Mic.ipynb\\n')\n", + " f.writelines(f'{item}\\n' for item in filtered_list)\n", + "\n", + "after = [str(m) for m in sys.modules]\n", + "\n", + "build_requirements_file(before, after)\n", + "\n", + "\n", + "def mlflow_model_fluotracify_package_workaround():\n", + " # set up python package\n", + " path = Path('/content/fluotracify/training/build_model.py')\n", + " pathinit1 = path.parent / '__init__.py'\n", + " pathinit2 = path.parent.parent / '__init__py'\n", + " path.parent.mkdir(parents=True, exist_ok=True)\n", + " path.write_text(\"def binary_ce_dice_loss_coef():\\n pass\", encoding=\"utf-8\")\n", + " pathinit1.touch()\n", + " pathinit2.touch()\n", + " # add to system path and import it\n", + " sys.path.append(path.parent.parent)\n", + " try:\n", + " importlib.import_module('fluotracify.training.build_model')\n", + " except ModuleNotFoundError:\n", + " importlib.reload(fluotracify.training.build_model)\n", + "\n", + " # clean up\n", + " shutil.rmtree(path.parent.parent)\n", + "\n", + "\n", + "mlflow_model_fluotracify_package_workaround()\n", + "\n", + "\n", + "# ---------------------- MODEL BUILDING ----------------------------------\n", + "# define custom loss functions\n", + "def binary_ce_dice_loss_coef(y_true, y_pred, axis, smooth):\n", + " def dice_loss(y_true, y_pred, axis, smooth):\n", + " \"\"\"Soft dice coefficient for comparing the similarity of two batches\n", + " of data, usually used for binary image segmentation\n", + "\n", + " For binary labels, the dice loss will be between 0 and 1 where 1 is a\n", + " total match. Reshaping is needed to combine the global dice loss with\n", + " the local binary_crossentropy\n", + "\n", + " Notes\n", + " -----\n", + " - This is probably equal to the F1-Score (need to double-check). After\n", + " tensorflow 2.12.-nightly this is implemented in\n", + " tf.keras.metrics.F1Score - might migrate then.\n", + " \"\"\"\n", + " numerator = 2 * tf.math.reduce_sum(\n", + " input_tensor=y_true * y_pred, axis=axis, keepdims=True)\n", + " denominator = tf.math.reduce_sum(input_tensor=y_true + y_pred,\n", + " axis=axis,\n", + " keepdims=True)\n", + "\n", + " return 1 - (numerator + smooth) / (denominator + smooth)\n", + "\n", + " return tf.keras.backend.binary_crossentropy(y_true, y_pred) + dice_loss(\n", + " y_true, y_pred, axis, smooth)\n", + "\n", + "\n", + "def binary_ce_dice_loss(axis=-1, smooth=1e-5):\n", + " \"\"\"Combination of binary crossentropy and dice loss\n", + "\n", + " Parameters\n", + " -----------\n", + " y_true : Tensor\n", + " A distribution with shape: [batch_size, ....], (any dimensions).\n", + " y_pred : Tensor\n", + " The y_pred distribution, format the same with `y_true`.\n", + " axis : int or tuple of int\n", + " All dimensions are reduced, default ``-1``\n", + " smooth : float, optional\n", + " Will be added to the numerator and denominator of the dice loss.\n", + " - If both y_true and y_pred are empty, it makes sure dice is 1.\n", + " - If either y_true or y_pred are empty (all pixels are background),\n", + " dice = ```smooth/(small_value + smooth)``\n", + " - Smoothing is not really necessary for combined losses (so standard\n", + " value is 0)\n", + "\n", + " Notes\n", + " -----\n", + " - this function was influenced by code from\n", + " - TensorLayer project\n", + " https://tensorlayer.readthedocs.io/en/latest/modules/cost.html#tensorlayer.cost.dice_coe,\n", + " - Lars Nieradzik\n", + " https://lars76.github.io/neural-networks/object-detection/losses-for-segmentation/\n", + " - Code by Stefan Hoffmann, Applied Systems Biology group,\n", + " Hans-Knöll-Institute Jena\n", + " - To be able to load the custom loss function in Keras, it must only take\n", + " (y_true, y_pred) as parameters - that is why this setup seems so\n", + " complicated.\n", + " - binary crossentropy returns a tensor with loss for each 1D step of a\n", + " trace, bringing local info\n", + " - dice loss returns a scalar for each 1D trace, bringing global info\n", + " \"\"\"\n", + " def binary_ce_dice(y_true, y_pred):\n", + " return binary_ce_dice_loss_coef(y_true, y_pred, axis, smooth)\n", + "\n", + " return binary_ce_dice\n", + "\n", + "\n", + "# define evaluation metrics\n", + "class BinaryFBeta(tf.keras.metrics.Metric):\n", + " \"\"\"A stateless F-beta score implementation by Jolomi Tosanwumi, see\n", + " https://towardsdatascience.com/f-beta-score-in-keras-part-i-86ad190a252f\n", + "\n", + " Notes\n", + " -----\n", + " - the F1-Score is an implementation of the harmonic mean of precision and\n", + " recall. The goal is to minimize type I and type II errors - and harmonic\n", + " mean penalizes lower values more than higher values, so that we don't get a\n", + " high score when one of precision or recall is low.\n", + " - FBeta makes it possible to give more weight to Precision or Recall. It\n", + " introduces beta_squared, which is the ratio of the weight of Recall to the\n", + " weight of Precision.\n", + " - beta > 1: Recall weighted more than Precision\n", + " - beta < 1: Precision weighted more than Recall\n", + " - from tensorflow 2.12-nightly this metric is in the standard library as\n", + " tf.keras.metrics.FBetaScore (and tf.keras.metrics.F1Score)\n", + " \"\"\"\n", + " def __init__(self, name='binary_fbeta', beta=1, threshold=0.5,\n", + " epsilon=1e-7, **kwargs):\n", + " # initializing an object of the super class\n", + " super().__init__(name=name, **kwargs)\n", + "\n", + " # initializing state variables\n", + " self.tp = self.add_weight(name='tp', initializer='zeros')\n", + " self.actual_positive = self.add_weight(name='fp', initializer='zeros')\n", + " self.predicted_positive = self.add_weight(\n", + " name='fn', initializer='zeros')\n", + "\n", + " # initializing other atrributes that wouldn't be changed for every\n", + " # object of this class\n", + " self.beta_squared = beta**2\n", + " self.threshold = threshold\n", + " self.epsilon = epsilon\n", + "\n", + " def update_state(self, ytrue, ypred, sample_weight=None):\n", + " \"\"\"this method is called at the end of each batch and is used to change\n", + " (update) the state variables.\"\"\"\n", + " ytrue = tf.cast(ytrue, tf.float32)\n", + " ypred = tf.cast(ypred, tf.float32)\n", + "\n", + " # setting values of ypred greater than the set threshold to 1 while\n", + " # those lesser to 0\n", + " ypred = tf.cast(tf.greater_equal(ypred, tf.constant(self.threshold)),\n", + " tf.float32)\n", + " # updating atrributes\n", + " self.tp.assign_add(tf.reduce_sum(ytrue*ypred))\n", + " self.predicted_positive.assign_add(tf.reduce_sum(ypred))\n", + " self.actual_positive.assign_add(tf.reduce_sum(ytrue))\n", + "\n", + " def result(self):\n", + " \"\"\"this is called at the end of each batch after states variables are\n", + " updated. It is used to compute and return the metric for each batch.\"\"\"\n", + " self.precision = self.tp / (self.predicted_positive+self.epsilon)\n", + " self.recall = self.tp / (self.actual_positive+self.epsilon)\n", + "\n", + " # calculating fbeta\n", + " self.fb = (1+self.beta_squared)*self.precision*self.recall / (\n", + " self.beta_squared*self.precision + self.recall + self.epsilon)\n", + " return self.fb\n", + "\n", + " def reset_state(self):\n", + " \"\"\"this is called at the end of each epoch. It is used to clear\n", + " (reinitialize) the state variables.\"\"\"\n", + " self.tp.assign(0)\n", + " self.predicted_positive.assign(0)\n", + " self.actual_positive.assign(0)\n", + "\n", + "\n", + "def unet_metrics(metrics_thresholds):\n", + " \"\"\"Returns a selection of metrics for model training\n", + "\n", + " Currently these metrics are True Positives, False Positives, True\n", + " Negatives, False Negatives, Preciesion, Recall, Accuracy, AUC\n", + "\n", + " Parameters\n", + " ----------\n", + " metrics_thresholds: list of float between 0 and 1\n", + "\n", + " Returns\n", + " -------\n", + " list of metrics\n", + " \"\"\"\n", + " metrics = []\n", + " for thresh in metrics_thresholds:\n", + " metrics.append(tf.keras.metrics.TruePositives(name=f'tp{thresh}',\n", + " thresholds=thresh))\n", + " metrics.append(tf.keras.metrics.FalsePositives(name=f'fp{thresh}',\n", + " thresholds=thresh))\n", + " metrics.append(tf.keras.metrics.TrueNegatives(name=f'tn{thresh}',\n", + " thresholds=thresh))\n", + " metrics.append(tf.keras.metrics.FalseNegatives(name=f'fn{thresh}',\n", + " thresholds=thresh))\n", + " metrics.append(tf.keras.metrics.Precision(name=f'precision{thresh}',\n", + " thresholds=thresh))\n", + " metrics.append(tf.keras.metrics.Recall(name=f'recall{thresh}',\n", + " thresholds=thresh))\n", + " metrics.append(\n", + " tf.keras.metrics.BinaryAccuracy(name='accuracy0.5', threshold=0.5))\n", + " metrics.append(tf.keras.metrics.AUC(name='auc', num_thresholds=100))\n", + " metrics.append(BinaryFBeta(name='f10.5'))\n", + " return metrics\n", + "\n", + "\n", + "# define model layers\n", + "def convtrans(filters, name, kernel_size, strides):\n", + " \"\"\"Sequential API: Conv1DTranspose, BatchNorm\"\"\"\n", + " upsamp = tf.keras.Sequential(name=name)\n", + " upsamp.add(\n", + " tf.keras.layers.Conv1DTranspose(filters=filters,\n", + " kernel_size=kernel_size,\n", + " strides=strides))\n", + " upsamp.add(tf.keras.layers.BatchNormalization())\n", + " upsamp.add(tf.keras.layers.Activation('relu'))\n", + " return upsamp\n", + "\n", + "\n", + "def twoconv(filters, name):\n", + " \"\"\"Sequential API: Conv1D, BatchNorm, Conv1D, BatchNorm\"\"\"\n", + " conv = tf.keras.Sequential(name=name)\n", + " conv.add(\n", + " tf.keras.layers.Conv1D(filters=filters, kernel_size=3, padding='same'))\n", + " conv.add(tf.keras.layers.BatchNormalization())\n", + " conv.add(tf.keras.layers.Activation('relu'))\n", + "\n", + " conv.add(\n", + " tf.keras.layers.Conv1D(filters=filters, kernel_size=3, padding='same'))\n", + " conv.add(tf.keras.layers.BatchNormalization())\n", + " conv.add(tf.keras.layers.Activation('relu'))\n", + " return conv\n", + "\n", + "\n", + "def encoder(input_tensor, filters, name, pool_size=2):\n", + " \"\"\"Functional API: Two Conv1D incl BatchNorm, MaxPool1D\"\"\"\n", + " encode = twoconv(filters=filters, name=name)(input_tensor)\n", + " encode_pool = tf.keras.layers.MaxPool1D(pool_size=pool_size,\n", + " name='mp_{}'.format(name))(encode)\n", + " return encode_pool, encode\n", + "\n", + "\n", + "def decoder(input_tensor,\n", + " concat_tensor,\n", + " filters,\n", + " name,\n", + " kernel_size=2,\n", + " strides=2):\n", + " \"\"\"Functional API: Conv1DTrans, BatchNorm, Concat, Two Conv incl BatchNorm\n", + " \"\"\"\n", + " decode = convtrans(filters=filters,\n", + " name='conv_transpose_{}'.format(name),\n", + " kernel_size=kernel_size,\n", + " strides=strides)(input_tensor)\n", + " decode = tf.keras.layers.concatenate([concat_tensor, decode],\n", + " axis=-1,\n", + " name=name)\n", + " decode = twoconv(filters=filters, name='two_conv_{}'.format(name))(decode)\n", + " return decode\n", + "\n", + "\n", + "def unet_1d(input_size: Union[int, None], n_levels: int, first_filters: int,\n", + " pool_size: int, metrics_thresholds: List[float], name: str):\n", + " \"\"\"Defines compiled U-Net. Includes option to define various hyperparameters\n", + " and the more abstract parameter of unet levels.\n", + "\n", + " Parameters\n", + " ----------\n", + " input_size : int\n", + " Input vector size\n", + " n_levels : int\n", + " Number of levels or steps in the Unet\n", + " first_filters : int\n", + " The number of filters in the first level. Every deeper level\n", + " will be twice as many filters till a maximum of 512 is reached.\n", + " Filters will be clipped if smaller than 1 or bigger than 512\n", + " pool_size : int, Optional. Default: 2\n", + " Pool size of the MaxPool1D layer, as well as kernel size and\n", + " strides of the Conv1DTranspose layer\n", + " metrics_thresholds : list of float between 0 and 1\n", + " compute metrics with these prediction thresholds\n", + "\n", + " Returns\n", + " -------\n", + " Compiled Model as described by the tensorflow.keras Functional API\n", + "\n", + " Notes\n", + " -----\n", + " - Paper: https://arxiv.org/pdf/1505.04597.pdf\n", + " - conceptually different approach than in the paper is the use of\n", + " transposed convolution opposed to a up\"-convolution\" consisting of\n", + " bed-of-nails upsampling and a 2x2 convolution\n", + " - this implementation was influenced by:\n", + " https://www.tensorflow.org/tutorials/generative/pix2pix\n", + " \"\"\"\n", + " filters = [first_filters]\n", + " nextfilters = first_filters\n", + " for _ in range(1, n_levels + 1):\n", + " nextfilters *= 2\n", + " filters.append(nextfilters)\n", + " filters = tnp.clip(filters, a_min=1, a_max=512).numpy()\n", + " filters = tf.cast(filters, tf.int32).numpy()\n", + "\n", + " ldict = {}\n", + "\n", + " inputs = tf.keras.layers.Input(shape=(input_size, 1))\n", + "\n", + " # Downsampling through model\n", + " ldict['x0_pool'], ldict['x0'] = encoder(inputs,\n", + " filters[0],\n", + " name='encode0',\n", + " pool_size=pool_size)\n", + " for i in range(1, n_levels):\n", + " ldict['x{}_pool'.format(i)], ldict['x{}'.format(i)] = encoder(\n", + " input_tensor=ldict['x{}_pool'.format(i - 1)],\n", + " filters=filters[i],\n", + " name='encode{}'.format(i),\n", + " pool_size=pool_size)\n", + "\n", + " # Center\n", + " center = twoconv(2 * filters[n_levels - 1], name='two_conv_center')(\n", + " ldict['x{}_pool'.format(n_levels - 1)])\n", + "\n", + " # Upsampling through model\n", + " ldict['y{}'.format(n_levels - 1)] = decoder(\n", + " input_tensor=center,\n", + " concat_tensor=ldict['x{}'.format(n_levels - 1)],\n", + " filters=filters[-1],\n", + " name='decoder{}'.format(n_levels - 1),\n", + " kernel_size=pool_size,\n", + " strides=pool_size)\n", + "\n", + " for j in range(1, n_levels):\n", + " ldict['y{}'.format(n_levels - 1 - j)] = decoder(\n", + " input_tensor=ldict['y{}'.format(n_levels - j)],\n", + " concat_tensor=ldict['x{}'.format(n_levels - 1 - j)],\n", + " filters=filters[-1 - j],\n", + " name='decoder{}'.format(n_levels - 1 - j),\n", + " kernel_size=pool_size,\n", + " strides=pool_size)\n", + "\n", + " # create 'binary' output vector\n", + " outputs = tf.keras.layers.Conv1D(filters=1,\n", + " kernel_size=1,\n", + " activation='sigmoid')(ldict['y0'])\n", + "\n", + " log.debug('unet: input shape: %s, output shape: %s', inputs.shape,\n", + " outputs.shape)\n", + "\n", + " unet = tf.keras.Model(inputs=inputs,\n", + " outputs=outputs,\n", + " name=name)\n", + "\n", + " optimizer = tf.keras.optimizers.Adam()\n", + " loss = binary_ce_dice_loss()\n", + " metrics = unet_metrics(metrics_thresholds)\n", + " unet.compile(loss=loss, optimizer=optimizer, metrics=metrics)\n", + " return unet\n", + "\n", + "\n", + "# ---------------------- LOADING TRAINING DATA---------------------------------\n", + "def read_simulated_csvs(filename: Union[str, Path], header: Union[int, None],\n", + " dropindex: Union[int, None],\n", + " dropcolumns: Union[int, None],\n", + " col_per_example: int):\n", + " try:\n", + " raw_dataset = pd.read_csv(filename, sep=',', header=header)\n", + " except pd.errors.ParserError as exc:\n", + " raise ValueError(\n", + " 'Probably the header parameter is too low and points to the'\n", + " ' metadata. Try a higher value.') from exc\n", + " df = raw_dataset.copy()\n", + " try:\n", + " df = df.drop(index=dropindex, columns=dropcolumns)\n", + " except ValueError:\n", + " pass\n", + " # convert from float64 to float32 and from object to float32\n", + " # -> shrinks memory usage of train dataset from 2.4 GB to 1.2GB\n", + " try:\n", + " df = df.apply(pd.to_numeric, downcast='float')\n", + " except ValueError as exc:\n", + " raise ValueError(\n", + " 'Probably the header parameter is too low and points to the'\n", + " 'metadata. Try a higher value.') from exc\n", + " # save number of examples per file\n", + " nsamples = round(len(df.columns) / col_per_example)\n", + " # save some parameters of the experiment from csv file\n", + " experiment_param = pd.read_csv(filename,\n", + " sep=',',\n", + " header=None,\n", + " index_col=0,\n", + " usecols=[0, 1],\n", + " skipfooter=len(raw_dataset)+1,\n", + " engine='python').squeeze('columns')\n", + "\n", + " return df, nsamples, experiment_param, filename\n", + "\n", + "\n", + "def read_simulated_csvs_wrapper(args):\n", + " return read_simulated_csvs(*args)\n", + "\n", + "@functools.lru_cache\n", + "def import_from_csv(folder,\n", + " header,\n", + " col_per_example,\n", + " dropindex=None,\n", + " dropcolumns=None):\n", + " \"\"\"Import CSV files containing data from fluotracify.simulations\n", + "\n", + " Import a directory of CSV files created by one of the\n", + " fluotracify.simulations methods and output two pandas DataFrames containing\n", + " test and train data for machine learning pipelines.\n", + "\n", + " Parameters\n", + " ----------\n", + " folder : str\n", + " Folder which contains .csv files with data\n", + " header : int\n", + " param for `pd.read_csv` = rows to skip at beginning\n", + " col_per_example : int\n", + " Number of columns per example, first column being a trace, and then\n", + " one or multiple labels\n", + " dropindex : int, optional\n", + " Which indeces in the csv file should be dropped\n", + " dropcolumns : str or int, optional\n", + " Which columns in the csv file should be dropped\n", + "\n", + " Returns\n", + " -------\n", + " train, test : pandas DataFrames\n", + " Contain training and testing data columnwise in the manner data_1,\n", + " label_1, data_2, label_2, ...\n", + " nsamples : int\n", + " list containing no of examples per file\n", + " experiment_param : pandas DataFrame\n", + " Contains metadata of the files\n", + "\n", + " Raises\n", + " ------\n", + " FileNotFoundError\n", + " If the path provided does not include any .csv files\n", + " ValueError\n", + " If pandas read_csv fails\n", + " \"\"\"\n", + " log.debug(f'Creating a list of all .csv files in {folder=} including its '\n", + " 'subdirectories...')\n", + " files = list(tqdm(Path(folder).rglob('*.csv')))\n", + "\n", + " if len(files) == 0:\n", + " raise FileNotFoundError('The path provided does not include any'\n", + " ' .csv files.')\n", + "\n", + " files.sort()\n", + " np.random.shuffle(files)\n", + "\n", + " inputs = zip(files, itertools.repeat(header), itertools.repeat(dropindex),\n", + " itertools.repeat(dropcolumns), itertools.repeat(col_per_example))\n", + "\n", + " log.debug('Reading %s files from folder %s', len(files), folder)\n", + " with Pool() as pool:\n", + " results = list(tqdm(pool.imap(read_simulated_csvs_wrapper, inputs),\n", + " total=len(files)))\n", + "\n", + " df_data = pd.concat([r[0] for r in results], axis='columns')\n", + " nsamples = [r[1] for r in results]\n", + " df_metadata = pd.concat([r[2] for r in results], axis='columns',\n", + " ignore_index=True, sort=False)\n", + "\n", + " return df_data, nsamples, df_metadata\n", + "\n", + "\n", + "def separate_data_and_labels(array, col_per_example):\n", + " \"\"\"Take pandas DataFrame containing feature and label data output a\n", + " dictionary containing them separately\n", + "\n", + " Parameters\n", + " ----------\n", + " array : pandas DataFrame\n", + " features and labels ordered columnwise in the manner: feature_1,\n", + " label_1, feature_2, label_2, ...\n", + " col_per_example : int\n", + " Number of columns per example, first column being a trace, and then\n", + " one or multiple labels\n", + "\n", + " Returns\n", + " -------\n", + " array_dict : dict of pandas DataFrames\n", + " Contains one key per column in each simulated example. E.g. if the\n", + " simulated features comes with two labels, the key '0' will be the\n", + " array with the features, '1' will be the array with label A and\n", + " '2' will be the array with label B\n", + " \"\"\"\n", + " # if not len(set(nsamples)) == 1:\n", + " # raise Exception(\n", + " # 'Error: The number of examples in each file have to be the same')\n", + "\n", + " array_dict = {}\n", + "\n", + " for i in range(col_per_example):\n", + " array_dict[f'{i}'] = array.iloc[:, i::col_per_example]\n", + "\n", + " if col_per_example == 1:\n", + " given_data = '(source)'\n", + " elif col_per_example == 2:\n", + " given_data = '(source, target)'\n", + " elif col_per_example == 3:\n", + " given_data = '(source, target, target)'\n", + " else:\n", + " raise ValueError(f'{col_per_example=} is not supported.')\n", + " array_dict_shapes = [a.shape for a in array_dict.values()]\n", + " log.debug('The given DataFrame was split into %s parts %s with shapes:'\n", + " ' %s', col_per_example, given_data, array_dict_shapes)\n", + "\n", + " return array_dict\n", + "\n", + "\n", + "def load_source_and_target_from_simulations(path: str | Path,\n", + " artifact: Literal['peak_artifact',\n", + " 'detector_dropout',\n", + " 'photobleaching'],\n", + " n_targets: Literal[1, 2]):\n", + " \"\"\"Script to read in source and target data for the U-NET for FCS notebook\n", + " \"\"\"\n", + " col_per_example = 1 + n_targets\n", + "\n", + " if artifact == 'peak_artifact':\n", + " LABEL_THRESH = 0.04\n", + " HEADER = 12\n", + " elif artifact == 'detector_dropout':\n", + " LABEL_THRESH = 0\n", + " HEADER = 10\n", + " raise ValueError('Training the model for detector dropout correction '\n", + " 'has not yet been validated properly. Please contact '\n", + " 'the authors if you want to do so, we are happy to '\n", + " 'support you and guide you through the code base.')\n", + " elif artifact == 'photobleaching':\n", + " LABEL_THRESH = 0.04 # TODO: check this threshold value\n", + " HEADER = 11\n", + " raise ValueError('Training the model for photobleaching correction '\n", + " 'has not yet been validated properly. Please contact '\n", + " 'the authors if you want to do so, we are happy to '\n", + " 'support you and guide you through the code base.')\n", + " else:\n", + " raise ValueError('artifact has to be in [\"peak_artifact\", \"detector_dropout\"'\n", + " ', \"photobleaching\"]')\n", + "\n", + "\n", + " data, nsamples, experiment_params = import_from_csv(\n", + " folder=path,\n", + " header=HEADER,\n", + " col_per_example=col_per_example,\n", + " dropindex=None,\n", + " dropcolumns=None)\n", + " no_of_traces = len([t for t in data.columns if 'trace' in t])\n", + " no_of_cols = len(data.columns)\n", + " read_col_per_example = no_of_cols / no_of_traces\n", + " if read_col_per_example != col_per_example:\n", + " raise ValueError(f'{n_targets=} suggests that {col_per_example} columns of '\n", + " f'data are present in the .csv files, but found '\n", + " f'{read_col_per_example} in the data. Re-execute the'\n", + " ' cell with the correct value.')\n", + "\n", + " read_artifact = []\n", + " params = experiment_params.T\n", + " if np.any(params.get('number of slow clusters')):\n", + " read_artifact.append('peak_artifact')\n", + " if np.any(params.get('number of bleached molecules (50% immobile and 50% mobile)')):\n", + " read_artifact.append('photobleaching')\n", + " if not (np.any(params.get('number of slow clusters')) or\n", + " np.any(params.get('number of bleached molecules (50% immobile and 50% mobile)'))):\n", + " read_artifact.append('detector_dropout')\n", + " if len(set(read_artifact)) != 1:\n", + " raise ValueError('The read .csv files do contain different simulated '\n", + " f'artifacts (found {read_artifact}).')\n", + "\n", + " if read_artifact[0] != artifact:\n", + " raise ValueError('The read .csv files contain other artifacts than the '\n", + " f'user suggested ({artifact=}, but found '\n", + " f'{read_artifact[0]}. Re-execute cell with the correct value.')\n", + "\n", + " sep = separate_data_and_labels(array=data,\n", + " col_per_example=col_per_example)\n", + "\n", + " # if n_targets = 2:\n", + " # '0': trace with artifact\n", + " # '1': just the simulated artifact (label for unet)\n", + " # '2': whole trace without artifact (label for vae)\n", + "\n", + " source = sep['0']\n", + " target = sep['1']\n", + "\n", + " if artifact == 'detector_dropout':\n", + " target_bool = target < LABEL_THRESH\n", + " else:\n", + " target_bool = target > LABEL_THRESH\n", + "\n", + " return source, target_bool, set(read_artifact), experiment_params\n", + "\n", + "\n", + "# -------- DATA PREPROCESSING AND LOGGING FOR TRAINING -----------------------\n", + "def tfds_replace_nan_in_traces_and_labels(trace, label):\n", + " \"\"\"Part of tf.data pipeline. Replaces nan values with zeros\"\"\"\n", + " trace = tf.where(tf.math.is_nan(trace), tf.zeros_like(trace), trace)\n", + " label = tf.where(tf.math.is_nan(label), tf.zeros_like(label), label)\n", + " return trace, label\n", + "\n", + "\n", + "def tfds_from_pddf(features_df: pd.DataFrame, labels_df: pd.DataFrame):\n", + " \"\"\"TensorFlow Dataset from pandas DataFrame\n", + "\n", + " This function was created to take pandas DataFrames containing simulated\n", + " fluorescence traces with artifacts (features) and the ground truth about\n", + " the artifacts (labels) as an input and create a tf Dataset\n", + "\n", + " Parameters\n", + " ----------\n", + " features_df, labels_df : pandas DataFrames\n", + " Contain features / labels ordered columnwise in the manner: feature_1,\n", + " feature_2, ... / label_1, label_2, ...\n", + "\n", + " Returns\n", + " -------\n", + " dataset : TensorFlow Dataset\n", + " Contains features and labels\n", + " num_examples : int\n", + " Number of test examples\n", + " \"\"\"\n", + " X_tensor = tf.convert_to_tensor(value=features_df.values)\n", + " X_tensor = tf.transpose(a=X_tensor, perm=[1, 0])\n", + "\n", + " y_tensor = tf.convert_to_tensor(value=labels_df.values)\n", + " y_tensor = tf.transpose(a=y_tensor, perm=[1, 0])\n", + " y_tensor = tf.cast(y_tensor, tf.float32)\n", + "\n", + " num_examples = X_tensor.shape[0]\n", + " X_tensor = tf.reshape(tensor=X_tensor, shape=(num_examples, -1, 1))\n", + " y_tensor = tf.reshape(tensor=y_tensor, shape=(num_examples, -1, 1))\n", + "\n", + " dataset = tf.data.Dataset.from_tensor_slices((X_tensor, y_tensor))\n", + " dataset = dataset.map(tfds_replace_nan_in_traces_and_labels)\n", + "\n", + " log.debug('number of examples: %s', num_examples)\n", + "\n", + " return dataset, num_examples\n", + "\n", + "\n", + "def tfds_crop_trace_and_label(trace, label, length_delimiter):\n", + " \"\"\"Part of tf.data pipeline. Crop trace and label to a maximum length of\n", + " length_delimiter\n", + " \"\"\"\n", + " trace = trace[:length_delimiter]\n", + " label = label[:length_delimiter]\n", + " trace_shape = trace.shape\n", + " label_shape = label.shape\n", + " trace.set_shape(trace_shape)\n", + " label.set_shape(label_shape)\n", + " return trace, label\n", + "\n", + "\n", + "def _scale_trace(trace, scaler):\n", + " \"\"\"Part of tf.data pipeline. Scale / normalize the input trace.\n", + "\n", + " Parameters:\n", + " -----------\n", + " trace : np.array, pd.DataFrame or tf.Tensor\n", + " 1D-Trace (1 example with 1 feature of length n along axis=0)\n", + " scaler : ('standard', 'robust', 'maxabs', 'quant_g', 'minmax', l1', 'l2')\n", + " Selected scalers from sklearn.preprocessing\n", + "\n", + " Returns:\n", + " --------\n", + " trace : np.array\n", + " Scaled / normalized trace.\n", + "\n", + " Raises:\n", + " -------\n", + " ValueError\n", + " If the value for scaler is not in ('standard', 'robust', 'maxabs',\n", + " 'quant_g', 'minmax', 'l1', 'l2')\n", + " \"\"\"\n", + " scaler = tf.convert_to_tensor(scaler)\n", + " if scaler == tf.convert_to_tensor('standard'):\n", + " trace = skp.StandardScaler().fit_transform(trace)\n", + " elif scaler == tf.convert_to_tensor('robust'):\n", + " trace = skp.RobustScaler(quantile_range=(25, 75)).fit_transform(trace)\n", + " elif scaler == tf.convert_to_tensor('maxabs'):\n", + " trace = skp.MaxAbsScaler().fit_transform(trace)\n", + " elif scaler == tf.convert_to_tensor('quant_g'):\n", + " trace = skp.QuantileTransformer(\n", + " output_distribution='normal').fit_transform(trace)\n", + " elif scaler == tf.convert_to_tensor('minmax'):\n", + " trace = skp.MinMaxScaler().fit_transform(trace)\n", + " elif scaler == tf.convert_to_tensor('l1'):\n", + " trace = skp.normalize(X=trace, norm='l1', axis=0)\n", + " elif scaler == tf.convert_to_tensor('l2'):\n", + " trace = skp.normalize(X=trace, norm='l2', axis=0)\n", + " else:\n", + " raise ValueError(\n", + " 'scaler has to be a string. currently supported are:'\n", + " '\"standard\", \"robust\", \"maxabs\", \"quant_g\", \"minmax\", \"l1\", \"l2\"')\n", + " return trace\n", + "\n", + "\n", + "def tfds_scale_trace_and_label(trace, label, scaler):\n", + " \"\"\"Part of tf.data pipeline. Wrapper function to be able to .map()\n", + " scale_trace()\n", + " \"\"\"\n", + " trace_shape = trace.shape\n", + " [trace, ] = tf.py_function(func=_scale_trace,\n", + " inp=[trace, scaler],\n", + " Tout=[tf.float32])\n", + " trace.set_shape(trace_shape)\n", + " return trace, label\n", + "\n", + "\n", + "def _get_pad_size_and_value(trace):\n", + " \"\"\"Get pad size and pad value. \"\"\"\n", + " def get_median(v):\n", + " v = tf.reshape(v, [-1])\n", + " mid = v.get_shape()[0] // 2 + 1\n", + " return tf.nn.top_k(v, mid).values[-1]\n", + "\n", + " trace_size = trace.size\n", + " if trace_size < 1024:\n", + " input_size = 1024\n", + " else:\n", + " # new size is the next biggest power of 2 → this is important for the\n", + " # skip connections of the UNET\n", + " input_size = 2**tnp.ceil(tnp.log2(trace_size))\n", + " input_size = tf.cast(input_size, tf.int32)\n", + " pad_size = input_size - trace_size\n", + "\n", + " # pad trace\n", + " pad_value = get_median(trace)\n", + " return pad_size, pad_value\n", + "\n", + "\n", + "def tfds_pad_trace_and_label(trace, label):\n", + " \"\"\"Part of tf.data pipeline. Pad the end of the trace with the\n", + " median of the trace. Set the label for this pad to 0 (no artifact)\n", + "\n", + " Notes\n", + " -----\n", + " - at the moment, the implementation of `tf.experimental.numpy.pad` does not\n", + " support the mode `median` (see\n", + " https://www.tensorflow.org/api_docs/python/tf/experimental/numpy/pad)\n", + " - that's why an own pure tf implementation of a median is used (see\n", + " https://stackoverflow.com/questions/43824665/tensorflow-median-value)\n", + " \"\"\"\n", + " pad_size, pad_median = _get_pad_size_and_value(trace)\n", + " trace = tnp.pad(trace, pad_width=[[0, pad_size], [0, 0]],\n", + " mode='constant',\n", + " constant_values=pad_median)\n", + " label = tnp.pad(label, pad_width=[[0, pad_size], [0, 0]],\n", + " mode='constant',\n", + " constant_values=0)\n", + "\n", + " trace_shape = trace.shape\n", + " label_shape = label.shape\n", + " trace.set_shape(trace_shape)\n", + " label.set_shape(label_shape)\n", + " return trace, label\n", + "\n", + "\n", + "def plot_trace_and_pred_from_tfds(dataset, ntraces, model):\n", + " fig, ax = plt.subplots(ntraces,\n", + " figsize=(16, ntraces * 2),\n", + " facecolor='white')\n", + " pred_iterator = dataset.unbatch().take(ntraces).as_numpy_iterator()\n", + "\n", + " for i in range(ntraces):\n", + " pred_data = pred_iterator.next()\n", + " pred_trace = pred_data[0].reshape(1, -1, 1)\n", + " prediction = model.predict(pred_trace)\n", + " prediction = prediction.flatten()\n", + " pred_trace = pred_trace.flatten()\n", + " pred_label = pred_data[1].flatten()\n", + " ax[i].plot(pred_trace / np.max(pred_trace))\n", + " ax[i].plot(prediction)\n", + " ax[i].plot(pred_label)\n", + " plt.tight_layout()\n", + " return fig\n", + "\n", + "\n", + "# ---------------------- MODEL TRAINING ----------------------------------\n", + "def run_cli(command: str):\n", + " out = subprocess.run(command, stdout=subprocess.PIPE, shell=True)\n", + " out = out.stdout.decode('utf-8')\n", + " return out\n", + "\n", + "\n", + "def log_cli_to_mlflow(command: str):\n", + " tmp_path = f'/tmp/{command.split(\" \")[0]}.txt'\n", + " out = run_cli(command)\n", + " Path(tmp_path).write_text(out)\n", + " mlflow.log_artifact(tmp_path)\n", + "\n", + "\n", + "def mlflow_run(train_data: pd.DataFrame, train_labels: pd.DataFrame,\n", + " train_experiment_params: pd.DataFrame,\n", + " val_data: pd.DataFrame, val_labels: pd.DataFrame,\n", + " val_experiment_params: pd.DataFrame, batch_size: int,\n", + " crop_size: int, lr_start: Union[float, int], lr_power: int,\n", + " epochs: int, scaler: Literal['standard', 'robust', 'maxabs',\n", + " 'quant_g', 'minmax', 'l1', 'l2'],\n", + " name: str, model: tf.keras.Model, initial_epoch: int):\n", + "\n", + " # FIXME (PENDING): at some point, I want to plot metrics vs thresholds\n", + " # from TF side, this is possible by providing the `thresholds`\n", + " # argument as a list of thresholds\n", + " # but currently, mlflow does not support logging lists, so I log the\n", + " # elements of the list one by one\n", + " EXP_PARAM_PATH_TRAIN = '../tmp/experiment_params_train.csv'\n", + " EXP_PARAM_PATH_VAL = '../tmp/experiment_params_val.csv'\n", + " LOG_DIR = \"../tmp/tb-\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n", + "\n", + " def run_one(dataset_train, dataset_val, logdir, crop_size, scaler,\n", + " num_train_examples, num_val_examples, name, model,\n", + " initial_epoch):\n", + " \"\"\"Run a training/validation session.\n", + "\n", + " Parameters:\n", + " -----------\n", + " dataset_train, dataset_val : tf.Dataset\n", + " Train and validation data as tf.Datasets.\n", + " logdir : str\n", + " The top-level logdir to which to write summary data.\n", + " num_train_examples, num_val_examples : int\n", + " number of train and validation examples\n", + "\n", + " Returns:\n", + " --------\n", + " \"\"\"\n", + " ds_train_prep = dataset_train.map(\n", + " lambda trace, label: tfds_crop_trace_and_label(\n", + " trace, label, crop_size), num_parallel_calls=tf.data.AUTOTUNE)\n", + " ds_train_prep = ds_train_prep.map(\n", + " lambda trace, label: tfds_scale_trace_and_label(\n", + " trace, label, scaler), num_parallel_calls=tf.data.AUTOTUNE)\n", + " ds_train_prep = ds_train_prep.map(\n", + " tfds_pad_trace_and_label, num_parallel_calls=tf.data.AUTOTUNE)\n", + " ds_train_prep = ds_train_prep.shuffle(\n", + " buffer_size=num_train_examples).repeat().batch(\n", + " batch_size, drop_remainder=True).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " ds_val_prep = dataset_val.map(\n", + " lambda trace, label: tfds_crop_trace_and_label(\n", + " trace, label, crop_size), num_parallel_calls=tf.data.AUTOTUNE)\n", + " ds_val_prep = ds_val_prep.map(\n", + " lambda trace, label: tfds_scale_trace_and_label(\n", + " trace, label, scaler), num_parallel_calls=tf.data.AUTOTUNE)\n", + " ds_val_prep = ds_val_prep.map(\n", + " tfds_pad_trace_and_label, num_parallel_calls=tf.data.AUTOTUNE)\n", + " ds_val_prep = ds_val_prep.shuffle(\n", + " buffer_size=num_val_examples).repeat().batch(\n", + " batch_size, drop_remainder=True).prefetch(tf.data.AUTOTUNE)\n", + "\n", + " def log_plots(epoch, logs):\n", + " \"\"\"Image logging function for tf.keras.callbacks.LambdaCallback\n", + "\n", + " Notes\n", + " -----\n", + " - `tf.keras.callbacks.LambdaCallback` expects two positional\n", + " arguments `epoch` and `logs`, if `on_epoch_end` is being used\n", + " - see https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/LambdaCallback\n", + " \"\"\"\n", + " figure = plot_trace_and_pred_from_tfds(\n", + " dataset=ds_val_prep, ntraces=5, model=model)\n", + " # Convert matplotlib figure to image\n", + " mlflow.log_figure(\n", + " figure=figure,\n", + " artifact_file=f'predplots/plot{epoch:0>3}.png')\n", + "\n", + " def lr_schedule(epoch):\n", + " \"\"\"\n", + " Returns a custom learning rate that decreases as epochs progress.\n", + "\n", + " Notes\n", + " -----\n", + " - function is supposed to be used with\n", + " `tf.keras.callbacks.LearningRateScheduler`. It takes an epoch\n", + " index as input (integer, indexed from 0) and returns a new\n", + " learning rate as output (float)\n", + " \"\"\"\n", + " # power: 1 == linear decay, higher, e.g. 5 == polynomial decay\n", + " lr_list = [lr_start * (1 - i / epochs)**lr_power\n", + " for i in range(epochs)]\n", + "\n", + " # log in mlflow\n", + " if epoch == 0:\n", + " mlflow.log_param('lr_schedule', value=str(lr_list))\n", + " return lr_list[epoch]\n", + "\n", + " tensorboard_callback = tf.keras.callbacks.TensorBoard( # logs metrics\n", + " log_dir=logdir,\n", + " histogram_freq=5,\n", + " write_graph=False,\n", + " write_images=False,\n", + " update_freq='epoch',\n", + " profile_batch=0, # workaround for issue #2084\n", + " )\n", + "\n", + " lr_callback = tf.keras.callbacks.LearningRateScheduler(lr_schedule)\n", + " image_callback = tf.keras.callbacks.LambdaCallback(\n", + " on_epoch_end=log_plots)\n", + "\n", + " steps_train = num_train_examples // batch_size\n", + " steps_val = num_val_examples // batch_size\n", + "\n", + " history = model.fit(\n", + " x=ds_train_prep,\n", + " epochs=epochs,\n", + " steps_per_epoch=steps_train,\n", + " validation_data=ds_val_prep,\n", + " validation_steps=steps_val,\n", + " callbacks=[tensorboard_callback, lr_callback, image_callback],\n", + " initial_epoch=initial_epoch\n", + " )\n", + "\n", + " mlflow.tensorflow.log_model(\n", + " model=model,\n", + " artifact_path='model',\n", + " conda_env=mlflow.tensorflow.get_default_conda_env(),\n", + " custom_objects={'binary_ce_dice': binary_ce_dice_loss(),\n", + " 'BinaryFBeta': BinaryFBeta()})\n", + " return history\n", + "\n", + " dataset_train, num_train_examples = tfds_from_pddf(\n", + " features_df=train_data, labels_df=train_labels)\n", + "\n", + " dataset_val, num_val_examples = tfds_from_pddf(\n", + " features_df=val_data, labels_df=val_labels)\n", + "\n", + " name = f'{name}-depth{n_levels}-first_filters{first_filters}-pool_size{pool_size}'\n", + " mlflow.set_experiment(name)\n", + " exp = mlflow.get_experiment_by_name(name)\n", + "\n", + " with mlflow.start_run(experiment_id=exp.experiment_id) as run:\n", + " mlflow.tensorflow.autolog(every_n_iter=1, log_models=False,\n", + " log_input_examples=True)\n", + " train_experiment_params.to_csv(EXP_PARAM_PATH_TRAIN)\n", + " val_experiment_params.to_csv(EXP_PARAM_PATH_VAL)\n", + " mlflow.log_artifact(EXP_PARAM_PATH_TRAIN)\n", + " mlflow.log_artifact(EXP_PARAM_PATH_VAL)\n", + " command_list = ['nvcc --version', 'nvidia-smi', 'printenv', 'lscpu',\n", + " 'free -h', 'top -bcn1 -w512 | head -n 15']\n", + " for c in command_list:\n", + " log_cli_to_mlflow(c)\n", + " mlflow.log_params({\n", + " 'num_train_examples': num_train_examples,\n", + " 'num_val_examples': num_val_examples,\n", + " 'scaler': scaler,\n", + " 'batch_size': batch_size,\n", + " 'crop_size': crop_size,\n", + " 'lr_start': lr_start,\n", + " 'lr_power': lr_power,\n", + " })\n", + "\n", + " history = run_one(dataset_train=dataset_train,\n", + " dataset_val=dataset_val,\n", + " logdir=LOG_DIR,\n", + " crop_size=crop_size,\n", + " scaler=scaler,\n", + " num_train_examples=num_train_examples,\n", + " num_val_examples=num_val_examples,\n", + " name=name,\n", + " model=model,\n", + " initial_epoch=initial_epoch\n", + " )\n", + "\n", + " for k, v in history.history.items():\n", + " for e in range(epochs):\n", + " mlflow.log_metric(key=k, value=v[e], step=e)\n", + " return exp, run, history\n", + "\n", + "\n", + "class TrainClick(object):\n", + " def __init__(self, runs=None, models=None, clients=None, mlflow_kwargs=None):\n", + " \"\"\"\n", + " Train 1D U-Net for FCS model. Offers two ways to train the model:\n", + " either by loading a prior mlflow-logged model or by training from\n", + " scratch.\n", + "\n", + " mlflow_kwargs : dict\n", + " Holds the following arguments for the mlflow run: train_data,\n", + " train_labels, val_data, val_labels, train_experiment_params,\n", + " val_experiment_params, batch_size, crop_size, lr_start, lr_power,\n", + " epochs, initial_epoch, scaler, name\n", + " \"\"\"\n", + " self.dropdown = None\n", + " self.button = widgets.Button(description=\"Use chosen model!\")\n", + " self.runs = [] if runs is None else runs\n", + " self.models = {} if models is None else models\n", + " self.clients = {} if clients is None else clients\n", + " self.mlflow_kwargs = {} if mlflow_kwargs is None else mlflow_kwargs\n", + " self.finetuned = False\n", + "\n", + " def _create_widgets(self):\n", + "\n", + " drop_opt = [(f'model {model.name} with {scaler=} from {run=}', run) for\n", + " run, (model, scaler, _) in self.models.items()]\n", + " default_run = drop_opt[0][1]\n", + " self.dropdown = widgets.Dropdown(\n", + " options=drop_opt,\n", + " value=default_run,\n", + " description='Model:',\n", + " disabled=False,\n", + " layout=widgets.Layout(width='75%')\n", + " )\n", + " self.button.on_click(self._on_button_clicked)\n", + "\n", + " def _on_button_clicked(self, change):\n", + " clear_output()\n", + " run = [r for r in self.runs if r.info.run_id == self.dropdown.value][0]\n", + " model = self.models[self.dropdown.value][0]\n", + " scaler = self.models[self.dropdown.value][1]\n", + " mlflow_kwargs = self.mlflow_kwargs.copy()\n", + " initial_epoch = int(run.data.params['epochs'])\n", + " mlflow_kwargs['epochs'] = self.mlflow_kwargs['epochs'] + initial_epoch\n", + " mlflow_kwargs.update(dict(initial_epoch=initial_epoch))\n", + " if self.finetuning == 'use_all_layers':\n", + " for l in model.layers:\n", + " l.trainable = True\n", + " elif self.finetuning == 'freeze_decoder_layers':\n", + " for l in model.layers:\n", + " l.trainable = False if 'decode' in l.name else True\n", + "\n", + " model.compile(loss=binary_ce_dice_loss(),\n", + " optimizer=tf.keras.optimizers.Adam(),\n", + " metrics=unet_metrics([0.1, 0.3, 0.5, 0.7, 0.9]))\n", + " mlflow_kwargs.update(dict(model=model))\n", + " # with self.out:\n", + " log.debug('Set %.2f%% of model layers to be trainable.',\n", + " 100 * sum([l.trainable for l in model.layers]) / len(model.layers))\n", + " self.exp, self.run, self.history = mlflow_run(**mlflow_kwargs)\n", + "\n", + " def train_from_scratch(self, model_kwargs: dict):\n", + " \"\"\"Train a mlflow model from scratch\n", + "\n", + " Parameters\n", + " ----------\n", + " model_kwargs : dict\n", + " Holds the following arguments for the new model: first_filters,\n", + " pool_size, n_levels, metrics_thresholds\n", + " \"\"\"\n", + " model = unet_1d(input_size=None, name=self.mlflow_kwargs['name'],\n", + " **model_kwargs)\n", + " self.mlflow_kwargs.update(dict(model=model))\n", + "\n", + " self.exp, self.run, self.history = mlflow_run(**self.mlflow_kwargs)\n", + "\n", + " def finetune_model(self,\n", + " runs: list[mlflow.entities.Run],\n", + " models: dict[str, tuple[tf.keras.Model, str, Path]],\n", + " clients: dict[str, mlflow.MlflowClient],\n", + " finetuning: Literal[\n", + " 'use_all_layers',\n", + " 'freeze_decoder_layers'\n", + " ] = 'use_all_layers'):\n", + " \"\"\"Choose an mlflow model from your files with a widget and fine-tune it.\n", + "\n", + " Parameters\n", + " ----------\n", + " runs, models, clients : list, dict, dict\n", + " contain mlflow-logged runs, models, and tracking clients\n", + " finetuning : Literal['use_all_layers', 'freeze_decoder_layers'] | None\n", + " 'use_all_layers': in fine-tuning, all layers are set to be tunable.\n", + " 'freeze_decoder_layers': in fine-tuning, only the encoder layers are\n", + " set to be tunable (freeze the decoder)\n", + "\n", + " Notes\n", + " -----\n", + " - the fine-tuning option 'freeze_encoder_layers' has shown to be useful\n", + " in a 2D-Unet with ultrasound images\n", + " https://arxiv.org/pdf/2002.08438.pdf\n", + "\n", + " \"\"\"\n", + " self.finetuned = True\n", + " self.runs = runs\n", + " self.models = models\n", + " self.clients = clients\n", + " self.finetuning = finetuning\n", + "\n", + " self._create_widgets()\n", + " ui = widgets.VBox([self.dropdown, self.button])\n", + " display(ui)\n", + "\n", + "\n", + "# ------------------ DATA PREPROCESSING FOR INFERENCE -------------------------\n", + "def tfds_replace_nan(trace):\n", + " \"\"\"Replaces nan values with zeros\"\"\"\n", + " trace = tf.where(tf.math.is_nan(trace), tf.zeros_like(trace), trace)\n", + " return trace\n", + "\n", + "\n", + "def convert_to_tfds_for_unet(trace):\n", + " trace = tf.convert_to_tensor(value=trace, dtype=tf.float32)\n", + " trace = tf.transpose(a=trace, perm=[1, 0])\n", + " num_total_examples = trace.shape[0]\n", + " trace = tf.reshape(tensor=trace, shape=(num_total_examples, -1, 1))\n", + " dataset = tf.data.Dataset.from_tensor_slices(trace)\n", + " dataset = dataset.map(tfds_replace_nan)\n", + " return dataset\n", + "\n", + "\n", + "def tfds_scale_trace(trace, scaler):\n", + " \"\"\"Part of tf.data pipeline. Wrapper function to be able to .map()\n", + " _scale_trace()\n", + " \"\"\"\n", + " trace_shape = trace.shape\n", + " [trace, ] = tf.py_function(func=_scale_trace,\n", + " inp=[trace, scaler],\n", + " Tout=[tf.float32])\n", + " trace.set_shape(trace_shape)\n", + " return trace\n", + "\n", + "\n", + "def tfds_pad_trace(trace, is_label=False):\n", + " \"\"\"Pad an arbitrary trace with a median up to a length of the next biggest\n", + " power of 2.\n", + "\n", + " Parameters\n", + " ----------\n", + " trace : tf.Tensor or np.array\n", + " is_label : bool\n", + " if True, pad with zeros at the end. If False, pad with median.\n", + " \"\"\"\n", + " pad_size, pad_median = _get_pad_size_and_value(trace)\n", + " if is_label:\n", + " trace = tnp.pad(trace, pad_width=[[0, pad_size], [0, 0]],\n", + " mode='constant',\n", + " constant_values=0)\n", + " else:\n", + " trace = tnp.pad(trace, pad_width=[[0, pad_size], [0, 0]],\n", + " mode='constant',\n", + " constant_values=pad_median)\n", + "\n", + " trace_shape = trace.shape\n", + " trace.set_shape(trace_shape)\n", + " return trace\n", + "\n", + "\n", + "def scale_pad_and_batch_tfds_for_unet(dataset, scaler):\n", + " dataset = dataset.map(lambda trace: tfds_scale_trace(trace, scaler),\n", + " num_parallel_calls=tf.data.AUTOTUNE)\n", + " dataset = dataset.map(tfds_pad_trace,\n", + " num_parallel_calls=tf.data.AUTOTUNE)\n", + " dataset = dataset.batch(1)\n", + " return dataset\n", + "\n", + "\n", + "# ---------------------- SEARCH FOR MLFLOW MODELS ----------------------------\n", + "def _get_paths() -> set:\n", + "\n", + " path = Path(\"/content/\")\n", + " if not path.exists():\n", + " raise OSError(f'{path=} does not exist.')\n", + "\n", + " # get all non-hidden directories. Currently the glob and rglob methods in\n", + " # pathlib don't support not looking into hidden directories (you have to\n", + " # filter them out afterwards), which makes scanning directories recursively\n", + " # last ages if there are hidden directories, e.g. the automatically added\n", + " # .trash or .ipynb_checkpoints\n", + " pathlist = list(tqdm(glob.iglob(os.path.join(path, '**/'), recursive=True)))\n", + "\n", + " allpaths = []\n", + " for p in pathlist:\n", + " p = Path(p)\n", + " if not (list(p.parent.parent.glob('meta.yaml')) and\n", + " list(p.parent.glob('meta.yaml'))):\n", + " # properly logged mlflow experiments have a meta.yaml file in the\n", + " # experiments directory and a meta.yaml file in the model run\n", + " # directory inside it. This if clause throws out all folders with a\n", + " # meta.yaml which don't have a parent folder which also has a\n", + " # meta.yaml\n", + " continue\n", + " allpaths.append(p.parent.parent.parent)\n", + " return set(allpaths)\n", + "\n", + "\n", + "def _get_runs(client: mlflow.MlflowClient,\n", + " exp: mlflow.entities.Experiment):\n", + " try:\n", + " run = client.search_runs(exp.experiment_id)\n", + " except mlflow.exceptions.MissingConfigException:\n", + " run = []\n", + " return run\n", + "\n", + "\n", + "def _get_client_and_experiment(path: Path):\n", + " try:\n", + " client = mlflow.MlflowClient(path.as_posix())\n", + " exp = client.search_experiments()\n", + " except (mlflow.MlflowException, OSError, TypeError) as e:\n", + " client, exp = None, []\n", + " return client, exp\n", + "\n", + "\n", + "def _get_scaler_and_model(run: mlflow.entities.Run,\n", + " path: Path):\n", + " try:\n", + " scaler = run.data.params['scaler']\n", + " except KeyError:\n", + " try:\n", + " scaler = run.data.params['hp_scaler']\n", + " except FileNotFoundError:\n", + " scaler = None\n", + " artifact_path = Path(run.info.artifact_uri)\n", + " artifact_path = artifact_path / 'model'\n", + " if not artifact_path.exists():\n", + " artifact_path = path / run.info.experiment_id / run.info.run_id / 'artifacts/model'\n", + " if not artifact_path.exists():\n", + " log.debug('Can not find model folder in mlflow run %s', run.info.run_id)\n", + " model = None\n", + " else:\n", + " try:\n", + " model = mlflow.tensorflow.load_model(artifact_path)\n", + " except OSError:\n", + " log.debug('Can not find model folder in mlflow run %s',\n", + " run.info.run_id)\n", + " model = None\n", + " except (ValueError, ModuleNotFoundError):\n", + " model = mlflow.tensorflow.load_model(\n", + " artifact_path, keras_model_kwargs={'compile' : False})\n", + " model.compile(loss=binary_ce_dice_loss(),\n", + " optimizer=tf.keras.optimizers.Adam(),\n", + " metrics = unet_metrics([0.1, 0.3, 0.5, 0.7, 0.9]))\n", + " return model, scaler, artifact_path.parent\n", + "\n", + "\n", + "def get_all_mlflow_models() -> tuple[set, list, list, dict, dict]:\n", + " log.debug('Start scanning all paths in the \"/content/\" directory for mlflow'\n", + " ' experiments. Depending on the size of the file system / GDrive, '\n", + " 'this may take a while...')\n", + "\n", + " allpaths = _get_paths()\n", + "\n", + " log.debug('Finished collecting potential paths. Start collecting '\n", + " 'experiments...')\n", + "\n", + " exps, runs = [], []\n", + " models, clients = {}, {}\n", + " for p in allpaths:\n", + " temp_runs = []\n", + " client, exp = _get_client_and_experiment(p)\n", + " log.debug('found %s experiments', len(exp))\n", + " if not (client or exp):\n", + " continue\n", + " for e in exp:\n", + " exps.append(e)\n", + " log.debug('checking experiment %s for runs', e)\n", + " run = _get_runs(client, e)\n", + " if run:\n", + " temp_runs.extend(run)\n", + " for r in temp_runs:\n", + " log.debug('checking run %s', r.info.run_id)\n", + " model, scaler, artifact_path = _get_scaler_and_model(run=r, path=p)\n", + " if model:\n", + " models[r.info.run_id] = (model, scaler, artifact_path)\n", + " clients[r.info.run_id] = client\n", + " runs.append(r)\n", + " log.debug('found %s runs', len(runs))\n", + "\n", + " return allpaths, exps, runs, models, clients\n", + "\n", + "\n", + "def get_current_run_and_model(exp: mlflow.entities.Experiment,\n", + " run: mlflow.entities.Run):\n", + " if not (isinstance(run, mlflow.entities.Run) and\n", + " isinstance(exp, mlflow.entities.Experiment)):\n", + " raise ValueError('run has to be a valid mlflow.entities.Run, '\n", + " 'exp has to be a valid mlflow.entities.Experiment')\n", + "\n", + " path = Path(exp.artifact_location).parts[:-1]\n", + " path = Path(*path)\n", + " client = mlflow.MlflowClient(path.as_posix())\n", + "\n", + " run = client.get_run(run.info.run_id)\n", + " artifact_path = Path(run.info.artifact_uri)\n", + " artifact_path /= 'model'\n", + " model = mlflow.tensorflow.load_model(artifact_path.as_posix())\n", + " scaler = run.data.params['scaler']\n", + " model_dict = {run.info.run_id: (model, scaler, artifact_path.parent)}\n", + " client_dict = {run.info.run_id: client}\n", + " return [path], [exp], [run], model_dict, client_dict\n", + "\n", + "\n", + "# ------------------------- QUALITY CONTROL -----------------------------------\n", + "class EvaluateClick(object):\n", + "\n", + " def __init__(self, runs: list[mlflow.entities.Run],\n", + " models: dict[str, tuple[tf.keras.Model, str, Path]],\n", + " clients: dict[str, mlflow.MlflowClient]):\n", + " self.runs = runs\n", + " self.models = models\n", + " self.clients = clients\n", + " self.thresholds = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n", + " self.out = widgets.Output()\n", + "\n", + " def _create_widgets_qc_metrics(self):\n", + "\n", + " drop_opt = [(f'model {model.name} with {scaler=} from {run=}', run) for\n", + " run, (model, scaler, _) in self.models.items()]\n", + " default_run = drop_opt[0][1]\n", + " self.dropdown = widgets.Dropdown(\n", + " options=drop_opt,\n", + " value=default_run,\n", + " description='Model:',\n", + " disabled=False,\n", + " layout=widgets.Layout(width='75%')\n", + " )\n", + " self.button = widgets.Button(description=\"Use chosen model!\")\n", + " self.button.on_click(self._on_button_clicked_qc_metrics)\n", + "\n", + " def _on_button_clicked_qc_metrics(self, change):\n", + " self.out.clear_output()\n", + " run = [r for r in self.runs if r.info.run_id == self.dropdown.value][0]\n", + " model = self.models[self.dropdown.value][0]\n", + " scaler = self.models[self.dropdown.value][1]\n", + " with self.out:\n", + " source_prepro = convert_to_tfds_for_unet(self.source)\n", + " source_prepro = scale_pad_and_batch_tfds_for_unet(source_prepro,\n", + " scaler=scaler)\n", + " log.debug('Predicting artifacts with model %s ...',\n", + " model.name)\n", + " pred = model.predict(source_prepro, verbose=2)\n", + " pred = pd.DataFrame(pred.squeeze(axis=2)).T\n", + " source_prepro = pd.DataFrame(np.array(list(source_prepro)).squeeze()).T\n", + " (pred.columns, source_prepro.columns\n", + " ) = self.source.columns, self.source.columns\n", + " self.prediction, self.source_prepro = pred, source_prepro\n", + " log.debug('Computing MeanIoU for %s traces and %s thresholds...',\n", + " len(self.source.columns), len(self.thresholds))\n", + " self._compute_iou()\n", + " self.iou_figure = self._plot_iou()\n", + " self.random_samples_figure = self._plot_samples(6, 'random')\n", + " self.worst_samples_figure = self._plot_samples(6, 'worst')\n", + " if self.with_metrics:\n", + " log.debug('Plotting training metrics for Quality Control PDF...')\n", + " mult_opt = self._get_run_metrics(run)\n", + " if not mult_opt:\n", + " self.train_metrics_figure = self._no_metrics_plot()\n", + " else:\n", + " client = self.clients[self.dropdown.value]\n", + " self.train_metrics_figure = plot_mlflow_metrics(\n", + " metrics_of_interest=[t[1] for t in mult_opt],\n", + " client=client, run=run)\n", + "\n", + " def _compute_iou(self):\n", + " iou = tf.keras.metrics.MeanIoU(num_classes=2)\n", + " p_and_t = zip(self.prediction.items(), self.target.items())\n", + " for rec, ((_, pred), (_, target)) in tqdm(enumerate(p_and_t),\n", + " total=len(self.iou.columns)):\n", + " new_iou = []\n", + " for thresh in self.thresholds:\n", + " iou.reset_state()\n", + " iou.update_state(pred > thresh, target)\n", + " new_iou.append(iou.result().numpy())\n", + " self.iou.iloc[:, rec] = pd.Series(new_iou)\n", + " # compute best iou and best threshold\n", + " self.iou = self.iou.T.reset_index().drop(columns='index').T\n", + " iou_best_thresh = pd.DataFrame(\n", + " self.iou.idxmax(), columns=['best_threshold']).T\n", + " iou_best = pd.DataFrame(\n", + " self.iou.max(), columns=['best_iou']).T\n", + " self.iou = pd.concat([self.iou, iou_best_thresh, iou_best],\n", + " axis='index')\n", + " self.average_best_threshold = self.iou.T.best_threshold.mean()\n", + " log.debug('The best average threshold is: %s',\n", + " self.average_best_threshold)\n", + "\n", + " def _plot_iou(self):\n", + " fig = sns.jointplot(\n", + " data=self.iou.T, x='best_iou', y='best_threshold', kind='hex',\n", + " marginal_kws=dict(bins=9, element='poly')\n", + " ).set_axis_labels(xlabel='Best mean Intersection-over-Union',\n", + " ylabel='Best Threshold')\n", + " # plt.show()\n", + " return fig.fig\n", + "\n", + " def _plot_samples(self, n_samples: int, sample: Literal['random', 'worst']):\n", + " if sample == 'worst':\n", + " index_of_interest = self.iou.T.nsmallest(n=n_samples,\n", + " columns='best_iou').index\n", + " elif sample == 'random':\n", + " index_of_interest = self.iou.T.sample(n=n_samples).index\n", + " ncols = 2\n", + " nrows = n_samples // ncols + (ncols % 2)\n", + " fig, axs = plt.subplots(nrows, ncols, figsize=(8.27, 2*nrows+1),\n", + " dpi=100, sharex=True, sharey=True)\n", + " axs = axs.flatten()\n", + " for i, idx in enumerate(index_of_interest):\n", + " if i < 1:\n", + " legend = 'auto'\n", + " else:\n", + " legend = False\n", + " iou = self.iou.loc['best_iou', idx]\n", + " thresh = self.iou.loc['best_threshold', idx]\n", + " if sample == 'worst':\n", + " title = (f'Top {n_samples} Worst - Trace {idx}\\nbest MeanIoU '\n", + " f'{iou:.2f} with threshold {thresh}')\n", + " elif sample == 'random':\n", + " title = (f'Random trace {idx}\\nbest MeanIoU {iou:.2f} with '\n", + " f'threshold {thresh}')\n", + " source = self.source_prepro.iloc[:, idx]\n", + " target = self.target.iloc[:, idx]\n", + " pred = self.prediction.iloc[:, idx]\n", + " plot_df = pd.DataFrame({'source': source, 'target': target,\n", + " 'prediction': pred})\n", + " sns.lineplot(data=plot_df.loc[:, 'source'], ax=axs[i]).set(\n", + " title=title, xlabel='time [ms]', ylabel='source [a.u.]')\n", + " sns.lineplot(data=plot_df.loc[:, ['target', 'prediction']],\n", + " palette=sns.color_palette()[1:3], legend=legend,\n", + " ax=axs[i].twinx()\n", + " ).set(ylabel='target | prediction [a.u.]')\n", + " plt.tight_layout()\n", + " # plt.show()\n", + " return fig\n", + "\n", + " def _create_widgets_train_metrics(self):\n", + " self._get_runs_with_metrics()\n", + " drop_opt = [(f'model {model.name} with {scaler=} from {run=}', run) for\n", + " run, (model, scaler, _) in self.models_with_metrics.items()]\n", + " default_run = drop_opt[0][1]\n", + " self.dropdown = widgets.Dropdown(\n", + " options=drop_opt,\n", + " value=default_run,\n", + " description='Model:',\n", + " disabled=False,\n", + " layout=widgets.Layout(width='75%')\n", + " )\n", + "\n", + " mult_opt = self._get_run_metrics(\n", + " [r for r in self.runs_with_metrics\n", + " if r.info.run_id == default_run][0])\n", + "\n", + " self.multiple = widgets.SelectMultiple(\n", + " options=mult_opt,\n", + " # value=,\n", + " description='Metric(s):',\n", + " disabled=False\n", + " )\n", + "\n", + " def update_multiple(*args):\n", + " new_mult_opt = self._get_run_metrics(self.dropdown.value)\n", + " self.multiple.options = new_mult_opt\n", + " # multiple.value =\n", + "\n", + " self.dropdown.observe(update_multiple, 'value')\n", + "\n", + " self.button = widgets.Button(description=\"Plot chosen metrics!\")\n", + " self.button.on_click(self._on_button_clicked_train_metrics)\n", + "\n", + " def _get_runs_with_metrics(self):\n", + " self.runs_with_metrics = [r for r in self.runs if r.data.metrics]\n", + " if not self.runs_with_metrics:\n", + " raise ValueError(f'None of the {len(runs)} runs which were found hold'\n", + " ' any metrics.')\n", + "\n", + " run_ids = [r.info.run_id for r in self.runs_with_metrics]\n", + " self.models_with_metrics = {key: val\n", + " for key, val in self.models.items()\n", + " if key in run_ids}\n", + " self.clients_with_metrics = {key: val\n", + " for key, val in self.clients.items()\n", + " if key in run_ids}\n", + "\n", + " for run, (model, scaler, _) in self.models_with_metrics.items():\n", + " log.debug('Found metrics for model %s with scaler %s from run %s',\n", + " model.name, scaler, run)\n", + "\n", + " def _get_run_metrics(self, run: mlflow.entities.Run):\n", + " mult_opt_dict = {'loss': 'Loss',\n", + " 'recall0.5': 'Recall',\n", + " 'precision0.5': 'Precision',\n", + " 'f1': 'F1-Score',\n", + " 'auc': 'AUC',\n", + " 'accuracy': 'Accuracy',\n", + " 'tn0.5': 'True Negatives',\n", + " 'fn0.5': 'False Negatives',\n", + " 'fp0.5': 'False Positives',\n", + " 'tp0.5': 'True Positives'\n", + " }\n", + "\n", + " metrics = [(mult_opt_dict.get(m, m), m) for m in run.data.metrics\n", + " if not (m.startswith(('val', 'lr')) or\n", + " m.endswith(('0.1', '0.3', '0.7', '0.9')))]\n", + "\n", + " return metrics\n", + "\n", + " def _on_button_clicked_train_metrics(self, change):\n", + " self.out.clear_output()\n", + " run = [r for r in self.runs_with_metrics\n", + " if r.info.run_id == self.dropdown.value][0]\n", + " client = self.clients_with_metrics[self.dropdown.value]\n", + " with self.out:\n", + " self.train_metrics_figure = plot_mlflow_metrics(\n", + " metrics_of_interest=self.multiple.value, client=client, run=run)\n", + "\n", + " def _no_metrics_plot(self):\n", + " run = [r for r in self.runs if r.info.run_id == self.dropdown.value][0]\n", + " fig = plt.figure(figsize=(8.27, 1), dpi=100)\n", + " run_end_time = datetime.utcfromtimestamp(run.info.end_time / 1000)\n", + " plt.title(f'Metrics for run {run.info.run_id}, created {run_end_time}')\n", + " plt.annotate('Found no logged training metrics',\n", + " xy=(0.3, 0.5), va='center', ha='left', xycoords='data',\n", + " bbox=dict(boxstyle=\"round\", fc=\"w\"))\n", + " # plt.show()\n", + " return fig\n", + "\n", + " def display_widgets_qc_metrics(self, source: pd.DataFrame,\n", + " target: pd.DataFrame,\n", + " experiment_params: pd.DataFrame,\n", + " with_metrics: bool = False,\n", + " ):\n", + " self.source = source\n", + " self.target = target\n", + " self.with_metrics = with_metrics\n", + " self.experiment_params = experiment_params\n", + " self.iou = pd.DataFrame(index=self.thresholds, columns=source.columns,\n", + " dtype='float32')\n", + " self._create_widgets_qc_metrics()\n", + " ui = widgets.VBox([self.dropdown, self.button, self.out])\n", + " display(ui)\n", + "\n", + " def display_widgets_train_metrics(self):\n", + " self._create_widgets_train_metrics()\n", + " self.out = widgets.Output()\n", + " ui = widgets.VBox([self.dropdown, self.multiple, self.button, self.out])\n", + " display(ui)\n", + "\n", + "\n", + "def plot_mlflow_metrics(metrics_of_interest: list[str],\n", + " client: mlflow.MlflowClient,\n", + " run: mlflow.entities.Run):\n", + "\n", + " mult_opt_dict = {'loss': 'Loss',\n", + " 'recall0.5': 'Recall',\n", + " 'precision0.5': 'Precision',\n", + " 'f1': 'F1-Score',\n", + " 'auc': 'AUC',\n", + " 'accuracy': 'Accuracy',\n", + " 'tn0.5': 'True Negatives',\n", + " 'fn0.5': 'False Negatives',\n", + " 'fp0.5': 'False Positives',\n", + " 'tp0.5': 'True Positives'\n", + " }\n", + " # metrics_of_interest = ['loss', 'auc', 'precision_0.5', 'recall_0.5']\n", + " ncols = 3\n", + " nrows = (len(metrics_of_interest) // ncols) + 1\n", + " fig, ax = plt.subplots(nrows, ncols, figsize=(8.27, 2*nrows+1), dpi=100,\n", + " sharex=True, sharey=False)\n", + " ax = ax.flatten()\n", + " for i, (m, valm) in enumerate(zip(metrics_of_interest,\n", + " [f'val_{m}' for m in metrics_of_interest])):\n", + "\n", + " metric = client.get_metric_history(run.info.run_id, m)\n", + " val_metric = client.get_metric_history(run.info.run_id, valm)\n", + "\n", + " steps = [m.step for m in metric]\n", + " val_steps = [m.step for m in val_metric]\n", + " values = [m.value for m in metric]\n", + " val_values = [m.value for m in val_metric]\n", + "\n", + " sns.lineplot(x=steps, y=values, ax=ax[i], label='train').set(\n", + " title=mult_opt_dict.get(m, m))\n", + " sns.lineplot(x=val_steps, y=val_values, ax=ax[i], label='val')\n", + "\n", + " if m == 'loss':\n", + " plt.setp(ax[i], yscale='log')\n", + " elif m.startswith(('tn', 'fn', 'fp', 'tp')):\n", + " pass\n", + " else:\n", + " plt.setp(ax[i], ylim=[0, 1])\n", + " run_end_time = datetime.utcfromtimestamp(run.info.end_time / 1000)\n", + " plt.suptitle(f'Metrics for run {run.info.run_id}, created {run_end_time}')\n", + " plt.setp(ax[len(metrics_of_interest):], visible=False)\n", + " plt.tight_layout()\n", + " plt.show()\n", + " return fig\n", + "\n", + "# ------------------------- PDF REPORTS ------------------------------------\n", + "class PDF(fpdf.FPDF):\n", + " def get_colorcode(self, color: Literal['blue', 'yellow', 'lightblue']):\n", + " colorcodes = {\n", + " 'blue': (0, 80, 180),\n", + " 'yellow': (230, 230, 0),\n", + " 'lightblue': (200, 220, 255)\n", + " }\n", + " return colorcodes[color]\n", + "\n", + " def header(self):\n", + " self.set_font(\"helvetica\", \"B\", 15)\n", + " with self.local_context(draw_color=self.get_colorcode('blue'),\n", + " fill_color=self.get_colorcode('yellow'),\n", + " line_width=1):\n", + " self.cell(self.epw * 0.9, 9, self.title, border=1, new_x=\"LMARGIN\",\n", + " new_y=\"NEXT\", align=\"C\", fill=True)\n", + " self.ln(10)\n", + "\n", + " def footer(self):\n", + " self.set_y(-15) # footer position 1.5cm from bottom\n", + " self.set_font(\"helvetica\", \"I\", 8)\n", + " with self.local_context(text_color=128):\n", + " self.cell(self.epw * 0.9, 10, f\"Page {self.page_no()}\", align=\"C\")\n", + "\n", + " def chapter_title(self, num, label):\n", + " self.set_font(\"helvetica\", \"\", 12)\n", + " with self.local_context(fill_color=self.get_colorcode('lightblue')):\n", + " self.cell(self.epw * 0.9, 6, f\"Chapter {num} : {label}\",\n", + " new_x=\"LMARGIN\", new_y=\"NEXT\", align=\"L\", fill=True)\n", + " self.ln(4)\n", + "\n", + " def add_table_from_dataframe(self, df: pd.DataFrame,\n", + " col_width: tuple[int] | None):\n", + " df = df.applymap(str) # Convert all data inside dataframe into string type\n", + "\n", + " columns = [list(df)] # Get list of dataframe columns\n", + " rows = df.values.tolist() # Get list of dataframe rows\n", + " data = columns + rows # Combine columns and rows in one list\n", + "\n", + " with self.table(rows=data,\n", + " borders_layout=\"MINIMAL\",\n", + " cell_fill_color=200, # grey\n", + " cell_fill_mode=\"ROWS\",\n", + " line_height=self.font_size * 2.5,\n", + " text_align=\"CENTER\",\n", + " col_widths=col_width) as table:\n", + " pass\n", + "\n", + "\n", + "def train_pdf_export(train_click: TrainClick, out_path: Path):\n", + " # save FPDF() class into a variable pdf\n", + " def _sort_train_plots(train_plots):\n", + "\n", + " train_plots_dict = {}\n", + " for p in train_plots:\n", + " for_sort = list(\n", + " p.parts[:-1]) + [f'plot{p.stem.strip(\"plot\"):0>3}{p.suffix}']\n", + " train_plots_dict[p] = for_sort\n", + " train_plots_dict = {k: Path(*v) for k, v in sorted(\n", + " train_plots_dict.items(), key=lambda item: Path(*item[1]),\n", + " reverse=True)}\n", + " return list(train_plots_dict)\n", + "\n", + " def _get_four_plots_during_training(path: Path):\n", + " path = Path(path.as_posix().strip('file:'))\n", + " train_plots = list(path.glob('*.png'))\n", + " train_plots = _sort_train_plots(train_plots)\n", + " skipplots = len(train_plots) // 4 + 1\n", + " try:\n", + " train_plots = train_plots[::skipplots]\n", + " except ValueError:\n", + " skipplots = 2\n", + " train_plots = train_plots[::skipplots]\n", + " train_plots = [(f\"{p.stem.strip('plot'):0>3}\",\n", + " mlflow.artifacts.load_image(p.as_posix()))\n", + " for p in train_plots]\n", + " return sorted(train_plots)\n", + "\n", + " def _load_simulations_metadata(path: Path):\n", + " path = Path(path.as_posix().strip('file:'))\n", + " md = pd.read_csv(path, skipfooter=1, index_col=0, engine='python')\n", + " new_col = {f'{c}': f'Record {i+1}' for i, c in enumerate(md.columns)}\n", + " md = md.rename(columns=new_col).T.reset_index()\n", + " try:\n", + " col = 'path and file name'\n", + " md.loc[:, col] = md.loc[:, col].apply(lambda txt: txt.split('/')[-1])\n", + " except:\n", + " pass\n", + " return md\n", + "\n", + " def _scaler_dict(scaler: str):\n", + " scaler_dict = {\n", + " 'standard': 'standard scaler',\n", + " 'robust': 'robust scaler',\n", + " 'maxabs': 'max-abs scaler',\n", + " 'quant_g': 'quantile transformer (Gaussian output)',\n", + " 'minmax': 'min-max scaler',\n", + " 'l1': 'sample-wise L1 normalizer',\n", + " 'l2': 'sample-wise L2 normalizer'\n", + " }\n", + " return scaler_dict.get('scaler')\n", + "\n", + " network = \"1D U-Net for FCS\"\n", + "\n", + " (new_paths, new_exps, new_runs, new_models, new_clients\n", + " ) = get_current_run_and_model(train_click.exp, train_click.run)\n", + "\n", + " new_run = new_runs[0]\n", + " lr = new_clients[new_run.info.run_id].get_metric_history(new_run.info.run_id, 'lr')\n", + " lr = str([m.value for m in lr])\n", + "\n", + " # first load metadata which has additional metadata if pretraining is involved\n", + " new_start_time = datetime.utcfromtimestamp(new_run.info.start_time / 1000)\n", + " new_end_time = datetime.utcfromtimestamp(new_run.info.end_time / 1000)\n", + " new_train_time = new_end_time - new_start_time\n", + "\n", + " new_params = pd.DataFrame.from_dict(\n", + " new_run.data.params, orient='index').reset_index()\n", + " new_params = new_params.rename(\n", + " columns={'index': 'Logged mlflow params', 0: 'values'})\n", + " new_epochs = int(new_run.data.params['epochs'])\n", + " initial_epoch = 0\n", + " new_train_nexamples = new_run.data.params.get('num_train_examples')\n", + " new_val_nexamples = new_run.data.params.get('num_val_examples')\n", + " new_crop_size = new_run.data.params.get('crop_size')\n", + " new_batch_size = new_run.data.params.get('batch_size')\n", + " new_lr_power = new_run.data.params.get('lr_power')\n", + " if new_lr_power is not None:\n", + " new_lr_power = ('linear' if int(new_lr_power) == 1\n", + " else f'polynmial (power={new_lr_power})')\n", + " new_lr_start = new_run.data.params.get('lr_start')\n", + "\n", + " new_tags = pd.DataFrame.from_dict(\n", + " new_run.data.tags, orient='index').reset_index()\n", + " new_tags = new_tags.rename(\n", + " columns={'index': 'Logged mlflow tags', 0: 'values'})\n", + "\n", + " new_model = new_models[new_run.info.run_id]\n", + " new_model_name = new_model[0].name\n", + " new_scaler = new_model[1]\n", + " new_scaler = _scaler_dict(new_scaler)\n", + "\n", + " if train_click.finetuned:\n", + " initial_epoch = int(new_run.data.params['initial_epoch'])\n", + " new_epochs -= initial_epoch\n", + "\n", + " old_run = [r for r in train_click.runs\n", + " if r.info.run_id == train_click.dropdown.value][0]\n", + " old_start_time = datetime.utcfromtimestamp(old_run.info.start_time / 1000)\n", + "\n", + " old_params = pd.DataFrame.from_dict(\n", + " old_run.data.params, orient='index').reset_index()\n", + " old_params = old_params.rename(\n", + " columns={'index': 'Logged mlflow params (Pretraining)', 0: 'values'})\n", + " old_epochs = old_run.data.params['epochs']\n", + " old_train_nexamples = old_run.data.params['num_train_examples']\n", + " old_batch_size = old_run.data.params['batch_size']\n", + " try:\n", + " old_lr = old_run.data.params['lr schedule']\n", + " lr = f'Pretraining:{old_lr}\\nFine-tuning:{lr}'\n", + " except:\n", + " pass\n", + "\n", + " old_tags = pd.DataFrame.from_dict(\n", + " old_run.data.tags, orient='index').reset_index()\n", + " old_tags = old_tags.rename(\n", + " columns={'index': 'Logged mlflow tags (Pretraining)', 0: 'values'})\n", + "\n", + " old_model = train_click.models[train_click.dropdown.value]\n", + " old_model_name = old_model[0].name\n", + " old_scaler = old_model[1]\n", + " old_scaler = _scaler_dict(old_scaler)\n", + " old_artifact_path = old_model[2]\n", + " old_train_metadata = _load_simulations_metadata(\n", + " old_artifact_path / 'experiment_params_train.csv')\n", + " old_val_metadata = _load_simulations_metadata(\n", + " old_artifact_path / 'experiment_params_val.csv')\n", + " # load metadata, which only is relevant in the new run\n", + "\n", + " artifact_path = new_model[2]\n", + " cuda = mlflow.artifacts.load_text((artifact_path / 'nvcc.txt').as_posix())\n", + " cuda_version = cuda[cuda.find(', V')+3:cuda.find(', V')+10]\n", + " gpu = mlflow.artifacts.load_text((artifact_path / 'nvidia-smi.txt').as_posix())\n", + " gpu_name = gpu[gpu.find('Tesla'):gpu.find('Tesla')+10]\n", + " printenv = mlflow.artifacts.load_text((artifact_path / 'printenv.txt').as_posix())\n", + " lscpu = mlflow.artifacts.load_text((artifact_path / 'lscpu.txt').as_posix())\n", + " free = mlflow.artifacts.load_text((artifact_path / 'free.txt').as_posix())\n", + " top = mlflow.artifacts.load_text((artifact_path / 'top.txt').as_posix())\n", + " train_plots = _get_four_plots_during_training(artifact_path / 'predplots')\n", + " new_train_metadata = _load_simulations_metadata(\n", + " artifact_path / 'experiment_params_train.csv')\n", + " new_val_metadata = _load_simulations_metadata(\n", + " artifact_path / 'experiment_params_val.csv')\n", + " train_source = train_click.mlflow_kwargs['train_data']\n", + " train_source_length = len(train_source)\n", + " train_experiment_params = train_click.mlflow_kwargs['train_experiment_params']\n", + "\n", + " all_packages = pd.read_table('/content/requirements.txt')\n", + " all_p_dict = [tuple(v[0].split('==')) for v in all_packages.values]\n", + " all_p_dict = {p[0]: p[1] for p in all_p_dict}\n", + " all_packages = f'{all_packages.values}'.replace('[', '').replace(\n", + " ']', '').replace(\"'\", '').replace('\\n', ' | ')\n", + "\n", + " pdf = PDF()\n", + " pdf.set_title(f'Model training report: {network}')\n", + "\n", + " # -------------------- MLFLOW RUN INFOS -------------------------------\n", + " pdf.add_page()\n", + " pdf.set_right_margin(-1)\n", + " if gpu:\n", + " gpu_text = f'The training was accelerated using a {gpu_name} GPU.'\n", + " else:\n", + " gpu_text = 'No GPU acceleration was used.'\n", + " if train_click.finetuned:\n", + " ft_text = (f'The model {old_model_name} was re-trained from a '\n", + " f'pretrained model trained on {old_start_time.date()} in '\n", + " f'mlflow run {old_run.info.run_id}. In this report, metadata'\n", + " f' for both models are shown. The pretrained'\n", + " f' model was built with {old_epochs} epochs, '\n", + " f'{old_train_nexamples} paired FCS time-series for training,'\n", + " f' {old_scaler} scaling, a batch size of {old_batch_size}.')\n", + " else:\n", + " ft_text = 'No pretraining was used.'\n", + " text = (f'The {network} model was trained for {new_epochs} epochs on '\n", + " f'{new_train_nexamples} paired FCS time-series with a trace length '\n", + " f'of {train_source_length} (for validation additional '\n", + " f'{new_val_nexamples} were used). The traces were set to 0 after '\n", + " f'time step {new_crop_size} to train the U-Net to correctly segment '\n", + " 'arbitrary-length time-series (for technical reasons of this U-Net '\n", + " f'architecture). The traces were scaled using a {new_scaler}. '\n", + " f'The batch size was {new_batch_size}, the loss function was the sum'\n", + " f' of dice loss (global loss) and binary crossentropy (local loss)'\n", + " f'. The Adam optimizer was used with a scheduled learning rate '\n", + " f'starting at {new_lr_start} with a {new_lr_power} decay per '\n", + " f'epoch. The model was trained using the {network} ZeroCostDL4Mic '\n", + " f'notebook (v {NOTEBOOK_VERSION[0]}) (Seltmann et al 2023, von '\n", + " f'Chamier & Laine et al., 2020). Key python packages used include '\n", + " f'tensorflow (v {all_p_dict[\"tensorflow\"]}), numpy (v '\n", + " f'{all_p_dict[\"numpy\"]}), cuda (v {cuda_version}), and mlflow (v '\n", + " f'{all_p_dict[\"mlflow\"]}). {gpu_text} {ft_text}')\n", + "\n", + " header = (f'Model: {new_model_name}, trained {new_start_time.date()} in '\n", + " f'mlflow run {new_run.info.run_id}\\nModel training start: '\n", + " f'{new_start_time}, training end: {new_end_time}\\nTotal training '\n", + " f'time: {new_train_time}')\n", + "\n", + " pdf.set_font(\"helvetica\", size=11, style='B')\n", + " pdf.multi_cell(180, 5, text=header, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + " pdf.set_font(style='')\n", + " pdf.multi_cell(180, 5, text=text, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + "\n", + " # --------------------------------- MLFLOW METADATA ------------------------\n", + " pdf.chapter_title(1, 'Logged mlflow metadata')\n", + " pdf.set_font(size=8)\n", + " pdf.add_table_from_dataframe(new_params, col_width=(40, 130))\n", + " pdf.ln(10)\n", + " pdf.add_table_from_dataframe(new_tags, col_width=(40, 130))\n", + " pdf.ln(10)\n", + " if train_click.finetuned:\n", + " pdf.add_table_from_dataframe(old_params, col_width=(40, 130))\n", + " pdf.ln(10)\n", + " pdf.add_table_from_dataframe(old_tags, col_width=(40, 130))\n", + " pdf.ln(10)\n", + "\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='Learning rate schedule',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.multi_cell(180, 5, text=lr, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + "\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='Logged Python package requirements',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.multi_cell(180, 5, text=all_packages, align='L', new_x='LMARGIN',\n", + " new_y='NEXT')\n", + "\n", + " pdf.add_page()\n", + " pdf.chapter_title(2, 'Model architecture')\n", + " pdf.ln()\n", + " tf.keras.utils.plot_model(model=new_model[0],\n", + " to_file='/tmp/architecture.png',\n", + " show_shapes=True)\n", + " pdf.image('/tmp/architecture.png', x=11, y=None, h=pdf.eph*0.8)\n", + "\n", + " # --------------------- COMPUTATION ENVIRONMENT ------------------------\n", + " pdf.add_page()\n", + " pdf.chapter_title(3, 'Computation environment')\n", + " pdf.set_font('helvetica', \"\", size=8)\n", + "\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='GPU environment (nvcc, nvidia-smi)',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.multi_cell(190, 5, text=cuda, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + " pdf.multi_cell(190, 5, text=gpu, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + "\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='System information (lscpu, free)',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.multi_cell(190, 5, text=lscpu, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + " pdf.multi_cell(190, 5, text=free, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + "\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='System information (top)',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.multi_cell(190, 5, text=top, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + "\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='Environment variables (printenv)',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.multi_cell(190, 5, text=printenv, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + "\n", + " # --------------------- TRAINING METRICS -------------------------------\n", + "\n", + " pdf.add_page()\n", + " pdf.chapter_title(4, \"Model progress during training\")\n", + "\n", + " for tp in train_plots:\n", + " title = ('Model predictions after epoch '\n", + " f'{int(tp[0]) + 1}/{new_epochs + initial_epoch}')\n", + " pdf.cell(100, 5, text=title, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.image(tp[1], x=11, y=None, w=pdf.epw*0.9)\n", + "\n", + " # ---------------------- SIMULATION METADATA --------------------------\n", + " pdf.add_page(orientation='L')\n", + " pdf.chapter_title(8, 'Simulated data used for training and validation')\n", + " pdf.set_font('helvetica', \"\", size=8)\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='Train data simulations - metadata',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + " pdf.add_table_from_dataframe(new_train_metadata, col_width=22)\n", + " pdf.add_page(orientation='L')\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='Validation data simulations - metadata',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + " pdf.add_table_from_dataframe(new_val_metadata, col_width=22)\n", + "\n", + " if train_click.finetuned:\n", + " pdf.add_page(orientation='L')\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, new_x='LMARGIN', new_y='NEXT',\n", + " text='Train data simulations - metadata (Pretraining)')\n", + " pdf.ln()\n", + " pdf.add_table_from_dataframe(old_train_metadata, col_width=22)\n", + " pdf.add_page(orientation='L')\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, new_x='LMARGIN', new_y='NEXT',\n", + " text='Validation data simulations - metadata (Pretraining)')\n", + " pdf.ln()\n", + " pdf.add_table_from_dataframe(old_val_metadata, col_width=22)\n", + "\n", + " # ------------------------ REFERENCES ----------------------------------\n", + " pdf.add_page()\n", + " pdf.chapter_title(9, \"References\")\n", + " pdf.set_font_size(10.)\n", + " ref_1 = ('References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, '\n", + " 'Romain, et al. \"Democratising deep learning for microscopy with '\n", + " 'ZeroCostDL4Mic.\" Nature Communications (2021).')\n", + "\n", + " ref_2 = ('- 1D U-Net for FCS: Seltmann et al. \"Neural Network Informed '\n", + " 'Photon Filtering Reduces Artifacts in Fluorescence Correlation '\n", + " 'Spectroscopy Data\", biorxiv, 2023')\n", + " pdf.multi_cell(190, 5, text= ref_1, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.multi_cell(190, 5, text= ref_2, align='L', new_x='LMARGIN', new_y='NEXT')\n", + "\n", + " pdf.ln(3)\n", + " reminder = ('Important:\\nRemember to perform the quality control step on all'\n", + " ' newly trained models\\nPlease consider depositing your training'\n", + " ' dataset on Zenodo')\n", + "\n", + " pdf.set_font('helvetica', size = 11, style='B')\n", + " pdf.multi_cell(190, 5, text=reminder, align='C')\n", + "\n", + " out_path = out_path / f'{new_run.info.run_id}_train_report.pdf'\n", + " pdf.output(out_path)\n", + "\n", + "\n", + "def qc_pdf_export(evaluate_click: EvaluateClick, out_path: Path):\n", + "\n", + " def _sort_train_plots(train_plots):\n", + "\n", + " train_plots_dict = {}\n", + " for p in train_plots:\n", + " for_sort = list(\n", + " p.parts[:-1]) + [f'plot{p.stem.strip(\"plot\"):0>3}{p.suffix}']\n", + " train_plots_dict[p] = for_sort\n", + " train_plots_dict = {k: Path(*v) for k, v in sorted(\n", + " train_plots_dict.items(), key=lambda item: Path(*item[1]),\n", + " reverse=True)}\n", + " return list(train_plots_dict)\n", + "\n", + " def _get_four_plots_during_training(path: Path):\n", + " train_plots = list(path.glob('*.png'))\n", + " train_plots = _sort_train_plots(train_plots)\n", + " skipplots = len(train_plots) // 4 + 1\n", + " try:\n", + " train_plots = train_plots[::skipplots]\n", + " except ValueError:\n", + " skipplots = 2\n", + " train_plots = train_plots[::skipplots]\n", + " train_plots = [(f\"{p.stem.strip('plot'):0>3}\",\n", + " mlflow.artifacts.load_image(p.as_uri()))\n", + " for p in train_plots]\n", + " return sorted(train_plots)\n", + "\n", + " def _load_simulations_metadata(md: pd.DataFrame):\n", + " new_col = {f'{c}': f'Record {i+1}' for i, c in enumerate(md.columns)}\n", + " md = md.rename(columns=new_col).T.reset_index()\n", + " try:\n", + " col = 'path and file name'\n", + " md.loc[:, col] = md.loc[:, col].apply(lambda txt: txt.split('/')[-1])\n", + " except:\n", + " pass\n", + " return md\n", + "\n", + " network = \"1D U-Net for FCS\"\n", + "\n", + " run = [r for r in evaluate_click.runs\n", + " if r.info.run_id == evaluate_click.dropdown.value][0]\n", + " # client = evaluate_click.clients[evaluate_click.dropdown.value]\n", + " start_time = datetime.utcfromtimestamp(run.info.start_time / 1000)\n", + " end_time = datetime.utcfromtimestamp(run.info.end_time / 1000)\n", + " train_time = end_time - start_time\n", + " params = pd.DataFrame.from_dict(run.data.params, orient='index').reset_index()\n", + " params = params.rename(columns={'index': 'Logged mlflow params', 0: 'values'})\n", + " epochs = run.data.params['epochs']\n", + " tags = pd.DataFrame.from_dict(run.data.tags, orient='index').reset_index()\n", + " tags = tags.rename(columns={'index': 'Logged mlflow tags', 0: 'values'})\n", + " model = evaluate_click.models[evaluate_click.dropdown.value]\n", + " model_name = model[0].name\n", + " artifact_path = model[2]\n", + " try:\n", + " test_metadata = _load_simulations_metadata(\n", + " evaluate_click.experiment_params)\n", + " except (UnicodeDecodeError, FileNotFoundError, IsADirectoryError):\n", + " message = 'No logged metadata of training data'\n", + " test_metadata = pd.DataFrame([message], columns=[message])\n", + "\n", + " all_packages = ''\n", + " if use_the_current_trained_model:\n", + " all_packages = pd.read_table('/content/requirements.txt')\n", + " all_packages = f'{all_packages.values}'.replace('[', '').replace(\n", + " ']', '').replace(\"'\", '').replace('\\n', ' | ')\n", + " all_packages = f'Package information from Jupyter / Colab runtime:\\n {all_packages}'\n", + "\n", + " else:\n", + " condayaml = artifact_path / 'model/conda.yaml'\n", + " all_packages += 'Package information from mlflow-logged conda.yaml:\\n'\n", + " all_packages += mlflow.artifacts.load_text(condayaml.as_uri())\n", + "\n", + " pdf = PDF()\n", + " pdf.set_title(f'Quality Control report: {network}')\n", + "\n", + " # -------------------- MLFLOW RUN INFOS -------------------------------\n", + " pdf.add_page()\n", + " pdf.set_right_margin(-1)\n", + " pdf.set_font(\"helvetica\", size=11, style='B')\n", + " header = (f'Model: {model_name}, trained {start_time.date()} in mlflow run '\n", + " f'{run.info.run_id}\\nQC Date: {datetime.now().date()}\\n'\n", + " f'Model training start: {start_time}, training end: {end_time}\\n'\n", + " f'Total training time: {train_time}')\n", + " pdf.multi_cell(180, 5, text=header, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + "\n", + " pdf.chapter_title(1, 'Logged mlflow metadata')\n", + " pdf.set_font('helvetica', \"\", size=8)\n", + " pdf.add_table_from_dataframe(params, col_width=(40, 130))\n", + " pdf.ln(10)\n", + " pdf.add_table_from_dataframe(tags, col_width=(40, 130))\n", + " pdf.ln(10)\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='Logged Python package requirements',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.multi_cell(180, 5, text=all_packages, align='L', new_x='LMARGIN',\n", + " new_y='NEXT')\n", + "\n", + " pdf.add_page()\n", + " pdf.chapter_title(2, 'Model architecture')\n", + " pdf.ln()\n", + " tf.keras.utils.plot_model(model=model[0], to_file='/tmp/architecture.png',\n", + " show_shapes=True)\n", + " pdf.image('/tmp/architecture.png', x=11, y=None, h=pdf.eph*0.8)\n", + "\n", + " # ------------------------ TRAINING METRICS ----------------------------\n", + " pdf.add_page()\n", + " pdf.chapter_title(3, \"Training metrics\")\n", + " evaluate_click.train_metrics_figure.savefig('/tmp/train_metrics.png')\n", + " pdf.image('/tmp/train_metrics.png', x=11, y=None, w=pdf.epw*0.9)\n", + "\n", + "\n", + " # ------------------------ QUALITY METRICS -------------------------------\n", + " pdf.add_page()\n", + " pdf.chapter_title(5, \"Quality control metrics\")\n", + " txt = ('The best average threshold is: '\n", + " f'{evaluate_click.average_best_threshold}')\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text=txt, new_x='LMARGIN', new_y='NEXT')\n", + " evaluate_click.iou_figure.savefig('/tmp/iou.png')\n", + " pdf.image('/tmp/iou.png', x=11, y=None, w=pdf.epw*0.9)\n", + "\n", + " # ---------------------- QUALITY CONTROL PLOTS ---------------------------\n", + " pdf.add_page()\n", + " pdf.chapter_title(6, \"Quality control - Plot random samples\")\n", + " evaluate_click.random_samples_figure.savefig('/tmp/random-samples.png')\n", + " pdf.image('/tmp/random-samples.png', x=11, y=None, w=pdf.epw*0.9)\n", + "\n", + " pdf.add_page()\n", + " pdf.chapter_title(7, \"Quality control - Plot worst samples\")\n", + " evaluate_click.worst_samples_figure.savefig('/tmp/worst-samples.png')\n", + " pdf.image('/tmp/worst-samples.png', x=11, y=None, w=pdf.epw*0.9)\n", + "\n", + " # ---------------------- SIMULATION METADATA ---------------------\n", + " pdf.add_page(orientation='L')\n", + " pdf.chapter_title(8, 'Simulated data used for model evalutation')\n", + " pdf.set_font('helvetica', \"\", size=8)\n", + " with pdf.local_context(font_size=11, font_style='B'):\n", + " pdf.cell(180, 5, text='Test data simulations - metadata',\n", + " new_x='LMARGIN', new_y='NEXT')\n", + " pdf.ln()\n", + " pdf.add_table_from_dataframe(test_metadata, col_width=22)\n", + "\n", + " # -------------------------- REFERENCES -----------------------------------\n", + " pdf.add_page()\n", + " pdf.chapter_title(9, \"References\")\n", + " pdf.set_font_size(10.)\n", + " ref_1 = ('References:\\n - ZeroCostDL4Mic: von Chamier, Lucas & Laine, '\n", + " 'Romain, et al. \"Democratising deep learning for microscopy with '\n", + " 'ZeroCostDL4Mic.\" Nature Communications (2021).')\n", + "\n", + " ref_2 = ('- 1D U-Net for FCS: Seltmann et al. \"Title of publication\" '\n", + " 'Journal, year')\n", + " pdf.multi_cell(190, 5, text= ref_1, align='L', new_x='LMARGIN', new_y='NEXT')\n", + " pdf.multi_cell(190, 5, text= ref_2, align='L', new_x='LMARGIN', new_y='NEXT')\n", + "\n", + " pdf.ln(3)\n", + " reminder = ('If the model was trained in this notebook, more information on'\n", + " ' model training is available in the training_report.pdf. For '\n", + " 'the models presented in the publication by Seltmann et al, see'\n", + " ' the methods section there.')\n", + "\n", + " pdf.set_font('helvetica', size = 11, style='B')\n", + " pdf.multi_cell(190, 5, text=reminder, align='C')\n", + "\n", + " out_path = out_path / f'{run.info.run_id}_QC_report.pdf'\n", + " pdf.output(out_path)\n", + "\n", + "# Below are templates for the function definitions for the export\n", + "# of pdf summaries for training and qc. You will need to adjust these functions\n", + "# with the variables and other parameters as necessary to make them\n", + "# work for your project\n", + "\n", + "\n", + "print(\"Depencies installed and imported.\")\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EPOJkyFYiA15" + }, + "source": [ + "# **2. Initialise the Colab session**\n", + "---\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8dvLrwF_iEXS" + }, + "source": [ + "\n", + "## **2.1. Check for GPU access**\n", + "---\n", + "\n", + "By default, the session should be using Python 3 and GPU acceleration, but it is possible to ensure that these are set properly by doing the following:\n", + "\n", + "Go to **Runtime -> Change the Runtime type**\n", + "\n", + "**Runtime type: Python 3** *(Python 3 is programming language in which this program is written)*\n", + "\n", + "**Accelerator: GPU** *(Graphics processing unit)*\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8o_-wbDOiIHF", + "outputId": "97646346-d727-455d-b4ab-7fec48b15d44" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You have GPU access\n", + "Sun Nov 12 15:54:57 2023 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 43C P0 26W / 70W | 309MiB / 15360MiB | 1% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n", + "Tensorflow version is 2.14.0\n" + ] + } + ], + "source": [ + "#@markdown ##Run this cell to check if you have GPU access\n", + "# %tensorflow_version 1.x\n", + "\n", + "if tf.test.gpu_device_name()=='':\n", + " print('You do not have GPU access.')\n", + " print('Did you change your runtime ?')\n", + " print('If the runtime settings are correct then Google did not allocate GPU to your session')\n", + " print('Expect slow performance. To access GPU try reconnecting later')\n", + "\n", + "else:\n", + " print('You have GPU access')\n", + " !nvidia-smi\n", + "\n", + "from tensorflow.python.client import device_lib\n", + "device_lib.list_local_devices()\n", + "# print the tensorflow version\n", + "print('Tensorflow version is ' + str(tf.__version__))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kEyJvvxSiN6L" + }, + "source": [ + "## **2.2. Mount your Google Drive**\n", + "---\n", + " To use this notebook on the data present in your Google Drive, you need to mount your Google Drive to this notebook.\n", + "\n", + " Play the cell below to mount your Google Drive and follow the link. In the new browser window, select your drive and select 'Allow', copy the code, paste into the cell and press enter. This will give Colab access to the data on the drive.\n", + "\n", + " Once this is done, your data are available in the **Files** tab on the top left of notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WWVR1U5tiM9h", + "outputId": "db84fbb1-4edb-4145-991b-44d4546af55e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/gdrive\n" + ] + } + ], + "source": [ + "#@markdown ##Run this cell to connect your Google Drive to Colab\n", + "\n", + "#@markdown * Click on the URL.\n", + "\n", + "#@markdown * Sign in your Google Account.\n", + "\n", + "#@markdown * Copy the authorization code.\n", + "\n", + "#@markdown * Enter the authorization code.\n", + "\n", + "#@markdown * Click on \"Files\" site on the right. Refresh the site. Your Google Drive folder should now be available here as \"drive\".\n", + "\n", + "#mounts user's Google Drive to Google Colab.\n", + "\n", + "from google.colab import drive\n", + "drive.mount('/content/gdrive')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6zv2yWb5QM4I" + }, + "source": [ + "## **2.3. (Optional) Download connected Datasets to your Google Drive**\n", + "---\n", + " The following 4 datasets are connected to this notebook. Click on the checkbox and execute the cell to download the data to Google Drive (the top directory).\n", + "\n", + " **Please check that you have enough disk space available!**. Note that you need around double the disk space in your Google Drive as the size estimates below, since the `.zip` archives need to be unpacked. They are automatically deleted, but if disk space fills up, it is wise to double-check that the `.zip` archives are permanently deleted.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VQgsQ5ncQ-2I", + "outputId": "1449d5f7-5950-4f48-e5b4-7ea5cc37cf86" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:__main__:Downloading simulated train data from Zenodo... Target directory /content/gdrive/MyDrive/unet-for-fcs/data/2020-11-FCS-peak-artifacts-dataset-train-split already exists. Skipping Download\n", + "DEBUG:__main__:Downloading simulated validation data from Zenodo... Target directory /content/gdrive/MyDrive/unet-for-fcs/data/2020-11-FCS-peak-artifacts-dataset-validation-split already exists. Skipping Download\n", + "DEBUG:__main__:Downloading simulated test data from Zenodo... Target directory /content/gdrive/MyDrive/unet-for-fcs/data/2020-11-FCS-peak-artifacts-dataset-test-split already exists. Skipping Download\n", + "DEBUG:__main__:Downloading TCSPC records without artifacts (Hs-PEX5-eGFP) from Zenodo... Target directory /content/gdrive/MyDrive/unet-for-fcs/data/2019-11-FCS-TCSPC-no-artifacts-PEX5-primary-data already exists. Skipping Download\n", + "DEBUG:__main__:Downloading TCSPC records with peak artifacts (Tb-PEX5-eGFP) from Zenodo... Target directory /content/gdrive/MyDrive/unet-for-fcs/data/2019-11-FCS-TCSPC-peak-artifacts-PEX5-primary-data already exists. Skipping Download\n", + "DEBUG:__main__:Downloading mlflow models from Zenodo... Target directory /content/gdrive/MyDrive/unet-for-fcs/mlruns already exists. Skipping Download\n" + ] + } + ], + "source": [ + "# @title { display-mode: \"form\" }\n", + "#@markdown Fluorescence correlation spectroscopy time-series data with and\n", + "#@markdown without peak artifacts - simulated training and validation data -\n", + "#@markdown **7.4 GB** ([link](https://zenodo.org/records/8074408))\n", + "download_train_and_validation_data = True # @param {type:\"boolean\"}\n", + "save_train_and_validation_to = \"/content/gdrive/MyDrive/unet-for-fcs/data\" # @param {type:\"string\"}\n", + "#@markdown Fluorescence correlation spectroscopy time-series data with and\n", + "#@markdown without peak artifacts - simulated test data - **3.7 GB** -\n", + "#@markdown ([link](https://zenodo.org/records/8074408))\n", + "download_test_data = True # @param {type:\"boolean\"}\n", + "save_test_to = \"/content/gdrive/MyDrive/unet-for-fcs/data\" # @param {type:\"string\"}\n", + "#@markdown Fluorescence correlation spectroscopy TCSPC data with and without\n", + "#@markdown peak artifacts - AlexaFluor 488 applied experiment\n", + "#@markdown ([link](https://zenodo.org/records/8082558)) - **12.1 GB** - for\n", + "#@markdown applying a trained model in [Section 6](#scrollTo=fB8QNLekkCyZ)\n", + "download_af488_data = False # @param {type:\"boolean\"}\n", + "save_af488_to = \"/content/gdrive/MyDrive/unet-for-fcs/data\" # @param {type:\"string\"}\n", + "#@markdown Fluorescence correlation spectroscopy TCSPC data with and without\n", + "#@markdown peak artifacts - PEX5 applied experiment\n", + "#@markdown ([link](https://zenodo.org/records/8109282)) - **0.6 GB** - for\n", + "#@markdown applying a trained model in [Section 6](#scrollTo=fB8QNLekkCyZ)\n", + "download_pex5_data = True # @param {type:\"boolean\"}\n", + "save_pex5_to = \"/content/gdrive/MyDrive/unet-for-fcs/data\" # @param {type:\"string\"}\n", + "#@markdown Neural network informed photon filtering reduces artifacts in\n", + "#@markdown fluorescence correlation spectropscopy data - mlflow records\n", + "#@markdown ([link](https://zenodo.org/records/8137129)) - **0.7 GB** - **These\n", + "#@markdown are the published, already trained U-Net models. The best performing\n", + "#@markdown model (which is displayed in the connected publication) has the ID\n", + "#@markdown `0cd2023eeaf745aca0d3e8ad5e1fc653` or `0cd20` for short.**\n", + "download_mlflow_models = True # @param {type:\"boolean\"}\n", + "save_mlflow_models_to = \"/content/gdrive/MyDrive/unet-for-fcs/\" # @param {type:\"string\"}\n", + "\n", + "\n", + "def download_unzip_tidy_up(url, filename):\n", + " \"\"\"See https://stackoverflow.com/a/63831344\"\"\"\n", + "\n", + " r = requests.get(url, stream=True)\n", + " if r.status_code != 200:\n", + " r.raise_for_status() # Will only raise for 4xx codes, so...\n", + " raise RuntimeError(f\"Request to {url} returned status code {r.status_code}\")\n", + " file_size = int(r.headers.get('Content-Length', 0))\n", + "\n", + " path = Path(filename).expanduser().resolve()\n", + " path.parent.mkdir(parents=True, exist_ok=True)\n", + "\n", + " desc = \"(Unknown total file size)\" if file_size == 0 else \"\"\n", + " r.raw.read = functools.partial(r.raw.read, decode_content=True) # Decompress if needed\n", + " with tqdm.wrapattr(r.raw, \"read\", total=file_size, desc=desc) as r_raw:\n", + " with path.open(\"wb\") as f:\n", + " shutil.copyfileobj(r_raw, f)\n", + " log.debug('Unpacking zip file...')\n", + " shutil.unpack_archive(filename=path, extract_dir=path.parent)\n", + " log.debug('Cleaning up...')\n", + " with path.open('wb') as f:\n", + " # dirty hack to circumvent large .zip files accumulating in the Gdrive bin\n", + " f.write(b'')\n", + " if path.is_file():\n", + " os.remove(path)\n", + " elif path.is_dir():\n", + " shutil.rmtree(path)\n", + " return path\n", + "\n", + "paths = []\n", + "if download_train_and_validation_data:\n", + " paths.extend([\n", + " ('simulated train data', f'{save_train_and_validation_to}', 'https://ze'\n", + " 'nodo.org/records/8074408/files/2020-11-FCS-peak-artifacts-dataset-trai'\n", + " 'n-split.zip'),\n", + " ('simulated validation data', f'{save_train_and_validation_to}', 'https'\n", + " '://zenodo.org/records/8074408/files/2020-11-FCS-peak-artifacts-dataset'\n", + " '-validation-split' '.zip')\n", + " ])\n", + "\n", + "if download_test_data:\n", + " paths.extend([\n", + " ('simulated test data', f'{save_test_to}', 'https://zenodo.org/records/'\n", + " '8074408/files/2020-11-FCS-peak-artifacts-dataset-test-split.zip'),\n", + " ])\n", + "\n", + "if download_af488_data:\n", + " paths.extend([\n", + " ('TCSPC records without artifacts (AF488)', f'{save_af488_to}', 'https:'\n", + " '//zenodo.org/records/8082558/files/2019-11-FCS-TCSPC-no-artifacts-AF48'\n", + " '8-primary-data.zip'),\n", + " ('TCSPC records with peak artifacts (AF488 + DiO-LUVs)',\n", + " f'{save_af488_to}', 'https://zenodo.org/records/8082558/files/2019-11-'\n", + " 'FCS-TCSPC-peak-artifacts-AF488-and-DiO-LUVs-primary-data.zip')\n", + " ])\n", + "\n", + "if download_pex5_data:\n", + " paths.extend([\n", + " ('TCSPC records without artifacts (Hs-PEX5-eGFP)', f'{save_pex5_to}',\n", + " 'https://zenodo.org/records/8109282/files/2019-11-FCS-TCSPC-no-artifac'\n", + " 'ts-PEX5-primary-data.zip'),\n", + " ('TCSPC records with peak artifacts (Tb-PEX5-eGFP)', f'{save_pex5_to}',\n", + " 'https://zenodo.org/records/8109282/files/2019-11-FCS-TCSPC-peak-artif'\n", + " 'acts-PEX5-primary-data.zip')\n", + " ])\n", + "\n", + "if download_mlflow_models:\n", + " paths.extend([\n", + " ('mlflow models', f'{save_mlflow_models_to}', 'https://zenodo.org/recor'\n", + " 'ds/8137129/files/mlruns.zip')\n", + " ])\n", + "\n", + "for p in paths:\n", + " filename = Path(p[1]) / Path(p[2]).name\n", + " folder = Path(p[1]) / Path(p[2]).stem\n", + " if folder.exists():\n", + " log.debug('Downloading %s from Zenodo... Target directory %s already '\n", + " 'exists. Skipping Download', p[0], folder)\n", + " continue\n", + "\n", + " log.debug('Downloading %s from Zenodo to %s', p[0], filename)\n", + " download_unzip_tidy_up(url=p[2], filename=filename)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jVGckx7ojEP2" + }, + "source": [ + "## **2.4. (Optional) Search /content/ directory for mlflow experiments**\n", + "---\n", + " This notebooks logs all trained models with mlflow (https://mlflow.org). This ensures that all models are saved in a structured way together with all important metadata. Each model belongs to a *mlflow run*, which in turn belongs to a *mlflow experiment*. They are saved with the following folder structure:\n", + "\n", + "- **mlruns/** → folder name, given by you\n", + " - **0/** → `experiment_id`, given by mlflow\n", + " - *meta.yaml* → experiment metadata file\n", + " - **0cd2023eeaf745aca0d3e8ad5e1fc653/** → `run_id`, given by mlflow\n", + " - *meta.yaml* → run metadata file\n", + " - **artifacts/** → here, your model, performance plots, and other miscellaneous metadata are stored\n", + " - **metrics/** → training and validation metrics\n", + " - **params/** → model and training (hyper-)parameters\n", + " - **ff67be0b68e540a9a29a36a2d0c7a5be** → another run\n", + " - ...\n", + "\n", + " **All models which you want to use in this notebook should be valid mlflow models**. If you use prior published models, make sure that you copy the whole folder to your Google Drive (here: `mlruns/`, it is also important that all *meta.yaml* files are present!).\n", + "\n", + " **Note: Re-Execute this cell to search for newly-added models, if they do not show up in other sections.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295, + "referenced_widgets": [ + "166686b860714378987a74bb1e6461a5", + "e408b7d616974146a15381310530a862", + "4d1f1318f6124231b7eed4d20302cc30", + "bb3b3e67ff1c484294f7e41dfb4b7917", + "a3290f6617c34ab5b154b0eb8049dc11", + "4ab482f9c0b44703a551240f7226e93e", + "50a8cf5657e54f3690042bccfa4ad42a", + "cfbd226d1cac4bf581a3c129b0d5a80d", + "6cc50affb00840afaaaa9dbbbcdce2c2", + "3713d61acd0145a5aca7ee38056e1770", + "bdc2829bf3b442e99f272dec592fe4de" + ] + }, + "id": "C2-nD03olfmq", + "outputId": "335f2021-182b-4f05-ddfa-a08988c1ea4f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--------------- Found experiments: ------------------------\n", + "[\n", + " \n", + "]\n", + "--------------- Found runs: --------------------------------\n", + "Found run 34a6d207ac594035b1009c330fb67a65, created 2022-03-03 14:16:13.734000\n", + "Found run 347669d050f344ad9fb9e480c814f727, created 2022-03-01 19:46:47.199000\n", + "Found run ff67be0b68e540a9a29a36a2d0c7a5be, created 2022-03-01 00:40:21.379000\n", + "Found run 0cd2023eeaf745aca0d3e8ad5e1fc653, created 2022-02-28 14:49:23.179000\n", + "\n", + "--------------- Found runs with logged models: ---------------\n", + "Found model unet_depth3 with scaler l2 from run 34a6d207ac594035b1009c330fb67a65 at artifact_path=PosixPath('/content/gdrive/MyDrive/unet-for-fcs/mlruns/10/34a6d207ac594035b1009c330fb67a65/artifacts')\n", + "Found model unet_depth5 with scaler robust from run 347669d050f344ad9fb9e480c814f727 at artifact_path=PosixPath('/content/gdrive/MyDrive/unet-for-fcs/mlruns/10/347669d050f344ad9fb9e480c814f727/artifacts')\n", + "Found model unet_depth5 with scaler minmax from run ff67be0b68e540a9a29a36a2d0c7a5be at artifact_path=PosixPath('/content/gdrive/MyDrive/unet-for-fcs/mlruns/10/ff67be0b68e540a9a29a36a2d0c7a5be/artifacts')\n", + "Found model unet_depth6 with scaler quant_g from run 0cd2023eeaf745aca0d3e8ad5e1fc653 at artifact_path=PosixPath('/content/gdrive/MyDrive/unet-for-fcs/mlruns/10/0cd2023eeaf745aca0d3e8ad5e1fc653/artifacts')\n", + "\n", + "--------------- Found runs with logged metrics: ---------------\n" + ] + } + ], + "source": [ + "#@title { display-mode: \"form\" }\n", + "#@markdown ## Run this cell to search the `/content/` directory, including your\n", + "#@markdown ## Google Drive if mounted, for mlflow models\n", + "#@markdown It will print all valid experiments, runs, and models. If at least\n", + "#@markdown one is found, you can use them in later sections for finetuning\n", + "#@markdown ([Section 4](#scrollTo=GyRjBdClimfK)), model evaluation\n", + "#@markdown ([Section 5](#scrollTo=1Tm3aimXjZ1B)), or model application\n", + "#@markdown ([Section 6](#scrollTo=fB8QNLekkCyZ))\n", + "\n", + "\n", + "paths, exps, runs, models, clients = get_all_mlflow_models()\n", + "clear_output()\n", + "print('--------------- Found experiments: ------------------------')\n", + "pprint(exps)\n", + "\n", + "print('--------------- Found runs: --------------------------------')\n", + "for name, mtime in zip([r.info.run_id for r in runs],\n", + " [datetime.utcfromtimestamp(r.info.end_time / 1000) for r in runs]):\n", + " print(f'Found run {name}, created {mtime}')\n", + "\n", + "print('\\n--------------- Found runs with logged models: ---------------')\n", + "for run, (model, scaler, artifact_path) in models.items():\n", + " print(f'Found model {model.name} with scaler {scaler} from run {run}'\n", + " f' at {artifact_path=}')\n", + "\n", + "print('\\n--------------- Found runs with logged metrics: ---------------')\n", + "for run in [r for r in runs if r.data.metrics]:\n", + " print(f'Found metrics for run {run.info.run_id}')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jKaeBnSuifZn" + }, + "source": [ + "# **3. Prepare training data**\n", + "\n", + "---\n", + "\n", + " For 1D U-Net for FCS the training data can be obtained in two ways:\n", + "\n", + "- a) Simulating new training data ([Section 3.0.](#scrollTo=hMZpkgEDKA5n))\n", + "- b) Download a published training dataset to your Google Drive ([Section 2.3.](#scrollTo=6zv2yWb5QM4I))\n", + "\n", + " Then, load the dataset into memory ([Section 3.1.](#scrollTo=UlZbSxToDlnM))\n", + "\n", + " Multiple paired FCS time-series can be simulated with the same base simulation parameters. These are saved together in one `.csv` file. Each `.csv` file has the following structure:\n", + "\n", + "| | | | | | | |\n", + "|-----------------|----------------------|------------------|-----------------|----------------------|------------------|-----|\n", + "| < header > | 10-12 lines | | | | | |\n", + "| | contains metadata | | | | | |\n", + "| < source 1 > | < target 1a > | < target 1b > | < source 2 > | < target 2a > | < target 2b > | ... |\n", + "| FCS time-series | Artifact time-series | FCS time-series | FCS time-series | Artifact time-series | FCS time-series | ... |\n", + "| with artifact | | without artifact | with artifact | | without artifact | ... |\n", + "| ... | ... | ... | ... | ... | ... | ... |\n", + "\n", + " The code in this notebook automatically scans for all `.csv` files in a directory and its sub-directories. It loads them, and separates `
`, ``, and ``. **Note: for this notebook, only `< target #a >` (the artifact time-series) is relevant - it is converted into the segmentation mask**. The other targets are discarded." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hMZpkgEDKA5n" + }, + "source": [ + "## **(Optional) 3.0. Simulate new training data**\n", + "---\n", + "\n", + " **The 2 most important simulation parameters are the diffusion constant D and the number of molecules**. For the correction to work on as much data as possible, the model has to be trained on diverse diffusion constants and molecule numbers.\n", + "**This is why at least partially pre-training with the already published dataset is encouraged**. If you do not use the published dataset, make sure to populate a `training` and a `validation` folder with your simulations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "affubmnwJ7Oi", + "outputId": "6915f66b-9005-4512-f78b-4214507a97fd" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:__main__:Successfully created the directory /content/peak_artifact-d0.5-n600\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "num_of_steps 16384\n", + "Processing tracks: [=================== ] 99% complete\n", + "Processing FWHM 250, num_of_steps 16384\n", + "Processing tracks: [============= ] 66% complete\n", + "Processing FWHM 250, " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:__main__:\n", + "Trace 1: Nmol: 600 d_mol: 0.5 Cluster multiplier: 5000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "num_of_steps 16384\n", + "Processing tracks: [=================== ] 99% complete\n", + "Processing FWHM 250, num_of_steps 16384\n", + "Processing tracks: [============= ] 66% complete\n", + "Processing FWHM 250, " + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "DEBUG:__main__:\n", + "Trace 2: Nmol: 600 d_mol: 0.5 Cluster multiplier: 6000\n" + ] + } + ], + "source": [ + "#@title { display-mode: \"form\" }\n", + "# ---------------------- SIMULATING TRAINING DATA------------------------------\n", + "def brownian_only_numpy(total_sim_time, time_step, num_of_mol, D, width,\n", + " height):\n", + " \"\"\"Simulate brownian motion / random walk of a given number of molecules\n", + "\n", + " Parameters\n", + " ----------\n", + " total_simulation_time : int\n", + " Total simulation time in ms.\n", + " time_step : int\n", + " The duration of each time step ms.\n", + " num_mol : int\n", + " The number of molecules in the simulation.\n", + " D : float\n", + " The diffusion rate in {mu m^2}{s}\n", + " width : int\n", + " The width of the simulation area\n", + " height : int\n", + " The height of the simulation area\n", + "\n", + " Returns\n", + " -------\n", + " track_arr : dict of list of numpy arrays\n", + " A dictionary where each track number (e.g. track_arr[0]) contains the\n", + " track data with y-coordinates [0,:] and x-coordinates [1,:]\n", + "\n", + " Notes\n", + " -----\n", + " - This code copies functions of Dominic Waithe's nanosimpy module\n", + " https://github.com/dwaithe/nanosimpy\n", + " \"\"\"\n", + "\n", + " # Number of steps.\n", + " num_of_steps = int(round(float(total_sim_time) / float(time_step), 0))\n", + "\n", + " print('num_of_steps', num_of_steps)\n", + " # Calculates length scales\n", + " scale_in = np.sqrt(2.0 * (float(D) * 1e3) * float(time_step))\n", + "\n", + " # Randomly generates start locations\n", + " start_coord_x = (np.random.uniform(0.0, 1.0, num_of_mol)) * width\n", + " start_coord_y = (np.random.uniform(0.0, 1.0, num_of_mol)) * height\n", + "\n", + " track_arr = {}\n", + " # This can be done as one big matrix, but can crash system if large so\n", + " # I break it up by molecule.\n", + " for b in range(0, num_of_mol):\n", + " per = int((float(b) / float(num_of_mol)) * 100)\n", + " sys.stdout.write(\"\\rProcessing tracks: [{:20}] {}% complete\".format(\n", + " '=' * int(per / 5), per))\n", + " sys.stdout.flush()\n", + " track = np.zeros((2, num_of_steps))\n", + " track[0, 0] = start_coord_y[b]\n", + " track[1, 0] = start_coord_x[b]\n", + " rand_in = scipy.stats.norm.rvs(size=[2, num_of_steps]) * scale_in\n", + " track[:, 1:] += rand_in[:, 1:]\n", + " track = np.cumsum(track, 1)\n", + " out = track\n", + " mod = np.zeros((out.shape))\n", + " mod[0, :] = np.floor(track[0, :].astype(np.float64) / height)\n", + " mod[1, :] = np.floor(track[1, :].astype(np.float64) / width)\n", + " track_arr[b] = np.array(out - ([mod[0, :] * height, mod[1, :] * width]))\n", + "\n", + " # We go through and make sure our particles wrap around.\n", + " # for b in range(0,num_of_mol):\n", + " # print 'wrapping tracks: ', (float(b) / float(num_of_mol)) * 100, '%'\n", + " # bool_to_adapt = (track_arr[b][0, :] -\n", + " # offset)**2 + (track_arr[b][1, :] - offset)**2 >= R2\n", + " # while np.sum(bool_to_adapt) > 0:\n", + " # ind = np.argmax(bool_to_adapt > 0)\n", + " # phi = np.arctan2((track_arr[b][0, ind] - offset),\n", + " # (track_arr[b][1, ind] - offset))\n", + " # track_arr[b][1, ind:] = np.round(\n", + " # ((track_arr[b][1, ind:] - offset) -\n", + " # (2.0 * (R - 2) * np.cos(phi))) + offset, 0).astype(np.int32)\n", + " # track_arr[b][0, ind:] = np.round(\n", + " # ((track_arr[b][0, ind:] - offset) -\n", + " # (2.0 * (R - 2) * np.sin(phi))) + offset, 0).astype(np.int32)\n", + " # bool_to_adapt = (track_arr[b][0, :] - offset)**2 + (\n", + " # track_arr[b][1, :] - offset)**2 >= R2\n", + " return track_arr\n", + "\n", + "\n", + "def integrate_over_psf(psf, track_arr, num_of_mol, psy, psx):\n", + " \"\"\"Pass an array of Brownian motion tracks through the PSF function\n", + "\n", + " Parameters\n", + " ----------\n", + " psf : dict\n", + " 'FWHMs' : list of ints\n", + " 'pixel_size' : 1.0\n", + " 'ri' : dict of lists of ints\n", + " Length values of simulated PSF\n", + " 'number_FWHMs' : int\n", + " 'V' : dict of lists of float\n", + " Intensity values of simulated PSF at length 'ri'\n", + " track_arr : dict of lists of numpy arrays\n", + " A dictionary where each track number (e.g. track_arr[0]) contains the\n", + " track data with y-coordinates [0,:] and x-coordinates [1,:]\n", + " num_of_mol : int\n", + " The number of molecules in the simulation.\n", + " psy, psx : int\n", + " The location of the focal volume in the simulated area.\n", + "\n", + " Returns\n", + " -------\n", + " psf : dict\n", + " 'FWHMs' : list of ints\n", + " 'pixel_size' : 1.0\n", + " 'ri' : dict of lists of ints\n", + " Length values of simulated PSF\n", + " 'number_FWHMs' : int\n", + " 'V' : dict of lists of float\n", + " Intensity values of simulated PSF at length 'ri'\n", + " 'trace' : dict of lists of floats\n", + " Gives non-physical relative intensity values (= emitted photons)\n", + " for each molecule during its track through the simulated area.\n", + "\n", + " Notes\n", + " -----\n", + " - basic algorithm for each molecule:\n", + " 1. Get Euclidian distances from each position of the molecule to the\n", + " location of the focal volume\n", + " 2. Choose relative intensity value for each position of the molecule\n", + " according to the simulated psf from `calculate_psf`\n", + " - This code copies functions of Dominic Waithe's nanosimpy module\n", + " https://github.com/dwaithe/nanosimpy\n", + " \"\"\"\n", + " psf['trace'] = {}\n", + " sys.stdout.write('\\n')\n", + " for ki in range(0, psf['number_FWHMs']):\n", + " sys.stdout.write(\"\\rProcessing FWHM {}, \".format(psf['FWHMs'][ki]))\n", + " sys.stdout.flush()\n", + " trace = 0\n", + " for b in range(0, num_of_mol):\n", + " b_dist = np.round(\n", + " np.sqrt((track_arr[b][1] - psx)**2 +\n", + " (track_arr[b][0] - psy)**2), 0).astype(np.int32)\n", + " b_trace = psf['V'][ki][b_dist]\n", + " trace += b_trace\n", + " psf['trace'][ki] = copy.deepcopy(trace)\n", + " return psf\n", + "\n", + "\n", + "def calculate_psf(fwhms, distance):\n", + " \"\"\"Calculates Gaussian of particular FWHM\n", + "\n", + " Parameters\n", + " ----------\n", + " fwhms : list of ints\n", + " List of Full Width Half Maximum (FWHMs) of Point Spread Functions\n", + " (PSFs) of excitation laser to simulate particles under a fluorescence\n", + " microscope\n", + " distance : int\n", + " Length of simulated PSF (from maximum radially outwards).\n", + "\n", + " Returns\n", + " -------\n", + " psf : dict\n", + " 'FWHMs' : list of ints\n", + " 'pixel_size' : 1.0\n", + " 'ri' : dict of lists of ints\n", + " Length values of simulated PSF\n", + " 'number_FWHMs' : int\n", + " 'V' : dict of lists of floats\n", + " Intensity values of simulated PSF at length 'ri'\n", + "\n", + " Notes\n", + " -----\n", + " - This code copies functions of Dominic Waithe's nanosimpy module\n", + " https://github.com/dwaithe/nanosimpy\n", + " - derivation of G:\n", + " # FWHM to sigma conversion\n", + " FWHM = 2*np.sqrt(2*np.log(2))*sigma\n", + "\n", + " # Sigma from FWHM\n", + " sigma = FWHM/(2*np.sqrt(2*np.log(2)))\n", + "\n", + " # is conventional Gaussian\n", + " G = np.exp(-x**2/(2*sigma**2))\n", + "\n", + " # substitute FWHM for sigma\n", + " G = np.exp(-x**2/(2*(FWHM/(2*np.sqrt(2*np.log(2))))**2))\n", + "\n", + " # open the brackets and square contents\n", + " G = np.exp(-x**2/(2*(FWHM**2/(4*2*np.log(2)))))\n", + "\n", + " # decompose fraction\n", + " G = np.exp((-x**2/(2*(FWHM**2)/8.))*(np.log(2)))\n", + "\n", + " # power law decomposition\n", + " G = np.exp((np.log(2.)))**(-x**2/((FWHM**2)/4.0))\n", + "\n", + " # e^(ln2) = 2 indentity.\n", + " G = 2.**(-x**2/(FWHM/2.0)**2)\n", + " \"\"\"\n", + "\n", + " psf = {}\n", + " psf['FWHMs'] = fwhms\n", + " psf['pixel_size'] = 1.0\n", + " psf['ri'] = np.meshgrid(np.arange(0, distance, psf['pixel_size']))[0]\n", + " psf['number_FWHMs'] = psf['FWHMs'].__len__()\n", + " psf['V'] = {}\n", + " for ki in range(0, psf['number_FWHMs']):\n", + " psf['V'][ki] = 2.0**(-psf['ri']**2 / (psf['FWHMs'][ki] / 2.0)**2)\n", + " return psf\n", + "\n", + "\n", + "def simulate_trace_array(artifact,\n", + " nsamples,\n", + " foci_array,\n", + " foci_distance,\n", + " total_sim_time,\n", + " time_step,\n", + " nmol,\n", + " d_mol,\n", + " width,\n", + " height,\n", + " nclust=None,\n", + " d_clust=None,\n", + " label_for='none'):\n", + " \"\"\"Simulate a fluorescence trace using the nanosimpy package and\n", + " introduce artifacts\n", + "\n", + " Parameters\n", + " ----------\n", + " artifact : {'none', 'peak_artifact', 'detector_dropout', 'photobleaching'}\n", + " Artifact to simulate.\n", + " nsamples : int\n", + " Number of training examples to generate\n", + " foci_array : np.array\n", + " Array of FWHMs in nm of the excitation PSFs used for the foci detection\n", + " foci_distance : int\n", + " Extent of simulated PSF (distance to center of Gaussian)\n", + " total_sim_time : int\n", + " Total simulation time in ms\n", + " time_step : int\n", + " Duration of each time step in ms\n", + " nmol : int\n", + " Number of fastly diffusing molecules\n", + " d_mol : float\n", + " Diffusion rate of fastly diffusing molecules\n", + " width : int\n", + " Width of the simulation in ...\n", + " height : int\n", + " Height of the simulation in ...\n", + " nclust : int, optional\n", + " Number of bright slowly diffusing clusters (only for artifact = 1)\n", + " d_clust float, optional\n", + " Diffusion rate of slowly diffusing clusters (only for artifact = 1)\n", + " label_for : {'none', 'unet', 'vae', 'both'}, optional\n", + " 'none' = no label is saved out\n", + " 'unet' = classification or segmentation (only artifact is\n", + " label, standard)\n", + " 'vae' = variational autoencoder (only clean trace is label)\n", + " 'both' = artifact trace and clean trace will both be saved (see\n", + " Returns)\n", + "\n", + " Returns\n", + " -------\n", + " out_array : np.array\n", + " if label_for is 'none':\n", + " fluorescence traces as clumns (trace A, trace B, ...)\n", + " if label_for is 'unet' OR 'vae':\n", + " fluorescence traces and labels as columns (trace A,\n", + " label A, trace B, label B, ...)\n", + " if label_for is 'both':\n", + " fluorescence traces and labesl as columns (trace A, label A1,\n", + " label A2, trace B, label B1, label B2)\n", + "\n", + " Notes\n", + " -----\n", + " - CC-BY Alex Seltmann https://aseltmann.github.io\n", + " \"\"\"\n", + " def _simulate_bright_clusters(psf,\n", + " pos_x,\n", + " pos_y,\n", + " total_sim_time=total_sim_time,\n", + " time_step=time_step,\n", + " nclust=nclust,\n", + " d_clust=d_clust,\n", + " width=width,\n", + " height=height):\n", + " clust_brightness = rng.integers(5, 10) * 1000\n", + " # simulate brownian motion of slow clusters\n", + " track_clust = brownian_only_numpy(\n", + " total_sim_time=total_sim_time,\n", + " time_step=time_step,\n", + " num_of_mol=nclust,\n", + " D=d_clust,\n", + " width=width,\n", + " height=height,\n", + " )\n", + " out_clust = integrate_over_psf(\n", + " psf=copy.deepcopy(psf),\n", + " track_arr=track_clust,\n", + " num_of_mol=nclust,\n", + " psy=pos_y,\n", + " psx=pos_x,\n", + " )\n", + " clust_trace = out_clust[\"trace\"][0]\n", + " return clust_trace, clust_brightness\n", + "\n", + " def _simulate_detector_dropout(clean_trace):\n", + " num_of_dropouts = rng.integers(50)\n", + " # simulate detector dropout\n", + " detdrop_mask = np.zeros(clean_trace.shape[0])\n", + " for _ in range(num_of_dropouts):\n", + " length_of_dropout = rng.integers(25)\n", + " start = int(rng.random() * clean_trace.shape[0])\n", + " end = int(start + length_of_dropout)\n", + " for mid in range(end - start):\n", + " depth_of_dropout = rng.random()\n", + " detdrop_mask[start + mid:start + mid +\n", + " 1] = (-np.mean(clean_trace)) * depth_of_dropout\n", + "\n", + " return detdrop_mask\n", + "\n", + " def _simulate_photobleaching(track_arr,\n", + " psf,\n", + " pos_x,\n", + " pos_y,\n", + " total_sim_time=total_sim_time,\n", + " time_step=time_step,\n", + " nmol=nmol,\n", + " width=width,\n", + " height=height):\n", + " d_immobile = 0.001\n", + " exp_scale_rand = rng.integers(20) * 0.01\n", + " # scales between 0.01 and 0.2 seem to work nicely for a distribution\n", + " # of total_sim_time=20000. if other simulation times are used, this\n", + " # number has to be reevaluated lower scale means faster bleaching,\n", + " # higher scale means slower bleaching\n", + " bleach_dist = rng.exponential(scale=exp_scale_rand, size=nmol)\n", + " bleach_times = bleach_dist * total_sim_time\n", + " bleach_times = np.clip(bleach_times, a_min=0, a_max=total_sim_time)\n", + " # simulate brownian motion of mobilized and immobilized molecules\n", + " track_arr_immob = brownian_only_numpy(total_sim_time=total_sim_time,\n", + " time_step=time_step,\n", + " num_of_mol=nmol,\n", + " D=d_immobile,\n", + " width=width,\n", + " height=height)\n", + " track_arr_mob = copy.deepcopy(track_arr)\n", + "\n", + " # do photobleaching\n", + " for idx, dropout_idx in zip(range(nmol), bleach_times):\n", + " # set fluorescence of each molecule to zero starting from bleach\n", + " # time for each respective molecule\n", + " track_tmp_mob = track_arr_mob[idx]\n", + " track_tmp_immob = track_arr_immob[idx]\n", + " track_tmp_mob[:, int(dropout_idx):] = 0\n", + " track_tmp_immob[:, int(dropout_idx):] = 0\n", + " ibleach_trace = integrate_over_psf(psf=copy.deepcopy(psf),\n", + " track_arr=track_arr_immob,\n", + " num_of_mol=nmol,\n", + " psy=pos_y,\n", + " psx=pos_x)\n", + " mbleach_trace = integrate_over_psf(psf=copy.deepcopy(psf),\n", + " track_arr=track_arr_mob,\n", + " num_of_mol=nmol,\n", + " psy=pos_y,\n", + " psx=pos_x)\n", + " ibleach_trace = ibleach_trace['trace'][0]\n", + " mbleach_trace = mbleach_trace['trace'][0]\n", + " return ibleach_trace, mbleach_trace, exp_scale_rand\n", + "\n", + " rng = np.random.default_rng()\n", + "\n", + " psf = calculate_psf(foci_array, foci_distance)\n", + "\n", + " pos_x = width // 2\n", + " pos_y = height // 2\n", + "\n", + " num_of_steps = int(round(float(total_sim_time) / float(time_step), 0))\n", + "\n", + " if label_for == 'none':\n", + " nrows = 1\n", + " elif label_for in ('unet', 'vae'):\n", + " nrows = 2\n", + " elif label_for == 'both':\n", + " nrows = 3\n", + " else:\n", + " raise ValueError('label_for must be in [\"none\", \"unet\", \"vae\", \"both\"]')\n", + "\n", + " out_array = np.zeros((num_of_steps, nsamples * nrows))\n", + "\n", + " for i in range(nsamples):\n", + " # simulate brownian motion of fast molecules\n", + " track_arr = brownian_only_numpy(\n", + " total_sim_time=total_sim_time,\n", + " time_step=time_step,\n", + " num_of_mol=nmol,\n", + " D=d_mol,\n", + " width=width,\n", + " height=height,\n", + " )\n", + " out_clean = integrate_over_psf(\n", + " psf=copy.deepcopy(psf),\n", + " track_arr=track_arr,\n", + " num_of_mol=nmol,\n", + " psy=pos_y,\n", + " psx=pos_x,\n", + " )\n", + " clean_trace = out_clean[\"trace\"][0] * 100\n", + " # add random noise\n", + " clean_trace += rng.random(clean_trace.shape[0]) * 10\n", + " if artifact == 'none':\n", + " out_array[:, i * nrows] = clean_trace\n", + " if label_for == 'unet':\n", + " out_array[:, i * nrows + 1] = np.zeros(clean_trace.shape[0])\n", + " elif label_for == 'vae':\n", + " out_array[:, i * nrows + 1] = clean_trace\n", + " elif label_for == 'both':\n", + " out_array[:, i * nrows + 1] = np.zeros(clean_trace.shape[0])\n", + " out_array[:, i * nrows + 2] = clean_trace\n", + " log.debug('\\nTrace %s: Nmol: %s d_mol: %s', i+1, nmol, d_mol)\n", + " elif artifact == 'peak_artifact':\n", + " clust_trace, clust_brightness = _simulate_bright_clusters(\n", + " psf=psf, pos_x=pos_x, pos_y=pos_y)\n", + " # combine fast and slow molecules\n", + " out_array[:, i * nrows] = (clean_trace +\n", + " clust_trace * clust_brightness)\n", + " # Save labels\n", + " if label_for == 'unet':\n", + " out_array[:, i * nrows + 1] = clust_trace\n", + " elif label_for == 'vae':\n", + " out_array[:, i * nrows + 1] = clean_trace\n", + " elif label_for == 'both':\n", + " out_array[:, i * nrows + 1] = clust_trace\n", + " out_array[:, i * nrows + 2] = clean_trace\n", + " log.debug('\\nTrace %s: Nmol: %s d_mol: %s Cluster multiplier: '\n", + " '%s', i + 1, nmol, d_mol, clust_brightness)\n", + " elif artifact == 'detector_dropout':\n", + " detdrop_mask = _simulate_detector_dropout(clean_trace=clean_trace)\n", + " # combine\n", + " out_array[:, i * nrows] = np.clip((clean_trace + detdrop_mask),\n", + " a_min=0, a_max=None)\n", + " # save labels\n", + " if label_for == 'unet':\n", + " out_array[:, i * nrows + 1] = detdrop_mask\n", + " elif label_for == 'vae':\n", + " out_array[:, i * nrows + 1] = clean_trace\n", + " elif label_for == 'both':\n", + " out_array[:, i * nrows + 1] = detdrop_mask\n", + " out_array[:, i * nrows + 2] = clean_trace\n", + " log.debug('\\nTrace %s: Nmol: %s d_mol: %s max. drop: %.2f',\n", + " i + 1, nmol, d_mol, -np.mean(clean_trace))\n", + " elif artifact == 'photobleaching':\n", + " ibleach_trace, mbleach_trace, exp_scale = _simulate_photobleaching(\n", + " track_arr=track_arr, psf=psf, pos_x=pos_x, pos_y=pos_y)\n", + " # combine all traces for features\n", + " out_array[:, i * nrows] = clean_trace + (ibleach_trace +\n", + " mbleach_trace) * 100\n", + " # combine artefact traces for labels\n", + " if label_for == 'unet':\n", + " out_array[:, i * nrows + 1] = ibleach_trace + mbleach_trace\n", + " elif label_for == 'vae':\n", + " out_array[:, i * nrows + 1] = clean_trace\n", + " elif label_for == 'both':\n", + " out_array[:, i * nrows + 1] = ibleach_trace + mbleach_trace\n", + " out_array[:, i * nrows + 2] = clean_trace\n", + " log.debug('\\nTrace %s: Nmol: %s d_mol: %s scale parameter: %.2f',\n", + " i + 1, nmol, d_mol, exp_scale)\n", + " else:\n", + " raise ValueError('artifact must be in [\"none\", \"peak_artifact\",'\n", + " ' \"detector_dropout\", \"photobleaching\"]')\n", + " return out_array\n", + "\n", + "\n", + "def savetrace_csv(artifact,\n", + " path_and_file_name,\n", + " traces_array,\n", + " col_per_example,\n", + " foci_array,\n", + " foci_distance,\n", + " total_sim_time,\n", + " time_step,\n", + " nmol,\n", + " d_mol,\n", + " width,\n", + " height,\n", + " nclust=None,\n", + " d_clust=None):\n", + " \"\"\"save out a series of simulated fluorescence traces and labels indluding\n", + " metadata of the simulations\n", + "\n", + " Parameters\n", + " ----------\n", + " artifact : {'none', 'peak_artifact', 'detector_dropout', 'photobleaching'}\n", + " For 'peak_artifact' and 'photobleaching', additional metadata is saved out.\n", + " If 'peak_artifact' is chosen, the parameters nclust and d_clust have to be given.\n", + " path_and_file_name : str\n", + " Destination path and file name\n", + " traces_array : np.array\n", + " fluorescence traces and labels as columns (trace A, (label A1,\n", + " label A2,) trace B, (label B1, label B2,) ...)\n", + " col_per_example : int\n", + " Number of columns per example, first column being a trace, and then\n", + " one or multiple labels\n", + " ...\n", + "\n", + " Returns\n", + " -------\n", + " Saves a .csv file\n", + " \"\"\"\n", + " path_and_file_name = Path(path_and_file_name)\n", + " unique = uuid.uuid4()\n", + "\n", + " header = ''\n", + "\n", + " for idx, _ in enumerate(traces_array[0, ::col_per_example], start=1):\n", + " header += '{}_trace_{:0>3},'.format(unique, idx)\n", + " for jdx in range(1, col_per_example):\n", + " header += '{}_label{}_{:0>3},'.format(unique, jdx, idx)\n", + " # Remove trailing comma\n", + " header = header.strip(',')\n", + "\n", + " with open(path_and_file_name, 'w') as my_file:\n", + " my_file.write(f'unique identifier,{unique}\\n')\n", + " my_file.write(f'path and file name,{path_and_file_name}\\n')\n", + " my_file.write(f'FWHMs of excitation PSFs used in nm,{foci_array}\\n')\n", + " my_file.write('Extent of simulated PSF (distance to center of '\n", + " f'Gaussian) in nm,{foci_distance}\\n')\n", + " my_file.write(f'total simulation time in ms,{total_sim_time}\\n')\n", + " my_file.write(f'time step in ms,{time_step}\\n')\n", + " my_file.write(f'number of fast molecules,{nmol}\\n')\n", + " my_file.write('diffusion rate of molecules in micrometer^2 / s,'\n", + " f'{d_mol}\\n')\n", + " my_file.write(f'width of the simulation in nm,{width}\\n')\n", + " my_file.write(f'height of the simulation in nm,{height}\\n')\n", + " if artifact == 'peak_artifact':\n", + " my_file.write(f'number of slow clusters,{nclust}\\n')\n", + " my_file.write('diffusion rate of clusters in micrometer^2 / s,'\n", + " f'{d_clust}\\n')\n", + " elif artifact == 'photobleaching':\n", + " my_file.write('number of bleached molecules (50% immobile and '\n", + " f'50% mobile),{nmol * 2}\\n')\n", + " # comments expects a str. Otherwise it printed a '# ' in first column\n", + " # header and importing that to pandas made it an 'object' dtype which\n", + " # uses a lot of memory\n", + " np.savetxt(my_file,\n", + " traces_array,\n", + " delimiter=',',\n", + " header=header,\n", + " comments='')\n", + "\n", + "\n", + "def example_sim_plot_on_button_clicked(traces):\n", + " button = widgets.Button(description=\"Show new example!\")\n", + " output = widgets.Output()\n", + "\n", + " df = pd.DataFrame(traces)\n", + " df_gen = df.items() # generator without cycle\n", + " df_gen = itertools.cycle(df_gen) # generator with cycle\n", + "\n", + " def make_example_plot(traces_generator=df_gen,\n", + " col_per_example=col_per_example):\n", + " plt.close('all')\n", + " fig, ax = plt.subplots(col_per_example, 1, tight_layout=True,\n", + " figsize=(12,col_per_example*2.5), sharex=True)\n", + "\n", + " _, trace = next(traces_generator)\n", + " plt.suptitle(f'Trace number {int(np.ceil(trace.name // col_per_example) + 1)}',\n", + " fontsize=22)\n", + " if col_per_example == 1:\n", + " sns.lineplot(x=trace.index, y=trace.values, ax=ax\n", + " ).set(title='simulated trace')\n", + " else:\n", + " sns.lineplot(x=trace.index, y=trace.values, ax=ax[0]\n", + " ).set(title='simulated trace')\n", + " for i in range(col_per_example - 1):\n", + " _, label = next(traces_generator)\n", + " if label_for in ['unet', 'vae']:\n", + " title = f'{label_for=}'\n", + " elif label_for == 'both':\n", + " title_dict = {0 : 'label_for=\"both\" - unet label',\n", + " 1 : 'label_for=\"both\" - vae label'}\n", + " title = title_dict[i]\n", + " sns.lineplot(x=label.index, y=label.values, ax=ax[i+1]\n", + " ).set(title=title)\n", + " plt.setp(ax, xlabel='time in ms', ylabel='intensity in a.u.')\n", + " plt.show()\n", + "\n", + "\n", + " def on_button_clicked(b):\n", + " # Display the message within the output widget.\n", + " output.clear_output()\n", + " with output:\n", + " make_example_plot()\n", + "\n", + " button.on_click(on_button_clicked)\n", + " display(button, output)\n", + "\n", + "\n", + "#@markdown ## Output parameters:\n", + "#@markdown Define number of traces to generate. Untick `save_traces_as_csv` to\n", + "#@markdown only view the traces in this notebook, otherwise they will be saved\n", + "#@markdown to `out_path`.\n", + "number_of_traces = 2 #@param {type:\"number\"}\n", + "save_traces_as_csv = True #@param {type:\"boolean\"}\n", + "file_name = \"testtraces\" #@param {type:\"string\"}\n", + "out_path = \"/content/\" #@param {type:\"string\"}\n", + "\n", + "\n", + "#@markdown ---\n", + "#@markdown ## General simulation parameters\n", + "\n", + "#@markdown Duration of each time step in ms. Usually, `time_step = 1`.\n", + "time_step = 1. #@param {type:\"number\"}\n", + "#@markdown Total simulation time in ms. Together with `time_step` this defines the trace length.\n", + "total_sim_time = 16384 #@param {type:\"integer\"}\n", + "\n", + "#@markdown Diffusion coefficients in $\\mu m^2 / s$. This defines the fitted FCS\n", + "#@markdown transit times for parts labeled as non-artifactual. common diffusion\n", + "#@markdown constants for point FCS range from $10^{-3}$ to $10^{2} \\mu m^2 / s$\n", + "d_molecules = 0.5 #@param {type:\"number\"}\n", + "#@markdown Number of molecules. This defines the FCS correlation amplitude height\n", + "#@markdown and thus fitted number of particles for parts labeled as non-artifactual.\n", + "#@markdown From experimence, good values are between `500` and `3000`.\n", + "#@markdown Note that higher `n_molecules` are the main source for longer simulation times.\n", + "n_molecules = 600 #@param {type:\"integer\"}\n", + "\n", + "# Currently commented out to reduce complexity. Will be re-introduced for a\n", + "# dedicated simulation notebook\n", + "# #@markdown Choose which label to save out. `'none'` = no label. Only traces are simulated.\n", + "# #@markdown `'unet'` = for classification or segmentation (only artifact is label).\n", + "# #@markdown `'vae'` = for variational autoencoder (only clean trace is label).\n", + "# #@markdown `'both'` = save out both labels.\n", + "# #@markdown **Note that currently only U-Net training is available in Section 4.\n", + "# #@markdown This training needs simulated data with label `'unet'` or `'both'`.**\n", + "# label_for = \"both\" #@param [\"none\", \"unet\", \"vae\", \"both\"]\n", + "label_for = 'both'\n", + "\n", + "#@markdown ---\n", + "#@markdown ## Peak artifact simulation parameters\n", + "# #@markdown Choose artifact. If `artifact` in `['none', 'detector_dropout', 'photobleaching']`,\n", + "# #@markdown you don't have to set other artifact-specific parameters\n", + "# artifact = \"peak_artifact\" #@param [\"none\", \"peak_artifact\", \"detector_dropout\", \"photobleaching\"]\n", + "artifact = 'peak_artifact'\n", + "\n", + "# #@markdown Only set these values if `artifact = 'peak_artifact'`.\n", + "#@markdown The parameters\n", + "#@markdown are analogous to `d_molecules` and `n_molecules`, but define the\n", + "#@markdown behaviour of slow clusters and thus parts of the trace labeled as\n", + "#@markdown artifactual. Thresholding these cluster traces yields the label information.\n", + "#@markdown The following combinations have worked well so far (`d` and `n`):\n", + "#@markdown `0.01` and `10` | `0.1` and `7` | `1` and `3`.\n", + "\n", + "d_clusters = 1 #@param {type:\"number\"}\n", + "n_clusters = 3 #@param {type:\"integer\"}\n", + "\n", + "\n", + "# hard-code the following simulation parameters\n", + "FOCI_ARRAY = np.array([250])\n", + "FOCI_DISTANCE = 4000\n", + "WIDTH = 3000.0\n", + "HEIGHT = 3000.0\n", + "\n", + "if label_for == 'none':\n", + " col_per_example = 1\n", + "elif label_for in ['unet', 'vae']:\n", + " col_per_example = 2\n", + "elif label_for == 'both':\n", + " col_per_example = 3\n", + "else:\n", + " raise ValueError('label_for has to be in [\"none\", \"unet\", \"vae\", \"both\"')\n", + "\n", + "pdir = Path(out_path) / f'{artifact}-d{d_molecules}-n{n_molecules}'\n", + "\n", + "try:\n", + " os.makedirs(pdir, exist_ok = True)\n", + " log.debug(\"Successfully created the directory %s\", pdir)\n", + "except OSError as exc:\n", + " log.debug(\"Trying to create directory %s cause the error %s\", pdir, exc)\n", + "\n", + "timestamp = datetime.today().isoformat(sep='-', timespec=\"seconds\").replace(':', '')\n", + "path_and_file_name = pdir / f'{timestamp}-{file_name}.csv'\n", + "\n", + "if artifact == 'peak_artifact':\n", + " traces = simulate_trace_array(artifact=artifact,\n", + " nsamples=number_of_traces,\n", + " foci_array=FOCI_ARRAY,\n", + " foci_distance=FOCI_DISTANCE,\n", + " total_sim_time=total_sim_time,\n", + " time_step=time_step,\n", + " nmol=n_molecules,\n", + " d_mol=d_molecules,\n", + " width=WIDTH,\n", + " height=HEIGHT,\n", + " nclust=n_clusters,\n", + " d_clust=d_clusters,\n", + " label_for=label_for)\n", + " if save_traces_as_csv:\n", + " savetrace_csv(artifact=artifact,\n", + " path_and_file_name=path_and_file_name,\n", + " traces_array=traces,\n", + " col_per_example=col_per_example,\n", + " foci_array=FOCI_ARRAY,\n", + " foci_distance=FOCI_DISTANCE,\n", + " total_sim_time=total_sim_time,\n", + " time_step=time_step,\n", + " nmol=n_molecules,\n", + " d_mol=d_molecules,\n", + " width=WIDTH,\n", + " height=HEIGHT,\n", + " nclust=n_clusters,\n", + " d_clust=d_clusters)\n", + "\n", + "elif artifact in ('none', 'detector_dropout', 'photobleaching'):\n", + " traces = simulate_trace_array(artifact=artifact,\n", + " nsamples=number_of_traces,\n", + " foci_array=FOCI_ARRAY,\n", + " foci_distance=FOCI_DISTANCE,\n", + " total_sim_time=total_sim_time,\n", + " time_step=time_step,\n", + " nmol=n_molecules,\n", + " d_mol=d_molecules,\n", + " width=WIDTH,\n", + " height=HEIGHT,\n", + " label_for=label_for)\n", + " if save_traces_as_csv:\n", + " savetrace_csv(artifact=artifact,\n", + " path_and_file_name=path_and_file_name,\n", + " traces_array=traces,\n", + " col_per_example=col_per_example,\n", + " foci_array=FOCI_ARRAY,\n", + " foci_distance=FOCI_DISTANCE,\n", + " total_sim_time=total_sim_time,\n", + " time_step=time_step,\n", + " nmol=n_molecules,\n", + " d_mol=d_molecules,\n", + " width=WIDTH,\n", + " height=HEIGHT)\n", + "else:\n", + " raise ValueError('artifact must be in [\"none\", \"peak_artifact\",'\n", + " ' \"detector_dropout\", \"photobleaching\"]')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 616, + "referenced_widgets": [ + "836599de3af14576b98f9d17fe4b0f8f", + "ff2d50b3707b41b696d635bcdf5b0884", + "9712b857502f4a78a3f8d69b153f69aa", + "9d427820c806445bbebe0e19e9d2204f", + "64bb41a1e0c3482abe35c7e2ce0fa454" + ] + }, + "id": "Y72h-GohOu4d", + "outputId": "67ef76d6-2279-48c7-f295-6ed8c82120eb" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "836599de3af14576b98f9d17fe4b0f8f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Button(description='Show new example!', style=ButtonStyle())" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9d427820c806445bbebe0e19e9d2204f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#@title { display-mode: \"form\" }\n", + "#@markdown ## Run this cell to inspect plots of the simulations\n", + "example_sim_plot_on_button_clicked(traces)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UlZbSxToDlnM" + }, + "source": [ + "## **3.1 Load training and validation data**\n", + "---\n", + "For training, we need **training data** to adjust the model weights and **validation data** to check for overfitting of the model.\n", + "\n", + "After executing the cell below, the following 4 variables contain the training and validation data as pandas DataFrames, all ordered columnwise:\n", + "- `train_source`, `train_target` $\\to$ during training, the model acts as a transformation function from train source (FCS time-series) to the `train_target` (the binary segmentation). The difference between the actual predicted values and the `train_target` is used to update model weights, and to log training metrics\n", + "- `val_source`, `val_target` $\\to$ after each epoch during training, the model is applied to `val_source` (FCS time-series) and tries to predict `val_target` (binary segmentation). The difference between the the predicted values and the `val_target` is used to log validation metrics, which are used together with the training metrics to check for effects such as *overfitting* (see [Section 5.1.](#scrollTo=ULMuc37njkXM&line=1&uniqifier=1)).\n", + "\n", + "**Note: validation is not equal to testing, which has to be done seperately in [Section 5](#scrollTo=1Tm3aimXjZ1B)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "8cb0d3deb8ab437ebb776aa6893d1ac4", + "855da811a95a461bb951bea17b982b63", + "5397126fb9e944d7b6cfe538d50ea3c0", + "a6338f4ca5f7423b99749589458b3ea5", + "91bc571c1a22496b94452ea1368b076e", + "b0ca703d7eaa4be1abeaaf00fe25630b", + "feeb46695b404debaa5d322cea22aa6f", + "9cd0e818af3049a392a9e62cb9a60f5e", + "0939ec84b8e1492eaa342afbed34a011", + "3da3406b3bab45f398df0e21418987aa", + "11d48b5d55d5446da45e6187d0e6d182", + "ba23641e0a5044fb89ddce8f9f3f2ca6", + "9c2477fbcaa8481ebdc27c82fa22ba9c", + "2ce279ea78814639ab5cff16bd0f4c13", + "f1c76ae5ca184f0d8210e80685795ee7", + "4f0dc784038b4308bf1b37d524c6907f", + "2341aab9fc6b40fd89a547a78d0c7a1d", + "5e8cb7b8e8ba4ab99508a4d1d7382eee", + "90c1fb6f8ccf4f91bbd577c65e07ee94", + "f64976b53f244203b06beb4fca531986", + "12354bba1b044086af677df17aaf2752", + "5fa34fa9bbdd438da848ee1af879e438", + "9a488e7b758d485fb864d70cf620b969", + "bcbeff954f524b11ba48f96a7c96886a", + "8850211aa32b441da6fd3344f1ac71da", + "5de614dbe6114240bd9c606605fae2ef", + "f394d2a4df2049be91ceb949138375d4", + "f63f7fc1c92d42b685313aa585123615", + "6e87c922482c4a00a3d3a00f651ca48c", + "3c8653b932364592960dcd908a41df0f", + "c544c5fa3d124238895c9624a7afb861", + "8e2a0fac32a64fa38b0700394f597eeb", + "094ccefcc59c4f2bbce54fe79aab926e", + "6d1d7420b1c943beb5331e6050a1f557", + "8dd056939e59447f89efb96b704f8cce", + "d096027c2c2e4f918fb62869513d7845", + "81227b8992a74614be3f40463af56afc", + "d94181512f7243c4914c307c70da1378", + "de270a7996324f89829caa3fea951d4a", + "88001984fe084ae8a619842027260d8d", + "589d48457cd840ec87a64245a6dcbfc2", + "f2848942381c4f3d9a6709b2e697c3d0", + "3a45a8089bd44504bf6ec8ca4e975d9e", + "08c37f12fcbe4a94adc97b33bbd79d7a" + ] + }, + "id": "bNZhIkr1HTSb", + "outputId": "a1f40554-1bd6-40f9-a4d9-44543e1123a6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:Creating a list of all .csv files in folder='/content/gdrive/MyDrive/unet-for-fcs/data/2020-11-FCS-peak-artifacts-dataset-train-split' including its subdirectories...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "0it [00:00, ?it/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "8cb0d3deb8ab437ebb776aa6893d1ac4" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:Reading 44 files from folder /content/gdrive/MyDrive/unet-for-fcs/data/2020-11-FCS-peak-artifacts-dataset-train-split\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/44 [00:00\n", + "
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\n" + ] + }, + "metadata": {} + } + ], + "source": [ + "#@title { display-mode: \"form\" }\n", + "# ------------------------ GET USER INPUT --------------------------------\n", + "#@markdown ## Path to training data:\n", + "#@markdown The paths to your folders containing the simulated training and\n", + "#@markdown validation data respectively. To find the paths of the folders\n", + "#@markdown containing the respective datasets, go to your Files on the left of\n", + "#@markdown the notebook, navigate to the folder containing your files and copy\n", + "#@markdown the path by right-clicking on the folder, **Copy path** and pasting\n", + "#@markdown it into the right box below.\n", + "path_to_source_and_target_train = \"/content/gdrive/MyDrive/unet-for-fcs/data/2020-11-FCS-peak-artifacts-dataset-train-split\" #@param {type:\"string\"}\n", + "\n", + "# Ground truth images\n", + "path_to_source_and_target_val = \"/content/gdrive/MyDrive/unet-for-fcs/data/2020-11-FCS-peak-artifacts-dataset-validation-split\" #@param {type:\"string\"}\n", + "\n", + "# #@markdown ## State artifact and label information\n", + "# #@markdown **Note: all .csv files in the folders need to be simulated with the\n", + "# #@markdown same `artifact` parameter**\n", + "# artifact = \"peak_artifact\" #@param [\"peak_artifact\", \"detector_dropout\", \"photobleaching\"]\n", + "artifact = 'peak_artifact'\n", + "# # #@markdown * `n_targets`: Which label information is given. This influences the\n", + "# # #@markdown number of columns in the .csv files and thus how they are read in.\n", + "# # #@markdown `unet` means 1 label, `both` means 2 labels\n", + "# # n_targets = 2 #@param [\"1\", \"2\"] {type:\"raw\"}\n", + "# n_targets = 2\n", + "n_targets = 2\n", + "\n", + "# -------------------------- COMPUTE STUFF --------------------------------\n", + "\n", + "\n", + "(train_source, train_target_bool, read_artifact_train,\n", + " experiment_params_train) = load_source_and_target_from_simulations(\n", + " path=path_to_source_and_target_train, artifact=artifact, n_targets=n_targets\n", + ")\n", + "\n", + "(val_source, val_target_bool, read_artifact_val,\n", + " experiment_params_val) = load_source_and_target_from_simulations(\n", + " path=path_to_source_and_target_val, artifact=artifact, n_targets=n_targets\n", + ")\n", + "\n", + "if read_artifact_train != read_artifact_val:\n", + " raise ValueError('The training data was simulated with different artifacts'\n", + " f' than the validation data. ({read_artifact_train=}, '\n", + " f'{read_artifact_val=}). Both have to be the same.')\n", + "\n", + "print('---')\n", + "print('Example of training data - source:')\n", + "display(train_source)\n", + "print('---')\n", + "print('Example of training data - target:')\n", + "display(train_target_bool)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GyRjBdClimfK" + }, + "source": [ + "# **4. Train the network**\n", + "---\n", + "When playing the cell below you should see updates after each epoch (round). Network training can take some time.\n", + "\n", + "- **CRITICAL NOTE:** Google Colab has a time limit for processing (to prevent using GPU power for datamining). Training time must be less than 12 hours! If training takes longer than 12 hours, please decrease the number of epochs or number of patches.\n", + "- **Before starting training, execute [Section 3.1.](#scrollTo=UlZbSxToDlnM) to load all training data (`train_source`, `train_target`, `validation_source`, and `validation_target`). They are assigned to your model training automatically.**\n", + "- After training, an `mlruns` directory in your `out_path` will contain all model data and metadata, and a automatic training report will be generated and saved as`_train_report.pdf`.\n", + "- Expand the following section for an overview of the `scaler` hyperparameter:\n", + "\n", + "
\n", + "An overview of the effect of different `scalers`\n", + "\n", + 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3tCMruRUVo73mWAO6YyDhcZGkNVQ9p1MM4htuE5kh7USKtT/XRQth2QfIds0zB4QrCCxZB8hw4hqHITxReIXjDdvbiTIsrJkc2U7ga/2MjmvBhXlRhv/Tr+BjZd5Isu7IZkbEPOm57+F0790Q03LlSZipIVwOsJkXJS+MmUtrSy3osc/tRn8UUb+NilNRzZGvb++I7c+xlWVYWIyEVyQYS5FGRvMXRLkWsyr97lmr7YpiPjObXkjgktndFpz49qJ3BSmnvNdfWkhaMk4MAuHSAFEqywjt91ay0kJrQYWvQ6bx7I04oBW/ivDEnqh5eJxMPhyWv/ABR3EfhzbJFiFt+nxk3kp7HmuMqvEv3ky86K0Te0zj6XUlLDYXRL5y6G0iGNjNkiipDpBJKw9BFJnbNcHmvSAwxxMojHcBD+MWpJTm2m8+KiIQI/NR/HtpI0JkIocAMc/wAhzVP7E1qEjz03Pw2oa8fTdfERV6hDRJdIxojtN1/hoL1jY7MQSJ08sSMt7YXwqSCSyCacZuMSvQm+1JHzrEBEWqaMkz6gV8c1oDKTKd8iUB7XUMDZCJgmPEvB2MKPDZCJ4TdZ3AvgqW+7HX1qQH6qajZDmfgNEWTTVVHy60+N0ikRuSe6hWEKnr1cNsg+Q7boR59TVRTuLAGYLf5bk7rCCXdC4lXIMSqIRM5rIp1fnN3LMiumaz8EScM9j9JYncpFLR0LEcxWz8L4ZOY1V3Y+Xjx06Jg3pbJmI9jI+NrF3PWTfG+ZjxnEIx7i97piOGWUh0UURLmtQpUhkekq7t2SN4HI78AjHcOjIVirMq/e+XTnx7Uyb6OKDmwkS86Xwy+GgIWCOV6zP8WjoI3cOQR8qJruB08KQJA3hc9cD9cqPjspxFbO2f8AK8QqyvKWRB/XR6tTZYtc7JI0lZOxoZVi7mTsXcuNl8M9yLiqq4WTvH4N7Wbog5PRGesbMgd+qjXfHqP495Z/kOX8aPyONsTMkTfHpZisqbX61elQFjCYxsbSA+fetFRpckDnG3Cb6+rZzKrTYrY4Re4uiZLc0AcT6fpajTOrolJycJ0trlK1UtMMEcSXfViwCPHcy81FGkrLuNIq5kTXN1BChB9/U8gc9qt0yB7DZqcFxj6JP1OyD5D5YPkOWI3NuyIOCYeqXjIHUaaOJ88rkeI6VvHgQfHF4ReLp7VyYZJZCwHrPOFJC2Mbhr0q+bGXAo0r971/GxYx3zyiC+HY4bik8Mzh8A6VooXCNPWflxvXkch/L5SuXpku6EV5aNGc6ZHOjb4GbBK5R5JB1IdIPxyzjrzBodzNOojQcq/e+XTnx7UTHPphvqHWwNlesDFiRquwWNHBNerUWDcTsNxv7yApHgMKtLkXfYSM5kax7pGxq5iNVzq0fczbLFzdlrFkm+bJo+XIMGkj7CFWDkDtQLgXp8MLiHMr3IyVvBBJ6LP4S+mP9J/2zUfx7yz/ACHJIWTbV+uBitCGe1JGsYkbPJJG2VkcTYWVI0gsbYWsk5LOfXh+AE+xdbL397eNsgMPpEgtJIsO7XTeOrFZyhttaI8RdkHyHywfIcPia6xKAaLNNHypSAebHHE2JCAI3JGMyOIQbmv8pEXKk/bJYkkbHC2NLWpbCWxiRs2wekrVbscA3muaj2pBHsaxrMVjWivBa8lWK1LBMFbuKcxWp++SM5aUHssq/e4y3P3u1CbIP1yd5ZJcAUWnPj1u1XV1TVKQ4MVsccTuOMyodXGDBeJWetkgkeOjp1qZGhYbjm/iIcitr2tlVzU8XK7hQgZIZ62riMxgL/Fwxc7G/iRU3Ly1TCxXwxNie9vAhbAolYS8Zry4wHMdPCk7OQpGWInA6sjVjadElOM3sJl+kcK8cLh1cNHIjJM1H8e8kBERUc/yH+1dbLzLP2EPpYf3aaJs8aJuTzQfIdpZkYUYxMZkEHyHD4kQ269eyAkiIVqf4/h4v5Gady2ajbDHN4cJajZMLYinBRr42RiPJy+Hc54QbkKt2o2xcJIj4I+KCKBvSLgPnEFRckhfb54X8myH8MVGxZHkU8kYLW/pTIljigAjIgnFYPbER8yIaJec6NFnrInQBZV+9xumkR0umB5IFooVOzTnx63K8HWzWUrZZzM5i8BKwpWTyBxtLsXl05TY2ER2MiV3jBx2vQSxW3EcGMEiTlCTxgWjE4hrY6Aqcw7nZpw6FxQJorDyDI4baqt/Ayy3ARzYrCR77HU0BQYB3hW0+4kuPcZbySNhsiriLxB6oySt1H4BLV8JUtkFDVDmFSBvvJ2k2iyI5K6yYOt3YCMGVd+Iu9NR/HtknErDBbmQTSg84tYsL49Tf3LzLP2EPpYf3b7EHyHYRE6aE2OxAEqh0FrwIXw3+X38rMR5ExwilK0NErx69C6kpiRgQiRubRjOgguO49OjJMSua+wtIeSQUM9uIOj7YATjm5P6meFqRkjoVBKMviiApH2fKZxKBLDX0XsSh1nfbt4bCMBZo4RnPks4GQ11+1XS1QaocW3jFQV0dWfErqyGPkwkwcywkY5GhBo4CERSNtX73y6c+Pal7JsYO+RvPk4WPWNea7lbdJ921mxFGqU32ln9LJBnt0pHEsjcHm5D/sacge211ZXviKxSHcGSEyzRKZK4SSV0uRwyTqALzZcJJUnGBK+Rqbk1H8e8s/yH+5eZZ+wh9LD+7fYg+Q+WD5Dko8c+ftsd9W10boQnsSRjRImuRNySDxSujHSOdo6tN1An6OOBCaxI2o6s/YViSGKDAsfkf/CqTdX5NXqVPDC2CMQZYLaeFpEV+m9/LbzFTeiBR+Htf4+StarY9lX73aSaOEmac+PSRtmjqtNMmJdpZiuqqlsNS/RsyOXRyqIPp162E2jY95OlJI0E0pMRFVafSrPLEiOhSuFbJqWojVxNV/oqGgkBmAoUltLvTqjTf47MQHU6ZWUVlYzpVZphpUN7RDtDg0jL4qy0o2SWbSJLXxVrW5Z1rLSADTbipI9KlSEJpDhirKOSykZpaRbOv0++Y+tqYKyMSh5R9TprkxQgSzPgqR4Ys1H8e8s/yH+5eZZ+wh9LD+7/AGIPkPlg+Q/clhZO1rUY3I4Ww4yBkcn3LaNqt22ib4/PV+926gY5pcYsy12nPj33XMa/78UDINqJu2bk37XNR23Ufx7yz/If7itR2KiOTYrUVfsQfIdrnIxsMzCIoPkP918bZE+3V+98oNdaABWDrgMDkW2ci2zkW2ci2zkW2EuuIJuRbZyLbORbZyLbORbY91w0/kW2ci2zkW2ci2zkW2DOuJ5uRbZyLbORbZyLbORbYE64Lh5FtnIts5FtnItssHXAYHIts5FtnIts5FtnItsNdcCQ8i2zkW2ci2zkW2HV1oeF5Ta3xZPSycZXnqf0snOlk50snOlk50snAq8+SHpZOdLJzpZOdLJzpZOV9efKB0snOlk50snOlk4bXnxw9LJzpZOdLJzpZOdLJwmvPZN0snOlk50snOlk50snGV56n9LJzpZOdLJzpZOdLJwavPfN0snOlk50snOlk4FW+EJ2EJK6E8K2Ig0tHNDpzpR0J/ItsGdcTzci2zkW2ci2zkW2ci2yvdcGAci2zkW2ci2zkW2WDrgMDkW2ci2zkW2ci2zkW2EuuIJuRbZyLbORbZyLbORbY91w0/kW2ci2zkW2ci2zkW2DOuJ5uRbZyLbORbZyLbORbYE64Lh5FtnIts5FtnItsrQphF81tNNz6QuQ2r22kxsNhFYltGbYnErXkqYBsIszwHsNNlMS9OID2SsWSNs9q9J7ieR4FiVIbsuLEmqOlNOFJbYnEqy6LJ2jzToNMZZxCvsifH0liUQRkjVfG0qzkr57ieR8l4YLHUTEtO+1XWJUpu05N4rLCxYzqZTTqE8qabZe+N5UF1ORj700UOrlnYbsvFJYQ21OBC8aYhlIVIbWYax8gq3Jz6zqZTTm3J8IlPPM5+Xss47VlJZeD2Z0NdEaaSXTGOsajLCWeC3banAheNMQx92dKCn2ppOVFRGlST7bd3KDDMsoin3poodXLOw3ZfzGDypeEtzqZ4zaYiWVmy3U5tnFYltGbYnErXkqYBmpZZx6QqxKYc+9NFDkNNgMpCZCgMsJZ4LeSax3tsJ5cfdnSgpsMlMiuYrEtozbE4lWXRZP2zQIbCMeCMWDatGIpaUQaQyafBlHREamx9CFK9aEJRpKIGR211II4R9IFJPDUijmbJqUUg5lADGNJp8GUd9OJIXsdpWvck1IKTEtQK44SoFBnx7EkYzTQMUD6QKSeOmDiJAqxqxPtQVAoxe2wr4LQV9CJIiVIqHA1Ita7YWFGayOgAimjowo5Aa6CuZseLFISyjDanQg/DjjxiQYQO0qFmma9kSVIqHRUAEMQYMIEWWNUPasGohBJ20QaRPoAnjRxthjwyoHPIZRhtToQfh5KACVn2wKkWsXbYVY9o1KUXnR0YUcgNdBXM2HVkFkkVMHCXFp8CEcQOIGHY4GF8yUQaQyafBlHREamWNdBaitow2kR0YUcjaEJowosYcGGVA55AlAGDF0ILw8lABKzZPSikmpRBpDJp8GUd9OJIX/AGHvbGwYmEyHbzmc3zyTRxZFMydn/BKNHAj8/OjSbyKZAj/+HCaOTJtkmjh88JkBH97WEbZaEjwdBe2lmWTV3RpFextui6YtLAwqt/VVB89rcC1kqTgVQUtqSRLqU+WpHNMBs7i0so3T2RFVPFeGtC04bNY10t0dWNInkr6weynsEEsLGzmFujbGLTxUp1B/auwRrDUYxqApGUVdPNvpx7W/MUaIckm9lryTKvTzTLeWS/nJFFQ+yGyS/nZdVtwXPe2trO5Az57o/rZ7wLxxiCDWk1nPqC+Irl8XKJcV9lYb6q3NnJ/uChCV+lJ7+eO5qnyVtDpWynsRUviFuamvnV4dtbGH6YkLMDnsrBkVuWRvjti4T+pWJ0VS5bHUmoyzA5Aip7OzBv55LwS3Lr9M2FpYU76Ukjrf9GLUYc8fXhs68NnXhs68NnXhs68NnXhs68NnXhsJswDYBS6oCMeanDT/AEnAlqC0eKSmgjcfWyFRPpoUddiPa20Ajd4uq5aW4STSWNdLktjXEZDPUDtjtwonDk1IcrXUrIYi6qBsdjXROtvCnwhWQQAXXhs68NnXhs68NnXhs68NnXhs68NnXhsl1GHBH5TbCGvb14bOvDZ14bOvDZ14bOvDZ14bOvDZ14bOvDYaTU2eSF1Uojpqd4yzU6kF2FcfFGdWw5z6jdAZVitktwpsWxrlTn1CEssK2NCzqw9JZKaeOaeoImLsK4+KWeonGlkpiJFsa5XpY1zWiwjQ3HXhs68NnXhs68NnXhs68NnXhs68NnXhs68Ng1uOWTtl1CDCoxLSoibccQnrw2deGzrw2deGzrw2deGzrw2deGzrw2deGzrw2COpQJefUeJfLTS4NZV4TGz1DCZSameBxNU8uK3CHZ1Ov4Gz1DBvEVHg5HUs0LzauSQompOklMq5yYp6iAjxdVyyrESdlF4SnXrw2deGzrw2deGzrw2deGzrw2deGzrw2deGzrw2BWENg3YlknVSLuEYzNOfHvuftn7+ZV3bFXd/T1H8e2SP5cYVpGZX1tuyydae9+7vRPPv2b/r/Sn+Q7ZxiLMukiJiGAGYLf8A3d6b/wC3V+92EDHLfFU5U5ZIkBsWnPj3l4k4vJXSOkjy07ZV9s8tx25V3JbWDCtPtXibn77LmZ8KeevkdI3y6j+PbH8SMr60zpVSAVETZiQJb+aKZs3ktnKyv2W/u/NL3U4zwmSFN/yfJpmDx7Emf1/zWr3Rgeaf5D5YPkP3QO9/d0/M8ip+7V+98unPj2wiRYoIy2ubj+77PEN8TlX6WWnbKvtmxVRqSERxw5cduMsFhlMl5lTB6OVPts1T7TzTTNgYBOyN3l1H8e8tp73aU9UNEndM7KzbWFOLHuO3bL6RzLGOdsrthJHh1IP8ObkvdbC0fJUf+45azNeDsT5NniE8Tta5HJcEO4YrBXFtejvLP8h8sHyH7oHe/u6U7R92r975dOfHthvs2erj+77I++ZVell5M2CqEe5rM5jeOeds4ZjkSjA9lcdusXu6nLH/AKcM5kjpDVW0qfbZqr2WWJKTAbYSFkKuO3k54h0cnk1H8e8tp73ae9IzKxyPXAJmxvxk7Xy0PsrjtzXI5uaknbHZCypFgR6krGQ+SlndxDzzumuMm7qVIj6nxUf+VB2DygIHK+s2f+zGezFf+uInQaFF3pN6NZ7C8fwlQ93rl3yZARGS3ZP8h8sHyH7tGQ0qz+7pTtP3av3vl058e2G+zDI8RLhUnKsIJOdCUQsDmPRt7lS7fHmre1U8zpk8U9Tmd2i9na+zr3Iodx264melnzXODCsfBGwTsJvqr2+aq9nk/scCmdNsF7jc9uIciudIi2H748zhP2aj+PeW097tuiW+ICnfEiruyH3uC9y0+R+K0mbNXVUvOBzU/ei53RLVLwzBSOWvk9oY7gsAVVwdnJyiHy/pK8lrTQCpIkiISMLYnyY32cbuGwte3+IbG+WeN0QMyR1N1KkxFZ7qEhWt35XmPhZsn+Q7bM8wI2nnmeoEz5r/AO7pD1fu6U7R92r97sW8ch5uo/B25JcAUWnPj2y2I8OGN6AXs7D3QXs7H+ZxHhbl0zWS007vFUkr5htW9qHcrK4Z/NmNdvt4vZ2vs6X+dx265f8A7B8ixuJVd1RNyJKZ3GJmoE3wZP7GCTnQhypBEhEeC9xsCknr3+6eQkdjTrurZzGOuqqR0oGaj+PbHvSNlTcvsJKnUfVLCzLgW3wklB1IO329nMsxvNWKG9Jek1ev51waosNc9ZLSq7g56S1biFj05K7/AG2o38V4Y7iJcQqKD7IqRIa873rSHDU1qTHK5/40a7c0X6E//WyncOHBbyRMhmbPqO1lWIAKZZrEs1Jgwl34+ZICY38Gni/VqfcpYRDRrqSXgbKrHDztJiyf5Dtbp0FmBhQgRQfIftkL/v8ANIer93TT29M+wCYw8byVfvdklG9xpNDISVmnPjz3IxtoS1a23L5ocC7oGGvjqQ378HkbDXyFuMHuu4Ndvnpu5iuVlJq7f0uPtgz+XFHI6Y2L2lk9HhacwwjxdLfP5hpCrOdK/hSv9Oi9h1aXx1sQ0oHTM3Np5zeZEKQjGLIrWWX1gAlbOcYvEM9/1tifwPOlmWqI/FTvTwGaj+PbHsSRlZSzASVtE8Em097l1LyZZ5eIrHLhTkdM7+d/I2VteSoksZngZ4Jm9Jll4a50qOvr7v0z0fI9fxAr+jJM8QAWvEXISsjJZeXEM3my/wDjwL+pyykSSF3tSCUjMsjmT1lc/gOM9aulRivermIczpBfqlzLBlnJvI/eMf0aUvibk/yHywfIftkfIM0X9fvaemSOz880rYItJuVanyVfvfLpz48b7Mxyvry5llzj4BGkOdCjuGNk6y0Q8zstp2SlUUjpZ45uRYwdj1d2yPtnOckovuY5GtGl9jXEKKJJM7kuk5rCY08a/AnK4UIh8NK7uJEywt0d2zD3KweX1j5OJmnMn9hJ6ZxiEAR/zrF/T1L2smzUfx7y2nvc1FtX67HfVXPRRmfxsV3RwemV6E5LIL2975kLlewL+LfayLvfh/tA1RS936SD3GSzccPEvCb9ZnS8Ywnuy3fq2u4VVfy1yd6SPtfbkevv/CCu8TT/ALzJ/kPlg+Q/bI+QZorNQyOip/uUD1feee07ZpHtXkq/e+XTnx6zk5YCyOVj13q72kbty/8A0iLXwA/uCPb00zkQj3DJd9Tq1f0ELl8P/wCXDLwzGOVCnSb4Nn7V0vc3JubX+0GevSmTI8o70KCZRxMKkVQy13FkLvfC9YxnLxVr/wCEns4vo+qXByFkIzUfx7y2nvc1D63ntfRElSWMn0CH82edVcZg/pwzclEX9O7+WWHtAPeFwtiEH9xtM9WP0xV4SS13G+S19Cb1f/4CXhCopF8dk/yHb1mv8PFMwiKD5D9tZ2E3eaMTcupeyfc04u+889p2zSC76vLEt4smyr975dOfHrjt2LnEvDm/6MdubC5GSke3pJUbIR9cY5PAXhvi62L0v/Kw33m130GWJrpJv5V/tGzKyHDvRAfwj4T7Qz3bV3I32fNXkTLwwyezb+9T/ML3uaj+PeW097mofW89r6Ia/Wf98l93g/p+Q1OIdrUbYWHtB2/n7SvWi9Jq8LppOdN5LZ34I90pKpwqI7fX6dfzCcn+Q7Wckh1PIRNXAQvhv/t0Xq5o7+epeyfc013vz2nbNH9sy59bZV+92OsSuomW8rLsklgkWnPj1x27P3803o1jtxM+Qu3xWHt4vS/8nJpOdNtSFqA5N/MFNwuwz0g/Twn2k7+bPiL+nwj271/TyRpEtSv44nfqI13x6j+PbJZEijprQoqakvVtibMli2+ah9bz2fpg+u6RJm5J7vB/T8hruGL/APQMbxjNVW+Qr1ovS81tgCcRkzFcWJ27S3q5P8h8sHyH7dCqLNmjv56l7J9zTXe/Pads0f2zLxd0myr97sfTiSGTU4hE+ac+PXHbvPN6Inuyv4jLvac7ihh+sTnbivK7t2TNVHgO4hthnpB+nk0rXj+Qj278O9CubwFDe8h9LUfx7Yqb0r6KOucJSRBlWnvc1B7vz2j/AMQHrCP2TfQrBXb/ACntVzN36+ZOKFRnRC7SvWi9LzWi75apiOIdCrrIZN1fpX1cn+Q+WD5D9ukKbAbmj3L4nUvZKt7pK37emu9+e07Zo/tmXvqbKv3vl058euO3ecp/LHgduIOfwGBr+Mz0oPRk935U7Xk3vq32uwz0g/T8xjuAZU4lsIuEYZNxI3vYk3Raj+PeW097moPeeez+pFVkPrjrvhn90+VrHifv5DXKk2yx9qLJzYNhfqxel5ZXcuIh/HPUes2JUI3/AKXSciOIyf5D5YPkP22qquzR7t5epeyU/avt6ecjLrz2nbNHdsy99TZV+98unPj1x27z2S7hR/VtV4SQ/UM9Ib0ZPd+SR6RshhSULEgaklWu8fYZ6QX8fNYL+kVvBOb9c3I04J3+wb/HUfx7y2nvc1D6/nORZC6vIvcByo9k3ujfcifv5DV/V7LH2ojuWBBLzocL9WL0tjGK9dhTP9eP9ZKld0+F+jo7uGT/ACHay3P31JjzwQC4C7/7UjuGOP1M0d9bDVEiMpqbtX26TvHntO2aO7Zl76myr975dOfHrjt3ntPbh+tb+uF/Mz0nvWIFruOfyH+0E9tsqf57DPSC/j5p5ESKT6kOYj8l/CaLN/sM1H8e8tp73NQ+qgqqGrfoyJ0iZHEj4sRFcpUTojavfvZ61Z6k3ujPdiPRuKn6fGh8QGG7vG7DInSiyb21Nf7TC/Vi9LYF6mRx8eFLuqQW8U9e3lmZO78zRLOI3J/kO1umkRwIbABYPkP2jpOSFH6maMT8/V3aqbtX26TvHnsjd+aNlRQ8vfU2VfvdjyJeq3VrKy+JdO2LTnx647d57X28MaxFWrt5I0/LkM9Int0PqeSw9oCu8XZU/wAx5OYRhnpBfx8xHuHevF6Zi7ni/QrNR/Htk0nKiojSpJ6PUjbe9s5n9XzUSfVnZEge4PTqYSxIiB4m9HVFate1XnXi8UgTuCNE3SVrk3y+7P8Ay5Il45TU3AEsSMmL4+jFcl1DuMjjdnCu90CSZOSyTTAbUbVC7nPKXfMHEs+TQowLK8GWWOONWTCt/Tywc2mhruAYSLiPSD6TN3k6L3Idk/yHywfIftWnbI/UzRnravX/AFdN2r7dH3fz2HcNFbL31NlX73Y+vFkIWCNUzTnx647d57FiyRyb+pWfu4/UM9Int0TuFzXcbcam9MP9oKm4fYBHyiKnYZ6YS+YWJZyJPzSZoUa6L0rJ3A8RN5Waj+PbQ6UQBfDR+KtPe5cjtmEb9KcPsVR6ra7xNlTjI+tPG4zGS9MJeV1AJiIzJInRSVLt7pvd2q/Suaj3WcSuhKhe86i9iwTwoFhGjzQQWEArC2G6b6j13BOIa2krEVYi/pNQD+ID/wDXt27IIUHhliR19RMbIyNn+od8dDYsVsGL4mQoKSGy021R7PJ/kPlg+Q/atO2Iu5Q5lID0b7jWPbKbtX2jimhCBkqGT57DuGitl96myr975dOfHrjt0beOTcq7URXLsKcqP/8A0bFN5EK75DPSn/FXc1FYP7fImK5uWHtIE3QbGENHJqXfj34Yv4xE3S7S2owrKnuA0fMJmbvZF6Vt+468JOaj+PeW097hzFlFp2rZmGmR1rdNEuJkFT/Z1Fj1IckeRZtRweHbUCPMDtwemHWT+IihHUkl0kXUbcf9VSSJ400qKe28HLIdUQPHFexJGPEZNfRRNhj5TFkEEalpZjeEmMhWKfTg7pa0lu8/SXajl8PpkEHxFLJI2KPTxDzFpiHxlKrydLJGj6eCFXanDC8Itp8kpHqthk/yHywfIftWnbMq+2aPevUdY9s012X7WpeyN9n5y5HOt9FbNTftsq/e+XTnx647dStR50HpBt4yzRFCcCm83FXckn4pZkRppf5kkK8Mpjvw/h6fF/EIdZAMoURxeFQq8PFTcvCuTs4iKr3IC757BvASN627ftN95BFvKp+4ipull9KH0rRPo6JWsRd6aj+PeW097moy3BE6YejbU+0cWdRlqLYxQcsjSJH47qwfWjav+uVFS2qj1cN+YYv1oplGaZMjjQYHFiB/zqiF61g50JUs9xDFDU2K2FzsHicw58Czas1DBwW+lSHSxyl80kUhqaTvrVxTEIT/AA4AhsunNJSq4exJjDuKsyWTOW6Gvg+YZdVszz9NwRrV5P8AIdtzCwmo0xLEsIDZ23/2rTtmcSs01o/uese36a7J9rU/ZkTcH5lXck0jZLfRWzVC7av3vl058esk3gsJSI8RnMYPNyrzUPuKmVrbgqLlzEe3rZHSk2EKtRvpI5OIxd+S+xiXKld9GR7XT/vJR3NnMjVlRHEsqSRqmSpubqQNglqsfDlTlkm84BvETGKjMHkc99eCpstqxR7N0zRj6OXjuGI0Vs3oRRL4W1YngTZGvkT9tR/HvLae9zV/8oBekS5Yguryuvr0/Tqq241IY0sGsRDJMtK9LIS4jawyGRsdVL6lH2yirkPia18GT38zcp7RtfKVO5Q9Ix7yNoqL/lFrUJaO0sZGORu/CK7fBGzmvhLb0WGZw0unJHNtbgththSx7zrMx81nB8wy2mbFXaXmZJUZP8h8sHyH7Vp2zAZ3y0uj+56zl3Daa7J5LE9laPLqBsZ9TfJOK17X7dT9mm+ofmM9pX/z0W38vNUGyvswZXTA5V+92TOhZe2hFaRfktndFpz49dSLFVmT77OUtRo4SlZZWDXHZIxoGrrsdOZO3irqYSWRSoOcGxOEYdnGp8qrDFJzs+rZOcvh5IeMSharDZ4l6seRH4WnEbKlmIwUCybwy6tjREsIEhgrJeCS0X/Z0rElsqi5/V1r97tHFumdcv4dRWfCwo0ttdqKdd6SovhIPrmqRlhErgPGmZqP49smc5kOnJX8+sbxW9nM/q+amY5W6g/LravTkMguqwkdFy14LkZkYDy1lF0xDzLTZbp+aKK82IsNYJ6WJUptFJuttUA/pMfG6NPxyMpaxK4bbCE6Kzzd0S5ZEqxVozyIwKCThkEaHqPUQTQyLFvT5gquQ1g9PMPYHd+H+X5rGbgj0Z7bJ/kPlg+Q7X23BYVp3UBTZ1GFa9HbCzWijdY4be3JXlQic4Cu7RpdeQVq93Oi0/K2GjGsYS40LbzBD2GPy4JlMnirnG1hgktamm7Fgksdg2cp5TIydVScFQdC9gAhCeGgIbPivajslmbDm/LC2RrQyFYXWWpAzAL0lDtR9xq+2ZV+98unPj2oO0N38y0TgsGqiQV1ksw13LuvLeROYKTzg6xEH04DHxNlRW10DOF8snHHTJzrM2P9cXvhx1syKtYavWzzeCQJ6ynVEUaRFDNLiuhV5usJEYNbgDT1FL9bS/3xXgBrhS+JeZQQLLaVNhLWqYYyS31G5r8Y9ZJee17y4+XX0sLimagTfUh2CA2Gaj+PbYq0SBRakIGW097l9G6Wptqbx415JE2ksGdUtqypbPBdVXj20QiRXtVTpWTcxvHkrUdaUZUY5k25DXFwl02jW7rQiRttaA1o1ife1UKi0larFSyH4dpRroLC8JePU6mrt2UoERdPpYGMmEaHw4/gv9oTXQlzarDa6CvmZXBPekbIDfG2DHcGp81on00gakc2T/IfLB8hwjUDICo9RyqLGc5SqGyUFCbOYhALuNDpNQPcRLaKoKnvmU41X0tXJx0wpcnhN6Dj87qL4zpG1IB8YodTZK2GErw5FjfSPgCuo23Y5jxG2ZikpC/ws8NutaWfarLdXli8wa4lY/TwtnL4aqsV5JN2j5OsLy0PlIKbY8NubYcyJ0ivyqSOOKYDlA2JHMm0au+ryr975dOfHtSv5dI2dPCFFOLlVP0yE8sN7ufA68kmrll4HQmOjrTtQitHIlFtMs6p9ePF6lXKwe1JlUk28lhMNlKWRgZfA+I5WoFOjLWq1BKArNTwTuCtJo5tQ3cNpAAeoqRyrEZYFrZHO/LnfM1cklkCkQlWY9v5duY2RJPwsMlbNNJOkkdRqPwEN/cQOCxF3pqP495bT3uXNu6uMg1HIw4wrxM1YS51w4wMAi6u/DO6opGoDLLqEdZbNjs/8rgaVHGpZdiIgJzJkIAGukHqQzIac0O2iAuhLScUwu6HKog7cYcF5/BDX6tWKO6vGkQVmp5hk1FbsspprF3QpLyE+o079afTB7AixLRp13b6nRjqfUziiZJoLpLQ6N9xJaeGvkVWrGSjzavVDCXalso7BumVVl3k/wAh22Bja4GuPkKkg+Q5aEtZYMnRY1KbgxiNyQ1YsYQj5PEo1JinI7lKsDXK7FV9fkEvLY4qN0UUqxzMK3PjnR6cfDkhPEry8HT6IvDiuV2EsV2b3yL4tXPfNxMmJY4ZC+UsVtyY5p2pKhjYYhj+HGHo2Od/NVIWozg5aK5XDww8zNE8SV+VfvfLpz49qhOKiZ6Q6Y2Fz0TeuQOdFHxLuVn5uzSfdtZxoo9PE3qR8KR20sTo6uOFZmubucz6IifihZwt2Iu7FTfsIT6V40hRGpqjphMbE4ivRem6Fz3qJHvka6Pe2ONzmgi86XCivEK0Nz3tTc3Ufx7y2nvc1ozdLu+jWq93Lkhjc9zk4l4Z5lnft0f2zUibrqqiWan+nDqaHw5PCvDsWXfD59IjuiQ0KQAhFVuyGV0EyFSpMQZIQRjR3ujY1IqsOZBy2NdK6jDcuosn+Q7bANtiDXASCyACQCX+WwfDbTVMkb469EmLr1hewL6qMu/wL3NhGWOPdnBjoWvx9XzHS1aJGKOkjHDMTDa3lvdWuYiBq7GB8Swh7nPiVi+HerXJuxojoonQtekYL34lW1YlC4c8K1MjD354REd4VyL4NG4MC7eVByEJiVCJGLG6CJZG6ZjWOtyr97s8aP4p5o8RGac+PTwMJiqNLqVM3SfA2np40p59LEDmf4fOoSUM3jZtFSI4nTU8LRdNlExU+n31dicDFYjx0oUUupqhjHF1G7T2nqKWCUeqZLqO60+tfINRTFw1GnVNEip4ekVmmuoQX1AOOLBpcp5VppVUlI0uayTS9U5CbqjZcYFpuUmXoBU86aSl5INTJZy1FJMaSBSymE1FLFVRhUUkdjU6ZXlwjvIePRwRRZqP495bT3uapCaRXCaVTwCafUV1tQMKA/xU1FsdKzjrV6bWwi/xCdsxOn5hYItITvnoq59YCXTCGzxVIsEcNPH/AJNqipmNmbT+D06BpeF9dKBIOcdSSQ2EOmOVFqmuj8PW6UZDNY6cZBNU6W5rZNKkynU9EVFaChoLLqqr5rF0uY2ETThpbLPTq17AtOkHRRUhM1dWaa6iMXVtWol07OykuqVglTQ1j57KAGIafJ/kPlg+Q5ZU/ipjqfxshFOPPMtexwI1FHIIbRDkjs0+qNCp44IDKF0xjaGNZehNUi2rlgRaR0g9bQKI2vqoXTuqozD5NPvcyDTA0LI6EeAommaSZ0IXxfRGxj1tPE4AgCOfC6RXzx0H5UVO5xFnQrLPegRc0XT7YZzNOQkzO0+54NxUxuGihbFHNXsmLkpONXaYilfWVCsHjjbCzKv3uyWGRtmfDJIcTC+eLTnx7Eajdu5N+cKcWxE3JtlHjnXYrGq5Wo7yIm5FRHJtgGYNsGFjFxIGtmyKFkDRRIg4uSxJtqhwO26j+PbJuPk6WkFjI05y+pWbp+r45qPb5WtRuxURfKweOOXbwpxbN2/ZuTf5CxYzR9ksLJ48REakMLB49j2Nkag8aTbJ/kPlg+Q/3ZoGzt2yxNmTz1fvfLWx2wFcbY2YAXPts59tnPts59tnPtsnsbMeXn22c+2zn22c+2zn22OsbNhvPts59tnPts59tnPtsgsbMiXn22c+2zn22c+2zn22CWNmZFz7bOfbZz7bOfbYbY2YAXPts59tnPts59tnPtsLsbMOLn22c+2zn22c+2yyjtj67y2o5Ez+fbY6xs2G8+2zn22c+2zn22c+2xtjZvN59tnPts59tnPts59tgljZmRc+2zn22c+2zn22c+2wKxszwufbZz7bOfbZz7bC7GzDi59tnPts59tnPts59tk9jZjy8+2zn22c+2zn22c+2xtjZvN59tnPts59tnPtsggOmtfLPAdDa8+2yCxsyJefbZz7bOfbZz7bOfbYFY2Z4XPts59tnPts59thtjZgBc+2zn22c+2zn22c+2yexsx5efbZz7bOfbZz7bOfbY6xs2G8+2zn22c+2zn22c+2yCxsyJefbZz7bOfbZz7bOfbYJY2ZkXPts59tnPts59tlUORC/wA1sTKx1SathX7bWxOryoboiYLrZ0+BkoaHsXUJUTktTZSf8mJmG2So9Y1vbNjiLqaIgK4JnN2WVwRVGvuDx5W3JsiN1FPOuwQywIikvinjyXJDT6i4JMnyTj5a3p6BF3U3OdqScdKs0qQr7VnbTjGbV/Yq5sxZibqfl9cneXsuipwq6XURKQpczRmU1sUZLstZpRgq65sHF/5HOKLWFkvJyTj5Y9vYFxJqOYyF2pJx0qzSpCsM53hn25qU8drL1pmozGDVRcxC5clkhJFeTRmJdmwIy3OnmrDepVv2ZZEijpbIkibbYvnjHrr4skv/ACOcUWsLJeTsvLMivIdfy7+unQsqTJSo9lsfYhnQ3REwXWzp8DJQ0PNQFk19QRcExnLqOeGF1udDPUGPOCw0ska1ItbEXGWs0kb9SlvE/fYXYGDWkF4TIC25NkRuop51+0fXtPQMSMAXb0T/AGS6fje1+m4XQxRthj2E6ejOk/x+Plu0yKqbUpeUAunBuMekhGM2GUTTjk07Fyn6bhdC7T46k7JNLJNGVUONGWihU4Ckhr58e1XMg0+8YVdODcbdPDJNXVTa532jaSE0rav1QOgcANJpgZ8C0UKnbCYXTxLpUZUSjh8fW0kVY/YeEyxFJq/Fkt00LlfWoBse1XMi0u2KF2nBuY3TwyTV1U2udhMKkQQ6XSDH1STGxaYGjgAAaAzLWr6rH0ZspMemoGQLp6PljDxhjfZVN6VlLFWO221UlvC+nWaRumhcr61ANtjXrYsh04NDPHpqBkAALQIdj69rjF0/G9r9NwuhijbDHltXJbApQRqQ3TQuf49HywQ2V42H1XjiwKNa+P8Ax+Pgk0wM+HYRTrPZLp+N7X6bhdC7T46k/fsLMerYBaDWaFFxBR7LKwZWDhHRnM8sj0jjBMZYA7InrIzCi4g2nWcFdgxMRkHksj46sBj1c/jXm/1Db0QAkUqI0dxcTS9h1xGCVBMwmHynnx18eyV6xxbFLiQyS3FisfKSfGKTE9ZImPV6/wD9D1IqxuM55c7auSYHUsUkqakV/SR6d5Qr6bww7ad0wlwBAlErX9Jq6mU2OmqHBLpcVHBSVRThjq08yorgzibOevVtba1xhJ2qEd0+z1G7wlWPCNTAUZANYTXLJXGaYEfbhgT10XgilEt4+W+WpZCCZSzBpptgrdS/0iSunankCnjAq6782sjJXUmq41ImEoXWUbq79BXUfNsNWisnzUcZhQdzTOLdqCubXj+BVXg1pSXWoh7WIF1KVDXSj8rVOnK0qGysiPAalt7FjrXVQjjqwqrcNLDT+Lmk063cUG4GGjFLavgBiK6eqeWX07kLolESj/6xZUYIoNkhqC2/jA49SNmjRd6MupJs6oTnVCc6oTnVCc6oTnVCc6oTnVCc6oTnVCc6oTnVCc6oTnVCc6oTnVCc6oTgZDwBeqE51QnOqE51QnOqE51QnOqE51QnOqE51QnOqE51QnOqE51QnOqE5KfPPEB/rc6oTnVCc6oTnVCc6oTnVCcBLaeEfYNAR1zD09b+JICLVBSzi2gBdUJzqhOdUJzqhOdUJzqhOdUJzqhOdUJzqhOdUJzqhOdUJzqhOdUJzqhOdUJwsh50PVCc6oTnVCc6oTnVCc6oTnVCc6oTnVCc6oTnVCc6oTnVCc6oTnVCc6oThsslhEIQ8EbqhOdUJzqhOdUJzqhOPupIf+jahdRra4UuIquFsxhSqLdVwhJ07TsTIa3+qSSwSD+npz49aCTzSNpyoq2epPSslEPIudVCQFUH9YgqIX+nfey/6tD7L+rqLs9A90lTtKn8MNBKhEH3dOfHvJqP49/W1FJvtvI8iOOX7t97L/q0Psv6iyNa67IjIp9Pzxsqdtr2ys7b93Tnx7yaj+Pf1TrJgw9/PzJ2SNkTbYLuuvu33sv+hdFvAqauWdCmTWT6dp05z6+weZS6dcr63+obLvv3kudgE/JFrDFPC2WvbKvtn2A7uWW58mnPjxp8NfG+0GjCW8DbHJbCxG6j+Pf1TJeaw6VsoYlolZJtuO7/AGHORjYp45/Jfey/6E0LCIRKocHB6AQWJ9ADIMxjY2UPsvuse2RPNbvVmpIZUbG1eETTfaNlr2yr7Z5zJVgEDnc+w08Y+dm3Tnx62hm57K8uCqnENiqSK8uQzVQzCKD7t/M8ep85T92L6Fgm4jbc92+xdkMgrqK0aANRyvnqtl97L/q0Psvs15ji12ETIOPpp/NA819Lw327dnGrhtKPV1bste2VfbPO016QRvXdpBfxbdOfHvJqP499mSRsTGPSRmzWBL4hPONwmS/VWHesxySN2WzeK389nO8YDUNmhVfxqjNO9n2X3sv+gqo1BjRzYUtQnCS24EEKKjkofZfZoV3mZCY2Yu07ZpHtfmt/wXx4q7io0HI0n27ZbdrqXtdXed3pRelpWRGWW3Tnx7yaj+PfZ1F2et7c93AwQlpg2tPT808qQQ1m5pQAqwTKu/K3t2y5lbBYxStmi82od/R7XtmaYm5lVsvvZf8AQvx5CqSpk5h4Fmglf4ZsNfCk5Fdp1qsrfs6e99gHe7Ttmke1+a9TedYp+luIlhtNJdv2W3a9KqnL89jM5ixpui033fbpz495NR/Hvs6i7PTycyrJ9tRdo1p6fmsu3A+9ZBxXnD+Gt7ds1R/OqXfWea57XYLipuXSS/odl97L/q0Psvs0xTBTXTMZIB3u07ZpVN1R5r5v6gQGIyv1F3jSUn5Gy27Xo6RfF+c2d3C8JG0ene8T2HLtdmnPj2pORx8xjtOyeF8LJxdb1U2d1B9nUCb6emLXCfbUXaNaen5jE4hBl3FeAijK/wDpW9u2a0/hprsnm1F2cyRHSaliSO10j7PT0z5mZfey/wCrQ+y+y6bw79ZKrW0/1srTtmle0EmoMR5ThmlQVnbr1EW10xFyJdl6u6o0d7/zEP5Y5fpOj36P073gpf8A5ls058e8mo/j32bztVDNzLI2ylfd0XaNafwwMiSS18h3spQ2MfN6KRq+GoXfWbNa5prsnm1Gu6nn9zqzuOmU5OaX9HL72X/QtTenVtaWTJNHcGJXsuDyJQ7OImp07Kyat+w9yRtlk5outl3R0q8Vhads0r2i07j5YpFkHre3akjdHaUrt1nsvu0aO7n5rLtxXoy/F6+PwxOoS3haj2ac+PeTUfx77OpSORWULnsuiHf/ACqi7RrT+GAd78li7hAm3uhVN6DQrFlN2vZrXNNdk82rO3BsbJc6tg4pqxiR22jyUnjy+9l/0CxYzhQqvwTotONjHXTsXKHgYLBQ+y+xZduT65rf0NHzLPlr2zSvaLTuPlBcyarre3H1/jLKuc2PUGy+7Po7ufmsu3WvsmwOJ09bs5Zeru+bNOfHvJqP499nV/p0S8dzZzLDqei7RrT+GAd78ls5G1hLOVFk9SscGnez7Na5prsnm1bKnhXfTVpkT32gHedJb47XL72X/VofZfYuV3VbfwrrN/EJpAmSOwte2aV7RdTMHM8ulE31qIjUyL5epkbTMu28VTo7ufmv7Bwhlsn+tq+2W8CSTasXfebNOfHrrfIS0qYmi8RLPFHZEvsdVTPioPs6wzTfv9Qy7rjShviANafwklZFgMjUv/JqztzIWSxZZdu0k5VEGKYVs1rmmuyeZhySxv8AluTGICfpXe64y+9l/wBWh9l9jUPZzY27tZ/QSvIeM+1XfVaQcq1dmQpNJ5JHcMek+3bHRpHq6EpS9UZc9q0d3PzalkV9tdjpFpHTa76XURCijHd2Rd6Zpz48SJAbE8AaSKSsDlikBGlJ1H8e+zf7lsaWLgutQdx0kY6Kw1p/G9lWGrG+X+TVnbgvZ4bGswenJfC1unZvED5rT9tMORaXzV8SzHWYbRdXZfSbptHp/tMvvZf9DUPM6GBJC2Kq6Xy5qgYGmHklJrdOo9K37Goez3sTYLHV0/OjFyweklLpV3DTTdn8hT0jG0n27ZP8sEn5OpufHz7ntWju5+a6gcTfaijVdN6a7Jqztz+OUysXfXZpz495NR/HvsmsbLdUbuK71Az9dpjvWtP46hXfRjLv1f5NWduRNybKZd9JpKRqh5rT9tKdo8s0nKh0/wDS31A5I9T5qruek3cu1y+9l/1aH2X2L6WNwV9BxWJ5EhkYy/Wb43Q/GyH/AOpoj5bETbZduoB2wVey3esOpEJYtyPOwjUV7IkdRo7ufmmb/wDLTlRKjTXZNWdurfc0Xac058e8mo/j32WyMMvQ1Sttb332lF/3GtP46hThpCiJG2fk1LF4gfaAf4WDR+UR8tiJrTNKdo8pn1Ep+16h9TNVdzrPwJl97L/q0PssONYDCNN4gaOZkzNiORyZqM9pB157qNfy2fhnl+M6cdwUE8q8Ol2Kyt2nO4Aqp6SVuzVEkPikdvdpVf8AZ6ncjaXTJXhbXPEs8VtVdyOLR+qS3LJSaYka6n1NFzKqqge8rT+/o+ac+PWhc8MnWZ5qrrp0zHagfNYaqJYPQbJrFGWRE7Roeczm5xIrstS3AgUZCTX1t/A3jdBpmVILfWn7ajkTwUEjnv8AJbTtiI22sCB2EJMkGaT7drRfrpAlJAt+7Ay2mw7TjImBV06Q1uofUzVsPCULHypcvvZf9AsqMEUS2jIj/wAmi8MRc+GjHnYVBQ+yy4vm2Mh19JAMTZzrW5bGuEWkn44jSfCDmqsxshMZoD0dFO1FUkdyu0nVTpBpib1KA2KCp5jeNx6xiUUqyC2nbKL607nIxLiZ8QV7O0iQQVJavThjILTWBUXhaZP9mca2LKI7nW6TNdLlvZvrmn2SvIFkRLGV/wDqqAzwgGpimtrASvDFwyc2LNOfHj69p6dDi8FJpqB8E2mq2crUfx7LWybVCy2LIdR6mvpJpwrPnalw0l3VmuRzdUTuYLp0pQ7LV3L8JM5XC0XdNaOTNTSq54r0RwtpBPJOf/rbo+SCRPql6iuscWeNJTLHwpR+5CDB/DM0m5HVmpplfdaPbyXn2ySV2lymPBDKYaM2RrndWXxxpsaDQP3Mt145gbDjfqs1JCqLwxMmX3sv+hahdRrYqskhI6guRsFKcC0OsiGqdOxMhrdk7t46yqqabuUUTUFpITJp06FEPunFuBassyyrwzs/UwJw2LCebouKycypNYscqmqoVbaM45LNkoFUZGKcbqVFymM4WEWDyWqTLNGf7QRiIJJG4d+oCXTzVBfJsLiy8YeCSsZVXZvdqYi73HXhqGZZmKQ+sYjoX+lXO3NIkUpOWkZNQqurs058e8mo/j2azduqYPXNfzDOaqJHfPMFeU51mRYtjpiLFxcYbEbBKS6Vyt/TB/hbq2dCDEIcY5qqyWvNVlo+0hZTWVk0ouG4jEqibtx5L7KXpry3vmJcr2WDOYTaIssFAW6B1g/9XW2sUQ6zOVK8qSKPT1g+CtitpRLUiw8K+V/MzltaHPgsixsJ4J5Kd/KMy+9l/wBWh9kT7YGFHzLvyMbiYBJypTSPE5XFfVJN09dHwwYX6UUe4mYh7Ymrlu1OKUd0U7C3tYs7lh8ZzJMi3xMzfuyw9pAm6C2w+ZZ4U/fOFfDNJfGSWW4iKcvjCRu9A14AX+iL6OEr+fT9uzTnx7yaj+PZrFvHWNj4QY4llySDhFhK5ETlVziDWziQ/iVPomGeoxnJHmer3V3uuQk9kjHMx8+9kZSoS8jigr/dpL+RsJZvOxZGjHnfiMbsI4hyd7o1eXI4mxJ5z1Z+EheBk374qo1KhVcbl97L/q0PsjWq4MSPlk1se+ZIGNydnLmr4+ZKxzoXqm7BGcsbJ/2j/m+BXObC7mW+clsqvYsbsRd2BgK522SJJYkTclhGjoGs5r3wOYsArXzSC8oKCBZgo4llxHOmwcGR7eUsIbvRF9HN/E+para7NOfHj7BoCOuYenyalgZBJcMin1H8ezVkayVv/wCSBDuFIjb4bhXOW3pg675BIv1ewz1XR70cE5MqmKkw677EuDjhRN+cpeU1qvUAblR7ZIN0mWkSNkbG6ZUg4SgRWTQ2sf1sR0Zj4eCFsT58SsVzS/3l/lhPoU/c8vvZf9C6LeBUxHEARizlcSlWsoDCkkB065X1r28bIIWNInbEGTMxWqZWOgQQLglvK9WR+H5glcCkkTY+dhTVajV/C72YYzZCLOFebxpEOTDvQUKOQm5rvDHMTjei714HYib29KR8VXwHT8KTucPyTCWMbMNXrJPPArcSPdHFWIIgw367901CyQYnmbxQGccfg3caruyjeslTmnPj1oJPNI2nKirehHQsbWGNstVCQFUGX0ayBV4ozR+YjR4ofGSE1nhbEIJp0ktfLFY8pOceOJACjFVD04J5U/NJ/FOFEyQYdn6t8n5ii8kkKriKDAHVJWs4om/jxfokQ3ETajKOiMR46CKaytZxZExilxVT4mvjR6+EadIZCnhK9vIHqmLK8l3AUQqcUI7pUMg4K2tl4LXL72X/AEJoWEQi1Iwbf8ar/CSUo0sEcbYo6H2Ur+XE4x0iPJe+OOd0TA08ZpoUcDnAWD3ASTrFVFv8IZDaIOGJbyGTE06i0bH/AJUidOuiOXYn2T4JwJjtwlIXG0lTA7DUFvYDSudYrFa/5AIgZJX4h9TTC5WFKGdCTylu5WJaXEDxiX3TfBy2MXMBtooJbDwZFWfTMr4CJmurCzlKGSZyQyyIOQl22WoXeuaa7JmnPj3k1H8ezVRD4KuUrhnLnSebmLlm4WE+2JBbWETPLgOnY46IvghW2ZEPHI69ZqMVoVgDGpRFcbHC0RzRcIJiU44/xMtBYi8VUYFABMZGGVU27BHn3Yx0I9nJGRYahlsAoyeAemmRzqmFxhLJmh2nWIZbyWdGi0uoIoEPCHItLodtZEVPyL0ubxBT5FlWttOnEans43rW9xy+9l/1aH2T28xmOjVHB18pssRMsCIqtVsisarlVNlX3PUjFfTZqaNYbqhh3jsYsr/3xUVqxSLDLjR5ZGbd30weueRBqesU0XN/0xTXqA0+Zo3Eu4IN55Mleg90UjkKWZVghDklqxBmhjZpz495NR/Hs1gm+szgdu8I7wvMdy8V7lbt0Z62so/1Ol/rcZZivi0+2JVHyKbls2PifH5tNBSBWepq3wRmPfx7CS5THTGSkQvesjgaoiwaHCjq3CSHlT1lZwXWX3sv+hNMweEOyHOij1ADKO+/BjGY9sjKH2WP05GRcy1EMxVTXqA0zSEc00Oj2csjSb4ZpdIhvz/EY0YzSnNbHp0OEpU3oynCYmpwoZK+oq0Dq63Sso509Mhd3qOi8S0fTc76qs0qjZAa1gcQ+mEJK6SJvF0rz4C9PQTVxOkI3RgVDYK2aJs8P+OQk2UmkF8WmjmNHfQvWxn0k5hX+NLBbVtTBVsmpoZ7EanQW1OoCENpQ2B12zTnx40+GvjfaDRhS6gAhHfbiRmaj+PZNE2eKgoWDRTaaj6f02N9bNo38M+j4HRhaaRbGbSIzssNLcKDaRhjmraiGrdMPGRkQkEOW9HHLa3VWp9dSUvSUoKLwcFppmSMsvTMsiC6UZ0+3F59bWachHdbUsUuBaUjHMP0vCYVNo5VJ8NH4mxqoLNtNpyI0EfR8j2maVihiqdNqasGk5HzVOm2zPghaPCJp+EMWOj8JS19FK8tkbY25fey/wChdCPPqWBkmo6uOJWII4Umvr3h0unWqyt/ucKIv3NOfHraGbnsry4Krph4zW05cD9VQvloP7rWoxPt33sv+qylkhzpZOV9efKB0snOlk50snOlk5YV58QHSyc6WTnSyc6WTnSycJrz2TdLJzpZOdLJzpZOdLJx9eeh/Syc6WTnSyc6WTnSycGrz3zdLJzpZOdLJzpZOdLJwKvPkh6WTnSyc6WTnSycsK8+IDpZOdLJzpZOdLJzpZOAiNAC8hwjTwulk4bXnxw9LJzpZOdLJzpZOdLJx9eeh/Syc6WTnSyc6WTnSycZXnqf0snOlk50snOlk50snAq8+SHpZOdLJzpZOdLJzpZOV9efKB0snOlk50snOlk4bXnxw9LJzpZOdLJzpZOdLJwmvPZN0snOlk50snOlk4+lkm/690W8CpqpZ2l7dRSzDBtsTACupnjNpiJZWbLe2MpiJTThSW2JxK15KmAbLJbRLBh85TkvTiA9lhLPBbtvDogfGmIY+7OlBTZK8lNQeIsphHWJBaMuiydvNtFgJtiJ4EtSpC6SxKII+1dFFeKDn8UJtsJrJpbD5ynJenEB7RLGwbjrc+cZLUqQvYUxZIBj7KMfqZTTqE8qabYYeYGWw02UxL04gMexKdYZI1XxoWT/AI/KUeNYyXhgsdRMS07DWPkFbZHeE6mU05tyfCJTzzOf/wBmaFhEINfBXM2nU41jNHUDRkxafAhHEDiBh2S04s5LKAGMaTT4Mo6IjU2OrYHYtCEo8lEDI7YZUDnkR0IMSdCD8PJQASs2E0YhhcFEKMJJp8CUd9OJIXsj06HFkunwJoXVArjhKgUGf7R9SLZ4ibk2x6fDhVaEJR5KIGR2xfqn+PA8MunwJoXVArjthI7CxyagUxUqRUOBqRa12x+nwpSFoQlGkogZHMqBYzsexJGN0rXNSSnGmljpg4iQKsasTCB2lQs02CyBKkVDoqACGIMGECL/AP0L/8QAUhEAAQIDAwcGCgcGBQMDBAMAAQIDAAQRBRIhEBMxM0FxsTJRYYGhwRQgIjA0QnKR0fAGIyRAUmLhFVCCssLxNUNEYKIlktJTc4MWRXCQgMPi/9oACAEDAQE/Af8A+ZClBIqf3PfF65t8dTakoSs6DkCwVFI2fucKBNP3w8/elxe0qgYj9zf6n+Hv8d/0Vn+Ljka1znV+52te51eekzVkfuF0lLaiIaJU2knx3XDfUmvqwo1aT190J5I8R1wt3abTT9xf6n+Hv8d/0Vn+Ljka1znV+52tc51eektT+4XtUrdDOqTu8d8gOr3QVAshPTDDhK0gn1fEnMEp3/uL/U/w9/jv+is/xccjWuc6v3PI6V/PP56R1P7hmCA0qsM6pO7x5jWufPNGyL1LpHN8YaNW0k5Z3kJ3/f5VIUh2o2Zf9T/D3+O/6Kz/ABccjWuc6vFYUVX6858eVQlYcvDQk/eVE59I6MkhpX56TIzYTt8y24HU3x94ntVDOqTu8eYP1qqbYcT9YoCCaBO74wzq07ss7yE7/MggiohyeOIREmsrQVHn87mR4NnttadkTDPg7pbrWkMiriQeeJoBL6wOfIy3nnA3zw6jNrUjmiU5Dvs5f9T/AA98OtqZWUL05cycznumkKlilhMxXA5H/RWf4uMLWQ6lPPWJZzOOrUMjbFxLqXBiBllT5Tg6cimlIQlw6Fd2VKSo0SMkszcaU5XlJVE1JqlttRCkqQbqhQ/d1ekJ3ZLO9bqhSggXledlVUdSOj4+YWq4kqiR1X3QmgrEusuNhSskuoqSa85ie1UM6pO7x1Kvrr0w43SYITtB4HJLKvMpOWa5Kd48wvkmM/dlcNOjJIH6sjp86yznpFWOgk9kWiPtKzu4QxrU7xE4zy3q+tTIyyGZpoDaKxNpKZhYPPEkKpd9mFuXCkc+SQQJiaeveqk9lILCX7QWF6B8BCGUJfYpoUAYYTRx4U9VUf6H+Pui0Wvs4Q2NuzryTarkk0r2uMKdJebPRxiz+UqJaVzU9dToT8If1sxuHdlZcS26pJ2mJHlL9kw/6Kz/ABcYl2ku372wE5LO9KR18MjCSuVSE/hVxEWijOXUDaruETys5Nu02U4Q0jOLSjniYazDpb5vuqvSE7slnet1ROahXnZTXjzD2qVuiR1WQqCaV2/cntWrdEnqRkleQd5if1Q3wzqk7vH0ISek90L9LRu+MEUNDEpqU5Zrkp3iJIgsjx1ckw4qksB+buyWfpV5xUutLSXthhLQZkiAKeTjvpFoJJbXT8Q/lESki4sh04AYxOalf/ud0IZW4CUjRAH2pg/l7otdkUS6IlGUtTTrQ0UiadHhSgdiz7oU0Utod2K7ostJTNTFfwK7oKRyolWS4ZZewITwMO6xX/tx/of4u6FJvEHmgJJBIGiLRws9v+LjCS0HWHFYgUrDKAl9xKMKJUfdjEipMxSZveUQK/O+sTLaQ28vb+ghtpbpogVh1hxkkLEH0rriQSSpdPwmJlktyLYVprxrFntqU443oN0x4OPB0ObSaQ1KCWmmyDpvdkSraXM5e2JJiVazbbe7jSJxNC2fziFJvOzYTtzcJl1MzQRpoRFs/Vy7y08yP5vurCipTZVzHJZ+lcTmoV52T16fMPapW6JDVHfkmNKN/wBye1at0Sbl0BHOTwySvIO8xP6ob4Z1Sd0Xj4Rd2U7/ABl8jrPdF0E1hzlK3xKalOWdN1sHpiQ1R3+O6aNqPRDuqTvPdks/lK82y1nZBdBjX4Q8yly4nZX4xM6he4w6yHEFJ207oSkISUjYPjE6wgy6vf1xZ7YVKXTtrCpf7Qgp0IETYvZsH8XxhoETzleYR9IJZLL6XUDlV9+3jDiSmSlgeYwhCjNYDSz3wrREh6IxuH8sLQVPlA05vvhuXUzIrSvf2DJLyNxi6oYmleo/CLbabRI3ANuEJQVs53YKD590Nm6srVhVKuBiwTWZ/hP80TKasL3RZraU3FjSQriIQlKi6FaK9wi0WP8AqSmmBtFPcIsdJAWSObvi0wS0mnOIShKZgKAxIVxEOsXkNtaPLPeYRo9/GJRlbbbpUNKYZ1ad0TAS4423eoa190PTbUqXL+m8Oy7CbVacmlIQOUpEWgKsUP4kfzCJ1gsumu2vH7pLaW9xySGlcTmoV52T16fMPapW6JDVHfkmdKN/3JzkGJXBxA3/AD2QpQTpiV5B3mJ/VdcM6pO6P9T/AA9/jXAmWFOfvyOaVb/jEqKMpyz+q64kNUd/jvatW6HFeSEw+i6EEbREgfLIht0rdWg7Keas30VPXxyOUWgo54BrFMDkbTdTTfxgawnd3xNO0tVlJ0U41ENqQ65nUGop3mLYbLsksJFThxEWgycym5oQIYbu3FK0gU4fCFnyYkWy3LoQrSAOEYZ9JHMeIjBQpltpGecl264E/CLGaz0nMNUqe/GJ6UU3JsqUKHEH3kjvix6pn0DfwMVESzWYaSg6YKQmpG2PA2lTinynGiffU/ARKt5toJid1J6uIhWvTuP9MKQFEHmhIoIbTnJUN86e6G/IbAMWg6RaEvQ83GJwkzTpVzmG1FC76dIMX76QeeLVVVxI6PujLobUgnJIcpUTmoVDGqT4pSpPKHjyevT5h7VK3RIao78kzpb3j7k7yFQwaPI+eeJlz6xDfSDEryDvMT2qhnVJ3R/qf4e/xq1lQfnTkdb+tKOcjtiS1OWf1Q3xIao7/He1at0ORNatrd8Ilk3Xv4fhDHpDvV5qQP2ZPztgKxyN6YrCjRUA4kwdEWhNqdnFH8OHGLOfWzZ7ijhdrT53mLNnFWiFMu6RTrFfn3wJht5F9s1FRxj1YTOoW+ZQDEfPfF6lRFBW987PhANDCzhBOyHXg/aLbJHIrw7okZbwVxw7FGLTlTOsBIOMONfsl9t8mtSr3fHGHZtptAdcNAYrBV5MU8isIOyJs/UK3jiIWPrAvm/SEKrANRWGhdATzCJmbRKN3nNBIETj5nXVTCMAgDj8TFkSaH0qfdxrUU92NYbsNd1wrO6Ju0B4E22k+UQPnsh+ZTOMIdSPOFtQSFEYHJLtZ90N88HDIATgMjfobm8QNOSQPlmJzUKiX1SfFnf8r2BkBB0ZAzVku10HLLGjyfHZH2V3qh7Vq3RIao78kzpb3j7lNmjJhrFaN8Tirswgno4xK8g7zE9qoZ1Sd0f6n+Hv8YIUZUJHzjkLBU6VHnESR+qyz+qG+JDVnf483qVQ4KEAxNovISgc8ITcfCfyxLLrMujzUotLcqFK0Y8Yl5luZvFvYaQ4+21W+qkSsyiYRnW4cWE1UdEIcS8AtBqIrpietNMmc3SppWMyXrPceSPKWa9sZxSGXJClVV7xGZVZEznEnySFUru+NIsFyra2+Y19/wDaNKYmZ1bM846zp0e6nwjwi9aNArySnDjCVJXik1yE4CKxKIDs88/zYfPuy2ywp6XvIFSnhExNuzQSlfqwq3VZtIQnyttdEVj1Mkw+0yij2hRpBguoZBccNAIacCgCNBgzjKUuOXsE4H3xbl5SUNp6T7hFnyLKJcEit8AmsS0siUbzaI0gxJySpl8srPJr8++GiptK5c+qfnh5xPIld5/miZ1695iz/SUQrScllisx1ZAaSLpPRA05LIQFvgHRhxi0mVHOJaGAJ9wiVYcUkN0xpCGSttbn4adsON3mGAkYmvGLRbuNM1GNPhktN0MoaUfwphb6g8pI5oklVSonniTl/CXbsLSEMOpGxWWX1yYbZvtrcryYm0JRm7o0pGVhkPX67ATkA8lP/wAfGLQabcfSz+KtffDDYZU40NiiMkzpb3j7lParrhgVcTvi0tYIknAoFMT2qhDmblguFKrMoI2j4+LNGjKol9UnLIKOKMs/qhviQ1Z3+O+5ebUFHbExy07hwEON5ynQawqvhI3RLrCZtfT5pybU2FtVwI7/AIVix3cy0+5zAd8PTC3lqWTp+NaRY80EOhpSqCnbWLRczco4einvwiw3rzBbPqnj8mJmZWzMtNjQqsWgnw2fS0k7P1iWl0yrQaTsidZLdpNubFEd0fSAeSyd/dDSxIziU7CjHqH6QxNmXYcQ2cVEd8LlDNNOPNYkLV1jCErUhV5JxiwnkpUtonE6O3Ii0GXZczA0CJi0USz9xzk3a9dYsdxTrK3FaSo8B4llS+cmVuKGAr74UlTSrqhQxIzJVJ5901pWsSlpiWlUNrFfJJ/5HDsienrjzF00Gk7j8mJiedmQjOaU1xi1n15phaTSorwidmW37PVRVTQcRFlKKpNFfnGH5sp8IY/Erv8A7Qy67NvAKxN1QH/aYlklDCEq0gCDAiVkSxNOPk8rvxi0GG2VX0DFWnzjbBblkBweUCP5onWQg5z8RV2GLP8ASURMgeDtH2uMJ0iGB/1Fzd8ImZdTLxbA3QzLKfknmhyjA05Po9yXd7f80TUtmWZlf4ge+B6cvd3QtkMNzDY2XYTyJXef5otBkOjHYFHhktw/Vs+yO+FFWdKoaqm8ndxEWG8Zhx5axjh2xMNqQy6TtVlltcmJf0Z7q4xPJN1lX5RDHor38PGC2sUNNOiJL/N9gwltSkqWNAhlAWlNeZPZD7dbQaKuZXZjGYSnPvk45wjviz1Nqm0IPTwi05UtUc/PTv8AuU/qxvhlJQ6Enni0jVyJJy44K7Yn9V1wcZL554Gua3DxZvUqiVNWU5ZDlKhtwrcWnYKZJ/VdcWefII8d4/WKHSYm9aOrgMkyfK6jEsaPjePNP0zuMAGSbfZc0+SO/hCUlaglOkwQUmhi0p/ONOsjnA+esRYLlHFo5xX3f3i3VHPpTsp3xZKVLmwpXqj+2SclS+tpafVPZFveiI39xi3PImgU83xjZFnU/Zrv8XCFSYEmJkaaxJLU0/fGkA/ymHrRKVG96yB1EiGXkIs1bajio4f8YtJxTkwa7MIsL0ZXtdw8SxF3s6DzxalnLWtcyDhSJIj9muJ24xeJAB2Q4q+a7uEIReCjzDvAicdS5LsUOgHuhC7iVDnHeD3RYbqy8W64U74eOceUU7TFjIK5sEbK/DvyHZkEWpoR193mhKkobWDyjSH5JKlMt02Y06ImOQN6f5hE8wVMpoMbx7STDDDbI8gfOETrSm5doHp7YRIjwXO6VGkN/wCIL3fCH3VTFqJYT6mPAxJ8p/2jDzK5dwtuChETA8hk86ExZksJR6YZBrQt8YtL0N32TwgtJSouDSQe6Jr/AFP8ENtlTEu5zHiqJnUL3GESSlshQHlE9lKxbiF32m6aEfGPBkrs1MwNKTxiaZMsoXdC0oPDvEfR5yjq2+cV9394tFJEqd/fFnyiHb2eTzUh+ziWi5L6anDrghcu4a6UmLOlw6wuu08MYn2krcZa2f2iUlVJS80dow7aRQBcrTmhxnyFPV9SkSqfszh6U8YoK1h51X7aQ2dFDwhx5Ty1K2FVYslaWp5tZ56e8Ui1ZUvBtCTipYPuSfh9ynElaUpHPCq+FYc4i0NaYl9YiJ/VDfH+i+eeL4DqCdghJvAKHiTepVDAo0mmWTTcdWmLP9bqyT+q64s/kq8df1jpA2mJ9oIWkjb3ZJ1V1STviWFXx5qYTXGLbVR+7zgd8WW1WabUoYY9gi1kJbnXEp+aisLWXFFatJglMmt5nqi0HS6pFfwp4V74sI3i4fZ78sxKNzjaUOaAaxb4PhKT0d5jZFit3ZSvPX4d0SjGfszN89ffXCLTbMu42jbcHfWHlhxVRzDsAGRSis1VFitKQwofmPdByLWlsVV0dppFloSkO0HrmJ70VzcYkl3A8fy8SBARVBX87fhks5jwlxTR2j4RLtB1Sq7AT2Q23nFpRzw0oy2eTWitHaIlsXkbxxixLvhOH4f6oUMKxeStKVJ0EZE6YtXQjr7vNS8telmgo6Me+CPLB+dkTHIG9PEQRWkAUETbBmGi2DEsLrKB0CG0Jzi10x/QROOKl7UW8Ng/pw7aRIYpUv8AFQ+8Vi3JEreQ8n1iE9cWghLS20DQEgQxLfa3lnQq4fd/YRaXobvsnhF2qa9BjMBx55K9BuwqXSEBCNhr21haQtJSdsBtIAA2RbLKnZnyR6h7x3xY0sl6QU06Kgn4RbjLaHGEpHR1VEfR8HwlR6O8ROozkusfOGMSqKISv8qe/wCMIBS2Qec8TH7NW/NPl0eTU95HERIM5lgDnxiaRVxpfMfnhDYo6R0J/qgM3XG/yg90OeiK9nuh2WTLMLCNBI4jI+8lu2b7hwH/AIwywtxtTqRgnT11iW8l5KjovRN3AppSzTyveSCO+J5pLLxQjR9xmCb7dOeD6V1xP61US2sRE/qhvhXofui/eX1d0M6tO7xJvUmGwUoAOUP5h1ZpEgryimErSokA6In9V1xZ/JV48ugLe3YxMqLjba1acYQq+kKi0PVhg3XQfNIoXkD8yYtpaVzXknQPjFnS6G2g2MaFWPWRH0gQ2h1ChpNa9gES+uRvEW2EpmsBs7zDxzqc4Nl0dn6RYLdG1uc+Hu/vDLgebS4NorkToi15N2cupaG2LSsluVlL7ew8aD53xYRJl7p2H9e+LK9DR18TH0gaXnEO08mlOvGLpu3oSqtRCEKcVdQKmGxQwsUMAYGJzVj2kfzCJSWUznK7VE++HUBxtSDthv6nOtr00p/yHwiXYceZcCBiKHjFxRSV7BEnJLlZhIOIuaem9WLGSFTKknRQ90PtJatVLSMBUf0xOekue0eMSjKy+AkV0HtEWK0pqfW25gQnvhZ9WJP0Vn2U8IWKGG1Bbgp0xa3qdfd5qW1CNwjb4qU3UhIgJoSeeJ2ZExMOkfNKfCG5lxuyi8jA4dlBBeFooYXo8vgCYmZdD92o+dMN6YmEJcQUK0GEcmADnlHoHf4ho7aKkbLlD74sZCm5aihTExasrn80UjG8PdFnMmTnG21iiik8T3CFAKF0wBTAQvkwhm6XL2hR/pA7oAphExym/a7jA1ytw78jyfqFITzQpIWKKyWi4Xppxym2nd3RY8irwVyp1g+PxhmTJkHJhQ2invIMWw+hssXvxA+6LUTR+9zjzRUE6clQDTJdNL2zIpV1JVAdzkwBs/SH6JdUvmIidQb9eenAGGVXFBUT+qG+Fmkqkc9IRyzDOqTu8SY5FPEfFHVRKuBpyqok1XlOK+dsT+qG+LP5KslfFlR9aT0Q9qW+uGdUndFoDkmCi4k9Xf5p1TTaJdQGN7Hq/vFooKJxzpiVTcH8Sv5jH0mURMt7u+G2DnWLg0gH/kYtl2/PqR+EAd/fEtLqflXin1aH+aGXVtWcVINDeP8ALFjPLek0lezDqEUwrA0Qhd5ShzfCsWqhTkmtKBU4cRFj3pdh0HSlXcIsr0NHXxMT7q37MYWvEk/GDqgOk90KbDcy8kaAqnGLPCW1NPHnV/KIkJhUwm8uHOWBvhRuIru7TSLReS2pDR0qUin/AHCLPeUp1/OGumm4Ew2vOVpsib9Lc3njDTCm5mYVTCgp7oB+zOe0n+qGng8CRsJHuizGft76uY8T+kTDSnLZojZQ/wAsWgnNzax0/rFnpRevDTcR3/pGeRLz76jp8jhjDc23MXlJ2U74klm5LgnDNnsKYcPlARZa76Gzz3j/AMotRwrUjdX3+altQjcMlcaQDXxUNKoHaeTWkPqQixkgbfj+kWM8ESzpI5OPZ+kSzqZtlLyfnZDemJF5xyYmELNQFYe8wjkwDXxLLP2+ZHSeOW1yRaDRHMOJiZn0S7qGdKlEe47YrC9EHRAh1srKSNhrlIrhktuYVLLZcRp8rhSKVlFOHaocDFjmsmiJ6XbRILaQKAD9YtWYLzoR6oAp1gQ66p5llatNO/zJISKmJp2jqU7MDAdzJzg2Q6tT05voR/FCklCik7IS84pxMoNCjDiM2soOyCyXWnCNiSYk0Fa03RjE419WXAOaJlKlNJdpho7BCMVRPrAbAiYlaSSAMVG52hUIaSCSo0whpxtuVbVtJPVT+8ZhCHmR+K6ffEpLIcmXFH1To98TjJacKqYEmkXM460n8wgJKjQQGlqF4DCG2C62tY9WLVkMy029tMPtlASo7RFn+tFoOYhuJCocKeiJdllTzVMQofGJRV6+emClJKQ2q9UbOEBZMyUcwieGal2XEaVE1izyEzqg9yaV6qRZbSVLbvCvkf8A9kOyafAHHSMUmg6PKxjMrYAbc0ikTzBMpnvzU7IfBTeB/Lw806Q5mW+ZXGnwiYZD9q5s6PhWGHnf2rmr3k+Vh1mLfq461uiylp8KZCtObP8AMYttJTaThG0d36RY6SqWmUjm7lQpH/SU9ffH0e9EG8w26hwEJO2nXF8JKr2gUhnWO7xwEBQNQNkSiriJo/nPbSG/sLIYBrRKjXcf1icoLNZu6EqEWbI53MvDEY16CNETSimddHOo8TFluKUl1oaLqvfh8YsnVHf3CDaKK3b1Cb9O6C8pUuyVaVFNYttaW3W3VeqQe0RZbmdmM4PWvH31iwUUk6cxMTiKPLX+ZXd8YfcCWieruhyglyPzI4KiQUoOutq51fzGJEXJmZcVow4Q+t1Typi95VT2RbrQbmgRtx4iEuplZ0SqRyh7qCLQ9JV1cBFm0KXEfO2LhDLaR/6axwiZmc3ONsU5QMWJqWvZV/NDjqnaXtgp5qWWMyjcOEX8IvY1hs5L2NICqxNTiZduqj/eJlpQs5i6NJ7Tohdx6z2GRtV3mJVzwZM01sp3074sdX2JIHTxjQBEtOZmeWgCt9XeYk3VGZmATouwlVBCFbDBNCIUcIsxKkuvvHQpXAmLwgrpFvLCVMuDTj3RM2iX5pMzd5NOw1iYn22Hm0H1oWcIVyRCDhANRWL4gEHJXEmPpAoqdRuhiSQzKqlueuO+JdAYQGxsET/oju4xLsKmr4SKkDD3juhDodZQBsw8zMYtmF0W5f3cMYtUpQvNoGnHjhupA5WMWe1LzDYcTiaUI6otN4JtNKjoRd+PfD0h9pv+qf1MS7qHJFxRV5dxQp0RYVntpbEyrTsickCqTfSnlKx9xqIkJNE9Z5aXh5XbQRZ8msuOlQ5ANfdDhzrSEHTSJF6+62Fabzf/ABwi0ZMyczmzo2boNbtIZZE0lmaSrAAdkLeRIzdymtp7/kxaF2YK0j/LST7xhwjPtvzLGbwNU15tn6wmWTLTjd3bXvjMpoU0wMMN5tdzmSnvj6QN30N001i2ZFwBnDQnHqpFnJ+0NoPOI+kEq0HwGU+VQn59xiTSb5WfwwxNeDJQpvlAn3UH6xYbKHn3G1jp+ffFjutMvBbxoP0iWIFsX9hJ/lNI+kDQbbZQ3hQmHplTjylpwBAHUIshlfkPU8m4R13zDsolEups4haq+9QiaZOYcppVTuETVm+EsrZT5ONR7ocstNXFjA4e4JA4jzVKut7xCUNi0Fvr9Wvz2wl0JnDNb+2J0maoo7BTsiVUGksPgYgf1GJ8Zx3O9CuEWaPskwfZ4xOBDCEybfq9+PfFkUDZAhxZYaWpvD6w8IbWVSTt441HdFnv5++pWknuhEw2h5bajjXuENrq1OI5l8SPhCllphpY2pWPeREyqlmXedYEWMSJdHtH+UxOprNqH51cYst0S6lBz1gscIkZptlFFnb3Q56Q31x4WghlH4TjFr3XFp2ikSzngywpIiyAlLCqbT3CLdQlhxKEDnV1mnwhT2ckg6vn/qi4lD7KU4i+OMSvpbvXxh1SEtvra9alevCkTuBT1xbrodmGyPwjvhVHbSStONP/ABhx1T6s4rbEotKM5eOlJhpaS2kA+oqPpC+TMpU2dA0j56YlHfBGWCdl7j5ty1rkuUN4KFBFmTiUyV948k/PGH7ZddSpKPJ+FIsyfS8gNV8oAdcWlMqlpcrQaHZEnM+Espd27d8GabD3g5ONKxaKHH55bbQro4Q3LBTDSHdKaHrESTean8wrYeANItskTFxHrJHGJF5Mih5helNTv0fPXDVtIW0guCiq04Yxm3GA3MJ6t4MM2opp9x67yvkRZ074Y3VXKEKUEAqVojPtqSFhWBhyfYaKkrVin564shIEokjbXjltCWRMsG9pGiJaTMw245sSIl1ht5C1aAYXNAS3hOylYTaEupsXlUNE4b4ftNqXezKuav6Q9ayVzDRQfI29fPuictISr6WlJ07YtKdUxLhTCtJHxhp++2HBoIrAtZQlkEq8u9juicmETzjjiRoSKf8AcPjkIpBAUkpO2LEZdaS4HE0jwRyT8hzzKxeSREvKZ+XeXtR8nhE294Q8pceAr8D8L2V7Of3xKTipRpebPlEp7KxMOLmXFvkbf7QbQQ7Lrbr5QRWvTSnGGl3Lx6OOESjHgzCWuaFC8KGGpVEokpb0EkwxLqa8NWtOCq98ERZwJmmqDaIn5cPWkzfFQe7GJuzltzDiEaAL3V84QzNUl1NA4BFKdN/4GBOLW8067jcp7gaw5aCnw6VYKVTR0RJekte0OMFINCdmW20OLLNzn7dkPspmGy0rQYm2m7MnEXMQMYtlwPvpXzpHbCVFNRz5GXFWe+HwnAgdeAr2wWygCu2FKUryju90WnMJcbZZ2pAr1gQGiUBQ6ezGJBIak20jm44wdAgpC0gHo7MgoVEQvBRA8y+opKFJ5xC1qcUVK05DohogsNU5u8w/qlbjFnuOIYWg+tSFKKzeUcYQ460/VBpUQXypnNHnrWEOpTLrb2mkWcSJlMTyrk4Vc1OAhp5TinVDC8ow/wCis/xcYUAqQFfx90SUy6hlN07aw4hRczq9JXxjNJvBQ2d+RTZLqV82Sc9IHsJ78ktMJYZSa4hfZSLYdVNrS51Ql5K7PU3zd5h1ZbW0sbFCLMUXJpaztB4iFrUkrSDgYmk33EJ3xaKlOlugxAp7os0kvIJ0/pAwGWcbLhRCiDLtgfm4+bZk89NobVhe4frE4yWnzIs6K8QIlLAVerM6KbNNYmZZdnTA6MR74bEzPOol3VHH+/DRGfmLKUuXHP8APvidUZlwzNMCae4CLFlyptU0vlKw6h89kZvGFSTSnUvnlCJmSTMutOk0udsWnZcw7NFbSahUTVmzEokKcGEeCXbKLavw1/qgWK3MyzakG6aDtidYcs15SW6gHb7jxh1959Svz7I8IfbAaJwSdHTWGpd6deA2q+axISplGEtK0iCipi4IebvpLdaXsOyLMlFS8qEOcqLQshDjanGU+XDc24uWMoca0p8+6PBHs0Xbug0PRAacdIp6xp1/JhFiP3HAsYihHTgcIYlX7UC1k1KQKQ1JuOTBlK447qisMzbsqhbadJhuyppSiFIOHwwixZPMuOFQ0UHeR1GHAKVMFFTWFJxwhCcItbB8bu8+ZS248bjQxiRoZCZWNt7hFhyiJhwuL9SnfBlGSz4Pd8nm7YQhThokfIiQYQuz3ysdPuGEIcLYUB6wp2g90KlLs54OcPKp1V05XIoDgYRYTSVXnTXoiRkixad1HJT8P1iZ9Pl/4uEPt55pTfOKQpkpdzZ56VhthTja3RoTTtNIS2paw2BicIsOTo2X3NujopUeJMSwmLlTySD7sn0glx5Mx1QpLk2lTw0ICR3RaMqZZ68TysYkbMS+482o4ow68RFsyxalWPy4dn6ROybYs5Lyk+WlIHaP1jNK/Zd6nr9lIlZVc4pSW9NKwix3G261xortTTjDSC0w2hWkAcIOgQNEHRCOV5opCtPiNoDaQkQRXAwBQUHioWptV5OmFrU4q8s4wwgoBrzwpxSkhB0CM6rN5rZphpvNICPMzPqe0Mkz6ntCLPWpEwmm2HOWYW2FqSrmh3XN9cSPpCYIph4jKCi9XafNsSzawy+dKU90S6GH3VTIT5WivVDa84gL54mZZibvJVyuTX/l+sMS6GBRI2Ae6HZGWm1OFacdFfcYYkWWGPB6VT0wtSJVqg2DDq8ZpWdRXfxgAJFBD7CZhBG0giu+GZVplKQBiBSsNy0taCEuFNKVqOcmGWUMIDaNA8R3lt7/AOkwCDoyLspkzDbqMLuzhC0JdSULGBjwVhNKIGBrkbaQ0CECkISlL66DSB3w7ZTbs2Jk6ObpgKBJA2RJnWe0YfNWXdx4QFBWAyIVfN4cwi1teN3x8zZ5AmUV+cIS2ZSTeQscon3HCJdluzm0Nc509RMOvhpgu6YYl2lTLaEE3SFDj8YfQhiTU3sCad0OWUtKmQkVBpUjpPwhyTQ5MImdqY/aLefzPV15HYcUlsXlQvZDjwaCRzkCPqnH/wAyO+HZlLTjbR0r0dUPMMCdZbA2qJ30rE6yxKyoaCPJr84xLyCnZozQ0BZ92kGHJpll1LCjiqKiAaxfBr0Q5aDbDS3zimv9IgPtqCSDp0RMSyJxIS5oBrFgIUC5Uc3fE3IszLqVPHAYe+Gm5eXcW4k4rPbE3LJm2s0rRDwaUgpe0dMZtM1I5pNMU9tPjFi/ZmHptacKYdVaxPqIlVEQ9a8qlSkX8R8KxMPZuYaRzpV3frDDyX2kuJ2gH3w48ElKPxVgTjaGg6fw3uoAfH9xzPqe0Mkz6ntCJH0lEOcs5Hdc31w24ppQWnSI0+flHCWEdXwizNWv2jElNNuN3R6oEAn9pdfdklX/AKx0L/F+ndktn1Ovuiz1FUskn5xyA10QwSpJrznickuoiXap+LvMTKxmF7oQopaYptp/Lksp5ITmtte6JmZSWXQg4jvizyTLIJ+cYEw3dzuzCHV0cbTXSe4w46rw1DWzT2GLOmc22tPNjDC862F88DWncO/J4R5aEc5V2RJPZ9kKrjthaghJUdkNWihcwcNNB2n4wT9ckdB7olnAtbnPe7gIdmnGnFJRsUT3Q0+t6TdWvT+ghldxZPQiH3Bm3ANg7oCyG19CB3xPOJdmFLQcP08y66WUFxOkROWul6WS0K3hc7NMWjbK51tCKU29eIj9pPZgS+yhHviz5oMS6lKV6vbD1pLXLloLrSJW1lMSjhJ8s0Ah63gh0oRjWlO+G51aV51ePlA9tYe+kDCygIqMQTurjD1rqfdutq8jHZ04RalpOJcog1Qaf8ce2PDy7JCaRhiP5oettDgxFFJVv54k54zVo5xGCFHgkwmfVNTsuR6tBDtqpbmnivTeTTcD5XvEO2w1PM5vQqterGGZ4oS60VYVr8+6HrRJmWXhsAHbDtuOvKTUUAr+nuiXtgtuBe8YxZVqquOh3ZVUOz4W1mxEjPqDrKFYBOHvUKwLbYopCeVUj3bd0SFqp8KQ2kXUHE76f2i1bQcamlsDReSfcBhDtpIXKhAV5RXU7om7QMjJpdAqaCJy2TMu3gPJ2e74xJ2giUqD62jf8mH51pmzlS4HKr24xNzbrNlsJSeVp6omVBbpIj9rKU624oVupp+sM24Gmczd9UD56jErPX3UNqVhQ090Pzjkw020v1P3LM+p7QiR9JRDnLOR3XN9f3GytC/4eMSMwhlty/sPGJWYDF+u0QqZrM59HPDs4ZedWTiKd0MKK5lKjtI4w5MLBwPrgdVItR9LrgSnZFnejI6+JjwxxlhtzSTe4wZxSZZD229j2xIuBxu9zk8YYfQqqCryqnjBeKZJBRpSr4mJqYuouHSpKYzn1UsnphpxRUEn83YqGHiw4HE7IbeGZeCjiqnGGZpCZTNk7FfpGcXS7XCPC23Jlog4D4RavpHVDDwaCwdopFnmssivzjC5sNqdVTk0h15KUsur0f8A+TEi6tyaRePP2xZKglDhifmR4Oq5z074lzdeQTziC+lc2EfhB7olFgTby9/GHV5xal88MO3JZ1NdNIQpJJNf/T4xMzJEw4UbcIcmA1VCvWQPf5qc1CsoSTUjZF46IBpoippTxJHWxaA8lJhha76UVwrD2sVvhF9uVvpNMf0hF4G+k6IUoqN5WmEqumvmZRJDqTvieaKVZznyOzqnZZEufVyFalAA7IzirlzZDk0p1hDCvVgJKtENIqTXYDkWu/TogN1KRz/H9yzPqe0IkfSUQ5yzkd1zfX9xbeca5BplGmJxaXH1KTohKihQUNkGYcIpXbXrgmuJhDzjYohVIW8Vtob/AA17YU8CwlrmMWQfryOiFulmbU4nYTF9RF2uET2lv2RD6ihiXUNlYE08FXwr5Pip5QifxmV5G5wMNtU2Xq9cOOKdUVq0mJh4OySOg09whp1TK76NMWSaJdO7vi+q7crhANDWPCXM4p3aYkNLnsnxZ1QUpNPwjzRAUKGGZQKUSrQDHgQ8rshhgJauLEGz1bDHgHkDHGEyhzgQqFWePVMLklDRzdsIklKAUTphmVzLhUDC0JcF1UZpANaROMDAp0kwtj6jNJiWlS2by4alquqWsbYmJS4Rm9BjwRS0IIG+GJO8klcBkZoIUIZkwsBSomZZFwrSMYTIqveVoh6RBIzeEKkVg4QGQLlfVh5kPJumGpQrJx0VECScKiI8AwGOO2GZZTx6IEkc7cJwhqVKnC2vCGWEsjCES11xSlHTWGJO6FByEtKUeukJYQkJHN+5Zn1PaESPpKIc5ZyO65vr++IcW0byDSCSo1ORbinKXtmEKdUtKUHQPO2esgrRsIPiSJ8pfsn75QHz4SE6PM0r+5qQCRiP3glakYpP/wC4DT/+AaGlfFnEJQpN0bBke1St0M6pO7xrO9KR18Mj7oXLV5405NGSYUU3ac48xOISgouj1R9zcbU3S9tFfEs9IVMpCh80yv8ALb3+O36GveIcczd3pNIKx4UE9H65G21PKuI05bx8Iu9HjyCQuZSFD5p97a17nV56VUVNAn7uygOOJQdphcuQXLuhJyJ9CV7XdlzKs1ntlaZJ/lp9kZHtUrdDOqTuygEmghDK1rzYGOSzvSkdfCHHbpKeisOGrKOuE8kZJ/WjcMk7yE7/AB221OquI0xNtLWErTsSnt+4MJBYeJ6OMTDSWgi7tFck7/lewMs8wmXcCE80Wd6Ujr4ZZk0cb3wtpTYSVbcrDOdv46BWGZTPS63QcRkb9DXvEOvksoUdNeEf6355skg2pMw2o6DXgcv+q/h78mZOaz2ytPEIKTQxZzQql7bUj/iYclAGc6k7AfeYKSnSPvLWvc6vPSWp+7y2vRvEK5E1vH82RPoSva7sq/8AD0+18ck/y0+yMkyoJaVWJJKTJLURoCclxV29TCGmlNPtXttD2xLJJtAnpPfE36QvfFnelI6+EPE51fswR9Sk9Jht0EhHRWESoEmp9XRT3xP60bhkneQnfkk2S3Mt3vWFew+I4yEMtufir2RZ3pKevhDPcjiYzSVpecOlPefPyqSth1KdJpxieBTmgfw5JttS0oUNiE5FNKShKzoMWt6R1RZ3pSOvhBBSaHIpozM0yyk4w+2XMwgc0TMoGAVA4VpC2kInw2kYVENC67MAcyoaaS3Im7tTXsyN+hubxCzVlHXF8eF3vnREiC4tBXjVXfDqQmbZA/Nl/wBV/D3xLa9G8Q+n7O5TYswy0XnA2NsEUwhrlpid9IXFlpvM15ld0OehH2U8YnBRLXs5HWVskBY+7yygt1wjo89I6n7vLa9G8RMM5lD+Omh/5HIwjOS1znWIdRm3FI5oYZDoWTsFYUkmzkkc+S0BRSPZGSe1UNtpbkPJ2obMZhAl0g4+Qo8IV6EnfDmvl9yYkNe/v+MTgIfWekxZ3pSOvhEwo51e74ReNykMLCCFH8PeYIpZfztMT+tG4ZJ3kJ35GvSJf2e45JptLdy7tSDkf9FZ/i4xZvpSevhDKSAD0I4wEESz6+kcf1yJl70uX66D52zWVZsr5yOwxMtIcKyoaE/GNMOag+wnjkf9FZ/i4xa7WCXuqJFtTc03e249hifbzcwodfvySn+Ks/PPEu0lwIUdIA74nxVBA/H3RMoSJlpQGJPwhGumdyuMS4vSyQeaJoAPrA54rdkHTFCWxv8AhCRm1pJ54sdxQdYSNFVfyiHGSt9Dn4a9uX/Vfw98S2vRvELTeln/AGj3RIekojNKWFuDQnvhDSwtJI5ommiucLfOeMWYgttqSfxGJ7UudX8xhxoEtlf4T2DJNyyXFZxX4T2fd5DSvz0lqfu8gznnx0Yw9rD/AAfzGJnXr3mJTUp9sRM69e8xJ8l32TEqzn5HN8/xhLSlIUsaBFpNDMod24CJdRUk15zE9qokAFMoB/A33w8nNoucyFd0So+xKrzKhzXy+5MSGvf3/GLT5KN6uMWd6Ujr4Q95Trg+dkBs3TWE1u4c3xhDeds5tvnCO6LSF16nQMk1yU7xka9Il/Z7jDqM24pHNEygurZQNqUwWV44aIf9FZ/i4xJsFqZb21FeMJ1KeqFAJlHx+b4RaXpS+rhEoEvSDoroJ7AIn0JRMKSkUH6eaZYL14/hFYalaSSs57Q90SLYbYTTbjGbC1rSeanaYsplBQXFDGvwicHkOH8qeJizpYPuXlaBE4kIkwkbFHiqJ/0ZcBJRNsJOxPcY+kAUjyxzd8Np+xrV0iJaWuzks/XlXuwGJYXWkiA0DeUrnJia17W/4QwguTL6BtvcYldQjcIUyH55SFaMYcZcRZ714c0STImErQr1QTChUiLLomZaRtBVwjbEk0l59KVjCHbObcNxGFBDjK2pryxs74kEByYSD80iabDcq4Oep95iXli2+0tOigJ66xNYeE/wwloutNgcyYWm9adN3CJfkHer+YxaGqc3J4xPvIlrqlnQFd0TNoCYQhKBSie2LiXEgnm4w80phebVp+6K9ITuOSQ0r89JkZoDzLbgdTeHnEpKiEiJFlQmqKGitfcYs6Xzb7ihoGEO4uH+D+YwqVQucWhW+JlNM2lI2ph5CnJlaE6amES6ZZxSE/gizPRkwoUbmt44xaXoiOrhFipCngFc5h+XXMNKuerj7os3VI9hHfD6b67vOlXdC0Jbl1ITooqHNfLbkxJJKX368/xi2f8AL6+6JZnwefDddHwhWMz10iYZrMvNo2FXZUxfolun4e9US/obW5HdFqekdUfs9rwcLGlV3qrFosqYUG1c8SarzIhqVuLQpXqikPslSnXeZXfAQFKld3dElg457XxibbU1LsoXp8rjEsKOpB/AI8lCOgQ45npB5bW1WHZE5Puvl06Au7UbqRYLZXIuo2kngItFJ8IUqmGHAeasxvOIdTzinGGm6MpbXzUyDnhgEIFejhCdAiyUKQXAoU0d8TjAmEBB+cDFrvFmWw9YgQ62rwxt3Zo7DH0hZLkpeSKkcIDZFnE85r3RLtLWqVWBgm9XthtNxIBhIw98TQ+vaPTDEvmZkr/Fe4iJcUZQDzCEMhCirnNeykWuQiRX1cRFitZ95xvnBHCKEytfzd36RIn7U2fzDjkkkFC1inrdxga47h3x9IPq1t16f5gYkpVbU0R+HvETiSqXWBzRL8hPspicQVZ1KdoTxMJSEqoNgHfBlV+Gh8aP0iX5B3q/mMWxmwhpTmi+mu7GPpA8VPJa2AVinlCJPGVb9kcItOXoS+dpp2fdFekJ3ZLP9bq89Kqo6kc47/MKISKmJHVecltejeIlkhMy+B0RLthF485PGLt55QPMniYLKQ4p3aeFIKaqrzfrCmg3aKbu3GHm0+U7tpSJQKlpb6waKxLLU5ZSlLNTX+qFt52Vu0rh3RYevG8w3qnvYVFm6pHsI74Uo+GIT+VXFMP6pe5ULQouy6gNieMN+kObk98TjIfeaQrRjCGxnFKI29whbmdms5SlST74mmP+plDY5ST7ykwmLKWXJJsq+aYRMsodn0IXoI+MD0Vv+DiIt0DwJSyMRTiIkNUd+SUSFOOhX4vjDfIl/n1TEoiilq51Hvi2f8vr7oa9IHsDjD2qVuMB65ZJG2/TgYXyVR9HVDMLTtvdwifSVIXdHrf0+asb/M6u/wAUYCAk50q6B3wrSn52Rbpustn8wh3lt7+4xM6le6GmFu2eGxpPxrFn6hHztOROiJnS37XcYOuTuPdCMEjJbPoLnVxEWGkJnnANGPERcPgK6bFjgYkfSWvaHHI23dWtfP8ACKCtYnWG35hrOJrSsNIuqWo7fgImNSvcYlx9ShXQmCL2BinlEwIaSUJoec8Y+kHoqfa7jFp68eyI2xZBvSTdfnGLX1A3/H7or0hO7JZ3rdUTRIZJHnZU1fT88/mHtUrdEjqvOSSL8wgfOEBCQoqGkwkUEDXK3Dvhaax60OS48JQ/Xo7DD2rVuid9Fd9k8Is1tJsyh6eJhnVp3RZUr4NMuoGNO/GJBtLhWlWgikSTdyXbqMbqR2Qv01Hsq4ohxF5tSOcGJYDMtnoEBFHCrnp35eTmz0nuh/8Axhn2f/KCkpWUmLH9Bb6+Jh5I8MZXv4QpspZS2MaXewiLaQXJBwJ+aYxZsktEu228n1ieqmRhAD7tOccIlx9S3uHCGcAPaVxMOJC3QlWiiv6YSKTVPy98PYtq3Qv/AAsf+53Q5imPo4aKdG6FpAS4fnRTzVj8hXmPpD6Ojf3QiYTMK8kclZHuBhwAoIMMpzbaUHYIoAoAZEaIcbzl3oNYOtB6D3QNGS2fQV9XERY4pPr3HjFoyzUvIvZoUrQ9oiS9Ja9ocfEc0wNEPirSgOYxLirCNw8X6QejJ9ruMWgaupP5U8I2xY+Egjr4mLSAVKkno+6Xgt9JTzHJZ/rxOahXnZTXjzD2qVuiQ1XXkdWUFNNp81Z3pSPnZkEUFb2Sm2FCpT87IcBUggRO+iu+yeEWVMJRLFk6VXqdSRCsKRKA+EzB6RwiXlfB3nKaMIToEFuswl3mBHvI+GSW1CNw8SYSEtop+JfBMKl2lupfI8oRND610/m+MWP6C318TCmwpaVnZktL0N32TwhKa0VzZGdc71cIltQjcIIqRCtencf6YzYzmc6KRNKuMLUNgPCFY2Z/8n9MK0R9HdY5uhzkL3easfkK8x9INQjf3RZhFXx+dUK25DyxkRo8W1UZyVKOcjiIQ2lu1DdGlH9UWx6C51cREkj7QjoKT2j4+IvTA0QRUUhtGbQEc3i/SFdG20c5+eMVRNTLKNnkg98LSUKKVaRFjrBkBTZX4w++Jmzs6nb8fuktpb3HjkkNK4nNQrzsnr0+Ye1St0SGqO/JMaUb/NWd6Ujr4eYmhWXcHQeEWYRnGkbfL/l/QwrZEugpU4o7T3CD62/uEDRHrZGkZtCUc3iLk/sCXV4m/X34HhkmeW6fzfGLJTdkmwenicrwCk3TohvRkZ1zvVwhpNxtKDsGQj6wHoPdknfRXfZPCCseA3Nt7uiclwxm7vrJSffH0dIzjg6IQ6hbi2xpFK9cLwUR5mx9WrzFvalG/uMWSftE0Pzd5hp9Ey3nW9B+OQ8sZEaPFtRwIZTXapPGvdH/AN0/+P8Aqi0m87JuJ6K+7GG1LQStvZTu7/EXpgaPH+kf+V190WSkLnWwYmWVLnnG6Ykn9Is3/C3/AOL+WGP8HHz633SW0t7jxySGlcTmoV52T16fMPapW6JDVHfkmdKN/mrO9KR18PMTWoc3HhFnYTrPX/VC4RCk0qeeE6IPL8Zw1spJHP8A1ZJpBDjqdt74xZDmck09GVzRDejI22UuLUdtOHizvorvsnhBi1fRpX2e5MWK3mpwp/JX33TEn6dM/wAPCHOWfM2RqTv+HmPpA7RaG9/wixzWYmT096osJ9V5bB0UrkVyxkQfFthCnG20p/GO+KH9p1/J/VE0guMLQNoPCDIuy8mt5z1qcfEXpgaPH+kBKnkNjm+eEfR9lC3luK0p0ddYXKrcte8nQKGLPTdsyYSfzfywx/g4+fW+6S6wlaK9OSQ5ShE5qFQyatpJ85J69PmHtUrdEhqjvyTOlvePNWd6Ujr4eYtB0MyrizzccIlF3ZphQ5+JPxiafSxaab3rJA/5Q3DmiE6IPLHjD/Bkb/6skyP+ok/nb4RYfoY3nK5ohvR49pOZqTcV0U9+ENtF5wNp0mLZly3KMVPJw7P0iRAE23Ta0O6JP0+Z/h4Q5is+ZsjUHf8ADzFvYzYH5fjH0er9aTtp3xZXpqOvgYRohXKEOTKGnENK0qrTqhvb4tqOKExLIGgq7xltr0JXVxiQf8IlkOUp+mGVzTA0eNMuFllbg2AxOOlyZbd2lon/AIqj6Ocp3q74RLqTNrf2EAQlVJObr+JcSjyF2UWwcU6etX3QaRkkOWqJzUKhjVJ85K4Pp8w9qlbokNUd+SZ0t7x5qzvSkdfDzFuLKZMgbSIk9eyPzDui3HC1OocGwDiYb0w5ohHJg8seK86llsuL0CJWVTMWc20cBp7a5EyjaXVPaSqnZhhFgKJlCOY/DK5ohvx7ZWPAljdxEMNhqdaA/KfeAYtQBQZB/Gnvi6lFpJp+ClOuLMcP7SmEb+wwrT5mx9Wrf5i1kLenlADQOylf0j6P+vuHFUWX6cinTwMS0yh5S206U6YVy4tWnhcqOnvEN7fFtRX26WR0jiMtt+hK6os1zM2YHOYKPaYlH/CmEvc+RzTA0ZVKCcTltN0Il1pO1J7h3xJ+U9j+FX8pj6OHynervyWmq7LrCf8A1DFm+izH8PH7mcBA05JDWGJw0ZMS+qT5yX1yfMPapW6JDVHfkmdLe8eas70pHXw8x9IPRU+13GLNH2hvfH0h9KG7vMNQ5oifeXLyanGzQj4ww4XUocO0A9ni2x6C51cRFneiN7sv0eP1jg6MrmiG/Hm30pZeaOlTiuykJ9Oa/g/lELaQ7S+K0NeuJgZu0EPK0BJ7P7xIzNLTK29CyfcT5qx+QuC+A/mOisAwpxKOVkWspWhPP8MhIAqYU4XZyZWNCUEdkWAVXlCmFO8xJkiabpziLJ9Kmd/eYVy4tJQNpS6N3GA6hsgKNKwD9YR0DvyGZuzIY5x8clqFP7Qlxtw45bYRfk1AfNMYccKbIQAdvxix/QW+vicjmmBoyzPJTvTxyLXdwi1lBDSifwnimLKbzs2lJ0Y8DFhnweYdZP4rvuvfDJPu/WPNfnr2xJEol3U9Kf6vubhuoJgacln8pUT2qiX1SfOS+uT5h5zlt9EWeryCnJM6W9481Z3pSOvh5j6Qeip9ruMSrJlp0NK2KEW8u9OU5gPj3wmdUyZYaQv37PjDmiLW9AX1cRElqGvZHDxbY9Bc6uIiyTekm6/OOX6PaxzdEm/n5p+6qqfJpzaMjmiG/HnfSnfaPGHDS0kH2OAgaItNQS5Q7UL4RIGk23T8Q4+asc4LG7vhX+IJ9n4wXUB+4Tje/pi2P8vr7oZUVtJUdoh1Z8ObRsp8YBChURNqCZdZPNEio3pofk7ospwstLcGxFe1UMoCX5cjbT+YiLNX/wBTeQnRjxhXLi11hq1GnVaAB/MYU5nTIrONf0iVNZl+nRDCitpKjtAhf+JI3dxgqAwMOBb9rimwjsMLWnRzERUUrFpulMgFdHFJhbwzSEfPzjFhvpXLkfh+KjDigpAUk7RxEL0wtwNN31bIbcKn3EHZTJOTTaFBBO0dhhawpsKTow4w+frUD2o+kDtxmnOO9PwiwgTMJWfnCJOjcwpfO8r+VUZ3FNNpI91fhEwnOT60c7lO2ELSlTzX5uFfj9ze1St0DTks/SqJ/VdcS+qT5yW1yfMO6xfsxZ3rdWSZ0t7x5qzvSkdfDzFtNKfabaRpKu4w9e/a1E/iT3RbXpy+rgIc0yPV/TDmiLW/w9fVxESqw3KoWrQEjhDaw6gLToOQmhGS2PQXOriIkEhMo0BzDLYzYZm32ga3cI+jn+b1d+RzRDfjPuBppSzDw8Im1BHrK4mJmUQ3Oy6k6Vf0gQNEW24GnG1HmWPeKRZ6SZtvePNWa8pt4I2GCoGfT7PxiZ/xBO9PdFochG9fGDOZiVaWjd7otF4pmqo0pHzxiUfusNg9PZFqTaBJlX4gKcYsVXhCn721MS7yJZq6o8priTGebQ6wuvI0/wDdWLAezs8pZ0qSeIgq+tu7o+kB+2J9nvMSbwebk/yqpwiScCVzDh0aeMMOJTLoJOwRavpHVCpjPzLNDWlPfEosJtJ5VPVJ/wCUTU0tmYWB0dmMBxTkg6o8/eItb/Dk/OwxerTf8IkZhLUlMIr5RiXI8DZHs90TbxS08tBxSDwrE5MjYOUkR/8Acx8+rFYdcLyys7YbWU2de+dMWqpSFNlJ5+6PpCUlITX5qIsU0da6SeBiWWHFXxtfPCJl/MpR7R4mvGFkftC/XC/Xq08Imk5q0lgbfnj9ze1St2RtV5AUYs/lKif1Q3xL6pPm3FhtJUYbXm1BXmHdYv2Ys71urJM6W9/mrO9KR18IWbqSqKgZSQBU5bReKH5dHOr9O+D/AIqfaHERajYW/MOHZdhLl/wKumv9QHdDmiLXP/T19XEQ3NIdkltp9VA4RJeiteyOGRagkprz5LZ9BX1cREoCmWbB5hwyy063JTsyXNpPGPo85RxxvnFfd/eAQcBDmmEafElyVMoJ5hkn/RlxIs52bUonkeV7jD7YXNsKOwL/AKRA0R9ItLXX3RKkIfllfPKV5qVWlt5KlaIatIPTqJmmhNKdZh+ZUlQUrE/CJR1TqlqVth8/ZGR7XGGHs+kqhl1sIaBOgKi15nONNNp2RYLjbbjgd0EUifzabiG9ASOJhOOdPR3iLEe8Hm0LOiLVtMyk6kNneItx3OTCF/kHfDDg8GbTX1+5MSL6M2+2DjT54x4S0iXCa40T2GLQdQ89eQdkJUUKCk6RCHnETSlDamnvMOLU6orVpjOKu3K4RNO5ySaSrb3YQ8jNqKYSCi8DshTyUMyyvwj4RJOF5qdJ6eBiVdW60L50YdQiefS2+pddnFNIYnM2UJc0UB/4CCQkVMBwuSDCj+bjFozynZwsil1A+ETrq5lsKJxNIlXlSwacRpCviIlZstPhkjALvdkTMz4RQUpSvaaw9rVfPqw04tx9C1mpI+P3N7VK3ZGdUndEgfrCIn9UN8SmoT5uc1CoHIPzz+YWTnnfZizvW6sk56m/zVnelI6+EWmoplzSHeW3v/pMTBusrI5jEtMCZBUnRWJo0YXuOQkJFTBtATFpoaVSiTh01ETSUotBT6jQJUjtx7omUqmDNZvHk+6EOJaXLOK0D/zMORa6qSV3nI4xZx+zzO4d8SjwQyw3+JPAZLWJDKSOf45LXdbQyG3PWI4jIDUVi8MeiJ83Jp72jFhLCJrHaPhFkKC5iZUOfvVBN4AwjTFaacstqEbhDrlGVrTsr2RaPoquriIsz0mY9lXdE2u4+0OdK/6T3RLuZ5lDvOAY+knKZ6+6JhsMyDLih5Q4YnzU4stqQodMSRo9Dr5ccK4lnLjohx4LZQ3+GvbEgvEoiZdLKQoRaHq9cS7AYET6cQuFLKQq7tiWVcCD0nui3HkOzynEHD9IbSXELJ5ob1fXFnPlDiq7RT592RDqVkpGyFTCQEqGgww9nX1ZXXEql2kDSK8YKb05880TPkOqiUeU7W9spEpPBjPhWhYI69kIWPAz0mHHytd3ZEw6VS/lbvd+kNLCpa7XGhiQVVJEPLDbyydojOKLiUbKiKFLbYP4oT6ZkmGyfLHMeESiU5pKqY/c3tUrdkrSWw5u6JDWndE/qxviT1CfNzmpMeoerv8AMKILzpH4fhFnet1ZJz1N/mpI0mEU54tuaACGa41SerGJx5EuEOuaAe4xO2kh1xLbSsKGvWMIsjVK3xac00GzLk+Uce2GHL7aa6aAxO+iu+yeEWeSq0G684i2ZVSEuP1wUU9gIizSPB1KV0fypiaWlQQE+qKdpPfC8aRb5pKN0/EO+LPdCUTLfOnh/eJRd4SleY8IZ1zu8cBFr6gb/jDbyS3nCdHwi3TnlsoScCR2wtwIpXbhCFg0Tths1Lu/+kRPpVXOHbF4puHnj6Of5vV3wnkJ3QTd7OIhcwVUKsAF090ST63lvXjUBVBE3NCVRXbFnz4eUJYJ5Kf0idmvB5Qk+sSO0xOTBcS+zTkXe2EPLZNUHlJNe2J0/amR0Liz1/Zm08yE8P0j6Q6tk7+EWs59kl0847h5q0PVhKPByh3nHdkeaLS7pjwr6u50fPZEpg8InHA42kp0Qz9YUt9OR9rPIuxMABSgIBAZTz1hWmJXVO7vjEs1nUuc4gXkUUIVNKF1W+Jd8NEqVC1G4hPzpMSA8pR8RHpa93wh+Xz5FToiRcCVFJ25EHyYSKmHFi4W+nuhKig1iTJDwETDgccKhAQRMAHo+MPOFTtw7FJhPpmR9QDaqxJKBZAGz7m9qlbsjaiWVg83dEhreqLQPkpESeoT4rroZTeMGbAcKPmsMTV5JU5sgEHRlnNSYVyD1cD47nIMNaV+z8Is8YKOSdcUXac0NkqQknzLjq2E5xvSItV/OuJX+VPCvfH0jeCJYIBxJ7KGG3TeC+YcBFkzAEutW3T2RPTOcfaWrTSnaYsx6iHFuHAAd8OTrU3KPFrYk8DFmIUZ1tWysW4qkmRTSREhMNtyZbUfKKsP+MNJDiHcOSBxEWrOuZiWmGj/AHwHxifmPCliX9UOIHvrWA4pp5SNB0R4Uv6kE8jRCHLrztOdPbhFqqCpcEc/xhLyESC0E4k/D4RNuJz0mOe5xi0ZhSC2oc57IkZhT8yL3NEkbyHT+YxPrVm0o2VibaS1KypG2vdFhTIadzNOX3Aw66pD8sgHAg16kw5N5u/ex8pNPclXxgWhnZT6zSpZ4D4x9HXioLbO+PD3pxV17ZFkOXLTKfxCnZXui11fZMf/AFD3wuczYfvGpUB3QfV3Hvhx7PPSyjpKCfeIlvRP/jHAxabudlWRWpFaxPID8vJpB9XuHmp0HT0d4ibwabEMSaSiq9sTzeGcimFYmEANtq6ILlUJRzViSTV2vNlf0neYSguBKRzngIcauqIMSyfqXFRZ2uVuida8gKTsyEEaYxI3RLM5lPT4iWyl1S+fJ6M/jsgJJEMoKgabIalVeQ4IeqqdvHaaxNthtWG2HvqlJKeaG2FOCqemEsLS6lRHzSHPSOtMJ9MyT6qBIiz+SrzglyS1T169WIEespPMSPdEs0HnktnbCklOSXllPu5o4R4RR4tnQIU2lci65zQlF5tSuakNalzd3RZjC3XfJ3RaEu6TRIrdrWJSXdCA2U4wyC+2XUaBBl1XUKHrRMSi5dKVK25LYSApptA9S9xhLJcazg01hbS2qD8UWU04+6ppA2fPGCwA2tROKTSCsBQRzxOmjMOJIbr7PCGULW1f5gKw60WrtdorASSCoDAZG2lO1u7BXIZIBg3tJu0/iNIMs41NmV0kikNPON1AhiYdU6kE1ib1i+rhDOqTu8zN6lUOVKATFrzGedQK1okcIvm6BFkTNG3s5sTxiZX9cSIlVpTKTCzop8Ylp4sNPM00j574s4hoNqWaY98WglDyG0KxBUIopl1IOkKPdDwzMy42DgMPdEzM5yXbSnQn/wAqwzMqW4FDTeB64tRoqtdaGxifhFSFgGJW0QGnHHcVeSfcqkWnN5wspbOFSfeow65QuAnZCJpxx9nOGt2lNwh+dE4lJSMMe2GH1S676Is18XFoVvi0DRKYf+uZRX1dESjhbdC09Pwiemx4Y2K1Abp1lJHwhU0tK1JPzhSL6iyEbK/PCLLf8FWq9hVPbgRAdUy4SmJebLEz4QObui0nr9mIUdqqwSSgGLwN3oHxgTzQWwtWgIp2Qibb8CASrEoSONYk0hTtFcx4R4aqjaCMEVp1+amgVMkCH5fOpCU7ImSkMFKdlIdGedKEnZ3wywFJunYqJljO3QPkRKt3ZgjmrDEvmVKMVFaZDrR7R7ollhK03jzwrlu/O2CtK2V3dlYs8fWndCyH3S1XCnbWGmUPOLB2GJphNy/zCJZml++MDGeRprtp4i3ClxKOesTKyloqTE6zpdiWaStlROyJJoLCscce2EJuJCYzf1uc6IW0hxQUrZE82Lt+GVBptAA5UE0FYS5nHLx2kQDSavdOS0PViQcobnP5puzs42hddOnd0dUKs1pV2mHPDvkoAQNo4iFyGeQEV0En/uJMMyTTJvgYxMyKlNJbbNSmvbEvZzTjIV6xpDaD4Yp7Zo4RMueGTBdpSsMrUzZazu+fdDcuvwdxQ6BE3L5sJSxoKU1I0FVaRLWcZMrQ1ySUEdRxh6Yam2n2mhiAQe2FS4F55PKIMKZU4X0J23YlpJaGGULNVIP9UTCL6CBpIIhyx6tUv0PPExR+YWoaGkU30P8AeLOs1tLCVqoa49Ri0bNSp5LyedPGh7u2GEiyJxtJxvDHrJi1C3LMLCKXia74l7KQp0Le5h0Y41HCJixm32qVKTX57ImLLfUjNNjk/CLKkVCTuzGlXDZE5LJcUyAMNEMyjaM5Lk6QO+BZ4KmsNAxgyjbTKnUfhhFnLl0PrWKk3adUTU2xKXAvb3f3iZK5hxU4nAVp7/7YxZlktvyt5Z5ZrupUYQLHcbllTCh5QPYK1i21sOEIb2m8f+0cYlG823SvmZw0YVAdOaUjd2Vhay4axTyIacutqTB8pKl1hM+6JdTIOBAHuhSyTCXCGlI56R+0whaV8qlOw6IefbmpjOJ6TFptgPKdSahVYTqfnnhhwJWAeeLLJdtJtw7SeBhy449fTohUwtN9IOnDtrxhDpgOHyhzw2qjqVboZmlNUB0QJxKqDfFlPKAeqcAgxNzCXkigizXLk0jeB2iJ2YKZlaRpvK4mC7feCz84RMpLb6knZBcwpFoNrl2WXweUB2ACL52xW6K88TU0pxpLPqp0Q6KSrahpqrgIWQo1HRwgqqEjmhibzQuq0CJF9PhCUjaDw83MPlpaRCZshYWeahhxd8kwys55J3fCM400q7orjExMXLhQcIz16YQAcKQ49nQ4oaMIZfAdTe5qR4agKIMAX1pUn8R7odRm3CiAq828rdxhExdaUnaYQ4mXWlStqYbfS0+pe+EPKQu/C5hC2CqG30JQgVgu0Ck9NYanqA5yJiZvJ8nYYZnFIrfxiafDpF3ZCnfqEoEGZS6yqo5olNS4Ik3Q2qh2wh4OP0QcKQ/OUwbiXnCtQQoQSiYvNc0POAvIRzQXrkwccP0yBdVlf5h3wzOBdEr0xOOh2l3piTwfHmk2rLMsgVqRTCJe0WnWM+rydkLtuXSDdBMSlpMPigPlc3zpianUyjZcVEvNNzKQ83ohU4yheZUuh0xPWq626ZdlOI+e+FWcpyTZbbwxBPXpMMVduWepNKKqe2vZEwybNWtgGt8Ye+nCvviQmW2Gc1MG6psn57YTa0otCTepshqcel3/AAjQlZrvxhi1mkPurWTdOjq+MSs4xNeW2ce35xhTqGwVKNKR4QhSQsKFIcnmG7wWsYRZTdUrmvxnvhICBROiK1i15Vx0Jea0p98X5qbWZjSUbd0ftxxbqMLqa47YVMthrPk+TSPD5Qp5Y2dsLtKXZXm1Lxhy15Za0IBwrp0Uh+cl5Z+jmBVwx/WHrTYl274N7ZhSG32n28DVMJtpjNBatJOjm6YtR5M65Vk1CR30+EIkJdDOYu4Q22llAbToEOs35dxpG2vvMWfZ3hYXnMKYdcJzqUBDqaEeZnRVhXztgckw3F0mBWEVSkxWKeVlkdbFoDyUmJVyUTKZv/NJH81YdTR5VOeLMUlh9lxw0HlfyxKISt5IXoqK++kTCUpeWE6KmBzxTGkIFMrEwpgLCfWFMiVFJvJ0w8So3laYaQpasItJBz2d/FCRiYtZSXLPlSg6BTsEHkwa3KQnysIfaSJBlW2qvhABhtF4muwHIiaWysON6QKQGySkc/mrQGKTkGOEUKQFxWsV2QpV418SQ1R3xN65UMJvMuDdknE3VJT0RTblrhTzEigi9WHG1NKuqypN0hQi+qt6FOFSr2S6aVgeS1e6YbVdWFGBU4CJZs+EE81fNNWOnONvHFJxIMKshE08VoVRHMOfCJeypQUUlNcNvGJqyGw79nXdOwV26eEMWElKgpxVeiF2SorX4K5QYYY6dP6iDYjjqb7q/L98S0szZbRW4cdp+EA1xEFpBUHCnHnhyXadUlaxUp0RM2MzNOZ1RIMOWAwR5BIhmXYelcw3ydH6++P2ZK3AhaBht0cIm7DbWb8uqldmyP2ATRTrm/fvhqyvCUh2VcqOHMIasNvBTyqnbEvLoZQG29AgikXDSHlAFLahW8adhPdEnLtsM3WTUQ/IS8wkhSdO3bCbGeCsyF1QeV88P0j/AOn2c2QFGvzpj9gJFLy+yP2NKgKFNPZuhixGgFB03ieyG7HZEwQo1Gmm+tMa9EGxn0uZttfkH50bfnRCLFlQTiT/AG+TFly7SFOFsUxu4/l+OMTDiWG1Ofhr8YJCl3QYV5OmG1BzkxamuA6Pj5lSQsXVQxJXyq8cAYEjS8PdDDAzNFDTCpJaVhO+PAFXBTTHgqr6UHb8YVZxrUGFya049EIlFrCTzwxKllwq2Q42l1N1UCXaBvAROMAUUjSTC2Ps+bGmJWWUkkuDCEsAzNFDCpiYlc0Rd0GESqnAFp54YlM4gqPVAYTmbqhDMpnUhVcImZVCUlxMJknCuh0Q9JYjNQuSdrSlYkWDevK9WJiWExSphuTUsmnTD0q4qQbRzEx4Cqgr1w2wp43UxLyynFXYVLlyRabOBqrjDEulkdMNyyg4tStBrDEngrOiEpKjQQmWSkJrs81Othbd7mhEl9Wb2mPBblxQ04Q/KhbYSjZHgTsOyS04oxEMymdBqaUjwBYOnCFyqkJx01pAkFFRBMSzRZRdMLl23FXlCEsNpBAGmEsJ8JuDQInGFOEKTAYzcubwxhuSSW/K0mC0pDlww5LFLlBorxhMndCTTGuMTrSboKRjohqSCSSvGHZQJKUJ0msMSVcXYMksrPNDEs4HQVDAQhu4SeeJ1moztdEeBOUJhEo6sAw9KZoFWyG5RbgJECXWpvOCGZPOpC6wtkZktogyigxc21iYlwhpKgMdsSzJU4K4bYS2lCiobfNSk6Ghdd6KRLWgJdKhdrjWGJxxiu2o90eEkzHhFIdtJTb6rmKYbfUhwqrS8QTTfWDaoGgbf+PxifmRO0F3CJOeal2Q2oaINqKS2mmJxr3R+1FBoK9avZFnzgd+rOnE9sftNKHC24NpxiXmfB5VDI5STXjE3NrSm5+JIhx+61LKOw8MOBhNqt1orRjxw7IklKkT9WcObZHha1IWleN7shi0Swzmgnnjw2YrW/spH7TK3UFQwH9onXqTRW2dHz3wxMKl0qSnbEtOMtMIS4eeDaiUrUUJ5qQ5aKUtIcTjzjqhE46Zlpbh8lN6vXFluEtqSrQmHbUvNkNih7oQtwTAevn5NYVaLanq0wFevR8IlJhS3XTsxMLcUtRVzmsImihhbJ2wmfSlRVT8PZExOOPLXj5KhSkGZzCc2MaoAha1OKvKOPmqU8Wm3xykKpXLQaYpXzKUBFabciEBFabYLqigN7BkCQnAQhtLYomKCtfEzaObzenT41KeOEgEqG3zy0BxN1WVSQoUOVKQkUTlIBwMXRW9t/c7bqmjVPiIcU3in/8Ab8tYbTeV+8FLSil7b/t+b1KolSSyCfEWq4kqhKryQr91TZ+uQPFKgCEnb/tmoGETKwtlVIlFJDKRXxH9UrdDOrTu/dLrwQlRGkRNKqpDnRAIOjxHdc31/wC2XFfaG074Kz9aPnTDSrqE+18IZczqAvK/qlboZ1Sd3mW5lSny2f3A4bwe6ocUFIRSEP5kncnh4j+ua8yTTEwlSVcn/ZAIOjx3zSaRTohKqBXztgYITvPdEnqRlf1St0M6pO7zDirqCoQ2oly8emJRwqBTzU+7zSilkkeYWdbv+MerDvK6hwHiTGua8zMqCWjXbEq+GkqKollFTQJ/2G04XL1dhyqVcSVRJmrZPT480aTAI6MlfJpEiatZX9UrdDOqTu8xnDdeB6YB2xZ/rfcyQkVMA1FRln1kICRt8wijhx2kd8bIc09Q4QDXEZXx9c31+YeUUNlQibevto6e6K4UiU1Kf9hyvLc35EuBS1I5oe1St0SGq8eYwmCo9EOo5HSIWLiimJDVnflf1St0MEFtO4eYOh+BoiRNHfuc3qVQzq07oJoKwhYcSFCLQ0J8dSrqSqGeUgQ0i6XEq5jkZ1ad2WYIS4gnpgEKFR483qVQ/qmuvJJKq1Tm/wBhymsc+efI1r3OqHtUrdEhqvHmtYvqh3kM/PNEwm66qJDVnflf1St0SWg9XDzDqiM6PnTA0CJPXD7nN6lUS5q0mF8kxLalMWhoT472rVuhrlt/O0wE1mHE9GRnVp3ZZzSnr4Qzqk7vHmNUqHf8v52nJIas7/8AYcusIWuu0/GCoAgQ1r3OqHtUrdElqfHmh5Sz0jhCGkuNoKtkTeuVEgfJUnK/qlbokD5ZHmHFHyumC3RhDgiU1yYU7R1LfP8AcpvUqiXc5LfRC+SYltSmLQ0J8dzkGEctr52mM0kKLm0xshnVp3ZbQ0JiT1CfHm9SqHFVKR86YnE0eMSHIMSiioLr+I/7CKrpJ/N8YtD1YY1q+rhD2qVuiS1MLcuKSnn8ZxAWl352Qzqk7omtav55okk3SobsszqVRIaw7vHWaJJheiCPsSYlNcmF+mo3fH7lM6oxKqq7T8sOPKL+b2Y8IltSmLQ0JyNqJdWk7KeK5yFQWwCyRCtBilRhEvqk5bQ9XriT1CfHnNSYVyhE/rBuiS8m+jmMSWhftH/YJNMTCjeQT0/GLS0JiXxcX1cIe1St0SWph7WN+Mkkpe64Z1ad0TgIdPTEsfrVjdlmdSqJDWnd472rVuheiFei9UNC4pCxtMTay3MhQ5vj9ynFXWt8StQ+PnZCj9rPXwiW1KYtDQnI1r3OrxXcG1boVyWTuyITd0/m4RL6pOW0fV64k9Qnx5/VjfDYBfSDE+nykkQyKPOU6Is9d4L3/wCwXtWrdktLkpiz1Xrx3Q9qlboktTD2sb8ZshTTqxtrDOrTuh1rOO46KQyQJlYyzOpVEhrTu8d7Vq3Q/wAhO7vMXSuXCRzRMCi2h87In9eN33K0NCYlcX0/OyHlXZqsS2pTFoaE5Gte51eK+aNKhYoGhuyKYolSz+btESmpTltH1euJPUJ8efV5IEf6zr74cSS6g74a17nVEhg8R/sGY1So0RaJ8hMSCyFhPPD+qVuiS1MTCglaCfGkdUd8aMg9M+eaM4L+b25JgVaVEhrTu8eadLayOcQ/qkfO2GdUndEwmpQemJ/Xjd9ytD1euJPWCJtX1xIiRcvN3eaLQ0JgqCdMNkeEODd4s/qxvgJBArke1at0SHIMIWF1psyWh6vXEnqE+OHLwQjmj/WdffkU5mnFqI2CJHF73/7Bm9SqHAPI3RaPIT880NLKKFMPapW6JA/VdcOrvsIqefxTgIkNWd+WlJwfOyErvze7JMapUSGtO7x5w1eMTKbsmmJTUpibXcSFdMO64/cprWN7/hEsKPgDZWJrWneYkXCl25zxaHqxNKutIV0iEemK+ebxZ/VjfDfIGRwFSCBEmbjS1c0Sir6VK6cloerEnqR47SbzgEPNhE4mm3HtyTRxWOgcYs/W/wCwZvUqiaAS4AOb4xPqvBI6IRDpqyT0RJYMqg6kb/h4qzRJJiQ1Z35VemJhtV2aO+LwvXdsTGqVEhrTu8eZSVzBSPnCJtP2YJGykSeoTE/qxvg1UusM6pO77i4AX0dcS2L8TY+sJ6TEnrxFoerE3qE/OyEemK+ebxZ/VDf4kvqHIkCLhGS0PViS1PjKN0ExKa4RNYTSFHo45J7WxImj3+wZpQKCjb+sTSauV6DDqy4MdkIg+jdXdEt6Mvr4QT9SB0nuiVdU6i8rxHtWrdEqkJaFMswbsyg7ovjP39lYQoLmajmiZNGVRIa07vHV6Z880Oalz2ok9QmJ/VjfDPKVuMSupT9xqHJgEbKw39S7fVoqYmuWd8SOuHzsi0PVib1CfnZDiyHFkeLOC8lKec+I07cSpPOIs/1olXVPIvKi0PViS1PjOcgxL6pz554mtKNwyT2thnC4Rz/D99OuBsVMIVfSFQ42ppV1enKQRpyTS84uqfnGJnl9RgN/UlY+cY0Kg+i9USmEuo7+EFWhMSQo11+I5yDCE3EJHQMs7KrKG3QNJpBFADEjrYnD9SYk13Hd+S+L1zb4gFcBBQVTQSBjU8P0i0JI5x5uXTgk1/4isSzakMIKhp+MTqas15oYSSqnODEpqU+aU9R1LYhlpT6w2nTF4Vu7cl00rkfcLTZWIljV8nZjD232j3RMpIOMSarrwi0PVicPkJTCVE3q+K5ipA8R9otuFMVU0cIkNUd8Wh6sSC6oKebI24HE3h4hBW2soxoDErIlWdQDhdB96axNNruocp5NAOvJPposKhIulA/N8P31OOpXdzeiHmlS7TC66R3CJqadeIKzsrkLgbwpWsWo1dWlyunupEsx4Q6G4l2TNP5tO0xNyKwXEoFacDEmFGXeZAxI4GClSXLqtMIP2TqMSbZclnKbK8IQjOOJQNsSqrrJUdhi4q7fphAlAt9tKOYExajYQ9VI2fGHdWqDoTuHAQElWAEWc2lyYouJtoy9m3V6Un+qFyikSYeVtizWC47hzGLRZWmWS8R5JPxiX1qYlpYruvbLwHCFhblovU2V91YLaggLOg5HE3JRcwNIIhh/7YzjQaYbaQ5O5xs8pSx2frGZU49NkDo/4iDJBTbaNFE8IclHHpdxQ2Cvb/eLMkXJp1N00CRj7zDiM2so5vMqWlFL0ISpc9dHPTuh91TS7qTo/vDIddmS3THEZJB8uTjbOwAwUlJoYfaSqQdc2inGLJkzMqNNg7cInmjmkuUwrFoMpTKMOpGJvV9+ESutSYtA8kRPM/UtqGnHsAhtCrq1c3fGIu12isNSn2oS7vzhWLMlUOpLi8dnCDgYuX3kjoV2CuQNLKCumAiWk8+znE6a9kfSFpKX03Bif7RPMlh24rTSJIEN488TpOfKTEgLpWDFxLCnktn/ACyREkn7Pf6aQ4kIVQKruioOETQMs40nTfFYlHQyiaSvmI6zhFlpTed3N/yQ3LpmJOVQdFa+69/aJqUupzqNqiKdZi0ZNVxojSquHXSG7OcSguLGAJ6qGn77m3L8rLgnYeP6Qs3gBDI8Mba8qqtB9+B3YxbEoJTNFJxx7onKz7CJhkYYwh+Xk3M2ziSoJPRWLAlVKmc8NCe+sBAqo00xJymYnH1Uw+OMCz0O20Unk8o/O8xy5ZSIaeSxJIQgeUsKr76dxiflBLv00VxEMvlphyXpyqdkSSFTlnoSjTex90MTLAQ1NrNAU09x/vFpkLSln1lHD564nJmWLAYFSpNR7/nthxnOy7ZPrFPACGZRDBUUbadkBlttQKExa5pJL6uIiUkmnbPQy4Kg498TEq5KkpVu4RbIQy0wxTya19394kmlLeSkmmMGZUhLjQ9YjsiTdzlohbnr1r/Fh3w8kJnnG2tHwETw8GnUsDZ3xOyhZstxG004iHCRmxShA4iLBaQti+oYhWHuEUAB3wU1bJ20MGXQW81spSGZVuWX9WKClOJ74tAATS6fOHmZnkCJPCZb9ocYnD9pXvgLN4qOmJBtiavvoThXQdkJfDVrX0igrd7v1h9tTDrr6hUXT3ROz6ZuVU0lNOScO33RZUuhiURdGkVidlL0kWUCtNES7OcsvNKGkK4mJaTHgjsyrYaCJ2jlF9Aht02i/dJ5Rc9xQAIKfLKTGeV5F88mg6qwtObmfDFHyDTtFIlH25aYVI7a1Hui0LqXM6kVQigPv0Qyth20kZk1BvcDhCZJvwotKGF2Ey6EozXqwykJU4Bz9wi2Gs/Nstc/xi27NvkzaObH4xZCit9Murk1r10i3yXZpzHk0p2fEmJdH1brmzDtrCppZCQNibvVUxZqEqk5lC9gJHuMWPLh+9e0YxZDAmZtxDmjE+4xbgDMyzQYJELWXXVuK0k1iTZQiXS4kYqSmvUIdReKDzfAw+gLU2D+LuMJl0EDOJqQSR76wthIl3EDpPbX98p5QiyZVDswpp4V8mvAwq8fJOz4wzI32373Kb/WvCLIezDyln8J7Me6LVnUz1zNjQKnrpwixLQNUyahhs7TCH7syH1Y+VXtixGEty2cHrfNMi9EIlAmcMyDpFImEXHlBBwqYBNAOb+8fSGXSoIdHK0cYmZNbEx4OMTxiUnXpdostmmk9kKmHFspYPJTBtMPOtqQmmbC/wCXDhBNcYaaCGktnZTsyUi2PQXOriIkwUyzYPMOEfSL/K6+6LVmVTbDDqhTld0DDJmFZgTSdhp3wZhWfVMJ2k9uyJtZfKZhZqpXdE3aOds9OcHlOV0dBhKLwUeb4gRYYuyZI5/hB0GEcnIvlRaPpS+rh5lbedomEM5uzRM08oKqOHGGZdT9676oJ90PygRJtTI21r3RK2gZRlKUabxJ3UELK1q8JVtJ9+nvidtRucknAE0xA7+4xK0WoMH1ykdUAXRQZHNMOSfgdmONrxP6iLgGEWL6ajr4GHJFE1ay2zgNOHzzmFS7l1dRycDDs5ebuJV+CnUnHthmfUmbE27if0pD05nJZwJ9dVTFj+nN9fAxm/rA50UyzzJVaMuQfkYxpwMKfRIWk44Rh8YtP6yecCeeAToyT7S5OYVQ8sH/AJVi84yaA0r/AHhMwpl9TkthXhWLafS/NeT6uHXUwpk3ApPNU++kXQ2gNp0CFbMriqNL3H98tCriRFmslibeb00CR2RYbBVMOFwaB21/SESbKC4QOXpibZzD62xoB/tFjMh99SFfhPwhlxyVczg0iFJpFmtZmUbT0cccioGmH7OcfnFoa0Vx68YcknWnwwrSdHvpFvhRlwU7CO+FybTzqJhXKTDrS2FltwUIjScISopNRFm2Qpa700nyaeI80iYQWXBgYSkIASItphC5YuqGKdHXSJSWM66Ga0iYs56XQha9uHXjEpINOzDQP4Qrth+zxL2e602fzcO4RKSipqz7o2Lr1UhiXU/eu+qCfdAW5MhuWGzR1mJKyX3UXl4Aj+r9IkpdUtLZtXOeMKIAx54Rycm2LQIMyunzh5lphb9bhoUisEf9D+fxRZEqUyRS6MF8CInGG/A1IpgkGnUIza8cNEBhH7HK6Y17wIk1VdQydBUn57Ys1g+HIbcFKY+7HK5piZZD7K2jthqw3VoJdN07P1iwmVImXK+qKdv6Qj/Fa/k74tGUzku4GU+UqnZCU3jSPB1ZjPnRWnH4QhCnFXUCpiyJHwdoOODyu408QtturS7tTWnA5Lfl0NupdTpVp6qRKyrk3SaGKr+O7TWFyJZmxLLOkjtiyLPZmpdal7cOBj6QM1U24kY6Ph3xbcklr7SDpoKdX6Q7LZuXamB61eww3LvzV5aBe5+uFWMAgpzmkXf+V7n6ocgqF67tyL5MPapzcf3yk3VAw4ltgOvo5RGPUI8Jl5ZhDisC4R14iG5hLia104cYeS3NzLcyjFKimoPT/aJWRbkit9RqTXHoibKZ6WuSwqq+Sadf6R4FnpBMsrDAfrE/Pplzm6cx7YamUrbQ4dovcPjC3UVCa7adleEJeSpkPDRphtwOsJcToOMKZZCvCHBikfrE40mcau1wNPjEzNIk5cvL2RPSIU4HgK3lJruGHu54VLS8sHH0oxNaw6yZu/mU6KYDrETEy1KgKdNKmkAg6IviLwrSPDylbt86NHvhNstuS+eQNpT2Vh5lMyhbStBiSlsxaLw2DvxiYbaVrRhpjMy8qrPg8lN2nRWDcmWiNKVQlpKGg0NAFIs+zGpcrqbx5J7IkJEftFSFDBGPwhp5Dyb6dETFtplEtNpTewxhyYzstLupwvODtJizXyQ4g/8AqLHfHhCbtemnbT9YmJ1aZeYebOKV0HVdr1aYacU8jOL0nzNlkB4hW0GH0Ny6GZNPJJpjt2w/NMSryG1GgoeKYnX20NUXjXvwiVlUTL62q+SpA34EfCLSUhEsQoYH+8Jki5aIeQKtimO4fER4OgP+E7aUiUtQvzASaeVh7qmCoAgGHnkJuqO2nbDjgQsA7fhXuhfKgLQ0tDSRyqwhlPhOfrjS73wqYRn/AAX1qV6tENSTYmczpRcpXpvRMNsS5ZZueTX5MWPJrDzcxsIPVsjwxnP+D18qK4Viohb4DJdRjTuhVqIlWUuKGm9xhM60twNJ00r1RaEn4U2Fk8kK4RYTd2WqdpPw7oU0wXgpafKrh1VMMKlZVIQ1gFEw4wh1SVK9XGLQl0TDBznq4+6LSlk+AqQ2nRj8e+LGpKyxecFLxA4U4xPrCA2pWi8ITbiHHSy4PWwPRe29UTTlFzJTpDY/rMZ9KUqUrZ+nxicm80lZTsTX4RadpKl3fB0jlgdpx7P3yTQVhq03m21NHEEKH/dC5hxbaW1HBOjriWtJ6VazbXPXspFhUdWlKxWh4Ywq1y8hSK8rRFkTwlXStzRiYXbTjEszdN4lJrvidmVPTC3jpNOAhNtlMolm75QSU/D3QmffdcTfVjXT2RMzLvg6LvklAu9x98WXPJmJMpTgUADs0w/bEqq+1oNIbtRLswy2xiLtD7x8ItafDrCpYHELPfFrWsuVmEobGgcf7Q3byH0uNu4V0fDvhieLcwc0ugIpE5aSn2Utg8k1hVqP+Ui9VOHuGyFWuVuB3bUbolrXUidU86fJVWvdwEGfcK1LOJMMvrbomvk1/SGfpE2CpSxsFBxiatB9UzfKtBHZFvTS1PIU2rAo4nH9YTav2ZxK9KsB1QzPBmyQ6lVVJpx2w59IXFoQBpp5USNqBBvuq8g88M2iyw67MO4XqdOjCJSbcRZr6kqxFKdZhbl9KQdkCYdCUovYJxHRDdpvyx8n8RV1kUiWtDNpbUs+sK/GJmYU4ty4TcUomJPUJ8zOqKWsInLUdmqV2EmsTL65l0ur0mH7RfmE3FHCgHuhucDEo4b1NFItC0DNM3SqtIlbSDUs00dN8e4UiZtp4rcaQaDysduiJWbXKrQtGw1ia+kBcWlxlNLvP0xKTi3Hs8oVpSH7RcYnQ6TVOmnvHCHZ0JbZmdh/8TCrdYU409zVqPdEnPKcz6vV8tXTsiRtBU5aod6KfPXCLcKFVUmvlFXvFKQ9arU8hGwp09kMWktuWuXsB79MO2k4Jxcwjb/4wm2XytKnDoFP13mP2osBQQaXkxL2spEitlRx2Q7NKdRcIiQnszMJcfNQPgR3wLebdlSBy6RY9pOKmznT5JGjYNGyHZ51qdU4FVuqVTrh20UO5htOFyvbFqWqZRTZaNU4V6R0Q5a6ph03q0rh3dkN2klLCpdw1UeBi0Z9tMi3LoreFOyLZmXLzCL2FAeuFqvLKoXOTDilrKsVaYFrv3VIXjUjsFO4Qu0EoS60ramkNqUpxFTop++SKimSkNtKcIA2whxaOScgNPEZ1qd8TYqyqEPONApQqldMTguv1MSoIDixsEeU4rHEmHnnJhecdNTkSbprkCVEVA8ZDRUm9sicZzibw2ZG5hTbLjI0Lp2ZM4c3m4zqrpRsMImVtsrZGhVOyG2y6q4ILV14NnohdbxrF7ybsJbJaKgNsIQG0hI8zP6ob8lDFw3b+yKmlMlfEs/SqLQHlJMSalF0AnI4Fty6KH5OMNLWz9a2aEd+QKoCOfKUkafGlGy275W0d8TjObXe58j0yt8ISr1RTItanMVQpxSgAdkTE05MhAc9UUhthbuKYbTVtat3HItRWq8YZZo+mow09nx/fRlAt4k6ILCVKvHmpDDWaqOmHJAKNUmkJkBQ3jC5EpIpjCpFs6I8BTQCu3sgSN69sxwgSraVBQ2ZAw0PVicbSWyumMMM5tq6YZklJXeUdEKl7795QwpE3LX/AKxOmEyii0ajGGZKhq5DbIbBTzwmTvqUVYCsZhvDDRCJK8lJVhzwuVQpu4mFyCaeQYaYCWwhUKF4FJjwRK3VJGFKQZDy6A4R4ALpxxgyxzlwdEKkSFAA4GPA7roSdBhlhLI8mFS6VOZwwhi46XBthyVXfVQYRLthtsU80pIUKGJWWCAFqGMKlE5spT1RmgW82qFWf+EwqQQR5JhuT+sIWMKQqQQdEOyWkohEikEFUMsJZrd2wpIVpgISnQIflkl1KU4ViYZzrdxMS8vmK46YlZbNpJWMTD0mQv6vRC5NRoRzdsIkhm6K0w+i82RSphmUSmijph+XSqhSNsNySUrvHRDsmlxV4YQqQ8ryThFwXr+2HWUPcqJeUS4i8Tp+MIkCa3jDkklIqnphiUznK0QmRJKgTohiUvFQc2QlISLohEqltKkg6YEtcZU3phqWUVpvjAwABgP/AMC6P/08/wD/xABREQABAwIBBwcJBQUHAwEIAwABAAIDBBESBRAhMTNBcRMyUWGBscEUICIwNEKh0fAjQFJykSRQgsLhFUNEYGKisgY18VMlcHODkJLS8kWA4v/aAAgBAgEBPwH/APuQSGi5/c+A4ce7z2ytc9zBrGYOBJb0fufCQL/viWW8IvrKGn9zf4bt8PPg9qn/AIe7NHtX9n7nk2Mfb66rH2x/cMYu9oKkFnuA8+R5xuHUnG8be1N1eZEwPxX3D9xf4bt8PPg9qn/h7s0e1f2fueXYx9vrqza/uGHaN4qbaO4+fKQJH8EXDkgFE+72jq8yk0udw/cX+G7fDz4Pap/4e7NHtX9n7nrdTPXVm1/cMAvK2ym2juPnz7R/10LcsVsJ6vmozdgJz0fPPD7/AFbi18Vj72f/AA3b4efB7VP/AA92aPav7PNnaG4bdA8+re5hjwnW4feWgci45q7Uz11WDymL1MjDG7CfvFFtVNtHcfPmP2jk8emQEdQ4fNRbNuej554epvfSE+rOnCqVxe0k9PreWPlPI7sN/iqabyiIS2tdTEiNxHQqQl0DCejNPLyMbpOhRP5SNr+kKs58P5s/+G7fBRStmYHs1Z+WHLcj1XTaoOqHU9tIzQe1T/w9yc4iRrVA/HI85pZ8boXRHQTnqR6MZ6szJmve6Ma2+OdzgwXcbZqmblJWx25r2/FUtY2q3WKa5rxiabj7u3YO45q/3U1pebN9bUtvE49fqGNxODVW7X7oTYXULi+MOOaoAa4W6FRbVTbR3Hz3OxOv1qRlpiG7792aA4oxnptbuB9Q7mlCT9n9HNR8w+tnm5Gvbo1gD4rJp/ZWDj3qfZP4FUc3Mht7t808xmpJSdxIVG4Op2EdCrjhfD+ZMjxhx6M1fIaemhw+84D9boTugycws1n5lPmkdBPfW0kKodijgN/eav8AH/weKybL+0F8rt2/szRnDUVB/L3J0l5WH60qj1lVNVy1Bidrd81BsqbifHPNG6SJrhuCr+bH+YKD2qf+HuVTM6HBh3uAzZS9lf2d+adwZVOLvxM7ismvEeJ53N8SqBuCkiudd+8qV/Jxuf0Knm5eISW1/dW7B3HNX+6qTbD1tVsD6iHaN4qt2uYNLr2+5S7NyptkM1TzhwCodp2KbaO4+fqYD1/JP9pb9dKItoVNshnptbuBVYLTHz3aimC9OT1+Gai971jaljpnQ7wnSmauBJv6WjhdZOcBKy/4T/yKrK+NgMTdJOhUe2Z/8PxUkzIiA860T+yzj/V4rI8xJdCeKrJnzUsUrtdyqSM+StI3sH6psodI+Le23xWVHB1NT2/G3xQcbYVVTNjFTHvL3d4UOyb/APFX+P8A4PFNdhBHSi5oIaTrX99Ufw9ylmdiI6ALfooKkwjEd+hSSnkhBbUVTSOMkDN39SpJWQi8hsop45gCwoezdiyg4BrL/iCpZhJXSluq3dZZQla2OOUaRiBXlJ8pfHuAupas1VJICNWH4qrkdHyeE63AKrl5SSQ9fdcKidcSD/QVG7DFSOdqHKJ1S2ekL9Vw5ZGOOogY7pf/AMfuszQ1rwOkZq73FSbYetq9ifUQ7RvFV20HDNBqfw+5S7NypX2Ab03zVPOHAKh2h4KbaO4qw5C/X4ec/m9p8FYXun63cVTbIZ6MXeR1Ku2g4efIbMJUXsjvzDuOai1n1c8vJZQZc2FvmoZnR43b7W7lTbdnEd6hnMbw4br+Ke4vcHO1k/JUM7xUN69HYsoyFtZibrFk2p/Z5Gu1vKpHYeUI/CfBSkGhjt0lf9P1TpoHRPPMt+m7uUTg+tqSOkeKc9optJ1TeCbrVf7XPxP/ACTHhkAedXKeCkqGTV0bmcPic1RX458TToF7do+ahnkfISTrUzvtProQO7rCl1KldaePiFlKVzsbDqBb3FPc5giLTpt/M5ZOqP8A2a2ac7jf9SssuBLAD0+CyWQJXX6CnPc6nLSdALe4qKfC+SXX6A/X0Qn6/wBO5VczJJYgw6nKbau4lU8vIh5trFv1RPox/ld8cSxWhjHQH/FZPNprj8L/APiVRTiaIW3W7vulTqfxGau9xUm2HravYn1EO0bxVdtBwzU+p/D7k/mFU/PaOKDS7UqnnDgFQ7TsU20dxX+G7fDzsIEHbmfrdxVPoiGeh2nYq7aDh58uzco5PsjH1g96mZhDT1KjPpELFclvR6rKZ/andndmjJjeH9CIsr6RmldidfqHcjswOPggPsyibQBvX4BZGkEdawk2GnuKybOOWcH63n5qeXFja3UTfv8Amm61WyCWoke3USe9afJ3DrHc5C7TfPEcNyqg/aAqN3pqTmqyqpfKJXPbqWIusDuXlkraJsDXaLu/Sw+ZVTJysmJUW3Hb3FN2DuI/mTXloI6f/KeblSu5OrdJ0O8VIcchITW+gV7oR1AJoWSG2jcev7pNEZGuAzV3NaqTbBTbR3mtc13NPn1exPqIdo3iq7aDhmp9T+H3KTmFRaJWqnf6b2f6SqnnDgFRbVTbR3Ff4bt8POvenH1vzSM+0LesKl2eeh2h4Ku2g4efLs3KNVGzjUDcMvYm7R3Z6rKA/andncraMz9WYC7URoCCa30U8XsEHcg7GOvuUbTit1HuXvLDb0la9liOHD9b/miLhN1oDeg2zSVMeU0qP0SVtBZBu5WQbpVzisnjeqPbt4HuKYfsyzp/r804WRFlMcRLukrDcoCwsnncnSarJrLFZLuHuHrBI0uLQdIzVE3IRGS2pDSL5iQNJzSe2R8CjqzVw9AKk2wU+1d5tD/e/nOa98xmtMIbaxfPUC8J8+Y/tcX8Sh2jeKrtoOGan1P4fcqk2iKj5zeKkeWTgg7lU84cAqLaqbaO4r/Ddvh5waTTgDMYS6Qu4Kk2eeh2h4Ku2g4efU7IqhbedrXBVLbta3rTW4ZQOpRuvI71WUPaXdnciLKyc3cmhWsrJrL6VqdZb8SJ5QWT17yw3bYoD0M4VkeaBnYbFAWWBWXv5m4g7Ew6QgnC6e1Yb2CYnON0TdFE2F1k3nuPrHc+r4D/AIql2EfAdyyj7K9N1DNlY2pu3M4XrohxR1ZsrPLKcka9PcsnTNHJulOlwH6lVVRG1xkvounzBkjI/wAV/go5MNROXHQLdyybJjlmsdF79+amk5Nsrv8AWU6UiVwHQqR12klVtT5LFjTHF9RE47256jYuUk3JyMjtzlSSOfymI6nEZ6iYw4LbyBmJu53/AMzuWT5ZI4HS68NrfopZDM2OU+80HNT6n8PuVZs1CLvCqdp2KsjIIcqLaqsdyTnuUJDqRx+t3m1BtEVDsxno3a256HaHgq7aDh58r7xuB6VSe0s7O5PZjt1J1+XHBRuAm439VWj9oP1uT9NkBZPCbrUg0oC4TdDUTdNPoqPWVzgiLuV7aM0gzYSDZBtwn6/McdGZw02RZd901ugrDZMGtAWcna0G6kdAR1oIou0WWSdOPs9ZJOJaqQxn0SD/AMVQzF45P8Ib8Qso+yvVMSaiUfl7k7mlTn/2bHx+apqls0IkJ4qepbT1sMzuaEdWb/qHnxcJP+KpqnlpqZn4SP5f6r/AM4+KZMaiSmkO/Enc+r4D/isnTGE6N5aO/M0+hJ+cp8lnl10HXifboVZM50Uce4Kmka+eJo3Nz1Gycqj2mDt7lQOGKZv+oqf2qD+LuQkYbi+rWq7+6/OE6VrXtYdZU0hY51ul/wAVBJbJ8ob0t+OheUk8hBu5MHt1LKMoFM7CdP8AVZNqxLeP/Rfw+5VmzCiaWyWKnN5VWVFo4gfeVDtFlLRjVL7Cfro82p2RVObxDPR6ymPxPcOjNQ7RVw9MHz5T6ZHWqc2q29mafX2FR6HM9VW7d31uXOsc7W71Io9SfqzA2UfPKj1Len60DpsjpCDUR6SbqUmvzHprtydzvMGs5n6sz9WYZiska5Ozx9UasB8jCOYLqCuc1k0l9+i/XdU/PPB3/EqgqA2Z1z6OEfAAKeeSY+mfrSqKZklRKQej4KSvPlXJami6k/7cz83zVLLyVPLfeP6Kt5sH5QoZmVEYljNwVTH0ph0PcspVJq4aeci1xJ3LJvtkX5h3oSuc1sZ1AjxVJ/hf41JKGz1EfS3uaqXbs4jvT61scxaT6IHxvZRSh5eB+IlYzjczcmOxN/VTarrJzgaocPBZRrHxYeRd0g/BQZSDZRHUarDT2XQcyojBGpwWUql0NQy24d+hZPmcyOeXfo8VV1TXvhlB1HT8LrESyqv0qKb02wW9+6q3ftUQ6ndyubWUL8NLI3pt3qWUvDb+62ymJMeHqWSqoQF73DQ1lv1cPn9yqmlzQ0dKdfyjtUu2KrdnTfXQqHaHgsqa39ip/RoMRQOIXHmVOyKhFo256VuGRzVRe9motqq/nN893pyG28qCEeVM+tWaqOEjtUY9Jnqq32h31uTNSdqUZu3NrsU1Pz4sJ0KLmrepVezkw3CGeQ6c7GOkNm9fwF05N1p+5HXmcbI5tdkdSlYWsDjvTTpRa5jnNdrBzFZI1ydnj6qpqcNTKWjX6PggfQI+t6p+eeDv+JQNrom5VJOKaUSEKqOKd56ypHu5NjL6P6lM0xYVUvxsivu0foVkKvDIXwu90F3YsnPdJHJIdZcSpqr9khYNbcY/X/yVk32yL8w70HWdbrC5cxwwPZrbiTahxe5794I+FkxxY4OG5GRxLid+tUOl4b0/JTkslKgcSCptSoX8nUsP1p0KreC9zP8AU7w+SkIdICOgdwX9psgpYBEfSsPAHuKyhPy85PRoVLJhjmZ0j671IcUIP+p38qM2KKTpc4eKi9rb+bxUVU6qqGF+sA9xzNH2akdY2Tzot1KCTA2RtucLfEHwVBK6aAPfr+4zE4mW6UfaEYw7lnndZTyYoqcHX/X+iodoeCyj76gn5SgkZbmhRbNvmVOyKYLMAOcS8lI9UZ9IhB7XEgblQ7RV+tvn07A+XSoYGCdx/Dq7U9uFxaq33UfRELur+Y+qrvaH/W5BjmNGIa1MHRnT0A/qLqG+G+5HUmc26CkU0ZhldGdxtmOtRvDRpTJMTlUsDWtPSPEjwTtahOsZpGOjtffmfqTSidKo9ofyv/4lOdqzO02TjY5i64T9SGmMn63oakdSq/Z4T1H/AJFNHvKt9qmP+p3emm4UjSxhv1LI/wDednj6qq28nE963ea52NxcUXXAHQmiwCeAXMH1rKo38iJz/ot+pAVLUSU+LCdFvkE/UoHujeHt1hO1okci0dZ8PMa4sAcNd1VG8zrKN1rp5xNKa4tOIIm+kputPmxCPDraP5ifFE3Nyqfmy/l8QnbBvE/y5oH/AG7Xu6R3priw4m5miwAUzvSCe70wFE3WslOvT4eg+qJDdea41ZsQvhvpzOOEXQkxzAbv6KWzZC/oIVC3yyOfD1fC6nOFsR6v5iqHaHgsomxdxVH7JPwHiotm3zKp/Jx4j5kwtI5U0jWPuVSnE55VDtDwVfrb59G4OlJCi20nYpto7iq0aiqtmCih7fVVbHOq3Dpsqh+KOHqb4qqdiI/K3/iFStvS9p7gidBXJYII3/iufjbwTnYXBZKiZNU4Xi4t4hZagZBWOwb9PaVfTZHWnswta7p+ZCj0OVbHiNO072jvcna1UU7WV8kMQsFvVbqjP+lv/ELCXnA3WVlCnZTPwsTOYTwTBjfbqPwF1k6B0jXyjU1r79rSsoQNZDAYxbVfiQFIzk7X3r3EXXAQGlTQmEgHeAf1Tz6Kbsb/AFvTNLVK21Mw9Lnfy/1QpTUUUBb0P7zZS0klPhY7ffwVdGMVQQNPKD4hyjHokrKjMD5B0Fo/2rJUYY1/G36eqqtvJxPfmtouiLebfchcyp+sBE4Db61p+pWAATta1eY/mDPHzCgwkXzM1oa0VFIGB4O8W7s4NtOaIYrhb1Nzk0nHdRiwWStDH8fUkgC5VRJaRo3KqIELr7woqnTHfU35aU1we0OG9TvEVUJOhpUb+UYHjeqqYQssd+hQSBlQMR0Kqa3Dfev+n3ttJHfTrUpdK3h9eKp6hkEgx71lCr5ScjUA5w/SyhrDHG+MDQ7R/VRVmBwidqXLvfDMfw4h+irKl8dLE0e8Nf6KjnEsYbfSAL9qyl7OSnODRdyMrGnCTpUtQ2KRjD711LVctUEDUFGPRuqL3lRYGnSdJVbYsDutTVUhgmcDzTo+CpnB+Jw6VFUNkDidGE/RQkvMW9SM8nLPZfQC34hVkpwS4D0LlXMJwG2vuUda7y6KIHQ4XPXo0LlmVF5I9Ruq2cB4h7VVuEtDC9ur1VZG5swm3aFMfsoj1Kogi/srlcPpejp7AqbDHGR+fuT2OMbnDUCoWGeCFg3D+Zyk5zVkY/tZ+t4X/UPtZ4BSQvj0uG6/YsBcG4dZuptlFwPeUWloBO9VTMclKP8AQPhdRQGX0j+Jot+YH5JzXPyoev5J5LdaqYf2QSuG5lv/ALVHCxsMU1vSxjx+SyvtRw8Sv7NfzsNwMF/FCBraicN1NDrLIjHSxSxN94EfArKcfJU3Jn3cI/Syy869ZfpAQ5qjjL3W7fhdRRvsX20YT3rKDGmGKVvQ0f7U678LG6yqahgjdq0Fo0btKhNwV5OZsn8vfmeJssnezN7e8rKlw6J/X8kXgzyOP/qMPeqel5SiknvzSPr4rLW1m/M3/iVHE2K+Heb+qqm/bv4nvWHSsOiyfmtouiLIR3Q55QuHkp2ktKkHpLWsPo3TxqTm6U5qATRpTxcALCUG3UbdYQZZuFBhLSmDSm85O1oiywlWtm6FELIm5un+km84LUsmj0HcfU1ZwwOKkmdINKfUPma0E6AgbDQjWStYIr2A+ajJwG29RZR/ZSwc4D+idNI60btV7qeTE6yNTyskTdwFu2ypK19DUcqzT8rqSoaIHAe9YfEHwU0voRqseJJHvbvLj+qjdeO/1vQOm68tbEJorc4n4qSUzwMBOlt/00KjqjStdYaXWUlTK+Lk3FPqnVNFJi3W8FyzrtdfSFPJyrMfS538qhNrqGb7EjoPemylhLr9Pcsm5Rka9plNwL93/hS5Xjkjsd2ntRLnAtJ0I1L4Gh0ZTpC1sjBv+aqHY2aOgeCp6hxxyv0m48U44gSelPOmyiq3PqGyDQWNt+jSqSccvHfU0O8SpKguLHk3NvEqnykWMih1jxLj4eqyhzG8VLCZaWHDrOhOhL6MUvD4LyVrGkA9J/VUsImgkjO8+ATm8nhA6FSj7eI/m7lkqEgGd2/Uss6ZQT0KNjZ5mNk0/ZjvUjA2uiwjRY+KyjT8hgY3UB4p9PI+CORouLeJT2Wmo5OlncD802MSzytO5zD+gKp23yni6GE+ClALP4f51OMeT4W9TO4oQOlpmhnuOae9V9LJO+8Y3eKj9nf2LyN4M7/xDQsj4o2P3G6qovKmFrjrWWS507epviVEzFEH9Jt9fquQw1hgb0Ef7VEwsopWu/Ae5VfscPZ3Kmpi2eLlOk/AXVHqd2JkRixg9JCF4smOY7R/+yihbAzk27lVsc/k8I1OCmY4SOJHvtX/AE/TgUzmyDWdR+upVcXlc1Q0azh7vVzs+3fxKcNKDAnt3prblFttCw703Q1X0o826i1JwxWKwaSjp0LDoThbMBZYU/XnabFE2R1K2myDSHINuLrAmtuE1tzpRasGlAYcw0rfdPKya7FCePy9TXezu+t6LrEJosFi9LCnNxFAYRZYSCinG5umnCcQTXYk91w0K6k1FMNoymvuAiLuusOggIMtZO5pQcWggb88VtKBsbppxtUPohHM7024Ve5zR3Ad1q6Ol6GsoPLHEjr+OjNpABCYbtBPqW08dRflNwumMbG0MbqGY6lQNLY3X6UG4zh6VFScm/0tbCU1rWDC0WCfFFNBaQXsUKcNm5UdFrJ8TnVDJdwBWUgDSuVA3HRBvTfvKfA2NsTTpwtVP7VP/D3JpIrzb8HioKaJ8XpDdb4qqc1scUTNQw/BMdgY5g35myAROZ05qLYO/O7wzVNO6edwtoLPjdGl8nia1v0U6FzMotk3O8Ao2CRkjDvaVlRgjpWMG4juKZG1wY4jSFTOwse7gpaUOeGN94k/qspgCB4Gr+qOnPSSBmNMBFTKT/p7vV1L7SSHrKa64xFOm0aE1+IIkNFwtD9KBtoUj7GyxrlDqQdYEJkgtpTZA7Ui/wC0XLWKY7ENKGhEC6Lg0J78RWLQsRTSTp6E+S7tCbNp0ojTiWIXssS5Zqc8MWMWxLQVyjVOXADrTL3sEHWFk12jSnFZH2B4+A9TXm1M6/1pTueFK6wssRvdak8+mFZYvRvnZmEl1I67UNmUDY3V9F1fTZdaldpt5gNs0J3IejoTTcJz7AKM3cU13p2V/TTnYVylyhpJQ1lHWhrTtXqg4t1eYxmBuEIG2kIm+k+a9jZG4XDQmRtibhYNCmeHkW6E2NrXOeNZXJN5Tld9rKNnJtw+pn93iM1P7/ArKLGvpnX3KPmBNkwtc3pUm1Z2qv8AZnoG4v5krw/DbcPV1J+2kHWe9VEToMLHdF1Izk3lnQg17AHbtfgibo4ow031/MhFxJuoYnTyAdfnTM5N+Hh3ZgcJRcSquJ1LKWX1om+nzIuZJw/mCII15sZw6VqVzpzE3Ttg09Z/lQkIbZFpaATvVYNn+QKnFpouI70WlukrWntwDCdxKyP7OePy9TlEE0r7fWlU8HlMwbe39E1jp8RG4eIHiooDLOItW5VEL6fQ9QRmeZrBvRJbcOQdYWX9mychy3b2Zo1Gx0hwt+t6bvUUPKlx6ASjG5sQedR8FFTPlikmGplr9qwvDMR3qhh8qkc0m2j5JxwjAVHSSzROmaPRarFEWWAi196Zk+SeRlONDrfzFGCRpcC3m61fBqU2jQmlxb6K5KRw1br9iacJumNe4+gFOzkZ3C2i6ERnmZA3WT3rJ7QapoP1oUWR6tzWvwaD87Kng5Wnkd0Ob4/0U8LoJHRu3Ej9EyEua5/4bI0cj5XRD8WHtJPy/cc/u8Rmp/f4FV/sz1HzBmk2rO1SRtlYWO1FDRo9fVxgVD+J7rrKm0Z+UeKrqSSOXGfeJRaP7M7PHNV0/wBnCWfh7tPjmyL/AHnZ4rKLQ2qeB9aMxbh1qcBrhboHcM1Q0Gplv+HwCpWHyhnFPaHTT33X/wCWbK8Li7ld1vH+qpaZwmiMg0O0/osogNqngfWhGnkDuS36VEy8cjrah4hRxN8hfLv1fELKVNyssbhv0Koj5KUs6EdkOJ8M3k/2b39Ab8VWw8hMW2sNyY0vcGDepsmvZTDTquT+g+SA+xceseKqYyxkXRh8SVFSRzRtc/e0DxUsDIa2KNmrR3lTMxsA63/DSoIzykRO8+KcwGRnW8+CoY3RU7WPGn+vqYohM8Ru1FUeSXw1LpTbCS/46lk7IzKGR7733dmgr+zYeXNRvuD+iyjScvUMDW39L4KHJsbKgTFlrqqyS2erjAHoC5KiyAXxB79Fr38FJRsdHyTNHokfCyh/6fnYHl9joIHHcockMgixyN9PRv6tKyXkyN0V3izxf/do+CFAIq40rzfQf+KgyI+J2g3a5vDoVZQilydyb9L2jvcE+gbS0VQD71yo8kGWmgwD3XXv0uGj4qHI8tDNymttrduhS0LXujlw6bW+v1UWTgKeaE+8SfgociRQNdY3Jt/X9VUZIbLGWatR0LK2Sm8pCYt9mqKhwS8oVW0DTFM9uku0/o02X9h1BLXu5tgf13cVV5E5Omkk1v3cLhZKydHLSsnOvC4fqTpUWTnsqi8t9EMsON1SZPFdWviJsLnvVFkcU0OEn0t/1wVbk59Xa3unTw+gqPJjvLmyN1NVJSRS5Unc4c3V2qnaWxAFf2Q1sUkbTbE6/wDRTZD5SflsXvE/XaFV0OCF8jG6bi/6qCjjp5ZJWe/+5af3+BVf7M9R8wZpNqzt+45W1s/i7lX075pI8GsjuVXTmcstuKbTAUvIP6FDRipomAaDfxU7Qyme0bmnuUdPGRpHuE9t1kqB0UZe73llL2p/Z3BeRRzzyx6gMPchRtdUvh3YdHwVfGYpcPQB3Koge2zw30bDuQhDq14fqc35BUlPifjGprnLk/tal3UpY2hpcP8AT8WqeEVEZjdvUkB5aEsHotv3aFNSyOq+VDd7f6rk2XxW0o0csdLMCNJPisk+z9qnhMrmEbjdZRFqp9vrQmUhlbE2/OxKKBznTRM1/wD+gq+JkdK/COj4WWWGlz4wFk+mPlDcfRf42/qqgF0LwOgoU7mUZf8AiI8VVxk0cMf5e5RM5ONrOhTxY6qJ1tV/6J7HAAW/9TuVLTA00Qfu9JRU5ms9vuvP6eqpNsM5cBYFYQiLqwvfzK3ZKgPpEKdjMLn202UWzbwT8ElTgeL6P6p+EjA4aCmtDRhbqTm4hb1NU4GJw4KilBbyfRmioWxVL6ge9mDA0kjesDcWPeo6VsU75x7yLg3WpH2AtvIzMZhv1oyWDj0fuWn9/gVX+zPUfMGaTas7fuMkMcvPF1qzHUqJjo6drX605oe0tdqKFPEDe263YgLaAnwxyHE9t0yEMkfJ+K3wTYSKh0vSFlgfYA9aZEJ6Rsbt4CwNBxW0qh1S/mKgaH1FQ06jbuRpIC3AW6Pl5ruaVk8WpWZpaM1EkuLQDht2BRxtiYGN1BU8JhrX9Yv+pUsTZmFj9SyuLuiHHwWBuLHbSiLiy8lj5JsW4LKGqP8AMPNoWlrX3/EfVAlpuFNVFrQG6yF5YfR+KmmLpcTShXN3heXekdGhOqhyZe1Nrj7wTawHX0/BPrGtJFlNU8szDZMe6M4mrlX2tdUkx0h2oBMm+35RyqakSDC1SVFo2sYdyp6rGDj1heVNa94J4Kars4BiMp5UvaVNVlhLWqnqH4wxx0J1a3D6OtRVhAPKJtawjSjKTit7yilMLsQUlVgA0a7FGsjDQV5dpPQpagRBGsHJ4wNKkqQ1gezSpZnSnSn1N2NaNymq8RaY06Ro706Z7iT0/uWn9/gVX+zPUfMGaTas7fvkkbJRheLoANFhmZG2O+HfpTYmMc541n1uUWNIY/eCPMrx6LPzD75cj15cXa/U3t+5yARY/vBzGv0OH/1gNX/uBuL282ie57HYjvOaHaN4qbaO4+dlL2V/Z35ppA+C/Tn15oGh2K/QfUUb3PD8R1OP3OOVst8O428zKDi2mcWno788PNfw8+T2yPgVI/BbrKL/ANoAzSythYXv1Z8I5DF1+flBzmUznNNv/P3uTYx9vrqlobKQPu8zzHG543BMqGkR4tbx4Zne3N/L45+WbyvI77XzUHMf+Y5odo3ipto7jnJDRcp8zGR8q46M2UvZX9nenyWJb1XTzeJnam6s2T9keJzUfPdw8+SRsTcb9So5WML2OOtzvh9wncRUQgHp7lTyulMmLcbZqH+9/Oc9BO6oiL3dKyl7K/s789OLxycEyVshcG7s883I4NGs2U1XyNQyIjQ7NJ7bHwKklJia467r/FfXRmyhI19NI0axbvGf/Ddvhm5YcryO+1/MBDhcLKUzrPh3YQf9wUdYTPyLhvI/QIODr2P3mTYx9vrqza/d6rYScD3JvPpOB/45ne3N/L452f8AcXfl+Wag5j/zHNTtLpW2VY5wro2g6CXZsbcWC+lSzNlgmw7rj4KpcBk4Anc3wVH7Ozgspeyv7O9SH7R/BEfZA9aZIDZvVdPqiaxsDeu/6XCyfsjxOaj554Zq2YSU0uH3Tb4jzI5i+aSP8Nvispeyu7O9T7/zSdwXLOY+GManDuHr6twZPC52oYu5UBDuVI/Ec1JK1jntOsvdmbK173RjWPFZI9n7VlL2V/Z3oEOFxmEwpqWWZ2pU8gj5d53FUtWZyGkabXTJnvoDI86bHxUpLoqcnpapZXSV7cW51vjmk9sj4FPN4mdqxjynEjOXQy9IuonF1JMT/pz/AOG7fBVOwfwPcoHftEV97AppRBGZDuQN9Kl2blQ+zMWVX4ZrdLfFR+3D8zu5URu6b82aKZk4JjP3eoaWRxg+urNr93qthJwPcqabln0+jVcf7RmqH8nVY+hhUT+Uja/pCqJjEWAbzZNcBlJwO8ZsnG7H/mOai2qfK6WvGLc+QLl3moeRo9No70z2135Qo9hU8X9yyhsKfh4BUZBgYOoLKXsr+zvUzjyjuCucNk2Xk5GdFkx7ZMp4m/XorJ+yPE5qPnnhml9nqPzeIzUsrpeUxbnEZoPap/4e5ZT9ld2d6mcCSOt/ci8Gpp2dAPd/TM6pw1Lae2sX9blOdvKhnQD8QqWV8YY1p1u+S1KL2gfnf3ZoPa5/4e5ZHm0uh7VXytkpZMO7R8QsnycpTNPRo/TNWf8Aa5vroVRK6MvaNRJ8Fk8gSAn8HiqV7jSytJ0AfNP2NNxb3KpOGpeR0qlJMDCehVBtVNPUUXCwaoZQXgqRxDJG31271HMGU74/xW+Gf/DdvgqrYScD3JjsNVT/AJB4rKHsr1yzWFkZ1u8FJMwseAdV1SShlEJOgdyypIJZWuH4QqHbRdv/ABCjmIEgYffHxKuqOpdG3k2/iHx/8fd67Uz11Ztfu+UJuRgOjXo+Cg2bf4/+IVLsI+A7lWbZ35CqXYR8B3Kt50X5gqqbkK/lLavknSta9sZ1lZMldyz4d2kqoAa4W6FRbVV7i2V5H45PBQP5STH0vb/Mqo/tzbdLVHsKni/uWUNhT8PALJfOfwb3LKXsr+zvVQ6z3k/WpF+E4VLrYqeTkqnlOi/cVkw4oMXWc1NrdwOaX2eo/N4hRP5SNr+kKmkETJ3nc5yEzNGnXpUHtU/8PcqycS0su6xt+lk/bO7U0l1ZAf8AT4FZN9lZ296qJsOUIy3h8Ssnvc+ma5xuf6+qnnEJYPxGymq71zeT/Kf1VdKZah192j9FyhjYxw6b/ALK0zxII2nRb5qjPpxj/U7uCylVGnjwt1lUTi+sLnay0dzVk/2piLxJRzvbvd4hf9PFrxgPT4KR37axvUVV1OKjqYLc3D8SFUnFM4ozEYWt6AFSezzcPmqh4jpqd53Ye5VXtD+JTZjBQNkbr0d6nnjkqG4TfQ5Tvw4bJpsCpNRW5V0roYHOYdP9VFlKSNuN+m5+rKOaOam9A7/BZQkMdM4jh+qpZDLVRk7rD9AqmqEkErHa7kDssqXT5L/GnSiGaQnpcmOwZLvx71Uc8cG/8QsnbWPi7uUDvQd+Zvio6h8fKEbyhI5h0dKhlbOwSN1fdG7B3HNXameuqx9oT6mRhjdhPrHODAXFV8zTSXaddrfqFlGo5SCNpGk6VDojH8f/ABCbVPZRRvbwVM+/KOcdzlC9sdKx7tQA7k+odUxNe78ayp7U7sTTeSl4H/isme1v7e9ZacWwEt6B3qGoZTytx+8bdpWUto/87/BQOwMxdDm/zJj3S1DXu13ao9hU8XKucHU8Fuj5LIv952eKqZvKMnmW1r/NVZvicFjxEPcnb+KZtD29xWSfZu1f2jL5SWHU3F22BWT5mzt5RvQqttpSparGx7W+866p5g1sMXS3wReWtqgOnxVdpjj/AC/JUcjZaiZ7Do9HuVSbwuI/GUXFzrqCwrIMWrB4FR+43oupJbTslPV3rJrh5Mxt9OnvPqsqScm+F3Qb9ylkvM6RnTfMehTkGQ26+9O5xWV3tkEbmG+vwVHOad5ePrSFRuwTtedyhlb5FJFv1/EL/p6YR1eFxsD3p0gOUg3oFvFVMrGCqjcdLsNvgpHB7i4J50/oqUjkJh1Kep5amDPw4e4qpN5nkdJT5y9ob0C3xuqd32lz19xVQbBpXvJ+o5q14exhv7viEdiOJ8F/0/8AaMkt/p/42VdVslpAfxeBVE4NqGE9Kqdo78zlRSBvJOduL+4Jzi5tzvJ8EKtnkJpzr/qqjnjg3/iFC9zTdqhJEZCvdpKdzisl1GICAbgT8fujdg7jmr/d9dUi8Z4+HqGjEbBVu19ZVbCTge5VLi6lgJ61UymTC3oA7liwwtI6XdwQmcY2Q7h33QcQ23T/AETZTJk12LdoUMrvRi3YrqukbNUF7DoKp/bIuHgUyTkqrFe2nxWXPZzwCm2kH52rKW0f/wDEf4JoHkj3f6m9zlBtmcWpj2tiqGk73dyk9mj4u8FRTGnhle3X6KfI7k2sB0W/mKGxsg77MfW9OUmhxVNM+LJ73s1g/JH2qT+PuKyEXeXNYDoN+4qu2g4ZqxxbFCW/h+Sk2lR9e8FWPu1jeho8FkX+87PFS+zH857kNaL+Ue225tu9M5zVPrCye5rJGFx93+f1WWv7vt8PNOkouHJNb1nwTdTvreodZUXMk4fzBU22ZxUs7IsomQ6h8rLKPtD+zuGZ2tUuqX8viENi7iPFP0uOaPnKc3aF7yfqOaSQPYxnR81c2sqOeSCnl5N1r2Ur8TWNG75lU22ZxCqHfbPb1uQOHSFf0QEVM4PdcdA7goecm81blJzlkfbnh4j7o3YO45q/3VSgGUA+tqRaF3qIdo3iq3a+srn4KZ5+tOhF7i0NJ0BONynbFvE/ypjra17qjqD5M+C3X8Qodq3im84JsjhUMI3WU21dxWVavymmiedF/DQsoyOjaxzdYN1WyY6iSx0YnH9SmexP/M3ueo5MMjX9BCqXETSDrKL7xtb0X8M/90UNmV7oUnOUDz5HMzh3psodM6Q6L4viCsivEdfGXfV9CyjXMfUSyQv923bfNO8mCK/Qe9VJtPJxPeptLj+Vvc1RuLIi5usFv8ycb0lz+LwQ1pnP7FHocp9QUbyXRj6139VlnaM4eoh1pgs1/DxCjJa4EKZ/KSOeN5VyQScztail5PF1iyB+yI6x4o680fOUnMTCS4XT9R8xmpHWoDaZhPSFUm1RJxPmw85DUtyk56yYS2qAHX90wlsLgenNX+6qTbD1tVsT6iHaN4qt2uaJgeHX3D1WUvZX9nfmKxG2HNfRZNNg763qMhrwSm84JovM08O9DTdVZHk1OOo96qaryiCO+vSnaSUJLU7oukg/oD881Vt5OJ7/ADGHQRwVzayB0AKTnJspax0Y32+GbJvtkX5h3ousC3pzTbCLt71U7d/EoGwKbsHcR3OXKHk+T67putN0PTdam3KLns4+qyztGcPUQ60/cm7sw5uZ2vzY+cvcUfOTtXmM1I60DY3Ur+Ue5/T5sOtHQ05n89ZPFqxo4933So1ScR3Zq73FSbYetq9ifUQ7RvFV20HDNBqfw9VlL2V/Z35tfnN1hajfgmqd4cyNo3DxK/Dw8SjrXu5pX8pI5/T5geMRHVmG5Sc7PES12JutP15pthF296leJJHPG85gfsiOseOZvOCHOUbr3UyOoFMN2g+pyztGcPURa1JqarW0Zhzcztfms1r3Ew2cEfMZqR1+fDvUnMQPo3T9oFRe2t+t33Sp1P4jNXe4qTbD1tXsT6iHaN4qu2g4ZqfU/h6rKXsr+zv9Q3Wn80pqcg69h0I617vnf3mZuoKTnZ2a0/XmkkDoo2jdfv8ANbzggo+c5Sm7U7mNUfMHqcsbccPn6iEb1JqanjUcw5uZ/mx6CV7ibrWIF1vMZqR1+fDqUx9FYrRp20Covbm/W77pUMLmPt1Zq7mtVJtgphaRwHrKvYn1EO0bxVdtBwzU+p/D1WUvZX9nf6hgu5O5pUYuxPTNadrQ5vnf3mYcz9VLzs7Nafr89gu4LUozdxTuaeKdzGqPQwepyxtxw8T6iLmqbcnc1O1pvNQFxdP3eazUc8XOTxY2zs1I6/OAubJosLdan1BX9Gy95qox+2tPHu+6HNXc1qpNsFPtXesqtMJ9RDtG8VXbQcM1Pqfw9VlL2V/Z3+oi5ydqKh5qfqTNadrQ5vmgXNk51n3zYjayl52dmtP8+PnLW0pm9e4pOY1N1epyztG8PUR6GqZHmp4tpTeao+a5P81nNOeLnJ4vJZOGE2zM1I687Wl+gZ42Xbj6CPH5J2pTjQM0Qu7sVJ7Wzt7vuY0lHVmruYFSC8wU+1d6yo2LvUQ7RvFV20HDNT6n8PVZS9lf2d/qIecn6ioNSfqTNaAu/SiLAjzY+cn8455tWdmtP89o0g9SHNKBshpZZPH2fqss89iEBMBn67IhNjc/m/V8zIw5j3dHzzAFxsE+Pkg1p1qVHmqXUEOamD7MlPF0R9kD1nwzNpsVMai+o/LMzmHPCDiusPp3UnOzM1I689Lznfld3ZmMLtKh0xHiO5ycDgJCmhxR4gdTQf1sPHNENR6lQNDqm/QD9zjGJ4COrNX81qotqp9q71lRsneoij5r+tVw9MHNT6n8PVZS9lf2d/qIecnG7bqHmpzbgpmtN2iOo+bHzlJzjnm1Jws0Zma0/wA9uoIc0o60xP5vqstDTGePgm/9vd+b5IRPMGMDRh/mWRf7zs8VOwRyuYNxKhjb5DLJvv8AJEFpsVRtLqhgHSspdPWO4qTSbLcVJse1DmqMXgPHwVRhMkpYLDSqoWpoL9anaGSuaNxKj/7Y/j4hBpIuFUtEcMf5UxjtdtYKsb2VPEHzys4/AhEh1IG9aqI8LGu6b+Ch5x4HuKZqUcZmk5Nu9SRhtPG8b75qOklkaXgbj8QmMLZCx2vT3KnH2Tz1tVHFykLz0EePzUkOGnkl+tYU0FojbfC0/wC5vyXJaHX3AH9bfNMHojgsmt+2v1fL7nDtG8UdWav1NVFtVPtXesqNk71EWzZ+ZV/u5qfU/h6rKXsr+zv9REbG6GzUPNR1OTNabtEVqzAXBzR85P5xzy80FTbszNaf50EZllawIaGppJDkdai03T+b6rKcLZIC/e1AWoHj/V8lTf8AbncHeKydz38Gf8UKLl6uVj+P6rJsAfSYZNIcfruVZBinkLekfFUrDBU6dYv3Ktfjixb7juKczE7R0oUE/JBzhYPIA7VUNwMt1oN+yxcVDse1VFOYXHF7zbqujLmU8Y16u5VEbn1DwBvPzWSfZ+1Np/J6aa4te/6blWMxRwN6gqWkZNTxk/6vjoRibFXxMHR4FUntM3B3enaI7DrU+lkfb4KMEyOPUe4qkhD5YWPGhxHfZUNKTrPNce5f/wAWfr3lZQxiGMRjcpGB2UsP1qWSWNe2QOHR4rJzTyDydRspW3yfIej/APIKoYYwWHdAO9U0HLuk/IO4W7kIJA7kbelZUFxh6x9zh2jeOZ7cLy1V3NaqHaHgp9q71cbDI4NCe3G0t9RFs2fmVf7uam1P4eqyl7K/s70xuJwarE5wCTYZ2Nu1xX92otQTtTkzWmD7RWTucc0bS4Ot0eIzR85O1nO9uJjVMNF8zNSfq8yoAbM8DpObJ/tTFrs3p0JjfsJHdbe53yR1qHen6j6qqYZIXMbrVPTGVskRNr/MJxZC3ABo0qkdfEoB+1zH8vcoZOUbeynhkL5SBrLVJByTnOdrJP6KSB88JEesEHvT4XQS2d0lVhEdLTE/ib3FVNPy4DG6yPmskUUVdTSMk17kyHDjY3c5SSY6iO+4AfEqsIfUwjoPyXk0r6gutou74tWTonww4XjTdPaHtLXainwMklAI0NCjjbE0MZqC5NhdjtpVNAG1s1tXz0qVuA4VM2zg0rJ8LpGTN1Yv6qsa2OaiDdYsO5VELIZDgGvT2lYSMn4XaP8A9kyj5Wmxs16v9yJsLlURL55XO6lk1hja7pIULf2NjR0+JVNA2opnxP1WPeqmkEsJmB0lmH4qmpfJ7m972+Asov8AubVUsbFWhjBYW+5w7RvHNNtHcVXD7MFUO0PBVe2Pq6TbBHnj66PUMH2UfFV/u5qT3uHqspeyv7O9ZLaHVAxBRcyTh/MFTDFMwHpCqqY0xax2u3zVKL1DOIzmDBAcO+x+CbzLJmgBEXBCYogC88E4akIS9skn4fHNkgB0zgej5ZoYnPBe3dmIsbLCdF96jbia3gpASNCk1NVsJITtStfVnqtvJxPeoo7zMY7eR8Vk32pnb3FNHpx8QqRmOCU9DmfzDxVRHyMz4ugkKDU5AEuPqql5jc1w61Sm0iklxvxKB+GQKOHBK+W/Ot8FRu0lqnkMTbhVvuqGHkgq1ukOVYxr6BhO7CVI7C2A8e9ZHf5OcZ3X7ioI3TYgNaluJbHoQ0zxk5myNeSBuTpmgB24qGXlJTnhjc2oleRoNu5FuKpVUz7VUR0Fo1BSsZjYd7ZB+g1+CyhO2ZmMb/mquYua5qjIFEB9a1G4GC19Koz6JCkcGSuv0Kne4hjN1/FZPBDH36CneyZo4neWsl3KraOXLvucO0bxza6jtVdsxxVDtCqvbH1dJtgvfHqGi0UYPT81X+7mpPf4eqrRemffoVCMMwt0/JUsZl5RjejxCpYTHUNDukLLO1bwWTWnypp49yqI8Ejrarkfom84K94zf8J7lG7UE1pc/CEwaUxQ893BOF2NKjFqaf8Ah71NsYuB7ysj7c8PEKSFwk5MDSfmqdvJ00lxpv3BMjMmK27SnsIu7ddSCwi4fzFVEbIp8LOhFvOU25P57uKaMXx7imU4bcN0kx3/AFTxayo6U1T7bt6yhTmGUuJ16VE0eVM/h7gsnC1TGePcsm00T5RiGoX/ANyoh+yTH/UxZQZ+0yO6Xu7/AOqhaMEnEeKEe0d0OH83qq33U1nIlsn1qzSxmN2FeUehg6lTbUKqfjY3CovTLWdeaWPlW4VMy9Dh6u5ZQb9jE7oAQ1LJm07QqiESSSneCe9WIs9OqHeieKhl5MlxT3HA1v1rVGNJPmM9pd9dCmh5W2lUjw12E71fcpXYrDoQF9CieOQwdaacJuqYnlAFM8PeSFSaS1RMAjx9IKd7JmhBMgsqxpEpP3OHaN45pGgTMI6fFV2z7VQD0iVV7Y+bJIIm4ijU2eWqGoxNJer3z0m2Cbzx29/ns5wUmpn5vmq/W3NRxgR36VIA15A9TUi8LgVF6MhLekqhcWvNt4WK0uLrCyjE6eSI7jo/VRRCnrLDV8wsqQgujZGNJJ8EYHRhjzv8E1jjE49R7lTR4yT0C6paaWSXlGj0Rr/QpjNIPTfuULLucCoxYk9RWG8QcsIwOT4sUEPB3w0rJLS2oIPR8lJE41zZdwHzRI5Gbrc5ZNp2yCVrugfFV1MyCmOHpVcML4h/pCrGNuJLadSI9J44dylFxdOGl56/FQ0vK8nh0eg6/wCrmqmfhqWg7m2+KqBpWSmhuKyyqPTTABURn/S3uCkAirLsFrfJZK2v8P8AMo4eRgqWjUHgfoVUaav/AOYe8KeExGUgWBLVR0xcKlj9BBb4+qqgfriFUaI2BRUzS27lWM0Y1bepmgMY5F92hvQqVt5L54dQ4BZSi5WJ3To71/ZmGDlC7Ta6yaPTv1hOZgfN+YeKq4/RDhuzEEa1pKgi5NvX5jY7SF/Tm2EvBRU75HtH4r27FJG7GWnco6c+i9CIQzBjdSqWBjuKk+zLS3oTIi8aE2BzXtNvqyj2HY5O9kzULblxVfzh6w1IHK39y3boJQ0ta4bwD+qqpTBC6Ru5NcHZqmqbBFyo0rlvtSw6kyVwrYWA6CnPwva3pupdqzj4rKU7Io/SPWqCpiAu42xWsqqoixGQO9FSkQyCJ+soVDcb2n3FT1bKlzmt3ZuVfKXsducfghFjjxjWpGmLXvCiqmU9zJqKFQTKxoGhwugwlpcqMXlUbgX2496mkZHLgO+9lFM2bFbcbIuaCGk6TmklbFbFvNswriZxh1DFf+EXQysJHg2swafFVVfLOQ7UmVLg4F50Kl2be3vU20dx9TUbIpnPTYSx7iRvWEYiU+QvEQO5wU5+2Lgq7TNAR1+Cw8sI2HddMjc6lLWjSQVRAjlfylZIAdRyjq8Fk+ISUkrraQO9RMtIQfrQooPtQx29Wa2hjPV/MUW2a5PbdkTR+F/cqX0agEdDe4KR9i8HoUcXujee9U9EaNzg46dHwU8DahmB6ynTnHG9vBVADixp6VV0sbGvcAo2fata4bwpoS0SYh71/iqU2iLv9Nv9y/vOxRw8vKB0H4b1TSGIXCqcMsgL+pSsBqrt1WC/vHLJkAEAm94/NGhlMc7G6y+/ZdPpJPLSXN0B7j3WVfsO0d68ibeR41vtfs9VUAmIgKaDlGgDcqRmGRosp4i6jt0lRQhwt0FTxcphsqdlpiOhQw8kSrG18w2R/KPFVDC5jrdSGlkY+tSbFyMrBwVXE1gLxvPgpqe9N6W8ptMH3eelVELcOLoUERGLENa5J+rt8xkYcxzuhU7A6UNcqqLXImfZiGw3d6qaNzJHPJ51/imjC0NWD7TGnRteQTuVYzRjVJFeK44oC5snR8nHYdBRF6bD1ZqD3lWx3GP1UuUuTley2rVx6+1NynK3FfT0KI4nkyHc7/iVHlAwvL7awB/9oAU9dNMMDjoVLXtbK+SQWDrfBVGUpWTlvui6kkHkbId+vvTJCxmFTylzoXA6QFS5VZUuaZdBbcngqCrbUgySHU51unDa6qcoisDHy84B4PaNCoy5lRE52q48E2ocQ2F3NBHimTtjFO927EqiujfPM5gs14+OFU8nJvBOoEFMyzaW+C46FQy3nsRzz+l1NVywyPhB3+KjndPGIydIxa+F/n8E4mRvBZOm5Spj5Y6NXBVOVntjLIdGk9ejRY96gy1JBLewcLDu0/FU+VadrhLIed8/BZVr2msxU+pvfvVHVOjbMSdOtT1kj+TqANRPgjlEhsunWdCFXLNMyJ+kYwn5SZUyQMjNgMV+1DG46fq6DrNwlPn5Mm3R8k+RvMJ3A/XZpVLVyx4Cfd1frdSVLau0rNX9fUyNxNIVLSN5OZxGoFNYZH2bvRpWih5Xf9BSdHWhG5wMhQ5OR5dbmt0dyjGBpshC1rm23L+zS+NzObe/xGtQ076Sn5I9QWTKd8dLKw67fNOH2310KngM0zbLL0bY42tHR4qfJxjbLI/Vb4qIMLBiGkDR9cLotwnEES4iypoXyH0RqKmpmyXI1o0jm37FlWFpMNhpLwqqis6PEd9v1WUo8dLJwPcVUQ2MUg/A3uVc04AOoKDTEEI/SuqY/bOspKbkw3rF1yJc3lNwRAMmIdSgZykz2/Wi5VCMNMwHoQbYuPSp6XlDiGtV8DhTOcdxHf6uCASscU6lBYWBMZgAClYOScOKZA4guYFBT4sQeNK5ENgcSNN0yLkjGDr0qWEmN2HpuvI3FoIROBrmu/D81E/lGByIwvibx7k6DFK124J7HTsIHSnwukhazgvJYg0taNabTOEwYU+B7nvICEdyHdVlJRXIwKCnwn0t4UtI11sGhUsBiBvvTYvt3PKlocZ5O+tVAwyRgalVxmRtxuT4THDdw03UFJfTIp6QMaXtU1OTGMW9QxkRPf0oQ44Bo0/1zFlmYOpTUhZdzdSpIjHe/UqvTCfVVDCZ38Si03ssBT4yhHdFthZYd6awayr+ldOO9MeTqVyL23rAblOaCLIs0BOxWwnUrXQagxO6E4l5xO15mG2haBoXJ6EG6bIB11gumBzMXWLIYiwN3BcmXFWLHYhrRj0potrWI3ur30pzi54c76snOssm7N3H1LIjMcANlSm9JMfzdyyRG1xc8jV/VEjemQl57Cf0VLGRRyYh9WQJF7b0+mLasRuGgn4Xz1uyVAfSIUmTZI64zgfZ6e4+KpaRzakSAejbwWXGuneI4xcqvuaZ7RrIPcooyA3GEyAvifLuFu9NjLpBHvvZZNp+RjxHWfDPPTNqCwu903T42yWxbtKc0OGF2pZVhY2JrmjVo7EIjURPffS23yToWQNaxqoqaN0kzDu0d68lMFRMCNR+GlVUeChwu1gDwXIObQ4iN9+yyo6YVLy3qVNQgVk0d9QHxFlG0RMawnUpH2AtvIzSUrJmGOTUTdF4Acej1VAdDhm1K4cS1Wsrb01uEW8yu2g4Kl2IUxwyx9uakdia53Wr7s9tN/UVrgcNlHIJG4m53DELFYG2wprA1uHNiF7I6ZcPUntxNLUbDSqh48nHX6qeT7V46yn4odDxpUjnMJBTMZZjLdCMiN2gFw1rlAo45Kl4ZGLoi2gq+5BxCE1kJtKma+J1nixQcSmyHU5cqNyqI30knJzCxWPoT3Ea0DdYgo48bXO/CL/EDxUmJrrPFig4hF9hdcsbrlL6ljKMiOLkxJ03H6W+ax6LoucquLkAzTe7Q79VTxunkbH+K3fZBrmtxEJvpalI0x85ZJ2BPX8vUtcWm4TXcjC5jhz7nsKgmbAxjG9qnnPK3adS+xZPGBq0+PzXlEVjDb0bWU1NgwlukDQfrtRnjfI2Vw0hNq2u0dafVMYSOhT1ImjtvUcjonYmozyEWJVJOTdrtybKDUco5VVQ1wAYdKqC2N0ODXpKgfHLHybhoCZF+0vldos4qeq5NwaO1Gd3K3aVLV8mS22lU9S9zgxydWMDbjWoazQeUT5YJwMe4qjcWCRttdvgmtZjxPF1ykUBxtGl1isUMtbLj1ENRrGkuG5VTgIXNO/R8FkyTkY3SkaD/VNnEddM8aRZqmndMepSVAMbQ3WLKar0t5Mpzg0aU6ocS62/1VHIWyYelPrPTFtS8px427tKhqSx5c/evLI1FWNdodoKmquSIsLry5tutNqWvOjVa6Na0AEBVEolfiCZPJG3C0ozPcQSdSdOfJsR1lUkzYwQ5cvjqBhOhSVjhJ6OoISBzMYUdQDHc67dydV3LhutoVHKcRDjo1qWsLgAzQoqouDnu3WU1ZbRGhWMDB0qeoYYyGnSU9+MAdCo5rfZW1ryyO4CfVRsJCiquUIG9SVTIyAUahjZOTKlq+ScW2TJjyokehVNM2LdZQTl8paTo3KplDYzbgnSOe0NO71VZQmU4ouu6qsnmoc04rWFlPRRz4d1j+q8mApvJ76FDktssDcehylp2PjDbXwggX4WQyQd53f7vksn0pogTi0lVmT5qiYyNOtDJTHSOvoGi3iv7JaZS33bfFZRojD9oDo0D4L+y3PjEkZ1gaOxT0vL1T5jzXC3d8lSUbHOx/hcVHBjmqWjeO/T3hOyTJa7deju0/FVrGVw+0Gnp3oUUbXsczRh+KnyaJ5uVLuheQ01uZvuv7LDIntadJ/8qigvScnINf14Kopm1DmufuKqqKaWoe6MdCbkpzmMD3dN1Hk1zpXxu0dB7VJQxCmlZEPSdht2LKsQEjXN1u/oock4ZAZDceKfHE6nMOAfQsm5MkbBa/pEjs1/NVlM1kUTd+hqjibG0N6BZSUjXzsmHup2T3OaG3/F8VT0UULGX5zTe6bS8u7lDos8n6/RMY2NuFgsPVXv5vV54cW3tnt6prQ3Vmc8vtfchE0SGTecxcXaSmtDG4Qrm1vMxu6fV6vO1+eXEgD1zHmN2IZwS03Gdzi43OcEjSFiNsP7nlibMLO8x8bZNDv/AKvznBgxH94OeGa/8v1OyKpyTECfMY3G4NThhJH7qqT9q0eaGkguG7/LNxqU7g6J1lTuAiHmQ7RvFS7R3H90yShrXW3Kodctegb+ZFsZOz/LLz9uwIuP2g+tajdZreKifyjA7PDtG8VNtHcfUsnJlMZ/cDzcS9ikN2tTJeSJ7PMg2UnqdSDg7V/kggjX58xtUNQOgr3W8fkqXZDPDtG8VNtHcfUPOFpKY4l9+Kpnl3o9Fvu9M0OlAPqHnacVuUnO/Tu8yDZSepncGxm6p5RG1xKgJdGCf8hxvx4r7jna3E4NVWLPt1efUG0wtmv6NlSH7PPDtG8VNtHcfUNcTyoK1FUXvfcybC5QN9Iz0LQXl3R6hvpnTvIzSa/07kNOnPDspPUSuLWEhVD+UiaVfRZU2yH+Q6fnv45nMLWh3Sodo3iq3a+fNomLuCkbzOCeMLi1UezOeHaN4qYESO9Q3XKjrVIbSfc6nZFRbNqAvoT2lji0qg1u89xwi6i5zQo2YS9p6M0WzbngBdG8DqRBabHz6nZFHYt7c1Kbx/5Dpue/NJsY+1Q7RvFVu18+o57+xSc2JTC0jlR8w54do3iqzWO31EPPlHFO1ql2o+51OyKhN4wmc4Ko2rlQa3efLs3KPnx/W8oNvM8dWaLZtz0nvdnepto7j58+zKd7Iz+LwzUfMP8AkOB4a91+lXANlJsY+1Q7RvFVm18+oGlx4dyZG17GX3Kp2pVGdBbnh2jeKrh6IPqIpC1zutSM+zD1TbUJxIc0bj9yqdkVC/UzqTOcFUbVyoNbvPfzSm86P63rkwHF+9blFzG56D3lV7Y+fU7IovBgazov4KqFpFR80qPW7j/kLFhuev5o+0Q8fkp9mzt71DtG8VWbVNjxNc7o86RuISfW5RbNqqNo766FSjCXDhnp9q1V3MHnuNmkpmtSNPIaelU21Ce48oxv3KfZlU5vJbqVMcfKAjVhVRtXKg1uzPaBExw3382TmFFgBiKOpWuFDsxnoPeVXtj59Tsim80qs2ipfRxM6FH73H/IJNtKJu0n63qV/Jyxv6CpjeJh496h2jeKrNqodnJ5wN2y9qi2bVVC0h61BtHDhnptq1V2zHHz5dm5M1qs99RjC5jutVLix4cPuVU7DGqe4lH1uVNMGSyRn3rfBVG1cqDW7NJsY+3zZNDCnaoszRbX19yh2Yz0HvKr2x8+s5gVA0PcQ7oKrG6WkKIWleoH4r/5Bl2bs1VragcVLF296h2jeKrNqodnJ5zLOjkcN91Fs2qSPHJ1WUdhO4Z6batVdsxx8+XZuX4eHiVVNLi8BTCzox9blWa/uVbqCp9MrVG7DVX61UbVyoPezSbGPt82bZuThYRjM6Gwc49fxVNshnoPeVXtj59YfRAVHtn/AMSeCZGHio9q/sUGiW3+QZtmc1SeYomg0DXdHzUO0bxVZtVA0uY8DzqTZnOPavroWMYsGan0StVdsxx8+okwOI6QtzPrepto7ipm3LD1qr1/cq33VS88Jjg2oxHcVWR4X4ulUHvIkDWpAeQjPHzazZhAAgXzS7Nyo+aU14fe2ag95Ve2PniS4Y3oVHt3/wAWYv5N73HqUOmZp/yDU7Ip45vBVPuIzFuTfR6beKg2jVXbTsWR3YmG/mnUqPZnPa1Ug/HU8M0G1aq7Zjj59UbylYbQBVW2cql2BoPWn6ncfuVRz41ALS24o7R3FZQlcKlse6yoPeVacOnrTvZG/XT5tZswmc0ZnjE0gKmOGNxVMcTXHrzUHvKr2x8+MYngKCPDObdBPwOaoOlw6h3qn2o/yDU7IqoAa8AdHzUn2j2tHRf6/RO/7efzeCpzd7Csoc/sWROa7zXGzSVR7M53e0hMdhqDxWIXwqDatVdsxx8+dpdMQEKaSRrYxrVXtiqzZhBrnRueotm37i8AysUG2RYfSk3XWUNNXGer5qg95V+rtTvZG+bWbPzINi9UZ9AjNQe8qza+cTYXVNtQqaFznufbRYjtzVe0UY5OZo6vC/8AkGoIwFiqG+nfqKpcUsv8J7invw0WDpd4BUmuPsWU9/D5rInMcqeQyNufMl2blTtwxjPMbVDViHK4utNcHT3HQqcXlaq7Zjj559q+uhR7Vn5VV7Yqs2YVK39jlcqfZD7jcPmBG66Z9lJiOq6Psx/Oe5SvxTxDoaO66oPeVfq7VWnBQDD1ebVDE0N6/MjkwBw6VRe8qeQyNu5UHvKs2vnP5pWS9r+iptTuJzVe0VZo8nP+kfvqR4YLppxNBUcjZW4mHRnBB0jNVSgvFlPzuwrIzv2wtPQsoNtcD8R8FR64+xZSNndiyLLZ7oulUgtH5khswlQOD4mkZ6uaPlAzeEDckKk2ipB9sFVsxxcM2IYsPmE20lcsPKbk6NHxWT60cnDJUO0uFv8AcbKoka+d4adXyVULxqmaTk9/WVTbIeqMtpAwKaZsDDI7Uri9s2IE2zSvMbC4KnfindZTajxKoG8pk11+tSnBUt4DuCoPeVadydI5+TvS06fNneGFl+nzJQGPLbpryNLVR7NUHvKubZ4d05mPDxceZLKxjDcqirBC4E6yR3qlkZidFf0r37M1YPSBWUG4XwN6h++pKrljboun15s3k9yDy2BjB9ac1S9zMGE6yFkqXGx0dub43VVP5NEZFVPwm6ZVxzQtLtDnaO1UhEeUGF+ix8FO4yzvsdGk/wC1ZP8AcWV5RFr36Fk6VsMpkfqAKp5WtiuSsbcWC+lGrLIJXSH3iAskyF8JDjv+Sm2buCotg1OcG6XFZSkfHT3YVPK2eqMjNR+ShtIJD0WUFQ2KT0lR1cLKgxE6f/Cn2blVVTWYod+EnvVDMDhxndZCRpeWDWM1RUuikDANyqpCWkDe09ybIW2v1LlmxwUgJ6/9xTa0tklfru7v/wDCrqlkbMG8oV8dJk8YhcuJt8FG/lGB/T6mom5BmOyfUMjl5Xcq2qMpMQ1JtS3ygyOOi3jmrdDp/wCHwQcHC4VbM5r2x7jdR1Xk0t9xVa5lsN9KyNO409REToA0fobqxJD+sKgcMTm71XS3nwbreK8ojFHyN/SJ8FHURyOwA6VLV/shqIvrTZZUq3wubGzRv70NIVbz4vzDMZWB4jvpKqa3yebk3arfFUztZcgBgaekKicHR6FRyssIxr1/GyrjcMKmq+VgjeecHfNUkrSOT3hQSieMSAa1cKOq5R+G28j9FVvDhYfiui86LfWlS1DqasqnjXhsO3D/AOVS1mJ3JP3NBv2D5qrqw6XBuG/ip6umqJGM3gD4i/77bzisRVHV8k2QP4jrTq51SbkaislzMgc5kh0myqKiSqAx7lUSXaGovNmi+pOkx2KbWPZDjB9IaB3dyZL+xk9HzVXVSTktfqFlDKbFPdjDeq6bVMhqOUf+HxUry9kjB+K/63WTZBDNjdqsV5XJjkcBoeqOowONvdae+6mrHzhofuv8UZ5JWlr3XFlHzlyhY8kJr76U2Z75nTHWv7VLQMRuDe/1xVVIJpuUCuY4w4bj3KkqXOmDn+8mVnKCOR2jX8lNVCoqQ/cFNJyhOnRYKU6VckhNdaQDdcJ8rpCXO1oyufDgO43/AFt8lk4k0rL/AFp9TlE2h7U7mlXvpV9Fgpa+XA1l7EfFOLnx+kbrylppGMZrBHim4seJxupHFzjdR1JM5kdovdNeWPxNUlUcDYW7zdNlPlkJG+yccIHV8yr2YCFA60wd1rylnkXk3vf1uq2TyjDL1KlqgymeHH0nE9ynqpJIg07rcU6tk8kErTpxJ1Q90nK+8p3FzYyej+YqM2aSo6i7GsOsJj+SbI4a7eIWTp/J723jxv4KbKYkDmu7P0VkZXskGE60yrdDDhbruPr4KorH0zn4Nd1TT2ZyjtZJ7gvdAV/Sso34Q4dPzCp3ljZCPw+IUsmJ129A+Aso53GpjeeofC375l2blI6wuMxdq61ILhRtw61Kz3lbRZSm7szNaJ9GyZKeTwFON1AdYTXXF08B5uVZYLA5sRBuM11Hzk7WVDvUQwkjPi9xD0bW3LH6IZ0JrLP0Zn6Xoa07XmbqWTfZWdvf6nKeiEcfmr+nhRNkHekQnMxFdSawtcndOdmpYiXgrlHEgqXmlYyIgg6xCDdKLPRwhBukKTmoSfZmPrv352n0DmtjYo+YEczHYrHoWgpxMml6juGWV03Sbob88QvKziP3zUbF9ugp5u0KU6FiOhNNwpDYI+lozPN3HM1HUg6yLha6gWIi7UDfVnfJ+HzBo9LNEfSsr4RdBwKc8gFB93gpzsL0TZaBcp0gCvd6aCToTtebcsnAtpWX+tPqcqm0I4/Nf3qkd6WhNJxK6v8AaJ2q6efR0Z2akDvQkG5THQnbNMdYi+a+my1KR1zo8whzPROaI3FlfDoWK7cSkeWlQnWFE6/ooG5IRIag8k6AmINOHFuzN1qDbR8R++XjE0hQMMsjGEaCR8SmUMktTIyIaGX7ipKd0brW1AE/D5qopZKP0JNen4K7pSGNVTSyUjwXjQQFis+6yfk91QOUv0j4KSmcyR8Y3Ow9/wAk2J9i626/xt3p0LmzGE672UkZhmdG7WNCAdIQxu9NBjJB1hU1K+snELN6adyiBmkbGqiLyaTBuVPTS1Ti2IXsLogjWsDlhNrr+zw9kOAaxp/RHI0kdRyLzuDvjZXw2KPMBTMTtDdajjkmIYBrTmOidhcLFXubqXEA09OlPd6ClhfC7A/WoMiOq3SyOdh06OCip+SqamJ2nDGfgAsowAcm8f8ApsP8q8mdit1Yvhf+ip6FjqinhkGhzLntxW7dSkjbC7k2ah6nKzS6AW6VSQiVksrhqbfvVNSTVED3RtvpHc5UtK+oeWjRr+CqGOp3YDrCoIPKZsPUnh0bcL9BVzbCqzJbaenLhf0dP62CDSQSBqUUL3Ymjdf4KOMvYSN3zt4pupCEvY+Q+7b4lPvhshTv5Dyn3b27dadiAuVSweURSuvpH9VI7QQvI5uQ8pt6Ktc2VimU5MzYX6L2+KGSn1Uzo2nVh0/wp1DKyIyu1A27Uzn4RvUpWFzmEjd/4RpZnYtGltvigbKnGKVrenQmH0tK5J078DdwJ/TSsnsMjpGt14SnZDfFGJoz7ukdeHd2qljBZTB2oyH+QLkHOc1rd/8AX5KkpOVcwO3uw/NZLyaKiPylx5hPwGj4/vkC5spsmQyyNlGggtP/ANqZTxskdI0aXa+xVOTYauXlJei3xuv+oIxyZcOr5JmRxC9slubrWV6I1UQZHr0BNyLHPUzYhhAcLcFRUzYKdkI1D5lOyIHVTpsXolwd+mv9bp1BBFG7C3R0fFU1LF5Q/F6WM4vEfosqULqesDnaQ8k/HUoMjVTCyXWL+KlyU6OCeSfQb3H6H5rJNAY5m1R1Fg/XR4LJOSWVdM58h1nu/wDKfkF8D45Ije2v5+CnoWywDlWXIN1SZNbBM6S3OFk3JcFxJhs7T+p3puSWsjMPu2I61VZIa+hbDEPSba3j3lChjaxrBoAUsDJLut6Vv6qb/p2SzWsO83PcqXJ8DabAG6wR+vxWQaVjYXtkbpD+4aD8k7JQ8ojczU3Se1TUJmyuYnts11/+O5Rf9PRskeTqv6KrslYxhib6Y6E/I8j2sjh0nT1KrpI35Sga5ug3v2BMjwOcelGniLnPw6XaD1qTJcFSPS/CG9gN1VZP5QyNYNOE2+SpqZsbI8YGNrQLqr2x9TRtDpNKo8lxUl8O8AWVNAymiETNQVPk6CndjaNNyf1UtFy9ZH6N9d+xUGT20s2INtdVWTDNUyyjVgP6m6psiQhkcsg0jDo3a1VUjKpj437xZUuQBGx0czr4ujqVXRsZDyLdF7qLJsc9GYgLO1X/AEPeoaEvlmpt4/8AyCZkKdscsN9drH9VWUDYuQHveg3q3qvoG0eSjF0G/wBdikyGHts11vRDf0N7qDJUlC9+8O1fFS5LiNRiDNf6alFk2M0baZ+kD/8AJHI8AY5rBrN/6cAv7LjcW423wlVGSmvr2TNGjeo6Zsb8YVfQ8tTOigABPzB8F/YMkVUCeZdZWyVDHRgQN0g9p1qGhilo2xubbE1t+xRZPfEZpHacdvgsl5KFWyQSizt3UetRZJbTxDDa9tPj8VPkrlpRK0Wbvt0rJeTXNrHykDBp+KyNTRWnfh03I7ExuFoamUdPG1jA3Q3Uv7IgD2vZosCP1N/EoZPdI6KVm51ynsa2N+Ea7/vkGxvmunyNjBJ3J0bX84ZiL+ZNs3cFSm0wT4Y5SHPaDbUqQ3hsqqxLGHeVZsbbDQAoYY6dnJxCwzEYhbMXNGgnznShrsKpJeTdhO/NJTNlmjmJ0sv8c3JjHyi5NuIO3hSUzJJmTHW2/wAVI8RtxFCXFCZB1plsIssOnEnPAlDT0J7i9xcfU0O0PDNdYxiwb1YXvmt5lfqaqE+iQqtrRGSBmjwSVD7j6GhSsjm+ykFwfDMW3IPRnBB1edVyCSP0elUkvKMw9GaGmZA57m+8b5mMazmprGtJI3qnpY6YvMfvG6kmZFocnutIxvHuzNaGDCFLLeF1uHx/fQqi2EAa0JnNbhHFTScpY9SZWlos4J1cbiwTa0OBuhWyDWvLTp0bvijWWw7+lGqkc0tO/MZ5T7ypJHCQMvoU02OTEFLWNczC0a02fBDhadKpajB6DtSdVNEosdClrNHoKSUvIPQnVeFrQ3ToXLSadOtPrMJcBp6Eypc2TGUyuN/SCknLpC9qacJuF5U5kbXHTe6Fd6NyNK8uOIaNCFSOTxnrTa0FpJC8rxRFw1hSzOm5yE7mx8mE+fHEI+hR1LMDbnSqh5fIb+qaS03CqakvOFupNqzjDiuVIk5QIV/4gm1zr+kFJV/ZgsOlNrXjWoqzUHJ9a4ghqlndNbFuTXFupF7nayoKgiJznabKnm5OTE5VE/LWVTUY3AM1BQ1YLPtNaZVtFwen4J9YeUuNSgfhkGlS1TnXaFDO4XxHcpKwuZYa1HVuY2x0ptd6OkaViOHBuUUzouap6p0b8IGr5J1cBbCEysLjZymquT5qdWgAEBTVWENMe9OcXG5T6lz3NcRqRqMczZNSlqGhrsB0hE30/wDuF1//AEef/8QAWRAAAQIDAgcJDAcGBgIBAwIHAQIDAAQREiEFEBMiMUFRIDJhcXSBsbLTFCMwNUJyc5GSlKHBMzRSYoKz0RVAQ9Lh8FBTg6LC8SRjhAZEVCVk4mBwk6Pyw//aAAgBAQAGPwL90QFrSguGygKO+NK0HqMLbStJWilpIN6dw+lpVosrya7tCqA9BGLKsKtt2lJrSl4ND8RDMupVHnQooTTTTT0wpxxQQ2kWlKUaACKi8fvDr7yrDTSStatgGmEstrWh1QtJQ+ytoqHBaArFYZmWFW2XkBxCqUqDeMSlKNEgVJhsBt1hbjeWQh0Cqm/tXE7ePdPTL6rDLSStaqVoBExMONOKZYZU8pSKeTfTTpgHbDZeVZC1pbTdpUdG4Zq04+485km2mqWlKoTrIGgGNm4Zelwp9p00tJupwmvFjZlaFTriVLu1JFLz6xubGTVZpXKXU4tu4aU4FHKuoZFnao0EOd4eDCHxLGYzbFutnbXfXaP3BHdk2xK297lnAivrhLjS0utq0LQagw2la0pU4bKATvjSt3MDjbLylVcVYQlCFLUo6bgm+AtpaXEHykGoxpaK05VQKgit5A0n4j142pZSqPOpUtCaaQmlesMVYZmWFW2XkBxCqUqDeMamVKUXUtF6whBUbPNp4sannlWGxr07ppLirJdVYRwmlfl++4JmV5rEvOW3V6kAtuJqedQiemZOZWmXfnJNhMwwrfZwCqHXvqRhCWOEchLy86gJ7rm1otJLIVYyu+F5r8IkDOzMxg/BqmFFClTSk23Aul7tam68X31hzuZ1aHpdbIFudWC4k2b0s0opNDeTwxhDJqDcurCtJhZeLICcgilVgEpFaXxICewkE4MU49/5EtNqI1ZNBezSfL46CEqaJdctzBTlLio5VemMFPJm5mdmBJvrmmysrUlyiainknTmxhOVacU4w/gt13JonlzBtilLyM0516QYkGJWZW4lbSlJeOE3EJtXZtoWipX3YwbMvG067LoWs7TZ/d8JttpK3FSziUpSKkmyYwMiXlphAlXQ868+wpoJAQRQWgK1rqiXUcGplW323kTbaZNaBeNC1k0c46RgqUTgdKSAEzRewctwJdCBfYFLdftaIYTMNrD6MoMm4LJoFqsi/gpF+DUsMPyTyZhlEitpNvNolRUe+HTnUjA7q8FpQoyKUhaZS1Yfqm9VBmnhMIytUTqWHEzARIOJLyinynbRSu+8U+ESaUsZB5TDeVqmiiqz5XDH/wBPhyUeawiHHRNvrbIDi8ku+15fBppGAGO4ZnKyEq427bbKU2wgAC0dNaaYnMlI9ztPYLeC22JJbAyubRJqc9V6r6X8MTUtKyRo/ghVsWN+8KWa/f08MTn7OkHGZdWCJltDaZVTWeSM2zQXmJouHJleT7kfEg4+toWRvVJVRGdXTEvlJXuttqaacWjJZQ2bWdm67owtOt4NythlvuNtbdj+FvU13uyMNIYki2y/gxYycvIrl0l3ZZJvVfp1xhBlll5iTdlGisy7RXaWHDqG+NNOsiMFpdwQwGWsIK3snk0qbyKs7Jq3gtUF+sCJf/xHk4UE64t+bLZsqZqqmfoIpZATqjAgXIHKKm7UzabzrNlzf/dvGnbCHWZRaHmsKUbUEGqGLV4GxFCeCJR79niWtTLqZkIk1iqCle/drRYNx0RghlnB6pd9ie/8kiWKPIdoa0zhfp4YkrMm+1PoS7+0H1NkB3NPleXVVCNNIwCpuVUwpeDymaVZIqqjdLfDvtPDCyg2VzEiW2VHUtKif+Q9UYP7mwa83NJwe/lkOoKMq53vWd8a+Vw6YwqzLyeQbmcGOJsNSa5ZGV1DON5v03RPOdzzzEsqRbbt9zKtVyhJFmlTwwEOSQl2WpoqbS3gxwtP5mlbGkafhWFP/sq/9l2O5Qk2K2976vJ0xhlhEqpMq53KptLEmuWSTlDbsoJJrQCuiMJtssdzyZDS0JSmiLVDap6hGGMlKOrwl+0EZCZS2SG81qudoTrrt4YatyEwvCQwqHFzQaNMjlM3P0WQmzdqpGEWGpNErMTc8ppFluwVDLGweIC/i/cMEuzUw1LNZGYFt5YSP4e2MJPtpU3g6ankhlQeMs39GLS7YvSkkadZ44wY9hCZWiWlsJPNZcTawEoyS7PfM0m+gtH5w13M4pChOtsLQudWVKQSBXI0pZv30S6XKpDrtm33UqWQm4nOcF4jASpp15SkzU01aRMuA2U26XiyToF+kxLtyswaiaKJ4OTq05FPfKVOcW6ml4H6xgpheELcu7NPCsnNqXVvJkhJcuJv1w1/5iJNAcSCXn1NBwUOaXBenj4IwJNvuPyrSpd9pBcnF5yw6iyLVc6t9PtCmyJIMrUluYceQ60ucU6sUSoi02RRs5uoxgaYamZiZmpuVdtJedKwpQRaTcdESj7U3Mz60YMmHHk5QrWF96qBXeq+7quujDQl5k5L9mLfQW55cxYWPvkXHgEZCawhNsS/cxcaWZhVXHq38eqiNF5uiSszAlFfs5uj5/h97F8WEPOIlUzSEzM2zOrmEWCk71w3i+yDsrEkxLTS3WXEuKbmFYScbSaEXZQWitWwXwZ0uqTNnACnMqk0NrbH/wBQTSZuZSqTLSmUJdIQnMSTm6DXhibyk/MMYQTPMoal0OkAs20eTrBqc7m4IkFH6JM60XPXQf7rMZNrCRYVLrz2lTyhl3abwN2qWRru+cSyp1+VkM5VWBPraS6Kf5oSDXghk4QmpnB8uqVZXKtuPqaKia2qmucrRcfnH1t79qftDJ9yZQ2e57WmxopYzrW3XCJwTL7hdnXG31uzS0JbZDi9YrZ0JFqlb4I7pZmirCDIlSzMGYs56DS2QK0zjxeGafawTOqadSFpNpnQf9SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SHX3cEzqWmklajaZ0D/U3TLCJV6addSpYDVi4Js13yh9oR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaQw6vAs4XGFFTZyjNxII/zNhMeKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2kKeawLhDKlNi25MNuEJ2C06aDgEeKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2kPqawLOJLy8ovvjN6qAf5mwCPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SHmFyr0q60lKyHbF4VapvVH7J3SpZqSmJpaW0uKLRQAASoDfKH2THiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pCXHpLCimUrS53Lbl8naToO+rpFdMKQ5gWacQq4pUWCD//AJI8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pCFKwLNKUg1SSWM06Lu+R4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SHwWHZdbLmTUh2zWtkK8knUoYsg5JtLBTaQW5gk3rShAIsXVKvgYEuiUQcIZVTSmi9mCiQqtqzsUnVrhLzUtaYSy2++pS6FtKiRcKXkUNdEZSYebYb0W3FWRGC+StdUfuxW4oIQNZ/c8Kcld6p3UlyV/rM+G7nr32xlKcFabtxsHORSuJ1pJz2qWhx/uc7yVjrPbqd5Kx1nvDLbSoFaN8Nn75hflQ/JaxTrinqOvlotqs/RZM2k8edU88CYRNoGEMqp1TpZzDVITSza2JTr1QllqZssKZbYfSpFS4Ekm41uJqa6cWC+StdUfupMNZQd9mNmi4wk7R+5YU5K71TupLkr/WZ8N/8T/nu578HRiwn/p9X9zneSsdZ7cTjzSkodQ0pSCvRWl0TjU0XbbS02UzIRlACnXYzduiJ8oDgsy7Ce+NqR5Tu0eGwr/pdXwzPAVD4+GwvyofktbrBfJWuqPCzK0miktqIPNEstRqpTaSTzbubaDhCEyZVT70SgrWwtxPVhvzRuJayAcq+lo12Hw2FOSu9U7qS5K/1mfDf/E/57ue/B0YsJ/6fV/c53krHWe3DjLyA404LKknWIXkQqqzVSnFqWo86jWJ3krHWe8NhX/T6PDDzz4bC/Kh+S1usF8la6o8LN+iX0RKeiR0bufztMtTnuhpob5DpJ54lUqWbKpTek6VV/puJVQ0iYSenw2FOSu9U7hS6FVkVokVJjCGWVM21NtOZJ5paEtb7NFocXHSsS6Sw42lMs/Raimi85rRQ19fhv8A4n/Pdz34OjFhP/T6v7nO8lY6z26neSsdZ7w2ECTUlSb/AGvDDzz4bC/Kh+S1ifDyJd2YcmViZKU9/aYDwqVH7Bb4tUDK5H9gd1uZO1TIfRJ5qW8pzw13X9f7il+4Mrv7dpW9+9vLXxiku42059pxFseqojBfJWuqPCzRUbNWyIlPRI6N3hHzE/8ACCOERKKSSFBhQqONcS61GqlNpJPNjl/Tp6D4bCnJXeqdypwISFqoFKpeYkuSv9ZnwcrQkVcvx/8AxP8Anu578HRiwn/p9Xcz1tRVZmlpFdQu3ctZNKugHdzvJWOs9up3krHWe8JKotGwWlVTin+NP/LwyGrQymcqzrpa8Ch9AISqtx49zhflQ/Ja3WC+StdUeF/1BEp6JHRu5qzvXDYr6omW203JWqgGwRLVFe8qHxVEr6JPRjl/Tp6D4ELSQpJvqIdSwKJNmws6RtrDzjhqS8TxaMeFOSu9U7qS5K/1mdxkKCzk7fxhDtLNrVDhGkJMMkmpsjEtylbI0Q2ulLSQaRKelx//ABP+cBxBqk48hS+xbrC5aybSRWuKe/B0RKMg97cC7QifdSmyldnTwXYpRTS8xazo148KJ1CYJ6cTrYrabpXnx1UQkbTibbpTJvov23RvSlVKxaSQpO0bmd5Kx1ntxNTahaDDanKbaCHMo+264k3hDC2rPMo154nygOCzLsJ742pHlO7R4SU9ErFOfg+cF11VhA0nwsqkX22lpPBnKPy8A87SthBVSD6Q7nC/Kh+S1usF8la6o8CpR0JFYadcNVqrX14nStRWQ8sX8cf6giU9Ejo3ZWKgKeUqh4YcQ0CVOtrNOEoVillEUzbPquxyvKEeAd80wktqU24FhkGmvT0YnU1vDlacwx4U5K71TupLkr/WZ3Dd1bbYR/uhka7+mHfNMMMU/ghdcU0SKWVFEMEfZAiU9KIlkWbWWXY4sTxrazsjnahahpSDRRNK7LzE4sKFEqUEqTsiRNa99RWP9D/lCnHV+RepR4sWFHReUISr/bGC1WRRbQVTzomfNEBS9+4aUTxxg30h+eOcacOfMTJCaauP1wx6ZMT34OiGbNM90INcTvN04nCo0AebPwMOLOhLVfiYyINFtml+uFrpWykmkIdpZtasc7yVjrPbh2XdFpp1JQocBh1wvOzLztLTr1K0GgXACJ3krHWe8JKeiVinPwfOJj8PWHhWPxdU+Am/RL6IPpD8sTYWqyXFWU8Jx4X5UPyWsU4clLnIOlhLSJmrpXlcmm0mzmgmBLolEHCGVU0povZgokKras7FJ1a4S81LWmEstvvqUuhbSokXCl5FDXRGUmHm2G9FtxVkRgvkrXVHgZr0SuiGOC10nE96dfTCOF0dBiU9Ejo3cur/ANy+hESfoj/yhSVXKBoYl/xdY45XlCIaA0oKkn11+e7c80w2iueZu0OZP9RimhwJ+ePCnJXeqdyoy0y1MBNxLSwqnqiS5K/1mcbjGhaBWEEqtAO0SeC1DFTpYIHtmFNJz1qzDwQxyUdMIC1UKzRIifGx09MLZJJFLQ4IlXVXLtHREp3Q6LTU1r1JpDiE+RS/Uaw56f8A5QtOqn99MLRXPV//AAw3y0R/of8AKFj7Qp8YSkkAq0DbGF6/ZQIlySe9Mt0p5tYmVC+0lQSOHVDTV12cYkW65gvpzmAXFWQTSpgFCq1Faa4/+UOtDFSB31MTJScwp6KRLu75IdSq6Hm61Shu1ThiYCkhJSEaOGJeyaWnkpPFExqJWLuKsTA2S6oqTRIv+Bha94VoXRJMNKQ4UrQs0odAzcc7yVjrPbqd5Kx1nvCYNccUVrLTlSfOxT3Gn5xMfh6w8LL/AIuqfATfol9EOelPQMWDuUpx4X5UPyWsQcfmmxkr2MkzZsm0lQKs42r0jZAmETaBhDKqdU6Wcw1SE0s2tiU69UJZambLCmW2H0qRUuBJJuNbiamunFgvkrXVHgZr0SuiGmLNQ86u/ZRIxPenX0w36UdBiU9EjojJ2jk+5bVmt1bW6QB/nu9CYS4UArToVrETfpv1iX/F1jjbWNKXkmHPSnoG7mFDSG1EeqJb0y+hOKY80Y8Kcld6p3BroianVsvSy1pS2hl1hbdlsE0rUXm86NES6i+44lUs/RCgmiM5rRQV9eJmqrKS3Q154eVWqiizfzQx546YQvWgED4wlSr1FVT8Iav00b5oKk6UUh9Kt88qt0TJBockekRLUN4UaxXbDik6LDY9SaQpSjQB8mv4oVB4vnCVneibr8IYUg0AzT6zitpOYgKsGm1MO1XVTtkHhvEFwaAEo/2/0i/XX5x+D5wxfoUIfbJzULRT2TEspKrJDekecqMqTeV2qxLKQqoKbXMaQ5U6WzSCkmqUqRQbLjEw7W13gevNEcw6IlQhVSl+ihDvnGCigOVbUmHCvWki7ip84lWkoUSMomvHB5oTQklIAJPFineSsdZ7dTvJWOs94TBvA051sU9xp+cTH4esPCy/4uqfATfol9EOelPQMWDuVJx4X5UPyWt1gvkrXVHgX/Rq6Ikm+Fxfwp/xhFtVm2qwnhMPemX0wjgdHQYlPRI6I/8Aif8APdIoL8raqfOpimz/AO7+aJccB6caOF0dBhz0p6Bu5r0SuiGm9iiroiScSgJS4wnRrOuHkU0orXn/AKxhCXUBYYKLNOEYsKcld6p3UlyV/rM4nObogw0rfX1g01QODFWtc0dEJFb7R+UMjUBCaXgrJrzQoIFSAVcwhTa7kqTW1spWAtV995MXXgweOHaaA6noMVSb0nHLX5pVEymlbhSJdShQi4+uEc8UrfCljQsikX6EpoILvkLFmvOP6Q2fspCPUIT5quqYc89PQqFj7QofXHMIWdJS6VU54ccrm1rDBrT/ALhXHCCDeIA2isOp8pJFcU7yVjrPbhS1qCUJFSo6hD6ywZfJu5MJUbyKAg8GnRE+WHm3gmXYSotqCqG07d4SSUqthAVWzw1xTY1UETH4esIlSb+9jcmyQaGhpu5f8XVPgJv0S+iHPSnoGLBvKkY8L8qH5LW6wXyVrqjwMx6NXRElX7w+KokJWzflm3LXORDvpl9MD0g+cSnokdEf/E/57ppX3/8Aniflwq5cwi/Zar+sI8442/SjoMO+lPQN3NeiV0QjnjBXovkmE3ABcolQp+H5xhjjb6MWFOSu9U7qS5K/1mcUyKkXJ6BC6aFVxHigjWITsh5WyK7VGFq2XQpKjSzWzCgofwlJu4bvnBVpFFJ+FI/FAZGkQ4nXUw4zqWQfV/3BOqsJI1kQtP3aw2n7FYN2+I0Q3thC98an1RbN1YTrCaQKi9UU1f1izz1hZJuFu/8ACYUgHMtJV/frgg6otQ6rTpv54VXXQQVDQIKjfqoYXU3pTdwwga7IiY4hineSsdZ7cOS7wKmliigFFPxETdh1xzLu5Tvjil0uA8onZE7yVjrPbhaQc5G+4MS3aWrOrHU3DFL7LCoTx4nxtRX4xMfh6wiV9GNzNenViNlQVQ0NMSWab5NquOXI+1TdynEuJv0KuiHPSnoGLBvKkY8L8qH5LWJ1yak32i63R1RUgpEuH266FVpYtV84wMrkf2B3W5k7VMh9EnmpbynPDXdf1/uKX7gyu/t2lb3728tfGKS7jbTn2nEWx6qiMF8la6o8C9fQqokeuJEnU6B8R+sSDijZQktkngtGHfTL6YHpB84lPRJ6I/8Aif8APdMtoTbWSDQedieeVmIDrTiT9qgvimxZBxt+lHQYd9Kegbt/8PWENBQpriVZTRAU+lA4LjDDemzJhNeIxhdPlEj4XYsKcld6p3UlyV/rM4pn8PQINOKLzBUNkE1oCIQdMOU0KizThhSvKUawprXWAuosqSaVhQj8cFSdNYNDmkVGNArdSOakLVzY7tUCuqBQXwkbI/vbiV9/NujmHRFVGidcJT5J1w+oGln9YA5421ighfmmLJ1RNp2AYp3krHWe3U7yVjrPbjCvmDqwx5ieiHv71wMXGoYkqVvUsrJ9UJ48U64DRSWboZJN6ymvDdEopRuspF0NNUvcrTmifKibCAk8V0TlFVQVWh8cU48oWv8AySLO2JxpBOawbOsWgK1iZW4q8u2iTwxbpUnNESizpUxU45bz4ZbpXKVv2RMWjWy8pI4sbNBW24EYnON/oieYUslYYqBwWYeTW8Om7mGLBvKkY8L8qH5LW6wXyVrqjwKOFwdBiW4HRDXmjpMPt175lC5Z4LoHpB84ZmCnKWEC74Rg9xBNlbIPNnbmYI00A+MS3mY32fIGfjb9KOgw76U9A3c4hxwWhMlCE8Ahj0bfUTDGdZyTqXNGmkMabGRP99ET1b8qsoFOPFhTkrvVO4KlXJAqYSh2W7nLjKZhrPtVQdt1x0XcOmJdpt5txxqWftoSoEpzmtOKbG0J6ohwwTtilboVHEYQNRgCKQk7TDHPHBZvhaQbyoQVC/OxEYkq2CKHRSsKPDuFEi7FaMJbI8mvxhvVrMCuka4bNdMG++Ew8PtKjbdSE8UI4oVxQpWmsTSgNIHzxTvJWOs9up3krHWe3D5bWcmtB58yMlS5ttu/jEPf3ribFbhY6IPFEv536wHSdAzjw64B0pLCxdwgwnjxO+jV1TEu3oyd1OYX9MSvnf8AKMHOkUJymiMK+YOrGbTPcQg158Tw2ziuiHXwu7KFNoaxE+kb1TYPqUIbZWAEtpTz3RKJSb0MUPqxyvnxJcauiJxGsPKMSP4+iFi0MzfcESvp0whBOcutIcp/mOD1xOkH/wC2A+IiZQSAFU0/3wwtCHk5Q6Ak33G+MEoofpUKtHjpjwvyofktbrBfJWuqPAt+lHQYQ04KLQ6AYoPJAB+MIChUPJsV2aIRwujoMDzQP98YH5Mj57mY/D1hEueCnxxzHmiJ1uoU21Ys04RfiR6UdBh9Gxyvw3cymuaHlmnPCaXCyjqpxX65Z4D1Q2f/AGIGLCnJXeqdwUqvSRQwlbsz3QW2Uy7VEWaIG2+86PVoiS5K/wBZnFM/h6ohxJ041p4aQoQOKBwDEg7DDfHjXxHoi3wxXg+UcaYUNZMcUHj3C4UuFQODEYbg8MU1UgxxYkebi5j0RM8YxTvJWOs9uFLCFOECthGkw8nJOMOsqsradpUXV1EjQYneSsdZ7FMoI+hTa47onXLVRaqhK+GD5i+qYXVQS3kk1PEAIFtZNxu9cTZB02acNIyVQ2ymoNdd0M+k/WJ2XP0eS+P9mJb0GIuOKtKKq/AQ96NXVMc8MN1zEKTd64wZ/qxhFum/b6Ewx6RPTCklQySW6kjTWtIUwm9Npx3j/ukLNbm1k046CEAXBTcLTtEJ8ynwhvIODykq44VbTbOTSUkbSBCX0aW1a4YoN4kn13ROvb5dAb+eJN0HeLNqnNWMK1+1CGKf/dBdYlk7ErPwimqJlvSHGbPFrhww3XRSJFStDTg6a48L8qH5LWJUt3p63QN1YW2ElS0JTeTRe/vs7I7gSZcTqX1NqeLZydAhK62bVfLSNO2O6W22kNMS7T76FAkm0TUJNbqWTxxlJh5thvRbcVZEYL5K11R4GXaRvlvAD1GKJ/zkV+EO8aerEl56ekQ36UdBgf3/ABIwcpVyWZdNT+EmEOJ3qxaG4mObrCJUJFBkwfXjnWrVqxm154m/w/PEn0o6DEz5w3bgR/FeNK8JiUcBNXK1HmhOKXUf8t1PrFIb9Ik4sKcld6p3UlyV/rM4pj8PVGJJ1QtI0RWFp5oTxCFc2NsK0C/GnhrFIR5g3BAvvgjZjX50K4ocOxMVxEcEHigCF6jogR+GEK2w35gxcxiZ4xineSsdZ7cO9zBBmLJyYdNE11VhYmkt90LVbccQ4V5RW05opxbInwwy2yFS7ClBtITU2nb8U0Ui5QLZr6oUOEfOD5i+qYPDHMYDhFoQ8fvmGUVNilac5h0jWn5RKrVSuRpdiccVvUVP+0QtQ1oI+Uc8IH3qxJLRvkFY/v1wtxd5UgprzUhKxpSaiFkm9e+htCdKm1Cp4iIebcvGUIIhhAFwBhXmwydOdT13Q4jWHlno/SLr8xA+Ah0a7aT8DCjdROaInEai3X+/XCDtcX/xiY1FxwGnrhHpR0w0VgWkpUKjiOJZJup8oWoaBphHEIar9qAtZqok4sL8qH5LWIqmZqYfpXJVsjJGoNU0TpuGmsCk1MJmQ4Xe6hZyhJFD5NNFBo1CGkoW600ltDKm0kUdSk1SFVFdujbiwXyVrqjwODggVBev/v1wqt3fU9Ih/wDD0CJPz09YQ36UdBhPmJ6wi7yWbP8AsiV9Eno3D/4ekQw2q5SW0g+rHPqyeUtLUNNNcPtUuUi1Xi/7h1DawpTZoobIT6UdBiZ84btdSe9d8HMoRgt502nCHL+cQy7SltAVSJXiV8oaWdAUnFhTkrvVO6kuSv8AWZxPecjqiAYyZ1E3wlVN9A445orsoIUcaeKCEjWIqDUhUJ4IENL8myBAOK6BwiK7YcJ1YnDdprBEOg6aU+IhdkVpQwTA2WdMHigAaKiFceIINxswjzqQ15giidkCJm6l4+eKd5Kx1nt1O8lY6z2J/wA89O6KjpJrCR9kUh2nFDKhcQ3T/cYB4YmW/IyVKcdIPFA44TDYrfbVdzJ3FpJvQj41iYr/AJqumGiBnWqc0NpVcoovgKFxF8VN5gwrmisTPovmIb89XQnE0tX2wT64CkmhgwtRh6/fikLcI+zQwyNiqmBTUcWF+VD8lrdYL5K11RiTbUE2jZHCcSUFQClb0V04rNRa00xOOm8ISVGkNJqS0SlaAfJ73/WJmYKbSm3maeqvyhcwkENuG61xCGnaVKKKpzwj0o6DEqkituwn5/KHPNPVMSvok9G4tkVz09O4mgf8wmLbpohSSmuyMIui4LWFdaG/SjoMTPnDEaEGlx4NzNKGjJq6RGCuJzpiU9EjoiWXqzh0RMA0K0uNZ3GlRgRhTkrvVO6kuSv9ZnElVL1kH4CGCSDlE5QUhIrW0LfrFYlF7VL/AOMNADT+sU2CHVDyaGHrQr3tw/7YaUaZ6bQ9ZHygq1CEcUNKrvxX40hYAqYDZNNUCJcDWlvqQOMwqkIPCYQ21Wzkq38cJHAYUkmlUlXwhS9SSlP9+qEzVquVqCNmnErjh/7NBSFccMn7Sa/EweOCBqVCuOEqppFKxc2bNki3S4ZsNByl6jo4olafY/4phJgu3WUGh56/pD+3KWfVineSsdZ7dTvJWOs9if8APV04rOulYqNyFeTWkJHMIcV9i+HiBvk0jmh0E1AVdCYqNG4mOP542uIdMIRpUSBSANeL1dEAw6D5aLPxx1xNKH3oUeHEtIFABHBQU9UVO3FhflQ/JaxPJTNsKUze6A4Mzj2QJozjAljcHi4LB54abcmGkOPfRpUsAr4tuLBfJWuqIK1qCUjSTEs0aZBCkO154mFKUBmECus0jB5JstMj/gKwlad6oVEMooCjudSjt1n5QhY0KFYdBFbaVJ+Bhi0qib9PFDrqRRxSkqVfppd84a2h1fQiBDCSb1rqPVEg0DSyqihzXQo6iKfKJVhYqk5pI8k1idOgtlaU04BEskG9xAqqt+qEptWnEpTarDfpkxVRAG0wElaQo6iYZbP8St+yAsbwZnqhpzUusTf4fnDMrS6mUr6xDrRNElq3Z9VOmJtxtYzHLKVI4xE66nQt60OKHilSaNKKTnfGHmbRsJZF1bq7fjBQhdkB5tPMQYwqMqczJkUO92wLCik5FejjjBqNaUrP+7+kStP8tIhpil9oKr64mVJ8pTKv9hxYU5K71TjUoJKyBWynSYdE3KTWD5cJtLfU6hNAOFC6wS7lQla1KaS+oqWlvUFE3xLhxttLYln7CkuEk5zWkUu+OKUcJzFOJb4YkE6sgOkxIEDSwCYZbRQWXHTfsAhJpWwzaPtUhOjvjanRxCv6Q+lIqoigHriYNLu5XL4kPRnrqhWryvjSGUoFSYlfMPWMJJ8oVEcFqA1QqVZrohCEJoiqQnipSGF0uUFK+Xyhf96oasJKnFuLTTmT+sINLsj8zEmwkCq26fC+JhC71MsqvGitIwgo+Rk1D10+cM1Guv8Avh5CiKtqUg02iFH70KTozCr4VgLKFBBUnOpdriUfVVLl6acFSRCqC+tOeH3Ef/kOpCdgTFx0ptRg4AacoeiJfi+cS1N7boTDbSaqSg5O1TgH6QyrWVKHRDx/9iOhULs+Uq0ePFO8lY6z24W64oIbQCpSjoAhSmVKNk2VBaChQOm8G+J3krHWexTA2OkfGFalCLXBSCNmIohR0AGBovhvzoaQD5eiJpH3fnBFdXzhBGusFP8AmL0w/naKUEJGutIsqN/DCBthVDeIeX5KzcI30aKilRDKxpvjKgaDohvZogX6b4b5oF98WtEU9cVBuxPqF9IRDiNoi/RSHb9UK1mkObQ58hiwvyofktYnViVmEJlFBLbRlljKpDqFOGtKXhFANcftBUs+ZNU24tLYYUVpq0lNqxSulK9XlR3MuWdL78lLsslKCoNqSpVxPk2ag80Ul3G2nPtOItj1VEYL5K11REyeAdMBSznWQk8whlive0JSeM0ivGPhDLKV0yYFTrgKrSzcTwQpIzXm03U9UKadctrqVVOmA/TOhpoVNlASB96Pxn5QvgSYwa2r6RKTfwVp8oQNeUBizXigX64mgEkh5SyFccSbKs5QJTxC6kTYQmrtkEV0a4lGCo0BBXXWaxM2iKps6OaEKqbaNBN8Bf2nVmnsw3TTWkMAJrkK2jx0/QwDlClOdr12TSGitVtSG9cPvr3qJUXbTmiEpSs0tlRTq1QrJqpVGjVE2hSilLqac9f+4SoGqcmm/wDAIL6jaXlkH4Kh52tMoupEZTyckRXniWTTegj/AHV+cSdTRDTaq/EwzVyriU6TtqYcFm0FAIp6sWFOSu9U7hTL7SH2laUOJtA80ZOWYbl261stJCRElyV/rM4sH8pTGD3hXK/RjZfWGFFyyptsN0pxQqys2k5RQrwiFqKQHVWm7fBWGVitUSrwJ4LH9YYXrK1j/b/WCvSlVyfXfDSUgJSGKADVeYZbsCwZUKpw2qxK2RRNlVw54ZQmpQlOk8cMOpRaQEEGnnGEK2uLH+2HnyqmSs3f3xRLtjStSE/GG+Bg/mxKtXArmFItU4okGyL+6XT6rP6RVtNe9008MYN81fRE6uoOVSQkCJ5p1AUKJqlQrHc6O9oFKUEGwgJtJtKptrpiXIrVbigfhDkuk1zCkE+ZEgxpsOpr8YlPw9ENKVWq3CfULolqClUvVp5sIVtaESq7ItNm1U6aGv8ASJYf+sGJayK0eBPFCyUmhmEUiSrUFSnDQ6tET8rUFaHAkKPBjneSsdZ7cTcu3TKONkJrorE7NvMKlcsUBLSyCqiRpNCR/wBRPhBcNqXYV3xxS/Kd2nFOAXHLK6YqTog0uiyDQwVVoSYDnlRYrwwQL4QDqizwxQeWgVhaTpF8NV01pCFjjrCjTTF+nEDW6FVVxwMZhR2QDsi1sEIFoBQANIswkAmxsi8ab4Ng76AdVNEAWr7V8KOwYqQpP2oXUUpDldbp6BiwvyofktbrBfJWuqImfw9YQ6dCkXwVRltVYXZNFEiFOcMLTXOyVaweKEo2QFDSL4AToJJh8kXUPQYENa86EVvBhxI0AW+aFCvkaOG1Da132KQ6TcpVNEN+cIUB5QocbNn7VOeCk6DCLN4F8JVtQDB4RiQoJus+uKnXfFTxQ2xxKPq/rAPHCE/aTDXPCSPs09YxLIuMJJ00jCnJXeqd1Jclf6zOLB9f/wAtHzgISKJGjErigVGlaiInPQr6IbW4lSVJUVJ4apEWUgJTsENLW3aaDJv1VrAdBoA3kwnnhp3yUpIMO83TCE7QofEw+SApSX1gKie/B0RnpCqNWhUaDWGbSLVUqTfstGGHFpKKYQ72NqT/ANQw8nMyNohKdBtacUrMXWGkqrz4sImhpUX85xOCmaWKA6q2olA3nZ5SeFRp+kNu6UuA/ARJIOhUygdMNIGhKgPgYZWRnJFxjBzRNAvKJrzCJWXaz12V3nSaARRQoQ0io9US3o09GORpvcpkydlaX/CMJn/9wek453krHWe3U7yVjrPYn0motPL6YyKNFYq7vaao+IhLaib4U3GU1G6FOHfHRBGrbAVXPhtVaWDBKEVBgWhdwQqumzWEEKs1FYoK2Trg08rVFhV1nVAG3XFDQwbqJpFKc8O2bqdEUVcvogqTe5shbWkqIpBVQ0BoeCE08o0hV14hZrUpF0ZIaYUBpg5hg5VBvSCKjbCQNMBWrXAsJuMVOvVCgNFv5DFhflQ/Ja3WC+StdURMBN5zesImT53RBUfI1Rk6ZmyLhww/UcPwhX3hSMmagWqY0RzQkk1rqgU3qdcNecYUnRUUizw0rC1akwE69EW1Uv0cG4RfSyq1iS5+GLX2UgRxisPJJvRdDP3c2ErIotKQIrT+J8oUE6aVjTfQ3cYhpJ0gU+ENc8J4oVxQnFhTkrvVO6kuSv8AWZxJtpCrJtCuo7hDKSSlOswUqFpJuIMBKRRIFABuShYtJOqAhAspGqJgOClp9axxQtYGcvSYytM+zZrwQ2wFW7OvwMjypGLB/K0Q5aFbN4hHFEs6TQsqJHDGDONzoh8cEMo02UAbibt074+pwU2HHO8lY6z26neSsdZ7E655SVq6YbUpIypRWsLRpsmkWinfI32wV/WLtlKwlShe4mteenyjJ0qnhhttINmoTUDRurFNQqObEoG40IrshNwqBStIoq8Xqu11gJGgbiY9H/yEXimJtwZqU6oIN4MbwXXi7FmimuEKCQCVqqdtyYymrZwwknyhURL8DCYleFQ60AkacVk6QoiFef8ApiwvyofktYlyyjL91md7lQ4G1WAMllakWr7q64UWUMB2XadcftgkKLayiib7q2VX36oyiA33CJhEsUlJyhKkg2q12qApSMpMPNsN6LbirIjBfJWuqIepwdMOMLOTStStEFAOfSo2qNYaFq9S6VhhpxVq2hWjnhEsnyhZrwQ2gpP2VEbawh3WmMuVJ0BWnViRCaf5dfhDdNkLRospWr1RwhUJvFSoCkSpKcxwqPHDktvElFbuMQV0pR03GFhNLSE2yngiovGJP3rxF6QlbbJWofiME2TcKngjO0JXC0KFCM0wkLNwFIWpKDVScoa7IsnRFixlOCkPshFlNSEg/CHFlN1K1GzXDFRcb7+KMisWSkXg8UM88IUBcYmF6A2L4So6KYsKcld6p3OUZcQ6ipFpCqi64xJclf6zP73I8qRiwfytuHuKEcWLBnG50QpC96fATvJWOs9uAty2QTSjbaln1JEIeZVbbWKgxO8lY6z2J7zlH4Vhn0QgrNCHVmlI/D88UmUD+DU9PzxP83zh0JFBdo4sQrxwKCmYk/7RimqitGvkIY88RP1G9tEe1iD1M0JpXn/rEqXBRCzaHNDoApo0cUZGlVhR0cUTCqVCUi/YbQh57y958RDCq0tmwYW39m6Eecr5Ynl1pYSi7jhSbNlPk8UJQNKjQQM8VRVahzD9IcP30/OJbTZyWnnJhpa6m0ylFPjEq2je3H4mEDRnun5xLEi5a9PPDP3phQ+IhtCxZUK3c+LC/Kh+S1ieWZ+bU4t4PhZydULpZqMz7N18IaDz7YCFNuFChV1KjVVq7WdlNJjujKOBNsOlgUyZWE2QrRXRTXS7FgvkrXVEPu2QqgpQ8N0BdRbtJdoNG2GVtnOsGtNRNRFmMHzLudYSi2dN1ImZiUdbVMIaKgEEGnDDTxudQsoWaaSb6/CJGU0PqUMqpJuvN0TDKlFYWpG+OgXm74Q4apfq1m8BtU9cIyFVKRLrUtNPKCbokHN6pxuhT/fHEsjRVQT8YcS6rvSStBNOOHJkp/jISDzKr8oaXLVCalZt6QTCQk5jdLAIvH2vjCqqDRyWhR00pU9MYSfdIyiFqKBW8gDV6oaebX3mbbzyryQpV8d8RbYIsqSIbaCQyha1WlEgWaC488MhxeYiieasOIQwcq4kprsETG1TVhIPnD+sNy/21Gp4KQruZJo4tSgDphCnE1QlSVKG0XRKvS7qW0kIQpGsJr/SJ2hzm84D8VPnChkbdpVSa36LhGD0WrniaKGjVClg95LZbbGkqJBiVWyqwsNIvHmCH1pNRcPgIZpWoF9eOGw8O9tZ4CdKjdCkMjPmkBdR/fHirGFOSu9U41WCErpcVCoBh1CJqUWpQpRLS2TSt+daVS6uqHGn5ZErZmXrDaCSLOUVTSBds4KRLqL7jiVSz9EKCaIzmtFBX1/vuD+Vtw9xQjixYM43OjwM7yVjrPY1IQ6thR/iN0qPWCIWZV2Ywk+shIDmRGTGtWhNeKsMtJbcasjeukFfGaXcMT9t9yYrLsGrgTdnO3XAYmOJfREvYvUpulOL/uGaeSupjudZrm3kQyBRK7VqvPDqRoDZA9UXj/7Yuc9YUtV2UoRxQ9zdAiZb3iEFFyeKHmPJDOaTq0RZ02UJFeaEuBshsoSbQF2iHQu9LjNegRbFyWnnLowmrY2f7+EFYupkx60wppRIB2RJlCe9t2geC66MqEVFtBrwa4tWBa01pE2Cmq1qFANlY/EYYIIzF2jD1ODoiUTaplSs8X90icZQKrpSn4hD1hNmtkeoxLjbUQ3lBSiMpZI4aQ8BpKDCl6cqpNAOeJNveklAv4oQitbKQKxKqs1CbVT0QkWTWr3REsF+ScpdCVp0tzJPNjwvyofktbrBfJWuqImPw9YY3FJFUti0o7IUm2aKFk8UVTcaERk65lbVOHcfgMS69YUR/fqiU9Knpib9KrpjKXUL9vm3vTDqh/DTaPrA+eIqpWqVJ9YI+fgZNwpIQu3ZO2iY7rrVDt3EaYrNdIsq4b64m21qqhvejZAliqrQVaA2af1gWjUgAeq6Dk0KcP3RWHwsfRNLXzgYmrqZNsIESiLX1jRwZxT8oAjCnJXeqd1Jclf6zP77g/lbfzh7ihHFiwZxudHgZ3krHWe3U7yVjrPYhlEBVNuMw0hYooaoKVXgihgGzoRk7/sxQaICloCiLr4edre5T4Qt6tykgUhB+/DTargptOiLVBapSsTPp1RhBKtCrIPqiwWwRd8NyrihnixTVrNSooKVcQhKECiRD99bSbfrMFtd6TEqOE/KLdBbpSvBFIQzeUovES3pk7l2op31WPC/Kh+S1uEGYfaYCzZTlFhNTsxYL5K11RBQtIUg6QYeU6o5Jl4oSmm+ptic1JUQtmzpGm7ivgMTDSba00c4bzSDZfQpN9NvBDdHqTHlBW9hiXfC0hSCpZTqvP8AT1wMk+ql9bfwhJTekMkrofL4PhEu4pwJS5eoUvSIW8ly22U2Ugi8QWnk2kGMoGEBdq3WmuGFspCHXnbJ4SYMk1vggU4Tp+MLdfKSFt2bHHSsTkxMt5gdVYQoXK4YbMqkqbcNmmsK/SJFSWy24SUO1FCM43/3wQ45MJsrWnvYPkm/SPVDcu82lxSEj1iGHnrSAQbSDca2v0h+YZZo6AKJRo033QkOr/8AHpepNytENGUo0jQsHpgBtSVizWui/ZEiVKJXKosimg3UgNOKUkBVrNh7vibDbi2jXaBcfXDzdQlKNC1eVxQxVdtzKDKX0FnXSKVLTdm1bKa66U44MupzvQTbygGrZxw5KzFWilFqov1/9wA2KrpQuHSYnH3FhbcwFpsDYTEyicS2u3cki8p4RCRZKEl0NWiNCjEuizbLF6FHTiwpyV3qndSXJX+sz++4P5W384e4oRxYsGf6nR4Gd5Kx1nt1O8lY6z3hbLiQobDASLgLsSrIpaNo8cOLAzl6fCsrpnJcFNwz6VPgML8qH5LW4yipd2ZaXJvMBLbZXnGzddorTTouhhkzLjLyUJCnW7JNaX74GMF8la6o8MKpBoaiurw6rCQm0q0aazjuxVpft3F4B1348Kcld6p3UlyV/rM/voqK0vEUN4xgkXjR4Gd5Kx1ntwVKISkXkmEOtLS42sWkrQagiJ3krHWe/fqKFq+vhML8qH5LW6l5ZE9KFDLaWwTKKrQCn+ZEzMJmZN1TTalhAlF51Bo+kj67J+5q7WPrsn7mrtY+uyfuau1j67J+5q7WPrsn7mrtYlUCZk1h5ywT3IvNzFKr9J92nPH12T9zV2sfXZP3NXax9dk/c1drH12T9zV2sfXZP3NXawzL90yZStta7fci7qFN30n3vhH12T9zV2sfXZP3NXax9dk/c1drH12T9zV2sfXZP3NXaxNIMzJoDLlgHuRedmJVX6T71OaPrsn7mrtY+uyfuau1j67J+5q7WPrsn7mrtY+uyfuau1hS1TMm2Q44inci/JWU1+k10rH12T9zV2sfXZP3NXax9dk/c1drH12T9zV2sTMwmZk3VNNqWECUXnUGj6SPrsn7mrtY+uyfuau1j67J+5q7WPrsn7mrtY+uyfuau1hK0zMm4S42inci/KWE1+k1VrH12T9zV2sfXZP3NXax9dk/c1drH12T9zV2sTEsuelAh5tTZIlFVoRT/M3TL6Jp6VdaSpALVi8Ks13yT9kR43nPZZ7OHkHCk53OG0FC7DN6qqtD6PgT648bznss9nHjec9lns48bznss9nHjec9lns48bznss9nCi/hScQvKOACwzvQshJ+j2UjxvOeyz2ceN5z2WezjxvOeyz2ceN5z2WezjxvOeyz2cSy5jCk43MKbSXEBDNyqXj6OPG857LPZx43nPZZ7OPG857LPZx43nPZZ7OEljCk4teUbBFhnelYCj9HsrHjec9lns48bznss9nHjec9lns48bznss9nHjec9lns4lQ1hScUhTlHTYZzU2FGv0e0JjxvOeyz2ceN5z2WezjxvOeyz2ceN5z2WezjxvOeyz2cPIOFJzucNoKF2Gb1VVaH0fAn1x43nPZZ7OPG857LPZx43nPZZ7OPG857LPZx43nPZZ7OJoO4UnEoS5Ro2Gc5NhJr9HtKo8bznss9nHjec9lns48bznss9nHjec9lns4efXNPTTrqUoJdsXBNqm9SPtHGoMLQ27qU4i0PVUQGy5JzSCsZRuypi0nZWq+DVorGDW5hrIvIYQko2Xa7hfD0yzhFKsqhKCJqXt0slRuslP2o+uyfuau1iaQZmTQGXLAPci87MSqv0n3qc0fXZP3NXax9dk/c1drH12T9zV2sfXZP3NXax9dk/c1drEtMKmZNpTraVlBlF5tRo+kj67J+5q7WPrsn7mrtY+uyfuau1j67J+5q7WJmYTMybqmm1LCBKLzqDR9JH12T9zV2sfXZP3NXax9dk/c1drH12T9zV2sfXZP3NXaxKoEzJrDzlgnuRebmKVX6T7tOePrsn7mrtY+uyfuau1j67J+5q7WPrsn7mrtY+uyfuau1hmX7pkylba12+5F3UKbvpPvfCPrsn7mrtY+uyfuau1j67J+5q7WPrsn7mrtY+uyfuau1iaQZmTQGXLAPci87MSqv0n3qc0fXZP3NXax9dk/c1drH12T9zV2sfXZP3NXax9dk/c1drClqmZNshxxFO5F+Sspr9JrpWPrsn7mrtY+uyfuau1j67J+5q7WPrsn7mrtYmlvvIedfdyhLbdgDMSmlKn7O7kZZl5Uv3Q4Qp1IBUAEk3VBGqGHXaF0iiiNZBpX4bhDcnNLdecClGWsIstosmiq0rvqaTthEq4/Mtz63WkOGYS1VsK1psizfZVtjuVM4WXGkzJL4QmrmTWEprdTXfT4RLTBTZLraV02VGN5Ew7MNvvvlqXtpaLVgvBIUml9QlQOdH7N7sWlSJhaDNBCLakhtKxqs1z9nkwZoPZLuaUZfU2lIo8VFVrTfTNuptxqSlamyRv00qPXCnm5qcmJMTDibTKGMpYSKa0gHOCvhDkzLzR7lY7nst2R34OEVJuroN1KRLOLftszTz7OQsijdgqoRr8m+u3HMupeLrIkH5gS5SKBSLFL6V1mFYPM4p1azL0mihNpNsrtXUp/Du87XHcqZwsuNJmSXwhNXMmsJTW6mu+nwjuwO5JpDsq0ZayKKytiprp/iXebjwmJnCjjaZWZp3TYbtWMmlVN7TSrZEjMzBmmpdLC1zKpdDVoXiyVBX3bVQmHHkv8A/jtzjcr3PZFFBSU51dNaq9QiSU8/lUT0suZydkDI0KLhTgXr2YlJSrJqIoFDVEqtL008lDswl9yXSzlCErUE3KFNWqHJmXmj3Kx3PZbsjvwcIqTdXQbqUhU6Xcs2szQEsUiiMnas01+RfXbD8pMTJm7LDT4cKQN9bBF2rM+Pg5Zxx+21NrfRkLIo3YJs016BfXbuF/8AkmUAvLws5o1766GZqZdm04OSlSi8221Upt5qlgiu9pvR6oy2WJYM4qV7msilAg3101qPVEr3Q/3QJuTE1vQMmbrhTVna9mOW7iy303fu58nbsWVaLd2mzBm2ZpapZqZl5XJLQkZS3k6qVdUHvmqgzY7sL2Vy8tMPJZUkUaKDm6L9d9YnJN99U1kQhaXVpAOdW64AavjjkhLTrrLj7yW8klKCmm+Wb013oOvZEymacmE4QsWkJfS3k6W7NpNjZaGmP2b3YuvdWT7qsIt2cjlKaLNajZohpx0gu5yFKHlWVEV+GJxLbypddLnEAEj13QJkTNhUrgtqeWAhPf1KCqg3XbzVTfRlssSwZxUr3NZFKBBvrprUeqGVKmcquclmnkkoTRlS1pTdtGfr+zE9LvOl8yz+TDqgAVAoSq+l3lYpR1mYU0O6WkLQEpIWFOJTfUcJ0QGGppcykoUt1pSU2WR5FCBWvHXXAYmXplGFFGXS53QlrMtqslSLAp9rTwQMHCcU2tDjwM0EItKCQil1KfxNnkxJTSwAt5lDhA0VIriwXYmFBh5xTa2bKaHva1VrSuoRMpmnJhOELFpCX0t5OluzaTY2Whpj9m92Lr3Vk+6rCLdnI5SmizWo2aIfeExklSMoqYVRAo+oKWL66B3vV9rwa10rZBNIZRMTHdPdEomZ3oFg6wKar9ezcKdM45IobvUttKSTwXgxLrwmZhlpwNI72lrJZQi8K8oZ3NHdheyuXlph5LKkijRQc3RfrvrE5JvvqmsiELS6tIBzq3XADV8cbDjSplEkhtxT6pUNlQ3tN/qpa0QieD2Wl3n5hlMvZFAGw4QoGlb8n/ugMKm8s4+iWUHrCe9lxyyql2jZX4xMtPOZZcu+prKkAFQuIrTj+GNhqTnXrbiHHSzZbshKU0Hk13xTr2wiVcfmW59brSHDMJaq2Fa02RZvsq2x3KmcLLjSZkl8ITVzJrCU1uprvp8Ilpgpsl1tK6bKjFNvy0wqWdZbU4FJSk1oNF4iYdS/ZZl5xiV7nsiiwuxVVdP8T/bHdheyuXlph5LKkijRQc3RfrvrH7N7sWtS3m0iaKEW0hSFqOqn8PZ5UVeVbcQ66yV0pasOKTX4YsF2JhQYecU2tmymh72tVa0rqEYUZk5pydcalzZWpCBYf1JFwHr4Il5NEzNNPrmS08t9DWVb72V0FBZ2bdcPvCYySpGUVMKogUfUFLF9dA73q+1jU01MKUl2TdcQzZTRK0lAF9K+UdMIlXH5lufW60hwzCWqthWtNkWb7KtsdypnCy40mZJfCE1cyawlNbqa76fCO7A7kmkOyrRlrIorK2Kmun+Jd5vg0oeCjZVaSULKFJPAReIbZaSENIFlKRqG4eme/Jee35TMOAKuporSHWrC1BwhSlrdWpdRozia3ccNsKZOTbtUo4oHO31TWprrrpgACgGrG4pxtbpWCnvjq1Urps1ObzQlnJrspWXAoOrt2tZt1tfGGCWKZFKUJSlRSmid6CBcQOHcMy1lxDDSbKUtvLTdw0N/PDTpZopsJCQlRCc3e1ToNNVdEKmkNkPKr5ZIFdNBoFeDH3W4HFPWSj6ZdmydIs1pq2QtgNKsLIJJdUVXb2iq1FNUNsKZOTbtUo4oHO31TWprrrphEyWu+ppSiiE3aKp0GmNdUzBtuB5VZt3fjQd9xeoQhp3LONpFmyqYcoofevzueBNlvvwNd8bNaUrZ0VprhbrLZStW1ZIF9aAHRfsxKSa0IpcaGMinukN1KrPdbt9dPlQ06WaKbCQkJUQnN3tU6DTVXRDj4a74u1WqiU52+onQK66QoS6Cm1SpUsrN2gVOrg8GuZbbIdVXyyQKmqqDQKnTTcKl5lJWyqhISsp0X6RCUuZd1KfJcmXFA31vBVfzx3Zkzl62q2zStKVs6K0urC1S7ZQVCl6yqg2CugcAxhLhdABr3p1TZ9aSIacQxYLQASAo2btGboJ4YeWGa5UKSoKUSmir1AA3CuukKSwlQtGqitZWo6tJvxtzCk1dbSpKTXRWlegQ9VtTuVRk1ZZ1TmbszjcIyNhdLeVt5ZeUtUpW3W1ou06IQy0mw0gWUpGrEppdoJVpsLKD6xfDDeSWUMiykKfWc37JvvHAbo7syZy9bVbZpWlK2dFaXVh5tLGY6mwoKWo5uwX5o4BBbZBAJtEqUVKJ2km84kJmMqQhVoZN5bd+rekQ681lgt0lS/8AyHCFE3VpapDyCha8tS2px1a1XaM4mopCGC2qwhRUCHVhdTpzq1vhKEJCUJFAkahiYfeyuUZNUWH1oAPEDww9VtTuVRk1ZZ1TmbszjcIyNhdLeVt5ZeUtUpW3W1ou06IYQWM1lNhKUrUBZ2Ghzhx+EUZdsotAJvWVUA0AV0C/QNw0mYCzk15RFhxSKK25pENuHLOKboUhx9a01Gg0JpXhh5YZrlQpKgpRKaKvUADcK66QpLCVC0aqK1lajq0m/GA/lCmlLKXVIChwgG/ngzCGaOGvlGyK6aJ0CHWUsnJuBINXFE0TvQDW6mqmiMkykpRUm9RUSTpJJvONx4pOVcbySlBRGbf6tJh1qwtQcIUpa3VqXUaM4mt3HDbCmTk27VKOKBzt9U1qa666YAAoBqxKl5gLUyrfJQ4pFfZMNvWFqcRShW6tVaaCam88Jh5YZrlQpKgpRKaKvUADcK66QpjJrsqUFlRdWV1Gg2611bYQyymw2nQMTD72VyjJqiw+tAB4geGFtM5dCF1qO6XDpNSRnXHhgtWF0LmVt5VeUtUpW3W1ou0wwgsZrKbCUpWoCzsNDnDjx91ry2XslFUzDiRQ6bgaahDrVhag4QpS1urUuo0ZxNbuOG2FMnJt2qUcUDnb6prU1110wiZLXfU0pRRCbtFU6DT95UtaglKRUqOgQl5h1D7StDjarSTz7jJW05WlqxW+m3wCLa0otmym0aVOyLba0uI+0k1H+BZSZfbl260tOrCRXn8AGracqRaCK302/HclGXbtBYbItCtoioHHT/BHW2X23XGjRxKFglB4dm4TlFpRaNkWjSp2btOSfbctptpsKBqnbxfvy0LSFoU8wClQuIyyISJKzKMCTedm2mBmICbNldka9PHGHJJ12asHBiplKphLNvX9jyTw3wGGZ+demG2FPlSUS+92rtBIpxXwjCTyshalQ+pSBWzVNbhGHZB96aSG5Zt4F8M5TOKgUmxUUuHDH/1DNpnJibclpdDgacS3RzMVStEj4Uieeq9ZErlWn5nIXLr5IbJqm/X64mVKmVzDyW1LDi0pF9NgESTCsKrHdUj3UpYZbqhQs3Ju0Z+uuiFTKHQ24cEtTQFkUDiiamJ2UcL2FbDLT6AlLaF5ylgjyRTMjDL7UyqVRIyrcwmXKEGqqKJSo37NR54wgxNTrjqUsMuodS0i2lS1qRZSKU0gUrzxhlK33G3pBSFIMylsrVUVyasnUX6Lr747pmF99ccVVmn0GrJ8YidXMrddmMg+8w1RssLs6LJTn7N9FteFZibcfLaW1MttWqn7F1L/AL0SqZgrK5bDAYq5Yt0yVc6xm1ztUYLZE8qXyyJtTi0NoKjYdSlNKjhiXtTokiiSMwpwIT31QUUnTquvp9qMGzL6rbz0s24tVKVJSCf3vBLU1LtTLWRmDYeQFD+HtjCMtKPviV7tSxKdzWVqtZMFSEW82la6dF8YHyk2/Lus4Sel8okNWrmXKE3KTXVddp5miy9Muy/dqJNYWloM1NxA8uuvZEu025MIffcsoEqlBWq4mmfmjRrjATjk28w4mamWipsNX2QsV0KFabLowclmYmXzNTKmbgzaavcObWgqSPKjB8q685JqdmXW1OKS0pxTYbtCtKpB/ukSSJeYLbjky2yp0pBNDpiYcXPqeRKYQalLBbR31C1N3qoNPfNVNES2SdmHZR2cVKEOJaDVRUGz5dQRxRMSan8qxLZQoUlIBmvu7MytDTg4YlmkInpKacC1hloMFZSmlSSolNLxrrGBHzOdwvPYOeKnEJTeQtGitRwxKYQfecZku50rdXKJbN9TVSgu+wRSlm/TCe4iQ6XBWxZtlOuzaza8cSUo1Pvy6VNvKW+ttvLKWhdko0WbtdBDzktMTD3cgbyoCGcjfTfVzrx9mJ/JKs5XCzLa7tKTLtxg+ZdnC8iZnnpYsZNIASC7ZNaVrmCMAuOT+UThFLi3JWwnMoitkXVuN1/77gaclWm5fCiyxYW0LK3SVC0DTfXVhjIuzDsq5O9yKDiWg1W8EJ8uoPNDhM/NqW7PPMtobQ0VWsuvRVIFT966JsTBUXJeZUxVyxaNwN9jNrfqiUDT8y5KTUy5L1cQyEXBW88u4p1ikYD/AP1OaTafm9CWrr1fc1xlENPZETimFtnIhpKAoprvspa1/KDNzU2p4rW4kN2EhKQFkDQK1ujCMz3cUobnhKNpyabDaSpAtm6ppaMGUcncp3PhCWCZtaU1FrUaXVHzENSqprLtowl3Mp8pSCtJYK7JoKVCqaKQ6qWfWttE682rubJZWwnRZt5p4YRNtT76ml4OYXehAygtL05vPdtgOoeeakWmit1UqGytPCQvyabL4nD3cqXZlnkoTLpSnPTZSqqqit9dVIkUpdmHpOdU6lJfS0E5oJqizn6qZ0N5B2xksCKmEZoNFjQYnWVTeXOQZcS642kZIqcsKN2oab9kYUknp0zrcuhkoKkpCgVWq1sgbB+5IcbTNrQsWkqEk9Qj2Y+jnPcXv5Y+jnPcXv5Y+jnPcXv5Y+jnPcXv5Y+jnPcXv5Y+jnPcXv5Y+jnPcXv5Y+jnPcXv5Y+jnPcXv5YUzMSsy+yrS25g91STzWIU3LSD0u2rfJawa6kH1IhQYwa4yFApOTwY4mo2byGkfspVho1bT+y3KI4sy6AwJaZDITYyf7Pes2dlLEKbawYtttYsqSnBbgBGzeQmZVJPqmUCyl44OdtgcdiHQ3gxaA79IE4LcFvjzL4KVMzakm4gyL1/+yEKTKzKVIRk0kYPezU7BmaLhGT7gesWA1Z/ZrtLA0J3mjggu9zzWVICSvuB6pA0DecJ9cPW5SYXlk2XLWDnTbGw5l8OZWTmHMomwu1g502k7DmaL4CWsHONpBCgEYMcFCNB3kOKRLzSFOG0spkHhaO05kOOsYPdYdc3628GupKuPMh1pOC1Bp29xAwW5RfHmXwEtyDzaQq2AnBrooqlK7zTS6EFEpMIKLQSU4OdFmpqqmZrhlphosIbrTKYGdcsV1ouFkwxKtNTuTZbS2msk9oAp9iPo5z3F7+WPo5z3F7+WPo5z3F7+WPo5z3F7+WPo5z3F7+WPo5z3F7+WPo5z3F7+WPo5z3F7+WPo5z3F7+WFuOJm0IQLSlGSeoB7O6bL1vvirCQ22pZJoToSDqBj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lhHdmD3Zqxvctg11dPWiBKrkHlyw/gqwa6UeqxBl1YNcVLk2skcGOWa7aWIy/7Ncy93ff2Y5au0X2IyU1JvzLda2HcHOqHxRCMnJPosEqTZwc6LJOkjMh8fs5ykwavD9mO9887MvhtLMi80lokoCMGuiztpmQnKS80uybQtSDxodu8hYMpMELWHFf8A6c7eoUoreabh6oMz+znO6CbRd/ZjtonjsQ0Eyb6QyatgYOdzOLMu0wgTMk/MBBqnK4OdVQ86IShzBi3EJFEpVgtwgDT9iG3XcHOOOtXIWvBjpKeLMujJTMnMTDWmw7g51Q+KIRLOYOccl0b1pWDHSlPELEBbuDFuLCbAUvBjhITs3kFZlJi0Vhwn9nO1tAUCt5ppCAJSYAQouJH7OdzVX3jM03n1x3e4lyqSopEvgh1oknWs0No04tMfRznuL38sfRznuL38sfRznuL38sfRznuL38sfRznuL38sfRznuL38sfRznuL38sfRznuL38sfRznuL38sfRznuL38sBhGWS6UlYDsu43UCld8BtG4dDjjiMmCTaZWKgGhs3Z2kaIDiAsJ/wDY2pB9RvgsLyynQkLIal3HKA1pvQdhj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/lj6Oc9xe/ljKS2C1y7n22sFuJPwRBmf2c53QTUu/sx21XjsQ/bwatWX+lrgxzvnnZl8FEvKTDCTfZbwe6kbPscEGYTg5xMwVWy6MGO2q7a2IDDuD3XGQq0G1YNdKQdtLECaOD3TNDQ+cGu2/XYiw1LzTaPspkHgOpDiO5Ziw6SVp/Z7tFceZfAlhg5wS4NrJDBjtmvFYjuT9nO9yf5H7MdseqxAacwWpbQNoIVgtwgHbSxDLipF5TjP0Sjg12qOLMuhDkzg92YcRvVO4NdUU8WZDcw5IvOTDe8dVg10qTxGxBfbwc42+VWsonBjoVXbWxwmMn3A9k8nkrP7NdpY+zvNHBDtlh8PLbyeUXg15V2w5t44ImF2H8o9Zul8FvNNpA0AJsnafXH0c57i9/LH0c57i9/LH0c57i9/LH0c57i9/LH0c57i9/LH0c57i9/LH0c57i9/LH0c57i9/LH0c57i9/LH0c57i9/LH0c57i9/LDhZt97VYUHG1IINAdCgNRGPuEsupVky6l00sKApWl9fKGqMgW3VAFCVvJAsNlZokG+vq24sF8la6o/dr7v3PCnJXeqcalWSugrZTpMd2FK5dsW7SXaVTZJBrSuyFpyLsutKUuWHgKlCq0VcTsMYI5UfyXfDDh8DTX+5yXJX+szuJpc/gyZW0kFphtt1sZtRU1t1tGg2UAh0TAcSC4S0h5dtaEbCqprfXWYnwguG1LsK744pflO7T/hWF+VD8lrGzNNtS6pZtlbWc8Qs2ik6LH3dsO2ciJeZcZedJUbSSilwuvrZGyMnMMtvt6bDibQjBfJWuqN1St+zcuWiVUcUL8U36JfREp6JHRunebpisWz3tT1LKCdioB27iSsLKLUyhKqax4B+0a0dUBusKcld6pxqsAKXS4E0EPyM22whLxeqpl4qoFlR1pH2oXMTmSDmRbYAZUSKJrnXgabWjgjBMxkW+6DMFJdsi1TIuXV3a7JrZVZPHuHSklJuvHHjwb6b5btjzFQxm2sq6lvirDTYvOSsHg8rEXFmiRrxlq0cn3Nas6q2t26pJKTdeOPdyXJX+szup3krHWe8NhX/AEur4ZlbiytZtVJ84+GwvyofktbrBfJWuqMbixpSkmJe1ct5NQObE36I9OPIX2rNvE76VWKb9EvoiU9Ejoxkm4CMqpXe9uJ3m6YfZCL0yxeCvlEgKbwuJ6Ib80YleerFLcoT0Hd21miYdQo0K3l05t1hTkrvVO6wRyo/ku7iTAJAJVUc0P2vIcKRimvTqxla/tGHebpx4JA0F39P1hYT5BsnGzdW2sIhpkpqlfTXEx5iokXlUy5dyguuzSf6R/f+XimUg5yLNfWMZ5J/yxZG+1Zt7gEGoOsQ6xdZyYX/ALhBZUnStSQRwCDQg0uO5kuSv9ZndTvJWOs94bCv+l1fDJ88+GwvyofktbrBfJWuqMb/AJiuiMFeYerib9EenG76L9MTvpVYpkrNLSCkcZjBCQSEqZvG3NGIornC+kTdmuYFIPqhAqKltFB6oY8wQ7zdMYQ4JSg+ES6q35VfQP0hlgJNpTAerwQ1LpqKVt8N10K89WKX9OnoOKas1BbUE/EbiYapc3Zpzw5zdMH0j3QIk2xSy4k15hucKcld6p3WCOVH8l3cSilXJFsn1RNEXgunE+gnOXMLpicbG+RSsHzzDvN0wCDUHXiwZrLarZHBUfpE84dAWTCUlNFWLdRCnVqz7Cr4wcTeStHRCLXkuhI9eKX8xUSAHkFwH1gwl0G2hRABTwppE8VKz2lLTXoidJNTmdOM8k/5w/5h6Ilqm9UsIU4bwnZFYX5phnzYI+00B/uhPpXOrE36U4iW1WgDTHJclf6zO6neSsdZ7w2FHEb0qRT4+GHnnw2F+VD8lrdYL5K11Rjf8xXRGD7qWLSP9oxW9NlhRhtei0kGGAADbXZMLB1t0GJ/gdVi/wBQRg60a2bSRxWBDpSSmsw2jmvh30YjCXnudESHmfIQyK3hIh3m6Yn6UuZCeY2f1jJk5iV1A9cSC1fRmWUlXMVfpAcbNpB0H8MK9IrFL8oT0HFhH03zGJ+0a2XVJHFinvwdEO83TChW/KO9EYPRrCCfh/TEiXs75Nq1jwpyV3qndYI5UfyXdwhH2UKv4xDASaBT9D6hiTylzoxTv4OiFs/irExZNbKgk8dRDRpSgs+rFL+jHSYeQDmrWqo5hCSbhkPnE0knNSi4c5jBvnt9EOqFxDh6YZJvNgRbGlLDh+EMt/fWfgmGHFXJQtKjxVhbIXmOKzk7Yfape4RTmxnkn/KH/MV0RIegA6Ye4oYbNbTgu5odAWCQDdCHKb1J+ENLGtoGJbjV1BEyEGnfwajhOLJpuq8L+P8A6xyXJX+szuFtS84t8GyhwrQikuXHEJRSg2KUaGugc86w86X1Sz+SDqgAVCwld9LvKpzRP22HJekuwKOFN+c7fcT4af40/wDLww88+GwvyofktYy13P8A+KHxLF+3fbKa72mi+la6YRI5JBKlNpvdo4q0aVQmmcBrv2xlJh5thvRbcVZEYL5K11RjVdW3mfCEf63UEMeYnohfJlQx5ieiJT0wjKUtUpdzQ22d8utIfZus3rh0rUVHLLF/HH+oIliDQ1Vo81MFf2plB60N01LbjCXnudESHmfIQ/5rfRDvN0xhIn7CelEWCL6xJeiV1lQy5S1ZQTT8MFW1ZOKV5SjFhH03zENrpS0kGJ1w6EvLMIzgLQtAGJ78HREzdZsuZPTpvEO/ijB613Wm0j11hnn6YZLZCqd6VwGpBhpSyVKvvPHiwpyV3qnGVKuSBUwlDst3OXGUzDWfaqg7brjou4dMLlskhNlKlZrtpSKKpRxNM0nVp0GMEy+Wb7oEwVFq0LVMi5fTE0KVyiwiGsnUUOSVXjh2tM02boZUNIctD1CA2lfeympA54lx/wC5fQICU1C16FDVC1KvUWUk+pMM8cTy06FPVHrETOTcKXEqG9NCLxDCa+QYb+6gDp/WHDtMISklOYEnhic8z9YkFnQkoPwh/wA89MNuJpaAGnjhVhQV3h0XcRgDRYSpddsHihoa6noxOLQaLGj1xbcz7Tl/FwRaQajuT/lDpGsWfXEudgCfUImkKKQoLKUjaBSMGf6kTCiK1U4IrwEf7oHmI6oiW85fVh0LOcpaVAeuJrJ97Us1rrBr+kZv2gqsBxFbJ24pLkr/AFmdw/mOlL5KnEl9wpJOk0rpjJspITW0SpRUSdpJvMTvJWOs94SU9EvFP8af+XhkItC1VRs69PgUvoBCVV08e5wvyofktY1OJmQJcvd05Et1OUs0010aDT4wpwzSQ04tpxxGSzqoIIsmtwu4deLBfJWuqIKjoF8EpVQrAKdukRLpIzlgOQj/AFuoIYWkAGoRD6lG8trvMNLWaJDYr6oaWr/8mg4oc5uiMF335M9WH+I9MTZSSDl1XjzoT6QdBiW4z0Jgr+y8g9aGlqNVFxuMJee50RI2TWiadETH4fnC3aWbWr8UTK0nMUux6qQ4TptE9MIp94Qj0R6sfiMKQaWG7dw10H9Ik3E65hF2yGs60pJUFeuHkpqA45aI4IlGaXrarXmjCl/laPxRL+hHyicWg2kmxfzQ6Ro7pVClnjMYPdbN4bQoc0JTaNj7OyEPKqe+WjthpNRaoTTnOLCnJXeqcZSq9JFDCFrmkvltpMujvVKNjbfvjdfwaIZcXMh1DDa22gG7KqKIJtGucbuCMEcqP5LuKTVqSq10Q44g6VlQONVDW89MHjiXUhQUM68c0KWNlL+MQy79+l8TLXl1CuaoicRW5SU9YQlP2UWfnHq6IKhA4h0RNjXk/nCUUuasCv4TDxGgrPTCE6LKLHHfWHlKqaoUPWKRZrpapH4ob84YmDarVn5iE+eflCVWrOYgeoj9ISRUZXejiMMk/ah30rkSqlXBKnK+yIqbypRqfVBljcvVw50DzEdURLrtUsOhXFASNSYWeGG+IQiXHkoKieG1ikuSv9ZndTvJWOs94SU9ErFO/g+fhpdJ0uMKSPbJ+XgFuK3qBaMXnQs03OF+VD8lrdYL5K11RD/mK6IkSfvD4w0nUhtIHqhsj7Sx8Ew0zXMTfThtRTbd0Qu1TNFiGmvJygVC3UG0ikSKlm0qwbzzxatFIyl9NlYm/TnrQj0nyMS3GehMZKuYRapw/wBmGfSNxhFJUAS45T1RL+cv5RNrSQF5lK8cNICs2xQj8RgnauvRFlAvUg+u+Ocw2eaHloNFJXd8Imf9XoMIVpCVhVNtIc9KegYpQpJByOkc0YQ5+uIZR9lhPyiY/D84PKD0QriiVABSW05P5wjiimusMlRA7wRf5+LCnJXeqd1gjlR/JdxS/wCL5bkwhOsKUeiHOL5wjzomPM/5CFQp03pFxp5tIVzdGKpiZ9CekQ556fnCuPEv+9cXXAIpC+BcNecMTKPsA9MWdUDiiWTWtlGjhqYZ88dMPJ2LWYqIA11MCAR9lI+EJ86ObE3C/M+YxSXJX+szup3krHWe8JKeiVinPwfOH1IUUKFm8H7w8LKg+TaA9SvATfol9EH0h+W5wvyofktbrBfJWuqIeOnNp64SgnNToEcwhvz1dCY/vbA44el6Xb6vOIb84Q75piVINCk0+MO+cYF9631GkND/ANnyhtOoDRH4PnDa/sqTdEwP/YYQiu9J+WNB/wDYroTDXm/rFDtMI5+mJxGoFJ+MOuHNtBZ9YMc8TRtWTS7FKVvzSPjExwrPTCvRI6EwtSTRQcQR6lRXa8eiFcUJ9IeiBCxwCG0aLBCfjXFhTkrvVO6wRyo/ku4mvN8Anjiap5Is/wC4QqHFjQpRMJJNTi54curbTZhY+8n5wcS+bphfEYdsilTWGvOG45oTxQ0TotiH/PVuU+dHNiSdlYapdbBr6sUlyV/rM7gzHd0tkEqsF3KpshWyu2EuNLS42q8KQagxO8lY6z3hJNxpQWgtLoRinfwfOJj8PWHhWD53VPgJv0S+iFekPQMUoEUo68EKrsx4X5UPyWt1gvkrXVEPc3TjCdQvgxSHOEU+MIJ0Aw75phlGtTl3whHniGk67az0RL2qW7RrSEcUfg+eJ/z1dO4Kfvn5QldM4a4c8+Ec/TDjY0LpXFzw4n7RxSnmq60P+eemFcIh3z09Coyeq1ahZ2Awn0h6I5oc4o/GnFhTkrvVO6wRyo/ku4mvN8Anzof9IYd4hiRi59yRtI6YuFO9/OF83TCOAg7lHFAOyFrpS0SablA4YbGq4GCDpEcQMSyhsPRikuSv9ZncYQtJwhKgTyX2ltSTlbm0i4FB1g6oaXNAh41rVNkkVuJGo0pdE/bfcmKy7Bq4E3Zzt1wHhMGeid6xxT3Gn5xMfh6w8LL/AIuqfATfol9EOelPQMWDuUpx4X5UPyWsancv3hM6mU7msilCkZ1dNan1Q42pcwzKS5aClNIQUkq+3W+mgZvDGUWHCP8A1tqWfUkRgvkrXVEO83T4BfmmJbVnE/CG/PEAbCYa4vmYRxR+DEtdKWlE03Fpd6rV3r/pid8+EYxxwePFKearrQ4saFKJxLH3h88TvmmAPvQj7yKw5xQ8NYpCTwRhTkrvVONSzoSKmG0TQa7/AC6ZlvJAiyDpSb79Iv8AhCqutpbUFFtnIqCqBVK2zcrhoLqxglijlsTBVXJqs/QueVSmJrzfAI44mfO/WHFJ0EfPEjFz7lPCoR/pfOHBwVglOrco4t21zwiHE6yow/8Ai6IlfxfPFJclf6zO6neSsdZ7wmDeBpzrYp7jT84mPw9YeFl/xdU+Am/RL6Ic9KegYsHcqTjwvyofktY+6i2ctpqHFAVpZtUrStLq6YQ642pS02f4iqGyaptCudThxYL5K11RDvN0+AXxGGef5wnzoV50IGy6E8UJ4Ruk8fzxOjXb/WE4xxwePEwkaUA19e5d804pXzf0gj7leiJr8MI4owpyV3qnHQ3iApEw+uylLacoUmy2NCNGj48MB5Lrqgi3kmlUst2jU0u6YwRyo/ku4keZ8z4BCeDREz536w4n7vzxIxKGzctgfbEf6fzhY2gwtavKpuUcW7QngrCidIF0VGgUMTA87oiV/F88UlyV/rM7qd5Kx1nvCSFpVhNF1J0X1/TFNp1EAxMfh6wiWUolSi2Kk+El/wAXVPgJv0S+iHPSnoGLBvKkY8L8qH5LW6wXyVrqiHebp8A4eCGTw/OEeb84I1UjnhMI4t0nj+eI+kRA48Y44PHu3DwUgCGa+Tmw36ERNc0I4owpyV3qndYI5UfyXcSPM+Z8APNhzmgQmsN80ISdKtEObmWGoqxqhCqU3COLdLVsFYbVoJZJ+BhzihS9RFIm6/aVDCRpTWvqOKS5K/1md1O8lY6z3hEcGKZ4U/OJj8PWESvox4SXrtI+B8BN+iX0Q56U9AxYN5UjHhflQ/Ja3WC+StdUQ7zdPgDwmGfPhB4IPFA44TCOLclR0CEI0DTiUvSVU0weBWPnhW7XCBxH4Qz6QQn0dKRMj+9MCMKcld6p3WCOVH8l3E15vgFcCYVxD5wIKRpTphHNEr536Q5uZYcPzxmArTQEwle3FzQjix0GyuN9yuiifXX9I/CeiF8OJwf+xXRA/vUcUlyV/rM7hcsoy/dZne5UOBtVgDJZWpFq+6uuA44Al0LW0uzotIWUmnqifLDzbwTLsJUW1BVDadu8Go7BCePE8dWT+Yh0HyykD11+USvox4SW8/wE36JfRDnpT0DFg3lSMeF+VD8lrdYL5K11RDvN0+AHnQjzoT5sHigccWkmhhtW0A/Dcr/vXDfFjc4sfPCt28jWXTDfEnoECorQ1EJWd6EGFFOhZxYU5K71TusEcqP5LuJnigzFbgqzSBGbfeBidV9gV+OIACpOqH6g3M/KFXXWfnCeOHoTzRL8cGppXRCD94/LEqYrvVUI9WKX21HTjcp5OcYZIqATf6zCOfpxc0I4sa/Rr6uKuoaYmfPR/wAov0UMLTpoadOJ5P3lHph2vkprikuSv9ZncPLM/NqcW8Hws5OqF0s1GZ9m6+EMN1KU1NpWlRJqSeeJ3krHWe8HML02W1H4QnjxTJ+6IHpB84lfRjwkt5/gJ6Vs3CVU5a9cPteUldr1j+mLBvKkY8L8qH5LWNT2XdyowgmWDNs2Mnkwd7o1lVdMSLX/AJTLLb6U5jLll60k6wKEC67j2RWXbbdc+y4uwPXQxgvkrXVEO83T4BPnQEHUoRxCGdi7oHHB4h0wz5o6Nyvm6Ybxr4ofoq0m6mIccK3bvnGEc0J4o421wjzhiwpyV3qnGtdK2QTSGUTEx3T3RKJmd6BYOsCmq/Xsh9CJxlyXUyFssIUkqGcRU66nTTVdGCWsg4Ed0E5aqbP0Ll2mvwxMHj+UOem+Qi2EEpyWkefEx+H5w6gaEqIiZcpnWgK+qCCKEaoYA+0DGErhm5MV/AqFq2N1+Jhvh/WHhQXnTCeaGXDvbUMHhiRrsVDqE6EqIEPed8xBIFaaYwcEj+ClXxJgGlykqp6opS+MIo0ANrN3AYl2KG2l5R4Lv/8AaGFXb5d/qjaLCuqcTTadKtsS6xpWVVxLWBdZUBXXUQpChRQChTmh4/eR0xNmu9Ug/H+sInLVxeydn41hdPtKPwMOV8lAX66frCxx9MTA12PnikuSv9ZndTvJWOs94Ob9EvohPHimvNEJ9IOgxK+jHhJXz/AYQ5AYnPwfPFg3lSMeF+VD8lrHl1SzSn7NjKlAtU2VhALaSGzVF294sWC+StdUQ7zdPgEJGkri77QhX96oleMRzweIdMNk6AkdEBQ0G/ErgGJf964bp9nG6mtYd5sQ44UN02gX1MKp5Sol1DSqteaEcUNn7qh8IR5wxYU5K71TuCWEKRo/iKuA0JF9yb97ogzFnvxRk7VdWmMEcqP5LuJS/LbvEOp1h6+F+Yv5w96NrqxNIWSKVVdwwUuAKQ4qtP74omSm4BSfWYqqltFsDhITE24u5aigK9kiCKnvjSqcd8N2rtF3xhXDCOaEDXDVdVYwc2N8RZ6IeAHlqj8RidqmzatU4tUSKPK7nSK88MEkigXo4bolUDU367jGE/QuQEbFKPwEIRaFu2rN5kwk0uDR6sLhpwK+jcr8BCuP/liQ2LwkUgopcTf7MTIULQzT0xM27kKWgVPnRJ8p/WHUHSnKj/aqH9BGQSOegp0Q6yQLYQVn1WobuudaP9/DFJclf6zO6neSsdZ7wc36JfRFYYdVvloCjTiia4hDfpR0GJX0Y8G48u4JENvAVKDWh8BhDkJic/B88WDeVJx4X5UPyWt1gvkrXVEO83TCU7TSDwY6C842LrrcHzodOykSvnfOOeDxfOCBqQB8Ia80YnKCtE1+OJfNDYOmyMbpVW+ujjhadoriA1Ug+buHki4BZGJnn6INTSznQwdlqEcUN88Nf3rxYU5K71TusEcqP5LuJ1Cb1EXCHwe922jw+WIS0Gq20uKA1XCsTqiAkEpISNX90idOvM6IW5k8mAuzSJohBoXG6XQCrfuOqUkjZSJtCBaNUGm3fQy1W6hI4qmHDooaeqEITpNo8eaYbDi7KQRah1CLw0b/AFxK1NKODpESrANVtqqR6oWqzRNpd54UxYcFlVowUqFUm4wmqcxuXFOO1dAQgUSIylkWxdaic1t2aEH718La2OK9V1IyStKVFJh1O9DiAAqnHD6Cq1RworH+oYeC8xV4HHahh1N7hQE3+dClqNEpFSYfedvWqmrjiaZs2aM2r9Nf7MSaQRaccs3+cYwe3qVOBPTDzWi0t2/jSYUbVoqCR6hE3ydf5UYN4WlD4rxSXJX+szup3krHWe8HN+iX0YpT0SOiHk+SW6/EQ36UdBiX/F1j4OY/D1hDvnp6FeAwukm5MoQPUP1ic/B88Uj6cY8L8qH5LW6wXyVrqiHebpgWgDdriZ8z/kmGQRUFYuhCFb4pqfXDHnjFUwGxRV6HBxWCTClk0CVJh6l+iGDqF/xgCFV2XeuHYLtRRtKa8+JwEVBbNx4xicXdZSU19eIiE6rWiFcZ6YI+6YmSNFr9YKdl0fhgU2Y3/PV0wyhV4UpNeeGufoh7zFdMM88I4oSYSSKLCqYsKcld6p3WCOVH8l3FJPIoVJt0rxCEj7SSIW+BQFJQEnUKUhmqiG1KooV5h0w+9aBS5Z5qCH2L7xbHz+UNuoAVVwAg7Ilafe+UKz7a1746oYfodFg7Bs+cOad9riRcABOXWPWEiFrGiMILJqUNWrR4L/lBiUKiL8z4XYnm21VW0aKFIl3km0y6uzb0UiZVdYsUSRsBu6cc0sjNXZoeaCLNpKaKPBmiHqCgtV+EPN/w2wiynZprD7pFMqsmmyFg6XHaD4H5Q+zbyjdpJQRoF18MgmpUqx/uJ+UFvKJLuSWmzW/XSH0FRNkig2RPqWLVtixTbWkSEsVd5Q6CE/i/rGDUrSUq7uFx4zB9Irq4lTaQMiJVaTfrsq/WJV2wnKAKAVS/fHFJclf6zO4nmXHUsIcZWguq0IqKViZaYRKZFty56SbsNO5o1X36tMT+XcbcPc7FnJtlFBad4T4Ob9EvoxApNCJUXjzYc9EekQ16X5GJf8XWPg3uNPTDlbs9HQrd1NwjC6kEKSZMkEeamJz8HzxSI15bHhflQ/Ja3WC+StdUQ9X7MSraFFKssK0+zWJgVA73Wp84RKtV0LzqaDshvzYZaO/Negw5S5NtSRDvmmEFRqckR6kUhxepSk9ED+9QgcCYQYTxw4OCJiuxA+MSnmnrGF+Z8xAbCSCo0APHDiaUWp8IPqMLp5KbRgqpm2qRK+Z/yMDJJspW3a56wo7Id5oc54OvvZPwghItqVK2+cw8FeSukU0IG+UNULbN9sZQHniVKgSDk9HmiLFN5/KYaspBy0uu1a41foIY/FDJ1m6nMP1h1VLw6kV9qFUNauVFNl8CMKcld6p3WCOVH8l3FLcSvlEnN2rYW2pdD5n9cSmlcaTtEdz3U7nyd411p0QyNFa19USqmjVpaieG6JSW0nLFxVdlB+hxFqtlVapOwxNIRvUrIhjSHcsqyR5qf6QvjjC3ovkqMIkfTIAsDn/pDUwLqKzFcIpEo6FWli2SNAIrQQ887acUugprN95iTatZgQVWeG0qJhz7KQn1/wDW4nTqyQ/4wxacsJbrUU06IcZVW09SzzVisAbICdsCWobeWyleClItAXiooYZSFEJVWorpuMOut7zQIlAbs8GES6qWWZtizzgwfSK6MUxaUElTagmus0htCTVTZIUNl9cUlyV/rM7qd5Kx1nvBzfoldGKfSpRUlDKbI2ZkL9EekRLt61KKvV/3Ev8Ai6x3OVcBIrSgh9iykpQjNNdKtkPuTS0pLZrcNRjNIVxY3uNPTDhpS9nqK3b3mHoie5D8kxNK2lI6cWTuCZc5pA2gGJdxd6ltpUfViwvyofktY1qUUjCQnU0J33c+TFfwUrwV4YlGG1yzc9abdVMFQC7Nc1CdtrRTYTwRSXcbac+04i2PVURgvkrXVETChpswXGrqGqT8YWlBFXE2DXZCH6VKLPwTEi7Swl5KRxEwxQEp0e1d84l0tpAK1K9d0ZcEWHEqpzQXQg5NDaqq/CYfP2E2/iITwqPQIXwIJiWWk6oDR3oUBBGiLNfo1kj4RKasxw+q+FA3d7r0Qh45rTbVSo6NcLSDaL00Smmz+zEylWkoArxwuzfVYN8So2MphhyzU3prWG7OtNq/iBixTfxTVnRk60ttkD2IYD9AcnkrWq6HU8NYmUrvNxEOD7Qp/tESJNw72T6omy2KZtE2NRsRJ8nX0riWVtK/lEp6X+WJlQRZbLqCKeaYlZcqsVFa0r5NcWFOSu9U41qSLSgCQNsMDuh2ZS/ItzDhcWVUWdY2Vvu0ZsPqlnX3JNCS24p11Swp2vk10UvrS6/gjBLWQcCO6CctVNn6Fy7TX4YiqybAa32rfpjBrZ02PkIDkwbeUCVppdSETQraFEHZS+CumZWlYwc8DRamgKfH5xLsBH0Nu/bWEqrTJpKvl88bp191PfKJVpvfl1ylfNTD6FHOQoi6MKOeSpsp9ST+sTHoj0iG3WwlKGibQHDTEkqSU2hUV1xoJSgbNEXijywLd+4mJm0LDqU5vCMV4tpaV8I85V0OBPkBSjXgiRmUkE5QLV5tx/vjhDTdbOWSRXmMCzflVKcP6RKqZzFql0kkbaUrCFNhRqVpqBcKJqPXEq4puoBSKp1UR+sL5VL9WD6RXQcUsim+Kuj+sTHnjFJclf6zO6neSsdZ7cPyxR9G1lLVdPBAeCbF5FKw44m8p2xpFRpGzFlh3waqGHZVyyhpCK2tZNK9ELSlXe1y7h47omnhpZKPUa/0jCXok9SFPK3uTXdruoYkXE70hR6Ilys2RUj/AHGGloJ77WxUaaQ+k3ZEAqMOJQN4dO3EuVJzUKW5U8AN3whc23aVMJcNoAVKtEJB3r7Q08xI9cPBw5q0g14v+4bbRQpKLR2iG2DXKOAkc0KH2lARbKSELyFlW3MMSgWrvjiBz3Q5ZrmLKDWAkkBR0DbiRa8pVkceJWlLffG1DadUTKNIXKFHFm/0hwNLydVA3QyX3itutCCaC+Jzzm+pEp6JPRiwvyofktbrBfJWuqImObpEV4TCgu5WofGE7bUMIeICWHmwFcF8POtqzklNCNoAiUc0oFpd2y4w3KKFEhLqq81flFVkJtJUbzt0RNoWLsgsERLA6Q6voRC/RK6DDSR5I+cMtG8KcFfXBCReTFg5q7VCIkS5VTmScuHDUCJLIruUlsGh01oKRhRDizYDQSlPCREsypZOeKDZeIyzTuVQ4AK0poujJrrStboYWnRc3ZhjWbRNDBmEiy4hq0Ak8EStRUFwChh8KH2ruOLeuwUA7LqRTVSLFKjSQdlYyjVLxQgw1MuC2DZKgnzRUQ262KN2UkDgswpZ0qviXSbg22pJ/wBx+cYPvGfaV0RKvWO9hajfxD5iH+KGZizlAhOitPJpiwpyV3qncKLUqy2VKyhstgVVt44LktJy8u4q4raaCSYwRyo/ku4n0oSVqNLkjhEMNNEILVwKvsw+2yr6EoRQHe3iH5ZlwfVk6dG/B6IW0pVUsTZ0jfUiVShNyF0Nm6iNcPovo0FWa676RMKBtJXSxtAgItC2RWzrxNAioOEF9KYlC4sJQla68FUgRhclIVS1p9KmJ3IosJQhaCmlL7MPHa0ekQ/IpeORDOcBqWFxhBC7Vpp/VsqboL94yLVlKdUTgfbCmnbKk1vCtJhSrdAl3Imo8rcSTACbD1u0TwCHnWV2VilFDzocnNNVJHFd/wBRNKXpZ74kjzTE0MpZeIUmlNSgL+mGmq2rCQmsd1VFMlk6cNdMNOOptKb0QiZCc9JCSeCJBCWvrCr+OmmFKOhIqYyqjnOTbSgOC/8ApCndIExk/XUYpQ+d8oXLWb3M61ikuSv9ZndTvJWOs9ifa02c1JA8rhiYVUZTyfuaYLqzaVk1JJJ+6YdFm0DFhS8w6QBD+UFm2BzUhdkd5SSmm2JWXsXHOtV4SIdJ0qFkcAiVUFjKBvJ3cdOiMKJ8ujZoNgVE8lK8xTKdHnJHREuUKNSF121N3RSG5cLNbQTnaL7oDdqgQap+MYOWqpCFOJNNp/7iYbdVaK0EWlXmt9IY+xlATzQ6GqtXZpGnT+kPTDiKtWVXUqa11Q8hDnelOFaSnjhhK78n5ValQMNX2rSb/XCnAEqJuAMIcaIGRCkJOnbfCWiUlOaqo20iQbSqqs005iIktamUkXj+9VInHK55FsV2xLuigWhahTguiaIWSVK73wCGmlkKSHwQeeJyZQSEFNbJNK0RCbVb1E88OL23Q/lKHKN0TUb010wsuHvuTSsDZf8ApfG+taD8IcP/ALT0DFhflQ/Ja3WC+StdURNHgHSIfQRn2kKBpsBr8ot+UqpMDbWsBGoqSqvFX9YmH8oC4k1I1muv+9sZNRNUN5NJGr+xCEJqNtImWNKXFI06heYdTLpC7bZVXReTT+sNIZq5kJdy6zSppdDDqlZz6TmUpZ/usI44aWs2UIXa9Ud1qRZC3NXRCn2KhtQtWVChCoCSbkJsp6YQoGy4i8HiiaSVVtm+um6JR2tEW02jsFYZbVnS6KiynhMNpAyaVlYUVqpZoLjzxL23VFlkjN1UrDAShSVgkcAH9gQ/nXlnJIrwqH9YSU3KF4OyA6d8UJBrxRf5JhKgaHXAfbXZcs3U1ao0ab4Zc8laiBzU/WEgGqUtJbHHZhsi5X9BAUnRk0D1JAiVSB9EmyfaJ+cBlxHem0XU0qVa/rAZRnF5FtJ4MVYwpyV3qndYI5UfyXcUmgGjaj3y7VDEw5nnJ5J3hvrdDqgKJU4pwDjiUXoNpCPhZjJFaWlO1cJrdEi5Lu2mFLNuxfUCl0SSW1kMrZqpHGCf0jCjjZWGrLdmurOEShdVRCWQ1arzw42tNltFRbBrW+JZ5gFxszy11poGaflDrAVaCDpjDjouC1oUK+fE0zZBdfWeYEXxLuuIIS5KJ3g110/CJyYNVtrtgFPHWMuFElSgpY0W4mHQb1JLdg6amJBCnBnJCCa3JISNMTDRNVd0h1NeevyhzupKnVlVUlPRHeVJCmpkFFDW0ANPrh3uhRf1pB21/v1Q2ltNAyVZ9d9/dIlpVBzTbDgp94ERNZRs0QEC5Wkn+ojCaRpsaPwmClQJLxSgUgpYdtsCXrTVatQUSi0uJKSkqGo7YalnkAWrg5XXT5xNydVJyaglSufV6okZYHPbctL4LomDlv8AxV6aXg5l0Ai4iFzAGaZtDgB41GGmnxZdWopzRdwQzk9CHFprt0aIZGitoH1HFJclf6zO4fmVgqS0gqsjSeCJhmYZSxMMkWkoXbTQioINB/Yid5Kx1nsU4K/xldJgq0ao2waaTqhRrdWMqILdaHTFlI0QhI4zCWT9qsPNoV9MinxgpJs01Q3fwQleomsLvNkxceaK1pCTUXaDC7Shfpgr+1F0CvFAI1Ra2QNW2Eq1AQ2LYupdFAqkLbR5YoVbL4PDC/KrshJB3uhWyFbVRmmoEWaXRZh4V30KrD1oU758sWF+VD8lrdYL5K11REyPN6whcEwspFQhNo8A/sxzQsi6oI9YimqKcOP8BiWXrCiP79USu3KpPxiaCRZQl1VPXEmo6FuuKHFQD5Q4dSE1PrA+ccFYUrgpFI4d0DrhIQkqVqA13QhSTmOivEdcLEN01CmJKCc1KqgRY1aYFTfSnwpGaCqmmgh4K/htLX6hiZupk2w3EqkEVmNFdWcU/KAIwpyV3qndYI5UfyXcUqraFCK6oCUipNwENPjNtKNkjTdT9YSCahIoICdQvgKpSiUp9Qp8tw56U9AiZ/D1RGFkj7KFeokwNsSrX2JZKemArUbsaW9SVFXrp+ngJy2NNkUPP+sKZdFFDXqMXGmJDid8ghQjK2zbt5Sv3tsZYnvhSATzUxKXSiQm1U6xWkB4b8zGjiH/APFDDqr0oWFGnHCW03kmgHDDytTCl1+IxSXJX+szuH5ZZKUuoKbQ0jhiYemHkvzDxFpSEWE0AoABU/2Ynwwy2yFS7ClBtITU2nb8UwrSFOKPxhu2Mmly8CkFCk0UNIVFRmg3pjbwQbBoNkWjWu2kKVStN8rFas88JtCpToipNIFDeNMKT5NKRZpWmuLria5tIBUeOCUK0XwBeSdUFs5tnVFIrTmih4orZITpv1xo54IBqnXGk2oFSYN0Eb4xU6NkXHNMCtb9EGiSBtPqhJ+1fFnRXRFDCtlNMUI8s4sL8qH5LWPubLtd00tZG2LdNtIRLrfaQ+5ehpSxaVxDFgvkrXVEKacTaQq4iH8q4Mi08WzTSqkTjYqEpIUyvSTpujJvNWHHk9826TSG2apXbC7NNdBDZSsZY75tWoRKy6krBcFV5u9ziISW3wvTpFOKEKobIZK3DpooDR0RLuCgQ6fZG2FulaXGrFEnXqgsujNOsaRGVSwAu3brww06wjvjztFCu+JjuVCQXUIrz6TSHHJptOSW3SwrXWh0QUutd4LroCaUrd/1DYaBWy4bI49kMPtJOe6Wzdo4en1Q4+4NKe836Tfp+EIafl02wkFVnTUcMMPZSy0sG1trah+aaBQQBRCdGmEtrzWSK5UX6oZ7jGYc1QJ0cMJQEZXNt1To4oLrlUKljYpqJoQYbtOFBRD1mlhC1tlVfKAu5q0h2WCQVNb6+6JcqNVqWA4hPkpjJNFKaot1VsrSMkCEApt2jsrSFyy6sOITazhwiLs54iinNsTzrtnIPhaaJN9CYmROtWVG5s2q04eiEpSN8oIrqqYlArPcl70r0VxYU5K71TusEcqP5LuIu+WzePnDmVIW8tNUAaEm+kYOfSlWUyjeUQL6bT0Q0zLgNlmtgfKBWzeuzcdW2FKY781qHladEPFTuRU25Y0V44TVaVtWwDS42dsAq+lU/kUJ+1ww6hboQhO9XStqMk5S2VFRswHXW6qpQ0utccPIbasJdTZXQ6RCmEo/8drPIJ4P1iXdYRbV9GYeDzaVv0UvRWz/AHSO+k5Z0BVql6OCO5liqwQDZ4f+4W2hJDJdShKiLs7REmuxafyqS6CbgnXEutpujtsMpCdl91IWqZIeTUhCPnEpLsqJcdyhtK10FQItzgUg1Iye3hh0ApS1aqFnYYbW6z3ptZqo6LomFhROWXb4oVO5SmTQE2Kab/6w44bFEi1pvN0NuBAS2vWo6tsPuWitpCQUq4a0pDq0KSLBKaHWY7sSLSNSU3qMMv5awhW+FL9MOScukAUzQTw1jIAByYDuUzTzRLvpasPCyHaHg/WGbZyVkB8VG+AVD7rYop6hVikuSv8AWZ3U7yVjrPYkON0BFoqrrNIaVbAsppogO2bC61UR5UdzqzwBSsIKgWnjvoKUIsOBNlKhAFf4e3yv0h1t2juVpaFLocdbsBKkgAbKCkOJIKGhZsUPBfCk5waCLlcMB0Gqc1HwhLjSt8gGweKLLigoKboqzthT1M5t489NETZNbTd6BwkQaWa3XV4L/jFLSySmyTz1hh1u4Nilk314YLxVpIqKQp+zpFMn5MPpSqq16DsFa0gh9FvKLylDqhmoADZ0U1bIeSy1RoUoNFYbSVBN5K6Q80oFIpmL54YVLgUAIXaPAAIl7CAmt1E80ArotFNW2LSaNIydiyga61rFgkZRSknzYlBodBS1b4ISnTZSE1hl64WNW2Ek2Cqq7yNuiGFLcNEii0gb6GkrNlTLxOjff3SAlAspGrFhflQ/JaxuN5B3Kd3pmssGyU5MNgb7mKaaYmUBh1S5l+VdadyZspSkprU+TSijQ7Ysofclz9tsJr/uBjBfJWuqMRoAK33Y60vxWqC1orjoLhuGytNqwq0ngOMKoLQ0GBUA0v3NCKjcOWBS2srVx4nLH8RZcVXaYU75ShQ4rLaAhOwQG2k0SILtkZQiza4Nx9Enf5S4eVtx4U5K71TjXk9/Q2eOGu51ISFSjYmb9L9fK+/vq64lslTuvIPftCmnKW00t8O+pwRgkZNvufug0XlDarkXPJp88RSoVB1HdXADXdiv3LjqU0W5S0dtNxapft3NaX7lbLu8VppjKHEhaDpScVAKQG20hCBoAxlK0hSTpBjK2BlLNi1wY5Lkr/WZ3U7yVjrPfvwC9RruAFitDXwGF+VD8lrdSsr3JJLyLSW7Xdar6Cn+XD8yuRlChltThCZtVaAV/wAuPqUn74rso+pSfviuyj6lJ++K7KPqUn74rso+pSfviuyiWbVIyhL7mTTSbVpsqV/l/dj6lJ++K7KPqUn74rso+pSfviuyj6lJ++K7KPqUn74rsoalu4ZS242twHutVKJKR/l/eEfUpP3xXZR9Sk/fFdlH1KT98V2UfUpP3xXZR9Sk/fFdlEy2mRlAWHMmqs2rTZSr/L+9H1KT98V2UfUpP3xXZR9Sk/fFdlH1KT98V2UfUpP3xXZQXESMoAHFt3zatKVFJ/h8EfUpP3xXZR9Sk/fFdlH1KT98V2UfUpP3xXZQ/MrkZQoZbU4QmbVWgFf8uPqUn74rso+pSfviuyj6lJ++K7KPqUn74rso+pSfviuygOLkZQguIbum1aVKCR/D4Y+pSfviuyj6lJ++K7KPqUn74rso+pSfviuyiale5JJGWaU3a7rVdUU/y91JOyyWlrYeyhS6soBFhadIB+1H1KT98V2UNS3cMpbcbW4D3WqlElI/y/vCPqUn74rso+pSfviuyj6lJ++K7KPqUn74rso+pSfviuyh2W7hlLbbaHCe61UooqH+X90x9Sk/fFdlH1KT98V2UfUpP3xXZR9Sk/fFdlH1KT98V2UFxEjKABxbd82rSlRSf4fBH1KT98V2UfUpP3xXZR9Sk/fFdlH1KT98V2UfUpP3xXZQxMokZQIebS4AqbVWhFf8uPqUn74rso+pSfviuyj6lJ++K7KPqUn74rsoDi5GUILiG7ptWlSgkfw+GPqUn74rso+pSfviuyj6lJ++K7KPqUn74rso+pSfviuyiWbVIyhL7mTTSbVpsqV/l/dj6lJ++K7KPqUn74rso+pSfviuyj6lJ++K7KPqUn74rsodlu4ZS222hwnutVKKKh/l/dMfUpP3xXZR9Sk/fFdlH1KT98V2UfUpP3xXZQzMzLMuy22y433p4rJKig60D7O6emZZmXebcZbb768UEFJWdSD9qPqUn74rsomW0yMoCw5k1Vm1abKVf5f3o+pSfviuyj6lJ++K7KPqUn74rso+pSfviuyj6lJ++K7KGJlEjKBDzaXAFTaq0Ir/AJcfUpP3xXZR9Sk/fFdlH1KT98V2UfUpP3xXZQ/MrkZQoZbU4QmbVWgFf8uPqUn74rso+pSfviuyj6lJ++K7KPqUn74rso+pSfviuyiWbVIyhL7mTTSbVpsqV/l/dj6lJ++K7KPqUn74rso+pSfviuyj6lJ++K7KPqUn74rsoalu4ZS242twHutVKJKR/l/eEfUpP3xXZR9Sk/fFdlH1KT98V2UfUpP3xXZR9Sk/fFdlEy2mRlAWHMmqs2rTZSr/AC/vR9Sk/fFdlH1KT98V2UfUpP3xXZR9Sk/fFdlH1KT98V2UFxEjKABxbd82rSlRSf4fBH1KT98V2UfUpP3xXZR9Sk/fFdlH1KT98V2UTrsylpC33soEtLKwBYQnSQPs7uVlWMllJlZRaeTaSEhJJuqK6Kc8MvqSELUM4DRUXHcICMg+l23YYDZygogm1Wt99Bo1iGqrZXPreQ2EqlltZEqGtKjXRa2VgMMiXE02Hy4paTYVk1BNwrdarw04YYmEiiXUBYHGMcyl0tIeyxZYYXLLRWrwbSq2TRQvBNNsdxJ7mE+h5bSny2bFkISu5NqvlI1x3S220hpiXaffQoEk2iahJrdSyePGrJqSlymaVCoHNCrS2O50vuNd0oknFgBIFSQF3Z1oV4IdcYyCpVks5U2c523S9JrqBG2sM5QNdyzDrzTYSDbSUE3k1vrZOrHMZTJuSiJJ2aCUpIXmWddeHZCpJ3udU6SzYcSg5MBdrSK6rCtd92iBLsJlkTKA+p0qQbCrCqXCt1qtddOGBMNJbEklcu2tCgcoS7ZvBrdTKJ1bcc8lb0o07LTFgulpVixYSrRa+9tiQccDUtLPsrW86uWW6jSANBFkEEnOhdA13E3NIlCKG2SpIzq12qApSJbLBrIzjCphmwCCgApuVffcsbMSsnQOUzbWisSjii0irj6H3kyrjiE2FEA2Qq7RrMLU3kHZNgsZSqaldsi9JrdSoOuFTTyGzJq7oCEIBtjJWtJrfWwdV0PSs5ki6hpt4KZSQKKtCl5Oizp8G+ppLORk0NqdtpJWoLVeEmt1AK667i7TDsuMjMLbU1beYlXFhu1aqCgKqTQA6dcZSTVLPlEoZl1wtkBwA3JF+b5WmtIyiA33CJhEsUlJyhKkg2q12qApTGt2WRbdtoH0ZcoCsAmyLzQEmH3GXJd9ElL90zByKkWxaXmgFWaRk1aaxWy0JEzSpayEm3WyTarXaDdThrDImQ1SZlhNNZIEWBdmm+/SL7sbj7b7TAaBWtTrRWLIHAoQw1hAsytpDd3crlFrUmpSF2qDijuhbTJllsPOsNtgpKbGgE11jipE1KzZaU8zYVbZSUpIVwEnYcSsnQOUzbWisSrLfcwnHO6Cpam1WKNOWLha11TrhE02213GlMutxDiSVnKUNxrdZqDov4IVNPIbMmrugIQgG2Mla0mt9bB1XQ9KzmSLqGm3gplJAoq0KXk6LOnE53OpCXqZpcTaT6qiJDCCTL9+aaJYyZtOLVTNSbV2nhh9iZU20y25RhKpZdXO9g1DlbNd9dwQhbqGSuZYQ9LhIIsWlpTZVffv033a4m2ZiwXpZ3JlbabKVZqVA0qab7FKuMFrJqfbacDiSSQpYTdfdpicE0pLbbWULbXc60qcSnWFk0PNBS8iXM28hgtKQkhIyi7NFX32dOqvBCZFvudM6FuhbqkHJ0RZ0JrW+2nXtiVm7NjLtJcs7KivglLOhIqYSmZSyDMS6ZpORSRT7pvv8m/cFbL7EuEZy3JhBUkJ9YhgTIRJNOIbISuWXnqUmtMpWg4tMd0LaZMsth51htsFJTY0AmuscVImpWbLSnmbCrbKSlJCuAk7Djlg2pDMupDinX1yy3gilmlbJFNJvOyMs6lpeDnXX5dKAnP72F1JNaEHJq1bIybyZczLqGFNFCTZTlF2aG++zzV4IfQ/YyzDpaUWxRKtYNOIjGy3Lql3UuBa8kWVWwlKdNbX2rI0eVDVVsrn1vIbCVSy2siVDWlRrotbKwGGRLiabD5cUtJsKyagm4VutV4acMMTCRRLqAsDjGKYmpUtZRlBcOWSVAgDRcRD1kNdxsTLUqtJBtlS7F4NdWUTq2wZx1plbTjDzrCUghabBFATXXzU4YMivudU6p1CEOhs5OikqVWzar5CteyLboSHUuLaXZ0VSspJHqxYPbSWu5Zham1ApNuthSqg1+7sjCDQDE2+xK5dGRaVmqvogi0a15oYbYeYXNvzBaUsyy2w3RFrOQVVrQDXrEOPNIZSZWWMxMpUCbVFKSUpvu+jVea6sbjKQ040ZRx5pIQbdpNnTffvtkI74yqecdQ1YVLLayRVfnJUqp17KwJdhMsiZQH1OlSDYVYVS4VutVrrpwwJhpLYkkrl21oUDlCXbN4NbqZROrb4NrvrjDrSraHWqWkmhGsEaCYal2q2GxQV07h+cTPTKVuiyU97ISKaBVNRt44dLkzMOTC1IV3SbNtNne0omms6tcIQmYmGljKBTyCm2sLNV1u1nZzQltAsoSLIGwYyuZmph6lckDZGSNQaponTcNNYFJqYTMhwu91CzlCSKHyaaKDRqENJQt1ppLaGVNpIo6lJqkKqK7dG3cMyjE5MS6GwQVIsWnK6Sap08VNMNWFutMoDYLCSLC8nvK3Vu44y4cdUApakMqIsNlZqoi6vr24+6HZl+xklMmXzLBQqlob2uoa4WFzMw4+ooImVFOUTY3tLqbdWswhCZiYaWMoFPIKbaws1XW7WdnNCXErcbbBQoy6SMmoo3hN1bqDXqGOZQvCU4pMw6HnB3q8gAU3mi4XcEZB2fmS2UlDgFgZUHbm3c1I7oyjoTbDpYBGTKwmyFaK6Ka9UZRLjrllJbaQsijSSakJu4BproxKAUUEjfDSILCMKToQVLUT3qucanyNpMNWFutMoDYLCSLC8nvK3Vu44WpS3HGlZSkusiwi3v6XVvv164WrLOzDi0pRlHiK2U6BcBtPr8HllOOotBIcbQRZdCTVNbujcbILLWE5y9Vq2Q0VV1neX14YQ0l59sWFtuFChV1KjVQVdrOymmO6Mo4E2w6WBTJlYTZCtFdFNdLsZQh5cur/MbpUesEQrv8x32omDVP8A5ArWiruE6KaY7pyjtMplshUZO3Zs2tFdHDSLSHXXbLYabDpHe0DyRd07Mapd0qDaiLVnWAa04jCHHZl5TSVpcEvm2LQ0aq6b9MOha3XWVocbSysiy2lZqoJur64dUXnZl10i069S0aC4XAYlAKKCRvhpEJSMITltKnCl3vdoWzVY3mgm+GyhTjTKQ2CwgiwvJ7yt1boWpS3HGlZSkusiwi3v6XVvv164WrLOzDi0pRlHiK2U6BcBtPrxLbS6tgq/iN0qPWCIkrOEpz/xG8k1XJGg9jTS6sNvuzLzqW1W22FWbCTSmoV1nSYW0XX3E5MNN2lDvKQagJu1GmmugQ53xbzjq8o465S0o0A1ADQAObE2jup6WCFhzvNi8ggjfJOsQl2YmXpoItWGnbNlNRQ6EjUTp2wttT8w6ohtKHFkWmwg1RS7UdteGEWZmYbmEqUszKSnKKtb6t1NmrUIaYaTZaaSEJTsA8FQ3iLSXXXqNhpGVI72gaEi7pv3Dban3WEoWF96s51NtoGohovzsw+htSV5JVgJKhoJokHTfDoWt11laHG0srIstpWaqCbq+uHVF52ZddItOvUtGguFwGPJ91PsNkFK0tWc8HjB+EWgpwsgqUiWNMmgqFFEXV1nXrMLbU/MOqIbShxZFpsINUUu1HbXhgoStbqlKK1uOb5SjruxuTNtaXVM5EEUzBWtRw6PUIdLkzMOTC1IV3SbNtNne0omms6tcIQmYmGljKBTyCm2sLNV1u1nZzQltAsoSLIGwYnZVT7rCHBZUWrNSNl4MJdXMvuZyXFoVZsurTvVGidNw0U0CHQtbrrK0ONpZWRZbSs1UE3V9cKtTUwuYK0ud1EpygIFBqporq1mEstlSgCSVK0qJNSTxknFKv8Adb7Hc6rSUN2KE0Ivqk6iYfSjCE0rKkqKlhu1aPlVsdMV7pmO6ctl+6s23as2fs2d7dohDQcebQGsi4Ekd+RWtFVHHoppONM4J6ZaUlsthtGTsgGldKa6hrh0uTMw5MLUhXdJs202d7Siaazq1whCZiYaWMoFPIKbaws1XW7WdnNCXErcbbBQoy6SMmoo3hN1bqDXqH7g2qYK++LyaA22pwqVQmlEgnUYX3O5aLZotCklKk8aTeISt5VhKlpbBpXOUbI+Jx5VaFuErS2ltulpSiaAXwsoqChVhaFaUK1j/rdKWdCRWJeabBDb7aXEhWmhFcdVNqaNTmqpX4YkKeVYC1pbF3lKNB8YRlspn6A00tw+pIMNvMrDjSxaSpOsbl+bdClNsoK1BGmFgtqSEm5RpRXFARk1WaVyl1Bwbf3Xud0vF2wHCGpdxyibxU2QaaDCH2HEusrFUrSbjCJUq7+tCnEpp5IIB6wxtsFtx1SkKdUUUo2gUqo1PDqqYbdbNptaQpJ2jdNrcCiHHUMiztUqg6calJbU6QN4ilT68aZUq7+pBcCaeSCAekQ1IqWoTLu9Tk1UN1d9SmgHdSjCgoqmVlCKaBRJVf6oSpTamiRvF0qPVC6tqRZNAVUzuEf/AMiYKeybriGpwLXkWlOEDJrFaJFdYjCmEWsHuOMKlmpdLT7RBezyVKsaaAK0a74n23JDKSaJyVfbaTIqaSU2hlLDSqnRWo+F8OdwYLyRaZQZRacHuFzbmquyVNh9UOBEqJyqkhTakFwUtCpsi9VNNIcZekA5LpwmytpBky2gNkItWUKrZTprzxhliSkO55tUyF20SpouXtIKkhQpW6ubagtpQpUs5Oy5Mu1IuSqEAKziEqJ54dlUIUwxQAIlWrVL/sDSNo2Vhtg4HYEt3Uc79nLKKWN/3PvtN3xjArU/KLW005NhSHWiEhNo2Kg1upSlSdUYDeTLOIf7oebecKTayVlyyFH7NyKc0YIVKSbzTwYX3U8WyA4goNkWvKvs02U1RIJmmyln9nMNt25FyYU05TOoEqFhWi/9ImGFzLbqVS1G+8FLmUpcom1tvpSJR56Vebbwi73ZMBaT3otlVgHYfovZMSxelVuvNuTRTLPSK3211eJFQN6diuGH1Po7nLjbfcy0yLkypjNFQhaVUQQqumJdYbcdyc2w4oNNlZoHASaC+EdxS05lHV5PKrkXqNbVFNmp4NvrhlmXDuSS3QZZBSs8YN9YwT3DLrlp1zBbjbq7JBylhFm3wg6KxPpwdg+ZlmlYNU262WVJLj11m7ylb7O4YKO4EmVEiumZm5S1p874xIvtSLjsw7gl0zSSCC89RuyFn7W/08MYaQxJlDD2D7m2JFcukuVN1kk1Vw64wxlcGmcy821kcpKF9Ce8pGUoAagX9EFrB0tMGXGCJtpNphSFFZUnUQLya3eqJhvB8s42l3B4ymTuyigsVv8AtlNrhjCXcUquUl+5Zfvami35TvknR+5vPOMTK21ybaEqZl1uAkLXdmjhEMImMGoU3MzMxMUelVTOQtKqlNhJ0munVGAJnCeD1urRLOsFS5YuKbXlEZOuki4Kv+MST4kO47TjyZkNya0UFlVLbpNF3gaolWVSLcw0Ur765KKmbKrrrKSKV+0TqiRXPYPD0z+yChS5hm/K5tKk69MYJDMkGZJtpSZlh3Bq3Bl6Izi0LJV5WdfGDe621zjLMmui32SlNcoLOaqt4Git90SqloL1i1RpUmqaaVo3yU6DsPHGbgkMTaJFCmSiVW+tK6E2ULSQEWT6+GMPTPcripqyyZZxKTaCgnSjh4owy0xLKbk1iSUlDQshS8sbVPvXJ+EOmRknpeQM5JqQzkS3nBzvirGoUs+owlb5yc2Jtay6mQcKlt1NBlrVmzZpdq2QzMoKJyeacUlPcrBRmrQUaLStCik80YblBLvFuTlFSspmk5QLUV5u27JjmjLNypmnHHhaU9JqqymzSqHtFPu8JiUVMnJziCvuhSZBwF647561ZUK0I5tESky4zMLZ7kdbtMS63aKtoPkg7DGBHEsTikIWp1RTJumyFNqArm7TohtkNrcrNS9oI02csmp9UTbKJNf7JTOtOLl2mzZU3ks6ykaRboSBwxIoXJuDBv7QdW2w4ggIayJ0p1ArrcdsPuCSWl39qoCVJSQQzVIVT7lK8EYQlW8GpXImfTYQuWU622gspJUG074W66NZjBIcYcShmfmKd5KAhstrs3GtkX3XxgaSwhgp3MlUF+Y7gW6ug/hAhJs8P9YKHJVxTCsM5QgoNCjuelfNrdCGpiSddwQzOvf+MlkrFCMw2BpTW1BsoU2nuqYolekd+X/i7sw6aNtJK1cQh4ZF1h1o0Wy7S0LqjQSIcebk5i224WywbAXUfip8YYLcjNLW8txtLYydcw0V5dPjAupwQss4Mm3UJcW3bSWgCUqKTpXtEeKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0hqXYwLOIZaTZSnKMmg//uR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SFtrwPO2ViyaOMj/wD6Q5kMCz9pw1Wtx5txSuNSnSY8UTntM9pHiic9pntI8UTntM9pHiic9pntI8UTntM9pHiic9pntIl5lAKUPNpcAVpAIrDXenH3XVWENNUtKNCdZA0AwzNoS46HqBttIz1E6r/7uiWe7nfKHnsgaBPel27FFX/a2VhDTks8ltaw2JjNsWjoGmvwiYmVgqQy2pwhOkgCseKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jJP4FnFt2kqplGReDUfxNojxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0jxROe0z2keKJz2me0gNu4IwiADaBamG2z60ugwhhjAk220gUSkKZ7SPFE57TPaR4onPaZ7SPFE57TPaR4onPaZ7SPFE57TPaQgvYMm2kKcQ3bUWiAVKCRoXtP+IzMsFWFOIKQrYdUTEzMIZDj9KpbcJCQkXeTffWMIhbcq2+6tbrNh5SxaP2swcESkk1Jys6GW7AVNqpZNN8M03+qGpWZpN0bCFl0Vyl2kwpttIQhMzMBKUigAyy/3ZTrpohOk/umC+StdURKTEtk1PS6yqw6opSoFJGmhpp2RKNILSpiVcDySVEJWq+0NF2+O2GWWkyzj5m+63bbqkpHfcpZGaa7IZedbl1ybVC2nLKqlXlKpYv2C/wDphAvMtuluXcUgrSDZNk3j93byirNtVgcf7m3yqX/OR/iznKpj85f7tMfh6whhS1FSr7zxncOu0tWElVIbdFwWkK8NgvkrXVG5wpyV3qn93k0bKH4/03LTSlUW5WyNtPDN8ql/zkf4s5yqY/OX+6pSVAKVoG2JvJqtWFBCuO0IlklaQSSmlddTuJv0SuiJT0Sejw2C+StdUbnCnJXeqf3aaUiinGKVSeHREjNjSpoKs7NcVSa69xgr/V6vhm+VS/5yP8Rm5humUbbJTXRWJ6UemFTBZslLy0pCs4aDQAROJZfdmZtqbyYcCWwuxbTa0izvaxKSjE4+wsofU466hvKBSCkWTdZ8vVsiVnciVuOsIdyTdK1IrS+FKKC2TMzBsKpUd+Xs/dcHt60havWP6RhZHkLNun4x+sSes911+Cf1ht8gJKq3DjxzfoldESnokdHgXJRaU2LakpI1U3OC+StdUQlbxULSrKQhBWpR4ALzCJsuVYXSwpAKrVdFALzEsvKKsTC8m2Q2rfVpQ3Zt918JlFOEPGg3hoCdAKtAPBGFOSu9U/u2HD95tPqNIkrPkoKT64dNi2VNMeqyK9O4wT56vl4EqUQlIvJMKyawuybJpt3DfKpf85H+IracSFtrBSpJ1iO8hwVNolTqlFRpS8k36Iebb7pCXlWl/wDlOk121tVHNCGC0qwgkgh1QVfvqqrU1hKEgJSkUAGqHOVTH5y/DVSoKGi7dyJSaGiR/uMTAOlaKD2gflEqa0o+u/mRDXGenHN+iV0RKeiR0eAfcTvkIKh6oU6rfLCyedJhbR3jSGwn1bjBfJWuqIkZlllUz3O4SppBAUQUkXVIGuJBPc5W9KvB9TaVJzq1qE36rWumiGUJkXXnlz3dSm21o72MvlKGqhfTZrh9sS5yMzNsTeXtJ73YydUkVrXver7UYQKi4LEu4oWHFJ8k6aG/n8M+ttRQsUoRxjwGFRXfPjpVCfOPyi7Rkm+oncYJ89Xy8C9bNLaShPHSJpbgNm0KAbSD+kMLcUVqNbzx42+VS/5yP8Wc5VMfnL8FMhVmrTymxTZjcdIqEJKroWsaFOqO7YUmhUixdz4gjUhZPr//ANYUD5LhA+GOb9EroiU9Ejo8Bh1KyVoRbsjZpEBQuNImr78357jBfJWuqNzhTkrvVPglLWbKReSYSpJqkioONppO8dOdzeANoZrsy3UcdqOK+E+jb6ggKSapN4OPBPnL+XgHnUb9KbokKJ+mKlV2WbvnBRXNJrSJf8XWONvlUv8AnI/xEkmgEZWXfbfa+22sKEGaE4wZYGheDosDnht5ydl22Xd44p0BKuIwCDUHXDnKpj85fgsJjVlvmcUwwAQpmzU7aiJv0S+iFekPy3bjqvo0rbter+kYPusocaAB4a39MPsi9KXCPVDnpT0DHN+iV0RLAKBKWkA01Zo8B/8AUHP0mEcUEHymyBuMF8la6o3OFOSu9U+CmPw9YRK+iT0QVHQBWG3kghKxW+JXjV8t244dCElUSaLO/WldeEKMYRZcAJRLr+WKV9Enox4OcXclGUUfZhDiL0rFobuYpwdIjA3G71hiCaUyainj1/PG3yqX/OR/iM6y0m24tpQCPtcETz4ZeaYfKbAcZUipSm80Iu1C/ZGE3kyM24pMyVts9yuJKqnNpVP/AFEs+Hp1E2FPOBTEmq9ajVQslFwv4OOGssTLTKmxbyVDYVS+lawpJWXCJmYFtVKnvy9ngsJ+k+asWFf9LqxN+iX0Qr0h+W7n+DJn4RgXi/liaB027V3DfDnpT0DHN+iV0Q+NeZ1fAYXQLgpRJ9v+sN+bDXEejcYL5K11RucKcld6p8FMfh6wiWP3APVdDvmmJXzYleNXy3c16JXRGCuL/mqMJtV37Cr+OkAxK+iT0Y5bzHupEp6JPRu5nzIwd6M9dUUh0f8As+Qxt8ql/wA5H+LOcqmPzl+Cnrde+PpbFNpKoQgqAWvejbGFf9LqxN+iX0Qnzju51VP4rYr+AxIF0VyaQoRMfh6oiYapvVBda7R/THN+iV0Q+najo8BME3lzSeesSMynSqqVfL5xL/i6piWlBTviVKVt4MeC+StdURg/u2x+z8sctlvo94qza4K/GkYNTMqBlUuo7oDp0NX2LfBvNMI7ss9wZOb7jtaK5TMs8Nne8GiGVvqb7tS9LAtKT39Wam0W1akXmop5KowhkXG0Dudy3bbKqiydF4p4KY5ukRLy1kWe5g5Xnh3zTEr5sSvGr5bt8HRYPRGCeb8xUPzQrlXEWTsgbKxK+iT0Y5TjV8ol/wAXWO7mPw9YRKJGlKaH2jCyPLAVD3n/ACieK1FVJpYFdl2JvlUv+cj/ABZzlUx+cvwTzn2ZtK/UVxK0NN/8owh5rPUib9EvohHnGJZqzXLKKa7Lt1hWulC7Y5kCJX0SeiJ2upKaf7Ym0cDdfUcc15sPej+Y3bi60spJrBiUOiyqvTEv+LqmJMf/ALc/8seC+StdUbnCnJXeqfBP83TCU0pYlQnj0H5x3LWy2hK7h5WZW+JXzYlONXyxYQaUqrbeTsjZUbl+hocmq/mjAi0ppvQSPX+sL80wmyKmqj8BEt5gxyf4/lEv+LrHdv8ADZ6RDfN0w36IdJicl9KmnNPw+UT3K1/LE3yqX/OR/iMzMhNtTaCoJ2nVE3LTJaU+xZz2klKVAjZU8MO5TIKm+6+5UqSghG+ArSvzhMk13OmdBetuKQbBCLOgV12067r9MS8+tSWGXWUvVWaBIIrphTjagtCpmYKVJNQRll+BKlGiReTDq9Fp0K68Sp875RPkaChjqRN+iV0QjzjGDPSK6N1huuorHwpEr6JPRDpF+VQDxf3SJ1O1KD8Mcz5sOeiPSN3NeiV0Yk8nT0CMHvJqFOOlB+A+cMPN0tiXurxqx4L5K11RucKcld6p8ERSttQTxa/lDAtb5F/FZhY2hf5cSvmxKcavliwr/pdXczJ2Nq6IwIa0zkdEERRSaKAfSeZuJbzMcn+P5RL/AIusd236UdBiUSoBSStNQeOJVSUkuKChdwf9xhQJuFUHpieFKHLlfr/6xN8ql/zkf4i7LuirbqShXEYWvul55xw1WtyzVV1BoGrgiYaXOzL2VdD1tdgFCwa2hRI100wgJmZht9JWTMpKbare+rdTZq1CG2Wk2W20hCU7AIc5VMfnL8DNeiV0Ypb8XyiZUbsxlPqTSJv0SuiE+cYwZ6RXRusLPoqA4XDQ+b/WJX0SeiEWknJKZKVLHHE43WlQAkHXjmfNhz0R6Ru5r0SuiJT0f/NUNtI3ypdIHqjAyTpC6fFMNegHSceC+StdUbnCnJXeqfBS1+tV0Sp+6eqRFofbKfWKRK+bEpxq+WLCv+l1dzM1NO9kXxgdGmytA+GKcfcVnAvuICdik64l/wAXWOOT/H8ol/xdY7thvWpdfV/3COUf84wesJJQjKWjsujCf+n0Q4iv2hx4m+VS/wCcj/FnOVTH5y/AzPmRfsMStNBBPRDTQVmOVtDbQGJv0SuiE+cYwc44qygOGpPFunQbxlT0CAAKAahi5z1ITLX5RSbeKa8ysOeiPSN243epDstYs1uqTpiSP3D1zEp6JPREgvWl8f38Ib9AOk48F8la6ojB0sXHGmXniFltZQTRCiBUX6vhGCgt5dH3ktOPJVZUpF9DUfaon1wWnJx9tlhqcW26l0pKsm5ZSSfKsjbp1xlpttzJtqlm6ImFN0LgTfYG+zla9kYQssOPWpdwEoKc3NN5qR8PBSn4vlDXEqHFpOhwxkaXs3VrprWJTjV8oTbUE2jZFdZjCaK5xDZA/DuW/SjoMMFSQoooU11HFNeiV0Q8K3Bd0O2K97WWzXaMUn+P5RL/AIusd3g9iyatOVKttVQnlP8AzxT7q0GyltvRrhB+8s/DE3yqX/OR/iznKpj85fgZjm6wiT29y1PxiT81X/GELaVYWK380TXoldELrqdIHqESRUsuLC1hRVp3KjsEOelPQMaKeUK/7TF4s5IKb46VxTXozDnoj0jduD7AA+FfnEqK1zkr9dT84lufrGGHQKlLyTT1w7xq6TFReMWC+StdUQWphlt9o+Q4m0IW0qXaU2sAKQUChA0Q00uUYW019GhTYIRxbIRMLl2lvo3rqkC0niMYU5K71T4LBQN/ffmmEITobWsX8UP+lX0wWNKHR0ViU/F8ok3BvkuIUK8Ria9H8k7lv0o6DDHmJ6MT7ad8pBSPVE65SuTJVTiETTtKW5hSqerFKfi+UMcFrpO7YQBWqxEnZJOUWlw14VnFOt/aYQfUv+sH8WJvlUv+cj/EZ7I2spkVbzfaNXDGF04JU0GVJ/8AFDJFkuZO+z/t54nlIbZdwU3KNOPJshSMoLZVX71KV5owaZlWD5RxOUfMtNtAoUtd5SBUaK0hpaEiVeW2lVhxNcmaaCLoVlCFL7pmLRSKAnLL1eBmObrCGm0iyhMsQBzKiXQN6GbaTtqf6Qnj+UPqTeCySPVEwRpC1dUQ16dXVTuXVK3qUEmHPSnoGOX8z5GHLq2nlI+MZG0MpS1Z4ImvRmHPRHpG7ebRetVKD8IhptKklTRQlYBrQ00RL/i6xhv0o6DBXvjSqjErr70noxYL5K11RucKcld6p8FIA0zQtVDBO1SvnDy9ReWPjDPP1TEp+L5RK+cnqmJn0Y6E7lv0o6DFBjwkeBXVh5vyg5U84/pilPxfKE+ed0temyCYYJuAtdUxIuqNEJSgk/jOIejHzhI+0VD4Ym+VS/5yP8Wc5VMfnL8C5LlXfFAKpwWhCVn/APGcu4gf1gFX8NuzcNX9mEjh+UHk3/GJ/wDH1BDCf/cvoTCnHaWrZF24mvRK6Ias+XnnHJqBpvekx3RWjeXt14LUWkGo7mHyPziaJ+zT1w56I9I3YOq0OoIwlwzyol/xdYw36UdBiY5OvoiX4vniwXyVrqjc4U5K71T4JtaCe8haCCNY/wC4U+6SGUOrRx3H+nrh/wBIrphji/4mJT8XyiUB02k9UxPrCyF0paF2sbmWaF1t9Ka+vcTbZJsut0CeH+6xN/h+cLcdpaDhGbEn+P5QPPO6f15h6Iwr6MdCokuSo+eIejESChcruoivsYm+VS/5yP8AFnOVTH5y8RUo51DZTtIFYadpS2kKpFtCqp247jXEgtlVLNj/AHGE8ne6sTV99kU9YgcZj/4v/GJxR0BSz/tENtaUglXR+kKrrcO4mFHQG1H4RKqGjJp6McuP4wN6uCCNkH0Z+UPgkAmyBw3iEizayoyfFi7nr3yxbpwbipuEae9hwUPGn+kPKO+M8VH2YZSDemtfXClV+jUFfL5wpITe5Lrs8OkRL14ek4sF8la6oiUl5bJpemFlNt1JUlICSTdUV0bYknG0tomplwM516EqvtHh3pjJsplxMtIfU6VpNlWTXZoL7rXPThjBrcshJl31BLy16UktKWlI4br+MRhAKDhty7iRYbUryTpoLufHLyqLKiqtvam6oguLrZGyMlaGUpas8GIpqKjVidfQAVJpvuOJqyaoz1DnIh0//u3v+MMqWalTZNfxKhknaE3cIpEp+L5RKN031VepP9YmSqqydZ4xucHBXlP3er+u4faC7VDWAptakaFQ56U9AiUGvO+UOM0vbVWvH/1iyiQQKkX7iYVbSaBSdPlU0RhBOlTjaLuOsSXJUfPEy7XfJpTi/wC4k2xeEzqh1MTfKpf85H+IuzDpo20krVxCJhTjbkqqX+lQ9SqRStbiRohxwysylaclRlQTbUHFWUEZ1NO0wzak5gvuhSu5k2CsBOk76mzXrENvNKtNuJC0q2gw5yqY/OXiSltNlLdoX663Rg1UuaWEEGugmgiSbtUFcoabbSsUmEEAuzCUEfd1w41T6M6eOsKdpapqiiDWrhp64vol55tTaajXDzShRWgwBwwqv+UsfExNE+UpSfWmG+eFuOKsIS4RU836xYrnUrSJlThJOUU2mkEKXaNq6p1XRN+iV0RKejEVUQBwwSg0JIvEOPI0VSRXmidmLs1SCnbrEC1oUmyKQhm13xLgqKcESh/9ghxi8LLRUD64VlVi5rJJ9YhTYOcnSMWYlKs2t/HDASopbdllrUk+aT8oQ5W0Mr0QtFb+6VKpzCLk1q4B0w42M5RIqNlL4wc9pSGVXfiXCF0paFcWC+StdUQ131xh1pVtDrVLSTQjWCNBMNyyXXUJaslpQpaQRr0dNYQ2l+YaUA4lbiCLTgWarrdrOynBEtMmTZS8wsOBaW01NEkCpp/dBGFOSu9U4sstJWK0oIL++TlLPyhco33tttVFfeh1xxaQ2htTYUNlq7FhJquaGkGnDVP6wCCCDrEZPyVi/wBYhblMwiyo88IoRlQvRzQzXyUED1mJc/8AsT0xKJrfnXeqJJu6zkrUPjWf1hDFvv8AYtUhUy1dstcdIQ23mm5doc+LBA1Zb9MSW7WerQIyarkFutddYKtak1JiV+0pkKPxHyhVD/EPyh5BUSEgUGy4RPpV5JSD/uhtwLRl0qylkcFYyNc9FSYQ8jeqhSQoFSdI2QyzYzVOLbJ4qRPt1H1pShfprUfKH/RoHwhnZkW+rGSX5DSV2yeAfrDLYTUJ0KB01pDwdvUJjKNcf9jE3yqX/OR/iMzLBVhTiCkK2HVE+ZsNIM6nJqDLhVYTZoKVArpOyHlzkvJzTimmmAwpZyaggk2jm8Oih0RLvS/c+XbyyQwpag22hwg0SaarI1eqJeQWlL7LTKWaLFQoAU0QpttIQhMzMBKUigAyy8cvfqPTCB9gUHrrDyXV2nhVyhN5iXVSzk1lSac0KqqmVoE/GG0E2ilVkjjhKv8AL0+qGxaObo4IeXfeRedcJI1GsTKafRkJr+IQqWHlm1xQAdFKiO56Zta9H6Ql9ZJowEHWa1H/AHDyibDan7Qrp1/pCbZpaSr4XxOpbTXL5udqGiEkDeSxu4a1hoLvKARXbWFJW4VJCbhC+bphKSKpUm8RfqMJUvylV9USqnFZiXKmJlaN4oih4oaeurla0ipVc9mL5h/SMHqcq2iiioJOmtQOiFq0ISLoQAc1thCBTiH9YqReFXQ558NiubaqYXlDW3phsJ0BP6wzU109OLBfJWuqNzhTkrvVOJPC6Ogw15wh5W1Zg0O+FD66w3ZKm1Iqkmu+grUSSVUJJ5oRY+kaUCQeeDaJJFDeawk0oVXmJhZuLjagQOKEj7p6TClVoaikSToutMpNNlYYBVoCwngEcMSrq1UCFAV+7CWLyo69W+hlP8QtX00A3xNLUurts3J01MSjaTmtX29daQh1K8/K0P6QHq0crWoiXJNTY1+cYZTthtecpbd3BZiZSFUq0aDnEO38AjDJWSC8MwcdqBwJsxMpSqlpFDDraTQh4U5/+owghNFFSzerVQxLTCr1ZRxXPQRXWaxaAvUlNYb80RMkf5VPiISqlaJSL+IQkj/MxN8ql/zkf4s5yqY/OXDvmmFJWK5leiKbLofrvm9kKV9wwiyNAqYDJF2owFm/OrFr7WMLhbCVkNWt6DccSD5WiMnpMWE3f9QGvIF8NkCmTCtPFiAB0CmIwvm6Yb80Q1zwwsim+xhY0WqRl281QVaHBEtXfIBHxJ+cJtDOcro44VwQog32oc8+BiRDXP04sF8la6o3OFOSu9U4mh/7h0GA7TOCqgwqnki1DTm3TCQNNupEZU3VVqhwAEXgRk/tqAxjihzb/URU7Ib/AL1QpJuF+iCabw3xQE+TTmEZZd5/pDlPLXUiEc/RCm9RUFY5c1xLVq2Q5xwcVdNoVi4lMOuhWc4omsBA3qK+uARsqfXGTG9pDfmjFU3Q2naodOJvlUv+cj/FnOVTH5y4fA0lCuiHU6aBI+EOWhoFDWHLt/phadQMKB+wYtDSMTY4K4k+cITxwaaK6YsazohMNunfgQUqFCMdXU5tNwQrQYoNEFRF6dEJTWBW6+kND7ls+uHUJP3opscr8IVTyRahtrZcIqc0Ea+OFJOmvzhfnwMQ44Zrdp6cWC+StdUQ13px911VhDTVLSjQnWQNAMMzaEuOh6gbbSM9ROq/+7oQ4liYdUQ4pbaALTYQaLrfqOyvBDSSw9kHVJSiZzcmSdGuvwjCnJXeqcSLJoUuV+Bj+9sELFy+iFJpcE3eqDdovgqpfWvxhCNRWIQlQIpfjHFC0GM7NNIWdgpAO1MOWE5yqYspqrSKAVPBAUoZ24SVDOF458SVjSrTBc0qtXxkifKpWFlVbzSG1DTojK100FIac+1WFEAqOuCEqrm00cNY5ob80YlQ1546cTfKpf8AOR/iM3MN0yjbZKa6KxhZDrqpwyaMqhawATm1obIA1fGH5eYwkoASzUz3TZQCipVaGilM3XwxJvJVNFq06VrYQ0HVN/wyQsU0bIRMMhU0lSAtFmgKxz0EKUUFsmZmDYVSo78vZChtEBa81CyLajsh1bSBk1KsCzrtA0iZGkIQkngqP1MScwull+mbwUEXZzizQQMilKk2gFZO/OpfXhhLRzbhCnluWEgKFKaqafjBU1nItBKdprohqvlKuhs1oLWn1QjjhIutqshNeGC2q4i4+uAT9mA/S0FKSSNkBIT9IqhMPMtJNlASRdpuhKKgFRoK6zChrFxjRqrBI0A0iUsA56aqVzQy0VZy9g/vVDya3GmiDsN49cJqKjSYbyYNVCwmuiFNOApOgiLA0UpAKjbyifh/YhQINEX/AKRdtswbSaBxSlCFH7S4f+4a/EfrFP8A15SvBSsHgMSxJqbOLBfJWuqIlJiWyanpdZVYdUUpUCkjTQ007IlGkFpUxKuB5JKiErVfaGi7fHbGUZVLmZdQ+l0LUbKcou1UXX2eavBErVDDuD5VKUsgvEKBpQrIs3nZfGEC8y26W5dxSCtINk2TeMQpfniHW3LKjkyWwrnh5nekJDla0uFR8xEuzWhWKV5oelVKqLAvHNCm10Ddgk1gW2ylAob+KMp5VKQlbT2U75YBSQb9nwgkA0GkxQ6RdDp2UPRAEOqF2TSDTngHXohKNumEtKSbKmtfrh8U+iotI9cNueSQYdcBBDVLfBWBTXFTcNsNtKutU+MOr/yy2i0dBuh91KgoNaaQ0K514FdsKB0GFWgK1uh0IbIsUKk674ST5JqIaaWSAVjRCkpSLrxC1qSRf8ImEJvUWVUEPoWAFJNi7gui77AhgDOU4m1/fqhxw77KWKbIl6eU4kfHE3yqX/OR/iK2nEhbawUqSdYghtK7zVVt1S7V1L6m+7bDktkl5FyzaGWXXN3orWtBs0QllSpgoTX/AO6dqa7Tav54ShCQlCRQJGoQ5yqY/OXC1fZFY75nqzr67YbSdKNCtcOJToWKH11+UVdNrud3N/vniWW3MIUr7NrfGJ5ld/8AHtnTWoENVRVbxNHK6hD+TXvmkgHTWoFfnASgEPhSSNgpW+JOWdQ1YDqTUJv0wt15Fh9DmbfquEEcMIS5/DcQTTmibUhVpKUKcSRdrhlbTgtJCELRS+tD+kBhHlb+o2aIZDirGfpVojOcCmktWRU3FdqG5iTucbdqQRdwGFzDZJRlspTRUVgMtJsE0SbWw76/ghxlCqy+VUpPDCrIt1SgALNyaJoYZduoDfX1QTGTT9CzRICTXhMWVcVdsNLSjJqBdQix5ov+MJcdVlSpoWrP2rH6xV9kuos89YTNt5id6jVr/wC4L+U9IpXqEIUM1xbi1U5xEq2qpU0CCpR03wprySoK9Vf1g3ZofVUcENuuBKphwmXUTm04eiCdVYl/xdY4sF8la6o3OFOSu9U4sw0trCSfj8oKmSUUWopUDqgrSLAKUggcQgaqRLvPUsKzVHoiZEo6CtagCB/eiMGHQVd50/ZP9YyJSlsMNLQTXSqyfnDLSickl3KKSOaHUN54UpuoVdUX1+UTym5dIesIASnzqmEBOttJPHo+UWU6bGvgETCXCQFt3UGuopErNrCqF1VRtAs/qYStmpaCbq6YBCiQg2UXUzdUTRt5JNxovUK/1EOzDoC3AT3rSbJOyJxls25Z9sXkX7yqfiYUmZQXGVU0aqQUWQhGRKk3Xhyui6A8pVpSRd6qCES6kDVaXrJhxr7akqrxV/WFFQzWGy7ppWmqHrN1GyuyOOFLdaygQpXe9G2G3A4pLRVRddG9pC1pF6XECu0EK/SG2HWL6/Tc8Jl65O23aUBdDrep0UbA4CDHe1WEpdobN3lQ86BQLWVU54rSmaBd6oLhTbNoDiTr54MohNKEKKhru/6iV9KnpxN8ql/zkf4s5yqY/OXCknQRTEsabOmGUI/iKKa8WnphQQspChQjUYqLjCwNCxQ+uvygAm4aMcp6VPTExQVNx+IxLWq8LCVDo+UYTd+zLKT6/wDqEpTepRoBivuhDg0pNoYitLa1JHlAXbiurFlq2W8oG68JjLJUElgKVQ6x/Y3CZT+GHMpDrFurbl6gdt1/wimqEMN0C1aLUMyqxUWmwrhrSsPZTf2za44S1dZSoq9dP0h11KFK76kZorqNekQ2ynQgUxYL5K11RucKcld6pxN+lHQcVaGm2FzA+iDmTrtgormVrTEEkmg0DcTXEIll0vKSmv8AfHDfmq6MWDVHVX/deIW7XeqCacdf0xOpp9Imz8QfljzkKTxjdUdFFLlrdnZnRlQqqXyVcR19OIV2UxJU6q0Upsi7VDTbirQa3tf74Iqo1J1mFKZRaSnTfGEHSL0JQAeNX9MSnV75WyJMKQrJlAdqfNr04m+VS/5yP8RW64oIbQCpSjoAha2lGiDRYcQUFN1bwq+HH0vHJt2a1bUDnb2gpU11U0wh8uqsLJAAaUVXb6qaVFNcJUkhSVCoI1w5yqY/OXifccQRLUtUGsmMsrW0WijUREwkhICnlLRTUmFLZeyQUa2Sm4Q6HXTbtd7KdQ4YZsrLralpSaC8DWYNguN1O2tIQnKWqO2jUaUbIm696VlaM1+zWGn20qQpvUDcYodEUEq1opemHZgt1eQAArngMLFFrHfOeEOuuJybaqimk7IyrrVqXDYp51YXNMAl/wApIvt6IeC2bMzbBRUjRClTWcEqICdShdQxMN3KbdcUuzZuAOqJpTwyLYfzEjWn+6QwcinvO8iUW53pQ+lbOvO/SBLNANqTvXKX88JDDlldupKtkNSz9HClVs021hbat6sFJicYbWppLVimvSIQltw9z0qpZ01rohyrxW95B0CFS7YUUoKEqUBWlU6YYbQ6pTblbTljemkMtKquWctZw0jNhQaqbRvUrTCZxZVbTSidV0PTiV3OppYpribsN2WEWnAql1NNBDQSkpKwFqB+1QY8F8la6ohK3ioWlWUhCCtSjwAXmETZcqwulhSAVWq6KAXmGnlPHJuBRFG1E0TviRS6muuiEypd76qnkmzfoFrQDGFOSu9U4lNrFpChQiEPvoPdN9yvJh5lu9dsraKvJrS74QmUdzk2AkkcWmFZKYqqmaFDXCck6pCwL63hUOImGjkEtJzgbiqg0fGDk1qbzaCt99dMTDjGcLCQ22NNbq/3ww0p1WVQE5ydFVQ8WSrvmpWqE5RIVZVaFdRgZNlCKaLKaRJtMpDSXAbVkXXQGGaJKCCgatkP1XlC4fgNEOKmUd+cBQU18mEiUSVNLBI+7QbYYWhNn/x++J++E/OFpcI7pWN/TeaLodSGw64lGZdUw0+4mrllJsHyVUvhtTUuFKU+kuEfZ1wXHFZVoXoRz64DiFZFFmikpHqpHenQGKi5WmmuO6LPfbFi1wQnLA1RWyQdEB9bh76kilN6bWkeqHMq7klVokUrzwFtWl2G12gTeo0upzwS8Vtt0SoEJ31REwlblA3chVmls0iabmwoFsgBSDwf9QhtAolIoImWUuKVl02SVatP6xMygOXUu0oas7V0CJUTLSkNOlV2g3CEpSKBIoMTfKpf85H+Izcu3TKONkJrorGFC5LrlO7UZJIUpJKKIpU0O06tkd1Kkyy40mWAYK01cyaypVL6a7q/CE4QEmp1azMVlQtNpNsos31p/Dv87XEpJZYocaYQ1lW6VqBSt8KSVlwiZmBbVSp78vZ++k0vOvwuC+StdURIzLLKpnudwlTSCAogpIuqQNcSCe5yt6VeD6m0qTnVrUJv1WtdNEF5MplnH0TKSyFp72XHLSa3+unxgygaLjTkxLP91WhROTDdRStf4X+6MIWX3GbMu4SEBOdmm41B+H79RIoNg8I3yqX/ADkf4ssM4Tm2kKcW5YSGiAVKKjpRtMeN5z2WeziWXMYUnG5hTaS4gIZuVS8fRx43nPZZ7OPG857LPZx43nPZZ7OPG857LPZxMrl8KTjkwltRbQUM3qpcPo48bznss9nHjec9lns48bznss9nHjec9lns48bznss9nEqGsKTikKco6bDOamwo1+j2hMeN5z2WezjxvOeyz2ceN5z2WezjxvOeyz2ceN5z2WezhlAwpOdzltZWuwzcqqbI+j4VeqPG857LPZx43nPZZ7OPG857LPZx43nPZZ7OPG857LPZxNB3Ck4lCXKNGwznJsJNfo9pVHjec9lns48bznss9nHjec9lns48bznss9nHjec9lns4UX8KTiF5RwAWGd6FkJP0eykeN5z2WezjxvOeyz2ceN5z2WezjxvOeyz2cTK5fCk45MJbUW0FDN6qXD6OPG857LPZx43nPZZ7OPG857LPZx43nPZZ7OPG857LPZxLyyCVIZbS2CrSQBTczEsslKHm1Nkp0gEUjxvOeyz2cJLGFJxa8o2CLDO9KwFH6PZWPG857LPZx43nPZZ7OPG857LPZx43nPZZ7OPG857LPZwygYUnO5y2srXYZuVVNkfR8KvVHjec9lns48bznss9nHjec9lns48bznss9nHjec9lns4eQcKTnc4bQULsM3qqq0Po+BPrjxvOeyz2ceN5z2WezjxvOeyz2ceN5z2WezjxvOeyz2cKL+FJxC8o4ALDO9CyEn6PZSPG857LPZx43nPZZ7OPG857LPZx43nPZZ7OPG857LPZxLLmMKTjcwptJcQEM3KpePo48bznss9nHjec9lns48bznss9nHjec9lns4SWMKTi15RsEWGd6VgKP0eyseN5z2WezjxvOeyz2ceN5z2WezjxvOeyz2ceN5z2WeziVDWFJxSFOUdNhnNTYUa/R7QmPG857LPZx43nPZZ7OPG857LPZx43nPZZ7OEB7Cc26hLiHLCg0ASlQUNCNo/xebmG6ZRtslNdFYnZR59U1kSgpdWADRQ0GgA/73GVYm3GHt60yhCVZZzyU3jopEyqeMxflDLt0byK6JrQEZ1bjpgMKm8s4+iWUHrCe9lxyyql2jZX4xMtPOZZcu+prKkAFQuIrTj+GPCEy+ZjuZAUZVIDeRXRmtD5e+CoVg8zinVrMvSaKE2k2yu1dSn8O7ztcdypnCy40mZJfCE1cyawlNbqa76fCJaYKbJdbSumyoxzLMhOvultjKFspauUpWaBm7AvTwRKybU3MsulxxLzjyG8qmymtm4WfKHNBmg9ku5pRl9TaUijxUVWtN9M26m3HguxMKDDzim1s2U0Pe1qrWldQhiYL+UVPSXdKQpAowq02NWkd81/Zj9m92Lr3Vk+6rCLdnI5SmizWo2aIfeExklSMoqYVRAo+oKWL66B3vV9rGGBNKDL0q4sIsJo2oFABF1fKOmJwScw9NpQ+2ht8JaDihUZWlQEml9/HDDMtOPMkMPuLcdbRbtoUE2VClNJNacF8d2B3JNIdlWjLWRRWVsVNdP8S7zcc401MPzLjM8G7baWg5k8mlRpUWdcEyk242GJNcwVOtptLWk0squ1WTWlIMwHSlhM23Ldy2RQhSEmtaVrVfqESSnn8qiellzOTsgZGhRcKcC9ezweQl5juWxLOTFoJBtEUoL9V98MPUs5RAXTZUbiZYkJp2acDRUtNhvvKioWQk000tb6sSsm1NzLLpccS848hvKpsprZuFnyhzQZoPZLuaUZfU2lIo8VFVrTfTNupt3ElITUxNMYRmVIDhdQzmCwtRLdkU0opfDtmayS5Rh9xSwhPflIcUgVu0Zl9NsGYDpSwmbblu5bIoQpCTWtK1qv1DGoB9ctryiLNR7QIiWnZqZnDg9TZeU62hnNSV5tsUrvKVsiMtliWDOKle5rIpQIN9dNaj1RK90P90CbkxNb0DJm64U1Z2vZjwk0JorH/jBq0hPecq6UGl19LtOyP2b3YtKkTC0GaCEW1JDaVjVZrn7PJgzQeyXc0oy+ptKRR4qKrWm+mbdTbDDhfqy/OOync9kUSEBedXTXvfxxKSlWTURQKGqO6HMIvIebcebCkNtlTyg4pKBSzwaqQy5NqfZkyhlJyAbLeVJIUFVzqb3RCp0u5ZtZmgJYpFEZO1Zpr8i+u2H5SYmTN2WGnw4Ugb62CLtWZ8cTiW3lS66XOIAJHrujBs6+7MpkjJtuvusJapbNKlVRWnmxlssSwZxUr3NZFKBBvrprUeqGVKmcquclmnkkoTRlS1pTdtGfr+zE9LvOl8yz+TDqgAVAoSq+l3lf40tpxIW2sFKknWIUllKhbNpRWsrUTovJv3DLzwdyjNQhTby26V070iO6KOLcvplXVrCa6aAmgh1lLJybgSDVxRNE70A1upqpojJMpKUVJvUVEk6SSbzjy7qFOq+ytxSkC6m8rTRwQtgNKsLIJJdUVXb2iq1FNUNsKZOTbtUo4oHO31TWprrrpgACgGrHMXKSZghTikOKSokAAXg3aNUBnJroF5S2HVhdrRW3W18YYJYpkUpQlKVFKaJ3oIFxA4cbD72VyjJqiw+tAB4geGHwGKpeSUKSpRULJ1CugcUZGwulvK28svKWqUrbra0XadEMILGaymwlKVqAs7DQ5w48fdLmXy1gt1TMOJFk6RQKpHczXdCGbqATTmbTYbV3NDbBZOTRapZcUDnb6prU1110wiZLXfU0pRRCbtFU6DTG7YM0kuqtrInHrzo+1DTSmO9tApAStQqDpBvzgeGBNls5etd+bNaUrZ0VprhbrLZStW1ZIF9aAHRfs8Gnuhsrsgi5ZTcdIu0g7Ny8Ud0IyyitdmadFSTUnfQGcmugXlLYdWF2tFbdbXxhglimRSlCUpUUponeggXEDh3CgUOrUqnfFvuKWKaKKrUc0MtKY720koSAtQqk6Qb84HhgTZbOXrXfmzWlK2dFaa8brDotNuJKFCtLjCMqlZSkAZMOqCCOFINDzx3Zkzl62q2zStKVs6K0urC1S7ZQVCl6yqg2CugcAxzDy0urW+my5afcII1XVoKatkJZya7KVlwKDq7drWbdbXxhglimRSlCUpUUponeggXEDhgzaWzljfvzZroJs6K8OJSTWhFLjQw2AmY72tTiP/LeqlStJ3391MNuOB1Zbs0Sp5ZTUaCU1oTww4+Gu+LtVqolOdvqJ0CuukKEugptUqVLKzdoFTq4MSml2glWmwsoPrF8NM2Xiy2AlLSplxSabCCq/njuzJnL1tVtmlaUrZ0VpdWHm0sZjqbCgpajm7BfmjgEFtkEAm0SpRUonaSbz/8A1D//xAAsEAEAAQMCBAYCAwEBAQAAAAABEQAhMUFREGFxoSCBkbHB8DDRQFDh8WBw/9oACAEBAAE/If4kR0sRlcwwkbDVgizWTEmk+CzR/kYjfN1bfhu2hrmd9kPKrtMkWRywRDNO52GCJVXAUZQQkTD/ACLSxcWFKgu2KULcY7KfTJomVgJaukUpDSQ3LOvCSIWwCjbkfwRFsBhZTcHiu4/VBSsF221TLi+2EiIZExpbJQE4E3qerakyYFt3wPAY0WiRF1dMUpCjyOnggPFEQ3sMYmFhblovxh6uxIinzCP8fD68Jdnmu1xHPwAZYTGJgy4lpSVcCReVkEuTtL/A1w7T2JiRMSetCniSTckzSNWhxkIGrItoPEaSyOhgJVhcWipgrQFIYbnPiCUlzNABmBucm/G7FXV0XOLPq8CZWAlq6RSkNJDcs68VIEG0sKAZTovy42AJGCpUAAuqoAb+LM7uGJUJ0ss9Mv8ANbuBTqVtDJ0mowg5w2HsIuEmTSlroWRoc27BlbaGuVAmPQBgS2ZQxZMgJJAgTlIV8iKkSNN9UnIiBsSTRMmltJwWRdslavuQRgCFoVzUk86YsHrwhBYtR8mwPECTRIGJN13k3wiiA1tFC9qsVXmJQr6/x1Q7mSIAMrV2JMTT0SJYkTQTgLvDn2YQze8UFideMs0lJFeant+KIQgTBGF20Xc1vQCMdoBbQJvC6WCOdUlxyOLW9Ms9kORnxFB0AxRK0DgcTddyL4xV7auBRJaTKhgtaYoktYd4EICUCTnU4u9y2lYAIyjZCpaNj225kwX2Vo3x2ADaSYIvpNNqsBInjVC0BM3UsLpYJVAgC3GQG01OILXNkQglhwCYcRQJeS9kBUxQgHVFXIjhfKlchHmKP2MKuTsFQxZL1KaofMrha2wG4LETSL+9SYhFE4EWRDWtaocFHyKDmoQzvNha0sLUBQkwz+KBSALEWBBSzegkDp3TztJiMBpinn6FQVK2sDIsKN/j7LB1Ruj2oNk/xMkOQsTIwu0GDJlGKjZxbLnFkTk4mopdjJCLkxvSFMA2gRddF9d6idJHrQfu2LVlnbdQBAktgxq3V516FaZyx55oOn+UQ1uwFhSLGwcmQnczilAX3Si00OYXFTQCZZ0Ax/ARUL8ObZWak709lyCSyFBKCVuGTaJFAoxN0iq3yRN0WWJFYZvFPpfvyFqMQBliigXVINWEBMAzlKjcw/YYbZGYJ1bwXAAR+irFluiLzeiCEQ6NyrOrDWhfLPkTI435YJDCNc3B+RRrDBNnR/O1rsrQhAkhZZmn3zKU0TIjCLLE3IXJRmUSCb5KIQYoQwiTZjm4B5FirXGYVlYfTNC8m/whbNjBje1pTAB3BC6j0TmmXboXMtSIZvamSuvi4Bg1ATzvU6pXcDIWBSwzlQp5dpF6+ZUjvP8AyWOyEwvYDFGuA8PJEAgrjDPpiqINGIR2rO8Uw+xofZZOGyhVFwIwAjmIgFcMW/gTwnmrU19EsfmjmBckpGHY/wBSePHjx48ePHjx48ePHjx48ePHjx48ePHjx48ePHjx48ePHj08wLkFKwbDxOyR2JQS6nFOv4zx48ePHjx48egvYUmV32F9/wCGePHjx48ePHjx48ePHjx48qdnECZro2Dxnjx48ePSUP8AJmN7bK23hPHjx52SO1KCHU5o08RDDKUhdztcv5Z48ePHjx48ePHjx48ePHjx48ePHjx48c3vDmC2eAhbJihj1Dw2R8Q8ePHj0sOWzAy0GFOi/gPHjx48ePQLy71hIijccG0mZAoUCH1Y1IoDVNQn3ORib9N2tC/W+iReYmwss0wI2xy7S05Yx/GKNKEvAXj+nhBtT7T1k9fHPitsYkk4SXMSxCT+nDBo/Kwt53J/MIJGT+A2F50EYBicNvNnOhNc1Gfc4CJv13K1L9b+IFpqLGwR/HhQRoTThnFuYEc/FaWwP9fCDJf1+b+OGCJMSkq4M3irVGCDjvhbOS96MyqSs6YUnMt+b7HN+YDGWPW/P9C2hchN7IoqRNraqJ8cfoNtErPWEpHIAcso719Bt4CuniZJTnb+rhBkv6/N/HDNUDh0yUAw09UIJYoDf84aU8z6M/r6toX02+vptvjkW6fOmE9vWn4YhjQW9mgGwxahFY3heBAYLPOP4SFPRkwg0DVo9JKZChEAgjJmt0GfWec7PoH8dL+vzf04YMpJArKz/OdbNEm3no8CAixyvX+f6LsulkXit47gy6/qIjCmUg5cPR79a7P5SMCA51UgK+m2+MFl7fekO/bs/uii60hLVvWp5WJqon+M5hEIMEQkwLrEvr+QMCmCQckP5Ev6/N4UsvGmCwOXjn7t9qM2/khgxFJEGzjh9tv+YI4zu9QT+FcKwy2R8fyG0L6vWvptvjQSELyxN9EKkQtbmEfYKf2GBumd2vvdvEQ9/wAEqBVgNWiZlBpEogcwSLYwXHaluw1NpCg5X/LCDebG1myt1NlMQp8UqyEBOlM2SZcvAX8pzV2FJSTXZvbwK/qcMRrHHVnlETFQ+BbLY/fFYUhKDKAlBGCYZWI4GkJS2EJOKp9rHNZexwMLUOLJI4tjnKwHA7vZzUmrYXrFkymfKhaxhJH8IaeqpOLhidKje+PMyQXLlYO1GZVJWdMKTmW/OtQcw5dJYPf8qFj60Oz1/AoMt7ZgmKc8j2T+I2hZmwo2KG9dQRhnxwPOAXMGivq9a+m2+MnwzoCKAGDpzjPPg5giwcz8OISAd7zrPj+q2o1vDEmLyuzSzSwCapXBgez6flhBhn27vETlRXhljz19vtTxh5vnEcBFYxrCUiMh5wIfapvmoHNZTGGeGKtXQRB6VIgnzxPallSG7SGfOsgTOnWOC5kXVgX4IIAAcKJpkhyBZjfyK+43ah0GygkvYr6HfibLqMLrbr2U44Suwy6EM8Pv8vCeokuhUBJVsM2arpxQ3VZK5zkswTWik1TF/GGvm5eJCH3qCWFk4xBEEumr/NDLWi/rfgn02+vp9uAi9wvQ7eFsKfQuFEEzLAlmgNU1Cfc5GJv03a0L9b6JF5ibCyzTAjbHLtLTljH4S+930ABlQeXGgUZgDT9Nt8cw3EnOKPsNqllJBsngQ+j50xc2Zwy9g8Yh7+xVnpwRoM8CLcp6f68cJQS2KVt0LVsytxDSTE32q54pThIn5o7p5IjSoYAZpqyfFD0yILH3PCUEm+xasL/gUN4QdLr+5TlCXLcKfFQmcCbYQCvWb1P8xbtEkUAc5XtauzfJOYMe6jHLARpAas+pbgrUT6Kx+KnAlI3hTI4PVJBRmgLFcQe5oEJ6CAunvQBybozLI22pqXEOasIsY3KB3KKtCYmOtL7NlZ7Cu6UyuO3tLB+6FTfxzIC2oUsBs+7olNJFVZkNd/eKmSiYlhcgH6q6TA48/wDatHFS6VLSMuxgZjfFYnkbdB0z3/ihg0uNdytx/IDoo+m3014F/YbPhbJvJzqnkbCzIPpAmuajPucBE367lal+t/EC01FjYI/HC+931EdIZ6j4IDf022v8nEZjePEZiAICmX1cxkzFH1vv4KES8ZnEk/gNu7DxzlTIJvwcM+nyPjhIGEwuBPar8sUSshCrJKEG6ufWOcrPqPAHlLWGfkFRzkK7KPalH07KAXwz8mmiuRupS1Rik9P6pwM0neBow1liFhIX3pBgaRhoLQRwaXWr3WpaQmdHi8juUXiYmKTXlFP79lJ1Zt5UYZBZtfgLO4i0Rg96ke8osHnaoXRe2ZCUWVReF1sKc5TC8qOlsFfQn/av5R2MSjSVGmaIodXsl55mgtNHfAYokARy1bPxS/diUp6K8kausPcrF97KOKVrBEr7LeolkiOSTNTxocZSsUO2KqAtSe9dx7q3Dn8YfxAwY1GULyu/kR0UfTb+OuSmz/CbQvvN1KuEuOrh9a1bHEk1cHCRV7in6bb40i3xVxnJysBwm1H70MDc9U8YxGB+AN/e76kvPdgfFE0cTbCXPO5XmO7YFI8FFk3WfT8UINMMzAOypiLNrxWxpEOYaHXMoogrcg8IzQM9CVb5oZ5UgmzgGiz/AJQCyPDsP3RcjYciV9KYcdG5J7VKdTcplaGfEXpIMau1DPveKf8ALAlIyY60AVYC9R8WyeZRCSqdUMU4FyDnUfNTJjB9GlBgBN6lKDBvBE/d6YDlHKFX3Wj0XHUESlyFszpAcUHwixF+2g/FEabHYp/eYIKjC6w+dXEkhbak0rqKLKrh600SAF9TavAjAzHt4gyTlOwBdWp/8yCRwgkh5KN+9EOdRh5fklLbhYv08FJdyR5v74UO0U33p4d8rCYdvzkfTb/4tvDaF9BuoXKCPUD5orrqroI4efT7U+m2+NT4RIe/hiwbboLHL2VKSsQz1/GbV/e76+zy4NTQLtqkk+hr6ff+KEGGQARDRP5AUd6lJyr7PMoF6M0uUwiJo2piFQu4+1KaykJ50DwyW6p+4qNt5BMGVDCzWm6vlWn7xTCZzPPb3q8IUSVvynsy/asDqgvKa0GVBiG85Ux8tC9Coam0NlopUUL3lmrp5I6P9anHHAp55QQOhSxS7jSGtQYMhQBds0GUJCnBQORXmQY91eplRkCJ0ozn3FLy4YctaS2h++9EY0xgd6cEcgnmqBFtPT/asmwznMX9+INKTEYTqCeVJxebWMmVzczY8IYwrBDmJOGik1RN6gHcng4QDV4SxYEldk4JLz2Q/fCj7Xbw/X6cMyAkTCZOA3epdxHFckn6hPnxwXrXvSvpd/4DeG1molJhJuQJmxGP8/0XZdLIvFbx3Bl1/URGFMpBy4ej3612fwlHptiObJPQaXMO73yrl2zYM8PPp9qfT7fEsnSotAOyz9nhu2Qd3DNrxRkN+tLPz+M2rUViauSx2QTslWFgQsUMUyLp3Zs0yrQYGk17n4oQaYNiJNUyeG6p6AiihcSgnenuwC+lX50E0u4fmhK8lnn/AMqYYshHWpQU6x5UeuoRbHrFT7Fms/bSrbQhflV075JQYI9OF2mSPNoGuryVyDpipYinFEqnyoI6FopPMM4ptNnwhSohKQuyuJ81g+1lMosMqlSBIypuQiKaW01HqvoKEWsizRLBm/XgTMkRfKrW2vvP8gYN9Pvr67ZXbHsrszgqgwAefBtoRORTtXBpctOL7/FLVmY2yNdQtq+KTSb8YITXXHChNYqUNCul27hZQhSi5PiXyqNjKRgwbtQAVFsuCWrCk+gww9quK+cGPAoyNMXaCaufvBoItUkxN3haBLp0HXh0iFUaDbpbtUACadUWPZ4fcb/wm0KIQMK+lBzVuLeT8cDtv9mej3dZr6fajsUHfE3hQBAZpKlfwvPHoCB967VxVY2BvLb4PxmxcvIQssdjpvwNd71ZLd6xSv8AbLP1yq5ApdzMe3ihJNJJsFDiiBfkaEOVxZCozZTZscDHnwSbsGnkYD71I6kC4Iw71OM2j4rBmXFW+AmoBdInvRGbhvS6YVFl0j4U4jCsdCpWCE6Qz8VO7Q/MpUwwlELbuDghsxE+rXVRqdTK3wXMBbzpIkhNGlkZSVoJwVlnLO3arYUdig94pEiITbyqBgoJPOriZ0qy8M95/VFBZTB5NMJsgKKJrPu1DfOVzjBHq0MGEl5/kBgyjk3EhB5KlYkJuu12x7KYqQiTYrZ1VJeZkF6Ric0QAKCRgh1wjyxXb+FtK6obs8xFQROxJD1UDq9GFiPivp99NYLpA8G9ek0+imULXikhPRKkSfVn7bUWBSC6sl7+tS0ADGGDx7BwaKjehycezwWWJlHkTV/361F+tsZglpqjfyQBqIc3vktEBapUXGweqsUlZEELUDEl6XWSPU/htoWOBQEoHJPX13yVjsBalh3ioxsKAaY7IFL7mnioI1o/Qj44/Zb1kLp7x1OBnkUAwl7jqP141vUA2Ld7FEMAABpwhIoeeGD8NQsT7xjxQgmkg3GhxZgOTqS5NiyBxDRvYqQ5AgtQVBdaRQ2aw5YfXpUe4Jpo9KMh4DCZm8qHp/Zq/hvH7r5UTc2keZWHSkMZE0dJceNGKcjoO1K08hX1Ongm801OQiMU7esNqcKyCDlen6AVIPYmiiMoI1COyO9IUuR50pxeW1RrsWotNfS5tDE86zfa6vt9nxBkeVCDpkoT1atRzfOg3YEMPENcijnc3PxVloDmqbehjlwNZdozhyHm0j9JBnRQs8AnQRTl+6TPeHF0tdLxV/V0NCV5p29PhXY/ar7SUn5dLzS1GBT/AFdGtLouaf3r7vOlCllHb/rT+popp3O0Y1EJtVmCRa3Ib50kHfE/VSLbsm9h9yk10LPT/tMYxubg/VagDABgR6JNFcdbFmr+bQqkSESTf9NRhWF5y+FYvHM3dS/wBeslqrwTEO6Uev5PnEVz1LdX+qkM7FQ+9KncSBfQku0UhJAmPP8A2oHoPVt80AjOxlB/PwtrjiQkGto24IyiakaRxhwZiQsbr4rCaLzkwWElG4sUwI2xy7S05Yx+Ei7GYKAvBJ0f9U/uaOEZpzquKcppK3rGsIe9SUwZNknw6SZDzYl7rxESHSxMQ8FsPudnxtayD02fWnRDDYIBHA1VGw3he9Q/Hsr/AB+KEGuetRxHcH3q9i5Y8isaqPYpGWVmlM9NjVwZL1KXTxXByFmtMskf9a+VQ+Y7VO759aUGP11FJsHbgpS3oypSa57I4LFAEDUV3ardYXuh81OtIh7/AKrJU82Gl0YE9q08likimzh1rvShHXLvRMLwn0qMQ4GX7WaEfRz4gyKXMBZ5AWJ2qWqKVYJm0QAIAGlG/eiPOoy8+CTaNdBL1Kg0K3oftwtMXbD6j8UZxp8lMPASDnXM73a1Qm/ac1JtGp2T5xQzCTyLVOHe1S1MiGYKDIHI8yXvX0dGt+oehf3TbZd3U+KMrPvovimZgicyppmVGbz70CZe5a5u6VAqwJzCfJRoIg6RvSSafsVbO76nnQQbK3q0K1wwNwn2qEnIH15lRNq41B/1pITVOjFNPZXaitUV9xe26xwtAc3McI4aEai7MZ5/+UnLAS+o1dSJIVlPQ8/A2VpCBkwICQxOl1pJWTtm20YwesJqYLe9aQLFuQspn8cK6NSzy9n0V3kq1OOJWKoRqcINi3SCMUDm6wxr73b4FQjNhnsgHjpEUYGdPHIHIlFY8jtqpRzaD73Z8bie5zVyKunkAibGPKkdlbGkk8ENVmnGr+KEGJzLlHIsRQlpgwKM3Cy7pB+q7FRDBEyp2OB2P8rq1j760Mg8BHRUHpS46EVPVJK7NvvWlVup++te/wDerdapzvSjQqlBeKSIS7FTHde9K9l6kUAVzakBJGKVOgLy1JmpFaRL1v8AChOQ0NYv+6TkDFTCTsOdAg3JUQUCx6V3ukACWzbyrOQfeaBsy+wf7w0NCC6o6shpgsFr8QMG+/3UJI1koXBjhjguaJUqERA9V+aBws2I5iP1X+EW3tQYfUUWpZFO0wTwGCe+FKPP96VFRV1R/g+A7bBHMXCoUJLnSjyJBkaLNKQnoTrL8FObLg7NIyKXV1r2fvTyJfZSMrqy13qtkc3x8C7GtUSAeEpSDIVlewvlHxQrACDtn91NhJdvaNTxuXao2CT553+fxNoQ6IGfUwcJldy3RzG/BA3whO8cBRRBkgTQ2QkalJ9aVqx2Y1qpaNRpkVO5SjWFWsTqTl1MDh7yh1Q5+hZ4GSjjORfjwZmHyyye9TkqpvkN/SjEQAckq4G/tdng8VHAOWz4Zh5KTV9btr6bbTjDFV2cPZrCZwGJAO3pSVnMfhhBhhRIDWA+K6AIwLHxUr64iNp3oipk69CHzTpCxbUKTkMD3pKdpuXo8ABZ3Gj6lJqhoNAuPNVyDldh9qyYljGLvhSBmiAOZWaKX7Nq9/70RWJHmhe7Rsuie1QLAt2oDlTtUZx2VN5fquuR2qymOTk34pCJ72RflSnxz0zEHtWSNK7970lu2SdbVIHU/NGZqr0vitglsdWmymtyIrzMqy6x9PMqTom5JVxL1Sl6a4nOigIAItqId0R99aO7AE5UQ91WhQpz9bv4gwb67dwhWA0yYv4bU3XedHKWfcX91oUiF5/8oLMHdpEfqjloo9qZ+nB0v/le+96ApK8Fh5r8SVDD8ijbfzYOtINbsFRLmlLLED81TdlBoRFsI0YPxxQhZLnDMxHcR80jvx7NOa31iAetWwxXzCmvk236R4GxC5UdJlvw50yr4LTEE40rtWc0a7tOMI7TyqApZShhLIviniaO4kUFWma4uD53DTcSBOTVsQjGjF6aIkgyPMpdENjRamkrSVYuoaqs6DapIOQPS9mojmAcbVaCGDYU+5Qa0Syup3azBUTdT9qj2TU5GfUoZYZHlHrS2p6VBJ5piJN1iS9Q60Cs7XPmlhXlICoS6QQWcUZO1O0CkQwLCOZn1mkyQ5OcP+lC5WmG1PLgpTEslZZm9O6s2TNNqGjwzclipzckCQex+KCpX7ckMPKsH4aAugnqFAowSWIRZtms/oVRqqGD6RYfKkFSHnQh7lIZT2PBipmEI5AL71YPBCQnwRS7EoS80p2q4PvMk+VS5lcM5hLW6pMOKidNU9+Q+h4C2Fs4LP6ozzA9KrY8pDNov2qRELmw1EpI5H/YoMCL5dBs+uPKkmRY1dFLpw2azVrN70reNain7a0i0nEGWKuFAUEHUCU9xoqg3cbl6PEB3GW1vOrZkYaCL2qQpvh2Ud6NMllt6qaJHRrEPuoxpAljFWeBCNlILtThXDyR8tZpX1kvYqi1LJzX7qT5pAk/yvMYrUYXWw6R5RHc5Wo0olbaTno0C3QJi60d6lu4roBX09qg2SB+1EcJomt/+fKu7+6jewLYTH6agRNz0Z6yrWEryP3a6yKi3qTTOrPiDNre+BEq+VYzxlkQGCyOMPENeFcXzUaML/lWeo8760rePlQjhmoAwmu9PmIiVqQwY6usv6oLABfjWS1R2GQ6jP7pYCSE9nzShoThz/xRmyQ9WKgAwdF390pLcLrGaz8xPmnWgYVmnN11iPFhuUyoqHmS1KgYmNasRKGhUHAMoepSAEgRcmpYnL/VB5cgHKvocqAKEFyhKEGpvmyi2phaM8qEAlxq2iAe1OiQBEc6kYXyY51njCFtagEMMaSLJWdqBzJHwDa413J5aYgN1OpEL1AIHdrO8ZtlpKrQIwjEyZQcJimUg5cPR79a7NEaTFo6wpPjJHWA+J86eM9azqnfNIDJj1oU1wFmcXHSPapsLAG6rvulNbyjFokD1vQdLzZxilgpYFsaU0P7ZCwNeoUH0cUWVkuPSpklnZeJ20WtPLYpnEgZXKoFVYNaV3QgCXqQkSiND8vSnheWxxRwNZ5t6FMzWNka/KaUnUErQrr1rT1u7ElLJZh51HfgNCyH22qeTEYWQdyU+KAHZf8A2rYZG4LB5lQvKUspBfvU4D55Ihc82sWMDEC38u6gD0MyEtRqQe1ApFgOTnULDc2s6xs1/MqiAteDGGfSPSjPF06THySknB4dbvfxQoWdR0JklWbg1uUQo3g4hgv381a5zJgMqS+XBIbqezT4ANAVyHtSXYKMpEx6k0P63WhhO1+6rpZb0B+1AvAWRvEru1GbEHANCmpMI2m4fWohcQQFv2owLkOdP5pHSA0R+2r66B0/1Si4oky2aIkgAvNUooKgnS+r7D+xB+WgMAFe0Cfuplebtlwse9fQ76G6c4rmtRE+xy05OtCoLYxAzRsTPHKUqu2uBL+9OWjnxZU5MgS3iVFm0rMUwpsMajlUD01BEsKik7DzairSgwIlus0I8i6OaS92pc7baCb0Hw2kzar3yIQ4fig8YgtKb9nxhrfKdwoWHktqjBEroOpCuuB0ozKpKzprQci3DAdPvolhChd6OMvtUhIhPOgVeh1q62F+sVFZjbpReuFjpUuZi+dXXeNXKaUJhAUXb/4UfzxVCQCozpa9dBJsRpfJ3apawLCFAFbBQm5iBLrEUqLoWLmhDbq8FmpWLhJT4MM0LWFNPNuSk7bVOESfM/2mSxEqPatwlearJJmZxeoQbgelABl5Nqnwnh1L/uiYWs+pSsiKltoe1AOJso26F6t7vCPwjaFEeVDbcg959KRZEwRsWKY8tCOW/rXsoFJn4pLDD6NitC6Qtlj91c5vM6zatXoZpssIVLRYu2LUsBKGdaJg7UViJQ96AZlAlBiDxNHZaBimysvRo6NBqDNWUKNtq++3pgIOoEj7hxa/7DFTHgQxUSEJF1q3EWjvSB5D24ImrjexIvD1qwCMXrUXsXdCKhzQa74e6hi3ls6A0xwsr6k19TnUKUrv2gffhCpBje1NmgL+GEGtfMHSB1YHCJ7qp5i8NSaMLf3NShFmwLzmUAGMBAVJTf06d9aFNCkxlQujOl70M4mJHoolWCYOdCUDgXLBbhszZ+7QWpzqL5MvMvemx5CNoF+2hMkiAIITwLNgybyIPng8ScELPBLju7zQTU2zgJlDCPXSoRABks3pHkSpm9Jl4vOxUWN6OkhpF0lGQQoJLMwZBFuj61IoaQuNPstnEdUOKJfZLqS0sjP4wMGhYLGTSV/OpO/DE5ZChSDYVxocSPtTagUhTf1mp7CiNS7EeHkFTQ0W7FDTIJbc1IZg10rUWEBmibXZGlIhysKm9TmYuQkXqLRBKJmS9L4wWeXFK/CVjemdcKJaI1Fpm9hpOaeRWNDFGM0RN5oPmp3U4czbuJBPqlCRqyss0l3whinYNSef+VFtZHJQOCsTm/WrC4CRrarZaAxNJbxUYxJNTJSszpV6Vu2NdKbAtnwl1t6ddvgAlVq6psOtIxBpgoYNm1VCpAf4RtCJOWwaIAl1ouH2Joc16EzrNOpIgroUvXyQ9JFAhGR6j8VFzKNTSYnj83xQuE1e1DangtFREWRl0/2mQ7VXjupUk1tpqNuEnzYoyF7A51MEpdcJPAd6wdUcBie+R6tSnREcgilvs1J+Mh2m5NYFYyjeP8piOykXNPNqC41Y0pfCNje9DoDi9WH3pNQNT6nOu2V3f2o/XtQQAwfhhBsV/jTBhOfEQTejfKhlbz80N0KBIjpQbisABg8JubBVFRwhpU4HeMymzUU6K9mCCrzrVumgURO8iZV+aifCEcfr9eH0/OlKRqsOPmuy+1M1GQaiI9qF76Rrm2c9Sr6ZHvB4N+DbYj8YYMpBgTzVRgaZZ1Y7VJ7dz3hitSEgL4N/JREJMFqYIKnaW5khUZvV3fS3zxKi+KYUInN0TQAAIDQowhZIvKhoY2qqxtYcOpztR6wEHg7VXESSuT6cGC7QWUv80YcCEohYdJg78DUArKN6hMHhfcUWbFijUUWEHUCU9xp20mW0TzVvZ8UuAJR5UCgCV0prFLo5RRjmv28Bs5Jw5JVyQDhLGKNQ/LZoheaVmhocsbwChYA5ILOlMCNscu0tOWMUQAJYXoGkZXBcxaptpgxhLR0X0pJyAfU0XAeU/wDVTCQ5kQu+lZSbKxk+KkxBR1PrSGIgnJV0zjh83xUsa7J5L9VMqaqFsg3hNQM3kj1q3ykrKXn271YhLm8se9G7yNxkpKnCJRhyedInyOsi3e9LrpJTsOjFMqkB6hKe40IlU2X/AAiijHMxalicyCNY/wC0k5iGyTNTROUTajWflYcqkOiRtQBDlkyoYixywfRSLmyCWAsKggZIDmSr5JwkJMvajyae4qQCKBTUqa9hG62/dS5I0dPHCMLcHTJCTZE8v5gb6/Xhn92a+/zrsvtxhEqRhKAgwW/GGAmIVt6h0dKP7GO0/rpxDZ8v9B1r7Pdq1hFV7tpp2MWNvPUWmgWkqEwASe7gc6j0lKBBhURSc4UJQDZbqPALAAk1wYJ1tLN6epnoTH98DIk60DQ93qTl+vWgFCVAjRVmwmFmA2qQ8xNT4JrOGHJE9GFLJez71FDM74iY1r77asTVueHmiNQ5gvNTCzOs0jyy5bMgUB1IHqfrTiCQbbSwO9XKVpQQR7qDRBkZ1qvRZL6B8KM4AxuIFCjrzy/61npp5ngbFhUwjNmC4mSIm03qxKzp1pWozIWEmool+ouQZAWGAxPGFFaYwSSgSbXozxABEkGdJi1RWFadeJna/nVjplc11BZ07p6pRzaAlsWfe9QqDqTEQ6Xc6Kk3GiCs8nvQyRgViUhpcoAT3YWkGu4CXG29AV1okgIed6QEowoSGb+RPKplwZYxP+qc8A65IFvOKlnD1nDtUhIgCEiS2lj1aKe7sBJu+sUvFEbMUjbRRWRrA1U9UUPEl3uRthIqzzWISj1p2yqyisdUaVbJMiTF75qaAS4zMX13jnmmvCwBSX2CVT3JIkwJPtXNji1l3pUBsl7hKQrAUXVR0iPZvTaU7AskaldIS1Q+RrzbUrndKWRq3xFR+gBYdd5ZmsJdwMTp+bUhYmYi4T3GjjBudLHlFPGviS1s2i0+TUnjEYibSafpSgNqitxPghAp4uWgpJJykokmogIW3rAzvDpVpokQmhu2i8msDn1jnKz6j/LSeGf3Zr7/ADrsvt+aENgAoDv6d4KPn5slhAYGq5jF2i8sLPk3VqVmx1o3UNATjY3WXnwAcz2lGZtDCMpmk0wJp2o3CELHnQ5yC33D29igPg85FEK66t1gaO24Dos96HoVM7sQNJ2pIEmL6UAXvSE5wNmIfFH8pLBnvNG+CwHT/Bq4AGnrIB7UhKQEtuk0ISQiMZXuVFgOXKzPxWBTuMLKTj+T4/QrmSwpmIn0qUyC5Uf9Ufu5UEIHJtQQkEr1DRFMIY0BQ9IIC2P1U8igyagBRtzEnqU4SEF1kJ870mBCelLmBqpb/VQG6Kldd5yUEU0vEjMWyetKIFGRmrzMmea6T61ms+F3E+x+VtCogrSDAUPd96L4Sks2Ih6hT1JPIoj2WrKbyRhE/efgcD74q6KO9Ev7KYgxc7KMLb39Lbms3Pko5AATan6jhs5vMa7xQxOniTuQXcTHrULSw7YgB5w+nBaCL7Mx/cHpwcSVOfzUAKna9nWjqiBeQAegURDLoqPSpfJWDREd4pZq+AeZg19VoPAYyTX+4865GEf0sINT7/Ouy+3D6jb/AAQwbFBIQxOaAABAcBAMpUdGMrS7QTSwbjQhm2ak5IoCABYChQ8hM2aJibs0IRUUkV1SUDAuCHyabJELlgoDyV3jaa+95UH0xDkqlyaBnNkF+nh7xWIRc93gfiRzDYfdrB0IZpHsvKIjR7UOrDkGMM0hl1Ep6DHfkonWEigqkiRvIzX3XPjHCf6aEmk+NtPDScokt3lxhHuWBSNI9sG6bPIm1AFnQicJK1pD0KOCcJm4k7k1pDCRMPnmkwraTVpbbfXlUVk3sRIcaVIgEMBvGw3zypJy96UjAIwvY4tXIiIxkebUomcmk18u9S1YMTCO41plw38/eta+fjWvppvTrwdAaL1HejwdtzqS0skc6ZDaJjV8xCfSi6HnZQvLRFBfCMRsMY0pM6UnjAJ9YViL2CyVqcxuMwrPJpFAOeBbKHJfSnGFyVc0Qzr2K11dSwTjLfp0pu6mbA1/Kal0s9khSnlRfGzhNpNerV1SNiOBRzFuVSYfA0LVPZO00OUpHkKzJvbNBQggwW5TmqIDeDeUclLPrMOidyhNbZNefvSlc4BIwnNS0BD8BlEs3PSmgwcI0HWn9M6GbunXH9NCDU+/zrsvtw7j2f5gYNcpcwa0AIKAaHAoXjzFmom0a5zGPys6QKnEt/Y8GAzHv/kbW1VNDoQGBhKzJKEsoKQNAu8q678xKNmZEy3PX8+S7b5yvEDAA5cL2KZFl48A4GiCEwmH+mhBrcJLhh3pwAlkdeIYG4kx+UMekZKAN2goNELXETJ/PDFhgAHc/hNpQuC4QXfagQjVKR82I8QoUKFCkEjPanqEeIFChQoUCGPtmU6snxBQoUKFAJG+1fUK8QKFChQqYCl3gLovU8IoUKFAhGqUj5sR4hQoUKFRAUu0JdF6HhFChQqELguUk338TkkdqUgmpxRrwPBHXeaeSDxA8ePHjxZWUOeogV5+I8ePHjwZxsxh5Bnwnjx482VtHjoINeXiPHjx48k07MYPInn4jx48ePBHXeaeSDxA8ePHjwTTkzg8ieXhPHjx5ySOxKQDU5p04rAGYO+oRtzKa/5Bxlc6LeZQa1chSasCFyG4iza+aI+vkRClb35FtngKASN9q+oV4gUKFChSIRqlA+TMeEUKFCgQjVKR82I8QoUKFCkEjPanqEeIFChQoUCGPtmU6snxBQoUKFAJG+1fUK8QKFChQqYCl3gLovU8IoUKFGi0wjBCffM6+Oa5hzj90KMjaetIugPgYQNJu8/AxXcQTTAxJIM2QKQveusXNCATY5LRUnAvaQQ2Ixi5akiK3eBjvxhD7OYfOLDvezojMEqAyHMIuhYWamemflQYIRmF9UcUuuAVzSCT1GrcnnAU7QW2JiymbExOcJgs7gvOKSlI0QMwLodxvbEcdChNrIMt6XWlbtAxIAb64cJsqTgXtIIbEYxctQYcEKmJyLzSQ1TPGUd5AGCSyNTpU8dvpFDqZATOlT4wUS1qhrNmNC800o+QlGcCllks8/BraIQr3htapPgXR3AwujLtTNiYnOEwWdwXnFM2C43tILkhI3tiKlUBNi7DDUNbpX8byDBDCkg1JI3sjHgRINjZBdWItqlHkIUkbHBmYMzGhMcEeExGGv5iWNaCFgOFPKMTai5vvfij5CPrnZq5no4ThoXHIWpZEgXOWIERCRviMVakpJxJFoW4wOvEc0dFMokweF6NKDUwhYJS5HOKbrWcZmsbQnWxN6lHIBArD0m7z4LKMuiSF9Q0/ZSQKJVi/nclJjgjwmIw1/MSxrUZzxCntCEKXupZoFfjdHgSRMsFg4F+SwHAkS2gaFDDmjQEjaFIFaKtMUZR7wSbCVyRum3A50Kr9kwMoiawzltDUcr8L1CHIwSQM4kLU0oNTCFglLkc4putZxmaxtCdbE3rRlKavgZRuLutSkHHX8Vz1utYKSULuN3CGlFzdd8HRDKzAM9SwSsBR7exgHZLKC2VppZEgXOWIERCRviMVakpJxJFoW4wOvFr+D9GVAzoStGqgSJ0i7llTaRSiabG2iEGF718tGtAMISgAmIsBycZWQO0QBbjn/ohe9dYuaEAmxyWipOBe0ghsRjFy1JEVu8DHfhe7GQQyGIWOdJSuyTZkLzGENjNLIkC5yxAiISN8RikTLZAwQX5QToZisenAsjEtKTYtM8L1CHIwSQM4kLUW5wGmV4i0KTjJvVh1qwCybkL4sdRbRlKavgZRuLutSkHHXg8ppyspcTqEqF711i5oQCbHJaKk4F7SCGxGMXLUGHBCpici80kNUz+MIZapFJWJClnClCjXhgIDwCciLWkNuIxa2c0tGukrLmGRaLNKteIn8UgYLuEyzNGWBAMBxUd+ttDAuQacG1MkQCphIEyijstioEiQuUqhS4BjwEm6VeEQxcd3erRVoMpiNyzA7KnlplVMWrKgVBOvE8PRNOavGEmUFOezsrG4whYkaVa8RP4pAwXcJlma38bMZzBmlZRTTiGlzxzB1ZH+AqaiMqUygx+e1sU0WJMNGFymbLJjWi3ujBrIohbogcGKCkve2S51KYzl2Wcz1iypiV3q0VaDKYjcswOyrBdBOSGQ3BgJ1q2YiDgiRUGmB+O9hesKpNsgwEufAgSETZAujkK7jlWyC/mpePmB+6cmLTQ0RWkaoaY5cBy4xP0H1YCcqgXpfto2UAbIU3qTSn/ANSEi7gEuau6CrMAl1QAF7cZTLGrNJjnZ9Whyb3aXkqdAir+/wDzBK1ORiiPl4AHBvy4XlVweTQYMbMyJJboRMNqXj5gfunJi01ZEYoglJ1AqkIaUyO6vWU1MZdDgY4Y7ZRTdQgk4oAJySRJK5EGLQRihyoK5HMVTFSEhuXoPO43DNMtzMt9aKco2ALAcM1HcUkmGLCLmGMUOTe7S8lToEVf3/5glanIxUSKbhSGMLgMTPyLbNQJXZjlEATjwERSR8BCSNWoBoAcCJOhuJ1qTSn/ANSEi7gEuau6CrMAl1QAF7cTjEm+mQoPKVaCzJ2AKSZAlAWoQ5MskpSLuESxFH5wMxEoFFcrxiME0IKAh1XiHGxS0a6SsuYZFos0q14ifxSBgu4TLM0ZYEAwHCwOGBcRCgpfGK197KJAUCliRN6k0p/9SEi7gEuaFSW65gFCgAiyoOriReaq3Vbq3eGajuKSTDFhFzDGKgNoWdkC4bwh51vHDvkrU0WxUSKbhSGMLgMTOJmzFOIoBRMmMg5paNdJWXMMi0WaVa8RP4pAwXcJlma38bMZzBmlZRTT+S6BisAZVrCTurmLCzfwCuyFwRbBDMSN/wACIchxs1hu2bcqLrCgKQYbmyJ/REWYGYmCdX4FBJcXMoMMwIvzPCkhcMQiTchjMP8ASco5KyCZWc7eDEF42TgTldvGkU+TJRjPN/Om3R4RIJqUNWe4ka0YS1k7Uu4AqSxImQ7AXvqaj7n5ZoG2iNGTm/DjcZhcVYEDP8gLMgXvJpBcMQpmdZNfmqHVFBsIyIJWMRlNKRNIEiAAgd55rWbeJxHQzygtCTQ3vsw1cMcqiknAEulhQSWbt2p00/0hVZj6W7HjikYAhyWJuaj9s/kASZkLbNpy2xAwWtywsF1yrFoodtBl74CEDIV5YrNgMy7AjHSyiM0O5wcgWYwSOjAyUQWzVBUhBHFydYQufrYlAFgxRBsuVbMrSWiCxd0/lgqF+HN8aqhoTBMJ1oDasVKkOmsiBY/OWTFFtKRcC+DK3A2NoqzD7y2h0kp2c6aaYEox5IQybmNGmxLmECLcYlzmxMC4xgFJRfNrbTlFNydDmhQkT5UBChGgCo2llrWM0gW5l4kU1mioeVT9gu2RJUCu4Et8QKskNSYUUc4mpspNWYgiTG6wxvSRMa0RhTALdclE5ozG8h+tRNFCIkExJ5mSTBGrUbmpBM0JjW6ZLaVG9OY3EX6FSTc+FYAQ70I4m9BdgGyZJLN5JZMlz+aFr8sL6MXOZtfSmVYR2sgIjKKho13sbtYQqyhoFioV2qBArzBa4W0qBpbTz45RpZX5UiiYjcyWJzgrM5YjRSzn5rjQBjDMEEalI5d72RAZY5a1LoiJXq3EwuG81g9f7PRA8mDWN48xWONkEHIwDepmFDYQggHVfnQAIJYxg3F9QurxBTBE8cONZgedVi4W9pk2eEsNWaR/0Hggqw0mulAEfYCV0v0xVorkBuICStJN0rT5COXSYheQWjmv8ECFUdRIjp/EbNmzZs2bNm45q2uWSVBuFd4+rqXaIkkjJlRi7VOaFcYsqdZ2qODUYEI0kRaKvvM2msgvJVjnTXoo6pkLwXfWh+vAUOnvUT6VPBs0ipAebHKaDkUBB2MwNPyUIxG81KjdA2ObegQYBwXIaJds7tMJEbVCpNdJW5tBJ3BSYozJYdJqTmEpAG4YAnkV1iwOVxN86WZoNsYTb5qJUMmBCYxAlsRQJySIEGhIF3Qo2PvtqGlBe8OcVnUc3GHoPxGzZs2bNmzZtCFQcRKrp8TbbfryIZXl+M2bNmzZs2bNm9YK15iYliYPSoERQURjTqZIE28NDPOkLRrlfodTZCNoKtc1euYYo5/H7KwsWWXWa6P4B79W5oJKClpChZJa1YwfbRwJsTehYgTDOJqFtzZtQbGOxJhbs0h/AqJEXeCxu1AjRc/jA066zaSIBtJB6lCswGOYEzhyq70st94grukr4mJXLGfjCuPKkQmLKJI0AE5goh5yhS7SLPzb0VwE1vx6kdS34zZs2bNmzZs2bOKcrCCETD63gRA2E0JLEhnkpOOUj0mnZRxTlYQSiJfS/IbNmzZs2bNmzZuzs47r06O9ekYW/JTCXQz9mOS500HpLjBKDYDoFRzZAOKSuzdvzaZw6fhVCjMrfm1lrYId+jIMqSwstjUq+dczYLWyjU51CS4IO7HNf9MTO3mnmb1LQWZ50BqYWDz+xXfcToG2u93BiJFLHg3BiME5gJ5tcyDXnMf1qW09uKgI3Fy1auaQme/BwXVVZfyDZs2bNmzZs2bNttt+vKhlefFaeyGaEagcg1rXPOwItxUwYhMT+eEoJbFCARkdTxAJWDd4AZQN3+NCYd7iOzoTrTdIjevYLLwtBGFZLAsBxMJFw/O2WBQVBOvjhMTfbhDIao/jhgMzgGfCNzcgmVk0Hb4wi6FzYQm1GZVJWdNaDkW/MMgRTJt/PbSYYj5JxRZj1TyqbEtJe0bSDlYXYaYMbY5d4aMEY8RQCKZJK8eFjcEVgnHD6bfX023xff5am9hNWfZAFQmPShJwJ4CYM8IUCeIyyPK34Jo79aG34YTSAK9DoLDHWGjTpPvBt2WeU8qTZreSNTAVuRqaFZB2b15FsePV0reBnwDlNiQ4fjVZLZyVjJftUI0Foyh81o9eFo15hOscdF4u7CfGRM2JDh/LDBvsc35kOdJyv8cbQgfEKOJCiHAIWwX38ImhatpExw+834fTb6+m28XbASroVA4QGN7OPfh9/lozi7G0illYCeYXfzX1G3D6rfgoYs+ME8KgXqxXT0r4L/AhNoOpg2aGsIgWxw+v045tog2NCvv8vFTtSeZ+VNsrdTXi4C/l8TrTwwQkyKB5caNvKwaA+eKm7IDtKeFKJjJe0iY44oGxpEkatFlmsxUVR2SAN6TLjsOH+SGDfY5v6ULaF9dur6fb4RO0+/B9Zvw82CEEBVsyCbQingIQxS1xW6oQ1LqWJTg3Yvr7javv8tXHIL0F/ZoCaYQaE0hcQyxKCOtK+FohGWn02/C3gzcIEXcweBOAuhlhNW9f2qyrADrgubKR+eE2Ta5GxVg5UJqQcI6QhjMA8E5dl3JKf2NCvv8ALQJhSDU4SQyp84Pu9Kxo7Mom6FJbMYpkK7GzMoYpHSjV1oFsSMmA4wNzLye/ASnW+uJhHdqwkkCIAosKQS8RUOELPzqG7Oa1c/DRXoJjKoIYSa+824F942XxX0WyiaXOFIMNsRf+OGDSXJCdYDPb+l62hfXbqGFz/mL3BopI0dYZqRSUTtJNBiDm0Gksh642fY4Rb6o4fV61ybnRZDUBCiDkRfWK+6347ekQMkDi1ff5atlF91uKTskWyUl7FAkuzEsKt5irg08zGvvN+Ak/AIHL81oiOK/o8lHkIVOSI9qgCy90vUIJGSslzKYzp5flhNgJEZPUhSdyodSCO9AFWAytff7OCs61xR7JGXQiPuKCdJHRQqn+Ecd72Q8tcFCggKrpfS5UuwcChEigE0ooiki9KYgECPKhbiJ5/oq7LhzA/wAVCmnwGEJSYTKjBKfAn67dWPzPbU7Z7lQCkLcWSzQmv0N5C9ThI6N7v1WLGK1JVv68PiUc9ZE/VAVhmM0pMueclieMGy+VpdUZdgSMTSY8MhhIBI0go7qGoJxsbrDy/N9tv+Yf4KtomHCTIfIsiWIvRw1Ab1lalwgNlMCNscu0tOWMcSGW40w4lXpQpml9dspTW/XbK+z50iko3NZj80+MW2MwS1aG7NrMhRi0BcwaK+r1pIWZUNAjCF35tCArge/747Ouav8Af5aMtQB2KQNIJG0N6Ndx6dIbqAzdVhIsXnwKQTuefGDYUnaSaA1UgZ0pBJM4Qx9eCydFmnQocdX50/2h94PmgkMc/XU5d4HBHekAi5JW7wwkmkk2ChxRAvyNCHK4shUZU0sXrYvJEyNihWwdi9eRfPBVSwkxE60k7AwM66TiJTyDFWBz5NylroFLLIo9zJVd5XkCT2aaqTG60d49mrsnlBpTU1lAP+9WQpHTr/ygP91UsbFyKuahRwzHeu0+9DGVOGbVU06ZFDnQKSxBOxM3pVnzaDkKIOr86Yi2TeilsNZaiJwl5D5qXCVBtAtRrMz7H2SiQFyNrGmdMFCzBD/vnRDmblH7U11nH3pXFjnOCpuEz6xHzwNKZX/kqJlE3MMqGhL0XgbHqpdILDmJj3rO8YhezHiDR0Ypx5kxJYQki0UpcmhdlJTmv5QyQSxi9OH22/5nPIhhO8fhQ1UDOyPj8DYam8CAceZZTJotV7wLi1w8iyZQSeMJXYNTyrJlrwjQq1WwTGG3eil0Mo4m9i3xULPumVD5qcxdeSoIiAIYhJ712XsUQbpqTfGvq9tDFcCQ4UwJrN6nAfeyLl0KXyRXe3D5tCao0QmcA2UmU4h8UG07OcnlNQxSftZVThEwvmvw19ZurBQw9KmERRJpENDXcvJQXuYTKMn2Sr+0s10e1OZIBoIFFp/qQ05e/vVxy08wo1Epx8qVYuynvR68NNU/NK8GSTk6NsVpGiZlQi1zro3jN4YQTSQbjUmkRUzPNyLCi/y16NTsG4all4tp1Jag1hpOoHmFJwSxNgGPIpDAZjqrvVJIeTJSMrFcl6pcBALkc9qvy4GNjTQoUhyp8S1csOVqw+xoynlXI5DvV6NPZUJKJBDzVFYGHUtzuUaSUR81YatRh/w9KPTQ+6+5ppsMjbSrwdBHqVe88AQ2Qlctz2aUpv7dJuxcE6l93pR6KrnmUU0iBbnavoeVJpLVykqR8pTeiZQA5Mj24GhAUUfd2qYggLG8tCk2ypXX7FGQE1AND0T+MGDDSk3X8yWDYbf4S/Ah6NU2CWokiHloWfl/I2hfXbqcmWPKAFOWLZdC96FBJzuZfNCfUYbl/RpVGJru+KT4LSNiIqdZ89nFW1BRiJgptKFqWidqS1O75ivudldr4Y8VFnUNDA93BA8iAufo4ejGFnVdPaiMCw2zc/VKtJgbTRBASPNFXJ9tKdmY7GKBhHKJy/twTBso84EuJs7rMJDRMOlI4TebzwB9LsruVTkRb5Ru9Gu9fNBksTFCxUCtV/lhNoUuClbtBNvQ5JH2a+jyVMXF/ZpR0tPs/elRn6n6NdtwpQyynDgrH0isi3XC3y/ZTWMhDo1O2WGeVmn9nU4AUJGV1VNSCVrMc/pQJJWGHZuxN7RX2uyldF6t/wApNYhPUihdET2rDorGSedCPtXYfZpSuipEZst6mDsndruv8cDBlramUjJzPy44j00nuv4Ppt9fT7fkNoVi7vhfNMxUVtJz7VDvI7cCgzOCPZT33xU3G0Guavtt6+y2q3ZCm0/3X2W9QKxONfslI6Jk+r91IyxE2KE+xWmqxkyOaKIrZXu/usnJcbSfpWnASnnu9EIIQE9K+tzUpDaZzRPsVC2IpcMI717Sgenije0cJMSvnYt9inAOC8AElzWNmhxWVPpoz1lfWbKYpwT7UK/Ql/dQzBatR+Z/FCbd+9/wfT5NRiS4nlT7T3ocMD5tKaQurfXh76ojmc4i5ftRXl1o8q3dTh9bkowX0krFsl6lfQb+BWU7FQ2YFfWpjyO7xnh2X2aUz5a09a13gu7UoUBDld7h4g3yYuplHRRRVho9xM/lDaE05N44RDaH5tHO5f4D6bfQCNCeBgmBNQ/ibQrPsw4GHyPalpl5BzYn2KMdFYNAzQE+4PxVg4K+tfZbU559molNZW40zRGef+KnYDYaAsfeTXYfaozom0V9du8DiMWavGzgpj7L5/VfW5qtwL5JP3y4fV0ashOptjh9Jur7vdQj5B6jw2Xj2nJiH49KjPKu1fUbKzdXtXYKy/iITbv3v+C76sNFBrPX2W/Dsf3w994b8xA7KXQSmxq0+tyVcJ/ZH78GboV2H2rmimu0ZqWfDBv0+n/aGYr9qnFQkJRIGle780ZxBydZ4gzwEwJHQSQ8scyt22xA3gkdCpRuoaAnGxusvP8ApPUdFH02/jb+w2fE2OJGQk1g1/MQxrSjJdE6izTYSXLiHA2xHrsPanNRff5eCuXiMm/QoDTIn8oX7PALgf6OBHYfavt58OcbKYlnwKGVlDFwM8FyfWai7z93j9nk193kcPrN1GAQCPN4OaBft+/D7Laj3s3tUbLZc7o0EE3QxRom5P0pnrqXt4IUj8+LYKFPbqyRKUjCib2U4SSt8oOkvALa90VZFHrl5TPDv3v+D7vJpWdP3pLFEieDtT54e+8IyNH3n44C60R2L/FPWUBXa58+DN0K7D7eP7PKjE7r6VCCYA9q7OntPZ/EBgxiIoXld/Ijoo+m38dbLNnxNlM7ZbAZ6G2a0awxfkICS5Fh4wvv8v4Pttqs6FCjdhXmJVlI711+aXkVMbk+/icoX4bCHZpj8RJ34/Z5Nfd5HCUaJ8yeMsRNtuH2W1aKH27UuxMn1Spev7K7P7eCEDAIQjrSEzKwsHWyy4NFHIYJjlZhumpgsRxbO3y/gI52peynLfV6NLtrj0UMlL0fvwcR2XhzM/uqV8W10UvIj0qDCBE1L+DN0K7D7eNipUcD7yorsqi8S41NWSFPocv4gMGHTxHIWL9eAmqB5L++FDrxVSrGv8Qj6bf+W3htC+/y/gJUnC3O1Q3rD1X7oZk3B5yprrUisNO1r6XX8BRc+sV3DwT93l47KTPct80p2WlmibwdP8oEhEs88V9HlUAch9vwwm3ZfwDhbD5qctvMvevd+1MxK3u9a+hzqVsllpau+PnwiVyU3uce6PetXQiOluIjrCuw+3icCJcT0pNhA0o+1zqQ5FG9WzoIymim1bT/ABAMGN1kQHr/ALwLk1OCj7fb8jPh3RH4Ppt/5TeG0L7/AC/gUgYB9/ila5PcqMMhe7wr7PWlPne9fa6+GwaEtPZUCzrPCC0hciEEUgtw9uOCnc+MYJm4dyjCcnqB+aACkjOPnVpwFgaXpdiq+n+qMC5j8MJt3r3/AAHIQTTyCa+5zporl/ap+p4lXee6mQT9NO+Pnwy7UvZ+uPcnvVkcK3hatFELhw9tXYfbimGUS6BLxIRCcHRRq3R76jGyOFwGBZjzeNYZyThySrkgHCWMUY+U1x5aK060b96Ic6jDy/GxeUSuxcA4KWjJN1VD2Kvpdv4i/Tb+Jv7jf8TaF9/l/B2v2aE9NXbPd4N9nk03gIQ9WncsxnhOPL9ldv4qxpDjgp3Pjdgz5GkRSIFVoeHIaE2hJ8v+1Bfkl9ln8UJsb3P71BL1viZqBcyT0vFSgStTKsx7cGGoiA1kHzwcsiAF1osNcZIiQ0oWgv5v9pR0Fd78td3SPPh702STCWresQunPlwqfG/NTCPXgoHMdPpoFxfgwaZ0JP3RmgAjX/j2r63NQTXtq7D7cfpt3B2wvnqYqXOx6lDjQQGXe3zV87mXAgjCHsoUZRvb58QYsKmEZswXEyRE2m9LhOCy0W3Ur1/IGgElE7wmu1cCtLgz5tfT7U+l2/iK3O2OQiKH03LaAD38PuN/E2sUZSwgvkxYsMxapORfYyGcQF2Zpa0ykHLh6PbrXZovv8v4Ow+zTMSmkq17R8/NKaJCU50r7PJr6DZ4gvrclXFsnfj2asPXEyFrxxHufH9lvTj7MFdn9qMB0B6U2D9HwwrnrdawUkoXcbuENKLm67QL6ACIgwAkwbBmlQpO5Lps/o58CNUh4MGk4hLUk9qJuJowk1nYa1CehS/mnbKhWRoQJTygy+1XjJAheJKc67mUArcNCHsDuIm3ftXeU2oDtd96gnEHuUwxFy/UolIKOQ12GhoY0NKuIXluc96HdyC3hTSISlEEXZxWlXnQvxQqug6i+3vSMhm5L/mkhERRKJg2ApdgEBoxQeXldEA4Rh4dZOEipOQi0Z1NPwEeb9US8LPvcUSQAijNsnaoRKX0bfFKT4SI1l8afWuVXvumEafZ/DDBvpt9du4WfZu0BNqRR9bt+TtH4Pud3iv3G/ibIXVJbyyY5UPiAJhBA7LcYX3+X8GB9B6NYHN9q9v7KX2NSsFPoNlCyuz0oWGEOAo5h6nD2/soTiCD24xFAET519nnwdUIkvmPEJchtOmtCDrYnm1PhgNMK7D7UKl5sOcPmonzPFCWnsc9zcWErNWKDDYFJioIxleLZSFiWcXJ7FC/jbDQgJoxSDQQnNDvlJ70mgHQse9LbgkfXnQS26iIR3qIArQic7tRaIgBzQKiVIu1Xs7NSpzL1r6HOgSzf5RUBJh+1aGRTvBq4rXJtiV7Vf8AdpSwlL0P+qxfwHRhSncXNeqPW0MuYCL6FH6OtEMQZLf9NXE2CV4f9aMiZFjF1S0DB7FFBGBGaogtqVAUgbnOmJbgdaxcaBrN3zQ0xsPKmD6NAkJvVvM+FYwZnjcqYMWHXU9EFJgOC56FFAJw6T+3/DDBvpt9IQyXKLAB7CUNfZ7vA39bt+NduTTdsd0oL5JwP4Pv+fFVE1j5fxNoX3+Wpy0qBECgljTiYBRgDLxieSMv3zp7f3KH6TBXPVGCmB2dqZKTFPstuEm1yjQhfh9LmUxBACPTiKiLHm1DHwS6f9oCoNzNNeakV6ie1JAc+IEgkGhPARzfko1wnNjZoNbMO1dh9q7X4UC9JPd+KE2GuXJM1AM+iXKgkRG2fHrR80ogkJB5ChYFgBpBoaBZtAz3pIlRGUZ96KAdICn3qBQM2oHzTASKcyyyVLZZ9o+KVaIg3EB2qdCWwuE37UhqvFuBG/qUSMl1bQVYebK15iNJk4XEYBpfoSqPcYNyrxyUGIl/aotngKSUxQRcKAYNZ82Lys1h+Ykbh9jVv5GWJGGmUKNWNhSJ90HQYO1ZfpYq6loSQ6FNgmiI1rNSFk2gXai2lZIbLelZNo44IjtVcE+nL+35pXSMIzCmk2iibLH3orlFtEWq+o3VOOreX6P4YYN9Nv4fTbaINuUcyL3eBtTPf+IKJkoAo2J/dflXLaF9/loZFmwJvXaaggyKiZJpTJtdkmWPIKQIKz268AZIC6tGdwwJe6J8yr62ZvJPxU1smoTaKF3QUbCqLfs0yFwvqf5mu0KDAR5y4FIkWzRphCEjwMAHTd/8PDlYxV6Qi5bkxSHxc76u1j2r/FA0lSE60ILGt1azdPxU4hLJ9+P126jJwgDpD919Pnrkaaijoe+lL8vtXlJO5USGIPOWoh3PwwmxbhRY3D5pXTJ/WJ+Kkf4WZGvdahIkSAyMnShRMV6DNOPUAehFvd6K30cNgrHpTi7gSpo1M5RtCyYg86tNJSbF/Mz6KmQUKve51rKCB2Q92kxnA5wRSmRMhZWedZQuhThSZN2Aew4Xyi5EOPPDRiJ4pgvLjcoyW44k3rihl9bRBq8hxNzVesVILGxsqF7tTDQxryTuJNBqxdqZqT4g8ajQgGscWEdUqelbIi9IuPKmnvx8tHQ7UbQcuAzj0aQCM7Egfal8EaMyvPmpM5WWGv6jdwZ7V8iwt5KfrMWwYZ8QbG1e0WW5YmaYcGHIUUCIZQsHQmObICzjMzzt0/H9Nv4LuOBITh1u3pD8gR9xtpihU1/GAyAXV0pkphSJxFEJCRY/E2hCcSSaRIdKmC00+pmWABV7U1zwmWQFD50CfX96fSwwj7YojmEjaH/a+y2qAsYeijsFTlFCNoRV7O8VE2UPeoJai+1OE5vmo95lSp1C5w6NSd1oxlCBqCmDdL0j5KWb/SJD5pp7ilzz800kjK9eWHQYuDHaposZEV9nnUU5+5qIx8JOpyIARKJpSATISIis36IaGO5UJgQnNWpuqGN0lKFo1bzQBHWZMNhNqgT6z8aNfun7ULMobsEl7HpSOEPROp7Vb0vwwm02EwjRY0GlEN8TzeC98HsFq6J1Qd5t76LSgkDkm/qs7KsiVmPNqAME5s3p14TfgDan+LQYwAjk3rIdTSAjTuvvwOayXt+5fh51Fea4GJnyUxIV+73AbQ9qvnAByS8j5qb7z0XvQKvg4TS6fAdEYAumKs8ggTS4TNsVfBBQWIZetS5AxStYaHvW+tpO+lbUj0XaE8yOYI+9W+tOyEUmtTMKhEiYImmfGv8AWaukYiXuGa+w38CkSxalgq2JSMljs/wwwb6ffwK+EmiOODAItuTojxKLMinOr/k+lCVIw4yfVjrRLkqe1ot1tRMibqefH7zbT6I/kfHj+v3UvW1Aar0D/XC2IBvk3L0plJQxEqF8LZSXTAN07mkCml+5hl6S0hzJlIOXD0e/WuzRQS4L80PmhYJimmj3qSRo5TzHpQSD0G//ACpzIAzmx3oBtES3sfdRizhcSsmoFg3foZqesJrFw870p2baJ0vmphCEB3/61D0XBciahMS8b2/2sEXoZvM1N0KYSpDNgpg/4qHsr3IUJsu0qKBfwt2hnzmkarAxEMz5KQ7CugZ/VGSejdorWuD3auWknrFvWh9LGuZd70KXFZnEDSZoS3ymid2qibv/AFRXUKO1wT3CnWu5Wm0ktCICf2VYaYO5+KsCuddq8udYFBPktMtVdeqdsA+vOjIGX41lptBbV7tTo8unI8MLmC1NFinmmJtGo26SNAL00Inrfc7ZDYkDLhUKTuS6bP6OfCaGoY2LY7VKGIyGSx/NREF6UYlPOY8qmAuI5ifX3qyFC73+lJiumtAXetIyCMswvbaRT20w2bWfWnELIkE+dRKF0KCyUTFb6JFpuxAt5Q+zhMpUbIozu9ffgMIsKIb9KiRY6Swrr5tG5khuJJg7+CdUBuAj478IQ3pAbqLecJUni0C6DUYgzNoJaKgw04tPOz6KIyPLMqe7TapJEiJT0HzUD8SG5LkYpuQ5cASOkmKPkoXiBM+Shf8ArLiuS9BM7WFPrNvyBg0EMMcsQbPN9KBhLMywxRZArGGanwSKBvLhHCVDCZ51G3N3NgXQk+lMLtuY0WplFynkPelv3raB8WwMmV2aMIwmdkdZvwndio5AZETKj7UmaKDiEmlsYtmVz04WJjtiJ5BetQsbISNMbSvrU7IqwhLobICrd4huzYHp2U4OdN2BB8mnvwDtuq08+X3n4qPcJBaBsVKUrE6orVhB1AKkUjI3hmOF/UiiNWKAqSSZKkxEIEwAdyULQlhYlFpF3IwJtNWVN8gsl6Z8qUxfZUxg+5/C2hJI8xSmbuITO0U8MjgLEbDzpBG+6mmcO11g9IozbVlHsCVB9pJsiPpUegYcrQjzZQguT1W9aG9Jh5UbUApVCRhPN/41fkkMc1f5WjQWYkLu01k5KtqtFUm8ZDbM0nlzlzLbJV2owzADL0a3OVwHfuh2l+wUPg9KUxHg9VZqXAiuXoV+tBmZUqd4hmsBf3Kgwza8ILmxHamDB0CRFikigw6DD3KgJbQMMo3ymlDOxodX/KmTzHAL2q+AuTI0AYzeyveLU5GDe1ygpVVISst71DlfbrL7AqOKMINBh8NSlzh3QDzoIaSQs9aAEDI3He8UKbBl50YL3N6CaCIpZRQ3vxbWokZi8qgSZEFiEPselA0hCUwhPSriNJU3+ru71PBOkoQEJzqwwc0SsjpBS5L56Rfd7LRs4SWJmfP2KRYCBK43jgDYSjhK1f5p2AvKaiUGooMdq8AGiNL0AGZVeFStxG/kVEqkxafMfBWh5u4Db0vSWyxC+CSkOvYTXEeDmYCBeIrRHGWLB+aWI40GMhnzofqYgmSxO36KmNQ7wEHqLK5WWYmCJpmJoxb5Oy1N8TJm2md8UhJA2C/5e9M8khzEL9BWc4eQU4wmDkMT0IVj2dA3n2nDS0Cayhsl7EGI/EGDIsM6NGOSdtqXshtgy6L6U8VphKoe7VkyDExLO/rScEDCA3Y+KTHMlL2IqB5t7DbErfrXbTbjVdOunNrD0I86P4lp6Fikh5QTmHvTWFwMFPckrTyDbA7Pkpg0uuBhLselGkREGsr3SoNgs3P+BpoC6ykP8Kac3DmfqWrzmTCsbzplU+4LWEp7jzrTuWMEEvtB3pPlT1gfFBKQhhz/AMFRW28oPYoqBMEJtfRHpUxgxV9XutHQHygzvQEknDnIUXssCFrkWOqU7sSjsynE3KmPqI6k39A9agPBS+iOlQptzQsD50Q7zr0E/FaMwD96FNIWSksPVE1AfaHK0umdMYrnvOBFARh/DbaFDuYTzKruLB4fIPoqQgsA3c0OKA5dYI7UmvDDch9OlMScZFxPkY9FWveGB0ONG3yqfaySWjpRdYBCwNrzigHUb3GJEzE3bG9KyxrhJFHW/kUxlXysGGrw5gd6uH3MTujziKHTLjQQSjyShyZLcFqzMT50mmAcYJV3WsD3XfdNQQpMkrRFQqCqcy+1CjHS0tyeaUbgBZXHrI6zQzEG8BJt63zSNSktw+9zzrWpgEhIT0nSnKoOreg6hQ1IRfOJryZmOVXW1IbFSMqWphSDmZKO3KiGOC3UK/oq2eYGQBnzmri2G/KplZk7RdTuNOkFEdVoDl5MTeSzgIXpSIJYMEjPf04Re4n8MJsdRJlLYfL5UTSWIEZyhrCd6vYfGSf+VKfSO0PZQ6ZqATiV0mOzTgjW1KS7miXHFtcWd7fd6UkJm2zQneakhMuYLk7XY8qVzRnWQIjRL1Ctk4SZvJelO0hBl4ifmgzR9kCmrpzZeINdu9CrgY5UGXlQiFedInt3qBVrODRqHFLMgURzzPQp6GHRID3HrTw5lhzkXyoE2QQAMeUKuFNLoGzakI2kpDqExsr6KUkILlyXPrNGvqU1EE+dG446E9YaJ5FQuaf2FbM7DK5eVPtnEE3F+TUyQNBxgdaj4wtLDVvI0jMXqA5tRdu7kVN6uSxOzs1PliuublucU5SiRMlPOwQMWFXErljc75jyqKs43bM1szSDJAD1vjxBnigdFMObijwuypKBzDGV14hspBaMzU4LW6aGMCqOsWrmghvyBzR/BlOcYqx1GTNACzaOZ1+aZZBIHzQdgEhPWamNSeBk0dqGX1jLJmsT0mDmnrJNm4NBN0w6ViHDMqhtes4pi8SDdCgYWAh0qRWVT5IslcoCFB5584qWzTM7UuWybszRsMHCpoyLjgiieai+tI6tpWEH471JSk58po4mFptaxYsQytKQnPDN4L3o8A571khzVYQiAtWSPdu1q0i3nQKgES3J+FtCjJic6r/P9qNULmGggEOOoHuKuxbbtirwaUjSB7NAX7qiKLPZxdn3xVwsd2hL+yrUL5jyLRuQILBdahPBmn6SqlMF57Nh6KkTcJo2SwvValRizFXhkWR43HOhjOjJwJ3qKVM55Yp7UE8WJZQj3U6FixWqMSeetW1yBV3VttID7FSDbLOX2an6OZ9NqhkNJSRp7pUTUniNVjvFLNLAgO5xnutW+42pXHnTkQR+GE2uHonRP3U16DH30poD4GVqfSQOSU0novypXUF7Er7rS0y8g5sT7FKeWy8uVBSb54teVVkI5qHRBLrZD2q4AzZeVo+avVN76IpJheQ8yJ9zhpTR5cXQPEliNOBlHJyuMJE8laXIDDucqclFuPCFe9G4yVA9iW4MkPN9aUuNQrBb5xwlnsUixt3u0nimJYiehcIeyQDU1nW2t+qJzmkZmE7z5eIM41DqphzM0+F2FJQOY5yulG/eiPOoy8+EsYXeapvckuCXjGmKYLa5he/nQEU93W+j28qZMzElBW3Ucqcol2RIoQsRYMTipGnOnF8hGp8OU2qTmJhvk3pRYBb6j9IvRMO9MrMsQImpOay7g7VNHkmBrUOCBu0o1laIatXIOM6qbq8EsbUc5o3VOT1VReYEEwjemExnQXp7UGNR1Jlna2GlgJ5RamOVcHOKOZLFo0omhXcowsOtUZCJmWpcn1n0qXxyUbQN3rRFTARNSQSjd6e5SrPvUEiKAi0yNJoSp2PC2TLJRXDqZjnR2SgAt5JcPGFBa8nWgEQIconoJTMEPgGE58w8qkPggyZFuiDVw0826OkzFMEkuIE57mtWjzA5Nw8rTPOmGwTdvZEuX0o0YCsodfR5tZVtObMi63t03qawWNNdHk605pgSCXcdKAi8JW3JyvjFR/mTGrM2Lz60kOwNczzV/WmowkZuXFtGutRgZ6hBNvWl5kp0ax6Y70mhfigWjpobUTwtIBOqzKoKJ3ARORnL6tMm2ULgIg6hWhYH1iL6NZWYRDVETvamK/kqXO/SPbnRg7EjLDJtN/MrE/Q0Izyt60PMbQkTWpbMLQcpG7Q50XI2RDCSJjPzR0y5C/rO5ffPK6aaAXtq7mFHEN50qVkC6M9ligjBy4ITOJtp6UtMX4Fu1rU4CI2pZgMa5bU0uyPDAL9xVxOwSVyw8pZ/FCbYmvPyUB7elEe78CDk1yaVZgoeF6q+X00mL0yojnLVhnlSUFpKYIXfLvSf6gmqBDXOeTU29IkchzG5UXlU9Yu9IvV7MYYgIxOdWohD6tTQm0VeBmhJoexQvzW4CJ1KaUXQcksCet29NfMWAEUOdx5VIQLBkyi6Bm9Ri4tjaGHeBMbVhMtaGBs3yTVn+ZeYR7KeowyHuRiaTE7+6ds6b1FGtkyxgwzQbrAG5JDvnNqHHyag4Gm3nU6bAKWQRI87VekOEyzjmbVAIGyjgg3SYhilhMgeSIprSd7WZ+io87wCRusR5XpCKYgnI2obqsQSyl5XrKDlSFuYxGvlSCXMWwmFjap3UjKUMttY53qay89E7jzpTAyLMRZLGhNLKOOQluXyab0opktgA9d6bq8M2km/d/EGDQPvU5AHtUgBNzK5hPVoSBIJhtRTGJhNNGmVrE4jpRkMaWGb7y+9KGMafJMaKBgwGwGD1c08LwbWkMYtV2RACp+ypORkUS/c1AryDW0JfQo8qh5Jbr0qaRrGbpbEUumCh9FqurK4gXXfOi5SRlZ6Pkpvotl9aIzp0ogsG7l06pexUI83qAhJq9En7xVhNQxgHsZq9HEGHT961BhpEaMOyg1NOFIvDyaedsNzBEelG6ckJNnaaH+MPqF6PdWwILQLUh5ZIImVh5RSl7OKRbfCkkhkgsJm+uahgRImMpt1vQjC4BdCrZQox8ndaCJSS9gPY3rSZGy9c5Mx02pTMt4iT9KOLgDTwtkg7BPpGBExc8sQzRV+FOdbwsYLjM0ynOZzpsO1ddwLPtShEu/FYoIsMXOHLJgvG3ECACwGngIGr16nfigmlELk5is+xCSYd/AAAQGApyJLIlnwXpXSZl54CxpdiZzUUhiJOhwuWRYYJbtXUEibt1YnzoMIioX5PAYCYHEt5dfFC0FrnJasJ7EFVb3TZOlfdQwc2fdFqkMJYycfDHP0cBgGhCRPEOhqqhF3LwhYDDJOnhAjZPoHgDQAyGF3ijInXgsEEYYx4TaWO5DZk7lY4RZ8gyOvCMAbBUvtYcBLPE2jQOR8qgMOuXzHr/KDBoBbZ/KW0gmHU8EMiZ6mPzNn0m123lGOKnnoJlKN9vEKFChQp8IkhgvNsLt4hQoUKFPGIBGQTv8AafEKFChQo8IkhqtNsDv4hQoUKFPpVE/yYy/CKFChU89BMpRvt4hQoUKFHpVE/wAmMnwihQoUek3u38ozz4lhvTgtbnIcacBTxiARkE7/AGnxChQoUKOMwCcAjf7x4hQoUKFPpVE/yYy/EKFChQqOegmEJ338IoUKFHpVE/yYyfEKFChQp8IkhgvNsLt4hQoUKFHGYBOARv8AePCKFChQEBgPGtBCeup4kIDBeNaSMddHgKPCJIarTbA7+IUKFChUc9BMITvv4RQoUKnnoJlKN9vEKFChQp8IkhgvNsLt4hQoUKFPGIBGQTv9p8QoUKFCjwiSGq02wO/iFChQoU+lUT/JjL8IoUKFDDeHBe3OU418ZUh0t4SbiEJNWkUn4g+QzBykY8CHwKOkysiCEN6YnKRjcxZ3oI6E60GRazgQItiyuSjwG8yAPzxnwkZaAyozouURkAnvPqAUbVvAVhNF5yYLCSjcWOIOyUuzRQknKSm96SVTDOqmKxQdmlGIkAVJNC2aUYWdApZAHgEWzxYgUsLfG0jPREFWHZqollau3UH5m0ocSLJeXJRK80wjwAwJTGHGQqarJWNnCYFpijBFfsTsCfOikicDmphaA5ILOlWXz5l0ypqZCEJHAqdMyMjRIaU5QNdL+IuSgUWHmSL0WACkli2abxwSeSURCthhmr9vEm2NwnzSWPxv5TACAAETcSQWifBKVmyaQyIAC4kCxYGQtN+NpIQO9SXKTRvQ5Y3gFCwByQWdOMcZGZwUlgBtUp9LVAX4aTWixWh/m0pxISczkUQ8AKIyVUjGuvbjIie8gtvc1ccMNyAZQ2lKxQ9MG6UvwZpsWIvmhSPDRmLgInnbHAqdMyMjRIaU+d28SakK1kXzRT7JEeKEF8pTzU3jgk8koiFbDDNX7eJNsbhPmkscEWDSWDeQjadSo6tPW4vJCTmpKBAcQlUl1gmFzoP/APeRKytXhdoKuw90FKJLCRLceGVAJpKgkSbj0pX+pkCdG82FSlI4yXSZm4bjai8s06JCETu0bqIKCTM20PKfxSPz4tgq2/UwsCqpg6M3sR4JkiCECtiPrNL6FZuAwNOEeqh6YN0pfgzTYsRfNCkeGjMXARPO2OL1WrU4rFR8FRS+AmSkNAcM3NEaFMWmit1JHkUkRP8AIAASpNlLeb8SQrmpew33VWUjG5izvQR0J1oMi1nAgRbFlclHgN5kAfngYoKKAWFw2vPlTpOXY2FoDBlMXFX8zewDoYwsEhG6niCTQwVwgcZ7KHXqtnOxgWYc+DjiDQGGAWESy3qbhgH3VDhJCrNrlS1tDArRpXFkwXvQZbujC6z0ChgJh4XoltLMKwKrAbF6O8XY5LCQMuIedPzNpQ4kWS8uSiV5phHgBgSmMPxue5aQcwIIR9QoyIWbK5rquV8CgJ8oFgSoSsJ5qkCuEq1gAg2MpTM0do7AlU2WBsIgsofYY8AQHFhHcJMCAsYnS60krJ2zbaMYPWE1MFvetIFi3IWUz4CkCrWQlLmWciqfQB/Km6tDAmAZK0zzMCLcVcmJRE8ZPSGGWDd+JFZyc5FJgYE5Kb0zR2jsCVTZYGwiCyrcilRri5oQZpji4WsEJgGaJ1Z6mTJLtbKGaTHzXqKLH0ORqAsssYmjZx5h+CDC5SsODNQBD1SRPUqIuNJJjnG4tcnNT6AP5U3VoYEwDJWU9yE5UBkyUSiJqefgqZsYgk3ulfxwogeGZTTZXIkYZPAJAVJk0o5YkqjVRzJuywYo2GuUbxWozIShJqKJfqLkGQFhgMTxSjIgX9ouVIkwyEN7OeRIxQLlMHAckMkm2RmJvV7DEA6SEWxeTZe3F8AoQRKcmhDyWhshYdqqdMELJoGmJRKQXIZMRBBSShDokFiQdNV14M1AEPVJE9SrmZFBYiDkEiEJFJjO/EpurQwkwDJWU9yE5UBkyUSiJqefgqZsYgk3uleBUSiB9PvA0yk2ghgmHSGpHVlg24i9zsq2Qz0pR7v8llBgUyZlijUeNRMOAsgWHCdrd2Uu9CDaOc04DuCzNeNhc5VZEjV7mE1JCd1Q6eMw3ltjARaiK5Y6WUB6H4gYBCEdaVrpPLuwFr5koJWPAkeKSDwCKSGIyFA84jErosEJiTFA0xKJSC5DJiIIKSUIdEgsSDpquvFaovXMgVRabob0WQ6aSagJlRpXqyJGr3MJqSE7qXQNRKXUA2LAQHEMzpkJilMllMmlvIFcJVrABBsZSmZo7R2BKpssDYRBZQ+wx4AgOCtHXdRGzXnaedXS/oQwGFmyaVqBpiUSkFyGTEQQUJCPcKpi2IRlemaeoTMtufIPXgszmNnEspcVk9b0vbZSGlgEumREEWKvLrkR7fBoW+d6z6+fUjplZMmtfiHQnLrZMpbo2tUgVwlWsAEGxlKZmjtHYEqmywNhEFlW5FKjXFzQgzTH8AzoJLSAxBMaUloIz9uBTzCrsFcSCNtwPPiJAokFAkGXVCpO/mQEFdFJLpNl8UiChRm1EswkAITGt+NmQEqsUG5L5zreG3CXirkyA7boKJuWU6oyyAXL0Bf60ia+EHpkCw2lKGYgTgjMFeVwxtek5gmpShN1zM4izfE/xIeyVFkEEJybVc1qEFQ+Zarqs4s+rxDfUMzEMsQtyBVkuSpISN/FApTIoZmXEieM+xww5ZIPVOMemX66BOM+tQ9ybxEoYLIhdPEhs2JItls971PsMsuWyT0WjgGUICBsLa8Xhs2xP/gz9r+Q6SEhjWp1wLcOLXlAMAVGo7ak9BWgLt+ZCsv84wBOjFLumKAYixDkbQMN2KkTvtgKnJamNQYqPA4ERCJKLYW5bNXiWMwECEiRBbm0Dq2QkWAegZoXELYLsjYSm9tG4qMO9YoXQxAILmJq3xrrMuXhnW0Zpd759hoJtgWCNlAwEWyZMm/mhclUS29Ak3GY4OS9rj/aFDWXkEw9qt6pipqHAktDzSrpzTkQUhyAbSpYDYTXXUaoBbFHnqrCQHdNZEK0kQ5wIMyUoEy3DNWI3mVtTiSaEIUzpHz2wiSCYmYXaLcWBNoXbxJl5qE8QYir6lBl5KLV+rvYOQOAYZinhqBJMhNQTYzkS1OtMOdhaJ0gToiogysrhB1IErKawqqum4hCPIlF1l/hGvfjyWSgxkjNP0gETIjWE3Qh3qIwsODYYsBsEuqU78oJRMw0CQHUmF+DUasQyG5gBqpIIPyZAFtHVfNBpnwMStgCMJ9QzEmTp4Uhgk5M0G7Ttwm4eg0RXPklDQwZl2YL5OiiRKlyVlutDSgeQJlPYkSszk5oXwpRhIoHIMBe506tZZU7n0JBYZE1bMDZXQkuFnGNquk0WOXQwdXUdDZ7ErFmJ1LQr15NIaO8ADQBcVfA4mYIbaTJtQhfn9lyxgFXJuFAKXjYBbVwAq6RNSt5fKhAaSTMWN6azpMiwbXBFYkaUuREeU5lxX43VYAdS9jNgiwR0aevoWXCGUpA2SHKmJ4iEswvJZcIG15BjmWwuT1GmlLtyGOVAFNQgQU2tncrpLAu5/bsKdASwSwa02C4C8CXgR0dzJSJ+8eSHO7NHa1kJsktmRiFNMpTTLJTMx5sgALEmQ/hHjx48ePHjx48ePHjx48eDNi2Bgln/APHjx48ePHjx48ePHjzFLRQhIbkzqUL4DJaCCQIME28Z48ePHj0Eez4kBhSb0RblpAxAIlT1SjvTPjYqAS8ykS2o3DD4Eud6OQuKgHUqUPU7TZOtQR7PmSCUJt/CPHjx48ePHjx48ePHjx48l2kMNraQf4J48ePHjx48ePHjx48ePQijgLCcqLaasrvHn3r4jx48ePMzHmyAisQYH+xYnbMkiuTW8UsrTHbYKJKTYiTMU4BxAjYlACXEze25yhKrgFhZXW651JAC9CAIO6TRIfLBRAGD+M2SNATlj5/iwhJDjBCQRWMyxGs0qjOYr65OF5KHGiTSMkbmoYmNKPjcDNIW0yWw1ctHKtzdeMPM/j6InzV/wCgbNkFQK95XwQMxX1EwTFGChA5JJ/qoUK02M/Xw2mwb0Je3/mWzabkEzeF2K2zGkgyFDbL5LiOseD7vdTlPvH+phQlAGuhDD5VEkLqSIbJ8+1AAlCx0ceAnNrjs/v21/FOZELLyG9JkWKBLBaEXGE61NtBrmwAnKMlPH4+iflqZSLhhvNNZWECGhMNdWnReFS4mST0X+Kepk8woYNrm0T7OhQWF2TcAaXmZGkIR8cfu91fTbfwqtYAN31mKz4YQQy1SKwNSBbGBdKiq9oluIFGSwTTzYSWWbW5Mw3tS4zgFmGAmRZMv8eFyP8AuXKhFXIujSURBygXm/KLPCMhAFGoKVcxs/tWxb3vkYhHyotEFW4EmGAAmYi0Ut4hlkswUh1kTrNGIJy7Owyl5WdaJGQaAGA/O2UCCpc3LJ47vAfRB7NIixuc/CVa7o5KPtd3H7vdX0238EFVzMSJq6kyFr/s1Evhi93M+GFN+S4x+6BGUtNWQPSs3IJniw0BAoxspBHBcl14vUjOZRA6kGME3Wp2ZHNXSFDlI/NevpcJa/Ac7AHNm9pr7XajQuRLDk/KNhU+0MpW7Unpb28zUwJSeVhn/g2zYxQAbWF554ouXTJAmhGQHHSXxycEV2G+HtSgKINznSzREd1JPs9aQiTkghfLx+53V9Nt/Bc0iGbu1YqV60rO5U4uDN0P4aFCBiPQAoMxQNR4pUCcbtid/wAETjjl0UifOiyBSajTBPBSNwoDCcWgaN2Xx+BVQkJJip+y+LSEiOtA2sD3JMe76/2qDYyAJVwVcEiXxM3LVPq9SkxDOJmu6bi5GHyoywJBhPxtlli4xwbq0Vi6Ir6bfX3e3jEgAMtrX5UCDzjI/TemZQlcok8GP6DdQukFTKSH8J/YfamOZ6wkfYf4kKEh97toc0SiogwBmV9dt4wtqwDYJpXgnpYBqQd1qSKGkuWdL197t4oJKTEwEmmQhViJEk8aFxMY8YBvVPPmfWn9o2TUCmbPfjzoAgdQJHA9AnGbUyJGFSEJItdBhpipatp7sHMsCbjqaDFdciFgCzORp0XgQuZgE9A/E5VzDgfY5q+m3193t4wSNQdYny0kukKMGSQ9B2fAz+g3VfYhONcfwOXLgzbCki3DSjm+6/iQoSEF2j7TpX2W1fd5/gI+930KEtuIOSH70wm5UjpH7r73b4BAhM7fT4+/UT0E1IyISyNT7dv06f8Ag2zZRkslsFTDwlbwJYr7HNX02+iWEK7zv41cPGj1T2pZrjrF4M9uCCCYakT9nrx+w3U1zILLm6D3/BlL48jLtRRQi5zdw8uBCTrRCMALuz4IXoui7WbYM25kU6+46eI41Nmda+2Zv229AWsY2riWW5Jyk1EQ3DOOhM5s9PxRQ2dSiyN1myK+22r7PP8AAQIiUaeakeMY1bwoGwAweR6UjPOS3pVn0bfxxUWVCfT1Yv8ANIo3TlaPihd+7KRqrFMYD/wTZsKBNUHFFZI6MWkqWVnWn02+vq96QDZAdEvFonT7PwJPOhH3bKUQSvqj/VWSRHDus93izhRtt1p2tZ+MYoOUaQUp6xTsbGnM/PBBtt/CEKEZDl7NfsY5QDi5KuXnIa+jz4oIowXuXwydEDZdQnbNSNjbqvzr7Tag1JkGwj2mmWs8ZfgNAAUFAnXjcxnPWBYWc/J/aNNkJWzYIrF0vFK2ZE0OXIIgzoOsUnQJnXZFtsKxlGk1cdmKySEo79QWMiGFXK2tEh8slEiZPwjcKowFTcSaNppyInU1EoDT7ffX3e/jZXY2aOT9gV97tqyxXIwIKtA/Df74/e5n4Df3u+s3UojITv1NFm5LRPyTyracJkuX8KFCYCSdnzPrWpAWxDW6HY9KM1nZOvo8+KH2ObwwblXdUaxXCM2/760TmEim3zWa4eU0p6b8i0UyNggkSsiyUukglvNR9SReaJ7tCZdCxid39m2YUaBhghh0otmd+gGEAFoGWZmoqvMF0w6YWBLYuznLzk0mTgxgItRFDcOLQQHp+Jt97voNh1oS3KtAAi05nwr6vfX3+/jpFraYl3yr73bS8o7nReoPUoLwDHlx+1zPwG/vd9dzri9Z0YvGkg0NRy/hpuFCdm08zi/3erI6D5fAoxlfIi/evo8+IX2ObwrpFlKLpAVKDKV3jhuMGCzA9n5EFoiK6CnJFAACAjFLJcCWkYmhBU3kxLRiH/gWzaXxNxVk2jF6JX3Tf71ASERTcyvu91ff70RjSqPCWCiMCAjWbY0AQBwyfe6nLdRa0TGfX04R6Yu9F/wG8KoMRJ2TSWTCM0/T7aabB553+FFDZgPBwlfQb91cEsrOzejN3zyGNIwJI25pY5AtQnZ6gSmVQqzvKGFeDOxYN6NjTyB5RvM8vx395tU5BFksiQVMti3ZQSOkY4RCqHZUSYCmWTfQF3ueLGAMFDQzw+930xE2BcWqygs31PLgLfgNBLBSHcV97m4KlNTDUh3e1Jbdmev/AMC2bRvVMBhKK02jUAaajR7iQHSV+6kO/vKFFJygSfLSjbqWUyXeSeHWuR8GNTlcfX/KtMpIeofXh9Lt+A2hwAeNoUTIQkekokhmxvQLuZGsC4eCJBCRNeMJmSCjWMWbUkPjbGEi4aUrIRVoxGI8lAqOGBdCTL+OEBIDcNRPJ3EiYcEQ6F25IQ9o8676otx0YSNXZ/FY0s934nAfZPOagpkJ0JxN2JogIeVJFw7+sEGVB5Xvnxt6dgbDL2rpIpjYHC3hl+j9dKBk0men9m26Oi+zKy9kxGtYz52XDLO6N2s1A0LVwQW0RfvqvXUznk5swQMAWoL+xJQMBlGMlEA+R7xIlg5S/hhcUNpRelG2CzRQBAXYfFfc0YaXG41R9CQ+J2SfWEzAeDGI5zrdkc60lE1MrLzt0TX0u34DZdkCjEuKhSj4gQeq5xIxyaMkhut6Fw4EnR/BhQlNfpsR3v5UzdlXaqXxYU3t/qr+lR72qY70kxLj4qwgQQFgOLoZZqGRxrkAe7h3/jw2LwXeCmMCQVwUHi2WQR7cPpN6SoT5Bn8f+BbNoho6OymjbW0H1/Co3MUggrB2VFrl0Uty0cdSoG4W9P3VYPixgiB+fB9rvqBSJu6r9OMwuhU2Se1DJFvjme1L5+kxGUOwqROHHuoPf8BtLK4x6UCKgiJ1ZONGM3PtKu/+7+DChD87vADWzBS3kaHKaFL/AGlUwbIq72tsKC0WWWVkAZjkR4UXCIWkg+fAiKbpJoT6NFenbLhhBED807DxtTCi63vdQF4JIniHfQbtLIRQykW931/umzYzk3bWIdq5xOJiSYoiB2DzRxmBDEjPAgAyja5+geDhJqgI9akS/wDsUruihGGgV9wTtqk/ZU6Bl26B8eAMgB6qFiFm4oyLI2pWO8+dWC0VJKz+6iwRCuU0HkPpRpagmLkZ7cNcbVa+M9fADIBdXSpaiyN5hCi4z7ofepmDk7S35o3kIRvehZTG+n3BK5wjjoSkhxAhQRWEQzOkVgPe0rAEQWSSbUzoUxaKIXUleRQpsCNhJYsLLTes7MjmjrGhzgcViTz4O8qdZDOq7HzXqUt7on14Bg0SyucLGLxhcHzRPdo3mvQKPSUBdVjuus85GpY0esIO7Xf1mbJANBUZIWl3nwzRKIA5j3HghibzGSe0xTaToWvFvd9aNQmzrBxUpsnNDdFEQlibFYnnc7MeAkPjDST5qV8QrCwk+U0bunBb2PlS+vKhQIGusJ/s2zCnQEsEsGtdBgj7ug5HRMlF0yhOziGWBIZCrV+GqV6N8E2om1DcMLQSPpxbSYhM+sjS3vSI470qxrV38kCFhHlY4J/Jmq/4rHG5dmX6VZ4oQ2JlimUC1l7fC9DeulJCDoTGu1QqWy6JpWp3EpDcvoACh9WDjcCo3GObyoYaJzv8KuYY5d43oDsdNzaltCwTNnZevo99IgM0LAq8qKiZymhDlQwKxBfRTxuSbJQ7npVxtwblKcK4pFr/ANlKaVL2/dPpBrxj9KRgM/bER71NIMdiccHipbvzA+aZ41FoQTzoK0vrhaPWiQjqDEe7SQS724n/ABUTpwHBC+PWiimRDWYe8UB8Ak2knjCc9y0g5gQQj6hSb/3e16qVvOSXerIkavc1mpIRsqehU1grxCRL52uMKxaJs3aEMhaMpE071Jaog5kPlCOtaPBYJ4TyM6YLlMOdkkahNMRGUjpSTZAwQxzpRblQbwqXsUJKTkwn91ry32NTImfdFDBYYTN4n2pBOQOdj8NEwqLjAkGPWkSZbFSLJXDoiNSnC1fJfXqSPng6DcOiS9qv+RkC3UPapjm8d5ZioQkCs6z7AqPB/RS+5ZpBejzpUQLTS1LfVyzBYY2xUEKcKaLWqnxfZT4pMQgTfVemAkVDeShPWroaRByjp7qWRcaZZ6tgOhBpIuJTMuM0JcJuFp0IKVkkT6Hb+zbMTtmSRXJreKgk9wFfJVV2WzSHIiHKq4VmWEbszU+MjAjzKztwGXZS1kQwq5W0okPlgogDBwm9Q5klj65U/sdwfJoKeJJcp7erUY0y5Gj2pVaaYa0MQIYgFN/ainEDJuzSKLFgOUzbtTr55XMklv1plUJl52pmbrgzLfNXWgUvknzipQOwNWc3S7MS9lTVxmZSD60tZDd6/wBlaCTW2S9qJ5GWg7sxFScTkrXPnRYm43c9qJFIRtZKUL9WVnaUKTN9PXyrc2AekNqjEVXByB2amwDt1j9UeCsToYnzra2nN+pafSDLGWyx17mhlVkgSWWsMvOk9HXF97+dEVWXZe7tGkxatrFd/pLURCbWf+1ckKYWyXoG0RAooIoF9pR+GFCgsYvc+Kf2NaunMifOteEr9tit/uDWWfutXbSZL1UKX6oGR7VBKcqy3+KFMD1moZAsWZcHrFKvIXNRI2wEdSruJmcsyw+tF9mwswp7tOEsJL1PPYJgWccpp/OgiyZHanpCDnhR6U1FxEsAvyu1aVELp28+VXBDfGS78KswfVID4pyc+VS0+Td9amJgloC5zytRdE41U/aaRuhA7FRtBZ7wR3oxFLnmSv3pVpRxguIzQ4kK1gX9vrV4ca8yAj17Vsz1uvnasnEyY5tFgEw3iKvaWaBV+yb71bLGEYQnuU5IcL82Pn+5bNnCjDnOlD0L8gqdnf8Ad/tZS2sNZ9q5fWJzrRxppl4mPan3Yi829aas8a3mixlc/EcBPnKuNmCPOnVWcnzCmSNCnD/wvTAjaiDNTJjCrq0bTdgc/rSUNwsp0+1LLLmpqhu+UQ8BAbkV9bkpgOQO1fd5USBLYdSrTwhgAXjMxNAGZwzKZpGdyJ0lNERyCKTScZ7h80jcXMdKv6mvd+/BBhixjrX3+f8ADChXwijscx5pMRWC7qnYogRlpfztTJVCIxJBUEIMzuy+9YUJH1+GoiphJNpoQBg4fd5tXlDCfOgmksCj6DQuG1NEp1kO32K0WrWhYe9AQiLhacKisC6Mhn4r6XNSFy5BiBPntxUiCz2vSSQ4p1Chw51geSm26Twmp6BizpRfgQbMcymbDGBLLNTGZbE1RPsUt2fTYfqoMLD2ShIcJwkDVpj4X2f3LZsj8FDzlTTUJyjqBIi68/5T6PN1DBCInbSifIIW52+ajBEhfRirS1oL1eGfkvwZE6hQZdI1D+0StJoB1IeqgqjSPmiZsK5rWvwRWWxTuTZKRw8QsS+CCBtJrREEAgKn6b7dKWZB6xUf5MxszSQmEhmWhzRLloWt6FIhYkztsrFd1TsUAUXeo0xWxkLljjyoj2XLU7/Xu/fhevq9EglCvspOMIi3LSBiARKnqlHemfGxUAl5lIltV0SNXuKTQhJ3VGfBZByefibJ14wrOAYzMW6S2r3QFjmKLEyoukWVvlguQ/8AaNKN7yFSAC6j95007VISEgk4/Z5tRcvER5lblZB+6UGJ3Wf8rmYNQILx0mGpsTFlrLWjPPP6oK5sAmoEpym4MW7eAIPPLoP1w1MH0RVwV502vWQAiDrV9xg6FmpDJU/r81bFzs8W/wAqBFc1sQ1emRZdvSDLtk4M1fX14l9p70IIx8H+zbX8U5kQsvIb0IJoX17oFgwOtTOSbe0PL0KTJaQBy6FcqGCgnkYo/wDcTICJK4OsU6LwqXEySei1zlC9Z5RiC6J9Jp8W4mUpz1al8yg2B7BT8J4+Q+76VK69MZcVfQUIgbcMSrWzcthtNBMmZJwo6eyo6Vpi1x8KhVEDzXT4aKZcHkop+vWmpNlaWhNIxsiqQ4QElyxihkMDUwijsDrKy3qWb6mD37VC0DfkDzoboLA71MCNnCab9qS8phNHanPfZUKU6E07n5kwRKj5OelL3I7lSekjR/xWUJvdagXaEQLt6TiRIzcoikhDU5U/IuShXK0etALiWaOZRHMqnJNvesAw+U3+KlcwPDexS7MwXxH/AFWpQxvRjH2V2Y4whJDjBCQRWMyxGs0qjOYr65OF5KY0KYtFUboQnMoOgcLt44BYQC7ls5Vubrxh5nABDCQDknzQlyM5VBNtcTSMgQMACA5zRleUlJ4R55o9qvzBbItA+SjSKKBhBh7Vvvo4mak4bAKwuTYl50n1hCxSQcBI5y1JaEe5+1AlNApiRIgiVBUejDGgDopYahQ2W0RmWloK0ryxSKMpTs4pbdEanB7Vzu2KDHS6sUzrfmgBPekYsBBAWr2KhJjHnw9KA2AWpWL0YuZXuUlomkmo0nwzFlQfZfMKDUMnPMfNQcAwbXl+awvSbcgd2hAg0mW1OoShKFX6TTjPHkZrQhd5pRDcASWYXfc9KWxJdNk/2bYt73yMQj5VEmMyTRdZtAXRFqXe2rEKRttIsL2vWo65AZFweSSgyKNgDAcG3Ml9BQm7JzW/uXzpXJdu4QQeUW609R6Lh7hQTBX7piCvSvgsZzskzFQEWOVdXrNIl5FcLGOc55UZWGVeZ3RotCAgsI3TLKMRTfQ6InsvLprSGkbMm3PSaFm3gx5NHA5ZYRd7UmvKyllqbNKCTrABLtHJWWkB0Dod/WpesltLQT60BAy8EJY3zFTXEQ2QrzJipRbgLfWOUlL7jvbF+HUc96hj+1F5WF8g9WsXavKTAG7ehY2PQBJPotKUTIWpmYPug3MytRGSJMIsc1Ng2M5sO9mT6VbUioLIk+VCsJMMCZfpmpDwAza4xFBOHmtyAJ3pziATcPp6VcQ3IykehFGyJ3vIB76b749ciLd2ns1EXC/SCnrUCN1nSX8VEKESJJxZC9nopU+VYZWO3vV00Qokm7lL2ssU6zRLTlZJMZes0gOc7xmD62qX5E5kBL1hSYxUx7vaVMQTkWw9YH1rAqQSIQ5QwmrUXGc5LUyaGtMre6rbxtOaX4qH228UsPenlzYHRKIkICMPeeYqeVKgWVCDbHXelQVraLgz1Ck+9MgAhCtrUjnRfG40ixRBMteeDGtLN82U9GxCXpv0J1mLygoauCUyLUwFPfboHQGO30qa2rDkHqSoxji+2Fj1qNRtTc2e9K+PKzQLuStawDnKE7Ku7EIJKts7Ac9KX4ll3Epbm39KtQDvShLPpFQ8kbWIMelqF0K1pMqSxQRG0AS+70eteichG+iI860FaRJSiedEi7ez/uWzY8qS4ACzMbTE0AJbZYgKegKY/Y7YSLnnRhEGRMlPnHU5D3CkQIwFxx+320BqREE4mfSaFNaIgEQ6AfJV7viC1KpMBquKVUtRgK2axk+TcZ4TAylaIJb8jwDXGxBfvTg1i3WXkcj3om04G0t+Agjfg+LqC5mIjpn1qWJ+YmS81vepp1RgdKYqZkoLC/FD/UcWILb1WijD6pem0QW6yA+2hvvTi++l6I8i2ES6vm3/AAwoSKCQZ5cEY32QhZgl9z1pnqRBWmi+wetAScg9Mk37vBlKeRseCfI+dq3H1Yhx9a0PpMuEbt146fCKEMDy8dYmkOlC3bjA4krAsS2/Z6+IXI+bIQA87VH2XbmWR0s4IlCws5EcI7dWxAwW60yScPIGLdKLGOlSVd2h/Jh2RafOj0uwCHggVmvCDYqbUT1WNjy/s7Ztb3wIlXyq4v6HQsQLI3MNXvET+KEsF2QZYmmPZ2VnYYRuCNaBORaQOE4tkYICwGvcbQ3puTAFZDnebtEwKHMkQdq1hwpdZiI5QVDySyskc02npmizSm9X9rz2q9cFMA1CfmahK5pApF6a2zzankpgXAcm95HtWJQFyRAtAwSrI1Z4aIyc5zRnJS2Qj+2gQruq+jyLeVM2pPSt3Leor5MkhOCa2miJYENWAjaDbNAMzuGFydMvmV7LDog21tWynRQBbtBTJVIkX1Q2GYPJoCRJhPQJd2xmmV30C5k5GzYelR6BUkk7pl9aehMuZMwHL5av6KExmIqVdijZIaJ8HeeOaj0JwaJLDpF+vShrLQI7WEvUa7Oy6Xyn7ii9ELyKV74XFMA3BpGq0DOKmX5kjhiwW/dLdz5C7ydPSprKFppFZ8u9MsIsC3DcdKzLg9lN0x4IQQy1SKwNSBbGBdKiq9oluIFGSwTUicmWSApF2QJZinNWhDhCoCZFhZeMKXeW+Gt37qwuY5m9FgXWExNV7QvQCgCqGQEOdqEdNIRdqmkTpmrYE1eL35XinGYRAmRWt/bR86dg3Oy0VEExTAITvYeryXZCjP0GNP1T4YxUkhMB60ARS2xhpVpzkEJzFRQ31kXL3j0rTN/thZ6NWlgxBYk9V6O6oCQPS28UXVIhuwPNp1o0aBAk2wHVHotBLDJc1LJjIk86nxZdeRFudQ4jOg7nO9/Ki8gamjoMYobqZRcbjmt6zU1ywNIQtmnpSRyjLfIzt1ruynXMRjNFzwsJkf4elA5ELMhmdIY1aZjBiukkZa3t/wApwwZnvXAzZbkeYn9GiKLC7MU4w0iBJjOgMTnWoS7aBVTkvlVBmcuRVtVajhBD6tRQzpRfAtvvqUGLpQ5K+0/urTBPY/s21vlO4ULDyW1MnozYl3l1i6wmKk4F7SSCxGcXL0F+0DFiVtrFyiymorAIDQimmpTovAhczAJ6B/NBUFphd/NCm/JcY/dAjKWmrIHpWbkEzxYaCPhs28RgkWvXwUVM1gmZVdcIE3kU7GvkDyjeR5/zgpDsBAf+CbMxHmyAKsSZXgeDONmMPIM+E8ePHkzjZhLzDHiPHjx48k07MYPInn4jx48ePJHXeYeaDxA8ePHjwTTkzg8ieXiPHjx48WVlDnqIFefhPHjx5M42YS8wx4jx48ePQR7PiQWAJt4YI9nzJJIk34Hmyto8dBBry8R48ePHkjrvMPNB4gePHjx4I67zTyQeIHjx48eLKyhz1ECvPxHjx48eDONmMPIM+E8ePHmyto8dBBry8R48ePHkmnZjB5E8/CePHjzMR5sgKLEGE/t7+KcyIWXkN6nBEr5OhCOmB18CwgGVRkGYk1WSyRIOvpjIoMYJjMOaUTTY20Qgwvevlo1oBhCUAExFgOTiE2kGlzhBbixYJ0pW7QMSAG+uHCbKk4F7SCGxGMXLUkRW7wMd+OExluxqMZ131IkeZQMFvlkH1STPTPyoMEIzC+qON6hDkYJIGcSFqlxwMuAEBAOa7703Ws4zNY2hOtib1oylNXwMo3F3WpSDjrwmx3MRyuZIScqga/MGJEQgIzC8VO3c96KmWCTELpRhwQqYnIvNJDVM8Z5vVjqQsnm/OpSuqKYtoIbq9k1lko0pp4OSzGheaaUfISjOBSyyWef8ZCuTzdl4utZDiEp3Z3uUMd/BFnQjcOAliVgLEkyJHmUDBb5ZB9Ukz0z8qDBCMwvqjwAN0cFwZQ6mkltwCbABBiqGJstsJFSyUaU08HJZjQvPF/7o4Jd+QKmuoBoMwwNQZpMcEeExGGv5iWNaCFgOFPKMTai5vvfi2DLbwmgICWdEZglQGQ5hF0LCzUz0z8qDBCMwvqikzZ2uYIXmTLEY24NbRCFe8NrVszOyZUVgQZTkpyUYfhgMykbL5M0zYLje0guSEje2IqVQE2LsMNQ1uleCyjLokhfUNBWlLAETEZhakxwR4TEYa/mJY1qM54hT2hCFL3Us0CvxujwJImWCwf3Rb3vkYhHyrCKEsiS1WAzg8EFJNZa6lBVmrubqHSYsFQhyZZJSkXcIliKPzgZiJQKK5Xjdwqj6ZUULJJNTvTns7KxuMIWJGlWvET+KQMF3CZZmjLAgGA46X9AAADYcjXdqJw8yTFRSKKclsVAkSFylUKXAMcc1HcUkmGLCLmGMVfrG3BU0kysRKv7/APMErU5GKiRTcKQxhcBiZxcGlAaAAIJg0yDkqXQIykYF4eSCjZco+UgYLuE7prfxsxnMGaVlFNOKzAcwCXTjYC2gbFZZBcUMe4ApMdZpMoCd0YXKZskTGtFvdGDWRRC3RA/GTbBrbFPMsiVZgkoAAgLAeC2aEEhAYMmSNsVE4eZJiopFFOS2KgSJC5SqFLgGOIkHDUysHclWXby5GXemRYA6GHcAUmOs0mUBO6MLlM2SJjXikAckkISS5Z0qMFybjICeUaXj5gfunJi00NEVpGqGmOXAcuMcU0JFQO8KwC60UyRAKmEgTKKOy2KgSJC5SqFLgGKCaZnKEAZRQQgl4MUFJe9slzqULrMMwRN8sv8Ao1JYRmysRCDIWQc1YLoJyQyG4MBOtWzEQcESKg0wODflwvKrg8mgzH4VMiQBtLal4+YH7pyYtNWRGKIJSdQKpCGlMjur1lNTGXQ/+h//2gAMAwEAAgADAAAAEJJJJJJJJJJJJIIBBJJJJJJJJJJJBBIJJJJAJJJJJIJJJJBJJJJJJJJBJJJJIIJJJBJJBJJJJJJJJJJJJJJBBAJAJJJJJJJJJJJJBBABJBBJJBAAIJJIJBJJJJJJJJIIAJJABBJIAABIJJJJJJIJJJJJJJJJJJJJJJJJJJJBJJBJJJJIJJJJJJJJJBJJIJJBJJBJJJJJJJJJJJJJBJJBJJJJBBBJJJJJJJJJJNJJJJJJJJJJJJJJJJJJJZJJFPJJJJJJJJJJJJJJJJJJIJJJJJJJJJJJJJJJAJJJJJJJJJJJHhJJJJJJJJJJJJJJJJJJPJJJ5pJJJJJJJJJJAJJJJJJJPJJJJJJLJJJJJJJJJJJJJJJIlJJJTZJZJJJJJJJJJJJJJJJJ5JJPNJJJJJJJJJJIBJJJJJJJJJJJJJI5JJJJJJJJJJJJJJJEpJJHzJHJJJJJJJJAJJJJJJJPJJJ5pJJJJJJJJJJJJJJJJJJHJJJJJJHJJJJJJJBBBJJJJJJlJJI/hJZJJJJJJJJJJJJJJJJ5JJPNJJ5JJBJJJJJJJJJJJIJJJJJJJIJJJJNJJJJJJJJJJJCpJJOZJLJJJLeJJJJJJIqYZ5HpL56RZODJJk5JJJAJJJJJJHO5JJJJJBJJJD5JJJJJJJJJTHVJJIMvJBJJJspJJJJJJoTCfotj9BYBJR3PH0VJJIJJJJJJIp/JJJJJIJJJItHJJIIJJJJJY8pJJOhpIJJJKsJJJIJON8tJzBJfGZ3nQAY5TgZJJJJJJJJJLP5JJJJJJJJJFZpJABJJJJJPGVZJJUNIzJJJBjJJJBJQaPhACKEPz7rTKRpiBMJJJJJJJJJJp/JJJJJJJJJIhJJJJJJJJJOS8vJJHOZEZJJJfhJJJJA9ZwD8Wp2BenHFXdaK/lJJBJJJJJIPJ1JI5JJBJJJFY5JJJJJJJJ9Hl5JBonIbJJJGLJJJJIkdvGgeH5wKddTV16CwgpJIBJJgRBCI/JBPBDJ5JJBjFJIIIJJJJCtcvJI/A5CZJJNRnJJJJLg5WFaCL80hG/7BSoD2VJJJJJDvJZpLhWYwTpHX5ZMppJJJJJJJJZhl5JP5DIVJJICKZJJJJfXvew8tTsxtoc/WbUts5JJJJIGxh4JdEdAttJOO/PUNJJJJJJJJFeGTJJpJtHpJJJIHJJJJGBeEMUAraMNtwB8cfY5XJJBJN8xUmzOy+ny8Q1NBtGRJJBBBJJJJ6DoNJFZGoVJJIKCpJJJIteNrsPHfpGsheh7pSPlJJJJImf8AHSIR2XlTmFnh476IiSACSSSSRDg9SSXyM0qSSQ6gSSSSRXcJdYl9jcyAyDpDbILwySSSTSccClbdX/zpN/tb0WEVSSSSRDzSlcoeyTyQPnxSSaKvySSSMPbYq1KPjScbmZJ2qdV2SSSSbpf/AMy6mf8Ax+nbmv31LNJJAJCnHzHOyDWNidyXcjLy5B5JJJJR+QSZo2NElznbdrjRcnJJBJJABb8AwqrlM8b67QPIRjJAJB30ZSHApBKZ+wLNQ359GJJIJIsnavu4KOysyl9KLn3IpJJIJIsce0gj0oikB4VToJNb3ZJJJL9IwO9u/oxB/B0WFkPn8pJJJGobqx4O/s/DFDqFF4YCJJJJJGj9kvJVAuZ+0iTmkq6mFJJJJ9ivpvUTj/z/AM8wp6RsIvySSSSSuCSRXF8SSOTT/KmSTSSSSSHSbI//APQG8/6XXHwg40OkkkghH1fHz5ottULkCBS3ZV0kkgkkkkkkkkkkkkkmnOm9kkkkkEk7wn/Y59T7/wBZPAB+v0lXJIBJTLz5V0w6L9f33oO76EaXJIBJJJJJJJJJJJJJBGdNJJJJJBBJ3IbLwBD1GMXKJwKYD73JJJJP/wAA/wD1RI//AB5/wv8ArRMa+SSSSSSSSSSSSSSSSSM6aSSSSSSSQecM3bHj9SSMQ2sQaSOKSSQScWbqtdG6G7IKp51btInaSSSSSSSSSSSSSSSSSTnSSSSSSSSSSSSSSTuGySSCSSGSGSSSSSQCSSSSSSCSSSSSSSSOeSCSSSSSSSSSSSSSSSSSSMCOSSSSSQSSSSSSSSSSSSSSSQSQSSSSSSQCSSCSSCSSCSSCSQSSQSSCSSCCSSCSSCSQSSQSSQSSQSSCQSSCSSCSQSSQSSQSSQSSQSSCSSQSSAQSSCQSSSCCSQSASSSSSSSSSASSQSSSCACSQSSSSSSSSSSSSASQSCSCCSACSCSQQSSSSSCSQCCQQSASSQQSCCQSCSSSSSSSCSSACSQQSCCQQQQCCSSSSSSSSCSASSQQSQAQCCQCCSSSSSSSSSSSQCSSSSSSSSSSSSSSSSSSSSSSQSSSSSSSSSSSSSSSSSSSSSSSSCSSSSSSSSSSSSSSSSSSSQQCAACQCCCACSSSSSSSSSSSSSSQQCASQSCASSSSSSSSSSSSSSQSCCCCAAQSSSSSSSSSSSSSSQSQQQSACQAQSSSSSSSSSSSSSSSCQSQACAASSSSSQSQASSSSSQSCQQCAAQQSSSSSSSCSSSSSSSSSSTxySSSSSSSSSQASSSSSSSOSSTxySSSSSSSSSSCSSSSSSSOSSSSSSSSSSSSSSSCCSSeSTxKSSdpzwySSTySSSCSSSSKSeTySSeuWRySSCSSSSSSSSSSSSTySSSSSSySSSSSSSSSSXTyaeJSZDw2eSSSQ+SSSSSSSeTyPyKybTxSSCaQfLSSSSSSSSSSSSOSSSSSSWSSSSSSSSSSTGeQTzSTndW/wySTsuSSSSSSQSeW/wdhedLyQJ8FcaeSSSSSSSSSSTySSSSSRySSSSSSSSSSaS5eeHvlf0z+WCGeE8SSSSSQi+R9CLQ1aXqyDQZIK8ySSASSSSSSSSSSSSSSOSSSSSSSSCCRvLfKOoUpfHOwQQbpT3ySSCQwmuuhwG51IMqfj+aad7ySQCSSSSSSSSSSSSSQSSSSeSSSASSP99hABcKshZ92CEvXtOSSASE8WEBatQAmZn04BZzBWiSSSSSSSSSOOSSSSSSOSSSYSSSSSTDB86YcJy0pMtyH+dyEeSSSSQf8A8deVX/X/ALZ+nCWJNFVJJJJJJJJJFOJJJJJJLJJJFpJJJJIyDwDSRoyTPTn/AGf3FoOaSSSSf/8A/wCx9PCB06//AOXfj7+SSSCSSSSSSOfySSSSSOSSSKzSSSSSeNuy265Dcdvs9vnrLMjySSST/wD/APyh99//ACt/8UL/APJrkkkkkkkkkkc/kkkkkkkkkkRmkkkkH/4/6GKu6/76n/3I596O8kgEl/8A/wD1Lv7/APrv/wDL/wD/AM25kkkkkkkkkkl8kkkkkkkkkikckkAk/wD/AP8A8Psf/wDx2/8A7r+e7xaSACRf/wD/AOYp3/8A0Z7+X/8A/hdckkkkkkkkkE/MkkkkkEkkkVjkkkkn/wD/APqCpD//AM6373//APepLJJJJf8A/wD/APTV/wD8fz/2/wD/AAauJJJJJJJJJI4OZJJJJJ5JJIjFJJJJP/8A/wDb9YH/APT5f/8A/wD/ADGuJJJJB/8A/wD2I24//wD2D/f1/FfRJJJJJJJJI5LpJJJJJPJJJEZpJJJJ/wD/AP7yXc//AJfx/wD/AP8A+wmJJJJJVJ35YvpDz5O4x7/v3F35JJJJJJJJBJNpJJJJI5JJIzNJJIIP/wD/AP0w53/7P83f/wD/AMwmySSCT9uGAZfJNIdLXeWe5ginySSSSSSSSIR1SSSSSSSSSQ+aSSASf/8A/wCnry3x8v3Pp/8A9C0uSSASE4pA2qJNS4XEtYt2BV7CSSSSSSSSRRcqSSSSQ2SSSXySSSSTl/v+IH63/wC37kNf+8OqEkkkk37ZIUaJeYvAFXRYOAn4skkkkkkkki6pEkkkknckkkXmkkkknG3vu+Ves3Xg9mX/ANRkA3JJJI6QsMtXfRO/JewcPtENMpJJJJJJJJF+j5JJJJPJJJGfNJJJJBE99UaH+GrnjZQ1L1wZQ5JJJCMwvIp/pGfpIQn8gEEpfJJJJJJJJIuzPJITZIxJJJ95TJIBATG2gNi8bt7/AD2C1aHR3wSSSSdnNvSOkEbyyTzjQn1AymSSSSSS6D4414lymNKSxx/numSSSQdlOjg8yHrsMdAFD5EYiSSQCT0Qg2QozfUKSRTJWjEKDySSSQQj9fDGw0jMbM3RxXX8jySSSBbO4brCiNZK7Amk+FR4dySSSDURSlfPyj0yYLlcC1gGiSSCSQ/w1RImzqCZM0QqnLvpaSSSQsEAj/BzZmj+zkFj07UDeSSSQjhMj+OPPhf/APHRH6v+tUkkEk6aGsm7o8DaykfcaPY3AckgkhVSLqVRjupvrwuCJnVoMkkkkmrntQohT90AWetnBEUCmkkkkkrTLG7JClpIIf6mGxYA/kkEgik8UDkjkjgn8k81gTkikkkEkYkkUcknkjE88kmEwckHkkkkkkkkkkkkHkkkkkksk8kkkkkkgkkgkkgkkgkkgkkEkkEkkkkggkkgkkgkkgkkEkkEkkEkkkkkgkkgkkEkkEkkEkkEkkEkgkkkEkgAEggkEEkEAkEEggkkkkkkkkAkkkgkgEgEkgkEgAgkkkkkEkAEkEEkAAggAgAEkAEkkkkgkkgkkgEggkgkkkAkAAkkkkkgkgEEkgEggkAgkkEkAEEkkkkEkkgkkAEgEkkAEAgkAkkkkkkkkkAkAkkkEkgkkkEkkkkkkkkkkkAkEkkgkkgkkkEkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkEkEkkgkAEEAAEAAkkkkkkkkkEAEgEEEEEEkkAkAkkkkkkkkkkkkkkkkkkkkkkkkkkkEkEkkkkkkkkgkkkkkkkgkkkgEAEkkkkkkkkEkkkkkkkkEkkkkkkkkkkkkkkkkkkkkkkkkkkgggkkkkkkkkkkk8kkkkkkkkkgAkkkkkkkkkkkkEkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkmUkhkkkkkkkkkkkkkkkkkkkknkk0kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkljkl0kkkkkkkkkkkkkkkkkkkkdkjkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkEkEkkkkkkkkkksqkqkkkkwkkEEkkkkkkkkkkkm0kskkkiskkkkkkkkkkkkkkkkkkkkkkkkkgkAkkkkkkkEkki0clUkkklMkgEEkkkkkknkkkldknkkkkmEkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkmk8kkkzikKkkkmbkkkkkkkkkIk0kkkH+kckkkcSkkkkkkkkkkkkkkkkkkkkkkkkkgEgkkkkk0yskkn9Uj8kkklkkkkkkkkkmkakkkszknckkhukkkkkkkkkkkkkkkkkkkkkkkkkkgkEkkkkmnVkkkNKk2kkkjwkkkkkkkkk2XUkkkpkkxkkkqakkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkku6kkkgscmUkkkaikgEkkkkkli/kkknCEl8kki2hkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkh4Wskk4zk7kkkAk8kkkkkkkkl3cMkjq6k/kkkrRskkkkkkkkkkkkkkkkkkkkkkkkgkkkkkkkBmWkkigUgUkkkkfkkkkkkkknU7nkkddsn8kkmc+kkkkkkkkkkkkkkkkkkkkkkkkkEEkkkkkjNMUkk8mkCkkkmUUkkkkkkkktSs8klsOk/kkkzi0kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkklmNikkB8AkUkkhqpkkgEkkkkintzkkXkqjckknc8kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkz5iEkhUl0ikkk9GDkkkkkkkko/Dkki86kAkkkvh0kkkkkkkkkkkkkkkkkkkkkkkkgkkkkkkjScBkk6kDOUkklHSskkkkkkkmDUdkk3k5gskkzsMkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkS1PnkkMk46kkkb6IkkkkkkkkFIn8kmknefkkk9jkkkkkkkkkkkkkkkkkkkkkkkkkkkkkiUnnyR/EkHktEsMkCJJsggEkV0kuPg7kkck3wPEkRkTkkkkkkkkkkkkkkkkkkkkkkkkkAEkljCy2KkzmoU5eDycHeWmkAAk+xkB2AjOnnlbvnJxWu78kkkkkkkkkkkkkkkkkkkkkkkkgAgg8ZeoooKpFb8u2X2X6uQEkkkn8sgpMd+3trq1YTd1tBokkkkkkkkkkkkkkkkkkkkkkkkkkkkJ1aYPGc6KtXb1LePiJfXkkkkLT63H7cKPCH7PQdjDbAkkkkkkkkkkkkkkkkkkkkkkkkkkkkmzFf48qOGB3/t9J0VC39kggElg6Cn+ujZ6f8Az+jDWXZ95JJJJJJJJJJJJJJJJJJJJJJJJABJKf4PraxpwwJ+pWLLDy5jZAAJC5ZeFchpW8xFPsXf+zDRJJJJJJJJJJJJJJJJJJJJJJJJIJJI4TN8kZcKtEl/pHKelYufJJJIzz6b3cFo/BbPuZeiKWfpJJJJJJJJJJJJJJJJJJJJJJJJJJJJ6fB/1A92Px/xyfekoNM5JJJF+Z//AN2e8X+//wD149zR/wBJJJJJJJJJJJJJJJJJJJJJJJJIBIJMTmiYObBU+Xv+JJS1gEpIJJJqD7NyGcJUF+qHJDztIOZJJJJJJJJJJJJJJJJJJJJJJJJBAJJJJJJJIJJJJJJJHJJJJJJAAJJJJJJJIJJJJJJJHJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJBJIJJIJJIJJIJJIJJBJJBJIIJJIJJIJJIJJIJJBJJBJJJJJJJJJJJJJJJJJJJJJJJJJJJJJBJJJBIBBIAJJJJJAJJJJJJJJJJJAJIJJABJBBIIJIAJJJJJJJJJJJJJJJJJJJJJJJJJJJJJIJJIJJIIJABIJBIABIJJJJJJIJJIBJBJJBJIIJAIJIIJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJ//EAC0RAQABAwEIAgMAAwEBAQEAAAERACExQRBRYXGBkaGxwfAgMNFA4fFQYJCA/9oACAEDAQE/EP8A+yEzwH/j6vhPSY/Mc8sdGHY2V4T1x/46QtzPCcfuz/5KwLTDHos0oHf/AObPtcGz6/D/AMf6fD9xD3T7f/BUK4L4pQroPj8yGsE9Z/3TndEe3zXiH4WgwdD/AOTPtcGz6/D/AMdz92H92Lm/+D5r014r0fnL7mPW1CDIvJ/qrLhw3oxPOD8GqN30/wDkz7XBs+vw/wDHau7z9zBzf/BVlFk714r0fm5g3Hug3nL5pZZCL3RGF0PW3xHp/wA9IJhpwZP1T7XBs+vw/G9MwRyIt+d8SQcEiH/JMFsrZ5Z8/uuxqY4TE/pIhAz4Y/yPefNeK9H5pHgo7R81EHYXsT6KJDve6eO9G2/kPT+hYu1KiSkoLMQ6m+czupNbq9Db9uqOi0i7vW6MXxkH5oDEiPdGDAL3scZiUTVzJkk8mK817PwgxQM9SduoWwazEzUdCkRqXT42fa4KDeBLpRlIGPFtjWbze8Sj6/m1HTT9tZqMy5HVDO0MxXQu7Evgst0IUjKJI3OBx1307UGjZ/x/NbPq60/SA/bDmqPK+P0RMTAvYr3vx/iEywVkuZ9uxqc/0r2leK9H5mhwp7pRCro6vybGY3R2t8bSMn6LwmgktAnVn1sC4v8AwftaDDrkY9aa0t/CvpN5QTnTh0mdjkTB3DNZlJPRZPDTn4qtZMo2KDbk6oHKihkBTfY4b56UEXopuKz4orUiDuY6bL0DgcEwHV2IFoPtRjGQ+VeAVJrYmXLMN298V9Nu2ndu8f75zFAZfrFfa4Kvs2yN5GeGz7HFsGKVM5tDLIA7hUILrulxHermRIJ3SxV053uk/wCL5rZ9XWvQ9n7fb9P6PNems/N+NjwcSg5/4XnvTSGPj7dn0m+knIemvFej87fUIp5yiNkK972/gIWNFHvPp/MyHBoJG7Lt/sbFZ5fP64YmsplTiIp5hipbhSL5X5xypnEwzwIvaUwNgNVv4xr2a8tSbcGV3H3TNQ72PCoIQKw721uwJ2osZIzfIL7qcYBzQAodKdiMkdUf8okEShOstJ7G8J3ifRRTMxzkPV68772e6WU+E+aXomTumxNIS7vTQIII+TgxpHemUgBrrBncVDAYYaaLGSwbmLUAy608KG04EwbvrUIGGF0lJicTF69X3UWlsd8d6IW3wkj1ShJAvoqF6SYQuXDGOY/YpoSLLSCZ17FWpmA4JENMAZBX4pf2i3h6Y+KcFKOpVP5QusnoWJRJ3ZigjIZJN8hvviB4f4rdSvsdnofNeh7P2+x6f0ea9NeY9Gzw/wA/4XnvTTQX8gdn0m+vEemvFeitW1Y0nV+RAB9yiKy5hr7XOve9u1QcgfdeY9H5sRkXqsL6RseAfrYEwmRmx8FqYCBOC2Be89aEj9oaxzNPRl4KKuCA5CKtPET5s9+1A8N7kqUqvYF8oyHaL0KNJC3SjiIFDjYPdCAApibhldYY3NAzDJ3hPDQKRlDi6De3LcSnD2XZOCOrCgmlZGv+wjxsDNNCZEl/hqPMsBxVUOeY4cCoZycSYfF3cpN5hdZLrLJztmpAmY6Vj8z1oyhdTzQt6Kh/luhFA2KMjisTNYRvU14s3ppgPpd2mlAYZ4ZPaFQhJB3wJ2pBCEnSfVWbn7Kg7jo7xHX4zXjvRTJAgmqZe6aMqwF0nToKgprFKzQotrCX1cxBuvbFEQSNGkABoDQlH3d/iMYvouz1PmvQ9n7fY9P6PNemvMejYo5P/C8d9UnmruRSHnEsHNr7TfQkcnprxXo/OKgb3p62C/RADj7dqgcnprzHo/Pz3prrBfVGCgDvr7Kj3sns/wC6xSWup+ogm/5KYU5/FGURFJ5kUKQ0pKRln52MiIuu6aCIwnt/VI8sUHG68E8ComYIJcZ/hUJwkb+FxifVOWAy15iwRvgOfOn/ANv13kVJI1oP7I8wDQLdH2/rSKVxs0oZrBemRQ8Y4oGN4LFrX31oTCDkj5M1a/s7N7B1XWpZti/TeVEwtZvifLMUES9zzgPQUTdBhvoVid2TdQaxeea15Vb6ffRBapOcJ6WmQd75WoiYQO8KG4oHiKtFshi1mN+DcTvS8SxdBg7BFSOgCPEhOzTFiID3odaPk/z/ABMRBMxxk/jsbHy916HspKjuPX4xshJJOo6nD8/Y9P6PNemvMej/ABJeE+qCRx90C23jqlfYb6x8z5rxXo/OPMmxZTb2j80lE73b4j015j0fn5701pr6nCkdaBD4l5mvtcH9SgcH2q4edTWXlQFQ0pRoBVKZcX4qEMCSmSITO+ZTFg4tIWIB6zdJmzoeqhZJNtALJvtktZa1FbcLb4HzPK5Zr50jiOVtDixedGm/qB8Rq1WQQ6qfSpdqQprWbgozmaXVSkEcDnjoVDSMoi0F2HqscL6xUOYsk43XsuFxrFNOlUdAaZCnTEb6jqwSc3jTLm8Ftabx3RTonWgChd/v+2jLqIRaJEjYJHOXx5pJjs5aKIDtFEicA0mWXgA4vQ82RNnAMKTI8jflRAyUasskzxzeZoV2gxNYCFvaYRNJmaI1z44CyyaqhM3ZqOgyibmCTliPX7M2xh3ww+dl0o3uk0JJsRgl2QEfpNYtlr3nzXoeyvDPx+zz2SEp2NpYI57WUb/h/OS1r7tee9NeY9H+JLPYWDz/ACaUp3DyPzRNoC7lKvsN9Y+Z814r0flHFG7LZ9tln4FOMFyiYmi7fEemvOej8/W9lKM+oPqisSQcrJSpaA7NA1mTxb+fqYuAJdxKhFyJLfYZOF6CQQBZcCwPKbTigKWZL5sxQC4BK8I1o7iYTham+GtSc4Q3MsRwsLihYmaAVYjHSVsU4nEEw2TLqnmrMssSsBBxwoumtA+YJckePbhQko60iUSzJ0Hu9nGgk5GiWFW6N8TfMWoSEMWZuZOmxIPuWhUB8bnUAUeHBhztESRLvIMx4U4cKg9YQQcCV6HKt0llwLxCN3txpkBur777AASCETdx2z0teCs+3qoWClX72MrYpAZuDwS1Pi5khswIxLdySVmpPul6C1vcEDQYjEDc13rBRGeCWXKrrAcsYKCBwag3QDeYbhBrDlLDffUQbPc1bj00a+P2fZblfbb2vKfTXlbCFTC/nzsLFB+VYthsykPJ3qkFVGNEbvAtVtwyRs2J1oAtrnGUVBtKebAJagkRg9CyeDOwgp0u9ILhRzCZjxTubsnrV+sAS8QQTnesb8JyJNvnUhECFt8sVhRJeKzLUKTsvBHfCLbAGNShJEOJmFizvvrSSycYgsL2/wASTgcnpopGgrwT21Pa8rHBj5rHzPmpYJgP5R4+HZ/EzDh5QrxtvDWfjb4j019zgfmt2JgcCNiDWYg7NKQ3Mvf/ACpy1Id5+P1SfQwMg3PKVheU50O+BzgidW1L+ztMwWLtBCOVSolEOMx3LcW26rEzP0OEzU2JbBuLjzSES0ekRyjzQwgIBL2hlxzvpB1JXc3V+aeBczmIT09aZgW+3qiBYiY1VmN/ydWlkkA3kC5kw4OvCoqkji4KR1jIayxeBBxhcaiBQo3wS8R9NkwROIlxnG5ya686FWlAwqqAdhz3rOqDFs/gQtsa/Eacpp4oNEhOlC5APesrjlipAxGrlBMGLK/SKeWtG6kC++A0cS9AyvIFlmN0BEWino5VjGj05tMBKqCTibc7UquUE6CA6EUChN7lDXnMWfV6rAY0A6tHtASbkAaMBypxPKh8JKN90hOEEM0MhoqvdI4wZcfsBabmYk4eJGKiNlst1mvKfTQhF1lxpdzihsaS+utZSjZmRbdaLcLF7QnS2pWLZ99vVIt5Y7pGnrnQVAx8dI/Jq8Wfmvstysn2yN5ZPDZEW/00gzVb7z1ikD6nh/SrTTlvuW2LpJwoi4HTlKbfIrwahJwl1Lvsr7XFQrL3Xj3Vv06UPeGeEsFCGl+qU81CCRg5p8JGi1BwbRFx1vv6U6AzhnC9VKSEGBuSDPLSP8LwnpoebgoYDQD2/NCIYx6+aUDk9NMkNxT67c/j63sqQcPTG3xCpATYdS+zByempN2fYfz82dB8n+qgEbtgAg6+sqQfqASm1p41l84MSzl1eqKKUAOLinRwlkcjuaAEW42C6xwg3X78DzuRR72yZzVPoodZ1dwkeDszfGt94XrYjrQJOGhyBFv5KPJI8NicQvEue70imOVQSLBcl6hexeKBHcSeCFF6XTLCKIc05DM1FobC94E8DrnSjLEg5BPta8x+AizIPef5QNISi8qEcsE54VfYscaxETG6ROlKwkEHAlY7q9aMRoDsD4oF959HGjeZAm6IfFWTm1y/4HWmm4tDSYX8tZJzJxlYtmsJxTyj5DZjybM+/qvP/VZfFhYZg73elWzGQ9wF92XKak6bBE+TanDAcgeVPyO5fXGrNDLi+zaQg7n+NHgGcImxJNtbTL/JbOVoAwAEuW+TdDAb5r7DjWtmR0kxwaCwBQwRKjLUCRd4zL5r7HeoJMk8gR4r7ulCJtH9OVG79oabxYg2GWV+Wame4lvqXdoaM6KPEBniW6dKtEN5LN0ziWbaRpalh7BLmj64UCPF0U/NXwWHkWZ7jFFpJGTBAghcRGud+8aEMHW4pHhqf2IeUPKYrGJKW0Nyg5xMouN7hc6Vaj/oH202hTh0mZq99Q7D+1EtbbtP9aEgtkaEtUOMX/1T+UVGcN46mJreN+4HlvwqJNcmNF27n/Czlo9NOz9EV6foryj2U+w9NRFZgYM9locYSe/4et7KMm4Pe7tABmLdmvu67MHJ6a8w/Mb0kdWsw0vANgMbj3I+agHE/VbnMgdZ/lWiZPaPz4pJMbDnJ4UfVChBI9QXlazeCvNZaE0siXrN+cQ1Ge5M75k+t1cnh2B8bZg4FmsSQ8EXEPGhILMZ5SezubBBvVQcUuYci9gosWAsatnQ3OVqM4weqnkoZqWUtvBB4pin+gHsaEKbDDQKOoDylMJFnoRByJY5tfTbmpNJvPrb5ptEEO8qOaBUgsppCjyFEAk7Mk8xTTsTCebFHpAIJIzeBzgaLwXof2oEOWa8Gz0D1ogDWhpyBHeNx2fJ6/W4rwIMc2D0Y50LuInd/jYKFJ0Z8J807TvPgqBEseK+qFirALonWJIndSrxLxJQS/4Yq5sheZqOEtuFQn3A1VMO7BF8QdMZYuQpUKDD1G28jzr7Hep4FB3j+UYLK7CeygPgEHc+awphHk2oFrY8LR6YvRXSkdIYOyMb+FHSRtyBwvJpVhIB0ACeEvenaMZ3/hV5YtP03xFNoCHYXwowYZeix3KGJi0iZTwETSb1PEzcneh8BQz14e4tODxeaEYFBTxtL70ns15ij8MjDpsGUA4l0n+2pTCnJID4pjeAFeA5p2UhFpgMtjVPDrRUQA9f4IQFvr0tWKdz2Vd0PRSkuJ7K8R6aNnL2KRa0hTx3o/D1/ZWc4A8bczJX3TG/E9n/AHUqZy4Vg5PTXmH5upf9CVcM/oFTMRIPcrLr+KF/Ent/UwWSbzUJBgHgzY+KgcRLIZGb90ZuBR1kauvYQ8LMaMPGvvN5RkiUrGqhPO1HpZ48zHpK8U0lZHYn4UJ0BdxOyw0KUpcqECZeG+JeFSRVmVi5kWmBCDRXQeMTByQ2HQs0MuZRvxSARZU7RPsoomRaAMTAXagI+3q4b6JR0oxPRlNjExuhA2zb/dClIE7lJBIVjiK+2KkbKhrBFg337TUAFwF4sx6e1ARAhhApQ5w9ijxlEm8kRQawwn+3N70mH6yqRcJo3Kz0m/8AKK6yJzCeEpOiPn+UiDX4lBEU8OSD1EHsiUYD+q+23FIwfulINnZE7CxAA7URGqfAfFFnZkEZEV3Ub1DjS1QwNbLX3h5oLEoqTMMVMGQHGGi84S+qAw5P+9iDcgibxIauA0ZJZGeTP2d/wCC5tNFxdynWKWKU1yLWvTT9ISCwrq5sIXcdaumrEzLZxJgd1t9KxkSHk0AAgKzUybhPSRPVY0igIMFeKoLN9Z2eekOyFJSkaWCawKFzSxC/ETHPdUjEICNLCXGuHmopYI7XtG8Lk4uFAaiGcoT7KYNAPx8fqipxLHV2JDbuDfGwdgYMTpO7YTGAXtTyV0Jw+2k9JYPaX1SE8FHWB0EpBjEPZo9h6aNQ0Pn4rxPhrxXr8FZvJ4Z+PwQLvfLNYMiR6aIzVHu08R6a8w2AZBx+LG/JOSlfW4leK9FJGjf4qKuRv5i+8qMfpgWuaC7I9AinF1D7/lThMyvdaMjEB6S+ajLqhBwp6EHSgRIeaZlw09JrLynN4Aewz0gvSK8kMa34o0AxxuAJ45xQ1blY+VWbiYc5Hyp+iWAS1QAlAnKX5vs6a+HlsXYaWpq/RO5/NZuUDhMPVWqYneAniXvTcARS3R+ak8F4RScUjsfKp6eQLIz0KNlQiwsPHnW/ahlYiiTkfKmyZDoZqLOLGlohoib6FCFt1BQ+ZjhFKNsIRzEe3lWRi0uYBPjSmTct7z8qMRIGtplmjzu7VE29ghNgInBJFlJY6ASwEpPhNHyAN7WAvIWNw0RqEfEf2nnZBNrh7mrr2j3GfR+r7bcbIUAJPxm65d4wMdmtcKBG8Sz3damcTnyV9c6lDDMObCjvWpSAUoLMWINxYtisVAJPwugzc5P++NqOQ0xbsCLkIl0JZMTNQGKdRHZ6pSDSKazsnztAKw7MHQjzQHdaZvQ1konovmakcRZ8KVIIRA3j8s0YspvQu9IWqirlhE+P0uHgKVjF8j8UhFKxJxI2opEXwNIDEsb44VmDSPMqIcI3LyyHS1IklSdmKRyOsQUdzScZw0LrqT3D3QaV92klvxJwpIRTe1fR/uhf3ojJiRPDrUx9NCJlsBpFlV4asVLpSHECF9/8croXcCTxXOVF7ZYgQijHCCKEELQ4MNtKSzhB5NvmixquhdpqtEygxbPanj0W3yp4iiVZOE6yR0b8S1HJFzznlEVj0/NeXeyk4wyjtHho1U6t5JiWNyeKBiRKoIyBi5F3Cb261enGDjZ+aG3QOIG1DZZIJISqx5tmgYgnJOqc9n+Uu73k0CRjCZxGlASgpGdDdRQtgQ1yZqAMLLotPCUfpCLKTy/2q1bkniSJ1COtTsnEWyY50Hlule0tKSug6F6HtVwCBOhn2rMqkObREoZe+31Qn7WKliXuNASjyihMoCitUTKLqHnA+kqdsQ+CgeABvS//ADxq0jco0Jf6UMQgvSQI77wOtBbHhULZM2OAM8GEO/XFRJNVRdi0RgScDf8AYqGSC6GWMah0aJ1SwZtNHSaBAgUTuSJ71ItuN5iVx1HsqmZc+S+jVy8sebssNY5hgtr1LyUDiAy6JfDUzcykskC8kaeqf7EXOLHY81KLAJnezS8kGTYAlTCg9CD7KzYCZ1j6qV5EB3Jd7hQu/SFXLxDkY/UA/pCiTdan0KREqZoC6gFdKaEFsTMSGBjAxltxoUMuIXULt6p/NCpzxZTUkeO/WlZJlOMgPzaFQxB5zfTPJpuCki1HM4Ip458Un9LBNizg0nWgE1oUoiG+onDeszgjuOl/tqHJmp2KBwgrssnhnvRqhgJmdQwZoL1XBEWlCW+FfDuokxs0hJ2cSYoSShm5RAVDLg8pv8VOCYBhduJ1uDHSm8sBzjWmMH2KSYNbtw/KDtpWW4q5yvkT9IhvD2VMcA6gOCFlCodIyxclgNCQYjOughbeR8MdrdKiJEQCzbKb1FnWd80PsLhuGfsRSgd1Vl1iC0WkoaW6BZN0DNwnU6tTHM3gLopvnF5/gbEjlpAbhs97u5SGCRNIRJqXueSoEx1xQHOb8hmiA2KO8HqmAXAcg+idZqV0oXekkTx8OlCQ4vBxaF0Z/jvy16m5kGpwoI4kjI6LL0NTKRZ4sgOWXY3g7FyARK2AzZlOs65ozLGry+EUHCXZC2QHHKpIXATvhrBRMgdY+QpfuIkaJC4w2h1bZSiRCGQ389+veoFQbe0EthbQJBwgmgQAErppHtIDKpvp81VLxIQ4+mdanzEeD/p5odIE3lLLcN9TxeBvTcB4KLqI9aLmCAvwfbUlMUDkAB32Cahb8FtREZxwikhuIxaYW63tTkMrHxHee9FCxHcNpfWFnjN706KCxvjhGMnTfj9Shn2aRZEq0X0i/BRcvFBwUxOqO2tYFsSOi9SqGm5SVJ41iEKj3fLRmtU9l/aZhRKzHAvwEUQaAcdCktl7VPurrGQt8w7BSpQEscAYmdKgvoJOsl8eqsrqTyH1xqPVkZ3KnpFRhYfmL8Ld4oDHM3c91H9lKaTQSDG918LNFo0TwtlE24xXjejQ3CRK5OkcKvMElwcRRzKAkYyJRkAl6r5Kg6CNm6S6WWKAFAhY7o+KFfDGupwVh8aWzlDumFL4IDnLuiiQDT1KEWy/dXzQbQai9pjDzieVOzhh6D1QtpAOKxBRNFJOF6hEzAmZBbnMUUnSBjW49w/ryrZYZAuw8kcxJvsiiCCsqyjOrmGuJo6Lm0Xbgi75hEhN9ISxGU0aaqReprqhqut7NsTRksojgFm2l7nBKVQNRiL67zPKkkJbDgCXlLdxLeKMyLNOA81HlIrc41HWTolHyCI2u3p5DsU6guOgIATorBHleggJMDYMnJYuwOYzlCVkN1FhFt02mOPOllhxNhDLfucZU3UzxiIhu0YVQvHMaeiASu4KiEYgZIXEDvm3Oi+gEjZZJAnKN0xaauJST3HoNoiLKoyIT5iGfcIV2FPNzBpgZ3SWrHmnszSTgQsZJCd6wc6gXnFTaIhi+SUxlioCSCSJZBSDF0LXyhF5pdYLi5eyNDCHUu3qXAEWUQKmIZiJyUaRQSQ2Rs0vBvs1oOEHck0+lFmCUypa0Mhpww0iOCSzOSNMiL27CJmpkURcgh5UhCCZIcX6YjnUizLchm2OG5/Ty0o2GDEMTMwbTYsLXzTqtsEbix3CXjupMTuhHNGrkiNzQhB2SV4bjktLmhu1iZhMGhBvilrOSCQqSHeIN8saVmmURv8AZAzzKANmGcS5XvQriSO9acyN0xY4EWoOEkk6wO3CEvv4jEDO+gqZ8U3ehfhE0dEOI6lPC5zvQng4T4mWR1ZiaRgkSNlC4TdMmbOlaJYtZegTeO03vVoMnKIsTK3Oc3os31hTILqTnCel2zzzHOqPg3pDUGGM0zFCQUlZdY1jdaoUwNjkmPNRFoh7j7DZOrjpkJWjDjLO6ksQCTiSk9x7U2bslwAR0IPeaunnk71kM97buNXM5RHKJ7PisPqXqJeXtXvVxSHcJ5NklYaJhAvv9KeQ/wBqQOVd2GUUNOYi85o80JP6Q0Ldmm24ESlqFat2p3gopzHo+acDKzy4R/uaUORHTNQG5n00w+Uu1EowYk2s2HfFfa4KhBMS6yvVo2EI33J7WjETvonlDzlK+LU4uusd2zSaHzsmrpsSEyCayiseOdRUwoNb467+NAAhZ1ukVmAX2ZrOCh6pUJRLm+GTtQJaw7hUclm4hP8AdZOQz3zRgHbGGJh6xfxTmSjLv/WRVCVESQLI6kGHfWbyUNVgCuhME77rUGxPBWLRtFs2W8ZKP1FRxgUTumLk9agSlN9JLuOrRDaBoXYoF1MDbkgdd0N6EcHDfvHRHnO6sqJBmcBzyQyzY8WUjSgRtwMuGdMavep2JTEbwhOkJxy86tDwkIRADMwEty9+9ADkxIje9ozgnERrNqyyJJoiYd7P9qW0osTMBZ5OL8N0KWHBK6XISMCOVQksKYMjexG8gC0241ZulFlgm++661BRUlYUiYeQx0jNNSMkxiVX5qQggqzFCpJIdU2dEBelHmiVRRi9gi0QDluvIksuHLvJgWMVI8mSBaFW+VXVPSm8ENYvAM669IvkpzCqB32SS62Xd9JghwL6gMmsDxjS9Qd2Sgm7acYnOVJYomxkHNj/ALAxacaVb7hFdIEYNG5fSIjcAVB4XkwWySAxv0mpupBKWlOCbA8uNXDgJVqJo+KgJOtECbv6QTMeBYxd8TQPLrniKDyl71dvkg0Z1ckEqf4bpTXqzelkpBXkFXoHxmr7MkNy8vZM9dzW/vHLA6jpNMRoYFLygCc2uaNY2aaSpyIycIpMkLILATh1ZLaRfNmrwGVlN9pMRPwq9+Pqq7cTSzEkTSp7DtWkYe2YrAIp5gD29ONXoUAxdYi+L76MgMjiUj39fwmcELxut12GDb5ndH34oyCWGsBcW4KzjpV+AmLQk3R5TnXhU9uQWsq862SQqTEYzjei5wu8UqI1ERMkm++W9nSEUng41RnkqE1f6KBiYQjz3o7DCxxOM4sM4i9Y6meYBr3qwVnoQPulKrL+mKjMMnPftb2p2pCgKEjQEEB+IB4GGnUxa+KCTlPRpfcsG6WWtUzs4xHqiAZj+z+Srnb9Tnsy+zNETjB5Z+CvPfdKBu1719rhQUze+mpK9PwX3wnJj9YA2gbrgv5jm8IsVjcL2HSdJzFpi1EMRAe5NHxhENWggkmLQ5mFoOKZDqwgnkVGxcIZsAmg3CYuENrVAEpVgMszewSWh4FW+g0FzCwTd0PyGTcUcyQeKMHAWA0p7hAISkIY8ak0StFYJgN/G686jVHEsAJVIVsI2ZoDYCD8PM0yspj+T6dkTpio1hVd5lZkIIqeoEJwogSWEBZjPOxfgbth4wqsarl+8sU7ESWCJZvd7Yvwp+IEKNSl2ZISyWvebtMhdQ84H0lAQdfj/lOB0D3+aeUxHm5SgS0WgKd5pzyvv9J2W87oPNWMEcHAwdd0x3okpeFQLkdAjOKGLAFznXN82vxpHJHIDELRdI8yY0per1lkhpldYib4qIo7okpe9wkRYLLGad7BJwSGOUKs/wCosoJdSm8BF8Z8bEALQhIGDq2PKFISFAjInVv4mmWJKbsR0eJbpyqaqZOhLPTy1N2ERvcBnpEY50IpYklFss6pkL7iMVDTextZAN4z6d8QiLwBxiXdKwceS1gvmgAmtEKO484H0lJM2pHM1wSPWiqpHLM7vtqVSzhvibPBm9TvFx1LjmSTzqwCLMSoi+l7cZ72hRG8lkwbsqzr0KXKiRtwZjriomp5MMnzHWi4BgC0GCxiNwtFq0BblpcAeKBxY3Uq8WPKHqoZiWLMO4TS8ZmR4VdhhMaSLF6AG8Txq0IRDkBJP3SslEQ5BM+qZdRfEoPWz/w/qc9n1OdeR8V5772fS4UxcYKVSuv7+BwPPwK+g3FCZhnSbi8dqFnq+FTeKcW3DfKwOgNixoQ2W+eb4qZo57jFKSlh7EdjYpKJU9aJOuvyW80fOLDxJPxsdNukH1wqaMMPDDPfkl6dmW+eCDsUyVhivpKl+TmoJgpbeE3ZjxX+1aYvVM8SGPpPUW3065GXKXHSvtN9ZCpMSYk7ppihQl7KwphXkXo6XEV5QLiJ5opzQq9n+6AVLltYJHioXiGYvLPJETSFXSeCrFTId1PE0hS724yf5ToanrD/AJUgBRD0fP6WwjBwZs9KCuBJS0hdUxKRRNMATebEi9i8muL0GQI4t4QrnNo5aTeiFETG0yAIcy7tU4UH6Bd7bE7wfjSKUpeFrJN+EDa2SLUJLJeAAdUMs5M/FNWkpvuSjdKbqYEBTTAGJZMxFwpNNC4yQldE9DS0VFWiJzFjpLNnFIdVgncRJ3JZ50WyyFwgXTBrAnHnCaYGzHt63zupxokGRuwzdyWYyVLpAohJCu4EZvQxSCuiIWX3gk8l41BQahaWCLdBbFQQx04Te+d/KkmYohvdncvgNpmgcza4wZOi+aOdMFZzMERz1nhFMyYZtNjSecR6mk0EgeCEsxYncUdKSIyOiRqznecavzhVlWaXNhSyLU7tskb9miQeZTSonOCMu+c/7ojSwDxC/EGJNZyUxRHBslklpOa9otWvQ8QjBbf6Z3xIlAjBLCZtnXvrjlGTduEz/K4J3gB80samiuVEZRrZfetbos2dRRevQTWhloPLQVlzpriIqFCAgmUYgdLAHuW9DJP/AIn1OdeR8V5772fS4f4LXn+ykBQSz2BHSg+AHP8A5NJCRkD2vTIMAjoS07/bSCyqeaKDnKpkFO+uaaubxzmYTpHmnNQcVMS8t+NBhuUDUlIZtimZtJjdMvmmBEHC3sozpERFtNKZKEnX/YU0It35KveaEMynwx81eKGfGwByh9UGQss4uJ81qhQ4wl91Y3Yo4rPs8K1LZESxEzEc786IeRRWwKj+G6lp3PmitWcc1M07le/hFMycTnMr/aXqEyvP+lCswLOhXu0wNIfDTAcyJjvBRGRLPWhwITyUJbXC4vLxFZWgO2oBtVjIksc2aCy2wc28HLPThQkEBqoT1gud1hjtatLgjoQ6XfH6vQ9m0cEglq2yskdC9I54vNq1u0z+HpaB1ovn/lRA7FpYyOMaVZz3uheZdIwwku+OTU1CYomcgRxlpOiplW6rlXfU1DRO4nz+lLlmzjAzW5PwQ+Y2S8qm6zIwhwiN+hsYKTDhSkrYycM/2jgIbDreLdI+6twV5XqH6h1D+7EuGAdqWS/m4+KCCP8AxPqc68j4ryX3s+lw/wAGRnlExwxSqy7GAWnflJD0CmBhQnMpsssdnikxSJSVpawMNmMUhS1jjKaBpdGdLlISrK9lFpLlc0pkSumNJ3xvr6fjSl3kOYkUNSJPK6W2Lt+24/HzCnKnU9GxscoA5hPINOzOShWIQyzqeaCNAmOpHzQIYCjUl0xpOJ5xSENKWJAKNrQkVf8Afxtl2NbmPT+pwcjTmXAG+HXhSjpFGGTNsYl8UBBKX7sdpoKwpf8A15zS1Dj7vp54U0VBFY0un871Ju655W854UKNwJeSbHO3nhUfgy4FHMhEG/SnYyURkzM41paeITxWpHkjujPlpvBCRHOFnljjQ2qlA4Zm/wDP9UyhKR1fiiABZOC7DFtPikFwpbhkudkqBJQO5Up4IyazNuiUSywQGM3YOFRoucs43X18UjWsP9/1QwY287v5UwVxB2hoMqEzarKoXUCz30rGsNXXl87qITykcEaxx3VAMQmY4xHOkrATMaTjnV2EE+T3eoIb6urV8wijgs1AkZsaxm5JbPigAkEJcXSoiTpdf/F+pzryPivPfez6XD/MnKWJKcPK3eex9OYA5GKanLHW7+0KNyehb2/hFez6f8xgKY/fNRiWXm7QDGyCZ1/BGR/4yGJKREhNqCimP/NZYSidHP8A+vOKzc/IFQGxQu/+OoZ/OGJi2ySY/wDHkx/5EyjbE6T+JGBLW3xnZ5r014r0fl9jipYJqfbMYOSTQwE2I2OwCaJJ5X/RYElWN97/AOHZ+IBycfgGxGbNzLb9jh+gjWCYO6o04HYOlKwYwTrtJUrXxxn8zmIzZuZf5f0+H7kflZ9v+PgNAerFNl0ZdbqHrZ4Da1I7iYnts+l3bPNemvFejaSKVxW8+kYxM55bPscVKOXHLxFQ3dD0/NeIbPut2zwPp/McEqfBNGpJO7gtrf8AwCqFCLunRSno1zZ2fZ57cHMJ54XrExX2OLa4zX5B80UVjJy2gUwn5oi1BAivKAl67tpM22XC5/1ty/iy4wDtNgFcjuJidubFODhMjV+nokSd6F4jB1ULboooWJJOJv8A8n6fD92Lm/4/228r7Le2eA2vtcNj63dsRlkTq0LhYLqS3jnsQDuMTpNWVXlybJ42oJjBO6EkJ515r3X2OKvoW6j+wsfygUXT0YoBBXeSRF4X619lu2eI9Ox1gidDk/Agt7nCUWoyX1dWJWLIZCN1wZ/ec0wTm0EHCAe7sGezHqps1EmOjDQs5PbX2OKnBwlk2CQErfcQ/DWsJJ5qYEHBzgZn/VW02znQXNCCgC80mjMuatoDCaIe4/NW4ZFC3GFRQCDoCycntQAwfIXaUAIv2Sls7dEY+SmVh3qRK0ryj3XnVGbw+vzX2O/TDdzZBmUnTDy5f4+EefCT92Dm/wCP9tvKaTLtpseNhSGAHcigAZknZim0SccyM0WFibyue02Kt1+TZ7z5pdGJDmrNQFIB5sztMV5v1X0++vt8aqAswcb19jiocegOj/tUk7YbdZmsI9nk+Kc51BOQp4Svtt2zwPp/ACz8SHNmdn2uCvpcVJEsn1FPaStISXaXwpEYaHc4iOWvX9ka04/A8hfFTDlU4MqfFAqDNff72ws9ApG5hHNmfuaWBEEcmCgOZl+TZ4j6qYV1HdeXikMSq9K1khd8ENmRhpEncoEIBe6WPoj4aUUWF5KSrsDxFZfkvYzQHbCXGEW2lD7beVaejdmnnPpqZhITvugin1gsGLXbd4Y5VMaECeQ/tZfwOcAW6leDUPybUJhD+9KRIUzRa7ERxuHzjh/j+WfP7sXN/wAdoLFvNCWoiLW+23tGPp4r7be1f9bFAKYuvyl8UGeGerBV62zgRC96YnP9K9pQiya1HFaLsGlEGVPB7K+n319vjURS/scVFEzB7pILE05VMTe+6rLRCndWF3Q7GwzJtoIIZknZilHhF7oLFIoU33/lfa4KRxydsSf6X40ZHh7lN1sM8wrI+rKLnyCI8UDoIsYuH9RBMSYZjThN+1EsDIwaaJ/mL0PtsLt6C9K0MpXWm2RoF3QiP7ml3WCh9CxyOsjHZKIWARyKPGPZWduB5lDDISITeXeEq5j/AFf980W7BH0b64KkUcAbDg2B52815/1RlLgd4UI+9YqSOThwFPVGzmRxE079OdWYTm6BBwvE8N2aVHH4pPHJdQHdGgZPL5qYI5nsp6rIUMcVW7v+7oqDoSw3MRYeDTmYZ9jyUCNSdzwQdKNFUhuRZyt9mgWT7ikaiF7S1HS1XYvxsEQPuX1YmBHFQAcWrYT87iY4RLgabgCRwQE+qUQwjHEH5/xGw7Dyz5/dcC9/b+kjjZ8MfsBm6wc2js0Y1BYvzSpA3U78jPiijKzGKQ9bDHK9SrEWjQF9BQsyou7W8MrxZh9V5T7aVEQRG6zryfZQdCYW+jUwAgudwvFm/wBmvp91Yc6DumjVgR96+j30fGJD0WLsDIa6+Mt+aKs4UjlA+aXWBvuCdYO9RbFRjwnzwydHYTn5KJMYrnUgYCN+HdmtAA33mj1KC4lJnvPpKtQUQ3s69xt1ohNmI5qkBObkU80YBv8AqgQQLjmGkzw97SXWDwf8qFSzoi7LC2/hRKeKBbCJxe9utSVscu9/vV71wnScE7/1QdZAdSjEMBHSHYS8L/7aFhDHorwqRstKQ03gEkb7HmoSZQerPoq22L1S+qQsTpeMleBBfStL/ZSexUw5Jt0hPNqbKir0d/s0jCtP0fxqU24Ubi14b02cHoKyyeg+j3rcHZ3AqY2L9umFIELC8ijjrz5BsU8IXQsdLlGA+00bLxn9G69OREF3sRW+QPa/xX3vGh7hg9qoBwQDhZQbk5nhic5d2wQQn1CTi9r4oDI8wlU9B3otNx/qmpc/CpGlwBwjfuJ/iea2OZv1f9xMah5Px+hXiCe1JYui/s+23lARBL6or5aN13PcHgpG/B2X4p1mlPJtGMlAa3E8PmiCV0ubK9Kjb56M1C4GZLcXlipDg5XLZQ2pNs78OTxr6Hc19pur6fdUkqy3U/rsWvBCfhZ/TYifCq+NLEeYpd2XD0reaIhgzmmJ1ip46xdJDnq5owxwPmsuQJ0SPAUAE3HQ3ORspCYVZvM0Ok15j0bC+EnZvQE3KpGX+M2A+o3qt+pZqCpEAm8ZuFjvXmHxSTNkk4Mb1h7NHyKFtuDP7QCNhgGjLwh1FvsrzvlU4YGeg0J5mvznqgbgJJtuu5ivZ2Dm6vHUG59ZqIHA2CakDcQjysVJHtI4EHtDrWF9YbFccxOgPY1Eo3bdv+0MEQ7kk2+w/FOKuy+8C8M/WvuNzQm+F2/7RjkeGaEaSD2/2sn7pTjJL0UnhrwFKlfrFBe8KVW4nZHxXg/X+I81s+rrTqQ29n7UA6T6Xz+jzXprPzfj9hOMXn2+KBKIJd8THurBxfKtfc76oYNZ9JRlyPmoCuvDSKPLeq+x3qClIjech6K8N6oW0HZS+A8STrGkxR/yhG8bOL9qi8BRvth0V2PpwcGef/aUjJ6j+UQ5gOz/AFWuyQOY8ZXznqhKwlk4i7IjAyheiT21lVB0kPYms+wD0QvA1D5VgxoZ7XG+ibEgRc6t/dVolnT4FCFQn9KJ70CNgPtQhcXqvscNBQOHupj1n2k+aOmWXr/I/V5Z6/R9HioDISM6spOF6xJNLlAHYioIQG0nKYi5oEjzRbK3yVtBtKQ79RWdBTnpbjh8V9zufi4qTPH0NRP6wbY2O3wURFrQ1UpNIVrkjgyHpf8AEXNI+zYYRy+a9D2ftcrz9P6PNemlK5vRsOdAPL9V3U9tiktx90EwXQOhMe2nJUJo7jRnyPms5yJ4r7HeqduThEhnvQADElG0KPY/0oANiAnWbt7FvSUQI3FX40bmZ6e9Rea+23H4GWAXpq2TUHgyRxyxOJYod/kedmLL1OOsE9pOuz7HeqfJk+SNnn+mvptxToNGfCfNfT76WAzc7ydr96XPldk0QR+oV7D2VLoPbXmvT+ryz1+gqR9ypw243eI9NZ9H6YXgiD3KoIi6xqwJeNjZFdp+QOPrT8fHyqaWtXumATyI/FtFR7EUBS8M4xAP9nShyhETclmh1abzl9EpcSALObE+P8T7je2ep816Hs/b7Hp/R5r015j0bPD/AD+r7HFsALH5Ehh9ykR8lG8QHkOle4oXMacQP2NelRj5V8fnZc2YBO+CPwCWuuhRbmUODdzor929W+4KbV4yrI3ETDWTZ5/pp8wA7EbAdceR/jZ9jvVJTwsTpMWOFpeJUUayM78o4WoSW6Hss+zvTnzZN0FO5RBsC+/0+Sev0eQoV0l3f+NX0aU2wj4/TAM0g6D0WwEkxC+jfEVZwgl3Qoew3/j4+X6H3pC9hTyUCZQZvVXut819zg/xXY/Yu2ep816Hs/b7Hp/R5r015j0bFHJ/q+xxfo+q3qfR1MgNZpSPOosmU+A+KuNKBy/IEEn99ibGzbVm2DWbRz41MYi48v8AduHnWTZi+aOgPZtgmdn2O9WmiT60GaZkdiDpMUs/3KvPff6Rf4/X6EJOBJzsff1riMaJkaDgyDHOfHF2KNhNJu/HKa+UB5StJtbOk0yGiOaiomAES8lkiJb8fHy/OX1RMc1v8uVEhMEr2sPO2+mBl35QHdSmzAj2FOet/iTdQZdZjybG9N816HspnEqH7PY9P6PNemvMej9kvscX6CIkkdbHlvwpdVw7J8UujsMb1RPCmymxx/lGxX2OPZNEx5l/K+o3/g5PzNImV8jpM1FtJBOKXrxGGFRceHDedKVxCl4siXoByKU/VlSEm99/p8n6/QfCh7VXETd87PSUrXrVa9umkws8Wbb2svxI5YKb4h7S7fM9FM7cEiZyfKJ/Bx8vyhLlhOJBSetNi1gaKtu+yyQSCb5Fmhk4ADTIQdVjrFSTooQ2uHBk3f4iUHYx5HzXoeyvHP2MkcfT+jzXprzHo/ZL7HF+ggLAeBM+wKf0WagbKw6JWbY4PyjeQEtJEoFm+5ne9tiWJCvCCUNlmFvPKkRqh2Xz+Di/nBzNxyZIelMFZ7inloR5EkcJRGsCYFoCyDdExyrdUq8J4WkLTf8Ap8F6/REMJ54El6zzRVlAgEpKEucsS4zc7J05Vg6UlI+/41l+KfcE7l8bXHM9ilh5tG+EirbQi/MYfJtcfLaOLEodVg8u0F7XHGQ7tJsLI+P1thRpAzhrYz7r6vF/huRKxbJq8PkoxOsHmfivDP2ed+jzXprzHo/ZL7HF+hJBuUAsmDXh6MulYedWrihzkGs6NA1D3gP4pIKXjtrXFoe/wcX8y6VA3EpTzm1uOlG3wp6REOAw9KBFDbnEl9PNPcgV+I6Nj9SscT1QRF251iPFSqOjHiaYhRZehE+9hoM0eifjYieAy0P0zAxgl8Ssxy4NSBg3+297cadVFrug+KazVcHStUhPuz4oo6YE6t7FIzv+n8bCRNQdzlPQI2INqnyjHzShnYzaHPPJ4GoL8sYckzOWJ6bIljY4+W37rd2HHUzHQmt2Q9xPdBkwLyS+aG73Mbw57LYm6S5GQ95O1b+QPSXs/wAPhuL4rFsNzgVj5nzXhn7PO/RJYw2e5QOqGe5/r9kvscX6PAUvVKSTX6ULExKBWQQZ3gjM75OZrDz2dKfq2flEqtxOyNvjPdTuG1JF0xoSl412uL+f2O9Shb/Iax0YyBOcH0NK3ce4H9TOi+nqvscaEhkFncxO7bnzppZRnXBeaDV+8jHGBPijDyNxplIuO5B5aRLsKgHysOj/ABWbBp5/ECjnLhPMJ7WsHSsygvIR8VLSSJXM5vcaBC18RrKuh5oNecoXEhccagLMrkPL2PilTO4Hqkd9KGQbE+M9tazZWDPAL5qKC4p8/DzThYDDOJkPZJeHCpjAqJhHE7mnNU+ITbNYuDHqKv3cbLvyVi8EjOvikylLHhCo95F2D+0kUyHK9TH5c/qpQ0XcDy0lA27Al5oSGG7o0T8wTlC+n+H5r01i2eEUoHJ6a8M/Z5H6PN/NfV1/ZL7HF+jFQw+mN9Lclzl/1sqY7Wal3hB5BWsMyJySTYwDV+F+NmspFjBN1QXuq7YgTJydr4KZc/JgYA84PNLr4CehPer1UkjjATpx3RWPlRASPWQvLfhQK2vdn/UfqFjdh7MeWmWc/Oilig5orCBkFPBD5LUfdAE8WX17VkOouk/9BUj4kBNZyjgUALFRjkFLc+jdNnG/c7531mFCwGS7WAyNoaOJiHVZDzSPCF3X+UQXDVBIir3ardE3Xm2KxUHsl1ixeWA82qzpfNBagS5m/wAVdITshQnCK78o7uadO72yiHCV714xUKN+v+KXRlAHKMjbgTfE0JjdI8b0JFYMMJFPuGjQSHTSrUynCgFQcUZ0Kmhs7gx0idrRUIpt7wp1CTN8Tk6U5TQeX4oBhUdU0D5Rn6AeFJPiPmvvuoI2C/uCvUP+H5r00MMlJmiD3K8ErxHprwz9eNopBSY/R5P5r6uuxxyP6vscVQ5oL2pQC5xx19G1EkBtBRNztH9emzgYl7fBQPQ1FKIRjgA+sw7GBK0z8kXS+nlCPGvsdzZNGJgc4bbFFKPeENGyILO0SlBkbzmKKFuEuaPPpSikpnhzpMCjHTQyu1Q5VHnBscly9lDAF2GY7cJ8UkF3HNb3RetY+VeNU/tQnqX6lPgN6M2vS4GY3I4pUFRRndnNJ/dD3mkJVvp7pWSIYzOhRlRl3xOO8UCpgAnEk+a14c6zntFTJMMzvkfLTAxj1Oh3sDyRF4xmOFHrsQwxdE3YGRHhwqQYmdjRVfJ0a1uDXlYfD2aRZ0scLj6d6EKeAc1ODG+jE4hT5QknMpk7snqPeKUmVmgWVNmJtPKo8yp5RO7mJ5rhAvayUzmfCaBTKCk3w+k6UtjDOJmJ+EDkFLs2LgEB960c7FgjqgiOLUsM03VUI7+acNYvSy3b65ngEvKiZZjWOlTpnC4lCPZUIVhfF5PDQTFE9ZkRux5pRaE8z/oYqxTj7UqpCVy3/j/D816dnivRTbsnyf2vEemvc9v6/Q9leQev0HA6L0f2vq67MOR+r7HFSQ0ZC1s15ml1UJA8YYozrIG/A36r0igWv/J2O3gLq4A1q844y+gZuZCI31fUh4wF2E8a1zVkRCBZHGKRmBF5C07BREmI/g/E9KKP7RVLDIRuIVmhHFK3CGE5bC0eBAzPTZAb1WIM6u0+kqUJn301HEocWVB27UxsiUTCMEUqeoPevT/KhJ3jzBt+23FNaKIc5fJsIQAYPZRRsh5alLfRaN0gx5qQn39NRmmY5m54TdLQyT+nK2ewFAA6j/figAWhAdyR8zTTWyw+QnlNQqjUnMp8VeXP4fih5m4dL/yrpn1alJMrnpMU4LWzu3nz2o8VjDxJH2FOHuO8PmpFVhPID5KdaUyzi8eKyj9RRIiJ88rRxkGxPbqGr/SROIzTM0i3Ibe9rQzdm6ZFKhEhD4fMVYQs/wAF91e6YE6EfFOSbO4i7pKdagDuDuPxTKcyE6DPmicpbfAdAUhgYETfWPHimXcJ5oLZs94Kntssdf8AdBJhh7az8307HnASdx805IhN9cv+H5r07JCqGrzHsp2PqzXse39fr+ygQHf6fmUCWlQkV6p9XXYwh+ojlo82oCuaDu8MxanYhk9unXShRZhe1ftqbD9WKsoEBDjixo9uVMxymdT/AE19jvU47l2seKagU41JbylDxlnOCitGIXP7HGacFSK+oqwC5JusZpNleHoQPBSsUXg/VFYEEqci0uGEebPgpM68OcL8UUDYD0x7GgQNHObO66oX8R3oELe4d0KeyhZ1meS9VZMxcdw84qRBwEQHPW9Hk5WMBpx/s1rW8DN4Sew0nQQVnMOjs9ziielH6nPYesUroGXfIekVNIIIOlnwU27fEUc6wmeaPFFKsz6KC8tLziP3Rj9Nz1fFX0mR/ju7EXS5VO3i3rMfW+mj3z6aQWUr2t/aJxIvKD+OwUbDk51jPF8Vx0I7E/FKXX0OFIgOBDq+bR1plZXtzIfkqOmUlwRYMboqZ5WPd2nTLAscaJ9xA7/8/AsnBQkwEusx/Ky5RjpNQxNMg3UgBmt+EHpZURZP+UImzM8bNYuf5Vh9ldEB3KAYIHUVrPzfTskZEjHZpErynuv+H5r07G8kBFMnN7KE31Xt/wBr2Pb+PHjFEoEC18jT450T0JeHHmrgp2+v7KuaPzngvqvo8KdpfOzcjj1BvWcpB8fpdtESOYZ42pim5d3rcoBaLJN5XYzEkTikQLr6fimxgW8OLxWeCKjfCeIaVoeiBgUmCkiSM2PH8mjCWGXrR34gOF5nsJ1o6YoGrh8+9zUg5IeL8c0KJrKxguCHMSL1N8XQZs5Vm3BKuDS4xzH5KuqalqSj4Rb791T+4h8F2phZLeoB8lGi0M4IU8rr76vjqHKFYFRIdVAe3vSzZDLa3muKK+Cp11nqgfFaQtvNf6pgpKLziRjWcVDcjW+4dmiTUAhOA7RjqaiEwvJghPiFXhViXOzPjzV4ByIAiW5bdBGu9pSiewi56daiErHYn+6aQCBmbjczuMdqak/s1Y3CaMWG3ZpBJpZ4k+UMxPSrVyRObhp3E/UgYWMtKLpzHoKk1NiaRr5x9saRkt0v81OyWxRZyg8TPdpCD5J/lRe4X4+dpJvvas7bQI0uL4pjTInYf7Rj6MlTAASnqnzsFERNZ0W8L/1obpdE/ev4ShsC3LZBqBeHHWGucX+0GbxemajbwLys/edNlxFdUWjDqL30oYqp7oi/edEpLo4EAkvGYqcFi+cf1Wb7Wazc307OME+o+a8r9gYrXGNF5znhuq4jPeCT1imxg3M4WrojDMO+LW2TgmTMlyOFqweBM8YH1PaoLywh4I+6hHV2MnuK8RQIizleBUiXSdOTU5uWCQCJV3WaYoEVGyF3XhegK3zzYO7Qb9NA1kY8tIrjjUxI99kCQQuLEvXmkU6VOq49TNICTAtroxG+anxNzLEAh9ZqXxPhjjx5UvMyjpU4b0Pn4qaSzY9VRuSIXO3uruloRufmloMEuhOJ57LF5FyM1Jiil6te2oObQoTDd8W7x2alPF/U0gCTEb5tpnh2ooTvo8V6P0ytcPZSa5sHYx0IChwIV43PloMjwohIBQeaApxnCRzA+aVDD5IVFdHHmf8Aj1R1CoIKrY5qsgPeIjqeygDxPcEfmU7VcXElnfiamEo40lUjmIdKXfKTuKzu1alLoWDKlfle7TksMdqd+6gESXWILx3mKyIMqyEhMSQ96w3QA4o06YGNyEH+81FiCx3yncYirUphL8adnrt0wD/aHmqRI2NhgFJ+8KaSGweY9xeo6ajBwitoyy1g1tV0iQJ1Ij7D4pQGxIcUB7w7c6W+Jw5hHbDxrchR4pxkJW3sfC61MRVvBb96dzmWhAdQeqvmpPhbtN7NNFseYpmHAL3EB3WnNRaEZRuZVCKTsWbSZeDi36jFltjmUayOLd9ikF0OUJW6+8sH1TpLN1gPdQC2GGIIWe0UGh2O8URDIxG/j59VFdvu2EQfs0XYAfICoDJMT61D2ACORQD7/kUC984AVLLPhlt116UlcEGnCkQkgTc5f5Qg4DubvwBWL3QmkdhIv1CkNSU6WT+VkZuOz430V2KHQEPuriTAHavXdZmaESXCmAly3S/zWRYvzTNAy0p9QX0J9WKkNFveTYbPn8UCx3p5fqI5CiHE2bM5X/0xJwBkZUgMyE3d2kUbFhZDjLBuuvVoLLZbgiRrChnF+FHrMkKu4Hhv0+K36oLTKXW0VIVAUumGxbJa80cQTDinViIjjU/GVsdIObF3WXlVn4qCwjctDIuo61aQi5iZlbmhE3xZrNA6Q3BC4WG1sYtUhUgLfIZAwGOPGkODggSobQqkzmM8aTLYJ4wBB08vChGhmJsOV8j1q7QdhsjKb3sR5L1DKwOp/QpMspI0IwGrOtrcaB8tliVRKGhDqsROWAaSY3QMdbjOkWaPGAtmgiACN4vx6VpwNm8RE6EHl4VDUsmqlXF8SC6gbqCBSUzBXxO65ekM0+UwjCQKFujWn8geW0gk33RLcbxUZEgkm4GeR3+YZqCJOe+IsTnA/WmJCa8GY9k6xQSmJONtbjKvQ4RQaIrE0xK3vPWOFA7ZYvbKLSTeeFRpQMbwDJxyA8xXVOAbGPGpltZpJKEVr2KV5ZRcWtzR0JIktATdm8Y3caCmU1ItIAMQguwt5daZEGWbcQtzGzxP0zbl7KllYVcLPk1vjZ80iG+f9UwmEF6T/fFNvBPmb9/dLJHFjVbxRm92KBGF0RLbrFZXE2dAOS0/WofWGON7Jzqf2UWSzfW+sdKVlLTIS7adaMuPQaPVMeVeMI99c9atTHgPgT/qiJDCX6mtGEZ/ii0aZcppy+M25szS4WGDLiCz191u+g4Sfd9HqDL2fmw09zkXWbxNJ7YHIU+Xidq4ez4VmbUPSpCHWajCMY1PaF6SZumhjpkdofmlEyHchM77lqGxMjgKe2jG4HYD5oyNEeV+ajSsdVWfT4p96nHKX+9gyT+kX4c8sf7q9b3F1k8VAmFXvSMO48HpTIEUuDm8/inUolmIuEWqcRKScxb+Ps0jFsh3P90iqxcnr/qolwE3zMOnCL0pORbpd8NGQyFARhV3VDG3Xsl6KiwY4ufFIK42RxZKixusuk8/vGj9qJGstvmeVLzIDkgTPipEza8z8UsQrMkeqC9JGOIDfvQ0vBzm+m58QUbC0r7/ALFKYu49xPdFto3sr/E7VBBu+Gmu4B3p1I98/wAaNTiIk7neVkbWmdY15vLdejbxIF66doo45UvC1qnOfOo6PihRkohSynlU8CExbHDvihtIR2i5waSHn6f1IxhCC8xeJgteb6RqSneAym8MkaXkhxv3U9MGCImzedCbMk3kGjAxBKby6Ex2dQpI5JsWlZwTuy8BpkJXcYuO5P8AZIjS4EMhYtzxxiZi8Reg8sBN2ZNAi8gLvK9EkrcMQuDEpYDMWpPLMpmDQ1wIzvK4Wyt6E8bdtZaCXrQQt0Z75QG4dayNGxyNruYL5mM3ooBkYQwFILuuab91WLB7CRE23NN6UcwNYwQG03ZNDKS75KACMlYjn3O5S5hQDJCtoHfNudLCnIm/bK8C9E5ZZjcC4b1nkUAKAQBgMQFKyo8yFXgskb4uxxtK035ANgAKd0WuxzrBEDjC30kgva89qK2IM8NOriMzbNIMEomWGy2dYid2sUlAAV1CNJNc2zplJS8iWQsQmSXN8RMzaKONCLxa6Eu6Xk1tSUIUuqFFlvuHr3pQghfkk33WcN6NZoTKUXu4RDjWCaygu5LpUCZvzHKlAC5b3ZbTO8GByUIMAAViWnLEir1VnwUrWoAkZLHhMBCbnSzRueoSQxMzxutzP6YFy9K9ygKtAFLwS+P7SuJ0pIG58kVIIpjB2+loXUinf/lR2bwFsCxwW5LF9Ks9AL3QhpXLus92N9M7F4mLIV9M0hckhzILDOMUYGhKiFLtN4Z3Ogpc42pBzQdIDImRMJSppTKuVbq0SDLp2ojQGSE2dc72+aFCpYhN5knZKMRaTKwMnWKCJ0d1lJu0s6B1mnEBMf8AKieodj+7GtiA1yI+2KADw90+KCCP0xbxPx/ahiaCgZajC0rHSP7SgC4x7+alG5TBwB2A+PwfceijHS9FIFuXZX4q0BrXA4naalG5tWndV7x/P0Ex7jw/2mBXPPEoUubFzgZ7UBevM9d9Ta3gPEedg6jYJ6SFurQBOX1P90GKCPZoKa2OtINh/I/VNIBvBSQLRAuHdrNg7yxBEQkGhOZjNotT0IRvSMYOF5HIqLFDcug4MrZdIHVagsAFhli97Wm5bFnfQTBuSwBBeEiXb6RNZ+yqwhLASpiMxrEWKCjaqQMWtqjFry8wAIkjQlE2IEmbDnV7tG7uFufnBnDekihmEvBA3HTlR2UM3Hfw5HI3y0pQWIc3FNwUC2sty1MniQQSUIVUN83Wl1oKZJgiHQdW/ChyipMCWWbnyit202AzuZkUQ2NZbsFrhpRpnICws78xo4XNqt1Fa85vnjM+rUqhoWUVDGM+8nfaHWgsRlmRWbypA2TpFABGFgFnGOEcqK2YYWSJi15m8sTkspTUqNtxANYZZIb8KINc3wk3F2Gzdk4WuLKbIvexus6yjJIWtStrAS0DddvjMhjGVvgFhCERCTEt2m9ptDurKWHCMibab2UVwQmS08mbw6WjKeIMSmYXS2VJC1rBFZhASciztTwS210cPJhjfFC+cRU1KRBHRHHqokq4Z7r5/S9CRphXAcWPVQzwIq0uZPIdKPZkX35Y6k1O0ZIcYJ80uyuycOe81qM53rYuPjNBEvOkaW3/AOqANEl4INvR3oa48Df1p+Iwg36ad9adYvJyrKzM9fumKeBgPFcfPenIsg8zLHO9E0kRDxhxw461m42caL/KjAmwc5xQSZR/HlntTajO65fkxUcOQF5nEqU4KZ5jHkovIiLaZu0At73T6U5Es2eHHlQAME29fclAdhs6wj2rHBK0jCF4hbpMUDyWX8Z30TautlrH9Ovi8iAkm+6YqKmBJnhMVY0MnJ8NSIvqeu6oWIxbN3+UqEDgvjN7PLNDgyh1cUV3OLj6fqQ6r++fvCjkUotwbxfWhozdIzGq+uFWMHDrk676iQYzHTfy806/IZx/vnTvkKMTjOtQMhkcGNX7NBnJAb+PWooAYYz5rXKs2olce+fLSoEwIe0VKhreB9vaitlx89s1CyYLviY/l4q9VgZ3YYiohyiY4x/aangB8OcYrK6BuIDXn5rXIAjde0YzVuloel/lN7vDwJCoMScO0M96PyCWeD8lX0I35ak3zhoB3KantAERx39aRbEBPO02t0okkDv3b4pJKQEPFYikZLSa3T78VHaTcXcxWDg56MW6c704K0W7z7qKL3rdtY51H0EHZ/d2+tLwBbIJ730YsOX6pSKEIBYFWe9JW5Bpk1zuPNHEWAErEMIcrWjTdTCgMzGckPijyOI5xmTN+53o6wQlkJDrcd4w1ZrUTG5dzqxpSywM5yzv7HLfSyZssEQq2v4pZjhEYL8mnHneooBSkvHJ4DONaVTiXAvKDXV7URuAHILFs4zSjbmWiSuDljkZoALzV3LM9y3qtD8km4W9zqUiCvA+Tq4xxosq6lk3JTMwxnQnFLlbEXtBlgjXGdKAXQXcVkcaS21tQKkuMDRmZiZ0zczVszOTlRlgiJx9Mi2DZJhFNMIq2CCCGI4kahar5xJReIWJ1l031OMkHFCyve1KMJdw5SXNFL9Jq+nkDVDFtYW24pnMMcC9uVqv7bE8m/fNi2L5pNQECS3CxyZbcXfRnFgdZSxFp/2rc5CO+SL5xbdTvYkhoLRwTbfdo9wA3UUWKC0a5SdyKey9JRdEt0RIiI3ZyXxrRnGUEcMPPfc8lKJy18fqBgbYFnZCbL7QAg/B5GYZOe1REXKRkfgEWKQSH8LU5K83ZxdK82k63FOuwuGCiJWP7NaTf8FNGZ67+f60BAk/IGBsQc/iPF4TxjH4QTMX2xOyBZ/F9idqY5NgBYoMUBtBQkaDiMInhu/yc0q2f2tnuidH8G1YUR5Of/1+SNAf+hfSJQc//n/W9lOpLf2/hYSYF7URGoPf/wAqfcYe7/r8V4vKOl3/AOZWCbuKmvMIPMSlAJVOsrHb8PJeq8N6/wDJuwwyc8VHOoY6z80LK/BxP9x/8yTugu4/ynblZ8f7Q7z/AB/VMoQs+42+S9V4r0fpEMiUOk/z/wACU8R2Y+KINAnmibEz2AT1vb8PMfj9IBSAqVlMMdf/AIgGVP5uhbndT1QAcpHg/FKT9Ir7nvb5L1XivR+hM0BexSnI+w02MCHUv/jpvCR7P0GQ3j3Rw5vxRiJ+AeQ/H6R1xBDnFYHJI5o/ym/lv7f/AINgQuHQxtJDAL2oCdV+eUifKahKWnSM9w/lMg6L6Hb5L1XivR+gNWQs6yfymgFqVy8Pn/DcNAUBJI7cGGXSH9EjC3lUu2af6rH9LKAiSO1Hm/D9GdUKGOZr6fNCFKz8f9a972//AAaU/q7sABePkmvNemsnN9H57gBl2P5ShEgDvN/dGPgU7SV5T0bfJeqAhx6D9Ht+2sfKjQdR+H/D9b2V470UCLSsRTXufH5kxgF7VEi5R7LS30niilXNeO9G3CcS8UZxJP5ytcPZX0+Jsi4ZJ8/P/wAHfzfnY+nwrzXprLzfR+Yn6sUaCCd897/NeU9G3yXqmQ/Vn6GDwr4/2oxyq9v0/wCH63sqXcA7WrwGvEr3Pj8/PemhWLJZXmKi01470bfEqc8l6/Pxqc9DQiMNOx9WP/g935DmtEi3ccYr6fCvNemiA5v54B/0UVkwE7V63opZBhHuf62+S9U/Jev0Qk3f6mh1RI/Hpr3vTUe6FemPn/CE9L2U2C0WesV4DXiV7vx+YEXc+qUQcKoPwR97FIwfuleG9bfd+K9j2/n63sogtD5UTDUGvP8AikFT/o/+ClvQPZo0mcfinKu6rzXprFzaBAzjlb8p00ZOg/5XivVAZtxTkd6u1pBur7XE/O5GB2aq7mfKfNe96adrj/wnr+yoLcB6fmrhQDqyvya8Cvd+Ni+WsdS/4ztbn1Rhbp8Pua8KhjDf4BpTNuNv3dK9j2/mgn4eyvW914T20ON/0fFfecP/AIECpAVxTDRQvP4piDdV5r01i5tec+vylTf4EfFeO9FKxeA/HxUHAen+9viV5j2fn5707MyZ3fRWvxD0fNZ/z5/4SeAyD5+KQM5PF38qIt5T4Fe78bPp8Pxcji9VNA6+hSSQ01ghs7Vv5W36ulex7fz8J6aJSRT3UoEqJ2h+WiLvLuLQFjW9/wDn/wAD571QLiszn8UhTcewnxXmvVYubXnPr8tZl6T81470VDQuQvWpE6EdA2+JXmPZ+fnvTsYMyU+ika0fmnj/AG/4W8b/AIpSOD4RTQt6dyPmvAr3fjZ9Ph+JIXR9VwPfTY7W4ojdBeve9u36ulex7fzLj2e3/aSAFQXLF3CSvpcKnMb/AP4FxyqLpdzU0Os/FF5tKegxXkvVYubTLwC+vyIg73ooAQY2e9oiVkTsiXCvMez885SGNJVv2osnB9q8V6KHd5++KU/Zl/wvu6V7tQaUvirlP7mWvd+KipxLHWjJbvof7/HwnppPExCc42ee9NNRXX4ri4R5mz7ulex7fzGCyzzZpx2Q2MPkPnxSRXf6P/wPreyo3V91ZyWjSoSfVOW4vVIodF6KZEiIZ+7o/FyNeU9G1LjW/lS3iIJ2nZ4bXmPZ+bgdAPE/NCKdR7y/NNZuPtpjiYHpryX20Ikn+DFN+ppvQPRr7TfW4D4S1j1/FIJkTw14Hx+Lwnprxz1sznInipYJuexUQES3vGzHr+KYmcfb+bBGpUjsjuWyPfW7D+0Pf/4GF7h7KstAzxQgcEjq/wAKxOfxQHYV6pKje+ivK+vxFjgPqvKejaex8NS6JlHdrXcJjhXhteY9n55RWPSkWCUHoRFex7a8J6alHe16cqbnr/BJXQX8/vSnK8X00u5oef8Ade/6ax6/inL8T2pTyPj8XgPTQAQbXLPH1TteM9w/jsx6/isXN/LgqTSB3j6aZjAezZi5HzUUb59T/wDAtVuh6QqNf/If7QZ0I6Xj3SiDj8VfW+9wV9kY/qkWSU9f38PLeq37rvXagjH+yPihea9Jmpa2j5h9NEx3e7V5j2fmGDxPSmEm/wDFex7a8J6aP0tGvA+X/BNOQPMt80hUgBzs/wBO9eW9tKT4fKsev4owbvPakqhxPU+Pxc3QPf4I02gjj/ya+P5pBklLdH5r5/isXN/IyPB9V4p6p9lx2YOR81xJP/2q9Lwwb4JqwkSD3KQFFludzarBh2FOWCHov+1i+9qRKxA9bPVEBza+nlSnt/opo0bveJ9UyTvfB+Gp3PqlR/6A7cgSG9j4metcVKV3J+KINcx7KhRJw9fzZ0dPSY/BGBK0tGMk8BaOEqvQSZOCRXb3cEu4qLgBTiSoG3g/HzTwF4XHJ7qV7j7f1BAlZnhBJ3qEmU54C/FathMcNiIYw4dGM7ABuRnmFIVlI7lGaKAZQ+35mie8DuR81j1/FHOMy9j/AHUyuf8AZ+JHHWew/wBPw4jTyT80RqhPvzXkPRTJPP4oI269/wDKBcFRItKdvwIK6MaQXnlR8QJEaoTzE8KvqyGmTHOL7OMRHb/tGLoj/wBq7dYXki7ZMtgw2zcKbMr0GNw8bp6anbLIwFxQxawGyMQVhM24kJc42vcaG8I43QHmavRAzeJiBf8AVML7Ea2Vl5Bd4S1DsFvaQGN2mYtbdTSXFyyNo1b+KYlAURyO7nSLX6LRdpN5ApXAKiVgOK6BrR2qILziMc65gROk5iiFAZ7Dv353YopDETBBK+TFeA+qCSVzcpeF6KBICwgk4uPPvFJAEDa+jHMed6dQuYjNmGbayY3UkECJe2IYONR0oIyZjRnRoXeNFWIoHi9CXCjMTuzAHgmjyyA8s7CwxA0va+tFLcwpwGZdLR0pZMhGya5IyLZwCsgTA3K2vXKpvRdjWYZni3omEA5oHwK04ilwrgeMMGuOHOkaZknZj9M6nCxa/iT3R4bua0M5cpovCYMjvAG8SYYaxE2CLwX2b2+mkss9kOlNDRMjmnUXY8kDTaG7DpdAXj31oKXhnSYVPFWDKFfTpGvOu6Hsos17/FMlNxTgmeks8KdFtCbhlaBu4cTi9AJzM8lie5RDE74oH2F4JzSOR0t3oSFEvxJ4HrYOcKXmwc72tVmJMYkiwXOt99PsYC7rSOBABBwoKMESbnHxTsGr0VdJYI5QVuAR80yfXCzIhfiTNInQLinPaHPBpYYmqkeT450MQ3M1KGHBROCj/hRb2yI32XkLpSiSTW4w+aJAkVaZAoTWYOTlQ7ogHAA7eSpHXjZhBDnNDPUpHKXONTSRL6Mf+zGlJLCWdxaeIcYoI+IE5yrPmOlSYg27SGgsoATggJmp4lEt0mZ3xV4oiZsmBI3kOJxbSkOrsbA9FEZjGFpO5rGthIt3uVLA3uJAX4Zt/ah5XEMQa4aWsWoj5EgreQbRwLbp5UBgwY8jWXUHhaza4HDNC2Q7JMW0kTpNXXBud0pec23UjMySdEV1vm3cp1DF+8AxObuRNoaC7oF7xm8QQSvpS0gaAKrs5QSxAsL5qyok8hid6USBqN2Ud5+lD0FWWL3HXdwxTLKUCpCveV1mDS14tNE/qhh0lJIby9P/ACFN+K2szfVBShqsX49OUt4ohrImZh1kWvZkdDNFqIIb5cVp4sUkFq2niOcxCFTwUyd8LScGlU5juti4QfcVPsAJMpLdnEMRiObSCQpW6SfFKEM3ccHoKIAmAddOsFLCQ0t8PHq0NLQhyedKuaJBGPIXu3/SZ5nw1e/rCpeCjsweq1PGZ42ZIq9g4AGCLwhHta+qEIOBYi0/HV3qV8qQ8BDuzRVoyaGWzGEnXlTBEkrzKhftutQLzHNPje0pREV97C+kepR3bhzxFe1jrwpMeexL/uheICckgBujqjerGxYk5LS6rADhI6Zy0ZlkA5mySNJ36VFBkks2gw7mCcXtSPBpThDLWEtuopvY2QvhIWCOF6yc5F9bEnGZz2pqiYRDulfmzmiRgji1ZtO67wRL2moStgAvaVeCCPKxEq+ZI2wynODGKHG4ARnUEdQ8t1XPlcKDOAMusxNhaSiJE5mRHABA3QJQbcYWQZ8FmnkZB3QBfrJypoGyw3sEcofBTFNBidJQvRxbkxLdjrUOCZXvAHKDdXUV4PmsQhLqIeSoUkIkxLEbnFI0w95T6S9u/wD7PmFCgCNWqW68N+pcosXsTgS+VzvqWzsWIiRubyFiKAdMLGJsgcaIldIvCEmMQhbXoeskjTJO+dIxxmSRNgcWnJjnVpitcYCxxQi9dviSCOMl5ncBEUS8EoliBQ8UrQiDgKryr13UFTccMIjzS021EGoMde0ZtUCYrJm8TlESRrypxbihzZeeWpUSBN51HGtx5aRqy0AkxF+SHnJOxCi6bIsowfhV7XFoltOCDxnjuxSVP3dsgciW3MwB+tNDhRGQvlb5FNJJqDqEYgiSBbhHHDrWko4BHvVuc71e3Gef+xQKctxwHWx8aV5VYtisivscH6YkkS0YYhJBKDYuYuYq95dZ3ZRxvahVuxdrLYcQZ5FIIoIGJgA2ws74iYxT8IHScQovNoRPekhKFMa5O7FadHW16QKJxX6ykW0owxFthGDuoYQjLGJSIsOInjOlQFa0JeMUhVtygE2Llstxh1oyQSOAsl+omcxvpJRa1eCYsbaOcTRlwDIWlmOWk9UNKWxbJqCCeTPDSaLJSQ6gXNKOeEedsDZZ7Xu4pwpAISNNQQtjkntK9Iq3MqDrAe6IMrPxP9dgLkGuIEeT0kGhZDA2UkQGOiU9TMxiZSBmdxNLicCc7kkcLx0xqgGmeUv946tQSsAciAq55DYoEtIvjeP/AGQYYlL5i+6pAQnsXj/GiiJEje4J2WzoxXUVebPbK814QQyEAOY9rRf+00yCNs3heGgBxIX0YTuTMU0YLQeT/p0qRWcvg6TGzE5lYqMW0KYtHRabzAbr76BK6FxCKDaYl33K1xlysPaURDoI5L2TmraHpWvAR0n1UYFcNCdxHw0+i4hYVYTFyDMwzBv/AAmkBfvbrNzlQUwBAcCpPB6sSDzt6qaEQsxMdJMtELEpEkkoYkRL54NThQtMi2ojoloqR4K3aAqNdxxd2iOwItLCMWb3tpVzwlZ3QmLN7290ShcnNMz18U0S1EjJAYS2RJzKsZmS2IbPFKxRMDi1i2TIadlODsB8/pUvwNpmNOs5pIVvBhYnQF4xITZwmtWvsEbwqCTqb80kKVgvAYh6yd6S23KdRWRzAP8AdIQF56KdoU0t2lIREQiZ0kOY8fwQBmHnJ5ipiiCM75hyiIbTe9q3/R84dNVDqVxYXDgmRl4E/wDaJExZewvmIq6KIBxUT29qPOTAEvYphMcpmQLE326Y1fwXpKhXsvcxF+mzUWeCYB7N+XOjJAjQGC0YXBvxapDoCTco114X51IvKxSwYpG65b2tvq8Qp5sIDrQTNdhYh3notz6SQXNawwF+JpweiKaEsuzK72ZdNasdWEm8jm3NW/CsunzTQEgCmsMwxuYY5O7Zkr6rd/7PDFGmWiQp5VsECTTE9SoRImWDQV76tJCESIuMcG8JpSoOCIgAORka6XC96ndJNxDRiJhxMxOlJIaACb5u+yHWYnJLkXdsMJD1KN+M3ojeSS+IlERuPNWOTlghNAKuYOco+3+6yDyG+IH1WDRBOYSSm5W0cwGgxO5zQsxlTGkmredvFYoxje4Dq2nGtSHKxAwCuak3N9qNxApSzN0vMC6FqsTEuBwNjMsIy5h3VCYAObPwL/uChZU1CosQx1QY81qt/wCf9ojpeA3gh5sUCVKSdEQXggd40p+2ExnfalAwW5MHYI6UKQJ1YIm/CCb1cYRQMxcLZlbSse6BFgJJuZGtFMN8BFS6pMhBCJIlzJLPI3yEzo6ObxLhrG6SpQzvdU+KiwI5RCQQWZ1m2pDkrOw6JwSSdcw0AFgvK/upVo8tuZv/AA7KdEAk0sDOT1TotGEmRXirP6VkgF6fQ1dQKiDkPTezaKNTFGgWA4GYW0DV1gAIN4nIeUxxNM0NEawQik2iZemGpMyhFt8uVhvkb8aHXZgkFsMywG+0aNZFF/uiZnn1xU2g0JsjeN3XpSRAuDfGYolFmDiq395VmicHdd0XWrgUOMWmCMEq8Vi95p8dixaM+U0kSub6tW9fXKrIs98wpOBLu8qIZmsrZkiWsSzMzUY5ZneFi8y5zd01cYpKQ6i53wTSTc5q8k3M1ZmS6yT4zU7YaQhRZk3IIW8dKUqUBukKdbY4UOkmN62X0hBw0uiM9UDeVBReQHYF/wCFRKLRm0lm68gotZVDnEUfSiRhiUNmzZm+vGnngMCAiGUbmOqeNB+I7iMGDFpTJpe8UnEBnkTSLBiWIsRwRzTGlKpiFTIgCbokptLGE8xdNFZEjnEN/wCHhfWrq2RoCFkXkWvbP/szW5Tc5o0gC9IsYu0m1l5LJDtQaIi+kstjdEcJqyUSJmyINt2L2hSo13CCCGpGslt9SSxES7KR5YqwQCSyBgViV63Oc1A6DhjBx0qdJkEgQwLGQBDnM0e4DJHGqdSzvGp0nAnIx0ICboEoC5Y1xEwi6Nr4vko9lk5LKlghb35U4kAmEuiukoWPHiwsa6chgcb34QdC6MszvhHZDGvor2PdQsCm2sydLaFZ/Bblm0cSbOS8VIoMrNiDhGe9QboeBlUHBm8itqaOKw6iJxGIjSauEqwaQEDhBraZlvWFMrOJi6BiW8UpNhKNJSTG+HNNOAhC6y78ETaUkN9qn5RkDaYXjE2jGlMQDCDvIMb4Degmlg1MQ0thejpqUNIEgRRTgNpm5a1TmElgEuCJnSXS6RiwqCqqGZiNL5A1I3ZoL0aeEOTNtNd1L2G30CiE833T50G7rL8FCxSZMsybr351GoMOUlZHXcrzzJanRksJjDLDFj3WH0CWLqimJj1wr2Pb+njqh7fioNERBI3AOwdZ7qNMM8wD4ouAEIM3wzmZVf8AVMgKSrTdl44l4XaH4oW11l32tPTM0OSJVMSy5rBbF81OaWC0KANDBEXnWkau0N8gPiTrUgsmMpDe1oTmnTMhWjGWBcShILFznSRpmdtKWd0YVGxnFDMLxusw9KCgmmXvCzhkmL6XisqTC4MdYsKd70l7C4cAmNeKsUi2YsohDqzM1O0ibohmEkcThE0dnFUC10s2bLP0pygSYN0Czv1nfTALNvWUM5LpGDcVp8hm7OIm+mHThRdYQGLozPC3zrahO6S709VL5YTlAMRukUBkTqQxlGF4XMxIXhrTrULNNxgazEZWmBBN+JIxmJq4AKa4ZEdDjQ3EVCGSct6DDNSDlWWBbEwQr3es1AKlhm4I3N0Os3tSFi1cRzzMlof91dtN3GSKuW3GKjm0q92lJwhG0hBeOUWjdhiktgYsXgIWQxoZJqTNYjm7EF8AfypWmQJZgnBuOH/sgi12LFDStQ0naF7DSKsTu7UKMlKENbeR+KVQFxt8V7poRu9lPaGIUnOYzl706YGHxHxTEQr5v8UqVKMt1Xe71pRJYJ5EGOH9b0iWaUTkZ2SoJy3Z/CGJ2aOyDq/ymnoSvM+mw0huHUnJHzsXGgZ8R/e9AzN5zkZ52qIbKnJJSL2u3tRXK7+U0R20h6xPzRB5yzzmlI0CveP5SYGwtyZ9lYig+9/0leQ9OyxMW/lXowY56/edazbOxSAtj8PGPmrMMkdv+0ixAbTbD/djEhIjDkVjvLEmJKml2EmbH4NnChHkfjbgyflHspeVn8q1GSb1197IuYiRuN/HYoJMEHKkbkw8W8VChYG9DV3v3M0WjIZoFjB5D+bFWVqApIPZ4/8AaBfv81z5vXHshwaQMEKTkxFJb84ixy+CoZcm0aHHfx90FqChyNXhUtNPuP8As1FcIl4W52zxagTu8E56nqjrR8udIJDQkA7U+LEQ9aMCu56/zFCwQpN7GOW+ivAjvO7lQhHiMzg8HejuOYmPuvehS5C23lofdZrCWItDpRidAby/htDRI47v3XGtZ7DDfd+KM2kw/wB80gEDOu7/AFp1q63DPWZocYSO9SnhzMl6aGFu65xHKPPKgwZ6NDrmlOWBC7pJWpj2RjEHzpUrsmv013M4pMatXNB2ZI5WoV2GOKj8UR4DLOkZg9URCJBeaE/qRnI1q0udNPVCuTK8Exbsa1G3iA7ETQQ5dJ3zr04ZoxIId/5RNqRe92CY6zUlNLc7zntb/dQF9Yg1m0+B5rwuvEkXOP8AKXbebhP9qHDMMnMqOTI3G+idhKYN1/mO1ONlIjda0dqiTlLwTHW9DkIOm7rRYEoekGF46UaWNZwHy/LQ1k67sMZ4ZqPIBb/VJjwLbmL+auAqJ5YenKlSyMHXXf8ANDUhFw5Wild8mcxr/qtGwieEzRgHEx1oHiHTEZDyI60NYOmvXOu7/lXTMEt6wxHX7vVK0gJbMnxVqhhbLHoYnNCDcITk/wCqB4S1PUYxfr/aIxky7rxb0UAMSc7E33TQoFi3/wArAMxf/wBAAQFv/wAef//EAC0RAQABAgMIAgMBAQEBAQEAAAERACExQVEQYXGBkaGxwdHwIDDhQPFQYJCA/9oACAECAQE/EP8A+yJwwf8Aj5NijnE/mmeDPMk2DzGE8/8Ax0RlnDfH7sP/ACVgmrO/xaUB/wDNX3N7Z9bd/wCP9zf+6JNY8H/glhhTzRYYF8/mQLYbzpBOSPDWDw/C6mI9P/JX3N7Z9bd/44j6MT92PwP/AAeyea7p5/O8WPfakBxF707DZw75/n4EE6/X/kr7m9s+tu/8cgRo+v3Y/A/8FyCbj0runn81PAKDcfc6UAbi80TGiHjb3Lyf7xliQO8h/Uvub2z6278bBRLc7/nZ0gO8Zk/0qYXk2eR6/ctQtYnfH6XbSkeJ/wBHga7p5/NrWDbxUY9he00gV1eadgeNtnFeT9GFAEklNQbWhzNZxpS7q9ftyczGc2dK1eFscFPVM5CLxSlyovsBwmExrVqYgY4k12zw/gkylYZYMbcjviZRMRU0AAzk2Hljs+5vUMMGZ5UZJAx2tsG6PLSTAnn502hwjwVhUh1idLJI2rCAzWDYdnNxrBatg4J1MUx5aUbEmZc/z9s2e/1R0pX9skZB7B7/AEbzEOrXiPf+QEWVY6LPl2CBizxXga7p5/MI2aetQouOpWx1ukdLbXH6K7RpyShLe/GxkWc+j9ozZctnHyori/yV9xo08YaM+cRGwRI7AkVhuwOYQ9yhRufFXgiE7AIl24CTxp84RB0uYX0jnSM4BRZAjvS3RMnRx57DvKJVvBK8jY4uQu+khsQaePUacJEGAktdDq191rtALY5/nCJpJB91r7m9Vgi8TozMb9n3NzYtUAzwJKJFZHolTYptk3tN0qzMwWNYJrCto5x/l7Zs9/qvL8P7fB8n6OyeawuB72HIzBLw/wAXYPimPN8u3AF8Xkrunn87byeKdhRErErz/L+GlQ5w9o9fndwqeAsR6/w7FccPf65JisAYjuRB7STNQzABWwWxGPGiVRJG9m8DQIXCyC0PHHLqV26sMxcBq/c8Kl067ilhUCGhe/VR60pEPAtgoeKUOq1cVQLzo2CXHJNvdMyGCYyQs41HTtI9Jjy0pnCDdA+bVZwXjYbJYI7j6oWAygzYuxwqQV0oIRh5hB808CVdTEPKslCM8Z9JTRbLxvpOECxLr9KnwySGcDExjEkV9PCptBc5GPSkZv7oJ89aYSCwvIC2oUDI4b8ceCfZo5YjBnIcMsNWsCUtvGZKFghTbcPgpNC8s+6FBCcgA/NNaFGFuwIxrhO6gcSAQ5EEtpMpv/yl/B6DZh5vVeX4f2+H5P0dk812Ty7O/wD+LsHxSROLsDZ9xpXdPJXdPNZXe2c4/IIIUFgy5X3ONef5douZr1XZPL+aBYg+NoDsz9YJmIs4XYdwqMWVeb4o8YcqcP8AaFYmxBzEd2lqmQ8UTV35s7WGmE8aRaC4gNAE3DbASF33m1MWQ+yhupQHddfFMEVAmLIgcpY6lLXJB0A9ymUBCt8CV3dZvuaEnYdgUC8iVOVAQptfwM99jYew4hGH5FNPUg74SP8AuPVoV8DsCpEP0a8/qlctgL5C38tTfwbmzSlgOExoRwjKdAMtxFqPYpLk2dYoSV0je2fAtT1IA0lXrQqMoOY8mNYXDwVPlTEzETLw4aV9pq1IUp+HV2wpyVsBGih8Tuq5S5HkY6TSOkJRlyVIVzYT91/yBJ/oGzDzeq8vw/t8Pyfo7J5rsnl2CeL/AMXaviknHupHqpqMwS8K+40pQ+LyV3Tz+aT4NXePGzGfWNCD9x2iXxeSuyeX8+wfFERrvQA91AZAjrnUWsPD/aCIZL6zfl+pk1w9FEg8PdRjmYxwZpITQxnAj1sAgzY6A9UhFiPcfCrxTsmLenyVK+IOkZ26Qe9DlUl7RNzOkrw4VI+P1nZUYi01d0OCkqZLCfr8FIRslysaxaa3uUIFt/yhmlyRq9tSSYrBco7ET9wmhTLWHCV8rTHQiS2ZCYsTga06ytAHAIrtK30+lAHlh4SeQUAJodgKlAkV6SoMyqnNoA4+xSiJkffNNcpD3UajZpMufQ+f8mIYxE7o/wCbATca8vw0QI1fxlYGGGGYTEd+78/D8n6Oyea7J5dne/8AF2L4pknf5aK1zp5hX3GlY3B9V3Tz+aZLfsWW49yfmmp4u3unkrtnl/PsHxWauw9FRFrJfHua+1u/Ucv1ZTaOFRskQFokqQKEMbj3QhKNlOKvOD1euSDgqDpM0hbx0TXpWM8KXJwqBHBReQDyqFKEIawt6g1ChgCIA6ATziaRpqSTDhTlt6LU1miHGVKCVj2fwpWyipFEIwxDPAH1uo8NSIq68yPVaEcZTUUs6cgUdo3PVqZcpaMrtB5/Zh7GTSSTtsxraOcUoNWwGSDZOM+sVibLzv8AVeX4a7h/H6vDYDA7Dz4S4OG0gun5wXbR4Fdm812Ty7O9/wCLHGMHf4pS7o8j7pAAQGNJuO6vuNKxuD6runn8k4UdUtvM7EJYlG+C9IYmS7e6eSu2eX8/H8lDKQpZ0aAy0g802nk6NCZx+LfqExfUKWE0JwKaZVYCmFKMM5UmNUgLAqBGSgRYifelTESvSgDQwJLzSJjsBK0hpxtAXKGaZ0GWW1EFda+umxiiZhWDr5pDAXqSXOpBjH4ogq0jgpXLWXjRwM6MhzDz+z7LVX1WiuwPJXZbEQHEfPrYh2KelY2x0YAjiLKhBQAnMCxvb1c1cBLl2Mt+NJ0ZsboSzUl0L4ElgqXTODm3RvI2E0T/ABp4sFHEJ/lIlznrVgEqwblFHhasdeV4sO3sKFSTK+kE1jahtwRBUgxsshPLRm+xh6NZ3dUuSFCJwtlFFfDuCUlDrs73/iUDieGrh1K+ri1Aq0BO8n1WNwfVHCTD5oG4LJ0/EyT7crt9rRlY7e6eSvsb383Y3mHAivu7lZ7Eh6Uhoy++KVTmHf8AUa/DwoSFAAqUkKMmsRrQI6UplpHLUjNNnS7vqEXIqLFpQSQ0iDsmdVJIUIAafhEAoRLVBFUwNfVSUAlGdBIacJKMOrT0KBa190pSUpWhMUSaJUnD5fH7GDurAw8S2DONTuQXet2uwPJSmbBA0mlqGjT3MwfXKsJoXYQhLyp7FCbXked8msbZ9NoqO7Q01hA/VudKBrj76EuHBNxHqvstVQWN8nRujfsgP7xWT6UntHSjDgfBKwchhwAO09aWWUx4wO3sq76pYcRuTY8Nfc3KVIeK035Vd9udJniwawS02Zy5AHtUrISXgDuhKyDGRncQ0i2lAsNmDfBNRoLLk6jIjiTM/wCLuPTQPHEolDI/tRMnHO+DLfPKhL4PkoyLqeSruR4/F4/kqQ7e3p5eSEcy+zF4PkqLcvf9/NNFLzQRTM9QNiCLn46crf7/AFeH4ViCli9DNRJQWGhc0gt2Rkc9guv2Gh5ediEg+41IWyaRKUQsrA/A4UQFMFpAWggpcKMUJMUS6iwTSjZz8dmDp5/WLA8GK6RL0sc6vDAE6lZrgYDkxnskQZFyuoF+KnGj67ZtljkwrGdW3LwRTU9JSZ0jGYuwwq4XiD5gu4ygkmkQb2R5vwK+w3Vl4k4Zw2d5SLFQBLMAkHCpkCzxwh6r6XRT1YMcVz3r6OdMIvNP01pW/tCjvXal0hhbScIml7kRb5LdjvSsiy/OQqENI/eTQkUdzc5gHw1bVdiQ3MG5J+aaaGsCWUqQxmcsNHJCJImVkGe5UQ8ZOMuyJrGSRXzdVW5TEJslrfZ51cr/AIU8FDnEZ84iKsHETqvilhOxL1j4KWFx9tlBGIDwmfdIdw9yp7ybMc116T/FiKI8NKyaPVeL4Kc8M8VHWeSlP0ZVB0tE9A+KMMJv+Hh+SgAaHe+2CmYt3r0e9mNwfJXYP5mFmO7UhLyz0fzYa3QdSPdJe1nv/P1Ls+FHFTuCmFaCCCoTQIHjRgNslaIdg8eVNxRM72hBSRQRhQgWhkHY7KUFyRdhpzHCsGsjfSwUYVAGlFLAtJcyrEoJIJRvC3mSkUUKcIE0SybMPT9Ylt4VTwEnMnhSmpR6Hy2CFAMyO4+qMo0fbU5IJLb6+pF2riLFjKYJjWKXEJ2aROQ8kB2KkftDkCJNcWbYy88dkXFBamAJeQS/YOFfS6K3wk6T804+AdUfDSlyuPS9VipoTiXpsr9a8+SbULxdIdVQtiY9aAF1eafylab6snN4+mkzUQrjdUe1ODJDzCeiUgzM4RwHuAHOLVAkRYjMF9rTQlpeiBTemnajtUEE3XttBjqV2ShRkDJnuNiJD7eoHUHgoljGB7pAJdFEzfkyxnKKaCUvn/CUh9fyasU6nkoIV7PP/lAykL0sPrSu6eSruc8lQDCC+sz8V2B4/DxfJTY2A8bZ9Eyvmo9WT0/7TAy4qxeD5K7J/OFrC/RKGJeGLCDPHnUGZKdGsXN6q+i0vput+rtDwpUQxG81O9GM7yQDs9aLmMUDlq+SeJWJRbmEp4+asY40rhTjSt0MbMapFvU8LmUS9bn6lMSgnMlobpTkOATggnZpQu0phUxGlMAM6c1mEjlQwzQuGvpoxXP+URYom4+qcBpymMlMKmXO+lBL0IRvL2j5oOk9ymkaGjDL5Ip1EaUq/VfVaqEkcPdClzZMbGxAq9aZjJHdfdOZrcFfJ7pjLyvptazSFbKVk4n82SNwgjojJThpTitx44kfD0/C12AnL+0CFN3y1mWVSIwGgawjJxKRKStYdEGxRzhRyGOc0jYjXeKUQ5+vY4mT0LR1ISi7FEZXJP5QRdL9CmR6kUesY9+/1Rkom3PYkibuGxMsSJjONdgq8iaXchN3000xL4M+ql0wWE4zcInpSReEu7XdPJQicwd59bMO0PH4GoTc8/ggHVqVNkijBzR813TyV2TsEZBw/HfOeSvsbmu6eaSON6qCuI3cWaMP0zjwQ5keSiIaHdU+iIHodCC4ynMPVQo5fFKBZOQuhvz84oQOc+qSjgWScvPjQAlx51FY3caQCmNV2pivCPQmkAtGyjHcYPVYlRdDAu5DnehvaC1BBwmADesUIZhBve9z1RDNT3TQsrQnU9Kiv4gsCObRmQiubrz2yt1pQSiJR4voGj08NFrZpERo0it+QCTtE75q1pskYxQSOh7CpAIbnUAHIeqaDQsKqGIb7DLaYvzttVQh9wpuEAWvcIcUF1SkcgJ3n4o4+LFli8RFWXvPoEeX9X1WraEUP4onVWMZffdc8pEZiR2PrYCIMSsekVD+Bu/cNt3M8UyylQxNCqnq80IUokMpxletqIGJsMJupbTYOCZfpXGb+aAA+oP0okgKVHAj3o/cSBxRsb6FdQaPInFgxnT5yATg3pAEyORL6oCkAHqTRwJnySNJUW9hhRmMSOOMWw3TR4i+zONeEsUoYsTkT891NDgCT0x3VmyUYzaHDe98alwQYt7oyvbOaMxgL5yr2wob7KCNyzxpcWRmTIg8d8s0yJXhuSXzmpQMkpMwBm2KLnFECks4dYtQJ7zSAb8ZpretdMZ5zG7GrHKr2/7WDk90huNgawC3olMSHme5UBZERpPlNZRGVW1iCraDsGryWOTJnvUb2Icxe9DxkYw4Mkm7fUmVThY3futFtd4EXWDjiOGM50IVLQcM0waWZdLssyKeOZJzuPREo/TbTKG+SWkPs3aiYNyvgY8KY8sIOwoH7CegeU61ZaUmeJ4aKkGfxQmtBQ4+1jSREtvzTAnGaQ4nAULlGUFhJwlPI1EWMvcpgs7imRj9Xmp4umfZ7NaakI3ko9xKVcZjXP3Cl9cOdy2cLG++LNFYsjQOAGaMUYxM4fZqbCS5iCccl5lMSDcMLxTzimJZRMagMdKjSWWcEpYeHusk4HkF3BV9sJGLYIrEkWN8hytDQAEpBzan8cYE3L4zMp3eUf0I7jRM4ybRjHim0N8HYwCBII5sjw1ZQWJyn5qA4Mg1Gx0aO1QWLxlxcf1Iv95UxBlUKDCOxZQqCpjursqKTT4oG+PX8rEfcKLkaeQKFw60immmSpBdKkElaqlIMRUKmJqOQ1GVIKsSjCqRFDE0pQ1hSSFo50w6aDgopQoSaibX0P08EPZQ6d8Hlh/d9WsyHFgldWZvp1pTzX7gVmiCZZdE6AxG7Sgsry72qC0GCC0SBvN70zeQyZWcImcM+NOwsUN4RZMUF4THItSMCRCOdxhybWezWNwQ5M9OdKaxjwmI7VgedQSVPnGShxTmdqUVq0dAQeuTuhmCQuiHhKRpkBOBCzPW1XmgGZurKzPCAN1IrJybYvtNLyFuFvgqY8agVZRGkn/FSOChSYt4RnjZ5RQmZaPFUd4rDtnJeWIKXuwl4rFIi4GaUQ6kOmNMEleN+vio8Ckbum68VNDltqBvuiTnpRgXQfMJdG1Fpku2yI6aVjaVvNv7oZSvJ4oZbCcbxG/K16ILiU0znpHSoERBc5mO0U7ZERLYxM4xItrpj+rsHhoRym5eWXhHSaaqCXuOSesWoZLG9qKisJSYGMDo3ihd1DJ4FqE1b2H5qOK2IblGeYUm5Fxc2jFh4xHiramACDCXVaAhVl3enGIzqLOoYyhvfmrpZEcR38Km/IGNRQ7malmSeYs336TUhTC3dh8Wpm2z3KoGLpwEMdSnkyY4hcLF9013PkUhqKwDeZzbG1ZwAG8bzSQKoZxwRpSbwcjwUCLfkAChPs2C77Z60aVyfjd2ax26kgCYOUxBNRXtTWTXzaETcsNzHqmWqYs2vFJOGHGgTyS8yvWn7yU7gmWkaAwutqmJxKGEKFngaWIiMnKx8SfrkRw9jU8ShMtDMi1EAlqKWWpJVALTBJnTmCrpuTUgM6GRYUgLpKCcKl2woJsUsiL0NhCltpRKDWhKCiPEpAxa9LQIiperoOFDbYUapnSWcWimAOvqpmlCaizRRKQ0UHV4/SccrwqLZNQBUaACcL+qIDQSmCxQmKlqKeIM9KRS7qWZw+aJAaVmtKWFQKcW3Or5xWe1RMwoIDAmu0aYCwh4SPkNqF2lMQpHmpWW40JDZApXJ80ymgCxUEyU9JPdQGGpM6NeFWkkOhXZ2KsXKbFiHj9IBEjDiYT1ogYsBsvRTQYlpwgPVMI3OtGCUBNLgPHdRkQZFiihEKDwTmdqIrARw3z/ACIoSYA88KkEwjyUbeAHUKJYRAxe5dNJ419TeqVkTDlGht2Up0kx1vOMxoVlmjhBA73qGJEZ5M7MxCO2yJrbEYMAHKEwno8KO5mb8QYbrWpwsgxuss9qwlAvMisIkByCpKw2dJIepTHZS6LSFXnnKQ46FYH5I6YpSU2myxiTlNu9HBAkN9v64Iy8jXDejsx0VaoqEyvKahDgVCJqIRhT6xRoq5SEKphGv8pTCTQT4qgCcqAtaeulXoojvVemgEkgl6h5SrhgqIGCijoms4xqAM5VcKVCMaQmwpFFrfUCpEJ5ZPBpoHKwBv2IDEp1gpK7r/SEnBbwqYPDzSEM6Au3pTFSkL9mkKLlRKHTbnpaWQFBdxaMcAqA0USgpINVKBLCpYfwv95GzF56iN5a5PTsDGj3m9MoNlahbOXugIuFIgDTs0hR9vXhViVhVe6ACD9MxKJIeG3GiLAKRCQlI1JX8XU5YlFIgwO9NLwBzoecGXWCDtWT6zdM4caISzH5AGG363HZ9rhTgZxHHDwtdueKYgwV9rdUZHQ8lRWr8N2gPE/W4XB8yi1Y4G9Y7RTPsyToxQAJR0S3aXI6U7vUDWBY+jKeda70YRRAoTEuPnp+S2kIS4oXu1jdpLmThwpFlxpMSxDul/tI1fh2umFhE/MeTYCoTOfn1QqkoYk5e9iYmgaqPlHyeuwE4LCThKeRp2Zkvn5oVmaePqjgLM9rJxoFQU+YA6RQhd/x+kbDR6Be1TPAF2OJO9GmVJgvQPEkpgwybWwvbdWH8oNrmMaGnesbFdDFeRLShIRSHUxOtIuVq7mNgA6FZtjhnrsCqFIglu8gl2FooypWMFORbvFFpZY7GogYuogjn2KsNALO4Ue40kqpI455KaAvHfBK17wzum2sBL8oViWwpGjlTKjBJwlPI0dhes8DKZYTkY01QZYMI19a5VJbxQTLE+aYBlu8iV6F6RCdtxiieFLAqNsu4mmkAFGOE2xxtFLhZHCy7wGXdQvyS9kncqYZhm5JqDebTgkJvqw4IZzhChzUlpjdVwhmDBVDH3OsKRc4rAeelEiCHcADld/4f1uOz7XCuz9ldueNn0t1EnOJQADA/fvsXs8mvqNaJIEjQ6tp60jle/motNElXWukATzVsGKgIYLYcHuoimhqJoyEEvVnq7DkTAjldqChl7Ivan0zdNzA9uwUSyC78FI7iQ34sN9uI2owYLYbwvVvUYklFs4BtxC1O3IC+ixc0nlNfzGifzRoIXPyMdZvpQBs4OMGPOvtNKwtQJcTI1iONRp6DD7vrFZQHFtS4FYrjIGMxwTNG5kHqfGmAGxfKWB3qa5liYII4pkKPSBLvVeqIXoO6KADY77oHzQs5PlJ+ai4CZOZ8fpDCcQ1Iuc6UuwAbwgyRMDOlTFykGVwTq2hbGNqXYs8baQgYYXXjnFqvUGLpcrOUb8qR0lGhfGDJT3nNEoEBeUYtvlLs5zemNILaqpkkgjBw90YKEHmBOsG+iRREzslEwXwmbL0GQUGcA2WTHNzvNBFiK8DcZwwuY0K4YkatGNRubynzLItkK2RLlKO7hJ+JC5Ph5Ww1o5pgmCSEkWMG5ODSKCTSajLkp3b6SmENkzK6yYKMcQ3UR5ESvBLOGGKvjTUq82QQbtONDMBDJaxGqyul4ilMYrDFweYdqWlEhIwiWZ4Ykb5pAQki8XTGOEz5iikQqN4AETjGrSAIanBM0HJhho7qj9WbCAFYvihN5vpRK3wUt1aYU4NCMmcIEg0jD+UgIZE3K25SYbxGDRoE8RcbsN4eDWb1I8G6IsW5l5YaAWtjfFghnC+GXTLG/CBYsR+zW+W7qlAugphgCJCcrh4ypJk5ixkghy5g5VaIRwzQYGGeWKzU+koUcBJlM7qviC1JDH/AIn2uFdn7K7c8bPpbv8ACQ4fgoVMwY6lnn9mrO8Z4R8xUqpwKdbUNJKZ5hvDl4KIuABwFTtvZ1wIHpFsKKmLEwwiRtrOG6hQM5OoOO27XOl6WCTkwBcLzNCHeDOsQntQzgnkLXE3M5mZvnnRtSF5fwaOcCz8QDpFJTYD7k+qtlJHdcV4yeaapCGGNkfVZCjdwg3sqvtE87gjwJzrIt0zBMxEzrFuFSITIF1AOXN1o59Xqm1CNcAcKMgtbuGjPGawwgPig+kCBwwJ4UvxKF4IHQrMKk7lGYxCimiEzgjZyrHCQ6jSZvahjaHeawkKi+SovV2ZgE6wRT3OXHSAueOBx30hlK5KrldAjW6T1vvrNop5hedjv+ry/DtUxCwVfGLjPNoDD9i9ZTfD8PIUhkk8f9qVDcvBODnjnV/CeKwQLIQSRhjpjxKN0pRHBsrO6Cg4ACALAGAGlREsx6I+v0gNuXc0rUbuF9bIvQBYIhJl3zOmbsKK+Kgoi4h34fFPCyQTK035z9yKlhxqb5B1fjYFpxL1oID52H3Syz/4n2uFdn7K7c8bPpbv8MDHCYnKcetACDYVQUa0AZOa0cUgjwcaNHDj3NyHGgAEBQ8CBLk40bWW4aQilS2AjORoGFcPhp7oMHwGiBCyJzjSdK+z3Ue+AeCppCFUcbIL42LY66v49o1EIyfLsGxIS4g9lKPOMClOZGwYZHaiylRPJn1TAYrRkS2JziZjhNAywaATKol7yM1Z9/HbBsCjE4vH9RhYShEsFdJ030AawSWDhfHGDvU40DboT1ilBKNv72woJNudfr2oqowgTnY/vSodnLDv2wp5MSI4teF+1DuSYb2lFQZl0oykNKyuIjlQx8yDcFQjCns27FB5ZGZ4SEccd1PfqEpiRFqATgJ5HurkAQ71i08fdE6kG+/Bs9RqUIFejUJiiQ5YX5jTroLdxwtd31Ko4XDHllRpc5fH9qWFL9tajIspes08GWIvR5kOQtzpWMlcjLj61plGRBjWcpqbYsxE7pnhQTiEROevCrdRY7Pi1T520yKi/DFneEVMBIu5ThZhv/aFWZYeQqU0ZzL/AMX7XCuz9ldueNn0t3+yFsYw0bOAIDcbDwYkri4tG3gzyIP2oYsRzb+D8JHjDy/7CQDj++KnMEHA2quOyWI/AWB/8YUwaRBI7UFl/wDNJJQI31MP/wBecaw/JQSuxQx/8eF/OSYm+ySY/wDHhx/8iITu3jOPxYUUO+k4bOyea7p5/L7m5SwTUs2cBwaGSdglxstFMMcf0XxIJORa3+O5czLiY/g3wTEWcG372/8ARrIZgOtBF0j3sSOBi44sZb9rBhe2d0fm5pEXLOD/AF/c3/uJeC3g/wA+MaiTuFo1smBlYL52dw2sjegiY67PuddnZPNd087XDwF1rQws42YjDjs+5uU5RgqQjSHisHhs+612dw8n5v0gRfixULBjdC3ytr/gfoCzNYzUZQvjgRs+rw24uco3GIcpia+5ubTS/UPxT5XcPHa6BKPgmb086AXzlYOWu2WuodF/m2eeYTulDa7EiYPQRMbcLtGHkcEwq0pzCYOlPsQZMgG+s08CYYdzo/6fub/3Y/A/z/Vaq+y0bO4bX2t+x9zrsABgzyKdASJkwWnWMtgiAgTGcNXGWtOpdG69FMFiM2GccK7F4r7m5TfTpXOzwfFIiXRRTkDQhnjLcq+612d68mwWkIcxcPwboRY3wm9Wr9WVioABiROthI/e/USHgUVXIocENjT2Q5AuzLpZ5JKRPi8Ffc3KMPI3HdsAVQBbVt7KyFK9qhToyMMUiP7V5pu4Zgw5U40q3aixYQ4A26ABkjw1ZhkYw3kUzjA3YZMRTey+iG1pNHPsqO9vzTM+mjlk0UABnXYPiu1qY0B5+q+x00SjVsniAw44nHj/AJ8UqH0/ux+B/n+q1UMaHXxc77EikqOjNMyRA9SaHwY1wZwp8LwnGz4HYITL0OzwNSRmI4ARUyWS75EXrE19ZrtUVMjck3Wzr7m5RI8ocmPmpp2w+ZqcuJjyVtzKnfJc6Ie5X22uzuXk/Dy9MxHAiNn3N6vrblFVuPyRHWGs4iuqDyoRJKTeATxy5fs3URdm54wd6hVBjvIEd6UCrAV9No2MgoYYWcU8CI+4UHuZi4klK0RD0bO+PNXis6a2h0WkSgA91WUhgyJZbOlVQjejTOSo8UhWXjoTsZXsfFIOwI96NdmBrCaTZdQ3SG+1pfVaqvDMOpTtDyVA5gY0slmg5qAk3kL23STxqA0jMcT8VgTqcJVh3wld9Ui8XJHES3C3OgMg4U+szM7rE+W//P5Hr92PwP8AOc+V/DKvSRCt9VopD9vGvqtFW/WxpnMrLcYe6THFjkS9qtkXbyZDpG6hARZ4rwNNbDlUBBgrqqjQWBvVfDtUVUx0/ublBpQ80jYYxThXR8tG8JuxrRLak6uxxtryaSIGOJNE3IrpFJJgRAbMW+a+5vUaJgd8ZKfNtClC7/FoNl0vWSmE31KiuCwuYPSaRUk3btkfqVBKOLCc98W60hUkJJnmj5s2ppJc2aJDnWYkY4lN+LChrIZ+MKPWzVFJSeEyhJ6jyprpTeLJXcPhrCklODSBgYUjo2dxqxn+3/O1KTmJ+jSt6LNNIS4hxGVOF+1dh80FqyXSVOfrXagxgYt4HzSEdkxkg49HjlamS+5oQbvdTW3FKROPqoChiOgfNYAazuALaMfszU5NgrqTIk3lAJidFj2ayyAX3FuLLG+kdAAZoruN/sNNuP1NENMj1QqcRkOrPeyQln+hpl4x7EqQx0/C/wAWpGW2PMmPNTsymJ3Kev8AISbc2eR6/clpa3j9KvHPif2ITACvApZFSsi3LcBvWH0j0wSO9IW1jIBk5SSeNqbETec1DurTowkvJWTsQ3ECea7DwKEayzOtx5vXZ+Cm2RLpZwVEJYDGpabluvCvr9az7zPQVOLKn6V9vpS65hDxApsjToWxwJ6o5myx0jtU8aMd2pzxPBXa1MDipAQmsMiCVmMMTXCmeY7aOZyetJbQMR0jzV/YIi1zLol+WNKUuTPAUsnBxY9vVKW6fNJPCseCPNFhR8ZSR4s96brgCXAAlv1opMyWOazRGVCTrZNW5WKM4xo0/VJlxEcGhOMUHnJsbaVv4U6qSfJXcNFYGcZKRtYQzoydqPCFzVzsZyQ+aiNDnacAG9ltnQex6qHwajEBFrC3tWEQ1Ypp4FEkXj5fkqLyzE6t7uWoMOPmawJHMfQ6VFrDvCn0AKmOSu29bDPKTzbnOzPCnKfaKEgpwdF5WouCLsdSa0HB1t7r6XdSdoTOtU45ULvZUmwGI343hBrskTdhJTc2+K1gI9B+WuZqt4z5oQ1kHfO3RH/J2zZZ1+v3O8yD2Hv9CjjLFAJGYfs+q1UwMsHkIHYpDKMdCzzWhacOp+6NDnJxbzjg0jGoeq9U6QIQ4EBzqZpG9OHipsUEN9IzvjSWf7Apb8C+NMWGJur7XUr7rVr63Wrs8D9eFfZ6UNoWM1u+HYCt4Cyc7s24TRskCcy/akiTMR6oJpwifrdTkne+qFlTARAHi6uLs5QoBKZOFJnFdk8uxMkY3LUTDxqE0+W7EfQaKwKOEi4+uNdo+6LLu9tGABZfVUfthZdjkSnlbickJ4a7b0oTwmnHA09g80zcpDF9bo48NrAiFd8odj6xVSt7scHn4o37j3U2zpXZetgmMKPFT4SljOxfm/8AKeGaNmGL/ZPdGusW2it3I+lfXalM6Zuv/KdcQ6kUhzgvg+KwH3OhGDDzCe5WJw2EpHfQh13/APyHbNnv9UDkl/D+0lGceQ9fo7J5rC4Hv9jFkyR9ONSJJINJifFXrcdgK+r0oDnJHkacHF9VP9lpZzR2ryV3hT0QoHDPy13ry0h6WMG2Jd4YynOJp94CHRLmNutTeRg01OYGw9EBiY4f8oi4PmfmmVcS6nwrLZjFr8V33xUyhq+qx/uVIZgLOKB8FYTqucA6sVh/KnMI7pUnQCEnMw62S2Y7C9TY5FvQApAGfsUpFA/0JDyopYi3srArH4vdIFd/imMOU+aSmBBy+Z/V3jz+jF4U8/MteKUXoMGI9WamzK7aCYmbglGe1FdX9FXKdmH9yrCrRZXZevxMSiwgeQqQfeXbOzE4V56cH3ShKOFK9Zg7yF8g/wCQH8INimfH1Xl+H9pg+Hk/R2TzRgcDy7FHMJ+qz7MGwQ33eKUKbCvNifBRg1KFDM5kdj6rDOEejXeFKFk0NScYaVnRer+GnYSamMoYLXb+RpKnWrcZtwMc/GptFfVavwWVou9BIYNNwnxWP9yrA4vdTHWHls+l0VbrAdmdna+WvttWjA5kdx9bHly8LHGIetulCSb6aqafNeB8NYKdm8n6u8ef0YvCjj3FezZjfnKJcfFKt+vqsP7lTv5+Pxcaoo5VY2JLHFn8Rc1J3GhEkopxxSOfUv8AJ9ho2Yeb1Xl+H9vh+T9HZPNdk8uzv/6vubmxW5/LuqXFxfyrNwp1Mcdyv4SsuNRjV7bLMxJY0ln8CauBxkfE7MPB8Upe0ekC4lkTBHbdr56wiEerOxjJPYPns7wo4t1SJZSdqFhopx1Mtih4/T3Dz+jGrsvihsX22zH/ADiqjRr39VMKA2fxcb8/V7pwqIO5Xg+a87z/AMgSf6Bsw83qvL8P7fD8n6Oyea7J5dhni/1fc3P0YPGvG9VjeFGE4VNgwR3X3RhUEvj+Ri+5bEKbvijC/KsbIh5pOztliNneGz3HzUA7/mvMrtzx+l2d3z+hYadt8Va3np2XrYIh/FiHRrTv9UoK61FjKfxcb8xCaYAZ0CF4U5Xh5rzPP/JACXByj07AS8fVeX4aAhBL+zw/J+jsnmuyeXZ3v9X3Nz9EAUJF+2pE4+qJA7DFrF/LP9y2ZW79hqIUMFaODnf71pzxFeZQQuh4/T2/9BbzV7PVYlEHFYlKgyrB+Ii+7524NS/4DG/KIWdTP0ubElFYmPQphWD7f8gAU2GON6ry/DXcP7Awd3k/R2TzXZPLs73+r7m5+gTTtqMorC2WPWL+KAKRthQaPdGOT8LGfmWNDI4+atkaNSr40Cb7hQQjp+nuXn9CBdWsn3SsenUs6xK7D5rAfib/ANw23GpgZxUrtGNteRmBeQS9jbPSwEcRfW+rOY87SgZp9re/xmArE2RD3+mmQZT4j3XcP7Ow/R2TzXZPLs73+r7m5+jE4V21YvHbQR4K3Cfjh/gMD8LGfmrpBVnM8tJgq8OMlWQ5H6je3Pmhg2LHKZ7lQgmZPeKIkTcI3yjxsZDhE5g97DBytgM6ezVDDbHdRIHfWJX28qx+dQcZ4PmkghhRELHwfPYomQTUwRzWdk/rpQLhsRAWMeaHlKchaPf87Vj/AHKgnDYY236jXsnchE82KEGsQJgD3KZLZHmi5PZsh4jxRhZg7Hh/x700O9Y2xWN7WNwfVd4/s7L9EV/EkdGnMmkdH+7O9/q+5ufoxOFA4zKMS1ot02Xu8V3j5/HD+5UI22BV8kN9tjPz7YryKxqMnMq58H9QGY+nmvubqO1slwzJvQvw4UBuKMAUBwFKsJgE7hcd6cHCWSgcmx6MvYoisLhzzKEG98Vd3XxRIKXhfdD95Vj0sLNHZWIkoNCWDkUwxl3pWC+A4CldkofDIY7qCzMPNl8tCjCSchnpnSYRdjvh1yrBkAiN6DtWeIT0/wC0rwS+KI95o2mmEVG4ignCsfRz5IB91dlo5CTaWEjLnNBhACm+VS6cOq/FGixJ44KCI4kRzJojOewo0GI79QR7dVQNpPzSS3/d+H+PsnmsbZ3TRu4PkrvH9nZfo7B6r3+tne/1fc3P0Ai0qGLRrB+50Ppx2Xu8UVEM180ikdhIcj2HvZh/cqSya7ZLs717vW2OD+RIyqdMXtTiXIrQIjvNY1CA3nmnD4fqVBYk5pPYp81L+g80YJoGKITBIURvSa5N6SAKMbiDrOTdWD4A5x/q76JEJS6Wgd2nlauhZ9TsUMSjFLoYsShrJyp10jw0ANSOgfNQY5/EqaYiGJwVicLybysawdUGsW125Xa9Xc71T3aUeA/qrswozxIpikQFuNdDCiNsc7Ai74DpX3migaN5BQj6PhGjAWJd1SErhiSM0eJKAWBy2MAqILfRQCmNKLIIpbGyk85vW81IKL77pUAWEg5N7xQOZBOh7rGJE8QFOKBIeavdQRcsxviY0qbvHn+f4+yeaSSGiKwFOjXeNd08ld4/rxCmjdcf0dg9V7/Wzuf6vublTdmh1aAKGGO7LyhtBBK7WyEP76o8KUFxoRwNlNDj4amIub7rvHZBeYk8MTZh/cqUsa7VKafNWFIo3NKeakgNptwABuF2GOb4ahj5jqo3MhclPTvWNWGhk/uH6itlDBWKgWcczXdRY8hRlYl61JgQWgMDcboCiY1zy/gomEAYpCqQ7YxjG4mrcpYTUFnnQTTANbC3WaAZiHDAnfamSsP3KTFgTejAvhMBR1WsoQSyZzZQHlurSBsdBCkTZ2T1NDe8k9x4pLowJeAO8nSlcaShykIeDVm8E3IsUBEYFILMSBi8cedOmAAjw9FntUroXpaKfJSnikIQADFsltYzqPplkibLv1WN850VxTVvS7fX/lDYSnB5vVId5kCwQNX7lQNgFGtd9UomRELO5Y7NJqKKJ9JYWNgncKfaBjlCGdce1E70DhH9mNGQd3ihzAIDAsv+PsnnZ3TzQaqfTXdPJXg+D9fl+GuwfP6AjmZ8vxXv9bMXG/V9zcoOBIW97mFdrpE1IyG6SaRi6TpiLcg5zTBD/sbAmxShuQxoCXpNJQTEaxnfTFfbUbrTG5JO3qkJ8aDqQidWUEUiY0Jci5HO+wE1sfOY8bJjRVyCMnWPI1BD6mo2ML9BoQTp8VE/JTpXnqUQZPaV2/VaqK8STwh6diHOfJQuYJ4zYpfybxrCk9qMtw81CmExz+jRc/TjCHoFEI5j80qEyjlEf2mmNlv4Ok1icMDSEd6vvj6fVGCm8eaukb/VE15XGsF7vj3QuzAOMRPRaY/TuZ7qFyC52DmxWOtRHGcOcUS20hDlfDrS0ono2MHfFQuukTSPyi3AbedsFqzdYQ9GkhiQv2KuUsDPaoLVBBxmeresLcSbYXRIqMkgSJ3L4qREkke6NWanc0risSJvnHanVOD5p5zQ6xTkbPZ80LCH314TybFGLYHjP9pLi9r8j/H2TzsgUS3zrsnhrtPZXh+D9fmeKUYNHyfmCsFIchD5p7/Wwt36hFGbtcqQ2XHKf6o+BLnws72KDG5vM/dAl+rtGyR7KApgG5vhK7wqTP8AgZRLoD5oFiPy0kt8+KMSUZ4z1TgZT5KkOrQb9F3+gWgSAeIMaWzrDzLcmaKWXLhIe6aUuRzx8JSDSiACC+Di0M7nuvdTjjvNSkibLonaJqJDvAyjDlbWhNkMVl+cRFpGMcZSp/A0c1auPAF3vNb9PZRKLAJvcBnRfUXaeNdDHAPekxDAOv8AFMkVxdbvFGH6cXN6q5EyL9c9iNcuFTtbnOY8U0Lj4aFWBXt9aBvgvCD4dhqrOVO4yLyg+Clj/wBQ/FGBX1d7QFMA6mOzRBMmzvIoULKciLBR3iv1aRKsC85VPpyOv/PwDLuKC5wDvh8U8xIRymp2nbH59UqbJHdfdSd6oHNd5KWN9yohGzPhrCboF7n4adjGXySK8Z5NjqJhJ6lIQsxHQP8AH2TzsLCFU0weDw0rowOv/K8PwflhIcsFt7p640CeI8P9oGB2+Z4asD7s/PvzzX2N9FY4+tmpePkpWGcKd/0gAskdbVCzSx1tSbgQ7lCPR2KiHEMsYVUL2608DsryKMwbllcTUGcNjW4metqBpY8ylOYm4rh7nlTB6MrIuevGpTOidqoN6vC1r2+ilCYX8lSCZr6oiNmXuHX3RCw38lR1GoDQ9XLJE4TeeFKMZsOR/axQEJMgq+DpRPJkt8rRW5M+7QsYF27E7rUQtF2NcMUTpl6UcEUotVmcpORR4RGfCzxFQTN9H5zHafmpE6R4K3hB+mhUWQlAWiY+WWrjP1GiZyGnG4X5JRYCltlUaYM92rtRu8eSMn6mExaMaLz08BWOmYShQcS3K9SjRRoxQ+fdIcZu9RegX172ux971AwsGcZHhaJtqsBaxMY/FODKh0f7SAc06j91YmCU842QiImrE5FHexY/gk1YHbZGPcXb/lH8gEjg5u5QhoZTyxqOWovCz940E9iR2aNRmXrlRwwo6ok0ec8nQE6zFMyy2G75Vgfa5WFwPJs3AR5n1XY/sDivYJzWvCN+tIwF0gMcpoYBRacMQqzCSRJpJN9kDoqRDZnfeokEAmeU+KxSmxrEJUq5Ooh8TXdqJS4TC0oDMGcZ8SiJcGWFWYA1uU0opAS4tjLfan+xoNYJehS7NAVyhF7BRit+OTjCdNgvpIPAWOV+tM06G+/Ch9AelOSUUrAjO5gdOVQaI9cco3caCuBE86iXQfj3URty/oqY0LA4Xe1WhbynU9ULAkgzYxjWNly4iHFwqHGkkuDWvkHgUnJAc4llYY4xHShRZlHXPlPDArfcU2L/ABTlzSjunn9M7O7yUIXFqBRdwhbfyoon2IrCDv8AdfxFEY2RfgHus6AVyJPYqNkEvafIUgxgAutnCmZEJ1xPFZCHyVXhLTddMdIp7JWaZyAjk40gJCpXWwdiowkQPWgy73H8NJU5HbT7pTtbQDijUjJB3ITUuBBSMIRq4zV/USNt1EwqxM8VPisCl7M6jfJN9YIOhGFA2UyDmKPcpOhVDeQjlSk4iHMM8btQX/V6UUEBR0NBu/8ANREkWQcCTtFRJggGglqC83bUPjEDhu4Yk1HsjN4uDnmF3tNCzNC1kTNSQwrG+llAupvQvYg3mN/1EpLbyVgH9PsUr0CGOEJS6MObof0aYts/OIqEBg9s6DLpR1iheMjEa1kFtjRD7FA0yp2K0d7R50yMQqI0msYgp5IIoLaEId0T5KPwszp8z2pC0oDxSJtQ54tMxDE6PwQ+MI5tArIzbktMbxOVn+Ux5Sg4WLG9q2OsRvZ7z61u4Art470ZctBSC5blf3QrYg5mkAZ1oEFzY83qAzX9IdjueHumGeFo4/qlsIGW4XLsMFv6HMiUg4AyuELFjvNNLq3F3AS62DkUjZkNOYLOUguGNt9KrmMgGqm/TP3WiyC8QgyvNCsFBBniXb4N7Ro09qxXciRGMzO7CkyZYAnLh6pIwi5IkKTa+WNRgGiTEAueImetXRygwAZBjCl73zqO6AAtguFcVx3OlOzIeJ0fQDkaU79wjdKZefYjOk7UjYumAdk5VZeOSXBCLWuydG1TGuCNz8LWSSMLNnFciMob6lRAhVu24nQ3zSGgHhiIO0ERvvVjcNViEF1WbCI0a0wHg6Ug1iR0sgicBQnitJSKFwkLJjWwJ3yRR4XgxGUN3NlULBOBeFA6WQyulpoywyQxZRGa5pziSKmuMjpMwsYXU+lWSI5vInjqPKaWRqBul77oA5u+aUgBBzxgLWjlO+kEkuWvgm8MWjfTRKbrvmE+KkLyx2qACWIZ3ZPQk486hYp5xSDmvqSlFyEArDDuvlKGhBQ5kRF8bIyX9JO5/NLJ2E5yM26E01GVWPFTiRObl4mzhg8eNJkOYek/NBSxILvZouJj3Yig3RlZvyvR5EiHWf5TSYCxHI8UwGKLDNHivH0q3SUm1tDwoWb4RjeIFs6gY0A3f4v6axXbOUI7rUUl1IfrVpSwiicVJm95Q080p45huoymZihZJBh18DWCpxfgRQybpIgxlucvFa3wb4aXBSbMdXi3ijsLCvKL1REoleYCegUtbKv1v7rBsx8tPKEsPzT3EQt+dBdx7j5CedOuiOcT89K3KQ8iuG09HopdGHremYzT2D1Uu9zyAPk71aghvR/NiQx+lhYmHGrFt5OgXqZMQDpRN2XdfNWyM3jV+3ojGAInKZvUV0IDuEPmjgd0uj/KNhdsdv7U6pWLYRb5tQuYYHh7pVSFpXsQHQU8xZOo2px7jx3GHejVZLp3ENQOlzrdi9+BRIyTO5D5wrC4K8RWIqFsL3aPdFMwRDPmmtmFncrh0p2De4R8+ZpGd4W0+zQZnaOiNMCsnlhD/wAobUEHRKO6honC8EfJSiKGRjU0aQDC8RlPo+lTgtfbG0NJTAQb71EcHlGb3SCQ0wJvM7BQiZBN8d/zSeai6zjvKArh5P1IH0loEFGdSMcKKJTyampGlEmNREqRBswQbiZ8q0LKjJ3lnyHSmyZDHcfXmgbVIcxSMBcoAJZKaSxPNgngaVkRUciUtkKcJopCkplW6uMrQBhQSWdQe9UBUghjNKcjBMdaWZipEBjB0kbXxsfFOc3FDegPWDpSDlUYsBk4n9pUaFRdTSlyrtLqiAcgB0AKsYrGDb4H8/SDnHPHC/qt5j4KunoQ6SS7UKDMx79VAm0XG44dEq38IpvxDUKWCHhI+QrGakDXuXwmdI2+QpDVB2/7U0EClSRSwxtCNLMzNQFQEbRKZtxWlvKRBjMj4u6VA1QgL3k+qG2BgdZIkjJJzpiaLijwTPEp62Q4GYpFFMccpIRtY5IzGaDZ3Xq38yBxMGk4SiEcEcRqBRIAWLG0botVy4CGpk5BSesxeY7QFCRmDmDB3Sm6pxDAmMTBtqR1pIyw95UtRJPFYO7Ur4ZI75KxKBDvVKcVYnS9YBQGkwfyam+QdX42BbMhlgieCaUBv9B90ss/pk4D38VJMUoFcKnG8BPOfigJTP8A5UJ1UCby9Vff4dk8tOeb5aNbqOoHurzureQ3rFQnVtCPWB0n5/Q7Pr6+KDJbxSDjsFcJIpS1aI5aVHpaXzOxMSusdpvyKSywPJ/lMdiidSkK8vVGHMnp/VNnjzNRiIieUp6q0mijxKE8OdhjGMcMbcbVZtSwFJHUlJ6icqjYMKUGlC2EurgZ3chpGhCVvKkAcagwU0pqbwIU3IJOlkq7GdEkrmPuhrCnjcjeXubmN97UvAUpIv8AFCJq9FOexBxT8xyoS5oInneUhZowZjY0ynpJP9pCcWouIwqa9MQlqitIW8kJy3I50BkMKECkTc33+ROtKnEiMpMHSL6tYJ1AeNzrQFQXyzMTiSTpJStBK1BQhFEzEx81KysuOg9fpBvCU1zViYBBztuq5yEkt6XwDWN1qTfhW6E8qxgmBHRuuadhrV6LCJpEeMHGmhLZDNWXtDlS9y2QzIyM2ymTfTTmgG8Uv5pZ49zpyoYRJX0zoBifcawfYjl9zxopNhJuCiuFLyEg7Y0paDibpMd/isF6EzuhK729NTgYRaCGet6iYukOZiRvnrO6gyW9Fm3Kam3wqHB3NRfIRHBJ80z0zN88LFJb2jn9aFm3Lm/dxpWcAIbyYeHunvYh1L7qCigSMsvilyCSb7+YnQmmbWS32c8efa9RZwMTm6Ro/cbZMB5lRyqRqQDel1X6jwPsqMNshUu0qvhY+aOSYxbcLX54UqVgLyMabLDGfq0E+imiMBvvLTSQ6WQcJ0PNXiDF6eWlTyk4Tz040Gekwx/lCCAk4xjhUrgSHKcio7gSOm7lU9BcScKymxFOLDxw451PGVJ1moJsru+jrTnwY+utMohYaTE/NprfAEa4kzU/7Xx3T8UGbEU7uE41dqJCy65cKyKCp1teeFXyWb5P7Qk2wDewtTbLv628U/N4Ybz01Z4Bbj/MSlSMEVNMws8v5QpZlY4Xi96bLKedKKLhLJuCZoAG8PJ++6mBDq4a1jGTDpnz7URqXPpHiprta95TU+xT3fGulYppXgU9UwtsH6oWAspLdQCMdKHuILTg5YYy56U8kJCwEyui8b3nPWicphE4NmfNJU5peE4Q4W6PSlbJKTZYHlZNEkpOtPPVYwyY50OJRYWCMjq3z0onsQbrMgF7d6EZjcEltxZ7uFqkhQCG08XdcMSIoAnQ31oS5ZHWmd5RLVE3mMcKN7YwzGBbEwlxcKaq0cNQiOjfzQ4lgh1V/Q5NSyhkT6eTdO6nQJkGAswOESaZsY0C5cm15EEs5Y4Z0lslkRkEJjnBDlelAQxYmLkJExGeFnCKvk4bDYEhizMY/WKssZcYkQc8Q1c0ZmSZNGclvVlcMJtMhMZQZ6VCEhW5IQGuF+NsLicgJxDBDZxQbZYxVhDBOQib5SF9WidiWd7m43qxtkxxWE0i7fG2FGKRlGCy3OJBfcaUwxkTKEMZvB8K1cWTSGbYY3130ByYJZoUoEXW1zOEKrrTQKC7zlgQ1EHw2oTFoRrMwjIliMG2OVPf2UJiKcOB7NHohkdf1KxO2CZ2Sxdbaqsv4HBxJDw2oUUuUKYP4LN2hRk/CV1MvF2bqQHAoslgHlsallohhlZzb8A82EctOH6xVI3/ACVxOwUw/FatiY54/hLETbbMbJYj8SmObZxw7FW7SZ5XaiJDS960zz1/04UAXP2jDkEeZ+B4MgicTD/9fnq2P/QgpxLH/wA/4/kplJb+X8LgRKHWldyU6f8AlcJR5/n4jxaE88P/AJlBE3an7gw8RKKVvKc5fw7Z5rvnn/yUS5hbjhUT5g+6C4fwE/Rj/wDMm6EXqfylaLfs+a3k+J806JE/O3tnmu6ef0nxaUOU/H/gTzeOjFDHyE70KImTpgnz+HZnv9KglbVNTmLf/EIQI/NEm7utGC5nsfVDDb3inkedvbPNd08/ocXEF7UxeL5DQisBDp/nB+S/h/QBDX2acPF9UYgaeD8OzPf6SBYiHGKwQZOqPxTmy38v/wAG6GYORtYjNDrSGsh+crxEeaipIyD5/wCUyhyX1t7Z5runn9AirSxzkploaV+T3/jBkgKAGA7VRjg5z+iLQt3U1dLZVg8PBSADB2uOGe/0YjQUIJjfpapCyffmvL8v/wAGpH6u7FO2l2YrsnmsLgeX870wGXQ+KUIECPN/NAVgPiu49G3tnmmBMV8v6O89tY1Eg5j6/wAfj+SuwPFIgZ1jpFeB7/MVeRNQIGKPRaTFIu0VjjXYHjbjjMO9OMYt+crO7yV97fsjyMFPfv8A+D7n27Pub67J5rC4Hv8AMT9GFH6cKczrPW9d16NvbPNFl+r/AKFLjJdH+0IVeR4/x+P5Km+7xau4K76vA9/n2D42A3jrvFRaa7A8bcPFUI4zz+fZ13tCQw07G/0f/B6igc1aSE3cK+5vrsnmmr4H54Gz8lIxwCV4/gpp2SPU/m3tnmj47z+hWiUp1zoQWaj1Y915/ho+GJ1Af8Qnk+SmwrQe8V3BXfV4Hv8AMiTo+KcRbvOoRxEfelXhXZHjbi5PdeH4Pz8PyUPcU3nZ4oVTOGu+9V3L/wCCZNEvSiSLT5pYRrV2TzWPwKYhwT3/ACGdkz0FdgeKAz6FPuDB2kZta770/nuoHYkJYi8pXn+GnZUl6n8P8Xj+SoTQTw+6EES4Od29d9Xge9h4Xucm345HR8UYDT0+ZrHoQxv7BTmfQ2+v3Xh+D82HJ8leVWBw9tHel/PVY+J/8CAVgVvsRoSxJJ0RoisFq7J5rH4FdgefyeZ9RFdgeKVxkH70pJuXht7quyeH8+wfGxFuN81FGKjwe6xUQ+T/ABWAMUPfqkFOJ8qKBlR7r131eB72fc3/AIuXufFSWnM8UkkU9ovb0p2+31+68Pwfn3Xpo5JMzcVIRdnt/wBaIJu7y0TYYs9f+f8AwPYPii9Y3j6p0O70hXZPNY/ArsDz+T+lB/a7A8VDSXIWnKsiNvdV2Tw/n2D4o81GMsr5plsn3TA4Hl/xanX1SlbvSUUomw627V31YuT3s+5v/FBJo1uqTxsRXcmcmdef5dvr914fg/M97fH/AGrT3eFCQsXdK+tuoJzy/wDgXHDotjU1mc+qfBeUc3Nds81j8CiXlQ8/kZB1fBQAQbPdRM5knYpW+uyeH83xUhjerfpUXNz5V3TzRaIPvanJ4fP+L2eq8qsm4ryb+Ktd/wCIrFye6hZRNudIAWPJ/n49x6aVBMYbOwfFNSda3QKczZ6/deH4PzCJxeWu18HYYCwfXekoaPh/+B8PyUUffOjfm9VDLUuc8HWKu455oAUzHlpWKUx6vr8VCa7j0bUBGfw01wi46Ts7grsnh/NAOUeJ90kc4Q9f+0Yg3eClDMB7oEjjQRJP8MUnX2VCjJ8KsXe8tZgkucvxWPk90oDifOu89/i7j012542Pi5E7VPZhfoVvxT1jZj5fdFJd3g/NwtSpUs1zD3sj1/Y/qh0P/gYXd3krBKH4oXKK6BXtZUrH3CgEwUpJI+pa7n4/EkMAa7j0bR0/TWAsUd6za+MV3hXZPD+eI0x4KL+IGOmvvlXh+Cu49ND0kEnnPxSngHj/AAkzlL9+5U5TvfdS9ROeNIEzqx8nurWfWNX8b2/jgcfTQRY2qV4+KJzI+T+bMfL7rH4H5Teiipm/w0E5Qj4dHhswuB7pVcw9f/gRaG7D3KmK/wCA/NOAEoIHCA60epR5EndK+5wrFxaCV30nx5fw7B8UULO+1mjp5ShtO7lM1My38PumMa+L12Tw/mWDvPCgsej3Xh+Cu49NQTsUOg/NfTx/wmRgOZ/2kK9iOz/OtfcaKIkwn241j5PdMUn1emwL5XfxhLMHn8EVbDv/AMmvR7pPjT8V6/dY/A/K/hvivJ7V9tu2YXA91cLH4f8A2laxvG+Ca34g9aGTFN+DHnabLJu3bLzNO7WH9bUcheUdpqVfXeo2vocKBFy+dLGSCZ0T72pEnV/AEsh8VhpQdrbTzcJXQcD3wasphHendw+KZUMJ8NSDMYvPzs4zE8sPwAqQFLsl8HgDvare0CxiwCxBYxYNWp8iwdzCjV0R9e6jMukb7B5qV3f5f1FiZmd1pOtTMwiY3oe6zm+MbtgqUkxMycOuzGKI80AVYl6pVqfe1M9yyp4B3blFmYHtVj5fdHZ5q9D+03mwIbiB/EzGJOtvf4FaUfe2FEDRnWJx9FFh8PdLI2Hj/tKGLXGd2/BwC8hfOG3Gi2mw4WsXlNWW3IZ3BPCbbI9aeP8AtAVkHRP/AGjQESGOJhOUUgLYozmxExpmTS6wTPdsUMEJwuXkvypbCljrJdooIWUi0xMof2rDbCtIIBRbgQxgmIlvbOmWhcmwXTLRISgouIykc5A3aUlG6+6veUDiyU2V0tiwNgz4VamJfXzWQLJjOMJpwCkNiaaYa41LTRRLLAdhNd28Uhc3+Wg4Ab7U4jKhIohjZOHSaywDHgPJTL/9ZKBwUYCNV8VDbJhDmwvEZlO1upm6MDlY6G1SmCwTm2g4tPtgKccNgvEU85ArBZTHFRztRqNifR+KYCgVagXvPGo8TMmbRK5G6zjQgIpCThF/i1sZqS6ADcylyx38KI4gA9Sf0g6luwpyrhyzIi3qhAgqO+H5LUwiFE5Rg2JYOJ2P90UcRwTCiW2ZcSEq9y5DrGpSoxG8ZwjeOlY2NC1p4hwOFGXITxZTw0FDcDG4moCRiDvUeiKZXiIXwBN8OtOLT8DbXGnQo0n5KWmVpDwQmCZ36UoGkn1LmxcYqDgS8LM3q8sOcwzchhlbSlRmvf8AtENqd09UhVnWZqK4Sxa0QODPqptkFQyiy3CKWHAl4LSgAlZxsp6oRQblSAiGP3L85woeo77EvfhbHeUVahJ7SpzGCuRChI5RLxcafWITvFZ+oaSAsiBmYDPCjG3kkiYDG6G8xe2k/wDszLNCOT4oCJcMPPmaLGV5nFFydZJ1utRVBMRjGU7yMah+KDv0xOtJbsFwMZx6R0oFMf8AlQ4tO5lbb8L8NKQpil5ZZIJ5402hAJA2jVuN73602pR4U2M2PIecsyrnTpmbJ9605MPcB6pC7ATesC09eFYd0RrZLwdaWYCTpPrrVqVjHIBC04pE3S1Y2dGovFijclInXBPSPrSUAEE2skW1341h/cq44z12x0kokDKGnUujC2GEcIKCzI1rIRbdJtlhNIsHA4RFGMkknmp7rgHfaBtvijBwbgd6OOE0PLEha+C33zb7NW5IAconvNMRMz3QBWYLbsXyrTIMSHS2fKWlrlM/e1GMNOgPSmZTj2QdC36Yk1HhfVOQ3NdVfrTA4CQ9Z+KRPgZRcQhvk443tUgRJMt232KWtE9PMRrcpkvV8b5UxaqXYhDGFzDPMKmJCPq5zLNEAiB9EDuzyqYMYnN/vSmeZDze6HrB8FT+73CI8VMCLdOhHq0pW6TEY1l+aiYsGsovuJm9WdW05kC6zrO+sO3Btldh3RGHWjhYlMmsB6uYUiMs5W7TuopEapacAyNW69jguK0eoPOFpgkixIxFkukLa07+ThnEHCzQZZ1ml2KIOFxi3/KTvFlwZm31ejgCoS3jHC/m1POXPm+xpgNlSmbAWt0yO56rE0YckXs0BJaHoruNAESdIB5wden/ALKhDR8Ug6nzRGOtQscKIAa+bUk5Y0EIpnHpFLA5bWd00KY+cKRS404ZH/KHJ0RygKAKmfqrQuMedkwkTPRx7bBAhnWH9ypS2+vV7oRt1NzZjVx9U0SXhDvM6w8g75V8rUxkPZSwhRgHSsHhWPsMua+5vfpcjc8UZPIlDGc2KkTlUgulABHIoxf79asN0ab7ZKMvRUAtzZGDZe4+KNBxqCSa92gfoxqGbIrH+50hlEcEAw3z22xC/Z2CEqySsmyEmjtWZUxdW7xpheEr1D4ouR+2msRxmvM7AVgox7ruf+yFDF6GpprPmoSFKcGFQLTGmtAEqGphsxPCsShBNI3FZqghg0BnBsSaIIV/wkYNLLLSEGdJSFCMUhb47VNHCgk3e6OM5sUn1cKagvHxSST7apUJgl3FY+yIhRgIbvJSdv0jPzJ2p9dKmnkqIZxahU7cv5RhZgaOavwoEFbwoEb6sjvq4lilio1qCWrcrfhMWJB5JJ2Z2OiypIMsU2IyoCHGrCUuBhW5WoYWKnUkXtuL/NYKCkMlBykiSdSSeJrsw6+z1P8A2d6IlKeQONjvepZ4GJwmLG921BGhkFkne0YUQwXEhkbL9XMG1QEuwBq4VGOiHKYJ6opk5YMEg3vTG0MFsZRMzq9qv0vGLIN1HGsJOEJ+H8rBHy6TKeaxLlUYSMNY/wAo6sUHcIiby1Y16x0MV5F4xypodIk7/o082yhwvjyolmuRc5Be81OqIr5Ee0OektKwIqQEJknkKT2rJ7YdZ+GmcF5WihOBNOT2ZGYhHEVjhOdSAY3pK7M8SeqiRSrAZ7qu3mwtiTG+Fs6bwjJ60rUUkFgQ6p6aWDm/WoQRp5D7qXgngmVLLcjKL5MmDWCAaUxYLGWElNIut428AaujgXoi3zdVE6UQZ3VhgOSMwpyEQgbgI/TDmYPhPKVMIWDpg9WpWCH3pIxrlYvcpSdBdRMdydBm+FO4VMcrk+6XcU9oI1hRjdSIlckjGhINajPOZFxaBYy50kRMTpOE06C5TuBf441gTGXhCzmOVWtprreADlE9qgmMb0twsPSybjzxqxkMjySzwaSQAiRnaznGWFFqbfNWOJYGTJCY0lj+UMDjhUQMWcKuUOhATne5UX5IFIIQYsyyFptrQBQhawQeV8d9JjnAqJEyowUgXgp5JUnVBIzzFjq6UoQzq4MTOpCnirGlXFQ3AV2IN8UEUojioUdxJtybk708ETnQizKjgioOsw0XlpWOCOebtWGFNTMlp8jfJlVnq4M1Bum0K9r4f+zFaqCjCjOShzm7jYoNpDxXQvWkkzNsYILmszvisMiS4yy8zxKDSMUhRcmcob6UPJNAbEDM7oJq+CCAQwlAmAeFnhFSEquOOJuzqNogUKzJGOCTJhhFRTWOATHJGTc3lGEgxRgkhxkjrKNqUuGMsZuKbCXtjbBoPBBmG4FyyFrcaBiRxI2FDcLcam4keSycRJ3ZN8vNohDhpOeoicvJrp9Yt1AS5RAzJzawzBahF53MXGzaaa8HBYXZd84dKDZA3iEFbyLQgXowWUoyMxjOMyYxVgMXHOVk75MrxEFqxwyEYxNhXGC1Wx4E5wNk6SYU8crKNgg0yzF4GF0vUOYzCXiVpyXnHO1GyXIpexBOkrolipFg6Gt8xzM8mmgIgoggrpLxFm8NRDsFy2MWYjODOwzjdntUARETOdsFcmdcKbEG5cUwkY4Ezf4o+yWlKKLHA8UAOELcClIIMDBhDra3CplIkwMBA5agO7CG9ATCkROJaXHxWR6CJsAg4xPnfXh+D9O7YX17pJSRRQllXqvKKEaJ4zsq+6TuXKXC2SMIgA/tSIiRWMMLTljbfhTvHjfLKDS949RTRCBBxghlYJxxthUggpDMBVzcWZtGVExYpaQqd4eVRmxJwQuS95HgPpCpm4i6ZwLChNmirREZeBu6yL1IIjQXCQ51JJMM6Tgc8taVzEvE2ztNYFxW3seU3QelqG90Zb1ictyt9IxNxUpMiIgp+EUWTIEoZ3OrMVD9ICUyXWwJDTy0ZilxdZNzTKNKNNdv5QEjAsM4urShxCRYjGYtniZutOllK5sJEb7+sr06TgafNY9ARgKsZ1hVMiY4ZJwEkN9nCYW0lEWJ0xIQkXFyidIoCCDbjAScJioRIImZBHm7qXYAkpIAwaCkkVAeEYMULhbsi1jlFNk8qGC5bfOMRbWlzECG86EjBvNQuIm3WIBgX3TUi3gDtQk5VneFltPGbzriTSM1EEySkri45mDFRQBEYWJltir81EgQTBEsYurv/wDZQBlsAgudZYAesh1SiwJ49aQSGhQuV+0e6AFTPb3TxRSXXw09oxkGMMJwwOlEQxJPfukGSB8e6AAAYFgDQ0ChEFljiy47/gtQjco1eCRsUgDx1w/CSY2Z2sLyPmjhpGHD7OxghYGTGGeGWwF5kjvS8HQ4XI70g7howYAza9i16V4Z/wApGt4RymPVJNhgigDzIHSfmlwC57keGsdpf0oOM8myExN6tViJ4ZfeFZBfYAVC7+HdNY6wZ+9KF4Kl4vibEADCJJgi40bt8YaMB3EOFx7dm+hPZPe3FU/lJcBHs1fBeJyy8bJsZmTq6buuwVBEs86FuHF3v3qVC8pyXI0PuEUoLC0wDi9i+dhnBKnKuL4O5/7Sb6eAfYrf2uo2TcA8bzR2VGc+fdSS1F513evFREhBeeRvqOgNTqsmHdfhfsVLhZHVHp80xclBhkpCVdaJFyZOVNPWMOXzjTNEiOE4/FJbDXpHzU4fccIxe7TzJFHH7/KdBnC+jeTxWCtATN5M6dxLFubdy8lYXifcuVYC2bSx7p5dHE+KCmYjLX76q0nJHKIpRxhmo3y4GDainuUywxnj9zpdCObN5YUAQyyDgwFQHEwJxl9Z1CwIrc+5GNPDk0p4RDPG9ImYsetMGRQRnOEvmlRTChwFj9Qp4Skr5cM8/NZGYB3xN+rU7ZZXq4VeMHOPutBUCPb5pC5C23SxPKKhojfhaMPdSYe7LuvHd7b6FiGbO75ogA/r/lS84kjk1PDM6tJrKES62/vWga9Mzre/mpcSIO7E8qlhgPP+UmVA7y5G7Op2zk4n18FQ2Dy1xv8AypMwFv8A2j12lvqTbtUahBRxxOfGhxwsXll9tTspTZe80JvTDCcv7WeYp54UmzxieVMcY6zgebPKmrgzy5Yd6w5SkaBJM8vuhgCLKY4Q+6uWHG+BPlMMKdFmx4n9pHjNGwJzbl8UyBAg1tN/LSkTBwuxzikas/8A5WVIm3/oKqVv/wDjz//EACwQAQEAAgECBQQCAwEBAQEAAAERACExQVEQYXGBkSChsfAwwUBQ0eHxYHD/2gAIAQEAAT8Q/wATjfRApC3XKtXA4aj0ERVm2ApQsZ9Fer67HWA5FRwtEMPUwehfCPJpHYoi7ctmztweYRbqxx1+rCy8ABVWALgD3L0HYj1P8jmqOFuUYIwFZocP7xGYVqQRXUNcV2snYOc4lEiHhSgMAl2D4G2abgFX2DBxoEniSiADO/qXMl+lE6wCwK9ByRX3haiAioN1aCR6FJyiXOHjbM9cygVgdUPoBqYVByABdNH1gowiCyvkYp8fQiegxGWQ2jQTkHhHqOQ4hUgkgVV1EPo2dEeZaNek5cT1a+gxVAgHIIQVKzgeMM5gfYwNDhUilAfzmMVlXPvcCWRec87CbAlIPMcNhgZ6kaSJTVwL47HUVnkLHkgJUMcJIcdAERgR7InTxEB0BYQ0TQQReF8NolQdQQiHFF4jGYrtZOwc5xKJEPClAYBLsHxP1BDG31AFWul41BG1A3SBAVQAr9UlA2SRMCJhUCwUB/zIO8iyp0CZWhSgLiMkbEmdYRQeSjH0LGTLQPUUUMiZ7pRYUZEq1oYardMi8NBbDCHNFQZag9AgbobcUZkwpIpBJJSjR6vBCruWgRCK6MAQUSrsxyRlwdKi/Le7IUKd6AMzohgpaz4FwI5zAVGaNKs0VLDW/wDHFfuwveVCABVQxBvG4OUP4AEpC7eWNWnimrnwcqJGXwlszCEq6QhjEdaRyNHshsoisGcGbASrcplx88EIuw1XTSy3D3uCYAYyiLoSCWWTDNsSPcxwCClynbVDJRsVrc1dIfZzLIcU5ayu8vaE6nYgTaEzZXSqUAQkL2LdJyeGOMAAiCt2DcENTFxANIzEqWYHVwg5YTqRhHjNPcwMRTuxhhGALBET3BW1YkOIgNhSo4WCVS06cUzeCeWEkk5LiAmJJiRFjkVN3C1nNhZjSQ8UmS+hFeBjqL1bsaEWmaeOCe+HuXMdXxu1GtARKNECrZsRkCA+14zvXrz6VCOgugQAh1HgW4DpAeq+uOqPF9QQKKHSZMAv43/IZJrQukrCjkovgiAAO3uETboEkZaKWo1hgwQJCK5UAXnqfU7wP+w2M4kQ1EkTNu7hhJwCx1ax2peHFZWCUapxAwVk4w/SaDt1TNZnQHXJER1u0Wn+BWlUAaEwUDC1jhnLX58DxUxCUpCzOo8EyBgGhlFg/UD0crNgTQWBg9AkLpoNzhKZGCdsFIcM7hu5Kt2BDJDZAWG0V0WoCz0aA2HG6ac0IBSCappbwWEKi1SfBWqwOIpAjZCKCSVYgLjdXptcAkahpbcONmRCWNUMqhtYgXNF/LdFvLlO0MjMugOpqR3RsE0Kiml5JpXSXCJs+vW7cRBoOCcoc1cgJbgTTAYsYNRjtFNtEeJkKiWemAoptBQEpNg1PtEYjJtRNuvu9wfoAvdJemRXW2uCcAE9ZUad4Kr0fgvFVmD15z7ZWtNYUrTmz/3uq4WmOdtfHlbSwk45YdATMmzSEI3UfzIDDSyZilBiU/1K9evXr169evXr169evXr169evXr169evXr169evXr169evXgDDS2bisRgV+oJeY0lKbCCT2Tf8S9evXr169evX8YvTDWAVECcjYJ/hL169evXr169evXr169evX3dqc5ypMPIBrRPqXr169evVrffI6UOBQcpVX6F69evCXmNJSmgokd119LFdRSgWEqwgctz/LXr169evXr169evXr169evXr169evXrwMSNyTE1FqS4WpVA7lCJ5P1L169evWxloxjNpeIxcL/AvXr169evAAQoROA13S0Qm8SuBFXQqRQwvBZ8JmopfiisBAATbOhzCjbAgaQYAcKFFxWFehcJ4KYTr/jfVlaxAV9UPf8A06t18uJnkCdk5+toGw0HVHDo6eC0dyMOIvDQeOP9O6dcgGvS6XShT+aYHcGn+AuMKSg7K2upuXHKnwGaik+aKwVClvZ0O4UbYEjSH/HVtCKovkZpXY3h0i9Q0Oq8YjIJkOCl/wBardf9zzf5OjpK5phiAo6QK8Rwx3YL30OY6UEOICckWgdWu/RK3Fj/AJhzTK4vd/DH+hXK0RmaCulvshiE+fk6TO6v1pw5Ii8F5IVPLtlNSQaKDXvffyx1f0j6KxLELZoE4aXW+H/Uq3X/AHPN/k6OhAFxRUT1HphcxRTMsgAGA4C/zuozNarf/jfzfpe5/qVyuTJDZzC078kPdhUBmQHBvX+rKiWyV/QlKvMvn9ExFl0Dj/hKwcI6hBYtpIBy5qzARagYoBBDgRfYkt0hBC17IO5/if8Ac83+p0dOmWMrUIqvK/zGed+c/wABcwmBdks3iLDR3Yv/AB+t3J4t99p/51j3RkQYoPdRUr0ePZyy4dpCfy7g5NSuDzVn8ElfILJ1Cfw4sbVn0HCWFCIQK54GNA5EjpPqr4uN2/w8VjyaG1KijTHipy/yOjQLzA2h7n8X/c8316Kitrg4F0HY7ryv1kYxlcal5f5Lp0irWI4wpwuj/AialoUHRPtdX+EG3Ekm5im9vf8AyFyvj/TX8GSDzp6CwEeQn3M0pqVrfHZSvQFeMdsTHcZ7A+30DBS0DZ/AGWFVIB3yWFau5Cc4LURnRFaFokhWus3KHwgqKsCoef8AKrdbXQOXt/Mk8sEX+5xIX2Y1dmGxGiY2hxdScr18HRByGV0L03N4qNUQ3Qi9Zc/b93ja/LNoG40mJad8j46nX/Xsfe3G0ENCar5jr348P3PNjyQgVTI8k3x3b0j+EnUiB9TadLKyqgVYZdfxAQgjKUfi9vFcN6DQJe58WKHLOm8PsUoFcgu9enjLa8WhhV1y+BRYlVAIZ0mzCiYSgGqaXq465aesaBjE8xP4XWmdq9c21SBXRbk6jSMYKLViXjRwTki0Dq136JW4sf5P2vc8RW0YGpBSC8g9/wCUhE7XYLDzpb6fwAPljgb7OlkxhKoIvT/EVyu52o1YKz4zVuQ4YWjXA8FN1dQ6BehdHTOP9NfwZM0AmVGDOs5yk0em0CeB7uL2y5HAOlEJfVC+/iHk8YvU/hcEFETufWwd4/v41VUh4S5K5I4ScVFXepjOZTECQ5BQHrXb+VW6oa4DZfbXQHXXOhk2Nzlmfuu/NSfPK9N974OphrUAh9dmWgKv6qrDWKhL0dX74wRavrbpN7AmuXwu0FgUOZHUUnqrtcK+PUa3QiOme96ZpTJaQhB3Sqd8YyLFVoo+d8NTpo9RBPofHhxIzPHD5KZPIWLSecTRro98MLEMhyA77Vp7u06Zoni2ulEiRvNFkIJRsC40KltHzc/c82dDhYs3kib0fRyEzi7BR9s5sTAhAPPWOEKiBBe3ZLe0889M0TYi9LM5yEXmBDv2+t1s9vStOmyi2bM2nbAgEOp6tWVv8rr9r3P8AT2LERYTr/GyYQwAGFqKaGqLbDg5T6VyybSSAoxnzOBqfCZqKX4orAQAE2zocwo2wIGkGAHChRcVhXoXCeCmE6/xYwbABHlaPsj7+IScQPnJb8h/BJt5VCDYRL7vz4OKl6VGiJrzPox/dd8MXFKipA77Pv5fWLLAd9+csIuaUrJp1PN8nw1j1u8UP15fWrcIAVVgGGa2omURK8nxdSMKKVuYj6Dc2Ej5Rb39c4oSOiiJyb588I4gLsQ84n2xKiWKEBVTaRo1p2eAJzRSKtho6VNuinfDGQrve/8APLjLppEE7Vb2caYekygJvTp74CnFkKCVvalvquHOoIiF5DsmDPlSvKo/v4ybuzvaLyn3MNMqKpGz77Z6Yqu34OPHB+DK70V/j74hMgQQVh1hvL5EzLwT5TLxBAL4LzKdpikXEmq6wgGpzfcMRulNIeoafJ5xSKqcggW84MbFiKFC9NDz2xTMhqFum5CXIB7ZRA5okaLX0KX1xURgCNh32vzwM4IIbicPGESqKU0Xh18l98USVRGpB4Cd3Ci2hge4HeGaUmCvIb+t4xdBVpdh+75ck1xfgOr7H2zS1sLK9DQphq5dnORSo3pPHhvm/wAV064pdSI1fIA9vAAJo/t/x558kDVQMvQ6H3+uiucq3FzkoYSYK7wnwGaik+aKwVClvZ0O4UbYEjSH+RWMAzVSFnw62p9KR2Ttb/lvjroXmfUF8Q3ACBgfHBqIgXJTWFfVB/f0+jELEBmoGXyp/Ad0/sgYGGPmY6VDpdgx/D8eCtaBfXR+H61acCSi0myKvoYjl39SKuRAQtmAi+xJLjSGlr0RN3GR9WkKm3i7OMsPjjCnPag9sBbwPgBJbQ4VBbzK+hnE9KhQLDRtxsAmDQ6B24Pre+SyXooAOu2z74vm2Bcu3dwHOIwn7w6D3xGmUVaIe2kffEsgKh3Wv5xFp3KQgY9kPbWKIMLADVXyK44DL9xn95Y7xGLGoAKwFZ6GPvroEJ67ET59vCKMacnPfiJc0KxnluEOQc+Ze7lUH85GKeVT7mXKPQmtT1WeuCpwOTss+9981XLr0+/obPvjQEHgeVOXffCeUkrlNPK4ElJiKyoPnJ6UUSBMtlB5OmBMUlG0juxPs5U/XvIY9XGOL9O1l97cbG64MCi2mg1selseHcz9z349YPkAIPRD1Lh8GdiwDcObd8DjdIjSEDl08EM4ff8AFwkQRrvlGt2pvqP+I6dCTD51Xj7I+/h+17/5M8+SEeEV2uzR/P8AhLlfV8YAEACbRZ9kt/pncdM8mPmx8FgIkWvaX9/wSeTd60RyvLjJ93wBZUo9bT8GKmoIXut918eam2vG/wDr+E4MBIq1CfLZfIeAjUB5tXm+uctpdpqJ5/h54JACyEwbbjp6v8St0jyBQvBaHz98l9SLcbNP2wMpBQBExelSWOVyrPPzPLFeqpHkH9B4EMoWm7SPZJ7YZZhlCgx/D8ZZLHrQn+sHmt3GjD5/GSuSyERtdgvfWTTLRdlvlt06GPU4FEBqvVebjDcexC2/ae+UC8EOnX/GUwNloVdD2PjA7UbOV54RSNCqb6YQ4Cl6GSfjXVeF31BZrVwBYbeq6zhoYjQlpFFPf5GVFVoDz0vky5dEU1v/AJj4PxbolHnPbEcoMDgDz2t98XKOdN9+HVfri9AnHROumr7503PgjQrUJhsGn50+1xSaFnmE/ccApSBpReeOmUIDktFz84vyaLka0+1PTETRQXsOvsYuoVcBjW/sYM2+6tvBhZEpoAB15t/U6MJ1gYoOgAVfLOug6vxxdnViKuJ4wEBpbESrZT+Oll32wJjkuuhEoQSX/ngcELehQx+758D7YSUq69fp5E5Jj5U4S8f4V8nZ+y7v+EuVuNgaqMXzCPdB75Twj8aXavRt6vE3/AdSk9YK8GOaNwOngBJUjsg/MUOSx30SGnpYbz5V9/5DqgY9HD7ni0gYgITB1Vve3rjg90/iVboXggkqt+FMR/KMXg/YwJben5ufcsEeXkTWymPtgowbp/x9spyag51TLL/1TJYbVgkBb5cnphXZaJdxvTDfNS7g4rdjWLA8lI7nzD6Yqb9inwwF+NYRXQ32GPMZyaHg+5hqOCuQGc8JT0PPEAUSN7UpPTD3xop0RTEWur9TZM5kEKu2A7SX/wCZpM8WQbHzv941xyTWkefTOtoazgDpy4PxlOSyBJy5E0CrAQp8jnHoC6A/+GWqzEuhQKHdh8GCTCqmAQ/45qJkEBZXoQ+2Hi8ieTE6Oz27YXIoGSLV/wDMmIpNqkZkL96Uppfvm0yHeZr0g4L6XLp5nzfsyF63RFnK87wXC6hVwXtzE6XHpJWOwEW94fnA36B3j9B6/U62Y3QAMOAjaM0nKiGC4Wy6qA0+mdddaJHkF7m8EQRo5zkAnMIOffNXyEeHIDR4Hv4Hc5pYBBb8fbP13c8NZmu+YvCe7+9p9bxGUyOtEnCPJyeFTegxQJPPe/GMlKpdLfZfW8E9Y1x5+D4x1jjxU5+y7v0rpM4i1dNoVFMjNf8Aj9buTxb77T/zrHujIgxQe6ipXo8ezllw7SE/hzEtgBpPQbTqXGMtp1QtfO/YzXDA1oRDbAdfwjUpvcfzYx7Y4ab3ONnaPTwSpa8CSCAdgqeg5QEMTj/gH8Z0RzGLGDvgtZr/AO6PfKhvesDBNFNE4zdDUIUEnwM88X/Ysi6r3/Kz+JW6J6LQ6F/6yqSMuh1ueW84QoWvAsH5zbvUjex/zHREewku/vkRIQg0QZ/XzcNBsO+Q2PtcUzua6aCfll0BgKvZ91xVNYGgdjDrEaVQUXHtZqThvvSf198EFGlGVpBNF5H9mXb0wsVSp84HSbKrvwY1OQeoY/GsRKkAlm118twwrHInXQt9vvgqEYmxMUCVDgvGb2PHqQ3+sjUkofK/GR4utnbXSOMsAYe9a4c8VIpSM7Y9NomdED0RD5Xvm+KHwVI68vXfGCYqVJya/H3xdKBzVD+8vuiBddz4XGR72fQZ84jSoa5Pm+MNn9JmqwDW1s17txJQ2idbJ9v53TrTIefru3gngEnRtj7g+3hrGQixCsOdGfqu54L6ZQId0XsnXpjnCroS2hqUGeR2xolWjRyNdm30c4zeFyq+tzgnPVAQHy6xUjqF5aB4pPg8NJTddBQY7PUdmbISlCCpLV80LeMWEaQFpOAqY6PZUkd0OpeGLyKMEEE69XxX7uzjSBYQC587owkeydR6CuJBA4Bdvh194qbNHmTwcarOr5X+8qhT9N3XAWa87h2igNEaTs/Ye3h+67v8Jcr66RquzTz1PRclUD/MIfcY75eYg2B5evER382/PwpKahBcSFjAE3HRmxc7KhAe5r6XRNSKdnvJYZ42+SxE6Z77T+OO8EAR8FGoc7VXfnZ4CU1GxvyiS9W5OMb6gl5W246ViGpSaFM3xOnufUrIS0vAKvwYVKbueg8tiS4gyXCrimcrYwBY9vC8SAOQafhfnNRimD1mHouVFsLZ6cYTOyCa3v4x3+XO9/8A1i94Qug7PvcB80q9phD4JBKSNffE3aN8ttxjm5HzEH988RMoJZoeH4fjDnpCxwo2d9fdxHIDdoKvXYzTFazqR1+9cYuVROTI6Yvelv8AXgkAYabG0+E+cE6VACq3R8GRbvITofROSkL0r/lxM7nBEwxPGnOl/rHdNfsHba0m5s6Z2Kn9XDx2oGlv/wAxFEjqXX/WUNjB3wuvXEVQD7BQ+2LQW0NEvb4wuIRRMiToB2ZnGaK81w0Vb8xD+8FTAQ5BdnpMnLDvjS/7/I6dI+TGi0pLEZTEEiEtlV9J4PKS6tNHYdLjUjEQT0zZRXdtGp9jB8qMqlXp1vbEWpUEuVsaV6d8/a9zwKJ5DchMFiZoQNqVdO2++A4I4vLWHsL7YOH7Sjor7vDSMIAAse0jzo8ILKDQ6Jd9NvDz88htvHrlCPM9R63HXjfcEIpkhujTgil7IkJx1m1jiCkXnp6eLVnl/E5+x7cUAztwsV9fiz9zyYqoKjB9RfJ59e2IEo9vDzeQz3RwQ74iKv0t+xzrGJMocxD9xT3wKB6gtLHa8XOjAOHmaD3NILTnns4qp0SBseq27v8AhrldgXBMIlXkeokffG328JEafbJi+wUVzk3YdJb0zVV2143/ANZv7HuEPuOXfyPt/ViRgOl7E/bxfuO/KpxonlQjn7GzwFCVWeTH+8IiL/Qhw+/5+t2BNOkRHmnxGEfIaAkB6eBVRo9Cb4T2xgBMD2o/P1KyEtLwCJ8ONS26nqfHYxuYN8HQEFWwdcyTYHyK7/GCyoADq3EYIciRMiBAjdw269YztKNej/7iKdKDzVv4MDldt7Eh9nw4hyq66n8GBXqBmZGKi+VH4zp/fGOAWK31Gn4MAQ0ADQ3z7h84FEOU8yZQ5gag1We/3yDRUzmFYOrQPS/Vz7z+PoKy8yve/wDMJkdvUoTA3Td23JL9nEoIC9FMPdX3yYxJseQH9YVxGaeof3h+IY6nGakvI7aP9Yg2lp0DH/XN3Q7BzXWMFIhe0n95dRoh859jwUAYCPmW/wBfwFArop0wJQ1vhwQO6YkwY2m2gBFeUYiHg6lJiDtCOko98GY4UrQt10MPVOr4fXly40HvJsJ3w2QTGnRoasAvOjOdx8aJod0DToO+Q+VMhwVGmgD3dwTQrJeWsrLogF0ym4Rvq6Yd/wCs4h9rqI7HErbJoqA6FWBozaF34cWBXN4igkXjnh7eCAIOd1RR9fwxJViiYQkCxlbR6U1LvGkbbwqrhEpedZ3jEXkIqivN1lM0rqwst9bDtIonZcD4+zNkASaKEh8n0MOBg7JJipaLryuPNUMMgb1NtDUkbqicMRdA+p8Digh6rbDy0BMsYS2ke7HO3H9YwDZDuRTs3KS5RqaDHwBmwvlFI1T3typhN5dgfvvijcMnCgP4fGWJAGTVxN9rXPlMADIQPHA9wW8hCrec0n2xF6zypb32PY+lcKwONLehKbtipCtFi4VYCiELZAtoiN2Y5il8PK4AcKFFxWFehcJ4KYTr/D2qFmA869ja+mRZvheCHnCT6YbuQiowC+ZT+jHQRo086/vA8hRsgQPKZ5YkybTGYU6MT6GFCk68LUgCrSvvuj38YTp3u6ZubH+NgrpBxT6AqaLOF5xU3cj01Ls5q+AffJN27fQSvkOJZ0uTmv8Aw/iVurm6gHr/AMs4Tyj6R/eM1YCe9PtTApSkrWrP3XHdqk+bm20HrDf/AJjxNE91L/eXXYPgTxsLWLmlI+SLlddE2fpyfOOjkGuaY9x/WDEKYrOWvuGD8XBThWXfzg8IPfAH8eFgKgV8tZxC0UKEPznmC7eTPABXvMhoO8diQ++fuO2SkCiHyH4Q+2PRK061r+2ffuCmUKnJEfyGGALp/SKfeY17SD2rMAZhB0bFPgcC+T+YwTTll66/1iggb3Wtf7wDolPmvgb5GMyHq58fVdExlgltKNVhZZg9Q1yB3HJAWYDA8YCAwkqBVth4GT0eUBgnqnl26JVas6KD+33xzHQGoDJmuR32Mv2gH3X+ExIMlwdJd4ldSK9rn2w+/Ny43TizVxZRBRZowOp/ViGGsKEEMV5DInNiPPDsR21IQ89YjIaDoG9wEnnj1OuAgBsz0/8AjYqdYQRVX8GDbe7Sq011192HaUQoIJr1MulSwcy+mtB12wX4hAW4/CF35YJiitDhKer7GDwD0LoAeSQ6uq93ET6MXzf+DnQmJWSGvbb2wMAlOEIB57fJgxap6AT2Ue2BvoMdwq39rgWIorsGt632J6s20b6hEHpv8YIRA49K2ABqzFCCrDzOTP03ZjmAgQNLOj4AYdasNmfdzZ8U6gmPziBsinEZ9t4CuiRaogY7mjQGrhrtx9C7d9EdSqpLJ77XDKqWNVNQIAYAMMc0efDQ2QaLDP41czkrSkD8vgw7GBpah83GOiG4KIRH0XXp2yxN+KgfjwOvxKz98Auro2tA7UTzPpEIGdUOQOwlRiCmnY8eJoqNNQw2OucGiK62jB718GExEK77BZHhNWInJhDUEJ5/wIhOajBZwE4a2TDqQuFFcQ2Dgw1b4qM4HrLn63vgEgpzAJLfIb7fxK3SXGwjtnpjVp8zf94OwRINLv7GN9Nh4BD2Puz9p3yBCBQ5a7+2B5wPm3/1il2gjtP/AIzzsL4Cc73yXK9j2ATdfLXTK7AOCy26sQnqxp4Br2af0z9TzYXme3OApOeHJOYh8yX8mLS0y/amHGPwFXFu6insn2uMiDyU1z73K6CrvFuEDJVEnUwi0zt5FDjnTgmKMjAA9HPIz9liNBV6EsP1rESOafNs/Dg4gMiBbf30wHhCR4SmQdQOzD1/rXHdFc6C/wDWW2aurIsGZUb9hj92Hf8ArHL+cIq7r39MICCCj104qDRkmr/EdOtssBtUh6Rv5McqKJRmkifHgKqKdNeE9tAd1r+cHwIn1Hc91gRJtQBbe433yxuYykOq74NRegNiX/z5xCisZYa/cH4PPPvH5MaVL1HJG/nEQKJQnTbDMg0dEAp2X4H6CsAMgI0OLqxwU2pDlKJ5Ys3ENdymdNG3GUtdq1nFOD474mMEfINH5x/IqVU8q595+DAUNT+XfuOO7WTuvPgqUEQjWwYP2fjwMjAMHkVmJTGq2dH2TU64vNgnxhnbAThQV77Y4AEC8DXBNYBoxs+4bPgxNLhjgK/DiISjugP2j2/iXK6GVzy4Pm/04sK8ZqMPYksOYNs48FIDUCwgs7VPCeuSqJBdWGHlgqyiZdrtvSu+Mdew4EucNg3xcQ3kVLYAvBL1Ozg47AQcgvnMt7LfXV/eaIdzUJa99I989mDAEjdP0RybdQfMfb7/AEATAc7v7QxmRA0JAgrLOu+TBm/AwZ139B1RHs1SwHRwxGPRPp30tBKX8ccl5qY9JQvdPgy3WrblYO5du8sxGKgr5z+FW6Gbl8xBvyyLNyreAac/3xBBlLQCOXTW9ZhqLp+CF91/8wsALHNRf69sdxIF6jWGIrV9i2+RX2wiroLD15gJ6YbQRbYrRpq1dJ6B9g4q7VnHyZ+x7MACrmjWbz6/fOWXVl8rGF2iOygPXefqebDj2peXT3R98ROkj3P+cMNvCL0/+Mg6Nj5BT8uBmChIQs0a5eq5WlNadyj8YU8aNSaN7sYURSjyjn4+YxwUXBo69REvaeeBD2N1O2j8vh0UmnHDfY8aSYgiUnmTLdq0HRPxzly6DI6V/BggYIVrRWON3U+d/wB4ApAglUBT9Oc2jqfFTTWzOvbE05rBDuh3+2N4SgSq6/Kvu4VVrx6NwdU2DZIJJLb7c9GIHa9gCHrf5J06kdPer6SzJWSnc0z6RPt+46BSejm3aJGwbPTherlBkAR6B/v7sXxyFFJL57n3xxs0B8xr8mBh5TZoh2NNeWfvebN4aRksZ9GyC1+wi19/t4qKCKMcG2BgUsTaTma53k2Br3A5wjvID5v9YxrVGaeTHkKyd0xJOrxsqfKP58XNrE7JlxmdCLwIh9sMVWN9af25Y0mw36uVBb0tH+xm9ReJtF7xIy3Hkwf0+hcxFcvqFKKGsSNzfcumjQq1EHke2KsCLVrhHg4PJ38VeiIQQ83DHrJiCtOSuA6YZ7JYmHuvY3pwAEMt7FyxUNXfxrAQiUKMfJwrJgNkSVh/Y5ycEYAEHz3lEdJHAlvTX3wIy4AjO/vXE2RN0hQTNEaOpc18pbkJL3fgwLKCTsC7995vFX7DWen5sF6HKGG6BQZHePMxjKAHYTfc5NstNZSepNIcV3ox5q/QhSl6m3I0OEjaAarUfXFuWAbblc0uJshz1LT8n74Sx6UDuroxyRBkkgBd1GemIX2OhCd+tDyyTA1kUoTXVP0DNxubajrxJJejkuoUfr3wqyhFLbQ40C3z+RbRgNBp63/LjNJtCAkOaja31yTK4gQVJXce8tw1oYi4m9AGXu2zGCWbmvzvAvWeRiIxdciynD7DXC5SlbgjEDtRQ5jccEVqoZ/c+Q8sNNPPTTHr/RlNxlJpl7IYbmUo0SeqmcVIRoAPokwQdgPoVq5WYgFHJ4CCu0N4jfCwAgdUEoNUopiu0IhBgjLJEtXh0et4rG2ahVXZN5Z7oTRSySTDEEGJaJnwYXfB3CT9j4xrISGVEGO4EvXDGBZrTU752Wu+LA1ZCDeml4Fo7bw5ajauAHmqYbirRqOL6xnpi3wJs9lv4y8BYd0DoegJ5ODJ4gqQfgyVukZ6f98s7R6MKfhb7ZAgQdl4c9j5yYwkEIemzsB5++CGIWUMC7YWb7ZGHogFievJz2emEZCa7z+0x/YwObCHRvz5K3J0aG3ptPnNSr7pfZ6BXSzu4O/ejTuVKknwsvlhQLJeiZgQACoIdJH3PuzaSQ1QNigy9hkIua/v/eBsnW1fugPvgJiWxUcQj7Y0MsxYFA5e5xNW4jpbaJoNsA2GEAXcXe/Hm5uuOx5JLwPjJqWFHQF7oBXkwAgRa+eHI4HbjAHVnkawsmXwVXDhCTycJw3K6jTIyTdDvP8ArnV+/Gwo7cGvqdVDRCGW6AFXywhRd2iUdOoEEo+LpecmIAu30I3Fu2WBqrEd+S5e8LbOUu8HYVEertb8ZVhDGNj2x7I+k6XU/fPKJm3AnR9+2K1bhaCAqcGIS2w5rYe7L+MDsiR6l/hiVo8wIB+fxgPQg1zDXyfbEahVGItvtM3mRU8ErOv/ABj+ARK25B02lzQ/d2CVX2bwnxrU632hMmZvGnQP/TO5QOsJ8awioISo3PblMvJ3daAV1hBSYGm9/byxmwjyEgL2txcqmLdm8LK3UiC06uNnxgGnqAYdzP0PPkDrqbCoX2DNrAdLZFM0YVQ6PTmusEAhU64XeOWAonCYSWDboo/7MuMuDR6kOnT4xErteyoPkF+MvkFIKqn/ALj5qaNjOMKgBd+yPxc1IAfxfQuYkYEitHZChQUsnS+wJgIjNwCAg6nccTnSTYLRMiDFB7qKlejx7OWXDtITPW8oS7GfdMGHmDUEnaqUm1hujFeUCqxbRnX1os2PPL+uuEBeO5pq+kT+mOt1ZSD5TzhjHvAqtQ3hTgoA1qvU3JHtzm0VziGpO9O+a7irAzt0S1eB45yJuqCYFMh4URVr7YdANAE9eeQtrt3cV4grXR+XGTUBbvgl88QcFuupK/Y+Mts1kNgD1EL5LvUV5aQCmz3OF582GKGHYE0EXSdufLOv8a3kjbBgcfaTjIKI0U8x92WDJ1FHfQjTeMRY3wpKA9Dj2xXUXtTj7GGnAqk+fV30ZrqcMdBwHBp1gOy4jbkqysF5XlWu8apYKC/FeWB61wqaB5xEcLr0Y5Tmo2CcCmh83u4F5AbrhD0vPTrk5aQOlm/fXqON7G2C7YEJoZPFvShqD95zZzfQ3bJzxgMws7c28uj2wCqRUikQ8w743eItWER9t5emWZwAAYCvQOHXXqfSr4RRtsgWAFNIPTH7Am1uDGFYbnQ8XUAdbj2/9ZwQsm1imLprTpbcUMokcYGj0pN04mwcy0cfQoHUXh76ZylNSMWIEPM0mGQqgp2Ktjfo7YTWPnisJ534DLlJdOFCTkjyMOygkwwBoJ0Mhu5iECJ3Svrhed1faFh3flglLTu0QoBf+M3XvTWpU5kG/J7Y0Gmv8Sb7/jhaaXt4ar02JOpvWxocQAQi+WMTCxVTp2Yp6LgIVS00a9UFper3yPDo2PI9oe2APVaFBdntgbc9WiPbK8mA3WPwY0VTiUYB9TB1cFQJgL1jvzu81Jgrl/MLNvlj9p1XS6Ltb8tGUzGortKnam57ZcpXIa34dKuKMKNntvkhfJQd7aX7Ps2rMgqNWcsDedhSI4P/AAw0bLwJBJYlnCA9DG/WkbD/AHjBTyW6JXkYzhUgEdR67Qx0C61lnHfF++M7AAM56koHXn63Sj9ZCM/cAJjBdOJ/LFwDEI4DaGLCnJEojRrr0wNwK+CNEJ4Rm2fJ74iJlirSP5fth3QtTaQiX4caDRVKzt3m7iCtQcU1h7fbCoHQuxhf7xYQIJcuT5xHgvsBv7u/PAIJqx4BkwYV05iH+8ZjgIcub9fgMZYXRuGgG/WfODzNjx2X2NvxiGpHSohOfS5H8Q89I49cdFSQ+8/eMRsBVehnLPFUYKeqYn6TkBKG+eDBAVsL12n9YKcMxIqsIV4MMKOU5H/3KKq09XmfA/JllYVPJuGWSRckU+6ZsxXy6YGt8OOOsxInb1gVI76+5jBoMyhWtnWQ++ShpRwUrrCKRGN4Lr1/DjTTG+hb9nfODiCRomaa1H8MV/AXyvN93GtyqbJxzm5bSr3Qv5yBxB7QVcoiIURdh/Zje6uF0H+R/hXK+5DC4gkoUCUbGl0KHfLWAOIS/AGBLSuelzb4T0wpaCh3vk5j3yW8q8l3Z6E9sd6+YsQlOoj39MrNCCDP/Bb7YBiRiCC8r8rjwwaPcaYu+yklTHkQymgnRhP7Ma4wM3rm4Wbwkuil9ivthZV9eZF/Jl9RC15TnY632xvPCoAYO/4XpicWgQDoXa7mdyH4IUTm7P7wt4wG+3JeEKDCH5Xt43IFkLDR/VxnqIcT0xep0SqulnlggRpGyjH3wU8BTtV+Q8B0g5IB9At4d1yt3KL8j8YopdBEIAPYmPTM7clkt7U9s2QyQcDD8P2wRDQXhV/d8HGKkk0p96KefhUlm4wK5PjGoVMeVQv8Kt0lqYV0jsOGaHKrA83b4Wvgf2cZwXQlJvyPxkzlFvMy1ANv9zYcJ1HKuVjbF0Hm4wNFWgUJxZUed9sBI6XAQRvEhJjX7gckSHz8Ylkip6MKfL84r595AFfvkJDGRF9Uob9Zn6nmwuNnCV2uB0TZi8gWKuvO0VpuhvRj4+XVPabexs60aOQm7gM0POl11fB5p6QmQOun4engAIdEIUj18OUadAIYPMGdsMAAHRYM40d+Oa5RxCmlfCcpPXOJ09oAUvXeIYlF1QC+esLw5fmWHqY7h5WikvUuKuieg8SLxA3td8BSTyFKI8I8+MZB5Lj6mFd+I4gpU5O+AYpKNKUL7nyfxunQGie5S8dyGPlkvIMWVgF41deuRgKzfvR1Nb46znHKwiBbQT2dcYeUAS6E4Outnrl2fCbOB16iXGQJNG7ovyY0+W5rQK9dom+2O5lqFsfi37euJ/HE7jeg135xPN6oeYhemz7uaPTzAYAjYG9hlEilY21OeC5va8CbDV9UPbA81kwFHk8ZoNYwhs7J11ksbJMO9E76nfAJLaI3lHztwhutiQ0PoP8AzAUJeYq6X1wEe7xvS+coSeRebi9umLJ2zQKEPKxe/li51QHJoJqQHvt3kmmsLtvYXy/vHgIijQNe6uOcJGoYOR7+0xbCXU6Or7PnJkYs2Nyg3vByqiQBW8X0H3S4kxUjb/xffIIHOpiknR/5gBQ7nSodHhKBrvjr2WQKfIQKdnD/ABK/IABe/wCclw2Nyx/0hgMgaojHPpqY3eJJ0A8T2yYY4fwi5WSddCDHd+g4JY2ybgEH5fnKs0YBROXog4pRH1kPU55zV2MdhV+DKw6BRtD2t98JeKScHA9xjAgUYrkLzrOPFq1Q3HqLxYwCDApBeunIgQwa2wLxdMYipR5ED8Z5CGbKJZj2IPelGLmpRhd0Afl9sSOnTppgb43gLqKE2UL8/Qs0R0LVa+/gAMUdxwP2cZ0BGNvIfZX1w2iSOolBj6XKqimbIJHOkHCFQo9SGzy3+cAuSqqqD3fkcXUcFsbwPpUyaC5SGgJ98DR5AcBN9GR1NEaUlz7HIgBx/RiRzT/axQLaL8t/vDEQIH8Kt10QqfZeSFY+fiKvAjko8gbElgHKwbV3A0UPIikw5vnQaAdAAPpDAfRU5yyMOnpV+64N84BhuLiyznLca7HtjpDtgVK+QnQTjnrgdB521dVm11cRFBjS/SAgBu6+r4cChHojo650tPfP0/ZgT1IsXVybF7eiC9UB+3piloJB4WxiqlMEmiX7fQiNjW2rdrTpE/jdOlFEkdaDfv8AOXSGdQmt6GAss1imrXJNip7Ye1IDdoWOPN7zrgjRiYO49sCJSaRGl1e7szHIDEQK1tdckPgw+q9BVKvkC77P1CptWzEvuXDbGgEA7GElKUwR/wDnlhKhshgd/Pb74VNwZkuLO4eV6ZrTMO2fQEuSqeePDYsEoKvuJ7eF/wCk0lIj3ovlMDKojqZzdmoNoR5z8HbwZ6MBKuVw4Xx5CFcsrL3e+QOQL5hFtIhE198sbR6MLfLS32w5oER0FS/KHvlirbd6/AZsNOIm1E9Tt5mOWRAFVylEpcFizHQ8JPi/r6F0KdWhs1EDDp6jKTZWiFoHNLDdTTo+IWWo5pSLrABwoUXFYV6FwngphOucEkk8kn2BcbMDIhAhuDHlsprD+wSCLRyUZC69eFOiZU7vsmVOxKdLhU50/ObOXNdobYKBZ1ma4aWQKDfIfhcTSWnkYZ6RTcsOibIrFHVIPfy8emCqszqnflgkF7a9j8j8Y/1+YBBfVPhygABBxV/vEZfRb1IOxAvSO+TVrbnkM6QTfa41qhJVbHYXjvjIlJ1NHc5HqYvu1FEAPU1DFF3Lgs8yt4Fl+UM5FkScU5wsqyqdS640PbKXBWAhclrXtDJi0swSleCnHfpkTQWBCND5TC0ip0RHqOJkAm7Jm+mzjzzcWi4BUkzZb1t1rE7Ejy02YbqOyub4PS+2F3YMCLovQKvo5MvQFPYAAvBryxmFjI7NR3EPjAP/AIKeJsfnFAVj760/vG7qRACnqU+TvicAXIUoPsr5eeRYWpK9P9/WrDiwV7QEqgtFDs/z3X8f2fJn6fs8P2XbidZyUZzzgBwgHYP43RZeusiwZkRdCbcCnY9hwiIKERQIiIIni6gxLFesfl8JgzELUXUlUVJRMAAOUAND+X5xQehU/fnBDgA1PUNK0e6eDyemR+7+sR5UoCusO6r6uKURHneRoiYPJ4+0ffJniwgoHuq+r4TWDexIPrlDY1OU2F7BcLgRq9Fvwj38BnxZOgj13Xxie1LSnqD0YuNmnlo08AVdYdVVfNyRbu4L1BQ1ihoQIYEnZQfS4k5v0O3Te9yetmbi1I6y1vtgEBCOwAY6V37+EIQUFArOmJo42bdi3U3rz8sWn4G3QF+Rt3bgeiULBQDfq4J0xNgqBai+1vSbYPG/Rb+GWGgnJ6uKdHbAsknxIdbsHXphRuMqvWdtB6Y4/Shele+nvgFuekDJ5jcNEEQ50Z7X5Yh3YyKVjjyT6F3QZuWNrACdchwFdujlpkrmqFRgdmJ7gBgMBTmL4q0ECj3oBpCVOoYLCut3EaXR26a75XzTJclxPQK2CndlqFHQ0JruR354Jur2mSR6CvLTGFM5YSJpdg8mynU7gOLHACbaoeTeJfQyiollLOg053Nmz3zWHlR0ntGTJ1gIoKCaNEOjgBQhpwjVt62ps3ioLqbtxXq4QnPmDMUsjtil8vlidGfKCoLFB6y++Hv1QeIfcXY+3FyNe7dKlTViL3OhpBJPKCAcb8vGhziAFOwoWEPJUB6FzSakoAZUFaPWXrhM/pF5KJ6tERuTCrS3KI8o9KD14MRNYUCbYhirCJub68rkCleVS5OgGWrI0deTUgS0MUjh0ut8VVZwxl0J1sRIkBBSYY7jl6zHRSiAiu1BodXpgEq4IQAUGnFS3nGltxSFYgAMaD0DBaH6qXWccHnp55v69yaz1oN1XUKwqW3KqPVspgS251X8LstAFXwgBuGE/cCBAVQlTkOty5TgITMHsD2w47LxLQq7K5Td9cbZ7i5isQNugWpjGCMVSRJySuDau3EN0wcAYgQvn9CsXjTEhhqTFCJqnODB8hQFb04QfYNFA00ypGyiC05CL7EkuNIaWvRE3f8AKCKDGlOHxh/Z8mfp+zw/Udv8joMNmp4LCFBGulkYh/p4LGoXiRo6Qa76iVLLMigr44EewO1OcTNI2NWpiEAKVDnGXo2IKUlqT7PPIOSRSAeDq0NYiVjrC7F7PftiMcxARsGL0674bpXpYkD4MGaTUtNY6AGuNdcSm6GNN6V5vTvWSXu2BbhqQsSF42jd/e43EAKtIUFtTWJfCdRLR7r74OCdyaaU0KKu63rs9qBA7JezftYGjXJURj5IcLQ4kBy87qt5AQwYK7p3Rb5ucdpTiAlHqMlSRARiEWuyauRw29AGHVuNuvGBx/KhCW8L21ga0kzNuvKpzgKpRbfpi8xCtQOjzswD2M9VPlXHyqq0Gu73a+ca9soGxBZ0WdKZcoX2NYSoAjQqPPuYVxCGECDIml8j2G+nSyqwxL3G9NDeuV4Fzf06BJBpzpd55ZopoVOlmC/UAkBHqNHr55flOsaYQ62OCL2fK0VHAGncwAsBgLd+aavf+VcrPqZ2+gAr1iTnXY4JjTOHffgFE4nm5XKIwdd77g99YtJFoqIFDqgQ7Xufogu4YWZRF0DdPX7maoj4SP4wQOBlxGxucWj+J8OB3WKqQnnW9BxVVWrjH+Sg5RHjadZNc+IihhBZof0fqBUvsH6mBBeLTkcdkHpC2G9wx6vK5ZlqadZpeT7PTwYbzgAIYh1Q57Y5iqdkBO0op3DjdpelAOlLsL23g5DDSKAoHqh6pgxmqCBq50P3TEVd6mPkzIa0qvN9hDzzeCCsWw774OmI62Vd4f6RW655n7Pkz9P2f4eZ06B2MtECQ9KYZAEANB4OEqQ+MdBdQtMceSZuUssgia8nAwAhDBtrT6uBAUAgBwBhs8g8CkdPHX+8WD1YcKnrVxxuVLCKr0nHy9trAJUNg3PsfGLViAiOS+YYMZDrc97ku54AaSmRSgpTyc2zTRWz2tNLer3fpCkFXR7ZL8sEnLX38EmllqJEHeg33yIRqlRWu3zXEQp0lunJvLfWZvyghMBs8wwTY4Oqs5/1J1du13m1VSnZwVG824CpOW31xAyX/n40KKCnD28CV48xYbL00/WuNCA0vwpU7N+Kt5Spo9kwp0NATaJOIg3XZNmpH1KGngAB2MtMjMkFBCHkdUdJp0ONhESSlh0m9diGy2DRkaQaUtN26iOKuJtS1h0NdqdXsMcdWwTs0NbvcoRKry03W6lKAeVaQoYDYCuQSnEmqc7kVohUSqNaEUC9nU3TjQBoDY+nRThcZI5RiAhffZ7VXneWDx7DE5pFrHWxcFlYg1yaSVb4N+kyi4BvYXPYa05a4SJT0iLQmhQmr1qMBYUprhwDQFWRvd3O8TiXRjbWoAwr9rtloDq7S66YFlksUihAXanmKJFMeekwIspou+vCc6QUaScgiVbLSaBMhmbsoIChub5R0oYWKU3fioiozY4ibwtHZ4UBu02iOBGPJ0dLUuwLxREl3crbNdEIaJs96emM5a9OEVUqUmltuFTZc+ZgaUCvkKJhEQotXNpLDS2qnELiaEoJumli8BxdRHG3lmNtFHqgF3xiI6/Qm0biaHZimo1AkFAVDOAoTsLZhKihB7bNwTXejk0frEjsO1a53OMaTJpMljFAKnTVlLVRvJxSsUqejR2/0yt1z9nyZ+n7P86c6dEAcGoBw/d+cO2saAEA9vCWvv60VbxY7bZISDg5eO/1hCGj6th5VaFE4b1O30MsorRZsf3Pf+RcQSmvrzAGno2FGwXpE5OA1b7Jhhqo3PL/ADcL3oqKbOGIp0Xv/OUSJdKb3S6+Dx2wpYIVavuq+AZBAwKCoL2Fdeb9A6hgAiheEdj0/wBMrdIt+4TqSLwxS+bgHxQ6DsnXAh4KZ1CK0RR6aU13/ldIWCCGKo6ACq4GP0q4vIgiIoj/AJ7oXNk0Eo+z9CXn+ZcpNT6IASgLAL0wUTPB6UaKFA88P1OnTp06F8DAGTjowFNjeo/S6dOnTowBnofTVSZpz01n0unTp06V8DAORpgQBdre4fS6dOnToSglR2xnQQTQdvL9Dp06dCiZ4PSjRQoHnh+p06dOnSlBIjpjekomx7OT6HTp06Emp9UAZAkol6fUEvMaSlNJEI7rrF5I2JB9HRFQhK20n0r169evBmBYiErbt6qYWH0r169evRGTAaIUgoivHL9K9evXgzAsRCBs29EMZH6V69evXo5V4REMRbLdMm6fSvXr168kbEg+joioQlbaT6V69evXg5V4VUNVLJNEmq/QvXr14S8xpKU2lQnsm/AUPHCeu3lBJKO5GK2to2NAxAUHpvYF5uAkQV5FRU36a2SdNac0uwx0r4GAcjTAgC7W9w+l06dOnSomeD1qVUqQ44PpdOnToUTPB6UaKFA88P1OnTp06F8DAGTjowFNjeo/S6dOnTowBnofTVSZpz01n0unTp06V8DAORpgQBdre4fS6dOnToSglR2xnQQTQdvL9Dp06dAdG3KQZoJbrITf1GlXXaXIK3SyDB2GiIctDROhH0I9D8EtC21CBlLkC721XvXbzQGEmorK4VaKFhmCgE/qb7NPbxqa4GXh9JobWDhCtJFvtEaGwEARyFpyMTV8RUg8WAMcNIGuOSDuOdL16uXAFXZ2dTvQU2G0icMDoLBtswfBK0IDdAgfA9r371AFxSmkCYSkZkyaULJniFGEmorK4VaKFhmM62qItImBcgUngmPxCp1pdBUgUu84fPkF7XVIWGgY+qVWmQkRMZRasCciPF7xUJQiUGPJtasQIKVGIjNmHWnueIt0wKGlcO9BTYbSJwwOgsRdCVWQ7RAboSGD7i/7E4iowEA/xkK7DohrdBaTErxrFb5y4HQ1AFfPBmp0wM5yNgJJTGtLUs3uT5L1dzJJyjfL5yB13i9DwddEbXZ+y32WdcVBdgumFCqglYsIWtAwkEWr0CjcOhEgoS0RiBialeDHfU5yfshAHpXZOxtSJxRXX3Qq4XmYda7u04naKOMiWyIGhxzRoa8NHOKWRVGUE0C2ZehBiR0VAlAGwGa0tSze5PkvV3MkXhHjKmKuUgQbCixHumtHKpV8Ap9fWWBJFWRuph4SWnagg0a7UMbsFLF7aFDWRQzCfIULvAqTs6ylAL7QMreynlPCFetJLqClCIqNwnY2pE4orr7oVcLzMOtd3acTtFFDFjd3VJARSkAdONAzkfxetlWsYesw2JtGeW5Zvj2W14mzlPfEJFEE8wMXnpNQiUYRcI08kLWgYSCLV6BRuHQiQUJaIxAxNSvBSAca13hVQoadGFceoZoEzdMEgqmKzKp8SALAhNAKdgjMkkGIqYs8T6tSz6BxytDpMOQLvbVe9dvNAYSaisrhVooWGYKAT+pvs09vBpsl1nSVKQ0IJbjmIZuBphi6Y7RCFrQMJBFq9Ao3BSgfhDI7KsLCCrThnsBAAKAAgAB4Qr1pJdQUoRFRuDXCayQJb5t8yYC1/ADjZUScrAUMWN3dUkBFKQB040DOR4AaxWYDBEibU0THIF3tqveu3mgMJNRWVwq0ULDMZ1tURaRMC5ApP4jckOw2fxyFUoojVXmCgegH0d+u7uQY4YFaTbCpyhFDawLC6wrh+I6EIV6AhsK3hoGGwBAA4DxbAImnopa6Ga5EBYVVuUCWlVVdMBk1RCkkuOpUBW+CUTHudPqhEigt2qtUoDKS7ZB3GxNw4qoWRYfWEkypV8S22UTAoRbUQmRS4CgP+dJeluG8K4fiOhCFegIbCt43rhNTDWk4s0QnhTcgEUXaCkk+CNFsIyJpSpI09jH0yKYImwy2YAAAgQWSM6XKNkFGEwk3moCKsXekEdiOGBf1i1DSSCpCVKAyku2QdxsTcOKXaBmAm5FKdU1zY3pVkQ4qAlGBX+NuzNaWC7MSCtO/oX7MyOEnESxkaZo4Ii8QCDXBEhwBgkjexLqdJCje9DMcsTlCMzHDG68WzI6WiRjBdlOGUMAoNPkVrkNGpoYPPjxE1ooIQ0hkhJhsSLgwwAADx0CKcPU3SwoQCCAO2LVPCs0WqhQeQgeo1tYgdsjkeyyuuRoUCu31avLvw1WaTFHsN5ClOFwI1mJpbrlozQrgkjexLqdJCje9DMfOrNW5PEgjQISBFOq2iYBWYHABkNBPNamN8qikcDx7HlQyiDsNCI33uZk+VCe2CuJFg4tT1+V9+UMEo3gYAHAAAeXgPT2koV7E1FRVTNsWqeFZotVCg8hA9RraxA7ZHI9lk2fAkwQVS01RVv8AFzijHkpzMZPKkg+gcdwTAMEJVQWmwcHfr7mEoNiK5XeDz48RNaKCENIZISYbEi4MMAAA8YGPV03XAkBFJFsYh5QBVhocWrW2eQCIUzxFUUreE4DSDWMIVS3nxLyphumFUbx2XUVOUIobWBYXWFcPxHQhCvQENhW8NAw2AIAHAeAl4UUiTyRC2uowyITyIcPTkPWghB58eImtFBCGkMlbzSNoWSBBCCmBySru1QlQoKKqqvgPT2koV7E1FRVTBNfGZJYllW1Xqc6RpuUg9sjke2ybPgSYIKpaaoq3w0heR8BdUILwgcKnKEUNrAsLrCuH4joQhXoCGwreN64TUw1pOLNEJ/kBw4mWqGgAVXQGAqJSYElrAIx5E6fQF1GNgIuEAiUTp/Abb6AYFUqJFYno4kbGaUYiVAdER2f6JqdVjSLBUYWs/gcRWANFdEUEEnk+keTFVQpQHewIRH/SFx+8NY0qcAHbt9ArCW2LLRVwNvT6x1JNV0FTsQnVZf8AOAScwoqaQUR0jky4MnVAXZAwt4GYI0yEpwkNE1MJ32FETJgOzAq2LW6U2wtF6C0wHB5LMxS7KgGKaUVXnGbkaJTYiPRlL4FxmB8cs06EEiKeUgXNAU1yMZJygeJrpCOJ8MALefgBFgC1AOLXNmirqCibRUBWZsLmP7YFhcEawMGNOGRwApbCcRSiLM3QdCmEZwMGpUaqdecD6rDImqbFdrYbY5BCCHn5VxIRWkhNuHueNE5t8lYa2AmiP7Aau5NiUOEsyPFGEenpgIL0FECGFKIwAXQH+XGlEAIEyAFjKVxlO2TmZKvawMQCtiIrgGBKt1aK4C3eXPhy2oIdQSSBvh0ZDPoSbwfGmJr3nL55oBceUDPb9ZxRkTUdoHNJ5uJzRgs9+yfStgogBmprN96a+GQRXxrQgyNFkLKYiacIrB/pemGowqjSCoONG8k/2oPBkcJRHGcamTUJIjjXpRpOcxNXcRBEVPblDVqIHR1xyn9YEqA16SFQKKwtUTwaSlooJHGunzZw1mV2ESacGDI74UpAVuB5NO0meOMCCpnop/mawoWvqC8GQHVrBmUDdmib6SaARyQH4gdJAJoiy4d2BsxfWrOulUAVFGGlB2SxWtLeyQNpW7L8GQGHIWZvqgjtuQCJ4hWE9IwRV7A1MakiTZSVLVJFQxQoRIrPaBlKkXD7D+p9QEVRqBQ6rpBaA9h7UySsAt7QZiZ2kNAGmu15VY0x6JoPOsMMP/jNtEmAewPNa9lgBEriFqgzrUtzmx6HVV1HBNSXKyHXIJAbopDEjWCAKQDhr/hAKrJkQyVCInI/xP379+/fv378UQCVhS4IKaQemWoFwlJrDRTZ1xwnBpxIVQFWmFwIh9l05FbFiu3ADcPCTGoegk1JgeCjvBGA1hKnlcMYwxYLXAkGNd3NvbGBWAIu+WOh+SUiDCIojjDfIGrWLRSYdLRgkIlG3LwlFSIujB1vA7hCMxMFTlT76YhUbiGUA0bcaAFN3AMkjoLTbic1O9IAAsDdJK4AWBE5UK3crA4DD5wlE2o1XezvGU/CMoNgqiGLgP1/dxAAIOwgwmPt+BCAFA+Sg1DBBdsIQ2jCIVC6bJUMFCgvJh/E/fv379+/fv2qsmRLJACq8B9XokyrEIpMgKvH8b9+/fv379+/fnENsXPvcCSxeMNKwsUsJGOzWsGvQ1UAnUAGlgdsXPhRSj1jauzEhg4FBvi2UpWMpc7cdMESMJBGltc6UBqRVA5BS3te+Mfw/wDiRGimKKOsHIg21baVcDZ0xk9bqwHm0XRmkQgBDLvSo6K0wFdMVxDcCsUJ5clzT87WvqG8S8triMCMBpB5DHl9pPrYCaCI8Y5eik02VaKxnXH84L7FlCVeA5wwkhvCCBJUjU6Ytn4lQm1JeIAYBg5fnU0YliJi73atGmslsKIGgNqlJ/E/fv379+/fv355OzMPLBoK66l+gfT1rJySo+OTCzaLUMaeOyw9Lh5OzMPLBgo76k/kfv379+/fv379+xekkgeSVj64sxE7bR3igIrSExAOs+3XW1Km8eMCkU3MiGKWuy4DOBAA4xaSdNhXbj/bJsQgGtBanVyN59CZCOMNc4cU+2W4AqB6qXbnlPjLmlUAFqgXOWnlpNRHZ3LvFuqL1Dk96453iPbtFAQYQAgujtmhqrnpHswDY4zuhV9rtnfek3kA/aP2lWarpOcZAh9hpE0KqFwuKNsm/FhV1iu2T3/cO5NDUoD0Lcr7YdTvgKokcEP5H79+/fv379+/f+qTKsQiEyII8+N6tJE+DgbA8FmXczxtCBCdOliv51bhACqsAwywqJRO/wBSMRcpA8HpTKkO3+MrJdbCYLNBUhUKlTB0C4V82tECRuEjq6nqEFGimaV/mXSRoVFRYd2C+z9aBYGKVtDl+58+CZZIRe0JWdtnz/jumilK+RJKGwEBOERFVaI2gE0HkrFOSJRGjXXpgbgV/mgtODuuKdP89dw1g9gEZoKpxJVxSURp+xjF7gAA4UqDiNKdGYRwAQHT6tUTY0UEFDmFN+Z9NPoQENB5F0fxSZPIpFGQeWJpXCRDUKAmw6cXLihTeYl8DKQqUbsYnz4LCVT2LUdUU6nOv4Kq6x1FEHkVn8Kt48C8vQoFghA3HjJ3rkzspIuYdbcP6gZ6wHAACOdgVvQLmpuw7TR2+vdLbyNCN88m/o2zgmFNieS+Kn6+X1/tPLOTyvCbOLZ0eeKQN7QwE10P7NTw6oPJFA0C8p4252VqA4nAxl6k7H1zctWCmxONL/qXTo5Zyj7gCr2IB0AP8dcrIwaqpKWdNZzJ/poDvSGl+lDjE3pJ49tt8vD9v2fVJkuRY/ACqvbOFEuQoCIKjHzg08OaIhRqhdlBt75yQrN81+Ce2Kp3/A8Ff0t/BHiCicn1tLrgrIDjzTLNsUNApeCG95z/ADq11sgIEdadZXA6MZzUS93n6XwmGeDougLLL1n0cxQvZG2d+PkxlR9BwHXc39vEgIqIkunpDAed3OBzOq9d+Xh+28skpuz0DXYhF3vxNWenSwPXXbx/V8vhsnXSSNXrfx4qBVgbVwOcBAHhE5M8oJ707bJPLLuSVOKHVa9sh0SEshj2Yn+S6dOx/mzUF4E+T/PXK5GlBj+r7PCNCD6sAPV+BemIDBVKKHDHZfBMsoHRKDO2sGiLyVfpvjf2x/zAoK0HLKWd8a33/wAXhyIazlo2Z2KHzj1qAdFAfB93BcRgSgrd2fTzzqkeg9Z51vt7+Iqo7N4BQdZQrVh439vo5RfFtVfSY6uyZvDk+IoUbAutu7f51a6p8xVgFYbdHTDj8H5EE9vBOQPJpDfTX58EWNawFoPXRgJiKA+Xg8hsU7RCiPZPDt0YJUvdMxTtdvkWazYiXQVoFs4Hl5y0hUiCJBODjE/j3VKVXvgcRICJDz6te/gp+lxkrqpJURvnap3yGbbwJ1zwDe0eus0pS9cIJ1KF50c8rNkpqoAPsHj+75MZog4jEcNBgxpbVX0TCf8AFKqgG/NwqyIXzz9p3Z9m/Lgy14Tye/hquTo6eQHhEMjoBBTYXk2a/wAd06YUEiR7Sdjyf5nf29n+QuVyKKkWau/hqrx5eCs6qtJIfjHFUEtEEX3zeADtA7J1oYBkYvY/kfbwE04FG21fv4cf6ayGlFoIgNcy84VZC6QLyIqeePH/AKbsz7ngWLcxbKU6a34c14zzSrZvbWnoc9awIyEtt539rLS1whJm/MR5YVqsIgBUXpR8eQa4RfoNyotJgmg1zLz4fuebPvsYiz67KL0a+HCBSByHCee3yYYAnCNMSaS6lDrp5+vX+RWupZS8zEveIUUgTfIfOGfFO6S8ovs9MBsZUQDuuMVNngpFxQg68sRvfM4pYR1G24WwtyEpG+eecFAikbTQ+4ePMmTGDry3iL0wO6/oAouWW5byooea/jseEqyRrihInniHxV1WNrnBWOFUQfLWUwha7xzNWZM6FWG3VZeR6Es7CUjHSeeayfC0ivrfH9Xy+EgkqGq6Ur4Uq3KoCI0XWuJcPAL9OlnlS+pjkT+40CXzwpvMKMTI4YH38GCWgNOkJrpr63A0E4BsZY+yfOOqLYKWgmuG/N45+t08U91i0IuJxQGOVRpO+DikztBXAewO1OcHdC0NGr/lxILJV+5/grjpVSkKNbS4UcWdSPB8q0ZjutR5AHChRcVhXoXCeCmE6+P0Pghe68iYjBAoTk8CQEaMNnv4SEFWd30wdYSkBRv0w9XQSSLZ009cNvHUPT9sk8sap8VDoF6F0dM4/wBNZEPSGBRdnkuCmHHIEb+ctRCzoqp8D5z9N2Z9/wACccS+H4c9J0UvBPxiFMdqoEROTGBIEHibF+XNCA0Rrru564IUDF0tTwKyBpF6n8L4vqqGiW6EXrznPQckAoY6TapNd/Zn7nmxhDJotinHmzejLyWl8hwMAMhSYnzhIIdyYsqSBDqAmzbs65MC6oWgMq86D6VZCWl4BV+DCpTdz0HlsSXEGMvt12byPutjOSVvQJmrmwaTZ38JuUBbZxmwZrzwdFJjOKedbN6dY4VhSVQWrvJZSIKVSnvhbMBBWVNuiJxrjOUva5PGbLDj1l3QE4zU5NQo1hrl8GTkKjJFSNemDaeHNiIiDrrzw3ng7Itff7TBGRR3bdv66Z63jWUHGkZCdalORnXl4LuQc8oRZeuspONPwsFOBCiKNejilU1cwYdhtHrya3kXiClUx0mjR6nznUUA+X/cugnOmwPuOC1aMnqH/MCzcgKCpB8vuMQMZHJ2Cw2jPJ7rhU0FCJTidGieeUtSbwS2usWeeSJKdHAOwox3WOaexKs7duzrtxpxhogst0AD4mGyQe67e1uG2jDGdZ9tvD66qMavp4tM6lq0X1Q98BeEtRGvCEdl66m7ziQKi0Ok5exg8BjqKiZe59To06N6Eq5gjRVBGYS6vqUGtpgHAH8ro5ydA6d9HsfB/gRPK594fuS6vF/hB04UN5UF5U+/8C5b3t1O55KbzaLD2PkyYLvEtFENPBXp/SVgKvwYU8QO1CjnQlxeDE+dSDbyvjGJQUe6R+c48E6LNnmh+ce1ob4Sb7qnq5p2tqw7Btzj3ECBB7pW/DYyCSahYL13Hfk4JjtHCBBTYnk445D32Nb3DwmG5BOLKLPbL9sTCoLD08MDsy92KTzHwXrDo4gPKHmxmixEOPU06E9B7YnRf+AK/YcRbEy2A0f02eHL9N5YYTXrCqmxHkTnjFNAKbto0cNLNy4GUcNBorzv+iZUgvZsib3GmeW3Ao0xOhX1r8Yr8EgmkRh6IX0x0eVuRbNFCDEc+Y4VQOOomORhuQd7w93NM6TuORHsDL+nxu3d+pCyXrciw7nYQV5WPPfG6IXDjtOZSX6VZCWl4BE+HFMyriiEPUlNOGt6ZFixY8ZhKkQTwXWymutWHsuBPgtErTyePBt0kK0LRnsfBiJmotEXI+iPvjRTT/ZjkeIVk32YG73IUHwDrBwt2UqBNa3dp5znjPOcwnJX16YqRP5HQPczsXphuEjjSavl3Oo4gxot8MJUOICPBkm9xPmYJAky7QFQ7FPkzm/LFQWeSzPOyDgoOSomqVDc9t/YzfAQkVNLU1FfK85RIPqsaGvSfGIomsfMf+YwupFV5qeA1Oqocm+W09HNWSqZbuLjDeGb1b2Tpj5boawyk0f+4HS4Vu6Pumfqu2OpA+dGM9+MLv51CqV98GR4SKqyJxAc8+H2cZtLQKg32fGD1p4BwPifOA5wvfFf+piugVn4ZsC1wQvQlVO/+I6dfve54OWoSvK/zUXU0UBu9j5U/gNkGFR0DrocPESldED3T3f5VyuRCzmZNAewB7Yxtwgi8h32T0DAwScdjv2WKyJLO3yOeA+cjbWk6XR7jNbqLpUNeewztL7WmvLtPxlb4FWhHSDyOKdzpUAnsA9s3HPYl3lDkhx9B8ZMhNhdtVEugJZvNv1dZGQ6ZakDvy+PAB2siFEiJ12cU8HQ6PTva9jBmBCmlFB8pkVJQ0bF763lF5rYYEh6a0g+wYOYc6CmjR5+Du3gk0BL58lmq+J2yBEgssTyXN6bUnn/AOrhMYrW8k+PKeKltf0tODaqzXDidEPe+uK/uby9kKCtg8P2fjAVMABDD1X+JWue/ExGTQfBPAReET5xRjO3Ayvr8WOelxJJCF7YQfcM/tfwYkUdJQwuvRbPJz9X2+HXw55DA+MNxdjoFc/M9fd8GpFOOsBYgEKyBu9WV9cGvqcdEN+6HvlOz4Q2c8hg/lE97i6Ok9IgF+BkzEOr1cINmVYwJeqrTsdvCZBRCPVaH8MfwOROiL7LgXdkJwIT8OfrebjbKypP7eXgjoe/4jIhQiDrJMZgqbDdBH4/xbjp1+17niO52CrhYNmlPf8AlckZqSLd7/4qkwlytAikhZyL9tvbNuBmBzp6xr17uaAk+AT+vCR0nvA2xP8Aufse2CPbHLEKPROP/br9L25+57872ZdECPqL5zX9nfETVzQAi/DzYzQTewf9sSlJTgEvc+DH7r8MlvO5lT18598iYeTuH+FjMaHnh+YrB3PM8IQ4shtDT+cX7nTK2Hw5EKPAg9mkW4W3plyCLgbCddgw1nV/Av8AWX04Tdpiddu+ngKMlhUSA9AvTGMAMGUtj32GWNpLTx/7Y3wKego46dl3dXJDuH2fACrUBL5Vgj2lHtP+LGQTL1Zv5046B/hK1wCSbrJ2GXm2lPLebj+tMHJBxIxU/OC8crKbyXw/Z887oKdSnQ3yeeEUAhW0LM9z5xiRotE9fB4BXiJoy0eyoqXpdjNP29PoBIdhH5c/ZdjGmpDsBXCeFus9y/vxUgPQh4LXhlI0VvsYpDrX4MNwiP3jYlxzh2s/I9vqdfEsyLwbY3bs1haKvE8LIHuP8rqvhd8Fp6Int4MzFST+afqKaDtf4WSolfLjT8J4CY9iHITemg3y/iXK1sdj4bCJGn2YmG9iIBe58GfseZinUoHfc/5jt5Eb6/0liF18FgBc/c9+A69kcEJH5xTypSq724YnWk4ET8sdA3uFCRViPL1w/Y9mNHsZ9n/MoXQt8ZF1PAaqXF0SP+fGAXXCUQREe/Lz3x1wb4ze2vw8CG7FKlVUHltH/wCvDiwdEoNppXHX6EKRUCpS78z2HwmlaWJw5HmbP0cUoIUeGJ8CP7nuyvK/sc/U9z/CVrgAhCuBA3Rh5Mn4cTK64fb4K+l/Pg/Z8/p3gC6eYZ0Di00Svno8CHZr7pTn66fR+27Z+x7MYoFEj1jcVGqobsResv0pp4z30mHWM046gPzj8nGdEYmNpXQ+G/gGDwDnKivufU6HaGtKfIQ4IdCI62kApk8HptAJBHsDtTnEzSNjVqfymP2vf/HnnySnnfwZQXciK4+y4SGa43c6gDU97WIMtLCsAAa7k8bmd3Q65Zli2yPj8uCqwPYIeFn0EUognuxQKvNRAV6/Fn7nrknopJwrfxPBP9j2eN+dwQIsLLz9CGPoYEZ3CJ4tnS+AehVfnLxlj15t/fiRBKIw+7eIB5dwwgiD578CO7keYR+Xh+578VcRLeknHtlpxRTWiPK4BokOpBb+T5zfmyOlU/GMKqS9VH0K+D1MrQs9jCkBmyAeLQjfEKEkHGJPVScpQawLRRJYYPkTV8tafBaOv8A+6OTtWlB5v/AzWkNCLI/rw/Zd/B+z5/SGXpIdQfgvCi4gtHxYDkUYOCD8D6P3nbP2PZ/B9TIavcKfcwe6vk2qv5GL9DoxX/EzdOgZho1V4+yPv4fte/8Akzz5JRnIvslR+z9S45PlWqIEVGhAADBLKkdRBDKslFEd/wAavnT9bbH7H++dKYH07NOFFwDe/D/edhDVvUvw9su5NXwTAcCX3oVf19QMAJaerwAqYk7bpDryfOVzR+rF/wB8fsWH3bwEXhtICLO+k8QmVSLWlOPy/Ph+5784+/8ArAtCsXJ0uUJOMfazEbfo8/V9n0K3+iCoHkTNir3CqMO27bGoyNTSrLDsE59EzxXU7RP09P4HJsPVmj9nPM4OBdogl4ds/emQHuXKmXR+UeCtBWJy1f8An0x8sxeCgPumdRcemr04YdKgu1GREaKTUoia6fR+87Z+x7PrLuKK8rX9dMuAUldKx+zhdFWs1A+VMeI2k7hx30P8SunRVIbdAGuGXbk7vgLnUk6jPy8D0BtyWNU7V7v+PfJ2fsu7/hLlfKghc92j7picN+CB+2FIKeHoLm+fcNWm8+w/hz9Hzz7d9JYOfZ/z8HYtBXtX/wAz9v38RfIH4c+7fh9YvbWBnne22RLFQvGN6jCKKmo/veAATwJVFfYD2x31/wA2NxIWd4/hVrjt6fwZ5TL7rNiUaLV2d5r6f5MWq9kryz9jz4pyI0bRpfO6z953+klUL0SI/Ffnx/b9mIxVNl5PL28bLeV/X9Z+x7PquWJOCiS++LWQ1cl9fOD4+F/BweQtwLRJZ1yEPdfvnQsqPIm+vH+I6dTLE+xbPlfPhKmIJ5n/ANHger+rr/IzCTWXf3An8cl5+y7v+EuV83CD7sXb5HzhPWD4uCEUPGnz77+TPsH4wy3sPuz7N+fpPuqIFcQwged2c92+DLrEAjSaaYuKSQCdov7fH7P+HPtH4+udBL42No+2IoSk92PvgWxoFE6HEY3MaQWQ7S/GcUXWusD8sQxARPb/AAVa4Raujha1L22pgh52ymSgw9ysnEUA1tUT7/GIZtSmEQBLnxfjN/3N4mr0+jWHWfUPEafc/swxIGVNnL7ZokrcARj9x8OL9NufsezxV3K3RSfA+KVoMlrI3yR8+WWozbl3J4MK/B5f68BpuT1JyfGfYfUOoU6tDZqIGHT1GXEzIICEoWFUAK84njAQGlsRKtlP44Nt+4oKZ+y7ngEQAEOBWNezjouoy6bnbB+u7P5P3PJ/jknf2Xd/hLlfLSbKD54glBnNi1YH6vmZ9iwPsuiMiOnycF4QBwWp9/pZIo66+gAm47EurXx+y/hz7R+PrTAUVoKr8/sxBQkfcph0W0ujw4RehdqCVZ7MSBX0c/EOj+JWuIg2nfZ/3DRFuOUbPcJjG2t67n9D5xtox7Rh8j3PDiViotv7L7eBoGOlDAA5XIq7t6NG9efhyqQkL5/+vwxFCjPXqY3f24/q++UOUW+M/vCugynYGGTxFPM1Xyv38LtqB6aF5q7f63kGsp2aJ9zCsCosDobfAzRFayEn7j5wg6BQVoXzrXm7eBCKBWLnF+m3P2PZ46eDu6Sys4AHnv7OEcUdHuI/OF2BgB01O7Ro8+zlNKmtVD/jwG5+1Cf18ZwDXV1r+L+p10GbljawAnXIcIjgJIEAF3AFUA1/I6UVIWzgF9s2/b2eCHCU7Bt+D48IX67s/ke3yfh/gpAAnOqnR6NvfNcD3OiB737Ph+67vqXAETWaXvkAJppxYruo73y5V3AgiDEF7qalOhz7mWzBtA3+L5RmwcOBvD8YU4iq98C7Ec7bRvqlz7F4CR397T6iGcqnsBB+PH9d3y4bzur5Bs3PBzzB+HPtH4+v9j34yTq/fgr+7pnI56ql/A4oKM/ZA/Sr9bKtYw9ZhsTaM8tyzfHstreISMC+oBCO0VGdMoOYXKtTo9lwTG5Pkaflz7FhuJSiQ0nbb0Hs4igRopx+jKz7VVpC+xgJ3JzZknHNY5BBsAxE6I46NkDzR7CfbB2xDchgvq4TYoTgov8AWCLlD1o/oxzp0/oKPJbfRgv6m8SNllNu/B6DATOzvPH+cfy2YTS4+4jiwFcbEgX0MGFn4+o2N7fLTvNtSuRpQZ2rFbJVNDm/KV8k74L2aVQAweYid6YeNNINAR6cMAedsNBsW8R107CkqqCwUAV7F/LijkgolBHDCJY+L/eK0r0ge6Dlpj7DuR20u+q+HUmkDYp0lebDO2dqp/bnAri3NS/rri8eOKRD5cvjPPeCaodlPg9sZSl52GfssQJrNwCB+X1MFDfPtyP95w5fe2CLfK/Ly/xHTqT+37ngUTyYAKo+fGn4H+WxbP0j/Bv5f8KjouQJkHMRb2RRqbdY22i5YBNxIJIKdf41fJ3DYOPNxBpq3o/+s+xyF64Y/b/w+CSEZmlgK4+KjRIxKeCOtCHeX4XwkqXIHhFB3Qr7q+I6k5MWM38Pj5R6ofs5rZaDqnf6ig1igje3oOOiBM9eWX5yxzeHTQJ7fjP2PZgIo9SQffC8AQXyb/X0q+cjlchVMiiYwNZnp51m6Lgqy75k8V136VoA8PrepmkilFdiHa6uIVIgpiGmCphFiZReQqM0LrN7N/eHLJGjryxwW8KUhrrjTfOYSX1ylY44dLrnFETjUPnKZ0uFXJ9RqgX0+e+UDPRwrg9DCfAOhxsMPbFo/W2Bpz3ey41eaP2wBakAzYQ/IZFYHxShXtvGUtWARq322/GAQSnU9MU/alFTAvbkeucyd8EBDzYjrz882Sjo4UNOhx/eEDTsikkG2bvIxk/M+zH30vY1OMrxQFomWczW/FHJmotJRlelc3YQQp5xhSSqNcJLdNTII2QL6ceQGicKlPcT2w0ohz+bOgxS4A/O16ua08XS0H5MMslWitfyFqcYP1H6mL8lV0hM8qYxWxuNwa6DfxjxAj5ds4WtnKMusBzVlrtV8nrOj6f4jp1Jc2OJ2TCGZggUBelfE0dM/jYgWpR7wMO4PfFZeEkOjP4P03fxibkAqvB/GXK+QcILJztDLI0IoKFexUPVPED5g6joA6vjq9h2FEPsr28M6rKwHsuWyokEOgAPxn2/8OR2j7B/zG1Ug6VI/G8/c9vhwb9mxqvI8HPOcQWFgiIKJ4rCAwFpiN4onZUn76YCB0Adnrm/kHZq13gGG0fKv7y5vC/dP68RlrNAGAe3gqu5Hw/1l0/Auh0l7Li+UgDhqG5+x7PBhekVp5h/ErXEwhmF9i5Z5wiax3jt5c3Dpwl7LlOqaHlxQaNhhB2AnkYlw9RANp9j4wtwD5iow2/jiJd0MjY7hFf+ZvGnDbEPnQT1wy6+MaMLq899nFObUZDdArGh3wY2gW9z77e+PkUFNg2IFQXpkBGWlZUa6JdDxMuQkBUhfN2++Oh60gdi9CX4cWc7YIHDCcY7YN1AYuqXVLiKQSkaaKToxy3y6UoRKbPbCKJeQoNckNONZyuiaw55d5KVFLuaHpy/Li1jA+EBJHRj0TneCwOs4uqunYzkQU6AQe1HGu9y5knePMxv+pcieZZAAdADEVYLV76f6MFTZGVJAc9L6C49iXkAFa6+/F5uHtnssSkNuh4yo8sQEnHTQPbG/KzCmsZCmkbBvTEJMFbwAecF8hYtK0PRNL1jgIb+Q2p9hlRuIlD3tu33nnmuYpSFVSaj/D/GdOpMnjwvDYCvOj5vA6j0qCr0DP5Dxu/g3Q6mjoD5Pv6eIqF532/jXK+T6MYQCI76j18Fgx5pCGidSW4Nc7VCEFD+xcLEoBdBV9gXwCAZQgByuBXgWw9sRBlJqc5K6OHAFXyG4QQJEjCI+mVLrArSfsYUPb36H/uQXmbxYLPk9sf63RzcSaVYh06K4IlAKKcln9OCAywBBEeTwbZpJC9AOum9Dw3pbV3jgcMKDVFHcons5deir1P/AEzUE1gsuinsn2w8JSFEYI5YjYHlAt5+87ZjYCU7Cl9gX6JAckgCspeTWEt2gRiSwBAD2f1nqMfszoZX+zHemMyvTghSiE9j0zR8gzt/CrXEpNAtWUOZt7YiegJ0cvs8XJ2F/wApt9MvWb3GwWagqCKt8bmQHEgQCVtEbbkUYBLIb1cD9WuthiOFksZXWAuHsU6B89PxhnVqfJnzGt6tfjJ1jeCika13PjyNB/cEqda6vdwbRQXHgTyl598gydj1f2jEVVKk5FeRB6HbJCIxj02YNcSCaGPPX5+/guVJsAtCkFCnbzKg6CIGarWoSd8CxLQmxB3US6OnHikzcyS/4HviOZvoiDHX98zeqgSYnvV2vrlyOqiwjz0DpdZxSrqE3LqFlyoFt5s17EX2x5llchgQtFyhU5wF7HBuIdmr5O+LOX3Mzmqc55J4MYKtCi+gVSHXfXIQhjaY3oRXyHrg0UUluyoq6OZJrFi8rQNDHG61Oz4Ill2CQpc3Ql4bNUCTpHizkG35fqdRZvtb1QjSpo5Oc4NJtkViKheQRwH9MBgbn20oaaTf8klEvTERETY4p5uIpXsieZ/3cM/kfRXaBAjFh9ER9/rPyKlAHKuAtdEtFE58RXRA27QlQ7FPk/iXKy8EQe5sfZByFTg1INdRR1/3CEcds8j5CwD6JIIZGkSsBEUhZtLn5fnEehNUQ6nu+MPmO7YLXwM/c9+QMesELddhXrN7xKmQLaK+6ZJARC84ORMRVOrH/eBwwD3wwHIp8ZBTYg9Na/vEGlC60EO6AYAkKnfAuKbaPSvNdNG85VcWtMJZSvn0wq9ovKj429slgB2bEic8D5wOZsgTSo+5vG9RuO646EFPPFqeuNKob8pg0+mYpXl13F/rNJ5iSzle0vtgtackGhDsp3298l5cBE1P3zxfHNirTVd2GsaxWqIGWTket35YcKooIqN9BLOvHnjCFEvdT2mWa7qQJLyOQ7PdwUuwL5DP5cJJoY2qT3dJ5Zr5iLbgHk/aYrRcbC4H0V74YOwPt/CrXQ6I0DgU195mhd9E4Ot0Brg6vGO374y+9qPZEyEOADKQoHQalOG7kzIYCWHuUXqGA/J2gLdbhteOO5kXtaa4pyo+x3phg0HqVAociD3vIYbw+BAZwPmOFIsRwYK2jSHyc/U93ha8APYtxTlUBmuvpkoqIEpUG8dcj7ORV+JUhQVErxtveUDUWoiVdoHXVBSqAZpxIFK7vUcdOWnNGA5Vp8/7v0EViSaKwL3Y/D2xera5GhDgyx541hkk6yyWrSwEHb0yzG7W+FNa9n4zh+0vdp+/2MicomKKNJ1rD3xjAOgLoHkiHz5YVCIJNCnsxejmwkvi7g1C0vGL23bhPQ7BRQdyWOscibz0gSeqAHm4EAaHGXax3Jox+H5YtZjDU5YOvLD6UGDyEN0Gn5P8V062tKAmjWOw0M715XFPOz2kwQ8AKHzZ+H6j60CObxhdaC3hCTMifVrItYWOt1ChswYRFWN6nZe6pPZpjYDjuI+ifQBszSFeVnuB9/rNHvibvyMzEtMdACPy8Ni7UbstUd6aNDzhZTRFOIaNro+lcClaw0Jdhqu7eGB879oK6Sqd5QLMQYoPdRUr0ePZyy4dpCZxZGKLKD6xZGABJyFB3oHPbOepQitQuqzd8vXGkj2xyB67m++N60CzB2Fh5LMNYwouhPoerDAyKqBlp/eSTQBZRoTW3Xz2qJBoQB5fQhfzmxUcLII9INvYcdEFIMVEHyj5MOUm0CK1vpB9sFQkLwIKb7MY0Ducuu9OPbLnz+087H4zRHBAWoj6vnjCWrulojTz/vEkrAe4nuI++RWIujKDwEDt0HKqJYpWPCOnvcTAB+VxgcdPj54JDFYV9BDsffEXaTTrAxsivkIBHo5VfKddViX0PCEOKw8g9cV8QGiUcdbowc2Ee+0zpBrx3W+JXtMAUC1AFb7BlvOVPbZVVUa/H3yatKEICa9r0cQIBJvEs/D3xIqWTACKvQhhMnJDRkcbCk5EydddVeEzUqYnct/DH5kQ98FA7MjBcOCZvq4jMt2Q+oWnn1wIB9CvWicLFPuMMEaUHXJMEeORoVqFLAdoQ3FDSDCM6ZQcwuVanR7LhPIQ4kp6MWv+MAEFpqMR63q8s2oYtbdbureQRF09wI1DgtaaCrNOsxVvPBCxQ9YnXHuYurMRQUet5WzZoymoEOoaBwD1e+sBlEm2/K6dfiOMEkbQiXyr8uUz4JFbfoOTp6tWMxYprWjHEkEeoQ7SXviCW1vlf7xyybDU0gRY37vCsGE1PAvK6PDHEQmTuSpQarxerMaF4RJkE1oZq+rz9EZFINQS64gm+rWi41E0A3d2BDk0UlOcPGCM4Wv8mFHqaCcTux4zV8OkknfQjC3oA4o0PbA3rqCHobxxJgKWsTmDPVhiln6T9OQWnXfKuKQlQmpx0d098Y3AmrlGgUBep5l5poKfr2xeHAmCC2Mf2b7fykunR8IrFISNMS18mbPosS4bDkBnSzfOCVq2UoNz1ySYIhQUE6UbvFhXRhMJfDzXIkF64jpLZAWXgNGd93MkB1sdtB663rpcc0LlSu81t9LV8s4NHTTrkvYnnYgBgvBvo8TLgbg2Ar2Z+MIDEg8ANebjGeSUB1QNkqaTrrCu5wEuyb0G8IZhVE1Ah0dvfwOitlygAHYe70zGm2kYQTwUxlb2GDsCbrgTPgKdVBTUJFlhA4U26I749c/MaJDBgp5nuNgOlohC9GOvR8qTDRyPI2/j74Zf/wABsXCibOlO5lcV0Kmmv333wngsgWdSPGzNbkWEFh1g7nh1dvxtY8uHeBVpQdl4uOJvE0wQpfRVui44GDYC/wA6zPPLbmikmCo0bsdMPOtcrqA1AjSC+QQXCShFxOuFxDDY8P8ACrlaURjb22fa4UlA1Tpp7CZv0gMRsNGEKF4TJTSiu6L/AEnzildophpZwg61LxVUCwiKJ2qh8zKzW8LRgddFxWF511w7Bt63p1qomMUBbOUnNjyxdaEKNGjuI/GeQq6EFPtgHwBf3WD4x1jLsqFPZHtjgdUNAKuur+mWTiwOR2y2VIAVD2Iwc4PaLUpGEck2czBRE7jtDTII8PZMZpbtBMAOFTXtLwY/CPsMFDi8nVi8GFfIpaEBttHkOMeqqEih42OM+JPLKB3oz2ObqGjUiEFRGaNI7wWNhaDiVgQk2vPJM9ZCIR50ua4meVKeSN6YuYq6xxJ1inoeuIOpk4QF+z4xQMAah64OXzg1ZSRqxeukHT0nC5NAc4SBtJXKkephN9rgIS6a1lljPhAq9d4v+GsN9JuRd7fLICk62076v7GNq4ZABLiBCnU31wGzUCjpnUO1wFmjIr06fUrNCNWnYwQ5O9u94WgoWAjqIELtB5PFdtVliZmDbAX2zXUaUAOqu3fPUW42+xRDlVtI3fuOcQDpBtINJIl6KbgBDSKPRgaXnVOuKhaSSJRqiQD6OP5lg2ANAVwCXE8+40RSoG0aOjO9CNYMYUEckFC+Z4AYR+oNE6kwpawvPB0QC9IrDeM/pCpYfhfa4jS8kVQaaCOvQynFKvLGbyTRrWRtCkZvuaP1ZrOtJGzqgTQ84QxtMgC3ro9W9dtxSekohrryb1yv2DCAdHe03xN3T9DFCgbrb6BVjRzS9DnCNacIwcjeQJr7RESy9PWTZAgG6QtqKEvkYvCKH0EOpoHtx1+Hwx9FZZZXEVgha+GfU92JlFdG1EdAjXG23HMXU02U4NRf+MIwcbVJQjbCa1j58ZchR+2BDUgUQaCjUna85KoWA0x9EN9PB2qhF3vs9vx3xAtAJCeaN3e7qHN1/C6dCJUOEtyJYpeHquaBxazgPkwg+/ZgSuoSKvYNeUxlEg3K0u0AeluvXFey2QE2l6dfTvcYwJWdKQK0Suv+BSSYFIIojpA7OI4WbbaSZs0tvYxCQR0oNCmtHoWprFihEKJsb2jfeuRFacPMfsAbey9GAHNyQfGa2wSjuwkEy2pWQ435Ob6TBVKR10h269Bg7Zy7yh2yvXonWjXOJFVSqCczUROj31hBNkgIitSPLy4DKYAGbCdnsHvkn1FnJ1WjCFReeuABiYApUOU5UIrK4C+pEKwdGnDW3ODn5rdKjbtBGhF7GAvQpIoaHofkcqAaDGCcPeB6M54w9hACqPQNruNOc3gOCPj8yQ8dDCS2ko1zjtTCKT/KU7SUCDho24Cy7YukmheHOnbGYz+mXTsCkzozZhSmJAk0DwHA6juuLBVsGQEhorxd84B1FiHyiizDnaTGdTPDkze9dPfHWoTGkCAfk9caIAJ0OrQCU6sSXHOXUWRWFQhrsgdxHRKy9LG7GDPLHJqB/CLlbmqPIkR9lvthhAbABJXnbZx7Clr1RwdAb4kl6Ys923yn2Db3wVUX9Q4TzW+oy4lDFFjNQA+fcKNUqUR5oqB28O65e83EFN1diLTtm1kX20zdKbfXq0UKATcumluJrkAiBMdUbO1URG2mbIBQEFC1WhISecGQDCNfrrlXAmSaSAF2Hu3rCz9SdNDqlPFpgNkk0IWAIWPWdMWogka513ZvvkJNOUKBsTksbw4XLmvGqS88naPkI2dlbAKs4gvjF0wdCRteQsNa1Srgqv6gUHaGyQAsqjkhF4YDbEaMaNAYWvlFguZVmhooqmUunzEEjg2C6vexTEpSIVB5k9se0sAr4wApQcWYWBrB25FnxlohNN1VvxiGA1oDdvzF+cMmXsSGwfFvnhhlEtGnodU7PHUBV6gsGuUheIat2GCbI5hz7L85OPpVpbHsa9echs6CA6eUBvqOA4cpPWUwaj76hhD/AGoiUOxQ9LDz4AEQIHr/AAq1zO/+lslykXRtg7iBOaWadII5MqDQ3EXEAcAx6aB98CUdatMXuJ74+KXJqJzE1Oi9blMdEWlxeND05N4M7pIJ53ihtDc5p8pB0tqEC1E6dnOf4YwangC+xS6gIQtVPcibNr0hdVUQRlB9uInfdhWegRICllCOlnTJM6Cg4ZSxMoAPpFA+iAL3dN1xJ5ggpbsdvGsJ7Mo2cSlGTk6sQSxP7BUOovR22KYeJkK1WF0OY6j0ZPSSMaUpIi9kXCZJEFMkOzTF6qdx5UHZA0ZoKNVs88qj90hZs4RzrgyfzTdZkFCMOiBLkTf2hAADRAjd9k2KEG1ti5NB47p0M17MQcIDoaLHkDOG5pcxH5I/GDjPybEspopeu+Mlr4yiNADZlP6wuy5P2iIAqJRtusravbN1KRaRonuMJp3O1CV3Up84oK8BgU1I2lHU+DeTsL4UN6KdOsXDRsfiDRE4cf8A4hVgUoMh1ydph1WDldlXRp03JNoLdHJF6w7wxcgGokkezR9TqJjWaIlNUguqlxQiyy1S0d4lIKReDosJug6OD0rj+GkqzYn4MfCjAkuucPK7cUd6O/Lx8Y1RSEOzwXsdPXLeX5k5PlgiWKIZNi8dnEcjN3YvDvY9scVTB4U3Hc0YfS9Ui/kmslRUk1EV3wuO+Myv0m7R773PIcgdRtYaNfLfPHOHwGBGgoL75waN3R7J54GWezRE6z43hSSuZj5e/wB8mPhRo0DtzTI20leGIh31wd848JQ7Ff7XBkQMHSJHXpjtLPRNHGTqVX1Lr0a++O5SogAG/wDuFgPbNBduHCVA8h19XNe5+2zYD5tQ4u+cCStI0IrPXnX/AHCCbsqeM81leyymS5U4Slxm8FMVB2t4hlpEmuhFPaa+MpgjNVAK69TCQlObCoGHxiCaKvJve98gnbDsEv8AecAEBXKN+QxQ0oAo07vjU88e4sahdr+Tf8K5Wtw9BZtx0SVeKOI+RzcFAgqJVvnRhuvliBWH3cjD3Pzi+b2BrS33QvS63ikISodeP+YtnMgPNf8APGBdy++GhwEeDaO9h74EyWQ2cB24wyZHgAAB0P6zesgDSt7Mk/7hiEiSLK6c1tzQ+iO8ZtbQcdNIj1H4XFRsG/Rsy0SM6CL4oAFiZquy3cJgoiaTHBeo7jX+sKfxAUIB6C47Im4CAWBGk9XRMbC3tdg0cTBFW7i8vcMaYTny2XGismHKlN763bFpC7eq/wB4A9UYAgBo8higLcUFCs4oDzQ64SbEAZvHac3liKvaZuSyvMVT8DymTGetCCCB7jB05tuyr0P4Va4didS4tv76YCZoJvCin5fGC4KDVMAOquBO9sVhtSMA8riYyUCOHaHuvdxMt6UwAvc+LDICQ+g/qQPvggEAiDyW7+DxEVoE8in94CEF9xV+7j+PLxi3qjDzmV2G8sFEPZ+7EXrslu+sO3bF6nSmgh7HzeF4dLcWxAOtUj2n5fqWFMKhdD1/B4H6tcAMA1oketM5NpA08OhVHfcTkc4Tg1DpE+4Pt4MGFs0DUOpQx7aseEEcFHodXfL7QZVUWchPkvhqpfULNckGPo9SYBET27vfah30OMErCYogKhYdXDWIBrVNDuwexkYbrPddgFrbovqdRoSzRApqkF1QuPEWGWqWBvVrBCAPGAgMJKgVbYeFPjuFquey/twg0kwlWo5KTudM0otdIA0TWHpu3IRlTtH1YdtB5pre+1+2VKnCtHXL6yPngNd60I1v0Lged4CVAvQWTvigFChCdEo/G8+aB9o/ONnLvu0N+fBziILNAaAkR+NZe9MUd9a9vY9cEZVDozr1X8+eNSk3FGrrvgTYVBKTuNsxpoNqRcivXZ67wlARsIEnHrvjvhi2dzOg6+X/ADFa7RFC7RvD/eB+K07Kl/Hzkrd0Ll0vpj32h9OGmc9vOA6Oinma09sncopge88skvA4jQYTrennzxm2WCQKGjrG7Jz5Yqmqrd3k89Ob9pF2ooj6N3kDNNMQdTz/APmGKjiglTm3SZfbZWpQeJ1S/wDzEJrXMKINb02OsdHGD6WPKgzrZ2pPLDjmxDoZr3E32wEkhGuu/tsPbI8QDHCdz96Y62FyxBe/HpTGBEtNI8L12JTqP0rqrTUNnTZ0eE1jPyhFWphOQHh7eKsmVVEj1ET1G4zo1tgA3uDdUvHXBx1ns6AJtyhUsmssFlxIMK0JeLwPEzfjA3FSRlS5a3XuV6KwEIE+k45jrYTbdyXh1g1hB02sz+dQmtsbBaQ2PGD1ExJzAbVARdoUJiMIF2pgh3Gg9ScNPvVbxXcIDatGFg04IsV0oa5LOFOFzaTwBN7tUmeU1om+VWTEpXA7fjm6NXFotIosIHYaMIoeJrRjXGgEGrLhdH5DB6SAITuTCP506qh8C8otcZb3ZkimSvMLIeZmcRD6XjDQEOB8zWHAdwrB1LTanBzkEwLIUjqcpsaxIYOUXSWz5W8Ggi+guSDwkQYVo3nV2GbYeBSTSt2BDZGngwAkiBRMIKZdFOoCghJChB3UtdmO6bKLaRNb1pvqOF0Z4yVUOwHZjoc48NUKzQqySAyaLDeJ3KRI6WxnQQxNJwthagIDdHYtnliccEyiWA27AUs5Oce6oEhNF12kMUNxuAENfdMA0QRrnbA4HtwI1pperHDzZcJInU5Du6MYkhBEpI0sXvF2y2KIBjCb0iLpDfN/hVrk91gu5hs3tZyTVco9T4qjclLiAR5x8svWUMKBoeAR28DQMSjVl5BZW+Zx5pHthKtIIElPTiyvSKJjAGgC6kRCAozRStUHzII9HmkFRqFx7OgTgOSPGUmIcGAnoFAZIrzoKEzSsJAjgr14uW6ya2QNodHgw2g8ogCBAIqT2IJYG2C9dRQbVVV3l9W+NKx2Jo7a5i4kEm3hejDbSbQ3SBr8jH6MaFDSqLBVTWibCy1EIijGax+JcixwmdYerMiTvoLQKs7quQsXG8wC8KyvKUG1QpnWhHTgCCvtk3xECo0EQDVC8vSAd89AAQDGvMtAMm3tA07TIp7kOIjG8MccKDaelSvWI4/gSpBdNRoPN0yOxDgKYTnYvuHSpjPM0pZ2XljhAA40lJ3bwhvlvCVLiR9d3yHLrUbgNaamYKrE0ByZBrxXOAulFF12DaGzgmka2aCPd0sDeH1Mag7fAeZIDpHTR2CoatRe/b9sVNMQJlgdIQzZ7Y+icDhBVBALArt1gfI+iEgKQVm2ixuGMS23iDoqz3W/xOnTxbAmzJpka6bbj76wyLQa9SkhhtA0EK0LCrVlZHpDsr7C80tm/tk3fKRFA3o4zqL14wsl1laCE1A1RdnnIIjAjN2m2kC86yMOAsKh1YiaPCRMu0NsgIhAgcdsdhYMMtrZw2nTWsKYo3oCt54SQ5JMPprQ7+XJ95wfnARARTOalnM6Y6mf0RrtsBBfjFup6oIHzpVac8OEJS3aEXmEOvxrGtSJ9dZerT0Xyyiz3yF6kkhdkcXePEoQAmi7DS76ATCmpe+4hHYEelfLDeTFpK8xL2hYduJo4hOABAtqf6dzfCxHQAOk0L6YXRshGgng4a41x2T/AJsQEriSl3q4eKFNRIFItNvdnG0cf16kTEFSIurZoyFC4AhAJtQS+T1Y3/pFTUdEBCE4zgKzJoBN83POuOShZrUNjxuq1Zq70CAkwJIKeDWTjV6gVU+UD5Qdnu3zknKpocL+Z54ogGGuyz0Ih74nCrUOks3bToqm+XjNDSwQptSdqCVvDIQFA6sYl0NeunKl+nhSrPdfpXFk2TCGnWApEeRqUm4d9WCIIEjFCsgSDmI715dkww1Ubnl8PmAMSjlTle/iOy1Ygyg9LD4PBQ6hiUprXMoM8vBBESj0w/IgcAcAdD6AJedukQ0dwXPWPIeIM0DU4hclhZzDOICxoOBeE7+ATjwBMaAgHYMNY8Mh2R5+jq3ZOhrfBo0a8BW9LaBR2NAHlhUzsUaTXv8Abw4+lfYUh3XNRgkom1NoKmEMmPaFSu1V+Ox9B3JIO5RaXvXnrfqV/EGJs2vnM15l8MF2qktszpegx8v9+qfL8WXqkcxxM3ndHVrGQ+PJciOkwIQ0fSCIIAKVJ1VVevgg1dQtdzs7+kDHlTZNSzQp9AKbFECwXlCvzkvhIhDFLHv4C8FHC1zHpYfSbTQJ0EHyB9sAAGg14alpjnQUeYg+2AHBMECaoIKtdernWh0RCh5qvv4x8aOfsrSZo2gg0CB7FXXl2P8AJdOgDgnXWDAAqqHP8q4ddxEp9A8RPUjVa/mXJwGA65C7bSsvLjyPOhACShSoXqfU6dOnTp7FMyhbMj6rXSVPpdOnTp08rM6BVvTKCSiav0unTp06OxTMIWXY+400kX6XTp06dH4omSMR7MOsSg6PodOnTp5HnQgBJQpUL1PqdOnTp0/iiYIzTox6wYLp+h06dOg4DEdcBdNpSzk+pZSvRoIgRmUWzXg6eVmdAq3plBJRNX6XTp06dHKzOgVbw2oEgu59Lp06dOj8UTJGI9mHWJQdH0unTp06OR50IAGQCxS9X6XTp06fxRMEZp0Y9YMF0/S6dOnTp7FMyhbMj6rXSVPpdOnTp0crM6BVvDagSC7n0OnTp0kb2+8IILtN0cv1JG9vvACCbDdHD4OjsUzCFl2PuNNJF+l06dOnRyPOhAAyAWKXq/S6dOnTyPOhACShSoXqfU6dOnTp7FMyhbMj6rXSVPpdOnTp08rM6BVvTKCSiav0unTp06OxTMIWXY+400kX6XTp06dH4omSMR7MOsSg6PodOnToZSvRoIlRiQG3f1rAELppjgRbJdj8xOBrbt3x3Jfo2qzLvjsJxxxYY3D0nxXMSgEQBR2ZmPcjBmUCKOcQ+OVI+YeJZQO2q8THGzpFA4CKpCgLW4NVDaIjdmOYpfDyviURCK3ZMnkM9znDMUPziaCSENZdAO9RgO64Yw2QQgKnzAKskNm7p4by8iQ3PU4JyHeFgG7PWBZa2uowAbtScgBVxUj3gPWogIIJVS6jp4pc6YIciS0cmhxm70OfaLgIQALpVrlBoxm2qqUi6xNyCc5BEaIrmnwIXgqzG2C1KCKXZgcLg7U5U9gN4DD9nUk33QdDIgaI8QgxRSUm7ucyFZNok8nAWoJ/H0saTgCQASQ+ieWh7F6XDAcIkqy9QIoSsDO2ZfRLgWDRZjTo+IWWo5pSLrxHieXq2fNCPpze2hQleGXY1SWW2eENrNUmTBRuTO/1CjqaZBccF8Dmd+UzZEC1RBJug0BoCgePwgxQVIcN5pneDDRnoLAEkES+thG4gpSGELwVZjbBalBFLsx+lMCSRrRUjc0MqvVIYBSEUgBgwDRHiEGKKSk3dzmQrJtEnk4C1BPCyPB5EV0oAbpvDqKdtwIL6r0qAXIOkYbT9Jq/X2K01M2I3IaQBIVzj+9Q04StyGJ4Q8bZl0+C4LA6nAOclz724TaG2A5oA63FQUzQIC7RwOI04IBo+lSub3ZHJPXgvWfxcHqZWhZ7GI9rUWxqsAkRIXxAvjqLHDoKpAdObY7ztVH5F1xOg4bzTO8GGjPQWAJIIl9bCNxBSkPAkDjyCSOJYORvE5Y3R2pFQwOUcqsDPRRBTWKghUbEpuBt1RUoUF8WYm1A61aKA4dSu4ek+K5iUAiAKOzMx7kYMygRRziHxypHzDwFM0pfxIQAkG2uMdJhpFHDxLQdEw8I50gYruMGNAheO2eNAgAKrSAhCUgcBKsYqAFUXwKxpavBO4kjSIZNjkwsSTHSDUpji93pL45gpxBSaMCiFtlBFLsuGqoUfLwZwZMA5dRQDkXFXAblkUBZEACbAA3ak5ACripHvAetRAQQSql1HT+OyeX6EkQCDbEiAix703l3KvUKvX6HntjBLCMlKlo1h+kY2bYSaZHKMB7f0LUB3NbJEmcJl1QA8gA8ToNsKlVamM77XDKqWNVNQIAYAMMc0efDQ2QaLDPFKO5547noklB8S4qgmoKSAkjzoR0XJpl3cfZQgSnThIjxew7f3SXLRQokQUSNEF1RCqvO8mwHt/QtQHc1skSYC5+3Rkg0gxoeIfawsoAkUAgBMVzlQMAhaa7x5wGNBMgDcIwC3MXOyHmVU9EgDIE8CTee0SBMJyUKbE1k1gG3iAQgIKQAACkgJI86EdFyaZc1lC2HQVxUGRGT25whPuG5FqIf41Z+WQVgleuxgHi66gJ1dyiX1HG7fcmC+YQLgYzuDXizCy91GowOzE9wAwGApzF8WcaoFIoDghGulkYhhya1x4RBAyVFMr4aqrz8hsFRv4N0A7sr1OijgrjxWCXIoQNE/KYRRASyOJUwXsDUNTWORp4KRI1CshbCvkhQBMKAwCqxVPgSbz2iQJhOShTYmsgENV8KsrNnocGGDkNEzbAdHyYMuayhbDoK4qDIjJ7c4Qn3Dci1EPhHYPsEVNBKdEvWJwmzXcsg4CDeXfjQ0JAJdLOAS2UhIAlu85iAusADErRHbCJEUGNC1cYspqFR4wobNkNZcjgBJAZpMIQLvEshHpnQX3RlEDAG0yxgFMAh2QmCANpUDud6A9v4n+iCoHkTNiDmcKuodm4ik8d92mYE0zdQqbJjN7zFpEEgDJw1jkaeCkSNQrIWwr5IUATCgMAqsVT4zILuEHB7AVW7EDf4OsIDEIRrGEshHpnQX3RlEDG+n61ELsACAAB4n1MsQILFCs7ZwfpGNm2EmmRyjAe39C1AdzWyRJnCZdUAPIAPASz/AOyjKByAIQbqlOvMAxB0NV14cjTwUiRqFZC2FK4q3HEbATAlE4J0S6LQAZSAqgEPC/vV1rTakEDQAYMKm5SBAwCTkIAVXIcdIpqb+mKy8N/Ih1FSb6NPBHi4V/bSSsr6oyRKP0jGzbCTTI5RgPb+hagO5rZIkwFz9ujJBpBjQ/wAGnUyAqHeaAq4YtJj1A5NkqcXOmvKkdhTbnRsoCngtzXSPe6jswFUDBTDNqMxZEo3gb+o1QqNQFZ56wgcXDpKQAKClsXxJcCHSoxiB5QRQZ0f6+5EKbI6LVDeVnpplNhhsQBecgUUIygf2fSYqAg9QgXtUPPESecgsIQFXbVBhEYbyIAx1CCOLBj/ABSdjBg5+gtzk8YMTB7iqJ2RE5ER2ZtNoLxQ4hRReIgzwejZsiKlbUpGszgqcwaICURiD3zj6RdUMAGDViKgMF14sAo4k4c9fId08fJ2qqU4h4tdgQYw9Heh4cAhUUPo58HbUEJkkITkFRoKhAOOJeu+nkOy5Y9GHlSxSg04lf8A4RyFVC3iKoRRGNJkhhDFfEDLYol+EZLURwBe0w5jdIS1rIpu40GbkU9Ry9S6K6uXrl08sw2AC7FJB8F9osQGbFTBMl8WA1T67t2pj/1os96Duh7B1xyWMY1XEs3BW1hOv+yUToOGCiQ1kmwGUhBLh6mz7KUIZZdT1wcmuqFKulivb1EhHYJr71374IQq3B+MwCrjXKHS2sU4Y9G4BoPAzRyAByDIBkQJXhRy4EuWEigRhk0DSKkMABA5ECYcUcqH1KxhtaAJiYs7bWLEEmxiAma3rSmNQNKfT4W1FE+JJqDNUN3h18hYtFF0pEmkErgCtljmxTBpsxW53KW+gBu6wGhYWC4oQJJdAzoG0TnJOFsFdCEoYi8diqRoyHf8OM1sfl8lKj0LxiBgHuWUHSZhaHFUtDFiAhRqricGbSoClunAEmJOZdO07mQ1Ki3DPG5QnVFRWi+LimoIKhcFFA08qZt/fvFfAiBhwCi4/PTgNYTchfOzHQQCkM6cBsJNWVWz/PJSE9hEhMcLJIw66CwpRQopiRXJPbix2SqDJZNlygzVDliWsAe0tALgNwpXQv4GAgUnymJ2YMXXACDAorcIIsygA5uwF62gSOBSW14K7WkIDbebiKHBv/GQ3ibxXxyNJWomyFRLkFdzHA0j1RGiF9VW/Q8GZAUwOGvkp/tIpOtFPFPV4IrAcG8aZDr6hFIMoAQWgOVaBkFlrVc2FNU6WPi8UbO6rtTNBMZ3bUODtgF4DEFyhw5bCLXY7f8AbrdZeSIG1DQbWBmn59paIhIrsSID/FWkiiMByFg6usGcsSIEinLQUIjgJAKorOGKU8lMKSo4IMqzoSML/hL169evXr169evXr169ev1teWNKJQ6qv+AvXr169evXr169evXr2UNgTgDJF2CciONxMHZpFaEgLAr9a9evXr14NIEAAIAAMUti85deJ8CWoBBtAUCvp+scNT7XrVBYYtxJRIBwj7YsLQpJSmgcqOtp3YDSBEACghBULKnP+EvXr169evXr169evXr1690OR1DcTwaxkaKP+AvXr169evXr169evXr15ACAs6ERE6G7HNe9gTVdqVVVSqqqqv1L169evaSo5IMqzoCsZ/sa0BVz0JAElKCZIsXN4rWsRAFpUNBVIVN1wMOiGE402C12GEFGy2hqftKIsLYG7cJ3yAhGAAAAQ/xtu1AsgNG+R/iq2s5jT+eEexcGjgSSm+VnIhG6aRfGIiuFM01Olqg47paWmBgogpBwFKVAGEDNCQdf47se1RdiWcGuXRq//oH7/Hb82cxFXbAD2+hniUaXbOlnOTMtfAgM67/1StW2jmw83JPL8vL6S5vFy9bhCbVnTq//AJl+/YboPHEBywKzpnzqESsFlNlPPFAHoUyz0oM7J3Pp5dDKv+pVasG/cRz11Rh09Eo4gB/VQ0Eu2PGApxNzCqdKb+hxYaHuifd/378T3wQjH1UCKUHZiRMaK0gE8h02ooMFxYsJFpSsqu8LMK8uNNSttNptd34BiPFmnjnH/wAKDKumOGDWlN/4oWNkeQ354fRWoqaJubHdb8jDopQmtiOjpMUyoaRyL3A+/wDKykw9VZGOfOjyxX0wQCIjsT6VZOSPIbP44SKQSF5u68CYMBivGW+yBTZmChptREDIACUBjBSHog0v+MrRRLrCcuK7EcJFqeekffFoqYIDrp0GHHNdR8df0dfwkPBDBFVXQB1x9aoAQLXfZ/tX9UUQlhuopE885jLAqCMqYjgzYBQrANU5IIEAM24+bVg+y7d2hgk0JCoA0AAB/O/ChkUjKE6iInRPrqjFCOl/dD3xRZId7ZX5GRim1TVb7YHcTj/kmUk0WCmwinamNYaTLeJ22nJczUjaNddhOwfSr3iGs0uw1m1lWCSQaxy+wFFAxBo9MC1cd6pENCFK0SR63AgEnaiqJTVDDAA17JOpv+baVu1TInFFPf8AgHiBsKF+IWLTkuJXKgAHGnc+hT97X8Lm61DcGhq1t1j0NPFQS9BAXo4Ezl6Iqu3QFe3/AODfvy/LKiz3F26iGuPGajVqNBesMfJzYACD8/XTJAKiiBwoqdkeuHJGiQFSnfYns5OLIRgdNkCGurE4iZ6kvy3v/J6kpdhVicBdnQ8EYbbKnQWENhTyUzaMnDYw/BQ+P8NWreptaA5XHqufQ1E8kTxEiiEoIw9ODrsdLfrphZTTUCdEXWCpfGULudiw9UxXGFNrHVFEeoj46cAovl+Mv4GdKPBULHTL1yKsqotAlopG8HnpoJ01DC9j52O/7SD9EzH4BtVeDGh+noiLtHUuscbIRUIkQgi2sypwNRpWTHzOAgY6KCiJyP8AG/ezAodi0feHw+HFvu9xzLoN3wkt2q8V6fXWGHJYeEbdcPbAZjIKKQl5ErhCjhkjrNVPL6bB4+0BVqAcMRj0T+B6fVh+x7MAwBg0jf8A8VK1eIy++gFYFZ8YPJ5weUxTk/gAc1RTFSD2M3TaFIVr0T4w3X5OPRTogjKIPOJ1sClWBA9gD6BCXRZSGSG3Q8Yn1SUhkjs0mn60VCB6o29pcfX34N6lmGyaprpm+rrD/Zv2VPKFXQsKbuzqwmqO6JYERR2YJBBiukGoqWgV2CgTtfeR16mgu2F4M5QQ3JEQTvh/4UGFMNcsG9Aa/iQoCdBv8cdJ1UdQRdch56D3xU2pL5un4c6oYMEYD5xvn84tX0ReYNia9BEZ2Y/D/Bb6AEUw0eEVPIzeF9q6f1hiUEJeu7+v8RWrxEoiCk2Kx+n78+w/ln6/t/AGGauS/r6ZD0kilUZ1n450Fa1xC/Xl9IwpBIA1XYCfJ9f6jyxkDXyg1/CYwp6BETkxIhLPXZf/AIM/fnqx2y7tsgRV8uuJiU9sZdYbfE6ScBOBy0r7Ae31qNQm6qA/JPTywu/6o6IHJRTy7UcxKmgCIQCHEC89iE+gpe5vREQHsDJ5H8CpTztbUQef34DVDmrpPAhb28MW4MV4cPG0Zvj6Ff6baN7VkkT+GIpXam/G1xw/64xfbfPu/IoxQTGZa2Qys+LW/VgJqksInd/E6QE2Owz+M0/EleQ0cSR9nvr9n359l/LP2/b6xRno6iCYxJIidkn7LjRbmaEcHLvq9krTpahO1Cn3p8YUaRLD4n6Tt/BPTAzhrB5EupoAB30PnFYIR6mgff74hZpmfp3zU4TgCDsHQ/8AwT9+5EqAWiJyY6bCqFgPRFHuLjnnCKrW3xk9lO6AFlJ13Ca5XpH6TSwE7rPv9wwDBC3zk/g5PJDT2fk5vQ/FQh+TxWEnSmkCe4phFhTB+3f6xEg5Rq1utTrjKVVZXrvLWI1sI+14vt4Y+ai+X/k/wlau1bEZi7MKtvV+NcyeXniNTBAMUWIVNaufbfyz9328G+OjAWxQrUHd+kE0gha0M6zNoBOkOtdWEN1XjEjnOierOYjYI9TyCfIxHSjV6BA+DxSGsan8E8aYCoq0O7BfZxXyj8mAEAKz54C6hPA6E29Wnmbc1Dt+H/Zn84Qq58iFBYwVjlJkvuWvJRdNDRDL6JHuUegWQdwega37FFlvS6BQPCLAxIJJWYzvkBCsRBERj/Cptb4Iqr0ALjkgktqk++DEoIe+SyAo0RSYp/BalJ0/yk0Z+rr4DNSEI7U/e72cVABK8iBPv8fs/wDDHRn7zvguQGguxB+cQLgICxL3FdvNhcoZ1AFKdF/wlasfdORpdXXpnq6RVodUhs/yPsscgoYnBh+2/ln7Ht9c5JyGvF1YSM0e6V56aB8umcqeU7OPxtkKbincvnAYIk9eX8YnsldrTA3j1kjRHSeWCZ6StRAdq3/mWoiUte990ffE41QxAEHlRprqf7N+t0t7IgbUdJsYmG3cjprBgopUoqQbDrcDvCUYTQMMLILuCFVedpNnBgbE016AD2/ifuYQsgulB5Cr8XEiskvTf/jJHRqtC31Qvvin8ChSgNralSOjQ37HbwGA5IMi4RQ7rQTv54/jvEp07kC66C+Ovo/4o6M1CIUMjwb0xukrcKBnx/hm1atCYjMbU+1UxJUKByEx9wPvhbVx+sR59l/LP3nb65wLawAp2OqoB1UxhFAJDRv28JCqkC80UQWj5f5MRPGRZ1QFR60/DhphQCAXm21hmet0rozzIv7/APuTTRlJoSnXi/8A4F+/2QqcQ51d9rcUM8G9wffOJkcpHRH2WV1cAQI1KRjqcdsU8MpltdcBQC+VTf00HsXA7AnRGgnbDoQMAEADgDp4b4w1+tfH0Pcz5LNXOlZdL0R7yfwHVuoaA8mdwjW3RxhWAUjSly+58nhN1pDxaKj5ympSRsd59CuQtUVMkInJeaIdcP4ngZIx03QjARrIMgEIiLSGrkbHpnSaN1s24FQrCyVrBN2DVpfxtsvY/wAsJAALIUTzEd+WMJ+UySicHDeg9D9J2yeIhP5JLvXk4sIz8ymej8/1WL1goOjHnF8Rgd/1EsrDpXDkkbA3FEWqiP2PBK9BH8GcmhikUIHkdXrino/CEZIVLXpTqm+/yzaos8c7/l//AAL9+VQhOJd6MYYVg1IVM54nmE4wVjACi8cl5wbhniwGXyRmgZTPXKXxpOwvlPfNAqxKgdj9vDp9KClDj1gv0WISp5aUTPu93EESPdhfIVrpDtfD9d3fwHXZQ4aqh3zVxNBmgdKJsJ6Cz2MRmZUroAPYAwOi1YaN7ye+Wtd7r1P/AJYP0SVA8I+KtyoDuVSakdmtYbIaGgLwAAIhCYX5DBkSpA0IJnKEJ1Glh6AnL3/jVogAGoiETs7xBFbXUR1FY4ifUee7K3AlgmozelHq6Z+u7ZM1KBUEpdlMU876lsMCUZXq+AcBumCQV42mdYuwXoBlkscZKCtqZ16y+H73tgAwb7tT4D7/AFuDreWRfYL7YyFan1pgcEJd+F4gQ3dDA60ThEAth6MH4X/Zv4/dHB/eA0m5mv4zFRc0NcZtxGs6QxIlN47mYb5BXXBFArQB8Yg9JzIa9qg4emE/p2pEmzUTHFef4S9UN6N0ap1wuVGDABXb05xOnWeRH22HrlX9W2aJL7KCMfJzyPh4jD9VARkBdYrPOH0WKnuvt/1jvpComY8zXtcACVdf8xdZ+u7v4Dqx0KIk2dCyb13mbbl2wwlgUnM9cd8F2KVWwIoVOC+28J+Kkuwk9RE/wVat4PptVAYdzQ+d6YB1ODorYenNjQi1OzxfJx08jm/qu2fOt5AoEAnD9PG63VMikHAIAcB4qbWF3VYzl0qwZLxt+Pz8P3vbH7HufV5weZsMvTjDvkngC1enGMse7pGe1Pjm6uiLNovn0e//AOBfv46sJVCqk5ePXthoXW7iBvqX/wBYxrQmsBBzIV3rddvLAj7H/ozzG/FxlCiKJ0wA0ZN6Sn8fvhnDo5lErxZewWtXxFI5yE2lZ7Gb9IA8jxSInKar9FI+TkLmJFN1QreUx1wgmjiCdWe85HN3CiKwfeH8B22o6AFQn3PnDZq6jRh3YL7PifY51yc9HxX/AAUFatkCMRZ03Y7j5YlXMizLo1rpTTeeYj5hMl75f7z9D2x0WkO4Tg5knY7hhNHo+nZFeIYinXq9voDkTeRC+kZ5wO0kXSPBKHUTTyV4s9Atasu8V+vfP0vc+pTbDNfIbxRMghsoZ+k7+LqsuROQFPUmjzd/90/f7YG9m5AM1ye/dMRm654Ps1ZZceQLcBRI35k8/Hdp2EKMSnZE9vBo6xOvXTsUntwmv33dh+kU6RReuj75aSD0rsP7cBY7P24rYpA2grPhw9khIQ6+/B7PfCCKB0BF8p9/oWiKAWAjimgQctAfx4uHEqaQLt5q4aOW84/da80uNWJGr0IfjBhLGEGB1aIdE9MUWHluncd7JNc3pHPlyvc+i9n0H5FSgDlXA00dU1wnIoR43gNHoAVaU9fuwgUHUWF8gdYchLGm6XyjfYwcpjIbEi+8dTOJJoCTWr7TxVt5zOn10R6F07ArRFdta6e3pUq5yrAz0CCusQFG4rBoCSwAzxCUt4UpqpghAb9sHV14zOy1ITJ0aNt0Peg7JGY6AZTqM/XeZO3C+B0jDCLYp0sfjw00Q5XXoI8LrhO9KidMet6jpjBuRX2/4yPZleGKdVsYIhlALWtvAN8rzxn73th/+oSFz5pfZwYswE20dvE6u/pLmvw2jX0/SfQWI7WKBKC/8XTjGMBCmgqTh0Xou+IMiCxeuRTCi6gYH7PxhsMOSPE60q+pjABLYyvYw6+Chy613l+iPs/nZGlmnDvZi7JAWPsQmLocK+/gEarkcqW+YNf2zkwjwAF+P9m/W6y8kQNqGg2sDFm9RhASoKortEQAx/cqeMIydC0G+CFItSanGogSRwYHxNNeih9/F+7g53SI4u4b58m09e1ABqWAql6vTCCVpRyGaQJJyXpgjw3J6b9VqQPbSnFMZoGLc2uJqYK5M7HDlHgV9s2GOEBJE9CX3zpSMFafWmii8t42tpzTwdOt/GAkvEm1nHroxWpAXsJ7AHtklGs3Ql8irnBBDZQ4xWv2DbQgVnJrXLrBSzi6izTtSYFIaBp1utG98665EFaBGou4fZccx1xAQo3YRw+iKOACrXsYxZtTUtR1UD0XI/eQFLHXdE1gE6CdKczQradz2ySEmarM6miXXbHSOj4GO5HysYxrL5in8MqBnko1W0d0wbyws0KPdWt13mrKvOlcuOngIwPyL+E0Up11syX6IV87SHn0LOXGS0XYxanaIPKYvVYPRcex+DyyLTvqBufXf58piKk06xnzhozEl83gkQ+57Y1J/eQEffxV2Ty/QkiAQbYkQMX6+38+eqYjkRhWQjwxoJ7o2iphAX0FjiPIhGjx8Vay3dCabV4IOB1sCFHIInI2WHGQ7m6riuqZ02u+2KEwBGzTutTZzrw152wN2B54X6YCYhIA8InOJjr12RQ9JH5fLL4cFtlaPJZ5OQ0UZDdDrXf6d8hQBTsX5XvljZKfW/8ATi9Ti1opGdqPw41qVrZiLZIfvkO6herWPb4sshdlJFfD5DoLxvHbpAmpolB9U32wuvDKu6Ymgd61xlyRUFmTRFINU3Pb7vCi7EVVAGcRDuc4QJuFC2B09Hvht5qqpAq9eP7wkJ4qCYH6G45ybhyd46ZaIrNRAOCpWdcE1QnRQW9tY8u8Ihc83u8Nzh/VgEo30T9uWB62HldinKwXuD1UgHSiO8YvkSnhCI2I/PdLPZFB0OogqdmGdCOppEvvMQQCtvBg6bX5cEEPVLMt44b8m5xOAejUOJsm285In/SStE6cw6ZW5/s39aAq56EgCSlBMoyuysiJUUAQaqr9nXEhx6Gk3AaRJMjiVPQgkQEOEWBmxWWNwnfICEYAAABDFgvbFKV27xirzeDQ16R7YjNh3if2sonZ+QQOh2w57GWU+A/VV0pG8igGPsIRTRFD37bwhearJKG4AS8jxiIDmTZBNdxuJFxiAO09i9R2O2BeiNgmum9L5Xd5zTf/ADjg+6fGOiOSPOZNRZPK9ct6yDbNxrcIe0HEXDV26L+ae2VCC62gJ1umrpHvi168PJQdsOzY3BGACcphi/0VwmUCmqfKTTuoZT95EAbwO4Xjjnogp6IMBTykWYrE7W0RWoQNPJ76WhqeYhnCxSu984xHJRg16gecBaHTAcUW4N5JPSu4k1ybMaZkYAAAdiHfWUQQwQFTi6g/npI12Xy88TNGRwMpS3Q69HJ+rRBPg1SVmiHOV2vaok6fkSGEVAaNgkC87GtevXT4BBai99b0ZgwHw5ST9nJ1df6scDZiKUF9B+TlD3YOoETjU+MZo45WG3XzXCJAK1QGPQADyP4Vavl9te2IwjYS9JxFAoTrvHD34YcNOzUwGjNMTVpuglbyupgJHOQBKrmEd9jLuAChFQjsouHSNKBNN9B/UwIYk1aHO3WLdv8AMwNvInzmNOMQyWGXqDmgd6PTTzjKBLwBUOs/dxQwyRSAh5pvzwMLEBsdnGUCSxrcGzv3XzyHGwrQbPWq0uJ91JQKe6Oj7mGjaJKyegpfJ64JdfoTpKnvQnfvg16KmEDR2jx9LhU46wIqnnVON9sTJrYj118pgsKG0JQRXflcFrkKEswhSLVdE9ilDActJ5mrHIhM50WU93AzOKUkFaISvkLMU6Ki28G+NIezEuEx1CGzXTjLrScbFIJxo9+Dak1kH7dAQaACYgmYwARN6BD2xOHF6Ao/3gLmL10A9NdsNPZvs5Ntcxf6SWWyTrV5H68YhQCjhKPsv9y/fu4EwkTfZhxiE3pVH3/OAdJoA4K/tfOUUxIhSOzvpZOo18CAEHqH5xJNbjEEnpEuBNKOAR2ebLb0HE1tPXBpV7QiBp5pH58LL2bDA5XjrG/gMQHdyIWNIujbvWME6IHYq/lcPB1FvVKPvc1AmfUhs+/xnqwqzX2kucHknXVt78vly4cmvMH32PvjMlTVwWbmGKoGdyj6+BgdWelH+jwIB+JDsgwV6MyFK00AJ9srpmzwKG2rGsH0242B4+QgWevxhhVmQO0HCV7TEUAYJko3W7M4lRmc4D9gqeGz76/OKo6/1Z9z+TwVmjcvMP8AFyrVju1my4mKIUBUbDzLWeWbgO25wWee8FcVnS1qeg/BioAQCMCzhb8emNqiulYAXpHOWAYkG3Rfj69O+nFEBKck86nxgkoAB5eBvpsDKwgt0RE9v7xXAUvOjLJFi06bc5W2OoPC613cc+qTqVIz1Hxjs1TRdjEddHrMosyEpR9Ol/GTiLbBADfr9mBvNdWC4AY8ElruzvyeIotxS63fIpgMAoiPXEeinKoLPlfbEbtZB3QDBbWgp3Sz8vgWRSaOrK8z8hjYXxdIgmvZxS0WoPscG3g/GahjwGwM7mr2wxAO5NL/AG88ksQrtIDfpnSqj+cqXzwSfKSBisC4vNlz7/7l+/ZIW3IgHITN0ysTXs5yZEAsp8L33mSWUt6Om+nK+7hpRIre19WpvGvEHlIU9sE2vFHIj5LcfQhJRzS/9+MqiUqkitPazwEZfzl/rBSNAM0ol/J84evuocC8deHjtjLAalI1A+WAA6NTps/lMP8AqhoaWJ6q6xI01XilPs5LkK6O2I4V2OyI/ZxYky4kicbIW8dPP6BHW3YutPTX2wfgQOgcGSsTegIH1/8AM58VGRw6XIrbCC0dvLJ8GRpqIj0TJEaXrBXd/a9sbkYVRSB4c7zYB22lhZ57wOJfTu2b74YIQE0SPmE+5gAkUPURPthdewo/Gfefk8C3JalvNf3jB0CdwHuI+/iruvE+BLUAg2gKBX0/WOGp9r1qgsawEeGVBfdGwRcg2JYLAGlJ1cHd4q9JO56UPO4/HI3eQSMTdoDc4oX3MUy8v6QReoYkLQo8hF9Y+cYwqA+RUz2AxRQ1LYpPSPG2lSISg+YfPj9gwWhvtt6vvnOwC+zb/T74UB8mD/rKCRh9qub0skik2vkXBVCaHyFnvJkmCmh5omekfOc4OkT14MchOvcbYd/s+foY4GjwCjpjUO+/hfQCF1YCez9sNqAsoGyfk9O+KY0kdxmzvjdDcsIbyju/bLY1k2uxAe+BbXggQyj7Pvm5YAOjgb8zp5ONDqzkVt+GuL9qKw1UeAI/Ofv9OIldmrw/a8mAUgEP9mvxPfBCMfVQIpQdmTGjztsgk6LA1KlQWzIEZQEmaAJBrgaycAhd03KdQKWbZKQOx9OMf/Cgyrpjhg1pTeMHL2lsTC72sY0qdADbye2CRTEUhV4TZ/3AKIN80iewfPXBnpsDKk7bQEZO63LHawcgpNnM355ofx8PLHcI736wudDYFCvdR354q+tbUCuxdBzHXCgQj8guijvmmPc+3jAp+S9sHsFEWJtDmYbzhWbXAbqJPevJvAhTmgtAa1yJhKWx3KCXbxmkWlQKEvZpTyw1DOai0HU5Zh1hBVKBEI7Xy6jg1AiQQUXvELqpghEYEQcmBMbizCoryFbwgpD1FFF5wcbEluQ4XpA5y41w3qGRVROSHrFhiQDqXXvgagDrg4k9KPbFIEJb7qp7YzKE1VrVynXb6VxvZemB/wAHNWx9RgQ98B2iCbLRO6rrxMMIbp0TkfTfnPXKqHMJJCb82e2ayQkeva+x3jLPUY+UvzPZyCkiBwYfLq9sWTRHO0qd9tzr23IpoBPKYjRq2OyL5Se3irazmNP54R7FwaOBJKb5WciEbppHlWBvoEFdYoqEyOfvkjrcZoWY5ClKgDCBmhIOvC28SSxHwdwe+I3VyGQ4IrhqCRwgcTrBZqhAdvLDjStdV63krq798VLbNaClsnV98nvFSNwZN7QnDhe9TcB3HfI/vIyQrmq5HrkIbZExF1QDo+bCoOLCKwr0qzF0nXWAEwFEFrzIa+HxhC5LnSr/AE5YHS4+rrfW7tnOE+tsOOFx3guyA0+7fhxXcEZEDfM2TyytuuSRWJa3jnr14xdMjJAll9Tfv5ZRIInmCXUVEFe5G51yNni3jDbqIEU535ZXWa51FfA1hhDN2SbzHq6r5YSHixUtkyKB2PPTAYo4W3V6AQcOgIwM6H5MqlDYdwnZCvt6YwpTSNogXQG+wHniRNFMkTEnfkAXuHZYqS1ACaDsz7sdNGit7Cdql88K1EYUSFfNxFiZkBSLd7dBzJoxx2GYSoieyY/rAEiRRzvk+8xT5AKoRR7vmFYOlDI7l/s39UUQlhuopE88pruoypsByABGJpgM3NgE4CkbLqRZBRJ9WBIFAK4SprAwAGgAADt4P9Yjo3zH+sbk1uQgISSGoc0a0j5013G37euCOAAcgoJaBT9G8WAAt3O48wFPIDgmKByogphBCwaLs0Y+1WIq7HNYN7286a/q6mEabpHDnzgYkANjIKR2Phsw4wFtmBWwyBycZ1/dRdXfcAH4ZYZ05kQcRUOvDYplHgi6NAv3PjHqKp8fDx1HpMD8wAMG5TkEhs5m8SKxNsoAtO087nUjIZa5hXvtr1Cajk77ntYisacgN8mPob1WgbzOlGPQuQEMFDTNBp54ChnW6feYsoSNOHY8ZEXTwajOrEIBdgMZbe/RhqHlodjePc7tiIhJjCUHq3DWKwqm0uoo8w08ZAgBZy3f2uKYk5obKqz3iQOmRJywhaIHo6ffAhNkJW28gMvKcBiVsHvRLYCu+yOtTJ5EwAUTsHTR51xXNh4Ugtt9lV1wqww+oIkBEUyhV2tb5CoM1jJN69QfNl4r+tcC7kG3y0BiYbIGkgehK+2IxoZlBiSaDntkD+tSjSbIhAeZgMLIhpRB82Pw9v4j1ateaKEU3BI0vRc1+9GnA0JojeSIbpV1yhEc1Uu7Xpm6oAaiRBvevTGmLnOKekDqdM1TccLHUHQalLy6YIBep7UXyO89fLCzcGj7bW2A5YHWHHvP4t4utCdq75OmpoF6O2kL1TThA460thhFLd7mJczZNFHThDtdr6YCqOm9i7LZfPHXEJe2ygyl/wDoc40MKJYLSdzU6OF4GTuFiM4A0g9cCnzoIBeUVrr16BEQaMma1EKluzvgxyBdlA0m+Xk7Lq8JxSnclOOTWEg911K8bqjs134x1o1qEatBuhaKlmAq2QAcoEIujubrzY0rXlgBSipHnUm0sTKE26dap5fbsnByuIKO0no98nzYSoNPt8nOKo27YBFjIh46YktwL6uDOSvAXhy9mKaFocHewhV7XPKKFCWuOuxfwruWFxoqbics1A26TvBFyVXiVKsaLJMVhDg1xoxtIwVLAXyuJWaaEMXmwV6q7zNdDMwNGqaHRuuma0r5EIA3su628YZFsb2/3I/f3k0kxiI/nwB1YxaAadioe5ksJjEk52U8+CubAXa0sTTpRSnJEHAPmXiGxHo5KSBdkR8j2xZOECFVQOhVddV8RctBlpUgVo6AS9AXBEEDzHnHIJ2FaCzTe9pMYeCDet77vnOeqJjoN91MuCvfEBzFGOymvTB/Ec2IBfc8CIM5KVQQoL2EX6BtyScKKfl8eDFHGiiLOxBanAOs0yyisAjhBJpFenPgkMkX28I8qAUKY6ArhytxpI+zK+uWgrdPU3E0KRCgqw7bVzZ7mA1agvC6ZSc199EGMKSdJM4sogDl4HG7gPASGxRbJIa6vOoRLlpAlAqAjWGhgWtbORw96LzX+FWreAlA4Sr7oe/gB7bKEIR4UETpHcyAA1lVcOkQ3t88CB1hsQI7gHv4C4cONWsOlefoNAPLZq6f7wSnINeWdmq438MUt0UPa/vw0L0te1Lj1OydcIepRtTG+VfJggoKBWdMFYWBkl532T38WPCIoCQptB/Qdz6k+SkhmHSLTpY7HCd+13PAnQPPI8VwOAlR0AfYPCyJds5dAsrbXfODLZkpx37Ih0r0kbfxYs1R2q8uB4G0KloO+EhWpjqFY7dMPMW/++CpsToAAAdgA3vW1xVZcEBShKKEPIbu/wCyf1DRCGW6AFXyw0pegSBNT0BBKNwvkdCEK0EQ0Naw1Af84C9JcN4Uwy6EhVQaRER8X6s13vBIGBnunRilK5VCWjTqB6jpNo/FZDLKaZub4N4yC0KJQ2g3oaiXZH8U0Y4kgOkCSAFcYMsSTathBJuCKuklizBDdYXZdpDHYIocGb3i7hJYIDrLisVInZiSIgYiW7wk1jUEQUWkukq1uFIKgUR5EwLOlUQR2pVDbXXOEiP4LNIMSc4yuMqNyJJFbRoa6duuBNUpWOxwXltSB1onfXMMpXRyCRIYtSQVDSXYVgdOLimqGIwF2hNSmiyboboZjo2gXdeRrd2UyyP8miIdBOhiFdoYBAgIQslDGBDAs4rwRJpEEjiJpz8YnkC9jgjkpDxUFsjDyRoNTKR6LHiQqoJU1RdQOiRJJMtKGjfMabmEGDaiug9GLgM31Iklq3TG6vXNbNT0gGcvtUWtYUaxCsFyVFRU3onW28FVdcUgCzshRha4EI7sAESUk4q87Uxv2CvEkPIYc7MHcoyMgrQbZOV7LwQIDSKbO1ckfNMAcpQJeB2K3i92MWOdLTsR0nE7THYfIwJq9w10+hWTkjyGz+OEikEhebuvAmDAYrxkxEACFE8BVAa1gZjHdMUgpHCQaXwVj6XBSCJTZzybMLeSusRgGKttc6kyOHJOSWKJVV5mFtj2hmwwIRbwc5HSJx+1ueFCtGzWBrsIIMI5dVKQgXeEMc4fZlAXOnIKGcvoEjnGy+iJvneRNggqhqCX+Z0IcVDyRFtjkEXVtHIpgIOyhepBm/Kujzpf1/qenzK/LgNKLcWJg1YWdjNObS1dUm0+1w3lQ8bdFgPPMDEI0ZgtZvWNeg6LKgSmplBdIuxa6Q1vFO7BC4upWbKVjguDHYhwArFAKa2mpU7ildXNEihijOHDnQIg3E80WTrMYp0bQbClPSaaHiCJHGa0htjsR36XKdwwBbCwgBJKUJsHiBkUKcAlEidCrjyOwpbdUhEIdPJJv9lV9nmWXzwhiabCF7O0KO+yig6AlzMuq8PmumHaB2RoodCdBKLhwZTEAp1APBNC3GpdcoYuBkWPoUSnyi1gFNfUVpp4ZQJNAywE43IdFHbM7qwAr1dbeuCcwKUgAGzbbyOMShLKNEFSI3yvTDv6CImuUrRYsRqKJ0PkJgBXfAfH+zfqP1kIz9wAmMF04MYJgSPSJCdGyCRk1FZXDjRVkNwkITJk0oSTPMKVXfgGK9W7eeMP/Cgwphrlg3oDX8yCRKfzmVgEACwXrKz1f5le8Q1ml2Gs2sqwSSDWOX2AooGINBKRkVT4qUbAJN4csjGJ0JxwxWigVhZK1gObVu0P55u/zmd2IB2A4/8AwT8pKjggyrOgIVngvRGTAaIUgoivHL9K9evXojJgNMAUEQTnk+pevXr16OVeERDEWy3TJun0r169evZGxIPg6AqkbGyN+levXr14OVeFVDVSyTRJqv0r169evBmBYiErbt6qYWH0L169eiMmA0wBQRBOeT6l69evXo0gRIAoICwC2Bx9KNIECAIAElEso8eC8GYFiIQNm3ohjI/SvXr169kbEg+DoCqRsbI36V69evXkjYkH0dEVCErbSfSvXr168GYFiIStu3qphYfSvXr169EZMBohSCiK8cv0r169eDMCxEIGzb0QxkfpXr169ejlXhEQxFst0ybp9C9evXtJUckGVZ0JSl/24nvghGPqoEUoOzEflibAmFcQsLUrxkvvlfdMkogbEsLFFWGzRl6gIaTFMVmVT4kAWBCaAU7BGZJIMRUxZ4grQq0DmdUq8oIlIzJk0oWTPEKMJNRWVwq0ULDMFAJ/U32ae3iI3moVX4tzlJbqwzSK+kMDLQxuARyFpyMTV8RUg8YV60kuoKUIio3CsXPpr0SRJWggLzMOtd3acTtFFDFjd3VJARSkAdONAzkeCLYXtGwO0koRMTkwSpvHA51oi4zDUCfxE0kBiMDOtqiLSJgXIFJ4I1HsDFrZMnRCkuX3mE9td0yiIjFA880AUAhGUWrAnIjxe8VCUIlB/FRXpGXkQK7W83aE0ltI+zT6EeygKG45fMOwWGaRX0hgZaGNwCOQtORiaviKkH0FfCMN8ZJE15o4rY6UVwhRbUnDQPPNAFAIRlFq8RggCYY0aRBFXSyO8AEbBtCsrUCtkJrS1LN7k+S9XcySco3y+cgdd4vQ8CPSkmjhBGrVWKYhWki32iNDYCAI5C05GJq+IqQYI6ujGlFNUeOIXHk2tWIEFKjERmzN7M5Bjb3WhVoXH35Yv7L0ewl2CLoSqyHaIDdCQwfcX/YnEVGAgHw0c4pZFUZQTQLZindfQF43XhFYSmtLUs3uT5L1dzJF4R4ypirlIEGwosR7prRyqVf91VFEJYbqKRPPBvEfaJBw4UABA+gmAo9kRuIKtZThaBi5kliToqWIpwplnkAiFM8RVFK3hOA0g1jCFUt58WNeJgUW6AHrVUFAf86S9LcN4Vw/EdCEK9AQ2Fbw0DDYAgAcB4gMc/kAMYGA5Ssru+VlkFKVVdXTAZNUQpJLjqVAVvgPT2koV7E1FRVTIfUJrDUlCtGaIHqNbWIHbI5Hssmz4EmCCqWmqKt8Jmg5pAQrYF4QSVHQzM1imlAQRhkBY5Bgr0BBRKLjeuE1MNaTizRCeAKQmjo5isUUTSDPwPKsklCZpQt5CsJihCWFLTAQAIEFkjOlyjZBRhP4iRom4QtfI/uiGAmMAQDoB9FYcyCwQjhpQETTF3fKyyClKqurpgMmqIUklx1KgK3wGygjGM9TLsCtwwuCcBesu3oYUEpKEzShbyFYTFCEsKWmAgA8F4m32mcRQqE6I4zTCwyekemkBwTBJG9iXU6SFG96GY5YnKEZmOGN14hByUnAWhjEmzgLCqtygS0qqrpgMmqIUklx1KgK1kD5qeExoCVFa3CTeagIqxd6QR2I4LzK+0Dyu75TmzIEWcokCA2wIEUu0DMBNyKU6prmxvSrIhxUBKMCvhqs0mKPYbyFKcLjzjB3WYOAIAEQDBJG9iXU6SFG96GY+dWatyeJBGgQkCKdVtEwCswOAD/+hf/Z)\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "ff8c22194efb4ab9935e37b682abe1fa", + "d315f4f52cbe4c938351650f953e7606", + "410897a876ab46bdb458699bbfc85577", + "3995ebfe19214d8fbf8fb1a4b37236d0", + "269767a1d80e41d79792e50efbd7a493", + "5494e520b49748f0a8b0288fe658bd89", + "8677548a120b4f2d92150a1f96c475f2", + "600eed09db4a4b70bf67ab83e087f820", + "ac7c2398d5a64f89a8c4ab0baaf62418", + "e537f18569c848288f7099b0629e1b12", + "7c0e6b02164140c6b132b152d06cafe8", + "1fb4ded1973b4614b33d4449bb56fac3", + "c0cc0a867a084abb8bbd4459e25b9354", + "465598a9bc494c97837f3c6a995b7a91", + "c229d42162d2457386f5072810f801b9", + "ff0ce6852f684ea791e2d3952b369c44", + "ad4db1e8947545e6b85a1b336594bb4c", + "5c03e244a7b14a159a6bf5a4a0a7f09b", + "5c2422515ebe4b388e5e6bfc937bf455", + "4d582b3e38614c3796564d810c5e8926", + "24f85e8e14e2470eb8dc33cd4c089047", + "cd625f20458941e2a0955f4367c7d407", + "cf62cbd0623e47f2b597b736278bb8b6", + "65b28f66827a4f38beaef4232a6407f8", + "cc42b6c814f54f099d1c03fa0c8bc0de", + "7bfc6ec0686c43ddbb9d4c623351d595", + "4feeb1b232934c7cbda666decaa8518b", + "a6445f26ab9c46c7a506df6c2aad6a11", + "72ebf480fc434ab792a9e4b2c89d110b", + "d3b92144b61d407b9a8445abd1112644", + "05e227932b494388b000daa7f05a0358", + "06603dfbf32c4f5283016c04c1d4e3d4", + "75937286e7a441bbb14a8c4677ee0b02", + "66070aa06e044a228ff1400bff389b9e", + "d2378a52b4e74e1da16eb16b0484e029", + "67852f0e5d424d6cb56abcb918618ff9", + "c165d562fb4e4bd4ba1db53a704acc6a", + "bdf5c3b3148a4c39a9f7cda3cc576508", + "a99b6751a34f42bda02ef7658cc31e73", + "d27beb563fd74929bb311e0f4031f3b0", + "a9affa222f9d48f9acff2c5bd08df0e7", + "26900002143a4fe59d5c5235e408562e", + "36944586f497452d8df81e0bf47172a9", + "a8d4b24422fb4811ab90555905e76be7", + "2f91f29462f74cc295cc0c0f830945c8", + "d387be7834ef4ae2bffc7bc60639f8fa", + "5953b3e065b649c69ad2173cd3ccb116", + "e813a52b8e754df78aba32fa8c262151", + "28b05426f1e04a24b439b6b089b95008", + "9802f322f8df4d40a86b8c04b34817b6", + "41d38730dfdb481085181c1e5eeaef76", + "631ac8b5903447b783787289b562c2ab", + "e01900761d71468cb8d4f1c8507f04a4", + "6e20147f60664bf2baa221260619cbda", + "47894a6b101a4abeb31539fd716c7a45", + "b57bf4bd9aff4dadaf1b700f614bb823", + "3389e17aa5f5413f8bd07aad789c9f1f", + "6312248c82c14ff9a1dbffc3d66297e1", + "27e8ca3c47bf49b487230defb4bd4fb8", + "e76f62ed563142bfabdea5f8a04373f0", + "427a9ff7f00545e881ba24e16a4be85a", + "36a503a230264332aa791b2f790a7fe7", + "f2d16f3e1f6c448fb0f6e679a99fbeef", + "aa8108f88173451b986c8e668a37d68c", + "4de6beceb0cf423aaa6e2f6bbdbe7da4", + "ce22f2f5288e4415b4119fca8bef1e6b", + "3cb867811122467a9ce762da95c81a9c", + "733271a280684f80a308461e2ea6caa1", + "199087047cf74d19a4907cd1d701aea9", + "da72b3d7de4444ce8f305afca2b3feb7", + "ea2f946266f34a25979656339a005745", + "13507952d4ed4140b65f324bfe0b877f", + "77f2290f6217420b88d6e9a7d7c4a446", + "710ed34d70f24d5d9eb85808c1e17f92", + "ef855ac5e2c443e79263b6b0756fda74", + "706b7ffe9f1146f7acf1edd4ddcde0d8", + "053ab88f379d4a36a5e0afb0337c8496", + "f5b77444c4fb43f487868d3bd9824758", + "e0872922f1444114b2e05d2db49fba77", + "5ae67475ec004c7684aeaf74d7ca3e1b", + "22e8352843f0439993c078ea782b8bd9", + "19c0001d9e2a4aff8c3b1699de16d3dc", + "3f2d6e5d93504e9ea544f9453461d238", + "fb0d26a209164181ba38ae639428e426", + "07932c559ea449549d280cfaea57a4ef", + "38755d4f74d34a13a690ecd85ed602d0", + "1f517206a35443e5a5e4bce9ae802842", + "01a80bbbd66b41cd8c0e38c3d7a48cbf" + ] + }, + "id": "i1sKnXrDieiR", + "outputId": "12a85c68-41c2-435c-d5b2-19bb1ca401cc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "INFO:__main__:Default advanced parameters enabled\n", + "DEBUG:__main__:unet: input shape: (None, None, 1), output shape: (None, None, 1)\n", + "DEBUG:__main__:number of examples: 4400\n", + "DEBUG:__main__:number of examples: 1200\n", + "2023/11/13 16:44:53 INFO mlflow.tracking.fluent: Experiment with name 'test_model-depth6-first_filters23-pool_size4' does not exist. Creating a new experiment.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/2\n", + "1/1 [==============================] - 2s 2s/step\n", + "1/1 [==============================] - 0s 28ms/step\n", + "1/1 [==============================] - 0s 28ms/step\n", + "1/1 [==============================] - 0s 24ms/step\n", + "1/1 [==============================] - 0s 23ms/step\n", + "293/293 [==============================] - 220s 474ms/step - loss: 0.7114 - tp0.1: 8757495.0000 - fp0.1: 12300959.0000 - tn0.1: 49616536.0000 - fn0.1: 1332687.0000 - precision0.1: 0.4159 - recall0.1: 0.8679 - tp0.3: 7600999.0000 - fp0.3: 6424773.0000 - tn0.3: 55492716.0000 - fn0.3: 2489183.0000 - precision0.3: 0.5419 - recall0.3: 0.7533 - tp0.5: 6094145.0000 - fp0.5: 3079954.0000 - tn0.5: 58837556.0000 - fn0.5: 3996037.0000 - precision0.5: 0.6643 - recall0.5: 0.6040 - tp0.7: 4392604.0000 - fp0.7: 1157420.0000 - tn0.7: 60760072.0000 - fn0.7: 5697578.0000 - precision0.7: 0.7915 - recall0.7: 0.4353 - tp0.9: 1842460.0000 - fp0.9: 174905.0000 - tn0.9: 61742616.0000 - fn0.9: 8247722.0000 - precision0.9: 0.9133 - recall0.9: 0.1826 - accuracy0.5: 0.9017 - auc: 0.8897 - f10.5: 0.6327 - val_loss: 1.4243 - val_tp0.1: 2317614.0000 - val_fp0.1: 3866322.0000 - val_tn0.1: 12932332.0000 - val_fn0.1: 544532.0000 - val_precision0.1: 0.3748 - val_recall0.1: 0.8097 - val_tp0.3: 2300013.0000 - val_fp0.3: 3662444.0000 - val_tn0.3: 13136210.0000 - val_fn0.3: 562133.0000 - val_precision0.3: 0.3857 - val_recall0.3: 0.8036 - val_tp0.5: 2272002.0000 - val_fp0.5: 3290997.0000 - val_tn0.5: 13507657.0000 - val_fn0.5: 590144.0000 - val_precision0.5: 0.4084 - val_recall0.5: 0.7938 - val_tp0.7: 2219701.0000 - val_fp0.7: 2812845.0000 - val_tn0.7: 13985809.0000 - val_fn0.7: 642445.0000 - val_precision0.7: 0.4411 - val_recall0.7: 0.7755 - val_tp0.9: 2054495.0000 - val_fp0.9: 1895984.0000 - val_tn0.9: 14902670.0000 - val_fn0.9: 807651.0000 - val_precision0.9: 0.5201 - val_recall0.9: 0.7178 - val_accuracy0.5: 0.8026 - val_auc: 0.8455 - val_f10.5: 0.5393 - lr: 0.0300\n", + "Epoch 2/2\n", + "1/1 [==============================] - 0s 46ms/step\n", + "1/1 [==============================] - 0s 44ms/step\n", + "1/1 [==============================] - 0s 32ms/step\n", + "1/1 [==============================] - 0s 24ms/step\n", + "1/1 [==============================] - 0s 24ms/step\n", + "293/293 [==============================] - 138s 473ms/step - loss: 0.5216 - tp0.1: 9442760.0000 - fp0.1: 12668733.0000 - tn0.1: 49264324.0000 - fn0.1: 631879.0000 - precision0.1: 0.4271 - recall0.1: 0.9373 - tp0.3: 8354224.0000 - fp0.3: 5814704.0000 - tn0.3: 56118340.0000 - fn0.3: 1720415.0000 - precision0.3: 0.5896 - recall0.3: 0.8292 - tp0.5: 6979577.0000 - fp0.5: 2808058.0000 - tn0.5: 59124996.0000 - fn0.5: 3095062.0000 - precision0.5: 0.7131 - recall0.5: 0.6928 - tp0.7: 4928653.0000 - fp0.7: 1069016.0000 - tn0.7: 60864012.0000 - fn0.7: 5145986.0000 - precision0.7: 0.8218 - recall0.7: 0.4892 - tp0.9: 1967299.0000 - fp0.9: 139061.0000 - tn0.9: 61794000.0000 - fn0.9: 8107340.0000 - precision0.9: 0.9340 - recall0.9: 0.1953 - accuracy0.5: 0.9180 - auc: 0.9318 - f10.5: 0.7028 - val_loss: 0.4927 - val_tp0.1: 2684145.0000 - val_fp0.1: 3133455.0000 - val_tn0.1: 13665199.0000 - val_fn0.1: 178001.0000 - val_precision0.1: 0.4614 - val_recall0.1: 0.9378 - val_tp0.3: 2474756.0000 - val_fp0.3: 1652758.0000 - val_tn0.3: 15145896.0000 - val_fn0.3: 387390.0000 - val_precision0.3: 0.5996 - val_recall0.3: 0.8647 - val_tp0.5: 2194586.0000 - val_fp0.5: 909056.0000 - val_tn0.5: 15889598.0000 - val_fn0.5: 667560.0000 - val_precision0.5: 0.7071 - val_recall0.5: 0.7668 - val_tp0.7: 1782801.0000 - val_fp0.7: 406484.0000 - val_tn0.7: 16392170.0000 - val_fn0.7: 1079345.0000 - val_precision0.7: 0.8143 - val_recall0.7: 0.6229 - val_tp0.9: 717690.0000 - val_fp0.9: 45898.0000 - val_tn0.9: 16752756.0000 - val_fn0.9: 2144456.0000 - val_precision0.9: 0.9399 - val_recall0.9: 0.2508 - val_accuracy0.5: 0.9198 - val_auc: 0.9385 - val_f10.5: 0.7357 - lr: 2.3437e-04\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "2023/11/13 16:50:54 WARNING mlflow.utils.autologging_utils: Encountered unexpected error during tensorflow autologging: Changing param values is not allowed. Param with key='batch_size' was already logged with value='15' for run ID='4ffe3304ad9941deb01823f099f69da2'. Attempted logging new value 'None'.\n", + "/usr/local/lib/python3.10/dist-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\n", + " warnings.warn(\"Setuptools is replacing distutils.\")\n", + "2023/11/13 16:50:54 WARNING mlflow.tensorflow: You are saving a TensorFlow Core model or Keras model without a signature. Inference with mlflow.pyfunc.spark_udf() will not work unless the model's pyfunc representation accepts pandas DataFrames as inference inputs.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading artifacts: 0%| | 0/1 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "#@title { display-mode: \"form\" }\n", + "# ---------------------------- GET USER INPUT --------------------------------\n", + "#@markdown ## Basic Training Parameters\n", + "#@markdown * `model_name`: Choose any name you wish. Mlflow logging ensures that\n", + "#@markdown two models with the same name are still logged seperately.\n", + "model_name = \"test_model\" #@param {type:\"string\"}\n", + "#@markdown * `out_path`: Path to create a mlflow tracking URI. See\n", + "#@markdown [Section 2.4.](#scrollTo=jVGckx7ojEP2) on how mlflow stores model data and metadata\n", + "out_path = \"/content/\" #@param {type:\"string\"}\n", + "out_path = Path(out_path)\n", + "model_path = out_path / 'mlruns'\n", + "mlflow.set_tracking_uri(model_path)\n", + "\n", + "#@markdown * `use_pretrained_model`: Enables re-training a model loaded prior in\n", + "#@markdown [Section 2.4.](#scrollTo=jVGckx7ojEP2). Either `use_all_layers` to resume training\n", + "#@markdown of the entire model. Alternatively, `freeze_decoder_layers` to only\n", + "#@markdown the U-Net encoder (An experimental option which has shown to be useful\n", + "#@markdown in [segmenting ultrasound images with a 2D-Unet](https://arxiv.org/pdf/2002.08438.pdf)\n", + "use_pretrained_model = \"no - train from scratch\" #@param [\"no - train from scratch\", \"yes - use all layers\", \"yes - freeze decoder layers\"]\n", + "upm_dict = {\n", + " 'no - train from scratch': False,\n", + " 'yes - use all layers': 'use_all_layers',\n", + " 'yes - freeze decoder layers': 'freeze_decoder_layers'\n", + "}\n", + "use_pretrained_model = upm_dict.get(use_pretrained_model)\n", + "#@markdown * `epochs`: An epoch is a whole iteration through the training data.\n", + "#@markdown For FCS artifact segmentation, good results can already be observed\n", + "#@markdown with only 20 epochs, a more secure number is 100 epochs.\n", + "epochs = 2 #@param {type:\"integer\"}\n", + "\n", + "#@markdown ## Advanced Parameters\n", + "#@markdown * You can either define the advanced parameters below, or tick the\n", + "#@markdown following box. Then the parameters which performed best in the\n", + "#@markdown accompanying paper are chosen.\n", + "use_default_advanced_parameters = True #@param {type:\"boolean\"}\n", + "\n", + "#@markdown ### If not, please input:\n", + "#@markdown * `batch_size` defines the number of traces seen in each training\n", + "#@markdown step. Smaller batch sizes may improve training performance slightly,\n", + "#@markdown but may increase training time. **Default: 15**\n", + "batch_size = 5#@param {type:\"integer\"}\n", + "#@markdown * `scaler` is a feature normalizer applied to all FCS time-series for\n", + "#@markdown training and evaluation. **Default: quant_g**\n", + "scaler = \"standard\" #@param ['standard', 'robust', 'maxabs', 'quant_g', 'minmax', 'l1', 'l2']\n", + "#@markdown * `lr_start` and `lr_power` allow to create a learning rate schedule\n", + "#@markdown according to:\n", + "#@markdown ```python\n", + "#@markdown lr_start * (1 - current_epoch / epochs)**lr_power\n", + "#@markdown ```\n", + "#@markdown **Defaults: lr_start=0.03, lr_power=7**\n", + "lr_start = 1e-5 #@param {type:\"number\"}\n", + "lr_power = 1 #@param {type:\"integer\"}\n", + "#@markdown * `n_levels`, `first_filters`, and `pool_size` are hyperparameters\n", + "#@markdown defining the U-Net architecture. If a pretrained model is used, these\n", + "#@markdown parameters are inferred from the pretrained model, no matter which\n", + "#@markdown values are chosen here. **Defaults: n_levels=6, first_filters=23,\n", + "#@markdown pool_size=4**\n", + "n_levels = 5 #@param {type:\"integer\"}\n", + "first_filters = 64 #@param {type:\"integer\"}\n", + "pool_size = 2 #@param {type:\"integer\"}\n", + "\n", + "# ------------------------ SET DEFAULT VALUES ---------------------------------\n", + "\n", + "METRICS_THRESHOLDS = [0.1, 0.3, 0.5, 0.7, 0.9]\n", + "INITIAL_EPOCH = 0\n", + "\n", + "if use_default_advanced_parameters:\n", + "\n", + " log.info(\"Default advanced parameters enabled\")\n", + " batch_size = 15\n", + " first_filters = 23\n", + " lr_start = 0.03\n", + " lr_power = 7\n", + " scaler = 'quant_g'\n", + " n_levels = 6\n", + " pool_size = 4\n", + "\n", + "# The U-Net is trained to accept arbitrary input sizes, even though the\n", + "# architecture demands the size to be >= 1024 and a power of two (2**11, 2**12, ...).\n", + "# To achieve this, the arbitrary-length user input is padded with the median\n", + "# of the trace to such a power of 2 or at least 1024. For training, we simulate\n", + "# this padding by shortening the 16384-length traces (exactly 2**14) by 10%\n", + "# with the variable `crop_size`\n", + "crop_size = int(len(train_source) - np.ceil(0.1 * len(train_source)))\n", + "\n", + "model_kwargs = dict(n_levels=n_levels,\n", + " first_filters=first_filters,\n", + " pool_size=pool_size,\n", + " metrics_thresholds=METRICS_THRESHOLDS)\n", + "\n", + "mlflow_kwargs = dict(train_data=train_source,\n", + " train_labels=train_target_bool,\n", + " val_data=val_source,\n", + " val_labels=val_target_bool,\n", + " train_experiment_params=experiment_params_train,\n", + " val_experiment_params=experiment_params_val,\n", + " batch_size=batch_size,\n", + " crop_size=crop_size,\n", + " lr_start=lr_start,\n", + " lr_power=lr_power,\n", + " epochs=epochs,\n", + " initial_epoch=INITIAL_EPOCH,\n", + " scaler=scaler,\n", + " name=model_name)\n", + "\n", + "# --------------------------- PRELOAD MODEL ---------------------------------\n", + "if use_pretrained_model:\n", + " try:\n", + " if all([paths, exps, runs, models, clients]):\n", + " pass\n", + " else:\n", + " paths, exps, runs, models, clients = get_all_mlflow_models()\n", + " except NameError:\n", + " paths, exps, runs, models, clients = get_all_mlflow_models()\n", + "\n", + " if not all([paths, exps, runs, models, clients]):\n", + " log.debug('No valid mlflow-logged model found. No '\n", + " 'pretrained network will be used.')\n", + " use_pretrained_model = False\n", + "\n", + "\n", + "# --------------------- Download the a model provided in the XXX ------------------------\n", + "\n", + "\n", + "train_click = TrainClick(mlflow_kwargs=mlflow_kwargs)\n", + "if use_pretrained_model:\n", + " train_click.finetune_model(runs=runs, models=models, clients=clients,\n", + " finetuning=use_pretrained_model)\n", + " def train_pdf_export_wrapper(change):\n", + " return train_pdf_export(train_click, out_path=out_path)\n", + " train_click.button.on_click(train_pdf_export_wrapper)\n", + "else:\n", + " train_click.train_from_scratch(model_kwargs=model_kwargs)\n", + " train_pdf_export(train_click, out_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1Tm3aimXjZ1B" + }, + "source": [ + "# **5. Evaluate your model**\n", + "---\n", + "\n", + "This section allows the user to perform important quality checks on the validity and generalisability of the trained model.\n", + "\n", + "**We highly recommend to perform quality control on all newly trained models.**\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ULMuc37njkXM" + }, + "source": [ + "## **5.1. Inspection of training metrics**\n", + "---\n", + "### **a) loss function**\n", + "First, it is good practice to evaluate the training progress by comparing the training loss with the validation loss. The latter is a metric which shows how well the network performs on a subset of unseen data which is set aside from the training dataset. For more information on this, see for example [this review](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6381354/) by Nichols *et al.*\n", + "\n", + "**Training loss** describes an error value after each epoch for the difference between the model's prediction and its ground-truth target.\n", + "\n", + "**Validation loss** describes the same error value between the model's prediction on a validation image and compared to it's target.\n", + "\n", + "During training both values should decrease before reaching a minimal value which does not decrease further even after more training. Comparing the development of the validation loss with the training loss can give insights into the model's performance.\n", + "\n", + "Decreasing **Training loss** and **Validation loss** indicates that training is still necessary and increasing the number of `epochs` is recommended. Note that the curves can look flat towards the right side, just because of the y-axis scaling. The network has reached convergence once the curves flatten out. After this point no further training is required. If the **Validation loss** suddenly increases again an the **Training loss** simultaneously goes towards zero, it means that the network is overfitting to the training data. In other words the network is remembering the exact patterns from the training data and no longer generalizes well to unseen data. In this case the training dataset has to be increased.\n", + "\n", + "### **b) other metrics**\n", + "The following other metrics are logged here:\n", + "- **True Negatives**, **False Negatives**, **False Positives**, **True Positives**, **Accuracy**, **Precision**, **Recall** (all at a prediction threshold of 0.5) $\\to$ for a good breakdown see [here](https://en.wikipedia.org/wiki/Precision_and_recall)\n", + "- **Area und the ROC curve (AUC)** $\\to$ for a good breakdown see [here](https://en.wikipedia.org/wiki/Receiver_operating_characteristic)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "a6b96e2c11fe4ce9a127346a8ebaabd8", + "006f452a5cac4c0eb5d2d1582decb174", + "461f3d794fda45cbaa6d161be272f05d", + "b9d1dff73a864095b425499ed912165d", + "02a0bc2318804b4a9164d59d837f2023", + "0728be8d8b2340afadbbef8ded000c2d", + "600202906123442eb1bc794838390a72", + "bfb01e87fc6f4aad87dc727ddc5a7e99", + "1f434b1d2058428097539efb4d39e3cc", + "a36d05d57fa849dba64904111f7227c3", + "dea9aaaabb854ca3ae4e4271e3737a0f", + "a829fa015bce42d9adfe9546724a9d83", + "f3fda2247f534374828df3e35a103682" + ] + }, + "id": "WCmi1tVFCFt4", + "outputId": "49d72aca-8773-4f47-85da-f57f55c7aae7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:Found metrics for model test_model with scaler quant_g from run 4ffe3304ad9941deb01823f099f69da2\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Please first execute Section 4 to train a model. Note: the published models do not include training metrics and can not be analyzed here.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(Dropdown(description='Model:', layout=Layout(width='75%'), options=((\"model test_model with sca…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "a6b96e2c11fe4ce9a127346a8ebaabd8" + } + }, + "metadata": {} + } + ], + "source": [ + "#@title { run: \"auto\" }\n", + "#@title { display-mode: \"form\" }\n", + "\n", + "#@markdown ## Run this cell to check metrics of the current run\n", + "#@markdown **NOTE: the published models do not include training metrics\n", + "#@markdown and can not be analyzed here**\n", + "\n", + "# ----------------------- GET USER INPUT ---------------------------------\n", + "# #@markdown ## Input\n", + "# #@markdown * Choose if you want to use the last trained model from\n", + "# #@markdown [Section 4](#scrollTo=GyRjBdClimfK).\n", + "# #@markdown If not ticked, this will scan the /content/ directory for mlflow\n", + "# #@markdown models (as in [Section 2.4.](#scrollTo=C2-nD03olfmq&line=1&uniqifier=1)).\n", + "# #@markdown You can then choose a valid run below\n", + "# use_the_current_trained_model = False #@param {type:\"boolean\"}\n", + "\n", + "use_the_current_trained_model = True\n", + "#---------------------------- DO THE THING ------------------------------\n", + "\n", + "if use_the_current_trained_model:\n", + " try:\n", + " run = mlflow.get_run(train_click.run.info.run_id)\n", + " exp = train_click.exp\n", + " paths, exps, runs, models, clients = get_current_run_and_model(exp, run)\n", + " except (AttributeError, NameError):\n", + " print('Please first execute Section 4 to train a model. Note: the '\n", + " 'published models do not include training metrics and can not be '\n", + " 'analyzed here.')\n", + "else:\n", + " try:\n", + " if all([paths, exps, runs, models, clients]):\n", + " pass\n", + " else:\n", + " paths, exps, runs, models, clients = get_all_mlflow_models()\n", + " except NameError:\n", + " paths, exps, runs, models, clients = get_all_mlflow_models()\n", + "\n", + "\n", + "try:\n", + " evaluate_click.display_widgets_train_metrics()\n", + "except NameError:\n", + " evaluate_click = EvaluateClick(runs=runs, models=models, clients=clients)\n", + " evaluate_click.display_widgets_train_metrics()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "smiWe2wcjwTc" + }, + "source": [ + "## **5.2. Quality metrics estimation**\n", + "---\n", + "This section will calculate the Intersection over Union score for the quality control dataset provided below in `path_to_source_and_target`.\n", + "\n", + " The **Intersection over Union** metric is a method that can be used to quantify the percent overlap between the target mask and your prediction output. **Therefore, the closer to 1, the better the performance.** This metric can be used to assess the quality of your model to accurately predict FCS artifacts.\n", + "\n", + " Two further plots will showcase the input, ground truth, and prediction together with the IoU-Value:\n", + "- 6 Random examples\n", + "- 6 Examples with the worst IoU-Values\n", + "\n", + "### **Thresholds for image masks**\n", + "\n", + " Since the output from Unet is not a binary mask, the output images are converted to binary masks using thresholding. This section will test different thresholds (from 0.1 to 0.9 in 0.1-steps) to find the one yielding the best IoU score compared with the ground truth. The best threshold for each image and the average of these thresholds will be displayed below. **These values can be a guideline when creating masks for unseen data in [Section 6](#scrollTo=fB8QNLekkCyZ).**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "8d4e27935c1745fea0f56a237f44b512", + "9ff4184231d4484f810b65b699edbd98", + "6cfd73d40ba14cff9ab70f4db7f32164", + "402be8f155a9421f95be1f9a5f1c02ed", + "4efd5acf6666419fa7f52c4d685ab60a", + "5658d1403d79489b94162f4df2b8a6a0", + "35cebe58059b4c67a8b4c824fd30a811", + "ae5edc86a8b24013891e2c2a25ba89e2", + "eafe144a7df04d4b8a577623aff9d559", + "8106c9a170674df5b629b831ee0231d3", + "4bdaff015e5747a6a1c1716aed8c8f4c", + "9ab87a5a00bd448d88a6601a0f4dad08", + "afd4d814f0d4451f93f020922198862f", + "827fed08bfe14f6c8320577c26ce77a0", + "3a25f3aad248490d9170349598f69725", + "417fda5b4df547f8a5a6e9983c704401", + "82f036fcfece4e86929721e9cb05efa2", + "ba9e3a2739104f7085325a85530d9c7c", + "d9c8c2eccc3c4782bd4e8b8e36c3aa3c", + "e8b45cad8346447c9f7d9969e2013285", + "ab52931931af499f9faf07eb6b6e8fc4", + "2a034941a48946c9819b9e1fcf7e5428", + "532d190fcbf7459ca728a2f72cbf6ba1", + "870571f84d524a3fb36b52650ef4c1f3", + "84a8fe83da98401993a80d86efd7add4", + "4970fadd60184c8287e5da7d9870af6a", + "0fd49137207e4b29a458c1a012803955", + "b4d3090e18ce441e88457087e5e7af1f", + "81f7463cdd1e4910ac853e8e5d176080", + "111efae1aacd4ef6916f4143877654bb", + "3e06a0f722404c9cba4890f8d547c3cc", + "4726817874b2426cb0fd8c63c7a71609", + "1e70b14ae7624a6b9d0faccbcf5c07e1", + "07dc9f5eb3244f6fab4232dd6bbacd23", + "4a0f76bf7117414e8650e8dc062c1ccf", + "0bf992cb3b0b4680a68263ac097c0f98", + "b772b78ad2f4473daa9c83da9fed743d", + "3d885f2943914ac797a3bb31b5aab10a", + "04c7f75e225f43258f3cf212240ef977", + "5bfd17fc08484f4ea81c8fbec4d67b76", + "c1f3c8363a4741b3a89565c7659f1afb", + "b612509b411d437da28098f30dd99ba7", + "c4e1c0914f0f44d3835757af12ce7437", + "a6a1a452e95a42feba2b31b2770c2b7b", + "3f19a671550348449d9b5e137575bb45", + "dbff7c5a51c8446985c535b2c1071f55", + "b4fe34d1fd524859b092be43761f236e", + "163cd35427964a2399b2fa5a20262678", + "be9c9326d96f4e33a9822d3901d7f14a", + "69d4c37abaa245ae9dbc70202fdcda07", + "7749b3931b744ade842f2e345b5fd7b7", + "21b1fe22b1f241c4bfb19926e3322b35", + "482397af23044a8cb77de7fbea67dd92", + "25ca6791d5da4e87957e0fff0bcf836f", + "dc74283037304f31a46f1e1114c6b0f1", + "91549810eec442b9957b67262367ead5", + "d509322fb5344a33b6cb5d8bfdaec142", + "88990362ab314078b6c05e89584b2c7a", + "01ecfc2b3e4a42f19ae7e1102844991f", + "17138529e9214c4aba957e2aba2dda0b", + "65cc0910b65044f2bfbb91b6e52a1c73", + "a10820d25eb54aebbd6ae32a3cc0e9b0", + "c2691f0f03174b66a39efa9dd2fea6a7", + "544073e7f41742749e22852ce53c8d73", + "a1ba50dd4c6240ab9659514dd443a151" + ] + }, + "id": "Z179Zxgtj0PP", + "outputId": "c2d13aa9-aa2d-44d7-e82b-873adc1d7bee" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:Start scanning all paths in the \"/content/\" directory for mlflow experiments. Depending on the size of the file system / GDrive, this may take a while...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "0it [00:00, ?it/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "8d4e27935c1745fea0f56a237f44b512" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:Finished collecting potential paths. Start collecting experiments...\n", + "DEBUG:__main__:found 2 experiments\n", + "DEBUG:__main__:checking experiment for runs\n", + "DEBUG:__main__:checking experiment for runs\n", + "DEBUG:__main__:checking run 4ffe3304ad9941deb01823f099f69da2\n", + "DEBUG:__main__:found 1 runs\n", + "DEBUG:__main__:found 1 experiments\n", + "DEBUG:__main__:checking experiment for runs\n", + "DEBUG:__main__:checking run 34a6d207ac594035b1009c330fb67a65\n", + "WARNING:tensorflow:Unable to restore custom metric. Please ensure that the layer implements `get_config` and `from_config` when saving. In addition, please use the `custom_objects` arg when calling `load_model()`.\n", + "DEBUG:__main__:checking run 347669d050f344ad9fb9e480c814f727\n", + "WARNING:tensorflow:Unable to restore custom metric. Please ensure that the layer implements `get_config` and `from_config` when saving. In addition, please use the `custom_objects` arg when calling `load_model()`.\n", + "DEBUG:__main__:checking run ff67be0b68e540a9a29a36a2d0c7a5be\n", + "WARNING:tensorflow:Unable to restore custom metric. Please ensure that the layer implements `get_config` and `from_config` when saving. In addition, please use the `custom_objects` arg when calling `load_model()`.\n", + "DEBUG:__main__:checking run 0cd2023eeaf745aca0d3e8ad5e1fc653\n", + "WARNING:tensorflow:Unable to restore custom metric. Please ensure that the layer implements `get_config` and `from_config` when saving. In addition, please use the `custom_objects` arg when calling `load_model()`.\n", + "DEBUG:__main__:found 5 runs\n", + "DEBUG:__main__:Creating a list of all .csv files in folder='/content/gdrive/MyDrive/unet-for-fcs/data/2020-11-FCS-peak-artifacts-dataset-test-split' including its subdirectories...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "0it [00:00, ?it/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "9ab87a5a00bd448d88a6601a0f4dad08" + } + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:Reading 30 files from folder /content/gdrive/MyDrive/unet-for-fcs/data/2020-11-FCS-peak-artifacts-dataset-test-split\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/30 [00:00 Fill the below code to perform predictions using **1D U-Net for FCS**. In this section the unseen data is processed using your trained model from [Section 4.](#scrollTo=GyRjBdClimfK) or a loaded model from [Section 2.4.](#scrollTo=jVGckx7ojEP2)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PpQlIcv5pbWe" + }, + "source": [ + "## **6.1a. Apply at TCSPC dataset (.ptu data)**\n", + "---\n", + " Time-Correlated Single Photon Counting (TCSPC) measurements are an important type of data record in microscopy. For each photon that hits the detector after an excitation laser pulse, it logs two properties:\n", + "1. **macrotime**: the time after the begin of the measurement. In FCS, macrotimes are correlated to get FCS correlation curves, which are later fitted by theoretical models to extract physical properties such as the diffusion time or molecule number.\n", + "2. **microtime**: the time after the laser pulse. This property is important for e.g. lifetime measurements. Here, it is discarded.\n", + "\n", + " Correcting FCS data in TCSPC format, the following steps are important:\n", + "1. Execute the *Setup* cells, because a fast Cython algorithm is used to improve the computation time for TCSPC correlations.\n", + "2. Load the *.ptu* data in [Step 1](#scrollTo=w17NWhLjuzBX). A helper function will plot a binned time-series, the photon count decay function, and an autocorrelation function.\n", + "3. Process the loaded file in [Step 2](#scrollTo=dyI-XR5iGyJq). Here, the magic happens:\n", + " - choose a segmentation method (either U-Net for FCS or simple thresholding as a comparison)\n", + " - choose a correction method (**cut_and_stitch** has proven to be the best in the accompanying paper, but the other options described there are available as well).\n", + " - if further input is needed, you can provide it after executing the cell.\n", + "\n", + " **Note: With this GUI, only 1 trace can be processed at a time. The underlying functions easily allow batch processing, as shown in [Seltmann et al (link TBD)]().**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "tXz1UaNWojtp" + }, + "outputs": [], + "source": [ + "#@title { display-mode: \"form\" }\n", + "#@markdown ## Setup 1: Load Cython for fast TCSPC correlation algorithm\n", + "\n", + "%load_ext cython\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "axQ-iXMOoiKW", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c6aff82e-1063-4bc0-b0fa-341dc9a5d31d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Content of stderr:\n", + "In file included from /usr/local/lib/python3.10/dist-packages/numpy/core/include/numpy/ndarraytypes.h:1948,\n", + " from /usr/local/lib/python3.10/dist-packages/numpy/core/include/numpy/ndarrayobject.h:12,\n", + " from /usr/local/lib/python3.10/dist-packages/numpy/core/include/numpy/arrayobject.h:5,\n", + " from /root/.cache/ipython/cython/_cython_magic_386dfd0d81e156967f328d5672ebbbf53379c88a.c:1215:\n", + "/usr/local/lib/python3.10/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: #warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-Wcpp]\n", + " 17 | #warning \"Using deprecated NumPy API, disable it with \" \\\n", + " | ^~~~~~~" + ] + } + ], + "source": [ + "#@title { display-mode: \"form\" }\n", + "#@markdown ## Setup 2: Load divide and conquer fast intersection algorithm for TCSPC correlation\n", + "\n", + "%%cython\n", + "import cython\n", + "cimport cython\n", + "\n", + "import numpy as np\n", + "cimport numpy as np\n", + "\n", + "DTYPE = np.float64\n", + "ctypedef np.float64_t DTYPE_t\n", + "\n", + "@cython.boundscheck(False)\n", + "@cython.wraparound(False)\n", + "@cython.nonecheck(False)\n", + "def dividAndConquer(arr1b,arr2b,arrLength):\n", + " \"\"\"divide and conquer fast intersection algorithm. Waithe D 2014\"\"\"\n", + "\n", + " cdef np.ndarray[DTYPE_t, ndim=1] arr1bool = np.zeros((arrLength-1))\n", + " cdef np.ndarray[DTYPE_t, ndim=1] arr2bool = np.zeros((arrLength-1))\n", + " cdef np.ndarray[DTYPE_t, ndim=1] arr1 = arr1b\n", + " cdef np.ndarray[DTYPE_t, ndim=1] arr2 = arr2b\n", + "\n", + " cdef int arrLen\n", + " arrLen = arrLength;\n", + " cdef int i\n", + " i = 0;\n", + " cdef int j\n", + " j = 0;\n", + "\n", + " while(i i\", bytes.fromhex(\"FFFF0008\"))[0]\n", + " tyBool8 = struct.unpack(\">i\", bytes.fromhex(\"00000008\"))[0]\n", + " tyInt8 = struct.unpack(\">i\", bytes.fromhex(\"10000008\"))[0]\n", + " tyBitSet64 = struct.unpack(\">i\", bytes.fromhex(\"11000008\"))[0]\n", + " tyColor8 = struct.unpack(\">i\", bytes.fromhex(\"12000008\"))[0]\n", + " tyFloat8 = struct.unpack(\">i\", bytes.fromhex(\"20000008\"))[0]\n", + " tyTDateTime = struct.unpack(\">i\", bytes.fromhex(\"21000008\"))[0]\n", + " tyFloat8Array = struct.unpack(\">i\", bytes.fromhex(\"2001FFFF\"))[0]\n", + " tyAnsiString = struct.unpack(\">i\", bytes.fromhex(\"4001FFFF\"))[0]\n", + " tyWideString = struct.unpack(\">i\", bytes.fromhex(\"4002FFFF\"))[0]\n", + " tyBinaryBlob = struct.unpack(\">i\", bytes.fromhex(\"FFFFFFFF\"))[0]\n", + "\n", + " # Record types\n", + " # SubID: $00, RecFmt: $01 (V1) - or - SubID: $01, RecFmt: $01 (V2)\n", + " # T-Mode: $02 (T2) - or - $03 (T3)\n", + " # HW = $03 (PicoHarp), HW: $04 (HydraHarp), HW: $05 (TimeHarp260N)\n", + " # HW: $06 (TimeHarp260P), HW: $07 (MultiHarp)\n", + " rtPicoHarpT3 = struct.unpack(\">i\", bytes.fromhex('00010303'))[0]\n", + " rtPicoHarpT2 = struct.unpack(\">i\", bytes.fromhex('00010203'))[0]\n", + " rtHydraHarpT3 = struct.unpack(\">i\", bytes.fromhex('00010304'))[0]\n", + " rtHydraHarpT2 = struct.unpack(\">i\", bytes.fromhex('00010204'))[0]\n", + " rtHydraHarp2T3 = struct.unpack(\">i\", bytes.fromhex('01010304'))[0]\n", + " rtHydraHarp2T2 = struct.unpack(\">i\", bytes.fromhex('01010204'))[0]\n", + " rtTimeHarp260NT3 = struct.unpack(\">i\", bytes.fromhex('00010305'))[0]\n", + " rtTimeHarp260NT2 = struct.unpack(\">i\", bytes.fromhex('00010205'))[0]\n", + " rtTimeHarp260PT3 = struct.unpack(\">i\", bytes.fromhex('00010306'))[0]\n", + " rtTimeHarp260PT2 = struct.unpack(\">i\", bytes.fromhex('00010206'))[0]\n", + " rtMultiHarpT3 = struct.unpack(\">i\", bytes.fromhex('00010307'))[0]\n", + " rtMultiHarpT2 = struct.unpack(\">i\", bytes.fromhex('00010207'))[0]\n", + "\n", + " # if len(sys.argv) != 3:\n", + " # print(\"USAGE: Read_PTU.py inputfile.PTU outputfile.txt\")\n", + " # exit(0)\n", + "\n", + " if outputfilepath is not None:\n", + " # The following is needed for support of wide strings\n", + " outputfile = io.open(outputfilepath, \"w+\", encoding=\"utf-16le\")\n", + "\n", + " with open(inputfilepath, \"rb\") as f:\n", + " # Check if inputfile is a valid PTU file\n", + " # Python strings don't have terminating NULL characters, so they're\n", + " # stripped\n", + " magic = f.read(8).decode(\"utf-8\").strip('\\0')\n", + " if magic != \"PQTTTR\":\n", + " f.close()\n", + " raise ValueError(\"ERROR: Magic invalid, this is not a PTU file.\")\n", + "\n", + " version = f.read(8).decode(\"utf-8\").strip('\\0')\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"Tag version: %s\\n\" % version)\n", + "\n", + " # Write the header data to outputfile and also save it in memory.\n", + " # There's no do ... while in Python, so an if statement inside the\n", + " # while loop breaks out of it\n", + " tagDataList = [] # Contains tuples of (tagName, tagValue)\n", + " while True:\n", + " tagIdent = f.read(32).decode(\"utf-8\").strip('\\0')\n", + " tagIdx = struct.unpack(\" -1:\n", + " evalName = tagIdent + '(' + str(tagIdx) + ')'\n", + " else:\n", + " evalName = tagIdent\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"\\n%-40s\" % evalName)\n", + " if tagTyp == tyEmpty8:\n", + " f.read(8)\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"\")\n", + " tagDataList.append((evalName, \"\"))\n", + " elif tagTyp == tyBool8:\n", + " tagInt = struct.unpack(\"\" % tagInt /\n", + " 8)\n", + " tagDataList.append((evalName, tagInt))\n", + " elif tagTyp == tyTDateTime:\n", + " tagFloat = struct.unpack(\"\" % tagInt)\n", + " tagDataList.append((evalName, tagInt))\n", + " else:\n", + " raise ValueError(\"ERROR: Unknown tag type\")\n", + " if tagIdent == \"Header_End\":\n", + " break\n", + "\n", + " # Reformat the saved data for easier access\n", + " tagNames = [tagDataList[i][0] for i in range(0, len(tagDataList))]\n", + " tagValues = [tagDataList[i][1] for i in range(0, len(tagDataList))]\n", + "\n", + " # get important variables from headers\n", + " numRecords = tagValues[tagNames.index(\"TTResult_NumberOfRecords\")]\n", + " globRes = tagValues[tagNames.index(\"MeasDesc_GlobalResolution\")]\n", + " resolution = tagValues[tagNames.index(\"MeasDesc_Resolution\")]\n", + "\n", + " if verbose:\n", + " log.debug(\"import_ptu: Writing %d records, this may take a while.\",\n", + " numRecords)\n", + "\n", + " # prepare dictionary as output of function, if no outputfile is given\n", + " if outputfilepath is None:\n", + " outdict = {}\n", + " outdict['trueTimeArr'] = np.zeros(numRecords, dtype=object)\n", + " outdict['dTimeArr'] = np.zeros(numRecords, dtype=object)\n", + " outdict['chanArr'] = np.zeros(numRecords, dtype=object)\n", + " outdict['resolution'] = None\n", + " out = outdict\n", + " else:\n", + " out = False\n", + "\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"\\n-----------------------\\n\")\n", + " recordType = tagValues[tagNames.index(\"TTResultFormat_TTTRRecType\")]\n", + " if recordType == rtPicoHarpT2:\n", + " if verbose:\n", + " log.debug(\"import_ptu: PicoHarp T2 data\")\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"PicoHarp T2 data\\n\")\n", + " outputfile.write(\"\\nrecord# chan nsync truetime/ps\\n\")\n", + " readPT2(f, numRecords, globRes, isT2=True, verbose=verbose,\n", + " outputfile=outputfile)\n", + " else:\n", + " readPT2(f, numRecords, globRes, isT2=True, verbose=verbose,\n", + " outdict=outdict)\n", + " elif recordType == rtPicoHarpT3:\n", + " if verbose:\n", + " log.debug(\"import_ptu: PicoHarp T3 data\")\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"PicoHarp T3 data\\n\")\n", + " outputfile.write(\"\\nrecord# chan nsync truetime/ns dtime\\n\")\n", + " readPT3(f, numRecords, globRes, resolution, isT2=False,\n", + " verbose=verbose, outputfile=outputfile)\n", + " else:\n", + " readPT3(f, numRecords, globRes, resolution, isT2=False,\n", + " verbose=verbose, outdict=outdict)\n", + " elif recordType == rtHydraHarpT2:\n", + " if verbose:\n", + " log.debug(\"import_ptu: HydraHarp V1 T2 data\")\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"HydraHarp V1 T2 data\\n\")\n", + " outputfile.write(\"\\nrecord# chan nsync truetime/ps\\n\")\n", + " readHT2(1, f, numRecords, globRes, isT2=True, verbose=verbose,\n", + " outputfile=outputfile)\n", + " else:\n", + " readHT2(1, f, numRecords, globRes, isT2=True, verbose=verbose,\n", + " outdict=outdict)\n", + " elif recordType == rtHydraHarpT3:\n", + " if verbose:\n", + " log.debug(\"import_ptu: HydraHarp V1 T3 data\")\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"HydraHarp V1 T3 data\\n\")\n", + " outputfile.write(\"\\nrecord# chan nsync truetime/ns dtime\\n\")\n", + " readHT3(1, f, numRecords, globRes, resolution, isT2=False,\n", + " verbose=verbose, outputfile=outputfile)\n", + " else:\n", + " readHT3(1, f, numRecords, globRes, resolution, isT2=False,\n", + " verbose=verbose, outdict=outdict)\n", + " elif recordType in (rtHydraHarp2T2, rtTimeHarp260NT2,\n", + " rtTimeHarp260PT2, rtMultiHarpT2):\n", + " printdict = {rtHydraHarp2T2: \"HydraHarp V2 T2 data\",\n", + " rtTimeHarp260NT2: \"TimeHarp260N T2 data\",\n", + " rtTimeHarp260PT2: \"TimeHarp260P T2 data\",\n", + " rtMultiHarpT2: \"MultiHarp T2 data\"}\n", + " if verbose:\n", + " log.debug(\"import_ptu: %s\", printdict[recordType])\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"{}\\n\".format(printdict[recordType]))\n", + " outputfile.write(\"\\nrecord# chan nsync truetime/ps\\n\")\n", + " readHT2(2, f, numRecords, globRes, isT2=True, verbose=verbose,\n", + " outputfile=outputfile)\n", + " else:\n", + " readHT2(2, f, numRecords, globRes, isT2=True, verbose=verbose,\n", + " outdict=outdict)\n", + " elif recordType in (rtHydraHarp2T3, rtTimeHarp260NT3,\n", + " rtTimeHarp260PT3, rtMultiHarpT3):\n", + " printdict = {rtHydraHarp2T3: \"HydraHarp V2 T3 data\",\n", + " rtTimeHarp260NT3: \"TimeHarp260N T3 data\",\n", + " rtTimeHarp260PT3: \"TimeHarp260P T3 data\",\n", + " rtMultiHarpT3: \"MultiHarp T3 data\"}\n", + " if verbose:\n", + " log.debug(\"import_ptu: %s\", printdict[recordType])\n", + " if outputfilepath is not None:\n", + " outputfile.write(\"{}\\n\".format(printdict[recordType]))\n", + " outputfile.write(\"\\nrecord# chan nsync truetime/ns dtime\\n\")\n", + " readHT3(2, f, numRecords, globRes, resolution, isT2=False,\n", + " verbose=verbose, outputfile=outputfile)\n", + " else:\n", + " readHT3(2, f, numRecords, globRes, resolution, isT2=False,\n", + " verbose=verbose, outdict=outdict)\n", + " else:\n", + " raise ValueError('ERROR: Unknown record type')\n", + "\n", + " if outputfilepath is not None:\n", + " outputfile.close()\n", + " return out, tagDataList, numRecords, globRes\n", + "\n", + "\n", + "def gotOverflow(count, recNum, outdict=None, outputfile=None):\n", + " if outputfile is not None:\n", + " outputfile.write('{} OFL * {:2x}\\n'.format(recNum, count))\n", + " else:\n", + " outdict['trueTimeArr'][recNum] = ('OFL', count)\n", + " outdict['dTimeArr'][recNum] = ('OFL', count)\n", + " outdict['chanArr'][recNum] = ('OFL', count)\n", + "\n", + "\n", + "def gotMarker(timeTag, markers, recNum, outdict=None, outputfile=None):\n", + " if outputfile is not None:\n", + " outputfile.write('{} MAR {:2x} {}\\n'.format(recNum, markers, timeTag))\n", + " else:\n", + " outdict['trueTimeArr'][recNum] = ('MAR', markers, timeTag)\n", + " outdict['dTimeArr'][recNum] = ('MAR', markers, timeTag)\n", + " outdict['chanArr'][recNum] = ('MAR', markers, timeTag)\n", + "\n", + "\n", + "def gotPhoton(timeTag,\n", + " channel,\n", + " dtime,\n", + " isT2,\n", + " recNum,\n", + " globRes,\n", + " outdict=None,\n", + " outputfile=None):\n", + " if isT2:\n", + " truetime = timeTag * globRes * 1e12\n", + " if outputfile is not None:\n", + " outputfile.write('{} CHN {:1x} {} {:8.0f}\\n'.format(\n", + " recNum, channel, timeTag, truetime))\n", + " else:\n", + " outdict['trueTimeArr'][recNum] = truetime\n", + " # picoquant demo code does not save out dtime\n", + " # - but for lifetime analysis, it is needed\n", + " outdict['dTimeArr'][recNum] = dtime\n", + " outdict['chanArr'][recNum] = channel\n", + " else:\n", + " truetime = timeTag * globRes * 1e9\n", + " if outputfile is not None:\n", + " outputfile.write('{} CHN {:1x} {} {:8.0f} {:10}\\n'.format(\n", + " recNum, channel, timeTag, truetime, dtime))\n", + " else:\n", + " outdict['trueTimeArr'][recNum] = truetime\n", + " outdict['dTimeArr'][recNum] = dtime\n", + " outdict['chanArr'][recNum] = channel\n", + "\n", + "\n", + "def readPT2(f,\n", + " numRecords,\n", + " globRes,\n", + " isT2,\n", + " outdict=None,\n", + " outputfile=None,\n", + " verbose=False):\n", + " T2WRAPAROUND = 210698240\n", + " oflcorrection = 0\n", + " for recNum in range(0, numRecords):\n", + " try:\n", + " recordData = \"{0:0{1}b}\".format(\n", + " struct.unpack(\" 4: # Should not occur\n", + " log.debug(\"import_ptu: Illegal Channel: #%1d %1u\",\n", + " recNum, channel)\n", + " if outputfile is not None:\n", + " outputfile.write(\"\\nIllegal channel \")\n", + " truetime = oflcorrection + dtime\n", + " gotPhoton(truetime, channel, dtime, isT2, recNum, globRes, outdict,\n", + " outputfile)\n", + " if recNum % 1_000_000 == 0:\n", + " if verbose:\n", + " log.debug(\"import_ptu: Progress: %.1f%%\",\n", + " float(recNum) * 100 / float(numRecords))\n", + "\n", + " # FIXME: Not sure why globRes is the output here, and resolution in other\n", + " # functions. Should be double-checked later.\n", + " outdict[\"resolution\"] = globRes * 1e6\n", + "\n", + "\n", + "def readPT3(f,\n", + " numRecords,\n", + " globRes,\n", + " resolution,\n", + " isT2,\n", + " outdict=None,\n", + " outputfile=None,\n", + " verbose=False):\n", + " oflcorrection = 0\n", + " dlen = 0\n", + " T3WRAPAROUND = 65536\n", + " for recNum in range(0, numRecords):\n", + " # The data is stored in 32 bits that need to be divided into\n", + " # smaller groups of bits, with each group of bits representing a\n", + " # different variable. In this case, channel, dtime and nsync. This\n", + " # can easily be achieved by converting the 32 bits to a string,\n", + " # dividing the groups with simple array slicing, and then\n", + " # converting back into the integers.\n", + " try:\n", + " recordData = \"{0:0{1}b}\".format(\n", + " struct.unpack(\" 4: # Should not occur\n", + " log.debug(\"import_ptu: Illegal Channel: #%1d %1u\",\n", + " dlen, channel)\n", + " if outputfile is not None:\n", + " outputfile.write(\"\\nIllegal channel \")\n", + " truensync = oflcorrection + nsync\n", + " gotPhoton(truensync, channel, dtime, isT2, recNum, globRes,\n", + " outdict, outputfile)\n", + " dlen += 1\n", + " if recNum % 1_000_000 == 0:\n", + " if verbose:\n", + " log.debug(\"import_ptu: Progress: %.1f%%\",\n", + " float(recNum) * 100 / float(numRecords))\n", + " outdict[\"resolution\"] = resolution * 1e9\n", + "\n", + "\n", + "def readHT2(version,\n", + " f,\n", + " numRecords,\n", + " globRes,\n", + " isT2,\n", + " outdict=None,\n", + " outputfile=None,\n", + " verbose=False):\n", + " T2WRAPAROUND_V1 = 33552000\n", + " T2WRAPAROUND_V2 = 33554432\n", + " oflcorrection = 0\n", + " for recNum in range(0, numRecords):\n", + " try:\n", + " recordData = \"{0:0{1}b}\".format(\n", + " struct.unpack(\"= NcascStart:\n", + " # Old method\n", + " # i1= np.in1d(y,y+lag,assume_unique=True)\n", + " # i2= np.in1d(y+lag,y,assume_unique=True)\n", + " # New method, cython\n", + " i1, i2 = dividAndConquer(y, y + lag, y.shape[0] + 1)\n", + " # If the weights (num) are one as in the first Ncasc round,\n", + " # then the correlation is equal to np.sum(i1)\n", + " i1 = np.where(i1.astype(bool))[0]\n", + " i2 = np.where(i2.astype(bool))[0]\n", + " # Now we want to weight each photon correctly.\n", + " # Faster dot product method, faster than converting to matrix.\n", + " if i1.size and i2.size:\n", + " jin = np.dot((num[i1, :]).T, num[i2, :]) / delta\n", + " auto[(k + (j) * Nsub), :, :] = jin\n", + " # log.debug(f'tttr2xfcs: finished Nsub {k} of Ncasc {j}')\n", + " autotime[k + (j) * Nsub] = shift\n", + "\n", + " # Equivalent to matlab round when numbers are %.5\n", + " y = np.ceil(np.array(0.5 * y))\n", + " delta = 2 * delta\n", + "\n", + " for j in range(0, auto.shape[0]):\n", + " auto[j, :, :] = auto[j, :, :] * dt / (dt - autotime[j])\n", + " # FIXME: why divided by 1000000, is this related to self.timeSeriesDividend?\n", + " autotime = autotime / 1000000\n", + "\n", + " # Removes the trailing zeros.\n", + " idauto = np.where(autotime != 0)[0]\n", + " autotime = autotime[idauto]\n", + " auto = auto[idauto, :, :]\n", + " return auto, autotime\n", + "\n", + "\n", + "def time2bin(time_arr: Union[np.ndarray, List],\n", + " chan_arr: Union[np.ndarray, List],\n", + " chan_num: int,\n", + " win_int: Union[int, float]):\n", + " \"\"\"A binning method for arrival times (=photon time trace) or for\n", + " lifetimes (=decay scale)\n", + "\n", + " Parameters\n", + " ----------\n", + " time_arr : np.array or list\n", + " arrival times or lifetimes to bin\n", + " chan_arr : np.array or list\n", + " the channel for each photon arrival time or lifetime in time_arr\n", + " chan_num : int\n", + " which channel to choose in chan_arr\n", + " win_int : int\n", + " binning window\n", + "\n", + " Returns\n", + " -------\n", + " photons_in_bin : list\n", + " list of amount of photons in arrival time / lifetime bin\n", + " bins_scale : list\n", + " the centers of the bins corresponding to photons_in_bin\n", + " \"\"\"\n", + " time_arr = np.array(time_arr)\n", + " # This is the point and which each channel is identified.\n", + " time_ch = time_arr[chan_arr == chan_num]\n", + " # Find the first and last entry\n", + " first_time = 0 # np.min(time_ch).astype(np.int32)\n", + " tmp_last_time = np.max(time_ch).astype(np.int32)\n", + " # We floor this as the last bin is always incomplete and so we discard\n", + " # photons.\n", + " num_bins = np.floor((tmp_last_time - first_time) / win_int)\n", + " last_time = num_bins * win_int\n", + " bins = np.linspace(first_time, last_time, int(num_bins) + 1)\n", + " photons_in_bin, _ = np.histogram(time_ch, bins)\n", + " # bins are valued as half their span.\n", + " bins_scale = bins[:-1] + (win_int / 2)\n", + " # bins_scale = np.arange(0,decayTimeCh.shape[0])\n", + " log.debug('Finished time2bin. last_time=%s, num_bins=%s', last_time,\n", + " num_bins)\n", + " return np.array(photons_in_bin), np.array(bins_scale)\n", + "\n", + "\n", + "# ------------------------- MY CODE ------------------------\n", + "def array_safe_eq(a, b) -> bool:\n", + " \"\"\"Check if a and b are equal, even if they are numpy arrays\n", + "\n", + " from https://stackoverflow.com/questions/51743827/how-to-compare-equality-of-dataclasses-holding-numpy-ndarray-boola-b-raises\n", + " \"\"\"\n", + " if a is b:\n", + " return True\n", + " if isinstance(a, np.ndarray) and isinstance(b, np.ndarray):\n", + " return a.shape == b.shape and (a == b).all()\n", + " try:\n", + " return a == b\n", + " except TypeError:\n", + " return NotImplemented\n", + "\n", + "\n", + "def dc_eq(dc1, dc2) -> bool:\n", + " \"\"\"checks if two dataclasses which hold numpy arrays are equal\n", + "\n", + " from https://stackoverflow.com/questions/51743827/how-to-compare-equality-of-dataclasses-holding-numpy-ndarray-boola-b-raises\n", + " \"\"\"\n", + " if dc1 is dc2:\n", + " return True\n", + " if dc1.__class__ is not dc2.__class__:\n", + " return NotImplemented # better than False\n", + " t1 = astuple(dc1)\n", + " t2 = astuple(dc2)\n", + " return all(array_safe_eq(a1, a2) for a1, a2 in zip(t1, t2))\n", + "\n", + "\n", + "@dataclass\n", + "class FCSPhotonDecay:\n", + " name: str\n", + " channel: int\n", + " bin: float\n", + " original: np.ndarray = field(default=np.zeros(1), compare=False)\n", + " scale: np.ndarray = field(default=np.zeros(1), compare=False)\n", + " no_offset: Union[np.ndarray, None] = field(default=None, compare=False)\n", + " normalized: Union[np.ndarray, None] = field(default=None, compare=False)\n", + " processing_prediction: Literal['none', 'threshold', 'unet'] = 'none'\n", + " processing_scaling: Literal['none', 'standard', 'robust', 'maxabs', 'quant_g',\n", + " 'minmax', 'l1', 'l2'] = 'none'\n", + " processing_correction: Literal['none', 'set_to_zero', 'cut_and_stitch',\n", + " 'averaging', 'random_weights', '1-pred_weights',\n", + " 'constant_weight'] = 'none'\n", + "\n", + "\n", + "@dataclass\n", + "class FCSCorrelation:\n", + " name: str\n", + " method: Literal['tttr2xfcs', 'multipletau']\n", + " channel1: int\n", + " channel2: int\n", + " count1: int = field(default=0, compare=False)\n", + " count2: int = field(default=0, compare=False)\n", + " kcount: Union[int, float, None] = None\n", + " brightness_nandb: Union[int, float, None] = None\n", + " number_nandb: Union[int, float, None] = None\n", + " autotime: np.ndarray = field(default=np.zeros(1), compare=False)\n", + " autonorm: np.ndarray = field(default=np.zeros(1), compare=False)\n", + " processing_prediction: Literal['none', 'threshold', 'unet'] = 'none'\n", + " processing_scaling: Literal['none', 'standard', 'robust', 'maxabs', 'quant_g',\n", + " 'minmax', 'l1', 'l2'] = 'none'\n", + " processing_correction: Literal['none', 'set_to_zero', 'cut_and_stitch',\n", + " 'averaging', 'random_weights', '1-pred_weights',\n", + " 'constant_weight'] = 'none'\n", + "\n", + "\n", + "@dataclass(eq=False)\n", + "class FCSTimeSeries:\n", + " \"\"\"Holds exactly one FCS time-series including \"\"\"\n", + " name: str\n", + " channel: int\n", + " bin: float\n", + " size: int\n", + " kcount: Union[int, float, None]\n", + " brightness_nandb: Union[int, float, None]\n", + " number_nandb: Union[int, float, None]\n", + " trace: np.ndarray\n", + " scale: np.ndarray\n", + " prediction_method: Literal['none', 'threshold', 'unet'] = 'none'\n", + " scaling_method: Literal['none', 'standard', 'robust', 'maxabs', 'quant_g',\n", + " 'minmax', 'l1', 'l2'] = 'none'\n", + " correction_method: Literal['none', 'set_to_zero', 'cut_and_stitch', 'averaging',\n", + " 'random_weights', '1-pred_weights',\n", + " 'constant_weight'] = 'none'\n", + " correlation: Union[FCSCorrelation, None] = None\n", + "\n", + " def __eq__(self, other):\n", + " return dc_eq(self, other)\n", + "\n", + "\n", + "@dataclass\n", + "class ProcessedFCSTimeSeries:\n", + " name: str\n", + " channel: int\n", + " bin: float\n", + " uuid: str\n", + " pred_thresh: Union[float, None] = None\n", + " record: Dict[Literal['original', 'preprocessed', 'predictions',\n", + " 'set_to_zero', 'cut_and_stitch', 'averaging',\n", + " 'random_weights', '1-pred_weights', 'constant_weight'],\n", + " FCSTimeSeries] = field(default_factory=dict, compare=False)\n", + "\n", + "\n", + "@dataclass\n", + "class TCSPC:\n", + " name: str\n", + " resolution: float\n", + " glob_res: float\n", + " ptu_tags: List[Tuple[str, Any]]\n", + " ptu_num_records: int\n", + " ch_present: np.ndarray = field(init=False, compare=False)\n", + " num_ch: int = field(init=False)\n", + " channels: np.ndarray = field(compare=False)\n", + " macrotimes: np.ndarray = field(metadata={'unit': 'ms'}, compare=False)\n", + " microtimes: np.ndarray = field(metadata={'unit': 'ns'}, compare=False)\n", + " kcount: Union[int, float, None] = None\n", + " brightness_nandb: Union[int, float, None] = None\n", + " number_nandb: Union[int, float, None] = None\n", + " weights: Union[np.ndarray, None] = field(default=None, compare=False)\n", + " channels_parts: Union[List[np.ndarray], None] = field(default=None, compare=False)\n", + " macrotimes_parts: Union[List[np.ndarray], None] = field(default=None, compare=False)\n", + " processing_prediction: Literal['none', 'threshold', 'unet'] = 'none'\n", + " processing_scaling: Literal['none', 'standard', 'robust', 'maxabs', 'quant_g',\n", + " 'minmax', 'l1', 'l2'] = 'none'\n", + " processing_correction: Literal['none', 'set_to_zero', 'cut_and_stitch',\n", + " 'averaging', 'random_weights', '1-pred_weights',\n", + " 'constant_weight'] = 'none'\n", + " processing_bin: Union[float, None] = None\n", + " processing_pred_thresh: Union[float, None] = None\n", + " correlations: List = field(default_factory=list, compare=False)\n", + " photon_count_decays: List = field(default_factory=list, compare=False)\n", + "\n", + " def __post_init__(self):\n", + " # How many channels there are in the files.\n", + " ch_present = np.sort(np.unique(self.channels))\n", + " num_ch = len(ch_present)\n", + " for i in range(num_ch - 1, -1, -1):\n", + " if ch_present[i] > 8:\n", + " ch_present = np.delete(ch_present, i)\n", + "\n", + " log.debug('TCSPC: this file has %s channel(s): %s',\n", + " num_ch, ch_present)\n", + " self.ch_present = ch_present\n", + " self.num_ch = num_ch\n", + "\n", + "\n", + "class PicoObject():\n", + " \"\"\"Load Fluorescence Correlation Spectroscopy files, predict and correct\n", + " artifacts, and autocorrelate the data. Written by Alex Seltmann 2021.\n", + "\n", + " Loading and correlation adapted from Dominic Waithe's FCS Bulk Correlation\n", + " Software FOCUSpoint Copyright (C) 2015 Dominic Waithe\n", + "\n", + " Returns\n", + " -------\n", + " subChanArr : list of int\n", + " Number of channel of arrival for each photon\n", + " trueTimeArr : list of float\n", + " the macro time in ns (absolute time when each photon arrived)\n", + " dtimeArr : list of ...\n", + " the micro time in ns (lifetime of each photon). By default this\n", + " is not saved out.\n", + " resolution : float\n", + " Time resolution in seconds? (e.g. 1.6e-5 means 160 ms(???))\n", + "\n", + " Notes\n", + " -----\n", + " - currently only tested for .ptu files, but Dominic Waithe's FoCuS-point\n", + " supports asc, dat, fcs, pt2, pt3, csv, and spc. With some testing of\n", + " the FoCuS-point import functions, it should be possible to adapt this\n", + " code. Pull requests are welcome (https://github.com/aseltmann/fluotracify)\n", + " - the original FocUs-point code supported bulk correlation. Currently,\n", + " this is not tested, contributions and tests are welcome.\n", + " - the original FocUs-point code does a cross-correlation, if there are\n", + " 2 or more channels with data. I left these parts of the code mostly\n", + " untouched, but it is not tested. Contributions and tests are welcome.\n", + "\n", + " This program is free software; you can redistribute it and/or modify\n", + " it under the terms of the GNU General Public License as published by\n", + " the Free Software Foundation; either version 2 of the License, or\n", + " any later version.\n", + "\n", + " This program is distributed in the hope that it will be useful,\n", + " but WITHOUT ANY WARRANTY; without even the implied warranty of\n", + " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n", + " GNU General Public License for more details.\n", + "\n", + " You should have received a copy of the GNU General Public License along\n", + " with this program; if not, write to the Free Software Foundation, Inc.,\n", + " 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n", + " \"\"\"\n", + " def __init__(self,\n", + " input_file: Union[str, Path],\n", + " ncasc_start: int,\n", + " ncasc_end: int,\n", + " nsub: int,\n", + " photon_lifetime_bin: Union[int, float],\n", + " photon_count_bin: Union[int, float]):\n", + " # parameter object and fit object.\n", + " log.debug('PicoObject: Start CorrObj creation.')\n", + "\n", + " self.filepath = Path(input_file)\n", + " self.name = self.filepath.stem\n", + " self.ext = self.filepath.suffix\n", + "\n", + " self.ncasc_start = ncasc_start\n", + " self.ncasc_end = ncasc_end\n", + " self.nsub = nsub\n", + "\n", + " # for easier numerical computation, macrotimes are converted from\n", + " # nanoseconds to milliseconds in the method get_time_series()\n", + " self.time_series_dividend = 1000000\n", + "\n", + " # used for photon decay\n", + " self.photon_lifetime_bin = photon_lifetime_bin\n", + " self.photon_count_bin = photon_count_bin\n", + "\n", + " self.processed_time_series = [] # list of all ProcessedFCSTimeSeries dataclasses\n", + " self.processed_tcspc = [] # list of all processed TCSPC dataclasses\n", + "\n", + " self.import_data()\n", + " self.get_photon_decay()\n", + " self.get_time_series()\n", + " # self.get_cross_and_auto_correlation()\n", + "\n", + " def import_data(self):\n", + " # file import\n", + " # key = f'{self.name}'\n", + " # if self.ext == '.spc':\n", + " # (self.subChanArr[key], self.trueTimeArr[key], self.dTimeArr,\n", + " # self.resolution) = spc_file_import(self.filepath)\n", + " # elif self.ext == '.asc':\n", + " # (self.subChanArr[key], self.trueTimeArr[key], self.dTimeArr,\n", + " # self.resolution) = asc_file_import(self.filepath)\n", + " # elif self.ext == '.pt2':\n", + " # (self.subChanArr[key], self.trueTimeArr[key], self.dTimeArr,\n", + " # self.resolution) = pt2import(self.filepath)\n", + " # elif self.ext == '.pt3':\n", + " # (self.subChanArr[key], self.trueTimeArr[key], self.dTimeArr,\n", + " # self.resolution) = pt3import(self.filepath)\n", + " if self.filepath.suffix == '.ptu':\n", + " out, ptu_tags, ptu_num_records, glob_res = import_ptu(self.filepath)\n", + " if out is not False:\n", + " (subChanArr, trueTimeArr, dTimeArr,\n", + " resolution) = (out[\"chanArr\"], out[\"trueTimeArr\"],\n", + " out[\"dTimeArr\"], out[\"resolution\"])\n", + " # Remove Overflow and Markers; they are not handled at the\n", + " # moment.\n", + " subChanArr = np.array(\n", + " [i for i in subChanArr if not isinstance(i, tuple)])\n", + " trueTimeArr = np.array(\n", + " [i for i in trueTimeArr if not isinstance(i, tuple)])\n", + " dTimeArr = np.array(\n", + " [i for i in dTimeArr if not isinstance(i, tuple)])\n", + "\n", + " self.original_tcspc = TCSPC(\n", + " name=self.name, resolution=resolution, glob_res=glob_res,\n", + " ptu_tags=ptu_tags, ptu_num_records=ptu_num_records,\n", + " channels=subChanArr, macrotimes=trueTimeArr,\n", + " microtimes=dTimeArr)\n", + "\n", + " else:\n", + " self.exit = True\n", + " # elif self.ext == '.csv':\n", + " # (self.subChanArr[key], self.trueTimeArr[key], self.dTimeArr,\n", + " # self.resolution) = csvimport(self.filepath)\n", + " # # If the file is empty.\n", + " # if self.subChanArr[key] is None:\n", + " # # Undoes any preparation of resource.\n", + " # self.exit = True\n", + " else:\n", + " self.exit = True\n", + " log.debug('Finished import.')\n", + "\n", + " def get_photon_decay(self,\n", + " photon_lifetime_bin: Union[int, float, None] = None,\n", + " tcspc: Union[TCSPC, None] = None):\n", + " \"\"\"Gets photon decay curve from TCSPC data, specifically lifetimes\n", + "\n", + " Parameters\n", + " ----------\n", + " photon_lifetime_bin : int\n", + " bin for calculation of photon decay. Photon lifetimes in the\n", + " time interval x to x+photon_lifetime_bin are aggregated\n", + " name : optional, str\n", + " The photon decay and scale are added to self via a dictionary with\n", + " the key \"name\". If None, the current time is taken as a key.\n", + "\n", + " Returns\n", + " -------\n", + " Nothing, but assigns to self:\n", + " self.photonDecay : dict of dict of list\n", + " - 1st dict a wrapper from name\n", + " - 2nd dict for each given channel:\n", + " - the original list of binned lifetimes\n", + " - the list of binned lifetimes, substracted by the minimum\n", + " lifetime\n", + " - the list of binned lifetimes, minmax-normalized\n", + " self.decayScale : dict of dict of list\n", + " - 1st dict a wrapper from name\n", + " - 2nd dict for each given channel:\n", + " - the list of the centered value for each bin\n", + " \"\"\"\n", + " if photon_lifetime_bin is None:\n", + " photon_lifetime_bin = self.photon_lifetime_bin\n", + " tcspc = self.original_tcspc if tcspc is None else tcspc\n", + " if not isinstance(tcspc, TCSPC):\n", + " raise TypeError('tcspc should be an instance of the TCSPC dataclass.')\n", + "\n", + " for i in range(tcspc.num_ch):\n", + " photon_decay, decay_scale = time2bin(\n", + " time_arr=tcspc.microtimes,\n", + " chan_arr=tcspc.channels,\n", + " chan_num=tcspc.ch_present[i],\n", + " win_int=photon_lifetime_bin)\n", + "\n", + " decay = FCSPhotonDecay(\n", + " name=tcspc.name, channel=tcspc.ch_present[i], bin=photon_lifetime_bin,\n", + " original=np.array(photon_decay), scale=decay_scale,\n", + " processing_prediction=tcspc.processing_prediction,\n", + " processing_scaling=tcspc.processing_scaling,\n", + " processing_correction=tcspc.processing_correction)\n", + "\n", + " # Normalisation of the decay functions.\n", + " if np.sum(decay.original) > 0:\n", + " decay.no_offset = decay.original - np.min(decay.original)\n", + " decay.normalized = decay.no_offset / np.max(decay.no_offset)\n", + "\n", + " if decay in tcspc.photon_count_decays:\n", + " log.debug('get_photon_decay: did not save photon decay with name'\n", + " ' %s, channel %s, bin %s because it already is in '\n", + " 'PicoObject.photon_count_decays', tcspc.name,\n", + " tcspc.ch_present[i], photon_lifetime_bin)\n", + " else:\n", + " tcspc.photon_count_decays.append(decay)\n", + " log.debug('get_photon_decay: saved name %s, channel %s, bin %s',\n", + " tcspc.name, tcspc.ch_present[i], photon_lifetime_bin)\n", + "\n", + " def get_time_series(self,\n", + " photon_count_bin: Optional[float] = None,\n", + " tcspc: Optional[TCSPC] = None,\n", + " processing: Literal['original', 'preprocessed',\n", + " 'predictions', 'correction'] = 'original',\n", + " name: Optional[str] = None):\n", + " \"\"\"Gets time-series from TCSPC data, specifically photon arrival times\n", + "\n", + " Parameters\n", + " ----------\n", + " photon_count_bin : optional, int\n", + " bin for calculation of time-series. Photons arriving in the\n", + " time interval x to x+photonCountBin are aggregated\n", + " truetime_name : optional, str\n", + " the key to self.trueTimeArr which determines the photon arrival\n", + " times to use for the construction of the time-series\n", + " timeseries_name : optional, str\n", + " The time-series and time-series scale are added to self via a\n", + " dictionary with the key \"name\". If None, self.name is taken\n", + " as the key.\n", + " \"\"\"\n", + " # update if method is called again with new parameters\n", + " if photon_count_bin is not None:\n", + " self.photon_count_bin = float(photon_count_bin)\n", + " name = f'{self.name}' if name is None else f'{name}'\n", + " tcspc = self.original_tcspc if tcspc is None else tcspc\n", + " uuid = tcspc.ptu_tags[0][1]\n", + " if not isinstance(tcspc, TCSPC):\n", + " raise TypeError('get_time_series: tcspc should be an instance of the'\n", + " ' TCSPC dataclass and not %s', type(tcspc))\n", + "\n", + " for i in range(tcspc.num_ch):\n", + " temp_proc = ProcessedFCSTimeSeries(\n", + " name=name, channel=tcspc.ch_present[i], uuid=uuid,\n", + " bin=self.photon_count_bin)\n", + " if self.processed_time_series == []:\n", + " self.processed_time_series.append(temp_proc)\n", + " proc_idx = -1\n", + "\n", + " for i, rec in enumerate(self.processed_time_series):\n", + " if rec == temp_proc:\n", + " if processing in rec.record.keys():\n", + " log.debug('get_time_series: skipping time-series creation'\n", + " 'with name %s, channel %s, bin %s, processing %s'\n", + " ' because it already exists in PicoObject.processed'\n", + " '_time_traces', name, tcspc.ch_present[i],\n", + " self.photon_count_bin, processing)\n", + " continue\n", + " else:\n", + " proc_idx = i\n", + " else:\n", + " self.processed_time_series.append(temp_proc)\n", + " proc_idx = -1\n", + "\n", + " time_series, time_series_scale = time2bin(\n", + " time_arr=tcspc.macrotimes / self.time_series_dividend,\n", + " chan_arr=tcspc.channels,\n", + " chan_num=tcspc.ch_present[i],\n", + " win_int=self.photon_count_bin)\n", + "\n", + " kcount, brightness_nandb, number_nandb = photon_counting_stats(\n", + " time_series, time_series_scale)\n", + "\n", + " new_rec = FCSTimeSeries(name=name, channel=tcspc.ch_present[i],\n", + " bin=self.photon_count_bin, size=time_series.size,\n", + " kcount=kcount, brightness_nandb=brightness_nandb,\n", + " number_nandb=number_nandb,\n", + " trace=time_series, scale=time_series_scale)\n", + "\n", + " self.processed_time_series[proc_idx].record[processing] = new_rec\n", + " log.debug('get_time_series: finished time-series creation'\n", + " 'with name %s, channel %s, bin %s, processing %s, indexed'\n", + " 'PicoObject.processed_time_traces[ %s ]', name, tcspc.ch_present[i],\n", + " self.photon_count_bin, processing, proc_idx)\n", + "\n", + " def get_cross_and_auto_correlation(self,\n", + " name: Union[str, None] = None,\n", + " tcspc: Union[TCSPC, None] = None):\n", + " \"\"\"Gets autocorrelation of photons and crosscorrelation if there are\n", + " more than 1 Channel.\n", + "\n", + " Parameters\n", + " ----------\n", + " name : str\n", + " \"\"\"\n", + " name = f'{self.name}' if name is None else f'{name}'\n", + " tcspc = self.original_tcspc if tcspc is None else tcspc\n", + " if not isinstance(tcspc, TCSPC):\n", + " raise TypeError('get_cross_and_auto_correlation: tcspc should be an '\n", + " 'instance of the TCSPC dataclass and not %s', type(tcspc))\n", + "\n", + " # Correlation combinations.\n", + " # Provides ordering of files and reduces repetition.\n", + " corr_array = []\n", + " corr_comb = []\n", + " for i in range(tcspc.num_ch):\n", + " corr_array.append([])\n", + " for j in range(tcspc.num_ch):\n", + " if i < j:\n", + " corr_comb.append([i, j])\n", + " corr_array[i].append([])\n", + "\n", + " for i, j in corr_comb:\n", + " log.debug('get_cross_and_auto_correlation: Starting with channel %s and channel %s',\n", + " tcspc.ch_present[i], tcspc.ch_present[j])\n", + "\n", + " out = self._cross_and_auto(\n", + " true_time_arr=tcspc.macrotimes,\n", + " sub_chan_arr=tcspc.channels,\n", + " channels_to_use=(tcspc.ch_present[i], tcspc.ch_present[j]),\n", + " name=name)\n", + "\n", + " if tcspc.num_ch == 1:\n", + " log.debug('get_cross_and_auto_correlation: Starting with channel %s and channel %s',\n", + " tcspc.ch_present[i], tcspc.ch_present[j])\n", + " # FIXME: What is i and j here??\n", + " out = self._cross_and_auto(\n", + " true_time_arr=tcspc.macrotimes,\n", + " sub_chan_arr=tcspc.channels,\n", + " channels_to_use=(tcspc.ch_present[i], tcspc.ch_present[j]),\n", + " name=name)\n", + "\n", + " tcspc.correlations.extend(out)\n", + " log.debug('get_cross_and_auto_correlation: Finished '\n", + " f'{tcspc.processing_correction=}.')\n", + "\n", + " def _cross_and_auto(self,\n", + " true_time_arr: np.ndarray,\n", + " sub_chan_arr: np.ndarray,\n", + " channels_to_use: Tuple[int, int],\n", + " name: Union[str, None] = None) -> List[FCSCorrelation]:\n", + " # For each channel we loop through and find only those in the correct\n", + " # time gate.\n", + " # We only want photons in channel 1 or two.\n", + " name = f'{self.name}' if name is None else f'{name}'\n", + " method = 'tttr2xfcs'\n", + " num_ch = len(set(channels_to_use))\n", + " if num_ch == 1:\n", + " indices = sub_chan_arr == channels_to_use[0]\n", + " y = true_time_arr[indices]\n", + " valid_photons = sub_chan_arr[indices]\n", + " else:\n", + " indices0 = sub_chan_arr == channels_to_use[0]\n", + " indices1 = sub_chan_arr == channels_to_use[1]\n", + " indices = indices0 + indices1\n", + " y = true_time_arr[indices]\n", + " valid_photons = sub_chan_arr[indices]\n", + "\n", + " log.debug('_cross_and_auto: sum(indeces)=%s', sum(indices))\n", + "\n", + " # Creates boolean for photon events in either channel.\n", + " num = np.zeros((valid_photons.shape[0], 2))\n", + " num[:, 0] = (np.array([np.array(valid_photons) == channels_to_use[0]\n", + " ])).astype(np.int32)\n", + " if num_ch > 1:\n", + " num[:, 1] = (np.array([np.array(valid_photons) == channels_to_use[1]\n", + " ])).astype(np.int32)\n", + "\n", + " count0 = np.sum(num[:, 0])\n", + " count1 = np.sum(num[:, 1])\n", + " log.debug('_cross_and_auto: finished preparation.')\n", + "\n", + " auto, autotime = tttr2xfcs(\n", + " y, num, self.ncasc_start, self.ncasc_end, self.nsub)\n", + " log.debug('_cross_and_auto: finished tttr2xfcs().')\n", + " autotime = autotime.flatten()\n", + "\n", + " # Normalisation of the TCSPC data:\n", + " max_y = np.ceil(np.max(true_time_arr))\n", + "\n", + " out = []\n", + " corr00 = ((auto[:, 0, 0] * max_y) / (count0 * count0)) - 1\n", + " out.append(FCSCorrelation(\n", + " name=name, method=method, channel1=channels_to_use[0],\n", + " channel2=channels_to_use[0], count1=count0, count2=count0,\n", + " autotime=autotime, autonorm=corr00))\n", + "\n", + " if num_ch > 1:\n", + " corr11 = ((auto[:, 1, 1] * max_y) / (count1 * count1)) - 1\n", + " corr10 = ((auto[:, 1, 0] * max_y) / (count1 * count0)) - 1\n", + " corr01 = ((auto[:, 0, 1] * max_y) / (count0 * count1)) - 1\n", + " out.append(FCSCorrelation(\n", + " name=name, method=method, channel1=channels_to_use[1],\n", + " channel2=channels_to_use[1], count1=count1, count2=count1,\n", + " autotime=autotime, autonorm=corr11))\n", + " out.append(FCSCorrelation(\n", + " name=name, method=method, channel1=channels_to_use[1],\n", + " channel2=channels_to_use[0], count1=count1, count2=count0,\n", + " autotime=autotime, autonorm=corr10))\n", + " out.append(FCSCorrelation(\n", + " name=name, method=method, channel1=channels_to_use[0],\n", + " channel2=channels_to_use[1], count1=count0, count2=count1,\n", + " autotime=autotime, autonorm=corr01))\n", + " log.debug('_cross_and_auto() Finished.')\n", + "\n", + " return out\n", + "\n", + " def _auto(self,\n", + " macrotimes: np.ndarray,\n", + " channels: np.ndarray,\n", + " channel_to_use: int,\n", + " name: Union[str, None] = None) -> FCSCorrelation:\n", + " name = f'{self.name}' if name is None else f'{name}'\n", + " method = 'tttr2xfcs'\n", + " indices = channels == channel_to_use\n", + " counts = np.sum(indices)\n", + " y = macrotimes[indices]\n", + " log.debug('_auto: using channel %s with %s photons',\n", + " channel_to_use, counts)\n", + " num = np.zeros((indices.shape[0], 2))\n", + " num[:, 0] = indices.astype(np.int32)\n", + " auto, autotime = tttr2xfcs(\n", + " y=y, num=num, NcascStart=self.ncasc_start,\n", + " NcascEnd=self.ncasc_end, Nsub=self.nsub)\n", + "\n", + " log.debug('_auto: finished tttr2xfcs. Normalizing...')\n", + " # normalization\n", + " max_y = np.ceil(np.max(macrotimes))\n", + " autonorm = ((auto[:, 0, 0] * max_y) / counts**2) - 1\n", + " autonorm, autotime = autonorm.flatten(), autotime.flatten()\n", + " out = FCSCorrelation(\n", + " name=name, method=method, channel1=channel_to_use,\n", + " channel2=channel_to_use, count1=counts, count2=counts,\n", + " autotime=autotime, autonorm=autonorm\n", + " )\n", + " log.debug('_auto: Finished.')\n", + " return out\n", + "\n", + " def predict_time_series(self,\n", + " method: Literal['threshold', 'unet'],\n", + " scaler: Literal['standard', 'robust', 'maxabs',\n", + " 'quant_g', 'minmax', 'l1', 'l2'],\n", + " time_series: Union[ProcessedFCSTimeSeries, None] = None,\n", + " model: Union[Any, None] = None,\n", + " threshold: Union[int, float, None] = None,\n", + " name: Union[str, None] = None):\n", + " \"\"\"Takes a timetrace, performs preprocessing, and applies a compiled\n", + " unet for artifact detection\n", + "\n", + " Parameters\n", + " ----------\n", + " method : ('threshold', 'unet')\n", + " model : optional, tf.keras.Functional model\n", + " Needed for method='unet'\n", + " threshold : optional, int or float\n", + " Needed for method='threshold'. Every timestep above threshold is\n", + " considered artifactual (useful for e.g. peak artifacts)\n", + " scaler : ('standard', 'robust', 'maxabs', 'quant_g', 'minmax', l1',\n", + " 'l2')\n", + " Scales / normalizes the input trace. If method='threshold', the\n", + " threshold is applied to the scaled data. If method='unet', check\n", + " the scaler the unet was trained with.\n", + "\n", + " Returns\n", + " -------\n", + " Nothing, but assigns two new variables to self\n", + " self.predictions : numpy ndarray, dtype=float32, shape=(input_size,)\n", + " Predictions between 0 and 1, 0 = no artifact, 1 = artifact\n", + " self.timeSeriesPrepro : numpy ndarray, dtype=float32,\n", + " shape=(input_size,)\n", + " The preprocessed time trace scaled according to scaler, and\n", + " cropped according to input_size.\n", + "\n", + " Note\n", + " ----\n", + " - for method='threshold': robust scaling + threshold=2 seems to work\n", + " fine\n", + " - for method='unet': The input size of the model:\n", + " The prediction is made with the trace padded with the median to an\n", + " input size of at least 1024, or if bigger to the size of the next\n", + " biggest power of 2, e.g. 2**13 (8192), 2**14 (16384), ...\n", + " This is necessary to avoid tensorflow throwing a size mismatch\n", + " error. The algorithm was trained on traces with lenghts of 2**14,\n", + " the experimental test data had a length of 2**13, so these sizes\n", + " are known to work well.\n", + " \"\"\"\n", + " name = f'{self.name}' if name is None else f'{name}'\n", + " ts = self.processed_time_series[-1] if time_series is None else time_series\n", + " if not isinstance(ts, ProcessedFCSTimeSeries):\n", + " raise TypeError('predict_time_series: time_series should be an instance of the'\n", + " f' ProcessedFCSTimeSeries dataclass and not {type(ts)}')\n", + "\n", + " if method == 'threshold':\n", + " if not isinstance(threshold, (float, int)):\n", + " raise ValueError('If method=\"threshold\", the threshold'\n", + " ' parameter has to be int or float')\n", + " elif method == 'unet':\n", + " if model is None:\n", + " raise ValueError('If method=\"unet\", the model parameter has to'\n", + " ' be a compiled tf.keras.Functional model')\n", + " else:\n", + " raise ValueError('method has to be \"threshold\" or \"unet\"')\n", + "\n", + " trace = ts.record['original'].trace\n", + " trace_size = ts.record['original'].size\n", + " if method == 'unet':\n", + " if trace_size < 1024:\n", + " input_size = 1024\n", + " else:\n", + " input_size = 2**(np.ceil(np.log2(trace_size))).astype(int)\n", + " pad_size = input_size - trace_size\n", + "\n", + " # pad trace for unet input\n", + " trace = np.pad(trace, pad_width=(0, pad_size), mode='median')\n", + "\n", + " # scale trace\n", + " trace = np.reshape(trace, newshape=(-1, 1))\n", + " try:\n", + " trace = _scale_trace(trace, scaler)\n", + " except Exception as ex:\n", + " raise ValueError('predict_time_series: Scaling failed.') from ex\n", + "\n", + " # predict trace\n", + " if method == 'unet':\n", + " trace = np.reshape(trace, newshape=(1, -1, 1))\n", + " try:\n", + " predictions = model.predict(trace, verbose=0).flatten()\n", + " except Exception as ex:\n", + " raise ValueError('predict_time_series: prediction failed. Check correct'\n", + " ' input model, input size..') from ex\n", + " elif method == 'threshold':\n", + " predictions = trace.flatten() > threshold\n", + "\n", + " new_scale = np.arange(start=ts.record['original'].bin // 2,\n", + " stop=predictions.size,\n", + " step=ts.record['original'].bin)\n", + " preds = replace(ts.record['original'], size=predictions.size,\n", + " kcount=None, brightness_nandb=None, number_nandb=None,\n", + " trace=predictions, scale=new_scale,\n", + " prediction_method=method, scaling_method=scaler)\n", + " prepro = replace(ts.record['original'], size=trace.size, kcount=None,\n", + " brightness_nandb=None, number_nandb=None, trace=trace.flatten(),\n", + " scale=new_scale, scaling_method=scaler)\n", + " ts.record['predictions'] = preds\n", + " ts.record['preprocessed'] = prepro\n", + "\n", + " log.debug('Finished predictTimeSeries() with name=%s, scaler=%s, '\n", + " 'method=%s', name, scaler, method)\n", + "\n", + " def correct_TCSPC(self,\n", + " pred_thresh: float = 0.5,\n", + " method: Literal['set_to_zero', 'cut_and_stitch', 'averaging',\n", + " 'constant_weight', 'random_weights',\n", + " '1-pred_weights'] = 'cut_and_stitch',\n", + " weight: Union[float, int, None] = None,\n", + " tcspc: Union[TCSPC, None] = None,\n", + " time_series: Union[ProcessedFCSTimeSeries, None] = None):\n", + " \"\"\"Takes the artifact prediction from the time-series and removes\n", + " the artifacts in the TCSPC data\n", + "\n", + " Parameters\n", + " ----------\n", + " pred_thresh : float between 0 and 1\n", + " If prediction is lower than pred_thresh, the time step is assumed\n", + " to show 'no corruption'\n", + " method : Literal['set_to_zero', 'cut_and_stitch', 'averaging',\n", + " 'constant_weight', 'random_weights', '1-pred_weights']\n", + " 'weights' : give a weight to photons which are classified as\n", + " artifacts, see argument =weight=.\n", + " 'delete' : photons classified as artifacts are deleted and a new\n", + " dict in self.trueTimeArr is constructed with the remaining photons)\n", + " The time-series constructed from this trueTimeArr will have drops\n", + " to 0 where photons were deleted\n", + " 'delete_and_shift' : Like 'delete', but additionally adjust the\n", + " photon arrival times of all photons by shifting each photon by\n", + " the bin size which was deleted before. The time-series constructed\n", + " from this trueTimeArr will have no drops, all ends are annealed to\n", + " each other.\n", + " 'averaging' : save out each unique connected non-artifactual\n", + " segment of the trace to self.trueTimeParts and self.subChanParts.\n", + " The actual correction is done with\n", + " `get_autocorrelation(method='tttr2xfcs_with_averaging')`\n", + " weight = float | int | None\n", + " Only used if method='weights'. Photons classified as artifacts are\n", + " given this weight.\n", + " 'random': Each predicted bin gets a random weight between 0 and 1\n", + " '1-pred': Each predicted bin gets a weight of 1 - prediction\n", + " None: (or explicitly set to 0), the weight will be set to 0,\n", + " meaning the photons will not be correlated\n", + " truetime_name = optional, string\n", + " Used for getting the photon arrival times you want to correct.\n", + " If None, self.name is chosen\n", + " timeseries_name = optional, string\n", + " Used for getting the prediction which is the basis for the\n", + " correction. If None, self.name is chosen.\n", + "\n", + " Returns\n", + " -------\n", + "\n", + " Notes\n", + " -----\n", + " - TODO: currently, there is no automatic time-series creation for the\n", + " correction methods 'averaging', 'constant_weight', 'random_weights',\n", + " and '1-pred_weights', because it is not obvious how to translate the\n", + " correction to a meaningful time-series. Ideas:\n", + " - 'averaging': would probably consist of the binned parts, for each\n", + " part a FCSTimeSeries from get_time_series would have to be\n", + " constructed and they could be stored in\n", + " ProcessedFCSTimeSeries.record['averaging']: List[FCSTimeSeries]\n", + " - others: probably multiply weight of the respective bin to the value\n", + " of the bin. Have to check, if that represents tttr2xfcs weighting\n", + " correctly\n", + " \"\"\"\n", + " ts = self.processed_time_series[-1] if time_series is None else time_series\n", + " ts_append = False\n", + " if (ts.pred_thresh != pred_thresh) and (method in ['cut_and_stitch',\n", + " 'set_to_zero']):\n", + " # new ProcessedFCSTimeSeries, because a corrected trace is appended\n", + " # which has a certain prediction threshold as metadata\n", + " ts_append = True\n", + " ts = replace(ts, pred_thresh=pred_thresh)\n", + " if not isinstance(ts, ProcessedFCSTimeSeries):\n", + " raise TypeError('correct_TCSPC: time_series should be an instance of the'\n", + " ' ProcessedFCSTimeSeries dataclass and not %s', type(ts))\n", + "\n", + " if 'predictions' not in ts.record:\n", + " raise ValueError('correct_TCSPC: The provided ProcessedFCSTimeSeries'\n", + " 'does not include a record of predictions. Run '\n", + " 'predict_time_series() before correct_TCSPC()')\n", + "\n", + " tcspc = self.original_tcspc if tcspc is None else tcspc\n", + " if isinstance(tcspc, TCSPC):\n", + " tcspctest = replace(\n", + " tcspc, processing_correction=method, processing_bin=ts.bin,\n", + " processing_prediction=ts.record['predictions'].prediction_method,\n", + " processing_scaling=ts.record['predictions'].scaling_method,\n", + " processing_pred_thresh=pred_thresh)\n", + " if tcspctest in self.processed_tcspc:\n", + " log.debug('correct_TCSPC: This TCSPC record was already '\n", + " 'processed with the same parameters. It can be found '\n", + " 'in self.processed_tcspc.')\n", + " return\n", + " else:\n", + " raise TypeError('correct_TCSPC: tcspc should be an instance of the'\n", + " ' TCSPC dataclass and not %s', type(tcspc))\n", + "\n", + " if ts.uuid != tcspc.ptu_tags[0][1]:\n", + " raise ValueError('correctTCSPC: time_series and tcspc have to be '\n", + " 'from the same file (same uuid).')\n", + "\n", + " if method not in ['set_to_zero', 'cut_and_stitch', 'averaging',\n", + " 'constant_weight', 'random_weights', '1-pred_weights']:\n", + " raise ValueError(f'Invalid method: {method}')\n", + "\n", + " if method == 'constant_weight':\n", + " if not isinstance(weight, (float, int)):\n", + " raise ValueError('Weight should be a number, but is '\n", + " f'{type(weight)=}.')\n", + " else:\n", + " if weight is not None:\n", + " log.debug(f'Parameter {weight=} is set, but not used in this'\n", + " 'correction method (only in method=\"constant_weight\")'\n", + " '. It is ignored.')\n", + "\n", + " # get traces, channels, photons\n", + " trace = ts.record['original'].trace\n", + " trace_scale = ts.record['original'].scale\n", + " trace_size = ts.record['original'].size\n", + " pred = ts.record['predictions']\n", + "\n", + "\n", + " channel_mask = tcspc.channels == ts.channel\n", + " channels_corrected = tcspc.channels[channel_mask]\n", + " macrotimes_corrected = tcspc.macrotimes[channel_mask]\n", + " microtimes_corrected = tcspc.microtimes[channel_mask]\n", + "\n", + " # get prediction as time-series mask and photon arrival time mask\n", + " trace_mask = pred.trace[:trace_size] > pred_thresh\n", + " photon_mask = np.repeat(trace_mask, trace)\n", + "\n", + " # match macrotimes and channels shape to prediction\n", + " channels_corrected = channels_corrected[:photon_mask.size]\n", + " macrotimes_corrected = macrotimes_corrected[:photon_mask.size]\n", + " microtimes_corrected = microtimes_corrected[:photon_mask.size]\n", + "\n", + " log.debug('correct_TCSPC: some sizes: original %s, channel_mask %s,'\n", + " 'photon_mask %s', np.size(tcspc.channels),\n", + " np.size(channel_mask), photon_mask.size)\n", + "\n", + " if method in ['set_to_zero', 'cut_and_stitch']:\n", + " # delete photons classified as artifactual\n", + " macrotimes_corrected = np.delete(macrotimes_corrected, photon_mask)\n", + " channels_corrected = np.delete(channels_corrected, photon_mask)\n", + " microtimes_corrected = np.delete(microtimes_corrected, photon_mask)\n", + "\n", + " if method == 'cut_and_stitch':\n", + " # moves the photons as if the deleted bins never existed\n", + " idxphot = 0\n", + " for nphot, artifact in zip(trace, trace_mask):\n", + " if artifact:\n", + " macrotimes_corrected[idxphot:] -= (\n", + " ts.bin * self.time_series_dividend)\n", + " else:\n", + " idxphot += nphot\n", + " log.debug(\n", + " 'correct_TCSPC: performed \"cut_and_stitch\". Cut %s of '\n", + " '%s photons.', len(photon_mask) - len(macrotimes_corrected),\n", + " len(photon_mask))\n", + "\n", + " # calculate corresponding time-series\n", + " new_ts = np.delete(trace, trace_mask)\n", + " new_scale = np.delete(trace_scale, trace_mask)\n", + "\n", + " else:\n", + " log.debug(\n", + " 'correct_TCSPC: performed \"set_to_zero\" on %s of %s photons.',\n", + " len(photon_mask) - len(macrotimes_corrected),\n", + " len(photon_mask))\n", + "\n", + " new_ts = np.where(trace_mask == 1, 0, trace)\n", + " new_scale = trace_scale\n", + "\n", + " kcount, brightness_nandb, number_nandb = photon_counting_stats(\n", + " new_ts, new_scale)\n", + "\n", + " ts.record[method] = replace(\n", + " pred, size=new_ts.size, kcount=kcount,\n", + " brightness_nandb=brightness_nandb, number_nandb=number_nandb,\n", + " trace=new_ts, scale=new_scale, correction_method=method)\n", + "\n", + " new_tcspc = replace(\n", + " tcspc, channels=channels_corrected, processing_bin=ts.bin,\n", + " macrotimes=macrotimes_corrected, microtimes=microtimes_corrected,\n", + " processing_correction=method, processing_scaling=pred.scaling_method,\n", + " processing_prediction=pred.prediction_method, kcount=kcount,\n", + " processing_pred_thresh=pred_thresh, number_nandb=number_nandb,\n", + " brightness_nandb=brightness_nandb, correlations=[]\n", + " )\n", + "\n", + " elif method in ['constant_weight', 'random_weights', '1-pred_weights']:\n", + " if method == 'random_weights':\n", + " rng = np.random.default_rng(seed=42)\n", + " myweight = rng.uniform(size=photon_mask.shape)\n", + " elif weight == '1-pred_weights':\n", + " trace_mask = 1 - pred.trace[:trace_size]\n", + " myweight = np.repeat(trace_mask, trace)\n", + " else:\n", + " myweight = weight\n", + " photon_weights = np.zeros((channels_corrected.shape[0], 2))\n", + " # for autocorrelation, only channel [:, 0] is relevant\n", + " photon_weights[:, 0] = np.where(photon_mask == 1, myweight, 1.0)\n", + " log.debug(\n", + " 'correct_TCSPC: prepared \"%s\" correction for %s of %s photons.'\n", + " ' Correction is performed automatically by calling '\n", + " 'get_autocorrelation()', method,\n", + " len(photon_mask) - np.sum(photon_mask), len(photon_mask))\n", + "\n", + " new_tcspc = replace(\n", + " tcspc, channels=channels_corrected, macrotimes=macrotimes_corrected,\n", + " weights=photon_weights, processing_prediction=pred.prediction_method,\n", + " processing_scaling=pred.scaling_method, processing_correction=method,\n", + " processing_bin=ts.bin, microtimes=microtimes_corrected,\n", + " correlations=[]\n", + " )\n", + "\n", + " elif method == 'averaging':\n", + " parts_label = scipy.ndimage.label(~photon_mask)\n", + " macrotimes_parts, channels_parts = [], []\n", + " for u in np.unique(parts_label[0]):\n", + " if u == 0:\n", + " continue\n", + " macrotimes_part = np.where(\n", + " parts_label[0] == u, macrotimes_corrected, np.nan)\n", + " channels_part = np.where(\n", + " parts_label[0] == u, channels_corrected, np.nan)\n", + " macrotimes_part = macrotimes_part[~np.isnan(macrotimes_part)]\n", + " channels_part = channels_part[~np.isnan(channels_part)]\n", + " macrotimes_parts.append(macrotimes_part)\n", + " channels_parts.append(channels_part)\n", + " log.debug('correct_TCSPC: prepared \"averaging\" correction, removed '\n", + " '%s of %s photons, saved the remaining photons in %s parts.'\n", + " ' Correction is performed automatically by '\n", + " 'calling get_autocorrelation()',\n", + " len(photon_mask) - np.sum(photon_mask),\n", + " len(photon_mask), parts_label[1])\n", + "\n", + " new_tcspc = replace(\n", + " tcspc, channels=channels_corrected, macrotimes=macrotimes_corrected,\n", + " microtimes=microtimes_corrected, channels_parts=channels_parts,\n", + " macrotimes_parts=macrotimes_parts, processing_correction=method,\n", + " processing_prediction=pred.prediction_method, processing_bin=ts.bin,\n", + " processing_scaling=pred.scaling_method, correlations=[]\n", + " )\n", + "\n", + " self.processed_tcspc.append(new_tcspc)\n", + "\n", + " if ts_append:\n", + " self.processed_time_series.append(ts)\n", + "\n", + " log.debug('Finished correct_TCSPC() with uuid %s.', ts.uuid)\n", + "\n", + " def get_autocorrelation(self,\n", + " record: Union[TCSPC, FCSTimeSeries]):\n", + " \"\"\"Get Autocorrelation of either TCSPC data or time-series data\n", + "\n", + " Parameters\n", + " ----------\n", + " method : ['tttr2xfcs', 'tttr2xfcs_with_weights',\n", + " 'tttr2xfcs_with_averaging', 'multipletau']\n", + " the `tttr2xfcs` methods perform TCSPC correlations (see Notes)\n", + " `multipletau` offers correlations of time-series\n", + " `tttr2xfcs_with_weights` performs weighted TCSPC correlations\n", + " `tttr2xfcs_with_averaging` performs TCSPC correlations of a list\n", + " of TCSPC traces, and averages the correlations afterwards\n", + " name : string or tuple of strings\n", + " if method='tttr2xfcs', name should be a key for the dictionaries\n", + " self.trueTimeArr and self.subChanArr. If None, self.name is chosen\n", + " as a key\n", + " if method='tttr2xfcs_with_weights', additionally to above there\n", + " should be a dict self.trueTimeWeights from correctTCSPC(\n", + " method='weights')\n", + " if method='tttr2xfcs_with_averaging', name should be a key for the\n", + " dictionaries self.trueTimeParts and self.subChanParts.\n", + " if method='multipletau', name should be a tuple with 2 strings:\n", + " the first key to the dictionary self.timeSeries,\n", + " the second key to the dictionary self.timeSeries['first key']\n", + "\n", + " Returns\n", + " -------\n", + "\n", + " Notes\n", + " -----\n", + " `tttr2xfcs` : python version of:\n", + " Fast calculation of fluorescence correlation data with asynchronous\n", + " time-correlated single-photon counting.\n", + " Michael Wahl, Ingo Gregor, Matthias Patting, Jorg Enderlein\n", + "\n", + "\n", + " \"\"\"\n", + " if isinstance(record, TCSPC):\n", + " method = 'tttr2xfcs'\n", + " fcscorrtest = FCSCorrelation(\n", + " name=record.name, method=method, channel1=record.ch_present[0],\n", + " channel2=record.ch_present[0],\n", + " processing_prediction=record.processing_prediction,\n", + " processing_scaling=record.processing_scaling,\n", + " processing_correction=record.processing_correction)\n", + " if fcscorrtest in record.correlations:\n", + " log.debug('get_autocorrelation: This TCSPC record was already '\n", + " 'correlated. The correlation can be found in record.'\n", + " 'correlations.')\n", + " return\n", + " elif isinstance(record, FCSTimeSeries):\n", + " method = 'multipletau'\n", + " if record.correlation is not None:\n", + " log.debug('get_autocorrelation: This FCSTimeSeries record was '\n", + " 'already correlated. The correlation can be found in '\n", + " 'record.correlation.')\n", + " return\n", + " else:\n", + " raise TypeError('get_autocorrelation: record should be an instance '\n", + " 'of the TCSPC or FCSTimeSeries dataclasses and not '\n", + " '%s', type(record))\n", + "\n", + " corrs = []\n", + " if isinstance(record, TCSPC):\n", + " log.debug('get_autocorrelation: starting tttrx2fcs correlation..')\n", + " # correlating with tttr2xfcs - 3 subtypes depending on processing\n", + " if record.processing_correction == 'none':\n", + " for ch in record.ch_present:\n", + " # only case with > 1 channel\n", + " corr = self._auto(\n", + " macrotimes=record.macrotimes, channels=record.channels,\n", + " channel_to_use=ch, name=record.name)\n", + " corr.processing_prediction = record.processing_prediction\n", + " corr.processing_scaling = record.processing_scaling\n", + " corr.kcount = record.kcount\n", + " corr.brightness_nandb = record.brightness_nandb\n", + " corr.number_nandb = record.number_nandb\n", + " corrs.append(corr)\n", + " elif (record.processing_correction in\n", + " ['set_to_zero', 'cut_and_stitch']):\n", + " corr = self._auto(\n", + " macrotimes=record.macrotimes, channels=record.channels,\n", + " channel_to_use=record.ch_present[0], name=record.name)\n", + " corr.processing_prediction = record.processing_prediction\n", + " corr.processing_scaling = record.processing_scaling\n", + " corr.processing_correction = record.processing_correction\n", + " corr.kcount = record.kcount\n", + " corr.brightness_nandb = record.brightness_nandb\n", + " corr.number_nandb = record.number_nandb\n", + " corrs.append(corr)\n", + " elif (record.processing_correction in\n", + " ['constant_weight', 'random_weights', '1-pred_weights']):\n", + " auto, autotime = tttr2xfcs(\n", + " y=record.macrotimes,\n", + " num=record.weights,\n", + " NcascStart=self.ncasc_start,\n", + " NcascEnd=self.ncasc_end,\n", + " Nsub=self.nsub)\n", + " # Normalisation of the TCSPC data\n", + " maxY = np.ceil(max(record.macrotimes))\n", + " count = np.sum(record.weights)\n", + " autonorm = ((auto[:, 0, 0] * maxY) / (count**2)) - 1\n", + " autotime, autonorm = autotime.flatten(), autonorm.flatten()\n", + " corrs.append(FCSCorrelation(\n", + " name=record.name, method=method, channel1=record.ch_present[0],\n", + " channel2=record.ch_present[0], count1=count, count2=count,\n", + " autotime=autotime, autonorm=autonorm,\n", + " processing_prediction=record.processing_prediction,\n", + " processing_scaling=record.processing_scaling,\n", + " processing_correction=record.processing_correction))\n", + " elif record.processing_correction == 'averaging':\n", + " autonorm_parts = []\n", + " for mtp, chp in tqdm(zip(record.macrotimes_parts, record.channels_parts),\n", + " total=len(record.macrotimes_parts)):\n", + " # with this if clause very short parts could be\n", + " # discarded and the correlation quality and thus the\n", + " # averaging improved. Since in TCSPC data the part\n", + " # length depends on the photon counts, an appropriate\n", + " # part length is a subjective decision, thus it is not\n", + " # used)\n", + " quality_threshold = 1\n", + " if len(mtp) > quality_threshold:\n", + " # only autocorrelation\n", + " indices = chp == record.ch_present[0]\n", + " y, valid_photons = mtp[indices], chp[indices]\n", + " num = np.zeros((valid_photons.shape[0], 2))\n", + " num[:, 0] = (np.array([np.array(\n", + " valid_photons) == record.ch_present[0]])\n", + " ).astype(np.int32)\n", + " auto, autotime = tttr2xfcs(\n", + " y, num, self.ncasc_start, self.ncasc_end,\n", + " self.nsub, tqdm_disable=True)\n", + " # normalization of TCSPC data\n", + " # Note: the maximum photon arrival time is not\n", + " # computed via max(tt), because the parts have an\n", + " # arrival time offset depending on their position\n", + " # in the original trace\n", + " max_y = np.ceil(mtp[-1] - mtp[0])\n", + " counts = np.sum(num[:, 0])\n", + " autonorm = ((auto[:, 0, 0] * max_y) / counts**2) - 1\n", + " autonorm_parts.append(autonorm.flatten())\n", + "\n", + " # compute the mean correlation\n", + " corr = np.mean(autonorm_parts, axis=0)\n", + " corrs.append(FCSCorrelation(\n", + " name=record.name, method=method, channel1=record.ch_present[0],\n", + " channel2=record.ch_present[0], autotime=autotime.flatten(),\n", + " autonorm=corr, processing_prediction=record.processing_prediction,\n", + " processing_scaling=record.processing_scaling,\n", + " processing_correction=record.processing_correction))\n", + " if any([isinstance(c, list) for c in corrs]):\n", + " corrs = [item for sublist in corrs for item in sublist]\n", + " record.correlations.extend(corrs)\n", + "\n", + " elif isinstance(record, FCSTimeSeries):\n", + "\n", + " corr = multipletau.autocorrelate(\n", + " a=record.trace,\n", + " m=16,\n", + " deltat=record.bin,\n", + " normalize=True)\n", + "\n", + " count = np.sum(corr[1:, 0])\n", + " # multipletau outputs autotime=0 as first correlation step, which\n", + " # leads to problems with focuspoint and focus-fit-js\n", + " record.correlation = FCSCorrelation(\n", + " name=record.name, method=method, channel1=record.channel,\n", + " channel2=record.channel, count1=count, count2=count,\n", + " kcount=record.kcount, brightness_nandb=record.brightness_nandb,\n", + " number_nandb=record.number_nandb, autotime=corr[1:, 0],\n", + " autonorm=corr[1:, 1],\n", + " processing_prediction=record.prediction_method,\n", + " processing_scaling=record.scaling_method,\n", + " processing_correction=record.correction_method)\n", + " log.debug('Finished get_autocorrelation() with method=%s, name=%s',\n", + " method, record.name)\n", + "\n", + " def save_autocorrelation(self, record: FCSCorrelation,\n", + " output_format: Literal['focus'] = 'focus',\n", + " output_path: Union[str, Path, Literal['pwd']] = 'pwd'):\n", + " \"\"\"Save files as .csv\"\"\"\n", + " if not isinstance(record, FCSCorrelation):\n", + " raise TypeError('save_autocorrelation: record should be an instance'\n", + " ' of the FCSCorrelation dataclass and not %s',\n", + " type(record))\n", + "\n", + " if output_format == 'focus':\n", + " kcount = 0 if record.kcount is None else record.kcount\n", + " number_nandb = 0 if record.number_nandb is None else record.number_nandb\n", + " brightness_nandb = 0 if record.brightness_nandb is None else record.brightness_nandb\n", + " ch_type = f'{record.channel1}_{record.channel2}'\n", + " if record.channel1 == record.channel2:\n", + " corr_title = f'CH{record.channel1} Auto-Correlation'\n", + " num_ch = 1\n", + " else:\n", + " # preparation for cross-correlation, which is not implemented yet\n", + " corr_title = f'CH{record.channel1}{record.channel2} Cross-Correlation'\n", + " num_ch = 2\n", + " metadata = {\n", + " 'version': 3.0,\n", + " 'numOfCH': num_ch,\n", + " 'type': 'point',\n", + " 'parent_name': record.name,\n", + " 'ch_type': ch_type,\n", + " 'kcount': kcount,\n", + " 'numberNandB': number_nandb,\n", + " 'brightnessNandB': brightness_nandb,\n", + " 'carpet pos': 0,\n", + " 'pc': 0,\n", + " 'correlation method': record.method,\n", + " 'scaling (trace postprocessing)': record.processing_scaling,\n", + " 'prediction (trace postprocessing)': record.processing_prediction,\n", + " 'correction (trace postprocessing)': record.processing_correction,\n", + " 'Time (ms)': corr_title\n", + " }\n", + " else:\n", + " raise ValueError(f'save_autocorrelation: {output_format=} not valid'\n", + " ' or not implemented.')\n", + "\n", + "\n", + " if output_path == 'pwd':\n", + " output_path = Path().parent.resolve()\n", + " else:\n", + " output_path = Path(output_path)\n", + " if not output_path.is_dir():\n", + " raise NotADirectoryError('output_path should be a directory or'\n", + " ' \"pwd\"')\n", + " timestamp = datetime.today().isoformat(\n", + " sep='-', timespec=\"milliseconds\").replace(':', '').replace('.', '')\n", + " output_file = (f'{timestamp}-{record.name.replace(\".\", \"p\")}-'\n", + " f'{record.processing_correction}_correlation.csv')\n", + " output_file = output_path / output_file\n", + "\n", + " # compatibility with FoCuS-fit-JS:\n", + " # with 'w': utf-16le (doesn't work), utf-8 (works)\n", + " # with 'wb': works with .encode() behind strings\n", + " with open(output_file, 'w', encoding='utf-8') as out:\n", + " for key, value in metadata.items():\n", + " out.write(f'{key},{value}\\n')\n", + " for at, an in zip(record.autotime, record.autonorm):\n", + " out.write(f'{at},{an}\\n')\n", + " out.write('end\\n')\n", + " log.debug('Finished save_autocorrelation of file %s', output_file)\n", + "\n", + "# --------------------------- PLOT RESULTS ---------------------\n", + "def tcspc_click1(files):\n", + "\n", + " def make_example_plot(next_file):\n", + "\n", + " plt.close('all')\n", + " # print('Loading and plotting...')\n", + "\n", + " ymax = 0 # default for ylim of axis \"correlation{i}\"\n", + " ptufile = PicoObject(\n", + " next_file, ncasc_start=NCASC_START, ncasc_end=NCASC_END, nsub=NSUB,\n", + " photon_lifetime_bin=PHOTON_LIFETIME_BIN,\n", + " photon_count_bin=PHOTON_COUNT_BIN)\n", + " ptufile.get_cross_and_auto_correlation()\n", + "\n", + " meta = ptufile.original_tcspc\n", + " inner = []\n", + " for i in range(1, meta.num_ch+1):\n", + " inner.append([f\"trace{i}\", f\"trace{i}\"])\n", + " inner.append([f\"photon_counts{i}\", f\"correlation{i}\"])\n", + "\n", + " axd = plt.figure(figsize=(8, 4*meta.num_ch)).subplot_mosaic(\n", + " inner, empty_sentinel=\"EMPTY\",\n", + " # set the height ratios between the rows\n", + " # height_ratios=[2, 1, 2, 1],\n", + " # set the width ratios between the columns\n", + " # width_ratios=[1, 3.5, 1],\n", + " )\n", + "\n", + " for i, ch in enumerate(meta.ch_present):\n", + " ts = [ts for ts in ptufile.processed_time_series if ts.channel == ch]\n", + " assert len(ts) == 1\n", + " ts = ts[0].record['original']\n", + "\n", + " pcd = [pcd for pcd in meta.photon_count_decays if pcd.channel == ch]\n", + " assert len(pcd) == 1\n", + " pcd = pcd[0]\n", + "\n", + " axd[f'trace{i+1}'].set_prop_cycle(color=[sns.color_palette()[0]])\n", + " sns.lineplot(x=ts.scale, y=ts.trace, ax=axd[f'trace{i+1}'],\n", + " ).set(title=f'time-series of channel {ch}, bin={ts.bin}ms')\n", + "\n", + " axd[f'photon_counts{i+1}'].set_prop_cycle(color=[sns.color_palette()[0]])\n", + " sns.lineplot(x=pcd.scale, y=pcd.normalized, ax=axd[f'photon_counts{i+1}'],\n", + " ).set(title=f'photon count decay\\nof channel {ch}, bin={pcd.bin}ns')\n", + "\n", + "\n", + " for j, c in enumerate(ptufile.original_tcspc.correlations):\n", + " if not ch in [c.channel1, c.channel2]:\n", + " continue\n", + " ymax = np.max(c.autonorm)\n", + " axd[f'correlation{i+1}'].set_prop_cycle(color=[sns.color_palette()[0]])\n", + " sns.lineplot(x=c.autotime, y=c.autonorm, ax=axd[f'correlation{i+1}'],\n", + " label=f'Ch{c.channel1}, Ch{c.channel2}',\n", + " legend=False)\n", + "\n", + " plt.suptitle(f'TCSPC record: filename {meta.name},\\n{meta.num_ch} channel(s), '\n", + " f'{meta.ptu_num_records:,} records',\n", + " fontsize=10)\n", + " plt.setp([ax[1] for ax in axd.items() if 'trace' in ax[0]],\n", + " xlabel='macrotime [ms]', ylabel='intensity [a.u.]')\n", + " plt.setp([ax[1] for ax in axd.items() if 'photon_counts' in ax[0]],\n", + " xlabel='microtime [ns]', ylabel='counts (norm.)')\n", + " plt.setp([ax[1] for ax in axd.items() if 'correlation' in ax[0]],\n", + " xlabel=r'lag time $\\tau$ [ms]', ylabel=r'Correlation G($\\tau$)',\n", + " title='TCSPC Correlation(s)', xscale='log', ylim=[-0.1*ymax, None])\n", + " plt.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\",\n", + " title='Correlation(s)')\n", + " plt.tight_layout()\n", + "\n", + " plt.show()\n", + " return ptufile\n", + "\n", + " options = [(f.name, f) for f in files]\n", + " dropdown = widgets.Dropdown(\n", + " options=options,\n", + " value=options[0][1],\n", + " description='File name:',\n", + " disabled=False,\n", + " layout=widgets.Layout(width='75%')\n", + " )\n", + " output = widgets.Output()\n", + "\n", + " interact = widgets.interactive.factory()\n", + " interact_manual = interact.options(manual=True, manual_name=\"Load File\")\n", + " @interact_manual(select=dropdown)\n", + " def on_button_clicked(select):\n", + " # Display the message within the output widget.\n", + " output.clear_output()\n", + " with output:\n", + " ptufile = make_example_plot(select)\n", + " return ptufile\n", + "\n", + " # ptufile = interact_manual(on_button_clicked, b=dropdown)\n", + " display(output)\n", + " return on_button_clicked\n", + "\n", + "\n", + "\n", + "# -------------------------- GET USER INPUT -----------------------------------\n", + "\n", + "#@markdown ### Provide the path to your dataset and to the folder where the predictions are saved, then play the cell to predict outputs from your unseen images.\n", + "#@markdown **Note: currently only TCSPC data in the `.ptu` file format (PicoQuant)\n", + "#@markdown is supported**\n", + "\n", + "data_folder = \"/content/gdrive/MyDrive/unet-for-fcs/data/2019-11-FCS-TCSPC-peak-artifacts-PEX5-primary-data\" #@param {type:\"string\"}\n", + "data_folder = Path(data_folder)\n", + "files = list(Path(data_folder).rglob('*.ptu'))\n", + "\n", + "if len(files) == 0:\n", + " raise FileNotFoundError(f'{data_folder=} does not include any .ptu files.')\n", + "\n", + "\n", + "\n", + "# Hard-code the following variables\n", + "NCASC_START = 0 # for TCSPC correlation. From ncasc ~ 14 onwards correlation is very slow.\n", + "NCASC_END = 30\n", + "NSUB = 6\n", + "PHOTON_LIFETIME_BIN = 10 # used for photon decay\n", + "PHOTON_COUNT_BIN = 1 # used for time-series\n", + "\n", + "\n", + "# --------------------- LOAD AND INITIALIZE PTU DATA ---------------------------\n", + "ptufile = tcspc_click1(files)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "30b7c821a1df40518cee0de2588fcdc8", + "d697e6dcaf384d77901637e1261f48de", + "70ec1b979583409c8f9feab1cc3568b0", + "e732d8d7da7549a2acc8d4441539bb39", + "8ae536833251402dbc687c8a22359357", + "7787cdfd110c4a3cb26bd5488281ab55", + "e534559445fc4a8dba83e4231d26b405", + "6352759d3ce249d483c1d860508c59c7", + "4420660a433942f687f171213c701842", + "1aa2ef8461694a0387ec08b8dbfeb362", + "ac496508a00b4b489c4d27eb76d9de9f", + "fd842cd46b414cbea31b2be490350053", + "7c76268bc5cf45519e0e5c2d44105857", + "a5d212465d5646cb920258dd47c85544", + "36d0ab9b28ed41da81a37b28cf789105", + "bdedeb1df03143eebdc4fd329898496d", + "8669ea73accd429280d14aba975c0da1", + "566f766b0ff04184a7776ec9e07cd352", + "994bb4869d7640c1a67f858beb4d92b7", + "72fed1fe5c6b4e9197b7a6fb7f005171", + "3cd8bb2fb1454336b0534af54a48a440", + "190b7474fb5c47a38f79c22da0bd51a6", + "607ecb14965d4c19b4f546ac578e3e10", + "8d76938076ef41fca0cee0418d37b01f" + ] + }, + "id": "dyI-XR5iGyJq", + "outputId": "e8c63d79-95e2-45c4-fbf4-9b685dd8c347" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(Dropdown(description='Model:', layout=Layout(width='75%'), options=((\"model test_model with sca…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "30b7c821a1df40518cee0de2588fcdc8" + } + }, + "metadata": {} + } + ], + "source": [ + "#@title { run: \"auto\", display-mode: \"form\" }\n", + "#@markdown ## Step 2: Segment and correct TCSPC FCS trace\n", + "# -------------------------- GET LOADED TCSPC ---------------------------------\n", + "# for first execution, the widget result has to be parsed.\n", + "# no new parsing if this cell is re-executed without loading a new file.\n", + "try:\n", + " ptufile = ptufile.widget.result\n", + "except AttributeError:\n", + " pass\n", + "# -------------------------- GET USER INPUT -----------------------------------\n", + "\n", + "#@markdown ### Load model\n", + "#@markdown * Tick the following, if you want to use the last trained model from\n", + "#@markdown [Section 4.](#scrollTo=GyRjBdClimfK)\n", + "#@markdown If not ticked, this will scan the /content/ directory for mlflow\n", + "#@markdown models (as in [Section 2.4.](#scrollTo=jVGckx7ojEP2)).\n", + "#@markdown Afterwards, you can choose a valid run below, as well as a **prediction\n", + "#@markdown threshold**. You can use the best overall threshold computed in\n", + "#@markdown [Section 5.2.](#scrollTo=smiWe2wcjwTc), otherwise a good value is 0.5.\n", + "use_the_current_trained_model = True #@param {type:\"boolean\"}\n", + "\n", + "#@markdown ---\n", + "#@markdown ### Choose your segmentation method.\n", + "#@markdown * For using the trained model, choose `'unet'`.\n", + "#@markdown * For comparing to a baseline predictor, choose `'threshold'`. **Note:\n", + "#@markdown do not confuse this with the prediction threshold of the U-Net. It is\n", + "#@markdown applied directly to the data as shown in [Step 1]() (or if you choose\n", + "#@markdown a scaler to the scaled data).\n", + "#@markdown * Choose `'none'` to correlate the trace without segmentation or correction.\n", + "segmentation_method = 'unet' #@param ['none', 'unet', 'threshold']\n", + "\n", + "#@markdown ### Choose your correction method.\n", + "#@markdown * `'cut_and_stitch'`: cutt out the parts predicted as artifactual and\n", + "#@markdown stitche the remaining ones together.\n", + "#@markdown * `'averaging'` correlate the single parts predicted as non-artifactual and\n", + "#@markdown compute the average correlation.\n", + "#@markdown * For research purposes, 3 other correction methods are implemented:\n", + "#@markdown `'constant_weight'` (and as a special case for weight=0, `'set_to_zero'`)\n", + "#@markdown sets all parts predicted as artifactual to a constant weight or 0.\n", + "#@markdown `'random_weight'` sets all parts predicted as artifactual to a random\n", + "#@markdown weight between 0 and 1. `'1-pred_weight'` sets all parts predicted as\n", + "#@markdown artifactual to \"1 minus the prediction value from the U-Net\".\n", + "#@markdown **Note: Do not use these methods for actual FCS analyses. They do rarely,\n", + "#@markdown if ever, yield good results, as shown by [Seltmann et al (link TBD)]()**\n", + "#@markdown * Choose `'none'` to ignore the segmentation and correlate the whole trace.\n", + "correction_method = 'cut_and_stitch' #@param ['none', 'cut_and_stitch', 'set_to_zero', 'averaging', 'constant_weight', 'random_weights', '1-pred_weights']\n", + "\n", + "#@markdown ---\n", + "#@markdown As a default, the TCSPC file is correlated after applying segmentation\n", + "#@markdown and correction as chosen above. Tick the box and supply a output_path\n", + "#@markdown below to save the correlation as a `.csv` to be loaded in\n", + "#@markdown *Focuspoint* (https://github.com/dwaithe/FCS_point_correlator) or\n", + "#@markdown *Focus-fit-JS* (https://dwaithe.github.io/FCSfitJS/).\n", + "save_correlation_as_csv = True #@param {type:\"boolean\"}\n", + "output_path = \"/content/\" #@param {type:\"string\"}\n", + "\n", + "#@markdown # Re-execute this cell, if you change the parameters above!\n", + "# ------------------------------- LOAD MODEL --------------------------------\n", + "if use_the_current_trained_model:\n", + " try:\n", + " run = mlflow.get_run(train_click.run.info.run_id)\n", + " exp = train_click.exp\n", + " paths, exps, runs, models, clients = get_current_run_and_model(exp, run)\n", + " except NameError:\n", + " pass\n", + "try:\n", + " if all([paths, exps, runs, models, clients]):\n", + " pass\n", + " else:\n", + " paths, exps, runs, models, clients = get_all_mlflow_models()\n", + "except NameError:\n", + " paths, exps, runs, models, clients = get_all_mlflow_models()\n", + "\n", + "# --------------------- PROCESS PTU DATA ---------------------------\n", + "\n", + "\n", + "def process_ptu(ptufile, segmentation_method, correction_method, use_current,\n", + " segmentation_threshold, segmentation_threshold_scaler, weight,\n", + " segmentation_unet, segmentation_unet_thresh, output_path):\n", + "\n", + " if segmentation_method == 'threshold':\n", + " ptufile.predict_time_series(method=segmentation_method,\n", + " threshold=segmentation_threshold,\n", + " scaler=segmentation_threshold_scaler)\n", + " elif segmentation_method == 'unet':\n", + " model, scaler = segmentation_unet\n", + " pred_thresh = segmentation_unet_thresh\n", + "\n", + " ptufile.predict_time_series(method=segmentation_method,\n", + " model=model,\n", + " scaler=scaler)\n", + "\n", + " if (correction_method == 'none') or ((correction_method != 'none') and\n", + " (segmentation_method == 'none')):\n", + " if (correction_method != 'none') and (segmentation_method == 'none'):\n", + " log.debug('A segmentation_method without a correction_method (or vice versa) was '\n", + " 'chosen. No autocorrelation of the segmented trace is '\n", + " 'possible. Proceed with autocorrelating the original trace...')\n", + " ptufile.get_autocorrelation(record=ptufile.original_tcspc)\n", + " if save_correlation_as_csv:\n", + " for corr in ptufile.original_tcspc.correlations:\n", + " ptufile.save_autocorrelation(record=corr, output_path=output_path)\n", + "\n", + " else:\n", + " ptufile.correct_TCSPC(pred_thresh=pred_thresh,\n", + " method=correction_method,\n", + " weight=weight)\n", + "\n", + " ptufile.get_autocorrelation(record=ptufile.processed_tcspc[-1])\n", + " if save_correlation_as_csv:\n", + " for corr in ptufile.processed_tcspc[-1].correlations:\n", + " ptufile.save_autocorrelation(record=corr, output_path=output_path)\n", + "\n", + " return ptufile\n", + "\n", + "# ----------------------------- PLOT RESULTS ----------------------------------\n", + "\n", + "def make_example_plot(ptufile, pred_thresh,\n", + " segmentation_method=segmentation_method,\n", + " correction_method=correction_method):\n", + " def make_subplot(ax, trace: FCSTimeSeries, artifact_seg: np.ndarray,\n", + " artifact_color_id: int, title: str):\n", + " fill_artifact = trace.trace.max() * artifact_seg\n", + " fill_noartifact = trace.trace.max() * ~artifact_seg\n", + "\n", + " ax.set_prop_cycle(color=[sns.color_palette()[artifact_color_id]])\n", + " ax.fill_between(x=trace.scale, y1=fill_artifact, y2=0, alpha=0.5,\n", + " step='pre', label='peak artifacts')\n", + "\n", + " ax.set_prop_cycle(color=[sns.color_palette()[2]])\n", + " ax.fill_between(x=trace.scale, y1=fill_noartifact, y2=0, alpha=0.5,\n", + " step='pre', label='no artifacts')\n", + " ax.set_prop_cycle(color=[sns.color_palette()[0]])\n", + " sns.lineplot(x=trace.scale, y=trace.trace, ax=ax\n", + " ).set(title=title)\n", + "\n", + " plt.close('all')\n", + " # print('Loading and plotting...')\n", + "\n", + " ymax = 0 # default for ylim of axis \"correlation{i}\"\n", + "\n", + " if (correction_method == 'none') or ((correction_method != 'none') and\n", + " (segmentation_method == 'none')):\n", + " records = [ptufile.original_tcspc]\n", + " else:\n", + " records = ptufile.processed_tcspc\n", + "\n", + " inner = []\n", + " for i in range(1, len(records)+1):\n", + " inner.append([f\"segtrace{i}\", f\"segtrace{i}\", f\"info{i}\"])\n", + " inner.append([f\"corrtrace{i}\", f\"corrtrace{i}\", f\"correlation{i}\"])\n", + "\n", + " axd = plt.figure(figsize=(8, 4*len(records))).subplot_mosaic(\n", + " inner, empty_sentinel=\"EMPTY\",\n", + " # set the height ratios between the rows\n", + " # height_ratios=[2, 1, 2, 1],\n", + " # set the width ratios between the columns\n", + " # width_ratios=[1, 3.5, 1],\n", + " )\n", + "\n", + " for i, rec in enumerate(records):\n", + " scaler = rec.processing_scaling\n", + " predictor = rec.processing_prediction\n", + " correction = rec.processing_correction\n", + " if predictor == 'none':\n", + " ts = ptufile.processed_time_series\n", + " else:\n", + " if correction in ['cut_and_stitch', 'set_to_zero']:\n", + " ts = [pts for pts in ptufile.processed_time_series\n", + " if pts.record.get(correction)]\n", + "\n", + " ts = [pts for pts in ts if (\n", + " (pts.record[correction].prediction_method == predictor) and\n", + " (pts.record[correction].scaling_method == scaler))]\n", + "\n", + " else:\n", + " # the other correction method have no time-series data for their\n", + " # corrected traces\n", + " ts = [pts for pts in ptufile.processed_time_series if (\n", + " (pts.record['predictions'].prediction_method == predictor) and\n", + " (pts.record['predictions'].scaling_method == scaler))]\n", + " if len(ts) > 1:\n", + " log.warning('found %s processed time-series for record with scaler '\n", + " '%s, predictor %s, and correction %s. Plotting currently'\n", + " 'only works properly for 1.', len(ts), scaler, predictor,\n", + " correction)\n", + " prepro = ts[-1].record.get('preprocessed')\n", + " correct = ts[-1].record.get(correction)\n", + " pred = ts[-1].record.get('predictions')\n", + "\n", + " if (pred is None) and (correct is None):\n", + " log.debug('No prediction or correction was performed. Skip plotting.')\n", + " continue\n", + "\n", + " scaler = pred.scaling_method\n", + " predictor = pred.prediction_method\n", + " pred_thresh = (pred_thresh if rec.processing_pred_thresh is None\n", + " else rec.processing_pred_thresh)\n", + "\n", + " pred = pred.trace > pred_thresh\n", + "\n", + "\n", + "\n", + " title = (f'segmented time-series (bin={prepro.bin}ms, ch={prepro.channel}'\n", + " f'\\n{scaler=}, {predictor=})')\n", + " make_subplot(ax=axd[f'segtrace{i+1}'], trace=prepro,\n", + " artifact_seg=pred, artifact_color_id=1, title=title)\n", + "\n", + " axd[f'info{i+1}'].annotate(\n", + " f'TCSPC record {i}:\\n{scaler=}\\n{predictor=}\\n'\n", + " f'pred. threshold={pred_thresh}\\ncorrection method={correction}',\n", + " xy=(0.1, 0.5), va='center', ha='left', xycoords='data',\n", + " bbox=dict(boxstyle=\"round\", fc=\"w\"))\n", + " axd[f'info{i+1}'].axis('off')\n", + "\n", + " if correct is None:\n", + " axd[f'corrtrace{i+1}'].annotate(\n", + " 'A time-series representation of the\\ncorrection method '\n", + " f'{correction}\\n is currently not implemented.',\n", + " xy=(0.5, 0.5), ha='center', va='center', xycoords='data',\n", + " bbox=dict(boxstyle=\"round\", fc=\"w\"), annotation_clip=True)\n", + " else:\n", + " axd[f'corrtrace{i+1}'].set_prop_cycle(color=[sns.color_palette()[0]])\n", + " sns.lineplot(x=correct.scale, y=correct.trace,\n", + " ax=axd[f'corrtrace{i+1}'])\n", + " plt.setp(axd[f'corrtrace{i+1}'],\n", + " title=f'corrected time-series\\n({correction=})')\n", + "\n", + " for j, c in enumerate(rec.correlations):\n", + " ymax = np.max(c.autonorm) if np.max(c.autonorm) > ymax else ymax\n", + " label = (f'Ch{c.channel1}, Ch{c.channel2}\\n{correction}')\n", + "\n", + " axd[f'correlation{i+1}'].set_prop_cycle(color=[sns.color_palette()[0]])\n", + " sns.lineplot(x=c.autotime, y=c.autonorm, ax=axd[f'correlation{i+1}'],\n", + " label=label, legend=False)\n", + " axd[f'correlation{i+1}'].legend(\n", + " bbox_to_anchor=(1.04, 1), loc=\"upper left\", title='Correlation(s)')\n", + "\n", + " plt.setp([ax[1] for ax in axd.items() if 'trace' in ax[0]],\n", + " xlabel='macrotime [ms]', ylabel='intensity [a.u.]')\n", + " plt.setp([ax[1] for ax in axd.items() if 'correlation' in ax[0]],\n", + " xlabel=r'lag time $\\tau$ [ms]', ylabel=r'Correlation G($\\tau$)',\n", + " title='TCSPC Correlation(s)', xscale='log', ylim=[-0.1*ymax, None])\n", + "\n", + "\n", + " plt.tight_layout()\n", + "\n", + " plt.show()\n", + " return ptufile\n", + "\n", + "\n", + "\n", + "\n", + "# ------------------------- INTERACTIVE RESULTS --------------------------------\n", + "\n", + "def tcspc_widget2(ptufile=ptufile):\n", + " seg_thresh = widgets.FloatText(description=\"Threshold:\",\n", + " value=1.0, step=None)\n", + "\n", + " drop_opt = [(f'model {model.name} with {scaler=} from {run=}', (model, scaler)) for\n", + " run, (model, scaler, _) in models.items()]\n", + " default_run = drop_opt[0][1]\n", + " seg_unet = widgets.Dropdown(\n", + " options=drop_opt,\n", + " value=default_run,\n", + " description='Model:',\n", + " disabled=False,\n", + " layout=widgets.Layout(width='75%')\n", + " )\n", + "\n", + " seg_unet_thresh = widgets.FloatSlider(\n", + " value=0.5,\n", + " min=0,\n", + " max=1.0,\n", + " step=0.01,\n", + " description='UNetThreshold:',\n", + " disabled=False,\n", + " continuous_update=False,\n", + " orientation='horizontal',\n", + " readout=True,\n", + " readout_format='.2f',\n", + " )\n", + "\n", + " options = ['standard', 'robust', 'maxabs', 'quant_g', 'minmax', 'l1', 'l2']\n", + " seg_scaler = widgets.Dropdown(\n", + " options=options, value=options[0], description='Scaler:', disabled=False,\n", + " layout=widgets.Layout(width='75%'))\n", + "\n", + " weight = widgets.FloatText(value=0, step=None, description='Constant weight:')\n", + "\n", + " button = widgets.Button(description='Process record!')\n", + "\n", + " output = widgets.Output()\n", + "\n", + " input_widgets = []\n", + " if segmentation_method == 'threshold':\n", + " input_widgets.append(seg_thresh)\n", + " input_widgets.append(seg_scaler)\n", + " elif segmentation_method == 'unet':\n", + " input_widgets.append(seg_unet)\n", + " input_widgets.append(seg_unet_thresh)\n", + " if correction_method == 'constant_weight':\n", + " input_widgets.append(weight)\n", + "\n", + " def on_button_clicked(btn, ptufile=ptufile):\n", + " try:\n", + " ptufile = ptufile.widget.result\n", + " except AttributeError:\n", + " pass\n", + " output.clear_output()\n", + "\n", + " input_dict = {i.description: i.value for i in input_widgets}\n", + "\n", + " # Display the message within the output widget.\n", + " with output:\n", + " ptufile = PicoObject(\n", + " ptufile.filepath, ncasc_start=NCASC_START, ncasc_end=NCASC_END,\n", + " nsub=NSUB, photon_lifetime_bin=PHOTON_LIFETIME_BIN,\n", + " photon_count_bin=PHOTON_COUNT_BIN)\n", + " ptufile = process_ptu(\n", + " ptufile=ptufile,\n", + " segmentation_method=segmentation_method,\n", + " correction_method=correction_method,\n", + " output_path=output_path,\n", + " use_current=input_dict.get(\n", + " 'Use the current trained model (if unchecked, provide path to model below)'),\n", + " segmentation_threshold=input_dict.get('Threshold:'),\n", + " segmentation_threshold_scaler=input_dict.get('Scaler:'),\n", + " segmentation_unet=input_dict.get('Model:'),\n", + " segmentation_unet_thresh=input_dict.get('UNetThreshold:'),\n", + " weight=input_dict.get('Constant weight:'))\n", + "\n", + " ptufile = make_example_plot(ptufile,\n", + " pred_thresh=input_dict.get('UNetThreshold:'))\n", + " return ptufile\n", + "\n", + " button.on_click(on_button_clicked)\n", + " displayed_widgets = input_widgets.copy()\n", + " displayed_widgets.extend([button, output])\n", + " ui = widgets.VBox(displayed_widgets)\n", + "\n", + " display(ui)\n", + " return ptufile\n", + "\n", + "ptufile = tcspc_widget2()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gP7WDm6bkYkb" + }, + "source": [ + "## **6.2. Download your predictions**\n", + "---\n", + "\n", + "**Store your data** and ALL its results elsewhere by downloading it from Google Drive and after that clean the original folder tree (datasets, results, trained model etc.) if you plan to train or use new networks. Please note that the notebook will otherwise **OVERWRITE** all files which have the same name." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mELG8z-ykCKV" + }, + "outputs": [], + "source": [ + "#@title { display-mode: \"form\" }\n", + "# TBD: CSV\n", + "def example_plot_on_button_clicked(data_df, labels_df, prepro_df, preds_df):\n", + "\n", + " def make_gen(df):\n", + " gen = df.items() # generator without cycle\n", + " gen = itertools.cycle(enumerate(gen)) # enumerated generator with cycle\n", + " return gen\n", + "\n", + " data_gen, labels_gen, prepro_gen, preds_gen = (\n", + " make_gen(data_df), make_gen(labels_df), make_gen(prepro_df), make_gen(preds_df))\n", + "\n", + " def make_subplot(ax, trace, artifact_seg, artifact_color_id, title):\n", + " fill_artifact = trace.max() * artifact_seg\n", + " fill_noartifact = trace.max() * ~artifact_seg\n", + "\n", + " ax.set_prop_cycle(color=[sns.color_palette()[artifact_color_id]])\n", + " ax.fill_between(x=artifact_seg.index, y1=fill_artifact, y2=0, alpha=0.5,\n", + " step='pre', label='peak artifacts')\n", + "\n", + " ax.set_prop_cycle(color=[sns.color_palette()[2]])\n", + " ax.fill_between(x=artifact_seg.index, y1=fill_noartifact, y2=0, alpha=0.5,\n", + " step='pre', label='no artifacts')\n", + " ax.set_prop_cycle(color=[sns.color_palette()[0]])\n", + " sns.lineplot(x=trace.index, y=trace.values, ax=ax\n", + " ).set(title=title)\n", + "\n", + " def make_example_plot(traces_generator=data_gen,\n", + " labels_generator=labels_gen,\n", + " preprocessed_traces_generator=prepro_gen,\n", + " predictions_generator=preds_gen):\n", + "\n", + " i, (_, trace) = next(traces_generator)\n", + " _, (_, label) = next(labels_generator)\n", + " _, (_, prepro) = next(preprocessed_traces_generator)\n", + " _, (_, preds) = next(predictions_generator)\n", + "\n", + " plt.close('all')\n", + " print('plotting...')\n", + " fig, ax = plt.subplots(2, 1, tight_layout=True,\n", + " figsize=(12,6), sharex=True)\n", + "\n", + " plt.suptitle(f'Trace number {i+1}', fontsize=22)\n", + "\n", + " make_subplot(ax=ax[0], trace=trace, artifact_seg=label, artifact_color_id=4,\n", + " title='trace (before preprocessing) and segmentation (by simulated label)')\n", + "\n", + " make_subplot(ax=ax[1], trace=prepro, artifact_seg=preds, artifact_color_id=1,\n", + " title='trace (after preprocessing) and segmentation (by U-Net prediction)')\n", + "\n", + " plt.setp(ax, xlabel='time in ms', ylabel='intensity in a.u.')\n", + " plt.show()\n", + "\n", + " button = widgets.Button(description=\"Show new example!\")\n", + " output = widgets.Output()\n", + "\n", + " def on_button_clicked(b):\n", + " # Display the message within the output widget.\n", + " output.clear_output()\n", + " with output:\n", + " make_example_plot()\n", + "\n", + " button.on_click(on_button_clicked)\n", + " display(button, output)\n", + "\n", + "# #@markdown ## Load unseen simulated input data\n", + "# #@markdown * Paste the path to the folder containing the simulated FCS test data.\n", + "# #@markdown This should be data which was NOT seen before by the model during\n", + "# #@markdown training or validation. To find the paths of the folders\n", + "# #@markdown containing the respective dataset, go to your Files on the left of\n", + "# #@markdown the notebook, navigate to the folder containing your files and copy\n", + "# #@markdown the path by right-clicking on the folder, **Copy path** and pasting\n", + "# #@markdown it into the right box below.\n", + "# path_to_source_and_target = \"/content/gdrive/MyDrive/Colab Notebooks Data/U-Net for FCS 1D/firstartifact/firstartifact_Nov2020_val_max2sets_SORTEDIN\" #@param{type:\"string\"}\n", + "# #@markdown * The simulated data from [Section 3.0](#scrollTo=hMZpkgEDKA5n)\n", + "# #@markdown contains test source (FCS time-series) and 1 or 2 targets (labels for\n", + "# #@markdown either training a unet, a variational autoencoder, or both). Provide\n", + "# #@markdown the number of targets below so that the data is read in correctly:\n", + "# n_targets = 2 #@param [\"1\", \"2\"] {type:\"raw\"}\n", + "# #@markdown * The simulated data from [Section 3.0](#scrollTo=hMZpkgEDKA5n)\n", + "# #@markdown supports training the detection of 3 different artifacts, please state\n", + "# #@markdown which one should be read in:\n", + "# artifact = \"peak_artifact\" #@param [\"peak_artifact\", \"detector_dropout\", \"photobleaching\"]\n", + "# #@markdown ## Load model\n", + "# #@markdown * Choose if you want to use the last trained model from\n", + "# #@markdown [Section 4.1.](#scrollTo=i1sKnXrDieiR&line=1&uniqifier=1)\n", + "# #@markdown If not ticked, this will scan the /content/ directory for mlflow\n", + "\n", + "# #@markdown models. (as in [Section 2.4.](#scrollTo=jVGckx7ojEP2)).\n", + "# #@markdown You can then choose a valid run below.\n", + "# use_the_current_trained_model = False #@param {type:\"boolean\"}\n", + "# #@markdown ## Save output\n", + "# #@markdown * Choose if you want to save the output. If ticked, provide an output\n", + "# #@markdown path\n", + "# save_plot = False #@param {type:\"boolean\"}\n", + "# output_path = '/content/' #@param {type:\"string\"}\n", + "# output_path = Path(output_path)\n", + "\n", + "# # Hard-code the following variables\n", + "# PRED_THRESH = 0.5\n", + "\n", + "\n", + "# ------------------------------- LOAD MODEL --------------------------------\n", + "if save_plot:\n", + " if not output_path.exists():\n", + " raise OSError(f'{output_path=} does not exist.')\n", + "\n", + "if use_the_current_trained_model:\n", + " paths, exps, runs, models, clients = get_current_run_and_model(exp, run)\n", + "else:\n", + " try:\n", + " if all([paths, exps, runs, models, clients]):\n", + " pass\n", + " else:\n", + " paths, exps, runs, models, clients = get_all_mlflow_models()\n", + " except NameError:\n", + " paths, exps, runs, models, clients = get_all_mlflow_models()\n", + "\n", + "# --------------------------- LOAD DATA ----------------------------------\n", + "test_source, test_target_bool, _, _ = load_source_and_target_from_simulations(\n", + " path=path_to_source_and_target, artifact=artifact, n_targets=n_targets\n", + ")\n", + "test_prepro = convert_to_tfds_for_unet(test_source)\n", + "test_prepro = scale_pad_and_batch_tfds_for_unet(test_prepro,\n", + " scaler=logged_scaler)\n", + "log.debug('Predicting artifacts with model %s', logged_model.name)\n", + "test_pred = logged_model.predict(test_prepro, verbose=1)\n", + "test_pred = pd.DataFrame(test_pred.squeeze(axis=2)).T\n", + "test_prepro = pd.DataFrame(np.array(list(test_prepro)).squeeze()).T\n", + "test_pred.columns, test_prepro.columns = test_source.columns, test_source.columns\n", + "test_predbool = test_pred > PRED_THRESH\n", + "\n", + "example_plot_on_button_clicked(data_df=test_source, labels_df=test_labels_bool,\n", + " prepro_df=test_prepro, preds_df=test_predbool)\n", + "\n", + "\n", + "# # correct traces\n", + "# sim_corr = pd.DataFrame()\n", + "# for i in range(len(sim_df.columns)):\n", + "# sim_corr_trace = np.delete(sim_df.iloc[:, i].values,\n", + "# sim_predbool.iloc[:, i].values)\n", + "# sim_corr_trace = pd.DataFrame(sim_corr_trace)\n", + "# sim_corr = pd.concat([sim_corr, sim_corr_trace], axis='columns')\n", + "# sim_corr.columns = sim_df.columns\n", + "# log.debug('predict_correct_correlate: Finished \"cut and shift\" correction')\n", + "\n", + "# # after correction\n", + "# correlate.correlate_timetrace_and_save(sim_corr, out_path, out_txt)\n", + "\n", + "\n", + "# #Here we find the loaded model name and parent path\n", + "# prediction_model_name = os.path.basename(prediction_model_folder)\n", + "# prediction_model_path = os.path.dirname(prediction_model_folder)\n", + "\n", + "# if (Use_the_current_trained_model):\n", + "# print(\"Using current trained network\")\n", + "# prediction_model_name = model_name\n", + "# prediction_model_path = model_path\n", + "\n", + "# full_prediction_model_path = prediction_model_path+'/'+prediction_model_name+'/'\n", + "# if os.path.exists(full_prediction_model_path):\n", + "# print(\"The \"+prediction_model_name+\" network will be used.\")\n", + "# else:\n", + "# W = '\\033[0m' # white (normal)\n", + "# R = '\\033[31m' # red\n", + "# print(R+'!! WARNING: The chosen model does not exist !!'+W)\n", + "# print('Please make sure you provide a valid model path and model name before proceeding further.')\n", + "\n", + "\n", + "# # Activate the (pre-)trained model\n", + "\n", + "\n", + "# # Provide the code for performing predictions and saving them\n", + "\n", + "\n", + "# print(\"Images saved into folder:\", Result_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "owyvVA3ndrwA" + }, + "source": [ + "# **7. Version log**\n", + "---\n", + "**version 1.0.0**:\n", + "\n", + "\n", + "* First version\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JbOn8U-VkerU" + }, + "source": [ + "\n", + "#**Thank you for using 1D U-Net for FCS!**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OOcY-deOQQ5A", + "outputId": "00b3f935-0cd2-4037-8357-576f1adc86b6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Python version\n", + "3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0]\n", + "uname_result(system='Linux', node='323e3cf27583', release='5.15.120+', version='#1 SMP Wed Aug 30 11:19:59 UTC 2023', machine='x86_64')\n" + ] + } + ], + "source": [ + "import platform\n", + "import sys\n", + "print(\"Python version\")\n", + "print (sys.version)\n", + "\n", + "print(platform.uname())" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { 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zsy/nVKNGDXh4eOilqVQqdOjQAXfv3kViYqKUvnnzZnh7e+Ott96S0jw9PdGzZ0/89ttvUrmKYh/LTalSpWBtbW1W3ue9P8vb21uvPsmv/B4HzREeHg4fHx+MGTMGQgijXVV1bt++jUuXLpn8PDEx0aBbanb52dbXr1/H9evX9dLMPUZlZmYiNTUV3t7eeuleXl6Qy+VmfwfmnAeYu/0SEhIAwKBMvr6+AKBXJnOPcXv37sXjx4/xwQcf6M1zxIgRSE5Oxo4dO/I9z6L2QoOYcuXKoXHjxvjpp5+ktD/++APx8fEGByad4cOH48MPP0STJk2waNEiDB48GOvXr0dwcLDUlzM8PBx+fn6oWrUq1q1bh3Xr1uHjjz/Wm88HH3yACxcuYNq0aZg8ebLJMq5ZswYdO3ZEbGwspkyZgrlz56Ju3brYuXMnAG3QFRwcjMOHD2PUqFFYunQp3n33Xdy4cSPXiiA8PFw64C1fvhzr1q2TKt8ZM2ZgxIgRKFWqFBYsWIDu3bvj66+/Rtu2bQ36qz5+/Bjt27dH3bp1ER4ejlatWplcJqC9WT44OBheXl6YP38+unfvnmt+U+bOnYutW7diwoQJmDJlCg4fPiwdSPOiVqsxatQoDB06FLVq1TL43NPTUxrwwM/PT/oOq1WrhnXr1sHDwwN169aV0j09PaHRaNC5c2fMnz8fISEhWLx4Mbp27Yovv/wSvXr1MliGEAIxMTGIiorCgQMHMHr0aCgUCr2ATSc1NRUxMTGIjIzE2rVrsXr1ajRu3FiqFDQaDf777z/Ur1/fYNqGDRvi+vXregdbAJg6dSp8fHwwfPhwk9tp06ZNSElJwfvvv4/FixcjODgYixcvxoABA/Ty/fnnn+jevTtkMhnCwsLQtWtXDB482OCelPT0dCgUCr3ABADs7OwAaO950OUDYLQitrOzw/379xEVFaWXHh4ejidPnuCTTz4xuT75MXToUISHh6Nt27aYO3curK2t0bFjR4N80dHReOONN7B7926MHDkSixYtQqVKlfDOO+8YDZBnz56NHTt2YNKkSRg9ejQiIiIQFBQkBYYF+S7N0bJlSyQkJGD48OG4ePEibt26hRUrVmDLli2YMmVKntPrtnfOEzoASE5ORkxMDK5fv44vv/wSf/zxB1q3bi19vm7dOjRr1gwqlUraZ5o3b46zZ88iKyvLYF2VSiXq1q2LU6dOSWnp6ekmfw8ZGRk4d+6clA8w/O3k/I0NHz5cuuAyevRovTp69+7dqFq1Kv73v//Bz88Pjo6OcHd3x9SpU6HRaPTKZM7vGTB/HwGARYsWoV69epg1axbmzJkDKysr9OjRQ++AbUpu35M5IiIi0Lx5c1y4cAFjxozBggUL0KpVK2zfvl0vn6l63JzjI6A9rjk4OGD8+PFYtGgRAgICDI6FH3/8MerWrQsPDw/pd6Pbp/JT35q7L+dHVFQU7OzspO8aAE6dOoXXX38dcrn+aUzDhg2RkpKCK1eu5DlPoGD7mKXKyMhATEwM7ty5g61bt2L+/PkoW7YsKlWqVGjL2LNnDxo0aICvvvpKCmR9fX2xZMkSg7wDBgxAtWrVjM5n8ODBcHJygo2NDVq1amV0382P1q1bF/g7tLW1RaNGjbBmzRqsX78et2/fxn///YdBgwbB1dUV7777rlnzMec8wNztV79+fdjb22Pq1Kn466+/cO/ePfz999+YOHEiGjRogKCgIAD5O8bpjgE58wYEBEAul0ufF9Vxs0BeRHOPrjvZsWPHxJIlS4Sjo6PUBNqjRw/RqlUrIYQw6E524MABAUCsX79eb347d+40SDfVnUy37KZNm+p1B8r+ma6ZNC4uTjg6OopGjRqJ1NRUvby6LiSnTp0yu6kzp+nTpxs0Hz58+FAolUrRtm1boVarpfQlS5YY9ANu0aKFACBWrFhh1vJ0fdMnT55s8FnZsmWNdoVp0aKF3nbUNe1Wq1ZNr6vRokWLBABx9uzZPMuxZMkS4ezsLN1bYao7mal0Y90M161bJ+RyuThw4IBeuq4/58GDB/XSHzx4IABILz8/P7Fhwwaj5Q0LC9PL27p1a3H79m3pc11z+qxZswymXbp0qQCgd9/AmTNnhEKhkLrrGPsdCCGkfSJnWWQymbh165aUVrduXeHr6yvi4uKktD///FMA0GtOX7BggQBgsI0mT54sAIhOnToJIbR9a11cXETr1q318sXExEj9do8fPy6lP3jwQDg6Okp9xLPv36bk1lR/+vRpAUB88MEHeul9+/Y16LbwzjvvCF9fX4M+t7179xbOzs7SNtT9bnP2DdZ1SVq0aJEQIv/fZXa5dSfLysoSI0eOFNbW1tLvSKFQiOXLlxvNn93jx4+Fl5eXaNasmdHPhw8fLs1TLpeL0NBQvWZ+IbT7fs7uZLrvYP/+/Qbz7NGjh/Dx8ZHe16pVS1SpUkWvzkxPTxdlypQRAMTmzZuFEEL88ssvAoBYt26d3vx0+2HNmjWlNFPdRJycnISrq6tQqVRi6tSpYvPmzdJ3n73uMvf3LIT5+4gQhvtdRkaGqFmzpnjzzTcNtlN26enponr16qJ8+fIGXaTNkZWVJcqXLy/Kli0rnjx5ovdZ9vs0TdXj+Tk+Gqtbhg8fLuzs7ERaWpqUZqpLjrn1bX72ZXNdvXpV2NjYiLffflsv3d7eXgwZMsQg/44dOwQAsXPnTpPzLIx9zBzmdnEqaHey7MzpTvbTTz/pHdvq168v/vvvv3wtJ7eyxsbGCgDC3d1dODg4iHnz5okNGzaIdu3aGT130Z3TZHfw4EHRvXt38e2334rffvtNhIWFCXd3d2FjYyNOnjxpslx5beuyZcvm+nleXZ6vXr0q3Z+le1WoUMHk8SEnc84D8rv9tm/fLnx9ffXKFBwcLBITE6U8+TnGjRgxQigUCqPl9/T0FL179873PIvaCx9iuWfPnkhNTcX27duRmJiI7du3m+xKtmnTJjg7O6NNmzaIiYmRXgEBAXBwcMDevXvNXu6wYcOgUChyzRMREYHExERMnjwZNjY2ep/pupA4OzsDAHbt2oWUlBSzl2/K7t27kZGRgbFjx+pdURo2bBicnJwMrgaqVCoMHjw4X8t4//33n7ucgwcP1rsC2qxZMwDIsyn+8ePHmDZtGqZOnQpPT8/nLofOpk2bUK1aNVStWlXvt/Hmm28CgMFvw83NDREREfj9998xa9YseHh4mGym7dOnDyIiIvDjjz9Kv83sXbp0/6tUKoNpdb+b7PlHjx6N9u3bo23btrmuU/ar2borgYGBgRBCSFdAHjx4gNOnT2PgwIHSbxEA2rRpg+rVq+vNr2/fvnB2dsaQIUMQERGByMhIrFy5EsuWLdMro1wux/Dhw7Fnzx5MmTIFV69exYkTJ9CzZ0+pC1329Zk0aRIqVKiAoUOH5ro+5vrf//4HQLudsss5lLYQAr/88gtCQkKkljXdKzg4GPHx8QYjEw0YMACOjo7S+9DQUPj6+krLzO93aS6FQoGKFSsiODgYa9euxYYNGxASEoJRo0bh119/NTmdRqNBv379EBcXZ3JEmLFjxyIiIgJr165F+/btoVar9bo6mpLXumZfzw8++ABXrlzBO++8gwsXLuDcuXMYMGAAHjx4oDevDh06oGzZspgwYQK2bNmCW7duYePGjfj4449hZWVl1rZLSkrCkydPMHPmTMyaNQvdu3fH+vXr0a5dOyxatEi6omfu7zk/+wigv989efIE8fHxaNasmcFvKaeRI0fiwoULWLJkCaysrPJcz5xOnTqFmzdvYuzYsQZdS4x1WcxZj+fn+Jh9HRMTExETE4NmzZohJSUl1+482ZdlTn1r7r5srpSUFPTo0QO2traYO3eu3mepqakF2m+Lch8r6Vq1aoWIiAhs2rQJ7733HqytrZGcnFxo89cdUx8/foxvvvkGEyZMQM+ePbFjxw5Ur14dn332mV7+ffv2GXQdDQwMxObNmzFkyBB07twZkydPxuHDhyGTycxqxTYlMjLyuR6n4ejoiBo1amDEiBHYsmULli1bhqysLHTt2tWskUbNOQ/I7/bz9PREvXr1MHv2bPz666+YMWMGDhw4oHeOmJ9jXGpqqkFLd/a82fOZO8+ilv+a9zl5enoiKCgIP/74I1JSUqBWqxEaGmo079WrVxEfHw8vLy+jnz98+NDs5ZYvXz7PPLr+ksaG/80+n/Hjx2PhwoVYv349mjVrhs6dO6N///56B0xz3bp1CwDw2muv6aUrlUpUqFBB+lyndOnSJn9kxlhZWUn3AD2PMmXK6L13dXUFAIO+1zl98skncHNzw6hRo567DNldvXoVFy9eNBkY5fxtKJVKqXm1U6dOaN26NZo0aQIvLy+DYb3Lli0r9aHu06cP3n33XQQFBeHy5cuwtbWVTgiM3XuTlpYG4NlJw4YNG/Dvv/9K3W9yc/v2bUybNg3btm0z2K66+610v4fKlSsbTP/aa6/pnXj5+Phg27ZtePvtt6WK08nJCYsXL8bAgQPh4OAg5Z01axZiYmLwxRdfSCcLbdu2xTvvvIMVK1ZIeQ8fPox169Zhz549Bt04CurWrVuQy+UG99bk3CcePXqEuLg4rFy50uTw5Dm/95zbSSaToVKlStLBLD/fZX7MnTsXixYtwtWrV6Vt17NnT7Rq1QojRoxAp06djJ74jho1Cjt37sT333+POnXqGJ131apVUbVqVQDaIK1t27YICQnBkSNHcr1fJ691zb6e7733Hu7cuYN58+Zh7dq1ALRdDCZOnIjZs2dL62RjY4MdO3agZ8+eUhcnlUqFL774Qi9fbmxtbZGcnIw+ffropffp0wc7d+7EqVOn0Lx5c7N/z/nZRwBg+/bt+Oyzz3D69Gm9bZPbtpw3bx5WrVqFTz/9FB06dMhzHY0x53ijY6wez8/x8fz58/jkk0/w119/SX3pdXK7lzP7ssypb83dl82hVqvRu3dvXLhwAX/88QdKlSql97mtrW2B9tvC2Mfi4+P1TtKUSqXZ9//lV1JSkt4FN4VCUeALgt7e3tI9FKGhoZgzZw7atGmDq1evwsfH57nLqtvm1tbWeud1crkcvXr1wvTp03H79m2D84m8VKpUCV26dMGWLVugVqvzvCBd2LKyshAUFISWLVvqBb5BQUGoUaMG5s2bh88//9zk9OaeB+Rn+924cQOtWrXC999/L9W9Xbp0ke53/uOPP9C+fft8HeNsbW1NBuvZjxFFddwsiBcexADaK2rDhg1DVFQU2rdvb/IGJ41GAy8vL6xfv97o5/nZkQtzgy5YsACDBg3Cb7/9hj///BOjR49GWFgYDh8+XCgBQ27yux4qlcroyaapA7SpCsJUpZHzKkp2V69excqVKxEeHo779+9L6WlpacjMzERkZCScnJwKVPlrNBrUqlULCxcuNPq5v79/rtMHBgbC19cX69evz/PZRKGhoVi1ahX279+P4OBguLm5QaVSSVels9Ol6Q64H374IXr06AGlUimdOOvunbpz5w4yMjJQqlQpqNVqtGnTBrGxsZg0aRKqVq0Ke3t73Lt3D4MGDdK7NyA/mjdvjhs3buDs2bNITk5GnTp1pO+iSpUqUj6lUolvvvkGs2fPxpUrV+Dt7Y0qVaqgb9++kMvlUp/piRMnolmzZihfvry0PrqrUA8ePCjQAcpcum3Qv39/DBw40Gie2rVr52ue+fku82PZsmV48803DU7iO3fujPHjxyMyMtKgH/rMmTOxbNkyzJ07N1/PQgoNDcXw4cNx5cqVXE8WdTd8mlrXnOs5e/ZsTJgwAefPn4ezszNq1aol3deS/bdTo0YNnDt3DhcuXMCTJ09QvXp12NraYty4cdLN/LkpVaoUrl69avSmWUD/Qom5v2dzHThwAJ07d0bz5s2xbNky+Pr6wtraGqtXr9YbzCW7NWvWYNKkSXjvvfcK7Z6wvBirx809PsbFxaFFixZwcnLCrFmzULFiRdjY2ODkyZOYNGmSWXXL89a3BTFs2DBs374d69evl1p8svP19c33fltY+9iYMWOk4B4AWrRoUWTPwJo/fz5mzpwpvS9btmyhPaA7NDQUH3/8MX777bdc79Mwl5ubmzSgSc5zhuz7c0GOEf7+/sjIyEBycrLewCQvwv79+3Hu3DmD33/lypVRrVo1HDx4MNfpzT0PyM/2W7NmDdLS0gzOXzp37gwAOHjwINq3b5+vY5yvry/UajUePnyod3EkIyMDjx8/lvIV1XGzIIoliOnWrRuGDx+Ow4cPG33glE7FihWxe/duNGnSJM+Td3NGDMqL7urRuXPn8rzRrVatWqhVqxY++eQT/Pvvv2jSpAlWrFhh0NyXF90V/8uXL6NChQpSekZGBm7evCm1HhQ2V1dXowMR3Lp1S68cz+PevXvQaDQYPXq0QfcCQNuqNWbMmAI9XK1ixYo4c+YMWrduXeDvPi0tzayrkLorbrq8crkctWrVMnqj4ZEjR1ChQgWpC9OdO3fw448/Gj0hev3111GnTh2cPn0aZ8+exZUrV7B27Vq9G/mzj+QHPPu9XL161WB+ly9fNlr+nE9s1z04zthvK/uVOrVajX379qFRo0bSyfjt27dx69Ytoy2bnTt3hrOzs9mj+GRfJ41Gg+vXr+udhOdcH91Njmq12uz9Iud2EkLg2rVrUrCTn+8yP6Kjo40+BFF3s3XOEXeWLl2KGTNmYOzYsZg0aVK+lpXz92lKzZo1YWVlhePHj6Nnz55SekZGBk6fPq2XpuPq6oqmTZtK73fv3i0NopKdTCbTG73rf//7HzQajVnfU0BAAK5evYp79+7p1T264CTnxaq8fs/52Ud++eUX2NjYYNeuXXpdI1avXm20rL/99huGDh2Kt956C0uXLs1z3XKT/XhTkHre3OPjvn378PjxY2zZsgXNmzeX0m/evGmQ11Rdam59a+6+nJcPP/wQq1evRnh4uEELnU7dunVx4MABaDQavQDvyJEjsLOzMwhqC3MfmzhxIvr37y99ruuZUBQGDBigtw8W5gVZc+sOc8nlctStWxfHjh1DRkaGXq8RU/uzuW7cuAEbGxuzWncLW3R0NACYrNNzG0ENMP88ID/bLzo6GkIIgzLlPMbk5xinq1ePHz+u18J8/PhxaDQa6fOiOm4WxAu/JwYAHBwcsHz5csyYMQMhISEm8/Xs2RNqtRqffvqpwWdZWVl6J0v29vb5PnnKqW3btnB0dERYWJjUJKaja3FISEgw+MHWqlULcrncrKF9cwoKCoJSqcRXX32l16rx7bffIj4+/rlHdTGlYsWKOHz4sF7T4fbt2w2Gn30eumFNc75q1KiBMmXKYOvWrXjnnXcKNO+ePXvi3r17WLVqlcFnqampUj/f5ORko/cu/fLLL3jy5Ine6BqPHj0yuqxvv/0WMpkMr7/+upQWGhqKY8eO6e3Ely9fxl9//YUePXpIacbWXzeaz/fff48vv/wSwLOWruy/ASGE3lCrgPZKSd26dbF27Vq9A09ERAQuXLhgtPzZPXr0CJ9//jlq166d54nT/Pnz8eDBA/zf//2flLZy5UqD9dF1FZw/f77Jq8K5ad++PQDgq6++0kvPGdwqFAp0794dv/zyi9FmeWPf3/fff28wLOuDBw+kZQLmf5f5UaVKFURERODx48dSmlqtxsaNG+Ho6KjX3WbDhg0YPXo0+vXrZ/JKN2C8+2xmZia+//572NraGr3fIztnZ2cEBQXhhx9+0Nsm69atQ1JSUp7rumHDBhw7dszg/r2cUlNTMXXqVPj6+po8Ac1Otz98++23UppGo8Hq1avh5uaGgIAAk9Ma+z3nZx9RKBSQyWR6JwKRkZFG71vav38/evfujebNm2P9+vXP3Z3y9ddfR/ny5REeHm5w7MqthVvH3OOjsbolIyNDupcoO3t7e6MntObWt+buy7mZN28e5s+fj48++ghjxowxmS80NBTR0dHYsmWLlBYTE4NNmzYhJCRELygt7H2sevXqCAoKkl65/UafV4UKFfSW1aRJk3zPIyYmxuhv6ptvvgFgOBrV8+jVqxfUarVeS1VaWhrWr1+P6tWr612hNzbEsrF6/MyZM9i2bRvatm1b4P3O2BDL5tIFxDkfN3Dy5ElcvnwZ9erVk9J095llv0/G3PMAwPztV6VKFQghDIYz1o3+m71M5h7j3nzzTbi5uWH58uV681y+fDns7Oz0zkeL4rhZEMXSEgPAZHeQ7Fq0aIHhw4cjLCwMp0+fRtu2bWFtbY2rV69i06ZNWLRokdRvMCAgAMuXL8dnn32GSpUqwcvLy2gTdG6cnJzw5ZdfYujQoWjQoAH69u0LV1dXnDlzBikpKVi7di3++usvjBw5Ej169ECVKlWQlZWFdevWSSdY+eXp6YkpU6Zg5syZaNeuHTp37ozLly9j2bJlaNCggd7VnsI0dOhQbN68Ge3atUPPnj1x/fp1/PDDDyaf+VEQHh4e6Nq1q0G67oBm7DNzvf3229i4cSPee+897N27F02aNIFarcalS5ewceNG7Nq1C/Xr18fVq1cRFBSEXr16oWrVqpDL5Th+/Dh++OEHlCtXTu8gOXv2bBw8eBDt2rVDmTJlEBsbi19++QXHjh3DqFGj9FrnPvjgA6xatQodO3bEhAkTYG1tjYULF8Lb21vvpN/YOp4+fRqA9oCvG96zatWqqFixIiZMmIB79+7ByclJCrRyCgsLQ8eOHdG0aVMMGTIEsbGxWLx4MWrUqGEwWEGLFi3QuHFjVKpUCVFRUVi5ciWSkpKwfft2vYPBDz/8gF9++QXNmzeHg4MDdu/ejY0bN2Lo0KF6v2tjNyXqTpZatGhhcDDUtUyeP38egPaE+Z9//gEAqStO3bp10adPHyxbtgzx8fEIDAzEnj17jD6HaO7cudi7dy8aNWqEYcOGoXr16oiNjcXJkyexe/duxMbG6uV3c3ND06ZNMXjwYERHRyM8PByVKlXCsGHDpDzmfpcA8Pvvv+PMmTMAtCc3//33n7SOnTt3llp4Jk+ejP79+6NRo0Z49913YWtri59++gknTpzAZ599Jj3b4OjRoxgwYADc3d3RunVrgyAwMDBQap0YPnw4EhIS0Lx5c5QuXRpRUVFYv349Ll26hAULFph1hXL27NkIDAxEixYt8O677+Lu3btYsGAB2rZti3bt2kn59u/fj1mzZqFt27Zwd3fH4cOHsXr1arRr187gxLJnz54oVaoUqlevjoSEBHz33Xe4ceMGduzYYdbVuC5duqB169YICwtDTEwM6tSpg19//RX//PMPvv76a72TUXN/z+buIx07dsTChQvRrl079O3bFw8fPsTSpUtRqVIl/Pfff1K+W7duoXPnzpDJZAgNDcWmTZv01qF27dp6XRl1z9TIrduPXC7H8uXLERISgrp162Lw4MHw9fXFpUuXcP78eezatSvX7Wbu8TEwMBCurq4YOHAgRo8eDZlMhnXr1hk9qQ0ICMCGDRswfvx4NGjQAA4ODggJCTG7vs3PvmzM1q1bMXHiRKmbzg8//KD3eZs2bfTu6XjjjTcwePBgXLhwAR4eHli2bBnUarVe96sXvY/9999/2LZtGwDg2rVriI+Pl+qIOnXq6F24XbduHW7duiVdaNu/f7+U9+233zZ4vk1Ot27dwrp16wBAOpnUTV+2bFmpy9wPP/yAFStWoGvXrqhQoQISExOxa9cuREREICQkxKxzJXPLOnz4cHzzzTcYMWIErly5gjJlykjT/v7773rzHDBgAP7++2+932KvXr1ga2uLwMBAeHl54cKFC1i5ciXs7OwMBnfIz7bWDa+cc5805xgVEBCANm3aYO3atUhISEDbtm3x4MEDLF68GLa2tnoDVxw9ehStWrXC9OnTpWcBmnsekJ/tN2jQIMyfPx/Dhw/HqVOnUKNGDZw8eRLffPMNatSoIT3SAzD/GGdra4tPP/0UI0aMQI8ePRAcHIwDBw7ghx9+wOzZs/W6/ufnuFmkXsQQaOYMwSqE8aF0hRBi5cqVIiAgQNja2gpHR0dRq1YtMXHiRHH//n0pT1RUlOjYsaNwdHQUAKRhgnNbds4hlnW2bdsmAgMDha2trXBychINGzaUnux+48YNMWTIEFGxYkVhY2Mj3NzcRKtWrcTu3bvz3A6mhtYVQjsMcdWqVYW1tbXw9vYW77//vsGwm6aGIDbF2DCr2S1YsECULl1aqFQq0aRJE3H8+HGTQywbe8o2ALF69Wqzy6NTGEMsC6EdCvXzzz8XNWrUECqVSri6uoqAgAAxc+ZM6Ynkjx49Eu+++66oWrWqsLe3F0qlUlSuXFmMHTvW4Hv4888/RadOnUSpUqWEtbW1cHR0FE2aNBGrV6/WG/JU586dOyI0NFQ4OTkJBwcH0alTJ7Oe3G3qd3DhwgURFBQkHBwchIeHhxg2bJg4c+aM0e38yy+/iGrVqgmVSiWqV68utmzZYvRp5OPGjRMVKlQQKpVKeHp6ir59+4rr168blOnIkSOiefPmwtXVVdjY2Ig6deqIFStWGF3vnHLbx5Bt6Mecr+xSU1PF6NGjhbu7u7C3txchISHizp07RofyjI6OFiNGjBD+/v7C2tpa+Pj4iNatW4uVK1dKeXS/259++klMmTJFeHl5CVtbW9GxY0e94ap1zP0udcPdGnvl/I527twpWrRoITw8PIRSqRS1atUyGCJTt+3MmedPP/0kgoKChLe3t7CyshKurq4iKChI/Pbbb0bLaWrfP3DggAgMDBQ2NjbC09NTjBgxQm8YaiGEuHbtmmjbtq3w8PAQKpVKVK1aVYSFhekNs67z+eefi6pVqwobGxvh6uoqOnfuLE6dOmWQL7cncScmJooxY8YIHx8faVv98MMPBvnM/T0LYf4+8u2334rKlStL67l69WppH81ZdlOvnL9RDw8P8cYbbxgtV07//POPaNOmjXB0dBT29vaidu3aYvHixdLnedXj5hwfDx48KN544w1ha2srSpUqJSZOnCh27dplMKRsUlKS6Nu3r3BxcTEYjtqc+laI/O3LOem2u6lXzuFvY2NjxTvvvCPc3d2FnZ2daNGihUE9VFT7mCm5LS/nkOy6IYbNWVdjcvtdZj+OHzt2TPTo0UOUKVNGqFQqYW9vL15//XWxcOFCs4cHz09Zo6OjxcCBA4Wbm5tQqVSiUaNGRoe8NjbE8qJFi0TDhg2Fm5ubsLKyEr6+vqJ///5G6+P8bGtTQyybe4xKSUkRs2bNEtWrVxe2trbC2dlZdOrUyaCu030n5v7WjZ0Pmrv97t69K4YMGSLKly8vlEql8PX1FcOGDTM6z/ycr6xcuVK89tprQqlUiooVK4ovv/yyUM+BCpNMCDParYmIiChPFy5cQI0aNbB9+/Yi6w5MRETFdE8MERHRy2jv3r1o3LgxAxgioiLGlhgiIiIiIrIobIkhIiIiIiKLwiCGiIiIiIgsCoMYIiIiIiKyKAxiiIiIiIjIojCIISIiIiIii8IghoiIiIiILAqDGCIiIiIisigMYoiIiIiIyKIwiCEiIiIiIovCIIaIiIiIiCwKgxgiIiIiIrIoDGKIiIiIiMiiMIghIiIiIiKLwiCGiIiIiMgMMpkMM2bMkN6vWbMGMpkMkZGRxVamVxWDGCrxli1bBplMhkaNGhl8FhkZCZlMhvnz5xuddv78+SYrl61bt6J9+/bw8PCAUqlEqVKl0LNnT/z111+FvQpE9AqIi4vDu+++C09PT9jb26NVq1Y4efKkWdMOGjQIMpnM4FW1atUiLjVRyaILCnQvKysrlC5dGoMGDcK9e/eKu3hUglgVdwGI8rJ+/XqUK1cOR48exbVr11CpUqXnmp8QAkOGDMGaNWtQr149jB8/Hj4+Pnjw4AG2bt2K1q1b4+DBgwgMDCykNSCil51Go0HHjh1x5swZfPjhh/Dw8MCyZcvQsmVLnDhxApUrV85zHiqVCt98841emrOzc1EVmahEmzVrFsqXL4+0tDQcPnwYa9aswT///INz587BxsamuItHJQCDGCrRbt68iX///RdbtmzB8OHDsX79ekyfPv255rlgwQKsWbMGY8eOxcKFCyGTyaTPPv74Y6xbtw5WVtw1iCxNcnIy7O3ti2XZmzdvxr///otNmzYhNDQUANCzZ09UqVIF06dPx48//pjnPKysrNC/f/+iLiqRRWjfvj3q168PABg6dCg8PDzw+eefY9u2bejZs2cxl45KAnYnoxJt/fr1cHV1RceOHREaGor169c/1/xSU1MRFhaGqlWrSl3Ncnr77bfRsGHD51oO0cvg1q1b+OCDD/Daa6/B1tYW7u7u6NGjh9HumXFxcRg3bhzKlSsHlUoFPz8/DBgwADExMVKetLQ0zJgxA1WqVIGNjQ18fX3x1ltv4fr16wCAffv2QSaTYd++fXrz1nUbXbNmjZQ2aNAgODg44Pr16+jQoQMcHR3Rr18/AMCBAwfQo0cPlClTBiqVCv7+/hg3bhxSU1MNyn3p0iX07NkTnp6esLW1xWuvvYaPP/4YALB3717IZDJs3brVYLoff/wRMpkMhw4dAqANYry9vfHWW29JeTw9PdGzZ0/89ttvSE9PN2ubq9VqJCQkmJWX6FXSrFkzAJDqC0C7/4aGhsLNzQ02NjaoX78+tm3bZjBtXvVTRkYGpk2bhoCAADg7O8Pe3h7NmjXD3r17X8zKUYHwcjOVaOvXr8dbb70FpVKJPn36YPny5Th27BgaNGhQoPn9888/iI2NxdixY6FQKAq5tEQvl2PHjuHff/9F79694efnh8jISCxfvhwtW7bEhQsXYGdnBwBISkpCs2bNcPHiRQwZMgSvv/46YmJisG3bNty9exceHh5Qq9Xo1KkT9uzZg969e2PMmDFITExEREQEzp07h4oVK+a7fFlZWQgODkbTpk0xf/58qTybNm1CSkoK3n//fbi7u+Po0aNYvHgx7t69i02bNknT//fff2jWrBmsra3x7rvvoly5crh+/Tp+//13zJ49Gy1btoS/vz/Wr1+Pbt266S17/fr1qFixIho3bgwAOHXqFF5//XXI5frXBhs2bIiVK1fiypUrqFWrVq7rk5KSAicnJ6SkpMDV1RV9+vTB559/DgcHh3xvG6KXje7iiaurKwDg/PnzaNKkCUqXLo3JkyfD3t4eGzduRNeuXfHLL79I+6w59VNCQgK++eYb9OnTB8OGDUNiYiK+/fZbBAcH4+jRo6hbt24xrTXlShCVUMePHxcAREREhBBCCI1GI/z8/MSYMWOkPDdv3hQAxLx584zOY968eQKAuHnzphBCiEWLFgkAYuvWrUVceiLLl5KSYpB26NAhAUB8//33Utq0adMEALFlyxaD/BqNRgghxHfffScAiIULF5rMs3fvXgFA7N27V+9z3X6+evVqKW3gwIECgJg8ebJZ5Q4LCxMymUzcunVLSmvevLlwdHTUS8teHiGEmDJlilCpVCIuLk5Ke/jwobCyshLTp0+X0uzt7cWQIUMMlrtjxw4BQOzcudPgs+wmT54sJk2aJDZs2CB++uknaf2aNGkiMjMzc52W6GWyevVqAUDs3r1bPHr0SNy5c0ds3rxZeHp6CpVKJe7cuSOEEKJ169aiVq1aIi0tTZpWo9GIwMBAUblyZSnNnPopKytLpKen63325MkT4e3tbbBfA9Db93Xl1Z1n0IvD7mRUYq1fvx7e3t5o1aoVAO2whr169cLPP/8MtVpdoHnqumk4OjoWWjmJXla2trbS/5mZmXj8+DEqVaoEFxcXvVG3fvnlF9SpU8egtQKA1GXzl19+gYeHB0aNGmUyT0G8//77uZY7OTkZMTExCAwMhBACp06dAgA8evQI+/fvx5AhQ1CmTBmT5RkwYADS09OxefNmKW3Dhg3IysrSu38lNTUVKpXKoCy6G5CNdWXLLiwsDHPnzkXPnj3Ru3dvrFmzBrNnz8bBgwf1lk30qggKCoKnpyf8/f0RGhoKe3t7bNu2DX5+foiNjcVff/2Fnj17IjExETExMYiJicHjx48RHByMq1evSiOZmVM/KRQKKJVKANpBOmJjY5GVlYX69eubPcIgvXgMYqhEUqvV+Pnnn9GqVSvcvHkT165dw7Vr19CoUSNER0djz549+ZqfrqJycnICACQmJhZ6mYleNqmpqZg2bRr8/f2hUqng4eEBT09PxMXFIT4+Xsp3/fp11KxZM9d5Xb9+Ha+99lqhDpphZWUFPz8/g/Tbt29j0KBBcHNzg4ODAzw9PdGiRQsAkMp948YNAMiz3FWrVkWDBg307sdbv3493njjDb2REm1tbY3e95KWliZ9nl/jxo2DXC7H7t278z0tkaVbunQpIiIisHnzZnTo0AExMTHShYJr165BCIGpU6fC09NT76Ub/Ofhw4cAzKufAGDt2rWoXbs2bGxs4O7uDk9PT+zYsUOvrqOShffEUIn0119/4cGDB/j555/x888/G3y+fv16tG3bNs+rnCkpKQCeXQ3VPXPh7Nmz6Nq1axGUnOjlMWrUKKxevRpjx45F48aN4ezsDJlMht69e0Oj0RT68ky1yJhqeVWpVAb3oKjVarRp0waxsbGYNGkSqlatCnt7e9y7dw+DBg0qULkHDBiAMWPG4O7du0hPT8fhw4exZMkSvTy+vr548OCBwbS6tFKlSuV7ubrBFGJjY/M9LZGla9iwoTQ6WdeuXdG0aVP07dsXly9flvbjCRMmIDg42Oj0+Xkcww8//IBBgwaha9eu+PDDD+Hl5QWFQoGwsDC9gQSoZGEQQyXS+vXr4eXlhaVLlxp8tmXLFmzduhUrVqyAp6cn7OzscPnyZaPzuXz5Muzs7ODh4QEAaNq0KVxdXfHTTz/ho48+4s39RLnYvHkzBg4ciAULFkhpaWlpiIuL08tXsWJFnDt3Ltd5VaxYEUeOHEFmZiasra2N5tHdsJtz/rdu3TK7zGfPnsWVK1ewdu1aDBgwQEqPiIjQy1ehQgUAyLPcANC7d2+MHz8eP/30E1JTU2FtbY1evXrp5albty4OHDgAjUajF1gdOXIEdnZ2qFKlitnroKPrJuPp6ZnvaYleJrqAolWrVliyZAmGDBkCALC2tkZQUFCu05pTP23evBkVKlTAli1b9C6mPO8jHahosTsZlTipqanYsmULOnXqhNDQUIPXyJEjkZiYiG3btkGhUKBt27b4/fffcfv2bb353L59G7///jvatm0rBSt2dnaYNGkSLl68iEmTJkEIYbD8H374AUePHn0h60pUkikUCoN9ZPHixQYtI927d8eZM2eMDkWsm7579+6IiYkxaMHInqds2bJQKBTYv3+/3ufLli3LV5mzz1P3/6JFi/TyeXp6onnz5vjuu+8M6o6c6+zh4YH27dvjhx9+wPr169GuXTvpwohOaGgooqOjsWXLFiktJiYGmzZtQkhIiN79MtevX9e7upuWlma0i+unn34KIQTatWtn7uoTvbRatmyJhg0bIjw8HE5OTmjZsiW+/vproy2gjx49kv43p34yVm8cOXJEGkKdSia2xFCJs23bNiQmJqJz585GP3/jjTfg6emJ9evXo1evXpgzZw7eeOMNvP7669IwqZGRkVi5ciVkMhnmzJmjN/2HH36I8+fPY8GCBdi7dy9CQ0Ph4+ODqKgo/Prrrzh69Cj+/fffF7GqRCVap06dsG7dOjg7O6N69eo4dOgQdu/eDXd3d718H374ITZv3owePXpgyJAhCAgIQGxsLLZt24YVK1agTp06GDBgAL7//nuMHz8eR48eRbNmzZCcnIzdu3fjgw8+QJcuXeDs7IwePXpg8eLFkMlkqFixIrZv3y71bTdH1apVUbFiRUyYMAH37t2Dk5MTfvnlFzx58sQg71dffYWmTZtKdUf58uURGRmJHTt24PTp03p5BwwYID3E8tNPPzWYV2hoKN544w0MHjwYFy5cgIeHB5YtWwa1Wo2ZM2fq5W3dujWAZ0PGRkVFoV69eujTp4/U5XXXrl343//+h3bt2qFLly5mrz/Ry+zDDz9Ejx49sGbNGixduhRNmzZFrVq1MGzYMFSoUAHR0dE4dOgQ7t69izNnzkjT5FU/derUCVu2bEG3bt3QsWNH3Lx5EytWrED16tWRlJRUzGtNJhXPoGhEpoWEhAgbGxuRnJxsMs+gQYOEtbW1iImJEUIIcfHiRdGrVy/h5eUlrKyshJeXl+jdu7e4ePGiyXls3rxZtG3bVri5uQkrKyvh6+srevXqJfbt21fo60RkiZ48eSIGDx4sPDw8hIODgwgODhaXLl0SZcuWFQMHDtTL+/jxYzFy5EhRunRpoVQqhZ+fnxg4cKC0jwqhHfr4448/FuXLlxfW1tbCx8dHhIaGiuvXr0t5Hj16JLp37y7s7OyEq6urGD58uDh37pzRIZbt7e2NlvvChQsiKChIODg4CA8PDzFs2DBx5swZg3kIIcS5c+dEt27dhIuLi7CxsRGvvfaamDp1qsE809PThaurq3B2dhapqalGlxsbGyveeecd4e7uLuzs7ESLFi3EsWPHDPKVLVtWlC1bVm879+/fX1SqVEnY2dkJlUolatSoIebMmSMyMjKMLovoZaUbstjYvqNWq0XFihVFxYoVRVZWlrh+/boYMGCA8PHxEdbW1qJ06dKiU6dOYvPmzXrT5VU/aTQaMWfOHFG2bFmhUqlEvXr1xPbt28XAgQP19lUhOMRySSITwkh/GiIiIpJkZWWhVKlSCAkJwbffflvcxSEieuXxnhgiIqI8/Prrr3j06JHeYAFERFR82BJDRERkwpEjR/Dff//h008/hYeHBx98R0RUQrAlhoiIyITly5fj/fffh5eXF77//vviLg4RET1lsUHM/v37ERISglKlSkEmk+HXX3/Nc5p9+/bh9ddfh0qlQqVKlbBmzZoiLycRFS3WBVSU1qxZg6ysLBw/ftysp35T8WJ9QPTqsNggJjk5GXXq1DH6MERjbt68iY4dO6JVq1Y4ffo0xo4di6FDh2LXrl1FXFIiKkqsC4hIh/UB0avjpbgnRiaTYevWrejatavJPJMmTcKOHTv0ntrau3dvxMXFYefOnS+glERU1FgXEJEO6wOil9sr87DLQ4cOISgoSC8tODgYY8eOzXW69PR0pKenS+81Gg1iY2Ph7u4OmUxWFEUlemUIIZCYmIhSpUpBLn8xDcOsC4hKnuKoC4CC1QesC4iKTn7qglcmiImKioK3t7demre3NxISEpCamgpbW1uj04WFhRk8bZmICtedO3fg5+f3QpbFuoCo5HqRdQFQsPqAdQFR0TOnLnhlgpiCmjJlCsaPHy+9j4+PR5kyZXDnzh04OTkVY8mILF9CQgL8/f3h6OhY3EXJE+sCoqLDuoCIgPzVBa9MEOPj44Po6Gi9tOjoaDg5OZm88goAKpUKKpXKIN3JyYmVFVEheZFdMFgXEJVcL7o7VkHqA9YFREXPnLrAYkcny6/GjRtjz549emkRERFo3LhxMZWIiIoD6wIi0mF9QGS5LDaISUpKwunTp3H69GkA2mEST58+jdu3bwPQNvcOGDBAyv/ee+/hxo0bmDhxIi5duoRly5Zh48aNGDduXHEUn4gKCesCItJhfUD0ChEWau/evQKAwWvgwIFCCCEGDhwoWrRoYTBN3bp1hVKpFBUqVBCrV6/O93Lj4+MFABEfH//8K0H0iiuM/Yl1AZHlK6z9qTjqA9YFRIUnP/vTS/GcmBcpISEBzs7OiI+PZ99XoudkyfuTJZedqKSx5P3JkstOVNLkZ3+y2O5kRERERET0amIQQ0REREREFuWVGWKZiAqREEB6IpDyGEiJffo3+ytGm956GuD5WnGXloiIiF4yDGKICMhMMwhENMkxUCdpX5pkbZo8NRbytFhYpT2BXJOR52wfVOoNXwYxREREVMgYxBBZCLVGIC1TrX1laZCaof0/PUuNtEzN0880SE9PhyY1FrKnQYciLRZWabFQpsdBmfEENllxsM2Mg31WHBzU8XDUJMAWaQbLkz99WedSplShxGM44YlwwBPhiFg4av8KRzyBI0IU/vAtqg1CRERErywGMUQFlKXWIC1L8yyweBpIpGepkZrxNF0vwFAj/Wn+1Iycn2meBiPP0lIz1UjPyIIyKxG2WXFw0iTAVZYIN1kiXJEIN1kS3PAszRfavy6y5AKtT6ZQ4AmeBiDCEbHQBiaP4YQEmROSFM5IVjgjzdoFqdYuSFe6QG5tD5W1HDbWCu3LSvu/rVIBNys5PP1KF/JWJ6KioNEIqIWAWqN9ZWkENLq/Qv/9szwaaDTQ/hUCmrRkKFKioEh+CKuUh7BOiYZ16kMoUx9B1XwsPCoFFPdqEtFLhEEMvTQy1RqDYCJ7kKALDHQtGek5gg9dUJGaqX76mbFA5FlAkqXJ7+jkArZIhxsS9YIRL9nT9znSdX+tZJrcm0NMSJY7IdlKG3CkWbsgQ+mCTJUrsmzcoLZxg7B1A+zcIbP3gNzeA1Z2zrBRKmBrrUBZawVes1bAxloOlZUCCrks/wUgKiGEENAI6J10q7OdjKuFQJba9Ml69pN7tUb/RF8/jwZqDXL8zT0Q0AsacuSR8goBtdqwHHrrIwyXqzYxrcHyhUBuD1uwRyq8ZU/gJYuDF+Lg9fR/b9kTvfeOslST8zh1pQWDGCIqVAxiqMjkDCqk/3O0OORsyTAIGrK0QUWqkXllD1TyH1Q8H2tkwQWJcJclwtsqCV6KZHgqkuAhT9IGILJEuIhEuAhtly0HTTyUIu/7SIzRKB0gbN0hs3OHzF77F3bugJ0bYO/x9P9sLxsX2CusYF/I60wlU2FcRc8ycpJs7CTe5Il4bsuXAoRnJ9mm8+S+/NyDCNPTUk4CjkiVAhAv2RMpUNH99YT2f3tZutlzTYUKsXI3PJG7IVbujjiFG+IU7qjlXbMI14WIXkUMYl4RQghkqoUUQKTrtUxkCwyednfK3hKRmiP4SM/ZcvG0a1R6jvkU54mDStet6Wlrgo21Aqps3Z10aTZW2q5PNlYCzkiBi0iAk0iAoyYeDpp42GfFwzYrHjYZT6DKeAJlxhNYpT2BIi0W8oxEwwWrn75yo1ACdh65BCFuBkGJ3EpVFJuJitj8XZdx83FyoV9F1wsi8riKTrmTyQAruQwKuQwK2dO/chkUcjkUcsBKLodc91emey+DlVwm/dWfTv8zU3kU2Zep0P41Op1cDoUMUCjkJvPIZbJs6wBtF9T0h7BJewRV2iMoUx5CmfYQypSHsE7RdvVSpERBnmV4L5xJSkfA0Rtw8AEcs72yv3fwhq3KEaVlMrAjKREVNQYxxUQIgQy1RtuaYKKFQtf1Kd1o60W2bk9Zhi0UuqBC6j6VqUZxXozMHjTYZL+HwkS6ylr+NADJHng87d6ULb82ANGfl0ohgywjKcfwv1FPh/19bHxY4NQngNDkf8Vk8jwDEG3Aki1daa89c6KX3oGrj3DmbnyxlsHgRFqRxwm1kRNxvRN7GXKc4OufSBtOoz9vc/IYBAQyGawUumU8CypM58k9WMieJreUrpJCaOupxAdAYhSQFA0kPAASo7VpSdHa9MQoQG1+ywlUztrgRApIvAFHX8Dh6d+nwQlUDkW3bkREBcAgpgh88utZ3HuSmi0wyXb/RbYbwYsrqJDJYBg0ZGuhkIKHbPdE6AccT4MHa8XTz3IEJVLrhjYYUVnJIXuek3Zp+N+YZwFIXM7nkjwGkrP9r8ks2LJsnI0EIaZeboCNCyDnM2PJuKHNKiA2OcPsq/G55tELFrK1DuhO6HOcpOuCBSrhNBogNfZpcKILSKJyBCfR2jR1Prqj2rjkaC3JGZw8bVVR2hXZqhERFSUGMUXg3+uPceOR+SNE6YIK7Yl/tq5PTwMC/SDBVOtFjjxWxls7CiWoeB7qLO0B2+DhiE+Dk2QjrSWZBRttC9Z22VpH8uiyZe8B2LoCigLcQU9kQkidUsVdBCouGo32wkuiidaSpKhnLSqaLPPna+tmRnDiDVjbFt26ERGVAAxiisC4oCpIzVAbDD2ra73QBR+6QEWpKMag4nloNEB6vGHXrOQY4122Uh4DaXEFW5bcOvcuW/Y5umzZuvEKIxEVPo0aSH5kRnDyEBB53SCXjZ2HecEJ748jIgLAIKZIWOTVVyGAzJRsQUgurSXZu3Xl5yAtkWlbPfSCkFy6bNm5Ayon3kdCREVHnQUkP8w7OEl+lI/752SAvWeOgCR7oPI0OLH3AqyURbp6REQvGwYxL6usjFyCkOz3l2RLy89INdkpHc0YZSvbZ7YugFxRqKtLRGRUVsbT4ORpEGIyOIkBYOaNijK5NvAwdZ+JLlCx9wIUPMwSERUF1q6WQKMGUuOMBCSmWkxigfSEgi1LoTLsmpXXze3s3kBEL1pW+rN7SnIGJFL6A22daC6Z4mlAkldw4skLMURExYxBzIsmBJCeaKRrlpHWEt29JalPYPYVwuxkCjODEbdn95VY27HbFhEVn8xU84KT1Cfmz1NubV5wYufO4ISIyEIwiCkKJ9YACfdN3+Re4OF/XUwHINLN7dnSVc4c/peISoaMZPOCk7R8PFdHocx2E3zOBy9m+9/WjXUhEdFLhkFMUfgnHHhyM/c81vYmghATLScc/peISqL0JCMBSc7gJCp/XVytbEy3lmR/CKOtK1uOiYheUQxiikLN7tqhhA1aTDye/c8x/ImopNJ1e5UCkuyjdT149vDFxCggI8n8+VrZPg1GjAQn2YcWtnFhcEJERLliEFMUWk8t7hIQERkSQttdy5zgJDPF/Pla25sXnHCodCIiKiQMYoiILJ0Q2hvdTd1nkj04yc9Q6iqnHM82ydaVSy84cSy6dSMiIjLC4oOYpUuXYt68eYiKikKdOnWwePFiNGzY0GT+8PBwLF++HLdv34aHhwdCQ0MRFhYGGxubF1hqIipsL2VdIIR2UJDEB6aDE917dbr587VxNnGfSY6HMirti27diIrIS1kXEJEBiw5iNmzYgPHjx2PFihVo1KgRwsPDERwcjMuXL8PLy8sg/48//ojJkyfju+++Q2BgIK5cuYJBgwZBJpNh4cKFxbAGRFQYLK4u0Gi0IxWa6sqVPVjJz2iGtq6mu3JlD054Tx69pCyuLiCiApMJIQrwAJKSoVGjRmjQoAGWLFkCANBoNPD398eoUaMwefJkg/wjR47ExYsXsWfPHint//7v/3DkyBH8888/Zi0zISEBzs7OiI+Ph5OTU+GsCNErqrD2pxJbF5zdDDy+bhicJD8ENFnmr6Cde97BiYM3YM0rx2SZXvq6gIjMkp/9yWJbYjIyMnDixAlMmTJFSpPL5QgKCsKhQ4eMThMYGIgffvgBR48eRcOGDXHjxg3873//w9tvv21yOenp6UhPf9ZNIyEhH8OEElGRK9F1wcFFQNR/Jj6UaYdVl4KTHK0l2YMTK2XeyyJ6xZXouiAzDZDJtY9K4OAWRIXCYoOYmJgYqNVqeHt766V7e3vj0qVLRqfp27cvYmJi0LRpUwghkJWVhffeew8fffSRyeWEhYVh5syZhVp2Iio8JbouqNoRKFU3R3DytBXFwYvPfiIqRCW6Lvi5L3D9aWuP3Fr7oFaF7q8SUFhl+9/a8H95bp8bm59uOmvj6XktU1dGuYJBF5VYFhvEFMS+ffswZ84cLFu2DI0aNcK1a9cwZswYfPrpp5g61fiwyFOmTMH48eOl9wkJCfD3939RRSaiIvDC6oKWht1XiKjkeGF1gTrj2f+aTO0rH7e7FR+ZkUAnl8DJaIBmbLoceQorSJPLi3uD0QtksUGMh4cHFAoFoqOj9dKjo6Ph4+NjdJqpU6fi7bffxtChQwEAtWrVQnJyMt599118/PHHkBv58atUKqhUqsJfASIqFKwLiAgo4XVBv01AVjqgztQGNJrMZ/+rM3L8n2Ui/WnwYyxd+msiPdfpsi3TYCARoR35MD+jHxYnmcJIS1NhtFAVUstWznS2cj0Xiw1ilEolAgICsGfPHnTt2hWA9ga+PXv2YOTIkUanSUlJMaiQFAoFAMCCxzcgeqWxLiAioITXBda2ljEqoBCFFDhlaAcvMZq/INNlnyZbmlDnKL8ayErVviyBFBTlp4XqBbZs6c2z5HUttNggBgDGjx+PgQMHon79+mjYsCHCw8ORnJyMwYMHAwAGDBiA0qVLIywsDAAQEhKChQsXol69elKz8dSpUxESEiJVWkRkeVgXEBHAuuC5yWTagUQsZTARjTpb61ZewY+JQKxIWsVMTIccgbFFdS1EPlqacgmcavcGyjUplOJYdBDTq1cvPHr0CNOmTUNUVBTq1q2LnTt3Sjf13b59W+8KyyeffAKZTIZPPvkE9+7dg6enJ0JCQjB79uziWgUiKgSsC4gIYF3wypErtC9LGF5eCG3QVagtW/kN0vI5XU5SMPYc/BoUWhBj0c+JKQ4cD56o8Fjy/mTJZScqaSx5f7LkshOZJEQugdNz3LdVJRjwqWVysa/Ec2KIiIiIiKgIyGTPuoLBvrhLYxTHoiMiIiIiIovCIIaIiIiIiCwKgxgiIiIiIrIoDGKIiIiIiMiiMIghIiIiIiKLwiCGiIiIiIgsCoMYIiIiIiKyKAxiiIiIiIjIovBhl0RERERE9Nwy1RrEpWQiLiUDT1IyEZucIf0fl5KBkDqlULO0c6Esi0EMERERERHpSctUIzY5A09SMhCXkoknKRl4kqwNSHRp2YOUJykZSEzLynWe5T3sGcQQEREREVHuhBBITM9CXHImYlN0QUkGniQ/DUyytZTEJutaUTKQlqkp0PJkMsDJxhpu9kq42FnD1U77181OicrejoW2XgxiiIiIiIgsQJZag/jUTKnl40nys1aS2JQMxCVn6recPP0/SyMKtDwruQwudkq42lnD1f7pXzslXOyUcLO3fvpZ9s+VcLa1hkIuK+Q1N1K2Il8CERERERHpSctU6wUbT6QA5Gn3rOScrSQZSMiju1ZubK0VcLWzfhqAPGslyR6A6NJ0nzuorCCTFX1AUhAMYoiIiIiICkgIgeQMtdGg40m2m9yf5Li/JCVDXeBlOtlYwdX+aYtIttYRVztruNgr4ab7304JV3vt5zbWikJc6+LHIIaIiIiICIBaI5CQaqx1RHs/ial7STLVBeuupZDLngUbulYSOyVc7LO1ktgppa5cLnZKuNhaw0rBp6QwiCEiIiKil05GlsboUL+6LlvZb2LXBSnxqZkQBYtHoLKSP+2Glf3eEWuDtOwBiZNNye2uVdIxiCEiIiKiEksIgZQMtV5XrNhsN7TrhvrNORRw8nN013JUWcHFXjuill4rSbYAxDVbVy1XOyVslS9Xd62SjkEMEREREb0QGo1AYlqWGUP9Zh9hKxMZWQUb7lcug7YLlt2zYEN3I7tu2F8XIze3W7O7VonHIIaIiIiI8i3709kNbmLP9mDEON3wv0//L+Bov1BayXPvpvW0ZUTXSuJmp4SjjRXkL2C4X3rxGMQQERERveJSn3bXMvUk9idGRtpKTC/4cL/2SoXJYX2zBynZP7dTKnj/CEksPohZunQp5s2bh6ioKNSpUweLFy9Gw4YNTeaPi4vDxx9/jC1btiA2NhZly5ZFeHg4OnTo8AJLTUSFjXUBEQGsC4QQSEjLyiUAyd5961krSnoBu2vJZICzra5bVt4PQnS1s4aznTVUVrx/hJ6PRQcxGzZswPjx47FixQo0atQI4eHhCA4OxuXLl+Hl5WWQPyMjA23atIGXlxc2b96M0qVL49atW3BxcXnxhSeiQsO6gIiAl68uePZ09metH7pRtJ5kezp79oDkeZ7Obq2QPRviVxpJ61lXLWNpTi/o6exEOcmEKOhAcsWvUaNGaNCgAZYsWQIA0Gg08Pf3x6hRozB58mSD/CtWrMC8efNw6dIlWFtbF2iZCQkJcHZ2Rnx8PJycnJ6r/ESvusLan1gXEFm2V6Eu0Hs6e7LpoX6zt548z9PZ7ZQKo/eOSA9HNDLsrz27a1Exy09dYLEtMRkZGThx4gSmTJkipcnlcgQFBeHQoUNGp9m2bRsaN26MESNG4LfffoOnpyf69u2LSZMmQaFgsyaRJWJdQERAya4L+qw8jEM3Hhd4emdba70hfp+NtGUsTfv/y/Z0dqKcLDaIiYmJgVqthre3t166t7c3Ll26ZHSaGzdu4K+//kK/fv3wv//9D9euXcMHH3yAzMxMTJ8+3eg06enpSE9Pl94nJCQU3koQ0XNjXUBEQMmuC+xV2oDCSi7L14MQXe2s4cynsxMZZbFBTEFoNBp4eXlh5cqVUCgUCAgIwL179zBv3jyTlVVYWBhmzpz5gktKREWJdQERAS+uLvgitA6sFDI4qvh0dqLCYrGhvYeHBxQKBaKjo/XSo6Oj4ePjY3QaX19fVKlSRa+JuFq1aoiKikJGRobRaaZMmYL4+HjpdefOncJbCSJ6bqwLiAgo2XWBm70STjbWDGCICpHFBjFKpRIBAQHYs2ePlKbRaLBnzx40btzY6DRNmjTBtWvXoNE8G0bwypUr8PX1hVKpNDqNSqWCk5OT3ouISg7WBUQEsC4getVYbBADAOPHj8eqVauwdu1aXLx4Ee+//z6Sk5MxePBgAMCAAQP0bvB7//33ERsbizFjxuDKlSvYsWMH5syZgxEjRhTXKhBRIWBdQEQA6wKiV4lF3xPTq1cvPHr0CNOmTUNUVBTq1q2LnTt3Sjf13b59G3L5szjN398fu3btwrhx41C7dm2ULl0aY8aMwaRJk4prFYioELAuICKAdQHRq8SinxNTHPhsCKLCY8n7kyWXnaikseT9yZLLTlTS5Gd/sujuZERERERE9OphEENERERERBaFQQwREREREVkUBjFERERERGRRGMQQEREREZFFYRBDREREREQWhUEMERERERFZFAYxRERERERkURjEEBERERGRRWEQQ0REREREFoVBDBERERERWRQGMUREREREZFEYxBARERERkUVhEENERERERBaFQQwREREREVkUBjFERERERGRRGMQQEREREZFFYRBDREREREQWhUEMERERERFZFAYxRERERERkURjEEBERERGRRWEQQ0REREREFsXig5ilS5eiXLlysLGxQaNGjXD06FGzpvv5558hk8nQtWvXoi0gEb0QrAuICGBdQPSqsOggZsOGDRg/fjymT5+OkydPok6dOggODsbDhw9znS4yMhITJkxAs2bNXlBJiagosS4gIoB1AdGrxKKDmIULF2LYsGEYPHgwqlevjhUrVsDOzg7fffedyWnUajX69euHmTNnokKFCi+wtERUVFgXEBHAuoDoVWKxQUxGRgZOnDiBoKAgKU0ulyMoKAiHDh0yOd2sWbPg5eWFd955x6zlpKenIyEhQe9FRCUH6wIiAlgXEL1qLDaIiYmJgVqthre3t166t7c3oqKijE7zzz//4Ntvv8WqVavMXk5YWBicnZ2ll7+//3OVm4gKF+sCIgJYFxC9aiw2iMmvxMREvP3221i1ahU8PDzMnm7KlCmIj4+XXnfu3CnCUhJRUWNdQEQA6wIiS2dV3AUoKA8PDygUCkRHR+ulR0dHw8fHxyD/9evXERkZiZCQEClNo9EAAKysrHD58mVUrFjRYDqVSgWVSlXIpSeiwsK6gIgA1gVErxqLbYlRKpUICAjAnj17pDSNRoM9e/agcePGBvmrVq2Ks2fP4vTp09Krc+fOaNWqFU6fPs3mYCILxbqAiADWBUSvGottiQGA8ePHY+DAgahfvz4aNmyI8PBwJCcnY/DgwQCAAQMGoHTp0ggLC4ONjQ1q1qypN72LiwsAGKQTkWVhXUBEAOsColeJRQcxvXr1wqNHjzBt2jRERUWhbt262Llzp3RT3+3btyGXW2xjExGZiXUBEQGsC4heJTIhhCjuQliShIQEODs7Iz4+Hk5OTsVdHCKLZsn7kyWXnaikseT9yZLLTlTS5Gd/4uUIIiIiIiKyKAxiiIiIiIjIojCIISIiIiIii8IghoiIiIiILAqDGCIiIiIisigWPcRySSSEQFZWFtRqdXEXxSIpFApYWVlBJpMVd1GInptarUZmZmZxF8MisS6glwnrgoJjXUCmMIgpRBkZGXjw4AFSUlKKuygWzc7ODr6+vlAqlcVdFKICS0pKwt27d8FR7AuOdQG9DFgXPD/WBWQMg5hCotFocPPmTSgUCpQqVQpKpZJXDfJJCIGMjAw8evQIN2/eROXKlflQMrJIarUad+/ehZ2dHTw9PVkX5BPrAnpZsC54PqwLKDcMYgpJRkYGNBoN/P39YWdnV9zFsVi2trawtrbGrVu3kJGRARsbm+IuElG+ZWZmQggBT09P2NraFndxLBLrAnoZsC54fqwLyBSGs4WMVwieH7chvSx41fX5sC6glwXrgufDuoCM4a+CiIiIiIgsCoMYKlTlypVDeHh4cReDiIoZ6wIiAlgXUNHhPTGEli1bom7duoVSyRw7dgz29vbPXygieuFYFxARwLqALAODGMqTEAJqtRpWVnn/XDw9PV9AiYioOLAuICKAdQGVDOxO9oobNGgQ/v77byxatAgymQwymQxr1qyBTCbDH3/8gYCAAKhUKvzzzz+4fv06unTpAm9vbzg4OKBBgwbYvXu33vxyNhvLZDJ888036NatG+zs7FC5cmVs27btBa8lEeWFdQERAawLyHIwiClCQgikZGQVy8vch2otWrQIjRs3xrBhw/DgwQM8ePAA/v7+AIDJkydj7ty5uHjxImrXro2kpCR06NABe/bswalTp9CuXTuEhITg9u3buS5j5syZ6NmzJ/777z906NAB/fr1Q2xs7HNvXyJLwbpAi3UBvepYF2ixLqDCwO5kRSg1U43q03YVy7IvzAqGnTLvr9fZ2RlKpRJ2dnbw8fEBAFy6dAkAMGvWLLRp00bK6+bmhjp16kjvP/30U2zduhXbtm3DyJEjTS5j0KBB6NOnDwBgzpw5+Oqrr3D06FG0a9euQOtGZGlYF2ixLqBXHesCLdYFVBjYEkMm1a9fX+99UlISJkyYgGrVqsHFxQUODg64ePFinldcateuLf1vb28PJycnPHz4sEjKTESFj3UBEQGsC6hkYUtMEbK1VuDCrOBiW/bzyjmayIQJExAREYH58+ejUqVKsLW1RWhoKDIyMnKdj7W1td57mUwGjUbz3OUjshSsC7RYF9CrjnWBFusCKgwMYoqQTCYzq+m2uCmVSqjV6jzzHTx4EIMGDUK3bt0AaK/AREZGFnHpiCwf6wIiAlgXEBUmdicjlCtXDkeOHEFkZCRiYmJMXg2pXLkytmzZgtOnT+PMmTPo27cvr5wQvURYFxARwLqALAODGMKECROgUChQvXp1eHp6muzLunDhQri6uiIwMBAhISEIDg7G66+//oJLS0RFhXUBEQGsC8hCCAu3ZMkSUbZsWaFSqUTDhg3FkSNHTOZduXKlaNq0qXBxcREuLi6idevWueY3Jj4+XgAQ8fHxeumpqaniwoULIjU1tUDrQc9wW746TO1PBVFS6gIh+BsuLNyOrw7WBZQbbsdXR37qAotuidmwYQPGjx+P6dOn4+TJk6hTpw6Cg4NNjnCxb98+9OnTB3v37sWhQ4fg7++Ptm3b4t69ey+45ERUmFgXEBHAuoDolfICgqoi07BhQzFixAjpvVqtFqVKlRJhYWFmTZ+VlSUcHR3F2rVrzV4mW2KKHrflq6Owrr6WpLpACP6GCwu346uDdQHlhtvx1fFKtMRkZGTgxIkTCAoKktLkcjmCgoJw6NAhs+aRkpKCzMxMuLm5mcyTnp6OhIQEvRcRlRysC4gIYF1A9Kqx2CAmJiYGarUa3t7eeune3t6Iiooyax6TJk1CqVKl9Cq8nMLCwuDs7Cy9/P39n6vcRFS4WBcQEcC6gOhVY7FBzPOaO3cufv75Z2zduhU2NjYm802ZMgXx8fHS686dOy+wlERU1FgXEBHAuoDI0pT8Jy6Z4OHhAYVCgejoaL306Oho+Pj45Drt/PnzMXfuXOzevRu1a9fONa9KpYJKpXru8hJR0WBdQEQA6wKiV43FtsQolUoEBARgz549UppGo8GePXvQuHFjk9N98cUX+PTTT7Fz507Ur1//RRSViIoQ6wIiAlgXEL1qLLYlBgDGjx+PgQMHon79+mjYsCHCw8ORnJyMwYMHAwAGDBiA0qVLIywsDADw+eefY9q0afjxxx9Rrlw5qY+sg4MDHBwcim09iOj5sC4gIoB1AdGrxKKDmF69euHRo0eYNm0aoqKiULduXezcuVO6qe/27duQy581Ni1fvhwZGRkIDQ3Vm8/06dMxY8aMF1l0IipErAuICGBdQPQqkQkhRHEXwpIkJCTA2dkZ8fHxcHJyktLT0tJw8+ZNlC9fPtcbAl9G5cqVw9ixYzF27NhCmd+rvC1fNab2J0uQW9lf1d8w6wIqKNYFLxfWBVRQ+akLLPaeGCIiIiIiejUxiCEiIiIiIovCIOYVt3LlSpQqVQoajUYvvUuXLhgyZAiuX7+OLl26wNvbGw4ODmjQoAF2795dTKUloqLCuoCIANYFZDkYxBQlIYCM5OJ5mXmrU48ePfD48WPs3btXSouNjcXOnTvRr18/JCUloUOHDtizZw9OnTqFdu3aISQkBLdv3y6qrUb08mFdQEQA6wKiQmTRo5OVeJkpwJxSxbPsj+4DSvs8s7m6uqJ9+/b48ccf0bp1awDA5s2b4eHhgVatWkEul6NOnTpS/k8//RRbt27Ftm3bMHLkyCIrPtFLhXUBEQGsC4gKEVtiCP369cMvv/yC9PR0AMD69evRu3dvyOVyJCUlYcKECahWrRpcXFzg4OCAixcv8ooL0UuIdQERAawLyDKwJaYoWdtpr3wU17LNFBISAiEEduzYgQYNGuDAgQP48ssvAQATJkxAREQE5s+fj0qVKsHW1hahoaHIyMgoqpITvXxYFxARwLqAqBAxiClKMplZTbfFzcbGBm+99RbWr1+Pa9eu4bXXXsPrr78OADh48CAGDRqEbt26AQCSkpIQGRlZjKUlskCsC4gIYF1AVIgYxBAAbdNxp06dcP78efTv319Kr1y5MrZs2YKQkBDIZDJMnTrVYMQSInp5sC4gIoB1AZV8vCeGAABvvvkm3NzccPnyZfTt21dKX7hwIVxdXREYGIiQkBAEBwdLV2OI6OXDuoCIANYFVPKxJYYAAHK5HPfvG/bTLVeuHP766y+9tBEjRui9ZzMy0cuDdQERAawLqORjSwwREREREVkUBjFERERERGRRGMQQEREREZFFYRBDREREREQWhUEMERERERFZFAYxhUwIUdxFsHjchvSy4G/5+XD70cuCv+Xnw+1HxjCIKSTW1tYAgJSUlGIuieXTbUPdNiWyNAqFAgCQkZFRzCWxbKwLyNKxLigcrAvIGD4nppAoFAq4uLjg4cOHAAA7OzvIZLJiLpVlEUIgJSUFDx8+hIuLi1T5E1kaKysr2NnZ4dGjR7C2toZczutF+cG6gF4WrAueD+sCyg2DmELk4+MDAFIgQwXj4uIibUsiSySTyeDr64ubN2/i1q1bxV0ci8W6gCwd64LCwbqAjGEQU4h0lZWXlxcyMzOLuzgWydramlda6KWgVCpRuXJldiMpINYF9LJgXfB8WBeQScLCLVmyRJQtW1aoVCrRsGFDceTIkVzzb9y4Ubz22mtCpVKJmjVrih07duRrefHx8QKAiI+Pf55iE5Eo3P2JdQGR5WJdQERC5G9/sujOmRs2bMD48eMxffp0nDx5EnXq1EFwcLDJ7lz//vsv+vTpg3feeQenTp1C165d0bVrV5w7d+4Fl5yIChPrAiICWBcQvUpkQljuuHWNGjVCgwYNsGTJEgCARqOBv78/Ro0ahcmTJxvk79WrF5KTk7F9+3Yp7Y033kDdunWxYsUKs5aZkJAAZ2dnxMfHw8nJqXBWhOgVVVj7E+sCIsvGuoCIgPztTxbbEpORkYETJ04gKChISpPL5QgKCsKhQ4eMTnPo0CG9/AAQHBxsMj8RlXysC4gIYF1A9Kqx2Bv7Y2JioFar4e3trZfu7e2NS5cuGZ0mKirKaP6oqCiTy0lPT0d6err0Pj4+HoA2UiSi56Pbj56nQZh1AZHlY11ARED+6gKLDWJelLCwMMycOdMg3d/fvxhKQ/RySkxMhLOzc3EXI1esC4iKHusCIgLMqwssNojx8PCAQqFAdHS0Xnp0dLTJscR9fHzylR8ApkyZgvHjx0vvNRoNYmNj4e7ubvJhlgkJCfD398edO3cssn+spZcfsPx1eFXKL4RAYmIiSpUqVeBlsS4oWpa+Dix/8WJdoGXp3yNg+evA8hevoqgLLDaIUSqVCAgIwJ49e9C1a1cA2opkz549GDlypNFpGjdujD179mDs2LFSWkREBBo3bmxyOSqVCiqVSi/NxcXFrDI6OTlZ5A9Nx9LLD1j+OrwK5X/eq66sC14MS18Hlr94sS7QsvTvEbD8dWD5i1dh1gUWG8QAwPjx4zFw4EDUr18fDRs2RHh4OJKTkzF48GAAwIABA1C6dGmEhYUBAMaMGYMWLVpgwYIF6NixI37++WccP34cK1euLM7VIKLnxLqAiADWBUSvEosOYnr16oVHjx5h2rRpiIqKQt26dbFz507pJr3bt29DLn82AFtgYCB+/PFHfPLJJ/joo49QuXJl/Prrr6hZs2ZxrQIRFQLWBUQEsC4geqUU4UM3X1lpaWli+vTpIi0trbiLUiCWXn4hLH8dWP6Xw8uwHSx9HVj+4mXp5S8sL8N2sPR1YPmLV1GU36IfdklERERERK8ei33YJRERERERvZoYxBARERERkUVhEENERERERBaFQQwREREREVkUBjFERERERGRRGMQQEREREZFFYRBDREREREQWhUEMERERERFZFAYxRERERERkURjEEBERERGRRWEQQ0REREREFoVBDBERERERWRQGMUREREREZFEYxBARERERkUVhEENERERERBaFQQwREREREVkUBjFERGSWffv2QSaTYd++fcVdFIszY8YMyGSy4i4GkcU5duwYAgMDYW9vD5lMhtOnTxd3kaiEYBBDJdKaNWsgk8lw/Pjx4i4KkcXT7U/GXpMnTy7u4uVKV3YbGxvcu3fP4POWLVuiZs2axVAyQykpKZgxYwaDPKJCkpmZiR49eiA2NhZffvkl1q1bB29vb0yePBmtWrWCo6NjnhdW/v33XzRt2hR2dnbw8fHB6NGjkZSUZNbyTdWbc+fOLaQ1pOdhVdwFICKiF2PWrFkoX768XlpJCQDykp6ejrlz52Lx4sXFXRSTUlJSMHPmTADa4Cq7Tz75pMQHjEQlzfXr13Hr1i2sWrUKQ4cOBaBtEf78889RuXJl1KpVC4cOHTI5/enTp9G6dWtUq1YNCxcuxN27dzF//nxcvXoVf/zxh1llaNOmDQYMGKCXVq9evYKvFBUaBjFERK+I9u3bo379+sVdjAKpW7cuVq1ahSlTpqBUqVLFXZx8s7KygpUVD7lE+fHw4UMAgIuLi5QWEBCAx48fw83NDZs3b0aPHj1MTv/RRx/B1dUV+/btg5OTEwCgXLlyGDZsGP7880+0bds2zzJUqVIF/fv3f74VoSLB7mRksU6dOoX27dvDyckJDg4OaN26NQ4fPqyXJzMzEzNnzkTlypVhY2MDd3d3NG3aFBEREVKeqKgoDB48GH5+flCpVPD19UWXLl0QGRn5gteIqHjcunULH3zwAV577TXY2trC3d0dPXr0MGsfuHr1Krp37w4fHx/Y2NjAz88PvXv3Rnx8vF6+H374AQEBAbC1tYWbmxt69+6NO3fumF3Gjz76CGq12uxuHOYub+nSpahQoQJsbW3RsGFDHDhwAC1bttRrScnIyMC0adMQEBAAZ2dn2Nvbo1mzZti7d6+UJzIyEp6engCAmTNnSt1OZsyYAcDwnpiaNWuiVatWBuXRaDQoXbo0QkND9dLCw8NRo0YN2NjYwNvbG8OHD8eTJ0/0pj1+/DiCg4Ph4eEBW1tblC9fHkOGDDFrexGVNIMGDUKLFi0AAD169IBMJkPLli3h6OgINze3PKdPSEhAREQE+vfvLwUwADBgwAA4ODhg48aNZpclNTUVaWlp+V8JKlK8LEQW6fz582jWrBmcnJwwceJEWFtb4+uvv0bLli3x999/o1GjRgC0Jw5hYWEYOnQoGjZsiISEBBw/fhwnT55EmzZtAADdu3fH+fPnMWrUKJQrVw4PHz5EREQEbt++jXLlyhXjWhIVrvj4eMTExOileXh44NixY/j333/Ru3dv+Pn5ITIyEsuXL0fLli1x4cIF2NnZGZ1fRkYGgoODkZ6ejlGjRsHHxwf37t3D9u3bERcXB2dnZwDA7NmzMXXqVPTs2RNDhw7Fo0ePsHjxYjRv3hynTp3Su8pqSvny5TFgwACsWrUKkydPzrU1xtzlLV++HCNHjkSzZs0wbtw4REZGomvXrnB1dYWfn580v4SEBHzzzTfo06cPhg0bhsTERHz77bcIDg7G0aNHUbduXXh6emL58uV4//330a1bN7z11lsAgNq1axstY69evTBjxgxERUXBx8dHSv/nn39w//599O7dW0obPnw41qxZg8GDB2P06NG4efMmlixZglOnTuHgwYOwtrbGw4cP0bZtW3h6emLy5MlwcXFBZGQktmzZkue2JSqJhg8fjtKlS2POnDkYPXo0GjRoAG9vb7OnP3v2LLKysgxan5VKJerWrYtTp06ZNZ81a9Zg2bJlEEKgWrVq+OSTT9C3b998rQsVEUFUAq1evVoAEMeOHTP6edeuXYVSqRTXr1+X0u7fvy8cHR1F8+bNpbQ6deqIjh07mlzOkydPBAAxb968wis8UQmj25+MvYQQIiUlxWCaQ4cOCQDi+++/l9L27t0rAIi9e/cKIYQ4deqUACA2bdpkctmRkZFCoVCI2bNn66WfPXtWWFlZGaSbKvuxY8fE9evXhZWVlRg9erT0eYsWLUSNGjXyvbz09HTh7u4uGjRoIDIzM6V8a9asEQBEixYtpLSsrCyRnp6uN78nT54Ib29vMWTIECnt0aNHAoCYPn26wXpMnz5dZD/kXr58WQAQixcv1sv3wQcfCAcHB+k7OXDggAAg1q9fr5dv586deulbt27Ntc4kskS6OsdUHbNp0ya9OsnYZ/v37zf4rEePHsLHxyfP5QcGBorw8HDx22+/ieXLl4uaNWsKAGLZsmX5XhcqfOxORhZHrVbjzz//RNeuXVGhQgUp3dfXF3379sU///yDhIQEANp+tOfPn8fVq1eNzsvW1hZKpRL79u0z6JpB9LJZunQpIiIi9F6Adj/QyczMxOPHj1GpUiW4uLjg5MmTJuena2nZtWsXUlJSjObZsmULNBoNevbsiZiYGOnl4+ODypUr63XJykuFChXw9ttvY+XKlXjw4MFzLe/48eN4/Pgxhg0bpnevSr9+/eDq6qo3T4VCAaVSCUDbtSs2Nla6wpvb9slNlSpVULduXWzYsEFKU6vV2Lx5M0JCQqTvZNOmTXB2dkabNm301icgIAAODg7S+uhal7Zv347MzMwClYnoZZKamgoAUKlUBp/Z2NhIn+fm4MGDGDNmDDp37oz33nsPJ06cQM2aNfHRRx+ZNT0VLQYxZHEePXqElJQUvPbaawafVatWDRqNRur7PmvWLMTFxaFKlSqoVasWPvzwQ/z3339SfpVKhc8//xx//PEHvL290bx5c3zxxReIiop6YetDlm3//v0ICQlBqVKlIJPJ8Ouvv+Z7HkIIzJ8/H1WqVIFKpULp0qUxe/bsQi9rw4YNERQUpPcCtAf7adOmwd/fHyqVCh4eHvD09ERcXJzBvS3ZlS9fHuPHj8c333wDDw8PBAcHY+nSpXrTXL16FUIIVK5cGZ6ennqvixcvSjfumuuTTz5BVlaWyXtjzF3erVu3AACVKlXSm97KyspoN9K1a9eidu3a0r11np6e2LFjR67bJy+9evXCwYMHpaGj9+3bh4cPH6JXr1566xMfHw8vLy+D9UlKSpLWp0WLFujevTtmzpwJDw8PdOnSBatXr0Z6enqBy0dkyXQXAoztA2lpaXoXb8ylVCoxcuRIxMXF4cSJE89dRno+vCeGXmrNmzfH9evX8dtvv+HPP//EN998gy+//BIrVqyQhmscO3YsQkJC8Ouvv2LXrl2YOnUqwsLC8Ndff3EYRcpTcnIy6tSpgyFDhkj3QeTXmDFj8Oeff2L+/PmoVasWYmNjERsbW8glNW3UqFFYvXo1xo4di8aNG8PZ2RkymQy9e/eGRqPJddoFCxZg0KBB0j42evRohIWF4fDhw/Dz84NGo4FMJsMff/wBhUJhML2Dg0O+ylqhQgX0798fK1euNDpkcWEvD9AOEjBo0CB07doVH374Iby8vKBQKBAWFobr16/ne346vXr1wpQpU7Bp0yaMHTsWGzduhLOzM9q1a6e3Pl5eXli/fr3ReegGE5DJZNi8eTMOHz6M33//Hbt27cKQIUOwYMECHD58uEDrTWTJfH19AcBoq+2DBw8KPMqhv78/ALzQOppMKObubERG5XZPTFZWlrCzsxM9e/Y0+Oy9994TcrlcxMfHG51vYmKiqFevnihdurTJZV+5ckXY2dmJfv36FXwF6JUEQGzdulUvLS0tTfzf//2fKFWqlLCzsxMNGzbU67994cIFYWVlJS5dulRk5crrHjNnZ2cxePBgvbTU1FShUCjEwIEDpbSc98QYc/DgQQFAfPzxx0IIIb744gsBQFy+fLnQyn7t2jVhZWUlxowZY3BPjLnL05Vz5cqVeumZmZnC1dVV756YLl26iAoVKgiNRqOXNzAwUJQtW1Z6HxMTY/Y9MToNGzYUb7zxhsjMzBQeHh5621sI7T0yCoXC6H1LeVm/fr0AIFatWpXvaYlKgue5JyYuLk5YWVmJDz/8UC89PT1dODg46N3Plh+LFy8WAMS///5boOmp8LA7GVkchUKBtm3b4rffftMbAjY6Oho//vgjmjZtKg2n+PjxY71pHRwcUKlSJal5OSUlxWDYxIoVK8LR0ZHdMKhQjBw5EocOHcLPP/+M//77Dz169EC7du2k+7R+//13VKhQAdu3b0f58uVRrlw5DB069IVe5VMoFBBC6KUtXrwYarU61+kSEhKQlZWll1arVi3I5XJp/3nrrbegUCgwc+ZMg2UIIQz2UXNUrFgR/fv3x9dff23Q9dPc5dWvXx/u7u5YtWqV3jqsX7/e4P44XYtO9vkdOXLE4CF7ulHc4uLizF6XXr164fDhw/juu+8QExOj15UMAHr27Am1Wo1PP/3UYNqsrCxpWU+ePDFY37p16wIw3p2G6GXn7OyMoKAg/PDDD0hMTJTS161bh6SkJL3ny6SkpODSpUt6ozc+evTIYJ6JiYkIDw+Hh4cHAgICinYFKE/sTkYl2nfffYedO3capM+YMQMRERFo2rQpPvjgA1hZWeHrr79Geno6vvjiCylf9erV0bJlSwQEBMDNzQ3Hjx/H5s2bMXLkSADAlStX0Lp1a/Ts2RPVq1eHlZUVtm7diujoaL0hTokK4vbt21i9ejVu374tdV2YMGECdu7cidWrV2POnDm4ceMGbt26hU2bNuH777+HWq3GuHHjEBoair/++uuFlLNTp05Yt24dnJ2dUb16dRw6dAi7d++Gu7t7rtP99ddfGDlyJHr06IEqVaogKysL69atg0KhQPfu3QFoA47PPvsMU6ZMkYYwdnR0xM2bN7F161a8++67mDBhQr7L/PHHH2PdunW4fPkyatSoIaWbuzylUokZM2Zg1KhRePPNN9GzZ09ERkZizZo1qFixot4zXTp16oQtW7agW7du6NixI27evIkVK1agevXqSEpKkvLZ2tqievXq2LBhA6pUqQI3NzfUrFkTNWvWNLkePXv2xIQJEzBhwgS4ublJ9ynptGjRAsOHD0dYWBhOnz6Ntm3bwtraGlevXsWmTZuwaNEihIaGYu3atVi2bBm6deuGihUrIjExEatWrYKTkxM6dOiQ7+1LVJJ99tlnALSPWwC0gck///wDQHvfnM7s2bMRGBiIFi1a4N1338Xdu3exYMECtG3bVq/b5tGjR9GqVStMnz5derbT0qVL8euvvyIkJARlypTBgwcP8N133+H27dtYt26dNNgHFaNibAUiMim3IWEBiDt37oiTJ0+K4OBg4eDgIOzs7ESrVq0Mmnc/++wz0bBhQ+Hi4iJsbW1F1apVxezZs0VGRoYQQtv9Y8SIEaJq1arC3t5eODs7i0aNGomNGzcWx2qThUOO7mTbt28XAIS9vb3ey8rKSuoOOWzYMIPuTydOnBAACq2LWV7dyZ48eSIGDx4sPDw8hIODgwgODhaXLl0SZcuWzbU72Y0bN8SQIUNExYoVhY2NjXBzcxOtWrUSu3fvNljGL7/8Ipo2bSptg6pVq4oRI0bk2e0rt7IPHDhQANDrTpbf5X311VeibNmyQqVSiYYNG4qDBw+KgIAA0a5dOymPRqMRc+bMkfLVq1dPbN++XQwcOFCvO5kQQvz7778iICBAKJVKva5lprqTCSFEkyZNBAAxdOhQk9th5cqVIiAgQNja2gpHR0dRq1YtMXHiRHH//n0hhBAnT54Uffr0EWXKlBEqlUp4eXmJTp06iePHj5ucJ1FJZ6o7WW7nBzkdOHBABAYGChsbG+Hp6SlGjBghEhISjC4ne1fQP//8U7Rp00b4+PgIa2tr4eLiItq2bSv27NlTJOtK+ScTIkf7MxERFYhMJsPWrVvRtWtXAMCGDRvQr18/nD9/3uAmcwcHB/j4+GD69OmYM2eO3rC4qampsLOzw59//ik9lJVeDI1GA09PT7z11ltYtWpVcReHiIhMYHcyIqIiUq9ePajVajx8+BDNmjUzmqdJkybIysrC9evXUbFiRQDabo4AULZs2RdW1ldRWloaVCqVXtex77//HrGxsWjZsmXxFYyIiPLElhgioueQlJSEa9euAdAGLQsXLkSrVq3g5uaGMmXKoH///jh48CAWLFiAevXq4dGjR9izZw9q166Njh07QqPRoEGDBnBwcEB4eDg0Gg1GjBgBJycn/Pnnn8W8di+3ffv2Ydy4cejRowfc3d1x8uRJfPvtt6hWrRpOnDjBPu9ERCUYgxgiouewb98+tGrVyiB94MCBWLNmDTIzM/HZZ5/h+++/x7179+Dh4YE33ngDM2fORK1atQAA9+/fx6hRo/Dnn3/C3t4e7du3x4IFC+Dm5vaiV+eVEhkZidGjR+Po0aOIjY2Fm5sbOnTogLlz58LLy6u4i0dERLlgEENEREQvhf3792PevHk4ceIEHjx4oHePmin79u3D+PHjcf78efj7++OTTz7BoEGDXkh5iajg+JwYIiIieikkJyejTp06WLp0qVn5b968iY4dO6JVq1Y4ffo0xo4di6FDh2LXrl1FXFIiel5siSEiIqKXTs7RAo2ZNGkSduzYgXPnzklpvXv3RlxcnNFnlBFRycGWGCIiInolHTp0yOABo8HBwTh06FAxlYiIzMUhlvNJo9Hg/v37cHR01BuWk4jyTwiBxMRElCpVCnK5ZV1TYV1AVHiKqy6IioqCt7e3Xpq3tzcSEhKQmpoKW1tbg2nS09ORnp4uvddoNIiNjYW7uzvrAqLnlJ+6gEFMPt2/fx/+/v7FXQyil8qdO3fg5+dX3MXIF9YFRIXPEuqCsLAwzJw5s7iLQfRSM6cuYBCTT46OjgC0G9fJyamYS0Nk2RISEuDv7y/tV5aEdQFR4SmuusDHxwfR0dF6adHR0XBycjLaCgMAU6ZMwfjx46X38fHxKFOmDOsCokKQn7qAQUw+6ZqKnZycWFkRFZLi7oIxd+5cTJkyBWPGjEF4eLhZ07AuICp8L7ouaNy4Mf73v//ppUVERKBx48Ymp1GpVFCpVAbprAuICo85dYFldUInIipkx44dw9dff43atWsXd1GI6DklJSXh9OnTOH36NADtEMqnT5/G7du3AWhbUQYMGCDlf++993Djxg1MnDgRly5dwrJly7Bx40aMGzeuOIpPRPnAIIaIXllJSUno168fVq1aBVdX1+IuDhE9p+PHj6NevXqoV68eAGD8+PGoV68epk2bBgB48OCBFNAAQPny5bFjxw5ERESgTp06WLBgAb755hsEBwcXS/mJyHzsTlYUEqOAlFjAu3pxl4QKgVqtRmZmZnEXwyJZW1tDoVAUdzFMGjFiBDp27IigoCB89tlnhT7/249T4GxrDWc760KfNxEZatmyJXJ7/N2aNWuMTnPq1KkiLJV2BLOMjIwiXcbLqqQfR6j4MIgpbOpMYNNg4MFpoFM4UKdXcZeICkgIgaioKMTFxRV3USyai4sLfHx8iv2+l5x+/vlnnDx5EseOHTMrf85hVRMSEvKc5pPfzmH/lUco526HWn4uqF3aGbX8nFGztDMcVKx+iV4FGRkZuHnzJjQaTXEXxWKV1OMIFS8eRQtbZgpgpdL+3foucOcw0G6uNo0sii6A8fLygp2dHSvPfBJCICUlBQ8fPgQA+Pr6FnOJnrlz5w7GjBmDiIgI2NjYmDVNQYZVjU/VtuBFPk5B5OMU/H7mPgBAJgMqeNijtp8Lavs5o7afM6r7OsNWyauNRC8TIQQePHgAhUIBf39/i3seVnEryccRKn4ykVu7KxlISEiAs7Mz4uPjTY9ColEDf38O/P0FAAGUqgf0WAu4ln2hZaWCU6vVuHLlCry8vODu7l7cxbFojx8/xsOHD1GlShWDLgFm7U9F4Ndff0W3bt30yqNWqyGTySCXy5Genm5QVmMtMf7+/nmWPS4lA2fvxeO/u/E4ezceZ+/F415cqkE+uQyo4u2IWqW1QU0tPxdU9XGEjTUDG3r5FVddUBhyK3tmZiauXbuGUqVKwdnZuZhKaPlyO47QyyU/dQFbYoqCXAG0+gjwawhsGQrcPwV83Rx4axVQpW1xl47MoLsHxs7OrphLYvl02zAzM7PEHHxat26Ns2fP6qUNHjwYVatWxaRJk4yW09SwqnlxsVOiWWVPNKvsKaXFJKXj7D1tUPPf3XicvReH6IR0XIpKxKWoRGw6cRcAYCWX4TUfx6etNS6oVdoZVbwdobTi1VwiS6BWqwEASqWymEti2UricYSKH4OYolQ5CBi+H9g4ELh/EvixB9D8Q6DlFG2gQyUeu5A9v5K4DR0dHVGzZk29NHt7e7i7uxukFwUPBxVaveaFVq95SWnRCWlPg5o4/Pc0wHmcnIHz9xNw/n4Cfjp6BwCgtJKjmq+TdH9NbT9nVPJ0gJWCgQ1RSVUS60FLwu1HxjCIKWouZYAhO4FdHwHHvgH2zwPuHgO6fwvYexR36YiohPB2soF3dRsEVfcGoO0Lfj8+DWfvxj1trdG22sSnZuLMnTicuRMnTWtjLUeNUs5SV7Tafi6o4GEPuZwHfiIiejkxiHkRrFRAxwWA/xvA76OBG/uAFc2AHmuAMo2Ku3REJpUrVw5jx47F2LFji7soRW7fvn3FXQQ9MpkMpV1sUdrFFu1qam9mFULgTmwqztyNexrUxOHcvQQkpWfhxK0nOHHriTS9vVKBmtnur6ld2hll3TlABRG9WK/ScYReLAYxL1LtHoBPTWDD28Djq8CaDkDbz4BG72mHKyIqBC1btkTdunURHh7+3PM6duwY7O3tn79QVChkMhnKuNuhjLsdQuqUAgBoNAI3Hyfr3V9z7l4CkjPUOHIzFkduxkrTO9lYoZafM2qVfjYqWmkXWwY2RKSHxxGyBAxiXjSvasC7e4Fto4DzW4Gdk4E7R4DOiwGVY3GXjl4BQgio1WpYWeW9+3t6euaZh4qXXC5DRU8HVPR0QNd6pQEAao3AtYdJ+E9qsYnHhQcJSEjLwsFrj3Hw2mNpejd75bMR0Upru6J5O6kY2BCRSTyOUEnAO0GLg8oRCF0NtPsckFtpg5mVrYCHF4u7ZGThBg0ahL///huLFi2CTCaDTCbDmjVrIJPJ8McffyAgIAAqlQr//PMPrl+/ji5dusDb2xsODg5o0KABdu/erTe/cuXK6V2Jk8lk+Oabb9CtWzfY2dmhcuXK2LZt2wteS8qL4umoZj3q+2NWl5r4dUQTnJ8ZjB2jm2LuW7XQt1EZ1CrtDGuFDLHJGfj7yiMs/usa3l13Am+E7UHDOXvwzppjCN99BX9disajxPS8F0pELwUeR8hSsCWmuMhkwBvvAaVfBzYN0nYvW/Um0CkcqNOruEtHRgghkJqpLpZl21orzLoyvmjRIly5cgU1a9bErFmzAADnz58HAEyePBnz589HhQoV4Orqijt37qBDhw6YPXs2VCoVvv/+e4SEhODy5csoU6aMyWXMnDkTX3zxBebNm4fFixejX79+uHXrFtzc3ApnZalIWCu0N//XKOWM3k/T0rPUuByViDN346UBBK4+TMKjxHTsufQQey49lKYv5WzzdDQ07VDPtUo7w9Wew8YS5QePI1o8jlBhYBBT3Pwbaodh/mUocGMvsPVdbfeydmHaAQGoxEjNVKP6tF3FsuwLs4Jhp8x7d3V2doZSqYSdnR18fHwAAJcuXQIAzJo1C23atJHyurm5oU6dOtL7Tz/9FFu3bsW2bdswcuRIk8sYNGgQ+vTpAwCYM2cOvvrqKxw9ehTt2rUr0LpR8VFZKVDbzwW1/VwAaB/Gm5qhxoUHCdqg5ulQz9ceJeF+fBrux6dh1/loaXp/N1vULu2iDW5KO6OmnzOcbKyLZ2WILACPI1o8jlBhYBBTEth7AP1/Af7+XPs6/q32AZk912qHaCYqBPXr19d7n5SUhBkzZmDHjh148OABsrKykJqaitu3b+c6n9q1a0v/29vbw8nJCQ8fPsxlCrIktkoFAsq6IqCsq5SWlJ6F8/eeDfN89l48bsYk405sKu7EpmLH2QdS3goe9k8HD9C22tQo5QR7FQ81RC8DHkeoJOGRpaSQK4BWHwF+DYAtw7QPx1zRDHhrFVClbXGXjqBtir8wK7jYlv28co4OM2HCBERERGD+/PmoVKkSbG1tERoaioyMjFznY22tf6VdJpNBo9E8d/mo5HJQWaFRBXc0quAupcWnZuL8vXiptea/e3G4E5uKGzHJuBGTjN9O3weg7TlbydNBaq2p9TSwsSmE3zSRpeFxRIvHESoMDGJKmspttN3LNg7UBjI/9gCafwi0nKINdKjYyGQys5rii5tSqYRanXef64MHD2LQoEHo1q0bAO0VtcjIyCIuHb0snG2tEVjJA4GVnj2090lyxtOg5tkDOh/Ep+HqwyRcfZiELSfvAdAOPFDZywF1/J52RfNzxms+jlBZsY6jlxuPI0SFp+TvSa8ilzLAkJ3Aro+AY98A++cBd48B3b/Vdj0jykW5cuVw5MgRREZGwsHBweTVrcqVK2PLli0ICQmBTCbD1KlTeSWMnourvRItqniiRZVnQ6o+TEzDOV03tLvxOHM3HjFJ6bgUlYhLUYnYcPwOAMBaIUNVH6dsLTbOqOLtCGsFB9EketF4HCFLwCCmpLJSAR0XAP5vAL+PBm7s03Yv67lWOxgAkQkTJkzAwIEDUb16daSmpmL16tVG8y1cuBBDhgxBYGAgPDw8MGnSJCQkJLzg0tLLzsvRBm9WtcGbVb0BaEdnik5Ix5m7cU+7oWlbbp6kZOLs0/tufnw6rcpKjuqlnKRuaLX9nFHR0wEKOZ9hQ1SUeBwhSyATQojiLoQlSUhIgLOzM+Lj4+Hk5PRiFvrwIrDhbe0wzHIroO1nQKP3tJ3NqUikpaXh5s2bKF++PGxsbIq7OBYtt21ZLPtTIbHkspc0QgjcfZKabeAAbXe0xLQsg7y21grULO2EWqW1QU0tP2eUd7eHnIGNRbPk/Sm3svNYUji4HV8d+akL2BJjCbyqAe/uBbaN0j4Yc+dk7TDMnRdrH5xJRGTBZDIZ/N3s4O9mhw61fAEAGo3ArdgU/Jetxeb8vXgkZ6hxLPIJjkU+kaZ3VFmhZmlnKaipXdoF/m62Zj0Tg4iILBODGEuhcgRCV2u7l/35sTaYiToH9FqnDXKIiF4icrkM5T3sUd7DHl3qlgYAqDUCN2OS8N/dZ0M9n78fj8T0LBy68RiHbjyWpne2tdYGNVJw44JSzjYMbIiIXhIMYiyJTAa88R5Q+nXt6GWPrwKr3gRCFgG1exZ36YiIipRCLkMlL0dU8nLEW6/7AQCy1BpcfZgkDfN89m48Lj5IRHxqJg5cjcGBqzHS9B4OStTS3V/zNLjxcmLXFCIiS8QgxhL5NwTeOwD88o72hv8tw4Dbh4F2YdoBAYiIXhFWCjmq+Tqhmq8TejbwBwBkZGlwJTpR7/6ay1GJiEnKwN7Lj7D38iNpem8nld79NbVLO8PdgfUoEVFJxyDGUtl7AP23APvmAvu/AI5/C9w/pR29zKVMcZeOiKjYKK3kqFnaGTVLOwPQ1odpmWpcfJDwbPCAu/G4+jAR0QnpiE6Ixu6L0dL0pV1std3Q/LX319Qq7QxnO2sTS6OSZunSpZg3bx6ioqJQp04dLF68GA0bmh7VMzw8HMuXL8ft27fh4eGB0NBQhIWF8QZyohKOQYwlkyuANz/WtsxsGaZ9OOaKZsBbq4AqbYu7dEREJYaNtQL1yriiXhlXKS0lIwsX7idI99f8dzcON2KScS8uFffiUrHzfJSUt6y73bP7a0q7oGZpJzjaMLApaTZs2IDx48djxYoVaNSoEcLDwxEcHIzLly/Dy8vLIP+PP/6IyZMn47vvvkNgYCCuXLmCQYMGQSaTYeHChcWwBkRkLgYxL4PKbYDh+4GNA7StMT/2AJpPBFpO1gY6RERkwE5phfrl3FC/nJuUlpiWiXP3EqRuaGfvxePW4xTptf2/B1LeCp72T++t0XZHq17KySKexv4yW7hwIYYNG4bBgwcDAFasWIEdO3bgu+++w+TJkw3y//vvv2jSpAn69u0LQPuQxz59+uDIkSMvtNxElH+sbV8WLmWAIbuAnVO0Xcv2fwHcPQZ0/0bb9YyIiPLkaGONxhXd0biiu5QWl5KBc/cSpIED/rsbj3txqbjxKBk3HiXj19P3AQByGVDZy1F7b83TkdGq+TrBxpoXk16EjIwMnDhxAlOmTJHS5HI5goKCcOjQIaPTBAYG4ocffsDRo0fRsGFD3LhxA//73//w9ttvm1xOeno60tPTpfd8uCNR8WAQ8zKxUgGdFgJl3gB+HwPc2KvtXtZzrbbLGRER5ZuLnRJNK3ugaeVnF4RiktJx9l68FNScvReH6IR0XI5OxP+zd9/xUZRbA8d/u5tk03slCQQB6TUUgVe6BpRIkSYIBBS516AioogFRS9Er4pYEAELWLigQBRBQECaIII0AekCoYUQIL3vzvvHJks2PZBkssn5ftxPmNmZnbNrZjJnn+c5z4mryazYdxEAG62Gu/1caBV0q8Xmbj8X7Gy0ar2dGis+Ph6DwYCfn5/Fej8/P44fP17kPiNHjiQ+Pp7/+7//Q1EUcnJy+Ne//sVLL71U7HGioqKYOXNmhcYuhCg/SWJqolbDwL8lLB9tKsP8ZT+4/z/Q6V+mMs1ClCAkJITJkyczefJktUMRotrydtbTs7EvPRvfGmdxNSnDPDHn4Yum7mjXU7P4+0oSf19JYtneCwDY6bQ0DXAxT8zZMsiNRr7O2OgksalqW7duZfbs2XzyySd06tSJ06dP88wzz/Dmm2/y6quvFrnP9OnTmTJlink5KSmJ4ODgqgrZKsjfEVEVJImpqXybwhNbYPVTpokx178IF/6Ahz4yTZwphBCiQvm52uPXzJ4+zUwtAYqicDkxw5zQ5FVGS0zP5tDFRA5dTARiALC31dIswJVWQaZqaK2D3ajv7YxOK188lZW3tzc6nY6rV69arL969Sr+/v5F7vPqq68yevRoHn/8cQBatmxJamoqTzzxBC+//DJabeHEUq/Xo9dLGW4h1CZJTE2md4EhX0JwJ/jlFVMyE3sEhn9tSnKEEEJUGo1GQ6C7A4HuDvRtEQCYEpsLN9ItxtccuZRIcmYO+2MS2B+TYN7fyU5H80DT3DUtc7uj1fN0RCuJTZHs7OwIDQ1l8+bNDBw4EACj0cjmzZuZNGlSkfukpaUVSlR0OtMYJkVRKjVeIcSdkSSmptNo4J5/Q2AofDfW1L1sUS8I/8DU7UzUKAsXLuT111/n4sWLFn+YBwwYgJeXFy+//DJTpkxh9+7dpKam0rRpU6KioujTp4+KUQtRe2g0Gup6OVLXy5H+reoAYDQqnL2eajG+5silJFKzDOw5e4M9Z2+Y93ext8kt9exuLh4Q5OGARroKAzBlyhTGjh1L+/bt6dixI3PnziU1NdVcrWzMmDEEBgYSFRUFQHh4OHPmzKFt27bm7mSvvvoq4eHh5mSmtpG/I8JaSBJTWwR3hH/tgJWPwT9bTfPKxOyGvlGmggCidIoC2WnqHNvWsUzjmYYOHcpTTz3Fli1b6N27NwA3btxg/fr1/Pzzz6SkpPDAAw8wa9Ys9Ho9X331FeHh4Zw4cYK6dWWSVCHUoNVqaODjTAMfZwa2DQTAYFQ4cy0ld2LOBP66lMjRy0kkZ+Sw68x1dp25bt7fw9GWlkHu+Vps3PB3ta+Vic3w4cO5du0aM2bMIDY2ljZt2rB+/XrzYP+YmBiLG/NXXnkFjUbDK6+8wqVLl/Dx8SE8PJxZs2ZVToDyd0SICiNJTG3i5A2ProKtb5lKMP/5uWlemWFLTCWaRcmy02B2HXWO/dJlsHMqdTMPDw/69evH0qVLzX98VqxYgbe3Nz179kSr1dK6dWvz9m+++SbR0dGsXr262O4WQoiqp8utana3nwtDQoMAyDYYOXk1OV/xgESOxyZxMy2b7Sevsf3kNfP+3s56c0tN62DTBJ0+LrXjC6tJkyYVez3bunWrxbKNjQ2vvfYar732WhVEhvwdEaICSRJT22h10OtlU8vMqglweT8s6AaDF5kmzRRWb9SoUUyYMIFPPvkEvV7Pt99+y4gRI9BqtaSkpPD666+zdu1arly5Qk5ODunp6cTExKgdthCiFLY6Lc3ruNG8jhsjctdl5hg4EZuc22KTyKGLCZyKSyE+JZNfj8fx6/E48/4Bbva5XdHcaJlbQMDTyU6dNyOqNfk7IqyBJDG1VaP7YOJ2+G6MqTXm26HQ7Xno8aIp0RGF2TqavslS69hlFB4ejqIorF27lg4dOrBjxw7ef/99AKZOncrGjRt59913adiwIQ4ODgwZMoSsrKzKilwIUYn0Nrrc8THu5nXpWQb+vpJk7oZ2+GIip6+lcCUxgyuJGfzy963qXUEeDrktNu60DnKjeaAbbg62KryTWkL+jghRYSSJqc3c68L4DbB+uqlr2fb/wsW98PBnpq5nwpJGU6ameLXZ29szePBgvv32W06fPk3jxo1p164dADt37iQiIoJBgwYBkJKSwrlz51SMVghR0RzsdITW8yC0nod5XWpmDkcvJ/HXxQTzJJ3/xKdy8WY6F2+m8/PhWPO29b2dbrXYBJoSG2e93C5UCPk7ImqjjES4dhKuHYO7elTYEAa5KtV2NnroP8dUhnnNZPhni6l72dDFpi5nwiqNGjWK/v37c/ToUR599FHz+kaNGrFq1SrCw8PRaDS8+uqrGI1GFSMVQlQFJ70NHet70rG+p3ldYno2Ry/dGl/z16UELtxI52x8KmfjU1l9yNRioNFAAx9ni8IBzQLccLCTVvuaTP6OiHLLSIRrJyDumOnntdyfSZdubTNwPrQZWSGHkyRGmLQeDv4tTd3Lrp+CL/vB/bOg08QyVTMR1UuvXr3w9PTkxIkTjBx562IxZ84cxo8fT5cuXfD29mbatGkkJSWpGKkQQi1uDrZ0aehNl4a3Wt5vpmaZWmouJXLogqnV5kpiBqfjUjgdl8KqA6abEZ1WQyNfZ/P4mlaBbjQJcEFvI4lNTSF/R0Sx0hNyk5Tjtx5xxyG5hK6Szv7g2wTs3SssDI0iszmVS1JSEm5ubiQmJuLq6qp2OBUvMxl+nAR//2Babj4IHvrINHFmLZKRkcHZs2epX78+9vb2aodj1Ur6LK35fLLm2IUoj7jkDI5cSsxXPCCR+JTMQtvZ6jQ09nehZeCtOWwa+7tgqys8631B1nw+lRS7/C2pGPI5qsScrBzL18JyHJKvFL+PSwD4NDE9fHN/+jQGB4/i98mnPNcCaYkRlvQupq5kf3wKv7wCR6Mh9ggM/xp8m6odnRBCiCrm62JPryb29GpimmtFURSuJmWax9ccyp3L5mZaNkcuJXHkUhL/22Pa185GS7MAV57u3dC8vxCimkm/WXQ3sBKTlTqm5MS3qemnT1PwubvMyUpFkCRGFKbRwD3/hjrt4PsIU/eyRb0g/ANoNUzt6IS4Y/Pnz2f+/PnmwajNmzdnxowZ9OvXT93AhLACGo0Gfzd7/N38ub+5P2BKbC7eTOdwXovNpQT+uphIckYOBy8kIH0+hKgG0m+aun0V7AaWElv8Pi51cltUmt5KWrzvBgf3ch9eURQUxTTBb0WQJEYUr24nUxnmlY/B2W2meWUu/AFhs00FAYSwUkFBQbz11ls0atQIRVFYsmQJAwYM4MCBAzRv3lzt8ISwOhqNhmBPR4I9HXmgZQAARqNCzI00/rqUaFEpTQhRydJu3GpRyZ+0pFwtfh/XwKK7gdm7mTfJzDGQlJ5Dcko2yfEJJGfkkJyRTXJGDkkZ2STlW07OyDZtm5m3bFr33yGtGNQ2qELepiQxomTOPjA6GrZGwfZ3YO9ncGk/DFtSYSXyhKhq4eHhFsuzZs1i/vz57N69W5IYISqIVqshxNuJEO/qX1JYCKuUdiO3NaVAN7ASkpUc5zqkuzci2aUh1x3rE2dfn8u29bieozclGinZJMfn5CYmh28lJBk5ZOXceRW65IycO36NPJLEiNJpddDrFQjqaGqNubzfVIZ58CLTpJlCWDGDwcD3339PamoqnTt3Lna7zMxMMjNvDWaWajxCCCGqQk5yPOmXj5Adewwl7jg2109gf/MU+sz4YveJ0/pwThPMSSWIv3MC+Ds7kNNKHVIyHKHQbjHlisdZb4OLvenham+b++9bP10dcn/a21g852pvi6eTXfk/gGLUyiRmzZo1PPfccxiNRqZNm8bjjz+udkjW4e77Td3Lvh8Llw/At0Oh2/PQ40VTolMDSfG+O1ddP8PDhw/TuXNnMjIycHZ2Jjo6mmbNmhW7fVRUFDNnzqzCCIUQNUV1vQ5aC2v+/IxGheTM/N2sckhKz7boZpWU+5whJR635NN4pZ3FP/MsgTkxhBgv4K1JpLgasRcVb04ZAzmlBHFKCeSkMYjTSiCpOBS5vaOd7layUTD5yEtMHHLX6QsnJs56G3QVNKblTtW6JCYnJ4cpU6awZcsW3NzcCA0NZdCgQXh5eakdmnXwqAfjN8D6F+HPL2D7f+HiXnj4M3DyLn1/K2FrawtAWloaDg5FXwhE2aSlpQG3PtPqonHjxhw8eJDExERWrFjB2LFj2bZtW7GJzPTp05kyZYp5OSkpieDg4KoKVwhhhXQ60xd8WVlZ8rfkDqj1d0RRFFKzDKako5TxH+bkJN/4j+SMHJIzC3ef8iSJRppLNNJepJHmIu1y/+2tKaKFPzdfuKh4c5pgLuiCuWwXwjX7+iQ61cfO0c3cKhJsb0uzIlpHXHOTEGe9DTZlKHluLWpdErNnzx6aN29OYGAgAP369eOXX37hkUceUTkyK2Kjh/7vQ/A9sGYy/LPF1L1s6GII7qh2dBVCp9Ph7u5OXFwcAI6Ojmhk0s9yURSFtLQ04uLicHd3N/8xry7s7Oxo2LAhAKGhoezdu5cPPviABQsWFLm9Xq9Hr5eCFkKIsrOxscHR0ZFr165ha2uLVltzbiCrwp38HVEUhfRsgzm5sEw68lo/LJOOpPRbrSLJGdmkZOZgvINGIE+SuCc3UWmivUwT3UXu0lzCQ0ksdp8Uh0DS3BqS7Xk3Ru8m6Pyaog9ogq+rB0E28vuTn9UlMdu3b+edd95h3759XLlyhejoaAYOHGixzbx583jnnXeIjY2ldevWfPTRR3TsaLq5vnz5sjmBAQgMDOTSpUtV+RZqjtbDwb8lfDcarp+GL/vB/bOg00RTmWYr5+9vKh2al8iI2+Pu7m7+LKszo9FoMeZFCCHulEajISAggLNnz3L+/Hm1w7EqiqJgVMCoKOToHLiWpGP/tSsWrSH5fxZVDctwJxlIPjZaza0uVvm6WeWt89UmUzfnPP5Z5/BOP4t7yj84JZ3CJuNGEW8s96d7vQKVwJqA9904651xrpCoaz6rS2JSU1Np3bo148ePZ/DgwYWeX758OVOmTOHTTz+lU6dOzJ07l7CwME6cOIGvr68KEddwfs1gwhZYPQn+/hHWT4MLu+Ghj0wTZ1qxvD8+vr6+ZGdnqx2OVbK1ta12LTBg6hrWr18/6tatS3JyMkuXLmXr1q1s2LBB7dCEEDWMnZ0djRo1IisrS+1QqkxWjoHUTAOpWTmkZuaQkmEgNSub1EwDKZm567JyTNtk5pCWmUNqlunfec/nGE1JzM0MIxk5t5+MaDXcGtOhtxz/YZGY5Ot6VXDMiL2t1tSrK/VabjWwv26VLT57HNKuFx+Aez3LCSF9TckKdlK1705ZXRLTr1+/EiekmzNnDhMmTGDcuHEAfPrpp6xdu5YvvviCF198kTp16li0vFy6dMncSlMUqUhUBvauMHQJ/PEp/PIKHI2Gq0dh2FemE9fK6XS6ankjLm5fXFwcY8aM4cqVK7i5udGqVSs2bNjAffdJtT0hRMXTarXY29urHUaZZBuMhcZ/FDfeI6ngmJDcdRVRihdMnTqc9TZ4ORdMMCwHmxca/5Fv2dFOV77u4IoCKXFw7S9TgpI3IeS145BeRMuKKVLTmOH8E0L6NJZkpZJZXRJTkqysLPbt28f06dPN67RaLX369OH3338HoGPHjhw5coRLly7h5ubGunXrePXVV4t9TalIVEYaDdzzb6jTDr6PgPiTsKgXhH8IrYaqHZ0QFj7//HO1QxBCiApnMCqkmAeeFxzvcWuged68HwUHrCdn5JCebaiwePJK8RZMPkoqxZt/Wyc7mwqb3b0Qc7KSO7dK/rlW0m8Ws5MGPEKK7AaGnWPlxCmKVaOSmPj4eAwGA35+fhbr/fz8OH78OGAaZPfee+/Rs2dPjEYjL7zwQomVyaQiUTnV7WQqw7zyMTi7DVY9bupeFjbbVBBACCGslMFgkK6lt6m6di2tToxGhZSsnAKtHvnGe1i0fBRITHKXU7MqLgGpMaV4FcU0+aO5RSVf0pKRUMxOucmKb1PLWey9GkmyUo3UqCSmrB566CEeeuihMm0rFYlug7MPjI6GrVGw/R3Y+xlc2g/DloB7XbWjE0KIclEUhdjYWBISEtQOxarlFfmojZUeV+67yJlrKYWSkPwJS0pWDhU1HYq9rbZQ0lFUa0iNKsWrKJAce2usSv5uYCUlK571i+4GZislsau7GpXEeHt7o9PpuHr1qsX6q1evWkV1pBpFq4Ner0BQR1g1AS7vN5VhHvwZNOqjdnRCCFFmeQmMr6+vlFu/DfnL5AIEBASoHFHVW3XgIjtPlzD4Ox87G22Blo9iZkUvspXE9G+7mlyK15ysFNENLKOY0sUaLXjUtxxg79MYvBtJsmLFalQSY2dnR2hoKJs3bzaXXTYajWzevJlJkyapG1xtdff9pu5l34+Fywfg2yHQ/QXoPs2U6AghRDVmMBjMCYxMinz78iZ6jIuLw9fXt1K7lpU0zUJREhISePnll1m1ahU3btygXr16zJ07lwceeKDCYrq/mT93+7lYdsWyty2UfLjY22BvK38bgdxk5YplknLtRG7LSgnJiuddt7qA5e8GZmsdhRVE2VldEpOSksLp06fNy2fPnuXgwYN4enpSt25dpkyZwtixY2nfvj0dO3Zk7ty5pKammquVCRV41IPxG2D9i/DnF7DtbbiwBx7+DJy81Y5OCCGKlTcGxtFR+sHfqbzPMDs7u9KSmPJOs5CVlcV9992Hr68vK1asIDAwkPPnz+Pu7l6hcY3tElKhr1ejKAokXS6iG9gJyCxjspI3dsWroSQrtYjVJTF//vknPXv2NC/nDbofO3YsixcvZvjw4Vy7do0ZM2YQGxtLmzZtWL9+faHB/qKK2eih//sQ3Al+mgz/bDF1Lxu6BII7qB2dEEKUSLqQ3bmq+AxLm2ahoC+++IIbN26wa9cubG1tAQgJCan0OGslc7KSvxtYXrJSzPQVGp0pWclfCcyniakbmBQLqvWsLonp0aMHSikj3yZNmiTdx6qr1iPAvxV8Nxqun4Yv+0HYLOj4hKlMsxBCCHEbyjLNQkGrV6+mc+fOREZG8uOPP+Lj48PIkSOZNm2aVFO7XYoCSZduDao3dwMrJVnxamA5IWRey4okK6IYVpfEiBrArxlM2AKrJ8HfP8K6FyBmNzz0Iehd1I5OCCFEASEhIUyePJnJkyerHUqxyjLNQkH//PMPv/76K6NGjeLnn3/m9OnTPPnkk2RnZ/Paa68VuY9Mgp1LUSDx4q3xKuak5QRkJRe9j0ZnSkzyVwLzaWpKYCRZEeUkSYxQh72rqSvZ7vmw8VU4ugquHoFhX5kubEIIIe5Ijx49aNOmDXPnzr3j19q7dy9OTjVv5nGj0Yivry8LFy5Ep9MRGhrKpUuXeOedd4pNYmrdJNiKAokXClcCu3YCslKK3kdrA54NLLuB+TY1rbOxq9r4RY0lSYxQj0YDnZ+EwHbwfQTEn4RFvSD8Q2g1VO3ohBCiRlMUBYPBgI1N6bcCPj4+VRDRnbmdaRYCAgIKTcTZtGlTYmNjycrKws6u8A13jZ0E22iEpIv5WlTyt6yUkKx4NbSsBObTRJIVUSVqcCFxYTXq3gMTd0D97pCdBqseh7XPQU5m6fsKIYQoJCIigm3btvHBBx+g0WjQaDQsXrwYjUbDunXrCA0NRa/X89tvv3HmzBkGDBiAn58fzs7OdOjQgU2bNlm8XkhIiEWLjkaj4bPPPmPQoEE4OjrSqFEjVq9eXcXv0lL+aRby5E2z0Llz5yL36dq1K6dPn8ZoNJrXnTx5koCAgCITGDBNgu3q6mrxsCpGI9w8Dyd/gZ0fQPS/YWFPiAqCuS1h6VBTD4mD38KlfaYERmtr6vbVbCD0mA5DF8OTf8DLsRD5h2ky657TofkgU4uLJDCiCkhLjKgenH1gdDRsjYLt78Dez0zzygxdDO511Y5OCCHMFEUhPdugyrEdbHVlqvL1wQcfcPLkSVq0aMEbb7wBwNGjRwF48cUXeffdd7nrrrvw8PDgwoULPPDAA8yaNQu9Xs9XX31FeHg4J06coG7d4q+/M2fO5L///S/vvPMOH330EaNGjeL8+fN4enpWzJu9DaVNszBmzBgCAwOJiooC4N///jcff/wxzzzzDE899RSnTp1i9uzZPP3006q9hwpjNEJiTBHdwE5CdmrR+2htTZW/8k8I6dvUVCFMZ1u18QtRCkliRPWh1UGvVyCoA6x6wvQN0IJuMPgzaNRH7eiEEAKA9GwDzWZsUOXYf78RhqNd6X+63dzcsLOzw9HR0dyVKm9w+xtvvMF9991n3tbT05PWrVubl998802io6NZvXp1iZU+IyIieOSRRwCYPXs2H374IXv27KFv37639d4qQmnTLMTExKDV3uqEEhwczIYNG3j22Wdp1aoVgYGBPPPMM0ybNk2tt1B+eclK/m5gccdMXbSz04rex5ysFOwGJsmKsB6SxIjq5+4wmLgdvhsDVw7Ct0Og+wvQfZop0RFCCHHb2rdvb7GckpLC66+/ztq1a7ly5Qo5OTmkp6cTExNT4uu0atXK/G8nJydcXV2Ji4urlJjLo6RpFrZu3VpoXefOndm9e3clR1UBjEZIOF9gQsjjJScrOjvTbPUF51nxvAt0cgsorJv8BpfRvHnzmDdvHgaDOl0Iah2PejB+A2yYDn9+Advehot7Ta0yTl5qRyeEqMUcbHX8/UaYase+UwWrjE2dOpWNGzfy7rvv0rBhQxwcHBgyZAhZWVklvk7e5JB5NBqNxdgScZuMRkg4V3Q3sJz0ovfR2YH33YW7gXnUl2RF1Fjym11GkZGRREZGkpSUhJubm9rh1A629tD/fQjuBD9NhjO/woJ7TaWZgzuoHZ0QopbSaDRl6tKlNjs7uzJ98bZz504iIiIYNGgQYGqZOXfuXCVHJzAaTC0rcfkmhIw7BvGnypCs5OsC5tMUPEIkWRG1jvzGi+qv9QjwbwXfjYbrp+HLfhA2Czo+YSrTLIQQopCQkBD++OMPzp07h7Ozc7GtJI0aNWLVqlWEh4ej0Wh49dVXpUWlot04C3F/F+4GlpNR9PY6vSlZ8W2Sr3WliSQrQuQjZ4KwDn7NYMIWWD0J/v4R1r0AMbvhoY9A76x2dEIIUe1MnTqVsWPH0qxZM9LT0/nyyy+L3G7OnDmMHz+eLl264O3tzbRp02rvLPSVZcV4uLy/8Hob+wID7PMlKzIGVIgSSRIjrIe9q6kr2e75phr2R1fB1SMw7GvTt1VCCCHM7r77bn7//XeLdREREYW2CwkJ4ddff7VYFxkZabFcsHuZoiiFXichIeG24qwV6rQBY06BbmCSrAhxJySJEdZFo4HOT0JgO/g+wtQcv6gXPPQhtByidnRCCCFEYf3fVzsCIWocbembCFEN1b0HJu6A+t1Mk3atfAzWPgc5mWpHJoQQQgghKpkkMcJ6OfvA6B/g3qmm5b2fmQb9J1xQNSwhhBBCCFG5JIkR1k2rg96vwsjvwN4dLu2DBd3g9Ca1IxNCCCGEEJVEkhhRM9wdBhO3Q0AbSL8B3wyBLbNNdfiFEEIIIUSNIkmMqDk86sH4DRA6DlBg29vw7RBIva52ZEIIIYQQogJJEiNqFlt7CJ8LgxaAjQOc+RUW3AsX9qodmRBCCCGEqCCSxIiaqfUImLAZPBtA0iXTgP8/FkARcxsIIYQQQgjrIkmMqLn8msMTW6HZADBmw7oXTKWYM1PUjkwIIYQQQtwBSWJEzWbvCkOXQFgUaG3gyEpY1BPijqsdmRBCVGshISHMnTtX7TCEEKJIksSImk+jgc5PQsRacAmA+JOwqBccXqF2ZEIIIYQQ4jZIEiNqj7r3wMQdUL8bZKeaupatnQo5mWpHJoQQQgghykGSmDKaN28ezZo1o0OHDmqHIu6Esw+M/gHunWpa3rvINOg/4YKqYQkhREVauHAhderUwWg0WqwfMGAA48eP58yZMwwYMAA/Pz+cnZ3p0KEDmzbJJMFCCOshSUwZRUZG8vfff7N3r5TqtXpaHfR+FUZ+B/bucGkfLOgGp+UPeG0RFRVFhw4dcHFxwdfXl4EDB3LixAm1wxLWQlEgK1WdRxkrLA4dOpTr16+zZcsW87obN26wfv16Ro0aRUpKCg888ACbN2/mwIED9O3bl/DwcGJiYirrUxNCiAplo3YAQqjm7jCYuA2+GwNXDsE3Q6D7NOj+ginRETXWtm3biIyMpEOHDuTk5PDSSy9x//338/fff+Pk5KR2eKK6y06D2XXUOfZLl8Gu9N9RDw8P+vXrx9KlS+nduzcAK1aswNvbm549e6LVamndurV5+zfffJPo6GhWr17NpEmTKi18IYSoKNISI2o3jxAY/wuEjgMU2PYWfDsEUq+rHZmoROvXryciIoLmzZvTunVrFi9eTExMDPv27VM7NCEqzKhRo1i5ciWZmaZxf99++y0jRoxAq9WSkpLC1KlTadq0Ke7u7jg7O3Ps2DFpiRFCWA1piRHC1h7C50JwJ1jzLJz51dS9bNgSCGqvdnSiCiQmJgLg6empciTCKtg6mlpE1Dp2GYWHh6MoCmvXrqVDhw7s2LGD999/H4CpU6eyceNG3n33XRo2bIiDgwNDhgwhKyursiIXQogKJS0xQuRp8whM2AyeDSDpInzRF/5YUOY+6MI6GY1GJk+eTNeuXWnRokWx22VmZpKUlGTxELWURmPq0qXGQ6Mpc5j29vYMHjyYb7/9lv/97380btyYdu3aAbBz504iIiIYNGgQLVu2xN/fn3PnzlXSB1a15s2bR0hICPb29nTq1Ik9e/aUab9ly5ah0WgYOHBg5QYohKgQksQIkZ9fc3hiKzR9CIzZsO4FUynmzBS1IxOVJDIykiNHjrBs2bISt4uKisLNzc38CA4OrqIIhbh9o0aNYu3atXzxxReMGjXKvL5Ro0asWrWKgwcPcujQIUaOHFmokpk1Wr58OVOmTOG1115j//79tG7dmrCwMOLi4krc79y5c0ydOpV77723iiIVQtwpSWKEKMjeFYZ9BWGzQWsDR1aaJse8JtWrappJkyaxZs0atmzZQlBQUInbTp8+ncTERPPjwgUpyy2qv169euHp6cmJEycYOXKkef2cOXPw8PCgS5cuhIeHExYWZm6lsWZz5sxhwoQJjBs3jmbNmvHpp5/i6OjIF198Uew+BoOBUaNGMXPmTO66664qjFYIcSdkTIwQRdFooHMkBIbC9xEQfwIW9oSHPoSWQ9SOTtwhRVF46qmniI6OZuvWrdSvX7/UffR6PXq9vgqiE6LiaLVaLl8uPH4nJCSEX3/91WJdZGSkxbK1dS/Lyspi3759TJ8+3bxOq9XSp08ffv/992L3e+ONN/D19eWxxx5jx44dVRGqEKICSBIjREnq3gMTt5u6lJ3N/RmzG8JmgY3c0FqryMhIli5dyo8//oiLiwuxsbEAuLm54eDgoHJ0QojbER8fj8FgwM/Pz2K9n58fx48fL3Kf3377jc8//5yDBw+W+TiZmZnmim+AjI8TQiXSnUyI0jj7wugf4N7nTMt7F8GX/SBBuhNZq/nz55OYmEiPHj0ICAgwP5YvX652aEKIKpKcnMzo0aNZtGgR3t7eZd5PxscJUT1IS4wQZaHVQe8ZENQRop+AS/tMZZgfXgQN+6gdnSgnRSrOCVHjeHt7o9PpuHr1qsX6q1ev4u/vX2j7M2fOcO7cOcLDw83r8oob2NjYcOLECRo0aFBov+nTpzNlyhTzclJSkiQyQqhAWmKEKI/GfU3dywJaQ/oN+GYIbIkCo0HtyIQQolazs7MjNDSUzZs3m9cZjUY2b95M586dC23fpEkTDh8+zMGDB82Phx56iJ49e3Lw4MFiExO9Xo+rq6vFQwhR9aQlRojy8giB8b/A+mmwbzFsewsu7oHBn4GTl9rRCSEqgbTe3bmq+AynTJnC2LFjad++PR07dmTu3LmkpqYybtw4AMaMGUNgYCBRUVHY29sXmhvK3d0doMQ5o4QQ1YMkMZUgMT0bV3sbNOWYlExYGVt7CP8Agu+BNc/CmV9N3cuGLYGg9mpHJ4SoILa2tgCkpaVJ0Yc7lJaWBtz6TCvD8OHDuXbtGjNmzCA2NpY2bdqwfv1682D/mJgYtFrphCJETaBR5OulcklKSsLNzY3ExMRim5BHLPydk1dTaFfXnbZ1PQit50HrIHcc7HRVHK2oElePwvLRcOMMaG1N88t0nFCumbVrq7KcT9WVNccuyufKlSskJCTg6+uLo6OjfEFVToqikJaWRlxcHO7u7gQEBBTaxprPJ2uOXYjqpjznk7TEVDCjUeFEbDI307LZdCyOTcdMswTbaDU0DXAltJ4Hbeu6E1rPg0B3B/ljWBP4NYcntsKPT8Kxn2Dd83BhN4R/CHpntaMTQtyhvEHhpc36Lkrm7u5e5AB7IYS4HZLEVDCtVsMfL/Xh6OVE9p2/yYGYBPadv0lsUgaHLyVy+FIii3eZtvVz1dMut6WmbV0PWgS6oreR1hqrZO8Kw76G3+fBxhlwZCXEHoHhX4NPY7WjE0LcAY1GQ0BAAL6+vmRnZ6sdjlWytbVFp5O/b0KIiiNJTBnNmzePefPmYTCUXoXKzkZL27qmxCTP5YR09p2/mZvY3OTo5SSuJmWy7kgs646YJtqz02lpEWhqrQmt50G7uh74utpX2nsSFUyjgS6TIDAUVoyD+BOwsCc89CG0HKJ2dEKIO6TT6eRGXAghqgkZE1NOFdX3NT3LwOFLiRaJzfXUrELbBXk4mFtr2tX1oEmAC7Y6GZRY7aXEwcrH4Ox203LHJ+D+WWBjp25c1Yw19yW35tiFqG6s+Xyy5tiFqG5kTIwVcLDT0bG+Jx3rewKmgY/nr6exP8aU1OyPSeBEbBIXb6Zz8WY6qw9dNu1nq6N1sJtFNzRPJ7kxrnacfWH0D7BlFux4D/YshEv7YehicJdJ0YQQQggh7oQkMdWERqMhxNuJEG8nBrcLAiAlM4dDFxIsWmuSMnLY/c8Ndv9zw7zvXd5O5ipoofU8aOTrjFYrBQNUp9VB7xkQ1BGin4BLf5rKMD+8CBr2UTs6IYQQQgirJUlMNeast6FrQ2+6NvQGTJXPzlxLMbfW7Dt/kzPXUvkn3vRYuf8iAC56G9rUdTe31rSp646rfeXV5RelaNwXJm6H78bAlUPwzRDo8SJ0ewFkvgIhhBBCiHKTJMaKaLUaGvm50MjPheEd6gKQkJbFgZgEc2Jz8EICyZk57DgVz45T8YBpvPndvi60q+dBu9zyzvW9naS8c1XyCIHxv8D6abBvMWyNggt7YPAicPJSOzohhBBCCKsiSYyVc3e0o2cTX3o28QUgx2DkxNVk9scksD+3tSbmRhonriZz4moy/9sTA4CHoy3t6nrkJjYetA52w9FOfh0qla09hH8AwffAmmfhzGZT97JhSyCovdrRCSGEEEJYDblrrWFsdFqa13GjeR03Rt9TD4BryZnsj7lpepy/yaGLidxMy2bz8Tg2HzdN3qbTamga4EJovsQmyEMm46wUbR4B/5am7mU3zsAXfSFsNnScYGo2E0IIIYQQJZIkphbwcdET1tyfsOammZKzcoz8fSUptwqaKbG5kpjBkUtJHLmUxJLfzwPg63JrMs529dxpXscNe1uZI6FC+LeAJ7bAj5Fw7CdY9zxc2A3hH4LeWe3ohBBCCCGqNUliaiE7Gy1tgt1pE+zOY9QHTJNx5i/vfPRSInHJmaw/Gsv6o7cm42we6EqoObHxwE8m47x99m4w7Gv4fR5snAFHVkLsERj+Nfg0Vjs6IYQQQohqS5IYAUAddwfquDvQv1UdADKyb03GuT+3xSY+xVRE4EBMAp/9dhaAQHcH2tXzILSuO+3qedA0wFUm4ywPjQa6TILAUPg+AuJPwMKe8NCH0HKI2tEJIYQQQlRLksSIItnb6ugQ4kmHkFuTccbcyDcZ5/kEjscmcSkhnUsJ6fyUOxmnva2W1kHuuYmNqbVGJuMsg3qd4V87YMV4OLcDVj4GF/6A+2eBjXx+QgghhBD5SRIjykSj0VDPy4l6Xk4Mams5Gef+8zfZF3OTAzEJJKZn88fZG/xx9tZknPW9nWibW9rZNBmnCzqZjLMwZ18Y/QNsmQW/zYE9C+HSflP1MrcgtaMTQgghhKg2JIkRt62oyTj/iU9h//kEc9GAU3EpnI1P5Wx8Kqv2XzLv17auO23zJuMMdsfNQSbjBEBnA31eg+BOEP0EXPoTPr0XHv4MGvZWOzohhBBCiGpBkhhRYbRaDQ19XWjo68KwDsEAJKZls//CTQ7kttYcjEkgpYjJOBv5OhNaz8Oc2NxV2yfjbNwXJm43lWG+cgi+eRh6vAjdXgCtjDkSQgghRO0mSYyoVG6OtvRs7EvPxqbJOA1GhROxyabuZ7mJzfnraZy8msLJqyn8b88FANxzJ+M0JTbutA5yx0lfy35dPUJg/C+w7gXYvwS2RsGFPTB4ETh5qR2dEEIIIYRqatldoaVBgwaxdetWevfuzYoVK9QOp1bQaTU0q+NKszqu5sk441MycyugJeROxplAQlo2vx6P49d8k3E28XcxlXbOTW5qxWSctvamSmV174E1U+DMZljQDYZ9BUGhakcnhBBCCKEKjaIoitpBqGXr1q0kJyezZMmSMicxSUlJuLm5kZiYiKurayVHWDtl5Rg5VmAyzsuJGYW283bWE1rP3ZzYtAis4ZNxxh4xdS+7cQa0ttA3Cjo8buqPZ6Ws+Xyy5tiFqG6s+Xyy5tiFqG7Kcz7V6paYHj16sHXrVrXDEAXY2WhpHexO62B3xudOxnklMd2iYMDRy4nEp2Sy4ehVNhy9CoCtTkPzOm7mKmjt6nrg71aDJuP0bwFPbIEfI+HYT/DzVIjZDeEfgN5Z7eiEEEIIIarMbY0QvnTpEo8++iheXl44ODjQsmVL/vzzzwoLavv27YSHh1OnTh00Gg0//PBDkdvNmzePkJAQ7O3t6dSpE3v27KmwGET1EuDmwIOtApgR3owfIrty+PUwVvyrM9P7NSGsuR/eznqyDQoHLyTw+W9nefLb/dwTtZmub/3KpKX7+XLnWf66mEC2waj2W7kz9m4w7GvT/DEaHRxZAYt6wbUTakcmhBBCCFFlyt0Sc/PmTbp27UrPnj1Zt24dPj4+nDp1Cg8PjyK337lzJx07dsTW1rKE7t9//42Xlxd+fn6F9klNTaV169aMHz+ewYMHF/m6y5cvZ8qUKXz66ad06tSJuXPnEhYWxokTJ/D1NQ0ib9OmDTk5OYX2/eWXX6hTp05537qoRuxtdbQP8aR9vsk4L9xIvzUZZ8xNjl25NRnnmr+u5O6npVWQu3lcTbu67ng569V8K+Wn0UCXSRAYCt9HQPwJWNjTNHam5RC1oxNCCFXNmzePd955h9jYWFq3bs1HH31Ex44di9x20aJFfPXVVxw5cgSA0NBQZs+eXez2Qojqo9xjYl588UV27tzJjh07St3WaDTSrl07GjVqxLJly9DpTOMVTpw4Qffu3ZkyZQovvPBCyQFqNERHRzNw4ECL9Z06daJDhw58/PHH5mMFBwfz1FNP8eKLL5b5/WzdupWPP/5YxsTUQKmZORy6mDsZZ27hgMT07ELbhXg50q6uB+1yu6Hd7WdFk3GmxMGK8XAu93zsOBHu/w/Y2KkbVxlZ8/lkzbELUd1U1Pm0fPlyxowZY/EF5/fff2/xBWd+o0aNomvXrnTp0gV7e3vefvttoqOjOXr0KIGBgVUauxCifOdTuZOYZs2aERYWxsWLF9m2bRuBgYE8+eSTTJgwocjtL1++TLdu3ejUqRNff/01Z8+epVu3boSHh/Ppp5+WeryikpisrCwcHR1ZsWKFxfqxY8eSkJDAjz/+WOb3U9YkZt68ecybNw+DwcDJkyflYmWFTJNxppqLBew7b5qMsyBnvQ1tgt1pV9eddrlz11TryTgNObBlFvw2x7Qc2B6GLQG3IHXjKgNr/uNvzbELUd1U1Pl0p19wGgwGPDw8+PjjjxkzZkyVxi6EqOSB/f/88w/z589nypQpvPTSS+zdu5enn34aOzs7xo4dW2j7OnXq8Ouvv3LvvfcycuRIfv/9d/r06cP8+fPLe2iz+Ph4DAZDoa5ofn5+HD9+vMyv06dPHw4dOkRqaipBQUF8//33dO7cuchtIyMjiYyMNH+4wvqYJuN0pqGvM8Pa35qM88CFW+WdD14wTcb52+l4fjsdb943bzLOvBabu7yd0FaX1hqdDfR5DYI7QvREuPQnfHovPPwZNOytdnRCCFElsrKy2LdvH9OnTzev02q19OnTh99//71Mr5GWlkZ2djaenp7FbpOZmUlmZqZ5OSkp6faDFkLctnInMUajkfbt2zN79mwA2rZty5EjR/j000+LTGIA6taty9dff0337t256667+Pzzz6vF/B6bNm1SOwShMjdHW3o09qVHvsk4T15NtijvfO56GqfiUjgVl8KyvabJON0cbGlX91Z559bB1WAyzsb94Ilt8P1YuHIIvnkYekyHbs+D9rZqeAghhNWoiC84p02bRp06dejTp0+x20RFRTFz5sw7ilUIcefKfdcVEBBAs2bNLNY1bdqUlStXFrvP1atXeeKJJwgPD2fv3r08++yzfPTRR+WPNpe3tzc6nY6rV68WOo6/v/9tv64QOq2GpgGuNA1w5dHcyTivp2SaWmpyiwb8ddE0tmbLiWtsOXENAK0Gmvi7mpKaeu6E1vUk2FOFyTg968P4X2DdC7B/CWydDRf+gMGLwMmramMRQggr8tZbb7Fs2TK2bt2KvX3x5fmnT5/OlClTzMtJSUkEBwdXRYhCiHzKncR07dqVEycsy7mePHmSevXqFbl9fHw8vXv3pmnTpnz//fecPHmSHj16oNfreffdd28raDs7O0JDQ9m8ebN5TIzRaGTz5s1MmjTptl5TiOJ4Oeu5r5kf9zUzfbuXbcg/GaepG9qlhHT+vpLE31eS+Hr3ecA0Gae5taaeBy2rajJOW3tTpbK698CaZ+HMZljQDYZ9BUGhlX98IYRQwZ18wfnuu+/y1ltvsWnTJlq1alXitnq9Hr3eyqpaClEDlTuJefbZZ+nSpQuzZ89m2LBh7Nmzh4ULF7Jw4cJC2xqNRvr160e9evVYvnw5NjY2NGvWjI0bN9KrVy8CAwN59tlnC+2XkpLC6dOnzctnz57l4MGDeHp6UrduXQCmTJnC2LFjad++PR07dmTu3LmkpqYybty48r4lIcrFVmcq09wqyJ1xXU3rYhMzLMo7H7lkmozzl7+v8svftybjbFbHjdC88s713Alwc6i8QNuMBP9W8N1ouPEPfBEGfaOgw+OmMs213Pbt23nnnXfYt28fV65cKbIKohDCetzuF5z//e9/mTVrFhs2bKB9+/ZVFK0Q4k6VuzoZwJo1a5g+fTqnTp2ifv36TJkypdjqZBs3buTee+8t1DR74MABfHx8CAoqXEFp69at9OzZs9D6sWPHsnjxYvPyxx9/bK4F36ZNGz788EM6depU3rdTLlKFRJRFRraBo5cTTUnN+QT2xdzkWnJmoe3quNnTtp4HobkFA5oFuGJnU8HjVzIS4cdIOPaTabnFEAj/APTOFXuc26Dm+bRu3Tp27txJaGgogwcPLncSI9cCISpORZZYHjt2LAsWLDB/wfndd99x/Phx/Pz8GDNmDIGBgURFRQHw9ttvM2PGDJYuXUrXrl3Nr+Ps7Iyzc9mukXItEKLiVGqJ5dpOLlbidiiKwsWbBSfjTMZgtDz99DZaWge507aeuzmx8a6IyTgVBX6fBxtngGIAnyam7mU+je/8te9AdTmfipuPqiTVJXYhaoKKPJ9K+oKzR48ehISEmL8QDQkJ4fz584Ve47XXXuP111+v8tiFqO0kialEcrESFSUtK4dDFxItEpuEtMKTcdbzciS0roe5xaax/x1Mxnl+F3w/DlJiwdYJBnwELR6+w3dy+6rL+SRJjBDqsubzyZpjF6K6qdR5YoQQFcPRzobODbzo3MBUNUxRcifjzE1o8ibjPH89jfPX01h14BIATnY62tR1N89Z0y7YAzfHMk7GWa8LTNwOKx+DcztgxXiI+QPu/w/Y2FXWW60RZG4IIYQQovqQJEaIakKj0dDAx5kGPs4MzZuMMz2bgxcSzInNwZgEkjNz2Hn6OjtPXzfv29DXObf7maka2l3ezsVPxuniB6N/gC2z4Lc5sGcBXN4PQxeDW+ExasJE5oYQQgghqg/pTlZO0mws1GQwKpyKSzYXDNgfc5Oz8amFtnO1t6FdvoIBrYPdcS5qMs4T6yB6omnwv6MXPPwZNOhVBe/EpLqcT2XpTlZUS0xwcLDqsQtRE1SXa8HtsObYhahupDuZEDWUTquhib8rTfxdGdXp1mScB/JNxnnoYgJJGTlsPXGNrfkm42zs70pobktNu7oe1PV0RNO4HzyxDb4bA7F/wdeDocd06PY8aCu4SpqVk7khhBBCiOpDkhghrJyXs54+zfzok28yzuNXktl3/gb7YxLYlzsZ57ErSRy7ksQ3u2MA8Ha2o23unDWhfb6j7ZHZ2Bz8CrbOhgt/wOBF4OSl5lurVGWZj0oIIYQQ1ZN0JysnaTYW1uhqUgb7z+efjDOJLIPRYhsbrYZJnnuITJ2HrZJFjksgNsO/hqDQSotLzfOprPNRFUeuBUJUHGs+n6w5diGqG+lOJoSw4OdqT7+WAfRrGQBAZo6BI5eSzAUD/jxvmoxzbnwH1mveYL7t+9RPvkT2Z/ez0ieS1FYRhIZ4Vs5knCrp0aMH8h2OEEIIYZ0kiRGiFtLb6EzdyOp5AJaTce4/X48Xz93F+OvvEKbdy4hrH/DDL3sZmf04BhtHWgW53SrvXNcDHxcZJyKEEEKIqiVJjBACjUZDsKcjwZ6ODGgTCLQgLbMX536ZQ919bzNQt4uWuhieyHyGveeM7D1307xvXU/H3GIB7rSr50FjPxdsdDWjtUYIIYQQ1ZMkMUKIIjnqbQkJnwat7oXvx9Eg5SIbnV5jT6s3+DHnHg7E3OTE1WRibqQRcyON6NzJOB3tdLQJNk3GObBtIA19nVV+J0IIIYSoaSSJEUKUrF4XmLgdVj6G9twO7tk/lXs6/QsmvUlSjoaD+co7503GuevMdXaduU67eu6SxAghhBCiwkkSI4QonYsfjP4BtvwHfnsf/vgULu3Ddehiut0dRLe7fQDTZJyn41LMVdDaBnuoG7cQQgghaiTpuC6EKBudDfR5HUb8D/RucHEvLOgGZ369tYlWQ2N/F0Z2qsu7Q1vj4WSnXrxCCCGEqLEkiRFClE+TB2DiNvBvBWnX4evBsPVtMBpL31cIIYQQogJIdzIhRPl51ofHNsK652H/V7B1NlzcA4MXgaOn2tEJYX0UBYwGMGaDMQcM2SUvG3PAkPvTvE3+ZUPuPnewbD5uTr5jlhJXcTE9/Bk07a/2pyyEqEEkiRFC3B5be3joIwi+B9ZOgdObTN3Lhi6BoFC1oxM1iaIUuIkueFNdnpvsfDfopd14l2m5pGSgHHEZc9T+lCuXIUvtCIQQNYwkMWU0b9485s2bh8FgUDsUIaqXtqMgoBV8NwZu/ANfhEHfKOjwOGg0akdX8ylKOb5Br8Dl8nwLf7vJQN4xlFp83dXa5D5sQasDnW0xy7mPMi3rcvfPW5f/GBW9rDP928lH7U9SCFHDaBRFUdQOwpokJSXh5uZGYmIirq6uaocjRPWRkQg/PAnH15iWWw6F/nNBX3yJZWs+n8oU+84P4Ob5YpKB2+iSU9SyUovHIplvmgvcmBe8qa6wm/+ilnVlj6O8y1pdrfkioMZfC4QQZVKe80laYoQQFcPeDYZ/A79/DBtfg8PfQ5220DlS7cjUc/QHuLxfnWNrbUv/Fr605RJv/iuoZeB24tLZgkZba27whRBCFCZJjBCi4mg00OUpCAyFfYuh07/Ujkhd7UZDo/uL7mJz28lBvi46JbUOCCGEEDWYJDFCiIpXr4vpUdu1H692BEIIIUSNJPPECCGEEKLGmDdvHiEhIdjb29OpUyf27NlT4vbff/89TZo0wd7enpYtW/Lzzz9XUaRCiDshSYwQQgghaoTly5czZcoUXnvtNfbv30/r1q0JCwsjLi6uyO137drFI488wmOPPcaBAwcYOHAgAwcO5MiRI1UcuRCivCSJEUIIIUSNMGfOHCZMmMC4ceNo1qwZn376KY6OjnzxxRdFbv/BBx/Qt29fnn/+eZo2bcqbb75Ju3bt+Pjjj6s4ciFEeUkSI4QQQgirl5WVxb59++jTp495nVarpU+fPvz+++9F7vP7779bbA8QFhZW7PZCiOpDBvaXU960OklJSSpHIoT1yzuPrHG6KrkWCFFxKuJaEB8fj8FgwM/Pz2K9n58fx48fL3Kf2NjYIrePjY0t9jiZmZlkZmaalxMTEwG5FghREcpzLZAkppySk5MBCA4OVjkSIWqO5ORk3Nzc1A6jXORaIETFs4ZrQVRUFDNnziy0Xq4FQlScslwLJIkppzp16nDhwgVcXFzQFDPRWlJSEsHBwVy4cMEqZ++19vjB+t9DbYlfURSSk5OpU6dOFUZXMWrDtQCs/z1I/OqqymuBt7c3Op2Oq1evWqy/evUq/v7+Re7j7+9fru0Bpk+fzpQpU8zLRqORGzdu4OXlJdeCakziV1dlXAskiSknrVZLUFBQmbZ1dXW1yl+0PNYeP1j/e6gN8Vf3b12LU5uuBWD970HiV1dVXAvs7OwIDQ1l8+bNDBw4EDAlGJs3b2bSpElF7tO5c2c2b97M5MmTzes2btxI586diz2OXq9Hr9dbrHN3dy9TjNb+/xGs/z1I/OqqyGuBJDFCCCGEqBGmTJnC2LFjad++PR07dmTu3LmkpqYybtw4AMaMGUNgYCBRUVEAPPPMM3Tv3p333nuPBx98kGXLlvHnn3+ycOFCNd+GEKIMJIkRQgghRI0wfPhwrl27xowZM4iNjaVNmzasX7/ePHg/JiYGrfZWYdYuXbqwdOlSXnnlFV566SUaNWrEDz/8QIsWLdR6C0KIMpIkphLo9Xpee+21Qs3N1sLa4wfrfw8Sf81QEz4Ha38PEr+61Ih/0qRJxXYf27p1a6F1Q4cOZejQoZUak7X/fwTrfw8Sv7oqI36NYo21TYUQQgghhBC1lkx2KYQQQgghhLAqksQIIYQQQgghrIokMUIIIYQQQgirIkmMEEIIIYQQwqpIEiOEEEIIIYSwKpLECCGEEEIIIayKJDFCCCGEEEIIqyJJjBBCCCGEEMKqSBIjhBBCCCGEsCqSxAghhBBCCCGsiiQxQgghhBBCCKsiSYwQQgghhBDCqkgSI4QQQgghhLAqksQIIYQQQgghrIokMUIIIYQQQgirIkmMEEIIIYQQwqpIEiNuy9atW9FoNGzdulXtUCqVRqPh9ddfL9O2ISEhREREVGo8QoiqsXjxYjQaDefOnSt129pyPRRC3B65P6gcksTUMnl/mIt6vPjii2qHV6KCsdvb23P33XczadIkrl69WiUx7Nq1i9dff52EhIQqOZ4Qla2460HBh9o36D169LCIx9PTkw4dOvDFF19gNBqrJIZPPvmExYsXV8mxhKhM1nbeh4eHF3ru3LlzaDQa3n33XRUiK0zuD6qejdoBCHW88cYb1K9f32JdixYtVIqmfPJiz8jI4LfffmP+/Pn8/PPPHDlyBEdHxwo9Vnp6OjY2t06TXbt2MXPmTCIiInB3d7fY9sSJE2i18r2AsC5ff/21xfJXX33Fxo0bC61v2rRpVYZVpKCgIKKiogC4du0aX331FY899hgnT57krbfeqtBjjR49mhEjRqDX683rPvnkE7y9vQt9o9qtWzfS09Oxs7Or0BiEqCzWdN4DrFmzhn379hEaGqp2KMWS+4OqJ0lMLdWvXz/at2+vdhi3JX/sjz/+OF5eXsyZM4cff/yRRx55pEKPZW9vX+Zt89/sCGEtHn30UYvl3bt3s3HjxkLrC0pLS6vwLw1K4+bmZhHXxIkTady4MR9//DFvvvkmtra2FXYsnU6HTqcr07ZarbZc1woh1GZN533dunVJTk5m5syZrF69ukqPXVHk/qBySFooLJw/f54nn3ySxo0b4+DggJeXF0OHDi1Tv/BTp07x8MMP4+/vj729PUFBQYwYMYLExESL7b755htCQ0NxcHDA09OTESNGcOHChduOuVevXgCcPXsWgJycHN58800aNGiAXq8nJCSEl156iczMTIv9/vzzT8LCwvD29sbBwYH69eszfvx4i23yj4l5/fXXef755wGoX7++ubk977PJ3+f1zz//RKPRsGTJkkLxbtiwAY1Gw5o1a8zrLl26xPjx4/Hz80Ov19O8eXO++OKLQvt+9NFHNG/eHEdHRzw8PGjfvj1Lly4t/4cmRDn06NGDFi1asG/fPrp164ajoyMvvfQSUPy4saL6gCckJDB58mSCg4PR6/U0bNiQt99++7a7gzk6OnLPPfeQmprKtWvXAPjnn38YOnQonp6e5ufXrl1baN/SzqWCY2JCQkI4evQo27ZtM5/7PXr0AAqPiZk0aRLOzs6kpaUVOu4jjzyCv78/BoPBvG7dunXce++9ODk54eLiwoMPPsjRo0ct9ouNjWXcuHEEBQWh1+sJCAhgwIABZbo2C3E7qst57+LiwrPPPstPP/3E/v37S92+rMe7fv06o0ePxtXVFXd3d8aOHcuhQ4fQaDQW3Ub/+usvIiIiuOuuu7C3t8ff35/x48dz/fp18zZyf6AOaYmppRITE4mPj7dY5+3tzd69e9m1axcjRowgKCiIc+fOMX/+fHr06MHff/9d7DcwWVlZhIWFkZmZyVNPPYW/vz+XLl1izZo1JCQk4ObmBsCsWbN49dVXGTZsGI8//jjXrl3jo48+olu3bhw4cKBQE2xZnDlzBgAvLy/A1DqzZMkShgwZwnPPPccff/xBVFQUx44dIzo6GoC4uDjuv/9+fHx8ePHFF3F3d+fcuXOsWrWq2OMMHjyYkydP8r///Y/3338fb29vAHx8fApt2759e+666y6+++47xo4da/Hc8uXL8fDwICwsDICrV69yzz33oNFomDRpEj4+Pqxbt47HHnuMpKQkJk+eDMCiRYt4+umnGTJkCM888wwZGRn89ddf/PHHH4wcObLcn5sQ5XH9+nX69evHiBEjePTRR/Hz8yvX/mlpaXTv3p1Lly4xceJE6taty65du5g+fTpXrlxh7ty5txXXP//8g06nw93dnatXr9KlSxfS0tJ4+umn8fLyYsmSJTz00EOsWLGCQYMGAbd3Ls2dO5ennnoKZ2dnXn75ZYBiP4Phw4czb9481q5dy9ChQy0+g59++omIiAhzK8/XX3/N2LFjCQsL4+233yYtLY358+fzf//3fxw4cICQkBAAHn74YY4ePcpTTz1FSEgIcXFxbNy4kZiYGPM2QlS06nLeP/PMM7z//vu8/vrrJbbGlPV4RqOR8PBw9uzZw7///W+aNGnCjz/+WOjvNcDGjRv5559/GDduHP7+/hw9epSFCxdy9OhRdu/ejUajkfsDtSiiVvnyyy8VoMiHoihKWlpaoX1+//13BVC++uor87otW7YogLJlyxZFURTlwIEDCqB8//33xR773Llzik6nU2bNmmWx/vDhw4qNjU2h9cXFvmnTJuXatWvKhQsXlGXLlileXl6Kg4ODcvHiReXgwYMKoDz++OMW+06dOlUBlF9//VVRFEWJjo5WAGXv3r0lHhNQXnvtNfPyO++8owDK2bNnC21br149ZezYsebl6dOnK7a2tsqNGzfM6zIzMxV3d3dl/Pjx5nWPPfaYEhAQoMTHx1u83ogRIxQ3Nzfz/5MBAwYozZs3LzFeIe5UZGSkUvBPQ/fu3RVA+fTTTwttX/AcyVPwfHjzzTcVJycn5eTJkxbbvfjii4pOp1NiYmJKjKt79+5KkyZNlGvXrinXrl1Tjh07pjz99NMKoISHhyuKoiiTJ09WAGXHjh3m/ZKTk5X69esrISEhisFgUBSlbOdS3vUm/7nevHlzpXv37oW2LXg9NBqNSmBgoPLwww9bbPfdd98pgLJ9+3ZzbO7u7sqECRMstouNjVXc3NzM62/evKkAyjvvvFNizELcrup83uedqzNnzlQAZd++fYqiKMrZs2cLnRdlPd7KlSsVQJk7d655G4PBoPTq1UsBlC+//NK8vqj7ov/9738W57KiyP2BGqQ7WS01b948Nm7caPEAcHBwMG+TnZ3N9evXadiwIe7u7iU24+a1tGzYsKHILhQAq1atwmg0MmzYMOLj480Pf39/GjVqxJYtW8oUe58+ffDx8SE4OJgRI0bg7OxMdHQ0gYGB/PzzzwBMmTLFYp/nnnsOwNytJK/FZ82aNWRnZ5fpuOU1fPhwsrOzLVp3fvnlFxISEhg+fDgAiqKwcuVKwsPDURTF4nMJCwsjMTHR/Lm7u7tz8eJF9u7dWynxClESvV7PuHHjbnv/77//nnvvvRcPDw+L3/M+ffpgMBjYvn17qa9x/PhxfHx88PHxoWnTpnz00Uc8+OCD5q4VP//8Mx07duT//u//zPs4OzvzxBNPcO7cOf7++2+g8s8ljUbD0KFD+fnnn0lJSTGvX758OYGBgeb4Nm7cSEJCAo888ojFZ6LT6ejUqZP5mujg4ICdnR1bt27l5s2blRKzEEWpDud9nmeeeQYPDw9mzpx5x8dbv349tra2TJgwwbyvVqslMjKy0Gvmvy/KyMggPj6ee+65B6BM3duKIvcHFUO6k9VSHTt2LHJgf3p6OlFRUXz55ZdcunQJRVHMzxUc25Jf/fr1mTJlCnPmzOHbb7/l3nvv5aGHHuLRRx81JzinTp1CURQaNWpU5GuUdVDuvHnzuPvuu7GxscHPz4/GjRubq36cP38erVZLw4YNLfbx9/fH3d2d8+fPA9C9e3cefvhhZs6cyfvvv0+PHj0YOHAgI0eOrLABeK1bt6ZJkyYsX76cxx57DDDdxHh7e5vH8Vy7do2EhAQWLlzIwoULi3yduLg4AKZNm8amTZvo2LEjDRs25P7772fkyJF07dq1QuIVoiSBgYF3VH3r1KlT/PXXX0V2r4Bbv+clCQkJYdGiReYS640aNcLX19f8/Pnz5+nUqVOh/fIqLJ0/f54WLVpUybk0fPhw5s6dy+rVqxk5ciQpKSn8/PPPTJw4EY1GA5g+E7g1rq8gV1dXwHQj+fbbb/Pcc8/h5+fHPffcQ//+/RkzZgz+/v4VFrMQBVWH8z6Pm5sbkydP5rXXXuPAgQN4eHjc9vHOnz9PQEBAoS7yBe8dAG7cuMHMmTNZtmxZoXhLui8qidwfVAxJYm7T9u3beeedd9i3bx9XrlwhOjqagQMHlus1FEXhvffeY+HChZw/fx5vb2+efPJJc39rNTz11FN8+eWXTJ48mc6dO+Pm5oZGo2HEiBGlDsJ77733iIiI4Mcff+SXX37h6aefJioqit27dxMUFITRaESj0bBu3boiq/44OzuXKcbiErD88m4SSnp+xYoV7N69m59++okNGzYwfvx43nvvPXbv3l3mWEozfPhwZs2aRXx8PC4uLqxevZpHHnnEXLY57zN99NFHi+yLC9CqVSvAdCN24sQJ1qxZw/r161m5ciWffPIJM2bMKPGbKVG5auq1oKD830aWRf6B62D6Xb/vvvt44YUXitz+7rvvLvU1nZyc6NOnT7niKEpVnEv33HMPISEhfPfdd4wcOZKffvqJ9PR087escOv8//rrr4tMRvKXd588eTLh4eH88MMPbNiwgVdffZWoqCh+/fVX2rZtWyExiztzp9eC119/vcjfP0dHR1JTUysw0rKrDud9fnljY2bOnFnkeJqKPh7AsGHD2LVrF88//zxt2rTB2dkZo9FI375972iOKrk/uHOSxNym1NRUWrduzfjx4xk8ePBtvcYzzzzDL7/8wrvvvkvLli25ceMGN27cqOBIy2fFihWMHTuW9957z7wuIyOjzJM3tWzZkpYtW/LKK6+wa9cuunbtyqeffsp//vMfGjRogKIo1K9f/7YuJGVRr149jEYjp06dsqhvf/XqVRISEqhXr57F9vfccw/33HMPs2bNYunSpYwaNYply5bx+OOPF/n6pSVHBQ0fPpyZM2eycuVK/Pz8SEpKYsSIEebnfXx8cHFxwWAwlOnmzMnJieHDhzN8+HCysrIYPHgws2bNYvr06VLiVSU19VpQVh4eHoWuD1lZWVy5csViXYMGDUhJSamQJKQ49erV48SJE4XWHz9+3Px8nts5l8p7/g8bNowPPviApKQkli9fTkhIiLkbCpg+EwBfX98yfS4NGjTgueee47nnnuPUqVO0adOG9957j2+++aZccYnKcafXgqlTp/Kvf/3LYl3v3r3p0KFDRYVYYdQ67/NaY15//fUib+zLerx69eqxZcuWQiWjT58+bbHdzZs32bx5MzNnzmTGjBnm9XmtqPnJ/UHVkzExt6lfv3785z//MVe7KSgzM5OpU6cSGBiIk5MTnTp1spj59tixY8yfP58ff/yRhx56iPr16xMaGsp9991XRe+gaDqdzqILGZjK9hX8dqWgpKQkcnJyLNa1bNkSrVZrLm08ePBgdDodM2fOLHQMRVEsyhXergceeACg0Dc0c+bMAeDBBx8ETBemgjG0adMGoFAp5vycnJwAypzUNW3alJYtW7J8+XKWL19OQEAA3bp1Mz+v0+l4+OGHWblyJUeOHCm0f17ZWKDQ52NnZ0ezZs1QFKXSxvWI0tXUa0FZNWjQoFC/9oULFxa6ZgwbNozff/+dDRs2FHqNhISEQteP2/HAAw+wZ88efv/9d/O61NRUFi5cSEhICM2aNQNu/1xycnIq12zcw4cPJzMzkyVLlrB+/XqGDRtm8XxYWBiurq7Mnj27yOPmnf9paWlkZGRYPNegQQNcXFxKvF6JqnWn1wJnZ2f8/f3Nj6tXr/L333+buxtVJ2qe95MnT8bd3Z033nij0HNlPV5YWBjZ2dksWrTI/LzRaGTevHkW++T1Gil4v1BUK5DcH1Q9aYmpJJMmTeLvv/9m2bJl1KlTh+joaPr27cvhw4dp1KgRP/30E3fddRdr1qyhb9++KIpCnz59+O9//4unp6dqcffv35+vv/4aNzc3mjVrxu+//86mTZvM5YuL8+uvvzJp0iSGDh3K3XffTU5ODl9//bX5JATTRe8///kP06dP59y5cwwcOBAXFxfOnj1LdHQ0TzzxBFOnTr2j+Fu3bs3YsWNZuHAhCQkJdO/enT179rBkyRIGDhxIz549AViyZAmffPIJgwYNokGDBiQnJ7No0SJcXV3NiVBR8mYLfvnllxkxYgS2traEh4ebL15FGT58ODNmzMDe3p7HHnus0Ky9b731Flu2bKFTp05MmDCBZs2acePGDfbv38+mTZvM38jff//9+Pv707VrV/z8/Dh27Bgff/wxDz74IC4uLnf0uYnKY63XgrJ6/PHH+de//sXDDz/Mfffdx6FDh9iwYYO5xGie559/ntWrV9O/f38iIiIIDQ0lNTWVw4cPs2LFCs6dO1don/J68cUX+d///ke/fv14+umn8fT0ZMmSJZw9e5aVK1eaz73bPZdCQ0OZP38+//nPf2jYsCG+vr7FjmcBaNeuHQ0bNuTll18mMzPToisZmMa8zJ8/n9GjR9OuXTtGjBiBj48PMTExrF27lq5du/Lxxx9z8uRJevfuzbBhw2jWrBk2NjZER0dz9epVi29uRfVW2rWgoM8++4y7776be++9V4VoS6bmee/m5sYzzzxTZDepsh5v4MCBdOzYkeeee47Tp0/TpEkTVq9ebf57m9eq4urqSrdu3fjvf/9LdnY2gYGB/PLLL+Z56fKT+wMVVHE1tBoJUKKjo83L58+fV3Q6nXLp0iWL7Xr37q1Mnz5dURRFmThxoqLX65VOnTop27dvV7Zs2aK0adNG6dmzZ6XGmlc2tLjSwjdv3lTGjRuneHt7K87OzkpYWJhy/PjxQuUBC5YU/eeff5Tx48crDRo0UOzt7RVPT0+lZ8+eyqZNmwodY+XKlcr//d//KU5OToqTk5PSpEkTJTIyUjlx4sQdxZ4nOztbmTlzplK/fn3F1tZWCQ4OVqZPn65kZGSYt9m/f7/yyCOPKHXr1lX0er3i6+ur9O/fX/nzzz8tXosiyki++eabSmBgoKLVai3KKRb8jPKcOnXKXMb6t99+KzLmq1evKpGRkUpwcLBia2ur+Pv7K71791YWLlxo3mbBggVKt27dFC8vL0Wv1ysNGjRQnn/+eSUxMbHEz0NUHWu6FhSnuFKrxZXvNBgMyrRp0xRvb2/F0dFRCQsLU06fPl3k+ZCcnKxMnz5dadiwoWJnZ6d4e3srXbp0Ud59910lKyurxLhKiiG/M2fOKEOGDFHc3d0Ve3t7pWPHjsqaNWsstinLuVRUieXY2FjlwQcfVFxcXBTAXG654PUwv5dfflkBlIYNGxYb85YtW5SwsDDFzc1Nsbe3Vxo0aKBERESYr0fx8fFKZGSk0qRJE8XJyUlxc3NTOnXqpHz33Xelfh5CHbdzLcgvPT1d8fDwUN5+++3KDlVRFOs772/evKm4ubkVWXq8rMe7du2aMnLkSMXFxUVxc3NTIiIilJ07dyqAsmzZMvN2Fy9eVAYNGqS4u7srbm5uytChQ5XLly/L/UE1IElMBSh4sVqzZo0CmG/S8x42NjbKsGHDFEVRlAkTJiiAxY37vn37FEA5fvx4Vb8FIUQFkGuBEEJRbu9akN/SpUsVGxsbJTY2tgqjFnlzyBWXUIjqRbqTVYKUlBR0Oh379u0rVIUrr+pVQEAANjY2FgPc8waix8TE0Lhx46oLWAhRKeRaIISAsl0L8vvss8/o378/fn5+VRVirZOenm5Rfc1gMPDRRx/h6upKu3btVIxMlJUkMZWgbdu2GAwG4uLiiu3L2rVrV3Jycjhz5oy5Qs3JkycBClXQEkJYJ7kWCCGgbNeCPGfPnmXLli2sXr26iqKrnZ566inS09Pp3LkzmZmZrFq1il27djF79uxyl5YW6pAk5jalpKRYlOI7e/YsBw8exNPTk7vvvptRo0YxZswY3nvvPdq2bcu1a9fYvHkzrVq14sEHH6RPnz60a9eO8ePHM3fuXIxGI5GRkdx3332VVn5YCFHx5FoghIA7vxbk+eKLLwgICKBfv35qvI1ao1evXrz33nusWbOGjIwMGjZsyEcffcSkSZPUDk2Uldr92axV3kDOgo+8gVtZWVnKjBkzlJCQEMXW1lYJCAhQBg0apPz111/m17h06ZIyePBgxdnZWfHz81MiIiKU69evq/SOhBC3Q64FQghFqZhrgcFgUIKCgpSXXnpJpXchhPXQKEqB4tdCCCGEEEIIUY3JZJdCCCGEEEIIqyJJjBBCCCGEEMKqyMD+cjIajVy+fBkXFxfzjK5CiNujKArJycnUqVOn0EzF1Z1cC4SoOHItEEJA+a4FksSU0+XLlwkODlY7DCFqlAsXLhAUFKR2GOUi1wIhKp4a14JLly4xbdo01q1bR1paGg0bNuTLL7+kffv2ZdpfrgVCVLyyXAskiSknFxcXwPThurq6qhyNENYtKSmJ4OBg83llTeRaIETFUetacPPmTbp27UrPnj1Zt24dPj4+nDp1Cg8PjzK/hlwLhKg45bkWSBJTTnlNxa6urnKxEqKCWGMXDLkWCFHxqvpa8PbbbxMcHMyXX35pXle/fv1yvYZcC4SoeGW5FlhXx1MhhBBCiAqyevVq2rdvz9ChQ/H19aVt27YsWrSoxH0yMzNJSkqyeAghqp4kMUIIIYSolf755x/mz59Po0aN2LBhA//+9795+umnWbJkSbH7REVF4ebmZn7IeBgh1CGTXZZTUlISbm5uJCYmFt9snJUGhkxwKHufWiFqozKdT9WUNccuRFXKu80oqXuIWueTnZ0d7du3Z9euXeZ1Tz/9NHv37uX3338vcp/MzEwyMzPNy3l9+OVaIMSdK8+1QMbEVDRFgbXPwfmdMOwrqNNG7YiEEEKISqUoCtdTszh/PZWz8Wmcv57KuetpnItP5dz1VBaNac89d3mpHWYhAQEBNGvWzGJd06ZNWblyZbH76PV69Hp9hceiKAqKAkrevwFj7jrT86CgYFRuPa8ohfcz5m6X+5/5NZT8x8jdRlFyn899Lcyvf+v5vG1KjSHfv8sUA/ljyf9e8+LKt00R++WPgQLvIy9GiozrVmJtGVf+91mOGPK9T/MxLN5f4bgs36dlDLe2z/sdsHyfCgpGY3FxlfI+LT6bon8HlNz/0RbvvZTfgSI/f2O+3+t8Mbz8YFMGtAm8s5MllyQxFS39pimBSTgPn98PD/wX2o0FKxy4LIQQQuRRFIX4lKxCCcq566mcj08jOTOn2H3PX0+tlklM165dOXHihMW6kydPUq9evQo9zvjFe9l1Jr7Em0chaoP0LEOFvZYkMRXN0RMmboPof8PJdfDTMxDzBzz4Htg5qh2dEEIIUay8ROXc9dR8SYopYTl/PY2UEhIVjQbquDlQz8uREG8nQrwcCfFyIsTbibqe1fPv37PPPkuXLl2YPXs2w4YNY8+ePSxcuJCFCxdW6HGycoxkZBsr9DVLotGABtBqNLn/Nq3Q5v47//O5T6HJ3Var0eQuA2hM++TbT5v7pawmd/2t7W/tl//ft7a3XJcXA5rcY+TbxjIuy/dR8PXzv0/MMebftuj98uIm9/mC71OTL8aC7xMs34Pl9ppCcVm+z3zPa/N9lsXFkO99anJfgyLeR4nvU5P3e1E4Bsv3mfv5a2/9zhT8PSr0mea+j8K/M0X9DpiuERVFkpjK4OABI5bCzrnw65twaCnE/mXqXubVQO3ohBBC1GKKonAtJZNz8WnmZOX8ddO/y5qohHjnJii5SUqIlyPBno7Y2+qq8J3cuQ4dOhAdHc306dN54403qF+/PnPnzmXUqFEVepw5w1uTlWMsw010gUSgiJv7WzekxScQQtQGksRUFq0W7p0CQe1hxXi4egQW9oAB86DZQ2pHJ4QQogZTFIVryZmczU1Qzl5PNXUDyx2vklpClw6NBgLdHQjxcqKelyP1vZ2o5+VEfW9HgjysL1EpTf/+/enfv3+lHsPXxb5SX1+I2kiSmMpWvxtM3AErxkHM7/DdaOg8Cfq8DjpbtaMTQghhpRRFIc6cqOQfp2JKVNJKSFS0Gqjj7pCboFi2qgR7OqC3qVmJihCi5pEkpiq4BsDYn2DT6/D7x6bHpX0w5EvTc0IIIUQRjEZTonJrjMqtAfXnr6eRnl1yohLo4VCo21eItxNBHpKoCCGsmyQxVUVnC2GzILgT/BhpapVZcC8M+cLUWiOEEKJWMhoVriZn3BqjUmCcSkkDwrUaCPIoOJDekXpeTgR7OGJnI3NaCyFqJkliqlqzh8CvOSwfDXFH4asB0OtV6DrZNI5GCCFEjWM0KsQmZeQmKGm586mYEpXzN0pOVHRaDUEeDqZxKV6OueNTTN3AgiRREULUUpLEqMGrATy+yTQp5qGlsHkmXNgDg+abKpsJIYSwOkajwpWkDM7Hp+YOpLfs+pWZU3KiEpyXqOSNU/E2dQML8nDAVieJihBC5CdJjFrsHGHgJ1C3E/z8gmlOmQXdTWWY67RROzohhBBFMBoVLiemm7t65R+ncv5GGlklJCo2Wg3Bno75BtLfSlQCJVERQohykSRGTRoNhEZAQBv4bgwknIfP74cH/gvtxoLUehdCiCpnMCpcSUy3mEflXG7SElOGRKVubqKSv1WlvrcTddwlURFCiIoiSUx1UKcNTNwG0f82tcj89AzE/AEPvmdqsRFCCFGhDEaFywnpBWakN41TuXAjnSxD8YmKrU5DcO5geot5VLycqONuj40kKkIIUekkiakEU747SHxKFt7Odng76/F2tsPLSY+3ix4vJzt8XPR4OtlZfiPn4AEjlsLO9+HX/5jGysT+Zepe5tVAvTcjhBBWKi9ROWtOUNJy51MpY6Li6Uh9r1sTPea1rAS4SaIihBBqkySmEuw5e4OLN9NL3c7d0RYvp7xEJzfZcX6IZh3qce+hF9BfPYJxQXey+n+EvuVANNK9TAghLOQYjFxOyLCYkf6cOVFJI9ugFLuvnU5LsKeDuSUl/xiVOu4O6LRyzRVCiOqqViUx8+fPZ/78+Zw7dw6A5s2bM2PGDPr161ehx5k9qCWxSRlcT8kiPiWT6ymZxOf+Oz4lixupmRgVSEjLJiEtmzPXUgu8ggO+vMHHdh/SMesE9qsi+GLFgyxxjMDdxQkfc8uO6aeXsx0+znq8chMhd0c7+eMrhKgxcgxGLiWkW0z0mDePyoWbpScqdb0czXOo1PN2ym1dcZRERQghrFitSmKCgoJ46623aNSoEYqisGTJEgYMGMCBAwdo3rx5hR2n290+JT5vNCrcTMviemoW8cmZxOf+vJ6aSXxyFtdTM7mW4s7zyf9hbNpixmvXMl67lhZpp5mU8DSHKLkMs1YDnk56c3c2rwI/b603dW+zt5VZm4UQ6soxGLl4M73QQPrz19O4cCONHGMJiYqNlnqejoW6fdXzciTATRIVIYSoiTSKohT/l6EW8PT05J133uGxxx4r0/ZJSUm4ubmRmJiIq6trJUcHiqKQefgH7H6ahDY7hUy9Nzta/5e/9a0sWniup5p+JqRll/sYLnob83id4pIdU1c3Pa72NtKtTVSYqj6fKpI1x66W7AKJyvnraebxKhdvppeYqOhttIUrfuW2rAS42qOVRMWqWfP5ZM2xC1HdlOd8qlUtMfkZDAa+//57UlNT6dy5c7HbZWZmkpmZaV5OSkqqivDMNBoN9q0GQWArWD4afdxR+ux9nD69XoXwyaC1HFyabTByI/VW1zVTopPJ9ZQsruX+zFu+nppJtkEhOTOH5MwczsYX7NZWmJ1OW6hlx8vZDu983dvyEiBPJzsZ/CpELZNtMHLhRppFgpLXqnLxZjqGUhKVEC/Lil9541T8JVERQgiRT61LYg4fPkznzp3JyMjA2dmZ6OhomjVrVuz2UVFRzJw5swojLIZXA3h8E6x9zlS5bPNMuLAHBs03VTbLZavT4udqj5+rfakvqSgKSek5uclNbtKTmlmgi1uW+bmUzByyDEauJGZwJTGjTGF7ONoWaN251aqTt94n96ejXa37dRTCKmXlGLlwM63AQHrTeJVLCSUnKva2txKVEC8nizLFfi6SqAghhCibWtedLCsri5iYGBITE1mxYgWfffYZ27ZtKzaRKaolJjg4WL1mY0WB/Uvg5xfAkAnu9WD41xDQutIPnZFtMLfiFNm6k29Mz43ULEq4jymSo53uVuuOkx4fc8vOraQnr4ubm4Ot3OzUANbcDcOaYy+LvEQl/4z0eVW/Lt1ML/H8zktUTAPp85cpdsLXRS/nrijEms8na45diOqmPOdTrUtiCurTpw8NGjRgwYIFZdq+2lysLh+A78ZAQgzo9PDAO9BuDFST8SqG3OIF+ZOe+ALV2vKP6cksYQbsothoNXg62ZnH63jnFikwj+1x0Zu7uHk62aG3keIF1VG1OZ9ugzXHniczx8CFG+kWCUpeN7DLCSUnKg62OsuJHvMNqPd10cvYOVEu1nw+WXPsQlQ3MiamHIxGo0VLi9Wo0xYmbofof8HJ9fDT03DhD3jgXbBzVDs6dFqNuftYaRRFITXLYK7Qdi05y6JVxzIByiIxPZsco0JcciZxyWX7f+dqb2OOp6Rqbd7OdjjrpXiBqDkysg1cvJlmnujxbL4B9ZcT0ynpayxHO51FghKSrwuYJCpCCCHUVKuSmOnTp9OvXz/q1q1LcnIyS5cuZevWrWzYsEHt0G6PgweM+B/sfB9+/Q8c/BauHIJhX5nG0FgJjUaDs94GZ70NId5OpW6flZO/eEEpBQxSszAYFZIyckjKyOGfshQvsNGax+kUbOHxcdFbzNHj6SRz8gj1ZWQbuHAj7VaCkm/ix7IkKqbExNHcBcw04aMjPpKoCCGEqKZqVRITFxfHmDFjuHLlCm5ubrRq1YoNGzZw3333qR3a7dNq4d7nIKgDrBgPV4/Awh4w8BNoGq52dJXCzkaLv5s9/m6lFy8wGhUS07NzW3RK796WmmUgK8c0sd6lhPRSX1+jAU9Hu3ytO7dadrzNk5LeSoBkTh5xuzKyDcTcyD82Jc1cpri0RMXJTmeeiT6kwDwqPs6SqAghhLA+tSqJ+fzzz9UOofLU7wYTd8D3EXBhNyx/FLo8Bb1fA52t2tGpRqvV4OFkh4eTHQ19S98+PctgbuHJ35pzLbdSm3lS0pQsbqZloSiYKrilZnHyakqpr+9kpyswJ48enwLV2vISIDcHW7m5rGUysg2czy1HnH9A/fnrqVxJyigxUTG1ZOYmKHnVv3ITF29nO/ldEkIIUaPUqiSmxnMNgIg1sOl1+P1j2PURXNwHQ74wPSdK5WCnI9jTkWDP0scV5RiM3EjLsqjWVqh7W27iE5+SRZbBSGqWgdTrpjk0SmOj1ZS5hcfTyQ5bmZPHKqRnGTh/41Zp4vzjVEorXe6S2+Uyf3nivHlUvJwkURFCCFF7SBJT0+hsIWwWBHeCH56EmF2woJspkal/r9rR1Sg2Oi2+Lvb4upRtTp7kzJx8CU8m1wqM5ck/pic5I4cco8LVpEyuJpWteIGbg625HLVPKQUMnOx0tf6G99KlS0ybNo1169aRlpZGw4YN+fLLL2nfvn2FHWPzsaucvJqSO+GjKXGJTSpbomJOUPKNV/GUREUIIYQAJImpuZo9BH7NYfloiDsKXz0EvV6FrpNN42hEldJoNLja2+Jqb0v9MhQvyMwxcD0lK1/rTv7ubJZjem7kFi9ITM8mMT2bM9dKL15gb6s1t+Z4O9kVqtrmk6/1x92x5hUvuHnzJl27dqVnz56sW7cOHx8fTp06hYeHR+k7l8M7G05wPDa50HoXexvq541RyW1Jyav+JYmKEEIIUTpJYmoyrwbw+CZYOwUO/Q82z4SLe02D/h0q9mZNVCy9jY467g7UcXcodVujUSEhPTu3dSezyO5t+ScjTc82kJFd9uIFWg14OuVvySm+hcfLyc4qihe8/fbbBAcH8+WXX5rX1a9fv8KP06OxL3f7uVh0+wrxcsLDUcY7CSGEEHei1k92WV5WOamVosD+JfDzC2DIBPd6MPxrCGitdmRCBWlZOcQnZxGfmnmrZceihedWAnQzLbvcr++itzEnONP6NaFDiGex26p1PjVr1oywsDAuXrzItm3bCAwM5Mknn2TChAllfg2rvBYIUU1Z8/lkzbELUd3IZJfCkkYDoRGmpOW7MZBwHj67Dx54B9qNMT0vag1HOxvqetlQ16v04gXZBiM3U7MKtebEF5iMNC/pyTaYxv4kZ+Zw7noa2QZjFbyj8vvnn3+YP38+U6ZM4aWXXmLv3r08/fTT2NnZMXbs2CL3yczMtJgYNykpqarCFUIIIUQBksTUJnXawsTtEP0vOLkefnoaLvwBD7wLdqXf0Irax1anxdfVHl/XshUvSMrIsUhqmvpXz28ljUYj7du3Z/bs2QC0bduWI0eO8OmnnxabxERFRTFz5syqDFMIIYQQxZAR3rWNgweM+B/0ngEaLRz8Fj6/D66fUTsyYeU0Gg1uDrY08HGmY31PHmgZgIeTndphFSkgIIBmzZpZrGvatCkxMTHF7jN9+nQSExPNjwsXLlR2mEIIIYQohiQxtZFWC/c+B6N/ACcfuHoEFvaAYz+pHZkQVaJr166cOHHCYt3JkyepV69esfvo9XpcXV0tHkIIIYRQhyQxtdld3U3dy4LvgcwkWP4o/PIKGMo/mFsIa/Lss8+ye/duZs+ezenTp1m6dCkLFy4kMjJS7dCEEEIIUQaSxNR2rnUgYg10nmRa3vURLHkIkmPVjUuIStShQweio6P53//+R4sWLXjzzTeZO3cuo0aNUjs0IYQQQpSBDOwXoLOFsFkQ3BF+iISYXfDpvTDkC6h/r9rRCVEp+vfvT//+/dUOQwghhBC3QVpixC3NBsATW8G3OaTGwVcPwW/vg7F6lskVQgghhBC1kyQxwpJ3Q3h8E7R+BBQjbHodlo+C9JtqRyaEEEIIIQQgSYwoip0jDJwP4R+ATg8nfoYF3eHKIbUjE0IIIYQQQpIYUQyNBkIj4LEN4F4XEs7DZ/fB/q/UjkwIIYQQQtRyksSIktVpC09sg0ZhYMiE1U+ZBv9npakdmRBCCCGEqKUkiRGlc/SER5ZB7xmg0cLBb+Dz++D6GbUjE0IIIYQQtZAkMaJstFq49zkY/QM4+cDVI7CwBxxbo3ZkQgghhBCilpEkRpTPXd1h4nYIvgcyk0yVy355BQw5akcmhBBCCCFqCUliRPm51oGINdB5kml510ewJBySY9WNSwghhBBC1AqSxIjbo7OFsFkw7Cuwc4GYXfDpvXB2h9qRCSGEEEKIGk6SGHFnmg2AJ7aCb3NIjYOvHoLf3gdFUTsyIYQQQghRQ0kSI+6cd0N4fBO0fgQUI2x6HZaNhPQEtSMTQgghhBA1kCQxomLYOcLA+dB/Lujs4MTPsLA7XDmkdmRCCCGEEKKGsVE7AFGDaDTQfhzUaQPfjYGb5+Cz++DBd6HdGLWjE0KIO2IwGMjOzlY7DKtka2uLTqdTOwwhKoTRaCQrK0vtMKxSRV4LJIkRFa9OW3hiG0T/C05tgNVPQcwfpmTG1kHt6IQQolwURSE2NpaEhAS1Q7Fq7u7u+Pv7o9Fo1A5FiNuWlZXF2bNnMRqNaoditSrqWiBJjKgcjp7wyDL4bQ5smQUHvzF1LRu2BLwaqB2dEEKUWV4C4+vri6Ojo9yEl5OiKKSlpREXFwdAQECAyhEJcXsUReHKlSvodDqCg4PRamVURnlU9LVAkhhRebRa6DYVgtrDisfg6mFY2MM0dqZpf7WjE0KIUhkMBnMC4+XlpXY4VsvBwdQKHxcXh6+vr3QtE1YpJyeHtLQ06tSpg6Ojo9rhWKWKvBZICikq31094F87IPgeyEyC5aPgl1fAkKN2ZEIIUaK8MTByw3Ln8j5DGVckrJXBYADAzs5O5UisW0VdCySJEVXDtQ5ErIHOk0zLuz4yzSmTHKtuXEIIUQbShezOyWcoagr5Xb4zFfX5SRIjqo7OFsJmwdAlYOcC53fCp/fCud/UjkwIIYQQQlgRSWJE1Ws+EJ7YCr7NIDUOloTDb++DoqgdmRBCiCKEhIQwd+5ctcMQQqisOl0LJIkR6vBuCI9vglYjQDHCptdh2UhIT1A7MiGEqBF69OjB5MmTK+S19u7dyxNPPFEhr1WdvfXWW2g0mgr73ISoDmrqtUCSGKEeOycY9Cn0nws6OzjxMyzsDlf+UjsyIYSo8RRFISenbAVWfHx8anxxg71797JgwQJatWqldihCVClrvRZIEiPUpdFA+3Hw2C/gXhdunoPP+sD+r9SOTAghrFZERATbtm3jgw8+QKPRoNFoWLx4MRqNhnXr1hEaGoper+e3337jzJkzDBgwAD8/P5ydnenQoQObNm2yeL2CXUg0Gg2fffYZgwYNwtHRkUaNGrF69eoqfpcVJyUlhVGjRrFo0SI8PDzUDkeIClOTrwWSxIjqoU5beGIbNAoDQyasfgp+iITsdLUjE0IIC4qikJaVo8pDKePYwQ8++IDOnTszYcIErly5wpUrVwgODgbgxRdf5K233uLYsWO0atWKlJQUHnjgATZv3syBAwfo27cv4eHhxMTElHiMmTNnMmzYMP766y8eeOABRo0axY0bN+7481VDZGQkDz74IH369Cl128zMTJKSkiweonaSa4GJWtcCmexSVB+OnvDIMvhtDmyZBQe/gSuHYNgS8GqgdnRCCAFAeraBZjM2qHLsv98Iw9Gu9D/dbm5u2NnZ4ejoiL+/PwDHjx8H4I033uC+++4zb+vp6Unr1q3Ny2+++SbR0dGsXr2aSZMmFXuMiIgIHnnkEQBmz57Nhx9+yJ49e+jbt+9tvTe1LFu2jP3797N3794ybR8VFcXMmTMrOSphDeRaYKLWtUBaYkT1otVCt6kwOhocveHqYVjYA46tUTsyIYSoEdq3b2+xnJKSwtSpU2natCnu7u44Oztz7NixUr99zT92xMnJCVdXV+Li4iol5spy4cIFnnnmGb799lvs7e3LtM/06dNJTEw0Py5cuFDJUQpROaz9WiAtMaJ6uqsH/GsHfB8BF/6A5aOgy9PQ+zXQya+tEEI9DrY6/n4jTLVj3yknJyeL5alTp7Jx40beffddGjZsiIODA0OGDCErK6vE17G1tbVY1mg0GI3GO46vKu3bt4+4uDjatWtnXmcwGNi+fTsff/wxmZmZ6HSWn7ler0ev11d1qKIakmuBiVrXArkbFNWXax2IWAsbX4Pd82DXh3BpHwz5Alz81Y5OCFFLaTSaMnXjUJudnR0Gg6HU7Xbu3ElERASDBg0CTN/Gnjt3rpKjqx569+7N4cOHLdaNGzeOJk2aMG3atEIJjBD5ybVAXdX/kxe1m84W+s6G4I7w4yQ4vxM+vReGfgkh/6d2dEIIUW2FhITwxx9/cO7cOZydnYv9ZrRRo0asWrWK8PBwNBoNr776qtW1qNwuFxcXWrRoYbHOyckJLy+vQuuFsFY19VogY2KEdWg+EJ7YCr7NIDUOljwEv82FMlbnEEKI2mbq1KnodDqaNWuGj49Psf3a58yZg4eHB126dCE8PJywsDCL7lVCCOtWU68FGqWsNdoEAElJSbi5uZGYmIirq6va4dQ+WamwZgr8tcy03PhBGPgJOLirGpa4PdZ8Pllz7KLsMjIyOHv2LPXr1y/zwG9RtJI+S2s+n6w5dlE+cj2oGBV1LZCWGGFd7Jxg0KfQ/33Q2cGJtabqZVf+UjsyIYQQQghRRSSJEdZHo4H242H8BnCrCzfPwmd9YP9XakcmhBBCCCGqgCQxwnoFtoOJ26BRGBgyYfVT8GMkZKerHZkQQgghhKhEksQI6+boCY8sg16vgkYLB76Bz++DG/+oHZkQQgghhKgkksQI66fVQrepMDoaHL0h9jAs6AHH1qgdmRBCCCGEqASSxIia464e8K8dENwJMhNh+Sj45VUw5KgdmRBCCCGEqECSxIiaxbUORKyFeyJNy7s+hK8eguRYdeMSQgghhBAVplYlMVFRUXTo0AEXFxd8fX0ZOHAgJ06cUDssUdF0ttB3NgxdAnYucH4nLOgG535TOzIhhBBCCFEBalUSs23bNiIjI9m9ezcbN24kOzub+++/n9TUVLVDE5Wh+UB4Ygv4NIWUq7DkIfhtLsj8rkIIIYQQVq1WJTHr168nIiKC5s2b07p1axYvXkxMTAz79u1TOzRRWbwbwYTNpy0LOgAARt1JREFU0GoEKAbY9BosGwXpCWpHJoQQ1VpISAhz585VOwwhhMqq67WgViUxBSUmJgLg6empciSiUtk5waBPof/7oLODE2thYQ+48pfakQkhhBBCiNtQa5MYo9HI5MmT6dq1Ky1atCh2u8zMTJKSkiwewgppNNB+PIzfAG514eZZ03wy+79WOzIhhBBCCFFOtTaJiYyM5MiRIyxbtqzE7aKionBzczM/goODqyhCUSkC28HEbdDofsjJgNWT4MdIyE5XOzIhhKgwCxcupE6dOhiNRov1AwYMYPz48Zw5c4YBAwbg5+eHs7MzHTp0YNOmTSpFK4SoLDX5WlArk5hJkyaxZs0atmzZQlBQUInbTp8+ncTERPPjwoULVRSlqDSOnvDIcuj1Cmi0cOAbU6vMjX/UjkwIYQ0UBbJS1XmUsTDJ0KFDuX79Olu2bDGvu3HjBuvXr2fUqFGkpKTwwAMPsHnzZg4cOEDfvn0JDw8nJiamsj41IWoeuRaoykbtAKqSoig89dRTREdHs3XrVurXr1/qPnq9Hr1eXwXRiSql1UK35yGoA6x4DGIPw4IeMGg+NHlQ7eiEENVZdhrMrqPOsV+6bBrnVwoPDw/69evH0qVL6d27NwArVqzA29ubnj17otVqad26tXn7N998k+joaFavXs2kSZMqLXwhahS5FqiqVrXEREZG8s0337B06VJcXFyIjY0lNjaW9HTpSlRr3dUD/rUDgjtBZiIsGwkbZ4AhR+3IhBDijowaNYqVK1eSmZkJwLfffsuIESPQarWkpKQwdepUmjZtiru7O87Ozhw7dswqvn0VQpRPTb0W1KqWmPnz5wPQo0cPi/VffvklERERVR+QqB5c60DEWlPysvsT2PkBXNwHQ74AFz+1oxNCVDe2jqZvQdU6dhmFh4ejKApr166lQ4cO7Nixg/fffx+AqVOnsnHjRt59910aNmyIg4MDQ4YMISsrq7IiF6LmkWuBqmpVEqPIJIeiODpb6BsFwR3hx0lw/jdYcK8pkQn5P7WjE0JUJxpNmbpxqM3e3p7Bgwfz7bffcvr0aRo3bky7du0A2LlzJxEREQwaNAiAlJQUzp07p2K0QlghuRaoqlZ1JxOiVM0HwRNbwacppFyFJQ+ZWmYkARZCWKFRo0axdu1avvjiC0aNGmVe36hRI1atWsXBgwc5dOgQI0eOLFS9SAhRc9TEa4EkMUIU5N0IJmyGViNAMZi6mS1/FNIT1I5MVJK33noLjUbD5MmT1Q5FiArVq1cvPD09OXHiBCNHjjSvnzNnDh4eHnTp0oXw8HDCwsLM38wKIWqemngtqFXdyYQoMzsnGPQp1O0E66bB8TVw9SgM+woCWqkdnahAe/fuZcGCBbRqJf9fRc2j1Wq5fLlwn/2QkBB+/fVXi3WRkZEWy9bSpUQIUbqaeC2QlhghiqPRQPvxMH4DuNWFm2dN88ns/1rtyEQFSUlJYdSoUSxatAgPDw+1wxFCCCFEGUkSI0RpAtvBxG3Q6H7IyYDVk+DHSMiW0tzWLjIykgcffJA+ffqoHYoQQgghykG6kwlRFo6e8Mhy+O092DIbDnwDVw6Zupd53qV2dOI2LFu2jP3797N3794ybZ+ZmWmusQ+QlJRUWaEJIYQQohTSEiNEWWm10O15GB0Njt4QexgW9IDja9WOTJTThQsXeOaZZ/j222+xt7cv0z5RUVG4ubmZH8HBwZUcpRBCCCGKI0mMEOV1Vw+YuB2CO0FmIiwbaapgZshROzJRRvv27SMuLo527dphY2ODjY0N27Zt48MPP8TGxgaDwVBon+nTp5OYmGh+XLhwQYXIhRBCCAHSnUyI2+MWCBFrTcnL7k9Mc8lc3GeaHNPFT+3oRCl69+7N4cOHLdaNGzeOJk2aMG3aNHQ6XaF99Ho9er2+qkIU1Yy1zJtQnclnKGoKmTz9zlTUtUCSGCFul84W+kZBcEf4cRKc/w0W3AtDvoSQrmpHJ0rg4uJCixYtLNY5OTnh5eVVaL2o3ezs7MylSX18fLCzs0Oj0agdllVRFIWsrCyuXbuGVqvFzs5O7ZCEuC22trZoNBquXbuGj4+PXAvKqaKvBZLECHGnmg8CvxawfDRcOwZLwqHPa9DlaVOZZiGE1dJqtdSvX58rV64UOceCKDtHR0fq1q2LVis92YV10ul0BAUFcfHixWo7d4o1qKhrgSQxQlQE70YwYTOseRb+Wm7qZnZhDwyYBw7uakcnymDr1q1qhyCqKTs7O+rWrUtOTk6R46VE6XQ6HTY2NvLNtbB6zs7ONGrUiOzsbLVDsUoVeS2QJEaIimLnBIMWmAb8r38Rjq+Bq0dh+Nfg31Lt6IQQd0Cj0WBra4utra3aoQghVKbT6YocOymqlrTpClGRNBro8BiM3wBudeHmWfisj2leGSGEEEIIUSEkiRGiMgS2g4nboOF9kJMBP0aaBv9np6sdmRBCCCGE1ZPuZJXAYDBIX8nbZGtrW3OaaB09YeR3sOM92DILDnwNVw7CsK/A8y61oxNCCCGEsFqSxFQgRVGIjY0lISFB7VCsmru7O/7+/jVjAKhWC92fh6D2sPJxiD0MC3rAoPnQ5EG1oxNCCCGEsEqSxFSgvATG19cXR0fHmnETXoUURSEtLY24uDgAAgICVI6oAjXoCRO3w4pxcOEPWDYSuk6GXq+CTk5DIYQQQojykLunCmIwGMwJjJeXl9rhWC0HBwcA4uLi8PX1rTldywDcAiFiran88u5PYOdcuPgnDPkCXPzUjk4IIYQQwmrIwP4KkjcGxtHRUeVIrF/eZ1gjxxXpbKFvFAxdDHbOcP43WHAvnNupdmRCCCGEEFZDkpgKJl3I7lyt+AybD4IntoJPU0i5CkvCYeeHoChqRyaEEEIIUe1JEiOEWrwbwYTN0Go4KAbY+CosfxQyEtWOTAghhBCiWpMkRlSokJAQ5s6dq3YY1sPOCQYtgAfngM4Ojq+BBd1NVcyEEEIIIUSRJIkR9OjRg8mTJ1fIa+3du5cnnniiQl6r1tBooMNjMH49uNWFm2fhsz5w4Bu1IxNCCCGEqJYkiRGlUhSFnJycMm3r4+MjxQ1uV2AoTNwGDe+DnAz4MRJ+nATZ6WpHJoQQQghRrUgSU8tFRESwbds2PvjgAzQaDRqNhsWLF6PRaFi3bh2hoaHo9Xp+++03zpw5w4ABA/Dz88PZ2ZkOHTqwadMmi9cr2J1Mo9Hw2WefMWjQIBwdHWnUqBGrV6+u4ndpRRw9YeR30PMVQAMHvobP74cbZ9WOTAghhBCi2pAkphIpikJaVo4qD6WMVa4++OADOnfuzIQJE7hy5QpXrlwhODgYgBdffJG33nqLY8eO0apVK1JSUnjggQfYvHkzBw4coG/fvoSHhxMTE1PiMWbOnMmwYcP466+/eOCBBxg1ahQ3bty448+3xtJqofvzMDoaHL0g9i/TOJnjP6sdmRBCCCFEtSCTXVai9GwDzWZsUOXYf78RhqNd6f973dzcsLOzw9HREX9/fwCOHz8OwBtvvMF9991n3tbT05PWrVubl998802io6NZvXo1kyZNKvYYERERPPLIIwDMnj2bDz/8kD179tC3b9/bem+1RoOeMHEHfB8BF/fAskeg62To9Sro5NQVQgghRO0lLTGiWO3bt7dYTklJYerUqTRt2hR3d3ecnZ05duxYqS0xrVq1Mv/byckJV1dX4uLiKiXmGsctECLWQqd/m5Z3zoWvB0LyVTWjEkIIIYRQlXydW4kcbHX8/UaYase+U05OThbLU6dOZePGjbz77rs0bNgQBwcHhgwZQlZWVomvY2tra7Gs0WgwGo13HF+tYWMH/d6Cup1MA/3P7YAF3WDol1Cvi9rRCSGEEEJUOUliKpFGoylTly612dnZYTAYSt1u586dREREMGjQIMDUMnPu3LlKjk6YNR8Efi1g+Wi4dgwW94c+r0OXp0xlmoUQQgghagnpTiYICQnhjz/+4Ny5c8THxxfbStKoUSNWrVrFwYMHOXToECNHjpQWlarm3QgmbIaWw0AxwMZXYfmjkJGodmRCCCGEEFVGkhjB1KlT0el0NGvWDB8fn2LHuMyZMwcPDw+6dOlCeHg4YWFhtGvXroqjFdg5weCF8OAc0NnB8TWwsAfEHlY7MiGEEEKIKqFRylqLVwCQlJSEm5sbiYmJuLq6mtdnZGRw9uxZ6tevj729vYoRWj/5LMvh0j74LgISY8DG3pTYtB2ldlRlVtz5ZA2sOXYhqhtrPp+sOXYhqpvynE/SEiOENQsMhYnboOF9kJMBPz4Jq5+C7HS1IxNCCCGEqDSSxAhh7Rw9YeR30PMVQAP7v4LP74cbZ9WOTAghhBCiUkgSI0RNoNVC9+dhdDQ4ekHsX7CgOxz/We3IhBCi2oqKiqJDhw64uLjg6+vLwIEDOXHihNphCSHKQJIYIWqSBj1h4g4I6giZibDsEdj0Ohhy1I5MCCGqnW3bthEZGcnu3bvZuHEj2dnZ3H///aSmpqodmhCiFNV/EhMhRPm4BULEWtg4A/6YD7+9Dxf/hIc/Bxc/taMTQohqY/369RbLixcvxtfXl3379tGtWzeVohJClIW0xAhRE9nYQb+3YMiXYOcM53bAgm5wfpfakQkhRLWVmGiac8vT01PlSIQQpZEkRoiarMVgmLAFfJpCSiws7g87PwSprC6EEBaMRiOTJ0+ma9eutGjRotjtMjMzSUpKsngIIaqeJDFC1HQ+d8OEzdByGCgG2PgqLH8UMhLVjkwIIaqNyMhIjhw5wrJly0rcLioqCjc3N/MjODi4iiIUQuQnSYwQtYGdEwxeCA++Bzo7OL4GFvaA2MNqRyaEEKqbNGkSa9asYcuWLQQFBZW47fTp00lMTDQ/Lly4UEVRCiHykyRG3LGQkBDmzp2rdhiiNBoNdHgcxq8Ht2C48Q981gcOfKt2ZEIIoQpFUZg0aRLR0dH8+uuv1K9fv9R99Ho9rq6uFg8hRNWTJEaI2iYwFCZuh4b3QU4G/PgkrH4KsjPUjkwIIapUZGQk33zzDUuXLsXFxYXY2FhiY2NJT09XOzQhRCkkiRGiNnL0hJHfQc9XAA3s/wo+vw9unFU7MiGEqDLz588nMTGRHj16EBAQYH4sX75c7dCEEKWQJKaWW7hwIXXq1MFoNFqsHzBgAOPHj+fMmTMMGDAAPz8/nJ2d6dChA5s2bVIpWlGhtFro/jyMXgWOXhD7FyzsDifWqR2ZEEJUCUVRinxERESoHZoQohSSxFQmRYGsVHUeZSyhO3ToUK5fv86WLVvM627cuMH69esZNWoUKSkpPPDAA2zevJkDBw7Qt29fwsPDiYmJqaxPTVS1Br1M3cuCOpgqlv1vBGx6HQw5akcmhBBCCFEkG7UDqNGy02B2HXWO/dJlU0WqUnh4eNCvXz+WLl1K7969AVixYgXe3t707NkTrVZL69atzdu/+eabREdHs3r1aiZNmlRp4Ysq5hYEET/Dxhnwx3z47X24+CcM+QKcfdWOTgghhBDCgrTECEaNGsXKlSvJzMwE4Ntvv2XEiBFotVpSUlKYOnUqTZs2xd3dHWdnZ44dOyYtMTWRjR30ewuGfAl2znBuB3x6L5zfpXZkQgghhBAWpCWmMtk6mlpE1Dp2GYWHh6MoCmvXrqVDhw7s2LGD999/H4CpU6eyceNG3n33XRo2bIiDgwNDhgwhKyursiIXamsxGPxawHej4dpxWNwf7psJnSeZyjQLIYQQQqis1iUx27dv55133mHfvn1cuXKF6OhoBg4cWDkH02jK1KVLbfb29gwePJhvv/2W06dP07hxY9q1awfAzp07iYiIYNCgQQCkpKRw7tw5FaMVVcLnbnh8M6yZDIe/h19egZjdMPATsHdTOzohhBBC1HK1rjtZamoqrVu3Zt68eWqHUq2MGjWKtWvX8sUXXzBq1Cjz+kaNGrFq1SoOHjzIoUOHGDlyZKFKZqKG0jvD4EXw4Hugs4Pja2BhD4g9rHZkQgghhKjuFAVyMiH9JiRegvjTkJ5QYS9f61pi+vXrR79+/dQOo9rp1asXnp6enDhxgpEjR5rXz5kzh/Hjx9OlSxe8vb2ZNm0aSUlJKkYqqpRGAx0ehzpt4buxcOMf+KwPPDgH2o4qfX8hhBBCVD+KAoZsUxGq7DTITr/1Mys133Jxz6VDdv5/p0FWwe3TQCnwxffA+dBmZNExlVOtS2JE0bRaLZcvFx6/ExISwq+//mqxLjIy0mJZupfVAoGhpjLMqybA6U3w45NwYTf0ewds7dWOTgghhKhZzAlGcYlD3nP5E4cyJBV5z2elgmKouvejtTWN1y7jFCBlIUlMKTIzM81VuwBphRC1l6MnjPwedrwLW2bD/q/g8kEY9hV41lc7OiGEEKJqGHKKSQ5KShzynksvOunIKpCAGLOr7v1odKYx3LYOuY/8/3YEO0fTz7zl/P+2K7je8da+5td0BJ1thYctSUwpoqKimDlzptphCFE9aLXQ/QUIag8r/7+9e4+Oqrz3P/6ZmWQmCSEXLkkIDIQqApabBBLDRdRGgmAWeI7KReVSsVrBVrNoAatQZB0DlWqo0qLWSs+pFMQfiOugIKZSLqZSEVpR4ChylyQgkpCE3Gb274+ECUPu18lO3q+1ZpF55tl7vntPZphPnv3sPVvK/Lf06hjp7lekvhymCQDwMberjsGhnqHi6kDiasEztFqs1QSHqkLFNcHBq08Nj9n8TXn2UUJMLRYuXKiUlBTP/dzcXDmdTh9WBLQC191ednjZhpnS6X9Kf50ijXpSuu1pycbHCgCgCm535TkWVQaHKkYyagoVV49+uIpqr6PJWKoYjbhmJMMrOFwTIOzXjHh4PVb+s81uyoDREvi2UQuHwyGHw+HrMoDWJ7SHNPM9afsz0ierpd0vSqc/le75kxQc4evqWod3HpOyD0lWm2T1Kxuyt1rL/7WV/+tXjzZb2V/lPG1X1lvXtvo8/7XPVdPzX7NeT5uN/3wBs3C7pdLChgWHGg+puiqolBa27DbVGCpqCA7VHjZ1zWN+Dj7jfKjdhZi8vDx9/fXXnvvHjh3TgQMH1KlTJ/Xs2bPR6zeacMJSe8U+NBE/u3TncskZJ737M+n4Lmn1aOneN6ReI3xdne9lfyl9u9/XVfiYpYpgU1s4akxbbcGqlrYGPX99nuuqddS6T8rXARhGWQCoMjg0IlRcHVRKL7fsNvnVFCqqCg5Xh5GaRj/KH/MLIGC0ce0uxHz66ae67bbbPPevHCo2Y8YMrVmzpsHr9fcvm7BUUFCgwMDARtXY3hUUFEiq2KcwgQH/KUUOlN56UDp3WFpzl3THEilhbvv+TyQpVSq8WHaMtuGS3KVlf+00XNe0ucpOQ+lpK2+vS1uN6722zVXFstW01ad/jYzyWkpbYo+3TdUGpuYOUU0Y4nrfIoXH+HpP+s7X6VLOqcbNxWhJfgFNGCqqOKTKL5CAjkZrdyHm1ltvbZa/9NtsNoWFhSk7O1uSFBQUJEt7/vLWAIZhqKCgQNnZ2QoLC5PNZvN1SaiPrjdIs9Ol/31C+nyD9MHTZV94EubUumib1SvB1xW0DLe7PGDVJRy5rwputfWvx3obG8QavQ3VtbmuWW8Vbarl/yTDJblcUgueDbXJ3bumfYeYPWnSsZ1Nsy6bo2HBoa5zMfwDy4In0Mq1uxDTnKKioiTJE2TQMGFhYZ59CZNxBEv/8ZrU82bp0zekodN9XRFagtUqWe2+rsK8DKOKkTnXVSNpDQxH1a6jset116Pe8raO0b7ey77ljJfswfWci1HFY36BnDwFKMc7oQlZLBZ169ZNERERKilpwfN7tyH+/v6MwJidxSINny0Nndlq/7NNTU3Vxo0bdfjwYQUGBmrEiBFavny5+vbt6+vS0B5ZLOXvFT9JnEimTbr9aV9XALQ5rfMbhsnZbDa+iAOtNMBI0t///nfNmTNHw4cPV2lpqZ566imNHTtWX375pTp06ODr8gAAQC1a77cMAGgmW7du9bq/Zs0aRUREaN++fbrlllt8VBUAAKgrTg0BoN3LycmRJHXq1MnHlQAAgLpgJAZAu+Z2u/XEE09o5MiRGjBgQLX9ioqKVFRUcSXo3NzcligPAABUgRBTT1dOz8wXGKDxrryPfHmB0zlz5ujgwYPavXt3jf1SU1O1ZMmSSu18FgCN1xo+CxqK7wVA06nPZ4HFMOMnhg+dPn1aTqfT12UAbcqpU6fUo0ePFn/euXPnavPmzdq5c6d69+5dY99rR2LOnDmjG2+8sblLBNoVX30WNAbfC4CmV5fPAkJMPbndbn377bfq2LFjtRezzM3NldPp1KlTpxQSEtLCFTae2euXzL8N7aV+wzB06dIlRUdHy9qCV282DEOPP/64Nm3apB07dqhPnz71Xkd7+CyQzL8N1O9brf2zoCnwWWAO1O9bzfFZwOFk9WS1Wuv8V6KQkBBT/qJdYfb6JfNvQ3uoPzQ0tIWqqTBnzhytXbtWmzdvVseOHZWZmempJTAwsE7raE+fBZL5t4H6fau1fhY0BT4LzIX6faspPwvM9ecOAGgCf/jDH5STk6Nbb71V3bp189zWr1/v69IAAEAdMBIDoN3hKFoAAMyNkZhm4HA4tHjxYjkcDl+X0iBmr18y/zZQf9vQFvaD2beB+n3L7PU3lbawH8y+DdTvW81RPxP7AQAAAJgKIzEAAAAATIUQAwAAAMBUCDEAAAAATIUQAwAAAMBUCDEAAAAATIUQAwAAAMBUCDEAAAAATIUQAwAAAMBUCDEAAAAATIUQAwAAAMBUCDEAAAAATIUQAwAAAMBUCDEAAAAATIUQAwAAAMBUCDEAAAAATIUQAwAAAMBUCDEAAAAATIUQgzZp5syZiomJqdcyO3bskMVi0Y4dO5qlJgAAADQNQgyazJo1a2SxWDy3gIAA3XDDDZo7d66ysrJ8XR4AAADaCIthGIavi0DbsGbNGs2aNUvPPvusevfurcLCQu3evVv/8z//o169eungwYMKCgpqkVpKSkrkdrvlcDjqvIzb7VZxcbHsdrusVvI9AABAa+Xn6wLQ9tx5550aNmyYJGn27Nnq3LmzXnjhBW3evFlTp06t1D8/P18dOnRo0hr8/f3rvYzValVAQECT1gEAAICmx5+b0exuv/12SdKxY8c0c+ZMBQcH6+jRoxo/frw6duyo+++/X1LZSEhaWpp++MMfKiAgQJGRkXrkkUf0/fffV1rn+++/rzFjxqhjx44KCQnR8OHDtXbtWs/jVc2JWbdunWJjYz3LDBw4UCtXrvQ8Xt2cmA0bNig2NlaBgYHq0qWLHnjgAZ05c8arz5XtOnPmjCZNmqTg4GB17dpV8+bNk8vlaszuAwAAwDUIMWh2R48elSR17txZklRaWqqkpCRFRERoxYoV+s///E9J0iOPPKJf/OIXGjlypFauXKlZs2bpzTffVFJSkkpKSjzrW7NmjSZMmKALFy5o4cKFWrZsmYYMGaKtW7dWW8P27ds1depUhYeHa/ny5Vq2bJluvfVW7dmzp8ba16xZo/vuu082m02pqal6+OGHtXHjRo0aNUoXL1706utyuZSUlKTOnTtrxYoVGjNmjH7729/q1VdfbchuAwAAQDU4nAxNLicnR+fPn1dhYaH27NmjZ599VoGBgbrrrruUkZGhoqIi3XvvvUpNTfUss3v3bv3xj3/Um2++qWnTpnnab7vtNo0bN04bNmzQtGnTlJOTo5/97GeKi4vTjh07vA7/qml615YtWxQSEqJt27bJZrPVaTtKSko0f/58DRgwQDt37vQ816hRo3TXXXfpxRdf1JIlSzz9CwsLNXnyZD3zzDOSpEcffVRDhw7V66+/rp/+9Kd123kAAACoFSMxaHKJiYnq2rWrnE6npkyZouDgYG3atEndu3f39Ln2S/2GDRsUGhqqO+64Q+fPn/fcYmNjFRwcrI8++khS2YjKpUuXtGDBgkrzVywWS7U1hYWFKT8/X9u3b6/zdnz66afKzs7WY4895vVcEyZMUL9+/bRly5ZKyzz66KNe90ePHq1vvvmmzs8JAACA2jESgya3atUq3XDDDfLz81NkZKT69u3rdbYvPz8/9ejRw2uZr776Sjk5OYqIiKhyndnZ2ZIqDk0bMGBAvWp67LHH9NZbb+nOO+9U9+7dNXbsWN13330aN25ctcucOHFCktS3b99Kj/Xr10+7d+/2agsICFDXrl292sLDw6uc0wMAAICGI8SgycXFxXnOTlYVh8NR6RTGbrdbERERevPNN6tc5tpwUF8RERE6cOCAtm3bpvfff1/vv/++3njjDU2fPl1//vOfG7XuK+p6mBoAAAAahxCDVuG6667Thx9+qJEjRyowMLDGfpJ08OBBXX/99fV6DrvdruTkZCUnJ8vtduuxxx7TK6+8omeeeabKdfXq1UuSdOTIEc8Z1q44cuSI53EAAAC0LObEoFW477775HK5tHTp0kqPlZaWes4ENnbsWHXs2FGpqakqLCz06lfTxP7vvvvO677VatWgQYMkSUVFRVUuM2zYMEVERGj16tVefd5//30dOnRIEyZMqNO2AQAAoGkxEoNWYcyYMXrkkUeUmpqqAwcOaOzYsfL399dXX32lDRs2aOXKlbrnnnsUEhKiF198UbNnz9bw4cM1bdo0hYeH61//+pcKCgqqPTRs9uzZunDhgm6//Xb16NFDJ06c0EsvvaQhQ4aof//+VS7j7++v5cuXa9asWRozZoymTp2qrKwsrVy5UjExMXryySebc5cAAACgGoQYtBqrV69WbGysXnnlFT311FPy8/NTTEyMHnjgAY0cOdLT76GHHlJERISWLVumpUuXyt/fX/369asxVDzwwAN69dVX9fvf/14XL15UVFSUJk+erF//+teV5udcbebMmQoKCtKyZcs0f/58dejQQXfffbeWL1+usLCwptx8AAAA1JHFqOkYHAAAAABoZZgTAwAAAMBUCDEAAAAATMW0IWbnzp1KTk5WdHS0LBaL3nnnnVqX2bFjh4YOHSqHw6Hrr79ea9asafY6AQAAADQt04aY/Px8DR48WKtWrapT/2PHjmnChAm67bbbdODAAT3xxBOaPXu2tm3b1syVAgAAAGhKbWJiv8Vi0aZNmzRp0qRq+8yfP19btmzRwYMHPW1TpkzRxYsXtXXr1haoEgAAAEBTMO1ITH1lZGQoMTHRqy0pKUkZGRk+qggAAABAQ7Sb68RkZmYqMjLSqy0yMlK5ubm6fPmyAgMDq1yuqKjI62rtbrdbFy5cUOfOnWWxWJq1ZqCtMwxDly5dUnR0dI3X6wEAALhauwkxDZWamqolS5b4ugygTTt16pR69Ojh6zIAAIBJtJsQExUVpaysLK+2rKwshYSEVDsKI0kLFy5USkqK535OTo569uypU6dOKSQkpNnqBdqD3NxcOZ1OdezY0delAAAAE2k3ISYhIUHvvfeeV9v27duVkJBQ43IOh0MOh6NSe0hICCEGaCIcmgkAAOrDtAeh5+Xl6cCBAzpw4ICkslMoHzhwQCdPnpRUNoIyffp0T/9HH31U33zzjX75y1/q8OHD+v3vf6+33npLTz75pC/KBwAAANBApg0xn376qW666SbddNNNkqSUlBTddNNNWrRokSTp7NmznkAjSb1799aWLVu0fft2DR48WL/97W/1xz/+UUlJST6pHwAAAEDDtInrxLSk3NxchYaGKicnh8PJgEbi/QQAABrCtCMxAAAAANonQgwAAAAAUyHEAAAAADAVQgwAAAAAUyHEAAAAADAVQgwAAAAAUyHEAAAAADAVQgwAAAAAUyHEAAAAADAVQgwAAAAAUyHEAAAAADAVQgwAAAAAUyHEAAAAADAVQgwAAAAAUyHEAAAAADAVQgwAAAAAUyHEAAAAADAVQgwAAAAAUyHEAAAAADAVQgwAAAAAUyHEAAAAADAVQgwAAAAAUzF9iFm1apViYmIUEBCg+Ph47d27t8b+aWlp6tu3rwIDA+V0OvXkk0+qsLCwhaoFAAAA0FimDjHr169XSkqKFi9erM8++0yDBw9WUlKSsrOzq+y/du1aLViwQIsXL9ahQ4f0+uuva/369XrqqadauHIAAAAADWXqEPPCCy/o4Ycf1qxZs3TjjTdq9erVCgoK0p/+9Kcq+3/88ccaOXKkpk2bppiYGI0dO1ZTp06tdfQGAAAAQOth2hBTXFysffv2KTEx0dNmtVqVmJiojIyMKpcZMWKE9u3b5wkt33zzjd577z2NHz++RWoGAAAA0Hh+vi6goc6fPy+Xy6XIyEiv9sjISB0+fLjKZaZNm6bz589r1KhRMgxDpaWlevTRR2s8nKyoqEhFRUWe+7m5uU2zAQAAAAAaxLQjMQ2xY8cOPffcc/r973+vzz77TBs3btSWLVu0dOnSapdJTU1VaGio5+Z0OluwYgAAAADXshiGYfi6iIYoLi5WUFCQ3n77bU2aNMnTPmPGDF28eFGbN2+utMzo0aN188036/nnn/e0/eUvf9FPfvIT5eXlyWqtnOmqGolxOp3KyclRSEhI024U0M7k5uYqNDSU9xMAAKgX047E2O12xcbGKj093dPmdruVnp6uhISEKpcpKCioFFRsNpskqbos53A4FBIS4nUDAAAA4DumnRMjSSkpKZoxY4aGDRumuLg4paWlKT8/X7NmzZIkTZ8+Xd27d1dqaqokKTk5WS+88IJuuukmxcfH6+uvv9Yzzzyj5ORkT5gBAAAA0LqZOsRMnjxZ586d06JFi5SZmakhQ4Zo69atnsn+J0+e9Bp5efrpp2WxWPT000/rzJkz6tq1q5KTk/Vf//VfvtoEAAAAAPVk2jkxvsIx/EDT4f0EAAAawrRzYgAAAAC0T4QYAAAAAKZCiAEAAABgKoQYAAAAAKZCiAEAAABgKoQYAAAAAKZi6uvEAPAtwzBU7HKrsNitgpJSXS52qaDYpcslLl0udmlQj1CFBdl9XSYAAGhjCDFAG2YYhopK3WXhosSly8WlulzsVkFxqSdoXC4pDx5X/VxY4irv49bl4lKvYOLpU75Ol7vypaZ6WLKVYP1S/tOfUELf7j7YcgAA0JYRYgAfcrsNFZa6Ko1gVASJsjBx5efqg0dF4Lh6HZdLXGqJy9lG67xG+n2pUX6HNFxfKlrnJEmHLiZKIsQAAICmRYgBauByG56QcPUhU9WNYFwuH+2ocuSiiuBRWOJusW2x26wKtNsUZLcp0N+mwGv+DbLbFGj3u+pnW6WfrywfXHxO4dmfqOO3H8t+JkO2i8e9n8zqJ3WPVf+IwBbbPgAA0H4QYmBqJS53RVioNJpR+ZCpitGNuh0yVVzaciEjwN9aHhr8FOBvVVB5oKgqeATZbQqw2xR0pX/5z4H2qvsH+tvkZ2vEeTwuZUrHd0uHdpb9e+Go9+MWmxR9k9R7tBQzWnLGS47gxu0QAACAahBi0Gxqm/TtNTpRwwhGlXM1yttKXC1wrJQki0WeMFDVCEVZ8Kh9BCPQ36/akGG1WlpkW+okL7ssrBzfJR3bJX33lffjFqvUbXBZYOl9i9TzZsnR0Te1AgCAdocQ044156TvKz9XNem7OVgt8goSQXZbRaio4ZCpSiMYVwWOgPJwEmS3yeFnlcXSikJGU8v/TjqxuyywHN8lnTt8TQeLFDWwLLDEjJZ6JUgBoT4pFQAAgBDTirWVSd+S5Ge1VBEkrLUHj1pGMK6MdthtbTxkNLWCC9KJPWWjLcd2SdlfVO4TOVCKGVV2iFivEVJgeMvXCQAAUAVCTDP49PgF5VwuaTOTviva6jbp++oRjCuhxL8x8zHQeJcvSic+Lj9EbKeUeVDSNQk24sayUZaYUWW3oE6+qBQAAKBWhJhmkPLWv3TyQkGTrrO+k769Rjqae9I3Wp/CXOlkhnSsfCJ+5r8l45pQ3KVv+UT8UVKvUVJwV9/UCgAAUE+EmGbQv1tHhQf5t91J32h9ivKkk/8oG2U5tks6e6ByaOl8fflE/NFloaVjpE9KBQAAaCxCTDN45cFhvi4BbV1xvnTqk4qJ+Gc+kwyXd5/w3uUjLbdIMSOlkGjf1AoAANDECDGAGZRclk7trTjl8Zl9krvEu09Yr4qRlphRUmgP39QKAADQzAgxQGtUUiid/mfFtVpO/1NyFXv3CelRcXHJmFFSeC/f1AoAANDCCDFAa1BaLJ35tPyUxzvLQktpoXefjt2uGmkZLYXHlF2FEwAAoJ0hxAC+4Copm8dyvPzsYSc/kUove/cJjqwYZel9i9TpB4QWAAAAtYEQs2rVKj3//PPKzMzU4MGD9dJLLykuLq7a/hcvXtSvfvUrbdy4URcuXFCvXr2Ulpam8ePHt2DVaHdcpWVnDDu2s+zwsJOfSCX53n06dC2/Rkv5SEuXPoQWAACAKpg6xKxfv14pKSlavXq14uPjlZaWpqSkJB05ckQRERGV+hcXF+uOO+5QRESE3n77bXXv3l0nTpxQWFhYyxePts3tks7+q2Ii/skMqTjPu09gp4pRlphRUtd+hBYAAIA6sBiGYdTerXWKj4/X8OHD9fLLL0uS3G63nE6nHn/8cS1YsKBS/9WrV+v555/X4cOH5e/v36DnzM3NVWhoqHJychQSEtKo+tGGuF1S1sGKUx6f+FgqyvXuExBWMdLSe7TUtb9kbd8XGeX9BAAAGsK0IzHFxcXat2+fFi5c6GmzWq1KTExURkZGlcu8++67SkhI0Jw5c7R582Z17dpV06ZN0/z582Wz2apcpqioSEVFRZ77ubm5VfZDO+N2S9lflE/E3yWd2CMVXvTu4wiVeo2omIgfOaDdhxYAAICmYNoQc/78eblcLkVGel91PDIyUocPH65ymW+++UZ/+9vfdP/99+u9997T119/rccee0wlJSVavHhxlcukpqZqyZIlTV4/TMYwpOxD5ac83ikd3yNdvuDdx95R6pVQMdISNUiyVh2OAQAA0HCmDTEN4Xa7FRERoVdffVU2m02xsbE6c+aMnn/++WpDzMKFC5WSkuK5n5ubK6fT2VIlw1cMQzr/fxVzWo7vlgrOe/fx7yD1vLl8pOUWqdtgydau3lIAAAA+YdpvXF26dJHNZlNWVpZXe1ZWlqKioqpcplu3bvL39/c6dKx///7KzMxUcXGx7HZ7pWUcDoccDkfTFo/WxzCk745WnPL4+G4pz/t3S36BZaHlymT86JskW8PmVgEAAKDhTBti7Ha7YmNjlZ6erkmTJkkqG2lJT0/X3Llzq1xm5MiRWrt2rdxut6zlcxP+7//+T926dasywKANMwzp+2MVE/GP75YunfXu4xcgOePKRlliRkndYyU/fk8AAAB8zbQhRpJSUlI0Y8YMDRs2THFxcUpLS1N+fr5mzZolSZo+fbq6d++u1NRUSdJPf/pTvfzyy/r5z3+uxx9/XF999ZWee+45/exnP/PlZqClfH/C+/Cw3NPej9vsUo+48sPDRkndh0n+Ab6pFQAAANUydYiZPHmyzp07p0WLFikzM1NDhgzR1q1bPZP9T5486RlxkSSn06lt27bpySef1KBBg9S9e3f9/Oc/1/z58321CWhOOaevGmnZJV086f241V/qMaxiIn6P4ZJ/oG9qBQAAQJ2Z+joxvsB1LVqx3G/LT3m8syy0fH/c+3GrnxQ9tOKUx844yd7BJ6WiDO8nAADQEKYeiUE7dymrYpTl2C7pwlHvxy02KXpIxUiL82bJEeyTUgEAANB0CDEwj7xzFZPwj+8qOwXy1SzWstMcx4wqm4zf82YpgL/uAwAAtDWEGLRe+d9JJ3ZXTMQ/d+iaDhYpamDFSEvPBCkwzBeVAgAAoAURYtB6XP5eOr6nYrQl62DlPpEDykJLzCip1wgpqFPL1wkAAACfIsTAdwpzpBMfl4+07JQyD0q65jwTXftXTMTvNVLq0NknpQIAAKD1IMSg5RTmSif/URZYju2SMv8tGW7vPl1uqDg8rNcoKbirb2oFAABAq0WIQfMpypNO/aPiWi3fHpAMl3efTtdVjLTEjJY6RvqkVAAAAJgHIQZNp7igLLQcL5+M/+1nkrvUu09477L5LL1vKfs3JNo3tQIAAMC0CDFouJLL0qm9Fac8Pv2p5C7x7hPWs2KUJWaUFOb0Ta0AAABoMwgxqLvSIun0PytOeXx6r+Qq9u4T0qP88LBRZcElvJdvagUAAECbRYhB9UqLpTP7ykZZju0sCzClhd59OnarGGXpPbrscDGLxTf1AgAAoF0gxKCCq0T6dn9ZYDm+Szr5iVR62btPhwjvifidryO0AAAAoEURYtozV6l09l8Vpzw++Q+pJN+7T1CXilGWmNFlp0AmtAAAAMCHCDHtidtVHlrKJ+KfyJCKL3n3CewkxYyUYm4pCy5d+xFaAAAA0KoQYtoyt1vK+rzilMcnPpaKcrz7BISWXVTyykhLxI2S1eqbegEAAIA6IMS0JW63lP1l2SjL8d1lt8KL3n0cIVKvEWWBpfdoKXKAZLX5pFwAAACgIQgxZmYY0rnD5ac8Lg8uly9497F3lHolVJzyuNtgQgsAAABMjRBjJoYhnf+qYiL+8d1SwXnvPv4dpJ43Vxwe1m2IZONlBgAAQNvBt9vWzDCkC99UnPL4+G4pL8u7j1+g1DO+4pTH3YdKNn/f1AsAAAC0AEJMa2IY0vfHyy8uWR5aLn3r3cfmkJxxUu9bKkKLn8Mn5QIAAAC+QIjxte9PVJzy+NguKfe09+M2u9RjeMVE/O7DJP8A39QKAAAAtAKmDzGrVq3S888/r8zMTA0ePFgvvfSS4uLial1u3bp1mjp1qiZOnKh33nmn+Qu9Iud0xSjL8Z3SxZPej1v9pR7DKibiO+Mk/8CWqw8AAABo5UwdYtavX6+UlBStXr1a8fHxSktLU1JSko4cOaKIiIhqlzt+/LjmzZun0aNHN3+RuWfLR1l2lgWX7495P271k6KHloWW3qMlZ7xk79D8dQEAAAAmZTEMw/B1EQ0VHx+v4cOH6+WXX5Ykud1uOZ1OPf7441qwYEGVy7hcLt1yyy368Y9/rF27dunixYv1GonJzc1VaGiocnJyFBISUrmD2yV9saliIv53X3s/brFK0TeVj7TcUnYmMUdwnZ8faEtqfT8BAABUwbQjMcXFxdq3b58WLlzoabNarUpMTFRGRka1yz377LOKiIjQQw89pF27djV9YRartO1XUl5mxf2oQRWnPO6ZIAXwZQ0AAABoKNOGmPPnz8vlcikyMtKrPTIyUocPH65ymd27d+v111/XgQMH6vw8RUVFKioq8tzPzc2teQGLRRo6XSrOLxtt6TVCCgyr8/MBAAAAqJlpQ0x9Xbp0SQ8++KBee+01denSpc7LpaamasmSJfV7stt/Vc/qAAAAANSVaUNMly5dZLPZlJXlffHHrKwsRUVFVep/9OhRHT9+XMnJyZ42t9stSfLz89ORI0d03XXXVVpu4cKFSklJ8dzPzc2V0+lsqs0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\n" 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\n" 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sW7eOdW6lSpXQunVrRqEFtAtaHh4eaNy4Matuz549MWvWLJw/fx516tTh1X+B8oOgxLzH+Pr64vjx48jOzmat1D18+JA5ro9+xBCax48fw97eHm5ubiav06hRIwDaqCf6KBQKpKWlmT2XRj+XwLlz5zBp0iRW+zKZDFFRUbh06RJr1cfX1xcajQZPnjxh5UpITk5GRkaG0Wc0hUgkQvv27Zl8JHPnzsV3332HyMhIfPjhh7wHUECbwO3YsWOsMk9PT97n8+XOnTt4/PgxNm7cyHLyNLw2PRkw57zr5uYGJycnqNVqVp4Wa9m7dy9Gjx6NPn36YNWqVSbraTQaDBs2DCdOnMCOHTuYlUVTVKtWDdWqVQMAZmeFdnw1B71CyecZFBAobwgyXpDxQNmU8QDg4OCAFi1aMO+PHz8OOzs7JupccnIyAHDuYimVSqhUKuZ9cnKyyXoAWHUF3h0Ec7L3mK5du0KtVmPlypWs8qVLl4KiKHTp0oVVfuHCBVy/fp15Hx8fj71796Jjx45mV6ratm0Ld3d3bN68mRXaNiIigllJskTjxo1ha2uLzZs3IyEhgbVKJ5PJ0LBhQ6xatQq5ubnMYEh/RgBGkVWWLFkCAOjWrZvFa3Ot0tPJzugIWQ4ODgC0E2hLVKxYER9++CHrzzApY0lAfydEL5QqIQTLly9n1XNzc0NYWBjWr1+PuLg41jH6XLFYjL59+2LXrl2cA6E5UxOa06dPY9CgQQgLC8PmzZvNJh774osvsH37dqxevZpJZsaX6dOnQ6VS4csvvzTbv+zsbCxbtgyVK1dmJmECAu8SgowXZDxQNmW8IefPn8e///6LUaNGoUKFCgCAGjVqQCQSYfv27azP+OLFCyZMM03NmjWRnJyMqKgoVrtbt24FICQ1flcRdmLeY3r06IF27drhu+++Q0xMDEJCQnD06FHs3bsXkyZNQkBAAKt+vXr10KlTJ0yYMAEymQyrV68GAMyePdvsdWQyGRYuXIjhw4cjLCwMQ4cORVxcHJYvX442bdrwmqTa2NigSZMmOHPmDGQymdGks2XLlli8eDEAsAa4kJAQDB8+HOvWrUNGRgbCw8Nx+fJlbNy4Eb169TLa8ufixx9/xOnTp9GtWzf4+voiJSUFq1evRtWqVZlrBQQEwMXFBWvXroWTkxMcHBzQrFkzTvvrN0WtWrUQEBCAKVOmICEhAc7Ozti1axenacevv/6K1q1bo2HDhhg7diz8/f0RExODAwcO4ObNmwCA+fPnIzIyEs2aNcOYMWNQp04dpKen4/r16zh+/LhZk6zY2Fj07NkTFEWhX79+2LlzJ+t4/fr1GVOFZcuWYfXq1WjRogXs7e3x999/s+r27t2bmVDMnz8fd+/eRbNmzSCRSLBnzx4cPXoUP//8M5PXAQBWrVqFPXv2oEePHqhWrRoSExOZAX3Tpk2wsbEp0j0WECjLCDJekPE0ZUnGx8bGYsCAAejZsyc8PT1x7949rF27FvXr18fcuXOZc9zc3PDJJ5/gjz/+QPv27dGnTx9kZ2dj9erVyM/Px/Tp05m648ePx4YNG9CjRw988cUX8PX1xalTp7B161Z06NABzZo1K86tFiirvMWgAgJvGK5sztnZ2eTLL78k3t7eRCqVksDAQLJw4UJW2EVCdNnO//77bxIYGEhkMhlp0KABiYyM5H39rVu3kpCQECKTyYiHhwcZP368UdQcc0yfPp0AIC1btjQ69u+//xIAxMnJySiKjlKpJLNnzyb+/v5EKpUSHx8fMn36dFJQUMCq5+vrS7p162bU9okTJ8j//vc/4u3tTWxsbIi3tzcZPHgwefz4Mave3r17SZ06dYhEIim1KDamItc4ODhw1r9//z758MMPiaOjI6lcuTIZM2YMuXXrFmf/7t69S3r37k1cXFyIra0tCQoKYoU0JkQbKWzcuHHEx8eHSKVS4unpSdq3b0/WrVtntt+RkZGssJeGfzNnzmR9HnN19T/7/v37SdOmTYmTkxOxt7cnzZs3Jzt27DC6/tGjR0mHDh2Ip6cnkUqlxMXFhXTs2JGcOHHCbL8FBMoTgowXZHx5kPHp6enkf//7H/H09CQ2NjbE39+fTJs2jfNZUSqVZMWKFSQ0NJQ4OjoSR0dH0q5dO1bobJqHDx+Sfv36MX339fUlU6ZMsRjiW6D8QhFikLZXQIADiqIwbtw4I7MEAQEBAYHyjyDjBQQEyhuCT4yAgICAgICAgICAQLlCUGIEBAQEBAQEBAQEBMoVghIjICAgICAgICAgIFCuEHxiBAQEBAQEBAQEBATKFcJOjICAgICAgICAgIBAuUJQYgQEBAQEBAQEBAQEyhWCElPIrFmzQFEU0tLS3nZXBASsom3btmjbti3vuvXq1SvxPpS1309p9IfvfY6KigJFUUaZowXePmXtORUQ4Isg540R5LyAoMS8ZebOnYs9e/bwqhsTEwOKokBRFH7++WfOOh9//DEoioKjo2MJ9rJ4+Pn5oXv37kU6l6IojB8/nvPYP//8w1uIyOVyTJs2Dd7e3rCzs0OzZs1w7NixIvWpQ4cOZvtFc/bsWeb7epNC/+XLl5g1axaThbkkseZ5FbBMcZ/Lbdu2oWHDhrC1tYWbmxtGjRpVZiYYAjoEOW+e8ijnk5OT8emnn6JKlSqwtbWFn58fRo0aVaRrFQVBzpcfBDlfeghKzFumKMLC1tYWW7duNSrPzc3F3r17YWtrW0K9e3cYMWIElixZgo8//hjLly+HWCxG165dcfbsWava+ffff3HhwgWL9TQaDb744gs4ODgUtcu8OXr0KI4ePcq8f/nyJWbPni0MbuWA4jyXa9asweDBg1GpUiUsWbIEY8aMwbZt29C+fXsUFBS8gd4L8EWQ82+GNyXn4+Pj0aRJExw6dAifffYZVq9ejdGjRyM1NbW4H8EkgpwvvwhyvvQQlJhySNeuXXH//n3cunWLVb53714oFAp06NDhLfWsbHL58mVs27YN8+bNw8KFCzF27FicPHkSvr6++Prrr3m3U1BQgK+++grTpk2zWHfdunWIj4/H6NGji9N1XtjY2MDGxqbUr1NaaDSa91IYF+e5VCgU+PbbbxEWFoZjx47h888/x9y5c7F9+3bcvn0bv//++xv6FAKlhSDnreNNyvlPP/0UEokEN27cwA8//IBPPvkE33//Pfbt21cSH4UTQc6XTwQ5X7oISowBaWlpGDBgAJydneHq6oqJEydy/vD+/vtvNGrUCHZ2dqhUqRIGDRqE+Ph4Vp0nT56gb9++8PT0hK2tLapWrYpBgwYhMzMTgHYLPTc3Fxs3bmTMB0aMGGGxjy1atIC/vz+2bNnCKt+8eTM6d+6MSpUqcZ536NAhtGnTBg4ODnByckK3bt1w7949Vp3bt29jxIgRqF69OmxtbeHp6YlPPvkEr169YtWjbVGfPn2KESNGwMXFBRUqVMDIkSORl5dn8TPk5ubiq6++go+PD2QyGYKCgrBo0SKURsTvf/75B2KxGGPHjmXKbG1tMWrUKFy4cMHoezPFL7/8Ao1GgylTppitl56eju+//x4//vgjXFxceLV9+/ZtUBTFGgSvXbsGiqLQsGFDVt0uXbqgWbNmzHt9G96oqCg0adIEADBy5EjmuYqIiGC1cf/+fbRr1w729vaoUqUKfvnlF4t95PO8ZmRkWHweaBONzZs3o27dupDJZDh8+DAAICEhAZ988gk8PDwgk8lQt25drF+/3qgvK1asQN26dWFvb4+KFSuicePGRr8Hvv1RqVT46aefEBAQAJlMBj8/P3z77beQy+UW78mLFy/Qq1cvODg4wN3dHV9++SWv84DiPZd3795FRkYGBg4cCIqimPLu3bvD0dER27Zt49WH9xVBzgty3hSW5PzDhw9x6NAhTJ06Fa6urigoKIBSqeTVtiDnBTkPCHK+JBGUGAMGDBiAgoICzJs3D127dsWvv/7KevgAYM6cORg2bBgCAwOxZMkSTJo0CSdOnEBYWBgyMjIAaDXoTp064eLFi/jiiy+watUqjB07Fs+fP2fqbNq0CTKZDG3atMGmTZuwadMmfPrpp7z6OXjwYGzbto0ZDNLS0nD06FF89NFHnPU3bdqEbt26wdHREQsWLMAPP/yA+/fvo3Xr1oiJiWHqHTt2DM+fP8fIkSOxYsUKDBo0CNu2bUPXrl05B54BAwYgOzsb8+bNw4ABAxAREYHZs2eb7TshBD179sTSpUvRuXNnLFmyBEFBQZg6dSomT57M6/Nbw40bN1CzZk04Ozuzyps2bQoAvLbj4+LiMH/+fCxYsAB2dnZm6/7www/w9PTk/V0CQL169eDi4oLTp08zZWfOnIFIJMKtW7eQlZUFQLuadf78eYSFhXG2U7t2bfz4448AgLFjxzLPlX79169fo3PnzggJCcHixYtRq1YtTJs2DYcOHTLbRz7PK9/n4eTJk/jyyy8xcOBALF++HH5+fkhOTkbz5s1x/PhxjB8/HsuXL0eNGjUwatQoLFu2jDn3999/x4QJE1CnTh0sW7YMs2fPRmhoKC5dumR0HT79GT16NGbMmIGGDRti6dKlCA8Px7x58zBo0CCz9yM/Px/t27fHkSNHMH78eHz33Xc4c+YM71Xf4jyX9ADK9Sza2dnhxo0b0Gg0vPrxPiLIeUHOc8FHzh8/fhwA4OHhgfbt28POzg52dnbo0qUL6x5zIch5Qc7TCHK+hCAChBBCZs6cSQCQnj17sso///xzAoDcunWLEEJITEwMEYvFZM6cOax6d+7cIRKJhCm/ceMGAUB27txp9roODg5k+PDhvPoYHR1NAJCFCxeSu3fvEgDkzJkzhBBCVq1aRRwdHUlubi4ZPnw4cXBwYM7Lzs4mLi4uZMyYMaz2kpKSSIUKFVjleXl5RtfdunUrAUBOnz7NlNH365NPPmHV7d27N3F1dWWV+fr6km7dujHv9+zZQwCQn3/+mVWvX79+hKIo8vTpU6YMABk3bhzn/di5cycBQCIjIzmP09StW5d88MEHRuX37t0jAMjatWvNnk/3rWXLlhb7devWLSIWi8mRI0cIIbr7lJqaavEa3bp1I02bNmXe9+nTh/Tp04eIxWJy6NAhQggh169fJwDI3r17mXrh4eEkPDyceX/lyhUCgGzYsMHoGuHh4QQA+euvv5gyuVxOPD09Sd++fS320dTzas3zAICIRCJy7949VvmoUaOIl5cXSUtLY5UPGjSIVKhQgXk2//e//5G6deua7Sff/ty8eZMAIKNHj2bVmzJlCgFATp48yZQZ3udly5YRAGTHjh1MWW5uLqlRo0apP5epqamEoigyatQoVvnDhw8JAALA6D4KCHKeRpDz3PCR8xMmTCAAiKurK+ncuTPZvn07WbhwIXF0dCQBAQEkNzfX7DUEOS/IeUIEOV9SCDsxBowbN471/osvvgAAHDx4EIDW4U+j0WDAgAFIS0tj/jw9PREYGIjIyEgAQIUKFQAAR44c4bXtbi1169ZF/fr1GcfPLVu24H//+x/s7e2N6h47dgwZGRkYPHgwq89isRjNmjVj+gywNf6CggKkpaWhefPmAIDr168btf3ZZ5+x3rdp0wavXr1iVpS4OHjwIMRiMSZMmMAq/+qrr0AIsbhSZC35+fmQyWRG5bRjbH5+vtnzIyMjsWvXLtYqkSkmTJiALl26oGPHjlb3s02bNrh+/Tpyc3MBaKObde3aFaGhoThz5gwA7aodRVFo3bq11e3TODo6YsiQIcx7GxsbNG3aFM+fPy9ymzR8n4fw8HDUqVOHeU8Iwa5du9CjRw8QQljPaadOnZCZmck8fy4uLnjx4gWuXLlS7P7Qv2vDleGvvvoKAHDgwAGTbR88eBBeXl7o168fU2Zvb2+0om+K4jyXlStXxoABA7Bx40YsXrwYz58/x5kzZzBw4EBIpVKL57/vCHJekPOG8JXzOTk5AABPT08cOHAAAwYMwJQpU/D777/j2bNnnOZO+ghyXpDzgCDnSwpBiTEgMDCQ9T4gIAAikYjZJn7y5AkIIQgMDISbmxvr78GDB0hJSQEA+Pv7Y/Lkyfjjjz9QuXJldOrUCatWrWLspEuCjz76CDt37sTTp09x/vx5kyYGT548AQB88MEHRn0+evQo02dA688xceJEeHh4wM7ODm5ubvD39wcAzr5Xq1aN9b5ixYoAtFvZpoiNjYW3tzecnJxY5bVr12aOW4O+rSgXdnZ2nPartA28OfMwlUqFCRMmYOjQoYwNsim2b9+O8+fPY/HixTx6bUybNm2gUqlw4cIFPHr0CCkpKWjTpg3CwsJYg1udOnVM2sPzoWrVqkb3rGLFima/M77wfR7oZ4omNTUVGRkZWLdundEzOnLkSABgntNp06bB0dERTZs2RWBgIMaNG4dz584VqT+xsbEQiUSoUaMGq56npydcXFzMPouxsbGoUaOG0b0MCgoyeY4+xXkuAeC3335D165dMWXKFAQEBCAsLAzBwcHo0aMHAJSp8LtlDUHOC3JeH2vkPN3OgAEDIBLpplD9+/eHRCLB+fPnzZ4vyHlBzgOCnC8pJG+7A2UdwwdXo9GAoigcOnQIYrHYqL7+A7V48WKMGDECe/fuxdGjRzFhwgTMmzcPFy9eRNWqVYvdt8GDB2P69OkYM2YMXF1dTa7+0zaTmzZtgqenp9FxiUT3GAwYMADnz5/H1KlTERoaCkdHR2g0GnTu3JnT9pLrHgAoMcdNmUxmcqWBXvm0FGrUy8sLCQkJRuWJiYkAAG9vb5Pn/vXXX3j06BF+++03I3vn7OxsxMTEwN3dHfb29pg6dSr69+8PGxsbpi5tFx8fHw+FQmH2Wo0bN4atrS1Onz6NatWqwd3dHTVr1kSbNm2wevVqyOVynDlzBr179zb7eS1Rmt8Z37YNBTf9bA0ZMgTDhw/nbKN+/foAtJOgR48eYf/+/Th8+DB27dqF1atXY8aMGUZ20Hz7Y2mCVBoU57kEtLsAe/fuRVxcHGJiYuDr6wtfX1+0bNkSbm5uvINKCAhyXpDz/OU83Y6Hhwernlgshqurq0UlQZDzgpwHBDlfUghKjAFPnjxhrR48ffoUGo0Gfn5+ALQrdoQQ+Pv7o2bNmhbbCw4ORnBwML7//nucP38erVq1wtq1a5kkZsX5UVWrVg2tWrVCVFQU/u///o81SOkTEBAAAHB3d8eHH35osr3Xr1/jxIkTmD17NmbMmMGU0yt8JYWvry+OHz+O7Oxs1irdw4cPmeP6dR89esTZDl2uX5+L0NBQREZGIisri+VcRzsIhoaGmjw3Li4OSqUSrVq1Mjr2119/4a+//sLu3bvRq1cvxMfHY8uWLZzmBA0bNkRISIhZJz56u//MmTOoVq0a2rRpA0C7cieXy7F582YkJyebdPakKU1BXVptu7m5wcnJCWq12uwzSuPg4ICBAwdi4MCBUCgU6NOnD+bMmYPp06dblT/D19cXGo0GT548YVaIAW0iu4yMDLPPlq+vL+7evQtCCOu+mHpeDSnOc6lPtWrVmJXIjIwMXLt2DX379uV17vuKIOcFOa+PNXK+UaNGAGA0MVUoFEhLS4Obm5vZfgpyXpDzgCDnSwrBnMyAVatWsd6vWLECgDbcIQD06dMHYrEYs2fPNtLyCSFMiMqsrCyoVCrW8eDgYIhEItbWooODA7NaXxR+/vlnzJw5k7Hp5qJTp05wdnbG3LlzOUNB0gm66NUMw8/FxxfEGrp27Qq1Wo2VK1eyypcuXQqKoph7Tde9ePEirl27xqqbkZGBzZs3IzQ0lHPVUZ9+/fpBrVZj3bp1TJlcLseGDRvQrFkz+Pj4MOVxcXHMIAsAgwYNwu7du43+6L7t3r2bCYPJVW/gwIEAtAPh0qVLLd6bNm3a4NKlS4iMjGQGt8qVK6N27dpYsGABU8ccdILN4jxX5toujXbFYjH69u2LXbt24e7du0bH9ZPIGYaBtbGxQZ06dUAI4R3qlKZr164AjJ/xJUuWAAC6detm9tyXL1/in3/+Ycry8vJYz5k5ivNcmmL69OlQqVT48ssvefXhfUWQ84KcL6qcb9u2Ldzd3bF582ZWWO6IiAio1Wpe+XsEOS/IeUHOlwzCTowB0dHR6NmzJzp37owLFy7g77//xkcffYSQkBAA2tWun3/+GdOnT0dMTAx69eoFJycnREdHY/fu3Rg7diymTJmCkydPYvz48ejfvz9q1qwJlUqFTZs2MT9imkaNGuH48eNYsmQJvL294e/vz4oNb4nw8HCEh4ebrePs7Iw1a9Zg6NChaNiwIQYNGgQ3NzfExcXhwIEDaNWqFVauXAlnZ2eEhYXhl19+gVKpRJUqVXD06FFER0cX7WaaoEePHmjXrh2+++47xMTEICQkBEePHsXevXsxadIkZkURAL755hvs3LkTYWFh+PTTT1GrVi28fPkSERERSExMxIYNGyxer1mzZujfvz+mT5+OlJQU1KhRAxs3bkRMTAz+/PNPVt1hw4bh1KlTzABfq1Yt1KpVi7Ndf39/9OrVi3mv/5qG3nnp0qULKleubLGvbdq0wZw5cxAfH88axMLCwvDbb7/Bz8/PoolKQEAAXFxcsHbtWjg5OcHBwQHNmjUzsk8uCsV9Xs0xf/58REZGolmzZhgzZgzq1KmD9PR0XL9+HcePH0d6ejoAoGPHjvD09ESrVq3g4eGBBw8eYOXKlejWrZuR/b0lQkJCMHz4cKxbtw4ZGRkIDw/H5cuXsXHjRvTq1Qvt2rUzee6YMWOwcuVKDBs2DNeuXYOXlxc2bdrE6XTNRXGeS/p+3b17F82aNYNEIsGePXtw9OhR/Pzzzxbt+t93BDkvyPmiynmZTIaFCxdi+PDhCAsLw9ChQxEXF4fly5ejTZs26NOnj8W+CnJekPOCnC8hSj8AWvmADtV3//590q9fP+Lk5EQqVqxIxo8fT/Lz843q79q1i7Ru3Zo4ODgQBwcHUqtWLTJu3Djy6NEjQgghz58/J5988gkJCAggtra2pFKlSqRdu3bk+PHjrHYePnxIwsLCiJ2dHQFgNgynfuhNcxiG3qSJjIwknTp1IhUqVCC2trYkICCAjBgxgly9epWp8+LFC9K7d2/i4uJCKlSoQPr3709evnxJAJCZM2ca3S/D0MEbNmwgAEh0dDRTZhh6kxBtONAvv/ySeHt7E6lUSgIDA8nChQuJRqMx6veLFy/I6NGjSZUqVYhEIiGVKlUi3bt3JxcvXjR7H/TJz88nU6ZMIZ6enkQmk5EmTZqQw4cPG9WjQ1NaAmZCgupjTYhlQgjJysoiYrGYODk5EZVKxZT//fffBAAZOnQoZ5/1Q0ISQsjevXtJnTp1iEQiYYXhDA8P5wxbOXz4cOLr62uxf6aeV2ueB3P3Ljk5mYwbN474+PgQqVRKPD09Sfv27cm6deuYOr/99hsJCwsjrq6uRCaTkYCAADJ16lSSmZnJ1LGmP0qlksyePZv4+/sTqVRKfHx8yPTp00lBQQHrXK77HBsbS3r27Ens7e1J5cqVycSJE8nhw4d5hd4kpHjP5f79+0nTpk2Jk5MTsbe3J82bN2eFARUwRpDzWgQ5X3w5v3XrVhISEkJkMhnx8PAg48ePJ1lZWbz6Kch5Qc4Lcr5koAgphdS5AgICAgICAgICAgICpYTgEyMgICAgICAgICAgUK4QfGIEBAQEBN445hIlmkI/uo+AgICAQNnlTch4wZxMQEBAQOCNIxKJrArlSlEUHj9+jOrVq5dirwQEBAQESoI3IeOFnRgBAQEBgbfCP//8wysrOSGECZMqICAgIFA+KG0ZLygxAgICAgJvHF9fX4SFhcHV1ZVX/erVq0MqlZZyrwQEBAQESoI3IeMFczIBAQEBAQEBAQEBgXKFsBNjAZVKhRs3bsDDwwMikRDMTUBAoOyg0WiQnJyMBg0aQCIRxHlREGS8gIBAWUWQ8eYR7ogFbty4gaZNm77tbggICAiY5PLly+9k9uarV68iLy8PYWFhpXYNQcYLCAiUdQQZz42gxFjAw8MDgPYB8vLyesu9ERAQENCRmJiIpk2bMnLqXWPo0KF4/Pgx1Gp1qV1DkPECAgJlFUHGm0dQYixAmxd4eXmhatWqb7k3AgICAsa8q2ZQJ06cgFKpLNVrCDJeQECgrCPIeG7ezbsiICAgIMDJ6dOn0aNHD3h7e4OiKOzZs8fiOVFRUWjYsCFkMhlq1KiBiIgIozqrVq2Cn58fbG1t0axZM1y+fLnYffX29oavr2+x2xEQEBB4X3ifZLygxAgICAi8R+Tm5iIkJASrVq3iVT86OhrdunVDu3btcPPmTUyaNAmjR4/GkSNHmDrbt2/H5MmTMXPmTFy/fh0hISHo1KkTUlJSeF0jLi7O7J+AgICAAD/eKxlPygkzZ84kAFh/QUFBZs/ZsWMHCQoKIjKZjNSrV48cOHDA6uvGx8cTACQ+Pr6oXRcQEBAoFYornwCQ3bt3m63z9ddfk7p167LKBg4cSDp16sS8b9q0KRk3bhzzXq1WE29vbzJv3jxe/aAoiohEIpN/pYkg4wUEBMoqgow3T7nyialbty6OHz/OvDcXbu78+fMYPHgw5s2bh+7du2PLli3o1asXrl+/jnr16pV439RqdcnZbv/dF8iINS4fEwXIHLWvVzbWlVPa5EAFFDDAyw2hBXL8mJ7FPpeigEbDgYJM4M4OvXIJQFTa1+HfAFcjgNwk7XsHD2DobkBiUyIfqzwhlUohFotLrD2lRgmlWgl7qX2JtSlQSuSlA7/4mz5eoRrQ70/ApylwdhkQNR8IGQQ8PQFkxgH9I4C6vXX1Ty0EIn/mf/1ZmVZ3OTs7G1lZut+8TCaDTCazuh0uLly4gA8//JBV1qlTJ0yaNAkAoFAocO3aNUyfPp05LhKJ8OGHH+LChQu8rnHjxg3We6VSiRs3bmDJkiWYM2dO8T6AwDsNIQS9Ny2FXKXAwZHTQFEUAOBy4mUk5CSgV41e2rJHh4GDU4E+6wDfFm+51wIC1iHIeG7KlRIjkUjg6enJq+7y5cvRuXNnTJ06FQDw008/4dixY1i5ciXWrl1bYn0ihCApKQkZGRkl1ibqTuAuf/4EsHXRvm612OhwtkiEaYXOX9EqlfH5YilgowRaNeNuXwUgdCq7LCZGe957iIuLCzw9PZlB0RKEEOx/vh91Xeuiukt1plxDNGi4qSEAYFS9UZjUaFJpdFegpEi8af54Zhzwdz9gehxwYSWgygeubdAd3zmCrcRYo8AUkTp16rDez5w5E7NmzSqRtpOSkowi43h4eCArKwv5+fl4/fo11Go1Z52HDx/yukZISIhRWePGjeHt7Y2FCxeiT58+Rf8AAu809f+qr30hBu4nD0ddT22EuSmnpuC1/DVcZC5oV60dsHWgtt6mXsD3yW+ns+8YT18/xZdRX+LLRl/ig2ofvO3uvNMIMp6bcqXEPHnyBN7e3rC1tUWLFi0wb948VKtWjbPuhQsXMHnyZFZZp06deDk4WQOtwLi7u8Pe3p73hNcsKfnc5faugKOHcR3nqoDEFs9fp8NOkg0A8HXy1Tk8qfKBrARALAPUcnab+jsxXFT0BaQlo+2XFwghyMvLY2w9+YZdvZJ0Bd+e/RY2IhtcG3qNKZ95fibz+s+7f2JInSGobFe5ZDst8Gahf0dqxdvtRyH3799HlSpVmPcltUL3tgkKCsKVK1fedjcEyihKNdv6wdmmAgBApVHhtfw1ACAmK4Z9kqoAgFbOZymy4GzjXDLj9ntGgaoAvfdpF2v+fvC3oMSUMoKM56bcKDHNmjVDREQEgoKCkJiYiNmzZ6NNmza4e/cunJycjOqb0iyTkpLMXkcul0Mu1030s7OzTdZVq9WMAuPq6mrlJzKDxIRAtZEAtrbGdezsAZkjSE4ORBKt6mJjbw+JqPDrlVNAHgVIRFqzMn3EYsBcfG5bGSC1LeIHKb/Y2dkBAFJSUuDu7s7LtOzcy3MAAIWGPbHd83QP631cVpygxAiUKE5OTnB2di6Vtj09PZGczF65Tk5OhrOzM+zs7CAWiyEWiznr8N051zeTALQTzMTERMyaNQuBgYHF+wAGWCPjBco2UgMrAXp4I4QwZSKKO37Rrie7MPvCbCwKX4ROfp1KrY/vKjse6czSryRdgVKtNPo+BEoOQcZzU26ik3Xp0gX9+/dH/fr10alTJxw8eBAZGRnYsWOH5ZOtYN68eahQoQLzZ7iFpw/tA2NvX/b8HAi0QjxHkYP8MrJaXN6gv1e+vk7r765nXsdlaaNtGCowACA33A0TsIrVN1dj+pnpuJd272135b2gRYsWOHHiBKvs2LFjaNFC61dgY2ODRo0asepoNBqcOHGCqWMJFxcXVKxYkfmrVKkS6tSpgwsXLmDNmjUl92FgnYwXKLsMPzQcwRuDWWXnEqMAaJUbD3vtIqZSo0RiTiIGe3vggINurL6ZchMAsPPxzjfS33eNC4lsX4gvIr8o0fYJIdAQTYm2KcBNeZbx5WYnxhAXFxfUrFkTT58+5TxuSrO0pDVOnz6dZYaWkJBgcZArM1vRGltolC4AEUNCSaBQKxCbpQ0QULfIjRLLVd5RrPlen75mP4dzLs3BhAYT8MO5H5iyg70PoopTFZMrg6ZQa9QQi4oeZCBXmQt7iXlTx3xVPl7mvESAS0CRr/OmOJdwDrfTbqOjb0fULcaTbRLybj/zOTk5LLkZHR2NmzdvolKlSqhWrRqmT5+OhIQE/PXXXwCAzz77DCtXrsTXX3+NTz75BCdPnsSOHTtw4MABpo3Jkydj+PDhaNy4MZo2bYply5YhNzcXI0eO5NWnyMhI1nuRSAQ3NzfUqFHDbACXolAUGS9Q9riect2oLFepXe2NyYxh5J1cLcfcy3NxVybDN+4ydIvWLjBVcdSa5vg6CXmIisLZhLOs9+cSzhWrvX+f/Ivfbv2Gle1XIrBiID499imS85LxT49/Sn2HR6VRQUyJy85crpi8TzK+3CoxOTk5ePbsGYYOHcp5nNYs6egKAFuzNIVhxAfDLbCyjRhE7QBAOwFXako307WAjhc5L1jvz788j/Mvz7PKvBy9sP/5fjR0b4iqTvwyg8dnx6Prv13Rxa8Lfgn/xep+3Uy5iaGHhloMKDD80HA8SH+AdR3WoYV32Y7ck63Umv+ozPlyCZjk6tWraNeuHfOentAPHz4cERERSExMZMXt9/f3x4EDB/Dll19i+fLlqFq1Kv744w906qQzwRk4cCBSU1MxY8YMJCUlITQ0FIcPHzYy6TVFeHh4CX06y5RvGS8AwOQKfXyOdtHucMxhJBVG2ZSr5EjJM85lkZqfCgDIUeZAQzSYc3EO2lRtg7Y+bUun00UgKTcJDlIHONkYm8y/a9C+o3MvzcUfHf9gdnoepj9EsFuwuVOLhUKtQM89PeHj5IPfO/5eatd5k7xPMr7cKDFTpkxBjx494Ovri5cvX2LmzJkQi8UYPHgwAGDYsGGoUqUK5s2bBwCYOHEiwsPDsXjxYnTr1g3btm3D1atXsW7durf5MYqHqQViE6sHFPisKrDrZFPasxzf8dXokua3W7+ZPT4+dDx2Pt6JuZfmAgD8K/hjWdtlrChmXHxz5hsAwKGYQ0VSYrY82AJAG1DAnBLzIP0BAGDvs71lXomJzowGAGx7uA0dfDu8nU6U499H27ZtWT4DhnBlam7btq1RiExDxo8fj/Hjxxe3eywSExOhVCpNBnAReD8RUSLMajELsy7MYpX/8/Rv/NByKmsBL8Q9BM8znxu1QZuRxWTFYOvDrdjxeAd2PN6Bbd234cnrJ+jm3+2t+ngsubYEG+5qox7eGX7nrfXDFG2rtkXUiyjm/ah6o0qk3UxFJhJyEpj3IlHpej3cTLmJhJwE1jXLO++TjC83PjEvXrzA4MGDERQUhAEDBsDV1RUXL16Em5sbAG020MTERKZ+y5YtsWXLFqxbtw4hISH4559/sGfPnlLJEVNmkKZAbJsAsW2Ckd+F4ePctt8YTJqxkFWmBhAnlSJWKoUZV/9SoW3btqxds/LG3Vd3zR4fEDSAUWAA7UT83yf/Wmy3d43eFuuYo5O/diXFQerAq76tuGwFcTgWewxLry3lXHkl77Gp4/vCBx98AH9/Mzl7BN5LshRZqOJUBS28WqBndXZo1vmX56OgMALZsDrD0L5aewypMwQAUENh7B9KCGHJx0H7B+GHcz+g4d8Noda8mZGQEIL4rHiWnKMVmLKISqNiKTAA4O3oXSJt+zj68FyALRn0zbvf1PctoKO4Mr7c7MRs27bN7PGoqCijsv79+6N///6l1KMyCKUT0Bqisdr3gph4bQqFQgEbm/cvESYXng6ejPkCFxVtKxqVOdhYViz8K2h/3H7OfkXqV3xWPACtX4wpUvJSYCu2RYG6AA3cGxTpOqXF5CjtNnh9t/poX639W+6NwJvmr7/+Ql5e3tvuhkAZ43H6Y4w5OgZ+zn74pfVK7HuuWxDa+nAr89pGbAOVRoVD0YcAAAqKghKAWE9ZeJD+AAuvsBf0aG6m3kQjj0al8yH02PxgMxZcWYD21dpjWbtlRscJIWXKX+Ni4kWjspIyX/+g2gdwtdNFe1WUYGCig88PYvn15fgs5DP0DtQuENpKytbC3ftGcWV8udmJEbAemViGoEpBqOlYpXBdQ6uajJg0E6cuXMPyP7eC8qwLqkpDPIuJx5ivZqNTo05o5NMIddv0xvI/trDaGzFiBHr16oU5c+bA29sbQUFBAIDz588jNDQUtra2aNy4Mfbs2QOKonDz5k3m3Lt376JLly5wdHSEh4cHhg4dirS0NKbdU6dOYfny5aAoChRFISYmpvRvUAniKHW0+pw8pe6H+yr/FW6l3jKqcydVa0ZglOuAJ5bM1QDgaMxRFKi1K5clGQ3mVf4rzDo/C3fTzO9S8SE1T2u/rr9SZm67vHgIOzxlhSZNmrxRfxmB8gEtp8SUGBozP9dzCefQ/7/+zK53nFSKpn4+mHORnSE8S8HtF5WvMpGzrYRZcGUBAOBE3AnO42VJgQEAEcfUcf7l+dj/fH+R2wx1C4Wfsx8aujeEvdQe89rMw8wWM+Hj5FOcrrKYdmYaXua+xIzzM4yOeTl4FSuAjkDRKK6MF5SYkoQQQJFb/D9lvom/PO46ijxjG32iFXwvsl/gcU4C9NdIlv84BS0a1ceYj3sj8XYUEm8cRVUvD1T1csfiPxdj79m9mPHlGHw7fyV27NzFavbEiRN49OgRjh07hv379yMrKws9evRAcHAwrl+/jp9++gnTpk1jnZORkYEPPvgADRo0wNWrV3H48GEkJydjwIAB2v4sX44WLVpgzJgxSExMRGJiInx8Sk5wvQkaezQGAPSvaXrnb27ruaz3EfcimNeddnXCkINDcDXpKlOmUCuw+Npi5n3wxmDcTr1tVb8i4yMt1tEfINML0q1q3xw/nPsBu57swuADg4vd1uWkywCAGynmbXYFBATefdREu5ghEolACIHiVWvOeg/SH+BpBjtypIqisOMxv9QMUfFRxelmkYjOjDZaoKEXccoKzjJdvhJ9k+fpZ6bzbiNTnolshS5H06aum/Bf7//g46wd+7v6d0W/mv1KPaca7QuTmJtooaZAWaTcmJOVC5R5wNySsQu1lsyvHrLeE2hjrNNmRAqKAu2iWMHZCTY2Utjb2sLT3Y3JPj5jyv/hcaF5WAcvd1y4dhs7/tmFAR8NYdp1cHDAH3/8wZiRrV27FhRF4ffff4etrS3q1KmDhIQEjBkzhjln5cqVaNCgAebO1U3i169fDx8fHzx+/Bg1a9aEjY0N7O3teSdOKmvQkbLc7d1N1jFnqkX7MJ1/eR6NPbUKEdf2/McHP7bKyVNMWV5ZOhh9kHl9JekKRgWXjIPmmYQzzOvimkMciz0GALoErgavuVBr1KAoymqzSoE3j1qtxtKlS7Fjxw7ExcVBYeC7kJ5ecsq1QPmHnuSLIAIBoHjdAjauZ82fVAS2P9qO75t/X+LtmkOhVuBk3ElWGa20lRVUGl1kyOZezbH76W6rzi9QFaD1Nq3ieXvYbRAQRMVH4UrSFbT1aYtsRTa+jPoSlWwr4dTAUyXZdYYdj3ZgQNAALLi8gClLy08TElGXEqUl44XR/R2BK7KGvi0pHwOZNRHbMaD9ALSp1QbOga2wbvO/iIuPZ9UJDg5m+cE8evQI9evXh62tzq60adOmrHNu3bqFyMhIODo6Mn+1atUCADx79ozPxyvzpOVpTePM+Z4k57HzFjX1bGpUh85dABTfXIoQwkuJ0d/dGVyr+LsmXEnKzr0sXg4BmuOxx5nX5sKOqjVq9N3XFx8f+LgUzc4ESorZs2djyZIlGDhwIDIzMzF58mT06dMHIpEIs2bNetvdEyhjMDsxlAiEAETpauGMopOWn1ZqbXNBURTup99nlRVHiSGEQKVRQakuuZQLtPkxgCJFs9S/p5nyTCTmJmJi5ET8/eBv/Hb7N3wZ9SUArWXA49ePi9/hQvRN02hLCH2fmLK24/UuUVoyXtiJKUmk9sC3L4vfTqKxbwQAwMENcPY2qnPfxgaEwznNmsnbtr1HMO2nZZgyeypCGoegjswGy9dsxKU7T9hdcOAX5UqfnJwc9OjRAwsWLDA65uXlZXV7ZRFaKNuITQc6MNwRoE2kAF1ggFqVajFlxV19W393PbY81Po1DQwayOuclt4ti3VNAPji5BdMklWamMwYtK7CbfJhjkq2lZBekM6cG+IeAhSO7+YiybzMeYlnmVoFWaVRlUKoVML6R6B4bN68Gb///ju6deuGWbNmYfDgwQgICED9+vVx8eJFTJgw4W13UaAMoe8TQ0cp1CgrQCTNZOrQsqO4TDg5AVu6bbFcsYTwdfY1km3F8VU8EnsEU09NRSOPRojoHFHM3mnRNwMryjilrzhse7SNZZJ2JekKq+7sC7MR0TkCUpH1MlxDNLiVegtBFYNgL7VnzYnis7ULtK/yXzFle5/tRW3X2lZfR8AypSXjhZ2YkoSiABuH4v9J7Uz82XPWkcicuHPF6BXJKYo14bKRSqHW6ATjuSs30aJRfQz6ZBBq16+NgOrV8CyWncCRi6CgINy5cwdyuS6k85UrbCHUsGFD3Lt3D35+fqhRowbrj1aKbGxsoFaXrS1zvjx49QAh7iFY0GYBPqv/mUkzJwnFXU4IYVaA9M8tboKzP+78wbw2Z1LlaqtbxSyJyf7V5KtGgQi4smvzoU2VNgB0oZ9tRDolUd8u2xCZRJvMUEyJrf9MgmLyxklKSkJwsDahnaOjIzIztZPR7t27s7JGCwgAhj4x2jKNohKrTkn5991JK/0cLQEVApjXEkqCELcQ1vHiKDH0bnxJhg/mcuynic6MZkJcm0JfmbiafBUKjekIZLdTb7PM1wwXZ/c83YON9zZynrv14VYMOzQMn5/4HADgZu/GOp6rzEWeShdgRwixXHqUlowXlJh3AK6Y6iJKhBxFDvOeGFTx8/HCpRt3EROfgLT01wj0r4Zrtx/gwsmziHkWg1m/rMKVW/dhiY8++ggajQZjx47FgwcPcOTIESxatEjbr0LFaty4cUhPT8fgwYNx5coVPHv2DEeOHMHIkSMZxcXPzw+XLl1CTEwM0tLSoNGUXJSskmLtrbWYemoqNEQDlUaFg88P4krSFQzYPwCbH2zGtDPTcCv1Fvb33m/kxA8AdVzrcLZboC5gBuWXObqdvOL6cug/F49fP2YNBPq8KtCtRO14pHN4XXljJYYfGm6Uc8gch6MPc5rU0T4t1rL32V4AOnM0/VW/PU/3mAy/SQ/6pR7Vp2wFDSq3VK1alcnzFRAQgKNHjwLQLojIZLK32TWBMoi+TwyNxCG61K43/NDwUmsbALNrDGjlvgbs8a8oSkyeMg8b7m5gwtTfTL1ZrD7qE1Y1jHn95DXbWuOXK7+gyeYmCN4YbDLtgH6I5kuJl/D09VPOejR0stLvzn6H+n/VZ42TP5z7AYuuLmLSCehDj2fXkq8BMPZZlavlGFpnKPO+mVczs/0QKDqlJeMFJeYdQGHgAK6WeyA1LxUpeSlMmeHi8pRPh0EsEqFOWE+4BbdHp/AW6NOlHSaPmYqPOn2E9NeZ+Hy45Rw7zs7O+O+//3Dz5k2Ehobiu+++w4wZ2vCFtJ+Mt7c3zp07B7VajY4dOyI4OBiTJk2Ci4sLk413ypQpEIvFqFOnDtzc3BAXF1eMO1I6rLq5CodjDuNy0mUciTmCaWem4ZMjn7DqFKgLUMWxCnoE9GDK2vm0AwCT4Rv1lQv9Vb/i2mLrT+CvJV9DhjyDs56+CVuOUqf4/nb7N1xPuW6VAjL19FTrO2oC/ftChzqt61oXs1vO5qyjDz3oqzQqYXWtHNC7d2+cOKENL/vFF1/ghx9+QGBgIIYNG4ZPPvnEwtkC7xthVcNwZuAZrGy/ktmJIWrr8n1EDrAcuZGmqDvJReFu2l3MPj+bVVYUJeafx/9gybUlJdUtFvpjmaHlwdkEXYCFvvv6cp7/7dlvTbbHhb3EHgCw79k+ANponnK1HIuuLGLqZCoyjc4zTIjs6+zLer/u9jpW/jEhgXLpUVoyXvCJeUcxFfeepmaALy78txGQ2AKFW7/rl87GlFVac50ApRK2hGDeEt0ENyIigrOtli1b4tYtnY/O5s2bIZVKUa1aNaYsMDAQ//5rOkN9zZo1ceHCBYuf622hv4U97fQ0ONtwmzLpmzvRmNpRoXPL6H9X+km+aJvdomLo1G9qwv8wXRfZjmuwLIncMfoBC/jCtcvi4eDBGojoqHCG6O/YpOanwtOhfEa9e1+YP38+83rgwIHw9fXF+fPnERgYiB49epg5U+B9RCqWwkXsAgDIzdcucBAitmpj9E3lgLEWNVHjtfw1q6woMtjLsfT8TfXHNHN+oIbzkEuJlzhzydDmwqbg8odZf3c9Nt7XmZGZGt/0GVZnGP55/A9javjk9RM8z3jOHBeCwJQepSXjBSXmHUQkfW25EgcKvZV7a37Kf/31F6pXr44qVarg1q1bmDZtGgYMGAA7O7si9aMsor9Ck16QjtcF3PeYS6AHVQrirEvveuhP1vWdDItrTmZ4/ukXpzEgSJubx1TIY67BsrJt8UNOFiXr9e00XdS02pV0zpb68fxNDTr6n6NUw5MKY16p0Lx5czRv3vxtd0OgHED/BCmRddG3Hrx6UPKdKSISSmJyQeabpt8UKexvltz8QmZx0FdEalWqhVZVWuFcgnEESn25DQCjj47mbM+SAsI1Vhku0tlJjOcb0Zk6E8NX+a+QlJfE8pVylDoiV6U1f57YcGKRgs8IFI2SkvGCOVm5gt+MiRIZr2A7mEtrXEySkpIwZMgQ1K5dG19++SX69++PdevWldr13gaGCoGpbWcup/7VN1czr8fWH8s6ptKoWErM73d+113DxASd7wqivq+L/nk/XvgR9f+qj0y58fb79WSd2UT1CtUBWOfs38W/C2e5g9T6qHa7HusSrQZWDASgnXj8cPYHptzUPdK/p9abk/H4rQgrdsVm3759UCr5TzwPHjyI/PyyuXou8GY5/eI0Bvw3APMuzWNkgEZhXZjlr059VRpd48Wp+FNYc2sN0/dpTXUJog0Xkj6u/TEq2la0+hrW+DJai35SS5lYxgoOo8+DdH6KoikFjoZrJ99wTPZ2ZOfoM7RkyFPmGY15J+NPMt9BVceqsJfa8+qvAD/ehIwXlJj3BAmPSZd+DVsrJmlff/01YmJiUFBQgOjoaCxduhT29u+nMNAXrB72HgCAKY2nMGXjQsex6qs0KpNKiSkTAr5b3oYrVfSO0M7HOwGASTamj34+F9pOuSR2MvjkqzFE/7q0CdnTjKesAc/QAZbm9IvTnO2UOIJjf5Hp3bs3MjIyeNcfNGgQ4xgq8H4z+/xsPEh/gGeZz5j1BHW+r/mTyhDjT47H6purGXmrn9jYcIHsv2f/Fcmvb/ODzcXrpBWYMq/WRz/QkCH056vvVp/3NQ2VNEOTNMOcLzZiG9YCGM0bCwLzHvImZLxgTvaO4uvsC0cbR9x/dV9rOmTFuWJYV19Ah/5k/Xj/48iUZ6KCrAJTJqJE+KnVT/jhnFaYaogGeco8o3YA07s9llataDwdPFlJUE0pRQvDF2LqKa1DfqsqrZi6dNQZc4OPIY/TuROTGSpvfNA3MTj/8jzG1h9rpJCYUuj0/WYEx/6yCSEEI0aM4B2ZpqDAfNhWgfeHlHxt0JpLiZeYMnlKZ4xsURObH/71trplNTdSbqB1ldZoU6UNfrnyCwBjX8Bvz36LhJwEfBbymVVtKzXWmdcVBz6BD9bfXW/yGC3X9RMv09Rzrcd5ztpba1nvVUQFKXRWA4Z+NO727pxjKj0uTjk1Bc42zkVK3inAzZuQ8cJOzDsOPcmTW7HKQAmWMkaYUjQMMdzi1ldgaGRi3Q+agLDi1OtjSungG35aX4EBTPuldPbrjBUfrEAl20ro7NfZ6NrVXarzuh4Ak7bbRfHv0XfGZ0xGDO6JKUVPP5maEHGmbDJ8+HC4u7ujQoUKvP4+/vhjODtbXvEVeL9g1jE0tpjY4Mu32hdr2XB3AwDg+3PfM2VcOVa4nOEtYWpcKQ1MLSZ5OeiCCxyNPcpZZ1OXTWZ3yx+kP8Dj148tWiDo+78Axqbdj18/NqvEACWXW0hAy5uQ8cJOzDuKmqgtJpwyhP55qygKBRRllUnZuw7f6DB8bJf1BaWGaFDJlp2kLV+Vj12Pd7H8SGxENkxCsKKYR31c+2PMuTiHNajQLLu2DBvvb4RKo8KFlxfQq0Yv1oBhjVOpqb6ZSgBqjuZezbH14VYAOkXEsH1Tttj635fVkX2E5/6NsGHDhrfdBYF3AI3e79XQJEijdIZIys/BPbhy8BtJbKkP7Yyun1tLP2cMjf4Ov1KjxOP0x6hVqZbZ0MRvMtKWKd+XUPdQ5nVH344sn08aZxtnBFYMxNK2S/FllLESqiZqk4Fo9GHt3CecNwrjvOPRDs57om+SXBKROAV0vAkZL+zEvKO8yH6BZxnGwpAvwk+ZDR/FoUf1Hrwm/IYmXoa7FE03N8WCKwsw47w2304NlxpY8cEK5nhRbHdT8lKw++lurL612ujY3w/+ZgYAWlnQX7HiSqZqClODwN8P/ramuwCAmhVrMq+Z/hkMQpeTLnOeq19P2IkREHh3Mfx1L2u3DB18O6AgsTdvBQYAFrRZgA6+HTiPlVaIdjoEsb7cPBpjvGNBJ3sEgFnnZ2HQgUFYc2uN2baLEta+pNFPdrnl4RbOOuNOjIOD1AEBLgEm2+GjXOjXmXt5rlFgG0PLBABo6N6QNVa8aSVWoPgISsx7gulpHPcEVZj2seGzqsU3skn/muwkouacIk8PPI2IzhGoWakmbEQ26Ojb0Wjnhg/mElayfEaINsnm3bS7TNGB5wd4X8eUbXRRwn1WdaqK5e2WA9AOLiqNysjO25RypD+g+Tj5WH1tAQGBsgudYyvULdRINrev1h5L2i6BMqMZVLn+vNuUiCQmF/6KEpjEGvTllaWIXnQSZP1Q81wYZqcH+JtFmyOjIIN3Xf37pr/bZEi2IhuJOaY/z9aHW3Et+ZrZa+kv7nFZoYS4hRgpNh9U+wB/ddH5UNE7/wLlB0GJEWCweUfNaHIUOZCrihduks9OTGRcpMlBYta+e6g74zBy5Cr4Ofsx5fHZ8Zh3eZ7JNivaVkQFWQVUtquMCx9dwKLwRSbrWkurKq1ACGEFCiAg2PN0D4YfHs6Uzbk0h/N8PsnFaExFETNHbFYs9jzdw7x/kf0C8y/PZ9VJyUsxe70Qt5AihXe2zLv5WxEQKA/8X8j/AdAmdMwuMC2HChI+4tVeRVlFeDl6sXY89OFaxS9JLO00+FfQKWN06HsuJUUfrp2YDHmG9Z3TIzUvFW22t2Heezt4gxCCBW0WcNY3Z+5GoyZqtNzaEp8e/9Rknd1Pd2PW+Vlm29FXZrmUTi5/l8p2lYtk6ixQduD17e3bt8/qhjt06PBOJTssq3BNpTQKN4hsUjmOmEcMQEaIVUEALOHn54dJkyZh0qRJALSrJbt370avXr2K3KY1bRSoChCbFQsAqFu5bpGvSQ8yIkqEELcQ3Ei5YVQnJT8FWYosox2ZVzlyRJyPAQB0XnYaZ6d9gCqOVZCQk4DBBwbz7oNCrUC+Kh+OUkdeg4Ml8pX5nI7y+iYApkjLT0OP3T3Q0a8jZrecbbF+UeyzryVfQ2R8JPPe0cbRqE5dV+7vVCqSopJtJTjZOFl9XUMIzERSFnQZAYE3Tr3K9fBJvU8QVDEI/1ulCwt/4dkrtKulm9wTtRNAKIAi+F92DvY6GcsQAHgtL1qC6JLgaMxRxGXHma0TnRmNHY92YEDQAGaCbmkRiUteWgqw8jD9IdLy00wmfbyYeJH1PiUvFU3nnsDXnYI56/MxReYbPTImK8bscX2z4Ze5L80ep/nmzDfoVr0br+sLlE14KTHWTjgpisKTJ09QvTr/qEYCJQchxV9ZIByvSoLExERUrMgvcdesWbOwZ88e3Lx5s8htFKhLJiwrPdnXEA0aujfkVGIA7hWg1BzdLtCL19qcMIf7HkbwRm7BT2MnscOA/wagukt1zG8zH512dUKWIgt7e+1lVuNMwUcRyVHmcIYs3v5ou8VzL7y8gBxlDv598q9JJWZQ0CBkK7Nx4PmBIjlMGvZNQzQIqxrGygFjSokJqxqG3jV641XBK8RlxaGaczUrrqx75vMoCv2reKJFfgG+f/X2JjoCAgI66J1i7WS7O1Me9SiFpcQAYMJttsvLN6nElAYqjQpLry2FiBJhfIPxrKiUNH0C+/BOuvnTxZ8wIGgA9j7bCwCIuBeBdj7t0NCjIWf9ngE9se42O+l0Um6SWf+e/v9pTZ33997PClNPY5j4WJzfAKnZckz95zb2fbUZHx/8mHWcT3h+Olx2cbE0xnzg8wHn2Pb58c+Z1252biXSF4E3B+/ZblJSEtzdzW9f0jg5FX/1U4AfXCoGRRUtPrycojh3YRQKBWxsbIrUpiGensV3kLSmDXrgKO6Wsb6A1E8IaQjXSpc1jvE0Q+sMRXx2PKLio5gyWkHiE2LZMFsxTahbKG6m3gSgDTlpuJrHtVp1tK+xoyltylDDpYbJPng4eMBRqZ00FEWJMfyc8dnxLAUGML+yeCz2GOKy49A3sK+VSoyOG7YyxEmliJNKuZUYIaFSiXHixAmcOHECKSkpRt/9+vWmc0wIvL+YykvF4nV7tK4nR3g0t2M5oI1MVtKoiRp/3df6W3wW8hkzFunLwobuDfHvk395tUfnPdE3CTv14pRJJcbX2RcUKJZMpwMJWMLULo+hb6M0syvzOrBioFH9XjV68bpeSWBut//j2h9DJuHOVaJvKmguuIBA8SkNGc/LJ2b48OFWmYYNGTJEiOf/FhHZpBmV6a+ftO03BuO/m4/x381HhRpNULneB/jhl9VQFgqBjg07YsHSdRg24Qc4u3pi7NixAICzZ8+iTZs2sLOzg4+PDyZMmIDcXJ2zXkpKCnr06AE7Ozv4+/tj82bjjMEURWHPnj3M+xcvXmDw4MGoVKkSHBwc0LhxY1y6dAkRERGYPXs2bt26BYqiQFEUIiIiONu4c+cOPvjgA9jZ2cHV1RVjx45FTo5uBei78d9h3JBxWLRoEby8vODq6opx48ZBqeSv7FWQVWC2nR+mPzRZLzVbgQIleweByzrP0hb6iLoj0MmvEwDdRJ02IStOBnpDm+hMeSbz2t3eHS4yF9ZxqUjKMumiMaWUGEb3oRW4ktiJGXF4hFGdHKXplT76vhUnbGYltbYPbir+/j/lgVWrVsHPzw+2trZo1qwZLl/mjvIGAG3btmV+g/p/3brpzDBGjBhhdLxz5868+zN79mx07NgRJ06cQFpaGl6/fs36ExDgwnBngN7p1oe87oI1H66BBECHXLbP4s+tfsac1nOwur1x1MZP65v20eCDvtzRX8jKVmQzr/0q+HHmEuOCKwCMJdlmaNpsqT6d9V4/z5Y+P138ifWeaHT3n2uxLtwnnHm9rfs2s9cuDsvaLkO9ytxJMQHg6yZfY8nVJZzH9O9JO592Jd63t8X7IuN5LU9bG+t5zRrzof/edSxFALER2zA7A0q10jizrlpnfiQCBVuxdheEEIJ8uu3COtqfn/ldEhuRBLaEnQV44879GDXof7h8eAeuXr+OsV//DI+qXmg3YiAA4Ne1mzBj0hjM/Hk+ILXDs2fP0LlzZ/z8889Yv349UlNTMX78eIwfP555PkaMGIGXL18iMjISUqkUEyZMQEqK6a3inJwchIeHo0qVKti3bx88PT1x/fp1aDQaDBw4EHfv3sXhw4dx/PhxAECFCsbCPjc3F506dUKLFi1w5coVpKSkYPTo0Rg/frxW6SlcnLl09hICfQMRGRmJp0+fYuDAgQgNDcWYMWPM3jua+Ox4SCjLP5fOy86hlrs7Dk8KY8q4FohCN4Wabaf9zvaMQzo9ONCTcj5KjCmBbmhXrL/9f7TvUQw9NJR1XKlRYt7lefioNttBlnaof5rxlFWuf4+WX1/OvC6KIsEnNHJcFrct+aHoQ8xnLY4SQw/L5B3actm+fTsmT56MtWvXolmzZli2bBk6deqER48ece62//vvv1AodPLj1atXCAkJQf/+7Ch7nTt3Zo0VfLM0A8DatWsRERGBoUOHWq4sIFAIBbb5rkhk/DsNcNeZkH2Xlo5jDrqJ/f9q/M9k21zmVNagvzOg7yupP97bS+xZC0nmeFXwCko1e65AjwVZiiwo1ApWiP8jMUeMIoJZskig2+MbjY0QXT39ZJM1K9bEFw2+YJmu1XWti8CKgXjy+gmvtgGtSdy+Z5Z9sn2dfU0qXgAw8vBI3E67zXlMf5wxFdihvPE+yfhyE5Zh3rx5+Pfff/Hw4UPY2dmhZcuWWLBgAYKCgkyeExERgZEjR7LKZDIZCgpKxk/CFM22NDN7fFH4ImaV/dcbvyLiXoTJunWd/bGtmdbn4LUiE+F7uxrV2dbNeIXD2aYCJCIxHKQOcIYIKGALDh9vDyydPQWU1B5Bfp648/ApVq77m1FiWrdqgq8+Gwq4VQek9hg9ejQ+/vhjxkE/MDAQv/76K8LDw7FmzRrExcXh0KFDuHz5Mpo0aQIA+PPPP1G7dm2Tn23Lli1ITU3FlStXUKmSNmxwjRo68yRHR0dIJBKz5mNbtmxBQUEB/vrrLzg4aCf9K1euRI8ePbBgwQJInLWPuLOLM1auXAmxWIxatWqhW7duOHHiBG8lJjI+krFFNo8ID5OyWSX5BjszaTn8IqXRAxCzE0Obk/GMmd/cqzk6+3XGrAuzTNZLzdcFgBCLxLzj5L/MMXacNEW/mv1YK3KmuJZ8DRVsKqBGRe0zwMfhk0uhI4Tg69Nf694Xw68rTay952kSg0GdnqCUQ8f+JUuWYMyYMYxsXLt2LQ4cOID169fjm2++MapP/zZptm3bBnt7e6MBTiaTFdlcVKFQoGXLlkU6V+D9RaNh/y7FHNve7fV8ZJwsmOJu7bYVgw8Mxujg0cXO3q4vd/QVl3ylbreIK7GlORr+zTYdo2Vkq62tAAAn+p9gTH1vptw0Ot9cOH/9fnL5zsjVxuOWSi0CvZSqrwDUqlQLX5z8AgDwX6//4FfBDwBQzamaVUpMO592vJQYDwcPs8dNhf4H2ONMaUehe1O8TzK+xEIsr169Gj/++GNJNWfEqVOnMG7cOFy8eBHHjh2DUqlEx44dWeZMXDg7OyMxMZH5i42NLbU+ljpWTJgcpI7wcvSCRCRBtiofSoMGmjcMBigKpFDmt2hUH8+i46EuNJ+p1UDrMK0mBEq1Erdu3UJERAQcHR2Zv06dOkGj0SA6OhoPHjyARCJBo0aNmGvUqlULLi4uJvt48+ZNNGjQwOgHZA0PHjxASEgIo8AAQKtWraDRaPDo0SNmwh8QFACxWDfgeXl5md0lMkR/lcksxHgQHfLHJdb7lxnGJg/moOPf841KAwBfn/4aFxMvciowtMmAIXyjxJjjUMwho7J/Hv+DbQ/NmxIk5iRixOER6L2vN1PGR1nTV8JoDCMNWb0To7eC+lJSbtZ5eKFQKHDt2jV8+OGHTJlIJMKHH36ICxcu8Grjzz//xKBBg1i/OQCIioqCu7s7goKC8H//93949eqViRaMGT16NLZsMe2zIMCNWqPGL1d+wcm4k2+7K28F2k+EL4Y2C4YT83qV6+HO8DuY2HBiMXvGljv6SoxCo1vxnnORO3x9UWm/sz3n9c2VcZGnMrYm2XR/k1HZqxzdmKHf9u1U3a7HL1d+waqbqxC8MRgn4k7wuj4N36AHvfb2YuU2swZ9ZfNswtkitVGWeN9kfImN0Lt27UJ0dDRmzJhRUk2yOHz4MOt9REQE3N3dce3aNYSFhZk4SzsBLAlncmu49NEls8dtxDpROqHBBCbmPUOSTgCI9MxYKto469ourKMBYE4te5GdCKWmAD4iEcsvhgB4IpUClBrG7niA1F778D7OjoOGaJCdk41PP/0UEyZMMKpbrVo1PH7Mw8HSgDcRgpue+Eul7MGOoiheDvI0B6MP8qxpvC6QIzdwnrdy9Z42J6N9YvgMRIa+LfrMaDED3579FjKxjDWIWzJx44ObnRunYsFVpg9XIAJL4UAB7iSahp+9OOZkFikjVmbZ2dnIytLdC5lMxrnVn5aWBrVaDQ8P9sqlh4cHHj407etFc/nyZdy9exd//vknq7xz587o06cP/P398ezZM3z77bfo0qULLly4wFo8MEVBQQHWrVuH48ePo379+ka/1yVLuO3Z33cORB/ApvubsOn+JtwZ/n5kG9c30zJUYjSFx/TrmJO3h6MPs0zKnr5+ihFHRqCybWWMCh5VvH7qTY7/t+d/ONn/JNzs3Vi+I5Z2iYMqBuHR60e8rsGUEaId30woMVmKLM4dGUth8B+84krCqfss+rvi+mbLZxLO4EzCGbNtm4Kv7E7JSylyDpxSHR9KEEHGc1NiSsyJE9Zp2MUlM1NrR2ppFT8nJwe+vr7QaDRo2LAh5s6di7p1TecLkcvlkMt1E7vs7GyTdU3BN3M7oHVMNHROBEcoRgCgRJSu7cI6ptbOE3MTkJhremv00o27UBau8GsAXLx+BwH+PkYPI/0DDw4Jxv3791nmXvrUqlULKpUK165dY8zJHj16hIyMDJN9qF+/Pv744w+kp6dzfo82NjbMzpApateujYiICOTm5jKrBufOnYNIJEJQUBBr1au0EUEC8AhvHZduXdZkQ3MyPj4xTTybYN+zfajnWg93X7FXqM4mnMVnIZ9h7a21VvVDH1PPuKm+vch+YbY9F1sXo7JhdYeha/WuaLfDtLOls8x4MDZUfoqSo8YiJZhLqSSoU6cO6/3MmTMxa9asEr/On3/+ieDgYDRt2pRVPmjQIOZ1cHAw6tevj4CAAERFRaF9+/aGzRhx+/ZthIaGAgDu3mU/r1QJ3+uSkPFlhdcF71/QA/1Jpyk/iKuxuvuiMfP7N5QVzzOfI1OeiUx5JmpVqsWUc4VHtoSh3PnnyT/4v5D/Q75atxNvaSepKOMXAQEFbiXmWOwxDD88HKODRxvtNlk7mZdRTtD/5dSsWNPqvpYkRVVGyosSI8h4bsqlrYRGo8GkSZPQqlUr1KtnOiJFUFAQ1q9fj/r16yMzMxOLFi1Cy5Ytce/ePVStWpXznHnz5mH2bMvJ+8oKRbUHjE9Iwi8//IL+w/vj5vU7WLF+G+bMnGyy/sSvJqJjeEeMHz8eo0ePhoODA+7fv49jx45h5cqVCAoKQufOnfHpp59izZo1kEgkmDRpktndlsGDB2Pu3Lno1asX5s2bBy8vL9y4cQPe3t5o0aIF/Pz8EB0djZs3b6Jq1apwcnIyWnn4+OOPMXPmTAwfPhyzZs1CamoqvvjiCwwdOhQeHh5IzEks4h0qPY4/SOZdd2TdkfB29AYAzGwxE3K1nNdgQQtmVztXo2MHow8WOx5+E0+tomqYNdqUHbml0J42Iu3upJNUF579wPMD2P98v/mOcMxPDCcPfKP/cCGyZMNZRnxi7t+/jypVdBm6TTlcVq5cGWKxGMnJ7GcwOTnZ4o51bm4utm3bxstsuHr16qhcuTKePn3Ka4CLjDSOgFdalDcZb44QtxAA2ozz7wv6UQobuLaCvoGMvY12oScpU+f3mplvOgrl61z2MX2fCP3J7Wchn1ndT6Od5EJZoR+dLC3fOJKoPvUr1zdryuxhb+wLoiZqiCgRNGBPzrv4dcHfD/4GAPxx5w8jJUZ/V95eYrxIVa9yPRyN1YXbF2vYY0sd1zqY2HAiriZdRd3KdY1y1FjLnx3/xKij/HfDirJYNaBmYR62y/OtPvdNI8h4bqyeA//1119m/94E48aNw927d7Ftm3k7+xYtWmDYsGEIDQ1FeHg4/v33X7i5ueG3334zec706dORmZnJ/N2/f7+ku1+iFFV/HdqvGwoKCjC442B88d18TBw1GGOG9DWqZ1MYzaRhaEOcOnUKjx8/Rps2bdCgQQPMmDED3t7eTN0NGzbA29sb4eHh6NOnD8aOHWs2t5CNjQ2OHj0Kd3d3dO3aFcHBwZg/fz6zG9S3b1907twZ7dq1g5ubG7Zu3WrUhr29PY4cOYL09HQ0adIE/fr1Q/v27bFy5UoAKJHM9nyRinQKm1qjFaiLjhibAiRn8Q8sMbnxZAyqpV0BaejREC28W/CalNM7IqZMsgzL/Sv48+4ToBdOs4Qm8cyAq/dAn0k4Y9FGOVNhHNnnVQHbTrduZdM7r5bwU2pNAf0URcu99KZwcnKCs7Mz82dqgLOxsUGjRo1YO+cajQYnTpxAixYtzF5j586dkMvlGDJkiMX+vHjxAq9evYKXl5d1H6Tw3BcvzO/cFYfyJuP58Fr+GteTTTsvvytceHmByXM1scFkrN7nzToeHqRdnJGrdBN4D2fTUat+3P8QU3beYt7rm3oditb59xVlJ8bJxomJMAloJ/kA/8hfMrEMDdwbmK1Dh/3Xl+eEw6QOAMY1GMfaXTJk4/2NzGuuccNwlz2PijGqMzp4NNZ2WGvWnJkmrKppNwAAaOrV1OxxQ4qyo1LZvjI+rv2x5YplAEHGc2P1TszEiWztXalUIi8vDzY2NrC3t8ewYcNKpGOmGD9+PPbv34/Tp0+b3E0xhVQqRYMGDfD06VOTdQztDPVtEN86PCeMGpUTRBLzJhJSiQQz5n+HGQtnoJZCwQpUefR64WqLgr2V3aRJExw9apz4kMbT0xP797NXzg3D6RkKVl9fX/zzzz+c7clkMs5jhm0EBwfj5Elux1a5Wo45K42dJ5ctW8ZZXx8N0SC9IB03Ht+wWBcA5BrdPc9TqOBkK8XKSONnLSVLDpRyYmDa38fUgJmcx16l4R24gG6/cMCQa/hFWrNEvkprYpGtyIZSo4RUJMWB5wcsnsflE1N88zHC8erdYfLkyRg+fDgaN26Mpk2bYtmyZcjNzWUi2QwbNgxVqlTBvHnzWOf9+eef6NWrF1xd2SuwOTk5mD17Nvr27QtPT088e/YMX3/9NWrUqIFOnTrx6pNGo8HPP/+MxYsXMzmenJyc8NVXX+G7776DSFRiMWjKtoy3En0zjHV31mGtR9FNRMsD+iGD773MAsBeJBMV3o8N56L1ysw0SCj8c+0FFvXX7mjp388mnk3w512tX4ClHRNTiPTWiVfeXIlwn3CzioQ+3at3xwfVPjAbXZKOILa562YMPjAYgE7ZMFQ6uu/uzryWiWUYuH8glrRdgiqO2tV9uUony2+k3EB9t/qs8/VD5nPxNCUbNdy1O+l8/BlFJRdXCgCMdp7MMavFLPSq0QsiSoS0vDT423REtOKoUZ6z8sr7JOOtPsswQU1OTg4ePXqE1q1bc66UlxSEEIwfPx67d+/GyZMn4e9v3coxAKjVaty5c6dImmNZhcsDgahKMtGoVqgXJ0xteSVXmYsCVYHZENimWHD4ITQa7ns2sIkP73b67euHX6//CgD4886fmHl+ptlkmzSeDp5o5d2K94BpLbSjJt8cB+ZCe2YUZODz458z70/Fn+Ldjy8afmFUZvisNvm7Ce/2TGEkKGlFqWy5xvBi4MCBWLRoEWbMmIHQ0FDcvHkThw8fZhxB4+LikJjINsN89OgRzp49i1GjjM07xGIxbt++jZ49e6JmzZoYNWoUGjVqhDNnzvDOI/Ddd99h5cqVmD9/Pm7cuIEbN25g7ty5WLFiBX744Yfif+h3lKtJV5nXXMkG3zX0lYyDj24CBhNX2v8l7pXO7zAhI9+MSRn7nvUN7Av/Cv4YVW8UWlVphcG1tIrBzkc7zfZLqVHii5Nf4GiMbpFPrVGjo19H5j0tt51snNDcq7nZ9gBg15NdsJOYD37zKv8VVBoVo8AAukUcc7vrcrUc91/dx76nuvDF+o7xi64uYl7ziYYJAB8uOc2rHk1QJdPpMQAgI886fyBrFq8SchIQuikU9f+qj3Y72+Fp9k3kv+xb7GAOZYX3ScaXiE9MYGAg5s+fjyFDhvCKflAUxo0bhy1btmDv3r1wcnJCUlISAG0CRNrvwlC7/PHHH9G8eXPUqFEDGRkZWLhwIWJjYzF69OhS6ePbQM7TIcrw5831c+dSiBSFoSGNEnKWE1iRYAqjtvClOA5/f1+Mw98XuRMxernY4f9C/g9rbllOCvvo9SM8ev0In9Ufj6j4KNxMvYmwKmEWlZNwn3CE+4QjJasAK2+uLMpHMEv1CtUBAA3dG1qoqcVcf8ceG8vypTmTcAYf+n5osj4AKF61hr19LtpWbYvjscex/PpyLAxfiFqVahl9bwXqAtxOvW20ssiH5ELTxuc21oVyLevQiWq5iIqKMioLCgoyOUmws7PDkSNHitWfjRs34o8//kDPnj2Zsvr166NKlSr4/PPPMWdOyYaifVfQV9jfByVGf/XexuUq5En/A4iuTF3408/Wiwj514VYVKtkD+5Rn33PHKQO2NdLN7HvV7Mftj7cChUxP5H/+eLPiIqPQlR8FO74aaPEvSp4hV1PdnHWv5p8lbNcn7qVQrDlonmTm8MxhzGg5gBWGb0D80m9T7D02lKz5+v7NOr3lQ7Dv+rmKvx5509s777dYn/14eOj1cG3A367bdq0P/THY3AynWaOgQIFUvgfXyLj2f4ZYlkK1Pl+qOtadNPjssb7IuNLbD9PIpHg5Uv+CfCsZc2aNcjMzETbtm3h5eXF/G3frvtxGWqXr1+/xpgxY1C7dm107doVWVlZOH/+vFGUh/IM1zSbEpuPfhX1z+9Y/ONU5j39EOTobefZFD7M9Pa8pRWhssqb9InJfW4cfpqLh4lZuPfqnlVt91hxltmi5xOdjGbqP7dRkNzFqmvxgd66p/u06/EubH1oeifWnEL4IJ0dupNPvhrFq3C8jhkIsUiML6O+RExWDPr/19/kLo5h5mq+BMu1q4GOpsJxv38blKVCeno6atUyVnRr1aqF9PTiJR18lwl00QXIf5Oy7m1h5CNH2J856hF37q+Dd0wEeNHL6/XppqvosvwMlGrdb51WDPNV+dj9ZLfJfunviDFNm5gQpual8trduHS5I37cbzq8MgA8y3hmNHk3l7neEFMmWAVqrd/m2ltrodQoseTaElRzqsa7Xf0dKFNw7cRokrTuCJXtKvO+Vluftuhfsz8TAIcPTzM4XAo4crwJlBylJeOtVmL27dvH+tu7dy/Wrl2LIUOGoFWrVkXuiCUIIZx/I0aMYOpERUUhIiKCeb906VLExsZCLpcjKSkJBw4cQIMG5h3lyhtcX6BIyg67SRERnM3kROH66boZhDZ+F1b5rA3jJ6Gs26jUKPlFCFod9QxxKdY5ij5KzoakMMgCnx2iPU/3oNXWVjgbfwVEaRyhjIv8F2wHR7PhP2lrKoqCQq3ArAuzMPfSXDT15HbGNLdKZrhLczhGmxMq1C3U5Dk2lSPhWHOm0Urj+rvrOScPVinhRH91W0v5CMJZfgkJCWGCceizcuVKhISEvIUelQ9omQAAXg7vjpm0KYwzqrNl+v7b3MrK9bgMznJ1vh/spFpF6Mi9ZDxMykbkQ50ipO/bcSOF7Rt5JOYIFlxegHMJ54yy2wOmZV5KPr8ky0RjWRnh2oGg5TYfP57nGc/59QWEl58LjaX5gqPUkbM8P19rdkx/BmWG5Z3+caHjMKPFDJO7KHNbz7XYBgDIPA6yzOsESpbSkvFWm5P16tWL9Z6iKLi5ueGDDz7A4sWLi9yR8kyp5KEoQcQqe1DgHxELAOxM+HOUN0xlp+eDhJKAgFjhMMhfSXqcnA0bK6Oi0oOIJdMGAPjhnNbG1N5vLfITBluorUWVHaxdjaK03z1tQsg14NArWVeSrrB2lWgHfUPM/UbqVa7H8vOhQ31y5YBh2tPIQInlRhm3r6dc55w88I0IZAj9jb4bv4ayyy+//IJu3brh+PHjTASdCxcuID4+HgcP8k00+/6h/6xvfbgV3zT9xqrJZnnDWPYZy9wPFkch1McFN+MzONtolF+Aa3bacYGoHVDJyQYH9JSfsZuuobqbA4a38EOburr2r6ewo79dSryEnY93wtnGGbUr1cblpMus46ZkHu8kyzx2BgghJhe15l2ax1muj+EuuOkL8XPW59uuqQVFjcIN0pRxmDukGfpdfQlQlu9VdZfqZo+3r9Ye9hJ75KnMW6hIne7j8PMo9KzR02w9gaJRWjLeammn0WhYf2q1GklJSdiyZcs75TDPBzrjaF6edckL3wQaZUWoC6pAXVAFIo2N2br0nou+yM0SFybCLBTERTXHKUtY6+OikCvgZOOETBU/53VzNPFjayyqTOv9M5IytaZN1vvq8JuCi2RJjAKjj6nMzzQzzs1gXt9J484anqcwrXgZDvZ0yEv9a7ja+BqcpX0+1Ro1aldiG07rO9fS0PkRrOWSnXbHLN9U5JTyv0FZJggPD8fjx4/Ru3dvZGRkICMjA3369MGjR4/Qpk2bt929MktMZgzrvTWmpuURrmiEhjxPzTWpwADA5xlseZ6QkY+5B9mT7uepuZi57x7iX+kWZWKzYll16AUUuVpuFNYdMG2qxX9RjN/0zFB+0nlo6OAr5uBrYVFBVoGJYsaHvU/3mj2unytHH7sqfyPf8QDEam7rAa7oYY9fP8bZhLNIyePe4bKT2PE2tczMf3PJsd83SkvGl8tkl2UFsVgMFxcXpKRofzz29vYlk11aZWLSqVABBQWsOnJwC0WiVIFoFBDJklEAIEYjhqdSAxTusCgBaApXOfJVBBIACkq3qpMEEZxUaqaOQq5AgZW7OWUBuVwOjVL7GfLy81jmF6YghCAvLw8pKSl4VvAMBZrif+46Xs64EqNn5ie2PjTx0+Q8SJz4+Yzooyngt7jgUH0ZZ3mvGr3MnheTFcNZrswMheJ1C0hdruBWimkbZ0PnV9oemh6cVbnV8QoGZg9EFzXPMKHn4mvGO8JHY49iARaY/RxcmFb/iKUKAlbi7e0tOPBbieHqskqjspgFvjxTEgFmqit1bYjtnkOdX91k9LJ7L02nKtj3TGt69OfdP9G+mnGyP66dGLVGzX8RysLioykKVAVwsnEyuSuuj6X5ytzWcxGTFYMPqn2AZxnPeClGAGAnLZoPrdg+BpRIiQy59rlW51eFrctdqAt34Jp5NsOx2GOscxZdWYSryVcxt/Vc9AjoAaWa/V1SFMVbWXuWmlOkfgvwozRkfIkpMXv37kVmZmap54kpa9AZUGlFpkTISOUul+UDdgWsOiqKQorYeJWBaLIAImKc/FMA5GsAFDoUqkEhRaI9T6RSQwSCNLEYCj2hJlGpkCLRPiIKmQLp0vLnYJurzGXCAKvSVFY5Pcpt5Jj/0JpMvqYF5YT2gdh4IdbkcT4QevfBytVWjcqlWNed3my6UdmlpEsWz5OntQdRuAFO9yB1uYLXBa9R0dayDd2J2BMYHTyaMZWROHDYbRfuGKmJGi29W7IcfutXro/babdZ1fmGCRV4c9y+fRv16tWDSCTC7du3zdatX9/6ncv3keJEVCwPNPZozO2UbQVbnZyY1yK7F1DnV0eOnFs+LDzyGI41LLfJlYCYS4l5mfvSiu9IuxOjzAyFtMJNk7UM26PfB1YMxJPXT8xewdLkvkdAD+a1oQwlamO/zkVHHmFKJ/Ohk70cvDCi7gju/oi0Csi2p2sBdIDydWvM7DwKM272BgD8fOlno3OeZ2rHB3q8SMw19ovSN4WTp3SAusAH9tXWG9VTWblAKGCeNyHjS0yJmTZtGp48efLeKTEURcHLywvu7u5QKksoDPHK/tzldfsA7b41qjPTviNyKt612Oy+LADZ2ghyqSIxJntpY4ZvfJmEikSDiVXY0T32JbxkysY3GM8r4khZY921f7E/LgIA8H3z71Hbi0fMRmhNBaNeRFmXH8dMVVdHtsCXOFofilxT4A0VpWKFxSx2x4rI7VTzAgkA7Kv9DlV2XdhUugAA+OfxPxhTf4zF8+6+0j7Lw+oMw8mrXrD15ghTWhhWVa5U4f4rg4zrxd4MFbZX3gShoaFISkqCu7s7QkNDQVEU58SPoiio1cLkggvDoBj6GeLLOxkFGTj78izaV2vPBOYoiXxl+5z07hExb7JFeAZr4XpuOc3GiPWKJiGmp2kiSgSZmD220PfISerEdQqL7tW7mz1+OOYw1t5ci1/CfzFSYnIezzaqvzLyqVaJMfM1zWo5Cy29W5q97sXkUwC0pmNfbrsNJ73H3FA5U6jZZtZczwhbWRNDI/fgvG5Z928ub7wJGV9iSkxp5YcpL4jFYog5dkSKRE48d7kqC7C1NaqTggxkOpgIIVlIFaUKtvkS5rxKFIVEV+2PXpr7ArYaDexyVKx8GLY58UhUFP74xRLY2hbdSf5tkZSfjUSF9t5UqVjFqs9Ah5nkg0ZRETAz2ADA+HY1sDJSu4pIlMYrd6ZQZgUDABRpWqEeVjXMZN07qXdYScsAQGRjHKWmW/VuOPD8AK/rn4w9iw98W/Psrd51pVkQyZKtPo/G174BlJkZJpQY7W9NAw1up95iHXqZU3Kh3p3VFiYcgk9MkYmOjoabmxvzWsB6POy5J2PvAmOPjcWD9AcYGDQQ3zf/nrOOrVSEvz5phgG/XeDdbpJET05rLEWJ5OeX4uus89mj85FxfTcEph3xicYGlEjfJ0MFS1O0Jp5NIBVL4evsy/js0JNEejLvoRyARHIcIhtjS4oZ52fgStIVzG3DHcFr6iltKoa++/pibP2xZvvCm8I57INE0/5NalZgIfZ3kJbHHs9oBYX+3Fx+U5XtK+O1vNCcm1AgahPmbhw+oQJF503I+Hc3jMl7QpaIQoEDOyEW0RgLPg81exXFgRBQBlnH2+sFKPAt3FWyydc+gDdiy6djv/6AYY0pGcDP6VHxqg2UWfWR+/xLAOaV2Ia+LsxrouFvM6xR8AuRDAArb67E5yc+Z5WJbIzNE++msXfuNCrTK7iTT/HLf8OF2E6nbIe6h/I6x9tBu/u34oQZs5HCFVSNhiA+m6308wktyhe7wt9Ibbng8FnS+Pr6Mjb5sbGxqFKlCnx9fVl/VapUQWxs8cww32VKxAezjEJHuNrxaAdTZjhBretdAU39KxX5GiU1ZQ2sqMvXQ0dQ4xpvxCKx0c4JAKhyayD32ZcGpVTh/00vpHT01VpHtKmic4ymd4Be5Wr9LmOSZGbDNf/3/D8AgIvMxWQdwLpdCicb87tAuXIVuiw341+jH5mNSDGk9hDmLaOMFEL/BuixXqFhy2q/bw5gsPdy3B52G9kP5kKR3sZk1LNcMwFoBKznTcj4IisxeXl5ePjwIW7fvs36E3izPJdKIbdn78LkPDK2G+VaYDAMH+ugF/qxYYGcVWvr5fI3kSCE4MVrrWKmzKpvVXQVvshTuqEg4SOAaJ0wv+5s2h6YpRRxmDEo0lvBX9TPqJyo7bXni7NB2aQioyDD5DXOvzzPUWr85RtG2hFJTCupxbOz131mS4MkDZ1z4U7WcdhUPsFZR5UbiILEPujka94coqSgTE13hIW7EqFdu3acCc8yMzPRrl27t9CjkkWuKh1zONofgCY5t+g7n2UVffOgmpVqso6JCydIa4c0KmLr5qdAlFjn2G8Ypp0OEmMnsWOZ8Rk6luvjKHVEqHso7gxnR3EkKgcQlaHpWuEOQ6H850IDDfJV+SzzXlrZeJ6sBFHLQCCGOUFFy9u6lU1nqw+uHGzymETEVqTvJmTiYYJ5wZiea92iUFScaYWnQKm9Fj1OcQW+mfrPbVAUhcqOdgBEENkYR5NT51WDRu5mVb8E+FNaMt5qJSY1NRXdu3eHk5MT6tatiwYNGrD+BMomN23Zqz85FAUNRUd4Yv+rfQ3ct5FCYacNWKCC9dG0uCCEsDIilyZ/X4rDxWe50MgrgyidEPeq5ENhSxzvwan2N3Cq/Q0ANRpVM21DXc1VNxiJZBzmf5QKCZpIo2JbD20MdZn7ITgGLMaW+zt590+ZGYri2zyZH5DMZXLWN4/gigxnbnUvnToLmdsxzmMauTeUGU0R7Go5GVpxyCsMrXxfZl1yUgHroE1wDHn16hUcHMq3n8eVmHQEfX8YU3feslzZSgxDLPPJIVWeMUxATEc+l0mKuB5rKReL3uqfYUAV2ryqe/XurIkzbYbMpVByBQDQXodLydV+JnW+j8nuxWTGICk3iRXIhJap+fGjkPN4NtQ5tSC2NW1uLiq8zrmEcybrUKBM+iPVr8r+TN1XnMXem6avZyd2xqtCJSY3+nOT9XQQvMg1vYhaUBh9NDlLG43NXBAXdeFCrUiSYXQsL34UFKldOM9LyS5ATFr5tEYpK5SWjLfaJ2bSpEnIyMjApUuX0LZtW+zevRvJycn4+eef39tkl28OYyHCdyHYT6lkdgsA4LVYJ/RlHBPJWzIZ9lTRJTksqRWKidtuIvJRCk5PbYeKDkULIcmXxUcfQZXXCOo8P1CUGgmZmSxForgQtQyURC8kI6WBVCLCv5+3xLH7yVgT9YxVP8BNdz8pqbHdLiXJggIc+QZU2j4TOlqNFZG25KkfQuLEM6GZCSyZEcxpPQdDDw212M6zjGfwr+DPbpvzmdaWZRYoILHwdSlLIymr3uf1MRWsQ3AALRH69OkDQGsSMmLECMj0lEW1Wo3bt2+jZUvzTsBlnf5rtf4aO6+9wML+Rc9MzYVSwTYT2vd0H/4v9P9K9BpliRc5OtNpRXorSJy0MtHNqaiLDBaUGDNKDj351xANbun55dGhjbMUxjJerVGDgOBmPNvEV1Ng2kpAlVsTLT0/xPmk40bH+uzrg89CPmOVySTW3YuXuZZ9CM358jTxq4TrcRmsMnW+YV4vHX1WXQdRapUSTUE15Dz+AY0bncT1ezUhcz8IsW2y2d0nU+QX7nZyjSkOAQsQvPEbwBeQvDS2dtCeaPq7bjpHaxFw9fsPUdlRWNCyhtKW8VYrMSdPnsTevXvRuHFjiEQi+Pr6okOHDnB2dsa8efPQrVu3IndGwAI8J04OgcbmZDUUSgD6SoP2ByvTABUKVydoEVVJrUY1lQrRek7+GoMoLWuinsGzggy9G1S12J+HSVmo5GADdydb7LulFZi7byTgk9b+Fs4sGiq1BnlKNTLytBNQe9/fIJJmYd0FFwRX6QFHWcnEs1DLPY1sa0UUhVAfFzSsVtFIiWGdmxsAqRM7qpYqux6knApHoXAtNEFTmIjiwZ0/RoTi2jxZOruqo+VnAOC23+fKAk0rTSZNuACIbOMhcXiMXhseAUU3ibeIR+G9pkz99t5dl4Q3QoUK2lVcQgicnJxgZ6fzFbOxsUHz5s0xZozliHbvKzEp7AfQKFLfO0aOUrdoRNQyiApNmepV4R8oRR/LAVZM/8DbVG0DTwdPVK9QnXXfDaNl6ZOrysXVpKuYGDWR3Q9zUdI0dgjEZzgPYyUGMDbTtT56JT9M7cS0CHDFb6fZZo0UZdpcjChd2O/VDhgTNAtjL1+DPJXA3icCROWs35rZfhW8HAB1vh/atNEGn+HyOdIfpymRHBq58T2iJNlmfYcA4GFiNloHCkqMNZS2jLd6Npebmwt3d+0DULFiRaSmpqJmzZoIDg7G9evXi9wRgZJDJDFO2BRosKJMiyN98dA1Nxf15Ap4qFU45OCAU/b6zuc6IfsoKRsLDmuj0VlSYmLSctF5mdae9Y9hjSGSvQQoFX48KMeP++9j9ccN0TWYXzJGvnT99QweJ9P3QM0IsFNPUrD+bDQmtA80fbIeTb2amj0uEudC6nKFVfZaz9a3sqMMaTlyfFjbWGAqXzeHred/rDKKMrHDwghW7XcQ84o7AZthcrOcp1NAlBXMKgN8oCiC1LxUuNlz78b9etJ8LgKa9Gx+fgH0dVwdbZBhwvJQbBcPmfsxZGcFg29qv91PdqN3YG+etQuhc1q+ww7Ub5MNGzYAAPz8/DB16lTY25fcTmlZRKHSwKaopk8cGG5ERr2IKrG2yyL6fikyt5PIzx9odRsf5ObhpIM9VLnVoc6vbrYuMTOB3vxgM/Y924fJjSbD0Ua3y06bvHFN+tPy0orkY3jobiL6tuiLlLwUo4STt1K5zRTtfX+DSJaI/ISPocyuY7Rops/kqMlmr5+rzGXtyKvydDstjf04VpHMRvkyDoBDLywSlQNU2bWgUfIPZqNRuAEaGajCdutVrsdRi2K/5uifY42FUOUE4eLz9mjiVwlikfF3X6AUQr1bS2nLeKulaVBQEB49egQACAkJwW+//YaEhASsXbsWXl4lOxkVsIwpIat43RQFib2gkesypetH3qB/wgUigJ52V1Wp0bKgAAFKFVrms8ML+7jrtsZNZTfm4nqcLpLI6L+uwq7qZjj4r4ZIlgQA+HxzySu+tAJDSTJh57NRT6kjWHLsMe92bMW2Rs6chrAjh1F4nKxTMHZ/3hKTPgzEwn46E5LhLWjhb9yurdduTvtnRXor2EnFzE7M01Tu0JRGOx1Eqr2OiQGFaPhn9jaXF2ZH9HJebfxzPR5KtRIn407iVPwpEEI4TdWmNZkGAMiRa58zVZ4f67hG5aAXGIG/glYUfwH93UhOBKuyEmHYsGFISEgwKn/y5AliYmLefIdKiXxFyU6CFJqS9/MDgBUnnmDOgbKxq6MfkMXQaf5KjG58qe7Gz66+fV4+lNm1ocrk4cOrZ2LUqkor9iE92SXSm0rR5lwFKuMQ/abMssT25gPnJNisRVR8FKo6GS8a1qzIDnZA7wRBVABKXABAA3AkptTnWCzb99AwMqe9xJ7VbzqJNgBOywaxnXWBgOSFfrKUuACKjKaQp3YwWZceczUqJ2Q/mA+i1CpRtEJvK7FFXuwobZmycEeHJacpiEwm7iYYtO4ifjnMnTLkTfnzvouUloy3WomZOHEiEhO1TlszZ87EoUOHUK1aNfz666+YO5c71rjAm0dTUAXKjOZQ5WlXmgiA13m6XQL933SaRDehvmwrw/xKFXHAka0tJxXEICFDu9KvP1dWW/BJUKkNjhM6bKTl2V98eh5WRT61SmnSx95vDSSO/JUWQxxtHLGt+zaTxylpJjtpFgFr9cankj0mfViT5fvzbbfaqOutv1XORuuIz6ZaBXfc/7ETc+9MZRW2EbF9jBwD5wEgIIRbEaOzIwPmnUcBICU/hXltqHiI7V4YVufkWlwKspXZmBg5EeNPjgegXeEzRFU4UNARnYjCYKWPSGAcW88yuQp+jpkvMnQDdKxUN0AL+krpMWLECJw/bxxZ79KlSxgxYsSb71ApcfqJcbjz4pCjMvahKy4aDcHiY4/x+5loLDtedPlZUtCLGgDwqsD05+3TgF/0yZ45uSh4MRzKzCY8auumSO2rtWcdoSNB7nm6h5XYkg7xzuUnSAi3EuMoM79YJpJm4lXBK2x9uNXomKGfYVxWXGGbtOyiYK3dq6PUkfW+a/WuGFlvpK5AY96flSv6lznkhc75tt7bYe/zF0TS1ybrqnK0Sps6TxtQxsbtMOz9l+J88mGmjjovELnPJyD3Ob3DpDfVJZQZszGtlNc3j9PozXHkKkGJKSqlJeOtVmKGDBnCXLBRo0aIjY3FlStXEB8fj4EDrd/aFbAGa6ZRbIMxwzNZYdgL/420t8MoLw9sruCEC3bGP/JW808alVlamVBq2McpaUbhC8uKSd8157HwyCN8++8di3U5MTLPsm4aeunlVfT/r7/p5kVKKF83Z5V1q29+N1ImEWP359oVPSXnSiCX3whVuMui/bmaUmKkYuNdA0qcB3W+cfQwo2RfIvOJPQ9H6wYIozwsVuzo0AM4Be1nismKMaqzIuoBshRZjHIkMoisI5JmgrlPViQnO/j8CK96f1/kXkUUlJjS48aNG2jVqpVRefPmzXHz5s0336FS4outN0qsrYSMfNx9mVFi7dHciNdNIJcd52cqWppsf7yded3Jr5PJemPDArCgr+lQwEVDJ49/v/076witUD3PfM5a2GF2QjgwtROTKy/aQh0ATIqcZHQNAJCK6b5TkLpYZ/GgLuwjHXmyXuV6qGyns+oQ2xmvqLOxTloq6J0YkTYKqumdEv2Il6LCupkQ2yYjJT8Rj9IfITIuEnZVIyCteEHPFJvVAlRZ/J+T1BxdZNZFRx/xPs8QpVoDv28OwO8bfkmm3zVKS8YX2zjX3t4eDRs2ROXKlS1XFihx6svlcEwzDjNr67UHTrW/gU3FS5znEY7X9210qyvxUu6JaWaekom2AwAqq3ZiNKBEWsVCbPcCMvf9ENs/5z4RQEq2VnicKanVSyuz8a6ItKw8iWz1dyEouNhZjrhG28RLnLlsmY2VmDyFVsDTzp9VK1rjWEg4k6XlRo9jvVekfWC2lespukHQMNSoWs7XjFTEhL8kIFBr1Bh8YLBRrd3PN2P6menMe0rC4QNUqIU7ycS8zeLi0vmZ3uQUcJudcT49gqtMiUBRFLKzjb/nzMxMqE0EsigPcJlLFnVnWZ+kzAK0mn8SWXlsU55P6o4qdturItkBSVKyzS9wlBZ9ArVRjapXMO+3QmMjEWFgE9Ph3i2xeXQz40KNboeEKwgJF7/e+NXkMQ3RGMlPAFAXeJtvVM8xnahtocqubfYa+v+CUCZ3401BhyKOy9bu6gw7NAyZ8kyT9X8f1tigxLqxdnPhwhFtHWDr/Y/JulKXawAAkaSwP4VjwZPMe+j3Xz9MiJwAidNDSBz0n2MDnxiTiamN+30pWqdQvXidb3ScL91+NZPc8z2gtGQ8r1/l5MmTkZvLP0b29OnTOZPaCBQTjgFRAkCksTyp9VOyJ2Yivabol/pTXQcN9w7LfANbUZWFnRh9JUfqcpl5LXM/AhvXs7D3XWf2fMCyyRpfrHVwv5/MR3li/4QkYv6zWsogsplG5cgZ5jE5S6vMBdp1Ru7zCejmazzxB0ytABLu7MQGCp2lqCxudjqnfr6DuSEfN/NFSo7OnydfaWJyRCQ4/eI081aZwTb7yH0+CaGF+XgqOUpZZnGAoZ+SjqwCfpNH/UmmmON3wnonbM+UCGFhYZg3bx5rMFOr1Zg3bx5at279FntmPYQQPE3JgVpDOGVXyOyjSMspXt6t8IWRAIiR2U0Dd46JuJW42LEXBa5EmzbtKU0cpdqs7yIT05Tc55M4y9d8rF3UaxNY2apIlK1qcC3E6s6XikwvlijUuvHVkuO+imNs5dotZ8EaFywsHGo0uJFyAzlEt8CWH285BL62Hz6FVzC+Ruttxr/D45PDAAAd6niwD5hYMJQnc+dh0VcUAAvR2goR28fRFwMAJOYbRgPVNzfhcNJP5oikW9hvBxudkmNqUctadMGG3k9KS8bzmo0sX74ceXn8HQhXrVqFjIyMovZJoISR5bujSy77+/NT6X6YUYXRIvR/54Zill7JMUz4pDT0edEjM1+Jn/brO4fym+A/SMzCrH33mPe5JeQMa++3FjLH54hJy0XEuWiLWbTNKU/q/CrIixsJotZO/jWKSgDERtmLrUGR1g6c96jwi6lo4waN3Bv2Iu6YwobRyQCgeXV3aOSeOgdHpk3Dn775fnev3p15bRzsgJ+d8NNEChef62ylcxUq1KrIlSWafd81BquUGoUrZIV+XFwTBr722Kcfp2LEhst4mcG+bwo9u2f7wglHo/wC60M5CvBmwYIFOHnyJIKCgjBy5EiMHDkSQUFBOH36NBYuXPi2u2cVf1+Kw4dLTmHqzlsm5eP5Z8XzZZGrNHCsOQs2ldg25kuvF/9eNQ/gHxmqNLlQeI+uxuomuFKRFM42zlBmNIRG7sl5XpdgL9z/sRM2jWqGO7M6mr3GtM61cObrdoie17VYff3rqO6emcturyZqKFQck2Irhg1KLDebhyW7QIlhh4axG7ewSEWjzAwFUdnDXuJoth4dMMjbRWeWPLNHHf0aRufkv+wHRXo4r34Yos437e9EBzfKURko23oTmoKUrlBm1YPiVWuo86saHddvDQAC3HWfPyGj+MEzCCEI8nBi3pfUwmx5orRkPC8lhhCCmjVrolKlSrz+rNm1ESge92ykyHK/wCorSOrBrmTBmT5LZPwYyI1CynKfy7WqRPPzfsPoNvwkdZflZxBxPsZsneZzTyDo+0O82tNH4rUZHyyOwqz/7mN1pOHKDRtzpnKKV22hzg2CoYM5Vy4UvohtX4DrHtFCmrZxpu2Hw7eHo8nfTfAgJRZbLsUhM994J+bCk3zYi52R+3Q6q9yxBltoMFvzJvBw0K206UdgA8DbTO/cPUecfqzb3cpTKiHi2tanCHoG9AQAqHJqgKgNow5RcLZxgkZeGTJUND6fJ8PWX0bUo1RMN/K50n0eOrTym7Qas5Rc9F2kTp06uH37NgYMGICUlBRkZ2dj2LBhePjwIerV4wqZWnb59YTWj+TfGwnMb9WQpMyim6XQUGLj3ZznmU8YR2SFSom/r11CRp5pHw0u3AyS+ZmT8aXJE/l+AMDNxBimrJZjeyTc+hYFiQPMnmtvo11yoCgKHzWrBidb7iUIO6kIPpXsOeW2Nqmhhpn0vsx5aRQdjeZxkk4xSclL4awDaH/b9V2bIC9uBKtcZKOVi/K0dgC0ESnNYeMaZXK3QqXRoIJML/8NEZkNFc3qn0aGnCcz0FpmHHGSIrqdqIIkbah6mV5QoKoVdcGA1HkBXK3z6gMX5pQ2U0kq9T+zOqcOChKGQJ7SHRp54aIY57ilLaOVDEKIkXmltfL5883X0HPlOVZAn/cxyllpyXhei4t0nGdr8PDwsFxJwEqMfzx5HAqI8nUrVg4She0rpIpFgIld0Ru22kFL/2elMaHEaAx+wEbRx/Q4ci+J3QKHsFHnVwUhxKrJf1qOHElZWlOkW/EZCPFxwd2ETMzcdw+eFcyvOCkzGjOhGHdcjUe+Uo3j95MxuWNNdK/PXvFXqjUwbajH/tyUOA+eFYqbBEvEeY9c7bQrOFmi67CtGokLqW0wAOORXqBdoZyyJxL3nruhbW12RDlNYVKxCe0DMe8Qd8hI5soWdi/0d1+i09ghnivaS8Bro1yUh/PPsuFQOL7lylV4mprDIYUIxGrtyqbE8Sk0Khejpmo5t8TBS5UR3LQaYpWRfK7ORLUxJPaV5UUXUvj3JpQZDQGssEp8Z/D29n4nIlymZuuUC6VaA0qaDrHtS2hUTtAUTsZWnHiKsWFcEz3LaDTEbGCUA3cS0SPEGyFrh0LidA+zj/XCk29+4t2+4Sqxud32NwKl2zFfGfnU6tPn9g7Gjz3rAhy3wFbKXkSZ2D4QywuV0K861sT0f28yAUYUGgWWXl+Kr5t8rX2f3hI2lc4zUbJo6rjWgSlqVqqJSdvuQJ1bC+oCT4httWOk2P45lOltoMpsAE1+VWiUFjL4EhFyHv4Ep9rfGR1SaTQIrxqOfc/2QaNwhUZRGZSZaF/6yNyOw877H+zPBoan72Qd06iloCTa544ODKMfjbOSg565HYeCRfvDFgXDHUcAkKd0pls2cZZpITq1UxCWXrzKKsuLGQui0X4u+jfAtWOSnCW3OM/Q5+Ad7XdsZ6PvX/UeCniUjoznpcQMHz68RC8q8GYhIhW2Ozmht54ce6rnuJ8i1v64zA5VhX43hrarkY9SMKyFH+cpWQUqUJIMyNyOgqjtIOYKd0xEiHqUina1+GcZfpWjW1l8lJSNEB8XdF9x1uw5qpyakDg+BqW3+pKYWYB1haEUx2+5YaTEGCpsrGMq7UoXHS+fEssR+VVb3p9Bv08alSNEkhwoMpqCKF2Q/7I/7Lx1A0jPIK3dsVKUCqnTfSTme7FWg54VHAcwGKcep8BRf55eqBDFFE7SiUYMSsRtQkcshMys7dSOeW0v0a0o5b8YAk9XR+TYmc4jQ+MQsBj5sWOZ969y5VBIuAI7EOy8+gKSQhN1ER3RTncYCYUOllsvx8HJtI8rC43cA3KVmrV6CAAxr0ybC4gL7/M1O1vIKQq2hs9EKYxFXEnW3kVu376NevXqQSQS4fZt889P/fr131CviscFAzOxdaefQ+LwBLZeu6HMroOCF1ozH3kxVmL33XoJe781Jo8/SMxCjxBvSJy0Jrm2XnvAOYM3QbrBzg2fVePsAiWcbPlHKSwqWQZBEU5+xc88SSLm3rUwVGJ8XXULQX0bVsX0f9nBV/6+/zejxNBZ3zUqJ0icdLu5H1TjDpKiUTkgLVuFgkLzaHlSL9j7rQUAqPO0YZI1CndoFLqxcOdnLdB/7QUQlROrLUqSx6nAAFo/1duJWsVLmRUMonbircToy9rk3GT2wUJlkhBKt5uhR8Nqul3x2u5VEGNw3NZzL4jKCapsy6vuIgmPhSWNfghprgq6cmnFs5BWvAx1Ti3YFrTCuHY1sDiSnVZAP/EpLea5rDHOPk1Dv0bmk3zr2tGdn6AXFOB90WHehIwXzLzfQaSVThuVGf4U1Xo/Ijqhn7ktZ1U29wPmU9Ges5xGbB9tNryj2D4OIyOuIGY+h5OdCXJ4hqMsePERxA7PIa1wXZcvhioZ/xqdPbbunsmszMSdHz8CECkAjRSUJAekUDFSZTYC9JSYP85G4/vudfA8NQ+UK5CSnc9EjwHATFaMv2Vt3+iqquz6kFYwFeLV/ERlzem7WDtYu7sa4MYeUKXy+oAd11lsRJJcuNv5gB6edlyNM1GTQK0RMcJJnRsAiQN7BXb71VjugAVmO6DE6I1XsWkUt/OzWkNwNYatpLfSS/rKaXxA+BpqCBgSGhqKpKQkuLu7IzQ0FBRFcZpqUBRV5iKUKdUaHLyTiAY+FeHtYstMkoevv8yqt+70c0gqaI/pRwlUFCPfxMXnryC2fWny+OqoZ3C2s6xQaDQEmflKVh4rAPj6H/Zko4KFto7cS8Knm65hQvtATO7AvdtZLPQmo3ezDsOx5j6osoNRkNgf/pX5Jbg0haHM7hHijWP3k9HErxJsJCKcm9YenffpdUVPCmhUFaDKrQGN3JNREpSZoXASc1uhiCS5yJWrEJd3BzLPCxDJdKHjjU1mgYiRTVDDTeubwbkAaAJvBz/E5GnHXKnzLQTbD8aN5Hje59OsvrWa9Z6oHUGJ5VCYSERJURSi53XF6zwllh57jBhOC2Xd/esa7MnsUnDx6OfOCPr+sMnjtDmYjbwO5GkO8Ksaj8QC7pDgUpcrEMtSIJaloJuXNi3C/0J8cNyCCQHXTgz9241Jy8WGc9EYGx6AKi7cA+CJBzrTwgQ938vi+M6WJ96EjC92iOU3zapVq+Dn5wdbW1s0a9YMly9fNlt/586dqFWrFmxtbREcHIyDBw++oZ6WPHxtMW09jD+joZUSV0u+SiVa5OdjRtorjM4w7yNBo286YYh2gCh5U4SsfN22tDnnfHV+dSjSPmTtMkhduENOG6LREGgU5sKGG38uEYdpn3lEhQ6XYkaBMdEbAACtu73MzEW+frQ52lbZwMZXmdEUAxv7MDtKqizuFbCcJ9MtXB84cl83aYo4F8O8pkT5yFPyNxNIytQ9LwfvvIRGyXFdioCS6EzWNCp2UILvutWBxOkenGp9DzufP3lfW+J0D2eepJk8vu70cwxcd9Hkca4nuUBZPm2brZGjERERoApzFdF/trZscwpCCGbMmAEvLy/Y2dnhww8/xJMn5nOMREdHw83NjXn9/PlzREdHG/09f246DPvbYv3ZaEzcdhNhCyNR47tDuPNCKy+5fGCkLlqzFYkT26STS54/TMrCkmOPkSs3/ZuSmthV0Ge+BfNRAGi3OAoNfjqGI/eSoFRrMGHrDWy+ZJwjydnCDsv3e+4C0PkClQT698aT0u0CKzVKUGIFkwOsOD6IAJCWy951kopFWDOkET5prd0ZMbWDA2hzmRCVI9T5fnodp7Dzqunkv5HRNxCf+xw2FS9CYq93rznMrxr7VWIUTMNIluaYf1D3exHZvMYnYZUtyncu/B3ZAQqIml6wNH3PKYpCJQcb0/MCvc85yEI4bMMdc6PjlQvz1uXVhSK1IypL2GZ8qhztFv3Oz1qw+iwpHKentG+GgsReUOUEQvG6GWy9t8Om8nFtNwulPddOzLe7tbtuPVeexcYLsUz+vAKlGkmZBUjK1C18XY8z3gHrWMej2M8tH94XGV+ulJjt27dj8uTJmDlzJq5fv46QkBB06tQJKSncjnTnz5/H4MGDMWrUKNy4cQO9evVCr169cPfu3VLva2Ze8XMBGPI0xXjZwBoVQd+xv4DjR9Q3JxfrklLRPzsXQQrD/nMrC1/vMr1FqFRrLDp9a1RawfgyIx8bzkVj/dlos/UBdqjcbDODPQBInG4zdsda+AmP9DwFNHI3k8fp3CWk0O+kpBDZxsOp9jesMrE97VhIJ3jUQMlatSAG/2odRBWv2uKrjjWZrWsRx+ptQWKfwgHO/H2ROOhWAv+9o4scZ+u9Cy8UF7hOMQGl5/dDoFGw77FG5QBlekvWc0OJ2E7QI1r66fprRe4fqZNhoAk2dJJL/TuhtPC4lEcHTWvlKAA4OzsjMTGR+YuNZU92f/nlF/z6669Yu3YtLl26BAcHB3Tq1AkFBaZzjPj6+jKDua+vr9m/ssbJh+x71WOlaXNWyijprhauCU7nZWfw64knZpWQkjI3jC00o/x00zX8d+sl9t16ie92G4+Nlhz7zS1kFRWFWsPIidjECrj/UruoYfU6kQUMIxMaYu5ei+1jIK1wEyKbNGZslbrcwNxjpp+FA/eiUcPd2HqB9rvRh16tt2a3SZ7SkRU8BQByVRkgBgtBfNh72dTYavn5S89TIPvRDKgL2LtS9L51FRc7hNU0Pb7ygRJrvztR4X268qTQLF4tQ/ajGVCkan1mmvhVYvVZLNLWi8u7B1uvPaAk2VDn1IS0wg1mnKN3YMylkMjSC72850YCav1wGM3nnUDzeScQvjASr0yEUTc0YSwN3icZX67MyZYsWYIxY8Zg5MiRAIC1a9fiwIEDWL9+Pb755huj+suXL0fnzp0xdepUAMBPP/2EY8eOYeXKlVi7dm2p9XP58SdYevwxejeogqUDQ0us3ZtxrxHIs25BUncoX7eGzOM/2FQ6Z6TsLK3kwnleHkUhWyRCJYOtPWnFy1C+bmFVf3uGeGN/tM55TpVTAxJHbsfMtgujTEbyMURfeGy6EIvP29bgrCdz3w8bV8MBhd8EgAIAYoPc55PgUH2Z0XGxbQIGNGiErVc5EjEWA5GNcX4lmUT7M63hXgHaoU4Dhd73Q4lpIWKcS0AiFmFw02rYcfUFpBWNdxmI2k6bcJQjoIA8pTNs3I5o/YgogifJ2Qj0cDIy45JWNHa6NP35kgEihkZZAUTtAJEkg3VcnVMLLXyCcZNar9dJ9kqwiBJhfLtA/PlEq5ir830gtuNvLpGnUP0/e9cdHzX5/9+50bvrnnTQlraMsil778oSAUGGggwRUUCQpTgYPxwgIkPFr4qCIqAgUwHZewtl711a2gLd+0by+yNNLrkkN9o7WiDv16twefIkeS6XfJ7ns94flr3IFnZ5mBcczwpnmKNyFKAtrCEh4pS2FEVh0aJF+OSTT9C7d28AwIoVKxAcHIxNmzZh0KBBosf9/fffou1i6NWrl919nwTEDKnn7meJ9pWqbm7N0nzijjTRRoHelueThKV9MjNfj0sPctCqagAUCmFIBzfP0BLOpoPNKzZiQ0ISutYJQbC3eIK0wUhy8hcJ9PjmUEnIsbnNGXCz4dVSEgTybk6BZ7X5gn2Egpa7hCqHzRkFAKU2BeNWi4dQa1UK+PvpcM9CzBMWchAwh7p91KMWJv1ndZjma7vfgyI3DZRJw7LXZReaMLVrLH6Qit6VgMJ/J2+bMmlgyGoEUzGtmByY2kHyWLWSAEh36NPbQ1d5LeckCiwf0RTtq9unwAxsEoE1p/iynSFU0GfQ6xHCLQkqdTLA/B7KYmhDNqHowWuc8SjYYE5lSZ0zxttCKAvNc1rJM8eQWdj77L+35ixv+156ARbsug5PEVY8ezypZcXzJONLrcTcvHkTt27dQrt27aDT6RxmmHIUer0ep0+fxocfmqliFQoF4uPjceyYuCX42LFjmDRpEq+ta9eu2LRpk+R1iouLUVxs1qDFKozawsLdtDa/8UyyU5WYssDyVUzQCiePdIUCv/h643cfb0QaLD0xjk1kJpISuGIpSpg8rlDR1kB7FRgAyOKEAKRkF0kWjlP5nBWOqzBC2FEERpICoc7kJWxyMbRlFczoWA+kIhtbs8SZ16xh58R26LJQmLskdpv9POgJMsrfE0lZAAgSj/OE1g/K6A19ehu4BRyG2vscTAXR0Kq7omGkH7a/1xb9dwiTJXXhqwAA+nRhsSl9egcoNA+g9jkPgMSHGy6gZ/1QAVW3Q0VEFXoQCiMoRSEABRQafniXyusizt3LhiLG7HXsE9sR23M2AqA9NUqFEh5ujGJDccIchAj3DEdSHt/KuSEhGUNa8C0/FEUh38bisCIrMbm5ucjJMYfgaTQaaDRCtrzSyFEAyMvLQ5UqVUCSJBo1aoQvvvgCderQNX7u3LmD1NRUxMfHs/19fHzQvHlzHDt2THKC69OnD2/bMl6aO584MyfGGTL++G2hsaH3kiOifUm9v6hxIiNfWnHIL5b+vmk5xdIFxwGovM/CmNOI19bz28NIzirE/P4N0K9RZUR/yA85/nzbFcnzbT77AJ1rOY9tdNbfl7DudBJ+OXwHB6Z2FO1TzCkeqfK+4LABzV6obdAAKpUEFG5mjxldD6xkXJ50KI0mcB+KH3blHEVhy/kUHuGIqSgUSm0KTBSJXVdSobW4nYbMVhjQJBxrOaFozPPfqWYlwE4lRuV5DfrHHWAqjILK8xp9HijQMMoNcFCJoQz+KEh9Ge4RKwAAZHEYih+a6+lUCbDDQ0RZhiIqoFYoWO+JLXzRtx5GtYtB/II5UPmcAlkUBrVPCdkCRS9fFV7nofPaA316GxSl9oY2ZDOUHreh0DxgCQgIjtJ78k4m0BG4nE575hXqbHYeZMB4YBIz6PWJQpsEQlkAU779OV/HbqWjcy0hYZGbqnTrZFnGi8NhlTA9PR3x8fGoUaMGevTogZQUOjlt5MiRmDx5cqkGYQ8eP34Mk8kkoG4ODg5Gaqp4clhqaqpD/QFgzpw58PHxYf9q15amS3Q27mcU4NANe6rEmxFqNIIghbqo0uM2NCEb4OZPT6z2LMB+LlFgACBRbSl8HFvCvb/uPLacT+EdZyucx158vYuf5Hj6nn3MKwBgyG5kuxPoMCGl9gE0QXtE9zMUibXDS5J2HQhrAoAawV6CtkaRvhCzMIb50EmDx2/T35MAhZ7fHhI9L0OrrHDLhCZ4C+txqBnCDyewrCX0VY/B/P1sNWNz6NeZ+1mY9c9lCJ4FB+K1GWIBRnkVjF8fiCIt32P0ZitzbDajMClKrGkgKBSl9hbWRiqBpQIDAIUixVOjP9yGLJEQUO7PWpGVmNq1a/Pk1pw5c0T7lUaOxsbGYtmyZdi8eTNWrlwJkiTRqlUrJCXR95Y5zlFZS5Ik+7dz507ExcXh33//RVZWFrKysrBt2zY0atQI27dbSe4tBZ60jCcN4rWM3ll5WvIYa0adA9etzxEKdY6gjUkqnvLXOVEFzBr+PvcA11Kd53Hec4VmvbpnhRWQ+/2VWrMlPsyXMb45x2BqKRctoSQIloESMDOSMeFt4hCTFPR4Y4J0gvwWilSiTXgLzHulgfgYFATPSEZRChjzYqXH7H6PNwaKUiDHIJ0LKAVjfjXJ+ixjOlinBz9fkiNmKoji76AINI02vw9f9hMvDMp8P6WCQLVKngAIGLObgiyuDCZHlKmT46MryRtS50AbshkAoFDlQRu6ER/3oDVJvdF8Py49sJbNT/cL8qKVgx8O0LkaHtHfwT1yGQg1/e7YE0J5+3G+aM5kaY39sowXh8NKzMSJE6FSqZCYmAh3d7MFdODAgU6fbMoDH374IbKzs9m/y5eds/C2B23n7cPrv0gnX1EQhgFEGE3wfCS0Uqm9LsPNzzrpgdj5re11BOsT6Iff0cW9FK6kSE8ao3+XXgwIIFEgzBJ0/RvpsTNKTIHR2mRmHaPbx8CNw44zsk0MuJMzZaInbKJkwZ6T6wNDdhyMBTEAJV6TxpDZBgX3RpVsSf+eDKUng0gfs3CiKAUMGW0R6OkGb22J94ygzK51y9/UESXG1xxeyE3eZ6DUJUPlwU8WDPLmKOkKIyiKQjprxaZAGfxhzK8BfWYzkHrb1catFTG1BgedbU8Uly9f5sktrhWurGjZsiWGDh2KuLg4tG/fHhs2bEBQUBB+/PFHp13jvffew+LFi9G1a1d4e3vD29sbXbt2xYIFCzB+/HinXQd48jLelE+Hu1rWKcoXUaYZlCXPhFBnQKGVDq/k5hTai/NJWaLtYgYBW8i0I19058VHKC5hwaIo8/t/61HJArSM04qXVoWaIV6Ir23dw6RUELwXnzK5w0RS6PEN34ik8uGEj4nJw5Jz0LXELCIUjD6w5Zgw8ZQWQjCPUUbzWkxTaTuUXDZHijAbfRwAWRwMtY95blVo0qB0vw1ClY0RraOtHAk0j6Y9Vpb1x3RqNS+McmDTSDbnp2NsEAxZjQEI5ycumBBxN7/juP5ZdyRn0u+KQoRGelQ7hjbZfIObVbE2R9C/TbGRxIaEJCRl8hVtJj/z2G3rddXMZxM+qKtPOOgSK4Es48Xh8JO9c+dOfPnllwgP5/NkV69eXZAI5EwEBgZCqVQiLY3PXZ6WliYZxxcSEuJQf4B20TE32NvbG15eQot5eYEAhf3XHuH2I2uWBHG0LSziLWnfzhSyj1mdF0qpjNiqP2IvFu22n2LSGsTCOhhwFUSphS6p9wUAeKjp56JZFfvC08TwYfdauPZpNyRMfwF/jGqBHvVCIKZ4MCQRpoIYFD0YBENGO0Ef9+jF0FTaBs/Yj+FeZanti1tQTVf28WU/U/emY1zHajj1yQsI9WGoI7n3w/LeSCsxHcI78LZ59Q4kEp4tz69VubFVswkFrby4KZS8vpQ+CCrP64JJc1S9UbCEtfo/AD88jisgdaVQftaflmYqcia8vLx4cksszAAonRy1hFqtRsOGDXHzJr1QYo4ryzlv3boFX19fQbuPjw/u3r1r1znsxZOW8cwiXCzskaIopGQXYsbmizhshTnPEbj5nYRH9BLJ/cpSWILFvJQA8P1+x4tPcvHOytPYdTlN0L7mv/scMUB/uJ9RAHNj2SwKF2Z1xfb3hHLUEnRiv/la+ox2WH1SuAhVarjeMb6cII2e7DmMpAlCeWk+/+5J7dGldjC2vMsP76Us8h1NRfw6LUWpffln5ChSsSE+VkMXuWCUCIAO4+Mynao8r8G9yk9oWuc+66mQQoQ//axzFVB9ejtM7CAMW97ybhusf6clfhnWFJZeFusgSoyAdF9BXiRH+eQyjTauQn/uGdNT5JT0b3c1NReT1p7D1RIPJGWivy9Vkvv0gQUNuRiCvDQI9rK/KKYtyDJeHA4rMfn5+TwPDIOMjAzJm+oMuLm5oXHjxtizxxzeQ5Ik9uzZg5YtxeNlW7ZsyesPALt27ZLsX9FBEMCIX/9Dp68PsG2PlQoUefMX+CpSGL4wKoQfm1lNLxRqpJV5wcESKCyMuXVKd6AFmJjW/dek2TXsgSZol+Q+I0lhx6VUbEhIQvyCAxBT6xghpigJSvfX0VYkT3XpFkIMJWXLqgECNzOTsN8wwr+kr/R5lNoUKD1u8ItZWnEdWC5ygtyD8EWbL7Ck8xKc+6QvpnSlLX9FBiHzmSPhZB0j+THvpgJueAIlqHZNn48CyVF2VAqV4JpuJWQHIEi4Be6CLvw3QVFMUu+PVmGtBKevW9kHOy5Ju8DF0LagEGJEs9bUmnP3szD5r3NWejx5lEaOWsJkMuHChQsIDQ0FAERHRyMkJIR3zpycHJw4ccLuczZt2hSTJk3iTZJpaWmYOnUqmjVrZtc5nkZsv5iKIT+fwIpj9zDkF/vo33vWD7WrH2kUz1mwpcSLwbIAJoNzSfZR8Uvh34upGLXilKD9ckomVN70QpFZkA9ddhKm/FgUPegHQ3YTwULfFaAVPrMcVWhSMH2TOLspY2gReFr0Aew5HmQVgjT6wphv9jQY86uiW116IVitkid+GtoEdSvzKZEVKnM4H0GQULrfLVGOmEazkmkqrgRDjrmuW60QX5s02QBQcG8kitI4YbkiYeoAUGCH923iCzUwuHkk32NEGOGt9hf09dCo0LiKPxQKAooSEgyFiv9cNYjwNV//7lswFUagIPFNusEOF3nRg0HIvTIXuVfmIi6wKQAgxCNEZH6QejfoazDPYqHB9j3QqZWC0PcngedNxju8NG3bti1WrFjBbhMEAZIkMW/ePHTsKJ6k5yxMmjQJS5cuxW+//YYrV67gnXfeQX5+PsvAMHToUJ6LbcKECdi+fTu+/vprXL16FbNmzcKpU6cwbtw4l47zp9dpa0akv/VCkFykZFunepTCTbUaBh1fO66u/9LmcSt9hLHA1qa21tVsh+mIQpDYVzpsL1l0Dl9uZ4ajBEiDdAy0wURi9O+nMWmtlYUnweRkECX/M0wnzqHaDfcTFs3qUa8yAGDCC2FQ6u5CoUkBoRQm6Su1KRYtjlkrX6r6EtqFt+PVRmDi1nlhgRZeOcLKd28WYiGceAqP1BNH0TTLJVASSh7DE0EQiPKqhsL7r8OYWxeaoD1QeQkTkynSDRql0LCiUhCOhSBC+k5au8PH7Qw5eNJwVI7Onj0bO3fuxO3bt5GQkIAhQ4bg3r17ePNNehFBEATee+89fPbZZ/j7779x4cIFDB06FGFhYYLETiksW7YMKSkpiIyMRLVq1VCtWjVERkYiOTkZv/xify2gCokSw4fSXVgL4Z1VCbj1yHZ1cgaP84pLcg3tgfjTua8UhqD918TzcCyLRTqrtECdcC2UWmZeo2XGncf5IItDYMhuClNBjGCh7wooFARerMf1epi/r6nIQplkFuwW8rF+uA8M2Q2gT2+DzaeKYMxujMLE0TDm06FOpvxq6N/YukffkoJZoc6C/rF5vaX25cgzigBZbPY8qBVqQYFiSxQmD6RDuCgl1MV0nooYEQ8ApOfa/o29tWp8/nI9DGlZmW1z8z8KlS0iBQ39bCos5rL1b5sXyqbCGBTcHQuyiFEaxZexSnfxsC1uaKROZZ5vjbk1JcPYWAZQhf1hno4Wv3YmnicZ7zA72bx589C5c2ecOnUKer0e77//Pi5duoSMjAwcOSLOzuIsDBw4EI8ePcKMGTOQmpqKuLg4bN++nU02SkxM5BUcbNWqFVavXo1PPvkEH330EapXr45Nmzahbl3xon/OQmCJq1UsHlIKLefsLdW17L2C0cKMf1brmNesZVV/7JauA4gfD9zC+oQk/D6yuQVtJgmPog7I1+536HqugrW6LrYIAua3n48pB6YAAApNdEhfjp7O68g32L8QsYYwzyCkW/yo0T6096JO1cdwv/8DjAVRKEoaLHK082HIrQNSHwAjR7hTHEVQn9kCCvVjAcsYAMRHxqOyZ2VeG0NLSm9QEhMNPx9JLJ67TdVoGPPqwCvid+nBU0rUDRS+647UdmF6HnTXoYAg4M6xYj/KLYa1t2jjGXFq3fKGo3I0MzMTo0aNQmpqKvz8/NC4cWMcPXqUlxD//vvvIz8/H2+99RaysrLQpk0bbN++XVAwTQrVqlXD+fPnsWvXLly9StdIqVWrFuLj459IYThXgiHbEKvKbg16IwmSonh1JX4+ZLuOli38cdLx6u1SOYkv1ArmhYO1+2ofzs3sUuqxMYgK0CKRdf6U7+9fK9Qbe0umBl3lP5CbQyfg6x934rFaKZhwJopPHXcp4zwMma/BEmRRGIwgaKp5B2v/EKpskMXmMB4VJweGIExg6Ov0mc3gq/VFZrH1uY1QFsGj+pdQqHKhUnrAYIIg5JiBZYFQa/D3NgIcG+uDwusAwiX7F9wbCbeAwyhK6cNrt1Z01K48V6KY/j6kG4a1ihLtUpg0HCrvs3AL3AljXm2OksQ5jVKajMISN0Tq+j0pPE8y3mElpm7durh+/Tq+++47eHl5IS8vD3379sXYsWNZ15MrMW7cOElPyv79+wVt/fv3R//+/V08Kj4Y7nmx5MxiowkGEwVPjfnWn5WoL+Bs2KLCtba0e6HKC0hunYdlR4STaHpeMeaUFGf7euc1HsuKyifBaQpMlkRIgxRMBdFQeFtQJHM8ASc/6oxmX5jdo5akCoSa79JuH96e/VzVm365C42l86BJoaZfAxw9MhcKTSo8YhaBNHoi1IsOD2QW8wRsFxFFSU8uRtYdiV8u/oIhtYZg5ZWVdo0ngGiIh+n855gyma16pvwYFOf2Ea3Js7DjQsH5SI4SKfk8EnwlRkzA2RMeARCix9ri/ueOq02hWekqslBimn6+G+ddF0HrUjgiRxcuXIiFC4W/JRcEQWD27NmYPXt2qcdEEAS6dOmCdu3aQaPRPPXKC4uScBdHa9zFzd6JAr0JLzUIw1ev1IdWrRQQu0heklTTYUyqPBiyxVmvHMXD3CJUsojx16gtPDGFzvHEaN2UgIi4V7rfhtL9NgbUE4aJugqEZMCKRfgvQUGf0RrGnDjrJ1QUgFAWwVgQA6I42CpFPAN9Rku4+ZvpcQmFUTL3UaF5zFaeV+qYAr7W3yWG1QsAikl6TpMq0uqIUqmw6Juptx7GayqojsKC6qgn4mXb/l5bXE3JxZbzKdh9JQ3DWtLGPQoKUKSSH0ptAV3kMqjc6XuRVBiFAE/hO6FSEFB5n4Pa6wooo6+oElPa3GAuFg50zvtoC8+LjC+Vv8vHxwcff/wx1q5di23btuGzzz57IgrM0wJGkIvR67Wftx91Z+5APqfS/N4rwsRGMYgt+vZ4CAXgFc1o0eNtGXuYs7cuKERzzuINACK9IzHjJXEq0jmc6tJ5xUZeFWTCAfcrF9vGt8WRaZ14bXGzpfNZxFCUPBgrOx1Co0pmWmWuW96qdQeANngrb3v9jfUs1SUzsdUJoHN+LD0OpcXYTlXgVWsaW2DT30PNjlNFMHkg1pnTpBDrH4vu0d1RJ5Cfp2Rp9eLiob0sSQr7Fi/FD7nJlNLhZEyNAyncz7kPTbCNQloSzx5TyMweBHO46x2947lFUosAGZYgSRKffvopKleuDE9PT9y5QxtLpk+fXiHDyWjaV/vAyBxSnVJCo24fmNyDf849wOdb6XDJHw8KQ9IYaAhLGvWXUXDvLRSniSQwlwLNPhfSzR+56TgZgUpkIrJkgdqQYPYWkXpzYUSl+y1ognYjV2E7sdpZCPMU9xyQBl8UpVoU6JPwCvRsWgiV1wUQqiy4BRyEZ7V5cI9YAV3YekzoaVtO1Ayl53kmBM0aTAVV2PBfhVsGKIpiq9TbA5JiCj/ylQJjfgmtsgM0jeHu/PLcPWu0ttp/0cA4VA3yEK2vVzPEG30aVsbiQXFYPrwpPnqRpk+mstoh79rnKErrzuvfNLAzZ4tLUW2W5A2CzArFV68FQalh1mKuI9S3Rev9rMJVMt5hJWb58uX466+/BO1//fUXfvvtt1IP5FlCESfp62GOWRkgSQqpJduXODzzP1iZmGxhjbdIrKuEtcDLhvV6dFYO1iWn4LPH6Qg1igtWhtfdw80sFNdxGJh2X36IVnNLFxrHRe0wb1T2FeaH2IvhraIw+YUaiAyi8KhQPJ6bAPDPOPuTQ+eenAtGGDKVeN3V7jg5+CS2vLyl1GPlYu2NFbztrOJMPC6kFwrmsCoStoSsPrMpCu7waQu7R3fHvHbzeKwspMEbPsb2loeziI8rgNr3JBRaThE2DjWyNnQ9oCiCmx8/KfmLNl+wn6c141QIplTmhGOCEsSVGwuiQBYHS4YyMHhU+Ahu/ket9vHQivsWk7NK5z0Tu+PWfoWHucKCpDLE8dlnn+HXX3/FvHnz4OZmjsWvW7cufv7553IcmThWjmxud1+l+13284Yx1hdxUrCsWi56HcLs3aeM3iCLQ2AqiOF5Th2BPRFO3OKMDMatTsC5+1n43/5bohTMSpETr/3P/P1yiwysx5yiFNCnc3JtS9rVilLX6XYYL8bGibZrgnZCG8I3pHBZtZhFdd2AurhQ8Bd04augdL8HlSc/f49Q2o4wqBdeojTbETplKjSTpRAKA/SkHhFe9rNoeqgZ+Wy5BqB/t4FNRchYJODl5gdDVkN2u0Go9WP7NKyMPZM7WDUSeGhU6FizEkvVvHpUC3p0FuueQLco8wZH8eKGJ79a81XsG7APizouwozTb7DspWo/i7ml5HjSghWuNLBVYPVZhatkvMNKzJw5cxAYGChor1SpEr744guRI54/VA0yv4Dj/zzDfr7MiSvmhrTojfbG6FtfuC7rugwtQukXWp/RCgV334KPxv7kxxCTCbF6AwJNJF7M4+d4HEqiefEHNo3E3bkvYoXEJC4s0lY+JQKHtqyCfs090G5NO9zPFV8A6NyUqBfuWHIow06i4iSa61S6EgatsuNG1g1BW56ejq1VEiWKI0HarM1Cj1NaWFZyp9nqOvlNw8Yx0qEZYeFXoQ3dwJt4FWqz9ZVQFrNeIy5eqmpmuWGeScpEK6WGrKbQZ7QCZXJnKZO1Si1er/kGCu+9Df2jbuzYA3VCWQOI58lYwlvLtz4y9KHHbtmfcP8fN3dM5HZam45KW4/mecSKFSvw008/YfDgwVAqzb9bgwYN2PjpigSdo7FhHDjijWFgK4zM280bBaSZPl7hlg7C7RE0wf/ALWiHw9cDgF2TpI0b1rDlfAp6LzmCL7dfxeI9Qnkm5on5Zu9N6I0kDt94jHqzdoKdNwRWf7pdqShVEEmpwM1X4VH/ishgTeABqH1pgw4ThXAx/SIe5jCKCskhLKCx9IJtSvzNt+hwL5WnbUprtwB+DRtHa8REetGKBiEwJNH3np/zah3hfjqQRvMc64oQ0TBfLRTaZEDBVwbdlOJGW+790Kq0CNQFwtuN7x2xZLo013wu+/if5LNbkeAqGe/w3UxMTER0tJDBoUqVKkhMLF0Rn2cNXEsTtzoyV3GxFZdfGvxz6x+EetCWbcroBVNhDKr6mCvr2vP6JWg0eD8oAEd1fC/IxXQ+raRWXbFfRAVB4LWtwmRKBmM7VuUlzIqBWXSLQW9yLD/HXnSI6CBoY5QXcyKeuCeG1PtBQzJhbRTqVpZ2WzPn3J/3EaafHCPZT6fmhrBB+Bng0XuKwV/rj7FxY1H8uCNUXhfoUDFKAUKVw9Z1KTIVYVzDd83jKwnBYbxQlrBnMmwc0pi3zSgxZxKtJ7lycUdtnggdfWNLwWT73CI5ORnVqlUTtJMkCYPBOXkWzoTWTVr+7Z3cHofe53gPLBY+P77exOHrGUkKmVYSqhmCES50lVfBzf8INIH7HL4ewDfGWSIxvcCuWhnLDt/BsVvpyCs2smHWYp4YAOg4fz9m/l0yz5QoCITCBEJpTpBmjEhq5ZPzxPDuLWmWB0qpgqIlHgxNEDcigV0Fl2oMrAHLTpAGs+LAHFs/sL5Udx5Yxi5KyfOUEwo98q5/gv6xvSSOFKJWqDfaxbqWRW7B2RnwiP4WmsC9KOYwtuXCTG8c7mcOu1crhMpNQloCb5tQWoYil02YN65iLnshpsQ/D3CVjHd4JVqpUiWcPy8UXufOnUNAQClpeJ8xSK2vuMLbFc9xjj7HYnFn5MXCPrQhB1d5e2JYWDD+9fTANTf+i25pCbSVKMjgjTZRdvVzNqSsRUz+hz1xqYTSuUn79uDF6Bcxuv5ozGk7h21jlBezJ4YStQIa82rh3WavAgDUvgkIq/Gn9HViXmQ/n06Tphs2W62k68RYWuy+bMun+PbT+uHtBm/DkNEOhDIfSm0KCHWmwNplpIpAqLJAKHNhCwoL0SVGpTwujp/USKjoxVC6DXYdflk57ufnc/J5EqhduzYOHTokaF+3bh0aNmwockT5QqNS4u9xrbFpLD887MX6oYgJ8kSApzlcokVV/rxoq1CgGCgKaPipYzmBSq1jtZC4+N/gRlb3j//zjF0hbnoTiVeXHkfdmTvQ4P92IvaTf5FXLB6qnJxVyKGbNr95muB/OL2YcLLSe8IcRXKu2etOGsyLUeFCl4HYgrdEdhAUjHk1eHumNJlicwwbem3AiDoj2G394xfQJFhaGeayaCkIBVLzU3H+sX15RMwinzT4oOCeuViwUncflMkTId6OKSXh/q5VOKN8zCFq3Lp07zV+j/0c6W9mBnRTCqmj1RZeG0qiRo5CVzrGyTdamw3/Ukr8sw5XyXiHn65XX30V48ePh5eXF9q1oyveHjhwABMmTMCgQYNKPZBnCT46oQJAEASPN1zr5rgQFnv09yUmobtPPIp8rmJP4h7WE6OptAOaSjvwH2ceGxGgwR4RJmAtSU8Mlzlxikfd+V4INtmvBDVD7IuzLk1eS0ygWeBU8tLYn1zOgU7i/jIV41+oHezwOZ8ElAolxjWkF9+fHP4EJsrELti57GSU0RMUpQBBkKBIN+Rdn4EDUzrCTZOP1PxUrLyyEvnGLMnrbLixwa7xmJViK8UuLbbZmGrRE9J9NT5XQer5BVgPJO2DZ/W5MOZVFzuSB8sQiYmNJ5bkLJlhomjl6pPmn2BhwkJUj/BEwmWbp+aB+82cUwlIhhhmzJiBYcOGITk5GSRJYsOGDbh27RpWrFiBLVuck2/mbNQP9wVA5wl+sJ5mQWRqg7m7qbD+nZYACGx5cAqnylaj94mjcy3r8lGKctkWiu0MnaYMAShK7QltyBbw3sIS440jieplBT9U2A6LfImMo0gVCIUR2/ttR/xvE9jjDdkNQRZXwopXh8NP64fqvrblXYxvDCY1mYRlF34HoTCCzG4MjUq4IDSPQTqnsGGlhjjz8IzovnqB9fBh8w/Rc9UHMGQ35lE3lxa77jmmfDuKar60dd+YX5XNGQrUBSLah1NQlDQrzgZSaPXn1osBwKOv5kKasc06uHrL8+qJcZWMd9gT8+mnn6J58+bo3LkzdDoddDodunTpgk6dOsk5MSWwTKBnCgZyi5QNX3YSJpJCggOhLWIINJFQF5pdvin50oXQHkoklBXZEaNpWfPGUV57a/jqFb6be9075hyN0sZlS4Eq4c/nhpJ90K2mw+fxdLOfnaisYBbs9QLrYfcru1Fw722A1KF3yAwAtJtf5XURt/MuYt2NdbiSUZK/YuUnsjccjlGgCK7nRxBOxl+Y7Ly3k7edp8/D+uvrofIxV+YmKRN6N+FbpFnFRISYIsyDn1BpGU4W4RWBV2u+ygu7uJ1NE2Y0DG6IfEM+7lLSnikpcEeikuPDXIbevXvjn3/+we7du+Hh4YEZM2bgypUr+Oeff/DCCy+U9/Csolols0Hn9iNz6FPjKv5oXMVPNHylIuDdTsLQDgbMQmvBAD4dbEaJF1PxROivCd5/NOh30O0JhpNxZUrxwx52HFEiJ0rkmJJQwt+DjgwgQMGY0xDFD3uieWhz1PCr4VCeCKEwlvxPIsTdvNAuftgNgZTZK0hYyGSuIma5YOdCrVDjRuYNKLRJcAvcB6WOnyIQXucnHE4+LHG0OLjKhCvAfDeVxy3oImmWq8yiTJ6ykvDQHC4W4iFUUHpX7Y1a/rXYbYrkR3L4qugwbcrIN9B93d8+uuRALw1aVwtAm2qBpfLEPgtwlYx3SImhKAqpqan49ddfce3aNaxatQobNmzArVu3sGzZMh7jgAwzmARfbpJjTpERVT/ahr7fW2dYsgUDAKoU1oEvH/JzDXIUhNVwGbHEUnuSW1uGtbTdp6o/+/mHIY3h72F+jiy9WgzGdqwq2g4A2vAVGLJtCNKLhAncuvAVgrZaofaz91TS0d4DP42fjZ5lg5E0sp4Eip243RDsEQzKRCtQSkIJrZKekHSV/8TW23/jp/M/seFh1kL+8gz2FeKyJ5zM0keRq+eHg2UWZ2LWsVnQBv/NO3b73e3sZyWhtGBf48PSembpifnixBf44+of7D0DgFlHZ9F9S8ScwWSd8UwMzGi75OUjkJR9Ma6A0WjE7NmzER0djV27duHhw4coKCjA4cOH0aVL2Qsnuhpce44YfffDAue7YQqThljdX0kXCl+Nr9U+73aS9gAwRqr2NYJ47Uw+2ZMp4SP0AlMmD5iKgxDk7i9+iAvAlZWmfNteE3MpBKpkmwArepxQZwQAlH4H0CKsBbtNmXTQQzoMlxtuezL1pGS/9xq/BwNpgEKVD5XHDUHtr2zytkC+28LijosB0ExgrgBXQVOo6FATE2XCsQfHRPtbJvEDdLHqIHfus24ZMk0/i3P78YsnS61PhGMksHJkc6x8s/mzU//KAbhSxjusxFSrVg1JSUmoXr06+vfvj549e6JKlSplGsSzD9dZcFtXCUdesNmt3DOmJ+a1m2fzuDNavjXgtloN0sq7ZemJAYBCg+1FIePqtYYwXx1OfNQZV2Z3Q7e64m5cS0yMryFo2zWRDm9Ue13GuUfnRI+rpK6JSS9n4ZuEb3A1g2bEeJBlPxVuMUmHtonlYDgT3PA9saROQpWD5KKLKDKZ83Z2JPLr2jjKSiMGUSXGhifG8roEJx5c6l3Y8vIWnnAvuPsWbz83HAAAPFQeiAuKY7eT84SxylqVljceawxPU7vGImG60BpEiRmDZTgVKpUK8+bNg1GC1r2ig/vc7r0qVFjEFo2XZ3ctwxVNgnAhbr4EAES4VxeN/efCTWVbPkiRn7jaE6Nwe8jSF7erbmYo1D+OR8HtyRhR73WXXp8LvjHInurwfCVGQSgQ4e/Otindb0LldQFp+fbVhxODyvcEvj/7vfmSiiLeKPWZfPZQ7nxlKUu5GPrvUGQVZ5WcVLyfoyQDAboAXBh2AR81/8ih4+yFFDOoikM5rlVaZ1QrNhXjYNJBTovlXEHf3SoB/Lp8tx7lsWyDYT7S11ArFc+l8sLAlTLeoVWOQqFA9erVkZ5uP0WpDMBZSyCxYpeFFqFgdQPront0d0E/AAjgMKL9aVFf5qFSaVXVssyJsQ8URu4cabXHL11+gYJQINhbK5nHYgmVghAUqjz0fkdUD/bCzc/FvzuTBBlXuTIuZh/A0gtLcTOLjvcV421f/aaQQnpUvVHILs4GQLNpuRJuSjdMbjwZY+LGsBaipNwkDPt3GHQRv4BQZeNk1hqr57CXfMEaNCoNPNWeaBbFyV+hFKCMJcXX8qoBFP93yyjK4G0zSoRSQUhSQ4d7hXOS9SmYCmNw4rUTODeUVkYt73eEdwR+7/G71bFX8aKNK+zkIXLt87O64PLsrhjbsRrrAeS+Z4x3MlOphCN+nOyCiseoVZHRuXNnHDhwoLyHUSrYesu4lmtGkXZ3K0M4lKIYusp/8Jq83MzyvPhxB7wR+4nDtrPXmgtreHhoVPiohznclrEDGF3tleQsoMUMaE8SXAu9W8B+9nPhg/4SR5Qwq5UoMwRB4M/e/0PulS9gyGoGt6Dd0IWvsjvRXgwKQsELJ9ME/4simEPJmdxPBo6ENLK5lxbGKobxzJZy/KTBVVa44CoNYZ7W67tYKmaGbC6zJYlMA01ikVnMD/+fv/MafhjSGKPbx2DN6JaSOcDPazI/F66S8Q6baufOnYupU6fi4sWLtjvLAGCb499eiCkxjmBPunQexOTgIKtnL1VFekUx/kv9z2qXZqHNHD7t1K6xgjZDSX0aS+WGAVOrhKRIHEuh3czXM2gKRrGfp1U1fn2S7zp9h/GNxiMuKA5ebl4uj/MFgOF1h+OdBu+w2yRFIuFhAjx9kjGslf3Fy2yhdZh0Ab53GryDY68dwwfNJ7FtpvwayLsxA7lX5qLw/psoesAn9LD0gjHKFP3TiD9ld7LvcJSNkoWe2p2dUIfVGebIV6JPU3I+8ZA4Gt5atdUFJVP09aROixSV/RbI2VscZBB4ztG9e3dMmzYNU6ZMwR9//IG///6b9/esgGsMamcRqmUvBDUsAHSL6sZ+VntfwIpbn+FhoXQYm2U+DEEAL9UXX+j1a2SuWG8qEZZFBlcrMeZ3lSfSCSNAGGAiHQ8NLS1CPELwRt03AABqP06IEiUuD4x5NcENiVUSyhIiAgVoaVgyV0ksvq2BKSLcNKQpO48xiPUzM3Mp1LQhKcYnBgAtC/tV74fWYa15xY7FYJl/yIAy0YaripbjJeWJ4Ra5ZvIjpWDpJSGLucQW5mfR0pj7arNIVPLW4sPutRDh746NY1phbt96gvMz9OLPM1wl4x1+i4YOHYqCggI0aNAAbm5u0FnUE8nIyJA48vmFsACk67D//n6kFYi7qSkAJgBbPMXZo6wpMQNiBzg+GNK2xabeb/WwqfcmVPWVznEJ8dYiNcdsiWfqF7zVLgY/HaSFU3SgNCNWs5Bm+O7sdwD4CuXlDPGF5md96gra2kfQBAO/df8NJtIkoGR8EmDCowxkMXo2CME6aQ4HANZrqRwedBht/mwDwFz40hrqhAnjiN9oHY1lR+7AmFsX+oxWcPOn87ssPUBchrOPO3fB1xe3Cc5FgTKTCIBCTI29GLVzE96q/xaahjQVGAJMpEmyhgwDJo+G6+GxhbbVA4Fb5u3OBYXQkSQKFQqHTAjrE4SVzGVIY8wYulbRggULBPsIgoCpFPlMTwrcHD5vrfUplQQJZQm5yNAWVXDwunmh1TImAMdup6NKgDtLBiMKzgL/jxf/QN3AurycMUKZhxNpBxHrF4tgj2D8e0i4qLK0DM/uXRctYvwxsk00qltUS1dzws4+3XIZXevYF/JbNnAollUK1AnzxqUHOdCG/gW1zzmsvpqL12s/uZCyHtE9sOziMp43lzIJw4e6hL+C9VfCoNCa33/GiNIg3AfnkrLLxLDGeKXEFKBgbx088z2RZ8iD2pdOZOcu3me1mgUAuJZxDVtui7NBBWgDpAs3l4QwVjQlplFwI5x47QQWnF6ANdfM0Qk1/e0n7LH0xLxUvzI2i6QOWc5DAR78sPJK3loMahaJaRsu8NrtZeV7luEqGe+wErNo0aJSXeh5w+5J7RC/gI6xFEv2LA261A4GxFM9WBxPOY7jKcdF951WE7jn5YlPA8WTIuOKi6EA8GJePsaG2F7Y2sKh9zujxz+2+7229TWcGHxCcv+B9ztgy7kUTP6L/vKda9FjC+HUgrG2YOfGpJMiSeOWh4b76fgW0/B27GcFoYBCwtvjajCsMibKhGKjbdrpATWkFU8fjQ8ODTyEbH02PNW2mdbE7m+TaiSW//cIlMEPA2PGYGOWOEkFo9RQFIXhjTtj0/2quJV9i9dHSSh5yg7pdh/HU66gX41+AIQWsMziTMSvi7c65svpl/ljtyOp9veRzWE8dx/YyB2/8JMM54J8ikkTzPkOtqmJuYtPS0Vi+YimuJKSg71XH+LbvVaobTmP8Zs738SkxpMwIHYAFFCChImtXzKx8US0rtwaY1JPY9sFfs0Yy5wWL40KBEFges/agsupOSHLSZmFuPtYhKff6TA/D+/EvYMbAV5494+z7DvsjHw/R2Be2JsXWpZJ7wAQ5VUTCs0DqLzMi1iNUoMvT36JguC9aOH/IvK1OtzNLZ0nhvFAiSlAWpUK7zd9H++vPwNt6EbBfgbcnJgf43/E6N2j2e1d/XdJRk8oNbRnr8KFkylUUClUgmfCKtW/BSznt6pRt4GTjGHV/MLRERhmxVAlwfhqibgIX7vH8qzCVTLe4bdo2DDHwzqeR3BpN9/49T/RpGExtKkWCEgYcW3FUbet3BaHkqW549/0dQMgzeoyOCcPg5GHwaHCiTi7OBs+Gn6Rq251QrD9knRBNe7kbg0FRitWR9CF5fo1DkffRpV5wqa2iHfAFsRC+zrV5CtsKoWCt2i2VlTsSYLxxAC22cWahzZHjxjrdKC+Wl/4an2t9ll+cTmWXliK3lV7A6A9VCqvi/Crsh4fnCyEZ1XAkNUYiaTZk2bpiWET6xm2HhGFSEko0Ti4MfLvjAFIDSgv2ltjWdSSgT0LAOa3VilU0Kl0MJlUsIeTjTsxZSsUKFAw45chwzrqh1svBMh99i1p6rVqJRpG+mHdaRtePI4ynm/Ix9pra9G/Rn+QFllbBtKAtPw0fPRSKPw93HDqbiauptL5OcylZ/eugxN3MtCzfiikYJkz2GH+fuvjcwY437H/P/3RIqg7gPZAGUKxygLG+8At7KtweyTo99OVz+ARA+gzzWHSWpUWmcWZSC14gNeb+GLjzRLq5VJ4YmYcpWn1DyYdxDcdv8H4fePZfQRB4OXqL2NilptVJYZLgvLHVX5ulVqhtulpqWieGAaWhi6ud2Vtz7UYsGWAoACyWF+AKVVRMqdxHv/KXpXBVWKkCC5+GNIYb680F5G2h8lVRungsDkjMTHR6p8MITLy9XjvT/HiUpZYODBOeqeN3Jp+NfqxuR8AcGrIKbsX4F3zaOtajoLAea2Qeet/5/4naGsSZZ1mOL2w7AQQBQazgmO5+G0RE4Cv+zfAxjGtLA+TxIEkYWJZgCf/+6qUBE/ZkSJKeNJQK9Ts5J1vsG4NNZicE4OrN+mRq89FodHMggbCxGNFU/uexumctex27QC+NVdBKOCh9oC72h3XMq6haUhTwXWUhBI+Gh+QRZEg9cEAQY+fUdwsQ94kQx44YJSmEI8QnBx8EicG8y2nc0Ril+kDzb/9Rk7opazEuBZ79uxBz549UbVqVVStWhU9e/bE7t27y3tYdmHjmFaY0qUGXm9hP1OnVNG7plG26IP5i7VrmdeQaxDS3iakJSB+XTy6b+yKT3vXZan+AbMsHdoyCkteaySZSwiUT1KyZf7n8Uf/luygv7vCjtpmzgQrb7hKjBXPrpsfPxaJMcZQoFgaeEdZvizRMbIjzg89jwmN6EKaXOMRWUzndM5pO0dwHHcu35+03zzGEmNTpFckPmg6DTqDsIp6Zc/KCNQFCtrLE3ez76L3pt5Yc20NBsYOBADU8KvBmzNqBdTChWEXMLrBaNFzWHpxuAZDLiwNdFKvTbe6Ibz3W87rp+EKGe+wJIiKikJ0dLTknwxxbDr7wGafv95uaaMQkvVl1MYbG3lUinqT3m6rSR29HrfVKvzgK25JtLRyXHp8Cfmk9foH9jJ4RXlHibb/fOFnNF/dnFdPxBL9GoejYaRza7Y8zC3mhZ0lpCVY6f1koVHRv68t71XCwwQcSrJS0dlOMNZCPkOQ+HMYoA0AALxai18PIEAXgOOvHcfRV49i+93tAusfYJ5E5vSth2ZR/vDS0fefCaEL0tFJ0Aytsj05STNbzuRtWy7UIvxsewopzuQjKzGuw/fff49u3brBy8sLEyZMwIQJE+Dt7Y0ePXpgyZIl5T08m2gY6YdxnapbVQYAvlFGyor7UgPrTEqWhQwBcc/E8kvL2c8kRSIt2yyPHaFILh9qWPHQE8agXVYFwFGIeWLcNfRv3atqL6vHUhTF3kOSIq2GhDkKnmePUOCPq3/ALWAvCHUOACDcM1xwjFSuCCODgz2CMaT2YOwY/A1+7/47+lXvx/ZJzku2K4fyScJEmXA7+zZ8Nb74pMUnOPv6WaztudahkEPLEDl+xIb58/3c+7x+sSHS0SBiRoPnGa6S8Q4rMWfOnEFCQgL7d+LECfzwww+oUaMG/vrrr1IPRAY/x8NevJmVzX6+mXWTZzEf8M8AAYOJFKIMRiz288XvPuIvJXfhmZKXgkFbB+Hnu6Mkz7drYjurfPRctKncRrR9cQJdJGvmkZmi+6VweNBhu4pRuqvEF7G5RQae0rbv/j6Hru9KcJVUbuXhYbWFYZ7rb6wv8/UYyxP3fvjoxCdfZmEhxcanN+l5xSgt913LuIbThd+gRdNjKCxR0pjfiJnwmeMtF23Lui6j2xUqdpL211q3aJN2sAbypjJ5HnIZvvjiCyxcuBB//PEHxo8fj/Hjx2P16tVYuHAhvvjii/IentNgpMwyUcrDoVQQmNJFWAfLDOEC311tXSEnQcKLQzpgyzKckpeCxBz7Iys87KTHF8OV2d0EbaQhAKYiPoHA3bkvok11+p1+0kqM2RNDASAxpEUkmOjuN+q+gfND+XTJPiq+IsosqEmKLJMnpmsUXV9oWrNpAIB/7/zLzpMKQoFfLvwCTaWdIBQ0E6nY4llKCQl254eR++l8EVcpDrH+fDZQnle+AoD5bZj1hlKhdFhBZIxlDFILxMPkr2de5203LKkRI4YaweZcU9kT4zoZ77AS06BBA95fkyZNMGrUKMyfPx/ffPNNqQciAyjQ22ZnWDCgAWqGmPNtJmRmY3Y9mvUhOS+Zl8SelGc/Q9IKby+7Lc226AoBoHqwF4qM9nlibNE3O1onwEfjg06RnST3M+FhUqF2JMVfiO9J3OPQ9V2J/jX6Y3id4agXWA/NQui461i/WIyoO0LQ1xkTPXfyNbeJ/x4MpaUYO15qfiqar2qO5ReXC/YBtLcmvTAdO+7uwNHkoyw9LDMZVfWpiilNpmBonaG8dgbVfatjVL1ReLPem+yYmcVCZlEm2v7ZFq3/4FNJH7ohjGm3BtkT4zpkZWWhWzfhYrZLly7Izs4WOeLpBFeuWAvTGtOhGqZ2jUUDsRwbC0/Me43eAwC8UMVK3iUFeHOqi2cUSNPtUxSFLuu74MWNLyJPb08WGbDejpDe+Frii2ex+mCU0Qf6jLaCdsaL8aQT+zVKDWr610TdgLq49H9d8FmferyxEASBnf12sv2r+psVMIIgeHmB2/ttx9nXz6JuoJAF0xaYGlxJufTczq1K37pya4HMF8splArFHV2fDrXKKsrCb5d+w8rLKwE8eYXRUTDfJ8+Qh4NJBwXeEntgaXjLKc7h7DTfQzcF32NjzaOZwvF8yp4Y18l4p0mC2NhY/Pef9ZogMqzDHm29b6NwbH+vHa/NGS9IDb3B7kWavUUUl11cJmgTs45LKSlM7gTXnW0PCgwF5qrDIgh2D0aUdxSvQBwXBPiCnktfWt4Y13AcJjeZjMHbBuPvWzS3epRPFHpsECbxOyNcwVIhAACp9UOrMHohk5rPt2Ll6HPwwroXeFZoLkI8QuCudmefY64Hz0NF56REeEdgWJ1hvHoYXBxKPoTxjcZjbNxYlpXsx/M/svuzirOQo88BVxV5nCe9kNvnrsMRnZb3ZB7RiRcyk1F29OrVCxs3CpORN2/ejJ49rde1eJrANQZYU2IUCgJjO1bDEJEcG1IfiKlNpuKz1p/h2KvHMLIeXVDYmlwmQSKY4+n/8YC0IYr7/lmrNcMgxFuLmlbCahiMbi9No28vmPv3pBfW7mp3/PXSX/ij5x/w0NDecEuvcLCH2ZPBzHOtK9OGE+a34YaSlUYRY5jDVl6hFQzmHO82fBcvVHlBsBYQu8bRZHEWyUUJiwDQsnL+qfn48r8vMeXAFKy6sortE6QLsun1e9LgeuXH7hmLPfccNzpazk18pUbJrkUs76e1NVtukfOr0z/NcJWMd5jiIycnh7dNURRSUlIwa9YsVK9evdQDeRbxZpto/Hz4jt39bSoRIuEvP/l449vz5njCaJ9o3Mm2/5oMSAKg7FWGRLq93b4qfjjAp809lXqKt61RavBG3Tcw/9R8Xvvmm5tFOf9DPWjGHG7FZHvQfHVzq/vfbvA2RtUfxbqQLfN9CKLi0Uhaw5mHZ0RzZJzBIsOcg0sUIFV0lclT0SodC4uM8KILd5orRRPoU60PsoqzEO4ljOkWw0eHP0LnyM68CfZW1i3eeWlQYB5ghUKP02mn0bBSQ16fLGMBxgfTz9y4zCy2fa2XJ17PESZQyyg7ateujc8//xz79+9Hy5YtAQDHjx/HkSNHMHnyZJ6Xf/z48VKnqfDgKTF2yNuudUMwdR0/VIky+mJonRcFfbkL+5F1R2LNtTUsiyFJkRjXqRoOXLftfeQabexRFpgaXkoFARMpPYs5wtBEqDPgxiks2b9GfwDAu43exeDCwagVUMvuc7kKjBLDkAwoCAVUChWMpJGVx4y3n/VoS+T6OApGKeLm2gDm36swaTBMhRGo7idck11MNxcqn95iOj49/ikAs5eHm2+44+4O3rEVcV609CyVRjm0nCstDauMR4ukSEx6oQYW7Lpeci3Zw2IvXCXjHVZifH19Bdo+RVGIiIjAn3/+6ejpnml82KOWY0pMKeJVvvX35W23DmtdKiUmXam02xNjSbUMADsvC2NILa0bAdoA1sLxUsxLqOJdBd+d/Y5NVpeCvZ4fezFyx0hcSr+EJZ2XwNvNG+/sfgdq33gYslrwrlfVh65nIsamVV5ISEvgVSIGgIcF4tZSZ1grmUlLT5q9FqG+GtwSeViY39bSAyRFk8yAud/M5JNemI5mIc3go/Gx6mWc3Wo2SznKRY/oHth2ZxsbbsdLftXdB1lIW7f/K56Dndtv4qPmH+HVmmYyghxOGGQ657u8VgoF5qUGYcA1hw977vDLL7/Az88Ply9fxuXL5iK0vr6++OWXX9htgiCeaiWGuzgqNNgOH/bWqtGuRhCvKKYUmOfcV+OLlmEt8ec183xMURSCvewzLnCVGEcWhH+93RIzNl/ExeQcwb4v+9UTDRuTwsIhIZh+3BwOvSdxD6Y0mYIGQQ3sPoezMeXAFFx8fBGb+2yGRqlB76q9oSf1rLcYoA11RtKIobWHYnT90awhrnNkZ1T2rIxGwY3w8uaXUWgsxNIuS1kDjr3Y2W8nNt7cyLJwMbJ1251taBve1hy2ZvQAZfQVVTq+6/Qdxu0dh8G1BmNA7ADEVYrD+uvrWeYuywV9JR0dBviw8OEzq8QAtJeJmVujfKLAVtsj9DiRStexM1EmjGwTzSoxGpXTgpmeebhKxjusxOzbx09yVigUCAoKQrVq1aBSPVnu9ooOR6kpPW1Ue7YHjJtZDIMKjPjTXfwahYT9qkKsXyx29tuJY7czMOnKXQDA7Uf5qB3qjcspOfigG51YbSkMX6v1GmsxIggC3539DgBw/hHf0sjg3zs0reaexD2iOR+lgZfai7WgGUkjVlxegTxDHrShm8xKDAEUGYvQLbobNt/cjK/afeWUazsDS84uwcnUkwjQBiC9yDqFtTPCyRpVaoTpLaYj3Cscb9esgzX/3cfLzarhbl4M8gx5aBrSFB8f/hgkRbL01csuLsPExhPZc9gKd2SZf0qewMziTHx0+CMEuwdjd39p+sWXq7+MRQmLWAsiM5nF+MQAMId0cJUoj6j/IffKXPo6Jrqg4IYbG3hKDBd/+JhDDh+pHL+fn/auA8xz+LDnDnfuOG54eRrBzy0zt88QKTLJwFKBIdSPsfLySoR4hCC+irngq4faA74aX7xU9SVolBoeDTtJkQjkKDG/DJOm3i+tB7dRpB/+HtsGMR9t47UnTH8B/h5ugryDEx91hq+7+LXUKr7MyCjKQHphermGMjFeif3396NrVFdMbzld0MdN4YZ85CPYPRjV/Kqx7a0rt2ZDyx7kPaA9NaUwWoZ6hmJM3Bh2m5Gtd7LvYPPNzWbDFUEJijgzaB/RHheGmYtx1vCrgQ+bf8huW/7+LcJaYFidYfjp/E8sS2RFgrOUGF+tL6vEjI0biz+30uuSqEAluDOth0aFC7O6QKVQyLkuDsBVMt7hX7t9+/a8v7Zt26JmzZouVWDu3r2LkSNHIjo6GjqdDlWrVsXMmTOh10vHtANAhw4dQBAE7+/tt9922TjtxYjWUaLtlX1txdyXLbVYSoEBACMhRWophEqhQqhnKAK1/ETNv8e1xsGpHfFOBzr2eVgdPmNWuFc4fr7wM923JJ/DGpgQAsai7gyYKBOuZlwFACw9v5StVu+jNn+XHvVCkWfIw5KzS5Ccl4wAXYDTrl9WMOxkExpN4E1EYnCGJybGNwYDYgegVVgr1AnzwezeddEwNBYvV38Zr9d+HTX9a2J9r/XY2Fu6uJot9ZgJ67OcEMQIArhYc3UNq8AA5smLofZmJjfupGYqFiYXW7LoSeVoFZRicpQnORlccJUY7qNRrZKnSG8ajSwYkJTaB/jyvy95uQoAMKPlDBwadAip+al4/V9zeO6nrT+FVqWFu5sKPw9tgm9fbYjOtYQFjRlwQ4gcqXoOCAt4AuboY8t3IdhbC02JYWD1KH4IsBh5SJGpCL9c+AVzTsxhQ0XLA9ZCXJlQrNVXV+PrU1/jUvolQR+WncwZFMsc2aogFOw5dRHLUL/JSpYAwBFYKjFqhRo1/Gpgfvv5+KDZB2UbsAvgpnTDOw3eYbdLO+91rdKV/UyAwOzedeClUWFGb3PoIvP+emnVDnkWZbgOpVJZb926hXfffRfx8fGIj4/H+PHjceuW64TK1atXQZIkfvzxR1y6dAkLFy7EDz/8gI8++sjmsaNGjUJKSgr7N29e+ZtFLSvEVwRQNhaaXPrFXH0udt7diV13+V45lVKByACzpaxDeAfe/s6RnSUpdnljoSg8LHgoqPTuDHAXERfTL2LjTXrx3TDQrCjp1Eq235NmwbEFZlGRlJeEu9l3Bfu5VrryooYeXGswb9tyUmweal6wfNjsQyzsuBCA4/f6sxOf8baZyYtRlNdcWwOAv3iijOZFGRPqUS+QX/TSTWISXOXDJ4L48fXGcJMIJ+gdF4ae9UPh7QTvqoxnAxqlxsKTYH4uowKklQUfDqvYP+Pa4LOXaa+N2GLtv9T/sOveLgDAqzVfxabem9Ajuger0MfXDrZZh4ZrtLFFU24PlErbinyrqoGs8YuGUObrTXpsv7sdq6+uFpCHPAlU8abDUIuNxSApEkeTj+J4ynFe+N389vPxa7dfcT3zOn699CtuZ9EECmcfnsWaq2tw7tE5c3K/E9kjAXrhHeYRhkivSBAKE27nXYTeZN3QKwbLGlwFxgKceXgG1zIqZlysWqHGmLgx6BDRAYB9hZDFkJhrphTPLs7G0JZRODuzC2qG8MMFZVQsOLxC27FjB2rXro2TJ0+ifv36qF+/Pk6cOIE6depg165drhgjunXrhuXLl6NLly6IiYlBr169MGXKFGzYsMHmse7u7ggJCWH/vL1ts6i4GnERvjgyrRP2T+ng2IEiSTODnJRoXLtYj7mP0rE7MVl0f1pBGrqtp5mhbmTewOQDk7Ep5VMw/ht3EatEhLcw3pcJzfqoubQCOuvYLHT+qzMbGmeP4mMvpBiyFJwJRUGYWWRKKxBdBUaJ+en8T3hp00uC/VwFwlFWNzGk5KXg5ws/8+oEnX90Hv879z90XdcV7+55FwlpCSgwFODUkFNY23MtPmjKt9ZZTopLX1jKete4v62vxhfxkfHwUtPKglRRNilIeT24Ew+3UCDDpmZpWQ1Q2Wd97lonBFqJELPFgxriu9caVVhPzJIlSxAVFQWtVovmzZvj5MmTkn2XLl2Ktm3bws/PD35+foiPjxf0Hz58uMDrLUan+TyjXXg7eLuZ5x/uoxHhbx/zXb1wH3iUFFkUs+RvurmJ/RzuGY6bWTcx7795dtfsAvg1M7jV3UsLewgMACCVV4xT3BPDfI/ykMtMPkixqRhG0ojRu0dj1M5RKDYWs30aVmqIxsGN2b6MAWfXvV347MRn2Ju416meGG55AiWhxLedv8XWvlvNz1kpxI9lDa7zj85j6L9DMWjrINzPcZy++EmBWyemNOAqlQzZgVJBsL+XTqVDt2j7ZVqgZ/kqPM+LjHdYiZk2bRomTpyIEydOYMGCBViwYAFOnDiB9957Dx988ORcjdnZ2fD3t20lWrVqFQIDA1G3bl18+OGHKCiwXun8ScBLq0ZlXx2iAh1z1Ysh0ES/YK3DWmNjL+mQHgb1iopF25sVFcGXJBFsklYYkvNoBYevVNCTzccvCtli1lxdI2hrG94WCUMSJHMQADpHgQup2iKlgSUTGYODKeY4bqWCYJWdisaRz4S/SeF06mn2s6VHpDRIyU/B4oTF+OLEF7iZSeeQnHt0Dt+f/R4P8h9gf9J+DNs+DM1XN0dmUSZqBdSyuXAnCIIt1MldXEX7RGNhx4WY2nQqAOmibLaw65VdGBg7ECdeo5MxudZKpfs9AICbSiFKHw0A6gr2mzsba9aswaRJkzBz5kwkJCSgQYMG6Nq1Kx4+FCeI2L9/P1599VXs27cPx44dQ0REBLp06YLkZL7Bo1u3bjyv9x9//CF6vucVmUWZkvusvTOVLBLypYolzjkxhxeme/TBUUw5MAVrrq0RpbuXAjd35fuz3wOwJ9RZGvbq8VymJzEWr2JjsXmhWg7vKMO6yCgxDLjyZcONDXh92+t4VEDnVjALalbWkCY2ssBSWSgNhtYZiiG1htDn4yh2zG9YGlIcpUKJMA+zt4753kbSiC//+7Isw3UZZhyZgcPJhwGUXsHlvoO+Gl/2c2lpvX8e1gTubkrMfEk6381VeJ5kvMNKzJUrVzBy5EhB+xtvvMFjHHAlbt68iW+//RajR4+22u+1117DypUrsW/fPnz44Yf4/fffMWTIEKvHFBcXIycnh/3Lza3YlKrMdHPkwRHMPzUfnSLMRR4jvSIF/T97LJ4M3qaQtoLdtyN5mS8Y6RGIUQ1KeT0YyzwjKOoGOF70SwpS1eIZSCkxRopDIUwQZbbquAqebnwlZkZLPjsXQRD4rdtv+DH+R1EWOUfBZY6b+x+dEC91D62Fg63ovoL93Pmvzth2m1YaF5xegCVnl/D6MuEZloXF7EWIRwg+afEJL3SnTeU2vD4jWkexi0rLcLcdWeJyrF1BxapUXVosWLAAo0aNwogRI1C7dm388MMPcHd3x7Jl4gvdVatWYcyYMYiLi0PNmjXx888/gyRJ7NnDr8eg0Wh4Xm8/P78n8XUcRnnJ+FNpp3jFI2ODaY+jrZDDD7rXRKealfDDkMYA+LVGuLCsZ3XkwRH2sy25yIWYAhHqI81sNq27tMe0d1wY3N3sW1Sm5Zg9MWIypshUZK7NUs6emJT8FLad+zvMPDoTZx+dxd2cuwDMigqzQObRVztpbuGGPlMUBYPJwM69pQ2H3vHKDsxuNRudIzujhn8Ntr2iRSYw2H9/PwBgbtu5vNwWR3DxsZl6mvv8sVTaDt7LuAhfXJjVFSNaR5dqPGXB8yTjHX7Cg4KCcPbsWUH72bNnUamSY5bTadOmCdxTln9Xr17lHZOcnIxu3bqhf//+GDVqlNXzv/XWW+jatSvq1auHwYMHY8WKFdi4caPV/J05c+bAx8eH/atd+8lr0Y4g1Gi2IpsoE48XnhvjybappJlnvvD3w4DKoTavybMalrj9zycJK65KLXYZHBx4EAlDEvBHT7423zqstcQRtkGBQvMQYZ2YT1t/ioMDD+LwoMN2nYdZKDij1oozYemJ6RrVFb91+43ddlO4oVFwI7SqbLuCtj3ILjb/rkxelNSCyJrVz8fNrFA9LHjIU3AfFz4GABQaC3Ep/RI7mdi69+82fBfebt6I9onGqHrWZQHzewLAH6NaYGqXWOxOpJnPFics5vX9/eFxiMF5QY3OR25uLm9hXlws7nHV6/U4ffo04uPNrFYKhQLx8fE4duyY6DGWKCgogMFgEHjC9+/fj0qVKiE2NhbvvPMO0tOts+eVF8pTxnPp0RmWo5Mfx1s5AvD3cMOy4U3RrS7tvZTyxFhbZHELMdqC2PstpQLtn9IBo9vFiO4b0iISiwc1tPu6gZ5mo4UYlW+xqbhccxW5npg+m/uw7dYs9Cy5SMlSi0tV7wxvUr4hn32mCILAO3veQaOVjVBoLORdtzR4ufrLWNRxEV6KMYctV7QcUQa5etoQMe3QNEH4sr3gzq3FJrP8JEn6mcvR52DttbUOndNRhlprkGW8OBxWq0eNGoW33noLt2/fRqtW9ELpyJEj+PLLLzFp0iSHzjV58mQMHz7cap+YGLOAfPDgATp27IhWrVrhp59+cnToaN6cXtzevHkTVauKVw/+8MMPed8jOTm5QisyvfPyoYz/P3x47hscTzluM5Fvs5d4CNtFNzfcdFMjT2FbSIl5YsT40m0pMQRBiAocR+K3LaEgFPi5688Y8M8AXMm4wrZHeUfBT0tbDSp7VmZD49j9XlXB5fqSWiiUN7L1fGVRq9SiUXAjRHlH4W7OXafz+McFxbGfmd9FKkfJmpVuwekFvO2BsQPZxPt119dhZsuZuJt9F4O2DAJAk0DUCaxjdWxv1X8Lb9V/y+Z3AIBjKWbh3bKqdbY5g8T3M1XM9BYAEMiomTNnYtasWYJ+jx8/hslkQnAwf1EbHBwsMBhJ4YMPPkBYWBhvkuzWrRv69u2L6Oho3Lp1Cx999BG6d++OY8eOQakUf4c6duxYqpyh4cOHY+jQoQ4fx6A8ZbylguCldXzBJSWbrBkRLL001iCqxIi09Y4LsxoSvfJ4Ij7rwyfNGN0+Bj8euI0vXq4n6K/kzD0vVHkB/4v/H97ZTbNOKQgFjKSx1KE9zsD/tfo/kBQJX60vPj78MdtuTe5ZhpMpoMDkxpNhpIylXmxz8dP5n1giBwWhgJqwOGcpZRZFUaBAgQDB8xhVVCXGGYj0jkTCwwQAfGZMBee5dGZ+rqOQZbw4HFZipk+fDi8vL3z99df48EOaWzwsLAyzZs1yuAhZUFAQgoLs4x1PTk5Gx44d0bhxYyxfvpz3YNkLxoMUGirtbdBoNNBozCE0OTnCwl2ugH2JnbYt4FzKWTEUSTxQG7w87OIAM5EmoSeGArQi1ZjtDWGgKIp3ToYityywDGXjFkkL0AYIlBgQJszv3wABHrQSQFIkdCodL8m1ImBk3ZHI0+expAe/X/4dixIWsfu5sbzOADcki2EtkmKLsxYewdSQYVAnQKigMIsBf60/FnVc5OhQnYYWXtG4UihkPzqvqbjMNJcvX0blyuYkX42Lxjp37lz8+eef2L9/P7Rac4jRoEGD2M/16tVD/fr1UbVqVezfvx+dO3cWPZctA5YUGjQoW8HD8pLxgG3Djj3QKDUI0gUJwkWtLTALDMJc0O13t2PD9Q34usPX8HIzM++FegrnR5VSeG5bXhaxOe3D7rUwrVtN0YWNysJqzfXEHnv1GNzV7qysKw/CDHu8WR0jOvJYIZlwMua3USqUGF53uNPGxMjMV2u+irFxYzFx30Te/tIqHR3XdkR6UTo+a/0ZzzBWFs+OK8Gdk/Yk7kHnSHGZYw2Mpw0Azxgc4xODblHdsP3udqe8v6WFLOPF4bASQxAEJk6ciIkTJ7KxxF5eXjaOKhuSk5PRoUMHVKlSBfPnz8ejR2aXfEhICNunc+fOWLFiBZo1a4Zbt25h9erV6NGjBwICAnD+/HlMnDgR7dq1Q/369V06Xkew+s3mmL/zGr7oK7RM2YMcBYEUTogCY5GXQqGE8CcJAqd0tis638u9Z2Hxo19qywkI4Fst1r20TrD/euZ19P+nP/y1/tg3wCz4zz06x+vXI7qHzXGx1yRNGLVrFG5k3mDbTg4+CSNpxOBtg0FSJF6q+hLOP+YX2DSQBrzS2MxSVSugFk4OlmbzKC+YKBNL9QnQ9Jdc+Gp9nX7NUfVGYemFpawnRkqQO2IdnXHUnMvDhMMxE255ThQAYJJQvoONFTegzMvLyy7mxcDAQCiVSqSl8WvwpKWlsbJUCvPnz8fcuXOxe/dumzI0JiYGgYGBuHnzpuQEN2zYMNH2Zxli+SaOom/1vuhbva+g3aoSYxQqMVMP0AQaX5/6GrNazWLbo334MfwkReKLl+sifsFBh8Y5uHkV0XYpBcSyxgxXiWm+ujlG1h2JeoH1kFmU6XD9micFhrYdoA01TA6jq2QbI3OZ8zL3TEWo0D+2v00iGCkw4/3kyCcsdTGAUnt2XA3umiSnuHRGifpB9fHntT8BQGAgYJ5ZR3LLnA1ZxovDYSWmsLAQFEXB3d0dXl5euHfvHn755RfUrl0bXbp0ccUYsWvXLty8eRM3b95EeDifDpV5qAwGA65du8ayj7m5uWH37t1YtGgR8vPzERERgX79+uGTTz5xyRhLi1bVArGhWmCpj1/p7Y3/XV/NbttKFrzuZraq/PogDWcq18Fi6rHd8f7HHhxD3UBuIn5JYr+IEsNYR/pW74tY/1jBfiVB12OxDHVoGtIU/6X+Z+eI+DCQBsGxOpUOBtLAhpflXc0THCfwzFRQnH14Fp+f+JzdtswbcUYyvyWYazB5JVIu9dKGeIR50kw4zESRVZyFy+mXEaQLQpC76ytEWxIIGCXehu75+aLtTxPc3NzQuHFj7NmzB3369AEANoFz3LhxksfNmzcPn3/+OXbs2IEmTaSrvTNISkpCenq6Va/38wDLRc/BpIOo4VdDonfZYM07UWiQJqUQU3C4OJl6Ei1CWzg8HjHDljVwu6+7vg7/d+z/ePt/ufiLzQK/rsSqK6uw8+5O9KraC/5af2QUZQgKMXeJ6oIYnxjUDqiNekFmw6RWpYWPxgcURWH1ldVQK9VsMeeywFI5YjwzExpNKJPHh/ssceeYpyGcrLR15bhRB9wIDIqi2CCY8jaw2YPnTcY7/ET27t0bK1bQTENZWVlo1qwZvv76a/Tu3Rv/+1/ZOeXFMHz4cDpGU+SPQVRUFCiKQocOHQAAEREROHDgANLT01FUVIQbN25g3rx5FaJOjDNh+braYg/J5YQFGAmAWXaaJEwsdbz41rQTKSdQP7A+BsYORLfIvgBFn0GsFgCz6JWK1Wbc1JZ5PIk5fEICR6oOWwqZF6q8UDI+8wL7aVFYxMC1tO0fsF+0urKzwSjGjPLiq/FFlHcUNEoN2lZuK+gnhpahLSX3MWPmhioM3DKQLVrpalg+MxfzH4j2q1Vsf15BRcakSZOwdOlS/Pbbb7hy5Qreeecd5OfnY8SIEQCAoUOHsqHCAPDll19i+vTpWLZsGaKiopCamorU1FTk5dHGgLy8PEydOhXHjx/H3bt3sWfPHvTu3RvVqlVD166lYwp6VmC5oPrh3A8uuxbz/vSq2kuw76/rf0keZxl687CAT8NqTQFyJrgMl1wiDgZi3qcnieS8ZCQ8TEBibiIbtj2yHp+ptXFwYwysOZCnwADAG3XfwOFBh/FmvTcx5+QczD0x1yljYpSKv67/hVVXVrHzvxQzqL3gzpd1Aurg9dqv09eroOFkXJSGVhoAPj32KfuZ65k8++gs/r37LwDnFt52JZ4nGe/wE5mQkIC2bemFy7p16xASEoJ79+5hxYoV+Oabb5w+QBmOwV43+4CcXNQp1kNZ8sLnSljNqur47kcDaQBBEPikxScYXWcKQNFxmWKHd4rshDlt5+Dl6i+LnpspQmipxHCT6gAIQr+swVLIdIvqVjI+BSvcGMEf5R0leZ6TKSfR9s+2GLlDSCdenmCSQY2UER8c/ICntPwQ75oFEjMxMh6zV2u+in9e/genhpzC4k6L8UP8D1jSeYnVugeftv4UU5pMYQtYNgk2W3oYZdbSyrf1zlanfg8pdIzsyNvu4SdO+X3TrWIx1ZUWAwcOxPz58zFjxgzExcXh7Nmz2L59O5sImpiYiJQUM4Xs//73P+j1erzyyisIDQ1l/+bPnw8AUCqVOH/+PHr16oUaNWpg5MiRaNy4MQ4dOuSyuO2nBZYKMpf1qLT4/uz3aPh7Q3x5kl+zo2tUV8xuNVs0/DbPwPc+c8lTLL0slx5f4m0zhChNo8TpVG9m3sSAfwYIjE9itPvWwJXdYt5eT7UnUvNTkZqf6hBRgbMgNl+dfXhW0K/IWITMokykF6YLxsnWH3MSvTJXZuYb8llZvfvebuxL3Ffq5417Xo1SgzENxmBHvx2Y0nRK2QbsIjQJMc8n9YNKly6QXmRm2uJ6S7kKdXkm9juC50nGOxxOVlBQwObA7Ny5E3379oVCoUCLFi1w7949pw9QBgci8Zi7PPjJk4E6+0LT1np7QQGgesnC/qFEfZjXKnfA3w9PiO7j8v+LhZNV96vOo3y2BDMpmCgTjKSRFcANghrw8mK4Va5tgTs5d47sjC5R5hBHJaGEkTIz3CzssBAv/00rWJaegiJTEbKKs1jqxooCrtKSa8jlMdzY+9s7ihejX0Tj4Mbw1wqLy6oVarSubJsSO9gjGCuvrERqfioG1BiAWP9YnEo7BYBDQ2qhxLQPb++E0dPoUqULdt7biTlt57BtnSM743b2bQytzWdB6RsQh7nJOwTnOKbT4s3sJ5cE7kqMGzdOMrRg//79vO27d+9aPZdOp8OOHcL7JcM1MfRG0ggjaRQYbOoF1UO9oHowkkYoCaXVBReXAMaSvMQyb8eWd5+RoS9ufJEX7uUouyz3VolZvEmKRN/NfZFryMWWl7fwcgOfBJiwU64SYxkauPT8UnxzxmzMXdljJY9UhlkQMxTIZQXXY6IgFOz8cCn9EsbvG489/feUqmgw15tBEAQ83TwFNcoqEkbWG4njKTQ1flVfcebZ0oJriHgawskYPC8y3mFPTLVq1bBp0ybcv38fO3bsYPNgHj58+MyFalU8CAX7LbfSU+qSANxLAsouc7Tpo3fvY0JGFo7fvY8ANZ+0IVefi0vpl1Dvt3rourkZQNCWptKQxTBKDMC3UFoqDjl6+xeOTNErwGxBZGDpYlcpVNjWdxvGNBiD7+O/5+2rqHViuAsKlULFG5+raEeDPYLRIKgBIrwiANCTeJGxCLezbyMhLQFp+Wk2zgD2OAAYWHMgzxPDPAeWSkw132rOGD4A4Mt2X+KfPv+gZ0xPtm1Rx0X4u8/fiKsUx+ur4oxj+mPzYk9djkmdzzoSExMlqX0TE4X1rp4WWCoE8ZHWa8LYA8ZQI+b5HLtnLNr92c6mxZhrXX6Qxw+ftPwdSuv1cJRBjHtVMZp9E2UyFx4sh7Amy7BaQMiyyFVgALMysOrKKjRf1Ry9N/d26pi4MpMAgb7V+/KYHUubw8L1FCXlJmHItiEYs3tMqcfpatQPrI91L63Dxl4bS30O7rHz/5vPfub+3h3CO5T6/M87XCXjHX7CZ8yYgSlTpiAqKgrNmzdHy5a0BXvnzp1o2ND+wlYynI9eVXuJPiQ7Ov4PdUUKI111c4NehC3Hi6LwZnYOPCgKwRq+IpBRlIFio/lcrWPpBWjfhnzCBQA4knwE3yR8g4NJ4qw2XOpGrmWwtC5bg8mAT46YiRv+vvk3r0K2JdRKNcI9wzG87nBBcmtmMV3N3Vluf2eBq7SoFWp+0mUpaMcdxbWMa2i8sjGarmqK3pt6Y9j2YYhfFy8IbRED83sbTAbE+MZgdP3R+L9W/8dOtEHuQdjWdxtbm8aZ1aEVhAIP8h5g973dMJjoRVl6YTr2398vIIJY+/g0+9mbJNGikLaahpiejlCCpxHR0dE81kkGGRkZiI5+8hWvnQWuPB4UO8gpeR3sQt7iff8v9T8cTDqIXIO497jeb/Vw9MFRAMCNLDN7o2XYpqX8FaPtrxpkO2y5YaSvzT5SEFNiSIpkPTRPQtZZgs03IY1IGJKAY68eE9BRWxbdZWSbkTSiwFjgdEt+t6huiPSKBEArjbH+saWiF7YE1xNjIA049+gcDiUfwm+XfrNyVPnhg4Mf4JV/XsGKyytKfY5qftWwrCtd0f5gsnnNwv3NrDG/yrAOV8l4hyXBK6+8gsTERJw6dQrbt29n2zt37oyFCxeWeiAySofYYrNr+8LjCwjx4OewfNPxG4TpgvBqjnAx/1ipRB2F9ToolsY0AgROppqph+f2rYurn3ZDiA+fnpmiKLy9+20svbAUJ1LEw9G4ViJmYXko6RDu5fDDErm0lVL4+tTXaLSyEa9NT+oxab+5qF2T4CaopDO71lWECpnFmWi2qhla/9GaFVYURWHm0Zm8cVUUcJWW02mneUxxrvIanUw5iSkHpuDnCz/jlX9eEe2z8aZtCxgTjrH80nKk5KVgXMNxvEWdWqFGhFcEG/7gbM/S6N2jMXH/RLZg6H+p/+Hdve/iq/++4vUzchZxUysF4riOfkdkFcZ1sKwVxSAvL49Xq+BpA3cB9HaDt1lvpjPOaemNsIfRcfSu0QD43hXGQ3rswTEk5iRiygF+3oOfRpgLY80pefiDjljzVgvUD/e1OR6pc4qFkykIBetBKhdPDGH2xKiVatHwKoZpkQGjDLjaSw6Y7wlXcS5tkvuYOLPXhWtMspybKwr2J+0HYN88ZA2WlNUAX6mfuH+i4BgZ9sFVMr5Ups6QkBAB33SzZs0kej/fUBAA6awoFJGZo45ej2saenF4J/sOtCrzw+Cv9Uej4EZATircSaEFiAAQQ9kOR1vbcy0GbBkAAEjMTcSSs0t4+8UKXTLxqQBNmSuF+e3nIzkvGTG+MQCAMXuELmuxXAyDyQASJBuK9OulX0XPfyfnDvt5ebfl+OTwJ9h8azMAWjhz6XUNpAEapYaXBHsx/aLk2MsDlopKVd+qSBiSAKVC6TL6y+S8ZOy4uwM77krHxNpzbcYTs+PuDrxS4xXRonqAedJwtifGXeWOAmMBzbakA6YepOtkcGsKAcDPaUdFz/H0REM/PZg0iTYyEASB6dOnw93dTHNqMplw4sQJxMXFldPonIPKnpVBURRG7xqNa5nX8OeLf6JOoLDYq6OwfOccCd/ietP1Jj3OPTqHt3a9Jdq3YSXHIizC/dwR7uduu6MFuItv5nO78HaY3mI6KrlXgoJQ4K9rNMtaeVD9MvJIb9Lj+7PfQ0koMazOMN6cK/WbuHK8jMJHEASOPTjGKyxc2qKg049MZz/3iO6BVVdWAXg6KJbLAub7ccMtnUHG8TzD1TLeeasEGeUCS7WmSXATqAgVagfUxuJOi0vqhqSiWVGR4NjeeXlQawn4mkzIUkpbimoF1JLcJxX6xVi7ASA1X1j9nEH9wPqI8o5CdnG2ZI0TPclnL6MoivW6nHjtBI/f3RJftPmC/Xw98zqrwAA0dz93oWww0UpMkVF4ryoKAt35yfsFhgJ0+qsTqvpWxaoeq1xyTXuUCXsmN64CJubhyi7OxqT9k1hPnyOEDvZAq9KiwFggCB20zJXKMokn3ZLlUCX8WceZM2cA0O/0hQsX4MbJ8XNzc0ODBg0wZUrFZESyB+5qd2zvtx13s+/ipU0vAQB2J+4ukxLDWIktF6hS3oldr+zC2mtrsfTCUsRHxsNEmvDBoQ/Y/XqTHglpCaLHNg1pyin0xx2D0KBW1veVe8oOER0Q4hGCCK8IXnQBk2NUHovpGn41MDB2IKr7VsdnJz4DAAyuNZjXR6DEuNgTcyLlBLbc3sJe63L6ZVbhAErvseIu3Lnzcmk9O08LxDwx3GgOy7pAMmzD1TJeVmKeclhOJRcfX8SZoWcsWgl4kxS+zCrEB750aMzbmdl4MysHCCKg5cwejUSUHWt4WPAQUT5RwvZ8c60BbviZJT458glOpp5EZc/K2N5vu2gfS/aofIO56OCJlBPoGNkRHzb7EHNOzrE8lDepfHDwA94+D7UHL/aaUch23dslOd7yBlcRqOZbDVtub0G+IR/nH9lPQ+0ouEqMVqlFkUn4jNgzWXKJHLzcvAT7TZSJ96w4Ox+JuX5pE5VlT4zzsW/fPgDAiBEjsHjx4meWHIbJRQGEbGCOgg0ns1gwiy3sJzSagBCPEPaaJ1JP4GbWTV6fYlOx5CI7LigOSblJCPcK5yku3OgCpvBjtE/Zcpe4IWRizJYURUl+9yeBpiFN0TSkKQoMBawSY2ngsbyPzDhdlcOTmGtOiG4c0hinUk/x9pfWE8MFV7ZXVE/MC1VecMq8zXpiJIyzTwvFckWCq2V8xXwiZdgNSyWGIAhsurkJ9X6rh7F7xvIs3hpO5xfyC8Ash7WcGcnfJL5UY2JkLTnYpaz0XI+HNTDHJ+clY8LeCbx9Q2oNwbqX1qFPtT68di4hgL+ODjUTU2BifGJQL9BcdExsoua2MYtb7mRc1onZFWAS34fVGcZb9FvzeJUF3N9YTIEB7Jssub+b2GRoqQh9cvgTQZ+ygFtc1bKYHnd7d+13UU3P9/4BshLjSixfvhze3t64efMmduzYgcISMgVXUBQ/STALb66cKSvrXu9qvfFFmy8ETGdi76Cnms7bYFizcvW5AtZGvUkvamjy0fhg6YWluJpxlf4unH1chYORkWUtBNinYWUAQPVK/FyT9dfXo/2a9ph1bJbo9Z80Hhc+Zj9bGlqaBDfBog6LBMe4yhPDnLddeDvUCajj1BBcBsdTzaHhFVWJYXJDuayXpQHz/bieGC4L4NMuj8oTrpLxsifmKYfYz783cS8A4GDSQRhIA6uscCliPbkvKecsAhrZku3R9UejVVgrBLsH44V1L9gcV+vKrXE987rNftx8mb3397Kfv2z7JXrECIu2AfRiNNonGney7wgKZTL4qv1X6FqlK29i5044cs7opAABAABJREFUDIsMQRBQKVQwkkaYSBNy9Dk8iufqvtJ1bsoDJtKEs4/OAqAXH9zJUYzRxxmwZwK2p8+IuiPYxZLYZGi5CGsa0tTOEdoH7gRlGaJ4M+smYv1jAQDBai9sTE7Ft74++MmPDqWIMBgQIKHgyyg7MjIy0L9/f+zbtw8EQeDGjRuIiYnByJEj4efnh6+//rq8h1gqZBVnod2adry2staxqBtYl0fowYD7TsX4xICkSDQLbYah/5o92fUC6yEpN4l3nJ7U8/IoGFT2rIzs4mxRz2WjSFoRyizKxOk0ms0vvTBd0M8RtKoaiL2T2yPMV4fjKcdxMuUkGgQ1QJGpCBlFGcg35GN0/dHIN+S7rCaWNTwseIirGVdx7MExts1S7oV6hiLUMxS/d/8dBtLAEjm4avFvuehmPPVtK7fFwo4LeTmfpUVWUVaZz+FqbLixAQDY2mOlhZgnhqswX3h8QXCMDPvgKhlfMdVqGeIQ0Vg1Fm0ECN5LxxWel9Tmz55soj+BxkXm+Nd/PcWpMxWEAg2CGvAsPQ0rNZTMRwn3NFMutwprJdoHAC6nXxZt7xDRATn6HKy9thZ/Xf9LsF+rpJMpi03Fopq8AgrBopixqKgVarSp3EbQbqSMaP1Ha0w+MJndZ61YZ3mAx+hGGniUns4IHRCDPdY9e67dpnIbBLvTFYPFJnXLBcGsVrPsG6CdYDw9JEUK8p64rELv39uEYaGV0KmgAIvTHuHwvfvYlpSCaRmZTh2PDDPee+89qNVqJCYm8hI/Bw4cyGPBfNogxbLlCnDfnx9f+BF/9/kbbgo3nHloDi++8PiCoO6WVOIyE4LGKDFcMTu7V132fAycQR8cE+QJrVqJU6mnsPTCUhx5cIT9Xrn6XDzIe4AcfU65WMRPpZ7C2D1j2RwUQPq3jKsUh6YhTdn50VFyBHvB3JvDyYdxLeMaq8QQBAGNUuOUOYEb+ltRPTEfNKVDxd+s92aZzuPt5o0OER146wPuO1zaUGQZrpPxsifmKceM9EwQdfthbeJOAMLEOwWhYHmSuxca8X2JPPJgQsgIAlMysrDGW5ijwAVFUai/whxKpiAUWNFdmpOdy2Q1uclkyX5S0Cg1eJz3GJ8e/xTuKnf0r9Gf3VdgKMCVjCsAaJYdbmJ2s5BmmNxkMip7VhackxHABtKAKxlXaOY2lCzSTRCEGAFlty46G9xJyUfjgyo+5qrVYlSozoBYUb2eMT0xuNZgFBoLseDUAgG1qBSYxYdoOJlFm4+bONFDacHEpZMgeZY2bpVxALhUkIJErRYGgkCnAudU1pZhHTt37sSOHTsQHs6vN1W9enXcu1cxaV3tgdjC3lrtKnuw+95uXE6/jJZhLXneSh+ND6J9otmkeEDcAFFgMBNb7HplF/y1/mi8sjEAoHZAbdawxBzLyMW4CF+cvZ9FX8udXixzZY6jFd31Jj0KjYVQK9QCYxg394WRCwWGAja36PM2nzt0LWfAnhy9axnXsOnmJoR5huH12q+z7VW8q6Bt5bY4lHzIqWPizgdnHp6Bh5o2QjrTK++j8cGijotw7MExlyljZUXb8LY2SX7sQahnKL7t9C2vTQ4hcw5cJeNlJeYZQPOAeqwSY2n54wo5xmvjRlLgimN77Gfc83zU/CNBnoolmEXivHbzUMOvhmS/jhEdse/+PkG7UqE05zBYhP5ww72KTEU8gf1tp28lBRl3EuIuzL9uT7sxxUIU1l5fi49bfCw5/vKAp9oTeYY81AusB7VCjc29N8NIGcsswKUQoAsQtHWK7MSGtPzR8w+7zrPr3i48LKQJH8TCHARsS062+q3usRoEQUCtUPPi2rut74Yv2nzBKrUkQ1lasp8CwDxhrqnEIyM/P59nnWOQkZEBjUYjcsTTAbEF0K3sW1YZH23hYNJBbLy5Ee5qd54S06daH4FcFgvzZDwx8ZHxrLLjpfZCriEXM1vOxOabm0GBYsPOGIXCRyd8+rkJ66QIjb81HEg6gEn7J6FRpUb4rTu/iCJLGwyC/Q7JeckOnd/ZYOYMJgQ6yjtK0CcpLwkrr6wEAFzNuIoJjSawda+crcAA/N+XAMEqnkcfHMVLG1/CXy/9xaOALg0IgkDnyM5OKaLpSrhq/uOuqdxVrrnG8wBXyfiK6RuU4RBaBJo9JJaWP26ydJ6CXpbpFZzFIkXhsRV6ZTFE+0SjwFCA1PxUyVAEZhy2QpGCdEGS+xjXuJE08msIwGzNfzHmRZ4SY+16XIHPVWhaVW6FVpVbwV3tLlC4nF1h2Rlg7guTDxTjG2NVUSwrLAuoAqWj7tx9bzcA4P2m77N1gXjntINtqSzQqrTQKDVQEAr4anzxWzd64ZScl4xCo9njwj5fJY/cP54eaBQdiXeDpZ9VGWVD27ZtsWKF2bNLEARIksS8efPQsWPHchxZ2SAmP8pq2WUplkXobtdcXYOfzv/EknyIyUNGbqiVZqXk6GtHseuVXdCpdJjQaAI+av4RK2esMTJxv4ujzE0PC2iDRsJDIb0zc16CIFg58KhQWO37ScLSEyMWqsWdY/6+9TfLpHk7+7ZLxsSVkQRBwF/rzxJHOKu6vJE0Ys+9Pdh1b9cz75UwmAy4mnGVJbOwhCU9vwz74SoZL3tinnLMCvDH5t1vsNsCJYYj5KoaTeiZl4/YYr5n471gswdiXVKKxRWEQuv8o/NYcGoBrmRcwQ/xP6B15daCPvZSYQ6pPQSJuYm84phMLDZ3kjWQBtYzY3lubpzq2D1j4a5yx1v13xLUYuApMRKJ6PaQEZQ3Movp3AzL2HZXQSw04b3976FX1V64nnkddQLq4O0Gb4sqO1wwCyqpUAcloUSniE4swYOrcnwAmhyiUXAjNnyGuwBjqGSZJ5co2Rafvp/tSf1JYd68eejcuTNOnToFvV6P999/H5cuXUJGRgaOHDlS3sMrNcRyYrjFdMtyTkvZaiSN+OnCT3hY8BBNgpsgxCNENASKkZ9XM66i39/9EOoRiu86f4eJ+ybiYvpFLOm8BO3C27HHWjPkcPc5GsJkTday9WCgcDrVemkhoE8WMeRIGWLuZN8R9HUGmBxDgJaXzUOb4/fuv6PlHy3ZtrKiyFiEDw99CADY3HuzqAHqWcHDwofo/09/aJVa/Dfkv2deaXuScJWMlz0xTxWEL1SBgoCRswBrHtqc140rxBQA5jxKx/CcXHBxg1N8KNZgO3Ht2zPfsjkpUhMc026LtSraJxqtw/hK0Lx28wDwQ464igqz4DSSRqy5uoY3eR5POY699/civUiYy/JF2y8Q40MLYK6F8rsz32HW0Vm4neUaa5mrIFWgztng1uXh4lDSIVzNuIr1N9bjzZ22EyptKTEqhYoN3RPLwykr3j/wPl7a+BJPYWaeT24+lDmcjK/MkM92nbdyRd26dXH9+nW0adMGvXv3Rn5+Pvr27YszZ86gatWysXmVJxg5yJVlXK9fWc5puWBef309691g9om9R4z8NJEmXM+8jtvZt5GYk4iUfNqAteveLuxL3AedSgcvtZdVJaJeYD383edvdKnSBR0jHbOmWlOOxDwx5Q1Lr5aYgvCki13GVYpDh4gOvGvxiH1KucTrFtWN/cwdu72lE55WsPMBZ0217eVt6FKlS3kN6ZmBq2S87Il5miBiFbBsUSqUiPSOtGi1svoqo6VGiq+fEQK2JqDEnER8fZpPrccIY25hR71JzyYtcq0jn534DL2r9caYuDH4/uz3bLvYdQN1gQjSBeF29m2eYP73zr9IzE18agRVsHsw0grS2MnL1bAsjseAO6nbU8mZ+U2+OfMNukR1QRXvKoI+UtXInYGU/BTczbmLfH0+Hhc+xvxT81l2pQuPL7CLMHYBxYy75H9K9DvKmo2z4OPjg48/rlj5Z2UF8zw7s66JVDiZ2DsjFk7GHM/kShQZi/DixhfZ/ZtubsKmm5twfuh5EG3M5xT7BgRBINonGl93cJwe1V4lpk5AHUxrNg1zT851+BrOhOW9tIechPlNXKmIcclSDCYDq8jSAyjdOQfXGoztd2nGKIYm+nmAJWU1QRCI8I4QnatkOA5XyHhZiXnKYTmxkCSJqU2nYmrTqXaeoWyLMKmJyBoLFRfrbqwTtDFeFKVCCSWhhIky8erBWF5Tq9Kia1RXvhIjYYFimMx4Sf4lk5MlgQBgpnKuSPi7z9/IKMpAuFe47c5ORufIztiTuAcAf1K3Z5LmKo5S/Q8mHQTgmsrI3BoAefo8bL29ld239MJSjG80HoD5nWLDyUr+r3jZUc8Ozp8/L9pOEAS0Wi0iIyOfygR/f60/vm7/NUyUCe8ffN8p55QKJ7PMjwDod25wrcHILMrEtjvb4KZwQw2/GuhTrQ/cVe64nnld0jPqynBOwHpuUKAuENX9qiNIF4Ron2hE+0Tjh3M/8OqKPWkIwsnsUWJc7InJN+SzUREECJx7dA4jdowQXN9RxFWKY1kbuWx25Vlk9ElArE6MDOfAVTJeVmJcDIIgRD0ozoLlma9mXkWL0BYuuVb/Gv0FNVukXvYwzzAUGgtZ74kULjwSFo+aeXQm+lbvC4BmxjKRJp7iQoosJ7leG0B8Al5ydglbmM1X48u2s0qMSOHMMXFjrI6/POCudncZE4sYgj3Mcddj48aySgz3njtLifn0+KcAXEOowI3xt3b+Pv71kXdjB3xLilsqSt5fk+x0cRni4uLYd5ZrhWegVqsxcOBA/Pjjj9BqK55hQQruand0ieqCA/fNxSRLu7BkIHZ/AP47xRhxlAolpjWbBoqiMLftXPaY9hHtcS3jGlZfXS1a+8LeMZ5/dB4T9k1AgaEAP77wI+Iqxdn9PcTkOIPhdYdjeN3hvLYFHRZg0v5JbEjwk0b9oPo48doJnEg5gfH7xoveIylFx1WemDMPz7CeF41SY5e3yFG4WpmtSOD+fhRFwUSZ8Paut3Ei9UQ5jurZgKtkfMUINpVRalAWAkZv0iNPn4eHBQ+F9QhKo0txFDAxhUTKmrao4yL88/I/NnnlxRaTsX6x7Oc9/fdg/8D9CPUMZdu4XpaXYl5CWn4aFp5eyDuHmOXrbvZdAMC0ZtPo3CGLvmJMa87k239aUSegDqa3mI7/xf+Pd1954WR2THRiiywpjKw7shQjte/6JspkVYkZH9IOH2VkIthUEhJZ0i4n9rsOGzduRPXq1fHTTz/h3LlzOHfuHH766SfExsZi9erV+OWXX7B371588skn5T3UUoErWywNLo4i1CMUNfxqCOpCcRfVlu/j+cfnkfAwgWdVZ95fMSVGQSgw7795aLW6FZZdXCY5liJjER4XPkaBsQBj94x16HvYa6i4n3sfv178Fan5qTg06JCAjvlJQaVQwV3tjnCvcPSr3k80B8jLzQsNghqw2672xDAyLdYvFj1ievDIcLjXLw0S0hKw//5+5BRzCGSecXHHnaNMlAkURckKjJPgKhkve2KeKtgnQaYdmoYDSbTlz7KQX1kgNvmW1WIudrwtNpoonyje97r0+BJ23dvF6yNKf1lyXkvFxJKymIuMogyrY3le0D68PS6mX0Tvzb3ZNu7zYM8kHeRupiiWshCqFCoYSSMG1RxUhtGKgxkjSZECDyKX5ccSbGK/nP/iMnz++edYvHgxunbtyrbVq1cP4eHhmD59Ok6ePAkPDw9MnjwZ8+fPL8eROob0wnQsu7iMLdIIAP1j+1s5wjYmNZmESZgkaOeFk3Ge1YNJB1kFY23PtfBQe+BB/gNW3onJPQIE9CY9cg25KDaK0+gDfG+KowafzpGd8e+df0X3FRnp+l9uSjfcyb7D5k1uvbMVnSI6YUDsAIeu5UxU96uOWa1mie6r6V8TK3usxIS9E2AgDdCo6PAYV3liLMOfuEQOY+PGltqLcjPzJoZtHwYAWNJ5SRlH+fSA+zuRFCkaPkdR1HPlnXIWXCXjZSXmKYflKyZKu+ukF040SVQiJKDIWMQWFrQmwMWERGZRpkPjElOExK7JLGLTC9NhIA3sItxaTgzXY/M84+yjs5hyYAqvzdHE/jfqvoFvEr6BiTJJPhNKQgkjjC6htmQmHqnJicHBnFsgdTo0KyqCO0WhXnExFqU9go9oMT95MnMGLly4gCpVhMmzVapUwYULtMEiLi4OKSmWFPAVG9nF2VhxeQWvzdULWgAsHT0AXi7OgC0DMDB2INZcW4MO4R0AiMtggjAXmWQ9NSLvJC/M10GDVqhHKFQKFcI9hbl980/Nx5pra/B2g7d5no0jyUdwJu1MuSgxiTmJeHfvu/BUe2LVi6us9l3caTFv21Xhv1zDDGA2LPlofPB2g7dLf16OIZEXYvWMu2K439VEmUTnNQpUmUNCn0e4SsbL4WRPE0QmkVdy83jWF0EImR0Ykm1fvZFWYa14FaF/7/47moeIL/L7/d0PTVY2wflH4slcDMSEIkP1CQC9N/VG05VNcenxJclzWCpSHSI6wE/rJ+jHCKjll5bj7MOz5vYSgW1pkexTrc8TYwCr6BDztLQMa8mytti7MLPFPsaE3VjmXjkD3AnfcsGVVpDGfp6W+DfeDQnCo5IisEEmEp0LCtGkSNoiLaNsqFmzJubOnQu93vwOGgwGzJ07FzVr1gQAJCcnIzhY2mNWESFqYCnjtFtgKECuPhcGEz8MjHkHm4c2R3W/6my75bvLvGN+Wj+MqjcKQ2oNYfct7kgvvgkQrFx0VbHL+kH1ceb1M/jn5X8k+yigEMiW8io4aKJMuJ19G+cfn8fRB0dF5yQDSbODPSrgF+asHVAbI+qOEPQvK5h7czv7Nrbf2c4qMZbPhqPgGqiUCiV6RPcAwM8lfRahVWnxT59/sPXlrdAoNeIKvqzAlAqukvFPjScmKioK9+7d47XNmTMH06ZNkzymqKgIkydPxp9//oni4mJ07doV33///VM3EVpD68IizKg7CjMu/A9ASRynpLVEvP2DjCy8k5UND1Jsv7ktrlIcIrwiEOYZBq1SazWJk5nQyup2LTYVo8hUxJsgLz2+hEFb6XCjlqEtecn3//b9F2GeYeKeGAnrUm3/2lAQCoFF0FVxzE8jLO/nqh6rUD+oPt6o+wYScxPtYnEzmAySzEqW4LKFOQsvRr+IuoF1UdO/ptUFF1MnRrbwPDksWbIEvXr1Qnh4OOrXrw+AttyZTCZs2bIFAHD79m2MGVPxiDasQcxTvebaGrxW67VSn/ODgx9gf9J+zGw5E6/UeIVtbxzcGN92+lZgwLH0oDNKjK/WF+MbjcfjwsdYeWUlACDWn85HJAiCNY5xayhZoiyemAP3D2Dv/b1oEtwEL1V9SfS8XI9QeYNrLBy9azTiguLwe4/feX2uZ17HoC303LSh1wZE+0RDpVCBpEj8ffNvp4/JMvyJ+a0LjAXYeGMjXq7+cqnOaxkq/FnrzzCr1SyX1O+qSFAQCkT5RLHbXCX9x/gfEe0TLYeSlRKukvFP1RM5e/ZsjBo1it328vKy2n/ixInYunUr/vrrL/j4+GDcuHHo27fvU1wBWlwJ6RDcFChRYhzOUSl5Ib1FFRghAnQBeKfBOzb72VvsMswjzKq3xpK3HeBb/AqMBey+Kt5VrNIOc8fCVWimNJ0i1v2Zd507AinWHS83L9QJqGPXOX6+8DN7jKfa07kDtAM9Ynqwn+9m30WH8A7Yn7Rf0M+y2OUdtQprvTwRZDLhjexcQX8ZZUerVq1w584drFq1Ctev05Xc+/fvj9dee42V86+//np5DrFUEAuLFMtBceicEoaAEI8QBGgD2OtyaZa5YKz0TLuPxgdreq4BSZEI8wjDqSGnQFEUfjr/EwAbnhjwPTGO5AtczriMDTc2QEEoJJUYBSH0xJQX7KoTwzF99P27L/YN2IdAXSD0Jr1oAeaygkeWQih4Y5x9bHaplRjL76pWqqFG2QgpnnY0DG4InUpX3sN4auEqGV8xpIOd8PLyQkhICPvn4SFN35udnY1ffvkFCxYsQKdOndC4cWMsX74cR48exfHjxyWPe9qwy12H9ff3sNskRToo9O23KtzMvIlVV1Zh//39WH5xOd4/8D7+S/1PtK+9npieMT1Fc20YMN+FO1lyFRojaeRNeJceX8LZh2d5LDwMuNYleyxKssAyw/KZmnV0Fu7l3MOee3sknwFLMIpjlypdeDH75YEonyh82/lbtAprxbYxiclsjaOS9hSlCit9vLHVirx52rBkyRJERUVBq9WiefPmOHnypNX+f/31F2rWrAmtVot69eph27ZtvP0URWHGjBkIDQ2FTqdDfHw8bty4YddYDAYDqlatiqSkJLz99ttYsGABFixYgNGjR9s0VFV0iBmVrFELO3JOsbCW+HXxaLSyEW5kme+9pXxlcv9MpAn/3PoHW29vRS3/Wjj/6DzG7xuP/ff3Q6vSShKhiI1FatsabmTSY1x3XVgrjGINCRXHE2OLcAaQVnSuZV5zyZh4oYkEHe71Zdsv2e3SgjtXUhSFPpv6oPv67vxCms8oRu8ajTd3vIns4mze8zxm9xiM2jkK+Yb8chyd/XheZPxTpcTMnTsXAQEBaNiwIb766isYjdLC9fTp0zAYDIiPj2fbatasicjISBw7dkzyuOLiYuTk5LB/ubkV2/L6t6cHFl8zJxkGewS7zOV79tFZzD05F+/ufRcLTi/Av3f/RVJukmhfZiFoawJqH9EeP3f5mdfGjdFmJmqpsAVu+Nyd7DsYtHUQXv/3ddzNuSu4VqPgRuxn7oSUUZSBxJxEPMh7wOsf5R1ldezPEyx/x2uZ1/DVf1/hvf3v4Y0ddMK+LdhTSOynF2jrb5hHWBlGK47/Uv/Dhhsb2MUTwFdUGQs5G05WojcrSrbJZySKYM2aNZg0aRJmzpyJhIQENGjQAF27dsXDh+ILlKNHj+LVV1/FyJEjcebMGfTp0wd9+vTBxYsX2T7z5s3DN998gx9++AEnTpyAh4cHunbtiqKiIpvjUavVdvVzFp6kjGdkE/c5KzOjI8xGGy6uZVxj2RS5Co7lu8uykpF6fHT4I0w/Mh0myoTrmdex//5+3Mu5xzvO2vsa7RONN+q+YR6bA9/NWpgat47E0+SJsTTOMEqGpUfuwMADcAZq+NdAoC4QAP2bq5VqtqxBWXKvuN/VRJlwK/sWkvKSsOnmpjKN92nA8ZTjOJF6AnqTHiqFCq/UeAX9qvfDqbRTOJ5yvMye1CeB50nGVwzpYAfGjx+PP//8E/v27cPo0aPxxRdf4P33pSsgp6amws3NDb6+vrz24OBgpKamSh43Z84c+Pj4sH+1a9cu07hdve6xrBMT4RVhl8WoNBDzmEiFXFmzFto6LzckTCyczNIT4652R/2g+rxziE0wL1R5Af5afwD8iX3OiTl4ceOLgnoIFWXyrAhQKIT3gutlY4qI2oNNNzeJ1uQBzIs9VzzD666vw8yjM3HswTEUGApwLeMaDicfZvczYzKHk9GwXifm6cOCBQswatQojBgxArVr18YPP/wAd3d3LFsmXg9k8eLF6NatG6ZOnYpatWrh008/RaNGjfDdd98BoBdoixYtwieffILevXujfv36WLFiBR48eIBNmzbZNaaxY8fiyy+/tGqYchacLeOtgVm8+mp80T68Pa+trOe0lE/JecnsZ67cFXhiShZhXGv7+L3jsf7GegDAt2e+xatbXkW78HaY2XImesb0pK8rMpYq3lUwodEE7H5lN/YN2GfVq24Ja+84d/7QqrRWKdCfFKRCarlwU/CVGKlIBGYeKis0Sg2by8kYL+3NO7QG7rPBnW+zi7NLfc6nBVxjm1alxcyWM3mU2o4SWJQHnicZX645MdOmTcOXX35ptc+VK1dQs2ZNTJpk5sWvX78+3NzcMHr0aMyZMwcajcZpY/rwww9510pOTnZ8krt7BGcenUWGIR8NPW+xiyIGe45eQmWNH2qWWJsfG3JxLpdDWuAuDGNqV1AIddIp4OpWHM++ibySPgcs+pIkiVH1RiFQF4jKnpX5JykNY0niCcBQCABQi7CxXLi5FbqHV2GiSDTxikZICXsJE/+rvHsEeHjT6iV0BXylUvnwCkBsBQBoS2oUFN49CGTRVoS997awfbPz06BLPIlVMa+hfeYtZBhpV6/i7hHg4S3BtRj6ZuXdw0AaHZepyqWZqS4kHeb1NaScBahnJ4SoLFDmCO+lMs/MwEMUZgJXt1o9B/XwKvvZdHUroBS+t1TuXQCAwlBo83yOQpFDs95du7sHXycexNqH/CJm+uv/Am4+MDLhiRYJ/rkKBfa666AjSbQsKgaubgVpLMR+kfcVR+exH+spFQgy0ee8rlYjSS0udlUUhXaFrvVI6PV6nD59Gh9++CHbplAoEB8fL+mhPnbsGE8mAkDXrl3ZyevOnTtITU3leb19fHzQvHlzHDt2DIMG2a75899//2HPnj3YuXMn6tWrJwgV3rBhg71f0SacIuMTj+Nc2hk8Nkh7cUI0PtASatTziECA2hOBelqO3kg8gEJlIHQlVvtTObeRURKiwixAuf9GaANQx4NeqKbps/Ewk34XFSnnAQMnJyLzsvnz3UNAGh3CpLQIrdXn03JUnWmecw4lH+L1uZd1E7Uf3UFt6HAz+Sz23NqN4kfpaOxNy89dR2lKVIoConVBqO4egjuFj3CuMA1UyfegqJL/S76Lu1KDdr40C5GJInH0/kH2eruOzgVFAZXcvBHnVQVkdiIAQPX4OmqqQ7C73kTUO8Eh8SmrbCjF8WoLwwuV/1hwHrXF80Dc2AWodAjVWyz+nSjbiEL6N1GnnAVZUIAPLv8IACgyFZX6OiqKxKcxr4AAgUZZZus9lXHb6XK5ooHxwB889T18Ve5QEATi/etCRShgpEjsPrkIldTeAICOfrVZRfVo9g0UWfHSxOgqIUpXUiutRjfARcbm503Gl6sSM3nyZAwfPtxqn5iYGNH25s2bw2g04u7du4iNjRXsDwkJgV6vR1ZWFs8bk5aWhpCQEMnraTQanlKUk2Mf/TAPu2ZgiSkRJ3RaoLJw93s3jqF/Ti5mpNPC56pOi/dCKpk7BAcJjjl29z7UhRnAn69hXuUQ3BDpE6gLhJvSDbH+sZjWjCPwGeuYsVA4GI2NeMQDc9mPWnedYGzrHp7Euod0rOWitEcIKaCv0SSkEk7ptNBsmwrY0LxrAJjs7YWvA2hWHfLUciD3WwCAT0gQoNMhbM8cwEArYb9HR7LHPjLkYu/+GYjJzoEmIgxQ0d9V/e/7gIF/3SSVEm96eeJfD3e4b5kMlFRkdwv0B7w8cTk/mddfd+pXIO/5KfRlDY0BJAAwEASaR0UAAHwSTwBedIK+28PLwHnrjEtaby+g5DdWrhshatqdWTkUcFMjNz8N+LP0DE5iYH7nvx8niO5XbRoLiiQxys8HS3194FYyPlWJ5fuhSoUJwUGoYjBgS1IK8OdrMAGYEBwpPNkNM2vRYo0GnUreiw1enljlI/7OeZAkjt8TD8+0hdzcXJ6sspRjDB4/fgyTySRgaAwODsbVq1cF/QHaqy3Wn/FoM/9b62MLvr6+6Nevn119ywqnyPh9n+Pn/MvY7yFd/6NnXj7mPErH6pLtzwP8AG8v/Jt+DuPPbkO4kZY/S0pkpRQG5uSiTslccU2nxc2SucLt6LdAgVmmq3VaoGSfeusUVu7+BVoRX+flCT1BoPP9ZLiTFBT3VuDnyHCBNx8ANMX57Pv3j58Plvn60DtK5rNJN+jFkIqiMDYzG4/0epzXuGGJn6/k94jSG9AuOQW31Sps8fRAPnNOAJNu0CHRXfLyEfcoHSPUavRQKRF1fylg/AEAMMTfFyt9vNGisLDssqEUx6sBvO3rgx/86HGTIjJPRxBAiXwEAM36kQAFBANoFFoJCVptqa8vhmICMIUEA1oNtPu/BFFUjLOc+bG011EA6MNtKDlnwOV/gGOrRY54duBWJRx6hQKf3t0IANCRJOLvJUFdJRxGhQKf393M9j17JxGMKvJpeCiS1NLkBxMysvAmU87i4zSHlRhZxoujXJWYoKAgBAUJF+P24OzZs1AoFKhUqZLo/saNG0OtVmPPnj3sjbt27RoSExPRsmXLUo/ZLgTXRtXHeSgmDSApCnoTCZVCASNJwk2pgIIgEO4RCuhoLn9vQo+GJMeCo+d8VqgB0kBbg8MaAQoVaimz4FWUxXYJiH0JDYIa4HrmdUT7RAvH4xsJNB0FpJwDkjjJXW6eQNvJwPk/6W3PECCgGnCP45EIb8Z+bA4S3ckcpMGEKKiQBBOMoKACAQUA34BYwJ+2LvYhCtHcZEJ4SEPYE1TXFyT2kJm4ShjQxKc64EMLg6FEMUDmo1pwQ7ZvZzITuaBwlTCgGqVCkHcVwEuHt1GAzWQhalNqRAULr0vBiBvKXHShVAgJNU80rxB6JJN5MICCAkA1So1QKPCSbzDg+4wkQpQRipI/NYAxpjzcJowYrw1CIZmLHJAYofYDwkWePQ56wIRzZC7qU2poKjcT7TOUKMBWshCDKF8gXMQCUAb0s/idlSBQn1JjqTIfg0zuCAhrAgBoRejhlXGLLW5Zp1iPnnn5uF+iIIcYjYBnMOBbBQCFBkWJgJsHoC9J+FTrAE7oibfGj15sdp+HsCOfokFRMc0KaBFWpKUoYLAwydkeWHoSZs6ciVmzZpXqXOWB5cuXl/cQHENQTUSbHiFLpEAug0hdMBBeld1+GQbcJXNRBApuIXFAyfInVpEDkjSw0sr8Px0UVsUjjJ0r/Ag9WpB5qAQFWvrXAfzNnpimoNCLzEYApeTJXWZZNZDp6G0e4xgyD0eJYqhAoAalwl3CCCOAntCy71+YogBxJK0sFRtJqBQE1AqzXDzuWwm1SA+EwIQ4shAE7zswfwRCVFogPAJBIHFLmY3GJInTCgOCKAUiKCUI0NZqhFdFdQDmSjc0PgDwgQH0qsWGrBFF+k2gMAPwCAL8HD9eDWAsgCBTAXYQRRil9gPCo3h9PAD0I7OxXlGIN0wePDk3DybMI3MxgNQB4dKGVEegAdBGkYfqpAkNAuuDAIGJpjzsI4rRjdLy5u+y4GNTAU4SxXjFKxbwerbDrMdQ+dhJFrHPrhYEEN4M75a086R2eFMwT3stZSYCRAsi0wguWacAKFUBclnGS4B6CnD06FFq4cKF1NmzZ6lbt25RK1eupIKCgqihQ4eyfZKSkqjY2FjqxIkTbNvbb79NRUZGUnv37qVOnTpFtWzZkmrZsqVD175//z4FgLp//77Tvo8MGTJkOAOMfLp8+TKVnZ3N/hUVFYn2Ly4uppRKJbVx40Ze+9ChQ6levXqJHhMREUEtXLiQ1zZjxgyqfv36FEVR1K1btygA1JkzZ3h92rVrR40fP75U3+tJQpbxMmTIqKiQZbx1PBUqtUajwZ9//on27dujTp06+PzzzzFx4kT89NNPbB+DwYBr166hoMAc/7tw4UL07NkT/fr1Q7t27RASEuLU2GoZMmTIqAjw8vKCt7c3+yeVJ+jm5obGjRtjzx4OLTtJYs+ePZIe6pYtW/L6A8CuXbvY/tHR0QgJCeH1ycnJwYkTJxzyeq9btw4DBgxAixYt0KhRI96fDBkyZDzPkGW8BMpVhXoKIFvpZMiQUVFRGvn0559/UhqNhvr111+py5cvU2+99Rbl6+tLpaamUhRFUa+//jo1bdo0tv+RI0colUpFzZ8/n7py5Qo1c+ZMSq1WUxcuXGD7zJ07l/L19aU2b95MnT9/nurduzcVHR1NFRYW2jWmxYsXU56entS4ceMoNzc3avTo0VR8fDzl4+NDffTRR3Z/t9JAlvEyZMioqJBlvHXISowNyBOcDBkyKipKK5++/fZbKjIyknJzc6OaNWtGHT9+nN3Xvn17atiwYbz+a9eupWrUqEG5ublRderUobZu3crbT5IkNX36dCo4OJjSaDRU586dqWvXrtk9ntjYWGr16tUURVGUp6cndevWLYqiKGr69OnU2LFjHfpujkKW8TJkyKiokGW8dRAUVUbS+mccSUlJiIiIwP379xEeHm77ABkyZMh4QnhW5JO7uzuuXLmCKlWqoFKlSti1axcaNGiAGzduoEWLFkhPT3fZtZ+VeyhDhoxnD8+KfHKVjC9XdrKnAWQJ20RKSko5j0SGDBky+GDkEmmFFedpQEhICDIyMlClShVERkbi+PHjaNCgAe7cuVPm4pC2IMt4GTJkVFTIMt46ZCXGBtLS6EKIzZo5h6pQhgwZMpyNtLQ0REaK1Kt5StCpUyf8/fffaNiwIUaMGIGJEydi3bp1OHXqFPr27evSa8syXoYMGRUdsowXhxxOZgNGoxFnzpxBcHAwFAr7yNxyc3NRu3ZtXL58GV5eNopJyrAK+V46B/J9dA4q2n0kSRJpaWlo2LAhVKqn1yZFkiRIkmS/w59//omjR4+ievXqGD16NNzc3Fx2bVnGly/ke+kcyPfROaho91GW8dYhKzEuQE5ODnx8fJCdnQ1vb2/bB8iQhHwvnQP5PjoH8n10DRITExEREQHCoggcRVG4f/9+hbNAys+B8yDfS+dAvo/OgXwfXQNXyfinok6MDBkyZMh4dhEdHY1Hjx4J2jMyMhAdXYrq7DJkyJAho8LAVTJeVmJkyJAhQ0a5gqIogYUOAPLy8qDVasthRDJkyJAhw1lwlYx/egPsKjA0Gg1mzpwpWVFVhv2Q76VzIN9H50C+j87FpEmTAAAEQWD69Olwd3dn95lMJpw4cQJxcXHlNDppyM+B8yDfS+dAvo/OgXwfnQtXy3g5J0aGDBkyZJQLOnbsCAA4cOAAWrZsyUvudHNzQ1RUFKZMmYLq1auX1xBlyJAhQ0Yp4WoZLysxMmTIkCGjXDFixAgsXrxYTqSVIUOGjGcQrpLxshIjQ4YMGTJkyJAhQ4aMpwpyYr8MGTJkyJAhQ4YMGTKeKshKjAwZMmTIkCFDhgwZMp4qyEqMDBkyZMiQIUOGDBkynirISowMGTJkyJAhQ4YMGTKeKshKjAwZMmTIkCFDhgwZMp4qyEqMDBkyZMiQIUOGDBkynirISowMGTJkyJAhQ4YMGTKeKshKjAwZMmTIkCFDhgwZMp4qyEqMDBkyZMiQIUOGDBkynirISowMGTJkyJAhQ4YMGTKeKshKjAwZMmTIkCFDhgwZMp4qyEqMDBkyZMiQIUOGDBkynirISswzDIIg7Prbv3//ExlPWloaRo8ejcqVK0Or1SIqKgojR460eszatWtBEAQ2btwo2NegQQMQBIF9+/YJ9kVGRqJVq1ZOG7stPHjwALNmzcLZs2edet6oqCi7fsNff/3Vqdd1NgoKCrBkyRJ06dIFoaGh8PLyQsOGDfG///0PJpNJ0D8lJQVvvfUWoqOjodPpULVqVUyaNAnp6emCvleuXEG3bt3g6ekJf39/vP7663j06JGg382bN/HKK6/Az88P7u7uaNOmjeizI0PG0wJZxssyvqLAURlvrzw+efIkxowZg8aNG0OtVoMgCNHr379/H//3f/+HZs2awc/PD4GBgejQoQN2797t9O8qo+JAVd4DkOE6/P7777ztFStWYNeuXYL2WrVquXws9+/fR+vWrQEAb7/9NipXrowHDx7g5MmTVo9r06YNAODw4cN4+eWX2facnBxcvHgRKpUKR44cQceOHXnXun//PgYNGuSCbyKOBw8e4P/+7/8QFRWFuLg4p5130aJFyMvLY7e3bduGP/74AwsXLkRgYCDb/iQn89Lg9u3bePfdd9G5c2dMmjQJ3t7e2LFjB8aMGYPjx4/jt99+Y/vm5eWhZcuWyM/Px5gxYxAREYFz587hu+++w759+3D69GkoFLT9JSkpCe3atYOPjw+++OIL5OXlYf78+bhw4QJOnjwJNzc3APQz0bJlSyiVSkydOhUeHh5Yvnw5unTpgj179qBdu3blcl9kyCgLZBkvy/iKAkdkvCPyeNu2bfj5559Rv359xMTE4Pr166LX37x5M7788kv06dMHw4YNg9FoxIoVK/DCCy9g2bJlGDFihMvvgYxyACXjucHYsWOp8vrJu3fvTkVHR1OPHz92+Njo6GiqWbNmvLbt27dTBEFQr776KtW1a1fevtWrV1MAqM2bN5dpzCRJUgUFBXb1/e+//ygA1PLly8t0TVv46quvKADUnTt3rPbLy8tz6TgcxaNHj6iLFy8K2keMGEEBoG7cuMG2rVq1igJAbdmyhdd3xowZFAAqISGBbXvnnXconU5H3bt3j23btWsXBYD68ccf2bYxY8ZQKpWKunr1KtuWn59PRUREUI0aNXLKd5Qho7why3jHIMt458ERGe+IPE5NTWV/I2vP98WLF6lHjx7x2oqKiqiaNWtS4eHhpf5eMio25HCy5xz5+fmYPHkyIiIioNFoEBsbi/nz54OiKF4/giAwbtw4rFq1CrGxsdBqtWjcuDEOHjxo8xpXr17Fv//+i6lTpyIgIABFRUUwGAx2j7FNmzY4c+YMCgsL2bYjR46gTp066N69O44fPw6SJHn7CIJgrYJGoxGffvopqlatCo1Gg6ioKHz00UcoLi7mXScqKgo9e/bEjh070KRJE+h0Ovz4448AgF27dqFNmzbw9fWFp6cnYmNj8dFHHwEA9u/fj6ZNmwIARowY8cTd/8OHD4enpydu3bqFHj16wMvLC4MHDwYAHDp0CP3790dkZCQ0Gg0iIiIwceJE3r1kcPXqVQwYMABBQUHQ6XSIjY3Fxx9/zOuTnJyMN954A8HBwdBoNKhTpw6WLVtmc4yBgYGoU6eOoJ2xvF65coVty8nJAQAEBwfz+oaGhgIAdDod27Z+/Xr07NkTkZGRbFt8fDxq1KiBtWvXsm2HDh1Cw4YNERsby7a5u7ujV69eSEhIwI0bN2x+BxkynkbIMt4MWcZXDBnviDwODg7myXwp1KlTh+e5AgCNRoMePXogKSkJubm5Ns8h4+mDrMQ8x6AoCr169cLChQvRrVs3LFiwALGxsZg6dSomTZok6H/gwAG89957GDJkCGbPno309HR069YNFy9etHodJiY1ODgYnTt3hk6ng06nQ/fu3XH37l2b42zTpg0MBgNOnDjBth05cgStWrVCq1atkJ2dzRvDkSNHULNmTQQEBAAA3nzzTcyYMQONGjXCwoUL0b59e8yZM0c0FOHatWt49dVX8cILL2Dx4sWIi4vDpUuX0LNnTxQXF2P27Nn4+uuv0atXLxw5cgQAHaoxe/ZsAMBbb72F33//Hb///vsTDVEyGo3o2rUrKlWqhPnz56Nfv34AgL/++gsFBQV455138O2336Jr16749ttvMXToUN7x58+fR/PmzbF3716MGjUKixcvRp8+ffDPP/+wfdLS0tCiRQvs3r0b48aNw+LFi1GtWjWMHDkSixYtKtW4U1NTAYA3+bRr1w4KhQITJkzA8ePHkZSUhG3btuHzzz9Hnz59ULNmTQD0ZPvw4UM0adJEcN5mzZrhzJkz7HZxcbHoROju7g4AOH36dKnGL0NGRYYs42UZz6AiyfgnKY9TU1Ph7u7OnlvGM4bydQTJeJKwdMVu2rSJAkB99tlnvH6vvPIKRRAEdfPmTbYNAAWAOnXqFNt27949SqvVUi+//LLV644fP54CQAUEBFDdunWj1qxZQ3311VeUp6cnVbVqVSo/P9/q8ZcuXaIAUJ9++ilFURRlMBgoDw8P6rfffqMoiqKCg4OpJUuWUBRFUTk5OZRSqaRGjRpFURRFnT17lgJAvfnmm7xzTpkyhQJA7d27l22rUqUKBYDavn07r+/ChQspAAJXNRflGWowbNgwCgA1bdo0QX+xUIk5c+ZQBEHwQrDatWtHeXl58dooig63YDBy5EgqNDRUEC4yaNAgysfHx+6wDAbFxcVU7dq1qejoaMpgMPD2/fzzz5Svry/73AGghg0bxuvH3PMVK1YIzj116lQKAFVUVERRFEW99NJLlK+vL5WTk8Pr17JlSwoANX/+fIfGLkNGRYQs482QZXzFlfGllceOhkveuHGD0mq11Ouvv+7QuGU8PZA9Mc8xtm3bBqVSifHjx/PaJ0+eDIqi8O+///LaW7ZsicaNG7PbkZGR6N27N3bs2CHKPsKASVoMCQnB1q1bMWDAAEyZMgVLly7FrVu3sHr1aqvjrFWrFgICAnD48GEAwLlz55Cfn88mOrZq1Yq1mB07dgwmk4lNFt22bRsACKyOkydPBgBs3bqV1x4dHY2uXbvy2nx9fQHQiYPckIaKhnfeeUfQxrV25efn4/Hjx2jVqhUoimI9FY8ePcLBgwfxxhtv8MKyALBMMBRFYf369XjppZdAURQeP37M/nXt2hXZ2dlISEhwaLzjxo3D5cuX8d1330Gl4nOMVK5cGc2aNcOiRYuwceNGTJo0CatWrcK0adPYPky4hEajEZxbq9Xy+rzzzjvIysrCwIEDcebMGVy/fh3vvfceTp06xesnQ8azBFnGyzIeqHgy/knI44KCAvTv3x86nQ5z584t8/lkVEzISsxzjHv37iEsLAxeXl68dobJ5t69e7z26tWrC85Ro0YNFBQUiFLaMmCE7IABA1hWKQDo378/VCoVjh49anWcBEGgVatWbFz0kSNHUKlSJVSrVg0Af4Jj/mcmuHv37kGhULB9GYSEhMDX11fwHaOjowXXHzhwIFq3bo0333wTwcHBGDRoENauXVvqyU6v1yM1NZX3Z22BYA9UKhXCw8MF7YmJiRg+fDj8/f3h6emJoKAgtG/fHgCQnZ0NgGaVAYC6detKnv/Ro0fIysrCTz/9hKCgIN4fw/ry8OFDu8f71VdfYenSpfj000/Ro0cP3r4jR46gZ8+e+PzzzzFhwgT06dMHX3/9NT755BMsWLAAly9fBmB+rizj3gGgqKiI16d79+749ttvcfDgQTRq1AixsbHYunUrPv/8cwCAp6en3WOXIeNpgSzjZRkPVDwZ72p5bDKZMGjQIFy+fBnr1q1DWFhYmc4no+JCpliW4XIwAsQyUVupVCIgIACZmZk2z9GmTRv8888/uHDhAhsrzaBVq1aYOnUqkpOTcfjwYYSFhSEmJoZ3vBS3vCXE4nR1Oh0OHjyIffv2YevWrdi+fTvWrFmDTp06YefOnVAqlXadm8HRo0d5dKEAcOfOHURFRTl0Hi40Gg1v8QDQgvyFF15ARkYGPvjgA9SsWRMeHh5ITk7G8OHDHZqgmb5DhgzBsGHDRPvUr1/frnP9+uuv+OCDD/D222/jk08+Eez/8ccfERwcLMh16dWrF2bNmoWjR4+idu3abKJ/SkqK4BwpKSnw9/fneWnGjRuHESNG4Pz583Bzc0NcXBx++eUXAPRCTYYMGaWDLOP5kGW8dRkPuFYejxo1Clu2bMGqVavQqVOnMp1LRsWGrMQ8x6hSpQp2796N3NxcnqXu6tWr7H4uxBicrl+/Dnd3dwQFBUlehwlPSE5O5rXr9Xo8fvzY6rEMuLUEjhw5gvfee493fo1Gg/379+PEiRM8q0+VKlVAkiRu3LjBq5WQlpaGrKwswXeUgkKhQOfOndG5c2csWLAAX3zxBT7++GPs27cP8fHxdk+gAF3AbdeuXby2kJAQu4+3FxcuXMD169fx22+/8ZI8La/NLAasJe8GBQXBy8sLJpMJ8fHxpR7T5s2b8eabb6Jv375YsmSJaJ+0tDRRqyXDdmQ0GgHQIWdBQUFsCAIXJ0+eFK3l4OHhgZYtW7Lbu3fvhk6nY1mOZMh4liDLeFnGAxVPxjNwhTyeOnUqli9fjkWLFuHVV18t9XlkPB2Qw8meY/To0QMmkwnfffcdr33hwoUgCALdu3fntR87dowXE3v//n1s3rwZXbp0sWqp6tChAypVqoRVq1axYT4Aba1hLEm20KRJE2i1WqxatQrJyck8K51Go0GjRo2wZMkS/D975x3fRPnH8U9Wk+5NS1toSymjjLL33rJBpjJFcIAyBBUHshQERfGHCqhsEGUrS/bemwItq4OWDrp3mnG/P9Jc7nJ3ySVNOuDer1dfzT333N2T5PK95/t8V0FBAfkw1L9HAIzMKitWrAAA9OvXz+y1MzMzGW36CbLelcnZ2RkAkJ2dbfZ8np6e6NGjB+1PH8NhS/TfCUFJpUoQBFauXEnr5+vri06dOmHdunVISEig7dMfK5FI8Prrr2PXrl2sD0JTriZ6zpw5g1GjRqFTp07YunUrY1VRT506dZCamsqoMv7nn38CAJo2bUq2vf7669i/fz+ePXtGth0/fhwPHz7E8OHDTY7nwoUL2L17NyZNmgR3d3ez4xcQqGoIMl6Q8UDlk/Fs2EIeL1++HN999x0+++wzTJ8+3apzCFQtBEvMK8yAAQPQtWtXfP7554iLi0NkZCSOHDmCffv2YcaMGQgLC6P1b9iwIXr37o0PP/wQcrkcv/zyCwBgwYIFJq8jl8uxfPlyjB8/Hp06dcLYsWORkJCAlStXomPHjhg6dKjZsTo4OKBly5Y4e/Ys5HI5LfgU0LkbfP/99wBAe8BFRkZi/PjxWLt2LbKzs9G5c2dcuXIFGzduxODBgxkmfzYWLlyIM2fOoF+/fggODkZaWhp++eUXBAUFkdcKCwuDh4cHVq9eDVdXVzg7O6N169as/tflRb169RAWFobZs2cjKSkJbm5u2LVrF6trx08//YQOHTqgWbNmmDJlCkJDQxEXF4cDBw7g1q1bAIClS5fi5MmTaN26NSZPnoyIiAhkZmbixo0bOHbsGOtEQE98fDwGDhwIkUiEYcOGYceOHbT9jRs3Jl0Vpk2bhvXr12PAgAH44IMPEBwcjNOnT+PPP/9Ez5490bp1a/K4zz77DDt27EDXrl0xffp05OfnY/ny5WjUqBGtQnN8fDxGjBiBgQMHwt/fH/fu3cPq1avRuHFjfPPNN2X5mAUEKi2CjBdkvJ7KJOMtkcfx8fHYvHkzAJBW98WLFwPQWeHGjh0LANizZw8+/vhjhIeHo379+tiyZQvtPD179mS4Owq8BFRESjSBioEtPWFeXh4xc+ZMIiAggJDJZER4eDixfPlyWtpFgtCl35w6dSqxZcsWIjw8nJDL5UTTpk2JkydP8r7+n3/+SURGRhJyuZzw8/Mjpk2bxkixaIq5c+cSAIh27dox9u3evZsAQLi6uhJqtZq2T6VSEQsWLCBCQ0MJmUxG1KhRg5g7dy6ZfldPcHAw0a9fP8a5jx8/TgwaNIgICAggHBwciICAAGL06NHEw4cPaf327dtHREREEFKp1G6pOLnSbzo7O7P2v3//PtGjRw/CxcWF8PHxISZPnkzcvn2bdXxRUVHEkCFDCA8PD0KhUBB169YlvvzyS1qf1NRUYurUqUSNGjUImUxG+Pv7E927dyfWrl1rctwnT56kpUs2/vvqq69o/aOjo4lhw4aR1wkODiZmz57Nmqo1KiqK6NWrF+Hk5ER4eHgQb775JpGSkkLrk5mZSQwaNIjw9/cnHBwciNDQUOKTTz6x6P4TEKjsCDJekPFVQcZbIo9Nnbdz585kv6+++srk9S25jwWqDiKCMCrbKyDAgkgkwtSpUxluCQICAgICVR9BxgsICFQ1hJgYAQEBAQEBAQEBAYEqhaDECAgICAgICAgICAhUKQQlRkBAQEBAQEBAQECgSiHExAgICAgICAgICAgIVCkES4yAgICAgICAgICAQJVCUGJKmT9/PkQiEdLT0yt6KAICFtGlSxd06dKFd9+GDRvafAyV7fdjj/Hw/ZxPnToFkUjEKNYpUPFUtvtUQIAvgpxnIsh5AUGJqWC++eYb7N27l1ffuLg4iEQiiEQistiTMW+++SZEIhFcXFxsOMqyERISgv79+1t1rEgkwrRp01j37dy5k7cQUSqV+OSTTxAQEABHR0e0bt0aR48etWpMPXv2NDmu1NRUvPPOOwgMDIRCoUBISAgmTZpk1bWs4fnz55g/fz5ZwMyWWHK/CpinLPdlTEwMZs6ciXbt2kGhUEAkEiEuLs6+AxawCkHOm0aQ85YjyPmqQ1nvy+3bt6NZs2ZQKBTw9fXFpEmTKo0iWdEISkwFY42wUCgU+PPPPxntBQUF2LdvHxQKhY1G9/IwYcIErFixAm+++SZWrlwJiUSCvn374ty5cxadZ/fu3bh48SLn/mfPnqFly5Y4dOgQ3n33Xfzyyy94++238eLFi7K+BU6OHDmCI0eOkNvPnz/HggULhIdbFaAs9+XFixfx008/IS8vD/Xr1y+H0QpYiyDnywdBztsGQc7blrLcl7/++itGjx4NLy8vrFixApMnT8b27dvRvXt3FBcXl8PoKzfSih6AgOX07dsXu3fvxu3btxEZGUm279u3DyUlJejTpw9OnDhRgSOsXFy5cgXbt2/H8uXLMXv2bADAuHHj0LBhQ3z88ce4cOECr/MUFxfjo48+wieffIJ58+ax9nnnnXcglUpx9epVeHt72+w9mMLBwaFcrmMvtFotSkpKXrlJWVnvy4EDByI7Oxuurq747rvv7DKZEag4BDlvGYKcr9wIct7y+7KkpASfffYZOnXqhKNHj0IkEgEA2rVrhwEDBuC3337DBx98UC7vo7IiWGKMSE9Px4gRI+Dm5gZvb29Mnz6dVdvdsmULmjdvDkdHR3h5eWHUqFF49uwZrc+jR4/w+uuvw9/fHwqFAkFBQRg1ahRycnIA6EzoBQUF2LhxI+k+MGHCBLNjbNu2LUJDQ7Ft2zZa+9atW9GnTx94eXmxHnfo0CF07NgRzs7OcHV1Rb9+/XDv3j1anzt37mDChAmoVasWFAoF/P398dZbbyEjI4PWT++L+vjxY0yYMAEeHh5wd3fHxIkTUVhYaPY9FBQU4KOPPkKNGjUgl8tRt25dfPfdd7BHsrydO3dCIpFgypQpZJtCocCkSZNw8eJFxvfGxbJly6DVaklBZEx0dDQOHTqEOXPmwNvbG8XFxVCpVLzOfefOHYhEIvzzzz9k2/Xr1yESidCsWTNa39deew2tW7cmt6k+vKdOnULLli0BABMnTiTvqw0bNtDOcf/+fXTt2hVOTk4IDAzEsmXLzI6Rz/2anZ1t9n7Qu2hs3boVDRo0gFwux+HDhwEASUlJeOutt+Dn5we5XI4GDRpg3bp1jLH873//Q4MGDeDk5ARPT0+0aNGC8XvgOx61Wo1FixYhLCwMcrkcISEh+Oyzz6BUKs1+JomJiRg8eDCcnZ1RrVo1zJw5k9dxQNnvSy8vL7i6uvK6lgAdQc4Lcp4LQc4Lct6YipLzUVFRyM7OxsiRI0kFBgD69+8PFxcXbN++ndcYXmYEJcaIESNGoLi4GEuWLEHfvn3x008/0W4+APj6668xbtw4hIeHY8WKFZgxYwaOHz+OTp06ITs7G4BOg+7duzcuXbqEDz74AD///DOmTJmCp0+fkn02b94MuVyOjh07YvPmzdi8eTPeeecdXuMcPXo0tm/fTj4M0tPTceTIEbzxxhus/Tdv3ox+/frBxcUF3377Lb788kvcv38fHTp0oPnRHz16FE+fPsXEiRPxv//9D6NGjcL27dvRt29f1gfPiBEjkJeXhyVLlmDEiBHYsGEDFixYYHLsBEFg4MCB+OGHH9CnTx+sWLECdevWxZw5czBr1ixe798Sbt68iTp16sDNzY3W3qpVKwDgtYKdkJCApUuX4ttvv4WjoyNrn2PHjgEA/Pz80L17dzg6OsLR0RGvvfaa2ViFhg0bwsPDA2fOnCHbzp49C7FYjNu3byM3NxeAbjXrwoUL6NSpE+t56tevj4ULFwIApkyZQt5X1P5ZWVno06cPIiMj8f3336NevXr45JNPcOjQIZNj5HO/8r0fTpw4gZkzZ2LkyJFYuXIlQkJCkJqaijZt2uDYsWOYNm0aVq5cidq1a2PSpEn48ccfyWN/++03fPjhh4iIiMCPP/6IBQsWoEmTJrh8+TLjOnzG8/bbb2PevHlo1qwZfvjhB3Tu3BlLlizBqFGjTH4eRUVF6N69O/777z9MmzYNn3/+Oc6ePYuPP/7Y5HF6bHFfCliHIOcFOc+GIOcFOW9MRcp5vaLEdi86Ojri5s2b0Gq1vMbx0kIIEARBEF999RUBgBg4cCCt/f333ycAELdv3yYIgiDi4uIIiURCfP3117R+d+/eJaRSKdl+8+ZNAgCxY8cOk9d1dnYmxo8fz2uMsbGxBABi+fLlRFRUFAGAOHv2LEEQBPHzzz8TLi4uREFBATF+/HjC2dmZPC4vL4/w8PAgJk+eTDtfSkoK4e7uTmsvLCxkXPfPP/8kABBnzpwh2/Sf11tvvUXrO2TIEMLb25vWFhwcTPTr14/c3rt3LwGAWLx4Ma3fsGHDCJFIRDx+/JhsA0BMnTqV9fPYsWMHAYA4efIk6349DRo0ILp168Zov3fvHgGAWL16tcnj9WNr166dyXF9+OGHBADC29ub6NOnD/HXX38Ry5cvJ1xcXIiwsDCioKDA5DX69etHtGrVitweOnQoMXToUEIikRCHDh0iCIIgbty4QQAg9u3bR/br3Lkz0blzZ3L76tWrBABi/fr1jGt07tyZAEBs2rSJbFMqlYS/vz/x+uuvm/0cuO5XS+4HAIRYLCbu3btHa580aRJRvXp1Ij09ndY+atQowt3dnbw3Bw0aRDRo0MDkOPmO59atWwQA4u2336b1mz17NgGAOHHiBNlm/Dn/+OOPBADi77//JtsKCgqI2rVrl9t9qWf58uUEACI2Npb3Ma8igpzXIch5dgQ5r0OQ853J7YqU8y9evCBEIhExadIkWnt0dDQBgADA+BxfNQRLjBFTp06lbev9DQ8ePAhAF/Cn1WoxYsQIpKenk3/+/v4IDw/HyZMnAQDu7u4AgP/++4+X2d1SGjRogMaNG5OBn9u2bcOgQYPg5OTE6Hv06FFkZ2dj9OjRtDFLJBK0bt2aHDNA1/iLi4uRnp6ONm3aAABu3LjBOPe7775L2+7YsSMyMjLIFSU2Dh48CIlEgg8//JDW/tFHH4EgCLMrRZZSVFQEuVzOaNf75hYVFZk8/uTJk9i1axdtlYiN/Px8AIC/vz8OHDiAESNGYPbs2fjtt9/w5MkTVjM4lY4dO+LGjRsoKCgAAJw7dw59+/ZFkyZNcPbsWQC6VTuRSIQOHTqYPJcpXFxcMGbMGHLbwcEBrVq1wtOnT60+px6+90Pnzp0RERFBbhMEgV27dmHAgAEgCIJ2n/bu3Rs5OTnk/efh4YHExERcvXq1zOPR/66NV4Y/+ugjAMCBAwc4z33w4EFUr14dw4YNI9ucnJwYK/pclPW+FLAeQc4Lct4YQc7zR5Dz5SPnfXx8MGLECGzcuBHff/89nj59irNnz2LkyJGQyWRmj38VEJQYI8LDw2nbYWFhEIvFpJn40aNHIAgC4eHh8PX1pf09ePAAaWlpAIDQ0FDMmjULv//+O3x8fNC7d2/8/PPPpJ+0LXjjjTewY8cOPH78GBcuXOB0MXj06BEAoFu3bowxHzlyhBwzAGRmZmL69Onw8/ODo6MjfH19ERoaCgCsY69ZsyZt29PTE4DOlM1FfHw8AgICGP78+gxL8fHx5t46DaqvKBuOjo6s/qt6H3gutwFA50f74YcfYuzYsaQPsqnrADrTtlhs+GkNHz4cUqnUbGBpx44doVarcfHiRcTExCAtLQ0dO3ZEp06daA+3iIgITn94PgQFBTE+M09PT5PfGV/43g/6e0rPixcvkJ2djbVr1zLu0YkTJwIAeZ9+8skncHFxQatWrRAeHo6pU6fi/PnzVo0nPj4eYrEYtWvXpvXz9/eHh4eHyXsxPj4etWvXZnyWdevW5TyGSlnuS4GyIch5Qc5TEeS8ZQhyvvzk/Jo1a9C3b1/Mnj0bYWFh6NSpExo1aoQBAwYAQKVKs14RCNnJzGB842q1WohEIhw6dAgSiYTRn3pDff/995gwYQL27duHI0eO4MMPP8SSJUtw6dIlBAUFlXlso0ePxty5czF58mR4e3ujV69erP30PpObN2+Gv78/Y79UargNRowYgQsXLmDOnDlo0qQJXFxcoNVq0adPH1bfS7bPAIDNAjflcjnnSoN+5dNctpPq1asjKSmJ0Z6cnAwACAgI4Dx206ZNiImJwZo1axj+znl5eYiLi0O1atXg5OREnsfPz4/WTyKRwNvb2+zDo0WLFlAoFDhz5gxq1qyJatWqoU6dOujYsSN++eUXKJVKnD17FkOGDDF5HnPY8zvje25jwa2/t8aMGYPx48eznqNx48YAdJOgmJgY7N+/H4cPH8auXbvwyy+/YN68eQw/aL7jMTdBsgdluS8FbIsg5wU5L8h5259bkPNll/Pu7u7Yt28fEhISEBcXh+DgYAQHB6Ndu3bw9fWFh4eHPYZdZRCUGCMePXpEWz14/PgxtFotQkJCAOhW7AiCQGhoKOrUqWP2fI0aNUKjRo3wxRdf4MKFC2jfvj1Wr15NFjEry4+qZs2aaN++PU6dOoX33nuP9pCiEhYWBgCoVq0aevTowXm+rKwsHD9+HAsWLKClltSv8NmK4OBgHDt2DHl5ebRVuujoaHI/tW9MTAzrefTt1P5sNGnSBCdPnkRubi4tuE4fINikSRPOYxMSEqBSqdC+fXvGvk2bNmHTpk3Ys2cPBg8ejObNmwMAQ2CVlJQgPT0dvr6+JsepN/efPXsWNWvWRMeOHQHoVu6USiW2bt2K1NRUzmBPPfYU1PY6t6+vL1xdXaHRaEzeo3qcnZ0xcuRIjBw5EiUlJRg6dCi+/vprzJ0716IUnsHBwdBqtXj06BGt1kpqaiqys7NN3lvBwcGIiooCQRC0z4XrfjWmLPelQNkQ5Lwg56kIcr58zi3IeevlfM2aNUmLU3Z2Nq5fv47XX3+d17EvM4I7mRE///wzbft///sfAF26QwAYOnQoJBIJFixYwNDyCYIgU1Tm5uZCrVbT9jdq1AhisZhmWnR2diaz2FjD4sWL8dVXX5nMFd67d2+4ubnhm2++YU0FqS/QpV/NMH5f5nyELaVv377QaDRYtWoVrf2HH36ASCQiP2t930uXLuH69eu0vtnZ2di6dSuaNGnCuupIZdiwYdBoNFi7di3ZplQqsX79erRu3Ro1atQg2xMSEsiHLACMGjUKe/bsYfzpx7Znzx4yDWaXLl1QrVo1bN26lZaudcOGDdBoNOjZs6fZz6Zjx464fPkyTp48ST7cfHx8UL9+fXz77bdkH1M4OzuTn5GtKev9yoVEIsHrr7+OXbt2ISoqirGfWkTOOA2sg4MDIiIiQBAE71Snevr27QuAeY+vWLECANCvXz+Txz5//hw7d+4k2woLC2n3mSnKcl8KlA1BzgtyXpDzps8tyHnDsZVNzs+dOxdqtRozZ87kNYaXGcESY0RsbCwGDhyIPn364OLFi9iyZQveeOMNsthYWFgYFi9ejLlz5yIuLg6DBw+Gq6srYmNjsWfPHkyZMgWzZ8/GiRMnMG3aNAwfPhx16tSBWq3G5s2byR+xnubNm+PYsWNYsWIFAgICEBoaSssNb47OnTujc+fOJvu4ubnh119/xdixY9GsWTOMGjUKvr6+SEhIwIEDB9C+fXusWrUKbm5u6NSpE5YtWwaVSoXAwEAcOXIEsbGx1n2YHAwYMABdu3bF559/jri4OERGRuLIkSPYt28fZsyYQa4oAsCnn36KHTt2oFOnTnjnnXdQr149PH/+HBs2bEBycjLWr19v9nqtW7fG8OHDMXfuXKSlpaF27drYuHEj4uLi8Mcff9D6jhs3DqdPnyYf8PXq1UO9evVYzxsaGorBgweT23K5HMuXL8f48ePRqVMnjB07FgkJCVi5ciU6duyIoUOHmh1rx44d8fXXX+PZs2e0h1inTp2wZs0ahISEmHVRCQsLg4eHB1avXg1XV1c4OzujdevWDP9kayjr/WqKpUuX4uTJk2jdujUmT56MiIgIZGZm4saNGzh27BgyMzMBAL169YK/vz/at28PPz8/PHjwAKtWrUK/fv0srpsSGRmJ8ePHY+3atcjOzkbnzp1x5coVbNy4EYMHD0bXrl05j508eTJWrVqFcePG4fr166hevTo2b97MGnTNRlnuS0AXu6CffOt9xVetWgUPDw94eHhg2rRpFn0WrxKCnBfkvCDnuRHkvIGKlvNLly5FVFQUWrduDalUir179+LIkSNYvHix2fitVwL7J0CrGuhT9d2/f58YNmwY4erqSnh6ehLTpk0jioqKGP137dpFdOjQgXB2diacnZ2JevXqEVOnTiViYmIIgiCIp0+fEm+99RYRFhZGKBQKwsvLi+jatStx7Ngx2nmio6OJTp06EY6OjgQAk2k4qak3TWGcelPPyZMnid69exPu7u6EQqEgwsLCiAkTJhDXrl0j+yQmJhJDhgwhPDw8CHd3d2L48OHE8+fPCQDEV199xfi8Xrx4QbvG+vXrGalejVNvEoQuHejMmTOJgIAAQiaTEeHh4cTy5csJrVbLGHdiYiLx9ttvE4GBgYRUKiW8vLyI/v37E5cuXTL5OVApKioiZs+eTfj7+xNyuZxo2bIlcfjwYUY/fWpKc8BEStA///yTiIyMJORyOeHn50dMmzaNyM3N5TXO3NxcQiKREK6uroRarSbbt2zZQgAgxo4dyzpmakpIgiCIffv2EREREYRUKqWl4ezcuTNr2srx48cTwcHBZsfHdb9acj+Y+uxSU1OJqVOnEjVq1CBkMhnh7+9PdO/enVi7di3ZZ82aNUSnTp0Ib29vQi6XE2FhYcScOXOInJwcso8l41GpVMSCBQuI0NBQQiaTETVq1CDmzp1LFBcX045l+5zj4+OJgQMHEk5OToSPjw8xffp04vDhw7xSbxJE2e5LvTxg++PzXb6KCHJehyDnBTlvCkHOd6a1VaSc379/P9GqVSvC1dWVcHJyItq0aUNL9/yqIyIIO5TOFRAQEBAQEBAQEBAQsBNCTIyAgICAgICAgICAQJVCUGIEBAQEBAQEBAQEBKoUQmC/gICAgEC5Y6raOxfUFKUCAgICApWX8pDxQkyMgICAgEC5IxaLLapHIRKJ8PDhQ9SqVcuOoxIQEBAQsAXlIeMFS4yAgICAQIWwc+dOeHl5me1HEARZ60FAQEBAoGpgbxkvKDECAgICAuVOcHAwOnXqBG9vb179a9WqBZlMZudRCQgICAjYgvKQ8YI7mYCAgICAgICAgIBAlUKwxJhBrVbj5s2b8PPzg1gsJHMTEBCoPGi1WqSmpqJp06aQSgVxbg2CjBcQEKisCDLeNMInYoabN2+iVatWFT0MAQEBAU6uXLmCli1bVvQwbM61a9dQWFiITp062e0agowXEBCo7Agynh1BiTGDn58fAN0NVL169QoejYCAgICB5ORktGrVipRTLxtjx47Fw4cPodFo7HYNQcYLCAhUVgQZbwZCwCTPnj0jABDPnj2r6KEICAgI0LBGPp0+fZro378/Ub16dQIAsWfPHrPHnDx5kmjatCnh4OBAhIWFEevXr2f0WbVqFREcHEzI5XKiVatWxOXLly14J+wkJSURcXFxZT6PKQQZLyAgUFkRZLxpBAdgAQEBgVeIgoICREZG4ueff+bVPzY2Fv369UPXrl1x69YtzJgxA2+//Tb+++8/ss9ff/2FWbNm4auvvsKNGzcQGRmJ3r17Iy0trUxjDQgIQHBwcJnOISAgIPAq8SrJeCE7mRkSExNRo0YNPHv2DEFBQRU9HAEBAQGSssonkUiEPXv2YPDgwZx9PvnkExw4cABRUVFk26hRo5CdnY3Dhw8DAFq3bo2WLVti1apVAHTBqDVq1MAHH3yATz/91Ow4EhISTO6vWbMmj3djHYKMFxAQqKwIMt40VSYmZv78+ViwYAGtrW7duoiOjuY8ZseOHfjyyy8RFxeH8PBwfPvtt+VTMO3ODiA/1f7XqUpoVcDj40DnT4Cz3wOFGUDjkUBBGpD5FPBrBJz/EWj5NuBSSXw/XzwARGLAp66hjdAAR+cBTcYA1epX3NjsSVBLoGZr3evCTGDv+0BgM0DmVLHjKmdUWi2uJ2cgspoXHGWS8rmoSAS0nVo+1+LJxYsX0aNHD1pb7969MWPGDABASUkJrl+/jrlz55L7xWIxevTogYsXL/K6RkhIiMnKzvaMiREQKCtPs5/Cz9kPzjJnq44/8eQunmam4O2WPW08MoGXhby8POTm5pLbcrkccrncJueuyjK+yigxANCgQQMcO3aM3DaVbu7ChQsYPXo0lixZgv79+2Pbtm0YPHgwbty4gYYNG9p3oJdXA0nX7HuNqkrcWcPrlDuG1w/+1f2/8FP5jsdabm2p6BHYD5kT8EkcIJUDy0J1bQ8PVeiQbE2hSIR5Pl7oXFiEAQWFtH0EgEPOTkiUSvE/Lw98cj0LY3LzymdgIrFVSow9H3ApKSmMoFI/Pz/k5uaiqKgIWVlZ0Gg0rH1MLTJRuXnzJm1bpVLh5s2bWLFiBb7++uuyvQEBATtyP+M+Ru4fiRZ+LbC+z3qrzjH93BsAgIbV/kWb4BAbjq7yodKqkFKQghquNSp6KFWKiIgI2vZXX32F+fPn2+TcVVnGVyklRiqVwt/fn1fflStXok+fPpgzZw4AYNGiRTh69ChWrVqF1atX23OYQO0egHeYfa9R1bjzF7PNNQDIe85sbzRcN5mrSIqygUel/qB1+gAKd91r6vtoPLLch2VXtBogaiegKgTUSp0SQ+Uler+31Nmops7GJrccDHCOpO07r87CJ0UGwfyttyfGhJSDBRcAwL1SZQp7PuDKg8jISEZbixYtEBAQgOXLl2Po0KEVMCoBAfP8HfM3AOBaqmULlwm5CTgSfwSj6o4i2+ZdnoFDNXZBIi4ny28F8Pm5z3Eo9hB+7fErOgR2qOjhVBnu37+PwMBActtWi1Tlhb1kfJVSYh49eoSAgAAoFAq0bdsWS5Ys4fSju3jxImbNmkVr6927N/bu3WvyGkqlEkqlktzOy7NiBbbrXPN9XjXYlJjW7wDHvmK2D1kDVLQQT4kyKDE9FwK+pS5l1PcxdG35j8ueqJU6JYaLKvh+s4qzsC5qHYbUHoKPTn+Ex9mPGX1y+i2Hu9yd3D5/5VvggdHqUiV/7/Z8wPn7+yM1le4em5qaCjc3Nzg6OkIikUAikbD24bvoxEXdunVx9erVMp3DGJvIeAEBAARBYNejXQCAkXVHQqVV4WHWQ9TzrGdSEdESWvTb0w8AcC/9HtmeXPQE11KvoXX11vYdeAVyKFZn1d94b6OgxFiAq6sr3Nzc7HLuqizjq0x2statW2PDhg04fPgwfv31V8TGxqJjx46cDyAu81hKSorJ6yxZsgTu7u7kn/EKp4CAQNVhyeUl2HBvA9767y1WBQYARvw7As/zDRZBiYg++dAUV/7aIfoHnP7PlkpM27Ztcfz4cVrb0aNH0bZtWwCAg4MDmjdvTuuj1Wpx/Phxso85cnNzaX85OTmIjo7GF198gfDwcJu9F0CQ8QK2I6M4g3z9V8xfaLa5GUbtH4Vt0dtMHiemeBocSzhG21esLrbtICsprg6uFT0EgVKqsoyvMkrMa6+9huHDh6Nx48bo3bs3Dh48iOzsbPz99982vc7cuXORk5ND/t2/f9+m5xcQECg/DsXpVv2okw1jnhc8R+9dvTn3SxTJ+PrSyxOXkZ+fj1u3buHWrVsAdOk1b926RWaPmTt3LsaNG0f2f/fdd/H06VN8/PHHiI6Oxi+//IK///4bM2fOJPvMmjULv/32GzZu3IgHDx7gvffeQ0FBASZOnMhrTB4eHvD09CT/vLy8EBERgYsXL+LXX3+13ZuHIOMFbIePow9r+7Kry0wedybxDOc+DfFqJLFo4N2goodgkh+u/4Cfb/FLUVzZeJVkfJVyJ6Pi4eGBOnXq4PFj9tVVLvOYOdOXcUAsNVhWQODVoepnXi9UFZrvxIKjzJHRtj1mO96JfIdz0lKVuHbtGrp27Upu691ux48fjw0bNiA5OZmWDjM0NBQHDhzAzJkzsXLlSgQFBeH3339H794GxW/kyJF48eIF5s2bh5SUFDRp0gSHDx/mXWX65MmTtG2xWAxfX1/Url3bZAIXaxBkvIAt+OnGT/jt7m8YFzEOm+5vsujYrOIszn0ysaxM4yIIAttjtqOhd0M08m1k8fFF6iLceXEHzfyalXksbGPT069WP5ue25bcz7iPdVHrAAATG0yEUxXLzPkqyfgqq8Tk5+fjyZMnGDt2LOt+vXlMnyIOoJvHBAQEXm5yS/hPTsM9DebsvqF9sfq2LvmHRlkNErmumNfF5xcxIGyAbQdZAXTp0gWmyoNt2LCB9Rjj7DLGTJs2DdOmTbNqTJ07d7bqOAGBiuK3u78BAKcCk1qQirNJZ7H86nJ0COyA77t8DwCISo/CF+e/AAB0q9ENJ56doB1Xoikp07hOJJzAN5e/AQDcHX/X4uPnnp2L4wnHManhJMxoPqNMYzGGaoGqzIrByP2GJDZJ+Um050NV4FWS8VXGnWz27Nk4ffo04uLicOHCBQwZMgQSiQSjR48GAIwbN46Ww3r69Ok4fPgwvv/+e0RHR2P+/Pm4du2a1V+AgB3gzBluXYYmm0IbWyUYT7nwcr3Pqyn8gwV3D9xNvlZIFORrvQIDAJ+d+8w2AxPgjfGKoYBAVaHHzh54mPUQhepCHIk/Qrb/etvgOhOTFcM4rrZn7TJd11HKtCTzQb/oczxBF/dgifzky/0Mg+tmZlGmzc9vD9RadUUP4aWmrDK+yigxiYmJGD16NOrWrYsRI0bA29sbly5dgq+vLwBdNdDk5GSyf7t27bBt2zasXbsWkZGR2LlzJ/bu3Wv/GjFVmFcloFCgchOTGYPJRybjzos7tPbn+c8RlR7FcZRuxazRxkb49ZZukuAh9+B9zR+u/wC1Vo28kjzMPjObs9/OhyaytwnYnG7duiE0NLSihyEgYBXUJCEarS7WJcjFUHU9KT+JcUywW3CZrump8CRf83Wp3fNoD9r/2R6/3voVA8MGAgB6BPcwc5TlyCQG97StD7ba/Pz2QKlRmu8kYDVllfFVRonZvn07nj9/DqVSicTERGzfvh1hYYZaLKdOnWKYyIYPH46YmBgolUpERUWhb9/yqvVQ9fj97u9os60NrqUIRToFAJgwRdubYf8Ow6XkS3jz4Ju09t67emP0gdGIy4ljPa7Prj4AgF9u/wKCIPD+8fdZ+3kpvPBm/Tdxd/xdzGs7DwCwLmodmm5uinZ/tjMoT2mjGceaCsgVsD2bNm3CiRMnzHcUECgnEnIT0GZbG159pWKDx36RuggAcC7pnF3GpYea+Wz9PfPFNxdfWox5F3Ry8Jfbv5DjPP/8vH0GWIpKq7L62B0Pd2DykcnlsqgkZFGzL2WV8VVGiRGwLytvrISG0JDCTECgMrDn0R7Gw+5uunk/78abGrO2d6/ZHSdHnMSnrT4FAAwOG8x9Eg3z4ZWjzDF7bQHb0bJlSyFeRqDSUKwuRr89/VCgKjDbVyqWQkRx0b3w/AKK1cVIyON2nRFBhFtpt6we33dXv8Owf4eR255yg1VGrVUjPjceT7Ofkm0EQeCvGHoNt6PxRwEAl5MvWz0OLmq41qCNx1oWXlyIS8mX7J41UgQRwjyEwuX2pKwyXlBiBGjU8axT0UMQqCg4Y5TKlzB3w0Nj3oV56LmjJy1IUb9SSIVvMOys5rNoK5VU9wYGWgWj6UbaDTzLfcbrWgICAi8Xbx952+T+nsE9yddqrRolWoNc+uj0R5h+cjrnsSWZbUCAKJO1d+P9jbRtqhUhKj0K/ff0x6B9g5CjzMGZxDOciz32IibTEANUFktMeUG8BFk6X3YEJUYAANDQWxcrVM+rXjlelWPSXCkm05QxVIrxvDo8yXlC284ozqAFmbK5EDTf0tzkOXsG98SNsTdQ060m/4EQUqgLajGa++4R3FJtjUajwXfffYdWrVrB398fXl5etD8BgYrmUvIl3H5xm3P/2Iix6BTUidaWV0Ivxn3h+QXO44nSRRO2RRoutITW5H59zZm5Z+di7CFDJtd3jr6Dqcen8r6OrdBndANsFDBfDo9mwfpuG+wl4wUlphIz+chkNNrYyO4rFiqtClEZuoDpqlrcSeDlJrXQUPPpQeYDPM9/zvvYD5t+iBVdVlhc80AEB0DrAIDuBiFgexYsWIAVK1Zg5MiRyMnJwaxZszB06FCIxWLMnz+/oocnIIB/n/xrcv/HLT/G4NqDaW3/PPmH17mLU/tBPx3jW+yyUFWIvrv74uPTH3P20ScT2P90P639XsY9XtewJcYpf20xrxGVgxZTFvc+AQP2kvGCElOJuZR8CQDQbHMzu13j9LPTdj2/QFWl4szo7nJ3RptxcOWPN34kX+sf1FwEugRaNxBCCqlrNADgWR7dhWzRxUXWnVOAla1bt+K3337DRx99BKlUitGjR+P333/HvHnzcOnSpYoenoAALdOYMSPrjuTcxwd1biRA6Cbk5qwrek4+O4mk/CQcijvEfV6tGtnF2Yz2QWGDrBpnWTB2zTr57CRHT/6Uh0sa3+9DwDT2kvGCElNFsNcPadoJoW6OQOWCLb7lfzf/R9s+FHsIH574EACw69Euk+fbcG+DVeMQQYqC2KkoyWqFyY0m0/b9/fBvq84pwE5KSgoaNdJVF3dxcUFOjs6Fo3///jhw4EBFDk1AAADQpUYX1vbOQZ0xs/nMMp2bULvBEktMUn4S9jzeQ27r3dYmNJhA66cm1Fh+bTnj+IrIslhVlYGqOu7Khr1kvKDEVFKMV5d3P9rN0bNsUHPWlztcsSaVIQZFKHZpd9KL0hk+5gRBsPqEP8x6yGjTr+SZyxRUy4MZ18ILrRO0xTWgTBkqJLywM0FBQWSdr7CwMBw5oisOePXqVcjl8oocmoAAAF1qdmMCXQKxqvsqOMucrTpnI59G+GdwqcsZoZuO8Zk0x+fE07KHpRSkAACjtlZKQQqrS1uWMovX+KjFKctKVQ2S10JQYmyBvWS8oMRUUqIzo2nbCy4usMt1+of1Z7T9cfcP3Ei9YZfrCQjcSruFPrv6oOvfXTHm4Biaz3F6UbpF52JLEWrM3FZzTe6/M+4OtvfbjhnNZtDaRTC4j8glwkTangwZMgTHj+sqhX/wwQf48ssvER4ejnHjxuGtt96q4NEJCLCjL7DbaGMjq45/LfQ1hLrrC/3pFpHMuccCwKJLdHdWvfXmRhr9uW1J7CBAr2sDMOchZaIS6zCFqkIUqgpZP3u+MUoCprGXjJea7yJQEfg6+dr9GhqtBkfijjDaf7zxI3oG90QzPyFW5pXFjsUuqVly9NuRvpFwkbnQrDC7B+7G0H+GmjzXpeRLrFWv9bT0b8kaY0NFJBKhgU8DNPBpgGup11iL0fk4+pg8h0DZWLp0Kfl65MiRCA4OxoULFxAeHo4BAwZU4MgEBHRcT71ucr9SrYFcyh03wwY13TuhlcNF6gUXBxezxyXmJ9K251+Yj+39tzP6HYlnPt9NYZwxbNHFRRgabloG88XYolGWZCmTG03Gb3d/g5+TX1mHBQBova01AGBGsxmY1GgSbZ9xQgIB67CXjBeUmEpKeUyatjzYgqc5T1n36QteCQiUB2ypS8M9w80eF58bb3I/n1VNKlQFhur+0MiXvtJKrQchYHvatGmDNm34VUUXECgPzKU+HrTqPA7P6GSyjzGJeQZlRJXVFnP7vI+BkQEWj40t82I9r3pltqSoCRukQS7FWBkwTpZiCQ18GgCwfbzKjzd+ZCgxgiXGPthKxgvuZJUUe8XAUDHOYS/wilMOsUiW1EDgo4AoNUra9gdNP6Btd6vZjff1AGBEnRHka7FRjNC/gw0pVhe3X2zReQWY/PPPP1Cp+GcXOnjwIIqK+N8/AgK2hFq4ko3oFMufp8ZxItau+rPFrtjCFWxsxFjznXjCpnDkl+RbdS794tWLohf4K9q0O3FZ+fH6j3Y9/8tMech4QYmppBhPzgDbmzXNmcftT2UOmBeKXdqDs4lnefeViM27Znx37Tva9pTGU/BV26/weevPsazTMrxR/w2Lxvd2I11Fbqqbhx5qquaj8UeFrDVlZMiQIcjOzubdf9SoUWRgqIBAWVlzew3ePvI2azZENgpKuBOIqAtq22BEGlx4sRdjDo7B1gdbLTrSnIJlLVanp2eBzTXYVFKWp9lPEZcTx7rvh+s/kK8XX7bvghK1RpmAZZSHjBfcySop1MwjerSE1mSuelMk5CZg7+O9GBMxhsyyMjR8KK6lXmPtX9XdZbKLs5FWlCZklapkfHT6I7tfY1idYVYfq/cJV0gUMJ4WUJWqL85/gddCX4ODxMHqa73qEASBCRMm8M5MU1xcbOcRCbxK/BH1B4rURYjJjGG4i7LBFXtXmPAWNIXB5Pbyzssx5/QcXmOgLkw61/oBB5/rEpvcfnEbb9Z/k9c5qgpsn19KYQr8nJlxLYWqQgzap6tlc3PsTUbCAXvzY5cfMePUjHK95stIech4QYmppLAVgjqWcAy9Q3pbdb43Dr6BHGUOHmY9xKruqwAAh+MOc/ZPLUgFQRBQapQYc3AMWvi3wKetPiX3a7QaPMt7hmC3YIgqoaWi586eKNYU4+/+f6O+d/2KHo5AOcCWAtVSEvISAACF6kKGcDS2zqi1akGJKQPjx4+3qP+bb74JNzc3O41G4FVDXyhRJqHHk0RnRiOvJA8t/VvS2rl+65oC+kJZcj7/lWTqs1Ms55eZkcvNtioGoJ9+dhqRvpEoVhfjnyf/oGNgR1R3qY5sZTbZp0RTYlKJseWCq97ytPrOapud81WmPGS8oMRUIWafnm21EuMg1glgapAam8uanjvpd9B4U2MMqT0EMVkxiMmKoSkx8y/Ox97He7Gw3UIMCR9i1ZjsSbFGp9Gff35eUGJeEb7r/J35TmZ4kv2EfJ1VaNqXtzyqRb/MrF+/vqKHIFAJmHxkMi4lX8LYiLGY3WI2qysnH1QaFQbvGwyRSIS/+/8NJ5kTZ98CVQFpdX2S/QTOMmcyW9bwf4cDAI4NO0azEtT3rs+ryry/sz/vMfeo2YN8rVU7QSwtBGA6pfvxhOOs7fZwby3rOdOL0iETyzgzRLYNaAtAZxVbfXs1XB1ccWH0BZrCeC/jHkOhpBLiFlKmMVLRf3dphWk2O+erTHnIeCEm5hVgze01eFH0AgAQ6RtJtouMYlIa+zRmHEutCkxl7+O9AIDf7v5mo1HaB+OUkQKmqHwWNUuwdPKj1jAf0Jb4gAtKjIBA2bmUfAkAsPn+ZhSrrXcZ1BAaJOQlID433mxhxRxlDvn607Ofou/uvow+emvAzbSbeO/Ye4jNjuU1jj4hfXiPmZr9S5nWz9Bu4rlVqC5ktI2pP8YuRRnLosQUqArQ9e+u6LC9g9m+p5+dBmBINuQkNSigb/33FmIyY6wehyXonyFV0ar1qiIoMVUIVwdXFKoKLTJXA8CqW6vI19QfJ1VATW40mWFW58OzvGfW/+DLwQ3tXvo96w6shC5yrxKt/VtbfIxCqjC5/1FqHo4/0AVpvshTounCo5izg57auVvNbnAq6IfCBPPFtwQFWUDAtugt6NZAfZ6ZS4vLJ7ZUHy867tA4nEs6h0Nxh3iNQyQS0eJZTJVLoMoQdU4TvB2msyZrCA2n25irgyttu3dIb3QI7AAQtnGp1dM+oD0CXCxP96yn/x5DIW2CIPBRc3o8ZIBzAKk09AjWWaT0dV+MXdRvpt3kvA7XQqslNK3WFABQ3bm6bryVuTKnAA1BiamE/PvkX9Z2hUSBfnv6odeuXpxZO8wRn2eoq0ENep/WdJpZZURvfQGAQWGDyNf3M5npHSuC2y9u41rKNVoaXz61RmyBSqvC46zHwgqOCR5mPeTdd0H7BQCAak7VeB8T6hZqcn/PH85g0sZruJGQha2X45GnVGPHdXrROJFIhNSEjgw/dzYEJUZAwLaoNLaxbporH8Bnkrr0iq44n94VKtI3ElMaT6H1qebILp+oVuH0Iu5YF3cHqpuVBMUaQ8phrhotjXzoSQhC3UPRPrA9ZBIZ/h3CPnewhtU9V5PxJvkl+Ri4dyC+v/Y97+Op71tDaDAgzFDQ8M64O/hv2H9o7tccgEF5qO2hy/Jmyspt7AZv6vPlyx+9/8C1Mdcwv918APZxzROwD7xiYv755x+LT9yzZ084OjpafNyrzt0Xd/HZuc9Y9+ldwgBg/9P9mNZ0mlXn17PlwRbytQgiWuEtNr48/yUG1x4MgP4QyCnO4TiifLj4/CJ2PNxBFujcPdBQY2fz/c34sNmHdh/DzJMzcTrxdKWNEbIYOyhjr//zOuc+L4UXMoszyW39w71faD+sv8fPr5ZvYbYHybn48dgjXn1NXk9QYgQEbEpZVsCpx5pbTCpUMV2yAPbJ84BaA7DlwRY082uGgWEDsfbOWnIfl8VHzGN9eP+Q/Qh2C6a17UhYZhiLRsUaG2Mc5E5VmKzNXmqOfU/2ITYnFrE5sfioheUZJgmCwKlnp8htY0uLflv/eRoroR4KDwC61Mr7n+y3+PrmMC4WKixGVh14KTGDBw+26KQikQiPHj1CrVq1rBnTK83TnKe0ba6qu3yLBn53lR7szFV7QyQSoV9YP6yP4jdhpAa+saVILC9iMmMw5Sh9dYyaY78s7gl80RJanE7U+fQuvbIUUelR6BPax2QwogCd0yNPY+/jvfjy/JcADBmDLIk74VvvwVbYQolJL0rH73d/x4g6I1DLQ5CXAq82ZZk8Uo81pwxxyRXqc3Vyo8kADEpDUl4SHmXRFz8yijNYzyMWm1Zi3mr4Fmq41ARBEOQEXqxIhFJrUK64sqEZKypH4o6gU1AnRHhF0CxZ/Wr1w4GnB2h9746/i0YbzaeTbuXfCk9znsJD7gEvhRcZLxvgbJ17mUwiw/yL88ntsRv3I7x2FN5rOgm+Tr44l3QOgCE+yvg91nLXycZ1Ueusur45Fl9ajGxlNr5s8yXc5e60++f9yPftck0B28A7O1lKSgqqVePn2uHq6mq+kwAv3mn8Dmaemslo33R/E+a0ZM9FH5cThyc5T9C9ZndsvL+Rti82hzs48WryVV5jSilIIYUNYN7/2J58fflrRtuuR7vKdQxjDxmqGheqC/H3w7/x98O/ceXNK3CUViFrZAXFAa3psQYAMLj2YDSv1hz+zv6kEpNbkkvru3PATgz7l70OTFJ+kkn/c1sT5hFW5nOsvbMWf0b/iZjMGKzv82pn6zp+/DiOHz+OtLQ0aLV0d4516+wzeXlZ0KfDNxcXVtkpS3C6tccGugSSHgbUhQm9DNI/T47EH8GR+CO0Y0PcQnAXTKiWmH61+sFT7knzfBhTbwJqfXYQAHB3fi8AgEhEX4ThUmIeZD6gbT/Ofox/n/yLGq410PGvjmQ726IOXyXxSsoVDNo7CFObTMW7ke+S1h5bPetvYS5uPQZ2PN6KG2NvMJQt43GeSzpn15pvf8X8BQD4L+4/3B1/l+ZOJqTRtx32kPG8YmLGjx9vkWvYmDFjhHz+VmJsXu5aoyv5mi17GBsD9g7AjJMzGILBHFEZUbz6fXzmY9r2haQLVq6glX3SzOUWUHYoYzMxuS9SF+HOizus+767+h0WXVxk64G9dLQLbEe+ruFWg5ZgwtjiWNerLm27tkdteCm8IBFJeD/kMvLLbrGZ3WK2TeojeSu8ATB/9/bm559/RkhICBQKBVq3bo0rV65w9u3SpQtEIhHjr18/QzalCRMmMPb36cM/S9OCBQvQq1cvHD9+HOnp6cjKyqL9CZhm1IFRaLm1JW6/uG2+cyXCOBtZWWIRLHkGPco2WFRmNZ+FdyPfBUCPydEXnDYVXyM2WgfOLtTJFqqLVy33WowFj5P3DbEvc3fr1CAC1ruCqbVqRhKbM4lnGP0slVnkZ1p6WGphqsXf0cctPza5f975eeTrTkGddNc1sqTp5aQ9YLtvpjebbthfRYP8XxUZz8sSY2mu519//dWqwZhiyZIl2L17N6Kjo+Ho6Ih27drh22+/Rd26dTmP2bBhAyZOnEhrk8vllbrys3F2Ear715109skyF5+e/dR8Jwu5mnKVkSnk++vfIyk/CZ+3+dzm1zNHsFswYrLKJ/0iG/rUkGz8/fBvAMCUxlMq1OXOOuwruBt4N8C9jHto4dfCZL9eIb3IWKfhdYYz9u8ZtAcqrQpqrZq31et/J+guIVotAbGY38N9z8A9uJt+l1y5LSv6CYG9fNnZ+OuvvzBr1iysXr0arVu3xo8//ojevXsjJiaG1dq+e/dulJQYFL+MjAxERkZi+HD699GnTx/as4JvlWYAWL16NTZs2ICxY8ea7yzA4H6GLrnKmINjcHc8m22gckKNLwHK5k5mqrYKFZVGhbln55LbH53+CN1ju+P7zt9j9yNDPOX11OtmzxXi1AJUtbHJwqOY1CEUnoEGJeZY/DFGHMneW4bq9ReflLqkEXQZlF2cTcaCUGFTIv6K+Yu0JuhRapRwdXAllbAtfbcwjjOH3vJy6bnB8yKtMM2iWjjLri5D31BmCms9+58aYlz01iPj92hPa4jxtXKUOXic/ZjcXnljJd5u9Lbdrm8PXiUZX2Wyk50+fRpTp07FpUuXcPToUahUKvTq1QsFBQUmj3Nzc0NycjL5Fx8fb7J/RVPZTZdv/ceeenbfk33lPBId7QPbV8h19ZxKPMW5T78aZ23xtpeJXQ/pLn6t/FsBMGSj4YK6AjevrW7FTq/4DAwbCEAXlGmJ255KQ58oqbT8VxZre9bGiYQTGLB3gE1WvVMLdSmfE/NNJ9WwJStWrMDkyZMxceJEREREYPXq1XBycuI053t5ecHf35/8O3r0KJycnBgPOLlcTuvn6enJe0wlJSVo166d+Y4CLxXG8Z76LFXWoJAqSDlgXAONClscy/GE49BCi//i/rPomidStjPa/jgXi2F1DC6vDzIf0FbznaROuBJrSGKiIRU3+nMiU5kJNixR9KhWJH2yFEsUEP0Ev6w1sYyPL8nSpdDXW8D06N3Ujd+jPV3WjZWYZVeXYcfDHXa7XnnwKsl4m82ufvnlFyxcuNBWp2Nw+PBhTJgwAQ0aNEBkZCQ2bNiAhIQEXL9uerVEJBLRPnQ/v8q9Ik4twlVe1POqx6ufqdWINtXb2Go4FmEuBoItb35eSR6WX12OexlW1pChEO7BncJZLxwrMmbIIuwYE0MN6gQMwbLmXBP0+6nKztpea7G9/3Ysam8bV73YdNMLIcYkFSQhPjceBSrLjmPjYKzOL/7C8wtlOk9eXh5yc3PJP6VSydqvpKQE169fR48ehkrhYrEYPXr0wMWLF3ld648//sCoUaPg7OxMaz916hSqVauGunXr4r333kNGBnvQMxtvv/02tm3bxru/wMuHs8zZqlplVPTuYFZZdAhmXM35pPNWjcNYUaA+H9VaNcQUWZtdqJ/g0+WvcZ2Y9KJ0bLy3kZbF0RKcZbrfKzUWIcglCM2qNcMPXX5gPUavfFEX4qyJ89TLOT3KlCHIe7AUU5tMJVNYUzH+HgpKyi5ruTDOamkqbrgiEWQ8O7wD+82xa9cuxMbGYt68eeY724CcHN1k38vLdHGn/Px8BAcHQ6vVolmzZvjmm2/QoEGD8hiiVehXZgHgg6YfcPaz1ofeOJUgAPSv1Z+lJ5Pf7/7OuY+tirBZbDBp1hfH4sJ4kkwQBNr9qVsN2HR/E7frBW1s3ONsE9AGuGF6jEJldyb6OKLTiafxJb7k7Kf/7KjmfZlYhgbe/H/D1AxAbMillrlySUU6sanWqnEr7RaZ2OH0yNMWF5uzVSrPiIgI2vZXX32F+fPnM/qlp6dDo9EwFnP8/PwQHc3MgmjMlStXEBUVhT/++IPW3qdPHwwdOhShoaF48uQJPvvsM7z22mu4ePEiJBLzn29xcTHWrl2LY8eOoXHjxpDJ6HJqxYoVZs/xKtMlqAtOJZ7CpIaTKnooFkGdHJd1IexqylVyQurvwm1t4PrNaaFFs2rNaJPYd4+9y9rXHM+zubOHlmj5xeQl5CXQ6py9d+w91kylxkhFUmzvv52RAMXXyVf3giIKe4f0xozmMzgLa+oX4KgWcWuUGFOZIweHDaYlIHqU9YiRzMi4NgwVV1nZEklxvffKhiDj2bGZEnP8+HFbncosWq0WM2bMQPv27dGwYUPOfnXr1sW6devQuHFj5OTk4LvvvkO7du1w7949BAUFsR6jVCppGm5enumiWbaGOunWp3hkw1wsARdsE2q9YvNm/Tdp6YktQR8EWd6YU56MrSDmauFYyopr5n94Ococ1HCtYdPrVnV6hfRCRnEGZjZnZt6j0j6gPb7v/D1va6Exq08/wdJD0fiiX33OPnIp3SDdta4vTsa84Oht+L2otWpaZjo+tSGMsVXQ6P379xEYGEhuW+KrbAl//PEHGjVqhFatWtHaR40aRb5u1KgRGjdujLCwMJw6dQrdu3c3e947d+6gSZMmAICoKHqCEVskUKBS0TLeHlR30blhVTXX1QFhA8j09McTjuNZ3jOrZSXV1Zltsc4cBEGQ1oqy8sG+zVYcRZcFH536CLfG3SK3+SgwgM6yYFxLhgq1PIK+ACXXfaNX+JxkTmSbqXNzwZh3iAvRoa4DUgpSGOmopxydwihgaUoG5KnK9vs1niNkFVfORCKCjGfHZkpMeTJ16lRERUXh3LlzJvu1bdsWbdu2JbfbtWuH+vXrY82aNVi0iN0VZcmSJViwYIFNx2sJh+MOk69NfbG2zJihP9es5rMQ4R2BqPQo/Bn9p83Ob0+e5z83ud/YEqMibGsVMVcZGgDOJp1FQx9uZbtSYudiX52COmFE3RFm+4lEIvQK6WX1dZYe0j34Fx94wNlHo6W/Vw8n03FpejeyqHS6IDZXG4INW1WGdnV15ZUR0sfHBxKJBKmpqbT21NRU+Pub9pUvKCjA9u3bebkN16pVCz4+Pnj8+DGvB9zJkyfN9rEVFS3j7cGT7CcAgHxVvpmelQtj64slLpq/3/0dQa5B6BPCP0MSAHg5cltLbZE2HQCe5SUAHAaCFn4tcCNOgpIiIy8BLX1SWhY3ZGNFw83BDWotU7nRX8N4rtGlRhecenaKlE/U1NOFqkK4OLhYNB7jBA4yj+u4jQPouRMYFzGOts9YgTFHpG+kRf2NoSpwXgqvco1PtARBxrNj8VN306ZNJv/szbRp07B//36cPHmS05rChUwmQ9OmTfH48WPOPnPnzkVOTg75d//+/bIO2SL4pkWu78W9smwpqQW6m91B4oCBYQPRyMd8MazKgrlJYIGqgJaG2UPuYdPrU1N1cuEis0zgv2ywuRJYElxqb97eeA25xQbllm3t4OeTBpnxJEc3Yfzt7m+0Pkl5SbCU3iG9AbDHbtkDBwcHNG/enGY512q1OH78OG3Bh40dO3ZAqVRizJgxZq+TmJiIjIwMVK9ueaB2YmIiEhPtN5GoaBlvD66k6NKn3n1RdTKTAcyCsXyUeqVGiXeOvoOVN1Zizmn2WmmmFre4spgRIBhuqsbuykNqD6Ft+8vZs6NmFXIXwhWJRBjdqiZzh4afm5a/sz9+7PKjyT7GykpuSS6Zrn7fYEMSHi5X59ktZmPngJ2Y0GACALp84rNwZxbCMPXcdN/8vLFLUBcAQOvqrRn7yppgxdXBFT93/xmBLoFwdaj6NQ5fNRlvsRIzffp02t/777+PCRMmYMqUKZgxY4ZNBsUGQRCYNm0a9uzZgxMnTiA0NNTic2g0Gty9e9fkhy6Xy+Hm5kb+VYbCnRHedF/IptWaYlS9URy9LcfYStA3tC8pvCo7fCxSvXYZVvKN3QWodQGsgU/V9qrm4mFrjGu9VDZiUvPQeL6hiJ2YRYtZ/l8MknNMvw9rVk71bqF8a0DZglmzZuG3337Dxo0b8eDBA7z33nsoKCgg09GPGzcOc+fOZRz3xx9/YPDgwfD2ptdsyM/Px5w5c3Dp0iXExcXh+PHjGDRoEGrXro3evXvzGpNWq8XChQvh7u6O4OBgBAcHw8PDA4sWLWIURSsrlVHGv6oYV2DnEyO2IWqD2UQY66O4y0IciTvC2k4QBOp61SWzHgJ0paq1f2u09G9JO0Yi4nDpMUqXTH1fs5rPQj1/5j1HaEwvdr0W+hoAneWic43OJvuypWzXZz6t5V6LbNvzaA+jX4hbCILdglHXqy4ZR6O39AFlK0iqRyTlVoRG1WXObQJcAgCAUQvHVnQK6oTDrx/Gii5M9/CaruVbw8sWvEoy3mJ3MraiNI8ePcJ7772HOXPYV0VswdSpU7Ft2zbs27cPrq6uSElJAQC4u7uThTjHjRuHwMBALFmyBACwcOFCtGnTBrVr10Z2djaWL1+O+Ph4vP121cr53bZ6W7IOAGDaf5WPb2FmcSa8FF5wkjqhUF3IsOpIxBJ81OIjHIk7gucFpt21qMRkxjCKEZqm7L7ufFbuqBnfjD+71MJUBLmyWfT4Fbtko7pzdSQXJFs0xleJAOeAih6CSbhKxhSrmN9j+4D2OP9cl8HIVFpX7mvp7kdbTAz4MnLkSLx48QLz5s1DSkoKmjRpgsOHD5OBoAkJCQzXuJiYGJw7dw5HjjAngBKJBHfu3MHGjRuRnZ2NgIAA9OrVC4sWLeLtt/3555/jjz/+wNKlS9G+vS5t+rlz5zB//nwUFxfj66+/LuO7FqiM6C1IevjIytqeptOyA8D2mO2cdcs+Ov0Ra7u+JhtVdr8oosTGiYBQd/riqbMDe+yNo0xKLq9plT4YsOocdr65E1nKLDT0aYhHz9hXobVqJ4ilOs8BYytQpG8ktIQWwW7BJmVNY9/GrLE9bIoNm9LIliFuw70NhjHa4Hkm9znFua+aE7OOyePsx6jrVdcu7pJqrRrJ+ckQiUSMz6M4ZRAmtTKtMFZGXiUZb5OYmPDwcCxduhRjxozhlf3AGvQFNLt06UJrX79+PSZMmACA+cVkZWVh8uTJSElJgaenJ5o3b44LFy4wsjxUJjzkHshWZtPaxkaMxR9RhkwR11Ovo9HGRljacSn61dJVVE0vSseo/aMwqPYgs9fotbMXro25Zii0J2bPLNHcrzmeP+WvxKQUpFioxJQdqtDRK2WmME5hfeH5BV6xGZZAfQgCumwrtiZHmYOnOU/hInOhZbCxHbaLiTF+6P3Zv3LHW7FZYnTtzDZapiErdHK9AlTebkDTpk3DtGnTWPedOnWK0Va3bl3OVXJHR0f8959l9TWM2bhxI37//XcMHGhYBW/cuDECAwPx/vvvC0oMT/qH8cs0WVkwDlbnY820JmifDzsf7kRL/5aMYs56ApwD8MaBN2htD9LYXUiLVAQUpa/F8nREPc1FXa+O5H7jWlVke2YnyKvp4mK71uhK29etRjc08G6AjKIMfHv1W873cefFHVa3KL4FdbOLs7Hv0QEcSziM9oHtMareKNriqDVKTKRvJM3tS/miJ+S+R1n7stUOO5FwwuTc4nHWY17KLRsvCl+g756+kIqlDDc9VU4kjt3wwlDbee+XG6+KjLdZYL9UKsXz5/wnvJbCx8xs/MX88MMP+OEH9vznlRV3uTtDifF29Gbt++nZT0klZn3UeqQWpjIC6NjQpyss1hQDADKKMkhzLZUHmdzB0GxUhMWBek0+aZ5TClJo27bOVsaGPerE/Bf3HxZd0iWnqOwVuo3ff3nFf1ibvnj/nWTW9jMPX2BsW/oK57O8Z5QLWn4tfXrPweGDLT/4JSIzMxP16jEz0NWrVw+ZmdbVxXiVCHMPw5OcJybrVlUF+LgH61Oc67mRegPN/JqV+drRmdEQQURzEQ5wDiC9EfY8ZrpeaQprMdoA0NzJlOndGLtL1OzPypKMTpA4xkHqGs34LP6M+RPro9bDz8mPVoqBjZPRaYw2qiJS17MuYrJi0KRaE0Y/b0kE5uw7BrnvKdKdzFPuSWY1s/Q5v7XvVhSoCjDl6BSyTVvC/QzY8mALo82cpVo/l7EG/bnVWjWmnTCe9Ivh7FAl819VOuwl4y121v/nn39of/v27cPq1asxZswY0kQkYD1ftPkCc1rMwZa+zB+yKcqiQCTls68mUWtz2HsMlpJRlAGVRsXLDYcazK9P56knwsf+Vrmn2U9tfk69AmN7bF/w0njSUV48z7HuwZavZI9z+nLfPTxOo7szjKlvCIC8nmq68C4bej91H4Xpoq0vO5GRkVi1ahWjfdWqVYiMLFv2IYGqA59nyLGEY7TtbdHbGAsW05tNt/jaGq2GoTiYf75w7TfIUYljPG3PzuuJ+GJvlPEBpYihKdalmDZe/EnITQDAL3vXxA2mixrqs4uxuY7dTcohlTD990GNm+W7OKR3la3uXJ1RlJpQc8ehGbsY8rlmckEyClWFVi1cmTrGwfskMpzW4+Lzi4wFUAHLsJeMt3h2MXjwYNq2SCSCr68vunXrhu+//97qgQjoaFO9jVVFvywNHqc+LBRShYmedJr4NsGtF7cA6BIMUE3v11KvoXuw+VR7ZeVZ3jP03d0XIW4hmNtqLrrW6IqTz5jp+95p/A6SC5Ixv918ss24vk6Ryv5B57U8OFbrbMT9jPuM5A+VCQ+FR4Vc90Ued4E0a5nx102AkuWS+jtafm05xjUYx3IUN+ZcOl8Vli1bhn79+uHYsWNkBp2LFy/i2bNnOHjwoJmjBfQZ804+O4lW1VuZ6V15oRZV5MLY4u4odWQoH8Zuw3zIK8ljKFFuDm4mJ69Sl0dQshhFqJYGsZxuFZm9w3Q2rRbB3rhbyJxcH0/QZZviZ9mnzwc29NlAH1/p+9QvMKXmGhZ8xPI0aJV+tH7ULJx8rn8v4x55bL4qH0P/GUrbrylkd/0aU38MuyXGjHL7MOshZp2ahUFhg7C4w2Kz46NiSomR+5zG1XTg6tHj+LLNlzZ3PX+VsJeMt9gSo9VqaX8ajQYpKSnYtm2bVanWBGyD3n+2pitL6kYWqIKIS4lZ0I5ZS+HrDl+TAYej641Gp6BO5D6LK9NbWeDoRMIJAEBcbhzaBbbDwnbsOc3fafwOvu7wtUkf6nkX5rFXE6aNjXuczarp3Bi+6/wdZx97KBgKieE72/lwp83P/zJw4I5l7q0EQdBSLbMRlZRLy55zJvGMVWPTo68LtfTK0jKdp6rTuXNnPHz4EEOGDEF2djays7MxdOhQxMTEoGPHjuZPIAAAnPEcVYEglyCEuIeY7We88KSQKBiLeOeSTNeQY+NUoqEuSh3POpjQYAIeZj1k9FNkj4a2xBMAIHbIYD2XptDg1ic2kYmLDWeZK6o7V4ebg261RKPV4EKS6WxsTOifR3O/5rRt/X1yK+0WAKD1N4Z0vBLFc+ifefrPw0lqWbFLaozfwL0DTfSk06RaE4bVBjDvZrj69moAwL4n+0z2Y6M8k6q8ythLxgvOfpWMc0nnEJcTh+Z+zVHfm380mcVVdCkywVPuiScv8vHv7ed4q0Mo3BS6Sf9roa/hqwtf0Q5TaVWkOVsukeOz1p+VeSJnKdSVsUOxh/DxmY9Z+916cQvRmdFo6NMQTas1BcC+ipSvyoeXxLo4DeqKlkwsY1Xk+KRhtpSBYQPx98O/dde2ooKyWWxY7JJVSTTBl3ujsPlSPD59rR7e7cy/+FxRiQZ9fzqLD7rVxtBmQZBJLFujCZ3LbzVoRrNZ2B6zHYDO+mgrcktyyYnLq0hAQIAQwG8lA2oNwL9P/616RXWt4GkO3T2XLR6Gy0XaHPq4j4dZD1kVGADIKtBC7mT5ApxGS3BmPqTS0OU1/NbjQ3J7e8z2Mi9yKDVKyCVy3HqWDTeFFDVdayIhLwEdgzri9rNsWl9VdjMQpe5keuWBWlvHOEMbGxefm3Zng6gEBU8/wIbJ9TD1+FSyuZ5XPdbMa9bGN/KBauXxUnghs5g9PuNM4hnBElNG7CHjbTb72bdvH3JycjBunGXuFAJ09j/djwNPD2BOizkWKTE1XGtgUftFcJG5YOapmWb7UxUBuUSOrstPgyCA1FwllgzVFbt0lDri0NBDuJJyhVRm7ry4QyoCJZoSWsYTewoaKlRzM1WB+bn7z/Bx9MHI/SMB6Cw2Wx5swaSGk0glhm2MfLO2sKF3rTuTdAaBLoGIy41j9LFHrJBcanio1PGsY/Pz2xJLkydsvqTzIV96KJqmxEQl5SAhsxB9G7FbfOvP01k1Zv19G0ObBeGXU09Y+/GlTS0vXHrKfKDNP3yCtb+jlF+xupjMGOx9vBfvNH6H1p6jzHmllJg7d+6gYcOGEIvFuHPnjsm+jRuXXx2dqoibg84NS0RU3XXJxPxEMv2/KULdQ1nlLJUQtxCrxsCn8CIvJAWMprc2XMX95Fyzh5Zo6AttphSYfrX68SqQXawuRlY+gcE/67Ih3l+0A8/zn6O2Z22EfEo/XqM0FCK29tllzrohdX4ExxqbMfU4vX3VzVVk4iHj8/Edy4mEE+hWk5lMgQvqnEDJkkZfDy3dtgAvykPG20ziffLJJ3j06JGgxJQRfdEfS2Nc7mXcw+rbq1mL5o2NGIvN9zfT2oo0BpO8TCIjF95PxdD9d4Ncg+Dn7EcqMfMuzCP3aQgNLx/m8kLv2jav7Tz4OfnhcvJlAHSB6iH3QLuAdrRiabkluXCXu5fp2rsf7ebcpw/etiXU1aoFFxdgWJ1hNjqxyKZWGABQE9ZboopVGihkOiWz//90LiJ73m+HpjU9TR733X8xrO2Ng9xxJ1HnLx/q44zYdOZkQ8/rzYJYlZhd11LhzGIg0mcKNMewf3XfVVphGnoFDseRpB0A7Jc6trLSpEkTpKSkoFq1amjSpAlrnQZAF3ep0dg+w9/LxLpzcZD7ADuuP8PnpotyV2rmnJ6DP3r/wWhXaVRYfWc12ge0h5PMibbPzcGNMfG1b4Fh8+YUqTNT/px+yG8S/LTwMt48sAjN/ZtjVvNZkEvkrBN7ALwUGAC4l5SHQ3cMmRTPP8pDkxo1sOQgRwZSorR+VaniQPVg0BJas5+vKYVDq/QBwfEZHo47zFrjhu2cmqIgSByZC2RPsp9YpsRQ3FLyS4pRhjVNASPKQ8bbTImxV32YVw39hFskEiGvWIXcYjUCPcyv8BaqC5GUn4TqztVpbk1v1n+TNJHTrkMRCNTsUcksGZ1kYhnqe9VnpFy+lXYL/Wr1g5uDG3JLcjEgbAC/N1lGqMUkJzeajN/u/oZuNQxCa3id4QCAqylXAdBXWhr4NMCanmvw1n9vkfv9nQ0rT9biKHXkrEzfK7hXmc9flSmLJarel4dxdGYn+LkbYoDuJ+eaVWJWnWTPrNc2zBv/TOsAAFBrtHiQnIcBq9j95xsEsCu2BEcoYUu/lqztXBRrinH+th9Q6gL+KllhACA2Nha+vr7kawHr0RcPVIqtc6OqLFBriVD5K+YvrL2zFmvvrMXbjejFqlfeWImNr22ktRWouBcnykJh3LvQlvhA4/IAYgdTaWENk3RCw89Cq6dAnY07uXfI0grdanbDodhD1gyX5I3frgCEwXo/edM1tArxwpU45nsgVF4gtA5Q5TQlPRioMUZ30+8i0td0NilTBY0L49+D2PEZ5353B3fa99chsANmNZ/FmPwSGnZlh0+pBeOxKtN6Q17tP4gk1qdqFmBSHjLenssVAlagn/ARhBiN5h9B+6Un8NW+KHzZ5kuTx6UX6uJUrqVeo8VljI0YS2Y1oUJdyd9xzXzqQLa4Bje5btKlTwxgucXBusD++l46N7sOgR3IeBC2YEB9Xny2STS1f0q+mffPIwGBqZUpYyvYq4a+FgoADK492OLje/5wBv9FGb6jz/dEQaWhf6eH7rLXdqHSK8IP07sbAm6lEjEaBXFb4Li+dpGIfcXoRtoNs2MAgP61dAUJW/q1RH6xwUolsjLRRVUlODiYfM/x8fEIDAxEcHAw7S8wMBDx8fFmziSgRySuWpMw4+cam8WBIAj4ORuq1/9+93f6fhCMCa7FSWYo7Biwg7VdAgU0RSEgNC7QFIXorpPLFYNEVWKcOPoYHVF6iFare6G3frDFiJhC+aKHyfHoMVZg9BnVtCo3aArqoPj5SAytzYwBMec2vu3BNjJmkA1C40yro2PM+Abjadt6BYohHwn2Z66lJQ3EIgeoslugKPFNKF+YyK5aPt7yLxXlIeOtVmIKCwsRHR2NO3fu0P4EyoZ+wh2fYVhN2HgxHn2DB5uM3cgoZs+SsvvRbmx+bTNa+tNXifU3lhhifHeIe1XEuD+VCO8IFKmLSEtPecXEnHimi0k4l3SO/LzYlAj950VVYgpUBXia85RmnVIRKlxJvsIZyMkHauCjMfYodknNNkOtg2M7bPddUt+/tW4exrfW9O038SzT8Bt5b6tpBeKvKW2wdlwLOFlQuKywRIP3u7D4jXE8PP+K+QsxmexubFRyi3SfR3ZRMRyD/iTbYzJe3cl6165dWQue5eTkoGvXrixHCLwMrLvFLCAJAIWqQlxPvY7Pz32Oxpsa49Mzn3KeIzqT6QXSpUYXq8dEEARa+7fm2Zv+XJzRI5zRrlXxs7B+3FtXCNBYibH0uaRXZLVqqvLER+6WjllkELa/n2MqBOaeZ0uuLOF/LRZeC32N0UYQBGadmoVI30jSCkRwyOEewWxKHDclai0IjSvUeY2gKaDLe02xwaKUZoeU/a8S9pLxFs8oXrx4gf79+8PV1RUNGjRA06ZNaX8CZUMvIIwVllbfryP3uTpwF4oyRiwSo6FPQ6zrvY7Wrs8wptGKwcciYjweD7kHQt1DaYrLxWQzGUnsgKk6G/oVrGJNMTnOi88vYtDeQbTChPE58Zh0ZBJe/+d1q8cxtclUzn32COynKqWDwgbZ/Py2hPrQyyuxLN2oHuOA2IN3U9Bxma42UFSS+ZoQrWtZHrtVv7orPu5TD3fn98LkjoaMPKZWVv+4y/TnN+bkU12hu01X6Ys+WhW/FduXEYIgWBdKMjIy4OzM7jYiwESrsi7LYkWRVMxe9PFZ3jNMODwB/zz5BwBQojWd4dBYxpZlQU0LLVILmcVfNCiGImA7xIpnpKIgkhiK38Ys7oMZPUqTrFAsDWKZ+WD+Q9M7QiYpVV5KlZjs4mwAQF2vuhaN38FbZ7kQSyluVSYsH3qKEt9AQez70Cr9IZLkQeIYi+9O6DKPBroEkv1s8jwzMZ5CdSGcpPTf/MxTM3E0/ihuv7hNuhxypbfuXKOzRUMZ/E8/uNb/FK71P4VjMN3KVxj7ITSFurIV6mzL6/cJGLCXjLc4JmbGjBnIzs7G5cuX0aVLF+zZswepqalYvHixUOzSBuiFr8posaNYBei/5mlNppGrHWqtGlKxlNPkLObQU0n3MJbVjJBPD+Db1xthZEtDzRnjFfT/Xv8PTjInFKsN7gtlMeFby9QmU/FO5Dus718/5p0Pd2Lnw524O/4uqwDWF4qzBn2qytoe7MW7APsoMdTaPsaxSmXD9i5N1BTTR+OPovU3x5Caq0T96m5IySlCuJ8rlgxthBBvZ2QUsK92bbgQx3l+fcA/F82DTcfPcKG32rgqZOjdwB+/ndX59BIaF85jDsUdwrLOy0yeV1LqD650uEP7deYXv1ruZAAwdKiuCJ5IJMKECRMglxssmhqNBnfu3EG7du0qanhVgmKVBtoST4gdshAs7QsAUKo1OHY/De3CvOHpbPvEIpUN4zoimjLI3FH7R7G2K0RegPstqPMaQpUbCYnzY5RkGOqkyaXsnhJFiW+avWb96m64WurepZeX9zLuQaPVoENgB94B/NyYly1apUFRkXrchqL6bqjyIgC8Re9n4Wdb3zMSD7J0ikdhwiSz49n2YBsK1fSYJrYYp5L07nAM2spov/PiDjoEduA9vrQigyuySMR8b4RW9/tJzlYjX6mGi7zqZgCsCOwt4y22xJw4cQIrVqxAixYtIBaLERwcjDFjxmDZsmVYsoSPGVHAFG4ObvBx9MGhO+lGewxfFTWVK1WJYCMx35C9g7pifzddV4xKJGFf4fpk113atrEGrZ9EUy0gKk35FLuksuvRLsglctZ4HBcZfbKpJbSsqR9ZXcF4FrvUWxnEIjGZUICrjy2huhhYWxOhvKA+9KbU+QapuTpF5UFyLrIKVbgSm4nu359G2GcH0eprZvyWKVJYElEYM7ljLV7n+mEkd7BqixAvcqXUdhAgCMPvZ9y6yzY+f+XH3d0d7u7uIAgCrq6u5La7uzv8/f0xZcoUbNnCrOAtYECXSlw3iddodf+nbLqOqdtuYNTaSxU4MuvJLTFvvaBClTGF8ZPQ2W+oid6WIxVJIUbpc5cQgVB5oShhMjQFXFYSqjsZt3VsWPMgPPpa5z7lUFrXqkhreO8l2hL0r9Ufh4byD+xX5bDJMQtlV6mlRFT6vKzhZIj9sVSJuZdieD5pCvTudtxTT2oJBT2XktnuY3Zrmy0XDUWSAmiKQlCS2RbaEh/M/OtWubnNvyzYW8ZbrFIWFBSgWrVqAABPT0+8ePECderUQaNGjXDjBr/AVgFuvun4DQAwcreLHQxKDTUziLkJMnX/4g6LyYq2xsGR5jC26LzIK8GJ6DQMjDTU7NC7qNkTYwG16NIi1gJUOYUqtPN9Hd/DYB1ML0pnFUB6JcaadNGkO5tIgnxVvsk+pihSFyEpLwm1PbktOlSOxh0lXxunHLUJNhTUMZkxiPCOwOL2i9HzW/7+3TvfbYthq027KG68GGf2PH0ams4+9/SbvsgsLIGPixwz/2LPjgQADxb2Qe3Py5YlCAAIrQwisQrqgjpw8DAUy7S0svfLwPr16wEAISEhmDNnDpycXl2XOmsgCAI/HX8El3DdYlRsps7FRp/ONya1at5TXAUH+aAprIVAlxqc+6s5VWPN2GkKNaGGGrrJOFd6YDoUdzJ5KrRFway93BQysiiv/v+j1EIoSvMY6J9XFiX9KF0YIQgxxbJgmRKjf4+ezrq074Ta4MJeWGJZynyx3DAvkHmehyqrPTRKP/bOWgfIHSScmT7pg2S3fNlSyXCpswgAoCmuDk1xEI7eT0X7pSew4712cJJJ8MupxxjWvAbq+pt28X+eXYR5++7hrfYhaFebmYToZcbeMt5iS0zdunURE6MLXo2MjMSaNWuQlJSE1atXo3p19iJ0AmVH4vyIfF3dxfA565WURj6NWI8jCAJrTj9By6+PYfr2m2Q7n4n1kXspKFZpkFOoolkm6nvVx7DVFzB39118c9AQVMmVXMCWUF2T9HTY3gFb7tM1+ciFR9Dnlz9pbdGZ0axKn961ji2uxhykJUYs5ky8wOezHrV/FIb8M4RWv8YUVPN6TdeaJnpWPMuvLcf9jPtIyE2w6LhAT/OpSX81Kmg5swe98Oeasc1hDrFYBB8X7sQMeqSSsidzfJyWRwaPagvZJzavIuPGjUNSEtOi+OjRI8TFxZX/gKoIF57oZK6oNP5B7nu4IodjMyxNACKXyPFhk9koTu0HQGRy0l/2SS7z3O90plt71fl1yKr3pjj6wJB10UFa+p61BllEgEB0ZjS+v2a5qz6ba5QFRwMA3Bx169zUzyyrkN36/cutX/D2kbdZ95FnleqsTGKWYqAAUJg4lneiGqk7+6K5Pdy3JYpkiEvjn57nFOP7IzH4Ym8Ufjsbi94/njF7/Ce77uDYg1S88furZ23XYy8Zb/FTefr06UhO1vkQfvXVVzh06BBq1qyJn376Cd98843VAxEwEMdSgE+dayhiueCAYfVW/4OlBv91DqIHti05FI0XeUrsu/XconFM2Xwd9b48jMiFR/AoRfcDHhg2ELNazMKzTN1KydbLhompu0PZCkbygU1A5ShzkK3MZrRLXR7Rtv2c/FgfYHkq3Wqlpatz1PEcvJOKvVF3TfYxxdMcXRaY6Sem4+tLX5vtT01hybdSPC/smOY3p8R8AD4VqdhypWF6j3DELumL2CV9cWteT/RuYFkNIA8n+xac7LHiDJn9x/hOdHd6dausTZgwARcuMBX4y5cvY8KECeU/oHIkt1hFuoFZyn/36OnhvRw9UVRSdQuDtq2mc63iiuVk4/Xw16GQKnD9TkNoS3ygqL4DRxL2cfYvS7wMAEZQ+s0ve2Lua/WN+jiAUHmy9qeSkW9w59ZbYgiNId5RIVFge/R2HI0/yjiWC6kbe7IEiyiNlZWIdfflpduhKMlsB2VaT4S5s7vQ/Xr7V7LANDemny8icRG0PL8eroQJ9lBiAEAkMzy/CpUa3EnK5n1sUpbBsvTiFc1yZi8Zb/EsYcyYMeQFmzdvjvj4eFy9ehXPnj3DyJEjrR6IgI4Jhyah//72kLrSJ8Raivl1+6UMiKGb8OgtEw4SB9Ryr4V6XvWwqvsqw3E2+kGnP5qCu+Pv4usOX6NNdXqWDhepBwAg0DWQ5UjbYhzAqaeohEDIpwdQ5/NDyCvWxebIPOl+tFKxlPXzCHIJMpzfwlU6/fl+PRULqRMzRe7eQXsxrek03ucr1hRje8x2tNnGPxOKvYS2rfn28g8W9Xew0PKxclQTADrXC5FIBA8nywOaN05sZfExliJxjNP9N7pflGrL3DReJm7evIn27dsz2tu0aYNbt26V/4DKiefZRWg8/wiG/srPAmuMfkqoTNMV1HUSe+PsI36V4SsjMpFuQcYS96ldj3aBIAgcvpcCiTwVMo+buJ/JXe5BKirrQgV9bMaJE2582bP0lXlXrqOzDAuODlJdP4IwjM+cd0Bh/BRGm7bEukQmdOg11goKfKBMHYiSjO7wkFvudm18XhFHZjEASC/gW6iUIyaGJe6VC+N6Y6aQOMWRrw/fSyEXcnlBuQXGrbvC/7iXCHvJ+DL7Rzg5OaFZs2bw8Xm1/PzsRWpeAa2YXmRpMT5CS3V3EUFL0IVMdnE23qj3BmY0m0E739OcOF7XvTWvJzqY9NUUITlH96M1fkiGO3cEQC9qaC+4lIzfzugyR5VotJix/RYAQCzLpvXREBrWCT81k4lBSaJIHRMPVJWmdOLJstpWz70ZMrM9Uc2pGufxXFhScbqqKDGFmmzefad2DSNdGfgS6lP2VLyRNTxw7pOuiF7Uh3U/NdUyH9juV5FEtxIndYmmZS4qVqlRoHw1FRmRSIS8PGb8Rk5ODjSaym1ZeJiah00X46C2YEKkZ89NnXvF7WfZvI85/zgdI9dcxJMX+ZTU4aWTQxFwPSHL4nFUBGzWJ7lYl4xFX8RYj58TRwxFKXmqPMi8zkLmqYuhuxrHPUne0IPbSsMP0wqWl7MDxIoEiB2yTfZ/uPg1BHoYrOgFSt19ro8/8XU0/Z6LkkZCU8iWtMQG1nSj+QUVqZj9/P7OfKzepccS7LLdMWgbNGKDhYW7mKgOrYrp/WHJ8/CDbTfNdypFongOa+unlagNY3qQbFnSipcFe8l4XkrMrFmzUMBbOwbmzp3LWtRGwDyGH6Dux77zvdLUc1oHFDz9EAVPZgKEmCz0pFccrqdex+LLi7H5wWasuXiWPF90Kt3dgKvwloeTA849Nh2YP2rDLnx75Vu8tesnWvv5KG+81eBttPBvwes9lgVuAWW4lY9Hs7uFabQadKvZDTsH7ER9L4P5n+p/balCEFq4DHnRi6At8WXsi0rKxfDVF5GWZ98q2vZRYso3A0uzmh607Xc7h7Guxn7YPZzRpkchs407VpCnE+e5Pu8XQZt4mKJIXYSBewfii3NfcPQgoM5rZHgQiwjkFb+aSkynTp2wZMkS2sNMo9FgyZIl6NCBf7rUiqDXD2cwb9891P78kMWW3FUnHlt8vTd/v4zLsZmYuvUG3B11q/YSZ915VEQh2oUZFqP43qsVwcpjzCQfR5M3AQC8ZYbFgvaB7VHL3XSGwezibCj8DkBc6vKTyZGqHQCtSC4XyhfdoM6rz2hX5dUHoTa/WBLmbzq75TdDGhliYErRJ2PQu3K9KEpFbkmuWatU/sPP6WPM1lmTSzLbmh0nN7prvsjXPbukrlFwqTcXzmHL8DCLvaAvLxdA/WKflp+sLsngrvmiVXlAlcOMebTkN3jYyB3TLCLr5HNilgVWm5cUe8l4XkrMypUrUVho/oev5+eff0Z2dra1Y3qlEemr5Zb+2GWkS40YWmUAtCV+AMSkoOu7py9mnpxJBpifTzqPVQ/fp5zR+Cu2bpVGEbAdmR7fYsuDLZC60n1uNfn1sHJnbbQLsH89B05TMUf1XioSsQTucncMXPEUUcmGlTp9ummA212Ni/OPswFCBrafktTlMRxrrMOvN9dadE4+OBO6yXxJdgtMajTJTO+KxUthugDflkmtsfv99tj1nuH+cVXoJmfDmgfR+s7qSQ/c1zO4SQDCq3HXb7ElSdlF0JawvydqvaJTz04hLjeOzAhI1oAqXV0USwvIo3QQdld4KyvffvstTpw4gbp162LixImYOHEi6tatizNnzmD58uUVPTzeKNWmFxSKjQqAFRkXBLOA+IxCSEpXxaXOugQXRcQL2kq58fXM8dHftzHo5/N2TyObU6jCTyceozhlIOv+qETDpM81+224qk27eWYVG8XbibjH/8bvl0GYmESr82tDnd+AUdSWIMQoThxPq6fCRWQQxaWLEOHaF/Qq8iNbMrOnda3LtNjzKVtAaFyhKTYk+yHUOjmozqcrYYemd8S0ruzZL50c6J+HOr8+8h99hsynbwDQucCKRATEDplIzGdP0PK8gE/MbanLnFbB2UNTrLNAqXIbQ1vMnWVOW+JDixUGgCDnWmjp35LX/WtN7JiD9ymLj2FDqa7c1mV7YC8Zz0uJIQgCderUgZeXF68/S6w2AnQSMnUB9ARE2PM+t1KQ/+gz8vWxhGPIUbIHTRtPtsQy9n7mENGyidBvG4nLfTjXXopZp2ZZdW5L4BJOfNJe1vGsg9TcYijVWqiyDRXv9z02uBeQRUCtGZuGKZilLg9xNp47bS8XpopnAkBukW6lUZNXH+Ee9Sw+Pze2D+w395l2CNetHDcP9kTc0n6IW9qP3EctVHlsFnNVrmO4D+4t6I0fRzW1LA1pmWG/1i89fgGgm3xEvbhP2xc69yAAQEYJvJV5XKK4PRJwfkULqUVERODOnTsYMWIE0tLSkJeXh3HjxiE6OhoNG5p2KalIjF3I4jO4F/tuJmSh3peHsXj/fdb9IZ8eMOmjn5pbTLtekUrDcMkiCJ27mR6NBcpIVkEJdt1IxO1n2TgVY9+4mkKVbkVbw5F62EFiiAv5J3E1DiXsZvTRqjzI18xMYNzv27nWdxCJuSeRYlk2oJVZveoO0BczADCyH0pYXLL83XV9RBQ3aJOLaqUuWSJpjtHzubRmUEFtlGS1RnHyEABAdXcFZvdmBuVfnNsNUfN7G51bBkLtBmh1ilyAh+HZVlIW987S72l4a+4kQMUpQ6EuqAWxPBliRaLJLG9aJV3xi88oRpe/u2DaCdNxqARBoOH8/3SvNYbvpvDZOJPHSRTsNdlCPj1AWzBIySnG8v+ikZxThJgUpgvVq1hqxl4yntcTU5/n2RL8/Ez7cwpwobfESNC0pokAPYIeTCiTsAcrEhrzpu8/xvNxA6MIEiOrh0SRCLEsG6efneZxnrLBucLCwxIDACfiT8ExaAM0RSFkW3yuIcDa0hVIp1orIIIWhfHvoCSzHeS+J1CS2RYlGZ3h4H0KDl6XkKq1PJCPK2U2CZnhSoRBq87j4PSOFl+jvGCrn/PPtPZ4b8sN/PxmM5PHDmoSgLOPXqBr3WqoXWpp2fN+O6w7H4dFgxpYFbxvC8SUwFR9tXQA2PpgKzoEdsDIve/iUb7he+dyYXHw1rl+FiWNhFZZ3eKV85eJgICAKpPh8si9FEzZfB1BRmnAH6flc9aM+GiHbjHj93Ox+KJ/BGufrw88wPyBDRjtNxKyMPQXZvB/iZHSk1ukLi1+qUOj4S/P5uw0LLZcis1A13qWx/LxRZ95UOzA7vqbU5JKvnbwZM94pc4Ph4PnVQCA0shiIZdyT3xFUvZ6XnoIiCD3+wdSF7qrn0ikhVj+HNoSb4AwnZL94tNMwF3nvq3lqolihKT0MyFoyhmBzAL6e9Oq3CGW5UBbmv3MJZxeZFzqFoVeIb1x8G4KlClDDO2lXh0KmRjFKsN9U92d6XJYz98V0Sl5GF5qCXd1lCC9dC0qIcP052cKTVFN1PN3haOc+xweCicUS/Mhkb/QJUEhxICIKRd1dbXo97cubgU4k2g67XGJRksuAOhjFAGDRZMLrZrdHR8A6n15GOc/7YZAD0e8teEq7ifn4nBUCquLsFzKb77ysmEPGc9LiRk/frxNLypgAvLHarjJ24V5k/UAuHiezfQBVuXVhzK1P61NnR/OSD2sd4FYN6EF3tpwDT3q++HYg1RaH2pmDpEDPd5J5q57+JVoLbRiWLFy7urgil399+H1/YPINoKQQFNkulZKhHcEtIQWyQXPIXWNBkFRAh9kPiBfk3VkaGPjHqfYIV2Xj19EAGRefjEItQccvKyvlr3n8R4sbL+Qc7+DVAQNAIX/P3iUkQPAxkqMnZeKGgd54Pyn3cz2c3KQ4pc36X7PTWt64n+mFPxyhqAEqV56rlNcqAoMAHRcdpLraAClVb0J2SulxNy5cwcNGzaEWCzGnTvc2aQAoHHjxib3lzdTNl8HwPR1v5OYjX6N2eulPX1h3kNhw4U4ViXmj3OxrP1VRu5rJeld0THcB2cf6awxajOpmwmCQGx6AUJ9nHHsgUGhUFug/FiD3sLgGPgX6/4Sgk8MgUEux6Rk0/YEeHK7K5mL95PIXwBypiVKq3aFc62fUBg3BZoi0zE6ydnFcHJHqUuazlVratcw/HzyCa5+3oP1GH38klYZYLgmocXhu2lwoDhUkN4UHBYKiSIR3w9vgoN36TWDZBJd/xtf9kTEvP9Mjt/RNQFOim3493k1LMcGaCn30bnH6UBPEwebQFMYju2z2+CPK+c5+2hlSZDIdfeixCmW02pGaB3IWDA2DtyPQr8I9hV+rvvbwYt7XAAgMmOda7/0BOKW9sP90uD9Jxy/+fL1Gqg4ykPGVzl18Oeff0ZISAgUCgVat26NK1dMr3Lv2LED9erVg0KhQKNGjXDw4MFyGql1uCpKY10aGgTZosHMH6JjjT9o22ef0VfptCo3FCeOB6GhrwoaKzAA0ChQZ9rtVs8Pj79+Db+Pb4Gn3/Sl9RGJDQqKXsDoEZtIl2hrJGIJei1/QGsrjHsP2mKmEqPON5jO72fcx9H4oyjWp7HleAAYZ8UxB1lQjBBBJC5dMStVRJVpugxXfIqeMTBzTEmpT61Ylg2pi67gaFGJxqKUkRXFb+PsnwDCnoxuRb/XqCunaoKpyCvTzD/xgz11FtOikvL5/iyRoxs2bCDTVuv/FAr6JJEgCMybNw/Vq1eHo6MjevTogUePmLKGSpMmTZCenk6+btq0KZo0acL4a9q0adnfcDmx6wa7u8njNOtXrwFAqWK/L1SlkzFCrXP70Zb4oEGAwVXHXP2Z0LkH0e3706Sro54ano54nJZvN3miXyPhik1ZeiiatZ2OQUYu2G+Ia1Tn1YMjYWpRq2wKWrNgL1R3N6UkAUOb6WM5DNea07se4pb2g68ruxWH2k5odc8hjVYLZWo/qPNYXIY54n7EDlmsK/2yUkuPk4PpZ1yojzPuPE+FxPEZxHLdYqaWsqil0qrx8c7bOPeIngiI6pbFDQEPJweIVPRMZvoU4QCg8TbUQOOqBQMAcnVd2uKqMR9feJdzH6nEiC2r2WIcD8zGhvPsCw7lzasi46uUEvPXX39h1qxZ+Oqrr3Djxg1ERkaid+/eSEtjN0lfuHABo0ePxqRJk3Dz5k0MHjwYgwcPRlSUDYpB2YjDUck4TrF6OOYNQlHSCFo2ljBfZsCyWEZPo5miMtZy+X+1LhQ/fL3JWSwWYRRL8CGVAZEBJvfbAy3LQ9k5dBVEEqbfqaaAvloWl5NgUGJYrCvqglpwlDgx2jnHQssKJoY6vy60Kjeo83SuIvpMcJqC2qzjNoWmyPRnT3+AEcgrVqH+vMMI//yQRddhnte2K0Rs7nk9I6q2q+noVsbfDfO3VpJliLmSVzsKmddZRh8A8HLW/fYy3P4HsSKpXCwxlspRAHBzc0NycjL5Fx9Pr3GzbNky/PTTT1i9ejUuX74MZ2dn9O7dG8XF3IkKYmNj4evrS75++vQpYmNjGX9Pnz61zRsvB1wV7BPEHivYXW3r+PFLRsH1s3yWVeqmSMoDkdGE0zol5H5yLnqsOI0P/+SfgtYSSFceE7EplqBPS0yonVCUOAE37phwUy39LIsSR1t2Ea3Oep+ep6I9M9kIKLWqSBSpEMn4Z2qNXaJfPNQNUve8koJgkTHGJQRo+4xibhoHuTPauBjRogb6Ny59tou0KCxR0+6plNwi/H0tEWP+uGwk3znqtqgNz1SRg25CG51MV+pLMtujJKMDNMV+NGVIr8yxUVhiJumBiPveN/wuLLv/RBLz3ibz/2WPedNTHgloXiUZX6WUmBUrVmDy5MmYOHEiIiIisHr1ajg5OWHdunWs/VeuXIk+ffpgzpw5qF+/PhYtWoRmzZph1apVrP3Lm+zCEry75QYmbbwGlUaLu4k5iEsMhjq3Gf69ac5v1/DViQlnSAij2BeeMSIA4MLx4F08uCFahXihTS0vFKe+xtgf4K7A0ZmdeF/HFqQVZMCxxu+MdpnnZUztGkYGfzcIcIO2hF73Rl3siwuP9W4CLAKdEFuULYhaF2do0xrQFNRBwePPoCkozaBVOrGQujxCZqGtUywaHhieTlL8d8+gCGfk01eXvjn4AB2XnWBMkEvUWqsrhRuj0RK4FpeJAqUaWQUGQV9VathYgrdRkC5B+a1pSzwxYf0ViI387qm1n3wcdMHMb9Z/E46lWYEIkRIicWGZslXxxVI5CujcH/z9/ck/aswjQRD48ccf8cUXX2DQoEFo3LgxNm3ahOfPn2Pv3r2c5wwODibdKoKDg03+VRW4Vtm5MJfIITGrEBotgaP3U1n36y0WIolOvsg8L9F+0wTBvvBjjr+vJQIADkVZmIKWJw9T8yCSsVvwH6flm8wuxgYBkS7+RM0ej6Rn7u67IF04lXzqmhjQexx0qetLugyGeLMvennIKM8elngOLozdjPTyk+oJoYctBTQAqAuYtay+Hx7J2vevKcyiyg5SMXxd9avwBB6m5kNLed6IKK9D5x7ENwcfQK3Rck/wKcU79V4D1+OzaV1c632FkowuKIydSXP1ph5rjFiRDFNWNXVefdxJzGbdR9ZtIUzHVBrPIWzB3qnMgo+25lWS8VUmFU5JSQmuX7+OuXPnkm1isRg9evTAxYsXWY+5ePEiZs2iZ8zq3bu3yQ+9PLkca1ihUWsIDFh1jtxuWoPu8//Lm83w/tYbhgbKxKk4KxK1vL2QSOynHGEQhsdmdcI7m69z+mfKOCqjSyVi/P2uLtd8yNwUKPzoq/y9Gvgh3M8V2hLvcnMpy1UWMQIuAcDbIwdzeutM7vrsViGf0q1Vu28m4FluAeR+YHXXEitSkJidh7p+/OItyPgZALV8XAHQV9w0hYaHSX5JEXxAf+DlFaugkEk4P39TuGfNRDouQlF9D7KKSjB7x21IHGNBaBVovvgYzszpihpejhCJRFh7RrfK8dW+e/h2mM7vVKXRouOyE3BTyHBkZqcy++j+cPQhVp00fC/LhjXGiBY18M+dOLKtMO6dMl2jsmBce4M+wRDhVMwLOAbTlVaJ82MgowsAoECrWw27n3Gfln1IJCmyuxJjjRwFgPz8fAQHB0Or1aJZs2b45ptv0KCBLnYjNjYWKSkp6NHD4Ovv7u6O1q1b4+LFixg1ahTrOf/55x/e4x44kD0Vb2WjP0c8DBd3E7mzRa498wTfHOR2qxKJmKFrCpdExsKEWkvAgecqPBu5xSq4Kcpa5Z7OT8cf0RT7GvLmeKa8DlV2M4N1yQTqvHo6+ep5GerCYEjkqTrXI1kuxPIUEFr2yemfVxLgUlefFMW6NdwGAR4YVL826vi5onUoe6p1P6caIDRyiCRKjGsTYvE1VNnNAZEWEijg4HMEUqPYD11tKXZXvOLk4Yw247pXsUv6orBEw6pEN6nhgZQn+s9G91nJCcNkXmuULGjtmacMF1sqtIyopc9duVQMY5VH7JgAQu0CkdggO0Vi7lV+qWsU6fXABqFxwoPkXDQO8mDs21GqpHMV3SSvb+MpcqsQL7tnoHzVZLzVn+bjx4/x5MkTdOrUCY6OjiAIwq7BSunp6dBoNIysZ35+foiOZhf0KSkprP1TUrhXl5RKJZRKw0o2W4VRW/FOaXAoYDBvOnifBMQqtKtDn/B1DDcIkR3vtsX4w/8jt2Xu1/HkRRvIKYsGyjRDysTa1Vxx/KMuWHnsEX5nd9k2iyJgh2GseRH4Y3wLNA/WCfDi1H5wqrEJWh5FwMqKwR2MTnYR09JRv7onEinbvq4SPMszuF0YI5bmIzE3rVSJoeznuK+pVoYeEdWx/HC8cQ/DuI0mp/ee56DfTzqllbU6vIiAVktALBYhq6AEmy7GY2izQAR6OEIsFkGlloEQ6VfLtBBJc+AUsgYAkPdgKTotP4nWoV7YTllp++vaM1KJic8oQGquEqm5Sqg0BBwY2Xz4r4R+9Pdt7LqRSGv7eOcdjGhRA/NuDybbNMoAfMRR56WqoikKQnHSaLjUWVzaovvOpUa+2vpJyI0ve6Lzzk8BADfTbqKakyEDlNT9Fm4/648RLcy4ErKQl5eH3FyD/7hcLodczrQKWCNH69ati3Xr1qFx48bIycnBd999h3bt2uHevXsICgoi5amlsnbw4MG0bZFIRHNPoT5PylLR2Rh7ynhLLZumAu9NKTAAXYFRpvWGvNp/UBZ50Fx/rBmTMW+tv2ooumwjqns44kaCDzTKahCJVKgTWAPPkq5Dq3ZjJCtgRURAndsEGp8TEMuy4Bi0hdzlXOvH0hiSsbRDDJ+D7v+y1yOx8JblY5eJdQtPfRtxK6xikYi8TssQb4uvoUwdDAAoKFKQVjYaHJ4WeQ+WgO3ZVsOLvoAmEokYk+lLc7sjIbMQzYM98S9Zh5QAQRDwI3rj7gPuuIWu352CK7thiE6pi5erQgbjX51j4HaGxUmrduVQ1XRjA2G6aKaXM7tl9OwjfinERRL6IoO2pGwJZRQO1hdkFmQ8OxYvRWRkZKBHjx6oU6cO+vbti+TkZADApEmT8NFHH1k1iMrEkiVL4O7uTv5FRHBr+rZkyqZrAACZ13nIfU7A34s+WXdVyLBvansc/LAjWoZ4gfrViSRKQ4B5KY5B2wAAS4caUvVO7xFuNg86FzI3Q+BkceI4dK9vuJm1xdVRnNoXyrS+eJRqP6UPAM5wCR8x0z/WOB7jWkIG9A8WAiIUJkxk+Ny+vYl/OmS11vAducodsGiQbtWiTwN/uMqlNJeIy3H0IEi9AgPoUjMyIVBYqvjM+vsWfjj2EB2XnUStzw7i4N1kpOcrQS2SKGLxj74cm4kDd5PJ7S51fSl7DcKjrKv/xgqMHqpbGQCAkOCD7uFlulZlQ+KYiAb+AShMmKBrKP3OqcXnqHg60Ve0g1woxTw1clx6ap1FMyIigia3lixZYv4gnrRt2xbjxo1DkyZN0LlzZ+zevRu+vr5Ys2ZNmc6r1WrJvyNHjqBJkyY4dOgQsrOzkZ2djYMHD6JZs2Y4fJjt92E9tpDxrhyrqfbO6sUFodXfVyKG0rLtCntxQr5ci88y38lCdL8DMQpjP0TBk4/Qo0Z/FD0fBnVeQ/x97RnNZYmV0hV9sTSXPfhbROC3M3Q/e32SAmVafxSnDEADP8vcyfSw1XgxRqnJJ92r9Omk+UKtKdP/f+c4erHFdNZmbeeLv7sCrUotS9fjSr9zkc5WzEcRNhvHCUMiHLZin2wLZ6aKXYpAoKaHCXcvQop8JXvcjP6elnlyfb6l1zfy2CjJtL6yPACUwSAqyHgOLFZiZs6cCalUioSEBDg5GbT7kSNH2vxhQ8XHxwcSiQSpqXTf4NTUVPj7swsjf39/i/oDwNy5c5GTk0P+3b9vOkjLUvbeTEK/n84y6kZceqp3RdL9yOVSpvk+soYHIgJ0weIEYyWGuXp1/tNuGGVk5tXk0x/YmiLz1YeNWWyULa2Rfw1IHOMhlmaj5w+m87OXhaikHHx/RLeSQGjo7gIlWa0Z/Te+Ra/yLIKWLAqmLa6OjzoORMFT4wKd/CchZx9mQ5nWC8oXPaGQyjC2bQjilvbD6rHNceurXpjcw7Dq4io3vQJDzaSmG6wW0aVpGk8aFZ57f+sNOAZtIBVVpv+4YXvaNkNg7g3KZIS6WrudNsmxnTWVamkEgNEtTaclrapIJQSOThsOVW4jRpVsYzbd30Tb3vjaRvI1oVXgyYsCq6ql379/nya3qK4EVKyRo8bIZDI0bdoUjx/rrEv648pyzhkzZmDlypXo3bs33Nzc4Obmht69e2PFihX48MMPeZ2DL7aQ8Y2C2Iv18XGF0mPbJA66362DVMSwxPx0nJlBSKMlMGeH5UV4ywpBEJi4/gq2XEqAYSVdColICpnHNTh4ncOxB2mMehwqI7chKeGOXk20EEm43I0IfH2QnsUyofSZq8pqC1VWe3g7eSD/8ce8Jt+0a4vNr6a/UD6j9LdsmvXbuOaQOD2FxPkhIOLInkWIMbF9CP4Y3wLFqX2hKa6OknR6MeCJ7UMsui6VtrX0C14EilUaxGa+gFieApHDCxNj4v/sGNuWGQOhS0dNp241X0abHh9XB5PfhbbEC9/995BzPwDI3M0lrqC/J5G0bIu09atz15kxhyDj2bFYiTly5Ai+/fZbBAUF0drDw8MZ2QxsiYODA5o3b47jx4+TbVqtFsePH0fbtm1Zj2nbti2tPwAcPXqUsz+gM9HpP2A3Nze4upoOFLSUGX/dwr3nuZi3j5khTewYD7FUJ2jlHMUr9VB9RQmNA2smDmPffT3Fz18nX4e7mymqyEKTYPrYpvV2hcztHuTVjgLQKRv5SuurHXOhW5Vidwf7fdgYRn8/NwUKnlJ+HCItHLx1Ky8K/3/RItiLLI5F7ZNgouo2lalb76IkoxtK0rtDIaOvzErEIoT4GgSsXGZawBclvoniFEPtG1VOE3g4mQhqLM3yQhBiaIurGz1A2N0xcilFtzZeiCNfL+GVzpQdU0HDV+LoMUJfDSgfq2Z5I/U8g1qeNVGc9CaUKYMBGL4fY7679h2jbUhtXUE6VY7OXcOaEj2urq40ucXmZgBYJ0eN0Wg0uHv3LqpX11mbQkND4e/vTztnbm4uLl++zPucT548gYeHB6Pd3d0dcXFxvM7BF1vIeLaMkQCw6SK/Z+Cj1DycecjPpYUPCv9/AQAacTZj1TyniLkaffBuMnZcZ7eg2pLTD1/gk513UFD6PMguVJGLMmKHF3CtPxee9RajUFUAqVMcxKUV0QkVPdZEa7TYJpdKkUKcMHFl5o/o97N0y4wIIsjcr0Pi+IzRl4uCJ7MQ4sYMnDdGQlFcpBbGPDat6QlF4BY41VxHjyehQECEev6u6F7fD6rMTiiMnQ5Noc7KPaWTbrHoi34RmNa1Nna9x+83SCXYozpKMjpAndMcy/+LQbJ0J5xr/QiXsO8h87jGcZR5JaZDbZ1rnXEcKKF2oqWq1xMg5Q6Cf7PB6/B1Zh6jR53bDEnZZhLqmEi60NK/JYynyGJZFtaMbW51hs0Pu1nviSDIeHYsVmIKCgpoFhg9mZmZnB+qrZg1axZ+++03bNy4EQ8ePMB7772HgoICTJw4EQAwbtw4mnY6ffp0HD58GN9//z2io6Mxf/58XLt2DdOmTbPrOLmISTFo8WxVXJ1DfiVfG0+KjSG0BgVFq3aHlOLuZYov+tWHKqcl1IXBIAgRGrkMMX8QgKLnw8jXGtBXYur76VZL9NaR/v87h4ZfmS6mpcOKVX8Rs8ouAHStx+6+c3n2OORFL0beg6+hyqZbZiAuhMyDXpBSBAKdlp/E7aQcWqs52HLvU93NitVmVl0JB2hLhbimKAiqzE5kEVJWSj+Hwvh3oEwdRH/om0gt+ddVndVl62UzLiY8Z9LmiunpkWoCGMGlLwud6jArm4tY3BvZYp+uplxFVrHOQqZXqPmmQrUWS+XowoULceTIETx9+hQ3btzAmDFjEB8fj7fffhuAzq95xowZWLx4Mf755x/cvXsX48aNQ0BAAMMnmouWLVti1qxZtJW+1NRUzJkzB61atTJxZMXAVrtLD59sYD1/OFMaN0GnrOlXCWjIzGKmuJFgexcxNsavu4K/rj3Dz6VJP+ifDNM3ntuNjD5VEYnE8HI2kVmKJbuZLrGNFjKvM5B5nYWLgoCT93VGPy3LZJrcV+INJxn74iAVqvUlq8CaBT2DqzD1+aNVu0CdVx+qzA5oW4vpSvVFv/qY2UMXdygRizC7d10ydtUSxBofKNP6oySjC24mZBsNjf35InEyr8BTXeVkhAf5Wp1fF0Tp+1Tnh4PQSqDKaYL/7rIvBgGAWCRGXQ/u36E+fXJyDr/MoCPrjkS4p0HJaOTTiGFdkrnfho+LHGvHNkc9f1cEuCvM1gzSE+brTGajtDevkoy3WInp2LEjNm0yuESIRCJotVosW7YMXbt2tXogfBg5ciS+++47zJs3D02aNMGtW7dw+PBhMtgoISGBjNEBgHbt2mHbtm1Yu3YtIiMjsXPnTuzduxcNG5q68e3HlkuGH3lBCXNSq8oxVCx1MGOJUedRqjoTEkZKV64Ci293rIW1Y5ujKP495Ed/ja61w/gMHeMbGzJHeCroD1qZfqxGOf83X4pHdqGpvOqWLzkbZ2kxh6+rHK1bnIZr/c/h4ENfuYvNfciS6Uw3psNGqUULlGrEZxhldxMpIfO4DKk7+8rUgLAB5OtileFBxuZfLHW/Dok8BUVJI8gimUsPRUPNWWxOXy1O9z0TGkcUJb6BosQxJtNrf7LrLhb8e4/Rbq1ry5JDD8x3AuCo4O9mU9VwkzugRFOCb0a5Q6xgVw5re9RmVeLe+u8tnEo8pduwMK2stVgqR7OysjB58mTUr18fffv2RW5uLi5cuECLJfn444/xwQcfYMqUKWjZsiXy8/Nx+PBhRsE0LtatW4fk5GTUrFkTtWvXRu3atVGzZk0kJSXhjz/+MH+CSsSWy/ysMWwLAI/S8hmyxx5kF5qpsWEDqDLlSGmKaJrsI+93MTwdSye3pRNkvduvHnk1+qLY+636o5avqUQyWjgYrfbrJpsEFH4HofA7ABVRAo04m3GkqforEGkhkZhfZBCLDNeu7WN5YD+puBjJBLE0H1LXB1Blt0bN0vTO+po1c1+rh7c71rLJRDk1l+6mR1UuRRYWiKRC9ayb2nwC+dpFISWfZarslsiP+RrFz0ehfW0/lGQy00ADgIPYAYObcSdXEEl199Dgn88z9pExbZTEAJG+kXiUZXC91JVQoH/XmqJAyKViiEQiHPiwI05/3BWTO/Jzky7PaLlXScZbnJ1s2bJl6N69O65du4aSkhJ8/PHHuHfvHjIzM3H+PPNmsTXTpk3jtKScOnWK0TZ8+HAMH85MOVgRFFBcrBgTYgCExqAcyKWmBZHC7wBliylUFeq6jDY9PSP8MPe1ehCLROhWj7mKzManfSJxectoBHk6oqYn3U/VQaJbEdOlzNRCrxt/uTcK3x6Kxs15PWGrBJ3Uh5tMLINKa/5hfD9f5+Ym9z1Ka/dwYvnxlT40jAN0G5RaluIoh4gkhVBU31OaHOArxqk8FZ5kwcud1xPxZgud695ao4BTAHAszf7moK2OnKQekDg/xNlHMOHyoXvYi2XZINTuINTuUOc15uhLZ/35OEZbYlYhalcz7Vaz8N/7GBBZHU1rGjK0sJ2LjSkNZvLqVxXJLclFWmEaltx+D07BMuTHLGL0ic2JhVJj7uFffo85S+ToDz/8gB9++MHk+UQiERYuXIiFCxdaNZ7atWvjzp07OHr0KJlBp379+ujRo4dds17ag7+uPsO4tiFm+31zkH0B4N0t13Fqdherrs03vuNFnmUT0aG/nMfu9y2rb0F1WX2clo9rcZmo5kqVubr7XSIWw9tFQWsTO7DXxdHTNSwCm+8zrSjG56YSEeCG/XcN7VRFQ5nWk3SHNoVjjfVIL4pEoIdplzJ9XJJW5QZlielaJOxQLTGmufZFDzxIzkUkSypha/F0VUPqGgWCkEKTX4+2T+57DCXpPTiONE3Pmv0M55EYrDISMQCN7vsQiZUQKxLxTsdwnI2/AgevS8anAQCkFqZCKuG2cjl4nYEyrT9Sc5n3esNAd1x8mgF1bmNIHHUujN9e/ZbcH+YehimRU7D+1j7acW6OIjQojUuWiEWQQIQ3WtfEwv22jZ22Ba+KjLfYEtOwYUM8fPgQHTp0wKBBg1BQUIChQ4fi5s2bCAvjt6r/qtKD4kdZyGKJcfC6QL6Wivnrl2z+9zVk3EUoRSIR3ukchsmdavG+eWQSMfaP/wyrBzInozIxRUUxSgeZr1Sj7ZLjKCtRpe5dhNYg+A4OPQipWIo5LeaYPFZE6B6Qutz6Bqq7sblu6JSDGEqWtfR8jgc+aVbn/hkVPP4MBY8/w81YXd+nL/Lx7WHuGJQScTIca2yGwl8nPHXF2diurXu4OQZtg2ONjRDJMqEI+BNyP/552an8cvIJnrzI5y4NDmDd+VgM+eUC5wSoUYgaLULogYt5D5Yi78FSTIgcatW4qgLZymzy9yqTEHBksbhoCA1abGlh8jyTOoTgwqfd7DLGqoBIJEKvXr0wZcoUfPDBB+jZs2eVUWCa1vQgX997Ts+WlcNh9UjI5LZOfrzrjkXXF4t091xJaS0iHQYr7r+36bF/9EyF5rlh7FLEA2O5OWz1RQz+hbrQaUi3SyoUesuDma9dyxH3RyIiUGJkxS4q0YCqFFCVGD4KDKFxhNT5KQpV3LVLyMtTzm1cHJcXhEGJoXldlCJ1MVjTFTIJmtb0tKkbqodrHhyDtkDhv5sch2kM+/MffqFzxQIQKKMn3ImoblgoGxJucGUP8XGCQloqQ73PwDl0FbYkToenM3P508fRBx80/QDvN3kf/zyx7nmnLi1pQZTWiQl2bogcpcGFPNA1EG4ObqjnOJh2nEJRxJBJCpkE6yaYlu0AoDCzMP0qYA8Zb1W1J3d3d3z++ef4+++/cfDgQSxevJgMABJgJz1fiS/3MoP5uaApBmZg878f39Z88KGtoK6oiFmqMKfnm3Ip4weZapJSnMrf2R83x97EuAam00Z39tMJS+PCWGyfsbZEZ5l6kmZwz5uymWvFj+7SxYdu35/m2dNcrQSC9lokKYDM/TYcvC5g6fDaFk9Sdt9MQneeY9t3S7dyVVhiWAWTukYhzvEL+If/Re8sKsE3w0IsGktVYGi4QSkTi8TkvaQm1Li/sDfXYSZJLL6NAI5kHC87Wq0WixYtQmBgIFxcXBAbGwsA+PLLLyutO5k+HW14NRe82Zq94nRiViEiFx4xe64v+tGz2vFNLkJiFMMmcXwKl7rzIXW/CgDYfpXu5hhWxtgbPrC5y2Xq066L1HCupat1VqJRUiYyhNF/do7H0xfGSjI6QKtyRY9q76DgySwUJ41kHPO/E49BlasinjGZDiX1oC4MIeu1ODvweDaXDl8sy4WXizWTNIM7maaQuTjs4HPKinPyp66/fsGP+X2UZBgWSOVSMWM/IEKYh27MLqLg0po9OmQUN3lnmTPc5R4AgIWdP0BDh3eR/+gTSOSGhBdtQpmueG/WfxNTGk+Bs8z6unTGNYM0oC++ikunxmsGT0OQzFAjKaOYPQV+t3rmA/2Nf+OvGvaS8RYrMevXr8eOHTsY7Tt27MDGjRtZjnj1iEsvwNbL8WReegBYtP8+MozrZpjAXc6ewlNPl6AutG1DnQAdwR6Wp062FqoywJYmkYvP9zJjM6hkFpQg7LOD2EVxqdIXmwpzq8d1GIMG1UsFoYhuejZWYjTKaqSSRFAecM+y2AMD9TnvneXWuAvooK7g0k9uiRKjpW13quuKNWObWzUeY+HOxuIDOjeYiHkGP3WZu07RO514utQaVPrZ1F6GJfdGIa0wzarxVFY6BdItnVTLqUqrQvea3S0+5+lEvgruy8fixYuxYcMGLFu2DA4Oht9Tw4YN8fvvv1fgyLjZ9FYr7Hm/Hf6b0QnujuwT28/38Fu46lGfPgkKq+YMJ5bYBk8nGUax1NjQWyZE0mwAgGONDRCJS+AYsAsAcFVf96MUapIZe8HX1VRDaMhJI1+Xyisp9HpeyrT+KHj8ObpUHwJtSTUQat1z4sJjIy8FSoyJRCzBmp5roJCY9un/pftqWvFaTyfLLCvFan6B5XTMuJNZsHBmDXorlViWRxsOoZVAnW8IfmdLPiOSZiPCOwI9avaATOtH+8zvZ9DdrqQiwz0ugzv5vekJ9WG6OOtiVUrHYzIBjWGfcWyu3rKoKayN4uQhGBA6mP4eSpVqHxc5IgJso/AbFxx91bCXjLdYiVmyZAl8fJhZMapVq4ZvvvnG6oG8THT57hQ+3xOF8M8PYeUxXaDYvlvPzRxlqP1SzdF8nMrllMu0bWNrTH4JPdDfnohEIkxu9I4ul7+Wv4DnykSz4Xwsdt9IRPPFR6HREviIUs9Aq/TH9MjPMLXZFN7XkUtLY3Yorm5Dag+hrQoBQHEiM02zaXTjl0u4Xf+cwxfBpd5cXX59FjZPao1149lM0aYf5iWZHSBWlgYUighasVO1Vs2IqVoxIpL1PDW86Cv/Ks5EAnQ+20N3cwv0MTwkHLwuQOp6H4CGTDhxKZndr7mq4uxgWAUsUhfRFOJpx/llP/y1x69lWk18mdi0aRPWrl2LN998ExIJJdg2MpKzynRFQ3XjUcjYH6WneaZRlknpx59/nIF6/swJ3OLBjbD0de7YN4XffgAgCy0SGp08HtwkgNaPz/PIrogM8kKlUcFT4YmSzLZQ5fBbfCFAwMWBObnsGE63QL/x+2WUqLU4GZ1GHqlHDDHaBbRD92DTCw73sui1RPi4v0jUhtoZlqZYBkAZJgGJEzOGkrAms6cFiGnFtPPIASlf9CZTORuPSI9zrf/hm8vfIF+VD5VGC6lLDLmPqoAAgKRUidES2tI4IqP9LDV2YrJiGG16HMSUBUXKR9Rk4VFWN2it0h+q7NboUasdrT0qXbf4cCj2EK6mXOW8niVIeSSEeJmxl4y3+NeVkJCA0FCmq1JwcDASEspWGfhl5IdjD2luN2wsLK30ri2t8j2rxSyzBe+KzKzuJOUnWTDKsvPb3TWQud6H1M0yX2425v97H7P+vs2a5ZdQe+DtJqPRM7gn7/PpEw+IZYbVyJH1RjIsMXK/fyCWW5AZqFRxoPpWM7qAgEhEQASC8Z1O7hgKF7kUneuyVB02Y4lRZXZGgFbvU0yA6iahJpj329BmQYw2APBypiudfB+O24xSNDerXoe2rS3xhoSSSS7M/eWKl0vINbz/ptWa0u6li8kXcTzBfBxYsbqYR7D/q0FSUhJq167NaNdqtVCp7J9Jq6wYZ8IqsKBOVte6vpCxxDOwxaG0C2PPdOXvrJs0EwRdpulT0TtIxXiWWUjKoLFt2N3fjs3qzKo82ZNPWn0Cf2d/tPecjJIXPF0xCWBK4ylQvuiOkoxOEMuTIJJm43b6VTjWWEeLDazzxSFM3FA6EaXIVUNaZ+ZnX8/TYOm/n0WfxJqS93puxGeTr6U8+htTEDsDeTFfQVscAKlL+QeNU9+jyCETyhc9UBD3Hs5NnYWbX7I9e5kP60vJlyA1UsCMP7u0Ip1yuf/pfqTJ/oRr/c9N9geAYeGGcg+E0XXreVE8NIysVSuOcis/7g50z5e+oX0BAGtur0GW0jBvkIgsi2vRu5wCgL8bvyxeLyv2kvEW/7qqVauGO3eYE9Xbt2/D29uaVIIvPyPXmF6FblvLG2G+zpArdKvWn579FFqC34o4F5nFmeY72QF90bXKxGuhr6Hg6Qwo0w0rbuEe4QwlRuryGCKJJRYs3XdkSrBJJAY3CWOXDv1qKHuQKvOhEOJNN0fLSlczRCBoJntqfRoq/07rQNvuVMcXCqkVq4QsHIo7ZNSihVhucCGjPgheBhr6GNK01/eqb1EiDj0zT82kfVfD61SOLIoVQUREBM6ePcto37lzJ5o2bVoBI7KMRkH0SRCXRZMtcYO/u4J3ySyu9LnvRb6ne6Fld2vbcikBHZedxMy/bkGjJRiWVD0eTjJEW+lqlp6vxJ6biWRq5ckd+cVldgzsCAAYRLMWMeUfVdnQQgtnmTM8fKPh4H0GjjU2wCV8KbZGb4DU5SEkjhxprgkRtCoPuMl8yAkymxKjn1wDgKvCAaC4OfFRYt7vZli0EbNYE8yiVQBaRwAVEwxOszZpHEGofACtDFdTr+JRDltWPfb3qCTo91JqIT3r3GuhrwEAqjlVw3PtSdq+z1t/TrMIAboClO0CDVaTup70LKytq9MTCdCuzZKlTOwYDwfvkwzrTjUnnTeM8XenIbhLEdQ0chdbOKgBIilyoaokKbEX9pLxFv+6Ro8ejQ8//BAnT56ERqOBRqPBiRMnMH36dIwaNcrqgbzM3KUVTqQjr3YQWx7+iiMzO0MtMvQzJyhnNZ9lcr+LzP6Bm2yoC3Satr2C2MSKRKy+vRpH481nk9HjpfCCrzwYIjHF5UniAC+FF44PP44zI8+ggXdpBhgR/xVUAIDWkeZaZIyUknVnxJqLtH11S1c82axuEjGz7diszobzut2Cg8ct8txUS4yxyV5PTYoSdG9Bb2x6y3ZFBL0U9IJqzrX+Bxmlfk4td3659KsKEd4R2NJ3Cw6/fhgNfBqU6QEV4a1LOFHWhYuqzLx58zBt2jR8++230Gq12L17NyZPnoyvv/4a8+bNq+jhmcXJQYpgyu+LqwgsW+IGuVRilHqYG32l80/60GMC+Qap7731HA+Sczn3O0jFOP6RTs6wZdmjUqzSYPOleCRm6ZIQjFh9ETP/uo3vj+gmhCYL4VIWXQgQKFQVIib3MiTO7KvlfUP7wsfRYLH2VugWTKu5ln6epRYWmd61l6vmktYJBY8/xbnRJ0w+Y6mLgN7Ocvi7Gb5bPp81ITEEgPP9bqhsmUSZjJe+lyAXgzXdx8X6OEw+0BfmdNeXut3B3LNzcSzBsGA1pk3N0ldinTu5EU8K6WU3jBd7DsXqzhWXG0erqwYANVxrMOTqut7raNuORoVHf7v7m2HUBP3+PRHNjMuUOj2FvNp/OJ5wnBYbpZ9fGCtRpvisL/03Oa5tCGb0qIMhTQPx+zjz2cteduwl4y1WYhYtWoTWrVuje/fucHR0hKOjI3r16oVu3boJMTEWIpLkw8H7DHY/3YxiDT0bjblJ0cSGE3FqxCnO/f1q9ePcZw+G1Na5NjX0ao6xbYLxNs8CUJbSq2kJfr71Mw4+PWjRcfMHNIDx6p5ELEE1p2rwVHiSLmfV3HWCj+5Wxf5daJWB8E7/Fv8M5k7z6KLQr4wyJ6hnH+mCTh0kDjg5gr4KxeZORvWtVvjvQUyhPusRP0uMu6MMu99vh3+mtYdzabGvy7EcFjsz7ozGsFn+SjINlp8AlwDG/qpOpG8kAl0MCTQsySioJ9AlED1qWldz4WVi0KBB+Pfff3Hs2DE4Oztj3rx5ePDgAf7991/07MnfdbQiUVGCnI3rTJlCWupKxidNq97r7I1WNdG61FVl9Zim2Pt4r24Hj0UYzpTx0LnFhfm6IG5pPzxY1Ie2T2uklPx47BG+3BuF137Ura4+TdfV8Dp4NwXJOUUmA/upEnXDvQ1IK0zD5tiv4Bj4JwBdwLW+6G8dzzoIdgvGiyJDfFGQq25C7yTT/eZ0Ncqok2/Tnz/1+VqsMZ0yWSwSw1OhSx0f7hnOmDizUaI1LJjxsdwYszvpGzRvtw5ixTOg9L31DjG42hmv+tsa2vxDpIXM8zzkpRnRCMqz7LWG1Q1JZFiSDRh7KVAVUSo7H+5kyE+RSEQu8AC6+8AYWgwMBbnYCepcehxorwhmBrH2tXXKsAgidK1pKNZ+68UtAOzf3fKry1mv2ZWl5p6zXIofRjahldd4VbGXjLfo10UQBFJSUrBhwwbExMRg69at2L17N548eYJ169bRMg4I8IBiGdASWvzY5UeLDvd29ManrT5l3aeflJcX+ut1qeeJRYMbmultPR3CdWZ9SyaMd17cwf60xXAM/Iuzj378X/RnC1rkRmImN7/UxEPVz80Qj+Lj6IOJDScCAN6o9wZq5H3Her7lw/RBvbrzLeu0DEWJY0GoDdYgvcm7Y7jugaEX1ADQrKYnGvMoimaqhgUTgvGAcRA74MHHn2Nk3ZFY1mmZBeequtR0rcnaTlV0jBFBRE4YXlVLjFqtxsKFCxEaGoqjR48iLS0NhYWFOHfuHHr16lXRw+PN8xzDZFit1WLHtWes/YKN3EI3XdS5PnWpYz6hi/5ecXeS4a932iJuaT90r++LG2k3dPtZ0u0bM2E9PcajGSVDoswotufQ9I7ka43Rwsb50sxfeUbxP0nZRWi75ISZURjOtfvRbspkkWKhKU10U8+rHkPe6yfH+s9Db7k2r8QQkBt5frb0b8noRZ0ci0Qi/D3gb9wdfxf/Z++8w6I43jj+vQJH70hRmoioKCAqWLGgYu81Jpao0UR/FtQYE2sSY29Ro9EYWzT2ksResHdFxd5AEAUEpPe7/f1x7rJ7t9fgaDKf57mHu9nZ2bll7515Z95yoMcBWBhaKNVXR3F2aaPTo/HswzMIhHkQCOT/UxODoufGWFy6/hU1zGowu12ADCKTIvM8thIjEABOlkYACmFgoRxtlP3dh/sMZ0wHFQmoFsD7P3Y0dcT8lvMBAM8/PFc6z7+aP/7oqBzZqketrpDlcRfObD/uXuUVFlkqSMQCpp/danZTaodPibkUd4n3O0jEIrz6pQtmdauHnaNVm7VVRUpTxuusxNSqVQtv3ryBl5cX+vfvj27dusHNjd9JkKBMn4bsCQ0r9ro0n1ld0gVVdvRGpSzkFLEztoOLuUupmrHt/7o5CmRyga4YWUwdqXmpuPDmgto6dK6bfKluOW3EGuydGSGuZN5AIbv6ZDTY2oAp8bf3x2d1PkOgUyCkUv6Bz8SQay7ha+8LWZ4TZHnVUZDuA187X+Z/v3pwQ/zcqz7WDG6otQM5vQP12R/Xi3I6aEDieAjPPjzjlHX26Izk3GR08eiC+ralp9RWJFT5xagLsvEm8w32PN2Dpk5NUde2auYREIvFWLx4MQoLdTTlrGCwHXcLpRSm7eP6jp6b2gYAsGNUEAxYkYpo5+TiJitUdG7Wlc9ZTv6KizI1rIt2HaQKOzH6MvHPY+eJYclJabYHhtQdgktxl/BrxK+cc2gZRystBh9/eiKh/LOlMf9vUWCQAkOv79D87yK/Cr4Qy+yxuDjmYJxrFuN8sYCW81JmJ4bdz4XBC0vUJ00IBIKiMVbADeEvo2So/tEs0reGFYQCASDkN2GmlYD2ru0xpfEUJYWO3l0a2WAko8SM8BmB+0PvI9AxEDJKhh8uyZ39KVAYdmwY53wbIxteP5h3We8QvZBrjfL3DfmiQg4r0bi5sfx5EULIm9ZC1100oVCAkS090NyTf8epqlKaMl6n/5BQKISXlxeSk/kT/hA004Dj6FX0D7327hrCY8PRpkYbzGo6S+v2FFcvDvQ4gDP9zxTLtKUkjPUbi6N9jmKYT5GQ8XHWbcVKE43crIuUGB2+n2LdHp49lOqwlZgeftqZPomMo/DOeio+P6o6NLOqlUF22MyIxAg0+asJJoZPRIvqLRDiGoKl/flDIt+JoR3kP058WD/hdjbTsKPrDiZCi5WJIT5v6oYZVyei+c7mSM1N1ep70UR9NA/RhKH1daWyPGkejkUdw7Djw7D+/nqdrltZURf6EwAuDORXpN9lvcOCVgswuM7g0uhWpSAkJATnz1fuPDkHxxVNjNssPad03N1Ovltaw9oE/7CCbJiytgZoRUcX2Dt4xVFnejesjsnta2PXV02VjglZk072Rkxqdj7uv1Ht66kZbk/pySIdZ0Rk+hzG1f/Gjsc7eE1VaTMjelJMm9DSk/9qFvyWCLTZGVuxaGDXANMaT0OgY5GPIHvyKhKIQFEULsddxpW3V5gxSB30Yp6x2LhYC4rMgohABnzciZGIJMz3U+X3qE/6ePVBfnJLUIVmUFRizkxpjci5HWEmEX8Mjcz/5NH38XTMaSTnKM8bFwcvxul+pxFcI5hRmgpkBRAI5DvU199xxxZ6x1ETl+IuqYwQuelSFPPe2Uo+7gsEAl7/Fz4lpqRKbVWltGS8ziF1Fi5ciGnTpmHdunWoX79qrLDqE1NDMWZ2rYtFx59g3WdtMfn6CgDyiGQ0y9os07o9gUCA5s7NceXtFUwKmAQva93MoUqTRm7WePhWtROpLtCKRXGUGFpBoeE7lzYny5PmoVCmpVmPQApKkIOsAtWT/W8Dv8WYv66BKuA6vktzi1b6xAIxY5c97sw4uFu4fzQJG8fUoRXCjNyPA+hHn5mO+zvC1MsU2a8mw9wiHnGZcUrmS5fj5M6VZ2PPcjLNq4dCoRY5Y9p42+M2T/nn9T7HtbfyqHxlbdpYXoz3H481d9fgi3pfYPuj7UrHV0esxpLWSzDt/DSlY8Wxm/+U6Ny5M7777jtERkaiUaNGMDXlBsvo0UN54aGi4WRpDHOJWMm8CgB2juKuFtd2KApj/L92RTKbVnTYrP88AGP/Uj15Y+/E5L7rB0cLI9ASSQTNebsEAgEmtucfN9g7M7Q5WXJmHhr9fFpju2xqO5jhWUJR5EdKxvUroSeQtDWbQJgLoaHqCJu04kYvEtEmtPROjErTTDoAAGsMqGVdC7Wsa+Gzup+h5a6WyJfm40NuUTRFKyMrDD8+nJlAXxp0SWMyalq50pQKQRXMDpOIAiU1gQFlBTNDMwQ4BEBGyZjvWZp87fc1Hj2KwL+Jb9HC0xY3ProkySgZjAxEMDKg7zXA5/MJAB6WHkyCy4TsBNgacyPYCgVCOJjK/UVoxY2tJJZk7Lj69ioAbtSrkVtu4sxHB3+B4XvEZETL30OAd1nvlNowN1QON/4y7SWScpJU+vcQ+CktGa+zEjN06FBkZ2fDz88PhoaGMDbmCqOUlPIJ7VuR6N+oBvayssyzMZGIMKCJC4Y1d5fbHysvYuu8i0Kbq8Rm8NtglxWFskLIKBkjePo1qsHYe5eURR8TvBVIdTcnUxSEfGY/gY6BMBIZoZZVLcyOjIcj65jK3CkfB0R14XVDXENQmK7sOCqpdoR5r+hYGp0e/fGdDPRm6aBAub/F4EBX7LkVy0luKRRnobnvOxxN2YSo8LrY0mkLx36aRtcBIS5V8wD8VauaGMMKQPND0A/oV7sfxEIxzsfKV11UOV9+aozxG4MxfmPwPvs9o8TQCwwAsPfZXozwGcF77pPkJ3Axd4GLhXI29qrAN998AwBYvny50jGBQACptPRXnvUBnwIDAM0U8ruwlYOrL5MxsiV/OGIziRid6jth/9fNP/oeKMOObnj0m25wtbZC013yz1KoNyMdpeK6NOydGNqcLGzPPaV6sw8/UNnG7q+aokENS9SbfYLVaa7MZPzCIEPUgi5YfiULW15AJfTCkaXEErZGtviQ9wEySgZ7E3v08OwBBxMHvL4nRnquwv/jo/UD3/ghFopxdfBVSCkpuh0s8o/oX7s/Ft8o8uvTZsHhdTrLh4SidPaLoZWUGV1qIzqmC75u44lq5kbIKsjC8tvLserOKvzU4ied2iwOqwc3xOrBDTEpvCgaqGKYYVtT1fJ9jO8YzLkyB6l5qRrHgSYOTXDM4hh2P92N3U93Y1/3fbymfuowNTBFoawQedI83nDIZ5gIZVKYeS7D6Y/pvoQCIVLzUpXqr2u/jmPyTbPj8Q5MDJioU9+qOqUl43VWYlauXFmsC1UlbNSEPzT96NNAO1CObjCaExYQ0N0RsLFDY7xOf41aVsqJhMqKHY93YOGNhejs3hmLW8sFvm8NK9z4IQQdll8onp3DR0a19ICxoQiv019j04NNAEpmTsZ3bh+vPqxdiiNKx/nRnOxSFYbWN5j32x5t468kkAEUbWYhfyYaulrjtyF+mK6wMNu2nhnu3wEepzzGqden0LNWT+ZYHZs6eJLyBNYSa2iC/W+acSASfRQkhL+LFe7GphZ1UeFZdbVwxZwrc2BvbM+YeCjuhH3KZBVk4Xr8dfzS8hc0c24GMwMzNNlR5DgsEorwZ+if+PLEl5zzxpwew/ntVDVk2u5+VkL6N6qhVqanZHEVjfZ1HXD6sTyfxoN5cp+BRm6qf7vsnRgPOzMYibUf1md2Uw6Ly4atbMlkFP64+Arnn71XqqdusUooFBT58gGYFuqN1rXtMZi1mUPLUHrCX8fJFFCjxOx9uheh7qFY3W41ACA6LRrp+emobladWe0f6ytD7Znc/FXDmtfAvneqxw+BQACxQAxjMXdxlr3QpI28b+rUFEGOQXA2cy6WYz+9MGZvboBh3X2Y8jxpHrIKssokSe6NdzeQlp+GhtUachRlxe/vYmOCud3rYtkzxRbkwQhoP1NNi2iBToEc5U9GyXTOvxXkGAQfOx+sjlgNiqLwY08fzD6sHHAArAAYBkIDWBlZoYGdsrISlRalVAYAbzPf6tQvQunJeJ2VmGHDhmmuVMXp5OOI38+/4j1mxIq9Hx4Tjj3P9gCQx0Qv7k7KrKazMNRnKDwstEsuVhrQgwI7tCQAVDM3kq8yKuc40prJHeSRrxZcX8CUKeYlUQdbeI7wGYGG1ZQTK71Ke4XvL36P0b6jedvwsDNV9hERaE52uf3Rdhja30BBahNQBfzJYO8m3lVxdtGPnj2ZaFvXDsb3jVEgK2AUhZepL5njMy/P5CgxtBLBF0q0X6Ma2Kdi1zCvUKYkIfaMacaZGNyNTcXQekOx7dE2tHdtD7FAjH9e/gNPS08m6k9VMScDgKY7i/wKfm//O5o6c/0MRAKRPGGbc3M8TH6ItLwivwL+pKeEyk5OgfoVxq+CPTmfXydr54tGw55grryzUmXESkW4ySX5Yfv5X32VjJ+P8CU61NSGvJEX8zvjaUIG6jlZID0/XaGOfGJMm4FpitSnGMzA3dJdqY6hWIioBV1w4E4cgmraIDkzH/ni52qVGJqmTk3xIlWuRVEUBSuJFbNSr41PhFAgxB+hylGztIXxfaGkkFEy5v4svilf5MjIL15CUl1YcmsJnqQ8wfr265n/x9xmc9G3dl+lut39nZSUmCO9j8DayBrZhfJIl7qOAwZCA7hacCM+rg1Zq/acs7FncTZWHhlPSknxRTM3NPe0Q/vlqn0xjvU5xpi0KaIqaEZVT1xZkdB5CTkmJkbtiyBfLT8dFsx7zJFlEpCal4q0vDS0rN4SR3ofwed1P8ePzX/U+XoioQg1LWuW6w+L7VOiSDGD7gAAwjrUZhxffeyKVqT4wmJq6puRyAhhjcM48eBpdj/ZjYfJDzEpfBJvG+s+D1AqE9DO9WpW5vY/2w+J3TkIDVI55bVMihx72bHwuRcomvz4sgJCGIuNcWPIDUR8EcGU0XbHNNFp0cz7h0nylajsAuWwyYv7+mLbl4G4M6sD2nrbq/weNIZi7nd9lpCBaU2mIXJYJFa0XQGJuEhhohXaqrQTw+ZRyiMIBUKOkkubidxOuM1RYICqG2KZ5syZM+jWrRs8PT3h6emJbt264fRp3XwvKiImhvyLHNM71UHXBk7ooJBDgs670bm+I99pShyPPs68f5ysvZKxfIC/xjpyB2v5+292aOdUrYjZR/ktFgnh42wJgUCg5Ctia2SLW5/fwq3P5Qly+UyB3C3cmfeqQvXmS/NxM/4mrsRdYfrft1EN1LA2gZ+LFQoo+Qq8phX+xg5FOXv+e/Ufx9SIz1RX33Ty6ITRDUajllUt9P2nL/y3+ePGu6Ld+1vxt9ScrR/Y/ka+9r5o69JWZb4vvsm+q4Urzr8pUh40KTHsMQuQ/48MRYa4OeQmgmvI51PxWfFa919GySAQCFCrmvqIqdZGyruc9Hf/5RrJfahPSkPG67wT4+7urnayXFlsl0ubWtXMMbqVBzZe5G5HerAcN+mVcYlIgg33N+DCmwtwMnUq037qC3UhilU9L7ZmEmgw2UY336L7wbap5ZuQq8LMwAzdanZTGyWGDm/bsnpLHHvM9YPZ+VVT1HbkibSmxU5M0XfnCnkPe2O8+LhzTvtMAMDvHX7HmFNjPp4rY86qw3d9yO9JvixfaYDofqg7jvY+CmczZxRS8t0avig/QqEAwbXlyotEXPQ9BFra/zV25w4AtA1znjRPazOCT5VVd1ZhVINRmBAwAStuywN40M8Kn7JP6Zhg9FPit99+w8SJE9GvXz9MnCi3Nb927Rq6dOmCFStWYNy4cRpaqLioynz/dRtP3vKaH5NNasvxqCIlRhfzG005rph6AgEKi/ls9m5YHd6Oys7R7Gd9a6etEAgEnMUOvuhb0enR+LH5jyikCtGnltz0d8LZCbgUdwnVzarD28YbHdw6YOr5qRAKhLj7xV2lsYf2qdTkn8Ge2Orif6kvetXqxbxPz0uHlJJy+qFq50CfMEqMTKrSQoFG1e4Ue2FG0z1XzL9Cf18jsREzlqhSIO8NvYd8aT7HdJcvrwyrZ5w+0maM3tbeePrhKZa1lgdXUjV3IRHKdKe0ZLzOOzERERG4c+cO87p+/TrWr1+P2rVrY+/evcXqxKfKtNA6nM+KGZnpH+aj5Ec4+OIgYjJikFmQicoILaB0ybMyrJm7xjrxLKd4tjmUos2yOiwllljQagF61eqF49HHee1cafMCvvCZYlWCTCQ3+3iTwW+OBSiEymRBD6aKNHduzhKQ8nNs1DhOBjjId4gUV/UBYN7VeYwCA4AJvQzIJxHTzk/DT1eLnENFIt0Fc+va9ribeBdPUp6gUFZYZLpWmMtM1KuSEsMXvvuLul8w79VFFarKOzG//PILVqxYgb///hsTJkzAhAkTsHPnTqxYsQK//FK5V0ONVOzE6At6lRpQXhH3tvYucfuFsuIpMHUczbFioD/vMXY/+XZd+MoAef6W/rX7M7+jQlkhCmQFiE6PxonoE0xUMRkl48g+mrq2dbGk9RKMbzhebd/Zpt1lna6AjVQmRWKO3Bk9t7Bo/NM1n1lxUIz09u/Lf9FuTzvMuqycAsLW2Bb9avdjPtOJu9nKqqZx4H4SN68SbVJXIC3AtXfySJeqlHShQKi0SPk45bHqtAKsfERNdjTBmrtrAADdPbvj87qfw81CnjupLO5zVaG0ZLzOOzF+fsr5Kxo3bgxnZ2csWbIEffpoG8L108dQLMRXwTWx4YLcP6aRm0KY3Y+Cmh3aT5fJeUVCnTmZqo07G1PNg4OzZdH9OBd7jnlfHGf6PU/34J+X/2BKoynwsOT6DzFKDI9yoSoJnYGNPCwXX1QTmqJdmqIJ6qpB/riXoxyYuKObPHOtSChCoawQW74MwO1XwDcKK7bvMt+h/3/9YWZghmVtliGnIAffXvhWqb3r8dfxLrPo2fKv5s+8j8mIYcxQpjWZBiOxEcRCgepIbABvHgkHCwN0/ks+Sb806BIzkGQWZGJe83mY3mQ6zAxLLwFqReOfl/8olbEnU0wSOx6qshKTmpqKTp06KZV37NgR06dPL4ce6Y/0HM15RUoCe/Km+Awl5ybDUCREvhbh0vUNHbyGD7YSE5kUiXq29Rh/sjuf30EDuwbo4tEFR6OOcs5TDG2sOA6w70W+NF9JAalmUg2d3JWfM0XKW4m5m3gXsRmxnHD57LDPfOGA9Q09dhVShXiV9gr33t/D+5z3Ksc7Okz2N37fIMQtBAB3J0PTfTwWxQ3CQO/ElMRMPi4rDlZGVvh3fEt0X8Pa6aG4bdJymZ3nDpCHheaD7MToTmnJeL0lJ/D29sbNmzf11dwngykrKotYYTLMt2Ve2ZUYvp2Mkvzg2Rmj2eEW+exY1THz0kxmgsm3mkP3n3aUZ6PK7EKWI8/1om5Vj1l5Z+3E9PSvjtnNZmN6E+4Pt793fwDAOP9xmBgwEQEuTgjrUJsTDAKQB09Iy0tDWl4afGx90NixMRo5NOK9/p8P/mTes8012Kt6tOOlJvOSpjWVAxOwAzkYiY04A5VIIIKDqQNMDZRzX3yq2Bsr+xX9+/JfAPLgHerMfaqyY3+PHj1w8OBBpfLDhw+jW7duPGdUHuhM4aUF20yUoiiOIpOUk4STk4OxuK8vTk7m99MsLQzU7OyyV+jPxpzlHItKj0Jd27qY2XSmUohdS0PtlRh1+bs0QSerBOST74Bq8h1v+m9ps/PJTnx/6XscenGIKevs0blMrk3DNiebdXkWdj/dDUBu3qaIjJIxiilbjtG5VLytvTUqI4oBKWhZyZaZ6ky3AXm0V77vwE4yLu+kCce8TdUOuSoTX1UO/wTVlJaM13knJj2d+wBTFIV3795h7ty58PKqOIkWKwrsh11xksi33V1ZlRh6gqxvx372PXMxd8GDZHk+AkdT7Zxeadj2tnwrQurMyYSqhO9Hx3t12+T0Co9AoDxBVfxfjz45GpHDIjGqwSiV7QGsXDms7/FTi5/Qx6sPvjr1Facuu2+Pkh8xQQTYgz3tZGsgLJoQaPsvo88VQABDoSHHZjnsfBjWt1+vZUufBptCN6HHIblJGZ149o9IeZSixcGL1T4rVXknpl69epg/fz7OnTuHZs2aAZDbS1++fBlTpkzBr7/+ytSdMGFCeXVTI1u/DMTY7bc1RiTTJ2yfOkDZFMvdzpQ3iWZpo25RRDFkL3uhKyY9BrWta8Pc0Bw3P7/JydOhaSfGQFAkE/nGoruJd3Em5gxqW9dGd8/uKvvnYl6Ur8lAaICG1RriTuKdMksmTe9KHHxxEB6WHkq5Zura1C31PtATeykl5Sy40kk/z7w+g0nnJgEA/uryF/Y+k7sTpOelQyqTQiQUqR1XFRlSdwief3iO/c/3AwCMRcpzIb4wyGwmBEzAqAajELRTnlxWlcWGRCyUf7+PIldXy47uNVU/OwR+SkvG66zEWFlZKWnUFEXBxcUFu3bt0rW5Tx4py55YcXudb9VfnfN5RcbD0gMr2qzgzXBbkqBp7GdtlO8odPPspmQKpg0mBiZIzk0GwL8Twxa2Y1t74sD5oq17ugv353YEFrI7J+Wcy0fRCo9cWk5uLw8XnV2QDV97X52/B1Ck/IqFYux5ugc/XZP7tdgb22NW01nMZ0A+IaD57e5vWBMit/11s3CDtcQaH/I+MEESDMS6/6PoHR0jsREEAgFHMbscdxm9D/fGD0E/oLFjY1VNfFJ4WHrgt5Df8M2ZbxgFln4+2L/3GmY18CazyJfqaJ+jlXYBQx9s2rQJ1tbWePToER49Koq0Z2VlhU2bNjGfBQJBhVZiWte2x+OfOsH9O21zTZWcxOxE5j0FijPhLI6s1BdqlRjW4h7bpweQKyqPkx/j/vv7qGlVk3NMcXxUnHwKBAImHHJeobIS8yTlCbY83IIObh3UKjHsxQYDkQGTn2z3092Y2XSmyvNKg8M9DzPj4Jn+Z3DhzQV0ral94IfiQt9bKSXl9VGiFRgACAsPY97vfLITPWr1gI+tD6/sU0eAQwD2P9+PZk7NOIEMTvU7hfT8dK0WL00MTNDZozMy8jPUyFQpJ0KeOjNfPo5EHUEz52Yk1LIOlJaM11mJCQ8P53wWCoWwt7dHrVq1INYhyVZVQUap3olp4dwCZgZmWH67KINpZZ3IWEos0d6tPe8xfQVdqm1dG7WtaxfrXPZ95Ys2YyQ2go2RDcwNzWGoYAZB78RYGHHPE2ihxLiau+JadBwomXynqqa9fEV0yNEhTB4CRY5HHUdqXirau7VntuPZ0AOCWCjmKCzvc97D3tgekcMiMfb0WFyOu4yr764yx9nhLgH5PfmQ94ER5r0bVgcVoZtQppUYVc/ti9QXZZKYrSLxPFUeFedxijzcLT0ZeJv5lvFLaufajkly2qJ6C87Kb1UkKoo/qVxlZWgzN7UJIPWJlcQKcZlxAIDWNVpzdvQ0md+UJuqUGLZpq0gggrHYGC2cW6BAVoBGDo3w54M/serOKk6ULt5rKHw/kUCk1iqA3hHQFMWNfQ/pnZiIxAi0qN5C7XmlQYGsgFGqqplU4zjQlya/tvsVQgghFAix9eFWppxvN4oOPkBDj4m0/0xMhnbpN2jT46xCrimgo6mjTtYXi4PVJw02kHDz7KgLuMLHPy//gbuFu8aobYQiSkvG6+wT07p1a86rVatWqFOnTqkqMNHR0Rg5ciQ8PDxgbGwMT09PzJkzB/n56iNHtGnT5mOc+6LX2LFjS62fuhLgEIAR9UdwyiqrEkNTICvA0ptLmTj9AFDM4DZ6hX1f+VZdfO19cX7geWzrvA1SBa1LlWM/vROjbkCc23wusqMmQJrFjRLE9kmhWdRqEQDg14hfMf/6fJVRz+iBmE95ogc7E7HcrOv7oO9524h8H4m3WfKsw3TiOQ873R3w6YhxirbrbHTNulzZoRUSBxN5GNQPefJdvekXi3yg2CubbBMYwqfBvB4+mivpgdTcVDxMLspIPrLBSBiJjTAzSL5bUNZO6Qe/ac68FwlVTy/YIYIfJT+CQCDAuvbrsCl0E4QCIaNEiAQitbtJiivhAoGA8c3jM2EKj5Uvwmq6L7SfIABUN6uOTR03YUunLVjdbrXa8/TFCJ+iecGH3A9a72ToEwOhAURCEQQCAUdePf/wHAeeH1B7Lu1vElwjGCPqj2BCFmvL/ff3NVcqAYrm3aqUfXUK47+v/kV2QXaVW6SraBTLsf/ly5f43//+h/bt26N9+/aYMGECXr58qfnEYvLkyRPIZDL8/vvvePjwIVasWIH169fj++/5J2hsRo8ejXfv3jGvxYvVa+jlTWVdkc3Mz8SSm0sQsD0AWx9txZjTY5hj+nKCWx2xGj9e/ZE3RLIm6Ek9oDnuv2IgH5UrioJCrdrjgx4kg2sEo6ZlTcxrPg9danYBoN6/CODuxExpNIVzjFZihtQdgqWtl6Klc0sM9xmu1MbSW0uZ95sfbAZQPN8leheHbeahaK9dnqvB5UE7l3ZYHLwYe7rvAcAf/rqmZZGZzJ3EOxh5YiQW3VhUZn1cu3Yt3N3dYWRkhKCgINy4cUNl3Y0bN6JVq1awtraGtbU12rdvr1R/+PDhSgtGfJFoqgrsyfX2kYGldh3aP4GNUCBkxpHS9rNaN4Tr6M7+3tpGbKejJMZlxuFJyhNk5GcwJnEigQhbO23F7x1+x5ZOW5TOHeg9EL+0/AWta7RGe9f2sDe2xz+9/sGdL+5wojEC8jHqZrw8+JD6HCLgRAUzEBrAQGSARg6NykwpnNxoMvO+/b72CN0fWibXVYWiEjXnyhzm/UDvgVjZdiXnOD0mCgVChDUKQ0f3jlpdR1XqAV15k/EGzz48UxncQTFwnqoxSlXAHACISotC0M4gDPpvULH7WZpUFRmvsxJz4sQJ1KtXDzdu3ICvry98fX1x/fp1+Pj44NSpU6XRR3Tq1AmbN29Gx44dUbNmTfTo0QNTp07FgQPqVwMAwMTEBI6OjszLwoI/aWB5cDP+Jv5+8je+rP8l6tnWw7+9/tXZYb2iUCArYMxjaOgY6/oyJzsZfRJ7n+3lTdqoCU07MWyGBLlyPhuoWFHUxpwspzAHEGYDggLlcsgjshzudRh9vIpCk2tSYtgmEQ0dGir0Vd6XRg6N0MalDVwsXBglhq3I1bKqxby/ES8XVmznWm2TXfIpMZMCJnHqVLWdGJFQhM4enWFjZKOyTge3Dsz79Px03Ii/gUfJj1TW1ye7d+9GWFgY5syZgzt37sDPzw+hoaFITEzkrX/u3DkMHjwY4eHhuHr1KlxcXNCxY0fExcVx6nXq1ImzYPT333+XxdepsEQt6IJbM9ujlZdyxDp9oegjQq9gS8QS2BjZKDnC65M23vbo3KAoGfGEdrU4AUHU7cSw82/Qq/RTzk9B/3/7IyIxgln5FwqEsDayRnPn5rwTykYOjdDdszvWhKzBirYrUNe2rkonbXZYYk0LT/Xt6gOQKzPFCedfUhR3mMojzPOqO6vQaX8n7HqyizeSKiCX7TObzlTyPypuf10s9LOIO/ncZPT9py/uJt7lPc62rhjrNxbNqzfnrUcvAqhbXFZlFl6eVCUZr/Ps4rvvvsPkyZOxcOFCpfLp06ejQ4cOKs7UL2lpabCxUT1JoNmxYwf++usvODo6onv37pg1axZMTPizvgJAXl4e8vKKfpAZGRkq65aUY1HHsPfZXnR274xHyY8w//p8bOy4sdSuV5qwbZxp8qXKmeS1wbeGJe6/UV69pgc+vmtpgo6aNaXRFLRxaaN0/E3GG/T5pw8MhAa4PPgy/hnfApAHlYKFMf93yEsKwZi2dqhnU0/ldX+49APMvU8hN74nCj40g0AgF4z05J+tWNAoKjFZBVlIyE6AgdAALuYuqGlZEz8E/QALQwtYSazgY+vDmJTQ9/vCmwsYd2YcnE2dGaUnuzAbBbICGAgNOKE7+3r1lb8pxk5MLataWNNuDWyNi8IvKwYsqGpKjDbw2WCXVdjO5cuXY/To0RgxQm6ysn79ehw5cgR//vknvvvuO6X6O3bs4Hz+448/sH//fpw5cwZDhw5lyiUSCRwdK/4iTFnJeIFAADsz3WWVLihOFhdcX4C17ddiTcQa1LWpi/UdSi86YHwa1yTWwtiAE8RFTZoYvEqT504zFhszq/S03MuX5jNKTHFkx+4nu/Eo5RF6ePbgKD4ck1cNPzWhQIjIYZE6X7u0KI/d7LS8NMRlxuFD3gfeSKqAfEHwScoTpQh5xVVifGx9MLvZbDibOhfrfHa/ANUBBQY2ccZfH621h9QZAisjK956aXlpGOEzAkFOQRh7uuK4ImiiKsl4nZcYHj9+jJEjRyqVf/nll5yIA6XJixcvsHr1aowZM0Ztvc8++wx//fUXwsPDMWPGDGzfvh2ff/652nMWLFgAS0tL5lWvnuoJqjao24WgBfWxaHk4xWvvrlXaDLF8K1u0/TFffhF1DGvmzltOT+qLIyAbVmuIrjW7wsPSgzeiiFgoRk5hDqNcOFpojhInzfJGC8cucDJzUlmnaNdH/r9+/C6d4w/D5wOlmDj0ZPRJ9DzUE10OdEGDrQ3gYOKAQXUGoUvNLnCzcMOubrtgJbGSn/vRFnncmXEAgLdZb/E+5z3T9us0ubMxO4ocvdokEGgc25U4GX0S48+Ox87HO5kyM0MzhA8oCgBS1czJtIGdh4JGVZZyfZKfn4/bt2+jffuiIBxCoRDt27fH1atX1ZxZRHZ2NgoKCpQWkc6dO4dq1arB29sbX3/9NZKTk/Xad32hbxlfniguglCgkFuYi1sJt3A7QTmhrkSsv12F2JRszmeRUMDZzc0tUG3KRq/ss3eKaNnFVmI07YKEx4RjdcRq7Hi8Aw+THiKrIAuX4i7hwPMDSmbH7JxWZfFb0yflEQWLyf0mLcD2ztvxb69/lerkSnPR/9/+OPzyMKe8JDtH/Wv3L3EABVr5VRXaua5z0birSoEBgHNvzmHzw814kvKkRP0pS6qajNdZotnb2+Pu3btK5Xfv3kW1atV0auu7775TsrFTfD15wn144uLi0KlTJ/Tv3x+jR6uPDPHVV18hNDQUDRo0wJAhQ7Bt2zYcPHhQrf/OjBkzkF1qvXcAAQAASURBVJaWxrxKUzHjTaxYSSd8fCZadJSmRm66JaZUBa3gFWd3Z4D3ACxstRCtXVrzHmcLPVUJrvjPUz+4KCa7TMrIZ5zhAf6Q2uwVSUBZ0TkSpRy+VfHe+Nv78/bnmzPfAAATbhooUpZU5sNRQ2ZBJgAoJbScHF5k013Vd2IuDryIujZ1saLNCqaMPTmjgy/o8twpkpGRgfT0dObF3mlgk5SUBKlUCgcHB065g4MD4uPjtbrW9OnT4ezszBkkO3XqhG3btuHMmTNYtGgRzp8/j86dO0MqrXiTxbKU8aWNsQFXNrBDLPPt9m0a1gTGBiIs7e9X4msHKMh1oUDA2Yk5/+w9VEErEezxjr14I5N9dOzXEDXqQtwFbLi/AQtvLMSgI4PwJOUJs6CmOL6yFwh1TZZcHtCBXgDtQxTrE7ZS6WzmDHdLd60d9IvjJ6pP+JJv+7ISXmZSRdHSFt1YhMfJj3nboeV0RcjhRWQ8PzrPLkaPHo2vvvoKr169QvPmcjvCy5cvY9GiRQgLC9NwNpcpU6Zg+PDhauvUrFnkAPv27Vu0bdsWzZs3x4YNG3TtOoKC5AmQXrx4AU9PT946EokEEkmRCYBick9dUTctoQU52xxI11B/FQW+laLNDzZjaL2hvGGC1WGoYrWQFkjFUWIoikKbPW0gFAixsu1K+NlzB3H2ylEhVQgDLfYkate+hVtpr+Ge1ZcTbYcNPUjT0VCSs/KYwdRAaMC70qi4E8POJwIAMy7OQGJ2IlzMXRDiGoKErAQMrDMQtka2zOB89/1d3v6w7cJp6OsUZ62Pdpw0M+TuLNibFPkBqPMNqQpYGVkxTv40bIWFNnMpyUCpuJswZ84czJ07t9jtqWLhwoXYtWsXzp07ByOjIgV80KAi59YGDRrA19cXnp6eOHfuHEJCQnjbatu2bbFWmIcPH84xcdAVfcv48qSmZU18Ue8LbH+0HYD8uaJNf7IKstB5f2cc63uMqd/Syw4P5oWqDX+sSMtadrj0Ikmp/Psu8gAegwNdcf5pInoHVEdMcrZSPT7oSTkdGhrgTjwNRAYQC8S8CQ/ZCBXWYUUCkUpTIvZOTO9avbXqZ3nCjmaoTbJIfUMrImzfzI7uHWF2xQyZBZn4ucXP8K/mj24H5dnWPS098TJNvkBcHj48bOhnia24Dmvmjil77wEAHM2KrEP+evwX/Oz9UNdWOYHonQR54IxfI36FqYGpykABZQGR8fzorMTMmjUL5ubmWLZsGWbMmAEAcHZ2xty5c3VOQmZvbw97e+2cHuPi4tC2bVs0atQImzdvhlCN06Aq6B0kJyfV5j9lCb1i1t2zO5o7N0djh08vIWDbPW11ti0O9XFE05o2aOLOnfzSgxC9QqQL2x5tYwIC8K1qsYVugbQA2ohgqdkFrL+fiLaubVQqMUW7EPIJqoFICCuJFda1X6fSWTI9Tz6pOhF9AoPrDMb+Z/uV6qy4vQItqrdAc+fm6LhfblN+c8hNZmcnrFEYJ/+QOhglhiVrtHXsp3diFM2j/O39cer1KXT26MzxlyHIYduY04sZJVFiHj16hOrViyIqsSfpbOzs7CASiZCQkMApT0hI0GjrvHTpUixcuBCnT5+Gr6/6RK01a9aEnZ0dXrx4oXKA07SApQo/v5LvInxK0AoMDVuu8AUH0UWBAYAFfRqg1eIi89BbM9sjIT0XdZ0smOMyGQWhUKD1bm58lvKKMHvl/2b8TRRShejv3V9tO1HpXJMxA6EBI3MV5Tw78lVlC4tbnjsxBbICjD09FlKZFAtaLYCR2AiZBZnwtvFGDbMaTP1gl2C8THuJTu6dyiUYAhu+4Djd/Jzw1/XXCHS3QTvXunAxd0FsRiwA1YvH7ISY5anAAETGq0JnJUYgEGDy5MmYPHky4xBpbq6cpV2fxMXFoU2bNnBzc8PSpUvx/n3RNjX9T4mLi0NISAi2bduGwMBAvHz5Ejt37kSXLl1ga2uL+/fvY/LkyQgODtb4z9En6ixEmFC5AjEmBFTcLNTa8o3fN/jt3m8lbsdQLMSur5pxygplhcwkrzg7MeyQwnwClr39HbQzCNe7HwZjba5iYGZ2htQoVYwS83EnRiqjYCQ2QsvqLVWecz3+OgAwNu2KOzFMn1kDNgBciruEENcQCAQCtKzeEqden0JkEr8Cuaz1Mqy/vx55hXmwN5YvJAi03IsZ0LgG9tx6g3FtPfGu4AwAZSWGvp+qFLWqDltpnnd1HoCSKTHm5uZaRV40NDREo0aNcObMGfTq1Ut+XZkMZ86cwfjx41Wet3jxYsyfPx8nTpxA48aaF1vevHmD5ORktQtGw4YN09gOQT2KK/QUKI6/hz58P1xsTNC/UQ3svS2XQ3ZmEqWABXS0J20XXdk+eTRs5ePee/mK+dmYsxjgPUBlO3TIZHYbTDsKzuhsuX/l7RV09+yuXWcrAOWhxLDNmm+8u4ECWQEKZYVIypHvyvX/tz9O9TsFkUAEKSVlQvUfjz6OJa2XlHl/2fDtxEjEIhz8psjXhi2DVZnxl8d9VwWR8fzorC7n5OQgO1u+ZWxubo6UlBSsXLkSJ0+e1HvnaE6dOoUXL17gzJkzqFGjBpycnJgXTUFBAZ4+fcr0zdDQEKdPn0bHjh1Rp04dTJkyBX379sW//yo7p5UXtJCtrCZkitS2rl1qbcsoGVo4t0CgY2CxlBhu+GAex34Fn56f7v6qsU0m6aQa+19GOLKUGIqicDzqeLHy3bAxEBpw+j35XJEfipe1F3Z23cns7vWvLV/RpEMrd3TviAM9DuBInyP4scWPrFY1z0J+6d0A//2vJaZ08Map1/Kw6mw/H6Doe598fZI3sSeBS//a/bXOpVBSwsLCsHHjRmzduhWPHz/G119/jaysLCaSzdChQ5lddgBYtGgRZs2ahT///BPu7u6Ij49HfHw8MjPlu3CZmZmYNm0arl27hujoaJw5cwY9e/ZErVq1EBpavvktPnUeJj1UKmNP3vU1CavjpF1qAm2VGFq5amDXgCmj5ShbMdPU/zo2dTif2Qs7igpegEMA1rdfj1bVW2FiwETtOlqO7O62m3lfHv2l/x/5MtXR4n669lO5m47xoSlNAcBValUpMWzfUVUomqbzIaNkuJt4l7OzU5pUJRmv805Mz5490adPH4wdOxapqakIDAyEoaEhkpKSsHz5cnz99dd67+Tw4cM1bku5u7tz7MxdXFxw/vx5vfdFV9SFTTU1MIW1xJo3QlVlZNK5SUplO7vsVK5YDAxFhiUKFzq50WTGvIpvJ0YgEMDcwBwZBfLdxXPvNEfxYJQYNUK8SEH9qMRQFP579R++vyR35uYztZvXfB4nmZgqxAKxkgKsaH8qFophKDRkopdlF8iV/K4HugIAxjccj/q29eFi4QJjQxEEYiEdSE31dUVC1K/OzT+hOJliDwpZBVm8AQwIctwt3DG72ewyu97AgQPx/v17zJ49G/Hx8fD398fx48cZR9CYmBiOue66deuQn5+Pfv242atpm2yRSIT79+9j69atSE1NhbOzMzp27IiffvpJpckDoXSgKIpxigf055D8RVM3vM/IQ+va6s2/2QtEC/s0UFmPnbCXZpz/OAz3GQ4bIxv8GiFfRPrv1X/4rO5nKttxNHXkRI7i7MTwKEAtqrcoceSrsqKebT1ml0ObibK+8a/mj4kBE1HTsiZORJ8AIJfr1hJrfMj7AEBuhWAgNFBaxCpv6GdJlT/uvmf7OPldIhIjVAb9ofG391fyNf2j4x9qE2LS7H26Fz9f/xkd3DpgeRvtzLxLQlWS8TorMXfu3MGKFfIoO/v27YOjoyMiIiKwf/9+zJ49u1SUmE+JrIIsPEp+hEYOjbCw1ULNJ1Ry1txdg987/F7idrILsnEj/gYMRYZo7syfmEod7hbuzHtV9rq0AgMAE3y+BJ5/q7ZNbZSY/rX7o1X1Vvhs3TMA8p0YxUheirCzRQOAk6kTr0N+Uq6ysy3TN2kBYjNjMcZ3DKqbVcf7nPfYGLkRb7PegqIoxGTIo7N8e0H+HWllykgsAqTaO/l7WXvh+YfnGNdwHPf6rFXQqh6dTBPlEflm/PjxKk0Lzp07x/kcHR2tti1jY2OcOHFCTz0j6ILiItmWTluQmpeKltVb4lLcJb2FEjYUC/Fd5zoa67HXUJIyVa+Ct6zeEif7nuQswlQzkUc3ZS9Gfl5XfUqEa2+vcT6LhWLUtKyJpk5NOf4aAJCZn4n3Oe9hIjZR6cNY0fgu8DsUyAp0Do6jD9Jy0/Bn5J9o5Fg0SRcJRXA0dWSUGLFQLN+xYW16KY5f5YGmpOGKibmbOjfV2ObWzlvRaX8nzljcxLGJVv4/9O9QW5NtfVBVZLzO5mTZ2dmMD8zJkyfRp08fCIVCNG3aFK9fv9Z7Bys9ChsxTXc2xZcnvsTau2vLpz9lBJ21XV95b+Kz4/G/s//D1PNTi3U+eyKtTbSM6ibqBzkpiiaf6pSYzPxMvEp7BaFBqryuSMj0pb5tfZXnsFGl9Cjag7OJz4pHz0M9MeLECHTc3xHWkqKQopfiLqk8T1di0uXKkOI9eJNR5MdDlBh+aGW8a82uCDsXhhW3V2g4g0DgohiW28zQDDXMa2BOM/lOblnb9LMl6+nH/NnBAXloeSczJ0Zx4bQhEODO53dwtPdRdKnZRe31xjfkTtIMhAYYVGcQNnbciN5e3Ahk596cQ49DPTDz8kzNX6SC0LBaQ7zJeIOjUUfL/NoPkh8goyAD52LPMWVigZijOIuEIuzuthv1bIsiZ1VE8zJFmjg04Xzm89ECgBDXIod1oUCotJg4/9p8bIrcpPF66nYHCSVDZyWmVq1aOHToEGJjY3HixAl07Ci3405MTNTK6aiqIVPh2b/hvu4hoisT7pbuALhhLUsCbSLBl49GG1rVaMW81yYXT7BjkNrj6aytWBlUr6RffnsZC28shNjiPgB5OFJakKmKsBfsEowBtQcwO3X0tvfS1ks5A4SHpQfnPPb3is/mRv+5GHeReU/niuHl4yykjbfmqIEURTE2x4p+SuyVLaLE8LOizQr8GfonXMxdcOr1Kfz54E9OBKWqRExMDG+eHIqiEBMTw3MGAeDu4rGDhdCyoKx3+djrQ4EeuoVWP/ziMMacGoNdT3bBQGQAFwsXjeco7tSoy/9C34vKlItt1MlR2PlkJ5bfKn0TJEWefXimVCYSirjmewIxHE0dYWFYNPejI1aWJ8ejj2Ps6bHY+nAr73H2Ttw4/3GobcXvz/tLy18wMWAiDveSJ/P8X8P/cY7vebYHK++s1NgfetxWDDZRlSgtGa+zEjN79mxMnToV7u7uCAoKQrNm8ihSJ0+eRMOGDYvdkU+VmvbKmbkB+Y9jxPERCNwRiPOx5e+7ow9mNZ3FvM8rlE9u77+/r5e2swvlvhzFzVyckFUUbpD2DykJVjIZfm7xM35s/iNnl0ORogFT/uM1NhBpHEwLpAXIlebi/JvznEmIjZENY1bwe/vfsavrLhXXArysvDjH2CtlfChOdkLqaE5cyzYZox0padjRyirTpKEsMTEwQRPHJhxnT/o5r2p4eHhwok7SpKSkwMPDg+cMAsA1J4vLjMOk8ElIzE7EncQ7GOQ9CD+1+KlM+8OWzz38nFXWOxtzFsOODcOaiDVMWUxGDK68vYIXqS/wKu0VotKiNCaAVfQJFAlESMtLQ3RaNN5nc58nOlJieYf/1YXUvFQA5TP57Ve7n1KZSCBCQLUA5rNYKMauJ7tw7V2RWR8dvaw8eZf5DpfjLvMqYgA3ap+7hbvK4DwmBiYY1WAUalrK8xV+5fuV1gk/2fz95G8AwIU3F3Q+91OhtGS8zr/mfv36ISYmBrdu3cLx48eZ8pCQEMZXhlBE/0Y1ENahNvaObYbU3FSmPCU3BbmFuWUWraIsCHUvilKx7/k+vbY95OgQAGByvegKe3KojV3qq3T1KwMCAD1r9URvr95qFSt6wKSTXcooijdbNRsZJcM/L//BsahjnMhetaxqMSs6JgYmMDGQB4Ee3WA0AMDTqiiBq+LOiJc1V6lRRNHsr0UtOyzupz4UOTvyi+L1XqW9Yt4TJUY97EmVKrOGTx2Konh/R5mZmZyEawQu7MlYVFoUotKicCfhDqaen4oXqS/KPIww+z9obao6imRCdgLuJN7hyAlatn3I/YCeh3qix6EeOk/eBQIB9jzdg+6HumPN3TWcY7TCR+SRdvClDhAJRPg+6Hvms1goxvzr88uyW1rBF2KZDa3QFicqZFlFkfzUKC0ZXyw7D0dHR6WkOYGBgcXuxKeMWCTEhBD5BJK9MrT01lLG8fBTMbdhR6DysfVR67NR1rxOL/LXSslN4XXsdLdwR3R6NACg55lR0C1FJz9FA2bRTgctQFUNpuxVIbbyJRFJeMOHflb3MwQ4BMBEzGS2UVqhVJXLplvNbrwhqwUA6jtbKp/AIiO/KBCCYvvX310vaquYu2dVBXZo8sq0SqwPwsLCAMifkVmzZsHEpOgZlkqluH79Ovz9/cupdxUfxR1UChQTKao8IgJq+1vni07GJ9uEuq+zqvQ/KErqS+SRNiiOIT09e0IgEHDGC8W5S6h7KLOoVp5oCrFM/272PtuLvc/24mz/s7A30S7xOgBs7bQVw45rnwOlKj9zpS3jP43ZcyWBvWpmLbFmkhh+KnliJCIJPqvzGVLzUjEpYBKaODZR8tsoL9Ly0pj3bzLfoK5tXaU6tAKjhECAAf8OQK40F/98LEoXCjDssNxxdH+P/Sonn8z/ViBfBaRQ9ByoOoft98J28jcQGTDhuL888SVmNZ2FAd4DYGdsp5Q8U9F3SJWivKDVAt5yAGC77GQIBGi+tQFsjWxxbuA5ANxVLkUhzZeRm8CPr70v1rRbgxrmNTRX/sSIiIgAIF+li4yMhKFh0QTJ0NAQfn5+mDq1eME8qgI+tj74o+MfeJT8CMtvLwdFUXia8hSAPIDHr3d+xVi/scXKrVUcBCreK0IrGGxZR79n+1EWR6lXlSfml+u/AADCY8N1brMqorjIFuAgNyPzsPTAhYEX8CHvA8wNzLHj8Q4A8khqQ+oOKfN+8qFJiVGM2qfrc6ZrtLiqvPtX2jKeKDFlCPuHQ4coBD6tB3xGkDyBUkZ+Bi6+uYhzsefKNAeGKtiKIm1nrI5a5m4A5CZlybkf8DjlMQAgWiyGe2EhCiFgHO7VmacpJruUURT87P0wt9lclSs/bIF6lZWvRiwQY1e3Xfjp6k/Y82wPknPkibgW3ViEhOwEfOP3DWpZ12LaMBYbM+aKAoEAnT0641jUMaa9GYEz8CH3AwplhbAysvo4ieBPCjrFwe7jvUhGZn4mzAzNmAHC1shW6Ts4mTkBqoMTERQ4GnUUsRmxmNNsDrxtvMu7O2VGeLh8QjlixAisWrWKBIfREUuJJYKcgpiJOwUKDqzIihsjN2J4/eFlp8QI+N8rQi+AsGUd/R3eZr5ltaH7CjaJBKUfvG28cajnIfQ63AsAMOfKHPTx6gNAHkBBMYjCqjurKowSQ4+7tNWDIiMbjMTB5weZeZilRL3VgSK61qdNv6sipS3jq5btQjmj6gf1qZiTsSmQFWDX013Y+2yvRufMsoCe8ANqzLhUhIaUsZRPekhlfyN1A23RtaTMia4Wruhbuy+CawRr6jZW3VnF9I2+DuNT81Ex++vxXzj1+hTW3y9KBioQCBDWKIzT1jh/bi4XA5EBehzqgXZ72zGhktkIWV+rbl7R6ihtx24kNkITxybwr+avdG5xo8hVVZ59eIbIpEjO4kZVYvPmzbCwsMCLFy9w4sQJ5OTIle+KIDsqA/SCA0VRSrlj2Mkvy6ofiu8Vof1VDr04xJTRMiMqLUrj+eogSox+MBAacAK0aCKnMKfCRFekx0ZVPlUWhhYcWavrHMxSYslJmqpJTjV1kkfr7OnZU6frfEqUlownSkwZoirx2Ke0E0PDVgj0MZhUM9YcLUsdbNMCVYPjib5FyZxeZBT50FiywkeeNZWbc1EC9W3RmBiYyHcqZHL7dMUJhiayCrIAcIWsqsAAipFPXM1dAQDe1vKVfUUlra9XX2YlVOnZpCjUsC5aPQrOLgowYG8s30Fys3DDn6F/YmXblUr9nhAwAQDga6c+OABBDm0myA7kUJVISUlBSEgIateujS5duuDdO3k+hpEjR2LKlCnl3LuKy6PkR5h5aSb+fPAnALl8UfSTKcvIViUx/VdM3qutic8g70EAwOwC0MqQojmZjZE85HNxkiWXNw4a8paVFnzPTnxWPBpsbYAGWxvgZPRJzjHFjPblBf0MqFo4BlTnadOWdSHrEDksEpHDIrXeMazKvjGlJeOJElOGVKWdGPaEWR+5YnZ23YkQ1xD80fGPYp3PHgRU+emoMu9aeu835r34ow5CqyKahFJ3z+44N/Acct/1l59HAQ+THmL9vfU4/fq0Vn3fHLoZv4XI+zDt/DRm9VLTIH/+jTx0N61Esk1KWlVvBaFAyLTBtxpibFikJP1kV2Q6oI0grmZSDZHDIrGj6w6NdQlFTthVVYmZNGkSDAwMEBMTw3H8HDhwICcKJoFLXGYcDr88zOSB4tuJUTeR0zfampN9F/gd3C3cOabGikl+td2JmR44Hbu67sLUxnK7elU7MXRkyytvr2jVbkVgepPpGOQ9CKvarSrza7/NfIuRJ0YqlbMXz86/Oc/J1VOWGenV4WzmDGuJNe6+v8sb1njbw214kPygRNfQRSGhx1dVPjpVgdKS8USJKUNU7cSU1ypLacJRYlSEOdSFnMIcjPMfhwZ2DYp1fpBTUfJKNws3lfX61+6vVHYnSZ7rpp5tPXyRLo/IRX0U1rpGz5FRwL3397D27lqOf4oipgamAIBh9YahsWNjNHZsDADIKCiKCMaOuMbHzic7AQAv014C4P5P2rq2lff/oxLDrN6qEMwvWc54S28tBSD/n7zJeFMh8gJUdmhHVDqyVFXj5MmTWLRoEWrU4AY38PLywuvX6p/zqgz9u3UydcKoBqMw0Hug0oKEqnGnNGBP7NRN8YbUHYJ/e//LkbeX4i6pbEsdYqEYPnY+jPLCJBZUYQGgKlJjReTzep/jh6Y/wMfWp8yvLZVJEZcZp1TOXjz75+U/nGiaFSW6Yl3busyYyc5FRKMyiE8pQT+L6sb8T53SkvEV44mrIlhKLHl9BWhTkk8JtiO9PkItTz43GX3+6YP7SSVPnqkuq3MblzYAAC+Lot2apx8d+B8lP2LKGIMNHReeKIrSKnP09CbT0cGtg1KSSgNB0YDhYq45ozUAmBvIc4+wnRHpEMhKSowW0JHeIhIi0PlAZ4w5NUbrcwn8lFeG9YpCVlYWZ3WOJiUlBRKJhOcMAlD0vFQ3q46JARMxvP5wjGwwEhFfRDCKcZnuxKj8oBl2SOh93ffh765/F6sPzZybYU+3PfixxY+8xxUjOVZksgqyEJ8Vz8kxV1aoSryrOG6x5zQVRYkBgBvxNwCACcrDRh9ytkBagA77OiBkT4jSLqIiVkZWJb5eZae0ZHzFeeKqAI6mjmjk2Eip/FNKeMkHHTGrJGhKEKkJbYUrbWIl0TKaj6admJPRJxGwPQDGLnIzOHaIZXWhtXt79cbyNsvRyKERvr/4PRMelH0ObeOtCb7reFl5cfpfHCWGtpf+FM0hyxp61bmqKjGtWrXCtm3bmM8CgQAymQyLFy9G27Zty7FnFRtalrB3LYQCIcRCMbPjUBF9YlJzUxGfFc+Z/A2tN5R5n1OYU+wofZYSS9S1ratykWd4/eHFarc8WHRjETrs64Cxp8eW+bUzC/gn5uxnbWrjqZydmIri85Gen85Jq6AIe3fyx+b8yq4mBAIB4rPikZiTqHG3M8gxSO3xqkBpyXgy+yhj2ru25yQCBD7dicvxvseRmZ8JZ1PnErdFryYWd8JsZ2yHDR02MGZaqqhlVQtf1v8SplIpgFNKxy8aG6FVTi5MKBnG+I7RSqkqkBUAQvlEgqIAKdTniWGTnp+Of1/9CxsjG3wf9D1zPWdTZ/jYaWdiMMxHOSlXB/cOAKAU8awIrkmKiKIg/ViXnlww/xMSiazEFEeZ/JRYvHgxQkJCcOvWLeTn5+Pbb7/Fw4cPkZKSgsuXL5d39yostOlYRn4G/nv1H4zFxghxDQEAeFp5Irswu0wXGbSNTrbyzkrsf74fExpOwGhfeXJE9k5Mcm6yqlOLTeQwfaQvLlsOvjgIAHiY/LDMr60qMhl7zGtYrSGz4wFUHJ+YZynP1B6nx66wRmHo7dW7WNdg3wdNwYtuJ94u1jU+JUpLxpPZRxmSkpvCa8r0qW41VjerjlEnR+HGuxs4JxTCpgShPulJdkkmzM2cm2msk5ybjD8i/4CDsR1G8Rynp/YWMgrjG47X2B69CyL4eCZFUZBBsznZ++z3SMxOREJ2AoAihYdu7/N6nzOZ3o1ERsiV5ir5VrmYuyA2IxaNHRozZfVt6+NV2is4mTpx+qApappvXh4ijOSTDE9LTwBFoZb1YeJX1Wnj0gbVzasz/9OqRv369fHs2TOsWbMG5ubmyMzMRJ8+fTBu3Dg4OTmVd/cqLLRcjM+Kx4yLM+Bo6oi3mW/x95O/0b1md3zt/3WZ9kdbx35aWWev3NPmbwAwKXwSOnt0xuLgxTr34dmHZ1gTsQbVTKphZtOZOp9PkFPLimtBcaDHAQDcxTcvay+YiE2YVAAVxZxMk+JeUssOgPvsXoq7hJ61VIdPvpt4t9jX+VQoLRlPlJgy5PmH55h6XjkzaU5hDgwM+XOUVGaORx9ndp1Om5pgQIZ6u1F10Csd6kyw9AHtFFqgYmVFpuNKk2KySwpFq0DqBP6+Z/vw273fmBDF9Eo9X+jIn1v+jN1PdzOx6BWvzV4l2tZlGwqkBYwf1i+tfkGhrJAxL1NlyM7+3rR5Cr1KSCg5xV0N/JSwtLTEDz/8UN7dqFTQygA7ymBqXipiM2K1SuqrbwQq3iui2G9AOfTxpTdcR39tSc9LR3hsONwt3It1PkGOSCjCktZLMO38NARUC4CXtXyMYE/eZZQMtaxrwcHEAQnZCRXGnEyTcsL4peppPjHz8kx08ejCMa1jU1GUu/KmNGQ8UWLKEFV2kxUlQZS+WXJjCfO+oISyTR8rJ9qgqMQo7k/IPn6PTIEAZ14choHQAF1qdlHZXlF/5ULz76iFuPvhLAD1q0W0cKVzHdCDA12+/fF2tHNtB1cLV4S6hyLUPVSpDToCy3+v/mMitRgIDThRyvzs/VT2gQ29hzYxYCL6evUFII/ktvTW0hLn8CEQ7t/n380TCAQwMjKCq6srcfDnoZpJNbRwbgGRUIQLby6AAsUJ55qRnwFjsXHZmZRpKefpnV+2T6FYKEaQUxCz8FXcCTH9XRUjUIXuC0WeNA87u+6Es1nJTZyrAtPOTwMA3Em8gzsJdxDgEABDoSG6eHRBWn4aUnJTYGpgiuVtliNPmldhFEdNyklpzCf2PNvD5CpS6s8nmAtQV0pLxhMlpgxRFSXG1ti2jHtSNuhz4CypT4y20O1nfHRqVOUpkiwSYeblmTA1MNVOifm4E0MrMADkSTA1nEcrMfRKTteaXXHoxSEkZifi6YencLVwRVRaFDLyM+Bi7sJrrng8+jjmNp+r8lraQCtvta1rM9eglSH/av4lapsgzx30LusdvKy91IYA/1Tx9/dnJq30JJw9iTUwMMDAgQPx+++/w8jIiLeNqkjL6i3RsnpLPEp+JM+HQRUpCPuf78f+5/uxvfP2MvuNcnxi1CghfAEJAO5kr7ir1+9z3vOWJ+cmV+k8HSVl2PFhiBwWCZFQhDcZb3A/6T5efHiBzPxMnI05C1cLVzRxbFLe3QSgWWlo4dwCNkY2SiZzJUHdYjQ9ZtK7WVWR0pLxZI+rDOFz2h1WT9np+lOBvRpiLtMtU70itElUaW/LcvLbAJAqjMOUwl9N0cmK7oHy/16dvTqtTCkqMU2dmjK7J3TZ9AvTMeToEKUY9GN85aGP1UVGWX9vPWZcnMEJHw1AHoGABW1Oxp6khLqH4u+uf2NCwASV7RO0Y/vj7Zh8bjLOx54v766UCwcPHoSXlxc2bNiAe/fu4d69e9iwYQO8vb2xc+dObNq0CWfPnsXMmcTHgQ/6d0mBUhpnyjJPDBttzMkUJ5ts+V5cJ3FVuwH0QhhZFdcetp8SG3aEzRepL7DpwSYcj6o4SWnZ/2NXc1el431r98XMpjMZCwV9oC7QDh0pMKBagN6uV9koLRlPdmLKEMVQlyKBCDXMa6ioXfmhBYmbhRu837wrUVt/d/sbhbJCVDerro+uqcTC0IJ5X8izkkhPB+idCU3jLDMoC3QLasDsxEi5SgzAsif/qEDRcfBX3lmJz+p+xtSj88JIxKq3aC/FXcK99/fQ3q29Uk4aNl+mpWNTnZZYfns5knKS0NurN2yNbT/ZXcSypqpHJ5s/fz5WrVqF0NAis8gGDRqgRo0amDVrFm7cuAFTU1NMmTIFS5cuLceeVizS8tKQnJvM7D6wzcloyuuZUrdsxbcSC3Ann8U1J6MXjtiyHCgaf0vbr/JTwtTAlHf3io6WlluYyyz8XX13FQlZCXAwLf/k3fT/2NzQHEf6HCnn3mgOnFMVKC0ZT3ZiyhBFc7JNoZswqM6gcupN6UPvJsxsOhPeBSXz+3GzcIOnlScMtczfUlzYygIF5f0TmcLAqmlnyMPSA7+0/AV5iZ2VjqlLQkcLYRMDE7Su0RqBjoEAgFvxtxCZFMmpowptVh7p/xHj/K9i4tApKxtNnZriReoLJioZQX8weWJ4duyqApGRkXBzUzajc3NzQ2Sk/Hn39/fHu3clWwz51DgedRw9D/XEohuLAMiVA8UJk6bwr/pEW72Dz7EfAFpUbwFfe1/eY9pCBz9hK29sxa4yOVnT5lkDvQeWy/U13as8aR7HeiGrIKu0u6QV9DOQkZ/B+/zfe38PF95cQEJWgt6umVuYq/JYdoE8cejup7ur7EJVacn4SvNrdnd3h0Ag4LwWLlyo9pzc3FyMGzcOtra2MDMzQ9++fZGQoL+HVleUtvnLMJNyeUBPkI9FHUNOBYlaogkDoQGmNZ6G7/3Gw4CiOOZk1hJr+ObJV6X4zKv4sDO2Q3fP7pBmKu9yrLm7RuV57F2sNSFrGJ+Wi3EXmTqaBphlt5cBAK69u6ayDl+0M019ogeFM6/PIOxcGPY83aPxXIJ66HtbVQe4OnXqYOHChcjPz2fKCgoKsHDhQtSpUwcAEBcXBweH8l/lrUgwDvICIUQCEUQCUYUxJ1PHhIAJ2N55O9q7tueUD64zGLOazgJQfHMyWi6yvzf7nmgyAa5IrGu/DjeH3MR3gd+Vy/WTcpI01uH4qVaQYd7ZzJmJwKlkKg1gdcRqjDszDrcSbuntmrcTVOeCoa0igIr5eywLSkvGVypzsh9//BGjR49mPpubm6utP3nyZBw5cgR79+6FpaUlxo8fjz59+pRb8jRFc7K4zDhkF2TDxMCkXPpT2tAT5APPD2CAgQF8WA+vLsRnxaPDPnlyxsuDLyuZCegTkVCEoT5DgfR3AL5FJksqG4mNUL1QLoAYn5gSrOqpO5ceGBSVC/aAoXi+qkFfXeZiZidGKas3dzV3v5kp/nr8l7zuRyXmZdpLnHp9qlT/H1UFdojcqsjatWvRo0cP1KhRA76+8pX4yMhISKVS/PfffwCAV69e4ZtvvinPblY4aCXGy9oL//b+FwBw9e1VWEms8GvErwAq5mKZm4WbygAWFoYW6Fazm8pki5qwMbLBolaLOOFu2UpMRQkDrA2qfFIqCgKBgLMTU1EURJFQxJhiDzk6BPeH3lcKDQ2UnX/U4DqDmd9jVV2oKi0ZX6mUGHNzczg6OmpVNy0tDZs2bcLOnTvRrl07AMDmzZtRt25dXLt2DU2bNtXQgv7p4tEFoW6hKJAVYO7VuZh9ZTYy8jPkk+ZPELZydstIUmwl5tmHouy7ZT3Jk7HGu3dZRduc2oqhpJwk/P3kbxjaxiA/uQ3nmLrEnR3dOjKmBM8+PIOxyBguFi6ccxQFsKrBWd3KD22Spmmis8XSgrGNptuj/1Ym84yKCmNOVkUHuObNmyMqKgo7duzAs2fy33v//v3x2WefMYtVX3zxRXl2sULC+JawFjCaOTdDM+dmuPDmAu6+v8uzQFF6sCVQcWT1sw/PkJidiPENxxfb/9HEwEQpYiTbTLMyyauT0Sdx+vVpNHNuViFzSQkgULuwVp6wx71CWSFHqdUmV5uueFp5qjzG59Na1SgtGV9xnjgtWLhwIWxtbdGwYUMsWbIEhYWqhfPt27dRUFCA9u2Ltqvr1KkDV1dXXL16tSy6q4RQIISByAAmBibMaraq5EifAptCNzHvS6J60FnrgbJZOdnzdA/+fLYHqUIhrKQy2EiKwhbfk8h9cgSQRxzR5KOTkpuCDfc3wMBGnritrkUL5pg6AWpmaAYXcxe8yXiDvv/0xaRzkwBw/WC0XaVTJzTp+zn7yuyPZmEqkl2yiulnt6xXsz5lGMf+MvKJWbt2Ldzd3WFkZISgoCDcuHFDbf29e/eiTp06MDIyQoMGDXD06FHOcYqiMHv2bDg5OcHY2Bjt27fH8+fPtepLQUEBPD098ebNG4wdOxbLly/H8uXLMWbMGI277VUddQ7DjFlVGe7EsBdS1Mn8TZGbMO38NNx4d0Op/OvTX+PM6zN67ZdIIML3Qd/ju8DvYCSqPCG6p5yfgmPRxzDv6rxyuX63mt3UHhdAwJnDVJRdroz8DM5vQzE4QWmMXermcnSUUfa1S5uqIuMrjRIzYcIE7Nq1C+Hh4RgzZgx++eUXfPvttyrrx8fHw9DQEFZWVpxyBwcHxMfHqzwvLy8P6enpzCsjI0NfX4EDPREssyRk5QB7JY4qgWx7m/mWeV8WkWXWRKzBiod/4L1IBDEAOyMb5thjQ7nSUrugALe/uI1T/U6pbato50QuuPq7/IDeteQratp8F0UHWFro9qrVSyn3Q3FsyNnP30/XflJZT8pqm9mJoQMHkGg/JUYxfn5psnv3boSFhWHOnDm4c+cO/Pz8EBoaisTERN76V65cweDBgzFy5EhERESgV69e6NWrFx48eMDUWbx4MX799VesX78e169fh6mpKUJDQ5Gbq9rZlcbAwECrevqirGR8WRKbEYuQPSHodagX7iTcwd5ne9HJoxPWt1+PAIeKF9b1dsJtHI8+jrjMOE45Ld9uJtzk9WXQhpzCHKy4vQLLbi1j5KdYKMbgOoMxpO6QSrlwWF5+FB6WHgAAF3MX7Ou+jymnTf187HxgamDKlFeUnRhFRSFfxrUCKQ0rAnV5Yv6I/EPp2qVJVZLx5frEfffdd0rO+oqvJ0+eAADCwsLQpk0b+Pr6YuzYsVi2bBlWr16NvDz9Jq9asGABLC0tmVe9eqrDzurKf6/+Q7eD3dBgawOEx4YD4OYl+dTgOFaWoJ0XqS+Y92Wh9NGDXMHHefvMRmHMMcW8MZqghaTgY4hlCtoJ0NsJtzHyxEj8fO1nTl2laGIAOrvLI5/1rNWTty1F51k26sIqs2HvxNCrSkzIUrITU2JmBM7A3S/uYqzf2FK/1vLlyzF69GiMGDEC9erVw/r162FiYoI///yTt/6qVavQqVMnTJs2DXXr1sVPP/2EgIAArFkjD0xBURRWrlyJmTNnomfPnvD19cW2bdvw9u1bHDp0SKs+jRs3DosWLVK7u64vSlPGlzW00kuBQmJOIt7nvMfRqKP48eqP+JD7AS2qt4CdsV0591IZesdRUQbSn8/FnsPU81OL1XahrBB/PvgTWx5uqZD+QJUJ+v41d24Obxtvppxt/lrbujZjFVDcYAz6RnFHKF/Kr8TocwFOXdRO9n0pi4WqqiTjy3UbYMqUKRg+fLjaOjVr1uQtDwoKQmFhIaKjo+Ht7a103NHREfn5+UhNTeXsxiQkJKj1q5kxYwbCwoomrXFxccUa5OZfm8+sJMkoGfJkeXj+QXnrjU6C9CkSdq7oPq60scYZExMsfJ8M148P8aL487jt7AgK3CSS1D99QFEU2ri0wcSAiTgXe45ppywmzLRiOd3eDh9EQqSdHccc22JpgX/NTCGDAKOiT6Kje0e1bdFCUiDKhYnHCqx7IUFSXgznOnyk5qXiRnzR9i9tbiQSiBBcIxgNqzVkjoU1DsMwn2FKOVv2dt+Lg88Pqp0Yj2owCim5Kdj+aDsA4DMbCcZSRgjeNxIwMMYuczMcMDfDe5H8e3hZe6GxgzxB2OYHm5k+EUpGSQfTjIwMpKenM58lEgkkEmVzw/z8fNy+fRszZsxgyoRCIdq3b6/SzPbq1ascmQgAoaGhzOAVFRWF+Ph4jumupaUlgoKCcPXqVQwapDmM/M2bN3HmzBmcPHkSDRo0gKmpKef4gQMHNLahLXqR8UemYmnCBUQIVK++tqAM8Y1UvmL9RFCAn0Tqd3zWFlrB6uPvfLYoHS8Fqgf7zjIjfC4zAYTZgBgQJL0AhEBmXjp2P90NABDc3oY9t//CYWGOynZqUCIsksojJ2WDwijxB7V9nC41hx8ll1t/CLNwRshdRKQA+HnIJ4i/7Z6L2TJ50I94SDFZXBRg5IHwY/Li84uB8F+ZcpEoHfj4UxClvwU2hqjtDx8iUMDHYbXP9iYQU0CGgIIEAgyQGuNzmTFEFWSyrZGP30NMoVj3oqSsN0gEBMDzxweAyHCmvLY4F9kQQ7xnBAARgsVANgxh/PcQVAQDHwsAX4tMsE4kD208bk9n/CA1QwAlv6GPDOU7EqLj3wNUCYInsKZvJ+5vxsTb/wAAtguz8a8wl5nbvBdIGUtt6bZewIjjgJr8bXwQGc9PuSox9vb2sLe3L9a5d+/ehVAoRLVq1XiPN2rUCAYGBjhz5gz69u0LAHj69CliYmLQrFkzle0qPhjsh0YXotKicD/pvsZ67NWNT41qJtz/TaSRhBNqOVYsxmMJjxL3UdmrYyMPu9emRhuce3MOQNlsV9cwr4G4zDhEGyorGfFiMeLF8p/Nz9d+xsnXJ7G0terETNYSaxiLjZFTmAORUQKSWGO+qug8AFDDjJsEld7Wr2FeAxduXkBEYgQ6eXSChaEFHE0d4WiqrJjXsamDGUEzlMoVGVl/JHY92YUCWQEiDURIFYmARHkys/dWlsz/SCwUY2OHjUrK0qecsLWyoDgJnzNnDubOnatULykpCVKpVCmMpYODA7PrrUh8fDxvfdosl/6rro4mrKysGDld2uhFxic9xevcZNw3VR1Z0jUzDXgvv6dZEgnuO6sPHVoYfxeQyncoXjo54L6R6kmOX8ZbICUVXYVCBIpEoATAZ06OyBcWydfqH2IQbSDGfStLle3k5GcDcXJ5KxMIEOnuoraPmYlPgRy5WUicrTUeWPDYs390ObHPSQXi5Q68+WIxHrg4K1V1TXwG5BWtkjtbWQDWVgAAl5xMICFKbX/4kACwca2OFJEI0azJIwDsk33A0LjimamVB2uNjbDA1hrz36cAHxe/ypKWDva4YGKMdh8SgfSXTPkWhXrLy7RX2vENgLPOjngqMcQzYSEyEp8xz25fOxvsNzeDU8JjoKD4OwMdqtnh1EcZMDAlCUiXP68JNlZ4bKkctdNCKoV53B2gGLsxRMbzUykcMq5evYrr16+jbdu2MDc3x9WrVzF58mR8/vnnsLaWO13HxcUhJCQE27ZtQ2BgICwtLTFy5EiEhYXBxsYGFhYW+N///odmzZqVSWSy8Q3H4/O8z5nPhiJDSEQSmBmYwdXCFQ+SHqC6WXU4mykL9k+Fb5t8i3Yu7ZAnzZOPI9EXUb2hF5CXDdh742tzawxKewNBdhIEVq7A85OAWwsIjCwhEAhgZyQ3g1jZdiVepL5AbevaZdLv5W2W427iXQiykiASiiEyc4C3tTcepzxGQWEe8PoyBE7+EBlbM4nZVGFmaIb9PfbjQeJz5OQLUdPWCjJKBhMDE0ZJ48Pbxhv7uu9DSm4KzA3NixQ6lzbY1W0XrCRWegttbGtsi/099iM6LRqCvEzUyc4AJFYAgB45iWj44TkEXh3halWTo8CsDVmLPGmeWnM1Qtnw6NEjVK9eFM2Jb4WuIrN58+by7oJutJ2JrxLvom+B6t2VaoaWgKn8f+JZkIXVma/VNmnexAv4uDsblh6FDKmaHRSJDWDiCGsAdNiRg7lJiMp5DwEAC7ExfM1cEZ2bBN9c1bk+TEUSoJU8qpJEJsWatGcq6wJAnQAX4KM/xKDsd2ibl6pUJz1PPil0MbUAzOULNXbSfKxlTYIBwN7QAnUCnTkZMr+USdEwIxoUKPiZuzH3QxdEAPbkp+FJ1luIBUIYCMQwEIogFojgaewAQSknTNYnwR9f5cViaR6eZ7+Dr5krUEH8XXRhY0EW7mfKlT+fgBqAwccoWNkJGCoAagaVLO/UfGk+OqY+RmphNto19AE+jsl9chIRlJcCwUdDMgEEyJHlo6axAwya2wPF8MsiMp6fSqHESCQS7Nq1C3PnzkVeXh48PDwwefJkzvZXQUEBnj59iuzsbKZsxYoVEAqF6Nu3L/Ly8hAaGorffvutTPqs6HStCB0+91PGUGSI5tWbFxW4tuUc9wEAW5+ighqteNsRCUVlumNlYWiB4BrKQ0cz5487eK5tdGrPxdwFLubqVzj5UPWdfdj3TE94WHowuz1s3D6++OC7R4TywdzcHBYWmpVaOzs7iEQipaS/6sxsHR0d1dan/yYkJMDJyYlTx9/fX5evUXlwDUID1yCtq1sBaKND84107A4AuH58san58aUNBgBa63A9748vbTCBdpNxAwCBOvRBFQ4fX4SSYQrAv7w7UQKswf9Mqw6GrBvGADqhl1K5Lr87bSEynp9KoVoHBATg2rVrSE1NRU5ODh49eoQZM2ZwNFF3d3e5H0WbNkyZkZER1q5di5SUFGRlZeHAgQNa55khEAiETw1DQ0M0atQIZ84UhbCVyWQ4c+aMSjPbZs2aceoDwKlTp5j6Hh4ecHR05NRJT0/H9evX1ZruKrJv3z4MGDAATZs2RUBAAOdFIBAIBM1UNRlfKZQYAoFAIOiHsLAwbNy4EVu3bsXjx4/x9ddfIysrCyNGjAAADB06lOMUOnHiRBw/fhzLli3DkydPMHfuXNy6dQvjx48HII8ENGnSJPz888/4559/EBkZiaFDh8LZ2Rm9evXSqk+//vorRowYAQcHB0RERCAwMBC2trZ49eoVOnfurPd7QCAQCJ8qVUrGUwS1xMbGUgCo2NjY8u4KgUAgcCiufFq9ejXl6upKGRoaUoGBgdS1a9eYY61bt6aGDRvGqb9nzx6qdu3alKGhIeXj40MdOXKEc1wmk1GzZs2iHBwcKIlEQoWEhFBPnz7Vuj/e3t7Uzp07KYqiKDMzM+rly5cURVHUrFmzqHHjxun03XSFyHgCgVBRITJePQKKKoOg1ZWYmJgYuLm54caNGxxbQAKBQChv3r17h8DAQLx+/RqurooeEZUHExMTPH78GG5ubqhWrRpOnToFPz8/PH/+HE2bNkVycnKpXZvIeAKBUFEhMl49lcKxvzyhnZ0CA/XhbkggEAj6JyEhoVIPcI6OjkhJSYGbmxtcXV1x7do1+Pn5ISoqqtSTwxEZTyAQKjpExvNDlBgNNGzYEDdu3ICDgwOEQu1ciDIyMlCvXj08evQI5uY8cfQJWkPupX4g91E/VLT7KJPJkJCQgIYNG2quXIFp164d/vnnHzRs2BAjRozA5MmTsW/fPty6dQt9+vQp1WsTGV++kHupH8h91A8V7T4SGa8eYk5WCqSnp8PS0hJpaWlahcQjqIbcS/1A7qN+IPexdJDJZJDJZBB/TCS7a9cuXLlyBV5eXhgzZgwMDStWbg/yHOgPci/1A7mP+oHcx9KhtGQ82YkhEAgEQrny5s0buLgU5VIaNGgQBg0aBIqiEBsbW6nNKAgEAqGqU1oynoRYJhAIBEK54uHhgffv3yuVp6SkwMNDOQkrgUAgECoPpSXjiRJTCkgkEsyZM4eTjJNQPMi91A/kPuoHch9LB4qiIBAIlMozMzNhZGRUDj1SD3kO9Ae5l/qB3Ef9QO5j6VBaMp74xBAIBAKhXAgLCwMArFq1CqNHj4aJiQlzTCqV4vr16xCJRLh8+XJ5dZFAIBAIxaS0ZTzxiSEQCARCuRAREQFAvkoXGRnJce40NDSEn58fpk6dWl7dIxAIBEIJKG0ZT3ZiCAQCgVCujBgxAqtWrSLRgAgEAuETpLRkPFFiCAQCgUAgEAgEQqWCOPYTCAQCgUAgEAiESgVRYggEAoFAIBAIBEKlgigxBAKBQCAQCAQCoVJBlBgCgUAgEAgEAoFQqSBKDIFAIBAIBAKBQKhUECWGQCAQCAQCgUAgVCqIEkMgEAgEAoFAIBAqFUSJIRAIBAKBQCAQCJUKosQQCAQCgUAgEAiESgVRYggEAoFAIBAIBEKlgigxBAKBQCAQCAQCoVJBlJhPGIFAoNXr3LlzZdKfhIQEjBkzBtWrV4eRkRHc3d0xcuRItefs2bMHAoEABw8eVDrm5+cHgUCA8PBwpWOurq5o3ry53vquibdv32Lu3Lm4e/euXtt1d3fX6n+4ZcsWvV5X32RnZ2Pt2rXo2LEjnJycYG5ujoYNG2LdunWQSqVK9V+8eIF+/frB2toaJiYmaNmyJe//eePGjWjdujUcHBwgkUjg4eGBESNGIDo6Wm1/Ll26xNy7pKQkfX1NAqFMITKeyPiKgi4yPjo6WuX33LVrF6eurjJ+06ZNqFu3LoyMjODl5YXVq1eX1lcmVADE5d0BQumxfft2zudt27bh1KlTSuV169Yt9b7ExsaiRYsWAICxY8eievXqePv2LW7cuKH2vJYtWwKQTzp79+7NlKenp+PBgwcQi8W4fPky2rZty7lWbGwsBg0aVArfhJ+3b99i3rx5cHd3h7+/v97aXblyJTIzM5nPR48exd9//40VK1bAzs6OKS/Lwbw4vHr1Cv/73/8QEhKCsLAwWFhY4MSJE/jmm29w7do1bN26lakbGxuLZs2aQSQSYdq0aTA1NcXmzZvRsWNHnDlzBsHBwUzdiIgIeHh4oEePHrC2tkZUVBQ2btyI//77D/fu3YOzs7NSX2QyGf73v//B1NQUWVlZZfL9CYTSgMh4IuMrCrrIeJrBgwejS5cunLJmzZpxPusi43///XeMHTsWffv2RVhYGC5evIgJEyYgOzsb06dPL50vTihfKEKVYdy4cVR5/cs7d+5MeXh4UElJSTqf6+HhQQUGBnLKjh8/TgkEAmrw4MFUaGgo59jOnTspANThw4dL1GeZTEZlZ2drVffmzZsUAGrz5s0luqYmlixZQgGgoqKi1NbLzMws1X7oyvv376kHDx4olY8YMYICQD1//pwp++abbyixWEw9efKEKcvKyqJcXFyogIAAjde6desWBYBasGAB7/F169ZRtra21MSJEykA1Pv374vxjQiEigeR8bpBZLz+0EXGR0VFUQCoJUuWFOtafDI+OzubsrW1pbp27cqpO2TIEMrU1JRKSUkp1rUIFRtiTlbFycrKwpQpU+Di4gKJRAJvb28sXboUFEVx6gkEAowfPx47duyAt7c3jIyM0KhRI1y4cEHjNZ48eYJjx45h2rRpsLW1RW5uLgoKCrTuY8uWLREREYGcnBym7PLly/Dx8UHnzp1x7do1yGQyzjGBQMCsChYWFuKnn36Cp6cnJBIJ3N3d8f333yMvL49zHXd3d3Tr1g0nTpxA48aNYWxsjN9//x0AcOrUKbRs2RJWVlYwMzODt7c3vv/+ewDAuXPn0KRJEwDAiBEjynz7f/jw4TAzM8PLly/RpUsXmJubY8iQIQCAixcvon///nB1dYVEIoGLiwsmT57MuZc0T548wYABA2Bvbw9jY2N4e3vjhx9+4NSJi4vDl19+yWzt+/j44M8//9TYRzs7O/j4+CiV0yuvjx8/ZsouXryIhg0bwtvbmykzMTFBjx49cOfOHTx//lzttdzd3QEAqampSsdSUlIwc+ZM/Pjjj7CystLYbwKhskNkfBFExlcMGc8mKysL+fn5Gttnwyfjw8PDkZycjG+++YZTd9y4ccjKysKRI0d0ugahckCUmCoMRVHo0aMHVqxYgU6dOmH58uXw9vbGtGnTEBYWplT//PnzmDRpEj7//HP8+OOPSE5ORqdOnfDgwQO11zl9+jQAwMHBASEhITA2NoaxsTE6d+6s0XcBkA9wBQUFuH79OlN2+fJlNG/eHM2bN0daWhqnD5cvX0adOnVga2sLABg1ahRmz56NgIAArFixAq1bt8aCBQt4TRGePn2KwYMHo0OHDli1ahX8/f3x8OFDdOvWDXl5efjxxx+xbNky9OjRA5cvXwYgN9X48ccfAQBfffUVtm/fju3bt3PMnkqbwsJChIaGolq1ali6dCn69u0LANi7dy+ys7Px9ddfY/Xq1QgNDcXq1asxdOhQzvn3799HUFAQzp49i9GjR2PVqlXo1asX/v33X6ZOQkICmjZtitOnT2P8+PFYtWoVatWqhZEjR2LlypXF6nd8fDwAcMwm8vLyYGxsrFTXxMQEAHD79m2lY8nJyUhMTMStW7cwYsQIAEBISIhSvVmzZsHR0RFjxowpVn8JhMoEkfFExtNUJBlPM2/ePJiZmcHIyAhNmjTByZMnVbajScZHREQAABo3bsw5r1GjRhAKhcxxwidG+W4EEcoSRVODQ4cOUQCon3/+mVOvX79+lEAgoF68eMGUAaAAULdu3WLKXr9+TRkZGVG9e/dWe90JEyZQAChbW1uqU6dO1O7du6klS5ZQZmZmlKenJ5WVlaX2/IcPH1IAqJ9++omiKIoqKCigTE1Nqa1bt1IURVEODg7U2rVrKYqiqPT0dEokElGjR4+mKIqi7t69SwGgRo0axWlz6tSpFADq7NmzTJmbmxsFgDp+/Din7ooVKzSaHZWnqcGwYcMoANR3332nVJ/PVGLBggWUQCCgXr9+zZQFBwdT5ubmnDKKkptb0IwcOZJycnJSMhcZNGgQZWlpqbVZBk1eXh5Vr149ysPDgyooKGDKu3fvTllZWVHp6emc+s2aNaMAUEuXLlVqSyKRMM+ora0t9euvvyrVuXfvHiUSiagTJ05QFEVRc+bMIeZkhE8KIuOLIDK+4sr4169fUx07dqTWrVtH/fPPP9TKlSspV1dXSigUUv/99x9vW5pk/Lhx4yiRSMR7rr29PTVo0CCd+k6oHJCdmCrM0aNHIRKJMGHCBE75lClTQFEUjh07xilv1qwZGjVqxHx2dXVFz549ceLECd4IUzS006KjoyOOHDmCAQMGYOrUqdi4cSNevnyJnTt3qu1n3bp1YWtri0uXLgEA7t27h6ysLMbRsXnz5syK2dWrVyGVShln0aNHjwKA0qrjlClTAEBpi9nDwwOhoaGcMtrs6PDhwxyThorG119/rVTG3tHIyspCUlISmjdvDoqimJWp9+/f48KFC/jyyy/h6urKOV8gEACQr+ju378f3bt3B0VRSEpKYl6hoaFIS0vDnTt3dOrv+PHj8ejRI6xZswZicVGMka+//hqpqakYOHAgIiIi8OzZM0yaNAm3bt0CAF4ziWPHjuHo0aNYtmwZXF1deR32J0yYgM6dO6Njx4469ZNAqKwQGU9kPFDxZLyrqytOnDiBsWPHonv37pg4cSIiIiJgb2/P/N8U0STjc3JyYGhoyHuukZER77hBqPwQJaYK8/r1azg7O8Pc3JxTTkeyef36Nafcy8tLqY3atWsjOzsb79+/V3kdWsgOGDAAQmHRI9e/f3+IxWJcuXJFbT8FAgGaN2/O2EVfvnwZ1apVQ61atQBwBzj6Lz3AvX79GkKhkKlL4+joCCsrK6Xv6OHhoXT9gQMHokWLFhg1ahQcHBwwaNAg7Nmzp9iDXX5+PuLj4zkvdRMEbRCLxahRo4ZSeUxMDIYPHw4bGxuYmZnB3t4erVu3BgCkpaUBkEeVAYD69eurbP/9+/dITU3Fhg0bYG9vz3nRW/uJiYla93fJkiXYuHEjfvrpJ6XoNJ07d8bq1atx4cIFBAQEwNvbG0eOHMH8+fMBAGZmZkrttW3bFp07d0ZYWBj27t2LefPmYc2aNczx3bt348qVK1i2bJnWfSQQKjtExhMZD1Q8Gc+HjY0NRowYgadPn+LNmzdKxzXJeGNjY5W+Nbm5ubwmyoTKDwmxTCh16BCIDg4OnHKRSARbW1t8+PBBYxstW7bEv//+i8jISMZWmqZ58+aYNm0a4uLicOnSJTg7O6NmzZqc8+nVJk3wCTpjY2NcuHAB4eHhOHLkCI4fP47du3ejXbt2OHnyJEQikVZt01y5coUTLhQAoqKiGGfF4iCRSDiTBwCQSqXo0KEDUlJSMH36dNSpUwempqaIi4vD8OHDdRqg6bqff/45hg0bxlvH19dXq7a2bNmC6dOnY+zYsZg5cyZvnfHjx2PEiBG4f/8+DA0N4e/vj02bNgGQT6rU4enpiYYNG2LHjh0YP348AGDatGno378/DA0NGRt92ik0NjYW+fn5vOGYCQSCZoiM50JkvGYZz4eLiwsAeQAWPoWNhk/GOzk5QSqVIjExEdWqVWPq5ufnIzk5mcj3TxSixFRh3NzccPr0aWRkZHBW6p48ecIcZ8MXFerZs2cwMTGBvb29yuvQ5glxcXGc8vz8fCQlJak9l4adS+Dy5cuYNGkSp32JRIJz587h+vXrnFUfNzc3yGQyPH/+nJMrISEhAampqUrfURVCoRAhISEICQnB8uXL8csvv+CHH35AeHg42rdvr/UACsgTuJ06dYpT5ujoqPX52hIZGYlnz55h69atHCdPxWvTkwF1zrv29vYwNzeHVCpF+/bti92nw4cPY9SoUejTpw/Wrl2rtq6pqSknZ8Dp06dhbGzMRCRSR05ODicyUWxsLHbu3Mlr1hIQEAA/Pz+9J7EjEMobIuOJjAcqroxXhN4x0uZ5UZTxdO6eW7ducZ6PW7duQSaT6TW3D6HiQMzJqjBdunSBVCrlbMkCwIoVKyAQCNC5c2dO+dWrVzk2sbGxsTh8+DA6duyodqWqTZs2qFatGnbs2IHc3FymfMuWLcxKkiYaN24MIyMj7NixA3FxcZxVOolEgoCAAKxduxZZWVnMYEh/RwBKkVWWL18OAOjatavGa6ekpCiV0QKRFqKmpqYA+MP6KmJtbY327dtzXkZGRhrP0xX6f0KxQqlSFIVVq1Zx6tnb2yM4OBh//vknYmJiOMfoc0UiEfr27Yv9+/fzDoTqTE1oLly4gEGDBiE4OBg7duxQWlVUx5UrV3DgwAGMHDkSlpaWAOTRevhWeG/cuIHIyEhOlJqDBw8qvQYOHAhAniBwxYoVWveFQKgsEBlPZDxQ8WQ8X1txcXH4888/4evrCycnJwC6yfh27drBxsYG69at49Rdt24dTExMtHoOCJUPshNThenevTvatm2LH374AdHR0fDz88PJkydx+PBhTJo0CZ6enpz69evXR2hoKCZMmACJRILffvsNgDxMojokEgmWLFmCYcOGITg4GF988QViYmKwatUqtGrVCn369NHYV0NDQzRp0gQXL16ERCLhOJ8CcnMD2t+BPcD5+flh2LBh2LBhA1JTU9G6dWvcuHEDW7duRa9evZS2/Pn48ccfceHCBXTt2hVubm5ITEzEb7/9hho1ajDX8vT0hJWVFdavXw9zc3OYmpoiKCiI1/66rKhTpw48PT0xdepUxMXFwcLCAvv37+cdFH799Ve0bNkSAQEB+Oqrr+Dh4YHo6GgcOXKE2aFYuHAhwsPDERQUhNGjR6NevXpISUnBnTt3cPr0ad6JAM3r16/Ro0cPCAQC9OvXD3v37uUc9/X1ZUwVXr9+jQEDBqBHjx5wdHTEw4cPsX79evj6+uKXX35hzsnMzISLiwsGDhwIHx8fmJqaIjIyEps3b4alpSVmzZrF1O3Vq5dSn+jv1blzZ97wnwRCZYfIeCLjaSqSjP/222/x8uVLhISEwNnZGdHR0fj999+RlZXFUcB0kfHGxsb46aefMG7cOPTv3x+hoaG4ePEi/vrrL8yfPx82NjYludWEikp5hEQjlA982ZwzMjKoyZMnU87OzpSBgQHl5eVFLVmyhBN2kaLk4TfHjRtH/fXXX5SXlxclkUiohg0bUuHh4Vpf/++//6b8/PwoiURCOTg4UOPHj1cKo6uOGTNmUACo5s2bKx07cOAABYAyNzenCgsLOccKCgqoefPmUR4eHpSBgQHl4uJCzZgxg8rNzeXUc3NzU8r2S1EUdebMGapnz56Us7MzZWhoSDk7O1ODBw+mnj17xql3+PBhql69epRYLC61UJyqwm+ampry1n/06BHVvn17yszMjLKzs6NGjx5N3bt3j7d/Dx48oHr37k1ZWVlRRkZGlLe3NzVr1ixOnYSEBGrcuHGUi4sLZWBgQDk6OlIhISHUhg0b1PY7PDycCY/J95ozZw5TNyUlherZsyfl6OhIGRoaUh4eHtT06dOVnpW8vDxq4sSJlK+vL2VhYUEZGBhQbm5u1MiRIzVmu6YoEmKZ8OlBZDyR8ZVBxu/cuZMKDg6m7O3tKbFYTNnZ2VG9e/embt++zWmzODJ+w4YNlLe3N2VoaEh5enpSK1asUHrWCZ8OAopSSNtLIPAgEAgwbtw4JbMEAoFAIFR+iIwnEAiVDeITQyAQCAQCgUAgECoVRIkhEAgEAoFAIBAIlQqixBAIBAKBQCAQCIRKBfGJIRAIBAKBQCAQCJUKshNDIBAIBAKBQCAQKhVEiSEQCAQCgUAgEAiVCqLEfGTu3LkQCARISkoq764QCDrRpk0btGnTRuu69evX13sfKtrvpzT6o+19PnfuHAQCAc6dO6e3axP0Q0V7TgkEbSFyXhki5wlEiSlnfvnlFxw6dEirutHR0RAIBBAIBPj555956wwZMgQCgQBmZmZ67GXJcHd3R7du3Yp1rkAgwPjx43mP7du3T2shkpeXh+nTp8PZ2RnGxsYICgrCqVOnitWnDh06qO1XQkICxowZg+rVq8PIyAju7u4YOXJksa5VHN6+fYu5c+cyWZj1iS7PK0EzJX0ud+3ahYCAABgZGcHe3h4jR46sMBMMQhFEzquHyHndIXK+8kDkfOlBlJhypjjCwsjICH///bdSeVZWFg4fPgwjIyM99e7TYfjw4Vi+fDmGDBmCVatWQSQSoUuXLrh06ZJO7Rw4cABXr15VeTw2NhZNmjTBsWPHMHbsWPz2228YNWoU3r9/X9KvoJKTJ0/i5MmTzOe3b99i3rx5ZHCrBJTkuVy3bh0GDx4MGxsbLF++HKNHj8auXbsQEhKC3NzcMug9QVuInC8biJzXD0TO6xci50sPcXl3gKA7Xbp0wYEDB3Dv3j34+fkx5YcPH0Z+fj46deqEs2fPlmMPKxY3btzArl27sGTJEkydOhUAMHToUNSvXx/ffvstrly5olU7ubm5mDJlCqZPn47Zs2fz1hkzZgzEYjFu3rwJW1tbvX0HdRgaGpbJdUoLmUyG/Pz8KjcpK8lzmZ+fj++//x7BwcE4deoUBAIBAKB58+bo3r07Nm7ciP/9739l8j0IpQOR87pB5HzFhsh5IudLA7ITo0BSUhIGDBgACwsL2NraYuLEibza7l9//YVGjRrB2NgYNjY2GDRoEGJjYzl1nj9/jr59+8LR0RFGRkaoUaMGBg0ahLS0NADyLfSsrCxs3bqVMR8YPny4xj42a9YMHh4e2LlzJ6d8x44d6NSpE2xsbHjPO3bsGFq1agVTU1OYm5uja9euePjwIafO/fv3MXz4cNSsWRNGRkZwdHTEl19+ieTkZE492hb1xYsXGD58OKysrGBpaYkRI0YgOztb43fIysrClClT4OLiAolEAm9vbyxduhSlEfF73759EIlE+Oqrr5gyIyMjjBw5ElevXlX6v6li8eLFkMlkjCBS5MmTJzh27BimTZsGW1tb5ObmoqCgQKu279+/D4FAgH/++Ycpu337NgQCAQICAjh1O3fujKCgIOYz24b33LlzaNKkCQBgxIgRzHO1ZcsWThuPHj1C27ZtYWJigurVq2Px4sUa+6jN85qamqrxeaBNNHbs2AEfHx9IJBIcP34cABAXF4cvv/wSDg4OkEgk8PHxwZ9//qnUl9WrV8PHxwcmJiawtrZG48aNlX4P2vansLAQP/30Ezw9PSGRSODu7o7vv/8eeXl5Gu/Jmzdv0KtXL5iamqJatWqYPHmyVucBJXsuHzx4gNTUVAwcOJAZ2ACgW7duMDMzw65du7TqQ1WFyHki51VB5DyR84oQOV9xIUqMAgMGDEBubi4WLFiALl264Ndff+U8fAAwf/58DB06FF5eXli+fDkmTZqEM2fOIDg4GKmpqQDkGnRoaCiuXbuG//3vf1i7di2++uorvHr1iqmzfft2SCQStGrVCtu3b8f27dsxZswYrfo5ePBg7Nq1ixkMkpKScPLkSXz22We89bdv346uXbvCzMwMixYtwqxZs/Do0SO0bNkS0dHRTL1Tp07h1atXGDFiBFavXo1BgwZh165d6NKlC+/AM2DAAGRkZGDBggUYMGAAtmzZgnnz5qntO0VR6NGjB1asWIFOnTph+fLl8Pb2xrRp0xAWFqbV99eFiIgI1K5dGxYWFpzywMBAANBqOz4mJgYLFy7EokWLYGxszFvn9OnTAAAHBweEhITA2NgYxsbG6Ny5M+ce81G/fn1YWVnhwoULTNnFixchFApx7949pKenA5CvZl25cgXBwcG87dStWxc//vgjAOCrr75init2/Q8fPqBTp07w8/PDsmXLUKdOHUyfPh3Hjh1T20dtnldtn4ezZ89i8uTJGDhwIFatWgV3d3ckJCSgadOmOH36NMaPH49Vq1ahVq1aGDlyJFauXMmcu3HjRkyYMAH16tXDypUrMW/ePPj7++P69etK19GmP6NGjcLs2bMREBCAFStWoHXr1liwYAEGDRqk9n7k5OQgJCQEJ06cwPjx4/HDDz/g4sWL+Pbbb9WeR1OS55IeQPmeRWNjY0REREAmk2nVj6oIkfNEzvNB5DyR84oQOV/BoQgURVHUnDlzKABUjx49OOXffPMNBYC6d+8eRVEUFR0dTYlEImr+/PmcepGRkZRYLGbKIyIiKADU3r171V7X1NSUGjZsmFZ9jIqKogBQS5YsoR48eEABoC5evEhRFEWtXbuWMjMzo7Kysqhhw4ZRpqamzHkZGRmUlZUVNXr0aE578fHxlKWlJac8Oztb6bp///03BYC6cOECU0bfry+//JJTt3fv3pStrS2nzM3NjeratSvz+dChQxQA6ueff+bU69evHyUQCKgXL14wZQCocePG8d6PvXv3UgCo8PBw3uM0Pj4+VLt27ZTKHz58SAGg1q9fr/Z8um/NmzdX268JEyZQAChbW1uqU6dO1O7du6klS5ZQZmZmlKenJ5WVlaX2Gl27dqUCAwOZz3369KH69OlDiUQi6tixYxRFUdSdO3coANThw4eZeq1bt6Zat27NfL558yYFgNq8ebPSNVq3bk0BoLZt28aU5eXlUY6OjlTfvn013gdVz6suzwMASigUUg8fPuSUjxw5knJycqKSkpI45YMGDaIsLS2ZZ7Nnz56Uj4+P2n5q25+7d+9SAKhRo0Zx6k2dOpUCQJ09e5YpU7zPK1eupABQe/bsYcqysrKoWrVqlfpz+f79e0ogEFAjR47klD958oQCQAFQuo8EIudpiJznh8h5OUTOt2Y+EzlfsSE7MQqMGzeO85m2Nzx69CgAucOfTCbDgAEDkJSUxLwcHR3h5eWF8PBwAIClpSUA4MSJE1ptu+uKj48PfH19GcfPnTt3omfPnjAxMVGqe+rUKaSmpmLw4MGcPotEIgQFBTF9Brgaf25uLpKSktC0aVMAwJ07d5TaHjt2LOdzq1atkJyczKwo8XH06FGIRCJMmDCBUz5lyhRQFKVxpUhXcnJyIJFIlMpp29ycnBy154eHh2P//v2cVSI+MjMzAQCOjo44cuQIBgwYgKlTp2Ljxo14+fIl7zY4m1atWuHOnTvIysoCAFy6dAldunSBv78/Ll68CEC+aicQCNCyZUu1banDzMwMn3/+OfPZ0NAQgYGBePXqVbHbpNH2eWjdujXq1avHfKYoCvv370f37t1BURTnOQ0NDUVaWhrz/FlZWeHNmze4efNmiftD/64VV4anTJkCADhy5IjKto8ePQonJyf069ePKTMxMVFa0VdFSZ5LOzs7DBgwAFu3bsWyZcvw6tUrXLx4EQMHDoSBgYHG86s6RM4TOa8IkfPaQ+Q8kfMVBaLEKODl5cX57OnpCaFQyGwTP3/+HBRFwcvLC/b29pzX48ePkZiYCADw8PBAWFgY/vjjD9jZ2SE0NBRr165l7KT1wWeffYa9e/fixYsXuHLlikoTg+fPnwMA2rVrp9TnkydPMn0GgJSUFEycOBEODg4wNjaGvb09PDw8AIC3766urpzP1tbWAORb2ap4/fo1nJ2dYW5uzimvW7cuc1wX2LaifBgbG/Par9I28KrMBgC5He2ECRPwxRdfMDbI6q4DyLe2hcKin1b//v0hFos1Opa2atUKhYWFuHr1Kp4+fYrExES0atUKwcHBnMGtXr16Ku3htaFGjRpK98za2lrt/0xbtH0e6GeK5v3790hNTcWGDRuUntERI0YAAPOcTp8+HWZmZggMDISXlxfGjRuHy5cvF6s/r1+/hlAoRK1atTj1HB0dYWVlpfZZfP36NWrVqqV0L729vVWew6YkzyUA/P777+jSpQumTp0KT09PBAcHo0GDBujevTsAVKjwuxUNIueJnGdD5LxuEDlP5HxFgUQn04DigyuTySAQCHDs2DGIRCKl+uwHatmyZRg+fDgOHz6MkydPYsKECViwYAGuXbuGGjVqlLhvgwcPxowZMzB69GjY2tqiY8eOvPVom8nt27fD0dFR6bhYXPQYDBgwAFeuXMG0adPg7+8PMzMzyGQydOrUidf2ku8eANCb46ZEIlG50kCvfGqKduLk5IS4uDil8nfv3gEAnJ2dVZ67bds2PH36FL///ruSvXNGRgaio6NRrVo1mJiYMO04ODhw6olEItja2mocPBo3bgwjIyNcuHABrq6uqFatGmrXro1WrVrht99+Q15eHi5evIjevXurbUcTpfk/07ZtRcFNP1uff/45hg0bxtuGr68vAPkk6OnTp/jvv/9w/Phx7N+/H7/99htmz56tZAetbX80TZBKg5I8l4B8F+Dw4cOIiYlBdHQ03Nzc4ObmhubNm8Pe3h5WVlal0e1PEiLniZwncl7/bRM5T+R8aUOUGAWeP3/OWT148eIFZDIZ3N3dAchX7CiKgoeHB2rXrq2xvQYNGqBBgwaYOXMmrly5ghYtWmD9+vVMErOS/KhcXV3RokULnDt3Dl9//TVnkGLj6ekJAKhWrRrat2+vsr0PHz7gzJkzmDdvHie0JL3Cpy/c3Nxw+vRpZGRkcFbpnjx5whxn13369ClvO3Q5uz4f/v7+CA8PR3p6Ose5jnYQ9Pf3V3luTEwMCgoK0KJFC6Vj27Ztw7Zt23Dw4EH06tULjRo1AgAlgZWfn4+kpCTY29ur7Se93X/x4kW4urqiVatWAOQrd3l5edixYwcSEhJUOnvSlKagLq227e3tYW5uDqlUqvYZpTE1NcXAgQMxcOBA5Ofno0+fPpg/fz5mzJihUwhPNzc3yGQyPH/+nFkhBuSJ7FJTU9U+W25ubnjw4AEoiuLcF1XPqyIleS7ZuLq6MiuRqampuH37Nvr27avVuVUVIueJnGdD5HzZtE3kPJHz+oaYkymwdu1azufVq1cDkIc7BIA+ffpAJBJh3rx5Slo+RVFMiMr09HQUFhZyjjdo0ABCoZCztWhqaspEsSkOP//8M+bMmaM2VnhoaCgsLCzwyy+/8IaCpBN00asZit9Lk42wrnTp0gVSqRRr1qzhlK9YsQICgYC513Tda9eu4fbt25y6qamp2LFjB/z9/XlXHdn069cPUqkUGzZsYMry8vKwefNmBAUFwcXFhSmPiYlhBlkAGDRoEA4ePKj0ovt28OBBJgxmmzZtUK1aNezYsYMTrnXLli2QSqXo0KGDxnvTqlUrXL9+HeHh4czgZmdnh7p162LRokVMHXWYmpoy90jflPR5VYVIJELfvn2xf/9+PHjwQOk4O4mcYhhYQ0ND1KtXDxRFaR3qlKZLly4AlJ/x5cuXAwC6du2q9ty3b99i3759TFl2djbnOVNHSZ5LVcyYMQOFhYWYPHmyVn2oqhA5T+Q8kfPq2yZyvuhcIucrLmQnRoGoqCj06NEDnTp1wtWrV/HXX3/hs88+Y5KNeXp64ueff8aMGTMQHR2NXr16wdzcHFFRUTh48CC++uorTJ06FWfPnsX48ePRv39/1K5dG4WFhdi+fTvzI6Zp1KgRTp8+jeXLl8PZ2RkeHh6c2PCaaN26NVq3bq22joWFBdatW4cvvvgCAQEBGDRoEOzt7RETE4MjR46gRYsWWLNmDSwsLBAcHIzFixejoKAA1atXx8mTJxEVFVW8m6mC7t27o23btvjhhx8QHR0NPz8/nDx5EocPH8akSZOYFUUA+O6777B3714EBwdjzJgxqFOnDt6+fYstW7bg3bt32Lx5s8brBQUFoX///pgxYwYSExNRq1YtbN26FdHR0di0aROn7tChQ3H+/HlmgK9Tpw7q1KnD266Hhwd69erFfJZIJFiyZAmGDRuG4OBgfPHFF4iJicGqVavQqlUr9OnTR2NfW7Vqhfnz5yM2NpYziAUHB+P333+Hu7u7RhMVT09PWFlZYf369TA3N4epqSmCgoKU7JOLQ0mfV3UsXLgQ4eHhCAoKwujRo1GvXj2kpKTgzp07OH36NFJSUgAAHTt2hKOjI1q0aAEHBwc8fvwYa9asQdeuXZXs7zXh5+eHYcOGYcOGDUhNTUXr1q1x48YNbN26Fb169ULbtm1Vnjt69GisWbMGQ4cOxe3bt+Hk5ITt27fzOl3zUZLnkr5fDx48QFBQEMRiMQ4dOoSTJ0/i559/1mjXX9Uhcp7IeSLnVUPkfBFEzldwSj8AWuWADtX36NEjql+/fpS5uTllbW1NjR8/nsrJyVGqv3//fqply5aUqakpZWpqStWpU4caN24c9fTpU4qiKOrVq1fUl19+SXl6elJGRkaUjY0N1bZtW+r06dOcdp48eUIFBwdTxsbGFAC1YTjZoTfVoRh6kyY8PJwKDQ2lLC0tKSMjI8rT05MaPnw4devWLabOmzdvqN69e1NWVlaUpaUl1b9/f+rt27cUAGrOnDlK9+v9+/eca2zevJkCQEVFRTFliqE3KUoeDnTy5MmUs7MzZWBgQHl5eVFLliyhZDKZUr/fvHlDjRo1iqpevTolFospGxsbqlu3btS1a9fU3gc2OTk51NSpUylHR0dKIpFQTZo0oY4fP65Ujw5NqQmoCQn6999/U35+fpREIqEcHByo8ePHU+np6Vr1Mz09nRKJRJS5uTlVWFjIlP/1118UAOqLL77g7TM7JCRFUdThw4epevXqUWKxmBOGs3Xr1rxhK4cNG0a5ublp7J+q51WX50HdvUtISKDGjRtHubi4UAYGBpSjoyMVEhJCbdiwganz+++/U8HBwZStrS0lkUgoT09Patq0aVRaWhpTR5f+FBQUUPPmzaM8PDwoAwMDysXFhZoxYwaVm5vLOZfvPr9+/Zrq0aMHZWJiQtnZ2VETJ06kjh8/rlXoTYoq2XP533//UYGBgZS5uTllYmJCNW3alBMGlKAMkfNyiJwncl4dRM635pQROV9xEVBUKaTOJRAIBAKBQCAQCIRSgvjEEAgEAoFAIBAIhEoF8YkhEAgEQpmjLlGiKtjRfQgEAoFQcSkLGU/MyQgEAoFQ5giFQp1CuQoEAjx79gw1a9YsxV4RCAQCQR+UhYwnOzEEAoFAKBf27dunVVZyiqKYMKkEAoFAqByUtownSgyBQCAQyhw3NzcEBwfD1tZWq/o1a9aEgYFBKfeKQCAQCPqgLGQ8MScjEAgEAoFAIBAIlQqyE6OBwsJCREREwMHBAUIhCeZGIBAqDjKZDAkJCWjYsCHEYiLOiwOR8QQCoaJCZLx6yB3RQEREBAIDA8u7GwQCgaCSGzdufJLZm2/duoXs7GwEBweX2jWIjCcQCBUdIuP5IUqMBhwcHADIHyAnJ6dy7g2BQCAU8e7dOwQGBjJy6lPjiy++wLNnzyCVSkvtGkTGEwiEigqR8RqgCGqJjY2lAFCxsbHl3RUCgUDgUBz5dP78eapbt26Uk5MTBYA6ePCgxnPCw8Ophg0bUoaGhpSnpye1efNmpTpr1qyh3NzcKIlEQgUGBlLXr1/X4ZvwExcXR0VHR5e4HXUQGU8gECoqRMarhxgAEwgEQhUiKysLfn5+WLt2rVb1o6Ki0LVrV7Rt2xZ3797FpEmTMGrUKJw4cYKps3v3boSFhWHOnDm4c+cO/Pz8EBoaisTExBL11dnZGW5ubiVqg0AgEKoSVUnGk+hkGnjz5g1cXFwQGxuLGjVqlHd3CAQCgaGk8kkgEODgwYPo1auXyjrTp0/HkSNH8ODBA6Zs0KBBSE1NxfHjxwEAQUFBaNKkCdasWQNA7ozq4uKC//3vf/juu+809iMmJkbtcVdXVy2+TfEgMp5AIFRUiIxXT6XZiZk7dy4EAgHnVadOHbXn7N27F3Xq1IGRkREaNGiAo0ePllFvCQQC4dPg6tWraN++PacsNDQUV69eBQDk5+fj9u3bnDpCoRDt27dn6mjC3d0dHh4eKl8EAoFAKB0qs4yvVI79Pj4+OH36NPNZXbi5K1euYPDgwViwYAG6deuGnTt3olevXrhz5w7q169fFt0l6IEtd05j+f0ZmOz7E0YEdCrv7hAIFZKMjAykp6cznyUSCSQSiV7ajo+PV3IqdXBwQHp6OnJycvDhwwdIpVLeOk+ePNHqGhEREZzPBQUFiIiIwPLlyzF//vySfQECoZJAURQEAkGxz58Rvgz/xWzBt34r8IV/e80nECoNRMbzU2l2YgC50uLo6Mi87OzsVNZdtWoVOnXqhGnTpqFu3br46aefEBAQwGyFESoGaXlpKo/djL+JZZGTQQlysTxyWhn2ikCoXNSrVw+WlpbMa8GCBeXdJZ3w8/PjvBo3bozRo0dj6dKl+PXXX8u7ewSCSnY+3om2e9oiX5pfonZepr5Ei10tsOjGomK38V/MFgDA4nuTlY7lS/NxO+E2CmQFxW6fUH4QGc9PpVJinj9/DmdnZ9SsWRNDhgxRa2OnaXuMoDvJOcm4FX8LT1K007w1ceXtFbTc1RJrIriKZXZBNn649AO+PPGlXq5TkZDKpCBuaAR98+jRI6SlpTGvGTNm6K1tR0dHJCQkcMoSEhJgYWEBY2Nj2NnZQSQS8dZxdHQs0bW9vb1x8+bNErVB0B85hTklbuPg84PodagXYjNi9dCj8mfBjQVIyknCqJOjNNZNzE7E8ajjKJQVKh3bGLkRGfkZ+OvxX8XuiyxP9cLuwhsLMfz4cKyN0M7Zm1CxIDKen0qjxAQFBWHLli04fvw41q1bh6ioKLRq1QoZGRm89VVtj8XHx6u9Tl5eHtLT05mXqvarIhPCJ2DEiRH439n/6aW93U92AwB+v/87pzxoZxD+efkPp0yaUx3p+enQF3OvzEWDrQ2QmZ8pb19WenkoaC6+uQj/7f7w3eZb6tciVC3Mzc1hYWHBvPRlZgAAzZo1w5kzZzhlp06dQrNmzQAAhoaGaNSoEaeOTCbDmTNnmDqaYMvc9PR0pKWl4cmTJ5g5cya8vLz09l0AIuOLyx+RfyBwRyBC94UiPos7jq6OWI3pF6YjJl298y4AzL4yGy/TXuL7i9+XVlfLhcyCTI11QvaGYNqFaWi4vSE23N+AlbdXMsf4FBtdyX3XDwAgy7dlyh4mPcTYU2Ox99leAMCmB5tKfB1C2UNkPD+VRonp3Lkz+vfvD19fX4SGhuLo0aNITU3Fnj179HqdBQsWcLbs6tWrp9f2KzP3398HAMRnxSNPmsc5llWQhWcfnunU3vuc91rXFRnH4eDzgzq1r479z/cDAL6/9D2efXgG/+3++O6i5ggbJWHFnRUa61AUhbuJd9Wa2REIJSEzMxN3797F3bt3AcjDa969e5fZ2Z4xYwaGDh3K1B87dixevXqFb7/9Fk+ePMFvv/2GPXv2YPLkIpOVsLAwbNy4EVu3bsXjx4/x9ddfIysrCyNGjNCqT1ZWVrC2tmZeNjY2qFevHq5evYp169bp78uDyPjisurOKgDA26y3GH9mPOfYyeiTOBp1VKNMZ+9Cv8t6p/9OljEySsa8D6gWoNO5qyNWY9ODTXiXKb8PJ6Ll4Wzbuxbfl4WSGcnfCPOYNgcdGYTLby8zdZo4fnpZ3wlcqpKMr1SO/WysrKxQu3ZtvHjxgve4qu0xTVtfM2bMQFhYGPM5Li6ODHI8fMj9AEfTonv5/cXvcSnuEnZ3241a1rW0asNIbMRbHlAtAHcS7yiV68OUQRFrI2v8fO1nAMCRV0cwxncMjERGcDLTb+buPGke2rm0w/MPz9XWOxd7DhPCJ8DJ1Akn+53Uax8IBAC4desW2rZty3ym5d2wYcOwZcsWvHv3jmOq6+HhgSNHjmDy5MlYtWoVatSogT/++AOhoaFMnYEDB+L9+/eYPXs24uPj4e/vj+PHj2udZTo8PJzzWSgUwt7eHrVq1VIbwKU4EBlfcp5+eMpbrmnxhb341derr177VFYsvLEQOx7vwFi/sRhZfyRT3sihkdZthLiG4EyMfFXbQGTAOXY65jTfKVphYC1XVoRi+a7Q1PNTler09OxZ7PYJlYOqJOMrrRKTmZmJly9f4osvvuA9Tm+PTZo0iSljb4+pQjHiAzsaRFVG0RlQJBBxPr/Leod8WT7is+O1VmKMxca85WP9xuKrU18plZfUcZJG0Ra7jUsbRCTKI2f0ONQDDas1xLbO2/RyLZq2u9sio0C12QpFUfjn5T+YeXkmAPn9TM5Jhq2xrcpzCJ8G8VnxuBx3GVZGVghxDSn167Vp00atX9aWLVt4z1GMLqPI+PHjMX78eLV1VNG6detinVcciIzXD39E/gE3Czd0cOuA6PRoAMDE8ImIHBap8hy28mNiYFLaXSwVdjzeAQBYf289xvqOxa5uuzDov0H49sK3aO7cHJYSS41t0AqMsdgYdsZcP5Z2Lu2K3TdD6yLfgvfZ/LtiqsZdwqdDVZLxlcacbOrUqTh//jyio6Nx5coV9O7dGyKRCIMHDwYADB06lOPoNHHiRBw/fhzLli3DkydPMHfuXNy6davY/4CqjuLEX9G5v5CS2/MKBdo/UvOaz8Pe7ntxsm/RjkNseiz2PdvHW19fUVUeJT9i3j//8BwCcENa0gqNviiUFSopMIoC5mzsWUaBoRl+fDj+e/Ufjrw6otV1KIrC28y3JHBAJeNV6ivMvToX6++tL++uVDgUVwwJJefZh2d62dVedWcVDr04xDGp0sSXx4uCtSjK3fKAoiidfFEUv6tIKIK9sT3zedp51VE0+e6TqYEp0w+as7Fniy3DKUp+T3PiBqnc9X+Y/LBYbRMIpUFJZXylUWLevHmDwYMHw9vbGwMGDICtrS2uXbsGe3u5AImJicG7d0U2ts2bN8fOnTuxYcMG+Pn5Yd++fTh06BDJEVNMTMWmnM+KToy0wKSd9bXBRGyCoceGosvBLsgtzAUA9PmnD06+5ppRSXPlpl362olhK1qRSZGQUqXr1K/oPwQom8adfq1sQhCdHo0ZF2fgu4vfIbsgW+N1VkesRuj+UGx7pN9dJELpQi8AED8oZdq1a1ehk11eiruEwy8Ol3c3tOZy3GX0/acvhhwdgi4HumDrw60lau9l6kudlIB8WZEM10X5oUnMTsS99/d0Pk+RAlkBMvIz4LvNFw23N0SBVPMC2R+RfyB4d7BS+YsPRSbtV99dRVRaFO/5FEWhmRPXEiQpJwnPPzxHXGYcpzwxO1Gbr8GDXIkxrr4LX5/5mrfGnw/+LGbbBIL+KamMrzTmZLt27VJ7/Ny5c0pl/fv3R//+/UupR1ULB1MH/NHxDyaMpKpdEV0SdYmFYmYyXyArgBGMkCvNVapXmFEPIqN3etuJUVwBPPD8gF7aVQXftn52YTZjTjH/2nz89+o/pTo2RjZIyU0BIFeETAxMkJCVgEU3F2FwncFKDpobIzcCAJbeWophPsP0/TUIpQS9M/gpODrrm23btiE7W7MCX158fVo+UfSz94O7pXv5dkYLaFMoetGppLIiLjOu2FG1XCxcdD4nZK/c3HJX113wsfMp1nUBYMq5KQiPLbLRf5zyGDXMa8DGyEblOXRgAzYX3lzAuDPjOGU9DvXAmf5nkC/NRw3zGky5SCjCcJ/huPqOm+ahzz99lNrd/mg7pjZR9mfRTNEODltJHFxnMDwtPfHz9Z/haelZjHYJhNKhpDK+0uzEEMof9g7Gi1T+gAq6mAiwlQe1AyEld3zU106Moj9PaecrSMhOUCq7m3iXeb/rKb+Czt7BoXeL5lyZg1OvT32SOXT0zavUV+h+sDv+fflveXdFLal5qcx7bXbcqhJNmjQpU38ZXWCb/PAtvlQ07ibexcW4i8U+31BoyFte3MWlkvh/8QV+0QW2AgMAx6OPo/Xu1vDf5q+TKZeiAkMTsjcEnQ90Vip/k/lGq3a3PtqKIUeG4GnKU1x8I/+fpeena+ybQMB//O8nf8NQJP//ScT6C81LIJSUksp4osQQtCI5J5mzxb35wWa19d9kvMHDJPW2t+yQzOoGQkom3zBkmyKUhDYubTCg9gC1dfRpItLYoTFqW9fmlE0+N5njm8NHVkEW44RJm9uxQ2Uq0salDYCSOYZ+Ssy7Og/R6dH4/lLFzkdRx6YO815fijqh9BEIBMzKvVhQ8Y0aJoZPLNH5quSv4gJUUk6SyjY6uXcqUR9odPG91Ibtj7YDkC8W+W7zxfHo4xrPsZJY6XSNrIIsXI5TLb8VeZ/zHv3+7YdvznyD6Remo8XfLdB+r+rwy5oUnNlXZgOAxnGHQKhMECWGoBWPUx5rlUeFNifrfKAzBh0ZpNbRkT34qduJKczwQfbrkRjjO0aHHqvvYzNn9VHqZl6eqTfzNZFQhPXtlZ22z8ee13guvXqmzeTWy0qeMErf4aErKw4m2oV+LG8sDC2Y96Xtn1VRkUqlWLp0KQIDA+Ho6AgbGxvOq6JC7zyX5/8tPiseR14d0WjWpS95BoATUUvxuh32dVB53pLWS3Cq3yns6banBH4fgK1R6UZt3BSpnBCyrk1dAEWhoZ1MdZOzyTnJOBt7Vuv6bPPSo1FHAQCJOarvmS4+RmSxhFDWlJaMJ0oMQStOvT6lVb1r765xPj9OeaxUp8HWBmiwtQEOvihKXql2J6bQGtJsL3ha6c+W183CTWMdbZw9teFu4l2026u8O6LN96GdvXOlubgVf0ttXXp1UiqrmhNhRcwMzcq7C1pBsezYq6oSM2/ePCxfvhwDBw5EWloawsLC0KdPHwiFQsydO7e8u8dLgbQAybnJAPiDd5QV3Q92x3cXv8POxzvV1itpNDB2XjD2zjIdmIL5/FGpkcqkCDsXhtURqznH/4j8AwP+G4DvLxZ/h/SX678U+1wA8LXz1fkceoyid2lqWtXUeA57d6S0FQcKFKR51Thl9O68IpXB/JHwaVFaMp4oMQQ8Sn6kFI6RoijOZFhb5/eMfG4o4YQsZX8QPgplhWUWnelszFlMCp+ksV5xoufwoSqcqTqzC0UKZAUYcaIoMy5frP8Lby4AkJstEMAJfVqRYfto6euZq2zs2LEDGzduxJQpUyAWizF48GD88ccfmD17Nq5du6a5gXKAvfCiz10OXaEnpKoSUNJos3CjjvS8onw6V95eYd6r8pW5Hn8dp16fwob7G5iyf1/+i93/Z+86w6Oo2uiZrem9B0hCAqEnFCmhSy8KVmw0FSuKggVEUREE+RSwoNgQUbGgKCiCFAHpIL2XQBII6b1tnfl+TKbtlJ3dFILJeZ7A7sydmbu7M/fet51z/kd2/9kCsZNLDYrMRW4dx8BZOpqdsmPV6VX45sw3rDNrauepGJc4DhXWCnQK7YTZPWY7vQ7fQVGTdGi575gPnUaHysvTBdueTJJmKCPJxjnONOHGoa7G+CYjppGj1FKKcX+Mw53r7xQsoEasHYHkb5JxMOtgjc5vspvw26XfnBool4oviQwgBhpjJoyRP+PT45/WqC8MSi2lyChzzkteaqkdETy5XOU1F9aoPodjfZHUYpcRWXOWKtdYEO0bDYCm8m7IGNB8AF7o9gJmdJ3B6kY0NmRnZ6Njx44AAB8fH5SU0OPF6NGjsWGDOp2kG4kbaXz2iOgBAEiJSlFs90znZwTv2we3x2s9X1N1DYqiUGkTk07E+ceJxBoZSDlv9l0XMnOdzJcXxqxLTOsyDQkB8qLMNtKG//37Pyw6tIg1Endd28UaYCfyTggMFDnUViTmu1Hf4ZnOz2DdWNdqNaXS4gBx9KwJTahr1NUY32TENHIUVBWwr/neRIa3Xq4OxrF4/IE2DwCg6xAcF+2v7XkNfX7oo5hy8cLOFwTePT4IfQkMAf+ykYaaQq3X9ONjH9fK9UjUfIGz4OACdAvvxr6X+i6ZNA69Rl/j6/0XcLn4MgBILr4aGia2n4hJHSbB1+B7o7tyQ9CsWTNW5ys+Ph6bN9NaUYcOHYLR2MSmpARmMe0sXaxXVC+sHkmnnEV6R+KH0T/g3kRlghMGckYaRVHYmiHWuDLZTJBa4zeUxfPs3bNlGTYBofHx7ZlvAUCk/1Jkch4N4hs6NYnWHco+hA+PfohXd78q2+ZswVl4tXxPsM1Rc41BU8pxE+obdTXGNxkxjRz8sDozsPEXyHlVtMZJz8iessdVWCuw+hw9OfoafGUHa4ZhSw5yRgwomvnHWbqEWqitdakto0kuEtMnuo9o2+T2k0XfNYN3+7+reB0mPU1Kz6Ax4stT0l7IJjQ83HHHHdi2bRsA4JlnnsFrr72GVq1aYcKECXj44YZJJ85foHYM6XjD+sFoSVXYlNNILxdfxgN/0s4ms92Mq2VXVdPLS9VqTesyDZPaT8KRHDHd8Su7X2F1sATncVg81zbLmFo4i6Lw9++8RhOwONI6j/51tPPr8MZ+d/V0AODTE3QWwqn8U7JtHv7rYWiNYk0yR3SP6A6jtskx0IT6RV2N8Q2fF7IJdYoonyj2tZW04lD2IUnue0emJ75njl9kababZQdrZxSQ2zK2ibZZCvoClJY9d6GpUFGQTA3UesRqmnfNQG7CZDx5XjovNlrQOqg1fA2+IoKEOP84EUkCRVECcdG00jQAtBZBsakYAR4BtdL/JtQt3j7wNr4/9z0A4J9x/yDQI/AG96j+sXDhQvb1uHHjEBMTg71796JVq1a47bbbbmDP5MEfz1wR+a1tMBGFTVc24Z7W8uLOd6y/g31daCrEyLUjAQAnJzpP6ZLSN3n/yPt4oM0DkmnAW9K3YGb3mfDUeQoiw47GUE3rdNyFK8KyfEIDV5Acmgytpnbq3Zh0bCXjKyUqRTbywsebKW82zQ1NqHfU1RjfFIlp5ODrG5SaS3Ey/6RkLrPj4BnjH4O/0v7CtbJrWJ+6nt1OUqRsyoArqQTPdH4Gu+/bDXPuSFAU18eMUue1LM5QE4+YO5CbvJgJbloXWr8h0BiIUXGjJJW/70u8T1BYCyhPaLWlqfNfgSsCdvUNxoABpIVRGyN69uyJ6dOnN1gDxhE1Zf6qjz7IjUPv/fueIK1YCn9nSFMDawgNBsWIRStj/WIR5hWGgw8exJ77OW0Ux0iMVLSGj1P5p7Dj6g4A9fsM8yNryaGuCWAySApNEkSaCIKAr77u0kVDvdQRmdyo6FcTmsBHbY3xTXdzIwffg3im8IzsYM1MgENiaA2Ar059hRd2voCJmyYK2tkpu2K+rdrJ4LFOj1UXqhMAxfXRQ+eh6nglfHD0gxqfwxXIGjGEFgVVBVhwcAEAYMnAJSAIAt0jugvaJQQk4IG2D+B0AVfcP7rlaMVFS0NYVN1oPNv5Wfa1miLchoDGlKu+fv16WK3q6wT+/PNPVFVJM/3dCPDvqXOF525gT2i4611feXolZu2apdhGTpdlXeo6ydqXOP84VNmqkF6ajuyKbHa7oyOrXVA7xevev+F+PPP3M0grSRNEcRgSk7rCpPaT2NcEQUjWIDrLCOgeKRzHU6JSsPeBvXUmjKrWOXc092gTg2UT6gX1McY3GTGNHHz1XoqiZAfCruFdMTZhLCv0xcBRsGx2j9my57CT9hqz+NSGgKG7fThdcBpLDy9FpdW1QvE4/zj46MWaJQQIDPhpAPs+zIvm+NdpuEmud1Rv1qhxFAdVSmG5kektDQX8ouWbRX/lZulnbeCOO+5AcXGx6vb33XcfWxjaEMCnxq7v6C4fQ2OGAgC6hHVx+xz7svYp7pcjnCizlMFT74k4/zjB9pzKHJzIO4HRv47Gk1s5ml9HI/1KqbBYXg420iYYt+uajj/AGMC+3pq+VdIJ4qy28pcLv0gTsNQRuYHaGs6Zu2aqroVqQhNqgvoY45tqYhoZ3tj7Bs4UnMHUzlPRr1k/Ae2jQWvA4ZzDomOqbFW4u/XduLv13U4Hyt7RvWVTEyhQqpi6RB4ugptA+DnG9Y37/rgPAF1T8+ItLzptb7ab8ezfzyIlKgX7HqAXCf1+6MfW2jh+F+surcPUzlMF4f45veag2FyM1WdXs+QJgFhUtAli3Iz6KzdLxKg2QFEUJk2apJqZxmRqWAJ9XnovRPtEI7M884aOS4zDoi7vHaWFd0pUCtaPXY+OX3MpWGcKzrCEA5eKL4GiKKQWp4qMJWeGgK/eF2XWMug0uno18D8+zjFTHss7JplBUGYV1gL1iOyBA1kH2Pd/X/2bNtrq6dZwpc6nMTlLmnDjUB9jfJMR08iQXpqOs4Vn2UJyvgdx2vZpksccyTmC3tG9AThnGNNr9LKF8yRFqkonEw2wJCf0RVEUrHYr9Fp5GuELRRdw13o6YnT4ocMwaIVCYSlRKbJMaPz+y4mZqWVJW312NfZe34u91/fCbDfj5ws/I9gzmDViHL+LPy7/gamdpwoW35+d+Ay/XPxFdO5iczEqrZVsTvnvqb9jVMtR2HCZ5lu/XHxZVr+hsYCfxlKfi4maoCHX7tQ2Jk6c6LwRDw8++CD8/PzqqDc3L/IqaUYqx1rGJYeX4FjuMTzf9XkkhyWjS1gXEcOWWiiN+2cKzkgW9/PHcQoUXtr1kqjNweyDSAxKlL8wwR1fn8+GWnFnPpr5NMMBHBBs4ztPtqZvxWt71Ony1DUaU9pqE24c6mOMbzJiGhn+zfkXALD438UYHjtcFVPXilMrMHffXNyTeI9TDZKPj32MEXEjJPdpCA0IgkBCQIIiR3+Mr5CxhrRwkZkZO2fgQNYBfDPiGySHJUsezxgwAHA457BI/FGJXrKFbwuklqQCAEI8Q2AjbYL0LkC9V3/71e3s6w+Pfija72isMdo8/EiMlAHDgPntXt39Kp2bzsMjmx9RxTr0X8YXp7640V1oggK++uqrG92FGsFO2tln9kYan4xhsu/6Pjza8VF2+4pTK9jtyWHJNYoWRXlHye4b98c4ye3874SkSJfTcAGwxlFeZZ4oZa0+IRXl8tB6sEKYgPRYzT/ObDej3Fpeo36MjBspu693VG/sub5Hdj8fTZGYJtQH6mOMb6qJaaRgQs9qcrkPZh/E9YrreP/I+/jj8h+KbX+58AsivSPxzYhvRPsivCOg1+jx65hf0S5YvqDzRP4J4QbKCNJG15Qw4fq/0v5y2m8ACPYUF6QqLTj4OdrP73geKd+niCZfpQmdj6O5RxX3y03K/EiMEhhjytGAaQINpoD2uS7POWVBaihoTOlkNzv40YeGnK74zRl6LFYi+xjQfIDiOcK8wjAucRyeTHpSsR0f/HuZAoVYv1hRG7UEJHxj4UbAqDViVMtRgm1/3vmn0+P430FtGA63RNwiu4/RHaMoLUzZyoxPN7KGqwlNqE00GTGNHK6qCKeVpElu//m2nwHQdR5LDi/B+I3jFc/jkueSsEGjE3qw1FLRSi0ulCYTx/ZVtio8u/1ZweduE9RG1bWd4eEO0gJPaovy3fVuNhYwC4ibiVL0ZuprY0dDMTgDjbSukJyXnqndeKTDI7LncDYex/rH4tWer2JMwhjV/eKPpRRFSVL3uwKKom6YSKNOowNJCucGR90uKfC/19pI4bq79d2y+9459A4AwFbeGsZwZQOrKRLThP8KmmbMRg5XPTJyHrGvTtNhw/yqfEEBOoOxCWNBUiSspBXz9s9TNQEw0BjEKsShnuo48aVyuf2MfrL1IlLRkQNZB1Bi4dhwFHO4VaI2aDZJihRRXDeBAzNRrzi14qbIAZ/Xex6SQpNudDeaoBINxYiJ9okGAKc1cEqijdcrrqu6lsaFJQN/AU+BQqRPpOpjpVBgKmDZvpJDk9lrdPy6I7p84xozWzOfZi5f/3LJZcH7g1kHnR7jmFJXUyw7tkxyO58lVOdzDgShPN7dDONhE5qgBqpWUuvXr3feyAFDhgyBp6eny8c1oX7hqkfGVpEAnbe4noUpKJfDb5d+w4R2ExDmFYYfz//o0jUJjdgQWXNhDWb1UNY2AITF3QwW9qWVY/lsOgC9iDxXeA5XSsS0n/zPF+WjLp1MCn4GP5RaStG3WV/kV+XLtpvScQo+P/m55D4toaX1eCh7g9CnaKhgFg3F5mJsSd+C4XHDb3CPpMHUiDEU2024+VCTMaGmYFgOnUVwlSIhF4suKh5bZCrCyfyTKLeor+ngG3kkRUoW/6tFhHeEwAhgPvOp/FMA6IyCq2VX0dy3uarz8Z1SzhDuFY5Ka6WI0OXrM187Pba208m2pm/F08lPi7YLRTXdIM9pQhNuUqgyYsaOHevSSQmCwMWLF9GyZUt3+tSEekSH4A7w1nurFr8iLUGAt3vXUstOJoLEoGwlrSApEvlV+YqLP1e8pTqNTqR7w4DvdTuSc4T1frqKe1rfgy9PfYntV7eLWNP4eLbLs06NmIach98QwL/XMsoybmBPlMGQXOy4ukNEQtGEhgv+/aUU5ahrMFpf269uR79m/QAAxaZiUTupCLlanC08i6e3Pc2mrjmDUWtE76je7HuKoiS9/85EKw0aAyykBTkVOXhyC1ePE+ZJj/n8dGhXBBxdMagmd5jsNO36yaQn8cnxT0Tb+aQwtTFeyxHiuHLu3fftvmlqBJvQBGdQndOSnZ2NsDB1nkJfX2lhrCY0PBAE4dLgT5qaAXAeRpdCdkU2Qr3UpYGpwUv/vIS/0v7CskHL2MnbEUpGkyMdtFKRab/m/VjWMse0AlfAT9twnHiGx3KRgg+OfCB7DgtJa/t8cfILVqeiCWLwv9/aqmOqS6w+txpDYoagW0S3G92VG4Jt27Zh27ZtyM3NFdUfrFix4gb1Sh4NIZ0so5Qzzvl1exM2TRC1rUkkhPmoStGebuHdWPZLs90MD50HZveYDQIE9Bq9JHOW2hqXp7Y9xb720HpgycAlojb+BmWDyF0sPLgQo+JGKbaRMmAAoUhoXRbTu1IbufTIUphsJrzd523291yfuh6Xii7h+a7PNwklN6HOUBdjvKoE14kTJ7qUGvbQQw818fk3cDBUyUoUvlKgKA3sVa7nEwO0p1CNx2h61+kAgC92KRsLDEPZlye/lG0jpTT9yF+PoPM3neGh8xBsV/KC842hHVd3KPZLCRuvbGRfO34XfMroXZm7BPu6hYsXtmsurEH/Zv3d7st/HfxFptoaqhuN+oquLVu2DLGxsfDw8ECPHj1w8KC8Y2LAgAEgCEL0N2oUt7CbNGmSaP/w4erT9958800MHToU27ZtQ35+PoqKigR/DR1yEdy6xtS/p0pul0qJrcm9xTxLSsyJ3npvjIjl6PWXHF6C+9rch3FtxsnqeimxbQGcw4YPfl1mfRFh1NRgLbOUYee1nXXW34PZ8s9vpHckW+s5N2Uufr7wM/64/Icg1Xr27tn46vRXiudpws2FxjLGq4rEuMr1/Mkn0l6JJtx4/Dj6R4z7YxyspBV7Mve4sbgjoPW85ta1f7n4i2Q+ryOYAtB5G9QV/wd6yKc4hHmFYde1Xegd3ZudQGykDTbSxnomn0p6ChPbT4SX3kvWC/XtmW/Z10w+MUmRoklp5amVyKrIwszuM9E7ujf2ZAq9j3yhTce85AfbPsi+dowKXSyWzllvFdhK+oM3AbN7zEbHkI6oslWhbXDbG90dVagP7/6PP/6I6dOnY/ny5ejRoweWLl2KYcOG4fz585LR9rVr18Ji4RaTBQUFSEpKwj333CNoN3z4cMFcoValGQCWL1+OlStXYvx4ZVbDhoprZdduSE1T/2b9WYOlU2gndvuA5gNYZ0ukNz2eeuncTyFSw/RXVGHFR7cvwsY02lHzy8VfUGQqQoeQDpjSaYrkMUoptUqwk3ZoNVpBWm9dPjvunNtL58UKM0/cNNFp3REAkJZgaAwFLl9LSXct1CsU7YPb40rJFczZO4fdvvf6XtzV+i5B22JzscvXbkLDQ2Ma428adrIFCxbglltuga+vL8LCwjB27FicP6+snL5y5UqR5ejh4aF4zH8ZORU5eOfgO+z7t/a/5RL/vjl3OGylnZw3VICagkI1DDihnqG4tfmtAIDe0b1l2939+914attT+D31d9k+WEkrmyMsl1Jmo7hUAIqi8MXJL9D7+95ILU4VtHvv8HtYfW41zhaeRbx/vGDfXa3uwoK+C1idGaWIDn+xEOcfhxKzdCHqjczFb+gI9gzGnut78MflP9i6gYaO+jBiFi9ejClTpmDy5Mlo164dli9fDi8vL9lwflBQECIiIti/LVu2wMvLSzTBGY1GQbvAQHX1EwBgsViQkpJSo89V3+DXc9yo1DJ+bYO3nitW5KdpBRgDAACeeveJdphItJIRc/BMOE5lCsepv6/+jQ+OfsDqezmCYRuTQ5BHkOT2+zbcBwAI8Ahgt6kRklx4cCHG/jbWaTtHqKnlpEihT7jSVolCUyEA58QJDNwxYAAo667lncD3574XbeenF/roaR22VgFNTrH/AhrTGF9rRszHH3+MuXPn1tbpRNi5cyeefvpp7N+/H1u2bIHVasXQoUNRUaFcz+Hn54esrCz2Lz09vc762NCRWpLKqjsDtEL8ibwTCkfQeLTjo/h86OewlnQBKOm0ALWQYgtzhJqQe5RPFNtOzQSz/ep29rVje74hoobdhiAIvH/kfZRby/G/Q/+TbFNprUSPyB6CbRQoBHkEqarP4KdtSKWGMIjxi3F6rsaMrPIsXC27itTiVHT+pjP6/NDnRndJEXWt/G6xWHD48GEMHjyY3abRaDB48GDs27dP1Tm+/PJL3HffffD2FjJ87NixA2FhYUhMTMSTTz6JggL1C7JHH30Uq1e7X3h+I6DX6CUFHOsTfKcLP12Mv/2xTo8BqNm9xRhpUum57PVtfvjpLCeGzI+SvPvvu5LHvPzPy4rXZYwAUX+qP4ujFo0zfHf2O7a20RWoMVIrLj/v1nF8mK7f5byRBNQSLvDBzzrQaqrnm6ZymJsejW2Mr7lYRTV++eUXXLlyBXPmzHHe2A1s2rRJ8H7lypUICwvD4cOH0a+fdFE3QD+oERFN3mpAepB3VhA4p9cc+Oh9oCW0oEj1of9Z3WdhwcEFgm1eOi+8f+R9p8cSIPDPBU4bhrSKCzatpBVFZjqPUo2IWuewztz5HHLD+UZT32Z98fnJz9HCt4WA0YqhRgaEE7lAldpBF2HN+TWC65RZynAuuxQVVuX+puVXwGpXN/ktPbxUVbvGiCWHl7C/oZ2yw0baYLW7Ju5a33DXo19WVobS0lL2vdFolAz15+fnw263Izw8XLA9PDwc5845p+s+ePAgTp06hS+/FNahDR8+HHfeeSfi4uKQmpqKV155BSNGjMC+ffug1crXUTAwmUz47LPPsHXrVnTq1Al6vdBZsnjxYqfnuJGoK+Oz3FKOi8UXkRyaLJnqyh9jjucdZ19vSuPmy/NF5zE4ZnCt1FtRlPxv6Rn9PUjqOfY9n3BEjnxkf9Z+UBTlcjE5Q7GcV8nNE3VJc+3s9zVl3QnKGqzqXLaKOGj0pRjWqgu2pG8R7lRBjywFx9/WUtAXhuBdMq1pvPvvu5jYntYZq2vnSRNqjqYxXhq1ZsRs27attk6lCiUldNg6KEg63MygvLwcMTExIEkSXbp0wdtvv4327dvLtjebzTCbuRB3WVkNGF0aGE4XnBZt40dmpHBP63tw26+3Ia00Db6JQGWGtMq8I1r4tRBtq7RVsuw1Spi34RyuZHBpbpTdR9TGqDXicM5hAMDfGX+zg7Ec+AxsjulkrBcKtHc1wjsCoV6hAiOmXXA77M/aX90h7lj+5MEYOQBtGDnmKW9J34K1mwbBs1kRdA4Ozb7RfQEABeVmDHh3BzxjSqHzAia1n4SVp1fKfq7N6Ztl9zVmFJmKsOIUFzq32Ol830pbJWykTUB92qDg5lqiXTthOsnrr7+ON954o+b9ccCXX36Jjh07onv37oLt9913H/u6Y8eO6NSpE+Lj47Fjxw4MGjTI6XlPnDiB5ORkAMCpU6cE+2qbLam2xvhiUzHSStNqqVfSGL9xPC4VX8LclLm4o9Udov25VRyhwO7M3ZLnyCjNAEVRigZyx5COsvv4OJdVBq1MVhqhschG0ZWY0cZvHI+Vw1e69ExeLLoIiqLcZmZ0ZKZUglFrdOpcsBZ3l9wuZRyQpuaoyhiJwYMIsRHjcJ22QW0FwtDTukyTvA5j1NHXJEBVZ0zoK/rC6q1szADc3HW5+DJa+jdJYzRENI3x0migM7kySJLEc889h969e6NDhw6y7RITE7FixQp06tQJJSUlePfdd5GSkoLTp0+jWTNphq0FCxbgzTffrKuu31BIqderqVHhT9SETnoy6hnZk1vkA3hy65OS7dQgo9AhOkSKvQ2PdnwUr+15DYWmQgyNHer0nClRXC4mMyHZKuLg71/MTgz55Wa880cO7mv3Kga3icXoX0ezx/A/W5fwLii3luNyyWVByti4P8axrxMCEnCtXJoAwVrWETpfoUdEr9HDZifRdd7W6k7SiwEl2mcljG452nmj/zByKnME7/nRuoZsxLgbiTlz5gyio7n0HbmCy5CQEGi1WuTkCL+fnJwcpxHriooK/PDDD6rShlu2bImQkBBcunRJ1QS3fft2p21qC7U1xvPrCeuqJoZxhGy4vEHSiFGDP6/8CW+9N5JCk7A+VVq42lmBfe+o3ig7NxcgbPBNlP/95eoZA4wBskXjx/OO43zhebQPkXcuSsFG2RS/dztpx5HcI2gf3F6kixLoESjJKPdCtxdEqW9muxlZFVny1zG7plNGUfSYzmejZGAIFTql+I4xQJ7NTWAskR4gq+ixQI0Bw4daw64J9Y+mMV4aLtfErFq1SvGvPvD000/j1KlT+OGHHxTb9erVCxMmTEBycjL69++PtWvXIjQ0FJ9++qnsMbNmzUJJSQn7d+bMzVEQ7C6ciTY++/ezgveeUWsk293a4tZa6xMlkT7miMTARDZf2rG4Xgr/O/Q/rD67GhRFsXz9lvxbkX3yJTT3bY5/LuTh9XWnsT97J5aenSowYBxRUFXAUlb66rmQCt/gkBJxYwo/7RUJon2XSy6jqJKbQEgTXZvjLuWl2kLS/yqUUmcaskhoQoD43lADX19f+Pn5sX9yE5zBYEDXrl0FkXOSJLFt2zb06qUstLlmzRqYzWY89NBDTvtz7do1FBQUIDIy0rUPUn3stWvuMSCqwX9pjPczSEsZOC5200vTcW/ivbLnCfZQToXSarQAZQAU0skAeY+qEoMk4N4zSVEUS1oA0OMyHxvTNuLhvx7GokOLRMfKUWKPainWg3mo7UPKGjuUvKNJycjae55EpDFRsI0ghEaEY6RJjuhAUA+lrYKt3DVdrHAvOvWoha84g6IJDQNNY7w0XHZHTpsmDGdarVZUVlbCYDDAy8sLEyaIRbZqE1OnTsUff/yBf/75RzaaIge9Xo/OnTvj0iV5OkLHPEN+DuLNDqkJZnTL0QgwBmD+gfmSx/AL4gGANIfAWpoEY6gwffDtA2+r6sPYhLH47dJvsvs7BHfAvrPKhhUAHM07yr5ec2EN5vRSrsU6kX8CJ/JPoIVfC9a7SVrCABCY/tMxrD1CTxb6AOeRj6mdp8JD54GZ3WcKFhHOCAkq02hNB8rmjx4RPXAgm2PsCfMKg53kJjxD8D8ApFMA1UBKX6ExIb1USOAhKABuACKFfPhpI1Fqz0Jl2hMI9w53fkANMX36dEycOBHdunVD9+7dsXTpUlRUVGDy5MkAgAkTJiA6OhoLFghr2r788kuMHTsWwcHCBW95eTnefPNN3HXXXYiIiEBqaipeeuklJCQkYNiwYar6RJIk5s2bh/feew/l5TTLlK+vL2bMmIHZs2dDo6k9Is3aGuP53u8bFdkbmzAWq84InYcUReFQ9iHBNmfpGurTUuXPY8q6A0Rz6f3NfJopEpS4A5IiFcUyGUZHV8Sci03Fom1tg9u6nVmQXpKOaJ9oeOo82WgwZaUNkRfWHId3wnVoBKUByr/TyLUjcXLiSdH27hHC1B9C5/wz6wjunmVJchrY2NgE99CYxniXR14pUZqLFy/iySefxIsvvuhWJ9SAoig888wz+PXXX7Fjxw7ExcW5fA673Y6TJ09i5MiRddDDhg+p1CRGTVktbOVtYC3qhZn9x2DJmWedHwDae1RoKsTIuJHo36y/ohHDz+1VwrZ0oRG1/PhyPN7pcdkULgaClAYNvdBnDBgA0HopC2wCtEF0ofACLpdcxqT2kyTTAi4Xi8/DEBRoPDIEBgxAc/mfzao9g7m2Fww3G5gaGAaushjxkVqcih/O/YApnabUkRYIVf1v/VADjRs3Dnl5eZgzZw6ys7ORnJyMTZs2sYWgGRkZognl/Pnz2L17NzZvFi92tVotTpw4ga+//hrFxcWIiorC0KFD8dZbb6nWEZg9eza+/PJLLFy4EL1705Tpu3fvxhtvvAGTyYT586WdLDcSzILPqDUKiEPqAp3Dpc8vFcE4mS9e5GqgweUS52ObHA5kHYBXy8UgzfK6YnZTM1hlROmVWM34KKgqQH5VPhKDEp22pSCs83FcgDPPuSv59lJsaLN3z1Y8xpwrv5745PgnSIlOwdKBSzF121SUXbsd1hIuSqbRO0Z43BsDAjwC0CWsC47kHgFF6kHoip0ew5cNYNYGTQX+/w00pjG+VtxHrVq1wsKFC/HQQw+pYj9wB08//TRWr16NdevWwdfXF9nZNFWvv78/PD3pSkNH63Lu3Lno2bMnEhISUFxcjP/9739IT0/Ho48+Wid9bOiQGsyLzEWotCkzlPFhLb4FlN0H4R7qjcguYV2wNWMrtl/dDitpFXilbBUJqLr2AL56IgTPbn+W1vPQVAKka8Jsy44tg06jc8p+tvbiWu6NndYMCvExIr9cWa+AjzJLGQ7nHMaR3COC2pO8Ko4pR1qzgB409H5iWusuYV2wck+a7DX9DH7oFdULf6X9pbqfjRn8xV3boLYCKmq1hjKDu3+/GzbShtSSVKwYJs2zXxOQzGI4ZCsuFg5Hq6C6L6ydOnUqpk6VVnvfsWOHaFtiYqLsAsfT0xN//VWz+/Lrr7/GF198gdtvv53d1qlTJ0RHR+Opp55q0EaMyWpHudkGH2PtR2O+GvYVCkwFsoX3fGOdSW2Vigrty9qHMb+Ncbsf5dZyaI25ACnv8NJ6ZOLYNRKQyDiTqsdk8FrP19A6qDUAYMBPAwAAa29f61TIl6IofHmSY1ByNGKYeU1OY0sKu69LkyMowV4hb3AxkfSZ/8yElbRCH7hfYMTwQVp9QZqjoPNR1r+TA5NqZs4ZBZ2vaxH86xXXAQDXyq+hY6g6kocmNGw0ljG+1mL0Op0O169fr63TifDJJ5+gpKQEAwYMQGRkJPv3448/sm0yMjKQlcUV4BUVFWHKlClo27YtRo4cidLSUuzdu1fE8tBY0D5YXDj53dnvBCxOSqAoLUhLOAyhGzHzyG2qr7s1gy5Wr7JVYUv6FkGRNWX1A0gvQVqA1sP5fSSVY733+l6nx/HTLAg9HVW0ka4taufum8uyuvGpTPmfSzLHm6Rnd0IrpljWElrs5NFKO6JVYCvWgPHUuS9a1xjxWKfH2DoowHVvI3PsuYK6cdB4UbRDQOdzEUeuX6iTazR0FBYWok0bcR5/mzZtUFgorRXScEBgf6p7IoXO0C2iG4bFDpOlD/7pwk/s6w4hNMmNO5ohTsE+MsqRglNp0mLSfCFOPloHtsa9ifeK0sLURJIpUAKigr2ZwvF/45WN9HYV8wIDNULLSmjmLXRAMBEoRg5A65kJaKQNOo2+DPYq56nUUth4ZSM7z0JjBUh6jrAWd4U51znxDQM1RD9NaII7qKsx3uUndv369YK/devWYfny5XjooYfYEFFdgKIoyb9JkyaxbXbs2IGVK1ey75csWYL09HSYzWZkZ2djw4YN6Ny5bsP+DRl7MvfU6HiCsAOgYAjc77QtH3ITGACQ1fnBwiiR85C6l04cqXHGsOMIjb4YKfHBMFtdM2IebPsg+1quQFQ6t1jeU/v5XmF0xlH9maGTBqCYB94EGvzv32K34MV/uFRXV42YZj507V1NFM+VkGR4GvYqmsjBYm+4pAN1iaSkJHz00Uei7R999BGSkpJuQI+cg01X0lhxpfy4k9buYXPaZnx6/FM6Qi2BruFd2dfni2gPfm1TUgP850mhiJ3SgLJJp439fvl39rU5j2MyulAkNNoZ8dBgT+eaK47P8ecnP3d6TIR37WvG6QO56M21CmHK3tPJT4vaG4K49sxzz8G9327xYU5jQ6MvZBnQAAqWgtoj3WlCE9xFXY3xLse/x44dK3hPEARCQ0Nx66234r333nO7I02oe/xwXpnNjY+pyVPx0THxDQcQILTqU68AYHjscPxy8RfJfbZyOo3AJDAkxAN51fW74BnFnUPKSEjwT3DNUNNYsVfkQXU+ifBri/gTabBHMApM9PlIisTt8bdLUppSdrFRl1aSBoArzrSVt5NMOwOaaDDVQEtwOS2/nRXeE64WrzJ1VnIGa03x27FMeFRnu7kaFfyvYNGiRRg1ahS2bt3KMujs27cPV69exZ9//nmDeycNDy0XdXDC6eE2ZuycAYC+99r1EmcQdAnrwjo4mHoOZwLG7oB9Zij5D6rRFwMKqZrx/vFILUkVGTodv+6IX27/Ba0DW7MGGDOuPtbpMWy6skmg2SXqE/NehXPCGQuamkgEafUV1LLoAw7DWtRHsq0z1jdHZjONMVuxuRzlfnaF43HV7QgKOt+TsJa1g8aQB61RPtoPAJHerjNNNaEJalBXY7zLQy9JkoI/u92O7OxsrF692i2qtSY0THx07CMkhyY7bcdfLMohxDNEfierUMy7FSUoKx23SC1Evz7ztdO+OJ5FzSZHyDFdPdTuIUGb2T2kC0ItheKIJWEQhlPlDBgA6BWqPpWvsaJTaCf29b58oSHpjO61vkFpS0Do6Lz9xmrE9O/fHxcuXMAdd9yB4uJiFBcX484778T58+fRt2/fG909SfgaAmE30YWy2jrmZJAT1eTrVzEQCyjWHEdy6PRZJfIJQlMFrac8scrSgUthzhsMa6nY6/rzhZ8BcGlkjD7KtbJrkgYMIK796RWlTB8LAKVmIXlK1bUHBe9JisS4xHFQQkXqS06vw8Bsl3D48eY3rZfDZ6OUSXYCjM4jVAABTbWem97/KDybfQe97xlQNrFoNAMmRdmgcS2boQlNUIu6GuMbpuJbExoELhRdQAvfFri79d1YfHgxqGqNAIoiQBAMo5L8qv+BNg9gWpdp+Pbst7JtCC1NBSmklhVPlFqvNOEGiua0l5vg1EF8HVt5G+gDjkq05SDHdMVw7TNt5NLbKFKcN+7onSQtgdAYxEyAEfpO+HV7G/g6J+9p1LiZqEK9Yj6BRk8vrmyNNJ0MAKKiohpkAb8czDbut9Jq6odZzhHXysRGA1/h3RWUWcokWcQuFl3kxnAlTRTKAM/o1bL7Y/1jYckfDAAw5w+EMYSj7//+3Pd4rNNj7HsmsiBHW++pCRLVBjoSAUixbvIFSgHAViE85tDlSlxIbQkolaY4aOUQOikSFxrSbJnyvmO7KRp6/2Oy+wsq1GZBiMc/nbd8nRFTz/nAnw9IUjg3oQm1gboY42stCL5u3bp6E7tsguuwk64X7L1wywvYcOcGjIgbQW+onsAq058AaaNrUkiKxJCYIZLH7z0Wh70XyxRV5w0BtJhjpGcztvZAytunDzgseO+l95IkKnAFlEXskaeceMIA4QJZbrFMUiSWHl7Kvmc8tvRB4s9HmoW52rayDpLnTc+Mcio41wQn0b+GBt7twNcK+q/jxIkTIKsjTydOnFD8a4goN1dB60GrYtdBGYoAcmlQTME4H1IpQWoi5u/9y6WDl1vKMX3HdGxO2yxbj+MIa2EfmPPkNSNe3PkiDCF0lIg0iWtT+Or0BEHATtrxx+U/JM9VYRZzOfOFLwFgcgdaE4OvocKviam48gxAGkBauOjG4Ys+KC7zhZddevyt7p3gHRP1kMKBrAMSW7njqzIdoz7Kz39VunNmVXul6/ITTWhCXaA+xvhai8S8/PLLuHjxYp2LXTbBPbhT7Ln/+n7sv76fFUIjNPTEQVbFoCr9cXjHLwFAh/E3XThOU3BWw5w3GCfzffHolX/x6oPOr20jeZz/Ct4+AGgX3A5PJD2Bjl/XjAqSosQF8qQpGlXX7odns+9lj+MvKPg5z6/sfoV9He4dLqhBshbxUx0kPp+DYWIt6QpD8C6Zjjv/Ph3FzxobTuWdkt13tewqmvs6FtTeSHALF6sbzoabFcnJycjOzkZYWBiSk5NBEIRkXQNBELDbG973crmI05eqa3mNf3P+Vd1WivhDTa0HP2Xt85OfY0v6FmxJ34K5KXPZ7daSrrIefcruDcouT36xKW0TdN4xsOQPgbPaQwKEgPnRERp9KU7kCRc+dF0h7xzVcx5/vJ6aPBWv7nkVtvJEkCY63KIx8OoiKQCUHnp7JKAVjyEVl58FQMBWEacY2WDQN1oiTYY3fttKOwPRHMOq1jNd3NxuZOtQSYtzMVzS6g9tE4FlExoA6mOMr7VIzLlz5xrkRNMEGs7U5KWwOX2zrJIzaeOU6u9pfQ8qL093aKFyVq9OSys0FSGznFkUKE9wtaaOLVGkStn8YStTZsogKZJlKJPTMmjp31JA62s38RbNkkaI0NNKmiNgq5DQC6EIqCEfqIvi3psJfCE3R4xc23DFbvVE41l9XLlyBaGhoezry5cv48qVK6K/y5fdF2msS/BHOMpJwXh9Qtr77xzDYrkoSmIgna8a7BEsiDbbSropnkOOnYzdr7DPR8/VbHQM7YiLRRcVz/XM388I3kf6CCNQDF0y34jRahhnEbdWoexceq+9Mh4AUGKQrisizfQ1TNfvU+wbez2CnquGxnA0x0rfgc47VbSNooTzXZmlDEsPL8X5wvPV+x3OSFCwlnSt3qc877uTodFYUWay4q/T2TBZm74ztaiPMb6pJqYJAGjvjUavXhQMpCfWjVkHg9aA7RnbYQwThv0tBQPY12oMqAe/+hMIp6mFSZNwMurZMgh86a4uYV3U91MR7uWADIkZgkpbJSiKQlKovMEj8H4S/EW1+PsgJFISdN7iB5tOtXPeb8fc78aGgqq60e2oS1RcmYqWXWrr3m74iInhBEjT09ORkpICnU44JdlsNuzdu1fQtqGAvzi2N6AswCulziMEUuAvhn0MtEER4R2BaB/12iX2ypYw5w2CMXSbTAvC4X8OG69sZIWQ/Q3+MoLBHBg2NgY5lTmC92sv0cLG/N/ptd2vAQB0Ppfg0WwVTNcegLUkGYagaoIEJ+nExshfYM66GxpDvmI7BnaKxJGcIxgSOwSXcky4bPoHSuM3aQkRkSNodBXsa4+o77Hk8GGsubAGX576EicnnhR9D1qP67BXxFef0AhI6JIx6PtDXwyLk08BbAKHe5bvw7lsep6+OH8E9No6oiT8D6E+xni3jZjKykpkZGTAYrEItnfq1EnmiCbcSEiypPDhRp1FywA6UvDVqa9hCBYqHfu0mofK9MdBmqOwYH02olu0QYFdLBbI6KEUVlrhDYAiPeF4Wz7RPx7P8Eh4Vp5eWSuiXKTZeWjeEcEewUiJSsGFogsI9QxFfEC8ZLu8SiUqSw0sRd1hCDzI9cUirOHQessJKxJQE0D10wc5bfNfxsHsg84bNQDsvZQPzjdLwNpIC/sHDhyIrKwshIWFCbaXlJRg4MCBDTLKT/LqlyI9Y29cRxxgtrlGgc+AP6YydYwkRaJTaCc0sz6Ky2WnofWWFmO1lnZgj7TkD5E3Yqqj0KQ1QLRr+9XtrMGhJbT48fyPojZKsNiFaxGGbp9RoweEEVq97xmY6C6rBmO8eMVIa9KY8wbTVMYemdAY82Ej7Zi4aSK7v+LK06Bs/rLnr8p8AMawjdD7SRfX63zOw0YKI/SOmQl0Sl818Q6lUfx4ZdYy/JEqXXfUBCEYAwYAxn95AN880qPJkHEBdTXGu/wL5OXlYfTo0fD19UX79u3RuXNnwV8TGiachY2txV0V9wOAOb+/4H1hhQU2O4mfLoj1Z+gcXnogtZYmIe3UeOl+VTIDMj3U0kWSwr566MUGljO+f3VwPRJTYCrA1L+nYtbuWRi/cTxO5UvXXjjmaxOiJALho0eRwjx2rZc4N5qgdKqLNk/kKzOs/dchVxDc0LBgI2esEoQVZpt8Gtx/GRRFSdbtFRQUwNtbXiz3RoKsjlxQdk/4GdRQ37oOSkX9myPcFbv84Rw3ju/L2geAZjrz1Hmi1GSFIWgfDEH/SB5r49Em88Uf5SC1kD+cc5h1trnDsPbrxV8ltyvrO7n2XekcWTIBWEu52kxL/mCYrt8Pe1ULAIDdgTJdY8wBxUvFBgBLEVe/SFmDYMoU0j47gs96aSNtYkY5SgedL527wI/iVF17EKQEmU0z32aK12uCGPsvF6LV7I03uhs3FepqjHc5EvPcc8+huLgYBw4cwIABA/Drr78iJycH8+bNaxK7bMBwtuh3ZMeSAkEIF+Jd3tqCx/tL1G1wR/Beyl2fWcxzhovWOxX2itbse50EfamrquuOELCFuYh/rv0DXXWu86XiS5L6BFZKWZDSECjUdyBNUcIGEosXc3EXkCZ1E47Vid7I9eIqzP39DLyMWrw+uj38vZyzst2scBRKVYvt53OxdOtFoDpVn7TJD7SlJitW7L6C25KiEB8qr8fgiJOZJfBOoF97xS7HqWI/3Atpg/+/iDvvvBMAvfCeNGkSjEbOmLfb7Thx4gRSUlJuVPcUwTLJUQTKTHUjQGvKugueUT+7dEzHkI44knvE5WtllGXARtqg0+gEC//M8kwUGf4E7UqSW/Rz4w1DACMN+nitpzI1vnIkWxru0aoTtGMocD9IK2dYUZQGhOycJQQt8ik+LwDYHDIGPKN+RpljXVE15T5lFxMyiEEJsio0hEaUrk1RGgHLJmnzhkZXAdISKknbf6n4kuB9qaUUfgY/UTslkCQFzQ2iGW9Cw0Zdj/EuR2L+/vtvLF68GN26dYNGo0FMTAweeughLFq0CAsWLHC7I02oWzgb4Pm0xtaSJJiyx8BaJlSI1nhcdzwMn+5UUZClMQkZYCTBXZ/QCNMCpKz3muqAMAWa7oJJS/j1krT3j1/UD4gjLY4gqifCwW2ZUKvEZ7a74q1Q/n4e/OIANp3OxtojmXjj99OKbW8mWO1WEWudlAGjxgie/NUhHL9azL4nzeH4YNtFXC8W55jP+e0Ulm69iEHv7axOEVOPqmsTQJH0oiO3zL1UoJsV/v7+8Pf3B0VR8PX1Zd/7+/sjIiICjz32GL79Vl5n6kaCql64E7pKPPnjZpd/dzUQR3Cd49YWt7p9vdf20DUjfHKA57c/71TpnQ810SOqmqLfEYzAcohXCFr4tlB9TUCY3ieFjFIpw4kAqlOaKZ4RY3OY+5Sg9bwq2kaBAEURLL2soL33ecF7QzAd3WIYyBRBUPjt0m/sW6l6U61HFig7/f3ayluj4uJslJ2drzp9+vU9r6tqx6Ck0opeC7dh9q9N+jJNEKOux3iXIzEVFRVsTltgYCDy8vLQunVrdOzYEUeOuO79aUL9wNmin29kWEs6w17Rhhai9OU0AnTel6QOhbW4C/QB8r+9PuAQPMI3yHSsehDmeb34jCr3dqMjD5aCvgLK4ZpGYvT+x1QzzChBrh8URcFWGQNddVoYM6nIg/7MH9zfGU9/dwR7CsQLAUPgHthKO4I0R4n2SfRAce+VfC7N4GSmC4QODRxz9s4RbbNVxorSQP659g/6N+8vasvgm/3idD6d92Us3nIBi7dcQNrCUYJ9vx3jDPwHvjgg2q8E0tQM9qoW0HmnYuvZHOAO1Yfe9Pjqq68AALGxsXjxxRfh5eXsOWk4WH+c+801hkKXf3c18FCIILo7BpI2X1ltkyIT7akvNhez24SpXdIGilDAV7qNrTwR9ip6PBcp1VeDWZRTFIWrZWLjQAnO5rjLJVION4Jz4AkyDWoWVTBn3QVz1t144O7B+OzUMsE+jSEf9gqxWrGtnGO6tFc1ExX40xB/xhKzcPwmNFwqt87nAvi+aoo0iJyEjtiasVVxvyNWH8xATqkZ3x3IwPw7aiZ70IT/Hup6jHc5EpOYmIjz52lPQlJSEj799FNkZmZi+fLliIysmXe7CXUHpQnPnDcYhIZjsyI01akRKvOxTdl3oOrqeIkULcLpeRhdAYLgUV5a/bF0XDKuLBiJRXfTudaUTZiik1tmwuJ+HyA5uOGkmjzb+Vn2tYbQoCr9SVAkU8/jzIikmXW8DDrJGiAAILQWyVoZybaE+gUOWdciF/WIswXiXHqpPHZHJiM+DqcX4bXf5HVm6gbMM/Lf+S1cwYQJE5CZmSnafvHiRaSlpdV/h1Rgw2EbL8WQ/t1qU6y0XELQkQ+5RfvpAuXIqpTIL4M91/c475gE7II0V+nx3lLQt1ojBnCklHcESZEuR9v9DcpkJnKUzUyNCn+O0RqzXLq2GPR3YJccW6WXXXxHV2X6Y6jKHAdrsSNbofB8FrsFVlKYymg3RcEQvFPyGlXXlOttAMBT5xrNu2Pdz38BJEnh+4MZuJAjL2TaBNdQV2O8y0bMtGnTkJVFP+Cvv/46Nm7ciBYtWuCDDz7A22+/7XZHmlC3UDJi7FUxEKRzaRmNEZXeKEoPW3l7VGU8CmsJn3JYzfFMv3i5w5QOYztHs2lkhRUWBy8Z8NfpbMz/Gdi1Z4S6PtYRLDau31M6TUEzD/rzszVIbL+dfBc8LYCRHWvHGaDsqSWh9T4PjfE67FCmMr2R+O1oJo5miPO45TCwxUBV7TqESCtyr9qXhrs+2cttINxL7/rlsJQXVQhmwWuMWCsb5WwsmDRpEvbu3SvafuDAAUyaNKn+O6QKWlAOLFu1yS53Ja9Ccb/c832lhKNYJq3i2ga5KIg6SI9jgrQ3OW0S3hiu8ZA2EphanlJLqcs9a+Uv/UwzOJAtrZ9Dmpqh4vIzqLo+jt1GucHWKXluEhjQbIBgGx0pcQLKAFtpZxB6x7FP+Js7GjCm7DGwlXaSTf+zV8bBUtBP8dJy2mdyuJkJFeWeobVHMzFr7UkMXSJNZNEE11FXY7zLRsxDDz3EXrBr165IT0/HoUOHcPXqVYwbN0754CbcMCh5tfT+hwU6JRTJsJ+4FlIntJUgtFL6JPLnocPdgL0ygaf2LGxfZrJCYxAOyhQoZJSnQushtuzrE7nl3OfNLTMhLZ+ul0grKAOhK4a1qCcsRT1FjDTcd0yDtHDsRqM7KRgxLrAVKZE5GCN+g1eLr+Dd8gPkeS+TbVcTVJhtsNjcn+Eu5ZbhuR+P4Y6PxQOfHO5qdZeqdlpCvEgpqbRizjqhF1vvf0z1tfmYsea40zaMEcMwCdFonJGYo0ePonfv3qLtPXv2xLFjx+q/Q27C4uKKLqukCn+dzhbVcxRWWHDbR0KWrxk/HccLvPtKbkzvFs4Vjptzaje9zdeoFxTAsyD4i2npMUofuA+EPl+xDQMKFLpHdFds44jm3sqLb0eWMn7NJ2mOZgvsAahmgZSDIWQrvBMW4IcLX2PHtR2CfTpf6doRKaFQDc8YsZYkwVaahPl95nP9pkh2nKcoDaxFvQBoYCtPkO4YZYClUPyc8RHuqV4XCJCLNjVsUBSFMcv2oNObm2knqQP4tZB8GIL/hlfcUkAjr73TBGnU1RhfY5JrLy8vdOnSBSEhIc4bN+GGQa/Ro41vX1hLOwkWzAC9UNMYuAGeKXAkrQGwVcbKFmE6wrPFl9D5VKsIk3qeF1B+wuI8TQSIalEuR+HHlqE+rGfMWtIJVZn3wVrcDd4t34dX7HJVfasr8As3e777DbSeaQCA/Mpi+LRaCEPQXpizb4P4O3AY+HlsMgRBCIpMhXBuxNgqY+jfTWFBzNeoIRTE0NxFmcmK9q//hYHv7nD7HJdyXY8QuePBZXDqegm0nmmCZ6EujQrJ1CMX0gD/SyAIAmVl4tSNkpKSBqkRAwCEvgBaT6ETxeai6mWvBX/j8W8O47djwvPM23BG1PaXI9fw8+FrKKmiDQa557uogtvujFDEVWgI6TJaYZqrjBHjewYeEetkz63jnZsAgXkp8yTbkVZfmPMHYFzrBwTbrVCOXPEjVABgujZBvrGTYbYq817F/YSmChp9CcosEulIDqxnlVcnwFraCeb8waKm/Eif6fr9sBT2wezds9ltJEVKRxMU+k/Z/FF2diEqr0p//h0Z6p1GwM2ZTrbxVDaOXy1GmcmGv05ni/bLsZQbwzZD65ENQ6Br31ET6m6MV2XETJ8+HRUVygMEH7NmzUJhYaHzhk2oN/gYfNBRPxWmzAckmcJ03rwUhGoOf1tpZ5iz7oStUolGmYNGzy0grcXdAJIxfpRqYsQGkqNHiuD9S1rCYCtNBmlqrqpPriDM14hOzeSFyKTgQXEMOh4Rv4HQWlB17X4U5vJTG7jPf393ut9s3VE1KEq4OLCW3IKyswtRdnahS/0BgKr0J1GV/oRIBI0PvjZBXeDENbrYNFOCyUs91EWdrHYSn+5MxanMEhzLPabqmKO5Yh2d3KpMeMUuh3f8Ypf6IMeKxC/8lkJOqVTUsnGiX79+WLBggWAys9vtWLBgAfr06XMDeyYPYYF0tSaWm7k1G04I06sOpbk3f1IUhd9OcnVhXi1WunUeOcTan4JGL0EEwhu/lKMY9PPkmIYHAL2jOS+tAUHwkKnN0OjLYAzZgcM5wmf4vENQ3pMQOlZbB7aGWgijoxwsRT0BALbSLrzMARpJgrmjmmKZFNc1Oc5v9vJ2MGU+IIgEMTDn00xzdlMkNB7X4N3yA/H5GGOWF6W3V7qnfg4AVrhG9FKLZWBcH+wkKi11p5nFr3mstLixgCYapmOlIaOuxnhVRsz777+PyspK5w2rsWzZMhQXF7vbpybUEcw2lQ8eb0LS+Z6G3o974BPC1Glg2Cqc59XaKlrCnHMb+77iylRUXp2Ix1OEuitB3ga2ZkAnoxjNoEuYYyGkemyc1tcljQ8AIKRypykd2keJJ7RVD3fH67e1x8iOYk0eQmPCW2M5w6cV73tm6Hf556o5+DnshGz43F3wPVnusiip1ez7em8aFmw6gdEf7sLZbHWLPylq0qxKCapUu3hhAQA23mL1Qq508eez3ysLjhr1TB/o78eUMwL2Cpk0kP843nnnHfz9999ITEzE5MmTMXnyZCQmJuKff/7B//73vxvdPRmI7+vT150vAM9cL0XszA0CI3fbOWGq09VCeeN/10U6xeh0vnihTVIkdD41p0xnUo4c014NOq1Az4QhLmGi9TOGtFalOSZVczKt83Ps6+zsaLx9UFmy4XKecE1yNldoxRRl9wBAGwCAsNbDWtYOGg/52iAp9jZLcTfYeDWfFMnNkz893gufTeDrv9DPtlXCiGEMITVgao20Hlkwhm4R7eenkzFzQ8+WQbAU9kXF5WdRkTpdfE59PnzbzoRX81WS17RXuZZO5iqcUWEDQP9F29Fuzl+ocEJu4Q5sdhIFvBSyK/niiP+qfU4IdKozJzIK1K+LGzvqaoxXZcRQFIXWrVsjKChI1Z8rUZsm1A+sdiu+P7FLduA25/AK5Bkvg8YsKiz083DOyk2RBmiNWTRFMyBZx0FaglGV8ZhAuZk0NYO9vC06RgujIc2DvNjokdYrA4bQTdD5npC89oncc5LblXBbUhTOzB2GYB8jNNUr56qr46v7Kc94YytvhWsXb8fLa//FG3vfEBTMBnnTgxytmUCfs02kLzz0WnSPDWI9bCxII8b35LxnF/MKQBjyQOiKYbrOS11QURND6EpA6IoVa2IchdzGLNvjtvdX8vw8Y8vVOgEpKBlCuzNOwrfNHHhE/YSfjknfF44wasQFz/xr6AMOQOtzVnZBVsHz3v11Sp7pTAlPf3eE1lDS0ROhvbyNpJJ5Y0C7du1w4sQJ3HvvvcjNzUVZWRkmTJiAc+fOoUMH5YLtBoHqxf7DK/9VbGa22THyA5oq3tHI/fucuvto6mr6uON54rorChRAyC/8Kq9ORGX6o7AU9gRplk8BNwTRLGWOdLwGLTee0Q2EL3q3cpZWLj9+fXSMq83z9zSg0KSsLUYahAtNyoHxjKH1Jy10n3z0nGNI73sG3nEfO+mrUODWEPAv9EF0nZJv25nQ6DlDp3tcEIK8OYOP0cqpsopFUBNC6UjM67eJtWgi/R2cJlpukcykagv6J2Bxo6+5/3IhQHqANEeBtISJjtEYc0Xb+FBFPMCDKxHl68VVSHpzM97ZpDxPXy+hz3nsanGtM4TtSRXeV9/uV0l0wauDsVfrzL3SpI2jGnU1xqvSiWF4nl1BeLj7iuhNqH3kVOTDO/YTgQYLH6SVXydDTwZ6/0OC2gmK0iKv3PkAR2gsMIZtga2sIyi7N2xlHYDI34SNNPIDn86J8q8xZAesJZ0l99lQCVt5IqAxS1Lr8hET7AWbncLMEW3gZaAfBW3118OkE2gM8gt7nc9FGEK24dfLGhjLOC+ZZ/NvcDif8VZyn8Wopb2P9CAqXJQ7ppMZgneznrfK9MfY7YRUKocDfFrRHswq21h466UFMvUBh0XbHvh8Py7OH+n0/GrAj6JYbCSMOtfZfvh3gZ2koNOK74tysw3789dCHwDo/Y/CUqiOcvtgajnGOAQLc0q5e9sjkhYxlUvn4xMWfPi3NHWrMxzJKIbOlx9ZbNyK11FRUTcZwyX9DJNWX9iruJRbk9UOrYaAXisea5/74Zjs2R5e+a8TnRkS9D1C3yctfIRRO3P+QIHzQPIMVc1A2X1pIpWQrTCGuqYJchlfCNjHWGp8XnqNxiiuMRD1wyxeXP99leuLVl+FjAJxNEpJ54TylI5AEdWkA8191acgV6Y9AX3wTlgL+sEr9lN2u97vNEwyXDLC35v+Hf48lQmDA6N1crMg/PX4KFAUhTd/F9Y+adSGn6tBgcLlXBssBX3Y6Nbw9hEI9zPia9logpNruJgqtfaIenKdN38/jTKzDZ/sSMXLw9tItuE7kx78gmaUG9IuHJ8LIl3u4++z7jmdfBMWsa89In6DKetO7G7cpJIuoy7GeFVGzMSJE2v1oo0dJElB42Sh7g5+OJgBvVaDu7o2E+27xKS8UITkGKbx4OXvU9K3BWkOQ6VZfoCrujoehuCdbESCMZgouzhFy1rI5UBO7h2Lr/akse8NOvHkX5U5Dp7RP8JeGQOtVzpigz2RKeNw1PmcR1Xm/U6NmMf7xeOBHkJV6ONXaSPBXtUC+oAjkkKJfBhDt0mmB2y/lAZ4QRA58fOkv1e9xGL85SEOAzTPxjGG/86+Ji2h1f8HsQYWZTeA0Iondjvp2mRkdVKUvPFkFsL8PNA1Rl5nQgr55RaYbSRCfFwrMn7sG87QslOU5GD1xvrT4H9ZhIJxzMcPhzIwf7hw24mrZvGIyGNd4tPV/nkyCxNTYgEAthokhfMF7YwR6xBouU2h9X8LJ06cQIcOHaDRaHDihHIErVOnTvXUK3UQRgaFz3O3eVsR4KXH7pcdoq2gC4rdhVfchwCpR2X6kwCAvRe5GkRLUXdY8oZBq9GCsoaCkDEkvBP+h/LzcwEAen9pgeIrBfKU5iRhAlEdOSRt3tDo6Gg7oSvDpJRYWGwkNEqMkRSBHx7riTf+Pgelpe+5kgO4mqeDPkC4XVGo0SC9ONX50l7/KqtzB1yglx5FlVbYq2JhvxYLQuecKMSULfXMMiLO4jFYr6UHGULCYHmkTxzm/sE3bJxR8xMoLTfAnDua3RQT7IUKiXqSXS8NRN9F25XPB0BjUE9p7yr+Oq1sQBy7WiyZ7bHljHuGhyNW7L4iadwVVVhw6noJeseHyFO58AhwNPpieLVY4VbNamNCfYzxNWYna4JryCk1odv8rXj7T7EoX01wtbASM9eexIw1x5FXJh6sraQwb9YRfKpiyu4t05bEB/dLR0AAwFbeHrbytrwtGrw0nFYmtpa2F7TV+9MLVINOg9uShAr0UkaMrbQzys7Og7WUvtElnJwCaD2UdTosBdKFZG/cXt1PJmdbpiaCAWX3gL0iXrQ9zI8ZiOnvcGKvGHbS0mo0MIYIJ5PiSvkUEAFznN2IxHBfASGCKWcMrCX092Kr4IpqLbXI6nQxpwxPfndEqKGigMwibsAf+O4OdJu31al4Hx+OzGR2ksKaf6/i+R+PCaIgPx++xt5zpNUXkKT4FoPQmFHFSwm7XlyFYQlCQ5KyGx1IMLjnQWqRIIXYmRuwNzVftL2kUpxmovNOxfSRjSedLDk5Gfn5+ezrzp07Izk5WfTXubP8mHOj8PuJLPZ2oBfW3L1UbrbhWlFVrQpfAnRdhNYrg00XqzBzz4GdV4NIVsrXI5JWf/RqGYwAL71slPnWxeLaCwb8SIHFgU1rzuh2CPExyOvEAAj2MaJny2BEeCrXftGXqd3vLzfXeSRmdCfhXCSVvvvsIMfvV6KfFENHI06lrbLIf65JKbFYP5VHQytR7M+Hjz4A5WbxWMK/947PGYqdLw5A86DqOcNeu4x1rqBFEDdvOdLvpxdUYOyyPbj1PWmhztqA0EDkMGbZHoz/8iB+PnINH2wTR9aLKxWM5ybIoj7G+JvOiFm2bBliY2Ph4eGBHj164ODBg4rt16xZgzZt2sDDwwMdO3bEn3/+WU89lcay7ZdQWGHBZ/9crtXzbuWFSBkKTj58PZh0HkJQkMiCP1izr4W3h9YjB11jAtE9TkkZmTvPnFEd8NSABGi9LkHnKxw8mMJ/i41E5+YBAlYwqTQMGjr2/M5ICgzBuxT3Axp4G8UpTr3ig3HktSGgWE+acj0HHW0St7FTTP/o8wxrz9VWTOkrZu/Zfl4+T5nQ8D4rQWHlw7egMm0qx0BDGmGraAW7KRL2qli2qVlBo8VSdAv72pQzRrZdhdmGaT8cxXububQnZwxMe1PzJXVSXCmCLKkSThqpuRV48ecT+PVoJt7bLMwNZ2hkKZsfIj3EBqUkNBaQ1d70XRfzkLLwb7yzUZgbQGjN8G65VHQdACiWMEIAYNFdYm/SA5+LRfbSC29s3aAr4+jKlStBEITgz8NDuLiiKApz5sxBZGQkPD09MXjwYFy8qJxmd+XKFYSGhrKvL1++jCtXroj+Ll+u3bGyNvD2hrNgFq+E1gStj5hwxB2msqIKi3MClmoHy08nuEilzu8EjAFHaTYsxboqAs8NboUjrw6RP71DMT8ffCPGXiWM+Gs0BBLCfBHk5fx4PaTTXBkcv1YsSTduzpPvNwBczpOnZT8iwUi4dFyyaNuxOdw1pLIInh8sNGL0AYdEbewVrWDKGQlbubju5bejXNbDL09yJDarHu4OjYZAp2YB7DaptDs+SqqsKLOUQ+d7ElqfascoAVzJ58YXfy89YoK579te5VwDp+Mbf7lNyKKECD9u3HCswTzmhFxGSs9FLSotNkXK/oxCem5af+w6lm0XzgN2kkKZSb0DbuHGcxizbA+KKy24WMv1PK6gsYzxN5UR8+OPP2L69Ol4/fXXceTIESQlJWHYsGHIzZVeAO7duxf3338/HnnkERw9ehRjx47F2LFjcerUKcn29YGLOXWjjv7bUS6SIpVWy2dTggQzjF1QwFx9W0h4oTz0Wnz8YBdM6CVN4cgXpUwMD6jeVgjCYUIy59JEArHBXiAIAnNGc4N9uuJil+7TteJKmPPVqbNLgaI0snUagV56aD3plDidxOKED42uEjo/cZiUsvmgIu1JVGY8AgDw8+QYxqQY0OKChQNG+2jphYh36C5E+tPUnhRrMJKwldyCyivTYOFN8hab9MBLkhQMgdzEa1dgklu+MxXrjl3HJh6X/thle2TbA9KLdgA4nKE+TcHRib3rEndfffrPZUEUhXOJU4gJcPCkqsCK3dX04oTyJKnlFcTKOSHaRYkJA6QgZ2AqafvUFlwdRwHAz88PWVlZ7F96ujAlY9GiRfjggw+wfPlyHDhwAN7e3hg2bBhMJvnIWEwMF52MiYlR/GtoyC41gbLxn2Px7+aOo+rY1WKW/lXjkQnSIpW6We3l13Jzid7vJAyRP8JsM6ODzNjBIMDLoJzOrBBJIQgCj8d9gcr0RwU09waSmz86y6SbkpYgJGgmVp9HuUbuaEYRpL7T4dHjFY9TIie5LJEmN7azkIlrYkosAgRGmLAPdlOEKA1MKqJlr4qFtbAfbBJRegrcZ+8aE4RL80dg98sD0a91qESvlceDzWeuw0wWw7PZd/CM+gkA4Oehp4v7ZUEIUmOlUGayoUjGUeMIV2QJDvJ+H8c7kG/gSGHWWnWkLY748VAG2s35C4MXO4/w2ElKlB5stZP46G/1xS/Ld6bi+NViJM/dgiFL/sHOC3nOD6plNKYxXlVNTEPB4sWLMWXKFEyePBkAsHz5cmzYsAErVqzAzJkzRe3ff/99DB8+HC+++CIA4K233sKWLVvw0UcfYfnyGyOSuO+yMuOKO0jNK8fxa1zB9+6L+aKFMpdaRMBS2BvG0L8F+wkd37giHP6nUZn+KAAgxMeIuWM6INjbCE+DBm//yTGN8D2STO7v8PaR2MEbU60lSQBFTxSMhyibx3AilU7GguIW7oZg98LOlVcngLIEY0Ci1KRBT9SKudcO0PtxBaW28tbQ+VzA5VwTyCr6wXx6YLxgYSEVaXq8n9Bj52vUARJziI3gPDtMrY5H5C8waSzQembQJArVuJJfjrggMcHGHon0JgZ5ZWaE+nIRhwNXxJPh6evuCUq+9tspjO8Zg5JKK05fL0HPlsGyiymrwyLf0Sn44d8X8VJ1YShDu631uI5Lqe0Anj1OWgOg0ReLzk9WtcCV/Ap0iPYHUwqk9RRTLLuKNhFi1W2AjvaM//IgvAxa7HhxALQyn7suvJ+OcHUcBehnIiJCmqmNoigsXboUr776KsaMoaN6q1atQnh4OH777Tfcd999ksetX79edZ9vv/121W3rA0HeBhRWBMBe1Vz2vlm85YJE6pEygn0MyK1OBzaGbJGpTyCw/VwuL+2Xw+GMIgR4GgCZ4UujL0SizD3KnV4+glRaZcMjPbvh3T/pMYSye4LQVqFvAmcMlJlkCu/tnvDUBFV/AmegJCMxH9zfGR2/lj7CbopULIy3eRyHlOn03OBWWLqV9iiLJQSEfRCmS1e34KV86TSEcBFM6WArT4DOh1sEk2bhvKPTatAsUEZQ2sk8lFeVi8RIL+Ay2LlxUkos/veXmMkMAL5+uDsmrjgIpV/AVkZ/Rsd0LwZmm13gAEwI9WF1wSiKkqz1kYSLZcHO6mnk8PIv6hnEpKI9FjtJ05o7+MdsFer08xb8eRYp8cE4mlGM5OYByuubWkJjGuPdNmIuXbqE1NRU9OvXD56enq7dvG7AYrHg8OHDmDVrFrtNo9Fg8ODB2Ldvn+Qx+/btw/TpQp70YcOG4bfffpO9jtlshtnM1ZRIKYw2NDz6tZDW8/X1p9miYwZsagNFwF7RGnAwYvhRl2UPdMWw9uHo8NEadlvZ2fmAwxQwrTqsvmpfOq5V10BYC3vDGLYJAKDT0O0dFX31/sdhun4/AMBTT7dpE8F5hioVayfoAYAg7CKaYEtxNxgCxBSnjsX59op4gDLCQy/vDbSWJkHvfxSkJVhSHFQOrJAZbwJ+cZiQhUWvJQSF+QAQ5RcMIWQWuRWJom2E1gydz3no/U6CsvmDoggQBIXFW8/j1tZiT+D4Lw/ClzcXG4K3w1JAR7We+u4w1jzBMXwdlDBilPDzYeVapLT8Cgx4dwcA4I3b2mFSb+nUBrNDKs77DnnK57K555KhEbWVJ+JaURV09vvhGf199V7p1BzS7g0vg8P96SK1KINRnSKx4UQW7u/eAjqZVMjxX9Kh/EqLHa/+egqtw50sJF1EWVkZSks549JoNMJoFOe+uzOOAkB5eTliYmJAkiS6dOmCt99+G+3b0/VjV65cQXZ2NgYP5mok/P390aNHD+zbt092ghs7dqzgPUEQAiOOP5/URNHZEbUxxseHetcovUUOm0/ncOJ7vPquyvRH4RXzBf2GsGLyykPQ+epFx09YcQBP9LsVO1OvoE2EF06ebwuf+Pe4BiqYpwitvEZNSZUNngYt3r0nCSRJYe4J+jeKDuAW8gevFMJTQmqEtAZwQVNnSSAEBVtpJ2i9rog0W6qu3Q/PZt+LDiFNUSw5iaMzgKIoWIu7Qhvxh+i4JwfEo6TKikFtxA4fwqHGTsq5VXWVIz7a/fKtOH6tGB/u2oHzJSfQKbw1LhAOXnxSxmBxAwQoNi2Wicx7G+WXdf1bh+L9+5LxigITOCPiuXxnKlcjWo031p/Gyr1pmNgrBm+O6YBrRZUw8dIfd13Ml4koSejDOLx1RixTHzgvkf5FkTTlsy/PiImw34mLGbQmnZ2kZJ1SAD1XLfjzHFbsuYL7uzfHgjvdK2BvGuOl4bJJWFBQgMGDB6N169YYOXIksrJopeFHHnkEM2bMcKsTapCfnw+73S6ibg4PD0d2tjQTS3Z2tkvtAWDBggXw9/dn/9q1E+e0ugs5z4Y74A8I/PxXOVwr5lK0SAkPHl+foktMAHRaDUI8+Zz/8gv+VQ93x6hONG86P5/aYqNvr5NF0ulFrcJ8MHsUvZrme8Du6SYuwGSMHcruBbspkp4QHaD3kS7aY/VqqsFoICjBXp6IiitTUXHlGZAW8bWkQFr92ImE0BfBI+p7GCN+FfeHcNBaABRZ3/jQVt4iuV3vR3ubaFpm+txqhPcAQMdjKTqURnt+z2eXSRafO8MLErUwfPCLJt/4Xfr3qjDbMPkrYZ6547Oz+1I+TFbmO2MWLhoAFCgrdy9L5aQD9DfEeG33XCqo3ubaJFppsaHUZIVHtVcyNljd4mTzmRwcuCJtGCtp+yihXbt2gnFrwQJpoUB3xtHExESsWLEC69atw7fffguSJJGSkoJr12iDlTnO1bGWJEn2b/PmzUhOTsbGjRtRXFyM4uJi/Pnnn+jSpQs2bdqk+ntQg9oY4y12CtBUslEYV+8dOXy0/ZKk44A1YACwNXiSERMKy3dkwpI3HF18J4iISRzTeqXh/B68u2sz3HtLc5apzN/PeZq03u80tlynBRaVFtoAoPM+D1tZB1gc9bQAAAQoigBFaWHOH0ATegAgbb54q7pwm6JoQUsGpVU2NvpPURqYsu5EeSq9XjHqtHj9tvbow9O4eaNaw0UkSVCdim3OHQprSSdUpD4P0sTVBkX4e2BY+wiEhGXAI+J3ZFp3O/taJLHq4e7wNepAmpqj6tpDqLj8rGQ7k80mYKHcM5P+vpxFACsuvYSKK09L7rOb6NX6yr1p+MchFWrl3jQAwNf70nHmein6vLMdf57knvHMYnkD2BmL43M/KosDA8CnO1Odtqlt2CkK3gbh+qfCBDAxAH4qvxxW7KFTlr8/6H60v2mMl4bLkZjnn38eOp0OGRkZaNuWc+eOGzcO06dPx3vvvadwdMPHrFmzBNGbzMzMWjNkhiypOesGRVGY+NUh/HMhD19NugUD2ygX/jFYujEf5TY6jEiRBthNEdB6cDcfUe3tspvC2ZqLKR2m4vX1zutOWob6YNkDXbDhxAa2+NlW3hrZxfTgSkrkNvh56LBlen/BNiWNhO8f64m3N5yFXtcfey51AAgrDEH7BW2YCdURWqNwIDaGbYa9SpmpZmBiGLaf52ot1ECjL4Uh4F9Yy9qBNEXCI/I3kDZx/QsAUXQnyEdYDBvv2Q/707JA2b3gEc6RUTBe2k3P9cU9f0n3ozL9cYAiWBVtV3EqswSjP3R98j2f7dyjrUQ2wEANDajFRuIZViyw+vehNNAH/QOP8I1su97xoTgkEUzSGPKw5vBVYZRM5e/MoMPrf4GkaA0DQImQQoy06rovW0UCDMH/sNvdrYk5c+YMoqM597eUh85d9OrVC716cQXIKSkpaNu2LT799FO89dZbtXKN5557DsuXL0efPhxr4LBhw+Dl5YXHHnsMZ8/WHptjbYzxfh46aAxcWqZBp0Hta4vL3AsEvUs65ZU7hqQoaDyyXL+sQjqZtVjoRGGivgPjOrLbbk2Mxj4Zm4bRkJnUKx6b5UnQWFpkqbpMrfdF2CvjYM4dCdLUDJQ1EDrf0yAtIbBUOzY+23UZltzh0PueAWXzqhbcpb8bW3kbWIu7y18cwKTecRh3Swu0nSNcXDG1JJYCKeOK9zkJeiwoMZdD58BzkBzrPGOlX+tQHH99KDafycYT38q3M1nt2MGSwhCIDqDn7qcHxsNqJzEwUbw+oB3hWpCm5iDNIdAYhenFHhF/wFpEP4cTVhxk52VHYpZNp8T3llI2LOmw09GoyS93HtlcsPEcHu+vkrwF7pFrOMJOUrDYSfDdAWXea+HZ4jiqMqZg54U83NW1GQ5cLsC2c8pCojVB0xgvDZcjMZs3b8Y777yDZs2EzCStWrUSFQLVJkJCQqDVapGTI8yLzMnJkc3ji4iIcKk9QN8Yfn5+7J+vb+2lfSgXrDtHlcWOGT8dZ70jk1fS3mq5PHw+ykwUKFsAKFsAlj/QBxaeTgsAxId5gLT6c+lQAIw6DQzB2+Ad/z/og/5xIsTGgB6YdD4X0C1WnsXsqYHKFJuOSG4egJ+e6MXlPDthDXMGgS6OBPiaJvYK1/pqLewNexWjP+N8wrJVxMHPQ5ga4qMLhSVvOKyF/YQCjtUaPm0i/GQNJLIqBqSpBaR8FDYVg/rjPH0WVzBs6T9O22w46XxRpTZNh9EO0FRrrej9Tou+7bgQaUOO0JVh2faaefWYOTi1mhGJyXV+954kp8e2i6QXQ/ZKLp0u0jsSYZ7qnBKO8PX1FYxbchOcO+OoI/R6PTp37oxLl+g0Gea4mpwzNTUVAQEBou3+/v5IS0tTdQ61qI0xftdF4cLP31Oc2lVTyKd1VYts2iT6TVDQ+R2DV8zHOGf6BV4tVgh2m3OHOb8wpZFt50gCsuXOHfh6yG/oFBnLbrs1Vklwln5Cw30CYDcpC2Jrvc9LMn8ZAg9B532ZZby0FvdA1dWHYSvhKNIXbjzHEZ8QJI7wiQIkDCMpeBq0iPT3QNnZ+Si/9CLKzi7E010nqzpWp6leWmnEv+HkvuqEwDUaAsM7RGLxvUn4faq0JIDJasP640wkgCesrNPi5eFtJJlE+caEuWAAbOWthfslySSAfv8TOpeqrOLsgUoF6nmpgnl34Erd4Deygp801Mj1kRQlmeqm86bnj/XHr8NktWPcZ/trnXWWj6YxXhouGzEVFRXw8hIvDAoLC2vVMnSEwWBA165dsW3bNnYbSZLYtm2bwILko1evXoL2ALBlyxbZ9nWJ9IKaU6q2nbMJayVCl/xCbDl48uo/wvw84Li4/mjkDKy7fSP2Tf6R3UYBIHQV0BgKFPOkGWx5vh8SorgFKOMVkvIoPtZXXVGcI66V5MGz+Qro/Y65dTyDWyXyn/l4cVgi2kf5Yf4dHbBo+COy7ShSavFCcF59JxMmZTfClPmgchveNRbc0YV9zTC8OU5CSjCpiIQopQTw8dOhq7jj4z2SukRqURuF7ARPkNIxmpJXJccMQ7e7LviszvsyTiLV0VY9wRm0GlAUhbu7NoPBSVQm2LvaPUvp4amjn5MVw1ZgUMwgp32oCdwZRx1ht9tx8uRJREbSKaRxcXGIiIgQnLO0tBQHDhxQfc5bbrkF06dPF0ySOTk5ePHFF9G9u7LX/IaBd6/JReGKalA3QzipX2FS2PhRZQIAoSuB1isD+Saxo8ZWqW7clTIeAGBkR+GCJdIvCF2ihJ5xSjElkh4PNRpC8P2Zc4cKnTUADIH7oPWUT9XRGHKg8zsGjYcwRYdJr9YacmHKHo3K9CcEjhm932n4tp0Jr9iPFPpJY/Pz/QBoQVmDcXfXZnhawvnmKJgMAFqGfU3C2eZjlKeglsKdXZqhowwD2JrDvM+u0jjrHhcEz+ZfwCdxNjT6Ip7DjQYjqMxggYym3ee7roi2KTlqHaUf3E2vV6JKdsRRJ7TNSrUsDNToPf1yRLkWtD7R2MZ4l42Yvn37YtWqVex7giBAkiQWLVqEgQPdp7xVg+nTp+Pzzz/H119/jbNnz+LJJ59ERUUFy8AwYcIEQTHTtGnTsGnTJrz33ns4d+4c3njjDfz777+YOnVqnfZTClI8444P8d/ncjBr7Qlerr9zbDiRJfIKSsGEPHjGfALPZivRPspPNOC1DIhDQpivgF7y00MbYAiqFjdUMUC2CvfFHcniAT2EFIbevS09lSk+FVDq8wN0PhfgESmuNeFjSd/P4EXK5wVrnVB8hvl5YMOzffFgjxi0CZM3eCgJb6je/19o2YlV+XPazZEwasR0l1dN/8K37Uz4tp0JQ+ltoKpzsYUTIDO4Cq/hEbkGHlGrQWi59K5/0wpxtbASr6xVz9Qih1BfI/LKzHjplxM4mlGMRZvOOT9IBhddmJDcgU4jlzFLf3cXBIWczieroe3F90JGYSX0Afsx78wozNhJ59lbnHgZ+7am8++1XpdRZaMNqbokRuHD1XF07ty52Lx5My5fvowjR47goYceQnp6Oh599FG238899xzmzZuH9evX4+TJk5gwYQKioqJEhZ1yWLFiBbKystCiRQskJCQgISEBLVq0QGZmJr788sta/w5qG76EdIqLM+0LZcjdQ/R20uYLW1lb2CvjQFGa6jGCI16/XiymPn3mVi7yNyxGJrJOkCAkGP0oigDl4fxZ9zRy9/GwwPmOJ6/+3y6gLAcIEDqHGj4Hh0QzY7Lgvd7vNDyjf4AheAc0hlx2vBtYTRziEfUjPCL+4JxADufTejpfePp66JG2cBTSFo7Cu/ckscbq4/1aIszXiOUPdcWbDsXvAC1oDEgbol6G2ozaSbO4KaFZoBcIfQkIjR3G0G0whm4Vn5OHT12ILgxqKx9J7r1QSCa0+iAtY2AnKTz4xX6pQyRxSmWtZ1ZJFY67YcQ0C/QUvFdjxMz+9cbJdkihMY3xLtfELFq0CIMGDcK///4Li8WCl156CadPn0ZhYSH27HFeMF0TjBs3Dnl5eZgzZw6ys7ORnJyMTZs2scVGGRkZ0Gg4uywlJQWrV6/Gq6++ildeeQWtWrXCb7/9hg4dOshdos4g9SA89d0RfPRAZ5Yl6+GVNGVI8yAvPDVA6PFJkynef3r1EcntjiA0Fui80kHavGHUaXFLbBBOOanbvlZUBk82Y0ndAqtDiPi79SWTWF4AS1F3nJj2mapzSUFbmQQYlYvHH2o7AYNb9sLglmvR8euOkm1OXlPPSNQ6UD7SIaURoA84AkJfzSKioLkAAPbKGKS0dGQmAwqtXBh81SPdMXE7PRmG+nBRUDY6Rgh/SJ3vSRBaC8x5w7D/cgFm/HRcMbriWDPkDGarHbfM5ya+NYevuUwly+BSbjlig73rjHaSkLtvqzdP+kra4yyFFcNWoLxY7jkgQIGixQZVgCFEi2+eAybB7nr5dfgZ/OBrqF3mMke4Oo4WFRVhypQpyM7ORmBgILp27Yq9e/cK6kheeuklVFRU4LHHHkNxcTH69OmDTZs2iQTT5JCQkIATJ05gy5YtOHeOXii3bdsWgwcPrjfjzlUwkZAWvi3gXRUIQDwWTF55SGUarhgUpTw9k6YWqLo2Ec2DPHH13Ehex+QXXWnWTQDoCO67AxZi17e7UWkXLgoJwipifqS3U0g37wHwkGK/vHw542Byrzb4i68vXd01KyWM3hrDpAr8hJ9jcKy0k1Tvdxp6v9OwFPSGOfc2XocdnTy1x341a2RbzBzRRvbeZGpipAxRb4/aI/gBQYE0RaHs7Nuovc/n/nlcOZJxvh5OL2LJVdSgQEXtDAD0WvC30zZ02p/w92CYVhnUA+t9raMxjfEuGzEdOnTAhQsX8NFHH8HX1xfl5eW488478fTTT7Ohp7rE1KlTZSMpO3bsEG275557cM8999Rxr5xDir9+69kcvLf5PGaPEhaVSqXnMLS07kM4oCeE+eKUE1KN2GAfcIE/dTdZsKd4Qd48yBPHq22GlwYOrNEN2z4yBP+aIKIo5sMg633nQKkMvTP4YOAHqLJV4aWdr4LQqGHtoo2OMD9PxVZ6/6P4391iMgx+gXeXFoF4MH4GssoK0L0FJwplDKS9V4S2CnqNHlbSiiCPIBRUVLBnue8z9R4utSiViCqqKcaXwlPfHUFssBd2vEgvUJZuVRYXlQKh5acwCGecQI9ABHkEodBUCC+dF6psJlAgYQz9C7ZSYe0KaVbO7b1QdAG7LhyA1tsCuwPVNRMpY4wYkV6EA2ZUs7hRBDdhPvzXw3jplpcwvp2yoF9twJVxdMmSJViyZIni+QiCwNy5czF37ly3+0QQBIYOHYp+/frBaDQ2WOPFEQRBwKiXN8KtdhIagkAOTwsrPtQbqXlOUozljBgHI2Vir1jM2yCV8iP+/k4VcB7j3MpckQEDAFAY3+QEgvnwM9JGeKvAVmjm5xi5pPukcxIJN+cOh9ZLWLMW7mQsdfy4zDjt2XwlKi9Ph62sAypM0fCO/UT5PCqhdH+eyyoH9JBMJ9Nq3VsV94xMwf6svex7a0lSdf2KmO3SORTa16DelM+ampZfgUAvA/y9lCNPrqYUz9twFo86pKP/eCgDL/9yEvPv6IAHe8SI6ZxloCqdrJatmINXCiVrlWobjWWMd8v96e/vj9mzZ+Onn37Cn3/+iXnz5tWLAfNfBKOEzs8XdYXlSD2ERozZyj2YS/q/L3nE3V15qWEqF/1tgtpgWpdpeLvP2+y26UO4BV+7UPXMIlIYkEhPipTdC6YcaQ/nH1fEWgCOyClVTx9ssVuQEJiApLAkVFyRprp0BOPJ9NJLL0SoaurTUWFzJGuaHAskZ/aZhPdHCCnMU5rT9TH3t70D97ehdXfGJIwBJ8agbvBNCk3CgxJ53fUFhqnLbLOzonOugZJ5DXjqPFFooo3dD2/9EDF+9OekDWBhqgdpCVMsNv72zLfYU/wpPKNXi/YZAmka8V2Zu+j3KiNLjoN4fYhdNkSQJIm33noL0dHR8PHxwZUrdL79a6+91oDTyejfKr00HTadfGrSiPd34fFvDiOFl04T7K1cx6gxZkErxyxGVo8pmipojFnQGYtZoghniPLmxC4O50gTeJBW6cJuAGgZ6sSQAKCpXlaQJCkbVXQ+x4nTpDqFitO2RMdIgElbo2x+rABxXaOkkgBp82HZOvkI9hA7+tTg0Y4Pw5zPRaPsFQkA5WYdsuJ8LvweY1RSxwNc8X56Aa0HljR3M77aI66d4UONIaGEtPwKVtCSSev64ZBzKuO+rUKgq6WaGFdw76fyWi3/ZdTVGO/yavmrr77CmjVrRNvXrFmDr7+WkdJtgix16tXCKuSVmTGFJ1hpJyn8fPgaBr23A7mlJqw75pyHHHDGkENfn1lUT0/holNJYdIpVx2jucksNkR9isujHR/FbfFcWD/U14gfR/+It3q/hZ6RPVWfRwrxodWTNUGCNEvn3+ZWcrnWe+8TDhiUnR70I5x59Xi4WHQRI9eOxKRNk0A5FD3KgSI9MKjFIPSKki56K7/wOsrOzcPQhGTJ/TqN80czxJs2hJoFeeCe1vfg0yGf4s6EO9kUNintCh8JfYb/9fsfHu4jLTpZU/BZ3pRwx8d7kJbvJnufYB4SfmaLnUs9sFN2YbqXhJFHKSzgrpXTC1VC65zIYOVkdYWKNgchWHcplm92zJs3DytXrsSiRYtgMHB1Xx06dMAXX3yhcOSNBPdb+XjL5+lfyi3H1rNCVp+DacoisoRGXM/CwBBCU/XrvC/Au+X72Fn4EYqD34B3q/kgdKWQW8wD/DQnhVRLBahZcJrsdN9TS1JBgMCKId/AXj1WM0yPzjywdHoZ92xE+0SjU6gzkcAG9OyUd0HFxVdhun63YPPSAUsR4a2OzckR5wrPwRjCRbyZT6sxZsI7fhE8W6hP0/YyKGQrOIyLY5MllEtlwCz4+XW6b8rogQFAcaWlxiKXjlkqZpsdr/wqX/v55cRueG10O3wxsZuq+/mH6tqdJtQMdTXGu2zELFiwACEhIaLtYWFhePvttyWOaAKgrI9xy/ytgknNYiPxwprjSM2rQPe3t2HaD8dUXWNCL9rLFCgRvo2uLlbzrDZiIvw57wp/YuNDq+FC/qM7RUm2UYt2we0wNmFsjcOHTEG+n6cWax4d6aQ14Gv0AWnm7ldrcQ/YKmMwJSVZ9TWtJB21ya7IBggrSBX6K6Q5BEsHLsWrPV+V3L/75Vvx5YSesoWQyS0CnF6DWVQfyTmCWP9YpESlINY/llfaK54c+iSIn91KWyWrXl/beOeujlj3dG+n7Y5mFKuiaJYG92zZyoQGeZsgTgfGYrc41GwJn0lCX8DpU7gKXu2TxW5B97ggHJ8z1OlhmUXqmOD+61i1ahU+++wzPPjgg9BquXsxKSmJzZ9uSPh+Sk+MbJWCNoF0KnCn5tLsUe6CssuPMcaQ6ohO9WOuJTQwkWW0sj3BGe2UhJHCjBmAvCFRU+HOIlOR4Bqdwjqi8vJ0lJ1dCGsJrTPjqVdT3M71Q12tmXK/tT5n4RH1vYrz1Bw2dmEu/I5rwj747r/vsq8thb2h80oDoS8ENDZoDIXQ6NUVvAOAQSsc7005oxBhaAdz3q0w54wW7JNaT8iBMWJe/U1doXu52Yb8cmWnUPMg9Q5HAEh8VV44sV/rUAxqG45H+sTBqNOqMmK+2K0cSWqCOtTVGO+yEZORkYG4OLHXNiYmBhkZTRarFCiKwvcH1H837vKnMwQBUrn4VjudOsPMW/y0FTkjJqucS2eI8XfPe1TbYIwYM7Lw2DbpAtPXer4meN8pgsufTYovwwNt78OE5OGqr7k5fTP72iPqB2hkRDUBgCykJymd71mM+GUELhRJ13g0C/TCoLbhsgsJNRPH0Vxa7HHHtR2C7ZyWjvg+YNIX+dh3fR8i/JwX580bq54QY2THCMy/owNubROGFkHuiW7OHNHGeSMAlN27+n9PkJYwxOpp46FjSEd8fvJzQdt5feZxbxxyv7We7o9f/AXj1TI6lUFFMM2hnqfxIjMzEwkJYvpakiRhtapP/awv9IoPxocPdIWXnn5uWof5sE4kdxDrkLKjZMTwWgGgDQW9Vlf9moTd1AyWwt6wVyRgcnuhrgk/Si037nfpeFr+kip8UHznFwAB3TijoaQqCuTwfGaU0s/n8sHLZdrLp5UCFLSGXOj9lUlhagttq9P7KKt7qWPOoPM9AX3AYRDaCs7odKHOU5TGavPF020W45Vez4E0CTUAlWr7GDDGgJ2kUGpS/7zml1ucRutzSt2n8HeEp0PtmitMsHxE+0Sj6pqyNEIThKirMd5lIyYsLAwnTpwQbT9+/DiCg+vmgb0ZkVdmhtVOYueFPMTN+lNS30UO7oZXjdV5+FI5nHaSfli1BD3Z/ZvDpa/JTWZ8jY0gj7ovRFMDb703Wvq3RKRPJMx26cGtwCRkOukSyS2GzxYfxU/p77h0zXGJ49jXzcOVvV333kJrNmgMhbhWfg13rb/LpWvVBgI8mVCtuvvonUPvOI2QnXtrOIa2UyfSlhIfjI8f7IoHe8SAIAi3mceeUKnMfHdnmhnNUkALwulAU+oVmgrZ+56B8F6vm/QTo5aelNWkBOoDhOyCjbUmpl27dti1a5do+88//4zOnTvfgB6pA/vcEMDcMR2Q+vZIbJvRH11URFL5GCJ6tpwvSBmtrP1Z+9lxHYQd9opWMOfcBltpMp7tIl/DJ2dIXDAr09c7w6AWgxDrF4u7WtFjX15VHl4al4V7hhxHUkv1dOI6r3R0C++GvtF9kVOZw9bwqIvmi40YR6fOoBZ1p8nUo2MGfBJfhWdzTmz07tZ3KxxRE7g+ZhRc7w7Sws3pxrCNOJD3Fyb1FjuobSSl6FiN9PdgI/x2ksIjK6UZH5ObB4i2eeq1TmtOQiWMnGe/P4qNKoSTHXExR0jpX1Tp3uJ5Sscpoqi/IyL95R2DjXGcr6sx3mV2svvvvx/PPvssfH190a9fPwDAzp07MW3aNNx3331ud+S/hEu55Ri8eKfbx5e54MngwygTiaEoCqTNF5bCFPS/hS6y59cKyE0K/Zr1w4dHPwQAlFvrVs9DLdqHtMe6setwoeiCrIHQLbyb4H2gh7jOwWQzwUOnjhowxi8Gi/otQqBHIN4+8DagkAH0c+pKVed0hua+YlFFtRjQoh/WHLkoWVTqLjz0WkmtIym0CvMRvPfQ102qGgCE+RqhYRcnGmi9L+CSdS0AILM8Ex/d+hGm/k0ztNgoGwqqeAaug6f3qYHx+Mp1cjQAgMHWHCTS6NMSjKCf6+dprDUxc+bMwcSJE5GZmQmSJLF27VqcP38eq1atwh9/OCfquBE4lnuMXVgzv5tWQyA+1AdHMopdOteMoYkC8UA1ETp+6qOnTo8yKwCeLsk/Lw6UdVAB7ukS+Rucp8156jyxfux69vwn807ikxM0eYzRWIl5feYJDKhpXabh/SPS5DJnC8+iUwhdC8NoPvEL4/sEPInkWC0+OvYRhIt5AuUXX4FPKybFnYLGQNcl9Yzsic+HCiO0tQ1fDx0IjQ0g7Ci/OAsjbinHK90nOz9QJTR6RiKAAvO5NS4MOKairkBRMjxbrIDOOxUafQn2Fn6D1/emwxBSCkv+YLatzU7i37Qi2XNpNQQbiTHbSBySaEtRlKj+D6CzTiZ9dVCxr7cnR+GTHUKmuvXHr2P98evo4SLL12UZqQpXserMKngGdUZV4S2ybdpH+SGrRLq2jaQA7c1BvlhrqKsx3uVp9q233kKPHj0waNAgeHp6wtPTE0OHDsWtt97aVBNTjY93XKrR8ZvP5DhvJAEPHcMKww3mBeVmxL/yJwpKvGHOuR0PtJoCQOiR1sjcBvEBnCf8col6wau6xoWiCzhfeF52v6MB0MynmajN7szdLl1zRNwI9IzsKftd8bH29rUunVsKo1qOwsmJJ3FyouvilG/1fgumzPGgrLUbPVPLIqN1mEyljvN007Dp20pY05NbZkaZhZ7Q9f6HofVMY/fF+sUiyoer5bKTdkzZPIU72M7lWr95e3vEh7qX9gYAAUauX3mVdART68YisbEaMWPGjMHvv/+OrVu3wtvbG3PmzMHZs2fx+++/Y8iQITe6e5JgREprAx56LUZ04FJ2Ca2808hWLk7J0GuZ9FMSGkMuJg0uQyWRgX4/9hO0i/bhirTVjGWOUIrs8ME3kARzTfVr/rZwL/kIb4RXBPZl0eQseg39Gf2NnCH1eK8U3NX6LjzWchks+fzICgGK5AkDEyT0AXT67f6s2qeddwST0qv1uA6fVgtw3vIj7zdyD18MlSh+JrgIU6S/+toROqquBWXlvstCcz7WXlwLnY+QrttGUiiulNdm+eD+zuwYL1dQb7GTsNrofvKdXI98fchpuppOQ2BkR+l09gNXlAky6gqXSy5DF/6LYhslCvXaZjy7GVBXY7xLoxhFUcjOzsbKlStx/vx5fPfdd1i7di1SU1OxYsUKAeNAY8bZLGUhxSDvuvme+DUxTLiy67yt4D8vzGDDFKsD8ulkzKQBAOsuravt7roFk82Eu9bfhVd2v8JuWzZoGR7p8AgAYH6f+QjzEhbLH8s7JjqPkodSCXxvV4hnCHSEOJipITSY2X0mAOD5rs+7dR01uLf1vQCALmFd6uwafKhdlJebnUcSq5zkIkuxqAHSKWbswsiYz07oAcYArBy+Eh5aLtqWEJDgYCRwv+X4njEgKff1EfiMsRXWiuptrhkxywYtw20tb3Pe8D8Gm82GuXPnIi4uDlu2bEFubi4qKyuxe/duDB3qnBzhRkFwL9XCmsSTR65B6OXnEFPW3dA7uHGZKAVBkND5ncAvmfPx0/mfUGIWpr8+3ulx7hoKz/PrvV6X3O6Okc2vkWGuSRAEfPT0YrZTaCe80O0FyWNTSzgPPHMeq50bX5LDkhHiGYKL13xB2RyiRJQWlqJbYCnqIaoXGffHOGzL2ObyZ1ELglF3Zmjca+H+SApNEm3jkzCo0fBhUEAdgT5gP8z5gzEtiSafYecyhwi1zU7JRuH/eKYPurQIdDo3mG0kzufQ9/QrI9uy29XUuyRG+OLjB7s6bdfQcEUh6kM2snSyuhzjXTZiEhIScO3aNbRq1Qr33HMPRo8ejZiY+uFev1lgdFIDUFdRRH7eqqOhT2jLoPM5jbNFxwCoq4nhQ8lbVp84mS/29PRr1g9fnqJ5xmfvni36PEzqAt8LqVMhiCkFvvfyw1s/xIxuMxBgDBBejyCw8OBCAECkd93pJzXzpSNM/M8FAJeKLkHjcU3AVFQTXJpPK3yrzVaQEnZ1FS8Pp9MeQ3w4g79liDe6xQpTAwlDHs4U0BSeFMlN4qNajkKwZzCa+TbDhHYTMLvHbLQMaCkwVPj6BxoNodqIebCNmFDCi+A8hRSv4JpBUjN/bH6+n+g4fp87hXRCqJc6Cu//EnQ6HRYtWgSbTV26YkMBP6/9YLZySowjvpzYTbRtZAdurIiKlWfqe/2uAJyZOxwhnlz0T8eriVEiBbFTnPOgT3Qf2XapxakCJxYA3NP6HkmnjTPwx1p+GhnzvGkIDc4WSol1Opyn+tpXyzkNkMFrBmP12dVoF+Wgk0OY4RX3Abz9rsKccxsAHSozJkNP0M/8mYIzeG77cy5/FrUIYnSAqg2CWhH1k5yneelkLjjmivWb4RH5G3wSFuGLM7TIIWdsChcPNpLCS7+I66ABoEM0bThqneRGVZq5+y7PCRsZg/VTe2PumPYY1fHm1CBMiZevEXe2RvyvoS7HeJe+SY1Gg1atWqGgoMB540aMY1eLFffXlRJ1QTm3aHXMP9V4ZMKz+Tf44sxSAMCY+DHsPjW1ITF+DcNQdZxY1YBZVPoZuInO7UgM77j7N9yPdw69g2JzsaDNmN+47/ZambwIXk3BRNMMWmFkb+rfU+Ed9xE0RjETmSvokxCCRXd3gq46zKA2stA1Rl5rBQBWT+nh/CTVz8iXE7mc48v5FSJvo2f09xxtLMFN6MxiiSAIvHjLi7ivDV2vxzdUssvzBOfiL/DksPmuzZjZ42XRdoOGe4akijbjw3wkWdo6enGkETeLQn1dYNCgQdi50/06whsBflRi33XXBOykIo0D23AR5CKrPBHMp2fnQa/VsIXpQ2KG4JGOj2Bm95nYM+NeTEyJBSBduP/d2e/Y10pj4LdnvxVE6wFgzYU1eGPvG7LHyIE/ZvOvWWmj635KLaVIL0l3eh5mkX25mEttzqnMwYbLG3DE9B70QbyiYYKE1pgHqzabjcLYKxIRVrTI5f67A0bKQKt13cCQg4bQoLm+v3AjQYG0BMOcOxzj27nClsXdG2zkuJr5s0WQJ0J9jawQ5MErztd7ziIxPRdwUS+1qVSdmgVgQq/Ym3ZcXHS3tK6RQau5aT9TTVBXY7zLbpWFCxfixRdfxCeffIIOHdRTrjaBQw0FamXhwUtHsDvmsRKM16taZ8Xg4LmSQXPf5rhadhVDYxtGWkfHEGlGEL1GL5p0GTATN9/b547Qmztg1OLrAmklaQCA6+XXBdvZCVOCYtkVfPuo0NhQG2EZ40QcLSVerFUjhyQeow1D1frLkym465O9AOgIIwOCIOFK3oaFNAPwZt+ricSUWkoRCbFn0JFW1hEECAHVLIN2LWw4VU0m8MLOF3Bv4r0YEtMwa0DqEiNGjMDMmTNx8uRJdO3aFd7e3oL9t99++w3qmTz4xqqjI6h7XBAOKuTrSy3k1DoJGJKVsQlj0TaoLeID4pEclszu50c4HCHVzhVsTNuIRf1dMwTkIjE9InrgesV1kCSJUwVCXZHe0b2xJ3OPYBvzeRw1Y65XXEd+1QlojZ3BzgC8lCitz0XYyxOg8z2LbI3QcVFXYAwCstoxUhtGjE6jw6jI5/DR2UzovOm6W8ruCcoWCEvBANzdepQLZxPfa8zvZNQTOPjKIMTN+hMAVJFU6FxY1Hjo3fsuXhyWiP/9JV8LeyMwe2RbzP9TGEUM9jZg1SPd0SzQC+ue7o0xy4T3sb2RpZIxqKsx3mUjZsKECaisrERSUhIMBgM8PYXFZIWFN6bQ6maCXmIxU2MQFpTYU6ExZoM0R2D9ses4kcnlQxPVgzozUIV6hWJ+n/lOqZPX3LYGGaUZAtHAGwmtRgsvnRfrxWOMsYHNBwr0XJxBnXiaGH2i+6hKfWDgZ1RnLLqDJ5OehFajxeQOQtYbZsJUK1oX6xcLAIgL8WbzeB0ZxgB1i6xx3Zq7TanMR5ivmFmtbXXKiHCf9LXkPF3ChZvw+7k38V7MPzBfsV93/343ekf1BiCsXakgLrKnC/IUP1MEQaesOSKtNI19vT9rP82s1zCCnvWKp556CgCwePFi0T6CIGC3u6fnUJfgR2IYOmEGzQI9cVBBI69rrHK0Ug06hHRwEG+lwTdilg5cin+z/8XolqMR7BksUItPLU4VHVsX4Keg8Z/Lz4d+Djtlx6ViMRHO4BaDRUYMYwCNbDkS7x1+j93ORnp4Tpsofw8w7g2v5itRfuFV6HxPw15NS13XcBx/astp9kifOPx4zYAiEnin7zt46iz9meeOae9qD0VbmLUBSZGqIgXtInmZDS4YMa7U7vAxokNEjY0YqXmNwfODW2PJVo6ecscLAzDg3R3K5wsXnm/xvUm4swtHJJQkQSvd2OphGNTVGO+yEbN06VK3LtQEGoFeeix7sAvGOljnavFE/3i0CvPBjDVC0S6NMQ+fXpoDz+b+qLg0CzPXOtaO0BObh477yW+Pd275euu90Ta4rdN29QnGgAG4uhCldLd2we1wpuAMwrzCWLE3C+levcjDHR4WCSgq4YE2D7h1HTVo7tccb/V+S7SdmzDlB0sfnT/KbbSR+25/WgmaHymYMTRRdIyzlIEzc4fBy+BerZEjhrQV12Adr07T1PHyr7sEDsOx8p/Y95N7t8Dq8/LMS8LCZOH3o9Zbuuf6HjgaMWW4yL6WWrDsOC/tAXaspWis7GSkBP3qzYRdmbvwUDuuVsrZOsXdhZwctqZvRWZ5Jvo268veQxqCTjmT00NxjFwnBCRIGhQ1BZ+Vi1/TSBAEdIQOJpuQhtbX4IsrJWILsHMYrSUR5hWGtkFtWWcSY8S0ifDFsevAZ+O7oktLHQb+xDuYIMHMgfWBxMBEzOs9D+ml6fj85Oe1EokBgOP5/6KIpGsA6XPaQegLkIPt2J4Ri4EtBqo8k3iMivaJRqGpkL1/Pri/M579/qjsGcYkc8yPrkRi4kN9kNTMH8evKWuu1QX45BmOmDa4Fc5kleCv0zQ7bGyIMFJQnjoDPvHvCbY5Pud3dFbOQpA6prGgrsZ4l5+siRMnKv41QRkD24QhuXkAjs1xL2Vk5og2uKurmDKY0NIedI1eZmBwiMT8V8DoFvRrRhdNS9Epvz/wfUxoNwErh69kt/EZblyBXMqaFFoFthLQgdYX1KSTMQYMQPcTEEZapBj0HG0YfgFxiI+h1gyY/q1DFT17/EmgfbTQExbuE4KOIR0R6SNdDMpPASII8aDKsNy5Cn6qnVRNTL5EMetfz4kL/a+WXRVta0LDBN9xckuEUC/C8R64v3sL0fGD24aJtrmCvZl78enxT3Eo+xB+PP8j3v33XZwpOKOYTsYHn7kPgCoD5tfbXRfCbB3YGh2C6YiRlOMszl8osFhmKcP3577HyYkncXzCcdYpwHde+Rp82deMEZNatQun3hyGoe0jJFLlyBqn17qCcO9wjEkYw5In1JYR89iWxwAAA5oPgF6jx5onu+LRwQasurAYX5/5WvV5gryEke4nkp7A3JS56BXZC8mhyQDki88f79cSLUO98UAP7p52JRLTOtwHUQHq6aAZ1ActsVLK9IPdxNko284J5TCkIlj3SKzXmlB7cPnJysjIUPxrgjLeGkMP5gFeBmx4VsgO4+vh/iKQMWLkUS3GRtSd8OCNAMPQI1fkDgAR3hF48ZYX0dy3OZYMWIJ3+7+L3tG93boew4QlBz5VqJpi1boAFwngJvK3xsrXrzHt+REOqbxlgiAwIJFjz+qdwNW2rJzc3d3uiuBsPuRPZrH+Qk2ge1vfi9WjVuPBttJFrssGLeO9E06Kay6swZb0LS71lYEU6xIf7R3Zk0BThzrCkR68MWHbtm0YPXo04uPjER8fj9GjR2Pr1q03uluyiPGLqU4tFBOO8NdbaQtHQSqDeMGdnRDuZ8STAzja8GmDWqF7rDp9p53XduKjYx9h3/V9rHPKRtpYA8rZwjk+IB7j2413iaLdFUFF9hhCw9aM8dPZGPgb/VlDZkbXGQA4kg0NoYG3nvaIl1s47Rz+Z+PXI52rZt8UPYMEyQqBMmyONREUVotY/1j8r9//ZCmkXQUTYT6RdwLP7XgOFl0qOjenxxZXmOMCHYwYO2lHQmACPhv6Geb1mQdAPvI+a2Rb/D1jAHw9uHvelUgMQRAu088zx9U1Sh2Exh/v35J9Pba78Pt9qO1DOKEimuTOZ/2voi7GeJdHpNjYWMTFxcn+NUEenZr5w5vHStM+SuilX3inNJsFgxeGtpbd985dyscyA7izAuSbDTN70HosOZW0R8QZG9jgmMEYFjvM7QgJQ50shUc6PCJYzLibslZTML/x+F70JN2/dSjCJWpMGKw+t7r6OG6wTZDJHZ46kBPaO8WruWKoNmsD/1zMV9wfHeCJDtF+6B4XhFAvrrZgYd+F8DHI5zwDQGJQIqf47eCZLTGXIKPMPUcM34PHF9hk0CxQneexvggnGho+/vhjDB8+HL6+vpg2bRqmTZsGPz8/jBw5EsuWLXN+ghuEYE/6Xvrt0m8CAd07utAL5TbVhurDvcVzY6ivEftnDcLLwzkP7/NDWuOnJ3qJ2rYPFtc88CMufCPmua7P4cj4I5jWZZpi3wmCwEu3vISvR3yN4xOOIyFALKIpgpvOcIudHgsNGmmNtJSoFLQJaoNAD/p5JimSNcY23rkRhx86jFj/WLY9fx5jjBxAWNfh2HGmRpCRC6hLxs3M8kwsO7YMG69sxPC44ejfvL/zg1SAMd6Yz0dSJGvwuTK3O46TJeYSmO3CaPHGU2J2y2mDWsn0y7VxS0oI8vSbwzC8vbSoJQDEh3rL7nOGcD96/nt6oPI9vueSkInN35Obz2ftniXY93L3l1UZKCE+8nNvY0JdjfEuGzFHjx7FkSNH2L8DBw5g+fLlaN26NdasWeN2RxoDPJzkQUt563a/PBDv35eM25Oi8EgfzivgOGYwlI5yYFJn/guRmNk9ZiPUMxS/3v4rW9h/IOsAgLo3HMK96Qmwb3RfwfYXur2A57o+x6rH30gwE/nANiH49akUfDq+q6iYkEkhA2jD7HDOYYE3TS41jD9opxVUSrZRgqNInxScpQ1oNATWP90HPz7WEzaKI2jQElqUWkpRUFUgyrMXHF+9EJg9kq77mTmiTfV16cVAu+B2Tvv45u3CRaWO4LzBjmxxAJcCN74nvXD64P7OkudtrDUxb7/9NpYsWYLvv/8ezz77LJ599lmsXr0aS5Yswdtvv32juyeJzPJMrE9dD4BOxXpy65PsvoGJYdj8fD/8+hQdqWkZKm1cy3mXR8aNZF+HeYZJRvf4tS+M88RG2tj3rqQOawh1tK+/X/5d9TkZVFor2foVuflnZveZWHPbGgxoPoDd1u3bbnj7wNsI8AgQRdiZqEO8fzy6hHORJMYI8jH44Onkp7kDCJJNqR6bMBZHxh/BkgFLXP4sapFdkY3lx5fjh3M/1Op5me+BofWnQLEkNa7M7YsHLMY3I75h3/904Se8uvtVpJWkIaOUduQMbCPWrHp/20XRNsC1SAwAnM0qFW3zNuoUtchcicT8PUNoNC4d1xn7Zw3CMAUjCRBnAbQK46Ll/BRRxhB+UkJ82RGMAdXYUVdjvMtGTFJSkuCvW7dumDJlCt5991188MEHbnfkv4RH+0hHpKSs9sf60YaJr1En6c1oFuiFMcnR+OD+zoKitMOvcjU1796T5OCFES+ErMXdUHZuHt4b8J5o382G+9rch233bENCIOdVeSrpKbQPbo/5fZTZpWoKxmjqE90H49uNZ7e3CqCNAj4Jwr2t763TvshhyYAl+PPOP9E98hZ0bhEID70WJqtwEVRiEobBJ22ahENpRU7PrePNMqM7ReKfFwfiyGuu13e9OExMHOAKNBoCBEEIFMk1hAaL/12MAT8NwKozqySPm7tvLvKq6CL7UR0SkLZwFJ6onoiYhWKH4A4Y3GKw6Fi+cOnElFgsGccpaPtpuYLOOXvniI5lnsi5Y9rj4CuDcHuSOFrD70NjQ3FxMYYPHy7aPnToUJSU1H8BsBqcKzinuL91uK9iIbESmJQegKb1NtnFRjlzrzAF8oD7rIuAPBlGTcEv7HeWjsZPE7OQFtax4Ahmwf5guwcxoNkA7lrVxpyfwQ9PJD3Bjtd0FIarC9Vr9Kr00dwF8znSStOw+PBifHvm21o5r2PaIkVR7H3gapZFclgypnScwr5PL03Hbb/dhsmbaLbLYG/1i++CitpxHtaGUPLtSVFoGeojWG/ptQQi/MW/96uj6Pn62VvptcRYh8L8wW3DMHtkW6ye0oNl8QTozI9+P/TDkHYcAU33OOk00HtvaY5Rnbi5w9vNMeFmR12N8bU2aiUmJuLQoUO1dbqbGq+Olvbk6iS80NOHtMZbY9pj43N9XcqdDPI2YNkDXfDMrQm4q0u0gxdGaiGkASgdjNr/hlfA0SsT6ROJH0b/oIpxrSbgezxfuuUl3NXqLvSI7MGG5/mU1a/0eKVO+yKHKJ8oNPdtDk8dl8LIT7d5AABaYElEQVS05aywANFd/Rr+GkSrIdAi2EuSBEAOzMT4UA/XUzl2vDBAtO3TE5+yr2fsnMFSFst5of/N+Zd97Zifz8/Dl/L6OUZJckt56Re81DSpAmnGi0cQBML8pBdPI2JHIDGwZsbdzYrbb78dv/4qLhpft24dRo8efQN65BxSUTMpUgcGjLH8zK3O07bOF3JUsia7ScDW9XYf2mvJppNBmE72ybFPMGrtKJcXzo41NB/d+hH7+paIW5AYmChw3KgFf+HNH5Ok4BhNkHuOX+35KjbeuREj40ayKX2AmBiDPR9BgrQGoKV/S9UaaTUBPy30q1NfsRG7moJvEALCdDJXamIYDI0diha+dIE+szYgq9cPf57MErUPlhnrfz0qL87qCrxrgRzm3m50GvXiezknkxzxwKN9W2LfrFvx/BA6Vd8xnZcgCEzp1xIp8SGie7HIXCSYJ+SME6NOi2UPcNHCxih0CdTdGO/yHVNaKgwDUhSFrKwsvPHGG2jVSjpfsgk0pIwUD70W43vFAgAu5paL9ithVKdI1sIXTECEHaAap7Vf1yioonNmlx5ZigntJ+CNlDew+PBiPLH1CUxqP0mgp9OQ6o9u6xSJDSd4kxIBUcAuPtRbMleZD50bhb0A8MNjPfHuX+dZggGtg0GvJGR2/PWhKKm0okWwWPHeEVW2KgBijyUDue0AtyjclbkLmeXiSTm7gs4RZ5iWbLy0NyOhXIsTE+w8n9tVEcH/Etq1a4f58+djx44d6NWLrgnZv38/9uzZgxkzZgii/M8+++yN6qYAUkaMnbLLLiZnjmiDR/vGqcqRX3p4qey+1oGtBdfXEBoYdfQ5TXYTCkwFyCjLQKlFnLKjBEcHV3wAlyrzaIdHkRKd4tL5+His02PIrsiWFStm4DhmyhkxoV6h2HhlIwpNhWgbxEW/GedMibkE2zK2YWrnqRgZNxJmqxYajEegCw6XmsDRGHM0PtyFiEACJJdO5sJ88+LOF7EpbRO6hXdDuHc4Msoy2HuIGQe7xgTim/1CcprxvdQ7n+7v3hw94oLx3I/HRPuGtgvH5jM5ou3PD2mNczlleIjHfOYqmHUW/zlTSneL9OcMayUJP2epvmqjSI1VJ6auxniXjZiAgACRJUlRFJo3b44ffqjd/M//GpzpbOjdXCACDhSfhF20QNX5H4J32C4sOXwBz3d93u3rNHaUWOiwp5W04uV/XsafV/5k92kJLf7O+PtGdY3FB0c+wPG845jYfiJLPc0fqAHptJM2EX5OjRj+IO+KP6lny2D8/CS3CHKcVK4XV8ke6++pFxRYCuBwnzszYi4UcWJmJeYSAcED49GUMmAY3NnqToxLHEe35xkxYYZ2gMRH+OnxXvj9+HU8N7jJwaOEL7/8EoGBgThz5gzOnOEYAAMCAvDll1+y7wmCaDhGjMRixFkditoiX+ZeBGijma9o/9Xpr7Cw70JBOlmQRxDCvcLhofVQTbHsCEdh3jJLGeL843Cl5EqNF+HPdH5GVTvH51ZpYb7q9CqcKjglYBxkoiy5lbl4fe/rCPIIwr2J98KnfmwXFo7fvZLzxBVIpZPpNDr4G/0FBAfOwBTx8yPTDOU2c1/HSDiN+rYKEW0DgO6xQTiYxkX3h7QLx4I7O+G9zdKOKT7F8scPdkHvePq8Ef4eWPe0e8yhjuA7jdXaDVqFNZhclLVvqxDsupiPiSmxqq7RWI2YuhrjXTZitm/fLniv0WgQGhqKhIQE6HQ1DwX+l3EoTTmFp0dLddSaUvDR+6CFbwtcyTdBqiZGo60EqctFfpUy81MTlMEPN/MNGIBmyXmg7QP45eIv9d0tAS4WX8TB7IMYHsfln7ZUwezyxu3tYdBpJDUtGPAH+c4t3FcddzRiiivd0+1xBFPQL7domNR+ElaeXgmATvvqGt6V3aemHuVQ9iG8mfImfQ2eRaeRMem6xwXJ5ko74ljuMYR4hrACro0JV64oyNs3UDh6ZmtLCwTg7sX3+r+Hg9kHBUbMhssbsLDvQgR7BCPGLwb+Rn882vFR1lB4Y+8bbvXHMc3q3j/uZemIpajr6wIaQoN1Y9dhzG9jAMinSK04tYL9TrSEFu2D2+N0wWnW+GO+vypbFTJKM9DCrwXKLeXQEBp46Dxq9beS+xx81JYR882IbzBwzUDWCUWBwr2J9+LeRNfqL6WMbcd0Mil+FblUqIMOa5vPxtPj6uP94/Hh3+L02n/TufYjOkTUSopVpL8HskpM6NiMdkyRvA+QVVKFpOYBTs+hFLGRi8R8NekW5JSZEe1E+0arIWAnKSRG1H06Y0NEXY3xLj/J/fv3F/z17dsXbdq0qVMDJi0tDY888gji4uLg6emJ+Ph4vP7667BYlIvJBgwYAIIgBH9PPPFEnfXTGUpNykWXeodY5k+Pi6k25UAQBDbcuQEjAxcDpETazX9U7LK+ocQAk1uZi9aBrbHj3h04Ol5e6biuwa/bYeDroXfKDObtYceSccmKi27+ID+oBmJ9jqmVjpOgu2AiMXL3+XNdnmNfOxotvaN745nOzyhSzV4tu4qfztNS4HzF6txyeTY0tRi/cTy+PVs7BcBNqHs4Lmpqk5SBLdYmtLK02891fQ5/3PEH7ml9j2D79qu0o9HVhboU7TwjClxbi3A1aOnfEnck3AFAvoYmvZRLc9JqtGzEhhnz+EbMqF9HYde1XZiwaQJ6rO7BMlnWJRy/+/1Z+2vlvAEeAfDU0t/Je/3fQ8/Inm6dR9GIYSN84uOcZZMwYIwSH6P0OHwqs1TUtiZIDPfFzhcH4vSbw9hr8p9Ox7WVHNzRdNFpNU4NGAD445k+uKtLMyx7QJqZsgnuwS13RGpqKp555hkMHjwYgwcPxrPPPovU1NTa7huLc+fOgSRJfPrppzh9+jSWLFmC5cuX45VXnBdOT5kyBVlZWezfokX1k3f+7SM93DouisegodaDC9Be3If/ehj2wLWifQ/2aIFnB9H5zf8FiuUbickdaOaWeH8xtWJqMf0MBHsG31BjkV/ky0coL5Xl1ua3io57a/9bTs/NL5CsiYiX48SVVyZWtFcDx6gFQz0qt6DUarTsYo3fJq8yD90juuOxTo/hzlZ3Kl6T+Z489NyzdMw+1+W+S6GxspPdlJBwzFZYnYkOqwPjDScIQtEY4ae4UBQFk83E1oUUmZyzDfIxqMUg0bbcqlwAtfe51OB0wWn8eokuAJYjouHPY1pCy0Zs+PopfFhJa70aZEatUZ3ujhtghJ3bh7SHr0EsmKsGUt+Bl552flZZq2RZ4WqDPawuYNRrYNBpBDp8/KwtnUojRunzvdPvHUxuP9ntPraN9MN79yahWaDz2s4mqIfLRsxff/2Fdu3a4eDBg+jUqRM6deqEAwcOoH379tiyxT21a2cYPnw4vvrqKwwdOhQtW7bE7bffjhdeeAFr14oX7I7w8vJCREQE++fnVz+hvD4SuaP3dnOeJhKpwqKXQqGpEIeyD+F88TE4spNpNQQIjXt50k0Qghn8mQGfj0c7Plrf3ZEE00dmsmPANxyYWhk+/rj8h9Nz8yMxar1ydQk+MxEfTBG+FBhaZsZj/c+1f3DrmlsxcxctnHpf4n2qrl0Xn16J3ao2sWzZMsTGxsLDwwM9evTAwYMHZdt+/vnn6Nu3LwIDAxEYGIjBgweL2k+aNEkU9Zai0/wvQavRwksnHAfcoThee3Et5uyZIxDLJEkuEiM3Zn924jOM3zgexaZibLqyCZ2/6YxbvuNqI5mopFpsTRcqZ38y+BO80O0F3B5/uyDtsq5xLPcY+7pXlHQ2Av870Wl07HtGN8rRiNESWnY8rI/UuFj/WKwcvrLWzzt//3yWbpsx8D478Rl6fNcD/zv0P9XncTRiPHWeGJswFgD9HT7818OSNboNYMiXxIlrYopefqRU7VzloZdfHwV5BCHSJ1J2f0NDYxnjXV7Rzpw5E88//zwOHDiAxYsXY/HixThw4ACee+45vPzyy3XRR0mUlJQgKMh5pOK7775DSEgIOnTogFmzZqGy0nWBvtqCoxqsFFwVjWLADNpXKy5DY8wV7Fu1L531rDSlk9UMzCQp9T06FsbeKEilkwFCemRnyvZy4HuqaosqcnQn9yeGnEohw82I2BEYETtCFRVsXiWtF/P5ic8BABuvbMSaC2twqfgShsUOY9vJKXvXxYReH2KXP/74I6ZPn47XX38dR44cQVJSEoYNG4bc3FzJ9jt27MD999+P7du3Y9++fWjevDmGDh2KzEwhAcLw4cMFUe/vv/++zj/LjcSQmCFYMlAomOiqEVpkKsLre1/Hr5d+xezds9ntTG0Hs1iQwodHP8TxvOP44fwPMGgNAjIAQJ1oKx9PJT/Fvj458ST6RPfBxPYTMb/P/Hp1fjGL8zDPMIEoLx/88VdLaNkC9exy2nnh+F2Y7CZY7HT6eX3V9/yeygmD8jVGaoJr5dcAAFHeUXhz75s4lH0I/1z7B5W2SmSUZag+j+P81SOyB9oGtcX8PvPRNqgtCk2FaB8lns/cib7f2sb9tOOagJ+GqXasfqxfS7QM9ca0QdL3Hf+ccvNCQ0BjGuNdHpnOnj2LRx55RLT94YcfFjAO1CUuXbqEDz/8EI8//rhiuwceeADffvsttm/fjlmzZuGbb77BQw89pHiM2WxGaWkp+1dWVnsK7JkKDEwM1OZuOoJhGwEAEMLFa1LzAHZQb0onqxkY9rGjueKaF0fdkRsFuXQyvjfqVP4puAMB40stLbhrUlszNXmq4P3GtI3YlLZJlUecUfcuMHHOhbn75uKfa/8I2vHz7/mQW1wyxdDuoD7SyRYvXowpU6Zg8uTJaNeuHZYvXw4vLy+sWLFCsv13332Hp556CsnJyWjTpg2++OILkCSJbdu2CdoZjUZB1Dsw0H3ih7pEbY3x5ZZyvLJLmNLsuHh2ho+OclosfO0m5tnSElrWQ+4IxkhpF9xOMjLcJayLaJsS/I3+ODnxJE5OPOnScbUNxsjIrcplKe0dwY8am+1mdnHJfG+Ov4PZbmYjMfWRTkZSpIAt7MVbXqyV8zJ9v15xHTuu7UB2RTaO5x0HAOy4ukP1eRyNGDtph1ajRYW1AmcLzyKtNE1SW8WddLIWQTcmfcqgc30tFeBlwN8zBrC6MXzM3z8f8w9wYtoL+y6sUf/qEo1pjHf5Vw4NDcWxY8dE248dO4awMNcWIzNnzhSFpxz/zp0TqiJnZmZi+PDhuOeeezBlyhSZM9N47LHHMGzYMHTs2BEPPvggVq1ahV9//VWxfmfBggXw9/dn/9q1c82bVVNICWKqAeNlAkBTLPPQs2UQG4lpSNolNyMmtZ+EYI9gvNrjVcH2h9o+1GCERLdl0AOPo8BaFx6b2JoLa0THvdDtBafn5s9rDYEpslt4N/b1Xa3uAkAvZKSKlBlMaj8J8f7x6BVJp6qIBPI0WlYAzh2sHrXa7WPdNQzLysoEC3OzWbrGyGKx4PDhwxg8eDC7TaPRYPDgwdi3b5+qa1VWVsJqtYoi4Tt27EBYWBgSExPx5JNPoqDAeeT5RqC2xvgZO2cIDGDA9d+vyCxdt/L18K+x+77d6B7RHaGeoaL9FEXhTAHtNNRr9PDWCdkHU6JS0DKgpUt9aSjgGxk/X/hZsg2TEspg13278OmQTzGp/SQA9Pfjo+eizXbSzkViNHUficksy8ScvXPY99fLr9fKeR0NMMaAcRXDY4cL2Oh2Ze7C5ZLLePvA2+w2qboYd3ysvRPEqfWxKjS/ACCnIgffnPkGZRbXHQ1dY7j5rjaC5gw9f2JgIt7t/y46hHSohbOqR9MYLw2Xc4umTJmCxx57DJcvX0ZKCq37sGfPHrzzzjuYPn26S+eaMWMGJk2apNimZUtuIL5+/ToGDhyIlJQUfPbZZ652HT160MX2ly5dQny8uDAbAGbNmiX4HJmZmfVqyPh5uOcl4kdiCIgHn97RveFj8EFyWLK7XWsCgMSgRGy/dzsIgsC8A/PY7d+e/RYvd6+/dEoljE0Yiy9OfoGRcSMF21+/vT2iAz0xJjkKT//jLyrWZUT0lMD3ztWU7/7ebs1w8EohhrWPAMBNxs0C1deF6bV6DI8djk1pmwTU1kqpbjO6zcCMbjPY92Pix2Bd6jr2vYbQ4NGOj+Lzk5/LnkMpYrL24lqX66Na+LZARlmG2zUxjmPU66+/jjfeeEPULj8/H3a7HeHh4YLt4eHhIoeRHF5++WVERUUJJsnhw4fjzjvvRFxcHFJTU/HKK69gxIgR2LdvH7RaacfJwIED3UpJnDRpEiZMmODycQxqa4zfe32vaJtcQbQc5JjH+JGVK/lialK+sbQ7czfL5gUAIZ4hCDAGuNSPhgSGJh2QT/1KCk1iF/Bdw7tCQ2iQEsXpULUJaoNfx9ApegezD8JG2WAhaSOmtoQnleB4X7ui4aIE5jMw+PH8j+xrxomjBslhydhz/x6svbgWr+99HQBwNEeYXeBYUwm4l0I8WCLS/tSABLz0ywmnxz66+VGklabhVP4pvNPvHYfzhmPrWTqdOMxX7EAUpL7VghXDPHMPtn0QZwvO4kLRBdX6R7WBpjFeGi4bMa+99hp8fX3x3nvvYdasWQCAqKgovPHGGy6LkIWGhiI0VOxlkkJmZiYGDhyIrl274quvvoLGDWFIJoIUGSmfg280GmE0cg9Eaalrqsc1xexRbXElvwLje7qWbylMJxNOpFUWO3pF9ZItkmyCa2AeypMTT2LQmkHIraTzTM12c4OIxkzrMg1TOk4RpZj4e+oxY2giACDcOxzXK4TeQTX3h7eBGzKCvWv2WRfdnQSKokSD3NJxyarP8eHRD90qpuaDYTRjoIEGXnovPNj2QXx39jvJYzSEBgQhvWD95sw3qo2Y70d9j2tl13Cl5Ao+Pv6x2+lkZ86cQXQ0l8bGH8NqEwsXLsQPP/yAHTt2wMODY1K87z6ODKFjx47o1KkT4uPjsWPHDgwaJGa9AuDUgSWHpKQkt45jUFtjvJ/BD6UW4bGu/n6OtSZWuxV6rR6FpkJ46bzgofNgIy4AMDRmKAiCEBi7bYPbsmrrnjpPbL9XqOV2s4Gf6vRQO+n07y+HfYl91/ehd1RvaAgNTuWfQlppGloHtkbrwNZYl7oOCw9y6T520s6OE/WRTuaYtj08tnYKoKV0c05OPOmy8QwAB7MOsgYMIDa0HA0mQF2BvGOUhT++Mw6qu7s2Q4CXHslOtFvSStMA0JpsjkbMFxO7IXbmBgBAkLdydK11uHssbnwwz9xf6X9hT+YeAOpFXGsDTWO8NFw2YgiCwPPPP4/nn3+ezSX29a35DaKEzMxMDBgwADExMXj33XeRl5fH7ouIiGDbDBo0CKtWrUL37t2RmpqK1atXY+TIkQgODsaJEyfw/PPPo1+/fujUqVOd9rcmCPfzwO/P9HH5uEobj7DAYXG1al865o6p39BnY8DW9K2sAdPQIJUjz4eUx3/27tmY32e+RGsOWg2B028OA0lRbuUcO4KZ4G6JDcShNDq1plusemrx3Zm7BYs8AE6pTXMqcvDThZ8wNn4smvs1R5iX0FPILCyfSn6KNWLi/eORWiJMQ5ULmvBrG5yhQ0gHdAjpgHJLOe5odYesLoYz+Pr6qmJeDAkJgVarRU6OkBAhJyeHHUvl8O6772LhwoXYunWr0zG0ZcuWCAkJwaVLl2QnuIkTJzrtb0OGlIeRhGtGzPh247EpbRP7vsRSAn+jP/r/2B8AsPHOjdiaQbOGRftE470B7wEA/s3mlNZHxY1i09qqbFVsfcPNCj7joJzBYdQaMaD5APb9Lxd/wc8XfsbU5KloHdiaTR2L9onGs52fRbvgdvh0yKew2q0iUc+6gEjsspaiP3LEPK7+3kdzj+KRzcLa5ua+zXF/m/vx/Tm6WJuhpOZDTU3MT0/IO8PaRdLfvUZDYGh79TWkQR7uCYH/++pgVJrtCPGp+YKfebYZA6a+0TTGS8PlVUhVVRXL8OXr64vCwkIsXboUmzdvrvXOMdiyZQsuXbqEbdu2oVmzZoiMjGT/GFitVpw/f57tm8FgwNatWzF06FC0adMGM2bMwF133YXff/9d7jI3NT4+9jH7OthHPKAdyj6EtRfX4nzh+frs1n8az+94XvC+oZAm7Lu+D28feFvAjuOIcmu5aJujMSAHb6MOvm6mPcphVEf6Wfbkaa+QFIlZu2Zh5amVssdJ9dlZKs2ItSPw2YnPMPJXOt1OUE8GbkHgZ/DDuMRxGJc4TmTA1DT64wgfgw8ivCMUa3lqAwaDAV27dhUUbDIFnL16yS8+Fi1ahLfeegubNm1Ct27dZNsxuHbtGgoKChSj3jc7+GlPDFxdILcPbi86JyOmCgC+Bl8czjkMAMgs55iCsiqyANCsUgRBCCLAyd8kY9Gh+tFDqwsEGAMQ7RPN1qypAWPsMNGDs4VnAQCBxkCMbDkSsf6xSIlKQf/m/eslnayujEip87qTgsrcU3y08GuBl2/hUqKl0snkEmCGtedSl8J8PaQbuYEJ7eiUIiY1+ticIXj21gTsnSnUOZNLWQrxMaKFyvobZ5Ay6hoiGtsY73IkZsyYMbjzzjvxxBNPoLi4GN27d4fBYEB+fj4WL16MJ598stY7OWnSJKdhqdjYWMHD3Lx5c+zcubPW++IKfn6iF05mluDN3+uHtY2FRJrLukvrsC51HZ7v+jwSgxLrtz+NBA3FiDlXeA7fn/seFfEVuC3+Nsk2l4ovAaAZ1RhNFWbbjcBDPWMQ7GPELbwozP6s/fjj8h/4A39gUodJ+PT4p/jo2Ec48MABxUgTk1ojB8fJ2dGg4+fhv9qTJnDg554DNDWur17eO1hmKVMlRPfU1qdQZavCgr4L6o3dbvr06Zg4cSK6deuG7t27Y+nSpaioqMDkybSQ24QJExAdHY0FCxYAAN555x3MmTMHq1evRmxsLLKz6fvFx8cHPj4+KC8vx5tvvom77roLERERSE1NxUsvvYSEhAQMGzZMth83OwQpvKC1l1wVH9ySLtRWs9gtgvvA3+iPOP84XCmh62JO5p0EBQrz9tP1eIz6vKNezc1Mpd8uuB023bXJeUMeHLWxNqfRTtVTBTQLo8lmwv6s/TBoDEiJTpE+SS2Cn/Y1N6V2hHABoEdED2y4vEGw7aE/H0KBqQCL+i1Cp1B1WSb8SNHTyU9jYPOB7L0b4R0Bq90KkiLRLtIPZ7JKecdJGwtv39ERhRUW3NutueJ1XTW3Vp1ZBYCeCwCaPWz6UPH6pQa6y6rByCvcDGhMY7zLkZgjR46gb9++AICff/4ZERERSE9Px6pVq/DBBx/UegdvZnSLDcLk3nF1fh3HfFidVXzNJorlukdt6abUFMxvLEf3yl/EP9DmgXrpkzPotBrclhSFCH/Oi1dlFVKSf/T/9s48vqkq/f+fJE3SfaNLurcsbaEUCjgtRRaBStlBRFAYUAYRFXQU0K+OIIzzU5BhGBkHERwFUbSyq4AsVhaFUrYCRbBQoBRKF6AtXeiW5Pz+SO9NbpY2LUnTtM+bV18k955777kn9z7nPOd5zvOc1YSjnbHPMGuyn5PWJUw/oEFDqNQqA/cvR0njM4lKtRKOulajOuHgdfz34826/pmiMzhVeAo/Xv0R03+ajjVn15h13MMwefJkrFixAu+++y7i4uJw9uxZ7N27l18Impubi/z8fL78mjVrUFtbi4kTJwqs4CtWrAAASCQSnD9/HmPHjkVkZCRmzpyJPn364Ndff7Wa33ZrZHaPhkP+G+ONI8LQu7XqWt6qwgXaEOt001P2TMHUPVOF7sPQzNAHu2qTKbfEuo/WBDfxwM2Wc7LPU+6Jj05/hL05e/HKL6/g1YNNW7fbXHQtJqM7jrbYeQcEDzDYdv7ueeRV5DVpPZbuMxXtHS2Y2Dww8QAOTT6EQNdAbH4xEc/1C9ceZ6KP6+Aqx5YX++GpxpQYM7QYxhhGbh+J5K3awXFjiVubE/q5qbRUImJL0J5kfJOnax48eMCvgdm/fz8mTJgAsViMvn374sYN4/kUCOuiPyNYKz8HIF6wjZSY9gPXgZpyeaqs1UYlezr6aaw8vbJF6mUpOBcy3ehqozuNxhcXvsD4zuMxttNYs88lFokNcv4Ys17tGLsDT/ygjQBlsOhVJBxAmLtWiht43Km6g4yiDAS4tIxpfu7cuZg7d67RfYcOHRJ8z8nJafBcTk5O2Ldvn4VqZr8sTV+KT5I+4fMPmeJKyRW88ssreKmnoddCraoWNUqNPOesK7G+sQbujMbQtSC2RBjh1oS+JYajtKYUn1/4nJcJLaXc6fazSqaEFJa57rbL20zua0pCUt0Jt4aeFVe5A3qHeWHDsRzNNZq5DHLe45H47Mg1vDUiutGySqY0CHufV5EHpVpp0sLY3NQUTWFUx1HYcWVHk5KK2pL2IuOb/Eh27twZO3fuxM2bN7Fv3z4MGzYMAFBUVGTWoiPC8ugLbi9XwxkZyhNjefY8sQc+ToYx8G0N58pgKmINN6slFUubvZC8JTDVKc+I0VhidHMHxPrE4rGQxxDt3XgnqYt+ng9TdPYSBgs4fvu44LtI1LyoYhzcoMeeZvssSW6u8fDSjDHk5rbOQYN+UtML9y7gTtUdE6W1LDy6EHkVeVh4dKHBvhpVDTzkHugX2I/PQzGvzzzM6D4DW8cYz5nCoTtAb29y3pQSw3Gr/JagnLWRS+RYPXQ11iatteg19SMp6tIUJUZXyWpsjZCuq1ZzLR6vDu2Cs4uHobOfa6NlTfVb+i69usgtEGSmMZ6PfR7v9H3H6tdpq1hLxjf5l3/33XexYMEChIeHIyEhgV8otH//fvTq1avZFSGaj1KtxPKBy9HBURPVZVRsEMbHBQrKkCXG8oS4h+DTpE9tXQ0DuNkqUz68r/yiCQvZ1OziLU1v/97o1qEbevj2ECSvezJSkw9Bd1F/UlgS/uT/J6zOWI2l6UsbPO/xKVoFpKymDB8P+dis+uiG06xUCnPsdKpaIfiuH/HMFJxQ5wYgTY1u1VaIiIgQRJ3kKC4uRkSE9V1ym0OIm6HrjDkuPfqBJHSpUlbhEcUjWPv4Wj7vlJejF+b1mddo1D3dwXJDM/ZtEX13Mn3OFJ0B0IKWGLEEA4MHol9QP4uuT+KsfPproICmuTMLLDF6uXie+vEp9P6qN84UatpMN5eROSGWTSExc+GKqXfoaqlpa2RCRAeT+yxJe3PTtCTWkvFNVmImTpyI3NxcnDp1Cnv3ahffDR06FP/+97+bXRGi+WQVZ+HNI2/ys8oyqRofPS1UKEmJsQ5R3lHY88QewcDY1nCzsKZmtLJKNBHqmpuTpKXwkHvgu9HfYdPITRCJRKhWaaJBcWsG/nrwr4LyNaoalNeVG7hX6qObD2HXtV2CUK2AJrqbMV7o8QL/uaJWGAzgvuwnuEm162KM5XMwBpdAjVNi2qslxli+IACoqKgQ5CpoTRhz1zTnnWposFmlrEKtqhbHbh8ziH7W2Ey77ox6L7/2NaHoIHZo8J0LdNFM6jk6tM5nyVw4y/ljIY8ZBAIRN2E4p/ss6QejUKqVqFPX8VYtQc7IFlj2aWpyzVgOtp/nDcTCUV3x8mDjycstyfH84zhy6wj/vSnJRQnryfhmTREoFAqDeNPx8fEmShMcXcwwpTYH/UWe3GBPF25A2xSTM2EeIe4NL2ZsaTLvZAIAfs371azyf+39V6w6swp/7/d3a1broTkx5QTu1943OgtZVlvGh1VtyozkZ5mf4dXewsW+hQ8KTZQGhoYORWpuqsDSIpYX4J5sF6AzCayfSNQU+pYY1uT4PfbNvHnzAGh+s0WLFsHZWfvbqlQqpKenIy4uzka1axhji43NSTp4peSKwbYRESPw935/h6PEEW8ceQP7cvahj38fbBi+gS9j7Ln+ZKg2tD43S9zZs7PZyVbbClOip2Bq16kANO/U4JDBUDEVfJ18se3KNrjL3XG78raB1cHe4KwijDE+qiRHU/p2D5kHwt3D8WjQo+jo0VGwT981T/ex07XKNBc1UzdYV1OTUF9f+pq3TnJ09nNDZz/r5inkeP/4+3zyTQCY28v4ehNCiLVlvP3GYbRDdKMZWRL9zpRbGMrRxc8Vjg6OcJG62L0QJxqnqb/x87HPY9WZVVh8bDESAxIR4No6cnucyD+Bmftnws/JDweeOoA159bgs8zPMCV6Ct5OeFtQdve13Xy42qZYG1/s+SK+vvi1YNu68+swvvN4o+Vf7f0q/tL9L4L1EGJZ4+sgTMG5j7XXNTEZGZqgCowxZGZmQibTWZwuk6Fnz55YsGCBrarXIMaUmO3Z29HZqzMYY40mnHWUOCLYLRjZpdl4orM20em+HM0CWmO5PMQisSZ/UvzbkIgkgmhVnCVmRvcZ6OjZ0eDYtoyugicSifCfIZpIqavOrAKgHRjbuzsQtybmp5yfDPY1ZfJmZMeRGNnReBRH7jl66eeXcHbaWeTf15kUfUgd5s6DOxiyRZPjJfPZTIP9JdUlKKw0PYlkS/QnmCpqK9DBsUOriUraWrG2jCclpg1goMTozWRM6B2Mlx6zrwhURPP5k+JP+PrS10b36c/e6XPw5kFM6do6wi5zGaWLqopQVlPGu4J888c3BtaTjb9v5D+bM1uYMioFx/OPY0rXKYjfJLQiL0lcYvI4/VlLAJD5/Mx/Xj5wOd488maj1+eYFDkJKqbiXdxau4ufpTl48CAAYMaMGVi1apVdBYdZnLgY5bXl+OriV0gv0ORrKags4J+nM38+Y3TRtLejN4qri7Fu2DqsOLkCTg5O/LoJXSXWWKQ6McRQQ42ksCSDdVdToqfg8dDH0dO3p8Xu0d7hZAG3Dsme8+cAxpMUA8DrfV5vcpCZ2xUaa7Gvs69AueOCIADAhyc/xKU/jGdjbw7v/KZdGF9cXQxvR22urYraCgz8bqDJYzmXQFuhP8E0ZucYnJl2BlKRfSvG1sbaMp58i1oQa7mK6CsxxtzJiPaDv4u/yX3GsozrsvREw4viWwr9DuNA7gF8nKFdgD/9p+n852ndpgktI2a4VcT4xGBm7EzcfXCX3/ZU5FNY8MgCxAc0zTVWXaPtXLm1CObO+L6d8DYW9l2IPv59MLXrVAwKGdSka7cV1q9fD3d3d2RnZ2Pfvn2oqtLItNZsmert3xuDQgbhs2GfYcEjmpnE3/J+4/cbiyRVUl3C5yXyc/bDplGbcGLqCWTezUTsl7HosVGbrNBYmG4uWMfYnWNx7s45wb6BwQMhd5Dj0M1DBiFq2zoHcw9i6OaheO3ga7hfcx9zU+fitYOvIcYnBoDG6nVy6kn8b9j/bFxT6/CX7n8RKASNoVKrMG7nOCRvSzZ4VnSDI8Qr4vWsXA9Xz8Ghg/nPXnJhKPLGwojfrryNkwUnH64CD0F7c/W1NNaS8aTEtAE431VXqSt6+PZAqFuoYD+9fO2L+zX3+c/6i4/110/po7/I3Vbo11s/ohOXwRzQzEIOCx/GfzdHidl9bTeWHFuCzZc389uGhA7BszHPNnrc/EPz8X329/y26vwJCK6Zgx/G/8DP9JoK9WqKOL84vBX/FiZ0mdCk49oKxcXFGDp0KCIjIzFy5Eg+EdvMmTMxf/58G9euYUQikdH3xpibyUdnPuI/D982nP9cWl1qULahfEeVdZU4W3TWYPumi5uw4tQKwfvRHqhT16Goqggl1SWoVlbj8K3DOHzzMPIrNM/R1ftX4ejg2KiLX2snpkOMRc6z69oufrJTPyBArG8s/znKK0q4sP8hr8vlpBkUPMjg/fCWa5Wwk1NPGo3Gt/HiRoNtLYVRKzkNrczGWjKelJg2APdyJYcnY9PITXg57mWDMq+kvoJHv30UqTdSW7p6RAvTw0c7m6s/+NfNrTK9m9aaMTlqMgCgi2cXK9fOPPQV78sllwXfdz2xi/8shhjxCq31xBwlJqMoA9uubBOEaTZnRii7NBv7b+zHH8V/6FRWCjd1T0R4RAhc2cw535FbR3Dk1pEGw+62B1577TVIpVLk5uYKFn5OnjxZEAWzNbE0fSneP/4+iquLjS7WN/b73ygznhBa103ITeqG+X3mY/4jDXfs+oryr7d+xYV7FwC0vyiUumHl+UicbTBXTlfvrgAMrRgpf6QIZHtj6MpI/WfFUaJx213UdxFC3EMeKqyyPtwawMO3DuNelTBHl4+z1h3uaN5RZJdmAwDGdNQmHz5085DF6mIJaILYfKwl4+3bQZQAoFViGlpgVlFXgbLaMpO5Q4i2g64fvv7sETfwcZG68C4w3Heg9bgi6q9rSc0VKt/n75znP4tFYoR7hGPTyE3IKskySxHjgh88qNNapvr492n8uPqZRFNKh+7ggIE1uj5nTuocAEDK6BQUVhbC29EbcX5xjdajrbF//37s27cPwcHBgu1dunTBjRvGB/62ZuvlrahV12JG9xkGsrdbh25G1ygkKBIEC/af3vU08iryBG6eQ0KH4LnuzzV6ff21j7/c/IX/3BYH8A3BKTEqtUqb2FkkQaRXJF9mzI4xSAxMxN8S/maTOloCTgkQiUSY3m06b5l4P/19PBr0qEG4ZFPoPq9isXDSZ2rXqRgSOgQ9fDWTYQoPR6PHNYdYH62Vp/BBITo4afO76MrKbVe0eY6mdZuGH6/9+FDXtQRGEzWSEmM21pLxpMS0INZy734+9nnM6D4Ddao6FFYWwkHsIBAOgHYwSyGW2z5ikRjODs4Qi8QGMfe55yDcPVzQIc2KnYUZMTNajbuF/oJo/Yh7nCVELBLz99HDtwff8TYGp4xw7nWdPDqZde+c8qM7gHRwP4tr8qNYdSYZz3bTuqMxxsz2vzhVcAorTq3Ao4GP4tPHW18CVWtTWVkpmJ3jKC4uhlxumB+iNcBNCEnFUoFLzlvxb6Grd1ejAz5960lJdQlKa0oFYcP7BvY16/oX7l4QfNddh9XuLDH1OWKUaqUgJ9qx28f4MjllOYjwaJ2JU80l534OAM2i+PmPzMeM7jMweLNmnUlT+nbd51XfnUwNNRYeXQgnByf8MP4HjO0ZiE3pmozqD2uTifaORoBLAPIr8w0m2HStkbrpAbgEn7bGmMLSmtfstTasJeNpRNuCWOt5dxA7QC6R4+jto0jamoT5h4VuCIzpJNWjn7zNIxVLkT41HWlT0uAh9xDsM6XMuspc4eno2WpDcOtbiLg1M9zzrFKrUFhZiLyKPLPOx91nZV0lAJitvHHH1aq1lhiRpAI14pvIq8iDg9gBUV5RiPaObnSWTrcD5H6P9hadjGPAgAHYuFEnwpxIBLVajeXLl2Pw4MENHGkbGGOCd0nX8tE/qD96+/c2epz+78s9x906dOO3/Zb3G+Ydmoes4qwG66A/655bnst/tvcoXE2FT/DLVAJ3Mv3JEEvkObElula8u1V3eQUGaGKySx3ri35fwCXyrVJW4XThaUjElm0z7nr6E2y6UdF00U1y2cnD+kktTbH7id3YMmaLYBtZYszHWjK+fUm6Ng43+2ZsIMRvs28ZTjwkXMfR2mPbq5kakV6RuFxyGQsTFmJn9k7e3x/QzoJzHeK96ntI2poEANgwfEOjrmG8O1m9JcbcQR/XoQrcyUSajkwEEVxlrtg6dqtZ59LtAHklBu1TiVm+fDmGDh2KU6dOoba2Fm+++SZ+//13FBcX4+jRo7aungH6v53u4Hj0jtEIdg3GlyO+NAiDrP/7cpaZF3u+iN/yfsPjYY9j8bHFyC7NxqSoSQ3WQT+Ucnp+Ov+53VlixFpLDKcYSkQSAzdUe/dE4HIJAYa/cXMtMfquh/tv7Oc/16nrBFPdD9ttXC65zE806VsxTPVJuutgjOVmaimkEqlBG5MlxnysJePt+422M6z1uH918SuM2TEG6y+sB2A4wwHoZAann7xdE+0djSWJSzCz+0zB9n05+zDpx0lYcXKFjWompLKukl/Mr2RK9PQTDthUahW8Hb3h7aSJaKOrhKTdTmv0/JzrDbcmxtyQyFw54ZqYeiWmiT287mQDPyBpp31i9+7dcfnyZfTv3x/jxo1DZWUlJkyYgIyMDHTqZLvZV1PoylixSGwwoLxVccsgwh4gHPR4yD14ZdxV6or5j8xHD98eDcpqXXdJLhgHh8CdrJ2tieHav7y2XGCJ0W/D1j550xhcEtNHAx81mHhpkhKjU9Zg/aFO8J86dR2yCstNlm0quiGS9a0YphQCXTez25W3H+r6D4ufk3ZSIsg1qNV6LrRGrCXjyRLTBiiuLkZOWQ4fWlmtNm2JsfeZKMI8hmwegmplNbaP2w6Fi4LfHuQahCcjnzQoX1pdikvFlxDoatuEYhy6HVx5bTm/WJfD38Ufhycf5r/rDuDMccniOh9uwGOuJcaYOxmHCCIwxlBWWwYAcJe5NzhoIkuMEA8PD7zzzjuNF2wF6Cugxn5nY88hN1Cb0X0G5vWZh6QtSZqoUiLguz++w/m75/l8GUbX1Ojk8NB/Zl2lrvxMtVzcOtcRWYsg1yA8FfkUtlzegr+n/R2jO46Gm8zNaBhqe4ZTItRMbfD7N0VB48qKRWK4y4XJB3XlUp2qDnVKy8kkXUVF//0wJbd1ZTu39skWTP9pOkqqS+Dn5AcnqRM+HPBhu3PbfFisIePpF2gDcC8/N8AyFoFMN6oJ0fYpry1HtaraqFXOGJyPdGtZk6Hb2a0+u1oQZQgwzCOj29GZE4Ev0CUQCYoERHhEYETECD46W2MEuAQgKTRJrz5ad7IqZRX6p/QHAKRPSW94rY3OxGN7XxNz/vx5o9tFIhEcHR0RGhraqhb46/5OYpEYjg6OBmWM+ctzcpizEOhaXU4WnsS+nH2C8+rTkPy+U3WH/9zZyzDHRlvG19mXT3h78d5FfDf6OwBA5p1MvPjzi/Bz9kN2abbdT+Jxcu9e9T1DJaYJVpKevj3x8ZCP4SH3aNAKrR+I4mGHD7rvhLlKjFgkxmu9X8NHZz4ShGFuaXLLcnGv+h62jtmKKO8om9XDXrGWjCclpg3AdYScMDLmxjArdhaKq4tbTR4Qwrrwg2I9q9yVkitIzU1FiFsIRnUcxW9vaD2VLdCvh35IY/39uh26vtXGGINCBmFQyKAm16u3f2+DRdtcvy4SiZo0SNLt0Ln2b68+1nFxcfwAnWsD3QG7VCrF5MmTsXbtWjg6GioMLY0IIsQr4vkZ8TjfOMNCRn7K6d2mY1TEKD56JK/UGHFJM7auxdfJ12Sdunp3xaXiS+bfRBsjxkeTCFI3SWKsbyz+3PXP+PLilwDsf2E/l9fqcsllg+ejKVYBHycfsxIb16nrLDpI1JXb+vU11feIRCJM6zYNwyOG81ElbQEnr9VMjcXHFoMxhrcT3hasUyJMYy0Zb9/TEnaGtQYoBpYYI0pMUlgSJkVNQoBrgFXqQLQuuA5O3xKTVZKF1WdXCzLOA0I3hdaA/iw2p8T4OvkiISAB1cpq9PmqD5K3JgMQDviMPf9WRaStq65QbqwtpWIp0qekI31KOlxlrmYd01bZsWMHunTpgnXr1uHcuXM4d+4c1q1bh6ioKHzzzTf4/PPP8csvv2DhwoW2rioAwNHBEZ8nf471w9dDJpEZdx0zosUoXBSI8YnB5F2TEb8pHlOip+CdhHfg5+xnlnvQmE6axH/GAlcsG7CM/9zenqP7Nffx3R8a60txdTFOFZziw7B/lvkZKusq8UTnJ/B42OO2rKZF0Vdi9CNRNkStqhbzDs3D/EPzDfIN6WItWTq642j08usl2NaQK61MIkOQaxB8nU0r8S3FpF2TsP3KduzI3mFgqSJMYy0ZT5aYNgD38nNKDL1YhCn3MN59Rc9iwC0Ebi1rMkwl6ZzebTqe6/4csoqzsP739fzaFN0Bnzkd74O6B7hTdQdpt9Nw9s5ZPOL/CCZGTmz0uPLacuSW5UIukfMuO6qqUHRQDsfA4IGChcSNhd8UiUS8u9mQkCE4/efT7dbd8/3338eqVauQnJzMb4uNjUVwcDAWLVqEEydOwMXFBfPnz8eKFa0j+IQuDUaENEJlXSVqVDUY2XEk7wZlEG3KyBwjd05jVpp71doM6FdKrrQrl5f7Nffxc+7PADRKzIx9MxDpFYltY7fBQeyAOnUdXujxAoLdghs5U+tGsCD/IWTF5ZLLOHDjAADggwEfCPZ19+mOjKIMAIaTYA8rnhpam6v7vkyMnIitlzVRHo2t8bUFRpNdtlPLeXOwlownJaYNwL1IXTy7YG7cXAPzJmMMWy9vRWlNKUZEjOA7TaLtYsoSYyrEMm+JaaUdxqCQQfCSe/HRmbj7MNYZmrMO6Le83wT5lKRiqVlKzJnCM5j7y1x079Ad347+VnO9Bx3hr3wEyeGJggmEpsyGS8QSSNC+IkrpkpmZibCwMIPtYWFhyMzMBKBxR8jPz2/pqhlFqVYivyIfYrEYQa5BOHfnnEEZY7//5qzNOJ5/nJ/91nV91H2WHw973OjMOrdwv7y23GCfbiLH9qYMG3Ol4mRgW1pvNjh0ML754xveZS4xIBFp+Y1HY9Qnv1L7HukryzEdYpBRlIHp3abjpZ4v4ctj2mzqD+uO19Bv0C+wH1ykLqisq8TTUU/zSoySKXHnwR28eeRNOIgd8Nmwzx6qDs2FcsI8HNaS8eRO1oJYS2nnBIOrzBW9/Xuja4euBmU2XdqEVWdWIa/cvGSAhH1jquPmlANT/vetxRKj32EMDhmMV3u/ik6enXCz7CbuVt0FILyPPyn+BADo4tX4ui/9+zc3xLKpRG38/iaI1CplFcbtHIfxO8ejWlnd+AFtmOjoaCxbtgy1tdq1T3V1dVi2bBmio6MBAHl5efD397dVFQWUVJdg5I6RGLFtBADjAxxPR0+DbZeKL/Ez4ACw8eJGfJ75OUqrS/mB+Es9X8LKx1Yi1D3U4Pjf7/7On0efDo4dtNeWG167LWPMMsW1J/fObruyTZAs0h7Rt2QMCx/G72uuDNFXeH2dfdHZszP8nf3rcyBZDi7P1g9Xf8Bveb8Z7Ofuy0HswE+2RntHQ6lW4lThKd5CZAsaijZINI61ZLzdWGLCw8Nx48YNwbalS5firbfeMnlMdXU15s+fj5SUFNTU1CA5ORmffPJJq+kILcWA4AHwdPREHz/TCf64F7C9zdC1V0wNtk09B508O+Ev3f/Ch+m2NV5yL8F3rt4bL27EuvPrEOsTC0A4e/1F8hdmn18/j4a5i2KNBUAQywtwX3IFmXdc0d2nu0GdTaFmaly7fw0AcKrwFJadWIbOnp3x0eCPzKpLW2L16tUYO3YsgoOD0aOHxtqWmZkJlUqFXbt2AQCuXbuGl19+2ZbV5NF369IdzMT6xCJeEQ9vR2+D4/QHPd9ladZxDA0dyp+rIXfI1/q8BgaG0R1HG+wTiURYPXQ1KusqDZJstnWM5cXh2pP7/4sLX+Bu1d1GE+G2ZvSfO91npbmR1/SP6x/UH15yL6NK9MMOH/7c7c/YdW0Xfr/3u1H5yN3X55mfY+mApfy6maIHRQDMC9piLYxNVJB1xnysJePtRokBgPfeew+zZs3iv7u5uTVY/vXXX8fu3buxZcsWeHh4YO7cuZgwYUKrzAD9MPQP6o/+Qf1xr+oe3vr1LUhEErzf/31BGcoT0754rfdrqFZVC3LEADpuWHoWgyjvqFblQy+VCC0jx/OPo6y2jM/2zC30t1THba4lhlP+dJVDB9ffkSc9gG1XShHrG8tvb0yJ0R3Q1qhqcKPsBpwdGgjJ3Ibp168frl+/jk2bNuHyZU2S06eeegpTpkzh5fy0adNsWUUBDU0KfTn8S4PnV/84fcQiMTp5dkL/oP54oHyAC3cvoLNnZ4PQzW4yN7yb+K7Jeg0MHmjuLbQpjL2/nGKja6Wx9+hkv976FYDWEsdFKwOaP0Gp3yZpt9Ow4pRmTcKcuDlwRLJO2YfHVCTMY7ePobSmFADw47UfEeYexisx3CSTkinBGLPJZOwLsS+gWlWNcI9wvHH4DQCkxDQFa8l4uxrRurm5QaFQ8H8uLqZzO9y/fx+ff/45Vq5ciSFDhqBPnz5Yv349jh07huPHj7dgrVuOWlUtdl/bjb3X9wq2M6Z92exdiBPmMabTGDwV+ZTBbDDvTmYHGb0f8X8EAJAQkICTBSfxzm/v4EzhGQDahf66A5RlJ5bhr7/8FddKrzV6bv2kaQ9jieFeKa5jXZu0FuseXwd3mbv+4QJ0Xff4Gf0W6hRXr16N8PBwODo6IiEhASdOnGiw/JYtWxAdHQ1HR0fExsZiz549gv2MMbz77rsICAiAk5MTkpKScOXKFbPqUldXh06dOuHWrVt48cUXsXLlSqxcuRKzZ89udKLKVvBZ4Y38bouOLcLmrM28y6MuDYWRnRQ1CWuS1uCn6z/hmd3P4Fb5LSvUvG1izJ2Mm6gQKDF27okwtvNYAEC8Ih4AsCN7B7+vKa6suui3iW60sntV9/SLPzTc9fTfBW69F8d/z/4Xz+9/Htkl2cI8YC0dfbKe57o/hxd7vojh4cP5ba3Znay9yHi7UmKWLVuGDh06oFevXvjnP/8JpdL0w3z69GnU1dUhKSmJ3xYdHY3Q0FCkpZleCFdTU4OysjL+r7zccAFlc7HWAOVM4RnsuLKDz/Ssn+yPgSwxhAZXmSvC3MPg4yRMGlZYWYg91/bgaF7rsFLerbqLU4WnAACTIichwEUTGlw/Ap/u87zp0ib8cvMXfHTmo0bPr6/ENXVNjLADFr7X/YL6ITEw0eRsPH+UTgfYkouPv/vuO8ybNw+LFy/GmTNn0LNnTyQnJ6OoqMho+WPHjuGZZ57BzJkzkZGRgfHjx2P8+PG4cOECX2b58uX4z3/+g08//RTp6elwcXFBcnIyqqsb99OXSqVmlbMUlpDx+lH+dAfKu6/txj+O/wM3y28aHmeiD9B9jk0F3yBMY2xShnuXdAMe2Hv/F9MhBocmHcK6x9cZ7LPU87L23Fr+s4qpBOd92Gv8L/N/fBAMfQXAmEKQnp+OA7kHBJNMrSH6ar/AfugX2K9JuXlakvYk4+3mjX711VeRkpKCgwcPYvbs2fjggw/w5ptvmixfUFAAmUwGT09PwXZ/f38UFBSYPG7p0qXw8PDg/7p162apW7AaP1z9Ae8eexfHbh8DoBHeprLhUsfYPvg++3usO78OuWW5gu2jOo7Crid24W8JfxNsv3jvIv7v1//DJ2c/aclqmqSqTjsrp4b2eeYsKCFuIfh/j/4/vNrrVYNjzZmpa647GR+K2oiy8TBWzpa0xKxcuRKzZs3CjBkz0K1bN3z66adwdnbGF18YX1O0atUqDB8+HG+88Qa6du2Kf/zjH+jduzf++9//aurMGD766CMsXLgQ48aNQ48ePbBx40bcvn0bO3fuNKtOc+bMwYcfftjgxJSlsISMr2P1lsD658FNZjibaMx/n3tuunXohvf6vcdvF0GEkuoSXC29ivs19wHY/4C7JTE2mOTkwP+S/4dXer0CoG14InRw6mBUaWvKvTlKTCcT1B0jGIRYNvsKxtFNWqwfRMaU7FMztUA+20qJ2Zm9E9sub0NlXSXWPr4Wax9f26TcPC1Je5LxNlUj33rrLXz44YcNlrl06RKio6Mxb948fluPHj0gk8kwe/ZsLF26FHK53GJ1evvttwXXysvLa3ondzsDf9y7iLJ682isi8Zv1QdynMjQmN98ZR6IcNYEGCipq8Cl+lk7xv+rf6nr3+t+3l3hUD/QOVF6GeXKKq4E9l/TmP2k5Vrl7OiZtejhkg2XKn8El5ZBpNSYiEW3zwIV95t2P4TdkXL+M1wovwFWfB2xbuFQQ615phhDlGsw/PWiF0nuaXysyyoLcCLjf/x2EUT4k6c22teZ+9lQNmAtCHXyhaJ+UX5+dTFyq+6YLCsTO6CXRyf+e3pJFv85t1p73BuH34CsXnlxqI/Ac/nuBazp9AzEajFw/YjgvKoHdw226eNQb7UEgMe8YxFW86DRYwBAVKZxVVPVVQLXjyDO6Vfku5zDAwCi8nzg+hGsvLYTx0v/wNTAx/i20KWXR0fIxFKwugp+m7hQE3WK1VQYlLcktbW1OH36NN5++23ttcViJCUlmbRQp6WlCWQiACQnJ/Od1/Xr11FQUCCwent4eCAhIQFpaWl4+umnG63XyZMnkZqaiv379yM2NtbAVXj79u3m3mKjWELGP8jXzCa7ihyA60cgrrxtUOb3Kz9CnX8WABDh5A9fuQfU9TJ6pEc0xkg8wa1uEd88iW1FJ7EqR5uEVpJ3Gig2tOYQhkgBzAoZjs9ual2p3wsby7/T2bc0z7aovMCs99weEeX8anbZoAcF6O3eCT4yd4P2kInE4BzKCu5egq/qO/xJWgMHlSNw3RXXqgtRUFMC6Lipa/8H3B2c0dNdY/2qUtXiRGkWr57kFJ3kr6Mu/B1Qa8Zu6SVZSLt7xmhd1SU5kOQchQgiMDDU5RwBk7rj5P2GXZk6OQegQ/3kQm5VEQrq19sYw0UiR4ybJvxvnVqJjDJDl+RF51cBABKUIrg4+eDs/WuoZaYH5CGOPgiod+cuqCnh+8J4z0gg7FHASi7d7U3G21SJmT9/Pp577rkGy3Ts2NHo9oSEBCiVSuTk5CAqynBBskKhQG1tLUpLSwXWmMLCQigUCoPyHHK5XKAUlZWVNXwTxti9ACtUuUh3qp/tqA/ykQNg5nnN56fKyvHuvRIAwAUnR7ysaDiaTFrOTbjWm1uXBimQLZMZlHE5twXw9gQAvHzhE/gEqvDDzTwgEwh0lONlhR9ku+cDtbY3xxLWRRbgBzg64r83dhnsW1p0F6MrHwi2SZwcAYUfcqqKMLNeWAOAo1qNkze0vvmvhwahWGJa+L59txhTyjUD8X3ubvhXB8NBPEeAUon9N7WDv+cjTEdG4zoL53tXAUdH3K0tQ91X4yDXmbxzCQtGpVgM39yTwJkxJs8FAFEiETZLHSBnDB2v5wLY3WB5jq4iYI9EAinLAy6NQbC/L646a/Iyyf/YAxz7BntDApHv4ICFl78yeo6fc/Pgr1JBKhJBGhaMOpEI0v0L4e7ng1euZ5pVD33Ky8sFskpfjnHcvXsXKpXKIEKjv78//vjjD6PnLigoMFqes2hz/zdUpjE8PT3x5JNPmlX2YbGEjPdIX4uuNbVQ1RQCX46BQiSCX3AAihy0XeqKa9pO+b079/BERSXiXV1Q5+yEoLS1qK1aBYSHAAAkW5+Dl5MT4KsNk+yw/QVAabtoTPbGbBHwWbhGhjxSVY2OW7RBgC4FBQAyKcSX9wJp39qqihbHITwESpEIUTW1wJcNyzxdOgL4kvty8nvBvk/kMkwL1IyRjpVcwjFcwn9dizCoqhr4Cvi2gxdS3E2vY+hTVY0NBRq3pRIHCeaGGM9LF/Tz+0B9uN1Z4SFgJrxE3DO+hejwOjzj7QUxGGTfTQNTM8xsoL8AgH8V3sGwB5qJ5M3envjSw/QaxdjqGnyTXwgAuC8WY2aY6aSo4u3PA0oV3qyX86aYV1yCGfc1rqoH3VzxgY83xIzhXM5N4J3CJisxJOONY1MlxtfXF76+vs069uzZsxCLxfDzMz7479OnD6RSKVJTU/mGy8rKQm5uLhITE5tdZ7PwCkNA6T10UmlmrNUMUDEGB7HW4Osr9wZ8NQ+Eq1iFKFUtv0/7v6a8CIDYJ5LfEy2qgYuK8ftEADyYCGPkCtyvVeKYRAUxAFe1A/KkYQjwcERfkQjjlLXo4tERlokxQrRmnoESlao6qFH//PD/i+DhFgQ4CwVoLzD0V9agQCQ06UshAXyj+e/hqIa3yrTLk4erAnDUiBUPByU6q0wrzD6QCc7dWSVc2Jkt0Vynq0oECUQIYCLMUnviQ1Ut7oMBPlHQfZb/XqPEdqkSL4l9AN+GJwWcARhmU2ocOYAQne++omqE16rhIRZjlNQP8FXgWVUdtopMz9BJOnQGmAguAJKVNVAwEWLdwpHAajHENbwZtYKBJWHx4sVYsmRJs85lC9avX2/rKjSJEI9OGFxUhDAmAnyj4QbgP7VqfCiqRYZEI/c7q7TPprtrAODkgCcBPMkAuPig1oUhUlWNOLUEPt5dMFAEJChrcF/EEKuWIMCrM0hWm48cwIoqJb6VKfGUyB3w1QY1GYpanFCpMcbBh+932wJL6mXeX1VugK+nRc4ZB2BPpRoLHWtRVm9DuafqgMtqINLfDf7SOkSpNPKNG3/oEu7gwst1qYghtj5QAFfuvESNDmqgu4d2grqLugpqaGV+vFKMHmoxssUM4+WBgK8IvF2hgwJqMHRSNbzGwtU9GHDR9HM+0jp0VpmWycESZ77OEjB0NnHuGLUYivr3MhTVcG6gL/R09gdkGgXO3UGJTqo6zYjON7pZsapJxhunda5K0iMtLQ3p6ekYPHgw3NzckJaWhtdffx1//vOf4eWlmenNy8vD0KFDsXHjRsTHx8PDwwMzZ87EvHnz4O3tDXd3d7zyyitITExE3759rVvhiV/gH00o3gvA1iaUX9rAvjca2Le4Cdcg7Jvh9X/m4gxgjRnlvmy8CM8T9X/msqPxIgCADSa2J9f/tSTG3qmp9X/moPsur3yIely8eBFBQdoZT1Mutj4+PpBIJCgsLBRsb8hCrVAoGizP/V9YWIiAgABBmbi4uCbfi10wfjVe0tsUA2BjE04hA7BN57svgP+ZKEuYhykZ8FoL16OlGFf/Z2lCYFrWP1//Zw6+AL4xo9y2xosIEAPY2YTyz9X/mYMXzOuLmvKujqr/exhIxhvHLlYOyuVypKSkYNCgQYiJicH777+P119/HevWaSN01NXVISsrCw8eaN1k/v3vf2P06NF48sknMXDgQCgUCov6VhMEQbQG3Nzc4O7uzv+Z6uBkMhn69OmD1NRUfptarUZqaqpJC3ViYqKgPAAcOHCALx8REQGFQiEoU1ZWhvT09CZZvbdu3YpJkyahb9++6N27t+CPIAiiPUMy3gSMaJCbN28yAOzmzZu2rgpBEISA5sinlJQUJpfL2YYNG9jFixfZCy+8wDw9PVlBQQFjjLFp06axt956iy9/9OhR5uDgwFasWMEuXbrEFi9ezKRSKcvMzOTLLFu2jHl6erLvv/+enT9/no0bN45FRESwqqoqs+q0atUq5urqyubOnctkMhmbPXs2S0pKYh4eHuxvf/ub2ffWHEjGEwTRWiEZ3zCkxDQCdXAEQbRWmiufPv74YxYaGspkMhmLj49nx48f5/cNGjSIPfvss4LymzdvZpGRkUwmk7GYmBi2e/duwX61Ws0WLVrE/P39mVwuZ0OHDmVZWVlm1ycqKop98803jDHGXF1d2dWrVxljjC1atIjNmTOnSffWVEjGEwTRWiEZ3zAixlpxytFWwK1btxASEoKbN28iONh0xAqCIIiWpq3IJ2dnZ1y6dAlhYWHw8/PDgQMH0LNnT1y5cgV9+/bFvXuWzxzO0VbakCCItkdbkU/WkvF2sbDflqjVmkgz+fn5Nq4JQRCEEE4ucXLKXlEoFCguLkZYWBhCQ0Nx/Phx9OzZE9evXzeayduSkIwnCKK1QjK+YUiJaQQuYkN8fLyNa0IQBGGcwsJChIY2nDehNTNkyBD88MMP6NWrF2bMmIHXX38dW7duxalTpzBhwgSrXptkPEEQrR2S8cYhd7JGUCqVyMjIgL+/P8Ri84K5lZeXo1u3brh48SLc3EwnhSIah9rSMlA7WobW1o5qtRqFhYXo1asXHBpIvNbaUavVUKvV/D2kpKTg2LFj6NKlC2bPng2ZkeS+loJkvG2htrQM1I6WobW1I8n4hiElxgqUlZXBw8MD9+/fh7u76SyxRONQW1oGakfLQO1oHXJzcxESEgKRXhI4xhhu3rzZ6mYg6TmwHNSWloHa0TJQO1oHa8l4u8gTQxAEQbRdIiIicOfOHYPtxcXFiIiIsEGNCIIgCEthLRlPSgxBEARhUxhjBjN0AFBRUQFHR0cb1IggCIKwFNaS8fbrYNeKkcvlWLx4scmMqoT5UFtaBmpHy0DtaFnmzZsHABCJRFi0aBGcnZ35fSqVCunp6YiLi7NR7UxDz4HloLa0DNSOloHa0bJYW8bTmhiCIAjCJgwePBgAcPjwYSQmJgoWd8pkMoSHh2PBggXo0qWLrapIEARBNBNry3hSYgiCIAibMmPGDKxatYoW0hIEQbRBrCXjSYkhCIIgCIIgCMKuoIX9BEEQBEEQBEHYFaTEWIHVq1cjPDwcjo6OSEhIwIkTJ2xdJZuxZMkSiEQiwV90dDS/v7q6GnPmzEGHDh3g6uqKJ598ks+gzZGbm4tRo0bB2dkZfn5+eOONN6BUKgVlDh06hN69e0Mul6Nz587YsGFDS9ye1Thy5AjGjBmDwMBAiEQi7Ny5U7CfMYZ3330XAQEBcHJyQlJSEq5cuSIoU1xcjKlTp8Ld3R2enp6YOXMmKioqBGXOnz+PAQMGwNHRESEhIVi+fLlBXbZs2YLo6Gg4OjoiNjYWe/bssfj9WpPG2vK5554zeEaHDx8uKENtSehCMl4LyfjmQ3LeMpCMb8cwwqKkpKQwmUzGvvjiC/b777+zWbNmMU9PT1ZYWGjrqtmExYsXs5iYGJafn8//3blzh9//4osvspCQEJaamspOnTrF+vbty/r168fvVyqVrHv37iwpKYllZGSwPXv2MB8fH/b222/zZa5du8acnZ3ZvHnz2MWLF9nHH3/MJBIJ27t3b4veqyXZs2cPe+edd9j27dsZALZjxw7B/mXLljEPDw+2c+dOdu7cOTZ27FgWERHBqqqq+DLDhw9nPXv2ZMePH2e//vor69y5M3vmmWf4/ffv32f+/v5s6tSp7MKFC+zbb79lTk5ObO3atXyZo0ePMolEwpYvX84uXrzIFi5cyKRSKcvMzLR6G1iKxtry2WefZcOHDxc8o8XFxYIy1JYEB8l4ISTjmw/JectAMr79QkqMhYmPj2dz5szhv6tUKhYYGMiWLl1qw1rZjsWLF7OePXsa3VdaWsqkUinbsmULv+3SpUsMAEtLS2OMaYSTWCxmBQUFfJk1a9Ywd3d3VlNTwxhj7M0332QxMTGCc0+ePJklJydb+G5sg75QVqvVTKFQsH/+85/8ttLSUiaXy9m3337LGGPs4sWLDAA7efIkX+ann35iIpGI5eXlMcYY++STT5iXlxffjowx9n//938sKiqK/z5p0iQ2atQoQX0SEhLY7NmzLXqPLYWpDm7cuHEmj6G2JHQhGS+EZLxlIDlvGUjGty/IncyC1NbW4vTp00hKSuK3icViJCUlIS0tzYY1sy1XrlxBYGAgOnbsiKlTpyI3NxcAcPr0adTV1QnaKzo6GqGhoXx7paWlITY2Fv7+/nyZ5ORklJWV4ffff+fL6J6DK9NW2/z69esoKCgQ3LOHhwcSEhIE7ebp6YlHHnmEL5OUlASxWIz09HS+zMCBAwUhD5OTk5GVlYWSkhK+THto20OHDsHPzw9RUVF46aWXcO/ePX4ftSXBQTLeOCTjLQ/JectCMr5tQkqMBbl79y5UKpVAGAOAv78/CgoKbFQr25KQkIANGzZg7969WLNmDa5fv44BAwagvLwcBQUFkMlk8PT0FByj214FBQVG25Pb11CZsrIyVFVVWenObAd33w09ZwUFBfDz8xPsd3BwgLe3t0Xati09z8OHD8fGjRuRmpqKDz/8EIcPH8aIESOgUqkAUFsSWkjGG0Iy3jqQnLccJOPbLg62rgDRthkxYgT/uUePHkhISEBYWBg2b94MJycnG9aMIDQ8/fTT/OfY2Fj06NEDnTp1wqFDhzB06FAb1owgWj8k44nWDsn4tgtZYiyIj48PJBKJQeSVwsJCKBQKG9WqdeHp6YnIyEhkZ2dDoVCgtrYWpaWlgjK67aVQKIy2J7evoTLu7u5tshPl7ruh50yhUKCoqEiwX6lUori42CJt25af544dO8LHxwfZ2dkAqC0JLSTjG4dkvGUgOW89SMa3HUiJsSAymQx9+vRBamoqv02tViM1NRWJiYk2rFnroaKiAlevXkVAQAD69OkDqVQqaK+srCzk5uby7ZWYmIjMzEyBgDlw4ADc3d3RrVs3vozuObgybbXNIyIioFAoBPdcVlaG9PR0QbuVlpbi9OnTfJlffvkFarUaCQkJfJkjR46grq6OL3PgwAFERUXBy8uLL9Oe2hYAbt26hXv37iEgIAAAtSWhhWR845CMtwwk560Hyfg2hK0jC7Q1UlJSmFwuZxs2bGAXL15kL7zwAvP09BREXmlPzJ8/nx06dIhdv36dHT16lCUlJTEfHx9WVFTEGNOE3wwNDWW//PILO3XqFEtMTGSJiYn88Vz4zWHDhrGzZ8+yvXv3Ml9fX6PhN9944w126dIltnr1arsPv1leXs4yMjJYRkYGA8BWrlzJMjIy2I0bNxhjmtCbnp6e7Pvvv2fnz59n48aNMxp6s1evXiw9PZ399ttvrEuXLoKQkaWlpczf359NmzaNXbhwgaWkpDBnZ2eDkJEODg5sxYoV7NKlS2zx4sV2FzKyobYsLy9nCxYsYGlpaez69evs559/Zr1792ZdunRh1dXV/DmoLQkOkvFCSMY3H5LzloFkfPuFlBgr8PHHH7PQ0FAmk8lYfHw8O378uK2rZDMmT57MAgICmEwmY0FBQWzy5MksOzub319VVcVefvll5uXlxZydndkTTzzB8vPzBefIyclhI0aMYE5OTszHx4fNnz+f1dXVCcocPHiQxcXFMZlMxjp27MjWr1/fErdnNQ4ePMgAGPw9++yzjDFN+M1FixYxf39/JpfL2dChQ1lWVpbgHPfu3WPPPPMMc3V1Ze7u7mzGjBmsvLxcUObcuXOsf//+TC6Xs6CgILZs2TKDumzevJlFRkYymUzGYmJi2O7du61239agobZ88OABGzZsGPP19WVSqZSFhYWxWbNmGQxIqS0JXUjGayEZ33xIzlsGkvHtFxFjjLWc3YcgCIIgCIIgCOLhoDUxBEEQBEEQBEHYFaTEEARBEARBEARhV5ASQxAEQRAEQRCEXUFKDEEQBEEQBEEQdgUpMQRBEARBEARB2BWkxBAEQRAEQRAEYVeQEkMQBEEQBEEQhF1BSgxBEARBEARBEHYFKTFEu+fQoUMQiUQoLS1t8WuLRCKIRCJ4enpa9TqPPfYYf62zZ89a9VoEQRCtCZLxBNE2ISWGaFc89thjeO211wTb+vXrh/z8fHh4eNikTuvXr8fly5eteo3t27fjxIkTVr0GQRCErSEZTxDtBwdbV4AgbI1MJoNCobDZ9T09PeHn52fVa3h7e6OsrMyq1yAIgmiNkIwniLYJWWKIdsNzzz2Hw4cPY9WqVbzZPScnx8DVYMOGDfD09MSuXbsQFRUFZ2dnTJw4EQ8ePMCXX36J8PBweHl54dVXX4VKpeLPX1NTgwULFiAoKAguLi5ISEjAoUOHmlzPJUuWIC4uDl988QVCQ0Ph6uqKl19+GSqVCsuXL4dCoYCfnx/ef/99/hjGGJYsWYLQ0FDI5XIEBgbi1VdffdgmIwiCsBtIxhNE+4IsMUS7YdWqVbh8+TK6d++O9957DwDg6+uLnJwcg7IPHjzAf/7zH6SkpKC8vBwTJkzAE088AU9PT+zZswfXrl3Dk08+iUcffRSTJ08GAMydOxcXL15ESkoKAgMDsWPHDgwfPhyZmZno0qVLk+p69epV/PTTT9i7dy+uXr2KiRMn4tq1a4iMjMThw4dx7Ngx/OUvf0FSUhISEhKwbds2/Pvf/0ZKSgpiYmJQUFCAc+fOPXSbEQRB2Ask4wmifUFKDNFu8PDwgEwmg7Ozc6OuBXV1dVizZg06deoEAJg4cSK++uorFBYWwtXVFd26dcPgwYNx8OBBTJ48Gbm5uVi/fj1yc3MRGBgIAFiwYAH27t2L9evX44MPPmhSXdVqNb744gu4ubnx18rKysKePXsgFosRFRWFDz/8EAcPHkRCQgJyc3OhUCiQlJQEqVSK0NBQxMfHN6+hCIIg7BCS8QTRviAlhiCM4OzszHduAODv74/w8HC4uroKthUVFQEAMjMzoVKpEBkZKThPTU0NOnTo0OTrh4eHw83NTXAtiUQCsVgs2MZd/6mnnsJHH32Ejh07Yvjw4Rg5ciTGjBkDBwd6xQmCIPQhGU8Q9g89/QRhBKlUKvguEomMblOr1QCAiooKSCQSnD59GhKJRFBOt1O01vVDQkKQlZWFn3/+GQcOHMDLL7+Mf/7znzh8+LDBcQRBEO0dkvEEYf+QEkO0K2QymWChpqXo1asXVCoVioqKMGDAAIuf3xycnJwwZswYjBkzBnPmzEF0dDQyMzPRu3dvm9SHIAiipSEZTxDtB1JiiHZFeHg40tPTkZOTA1dXV3h7e1vkvJGRkZg6dSqmT5+Of/3rX+jVqxfu3LmD1NRU9OjRA6NGjbLIdUyxYcMGqFQqJCQkwNnZGV9//TWcnJwQFhZm1esSBEG0JkjGE0T7gUIsE+2KBQsWQCKRoFu3bvD19UVubq7Fzr1+/XpMnz4d8+fPR1RUFMaPH4+TJ08iNDTUYtcwhaenJz777DM8+uij6NGjB37++Wf8+OOPzfLVJgiCsFdIxhNE+0HEGGO2rgRBtFdEIhF27NiB8ePHW/1aOTk5iIiIQEZGBuLi4qx+PYIgiPYOyXiCsB5kiSEIG/PMM88gODjYqtcYMWIEYmJirHoNgiAIwhCS8QRhHcgSQxA2JDs7GwAgkUgQERFhtevk5eWhqqoKABAaGgqZTGa1axEEQRAaSMYThPUgJYYgCIIgCIIgCLuC3MkIgiAIgiAIgrArSIkhCIIgCIIgCMKuICWGIAiCIAiCIAi7gpQYgiAIgiAIgiDsClJiCIIgCIIgCIKwK0iJIQiCIAiCIAjCriAlhiAIgiAIgiAIu4KUGIIgCIIgCIIg7ApSYgiCIAiCIAiCsCv+P96GHfDEgUsBAAAAAElFTkSuQmCC\n" 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}, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:get_time_series: finished time-series creationwith name TbPEX5EGFP 1-10009_T177s_1, channel 2, bin 1, processing original, indexedPicoObject.processed_time_traces[ 0 ]\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:get_cross_and_auto_correlation: Starting with channel 2 and channel 2\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:_cross_and_auto: sum(indeces)=325740\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:_cross_and_auto: finished preparation.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": " 0%| | 0/30 [00:00", + "image/png": 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\n" 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Cut 63145 of 325731 photons.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:TCSPC: this file has 1 channel(s): [2]\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:Finished correct_TCSPC() with uuid {22F3D666-93A2-4DC4-B931-E3197DBD6257}.\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:get_autocorrelation: starting tttrx2fcs correlation..\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "DEBUG:__main__:_auto: using channel 2 with 262586 photons\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": " 0%| | 0/30 [00:00", + "image/png": 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file diff --git a/Colab_notebooks/Latest_Notebook_versions.csv b/Colab_notebooks/Latest_Notebook_versions.csv index 2f2a50b8..154216c2 100644 --- a/Colab_notebooks/Latest_Notebook_versions.csv +++ b/Colab_notebooks/Latest_Notebook_versions.csv @@ -11,6 +11,7 @@ DenoiSeg,1.14.1 Detectron 2D,1.14.1 fnet (2D),1.14.1 fnet (3D),1.13.1 +U-Net (1D) for FCS,2.1.2 U-Net (2D),2.1.1 U-Net (3D),2.1.2 U-Net (2D) multilabel,2.1.2 diff --git a/requirements_files/1D_UNet_for_FCS_requirements.txt b/requirements_files/1D_UNet_for_FCS_requirements.txt new file mode 100644 index 00000000..1c1f61ff --- /dev/null +++ b/requirements_files/1D_UNet_for_FCS_requirements.txt @@ -0,0 +1,59 @@ +# Requirements for 1D_UNet_for_FCS_ZeroCostDL4Mic.ipynb +astunparse==1.6.3 +cachetools==5.3.2 +cffi==1.16.0 +chardet==5.2.0 +click==8.1.7 +cloudpickle==2.2.1 +colorful==0.5.5 +cryptography==41.0.5 +entrypoints==0.4 +etils==1.5.2 +flatbuffers==23.5.26 +fsspec==2023.6.0 +gast==0.5.4 +google==2.0.3 +h5py==3.9.0 +httplib2==0.22.0 +idna==3.4 +ipywidgets==7.7.1 +jax==0.4.20 +joblib==1.3.2 +keras==2.14.0 +lxml==4.9.3 +mlflow==2.8.0 +multipletau==0.3.3 +numexpr==2.8.7 +numpy==1.23.5 +oauth2client==4.1.3 +packaging==23.2 +pandas==1.5.3 +patsy==0.5.3 +prettyprinter==0.18.0 +pyarrow==9.0.0 +pyasn1==0.5.0 +pydantic==1.10.13 +pydot==1.4.2 +pytz==2023.3.post1 +requests==2.31.0 +rich==13.6.0 +rsa==4.9 +scikit-image==0.19.3 +scikit-learn==1.2.2 +scipy==1.11.3 +seaborn==0.12.2 +six==1.16.0 +sqlparse==0.4.4 +statsmodels==0.14.0 +tblib==3.0.0 +tensorboard==2.14.1 +tensorflow==2.14.0 +termcolor==2.3.0 +threadpoolctl==3.2.0 +tqdm==4.66.1 +typing_extensions==4.5.0 +uritemplate==4.1.1 +urllib3==2.0.7 +wrapt==1.14.1 +zipp==3.17.0 +