{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": 1,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "'/usr/bin/python3'"

      ]

     },

     "execution_count": 1,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "import sys\n",

    "sys.executable"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 2,

   "metadata": {},

   "outputs": [],

   "source": [

    "import numpy as np\n",

    "from collections import Counter\n",

    "import pandas as pd\n",

    "import sys\n",

    "import os\n",

    "import re\n",

    "import json\n",

    "import openslide\n",

    "from matplotlib import pyplot as plt\n",

    "import cv2\n",

    "from shapely.geometry import asMultiPoint, Polygon, MultiPoint, asMultiPolygon, MultiPolygon\n",

    "from shapely.geometry.collection import GeometryCollection\n",

    "\n",

    "%matplotlib inline\n",

    "%load_ext autoreload\n",

    "%autoreload 2"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 3,

   "metadata": {},

   "outputs": [],

   "source": [

    "from slideslicer import RoiReader\n",

    "import slideslicer as slsl"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Background\n",

    "\n",

    "The histologic slides are often huge. Loading a whole slide into memory might be not the best idea. Here we use some tricks to sample tissue based on hand-labelled features as well as grid sampling in combination with color thresholding (to remove blank space)\n",

    "\n",

    "## Overview\n",

    "This tutorial focuses on basic operations (read slide & ROI, plot, inspect ROIs, geometric preprocessing operations on ROIs)\n",

    "\n",

    "See [demo-feed-pytorch](demo-feed-pytorch.ipynb) for examples on how to feed a ready-to-use slide into a pytorch model and [demo-feed-keras](demo-feed-keras.ipynb) for use with Keras.\n",

    "\n",

    "We will be using data from [Open TG-GATEs Pathological Image Database (Japan)](https://dbarchive.biosciencedbc.jp/en/open-tggates-pathological-images/download.html). See download instructions below"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## ***<font color='red'>First thing first!</font>***\n",

    "*Please start downloading the slide examples from a google drive folder*\n",

    "### Option 1: using a terminal script:"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 4,

   "metadata": {},

   "outputs": [],

   "source": [

    "# %%bash \n",

    "\n",

    "# mkdir -p ../data\n",

    "# fileid=\"1-s87PeyZY2C6ZejCyn4Cy0iYEkTeEFrP\" &&\\\n",

    "# filename=../data/14112.svs &&\\\n",

    "# curl -c ./cookie -s -L \"https://drive.google.com/uc?export=download&id=${fileid}\" > /dev/null &&\\\n",

    "# curl -Lb ./cookie \"https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}\" -o ${filename} &&\\\n",

    "# rm cookie\n",

    "\n",

    "\n",

    "# fileid=\"1-gPiqRvbiNT_s1OvFB9Eo8OLEl-g8aRM\" &&\\\n",

    "# filename=../data/14112.xml &&\\\n",

    "# curl -c ./cookie -s -L \"https://drive.google.com/uc?export=download&id=${fileid}\" > /dev/null && \\\n",

    "# curl -Lb ./cookie \"https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}\" -o ${filename} &&\\\n",

    "# rm cookie"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### Option 2\n",

    "*Manually download from this google drive folder, best one file at a time:*\n",

    "\n",

    "https://goo.gl/zwPAo3\n",

    "\n",

    "after the download is complete, make sure you move the files into the data folder (please execute the cell):"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 5,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "/slideslicer/notebooks/../data\r\n"

     ]

    }

   ],

   "source": [

    "!echo `pwd`/../data"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 6,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "ls: cannot access '../data/': Too many levels of symbolic links\r\n"

     ]

    }

   ],

   "source": [

    "!ls -ltrh ../data/"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Glossary\n",

    "\n",

    "- digital slide: a digitized image of a pathology glass slide \n",

    "\n",

    "- ROI (region of interest) -- a region in the image that can be represented by a contour or a mask.\n",

    "\n",

    "- countour -- a set of pixels surrounding a ROI\n",

    "\n",

    "- mask -- a 2D image with 0s outside and 1s inside an ROI"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Read slides and annotations\n",

    "\n",

    "`slideslicer.RoiReader` class provides an interface for reading, manipulation, and display for pathology slides and their annotations.\n",

    "\n",

    "all `openslide`-readable slide types are supported. \n",

    "\n",

    "in this example we will work with SVS format and annotations from Aperio software in XML format"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 7,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "'/data/14112.svs'"

      ]

     },

     "execution_count": 7,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "fnxml  = \"../data/14112.xml\"\n",

    "fnsvs = re.sub(\".xml$\", \".svs\", fnxml)\n",

    "fnsvs"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 93,

   "metadata": {},

   "outputs": [],

   "source": [

    "!ls -ltrh ../data"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 10,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "---------------\n",

      "counts of ROIs\n",

      "---------------\n",

      "          100\n",

      "tissue      5\n",

      "dtype: int64\n",

      "---------------------------------------------\n",

      "counts of ROIs after removing empty chunks\n",

      "---------------------------------------------\n",

      "          99\n",

      "tissue     1\n",

      "dtype: int64\n"

     ]

    }

   ],

   "source": [

    "rreader = RoiReader(fnxml, threshold_tissue=True,\n",

    "                    verbose=True, # prints out counts of ROIs below \n",

    "                   )"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "# High-level / low-resolution display of slide and its contours"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 11,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "<matplotlib.legend.Legend at 0x7fdc782dfbe0>"

      ]

     },

     "execution_count": 11,

     "metadata": {},

     "output_type": "execute_result"

    },

    {

     "data": {

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yOa9kTqHA57/3bb75tW+haxKcA9j1pkwIzdsgjEE84c3fx77X+491njkVw74jXB89RvpgWZhpKTz2GT5+V+qjlVkLPCHEAiHE74QQDwohHhBCvHcy/GNCiAEhxD2T2wXTjrlcCLFBCPGIEOLF08JfMhm2QQhx2eM7paefdGtM9NA6oq2bcc47GXHTNtb+4c6p/a94xSuJ1o9AJkVpQ+P6TchikVSUiTszGOnQuO4+yAl0PQLfIBOFlcBLLziXnq4eAK746U+4/aG7AHjL6S/luCVr8JwcdlcncuMYiVI0tm3CnjefdGwAMeaT3rweb147+BHCkwgrxY1D0jt2EQRZ9JwOTCARPe0kxy/HHa2Sy1iQxsTd3YSuhesVsOrj6ARk3sPYNiqOEEEDt2iTqVRRxlAqKDAuMrFIq8PIk1ZSkC4fOvvPOK13KT+/8ldc/q63Eww9xgwukxMTaK0f9XJp0eJweDwaXgJcaoxZBZwBvEsIsWpy3xeNMSdObr8EmNz3BuA44CXAvwghlBBCAV8BXgqsAt44LZ3HxWO/l59ANCA0JIZ43VpEto0bbruOf/7Xf+NXj9zEprFBALKOS++cPnTGIg2yiHwWq78HmcYYlUFv2Q1OBB1FROAiMllMmmI8jfEyiEyBV539QgBOm7ucyy79AN//1P/j3NUrqQdlTIehMb4LU8xjIkP0iwep/+g6RN4iHagjnrsC4gCxo0KqGuiswY4lTsZBJQ3iMEZh4ZU89AMbkUZQzlmUI0MwuJMksQiDIWTkIuZ2oHKG7JlLSFMbUUsI6i62ZRPbIWyooKwEbUtEoRv/v39NOhJj5xQXPu9PeM2ik3hoYicf+egH8TdOoOv7Vcc908RP/hnk5Dbtb1IA+nfcj5/OLAwFM+pbB4h1KDTj7puueFwrmh04dzE1c/TekJZ2N1tm7ZZijNkF7Jr8XhVCPATMO8ghrwB+aIwJgc1CiA3AaZP7NhhjNgEIIX44GffB2Zbt6UGAUaASWNDJI7+9jf939U8AuH5arP62HoxnEEisHo+0Ucfp6iB1uhHrd2AchY4kyklIcwHSaGJbY2mF05dn481385NrrwHg+L5+Vq+3ERmDVkX63nIC9W0NspWYalWjRmrQVkLuKiNcQbJliGR4DA2kOQfV3k5W2ExUh1FhDZGxsHeVSZZ2IO7Zim9bWMuK5HaMkrouQbWBW5DojEN4TA5x/yiyHiEmBtG9RcLhQey2InHewfEdUtcnXdyG2VzGsRSi1Imxi5jdMWaOxcuPP4Hu9hL/ds/1fORTf8vff+WLZLCnrpU2ZubG2wxyzTnrePCPRO1Ps/eEW4Lu8fKE2PCEEP3AScBtk0EXCyHuE0J8UwjRPhk2D9g+7bAdk2EHCp8pnwuFEHcKIe4cGRl5zHIdyB4yc7WZtBfpGY6QuqnPGoEQBqHlZD2cNjDMNRCkBJUQPThB+YSefZJYs/AYFhQ7EZaAWky9Xid+ZACRdzA7R0h2j2Da8mjHxslFCJFBSAvIYpWyJLUK/sMTfP2qKxktjzOvq5fnXfwykrYEOm2yC+bAUEK8YRd27yKSwWGSNhed1gFDOFHBlASZFy0ms6IfZ14vdiFDbWwct1pHKA+FjcxlSLZM4CuJ7Cihd4wwbEnKWUFGFPG1QQSazHqJak9JOly0HyLyDgUEuh6gA02kFLo3h7srIJzXR/Hi15I5YS66McRYOka6YYB4R4OT84u4cPlZrBvewaUXvp0dm3Y2h9lODjsT+5jXmg9/03ba3DACkxqUn6IOVR7MaLLbz4dlxgP26GGH52jSPMpMzRYjpumDB9Ts9py7aFaz6fbB1mx4s+dxCzwhRB74KfA+Y0wF+FdgCXAiTQ3w8483jz0YY75mjDnFGHNKV1fXLBPh8F2YtAQbkLpZUXUyOQ/ctEro2tx646386Ztfz+Xf/BoP33LrPknkSu1kpEWSpOhGROmYeYgFXSjXJg4DTEJzPdfIEPs5dIeFTsA0Jki1xgBprYFnN2dGOf/MM8jsUoiJGKICatkc6vO6ECYhuHEn9qqVJI0GdncHwgLZ0Um4qUK8dZz63Q+RW92Gf/8glnCIuktEpEhLEpWrhGGEdm1y2hBryFdirAmF6XORi+ah5nci+gW6YVAZSVXERPUaaaGAandxVywgjEZxRyKoQWF4jPIn/4PxuyZIS224xqEsBHUjMX7IKd1LuPDYc9g+McLfX/Z/CMdSTO3o1mb2rPErJtflfbJmqj7aeFwjLYQQNk1h95/GmJ8BGGMGp+3/OvA/kz8HgAXTDp8/GcZBwp94DmOYURzH3HXXXdx0883ce++9RFHUfN0KSNOUxf399C9ezOo1azixewluW7M5tnFkB+1DnVx48gu5ZXw992/ezlzVSc0a4pGxXSRjFfzxYfKZDhqbB3C7ejHVMkZkMe0WadBAbKuTZi1UsYRRzWHjYTjKe173Rv7huzW+c+WVLOxaxPJjj4OdOwgeqGP1eBgvB/NB3DVKcf5ckg3bEMUsolzDOuVYlNYEUQVryVLc3+7ERCmEMZaQVGsxrrJI0dgYqJSxLBvlaXJLioT3D2AmKmC5+O2SnOlkPDNEfs4i4uEJwlEfbfsM3QH5fBa5egFKtVG95V7EgkU4NNDbG2Qcm1xbhnigisi7JPUqx7td/PWx5/CNh27k/e+7iMs//FHmLZn/pFWDZzxmrx7XXCPkSGyuP/WI2a7qLprOZ98Gxowx75sW3jdp30MI8X7gdGPMG4QQxwHfp2m3mwtcCyyjKXrWAefRFHR3AG8yxjxwsPxPPvlkc+ONN85ctsnPwzqzaZF1Aj+74if85Cc/pl5vAHBc1zwW5rowpIyLkC0jQ4hUsNufmDru5X90HguOW8pXvvhvAHz3he/mq9tv4n/X34NrO3jSwk8ivv+v3yTeUQaZYFKNlcugkwC0BQ1N6jRteDpMkZaHiEAIhTVHEkuPMVPn0ksvBSn417/9LOLmDeTfejrIlMr9Y+jtASITYFkest1Dbxgh39XDxM7tdKyZQ12CUyqx6+d3IjcMkkqBUTHR3HasLVWwEmSYojMutiWIaxFpVuEbTclzqBdLuL5FZomFnLAJRoea5QtT4uf3IG4fRGgfqXOEeQcxNIZXyOGP+BTaegg7HES5QhqnSOVjxQm64eAt9Li1soNv3XsDjmXxD5/8JAtXr0Q0wgPetj0ukEKpA8bZ/xbv86470Lzrh/BC3NcJ5QlaV0AY0JDoBIHAth2SJMEYg5o8R4PhnLPP4e677z661eBZ8Hg0vDOBPwfWCiHumQz7EM1e1hNp3vEtwEUAxpgHhBA/otkZkQDvMsakAEKIi4FraM4F8s3HEnZPJtu2beNzn/scm7dsZmGuxOuOP4Mz5hyPU98NsgdZGsYoSItLyfgRjUUd3HfXbVz7h5u48tfXwq+vBaAv2461qMAl51zMOcE6PvmpzxDSdLTdvms7fSaLHi8juzuJgxSZcYiNj4pB2i7ENsKSmFodWWxDiwB/U0y23+CtKyOkpO432L1tKyVdJf727WRX9MJEDZVrg1GBaK/C5ggzLolzI4hKnaRaxGzdCEsLZBwIi1mMH1KbZ1MaHCfQEpEYEjuLRUQSg+jJ4La14YUBoyYinxGYpRJ2W5jaLlRHgXBoAlcqwls2oEZzmP4eTNRAxhqlJAkCLy8Jk80kOzugYNPIp9ihS14qzDxJ5IecSBsfXnQBXxj4DR/+4GV84p++wDFzD9YXdqTRFJRKSJJEg71nWq1pky60xNysmbUNzxhzszFGGGOOn+6CYoz5c2PMmsnwl+/R9iaP+QdjzBJjzApjzNXTwn9pjFk+ue8fZlumA5qUZxgL+yh/0zTlql/8gne/591s376Ntyw6g0+c8WbOcvqwkiqJKZCXNdIJFzOeJRkcIhwfRdz2MGuqDu875oVc9JwXTmW5qzHOL26/Eek1eN6pz2fFgmOm9lnd3UQG6JqDnggh8hHlmI//8z/zri98AuEK0BFJmKItG/wIYyt0ThFOBPxh4H7qfoMTlq5kYd9cnHnzyUQx1olzyb1sCWZXnaQvwJgMkS2go0H9Z3eSWbCScKWD6m0jUqNkzj6RtJhF2OBuStCBROuUqNMiDWoklk3sOCRLl6K3jFEPBd3Gw66FWL/cSDSwhWi3QNZSHOEROhKlCsgeG0ZGCU1IVmt0sUiaaEIng7bakLbGicvkShm8qEYagGW7mIk6mc4CfYvmcMmqV6GQfPR9l7J1yy5EsFeDa84ELRCTHQlCqNkPjT2Q3/GjxlM/VjL7Oh7PfNhjJxTFmiCKEVKBaK4yJ4Ro+h+2/I0fN62RFpNcceVVfPXrX2Npey+fetsHOHfBKowNUkSIkQncniKNapUk1ehaA1WZ4L8nYi4Ocvy31YbAcKpZyNfOeD1/fsw5AHz/gZu5Z9NDVB7axbzj+qfymtvXAUkCXTlEl4O9MM/1D9/NxoFtTNSr/Pyaq0E5jI2N87v7b2UiKSMSxY9vvpofXXU1y89YA0BXRwfBcIXM0jlULENy1w4atw+hFuXJeD2oKMIVKUYUcd90MiZXxdw6QrR5jPQRjRrwQUmMm0F1WtRtBcqQ351SmtuOCaFhu7hbtmI8h3yckkoDgWHev78T03kc2VPnErshpt0CraDLAd+A5+HHNqkHKjUIaZBuhJUrgNHobB7KCeJFxyIyGl2xCee2oZ9/AnpikK76MJcecy5KCD582SVsHBx6GmrFU49jWXiuizEG27anFjWfrempxb7M2ob3dHMwG96jONg5Sti8eSPveff7WTNnEX+z7CzS9jyiYTCIpmZVb4DWJLZDxq/hF3MMDTT488LcZhLG8CsxhPYc1MgYMRliBz69/RpGyxX+5Ttf5Ttf+gY3/f4WABzHYfn8Rbz1HRexYM5Ctt5wNx/4xqeninRM33w+feG7ed3HP3jQ0/qLc17JC856Hp7MEmwaQ0TjmPk57EoGq9tB9naRrt+J7CmRWz2Pxi9vJ57XRdYtoi2B3eey8xvXUU4nsH2F1XBIfB8dxGT6MqS7fMpzMxSqCXYeLOMQ1+rM+cS78LaMs+XXv6Xr5GWUt5ZpbNuI2zsff2ScbL2BXSrhj1VQeQ8RNQ3u2sSkQuCmDtLyiRcvxOwegsgmJUZ02jRGAjrDCEcJYmPYnTb4/CPXkiYp//ezn2HRqsWo8FDq7F5VaP/YB5tm76AmvP0CZ1p36dBKduACHNTlZNquc85p2fBmw1Gv4aWR4R8//TlcZfP25S9BZz2Un4IUJJUyetsQohqgqj4M7+Y7lBgoN+jKZqbSODGNUKGP3FElcQoYEZKVmv9vwfNQCN72xr/ipt/fwpKOubys/ySy2Sz3b1rP5R/6EP7wLnpXLmD10pVT6WVsb0ZhV8zlWbN4BQArVqzkvLPPQw7HhLsHUYvzGMshZ2u853bhKpdk/RDWgg7sXpt02xjueachN+5E7NiE7SrSwYiuc1ZROOMUvDhDnDVoqXFOX0CUzSLbHdpHIhzHIW7YxItzyLYusr0QdtRRxjByy0OUt5bJlCTxQ4PIRkJi24h8loqjCXYOYYIQ4wgw4GhDcvp8UpFB7B7BNhJbJnhJnWRHHS+skuqERhCQBhFz3CKXLDyPBM2nL/swUdyacKDF7DmKBV7T4ver3/yGgYEB3rjquRTjGoxOkE7E6EoZWznIngKikEU6Np/PL+TfyfFWMZcBB/4n3Mk74yp3Wy5vdeYy4gmU1nhhROKn9JUF71vwfDqzeTpzRS7+6CX86Uln8t0ffIMLL3gdYRzx2+uuQ2Rdli9eMlWyB7ZtmPr+lff/HR//zKcAOG7xci595Vv57EUf4KPnvArVqGH1FKCnE10PsZd0Uv19lei6LYyrGKdgUX94AMvKwaICusNF4rJ9fQO50kLMtxDCx95Yxo/KqIkENxUEG328gYCGEiQZi7iWYjKQGfBw2hT+YIWJ792M3laFkQpEw6hxhbIDPNdurqmxaYj2isY583hwXWJZxqQRasEy1NZBwkyEqkhEmBDUA0ypnUJnBiu1kAUFXhZtJJVqmTlZi7+cdya7ozp//67L0UTIUY3WUdN/kXiPd+4+s9DtOxz/sSaDeGwT3v5T9005EouZUj6YW/EhGQUnfeFQgLHyAAAgAElEQVSn9c4agxCa1MQt5+NZclTPeJymKT/4z+/SmS9y3vNOhR0xiBg21qAgMK4FdR8smyCTIQ3M1CtivXBYnE0YqgM2bBUOP8y08w4vpD4kKPQZ6J3LvPExPrLwNRTWLMO+bjdxWSESm0jHAPzPjTfwnauuAMC1HY6dfwyRhPNOOI1//tl3+OT3vsprzn4RAAs7+7B8nwV9fcSJh9EBqtLAdgSJZ6GyHmJRJ42JUSzRi3tyP1beg3qF6PYxWNFA/fFxtH32OirXPUIubkeePJ+2eJDkEYc4b6G1TSauEbiGTNUitepUCh6FcoGgZxxTsdjxmZ9gaQ+xIo8a9cl2lAhCSHWdeNc4bn+JqjI49Ri9fReqoIhKi2iXW6mq3dhbItTqHKrhEoYJ0lWkFR8dJ1gNQ8WxcDJA4pKxDLKjixNViZf4o1y9bS2//O7PedmbX4WybXRoIJVHrDFfSEEURaQ6xbUcglAjRIyZaURQi8fkKNbw4Nprr6VcKfPaeacj752A7g4oG0yhKcXiMATloGODnSa8c16Wd6dV3i/GeUFSgSjhzas15+qAFSbi9MTnA3GRVy1czovsfm7aXUXECcXdKf5gHdMGllNjYPtOvvWrn9Pd3sEbXvryqfJ88uK/4bKL3sZH3vBmzlq2nD8581wGhgf50k//E4AuJ0tSKtIYrCKNwYpDjB8QS0lS0aTVEGdhO5YXk/72XgQxslrH1MAUNXZHF2q4jPOyVejtNerrN5AOCDIdK7D6Ouha2Yfn5MkIC+HZCGnjGEV2qIx6QZG6n5B0ZDHGpbKowOjGKnJniNxcwxqL8ZwFxDkbU2ugayE6NWBDtH0H+Z27SEZKiARMPsYazVFdorAdg9QGiUYqgejM4qYCqV0cCmSWdpF9wQmItjZeM2chvV6Bb/70h4wNJs2lHSWgj1BpB1OKoOu4GCFwLYNteS39bpYctQLPJIbrb7gBW1qc/Vd/TNKVR2/bydcjOFccw9e8DEQ2sUmJPYsoDGmvVLiABhekGtvJkzRC2sY0/6ejwT+WAk4vwQPTBr/fUOzCyrcRL/AoaIMOLH47sImL3vl2AF5+zkvYPdDsfbzsze9gbq0b2deLdgqEVoHXP+88zn1x09Vl5cJ+XnDWWai4gVfKY7V1wpp2VMkl60d4nTbCSNK4guiZS+oohr7wS+xCltQfR/WXiO9bS1QrUTy2Bzs7h3rqkPR3MnHlz3GEJA4nkG5KnM2RsRQmH9Fo70T3z4Xrd+N2FMlVNIFU5LdPULIUjU6Ijs/S6JJEI9vIxilJoLFcDwtI8TD5ecSNmFTFuGWBytqkQ9sRW2vo+QVsEry2IprmmrVuFCMqDRruKNqdi/+D6zHlHZhjF/L2M16MkooPXvJ2KuNBcxytmmw6PgFSYKZG6AEbpubRzdxpezlw03WmFA8cVymrmYfRIC2UVMjW6m+z4qgVeNXKGGvX3sepy1Zhrr4PPT6BDkK+R3Pq9bviDKIomrMZx4Y0XyCpB2jXRaExHQ7Woj6cF6wic+aJ5Bop/q6Aj2VDlqQxHUbzelND2xZmIiYsVxGVKldvmpy/7ryX8fzjTiKhOUZybkc3pjsh3Lwd0QiQOsGys7zrre/gm//wZT72J+/DjyOEUpjOIqY8iH6wQqotfGGj6ylWPoNK85BzyKyYT5JVDP7Hb0g7i6TaJ1y+HOMF7BoZpbx1HZkV7ZR/dD2Z1SfjtFnYpkAjl0J5jFpQpV7sIPF9stUyVrtLdqzB8O4x0ryF9DLI7nakp7HvHsUeitE5C3tJH9pxSdoUwZwCbBoDN0DZRUxsiDyQ9QBR6iAZreE/OE5oJJVaSAqkrkPkedj5PNneEmLdDuQp/SSRQ/SHUXrLOd574kso+3UuffvbGa+Ok1pHx8O/Z1ytEYY0TZ/u4jwrObIF3kEWiLnnnj8A8LzCAsi4CNtgnALPTZpDyV6dlNH1OmmsENUJ3GMX4s+fizfPJplrYYcWqBAnU4S1G6FuoN3leSrgy/5WvhkPs9gyUAsBidhRxuiESCcsWbKYk489DdvXdLW1AVBvVAiUQtk5hCkhrYjIJAS3PIA7WiV1fKJaiMoVEFIQD9XRqY9zbCd4IdokRJUqcWMc6WtkKYOzah61LaPs+OIV+A1orNuK36FQAwbvNWuwli8mf85qqjffS7BjAhpV7J1Vks4CubCIZ0XYEchiG76t0Y0q2Y4MPQUPncawaxSTbSd2s+jQ4CYJ0ZZRPD+BcowZrKC6O4kjmyRNCNpS1CPDWJk8pjJMdm4Je1EB4gaO0EjXRUyU0aUS5uS5WEGRaj6lcdcGLKGRrkHZIf1+ifee9FIqkc8H3/F+RhsNjBCINHnU3HeHJArFo+POdPyeDoqZIh12nsC+WuC+c+zt/bI3jtAGrROiKMayjmrz+6w5sgXeQdj9yE4A5ne1Q1cOiiWIany0w+Kn1ggvtDV6bhY7BuMIkptuwNu1g/ThCmrUIqkFJA2b8TseIpqIwfKxGhCVfaTwcHMOKjKUHYU8sUSSjzFJwolzFrNx42be++W/Y0tlF/3FXgCuu+N2MpWIeKyGzzjRhMZOIpSbRxYcjC3pOKmfxlCAiOvYJ/SgU5vaHbtRFQfXzWJig2ovoR2XQFjIUg562kmFw/bP/YJo8xiyw8XqbsPOdKI9j/JP7qbr0lNJ8g71CR8r04YfB4SuILGzyCQiHh6H0Sqi1I4sR7BrjDiGetDAG6zhlmKU50OuRJozyKzAqIgkTfGrO0nneKj5BXJ2gcbyTsIkQooCwssht48i20okrk0kIV28iIIJSe9eRzA/QY0J3K4udBKiHUkcadIwYLFX5N2rz2E4qPCpiy5BaINwvKezSj3pJDTHXpvZjippcXQKvCRJuOJ/r6ErX6RNtMGgRpXrfNkUeGnFIcTCdlzkUIK2DLI0B6u4CMtksFbMg9TB6siRJAmdK+cjnDbSvg6sdhftuYhiFrmwjVj6lMp1oo0j5Fa1QZowGtSmyvEfV/2UVRecS19XDzc/fDdBUWGkIVdow1nVhzQZtGcBCu+EpZSvfxhMhWg4gkqK0qo5ycCCLKLNwbZsrDCEDNgiJh6L4Zg+vDOOxbR5RPc8wu5/vZn64E6MSkhv20bPq/sZv3oTTkmSpu2kIiK3vJ0kjHHGxyn0tGGwSSxDvRHCgiKVRor0E7ImSzgmsWoWZLPoWGLZknBNO9b8PjLFTtzhlI6qg1lXo63bo/3YLDJqTkwQDOwgRKJTRdWMk3YH5M7oxY/LiHoWsa6C5yrSeoDJWEhHEmmNU7LRownL6OCNC09l3cQu/u9FHySNI4xumrqm8yita5pipYQgiZPDGUU2Yzr75zbz3/7a36N1wv2Tm360rRw0Ctuyjthe6Sebo1LgXXXVVVQqFV678BQs16DbfOpWhp/q5lxz30tcRBwh+4vIvMJJE2TeJfLrRBt3kiYR8aBPdmWOaN58Ui1IhmqYOCUOQ8LEoAZDLCPA9bC1It5WYCio8sDw1qnmSBTHpF2KiVoFPwyoTdSwjE01KqN8hYk1xjKYmoV5cDOit4P8yjlYCBIVEbkR7sm9pNsCGrvG0MIQ2xmUr5GxJBgfQykNjQadJy2j4rroXSMM3ngPyYN1dL9BrjwG/5Fh0j8MI7sa1CZionuG8bos3Fw7dUchvZhcKIijCLG7SqEtT2p8pCehfw7V3gImgrjHIh0tE1U9Mo0E67lz8Dp7EK/LoZYYdqYW8R0JgWthhT6q4JDJC7L9JUoL55FuS6h86zZEOQKnAWv60FYZ42rivA0VjZdISFKkTEklnNvRz8vnn8Btww/zn9//9mHXBa015hBmWnkmEAYhaZocZFnQFo/FkSnwDvKajrTkxz/7KV1enjP7VqLDiHhnyCMTe6cgWh6mxEEGd3uCOnYx4fI+6F9GUbuoVBIvyfE5A7duDlCWxG5PsV2PwAXdMBhtCIYHMZYFhQgRCpwXLmQkao4SuOzyy/n8Oz/M0r4F/MUrXo8fBLzohDPpzOZIqzWcisDsHMW0KczGRxB2GZ2Cl/MId6WknRnS3VUsZeGUFU6HS76jHVXMYHXnSSyN74K7vITtSVKnjK4bCqctpmFlyEnF2K03IbMFom/eTcdl59PoLlCdsHAk6FhSPP1YTD7FRDWiShE/Z9NdyEEc49fqdHRk8FUCiyxy1TK2DnA2lrFVJ9mBGqO7duLuHkfMiYjutRG7AwrdishzIUhIXQsVpyBdgp0hyaYJwpokmZNHlrogFti/Xku1CmI4ROxy0QsgxSWRBvIKmfVIULykYzlLC3P40U+vYGjzEFLIvTOjT2p807W+6U7JmifGGXVvlTuYjngwC99j65ZGWbiuPeXo3OLwOTIF3kG467ZbKI9P8IrlJyE7s1BJsFzFvI4sarIa9ecSnO6EePUi5p6/jKybInZvICxJpCP5PxtTrhEZLq9kGfvFb2k7ZQWZvI2MBaXOLLIgMY4immjgd+ZI2jI0bnwQETaFqkgNbVLxu7V3EE4OlaomPr976F50WwZLSaJEkxqJu+oE4nW7SYWGMIWoDuMNpOfgFnuo7RgE7ZP4ddLNZbyJBo62UFWDrbPohsCUQXcL7JEGC996GhXtYu0wTHzhSsbKm6n94zXoYgkhY4RISS3D0PAY1aGYuGJDxsfWAmnbOF3taBlTH/fRRZfa2s2UyxGmXMdkLfRIlboYw8s51AY0mZe8ELF1C7LUi4Uiv243CE0U1XBdSYzG1GroxMGVHZhYEklB6tio9jx2IkhxsHNVhB9AzuCc+xz8akgahJCmCNvmT+eciAA+/amPkyb2gW7/sxolJVIIUlot2tly1Am8+37fnHr91I4lYCWInhI6SunZPcbXCglfcXyWZRXac7FWzGP9129lfP0QujJG2p6n0dPFw3KvTiDLAmtRL+qM49AjEXpJDyZURGVFZm6BzNo6ymgsq8pmfwyAef0LuXPn+n3KddsDd/P1K7/Hbfffi4wEojsPOYll5XFOWowZGIG4AcaDmsbryJFsGEYoB6HyRGVN7MbUwogwrUFWY2VTlJuSSdoQeBhXkgZwzNvPpzynD9U5j/Yoi9ejyYyOkikU8PryZDMZsr6mu+DgFFPcBVnStOm7qBsVRAUSV6FtD9tSFNoyiPkLcDIZktMW0v3c1UhfoOZLGr+4GjEuENEE9QeG0H0S28riJja1YdChJrVikjCgLdyNJ1PERIxVcgmKGSgWiPoEyeJ5FFafSn7NIurX34sRhsSRNKdWTFgo8rx2/vFsGN7BN/72Y09RbXpqUVIiaE4a2dLwZsezXuCZSV8BAWidTs6RdmCGG3W6cgWKHSVGtwX8bFeDKzva8UslFo6McmxPEddziIKE4N//l0I2wYvqiFIn2ZyLM17mtYScqQPe1g1iN4x/7le0Le1Fmgg1NI5bS8i7hmTIRx0/FxNOYEcFoqTZrurt7ODBzZv3KdeeSQG2ju0ktkAGNdKGINixAx3GkC9S3zoCToooJDTGqsQ6RVQjpBWR9tg4fV3IJCCT5HBcl1R7mCRFtUPWtcjP60RUhgh+v4G5z1tMrb+NqFymUc8T2DmcvMKkCpGkdL7sBEb6u9CNBmZnTJKVjPpj7PQTEp3gmjayu3yksLEmUsxEhageogaHady9mUxG4Sob5RRp/8uzEKaOVUiIxlMaSYhVKGB5YMIUJ5dFOYq4qw1d9RFuBMM+yXBAUC1jRZ1kBocIR33qt/wBV4AtXTKxIo5SSCAueZy3YCUndc7nyodv5cHNG5rOHraFNjFKPkZV32++xBn7I/YLkAIQAp3q5nKRZq97yX6J77fNFD5zHvunY5qzAR58BqAWB+RZL/DQGgnEUUTgR4DBpAkmnXnRk/Ub1tOb6SDeGfAek+WLpsgXdsPmUhGlMojtmzCNABn6SJ0iz1xGduky4p0Bww+MI4udvMXy+Wha4zXVYXSxQpwLcDICo2z0hKbWm0MumIN77CqS/gKi1IH7qhc3VywDhAUvXnE6zz/htKlyrd38CD3tXbxs9fOxswZdN+T6XcyCEibTjcllcHt6UdgkjRiZuqiVBQpnLyLRIUQR0XAZd+Uikl4LtECMxyhXIi0XrzMPNRuv4eEgcPpijvmLE4ncLMqvUiwKrNEEUTfk2nNs/MIVcNcAxB1U/To6sel6/Xm0SxslFIGjCbNQSC1kXhKaGIoKhSKqapyT+gnaQ8JGxPD/PEia74HtBSQCD6hPVEkscBOPWCWoSoUwjLCyFlpooqLC6i1QdC1EtI1I5oke3kAq+4iSCJlE6CBCKhuTaNLxCmkW3jLvVDxp8+nLPkKShBCFeMomEU+8gNizJriUewb2P7kNTa012hiEMa0m7Sx51gs8gUBjsGwH17Ex2hClmmT6osxSAwZtYiqVCr1tJUy3YXPSPP0ihoXju8BL0fkilRDOb3TxorZ21l73MPHDQxjtUTi+hGYce3kJa0knqeUgggx6MCTSKe5Z/aSijisioto4YvsWolu3ICyXYOcmTNqsptrY3D7wMKuPOY6+3j4ALn7Fn/GpP3s/+Y48sphH52IYs0lHa2h/GLSP224R2wopHKRq4AxHlH+3A7SDmyqy89qJd4wgfEWCwsnZqHxMtrcbPTqI60SIuk3hgn7EbkV66zipV6QhbHRbjnptlDHbYJRHm3BJkyGkG1JqK9CeMWz7xW1kPUGpp0CuppFxhsBMEAUujm0RjPtoE+HlimRPW0H7ouUYC0QegoGdeK+ag/fqVZiyT182T7bUhloQIm1B7vTj8EjQeZvUAVtodCpIY0hMBh3U8AXoXIDWCq0zxJFExAYT+mTaFOlWTTaT582LTmU8avD5D/w9qbKJMIgo2acjY5/lXverUdMdgA+0hOLe2YcNKImUanLB7D1KYnORcK31DCmJfZLZ36vFHECaCSmQSqIxB4zT4uA8Ecs0bhFCrBVC3COEuHMyrEMI8RshxPrJz/bJcCGE+JIQYsPkurUnT0vnLZPx1wsh3jKbsihLgRC4tsKyp7kaxAJSw/9e/TuSJGFh7xxUoZ0PtsNzZcIXs2W8NAEExBY/MdmpQzfXDEkxxF7RTrK2ggw9GjvGYKeP3TCI+Q5qUZ51X7qa9vNOxPXaYEcV34+xkix2o0aj6sNohd3ROEpKHnrwAa646Rq++vP/YNfuXbz8lBdwyrzjsK0Mzspe4iiAaoawUUM2DLpqEcQKWUvJ8f+z9+bxlh1lvfe3qta89nymnqfMnZEkDGGGhBBQJkVFBEURkMuroIICrwgXBfUiMrzgFa+IIDPKTCQQhgABE0ICIVN3Ot3p8czn7HHNVfX+sU+nOxAgJshF6d/nU5+zd61hr73Os5/1VD2/+j05oQVrFMHObZighLJCFJqkW1BJRdiu4buCojskkC46LlHNBqG/jmCiRXldiuiCCCp2/N7jcWbq2N37yVUDVVcMV1coRUZ4zkmESUXuWarlCk8orICk6NH1+tTaGSaZZFWPcH0fJ3KRjoOKJWqyRv2CB+Cf2qYaDhFexMJH9jN35Xfw0Vy9/zvYuUXy1QZMdUhu2k8eBpjZAk8HeJ02Ii0RnRhHBchCoByJowWBNZgoQASGUoAJPcxIoU6K0XnOhZ1tPGrqdK7e/10+9/GPgnDBu2/2/UNxD45T67V6uWK8Llj8qKH0fcBY3v6Ex7sv+HH9Nx6zVtPiwrX3Lwe+YK09hXF1spev9T+BcaWyU4DnM65hixCiA7waeDDjqmavPq6A9w/H0SIWR9ePm/FQ4+7DCwmez1eu/yYCeGR9O2ah4omNjL+MumzzI2zPUG6ZwHolZ1cpAB00F5kSDhUMb9mPqJfoIiFsNhklGrwSI31EWEMu9Zl7/adIRpoEQzzTxHQ0nVNnEL2UNNcsJyM216e4+cbvAnDpBY/h13/+aTzl3EdRP2sGqVyy7/QQpcZGBbKssM2Q0Clxg4KBY8kLMK0awbZpRodnqe+YwWtM0njkdvyGxCwPGC0tQVKiZIeq58LIx9qAQXYEuT4hayeUYYktBMvX38623/15TDvGWbqDwcIKiQNZ5lH2E6pIkQ0kVSiIRiUrSU7ShYmgSW+xS9go0TmM0j6yqshkHbF7CS0samGF/M4+YqUi8EJqUwqvEGjZZNsZ0/Bbj0E1FfnCEKe1CbeoEC4MnZS8n4IpyAbFeC1z4OIuj3BdhUn6mO0C3C7KCmQckns+zmKOrAzIiqdvOpstQYt3vPs97N99GKv8YyYjj2tHTUeItfbDFZHvBsndAjchBFKsOSMhMGU5rilsQWuL0XYtChyPOI4P8awFYywCgTyObGy0xlhLqTVlpRFCIk6Ed/cZ/1lD2qcwLuHI2t+nHtf/HjvGvwMtIcR64PHA5621K9baVeDzwGX/8Y8dm69U32OJErCa/QcPcNrGLbhSgkmZ7Vv+36TJ66xP5hnU8hK6P+RCWfGPcoV/mk7wrYVWwO5agzzJkO06opvjT2p05CG+NY/u9Wk+4GxcUsIQGpvbKBOQzg/g5K3UcFDVeFI7CEI2b9sKQLMecvGWiwg2TjCYW8KdsjSbYKSLX4YYKlxlKCsf4UmiWoh2DLYoGS0M0WlBun9EcfBOFq65k1yniMDB8SErljBTQ2wKcmkR0x3iTDQxRyrcRYnbnMb0DGY0YO5z32bqpb8A6zYzZUD1CjoziuaCYSg1rgd1VyGCAFdKUmHprw6QbgPtSGrTDl5Yx6k3aPgWeVoNe6ji0FveTzCvqfkuZVlRtQviqkJujiiONDAf+DamGqKWUooDt6OURNcUatVBL6xABbXYJw/SsYTUVIdq2EU+/eHIr3SRNJC+hUyjZIodjKcytCNxZcDvPODxKCF5/Z+8DOv9hApZH11bay3ScZDWjgVRxVrhIfE9dnm0reH4GhZH5wWPOtITuP/4cTg8C3xOCPEtIcTz1/pmjqtWNgfMrL3eCBw87thDa30/qP9uEEI8XwhxnRDiuqWlpXu4Eou4h28kXVhZWWV2dpaTJjdSZhlKVPxj3+VrxuWLXcsHwhZV3/J4uYlLTYdbo4BwaHGl5M9NjZeWPk9yNrDU7UEFJnFRq/P0d9SInnw6q7ccYvoPf4PKpGjrInoHGQ0S9MFZzMQEXt0BBIuDFS56yHgkf8Pt3+Vzu6/iLR99NwvLfbSuKPQAsZyQDDOUteilPlol+OfsZCgLotNOowwlrU0bsZVC1TJE3KDZ9zCDFDUZY6VEVQ3sngIV9zFRjCklamlAXrm0NtaxeZ/cA3wXFUWYQwWTz38Kbenj9geMegqdVrQmYwJSFlRClSV4RUm9UcfzBUEjoFtU1HJLmWnKtE+yoc5gNWPlQ19EFB5F5FAtDfBliB56aAeiDRE1Kcm9HDUhUds91M51lGdtJLIFrs4QhcKWBUVvhPfALSg/ZJAOEW5A8em9OC2FHRWUwsEUoKRHUrPgx8hhQWYs65XHr2w4h9msx4df/mbIx9GSvScy8vc4mvuKYxGiHY9qlURKiZTfHz1abY+1tYOtNWPnKAxCHKOiCCmRAiTihNrx/cCPw+E93Fp7PuPh6ouEEI88fqO1P76lztbav7fWXmitvXBycvLeH1fCbbfeCsDOqQmUE2Mqwfxx1hc0Qhz32O34oo5QONB2WS6PXf6NpUdVGNzSkPszTBzsM/y321h3epPkqquoZA7DFZxmk+ZZ66h6Dl7cwx0atsRNlpM+u/feCcCewwd535Wf4dqbvsWf/p83kKxmEHRwLj4N3+9jlYvwA7zztlF+ZR9hCsWBfUjHgaKPzAtEtA7/MachT6mjyhC1mqNSn3Bjm/CxG0hXQ/x2jdrpGyhDF9cv6RYlZquDPx0gAg+GJYP5fbSamqWgxWqngxqm6I6gGAwRG2ZQrk9hQHQ6BNqi/IhEuMRGkiPGOpyNEOfWg2BC9PwSo0pjixTtdjFPPAmVVajpCQa3LeNmBmc4oP3sXyXqxDjpiFq9ThrUcFt1cB2cKMYxGr51hCKGSLkYLUjbA4qNAu1JTFkiGi7SAeE5UOUYHeCUmnI556LOKUz5dT554zeYG60gvJ+eZWTHhtHHnKG4i3EyZtsd9ckS1oa+95hpOYF7ifvt8Ky1h9f+LgAfYzwHN782VGXt79Eae4eBzccdvmmt7wf1/1hgrOTm794IwMZ0AjPUVGXOc/SIR5icPxMDfokRrgj5DX9sUOc7DmVgMbnkOTLFXfPZvrRocsqWoHn+OeRbIvTI0O9bFq+fw8Z1nKCF7qb4yZDQyQAH8e/fpSbGhGVTHLvtp5x0CmduOYmsKFgYzSK3NMm+djtOeyOFjbHtGLNngHZLTOmiahFmfplRlYKqke3aj7ntDsqFAXmgsXUovQwxGpHdsIoz0aQaJqTLfbK5AdnqAqLXQ8xa1CaFaIcYIZHLgt6Xb2Hbn1xGHMKyNKzM9ekdKZDJENsfUbkCZpcZJgO0a4mlIp6J6TVjPM9Ba0M+kqT5iHBiHUK5oBW6sR37kV2I3gjVHRE3WlSRJDpjK7GuyL8+JCtKqhvmCSqByQs8kaHyBOW76H6JuWOA0Aq7sU0zi/EPajItsVFAUBqyyUkILNRqmC11Ql9iN7eRuuJ3tj+MUZnxhhe9DDH46SkCdM8OT6xFmOP1FMc7PGM5oYN3P3G/HJ4QIhZC1I++Bi4FbgI+CRzNtP4G8Im1158Efn0tW/sQoLc29L0CuFQI0V5LVly61vej8cNlZ8dwNNfdcD2bm5NMdiaQWydQjuLCuuB1Uc4jlMGRJUZrfiPs89kw4+l+ghs0CbZu55xtEZ8oenxms+Thp03gGAEDy+7vfBcnaYIZEe90iRo1aonAm2mi/QadFzwT51GnUnVX6D/7ydjaeOL85C3HinI/5rJLUc5Y1qjoS8QoQUqf0gzQxRzhTINKj3CaNUS2ip3NQMWIO3MwmuDM9SRHSkbGJehbTO5DoqhGJWaYUuY9bDainF2mcfIUwaMeRtkwbkMAACAASURBVIHB9EcUN6VUBwZ4skDVYoooRpmADc//BbZMxEy0Q+rtkO7siFiGiMgh3rSOnBC3tJAmZAeWaWjIl1P8Xg/jVTQ8TbG6iENC5ipsv0cmMyAg2Nyke3gOv91hOEi48x2XI5ogXY9hMkCNQoTJSQtBIiFBU5oKPVOiqwIz38c2HUzDI9wwjRpU6NzQuDmlMbkF7+QpOpddhKmFmAPzVIXgpKedz5NPP5/bs1U+/81r724+96CuMjar+zu8/WHElnvot2M+ikAgpEBrqCoNxoyv4+hR8sRC2vuD+xvhzQBfE0J8B7gW+Iy19rPAXwKPE0LcDlyy9h7gcmAvsAf4P8D/ALDWrgB/Bnxzrb12re/HAlNYsiRlR2sdFBoTjZCuQKcVoiXRtQj6wHSIaHUQRYnqDRCzJeXhWxGrBd76gCg3yENdtNI8wdT57VGDt0oBUQBfSyl0wWg5JzvcpQpdBh+5GnvaZtpPv5TGpphiOI4u1OQxi/37t7+dG/feDEDshSRHBggEOvDxdY3kziO4ysV0Rxih0IGLGQ6oP34juR1Q3rlEMOlTXyeQky2sLrASTEvhToaImkdpXBwvQM8WlNfegjdQiFgh6i4qjOBBGyjzIc5UwOon/p1GYCjXbaIsKnAVbquBcAKK1FAZRRBDvlBgCo3TqCEXlmGrjynbOA0XBjFFJRiuAg2obIFfC2hMVqzunsN3DXm2AL0BTh4hD2eYUUFkU5LRElUaEm2vEaw42G6OcjRqfohtB1RFSTU3QMiQClDr26RVSfaE9eQ7JfmgovsvnyBlhBO1ydyQ5Fs3c2lrM7Hy+NDb3vZTXdTaYJFIlFIIITDWYqxF26NS9v/lqbP/V3G/7p61dq+19ty1dqa19nVr/cvW2outtadYay856rzWsrMvstaeZK0921p73XHn+kdr7clr7V337gLu3XUakdHvD3B8B6OWx4Re6SKtQpy9FWc1g6bD5b2KDw5dwodPI9ICHeewT+FmBUrk6KVDuKJi6LfuOre0Bq9UZLoiCtr4J08gm2IsqT6hSP7mCyx/7hqqtOSG7gFmah1EqTj/ggvuOscjznsIAC9711/y//z1q/i7K96HGAqk72F7Bq/TRJYCXUqqckjzKeeRfGURpyYwSlJ0C4pDoFSG02pB0Yehg6hJVJZg8xHGl6jtAaIZYvb2EYXFMYpSafLP7UOdP4W58yDuxkmSPQtseO4DyVohQ5HgkJM7JcH0FEXZRaVQ1AXWFix7Gq/ToOw6SNlHJRqmLdKvaM7E2LIibkWIbMBAeJQZmFRSFTlyIkQPVylnXLJexUhIdAHxyQ0Gh3LshCJohigVIOsd/MTiRQrREkhR4h4eIIyk8zuX4TYCqvfdiodGiRi1VFAMVgmiElZjfMflqZvOZaEY8YG/eju2rxH3VPnrHgjJPyjSu5vK8ffRWe6Nut73Ljkb01KOKrlIJXGUuks0YI1ucEI54H7gZ+JxcfDIYSpdsTmcQmYdhMkQWpJQ8J1vHSQn4l+7AW8YebxjKefgsEKt6+BMT7HuTZdRe8klOJ0I2m3yIUy2pvHWDHmHKNCjEjmZkftgDw2wowqiAHPDAYpQ4K7fwvzKLIvZgEdd9hicyme6cyzpcslDH8gLfvHXAcjKnK/deC2f+cZnKHUfAkWxsILxFVJVNM4/j+E1XUZpitN1cXwBgcLEBbbRgu4At1lDuhGm6CO7Lu6G9dhUU807+LUGXDgB6ySqLrCBwD95CvONVZyTT0IpQ5EbDBWn/f5TcZWEwmClx0SaELfqFHWH0AtwYo/6SLOYGXxZkBJAZkmODDhQCqrVAlkqdF4xrDnE3QxnoweTLZwtEeVcClMg52YxhUb6Amt9zCBHRAotLRaFsAabl4goxFceQkxTrJugfv5mOs99BPV1GzHXLyDadUYrA5zAx2qJVRbrOphtLiaTPHzzOZzcmeEj117JvsERjPivNR/2PQyWE7gP+Nm4f/mYZt+OPURDI3OLcHzeYtu8aCXiUqEovGMKKO2HXkghc6pDyyy97gpkT9B65mNZ9+uPYebFl+A9fgd/Xst5+3Mu4CmXbSK4cD0oD9vtI12NmVEEB3Ocykf2EuJFy6c+9HEAHvGYR1PIESefevJdn7f9AWfzvis+CsDPP3xcg7Y3TIlCHyvHpFW3WUM1OnS/fg1GriJMge4IUich0i6DI3PkK0t42mKlxmSLiF2a6PxTqBaWERMhymb0dg9gBNVNIwbDCpUcQc6muDMB1cIqJoCCJRY+ehNGwY5nPwXRCSGAIIxZzkvimYihNQyMhDxhna6oXJduPiSjoHQs8aik76mxJFa7Tj11SPMQZyVEpF16dwzRUUi5JyGzkriM8KxAr8/ID/dxFwTaMUg1VqxTgYecz0g3RvhNF9sq6DlD4nV1lt74MWxH4IkSrzvCJBLTFhjXwxYVdk+BTiqcacvzZh6MQPCml70am/lI42Ay8/2R3T2sovgR6/rXNv7gqhp36R7fw5zz92gX3CO0NePaHSdCvPuMnwmHJ+x47symFqsF2lis28WpHzOcZ212+ccpeNvDpoje+SUGcw5l5FAgWPjQlzn4ji+wfOteFv7h31j+7FfwuhEb/+0qgi0t8mQVKyLUpMLdMUE96uCEmuYrH4tBsKgWufqWaznjtNPYsmUbjnBxxJge8dSHXoxYGJKl49UdH79qnKtJy4x/ufrrpIcyDJIyHVAuzuMUFuGFqDimmi2hqsGmGlHD4smQVGr8c3agAwfvaadgv3MHJktROUgkodPDlAnO+hA/SbEjHzaHFP0M13dJZ7vEU5sId05QfX0vwYzP9pc+hXC1y+FyidgXFJGkXo+ouRK33WbZGJx+wklhA9cLUMawfiLEKQviRoPywCr92FJshLyWooyhFdRwfQezQ+LqGXS5SjzZpu54iClDGmsK5TAyCmeYo3KHshnjLTrk87N4d/apzWbkeYaY0mS7F1gJImxqMYMEVWqcUuINCqSUEATobw5pbmvyjAsuYX+6zHv+51+jqwzl/WQcyP3l+BlzgpJyf/Ez4fCIQwB0OH7qO4HFpooXd/u8UhT8k58yWkz4dCnYe/0RvjAo+SUZ8oSu5GapEBX4ZYm+ci/peTt5wWzESxzDU+drDN97C+WyQrgR/lmnU+7uY9sdGBlGVx7CNfCF7j4qo3nmL/06orBUmeKRD3skL3328/mlZ/4i4XSHN//Ba+92yVfdcC0fvOJfecE/v4q/++S7KUcVVeyjai2YXaYsErxTmjj7fIZ3HMZ66xkcHNK8aDvVvx9EJprh1xcoTj4PKRTe9jbFgS7hOacjrEtZrVDMF7jnnU2eSvytbWyng7CKakOAT43+0gjlOpiywLn0Qia1pMDiLVtMWaAnawjHJStSKl2RIkmKHN8R9NKSZugyGg3xOzUCJShGGjfTjNwKOaHIV0vcgYsMF1FohjevYPOS4aqiJSBIK3xXYDsdBm1LEnQZRSNGbsXEcx/Hht99KoO/vZxqqUvg1Qi0RZ48gY0DbK1BsrlDOVWDshyTfycD9OGUR+x4IKe0pvn4rqs4MHcEa38y3Lwfh8M7Ie9+//Az4fD683MACO0h0pSsrzGO5ZvK4y+sy3PykCeueny0a/mb1OXrjjcuCg38fhog8TmoXV5bufzh1w4xb4/dtsXuENXrkS/soljaQ/aADdQvOhMrPIpdt6GnWtx26820/JhzLzwL4YDjGpTjcN6WMzCFITuUMLmtzSPOfSAAU80O73rnO3jF/3gu5245na/fegN/+E9/yS0H9mBkjvBrNCbqOIMCzksJmjWsznGtYPXrcyRCY6Wl/uAd5Ld/HenWqRYWUVNNcmVxNtaQbozqBFQ33YlX5ehal9QZIKMQec1e8siiIofBNYfxrM+mB51M848fR9YvKYZ9EtdBWZd8UFFrh7i+R6VLIhEj6yHNR57MQjmCYsj8UkqzUMhRTr0e4SmPbNESJyV2mGNP2kz2kJPwtnboZxVxTZIHAmdqAtX2gIKW8Yn8Bu1Gh9P//Fcprj/Arnd8nnwO7KZpSpXiWYleHFJWBeQJejBHKGP8J52FTFJEoPG3TWPvmOW3Nj0EKSX/6xWvwt5N4/0Hyqgg7Lj9MFhrGI6GFHmCtSCkOU5dZZzZuCtF8cOGsPcwfnYdZ42i8tObZf5px8+Ew7vjjrHY5rZWi0r5iMLAMOdzRnFPymIXe5oz1wpkP0NkSKfiK6slXyZkt3VorBncA2NBOwankLh+k/KGIfIbR1h698fQgcUc0riOxx3797N1y5a7PZ2l6+FkOWpJoCZCir0ZL3zGc/nff/gXvOW1r8Of1+x0TuY1b3w9v/LUp7I66PHGD/8Di4MhsqPItEJnEnaVpPOrNE/dSv3npnHsELWaIWt10ht2467rENY12VyJFinOvnmKAug7qEjgNHwKbSl3WYJeic4yChVjlruwWjCMU/K5PqUb4mSC8//oWWx8zc+jsgRx5xK+IwhjRSktga+o4oJylDB/9V6cRhvreTQ8QVkkNGKXYT9Br2oiJUn9hMJIVleHtO9cJj98BJkZysJSlmAWEvQArNehHKRU/iIzL3wixUGX3r4R7nJF1pYkexOKrkALhXAFog6hU6PR2U7muJgrDiGjGiWG5OYFbAozjsuTN5zDwaTL5//+PfffyO7yXGN3pJQHWIw5Xm7+e+b0jiMe3xvI/wTllZ81/EzcQbMmvNlozxCc2gbPIoKIJ7rHsnR/GqacJTX/kz7no3mzm3CF2+c3VQUjjdTHjPJvqwEfqRe8LlnG9jRyx2aENTRO20gwmZMcyEBIxGTK1+dvIdMlD77ooXe7JoHGOW8jRhpkanAmQ2g6xBZUM8QkGc5kSPf629h70+67jrMUNE7eQXZ4AdNykFsC3AmHletuZ/jVBcrlCn+qST4o0UphDq9QHF4g3jlJPNOg6vWRc7NUVU7eLbGuAZHhnRShEdQ2r8dNfcQwx1sviJxJugf6yKhCOQ0KvyDWdU559a9Bx6eMXTI0ThRifR/f8fEmm/iRJh7mOCYmFQU96zKqBE0vJoxDRqbCL8B6fWZsTGYATxFN1jGnbkStn8RsSLFFhX9ajDx9Eulv4M43foLVf/4s5ep+/I7CyRy8yqJaBpslyIkmISGpKahGQ9zhCkbmlI5BpAWebxGTAp34XDq1iVB5fPjKf8OWJToarWmV3PPP4ihd5Nj745oQY/VtJLUoRh6thGbXSkDa4/de2/TDyM1H1X+kxYq1fe5KWJwY1t5X/Ew4vFGyVgs2T0n2LONEHhbD+UXCFe4Kn1SrXNIb8CZ3yEVlgqxyMBU4CpmmYCoer1JerhLeL5eYnra0Rn2EsPiNGsPbF6HKGB6axVUdvLaHnJQY0eCzt1xD5AZc9tQnfs9VCZx2DLGkMgMoE8ThAdFJ2xD7hnzppm+RDIZ89Euf45t7bqHVaPD8X34OnfoGZq+5FSdwxqytxCCa04RhDa8QuOsUWdpH9PMxo6vdpJjYwPCmeXrX7KEoKpx6hFerAQXF3i5OVUf0NKKqSLMc0RY4QQ1OP528Sojzw1Rzg7slHkVZsu2Pn4bfmUDPWgqpyApD3JgiyCtkFhE2PdwHb8C1AoylLS0ruqDyLDEF4OAEIVlR4FUlqlHDq0VUR4bUQweh28jVAdFJE+hBgpsqrFOxPEqIbMSgt4LjpNhU4G1fh6xc7HKfzEBw1josJWayTkkfkgKLGC/GT7qImTq+iHjS5nNYKIZ84bNXolamf6J2eW9wbxYSncC9x4+jQt1PPfbcdAedqI5vc+woQAcSqSxuIDFpTjzRgofX4WAf5zaLTTSyEZGagjz2yPyUKJrgsfkA40bYXkpuLK7jUCQjXCsZJZLIsQxGXeS6JoHyGVSL9EYDTtp5Ko52MWuxg6DCKlDawelptKiIHrSR/FuL2IMLfOvIHt758ffxr7UGm6fGish/9AvPZevkDCYaITKJtQFyOUO4ErtvGVtI1KSLSA21J+5gdNWdmNDFDgrCWoDY3iZ1QTpgllKouwQTDcrRAKElLA6oKpcw0hSupjiY4NRmMXck6J0zDG8/QuPBE4AGazn87itx9iyy7lU/x5R7LnN/+QU8VzJYnUMJn4KK/sgib5oljj2EKylHGjHSpKc38RYVvjLYBbBbQfohnUedg7ehzfp6g0J4lO/6POlUjeLyOyhHFZmziOh7BFMak8eEiUPVTfB3tundvop71gTq1nmUYxjdsoBwHGgGBIs1rF/hndzELozQq1ANhhTS4dHrT+fyQ9/lg+99Lxc/5VKEGUdmx6vu/GAqyj1FZuL7tx2vCvADhq/j3ddk4uWxD5VW3G3ezq6Rk0/gvuFnIsKrbMlMexpGoN0BaiqiykpywI086K5SffUIdqmikCV2Q50DUYHOxxp2k0Ebv4D+0GKGOY5yiHyXzBG4UuH6AtcK5FQTL3Co+hlpVRBPNzhjZhvf/c6N/P4rfp9//fiHqGSO8Y7J70pnFW/DJNXeFKZjKl9QuuNJ8+6wz3f37QKg2WiBE2EShbuxgXByTKmxQmKnBGzwMdIhFQ5L/3YbTq2Jns3QvZKyAHtghcpkyM4UwTlbcWseOskIHrQVnAzdK/CnHbq3zJKvSExQwaqPaueIboXMHUa7DkGiMYWk9H3K9W3m3ncN1Tf3MfN7T2Qx9HA8F6sUUeBjMLh46NiitWW1lBR1hb9/QDpMqLo5XtMSripUBL3Lb6Bav4XRbQdxyhRxcJkqgOjSxyI3Qn1ocUKFtxRSljnVcAgTDmxu4IYV7o2LyMpQmopGswMrfcyhVYznYgNBepODziZIIom2JcFkTGg8nnDKheMo732f/b9hnsAx4YC7BG1P4D8FPxMOr9vtIvIcNTGDcEPMDYdQ1rB3aPiEVozaElnlyHxAN3a4ZNnjN1YbvEHEuNqQDoY4oqDuKpSE0gi071ND4gifpB6Q4fHdZEQ2FeG0LX7Lg7rDC1/0TB619Sy6c8u85z3v5Y2v+SukORZYexc+CCsSdFWiuxl5XqKK/Pu+Q5Eacp3T3LkeNVjB9wJUx0eWFZiAfL6PE7iIw8t4boATWSrX4jlgkwxtDJNPfgCMRgwOLpKvjBDKw9x8iLyeoKccRKKZOHs9QibUgjq6dwi5cSd5kaBJmNywAaFqeCIjLCtK4ZMZh/ldq8y/76uc/fInIHKLoxVmVIDyUcZAppClR9M3OIWmcAReq4ate9i6Jm1aVsUGcrdAfH0XgxuPkCSamT9+HL5wOfiNL+FmPvaUGUTQJHrcWUTGJ/dc9BDyL+0lOlSgzliHbPoEBPRvP4TjR9i5EaWyFEc0wcXrMNkK1sQIJMPlEXlquHjrTkLp8sGPvR/8AOt7dyMe/0AJgKPB3PFJ3aPjz+P1ntb6xVp1veOVUb6vcXdi8VH1lLEzlGtCySc84n3Ff3uHVxQFhw4fZks0ie0OsAsF5pQ2uqZ4ntPgTUXEn/Vq6GaEtZKuPpZVy5REupJAOZRK48Y1jCtwAwVWUwjIC02YCS53Ql7Sq/Ocw5JBHJHkBbN3HmL5K7P89gMv4a8f/Wwes2EnX/v2N7nqi8eEYIQ3QndHqMDHpAKvU+OU2jqUOsYNk0Lyug+9FVRB/6Y5ypHAaoWd72Jr4MiS+qYW5uAizkSTIjVkmSI4Gi04FdaB0ZX7QSaEfTVWX0lTTFjDdh04JKkqzWDQx1sPeVkSTW4kXdqNWPAp44AD192OrBUYJ8A/YxNew8FbSNDLCZk2jGZHbHvZz1HKipawSFeiK0NWCxFxTlbkOM2AOJYkKyli9xJDEaMKQzy/i22XnoMoFe3zT2FifQcja3hhTNMJqI50yfsC9i9gvvgd0rlVAlXhbaooOi5lTWDu3EXm+gxLjdw5iVYR9ow2Yr6PaEuyK/egM02cJ2ivwk1TRGxxlro8adu5LJRDrnjfB5DpT4+E1An8ePHf3uEdPjyW1ZuYmcSc5OBMuIiVVdARW9fWUv6mylHWIMKY7brgVF8QYHlGvSBsxNjSIsqKcpRTCkXpuriV5UAh+VijzjVxxL61OZtFI/jwIUNt3QTt1gSddoeyBq3nPYFnnfc42m7Eu9/z7ruuT4R1/IlJBvPzCG3JFxeJZ2Z4xbN+h0ec+QAuvvAinvToJ7DUX+WqL391rHiblRgNNvAxKwl6z4hkJcNOuFSeQ+hHVItD0v4KaIt0Y6qljKRfoCZb5DqhHI4opaLUGcFp03BejE4lfjIkv8ViIsWg4RHYGt4zt1Irp2md2mHhs3vQQiNP2kwxzJHr1xFHMWpS0n//tdz+rn9ny4ufhj51iijXlDbHW9IwjBBeiIvECwP8hku2uUXHenQ9B3cwyeLVRzj8iSvZ984rueOVH+bQ6z7G6MAQQUW1tY5M5wimBSOlMRMhMvQxSyDLCKfeQO7YhrspQvVLVOUQtize4gApKuSGCZiSiG1TlFWJpyZQdRdGOSazPLq1lZrr8+EPfRjtfp8ZncB/E/wXd3g/igUquH337QCcUpuhuLOH6Wus10AujXib6vNBZ5nTK02VWez6GmXH562yx6fcEecMc6rE4LoedgDay/GU4HLtcOOw5C9syN8nkj9dhEucYxSXbWiW71zA83zKUU61pOn9/eeZeuFjecjmU1lYWWa5O4eRCjHMcTZMIKzExhnKj2FLzJmPuphzL3gAX7juG3ziS58B4LSzzkLOxKhGg6qjYSrEmdpA8LgzcddHmJ6HEFDNryB8jT81g540aFkgOhGBZ6j2d4lmAhASXzjY1Kf45n7UgRG2Xqc6dSOsk4RegdtdxHpQXjXHcrGX+evvhHqBkSmNZhOrDNnCftJmgbNrieGgRMqE0YE9rPuViyiEJWqFONJQyYogNmRlSd+Oo9LWlkmKqTbeiqHxkscSTiXUNrfwG5Y0sqi2Q5Wt0t/fpz5wcGcmqEYSgcGpDFU9xE608LyEqjvEdTv4i32CiZD0tkXKuQFkBXZDE7E0IEsdbC9Bba2Tpj2qniUpBSgHrOKyyVNZqBK+/Kb33qPWydGh7V1UkjXhknuix33fitofxbWzY4HP76W+3H0fc4KRcj/xX9zh/QhYw569exEINk83CdIMGXqIsoQdAXEHJrWicg259TCLy3hLZlyA2zPYXCO8lGJ9jIwjZKdJoTVv7UterDoIdez2XWuOJSKCLMddTugvLiJ2TCKSAfK0KVTisK49Lu9RpIaxMoBEWof2I85BVhGyXsceHKHmZnn4RQ9jst0B4HmPfBrbNm7DzqdYWeL1NVJqym6P4pt3MDiQ8BHf4tUF3imbWRhpXl4Z7MQUZa+PWR7gnjeDs3EjSRWCSSjIiLULso2tu4hDy+g7l6kpgY1bqMohW13CVR6+V8NrWXw3IvvCfqQdEjdbeOfMIHserA9w13uIWY+lL9zK7+2bY3jWFj64cYorN0c02g6Zzvj09nW84NE7efSlD2J+bglz60E6U+M6vq93TuMZZ+7g+Q85i306R48MslHnTx6ygcc/cgcvne6gBxXKKERcImbqrCzl9ITB1AzDvXdgllJMo0e8yYfQJTU+NnXRvsamQywl+rYuQgrcDR40NGXdwXUVj914LgL47A1f+YmaKUBRlcfpG5/Afxb+ezq8tUezlC79bpemH+EPA2wRocMMxwrIKsyyxJoRclAy8cuP4vHLDS4VdZbO2IFmRKUNxarBOVxBqYkGFZ445tj+MCh4gAeXTDsEHIvwPirq+CdNEDjT4BqsC9HJm9D5CgzGIgHD/hCHIQiDFRVukSOqBKlKVKDRhUEGlt/6xV8FYLZcxQkNldToXoacbOHOVYiWT3D2FgZOxb+2PApTYQZdNjY93ri+hbNvRFD5mGGf5OYjWJMR1AUqaoInoO4hQk2xmqNqgjLJqZSgOjiLNSPiZgMdO3ieR+x1kFLgnbMVL3JIRiPKW3qoWoLIBKKCcsbgNBT7D/c5/xnncGPgccGgIk0NaelxxvwSb9nYZJNy2PT630JXI9JFl899/lYOuilvu+q7vPS2Wd564WkYR2DtkGfsnuOV1+zGMZpSFvhS0OvF1Gt14hIaCzmltsiRg5xZjzhnO2bBohcTPM/FmVY4cUg46SNEiWrXkRjkUoZfpNjIozISz4NHzpzBrd15bt+zDxGCEPr7VpvdUxLje5MP/yFDxWJ0RVmVx869tslYM1Y5vmeRlRP4D+K/p8M7Cmm5fc8eNjY6WFeDN8LfvB175gxaW3QcMZzaBNOS5J8/etdhwztX8VIXd6KGDhQFA9hQMXIKXGl4vzviY1tSzmx4vCF3eNVoxIPCY5Z4kwZzaIiYMZjdq+TN9cxdeR1lGLBxw3YAdu2+FavDu47RHoQP3YbKDEb6UOUU357l1OZmzj/jLD79jS/zur97O+/69IeYzXs4uJgJgxyW5Lft4f/zXA6VFc+uJH9j4eByztMWu+AZbo40v71pHc9C8fTlPvtmh/R6Q/4An6f1u/xKCF+sOdhRxTPWtVmuBGVZcbMUPE+6uAuLjPoFf0rFr6UFT7v1MJ+aHaJ8i3vSZrKhpBxJRFrwv3ZM8pvbt3Cw7nPpNw5w/ZYOLz9/G5+civFMyBYN/puupuwNOfCaT+JPd3CzgqvrEZfsWyIaWHYmI4ZKsTgCvTHm3J4m8jyskMRU9LspcdtnZd8ccipFPGELcb2B8CsSZ47RFUvkdRe3HjAoXdKDOeWBDHsoQ7QVSjjItKCYrmGyCLXYQ2iNGSQ8qTX+/3z4L96IXdSQ/2R+In7k4zn/GdXCT+B4/BcnHv8A0tLaA7aymv6gz871p2OVQTdbVN++FSscrtY+rzYuYPmwgMkw4p/DlCOrOafkitKVVGmCJz1KCRwGNVVRGZ+Wa/mTgw7XW3hukPJ0aTm7UPyVD7ucip8PSggbOMOC1GhIu4jTJlj+26/SmRrXtSiNoHQ05II9t9/G5o0zhM0m6vQZiusWsS2L7Phl/wAAIABJREFUE7UIlka86Cm/ysvn3sR37hxz8r584zW88GnP4qFnPQhrSmTQ4sVpzl5f8N6RQVrN3GQMaU7Vt3yq6fEMJbkkyFHWp3A8rg4lE92E/92AQc9SSQexzUVnlqKX4T90PXJ3iSkqkszyTqfkgiTjzzzFSheeEy7yt/2SYG4BtxUgihSlFa86NM/Vk00OLlgukqu8/1EX8Mpv78F0wUQuQhR0JxU4CgbLYHyqGcFS4DExyghP6dDr95keZaycvZ7OV3fhT7pQRlSVQa2bYNQVBPsXiU9ez6Cb0bw5JxMhIraIQYmnM7yN04yylHgwwKsFjAY95LJBHJFYOUR6EXY+xUOj6zHlwjLuGSfRmN/Pzs40N3SPoBs5TiGB4Eda4lErPBrbHV8r+wcJDtyNm2yORXHHn0iu0VKstVgBYo2acgL3Dff58SWEOE0I8e3jWl8I8RIhxGuEEIeP63/icce8QgixRwixSwjx+OP6L1vr2yOEePn9/VJHcfj2g2RZxqa4CdbF9PsYWaNyQr5x3JzbB4gRSrGun/GAKYdMWUTgI4cKLQL8oIEoNLr0eXw/4olJzPV2nMp7Z+5yoLI4rS7nnxfwm42SM15wMa2tHkuDFZAaJw5wPYeiFqCXyvGHphoEvPnNb+Blr/gjnvf8F3Ltt67HbQm0t4iTRVihcTc38eoBr3vVa3jl81/Cm9/8DwC873Mfh0GFtQW5n0DHG2dkdYZTD9H9fFwMpqM5Kyn5R6P5wCjk4HKCN9/npJWcaz2HN8xrdvuCoC8oE4uwENR9OKSppEQoidWCa1sh72nX+eVOzPPXe2SV4VDDx3UK9KFFzDqfbNLBcTrscl22yITDE+vZcvMc23/3yZhsBSkq9EATjQZgLc7WGQpSrB3/2B0BLB6BvSvIqTr54UWiU5ukuUuZlqAUxVyX1lkz+M0Q2wHfdfAfthG/t4ztlsiFCmlKlhdXodDo1JDPjhA9iRO4VEZg82q8PhVB5fqQa7yawvZGqHUTXDS1lcJoPv7OT6K96L4Z371ReL83p1kbxp6Qwvvx4D47PGvtLmvtedba84ALgIRxmUaANx3dZq29HEAIsRN4BnAmcBnwt0IIJYRQwNsZ17XdCfzq2r735ip+6NYb94yL42xoNjHLGdBESYObpzzyuEr0DaWoSh+pA7xK4VqJKQNs3UCoqbIFVE1h3WN8hXBtUkdhqdsU1fMpbplDnbMFnUvUk85msjaDqhL82EXfNkfNK4mCMb/OKpjbO8dVX/sq29szKOnwute+lne9/h1MPuIc5DqDraDIDTK11LrQ37+H/V/9BgBFWWG2GJyaS9yYwegaVgg0oIcD1EQAWmCWLU/sCN4cKYKJBi9u+Hx7S42tTsr7E80pUyFv0ZJ/mNCEm2McJSk9hRNF+B2JUuDX6iAUfz2yvM9VfGR6kk+rmLOO9NGVIDivjljp8e3Y5Te31/jYdJt3nrmFN87Uubzj84xv7mHLS36NKMnwOwF5PI5yR3OLeM2IrIJOmtFrNMnyGNyQI6OMTSsjqp7E7RlkWqEchZYB4obD0B8y/OosNbdN/1+uZSBqiIdsw2vWcYM6naRAHVpCJTmq7iGlJeu0KH1B8dCNKN/D6BKxo0UlXURXYg4sYYcjLnAmaHkRn7/i07hGQAHCeFhjxo3vr0ZxvEX+KB8nYExo/mFzfmsnqtAYYZDKIqxBiXvS9zmBe4sf1wTFxcAd1tr9P2SfpwAftNbm1tp9jCuXPWit7VkrCFQAH1zb937ja1/6MoHrcW68ASM0blVhqNCe4iEbPP7KHfIW2eNZYojTTMnrgstnc64rBN5EDkZR6oIyUnzKD/gSIa+PC363WfG2RsFvOylvlSlX0OARWYM/yNssX9el+NYhokGL0WgVp3SxpUW6AWYhI58br6JwIofrbx7XMHr6aY/mtU95ITunN/PxL36Om6/fhd7vg+Mg/n/23jzasqyu8/zs6Yx3fFPMkRkZOQ8kmYwmNAiKE6IoSulCsZcW9lIpxbJWldVq2WhjqyWtpVZbilQ5ixOloAyCQ4KMyQxJZkZExhwv4s3vTmfcQ//xXmYGaTIlVK0FFd+79rr37rv3Offetc/v/PZv//b3GxRBpBRzll9/61/zy3/xWgBe8BXPQp4ssMrgZgVzC54CiM0cQg9xsx3ygO4Tu5y+GNg3a3nhmfM8NzXct12zPBZkWcy3X30VL2kD9yOptwR7reOEVlSnV/ibiw22ahFqxlOmU/4wqvGTivrSBg/UI+okQe0Z0G7EqLk5nnK64rWl5Oqy5rc/cYHDVcFvLZ/jNQsH0EnDJoFQOxg1BCCREbPGk285nld43rini1Wak1cP6baBQ50+1doa7VLA9QVSCbL9Q+xiTDvskw4V9fo6IemQTyqatdOEyOCHHiUNotPBxyn1QooSkmRzE6kD5d8fRxSaKASaMytYbZCDDlIJ7HaEEhl3XX87y/WEd7zl7/G5xEeTL8aQBHanrZ+j1dIoFAqBIgSB9+Ey7dor+HzxxTJ43wH88WXvXy6E+JgQ4r/u6swCHADOXdbm/G7dp6v/ZxBCfL8Q4gNCiA+sr69/xi+0vLzMJ+77JE9fuBbhBUZIGuch1ngFnN7kSS0cETsCNc2G49vKmF/MB/x4m9OeBtWPUA38ednjVyaG/7hm0UXN89uWw6MZL+zA9abid6udUOgDTeCBYpuLx+/l1C+9gZyEcqFDEwtcFRj7Arm0Ew+SNrC1tg3A0XSRZHXEDz7zW4mk5td+9tV077wKW0wRk238rMafn3HNgcMP/767Dj0N340JKwV1OYHja9xuPd+WlPyKnqBReBmo7i94+5LixUryvy8NOD5p+JrKcVYpXuIdL7jvJP81Fvwf6w5xYoPvd/BLBF46yBELBqRHjhwvKwJtULwkNXyHkfymD4jKU48qzEYNgww5zPnkpbMcnXqa+S6ImPrempXfeive1fSfdwd/cfU8L3veE1iPDS977o386i17Ebrm6APLzG2P+b5nXccv33qYf3XPGVa2tki7XX7sK27kVU+5gQ/OdfimOw9zj5ckogQaqixBx4K2ruheuwfXGSMuNsxmBXVoIYqJz24h84Rm75AcAUkf0hrrNaoORNbR1DNCFRCFhTTl+XufQCQVf/mHf0i0NYXNL55f9Vg6uJ+2LQ6Pw+4KczvnaZrmCvPx48QXvGghhIiAbwL+/W7Vb7CjMRt2n18NfO8Xeh6AEMJvAb8FcOedd/7zW9xlAd+7774bgOcdvQ3bOkKkeGWh+adRxA+biufnnm+dZYyd5FupeUXPUkzEbvdAaRTdpkDKjD+bPbLNa+9cRBEq5mWHaO8+mjPneKGs+Eu/G9hWCuMlIa2otSFfHiEXY2aJIp6k3Lt8FgAnAydPnSCNYvSenDCXkG03fO0NT+ON972Lf/z4W7nriTcyedMZhBrQ37/AnUdv5eSFsygpiUSLuamLX64JrYJiyi8MNa5Q+BMjuMbyupFGJYZ/ubfP9z1wHF/MoYQjRJo9WvDkUGNCjJuBkDOCLriz0PyZiKCW+OkEmgTbTdAm8G+3JshZhez0SHpDxkcnuLUKLyv81hg/bTmUe/7VbBOxXPALG5uopYhGtfgiYf6rbuXrfuD3eOkly1q5ht62aC0RUUq/73nFu0/R657hUuNAS4YOCl/zyg9+jM6WQjclamGOstclbFfQ6SEiiW9KmqP7ad+/SkdGFD1L9sTr2DqxjBoVeGdRkURvFTSzirnEULqAW8hQa2O0LQk6RgwElQEVGrKNCV+xcDV3rz7IyvaI4fxexEMD7CGDdbm78JDHJR4aQ58eQn06cW7++QpH2NlZqwCpJN5LhLgS0Hu8+GJ4eF8PfCiEsAIQQlgJIbgQgmdHbPupu+0uAIcu63dwt+7T1X9BWFldJdURVw+GsDTHtoS7iXEItrygWVxgvPvzdQi0XvCXYsJ3yor/mFvSa1JclGLFjDd97ZAfX1T87n7B4siSbBtmRUX3q5ag9vyAmvKf7Aa/nk25rW2xKzNoDaIU+FhQXpgSbRfEHc2s2MnD63c6rK6tcmR+CWcsYeawruCrr72DVMe87ld/H93dT++Om0mumqd1JW+5Z8eIf/dzvxk9HGJXLHqhi7MOBgOKjZbslr24vTlqLSB7GkmLGm0QrhtgUOioQzyXYWcOMdW4YopMG3wDsreAd83O1CkqiU1G1Pc4U9Odz4h7ffRCglYN5CXi4ggVClTHw2aNXBxgETCtmI1K2G5orCRqoZyukTZTOvNDttlCLqQEnWKtRcQKXcRk+2JWXSDSHqyjVgE1a1APSnR/DjsYUJlAfmET09+HbVrYcOikT3qgJRsbxusj3GQK7zjBkAgxq3HZEsXGNlXjCKKhaDxUDZSOdrFH1UaI2uJFQxQM4eghylHDkxd3lOXe8c5/gP7jXLz4AiGlfLh4v7OrQ0p5ZUr7OPHFMHjfyWXTWSHEvss++xbgE7uv3wB8hxAiFkIcAa4D3g/cA1wnhDiy6y1+x27bzx8PkVPWkhPHjrPYHRKuWsLNNpnzKS8wnpvx/AtfEV3c4D8nM/4/NeH7TYNoFL09gu+/WnNb7gmbqyRf9yTiWxdp793kq5oJ+89N+GAT+Aaf8T2+y3t+8x7kwR5VkFzbz7k5jXBjh+lEyKImcQ1N01LMG4JKkTIgzM7q8PEHT3P+/AX2zh2Amcdrjw2OOFF8w/V3cH57jde8+jfQ1wzwH7+E2BZos+Np/s7b/zsv/88/wRv+8o287fVv5zdf//scO/4hTKQo3n0aU0vK1uODgCxHOJCnIpgVqKsSQtVifYtYiNA4nJfQOuy0RS518cWEiIwmKBrvkTJicnGMdBonFaG0dJYOUpeWtrF4KfB7OiT7B/STDD8uMPvmCPMRZq5HvdRn+uZjVJsx/Zc/jUpGyGlKHc2I05haSMpZg5IJHSS5yulGCbGO8VmC2aOw1ZRURHhhaAcJ1caYpvXE+/uozOA+ukbbbhKblPSaOeh4WruNTQztZB28QrUWmw3pCkVeGrKmJpk6ciWw3SGhsMi6JPrkGXQ34jozQAvJu99yN6JsLluR2M1EdrsvwyMSjJ8LH/FnavPo41xOAPoQpd4VY/f48QUZPCFEDjwPeP1l1b8ohPi4EOJjwHOAHwUIIdwL/CnwSeAtwA/teoIWeDnwVuA+4E932z5ulE3BuQvnOXroIOrBCUlHEg4L/s3Vkl8b1KRDjQxwSEv+2kf8ahtRtC1ubUpYK1CDLkQxW697N+HeTapRic09zR05Pyk7AKwh+Y2QIS5M0EoRBjPiFz0BfWMP3QqcUVg0KpIkTUtZNVwqZtwyd5hYGd7wxr/CGMNXXf+EnU3sp6boeAnKiOc//dncdegJvPGtf8PHPvBRkm+5Aylr9g4XAciznG6W8+fvehuvfdPreM/HP8TP/cFvs3p+m3azxI5a9HyO2Zthg6TZEuTPXKJKBG6lpawdUsdIW4FPYOpw+xJCWgEGuTRPHRt87GFU01SWTqqpN7dItcROGopZRdbvobtzYLpE5yXNpZMErXGNxMkWEedIq7BrMzZm64hsRKcwzG83ONsQu5TGKFzTogJMWolf6hB1IhovaaIG7zWJ81hf0ipo1kfUm1O00ZhJS1s3TE9fIiiNVxG+bik/2dB6S7jmICGPSYxGxRrRWKRzTHVKdfM+mM8RdYusp8h2FdWLsEpiHQRr0cHxtLlD3D++xOro/BcyJD8F3vsdycXLy+fZ/0oI7/HhC4rhhRBmwPyj6r77M7R/FfCqx6h/E/CmL+S77Bxo5+nsxjm891zDPO2CQY49IlXIKOH+yYTDiSQXgn9fGO4NGgIUCv6dEminKTbGSGtIu45RUzMQCdPaEZ2c8lod+D7bA+Aq6bE6oJ1Abncpfu/9VNJQTTwq1gwWUkabU8gyAquYfJ6safiJO7+FDx9seebTn0v/PQ9SuhZ1TQ5nx4i5AJvbvPArnslHV4/zi6/6v/m3P/kT3PJ1T+Al29/Ee8/cxzc/7WswTc259QtcdBv85dvezsW1NTanE/bccgApc9qNMWEyQzaWajJh8283MXEXt7qNEC3J/iUsApNsoIyh3nJUMmJ6ZoVeb55oMaa64BDbmmRB0ypPstRjvL6OCYLmE+cZLRnERY9XU/RNCcWZgn4aoaSgairaWYsQjl5/Dts4to+PGdx0Hf2Xfz3V/3s3ViYIVZOqmNpbqsk2czLH2ZYoD4QwoHe1oD4zJT/QJ75QU3cisklNXVVkCwPkPhivxIjRCHo5/sY99O/dgMGQ7XecJr1qnkhp2tGUJDOU4zH69kOE4yvM6m1EKxC9HuiYtnS0jScTAYnHG8Ndi0d418YZ/vbVv893/uy/RrEjDA5clmUMYTf29lhsxDuCZQIRwk74L3h8AHEZ64AIgSAuV9N4DIsmBM773XNdsXiPB1+WW8uOf/wEANf19sO+HKkc4dSIV358ix9yXb5tlrHeCr4xPEK02bgIE8UUNx9EzyzatiAknSylVZDXDmaSq4Tnz9nk1fUGP2y3cVW1421UFiMC0giSuMAfdlzYLpCtQUymxDIjbhucUvQb+L7vfRnXHTlAKz1xPkd0aorKW6TU+Jlizkp+5GnPxxL4qf/wU7zl9W/n5hc+ixc/47nEgxS9Z57rnv40Ni+Mubi2xnNufyq3fs0tCG+RkylaQHNxDJ2EpNMjmd+DrB0hFjupJetjms0trIc27ZIdjIl7fTqDHkooXGOJrolRT8xxGxNsJYjmPYlYRAhBtXwRTq8Q+xKpF5FjS0KXqhfhk0BwDjupiLpd2kmBD5btP7gH2U5xczFltIIMFa2wNFVJOkzoykApAl6B1jFd3VBc2sIpSRWgUg2itCQH55ES/FLM6PwIta+D7g/RShOvt/gOTGcVURoRTUrsaIeGvyl3xJvM/ZvIUUlIO4SOQM7GiIlDB0icw6iYcPUQXVkOx3PMRxlvO/5hdJ7g5ePf/uV38/geC4HP/WIUO2olj/t7/K+ML0uD53ZXsRb2zhNOTahrydYI3i53El5rBIWBr+wHftoU/Gjf8q+TTcq2Qd5/Dp9KgnVwccJ0pURtjAjWYdJA7WAYa27qGaq2RXc6VNMpG62lTlLKyQwfYszpgKwtzVxAeks67CCkwgSPjWN0EEgTSAdLqMoinziHuHoOVgvCDQdp0oxDtz+Zn3/xj3BkYQ//5TX/id/+b6+h/9Rb6PRT2vEW7fIGW82OQNGzn/AU5NkZNja4NCV0clSr8Y2l9JJZPSUs5AgsYtgDFdBFQ+xjOvPz2EagnaAXG5z2+KaG2u/IJlYlVkesHVtDJDNCmtBsSKJeD78/4M+fIqxt4BZi1MoMG+bpdYaoXDGdFdhJjbUJzTUG0g75zHL4276G0nsqaxBJh6KpyPfPE3uHXpjDlhVjO0P5gDIKuTlmxpRYxhSXNulGGdFUMDhyLeHiGm3lsNszmour1PsXiCY1JtRMN8Z4HwiRQs3leCmQfY3ZNyRhR1fEmZxgBHFwMMypJIhzI6zUZLHirsF1bLUF97z7Azj/+D2rzybE/Xkd+Yq9e1z40jd4j5Huvrm6CoATBeLIANU6VMchL2t0KJNE1y7wDOP4prIg9wbdAJMGEQQ2BELPkHcibB7ThEDrLHFuaFuPnzWYuR4mBOpOQm8uwkwKsiBRkUEKTz5p6PzIHeilIeXyGF9UBKFJlaF1DVbG6CdfR7M9xm7WSBFQB+fwF1dI986RrG4x6Ab+zXf+AM+59jbe/IY38es/9Quwb56fes2reemrXsHfvuduhnmfq66/kaoo4fwYN52iCk/IDH7sifIaHSX4ixvUpUWmDtUkeKWZCUkbK5RJSJOCMlSoPNBOHPiW2lsGz7kFubxC5lLspS69Z1xP/ykLRHM95HKEXhwg4oOoSCG6mrjbUI0mZNsxaT9GI9EzRXahYPvdJyAVpPv20u040tojRIUtS6pJwrjXQbuA8J7gU5zpoGNNsi7QIacpC1Tao3nydZRUjI+dpfOsa4lDQdxNiWWCPDfDpxEQES3mhArIU3jWfqI5Q6syqr0xKYI07yNzgwxgXUBKSakCVjrQNU0DX3ngKADv+50/wvj6M6867Bq0h/Lkwu6KQ3A7bDoCCELuTGdDwDv3yALFZ7ViAdgR9r6Sh/f48KVv8B4DJ06dYqHTIy8i/PsuEoQnNhJ/2SiVkYBTa+ggsEbgAvhIIhTYqkFGCUJrQvBoJdFtQHdyfNESgiNLDdHKiMl4yoKIka2nFpIkVygV0Is96EH18x/GpWDKhkRHjJoZqqMxqUH4QHpAEOqdY7pL29i2oq4N0+XzuGmJ2wokOvBdR7+S593+VN7+sXfwJ3/6x1zceCTx+ju/+VsRF6aIqENIckIkafSUqJ+QHxxSX2pxY4VI+pBphIsRSwIvBCoqKP/pI4QzU6rC4edS7OYE2VjsVkucQ3mmpjECqT3NjVtEIrC6MsZOS3wxRfYcYrqBHBvCcgXbDdJ0cF2LkJb0cEIYVrhhwvmPfBzhIF60lCNBp2ewM0dsusTVCNa2KMuSJMrQlUDIgE49MxVIjMGkBiVKVLmGXJ0gTQd7bEZ6aB/m0D78/i5h0SBVROgmCBTiyABRFYQ3nEU0CjnaJj6xxaTTYaRrghfIOMYlGdJbMhloQotPYiLd0lMJB9IBH1i5ADp9rCH3uCClRAixs4BxBf9T8KVv8C6nld3FAw/cx6HeIr7bQQw0sg7EieYFouLpWeDP52tYAR9FiEhh6haFRszHyDgBvUPP5LSh0TGzvR1abWguTfG9BdSgS4gVvpPTWcgppyWmaDB1QMUdwsDA6RHkhlBu4S4U1IsZRsJiklMVNbYo0L7BiIgwPw8ThbxYEHox6aVN1IWAmpRU2uIbgegP+NanPJfr5g/wZ3/8R/zKr/wKL/2Gb8cozWv+9Pc57S9BH5IjfYwVKJdRXphSm4pof0ouGuQwIpoEJC1hs0DhaeuUuJ9QSYufT6hXJuiZQww7+EEH5JDiwkkioWnahuS+hpBpZDVFRwLRibEXxlRZjL5LI+KMthtR5RXG1IRJQjNSqBCh12qSrkcISVMZDv/YC5iogPGaaqOmNRlxHfBbDtfJSDoBIwN2uUZITehG1An4g9fQHjuPqDRNOyKbCZqNltmFTcQhhak0bScgDs9x9P/8eg6/9Bkc/KGvgW7DvuffyPwrvpG9P/MirvqXT6V7YD9K9BASTCQJvYzCBxKd4bctdmpQbcOT+gfZqKcc+8j9n2U8PuLZBTzhoW0Vj+GR+RAQcsfbc8596iDenbZc/iCAEuqKd/cF4Evf4D0K6+vrVFXNtQv7UasTlN2J22FrXhG3vCqqmBNAR0MtCGWJz1J85IkbhystWuxMO9AKVTd0R1OyvfMgYsrpNtYXtJXFSUHrJSLx1LGnqT1O59QbE+pE4W2MSOawFkxkaLWEukIP+zQ+wgmFd4HIVsjK0lx7BKksXHMVycI+wlfdSlR1ETKm1h63LXnRk76SAPzVa/+AF738xfzkd72C1lr+/O/fQluVuAcnuI7HDWpEHzgLYR1c67GTgpAbECneSUQ3IZ3LmcUBmcWwXJD2FmFfj3yQIDe3cG2FpINIwfchSmNkaeFUgIUFoqv3EwY94jSjfs8KzGti12KOT2E4D5HGDy2yNdj5LkYnVOOSYC3pXI6cStq4Ib5KUTdTsmGPdEkjtifY3f+njiWRdsjSYUSCPbNGbTPEYsbe59xEkXtk2yDjiPDhCrvgya9Zwp9exjYCd3aLjTe+j33f+5U0Sc7qH72H2T1nkG1OmAT8xZpWKZBQWksc58jaQ6zw/QSvDbd2dkS6P/DXX3gywafDFU/vfzy+9A3eo2J49z+wcwc+sriHWjmeseF5ZtXhz+IOUhrUrCbYEtdUhLpBDRaQ+wT052m7ffS8RuQe1TGoUUN0WFFXlh+6UPF1OuO3VEaLwYmAMAojNc4rmuEQMxRU62fpvvT5SDeFaob0DXEkEaFFBE8bJGpco7Ul+EDtHegMubdDlI4ROkbMatQSlO89SVATqo+sIXqaJDcc6ezn2j2H+djJ4zCWvO4f/wqA22+4EVVHNExwRUNwKdFEU9lN6DjyW5cQPUG9MaOejsj2DqgujfBVgZmB1CleCZSsEAspzfFNol6K3y5wDlQvB5tSdAxVmBI/MUNgiW1BfP0+mvmAMBLpSppLQ+ShJXTmEfY8MgwQaYOyjngW2HjffQTpQVgaY7GVRE4kqhNB2VLlGrE0pCkMJBnGt2TXHmBWOppJTZIppJEUsynNqATrOPTT34Ke6xLmBOpiiRtNifd0OPeeBzj5F+9H7r+KzT+9h82/uY+4mzB6z4Oc/rW7cZMVxKEaOyqophXpVkEUK0I9w/Vjpni8FlwV94mk4pMnj+2qdH/2S0cgkVIhgtiJ2QnxKVE64R8pSspd/27Xs/sM7MZBXiEPeLz40jd4j8Lx4zuiPdfIHohH9sCeHAmCDbi9GaoWaGN2tpTVI8LE83NbDV99quGPC0MTDel9+zMRgy7ufk+00OMTbuevenMl+etNj4kMxuQ4B2Y8wy+v4r3BEXHpr++mGQ7Q3T7eSaQJmDjDO4cHWucIY4UIkqjyCNMiOjPaix557zZhILDdDvFChzqPiW+bJ1YNzGeorx5weHGJtckWl5aXue/McYw2fPUdz0BOPdokCJUQplOsEWiRIiODn3nEakPcMZgRTJY36R1YwKMIlaORM7yTjM5MwMSUucEWLdSB5MYus7Nb5PM92AxU/3AWPUsI956nXtui/vAG0aEO1DneF7B3htobcIMuammIsTPkhkRmHapbNO78FB/vBO31Hok62EEKz9QVVGGEOzVDTqb4Zg3bkURZj+LCBpmRyP0xTVshlUen0J5cJnn6DVx43zLOjvATizMduHeM39JM79lAH1mk+cQpJlmMnM5w0lMXBSpsztQ5AAAgAElEQVQtUZsFUqUkyqBlRCsCTEuIMuTFmjzKkLOWaH7IDdkSy8UO4cPntaS6e5XJR1NByUfK50MNf0VU7fHjy8vgycDFS5fITUy3H6NdxY/2Kr5RN/ygLEA51NoM4T3gqIJHTkGOPXfvbHHlNaXGz2asvP6D/GPVMDLQTBO+S7cPn2bUz6jPOfz2hPTqLnJPShYMrpvSMYL5sSduW0pbk/c7UMK2r3bSUrwg6STojkN0BaaXQxWQFzytiBkNFKFpsR87C3vmUZOcMGuphCWSAe6F24/cAsAv/MavAjAc9GnLCD+Ctqmx2xX1pSltWCd58lGiqaTcXsEc2Yfb30MnLar0NI3F6EB2eA63MoKoIEsb6nd+CO0L2v17aEKFOLFKsidhemqV3m372JxpRK+gzTpMNyqU8PDRTbSpKZoEqSbU+zqoicRvOlyiYCHGuRpOKNz2Flo6vJfIIiDFlDgk9LcjOt05rJ3QNCXN0pDswhajyTZt0VLukcw/6waqusQ7z7SxhD191t79QbY+dD/JSsHgzqM4v8JW1BDchE5uCcc2mUy2MRcrtFbUZyeoTiBuK3TaoS0mKG/BBERkqL1nemgf7qZF5HgDn0j8uKSXpIzaknp1G9d+Gu3ah2ccn0om8NDeMLnryQl2HMWHykPq3M75T9Xx5rIQtRD44BjNpljn/tmpr+Cz40uc4v1RsIITJ05w9dwScqXBXr3IC0djrK3RlQULbj5HOk8Tx8i6xh8cML20wf8mPe/0kmdHnqSe8NUbQ0BznUr5tXaT704T+nZKQcI3lAXJrSnyUkX1wU3c0R5F25JvT5jEkkQrwiAnGs8YxQGfeFQlCMrQyBmmDJABM4HoWKy1uGv2oU6so8cbiH1HMNGIKkyIOi3+hCd79n7saIJYyLl56xB3HbyBdx/foXx/yQu+nSgOqJsi+jffwcbfHUfviQhxsxPHaguEySgnY7KioZhLYdyiZgnSWDiYotscKWJMmuCP3ED7yZPIk1N0I7EevNWYAzHVyQ1UPEYMumgZ8LIPFZibNKobI7cN9u415OoyzYF5nM4IqwWBFhlp9EpNFTmkVXgBvjLEkabqWEIaMy5q4uGQmW0xGwUqT4gaS5CBeSdYedsxBoWmqR3dxNCe3EaYmGFP0IgI/8lzmMKRyx716ozcV0yu6zLYcNTVmLbTx65v0VlXOJ3h2opgA7UwSOsxWkDeJSpHqJUGZTKkCfjWc5Va4F3+JPdvnuO2w0+A8vFNKz+TJ/eZPnsobcU69/lxTF3Bw/iyMnjL68usra3xpNvvwu1JUTrGBY0KliAk1khCVRFcQGhN1DSEc1OSquRntGNqGxLTgeKRu7cwEWKzRGWbvPiOW/EfPIv1AVl7wmKDGQeM0+SyInSHlFszomrGyFd0gqIZT5FTiDOwWLJeRjVp4KKHRYkcKxpXMPCeqYRmQZCtnqdY6JCcKXDTBnFLRr2xSbM6JicQHxzwPc//Dp69fI55uvTnu6RpRJXCxoePY0xALWaIicHPSpxxhPMzzHyCUzWmMfhI4KYVlY8wWxNwHdpmk2K6zeBog5aKunOJbG4/zWqJkIK2haLdII0jwv0N27agN3WENGF0fETnwBL20jaVGtHxOe32GnrmqCzEC3PIuKHUjrhcoo0UxifMy5hSJVTTLRIiBC3j1jFsHSFLaBJN5h1NrmjXFP3xBdrDe5DjClcL0sM9/KUtqrNjZCbQKgYxIOyLqfsJ9dY26mwBkcZMBWKyTW9pHusL3HhKkAKCQ9QWOcgJTY1pCyIrKWXBZH6JbGuKkIpbrj0CK+/no69/+46XHT2+CdLDOXqf5jPvPUr982OHsEP+2e/2MOaK4M/jwZfVlPYD7/0wAHcdeQKiDLC8gWgtstWEHsjrMnSvg/SgXcA1liDlDpFmyIj35cjg8Sbl9dmUV0RT/oMfI6OSUBqaex9ADj1uMWa2OSISQ2ZO0W4XjGygCNvkMlBGsCA7VF4gBaRRSuN60HqqzQohJHY+wWNpncOQ0ziHyhK64iA+a9B7DhJOjlFPXSKUhih0iESLCxHWW2KVcuMTb2dwYIDRA8I+SdzP6Fy1SCkbKhWYnd+mrTXCBpKb9+LHLS0Sl+aQ5kRPX8AfzeGwoneTon/LHPlhSdOs0R5MYN9BpvdtIm2gd6BDelAT39JH+kDrWvrKoo/uRw46DF3M7NwMN5mieh3aPRGz2uP2LuITSTvewreOSEmi1NM8uIWrLfb6A7gmoI2kkSXBCwZSooWimk3QoaVFkeY52802ZadPdbKkMhLpA+W6RyiFyxQiT6j8mCaThItjdDmjzSO6TlJVgkY5GhOoqhnKeZARCLOTDD7sAgHRjxGLOa5UiOfcQXq6wVagosBiDV2dcPzBByH9dFvEdh+7CxQ76SmPpoR/KM3ksnLZypsQ8uF+l09qH2LaU1I9+rRX8Dniy8rD++CH3kekDVeXAW8b2iTj59sU6Wq2xoaranhhPWUxM5jZFJOk0BZIUuQAfJpSjxts0jJwkm9MGlylYE4htyOgorjoSQ4liOGQ2alNQiyIgoQ0pxUeZ2fkIse2BYkOeKWo9AzrSmI0IjGYpsUDCoEYOoyosZc20NrjSNDPfxbyzR/BPbWPet869d5AszZGmyGMZ5Rao9sZIt2HiRN80VK8ewMKR1iQRHXE7NIynbku8mCP9vgm9vQKCEh0iggOl4B98CxKLOL2LNGuXMBdqnDDAWFawlaDOgDqQILf3GI2ErhKE/cNk2VHcnSO6tgGYeMSNpVYmRL3WtrtgD4yhzh7iTyk+NoT6YTQEbQzR9xJsOMZk3+6j/glA2Znj+Gv3YM5JqBxNHFET0JdliwdPMTo+g7yfcvUpmXQnWdqS2oTobzHH03ojjzBa8JGgV+3qNTRWEfqAs47kpuWmDy4RRYEM9+QGoMva6zStFISZw7R7e4QHWQpYrsBX9LurZFvOYFaCrhBBidnqEhysDPkwdEqzqYo0X62Ifn540qK3f9QfOl7eLs3wLIq+dCHP8IdB69FkxBCyvHtlreOAm8OEe91hj8pDH8T5xAFWusJ2hGSiPc3lv9rYnjH6YpcQR7HFL6kKR2WQGMjWh0wVY6IAn5WUZ9dQytHYjRbjQNX0ZvWCJOjegLRMcRJRKhLsiQlERoxiLC9gMg8sRB4HL6SeKswJ0BKgZuLqN78ADYJ2NMWGyXoypHFCX4uw00dmpZ4YQ4hpzgTcG2LOjpAL2XITCDzms6+AVMDajwhPiCRhSTYCZxeZuZarGsQqxGisZRnzuBWpuhDGjGZ0rljHhfViG4fk5e4xFBfWqN9cIRbb8mXBPXFdSKtSJxFuhxhNvCnS+affRtm0WAPptSHW7wpifoZfn0D1UKztoXfZ2jXpoSpoChbkgdGSCyt0oitMXUUE6IO29UEfWqLZDojHHNoX+PrisiuYgxE5zapR2uEWiIPD7BdQRlpkn0Z6Q37Eft71KeWiRSE4FFG4ZRC9XJskZGPAlJoVGlQhxaZ1RohAj7T+KlC9QSm9DSVYjbI8CpwQ7rA1Nac/eQnPu2ADP5R3ttjKPs8Oqtk5/1OX+8cwUPwAec8zlmc8w8z5T3sIV7B540vfYO3i3e+850AfMXSjdjFHqEfscdOUY8aGLL2qO0aEUeIVmKd55Uu4x8bwc+Rcc4Htsae/2Yzfq9NKXXAEFAtlK1DyRo3qUm6ApIeIk0R1uNDoAiBJHjq1uHahhJLpDq4yqLjFFfWxIWhrCSursH7HVqgEJC3zuPvPEx0vEBPLapIkfkIeX2C0QK3fw/pmU30VQrrwRVT4m4HMdfDmoh2eZO23MJNG6ZbE5xvMVLjKwjFkHY6hijD3XY9cRPwVuI7kjq06BaQJcW5Gd3r9qBVhKw87mOnadiP2b+X+NqbiBcV9cUxVQiYxT6Fr2kGB1GnzyCzCHFnl9nWKcqPnCY6PYFpICwL6qklCV1IA9JLgs5pbECkkkhpatsSGocxkmhvjjMx7qlgegLXljuJ0XlJ7Rp0J8fmEalQSJ8S5SmTzRHF2Q2SqqCnDXZlwujkSdhsKKuAGmpCSImKnYlhlQmEa3G1wFYakhp/4QKmnFE3FlXUmCwlBJh2u6iuJKLCB8ENnQUATv/tu76o4zeEgHMO7/3DW86EfETV7Mruii8OvvQN3u7d89z5HYLGO269Gbk9RlQz+s85wj/EE97OKj98RPEDScER43iRWuK10QCbpthOQPNIPGa1NfyLKuEvmpTXkfHHpcEUAl+XOOFoKoP3irWtChmB35oRS4mfScgTxkNFO2sI1qNagZMBYQyTrXW0yKhqD8kOyaQUMQSDua1P7VrkvTPEyggVYuS1HcKeHD3sEEYx/swJrBsiTtf093UptxtGJ1bRUU22FNG5rkeqO2R7MvI0RtQJJvYUUYs83Ecs9rFpl3ZthdIVtMtbqFwRX30Au1VjVJdkzzxFXVCeG4OVOAJqNGb84bMIuUI7OksjtohuOArbAjcpsLN1xPWHMBsp7QNT5Cgn3XMAOUhI5F50X6F6DSEdIvoR1hr0rMInHqwnKgUGjYwTbFFii5aOqwgf9fiZRLcpQScgUprYEA0iUidRrkEYSwgBs3cOtVnhRE5bSvxahRMJLR7VyRD5AjZXlJmi3i8RDZj5iLA/Q2iLXZ/hvSYIiWpbgoJCJDRZwDznRsT5mqhwiOAZDOcAOHH6USoEuyTIEBDy0cbpU108EXalFhUPX4EigPQBpdQjfR7ObJFICWF3N3gI4TF5967gs+NL3+Dt4vixY/TTHLO6DlpDHZAfP4MwKTKZ48Va813dkldWGZuN548mgY80DXlI+HdZyzO6gZfJllv6lkOXGcDrtEKFBhtHTIwj7Qpipzhy9BBFO4NMY6ME7xp8UdCrWoQRhF6GjzRtVTAbjRl2OpSjdcysII4MxAFMIFDRrmrMxXO0gL3lII3doDlfkxwfUH34DE4G4nwfbv8Ef3gf43sL/FyPbL6PmGn8lmN674xWDmhrTci72Ok2YcsTVRGc2sAc7qDKbeppCxsFKkRUQlGvXiK9bQkpOri1LdxGQf2BFarpDLEwR7m+QbqUoNYiZOgTaUN17DSzQyX53Byh8DTnJrh5RXzzHNV9l5h86B5qsYWYbCNtg28SqrCNlwIFKCeRVSAICIMY6glBB2SSMisBp7CzFhRorZC9lDDIsG1LcnyDbH6I1ZJ40MF2Yio1RV49T9BgqeHaAVG/h2osiVWIjTXUdITtRCSVQftAqCyhEYRcEUKKrQW5rJCRAatI5zLUpYZY6B2x8digpGIvOZ045ezs4uMfrEJA2Nnu56zHBb8TmvksV+MXQdf7f3l8TgZvV25xVQjxicvq5oQQbxNCHN99Hu7WCyHErwohTuxKNd55WZ/v2W1/XAjxPZfVP2mXFv7Ebt/P/fYloG5qHjh2jBt7+2krjfWBX7cpz1lf4JeagOtYmuVt6irjZvGIALecVDAruKso+JlmzEu6Gi5t88qjGd+kSn4sjHmuqKi8JB1EDOuMMA5s7RHM1qeY2lAriatKwp178IdTprnByIhq0qA3amwu6C7MMZGKTKfohQQ7lyELYGoxRuEePA2Nxo/P0uYV6dEevm2ZRmNEoogOd3FDyIc9dBgj/QyWtwn7oLN3iNAGtRDT9gqqC6tUoxrdyzCppz1zjmJjSvvABGd69I/ux5gUv99h7G5EKARCN1DVimRfhrk6QWUZceuJugcQ6ynFRoHsL+GcQLee8N4EP50hjUXu7zDZ2oAPHqP31EWSfdczcx3cVoWOIBcW42vESGObTYpewBuDq6akwVF25qkKx7CTMASSlz6Vq372RbT791IWI6yViP1zmCSmSLpMtjcwosv0wggTMnqlpsahM4G7voNZ3YbVCzTBItIUZVNELyMbgT03QgaFzxNcVpA1BjUHSnhC1IXMUPkGubyO6GraN30IOQvIxmGtpdWOa+IBF4spj7h1j7Vie1nK8ENZxLsICDwBJeVOETuX4UNxPCklUoBzAWTAB49A7jBDhYAUXzZ+yv90fK7/3O8AX/eouh8H/i6EcB3wd7vvYUfF7Lrd8v3syDYihJgDfhp4GjtKZj99mWbtbwAvu6zfo8/1GTGZTLDWcsNgH1JG3K8kf1IrHPAOa9ClRQWPaCp+Qo55mSr5CVVw+1UDqrYmJBqeeBgbarK9iyycOscPjuBrM9A4GGR4WpQtKG5ZoAfQVYgEtFN05jM4NsZXCp128CKQpwYO7CTl1mWJCi1yf0ptW+Tp0U7iqBAIHVALQ8JSjOwcQloo1wrknETmM+SaQI0t4VzL5sVNmgqEz4nDkPCuio13PIgKBc3FdeTWjPjIPNaBzvs0VUbYM48+vETbD/hyQuEkcq5DFPr48RhNxOzeB6k7BXOHu8zOj3fYXzqKelIw6kyRt6X45QKKQDdawK3OiBjhdITupNTr26Qipp2fp6gq1ELNcLtF9zRV01L6FjOYR8cSn2vUyRGiLdBRxnSzIRYNSapY3yypDvS49MtvZOv/eT2sXCJOM7JIEk6uEVsDezQq7WKZMs5bJk2Bch7pA+NW0F4Q2KW9CNWlP9+nNYpat/iiRs4b4k6KdDO8mGBKt0NuujlFNBY7mmLHBaBoY3BJSiMcdSQQPuBkgBLm04zVcsR0Ov18hunDeCif7rGwI9QTcAHiWH3KQsVDAtxFWXHF13t8+JwMXgjhHcDmo6q/Gfjd3de/C7zwsvrfCzt4LzDYVTL7WuBtIYTNEMIW8Db+f/bePMjS7Czv/J3lW++eN7eqzNq6lq6qXrXDIIluCZCEhYwXGDsmADtsD5I8gwkwYgILTcxMEKNg6IGwJoYYkMCeUIRDm2UcAQhGgJAQi9Td6m71Xl3Vted2M/Ou33qW+eNWtxr1gixZhrb0ZNy4N885+d1z857vOe8573ueF956o67tvf9zPz8R/f8+51pfE4qiACCKI5wUNJ+jSvs9TYGfjufJjEvPUqz54bDmrkSgLg6RIsDnlurzTyBOLZGNhyzUMa4rUN5jGg3qvW1qEWBdjbo0w1YOYRSaANUTUElkyxCOamwxJevFTCuLLB2y3YLKIr3ATSyRD3BqnncVAUEmCPsNXB6gJ/voqSPUnqDW6KiDOqwo9idkYotWfwG/MyNLd4iWamyzpNYa027Tv+sVaNegvH+H5qEmfrxFtT9ElLuwOyPIJcXMI7fHWHLYcYi6pN4a0z51luCKY3B1n8n1IclaF2kC6kjRGNa4J7axicfEIzJbwywkSGJspKESqNIhGz38QML2lHIIVTMlONYmiGOEbhCXBWErRJ/pYxfamFYHFwTkhSGqpljjiJylmhaoeAm/voyyiqyEQjr0kSZmKYGxRdc1WaRo9VrUoxH6YA/tBU1jSDcmhPku4SvXKDfHmKeuUpgKgUS1Z7hpRTl1JAnYcQUR0GpTpAG2EeLwtOuKMC+IrCfIDEorXATSa+pEclNzBZhPtF8PXloVZR54PNgbM5tOkTduUYdDKTWXkfq2A+PrxjdiG69475/ZyNgEVm68XgOuPKfd1RtlL1V+9QXKXxo3NjS8lDzy4EMALDY7BLHn0FqD/yWd8p6w4GNj+F5xkMpZglQT1RqdxkhpqBNHvdajuuswsttFPLZFUAk+qwT/1IX8hGkyHU6JGl1Ebgi6bZIARKVRSEgEjGp8MyIO22gfEpawUAqCdkwtI5K9Kb4McS2Hsw4bSEQSM358gLeeQgqmV7fRsaUcF8zKCb7dxxYl0ZlD2CxCLWjanZNUF6e4LCPRC7hZgdvdQboMsT9l/MjT1EFJcusqxbURw6knXQwIun2snKCPJkRNgSum6DCgZoZrdvGJoMh28LpJcrBFUlkQEXVtCUOwiwHx0hpiNkEWFWKyR73ooVXR2djHZh61uUc9GBCUQxwB6sgCsg9WT3ClwfYEE+uZiBq/YUkDja/2YWdMQ+WUjQVEGFGHks7hVZKwZHx+m3qU0ew0ENZTXdhEPbGL6wf4tiLBk5aWdiWpswk+DikjC2tQnVgiOzfA5YqFH7iVoNHAS0O9rXGhxEtNPdZE/TZlZfA+QwoIqzFhLyHzAbLVZJpN0UkAWkCpCIwlLCvkS51yeI783Vefh50PW48UAvnVpCUFCIn3jiAIaLcigjB6NvxEInFuXpdEwYsqqXwbL43/LJsBNyyzb/pXIIT474UQ9woh7h3cUPzVxvLwE4+hhOR4ewGH5LEnd9kVMZ+o57oSFrhWJTilqCY5tXAIcoIyRGzvc+XL1/lYLblaW1wS8IGgyXUnecQofqO1hFIhzTTiJ1zM3bOILx1p4AVUhUGFEXaSERiBTwRIwWw4xe/PyAZDhjLGn4owF8eY2QybFVR5hb1wFSEjpDLEh5fIRIBYT9Bew/Ii4kiH8rPnkTrHm5CyWdC+82bE6gJ+GdQbj5G+7jb81FBft+S7MwhDZO7pHj9EKzDY2YzRxpBmlCI2CiSgO23KSYVaSojbKVom2GtTbMcSNBLCsEuxMyFIBFVWwZO7TJ58BG8rysYqfrmJyiCLJPtLAToyqFe9AhGFyE4XGUfEo5p4HGPUEug24dYEdTQkuJrRTjqMtvdo1F2u/ebvoXsdpDXYOkNSUT1+nfJUQtc6TvzM2zj8I3dx5F+8jf4/+A5k2kJHK7g4oKgUpZaYm1cxUUxyyzpKB5hFydH/9tUs/KO7sT1J/rmriEvbkPSxgOjNvdc6gTr2hJVFFDXBpGYmeohhheqleFsTxCE2CBBTh8BjgxjrE1yezcf9N2GMSymp65owSpAvcqLC+68l++238UL4Rghv65mk2zeet2+UXwMOPafd+o2ylypff4Hy58F7/2ve+1d771+92J/HQxkEDz30EMeX1ogXmzhj+ZWp5l/PNOf9fMAcl4690PN9k5TfbzYJC4OggVyO0CLkp/eafLAM+dGqRdiMOCq/suSIs5LwSEh9JOTL9XyQffC6JLzjNM1jDbTxWOOpzAxXVeAcMgCZBISxJ5AO86TDLy0QNRoESYMoChFLDZSD0sr5Dk0oMZemqNBS/8k5/AMzorecxO7tUYoCORkxu/AYKivIBxHTP/gS5VZBuBbjk5wo8bj9gmpWkG1dRR1YRa0t0zrYx04FcilFNRuIwiBpMBk6yp19wiSHAHwkqDcM8mCCcKC3POFiOj+burhI+2CHbiNHXavJt6YEuxrVbKJff4BocwcbeZr/3XcQK6gevUZtZ8idGp/WlOMaVQTQSSn3t+j2FilbHhE0ETJFoNBJjFhIIPVwboT+Z3fiG5I6VlhC4rUlDv7CW1GzTXRb0/7OE8Rhh8VbT82J6U+3CadNQlqUGTQXugTddWQvIvmes4j1mFwGKC3xBLjc4YoKF7eRZYRDE0YVIk5QYUA91nMVGJkgU4FTgkx7vDaknRYAD3/uC3gD9rnM91J3lPdg3bN7cc8ypgBrLM47jLFIOU/p+Lw/v/EzNw6/beJ9PfhGCO8/As94Wn8M+K3nlP/oDW/tdwCjG0vf3wO+TwjRu+Gs+D7g927UjYUQ33HDO/ujz7nWX4mt4XX29va5dXENBhZZVLxVfcUT+6vNil9rlfzLIsEg+D/qiGKphfcOXyrUsR75jcEVCY9wIT8XBvz8gYiPHNT8865k80+u8ORVwd9vweuW2vzUW47Tes06v2AXuMcJvpBEmEojew3wEhGG+LLCa0uqwK9khD/4ClQkUc5hQlAtPZfrSyUYR2tikOkSVR3CSofglgWmH/s94ltOEpwvMdMZoWwQvfIAaZChfRO1uU++J7AbxVx5JTd4Z+fqyYMBrlBUG1NmdU0x2kdMKpwU1HJC5D02BGcSzLWMsN+ZJ++ZZQTeMRNT6h0/z/K1XTFdUswEyJOLxAcTXJkTzirqK2PKMmf57lspdjSmTFGdAKv3cW5KECpcGlFcm0CzgekIptub6E6LMNY4BSIAYQxuZKgrhTEe8/vXyK7kZPtblA9fIPQeY0tmpkJcK/BPPoWvR7jpFtFyDP0uNskImits/cYfcPXX/xxVTlh75/fSeM1plBCEUYjPDUprZDdGri6DMdRpRLjQJmw3ca0Yl2XoxCKSJmJvjK0tSkuaXhIgOL60CsD0yiYEAoV5oaH5PPgbku4vWicESn3bevtm4ms6SyuE+HfAXcCiEOIqc2/r+4GPCiH+CXAJ+OEbzX8H+H7gKSAD/jGA935PCPG/AV+80e5/9d4/4wh5N3NPcAL87o3HX9Gp+eP6+V0AjurOfA8kDvh7tuA7lCFRFd2ozbSueaU03O80i9KR7E4xSYjJaqJY8j/1Bf9hBu9YCWCxS+Pcdb7bNXEqY3TmKH9voGBrPjvcE4z5b47fzj/8zft5dJABIf+hhP9vbUpdSv4s1DxUB3RNwd9theSuJClL6o/+MSNiWr0ABkN0dQQTZMTjADmwVLlD3WKQj1X4Expz/zbN224nH+4g3hBhBxJzeUi1WVPUY9rfdYbqoYdJ33CW6afnjpQwEKhuhJdNGJRkl3ZIvrNPtFNTypBRMKYha5qux2xlhr6i8McjwjuWGd53lYM/eIr6whLTa09gRhnBySb+IQGxQF4J8UsT6kFFYALyYA+9vIC5uI+6eYnBv3mQ8Khlf6NE9VOSkUfGIcZmyFBgJp54sAmyQXagiduQ4Ay60cQX21RVPN/bUoIobeHyivx3H2a0v4sNHK37evT/9p0EozH+4EEoYxqHQqYbhvzqNs2zy+zeK+kLQ+/0EqPLOas/fjdVNcXuzKj2B6TdAwTri/inB9i8JlaGzGaEhw8jrmzj8wTXKlHGkPdSbCuiObHQTBDTDCMM2nvsaE5wMgiRKPxzw1KceHEz4kb+7GcNu+ck8tZaP6tiPPfUPlM3D1J2xiBvyNB7/Lc58evE10R43vt/+CJVb36Bth745y9ynd8AfuMFyu8Fbv1a+vLVeOLpRwBY6/ZgLYDHprim5jOvdv8AACAASURBVEBZ49tt3KUdwkbMz3dj/qDyvCYdUtkWzcPLuKUOxZ+e5y3NmDflFTLU1FFJ5iTrbz6JDCp+5y+eBuB0KHm8cjw2y7nj//40b7Yxj97ow8mVJo07jlNuVPzPX7rCfOc6BOf5odkWPjxKIDOSky2yK7tIFLPz10lfcwKJoSpHqOVV/IV9ZKdF/khFdCyi8Dn+yYzKpzRajlkYkx7rEFyawMWrkC7DH14mnCrCsEX4xh6T37sCKyNwiui4onrkMqUOCEWfXj+mKDzhegOzMcL1muRXpzQ6TXzDMPj3FyjtjKjUtHrL1E8KhC5xQiF8hTs/oPF3X8/w4S2CkcZlOTqJEDOBSD0Vkvb6AnZ7hiBGRZ56CHrscLZCqDUqt0M8jlHdEV50KHc2SMKERj+m2qrRVmCGNSw79grHQtgi91PGRcn0I/fROt5jPPCY2RQ3Erj1BeLbD1BcyVi+uUl9YUC2uobPz7H/b/+Y8dNjQjKUSymPjOFLBlXUBIc7VJNdxMISwf4EEYLtO3wREt6xhrq4Sz0o8DGYXoKrC3Q5Pwmhyrn4ppPz/WEv5ocmvil4hhVfxDL8Nv7T8LL/L55/8mkSHbAWLcLta1BaPApfO/SwhrYmWFmiK0p+ICk5cedZoisV5RObuMs7WJFT7JV4meGu7JBfHNK7fY2937qfvY99keG1uTbe49V8Fj/0mlOo9RV+sBfzIWH4xF0LfCjeo/nGVZbeeuAv9U2NM2TQpe7WiNBRbs4IbzmG0DH5hYv4BMq2IXnDEcRiBi2BmE7onz1IVlcIHRCd6FNrT/Q9ryWJDXprk6KosBt7JN0W1auOknz/IapoSv2lMX53iNsNEY0Uc2WArfv4riZ4ewtb1oRra2w9dYWAlHCtheyFTLNN4pWjlFVGokKyZka2vYW7vI91AbYW2FGOPnIno995HHlum9BZ0n6KH5TYlkfEArOdER1bYPXUTah+E7XeQRUZ1YkeUVVRJBLX6RMtN1BXKmKX40qBaR1guj+iFI4i1IRCYCpBj5RZNcVvJ0SHQuJmh7x/jPV/8kp0rdAWgp1dmuf2iAODvTwjnA4pr38BUcdU2ztEZ1LqMwdwqwGu7fFSIpcFee7xUYzVFXKtTfN//Nus/4MfYOVHvhczGeJMRugVVBWMSyweGwRYpfF6TkIyAOVAfTPVmp7r7v02vmG8rOWhrLU8+ujDHG+t4FIDH30EWoJwlpE11DycYFth8hFBFRHoPWYPbpCeSTG7GWY8JTQCHxbk7RipUvrfdQjxu4/hY0VVad6kNVWacc0ozoYVb3zgEqZVU9iQV//s95I98Rhen0LWCuqI/6ud89tVwOl+wt0k1FdytCioc0tc1UyLkihNcOooclThkoTqD89jDqzgignNmWH/iw8Sra7grw5xRw/SOmYZffYzRIePMNuaous9RNjH5HvI8yHT8RQtl5k9+ghqfY0gdQipKaMmcSvD7AaYT25StxXF+cdIkkXKSUH+yDXCQqHqgHz1Ao3VLrqToB8eUVeSuthjKVqkbASohYCiUZDkU+LT60wGGaIISd54guqBKwjnIW7AoxfZn4X4Qxpz5QpiaR25NaE62CbyBeQGPSzZW28SuJzgcJPA7qGTBnWVkTUEaRIQmCn78R5Jbxm7mKEfKbCJw1y5xubONg1lMEhmg32KtIXLS6JmA8IYmTdwuaDULeylEbFz+FaXZg31uqBoJ5jLOfbJMQf/xd3ks01GH/tTpkeWMA8/irvah9uXESZD76T4wSaCBq4FUTNmdsch+CI4JN7VuMrzjEP1hQ5BPLuEvSHq+ZLU9RxHxvz3+ZL2GWFQ4W4ICbyAz+K+++5b1lp/kPlq6WVvzHwdcMDDxph/+qpXvWr7hRq8rAlvPB4zmU44e/oWnDSQxGA9Bk2cBdiswB5KUVOHMRXiSEL45Bhz0yo6cDCpKGON9oI07eMGu/Anl/CpolIhVCFaaN4y87hWzbGf/Pts/8pvo1RMIAKEKGnccjN+UkOgsaHnzpbm9MAwu7ZL2i3xrR6BduQ+oxJNwi+fI5cJadKkjkO0KRG1xUzGpL0uptjHBYvYayPUoQbWZKSnT2LvexK/PyQKPayt4RJwkcR8eUh4dg1XXCJ86+34axNMswPVGBUY6h1H1IoZXNtiobWM3o9hacpoe0B6cA2xPNeNC+83WLvNvm6hiyELP/I98MUBhS6pt69QDzVJd4JdX2H3oQtE3T6oAPPF61RuD99JENaQn6sJb4qpBiXNsoGNS/w0Izq0hC92QbSQ0rA42mIctIgPLVE9dR07KdGNlE4pqA+l5JcU6f4WNpvRiRWq26Eyc9mkZNshDkfoukDZCCFrkmYbMxwhmg0QAt21iFpQdXuIssTftEr5wEX6b38t+398PzGO4OY1tj76WeK1FYLAk9/3ILLoEr5WYS6PwYVgJSpdQPmacpxRqxbyL/bnA7CoQCqQ9r+M01Tw7D7gC0Fr/cHV1dUzS0tL+1LKbzk3rnNO7OzsnN3c3Pwg8I4XavNfxSzQPLuO8F2QBh95XFCyLUpsXKJ3cyZVjWsp5FWPCwT2+ga2GGGbmqQRYVONuX6B9Aduoyqu4kwKg5LAOMhnRDKjk4WIPEMcWcIPHXEUYSYlwjikStn8yJ9z+V9/gmI3RzS7hMpTziLKfEztLGGjSZV4yDSRSTANkFim2xtUaQtCQTFRlEsdwsLj1hcRhYdAMfyT+wjDNr6sEA2DbSWwvUk4GyNOtPDTfUTah8u76Mku8uo2at8Tpm0qRpTDPRa//xaq/RkFU8K1BaI4RpQZcloiBxnGjRHrqyiXIeKj7H3kz9Chxm2W+GFEmLQot7fJJxnKBlRJQLmbUcchse4QT2I4fgz55hNMywLd6VA0C6rtCeq1R6iHlzHNHmGQUgUB0+WDREimGzvokcOFDapJTSUN4cU9IucQh9ax04qdWcVgZ5dqfwzWkReO7GqGH4FVGr3Ux9YVwXIPkRkmTYmRgvLYEtF0TLXnUV+4iu7C7L7zyEnJyEK5PSCYhvirE+qZQ4dNgobFPLFBdKhFGefMXIE+1qZuCMKFFrbO8NtjAOwox2mPvuFMeMah8Ixa3dcVnPo8J+1XriSFQNx4jxc5mnbr0tLS+FuR7ACklH5paWnES/gDXtaEV1U3ck9c3kJSgqshjfjFusnfKRvcbfoQBLTbIcFuRm4CwmaIysGXIXaWY4YOlQuWb3sd9V9sYIYNyskAqyqqvSkaiQraVMciNj7yR/grm5hOAxHA9j1/SPml8+z+uz+FhzdoL6T4EEyVESx1INIgPMI7HIpg4DE2IfquZVxZs/Gbf8DwUw9ihCbYc2hTwazGRQXBAYkIQuTlEcGVgno2JggWQR5GBjleLTPbzwgPNCkfHTJ5aoDYH1L7Ct58nOaP3gx1gNwJiF6/THn/ZaQpkQe6VHuGsBNDRyHcFHF8AbfUxE33CVoO4Qd0lg4xOr9JNTLk0xyx3kT1+6jMoBZTIlsQv+EYYmNKtRBS95rEOwPqSzukpWY2nhLe1EUtGdI9g5v0EEOHnRaYoibY2MX7ktDUzJwFKkQM09mExj/7W5gjEXZrh1Ap2ghCITG6RMWCoBshRpJK7KKX22TTfaZ1hascRVnTGhlkt4UcTtBpiDAT6p5FzzqwbXEH+hTTAokG5yDPsdMRrvKUVYVtNLFXx0SVIEokxUMj9CRB1Ja8zKjDG2EoNXNvRfHXdAM8H/JbleyewY3P/6K89rImvCeenGft6hcx9aiJyJvkespvV19ZqRe9Bm5U4CNF2k8ohMK3U2SkUCtdqEPkas3Olx7ATMdIX6OcIowEOg0gB28K/LkZPlSErYqFpZC6mBHducr2HzxMPRmAU0y3FWXZRsx2yfb2SPKaUEf4Cowx5CIkParJ79/G4/Aiocxikkjjm4rSGuxghI4SqlyiltrkKxbXa6OORahbQPUFaRLh2oaovYA5v0nalMQdqKMAo/vY+y4z++DDFHJAstAiv39MqEKc0PgrA+oLW5jcI6YanUfMnhyQ3HSUut1HJDEUAWp/hsum1HZK0tSYhzcp84yk18UdLvCdBvbzXyY43oIpWFEjL1YkxKjTR1gIG/inNLq5QrHSImyVqP0Jyd85iGwE8xwemcPNWkRpAykNUQ0t1eT6r3wS/cQuURUQtyJm1RSswBmQJsSOR8QrDjVuEo9HiO0Cme0SH20SpRYXVpjt66h9w+R8jn7HzRRVg8l0A2v2ieuUheUutBw+LtHLCbqQlNIgowhFhRnPUMbAcIZa0hhdoGRI1FxA6Pn48t6iaoOJvvGxLJjnrX1+igvxAo9v4+vFy5rwqrIEYL2zhgwE7mifeNZkRc0nuXcmNfEkQ652MVGAMJ6wLlCpn8dWXaqRzT04V1LPFG5vh0l8Qy6pBqU1JlBMU0XQFWw9OuVLWcR0b4fq2ohsPKLd6c3jsV57BFXtIrNzBIcWiMIGpZGU+Vw4UjvBYl8xrkqaCxHaG9idEl6YUNsdqkASNArUmYOIkSWWCjfNaYU9ggNNtEjxuxnFxX3GX7xAvZchRIKbFJSpQpYJYSdClAVhFON7Ev+0oKq2CJSEYy3qboLopcjQEViH6DYpA0Gn02L4J/fSdAH53hgdarKmIWgHxKnCZYK6YRHSwawmbK6jK4U6eAib1zCqCDzYc19Cv2oB8/Rl8t0SjjUw17fg3gtUzhFOQiZ/eBnrPCQBVStC7e1jrEFqjbMWOxmROkupJLk1FLv7iLGh6oJuNIl6EQpLmZeUOOoJNPotgrUT1BsFTCFtdjn6j95BcDShffdR3OYUTEm4HGFCj8t2CHdznKlJZEQ2qHELMdpIbJmhclBxQBVHqEYDO83nhtzhFlU0RT8ThGIt88C6b2mj6lkMBgP1/ve/fwng4sWLwVvf+tab/rr79NV4WRPeMy4xrzQ0c8TOAD3J+XjP8vvBHj/cjKis47eu5/zkNOCprMQ0m2BbJNIyiw1lJhCrLcJWhcpDurnFdAR+lFFkFcIMWWy2+IIX/JhI+R82Nb8yCHCuZrHVpTIlduM6+VPncc1lVHIQHt2ZWwtLMaLdZLewVNYTUZCuH2DSUkx2SkQjwh1bxKkuSSZgXzD+8gam3KHav0qGwt9+C5PxLvUTQ4ITx7A3CVw7pHPLIvVsiLQB7gS0lxbJrkwQq22KKMPpmubrThFECyQ/8hrM/UOah1uEh7oUcogYjsjP7RLoHn6whTKK/PoTHHzlHTTe/DqkybCpw45qdKdB+6bjxKvLmHqH+IEpxUZB1RPkQZv0YA+7JDGnzqI2AsJX3Ix83QLVk1Nss4FtOaLOMsH3HUBlOSEeUwuqYUay2iJWFWHuiJVDeYFrt1mMQjCO2ht8AJMre2TDMZsbI1orCUEMvlFSlwZpYcEK7OWraNsmryKcj1h+062Ujz5NcM3R7MdEE4XMHFMqRGwpdwssAaG0yMyjSoeSEu0BKZH7hrDbQbVTyB1imiFKDa1kPvwqi3ca8TXu1D3PPnuO0fa8TGZ/xXX+JmJ3d1d96EMfWgY4evRo/alPferCX3efvhova8Lzz8jslCMYj7EYbCCx+ZDg8BL0Q4JS8H+ahIeN4mfHIYEDOxuTGUsaSpQQuGGBNh59sE8Zp7A1hTsWUYElOHGYYmZ5pA4xN4baJzMIw4DZ1j62qFGrK7QIiQNLXcwIDnTozCR+L0dNxiircOWU0XAC13eJzg0J202oSkRdE0xmrP3Y6zAtjcj3kDpAF4JutyL7nT8j7bbhVEr2yS+imi1i22R2LsPlFcqHRPEK48E2sitxY0cwtVQ7I+pBhmgm2E/+EUIU+FmOf/wqiTqE6reIV9rk+xU2L9DHj5HHHXb/7EFml+5H5w3IagQ19miMaBYIqxHpEdzrejRubyOvVoQbA6aixjsFZ45S7Y0o73uC9L4r+N0JMpR07zyBHRqy60Ocitkd7JIqS2+pTZ7nlN6TG8t0d48Kw/71y2wMd7F1DY0mUoU0I0VSedoats7vUUxqunUTbyq8rwjbAfWrF5g1RygtGfz7+9h4aBt/7Dg2lKh8TK4M4mCDZhZQl5Y4EpSyplhqk5c5PtZIrykxlL0WVVKTz3YReY1dSLClJcos5POVBcE8py3Pk3T/1sRP//RPr1+5ciU6ffr02be97W03nTx58haAe++9N77tttvOnD59+uypU6fOfvnLX47G47G86667Ttx8881nT548ecuv//qv9wDW1tZu29jY0ACf/exn09e+9rU3A4zHY/lDP/RDR2+77bYzZ86cOfvhD3+4+/X08WUdlvLkuXMAJDcfgt0SbSymrtGdHq8/bwHLXTqhgWeGYObgN7ZqjjUj3rTSwF4eIUxMLWtUt00+KwirHHNqheDgIYqH7ie4sE2dFbxaBnxaJFz1kp84EhNeH1DrHja0aCkxuzmIANWNKHyFGDtsWpEEMdlkhl5dxExL2JGUzuJjhUcTEOGXJed/9VMoExEpTbV4DHX+KsFtS8gFTf97bmL7k+eIj/aoP/04eZYQliXOW+qzKer8HkIZxK5DJlPKUMFSm2prQPyDZyj+eJci3KS1A2bTEy4V5HlNMpkxcjtIJUkXD6HKCrQjTvvs72wRHE3x7SnuwW3yfoI+uYp98hq7n54SL0GwE2F6KXI6ROyVyLiJWugjJ03qpRrSDH17l9lnruCaq4yvX6a92Ke/doB8vwDnIZAkPqKscsTyMroqML0WQZGTqAYGQXexQ74jqQOoAkmjEVNeHyB6HRIXIbua4twecr1N0S6Jj/aZPPgkvaeH1NsFtQ+xYUQkodyYUnYgkil6v8AtNhGbE9xKDJsT0DG5hsYwhypARyG1y1GmhiCklgKCG06LMAKhwLi/caT3i7/4i4eefvrp9D/nNY8dO5a95z3vufJi9ffcc8/Vt7/97cnjjz/+6BNPPBG+/e1vPwnwgQ98YOnd73731rve9a69oiiEMYaPf/zjndXV1fozn/nMUzC3Dl/qvX/u537uwN133z3+2Mc+dnEwGKhXv/rVZ97xjneM2+32S4kLPg8vXwvPf8VL667t48IxQkVoHSBm+bPNPmMk79Fj3hzWnAk8v1mHvG9f8B8vZQgJrqNA1NjM4EWGiR3i4gT3+w+RHl7BTh2RkNzaV3w4gM+kI35gMKPWKQqHnJUUWxOMVdhmTVoGiBJINUG3iRGWoK1Q+wNYUAhVESdN4mYHk+dYLfBZSWOxi3QzdBAiL18ieNNpivsfhfGU8Uf+FOFGtI6sE632UUcTfCzxyz2CBwZIwHeWsL0pVexQkwluZKkKSfC5bcSFR0k6farxCH2kRxGUSC8oy4wwXUKWhukXvkhejAjOnKC4PMGtOIq9LXTRRAUR8fphxEaFbkF84AA2j6hSEJM99NFDyIPHCFcXqc9dwXcbVLs5Ub9J9bktysUaqS7TXDCYbJ9qkKPXBC6SRF1B4AKkBC09phLEzmHHjlxYQmMZVTPsQoQNPMJYxHCGbfaYVFNU3KAYwrg2hCZjYf0w4qlHSHJFdmmGrzxhy+JnFVVdgrbEsxhfVIy6AjvICKUmciWq28B5TyxTfKjxqaMyhqAhMCWoyhE1Cqjn+WilFoi4Qrjn3kbPdyw8fyk7Pzkhbhysnad15Nm8Pc8m8PYe95eCXJ4Z+n+zyPWvwnd+53fO7rnnngP/6l/9q9Vz586FzWbTv/KVr8w/97nPtd/1rnetfepTn2r2+337Utf4zGc+0/7lX/7lA6dPnz77+te//uayLMVTTz31EsKEL4yXtYXnvCfQmni1i7pqsF2BN6CE4m5t+COj+bHQ8EaR8t2HFvnZjQnU8wnhVwvB9wYFogaVNqh3LHIxIihn2MhiAoUZThALFr0Vo1b6yGXD5EKFWpGEtke+s4N3giCCKrToDYs43sHvGSIJmavRsaKxEOLqADEeoxpN5Cyj2plihEBfvI56dR8/cZgwQG5v0fju76J8eosg89hGjTt9FLEH2fY+PHqV5kofg6A2FfWBEFFYop0LKBbxTZBFn+TWPrNzT1EuNHAnjlBPryDGnmp3B1QIiwnBwRAubiKPH6EajIgKS/3QE3NB014D0W5h1JggOEDxZ19GLy5gx5709jbVoQge3CJPE4InM6KjXax26FesUP3FAGFTrBC4BEKgbsXIPCcQkrwfIzc8Qu9jrveQAjwKJ8C6Aj1NaC6lCCdx3qEqSTYb0W53sViqwhOlHru2gLhmsMWERPSRkaZ84jziSAs7nGKUICtDwl6DaOTQwx180keYiuBYA3dlb66QMq0IL5fUbY/rNbD1CFeGSA1CK8hAN+38+1pfJLvv+nwArrTBBPMP+J9kZ3zz8VKW2H9pvPOd79x7wxveMPvkJz/Zefvb337yAx/4wKV3vOMdk/vvv//RT3ziE52f//mfX/v0pz89/qVf+qUNpZR/RhE6z/NnZxLvPR//+MefuuOOO8pvpC8vXwtPekxlWYo7hFbhj2h8XWKtpGq2+JcLCb/eKPjHUYmvJ9hsxJvMiODGTDmxno0kJQgFdlwQJRVMp1QEuNqCsFBY0IoqzKgfucb44gzlLeH1nGoyxOQevZIimormQg/Rk7hxhktSsrpADgr8KEMPa6pphlUhbuaxIsL12oTGwGtWqfcls1JRJuBsyuz+LRrGoU6uEfY62G1DeusC1dM7mHZM2QipKovIaoTt4a2kzGPcZIZrJLi4xpzbhElK+cgOKnSIIEQ3NJFoIBJJsJfjCo0tQoyxRFVA5UKyrKBwHhs6ZpvnUA8L5PY1ok6DancX2W0zevgcHos61EJF0VzaaLxN9sBjlA8VyFOrBCsKEaQ0lg4gszZ+kGGGEpQnnVUEMsBVDewKzMoMQoEVgkang9M1+ajEeYOxkmp3RNJOqSPIyxllK8A2I+JxgQshTdrU3Qx3uaIKO1SPT+jcfIj4wCpRC9T5TVw1QywvIbVF9QLKS1PaRYQfZmDAHemjrMCfXCGctEjDirhjiEYGuZigqIluXoOnBjySz4W+j992G2ImUC9gN7xkEMkNx4R3bm7J8RJBys84MJ4TaPw31b7rdDp2Nps9j1MeffTR8MyZM+V73/ve7be85S3DBx54ILl48WLQarXcu9/97r2f+qmf2nzggQdSgPX19erzn/98CvDRj370mZw33H333eN77rln5Rky/PznP598PX182RKe9xAoxXY2JAsUHEhRqkId6RBs7dPd2+Nm5/Ep2GaE6oS8LY74obB+9hp1XlL5EBrzGNIwDUksiDLEtxv4MEIMcpLEkkfQ6kt8Zaiqinq/JuwoxG4NE8FuUUIQk2UVCIfzNTaVmLiFDRTWGJqLXRQWJSzhXk60sDYnjJadJ5lJEpwx0FCUokldlLDUJuiFZH9xDjfJwFukqVFVhdACyzbtjiZ41UnCZoCLAsKTB1DHFuj/rTNECxHmwghx1YIPsB2FUAK5HiMvDpHZhGpriMwzlLWk3qMWAvz1KY28D3e1cCOPiApkoCiHA1QcYauSmbNUJkOlBcWkJl47jO4I9N4A32ojshlua5s8MuhJTVKn2EZAWQOvjWi0BW48RB1po5QgUJrpzhiNJj3QwtqQaiWmPnAcc2aFIpsR+pDQVPiyppoYbKQpmKBsTFFkJKagfaBH9egQf98OUZmD1hif4oclztXkOiAkII9qhANf19jBHlZL1J8/hYtmeB/gVYL3jmo0ZaoCeOwSWMHT+9toqbjlNbfjOhIvvjY9vP/asbq6al/1qldNT548ectP/uRPPivq++EPf3jh1KlTt5w+ffrsY489lvz4j//47n333ZfceeedZ06fPn32F37hFw6+733v2wB43/ved/0973nP4VtvvfWMUurZOeD973//dWOMOH369NkTJ07c8t73vvevTgPxAhD+ZRpD9MpXvtK/52d+hv/n136Nd73i+/iuzim8krgzKygxo35kgJRqnupOa7AGnxXQbfNUViO84FgISLAagkYbKku9sUEYNfAmQQUZNgqo8gnB4jJuf4qb5QgV4X2JSGNMqghrjatL4gNtpk9tk3TazLb2SVa7lEVFiCcrLG2tmRiD68YE23skd5+l/uMNinaMCipEVCIWjuHLEWq6SVn0SGJL9MbbqP7oXjh1lOml6wRSEmaOermP3B3SOL1GNi1AGJSpcGOD73bwm1PkgsGurRB9dhv5qj7FdjYnXVfidAs9GDJsWFIbEh9pY8/tUQkwCymqHGAJEUkbbxuYeowthsSlRESCIGwx3ZvQOrWCGeSIloQ8x+/vUR05TnR5mzxoYqcT4maMOtDBPXEF5wvGBcRVhV+MEENB4xVHsI9dgcyBttRLCzSuTWHRI4mpxiVxr0WdFRTTmsZCSjUaY51AWIc83cVvOkw+JLz9NNW5a0TtEBXEmA5UFwp0WhPbimJxGX9tgI4iTJUT2ghjp6i0BVWND+bJr7UFQo3rprhZgVQauTvif3/kdxmXJb/2kX/zAiNT/KWnr3oJvLAl97z78IZF97zcFzfwxje+kfvvv/8vVT744IMX77jjjsGL3TPfKnjwwQcX77jjjqMvVPeytfAA4mRu1S7fdASVSGQYEJy/xhe+NOKdsyb/tg5RyhN4g3IeltuIouCY9pzsaoKyQjlLVFjEtRFma4egkVAZi7IldRRhpwWaFJVJjKyQ/YDw9jWCOEFNDDKrqIopMtCUT4zI0xBT1MhGSLUzIW4nmMzh1hVWeeKGJ60tLC4wfOwqRexx4230nSfw45A620fNCtz6Os2VDnkeUH75KWgtoa7s0+hHxHUDFwii7T2ENexO95DWEYwNw6sz/FKK3dxDLioEFu6dUN/myc9vYMdTZOnwGzOqrUtMfUja7RMMM/JaU545SHhoCWFDTA4NlkkXO0RlRWM4o7ExRPcaBCcPUdkK326g8ykkgmrXILIu4vDtpLsOfXQdvz3GUeHIMXmJ0yFZsoAUNOGvvgAAIABJREFUKa2bV2mu30rjtWvMHruKLjT1UYdXS6SNGcOuYLKfM5nU1NozznIGVUnaT9gRBdNQ4VoBJAlsjUGVCCeQ1wZoUyM3C9z+DsXeJrEAOc6wtSZ6aocgaqCLmjhzeFugGyE2dnjcfIGqoTIllAZ3eRcZC+RwhF1oc260yU2drysq4mvGt89UfHPwsia8pcV5Xovtxy7iA4MrMwrg/T7hcSQfKgLGLqTKSqwAUVsCoQk9iFkNWmHCBDs26EAgMHgrCGJBsRqiqoq45YmoqfIS7SFZ6FNc3yXPJlRlhq/BC0+2O4DVFgcXO0zFmDRNmAmH259RhzWtkaCwHojIZ5aZgCjzqAWF6jdwX7wXUTlUZtGrPfSVffJiSOMQ6NVVZE8wlQa/4xhvXkQstXGdNi7QJFaiVjoYFbJ62xoMx6TtFlzPEDseebOF/QkutxAYxLDE+xLVS4lWClzmKFca+Gs7WBNQK42ejTnwpjdht3KEHFMwAV3gyxbpbevw+HnUxNObFGQDS241+Vji3WXc/hWK0S7jy1fQakpSzc8S64sjrJOk0tNab+MHBfXTl6i/tA1hzCQomWUhCz94nGJY0jYxadwmjKG5vojbNfQrR2Fm6KKi03TEVYxZEOjlA/h2m6jbBTTOecqGR7e7tH0LXIasGtQGiqbAVDW2G1EqAUphK42VHteQlB4oMkIzDzZG18hz+7hYcW3rEh44c/DwC+fhfg5TCSHmKsVfA74ieyduPF5cAs9/jYHO38bz8bImvMOHDwNQaQsadABRLfmZpOJ2aXibqmnKGh0HyNoghjNcKMCWuLLCLHaQriJYTKnvXEQsd5A+xIgAvWuRnQTX///Ze/Nn29Kzvu/zTmvew5nPHfr2vT13q7slUKMBEJIAMbgAYyqVIpVUMIHYxFWxC1IOhvwDuJxyYuNKnISkHGNHlcQYMzjMBgHWLHVLSK2mp3v7jmfe85reKT+cK3VL6pZaAgMt+ntq1d77Xe9aZ+1znnre4Xme73dE1yWIKkEvJMvJgjgFNRojtwpsFmnThOGgQNQTjm+eMFRr1JMZuVDYZkWaaNqDBoEn+g5Nx7qKuCjwC4/uJXop4YymJ1Cvlvje42uJXVriR55Gnd8lWxtAWDG6dA922YMKZFsjxCClf3qPMGnQpSZZaLo1hVyPOCWobiXIvZTiHZdIfMCNHf3siFBsoC7ejVodoVHkKSRmhj6T008aZnvPMRvP6Z7zOCdpo6GX1zn8yHM0eoj1C7yAvFCUdsE4b2h1QR89qiioHr4bdz4nygHFvTuIUJM8OqDtLWHVMu9bkiQnFClxLYW1giqrOPg3j5Pbs6fC4WVDKEv6+RLqgDMGU0uG2RrT6xZWS0QfiNd6etNQ09N3x+jSo0Yp4tDCzGG1RO6AWfZIGxFEgo3ITBPbFtfPiIyIrsLEHmUGdHlKM0oReUU4WyCsZnJbA3k9P/+lTBMRT8lGw2cJLv5sEEIIf6knhre//yvGzL+swxNC/B9CiAMhxKde0vYPhBBPCSE+KYT4RSHE+Hb7RSFEI4R44vbxT19yzZuFEH8khHhWCPGPbwv2IIRYF0L8lhDimduva1/8FF8GkxX4gujAqshbdeRn9ZKfLjqEE0ilEQGCMsQzW/DABcLmGvH6IboX+BrkUweoGVgtMamGUcTFHLU2JCkhyxIWZUd2KUUZGJ3ZQIqSbKGpAjTnzlKf20VMFrSzjuKhdeKgxAzHaJHhZE+S5aBz5HhAPJkhOgeTBbLpkON1sBa7svQ2ZTUoEHlLHA8I95zHPv40cTqnS3KgQ7YSv54Q25o0LfChw4cplCk+q+muLAmuIWpFszrBbVSE37hMGAwxy0j+9W+EEBBHC3To6OYH0JYkNwSrjz5Lupbiri5R3tDqDNmG02h4sYG+6wx5tY648w6mmaEZauxwwCx2ZEGRVAl+NcN+8DNIUaKkZPnkszCqqH//iMKAmdesJRl2uSKZO/LjjrR2mNkCeeLJv+dBtIqo4xSXDGEJ2ZqnHyYsrWW5mlBuDZjtlJQmQTKnPGgZK0127i5inRCuzLGFo+080XuSh+8lrilUniKDw89WSB+gzCnOnydzLXbY0mmDLwuiFKRRkvgWDhuE7nl+cRqhfeBvveNLmmTtHL6uEVn2FZvznwCfOjw8HP1ldXq3+fBGwKdeqc+rycP7Z8A/Af75S9p+C/ipGKMTQvx94KeAn7x97rkY45te5j7/M/BfAh/iVOjnuzgV6/l7wO/EGH9GCPH3bn/+yZe5/vPxkjh+e1d5qhJWGow9rbYQ4wp/MEUkkV6luFGOXi5xz+7DWKEnDbYs+KhXmKh40EXU/WP0zTmi6QlSgZjQHRQIGQjzY6p0jf5Zi9SOdjrH0mPGGmkd2cxj6w45XqNdLegOoVSS2cJCDAw212naniYIdFOTe4tYTkgGA1xaEF2LP2pJyhy1bJED6KeRgjm+XsClC6ijPYTvsd0SWw1JD+bMhaM4nGDKCms7Tn7nGczFTRLhMVfWMdsWWxu6/jpydwcVLG2Vog9OEDsVzY0pfpWSbhR0wpFd3ISn9ii+/y1MfvVJxKqn2op4lxLziK4HpHNPbGdIERi2jmZqyOp9qk7j712Hx6fId92J3Fth9xaw7dHzHOsdGo0YFHgtaJYNXgmSGuKowMw6msyQ35Ww994/YFiMSENPf+06XaUoqpI470mURAhDMIo0A/3wLm5fEJ68QvId95HdfSeTXz9A64v0n7pCdXGDfn9F/dGnkeUGYX+PUGaYkBH7FoYZ2khc8KQnHWrlYXqALhOwYHONXFp0kfLc8oRSJayX5zmN7X/WHuXp9CEGpFC0iyUHy5qLaUI8TTH+YjLjzzPoV++jxCv0dc796N7e3s/t7e39pWc8fqUOX9bhxRh/Xwhx8QvafvMlHz8I/Edf6h63dWuHMcYP3v78z4Hv59Th/VVOFdEA/k/g93g1Dg+oqoo0TXnu8h6849thekgwEqEMNB3uvjsR7Qq6HrNswOTQecQyMguS71u+mMrzM6HhsasrpFYIkSHKiNAFQnbYrYJEC+osxz43ZW2tZDWfk3Qg2gBbCfPJMWmqqd2SuL2Jfn5CPFdRDnP8smcJzJoFJZokOGbDIePxEHdwiKgVK2tRQuCEJwqDFCNUFWi7iLi/RB82uGUkHRZUBBYlJLMU6wrICuL0CNUJkAn2qQPW/7O30dx8grC1g5sfMxhfol/NCG2K8D1BKFSWk2fA28+zet81MuFgeUJ24QLTf/c05fltvDzAvTAlphkxKGKV0xRLsuuG2HvspmJw3yb1B4AtQzxckpxJsAcrnINqU9HveRKb4s4auqObmM6QyRyVJKwqgVlr4WRBG3ockXi8ohiNCNlpCEFrjV51yHFCslkQ95YIqVg7v06bCeo/vEpUEoRg8r6nUX94mWRyk1D2ZPeO6S7PQCXI0hJEh0sL0lwhhyl2v0csJX3sCK4mNhafJaiQ4hYtcSvFhB7937wL88IBxx+bcnFtByX6U/WeL7Z2ILKxvUueL3AR1J/RfOs2rfnLMv2+jlP8aYwC/wWfL6t4SQjxuBDifUKIz877zwHXX9Ln+u02gJ3b2rQAe8DOK/0iIcTfEEJ8VAjx0aPjI4wxnDt3joPFEVHv31ZEdtD3iBDRxyfIkxpR9ygU716VfKsb0nuHSD//q3+MlExEgnHYYInB0s08sne0tUZcmSOu14zPjFjNLUpIKDSus/iDltz3UHekOqGcLVF3V6Ai+A7pFNnKsTUaUqwNiVVJVWiGZc6qLMEkZLtjhhsbDNSQcgCCGeH5jkQ61FP7iH6GSQJJ19FPLf2BpRc12bvOIfcPMBsC6TXZhTHpTkVoWvzOiP7yCbJpsdMZaqkRmxE/TFBdjbw6pxGR7kaHEi1RatxwDX+rxZy0iLUUv+xwY02QAZol7Fakh+C3QQwdGEP3wT3MlkH0S+zBnG46JVw9Jps0rD41x/uGMOtQIpJUA7JG4ZWjnTQoN0W/9SIhS5ATzZYa4SYFydEUedRjVEQah04c9rhF7y+QRYDeMvnj68QrC6r7doAR8psukp2pSN+wgzlzN+3uGFDgDcEGYp8jnUSPoJ+1dPUKWaaIrKfpLH4jIExF7CPe9ZjNDBkjUUjsz3+Soz/4DHvNgnObGwSvCQFiFAjkKQsxpwGHCETXkecJWrw4H/vCyKuILx4v2vhtmUa+OH3lq2JQfh2fhz+RwxNC/HeAA/7l7aZbwIUY49cBPwH8X0KI4au9322Jx1f8n8YY/9cY42Mxxsc2N04jtJfuusTzkwNsYyGTkBU45YmJQjQdcpChhKFJzOfu80dJxjCRfKt2GKCMke9Yl9hEIXWKLDKsT/nZOvKLekiFxkpDOijxB0sSamQv6WWGH6W43BOTIfHSGdy4Yh46ep/iMkNvI+VOCmGFTcSpRGAfiKliNp2xNRoyHA3xE4kPgigbukVH8JGyDNiZInnsPKFNaKRirix263SjX+QpXN5Drg9QyTqhGmJLTX/xLKv3P4u9ekKQBrW7QUxGRBUwE4k+afDlGM6PESpFHS6ReQ5DQRykqPmE9I4R9gPXCJtbp3Wu77kbRiViqmBhUV7gWkc8nOEqRz+fIBpPKIYI6zAb69SZJRm0aJvgH1bYWxHdW1bLJWFm6YVj4HLqX3sGawrMlkY5QTOucW1CPcpwdUDOO+oo6QtLciEn1hnJeopRFdEmLJY9wzsNw0ubjL/tUUZvuZ/Rj76NO7732xg8eDc685BorGsJ8zn6UBASg5KGYC3BC9LYkIWHkDKiXcSuJejQE1uJ1wlRnlBXp/tx2/ka2MUpiSuB8NW6oZeUY0j5uhLjnwW+6j+xEOKvA98D/Ke3HRUxxi7GeHz7/ceA54D7gBvAS8Na52+3AezfXvJ+dun7smpDX/wAQB949L4HALh85QA5rhAnS9JeIbpIgkN2PapNyI3h7+olf/2uAY8Zg8szfnrD8XObgW/eMZzzHitWiP0lznU85yK/5hX/6LpjkTlkAU45TN8Sy4peCbL2mNxL+tEGUvTky47QBMbViGga2qMFnRP4dz7E5k9+D9m+JZaa4Baokwa1sUYrBNEpslWN3RjQ5hVZWTIwnj7TuDMp/lDCXZJMl2gv6KYdg80x9vl9uhONxTC7foWwLmg/8gn0p6+hAujNCr1eou885XiTfU/vPZkDvTUAE1HVBk50iHqCH7S46wf4a4rV0pM8eAYfHWKa0f3uH2GnDm074riAekVyvSfdHCFPEvLlkBCAtEPdt0s3PUbOK0yyhWo6qqYk8x2FKKBM0YuGkQg0rcduD8inx6QCjkY158/ehd7gttZEjT+3RnHHDqZTxFaTFy2L508wWymsB+TxjPbBezh831XqD13BzRpCULCasbx8hepH30UcabKiwu969Po6lTzdcwtRou8d49H4vU+SfOd9JO96M6ZtYKdC+4YoOkSX8fwLVwC44+sfwaclMX6WoTh8yby5+AXHy/aJgvgyS+SvWhvjdbwsviryACHEdwH/LfDOGGP9kvYt4CTG6IUQdwH3As/HGE+EEHMhxNs4DVr858DP3r7sl4EfAn7m9usvveoHSSVn77wEwNXJLe67uYPIDM46pD+tWSVR+HBMONR8f1biJgcwHCCWNZPxGj/0XAM4PiAT/nUFIm8QjeCecxXDWc8CSblo6ZwkPZrjUkVctuTLOa5ax7maDZvSN9CFmq2tnJPnjtFSIY2mTDSLX/wgbZpTywwjW7pkiB5uY7tD5KKjTmrqCKqeYWYCWSjarsdcuoC8eUxdHyPDCi5eIF4v0NrSHV1DXjiHLAXhyvPkG1v0bcvou97O6mMHiC2Ff+YqYVri9gpEleKlIi4s01Ekn+0j5Dn8/rNgJarYoZu3xPkMeQ+E/UPqvRPMoCKeaVAvBLJzGrd0uDTFThPirkFfq9EiULtAcmGN4uSYxVNz0tZh2ccNI3bV4G5NiYWjHA/g6orlTkZcabS35Ict/twu3JqS3TFi9vgJUQXUhQpfDGHWo7prKHJEE2g7g6w0UhjsCzNoBPbqISKxHKsl5YcmdHVBFuck959h+Vsfwi0bkjMZrEb0cR+xEJgq4KoccXmC2NQwK2h/+ePEN9yB9JHgA9ZIVG2Jac5zN093Xu55y6OnspSv4zWHV5OW8l7gA8D9QojrQogf4TRqOwB+6wvST74F+KQQ4gngXwE/FmM8uX3ubwE/BzzL6czvs/t+PwO8RwjxDPDttz9/eUQgSO556B6GoyF/MHmeaDTYgPan6sh91xNxuGpI8t130mpLcBHTWESS8P5r88/dbhEi7kwGYYgUlvSo5pfOtvxqviIJ8ZQrDom6sI5ZeVS2iTQFRVZRz5e4kcElivlshjEZvrUYJL5v6LyElcP3C+xRx8ZwQHJwSColarBG1BnDUUqpI9VGfipYXRnsEzdALBG2RNQpvo5ovcQZQ9zZJc/nxOsr/IW7Ye8Y5h55YmFxTG0D4bE343uHTDq8W7JopoRxS6YN6lvehJh2ZGubVPMEe/8I6SJ+O0fefQmjRljArZYkywK1kzE9kQS/JN66iRoFYuco334//ZZAP7RNMp8iUg/tinQ7o8oT8vmAfDwmUymZKHC2Rw4tHFjiPSPUWooTPeLmMV3XkH5mhl5fUO6cB9fCZIpKJbNsiKennk6wlUKoIcvFHKs73NiRPH2VdjJj8GzG6rJGzg5gIXDvv0Zve/ThDOG3UefWURfvRJzbohWQjkt8kKipJVjQZYp9ak7fSfz1FtFHRMyI2nLsO9aziq07zkNlXskyX8dfYLyaKO1/8jLN//sr9P0F4Bde4dxHeRn5tNtL4G/7cs/xshABbSXf8HVv5d/93m/T65x8KxKOG+J0hszGCOfJ71lD/cYtkke38c/v03cN0lkeTlM2rWISBY+pSK4D0XWo8+cI144INpBvGPzRCr0+QtQt/mABmcatGvQYRNNAEIjlgiTJoVO4EnAJcpwSakc+7dBVxZrsaLIBHk751lRKv+pJ+gDjId1iRpkEVnUHqUfuDpGdo/qWEvuL1/CXFfH8CLu/oFo0NGlBupuR3nGW1bVDROLpaOHCmDLL6N/3HKM37VJf2YdBzvp9l7DLOf7EIj7zAmbu8G2kP9PjPnyZ/Hvvw+95kqpFPlARTwzJ7jbdSYf6xJOM3n4n9Sc7hEgRfc/owp3Yp59FTleI7ib13CFTQ3m2QK5avBV401I0Dnkxwb6wgFGCXq3TZw36iROkm6OSjM4uCedHhBMPrmAxP0B1Eitq3DSQW0u2tYGdzrG9RMYlwkn6psNoRb+WkPYeYRuMihg9Ju43yPfcjbl8CzfIiCOP/fQ+IQFdd6RJBsseUQ1R8xkxBdF48rTFCY/SCjssCbM52mVcPtljOxvgm/o0FvuSPbjPBitend2+mpNffLe/lMl1f8p4zW+TKiN49MEHiESe3H+SOO+gt5DmJG2DcoL5h67wwybnHY8vOZCBkClEUXGvt/zCYMlvbzn+wTjgJ0twDjuzRLdCaIG7eVqCFtol5BplBXpTIs9UMPOIwYDMaFRZUK9q5HaO7EE4Tb2cMlqvUFspLQ2NHDFYT1lOW3QiUX1LaiCmCb7uyUY506MFOjOo1BBeOCDMp8x/d4J8zyPIsSLIlDQFO95A7BiWh0csH/84skjR927g/3iP7E2XGD94J6FocYsUubGG1CNWB1Ps0mP7DlMaxNoQkXhCUyKLdcKvXcf80TXcbx7h3vcMLBrqyQE+zODuNZpbHfKsRg1GJA/tYodLOgfx3nPQQsw8aTFANWBtiijArlawnrJ64RiGFe2qo523aFGTbab44RDTO7oiR16rWcyn9CcN5qinb1ZUg4RyTSNKTT2d0UaFUYLw1h1cN4CzI9wgJ+51DGtNP2pJ1wq67oR4XtK+/3mUUYTdDH90iK4ksvPEQYpzDc2aRuU9YS1Bzz1OaAKSZFjhd0enEfusYNKumLRLLo63/7xN/nX8CfCad3heRN78jW8F4INPfwYxXyB8iqoUREuQkg9XWzzZno6PPzatEK2nfQEkA2I6wowzfAioY49fT1HTKXJ9HREksajI184hJ1A7RYOlnQXaWxOa6HCbJcu1ASudU7z1PsJiii0gqp5EjZhMHSBAa4RyuKknyWq6VYsNlrZdoAvLol+xvDXj0E0JV64wv76HSRxSQ+wX2D++SrAr1HRO7zPcyTHiEwckNiG7uYbNGsIzh8jdEfWvfIr9932C4u5ztGqB9Apxf3laKbGaUG2fxT9b05xcJnvXm6k2NcnFIZ1qiPcO6M93tDs5YdEgb80Jy46+VyhWmDCgWc7haQHPTBB3aOTHe+xqhfCKRvb0qSXmjtqB2MgJvkfIFKEd2dqIdFTjhwP8yTFKWfr1io1BiS4TkqrCjQfYS0NQkmgd9rBGI9F5iXEdnXHom5FoD+Hhi1hVI7QiVOuYvYA7mKFWKRzUuMTRX1lhrjb01xbUNxZE4XCtIyhNdqOmaz1+3uKqcMpUHQSeGnO4Ij+ukSvLjWYKwL3nznzO9hQRBacpSq9U+PoyiCHSdT3OxduBii8Ma7y+P/gfCq95h0eA0WjIo488ykcWV/CFBuehbQijEaFSbJvV55YDChAhUj1QYJMZoV7hrYcmEAYp+JZuQyDeMCLcv4asG45O9uk2c5QL5MMU4xw6KRC5gQ8fUCxXVMxp/ugmPm5gqDBRnjJ32EjqUlSn6KXH9RCcwmUOdwL0itUyYKQkE4K13V30pTvJtncIZXUqTTg0qKOAExp374jcrhj/4GPIh7aIhcLe3aLbgt6ldCcN6daQ8vyQxQsT5MkStCd+ekrnGgbZDl2zIKocTcH0Dx+nsQ3uyg3cqEKXa2hhUK5AX1zDLWvStRRRgZ0GynevoYTFbjq63Y7u2oKY1XjX4gIE6xCX52RAFntUB21IMXeNiVLg+zmtMmRzRxinRCUQdYOfLJEx0ntLlUba4wWmKJguNL3U+GVHO1/R2RVZmiJuHmNChN/4NGFuMIUgeEkzNvhEoHNBJyIxH+HpcVs5fqQZP7yLyxO0PK139baBxiJ7i7IR0Vh6JZA2xRuNum8DpRI+c3LKcvzID37/i7b32VySr8DZnV4m0Up9Lucu/AVjS/5axmvX4X12/+T2gPjud72L3lp+99qnUJsS8baHEW/ZRi08Dy4i/2Jo+bt5zf+QN4gQ6JoFuldIfZob50eGkBfEvCL3cPDBCd/28Zb3hJynypxsBWZnAHiszhCJJIsRziooNMnOHSTDHLUhUb09dWixgVVLH3v0uqEcGZzpkUlKUVUkgxJTZmRGkCUJfMMlRNMigiBXnkRYiA0pEr+eki408vkJ81izeO8HaW4tqbMatz8nBoMJFWnX4MYpzeUFHoe7uU+/MSQEj8g0nQ+ny9iiIZQpIkqkSFEP3k2yvoZaHcC4wBY94YUTpEnpnz5GqyEmE8x+5TOonQ186MlP1siWDi/miCwjDY6BkWS5xPY1yYZBCo3YzGmevIVdrBjICpqIlT16KlErQV86/AM7eKnIigr2W6o64HPNaK+ljYFoFPlGTnH+DOFkgV5LoMyQ6wlmK0dmBbE7xCQJ4//6u3GhJ8mHFEdLvJB0IaK8oLu+QF0+wgXHqm9ARfIsEp3Eb6W46JGjId3GGjLL4FYKleGDR8+wnQ/Y2TmLkgIlTxOOQbx8wcWXQIwRpRRKnRqw/JICQC/O+j4n5fg6vmq8dh3eF+Dd7343O9u7vPeTH2UyHBE/eRn5ewfgJclDlzivA9/rPRdkxAGybYhZQsgL2iRFjQqYLJE+4KXh0/pFI/yNuUJgkX1PNBncV2A3BH3bERNNcI7u+gl60sBJjVvzmDAkEYZsc4yQGts6YhiSjwYErZFTjwkLQu+IKqGbtbQfvkWRFkTR4aeeflgSBwOWsUVZTychTQTpHPpFi2hqtNWojR2qlUIIjRoV5KMBXaIxpUDslITfv4JKGiQ5VgTiSUMQDqaezIOcLemfeBwxndJt7yL3QZk1UlVhMosrFP31q8jJEhEHqHqKOKxZro6JZzagHGOSjA5Nu6zphiUhTYkhJwqBvjZBbW8QyiH9IGOwkZOmJVFbwk5OKjZhb4rc2sCuQcgldQiUMoP1ERtCkg1yumlDDI7GO+q9JUhP6y3p1QZ3cATLGn8y4+Tnn6KZO/xdCc2ZirCVkz10FtV6vAjIe7YRMWMcUlyR4YcDYiWI+x1JEuinS9JrJ8SrE3y9Ym9+zEm34lsuPowq/kzZT17HnzJe+w7v9kxPacXf+Zs/RuM7fvk3fxW1MgRWRBOxL7yAcpbEKITRJGl+ug/WBJhMUKFFzmqSzqKWlu6w57FVw3kdEES+IS7p79/AWgMnPdmnF6RLhx7lxANN3A+3tUoVuvJkfYpoPY311A2IKqJ0gmx6VtMaEXvoa1j1hM0xrRP0OxmZirSrKbNZR58L8t4SosMc92AMMlgWk4CpBNkgx6yNicMR3tYcNFex9oTuZktIBGnsoDNw3EK2wqcDZJXShyUjYykfeBS5C+LB87R2DakHiKZFf/ga9fQ6+UAyw+F9wciVyLQg7GxhsgR/7yVwC6o7duiPlvhFjxwXDErDVAlSlWKjpukb1KpDxYxsBzLj6A8bmnBadu/GGap1iNigV5F+05OmazRekAwEfd3j70g5sSt0rlARRKFIe0nxxjMkjKlUiS0kdmeD6DrsfEl65TL5WQHPzEiuzSj2lrgP38Alkb4P9A1kRFzp0ULhr54ggzstD7OS3Fu86YnrwEjzW88+AcC7fviv4dAvLmUln1tlfCn97FcU4H4ZfG4H7/WtvP8geO07vJfgkcfexKMPP8Kv33ic5+wtKFIwCplE8AKr46lwctNgSnmak5NnuNZiZ5b/PhnyznnGD8Scq2XKe0v415fgu7c12WxBJmqkADvQuNoT8wKdT+D8AGkSzNmCME0+rv8fAAAgAElEQVRobYffiKRR0wkHVtEbiVpC/p2PkL39LurhiFWWom3ADAM7PiNmimYFo8oQlj1Np+hutfg8wZ7MMYmgwCNHA7pVR3N8QnL9BN0tKM4mqMzhz5b4J15AJJHEWbIzZzHDTYxKCfsLhvfexXR9wPLffxxx9xmm12+g5AlRSISW2OYY87YzTI6mMAhYZanrfaRrcbdu0h3dpH//46yEYPXsAWK2JKkM7uaCvhYMj1oS31PGgJkokvGQOAjIfY+b95hRigwWrCPTBt97TFSEVrLxnCCdd8jgicMR3f4Ba7MVxfqQvrNI3REuL5B1oL9+gBI1bjqj3RmR7C8R5+6lGG0S4hx5QyBmLWEtpbcObQKJNORDiaolzY7CBFhaR2cktCmM1sAAUpBJdUrx3tZ85Ph57hpscuHio6jV6/l3r2V8TTk8guDv/MTfpixL/uGH/i2tEtA1WJUQNhKUEGAkGEW3lPixBqUovSHbyPil1emw20bBb7eSXvYUURJnHdEXsLmO1Ra5lmKiI4TA+D9+J9XDm6ihoRY9aEOqPCqV9FVK2QUYJYiLY1abAfe+Z4k3JEpY8mFGLA15E5kmHX1rMWOFHGaUOqE0gsF2hl9ZgtHEQtF3FmcDeYykIiHoIemjZ2gmNUK0yOWMbmnBJdTaspgucLrHTk+oHjhHfzRFrwLq3jMQl+Q3a+rGgXZ0ooDBHZiPJJiNMclWhgqRNt8mnL8IwwH9I3ehU01JhfceRjlxFJEm0Kslq7xg0Xd0rSNdL5jIBW2n6L/uHPLiGRbzFdk4JwZBvwogNSHRqKQmVif44xMEmnM//M0Mdje4rCLz/Rl2YRFlQdQp3UAjsoQuqyjSnGJvTthMcc0hblSg5RixHkHnxIOG0N+eLkmJrkG6BrMMzL3ESMgSdUosNJkhRkM6Y/AyQ+icx2dXWdiWd9zzRsRagxz/iVQCX8efM762HJ4SbG/u8Ld/4sc5Xs35+Sd+h5hkmEWNnDuCC6ejthSYrEcEj69Smj4QZy0/mb+4PzNIQFcVySilm+ek33k3cZEgevBHLfLrHuDMOx5A3q+xTzf0JGg0cthiqyEmaoQ4FbaJ16ZweUrWecKqxT93A2EhS3LMrIE+UtiA62vkAtpbK8gkrZOsFj3DYY72gcVNB5cqGCakSYL5+gcI/TGrqx25TIl9gusNPi+pm5pyt0Q4RXrPBbyDeO0IM1mgN9cQTy7hWYt48E707pCmiZj0NMgS9QpzcJWkV4hlgRIWeesY2fXkNybIoFlNJ8i1AdhI0UnEzGMGY0oEZvO0ZrZe1VQTizSO8InrrF44YrBVcrA4YfPN97D+Q99Kn67onIc3nce2JWxvErXn+j/6feyqo6odo7zCx0gochYxMkwC4swIZhNs74lJgUZhCkWctoSRwPcCNwoQW/RagoiSeNJh+x6RetqmQ5uMAoVHQG6Q2wLfWUhTpE5xMvKB688B8J0//UPEWhLaV6Bs/wpEKGJ8mYNXv4J9faX71eNry+Hdxjc+8g2885vfyfuuPskz6RQ2RiDDqWCy87xnmvGtk5wPTyPZHZvoNiJTyXcnnn+SNfx45fnBPCDWEuRsQX5PSvvLz2CzKWGYIcsUtXfE4e98mMXPPoGYdOjJMakw2Ezg947pQo+caGJniYUh8Q5bOtS2hIcLklGBUz16WtMXAVd7qnKEPVuhB4YyTRE6IpzHtwLlBdl6R2zBHUj6JBCev0whE8p5R+JqovXIlaM8ihS+pzUGU2SYpiesWprC0Ccl3eIEm/TEbzqPu7XELJfIoWb6/IxQSezdgiDHtJMZIp6QOoFbBvxmRhgViLxguDuguXFMvX9MfdzilcUeL6lKiXgho3nDGLdeYYoRUQS6zjGOEI6XaDVn9vycgz/8CHpjk6zIqG+eILpIR48koC6dxW5XyChwpkMJQ3urJlMdfTlAHTliMaTfHRDXetxiSZ0bTNdCjMhMka78aRWMA9e1xGEgenPKcG0NadMQrEUDKlqSZYIY5Ji+xc9n+D7w8f1nedPmRUa++PM269fxp4CvSYcXE/ibP/Y3kELwy+//vdOaTKGhj6yMpr09FH9CGOITV9ADSThTIHTkgWHBX0sdufCwb4l7nnYyxWURNtZIjiTFG8/TmIAyKYtlw3LU4qUhLjvkRGNkhvQlertAVjlERWhT0rbAzFP0pINZR7je4MYlVTuAQUpS5gwnLVZLahvxsiPg8ONAbztMk5JPGoqkQ5iCpuvBtfSzFf347OmyTwnqcok4GqH25jDQrPZukqmUPi/Iz4xIyzU4b+l/5WkK09HNHUmqGI0z/OFNxJM1Ya/GqXVc2KUzS/pyTkw07M3Id3bhoGP46J2YPGW+mKITTWEkxycrmtLSfvoGo5MlFpAuMspzfJojhinb9jxmMiH92E38U7DxY2/l3BsfonWC/LhHlLuYZ45IjjtcYREixWpPsTNGTiRRKMILc3Sq8a7H2Qy1ey9xaunvHMBgCHJAMs6x585DIkkGEp0OUFlAqA6TRGRuELVDrU5n/z2O7C2PoqWEt5/l40d/TAS++a98OzFJviIlsVeVSvzS0rRXde/TXl9BEdvr+AJ8TTo8gMFgwDd+8zv4xI3nmXYdMkuRScr/1rxYPnzJtSiT0KmAmtfIqkDVNV4KhDRQWPylkliuowZjqjc+hKPFPbuH2O8IJ45cStIoUGsFbSrpZsfIWYvyEdcsiaMKYRJU9PRWYqUjHLX4NUlbBYK32NmccFzTT49pUkFqI157wsqjoqZfdAgjETJSW4GMEWctiUyQUhGEQvae4ThlKRpUrujFCX60AWWBP+zwIUddv4rzYA+X9PseeSI4vjbFZA651+LqJWqZnFYnnEvIj46Jb+pJek0eKnimRRnF6uAycTlHHpygjSZXmsWyx7c9anNEuT4kr1enWiKlQWqDlhLXNRiR0DmLbWviRsaZH7+L1HZkb9hm/N0X6PuIHkRUnqPeMEQFSMuCVEr83oR4UdAve6RXzFSOGuWY2BJvHDMqd4ne00ympKllOemI4QBrJcwSghRo26GSAtFaCCBVpBf+dAVQGsJvfAifZ6gXBL929VOkUvOOH/gOwivrwryO1xBe0w7vVAfopcfn43u+768A8OvPfxyRWIQRPNe9ODreU2mE9xiT0tcOuRIIYcALfFR0xxJ5w5GZDjk5pv6n/w9OSlb7x5jtDDdICIkhLh3uyoRkvmSUDfGbKWFVY7ICbuxjEoPVHflWgsoNTRtITnqqdI28qMgvVLg1QyhKirwgdoaMSH5mTCFS9KCiyEYE5UmVQ2Ypfe9xTUs3iYTUIboeFSLlQiGpsVvFqSO4dUQiAtYfoTckfOI6FD3JUSDuBrIuIDfXiedy7K2IfOQSupDw6ct4IUg/07KYd7BYkRhJODNGTBrC3etIEdBFQjYaka9VHMdIedQwO57SFgUr2RNcgx6WtPOa2PW4NKG9cwN9xx2U95zHXg94meGHKeUbzjH4ybeiminKOsQfdwxMQby1xHcOszaCWpFWDv+WLcxsBhNPphKCnNMtb9K0Bj001L1EDiTaQh47OJcSly00DnebstPbHoREEBF5gZ54vG9QexMef/YDXJkf8p33v5lSpAijeXGGJV5RV+Irwkumf599+zlr/qxc40t+JCBCILxOTfVV4zXr8L4MOTIAD91zL48+/DD/9pmPsV8vCS7yV0v/ufN3I4kiItoaOThV/lLR0u5s8e2H8K/Wh4gzEnljiSwlYmuIDj2JSvEHHhMFZAaSFr2d4lRE3r2BsBYxMsToEVXF/915vqsf8M4jwRPzSFIYogi4swM642mWHXkfMFlCFzv0yGFri91vCCONP5zRmw7VwqpuCJ1ApRKNog8drm1om56Z9ejthCxU5JMl8cqc9uYCt2zISUhmnrA5AKdpVku00oTdAeax+7DPrDBvGtDeuIo5SKgeuYt0dxO7tYarBJUUdLsS+9R15JoknUEWK3RtcEVHaHuyRHPDOcp8gEaQu0gvFP1kitjeorpjyKo9QV05QjT7hKMTmv/vSXSaIFqJtJrRYIzfKmn7OX3e0R/NCBslxfYIO51S3H+W9viI5tPXke0CNVnQhgyRrhEHJQPbI6cdyvTIYkQIKS6rEEtFspYjqoo4MEQTiYnEe49EntZghwidg9LwS1c+gZGKH/ip/wp0RLg//xleJNK5QPgL8CyvVbxmHd4pHc8XjrKfvyEipOFHfuRUwOjffOLfo3fP8O7e8pv3aX5/3QEtwXlEukHlHMVGTjLI+fvPnxCB/+XKlOcWCrGxSxQpJq1IRymOEV7V6KnDYdExx6xvoWYJ/Wcm6LObhIOOfKeklY7/afXiMvpj2nCcGMTd6/jnbhKPZtD26IVDzXp83eBWBpEbeulo2hVS5qRFRfQpsbXIboGpHa7QmN0RdJqyD8hYYILBCYOtEjKT48qCJs3wmUE2ObGbkCwaKpew2j9h45E7mP2/7ydVDX7PEU4c3WiOP5qx+uQtxKen7BKY1i3jY0ciJGk/ZDHQLA5mYCR+36CkRk08w42E4B0DDM3uNrlUOONRAtzK45eQpAVRV4SdDCs91/7HPyRGD6nG2YCuVxRvuZfar6OqHbRvOLixwNy9SbjckDYZZjhEvukifS4pfE1zwVG+6wFk7xFJSjwKqHZJamuEcDiTEI4nNGVA3QykA4NoNcpIhI6E6Ij9kpjnfPzgWS7PD/m+t307a+vbRCeIMn6Rmb1EPftP3b5fvoxMkCYalXxVvL2vg9eww3u1uOuuu/imt7+d973wFJ++/FHkRkl11KKiRcgEVo7AgsVmQWN7/MmC9ZcY963Jkno6IzT7MFnAcIAUM/yopDEaM+mJMdIfT7Aj6Glx0wmMUuxNi1h+vnG+IYlcKLJTQWlykotDuHQnNjWI1GC0oXMdzlnKokK3CaSSZtGgtCOvBszmFm9bciT90ZRimDLrPFmmse2M0PekZ87gZU2SdmTZ6TJ6WS/waYZJchgGxFiz+t0rpKHGmSGJUwzXK1SZEwdroFviWzN6H8jKMbPplCg8tq2p6g6/a6h3JSr25Os5eZUw7iSmq+lWS+Tj1+j0itRrwjmNRbCZp5BpYt0R6lMJS7E64vAf/wqH/+x3mf7D99FNYH7tKcqhI2w4WmlZHw2IlyVdd8BqPcGFQ+wHrpCaAc2yIVlYZu99Eo9DaYOUPW7eY5XCSfn/s3fm8ZZdVZ3/7ukMd3r3TfVqSqVSSSATEAJEBGRGIqhBUQERJwhCozTgQNM2NqLSogYJMiioDa00KEgr8yBBIoEkhBAgE6QqqVRqfOMdz7iH/uO+JJWkKlRCEAn1+3zO59239r77nXP2fmuvvfda64e4+jpYWqb5mAcRpqFYygl+vcxJZC2RjRhblrz7m5fRTZr89KtegCjH/8Ej9ji+m3jAKzyAC37xZWht+MA3LkdkHmEcopMi8hpOmMa327B7Bb8isa2UX9M1L0g8v9hwnGUEIgFVSHwpGI76OCdJsiHBOFwnxeUlwkSIskJ0UryOka2YEOekM45XT8NLmo4/jiznAqsHxoiDA6JtU6h+jR/vpk4UPlYgFabbRUqBjAI21MgpTTQT05OWSKaEVOIjiWtYjJRQSvTcFPHaCjKRuNxir7keVirUKCGqIY0Vzlmk96xlOa3WFO1lRWNGUMiUamVEyPai2x06UjAdKUzaJto1hnQWBiN8mjCOHbYAj6FczGmPLPHWFFtVE0UdMrRMCHMd0l84jU6mSGPJeA26z3sczngsJXWwFHs9atssMm7hkwSyjFzENFpgmIaVAeW+NUKWUNYDat+j9djTmeqPiZYTYq1Q0zEmaSJ7Fe1UgovxsUMgsC5At0Nd5ojuFH5hHvvZXdRpSXvzyTAtEUqBUVRb2wQEn1z9FoMq57kPfSydzgyC/1Ai7eP4LuP7X+Ed8czizoLZjTM865k/zg3Le/nCniuhlxGaCS4RE9Lr5QHapAhfYSlo4HjhjOWCjqPjK0QWcHETf84JJFZivMWVEh1K9GwDBXhfoYOGWBGtlOiyRec5T8MsRDydkmcrzznbm+DG5M0xIdZk+1dhdgHX0zRakqKyhFojDy0RnbudfBRQUYw7UCJGNV3TRcxr0lYD5qfJdw3QiaHKLYkOlK2UsWsSooAatxGmQrUCpfKUUUbSbSHGkoaVDFRNtSNikA1RlaDRFMjWJsp9i4wOlQxDgMQT8oDcEiFmU7QSxMsO1U3II4irMfWtGdUtGdXaGD2VUMcpLnjiLV3WPn0zhxqSztkPZfPpp9BoN9j0y09FjgVyxwaaJ2qY14hUInxFiFPaPz5PsSyRcYFcaNPsttFpTZSNUKZmeMsatWpSJZq6bfErqwhKiBXOZWhfIjJLLWPiNMAoI4kTwnSEFRq8pZnHZIf20WhqFJZgUkJRckvR5/9dfxmnzG3lvN99CaFfgXQcC+5GwXiE6/YycYTrGA5CjofXfuc4Fk6LvxVCLAohrjlM9johxL51PourhRDPOKzsNUKInUKIbwohnn6Y/Lx12U4hxH87TH6SEOLydfk/CCGi+/XpJEhZ8wu/8ivMzM7y/l1XUUQKmWVIK3HeIkTAn9AGoVBO46YbiNpTu4BsRAQHYewIN+4hZDkyNsgkxqKo9yxRqxbS17hIokc1vhHRfsEj8F+5BvPkR6BammhLi8YoYDB0l3L8KAMbCDfvQcgWJQJZSJKNHeSWFvZQAyFrtNDEmxo466AaoFb6+MojFkckG2awDUm0OUOGCiJJNR6DK/GJpLCCRlrhrULeAmWeU1MwsCNMKYiWAmVe0drSZOwDYVxgmhJfeuxyhsgtoRPhr9tP3fdESZNotkteeiLjSDpN3GyKaAhk3CBxniADwSjCziW0qNj68LOInriN5KEzCKC2gbJb4Q72qXfX+OuW8GWO8hLXd1Qf2Y3eWmN3e9yuAZU00AhUfh4W5jA3HcATEycN0lNOpq49tS/wIwuJxxhJsnmOBoG6VxOFgFgdg3cYP0kzVbXb6DOaVF9fRdYCOejT9IYP3nQFIcBvX/ASBJPJ63he9QcWjsXCezdw3hHkfx5COHv9+jiAEOIM4LnAmevfebsQQgkhFPA24MeAM4DnrdcFeON6W6cAa8ALv5MHmuDOZ/1eCJSEl730payWY/7q+ovxlUNECm00vnKsHco41HDoCExVI0SEQiCymjgJBOEwuolQLURtEXVJQoQf10iXESURumGQSmJbht6nv4R77Bay91+MH0eEpRx7aA2kI56dxjXbmM0prtPAxBZjUkTDU2clchAIN+8ibUtcJ4HSIVKNR+CJ0UoQTbcQZY9kYNDZFCYyGAIzTQNVIFBikFS2TeP0LcQbAKNpzSnmu1PopI0VFt1oYMceHXv0QoMqK7DbUhrNBDsdwSAjnp8nqIwwttRFICoyXD9HRU1wHltD1DEQJZhMUlcOLSIS2aH4+j5EJlBDO3G70JKaaEL3WDuaL3087gZL5RWyrshiTTHyRCdGxJvbiEFFGErSZAi3rkCIcHHOmJLyi99C7NiKqMCqQJ0Faq0Y33ALRT5Ay0AoPSLt4BMzSQdWWGTIsF9fojqxQfS4R6Jm5/nswev5xsqtnLfpLDY/+eFIAiL49QOJe+UdPMERTLw7zjeObv/d4Z5yd4vv6A5Yx3Gs+LYKL4RwCbD67eqt43zg/ev8tDczYSg7d/3aGUK4KYRQAe8HzhcTR7onM2E4A3gP8KwjtHu/4Nxzz+X5z30Ol+/byfu+dgkiEngX+DSan9wr+NnFmBeuRtRVQI0LcAKCxBmH6jZwMkBbUXuP6yb49USOvpWQlw4nwMYSvaUDkcR9ZREr2oR2Th2BjyxWC9ypcySNGL8MxsSYzbNEXiC1wY9LbFwQb0hRDzmLhV97IqLpEUGjdIO6EtRVjhzktDvzFI0hrhsYFRV5luM8aAKinSKm2njWUDcfQIgIipJssc+qiwmtyampCALpPM1mi97aiCJuYvYMocrwa44gFC7LiBoRK7ZEippokKESRZCORhoRywpbWMqlPkEWNGNFWY/RbYffEVOXY+p04jDtSoh1hF8dkcy0yd/6ecJcBYOaMrUoHASPasSMygzX8gih8VpBW+JNIAoGETyi24Ybb8aVjlQKKuHBOUQISBVBM8a7GlONwQXkw7YSZEVYG+FFCyObuMuvYrHb573Xf4GTOrO8+C1/AKPs2w+m4/i+xHeyh/frQoivry95p9dlW4BbD6uzd112NPks0Ash2LvIjwghxIuFEFcKIa5cXl6mro6WjPHo8+Bzf/75POYxj+FjN32VS3ddh5xucZlq3l6+xwmEkcgnPmTCLC8DHo3tZ4SqQPRHCG/Q55zAoY7kV+omTz0k+PBIY8kZxZb+7lWyvTlv+8JB/udS4GAjIQxzkodvIzQqqm8u43s9fFPiNqS4fk2RVVjrmJqbRaoOdtSHK29A9YdEqw3CYiC4AtONUSrC5bC8d5VmmCWuYmY7DUzUIotjrIkpK4eQHqQi0yVVf4woh6hRjBusYlf7tKKIdm4ZTddUvR4Umtg4kA2GeSBpJES9Pq6okBVMxzEuG6K2b6DVniZUHudLRDKFzQpkomiZFrLTQilwg5p87wA5PY90EUEo9IYC3xtCOkdxYIWwEKPnm5jTZlBKIYYlci2QHziErGvEuEJgcdNNpB3jM4Eqc+LaomJHEI6426QQksRJRK9EGI+qQfZrahFRUSNHJe6Km6AxBXEL33J4neGU4B0f/TA+BH7jx56HigRBRveaXeJude8jTcUdibyPnt34+D7efcd9VXjvAE4GzgYOABfeb3d0DwghvDOE8MgQwiPn5uaI4/g+tfPKV76SHTtO5l3Xfo59K/vZXhUAdETgJV2B0h6/70bkdEqII0ztUVWF6LYRIRBSgb/0Vv5uZaIgAS6qFS8+lPITexLOX9X86dDx3kLwhVzx89d7Li0l1ZV7UGKaprXYXolZE7C3jy96XCUDzxoZXrOSk5UB2Zij9RtPwM5I6nZNss0TkhSb5SAEVkla3SmGvYysn2Mz8FNNpkYF6UyTDZu7VNkQOdaYqQ3YhQ5Rayty1tBpt5CxJtvSRsy1J4Q40yfQ2BITBgNkDO2FKfJiQDXdQKeKUHtkKdCqQb17kf7e/YhWE+FBWU/cbqHwVBtm8JlEn7AVh6S5MubWv/sXdv7VP7D7wn9i9BXH7Jknk77ih3FbG8RrMbLRotrfQ481Mo6Q801SOUNaB2zl8FFElReYlS56riZXCpBkhUOTYpdKosphJYRuG5lKwlyDOnY0VI21cpJGPasR4yFxGBBXnriWvPe6S9nZO8jzH/J4HvTTT7+HUXMcDwTcJ4UXQjgUQnAhBA+8i8mSFWAfcMJhVbeuy44mXwG6Qgh9F/kxwd8lxawQoI5hgyOJY37n1b9Fc6rNH3z+A5zXXOXShYpPbgn8rAYyj91TI/aP0BnUBnQc43cvEh61DVmVSF/Ttnc+wduk77ifG6o7v9r04ZsJaQs7HlBJT2OmiU8tvqiw0vHGLCYPgovHYLyhKoasXPQ5lt74WcJczLBnqYWlceZGVGRQbYPoxiSbFKHbJAxqlg4cIneetX1LjHsZaIMLY7hlGbU6pvIlcn5uwqvRSyAfMlgbk2zdiF/s00obzMxspC4kRTnCnTCN7ghcryTkPTI7RDRbyLkG6dwM4uY92NLjpwKhUkgxR3bwVrwcodcGxJvaiKBJ5mcQ3VmUkmQfuAK3+wDDCy/GL43phRHeltBuwlyEiB1uKcepiMopBBFiVGJsoJix1IcESUMgi4KkElRRDRsiiBokwSGFwp+ymbqfEbc6WC2JU42dn0FISYpCz2+CADdVK3xm77U8dmY7P/P6VxKScNg+2b0LIbt9TXGk7bn1656yIt/OW3Fbndu3oY9zWdyfuE8KTwix6bBffwq47QT3w8BzhRCxEOIk4FTgCuDLwKnrJ7IRk4OND4dJfNjngJ9Z//4vAf9yX+4JJuFmxxpnuGXjFt7whj/CafjTSz6MXY/bcK4APEIBUwaUxyiBtTWm3cJ/5RbqIiZUgV/clvLaruL3WzX/Ou95nrpDAT48gVdMSZ6Tel6RjDn36pupswpZp+jZeVxlUdtiTNokrApOmSQP5xeVQ3cqjNfUzhGMQS1JTGExLjDadQBR1JhUUNGD3JOsDqmEY77VxLuATmPSmRYqAtOdIxhJJBRqYxtxYExspiijDNXzmE4TVTjUvMGuVeRmSGVXMJWnvVricgcdjXjICUTaUNcZ/WGGbdXknRRf1/ihRc5maNOn2enirKSuLKJfUlpHuHqZ2Z5FTjXwxiHnukQNTbwxJkmnUEgSDOw9hHAeUXvs/kPgLHFiUNIhrccP+xAFKi/xkcGnEhOSiX9hXWCjFJdY3N4KlzYZW4vOBSorSAlI7/F46rUBIS/4v9d8EYHgl375Retx2UfB8ZOCBwyOxS3lfcCXgAcLIfYKIV4I/IkQ4htCiK8DTwJeCRBCuBb4R+A64JPAy9YtQQv8OvAp4HrgH9frArwaeJUQYieTPb2/ua8PE0KYOJseI7Zu3sorfvtlLGd9/uSqj2DHJUGBiAwKNZlqN01PTuxig8xG+AT0rCc0Dc1a8qOU/EgroJPA2e2K/3OC58LTUv5rN/CsZs0FTc8vtyXJ9gVUajCnzFObHB9q5CjGqgzdafHHseTiFH6umaNHHhGLCWdpXcJjtiIiQbS1hRvUpAsGKSPEUBHFLVwAFwfG2RgZcrQrESEgmjEtatRUE5E0iZaW0VMKW5UEm6N7I9xyRrGUI2xARorgNHF7AXn2NCYSpDSoTRdlBNGJs7Ra02zYMo9bimjNzE8iM4zGFDMM2ymj1T6Vh6y3SjWqsA2FTbrUTpOIQJF4RsUKlQO/qKmNRM42qKpV1MIs1ipkM4UYnFYo6/AhwvmA3tCl3WkiBURqsigogkU6ixIKLL8AACAASURBVLU5fqGFDTmNgSSe65CU66xipgl9h25rXGKIXZN/G93Kt1b28pwznsDmh552zwPluMJ7wECEo9vY/6lxzjnnhEsuueROsuA9+Inrw50L7vkZP/6Jj/OOd/wl551yDr/woMcSEgFxhBp76jqD2CCKQK0cA5EwLR0uc0gZCFoj0xjSAHE0yXNXe6SXSO2xOqCswBUVVAqxqYM8mOG0xcuIEocvKuZe8hS+8LbP8OjEUCOpfInyBb6T4lYKAgliKibq5ejpNtmBAUkKZawxZUHhJUmaUplAOR7TiRWFiEiVwS4PCCqhrjKaTUPVGxO2dRGrAtMMVEt99I6NKOHp7bwFnWxg7DNa1qNbDdRMzPBgn85Mi2rRUscKE43JRZPWtMGsBqwqEEMPUwnRVITNLKLZJuxbxc1G+G4Lsb+HspayVdDQ0zgTUWeWmeefy/ijl+H2CYJdxdPChjEmaOwwxyTgkwbGO3qRIt7UQiyWGFURMsAFRKyohScOGgqJOWcD9bV7JpEnTiIdCDxp03DjwWVef9WHOKE5y5ve8zdI5RDizmPmHi2+24bV4fWP/uGwmkdv825L1iN85bbldQCe8PjHc9VVVx1Xw/cS3/+RFodBSom8q7L7NhDect55z+RpT346n9x5Ff/8rcuQrkAOxjhfISqF6GcEPL8/SHnWkuKFywaNQ7QSVCQRRQWL9cSLsPDIEOESTz3O0U4SgsQHDwbCYA2fStCg3IjOVMKu6QaPvvCL/FbR5IpQE7xFKYFqTRFFc1gRoTsFrdJDK4HlNZLNCY6EWGt8nNBuTrHmMpqFJI2aFL0KGTTDtQzZAFKovUFu20BnQ5cwKLBTMdVYE3UUoncQt5yRNKfRVUZbeloLLaQx5Es9Np20EfqKqGVgkya2CZ1piRpVk2B9F5O0FC4P1Ady8Jq8P8DhCZXB7RkRTXcIqUGMYtyhApHX6DSneOu/I3yBjyuE0YzaguAD3kSojXOTxA1+suUw5QTJdT2iPEPYDK8EeTvGOYEWAj/j8BsUxY0H0bqJtwYdaqQEXWlGledt130aKST/4y1/gNIOYR5Q/wbHcQ94QPX00U//jxjgAwiC1EgR+PVX/Bce9agf4oM7v8ylw/14oRDOI3UgpC1EFLFv3XlmpxNUUkJRYFVgMYJPtWKWezlfK2p+fsXx5D2KXSFlHCCUNYqYejbGWk8pclxeYRsp+smncHB8RzcY2cRHkmQqwY097sAajfmYuBkxUpqyKql1QoRGRhlOVcgx1IMhjfY0eT9HN2qkSmluSunOpoSxQXYFuhuT3dqjFzdob5unuX0TulMSZmcQYRYRx1QETEPiYkU1HhEGgUbaJBtWWFtRh4x4cYT+xXOp7QCpDbXNoY4ID9pC9ymnUycOl3rSRhNqgenOoeZiyqUh9dDTOmMeaxy2lITWLPUpXaoDmuDBJC2i3OK8gHyEzHvgK4SU1NLjg8dpga0Vvp8QCovqZYSmRFUBVkCtjJGmRNUDwmCMHEnCVERwmv937eUcyge88Ik/ycL0FkCC5W64t24pd3gV39P69xhaPB4/9l3FA0rhfSeQieIVr3oV20/cztsv/iifXrwOkggiiWkIVKp4/YLkSYnnFY0ak0gIBpXV/Mx+wxv2eZ49Vrybxu2K8SYRiIqAVBJSjd63CNZjak3UacG4ov74IR45p/iVRPD7jZrTx47QDqi5DQilCL4i1BJTb8HnI4gkUihsqvC6QRiAVRn1gzqo4QB7UoI6ZIgWBMX+AeVSiesqxHJJIxuhipJycZnxgTF26QDaJYTFAroGbzxEArljG1OnnIZrb8DMRxSVILu1j1EWFU0hCxh94itoTqSSAWUERMtE44pyukIpTRhb7PAgTEny4XWYIiPUBdrVFF/ei64Dejgk2Z0TFg+ipyUh9wQ7iYxoFo44MtT1ZE/V+RqhPN4XyJkGnNvEty2kBpUadG8MwSHEJMIm3hfgwQuoKQi6QO4v+ZLfxSf2f41zpjZz3itfSG3vvyjG4/j+wANK4R11br2Te8Bd85etF5SeTjPlTRdeyMmnnMz7rvw81+3fjaCmHkr8aMTWvOD1uuKnEj/hcXXubtbBM8KQx0wbfjlxPF0JVBSwhccOckKri1Qa2ukkAqAu+er4IL+8s+TjdeDRqcAEixCacvduzHwT3ZW4vGBcLTK3YwsN3aBMHeM9Paz3hM2z1FFM60AGfUW8OCCPRoglifYWLzQ6W6OyKU4nKKXpbNpC3l9EnXQClgxrPPlwjfGqgEow3r2T7OadGOMZVz1SID51ilorWtQULWiUXczKCD/UMHZMn386rac+gfxjt6C8wisFpk3lQQ+nKbKcumXw02PE409DJpICi5UFfmYj1VJBNBvhBg6xuoiZaTKIBXKujRAJSkl0iDC6gR4WRFeOiJMpyC2RcFQ2UFlH2Y4RwlAsdAjfWkPVBpIme82Av/ryJ9nRmuE173ozwltUVBxtkNxzGNcRC25zKznSdWeGsiM3ue4Cc5e2j+Qec3zj7r7jAaXw7g8YY3j97/8eW044gT+78qN8c7iGFiX60Q9D+xw5Y1DTBrW+US5iw6saNQBnp5KnnXsif6JHvKgFoi6gDsh4QhMotUQoOdkTjDRMx1wWIoZBcMjBx7JA3QCxfwAjiRpXSKVxuUX3c4bDARUTzluVxsjgaBlo2JK60tTVCNWdIZmapWprqioQ2sD8NpJmH1F7KhNQoUejNUP4xrWE4FEtiThzO0bGNIoYdcIClS8p9/ZoNqawayWun4HUVFrSbi5Q9g4hUkndWUOd3SVdOIGlSy7DhwI93aAzO4tqJsROTrh2Y0m6PEbNPRh/4yJ+pcKUJUJb7NoqWkeIqZRGI0Zt7lLHGoFClRaBxJc1oXR4H/AqgIQqK1AntEHHpGlEo90k7df4rsb4HNtpEJRgOM740ys/SidKefUfvv4+O6wfx/c/jiu8I6DT6fCHb/ifpK0Wb738E6xtaMFVN4Bu47d2cKFGBgUq4CvL4xqCv37cRi5KSsSghlYDGyrQQFmjrIM0QXmBqgIqNciVGh/N8WNbOnTWE44+NU4wkcZSU6kxY13gt2yiuaFDmdb43oAwzpB5hhAlSRCMDpao+Q2IlmJm0zaqqoTBiKrso4XBUWH37iN55IMJsiZaEKxZR20VKumifESxmNG65ltw8Frq1DLenzPVnIG5BBtqkoWE2g3xVaAue1RZn3TLZhpP2kZLzFJcs0b/6jXyaYdJO5goYRAyzAnThKaCxZxEJdjNjvDNWzGLI8JMiUdTVzXp4x4Ga33qm/YzVI74bIPNAs26hMwS1pZwShBSjS0LHA4fayqtqQsPyiPTGKslysSocY7SMa6wlJXnrTf9G4Mi55VPeTYbt576vR1cx/E9xQNK4R11v7f24AKII6Xp5u5rBK/otmZ5ze/8d7JQ84cfegd7XJ8QJGr7AmaxRNSBYOFGL3jOsuJFXzjIs/oa9q0QRmNQikhqXGoIKqBjS5hrEJpmogwjh6pLtmU1/3xKxCdaI1p+hJtpETemMOk0DCzu4CIaQyvqopsdWt1pym6XdMc2ijQiXogo4wZhNib3Y0wS46ZSWlELn1qS6Q56agu9L+/C1xY/btPuKTrtBt5YinGN6Sa4TScTnfZgopO6dKcihtWA9FELiG9CVceodpe0qfF1GyU9weUsf+5GbNUnbU+TffVGxDcGhE0thqvLhJtWUCsK1V9CPnIroRTo9ESqDU2skYgwhWlpdB7wn/wadscsLmqCr9BfGpPMNPGxRCkJG2bxMx38qMIojcgkvhFoeU0yrgmVQniQ2QDqAjotqpAhUs2bv/WvfGtpPy//lV/j7At+FmHs3Ravd1pK3gufu8Or3225eo9t3dPx2m33c9etl+O4P/CAUnhHhZGTJ3X3jvzkzDNP43W/9zp6dclrP/Zerq8WCR+/EasjSD3Cey63k9TlAGtewLhCSEMIMA5+EsMZLNQBsZwTVISxgtbjz8G6Aisyyiwnno6QaQc9GoHz+EgQRym+X5AFS1lXWJtTHxozu0FRFWNMJPE3H8AfWKXcdwg1nyJaEYwzaArS6QSpwc87RA9UN4JiRClGRC2FaTQwSUqz0aDefSuMSsTuFbxMUQNB+S8H8E9OCXJAG0O1kBBiSxUCCIWRipDOYE90IB1GG8S4JNQVjVTTeepW4pNPorh6J0VHT1JNMSY2AoentGDTWULL4fOcKlT4UGN7AbnSRxtDXgcGPqc61MNIgfcC30jR2zcQ1lbBaFRlgYBVBjsVIQYeHxsu/NKHuGF5Ly8/72d5yvnPREaCwNESThzHDwJ+MBRegElCMnUPlY6Q90zCmWedwUV/8RY2bt3CH13893xk178jOg18NGG0/8ltUzzROE5WntfPB3QnIrga7RVRPQmJqhKwznJxT/CBylPLNvG580SbY0InQulAyMDVJd4qrJD87zV4dU/QSzuoDHyrSfMhJ8H2hPH+AtkfYQcWlzQRKoU6oVoZ4vf0QLaoD+VgZ/AH+uhxTjKncKGLVDGNExYYExhfN8QkgXxQEVJDo9OmStvIbICZb2HmJfEtI0IyhTUNorKJXBUktSD4GDG0NBoNwj6LakT4oo87tIbfEtH96TOot82xPMjx7Zi4rnD9krAiKVSNchFRSyLFKnEJST1xK5EuQNvjfUlVRDQSRdPETLWbOKMQyhPVI/j6QWQ7wecjQjrpK+MNKvPsr1f43c/+Azcs7+Plv3oBT/mNXwApJlb+hOzwbn19TAbe3QJmv/s4UtKV414r9x0/GArvO8Tm2Q38r9f9EQ8/52z+YeeVvOlz70dUAtn1dFdHvL6Z857Zgidsm1pfrqYTYudWA5QgsYJ3ZBGvK+HthwJ/KxS9N15M2KnRkaLRaqPmYmTskWXgCqn5+1xxWQUX9SpEbWk/bBPx1jahGpHEEudKdKKomxJT5YR2glSBwlv0gzaCAH9Ln7AxEMXT+OlpXD3E1xXeWtTDNiF2xBRegpHIkaWKPObAXoRoQj5GtBPqVUc1WJtQFbYkyfknYrvThFv2YQiM9y/CuMQuDxBpimg1iQ8F8ukOS2/5PIYEsaFF6JWoqQgpQZQRQpSIkYIyxg7BViWpEMhYUFuP0xFxYpERmKwkZCWuEeNjhTABYWsEARUAFYFUyI7iy4u7eP0V/8S4LnjVcy7gqc96JqKGUNff62F0HP8J8IOh8ISA4HHeEwJHTjBwT9O7VnRmOrzuta/jqU95Clcv7+HPLv8wudXoE6fRc11kJlDjfejRPNJavAt4k6PWAl4obnF3NLytkzB1wdNwp0n0rSOqtSEiB+klvulpHpZ1ZVOkEJs7lF/eh+psp/viZ1J3PdJ1CTganS7BOOK6ItQWvSGFzEIngqediC9bDPvLlDZn+hEb0dunESuW4rPXEaqKKF8j1AUhiaiHFeL0BRwVzqSITbPE2+dpTHWoxwOUK6k/sYdYZNhHbmBQlMgB+DpC6iaVtZPknls6hCv2EJKKtByhbhkTbIrISpAC5yq8AZfUyIaiTgIqaIQUkEuUMgTnqHoFbuM0vhPjg0cUllBbnI5RzQZuuoGvPSoB0464/ODNvP2az9CKU/7sDW/kic//8YnbkAh3jsC5l3t1R3UqPtxQXCfOPrZ2j6XScVvuu4EfGILLsnRYV9NoJoel2b53kFLy8v/yUmZmZvnABz7AG/7tA7z4zB/mxHgBFxvCWkVYWsNuFixZhxlLLu2kjMYVz9cFW0zEQxcaPH5tkdA2/MVNksWh4uVdyVwtkD4gx46zguN9myQ3Z5of3qQwwworAqN//CT63DlMElPPVBQ9MFUGyWTDXAVB5MDu3Uc8lVJccS0iqomaW0Hk5DszlJKkc02qPRaZaKrOFGkk8dZjhxVVpjE4ZLfN8IZDNKcVLqtIT59meN2+SZaWpZLYdFEdj2gGfDRxExFj8P2aqGmoDoyIRjm5j9ENhYhzbFVi5rvI1RIXaRiVsLICM1NUDmI8SgRCmRMiiZqeItywG3nqVhhlqNk2DA3Sgnc1UoCcj6lzx99dczGfvelrzMYt/vC1v8+WU0+aLGG1B/+DMa8fx7fHAyp5wO246zMdy2mX4LBJ9du/k0su+RwXXvhmQghccM7TeeyOM9ElBKX42NjyhsGd9wsv2VwRykBQktCQfHilxZ8PcwB+teX4JRGwtkA1YoIH1Y5AgZxp4vcNkBWMEUz3NXkYwuYG5fIIVQtUN6I2AudqIp/iGoKLV0tOnVJsbUzDrfuZ//GzWPzsN5HOUpcV7a1zFPst8UkpdvUAxE2KUBHfkjOYnua3RopdTrDDwLtOj4lO2UT+j9eQzkpcVlDVBtPxeK+g08D6AltKRmtDFjY8iLLjEKODRF2F25NBHWFnBFFjCgYFYt8ackpSeT3pHiUJsUcNMrI0RSSCqO8wQuIBFQlqPKEOGARBCIzS3FKv8davfIIDeZ9ztz+I3/6TPySJY440od0eoH/YeLhbrP+Ruv4/2Wnp8eQB9x0/MBbe/Y3HP/kpnLbjdN701jfzzqs+xTUHdvGix5xHfO7ZnHjx14A7FN65iUf2cvz8FIwqGDvOFxWXJIJzjOeZiaVWHlFGeFci4hQnBU+62cPNI941U/PgrU0ir8hOcYSdFjxEzQQigR+UKAPKSxQ5v7ovZrePYAxvnF/iEXNTDK9dQhmBt6AbXapdfXzX4dcC0UpCIWpaO+YoOz0+WQZ2rS/Bb6rhpl01J99yALE1wQmoigKmSrxI8EYhshxciukE5jPF9NldcpdTXNbE7S0Qcw1c0UOqjfj9h5CdFm4WrE7J3ZhkrolZ9rgsTCgTK02jESFFH28USmkqNTlkj6KAkAnCV3yzf5A3XvEvEODlz/slfvRnn01Q358T+HH8x+C4rX8fIZ1iw8bN/MFrf48fO+88vnhgJ7/+4Xdx7Yc/xUNbhr/eCG/vlPzb6fDnyZgrTz2FlxyEIoCoAkJ5/mJTzPMiS3No8c6hY4E8aQqE51vFHZP3n45iZF+jFguqmwJi4zxSRngDjCwh0dieBwd1Yth92BLu8jzBJQq7t4fMM1S3TcNmhOkYvX0ztj/GnTmFfkSX6sAQFSlOju88LLZkI+xoGR8pijohChFifwS5RLsmZVCQjEmUJX7cFg5+5qskdUFmV7EzDjcsESduQvb7uFaEzC11rimGNWk8h7ypRoWAjA3i5AVavsSXA+pGhMRjjUCIgPCBUAnkaMAoz3j7Vz9FLDRvvuhCfvQXngvlf1TvH8f3Kx6YS9rvFPfmnaxXveKLX+Qv//pvWFpZ4qmnnM3ztz+CyLTwRiBO7PDYKyZMWC/tel6wrYld7uEHFSKJsHWNUhLfibF1RRxrduURv3pg0rgELp53eFchoyZhXOKCw7cjVADjS4qQYmKDyjN6ceB9y4oyUTyj5TlJSdQ4o4gitK0RszPItQGun2ESST0OuMgRVYpCt2n8yDQf+sQStzYVz9jW5uSWoLA5elEQ9YYUSY7IQTQjVNwEWxJsQC2kaFrkB5cwq6tU89PEnYiwd4yLJhyvFhAGoqiFGxXQSZHLPYTyyAC18gQdofJJAtPJKx7DxgX0ygBROXaN13j71Z+iV4z47y/4dR7xM084bJ9O3OnHEbv1CGW35787Ut9/D5a095RB7/iS9r7j+JL2fsKjnnAupz30LN7xV+/kXy+5hBuX9vGyRz+TLVWbcMuI/3VSxEW7K87yNfVyH1EriCQhQKQUlQzowqKKAtWd5pQafjcqucIbHkeJrFKCDghd4LWbEEiPc+RsBzF2JK7COXAzKZ2VmhcFh9zSRKtANSrwXhPbCllLspUl0pBQi4CWESH2JBu7iEISNmrGXx1x/mwgJCWy8qjliKSXobfMYe2YSDaRZcVYaqJhRmgmeErsck1oVCRak520jXTXPoqBQkcNAh68R7Y0fjgm09NE+RKaEjuVooSnkhDVk3RaIQ0wzpFa4bVA7V5CKMNlvZv4669fTAjw2t94Bec87ofwpQDzvR4Bx/H9gOMW3rHgiK/oKBlqgX/7/CX8xV9cRF1bnn3WOZx/6rkI2jCscdsj9Nat1JfeODHdqpIwpSEPSOHw2qCkJDgmxNFrOcoEaDTxwxFhqgP9FbwySGHwHYlYzmiesY1yaYwwAl+WhKYg5C2CGSAKEGs5fr4B4wqVT+INdB0QHUnQAnfTiOiJmykOjpCZxM80Scx6evWlATSbyKbGX38rQgd8N0FWFdpHeAJEMcF6fFOjYkW4dQ1vEkKvRqQW0UrwdpIsFDvAn/Bg6q/cTKvjcUojjEFUjiwrSU1M2JzCIEd7zyRvoeAjt17NB6/7Eps70/zRm/+MuWQamrftlR7dsrvHbrzt23e18I4p4/EdLR4L2c9t9e+p7t3Jeo6cJeXxxy28+4Rj4bT4WyHEohDimsNk/yCEuHr92i2EuHpdvl0IkR9W9peHfecR6zwYO4UQb1kn4UYIMSOE+IwQ4sb1n9N3v4vvLzz+yY/lbW9/B2c85Aw+eM1X+O1P/V++fPM3kNMBc0ON/cr1yNghtUc+ahu+ABcpXNLgEpHwcyuGt2eatUGGb8U4B7a3RphrYIIjqgRGGdAG5aEOgd7ORYQtqbMMTw1astaCC/cI3nQIskSj1koYO7xh4pcWPLUEmRW4ebBXraJ7k6SnCTVeKMLNQ7o/dia67KNTiXQapSVSSaRS1AoQEi9qdAFGpITlDKsEWgX8Vk1IFaEooQrocUVZJshuQbqpJGiNTzV15KmaknQ2xncVdlzhrEMCZUPy5q98jA9e9yUes/FU3v7edzKzYRYR31PkzHEcx91xLIcW7wbOO1wQQnhOCOHsEMLZwD8BHzqseNdtZSGElxwmfwdwARMms1MPa/O/AZ8NIZwKfHb99/sFtzmDBu8mlI7iPp7R3DXu6NvMq9IrNi5s4I//6I956Uteykg6Lrr6E7zx3/+ZQ2Ev2mlkUBNj4tQdKD1EVwKZVfzeIhywgvdnkrflEWYwRkylCBXB0oi6KKlSjQ8SJxxBKyIZEXcU9TAgKBGZwt6Y8d7dYz5WCj5SSd6dGWiC84GgPKoWCJEj+wOCj1GtLqoTEE7jhxVuOaC2ncLsyx5NtKONWLPQG+C7KS5tEMYOkWl0mqxbbQK5dAuuGiGER0uJqDUNNKH2CATBOVyDSUTFlw8QxhEuaNR4TLqak1qPi7pIF9AaTCuiCnDR5R/h6uU9/NSjn8TvXPQnKB8hnSAcviFzH6O9/iNi9I/uQnxPwWLHkJfvOO41vq0GCCFcAqweqWzdSvs54H331MY6rWMnhHDZOjXj/wGetV58PvCe9c/vOUx+v8HWHu89iHuXPOBe4Sgj8xnPeAbv+fv38OM/8RN8/eAt/OYXP8h7bryMnstRHsInL0M355CRRooOr0onvnkRgR3CgRaELMcQEHGMKGtEbvHeo1sS2bfoE6YRvRzhDH6pQP/QibQetpF03Lv9Pj6aC3wIRMojRhVB5JRmBpFFWF0hB31sEIQdAj0TERYa1B+5FJkXKJvipSNfCchQobICHSTKA4M+wQmUCMgd25FFDVWNkQGROULLIJVECUkwCpV7vBeEyk6SoVoLOsZNScRDtmDEgOjEOaKhZ3F8kN/697/n2oO38hsveBEvfM1volrH//WP477jO3VL+RHgUAjhxsNkJwkhviqE+LwQ4kfWZVuAvYfV2bsuA1gIIRxY/3wQWDjaHxNCvFgIcaUQ4srl5eVve3PBTzLOJklCpM39G6lzV9Pgtt/vdAECImn4tQsu4C/f+VecedYZfPrGK/jNf303nxnvhIdvR3qHPnkDslvyU23PpXOWz3Uczz85hs6EvMa3G4iyAufRSUwkKnQv4A/sol5cxTYkslUgTYy4ajd+X8HzphNe2q54TVrxiekclWtsKwKnCJ2Y6LyH4qZBxC20jpCVwt86JgxKom4H+8h5vGtRFRbRbhLZmlB7bCZg4CkbffI4oVYVQo4ohxkE8GlEnQns9BDXH2GUQFQFxiv8IzdhjCVEAaTDNwPKS6Q3uOv3oDOBOzTiswdv4LWf+2cqZ3n1f30FT332MyYHH1be+R0fi/2zXvf22uuWv7incLHvKtazI3NP1t/hNY8Hmd1f+E5PaZ/Hna27A8C2EMKKEOIRwD8LIc481sZCCEEIcdS+DSG8E3gnTA4tvm2D6wPXC08IARzfU8/DzRs28oY3/AHXXbuTt7z1It7z75/kQ1++hPNPeRRP8oKor1DEhFTAVlD7BJx2KuqbN+GLCpm08C5DbJuldhHqm99CnngSFB5xq6NcAD0VkTtP7Ho0VMSzg0D5IRQCrwUiV4RmBD0Bl96IHBegWriwvoQyBmYTwt59JCsB9eyI4f9v78yD7DjKA/77umfmHbvr1Uor6/YhIzsSPjgcWy7blEEctghxgFSiQJWdQOFKwhlwgQN/QFGVKgiVSopAkiLcELBJQsB2ACMbZGyDJVuOD9mWbZ2WZN2rY4/33hz95Y8ZSavdt7tvV3s97fyqpna23/RMd0/3N9/Xx9df/x16tBe/o0TSFUGnj6tUkGoHpbk+SRxSKHZSrUYkJiF0Ah1KwXUSVyp4xNhyMRXY//cKqum+HDZR9ByfJI7xagaSgJeCI9z50Do2H97DknPmcPunPslFF1+SejmeQAU9Z2YwZoEnIh7wLuD1J8JUtUY2/VNVN4rIVuBiYA+wuF/0xVkYwH4RWaCqezPT98BY0zQ4kYBCXIsRK2lH/RQ2Ghd7mELCpStexVf/6Suse2gdP7zrB3x/04Pc/dIGbl52HW9ctJwgBLY7ktkR7N8NCsY5jA3RMMKF3Xj7j0PrbOSCcwm37yNeUaK44xCu4uP31ojmdCBxH6aQIKaMSoxzIaZWIG5LMOUAjuzClNpxLSWS3hDfCFQj5FCIo0bUWuLwF38GcRFXCkiOJjjxIQpgocEcjUmOqQSUfAAAESZJREFUK+pCenti/KCIjWqYmiCSoNUKcm4VdzQmtAGFyIdZBq2B51u8MCSpWmyr4aCr8c1H7mHT4T0I8O6Vb2TN33yAYqmdpOYwoaTrYnNyzoAz0XfeDGxW1ZOmqojMFUmdzonIUtLBiW2ZyXpcRFZm/X63AD/Not0N3Jqd39ovfFwwIqjRU2tlpxATxBBZHBYbGFatehPf+Jev8dGPf4ygpcz3Nq3lIw9+nV/s30SyxOLFHrYnwZQ83KwyFeeRiEe86Qj6mgVIuwdb9uEnDr+3GxuC3fsyibGIKNYTJA4wlRDpUdS2g42RlhIcriLFVlxBCI52YdpAEwcmQDyHXflq/Irg5nqE50BcttgbLoBSgBQFs1eQyIekiqt5aGCIekPCQpnAs9AaYNpifDsXKbTSEoVIZ4BNPARIrMG1laGo3PviRm6/5+s81/UKb1l2Bd/49rf58898nGKxBTTGBJkTgHrm5lAm6GnW6sQYhfU22Kl/3elJVAQ9LbYMeXU+aDG+jKjhicgPgRuAThHZDXxWVb8BrGHwYMUbgM+LSESqS/2lqp4Y8Phr0hHfEvDz7AD4AvAjEXk/sJN0EGRcUOdwxuJ503h+ted48/U3sOram1i77ifcddeP+f6Tv+Lu5x/lncuuYtV5l2J9D9NXwyRCXBKMGpL1PlFrFUu6NaHpCZCFAe78eZhahOzfj5Y78ItK4gnqHCERpbLFFj2wgiQOv+oIMdgjFm31qMXg+dD20DN0r1gEz3VhigmeU0xrG14MYd9RxCsQF3y8bovfGmISiBfNRl85RFgsY9URB4bk6DFYNItkZwVRH+v6cCIQ1vjfLc9y784n6a5VWNo+j9v/8fMsmTsPnIWkli7JyMkZR/KJx5NBI2WcXeKc49f3r+P7d36PQ4cP01Ys8YfLXsMN868gKPpEUYSPjxQNsQIFD9sTEbcV8Xv60LKQJC3YvqO4lgDmFJFtFYQeEs/D+T7mWA2Z1YKphsQoJAKBxRiDi3xkvkfpYETsF4iTgxi/DdcdwuI52B1dJO1l/KhCFMWIKImxWCuIMUhPFRdk++maFrQgBNjUlqgakpaQB5/fxP27nmJXTxdzS+fwZ+9Zw5vfsTqdQtSvSE5qPmNRb+rONRluwVZzkU88Hhu5wJsMRiHw0nMhtjEP3vsrfvCTuziw/wBWDNdc8Husuui1XFSah1hBNDXzXG8VRTBaRNodoVrE9WCPOVyhgHgFXPUo3ux23PEaRpXExRT8ArVKAq2COoHIYYMWZH4Rf1cP1bJDK0VMsYLrCUkioRgnxKU485QCLqritc7CHOuj1upRTExqpAWWJI6wlQg62ogXz+Gpx37Ltx5/gGPVXgrW471/egvvfNe7UKuIcYOKIRd4Q5MLvLExjW29CeZkWzKIONyk9Ief3pMz5E8onrOsevtbeONNq1i//lF+8cu1PLxxIw9vf45zz+ng6vnLWHXR5cyetShdeqYO53lIbw0bVvCcJUliCkCPV8O3AZgStCUk8VFsT4kwCZFiARKHrYaEkqAa4x0p0NdzBBvMxrbWcMcj/E4f2VsjNAlFtVSpEohFO2ZhI0WDMuUgJKn04pVmk8wv4O3pY0/YxdrfPcy6vZuJk5j2YpmP3/IBrn/3O/A8B7HLsl7Hf91o5F0DFzW2amx6CMV6qZgeKWtuZq7Ay0iSCFVJtwOchhhjuOb3r+ea66/j0M6D3PfwfTy87jfc8+IG7nlxA8s6F3L14gu5qmMps9rnoH6AF0VoWwETJ9R8S9AXokEAh7oxcxWSNtRXrGfS1hMbXLlMEDsip9jlc2D3NqJWRRPBawuIexxJR5FinFALY2yLxWgZ8cH1gMxTOOgjZcvm/dt59pmdrN+7jT3HDwPw6nPP59o1b2HV9X9A2TPpFCE1eevNmVRmrkmbNbQoDFE1BMEEyv66fomGKfcBl2sE0pKgFYPgQdljx9Mvce999/DIIw/TU+kFoLOlnUtmL2Dp3MVc0b6Q+UEbIGiLD0mCio+pxZgOiHodTl26S9jxEO+GKwh/8QhBSxtu4Xxs11ESF5OUwIstbvYs5OAh8ApoJCQmQS0UXIWatWw+fJjHD2zmsd1b6a6mq0XmtLZzzauWc+Nt72XpgotIvNRxKeoDNbACTupmvX/+x6bh1dEYs/cgw6p400OPGknDy03asdH0Ai9JEgCstadqRyNZGs21Z0yd6itkcwLPLAFJErLpmed58tlNPP3EE2zZshWnqX0+p9TGio6FXLJgCUW1XNh5Aa19IWZhGyVXxPR1oy5ErliK7j4GIWglhPYW6OkB50GLB5UqXLoEff5l+qIQcTFbjx/h4OF9vNjTxWN7tlBL0l3BLp63hGtXv4mrr7mORefOB+dSwdafYZrpUKtK05M6L21Q0Q5982YQeI0+PRd4Y6PpTVpjUlM0HeFrTuF9JnjW4/LLLueyy5cja96DErJtxy5+e//vePLZjTyy80UeeuWFQfEWtHRQLpVYVDiH+S+/wIXz59MeFHjq2AE69hnmt7RDIGzbvo/usI+t6w+x59hBDld6TruPFcOyhUtYde1Krlq9ms6OWThP0WoJtJZpt3m7zJkeNL2GV5dGtLeBbXA6FUOj76SeLBkQtVKpcPTgMbp6jrBtyw7QiL2v7Gb37lfoPtjF1gN7aKQOFL2Aixedz7JLl9Nx3lxavTKXXLaczjlzKXo+J+ewT6RsO8OR10b80U2VhqcDzkaazJxreGOj6TW8nOEplUqUFpZYYBawYulyKCkUDNQcVBXbruzfcYjNW3ewY+sLXNZyPnL5AsJj+zA2oKU8i1ctXY7XbtDeBIIY8TykN04FpSlmnYxTndOcnJE5uwReI41uNA1TQFQR4xEnCZZTUyUmheH6mlSHXlI1EB9AkVJ2QS1TDYuCq8HcBfOYu2Ae1193db9IF/d/GFSS1J1g7EEMKjYtC43xAotz6dq9cbUYJshR3elz/QY9dJzuMzqkzlkjz80ZHdNzLsY0QQBE0DjCGCHJq9opFMQYvMASRyG4KjJWB6s5OZNE02t4iSpGQETTeV1w6iN5hiOxCsRxDOoo+B7qJqkDftBoZAPPHG7XrfoRhghvML4VxBqqvV38w5e+QNF43PaJD9NSmodzDmMMptdRK/t4xA0kY6wvq/H30ci+ExPz5PEm//COlaYXeJ4H6gTFIONcEQzgGYNTg5ssYddk9B45xstbt1Nq86Eapa4hMpwkiPp5seVMG5pe4PX1hQS+h+cZdJw72ByAMfjGSzW9vOUOouO8C/jEpz5NoQCtneelLqYynO8jUQRBXm4504PmFngCBethMBMzmpApjEkST8VezAwUsCd0TAHUSP2Jy/USWndz6SEeOQolWZMEnHLxZVeAgTiK07XJxqRp9dO1yoOef1oaT/iqG9gPMbMZfgpNXkZjpYkFniIqGOOBD5qkYTmTizohcSaTYUOMHA9/B/Kxs5zJookFXraywpAuRE+DzuR2p5imcvO0bv2TSlEDk2jHqp4Oije4YNIrElQaKP6TGVAwBpyjGsWUgmDQxNvTI8wcGlktNPNKZfzIP605U4pn7XT9vuSchTSxhjcJjMUZQaPXTwppopwDY9KNdUQMUZTgnBIE/uCJwsOqD3V+PLnJ3DARB2qKQjZxWvCsHfkZTYjW+U9I34UqGF8mdJvknPqMqOGJyBIR+bWIPCciz4rIR7Pw2SKyVkReyv52ZOEiIl8WkS0i8rSIvK7fvW7Nrn9JRG7tF/56EXkmi/NlGd6dRc4oSUvTYa1FVXHOje+KiJyGyGQ81vT7TuRMKo2YtDHwCVVdAawEPigiK4A7gAdUdRnwQPY/wE2ku5UtA24D/hVSAQl8FrgauAr47AkhmV3zgX7xbjzzrFF/s6qhNrDSU4dn0mVSY97sSgYco0RVU+8vzo3cpyOS9ocNQxxHJElMlACJEhSL9bW7QYkfLo0gYhB100ehHQIRmXABrwOO+gk5cUgD9WqMlSdnWEYUeKq6V1WfyM67geeBRcDNwHeyy74D/FF2fjPwXU15FJiV7Tf7NmCtqnap6hFgLXBj9ts5qvqoprXyu/3uNSVU44jevsqUPV9ESJKExDXYSEfwT2+twRiDRhF9UYRG0TikMWu31uKZgWbp9OLEyo+cnFHVAhG5AHgtsB6Yl+03C7APmJedLwJ29Yu2OwsbLnx3nfB6z79NRB4XkccPHTo0mqSPCs8I5aA4YfcfCREhjmPiJBnZ9FFtUOB5iO9T9n3EP/PtD601VKt97Ni1m7BWPeP7TSTppPGcnFEIPBFpBf4b+JiqHu//W6aZTbhlo6pfU9UrVfXKzs7Osd1kKEvhNDPUIv4oNYJh7JkkSUbX6JwDEQLfgmnArBlBe1FNTTpRRY1JJwyfIeocRgweluN9fbioipHpqen5vkWMZvOfx6+aqnN09/ahp31wzqwpNLKxd87YaahVi4hPKuz+Q1V/nAXvz8xRsr8HsvA9wJJ+0RdnYcOFL64TftZg7eg2A3ci+J6H8abvlA3noFAosWjxPGrVkP1d3YTh9NT0jFjiOP3ojGdXnhqD9Q2am8tNw4gej7MR0+8AXar6sX7hXwIOq+oXROQOYLaqflJE3g58CFhNOkDxZVW9Khu02AicGLV9Ani9qnaJyAbgI6Sm8s+Af1bVn42Qrm5gsO/y5qYTmDhbferI8zX+nK+qc6fo2U1LIwLvOuAh4Bmy1ZvAp0mF04+A84CdwJ9kwkuAr5COtPYBf6Gqj2f3el8WF+DvVPVbWfiVwLdJfW38HPiwjpAwEXlcVa8cVW6nOWdjniDPV870oWn3tDgbK9vZmCfI85Uzfcg7H3JycmYMzSzwvjbVCZgAzsY8QZ6vnGlC05q0OTk5OaOlmTW8nJycnFGRC7ycnJwZQ9MJPBG5UUReyDyr3DFyjKlHRHZk3mCeFJETU3TGzdvMJObjmyJyQEQ29Qtraq85Q+TpcyKyJ3tfT4rI6n6//W2WvhdE5G39wuvWSxG5UETWZ+F3iUgw0XnKGQZVbZoDsMBWYCkQAE8BK6Y6XQ2kewfQOSDs74E7svM7gC9m56tJ5yIKqXea9Vn4bGBb9rcjO++Y5Hy8gXTi+KaJyAewIbtWsrg3TVGePgfcXufaFVmdKwAXZnXRDlcvSeeqrsnO/w34q6mujzP5aDYN7ypgi6puU9UQuJPUO0szMi7eZiYzwar6G6BrQHBTe80ZIk9DcTNwp6rWVHU7sIW0Ttatl5mG+ibgv7L4/csnZwpoNoE3lMeV6Y4CvxSRjSJyWxY2Xt5mpppJ95ozSXwoM8W/2c9v42jzNAc4qqrxgPCcKaLZBF6zcp2qvo7UOeoHReQN/X/MNJqmnx90tuSD1CHtRcBrgL3AP0xtcnLGi2YTeEN5XJnWqOqe7O8B4H9ITaDx8jYz1Zx1XnNUdb+qJqrqgH8nfV8w+jwdJjXlvQHhOVNEswm8x4Bl2chXAKwB7p7iNA2LiLSISNuJc+CtwCbSdJ8YobwV+Gl2fjdwSzbKuRI4lpmM9wFvFZGOzMR6axY21YxLPrLfjovIyqzv65Z+95pUTgjwjHeSvi9I87RGRAoiciHpdgQbGKJeZhrvr4E/zuL3L5+cqWCqR01Ge5CO/r1IOir2malOTwPpXUo6avcU8OyJNJP27zwAvATcT+peC9IRyq9m+XsGuLLfvd5H2lG+hdQLzWTn5YekJl5E2h/1/vHMB3AlqXDZSupxR6YoT9/L0vw0qZBb0O/6z2Tpe4F+o8hD1cvs/W/I8vqfQGGq6+RMPvKlZTk5OTOGZjNpc3JycsZMLvBycnJmDLnAy8nJmTHkAi8nJ2fGkAu8nJycGUMu8HJycmYMucDLycmZMfw/byh++ThKz+YAAAAASUVORK5CYII=\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    }

   ],

   "source": [

    "rreader.plot(labels=True)\n",

    "plt.legend(loc='upper left', bbox_to_anchor=(1, 0.5))"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Inspect regions of interests (ROIs)\n",

    "\n",

    "`RoiReader` object stores ROIs associated with a slide in a convenient `pandas.DataFrame` table.\n",

    "\n",

    "the columns are derived from the annotation file, and not all of them are useful or meaningful. however,  following columns ARE useful:\n",

    "\n",

    "- name  -- contains ROI name / type provided by the annotator (in this case all contours have name 'glom')\n",

    "- vertices -- a list of vertices of the ROI contour\n",

    "- polygon  -- a representation of ROI as `shapely.geometry.Polygon` object. this interface allows to easily perform manipulations with ROIs, such as intersections, unions, deriving convex hulls, and morphologic operations.\n",

    "- area -- areas of the ROIs. one can use it for sanity check\n",

    "- length -- contour length\n",

    "- areamicrons -- area in ${\\mu}m^2$\n",

    "- lengthmicrons -- contour length in ${\\mu}m$"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 12,

   "metadata": {

    "scrolled": true

   },

   "outputs": [

    {

     "data": {

      "text/html": [

       "<div>\n",

       "<style scoped>\n",

       "    .dataframe tbody tr th:only-of-type {\n",

       "        vertical-align: middle;\n",

       "    }\n",

       "\n",

       "    .dataframe tbody tr th {\n",

       "        vertical-align: top;\n",

       "    }\n",

       "\n",

       "    .dataframe thead th {\n",

       "        text-align: right;\n",

       "    }\n",

       "</style>\n",

       "<table border=\"1\" class=\"dataframe\">\n",

       "  <thead>\n",

       "    <tr style=\"text-align: right;\">\n",

       "      <th></th>\n",

       "      <th>analyze</th>\n",

       "      <th>area</th>\n",

       "      <th>areamicrons</th>\n",

       "      <th>displayid</th>\n",

       "      <th>id</th>\n",

       "      <th>imagefocus</th>\n",

       "      <th>imagelocation</th>\n",

       "      <th>inputregionid</th>\n",

       "      <th>length</th>\n",

       "      <th>lengthmicrons</th>\n",

       "      <th>name</th>\n",

       "      <th>negativeroa</th>\n",

       "      <th>selected</th>\n",

       "      <th>type</th>\n",

       "      <th>vertices</th>\n",

       "      <th>zoom</th>\n",

       "      <th>polygon</th>\n",

       "    </tr>\n",

       "  </thead>\n",

       "  <tbody>\n",

       "    <tr>\n",

       "      <th>0</th>\n",

       "      <td>1.0</td>\n",

       "      <td>16592.0</td>\n",

       "      <td>4085.2</td>\n",

       "      <td>1.0</td>\n",

       "      <td>1</td>\n",

       "      <td>-1.0</td>\n",

       "      <td></td>\n",

       "      <td>0.0</td>\n",

       "      <td>531.8</td>\n",

       "      <td>263.9</td>\n",

       "      <td></td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>[(3161.0, 3197.0), (3161.0, 3200.0), (3161.0, ...</td>\n",

       "      <td>0.41</td>\n",

       "      <td>POLYGON ((3161 3197, 3161 3200, 3161 3202, 316...</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>1</th>\n",

       "      <td>1.0</td>\n",

       "      <td>15356.0</td>\n",

       "      <td>3780.9</td>\n",

       "      <td>2.0</td>\n",

       "      <td>2</td>\n",

       "      <td>-1.0</td>\n",

       "      <td></td>\n",

       "      <td>0.0</td>\n",

       "      <td>516.0</td>\n",

       "      <td>256.0</td>\n",

       "      <td></td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>[(2893.0, 3371.0), (2890.0, 3368.0), (2888.0, ...</td>\n",

       "      <td>0.41</td>\n",

       "      <td>POLYGON ((2893 3371, 2890 3368, 2888 3368, 288...</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>2</th>\n",

       "      <td>1.0</td>\n",

       "      <td>12731.5</td>\n",

       "      <td>3134.7</td>\n",

       "      <td>3.0</td>\n",

       "      <td>3</td>\n",

       "      <td>-1.0</td>\n",

       "      <td></td>\n",

       "      <td>0.0</td>\n",

       "      <td>470.7</td>\n",

       "      <td>233.6</td>\n",

       "      <td></td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>[(3320.0, 3419.0), (3317.0, 3419.0), (3315.0, ...</td>\n",

       "      <td>0.41</td>\n",

       "      <td>POLYGON ((3320 3419, 3317 3419, 3315 3419, 331...</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>3</th>\n",

       "      <td>1.0</td>\n",

       "      <td>19654.5</td>\n",

       "      <td>4839.2</td>\n",

       "      <td>4.0</td>\n",

       "      <td>4</td>\n",

       "      <td>-1.0</td>\n",

       "      <td></td>\n",

       "      <td>0.0</td>\n",

       "      <td>646.4</td>\n",

       "      <td>320.8</td>\n",

       "      <td></td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>[(3681.0, 3590.0), (3678.0, 3590.0), (3676.0, ...</td>\n",

       "      <td>0.41</td>\n",

       "      <td>POLYGON ((3681 3590, 3678 3590, 3676 3592, 367...</td>\n",

       "    </tr>\n",

       "    <tr>\n",

       "      <th>4</th>\n",

       "      <td>1.0</td>\n",

       "      <td>11433.0</td>\n",

       "      <td>2815.0</td>\n",

       "      <td>5.0</td>\n",

       "      <td>5</td>\n",

       "      <td>-1.0</td>\n",

       "      <td></td>\n",

       "      <td>0.0</td>\n",

       "      <td>437.3</td>\n",

       "      <td>217.0</td>\n",

       "      <td></td>\n",

       "      <td>1.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>0.0</td>\n",

       "      <td>[(2194.0, 3697.0), (2193.0, 3698.0), (2191.0, ...</td>\n",

       "      <td>0.58</td>\n",

       "      <td>POLYGON ((2194 3697, 2193 3698, 2191 3700, 219...</td>\n",

       "    </tr>\n",

       "  </tbody>\n",

       "</table>\n",

       "</div>"

      ],

      "text/plain": [

       "   analyze     area  areamicrons  displayid  id  imagefocus imagelocation  \\\n",

       "0      1.0  16592.0       4085.2        1.0   1        -1.0                 \n",

       "1      1.0  15356.0       3780.9        2.0   2        -1.0                 \n",

       "2      1.0  12731.5       3134.7        3.0   3        -1.0                 \n",

       "3      1.0  19654.5       4839.2        4.0   4        -1.0                 \n",

       "4      1.0  11433.0       2815.0        5.0   5        -1.0                 \n",

       "\n",

       "   inputregionid  length  lengthmicrons name  negativeroa  selected  type  \\\n",

       "0            0.0   531.8          263.9               1.0       0.0   0.0   \n",

       "1            0.0   516.0          256.0               1.0       0.0   0.0   \n",

       "2            0.0   470.7          233.6               1.0       0.0   0.0   \n",

       "3            0.0   646.4          320.8               1.0       0.0   0.0   \n",

       "4            0.0   437.3          217.0               1.0       0.0   0.0   \n",

       "\n",

       "                                            vertices  zoom  \\\n",

       "0  [(3161.0, 3197.0), (3161.0, 3200.0), (3161.0, ...  0.41   \n",

       "1  [(2893.0, 3371.0), (2890.0, 3368.0), (2888.0, ...  0.41   \n",

       "2  [(3320.0, 3419.0), (3317.0, 3419.0), (3315.0, ...  0.41   \n",

       "3  [(3681.0, 3590.0), (3678.0, 3590.0), (3676.0, ...  0.41   \n",

       "4  [(2194.0, 3697.0), (2193.0, 3698.0), (2191.0, ...  0.58   \n",

       "\n",

       "                                             polygon  \n",

       "0  POLYGON ((3161 3197, 3161 3200, 3161 3202, 316...  \n",

       "1  POLYGON ((2893 3371, 2890 3368, 2888 3368, 288...  \n",

       "2  POLYGON ((3320 3419, 3317 3419, 3315 3419, 331...  \n",

       "3  POLYGON ((3681 3590, 3678 3590, 3676 3592, 367...  \n",

       "4  POLYGON ((2194 3697, 2193 3698, 2191 3700, 219...  "

      ]

     },

     "execution_count": 12,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "rreader.df.head()"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Visualize the shape of a tissue piece\n",

    "`polygon` column stores ROIs as `shapely.geometry.Polygon` objects, which can be easily displayed in a notebook"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 13,

   "metadata": {},

   "outputs": [

    {

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      ],

      "text/plain": [

       "<shapely.geometry.polygon.Polygon at 0x7fdc6cedd080>"

      ]

     },

     "execution_count": 13,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "rreader.df_tissue.iloc[0]['polygon']\n",

    "# we see that thresholding is not ideal, \n",

    "# but it's OK for our application\n",

    "# you can tune it by passing thresholds to the RoiReader class constructor"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Perform descriptive analysis of annotated objects"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### visualize distribution of glomeruli areas"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 14,

   "metadata": {

    "scrolled": true

   },

   "outputs": [

    {

     "data": {

      "text/plain": [

       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdc6cd65320>"

      ]

     },

     "execution_count": 14,

     "metadata": {},

     "output_type": "execute_result"

    },

    {

     "data": {

      "image/png": 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kG/mxi4gjIuLOiLi3yvYXa20vIg6v7j9SPb6xbuYG2a6JiMeWHLeXVPOH+vtQrX9IRNwdETeNy3Hrq8w8qG7ADuD4ZfPeC1xSTV8C/FU1fSbwz0AALwPuqOYfBzxafT22mj62Zp5XAacD2weRB7izWjaqdV/fMNu7gXeusOypwL3A4cDJwNdZfLH7kGr6BcBzqmVOrdb5FHBuNf1h4A96yHYCcHo1fTTwtSrDyI/dGtlGfuyq72Wimj4MuKP6HlfcHvBW4MPV9LnAJ+tmbpDtGuCNKyw/1N+Hav0/Af4RuGmt52GYx62ft4PuDH0V5wDXVtPXAm9YMv/jueh24JiIOAF4HXBzZj6Vmd8FbgY219lxZt4GPDWIPNVjz83M23Pxp+njS7ZVN9tqzgFmMvNHmfkY8AiLwzusOMRDdWb0auCGFb7PbrI9kZl3VdPfBx5k8ZPGIz92a2RbzdCOXfX9L1R3D6tuucb2lh7PG4Azqv33lLlhttUM9fchIk4EzgI+Ut1f63kY2nHrp4Ox0BP4UkRsi8VPoQK0MvOJavpJoFVNrzY8waCHLehXng3VdL9zvr36E/fqqC5p1Mj2PODpzNzXNFv15+yvsHhGN1bHblk2GINjV102uAfYzWLZfX2N7f0sQ/X4nmr/A/ndWJ4tM/cft/dUx+39EXH48mxdZmj6nF4JXAz8tLq/1vMw1OPWLwdjob8yM09ncZTHt0XEq5Y+WP3LPTZv3Rm3PMDfAb8EvAR4ArhilGEiYgL4DPDHmfm9pY+N+titkG0sjl1m/m9mvoTFT2e/FPjlUeRYyfJsEXEacCmLGX+VxcsofzrsXBFxNrA7M7cNe9/DdNAVemburL7uBj7L4g/0rurPMaqvu6vFVxueoKthCxroV56d1XTfcmbmruqX7qfA37N4/Opk+x8W/0Q+dNn8rkXEYSwW5nWZeWM1eyyO3UrZxunYVXmeBm4FXr7G9n6WoXp8fbX/gf5uLMm2ubqElZn5I+Bj1D9uTZ7TVwC/HRE7WLwc8mrgA4zZcWusyQX4Yd+Ao4Cjl0z/F4vXvt/Hs19Ie281fRbPftHlznzmRZfHWHzB5dhq+rgGuTby7Bce+5aHA18EOrNhthOWTL+DxeuBAC/i2S/2PMriCz2HVtMn88yLPS+q1vk0z35B6a095AoWr4FeuWz+yI/dGtlGfuyA5wPHVNPrgP8Azl5te8DbePaLe5+qm7lBthOWHNcrgctH9ftQbWOKZ14UHflx6+dtqDtrHHbxFeR7q9v9wGXV/OcBtwAPA/+25MkPFv8Tjq8D80B7ybZ+j8UXNB4B3tIg0/Us/vn9Exavm13YzzxAG9herfMhqg+DNcj2D9W+72Nx7J2lJXVZtZ+HWPLuARbfjfC16rHLlj0fd1aZPw0c3kO2V7J4OeU+4J7qduY4HLs1so382AEvBu6uMmwH/nyt7QFHVPcfqR5/Qd3MDbJ9uTpu24FP8Mw7YYb6+7BkG1M8U+gjP279vPlJUUkqxEF3DV2StDILXZIKYaFLUiEsdEkqhIUuSYWw0CWpEBa6JBXCQpekQvwfUrHg57JZa14AAAAASUVORK5CYII=\n",

      "text/plain": [

       "<Figure size 432x288 with 1 Axes>"

      ]

     },

     "metadata": {

      "needs_background": "light"

     },

     "output_type": "display_data"

    }

   ],

   "source": [

    "rreader.df[rreader.df.name!='tissue'].area.hist(bins=20)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### find median area of glomeruli ROIs"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 15,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "17923.5"

      ]

     },

     "execution_count": 15,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "med_area = rreader.df[rreader.df.name!='tissue'].area.median()\n",

    "med_area"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### what is the typical radius of a ROI?\n",

    "$A = \\pi R^2$\n",

    "\n",

    "$R = \\sqrt{A/ \\pi}$"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 16,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "75.5329546953856"

      ]

     },

     "execution_count": 16,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "def get_radius(area):\n",

    "    return np.sqrt(area/np.pi)\n",

    "\n",

    "med_radius = get_radius(med_area)\n",

    "med_radius"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Selecting a labelled region of the slide\n",

    "\n",

    "in this case, I have labelled only a region of a mouse kidney for glomeruli (I made sure all gloms within the convex hull are labelled)\n",

    "\n",

    "in order to make sure we are using only the labelled part of the slide (~ left half), we need to select a convex hull (envelope) of the glomeruli contours. we also want to avoid blank space that this envelope might contain -- therefore we'll intesect it with the tissue polygon.\n",

    "\n",

    "for such geometric operations, we will be using `shapely` package, which `slideslicer` is build on.\n",

    "\n",

    "to select the convex hull covering all glomeruli, we first collect contours named `\"glom\"` into a `MultiPolygon` object"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### select and visualize labelled glomeruli"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 17,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "image/svg+xml": [

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      ],

      "text/plain": [

       "<shapely.geometry.multipolygon.MultiPolygon at 0x7fdc6ccf8940>"

      ]

     },

     "execution_count": 17,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "glomeruli = MultiPolygon(rreader.df.loc[rreader.df['name']!='tissue','polygon'].tolist())\n",

    "glomeruli"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "note that this polygon collection display is turned upside down compared to the plot above, because the convention for image display is that `y=0` is at the top, while normally (like here with `shapely` display) `y=0` is at the bottom."

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### select and visualize a [convex hull](https://en.wikipedia.org/wiki/Convex_hull) envelope around labelled glomeruli "

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 18,

   "metadata": {},

   "outputs": [

    {

     "data": {

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      ],

      "text/plain": [

       "<shapely.geometry.polygon.Polygon at 0x7fdc6ccdd208>"

      ]

     },

     "execution_count": 18,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "# select tissue\n",

    "tissue = rreader.df.loc[rreader.df['name']=='tissue','polygon'].iloc[0]\n",

    "# find intersection of the glomeruli's convex hull and the tissue\n",

    "labelled_tissue = glomeruli.convex_hull.intersection(tissue)\n",

    "labelled_tissue"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "additionally we might want to perform [morphological opening](https://en.wikipedia.org/wiki/Opening_(morphology)) on our convex hull before intersection for about a  diameter of a glom"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 19,

   "metadata": {},

   "outputs": [],

   "source": [

    "labelled_tissue = glomeruli.convex_hull.buffer(2*med_radius).intersection(tissue)\n",

    "\n",

    "# intersection might create several pieces, for simplicity we take the largest:\n",

    "if isinstance(labelled_tissue, MultiPolygon):\n",

    "    labelled_tissue = labelled_tissue[np.argmax([x.area for x in labelled_tissue])]"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 20,

   "metadata": {},

   "outputs": [

    {

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      ],

      "text/plain": [

       "<shapely.geometry.polygon.Polygon at 0x7fdc6cceedd8>"

      ]

     },

     "execution_count": 20,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "labelled_tissue"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 21,

   "metadata": {},

   "outputs": [

    {

     "data": {

      "text/plain": [

       "0.2462142805310981"

      ]

     },

     "execution_count": 21,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "np.mean(rreader.df.areamicrons / rreader.df.area)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### replace the 'tissue' ROI with the convex hull of labelled_tissue"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 22,

   "metadata": {},

   "outputs": [],

   "source": [

    "rreader.df.loc[rreader.df['name']=='tissue','polygon'] = labelled_tissue\n",

    "rreader.df.loc[rreader.df['name']=='tissue','vertices'] = \\\n",

    "        rreader.df.loc[rreader.df['name']=='tissue','polygon'].map(lambda pg: np.asarray(pg.boundary).tolist())\n",

    "rreader.df.loc[rreader.df['name']=='tissue','area'] = \\\n",

    "        rreader.df.loc[rreader.df['name']=='tissue','polygon'].map(lambda pg: pg.area)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## save ROIs as a JSON file"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 23,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "/data/14112.json\n"

     ]

    }

   ],

   "source": [

    "json_filename = rreader.save()\n",

    "print(json_filename)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Visualize the shape of the cropped tissue piece"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 24,

   "metadata": {},

   "outputs": [

    {

     "data": {

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      ],

      "text/plain": [

       "<shapely.geometry.polygon.Polygon at 0x7fdc6cceedd8>"

      ]

     },

     "execution_count": 24,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "rreader.df_tissue.iloc[0]['polygon']"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Sample points within the tissue piece"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### Randomly"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 25,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "244\n"

     ]

    },

    {

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      ],

      "text/plain": [

       "<shapely.geometry.multipoint.MultiPointAdapter at 0x7fdc6f1dfb00>"

      ]

     },

     "execution_count": 25,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "points = slsl.sample_points(rreader.df_tissue.polygon.iloc[0], n_points=200,  mode='uniform_random', random_seed=42)\n",

    "print(len(points))\n",

    "asMultiPoint(points)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### On a grid"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 26,

   "metadata": {

    "scrolled": true

   },

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "198\n"

     ]

    },

    {

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      ],

      "text/plain": [

       "<shapely.geometry.multipoint.MultiPointAdapter at 0x7fdc906d6b00>"

      ]

     },

     "execution_count": 26,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "points = slsl.sample_points(rreader.df_tissue.polygon.iloc[0], n_points=200, mode='grid')\n",

    "print(len(points))\n",

    "asMultiPoint(points)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 27,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "198\n"

     ]

    },

    {

     "data": {

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opacity=\"0.6\" /></g></g></svg>"

      ],

      "text/plain": [

       "<shapely.geometry.multipoint.MultiPointAdapter at 0x7fdc6cd2ad30>"

      ]

     },

     "execution_count": 27,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "points = slsl.sample_points(rreader.df_tissue.polygon.iloc[0], n_points=200,  mode='rotated_grid')\n",

    "print(len(points))\n",

    "asMultiPoint(points)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### execution time"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 28,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "1.71 ms ± 13 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"

     ]

    }

   ],

   "source": [

    "%%timeit\n",

    "points = slsl.sample_points(rreader.df_tissue.vertices.iloc[0], spacing=512, mode='grid')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 29,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "2.38 ms ± 43.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"

     ]

    }

   ],

   "source": [

    "%%timeit\n",

    "points = slsl.sample_points(rreader.df_tissue.vertices.iloc[0], spacing=512, mode='rotated_grid')"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 30,

   "metadata": {},

   "outputs": [

    {

     "name": "stdout",

     "output_type": "stream",

     "text": [

      "2.26 ms ± 8.24 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"

     ]

    }

   ],

   "source": [

    "%%timeit\n",

    "points = slsl.sample_points(rreader.df_tissue.vertices.iloc[0], spacing=512, mode='uniform_random')"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "### visualize sampled patches"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 31,

   "metadata": {},

   "outputs": [

    {

     "data": {

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      ],

      "text/plain": [

       "<shapely.geometry.multipolygon.MultiPolygon at 0x7fdc6cca6b38>"

      ]

     },

     "execution_count": 31,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "points = slsl.sample_points(rreader.df_tissue.polygon.iloc[0], spacing=512,  mode='grid')\n",

    "slsl.simulate_patch_sampling(MultiPoint(asMultiPoint(points[:50])), 512)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 32,

   "metadata": {

    "scrolled": true

   },

   "outputs": [

    {

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      ],

      "text/plain": [

       "<shapely.geometry.multipolygon.MultiPolygon at 0x7fdc6cca2128>"

      ]

     },

     "execution_count": 32,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "points = slsl.sample_points(rreader.df_tissue.polygon.iloc[0], spacing=512*2/3,  mode='grid')\n",

    "patches = slsl.simulate_patch_sampling(points, 512)\n",

    "patches[:50]"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": 33,

   "metadata": {},

   "outputs": [

    {

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      ],

      "text/plain": [

       "<shapely.geometry.multipolygon.MultiPolygon at 0x7fdc6cca2e48>"

      ]

     },

     "execution_count": 33,

     "metadata": {},

     "output_type": "execute_result"

    }

   ],

   "source": [

    "points = slsl.sample_points(rreader.df_tissue.polygon.iloc[0], spacing=512,  mode='uniform_random')\n",

    "slsl.simulate_patch_sampling(points, 512)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},