{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# One-Shot Dynamics in 7 Minutes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Welcome!\n", "\n", "This tutorial will take you through a complete *One-Shot Tissue Dynamics Reconstruction* (OSDR) workflow by reproducing figure 3 from [Somer et. al 2024](https://www.biorxiv.org/content/10.1101/2024.04.22.590503v1). We will download a dataset, estimate a dynamical model and plot a 2D phase-portrait of fibroblast-macropahge dynamics. \n", "\n", "Before we begin, let's download the dataset we'll use throughout the tutorial by running the following code block. \n", "\n", "This could take a few minutes the first time so continue reading while the data is downloading.\n", "\n", "We'll work with the breast cancer IMC dataset by [Danenberg et. al 2022](https://www.nature.com/articles/s41588-022-01041-y). \n", "This dataset includes 793 spatial proteomics tissue sections from 717 breast cancer patients, totalling ~864K cells.\n", "\n", "Fibroblast-macrophage dynamics in breast cancer serve as a useful test-case for our model because [Mayer et. al 2023](https://www.nature.com/articles/s41467-023-41518-w) experimentally discovered the dynamics between these two cell types." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from tdm.raw.breast_mibi import read_single_cell_df\n", "\n", "single_cell_df = read_single_cell_df()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(859710, 61)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "single_cell_df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview of OSDR\n", "\n", "OSDR uses statistical models to learn how the composition of a cell's neighborhood influences it's division rate.\n", "\n", "Thus, the core input to OSDR consists of:\n", "\n", "* x,y positions of cells in the tissue\n", "* cell types\n", "* cell division labels (1 = dividing, 0 = not dividing)\n", "\n", "For datasets including samples from multiple patients or multiple tissue sections (\"images\") per patient, we also require:\n", "\n", "* subject id\n", "* image id\n", "\n", "## The Single-Cell Dataframe\n", "\n", "To use OSDR we first prepare one large table with a row for each cell and a column for each parameter above. \n", "\n", "We call this table the \"single-cell dataframe\".\n", "\n", "The module `tdm.preprocess.single_cell_df` contains utilities for preparing and validating the table. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The raw data we downloaded includes many columns, including levels of various markers:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['index',\n", " 'ImageNumber',\n", " 'ObjectNumber',\n", " 'metabric_id',\n", " 'cellPhenotype',\n", " 'is_epithelial',\n", " 'is_tumour',\n", " 'is_normal',\n", " 'is_dcis',\n", " 'is_interface',\n", " 'is_perivascular',\n", " 'is_hotAggregate',\n", " 'Histone H3',\n", " 'SMA',\n", " 'CK5',\n", " 'CD38',\n", " 'HLA-DR',\n", " 'CK8-18',\n", " 'CD15',\n", " 'FSP1',\n", " 'CD163',\n", " 'ICOS',\n", " 'OX40',\n", " 'CD68',\n", " 'HER2 (3B5)',\n", " 'CD3',\n", " 'Podoplanin',\n", " 'CD11c',\n", " 'PD-1',\n", " 'GITR',\n", " 'CD16',\n", " 'HER2 (D8F12)',\n", " 'CD45RA',\n", " 'B2M',\n", " 'CD45RO',\n", " 'FOXP3',\n", " 'CD20',\n", " 'ER',\n", " 'CD8',\n", " 'CD57',\n", " 'Ki-67',\n", " 'PDGFRB',\n", " 'Caveolin-1',\n", " 'CD4',\n", " 'CD31-vWF',\n", " 'CXCL12',\n", " 'HLA-ABC',\n", " 'panCK',\n", " 'c-Caspase3',\n", " 'DNA1',\n", " 'DNA2',\n", " 'Location_Center_X',\n", " 'Location_Center_Y',\n", " 'AreaShape_Area',\n", " 'x',\n", " 'y',\n", " 'ki67',\n", " 'cell_type',\n", " 'img_id',\n", " 'subject_id',\n", " 'division']" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(single_cell_df.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the function `restrict_df_to_required_columns` to select just the subset of columns OSDR requires. \n", "\n", "A preprocessed table looks like this:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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xydivisioncell_typeimg_idsubject_id
00.0001210.000004FalseTu1MB-0282
10.0002220.000005FalseT1MB-0282
20.0003540.000006FalseTu1MB-0282
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" ], "text/plain": [ " x y division cell_type img_id subject_id\n", "0 0.000121 0.000004 False Tu 1 MB-0282\n", "1 0.000222 0.000005 False T 1 MB-0282\n", "2 0.000354 0.000006 False Tu 1 MB-0282" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from tdm.preprocess.single_cell_df import restrict_df_to_required_columns\n", "\n", "restrict_df_to_required_columns(single_cell_df).head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are `x,y` coordinates in meters. For example, the y value of the first cell, `0.000004`, corresponds with 4 microns. \n", "There is a boolean label for cell division, a string for the cell_type (e.g `Tu` for tumor cells, `T` for T-cells) and unique identifiers for images and subjects." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check the dataframe was processed correctly using `check_single_cell_df`.\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[94mValidating single cell dataframe...\n", "\n", "\u001b[92m[SUCCESS] Found x column\n", "\u001b[92m[SUCCESS] Found y column\n", "\u001b[92m[SUCCESS] Found cell_type column. \u001b[92mNumber of cell types: 6\n", "\u001b[92m\tCell types: ['Tu', 'T', 'En', 'F', 'M', 'B']\n", "\u001b[92m[SUCCESS] Found division column. Fraction of dividing cells: 0.016\n", "\u001b[92m[SUCCESS] Found img_id column. Number of images found: 791\n", "\u001b[92m[SUCCESS] Found subject_id column. Number of subjects found: 715\n", "\u001b[92m\n", "[SUCCESS] Validation complete!\n" ] } ], "source": [ "from tdm.preprocess.single_cell_df import check_single_cell_df\n", "\n", "assert check_single_cell_df(single_cell_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** \n", "\n", "In order to apply OSDR to your own dataset the only requirement is preparing a single-cell dataframe with the columns: `x, y, division, cell_type, img_id, subject_id`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Analysis Object\n", "\n", "The `Analysis` class is the primary entity in the `tdm` package.\n", "\n", "To see it in action, we will reproduce the dynamics between fibroblasts and macrophages from the paper by ([Somer et. al 2024](https://www.biorxiv.org/content/10.1101/2024.04.22.590503v1))\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[94m1/5 Constructing tissues \u001b[92m[V]\n", "\u001b[94m2/5 Counting cell-neighbors \u001b[92m[V]\n", "\u001b[94m3/5 Filtering cell-types \u001b[92m[V]\n", "\u001b[94m4/5 Transforming features \u001b[92m[V]\n", "\u001b[94m5/5 Fitting a model \u001b[92m[V]\n" ] } ], "source": [ "from tdm.cell_types import FIBROBLAST, MACROPHAGE, TUMOR, ENDOTHELIAL\n", "from tdm.analysis import Analysis\n", "\n", "ana = Analysis(\n", " single_cell_df=single_cell_df,\n", " cell_types_to_model=[FIBROBLAST, MACROPHAGE],\n", " allowed_neighbor_types=[FIBROBLAST, MACROPHAGE, TUMOR, ENDOTHELIAL],\n", " polynomial_dataset_kwargs={\"degree\":2},\n", " neighborhood_mode='extrapolate',\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Analysis` object above counted the neighbors of each cell, transformed features and fit statistical models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysis Arguments, step by step\n", "\n", "\n", "The single-cell dataframe we prepared is the primary input to the analysis object:\n", "\n", "```python\n", "# Don't run this block:\n", "ana = Analysis(\n", " single_cell_df=single_cell_df,\n", " ...\n", ")\n", "```\n", "\n", "We also specify the cell types we wish to model:\n", "\n", "```python\n", "# Don't run this block:\n", "ana = Analysis(\n", " ...\n", " cell_types_to_model=[FIBROBLAST, MACROPHAGE], # F, M\n", " ...\n", ")\n", "```\n", "\n", "Providing `cell_types_to_model=[FIBROBLAST, MACROPHAGE]` implies the following dynamical model:\n", "\n", "$$\n", "\\frac{dF}{dt} = f(F,M)\n", "$$\n", "\n", "$$\n", "\\frac{dM}{dt} = m(F,M)\n", "$$\n", "\n", "This model neglects other cell types that might possibly influence dynamics, such as adaptive immune cells. \n", "\n", "To simplify dynamics, we only include fibroblasts and macrophages from neighborhoods without adaptive immune cells:\n", "\n", "```python\n", "# Don't run this block:\n", "ana = Analysis(\n", " ...\n", " allowed_neighbor_types=[FIBROBLAST, MACROPHAGE, TUMOR, ENDOTHELIAL], # no T or B cells\n", " ...\n", ")\n", "```\n", "\n", "The OSDR dynamical model is based on statistical models that estimate the proliferation rate of each cell type. \n", "For example, the dynamics of fibroblasts are given by the following equation:\n", "\n", "$$\n", "\\frac{dF}{dt} = F\\cdot (p_F^+(F,M) - p_F^-(F,M))\n", "$$\n", "\n", "Where $p_F^+,p_F^-$ are statistical models that map the density of fibroblasts and macrophages to fibroblast division or death rates. \n", "\n", "By default, $p^+$ is implemented as a logistic-regression model. \n", "\n", "Logistic regression fits a linear model in order to estimate probabilities. The connection between features $x$, parameters $\\beta$ and the predicted probability $p$ is:\n", "\n", "$$\\log( p / (1-p)) = x^T \\beta$$\n", "\n", "In our case, the features $x$ will be second order interaction terms of fibroblasts and macrophage counts:\n", "\n", "$$x^T \\beta = \\beta_0 + \\beta_1 F + \\beta_2 M + \\beta_3 F^2 + \\beta_4 FM + \\beta_5 M^2$$\n", "\n", "The following arguments produce second order interactions as such:\n", "\n", "\n", "```python\n", "# Don't run this block:\n", "ana = Analysis(\n", " ...\n", " polynomial_dataset_kwargs={\"degree\":2} # 2 is also the default degree, so this argument isn't required\n", " ...\n", ")\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Plotting a phase-portrait\n", "\n", "To plot a phase portrait (or any plot for that matter) we pass the analysis object to the corresponding plotting function:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tdm.plot.two_cells.phase_portrait import plot_phase_portrait\n", "import matplotlib.pyplot as plt\n", "\n", "plot_phase_portrait(ana)\n", "plt.show() # required for rendering from terminal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tip: Cache the analysis for faster loading" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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7d6hJkyZssKC7g6uEN3ny5Hg9flKTHsxe+H9/++03KlWqFI0cOZJ/LFJvPQkZO6crMpXPEpF1XFkEso06DmYhPZAC6t/QN8+TePbsGbVo0YLdmpBAQ5KhK0DjWxhER44cIa/U3mzWrBlvZoJyQxo/riI9+chJNoFsoyxInfQ86VLEHAXSa9WqlSFZr47cy9DRREINOie4auHt2rWLx3v77bdpxIgRZAQ8r1djUihLz9hEFpUgIx85yXYcjBoX/eA8bemBZBASat26NXkSgwYNsnjrKlas6NJY1nJlKGY3Sq7M45aeGaEsPXndmzJZkIqc5Lb0PEl6SGBJkyYNNWjQwGP7MH36dPruu+9oypQp7N50BYGBgRwTdIdcmSI9G1AxPeMtMkV6ipxk3V8zkB5cmwgLeWof1q5dywmHEBPB5goQC4Sb9uHDh5zYaLRcmXJvurlOT1l6clqQMpGeIifvjulBlP/YsWMey9r8999/qV27dtS0aVOaOHGiy3MBRLKRiQpZSrRGMhrK0rMBZenJG9MzqsAZULJpco5rBOlhfvBUAglcm2jD9tprr7n9u2/cuMFkV6RIES4xc/WcDRs2jBYvXswZm9WqVSN3QJGeDaiYnrwWmVHj4viKPndGd1mQpZ5OtnGRyAK3oqf6woH0Xn/9dUqZMqVbv/fx48fsUsW9sGrVKkqVKpXLajKo0UZc0J1Wq3JvxgNl6clHpjKStIzuTVnGNTKm56lYGorR9+3b5/aszaioKOrQoQO3DUItHiQkXcHff/9NPXv2pN69e9OAAQPInVCWnhcossjohpTJvWlUBwDZSEQ2N6SR7k1PxfPWrVvH9w5cjO7EsGHDOHllzZo1VLp0aZfGOnr0KEunNW7cmDM/3W0xK9LzEvemspzkHFcmEjHaDSlTIounLD207UEP0nTp0rntO5csWcJt3b799luX44g3b95kwi5UqBAtWrTII3FR5d60AZXIIq9FJtu4uM6UQHaUlDE9dwPzx+bNm6levXpuzdR899132bX5ySefuBwT1C1UETFBZ6EsPS+x9FTsTT7SM7JbuEyWk2zyZp6y9E6fPs3Czu4ivcDAQK6fK1q0KCeduDJ34Vy89dZbHBPcuXMn5ciRgzwFRXo2oCy9GKiYnvHkZCTpySLgrGJ69rs28VvQJd1oREZGUvv27enJkyfcpDZFihQOtxmCRYwGspj30FcPySsiYoKuQpGeDaji9P/GlW2yV+PK595UMT37ANcmdCndUarw2WefMdmhL54z/U0hGg3X6OzZs+n27dssWQZrsVGjRuRpKNKLB8rSM84iU2QqZ1aoqiv0HOnhngEJIcXfHd3PJ02aRN9//z298sorDn8+ODiYDh8+bOnAAAwePJi6d+9OZoBKZLEBFdMzdlzZ3Jsyuk1l6jYhW0zPE4ks6KqAGJvR8bwDBw5Qjx49qGvXrtwF3Rn8888/PHfEzdqMiIggM0CRng2omN5/48pEImrc/yZ7Iy1IWSw9b4rpwbUJoq1evbph33Hnzh164403OA73008/OT0HYl/jXtdz5sxhIjUDFOnZgLL05LTIjDwOMpGebLFCFdOzL4mlRo0alCxZMkPGDw8P54JxWGOQOXPle1COoC849PsRdYU1a9YkM0CRnpstPUWmipxkHhdI7Aky7rb0cHy2bdtmqGuzf//+tHfvXm5ZlDNnTqfHuXz5MneB0FG5cmVasGABJ7O4Ix5pD1QiixfIkCk3pPHjGuEuNMoNaeS4gFHF9LIksrg7poekECSH1K9f35DxZ86cST/++CP9/PPPLlljmIfef/99fl6nTh1OhIGr1GxQpOclpKfIVM6EExnHlSVBxsiYnjtJD65N1MnBahKN3bt3swX2wQcfWAjLWQwZMoRLHOAeRWzQVfUW/GYjrmPl3owHKpFFlSyocV8cV6YEGW+x9EB6KEgX7VK9efMmd2uoUqUKW2WuAFbiuHHjuKmsq4SHTuqQK+vVqxcZAUV6NqCK0/8bV7ZJWY1rrDtWxgQZmbM3kViyfft24a7NsLAwJjwc899//92l3wOVFViLUF3p16+fkE7q+/fv50cjoNybXlKyEBAQYMi4Rq3sVSKLsbE3mchUtkQWd1p6R44coadPn1LdunWFjvvhhx/y2Kipy5Ytm0vxxnbt2nFzWRS0uzpvwkWqd1I3qjxDkZ4NqCzL/yYjI25u2chJjWu8pWdEtwlvqNNDsTj2v1y5ckLHbdmyJautuBInRENbuCGLFy9OCxcudPnagFSZ7iI1spO6Ij0vsPSMjL0ZNa4Rk4ZsJCKbe1O2/TWKTN2ZyHLw4EEqVaqU8Pq8Zs2aufR5ZJM2adKE7+OVK1e6rAeKbux9+vRhF+nHH39MRkKRnpstPSNubhV7U+PK7N400oI0Yn/d6d4E6VWsWJHMhIiICC5kv379Ou3atcsl96j+G9HRoXnz5kJcpC+DSmTxAktPtlIIGWN6Mk32ikyN3V93uTeRxQjNTTORnqZp1LNnTy6WR2kCXJuu4MqVK2x1lixZUoiL1B4oS88GVEzPeLkwoyZPIxJ6ZCNT2caViUwxN8DScYeld+zYMT42ZiK90aNH06xZs7gTg6sKMUFBQfT6669T8uTJ2UXqaM8+Z6FIzwaUpWfsuLK5C2Ub10h3oSzkZJR7E65NwB2WHtx+2P8yZcqQGTB//nwaNmwYjRgxgjp16iSkZAIi13CRZsmShdwFRXrxQFl6ivSMHlc296Zsx8GIcXXSc4elZ1QSizMAMaEfHjolfPHFF+Tq4v+9995jNRgouBQtWpTcCUV6bixOl83SM3LylGl/MXnKVrohi+Uk2/7CQnEX6aFcwQyuzRs3bnAJAZRboLzi6sL9yy+/ZKtx0aJFrDTjbqhEFhtQ2pvGjyuTJSIjOcl0HGRym7rLvWmWJJbQ0FCWFcNxdFW5RRe3HjVqFI0fP54zNj0BZenZgEpkkZdE1LjykbRMijfusvTMkMSiaRoLUWNfduzYQVmzZnVpvPXr13PmJzQ1Bw4cSJ6CIj0bUIksxo6rJnv5LBxA7e9/pGe0pWeGJJYpU6bQ3LlzuR+eq+QLyTPU9iFbE+LWRtfiJQTl3rQBZenFQHU4l3NcRU7/jWuUe9NoSw+kh9o1TyWxIMEE1hi2t99+2+HPI8sT4tMo79DlypCwgjieEdeQI1CWng0oS8/YcVWCjLzuQtmOg6zuTU8qsVy8eJHjbQ0aNKCxY8c6dZ1AWQVC2XCNoms66mdXrVrlslyZCCjSswFl6f03rkwWjhr3v3EVmcpbp6cnsSCt39148uQJi1FnyJCBrTJnzgsIG4Sn9wLE3LR582aX5cpEQbk3bUBZevJaTjKNK1N9mmwWmdExPSMtPVhHcAu629KLjo6mLl260OXLl2nFihWUPn16p8bZtGlTrOOOuRQu0hMnTpAZoEgvHihLzzhLT1mQco4rG0nLaul5Koll9OjRrKeJGroSJUo4Pc7ff/9taesE4Dm6tLta1C4Kyr3p5uJ0mcjUSEtPWZBqXBnHdUf2pp7EAk1Kd2HFihVcNP7111+ze9NZhISEcGNaa8CtiTIFlD+YAYr0vKA4XUZhaDWusZ3T1bgx44oWIHdH9qa7k1hOnjzJWprQwnTVGgNpYt4AGjVqxB3a0XfP0xmb1jDPnpgIssXeVEzP2HGNlN+S6TjIlngjY3E6xndnEktQUBBbdvny5aM5c+a4dL8fPnyYfvjhB8qRIweXPLjadsgoqJieDahElv/GVRaknOPKQiKyxQqNjum5M4klKiqK3nrrLXrw4AG7N1OlSuVSXzxYdHDLnj171mXCg4v0woULZAQU6XlB7M1IElEWpJzkpI6DcdqbGNOI6xc4evQozxGlS5cmozF48GC2yBYvXkwFChQQ1hfP1Vo8ED8ayyLGaASUe9MGlKVn7LgykpMsloiMbl6ZxjW6a/qZM2coT548hjdUXbhwIX377bc0ceJEatiwoUvHA4LUel88V/U5r1+/zhYjSHj69OlkBBTpeYmlp8hJjWs9ropBxpCeaIKCpWdkEgtcg0b3l4uKiqLvvvuO3nnnHfr4449dmnfeffdd2rNnj5C+eMHBwUx4mMvWrFlDqVOnJiOgSM8LsjdlHFe2yVPtr7EWmRGWDcYVnfbvDkvPFcvLHvj5+bFSCsjblXlu6NChrNoC96irffGwmED2KHQ6d+7cSdmzZyejoGJ68cAo96ZMZKo0Mo0fV5ZYlozH16jidKNID/t7/vx5t3QST5s2rUti1nA9QpcTFmPbtm1dnhfRlR3ti5BQ40phvD1Qlp4bi9NltMgUOcmXcCKjZSoL+Rvp3oT8FzI33UF6rgDC0X369KGPPvqI+vfvT64CtYFQgYHFWKdOHTIaytJzcyKLbMopMk2ealxjx5WtXlG2RBa4NoEiRYqQWXHgwAHuwNCiRQvupODqfAaL8ZtvvmGLsV27duQOeJT0vvrqKz5o1luxYsXI05DNDSnjuDJN9rJZkLK5N2XK3jTS0kMSC2KQuXLlIjPi0qVL3BcP5RRoLOvqtYDyBliMffv2pQEDBpC74O/oZLVt2zYuHEQx4rNnzyhz5sxUvnx5Dr7mzp3b4R1AMSMyfyw7ZAK5GtksPRkVWZRlKp97U0ZLzwjtTSMtPVh5RtUAuoKg57V4yKgEWbmaeLRv3z62GFu1asVlE+7spO5rr4joqFGjmNSQUrp27Vp6+PAhX1AIvA4fPpzy58/P/4f0VUeAmwiCpPqWKVMm8jRkIyeZLDJdl0/0uDhn2GSZPGUkaWXpGZvIAtIzYzwvMjKSlVvu3r3Lcz8MHVcAzkDxOYwlxPKMuKYSgl1XBFYf1atXpxkzZrCIqC0RV1h+KHjEwUEqa48ePezagXPnzrFWGzKJ8B1jxozh4sz4Vlm69h3w6NEjMgKK9P4jEdHj6i1HRF/oRo2rjy3buDIl3sjk5jXSvQnSq127NpkNgwYN4h5569ato8KFC7s01r1799hiRK++v/76y62dJHT429sf6WVaannz5mVZm4EDB9LVq1ft+vKqVavSr7/+yqubW7dusUI3TjoEV20VJoIQ8R6jodybxllkRo1rFOkZaUEqcpLPHWuUexMLeMyBZrP05s6dy+7HyZMnu1w/iHBY8+bN+bfCI5gxY0byBOyaKR0RD4UVWLBgQbveC8ZHjQeaJb722mtchQ+36ZIlS2y+H6SKqn19QyGjEZCJnIwaVycnoyw92SxImSZ7mWJksglkG2XpweNltszNvXv30vvvv8+qK0g2cfUco3s6dDVXr17N4TBPweGZByYuigh1TJs2jcqVK8c/CMFOV5AuXTo+6fD52gIutjRp0sTajEJit/Rkc0PKNq4+tiwWjqyWniyJLHq5glksvZs3b7KmZoUKFeinn35yeN5Cpuf9+/ct8x5IE/V9S5cupUqVKpEn4fBM+emnn1piaWDtTz75hBNY8CNdTTt98uQJt5MwUoLGHqjidOMtPVnICROnTG5eQFl6xo1rVCILSA+JfEYu5O1FaGgoEx7u/T/++MNhyxbzJ4rMS5UqxRwBYesff/yRyRNc4Wk4fEWA3HSZmGXLlnEWDooLDx065PAPQvwPPl7EA7GyQBYobqoOHTqQJ6EEp+UjJ1nJVJbJ3shxZYvpGeHeNEvmpqZp9MEHH3CLI5SmgYidqTdEtwTMdZUrV+ZjNmzYMLuTG42Gw1cEVjkISAKor4NSN5AhQwaHsylxYEBwgYGBnAYL0VIEOF1NiU1MrYWMsshkG1c20jPSbSqTu1DGmJ5Rll6VKlXI05g0aRInr6CUwFk3JHgB8yfmOxAenpupi7rDVwSICW7MmjVrcoEh9NJ0dndUSQAK3WaETJaeSjhR47oz21QmMhU17uHDh3muAtnduHGDrSBkMyIHASVargg3AzhfmD87depEnsT69es5fIWtY8eOLo0T9/ch5wMLhi5dupB0pDd16lTq3bs3/f777+yjzZkzJ7+OosXGjRuTN0BGS0+WmJNsJCJbVqiR2aYyuk1FjItsw/Hjx/NYGBMJGmh/g/sZmedI9nAFIFJ4zzzp3jx37hwTOLLoURrmLHB8NmzYYMmL0OdRLBggcmIGOHxFoHAcWTi2zGJvgUyWXtyLSxb3pmzkJEuCjFHjyhZ7E0mmaHuD+mD92OqWNOJVUBWRPXPz0aNHLCCNrucQGHHlHKM1EBJhdEBLGfqasBxhGZsBdl0RjsTqzJB9JAKyWXqyuTeV21QuCxLXbmJNZEE2OWrVZs+ebSE+HAtkJboyT9y5c4eJAKSH+mZP1K5FRUUxIaEwHnV5rhATyA7Z/AAK2UeMGEHVqlVzq66mPbDrisCBeNmO69aRftPJDCNLFpRFpkhExuNgdImFmd2bwGeffUYzZ87k57iH4QZ0pVs43KP65xETxJhorQNPGsJH7ipSHzZsGLtvsbliaeL6QFIjiBxZn652UjcSdl0RaC2fmCBjTE+2cWWZ7I1yFxpFprLtr1GWHq4zbKL2t1ChQtSmTRsursZ9PG7cOJfGg9tPjxHq7sDly5fz2Mh0dAfpLVq0iON3iFdCHcsVwMJDCRs2VwkPxwDlbFD2wgJANOy6IurWrUuJCTLF9JR7M/a4spCpbJapkbFCIyw9I44vZBBBeqgrRgKLK4DuJHrTIT/C+p4A2cGVajQOHTpE3bp1Y9cmCMYV6NqcUOdCqyBXMWHCBB4T4xkBu640pOjaC1cvBjNAJsFpZZHJWf8nWyKLkbFCI1pYGbG/ZcuW5cdevXoJGQ/p+0j80IHjgBo5o1oX6QgMDKSWLVtyL1N0znFlToK1CCsPCwIRVhma06JkYsiQIYZYeYBdVwS0NfViw4SgYnovH1cWN6RRFplsZCrruLJYekbvr8hxIyIi+FEv03IVULBCN5nHjx/z37C43KFLmT59es6oRF2gK619tm7dysTduXNnGj16tMv7haJ2WLkYE/1bjYK/vdJjiQnKvSkfmSpyknN/jbZ4Re6v3stTlCUGObP69euztQcickfbNP3e+/zzz8kVQFMTrkxobCLBx9V57ciRI9S6dWtq0KCBy9bny2DXFQEfdmKDSmRRFpmM4xql6SmrpSdyf6EoAoh0P9aoUYNJ78svv/RIQ1VngJZuSHxBiQUSV1w9HjCqMB6yRxEztdWkXCScminnzZvHMmToeI6O6QACmdb+aZmhLD3l3jR6XKNJRJZxjSZpkeMaQXq6dCMK4GXAw4cPmaBwXFHm4GpdNtRtoOSVMmVKHi9VqlRkNBye0SA9Bu1N+KNxAPSbAbV8ID5vgCpZkI9MZZvsZXNvKgtSvHtTt5owdyK2Z3aEhYWxSxMdcSA7CaNHRCd19GGFXmeWLFnIHXB45vnhhx/Y5zp06NBYFyoCsPDzegNUcbqyyGQd1yhyko38jbT0RLYWunr1KuXOnZvMjujoaE4wQReclStXutw1Aeenffv2zBlr1qzhmjxT99OzpTeHC+Hp06fkDZDJ0tMJWhaLTJGp3OPKYunJ4t6EpQcVFrNj2LBhtGTJEm40gNCWq3MWyj5gLYJA3d1J3eEZDcFLZNrExbp160zVM8lspKeEoWOg3JvGjqvckHIlsshg6f3222/cKBwqNMiwdBXQ5ETGJzZXlWCcgcNXGuJ5qPGAdA4mcvTUw0GBnI2uTecNUKQnV7G3rCSi3JDykKlRMT24+cyK/fv3s3ILavFcVW4BEBr76quvuK6va9eu5Ak4fEW89957nFr7xRdfcCASzQER0JwyZQr3Y/IGGGnpyUZOsliQiqSNHVcmcpIlpodw0IMHD0xr6d28eZMTV6BE87///c/lORGuzJ49e7LSChRcPAWnrgjotWED6T158sRtWTcyJ7Lok7Jslp5yb8o1rtEWpCyWngwxPVh5gBlJLyQkhN544w2er/7880+Xu8MjAQYWLeTPvv/+e4+2G/J1JpEFXXaBFClSWAgPr12+fJm8AcrSU5aTGjc2lKVnHOmZLZFF0zR6//33WXMZnR/QT9BR9O3bl2bNmsXP0S+wWbNm3GEe2ppGiJY7AoevCPhh4eMtXLhwrNfRgBAxPeixyQ4jhaFlsfRkc2/KSiKyWGQykZP1uCLPm+iYHpJYMB+I0vIUhW+//Zbmz5/PYtLOZFaCzFHaBpw4cYJVW2Ac/fXXX6ZQnXH4Sjt8+LDNlFV0yP3www/JE4CLNTg4mNUBnCUVEJ3eIR43OFZ1GBM3I9QCnAXGwv7BXQDgEeMiLuCKywD7B/cyBAIAfAeew/p25abEjY1xsY/W4+IYuCIPhN9tvb8Q2cV+QoHBlQkPY1rvL8bFRIdxXZnwEG+Je3zx+3EcXBkX42BsFOTqf+OcYXN1XGxQuND3H8fC1XH1cfRx9ZAGJi9Xx8X9dvfuXcu4eA33hKvHAeNa76+IcY2y9LJmzWp4VwVHsGrVKtblRB22swk2f//9t+U5WgThGjx+/DhlyJCBTAHNQaRJk0Y7dOjQC68fOHBAS5UqleZOBAcHw2eo+fgn5ccxY8Y4PdZ3333HY8Td/Pz8+Lc5i3bt2tkcF8fq2bNnTo9brlw5m+MWKVJEcwXZs2e3OW7t2rWdHjMqKkpLnTq1zXGbNm3q9LhhYWFaypQpbY7bunVrp8cNDw/XUqRIIXzcyMjIePe3ZcuWmitImzatzXFfe+01l8bNli2b8OsBKFCggM1xK1as6NK4WbNmtTlunTp1NFfx66+/8li4PkSgW7duWpUqVTSz4Pjx43yf4lrEPess2rZty/Omfux9fHy0Fi1auDTfiYTDviuoaqM8wbqFEJ7jNU+3iIfP2FnYKrgHsLJH12RnUbFixRdegzUKcVVXLD1bbgeMa+v7HAE+b8tadqWAFC5SHF/R48LyQv9GW+O6chxgeerttESOC0sjvuMb3/VnL6pWrWrIuBBEtjUujo8rwFxha1y9Z52zgBfKiHGtLT1Rrlgz1egFBgZSixYtuLkAtJWdDWuAC1Czbc0P8KLBtWkamUpHWfLEiRNaxowZtYIFC2pdu3blDc8zZ86sHTt2TPOEpeeXJLlWuXJlLTo62umx8Nnq1avHWqH4+vpqI0eOdGkfHz9+rKVLl+6FlefatWtdGvfSpUux9lVfUZ06dcqlcQ8ePPjCviZNmlS7ffu2S+Nu3brVprUbFBTk0rgbNmx4YVxYPY8ePTLluJs3b35hXHhPHj586NK4O3fufGFcWKv37993adzDhw/bvB5u3brl0rinT5/m69V6XH9/f+3y5csujQsvlBH7C0ydOlVLkiSJJgpFixbV+vfvr3ka4eHhWr169bRMmTJpFy9edGmsXbt2xZqP8JglSxZt6NCh2r179zQzwGHSA27cuKENHjxYa9Kkifbmm29qX3/9tRYYGKi5G9buzfXr17s83qZNm4RPRsC4ceMsFwAe4cJxhaB1dO/enYlZJ+gOHTpoIgCXo/W4AwYMEDJurVq1Yh0HXDeuAscRCx7rcceOHWvIuDiPIsatVq1arHFHjx6tiUDdunUt4+K8DR8+XMi4uM+tx/3000+FjNumTZtY11mfPn2EjPv666/HGvezzz4TMu7EiROFhnAwFsIqnkafPn14wYGFqasoXbq0Zf5s3ry5tnLlSi0iIkIzE5wiPbNAJ71iJcsIIRGMUaNGDctJc9XKs7b2rGMurlp5OrAq029uEVaeDsQw9X0NCAhw2cqzZeUgtiViQQGsW7fOMi6sahxvEVizZk2scZ88eSJ8f3FdiNrfLVu2xLKiRR3f3bt3W8ZNliyZy9ajjn///TeWlXfz5k0h41pbG8mTJxe2IMdiKkOGDELGevr0Ke/fvHnzNE9i+vTpvB8///yzy2PNmjWLxypbtqywc2kEvIL0xo4Tt1rSrT2QiahJA8CqG+PCDSyCoHXUr1+fx61ataomEkiIwbiirEcAv1tPlBk4cKDQceFyF7lQ0cfFJMfXmADr0db+jho1Sui4eiLHkCFDNJHIkycPj9u7d2+h48LFh3E7duwodNycOXPyuB9//LGwMUeMGMGJPSIANy72T4SHypVFkr+/vxALGxYdwi3vv/++kPkN7uiQkBDNCHgF6f29aYuwMXHC4LcvVaqUJhKwlrCvH3zwgdBxly5dyuNilSUScLlhXKyaRaJfv348LmKSIgFXL8YV7WZHzBrj4loTCWTuYVxXY5px0atXLx5XRAzLGlikYNyzZ88KHRcuboy7f/9+oeP27duXx71y5YqwMb/44gsmfxHYt28f7x9ipp7AhQsXeOGFRbOr2aiYI2BRt2rVijOUXQXu4RIlSmidOnXSjIDYymMPwYfEFXwj8wvZmg0bNiSR0DsM165dW+i4RYoU4cdSpUoJHVfPUtQ7O4tC9erV+TFjxoxCx9VrR13t5BwXekay6Caf+v6mTZtW6LiolwVE10TpWbaoKxMJPQtUtCpJlSpV+DFz5szCxkQNq6iaujt37vCjJyQcHz9+zHJgaF67dOlSl+pvT506xWoruD4WLlzoci2k3lgW9ZvQdzYCYmUQFBQ81G3C6C4WosfVU7qNGlcWZRqZlF5QsiCK9PSifJGkbC9OnjzJogvoVu7K4ujGjRvUuHFjbjiwYsUKl9VWIiIiqF27dvTvv//S5s2buazLCCjSU/AKcjKS9ERLsRk5rmyC3kYLTovupyeS9EA4rlhZrtR0nj9/3qVuESBNEB7uOzSDTZ8+vUv7hHF69OjBRLx69WqLpW4EnLozcLJQWAns3LnToknnTTCi04LCf5CJ9IxQhDeSTLG/RlmQMml64viKPMYgPVFthUB6nuxOk9SF34FeqnCPovUQSEpECGTQoEE0Z84c3l599VUyEnZfEaNGjWIxafhcrQWZ0fkWZq43wZNtL8wCoy0y0TDKDYn9NYqcjFCbB4kYMa5MFpl+HEQTqWhLT8aWbFFRUdxWDs1lodNZvHhxl8ecMGECi1xDsQX9WY2GvyPiqGj+B7MYNyyERNu2bWvYStgRfPFXMI3euJn8/XzJ38+HtwA/Xwrw96UAPPfH676UhB99nj/6UhK81x+PvuTv7/P80ZeSFXmdbvrlo4Xbrvw3RpzxMEbSAGx+z5/78d/663j09HFJbGQqk0WGycOocY0iUyMtSCPIVPSYiZ30NE3jpgKI36HlkJ6U5gogeYaO7Ggq269fP3IH7Ca9n3/+mR+hXl6gQAHu+Is2Q1DPRzd1mKTQ5RRxIJxBRJRGEVZ6b64gWeHGdJOIflxz3ukxMDeA+HRCBMGWe3cGLTuXgXZNP0hJdXJ8TpYxRBnzPHkSP8uWLIkfpUga85hcfx7gR8mTxvxtNGRxQ8pGeopMjbcgzW7p6ZnXsmDkyJE0ffp0+uWXXzhj01UgFog2ddhGjx5N7oLdVwV65SF9u1ixYnyB4gAgzRip3I0aNaKzZ8/ye/QGs+5E56op6c3GNSkqOpoiozQKj4ymqOiYx8ioaIqIBCHiMTqGHPkx9vPISI3C+TGa5s5fSDlz56FKlav+9xmrcTAutrCIKAqP0J/H/B393HuHuT3mtWh6HBJzY6fMnJ/uhhDdvRTTrkYE/H2Jqnz0B329+jGl277bQo7Jk/rzY8qkfpQquT+lTOZPqZL995giaey/UybzY+vXaDekkaRnZIzMiHGV2/Q/C1L0sTA76aFkQSZL75dffqHhw4dzmAsk5SrQSb1NmzbUpEkTNqjc6RWz+6qAOYs+S3rPKgQcdf8r0kxh/XkKqZL6ULb0rrWzt8bPg3+nfNkb0hftuzk88eqkCwLUSS8sMooePQmhRq++Tp9+PoSq16xtIUmdMMOfvw/PQ8KjKDQ8ih9Dwp4/Pn/t2fPXQOpAZDRRQPI09DBEo4chz1z63ckCfC0kGBEaQCXajqVJa25QpvTBlCZFAKVNEUBpkgdQmpR49OfX9A0WrSOQJeHEKDI1kkRkG1c0ORk1LkgPveFEXKv37t2ThvS2bt1KPXv25G3IkCEuj4favqZNm3JXHDSqNeL8JwS7vw1BS+D06dNUuXJl2rVrF8f1kNiCA4GElrp161K+fPkosQKTboB/TMwP5GGNkJAACr56mPKlCaP6ZVwv8IUFCiI8eOQYNW3+Bs3/bTHlK1j0OVFGUmh4ND0Li6SnYZH0JDSKnobiMTLWo76FRsQkgeAxNCKcAh+jhYovpc9fiXafQ2PdmOa6CQFWZQwBPifD5AGUNmUApU+VhDKkSsKP6VMFUFCoH/klTSncklTuTXndm0aNa1ZLDw2EcTxlIL0LFy7Qm2++yXP7999/7/I9htwQhMJQ2+epTuoOXxVwb+JigkmaPXt2rs9Au3swdt++fS0drBWMBSfV+PtShpR+FPLgKuXJ4E8VCjpXKwMX8NPQqFikuG3HHhrz7SQa990k8vFPQY9CIij4WQQ9fhbz+OhZJD3C3yER7NLVrdE7L/XcZqVq/VbQa1/vpPQpk1D61EliHlM9J8jUSShzmqSUOW1SypQmZkNM9GVQpBcDZemZP5FFL0w3O+kFBwezOgrUk1xVbtF79r322mt8XlDq4Gptn7Nw6mpDbA8NUHEQMNH06dOH3ZvWjQMV5AFieWlTYvvvor55KorundhAr5fLSNmyZYv3s9HRGpMkCJCJ8SmIMJKJMfhpOAU9iaCgJ+GW7U7QU4qI9mU38L1HYby9DKmT+8eQYOqklOk5GYIY8ZglXVLKli4574dM5CRjTE8WcjK7pScD6UVGRtJbb71Ft27d4vibqwT19OlTTn6BWxe13bD0PAWnroqVK1dansO1qUvZGHHxKpgbvr4+lriePZg9eza9935Punb7AT0KiWIifPA4nIJAkI/DKfBJON0PDqP7zwlRTwTCdvH203jH9fcpRsXfnkqfzj5CWdMl4xhvNn5Mzs/hYsW+miVWKJtFZhQ5yWRBitLe1ElPtI6pSHz66ae0YcMG7oLujBzYiRMn2BCC+1KXFzt27Bht2bLF41mrLl8VqK9Q8F4Y4jKMDGcyypHR96UuS1iR94LDKPA5Cd5/FE73HoXyI8jxzsNQevAknCI1P0qeMS/tPh1ocyzUV+bIkJxyZUpBuTImp5zPH/F3lrRJY2Wuxt0HmSw92WJ6RlqQZrb0MI5ocXRRmDFjBheKT5061SnhfSi1lC1blkXrISn2ySefMIEiLwT5IJ6G0t6MZ6LXFT5EQZ+IRI+rk5KM475scsZ7UycP4K1AtlTxvg9ZsOOn/EzTZi6g/81ZQrcfhtKdoFB+vB0USveCQ7ns5Mq9Z7zFBQQLsqcHASan3JlSUN4sKSnf883o0g0jxpWJpHW5MFlieiK0MvVyBTOKV2zdupVFSLAhbOUMYB1iMXPw4EG2ElHTjQ4MRsuL2QtFejaAm0X0ZK+vOvViXFHQb0K4EERCX9GKHtd6f0WtxJHokj5ZND28fJBaVM1pM1HnbnAY3QgMoev3n9H1wBC6EfiMrt8PoZsPQrhs5Nr9Z7ztptiWYhKfipSz8Rf03Z+nY4gwawwZZkydxKVJC5+VSd9Vxhik6HFxzYrQ3jSrGssFq0xNWHrOAtYdjj3OAQgPLYzMYOHpUKRnA7i5RSfl4CLARCea9IwmJ6xujRgX+ysyXRkEGt8xgOsSrk1slQvHbqWCBBi4TUGC1+6H0FVYg3ef0uW7T9lKDNcCKGX2krR8T2x9WZRmFM6emgrlSE2FsqeiwjlSUd7MKTmj1t5rTCbSMzI7NjEmspiN9IIFZWriHkRmpvX8if596OwAvU5P1nPrUKRnA/oqRTQSmpjNRk5GkamRJI0J1FGLBMktSHzBVqFg7P9DneOYyTNozpI1NHT0JLp8B2T4jAkSJRsHLwTxZu0mzZ81JRXKnpqK5kxNxXOnYUK0VXKBfRTtTTASRhbpyxKDFEV6yGDMnz8/eWOm5q5duzhTM64BgWMHsleklwhJTzb3plGWnuhxrd3HoqSiINWWKVk43T+1iT5oXChWDBGW4LmbT+j8rcd0/uYTOnfzMT0Ni+LXsK09eCtmv/x8mPhAgCVyp6XiudJQnswppHRvymTpmZn0UJyOJA9vydS0xtdff215jusFrlJoM7/xxhseKUR3mfTQ0RblCihRQApqpkyZLP/36NEj+vjjj2nWrFkkO4wiPUz4iZ2cjCJpfVyRpBfftQDLrWjONLzpAIHBHXru1hM6d+Mxnb7xiE5de0QPn0bQ6euPeftzd4yLFKLhKaKKU/oyrejQhQdMhtBMTYzuTaMsPSPcm7hmRSSygPQ8VZgtOlPTGgcOHOBEGJxPZPWjKWzevHnJbLD7qvj777/Z51u4cGH20X755Zfs+61Xrx7/P7otQI/TG0jPKNeTjJaekYksMiQK2bsAAiFkz5CctzolM1uI4lZQKJMfb9cf0Znrj+hZWBQ9o7SUqUI76vu/w+Tn68Pu0LL50/FWOm+6WEIBZkBi7yuI/RS1oALpIbnDGzI1dZw5c4alKJGwsnnzZkqZMiWZFXaT3ldffcV9j9ACAjcAmv61aNGCiQ9t470JMlp6sliQRo2rk54RZIprwRlLB+/XE2galM1qySSFa3TCjKW0499rVLhiA84sPXntEW+/bb/K7yuSIzVVKpyBKhdKT2Xyp7NLis1IGFmkL0NMT7+uXCU9FLjDQPC0pXf+/HkhmZq6niY67SA5Z82aNUIIb9myZazjbIQb2N+RCns0/ANw8X/22WfcJh7tIaC7aaaU1MQY01Puzf/cmyKhT5yiLBJkkiLRpUDyQPpt0yQ6+vuX7Bb99/JD+vfSQzp66SHXEp69+Zg3NDJGB4vS+dJSpUIZOPsUhOiMuowrkE3bVLR7U78PXCW9hw9jxGk9aekFBwezwSJCU1PX08Q5hDcQY7oKxBaRWINYoEdJD/Up+gnTgdZC+LHt27fnlu+uYOzYsZbuua6uPBKTpYd9xTmQxYI02r1pxPE1wnLQSxas3aKNK2Tn/wt8HEYHzwfRgXMPaP+5B1xSgb+x/bzuAtcI1iiWiWoUz8TWoDuaCSf2FkuiSA+uTcBTll50dDR16NBBSKYmWsyhHx4ai+/YsYMbD7gK6HK2bt2ax0VXByNgN+mVK1eOddPiMi8YGTdEly5dnN4J1G+ga0OZMmXIW4vTjSpZ0IlEFjek0WRqlKUnehGUUNw4Y+qk9Gr5bLzh3kLt4IHzMQQI4kPrp5X7b/IGK7BiofRUs3gmJkKjIFszXdGKLPr16moii244eIr0pk6dyh3LXc3UhJsW5ITeeIgNitDTPHLkCPfZQ03f4sWLhSQNuUR6vXr1ou3bt9v8P6wccHMiE8iZ1ULHjh35s+jK+7IDjc06Y1SW4nQAJ1H0pKyPK0v9nztKFmQgPXtLFvA+yKJhe7NGblaPOXIxiHaeus8bXKPQG43RHD1DaXzKU6oit+juw1DKki5Zorb0RGbxirb0POHeRLLJoEGD6MMPP2SXpCvHtnPnzswHIE80g3UV586d430qVKgQrVixgrv4GAW7r2LUWUyaNCne/4erE5ago0DWENjdnnTZMWPGUNq0aS1b7ty5KbEXp+s3omwWmYzuTTNkCMOyq1IkI/VvWZSWDqpBc/tXpQ8aF6RSedISDLFHWlpKX6EDtR6zk3r9dICW7rzGHSsSm6Wn3JuxgcUgvHG5c+emcePGubT4wZyNRBPkcrzyyisu79v169c5EQalcLBCjRbi9mhxOg7aoUOH2L1pDxDzGzBgQCxLzwjiw81ibVGKgrL05E1k8ZSl97IxIMSNrXO9fExun46dTcdva5Q0cxE6djmYt+9XnqXyBdLT6xWzU91Smbno3kx1ejK0QhKZyIJrNUWKFOROjB8/nufZHTt2uPTdKFVDKOqXX36hVq1aubxfiAdCiBrXFxJhMmeOKfcxEg5f/Vih2Lr48RpMUpinXbt2pXffffelaa5IWoESgL2mLJJpRAi+eltMDzei6HGx+sZxkCVWmFgsvYSAprq5/G7Q5k1zaf+/Z2jr8bu05ehdOnYlmA5dCOJt4nI/qlc6C71eKTuVzZfO7ixQ2bo3iG5ZJKpkQa/Rc2eHBYiKoOQMGffVq1d3epwpU6ZwCAoE2q1bN5f3C0YLavuQAfrPP/8Y5rmLC39nmB61etjZKlWq8Gv79u1j3y7M3kuXLnH8DystVOTHB7SdgBabtT8YFyr8xAi2wtLyVFNaGWN6oknEKDJVlp57BKcRz2tXKw9vt4NCaN2h2yyNhk4Taw7e4i17+mTUrEoOal45B2VInfBiMrE309XvL1eTK9ytxoJ59J133qHixYsz8TmL+fPns+IWiBOyZa4iNDSUWrZsSWfPnqVt27a5tbGsw6QH8xhs37Nnz1ivw+SFeQpfL7IwkW6aEOk1aNCAO+laA9ZhsWLFONjqyS7sMsb0ZMkKlVGRxSj3prsEp9Gwt2uD/NSlfj62+tYeuEWbjt5htZgZ6y/S7I2X6JXSWah19VxUOm9am+QmY52eWd2b7kxigRYmMizh2nTWS7Z161aem7GhtEzEuUGZ2969e5kzUBngTjhMemgbYSsQChJDh1wANRaff/55guOkTp2aSpUqFes1VPKjuDHu695UpyebpSdLgoyM7k13C06DtMrkS8dbvxZFaMuxu/Tn7uusBLPxyB3eII79RvVc9Fr5bLH0QGXssmDG4nR3Wnq7d+/muXrEiBHcydwZnD17lksTkLACw8bVhQ8WOXCNQrkFWZq1atUid8Phqw0ZNhCdjgtdiBpAawmQmqyQLaZnRCKL0e5NoyxI0YsKoyxId1p6tgBCQ2LL/z6sTDM/qkxNK2XnzNDzt57Qt3+cpjZjd9LsjRcp+GmElF0WjKrTk4n0/ve//7FSFjxnzuDBgwfUrFkzypo1q8vKLfrCCS5SuErnzp3LxpEn4PBSaNiwYRyzQ3mCHtOD6Qzmnj59Ov+N5BRoujljRpsBssX0jLDIjLIgMXFiMlKWnq+hbkNHUCxXGhrctgT1aVqY1hy4Rct2XWPX5y8bLtGCrVc47hfmE9MOKbFqb4p0b7ravsdezJw5k4nLGYs3PDycLTx8Hm5IES5ZWJw//PAD/fjjj1zb7Sk4fDQQpytRogQnm/zxxx/8Gk4igpE1atTgv3U3p6wwMqYHsVmZLD2jyDSxx/TMRHo60qQIoLfq5KE2NXPR1mN3aeG2q6z/+fvO60TpmpFf8dx0/f4zypUphRRdFoxwb8qUyILj6kwJgKZpnLMB9+imTZuoYME43ZWdAHI8kEiDJEgYTZ6EU1dFzZo1efNWGBnTg9irEeRkRF2hTOPKpvSik54Rlo6rsUKIYjcsl407Q0D/c8G2qyyD5pu1PHWcsIcaV8jGiTHQC00s3RtkJD1nMX78eJo9ezY3GHAm5gZdT3RwGDlyJOd6wJWJ8jR06UGttafh7+wFtXz5cs4KAkqWLMmq3Z7MuJQhGxJFoUjVFQ0kAD179kz4uKlSpWKZOCPGRdxXJPR2JqKPg97tWfR5s65XFKkxqLdCEgGQUeUiGXnr+MGndDkqP0VlLEqrD9ziEohmlXNQ1wb5KHPaZF4vbyYipgeCR22aGXrpxQd475CE+MUXX1CnTp3IGfz+++9sJaKsDeUNSKbp3r07k6kZvBq+zvRhQs0Haj9wgLDh4ID4Lly4QN4Ao9x6ID0jyAlJQ2jsK9O4onVTIXCAyVM0SevqFaLPm54+boTFa8iCLfohRR6bQ9N7V+IWR1HRGq3Ye4Pe+nY3/bLhIoWEi/eMmMltKkJwGl4ekLxZLT10Psdc3q5dOy51cBZ//vmnpfPLN998w00KRGR+eoz0+vbtyz5eKKpAQgzb1atXKX/+/Px/3gDZSM8oiwwaeLKQKW4oI46DbumJPm+6CpFoC9Io0tMVZErlTUuTe5SnqR9U4Jq+sIhorvPr8O1uLn6PjnZvGYa76v9wTDGeK2OaoZdeQvqX8NaVLl2afv31V6d/J4gd6irWmclIdIS71Cxw+JchYQVmql6eAKC2DkWL+D9vgCI9OS1II46DbJaeEY2KbZXxlCuQnn7sVZFGdixF2TMkY93P0UtO0ntT99Oxy7H7biYEo2oVjSA9V93QejzfbKT35MkTat68Of8+1M7pCz1ngDruuNcfzjESIGEcmQG+ztystiYsHDiRrTw8CUV6xrkh9XETO+nJaunFta7rlclK8wdUo95NClHKpH509sZj6vXTQRq/7BQ9embffsig9CKS9IzuIuBo7LNjx44ctkKtdbZs2VwaD+UIOnD8cW5RvobaPHdpa74MDl8VKFZ8//33uXYDDI4NHXiR4grz2BsgG+nJZpEZSXqiE2R0cpLF0nMn6elIGuBHb9fNS4s+q8FF7sBf+27S29/tpvWHbrldecYI0hORcKQvIM1EeoMGDaJVq1Zx01ZXm3hfvHjR4u1DuAvxPFh3qL8GsUob00O9BWJ6UOvGhIAN5QvorgAVbm+AkaSHlb1oJQ7dwhE9ucgU0zPK0sONasRiRSdTI0gP15foa8yerhDpUyXhInfE+/JlSUEPn0bQyMUn6ZNfjnBTW1swihCNyN4URXroBWoGzJgxgyZMmMB9Ul1VR7lx4wb3REWoC+otSGoEoebKlYvMBodLFuCPht8XnW5Pnz7NryGbE6TnLTCS9AAUqOsp9qIme0xIIFRX/PHeYOkZ4ebFeRMtKqBbeqLdm9Z1hSLDDY60QkK8b3a/qvTb9qv066ZLtO/cA3pn0l4a0KooNSqX9YUVf2Jyb+L8GNkV3F5s2rSJevfuzdtHH33k0lj37t1jwsM1h+45efPmNW33DcDpq6Jw4cIc/MTmTYQHYLLACRS9WjYqPoTJHhBNJCAnkL9oa0RG0pPJvQkY0V/RkfshwN+X3qmfj2b3q0LFc6ehJ6GRNGLRCRo2/xgFPRG/oJSB9GDpwcrztJvv9OnTXDyOwnF451zZH2Skogksiu43btwohPAw5+jF7R6z9Ky7lb8MEydOJNlh3QlAZNNao0hPF/fGhJ8lSxbh44KgRB4HI0nPiAwxI92bRiSymIH0dOTNkpJ+6lWR5m+9wqUNW4/fo6OXg+mrt0tShYL/ZYAnFtLzdDzv8ePH1LRpU8qZMyfH8VyRasN8A7folStXOG4noicejvNbb71Fa9eu5dwRI2DXLz58+LBdg3l6BWOEWoYMpKdbeqKtHGvSy5Qpk9BxcQxEuzCUpWec8LYrIuyQNYNsWY1imWjk4hN06c5T+njGYerWsABFJ7KSBU+TXqpUqVgOrHHjxi7FFrFYa9WqFfdEhavU1SQY/RhDiBqJNShwN6oLg12kh44KiQnWpCcSspGefoOKLluwtkxFBvVlIj2jLT0jtEJddfcXyZmaZnxYmSatOMNSZjM3XCT/vK0p4O5GkkGRRZR705Pw8fFxWfAZxwJNYHfu3Enr1q2zdNtxdcy3336b/vrrL25EDmvUKIgXvfMCyEZ61hZZYh5XJtLTrzFvjekl1McPGZ5D25WgpAG+FJk6HwXlaU/nbj42vaXnamKQGdybrgLWfpcuXdj9CAlKZ1rIxQUWaJA/Q4IkdDuRJ2Ik7LoqUIMHmRp7AD/xggULSGbIRnrucG/KYEHKRHpYccPFaVT2pllJTwca2MLq8w17QNEBqanXjwdo2/G7pu2yIMq96WlLzxWgvARWIub4hQsXsqC0CMLr3LkzE+iSJUvcUuttl3sTPZkgKI16PLBwpUqVKEeOHOyiQdbOyZMnaceOHbRo0SJ+HR17ZYZspKeXP8hCekZaeiAR3Egie6nhvOm6iSIB0jPK0jOjezMuCmRLRSnPL6TQfC0oNGUeGjrvGL3/WkHqXC+vy/kBZozpyWzpaZrGsUDU9kGbs02bNi6PiWsUjQtQ1wfCQ4zQHbDrqkDq6NmzZ5n0IDNTrVo1ypMnD2cKooEsdhzV+CA7qLOICGp6ErKRHvYXmxEkYmRMz6j9Fa3KYpSSDhaN3p69+TL4RIdRupur6M0aMUXM/1t/gcb+fooio6K9UpFFVtIbMWIEZ+aj8zncmyIs8a5duzLZwVhCl3Z3we7lcNasWWno0KG8wbpDajgKdpHVB4UWb8ncNJL0jJK00olEtKWHRABYkTJZejrpiXQjGUV6Rlh6srg3reFDGvVvWZTyZUlpSXJ5FBJJX3UoyRJn3pLIIqt7U9M0LkCHrNiHH34ohPDeffddJrvffvtNiNXoCJzyAaEflFl7QolMJxdNekZJWsnWacFo0jNCdFoW0pPN0rOWIXujei7KmCYpfbXwOP1z4h4NnHWExnYpSymTOT5NKfem2Hnrhx9+EGLYgPC6devGeR+IC7Zt25bcDZW96UZLD1CkFzPZYwJJ7KRnpHtThpieDuvJtE7JzPRdt3KUIqkfHb74kPrNOESPQyKkJz0sbjCfyEh6gLOEd/fuXapTpw5t376dz8l7773HHRewoezBE1Ck50WkZ4TKCW5SWdoLyUZ6yr1pGxUKpqcfPqhA6VIG0Onrj1mw+mlopMPWo5lIT28rJKN70xUgzwNNZZEAiY7sc+fOpXnz5nERuqegSM+NNVT6BCo60cJocWijSM+IkgVA9HHQz5nojgAyJbIgPuasIoszKJozDU16rzylTu5PJ689ooGzj9CzMPuIT99PM5GeGdsKGQ14G6ZOnWq5J1F0DjcpitA9CUV6blTL0C96fdUnEuhk/+DBA+HjIlHp/v37Uoyrx5kDAwOFjovOIphIRS9WjFio6F02RFummPCN6MgOt1l8FmThHKmZ+FIl86djl4Pps1//pbCIKI9Zeq4Up5utrZA7gGLzO3fuWM4JzgcsPSPmVUfg8FUxfPhwFhj1ZhjlJgPQb0r0pAyg4/Ht27eFj4u6y5s3bwofF4K36MElerEC4rt165bQcXURb8QnzL5QMSpJCMcW2dqirV24YxOyIIvlSkMT3yvHXdmPXHzI/fmio93flNbVkgUzdk03GnE7OGBxg+bjIjJA3Up6YG+UKKAtBbJvjHABehpY0eECN4L0YOEo0jNu3OzZs0tDeiBo0aQHVyxW1EaQHgjPCLfpyyzIErnT0pguZcjfz4e2HrtL01afS/D9RjSmFWXpJRbSO3DgAMfydAtPJz/MfyLaD7mV9I4cOUL79+9nhZZ+/frxZAtpGrzmTUB9mhGxNyMtPYwrOvkG5AQ1EtHuMqNID8dBNOlBkQhArZLZLT1MLka6TY2QTbPHbYo2REPaluDni3dco6U7r730MyJrh3FfuUJ6+nmGq9zbcf36dRYy0b1m6I0HUZNTp07xwnHYsGEe3T+nnN7ly5en77//nietX375xfIjocQCk9aImJW7YVQJgFGkB/EAI6wRkBMgmkgwLvZVtOUAS0+0xau3VTLCvQmhB9EwgvSMinO/zL1pjVfLZ6Ner8c0rJ666hwdPP/AbZaeq+5NLJhAeK7W+pkdp06dYi7AORg/fjxbuFBdgX5zsWLFTCFi4lKkV3d34ILAc7hrkK2TO3duFiWVGUZbeqJvTFg4gOgJXyc90VYZYnpGkKkR7k1MzDhvRpAerjEZOtPrpIe4nrvdm9Z4u24eFquOitboywXH6daD+PdH5ATrqnsTpKd7DLwVW7ZsoRo1avD1d+7cOfr0009NQXJCSO/gwYMcjMQE079/f7b8wPDbtm3jHzt69Gjq27cvyQwjLT2sbEVbwzrp6dlSoslJNOkZRaZGuDf1uJ4RpAeItva81dIDMIkOfKMoFcuVmoKfRdDQ+ccoPNL4+kFX3ZveTnpz5syh1157jZsRoM+ep+N2QkmvdOnSLDh96dIldm1eu3aNxo4dS4UKxbgdABQeio5/eAvp6a4y0S5O3FCYEERbephAYfXKQnpYiOG8iT53RpCeXmJhRAanLKTnqKUHQI9zdOcylDZFAJ298Zh+XnfBLYksrrgmUZ6j3/veBE3T6Msvv2TxaDQeWLNmjenLMhwmPVTVX758mVavXs2tIGyJuuLkuku9QUb3phGkhxsSx1006YFIjUg6wXHAyll02QJIDxB9HLCoMCKRRRbS83QiS1xkTZeMBrctzs8X/3OV9pwJNHUiizdaemFhYdz8FV14IEaNtkMyxCwdJj09dhcX8PWj/YS3wEj3JmBEwbdMZQtGkalOekaULSj3pviYnqPuTWvUKpGZWlePaUk0eskJevA4zNBEFkV6/wHzV6NGjVhlBd0SBg8ebMr4nRDS+/rrr22SAVLa8X/eAlh6RpKeURmcspCeUePqsU0ZSM8o9yZqwbzZvWmNPk0LUYFsKSnoSQRN+etsrP8TNQmDRLGPrmZvegvp/fXXXxzmQh7H5s2bPSYc7VZLz9bF9O+//1pWrt5i6Rnh3oSbCAXEqkDdmHERT8AEbQTpYeISaUXAcsB1ZoR7U7SuqVkSWWzF91C/5+tDtOnoXdp16r5wS08vq3HW0sMxwwJadtILCgriBrItW7akihUr8pyPbE3Z4OvIqhSkBsIrUqQIP9c3TDQwdRHv8xYY5d40ukBddPambFJkuD6NqNUD6WG1j0J9s6uyyBTTc9XS06XK2tXKw88nLD9DIeFRQi09XfDBWdLTY8Eyk97atWupVKlStHz5cpo9ezatXLnSkowmG+zuzjh58mReQaEBINyY1hk6uBjy5ctH1atXJ2+BUYkssupvYgGAiVTXdkysqixwcYpsoGyU/qYRPRCNiumJELLu/moB2nb8Lt0KCqW5W16u1uKMpeese1Nm0gsODqZPPvmEM/VRkoBkFdRhywy7SQ9mLZA/f342aWXI0jGzpWdUIgsmO5A1SNuI8oKiRYsKHRc3lej9NVp/U+QxMIr0kFknotu3NTlhM5t7U0fyJH70yRvFuNv6in23KVk6cVZIYrX0NmzYQN27d2e3JvrioQGsLMkqLrs3reMDKETHag+v2dq8BZiEkZxjROmFkZYeINrFaVRNnVESZzKJThshRWZkpwUzujd1VCuakbfIaI3y1u0hbIJ2NaanL3BlqdN7/Pgxy4a9+uqrVLhwYTp+/Dj16CHueEpBenDn6Dc79OPwd9xNf93b2gsZ0THbyE4LgGgXp14GYJQUmei4nhFuXlzfsEpkaC+kK/kbEdczq6Wno3eTQpzUkqlobbr5xG5Hll2WnivuTSSvYTM7tm3bxhrK8+fPZ5FoWHtmVldxBnZdFUhL1TMzoa+WGGDdU09/LovotOgJH1YvYrgyqbJgohHp3kN7FCMK1I1KZDGyp55IiLT0gALZUtFr5bPQ2kN3affNVPFmm7vbvSmLa/PSpUtMcps2baICBQqQN8Iu0qtbt67N594MPcZkVK2eETE9LEywcpYlgxOTMxYURhWo4zjkyhVTvGzmAnWZSM8siiwJoVPtHLR633W69TQJHb74kCoUTO9R96ZMpNelSxeWExPZdd5scPiXrVu3jnbs2GH5e9q0aVSuXDl6++23DWmT4ino1p1RUmSYPES7TnGhqgJ141RZMHEZQXoogxAZO5aN9ES6N4EMqZPQnaNr+PmcTZdM4d6UhfR8fHy8mvAAh38d2kXoCSvHjh2jAQMGUJMmTdgsxnNvdG/KIjotY4G6EbV6RsU2jbL04IIT2XVDJtIT7d7UcX3vEvIljQ5eCKLjV1w7tonJvZkY4DDpgdxKlIjpYAzdtebNm7PYKCw+FDB6C/SJw4iMVD0T0AhyAolcuXJF+Lh58uThc2/EuBcvXhR+fDFBQRhdtAUpmqD1BZBIlzSuXazYRXtekIgh2vMB60l0I2FYKuGP71LRDKEWQWpXgPlu48aNXIvsDBTpSU56mEx0txwuBKS16itWbypZ0BN3jLDGMNEDV6+6djPaAtRy0NNQNIoXL84TvuhzjAkFGn4ipaNgPSDV+syZMyQSBQsWZCIVaZno1wJadJm96S0yWEUr0oBI4d4USXy6ZFrR1DGkv/3EPbr/yPlGvUjiatCggdO1pIr0JCe9WrVqsRsT7ST27dtHTZs25dfPnj0rNGnA08DNiBRtI0gPExLGN8IiA+lhYhbdjVu37kFQoseFC/n69etCx0UBuWjSQ89IEJ7IxQosc1hlohdARpVtiCY9Xd5MZHwbix4oyCSLDqYy+dJyl/UVe8Va6PYC1wssbllq9BIDHCa9qVOn8kry999/p59++slSawXXZuPGjcmbgAvViCxLTHJwlRhBepjskRQh2mWIcbHfRpAecPLkSaHjFitWjE6fPi3c0gPOnz8vbEx4TuA2FU16SGgSncWL8gojLD1AdFIXxsWYb9aIkcz6a+8Nioxyf49P/Rzo5UQKnoe/M+6YVatWvfD6pEmTyNtgVGkBgFoYoyw93fKGS1LkJAKiFk1OOA5Y7WNcaPuJJD1YjyLrLHHtY8F34ULsTt0ixjWC9ETHH41ybwKi6/900qtTMjOlSxlAgY/Daf+5B1S9mHstLj0ODvlGBXPAKckCWBJY7SJmEDfVuk6dOuQtMEo5RZ/s9+zZI3xcTHZIZADpiQZIVDTpwRUFgjLC0gNwHCpUqCBkTBAeiF+kpWck6R08eFA46RmRHGOkpRfg70sNy2Wl33dep/WHbyvSU3DcvYmJGrENTIAguFdeecWy1atXz6Gx4B6F5A1kk7ChS4OZMkBltPT01k+i41m6K1I0OenjnjhxQuiYuii0aBcnrn0ZSM+INlNwb8IiExkvNiKmZ62dC7xWPqZu858T9+hZmPjyiISAMAMWIDJIkCUWOEx6ECKtVKkSi5BCSQIrP31zVFkCiS9jx47lFemBAweofv363KBQ9ARoRksPKz9njpk9KFmyJB09elT4uBAhwMpV9DHBuGhIKTKDD4sokMmRI0dIJLCgEE2kOumJLNLGRAtXpEiCgqUHiKwpNNq9CRTLlZpyZ0pBYRHRTHzuBO4Xb5XzSjSkh3R41OXB0sNNgHRe680RoMYPhe1IL8dkMnr0aI6/xOf2ww3szq4OSDM2QtILQENGAIsH0ahcuTJP9qIzOPUuyaLdshgXE5Rooq5atSrt3btX6JjoMgKXqchrD65YFECLTD7SdU1FxvV00hMZ1zPaval7P14pHVMbu+OkMZ6b+IBzqlybkpMeJhLR7h0Aq9xFixZx8Wt8zWjHjBkTi2CNbmaIFThIT7QKBQCSR2EuVG1Eo0qVKmw1iSYRxLPgNtu5c6fQcStWrMhZjKLHrVatGu3fv19oXR28HMDhw4eFjVm6dGl+FHktYCEJiKzZ1LuoiLT03UF6QK0SMbG8vWcDKSIy2q2kpyw9yUnvo48+4k66v/76K7slMbFab44CNzqsO9TVwHX6559/WtLY42Lw4MHsWtE3kQW9tqArMBhRRA7Cg7VshBuybNmyPD7qKEUCK2ZYZbt27RI6Ls49yET0uFigwW0mkkxglSEOJTJJBK5IuNJF7icWhDiuImO7+iJT5H1nVEwvLukVz5WGMqRKQs/CoujIRfdoBGOxDOk+Zek5B5GCFS6R3ptvvsm1Wt26dWM3GuIxcPnoj84kHMAVBzdUr169WOU7vmQJ3MR60ou+GQn9YjVCfgtAEo8Rlh6OE4hPNOkBID2MK1o6qmbNmsItPWRtIuNSpDsW4+FaF0l6WEzA2hN5LSArFkk3IrN49XCGyPsB1yp+v5ExPcDX14dqFo+x9naeco+LU1ca0pOqFOIHvAd///03e/PAMUj0W7hwIZlGezPuBhNef3QUcGvh5oSLCz8Yk/WUKVPIDECiDSYP0RqOOvSJzoju7HBxwnISvVoCOWGCEh0rw7ioqxPpOocVAYKy7goiArhWkXglEqJJT3ehiy5dgfdD5P0AwgORik6OsqUTWq1YRn6ECLU7gPsPHhdcL96MBw8eUP/+/Wn37t12vR8xYfTrGzduHLVr147dv/B0oE4XiY1I8MPrIuuMXarTM7qLLghAdAKGK6t6EJ9Rlh4sERRPI1tVj+uIAhKE0PkYiTIix4Z1DxWexYsXsySdKDRs2JDTzBHX/eKLL4SN26xZM5o4cSK7mnRNRhHWLpSJ4PbWtTNdBc4RxoR1Iiq9HRaG6NUyvB+i7wcjyDmupQeUzR+TiHPpzlMKehJO6VM51zXBEdID4Ym67syIjRs3UqdOnSy5D3HzMZDwdejQIV4kYoOHRF/YIqyFOfCNN97g8AaOFQwgo1sbOTX6vHnzeGWODDG91mzy5Mm0YsUKh8ZBjG779u28csQqF39v3bqVOnbsSGYBbnKjLL3atWvzif/rr7+Ej92oUSNeQS9dulTouLB833rrLSY9kQkiIDxc/AsWLBBqnbZv355vPPSBFAXozcItByk+UQCRYsGHFbBIMgExi3Qdirb0jJKMs0V66VImoQLZYkSj/70kVlnGFuCu1zOevQ0hISHUr18/nmcgqA1g8f7PP/+wOhfmcCy6MAehfnv48OGcSYx7B/wB1y/yMrZt20YTJkygDh068PXqjl5+Dn8DCsr1HnowU/XaIvj7QXyOAIou6NKLgwMVc2TarV+/ng+kWYATgUw9I4KqmDhh0htBenAbo+YRpCd639EwGBe6yAlaHxeTn8jMSEyocJnDghQFxJJx3kQuKJC8BXeOyDH1shh73U6OLAJFuuR10hN5ndoiPaBc/pgM1MMGJ7NggodB4G2kFxERwUmMSGr6/vvv+TX9WgDhQbBkyJAh7A3APYL3wtuEhSf+HxwByxDn3GPNajUHUbx4ce3PP//k56lSpdIuXLjAz48dO6ZlzJhRcyeCg4Nxl2i/Lttk2HesWrWKv+PIkSOGjD937lwe/8aNG8LHXrlyJY999OhRoeNGR0drRYsW1Tp06CB03IiICC1z5sxav379hI47ZswYLUWKFNqTJ0+EjTl//nw+tleuXBE25pdffqmlTZtWCw0NFXaeChUqpHXu3FkThRUrVvDvvnnzprAx//jjDx7z9u3bwsacOnWqliRJkhde33jktlbzs41atyl7NSOxdOlSw+5roxEVFcXX9YYNG7Qff/xR+/jjj7WmTZtqhQsX1vz9/fl3xbft27eP72Mzw6lEFltZmrBaRDeYNANgdaI+Ce48IwBzHy5Do1yccEGjxETkyhzJB3379qXffvuNNmzYIDSGirIVxCJFWntwx2KF+vXXXwsbE8IKuOZxDEShTZs27PJBnETUeUKWNdywolRU9E4TIlWTdJ1UkS5OWHoo+I/rgi+WKybj++LtJ4bW68G6gStYFwkwGzRN4zgcrK9ffvmFPv/8c2rdujXHlhFqQO4G5g/c52vWrOFrCfHxH374gWPkyLLXS7owf1knj+E+NjWcsfSWL1/+gqX3/fffa+XLl9e8zdIDunfvrhUoUIBXzkagZcuWWrZs2bQ7d+4IH3vdunV8jKZMmSJ8NdiwYUMtR44cWmBgoLBxYeXgOipWrJj29OlTYeN+9913fBzWrl0rbMz3339fS5kypXbq1CmhFvQ777yjicL169c1X19fbfr06cL2MU+ePFrPnj01UQgLC9P8/PyE7SOwaNEiPt+PHj16Yf8bD9/K1t7p68Ga0fOTGQAPx969e7WZM2eyF6VevXpapkyZLNaZj4+Pli9fPq1Ro0Zanz59tMmTJ2tr1qzRzp07l6DVhmP577//asOGDWOPAsbavXu3ZnY4THozZszQcubMyRcVbvjffvtNGzVqlOW5N5Le33//zd+zf/9+Q8aHqwhuvcaNGzOZiMaHH36oJUuWTDtx4oTQca9du6alS5dOa9eundAFwcmTJ7XkyZMLnVhxXF9//XU+zqJcc48fP2ZyLl26tPbs2TMhY44bN44JAG4iUWjSpIlWpUoVYeMNHDiQj6NIN1aRIkXYjSYKf/31V7wu077/O8ikt2LPdc2bEBkZqZ09e1b7/fffteHDh2tvvPEGkxFITSe3IkWKaG+++ab21VdfacuWLeM5ISQkRMj3379/3zDD4GUIv3Bce/LbJGNIT49n6AcTG0gQqwh3w12kp8eaPvjgA8O+AxYIfsv48eOFjw2LCRY6LnjdMhe9ogZBhYeHCxv3p59+4nFxc4q6kWBJZ8+eXatVq5b28OFDIWMiXooFhahrA8ewQoUKTKaiiBSTG47loUOHhIyHxR/GQ8xHFFq0aMGLPlHYuHEj7+PFixdf+L9pq88x6X37hxgL3RO4e/cu/8ZJkyZp3bp10ypVqsQLRd16w3zVoEEDXkjMmjWLz5lIz4lZEHn7qvZo2mDtfrdqvBlGejpwEI1wyZmN9ICJEyfyd23evNmw7/jss884UPzrr78KHxsrQCxU4NbYsWOH0LH/97//8X7jJnvw4IGQMUF0I0eO5GOOhBlRq9GdO3dyskipUqW0q1evCvv95cqVE5Yog9V30qRJhVk+cB/CbVqtWjW2BkScG7j733vvPU0UPv/8c3bxi1rgwM0WXxLXmgM3mfQ++vmgJhNwncEFmTVrVgu5YcFVsWJFrWvXrtqECRN4ISIyIcisiHoYqD2eN16736NmDOF1r649/mWk8aTnabiT9OAeq1u3LsczjPLVY5X/7rvvWiwnUVl81u6HOnXqcFYbiFWkK2LLli1ahgwZ2JqEn18UlixZwjc2JmzEp0SRSt68ednqE2H94DiCWIxYZG3aJObaxkIHXhnEa0Rg8ODBfL5FWfeYrPF7Dx8+LGyRh/FwXcbFscsPmfRajvpHkwlwfbdq1YpjaLgvTp8+LWQRIxOiQ55qT1fM1O73qm+x7oInDdAirp2zewy7SA+rWCQX2LN5K+kBly5d0lKnTs3EZBQwgWJFB2JCHAY3r0hgctaJFe6k8+fPCxsbge+SJUtyTAoBc1EuRMS3kDCDY4+EHBE3+q1bt9glBEIFwZht8sAiC5ZzmjRptF27dgmL7aJ0w5bLz1GghAfXEEp6RF2XSIxDfoAI4NrD/i1evPiF/wt+Gs6kh+1pqLnT6xViEB0RoYVsWaYFftzEQnZBI97Vwk85bq3bRXqIq+gb3BC4EbHy7t+/P2/Vq1fn1/B/3kx6APzj+M558+YZ+j2Y6PPnz8/kh5WdqPiOdb0VrB240b7++mth7kNMXliRIrEJbpg5c+YIsSiDgoK03r17s7WCmJeIRA+45+FCxJg1a9bUzpw5o5kJyDysXbs2H8utW7cKGQ+eCmTdupowhXNauXJl3kQlXyHxAnOJCGD/cO/88MMPNv+/2YhtTHqnrpkjw1LBNqKjIrXQ3eu0B5+/aSG7B4Pe1EL3btCinbzuyJn0/S+++MJmYa2RFpBZSA83U5cuXXiinDZtmqHfhUkZxxo3Lwhw9erVQsdHDApuqoCAAI73LViwQFhGHjI727dvz+cHEyPKXERMjki9hucBxx/JIyLictu3b+ffb0arD+cIJIUkhfXr1wsrYRk6dKjLY23bto3HwnUjAkiGw3m9d++ekPFy5crFC0Zb6PXjfia9DYdvCfkuBfHzbOjBLVrQF29byC6w3+vasw2LtegI11zqDpMeLDpbLje8hv/zdtIDMHnDfYfvRmqw0Wm6sEAw8eH7sBoWqQKilwggrR3jI0EBmZOiLD/EVGCtYGy4PqFA42ocCMSM2BRiSiBskB9cz6KsPliSKFPxVPp1XOBcNGvWLF6rxVHAEsf5gNfCVSDGBOtRhCcCpSQivSg4j/Fl1Q5feIxJb+G2y0K+S0EMOD5+dJcW9HXX/8iuT0Pt6crZHM8TAYdJDy6r2bNnv/A6XsuSJYvmzoOD2ARukh9m/+H2CQrf980331iSToyW3sH3IT6B5AvEZcaOHSs8eQJJBLDOUMyMTDpMjqKSdpBIgYkbxwtuVUzgrk6UcNdhH5GejexReCFcjVEiu7NGjRq8n/Xr12fL0gwQWb+Ja6lHjx58zFxNlMGCDONA6k0EkIn41ltvCRkLMWssEm1h2uqzTHpT/jKXSztRk93xvdrDb963kN39XvW0p8uma1FPxLqgHSY9XNxwA3300Ue8IsOmB8hFXfgvi+1glV+wYMFYmm/4G6/j/90JuGRAEnC5uWOCBAkhjopkEbjkoDEYV3XCVcBqRzo6rCgUn8MVhrogEUBmJ0oQcMxAVqNHj3b5nMEFiHRtLMhwXKBo4kp8DjcgiptR1oBrq3Xr1mwNexNgbb/66qtcvuGqaEHfvn05yUhEqjzCJLjmRCwicR0gVmsLi/+5wqQ3dJ5YXVoFJ8ju2G7t4ej3/iO79+toT36brEUFi1N6crlkARYHVsPp06fnDc9tZUmJBuIRCOrrRfHWpKe/hv/H+9wJFH7ClYLvh4yPqKzFl5FH27ZteZKHWxnuVmRPigRKBAYMGMDHFDElTG6I1YkALDK4nhCvxP4PGjSIMypdASxHZHci0xOk+vbbb7s0oSO2B3csJJowHmLWol3LngQWUFCTQbzYFasbpTDw8ohQFEKCEu5nuJdFKMdAJNkWthy9w6T3wVRjVJYU7HNjPhzV/T+y+6CO9mTBRC3qgZgFtvR1eiAyTPCYfBJS+cb/433uJj49zoS0a7ggUUfjDpcrEjmGDBnCHS7w+xGbw28X6Q7DpIbYJRY4IpIp4sZxUJQPSwHuSVExMKjD586dmxcicJe5kpyCekloy8IyBUnD0haVbOFpgMRxrYpKkPn2229dGgf3DIgYCi2uAupGsGRtQa/Ve2O0XLV6XkF2R3ZoD0d2i012v03SooLcc09JQXpwf8HaeBnhWRMf3u9uV6dOQgjuYz/gPsKK1Qg9zbjASh2JCXCz4rtRJA65OJGAG9EoIoeSi6uWXlwg5omaR5CUKK1NqMSAoLGJlOHyBuiKQq6Wk0DfF4sVVyXzkFWKe8FWXPr6/WdMeg2GGqewZDZAGP7jjz/mOLhHyO7wdq6ts5Bdz7rak0VTtKiH9926L1KQHiyouO7Ml214v+jOAo4AKfrIVtQzIpF44g7JNlxc//zzD7s+0VlAQTxg5cEdCwtYIXacEIIKuN5dcfEjkxZeBbjWXQFi7Lj/Dh48mGCBeniE8YtSTwJeKHg+YPUSEeu6urXObt8GLeird6zI7hXtyeLvWUrME/DBP2RiYPcKFy5MFy9edKizMvo/FShQgM6dO8fPPQHs765du+jnn3+mJUuWcE+7N954g3vGvfLKKx7bLwUFo4D7FP02X3/9de416Ow1PmjQIL5vrl+/TqlSpXJqjKCgIMqQIQMtWrSI2rdvH+v/oqI1qjt4Mz9fOaw2pU+VhLwRW7dupT59+tDJkyctr2XNmpX7IaLHJHoOWj/G9/xl/48NwPnG5q9FU9GnN6jswwuUNiKmz2qEjx+dzlCQTmQsQmEByS3vjW+rV68eFS9eXPgxMT3p3b9/nzJnzuz05y9P/pwypklF5OuHM0Lk60s+Pr78yK/h7+eP5Osf8zeaIuqv+fnHvJ9fi/mMj58/kdVrMZ+P2WI+6xv7bx9fCn7ylFasXEWLFi+msxcuULr0GahytWpUvWYtqlG7DhUpVpx8/ANivg+fV1CQFGi4jMa9a9eupcaNGzs1xpUrV3jROm3aNF4kOgvMHWiEOmzYsBf+r/HwbfQkNJIWDqxGeTKnJBmA6RrNuh8+fBjvhobBeETzYDwaAX9/f0qSJAkFBARYNiCFH1G73KnpnfxpKHOymGayQeFRNP/iQ5p3IYgehEXy4v+5lzHBDYued9991zykB4ZHF3V0UjayUy5WJKVKlXL687veKENF0qcgmaD5+JKPv38MuWLzD/jvuZ/ff+Roec2f3//fa/r/++HqJB/8jTECksQ8+ichCtD/ThIzHh4Dnr+O/+f3JyUfXMyW98S8xu8BqSsoxINNmzZR/fr1XfJmvPnmm3Tq1Ck6duxYrO7cjqBmzZo8R82dO/eF/2szdifdDgqln/tUopJ50pKngS7vIPvz58/ThQsX+BHbzZs3Y5FaVFSUzc+DeNKnT0/p0qXjDcfu8ePHNt+7dOlS7nKuE5Y1genP43sN833c8xod/IBCNy6m0C1/kBbyhF/zTZ+Fkr3WgZLVbkE+ycwzBzvMVs+ePaOPPvqI5syZw3+fPXuWV2R4LWfOnNx2XiRgigPffPMNvfbaa3Z/bv369TRkyBDK3b4npUiVIsY1Gh2FpVLMI1Yb0dHPX4smior67++oSH5Nw8X1/DV+ruH/8Nrz91q9pj0fU38t5rPPvye+z/L/v3gB+2B/IsJJiwi3vGY6cxykFwCiTEo+SZIRJUnCjzFbUiL9OYg0qf78+XuT6s/1z8Y88pYsOfkkTRFzkyRNrqxeSdGgQQOXx/jss8+oevXqNHXqVOrXr59TYyA0cvr0aZv/l/K5JfI0NJLcjdDQUNq8eTP9/fffdObMGSa3y5cvM/EBIJb8+fNToUKF2F0MMrMmNFtbsmTJXiCjq1ev0vTp0+mnn36KZfXVr1+fXb+uIureDQpZt5DCdqwiioyZr/yy56VkjTtT0mqvxiyWTQaHLT1cfDt37qTJkyez6+Lo0aNMeitWrKCvvvqKDh8+LHQHsXvw65YrV4598/YCPvx///2XVztmjp3x4QfJRkWSFhVJT4If0r3btylf7lyW1yz/Hxlh9VpUrNcsr0fG+Qy/FhHzvgg8hsf8DUKNBLFGPH8Mj3lPnNdiPhvOJMwLBncDxIctWQwR6oTI5Mh/6/+X0vKc9P9LlpJ8U6QmnxSpyAePJrwBFRIGFtOzZs2i48ePMwk4CiyWJ0yYQIGBgS/8X48f9tOp649oXNeyVLN4JjIa9+7do9WrV9Nff/3Fi3IYEPhNZcqUYXLDBqsUj7lz5xbqQQPJwu08adIkju/duXOHSdRZRF47RyFr51H4vk0xRgOIukBJSv56ZwooV9vUi1WHSS9v3rx88KpVq0apU6dmYgHpYaVSoUIFevTokfCdnDJlCg0cOJCuXbtG2bJle+n7b926RXny5OGLHf58BVHkHBVDhkygeAwnLTyMKDyUH7XnjxQRRloYnofGPMfrYVb/x+8LJbL6TMw4IaSFYntmuZGEIkky8k2ZmnySpyaflM/JMHnqmNd0YnxOkr4p0sS8liot+aZKG2ORKrgdcM+VLFmSihYtylaRowtYxLXatm1Ld+/efSE3oNdPB+jY5WAa3bk01S2VhYwAEun+/PNPJjoktQGwXlu0aMFbsWLF3Loo1zSNQkJCKEWKFE59NuLEXgrdsIgiju+1vB5Qqholb9KZ/IuUN7WBocPfmdVKliwvXiAIrhr1g7t06UJffvklvffee7R8+fIEV0BwD/To0YNP6jvvvGPI/iRG8LnFcYc/3x0EC3IMfRazhcUQYaxNfy0s5m/i12O/poU85fgCHhnhoRQNsg265/hOJU0eQ36p0pFv6nTPH9OST+p05JtK/9vqMWUaU692ZQEW1khoaNKkCZOeIyEOoEqVKvy4fft2jhFaI4lfzPmJiBS7wHry5AnHzGCh7tixg+eiV199lX755Rdq2rSpzfnTnfdxCgcJDwvWsN1rKXTjEoq6dfn5QL6UpHIDSt64I/nnLUoywWHSq1SpEpvocDsAOtHNnDmTVzBGAP5qpPw3a9aMWrVqRTNmzKDs2bPbtPBAeHAdYB/xOQX5wNfU8xgfpXE97gAXr4UAnz4m7dljin6Gxyf8/L+/47yG9z59FOMqDguh6LAQosDbZDuNIA6QuZsmPfmmzfh8y0S+aTPwo4/lNWwZlBX5EqD8ASEVZ+YXeHyKFClCGzdufIH0/HXSi9KELNR2797NRAdPGIyAhg0bctlGy5YtOWlENkQF3aWwzcsodNvymPsA92ayFJS0dnNK1qAt+WXOSTLC3xkfOS5C+IVhVcH1iOcw3bdt22bMXhLxCm/VqlXUrl07vpBbt27NFzH80qjHWbZsGf3xxx+8igHhYWWloAAgoxVuSsLmYPULp0+DMJ88pOjHD58/BpP2JCjm8XEQRT8Jtvr/YCZMTlh6eJ+isL1s/5KnIt90mcg3Y1byTZ+VfDNkId8M2cgPjxmzkW/6zImeGGvUqOH0Z0E+WAjHRYC/j8uWHqw6WHBIFkHCTL58+ejTTz9l7xRCQTIi8tJJCtmwmMIPbIoJaWANlykHJWvYlpLWak6+yeUo7xBasoB02rFjx3I8DycdsTwUk5YuXZqMBjKQkH78448/ctaTDvj8e/fuzRdb2rSeTz9WSLxAzDP6cRBpwYEUje0hHu9zWjceteeP+FvPeHsZ2G36nBT9MloRY+Yc5Js5J7teFWwDIRGIQqBw3joZ5ot5R2nr8Xs0oFVRal09l0NjIkb4ww8/cB0h4o6IGyL8AtEJXwnd2lpEOIUf2kqhm3+nyPPHLK/7Fy1PyRu2p4BytbymTMn0xenxAbuNmhZcxGu2H6bGtcpKEURVUIhlRcKNChIMukvRD+5Q9IO7FB10h6ID7/Bj1IO77Fp9GZCAA/Lzy4Itl+W5b+ZcbEUm5vgiirUzZszIafsIf+gYMvcobT9xjwa+UZRaVbOP9JCwN3HiRJo9ezbXDr7//vv08ccfs/dJRkTdvU6h21ZwyQG8FQw/f0patREla9heunidIe7N+LIzQThJkybl4kV3AN+n15mkTZdeEZ6CdGC5pZRpyDdlGqIc+eInxqeP/iPFwDsUFfScHO/foqh7N0mD9fjsMUVdOc3bCwhIQn6ZcpBvllxcQ+WXPZ9l803hnMSXTIDnB6oumTJlekGKDPB3YEGAjHCEUaDw0qtXL5fS/j0FLSqSwv/dQWFbl3M2pg640ZPWbknJ6rbkhZK3wmFLD6Z7QgSTK1cu6tq1Kw0fPtxwMx8EjAt659HLVKO0nP7zuIBEj4zuEQXPAdl1KBKOvnudH6Pu3qBoPN67TtH3b9sUQNCBpJoYAowhQ38QYfa85Jsus9cvJAfOOkJ7zgTS0HYl6PWKLybG2cKDBw84KUXKxJT7t9iiC/3nL443M3x8KKBkVUr2yhsUUKZGjKKTl8PhX/jrr7/S0KFDmdj0dOB9+/axQssXX3zBJQ3fffcdW31QRFF4uetlz549nNqMDDXESW/cuMHqCgoK9sAnaTLyz1WQCFscaJGRFP3gdgwR3r1GUbeucNp55K3LPPEh7hiJ7fTB2GMmT0l+2fOTX57C5J+7MPnlKUL+OQvyd3kLdEvP1wFuF6Fi4k5Ehzyl8AObKWzXGoo8e8Tyuk/q9JS0djNKVqeltFmYbiM9kBtMfGRR6mjevDknsaCeBpp78G+PHj1akV4cwKiGLJBOcNigK4jX4XqBTuDgwYNZsVyRnoIIQJMVMT5sRFVj/V/0sycUdTuGBGPIMOY5rERkrEZePM5bmGUwX/LLluc5ERaJIcI8hck3tXwuPmvS83OE9SQAJBEjTu5nogs/tC1GTQlA94OiFZjoklSoGyMRmAjhMOmhNAHpuXEBfTjUqQC1atXiyd1dgMuByHzuTZAZZNpQyqGTHKw4PdsUxwlBcJAdNAK93Z2kYP91A4kqeAHibnDp23odXpaKFSs69D2I5/kWKEkBBUrG/v6IcE5wiLpxkSKvnaWoq2cp8upZ0h4FPSfIyxS+d4Pl/T7pMjEJggCZCHMX5kQasyfPeBPp4ZqJunKGwvZtoLA9f3OcVwdc10mrv05JqjcmvwwxWsaJGQ6THjThUJeCkgVr4DX8HwCdO3cGeAMDg8hMMTmQP4Ld2CAii+QeFPV37NiRCQ41R3GD6greCVjtt2/fZqV8fYOIgt4CJj5Si09JX1cpQSzbetOFikUAFoB/zgK8Ja3S0PI6yi9AfkyC185R5NVz7DKFmzQC27EYmS1G0hTkn7tQjFXIRFiE/HLmj+nSYRKSCAoMpNDgexT6NBdpWhbpFp0QroclHn5wK4Uf3ELRgbct/4e61KRVGlHSGq+TX77i0v02U5Ee4nWoSUGvrMqVK/NrBw4c4MJM6NwB+/fvf6Fpo7dPbLDmUCCPmiBMctAIRW0Qttq1ayt3pReec4j26iQWl9T054hxWwPtWdA5BLEhkFWaNGk4+Qv6knGJzHrD+/AIwnO2zY6rgIJMktLVibA9B+TeIq9f+I8IYRlev0AU9owizx/lzeIe9fWLSZhhq/C5ZQir0I01hlhsIESDGjvUGwNNfiYWeobKFOp8zazkBCs84twRCj+8nV2XloQUIEkyPj9Jq70Wk5SiBNbF1enBekH8Ti8Oh6vugw8+YDUCd0LP3vx12Sbq0rq+2y06ED809iAmC1UY/H6oxEAtBoLcKgtTTuDcgrD0fmYQOo9LaChOtr51QESQxsuRIwdv1s+t/0a9mLdfF0iJj7p9laLYGjxreYRajS34ZsjK5AcijLEMi7ISjWjrBKosuD/hOub9tDp/+ndB0QmLV0c1Po3Ouow4tpvCj+2mCCQcWdVtIuEooGwtSlLxFUpSstpLE41CQkK4mN6T+p+ehrTF6Z4iPbiR0OIIcmxoW1SiRAkmOWxof6TcCHIA5xFxZ53YrJt2QrkDrVgAnM+4BGaL1OCu9nYycwWYZqKD7lHUNcQHz1mIEEkz8RXb60Soxwr9suWLaZbsJOFB7Jn3A/0sX1KSBSlDTxEfBAkizx2hiDOHKeLMIYq+HTs/widNBrboklSsRwElKtuVkAKiRy7GN998w8TuzpwLryE9HEQcOHRQtwZ6Q3kj6YWFhbFbZNy4cTwpQvwapRuw6BTMCZyzS5cu2SS2+Bp2xu1rBusd5TcKxoAzSNktes6SMBN181KMyHdc+CeJyR7NmZ/8chQg/xz5yS9nAfLNnCNBiSy4NOFChpWTEOFZEx/q8K5fv264q1OLCGN3cOSV0xR58SRFnD3y4kLA14/8C5amJKWrcRsfLAbsTRKC8DUkG5GDAW+UpmnsIjeiBZwscKq10LvvvsuuPVtIKABvZiB5AJqemAjhqsJEiA3kjpYmR44c4VhdmzZt2P0Bq07BHMCkhixZ1Dii2ai1W1Jf0yGmqhMZVO/150Y07FRwMIO0aHkKKFo+lnYpiC8maUZ3kZ7l+GHU9fO8vUCGiBWCBLPm5rozECHKNGAVYbGK+9je9T2IEe/HfOBMP058D4wBLLosW2gIRdy/zVabz/0b5H/3GiULvEbJgu+Rb5zekdHkQ4FJ09L1JOnoim8quuqflsKDA8hn5xny2XWWLVHdIrW16f+HPn5o6h03ySkiIoJjmrjmEWOO7zGh/4v7HiTrpUqViv/2OksPGYjQvETndIir4sAioD9q1Ciu34MLQUZLD4ko+D36hcPyT1aHBpbd+PHjuYu7gmeAyQiWG8gNixA8YsP1CODGw/lBKxlrUsMGd6RyP8oLZCpGB97iMgomxJuX+JH7u6EBcXyfC0hKVZYcpMtBT8iRiQ5zAJpjowksnoO44Ck4e/Ysb7ACUSoF6yk46AFFPnrI7amSRIRQ5gAfypkyCWVPmYQf86ZORoXSJqMU/rat0fshEfRv4FPe9t97RsceRVCEXwB7GLDhutXnI+sN94Ot17l8ISrKZrd4HbBkQX4is36BlClTsnX8sg3ztq3X3CFj6TDpYfJYsWIFq7EgowyZm5hkkMwBUkDhtYykhwsIXYxhIcQ9JGhGi9WigvsAtwwK93ViwwZrDl09AATiy5YtyxusbjwioUqGlaaC2EJs1iBlErwcU18ISTZsD+5SYEgYFV0YW23GEaxvW5X8oiLoaUgo+fv6UBJfH0oe4E8pkiah5P6+lMrPh5L72jeFaj6+FJE2M0VnzE5a1jzkk6swBeQvTkmy5GRPBCZ8kQszxKXRe3TEiBFMgPq8lj59+ue1zc/r+6KimPxAgjoR2npM6P9g2SJBBl6Xl23wqsUHxBt1Ehw5ciTnSoiGvzOTkZ75g4MHdydID4oshw4dIlmBi23gwIGchaoDbs569epxY0gFY4CbDqvmuNabvvjAOcBiBKQGt6ROdCgJUVBALM+iOFOudqz/g5v04eF9RAtrOT1+ZgqjPOmSEaWLzwKxygBNnpJ8UqYl3/Ro+5TZ0hvRL2M28suWl3vSOZuI4wxApCjDQMsjZNuDRB48JzvLPkOl5Xkox11lVSBZa4IECdoiRySIGQGHzwBW0yhVQIAfkw8OJp4jM8hWN3NZcPDgQQ726q5NTLYgd3Q+9lRdlDcCbiJotW7fvp1dyvAUwEUEYHWHawpNinVyQ/2aqnH0fsBSQHIRQiX379/nDQtqW8/hZUJ38pcBdWpp87vWGidb75GUKnUqouhIbrnj4xeAYsvnj0nIN0Vq7pThkyKVacWa4cqE8hPaIP3000+WzGRPAfOpbs15Ag6fpX79+nGtEoBOCo0bN6YFCxawaQ4xatkAgps6dSpbecg8xaoIWZkgPxSaK+UU14CMOQhqg+Cw4TluOrjGIcPWv39/i3sSCSWq5MN7gRACrHo9Lma9IVZrnVmptw7LnDkz34PYcJ3gb0eaVaMuEvFdZFw7EsnRY3rZ6jb1mmsSrsNPPvmEEjscJr1OnTpZnkPrD0kEUGOByLRsBIGbDAoM8+fP55WQLq2Gmh7rLhIKMccKCwOcY8ioQY0HQeu4QMwNMmw6ycGqwyoernAo00CIvG7dujyBKQvaexc6cFHDIwRC0x+RFKJbGXCngYwQGmnVqhU/IuFIr3nE9SLi+gBhwcWHxZWjQOamtxCewn9I1MXp6GiAuruFCxfSW2+9Zcg+etNEhvoevSQFMdBSpUrxwgeEiIkKSUxwEyO4jRV5nTp1mOCw4b0qe9L7gOkDbkksdGDF4xGxWT0rENY7CC3uhpCIu8pEzFynp+B+OHzVYdKDGxMthCDFFPci2rx5M8mA2bNns2UHLVFFeC8HJgFYZyA1AOcd2ZTYACw+4OqG5QySQ+mAWiV7H0AciMOC3PQNcTgAllr16tWpW7duVLVqVU5AgkvN0wBxoba2SZMmL32vXuMGsXhFeN4Jp2J6ID3U42H1LuPEBrcbsjQRvxswYICnd8f0K3kUfGMSQLG3LfTp04eLXWW8FhRebsXpFpy1FQfXNtz/3bt3Z6KDMpGZwxuQFEOHE7T3iqllw6svam9icYdr/dVXX/XcziqYi/SgO7lkyRK7Vk1mBOIKqP1AfAnyPGqifhGw4jDRQXgAG4py4dpEz0RY9wDiLUheQmcNWa8FhYStOFwDUCEC0O8R5AaSA8FhwSuTig08VFi8IS6dNVsOGjpiPIU+vGn5fyStIIYHTwW8FgreC4evWkx0cGPICNSoQFkF8SZM1qqQ+T8g2WTr1q1MchAfQIYujhOSDGDF1a9fn8sN4PLBShntcdatW+dQJp2CuYCiYpAbJAU3btz4ghUHN6UMVpw90EtjUOuZp0g5WnarJPlFPqE5H5XlTGJkiqoFcOKAw6SHlNcpU6Zwmr+MFwkmacTy3Nnk1qyA0AAyVUF0K1eu5CJRJBggxok+gMjStM6ggyQSLGR8btWqVapAXEKgLRJIDtuGDRs4GQz1qHDn6a5KWHHellkL/VyQG8j8yOVHPHflzpGVhcYVEhfsIr24UjBIVsFNg8LhuNYS/OH2YsyYMfx+lDzAl45JFtmUKIA3AljN6Y1uEytg7YKwcNwxEcClhUkOrh2cZ9TLJbSYgUUAt5aMC57Ebs1hQykBkjWQaAJXH1zTcFt7e2YtrvUGDRrwtXv7QUzZRNb0SvQgMcIu0ovr44YVICqhBEkQqPmCW2XIkCG84jx58qTNGjAF5wBXJUgOFh1cmIhvwGX11Vdf8blEvMZeKJewHNYcXM9r1qyxWHNwVUPp5vPPP6dGjRpx0XZiAX4/iB/eKeDq/af8mCeT5zNLFUxKekjvNwK4Ma2BrFC4WpAWjxovBeeBRQQmPQjO4hEreeiIIj6HuIZRunYK5rPmQHYVKlTwemsuPmzZsoUXenpG5pW7MZ3T82RRC+vECKfTr5DFB6UFAO5IEe3ndfVtuCFtQe9NpSMxN0KMD8i0hEA2Fiqw8FA8Pm3aNGrXrl28x1VBXmtOj83h3oE1h1rJQYMG8QQvszUHQWLIG6K0yFVXOlybUH9BhiZw9d5z0susLL1ECc1BBAcHa506ddL8/f01Hx8f3vC8Y8eO2sOHDzVnERUVpTVt2lSrWbNmvO8ZPnw4Cmte2H5dtklLzAgJCdF+++03rUGDBnw80qZNq/Xu3Vs7dOiQp3dNQRCio6O148ePa6NGjdIqVarE5xn3XrVq1bQRI0Zo+/fv53tIdkRERGg///yzljVrVi1ZsmT8m109bvny5eP7AQiPiNLqfL5Jq/nZRu1OUIigvVaQCQ6TXrt27bTChQtr69atYwLEhudFixbV2rdv7/SO9OzZU8ubN6927dq1eN8TGhpq+U5seG9iJj1MCB9//LGWIUMGPg61a9fW5syZoz19+lRLLAgPD9c2btyoXb16VfM2REZGajt27NAGDhyoFSpUiM9xqlSp+B5csGCBdv/+fc1bAHJavXq1VqJECf6dWFhfuXJFyD2C8dauXct/X77zhAmv4Rdb+DsVEh8cJr0UKVJo//zzzwuvb9++nf/PGfTp00fLlSuXdvHiRYc+B+JLbKT3+PFj7ZdffuEVPn575syZeVI8deqUlpgs27/++kvr2rWrhfAnTpyoectvW7Vqlfbee+9pWbJk4d8Gq+f999/X1qxZwws/b8Phw4e1hg0b8m+tW7euduDAAWFjjx07luclHFdg67E7THrvTtkr7DsU5ILDMT3ECWwpFuA1R2vfQLpQQNezClXNTPxANwt0pp87dy7XySFms3TpUmrRogULBng7EONBQg6yUFevXs3HAPqevXv35lIL6ILKCggi47ehlRVidOhUgYxadPqAOAASUrwxCeXGjRv0xRdf0Jw5c1iEGqIIzZs3F1oOg/IcZKvqPRnP33zCj4WypRL2HYkZjx8/pl9++YXrOjGXSwFHWRL+dqzKbt26ZXkNz1999VVt+vTpDo3Vq1cvjj9t3bqVx9C3Z8+e2fX5xGDpnTt3TuvWrRvHTTNlyqR9+eWX2uXLl7XEALjvZs2apTVr1kxLmjQpn+uKFStqo0eP1k6ePKnJjOvXr2vTpk3TGjVqxOcWv61y5cqW3+bNrrdHjx5pw4YN05InT87XNI4D3NRGXD++vr7ajBkzLK8N+vUIW3pLdsjhDkecdtGiRdry5cs5jIS5cs+ePdqRI0e006dP81xw+/ZtzqeAF8Bd183169e1zz77jN3tuHZz5Mihea2lh86758+f5/552ICrV6+yWge6G6OTuo5Dhw69dCzglVdeifU6Mg+xyk3MQME+es+h7RGy8lC0j0w2b69fxOofFg8sOtRxQgcUzWahooOawrx585KMgFfj1KlT/Nuw7d+/nwulUUYChSNY7Gh/4+1lNLi3hw0bxtYtetyhbtAorUtYzbh+II6v4+yNx/xYOLsclh7EIxztAgOr9mVb8uTJuQMGNswp+vOXbaihhrcJxxbQW42ZoZuGvXCY9OBuEQWJW/kZhmPHjtGoUaPYdYlausmTJ3M3CFyk3lxmARc32r9A5BhkAK1PlFrgeoPOp6zAJPHbb7+xSDsaqWKCgQoKmhbjMTG0r8F9jvKKTz/9lE6cOMGNqLGg0xfNRgHu0kqVKlH27Nn57+CnEXQ3OKbkqXCO1CQDQCYozULzXWc3lHlZ/x0SEsIb1JmePXv2wobQgT19B62BbhxQc0LpGhbpCT1CDs6Tik4Ok97w4cON2ZNEjsOHD9PIkSN58oc1AysY1i4saG+E3q4IG4qpsfpEjRlWkRAFl1kbFRMAupGA7NBvEJYM4o4TJ05kKSw9vpQYgHMLskMtIfoswsIFERkNTO6wRqDypOPcrRgrL2fG5JQymRwdIkAO6HCCzZ2LlPDwcJuECEsd1zZiedaADi88MvD2oYYU5x213IGBgS8YN8hBAAG+jBwR5zWittjpMw/VFLhrAGhwQr9PwXHs3buXyQ7JGSigRVC4c+fOXin3hZUl3LUongfJ40ZG4gLcXSA8mV23aKQK6xxEt2vXLrbM4bIcMWIE/zZvXbzEB0x8SFKByhKScoxIUkkIaHINi8XaMyWba9NT8PHx4esVW9zFJwTJoeoEL8zXX3/NSVewCsEBeC0u4P4E8YEMQYK2HhEeA5/gb8wROqAmBS+XcDgaBLxz545Wr149LoxNnz49b3hev3597e7du5o7IXMiy82bN7neCvtfrFgxbd68eVyY6421ZuvXr+caziRJknDSRqtWrTgwL3v6PZIHZs+ezckoSJjAb4PAAmroUFqSGIEa0a+++orLBJCkMnXqVEOSVF6G7t27a0WKFImV2DF4zr+cxDJ/S+JIBDMaDx480D755BO+7t955x0hY2IORDLj0aNHtXv37mmmKU6HIoR19tyJEyf4tbfeektzJ2QkPWRj/fjjj1qaNGm4xg7F5CAGb8OFCxc4Qy937tx8jooXL6599913nGkmM5BZvGTJEu2NN97gjFIs+F555RXOavamYnFHAXJZuHAh19ticYPMPlcUmlwB7ifcW9gH6/1rNmIbk96/l4I8sl/eitu3b2tBQfIcU4dJD5P1vn37Xnh97969XH7gTshGekgzrlq1Ku9zjx49tMDAQM3bVvlz585lTwB+Y+rUqbmoGinWMqfgw1JBYXjnzp0tKdpY5E2YMIFTtxM7MB/UqFGDjwsWA+fPn/fo/kAoA/uya9cuy2vX7z9lwntl8CYtNNz7FpkK9sPhmB78t7biTXjN0YyfxALEFuD/RiIDxLn/+ecfDvp6A7Bw2rdvH8fpEOBGphnS8JGQ8uabb0qVymwNXMs7duzgGB16MN6/f5+KFSvGSRkdOnRwqB2TN8ftBg8ezOe6TJky3GcT597TQEkIEitQ1K/j6OUYMfuiOdNQ0gDvapCr4CA0B9GiRQutTp062o0bNyyvYbUL+SDEatwJGSw96AlCUxTiuSg8DgsL07zFpQF3pa6VCLcW3Jlwa8qMS5cusQCA7pbFI9xkkMqS2VoV7eKF8HXKlCk5bgdRCrO46HGOChQooH3wwQexXh+/7BRbej+sPOuxfVMwBxwmPQj7litXTgsICOCLCxuely9fPkGx6MRGelgUtG3blvcPiQ5QVpEdCDJD8xKLGwSvEbtBggoSVcwy6bnSpQJKQ4jRwS0L9zM0Zr2hc4FIQlm6dCkv4nD+BwwYYLpYDhIgrAWmdXSeuJtJD9qbCokbDrs3c+fOzUorGzduZNUQABqIDRs2dHQorwUKkXv06MH1WEjRh6KCJ4sxRaTjI00Z5RS3b9/m8pRJkybR22+/LXWPPtQS4TfNnz+fgoKC2OUMN23btm2lLp8wAigxQUH99u3buY4SPepQR2U2oFQBqfbWbtbAx2F08XZMt/Qy+ewTA7h48SJ9+eWXnIZvlGKMgofgaEDfz89PO3bsmGYGmM3Sg1UwePBg3idksiKlV2YgIQEtn5CliASODz/8UPoefbBMkD0LDU+9gwHcl9AxVLDtxkbHB1jAyMCF/qOZgUQahFqssf7QrZjOCpPt76ywbds2TswrWbKkw91fFLzMvZk/f37OQjQDzER62BcII2NyGD9+vNTxn4MHD3JpCmrPkPqN+I3MBI5zAaFeZF8itorf1bx5c64V9EQNmQxADSWuY7h6UYv7/fffm/5Y4TxnzJiRY8vWGLX4BJPetNWOhRjQrqtgwYIct0RfQ4VESnozZ87UmjRpYop0e7OQ3tmzZ3kVjJUhUttlBCaMDRs2WPqaIVYLi8jejhdmBOKq33zzjaUBKx7xt3USlsKL1wEWA5js4dWBdS9L/aHeMBbXsfXvaTXqHya9fWcdn7Pw25G4h/g1ynEUEiHpIYkFri64vKB4gAQW6y2xkR7cPenSpePO8TK6yJCAsnjxYou7D+cQrUxkVYeBNfLnn3+y1Y1JG+1rYOHB0pPZ+nYHELZo0KABXwdoFQYSkQlYpCHB5smTJ5bXLt2O6ZReb8hmp+vzkHGN9l44Lm+//bb0ba0SOzzaZUFmYMGAurvPPvuMtRWRsCJTwBsq62je+d1333GXAwghIzkBCUkyJt3gN/zvf//j34TEG4gaT506lWvqZDovnkBwcDB99dVXnKxUoEABWrlyJbfjke06QJINzrt1EtKeM4H8WDZ/Oqfr8yCQPHPmTNadRL0tdCbbtWvH2qKlSpUStv8K7oHqsuAkYbz//vuc9Yd+YGgFhM7BZgJEXnGuIiIiOJsNmaR4hFI6+tShEzvEXVFAjqJydyjfG7HwQBbx999/z4LdaNODtjXdu3fnNicKLy/AR2H5oEGDWEAB7X6QoSmjODauBZAezr81/jl5jx9rFs/k0vhYAED8GGLwENEeM2YMlS5dmns8fvLJJ1StWjXTzQEK8cAZySHISsUFXtu/f7/m7e5NuE4guQS3GbQGzQrEGXFskLSBOkq4fZBkg9ewwf0na+0gxJzhykIcFb+lTJky2i+//CJ1/NHdOHDggFatWjU+fh06dJBeTg2Zxvgtq1atsrwW9CRMqz1oI7s3bz14JtyNjmsOsU98L5J9UJeLLu1XrlwR+l0KYuEw6VWuXJkLVONi2bJlWpUqVTRvJj1c6K+//jrHNG0Rv9mAuAziWjrR6VuvXr00GYHUcai6I2EIZN66dWtOLVexOvsB5XrooWIBVKpUKY51egNmzZrFv8m6WH71/htMeF0nG3evIva9c+dObfjw4byIwHWpd07p27cvk7B1jFFBQtKD9JAtqSlMSCADbyU91OB16tSJrSbr7DAzy2lB/d+a7ECAsPBkUhkBoW3atElr2bKlpZ0V6uouX1btYRxNWIJ1jOOHRQNKEGRNVrKFrl27cpKdNT5/3kpo5t/uk8ZDac/vv//OCwso1+C+w5wBEfYxY8ZwOZAn77/g4GCbDQMSExwmvQwZMsRSL9eB1Q6yGL2V9GBhYNJFZqOZ8ejRI23IkCGcXZstWza+8bD6BOHBFeOpdi/OdGxAux4UB+Mcwyr53//+x68rOAbUmIEQcByRhYiemLLAXiseLm4IKeh4EhLBGZsgvbM3Hmme2vczZ85oP/zwA9eFwmDAOUD5Q+HChVmeEHJ30ORFD0bMoSinMYIUsQj++OOPuc8h5jEzlJx5Cg4nsrz66qusrI5OyHpW3MOHD2nIkCHUqFEj8kZ8++23NGHCBM5ua9++PZk1KQGZizgPOB/oBoAEhQ0bNlDr1q2528GqVatMk8l4/fp1vl7QWRndmSFnhn1DktCxY8c4yQYdG1q2bMnH/ZVXXpEum9DTuHXrFl8D8+bN40SlPXv2xOo8YHbs37+funXrRmvWrGH5w/iAxTtkw5BkomPb8bsUHhlNeTOnoEIe6pSO6xVSbdg+/PBDCg8Pp927d/P1fenSJbp8+TJ3DF+2bFmsjuFIJMqbNy/ly5ePt6xZs1KqVKl4S506teW5rb/xWf0+wXHZuXMnZ5mj84Svry/fb4C/v8NTv9fA4V+OFPc6derwSYEGI3DkyBE+Mbi5vA0gEpQlID0ZF64ZgVZFyLqDJip0PseOHcvnBwBpYOKAniRa45gFIGFMVJgIbAH7O27cOMqfP7/b9012IGMXCwWUIWASnDFjBl8DmPRkAs5/WFgY5ciR46WZyk+ePOFyCx0bjtzhx0bls5lmsYTSh7p16/IWF1jgIaMaRGi9gfjR1urx48e86aQVH0BmOgFCJzcyMtLyf9afnTBhAmc7p0mTJt4NY3hjRqrDpJczZ046evQoLViwgAV7kydPTu+++y7XQ9nqsyczkAaP9HeIR48YMYLMBtwUIOSlS5fySh7932rWrBnrPZjoIKpsNsCyg2A1Uubj9mEEacNCUXBOcPmjjz6iM2fOUO/evfm6hSUtG86ePUt//PEHTZ8+/aUTLxZPgL5AgsD0wfMxllOjcllJBoBkUAKBLT7AcsMiEQQPAsSjvtn6G70gz58//8I4Pj4+XHcIosV7EwKITydBeGISIsnMmTOz8YNehng0q2i7UzYufgzq1LwZcEPA2mjevDn9+OOPplktArigQQxYrWXMmJGtUdQnybKSR03YTz/9RH/99VcswsMxhjWtCM9xXL16levF0PAW3SJg9ctcqwiPUpYsWeidd9556XvhKgR0S2/Tv3coWiMqmScN5cwoZxNjW8D9AcsdG+77lwELHhwb3E9YGGPxEBUVxRbejRs3+D34G8QHAoy7QbTA1uvY0EA47msg5bg8AfKzJsL4/ga5ugtOO3ZPnjzJN1pc91SLFi1IdqBlEhQpYD1BacUs/u+4cTtYedjcecG4AtxcaNWCCQ3737VrV/YWIK6BGxqqMIg/KNgP3H9Y/IwcOZInM3hg4HUx0yLNmVgkrnNM2hBVeBlg6cGahSWCiXfdwdv8eqNy2SixA9YvWp1hEd+vXz92l1rPFyBCHDdXY/2Ym+BmhksVikhxN7yOmDKe37179wU3LcIdcYkQC54aNWqQaDg8m+MCgwoBgrG4sXR212+yl/mczQ74wGE14aDDEoH71oxxO8Q78uTJQzIAq0BIgoHQ8BzxJSRDIe6IADuuJ8QbsRo1ywJDBmzZsoVdmHBh4dpA/zckNsiOyZMnM9n17NnTrvfDmtFdm6evP6azNx9TEn9faiiJa9MdgITa3r172RMQ1yITAXiZ4N7ElpCLVidIJO4kRJD79u2j1157jQyBo+meqPNCzRSKXFGXB/FVdJhGYfr27ds12UsWxo4dyyn+e/fa33vLSNy9e5fb/OB3QhgAac2yAIXCI0aM4NowpGn37t1bu3r16gv1Y99+++0LryvEj1u3bmkdO3bka6JWrVqm6W8pAiipSZMmDddi2guIZLdp04afj1l6kssUvv5NLrFsBffBYdJDv6p///2Xn+Pi1DsLoIA4bnGobKSH/lmob/v00081M2D9+vVca4d+XnPmzJGmqBw1QF9++SUXQaN/HZQpZJe5MgOwQJg6dSofV1wTs2fP9jo1Giw6sUC6efOm3Z9BGyzcs4+ehWsNhsbU5h25+J8yi4KCS6SHAnS9kzAuts2bN1u076BHKSvpYUKBpiaKRj2t4YgGniiG11u8ODIBeBLoPTZ06FBuPIproX///tLsu9kBXVu9/RPUPmTpcecIQkJCeJGHTu32AqoyEF746aeftN93XmXC6zRht9ctBhTEweEAClppIPkAPnQUuo4fP57rT9DWxbpORjYgwWLXrl2s1O7JOB5SzZGIcPz4cU5QQKzG7FmZ9+7d433FMYS/vk+fPpxJiLiogmtAws/QoUM527VMmTKckABFf28E6nwR04GwgiMiB8gjyJcvP83ffZ1fa1k1p9SJPAoGw5mmqRCXBqDSj+apkLWBuwUuThktPWiJQp4HXaI9BaxModCO/cAxPXTokGZ23L59Wxs4cCDvM+K7n3/+OccgFcRcD/PmzdOyZMnClvPkyZO9SivTlqcFne0hIu4IEGrBHDBr+U628l4dtoUlyBQU4oPDpBdfDMcT7gQRpIf9rl+/PmtUomWNJ4Dj9+abb/JvgRaf2VXZ4bKE6xIuTMR1v/jiC690t3kKSA7TxcLbt2/PeozeDnRuwe91NIFMJ73O47cw6U1bfdawfVTwDviLUteQFVAm2Lx5M3cN90S9Gxq6okQCBdtIJ0ZTV7MC5QZoNIqmrSiQRY0gan9kVPwwI9DgF/V2cBWjnGP9+vWsdZsYMGnSJNZXrVKlisOfTZWtKF28H0V+vj7Upkb8Gp0KCoDdpIfaKnswa9YsaY4sxI2HDRvGRZDuFsuGPuLXX39N33zzDWuZIp6RkKiuJ4GYyezZszm2BDUYkF3//v25GFpBDFAT2rdvX65Rgs4rjrE9hdneANQZIp4O2SxnkLNqe4vkWJZ0ieOYKbiB9H799VeLyLQRxY2eAFQfkIQB4nMnUOAP3ckDBw7wyv7zzz83rbDr1q1bmeAgKg6LdMyYMZQrVy5P75bXACLDIDuQHopxN27cSIUKFaLEhPnz53NRPcTRHcW1B5GUqWgdQtrK23VjRNYVFISQXq9evXglBvUDCExjApTZrQnlFWSeQl/T3ZMMWolAigcC0WbNxAMxI4sOor/I0vXmrEFPXX9w6Q0fPpzvI6jRwLWd2LIOsYAG6bVp08aprOnVx57yY7lc/lQgmxxyfAoehqP1YwsXLtQaNmzIGXtt27blbE5P1cS4ksiCpo347OHDhzVPZKqh2asZgWM6aNAgLhDOmTOnNn/+fGmK4mWquYOQA5R/0NjTrNeCO4CG1LgPncn8PnUtmJNXagxcr63cuNuQ/VPwPjhUAIbkBdSQoTEpBKdLlizJ2n9odPiyFhVGwlF3K96PLgWNGzemcuXKkbsBV6bZNBIRt0MLIjS8RKIKtDFRM9ixY0fT1wnKAtwjAwYMYMsZ1yAEeGHtme1acCcQy4a7HEksjmLm3zEthe6d3EzZ0irNVgX74PRsholQF5z2tMj04cOHHHo/OjFDMBsTu0JMBmnlypXpvffe404HIDs0IDVrPywZsXbtWhZ2QH84LLigdo9jntg7RCxevNiphdWRi0G050wg+foQXdvlfc2rFYyDQ1cauhgjrodMR1gEIA6o56PFkCfb26xaveqFRqQJAckYUB2vXbs2JWYgPotYClbZaACMDDq0pjFrFqmMgMIIvCNNmjShwoUL8z2DWKlRDZdxjyLpSJaFANT2O3fu7NDnoqI1+n7lOX5evWAyCn1406A9VPBK2OsH7dWrF6vllylThtUh0GXB09BjeqmyF9WWLFli12fQEQKf+euvv7TECsSQoJ6ix+2g/KHidmKBOPesWbP4noFI+9y5cw2PfUMNp2bNmqxf6Wn9WHsAQQZnROpX7bthUV85ePQ0388bNmwwZB8VvA8++McecoT7Af3bULKQUIYZsv3cWSyN5oepsxejHGk0jjO+zE2CXnQnTpxg/dDEGKtatWoVd70PCgqyNKFVbkyxOHfuHH3wwQfc7w5WDIrN0WfMSODab9asGRe4r1ixguOGZgauPzQNhdcFcU578Swskjp8u5sCH4dT7yaFqE317Jz1Cbdxjx49SBY9VQhRQL8YGzwrRln+Ci/C7ugvCrjNmk6tkcZxKJQCoAThZQos165dS3SEFxwczOLVqLeEqw0CxrI0oZUFEBz49ttvueN3jhw5WOXHHaIH+B5c96ijBdHi0ezApI+yDbh+HcG8LZeZ8HJmTE5taubmZrEgDZTYyIKzZ8/ywlO3NzAX4TfoJIjEQP05NiwOzFrHC6DeGETesGFD8rridLMiICApWyuID7wMiD0WL16cEhOQbdu9e3e+MJGhiTpLsy5gZMDhw4d5u3HjBt28eZMf8TcU/3FcBw4cyIlAKVKkMHxfsHj56KOPuLAd8fY0adKQDIBHqF69epQ9e3a7P3Px9hNauO0qP/+waWEmPADEgPi0LIDUGtSgkAtx+fJl3nd9gxcK3hiIZsSdt3Bu9Q0eLuu/E3pdfw1Zwn6CyBMLPBgZEydO5KSsjBkz0v3790kGeEWeL9K+u7Su7+ndMGWKPNyXmBjr16/PEnEyWAFmBzJc4Z7z94+5fWCx6Pjuu+8cctc5C2RMo33TlClTWP8ULlR3WAPwGGDydMVTggnzn3/+cUgJKTpao3HLTnESS+2SmXnTgZZmSBCSCSj/QmITtvjuXZ0QIWSBUI71hvOARyy6rP+GTGBCEauUKVNaiNCZDWQNAwgJjNgv/TqQSTLPK0hP4UWgLyAsOmg54gKFok5ic+kaBSwkhgwZEovsYOF16dLFLYSHiQ2xaQhS//jjj3xu3QX8RlxHrsTuDx48yALrsPTsxfK9N+jE1UeUIqkf9W9RJNb/gfQQx/QmwLJDiQs2R4AsdhxbayKMS5bBz1/Xn2MBB4LV/8YWGhpq9/fp1yT6aGbJkuWFDfFsiNKbxbukSM/LgJUYhKEnT55MNWrU4IkxsWk5Gg3UlUEoHDe6Dlg/iOe5Q6uzefPm/Ih6U3d2YVi5ciWTy5IlS1waB3FHTOoVKlSw6/33gkNp+trz/PyDxgVfEJWGezMwMJAnclncu0YBCxJci9hy5szpUunLo0ePOCSiEyGeY16BJGHc2mwQ2s6dO9n6wxb3/+EVAfnZIkVbm5GhAUV6bgQmC9QHGlUHt3fvXl6JY9WGCRiJK2YOgMsGuI1Qxwh3YtzjCh3XTJkyGfr9OL8QZcaEgImnRIkS5C4gKxTC2CBZ1Ha6KmKOGlndPfyyYz7+j9P0LCyKSuZJQ62qvSh2DksPgCuwbNmyLu2bwn/uV5BU3Kxj6MPCewRPB9ycIFkQHDrFQDRdt/5AkDoB2toQz0QCDJ7D0rTlhoUXAwmUwqFJDFGd092BX3/9lTvMf/nll8LHhibq4MGDWcuxcuXK3IRUQSyuXbumNWnShK+3t99+m+tU+/bty3+j1gx6qkZi8eLFWrJkybgOzxPd6YcMGaIlTZpUO3funEvjhIWFsW7v+PHj7Xr/n7uvcU1evSGbtQu3bDd5vnPnDp+HP/74w6V9U3AMBw4c0KpVq8bHvkuXLpor1wQaJR85ckT7+++/We934sSJ2tGjRzUjoEjPDZgzZw4THrqiiy4CP3TokFa6dGktICBAGzVqlBYRESF0/MQOFJT//PPP3CE+e/bs2ooVK2IV+Xfs2JG7dxv5/SNGjODrvFOnTlpISIjmbpw6dYqvr+HDh7s81s6dO/m37Nu376XvvXL3qdZg6GYmvcXbryR4jFKmTKl99913Lu+fgmPAsV+9erV25swZTRYo0jMYUOIA4b333ntCCS88PFz7+uuvNX9/f1bJwSpJQSwuXLig1a9fn6+xbt26aUFBQW79fhAcSBXfD+LzRDcTfCeOQcGCBYUQ7ujRo7XUqVO/dHEWERmlvff9Pia8vv87qEVFJfzbK1asyMdKQcHUpLdt2zatWbNmvILGjf3nn396FelB3guE1717d6GEh1UVbnI/Pz/tiy++YPeAgjjgXE2ZMoXdcHnz5tXWr1/v9n3QJcXg0ly0aJHmKaCVGO6xtWvXChkPbcmaNm360vfNWH+eCe+14Vu1O0EvJ1u0w8qSJYvL99nu3bs90m5MIZGQ3po1a7ShQ4eyL97bSA9+acTYYCGIJDxMPmnTptWKFClil4tIwTGcPn2ayQbXVZ8+fTzS6+7EiRNa/vz5eRLfs2eP5ik8fPiQdTzbtGkjZDwszpInT659++23Cb5v35n7Wq1BG5n0Nh65bdfYmzdvdrk/JqzaunXr8m++ePGi0+MomBumcW96E+mhQS0I79133xVGeLghMVlgXCRUYEJSEAe428aOHcvJGoUKFWIvhCcA4WTEDxGnvXz5suZJfPTRRxwrQxKPCOzYsYPvVzTRjQ93H4ZoTb/exoQ39veTDiVzYV9xDl0BkmLgyi1cuLBHEoYUjIdUpIcLG0Snb7gZzUZ6OuF17dpVGOEhloIkBvxWuHGMzhRMbEAiCtzFOG8DBw7Unj596pH9mDFjBsdoGzduzNe3J3Hw4EE+HiKTQ6ZOncoJMfHF8xDH6/Xjfia8rpP3aKHhjl3ncJsi/ugqzp8/z1Z2lSpVtCdPnrg8noK5IBXpIXuM9aXjbGYhvd9++40nCqTviiKm69evcxkCYjsgVAVxgLsN1xSIpkSJEh5zJWJxhFZPuJbRwsvTGbjYH0z4sDaRMCUKn3zyCVtR8WHa6nOWlkHX7jm+8Pj++++5XZYIokI6PixHeFWw2FbwHkhFema29JBsAMJ75513hBEeJmEk+aDnXUIuIQXHgeNZqlQpJjwkA3lqYkPfu7Zt23LCE2qTPJGhGRfTp0/n+wq9J0X3z0Miiy0gdgfCw7bl6B2n47HY71WrVmkigAQmkGilSpVUjM+LIBXpmTWmB3KC2wYp06IID8XsuOGqV6+u3bp1S8iYCjEk89lnn/ECpXz58h7N1EP8CMW9SO5w9No3CohjofEt4tGiUaFCBa5VjYvT14O1+s/r8aatPuv0+FgwINsWogGiAIsPSUXp0qXTli9fLmxcBc9BkZ6AiQuWGMhJROkAXFsDBgyw1IYp14o4IB29aNGivJj45ptvhLruHAVUczCZIlPQTFY8rj0k0hiRxAEyRZ2eNQIfhWpvjP6HCe+TXw5rkS+px3sZ3n//fU5CEWkxoz7zjTfe4HsSx8eT142C5KT3+PFjXmljwwUF9w6eX7kSv/qCmUgPBPXKK69w0BuxN1fx4MED7dVXX+X6O9SJmcHV5Q1AIhASgHSZNpQEeBIbN27kshO4Vz2doWmNmzdvcuxYhPJKXCDbGPcq6v50hEVEaT2nxSSudPh2l/b4WYSQY4vvQQmDSOBenDRpErvDYZ17spREQWLS27Jli83EFHt13DxNep9++ikT1NatW4Ws/JEqj9UwblwFMYAVhSQVuJ9h3Xk6SeSXX37hiROLG7OVnaBEAW48e/br0qVL2uTJk+2uY4RiEO5VWNsWebVFx2MK0L/cypJjIoBxkYDTokULzQiA7OAtwG+pUaOG9vvvv6tsaslgGvemM/Ak6S1dutRinboKBN4hzVSyZElOl1ZwHXALQ/gAixLE7owSr3UkIxKizbhm4IIzm4vs6tWr7PaFfqs9gCfCkUxJhC7w2/X49M/rYhRX6ny+Sdt75r4memGBxCBXxbETOpfQYK1Tpw7/JripcTw8IWSg4DgU6TlplaVKlUpr3769yy5I3Cy4QbEyVTeNWBFuWFTQJ/U0wcC9imsF1yoEBszotu7Zs6eWMWNGu69BR2vi4BpEwg5++/I91y2Zmiv33dCMON6ZM2dmy9UdnoQOHTrw4gpzQvPmzbl0AiLdZjzPCor0nPpOuDdglSEm6QomTJjA+4+CaNHdFxIjQG5fffWVRYTbDBqKSAiBGwyxMrjCzAi4KuH+HTdunN1WNHRJHVE/gZWLzMpdp+6xdQfCm7n+gmYU0MILdXbuEgmHpYwkHciY4Vjivs6dOzcno6F+F62oFMwBRXoOACu31q1bsyvS1VYaOuGhD55aEYpRVYEbEyvuYcOGmUKEG3VjBQoU4ESnvXv3amYFBNGxj/a6Kp3RuQTpFSpXR2v4xRYmvNGLTxh63cONCvLxRLshLIbRbqdfv34cT8axgjcHJRtIqFq3bh3X/Xkivnz8+HGPCpibAYr0HLAioCbhTGlFXOBGVIQnBpg4EIfCBAfrG3VVZgCStJCUhEkPlpRZgbgXFgpwP9oLqMc42tHg4yGjtWp9/2DC+3jGIZYcMxoQisiTJ4/Hk5eQ2Y26WzQfxnHTE/Zw3PPly6fVq1ePLUJcx1Bd2rVrF5O2qLkB5wl5A3qbLCJy2UslM3zwD0mKR48eUdq0aenXZZuoS+v6hnzHs2fP6JdffqGvv/6aAgMDKXXq1Py9zmLChAk0cOBAGjJkCI0aNYp8fHzI3cAp98T3isbJkyepS5cudOjQIRo0aBANHz6ckiZN6undojlz5lCPHj2obt26tHTpUkqXLh2ZFe+88w5t2rSJzp8/T8mTJ7frMxUrVqTixYvT/Pnz7Xr/7aAQ6jh2M4VRMiqSMzX98H4FSpnMn4zG0aNHqXz58jR27Fj69NNPyQyIjo6mixcv8nbp0iXerJ9jjtGRIkUKypcvH2XLlo3nHWxp0qSx+znu8Xnz5tHEiRN5bD8/P4qKiuKxIyMj+e/ECOOvPEnx8OFDmjZtGpMUnutrA1xEzuK7777jm89ThIcbbty4cXyTzZgxg2QFblycl2HDhlGBAgVo9+7dVKVKFVPs1+DBg+nbb7+l7t27008//UQBAQFkVpw+fZoWLFhA33//vd2Ed/fuXV5k9OvXz673Bz4Oo49nHGbCCw++QRO/fMsthAeUKVOGBgwYwNdJ8+bNqVixYuRp+Pr6UqFChXizBSyodQLUCfHevXv0+PFjunLlCv8/nmPD87CwMLu/Wyc8X19f6ty5MxNjqlSpLGT5sud4xGdlh7L0bACTaMOGDSk0NJSJwho3b96k7NmzO014Q4cOpZEjR7qd8G7fvs0XOlb1IN0RI0ZIeQGfOXOGunbtSnv37qVPPvmEj2WyZMk8vVsUHBxMb7/9Nq1bt44JGaRgdmu6Q4cOtHPnTjp37pzdFvLChQupY8eOdt0Hj55F0Ic/H6SLt5+SFhJIB2b3prDH98mdCAkJoXLlylGGDBlox44dXmfdREREWAhQJ0P977lz59Lff/9N4eHhsT4TEBBANWvWpCdPnljerz9/GR3A+tTJMC45wqOB45w+fXrebD3H+z19XyhLzwYyZ87MJyfuKipLlixSEt6GDRuoU6dO/L24CUDosgGrVFgkIOzcuXPzBFajRg0yA0AaLVq04IXF2rVr6dVXXyWz4/jx47R48WKaPn26Qy5hXD+woF52HzwLi6RPZh1hwsuYOgntmD+Ewp8E0pEjR5iE3AVYsLNnz6ZatWrR5MmTeaHkTQCBgVCwxUXbtm2ZyL755hteiIHQIiMjKUeOHLRly5YX3o//RzjHFhnGfR73b1ijuA8ePHhAQUFBvAi0BX9//wRJ0fp5hQoVKFeuXOIPmiYxjExkQSC5atWqlsAvJKycUXkYP348fx6F0u5OWkHyDZIOkDkGBZDbt+3rQm02IFMW3czxO5AR56l+d7bw999/s4oJylhczeh1J9DxAEXVjtYwIvGif//+Cb7nWVik1mf6AU5aef2rrdqcpWst9xHq2DwB7DPKRmQ6RyKBZCp08yAirVy5coZ/H1Rq7t+/z4lSyFxGxipKN6ZNm8YJO2gzBVHzVq1acZkH6mpz5crFpTD6tQKRASOgSO8lBbuo+dJPwsiRI50iPLSucTfhQdMRItjIEEM9lYx1gLhxoHiDyQqZs9u3b9fMApxPCAvg+KLpq7vqwURA17qdNWuWwx0qsPBIaDKyJrxGw7ZoJ6485Aa9+Jx+H6FBrbuBhRJk/rB4SsyyYXv37jVNhnN8QLkRFuhGiXUo0kvg4sCNilRu1LU4qobvScJbtmwZWx9cDLxrlyYjzp49a1rrDsXZqG3D+cWKVbZJtE2bNkwAjqbyHzt2jH9zfIuPp6ERWp+fDlgawR67/JBX+Na6ulhEQs3FE8B+6/e0QuKFIj0bwGQAFwCKnZ2p8YGyhScID/JLffr04e9GET26NsgGWKS6ZBUKu7dt26aZCViBgoyhO4naK9lw48YNtk5/+OEHhz/7xx9/8LVly00OwuttRXjHrzzkax9dLRAaiCsqv2/fPs0TwAIK527NmjUe+X4Fz0ORng3ApYYVoTMqGjrhQRXEnYQH9Y+yZctqSZMm1X788Ucpi97h/69VqxYfPzQCtVchxJ1uQUhLwerXuwXIBsi0QZ7LmQ4PcJNDjSjutWWL8ADEz2x1UcHWu3dvzVOuM8TmQXxQTVFIfFCkZ0NDD5MCLCazEx6sUJAdlB5gGRUrVozluGS07tCmxqzWHbBkyRIOsiM+de3aNU1GIGklR44c2gcffODU5+HSxe+PS3i9ftxvaRF04up/ZAq3L4hl8eLFfPzgcodrFe53vduCp4ivZcuWivgSKRTpxQE6JGfPnt3hlfDPP/9sOOHt2LGDV9udOnViyS1d2BYbJiOzWUb2Wne1a9fm3wBVfLP8Bix+EMv98MMPeRGE/XvrrbdMFVt0FCAb/A70tnMGOE/oKKAj+Gm49sHU/wjv5NXgBD8PGSxkjZoBID5kDoL4INGlkHigSM8Kf/31F4+HlamjZAQC6tWrl2GEB6tOJznEZKxdRZiUzSCw7Ip1J6IRr4iMV7QAypo16wvuOLTdkdFlbI0GDRpwxwdnAbcuuhcAgY9CtS6T9jDhNR6+VTt1LWHCA7CAKF68uGYWWBPfypUrPb07Cm6CIr3ngIUBcVqknzsyuSExAJMBYlFGE8+YMWNsxkeMqmdxh3WHidAs1h2y++KLQSELUWagryB+B7p7OAOkj+Pz8+fP1249CNHeGr+LCa/5iO3a+Zv2iRcj1ozsTTMt0LAv8O5gQamIL3FAkZ5V5iNWsRcuXHAodb1atWpazpw53VL4jWzMTJkyWSZiJNugLMHTTVIdse5Q2wbrDoXR6ERgNkBEwJrscIyLFCkitZWHTE399zgbw7py5Qp/fsEf67Q3Rv/DhPfmmB3atXtPHV5UeKJOLyHg/gHxwYPyzTffSFnTqmA/5BNfNAjQb0QnBQgY24uPPvqIxXeXLVtGWbNmNXT/Tp06RdWrV2dpNF1BHYsWyJqZWdRYx4ULF6hevXqsSdmtWzdWwH/llVfITIDOKrRcrYFj/Nlnn3lcL9DZ3wPBZVynOuLqMDoiA5cicwGac8iP7gaHUZ7MKWhaz4qUK1MKu8eoWrUqS0yh84SZgPtnyZIl3KkDUoGvvfYaS8opeCk0ieGJzulxE1fc4VpEfVSqVKm4NxuKtiF9BQsEKiVmL4xGLBJ1d8h8hIQVGpCaEXfu3NFef/11S7kEMmHxHD3x4AWQDVBPQbzKWgkF28yZM50ab80/x7Sqff9kC+/dyXu1B4+dc1EicxRhBLNaUxs2bOCed4jzIbFKJqUdBfugSM8J7Ny5k2MARtcagdBQ4I7fiFRv68aPS5cu1Q4dOqSZGZA7QrdoTLyI3Zm1ceWmTZs4Yzdz5sza2rVr+TV0tkZCC5R1ZAQIL25cEu47Z37P1mN3tFcGb2TCaz/qb+3RM+fd6f/880+Cqi5mwM2bN7keUT9uIEFo1w4cOJAFCbDwVJAXivQchLsSVxC/g+UBNQuUKcgUUwK5ffzxx7zvZcqU0fbs2aOZ1QpFDA+kjMxGTHbWkCVWagtz587lWHNcCbBBgwY5NM6yXde0WoNiCK/YGyO0kaPHurRfsPAQh3a2VtBdQMkSNF+tj5+ePY3XzWqpKrwcivQcABJXIOKMycTI4lpoHMJ1Cdfa+vXrNZmAsg+oliBZBVaFWYkDiRmQE9OTF8zuJnYG+E2fffaZJSEHjz169LDrs1hkTV97jsmO6/B6T9XIx1dr1qyZy/s1ePBgvrbNlMVpC99+++0L7mFsL+syoWBuKNJzAO+//z77+o20XKBcgbo7WEiOZJJ6GtevX2e9T5wPlH3APWhWIEYKdRDEluCq9mZAyEC/luAqhyv3ZYiIjNJGLjpuIbxJvx+y1IbiEYX7ruD48eM81vLlyzUzA0IEcbOl4b2A1B9CGzLdnwr/QZGeSRJXrFflUL0wS+2aPfs9depUjoEgBgYVE7O6Yr1BkNtR922GDBnYhWsvICvWf+YhJrs6n2/SVu67ob333nsW0sOk74xEX1xA0B1xMrNeKzpQYgOyw5YmTRqW+RsxYgSTIY4Fiv3hMoaqi7dfT94CryC9lVuNTehAex5dccUIoNlio0aN+CZC8bDZJwIdkLPSG+0iRmPmm/7UqVNs8cgsyO0o9Lo4ez0T94JDtXen7GXCazB0s7br1D3t/PnzL3RJwL3gqnt/xYoVPNbvv/+umRlYKCGGj33FPltnx86YMYMbs+r/D2JEM1RYgQsXLpRWo9Xb4RWkt/PoZUMTV5DZZ1TiCpT7kcqPlaM9riczAFbop59+yqt/lFFAhs2sALmhWSpKJmQV5HYWOEewvu1Jujhz/ZHWalRM0XnTr7dZhKM7d+5sM66FPoKuAt0OEB83qlmoKOC+hKcnoWsMiwNkdkKUG2IG+nHCvQ0XMz4PIXW4RGUsgfEmKNJLACA5IxNXFixYwAkfSOuH7qMMQEo/bmRYTKNHjzZ1MgKuD3SgwDXSrVs3aVzGolC0aFGehF+Gf07c1Rp+sYUJ7+1vd2nX7z+z/B/inrZk2bDYcRW45rEY8cbEENR9QuAbv61SpUov6OVikQsXLxrqwksClykWZ6jBPXHiBF+7CsbAB/+QpHj06BEraOw8eplqlM4rfPyePXvS7Nmzafv27awmIQqRkZGs/jBx4kR65513aPr06ZQ8eXIyM6BQ0b9/f1q0aBE1aNCA97lQoUJkVhw4cIDeeustunv3Lv3888/UoUMHSkw4d+4cFSlShJYvX04tW7a0+R7c+ov+uUo/rjlPmAUqF85AIzqWotTJ/1P4efr0KT18+JCv2XXr1vE9sXXrVipbtiylS5fO5f0cP348DR48mM9X+fLlyVvx7Nkzunr1Kt24cYOuX79uebR+jmvVGlBeypkzJ6VPn57nB32DepT13y973db/JUuWjDd/f38p1YZcgb+nd8CsmDFjBk+WM2fOFEp49+7do/bt2zORfv/99/Thhx+a+qKDlBWOAUgaN8i8efOoY8eOpt1n7O/kyZPp888/54l5/fr1VLBgQUpsWLlyJSVNmpQaNmxo8/8jo6JpwvIztHLfTf67VbWc9HGLIuTvF1uZMGXKlLwBkOcCnjx5IoTwACyk5s6dS7169aJdu3aRr693KiOmSJGCihUrxlt8gMTgrVu3XiBELDpCQkJ4w0If5Kj/HXcLDQ11aL98fX0tBIjrRX9uvdl63dZr+I1xN1w7cV8D6XryPCvSs4E9e/ZQnz59+Ebs3r27sHEjIiKoVq1afBFv2rSJ6tatS2bGwYMHWStz586drJeJVXnGjBnJrLh27RpbImvWrKFPPvmEvvnmG0qSJAklRoD0YJHrhGWNR88iaNj8Y3TwQhBh7fJR08LUtlbuly5k8ubNy56VI0eOUNOmTYXpXsJrULt2bZo2bVosndDEBhBJvnz5eHMWsN5BfLbIMO7foaGhTLT6c1t/W78Gwo3vPdhgzdrrOATxvYwge/ToYYg+ryK9OAgKCmK3WKVKldhiEAnc4N999x27cXLlykVmFoeG8O7ixYupRIkS7M4yM0FDRBmuYohvY1JevXo1NWnShBIrYO3u27ePRowY8cL/Xbn7lD6fc5Su3X9GyZP40Vdvl6KaxTPZNS5IEaLhCxYsYJekqNU6FoIgu48//piyZMnCnhAF54BzpLsw3Q1N05gQQX7WG1zkcV972eswDPDcqB2VFqITWZCFhc7OKFyWJbFEdPAdGpmQq0LyDmoSUetlZkAgGAkbSBQYMGCASgB43vUd90XcjuBIWGk0LCZhBe2Bzt5wPGsSxfxGFJaj3hOZojiPZi9jUJAbivSs8NNPP/F4yLpKTIBWJpqMopND2rRpWesTahRmBmqg2rVrx+erTp06LN2mEANkAOK4oFkvEBUVrc3acMGisNL7pwPc+dxZoAEw6jNF1zqC+CDMgEWX2dVaFOSFIr3nQP2WLi+UWABdTBRqo5YL8mqovQoMDNTMDJRIQNMTUm3Yb3TyTgyF5o42jUUBOaz0JyER2udz/rUQ3sTlp1lmzBWgES3uu61bt2qigX1GRxHsf1xLVUFBBBTpPS+2Ll68OCt2JIbCUZAEND4LFSrEhcddunSRwp2LImGcJyiE9OvXj5XwFV4EXNSoo7ty96nW8btdTHavDI6RFBN1/eBeadiwoSELDizG0BoJCzHopCooiIR35gc7CGQoXrlyhWvQkHrrzUBSSrVq1ahdu3Zcx/Xvv//Sr7/+ypl5ZgXStlFnh2xEdN5Gt3okGcXtcq4QA6S9Zytem96fup8u331GmdIkpak9K1KzyjmEJUuMGjWKNm7cSGPHjiXRQMIXkqiaN29OrVu35gxqZA4qKAiBlsgtPWjkYQyoIXgzTp48aekMXqVKFW3Lli2aTHJaaOQ5Z84c5cp8CRC/a9xjjFbz0w1s4fWctl+770L8LiEMHz7cUP1MnGvoWyLWDGWYzZs3G/I9CokLiZr0EOhHdwBIVXn7ZAqSK1y4MHdcl+23QpsxKCjI07thegQ9CbN0SMA27veTWniEcc1OcR299dZbLKW3f/9+w77n0qVLWt26dfle79u3r+mTrBTMjUQrQ4barho1anA9CNxladKkIW9HVFQU+fn5eXo3FAzA8SvB9OWCY3Q3OIx8oiPJ59Jq2v7794Z/LwqdUbsHiS24yjNnzmxY7SEUjFAfCFf3rFmzDPkeBe9Hoi1O37t3L505c4a2bNmSKAgPUITnfcCademOazRtzXmKitYod6YUpJ36jW7cOeyW70cRNPQ958+fb6haDwrhUbzeuHFjr4+7KxiLRGvpAQ8ePODECAUFGfEkJJLG/n6Sth6/x3/XL5OFBr1ZnPbs3EbBwcGcBKKgoBAbidbSAxThKciKczcfs37m9cAQ8vfzoY+aFabW1XNxZiWyXBUUFGwjUZOegoJsgGPmj93Xadrq8xQeGU1Z0yWjkZ1KUYncqnxDQcEeKNJTUJAED5+G05ilp2jnqfv8d41iGWlou5KUNuV//e8UFBQShiI9BQUJcPD8Axq5+CTdfxRGAX4+1LtpYWpTI8adqaCgkAhID26ewMBAfv4w6AFpWh41AShIeQ2jKWuqVKk4+zHuNYxmr79suEjzt17h7uZ5M6fgdkCFc6T22H4rKMgM6WTIUFc3ZcoUKly4MBUoUIBfa1q3Av+N1/H/CgqyXMOoa8ufPz8/xr2GbwSGUO+fDtK8LTGE17xyDprZt4oiPAWFxFKysH79enrzzTctzQWtd11fIaPj7rJly+i1117z2H4qKLh6DQ8eO4O23c1Oz8KiKFVyfxrUuhjVK5PVY/utoOAtkIb0MFk0bdqUJwmoMyRUxIrJA92zFfEpyHgNE5OfD5VoM5rq1GtIw98qSdnSu78TtoKCN8IU7s1p06ZRvnz5WGmhatWqtG/fvlj/D3cPVscvnSyeyxXhfXi/cnU6jtOnT9M777xD//zzj6d3xavgyDXMvkxNowsrR9LIdgUU4XkIOFebN2+mTp060eXLlz29OwreQnpoITJgwAAaPnw4a2CWLVuWLbS7d+9a3jNnzhx2B710sngOvA/vnzt3roF77p3YsWMHzZs3j+rUqcOaior8xMDRa5hIo/CwEFq4YL7Be6YQH9nVqlWLC/0XLFhAe/bs8fRuKXiLexOWXeXKlWnq1Kn8NyaF3Llz00cffUSff/45X4AI8F+8eDFW/ONlgIuzQL68dObwAZXV6QCgoTjgk0/4uZ+vH0VFR1HNGjXos0GfU/VqVT29e1IC123R8pXo4mUkpKhr2KzAudmxcyeNHTOG9h84YLn+gek//0ytW7Xy9C4qvAS+aTKYm/TQ6QBB+99//51aWV1QXbp0YXfQihUr6P79+y4ptx9qW47SJ5W2MkPBC/AgNIIq/v6v059X17CCgn3IMG0TpU6dOsFFokfvJBAa2t1kzRo7Kw1/I7YE3Llzx6XvqLD0iEufV1DwNNQ1rKBgJ+anZbH1hDrnmH75qBPiN99841A2JjLlhgwZQpcuXZJOWBrdI+DivXbtmte0PfLG32Tv70IBOmpKZbqGvfF8eeNv8tbf9ciF3wRLLyF4lPQyZcrEPd7iWnP4O1u2bPwcKhVFixblBpVoIGkvxo0bx5/LmzevtPEQnGxvuYi9+Te97HfhJpT1GvbG8+WNv8lbf1caA36TR7M3kyRJQhUrVqRNmzZZXkMiC/6uXr06/42bvVevXlxwfvv2bbvGvXXrFv3xxx/Uu3dvaQlPwXugrmEFBfPA4yULKFeYMWMGp3SfOnWKJ4enT5/Su+++GyuxBQkv7733HkVGRiY4Hv6/R48e/H7UmykomAHqGlZQMAk0E+CHH37Q8uTJoyVJkkSrUqWKtmfPnhfes27dOs3f319r2rSpdvPmTZvj4HX8P963fv16TVaEhoZqw4cP50dvgTf+Jkd/l0zXsDeeL2/8Td76u0IN/E0er9NzBAjst2vXjot8W7duzQoX6dOnp6CgIHYdwR2E1fHSpUvp1Vdf9fTuKii8AHUNKyh4FlKRHoD6PSit/Pjjj3TmzBnL6wj4I/4BN1LatKqLtIJ5oa5hBQXPQTrS04HdfvDgAT1+/Jiz45DSrQL+CjJBXcMKCu6HtKSnoKCgoKAgXfamgoKCgoKCuyA16b2sJZFs2L59OzVv3pxy5MjBbq7ly5eT7BgzZgwLisN9lyVLFtZYtY5jyYqffvqJypQpYymeRV3p2rVryZswduxYvg4//vhjkhlfffUV/w7rrVixYiQ7bty4wW2PIOCRPHlyKl26NB04cIBkRr58+V44V9j69Okj7DukJT17WhLJBtQn4neAzL0F27Zt4wsWrVk2bNhAERERnJWI3yozcuXKxaRw8OBBnmjq169PLVu2pBMnTpA3YP/+/fTzzz8zsXsDSpYsyQX/+oYWWjID2b41a9akgIAAXmydPHmSJkyYwJnAsl93t6zOE+YMoG3btuK+RJMUqOfr06eP5e+oqCgtR44c2pgxYzRvAE7Nn3/+qXkb7t69y79t27Ztmrchffr02syZMzXZ8fjxY61w4cLahg0btLp162r9+vXTZAbqvcqWLat5EwYNGqTVqlVL83b069dPK1iwoBYdHS1sTCktPbQkwgq7YcOGltd8fX357927d3t03xQSBhTQAdlEwBMCOoUsWrSIrVddPk9mwDJv2rRprPtLdpw7d47DBhD+7tixI129epVkxl9//UWVKlViCwhhg/Lly7OylTchPDyc+3t269ZNaFazlKSXUEsie7UNFdwP6KoiPgS3TKlSpUh2HDt2jFKlSkVJkyalnj170p9//kklSpQgmQHyRrgAsVhvAeL9v/76K61bt45jsehaUbt2bS4VkRVoqo3fggbbEDyAfGPfvn1ZztFbsHz5cq5p7dq1q9BxTd9aSMF7AAvi+PHj0sdTrIvJjxw5wtYrGiGjqBwxTFmJD21c+vXrx3EUJId5C15//XXLc8QoQYLoXLFkyRLq3r07ybqAhKWHdlUALD3cW9OnT+fr0Bvwyy+/8LmDhS4SUlp69rQkUjAXPvzwQ1q1ahVt2bKFk0C8AegSUqhQIe4UAssISUhTpkwhWYGQARLBKlSoQP7+/ryBxL///nt+Du+KNyBdunRUpEgROn/+PMmK7Nmzv7C4Kl68uPRuWx1XrlyhjRs3skC7aEhJeva0JFIwB5CTA8KD62/z5s2UP39+8lbgGgwLCyNZ0aBBA3bZwnrVN1gTiIHhORaa3oAnT57QhQsXmDhkBUIEcUt/zp49yxasN2D27Nkcq0RsWTSkdW+iXAFmPG7KKlWq0OTJk19oSSTjzWi9+kTsAZMNkj7y5MlDsro0Fy5cSCtWrOBaPT3mCm1J1BbJCjSDhesF5wWxIfzGrVu3cnxFVuD8xI21pkyZkuvAZI7BDhw4kOtfQQg3b97kMicQeIcOHUhW9O/fn2rUqMHuTQiYo0b5f//7H2/esHicPXs2z+/wMAiHJjHsaUkkE7Zs2cLp/HG3Ll26aLLC1u/BNnv2bE1mdOvWTcubNy9fe5kzZ9YaNGig/f3335q3wRtKFtq3b69lz56dz1XOnDn57/Pnz2uyY+XKlVqpUqW0pEmTasWKFdP+97//ad6A9evX8xxx5swZQ8ZX2psKCgoKCokGUsb0FBQUFBQUnIEiPQUFBQWFRANFegoKCgoKiQaK9BQUFBQUEg0U6SkoKCgoJBoo0lNQUFBQSDRQpKegoKCgkGigSE9BQUFBIdFAkZ6CQ3jllVe4PZCOfPnysQScEUAPLbQXiQ+XL1/m90CqzdNA+5NWrVq57fvQKgfCyUahc+fOFgV/o8+zTIDUHK45tLxx9DygtVG5cuVYZkvBc1Ckp2BzAseNHXeDLugff/xBI0eOJG/EV199xZOSDGjfvj0LDBux7//++y+tWbOG+7MZCTQ9RV+79OnT84amtdCQdAQlS5akv//+m5+/+uqrNHfuXDIrGjduTAEBAbRgwQJP70qihiI9hXhv0Fu3bsXa0CEB4tcQJnYFERERwvYzsQJi3VChNwI//PADd+RGg1yjrSaIPqPd1O7duyl37txMXDdu3LDr87C2QPzVqlXjtkcYA90HzL6gRKsmBc9BkZ6CTaAbOHoTWm9Qpo/r3gTQZQCTFxT5c+bMSdOmTYv1/7AS0eW5RYsW/J7Ro0fz63itYMGC3CoKDVnnzZv3wn6AbNHNAJN8gQIFuFlrfMDEh6agIGe8H2PG7W+HiRZdObAfcEthkkTvLripvv76a7ZydMsWr8X3Pejygc+jA8Fnn33GLZSsARcWeuzp+4Jee9b7rrvJ0A4LnUJSpEjBqvnW7WKwL/Xq1eNFRpo0abid1oEDB15wq8W37926daNmzZq9sOAAWaJBZ3y/DfuJrgQJAX3bWrZsycSIfYPSf9z+lqNGjeLvwv6jL9rnn38eyxqFxdO7d29+rVixYjRz5kxLizB7sGfPHrb08P1wceOc4np6GXbu3MnXMY45LMzXXnuNgoKC7DpvL0NC5wzAccXfaG2k4CEYImOtIDXQ1aFly5Z2qe6j00Dq1Km1MWPGsCr6999/r/n5+cXqOIDLLEuWLNqsWbO0CxcuaFeuXNH++OMPLSAgQJs2bRp/bsKECfy5zZs3x/pcxowZtRkzZvB7vvjiC37PyZMn+f8vXbrE7zl8+DD/HR4ern355Zfa/v37tYsXL2rz58/XUqRIoS1evJj/PyIiQkubNq02cOBAVtnHOL/++ivvz7Nnz7RPPvlEK1mypHbr1i3e8JotjBs3TkufPr22bNkyHqN79+58DKyP2ahRo1j5ft26dfyb0VUCavhbt26N1VGjatWq/NqJEye02rVrazVq1LCMgX3p1KmTdurUKe3s2bPakiVLtCNHjvD/YTz8FiC+fd+5cycfr5s3b1rGxHFPmTKl9vjxY5u/7dChQ7xft2/fjvU6zvOkSZP4eVRUlFauXDmtVq1a2oEDB7i7ScWKFfna0IFjnyxZMj7nOHdff/21liZNGq1s2bJafHj06BF/Bt0DEkLp0qX5tydPnlzz9/fn5zjP+K14jv+PD7hWcB569erFx/L48ePcreXevXsOnbegoKAXzsPLzpmOrFmzSt9lRGYo0lOwSXqYQDA56lubNm3iJb3GjRvH+jxat7z++uuWvzFJfPzxx7Heg8m9R48esV5r27at1qRJk1if69mzZ6z3gCQwYdkiPVvo06eP9uabb/LzwMBAfr8+gcXF8OHDE5yUdaBNzfjx4y1/g0xz5cplIb3Q0FCehHft2hXrcyDHDh06xJo8N27caPn/1atX82shISH8N4gUpGwLcSfb+Pa9RIkSTNI6mjdvrnXt2jXe3/bnn3/yuY+Ojo6X9LCgwXuuXr1q+X+QNvZ93759lvOEY2+NmjVrJnh8cV4LFChg+f3x4dq1a3zuQTBYEOF5vXr1+HfiOf4/PuD4Yz9swZHzFh/pJXTOdJQvX1776quvEnyPgnFQ7k0Fm4CLxrqDdkJxiLjd6vH3qVOnYr0GF5418P9x4y/4O+7n7BnbGnCtwqWUOXNmdr2hqSZccQDikYipwJ0FNxNcn3CfOoLg4GD+TNWqVS2vodGl9e9Dws+zZ8+oUaNGvA/6hiSLuG6tMmXKWJ7rnbzv3r3Lj3Chwi2IBI+xY8c65RLD59GQE4D7ce3atez2jA8hISHs2oaLND7g+CP+hk1HiRIl2N2qnxu4aeFGtkbcv62B37do0SL6888/KVmyZAn+ply5cvF7cDzeeustPm779+9nFzuyTPH/8QHXMjrE24Ij5y0+2HPO4DbF9yh4Bor0FGwC8ZFChQpZNn1CdmU8o4FJE12yEddDRh8muHfffZfCw8Mt7wEBIOEB8bPFixdTkSJFODYkEk+ePOHH1atXx1o4nDx58oX4ELL5dOhEo6e0IyPzxIkT1LRpU9q8eTMTC0jBEbzzzjt08eJF/s3z58/nWBUyJuNDpkyZeEK2PmZG47vvvmOCwDmzXgTYQs+ePZmIEN8NCwvjWDMWMzjmxYsX5//TFzm2AMIRcd7igz3n7MGDB7woU/AMFOkpuIy4pIG/MQElBPw/Egqsgb8xSTg7Nj4PMkNyRPny5Zmsba208X+DBw+mXbt2UalSpWjhwoX8OhJqkMiRENKmTcsLgL1791pei4yMpIMHD1r+xm+AtYTJ13rhgM3aOrIHIOX+/fszIbRu3dpitcVFfPuORBvUD+JzSG7BIiAh6IkmmOjjA47/tWvXeNOB9yObUj9/SCKC9WWNuH8D48eP5xIY1LDF9QbYwogRI5iIkKCDhCo8h/XeqVMnC0nlyJEj3s+DVONLlBF13hI6Z6GhoXxN4hpU8Az8PfS9Cl4EkA0mL0yuGzZsoKVLl/JqOSF8+umnnPGHmx+uoJUrV3IN4MaNG2O9D2NhMqxVqxZn+6GOK77Mw8KFC7Mrav369WzRIBsUEy2eA5cuXWJ3J7JIMTHCBXfu3Dm2hgC4xvAeTJxwkSEDD5NgXPTr148tE3wfsg4nTpxoKVYG8DlYnJj4YLVh3+EWxXFCRl+XLl1eekzhZsQxatOmDe//9evX+be8+eabNt+f0L7D3QaSACm+7LthgVSoUIF27NgRb90fzlfp0qWpY8eOXLAO0sdCo27duhbi+uijj6hHjx78t25VHz16lC00HePGjaMvv/ySFx3Y/9u3b/PrulvRFpANig1j9erViwkJLlV8F56/DFjsYN+xv7AasVhAyQRKNGDlunLe7DlnWLThvMR12yu4EQbGCxUSSfYmMvOQhIIkgGzZsmlTpkyJ9RlcZkiQiIsff/yRExeQxVmkSBFt7ty5L3wO2Z2NGjXiDLp8+fJZMjFtJbIgEQFJGkgsSJcuHSdGfP7555bkCWQktmrVihNRkiRJwvuObE9kI+qfR9ILPotx48uwQ+IKjgGyEfHeAQMG/L+du0dRIIiiKDyTG7aJYOwW3IGZsZGBgSbiCozMhY7EHbgOV+AmDN2A0ZNT4KDiT8+gDljng5lAUKqrpB/1+pbR7/fP5owgSFmW0Wq10vXV6/XodDqxXq+vBiLAdfAa17Xf76PX60Wz2UxjbTQaMR6Pf0IelwGKe2NnLFzraUjoHtal3W7fDLKAxGu3200hJ8IbrP9l4nM2m0VRFFGr1WIwGMRkMjn7XD6TsV7+Ecq5h3Qqc0JClXkixbndbqMqgkwEqfhOMV+sy3Edfrtup+vwaM0wHA5jNBpVHque75t/7yyykt6LZ1Wcn6TNRrvtEXYstCfZnT1zR0JAhGdw185j5mC326V55Zzesfug97O9KX0oWnTcaOfzeUpW0tatgrAHbWLe+1eEYZbLZUrK8qMGq9Uqta5pf+eK34pdLBYWvH/mTk/64JssN1ie8RFiuRXVfwV2ixwL2Ww2KbzBDmc6nVbaaUqvZNGTJGXDIwuSpGxY9CRJ2bDoSZKyYdGTJGXDoidJyoZFT5KUDYueJCkbFj1J0lcuDrDiX8xK2TltAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ana.dump('fm.pkl') # caches the analysis\n", "loaded_ana = Analysis.load('fm.pkl') # loads the cached analysis\n", "plot_phase_portrait(loaded_ana)\n", "plt.show() # required for rendering from terminal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Summary:\n", "\n", "Overall, the complete OSDR workflow amounts to: \n", "\n", "1. preparing a single-cell table with columns `x, y, division, cell_type, img_id, subject_id`\n", "2. fitting an `Analysis` object\n", "3. passing the analysis object to a plotting function\n", "\n", "\n", "The 3 stages are summed up in the following code block:\n", "\n", "```python\n", "from tdm.raw.breast_mibi import read_single_cell_df\n", "from tdm.cell_types import FIBROBLAST, MACROPHAGE, TUMOR, ENDOTHELIAL\n", "from tdm.analysis import Analysis\n", "from tdm.plot.two_cells.phase_portrait import plot_phase_portrait\n", "import matplotlib.pyplot as plt\n", "\n", "# 1. \n", "single_cell_df = read_single_cell_df()\n", "\n", "# 2. \n", "ana = Analysis(\n", " single_cell_df=single_cell_df,\n", " cell_types_to_model=[FIBROBLAST, MACROPHAGE],\n", " allowed_neighbor_types=[FIBROBLAST, MACROPHAGE, TUMOR, ENDOTHELIAL],\n", " polynomial_dataset_kwargs={\"degree\":2},\n", " neighborhood_mode='extrapolate',\n", ")\n", "\n", "# 3. \n", "plot_phase_portrait(ana)\n", "plt.show() # required for rendering from terminal\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Some final technical notes:\n", "\n", "1. In practice, because quality death markers are lacking, we estimate death rate as a constant based on the mean division rate, see the paper by [Somer et. al 2024](https://www.biorxiv.org/content/10.1101/2024.04.22.590503v1).\n", "2. To increase sample size, we perform the following trick: instead of excluding cells whose neighborhoods intersect tissue limits, we `'extrapolate'` their neighborhood by scaling values according to the fraction of the neighborhood that's observed. For example, if a cell has one macrophage neighbor, and we see just half of the cell's neighborhood, we'll scale the macrophage count by 2.\n", "3. In some cases, we find that the dynamics estimated as $\\frac{dX}{dt} = \\frac{\\text{divisions}}{dt} - \\frac{\\text{deaths}}{dt}$ produce a net growth of a cell population towards infinite density. We assume there cannot be positive growth at the maximal observed density, and apply the minimal correction required to keep the dynamics confined to the range of observed densities. To apply this correction, add `enforce_max_density=True` and set the power using `max_density_enforcer_power = 8`. For more details on the choice of power see the supplementary in the paper.\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 2 }