test_im5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found model '2D_versatile_he' for 'StarDist2D'.\n",
      "Loading network weights from 'weights_best.h5'.\n",
      "Loading thresholds from 'thresholds.json'.\n",
      "Using default values: prob_thresh=0.692478, nms_thresh=0.3.\n"
     ]
    }
   ],
   "source": [
    "# Importing all necessary package\n",
    "import os\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n",
    "\n",
    "#A generalist algorithm for cell and nucleus segmentation (v1.0) that can be optimized for your own data (v2.0)\n",
    "from cellpose.models import Cellpose \n",
    "from stardist.models import StarDist2D\n",
    "\n",
    "from csbdeep.utils import normalize\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow import keras\n",
    "from tensorflow.config import list_physical_devices\n",
    "\n",
    "# Setting the random seed for reproducibility\n",
    "tf.random.set_seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Check if GPU is available\n",
    "if len(list_physical_devices(\"GPU\")) >= 1:\n",
    "    use_GPU = True\n",
    "    tf.config.experimental.set_memory_growth(list_physical_devices(\"GPU\")[0], True)\n",
    "\n",
    "else:\n",
    "    use_GPU = False\n",
    "\n",
    "def load_cellpose():\n",
    "    \"\"\"Load the cellpose model and return it\"\"\"\n",
    "    model_c = Cellpose(gpu=use_GPU, model_type=\"cyto2\")\n",
    "    return model_c\n",
    "\n",
    "\n",
    "def load_stardist():\n",
    "    \"\"\"Load the Stardist model and return it\"\"\"\n",
    "    model_s = StarDist2D.from_pretrained(\"2D_versatile_he\")\n",
    "    return model_s\n",
    "\n",
    "\n",
    "def run_cellpose(image, model_cellpose, diameter=None):\n",
    "    \"\"\"Run the cellpose model on the image and return the mask\"\"\"\n",
    "    channel = [[0, 0]]\n",
    "    mask_cellpose, _, _, _ = model_cellpose.eval(\n",
    "        image, batch_size=1, diameter=diameter, channels=channel\n",
    "    )\n",
    "    return mask_cellpose\n",
    "\n",
    "\n",
    "def run_stardist(image, model_stardist, nms_thresh=0.4, prob_thresh=0.5):\n",
    "    \"\"\"Run the stardist model on the image and return the mask\"\"\"\n",
    "    img_norm = image / 255\n",
    "    img_norm = normalize(img_norm, 1, 99.8)\n",
    "    mask_stardist, _ = model_stardist.predict_instances(\n",
    "        img_norm, nms_thresh=nms_thresh, prob_thresh=prob_thresh\n",
    "    )\n",
    "    return mask_stardist\n",
    "\n",
    "model_cellpose = load_cellpose()\n",
    "model_stardist = load_stardist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "No such file: '/Users/afaf/stage-afaf/sample_img/5.jpg'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [2], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[39mexcept\u001b[39;00m:\n\u001b[1;32m      5\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mimageio\u001b[39;00m \u001b[39mimport\u001b[39;00m imread\n\u001b[0;32m----> 7\u001b[0m img \u001b[39m=\u001b[39m imread(\u001b[39m\"\u001b[39;49m\u001b[39msample_img/5.jpg\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m      9\u001b[0m \u001b[39m# CellPose\u001b[39;00m\n\u001b[1;32m     10\u001b[0m mask_cellpose \u001b[39m=\u001b[39m run_cellpose(\n\u001b[1;32m     11\u001b[0m     img, model_cellpose)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/stage-afaf/lib/python3.9/site-packages/imageio/v2.py:200\u001b[0m, in \u001b[0;36mimread\u001b[0;34m(uri, format, **kwargs)\u001b[0m\n\u001b[1;32m    197\u001b[0m imopen_args \u001b[39m=\u001b[39m decypher_format_arg(\u001b[39mformat\u001b[39m)\n\u001b[1;32m    198\u001b[0m imopen_args[\u001b[39m\"\u001b[39m\u001b[39mlegacy_mode\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m--> 200\u001b[0m \u001b[39mwith\u001b[39;00m imopen(uri, \u001b[39m\"\u001b[39;49m\u001b[39mri\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mimopen_args) \u001b[39mas\u001b[39;00m file:\n\u001b[1;32m    201\u001b[0m     \u001b[39mreturn\u001b[39;00m file\u001b[39m.\u001b[39mread(index\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/stage-afaf/lib/python3.9/site-packages/imageio/core/imopen.py:118\u001b[0m, in \u001b[0;36mimopen\u001b[0;34m(uri, io_mode, plugin, extension, format_hint, legacy_mode, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m     request\u001b[39m.\u001b[39mformat_hint \u001b[39m=\u001b[39m format_hint\n\u001b[1;32m    117\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 118\u001b[0m     request \u001b[39m=\u001b[39m Request(uri, io_mode, format_hint\u001b[39m=\u001b[39;49mformat_hint, extension\u001b[39m=\u001b[39;49mextension)\n\u001b[1;32m    120\u001b[0m source \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m<bytes>\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(uri, \u001b[39mbytes\u001b[39m) \u001b[39melse\u001b[39;00m uri\n\u001b[1;32m    122\u001b[0m \u001b[39m# fast-path based on plugin\u001b[39;00m\n\u001b[1;32m    123\u001b[0m \u001b[39m# (except in legacy mode)\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/stage-afaf/lib/python3.9/site-packages/imageio/core/request.py:248\u001b[0m, in \u001b[0;36mRequest.__init__\u001b[0;34m(self, uri, mode, extension, format_hint, **kwargs)\u001b[0m\n\u001b[1;32m    245\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mInvalid Request.Mode: \u001b[39m\u001b[39m{\u001b[39;00mmode\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    247\u001b[0m \u001b[39m# Parse what was given\u001b[39;00m\n\u001b[0;32m--> 248\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_uri(uri)\n\u001b[1;32m    250\u001b[0m \u001b[39m# Set extension\u001b[39;00m\n\u001b[1;32m    251\u001b[0m \u001b[39mif\u001b[39;00m extension \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/opt/anaconda3/envs/stage-afaf/lib/python3.9/site-packages/imageio/core/request.py:407\u001b[0m, in \u001b[0;36mRequest._parse_uri\u001b[0;34m(self, uri)\u001b[0m\n\u001b[1;32m    404\u001b[0m \u001b[39mif\u001b[39;00m is_read_request:\n\u001b[1;32m    405\u001b[0m     \u001b[39m# Reading: check that the file exists (but is allowed a dir)\u001b[39;00m\n\u001b[1;32m    406\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mexists(fn):\n\u001b[0;32m--> 407\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mNo such file: \u001b[39m\u001b[39m'\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m fn)\n\u001b[1;32m    408\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    409\u001b[0m     \u001b[39m# Writing: check that the directory to write to does exist\u001b[39;00m\n\u001b[1;32m    410\u001b[0m     dn \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mdirname(fn)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: No such file: '/Users/afaf/stage-afaf/sample_img/5.jpg'"
     ]
    }
   ],
   "source": [
    "# Load an image, run CellPose to detect muscle fibers and Stardist to detect nuclei.\n",
    "try:\n",
    "    from imageio.v2 import imread\n",
    "except:\n",
    "    from imageio import imread\n",
    "\n",
    "img = imread(\"sample_img/5.jpg\")\n",
    "\n",
    "# CellPose\n",
    "mask_cellpose = run_cellpose(\n",
    "    img, model_cellpose)\n",
    "mask_cellpose = mask_cellpose.astype(np.uint16)\n",
    "cellpose_mask_filename = \"results/sample_he_cellpose_mask.tiff\"\n",
    "Image.fromarray(mask_cellpose).save(cellpose_mask_filename)\n",
    "\n",
    "# Stardist\n",
    "mask_stardist = run_stardist(\n",
    "    img, model_stardist)\n",
    "mask_stardist = mask_stardist.astype(np.uint16)\n",
    "stardist_filename = \"results/sample_he_stardist_mask.tiff\"\n",
    "Image.fromarray(mask_stardist).save(stardist_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qyOPO0wEa7xjzc931KD7T3k/dKy/jywX+rdcfwUhnfZYSEVgurnoYj1RyfIReXGIXF1c9lA+Unp6Em3zWZy4vBf3iK0g17HCFLydvoi27+G7TJIIXQXeG7OoVjPaj6/p/qn26n2uzZs2aNevJ1GMNGf/kn/yTvPvuu/ylv/SXeP311/nKV77CL/7iL/Lqq69+6NeQdB5Ju/TX6BOXnffiwr4oHowgIWBCIQ3YRvXkJA065ZceD3iGWRoEG5HKkknLC2gJirFfDtj6t4XTsWEJa9bLKLnVaSvadlj2ZTSNi/7oExdj1UUHOxmv81tfwAAFk+tNErKNLrC9XlBGgJDSBTWo5JckkxEM6OiYS6dhOjjdoiy29dQLA1M420Q83Fijs9Oydxujc0pGONtcZ+21c79oTzk+grrBLy9QCeQhaUt1QIrmxnsAV5PGtwLgmKEJc4JY2bhPRqcVHiU4EqC3QeEGjkWFoQ0KqJIOvJzX0kpoZdwQjCiRBhk56GDKZm2c3Vlz5+yc82e+wedevkF/cACXO84fnHHw7C04WtL62ak3F1yAKqsGuwFRRZeLWA9m+GC0nSUCdOF+FRNQpdaKosiioHXJsN7RSfRuK32HuWNDxWqlE0lAJOFEzRJ8JF7DqyHieHvdIpSScL/WJIJtJYcreHQBVwsAmVY+aaW4BoMZaJZgJwyU1tcPb/8PRmgNDoZNsFYfHxfrMEv6c5808A1xAyLgeroQ2yGR29IgZTTvTPguIBYu2OiKsNcqQKYxqWrCzOgL6lLyeJDxTkUDl572xgCfAWaVjmoOekRdnuI3jlAbGN65gw87aro0Wz/XQkctHVjl4PYRw4NnuHh4l9I7B51nyToMNqAO4tbuU0yAtrZjcDpfjXdWVON0104nLmjRbO/g43EEPppJwyGe85M3HshzQ7yDPjUX+I/iM21f0nV4rZSTE9Y//Bz9/TVPx0xeM41W62skEfTWzScCMlIe26Lbay8/6Nk9e/jRntMJUuXpcTo/5nrUn2uzZs2aNevJ12MNGQF+7ud+jp/7uZ/72M+ve2AhYJkTRY0Kmj0JS4cO7YKfqT+hgKinYW3qs9e8exPlmAAUGfAR8Guv56K0Dn1KQ56yByzjq1KJV9bWt9FoVdtRvpxj6BRqAKcGHhFFSkcLYJCW0pBuyZEkJICLaUmgQxtiu8jJsJHWq9Fb6WQG3UjrL5kQ16cCU6OStA7x9DjK9D7xOB3Ls5u7UcxHp5OnM0rSLYVmeXN/EKVk53eR7QbExkCRAMLxvICgUco6gpsEMU7MWyEhUAMpORfp3UyHXszT5HhMtpcuNCmOSvRGzKlOViYBnvK9G7gK+6fiYmNZtThsL9dcroXtb7/Djdfe5eQLt+luHKPnFVtvkaNl9O6zhGclXIzy8JJy5yG+2cGNwwDGQ5T1dkNi9EWHnh5TVx1S4ku5bQa0Rnm99j2UnmGI4CPtC0ONPo7mTq+FutvG+ul60A4vGSaDgxpQsq+hU1r/0SFhru67AAkgXxLoNZilCaeqIFj0/iwl2/kZxpBrpEHFeL6lw85q7COjYMMEu/YOrLx5EM+2FoCUpbuaayfWiEH24dQSpdDW1mr+0zzPADKeBcLMlz07vS3sLL0XF6xWpCsxfs/ApqR1mi6+5uiLlgUJsmVFZYkenyKHBX9wl83Dc3w7IH2h9LFOnWCgbopbYXcmyOERh8+tOH/9gPMHG+RkYLWI85x0BR88HZ01HJmU2Cce5w9XCWCdLktPKKna5TkigLG5IyWJopQ0Nmoe03nucX/PTQTXRiDj93G/4JpBlvfRJ/1Ma+pe/QLbH3oWqjMsC3WhyMmSRSkB9590WbsJ9BQAoGtQ3v59tejRw8MnAzTO+mylUI+W2PKjH98ugnWKbq/Bee6a6FF9rs2aNWvWrOuha/rNdlIgRSDL/qKnWvxG8kaoS49Y4sOxXDRdN+OzSzjRhHSVvRdgTWEo7SKdfF7gPPMMleG7S4jTyZg9zGjOOSHDmGtyzJaYG1hD86Lfber3Nvn5JnNiA6Rj07d0iUV4jY0X9/v5Mq3cWyTKXhGNnoYjbJMWwpxuP0+XZkv0bdvdAKlhHuXI7hEkgbTnBhwJOJvuNHfwGhdeQ7OB9bA6Qm2LnZ3hVEpunyNYtXBU5VwLkk5HxakjYCoaJbhkCfsEiSzXSCTnltKFwc4yeCUBpY1cpJVPC5iEQ7JkFIsnpB1L77OE1j28ocUxixRi6ZXljQV6uaPe2XL59jnHX3oR64XDW7fYeYSQ1K1Rt1skXXPd/Qu6N+7R3b+MAJ+jh/jdhwF2rIZDUAVfFFh2cLiEVZf7LsYsUhhuHlMPlyzEI3F7l0Ex4njfMxjYRSZ19wWXPo2HGsdIK30d2j5NwJ2J0LQAo3SAWgPKCbBjOWc6tyYIF6MkiKtSc3E0sJ/rOnuctv0WgLki2o2ORBPQCIyfkt5h7K8ZJdVT30DJYBnzLFVuSeuazk1PoO4NMEYAkFj2LfSWPJ3uTloZMKh2DFbzHBGQlBxXFaGII645F4Au2bFCl8eU0wOwNfX+u7De0OmAHCxiHM356x6hRWoIG3wjDPcqerBgdVRYnzlnF4Z0hWUPxQr0oO4JjGN9tiMizhHpqJQ8H2qXNwpsDLOKKbE8V8no6BayF2rEcO/dVGknhTwnj7dcZn0U6WrJ+kvPU5fv7UtWl4ocHeIPzq5oZI9Y7YPsGktEok/tNZXeuoldXn78fdm+G323bAZMT6w6pR4tsP7jr3vvwkEvg33wg2fNmjVr1qzHXVrQn/gy8ua71LffvurRfGJde8gYFY4Bd0ZHlQdM9GIwOL5YYpsOrUPCwijxJR1WwUFsdEV1Rak1r5QzS0Vdm5Exeot5dOJzKYBlH7sES1neq+l6cne0EuMhHIzqDbq155HuMhtLMSV7yxmF3QA9Ojo2x+v4UuK9s+edpNUv0l2z7DPLjoO9ZTq2RY9B0VbCCs2V955EoeZEkjo6LjVn3CTdlkDxEiBUAvuq+QhXjYmDIopJ1hObRV/KolRbwPEBfvkA6oYylnlL7iMBj3LfABoxxghfKUAdXag6Tg5ZAu+tWSAiHmWkYogXSBDrgFmmOXc0Q2iApVqznV44b8bS6tYCL9tjSnNGSsGKInXHonNuPNthx0suLpSy2VC0g02FUugM7P4FoBy6MgwVuXuf7ndeR8+HCJ4ZdvDGFt/tYEgApkAdQEo4A7O3I6XQLQ+wroPVit39C7obx3CyRE8O2LlRiqJaAhwidH2HXO7wUmBRkK0zWOzvjLqmZFJx9DwNh6iW5qZtTrgcFzoC/7Y+WjntBOZq8q3sxdjct7mOowyXEUoH2GzHd4BlVMa+qgaxZsxyjvI4qRGY4yUTuomUbTdP4Fjz7zIeVNpcySLhLFVFUQyjqEdyeQnw76PLN4F+C7WxPO7LFDTlZrh0VD0AOURPD5ClYg/vwOYh6hUpASNbL0urBkOsr4pTxChUlotC2a3ZXG7o+gXLwxWbc2PYCsuuuT0VTNAuWwUkZLR02KqH4zZczHEujXRpcosYnZrtZGvpcGxQebzNM9480engG/+e47m+nOWRS17+3PcARiACzG6ewHWBjLVeX5dfUyl7BP4aatEjiwW+2Xz454hQTk7wV15iuH1A/T5ut+5sh24Hyp2HsN5gd+9hm81TAaafZPmyUI8XjyRkx3vNFjHzPp81a9asWU+2vvM//738mf/Jf8L/586PcPfP/x70b//Dqx7SJ9I1//YOrjYGZkQ1Y8GrYbWCd9RhiywPqbJAdAhnnRMX8lnOF3EI7eraoitcadAxLqhbD8KxPLDZJMWTX2XwSjN24a2qOJ8PLXgi4E2iwvZYpmpCIwFPup5ayrOJUESRCp5BJtklMQM0mlOqtpricBO2MlJaBARYS2NOSibuuJcsJbb3gEbxgZwoCC9fwssGH5i2i4YdDDJVuG2j1Rrpw0yvo0L0oTw4QlUYLtb0IrjXdFOlFyr720m6Hlohqyagip6SDQQ7lGSLNYBNAEahzXYMK0t0Ja51zT1CYVr/P4fKkNV84QYNyFJinXnNUtapLNtNAkbF6HBRjnphcVAYTgt1ewn3ztDnbzHsBnTVo1qolxs26x3ddkf3jTfp7pzhwy5Gu97Aegs14FasndyvGn07S5dzslzCtuLDgCwXLI6WUDfYwyW2PqUcH2C2xVUopW/slepGVwosOgyh6wpjj0ITvBpVLN2JGQOUQSyKvuciwMWhjTMXxeRsTBDfbLWtkaKHozGODbDiUU7s8fpOuBslgVaRQrWBdsRClLAn389167h6TpcHXGuuYoj3TDDqcYDlsRGl2kWyPl4LQ4Lm5syrFm/WGDYoLkOCX0G0a5MRoBNAOkwPsMPbdMdLqA+xe/fx3ZqSWUPSji/bxYaYo13s5yDJ4R4Ur2gHpcJuu0MXS7p6xOXFJV2prHqQLufFsudm64s5nrfiuLBsvaCW75MJ9jRvqab7sjmws8dDuLbTaZr7QFtfVknoL3mO8XZmmPVhNDx38v6/u3VI+VbcpHnS5XX63LiOEpEI57rm0tPTD31Xvty6xfATr/Lw2QXWvf/e357EdwV59TjeY/sKi/s7+tfvwTt3qA8efmi3o19eIjeO53TpT1l22GMH3SOb57lsetasWbNmXRc9/LEtP7n8Nj/5uW/zN//3X+Wf/ls/hfzyr131sD62rv2328j1yJJO2wsdKB2DQ92CHiqlBRb4VM4cITER2OLZcy6+GyVAyMuf+FmWTiZwlJEuZDlja2SYzr/pQlwCuDhQnZIX3paQJfr92eiGij5+CT+TQ3bJFyIrIgI4ChHwoBqp2pjQkrOl7JU0JzCU6MiW5ceN/TglYV1U6YZryc0iqKGFioSlKTZO6uh8jLTuvNAde1IGzCmUhI9Zai0lnH46ucSEAL3CERwdMZxf4mcVWyil60aX57QPoux2TCNOeCsWgNCSH7k7JUvfEaMCRTTnN7pmxhByPjKxO9x6bdcFhCk+AeJqlU5jHVSvgb48Had75q1O+uzTmNAGoXjFgDpsEioJlCUmyrDZwuWOfjD03TPKu/eR9SXstth2B8OQIMuxQShZAmxWoympRZ9MocSuurjEN7tw790pyOoeenJId+c+dbWkHB9Ruw5fLZGjJfSKaocVxY+P4O4C80sURa3GQVYk+3dqQEC16AGZEFklSpsml23u54xS9lxfrmPW8OSsrZMDzrAouc4+jJ4hKS0JPW3HWI2/j/PueTJobQ0k080p4VrNGwHTcdGgeRyDY8lw3iiIpOZKEcXMKAnHrUZ/Q8mgpTjn5BygmEIdKpqgOk4dirHA5RA5vUU5KvjFXez8HmpGkTwObeoBWd2xnWFWA3znHHkeZaoKg9GVHTYM+CX0h6ds1z0XFw/QY6PPtOvWrtXZa9eA4VRUyvie5hatDcRzGhM0jvtvWuea56p4suYJyqIfKW2/tPNpgtvxFsesD9LuuH/fToXDcU+3WuHXoQ+eZcuF/YTZ66Tr7mJMyeEKtPxg6KeF8uUvcv7lZ6jLDz8n7eZrXQqXzy9YP/c8Ys/Rn1WWr93Fv/3GB/aEtAcP0ReefSr2xZXoEZRHv5/msulZs2bNmnUd9MW/Dq/9gWd4pb/DH7/5D/j7/4tXuPmnX2b41revemgfS9f+qm7sI9h6zhWi/EoLTsfFgzUXFzsGSSApmr36AtJl+gQyGu4yEdkacDDEa5RVZtkjMEEKM9QtLsqF8UusZzxrlE5GH7/W35DmttIcb/YOFA/gZa4YiosEBFCj0x2lRNhJrQEhHB/BpuRrWaIVTY9XbGsJEOBR9q2UNth4nRpJfl7D0UaRMDRlJaQn3GtZJ1LD+WhWo2delg6jDUZk0IW0YIgGdaP3o1vCF6tU69gtj9Blj59dYGwDcgSCpOFQ2QO6NkLZffAa7yuiSAbstAe0gBlvABcSujiW7tKWZB0ux8RkuV0BVdPB1krtbSrP9izVlnyMkWvNHdWS2+KUTujdKHfPsXfvwTv3KNVY9kuWhweoKnr/HNlt8e0Gv7yEYQdeA5QBUmjWOdQNfIdLDfcqFYYt4kZXhE6heIXLc/TOHeQ7b6DffIPuO2/RPThD1ltkXWGdpctF0eMTrDvEvYvtKAUvirqO2xcJxEoYLeM4sBpw0DygpFqNHoLS1kaD/NmjtC0wb6XK6XqjAGVyQni67UY8GMeVFEWI/Rbl77n+JdyV1Vpad43j03KdVsOt0sJjAndaulTTzKjxvtIWmYF7jeflr8MprLnNURpe3SilUEqHimKuVBF21rHTU3j2OfzQ0XtvoWex7wsea88sIW3Mi6ogWildc4MKtQ4xLhy8Rm9QYLF0Or2EzQVltWJYHHO+KWwaHHRQFwoS+3B0K3bU8XCIMvyIdmrHdUxqjRmidaEkz4PjuZDsbZs9afGAxbFjrGHRiQjP+kD9oIwcK4Icf7S01sda17n3nl77r2AhVcrx0Q/4fUG++uOc/eTtjwQYv59c4hjY3Ow4+93PcfmHfxL96Z+g3Lr1/s8ZdrDdfaL3nfX95auO4WT5qQDG8T16xa9xX9NZs2bNmnX9tfh//yr/m//zn+CtGtVKf+nL/0/6f29Av/JjVzyyj6dr/6ncmEAAQh+hRvSAg81Z5ezuhh19hpdogrmEVhKJuQXJCtl0Nu65nSJVWElumD3dAn64++hAUslAjHQwxu8CyGm64xRHvEZ/v7HcMHr9mQcsQ0oEVnQlQhe8orYNn5d01BoX/fFVPUlCOh9FJ4CodGCavNSjxDeh3AhA8yLINXsnImOJtoij6T8SgeIONgRwcUv04wmVwulEyb6RHiDKUSTdUiJKVJkWqnnAwHIIN0/x7QM6zlgeWLToakE4NPob+yx4y7TtDT5pc0VazBfN2SaOaJZLI9EDktZjL+cie/1FyXgAYRv37Wjj2iuVVzTDcsaa+Fx7sTtrevKI/oVqdCpINdhs4J17+G+/jf/jb8A/+k349ltRDv3wEnl4jm7XsNugbgm5wS17AUI6F6OMu7lUWwJ2kSi4FQ0QJRjFDduusfUlPHyIvXMH+fZblHfv0V9ewtk5ZQdSCnJ6BCdHmHbZ29TBIo27WkLoBmzH4yxBmOQayJplz7AQJcKA3A1Xz96JjPs2VnOZHIY46iWgeAI4rMa+tQSOWX6t2k09RS1creIZ2tPaDGAM4lDTQdv6sGb579RXNLmnxbo2yW3ItRsJzbl+LMqOVQuqmkFJhe0AlB4rJVxMLKj9TfT5W+CX8PYb6OYcsZrHU4SqgCNFMBlQif3Y94W+KJrrXjVcz1jN5omKdkrXwerQ6ThH1ueUbgV6xG67ZDAlEuEj7dutZruDdgzl9oiipRBG3bipYBJl1kqX+zVufDhMKdW57kQkk+Dz3JMl9iS8DEg+u4gelez0GkHG65CU/X103QNfvltycvy+v+te/TznXzh65AHzLjAcKGdfOuH8D3yZ7uWX3ueBjlx+hJ6Rsz6U/KBnOF5ENc2n+T4iePf0HEuzZs2aNev6yYeBl/93f4+/+Nf+NdbeU3D+rc//57zx70D3uRevengfWdf+UzkuYIHRV5a8J11ULpXdww27bkltDeYzgSCcTo1PBOSjOdGCHQb4y8TZ5m4by5mzpHi6+LYRvCRCiV59e/0EK80NJYxZE9acPwESJKlH9KBr0Cb6TMpyEU4k9+ytRgYwRHKuZ89CpDV5yy9/I4lNDKBMF/2txDzdfNqKUxM2BFAIN5dkyaq3MjeABJRUQU2BguUYovzax7kMYuaIKbUWfHkcsOzsHoUBZQBqBN6UgIDNcSa5EZL7IiAgew7TcJSWBiYTlJYCzQUJAUBrBVxi//jIaWnl9K3SN/dE7sIS68E8IGqukQZe2lzEOmnPC1BoPoBazPHZJXZ2Qb1zye61u9Tffgdefwd5823kQTS5190wrjUR0FJyXVm+RQ04JoKmG7fsBbFIg2m5pkvpI/ClOGwu4cE95LXX4Ld+h3Jxhq8vkKHCsofjI0wWSLcAVSqCJVjVLLeNZS/jXGGx1iU5YW2u0AS3OvYc9EhcNovU9LaeJHpzhlHSUK15DDQIVuL1xyNL8m1bqbXk+OI4QIj0b/eA3AaWLtBIQ29L38duAK38vu11yePU2p2G5pjEGBJwS/UsMVboFKPn4sw5v1Q2rNj2z1Cee4ayW8O9t9HdBmrNNVHjuM81HseU4bVO55ns+WrtMFaF0lGHgTrs8jA3+s45PBpYlYewfohRMBZUW1K9xwiXaZzzcs0XSZif/Wa9OXaZgH7OkbR1pYKU5jSVac7Gc3BG+2ghAoDi9YqDcD1h0lWoHi+vj0vuuoY6dN30+fs0qO/Qo+91M5bbz3D+48/HDdRPUXUpPPzq5yk/8SPfd97t7r24UTXrk0uhniwZjvrP7C29CN6/XxOJWbNmzZo16/GXDwOv/rv/mJ/75X+NrRcKzv/2K/8Bv/HzX4zq2idI1+Qq5P01BjkUBS2odlFqKEYpleVSkCEqZYxF9rCD6IuYTCYBQ1YC7vXsS1eeRP8yb24sLPsXJiQUodrU73AEFRkQ0uieZxiKeSbTWtKVhDOTHZFw15V4b0mYprXCYoGULsFSC2+J54UzUdijl6PDzJpLb0zRjr5smo9pjrkRG+z1rIuizAY+Y9zNCOo+RUR4AiQF+iKoOEVAVWKfkLw1KafIEg4WlMsHdJtNOMMYhxzuMZ0Ao6omYGt9NVuHTIvyTsm5ZW8/ONG/z/cCfAQGB5OCoYguMO1H12WRks66LmCJpCNRoJPcFtUIyCmBZOvOsV2UkHubd83ZyzJYFUfVKLsdcrnBtrvouXjvAv/mW8i338YvzrFhi9sAVtGi2fswnG8BiCxSixMkatEEa1FGrF2hdB1d1yES7rSiWbKt0OEUN2S9xt98F3/tTcrlZZTfCnTHB8hiiQ0BsjuJ814wziwjNgn4THMMTqS2VUiLtL3jE4h1wDWgp5Z4nJMJkpau4LHYnea4NbcRBirtSeG0M7K8WPYQoWikXzfgLyVKhfO41gxMSmNrlMGr4EXHcCgSgIYDNn5YzZrxMNZTJ5hG6M5u2/Pg3cK7b/U8OD9iWDxHee4Wsj1DHryD2G6ElFUiHTscuGBmY+uB6XZJOCpFoqdoA8duDkUpJW9kUBBTeuBwsWVZzpHhEtcO+gO8RPl7TEVFpKZLGUYHrhAuX+mIVr4CNiAthErI82COKZ0r7pY3OWr0CG3jz36kNp1G5mrpRyjrFbkmAMuv4cIQEeierC+Ln1gi6M0b7wF8enjI5X/rSwwHn81X0boQzn70FvqTP/o9oNEuL5H19jMZx7VWEYbTFfZ9EsE/bVmvs6Nx1qxZs2Y90aoPHvBj//Zv8z/7u//6CBr/yH/nV9GjJ6tK6dp/Glv18UJWErpJ1k1qMRZLoSjUQaiyjF5zVSL/N51/4q3Uj/GCuwUkYDUhX5Q0k+m2DTxBoKswlaRDyKLX3+SKS5joGrCgOF6msmORSI3upKX2JnTxFpGRkGO7i0AWj/5mDW6EE6+5EglEIy1gI9GaVWqWSnpCR2upr0n+Rs4pEd4ywdIEpDRHI5BATgnHEg12tsdadHBr5ccujBfF3gDNcgWrgpyfp9OzBV+EW1ATDjlZwk2M2fL51oAvrZRUwkGXu9LHx8kIq/InqHr067PoZajiGUzD2CNQmPZdAM82N5HgrSOLVbTvokG5aATvuCE1nZYSffBUI7BFtxtks4k05G2NYJeLNazXqFewmvsn7ngwDFE2rM0Z6Dmm0UiLikQ4icT2RaJ2RbR10guA5RbhKJKOS8zg/jn21j10t4PdjrLqKccrSteNLtkozxVKX5Cuiz+lxPYmPa7ZV7AtGyzLqseSwXS8SrgcqzlmUcJuuR3hjkwI70D2HQyYn4grnZDkumiOVSd6MSI69VRtbst2vsih1VrT1RjHd0nXp1RD0zbo5tR09FnuS9kvlZdsvWBgdAy2YuNL/Nln6F59nv5zJ7B+gN19l7qLXpmxJANguzWDpIxrRNq6k5ol6XkMKzGPquN5TrN02swmEN8JB4dGp2vcBnYmmC6xcojJAjPBLPqKerNH0tLrPe7BeL5h66MoLUQnC/PF90J+ZFpXtA2a9lkr5yf7pc56NKpLjST56yCz61cy3fdPl4uxadEjiwUAslwyfO1HGQ4/2+PeFR5++cb3gkZ3/Ox86ss866OrCMPJ8kpBn/d6fYOiZs2aNWvWU6F69y4/+me/wb/9f/03+U8f/Ev8ra//GMNP/y7K6elVD+1D69qnSzeo4p7luRJOqwifFZZHwmHXYcsOWRaMC7TmhXKDXVlyrRnkECEIU+kfaPb1m0BeCzpBsoRRyVCJgIYNao3/beG2atf14oBaptf69HhaorJgZmk0ytLXbUUOYPAy9iBslKm5kiK9OUFBhkSIt/55YyF0OkCVajYFrHi4lbAsD5bst+Y1yx7z/ZrRygMKBUjLtNlED+aWgDQdUhHjjKPR640eOzkE2+F1E/PoJUCSt/LkybEYP7Bx+msr8UZB91KoR+g6lQwH/x1QNNo1Zj/B96CnnGNaFXjb1ebZjy+DdpS4KJYoG1UpOBWzVkYf5bOimsnn6TZrzrhqeBkQ2yL9Edqlw3GzQesOtXCOVSHTq6fBKGBSwznrFq5GLEtrE3p7Fiq3qO0cJ5Yp5Ej2nowEYXODYUt95130cAFHRzgKx8fYvbuoBkYezLDdAJ0yeINHdexn2OI9JEN7mrPWUaQF/eTxSkscz7Lm0QVJHEOjszdLeUfHcPu3ZP/T7OVIQ82aTr98XCtxLqpUq7FPsqdkuOza3YmEY1om4IeEO3qvd+tk7jWsGpQ+QZ0y+IKqC1YvH1JuHNH3Qjk7o957m1K3WSqfa0AEH2IeZHQdN1egUr3GrRLf2/PJuwN2J/4foaTlecpQCipO3wnrs3N2W2c4KhwcLlBzdHCKDCCZGt6apIlg5LmqXcDl2nPNtg25ttvNjwgAqrG/SxcjyPOJSOtjAO6aNwpmyPjIJIIsF9cjYRqulc31aevF+N3Sw0PqZoP82A+zfm7xyPswfhg10HhiX6b+038+/tzu3UefvZn9cmd9JD0GgBHipq31Bd0MVzqOWbNmzZo165OovnuHL/4ffp2/cfL7qEfGv/gfwcEf/gqv/sd3sV/79ase3gfq2n/Tlex3CCQLyAvaRGedOMfHwtGhUg5WIBlikKAETbeggZhEQItLXMgrEZZS0iUpo6cqL84VKthgMNgIA6NFXiTfjt+vR8AouClCh1DGa6tWauxmY4qvShZ1SqTk+m6DWEUWOgKV5kCMl5YJADL1qjS3CKhwQVu5a/yK1uewQUZtUCjLcrGaiR+WwE0yBEcmZ+Lo4px68CUii7fxCLtxk7F02VZH6MESPXsQAScmCVHKWCZte6WaGVUcPfdav8FOo88e0mJWJmebN5dZ9L8TNN1xU29HvAGlyfnZXJOje3HPfekQ+5l8aQ+oIibhhGupvd4F9EkXYfOEhrvQUCqdO6vDEoBrt0PqFhkqUivUGgFBEuMiQVLbp9LK9dUpEhdLXsClgtcsO4/1M65t1wwgqhHmY+F0xLeI7dDLS/ztOxSrMdcHKzg8xlxxc0pxSheOtODMGQDTXHUWwDlcdtP60pjK7I8YxxambfJwH7BhwAajeg0Hba5hr9bMs1nC7FmKLWgrHc9jZgSLo7MwXZNEIrhIc36mo05yajVgu5cYk7hHn06m92sl2hG8FL0YtVNU012qHVWX6PGKRW8sthvKbo2f3aP4QOkjxAXNFGZ3tCPaIYxrg72S9572joKhzafsjtea48z1m65DbzdFWocENXw98PDdc87u73h4f8dgC0yWAWTzhsB4/nSnOBRzSusFiozHe1uKLQgpPKXD2BYwzmWaNwR8D7A3iGnTeXrWJ5YL+OHqqofx6HSd+jKW1lviKVXXoasVly8fXwlgbHKF8y/dpNx+ZvrZbjsHwHwcKY8FYGya+zPOmjVr1qxroUWPt48zhcuXB77+b9yg/PiXr3RYH0aPxzeCT1FpjMrSvIBczRko2Uet18piu0Xcsb7HVaCV+Xp7gUytTeDUXGlgFC3ZryxAjYtg7U3HcJUEBtk/UfbKNZubTdJZqOmcdKIcM1ozJqDRcGbFxgVI86wOUd2iNqCLDlefei9CwgrDrQaAacZC8WioZ2Q/yVbCHC4zVKZyVpWxsruRSHeiLFsFp4zExdNpRpabZ1xwgp50MiaYdQLumZAgr0OOT2BYo9ttlpDCGODhHT5Ef8XooZgwauy9aCPr1QQeuI3z5h4RE0aU3mISZaIusR2yD6enx1SHyIMZiUqshcZuvbZJDWiX68/E0rmZJjBNr6gWpGikKXv0WIxS5YLmOtBFj5SKekVkyNcveBXMhnE/vKdXnzcznoxjbX05m9Mv+gZmKwA1KI5oc+ZVkBxPvo6xhXsPsDsPAiz1HZwcga6oKsmbA3AVhF5jztxqvHcJTyFWY54yoEbcoVZco3Q6Do92XNgEDUukG0dfzC6ONdHcfIvn5tqMvqJtbcSxXohjTsRxrbE0PcvWPdZEdDuQKaRG4wUF23PLSkJLH48rIRy/8TcNmzQeryWF6gvK4UEkN693cSxuL2F3TsGRoWK7IQB7g525dgN45hofiXosopZk7YD5gPiQ7sO2NgOYeh7bUkCKo6p0BWRhDJsNlw8Hzi8q52cDg3dUP8Rqh6V7sZ3qhNLY+xQUMx6zDcBHSFUDuu3c5WNQTB5PonGVn2X9cWxfI5D0GMhuPFm9W36grkm5tIhAf/0LSH6QZLVEXnmZurr6r591IWx++ovoycn4M79/NpdMfxQp1KPHBzA2WZ83B69Q8fVrXkuzZs2aNetjahiQ3XvvyNYj47f/1efoPv/yFQ3qw+nx+lbwKagBqlF5jd6cPz2RcFws++0tliBdADUlqVAQKyeBSUIJ0vVmLeUZzzIbb1WPWYYcwTDqLaTER6AWdi4Jp1nhvSEtZI8zF9Q1Ahz2LuYlN1BV8Q6KGsUq2mcgCbT8i3TQNdddjEcSkbQJ0Sx/boEhQoAnJ+bB9lxV7XtTc0OGu1DHcY/pxelcanBRJErHVWWvZ6UkRCyI9tTVCX6wwB8+jHATD2degI0EZBouQx9LNxPjhk0rIWkJPCQBjEVa+EfNAPEEj2qRLowH7AKo4dRqJkwTz32QLlYJmExzK0qmGEtH6/up6mPp6og4vSHlyZEXLCpKcQUiXXgwpFPKolAUzAbcooRViuDFs7w0nIOxTzwAl6WbsvXAdAmojeNacF0weKGK4KU5PFt/T6JvogfUjnU/0AnYsMHeehcuLkChrI6wgyNcu4Ds3SLmfPCx/F+0BEsyY5fhLOZRThy9ODNlGol+ooWAiSJZnlxAuzgSLEyU4RZMHlhinIomrEoQ2IBcQjvbh2/pAo37Ah2oRp9Njf6PscNygauAdfHfJfdkTrh79B6sdYi1jUbYjQYIHVwY/IhhdcTOlLpVrCb4vXwYuQ8C1SO5XkpBs2S4SgBSxbFduGFFlNZdMfZmO6Y911rcL4g+lyX2X4OlNOjr4IaqsTrt0CKsH6y5PB9Yb5WLTcG7QwY5YbAOat44EMW8AEucMjo/3W3qPeoC2qENAmvJFgdxorB2k2XaDbHiFarY0+3u+hRUD6Mv6nWQp4P7idfT7mIEfNlz+cPPXKmLcV+bmx2b3/sjI2is9+5FD+RZH0r1aHklIS8fRr644v6M7ujlXLY9a9asWbM+nuqdu5z+Jt/jw1h/buC3/qevPtag8fpDRs9eaSJjCWekMieAEUN9CIBQDVfF6KKfGVFy6dnrb3IRZW5EK4+2dP/lxf+Y8toCD8wz5KKVBGq4gtKFFK6o6IcYjqUsoW1hHkQAhdswQsMWqBBAMl1qnqEh2gN99EczR6rjwzD2cGzBLDYaHT0DORJEeYOJ2deugQFt/RIzaCV7sgXXCaDmYpP7Cceo8fgGbDNEJ4I9wDzLn0cO0uMnpxgDvr2MEt8ERlFy7KhGEEtpPfSY+vshJXs2NlcesQ0ygZCoBA1g7MSFq2XpclTBZ6fNVt5tBOiV6GlIzXLPdG25ZgCHldFI1mJkGuhr6dkx25ZzWzCJMJjm7NQGBdRQBtQrpRpdNUor+ZYKyBjQ0zx1JDhTmd7LWjfEcegd9+8M3Htry9b2jgsirKYV6FqWXOtoMKuoVLg8h3fuRtn0qkNOTvByCNKHMzQvoK2FGyVCHPskiiDaRWhMQl8pWeY+gr4YR+vtJyqTky/L4VFivjKd3XyI5O4MfGkOR8VHYDiCRw/oGS7XCDsyyT6ebeYyndtcGWqdXjMdriUTu6VIpMT23QhmxYVanUGW1G5FHRy73I7Huu822HpDrQGOW5iQmEeleB6XIun4bddvHtvuo7PSxz+RDi9phhW0xL5T2TsfxULE4lBmeQhHzy1Rd3bnWy43A5utc7k2fHUEekIl+nVSB4QB8y0iDfYUvEaPSMSQscTb3jM2iMAlBCjgRbB2LpTxNLd3vM56FKq9Xi/X3HVYH3rtv3J9sPruM0uT/rDa3uiwn/xi/Ic7fvfe7ED7EPJleWwBIzD2Z7yy91ehHvZX9v6zZs2aNesJlzsv/vV/xvLt7/osE1i/OPCNf/1V9PDxrFx6vL7pfUoy9yhFTvgT17ZZFmmSDonoLeg14FrDVuZ1z3UT1wiRpZJuqBH4yZ67T0anZNabJiBhdAqq6jiOsa9fc90ljPEshxSNUt7WR64Bzig6zX+kLcCGTYCL0gWkEaJ0muzfJqBFxzANn6hY83gFBExAKtYcZlmcWbNUPF2EJoxBIZMLstBCLKL81TBvpefpqPKWKJvOSo8wEu+OkMMVPHyAZmhEC5toMNWbU6228JjJOUkGnogYIjVmzKNEM4adcLl2KD1Rvhk9CDXL2MXSeZlgRjJNupUbR98+aOCzw1Fv4TlTT8hWkt/cqFHCK7SuoK6aZfYywTILWNT6Aqobvlmni1GSaQfEUVWKRq/AOoxxywG+NNafNteYCNCx2yr33x24/6Cy3fjYvy96+1lbcbSXEPcRMkcZ/w7u30fPLihdobtxgh+fYrLkPYRMC5Yl42UPiOMB4zDHq2FD9PJsnlavDsNojQuwW8e62/HYEClounU9y3azKDoB7zCC1rY2gveno671QHWJsnCDki5GJ0KVLMGtlpLwWvEscW81555OZjXGvp/xXkK1nrML4+5bD1hvtyBOWXaw3iAe/URVWhl5JmK3ewuimQie+zjPKUqcn3Q0RDlkojXjcaCIdrn9DfzKOGZ1obiwQrlxqhzf7KKn1nrL5cMtlzunOvhqiesCk248eSm0fKUsT48FpvmnBecEJG6HfBZTu6MoRSNspw0rAG4rfZ/1qGS9IgcHVz2MR6fdk+0IEtW4IfG06zF1cl6+uKJ8+YcBqPcfIA8vZtD4g6RQDx5/gDb3Z5w1a9asWR8k+T0/yb1/42eRn/nKVQ/le1Tv3uWH/+/vopffhe0Ezl8duPgjj9+Y4amAjK0noI7X2YzhDoKpjYELUjSdVY7bQK0+wkN3H4FXML24uA70VxGvI1zyfD9BR2AZDEDG79eeDsQoqU4ckGWq47gTVriTLSL3yh+V6Oknmv0XM87Gd8h2HV+sAGcAhuyD5yDNsZblwen0DF5h6XhK55tHmXAA2SStRbMcVBGPMlVPwOHZjw83WgdLvMu5D/jk7gwW8DYSvGv+rsPKEj85Bt9RLtbp5sr5siyVVqIU1xKmjb0IoVGNAKiWPS4b/PWxVDr6aBouuwQwUV6dbTWDxUj0YJycpg3CtqCWcJm1tzXLfpbZG9Ca0zPnM9KNs9y9rUGz+OOOaeJvF+pQMY8Lahl2MGwjTCXLz1sGeGv3qSqUMl24ee4rVd+bH8dQtgPsAF9pZMvvuw3Zc5clGIwgHUa3arGK7jbY3XeR3RpRpT85xRbHDGWJdx2mmuvWoyzaakD5IhGI0kWvJNOCSYly5VImp1IrrZXY797gdHMhArUaVmsC6HidzBJJaN32e5v/cJ0ihmafpnjtcMRK69nplgE1Mga7pM0u3L5Zpz32SE1nboQn5di8gvZsdME7b17y7p0dZ+dOFaf0iq/XqDgdrYx9WuithYDT0qplOuZHd3QmPwtT4FQLYmp9Z4fKgLCTgnkCQm2M1vMtjYPeuPFcx7JzbLtjc7Hj/MGG7TYTqbtF7KOE3lGRPwH3EWajbdGPNxxav9Wg5W3nEE7gPIdOwTaCzy6vRy4/PbrqITwyuU+u5CdS16R0/ZPKDhcgH/y4z1pWhPMffZZyegruDN/8Dmy2Vz2sx1Z2uHjs+jC+nx6H/oyzZs2aNevxVPmRL/Hav3KDez8C3/ojp4+lM7D++td58ZfzOnRfAt/5g4XuC5+/knH9IF37T92WaDv2DNt3HLbHOMh2gDpQEpa5NTeVRVBKwqEAGaOniNbnbYJR6bZqPQNFJ+DRXjOfMZZUt4v2hAn7qcUt7VY1U3sbXEAA33s8gFPrDt+tkVIw7VAvUH2MicDrCN80IWi47jQciO1/Oo0tEEsQinBSaroHcy6kR6RD6HBLkAABzCxcaOqMbkC0ol2Ua1eMwaOMl+UJcnKAb9YwbOPhWXY7jsICApnI5LKzcKq295UWsqLZDw6nCIhn8i6MfRWbdayVGPvolGzvGPMcUMVGINwca45GxnXWFUv2aRyTm92Dz+JRztsAZR2AyWkpO0+HmFMWGk7CYQe7DWJb1Ctd0Qz/aWBp6qNokoExCTLDsBlwp+i01ofBkD5cbnWzDZCcvQVbebMk/B3BY5aUq+UJwwc4P4O33o3k61VPOTrE9YiddQzWMbTXzTkUNFLW64C5NVNmOi09GttWQ6qPDj/JY0mFEaC19OLSLhjcw8XnU6E36VCUBrVEMHe0sa6EiyKemUc+9W1SxuCSNo+d5yGfIFey5L56QOUpgVlwU5yeQRacbYV3vnPOnbcGLreOHnTI9pyu21F6y9T06E9pbmiRsQy7rR9Nd6e04y1Ly90z+kWSbOJoydYFCtqVeJ08d5WSoJDoc+mZXF9EOTkWbr6oLEusg+F8YH25Y9CClR73LsyeFjcqpkpoH0Gs5fnV846Iex/uZRR3RT3Lz/Oc0cCoewuN0fH4nfXodK3ArbUArydPktUAsxjD1R5H2ULg5RfyPyq8c2d2M34fef94l0l/P115f8ZZs2bNetykBem6x7bC4LPSg3/pWeoyPuu98HjeFHbn5D/+VZ79++V7QONwWjn72ktXM64foGvUsOn95NNFLeGQshFKJT1I0OHVGNzwndNbh7ADpl5wkOWn6Y5zi6ttJ2DiPhiMcJJ4rIxlnek82n8NkfGC3TQDZJwxjbgl3GIezjlt4CsZme05nzAUoQ4DNjhuhVp3GSoSF2gNnrjVKeQCzYpayd9PLkukjE4ta7BFsww6k2FVMrGbuuccnOYiNtDGMmtFYIhdY5kWXK3A4RHSCbrZUEQzxdeyNDSBrhEwSmWvL2aAFHxKE5acszRa0YJyxnXgTqdTWTy5H4LXRMm15fy7pOswHZnmLUsX0Fbum/uE7GPZ9neionDKZt+9uuea9ISlXez3ALwxJ0bBbcjAIMdqnZKvkb2E83S5EdssopgllNMGiqOf5XIJJ7d6NuuKSiYi5/6KktaEdMpYou1Z7qpZju5u4dx8+BBZHqAnB5SDBTYcMTysbM4v2K6dUgqrpbHqAlAiQ7xWm3KA0XHr4cz1Zh9lhNLWnIyq0ClODcDaAD1QpKWTBwTcB4vuEY6EVZzwGKNREi1jqEmOsQhQE5aHAzn9uVG6TnhCW39Ga9vhxDnAO0wWbMuKe++suX+2RcsCPe2R3Tlaz+j6Aa3htmxhSgFAfWzp0IBbO/e4efRmzP2tDRt7u/GQ/90Wnlfic7KBPDBLp7XGucRdocZn6c2TjiLO3bcGBivYbofIIXQ93h1QN1uKNwdlOxFEYFL0rmznljg/acnjnoS4Di7pLM4Eb29rtjkdu/li/pHrunGtao/nl78PUtfN/RhTdvD4fu10ge2LJ3Rf7/BhoN5/QPfMLfxwddVDe6xkh/3Yf/lJkYvgRdH8HjVr1qxZT7OkX/Dgv/9VNjeFw7eN07/7DYbX37jqYX3mKjdvcP+HM7QXWNwDX2+udEzvJ99suP3v/wO8fJV3v9YyKwCJaozHTdf/W2/rx5hprVEGyVgOHCWuQkW4fLDl/lvnXDzcsBsqtdYRcLQAEPN0Zkn049MEPHsNxianT/tvAZGSZdoyQcsSsMMVahCXKGFuv08HJvuA0LP8Ot1T0noVNuiE4sOA1x3Sd7jAUBONuGdPOsZyRZGpR2X8rJVRN2yWJc0ZnjL1JpxKpC3dYy4azpmsy/Tm/qPBhgB2hrIzZVc7dkOh1g4/PA4XYx1guwavEZTTQlVaQnQrZZU9JxURLOFZkj4yztxnwR+zMDPBhgBeiYm3ACae9Kv1hoty7gBqQuynmJcM9ElQrcjonnRx0IJLSWdoQCn35rwjHHSttlY8oA81ynwJMGYad1M0+2gakTg89ja0BrwYf+Z7Lp/o+ZnULV1wwo7VauDkaODmibJaCUq60ybPZgYENSibk+nZtzTrtMUNGbb4+gLWmyjZPl7g3YLzC+XhxYIH5x0PL3vWO6V6OGVb8jUEZC2ars0sK3cVrGRbgLY5JY4VN7BqSCX6ZqaZuJU/j+ntzQWprU9hzgVZ4m/AYOPx2UrKpdOJA+QhPTp5x+MvQnKkyLgfpa1yKXi3YG0LLrzn/htbdNHz7JdPuHU00K3PKG4wGCbh5PUEJl4d37X+k5H+3hyf7TxBuhdRTUAXaysCc6JfZHRGzLVKa4dQo0WCEz0wPZ7TKaiGo7KXyskR3Hq+5+RU6XyHX2yirL0sMFlRXUfXbIQ/ZURScyc2uK5g+X7NpR1rLEq3a57jxn6n6bSdezI+etWT6wVHfBievJJp89nFuCd7zEtsN7d6/Ks/3j4AsDfeQq5Dsvkjkh/0WP9478P3k3ePr4t21qxZsz5LlRee4+wVZXNLuPsjhW/9iR/G/8BPI/3iqof2mWr7019ie9IAo/DS/+ub+O7xbZXimw3P/z9+g8NvdlMFmEF38fhV+jyZ3xQ+gixDRazWvQve5pwKSFitsttVzt7acH7P2VZn8G08pmTpYjoVx4vqPa/qhBcJiKXhjooL54BWypSqTI5APFxk4hEEExfbGV5RSjg2VPZePN4oWsRNDkbJXo8BHiuqlbLdoIsO+h6RgDNK9JDzBBHiFm5BGyD7NLYQGbNwZ7awkvi/ZT/AAH5ICaiYQSVFSg5rgj2toZ+74gNUL+w2hYfvGvffrpxvOnbdCr9xE1muYDsgdYfZEPAExQcNxkUroZ3ceQ3lNkg1QsKEQ5bptgDu0SuwJd5GaEcDdQ0gxT5HA2qlBzBcpDYlOWsCLK8TlZJ0UDrJLhUs10KbRy8epdptrC0Uo1EtoJrH3AJ12Oa2ZP/LttqyJ2ADquNM7N3VCMOe5u+jSLbHOCgDxytnRWxzyXUac5Tz6hN8FU1HYCafq2SJcq2w3mBbo2JIr8jBKvoyouwobHeF80vh/NLYVg8Q1+Aq0ZvTqlFrHqdmGcQ0wfYxhAiyB2mAWtKhGEEn2XvQsgdlIV/PRy4r2cZghOBIGvOaIzkckuoTdBaX6ZzRnM/ZAmHY1SwrN4SOoS44v+jYLY/ZrQ7oTla8+COnvPQ8rC4f0MtAsUohyv5LyeO/K0jpwmWhmmnTjH1ckbIH6FvP13bMF9wjWEhMUFcKiniUR0cLhiwN74SyCGeyeEEoaAYyKcJCjRsnzo0TZ2kD9eIioGSv2GKBWYeJIrEFDBlGRcJHF8F0miMzB8/etImwRT1Oa91+8EtzO85Xf49a0f/1Gn3Mm0UZ65OizQa7ezduHsx6IuQC6xcOKLefAcAuLuDO/blsGqAIdfX4OlE/SC4STtq5bHrWrFmz3qN6AN/5g4fs/tu/e6qOegpUVwUEunPhlb/xbYZvfPOqh/SBqu/e4dX/49d54e8oh691vPi3lYP/4r+56mF9j57cbwsfVgk0okxvwDQdVeksGLziXtgOzuWlYQew6qMHomikBtvYd03yIsfHHojhzxMiUCUuqFuoy2iUYsxHYGxGRzihWle9cEq1yNjsbdZKJp29C/VMsW1pyU72dky3o5To88Y2w2E6VIfpIqc5xogUZdsrN0UsgdgEb8x3WSbL6ABEJErCLcqKq5Burv3+dM29RI6ZcFo5bDZwdm6sL4VFhXL7gIObp9SVwrubSJVuEI9w+gVMi9CLVg7bQJSHGYri0/w0KBNgOFKqPQGNVwex0RHZ3IDSgGSCKiODT6LedsTKLYwj3jgeJxZwMkrWS5ptcjxiaKZ9Q7odsw9iuDMTWWu+txSsKMWzrDXBaQPJLprl4dPFdpTY5vhz/uNfnlX7kq7egNQBp7MsWMLZ5m7NMJfONA/nrAuu1pb76K51Bd1uwHZoWeJAv1qwPD1gvXa2l9ssO++oZpRlR5cwT1ridQE6zT6HhRbMM5W4Z2iQg4glLJRxnVoN6Dm4QRHEAm5J6yU5gn1JR6slFMsydomAHR2PW9Kx2nqT7rlgc4+ah2vWRShouoOVasogSnfriNODni985Qi794DD4ZLloSJUXIYA0dWgb2sOvAQcrLTS6XFHxPuqxqqWTKCGhJsNuo++zH0+PDp9xzNROrMlGynuu2FdoIhxcFBYA0MdqJcX9EeHeL/ANwvctrjGa5ZoEpv7KaGwkVAzbjCM57Ecd/xd818yusnH42LWrA/Sbojy48ddw0B98DCg6AyogDjP1dXjD73rQtj92BfQX74HVrE799BnbjyZpfqPUPVwgT+GJVkfRS6CdYpun6CbFbNmzZr1iFXfeJPDN17h/OW9c7rAO19Z8vI/uEm9e/fqBvcZ6vAff4fnDz/P6a9854kAjE317be58e+9zc1+ge+235MH8zjo8f+294jUvuNLWNYS1lSkCmYFG2CHoYdL+q5LEGPRf68GwNF0j8X1v2dwCeOFtIwuo3SV5UW1uGXJMWNPvfaoduEv9IxuOGnxK4AJQhlhS1yvB+ASFLQkgIn+cGYGBmoV3+zwxQLXACG1JVXTjG7hjhONBGjxgllus4BLHWFjuOISrGE4Q7qt0sHYACp7rr0s4W097ArZf7JTpFswdAfs+iN47jZ+tAhH2m6d5XC+59CL+VSXFl/D6IpCkCwxHd2BezA03ruEo6o5R0lXWwK8+ImOYKn1lFQpk7tKGrSLsthwaKW3UjTmhnQOpvuvASSVLvdrurqsDxhcepzCdufgBdsZNmQ4jJTsHUr0RmxutNEYma7LXE+MY556g3oSQTMfHzvBMsNN3+OqjDm3BHQ0qpku3A6lbUeUKhcR2G3g8jLGXDrKQcfB7UO0L1DDLepaqCJYl/NpDXMHsGv1+4EFPSuGcy0hFNd0F3qYsszxmrBXBVMoXfQ6HZcgHo4+oBPJBOmEhNKccx7wTluadRx7WhNYm1NxWp63SYSYiAnsKp2WnFOPwJ2i9DeP6FaF8u59lnfus7g8p5OBwhAfAj4EpCs5DwJSCl3eRAgWp9R0Z4YL2hEfaI7X1o+xHZftQKniWVqt47qMydBxLrPPQLg2JR2hrU0AcYwXrSxX0BWHzRbb7WKeuxW7rUa4TZ5rvM0bUFBKu2mC73EVzzDsAN3TPo8WD+qGeEXscfyInPW4yWt9/EumtzvqnbtPluvys1Cr9HgCtH5+if7uHwHAd1vk7oOn283YaYSnXAN5N6dNz5o16+mWDwO3/+t3kO/6mlJXYD/8+IWIfFoavvVtDv+j/+qJAoz7epxLu6//p2wGprg2929LzDXEWzOwuGAWcbqjEgEPlYRT6cTxFvLQXGjQerlNqaotmbelrXqmH7cy2qAgzYA2uXwaTZv6lcWFeU04mY6jVvq51xswEnKbM0jRopmYOyDri3gfa6EgyrBr1agZlKHpCEzHJpJhLRrll1Y9+sXtuQdprrnsT0emLqtKwJPkGS4K0qHSj0BD1OmWwuqZJacvnHD8u56l3DoKF2DdwGYX7sQi4dwq8Z5t3mubB4K2jW43JMszE5y4B7hwEirHv129cVCmy4VWmqt7P4v+mNYcoOnY0oSFU9/MAHaIJPiK8UVicHOLZb9FB9C93pHhgPPSjf0gS4K4HHYsC02XXVsuudasRFiQ5/bEnPu4XsKpxwjFp5CbBKaSjthktGP6OgkoRyCVfTcjCnpyp3qU2tv6EqkDqFE6YXXcc/ryCaubPdgO2w3Z33TAqVhzIkrOR+tPKg4W7QI0y+yjL2HNABthqAR860o6eKMVgNeEqCqYZil/9j8191j07tNNAGGEYZLw26vjNcqkwwHaStTbWokxefuT54GiCjIgpaM7PYLzNfXuHRacc7DaslxVejW6Tih0cT4QiVJ6LVRrpePRH1ayd2SDcFGmHaZPNcvzV7hn1bIXpQdMlexBy946h5yHPC6xcHyO6x5G92P1eHUVpyuO2BbZbGMCFks2smRn0WqilAn+QgYxQa718BObWayvvblG4txTa6UOA1SPPptP8wX8rI+mYbjqEby/aqXeu7d3Z7N9ds56kubBBc5/6IRy6xYAw5tvIQ/On9rzVH0Cw15+kOa06VmzZj3tsq//Njd+67soo8D29sHVDGjWtdITUHP0CSVT6WGrVI6AjHQJiaBS8QpFCn0XTibxKLBt5YreXD+jA6eV4gZME0r2bWxvG6m4lv0SgQxtkTQWeYZg5C8zByR6NE7lwTHeBo72vhC13JB0Q6mM/rN8pAe0W2+pssB2zmADWgTZVGTRoZ0kOMtcCauMpakEvFIv0+tZ9CJscDSAAgmMAmREOWrMgGS0bwNeaTyj047jxSGrk1vIq89RFh104GcVX9fIYvY6lmPGe0WCiztQCkXIcuMGjXyEbK2ydyrZbu6uvblt+28sR9Ys37SsxHXcSu7D7HuZYS1j/0JN6JzbbQ5iGrCXGJ+0npSaz7G90Bxiu7pSAmb1kuXnHVIWCVYrrdx///t9CzKJ987ejq1RvU/XciplhIvx3tAKyrXNzhgwlO64HJ3WWJ9VfCxF93wPd0cSpPpui1+co11Blgf0B8rpc4IPlQevDXjdsDhQOo3wIM1AnObodbFI75YIfSnSjxB9LK1tvtHcNyOAbcDPAnQ3uUdytCYQjtaD0mhqzI/GfDfAT7rvtCWR5CK35khNmBfMT9ASKd5eK6pL/OAw9vW9u6z6NXQBuYsqxSL4ZDCPMBXiBoTilKI5XqaACM9/iKDiQJxbjICfeEBe2hGQx8OYcN/uA2gAvdH/KEQy+14vUre4YeBScknFnJTe6c2x3QZZLEALsjxkc7mj6zKFHEO1UGsAeG+9XRvYH+/jxD9bq4Dgy1H6b3jwa3m6SxFnfQTVCvRXPYrvVa3YvfsTYIT4fOzmtQ3gqx7rnxywY53gr34O7t6Nm5xvvIUev/rUlU37smCL67XNc9n0rFmznnb5MHD6t/4Z25Of4OKF+O6vOzj4jTd5jG/lznpCdO0ho2iU75lVKA0eCtFDcerTJ6ZI16OqlE4pqnSeF9JSAww1B11ehOcVe4KZBIZ7acZpGqKVObfLbW0uqgQkkXZt2TdwQoXavFaZYizNvSfZVy77sJlZo6cJUPL9asXXG2xxwnqoDMNAv4JOwEu6jyygWgNoAWs03UcxjvZPac4yAhpIc3l5lHQ21xQapcji7912z5AKkQV6eEL/4jPIchGvrkJ1QzuFIZ2f42ZZlCOL0xXNnpRt/htcmzrPDekalCwbdWmpzW0sjSFleEaWkZtnoAkBllwikMe8Tvk7abgTYUweH9OoSwNWbe3pOHuSRKeK4FYDtJUONaETiRJ1cj2UDu86fLuOFOdkqeYJeXNfNHQDNr7vFE6SUDDXxNjL0DwCTWjdQAOuqewlBefjTUkQpiOwxWOMmIEKpUgkYz94QFUJh+FyQe/GrReO6Dtj++CMpWzp2cXz2lotGWZSc195s1YHQYxy8XQAZxBKVEsn0E0IHWXDCUfNAii3UuZx+3PVSoP9ZEhRRdHJhUceP217287Ohd7gP0g6agEpbKzgiw6//wA2F5TewlWY7sLwgDaHtAcf0XgNrTVAnTK+n5unycKTNebaFc21LZPTuh1h2TIhV1zCWEG1Pa7ma0iEG2V5smQjzvZKnuNWVXTp2Kbimw2yXFCWC7aXUcpfuoT2VnO5TX1UY5sDpyvplLQkwx5zIQ32ZphPxH7PmvXB8lqR4THrzZiA0R9nl+VV6wl0wp2/eszJmy8yvP5GlE0/OIebJ9fK1fdBstVjCPQfgbzTuDk3zJ89s2bNejpV793n9n/wj7jxe77M+edXnP6LhwyvfeuqhzXrGugx+ob+6UgaExTLgIZ0sFnNEuBMpy1CWWn0ULK0PUZywtTzroU0iIRzcHxcgowGJPISv/mHnHhYYYIdNPhgNn7xNm8lrTQCQktltfx7AMlMSbYa79nSOhphkAyyEI30aDX09BDuRIo0JWNNagaPilD3IKaJMux2EWayjM52URoa7+UAqtSEHxEsEsW/3kItMhhjAlsFd8Vkxe7glPK557BbhzEAje0uu+i1FxbKQuvf5tL2kwQsJuCtO+lEy/3SemOKZj9EH38H7PUmzBJdPNykwtgzUZMTx3zY6OwKVyKTC85a/0YbAY25R7/JsOdR012nCSKtzVdpFi/NdTS58NwZE8W9DtmnrpXnCrW51Rr0bJuZpePjmp/QLo7Ren1GYjLjmhxdfAn+zHxMpB6BJYxBLdZKiVswjxvqjm/XlIsFrJbUZQe9slgoN04X7HyJrXcBzRqAFsdsyBjl6N3Y9qG4ZTluN26XtDCYdhy0dSitjUE48rwdV95K4GWEhMF5m0vRxnXUgHjrCOA29eoUCeA+Buh4uxhJuIpHn8b+AK8wPDxjVYZxW6ek8zwflGhH4DK9fvgT8zzgJWG3gNt0/qCVyzcnbuPKbU3LCJ/jVJXuVt3vkSjj2EPNy5rzl7B7WohZCq5GtQHfFXRRYHXAen0X7WHRQgByUE7eIIiDczwuMhILQ/M92h/JnrWC2/Xv3jHrEarWxwcyms+A8UPIF4/J/voIsk44/+oXOPyvdtR33mV4/U26rsDx4VMBGn1ZsP76npu9zwqVOQF+1qxZT6ns/Bz9O7/KCe3qcdasT64n7xvfx5CLBsSwgDH4VKbcSk27IhygdDagVKoZmu43sgx68vQFhBpdca1sd4Q2e7Yg9oJI8qLaE0iKR1I1WrJHYCMd0yEu4riWKM8eiZKjJfoLtn5tmo4va6YrlJ13rM+M3eUl5dmbyGKByIZ+qSgDoj5+SQ52Eq8lZmgpyCIBZ7KmUko4HRF2mwHpS7wGLRhZ9sojS8KhLImsyqCH1JOb+LM38GeP4GAR6bxWwQdMLNxRkUkRMEIiTKXuKlLCYara+ruRMAwYAWPCp9HR2UpgYdxB3kJiIo07Zz7nP5/rAY9UZXTHZaPN2F+5y5s70nPXufm438Zg5Oq0/xCJ8v2oUG1rKMAMud+ggBk2DLGv3fAWHpPLq0Fvx6c2AOwxNU9LbfbMnBylrVR16j8pDbgmgG1TNSalN8CW4/eh5t8lOXy8h58/DCfwoqceH1GrobsF3XJB3XQIO6Qa0XnUwpVpmWjeZUGvG+5OtSibV402AK09QeOGEXwDk3GzAU+dWgm0/W/sOVTjZ0Vif6nltku4ZS37Umoez+7plmy7kEyrdssybaHWDj88YLjY4NuHlMP8vedNgnQshmzCvy0EJ3okTBhONFOhyf6QMAUQZRBMu2HRgLDEDYvWO3Vs9bAHRYMlx/ZZ7k/GGx+ePV9tcjVL9H3sFMx2+BB7rqwO2J5d0m22dAeMgS3e5klIwJhO2XYDJs997cZJxSl57glofP0v2Gc9OvluQPrF1fdVM8fPzn4wYBwq9E/F160fqLos46nsSdKwUrZfeZXy/70XadNvvo0cvTLehLu20uvrYmxyEeh0hoyzrlTSL/Cv/Rib20sOv36H+s9/86qHNGvWrFmfSNf/W28mBo+hCtkrzNPGJZaliZ0gXiNcIWFbCxeJl5EADqPDqYEZ0g6YbjIBkfwivd+XySV7+0G++AjnwvUTqbUtjIMs6axm6fyKBFzJsmFtibwSZaEt6TX6zsXfLh4MvPMa7Drj9Mg4ffEm5f491DeoefZ6k9amjjR2BrAQR6WL4BMHLSV7voX7Sxd9QgtLluFIjX5zknbA5sozKVQ5oN54AW4c4CWS/bqiEQJiBdsaoPQuFMkUaQno4T5EWnEpuO0ieiR7SY4oLV2KpfV+pMHGxmeEZnkbe/ppCw+JMvAAogGnCpGCKTQwTe5jn1xnYtQgdpinuzX31egO23OXNuZXI1oD94B109xLgtIoCdca/e2ay7GlVTOWtyZOzhLaNkaPWuJwVu71zxyrfkUT/3g6Cmu6YmNtSYK18X0EqpDrxUe4OZYPt3XpA/7wQaQ99wtstQrEXh3ZVLgYcDaYKF2RSOBOR6fn/qv5Hp1GOwBLujfCcPd0nyYMSxemj0Aw97+GKzNKqS2habgcEccyVCU2IKCvjj02o3xax/OHN+qdLFsCHGueW8oK63rs4V2Wixxfcyu2ku4CJFht+yjidOKY0gZExccE9PFOhUS6fOvBOJ1VJudzuAPjnKFEP9XJJO3j4xtsbHR+6vQqMaejq9pGoIsWFEN9YNg6UjpYHrG72FEXRpHoidlaOogItTqucXQqzdWrE7iW1sORdG0yOnFnPUKVqXfwtdQwwOIKIUgCRluvf/Dj5iRbgCe6r9/2ZsfJSy8yfOvb2HpNd//h9S+bVr3WLsYmL4L3BdnN/RlnXY3u/YmvUv7UWxz2d/kXbz3Dl37hx7B/9M+uelizZs2a9bF17b89uDk2DFGCmRftkXgaf8I75pGiDJBQpEhLuIVpmtLl5OyBgPhxC3URiXTg0dGTV+oiUFrYTEKu6AmpmYArVA9XVLWAVtZ64+HRVzDLh/csaAG9En6AYhZuxmpwcVZ5WOFsgPvvXLD1ghwfxoW+lHjvBIHaKdpFKW9AslZkWUbnnqdDUwZDiLAON0eafVITWmWpqkvHwIqtnlJfeBF75pjdZaX2gvQdlIIuFuGqE0HLkqEq1ZzaAGKmVmtzfmZkbithtm2EjyQfi8ReGk7JafIMdBkdfxrblY4uF1BtwRpQIQFSyMj08fSQ2TCl57b+hRBAuEGdMQ1aBFfFda+0ekw1t6gWL0xOw+DLGQ5c03lWcjty/An3JAOI4rGxT3DGtOkGSGVvWxBoITETfEinL5MrUTzZnDDNW7rryh6cbU7aKMk28ApnD/G374Qj9vAQuXGK3rwJB8e4x5pzE2wn1MGxIfr6mea4ct+ISJYdEyCyQX9VRDrMAi77uNNisGpgNeGb2VhGj2fATFpprEG8ILu5jrI0vu2/DImKGwyxegSndB2iJd7vYIn4lp4tRSzGRJxDfKhxrrD4E2EnEbajI1xsNxw0zhEE7JxS1GNNtf6O2eFgcp3SwnQKrU9qNjnEc41gghOJ2wFyA6pLwsY2BzJeME/QM4CgU3RA6xbON/QHS3a+ZNhMUFQ0wHxmX+e4HNW8ueL5vgJKAdcxxCZq0mcnyaPWsCpXC+E+ZfkwTG1CPmvV+uEAo9VwMj7l8qLU1ZP7ldOKcPa1l+le/QIAw3deR+7cv9Zp0/Xw+p47vlvWK949uetz1pMrPTnhrf/ulsN+B8Crz9/hN//Ht66/U3rWrFnXWtffyTgCs3DdWU1Gp9njzysQPRqr91RzBgfN1BHFqdTsDZeVWR5On3YHW5HkQOG8GwMVSkIcgxZAIl6xLnqxiYWXSVCswm4jbHcVFaXvhdK1SrB4HS0VvIwliHhNCKAjPENLOLUEXB3xihsM5xsuH645euYQ1usowd13D3kN2OaCGKPLMQyF6fCzDETJ3otiZDJx9C5Ul7GdpQ3K4Cs4vIE9exPrlOFyhx8s0NNjOOyBBB59h5pjhwusW1B8i0olyFEAG0XxGr38puJzxUsLrwiwoe/pkQlFiP3nU8n7VHa+9wEuhptM5abZK0/xDECJldDoZaQDJ6ZxYTCDkmMVwb3Eu3hbO4J5CSebWMw1mvA7Q0ESZpkCdRtlqAkR3S0cpOi4ngMkxraMETANBPtUFq0muU4sAWUDbe0PqHa00CFvAF1inWs6PNWB2nodKqqGWUlnoWDqkaJcN9jZPbQrcPsWvlzhz97Cge1ux9IfUrHoxYeHSw7Fh3YMOa4xtqknY50AmLUIk4yukT3ujodj2DMgKHGXZ4m8tx6FY+/JeCXzJMwOXofYvhKl18aQUBtaqS8SUMx1hSyX+PqMXmJfKwI2xDHRytKzBNoTbBcl3LItrb7tSRtG4OzSMGC837Ram+u2BdTICB4rBPQXHzsutp6V0tzcQYUxam5NOhoz9bu1k3ATKkYRoxSlOHhXWV+uI+F9dczlxQbtBlZd69Gp1HYuGVsxRP9NpYCU7ANLw+LRs9Qaap016yPIDDYbOFh9tu87DNj9B3MPxo+ivqMun2yIY52w+/xt5LVvgTvDG2/SlQI3jq+fo7FI9Ct8ijT3Z5x1FRIRtHvvmtvdrHlhM9+gmjVr1pOp6/8NYrQdtsLABhwdp44JzkgGluzXnZLlfNk7rZUKt5La5hRrRMaS2UjRrL0sKOFYomQfNatI9ak/ZEtkrc4wGMNW2A7Cbm8ozeRjlk6t1iMy3VbqHu9ZhJIVpKXAwY3C4S04OKgsZUfZrKHvsBu3GLpDTPvo94jgVVGLYvEIHtEot/R4XUlA41qi1BRJ59xuZHXVBLwDO2DobmC3nsU/fxsOOmww9Ejpjp1Otuh2G7ulFKxTvCvUwxXb5Yr1UNjmfLeEaPHoD4cnFElHXqEBFBnDJZrjj9bPT+JRZi3NNntFZsBGmApzH1uWXHs4y1yEYvmeWckuRbNvXiwGE0dLvL8kXJRcc+FOy8eKpCtMR8hnTOsk3HvZcS+XoEYtbZr0Wo/JcOXmWwDNOQrFGYGNN5MazYmYy9pATVELR6BKOvISvCfeAyQhmee2pZvSPPtTKkVbenGW+7cjZ3uJ330He/sdpO4op4fo555DbzzPUA8x13B4FsW1ROq2SMD/LkrjKRrJ6eZjgAqkSQ/Nucp1SsBYsodk3YNyUzvDlnacPRndIde8aLyleB3L1y1hdWF8gXH73CteBSsHDC7oxQY1o+B5g2JKrCbdhCJxg0BE8bq3P2B0WwbcywR3yZ06AuE4l2krE8/HiHQYBQS6dEmKduGWzHPdfsdDdx/3qedxEuuilYPnkhPinBDNRsErRSt92WKXF/hCGZa3OH+4YLBYO+EAVjo0blhkqbvSjdvgLlhtTvJ4v9qaXs56tBJB+uvtRvJao2z6M5KIIIeH6OFh3tST2XHyIWSHC7gG03T5/HJ0M+JOffuda+lU9b5cP3D6AXIRfKFX3+d11lOl+uABL/zNFfcuDzAXLnY9z/69Ei74x1h6ckK5eeOqhzFr1qzHVNfeyeiDx1aWhICqSK1Q02EWHf6YUm27MV030oT1vQ6v7MU3hoRIgKLm3Ilef5KlzZYOoyh1rAphU5t6w7UXFpyuAIeOd0JXokzYvObD0u9lmi6v6FkYPfWm2lZJV5Ba5fig0L1aGJKydIuKDAO+WuLd89iD++jlBpEt0gd0dasJ8DS33ZFKBL4IUdKqYZnyDMFwwKviVvDFEcPhCdw6QRcKwyV6vqZsN/jbAz7s4qL3oMNunMJLL6I3TkGE2hfOzp3Lf/6Qo5uV288qC3U6HFqJbvYzlCxJb7AE34MoMV2jy8sTfmn2GFSR0QmHRUluwMP4/Zj0LOH483TL5TtPa6uVG2erzub4DIco4fZUqF6jytdlfHCyzoAw6QR1F6qUKGWvQ/PpBbhpEEr3vGfNrZZvOsK1hN7hnBvC96bZG9EDlDcHmbYek0723EwYmo5CyaAfyb6GLXClleq2wKCx32P26ASD4RIeOlo6fLVAjlf0L92kbtbYwwqyDjBYoKW8FycdiGQZ5N5Ysp9hHHrNLdxla4K8kZBuPYXsEzhBtnjiBARUwl2s3qBqpoprPtYS5rW0b5do+p8U0rynLg+xizOoF0gnsd0a7kvNBOxWyo8092KMfjALSNsoqDd43sW49wKg3Fu5NHH8J1yeXJraVjRtiyHd07T1PP1+LJRPN+Q4txJuxKhAnQbu2QO1U8UXxuZyi1xc0t88ZTg31tsL5GCIGx4y9WZsR6jH6QrL7W9rVzy83FEOPzsZH7VcCJffg7OrHsqnKt/uon3BpwgHRJX68rOsXzhgexytT7qLPA+Zs3r9Av3WW9R33nnvHYRZAHFD6RqwG1c4+6kXOdlsGV5/A99s8G9+B3n1Zbw8uT0n3yOFenC9b068n1wE6wu6mV3Ksz47Hf+Hf5/yqz/Eg9/9OQ7f2nLyd//+Y/2NSPoF7/yrX2FzS3jx756jf++fzM7+WbNmvUfXHjJCux7Pfmvegl8CVGhc9lOKYNv4yTAY/SIZIjX8T854YS77oSTERX9JyBROnXQAZsl1c6bJCJkSCo1BJCDF6EXoBFyN4o4kbtFoGoiWkq6tBgV8DA8ZN1TSsSVO3zm9WJRLqgaA8hhPXXTIc7fxB5fszs4pfplJ2tm/UMsUJuEZLjJCigQ6UjBRbCiYL7HjQ/SZY8pC4ewe/uYZDAMClFqpO6fWSrfqKTuwuw/Yfudt9NWX4HMvYsD5Gt59INy9s8HLkueeKahEybe0VPA0drUgmtY90tOd1uamufcECYPm3opQKe1Z4z5tpeNOBne4ULOsVprr1X2E0CPVaU0Lrb1nurq8gWmNoBDhu8q9w6Ya71pweqoVdoNQz42FKX0JUB2lp1msm9ssYzLyBMD3sFS64xKAjz+b5jCcepW2CCUdjS1QJoJBdHQRWq2BjNr8Ti8cMNwY57BotCDwzRru34HjFWXxHHK0RJ+/xa5WbH0flW0C7dYH0ZGujLAzAn1aQ0MfX7+lTo9r09PtiIzzAbGNjcm1Hpbj8UcAybG01xyk5FgSSkP0QdTsf+oJVl2R7gDrCqzXrBaKDwmGS+6L5K3t5obWKFkPuNvKlg2kNOSXQ3NySmg9GgLJJdDIUCiXvdCWXJBme/023REtUw/PBHzkdou3fdzOjy3AKs5XFR9TucfzFEJRZ7Uw1ufn2GVheesGw8OezfYhi8UWpVCtTus8LdmSwBwH9RJ9TiX61dow7CVwz3qU8qfBldPKppfLRwoaRRU/OqA+e8LlCyt2R+8FZfXG9KmyvnWC/ugJh29+geU/+w71nTv4bgsQ54Zr3Bvzw2g4Xlz1EB6ZrBPWP/4y/dvv4MOAXVxQ3noXeeFZXPWDX+Axly86vDwF54330RwEM+szl1XqP/9NjjJV+nu+Denj5WyUvmN7IgwH8O0/fMQLhz9F/5//ylUPa9asWY+Rrj9kVMJZhGevxOZMi7JCJ9xapTe6TTq7sqfhmDLtCdea+Y0opSXTUqNstuIt5Rei9DDZU1xjB1Bo8MlazxcNt1sr4/URUjYTlY8XTZIgyBgSWEVIhlPHbUIifEHSRel1D5wR77u9WHN5f0NFOLx1SH/7Ger5Obq5pMiA1AEbwl1n7qBKNckAlYp6h1OopUB/gN9YwsEClQG9OMPePIPtBYJRpOSnZcx96RVlh+wMr4KuL+E3t/BwjbzwHP2tAzhccnl3zdnZwK3bHcsM03ECCEX7y+jP6NrcbrFvVFtibZba+pRyG67GvFPdHKzs5QZIwpgG6/ZcfTGHUXI/BoRIJm2ngY0cXxK7ZEbyHrCneD5fJ0jnQnWoruys8PB+5ez1+9y4rZweOotGq3Q099E8YpE6ToJzm5xnrTkmDa7buAW5QEE6XLoo4W8LPX8dZVKTK9K8bT3jXJCw06xFqAzRW8YTJAFWB3x7jr/zNhwcI6slHK/Q527h7zrD5pziAyoROBON1w3xDrz16rOcy1burunuDJAZY0twl85Lx/IxilmlJFu0BuZEMEs/q7SysNhvrWTcE9ibG+qRBi5IpoB3DIsldbvBLy8ZSkWqIV20R4jq30jObgvFi4zlyyNczhJ6kOiTmut4PGdkb8q4qZHOyISDJu2GR+tNmb1Tk/xGa4fmRGwnsMmVSzqSSfBo0oB7nm+EETg3J6ZnL4K+OEOp7C43DEcbZLniYut4LfRlQ9dplH07Ux9QUcRqHMfGGEgUCfLZz3bWI5edHCKvX/UoPn3Z5Rp/eI6Ugh4ffaLAGzlYsXvpFpvbywCLeRh/4BgKPHxpwfmLP0R/8Qon/+A7DN/8Fn5xiRwefOzxPPESwbvrBa22NzuWX/4i9de/DkB99w5FBHn+9hMPGp+KGxMfIOuz3cgMGmddobrPv8y3/oevMhzAK794F/u1X7/qIQFgl5ccvGPsTuLz8f6XFjz3X3azm3HWrMddWijHR9QHDz71t7r2kLErJQMPSAcWCfyA5kpC6YtweLpAilNkcj1aQockE0DCvgYA2oW47AOdKCl2sxFgtWTe5jpshiodv8zF5XxizxjeSCwnZ1mEkUBr+BZAbNqusaxSJXusTYm5EOWKZ69f8M5vPWDtwu1Xdjzz8gnL2zfQ4Rhb72CzRWzIBG6oQ/YzLBVddtSuIIsCXcKeYQ337qGbNdQBbSEOXhlpjQaYMK1RouzEPBeH7Tn2nW+j64GjmwesTo6o9y6oVqkZWiI6ORNjnj0cY+1/Hk4pTShkrVRaph6NwW3COSU5H5pORCEcoaqZdjsy4HSjWpant80qsRAi5MNHCIqFS9IcaroolbK3b1tgTEJBDygkVTBVtvScPdxycWGsboTjLMBxJj6PwKgtOQtzWHO9tZRomLbPglqLtmCP9vgAP2PJ7Nh/qVHTEcPGT/W9Ja37TtBY6Am3xrWe46gD+vAhdv8+sriNLAr9M8fsqPhdoW434EOE/bTQkXyyFB9JcPTobP7e6GNpSq5txdwSwEUgUuwAGZ2STEdKOCDToewWKd8krNyHCU6YKNvRaXluwDq8X6DrM7QXelekS0dmTd9eibVZiZ6dbV0KeZNifD9BquGqueXN8deKvNv6jh0nOcfaziM+jTV6qeZ5SSRK5WXyzooaNoZaafS9TIem7O1xiH6ljHAyoaPF+ApO30Hd7rCLHWsTNg92cPsQp0e9ojJQfddoZZhRiVYTUT7t480U6QulXpNSw8dNT4sjyQ0sWn7Uu1tkuUSXy4CN8v591kQESsEPltjNI3Y3lqyf6aiLjz9vrrA9Vu797OdZ/MSLrN68gPvnH/v1nnS5CnXxZIO375YLXPzwTQ6+dYKdRTuC+s670f3jSQaNRbCDa39p8KFkvaLmcxDMrCuRdB2/9adfxX8izi//7NUTfuIXvsDwjW9e8cgAd05e2/Dghw5AYHck6PER9d79qx7ZrFmzfoAe/g9+hjd/n/D5v1VZ/qf/NSNU+BR07b9JGA5Fx1CI0Zvj6aQp4arR6nS9IuwCOOAIhUZkAnrIHnbJv5u91/HWHD9ZYlnNAhKMjdnSLTeRoRhoK9VtrslWSi1TrzX3imfStUg6rVShOrXGOJUAM8mQEvBFurEUsFrZrHecb43zrSHvrjl6pmexKnDzEDlYIH4I1eJPK9M1C+BmBr6F7RoebvH1GmxAvWZPy5y7LMNUN0QDZGAaKcKZcoxHmblbxf0SvfMWq+c+x41XTpCLC/rFGhXNUBVPF1WCibHfYpt+HV1nLg0gxiTZWAbtI4xrMMfTNmrjorCxz6GIZkm7IhIdKE0Uy3p7lZjjgDsN0gXE1CzLh2kf1gShQkBb1XDTxTpU3ASTwnC5QzthuexQH3JBMZYDC74Hl6b1E2+nWbadUNkDbrb1r4RrkvZU90xVDuuhaKzpxOljv0OREv04aQ7FmnOnZBw5GvbfdFV6JLdnD1PqDh48QE5PgQBy3e1T6rZSu5663SC2Q2UbPRVFozTEZHLRtTWd7r7Y/IbG0oHXeiE0U6fGzQUfopy7Zll2czKbCIzgTXKVTHBNVNIxGMevZiBSLSuqgO42RACSR1lznY65tr6K6tiflfFGh+Xe0igJL920L/NvjfEJpIs412w77tuaGG+EpNu3pDvSJUBiA8VuI9hOS+R47gLNNPOWdN76NrY13ci2RroQRllIBB5sd1jf8c6bGy4vjec/f4Qvhd7WFJUs8SnUugMH7fZutLS2D1bHIJhZj1Z11dGpppP/6ZFvNtTNJo6PUpCug1KQvkNWK+zZWww3VwzHPbsjxXr50I7FD6vaw+WzHbvjE07/6Q5Zbx/diz9J6q/nV826VOSlF+A3pp6nI2h87pknskejL7qnLvDlB8kXimx8r+Rl1qzPRuWF59n80IbWaGL5zCXf+Ve+wPP/7mMAGYHuV36D1Vd+ivXt+OzkCTzfzZr1tOnBq4V6a8s3/nvCD9nPsPjP/v6n9l7X85vfnrJKlNFBk73Smm1nv5S1IHl5nW4zd6Bm+XEk2TpTv7SxB1wDMe2y3BN2JcxobkVPB+V7OucZo4NM0nnn3kqtZeIBNNAIkVTRYJUFRC1AFdQaXUnokf0BC4Wtgw0eHwS9Us8HTJyqUHdGFUXUkE1FhjaOTJUdNrA+xy/XUHdocbxGUnbjFCIOGRCDBNisXuk8oJVoSVDU5qaQaRAUN+p6Q3nnLs+8+BzL3Q3KRaVrwSRS8zuejS63PQITJhY8g7HL5OrL5zT2hLS+igFmapbQS8lemdhYqupi6QhlBIZkAnPzmzagGfsvy6lrzVRsxhLQ0dmX427mWMl/Go5Lh5nTqXPw7ILVAWPP0OjZmJA1XzG2t4T7r3ExQBpIGsF2/k4DeE7XD5FkbWKxL8bxpD9RmquvTAC9+RcTqrXxR6V1nVyaVsdXcu3iC/rFBru8QPoVpPNPbxwilx0yLJHNGoaC1G2U0BqZKC4jLCQhrXv0NDWXKQBnqERAjYbDVGOs1gJkEoh6rdnP08N9mGDQ8dYWMY/XPD9IwtS2l12RboHstkitFJE4/nCkgKDZ0zPn0zIASn2vfDjvJmgLE8rF3GDqHkiOZOoA6dbAdULsKIs3tGgCwikgpq0SyLYHCeYDRe71f8xfmTA6gilRqt76KXpCdCEutqI7gNL1lZ2vkdrjRbn79gYbjGe/dJvjVU+/uUS5BNsiWigqY4I32mEeq0tUJgA765HKFi006ymVOz4M+DCgh4cMP/FDrJ9bfk9/xU9Tw0q4fOUGh19/OkNhfNljn8AZ+rjKBTYv36D/zfeWCdZ33kUvLtEXn8cPlk8UtKvLGRTsaw6CmXVl6juke+/Nwc3NqxnK95NdXPC5/+Q17vyhL3Dj6w+p79656iHNmjXrA3T4pvPwJ4De+dYf7vnS2z+J/4N/+ql8N732kBGyRNL3EoUzYdZswGtz9ni6wRTQKM+1mmAoXUK+5y4aL9qi/yFjiTABOXzPbZZQUxFMPBxfkBfuWRk5Xu0kFsgy0HZh30IbWqjDGCwi2d9RZawG82zSJ0L0LPSM/XDYXQYE6E+XHEhBpWPYdNjtQ3zj+MNwZskqw04uzuHiAtlehhst51SsVeFVxFpydwJaU1oHwAjoqKgXYC+kJp9rrYeeg3RGf3GObo7obxYoBxS9CAQjAc+0hetIKyxPp6K3Ppm0qvIYZ0JII8JsWriIo+FqSyDltg859iFwlmkjiJV4YSlRxh1tOUf3qbeehhrAagrNCCdYuNESF4thaqAFN8UpyKqw0oJ+rmPpO1ZUuuz96SYIAxCp5ZBuv1xzIpFSrSJjurFrjLu5GVvPxSKFKs3lGM+NudrbmAagaKX3Nr5Xaw8QQMqSlNecrxruQJl6UYaJVfBhBxdb5NZBgNEqyMECzCh9D8sFvl5gmw1Sd8AG9004EduBkpBTLGyXMbQa4y8l5mWs9E7HbBC+Eb7F4WB4iTJlTNLpKzjpnszuieHYhFICqu62jnmPH3WweYCU7I/6ngM9V2beFTA3HIvjPh23LZyIWJ4JFlsyd+4Ai30SuHzq99nOP0Dk1JhRzXI/Zg/JXHMkMI+VK+0/A1Z7W885V5B9En3cb944PS00KFyU0XsT+pxDkx03njvgzdfOuH93QL7xAL70DAeLQ8rDHcUG+jw/WK45T2e4tdYPPN3BGJ+qWv/Np1h6dMT6D/44l89ezdee9TMdy2eOKe9e76Tv76fheHEtkqW/n7Y3Opa3blHffvs9P7eLC+y3v0F5/jnk5in+BLg5vS/ZF3nWvuYgmFlXoTs/+xJdf/Gen+35Gh4LDd/6Nqf/t28/vTcxZ816wnT7F3+Dh1/4MS5+15bh+R2/8W8e8eX/y08h/79ffeTv9fh/6/mEak5Ab9UOYlnZGHCnJnzyreGdY1Jwi55jXogL8XRwSQLEuND2LOdMdjM6fZgAozQXVSvlTRdY6+VYiX5vezbF5hgb+7KlC08h+0MGQBor38TSRdXoJ2BC9QEtmiEcHtXPVbjcVc5ev2Bw5eTFEw6fOWL5/DFCOgl78GGL3D/HN2uK71Az3GpCuCijFnOkaMSnuO1FN2dPwHRoebVI2u5irFEFnq5BAW32+lopPiAo9f45/XIFhwvKdoP4AGhATW+gOPshjgQynYQ2OfEcm8pMRRK4+gjVYsoSOhPbhLQMl71+liPIm5yAXg3bCd5HOI9Zc5s1JNOATgtdSZhnEmsOwNIV6QJdofQdS60s+x1lt6Ok68yDUEeJdUKolvg8xdIIEWCSUD0a4I2/cyKQxd2pxcLVNx4jFnPpRqRsR5qzaolybmvOhtj/reRdWlKxNyda2uHG+Q+oFg5eAXb4+gLTmxTTcGKikdBcHRY9vuqx7QpfD3C5xu0s1oVbwPysgfaSqdJj0Ev2vBx7Y1rCRIkeodYCnTxBdJR3e8n1Q0JidOxGqTmnhuE1Antc+/hDRWqlGfAivASKaI6EbHkQQDHAZwYRkQ5EmUJYRr+rN/CYjsKUtee12W1J0O14EgLsEttTW7/D6ZQQ62VsHUBsQ1prdTxyfXLdlHB+hqORsUVBAMooA5dFAZRFrRzeOOLocMmmOps1PHxnzeKLz1B30F1WCpXBS5x/ddo6KZnGLu0mxqxHKVsocnCA76433Hq/dHJZLtEffoWHP3KL7fHVARRXuPzcIUf3L5Dh6YEVXpTdyfW9geAKPHsTvgsyxi+d+uZbyL37lOefw0+PHmtXo88uxveVd4K0u7OzZn3K0qMj3vz9zmKPKroLz/43s6N21qxZH1/13Tu8/F+c8/Xfld/LFsYbv++Qz/3ydA36qHTtIaNI9GELPlKixHMsSTbUsrdZUUwCe5gFxCsa5chhCBK0lLxoH4llhCZAlv4GsKkZxrGf0hrKcl636D/mntBKJkboJUo+2d/XgolGKbV5ONUA9xrhKejI3owWqqB4zTJHwOjYesflJVxuYOfGyXLJ6avHHLrT1R1l+xAuL5H1GmyLlqm3oEu6sRJaBiTLUmlPp1s6rVCNVn37rrhMdRYCYEXPvKnfXhVBB8ekIps11nUgC7xbwbAL6KsK1SaHXJFwoaUby6wi7qh0+7wmQJan+y1L4EUa4EsYSQBCFx2DgjLzBZGEugmHISrOXSrhEWt7tzn9ojGheXOcxkBMJINcunG/OIqWDi89pVN0u80m47GewjXWsJ3iVsOlNgI131tjkmE/raeiowmxRTQCUzSgZpghY14KJebQLdZzbq+KRS9HJvjYol5aiS2SQL0K6nuQFqG0nphxVCAU5GIXYyyKbXfhGiw6pX0vCqyW2GKLnBwzPDzGLi7phkukrlGvuNaA0UnaXTLAxaMU2hgCYiWQ39VwOhbCDWw4pUu36S4cijFHMoYdaWnHtqDa4cT5wKviXYcPO0qNccTdgj6BYYQPhYE4j3+vVCol7lokGNbYx5aA1istSMjTMt0wsaQrUvJmhkoL4Gl9GuNcMzpPs8eijWnNE/jVPBmNLus8NluZvFk6fvN8pJbANI8N3uNmjVfuRKnmHPbGzVdOuXh7Q1kq/VIpq4KcPsv2mwKX79DXDVp6bHC6Ettlnr1en5aAklmPXp43/b5L3edf5vynXmJzozwWTrrtibJ8/pT+O3eveiifmXy1pK6utztud/uIH7SFvtkwfPNblNNT5Pnb4dp/DGGj9TNkfD/tl01ny+NZsz49ffELlNub9/yofuuQw//yn7RcylmzZs36WOp+63W6t7/E8FyYO7pz51EDRngKIGNz6aDRz0wkQjcCcmn0FjRBbAjI1BKTNS6kow+aRSiCaXABIC7LA5qFma7ZCAGv0SsuL/hl3HE2lTqbJhxr2KZBrwBo1qyKEl9uxoQTJcqSRcE6zIcReAbjC+dguCVl6rdWBJeewRy6nsXJksXzqyjJ3Z7TPbiD7rbghmJTqWWSNknYGrEQlvAry7i7KAVGGvBppcGACNWcOgwU7SlojCkvCNu8qTtDiVLX4juqGwNC6VeUugYuM9HbUQ9HIKYYw+gebCXkVSJoQkWit2TUBMf7pUs0gjFKQs6aqcE5dloJaIKn7OFowhicUYLjULMEvBkDXbN8nRIJ1OlExSwBY/Y+TMqjJXp9juE8ww51x8Z+h4zlsa3/o2dpa/Je1DP9O8FQa0Dp1RLsTkRJsr+h57YUi+RqMNyGcGBKQFJv66pFau+99kgag+6NTjtNSIvlutaYb/VwztVhC9sKRwt2G6c0wNvpBKxVsEXH5sEOWy5ZHCzx8yVcXiC2Bd8CAww1HYHZjxPH1cN1OATKM49+nVLij5JhKBY3AuJYyisGi3Wr6plgrQHHPcJ5BsC8g66D4RJkQEVQXWTAUwBLI9aHZ7uF6F3Zx7HtHmXZbR/h4QpuDs10EkpbUBni4+3GgnjchDBpi5mxj2hEnhO3Qdq5ptU7x/PamSLOiXlMexJMGoS0sWTcyb6kcbAyJt6LYK6IdgjGYlFwtjzzwhGLRYT2qMD2jbssPneDxavPsfumIQ/v0bNDZMCLZK+IcJVvL2cn46ema96Q3et7AaMeHVF/+svc/fwKe4y+5bjAxYtLTt/pke3Tsd7rjVXr2HJttTvtWXXv7cv4/VQfPEAuLigvvgCnR/E5+7jAxk75gaR0FgDlt9+AzQZefgE7Wo43E7+f9GJH99Y7n+HoZl0bFUF0wonbiwU/+tfujUn2s2bNmvVxVd98i9/179/mO3/4FjrAi3/j659KR6XH6Ov3p6Pm7LF0ALaLcEnXnYmCGopTNEoMByNLPSPwQjMtFpya/fBa6i7Z4088+ji6JQCq4UZCmtcsXjsrKMM9STq93FFNSLHnTmtwC2s+JE+3XRfwI0NWRMr4mi5dODQbcHDDuw7pD5DlMQt3TrsVq1cPuXECi/tvUs4fonUXDimvMa4McYlJbMChpQs34KEjbFD1dJLZ6LSL1xHwga4r0e/NPQFPAj0hSorcwlEnRseA7baU5SHDDrrVCb7eUXwIZ54GdNteDtA73SJGJx5Vt+GgtOh12CKgZapyEdEMKPEE91OASexSTzA0yUkw3Xpd5n41y1Rlcl/VVlKqU7lsLpVWLq8UnHAzGhL7bLVCzTEb0vU37tB0r7IXSDMBZWiBHWQq9t6aJMKM9i9kvPWHJKFsK2v3hNmJ4ZC9NQfhTvXmim3cSiHHqtKAu4x5xvEahbFsGiJIaDdQDS4e7ui2A4tloT/ssVWPd4q7cnnngvvfPmf7cM3h80fc/uJtfHfM7sE5cnmODOsIKZLsOWAVasxVHQKSmTkslLLUuIng0HoiAtEr1HRys0qu9aiHj/OGKmZd7v/Yey7gtgWxSBsfKjZAWUaPw9jSlrTeYDuMqTKeNzva2pI8fzjpUt4LjZHpuGvrYSzv1/az9pg4P0QV++SAdVqi9nRWbDC9nQuTQ9JKz/NOwAhk43zUbLHgNf/tQ/Z/NA7YITowHHWcvzuwfnDJZVc4XS04/vwxvPoi/i3FNw8pvgmEaY55x8WF853fenpKSD9LuYCfHMLde1c9lE9HteLbSG2WrkO/+ApnX3mO3eFjAm++S8NK2Lx8g9XvvPup3Dl+rCTCcHTtv2aG5MMROh8Ghm99Gz08RG+cwskR9Fef6OydXvkYHneVyx31nQxvOjtDDw8pz93Gbh5ji/feyCnnW+xf/A7D+uEVjXbWkyz51psMb3+Z/vlLtveXvPo3wf7Jb1z1sGY9hZKuQw4OoFbs4uKDnzDriZD92q/z4q/F3z+tq69r/+3PTKPjnEgz/IVL0YhecaLh9qlgW2PolO3Okc4pfQIYWmDBHqBRH109qvHbgEA+Iav8vlbTpQSMzwnAmCBRJcEDCWZaySOjW6nB0szLzfdIIMHkYHPx8Y3FhSqKS4csVixfPOGZFzuGYUfZPmBx/y7dZp1lnw4JDIJhBDRzAo6pM/63JySpFdabSq1Gf1DohfQ+RepvG502GFKjJFRUqLXGCw2t72GMvajBIJS6xazHZMG2P2C5XUA1zIcoexVDlglx3almWSKbiNaFYXBUdc8ll9cBFnBogi0Z2WFTsEWUhtrYO685+HJmAcUyNdit5r7rQByVPliSD6Mb0LNXX/Tvq5h0VO/DHdv3SN9HqbrV0RmoGXQikCnCPr67JBQcOZNk0EsNl2EY05SRb/kUIjKu0CxX93GF69ijL3hcg+kJvs3SgUnOX53Ke2kJxoEgZQT6/p4WRkOWIz9845w7r92nB27c6DiUY3SxROnYXOw4f/ucy7fPAxIPRn3wkNVLt6inK+z+AfbwEtnskO0Wr9vYgzIwDGtA6DqIoKFgkJhS1SkCUj3DUVr/SUYHortRpB3TgdzMKhWhKwr9ArcBtSHAnQpWQLppZch4nEsjvxnuFPNWnIDRKnvPiH+a2+ieDcCYZd80SJhBR+M+ldFd2xzWtfbsBo+SZXHcBsSy7yaAhsPV4u5FAup0fo43OOK1xginlm1VBEOpYhmYVKlKwkil2xgMBdspWxbIUKmDUIuxfP6UoVfqm2ds79zD15v40kLP2blxd7j2H0dXp+sKD2rF1muk6yjPPcv6J17m4oX+sS9lvHy+Z/HuAfrgen9h92XPcDjb476f7OIiLtjeLJTbzyDP3oybTFd0rM6l0h9CPn0Pwx07P8fOz5F+QffSC9TnbuAalTD+O9/CN5sf/HqzZr2P6rt3+PH/9be4+MpLHP7aNxlef+OTvaAI3csvYTdPkG+/Sb1799EMdNa1lvQLfvN/+TV+/Gd/mwebFWf/4ed49v/0975ve5pZH13SdeiNU+z+gw+shHgSdf2v6tIZZJ5ORgG3TNPVTM5tpcgAUhh2BdEdvRh9NzkPW3pMK/Ft1Yrjv615EAGiRFSyh5qJ4JV0su25h2Cvb58mUAwAKiN5JAFi/Dt63zkZGTt96Qn2FW4rr3jdYd5T9QBOjln2wuLinOH+XfTyHB22FKtTuXGW2KoGEAvnXet71wASUIRdhcsH8Na3N6w3AzdeWHHrhcJBHx3+Wr/GkIfdTgOMec2egRL9+hCyz1y4NkWNImtgQe0PYXFAHU7Q8yHDPTxhXsK2/z97/x5rW5ad92G/MeZae+/zvq+69eiq6hebTbKbZPNt0TRJSyYl27LltxElCAQIiQyTSgjKsOA4CEQjkCDbsPyHETlOAikWIshAIkGKIcmmrZgCRVGk2qRaJMUm+931vnVf5733WnOM/DHGXPtUd1V3VXdV3XtP7UFW33vO3Y+55ppr7b1+6xvfVxNy1Qb0SjxmndkzATYyibjNvpQEzeYR5tOgikc8S2lq0NhwJnGme4R81GhvDWiToM4hsswt2km9jSPmMJSnoUobXSndLCCUDcg44mZoSTBK7PeAmeEZ2FiRJL2Tprr0UJOG6FYm5eGkqJQLm54+hBOoRNN/ccKSuY/IFGqZthP3bAdOEG0Wnpfatis8N0OBmUpiwkNSy4zlyrnz0iFHd1Z0JXwmvfRs7W6joizPndWpYwLbNxZs70eb/Xh0DvMe2V6gfYdZxUfDT0fG0yV2cs64cooOaDG6GCSlhO9jHH0VZrlfq2eITUDHSJQPf0gVRWokiHcLpUNwV8a+h2FMYWIkOZd2mJpjXvGS8DHtRzVTy1soT2thny69pbV2k+eq8Axtxk+xR9qNg+mskT/H39v6Xg3CvduVk5PK3hML9nejjVwasPSa7fZlUu22GwmS4VXI2s+S2vxrwbWg2kXoS1fwscZjzEEMEUOpzLe3qdeUsjJKJ8yvbFNmC0Q6Zlf28O0t6s0DxpMz6tkSqc5idHYXwN9lU+9AXdbEWB9HpJ8x/Oh3cv9m/8i05VqBs6d32f70eXrwXs6y7dnXbCe9NOWsvx+91bJKvXULOTxErxwgB3sPRNnoG0/cr1t6dP66ig8fVoxf/DLl7n1kZxs7PMJOTt718W3qctX43PPMnnuetwU9/NB38tw/tYv1sHj1Kjf+m3+0UaVt6uuWf9+38b/653+BG3206d/96R3+9u0fY+f/8w8e8Mge/ZL5nPGHP8bp4zP2vnAKv/ypBz2kt70uPWQMyNLAWbY7psJq8tFLuKKp1nItjOPAMAbI6jLc4qK7Xeskbm2prSVXUp3UIjIakApQoGiGKuiE79YQcU2BJNWNTbkY42sKwmAP4RkHTcXYAiCIlm1R0ILKLn7lANmbU06PsFdvM1uepjLTaJ6SAnkxINnqvW731Gm0I+aOWWFYKUdHxr0jOB8FOXcOuoS4qQzDHastVCWgRUBMpolXLZNCUBRsDC/EIqDDETqbQ9mG3T3q6VkqIQ2ouFqo0giKp6lAjH3jFJVpG0AymVnXcx3SU9yM6h4B2KQisLWIe4bfEEpYTzo8BV8UnRKs3Z2KUbSti5Lt0NDacD3fWjCKDgGjegJI1jEw5eSj2L70e3tSQvGAiY14tvVLHadniHukMedjJjUj6/WY+dyRWp0t6N7asT1DUGhwMv1JvRKefREm7VYnH1HydTOeG9OAwSKOWgThUBasTgfG8xEGYXDn7NTRe0vmT8ToV6Lo9oKdubJ/MGeec16XNZSUNRSmtTpFCro/o+xusXr1hOXpSBkMiuEqLPoZ+BhbahWTeH4R8BKqBK/p22pt20GkCzAzjhSTDI/qcO0QG6jLAe3Dr3BKSC8ZMkTNtZ2ENlhhnD9SDUgLS9KmILS1+tNfC3m9/XnhXkLzH8VD12zp2Xg6KLfvDZycG7K/Ym9vjnoAQBFF3bKdOgDoWj1tNA/atReoYCqk7Sil+XQ2QWQGYUG2Y3eAOoudHmYdC3O07+hu7CB7C7yAmqFdRRcGq0oZRrCBvhu5+SSbeoeq7i24dDolVcrTT1EfO3gk1ItfWcuDwvzGHt3L9x/0UN6xqtuzR26/fCM1Oxy+aRWCL5fUl1+BW7fpHn8M9nehK+8KbPS+XNobEW9nyfA19rEIbC3w/V1ktUKWy0upTNnUI1hauPMdO1iGya4OBNlawAYyburr1Is/vDsBRoCr3Qm7P/Uc5ZPPMn7hSw9wZI9+6Yffz/FTMwDObyxYaLl0CtG3/VvFn/kzf4Yf+IEfYG9vj5s3b/Kv/Cv/Cp/+9Gt9JNydP/Wn/hRPPfUUW1tb/PiP/zi/+Zu/+ZrHLJdL/vgf/+PcuHGDnZ0d/uV/+V/mueeee8vjEaL1sXkjureQhZSXXVAl+lhRE7wUhjGaQY2AI9mRmDes1xCgtS836NTAlWqq25q6LFWNcUHfGp4VUuVYVbAGlzxSpJtgUT3CPcQFsVBnUaHRAkuYZB4jERW0gvoM2d6m399GhwG/fRuWZ5TqqFU6BykFWmNzhtVESEy0SBdCmRb4IxJx3SMExVIFON/u2LnR0+ExY2ZxQWue4yYgBy1kYwzVk2q0WaZ8MxRTntvsqA+U8/vocAyznrHf5nyYsawdgxesCpUAmFPARa2oWABGYfKiU/H4T6UJUsElvCElg34kE6zJufYIwKnE+3jCw+hclraYqQiW6eJd0UwmjkbTi2DR83kuiorSqdFJBQUbhmit8bqGiJlAHuJAo7UiT/QJW/+9kaj294Sebc1aKkCbKs3cLvyX22Tt+fmnlIDaeRx4C0cB3CNFfe1pGfsYmrKTKZjGLEDe6D0r7Ti9fcR8rpSZIiY4HUbHMIz4OLK1JRw8u8+1Z68z25kh2z3SF1QFnRW6/S100dMdzPG9gveKzgr9zV36Z24y7OxzvpyxOu9ZLQujKWPCYzTWijeI3+ZDA8rHYilT27mLUikgC2q/i0rBxzHSqSX8R82yrZ6AlVKl7cZUKZLHs+f75smptJApmVrjG+hr+zHUzO1WQ0LRfAFrIsIMYXKUZS2cr0AW0C0qomO6YhrCOM0BIlinWIlArGkf5mohk+uVdZt2QOuRpva1SX2dsFtAqWgvzPa2me3tMbtxBbm2i3SGnp5SX7nN8rMvcPqbX+L4t77MyedeZHj5FuX+Xeb377zl8/u7VQ/b59qmoH78Q9z54ae4/5GdRxJkucDJ01v4rH/QQ3lHyosy7F/6+9hvf1llfPEl6u9+Hl5+FVkNFxTs70z5/NLdgnhHyg9fP3SjPPYY4+/9Xl79Ax/mlR95jFf/4Ec5++e/l/L4zXd5hG++Np9p77G6cArZumXY/cMHN5ZNPRolwtG3fPWNkn/piU/xpX/z6QcwoMtVq8d3p7/PjoZLBxjhHYCMv/ALv8BP/dRP8cu//Mv8/M//POM48pM/+ZOcXGgd+I//4/+Y/+w/+8/4L/6L/4Jf/dVf5YknnuAnfuInOLqQmvUzP/Mz/LW/9tf4K3/lr/CLv/iLHB8f8wf/4B8ML7+3Uo2XSAA69bhwVm3tsQQkyAtpL4ouFtSxYGNhHGC0aMFFNB8X6jdgglSt5XCtkkvVkkV6bASqpLdjKvxae6QbiMnU+uipmktEmsxHI8xFIhjDJVu9iyClxO9Cgoc7VOmw7Sv49Su4GvrqnUjnldokaKFUa0rMKRzEox3boboneKzRfu0JRhVKX9k5cG480/HEh3quHwjzxB+R/hztxJptupKQr31Zlgb3ct+4eSimIlUD9xoMbXmOHt5BxhV1d4fDU+XlL6+4/XLldAW1CnXIhlJVRAPgicTPCNmeHvsoFGkFoQMv1DFanHFNhhdBNjGfhA9hzvXFMbeyUjBXbIw15ADVpn1pNVJ2J21irgkza5LP6HxfjWhCRfcWtNEkZ0HrxJonJFiCJ2+enamUFGnArykY12rYFgDS1HUyqTEbXsqW6HzPSCgPBaomXM7ZXKsqtWGpHCdNUZvHgASYrtqzpOfuSyccfvEYRmP7iW26vTnd/gy2Cqe3Tzh78R7j7WNKb8ye2Gb21BW6KzvI9gzdnSGzDFoZRux4iZ2uIsm4h9nenP6xbfr3XWfcu8p5nXE6wNIUo8NqQYkAIieCYZwSCj8CstYK5oJpbq8UVOdUFoyLXUyVUgJW99Kjnm34mQ7uCFK6aB2nhLK27X0pkxo5lma2V9v0iLgZkYd+CxdKQShtFRUP9axKpJLHCnZUDC2VfuHs7Rm7u05XB0zGCVSaZxs/db0OvClXc7k5uU7GaK8m26VT3SyyXkO0FPRUQEod0bqiu7lH+eAN5KkDpHPkzn3sc1+kfuFL2MsvM969y+rshDos0fGUvp4wGx9e/6qH7nPtLVYcupdIqfTBZzh7crHOQXpEa5wLq6f2uYyemb49p84v0Zr7GrXa75HubQaqVqmv3qZ+5gvwyh2k1ncGNirYRsX4dUuqh4/wa34plO/4Vu78/g9z/wOzKcneOji5Wbj9kx9m+RPf++4P9k3Uo/6Ztqm3UFa58cm7zA6dnReNa3/7d94VlW33zNOc/qs/hP0z30O5cvCOv9+m3obSQvnIhzj8w/8UX/xTv4cr7zvk06dPfNXDTj728H5ff1Sqv3sORFfZ7Mt3H/Bo3pkS93f2FumtW7e4efMmv/ALv8CP/uiP4u489dRT/MzP/Ax/8k/+SSDuhD3++OP82T/7Z/ljf+yPcf/+fR577DH+0l/6S/zb//a/DcALL7zAM888w9/8m3+T3//7f//Xfd/Dw0MODg549af/E/bnW0CArAbspsAUqbgXRheGVQ8HVzkdjOWLd5nNBhbdSOmcvhB6LrkAACVUkgJ4jTbSCe5o+pwl5NHmCSjrVsQGnyxVi5rQpqnQFIUSbauRhhxqR2+uMO35Lrg1kGYwKmO/hz7+OGVH8Xu38Xt3U4kUELD5LUpLREkF2sUwkJYTHL5r0c5pCRyqxTxUA5kJc0AzfVawVC82f7lotm4hIZMOtLV/SoSojD4iKKWEKtFwpHS4CnXnBsurN3jpV17g/vOH4CPXnnSuPy5s43RdiXZOB094HMElGRIjXbT1SsyVp1dmU/c1BWpQlpLwrKJTSrhOqc7uMf8qgmmHj4r7gJJt6q6hiBwr2kkEz7iASYRPF4m2Wwq138L39uDokHJ0D7cBik/t2tqgXtKmFq6S2llES6wjq2thY7brSgJiy6eXvKdQCV9IANUSisvcFxkeHNgqf6cWgKnBX6GNIdSp0R5tVDV0dFRC01rdMBPqqmfUGWe6xfNfPqUOysEHrrB1fZvV8SqUgSKoOjOFUYTZwR477ztAt3vwHhtXFFG8gp0M2LCiDrEmdauj25tDgdVZZXVWUXf8+IT66iH9eMJibvTFsoE9fTkdbBxSYhrHrYmHf6MJVgPkufaMuo1d20dO7tMd3UFZBhRMAJ22oJSQzUYQUR7unqC6za0QfpvW8qU9VbikkredK5ALx6FeAMPZBo+s29cBp7DywskyWpJ3eqVLb1QXC0Wq2aRWjN+UxJuG2Ri2Egmt8VgrGJF+3RTHbQZtHRQUN0AE1xn14Cr2HR9l3JmhJyvs1qvw/At0J/fjPRxGjOUywPnW3JipcLRc8sH/8v/A/fv32d/f/7rn+AdZD/pz7ff8xM/R9Ys3PV6tzuKTn/vqi+RHrdzRq1e498PPUGeXA8xphf1PH1HuXa4U2tXT1zi7OXvQw3hXqjs3Fv/Dp9ZBHyJ0j99k+NAT1EWQp+50oHv+Dnb7zjfkhSbzOeXaVfxtTqP2vjAezN+W17rMpauK/spvruGMCPJ9H+P2d+59TS/YujrnU//1f/jQf649qM80WH+u/Th/iE4up7L7gZcIMptFrsCwelfe8vwP/iD3viXOfwefH9n6/37yUqq1Lkvpx7+NL/6ha9TvOmZ7sV4jfVf5F5/+zde0Tf+nf+8P8K3/m199EMO8NCX9DP3Qs8j5kvFLz8E7i+Pethp94H/ir7+pz7R3vJfl/v37AFy7dg2Az3/+87z00kv85E/+5PSY+XzOj/3Yj/FLv/RL/LE/9sf45Cc/yTAMr3nMU089xcc//nF+6Zd+6U1/cEHrMLW16kY02otTPdfUW4xx8e8U+sWMupjHnUsZUx3m+XoJaJoKjwR7QMZzJCRsKkei+zCQULZrp+LsItySSJ6NMYyoRvtzgz+igRJsaqfM8Xgo3kQz7MQ7bLYN164jPXB4H44Oo/U3X6vm+wUYyLZQoj061FMBxFIrF0m8TSU4pRwbpRDBOHQINT0PBVyn9uAAKqGONLeEdK/ZQ/GfJqhwj3ZjM9RijsxATu6gszn9U/sML50gWx2HJ6fs1cJWSUWoe5iXZ2hOhJXkGDL9W1wjGZySsNAz8CO9DVWTmYVnp6mg6fEYrbHxASkJX6XWBEOhQtQEMLYc0ZmCekLiMqm+IngooJD0i2gFHweUES1M0O+CRnBaX7mcYu2oTq3lzZuvAWNo850qz7Y/pKkMPYJLLFr5fUpXZlqbDTR5Arm23zz9GRUBr5MCtnPFSszOOMJqLKxOjOWJM8xg2HZWZ45sFcqio2dk3o0YA3ilz/fppaesTuheNXzWoduLSaXqWvDecTp0lgh2Foo+dWM+Ezq6GOP+VcbdOfXWnOH0GGOFYvRSUqFKtEbjuW8cb1+AVKkVloMiZYHsb4ea8sSRIlgCNpFUQqcwVbuA7AWfWsnXrcSxRsOzkcmD0dE8vi13rkw3IwRdKw0JL1LafmkA0ZWmRZ0Xo98F9ZLnqjGOZw8VrkqX694CL6Ypo4sgUqa1Zhb7X6WEalYklJluoVBolrBSqJbrTqGOxlidoiOyrAwv3KN+8Tnm4wmwoksbgJnHucMJ71SrfMO5CQ+iHvTn2nu1fLXCbhxcGsAILQRmh53jM2S8HBdgXpRh773TKn12rYPf+11svXBM3Z1z8tSc82uKdRfX6Rz5nh3K+TPsvDKyeOUcORvQV+9iR8f41/Hw8+UyEmZfLpRrV5DrV98Wz8ZNq/RbL5nPGX7k4xw+O3tkwqa+Xm0+0y55ub/raec6rq9ejp7p2Lt5g/Gll9/VMWzqzZX9yCf4zL8j7O99tW3RWJXjOqeIUV35W89/Bx/9r87x13mdTb358mFF/fRnHvQw3tF6R78Fujs/+7M/y4/8yI/w8Y9/HICXXnoJgMcff/w1j3388cf54he/OD1mNptx9erVr3pMe/5X1nK5ZHnhBHp4GH4TLuHJptJalSuiXctpmcJOVDp0vsBmHWU+Y3HjCvX+Xboeio5gNgWKOIZSaJ5ukkKjAD1RmoEX1ZrmiEyXDYVYooZsJ24JvGSybaH5IJo3AElCLDIMOxWRFrDUxEMdpws4uI5c3YHjQ/z+EXUcKR5PlKKIWbRupiIqIGrCB48xZQwuxkgnhDelkT6Fqdgi2IJRM0BCkYQtDY2JAzUASQOnEa4RrdolU5TdApw1D7/WJh7wQ9Hq6N3b7F5/iu0bu5zcOWJQYVUN7wW8IGUNfD1bvZGmVCyIRZIwBubjpNgSD0gWyrAYtKri6NQ6HlwuYJJKQEvEcU1dV7YGW8IWEY2gEA8A2aw0RZRaYr3VssAXW7Eeasy9NbiX3Ka1TiMe+w2dWqa8adBStSh+ERK29ZieeqmWiwsSm14fkTW4TjUdOKa5P5rmVx1xxa2DbJ+PFdv6FTPBXZQKjCOcHI6cnWn4IqowHFaqw/bBjMW2MOecvqxAhgiSyXZg6wY6gXJu+Dn4yRFFNNqQZz1lPoOtDnTO6IIUR0klpwjdVmGo0ebb7y7oF3PGwwV2esqwPGdYVTrzUN5qHqfLgWaX6COMFZZjYbXq6a707Bzs0gOYU1fh+yniUMIrsqrR6YWbDEA1ozRbA1KBnMfJRNs9U9JFES9YsQnutuCnmqnUapY3AcJ/cTq+1qJgxI3O4jiItwjLgsnTUWSCn07NOY+bGyYR8RTnh1BNW+7liBjPdVM0LRdiM4pGG3W1ntWpUw80YMnxOefPvYAeH+G6RLVmaFOqwC/CVCU9PR/+ehg+195zVSs+jrg5yyd3v/7jH7Fa7Smzpw6iZeYRuZv9NWs+wy4RCP5a5UUYdqNl+v6Hrn7tx6owbsP9D/Tc/0AotsSuUpZQVs7idqU/Hemfv4e//Cp2fPzV6yHbqOXoGN3fR7cX+HwGXarJ3yR0FHcYK9a9eUX0e7m8V/RgH64ecPLtj3Fy8/LA2XfzMw02n2vvldr+R1/m/gc+SF0I1oE9cR02kPGhq/Id35qA8ex1//30fMb/8H//PTz+D+6j5yPXXniFevfuuzzKTT2K9Y5Cxp/+6Z/mU5/6FL/4i7/4Vf8mX/FFqCn8vlZ9rcf8mT/zZ/i5n/u513lSwCwTUonDdEUeoDFCO8wLtXRI0fA2u3kQF9zDEVLPcTuPC/oMahASJAUhmvyuBGhtjdWyLTPVQpKAKBuKJ3Xk2o/Js30xYQAXWosBLdnC6i3tWXBVOikMrrhsYfsH6GNX0GGJ3TnGz5eIZtKtE7C0JT6XUFB5TkZTSAbjCKCgrgG6xmjTjHyTVMIlpCoJVXAL+GHhOWfNww2QugYLnjAqYFnzxlsrPDX9E4MHhkKwSKGrA4uju9z4yBXG3xyRldH3QikRNON1DAAiJeYmSbKijKlMbFOt04YnyE3F3pTq7D41krp6NgZrgMVp30z0MYBQAstosdXYroSkniE/LorSYTbH9vawvS1YrRAD02xplvAO9RakEoLGbHttrfJCSTOy1u3b9lfMeKw9bQCSHKN5JCzHnp4gU6jicntymyblIkxqzlj3gntJhVwCZiQgZK7tsQqjKasVLM+dstVTV8Z8d4u9nY7t8ZR5PUVrwr2cp/C+7JE6wDhitSawDeVg3/fU1oc8nzFbLKhA6TtcC9LPYDFH5ulRqjVCR2QHm/eUYRcfHR+MOg65tiuMlWG1YlwNVBe87wmJbMfs6hb9rENshQuUWQdicSPBQMzopSUvr71Ni7bFH/C0qVnB1zA5JzjWqiLT/nNaYnzTLXuuRfGmaA3oKKprRTOhTpX0c0RkUiXGco2VbHkGgubY2dYCk5Ky+ZimWWpCTyZg6WashoF+0SNeWC2dk3On1EI5PKc+9wp+5y5Ssg3bx+mYK+n56e4JGyGj4B/6eig+195i6erRALivW7Vi5+Fd0z3zNIdXL8/FfSsXOH5qzsHhzqPfNu3OuL/IG7iXv1a78k1tq6swbsG4JSwPFOjhY1uU1RPMD535nYHFi8fw8qvY/SM8LT58uaTeuhUvIoLO58hijl45wHe23hA2ijtyfIofn8T3wxsf2ShS3kS5CONHn+HuR7cvjXqx1bv5mQZv3+faph7uGl98iZv/8AYv/Z79uLe8HB70kDb1laWFz/4vrrO/98bQsH5+l8f/b/8wlHfv4tA29ejXOwYZ//gf/+P8jb/xN/i7f/fv8vTT6xSiJ54IA9GXXnqJJ598cvr9K6+8Mt0xe+KJJ1itVty9e/c1d8heeeUVfviHf/h13+8/+A/+A372Z392+vnw8JBnnnkGEY8bvKm0WqcoE4EpCYa8hMeNr0bsZIT9QrlxhfGFVfjBaaGQjCDVcnHdHhCnUgOQZRCE2fqivunO8JptzcSXxFQ+NQCkLgS1WCvV2vvgUFMI1S74m4TSSofqHLavIDeu4N2I3b1DGY+wmVMq+DgmHHK0FETL9D4NiobyyRJSML2HixLp0CRIYVIBhu9kDTWgJnRN9aBqE3gmaG2wg4R0EvulOhQtiDpmzlhHSlcSsrTWZ0UZ6U6P2N2e8eTHrjLeKfR6HHBTCChnIF2CUIuwH0skI7IO2TANmNlA6YRaGlxJVWFp8y8kWIpwHbMI+qBkwEymEaM+8deY2gaUGuQrDDqjli3Y3UN253DnLHeCBgAWT9/E1h7f/DhTfZAQyJt0kdhXAZYykbodCO2xNIYj0zogfTkDyDIBprX3Hmv7v1yjMinpQqWrrrnGQxWoVpDRqSNUUWpxWPTUouisZ75w5rKirAasnie4LqgkXHcFs1yNFa9jwEM0YORqTEjm6LnCUaGUgvQ9LTzF5z1lfxfb2YYS4LNs9fH35YhqtP3KOBISX2AEq47WES+FThVfLcEyYf30JFSEVRkNtFZEnb50eLcGjAF6Yx23Nun1fHrYELR9MoF9IH0XG1zPAyxgoCX8zpCdkFS3ICLL81EDyLleNdSsls/1ptDNc48k/Y/jN4KwmBSO8VoqmSsu5FpbQ1FEkC7brwWqK+fnzsk5bFVjdvs+4+079DIwLxXRgJ0lx1fNkFRZtjUsPPwg7GH5XHurNf/0C9T7hxFOUR4hSFcrlooX6Wccf+KpS3eB38oVTt+/w+6j3ja9XFG3u0c6kOfNlDjUXhi33oENFahz4fQx4fSxOXx0jg7XKCuYHxrdqbF48Ri9fUh9+RY+rALEn5/D/UPK9Wvo/h7erY91GUZYDdjhETUVktK/Nzwz3646e2Jx6c4/7/ZnGrx9n2ubevjLP/mbPHXyLdSDLex3P/+gh7Opr6ju8ceoH359BSPAUAvv/9vLd83Hc1OXq972j0t356d/+qf5q3/1r/J3/s7f4YMf/OBr/v2DH/wgTzzxBD//8z8//W61WvELv/AL04fS933f99H3/Wse8+KLL/Ibv/Ebb/jBNZ/P2d/ff81/ECqjiIyt6UuYPoceIGOsitUAdaId4+E5y8MVq9tHmDl67QDr5pj2WANuBPSwTGd2De8ybW235tGSi0QQiAWAAZnan2VSOUGo1SJx2MY6tbq28A4sEnpVCgEiBKfgVjCZU7s96sE15OZ1bKuHszPKySkiRqkVN7DSYVpiG4JUINl+rZ5jTiWUZJAMiW1UIrwkjOMklZQFS6+4gJQBJE2IpN0gHVMIrTSpHRJzM1Y8wVxxx2vFa6gsGzeTNKJ0T1UklU4r/ckJVxZw8NguPTNcOmr1VMLFl3/NJGYvBVQpXUlfv5LIMluKLQJNhACS3hKcpzblphBziqePJhZt3qrhVSmC6vq/dgNXVFBVipdQctIxypw628GvH8D1XdhZIKsx2/lzipQMrYFOS+4TolU3W/LbvsOJpHPPiWrwtvGgC9Oe2jbUozVZVcle3YmkSloA2OTzGGu8TYaxTp8OptZgZQzcJZTB1cBWjp2DbvWUomztCNvzylyX2OoEG1aI14DvBJizMdZAA/mqARdj5YO4oQrFHFYDOg6UukJWS3R5jpyewL178MLL+Jeew77wPP7CLVav3mU8PcXGAfdxvf5rKFa7eaFfdGzvbLHYXdBt93QuyMk59f4Z490z6rIyVotWco2xea342AJcPAHgGlart7XkF5SBmvNV4vhIKEhaLDjtRkN4fEr+m6W3rDc7A7EUPMb6UGk63AgfglyPSrSUl/QtrQ3mRpv01LKcz9dMivYLatYAzAE2iygdgpjTaTiylly3tYAWkOMTZr5kMavMeujFKBfWmpZJJ0xpifD68MKvh+1z7a1UfzxSX72DjyN2fv6upEq+HdXG2xZhefwxVruX7Ar/K2rYVurVnQc9jG+q7PAIXT7CkPRNlhVYXnn3SKr1wrAjHD9ZuPfhnpd+5Cov/kvPcvivfS/lYx+le/IJdLFAZjPqq7cZP/cF6u9+bvpv/PwXGZ9/ATs6Wt+chNf+fVNvXALD9uUh5w/qMw3ens+1TT0i5U79J78Lv/ypR+a7x3uqSqGUN77Bf/r8Lv3f/613cUCbukz1tisZf+qnfoq//Jf/Mn/9r/919vb2Jl+Og4MDtra2EBF+5md+hj/9p/80H/nIR/jIRz7Cn/7Tf5rt7W3+8B/+w9Nj/+gf/aP8iT/xJ7h+/TrXrl3j3/v3/j2+8zu/k3/un/vn3tJ4RENtFonQnhf88UXBE7xUU+jmeJmzOl8x1IBZq/undNd2kWtX4NDxZY00114zkKROnDBgm6VCMpRD2r67tTSPphRyx7VOiiVvqqQCVA81W0utlgx1qVAtWke9FKz0FJnhW3tw5QrszUPtNQ7I0WnADw+Y6SLYmMEkRdASyirMQoGV4DMFSjEms9gOs1ReKVI8vQUdW1q0jea2mqUI0xxKvm9s/ARZkAQeHn5vNfuXRSS92hqMhNZ63b7SOdHuXHCKn8ERlCtXqct9bHWXokRqWcmL0PR6ZPJmbGrFGINrbptKrgNJL8V0y0xPyuprgOSeikjWQNBTnWkeryVtneW2mzugiPYMOmdc7OI3dtGdGXTxHMZhwk4xnprQNn0wSSWpRLgJmZLexhFiMJuUmNHNelHpxgQPSf/IjDaPQ6Ip7RJSTqrGhMcpQs0tanAs1XWenpf5epbefqIxJ7o1Q/YXYDCbOds6stAlvRvaaeyuHEfRQnMYAEO0y4Gk/2H6F9a2/Rq4zi1vIuSxFeFEI1LPgY7qS8yUShfLXh2dF2xe0FmPzhdoKbEOmiIx1XySMNlXI+JGKcZMoJMSc1UtQa5fOJ5DlSoXjnfSq3RSkApIEMj4N7Up3CX8QIM+qrQVl/vUW5t+g9/R7h4Qs63Ri+caY71YG0yMOW1KYXe9kLge60czFGi9TXlsNohucZ4bh4qLUkplayHs9x3zDvT0nE5HOnK7JG7NxJx5C/OGVPqaOVYfXjDxsH2uvdnqzir9P/os9cJdaF8uQ1Xed+u7EQ9TuePD+FV3zof3P3bpVERfWS5w8uw2+ycr5PT8QQ/nrdVYsXv3sZOTBz2Sd7x0dM6ul68Idnn3y1U4uyGc/fh1pIbSUQwW9yrz2wP9P/kS9dXbb/z8YUU5HbDZw3uD52Ep65XaP4Tny2+wHtXPtE1talNvb73RfSZz4Ym/J5NdzaY29VbrbYeMf/7P/3kAfvzHf/w1v/8Lf+Ev8Ef+yB8B4N//9/99zs7O+Hf/3X+Xu3fv8kM/9EP89//9f8/e3t70+D/35/4cXdfxb/1b/xZnZ2f8vt/3+/iLf/EvUt5qq5dHinLzHSMVg54qIlGFrW3Y2Y70YhHk9BwtimqBEbqrV2h8hnpKa4Fuyp9AMRnG4OGTFm/t6S84ufcFGNBsEVz34bYsjgCNjfglfAzPvR4v2VCoC7yb44sd/Oo+HOxCJ/j5CsYBX57jq5E6BNzTTtFO8TrQlRLNpiaM44h2JZSMSLOLA9FQIxHJy9EBHS2xRTVib/rYD5GCXHDGC2m3DcyRQTcJ8BJeuUTLdmyYTUBFBIoKY7UEgxb7x3WS3DpOV4w6nsOwpL+2R71TseEMygAS4NetIGJrmKHpLZnQza15KxIKVM225Eh+mVqCizSFZqybpqgLHWz6SSq4rscYvowy+YBWD8WpLXZjX4lg90/h8Ay9vh3jrQbVLtjSWbZ5axC+9t22tUkLEWIkMoVwxOzY5Mvo4lMoTIjIPOeg5Bww+Qe21vn247pzNSEjF97flfAc9em9Wku7kcrOIsznymxvj3E2g3Gk1zN6WTGTSpF1QrVqh2eysXaR4O2uQI35MI9jLv0FW1uyqFAjup1iCWjz8NFqeI1t7sSYmzLKKtSn1bDjAU4FLz3jbEbZ2oHtLWQ+i+dqwfsOsYoPNcCvhcKvZGuz1THUqg34Nm/EnJGcvtjf+Pp3RNuwp4LWpZkCrB/TvEs9w18a+A3PRkuQnAdgrpTa3q89vt0cICG0OF6iLb1YXbdtN/6o7WbMOmwo3lQvKJYTkpqnfyYBk73QibIzj+RuxiUw5lgCdhotVEqmdWYex7w0n82HtB66z7U3Ud1ZZfY/f4b6Oqb6DeDJrH/b3/ebqlqx1RA3jC6U7uxw9OT8AQ3q3a1xLpw+u8/2766Q+vBbCAD46Rn11u2v2m+XscrS2f315zn9A88+6KG8pryEtyPAsNNx9L6O2Qe+let/89PU21+dGDqVPRpr7EGX9XKpbnI8ip9pm9rUpt7eqrdexb/wLHz7V7dDn3zmgPf9zd/a+DBu6hsucX8jhv1o1+HhIQcHB7z60/8pV/pI8K0eF8TmgklP1R7KHD+4iu7vYyp431FPB+rJEpn30Kfyr1P8pVt0J/dDSUdtHdCsA1oiYMUt2z0lYUOI2SbV16Q4CykcoTjKNkVdK+4cxehwnUGJtmDXDt/eQfZ3kf1tdHdBNYdakTrCvTvICy+g4zLeyyrUinbpkWiOi7bsWUBQy0TbDK/xmgRDAuhoKrCiIzfbrTXakKOFUxhGcDO6viaYy47O1m7bVICiODXUVy641fRvDPjg5viYikSRAGISijf3GuDOOoZqMNuiXL/J2G9ht+4jZ0eU+UhBqcsKUunmut6U1l8cKC79GZkAh40ZIJNwpQEVVDENOV/448WLmadX4kSWNFq2ARdltEKlo3Zb+NY25dpBfFE9XuJjKEX12gJ5+VX09iuorBJ01pgPoj3fa30Nf2nBOZjh6ZWJ27R9EtHQ4euYuzK8JDMQxKPf1yAhrMY6AUi9WVuTLg18tWTubPs1o4419rF4AuFYs2OF5bkwsECvX+d8qOj5GZ0dMisDWxrPCyKtiM4CcEn4pYa9QILp1E5Gq7tSzXIMAcuap6DQhHq5zzKdPH7l6WCQx3JTA0qs52pxx672C2Rvj9neLra1hS2N8e4RdrbCzs8ofaFjSTccQ10hjAj1gm9n7q92LmhnhWaC3g53WN9o8AS9qgEi0/eygcG2n5riUazt/9YebxmqwgTNRaMNugW0SIJpy6CrsI6o4IqXdTgNlIShTVXolNye5tEY1gVxHgHDikAN+4RxpawWO9DN4OQ2i+6cQqhxIxxLEiwz+YKqtDMn3F8t+eD/5T/k/v37m/apN6j2ufZ7fuLn6Po3ToXtT0f6X/vs6wLGiyVdh8xmDx7wuuOr1Ru2U3VPPsGdf/YDl97nr5U47H3+jP6lew96KF+73PHjk4BYF75K2o98grPHLx8U7s6N/nikf/WUl3702iMRbtMfO9d/7S7+W5953eOr+8CzLD9w4wGM7NGq8+s951feOmWsq3M+9V9vPte+VrXPtR/nD9HJQ3bj67LWBT/sTb23S77vY/zO/27OwZVTIK6HDl/e5dv/83vR6r6pTV2o0Qf+J/76m/pMe0fTpR+GGmphJX0EUWBo12OuwAzTBb63Q7e7A1tzdN7jCrK9wHa3YnaOz1ieDcjOFv37nsBfVOQUGJa4j9FSGCgt0mQvKplaKyoR6mJSab6HwAR8zJWxhkaqdAA9VXpqmWF9gW6GbC2QnRkyWyBbM2Qxg1mhJiwRKXgPDCu6kOehTfBU1ipKk/BgzM5sxCeEMY1NMonaU72UDbcBGqW1OibAqs7SO47uGGenzu412N1XuoQUmkkPJmRacwBUs4paABHDszVTEDpMZIJQ1TxUht7UWAFHRQTxkXp4l/LEDvLkNerL4OMJaKUsNFVvJebZLP0UU51lNf5aIgQkkzEw0VBD5rXD5DZYhZrwJ9q8dcrf0amtOX6u0oEXXGcMZYHv79Jd20e253B0FvM85joY0xOyE6S29ufmm+m09OZQvCVgamvLcxu0Bbe0dm2oZJqxjzQZXGNw0lSLmTod2x8tt97UkTlVLprrJuExhE1ABn9EqnaoQT1bY8WFIgW2t6AoM6uoLpm5gdVIjC66VsKJAR3uLd2YXHFK+1HSN1Ra33ZuQ4D+BqoSlmf/vqrFcYGgOY9e14/HQW1AECogg1NvLxlOj+mu30B29uDaAXZyjhx12LBk9MLqzGE10M+MMg/1a0BhCYhYkiZKAlP3zHLWyY4gKj1ME4wGqMx2dgKwW/s3MrBH4zFC+kGaU4e8cOyi/VUREG9N1LGpkzlnvg/dlF4e60lwMTTViiKSEDoyyOMck2npKSY1l/RMjfFoB7NZYRyGWNP5fFWhJpCXprBsgTJt3qClam3qTZSOBq9zLSZOJNF++vPUN9Hi4mMo0HXWP5hAmFpjDF/Hq8keu/KeAYwQp4DjZ7c4OFmiR29syv5Aywy7dRs7PX3QI3lXqj8xtn7p07C14OU/9OFHAjACDLvCSz9yjatPfIL53/90eDJerEc5ZOhdLLtErdKbegfqtUqABzeOr1cidO97Ct+aI8enjC+9/HCPd1PvePknf5Nv/7kPcOufeZLDDwm7X3a+42+Fh++mNvXN1OWHjDbjrM4w6VJbpOiih/ksTrI7O/iiQ+cd3hEJtJ4hIQKrwyXnR0Injjy1TfeBp7EXevzwLrI6J5oUQyEWcM5SwVQjoCPVWdUd8YKTrdsO4h0mM4ayRV3Moe+QXtDSM47C0b1jzg+XDHWk3zeu7izY2p8h5MWgRyaMli7aF82w1QhjqCyNmoonSXCRGqsUL5Wa/nFI+gkmXMo2SxdbQ51W7mDhwyZ9DyqszoW7t41z7zgblkiBnV7oik9+dJLtlZ7zEi3s1hBsNPpaBFVMjEWFIkIdawCNorhpgMcghZRxRb13F33ycfzpJ5BXb2Mn9xEZsuU9E6+VKcykbVN0DRstgGIK4Mj9dyGjOR6fEyewVoW5MJpP3pduhVG3YLGFby/QnW3K7g7eFbwIPu9g6DAbKaIwZuiMC4om3PMJDOY7hxdoQirP9mHVMkHC8PBs+DoBpghuHXjNl2zufkrEx2hCS2WCTWv9W1xEuUORhIgJ/EICSEnvwFhrBUnq4fQw7+l2thhrpa/nFJZ0OhK+gprKvnivQiaA5+uJd3kcxWgmhR51DUjblqYKVWJXxu9EwhfUde1RKARh9AxISSVdA14FRyWCYOxswF4e4eCccvWAsrtgOQz4conRYdYhFPoSkN6zhdwraB+KUcvjk5w3g1Q8rqFfHnkJ7RsFLms42B47pYhnqE87lpv6uClMU81o+HTYt7UdLgFNv6yIxFy2o9vzvNUsA0LNu4b70o5Lj99ZdarH2a+UWJ9eZnFesSE8Vs1xVdwrmuestryr+WQLOi3zuvmi+2Zr8ZvPM373h7H+tcqa+cun2Kd++61dNFjFzuu7q2r8OsrFr3p4/95rvbMOTt+/z84/WT58bdPDiL16+w29msrJAFweJWNZOtu/8QLj4SH+nR969ICTwN2PzNi69jGu/r3nGL/83PRPfniE+M28gbup1ytXoc4287OpN6gf/E5+59+Zo30A+/mnt/jA//V3qbduPeCBfXXp9jZ+sBvfW+czytkZ9d79Bz2sTT3gGj/3Ba5+7gtcbT8/0NFs6rLU5YeMvqDO9/CuQ2YdLHqk1wg2wPG6hGr4yvBlgBXfniOLHlZQdnbpu4HuxjbddkFKh20vqC/v4rduU1bH6FgRH5rFICKGScnWT6W64jIPsFaglh76jjIrWDfD+44ikXjrtUInHN8deeFzx9y9taR6YefqOeMgPLmYMd9dIG7UCpQOG4doHzWJkBgVqjXUlE2b7glZPbZXAR9D6SWaLZqR7usaKjNI5aM2GBXKt9ZeGQqz8AYc3ZCdnnHVcT6MbM3zMRlYIn2DISV87shWySYB1PRD9Ip2Mn3hjdZhon3ajFoD12iXqdQ+wMkJdnSK37iGdY9TX3K6s2OkrjKA19GSHn8i2Q6+jlZ3AkCGCq4pO2O8kSIead5og1mJh0QYXTCdBcwsim0tYL4FfQkvzJlAifRo73rY6nDvY3+4oEPAw2bG6GaoRGty8N6m7kuVrDddaQsDyY2QcHFUbyAqVJyu0eYee76pxtaAK2BbzVCPJgJsasELgSMXQm1il5XpcdFRXqLd2XrqbIH0M+h6WJ7Q1WW06rtG0nu2BYdy1DF1PF0/mmpOJ6/IBkB9AmHkmlNt4Kop/Rqei4AUc0t1YIavuKeatYTST8AZUfdsfc/AExWwJX7vVWRcMexeQUrHaiwglTLv6bpC10PnitXK4JWuC1VsU3oGiw1FoGhsKzmfwadbq7dM4zQsb0jIpF6UvEcQwFTjuJ3WRu7TkCBGUIxZqKbRALpxZOd7loCrKM4YEJHwTm3lmvNVAbFov9cEmtnC7SUS4kuJY7yujEFAe0d0REvaK1iNVOloVifvOaxVnZIKVAedkrI29fWqvnKLxa8p9uyTrB7bos6Unc/exT7zxW9YleDjGNYMpUAp8edbAA9SCrKzDVsL6rVdAIb9OasrF75mOOz9zj3qP/nMe8K/75ut1Z6yowrHp7D1xu3x73g1hfj5Ejs+wY6Pv+Y6K/dPEN+9FOrT2VFl8aufZbx7F4Dzx2YPeETfeJ1dV1Y/+Qw3f3kH+53P48MKOztHBsM34S9vWNYLdumvljb1jdbz/+we/8Yn/v70s32H8Ndu/gDf+ifuf1WA2QOvFES0kt1d2EDGTW1qU+9AXfqPTb9xg3L1APERYcRlpJihyxXiFZEOzsFLtCXrfCsUT2OPzObo1R4tC7qtHhenulMOttDFTfzxK/i9U4bb99DTM+o4oDak0k+QbobP5/i8xzWAnWiqjMzw8Qw/P8KPVnTDGVIruFFVWciMGx/umb1/welqhtbwkxwOT1n0BZ/3ARnGgFHalB6pwIxQEoCAqQ3aRGNqU/dFgIWQbZ5EQi0NOkn8m1XL5OUI6rDkEZ6ecjNxtvac4+MhYIcWpAslmqbHnOYFSdgsNmDWQmU8gmayXa+aJeRk8uVrOo5SNKCFhNJQvFBsyXjnLnJln3pzH7Zn1BdepRzex2xFsTH2KRqtgdrRFKhNy1WawlRSX+ka7ebap3+j41ZwLZgSysS+A+1irOagFVXHVsf44TkMFZcZdWtGt7+F7u0giy10ex7X19Vh8DQLlSn43MQmeGgWnnsiDUclnEpVWYQMXYCfRuwDJyGyTLBwgqguqKbwTwL+GU3RmG2xqcLT5glo7TFNcdjAc6p1AZEZdTbHuxm+mKFWUa94HSNAKNeXdqFilFQU4gFWLQ3oVUrCrFyPuZ7ddHq/sJ/0JmhNaJ0SXaIFvUxrae3LOa1BJKFsamkF3CtuOZcO2BK5O9CNhl+9hmz1rE5WdLMZZdzC7ZzRBkSFLvdR2A1kqBGhqFRkAns26USbgq8kbMttTQAe/owpwHQSODpuY+73tu9SfeptLj10wVPruadvattPNROq4+CaEsVbC3aqVH2C1dGivWa9Sq0Wyz2hJlaQIkiZUc3oayipNRPmhWhbl1zXVj3a6XMmLNPnZaNkfEtV797HP/oM9z7cM+wJpzdvsPv+K69Rn8/uLimvHuK371KPT74+2HMPdeE4xilIJGFjgOYJQC4WcHUf21uwur7FuKWcXy2s9iQA9Nf4ZnHy+DW2vuP72Xn+nP5zLzG+/Mo3DEYve3nefBhfehnd20P39+AdDOvxvouOgXnPuDvDe+XssRnLPWHcEWSE7VuVrVsD8y+8Sn3x5Ugr/8rXuXMXsZt4eXQpo1Zn8fKS8hufo2aLsS4WnF19tGFcXQgv/th1tj5+lSv/0+ewe/fXNys39bpVF3opgPmm3pl6///ri/z1g3+KnY/d5Qef/CL73Tnf+z2f5eyJm69RDT8M5Wdn6DDGuX5Tm9rUpt7BuvRnmVk/0K/uI8MZMq7wOkKtFBcis6CgRRHpMOkZZUmdLbCuoFtzfDsAI7KgbC3weYeUgnYFm/XI7g48fgU5H/CTc+rJgEtFbcAV1Fbo4X3k9Biv59HyOQJuuA+UBA7Fmzqt0o9C70vm4uy7MPY9vjVD+h30WBnE6a4fIDPBO4MuoIx3kkoluJjnI6JUGygiaIM22a7ciEpLrZWuS3VhU721HlRNWJXqsFQlKbCYG1cf6yO1W6GfCaWU8KpLQVoDkiIWKrhMlRZl8v1rbeSTRE8i7VbcKak2c7FJ+RSPqghOWZ4w3r1HubKHX9+HnS3qizv47fvU00PEBlQdKeH/GBCLaHPNhGQzEM22eukYOw11jihalNGNEZmUlyD4uMRXAzKOqBtWh9iuWnNOV8ggcHxI7WbYwQH62FXKzhZ2NkIdEQt14KQOhVC9pZotkrANlwJN9Ub4G2ZsNJgHcCpk+67nHGXfLCRQDRBnnvo/J70mJVtuFVfNVOpyIfk4NYIaIMo9/CsVjX1UBJ/NsPlOqAitghkiNVS6LcwmYZU5aEJVb5JYSZgqHorcprn0dg0Ua6KpPKf1TVuuFkrPao09BlxPWDe1wqc3qbhkcExCrgw2URtxiTVca6Ue3kWr0c/2WN03VqZ02sEYisnOI4VdpFANMlY82szNMSLIRtwv7AfWascM5cEaPGZSjnoCX/U4Z5iPTI3xCRslj6OYiJLHTZxbqAEd17LBdswzQaTY73lspD9py0iqCTHFWkBVBAyJC1TwIdauS4dsFXRlWG0BTTS9LUiMXVWRroR1Qe4fbee+TbveWyr/oY/z/I/tTImnJ08KJ0++FkCJz8D3KMv3sbjt7D6/Yut3X6G+8PKbU1g06JilVw44/H3fxulNZdyS9fH7FmrYFYbdwtGzO8gPfJj9L32A/b//hfCG2tRX1fDYLvppsKMj7OSUsruD7O/BN3qRKIIXxXcW1O2e1dUZ51cL45aw2hfqjADFszxfTfs3fj59UoE5Oj7N7N7TXP3MwPavfOE1rYH18JjuuLI6ePS+YurozO6PzD/7CuNzz0/nKgC9cZ36AAWlb1sJnN1Qxj/wYWYnRn9UKcuHrCX/IapxcYlipTf1ttf43PN88D98Ad3d5fPf+a2cvG9Bf2wsXvy1Bz20ryofR3jlNvLkY/FZMAwPekib2tSmLmk9et8A32J1d16mWyxwqXitdKnsAkKpNXi082KIVcwrq1Wo1nRxTnd4BFsderBLPdiFvd1QsVmoB0UV70F35vj+FnK8QuoSvX8fv/0qHN6G8RytFoAvmwVF4m+hnAqYsvbMc4oIM3NmRXEGfKzUcaCen1FPzmAcKDevTSEthsG8wxcdVhQ1TcBXMYWiCSIJGEKqAN1b+EO2sErAI/EMBMmpkgQNASazxVYKIkLnxu7OyOwDBbOOmTrFLfzqYIJZaQxHS5leR8oQLZhOgjRSeZgqLgv/N+8CDMfjmorKMjyj4nfvweM30MUMW3TUp6+H2ubOffTwBDlfRQq31AiN8TGUiEWR0oUHYUs1FkGaCs9GbBjQaplOXpFhiNCCXE6lC1DUHqPV0a6neqV3QbynDuAv36YenVEeu4rs7eKLHtta4GcLfBynbaIp82KZhqpMLrS9Wig8wysx/y/DPki4s1Ya+qRKbWE+kr6bU2s0iluhooyDUmsA1k49g74L5jAiMVei0M0w7bCxIr2EgnVnCzs5RcYRZ6T4SNOzUW3yQ2yrKtLG4wjQBJiS7eKtT9gt/QMnIC6TN6N589SM7VIkoXWCRquhZMz30wtr0j0VtpqqWst1ONa4CBejFGHmI8PJXXozynzO+dEK6YSdfkYxAxnj+WO2kjuUGWAJtCXBYgt9UpmUqVPatDTgmVAvfgmS+sJs/0bKurHZ4wDxdgw1tSNQpAsILZbHmWY6dK4pj+z6UM5KNjMH0GdaN8GELcF/8tpcLYaJUFUYB6dqD5Q494kxJX57hD/FEtRJodzG7C3Zfn0m2NSbqO79T/PiD68B4xtVU7yOW3D8tHD89Bz9/meY33mam790F//t10+cfcO6cY2TJwvj2wBaXMB7uPfhwunND3Hz718NP8lNvaaG/Z5F18V+shqJ4UdH6NYWerAPizfwPhTB530oIQ+2qNsdpzd7hh1hdSAMO4RXcbl4y+bNS9qsc85vwIs3Orrv+gg3fuOD7P76C6Hcscrs9jmrg91vatvfrRKHcm7MXz2nfO4F6u07jK+jrl198OYjE/jyZqoB//lc2H1htVE0vk55EcbF5dnnm3qHyh07OkJ+6R/RznoP6+FU795Fjk+QotjrKNE3talNbertqEsPGUtdotaBKpYebuENZlAEKY6KZZiGYKsV9fQc6TvUhNJ16Fjg5AS5f4w8cYNxd450cyhzKIWu66lWowN3LujL95EXnkNOjlBbAeFdRm0AJVoINdU7Tg1YkraFJCBSJWiSKoihVIQl4+ou/vI5tjxHn7gJ21vofIYsxwACmh5qFgqnooQP4YUvzTXb5lo3k2OYdoiHr19rr8384QAbCuaBjFqgimeP78JgJmOoKY1IIpakE3ml29pQ47WZ2oNpQROumVTbIETCTlpoDNS69uIzwEu0cLuCnp/jd+/jezv4rKebz9CdLeSxPTg+Q+4fY6fn+GrA6go1B7N4zzrAaoWchSoRq6E6tQTAhBeduFMswJ2JUSTmQhsUlZhzLYAaxQWrDnWk9ELRjtXpMfWFFXJthVzZx3e3Yaz4YSRuuw+pNk3o6629VhqZiZ0m65/jD8ea16Ctk6qN1v6aAEtTP9u6jV1wOqrPWA6Fs5XBrKebLzBxOjW0KC6KlQ6Z9WgXis+mwPTtgo0Za1SNMg6U4QT1JaWL8WHAmAtAm4eioKWkotRAy2uVUZq+ogkHmzZOzJq1Ybb4x8Mn1UkuouiUznAWy3lQXSsbq6QPZm6He0BKc6oMFCkoSqcDnN5ne+s6w6xjEMXUoBbAMHGkE7pGgImW4hxUwkXiGG8KRppotx0UPlmfrfeXZGBRKgxbsjnthkUiSQF3S8tEyeNG4qBtStYEsq1FugUXBdDMcw6eikJNKNrUoHKBNsY8m+fNh66jeoevHBkH+s4pGjcEyCCaUHWuE+rXNy8i3MmsJbxv6s3U3R988hsGfVbg7DHhS//SNXa+/we48at33pRHYnn8Ji/9xBNvC2D8ylrtCS/83mtce/L7WfzS6yTgvodreVDYefzma5Me3bHTU+z0FN3bQx6/gV3dZXltzrhdOH1MsU5YHRDH6+wbh4lvpsZt56UfLJTvepbHP/kkW7/2Rfjsl+me+Cjj9sOrAisrZ3HrnO75O9RXbuHLJW90FOjODnc+chlkjF9dyytKf9Yzv7NRNX1l1blu/Bg3denKhxW+Odwf6WphfW8lRG9Tm3o369J/dAbscUSM0liERMuvt55AizZix5jNFNQppaJaURmjLVJ77P6ArZZ0+7uMe1fgBngfHn3mldIX9GiAF1+G8xOECPzQVPK5Tpf2DbmlIktoHnc6tYKu4Ycl4NL0mZMxWij9MNKk9fEbyPUruIZSS5EIRRHPNs3WjtwgZwZ54KHKYw0VomUzQYOH0ghChdSA4tpzLtsmUy0DkVvshQAULngDnu6h2hQCGNUxgjxEcc0/E2JMcLP9O5IqPUdLCTCWra2YMJqgWikow9EpnJ1BtYDKu/MAicMSTg7Rw0P89AwZVjA6aoZLJcI0AigWMjF5UrdJ9qsaXYaGmFRUDalEmInEXEiJsZs5klBHXQOCBmWjV8OqMdy+x3gyItvbdPM5urUN5wmCGJkw0cRqc04nCpchKhJhLaFQrRNI82bt6FNXbOjVvLHJPAhKx6BzVjLjeAmrCoU5ZXcHtjpsNSKLEoFAotBJBP1IwWezCK9RBxupNfw1dTUgPoBFoFG0QUt4hsIESBtkE3FcS3p01uiq1qbPYw1FU/VmbcFlW7hka338KmGd5JomVLU5FRnOlK3JkQeUUDvXetFU8HkGDUXYUJFoy9/Z2WO0Du92GEZDViOdBRwvAtIR60AjUMkz7GaqPOZkag+P46zlnkyPdE/oH56ozS8xEq2J/JpMlY65uRAYBKlczNdq8DLXS1sHSALJ5sEok+FqrP9U0Zq158YNB/eEmQaldOAFWw50pS3W+PM1N0tEphCitoI1/TKZtI+bejN1+OzFqJ5vrFzh+H3CyZPXeezZfRY//2tv+EVVuo77P/pBxu13bh9ZB7c/PmPxxMe58Td+m5pBG+/1cgV77ApchIwXSg/2ufVPP8HRByMA6ivbm9/NqgvnxR/u0O//Fp7+H0/oT0fG7YcvKEUMFq+u6H/neerLr7ypJE153xOXWtF2ekPpDwUdNzd7Lta49fBC8k1talPvzdLv/na+8H/s+JFnP8f/+A++j4/8739lc6N+Uw9dXXrIKKkaFAjg1QCECsVlapuMJGHoFLreER+xWkHHuPrpA0TqqVGHFSy2sO5qUEwzigkM59QvP0d3fIyMQyjM9GKbYcqrJrFVKuRaEIO0Vur0XEslkzYgqaHMMnWqV3QcGI/vJwFw2N+dlEqesK5ky2ZTRUnz64ufQg2oTNAjaUKoH/IxQoMtBOywSkkVmovhns5rCUyZfk766CS+q+lLZwkoYrsCJCZAaqE4CbSiu3fdEOqtHxUBCzAZPoGGl4quzuDkHBkEX3Rw7wy/c0h9/hXK2RnqAyojxR3JMB2RgLtYhmKkAmsal3qoAS0UlZI71Dz3Sc55MC0L3Wde6YUyLFtdVxW6PoJr3GBYMSxP0EGp88JsPmOsW6g7WmM7a62T7WJbMbT3u6hKk5h/8QaHYxum50kqAhPmOoKXnsqMofScLIW7t1ecHDkstji4uUP32B6ljtAX6ASkUM8GxrMBnxfmuz3SKZwuEbOAwH2Jg2gcCQ9BaKaCTlNjpkrvosdkArEWyjJ9WK5Fb3n0tINnrbb1BsfzQZLPCwGtBuBv3oviCbMF8thrbdmhFk51a1MRZqu5i1JHGE4GxsUSXcxxOqzfhnGFqtFfUO6W7GOtXsOvkXidWG8TXc3zTkJ4d1xKHNvZGq8p+ZVUM4oqSAloekHRGt6pLcFZEArudZqniclKAHBtqsnXwM/0pKSZNoBaAOQYo9LCZXKXxZlVOiKFfUnpK6VMTD7HFgC+4tl2v7YCqLZWbGp5tAMV3s36em3Sb/W1bn2i56mjj6O/+Ouv/5jv/XaOnvkKlfE7UC6hsrz3+z/Klf/u02vQ+B63i1td23rdL2vlIx/ii//6Eww7ky76gZcL1Lnzwo/tsPcloywfjnFBfMXaee4U/cJL1Fdfpb6Fi7LV+w7WH0aXsKwXTp6csffc8mFZSg++hEsNlje1qU09WiXzOff/te/h4z/zj/k/3/z/AfAPnn02Lkb96wT7vddKC2V/l3p4/PVDDzf1jtSlh4wN0jlNWRYX2uEvGLq1uDAPr761zCvBjknAq7yqs1rp3LF795Br+1O6sA8D+spd9M5dxAdELyY6S8Af6qRcaoEmmu2wDT7mo2nhCxVD6BDp4qIfQolJKLU6YFwe4a9ojHWmoOEvWCzBhAQMFDfUdIIqARdTlShcACAJqwDXfK+LwS+0RNgUMSoT9EIkrRcjrIQEHiIRChO9SJotxfHerRGWhIqth9uzzXQNG5neg5rqqxLtwVVLtL0PFT8fQk6mhr96h/GFlyjDMsBMtp3Hy6QnnxHqRAI+S0I6KSBdzHpBqD7iNfahSKTkTi3IIlSR+HdC+iV9mURd2sZvNYJVTPDBsNWK0c+x84JcmVO6WcBIGcEHWshHA0KekC6UdjYBdGDaZ9PapYnEdN1SbIqJgM6ouuDcZtx5ZckrL55zVpXZtW2ufdt19p/cofNKcaPqiCHY2ZLl/XNsVHTeMcw7+p0FpmfI0RLZKqAawSnYFPzRxqIIk1COi2Ns2jZfK35V1/ANu9Dq29ZhqwbM1wqeyffPmkp3DfRFfPp38WjlFaB6pqFLjiSVd4W4aB7dGIfC7ZdOOTxfsv/ULvuP7TLf26afGTreRccx1r4TPpVhcphKQ0W0xh40waQwulM07Bos11AcpxL2Bkq2Fjeg2hKZ22vG+C3PWdPNCIfKmDdUNFWMrRU7If104yPUi82LUTTXWbZwW0JQUcHGaIVXDZAqbrjPGLwwOgxnA4uO/FTxBL5CY8bhQ5njkQSvE6C/EDK1qXe9XOHVT2zzxK/tYCcnr/m37pmnef6H9t5WsPn16uhphZ/8KPt/9X/GhxX6wi30o3vYe5RDr650zBYL7Pw8fiGCfOI7+NJPHiRgfPhq2HHufauy93lndvxwHNs7XzjGf/233hJcbDVuX/7Ft9oTzm70bN3a9FEC1JlSZxvIuKlNberBly4WfOY/+h7+y3/9v+KKngHwF179Z3jf/ynDNje1Li3wgx/j9Pqc/nik/63nXhNOt6l3py49ZFTxTC+4ADZsisoIcJFqx0xiQUrJC/eAWwWJxNqUSlVboYfH+N1DrAuPOk7PkFduIeMZykhB1hfRqWZT7/I9A2CEAq+1B3sokNKbLgOPJzVdI5/mlsosAwa0QI8xnh/id2fIEzcwnVEsWqeweD3NdNgmq/SGVyYvtky/FQG6BFlt/Gt1pVihYpim2jKTeQMcNKVkvIxmC6tjaMIwKwEb1ttw8ct+qC41vQgDJiXkaDNQFbeahvUBgcRrDNsVGyq2dJg5/sJt/OWX6VihvoIabaamnsnG2fqZyc1NXSWeAMQtVVexjzotjOMY+011SsXGUkknAXjFHbqCj81zM7YtBFtGEaWKoTpQ3BlOzrBuizM5pzy2S78FemYwpLdfBmaoeST0iuJSMwBl7XWpqeqzkspFBxpExzBRTOeYbjP2M4ZRufXKOa++XDmfb7P7xA7XP3qV/cc6+tMBOT5jODmnnp3jNUBVJAkraI+cKL49Q3fm2NEZnSUQG8dQYnqN/ZMeqC38JPwY265vECohe5Ec74RUwWQCqJLHUvEEcJl+3JSHedTjbpSMsg5RXVMvhn53Us+JYjZG8jhMoUKkgjAapTuKKKbC2WCcnMBwa8BkxZXSI/OtaBn3Y8RHVKFQLxwDATBr+iU25a+2dmmyld5rQH/tYgw2UPyCzySRiu1546SFJ7XzSQqyMSotUCeeFu8wvQ/EuSbn3S9IHS+qnuPFdA0mc/YiNTy8K6vDygpDrdg4hNLY0iagrceWWk7YVtCAcAZHIe0cvKkHWat9WP3Qt9H9nU9Ov9PtbW7/2NPUN8gWeSfr+Gll8WPfyewX/jH11m1m99/P+bXLD3per8aFoI8/hn3xy0g/Y/yRj/P8jy6w7uEEjK2sOCdPCfPffnvVt2+1XKMp5ezpHXZOPsTrtpXduo2dnL1u6rr0M86uX/qvyyBwdl3pTwrd6eaiddgrD3TdbmpTm9oUgHQdn/mPvof/x7/x59mRFYMX/vMXf4Lbf/JZ9FO//qCH99BV2d3h9Poc64TllZ7x+z7A9m/OI5huU+9aXfpvTU0BF2At20QzVEGSKsQfNcGW4rX52gUiiUwYw32EbEe01RJePUS2twMqHd6DkyNKHSkJsEgoKRmu4Nou+1t7ZIC3sCxreqwKF9RepPrIGbOd2bEG/ASEiplQfMXq7BhZ7uM7C2wpdGmo5tYu5FMtpjEmyzTr+NlSwbT2cRMtVBy1McetofTL8Am3GunG2eaN1fg2n357QrR3t/0g2YoZY7IJKDbe1Nq4fRyRUhAp6elmCXwLXmrgIpVIuG1qrRqQ2OfKsBoYXjzl/DMvMZ8t2d0xtFh42IlOqbrgoRgTEjiWUL95zfAN1u3lCQjRwugBN7sUQbbE5OgETxjTCVYUHy3TqxtIE8wqnXSMUumKIsMKU2U47VkNI2V7i8qI1gHxMVRfIpg60Qabbb2a29xCi2hILVvmvURHsivGjHG2Q53vUvueujTOxsrQz9j60A4HB1tceWqLrYVjd44Yjs/x+6fYSbROuTZlW2FYVnQxY3W6pLt3Rn9tG40IamalYKqMZpRaqUQ4TtF1wEgce+kZGnR02o8BEy3/9Asd+AG3Wnuxt7sGsIZpbdvN4jjyTDHP492dCPshFHpFmlIvGZqEMlAbaxcNq8lquCvaVXQBchyw+vik4i8ds3vQs32wh58I6vfprTIUp29DcgkPUQNfOVYrZd6HIrB6pl/H8WlioW60UALbdBLILXXBS7tZUtfzmO3wzXsVN1QvBPMkOG/b6Qn5XUqsf6vTzQbS101cQMNr0vJeDV7zWAX3SGUvfaE7PWVr11n0eTNg8pYFRalKngcvwErihcydomnVsKkHVi5w63vmPPNbjzO+9DIA4w98lNPHH8xVtgu8+l1zri4+wdZ/9+tsPX/E+bUrD2QsD7pcYPXsDboru7z4Y1dZXvvKIJeHtwLwpVczcXPw3SrrhLMbytlN6I9g2IP6IzdfZ5DQnT1BfwS7LxhXPnWH+k9+9zUw8p22CnhYyotw8kTH3pfsPe3PaJ2w2t0Qxk1talMPvuwHP8Z/8q/+JXZkxZfHa/zs3/pf8u1/9svo87/+oIf2UJadnlLODNuLLx51Lpx855Nsby+ov/v5Tfv0u1SXHjLSAg7w9NtrCjmyFTP+LWnMdIGsGiEKVCOu6n0SAqqD+sh4eIzfug9XttC7x5TlgGBYJnWoB/RI5japJttb0tq2M2AhHt0RaSKE5Zrrur1TAjDiJVWPFm3LXlAGih2iJ9uU3X0o29RltG1Hr262e1sLeGBSKqonoJoSfgNauDrFlRYoYjlPnm2ekXKb2+UW6cMCKiW9F5s6SaKVu0GlBhe1TK2ZTVVVtLVTtrZrpn3VWr8hQl5q2yFN0SYSKeK1cu+zr1CPT5DdCotM5051n4hPHl/N81FqBMC4NAB5AYQIVI9AmJba66lc7D3Ul4in+jTa0LFQ8dVOYAxlWVPKWXUohqqhZaTTQh1G6DqGe6f02zNkvoOYYstjRMaEa4XWOuwqYGEEoJoN5xIaN9pKFsW9UMsObF/BFjNqdWzlMA7MtnquPjuj7G/T7RR0uaS+fIQdneAnq/AkTfWhjI5XMC8MpyP1dERc6WYrFmdL+qu76O6CuuipCKUriGp4E3oNoC55PEKsnaaY81S9wRpuyYXV42SLd9vXjhKq4KaWW7fa1te0AcexLbTE56aMbTcdxBPuEQrWCTCaT8peQXAb6FTZv9Jxen/ExRhH4/DVynA04tqzs7tHdzxgfoKqYzXsAmys0ClSFRWn9LEdnucfcUc1lZVcUNK2Y6sR+/xNcYEitL0t7abGlKKdBhHepjsAcfh15nwpqGveQIl2ehGmY661qosXTJi8VEl7BzNlGIXa9cjM2ZqPzCSPEeJ2SRMqRzp8HOvhY6qUKT0mtsd83fa/qQdX4wJu/74PcuW/uQ3A/Q8sHihccYW7H+2Y3/72sAJ5D9fxM3OOnt16aNuj36jqwrn1iY4P/tdfxvuO448/9s6CRoFhWzm/KiyvO3UR81W3vvZzxh1n3IGzJ4RXv+s6V//Jda7/7c9Ei5W+t6TW45ZwdrNn58XVdDPvvVbjTnnP2jNsalOberiq/9xL/Ilf/jfp5yNP/D8XfORv/UPGDSh7w/JxZP7cPcaPXp/U6NYLJ996jcXBFvJrn37droVNvb31HoCM8JpvSVN7oGfSbYAv8QBRKaCK31dPP8AxoJIrlhftRSvqpwwnx7gafnwSARA+8UMkwVqitUACCfmM5m8YV/WWF/jWVJaarZaUCVJiili02kq2L4ZH40i1iixH5O5dZLaFb29DPcd9FYq/6gkgsoXXKqWHRr7WXn/Z1pjhJc0vzTyTetMPsk3ppOBLYCQeSdsqAS8DPrbAkoCBk48hqcKzts3ZgN3aY1VQKZjUCcYIjnQRHiEJMJCmxCy4Fs5PzxgOTyiMlGJI5xMcxmu0OzfII7FfIsE7krndAvo0tuyp4nInUoPTwzICXYB8PQG8OGIxj2i0tVqJFtkAsAJdOOkVEWadM/SGrQYqM2xJeN8t5pnMDXJ+glsECU2vUWPbAzqnQszBNfeHK86CodvBrl3FEezoLNWWI0JH3xX6m3tQnPrCK6xu3aeeZ4iLCF0nqIK6Y2KMAlpHut4panhRimd7+kmBnYLYAu07GD0AudRUehYCgnnAbEu7gOSG0cUvF8D/GvIGP5a1Ii/nPnh1NkKn4nE6fixBHuHZOS3ZpuIjlXjZ1lxU4hhhHUTi5gl0ndJ1KJWDPWV4Qjk8PGesiknHqjqHt+4z27lKt7OHHy2hjtTRkaKZ1C5IsUi7R4Cy9gXLIJrQcIZCNQ6paHNvJxWrrBfjGM9oXo6xhuPf2uuUdnwVRUK2GXNkabkgqVxM0Ni2tfmANmWlE2BcAc20ehtgeW7IFsAY66Gd+/DJT9WbvtY9rARaG7rnOm77i/eOUuhhr6NnlSuf+DbKi3dYXn3wO8UF7nxsm/7kPUo7smovD6Rt/e0qPzmlvnqbvbFy8u2PM26//dD4/JpyelMY9jw/8L+x8g7ufCccfeAjPPlLz7L47RcZvxakvIR1fkXpTztm995M9vYlK4Fh5719U2NTm9rUw1PjSy/zLf/rV+KHb8BT+L1Y9rkvsrW/xdkT21MnhSucP77Fzkc+QP2t33mwA3wP1KWHjJNyKRVrDXaFf9naywxgaqFNVR2tdVMdJLzFpoRhnFJH6nKFdwVW5+DhSTZ5LE4pugnZ8vWiLTFVVAmpNBVwYqkc81QJeg0/vJLAifCjIz0RNZV9bhXMsLNT/OiIcrCHrXbRs/sBM12wVY3ZKErpQwvoTW056aKYrvZFAlpZPkabukwam0yPyEyalrXwb8ILbS4C6NJmH0swFiqnfJY1/ZamvyNBXj0BpgTgqxZAKmBQCw2pmICpcH77nNGNrh/p5opYTcWaIkUba5nG6hdAxxQg0rwOuaBs05Lb4Ov0Zog1km25WhOqlgQoFjCx1uaXaNHmmlCw18JiBmKV5eoMFlvIcoUe7DK4UCwgqJ+doDIiDEwCvRp/sQbSnPDQk8LoC2yxD1f28MGo5+dYrQlRhbI3Qw56/Oge9c4Rdu8QWa4oYnSzwnzeI7KG4CpCNWOsMM5beEh4CGoRlFPKoSFdgVmPdQWGBvM89q2HSjRcCMIzcAp9aWLUnHP15jXp01pwu6B2c0sgmenswgTG3FOFyLqNPbhwKAmndPVUt8YuzKZrkYw28gTKqaJMH8KtznnyprC7L5yvliyrcHoqjCfO6nxksbfFMD/ATi3UxQil7zHiRoWb5M2DGK9qU2ImJfX0YEyQKg6M2UquDeSnorCd3Ty8Z+NGRDuPxbFc8leWIN9S8WuMk2elkeeFTOIm95lLqB2rB2isGcZtEqlI2s2QWQEb6RR6yfMXqdhuLfDtmGmq0gTLeB576skDNhd1D0O5wq3v3ePgc3PsIfmGsLwiDLvC4rbnGnvvlXVxE+tRr/ELX2L7/hHDd32A8+uzb/rmwrgljHPh9Elh2P3m4OJX1rDnPPfPzrj6xLP5mf62vfTDXwKnNwqLV5Z4nzfL3iNVZ8o4f+9s76Y2talHoDZw8S2VjyN88jfZffZpVs9cZ3Vlhpf4jrt6fJfy22XTNv0O10NyCfHOlYhMfwaEcLBU0chadehuEVqQSclKN0Gk1oqp+W9mBh2YhI+YDitkHBCvIHV9IS0SbddNyohPPpCeF+5otB9LQquAWZriJkcLeLVonRWHTqa2R0TwTpAxoFWnMNYRPz7CF3Nkdw9fnWNWozW3b0+L9lUzyTbNhhl9CioJ20VNoEFCtNgG9/SFzECdpHOTJ6G21qIEQ00v2gIs1jCnvTOTZ9wElGjecpG06whVPV+3ROOw2YX3Utw7qheWd+4iPtL1hlRwlwiKsQz3UQ0op63tmVRYxuBin0lCrZwfVWqmdasq1pSeYvn45ne3bkeP1tIIwijFqaOFraAw+WAila5zxmL0JngXnVnIiFxdUO9UZL6LdB2cnFHqOl2UMub+ECoBUc07zOfY3hXk2g7jOFKkIDKjuOHn53QzKLKEW4f46Tlqlb4PryyVghal+IiYBawrChogs1eH1raYATyNbtn5CrsHZW8fyhzqCjxP4BJt9K4SoUHT3Eu0fvu6jbn5hK4lh+vYFmeSy8XLmkdquuikzDOPC83JnkDSF9XDIsCltW77dA5oUrr2tgHIWiiSTu/bF0XduLpQhi0YhpH73YyzsUNxxtNzSjdnnO0xq6cYK7wGSAvxpTBWi2On6KSuNDPqWAm3gFCQtkBw0S6PpfCRbN6q2s5nQLWa6zBf053qRtEyzZfluawBWTPSTiGBr4U2UhPeW63pgxmPE3S6AVO9QJkBig8D0tdUV4dSFSF8SC3Dn8QJfJtq7hxTOz87vvn+9BDV+XWhzh8u2ZwXGLegP/n6j72MVRfr4/1RKxeQC+up3r1L+aUTdr/jwxx/aP8th2tYJ5GE/JgwHKRVyTtEAG0Gt79T2H4Z9D3WXVWWoJ/8bXRrAY8/xurJ/Qc9pHe8xJ1xZxP4sqlNbWpTj3y5M37xy+iXnmP3fU8xPHMDWxTmn7u1aTd/F+ryQ8Ya7bJmngq8dlF74e8Z+tIuziepTaMPVgKWSEAtSUWjUxHCmw4faa2XyBqGuEVARbMNtIQo0wW2xXsEVCHCTXBE00POQaSEYizTrqemxDURofQdPoAUx+2MenyIPjZHd/fQ+wOUbLFuKb/ZqhjzUhL8QTZgJmSb6CiNIk4txs1PrsHaNrcX5IyxHSm78zrNf7QeZ8pttlY21aNKU0Exqdu8hNNbHSqlFFRrtDdDQBAn0p4pjETba98581kXvpWjwEzRkinEorHPE3LR5j6hlqTPY2vHlqY80wSWOU+4Thws9yqNfnlNtaKl0hCndBLrUQLm1FSzKsZiXlB1qg+oG5yvEO1wUYZhYLa3g/czxuOCLgWVAaTDvWJAHTrq2FHnM/Rgh+7xK6ga/cmAWKVIpS6XyOocKRVZVqhjpD93itf0lXSmNeXmX6X0aypBTaBqE2jM+Tg/wRcLmPX4KuZaaB6D7WdNJWhrzSch5FoB596CXdrBE3AtdHGWQsY8DnLVxsSnIlUCKI4XfBUnH0Rp28h0Lmj99LHN8buLLoGSit4GngWjM+j7gnbC3u4O9cyx0wH6QlnsMZwX/OSQYiu6rQ6VGusv4aIK0U5dWxpzS4t2xNqR0tR+cfxVywAFC7gt3m4KtPOGJFRPWwY33FuKtlFUcs5LvHdOQJzPYlAugtdUgZbYvWbN+gCEQl05daZApZP0fvWWaN3OLTWhSKqgc95F9OLMx6EuMgHHTT0c9bC15rrAuCN05yD10YRt30w9yu261jvDs48hz78w/c6HFf6p32bv5AOcfuQGdat9v3mD1+iEYUdY7QvLax7r892CrgrDLszv8p5SM9YZ6HxOvXcfOTmjX3yY8criUqsaXYXVplV6U5va1KYuT7kzPvc88tzzFJG4NtzUO16XHjJOyrxGHgDPftPWEd1CKSJEIgnXlLascVGcACbzR1LlVBEfgT5FUam1cls3/nmqtKaL+wRZU29oAztMba/uNrXweqqK8Oan6KTdX6AVa7KuLlRKCiIjsjyF+4f41WswnCPnIxFFHFf74rYeQ1Nz2bo1NgefXpUJ2ARSEpggJx0t07txUvF5KJhUZUrInRSB8bLhP9mCUprCs71RvrcT7ZsloSxFIunXJNOMa/rW5cyWMFEsDMzmI/OZMus1wIoH0HU8IBtkC61DDXfMFrCDhf+cyRpMtUTuWEetBb+laudriYN0a5hyQa3lHgqxkp6JQnjmNZoqAvO5UKmIOr4ckb6CgQ3GypZ0ixl6dZ/xuIPzFT5Uao3U8bFTfKujHGyhs4IPZzAOlPMBzpfYsKIbB0QMhor7OIXzYErBEjjLBHBF0yNQyDbrOu1Hm8Bhg/SKqmB1iZ8eQ9dhWtDawoFqQvGmXE01Y0LCpl6VBhWRCcZP6yG5oyYI9gbRJJrzczllMjWZ2H5RDekt/HtaN20XttCeKT05j+XprgEgpURoD6FQjv0u9MUpnTJsOaaZgq6CXL/CODq2jFZ3ZQjA39LNE/Q7sR1SFFzz3NF8GXONeHpYttGopJJ3rbJst08mtWOD6TG5lAnyyfScRvSlActq1ASppQvtYc027+n4Beg7ZD7H6xlFbLrp4Alqi7b9uj7/et6kgDwGJGCzaJvmTbr0pr52WYFhB2aHD3ok73IJWP+gB/HN1dEHttj/+1/xS3fqZz7P4ssvUB67wfj0dU6f3JpUZF6EYUs4uymsDjzm4AGpOccdpz8SdHggb/9Aqs4Fbl6Hw8OAwv/4d+g/9CzD+65cWtC4vNJTZw96FJva1KY2tal3pDaA8V2rSw8ZxdNhbc0KQk6YV76hocu+XzeUEt6LCdS8KYVo/xNtlKjjNVp5BaJ11Boja+gkAZg01VUqoabAkSjNNOrItw7VW/z/WpUV47EIU8k0WdEEDxagS3pJZZyCDPj5KfVsm7K9A+cnuAzpLxivOYWIXABGTO+VeROlKQZju8gxmjbPyHXbc2utDG4R7djRMt4AFQ3dcIHABWxKn0sRz7bNaA8Pj72K5v+5pq+faIAjt0xlEbzM6Gxkd0+YdzO25yNd0SmUGG/+jQ7UDL3IMWCp1rsQZJPwVTTVl+S5yda6xcReCWHDM1IyJKftc7ELwA5wVdQ8ttkiHKR4BKyojBGyYjXalTuotcKpsVwOaK+oFmR/lzoO6Gj4OCK14rbCjwasQMk0dNzw1YDWMbwg3cDHKZU5drRNajJE0i+0BXc4GFSx16yTpk9McotWsAx58eU5UnZAZ9RhiWqlpC7PEtA2gL52Ak3gNh1sMAXaSKPgCbVjBU7t902B6pJtyXlDwDJt2/MQF5rfYVuJAcFFiWPGw2/QMhxIEnJbAakB+ZEuwmVS2Snq2OocTk+Ybe1gW32s+5nSbfXo49cYXoJxPMN7pWstxYQ9g6qhAqWUHFONmwkmAZvxDEFan89A4hxUUuXYbm7kv6vksZMHsjZbg5xDIawALG8cTNvZYLqWuPivGYYkBRPFzBlWFaSgs23KvEPOK53GGK1WpE/vSAlo2YC9Y9N5RZrPZkgkE5AW5AHBg009WlUXgp2Cju+d9eIq2CMOPo6fUa5eOaDeu/9V/+bLJeNzz8MLL9H95Pdy9HTH+Q1h2HH8IfGidIVh7z2mZhSwve31z1apn/0CM3s/q/ddDZ/KS1SuoZbd1KY2talNbWrqztyAyW+oLj1k9NZ62VSMTTaY1FBggoBN7dgUbROEEcG8pCKxQkn4VwoUxYaBYpYX0xFmYV6heZCRsKoRlJJpyJb+aC2coiGX6X0hQCcB12jAUyZfNchwCqsJ5NbKMupIPT7Hrx+AbKN1QMUuBEg0gBN/Tu2K+fdo20yl1JRa3OScTcWYisNUQ7nFe0/jRC60ZAewEndEOqDS4IhVT5iRwMRj7lwCwtnoUCtFAybZxRAQA7xgWuDojN1uZLYNc7pUFAZMvji/km2rlj6LIhqem+ZrZVWCj+bZiWu0v0t8GW37LfY4CX8DqASszDXVlJSxCBD3aL11KO7YOKJaUByVgeXqDNmah1/i9haiyrg6R2qHmGDiqFfK+RkslzDGWlOV8Aw0EhRW1ATzGqDWw7+Q6pEirJFIrAmxRHNukshNoNGjbTp+MvCKptdfUwo30aC6ITIwDAMyX2C2SmVujbAfZ4Jbmmq7BkMh5lW9ZDN5nYSItHRubxC8qQBbGzcRltS8PiXAobVDPc8BzcKznQNEQ+3niYBV/KLNaBwLJlP6e4TdyLqF2cYI1Dk7h9kW3bzgnaKlgAs66yg3rzHePebs/jHD6RnLsxHKyM5BYX/X2e4N8ZIK0oDPqOb5qMa2m0w3N6It2QKMtvONkPum2RkkDm5KwgnWSh67JebELZTd7mjpcp7iOLWE8uaOGYyjU61QqyA7PcJAryNptpBWEuTaSXjYjucgotO5TYljSUruW6Ile1MPRz3MvNcKjNvvLTVjnT0coO2bqWHXqd/2fvjlT73xg6ziCvc/wkO5CIf3oJqRrwSJ7oyf+wL9ySnDR57Cu8vTWlznSp1tPoc2talNbeo9XVoYfu8neOX75iBw/TdHFv/tr25g41usSw8ZW6253Tr1uenyJi2fphqvXZCnB5y1C3qJi/t2sayAaY8PS5AV6mAt1CEhhnvlNaopT1hhqWZSaDIrMahCtuZ6Aq1EVtle2qBoA4zB7daBLa2V14FxGDm8dx9ZFvrdLfrjQxYdSMn3MpsCcNTbwBRqjrU4pgbepbpQaIm8k28iEF6UmiEvqSjjAlRFpguGKfy2AZH2GPMIsZmYZ/O8DMml1QQlxSNJN6HdgFFMMSmogLKi9JW5KFKbX2Nu5zQcpSZcC44ryV4mAhUKVq3hPWiaMMzRktCU2BHqkbDtIa8M2DapPpuvnq1VjDRAKqkCDWVqVSgJMlkO+GLM1zd01lFTHcmsoMUpJ4fo2XECMqc030EHGQOcYhYhK5LznbvCmpLXLcCiePh9MjXpZjBLTbWdBFhsx4tG274h6MS9LNd3h9URWa5gZ47rPFLNyzJONpLHYJuTtg8SmAXMrAkQcyVbGvt7tDurW4QgueCuaR/QFIzZHoxG6M70nk0V7Ani4vWtwWwfwzfQnEJYJFQn27p9WteK4CWOjzindKgodcjjXLuAyA1pd4XZlRm6s8WpF+7eNk5OgdE4XlZKr8w6TUuAVId6QmJpNyLCm7MlUos46l0e++14a/AwgKRn0Iz6Wk3dEtBr/oxasl3PY2eMx/raRbMFGgUQFEpRTHtk0cPyEMFTEd0mOhWKuV8t110D73EeUMzHOKZ8nSS+ubTb1JutcUvoT3jPeDM+yqEvrVzg1e/e4cavvHGiY/fB9/Pl7+sfSsAIhDfje0zNePbENovX+X19+RV6c4aPvu9ygEaB5ZXyTaedb2pT32jJ930svr/++m9HMu6mNrWpB1LdM0/xmX+xx2fxXeXsCeVbP/9R7Dd++wGP7NGqSw8ZPS/ESb1dA3NJHiZvtuad6BOOIFU+EkqvfDFJKCSiCXAMrWd06riPeTGdSiJvnorJd7JFdVK4GVATXqQXofoawWgpCQDjFWQa2xTpm6+tGOOFRN4Y51iN+6+cc/LKba5/9+Mc7O5gK0OsUiV8DcMHMucgGrUDtq5HTQvOCDyRv62pQZzEjx4XBq5MvCE3EZ1mdGqJDD1pbosK4gG0XKOVMx6cEEoc7Yhx1Pg3A6oJVjsqSi09CHRlGWEm1dEpG0YmEOOpJtTWQ5p945qz3lKPEV8rxSaoGco5TcDqU6hGUNuAOtH0jramXp+Uc+t07QbTnBZ+on4BBvoIZgzLJau758iVXcrj+9jJCpl36PIYPT3C63kGnIQ/n4iiXgKgNfNQsr06VagtcEeRWPsa6kJJ8I6lhUC2KktKNKVRZbmwDbBWCSdYwg3pBB+H8H6cbTGMTqkjnQ5x3EyH19rrclobVlMdul73llDbUypapeAOtcaxVnCktPlrKeZNkdkUp83nNPBZiHFjfZk378DWvB3qPRXFNYFsJiJXyzZ7Cf9Pr07pC9rPqDs7+MEOq3snDPfOkbnSb80ptaDbHVvP3ODwyNGX70GvuIzU9ANVMcwqBaWZS0buUpnOWW6GUSfP1KIl4F9EUkdoDUwKbIdo+zZDzEEt5rKtSGvUOSa7egDoSdHdCV47RGw6dtxm6Hw7gl5Ktnw3/Kw+tZIreTyoRhJ9vGGunfU5J0B77q+HFSxs6qErVxgX752k6cviEXd2Qyj7u6/bMi3zOS/8i+9j2Hu4zwPvNW/Gs+uFrfkcXy6/6t/qrVv0VrEPvY9x59E2DR0XhXFrQxg39WCqPH6T3/4ju/h25cbf+wGu/6VP4sN7LM7+Ea9y5QCAev9wo3h7hEq3wxLEa50+5/z4hHImjLO8nuqduj/fiCHeYl1+yChk8mleWKc5Y0MlSEU822ZVyQbBhBPxAtbARGvRlaZUisAGtVQ1ieLZChvKMqXhOfcp/zbH0VSEkk3DDTylUrKZsGWL6TRc1WxBzH9XR8ygMtkcRnvyCAjLwbl/1/Av3mX3267gd5agy1AukmpD8yYfJNRkCWpMM+clQIKkIqkx2hBftRCdDFNpasjWgk0m+k4zngm0E2xsrdb52kIoC0m3TPcpkESkNBbMuDROTyvDyrE6p7uhzN0iFKSEV6Y18WJJQNJawlvbZmnqv8Ch2rhcPqRikzefk0pHEsbldAlk+E5sdkAovQANPadCJnijCbXNwk8yUqajzdwsUsXH1cjhvZH7L5/D1hlXnr3Czv4W3ULw2wEY8XMKHa3l2KkJ0jKluMikqm1rN/z9GhmO/e9u2e+cG577QqfWeMc0fQi97bv0X9Q1tEKY2sh7dZbnp9jeFWo3h9VArY4XQ7UEEEt14kXPCw0ytYai+HSnIFZT+KSuVpVxCaUIuttFS3jNsTdRRYo33RtAT6VoQv7Y6wnVvO3j5qca8xYBRQ2mC6gGgEwQjYbnp3WK90qdFY6PBu599hBT6BcdW9uF7etbzK7vcPXDV7GiLO8c0nOG9kBdxnFjzlBHSleild1ivZRO076ACU47MFjNx2XSdCpyL8iFJxVjBBgF8o60es026dxveUxK0UldHDcILJKmS4fUjiozZG8LXZ0iXulEkOYxSihNzQ1KanizX93yXKoSyt8ImtGWER/gdpP7sqm3UOO20J0RAP0Sl6swbn/9xz0KZTPg8cfgKyGjCCf/wic4fubh35eusLoCi1d5T6gZreNC589XV719B7l/yPzJJxievo7Ny7s6vrelNirGTT0MpSCd8eqPDNz4lQ/hG9XUI1PdM0+z+sBjoNC/dET99Gce9JA29SaqfPtHOPzYNawLu6fF7YHFbz3P+OJLfOivHvHFP7jP8kaFKnT3znj9HoxNvVFdesh4AYFM/mD5w1rBiEzKNk0Q1OCQt4e4rcWD2aobvzasjpEU3LhDEgy5+PzkN6Ey9FT66aQSFGltuCUBiF0Yapm+4AUbbMofSa/AVEK6Y7VSTSil0Ikz3xHGFwbuP3/O8bcU5lsLurPzHF9TWU7Itb1LzE+CmAimacEfbUMat21NrZIKujbFCXPdLkDRmKCmmGsQxVMFOY0nIZh6zEmArYSTWrDBOD0xju8bq0GotbL7ZM98GCNARWLyzVMNOu37xooacI79KhcDfkRp7ebRWtvUekzjiN/5WrGp+ZfMoGlJL20uXNa+hk1hZzkv7kafoR8NsKpVfDSOj0dOzg0dVvgX77D4rqfw8zM4PUWo9KpraJMtrk15qAmt28KbkiCFTPzJ3VRtbdVHQLp2mHjOm03HTV0rUZvxoWQysjfQCdQRBEpdwbhEZj1S54zjQE+2FUtr/s0QmVRf2oW5jfTpeGttijcJS4LhTDg9CTXd/mLGdq/RXtxa8PM1zNf7fsKHVoPLFqVkiBJSIrgkl/rkywoIJY8zm+CkpKcmhIeiu2PLM8ZhweH9FUcnFe+cMhgnh7BzUrluhcX+nCc+cpXV6Q527wg/O2LlSqcritbJK9QFpCt5fjGaOjmOj7buYr+UUjCrMbZswW9t/JJKVXfJdul2jAd0rqmA9bibENthinv4WZa0ABAK41BgezuDkCxupLjgNc6DFBB1OteY+GYfIG0x5RJUndbXdBzmDZdNberNlheoC+hOH/RI3tkadqDOLwfNcnVse/5Vv++efh+vfK9e/BLyUNe47Qw7Qn/8oEfycJSPI+OXn6PcvkP39JMMj+8/UqEw1ivj4tEZ76YuX9Vbt9n5wrdw8vEBKcb9j19h7zce9Kg29aZKhPr4lemcNzy2S/fCHnZ09IAHtqmvVbpYcP/j1xi31nYfx++bMew9y/Z/+yr+D3+DD37+Gsvv/iC6qtTf/uwDHO2jWZcfMspaIeM0D8Am2gsq0jSGDStUs9TXxc8tAMPcUwWUr+YN0KWusUE21i2gfuG1pzbESZmYgBO9oPabEBcOU2tkUzE2YZc0TzhfCx/dHCkFIWBBJ5WDqz1bO87x0QlH95YcXN9jXJ0ysxXNjXINkxJkNKbkXFBWRukE6KZm52jrFHDR1uGcgRqhZFIFLRlg40boNUMVliiyvV3CnOndUA3Q2Fpoo4XdKVs9uhKESrcFs5mj9RyVSFE285A+e419NrXNNnLbEFcrRwvgldaO3lSBIg3qJnhuu87z9aTttWyFV8HEpgeGSixVhhbrpcWoMM2TTW27uFHrSB0rw9kKnSl+7Jwtz+nP7lNsRNXCw7FBXy8X2o7jdVQ0vQdzW1MNF2rJgIeq6xb+EDlGeI9M6dShYJ3aaMmoFSm0NBQRmUJdAgwaQg2fxOUxde8aNm4x2oDZKXNtjn9tH9R8f8kWXrsQZh7K4pqbqqKMKKvlyNmq0m0VlsNAr0KXtLQtn5prDUrsHyFgXQu8MZluKrinXyqpCEyg2awwtcQPbh5el1ahdAknY38NpwPjAdTBAq6NjosyFqEuZoyloNVYbHVsz2eM+1eod+ec3zlGz4+ZlSHW72B0faGbpUq45A0JmKwEAiZG67b7OJ17PB9XvWbKc56hLFSZAfrDBsJFIlSpwXE0Zj6DoYqEgtJHqGOHzRfo/g4MJ/i4mhSVDbReBPgN9DePScvzaHt8A9STOYXAJvflIapHgGm5wLAjlCWX2ptx2G8fyJe0RDj8/vdhX80eH+oa9pzuNMOuLnvpm/NctNNTyt1DeHL/HR7Q21fdycDpla11B8SmNvUgyipP/MoZn/m2gnTG7Y8L+29gU7Cph6ukFKxfK7i9U/Sx6xvI+JCX7O1R569z4XHh61a9fYfu79x59wZ1yerSQ8ZQloXPnjfZ2cXWjyY3TPWeJVhyb/ANsOant4ZyNAhjNdQ8Db5JtEpPyp1J+RTAQrLt0yQTh9trZVCDEz5/kgo4wyO1tUE2EcQs1X8ytfeSKjRIpVONrV/0xo3HZ/hLlfH+En96Dz1dINUj2CODHUjkKA0MTHCqae8a/FzDpva/rQM5WNy6PdwEtNPw1mt0Lv+qHsEW5tHWLKLpi6hfAfHW+6gFaogKs4WwW4XFliJbW8w6p6+V0tRq4lA0O4klRZQy7ZM2EGkt6tJYqlxQaMb+mLw0G66TCzt2GisTBAsRalOaEtB3Cgxqqr1sXc2coWb9JxbpzcWM0gXgMXEojg8j9eQcrQPmI1ZCwaoCIw2INghsoRSd/DUvsGMhE75ze2nehpJt1XkcuK9DfKQBO1n/pw3Ya/jyuSE5X5LrcBxX+Ooc2b2C9IKeAywDXHoAaCECgyzhtLRW8nYc0bBvybWhSNfRLWC2XRA3hkFYLg2vjvRCPxO6VDdOCdMt8Ca9S6O9fK2gE3L+cn4sqCkFC4A/ob4SILkargOjK+Oo2NwoCou9OcuTkTITZnuFrf2era3CvAx0BTg7p1boup5up2PJNuNp4XysiK9Qt7Bi9BFtlJM0KnACWF8AdUr6N8KFmyG5v80ZxzXc0wZavdkWpE2DQR3GgKkdKAWr8ZjBwMoM3d+jbM3x5SFikf9dhLCjyPkVkzz2fQoOp92yacDefK0Sjh3CWlW+qYehHhV7TOtC6XdZk6atE8atBz2Kd7a6Z58OFeMjVjaDYffyrr1WdS7o9WvY6VoyLP0MvXKAvf9xbFYoJyv0zhHLD9/ky//0AnHYftHZeWmkO3s4G8zEne7+Ej77Zfq9b4Xrj2Cb96YuVZW/94/Z/8QPcvS956yuVXR/n3rr1oMe1qa+Tnmt6Pn4aFpFvIfLj46Y3zPOr8Z+09FZ3K1s/8YLjJvgpbelLj1kdEslYkmvMI/FlDZ/mDTtTwMmTZ2W6kXzyY8wuRcFZfQRvCJjhKEEo2wef5YAMRV0LbM3pToGE2BsLY+eCdM6wa0EVinDbGArOJhMCrvmg6aaCqUWdpLecvNiXH9M6LfnlO2Ae769ix+dhbqwNoVetg3ntqhlsnQbx7oPOseTzCxB3YQHSmybEL51IpnKY7h1kQABAABJREFUbfEEvaCGiyCPeF8c3GRqQRUtZP9npHZbxXzM1toAG7IDUgtj3yNeUa+pnJL0UpRQM7a1MP1NUtHGBD9VZdIXkoq8gJLGxYTweOoFNeRE7XKtqb5mqprCVRC46B2W/p+x3T51i7rFWlMzZvMO1VAbzndn9BKYretDAeruGQZChLtkH+3EZfHw6lRZ+/V9hSJGElIl8QlALA24+bT+ugtrIJSLOXVNwdba4i8kOpcuwGE5P8F2dtCDPdxCaSpUirWwpBhXeDTGPoy25gTEOcI4Gpyixu4VYWYFkZEiilVhNThnJyO6BQspbIvSddkirBmUUh1JhaNbKPoirKh5aApuFaVO0HWt5NUEq7a+sSANmHsAdXOu3Fywu2uIrSj1jNl4DPcNrUYp8XKrVeF0KIwGdB3SdZS+ZzbfomSy92hxjvE6InWgYOHdyZDQF8ax5rEfa0mlrYsG1YUWBT8B2raWE/6712gdn/cTO64ujFUBpc4XlL0dyu42LbW8dB02VrwGWFUNxaVZeC1CtuG7TOcIS9mz5PnML4wjYPt7QRK0qbe7xq3wZtThESGjb6HGLbDu8m1XK+k6Xvznn37kVIythgOnOxf0MuczCPh25EuX/X3Of/Aj3PrEnLPHHZvlDfK6hdQDrHeQlsYJ9047tl/s2X3RmB1WyrI+FKJcqc7sy7cZv/jl+F5aN589m3rw5ePI0//NZ/ly+TCL20599dUHPaRNvZlyR1cjEB9k4o4fvUdS6R7hsvNztv/Ob7B7EMp7Pzuj3j9k9IfgQ+qS1KWHjHENa41XNTkgqoEtFBJOOZ7eh+oNAkokFJd4YigM00NQFfOSr+FIfrFqmqhgZfIaH77wxktgkqBmasq2pgxrIDHBYmtDbUyADKZpSksa0dJQoXmoDl0NDGZFONiGra1KlTPK6RlsbzOezin1NENIAipFS+N6niLpuq5bmDUAbQutaaEpzji1JPskG5NJFeaSijiBag1keT42N8yYnisSnm4m6VHnqaUMaVac0PO/UZUy34Z6Gq3IHqo4XKk2xjwTKbcBxzJwp2aTvCpUmbYpoGDTMNoEj/HW1E2mgXv6IDqqXWBkhQZXYhlkgjOhUHRSvSeCjDaleyPgaoh1QVt8BuYstnv6xQzvhO0ndil1RMwoKnSmIB6KPwcYIxU8E8mlpqKxqcxix6UiNvVjraVfGyy1hKF2UajJRFMTwOezpsRu8Oa4yRT6g2DVoQidG/XsBPYPGOo+470V6mcIQ0LbgGGSCtCWwu2EGlIsGqgtgVTxinQwwxK0w6iCLYRV7dC+hajEXBd1IvPbwzOlTkNcq3JFcM1j1BOC13h900gQV1XcR6ylzksCUFFqX+jmBV0N+OoQXx6iXileIxiq1vC7tFDX0vecHJ9zcuisRkf6nt0rM/prC7rFAopSfRYg9+wMTDgfYXW6RNxYzAp9iaAWtMax6kKtrRU6VIZgaFfC79WiVb5Pn9JhUukmZLWYcNeC02Olx+Y9ZXsX7cP3kToiNmI20JJ1qjhWHS3lgq1ioba10lro08fTJc81lGk9JtlnU5t6q+UKq31hcccfCoDxdtaw1245Xp4aD+Y0vYd//3dw/IEHOZpvrmLtweI2l203vaZe/T2PcW13zhf+hQPOH6sTSGzlxdv9+3UJ1B3j6Fvg6ENCWXbM7vVsvexs3TFm98d3PbRJ3CknA/qFFxlfvf2uvvemNvVmanzpZZ78c6/EDxvY8ejUK3eQa9t4EfRsxA4vucT9kpSdnr5Gpf92lHQdw499N/c/NEMq3Pz5LzN++bm39T0elbr0kFFU03cuU4/zIjhASyQBeyoC3RMmSWNZDTglKJQGGh2hw+mASEwtloKhCYplGISFei91khg14RPpeUZcjIsn3NHpfUNbF082JRRxbsiUVJ0+eqqTV1tm0sQFvCYpEmcuhtUz7Pg+tthBd29gd19COUfoiIv8GoACYhsm2JST5k0dpZjHdkTrra7bmml52uttbvJIz+CM8KA0arYPB5M0pMtECzLd2nM+J5VfU4iGx5ybQL+FLDr8KKXNbcxtXtbsNuBgQrBQSgZ4cXeUjhb8MykVJ4Vf7K+1p520X09wlhZWYr5OyE6SpVMfe0C/gMgS2y+holwDlliZUo2tuXLz2yKNeOfqDH/lLtXAuw4rI9pUqMTcIBW8oFIQNSZ8k62pLeFada3uc4t0dMsxtX3WWqOlKeMcRNox4AEoG2iWTMhOFadIBI+gaVPgRlmuGJZn6P4Ww2oHORnp1NALAoLmaVmk+aQG3PQiYAG5NBW60RYfqohCQcWQbSWe3CFYeDTmprr5ur2bAH2mbRJybibIqOFpWHIfWoSooCOeys71nKT6VgsUwe7doZzeo2NEZe1ZWt3zhsaQUubKwW5YIZyvjGojvY50yxGpp1gp6GwL056qheXKObx7zvHRgHbK3k7H1lzp5868hPJVcbRUVEbKZNgqVHOGlaDSo2VkbLNtHWMCVS0lsJ90GHPqfAef9ZRZKIrHVSg/ezFKu3GAU1IJbtpur7SbIeuVBJ6K6QvnR4m0b5WSwzTkPWFutql3oqyH1Z4wO7w8F2XWX55U6Yt1dnPGLlAee4wv/t7d194IewRr3HLqLLxBL2O5wr1vg9vfvYd332Drszp1yznbgrMnQUZh/uqcxW1n52WjP6nocNGP++0tcUfPRrovvcL4yqtUu7AdIgy7l/5SaFOPUm3g4iNX9dYtehVkZxu7dRvbeGm+Z+vwX/9+/rc/91d5orsPwF/8o/80n/rbP8wH/t+3sN/9PP4easW+9J+sYjRCF0o1z4ABkQQA9hq14OR7lonB0oijkKBSEoBFcqo7qAljgwitLbH5vnk4zvnkb7cGdtZSVwGRVLrR1HwJhwhuVlJlGd6SeQGfkkdPdRC+DjcJ7qHT48RBqfjyhHrnFnrjccb5PpxXZpLJuakoRBOKiOASycch+AqfxSnAQfj/s/fvwZZk13kf+FtrZ55z7qNuPfuBxqPxIEiCBKgHKZKmaUsi6cdYGsrjmZAmxIixZXsiJqTRmGF7FMPQTIT/MRlWWLJCVtgTkhmWZjQeOibCGoclx+hliZbEhyBQoAiSwhtoNIBGP6q66ta995yTudeaP9bambcBAmgAXX2rbp2FaHTXrXNO7ty5M8/NX37r+1Jx6JT0Ikxx46z+yxcK8flmhqlmq+n8HklI6TTQGEBHJzfuBBAdqBUMpUpP7Vaoj6EkVU9dqeFT0nDOQZsHCTVobG5uvXZJb80GCfMgScI/M1BvIScwpV2nn2C8/Nx7E06hRKu+xTrTCi41UqQJYB2KsAyOkWCFdRzo1Lh2dQn7C46/eMz2xftcuxkehDopQtNH0AATpASIhtYCXHO8Cb7FcSyUJKkiMMn1P7VDt//OBe6WasdYSK8JU0oIHyLUDEzyaI11G0ELUo3qA5xsGBGqdWwXB8hWgDNKLjzJbTclY7SQB3i0hICRHF1DkZpK4RiCUXCWnWBVwuuxzP6QboaNqTJtoFI0Q5AT7OaDBi8FMUNqQH+TERHFvIWreM6FYuLUrVIPluhmxE7u0rNJr8lY+SVl1E4c90ghr6w6WFxRarbli4woNc7jAdzWaL9ClitGWeJVce0Zi3PaF9ZmLCmUMdq9ump0XulKT9c7Xb/E1RmsMlAp7iwkLSLMqQhVFShsTZHVgm61wvoelsuEhSNsBswEV4kUawUd2zkQQVlFJdvQNdujPUKNLNd5ad6v5IOMpoxtdhCF5ie6q119o+UC436AnrK5HDdom2tyqVulj3/43WyvXYL9U9hec/ZenJ4mX6oa92Hct+kB/RtR3jnrpyvrp+Dudwi67envCcu70B87q7uV7tS+afCogyFDpZxskbMNfvcedvfeb+2zJcqwv1PR72pXu/rWqn7pxYsewhta+j3fyfP/6g3e/t99kfqJT1/0cB6ZOn1S2dcZMv9bT/1D+Df/Ib/2h97Gf/n3foyrv1F46pfvIh97DoYBiNbty1iXHjI2VVlTnsEMtAxJ30NPgODnoETcJMcfzqXN1sqUJNwp1aGjKbusMbN8W/jM+fT7maQHIzGeVDtqJveZW0KtDNBIlV/zTGxqstmdLsMomkdebgNNgJafJz77SqqOlPU97G5BrlylbtZUP5m2Z40yJkSd9XVkGnQkyRZKtF+KZTBNKkCVUClVz+TamNPiOkPaBIxSErw05WYmRbQWYDFh1ARVrgGxarxGVMEKZbGCcQO1RuqvJxJNGDaFPJ9vd+acDyFM26UBZpFz4SBM0NbTR04SGkm2H3uDj7T1E+stUoqZvRh11niVVHS5EO2qOe0AJjUSDTxSnoeNce/5Y8a7G/YOFxys8ih5zINISSXZfBcgCcVj+ScYxNJvMPR4Uyu6aAgAz0NGyHCaBEaTqvPcghDiM6VEYIjksY9JC5cBD5WbujGcnTDWwvEXTlgddcjhIaszA9/ymmR2a8dFmoEpXQJHo74mhEnyhFYBt0rfdXiXELm14OesCwRclRqnieVDAZ9Vyp4BNnidhM/Fs91YCL9EZP5cByuKasd4coKOZ6H2SH/RKTRJEsBOQU0BrQvOQn1SBmJxnpTYIWy7Rbb32e/26Z7Y59qT+9SxcHJ7w73bJ5y9umVYV3w0+uJoERZ7PauDnv0n91mseloIU0EYvDKKUDzaw62OjGbUGiresVbEor2cvQViio8BzBWB7RCt/h4PW9RJNWJcF9oiFs1WbKC14Ruz32x7ZXuuEecJu9rVN10uoWZcDbzpbZhveEkE2lzGGldCuXWT298ZJhaXoeoy/DO7N7br6sLLFbbX/Q0FjK8pCeBYO6fuw/pp4nenqpR1ob8XfqvfyPb3XjIOn9/Sf/jj1JOv74umix7rHswO6gjLFx/O4Jtd7WpXu/pa9dzvu8EP/oF/yt/9ru/gO/7Yi9jruJ7uCp7+Cx/iZ/wnuPK//CL/+lt/le9YfYGC84HV8/wX/+pf4pnff5dXbcX/8OrvYPDCWe35n//6D/Hsf/yPLp3K8dJDRlrgRSrsSMWWW/yhSqiXzoe/zErE8BGrBYq1z0kC0/zL0p8tsI0R0RwBoaLD2RnNsPRwkwSK7k3Jl8Etkg6RjeUk/CHTkS1bsJu55OSHaJ7bDgBkED5tgFVHvM4QzRuYqsj6HsPBHnp0jXpvpCPTdL0prfKXrjEUgkaGzUAo2xIgBYxiUm0Kinn455VprEbVgICQN4Aa+xKw0BK2ZeRHpoq0dF9piid3vLRWdIF+iayW+PEpJfdbms9jqvCkSHw+PrXDYmTbsk/+k54wdIKLTaHpOilgW2t6ksdzEDLGjiZUhUmpBY5Xi/EXQUoJv8RslTaRANTNv84cGQyvA3U7Ug6UYT1Sa8UXzrjdUvtK3+cce+hng9o01aZmslELLWkLNpdJzr17O4ChMo1bvgZjAyxN4cvTvObHTK2tGkEuqTK0fGlB5ttHFYqFym5tlfWpc//OMU981w26foWMFbUa5wPRBq3tvkZgar7PNm3VlqYsCbagTv6eAcjM0v8yP1FcMK9TsrYPcU5KJ7mfLfQnAaQwnZ+eila3Svtw1WhDdoTS9Qx9Rc5OKSXaz0PBGuvQRsEGgc4oJecwD41oqkPzvHNJb1CLsKo4hQwZ7tMNa1ZSGLTQHS5Y9MowCuuthv+lC9vtQO0E24tkbd+eYUPFxRm8THRPCcWnVWGojtURzW0vilCuOt2VVSqwFR8HSl9Qsq1ZNfwlgSmB3Ryhkr4CeU4ItZKt9g265jHAJ1VjdExeDuDwqNe5vKdHrqwPBeDy1UcbNI5LwZaP7vi/Vh2/Q6h/4NsDXl2WEtgeOd2ZXKrL2Lgf7eBvaiV4HA+d8fAbf/vxu+DF37Xg8Ht/G2/777++AkevX2NcPRjIaB2cPrlLnN3Vrnb16NX1j8cDkh/69k/yuR/5blb/wz+64BE9GuWbDU/++V9A/6sVf/vtv53/8t/9V/jh3/0Rfve1j7KSgWe7O7yzu88fv/X3qQ4v2ZJ/+NvejXTdDjI+auVmU3qvNlwhqSjzSGGNQBNJi76J8qXXWqQrN1+7uI1W1ltlW6ETo1r4K5ZUk/nkJ1jiRloDWhZS8Ue2EZtPIGVSIJL6rCQM3pKqveWuBCBtijrzMVqYI5o33xv7os0McrqBD0AX/oYj5dVX4eaT+PaQOoyoj6FwMsdo7nOhchOxubVxUrMlLKsGWnCd1YIFg3GIGc/xeLZXNsc/8MmP0CyUXhZ0KLY1Qc2mGgwg5yKYLvHFClQY10OGamTbZjI1oanJAjSFb6VEG7k3pdVMs5oSdEoNZgZ/ND9Ha3ffOc80IBxwz6UF8iTU5rx6NY9B+7NIKMZUsGp4DSjjRahbwzaRSDxuRjqFbh/2loTvh9n0OY0JiHj4Y5qf46z5mqbSbeNsAT0J6+ZUaEkFbQB4yTVAti031e08X0znTqz9hGQi0HXxHlG8E4pUig34Ak6PnXunlf3rB+i9DcIYeN7rBHRNo73brDV/x+KLpnrBUjUpqdwlIbfnnId6diT1iZP/Zksgn/bDPXZRJj4Wc5VrJ6ampqKTef16ct1lR4+hxdBKwvgEm9GrjpQUSBLq5lDDNt9WUgnbAG1bLhW3uP4E591SUNSg1zWHS8VXgmtp2udIq5aBHqOwyXmK81k0AXMRioVSeewLy67HS8Gtx0bgzKinRn84UvYWjAcLKKGI9dGR0ae5iQkpcbVoCs0G5YmAJA0TxxwLeJ3PoQjQabDyQclldvU4VV0GHOnvX/RIvvmyxUWP4MFVXTp333vRo3jjyy6hmnE49MnL+JEpAcS5/87KJ//I0zzz92+x9wsfpX6VIIb69E0mV55dXb4SQQ8PERHq8TE7v8Nd7er11dE/fZmXNoc8sbzP5//Fwnv+muzOn2+gbL2Gj3+Kd//UZ/nS1SP+23f8KL7o+NIPXMF6GPbjnvMtv7Tm7b/8zy5ly/Slh4whSNJUL5LBKA12ETAk5WjZ6Bo+fh5KMAOwgQhSUcYq3LtXuP2Fil+tXL3hFG0JtOmxl4EoTsADfU1zbo5BQKyBk2h+1gbtmFOpIeGHEMEX6RUZLZsx/tbijWqmBMdrRBWrTTLVhHWON181O8NPjvHDI/zuBuxkSl8OyGLQBSxIO76YL81gj+CBE5QDApyqRIu3jRm0kgBVhY4yg1yYeiUDHuYHJmRtIFJS5dfaLwMMd2i3opo1AR9F0ksvPQStqQ7RUKY1eOrn0pBTndcCPKwhJAftSqhhzCbgO6koNXO+zwn8LNuLWyBKNtVPPnzSgF9bG5MnYqoaPX7mCq7K9v6I7w/0xdk/MLpaWZXEaLnGWot9yX31VKpNaz8PTfWmFGUGQunhScLDgMgNwCfE9PZZlZlKxuxJzp+dD1RqHonM62LqFsdZ2MD+fsf6dMk4KsPhAXJ2n7Ld4j5mSrc0CV/A4Jy08Ni0gGUlz6rWEq6S5+0MP70dXssHCk2dKAmasRn2J4V0Y1ICu9U8dWK7qpLt/wEaRZO0dQtkXKOjJQzMM1Tmc9kkFM+GYzXboTn3fS0ywfvqTsFT2dj8Itt1wIhYmxHVEmuqZkq3BFBsDxO8grlMKsGSQTZSC2rRol0dfOiQscO1o3Yd9eqS0Qvjeoh2a4DNQDVje7bFzVh25+B23oSqZzu4C65zSNB03iIzAG/nX7smpxJ6VxdfTfn/KCuyhgNBh0fUn1FgfUMmT+ZLV+e+oy5bXSY1o3UwHjzaOzIcGZ/915TlD76fp39xy/LvfPgrlCLrpy9hutKuokTo3vE27NohDpTPv0jdpYrvalevq/y5z/NrH/sAP/KB3+TWd79EuXljd/58M2WVeucO3LkDwJMf/C1e8iYP6c2qSw8ZQ81lqWBM3z+LNuiAc0nlZIYTsymdTGnKkdIqbDaFV744cMYeSy2hLnTBLT3KzovWJODN9HEY4UGoGVAS26pWJ2jUYNqs7ElgNw8u4IU7SJkAZqM8bfumAVOlyyAGJ/Y7U6orI14dv/8qtevQoytwZ0tnG5CAaaUp/Bq4PBde00At6SFpli2U2oJnAi6EMjEhmyoUz3mYMVy0xoZToGUbaktpVkoA12wp9WxRlcUCX3RoPYsW01rDb7DLgBwJb8eCZgBFBMGIzvPeWpQl4QcTIEtoW+M97ei19OSmLPX0sWwwa1Ivpoorhj1DUhKmnFeBtTb48O9MBZhHG+yIgFWWxTg6cIoVeomAFKs5ck1QSGtEjeNr2QLdWEHbfnttC2xpkrxJ0Zlt4ADq8boWcuJtXWf28wQWc9s+mY/mGrAITJFOMBfA6Bm4dm2JHOzD9X3kYIW/0oP0eW7IDMSnBwHzf08AXzyAcgxnes2kIs6K1mvHPFrVkaZoNaQIomFv4B5p0KhP1wtpMyOhTjSJVu2atggiUHUJAsqIqidMzrb6xswSUIo0kFZivFbbWUttx0kV9TopdnW6FjRgqXgdKYGO5zUrfk4ZHeekSntYkQeuAVKp1FrnK4wYKhXKgFal60YWpcMWB6DxQGKzHhlO14hWvOS8Jkxv3p+I4CUfSOR1Q9qFK4E403lv01VLmh+u77yrHpp6xCGjK2yPhNVtkPpo7chlbpUG5uviJSxbhJK2XAJBwngAfhmChwQ2NyvP/S86nj76Xo7++q9hpyE3leWSzfVdO/NlrXLlCnbtcBYTLJcXPKJd7erRKVuv+a6feYlf/FPv5OzOHtde/cxFD2lXj1hdesjoXgFDaenMoewLAeOsNpJziivk/O/BmWib6q9xIDzMGDCW2KJQqiJWMB/RBBlxDy2p+5rBRbQAZ0unnGsvhdwOASMaAEqgJZIQyZlu6MlWS1TI/Jp4TYOVX3anKCqpsBI64kasisHZMXbtFrZ/g3p6G2ebidbZIuwJrdonpveelBIQIVshp+2IRPAFyUKbD6XE/AuOaJn3Q4Xq2dYOKKFEVCnTQZFstTYE61Z4t2TcrCl1g4pDEbTvwv8QB9cMo/GpRZ2iEZiiTfFWZ/UfcdyaarQprqyB0AnkScApQiU2Ad6EryVYDmatTV4nuOM0pWML+mmtpQ0KKu6KmTIOyrYYPU6pAwsd6UoEdgglUkuah2GuCVEygCi8Hlu/v7vNyhG36ReupuTTPBdirlOa2WRmCSTFWuDNvKLaMUE80rwllZ2uiGQ6uWcicfpEFu1YyQZ5Yh+/uQ+LjkrHdjSWXShOY0010Bf74dQJgjoRDtOChCLcKI5FlTzr8u9aaI8UzQAmg0R6koElFEWsi/P0nJo2VKiOWJ5NDYB66jpNYG8F45CekjGZIZJMoCikf6XP57klWNb00ySOWx0rVS3aizN9PASNPkPU9kDC2/za3PrOrGqe1LLYtD0pbVyhNlYErxX3MQ05A56yPoG+UMYtst3A/lVk2bO5f8Z+X+i6QpEAtTioJbj3Gudia2MnWr2tyZUlbubMNNeOhSK6XTMetba8y1yX4FBYF6Bx+ao/UvtT9y6xipH2fXtJSwIydqc++QU/iuWZmH2ZgLAX54s/LBy/47fz1r9xG/v1j6LLJVYu0U7u6rXVd+d+393Vrh5M2b/wO/jCD+/x7P/9N0Oxdolq/NRneOdPvRsZ7jFeMr/AXT34uvSQMSU00YLaZGaEEqqpF7Wp9cyyxTMjYD1UPqlZQ8XoeuPgRsFecegU7Q01R4pNN//U2T+upTSHmk4SWLTWzYatAu40NSP45HEHpDwxIaXPCqkADQk1E2w00a24TKxogkEJXYLJSSRj+4jVM+r9Y7hyHbcRW9+lMAKKSbY/a5cQNMdq55rO23aaes4DNpmAajd5HzpG8/4Lr0mdFXYa4LVaRWkdxgGnPFOsXYQ6CFV66mDYdsDLlgVO56SSK736sNg2oa5sPo+S7aSmcWxawI6XuBFtSdxOKFMt10dQ1UpLjoZsGm7qP5raLI5jKXNrdwuYiUAbyUOYEFDbmgw1bAWG0RlGi5ZTGxDfRuCG+uQZ6Ak9VRT35t3ZxtDWjuc6nxWutPU3taBPK2tSY4aycvrLUP9mII65QSm5PjNV+5zVQKhS889pOTC1HKsiGr6f/ck9OFxRD65hByvYhC9lg5NNfWr5IKBBbpVsuRdLBeI5KFh6InioJo8cI0g6qHFOXM350ziXFMRrprM7SA2fUJqqlATIsSZipmqqUffx0uH1FHxoRzE+v7V8nxu3ESnPMukkA+BL+r7Kso/9an6w0tbVuatZqoMjqdpSmZqvcUNFX/OG5v1Invf13Bq1TPVpIVLBV8fwgRzB/AyzATWhrJaILnEdcB0hvS7jgUMc7+k8tvTAdCLBWgpitWHbGYA29jOFc13SHspHrGbLjUe/6hI2V5XlXXs09klge+WiB7Grb6WGK05/X5BHWJg9HkBdXcIGLg2vxo//kWu8/W9+L2Vr1Evsf/q4l5+cItsRX8Strm+HCx7Rri5bdW97K8/8px/jf3/9I/ypF/8wN3/2Fy96SG941Y9/6qKHsKtHtC49ZBSaAssnQOQTigloEyCDDH+YtGkBKqwpDQMxrFbGrafg8IkV9WDB0s8QcYpBMy5srajthlsooTbygvmAaLZWZ54vqT6KtlpttoyhokqAJOmTOOGs9DoLNZqC6dRiGi3IM8owCx/Clg4tEpCqZJumekU296hFYf+QcWvU4ZjSjXOqNjZ50TnBZ8xCWdVawmmQVSOAo/kezrwkYGVMtTAGwZp9AVPVKK7TsZHmpYfgohiFajCMFtutHnA4pVMtCVgytMQnkNigmlJTQSgqWK2ZFlzm5OFUg0YjrSVIFMQ05yHCeMzTC1AsALL41MoePHZuk7amhLMR8ZZAPj9hFSegtRZsG4AtYJ5RfES8orUp1XL9esFNCLGkhnWkNjIWpd7Uq02dGccvmeoEGElvxzaYqd1V2nmkVKk5D/HDpsKcYjtS0TYF0iTgCrtDi1ZaU0S2dIMxfuklukVPXfX0onMSeLZox9y1dawzAPUax/1cK66qpj+lhxLWG/iyltsTjwo0vA9b2/gEjGVu+xXRCTo2hTC5omutkUqtBdcOfERtM7UFeyp4pSToTtI/qy5j268RKlkoKMWVjmiTrpmw3c4PmVLLbVYG5/9pBif5BIcDZmu7TjgJzZlh4PkHEM2ntZQIdynteulhGXB6TOlH+v1VJGqL4mMqGfP4BrwvU5J67GOe1wiTUpgE7XFKTTYOcc3YKQ529caWC9QV1FOhbB9+ymhFLnXoC3Bp/RhbeYkAmEc5eGg4vFwqxi8vWzif/dc6ylo4+iSPNBDe1VcvW6/RTz2H3roJ48j4yu2LHtKuLlmdfM8z/AtXf5mFVO783jU3f/aiR7SrXT08dcl/3Wt6oVD3mNVsW/ZU9khrCA2ok6CwZSs3QBbKqNA8dgj7Khz1cNg7BVDL0JLmfSiGaHtPBFK4C14sfeGEItEaGr5pCb1Epxv0ULYxj8MnxIJresqJTTQ0QEEGWbieS5yO8bxGRUiClASSYqBiyPo+rE/Royv46ojRluELiMzAz2u0oHvApNLHfjUfR5HSmiNpnpYx5hEp+YurZHs0TdXkTcyXSd7Nw2/+e8cQq+gy378ZQCwUVRJzpueUkWSrNDpmMrgEGSVaa6vPoFYmD7tQ/o1ew9OxyQYz/CX2S6b2eSmGydjkjHNr7qTclBkY49E2Knn8+g7XCNcptDHEOMcRRlNKV1BVCpm8jaWiM+YpZaMJwh1Rx6UG9GyN3lqn18SoJ2Y2K+Xa2rNc95JQbEooirE36NduPlL/OEFjVw8po0f4TvtssPDNRBCN9l2tA3p6n/qFF6FTaregQT/zek5OFcdIaS22El6iLkgXSlXM8xxtyeQg9AGLG+FO5TIiUATXSGOO60E7U2ON1Foj7TtPmTZzZnG+RVdvQRcrzD38WIFqwliFcYA6zuuhPcGI7unQ//oEwds50aY7PDzbedAuYq3FMAB6E/21ln4Lr1PxWdmZIT7uHms95yAgq6TgedZmat9NYxJ3pBrq4RapPtBvTzncd1ZX9rGE0e0e1CYQ7wnvw7+ytQuKGRQLhbI30Nq2Hf8Wnx/97GpXb2S5wOaGMO4//NTEFoSq/hLX5d67qPHg0YV0rlBXj8FRUqfuGZvrj+iB2tXrKluvGZ//POMLXwLb0eRdvbF18nTHUZrwludWFzyaXe3q4apLr2Q0b9CBCaQgTbWV8kUbwwMt1U0NDhntprxMfokgdBkE46KRYCwNYPlrXtcSU0XzcyZmcE7Bdu7/mwoPSADW4CKTChEcqQHAok01Wnib2q+xFBK+NCVRA18N8k3BC0UDOBgUG6h2HJ6FR1cYjgu+PcVtE6BLBWoCxi7VbqPQaWuPbm3nOR6vc7tmeltG+EWCjfy7SWXWvBNJgDZBoOaVaOGDuCh0W1Cp2QacYRLWoGDOaY33FQ2gasnHpCUT1zG6zHVW8alqQqzw4muA0gDPVG1JcBVwZFoxIDXec+4IO6FuE2LdVSK8IznoOcVjIJfqwlAd6RdIFy36k9pVXrtuujzultI0aa2vCaIDaqUK1+cRSbbBx7g8W2xtGrCk72ELImop1IHZzw3AQYuk6o8WZRTtr+Rh9wj2CU5r2dLevPxG+uNXqIuCL/exzQmiW7AM/2lYVDW9LdtckOrAGqDdQ0WpEm6LSIcVhTFAoXYBSos0sHguvb2pZvMcTt1nKvNiR9X0HAAElw4L2g3jlnEbKL50QrcU3GpauPYT0HUN/8EIx8njn4EwidlzHWU7fVP2QiaYp+ckTmltzqKYST4YSYiYr8M8bRokVNbN27TkpSGnstk2WG3uo+3zKipKcY2gGx9ge5/V1ZuY7WHjSTvrSAvN2J8E5ppt3S2NXqwpivOcsvYQoRHrKb1nV7t6w8sl/Bkh/PIe1qrLy+3HCDyy8O0bqbp8dANgxoNQ+j0WJbB+wuiP9dFMot/VrnZ1oXX3vfN/L+88Bl9uu9rVN1CXX8l4Tiknk/9Z8yFzYISOaDNtOi9vqkQFCjapjAAVRnGqKqbdrI6SaHMMMaRMLYGthRlp2rbAPJlwEa2GjYJ++U32OfFVqMOy9bD5yXkBOkxmRVC8LRWHDU5B+CUmgpoAypTQm9BFDbUBPTsGtnD9iLUest7ssa0LxqrUBD5ioSib/Cyz3dRspEoDg5JqyvMQLFq3G9xIRght7nLOiiqijkp4P2rCDPMRKcJyryfY0UijHFoEK5H+i2gQFe+ore9XiLlKZaJI+mUG9opQGUt/PDSVhgX3nnFYMFRNiJUg1GWaY5fmqRhKSMOn9ltPqGMWKjKThHIJgUP1aYiFbnUchNp1OT/pbzfpP2avQMv/ifrUJqy5TqQS7dVu4SbY1GNNOYtQVAKqCtHinBI5I5RopHKwUb0WXML5Nd2gukMLBApu2tR04auI0Jwuo0nfK8gIvoG7d5C+YGUR51wqSn3yfEz1LPE2TMKv01Id14J00qpApGQLbwNcccxrDl4TGE7nisR1QNRi7U6LMtZQVccyld3MsFEwXVGtUnyk74V+oRRxpCZY14Cq1YRxDC/RscYVQJtBpuSDilQttmijmNoZLsf6yOuH+xyk0tZBXJhQE2S0CSgqkuD+3PmWQTGxWpgArijUtB1wTdQvStU4ZoKjwwY2Z8hqhZW9sBS1+fyEgORWma4Jk0rzfO+9SwqLz3l/ptJzV7t6UNVA47j38C60cf+iR/CA6+Gd+je2BLZHPHr76zDuP7oqzG+mbOlsbl70KHa1q109cqUF3nUCwNYLh5+/hD62u9rVt1CXHjIGbGJqM+UcNAkWUNJLLtp8zX1KmVaZ4Vz8Id6qU+KzpAdbgr/0+VNNBV22RAfkC6/B8KlrbaA6QYQGAZCSLbxky2oAz9Z62f6B1mJ7zsssVXWOTymyJNCalI7n/9812lDxCMkQR9UovqHcv4cOA3p4yLC6yqlfZc0+VVaM1jEMBIzS2H/LNlUthZLjpyvZlurhG+mK1UxllkIDZg0/xQg9lZWefoKKaAEtiChFQxWo6qiOlBK+iFIyVduZWsENS5ViHJciSkGmuTYN1ViAqS5ha/NVrIgrlY7NsGA7lvC9bG3cHrA04Eh6ejaju9xPD1o6wS5yP8KLM1Rs89op8XpX6qiIFBZ9gKswWxRUCoLG/rYW4Aao23HPsTSFnFg7/jKpYV+7knzil40JqYRad4KTmgEflrYCTRA7Bd4wwVqQnBeJY5jKwNrUq61V10FdURd0OzBut/hyFfk67qFQzbXT1r95xaVG621EYgdaV6GqUOlwSrYS1zjmpccoOKGuK+28TuAVcDWBvTe1cSgWJY+zEGMOW9WC90u8KGIbJK0UOosW8cjV8Tj3i6NlQLsaILg04pZz46TXokx0XUUDBHoe09b2nOsr4Gn4byKgxSmdUFQTAp9v/U59ZIazWH7upGpOFW3TdJdOkS6vFekpGaC4AgPYBju+F5BwcQAUqsR5CtGuT7Nw8FSsEsSxKZqjS9vzqnXu2rXzZNzVm1AuMFx5OFunXYW6vOhRvAn18E39A6m6dOwR6xWyRfoxPma1vWb4I3asdrWrr1qS9xu7eqBVjg75ve/+OAC/fPwebvz95y94RLva1cNVlx4ytoCNUM0kmBECLNDhHkAnRGWSEFATYDQ1USZHNxUZjkqlaEVtDCCFJ2f0DL5IyOczsAhgFmnH7lDdQ3WXdnwBBmqCBU3wFS2cmv6KzVUx0oRtgoiB51rTJZjma8YaoTMCaPogWmr3pPlFtpmybGUE3W4o927Tdxv6myvsYI9TX3FmK9bjgsFKKAW1ZLuuJowJIKPIFAIRQw4gixSitdnJ7lnMUnE5zcEM8SZomv6O6oXiCsMW2Q4wjDDWVPflMVCJw9uORVMxRg9rHKtJcpf3PDYgDYhM4rzwsxttxHSkFKMUmZSLlsnXXitWhwnQwYhIS9P1AEk+Jy+rQGnwWUuMSQW0w6xDVCm9Bjwi2mwj8MRStXlubcKkcnNyLv2co6jmCjJmID6fHJO6dGo1p3kOTot2VqFNECzbyXX25hPANQNtGjzOltk4qhpt9HhA0vxpKEoNTs6wfomVJZOVos5t88FPdVrn0uYh144WjSAgiFRqD99PE0VKj2pJ6AZNChqMLSF+A93e4GXu2XkQ7QWxDutWgKM2ABURn9WpGEUdYUC9Bt70StH0zWwfrjpfj9xDEViZriMgmEUrejsUzTrAW2CSg1QLn6G8FqgqSMD/FvEsuV+iOoE9FZmVg57zSYBStfizuNFJqImRShEL0Hh2gi9WDGWFu1ClzY9T3JGQm+bnxXWvndPkug9YnetWmnLyMaEPu7rQshKKxrp8uNabdTxyUOobrodryh9sKQwHFz2Ib6zqCrx//CCj9c7m2uO0OHd1WUu6juf/L/8cH/9z38f4I9970cO51GXveRvfcxhg8cN33oa99PIFj2hXu3q46tJDRvEASsUkFYgZ/iEOaqnc8am9skE912iZbanIIJR8MjSrmwyt9Zw6LFRGnt6CUlJhlKArRIweYxBNJWPqiPImv22tbVM0Ay8a+PAGEaFKeLTNO9skej63bKfYLRkEKjoDl2xD9VSEiShSLZKMxVj4msX9V+hlzYjxpU8c89xzW14+XXI2LBlGmVqfm/elTxuMVmyqz9AqJjmBrU2BN07CRjy9HQNauOZxUGhNoiaKL7s4fnWAIhGmk/vs3lrho9U9Nxr7L9GQKkXjPZMSUeabn/Tq04RvWozFXmW5GAKynJtQiV7VULlZzEGNHtTYL7MArW19ZLtyzHlCVxdQTflYNp02hVqRaL+lQZ8AbbFGU3mnMzzShEdki3nzRKS1DTebgKa4lLbLMneAa8whmdodlEsaJkxFWhNPzmtPkFg3xLlU9Nx8trnKt1QLCCWMaFc43Rovfuour3zxlPXyCPM+PTMt2r5VsyVaIgXcOQeg2/YjvChNMhPqRsgSJeZf2sCbqlN0gqa4ZYhLtpQnQC0IJfc9Lh8LvOvCx7VBs5zLaX6NdjQgW+RlUj/nOa9x3DN/JeZGm/KwfR7Te+ZnI3GMpT0tmLBxKmNzSUGICgup4lVNxaFk0nyeGRap8SbRTh4+nTKt1yZmFJ9B4ckrJ2yrMy72MV3Q2uoNjcZq0YTdDQ5bwn5Sxdiut3nc3MHq+eW0q1090HKB7dWHS9FYV+0asKvLUuPBI6RmlMcAcn+1EthebU+cd7WrR7ek6zh9z5Z3fPuXePGPn/HiH/0hure99aKHdSlLn3uRj54+DcBnf/UZbP0ImvB+gyXLJd1bn6F7y9NI97h+YTw6pasVun9xPjyXfoWE7sfPBaIERGk+hvGigCAV0rMuEAGkx5tH62AktxI+Z6LRRVg9wIzBHGoioM0XMduoczRY88FrI6N1NQIN+KSaSloIzaxVFLFsu24+bgEop3brTOEQmBSSSkI/19xeKNxiqNkODglYsn2aARC0DvT3YG91HVv23PvCKesN+FMr2DpXykDXAKpmuIun96G2AegEmWSac0LBlkCuZkswMIdGGEiJuZ2SlKUESKsBecxmgMW0H+3oa4Zm5MG39EiE9LxLZR2hgvOETO5MqrgOi5blIhNUPH8b6C1p1wmg1EKBCI/IyYqulFAA1kx+LoAH8NFSJjhmkdZB1+nksRgbsEl1GHNhNFWo5JimwABR1FriL5MnpaRiDHdMmsKtwctzfoqS64l8X3sWMbXbMrcyw/T3DR5PRzlbaVsOD+LpSalx/AgPy1dvV1744kA5fhU5fILSH8JwjDKm6jfXZXLhyRvS0yfSHau53VSZNs1eC4cJRjbhs1g3aXfQWqTjvIl/13Pq5TbHIlCT/rmPhFJPcw3JnFfUQOG57eViwZBZAZrK2+bdGR6X81nSQGCbdIEIXpIS+yQ+PaRoPDk3g2QgjGAULUyZim0NWwDL+EyLNOuEtxh0fQEqZkSLOoJZx/pUuP3ihu70Fa49e4SwYFG3qPo0bm1J5XldEreE/qTtQ14jreleZwC5q129WdUUjS7Qn7aneBdXATx358BlKu9guALLOxc9kq9frrC9/vh6itU9Z3NdWd7enYO7enTL1mue/e+E9f+x4+bBKfzrp/zG73wL7/upLfWlly56eJeq6iu3+bs/9wP8ze//Tr79v3qFxyG7vNy6CcsFAHr9+m5NPcTVvfMdrN/9BK6w94mXGD/z3Js+hgeuZPyZn/kZRISf/MmfnH7m7vxH/9F/xDPPPMPe3h6/5/f8Hn7913/9Ne/bbDb88T/+x7l16xYHBwf8+I//OM8//437HQipfEKo2SIokn5wEwLwWZ0FAREk06UTGCKphkq1YIifnNEMH5vCrcnBWuBCbLOKRPIyoRB0CdA0bbq1YktLqpZZlEimxBYJ5WWOp6mZ2n25EoqgFsKgbqidU9vJ7NnnkoZucTAIuVKGbYgx+vm2YkeGMxbruzz9nqtce+shBwdKWQlydETVfSpdetY12MUEDEU0Pz7bJy0CTkTn9vOmsMQtVU02QSpJ2tLaWcMLr6I+UtQDbpScH1LBFvIoXGrMdYb6NMVeS3oWSZVl9RSFScJbpZpP7ayaoSrRPm2ZOp4ekJpKs1QhOrFvUrpUhzasmq3KquEviSNdQFRJGOMSASGgaBct+ZhR21nqBrlPkyDRDXOLffIGtHLNNEo1HRSdl6kklGwZQg1W5bpux05zvpyaOSihimwwr4HaCaeleq390xKOyfUpYsFHk2hWF4Z1RRYB3U5urxn29/BuD/eC14DjNhpea6zrVCG6aLTqTqSe1NPlPGjC3Gmtk0pX4piKoyUOoieMFtGA4iIJ4wyrNc5lU0w6wCheKe1D0fA7bLYEk1IxVbnZbq0IHQ424gm8hfBvJP0421WrgV9Jj824hiX8daPhz7ZrTdkZotV8yJFt55M/YhtPO7dTVdgU1oohms8pcl9cUt3oMLry6r3K6ca488opJ3fX2GIVAVgeB7XTOMZFhUaXXZTmLdmun9PDlHzUYsgjwxgv+jttV29cNY/GzVXFywXKmATG1cVt/s0qf+C/cT58Ne57ZPQ95GX9Y6xkBLxztkePyJfQA6jd99rlqeX/71c4+xtPZiggPPuOl/ncv/nenU/jG11WeeY//QWe/UMfof7mxy96NG9O6fwlLgkbd/Xwle7vs373E9hC8U7ZvOsW0r/5x+uB/sr3wQ9+kL/wF/4C3/M93/Oan/+pP/Wn+DN/5s/w5//8n+eDH/wgTz/9NP/Sv/QvcXx8PL3mJ3/yJ/mrf/Wv8nM/93P8g3/wD7h//z6///f/fmr9Rp8VOKVohLEASQs5r7rRhArNv29qfSaUWeG7l4otzQxYN0YDozCikSBbUwmXSp3Wstz6F5ulXvwhfq7Z9hps0miBHjINNf87lUGWny1uyW6EFlrj1MmTcAp11vnzMtAa9fBEjNv9bO/2lmzRxhdt08WdzkcWw32ulGPe8m0H3HjLAftdR79YwtE1XA8Y6RiHksmyDVLJrI7yCN9orbutZVUlg0Ia4MlqCeABkDRBXkEXHW4D+ABSKSnsC2BlVMLjr1BRae27MnMvb2qqDIyRBEpeiHCahFCakMcDgITnXioh0xtPtY0ruVJLay5lUgC6aHpJhvdnRhvHIRfoJL0w/VybaQfdstAXMi2aSRXWpJgClAmoCkHTIsDErYHwUH7GlFeEmuD2nEdkfpblHIg3r748FyT2XSiYNRCUKrk6f5a7TZ6P3lqCU+0r2XrtXpFsH9Y89p0bqz0oXlHfUuoZ6lAXC9A+gla8AjWBagUclQ6Xkkgx5la1izluO+epgg0En+djnEsB5XUC4wFOs325taKHzBBcqOLx2X0X/p0JzSG8C5US853g36wFA00d53E+aFNKnzdHaLhxvkYExJ6BXPDEGYI0S4a4QqRTq6VyECJ8SSO5GrdUPMdMlLa+W8iRND/ZDMZJQBlp3AWXvMYhnJxuGbRSK5zePcW0R7WH1H4jbU2CaEFdJ5gtxDnVgqKsjjgVnzD1w08gHo7vtAdb03XmMSkXGPdgfVMYDi5mx60TbPf7+qUs7x4Nb8a6gMmQ+jGtuu8PlYXCm1WPw/faY1VWeet//RFe/OWnGSx+r6o/eI9y49rFjuuy1qPyhPwNKN9u5z/szvGHtvTqEbb4snsqffO/2x7YXd39+/f5iZ/4Cf7iX/yLXL9+ffq5u/Nn/+yf5U/+yT/Jv/Fv/Bu8//3v5y//5b/M6ekp/81/898AcPfuXX72Z3+WP/2n/zQ/9mM/xu/4Hb+Dv/JX/gq/9mu/xt/+23/7GxqHTyEExB08RhWnJpmyxBQytSxKtAjOEru4QU44ox5BD9WF9SCcnArH94UNkknCJZRJTfXVJIk1gYPEZ0e2Q42b7ARJoX4k4WIo/tSZvPXC243JBzHUSBW3MdpwrQXTMAd/pFISnRpFCUijk/ByUme2VGfatiIFWXCKGcvtfa7YMUeHyt7RXqoMC3Wxx3q74PjEOT2DzejUSkBXLFSGJVtKPeHXGFBz8oezgG2WASWStEKbx6Io1QtmoWqrZhEkkscoBI8Jc5tS0yOcxqWFjSTI8GjnjMCZUG9FS2iGkOR8RwnqoAnQAsQImE0J1kxwmBwz0Urf4PWkIs0JT3WhW7Tc4qk+k4BLXSl0fYJBqaFIlaaaDBUeHtv2ts9Mw00G2AB3zZUwNapPCsQApwHQJqWrzgqzoGUBBVtrdIN04jFX7k619DOUDOtxn44ZnZJ5LblNRzQTk2tFbMv1m/DMezpuvQ1uHo506/tIVzCR3E7TiAatM4exBS61MBRxqjnU9AJtsF/OweXcZ0uwbdP/5vkJQFgnEB3t0JLhO114NSYwje2GbYFnmnUA6DmUJx5kBJQdGfE6RlBLrp7UGKa1Z7SSe1OX6twWXjKpWSyVyFLTbiAdDlWzpd8Qq5gP0zGMYxLmiu6W53z0dltTuTr5eUYpCc4RRknXV+kwc8ZNZbPe0smGg8VAR2XoltMTcifXB9GibhKhQ+I1zpYExeaGZTCUYyngfrh/UXtYvtPelHr87rOxEqrG7VE8DHgzqy7Z+TFe4hqOHnJvRgnF5eN43p8vWzibmzxW8/C4fK9J19G961nKe9+NHjwC1P9brHrvHu/8j38FfvYJPvvJJ+l+8Qi7e/z137irXX2Nsldu4/eO8ZNT6u07Fz2cXX21aoqXrMUX7uGbzZs+jAcGGf/YH/tj/L7f9/v4sR/7sdf8/NOf/jQvvPAC//K//C9PP1sul/zu3/27+YVf+AUAPvShDzEMw2te88wzz/D+979/es3rrmwjrHlDP3uCpSrQIjzFm7ou3nTu7Y5Wo3iGP1TDXTHpWZ9UXvjEGV/4xBm3bwtnG6E6jO5hGdhSHcQDQKrm/Xy03UYYhE+AylvLpBfcyhwMUjPIhWxRbcrIbG8smh6CWhIIzCEoE3WyhGt5yN0MseyfVE3FW/gSxphkahe2BEqdDyzHE5abeyxkoOwv8L0lXL/Ouhxy/6zneC1szpyh0gY2j4EcemkqrVA2UQOWiRVEYh+sVnQ0TByTmrAp9r2ut4zbyjg4WCgFRZwi0ItAbYo7BxuR6gmJWjb3LAkMsGRT6ygNALe14zYrHzM0aKJ9pNq1JTNne3dTxoWAqyn7mOBLCyByWnp4qEtFCmWhLK/27C0Kxa3F1tDagIU2J00lZxMUjO7obKVvmTeuU8v2bA2Q4DznQC1Ssk0tvTRTxYdOALqkd+YUDiMllI9SwCPMJdS1AalDGRvt8RFCEzBMEjJbkWhPdFip88Q1eMvNwpVFZTFskO1I7RY4HeaaAUDlXEjPDMviPCrxkEY8FamWqcikQjhXYTsWBEwMwhcgvUU5t5RzJ71FyXCdvouHDg3+t1U9/V+G7aRidPJnJaH/NPY4DC0QSaQxQqdk4FS79sR/jLiPAZXzuMn5z2nnUZ5fgQU1fSrz+pIPKVJyGz6dFsE3zUZBCQVkQPIAtQWhE6MAy965/kTHlSsjt97u3Dgc6YdTSr+iyjLG5YplsJVLrgMC1komn6tbpKs3i4Opwf3hhiwPzXfarh5YucBwIKxvCNa/eaShLh4PquGPx25+RXmB4YiHFl65hopvV7C9ZgxXHtID9QDqcfle029/N/d++9Mcv/8JNv/8+yhHRxc9pAdevtlw+P/5Zb7jj/8Kb/kzv4gP26//pl3t6muUjyP15VeoX3rxQqDVrl5fjS++zPL2hrKp7H3uHvbJz1zIOB7Is9Wf+7mf41d+5Vf44Ac/+BV/98ILLwDw1FNPvebnTz31FJ/97Gen1ywWi9c8VWuvae//8tpsNmzOLfh79+4BARXcNG5jU1jYcjOit8+mtl4g4IRqtpxGi6mJAYu46deS6qWOinJ/U1mfGGdyhnRLrl+FlQhSzgOibJ0t2absIWWbQjIS6kXrsk9wIaDhrIoyb+2oEagh0ck5Kfgm7OVgUkkp0Qw3QmYEokgGdzRfR0mfN2t9nEYq5SzTqAXXMHkrdYuc3QUfsXIABwcsnrnOyf2RYe0MS+gS5hYcswZSE5M1deXkoxegLva0S+5ZqDXbPNVRE2pRVAaKbyld7GfdDtFCXQIetYyJ8JiTCd6GLaWnii8TOiT3FT83e0xgZAZF8Tfe/PEgFZFgXicopOjUatzs03PKUEuo5aF+a+3gZtH+61onpeTqcEnRER2HWIMSx8YzrCaOZ8E8Qza8BdqkH6SXUMtp/tQ1AXp6NxJeg26OaAE8urhz/UdwTUIxg7AQsGxtJ1c003wULcEz0y8xxtjWbKW01mMEo+Y5GetbxTCEPoGmeGW7PcUGgdUetfSo1Sl4xSa/yDhsTqg/Q1Ea60pqTXhmiJSEaw6ltXeX1EU66YMQa91qrPUMm/EYZCoHO2rz2Wzeo5RYvWLTOWTS5jqPeyyjXCEV6JhCjDy2r+nnGa9IKGetGdomFalKSUBuoazN8zqOv7SLQQY95bHw5nuaD1EcbPRsD0+Amp/RbCBCLZsPHGTMcJ3KQpwnn4KbT63ocXo36rih9ku8P8TWA0WH9LMNBadrwNLRnZJ+qyIFk3yw4C0l/eGGjBfxnQZf/XttVw+2rIfhUChn0K0f/Lqsew98Ew9HPT7s5itqOHS6E6E8hPdldfF4+zGeLy/O2RNOf5+pCeqy1mPzvSbC5ukr0x+Hg0L3/nchv/hPX6P2uazl43jRQ9jVrnb1ZpZV/EO/QadCvcDz/w1XMn7uc5/j3/v3/j3+yl/5K6xWX93JvIGxVg20fa36Wq/5mZ/5Ga5evTr98/a3vz3ek+2qoi1vNv/dBGkoXgE0WnCZaAtxa17IuAZIRZbSAyXUiX3hbOvcewlefmFkMwaEbALGyaPNfQqx8MQ9TvMBLDGuVDS1sU2AUVrLcMCVMqkCM0DBE0qELC9gT1M7ilNrze/RTNyQuUG0pWw35icZOmNeY87OGQWLKFoUEUcZ6LanlHt3kDt3WBwKV7/tFourB5RFT9EIDVHPfZGAYE1cGeEoJeHqeW9MT2gnSEnwZLFfopFKURS0AymG9AUj2r9DKRr+h0gJ4NOSTShYQiEmuKvpOahM4SHZb64JnkMhqtEGTWv9bGvR6XLcASFjHQXGjPl1C+/CCNIIv0Zv6kcLpFQ1fO88vTKLJgyzBILzEcgVqUya2zTxa2BITSbAqi0gRNo7E7BarHvRBuLaCQbnaPS05tqxn30053OhvTGOoeT68xkAuuDV8XHEbGAK35EZpHcaijos3ltwZDxDzLCyByzm5HI8OswtPDpjUzFfnPvH2rz4SAvFiX8sIZjksdI4b8xBdUb68TwhwptdY+1Ij3tFqeFb2FSPnvuT++wTdG7q2Gwvn/6e3HqoMMMXMtqhpzbqqTU+BqEoUzelhyq0+blakEZaQ3z8Ta4SaUCxPdbw2V4g4WYDx47GOZH8XUUo3tZ2jG0lwoHAwslVONKtz5Cuoy720mi8TsfDAVTpUgGcV51zok6d5vu8J+vDVBf1nQZf/XvtQVddQl0FbHsUQiseRI0r2F4TtlfOKZAfQLkKdfngPn9XD0kJjAc8lKB1PAB27fpTjQfOcPgQHqg3sB6r7zV3FrfPXvOjzY0lenj4YLe7q13talcXVVYv/AHDG35X96EPfYgXX3yR7/3e76XrOrqu4+d//uf5c3/uz9F13fRU7Mufcr344ovT3z399NNst1vu3LnzVV/z5fVTP/VT3L17d/rnc5/7HBBKwsmLMGVoITZrkCagQAR5ZNAFEqm/CZq89fhkEITVSimw2heOrilH1zsOn+rYv1nQbiRoQ4Z45D9T262FYkrcQsUmhqiHd6TXc7BgBkQ0FWb77bQ9eUseahkoYTIDg9jPfJ1CZtOmgqriNm9Lsq1Tik7egFpI0JXbk/w8d0Ls5aht6cYz9OQu5eWX2e+3XHvnDZaH+5S+THgsAmdyLJ6QtIYqraa6sIHYBqlcYjtYBVFqtnl6HcLXzn0KWxERqIYM2ZbbWrQtfPrcQo2ppYPExmQYSQNqJukpKPHfo6XXXx6zlnjdfm+SBpCaklTaEZuhoBABJ4ikyGwG3KEwJNqNUQwNqz5IX724MIi1gA+Z/ofHzyMsR2alqXn4DWp0x1dIG4BE5EYGE7X9ztCgBoOEBKoQ6dwkFPbpM8DTPzBfTwC2KS1YU12X+9e0k817M9S6saYaGA8/x9bKDhSj6wfwLbpa4l03qZBJL1HJYxwQPPFpwnaa56Sn8tENxSbA6LQEds/xBogO0J8eizKdOLGmrLX+V2BIBWJ8dpyS5x5kJISeEqOo07kYae95iRDHNNZczZTw1uYdItS2RqM9PR6R5J/TM66h0oD1CVFzlJ5YcVq47Rj5uXlvZHSKSGr6a8fMZn/KfI05eSwKXiWDlzbo5gwWe6DLmLdcjzNtTkCtqaI+px6IVvrY3sNYF/WdBl/9e+1B1/qJytnbB06fHTl5duTk7cb6ljMegD9GiicXGA+EzdFss/CGb6M8JiA3HzA+zjUc+MMHlAXq3g4wvqbUOXvSH6Ch1MXX4/a9Jp/5PN3Z/DtGd1bx9UMoK97Vrna1q0tSb/hX6I/+6I/ya7/2a3z4wx+e/vm+7/s+fuInfoIPf/jDvPvd7+bpp5/mb/2tvzW9Z7vd8vM///P80A/9EADf+73fS9/3r3nNF7/4RT7ykY9Mr/nyWi6XHB0dveYfYLqJtgxraAqsCCFoyaZM7YLgqBjUmh5ocu5GvuCED52Ujr3OeOqZwns+sMd7P7DgmWcKhwuh75xSUq2XQReiyTPE0Yz11XJeKdaUZvnnCSgmwJi54hxEQwN44Z92PjehumM18dS5dmmvMkNTUpUXaSsJAlORlfknYqnmk4ASqqkIS/ioOL1U+mFNf+82SwYW12/i3QGMHSYd0NGSpOXch5uDdB2mAiWSmbUoXS/hDWdGUSH8C3tMClprQM5s6/Y6J/o2sCUNanQF7QpSFCcCe7S1lksASp2Uh4Fw4s/lHNKT6d5ISabUQLVHy2nLxoWp0zyhXdOtzZMdn84EigNgObUKwzahlxteh2gpbQApD5lNwLeNgZgfn9tnmbafANM8oHKCxQB0DUJbtoRnK3lCtwmIeq4v9wxLYQJGDVh6k+BSwgrAmdLLVcuUwq2UBKie51Oenw0gqiDaofShyvQBiuD9Hi4d1lR7rSPbPH3+Zu7uTvqWEm3Sk5PldDLRIpydhHjTU/dcG/lnMxiHSh1rvAVHhzECiyz8MKFmgE/MoWQqu/iEoWOduIClclDynM4W9FgTCRFdQo1qDfAmXk51NAn58ohN61Kzf1wnWB+vVc4loOdDhUhBDx3jpIC2SCsvhGdi/NzyvTN8VAmlbPOHJM9pr6eoVWpZIrLM6276zuZDklaRPZMy0RLWEVM20ENYF/WdBl/9e+2BloIv8iKnDp3je5Xxxsj6rQMn7xhZP/kQwpIHVC7RzvygQKP1YGUHeR6LUtg+ZN6MVqAuduvvy2s8ME6ferAq5ousx+17rb56l9X//Osc/eqXOPrVL7H4Rx/beRTuale72tUDrDdck3DlyhXe//73v+ZnBwcH3Lx5c/r5T/7kT/LTP/3TvPe97+W9730vP/3TP83+/j5/+A//YQCuXr3Kv/Pv/Dv8B//Bf8DNmze5ceMG/+F/+B/ygQ984CvMib9utXY/J/3iSK+16Ht1E8iUXBWdWhpFNZV+RFBChhZEQAbYUtFhy+FeUK0CFPcAKnBOzxYfKNJ8/sLvLVpsffrzJCWjpdxaetxli+45quQJGKW9zzIlVlOD5AkpVSb4NqGyWdhGCzSZf+ZTG6u5TK3bpNpQEo5MloUNfolRHLyO2N1X8Ks3kGs3sTs9bqd0tslhGkVDNSoKWsE2I1pSPVeB4hQRanWqK1JAqmCD4MtQp3XueK2ohBejwAT0RHyCqNO8JmBtvytWy3bkqYXTslUzlaap7Jx8Chu4Mybo7OahPmkAONuImyr2fNuuuGT7dToBtpZiDPcRLR3VssVeC6I6h4KU2Lfmn6eSQM0tQJTmn0VpaSQ+rXfAAwJFwnVOh4Wa1PPndm5+GhyM84Zp/sKvz2m0U1IpOGwHukVCwQbpcERD2WcWvoZIBJJodi5Ladq+oGbSoF+cIrgsopV4HNF+ga0LSs2xaIQFZXszCmp5M1B0SnIP7jYrmFMuG4C7KfZcgPBhbTadk72BEwpC7bCuj91ogNI91q0bXlMFPZ0YcW6E3WeCWyfmBFKFmqB3kovmcZOSasNUIKpEUDkByCNxW6e13GCj+Qy6z8NxrKWnT4gZrzaB6km5jaT3ZLNdYH4QkNepqfXeY6ymgpFAUgZ8OIP+IB5u+HZaD7FQHUQDzlandNGuL3l9srS1eBjroftOe8AllflL4reqzhmvjYxHgq6VxR2lO3nThnchNYFGlOU9e82zo2+1hv28AO/qsai6cupSKOuLHknUuA/e7dbfV5TA5paxuKd0J5dvfh637zUAOz3FPvWZix7Grna1q109FnUhjU9/4k/8Cc7Ozvijf/SPcufOHX7gB36Av/k3/yZXrszGvP/Zf/af0XUdf/AP/kHOzs740R/9Uf7SX/pLlPIN9hVli2Bh/kU+km8lIVOAsnpOtRWhzy38gVQkMkGiJIrIekQxOphaRoP25WclXDH3SHLGYpstGaGpCxMcKcSNfirTWnyEZBAFNOVhvlcSkmjCDpgUg4hE4AqEjqupp5oarUFXSeAhQmvXBg3AGHKx2KUcTYS4SG7SU6UVyEYNnAG59wq6fwO7eZXxXgdntwMgSI3k25bmXHQOnjGNxO0gQNGySnrPFUH6Bd5ppvTGHJvXgHDMAE8s21a1HW5J1ZpmYI9jWpERGA26aI8Ob7wWekEAwsZIch/nYBNAdW7NjVjjgDDUSTnXoE5Lf05ZWwKhcxDbiVCgkmA4qZBYOtbJdLgzNCfVuI00BRXAE6g3wOmZOtK8Etu5oGS7LE11ac2pNEac66v5dYaloUcasCewTNioCeO1JYTnZzcIrukHSBVKneF78+xpKkoj2v1LS7b2Eiq8ccAOltSuRzbbPEdCDUiqf72mUs7yk3JsGaGCmE3zPPkTeoP/0LwZQ5VqyUQF6FCPVmnplhH6hCQEDp9FENBC9QxIIUNTUqE6iUslrzve4GK7QM1w1aXMoJO47ng+wJDa3hBqSPX2ngz+kXzKgKMtaIl2jfB8Zy7Bxo/zM0s7RlKmBxSJwImG+qZGTBCZp2DRVGVKBDMxbqnSY/0S2SxQ2UYyvM0PEWwID1TLhO5aK30RuiKcy3V/5OpN/U57gCUO26NzT2S+Vqlj+5X1XqUcFxZ39KEMtXij6kGBxnH/jfmcXT0iJTBcIc6Vh4Bd1b3Xeb4/huXFOX3aOfo0lz4E5reqy/K9tqtd7WpXu3rzS3yOz71Ude/ePa5evcqd/9Of5mi5l7BvbtOd2jRRWoqspvqrEsnMkQUx3/wGDlGqrKhXr2PH9+nslM4HSt7MWwuPEbI9+1zibiqASvb6esrNPOlRS4uOlt8GCkKZ17zUmkpOslUxgmMyDZj0xkufwqmNM49wBI4wt1tn8InouaCaljib4CEAW81W3AhVCcAlVCeAXv6CqhaqK3PDpMO6Jb5/Da8Denwf9Q3UTSqiBNfUWJoiUhJ6CVIT6HVzi7otj7BSkeN7lLrOeY19QmQaX6CWCE2JoJlol8bSC1CIvtChxmtK+Po12EUC04nq0YJMon91MrP2mQZVCTCjQJFIOrYJmOaimtSLkq293iR7oMKWPU5PBVb7LK/voffvUjb3KRnWI2Kt3z4gUFCoHGf6EzalLjUt+GQCmaF6bcRrBqCDxP4WD6iER8uu69ziOrfRh4emFI1goVyParFuJEmfe8U0FIAl56vWOm3Y29yRHpb53+oJfKVkIrzg0jFevcG4HdHj23ReoUyUPSB7A/rAdCcgCWUtPEg1AWOcmu2EaGBumpD8mzw3pceqYlbg6tVo01/fRcb1tL7aFhWmNvS2fUsoPc2flgh/SngfyeES15qpPb09bWhPExqMTYFmBhBh4d3o6VEZoF/yAYVnkvq561w+tJh8WXGwySkyx664jwgeYUgN81uM2YtQvcZ1Y3RKUbwkfKwlIKJ2+PKQcbOl1OOwivD0Jq1OrfGQxvKBi5nRd4qqc7ze8vb/Irya3pS24Eew2vfat/2ff5qy/Opm/d90KZy9ZYRvRtlkErDx1YczQfeNKnEoZ7whoNF64fhZwR4HJZmALS56EA9JGey/IOhwscPwAvffYdjyMVh/32w57H2xsPfiNzdHdbvmn/4//uTue+1rVPte+z38ATrpL3o4u9rVrna1q69Sow/8Pf771/Wd9uhKR77BaqDIU30kiYW8JS7T4mGytQ+f4JmLRUozlYpQpWdsacUVMGkWhczBHpb8J4MqyJZXzYTXqV2zAUUSciSQaHKmc/DDE3dKU9pZDQCIzW3RGoDJrUZLb94HWYKTc+wClxEkQmCEhBvSQAQJxub5ajAz8m9zHJZALdt/AzNWCiP9cIwev0ARwVYHVFYgS8RCEUbuOyIZKJFtxZ0iGdISMHIJfR+udcXDv0oDsGTHcyRZk6qvBHpW01uzVkSbF2ZNqBwBMS6SirSKeihTHc9gmkrzldMEeq3tdJKnqaRSUIhgDMugmPyFNJN8gyumQnFS8xFhNoQyDrcIACFAYcDdlpLc4Fzz4ZNJ7Yk37WSGB9m59viEpEYLt4mW2wC6TufZesw0yHgd6cF4DoxagippATKWKeYN1oun4rNNVLSLm1l6jpZUw7XzL4OHpPHWfI1XRC0BVkU295FFB/0+1sXTcdc8Tj6CNOAX4E2SEnv77NbC3kSeZqFu9DzfFaSQ1wKPhG9SGimG9QXvCjaO1NEZx5HqRrUxAWbCRs+z3gUqU9p7K7Mxryk5FlF8SkGXGRi2sJcGYfOMazC4AWqfdqimN6VM161Yp0ZT/XoqDpt0OGBmnBcNIEMNlWjGwhea/yLU1oXuzVcyUuupEksu0CTiA2xP0eWCWvYwi/0aq0UIU4m26CJOEaNPdazbORC9qwuruvS2yL7xUqdeHTl7+8jZ03ZpPRsnReNVDfX5t1DjiscDMMJOLXe+FLZXufA5GffBdn6MX7sE1k/apU+b3tWudrWrXe3qjaxLDxkDjmTjpGr6CNoEi1R0+qclADslb5pTWZeJuJFWmzf/SijMFCxb/UQ1giokw1LyHxIIkX6NggVESmDUOIR7tvtOwJFUqwUcm3wHs526gcpoke4S0KQPnge8aqS0uEawQ4Y+RBJuCY7SZIzS9GFk+rJPvwO38Bg/p0RrysDOQRI6IWkN6COFSj9u4O6XgAHf26eWBdb1QI97j9NlaIgi1oEXCj3a9VD2Mfbx1QFSuujmHqN91ZEM0GBS6zWVnKjiXYG+w1sneIN/CS5RxYtGarQHnMRS4ZUQLlrem6pTXuvbJ7OqUJv4jAbzGgCc2E2oxsSw1n7exHyZNu4qqYgErRbHJQFl8MzWrm7zPJMpxKlwVDKYYzLwnN4dAR5SJkraoDHuFO1p3qXT+1JZqwmaQg2Zbe6eoSDZRu0iVPU4jtKDFKSlWlcSThdMhHCD1NbRnYwsthdt3Kk6zHZk8Ypst8E9ygK84CZo1VD6qiayHVMVqalqtHmBkm3b1bIVXKfU6/M+gV5DLigS6ce2HUOluNijSsfx7cqdF7es10atPvl4ksrRSPtuC6fNMbSGaKVk0EzN865QXLNVnfi5t/Ntvia04yXaoDG0xxHtetDMEmUC3nk+5CvbQ5U4ITRb8mM9TAFVeZ47sV6nfWhAPem6INP10j2S24s6UhJq2ogMG8reIdpfxWoJ+4keVEYk2/Mj9CbOuzrYY/Bt9PDXG9I6qU49qpy9fWRz0y9lGrULjHuwvi7fFGjsNs6Nf3Kb7iHx5Xszyr/VdXXJatx3hoMLHIDAcOUNON8fg/LOOX3L5byW7WpXu9rVZapy7Sov/OQP8cn/9Acp73vvRQ/nsa7H4Csz/OpEdIaNkm2JtITb9JUD/MskhBFMEHLAIl2ETSw6hErxbEttKqBzrZeeerQGANSFaqmYfI06cYYRk9qxtVeKYIyU9PBrYRVTe26m7bo4lEzANUXVwk9NZG5hhXP2gaHWcoEWU9NUZ/gMFAUN1tCGo+nJhkC11FplR7Cnkq2OCcGEkQQpNuKnr2KHt9C9Q/zkHiYV1Vh+lt6GLpqBOILTU1FssUD39oBowfUiyOixvYSK1ozvEnzgAVXdK6iEt6BIpKbamMnGOgeZSChSXCVUbiKpbJNI+cmDag3iQMxXApgu261dMsU6Za0O0aaaMOZcwyrZKQ8ZkBLQLlVtDmLZSltDx9baX/FZCdjapdvxcQwpBYUpcMYywXhqtZf00PSZ6FSiFTtCSFIVqKnetJTCep0CP6ZSA0rMN4UgZzmuONOmNmqV2A+XsAGo5ucQaLb2pjeo2xgt024BLHFYnyFdh489YiPVt7k5RVUmWN7ApYpiHisQ7eJ81PDLFOkmRGcZ5KLE57Q/O2CubLc9dtCxvT/ywuePoQ7ccEd0ZEVBS0JtWihPTftLmcJYYvml4jCvMZ6kuWbHezv/kfRKbarjVDjGuRwnseRTifkBRbxvtmqNZGwS5LVyZh/PWK+OUGKdiNPMOIvkQ5UaAFmRDIDJbUv6fZriEiAYUpWtQsGpPoAvqcsVth7QMiLjGKrhovMYHEZnunbt6gJLeGPbJtUZbo4MV4X+bmFxV8IL9xKV9QEal6+Cjq9v7srWuforX2L81Ge49eJN1v+772Bz/fKryeSNzct59EtgOHK60/RBfpPLOhj3dkfk9VbdN06eKRw+74+lP+OudrWrXT0K9cL/9rv4Q//236GI8V+/4wd51x85wE4ueTrhQ1qX/64uIYTTQlAC6M3qIeYQhgmmlVQGRvOsuVC9o46KjQLdAqmzV5lQiBjkZFlKtknr9Ev1mJAm5ZKh7HFJkNB89xpwSBVW+3dL6GAGTB40ApFsobYKYpiN4BZtwzQVVSrUpCGOHKelGlJ0Ukw1CZUTwEDSx25K/s0QHJn+F/8dfpShfisJQEPx5IgYHSPdyStQBNs/xL2jWsG7JayuwPIIFofUxRXGxT7jao969Sp64xqyv4f0HdLrNC7XaHc2VVw1J12waqFM87lVVMyhjpGqC5PST1N96oRXXK0CdBlu45nAPPs96pwm01YS6qEGDXBpKEoRoUgD1OnFqMyeihEZnSE2QvEYn5ZsddVUz2aiOe0wMYsOpiWbazbjREIVKalwxNNvMoNrsl1WkZCbagNNqUxMZaSmanOGVw4lAHvzeUzt6bTCrCHDbLVuCrocSCbMWJwzlkFBee6JQ3FBU8mr0agb++MGNiDbUzqLcVjpcQoi3QS+NNWDc3J3HGFoQUWpKk4gmMakKF2c6ypQmlqwgPdUFozdEpYHnNwbuHdvZD0I661Sx3Y0FHfNORRKu7K4pyKzSRMbvNdUlTa439LMA+5GsJMlAA+I6jUeCrTLgJyDh54gM5NbMhQn28ZTyijT383HpqlwyfZ5b7DdCZsBD/Vn0R6X0qJxAi5qHjtzqDUeKrhTVHNdOCoGwxmlU/zgkLFmmzuhHK3mkyJaRJCu43H4OnqYy5UHkzLbBWw8fXvFO9Dx3HfuJSjrYXNDGFdfXRImDmWAgxcGrv7dTzBmwml9+RVu/LMLNubb1YWV9TBekJpxOHxA5/tlLYHt9cr6psy/iO1qV7va1a4eqjp5O5R8cvfPv+PT6BM3L3hEj29deiXjlGDbYJkzwb/wGwvo4hBKt0lbGDCNKmwH2G5DsVf2F5F0bANFmyoJQgEUISlNhUS2KU5gzGWCKpLKOBoMQOIuL1WFDYAmJ5jgiZQMu5DoaIywh0iXLRPAdJyKSoAsy7TmIg16kcrBtv2AJbMgMD8HaEnQoDlfEdjQ2ipfEziTQLQJBCO/IpR0FUd9gx2/iHdHcHQV2Y4ByEpH7ZfUfgGLLuBq36F9D6XgFj6PssifbYZMgpZocS4tpMazxdhz+yU8EltIiVVKa69NVWLsc2IymVuDp0OSDfM0xViCQyllAnyTg57PKsXwPnSadMy9+WkqVQwpjpaSnCmO32KhjCpTirFZg2eSsdK5ns1blHe2RzPBa7K7Pvw5Y2229tbmSdrWT7Tg6pQe3RJvtCnt8nhPvbvnWnI9FY5tX3GokuEjzK+Pte9EmnvOhcSroj1d8rxrnCyDmKylRIfHqdiI1AEpPWPtEO1hHNFCeEomG2yt/7VaBP7kNmsLXAmNYh7jhKRmk+/qfJgLlBV65Qq6UPb2hOtv6ehly/4B9KuCapnIbwtkqp7rCs81mesyjwm576GkDWVjiQSjaKNXnxR+beyl5LkWh5CM0Z7Os3ZM27qODVjAzPkxQJ4XcwgVYgHrz12TSFUoTTlL+rU2r1Bp3LspN2MtqOZ61Uy71lQbj1u6vQPW2yuMY6XzIXxXJT5PjCll2s75V+7qzS9bxIOHB1XeGyfvMsq9jv6+0J1xaRRBVmB7TfD70J84OoBWpz+udKeV/sVj+OKL1ONj6pet8/3P3EV+8Oaunfgxre01p2wE3b4525PqHH1u5Es3+x0s+0ZL4PQtRtko/b3d99WudrWrXT1s9exfO+Vn3/PP8YPPfoZf/jvfzTuf+0cXPaTHti49ZAQmtVq7K5+0aE1k1eBc3vE0wGgIgwsnx3ByN9RF5QnhQDv6OuLaTeojsZHZA+78/ze/s5b8O6fpyiTnOOdRN1GiBkXqDA5bK2anqW70SRmFgXmDZTbBLSHaH4G51VFC/dcCX+Z7ngRVGUHdNJC0kSdsDJiRuqqEoMmKpoltykc82riLKiaC1A2+vo3Vq3TXrmNNrLnaQw/3IvRFPII+JDwu2YwwjIiG6qxScHPULZO6CZWiRLqzpCrTElpFi2mCpZIBLtY0nvO6aBBKs7W+iQgbGQ4Qna/KYz0F9TSR4zQf0Wo8MZ/26enJ54TqUoqGTV6dg2tUBboOzYTlmFudYLMmxMLngJBGxzxbz1tbd6x2pea4AqwmrWr7nGBsCkcRwpsQyZbr3LFcB7QOag9o1Va9yHnPSs0t26R+VTRUcgR0Tuod7bjTxKXvIzK3Aqddn9sW7RZ4t2Q4qXRjpVOheaO28KII6QnvR0sQG44IAX2bmu9clz3uuS7S3sBkQbWecrCHKlxZjey/peQxgS6hv0sAYUn18Lz2ZyAbS+zcQw6Ic78NwNqZyqRulanvmwkOtquI0yBx/jczMG/HZQa9edS8oUbygQdBRnA6jYcjQnjJRoBNi50Zp3Z8VGOubF4rkGFNErA51kTsr4pQx4GyMBbXr8N9YHMPbES1+dvGXGjRB8m3dvU6qq7ehAMgUK+O1COQrbL/fKFscyk+4uUCw6Gwum1c/Vv/DF9vsHWYLn7NSKNXXkW3tyJ0Z1ePXbnC9ghWrzB/zT6g6s6cJ37pFeo/+yS3lt/H53/vg93epSx1Tt5mHH5W6U525+yudrWrXT1MJb/4q7z7Hy945dYN3nX7V1KotKuLqEvfnza1JUKopKaf532zt7Zpe00qr0vzO4xk1bPROT6D+xthW53hzhl1a/joE4zyBAMyqZBSuZVAUBLKNLgQDbuROBzioRpBF56ptlLJuGSkSPgLUvE6Atl+bY5Vp4pQSRXSGCm/LXPXPdJpZ59Iwy396hrIOQdeYsw+qaVaUEnzQEQCeFr1hCepCHtNayjMMhVrqTkolbIc0c0duPMStgD6DrpC7TXCWpYLbNkxLpVxoVQJ30bvekbpsZrQtVfospVzgrIJO1Sp6R0nKlPQizXEpCT8bW3QMQ/NS09FES0J4pjajxv7aXy4vffcrk/qMHUSTto0x9KOpWh0w1cjh9dwYVwQhXOgMP/bZfJ4bP8wAR7m97QWZSf3o7245D/CFBpEhBh5U/R5tJpbqnwbmGzzAHkscoctKbEnWGr9tC5KrQGSGvD1TH529ynhvPk4Fo3wJZFoljZLZak2ywKn+oj7SFmtkOUhpnuMGQI0WQHkOYHF2hQ0/R9lOnatTRuLFn9L5aBZQejRsgeLA+TKFfRgBV7pMJbirIrStfbrUuIBhTlQJs9UlZIgOB8eSAm/SQqiJR56tOuEhX+iEUFG6oJawmBtay6Pvc0LTVDQkq3O+aBhngYa5Gzt+p7XBsljJmKoEsnYFupkPFv0dYbUSSPDO1R8UmYi4ULrWrDSTWre/DjEFbOKDxvq2RmlE3x1gJVVAuiCdH1eZwP07lQ1F1gC9mZAxnPb86Vx8s6RsyecuuRCfOne6HKB06cKcuXKBBi/XtWXXmH/iztY8TjXuOfUxYPdxvKu8cT/8Anqb3wMrNLf3914fbNliwiCufx3ULva1a4ei5LL9Qu4D1vGL76AbzYXPZTHui69krGpehKDxQ/Tq4+EZu3vpPkdeoUEgJ0Ye/uwvdHBaWF5/QAdK9QRky1IJUNfZ8WPAzKmmLCFWYA1r7zaIFEGsBDthk011+7rozt1ViW1llymzYTisP25QSVPldvUoqmh0pQmuJO2vwFHclLSD455ex5j9nyPnANtM5C18IxjBreNIE3gTUvMxRhjKDbG328L3L2PH11F64BtO0bzCHgZnbodGE+3qMNCnYV0yGJBt7fCB4sW2nagU6Fq2QJbJNtQs387QmwSQmYYUChEmVRZM4DRhFJN4dcwtTaCMq0fa23WOf+W/xYBKTlD1fPlqRy0CASqtU6w0gg/x0Gl5W/EWNL/rpqBRjCONcjouSYkPSkbLG6t9SKxr95WT9PCtcWVoS/5Z59A3TnVnebxrOnb6IZb+Pg1NjsFwqT/o7mxXQunx5XV1Y5lnz6gZqEQRScPy9YujQdYJFWOWmbw6ximHeqxP9L36OGCMVg7imdreWDRibG2lngLOOuttdzC0iA6jwWkg9JDWSJ9D12PlxV6sKIsF4zHx9TtiFrNc7W1PDuJyALMch46zwpDz9Z+a8o/QjHY1MreBizxEGG6b0l7hel8F9KvM8Guh9pw1hwHvA71Y+hPY56nEzuvEWm5QBxPwxApAbxLKIlrslN3Rd0pQUQZTXBvCd6AKDIlbacCUs+F3oghwxqGE1gdMpysMFtTpCLmkwo7TpQdaLmosp6L8WcrznBrZLgmrF7oWL6aY3mEf98dV3DyPW9h+fzn56dPX6uscuvD97j/7NEjvd+7+hZKYXPD2XtRkDeY/Yk51z+6Zfkrn6C+eveN/fDHuMYDY32zsHpp9721q13t6tEt/W3v46P/9lXe/reM1V/btRbv6o2rSw8Zmy+ZeSiWJoUOMyBobdL5jrwBJ5SE6uyvCt2icHBzBdcWFAbKotKL0UnIgzTVYe4RnykRvzop/yK9F0igGAAgIJ64oNZ8BTVHVGfKIwRpysTgQmvpDBVTNWGzDZXmshtRFdQMiBCQ8MPrkGk8lkEiJdOiSwyxtVGS90apXHJNqJSei5ERoUyyJQeLqGSs1mwbZYYoCZawTLAW6CiYbNHNMXbWw4Eip2v0YI/t8YZhC5s7a4azgdIX9o469KjQLfaop2eoFFxqpi9nu6wTc1Q0AJ+dDyQ51xqMpqGlJERtkNARKa8JAvJzN4mSJFKyHdmjHzsBS6hHk2JhXimi4QWYnnveIIpnCIeGmoxUuVUHtAQwLF14gGa7skhbAtlibdnKLhH2EtBaZ4CsreWWiUhq0vD23pZ4bd70uzR55LTPPiWih5+jpmpRNIJXou1ac/8Md2VbhdsvbTi5N3KldBwdOXvF8xikn2fC8+aR6bmmAlgqNqluC2IFLSWOs1UQp7t5nXHvgOHlO9T1fWS7pSuVomMkHktBmymiOtE+Hf6BohGmYg5Ih3c9LPfQ5RIrPVV7fG9BWS1wjf2rHjBPtAWt5DitBkhrlDobjUMZ2HwQZ/uAOSU8V1QDn5Dq0YB85obmWjRJf8l8+BAI0eKhBczHK9dlKApLgu2mtJbp0MbU5zVP83FFHVNV3ZSNeT7kn0Odm9dPJf0YQ3UbsDCvXw3ke0WkgDmqW2QYoRfs4CrDdkthg49jeOJ2zclzR1guqmzpD9SP8etW56zfNrC9Vjj4vNKfOOO+PLLQ7c57e97yO78L/9Cvv67X6yefpzv9boaDSw4s5memu/qyskW0TS/vvHGf2d93bn3wFepvfvwrvEB39S2WwPqW0R8rZb2b2119A6Up7ti1ce7qgqt721v51P+14/uf+RgfvPEs3/63V6+7C2NXu/p6dekhIwQoMrx1iAbkmbzrUgWUYp+m3IsW3FSiqbHSaPW1UmGzoWhl0SRr5iAjjdQEnwu1Wvx1gz2Wyrn0fMwe1wA3TfaXgRcVWitsk9JZgswqjphAgdGU+3eVl17Y4B3cfFq4su90XhGJ1ssmjfTWfokGBJXQVdk57zck5sFJLzvJlko/J8posKvREYEioeZK3sRkNEgNyCkWikrzhJmh3DNfIyevwmJJ2esYtwOihfHkjGEzUMfKsDFcnX5PkWVP7ZcUG+hLAIzojhWseqQ6S8Vr42s6BXsEiQzPOSktNKdSJp/JpmTNdmCvc3t7AlpJ5WcAw2xxbcrRVAa2tvjgy0FjQqUXIMXTj0/UM8VZUVcqHVWF3oVBO4oHGm7L1jIUpOS2pjbhBu3cJiYdStMM4UiwqDofr2ihTSik2bqbQNTz7xXwUlArjGVIYzHP5PRUFWoAJS+Zru7Ceg1371W25uzXMY6N1wlGzoBtFq+1c9DcMum4C7iVSlzGEUxwqei4hYOO5RNHbK4eMn7uRTg9RmSL6Ih6BRdGLISsFgBTRXDtGDMRulKoXQd7e+hyhS87ZNmBFmS5iKAjN6pKrpUEdmJze7i0fbFo96bNbTxcmOJuLFWlLTk8Zb/NizKCk0DFssU8AKmdTxrHM1Va8yzOY6wyHcsGFKv6lPJuqZY0HzPgXMB1UlhKe2jhliratAzQWKtNdRnHyXL9SK4nwaRS0hKidHk98VCXgoSP6mD4CIvVis3BVYazuyyKUDRUxWED8CYlH+zqK+pN8WN8HWWHleP3GssXCwfPB2i0R/C3FOvgxe8/4unnn6R+6cWv+/r66l2uPGfcft8lJnAPxxJ7qGvcd97yC1u2VztOnlK8fHPrYXnXOPrkGeUjn6IeH7/Bo9xVK1s699/uHH06bgG+vMRBf4uf7+rxLV2tkEV4I9hms2vn3NWF1su/9x38tmc+CsDVq6fIwT7sIOOu3qB6BH99/8YqFFMeQQNogj0FrRNkwywhZPxCNylqUr2nGRZROsHVkbpFPaHZ5KMIs6EhswJx+vFrQ1Sawi8I3XwTP7VBTsAgPsBTmdbe4+LUEU7OhC98ZsOd+yP9Uuj2hOUiAGFxEl56JOG2wUj4QIolREz451ITeOi51u9zGiPP/2vKrEaIRELRlTzOLRRV1gSiUsCjVfb8HEtq1qptGO+/iq6WlHGkqND3yiAwEIqz7XrL4D3Lrqc/PMSGDcIAXvCayioxjIrQxRyLTSnULam45pjdhJqqUsQCuqbKwrNFWlwzTZuplT4lbHEMEpS6e3oHxufE3ETASyhGZUqubgDbrIW5BPh0E6RfUKSEslX7UKrS4G/Tx2Xbs5MQcQ79wD24roa60tIKwDyVjTlmmXq6s9G2eYkioWIrJWCqEGsIp5MOk5HmzTjZC0zmkKHIdR+pg1AHp9tTlntCT6jdrITCThJUSQI6rzHGri/T9MZfxf5prklTsFqRWgOoHixYXD+ku3XI8JkXsbvHWF3DsKUOlbqtjOOAeaXrlFIKdAu8X2HLDt1boqs+fT0V7QUvgpQCKtShBsDTjhGhp+QDipqqwPSiRFC6PL55XnsLnElfRHWwWGOJ5vJ0b2uDVKtqvl7nxPRzkxJzpjT/goag43zL1ncVumxJb2hwuuZwDnxKoVKn9RnK3FxrU+BMmRSv4SHbPDjHXI0BCAG0lBlOe7t8aDTkDwN13FIWPeXoCqODbY8prMMv1R2X/re+iO/qwZaCLR4iQ0R1Nk+PbK8rVz5V6O+nqvER8z8bDoST738nq7/+yutSrFz/0Mvc+Y4n41qxq8ezFFafeYXuM5/j6Nm3cfK+J7n/1o7atwdaX73EnP4Ern78lPJPPoqt15clvP2hrrpvWFfoBv8K5fW1T66RX/nNixnYrh6+0jIBRgBZLHaQcVcXWvfeHRctc2H8ezepr3z0gke0q8tUlx4yAswtoO03gNlDLFRlmdnaGOGk7CNVf2miqH0AwZrtqZ7+fD513wasSSGiTmCwKR5bOy3nWhkD1mi2U4fw0KYb/AlUEqpIlGwbdVw6xmoc398yZJtk9Y7BKn0Ppb1HJP3pAmhN4TZE4EMLE3FvHo+Syr9oJBcPVV4ngonhphNoUo2fcS4oJkVfoRyrOc/ZFuzVM/0322ETkHTjgG3WlOU+3RAt36LgozFuIy27boy6B7q/wk4X2HaL122M15oqC8zG9KIrjX9FF6g4paQPYx4rn+DLmIEcikoqzCSd7tpaIQHKtKxkApDBX5sKMI+PO9JpNuWnOs2hNsWrRUu3UkEK0ut87IsHCBVDOg11HjluSxCW6jlLGi2pLFMpuBklj3szBsAqkp6SXvoJYkvCZE+lnFuFopNaE9UpgV2y3TwDt9ubEUp8tsL+vnLjrT2LVcfBnrGQOGfiNGqp3ZoSQ/AilDyGjmMKao5aA9pClRHRHjWo6wFv7eg45akD5Ojt1JfvU7/4KttX7+O9MeqWYbPFq7ERpfQLFlcPkaM96JXxbMNw75TFcomsFtjpiHWC6IZubwldQE/pC6VXGDvcxlQ/z23TqDK6hWpY83w+dy2ZfFFLXi5yv1M0OKll3cjzLu0UnICpEueOiMxt0lKQTGcmFYfRIh/XkdosCiSAc7URweO0Uw+oKBYKS081YVNEkoFYIhgjzT/A8yHHBCm9BrTX2IdYDh7ek6rgZW6N1wrjiI0VXfT0N65hJx315Bixge0Ad+6cvp6r+a7e4PLCQxlg4Evj3vuM/nbHlU85OsLm2qMFG1/9to63vudZ6sc/9XVfa596jr0Xn+T06TdhYBdU0r57d/Vblitsnr1B+dRnGD/9WZaf/iz716/Dkzc5fc91hgNlcxS+u2UD/amhg7P/hTPKF16hvvwKvtns4OKbXDd+c0PZVF597x5WoAxw9Ok15R/9BsP6/kUPb1cPSUkpr/2B7c7UXV1sPfs/HvPLb/k2rnys423/9W/y0Dbwa0FKwYddx9OjVJceMkaqayh/MvIiQU7eGGcAgjZQhEzgJBqUk/KjWL8IBaBHOmt+WogWW3vgJEJoUp4AU+YeKbAN8RlITZ2atDCPbGNMwBWKolDlpSAzwIERCkqg9JXFCk7vgy6Ffg/6TijiUztkk0E1tNjUnaGRqkzaQg+gE0q+0EGpCy00wlzzOzFasSV92kLp2UBaC3yxhF/piVnJ/Ym2zEllqdn86RU/W8PeASJQ+g7uSsxRrVQ02khF8a7gi3226zVqhc7GhH8OGunEoY4SbExfyTKPN6Yk5loSJDY1VeJhpglv60LyPjzVnH5euTpr7yg18VOvmSpuM+jM9nNJoCdN3ZgtwuZgImh16HpMFpgPCZOS3KZ60NwTSlvCxYJZHFHPvh1PpaR5ht948wpk8iYUc6gJv5UMIXHEsj1cFCx8Nq0pMoWEqDkxgXZTaecse+HWdUXV6OI0yvMwW8u1MAeVCNExW3GJhHQxCSyah6ElMTf4iTgyGPX4PjaOcHWf7sY19L1Ps3nyGnrnDN9s6IYt3Rit094Vur6j7PeYGfc+/Qov/9qLjHfOOHpmnyfecwuxip96qG7XI+VgD18tUO2Q0lO3ZwFCtXmwzoA5wmAipGa6jrSzQiSOTVtrTZk8rZ5ZuSSu+UAgj21eg1pCdKge2zs1W5z9NcpKUIrrNBIaPCbWYAiY54cuU+q8JPnMMc6eqmTrcz4gSMDaQl68UZ8WLiPNciBCjzTXmdcNrgu6riDLHl/cYLs8ZLx/xtnZGXfPdi0aF1F14Rfrx/h1argxcvuqsP9cx5XnjM1Vpe49GrDKCtz5vie5+tznv65ixYcth1+onD79CFHUb7Qe3mX20NSr37bk1i8sp/VS79yBO3dYfhSWIhzt78fvK9vt9BoHvtGu3LKp8YThETiPHvoSkF/4VZ74zFtgucBfvk09Pn6Np/euduW1zt7WAPWhRTq7ekzKP/hrfPs/jt/3H9bV2D39FPUtt6BTuhfuMH7u+Yse0q5eZ116yGgu5+6b0xvQmdpjJ9iIZ/tvU68FVkk9ECrK2C+xOmbSamtEbKqmOUAFmVWRxB8j4KLdFUncoLuUUBO24IwJbJEBME1hGa3XVjOYRR33UJP1vXHrbR28OHJwSzg6rCwKlMQAbfu4I6UlLbd2xtkXT9MnbkrYboN3n8ZtONUCamkLhaCp/uC8UnSag4RyLQW5+SCqCqN7AFsqbgO23SC10pWOfqn015Zs7qwpCf5srLhVXDrWVTl9cWDvinBloWgL3BGidblGM6eWpnKM+S1SAnppjFxyAqaU8ehDnjwOm4I0vBhrdqkqVhtQCWDXwBEl56/WVKJprqJpZvKXjJaCHPLUoQrbjeFLo2i0S7Pcw87WIBXpGiIPGCk1Q4M0fSI916zY1CaNtxgSprFaQi9xhzHm1TXBIQEPJ0Dc1l8bc54irnNyeFsjAFUTSlNZFMFqZGa3s0QkE749YHd8rqVPYYyvmGbbfgPrDbDl+Zxws56tGe4N+HqE6/dh0SP7C8rRAjlYoO6MkgEmFlDfhkgqP7t9yhc++jKvfv4UHwZqBwdPb9nrY8Eagm0qVTYs9lfQ96n8zLZ01QkMNvgbnpfMCkSZIWxrq2fywWwIcrZFmNwSMmypPZyY2o5zpbS0ahK4x7XLmF7lBFz3Bsjb8Z+a/anNtcBfq7B1sfBgpD3oaADR04ZAMFGgNhdThBJHN0Gnp+dp85stnSIKdRxx2+LDwHCi6DCgRwfoU1eRm9dg7wQfdk/1L6Lq/iNwI1yc03cNnL5DOPqocvh5Y31NGA8e/nCYk2eUqx94L/zjj3zd1175jVd4+bc9iZVH4Jjs6oHUvffAk8++jfqxT37lX7pjJydvyHYWn3kZGd+K97u19i2VgHXxe8D4+S9c9Gh29TCXVRgGWCzwWrHHpFV6+LHv5Uvfv+Sd/+/PM376sxc9nF19eT3ED0P04IDxHU/iJR++Pn0d+eIL+Lgzu30U6tJDRhGQkp6DCcy8gcC88Z4bd8k7+VTBOSk5VKyl0K7P8EQneLQEW96QT0ozmG7Sw2+vQaw5YMRTGifeFEXehHMxGiF99DTH3FKamyoNYGRZ4OYt58pRoeucZVF6UrFIS/FNkGrngCox9gAHksAxVXXIBAinXNz03lMpE8BC7NzFKf3+cm8sO8xpPxPPII9QRjoSnnUJ88DARnwYkNKhmy2HTxxgJ8b9z49UURgEGwTrna0W7t0tnN45xZ8Sjg6V3prCCrwoo4O6xfEn1GbmhqpGKzHnILHopA6dWq+lqUab6iye+kdQTqYkS7SeRnnTrs1KMfE5iZuYgxYGE6AzlLZVFmzOHPGRxZWCA2VvH1vfj3mtBlqobZGoUCRawyM5OlqNvaljSWCqDeolhoqDkaPzUAemotATCFuLEDdLyGeolty3tk5lbhNnhvPNGSDmMD0jkSm53KtPita2Xhqwj9DzGZJ6zlOnAa6qO27Kdm3ofaOuK/RKf2rY7RO6W0fhZyaZm6QJX02QsaK1w8yoRTnr9tiwoeuVcVkYRFh/8YRhM1K90C+VbrXlYLFgb7+nutCJoqULxWM7vVu7eGutn6Bew4Lz/8+qYtJTMwBfayk+/85maoAExGvKSa/nbB3C7TCOo7fPBDL0yVWn1Qg2pZtLe7iiAY9VNMFlDLZdklzSP9Tbg5c8wEj6j06fnN22heqh2vSidCpIrbgq2nf0ArV3qijDxulOB/TA6G8ccHBlydH+DjK+2eUFbPUIzXtx7n3XwPG7lWu/KRx9pnL/mUJdXvTAvnq5wJ3vusL1D3df9xdj/+zzLF95krMn36TBvdn18N7LPDTlCvXa/oPf0HZAR6HuIOOudvWmla3XsB0eq2TpT/8h5ad++P/LX/4Xf5Br/+5bGZ///EUPaVePeOlqhbznWYZb+8hgdK+cwBdfpN67d9FD29W5usR9OVGTWkwUlwKuoWZrgQQZBCMtgCOBW2tdlVJw7ajdHhSljGOo+NrNu5GpsgkTU7cWah6Pf2IkmHgo3TQAm6hEyypMTEJlVhWGpizwTQt1UM0WSolm2M6dPYGrC+NAYOEkHPJJMYU01VR4DroXWsJ022+yDTOCPNJHrinwJJV3hEorxVyTskpn6hGwJOEqlLm9svnOTeqthIsZ4iFudG7Ieo0Vp6jQuXH4rqscvvsm+zcWlKUyFsWk0B8d4Ff3ObUVd24766HDpGDiDEPFthWGilsA4cnzz1vitU+toU6o7lpraAAhBWlmZfPPyeRf1ZY6HZ9dGuTJ+ZcEgYYwjgIW812ItUcJuBjQLTalvaDm4c23Ncqqpyz6mM+W9CtQ3BFqAkvwatSaXo4qcXDSX8/dqR46NLx1RYYizQj/RbdQDoarQCQqR3hPif3U1Pc18C2xJiqeno82+YRqegLGIklf0ga7RCiSqN0rEww1pvRkw5Dqmf7e7knD+1N7gV7o9gouI0WNrs/PHz08H3UR55oTfp7rAd8OjKdnbO/d5fTl21A3vPV3Pc2N3/kEh++6xo133qDvlFdfXPPZT9zjC58+5sUvrXn11YF7p1uGTtHVAu06OtdQJTafTov1S55raqkKbMrE9uBBoXkb0hSO+TntWmPtwUfKLy1VlS1N2nNixfN6lorH9o+4ZJJ7bCQgrsWDgxqp5xk1xGQOkUBRta352bdVm2epTM8YgNb+384Jo8tjW+uIjeFZGQ3xFe1kiqARj/mREm2kVg3fDIg43dUVe89cfZ1X9V29UVX3p4vCI1W+Mu789pEvfb9y8CVj/0UP24WHtNbXhfLEra/7OluvufGbl1PdIib0xw+57PQhqfvvPHjg26gvvczizu54fKslVVi98MaoS3f1mNRjBBgB9j/VYy78m8/+Eh/9T56ke+c7LnpIu3rIqxwdUb77O5D9fbrPv0I5G5BqlBfu4OaM3/ed3HvfNc6eWHD6zIp7H7jJ+P53XfSwd/VldemVjJa31aHiilbfpuhBNfzuMgHDtf1shmdeK07Buz18O6JjDXFYa0f01lQ6t6hOGqbpP9oNvE1KJ0swkJgF8Dk0oanokgCY11BMJdxs6rREZ6kcazKmcz6MTa7U9Eb52VOrtNIGGC3D06w1wBPvix85Tp1Vewk4ZjXbrOYjvSA94QnZzhvmlBb+bjVea1S007jR9QE5O8WHA3SxopizKlDeeYP1C/fw6nQHC1SF5WLJwVNXuV+Ns3vO3TsDiyc6VmWky0wRqoEpI9HKSqZJe7VpntXSFzHBZ+m6aMc1ad3zEbpSSkq8POctj52HwtCtJT9ninbOiXsqAtPvuRpzUEseH1dFtGPVL6EpPKvDXke3WiLbDc6A1+aFqQme6zT/TZUY6j2fW3Alk8ZV8Jp+nO5I0UwrinWW7qJ0InRaEr47VgMQh5dm7nfCsuLNt1onqBpqzfkEsFS/tfRkU0vxqucpGLBfJTwEw8azeQH6pBSWlMYWHNtW9EComgE0WtC9HhkGfAylalcr9fQM3VbG9Zb7X7jLy594ic29Nd4VVk/u89Sz19j79gOW6wG7v6G7vsRe3gRbRjDtWd44pFxZoXeX6OkC3w40MWn7d/NPNE9lsCV8LrMi+JzZYZ6DGdZCKJxN8hSggXw950nZLiXZ/t/gH4DpBALj4/OztUHz1CJLqFwlIf8E0b09ypBZwdg8g0SnRPTmISQyq8I1YbPnwwktieRLu4TkgwrvYo1Zz1DzAYc6aIeNgm5GyoGwPNilS7+pJTA+yupRgfH6yBd+RDj4dMeNfzZy+kRhe/Xha6G2Hrbf/hb0iy983deuPvQpFr/zfWyvPHrw92vV8g689W++zGf+17cYLtm+vdFV34RLoY8j1z5pnD3z4Ld1mUsq6O3jXdjOrnb1VerZv/hR/p+/+wf4t5/9Bf7E7/wb/Cc/+eN827//+ccOtu7q9VV577v57P/maYYjR4cbLG8Lb/mHx+hHPsl4enpe9TCVVOg/98o37E28qwdblx8yNlVfkxZZxCkgkv56QmtllZQghTCpg9LUOj2slrA5o0gNMCXt5jyBlkv65nn42k1gwCcVGl4moBk357FdJcFRprxOoRgJ8pT2HnKc2f4rbdsZNtIgIpAULcbn4OnSiNSEmNki3VpTaZvMNlfyw32ameSl5/waWzusKJin/988joYZWpKzpL+emIWCr2jMcaoypRrOBjk9xRY9xUr4Bi4KdmMPuz+i/QJhpOuEg3dcZXvvjGGz5uRk4OAq6LLQCQgV6zK8xnLe3PHRocg0l6YTmw0k0gSOxfGxvUyCJXqo9sRbC2kCopq+ldKa7pVGbCRVoJEoHGBXXajuiBgihWEURgVZOdoXGAKUiQuyfwXfrhnrEGq+bF8mA420CNXqdADr1B4v567BEtkupWSrNqEkFPBSUJo3qGd2R4JOkfDpc02lXqjiJqVewiqXmLeplZq2oFI9C5gGsA7/SEO0TLCsaHoIIkCdFHIB2KLtVxy0wFidOlbQju5oESDsaIXv99h6QLdnsB2weyfYyRmcrtnePePlT97l5S+eMVqAzTufeIXDG6/w9Huuc/DEkqKFJ991hPc9917asri65Op7bnL4lquUIthqD/dQf2qGrTTKKJLq44SCQmsDJ8+/c/6MCZvD0zQ8P+PPzFQ4V5a3HuhYSeEooESAVKaQo4J683tsDweYPBsjLGbM7+Q8pyWONTXPZTWqCNAx913PINOncVkoRCX9J82xDCWaH7JEiriq0NLWBzO2256zKsgTCxaLPtrO9xaYdjCm72l/6b+OHqryDnxxCWCPOifvGTh7qvD0Lzn7L1XuvrPDHjJmfee9K2790vLrBsDUO3d45u8d89y/duVSeTNe/fRI/Y2P8a7TNZ//8bdx8rbLs29vdknXUd76FtbveZLXfNkAy8/ehuMT6kuvfN2b+Ku/+jIv/HO38G53LL7Z6u8p9tLLFz2MXe3qoa368iuc/I/fwfr/0LOSgX/ln/8wn3nPs9SPf+qih7arh7CO3/8E26vxnVQXcPq08+kfP+Tth99J+bu/Au70v/Yp6t572V4paHUOPnVv14b/RpUWdG+FnZ7ObXnfZF36u7pS4qY/OJNne7IG5HHBknvLuf/VpDNqgnmP7+1TFh3c38ZNNC2cIROZLQMTMmW5QcumQhKxlPfUbGE+py40x9URL0CZVXB5s98gl2TL6hyCEQow8mbfEwpNHZnFIZVLPoHBGQK5ORTHRNPir4ECSSVcKqGkAn2+NQEFMoE3kRLATWVO3CU9KNv+u1MzwRuIbfaOGZmcnGBPKiIjMpxBPcI7y1ZYo6yWUAUdR8pK8U44uLFi+9YjTs62+P3K6aaiVlktnWXX0fsY8FPSB7GBZc4DwShp6kfIxOeYLymagNbC748ZTge8DYnmBHiz7XqKMidEidpUfirUFtzhAeBUO6TvKHtLIjm6xvwPFfZXyPYQOTPcNlSrlFTaSdH0ayTHBOIWLerpAVol07Jp9yKpWfQAhGpAF8czLCJjvyogZpMKr9ZIBC9FqR43LlNCtQsqCb3IdTIh9jkQKfxFK80p0GdamYEx8ccqnAuv8Ty/FN86oj2jCWd3TyiHsLqyj6x6/P4Wf/WMbr3B799Hjk8pJydI3cJ25OaBUW9sufvKwOa+MA7Getiy3t9itqJbLjk8PGDxbQds3nMN2V+xfPIK/apHMNhfMZaCegeMAWmbdCFtEUKFGarRNPOMtS2kbQBTMvTEFElYaQ0oeuawhB8oJQ9ca7sGzMdUUGeQUhy+134XNHjuDhneAvXceauYDQkpJS0acvvM6sXwpdX2uCAe1uTP2riaOjJe26woNGD64Nw7dj7//Jradzz5RMfy2hF6uEQWPdopuuxguWAcd1qQN7Pqyh/JVumvVnZY+cKPCIef6nj6F884fnbJ6ZNKCz+/6BquCOXWzdcXDvGPf4Nr7/ld3P6uh0yS+U1W2QpX/vHzjMD4med46181Pv1vvWOnaPwqVZf5hX7uot48qF753hu8+u2wfWJE9r4SIvr2JoxPsP/ct3HtE8a1X3ye8fNf/C2Bo33mcyxffpL10ztF0Tdbq1d4bAI8drWrb7ae+Usf4T//4d/Dv/+Bv8NvP3yOD/3g7+DaDjJe/hKhfPt78GWHfOYLr8sz8crf+xjra9/J/WeFcZX3lT1sr3bs5Wvqq3dZ/N1/yurqFRhH6qt3H+BOPD6lV66gt27gfUd37z7jC1/6lj7v0kPGdjcvLTwlAZGkUk8lpUUNHOVNOyKYK5Ul4+oKNo6UukEw1C0hlTfLPtRlAoQN9AVsCKDZbuandFhINVoNa8JJ+piKrwYJRKb21NaW2MJiQsRYwztv2t305QPQCq5oUzHljjY1pZpmsm+DgwF+vMkPhTizc35g3nZ8vqfCLRWNkyry/I1DqtU8wj8Mg2IZ8pEwhjY+xWxEtwNWR6SU5HJKt1CkW2KnYwZOCKUTjt52FR2N+kphtdjQ2RphxGq0tUZPZ5eKy3OCUEs1YsLCqYXUEzgnJJ0UjxJt8w3GznsnqVyTUJdpQMea/pxaNMNyQplXR4sWYwtopH2P9gtK6bG+R2uligUYNqO4wMEV6uCwgaJDzldAvXGsFGnp0OkXmMcmVJcANcFjW6x5/Cznvla0K7FOWys3GWiUQL21L9cxJYVCqm8TP1lreQ6VpbYWadUZ2KpPkI1zIT0T+MZwqah1mDUVqFJzPZoJboXNVrhze40v4eCGc7Ts6fcVPT5FXn0VPTuhsy0MI2KVBUa3NA6eNs5uwtlaGKrS9XC0PGM5bOhqR93eZX91yHL/ChxAZytq7ZCuw/dX+N4KuXuaIlDJtu7GAoV2m2aeyeYaISglPSkn78QG/VN1KAl8W4hMCyBS0QxL8ghxEQc3esK/0ds1g4CbE7Odzrt2rYt1GsnnNkFJtOA+4tnLr6QAehJQZhs14YnpphMIbYFA7fGMWSozpe2PYVK4e6J87rmBe7ZCloXr0qFXlnRXr+DdeQWkR+rirt60qqtLCHjUuf9tA596esmTH4Qn/8maO+9dMly5+BZqV6hPXofXAxmtcu2v/TqnT36A9a1H/zgdPO+MX5x/WR0/9zzv/G+XfOYPPbUDjb9FnT4l3Ox6fNhSnniC09/1Tr7wL3QMTw5IH0Drqy1nWRgsjLPvHDn7TvjCjzzD4Sffwdv+xm3sIx9/DWz0zYblHVg//Sbs1GUsh6PPjt+y2mNXu7rsVe/d493/tzP+7H/8Ixystjz5D7+4a219DKrcusULv/cJrBf677vOE3/9k9Qvvfg131Pv3OHGX/olnnzmLZy+/xnWNzv2Xh5Z/oPfeI0thQ9b6suvPNgdeIxKDw6Qp25laCikoulbqssPGTMhWAWqtvCTdhOdiKC1DrcwhfyZ0VMX+9ie4vdO4sY/veomGCNCJMDGL27Ne05aokLek0v6pNHebjVbFdtrGi3Iz27SNJgVd87UnkqigZqSJfdzw3IBxrYpGlhU8Wh9jczYgGDSpFLgrVVVWs+lTjC1AY15WJYtok0ZNW2smVSGB2D+2TDcQvWGesJNaKnb0Z5ZERfG7YisB6zrw8TQDTGjiEAHtUbasXbCYq9w9R1XsCuC3r1Hd1bx7SYTvxWNiOlU3CUENYhk7ITOOidLNxhrZvG+PN6FUGxWq3P4ibfk6NYkrVQNiOgIWuJzvSlWE7ppibkoXkA6vFtQteDLjrOXNtx/4ZgqxtWnj5B+ZLG3B4cGDEhNMGMjLiPalQmSIkKlteGea2NNOB1qUQk14dTur1Q8AkuYgXOD1VJndWL4PVoKI5vPaSoNIYKNvJ0DOqlhZ9VitF+H7C625wLGGAo5EXwE1zHaxdFMLg6VXDWnmrCtHevTgb7fYzuMjGdnLDZn6Kt36E7X4CMFEqAZirEQpxfj8ECxfcBkUvMucLARGcDHU/z+HezVQ+qrt+CJa8jREUU6bP+QenKCyBYdjM6ZfBUtgVyA0YJbQlttazwW3vnTBFpbca5Fc0yNIjrB4XRimOAkPsUxJTAM0KtNYZhAt0q0Yrt5tr4Dma6Ogxq4VMQzWbpEyrxYtoKLh1+nx3XGLBWo0vwy2zFO5WO78OWlTAWqd9w+GTgenHKlo9tf0nUKZxtqJ3HNXG+RWqEodnb/Ky7fu3ow1Z3B+hK14n552WHlhd8De8+veOvfPWV7bcHdd3UXmkLtAvWgf91pe3Z8zFv/9it85n91k7p8dI9V2Qq3/smr2Jcp6erHPsk7/1v43B94ivUTj+7+PYgarjjjD7+f47cteen7DY4GkPpVweLXKtkfOfnAyD/7jkMOP/oDXwEbj54bufu+y6GYvYgqm/Dl3vnL7WpXX7vqRz/BO34iOrbG9fqih/OtV7aVAtjZencN+C3K3vk01sc9yHAg2NuegK8DGQFwZ/z8F1h8/gss2mc90JHuSq9fmwEjfF1rn9dTlx8ymgUIazfpZCsvTdWYkE2IFmpVxAuVDtclvlwGJtluKBI34A2gQPNsDFDW2oRdM4whwaGqxE17GgCGd1qZgEBAzfy3ayjqgAkOqoZSaEqUTa88nCmdJMM52ieKlGSlytYcoVBKhDWEb6JMbDO6ckKBNwVaTLkz6cfoTXwXfcWSgA5LZdaU1JwkMpOGTSxhU9uv3Mc8HtUIZZiDaIBGrVvq2Rq5chCgj4oPA9IVukWHbUa8eoCevlA6Ra/sZ0KvhabMNjFgNbS0pRCDNDcKihZNGOloAjRzP+dHmGEc1gAds0q0qT9rtqNr+izWVI+pRPJwTQ/MVGWGt6MkfFLoVli3pOoeZyO8+Jlj7j1/gogybk954j09ZVXp91eoXcXXp5iPqG8otQXNxAELVVjqDxvgawEuZtB1sebtnP9nvndSE9Y8nkGUJk/FAFagWlJZqxneE/6R07FPftgS1aWtpQbcW1JymeGb5lpyd7wkYDM/N8vEmut7hnHB6SCMHse9FKM7uYNuj+m3W4qPMe80z8xo3VapcX5W6KUgGknkLu089YCGOMqWurmDfekE7r4Ct55Erl1HVwuMJTpugHFKgyYVfFriPHNr15tcKU05nfs+qQ8THoa6NM5rbWkvOYfWPoN2js42BO24tRTzoOSOmaPt0q7noKZbHksSLMcxDZjuiGfKuOeazmtFzYR2q/Ya+UybXZHQz7rb9JDCpFBNcekZxkJXO248teJg6cj9Y7h7GzYbfL1pT0aQ7Sm7enNqcdfnxXVZS+Ds7QOf+MMLbv6Twlt+/jZ333eNk2cU+9Yf0H7jw3Gmhzavt+qvf5R3rL6bz/0rVxn3Hj0QV7bC2//WfezDv/Fb/n392Cd5+391m5d//Dt59X08NK3tF1nWO3Zr5FN/BOCNa8OVhXHygfUEG29+ZGDv53891+QlvxY8qCrw6T8Ii3/x+9Gh/U4IVz9pXHlujf7yR3YK/V3t6lz5ZsOj9032W5QWys0bSBe/TMhqSX3l9gUP6uEqWS659+6D6etledeRjz13OY7/JS8Z6xvSgn7pIWPed+MiWHrIRRtjeuq19OWpVTFgoEuHLZewWuDDFq0DUDOdGBxNVZih1vhhuhI6oWBLKNWUPxHmIAGELH0PpUEInwSETQUZILMFxJwLg/CaANAzHFhmeCES+4qjKNux5/79yugDeythbw+6pjRMyNHCXshtW1KMBqrMw09RE9Z6S6JuhFJjP4ONJJRMD0kSUpFqKwvjtgAoHp6Hc0JypH8XN2zY4rWivcZnSbRsmoJ0QWC8erTXqkKv6OEK9ysRKrNWqm0xHekyiCPAslNKJjCbp3edYDWeQGm2KccBSy/A863r+OSvh0xdz7TU5vDNFMQSVJaS3cmFuh2x6uhSgB7XFbLcwxY9daycnji3720ZiyJi3D/bcqVWlkOE38iiQ3Q/Wq6HnrGuQQztKio10qcJZaOWgLy1qQsndWqDwgEfw9cxTxa39KAkE3ACNqUTYCZMG+IFM8dVUEpb8KGaa76VPs/3DLmkbaYt1QSTMrfMmqe+Mo+DxrkWyr6CrQ5Z34n5OLiqXDka2Vsf048bxEYkVZrmAb2aG2FrGg5vTMm24PTQVDIqm3iIQLhwdrVitsG+eILfv4ccXEdWe2zO1vS2ZgHZ5hyqYFKtGq3ekr6JOe3Z3hzhSLntBLAB/icnxIB0hGJR2nma39LRrl9zf2TijA3JeoL0FGNPidGTP2p6sEoDgtrOvVAbF5FQMDZanLJEr/nv9kDEAdPURLfriMS52K4/xbhxc8UGYfnEVW48seBguA/3T/C6Rcamjowd2K53kPHNKHG49qkNt79n8fVffBmqc175XSP33nWdZ/7+yJVPn3D7uw/Z3HjzW6htWfhG+aZ/6Nd55yvv4Pl//W2sbz46v553Z8Lb/qf78Ev/9Gu+rt65w43/1wc5+uHv4bP/6rJZCD+W5erYYUW6B6fZaLDx5P1w5dt/O6s7+fRzxxm/sVKQoy39osKV7WtUNrc/AC9V5dr7fhc3/vvfhNch3NnVrhBBl0soBR/HN0RJtKsHU9J3E2CMP/dhrTTumsBb6TveyumT8YXe33du/U/PMR4fX/CodvXVyu68inY3oRp2+84bspYvPWR0KQnnADQhH5Maa4Yo829YXhbUsocfHsJqgaxHJBODq3smwWZ7pFso3hpdzI+J7aVasKZSrvRYrQEgmqcjds7fO1Odp5AUAVca8xK1yQcuacYEUZHYl+muyWHrwu0XB770woZydcmVA+O6Ovsr6D09EVWze3Xe/9YyHLsgmT4cP2l8rdn3BTqVWQmXARhpGDlBHRWf2z5JSOuW6k9DBVRKhO4UCeZj4d0o1ec5cseVVGsl7FDFiyCm6OE+rh315QU2HCNsEELh1nUyB+TUGGekNdcJBuM2BZqgXcxCSyi3bO8VR8zxangpuT85d82fUgWV/z97fx5tW5ad9YG/Odfe59xzu9dF86LJiOwiW6WUSaYaS7YQRhJlWwYbjGRARhjsEmaUhLA0AA08XAZ7JJaH6UaBVRbDLgpcLhtjhFwDsCywkVEDklJtNsq+iz5ef9+99zR7zVl/zLn2uS+yi8h88bo4U3oZ7917mrX3Xmufs3/7m9+nuCtmhpljpljXUUqHMUFnOzDbwk2p8zmyNUO6HkpFUbpJejV6WUO5LsblpYfJThK7FTYscZYBTmWIcBYxpMY+Fo9wElTTprILZZrHMfW2Hxty0jLOxlC2QcsbvsH3M58RT28gLBW7Cbw8oZS2WaTpferGGClidbT7awraAGgxhgFlKTNW/Yxua8H++Y4zp2B6dEhXV3SaUI4AlUocaxHB6pDzdJzh4wErqjh1XM8REu5oS4g2QRYVt4ot5uj2WfrdbXQ+QHXch3g9KYxtw2nL0G4QtKWlDdeKNzkhjR/Wlhadisf213bzQGR9PhG6ZJGe8HI8c+VNCx39RP3EWm2AEa/ZEt3GuP79mD/TfGDzHUNNbfmiiW2lbadA3lRxCB/RHOvOtPDa1+9CL5T5JXR1FC3ZCaHjFBTbq5tWk1e8ti46Zz6yZPqRZ4HHbvdwbmmt7hv49O8S9j+0xyP/4FmWj5zm4tu2GLZvzfu7gHyZ4UbDpz7Do//jkuf/lddx8FpuORx9OaWDcOrjxrl/9ImXbBruw0D3M7/Oo1vv4rn39Kz27x6YerPK1an7FZneovOgwMG75xw4+HGHrO7gSXWnVQLGbvKFj1UpxrVvPeTCux+BP3oLx7apu7Z0exuZxM0/mUziMmoDGu+acnv1fW590Upv/u6688BPfWaT/nyHlx0e3pRE6ZN1z0PGUQFIpPiOcGwMeWnqqrhYNg0vRtvawbZncaditWKoStGCM0QLrjXlmrdr9fZ2IzkQ8qQj4V0IFS1511g90F2OpwXOSI7pZEs3J9RzzeGOVEo1yNCUTE0O6SgrF65eW3H10JnNlDJR9OqKoor2eaHvTeXkUFKpl+9CyTbopuCD0WNw3dJ9QvGmJyKVi2BDhqcUGUNGVDPIxi01cM3jLXGGBQwb8j3UgVXAExuMajUUU6VgNWCslBI+Ah3gCtMeffAUzBS/foCb4r7EZQj/QI80cNESXnbKGiwiULoAVjUVWVoi0Zf0N0zPRdETxDUVpG7pZUjYc5iBlQLiaN/h2mO6RdnexqZTTIS6qtiy0u0qp87tcH010PfCzqkJk75Q+g58wFURq4hUXA3zGpajZminSL8T830YcHesrqIF1gZUQto2mFNKqG+LTqlU3IdU3qXXpEV4SRzznOOj6lSb++B4TGnHPtvorSZlPsHuY9ZmK3dCsPg7UJt3Y0sZ97X5hgiuSq0zrN9HusIDr50ys2O6+RGlLtFax85PG+0DYinUWseWfsnW6BHnNZVx2kS29RrgvgHYLjwsh0PsaIXUSjc7Db6NL1ZIzVYoS5Vf2htIgntP4i4umK09TjFLwJ/ekCfh6igiXOPrBv5cJNLK24kjH+Ea65MiSKZJN9Wktv3czhWWUDTVyo1IxlIPxbG7ZThPKBOrNXuIVELCqFJ1b2efOM9W81CC6xTpJnSyRI4P0dWCIjGyANElz8+p4txc476ipRXO/+/PUz/ycfy+c7d7OLen1Ln29hWHr3mA8z/vPPy/Ps3BVz/A1dd1t6WF+uXU8Myz3PffX6H8W+/kyptuf5DNi0sctp/JOfbRTzK8zJsGPgzMfukTPLR6LZ/8Nzp8alAFXaSv9lLwMt7/uLfKoJ6uyNZtuNEiILMBtw6pd9ikuhPrJQDGViLQb23apTf10kpeFLSgsy3qcnlTL/o3dXPKl0t8qKOa0ecbT8YXl336KR7633t4+jmGTfrz3VE3+Vxzz0PGUONlS66MksO4eEYD1pgjRUAnuPb4dBefTvHlKtVzFbpII+7MU6GVMGJsQz2RHN1Uffl7FcdtSGWV0vpFfTRX81QfJXazfIGUdoVHYCqO4q1y7KEqElkHSkQrOOCKUuhmHZ0P1BqJukdHK2a7St+Hn91aMRn7K1Jkk3hk6kS0C3uE1XhNtR4Z+BLQUNtrpNhJimQARkAe8xNQ0iXaanPb3dPXL9upDULxh2A1BVcJhW0xIMvKUBS6gswmCYg0lZRN7eXozgzrFDs8huUcbInIMvbrCIYds+AdDfY0imK572N39C0tg+Zd6bW1revaL7NPULoyKoKXLl68dGiZUGbRgl8rrBYDTHpWKhxfr0g3577X7rI1FbrinNrrKQ7qNeZgrdjxMSyPoQ4ZxhFeek2N69Ih3RS6njrpsclWDDshNgKDBYQs7lArwioVbHnMNdWKVMxrzpFQ1GopqURrqsQMEhrb4HPfjh6RkmngHuDSPRR4eexrgizJVnNE6EZgW3AXjBnDdB8rhe3Jkmk9QOfHaTfgeGeIG2phEmASAN885yUtoCSCT0Qs2+JDQYgY1BiHeviCWgK/SMlOxbCtGObXYl9un8VsD7Ul7gMuFard0C48JsHnTQST8VZAzulUIlqLxyHXQ4ZCUU7k7KzXKNRYl41MZjt/U/pCntPa+Sle9cSNiLS8lLgZIamKFCT8QmUNDEPN6alyBbGM8WlK8DzzqBRwwbSEDytbMJmhWmFxHWVJkXgtJzxlJelnO4dqg9abuuklBqc+YdSPfep2D+WOqLpfeerbhMtvepjHf+ICux+BC193jvm5Vw7eiUNZ1PUy/jLK5nPO/J1fZeeb3s6zXzdl2L79F59ahelFePAXr6O/+hHql2HoXx58gOe/4w1c/BeXbO/POdXFxZq5xB8T5scTRJ1h3oFDudxhW46s4sYfHuenu61cHdvx2wMYWwkwq3DUbdz1v1i9DMC4qU293BpDAluJotMpdi+EpNxr5Y5duYqe2oNq1Gub4MIXl6+W1A9+5HYPY1O3se55yBhX0Err8/WMQo5gA8Vc8iJ+Si1b1J1dbHsbX9UIJTCn04IXRYcWcBLwIBKlw3cwXqUBDVJJJdkiGgnJ6gXz9UXGqFa0LlRLkTKC+xCwD2hxzkJwBnMLKCHZjpup1iOwG0ItpgaFytmHCsuhcPXyIfOh0O93DAkCSyIYafBDhUj0DRWaSwlApGmil6EUbVwyqkE1oEGYASaYsICrRaNN19aJx/EhWkdIEgnXkQMc6iZFSwl1WCcBOKpT6wpfVjhaUQXK/nZ4xWXis4lg0x7tenS5xOqAdB1GRy1ThuUSXS2hrsIPrpAKv4ywkEpJNWeozyIM6OSxMAQfUkmWkrPMGaF0PV4zCVnBS49rh056mPRI31FVObg05/D5Q8SMybkZZW+HlSv10nX2Zx33PzZDl0PA2VqpS0dkQT0+hOUxWgeKVUQL7jVUjjXG66XgdYUvoRRBrQAFtKNqB30HXTmhbusxnwFddIXXTHpO2GSrJYJR/ARYV0JRmesr9thAM9dzN2yodH0X3ogWClBvYrVsDXbPdSSOSUzKWC/R/u4ow2rCUneh32KyDf3qKro6onMy8CbhcNPsOajG8dSEyCpOdVKFF3pJw06AaU68jmeqso1rSltrv1eKLPDFFazrsJ0zLG1FGa5QvCLqAYMTGUoD1u2mgzTRazaJpxLwpCdrqEB9lOuM+yf3PdXafZL0Q8xzUtvWPLtoGptZnmcaY4aTgJEmn4x7A9nqEZvdJKiax719+U1f2fydm8U6lXCyNFe8bEHZQXyBLA/ptAYEsNzGVClbHWI/qeaF7ebq9pUoMXjon83pfub9GYq0KQDUOXrdit/83rM8/H869/39j7F68yNc+Jpt6hY3HzY6lGcu8ZW63Nh8TveP38fj73+AZ//NN3D4yE0Z3cuu7lg48+HK/q+/gH3mKXyxeNkrWLqO4V/6aj7yXR37569w+kUyRZU4f6Mw2TuOH+7Ef+xcfpOw+M61WhVWxz1y1MWNz6UilbC8uP0s9vOWC9RdQ7Zvv4+XFMe3B+SwO3FDa1M31N5qAxg39YqVHR5RdnfgpKKx72EDGW9b6fZ2eC26Y4dHN6gVfbWkXrh4G0e3qU3d2XXPQ8bm/WWqoZSqAnTh1Sh9tE5KT+1n2Kl9uO8UFcOev5Kea6HK66SEikdLtNsmXIyQihZukhfiqUKM0AYLFCCt8TTgSJH84utKDSO4gH0KxYS1R1wSBA+lkKRSS7D06nbMm3wwtlmJwI++d/Z7o39dz959ymIBXREmxTN1mFGRhkrGSeQ4ONG+agk78BuUtAFHNFRNMnIpIAWBHq/RpZelNT9GSfYr0bYprhmcktuhJeCgVko/gVKwoeLLAauGr4yhVmQ6IBNNT8aCFEFdMSrSQfFUXG331OocH1bmx4r7BC0wnXT0Kmg1bBgosqKXirqjYoitwtvPPT3sUolaMmBDCOe+ooh0VCmggmiPlA6ZFOgbDAYrHYdXljz3yQOuX1phdcHeceX0Gyd0ux1Hzw8cPHdMd19Pv8U6lMVW+PwQH47QOkRiNSB1FcpVj9RncIoY5GVscLsBdw07gKrYMuYkWmgK35UVqvdIUbouAl68KzDZQstOtPpqwQfHVwu0DvgqU61dEF/Ss0RZjCndilJro4qh8JWm3s0JEiI7b7OFFrnS5nOtUxZlhzrbo98WunqVbnWcczFDXRLUK46XON4q68nYAHmbvyH+1fQBFVwCloa42EboFoLMnNAjFA2IZ7Wi80vU0sFsBzteofUA8QStZqkGlXEdZdpK/MvztZL0hpdjjKtSKZ7AT1obeDtXxK4LB4aEtjWPVyLTgJipFJRCQ8App8yl7vk6DWbmOCVuBrTxEFriVOnaCJRNPFr3QwfJ4I6WgjOBMkH6gtUjGI4pXiNlPscQ89njLkhOAiHOsb5RMt706g/hoZ89wH/p/WNb+6ZuLJ9Vnvp2uPymJ3j87z7PQ3/7Ka598+u59ni5qWnHOoDfxKTZ+tzznP8fVzz7XW+5ZaBRB6E/gHMfXLH9q59hePY5vlzkIl3HxX/nazn81w443b/80Kd2HtMSI5h0FWZLONvsPmC16qhVGOZ92ETM9Y6Bjy5Qd+odARhbSXF8qyLHd7h3wG0o36lMpnfOsdrUPVhWqdcP0dkW0sfluS+Xt3lQt7lEsG/6Ghb3Tdj9yBXk0lUA7NpB+Me9km/ddcj2LIVEUCY99dJl7sb29XLfOaTvcTPq8y/clduwqbuv7nnIGEqcE39UMCl4N6HKFtZv4XszeOA0dm4Puo75p59ndXVB38FEBC0TrJtgRweIrFNkQ02YxCy9yprn2tiamMofQUZfOE1xYwAGCdigAbaC6gVQjDbItaIqPNOaTxrgli29/frGcyqUSqrKBGG7d6ZnlSH98nqE0hKm8zFjaySe8K8B1DX7C4gaarEWBKKljSfhxPjY1nYdw1LR9HBzzAbUo8U4rAKboqvDTbCyhU6mqfwUvBdqFTwTwa1TSp8wy8OzDwPvgOUSWRosB5gv0GEJRfGJUCawvFaZz6PtuRw7ZdolPJyytbPF1kzimNdKWa3C788NtOY2ANLUY+EHKUWhL/H3bMkvpaTqdIDVKlVuheXxwPLQOT42lgP4pRWzwyXbezOmh5XqwmrpTLtQ5JUCOj9Cl8dgq1DoafpSaqF5+NlQ0zdRxpZ1d0O0pGrO6MRovsSegHCoFVsoq0GYbCk6zYs3k7iDOp3i/RZla5dhbwuZdywuXGexdA6uzBmWznSnZ3s6YUt7Oql0OiCSfwg/Uc/gkDI6cdaEdxCYXtqSQL1jMWyx1D1sa4syKxQ7oFtcoyTw9aZkTFBlCRBVffT3xJ2iilmNkB+R0WtRBdyGeN8MIGqp7uaefpupwB2BKOljWPHVIeXaC9j2WZhs44v0g/RKU3yqkQpbTyiaNyPaa504TVnzLJWCmyXgjHHFQvQMLQq9J2YRoBOHNmAzHmno7gglRZSpMB5VWankHlWlsgaN1nxeY9+5VEQa9CVbneP3rcU5vE07hqGjlgml69F6RGdLRCPx3FzHbm8hjlu7QSIiWIZZSbnZ0rFXb/VHcPZDK2a/+HHqxUu3ezh3fgkcvW7Fh/+D+3jon55j/6c+xN7rHuGF95xmefrmtFB3x45duvKVv9CJqpcvc/5/+jDP/L63cPDaPIcsXwTPvlJ2L1COhUd+5pjJpy5gL1zEjo6+IkWmdB0X/+DXcvwd15iWm68MawCyTBLqzuJC/WT79WLRMxz0lIMSXo+VvGl204fzOTUCxt07D1rJxOI76/xVHPP9ovKdymTnVQ57NnVryip2eHjyS+ftHc/tLBEu/uFv4D/+0/9v3tBf5DeXD3KlRlLbf//U19H/qT38fR945d6/FMa0U4hAzLusfV26jid/6Ov43n/n7/OayUWu1S3+4l/7Th78a/984yG5qVe87nnIWOmookAH0iXD66jTLez0aeyB07C/DbMJhmPVWa0GFqvCYEbZLYgKZbaFHRSKSyQZN087cYyClYCHMqqDgBG6tXbtUFWpZ8gMFZW42A6VSQbLtNZPfAzICHhJkwi2V0+Q4IRx4gnVUv4JlVe0WncGdKFPFE9FpjThpWA0dde63dO9rhVnAqY+vrwiqIFRE761lnGPv2sLdbH0p8vQC+0DkKZvnKaaKX7bwXSGawnFU4ZQ6LSjDo6unG7W4ap4l8cCoA74YoUer6irimrBV6FKFFswAco2lPt7rh8PLObO/HBgfn2FIXQTpc6d7uwW/c4EKx1GCaBn0a5qGfSjRdGidKWAGFq6uKgRiTZiM6iraONOxaPQITIw7Z3JntAvC7YKljcsjP6BwrnX7DGfO4WKdIZ0gi7nlGEOtoK6DP8mh2jLj3bTCCsRUKUSQUTRnhxpwZocKUN/qUPAWRWhFEG2nYmH0ra4URS8F9yOsaMlqgt8fozOthlqRx0GDq4e8cKzC5gWdkqPdDOYKZMCdZjT2YJO50iOEU/Q7D6CdCklYVb69HmFssVimLKa7uFbU1w6ih5Rjg9QSziVa2xMxc52Ykt1o3h4nFYJMwGRQktibmA9Jn6ogj0VLU3dW0QwP9ESoeC2DjkJ24IBrUeUuVJ37qNOtmFZ0XpIVyw8S5sfYtv3xDpwixbsUPZau4dAaaFInWZba6ivPW0MtAHlGlYEeAYradoVpEpSJZ+fIK/d8mhBMuprJWNLh/am+nUj9bmIKyY5ZvdUTBoqJQKNMKDDy4ToZe/x4Yhiq/DIBCp5US8RiOOW/rHm5GkwjpkIt+Tq/h6p2UVn+fCNPxOH7hDOfngNF7/gV0j3DLDa1Mmy7aZq/Coe/7vP88D//EGOvvFNXHljT518Za+9++SAr24yqNDCpf/Lm+DbLnF/ArWjxWQ8rmbK4ZUZ0t1IGv2oQxbxGf3i8i27IeF4srWiuiD/RBk+/dmbMuxrv+c9HH/HNfpXADB+sbqh/bqr2PYCfzD20zAoy3mPLwqsZFQ83swwFAlXFpZn7kzA2EomFa+ySZxmAxjvitJCuf/cvaXQule24yso3d3FftclHusu04vxtsmzlPye+O43fJrv+eY/wfn3vYIDqCGGOAkaZTK5q9rXy6MP8x//4f8P3zZ7ZvzZG/7EX+XP/vIfRn72V2/fwDb1qqh7HzKWXWrZhq7HuwlD38H+Nnb/Pty/R9neZnDH6kDpJng1dNozDM7hpQVVnP2yg2xtoaXHV6uEieHB1tKfi0naP8r6yu0G9VNCgFQNQngRipy42Hcf2zpHgOIlYaKsX9Oz7TBDVwIdRFtjg47NAy+e0tohU8GGhkoyvR2bnaR6eqxp000yqhRboIQiIzwJ7WJ6LaaiUzTfy9fbnTLFeD0hQz7isZbbEh6MHVWn6Gw7fu8Cy4FSFOtBd3usdqE+E/BJSahkMBi6qPh8GerG1hIKMDhWQxU2mzhb00I1ZbmqLFaVOsT+KlsdW9tK74vsSe2gEF6P1TPUI9SFSEWpFBdkGBJskUnUFu+XMFCbcsycrb2esw/vYj7n4MpxqBF1wrASJr5if79Du54eQxbH6OoYhgVSV7ELrYULJUjFkVICJEoAneoWbba21tNGa3FAzaaohZgDXc6j1k4sZngNqKQY+IqyMOpijpaOST/l9LmOflejVbwUtqbCZG9KPy1I3UKOB2xxjK/muC0RXY3hIlYt5kauGVKBJ2WLJTNWu6fw3S1Yrph0A/3RAf2qxrwn1XzZ3iwaczhUhJLsULMdOeCYhWwu4JrouMagccdUCSdIa2smWrxTfxcLNeFbSXi8hHoNPxR850GG7T3keqUyH78IRYuwRap3zh+FUHF6+MI2AOoZLOQCRXVsMcdDqRjg3ygJBW+QWFq8nubNDGn+mO0GgevI8MJzNea3s54nTRUaquwc01rymGpLCccJAaPDZQL9DkyUMhxTfJ5t17EeipYAFKnydgn1I9qgqaSyst1N2dRLqQd+/CMM73kLq/2Osohz7OwzB/Dpp7CDgy/ZwlovX2XrBeVoZ3Mn+3MqVY2/+cfu4+H/8xx7/+v72fngOS5948Mcntcvq4W6m8P2L33qy24t/rylhau/72tZ/duX2OnXwGpva3HDw05vH9+Ut/voHznLE/Y1lF//WChtvsyS93wVz//OBXu3GDB+vhoVj6XSl8psGqC2KR6HoTC/OIsbpnMNleiXES4jBjtPCud/5gqyHPjQ95++mZtx80tAtoeA0a9i0LgBjHdHPf2DX8+/9d3/hP/p//stPPKf/9ztHs6mblLZwQHnv+d5vv+bv49nv6Gw9YJw8PYlLJWzv1J4+L/71VfUyduHAT+eI9vb658tl2E3Nfq239lVn3yaH/kLv5+//d0f5fHt6Gr5u+97N2/7+Ge+Yn/oTW3qS9U9Dxn99Gl8dx+fTvDZBE7NsL0ZOp0gO1NqiQvcYSloiQve7tQUnxTqSrn63Jxuq6Pb77FuwjDMKT5EcEF6tK2RWwNwcYFfUrVkRAttgIz1BXWRUAaNrYwC3qBeCA5xzWCaVNGF4kiaxRviY0zyCCBbC3FYrDUFUkuhzvbtfJ0xAbjtLydCRDLsxLO928c27kz0TbVjwIYAl6NSUUA0AkcCJlWc8L4zM2wcv2Q3e0dFYz/NdpDJBDfDbBnpzEtDrMBkgqgiUlDRBGcW/nVHC2S1ojaZ5cpgiHbNCmCKzZeUaaErSqfO1gx8KphZwhRBhkWAJhVEKlYd74S+b+2zlSIBmam599JfM5K9K0hBS4loEV1DWl0NFIX9M1Cm22xd67ElrOYLLj/tTBk4ff+U3XMzSq2wXMJqidowppO3QBw8PBFVBaxm66ys0XAQz1Ht1uaZeLbKNxsBB7MB0UhW1qb0y21q7fo1E5l7jH4YmDnsTENV6RR0qdjFmCLS9+hkhm9PcCYMFWS1QpYrxBcgNVudNRW0YDKF6QzbOw2zCX79mNIp3eKAbhgimbiF0Xisi0j5DkVf6oTXCwlOePy1dGtDae0P4T06wq30tiwjTMvXicUX/9SmhLRxPYobOlylzntk5yzue9gSlCXmK/DwcS1pbdB5boACXkORKRrKvirjXDHWCtQQQXrc1Eg/gjj/NBVkLNBYG03LHIEsZqlEFE9FYjgpxhMrLbAqkWTeBZBUHIaqsd3QaBA7Wtt7nClMtpBOkMUhpWaCtMjYyq2que5rtmintULOzxhT85/dtOe91KqXr9D/o/fRn/jZy/qybZXJNXhlHY3u7vJZJFBvv+lreO1/9xlO/U+/zP6738Lz795lmL2815pcdeqFCzdtbLq3x1P//jvof9sFZv2tuVR45OFLXP4zHReeeys7H53w8E8for/0oZelziz7+3z4D+yyv3vllRvoTaimeOwmxtZDq9HjcbHoWR5Owttxrkg90V79ouvN7ljYegH2nhzY+fg1/MOfwFZLytvfDP3d4T8rswHn1QkaN4Dx7qnjB41/89Qv88Z/9zn+69/8Pcz+3i/c7iFt6iZVvXyZ2U/8Aq/7ifj3Q6GWAW5NVKAdHiLDgE6neK3wltdz/Pgu3WFl+vO/+RXdcLsV5cPAff/1z3P9v53wAY1vjG9a/hLDXQBIN3X31z0PGWVrAnszfNbD1gTdnmC1YsslNi3hAehCKX0oFIaBbmtCf2abg+cWrK5Xrj2/oOuVWT/FDsA76FRG0EgCPJAAfAkePa6yRyWfpirIgRsCF8bFnhf7pnnBHwClPSdUTakYbFigqYGcBF6a8NExvVEl5OkhKa13NhV249uvY2ozCMPDC84aNmX0hWuKTIm42DHNlmyv9gZAhAiMRsPTzULBKAkcWkpz9Q76GbK7QzWgGvgSrh6gQ4Wuw3dm6GyGbs3i9QbCb7FWdLHALdqjzSJNWHF8GCJUIo/HkAEWRSU6xTzhizD6XZp5opcaLawN22TwjqZHndFgUezDFnTi6rgWBnd60VCeUfHidIPBytndUko34eJnD1lcXOAzYzVVtkzZKT22mOOLBcUG3IeAavEmse+LMIZ6NOCt+V4ZNBPzLH0PfZxtAZDzMWpACUWkS6g2pbXhCtni6gHW0TwuMX2iKU9wH0KBRwBqloIfXkf7KUymWD+NxPbdDvFUwpkjNTOTux7dnsLeDLqCXJ+jOx3l4Dq6PEJtiA3QnPPuo3I0D2y8zjjPE6iaZHtzqodPtC/HLsm1qwmsxcdAnwCYJABLEOlEa7q0+STERHPK4hrWT/DpLvgudXUdxdJnNZKpVccjAFaz5TnWSSgxjW5SIjimbSftfBIqx6YatlxcTchItqLXE+EpwQvXNyGMHL9GO7h6zA8ZvR7bXG7nNInj6rneRVPpWbDaY1vbiBqyPER9SIVvC0XSYOFulNx5I5A/4TXUGGkMYfOl51bW3pMDV776do/iDq9MoP7wH3+U1/zUeSY/9Ss8/Kn7uPpNr+XgsYK9xHyM2YUB6XrKuTPU1zzA9ce3We4qZz54nfLp515Wm185c4ZPfv9bOfsvPDveHLxVNe0GHnnkEjwCF7+p4/qvvZs3/rVPMjzz7Et6/vO/9+3svO7KKzvIV6Ca4nF7a8n21jI6Bkw5Pppw+p9ssfNsZecTV+LzsdVzL1CvXQ+PtROvdfHdZ5H+RrXpHVuvRkWjCT6r9NsbwHi31Ot+YsHT/8Yp3jF9iv0f+Cz1H+9hBwe3e1ibeiXqNsAxXyyoyyV8/Tu4/OadcCc7U5g+8Tj86gdv+Xi+nLrpVi2b2tRLqHseMtajQ2pRbNHhvcL1Hi8dg4Nc3Yr26dmMsjvDl05BWbnQb/fQFRaDw9GK07PTVF+ik45SKoIlIMu7KR6BFqFACphQyYAYEkZki+NJcNVAQjPNi0AVGaFQ0wk2SCLZ+hgX/T6qF41QRCmarxFeEpLw0bPVeVR55QW96otBYwvUANHwjqN55rEOnQkwIKMizeIJBJwICIVLE9PhhHJTRAJkqcSXOUkvSenxnVP4pIP5HD88QOoR6jW24Rg4LrC1g58+g852MFGgwGqFrVaoZ/t2jXCQuhoQq/jKApB0cUWo5khp0Db3m+rYVtsCQFoieHA8iXZXd6gKlRvBkxGJ16lgzHxnZDC8C8yoncCwZJivqFrwVWF+bcX8cAnLgen5HXQK7gM2X8KwxIYFmmnmLVSmtQy7pzLNnVIC8jawi6w1tg3GNTBtZkhZH8P1b9r2sFbYtW0kgokcAl47oSD1lsIcvoDRnpwK3cGwYcHggky3kdkuPttDTu3BzgzpFel1DNSRrsPNKB34c1fR+SGlDoGv8/qmta2LNfAbCr4AWzExNcG9nQDwojFPPQGetOPT5l+tCffa+uXE+lCaurDtzJOwLKDngm5xOdSFZQuzrVT/rtBikfTdaHQC/3yHuAGRiuqmPBRryzHPFFJRCiKFUf08AkEiqObE8x054feabyURDCQOpaXCE4qd8MRsp6g8TyT98zy3xHpQkCk+2wnvz9UxJQN+2pmmtX2LFdyVKhZWAus7LPHayvgsacdiUy+rpOso5x+ESX/jL9yxZ57DlqsvaO69++HL8O1noNvA3S9Vdb/yqX9DOffY1/HA//gBdn78l9j7qid4/hvOsNr7EsEwAk/+yx32r72L3Uev8eDeFXblEirOygpPXb0ffuGNPPY/P0v96Ce+6DjKG1/Hh37ofh5+7a0HjC+uWT+w9Z7n+NCff4Q3/ZVz+K98cQP+8uY3cvlb5uzfA96rKo6WStmdc+lfEla/usXurx4yPPnUF31e9+gjXPzqu2/7X02KRp8Y/f4Cufc39Z6pyUef5jfmj/Lw7lV+6DU/yf/9W/49tv5/GzXjpl5a6dYWXu2Lgrhy331cfGLnBrsUObx7vBk3tanbUfc8ZJSrF+HoCC3hQ2hSqDLFdMLSjOPjgTrt2Xpwn9kDe5TTe3T9hH5/wt7DOxxfPuLowpyr15dMzu6hB1dhtWIMSBBFJQJAGkyrDfQkoLEWfFIYFV/SEldobZukH6IjWhIe1dHHLmoN+zBJZVcLb2lv2EIbPFoWlfSSa8AlYIucAJQjs/D0nMuWSXNHzKLlNwGBp29kAyyarZGwVn2NMCFfGxdU1iEVDYLgNdqSvcf7PWRnRh0WyNFVZHWE1kWAHxVUO6Qaw9E16lDx0wY7ewEJLdRosox2zQEYMhU4hVeAIF0X+1Obj2Xz+AtVn4jSWsAbRGqhF20PW+lxLVRWoVAD3AZsNWDaMyyc47lxVJdo75w+NWWqlq3xRBv7pAS8EhhWFm3VDtoVJtMJtlyhi2OEIbz9YnBtRkdrbYJPSUVt8+A7MenirxowcvT+jIOWiryTC0ViX3vstKY2VV8/zzMcRqTNSEkpZMIuj5ZqzALI2hCqUC3Y4ggZhghi2QlIpXszKF2o82rOhcUKWy6Qqwf0w4LiNUJNEhZqznVvCkEESeXp+kLfE3oyHiOs4fp8UO5XI1p5VQRKJnG3sJ6kc94CirIlPRCcjzC+7b5+WFD9KrUzrGxB2cXrgNtxeFJGak0qCm9Mfda2vnPfiqw3KADpeo35eN6J49HsGZuSOZdZ7KfxqZ7q4RMt4hI7oIbsd+1HqZrnqeY5I4ik/QEThjJjEEEWcybSQp1aW72PCkqTTO4WDauGEeon2M7d19SmnFBhbuqLl/QTjn7n1/Hkv1Z55JFL7E5uVEa5C5947i0M1ybsfrxjcs05+5tzyrUl8uFPYkdH2Mc+zdlfu49L76qfNwBkUy+q4lx8z8DhQ1/Fa//eJew3PsyDnz3N0Te+kctv7LH+8zxH4PoTK970hmco+rnzu9fKY2cuw++4zFPfcIr7/h/vpvvHn9/JvnvtY3zwT5/jkUdvXtv1V1oCPHz+Mh/7oVO88S++/QsnfWrh07/nAfb3762kcxXn1Okj+JYjnvTHOf9Xnv7CShstPPlvPY49cBdemDZF43FBlvfuzaBNi/SdW+X++7nwr74RgHO/chn7jQ+Pa2147nn+9l/4dt72Z57iNd0Vdv7DJ/F/slEzbuqLV3f+Qa6/53HmZwrdwjn1f3yc+sILn/exsjUNLVFWWTq8cPEWjXRTm7o7656HjL3N2aJQV3EBbwpiS1YL5fDSwNFSqb1w/NmrDK/ZZ+e+berpLSZ0nD3fIcMOz3xoweWPXWXv6x5gureNXT5CiHCN0jzNfN1yOH7FTOPEph0kYc8JBzSE9GNLj8TWEtmelw3FUSOYlAABEgBTE455KhpbC3K0R6fw0dYvoUTbsieIVE0ohUCR0atOMxDC02tRPF8s27VHgOGhtGrwwDLVdtTIpVrMtObjWwu54lIYZAc/tQ9DRS5eRPwYEQtlonjAyGosa7yWDmArhxXI9iz2ew3lnGP4ZIsynWDDUQCi0qEiqNUAvV2ETozMSeIxlvCuoTrRBIu5/e7KQGG1gjp01GCxoSJcCculc3RQOVo6y1qZbMFix+m68E60GunlpSt0nTBooZ9N6JYw2Vb2Tk/pO0VXc1gtEBvywDUIE68hWpASwFi8QV0PspRAVZva0LtQ11lCpyaRXUvlMg0m4XK7fsi2/0qC72x6DTUgI4RzH2NTEiITKskhAGCoAg3VSq1zypUrDChs99jeFNmeYObY0inV8eMlcumAslyAWE5bRVapzG1ehjCuN83ZFuFKAVhIsG/jsgkvQU+IajQyFxC0rUFxX8M7qeMqbQksLY3ZLNrWU9ibnewVbB7rpl8i/S7S71KHKXV1nc6XuK9y0KkWZOToSLNUyBsHjOeWyGl2CUUuHopCz2XbxttCpxprtZMS5ZSpNq457qcEm22dY3ljgrjJoBKmBtUKpjNq2eLwuHJ45SqzbaXfl/WCaecJmh0ClFICCKevgBSB2o5TA5KxzfqFLs439Tn1zB99F0/83k/xmu4LXxCfezy9gt4R/zGE66spv/nUm+k/OmPnSWd+diPXeVklMH90xYf//VM89g/fw/R//WWmf/8XeeSJ1/PCv/ggi7M3qhqPHrIvCBhfXI+cusqzf9zYn3wtWz/1K/iw9lrUvT0+9u89wiOPPvdKbNVXXOfPXeVjP7jPm/7TJ6gf+ujn/L688bUMX3OdrzCg+46u5TcdMPzGb/nCkPiRh7j2luG2K1C/kpJZja+A83sLNMpKsDMrJtur2z2UTX2+EuGFf/2NXH57fEe4/NYzvPYfvhP96V+J37tz7m/9It/3234/f++b/yv+9OP/gD/3nj9M+T9++TYOelN3VL0orKV76DwXvvV1rHbi16sdYe+xB+ALQEY/PkYqeBeXA3ufPKZeuXqrRr+pTb3s0u1tVl//FuZne3Y/fYj/8oe+YGfTK1X3PGRUq6EIa0EEgIjRF2W4vuL6oWOzCX60gsUx3bWO7VlHP51Stra4b6envHGPC88sWF1ZUbd34PA6LFZAKJtCIdku8H0MXwDG9sgiimm06vrYeukJQFr7ZYqXmjpPunXwa9PdOVAa9PIxuXiERJZNi14jVMNb0nSMQxJ8IowJzO1ifwRMmZ4Y6rEV7qFikpSEqfbxfE8PQzwCNRIKhgou/gSQSrVjYx6eqsfiwAzb2QdfwdWLlHoQ+6/m/kkfRMQpnQZkoyLLQxaXQe0sZTIJ2FIN98pQD6kmyGKATtAS/E2JIBbxBK+pbg2QSJOPBTvSMm6jhwwQA+aDcnhlwdULhyznlf70lNMP7tAhXL96xNHCWK3ABgM6hpZ0nX6Rbo6LoQJbU+XMa7bZ2uuYbBd2dzpUBmQYCK88w4ckeqk8czQToJ2KZ35LAOymXnSV9MkUTGwM+Wnt3+YWmSMScMtLA0Nr8BX/aa25FaMipgnT1wnX+c/gwCKoeajoNCCXp/elm1FUWNVj6qWLWK2UvsNKQSY9uGPLJXLtOly7iqzmqFuCs5qKzEaz2ihj7lsLO5FMkD4ha9SE+LFLbISGMfUDgHqCc5VMd044N0YiaUWkjK3I5o4UBbPYNg//U03QiS3QYUC94p0hkxne7TPMj8DmFF+FijH3dyyNmOuOUUQRi7b0WEOVMW7ewNVHZWrz3Bz9DFUxi3k8KpdH5aJkq3ueR4hW8/XZxbONvp10JOKadEr1KUM349r1gec+dZXBKrtbQtGO3V2hqCOuFDJ1vhm5WiRej4nWzYcx11nzrw116ks7p28KHv1XPs129/KUn4qz38/5utd+Cl4bP3vmaJ9Pffr+8Zy/qZdWPjU+/R3Cmce/jvN/52PUj36C+z77NMN73sKFr96mzmC1Bw+97fmXBBhbnd874OhPLHjhsa/lvh/7Z3ETZG+PT/2H7+C+97w038PbVefPXuPD//4DvPk/vUC9fPmG3z3/Wx9ga3rl9gzsFtXWZMUn/sCUt33ytQyf+NTn/P7Sv/Qocg/4/Mm0xve8Rbk1yQuvcHnv6Nklk+72p51v6vOXdD1HD64lHN47T3/jjNf8sym+CBW/DwNv/Y8v8L0/+gf45vMfZ/LsAZsjuilE0Ok0LgIBm8d8ufYNj4+AcXxoPXHt/qKqFy9x5v3XmJ/fZvbMIf7+j3zBx25qU3dC1Xc+wfPv2gKBg9fs88jRG6gf/MgtHcM9DxnNLMIokFHpU9zpO5hsDdRn5hxdO2Q6E/qtLWadMa0DejSH69dAO05v7bD12hneO06HzfYpywXiiwRI64RYSkkBo49QRMZk6QQcWAK7hAAlVEnR9impomoX3bKWRkqAAKXg1FRylRsCMNpdGhHFV5G1nIKoKB9jaVCThlwSUganivAYSxVhl88JCIIUqqfPm2imEqfPoYSSskhJ8OJEsEUANyfaq4M7dph0+HQXmSpcuYAuDxP0ZGurFOoyAJOW8NnrO2GwAaPA0TG1HMK0olvTAIK2QgxUO8pWj5hRGjAsMiYyB1Cz9B2MhGrLY9GkXurhARit5eEbuZwbl5454uILx1gRpotKmXTMZj1LF6xmwngP/U5HvyUoQ7hWlkiHlkFQKr0tObsvLLYmDAtDVwOyUmxpsHSKJERsCsPaWk1D2VYaBPMYe7TQwjqYZO3J2cI9SEWfpcoVQM1wzW13z1be5g9KJgqHHFbUxumoesIfUAW1pvhLFWgqA6Up5kzoAGfB8tpV7BNPU6YdnD0TUHYxwPUDusUxBUuFno3jhGjzHlWba83pqNL0ts4yAKmFFxkaMLGR7raWXHPeJ+qSAHrmsac8lcKxpjJIpq2ZHINbADZp245nyvgcrTAcGcPWPmyfxRZzyvKY4nOkLlExSkklZmLNUW4YdwVGqCvpZRot3bGtOgbU5JnFLKWkzfe1nT/yy5MQim4zRLpcMwNIKDhju1uCdaFaoZYdpO+x1Yrh6BjMKZMJK6scXFsx2+7ptCmkISKm84jldsho+Jg/9AygOnHsNvXSa7+fw03QhT20fY3r56dceOYUm0PwMqtzLr9r4PDhN/L6v3Mf9uu/if7Mr/LQB84w/9o38Nz3HrM/ffmtsdv9kvnveoHlp9/D9MIxH//de9z/W26/B+NLqfNve55nft9beeBHf36tGDn/IBffM3D6HvBi/FK1f/aQJ3/nw5z/K59eq8iB8uADvPDu2ziwm1wysbg5edSFP/XdMDk/T/nE6PbCN3lTd255rUyuwuK+9c+2n3V8eSO0Hz71GU5/5x7v33mQ+tzHbvEoN3XH1YsAI4CUgu7vcnz2RjV2Nwf59DNf+LXc8V/5AFPuiXsrm/pidSKc8m6uOuvGz2YvcPE95zjzke6GLplXuu55yCgOdVkj8EACUtiqUhTOnHWW5szN2T1TOL1X2etKavOgK0p1Rw4vUSY72NSRfh/b3qEujpH5CpEh1FI3eFo1XVFeOmdAhzGMoTDudqJlMsFBQrC1uqcpfhKIJTG0kCuGirBoQo669ngbE2VlTAlundZmrUVRTrxPhJ40CCEZ4OFN3XLSg6+wBpXiRKp2+j+KgCs1MUWIpzTCX8QoXjCPveva4d0+bO9g1y6ii+NUbAmVgFyVAekFq6Si0QFDpcPd6MrAfH7E8bFRZhbtm6uCa6UrgpRshdW2L1I95ooVUNeQz1uo3EiVoxAJuUOCOMk+YFNhtaocL1dYF5OrQI57gmmhqmMG0y1l/1xhKgNac0HX4D+Uilqo+ybaY6oslpV6uERmhYk7NgyslgNl4qjW9Jdcey96o8J5/JrPZDQpZ7lky6+OQMosWnFVxhgYqhtSdS0zbWFDHsepo8espttnzO4Q/SWuttinlBr7ztfzHzIN2Wq2L1c6OtwGVpeuYFfOUM6cipbvo2Pk4Drii0gqRpBKKDbFbjjhm4cyUhKimUUrdahSPdWbkDHpuCY0l0iAD51n0vmxHV4S0Bkjk3VJJWT+oHmLjko8wVXHfUaurULBq2J14Hix5IXPXqCc22H/7D6zfgtbXMfmV+nI1GlVanWGaoit0EJsX65rkRNAWUDc1p3tOReCv+b8UAOLNaMCpppt/5ZnG4m09OaDKBptcCk4NIg1urWHdFNkOGbCkv29aL0/uLhiZUOsC8mZIYLp2td1fe+j4V9GVaVoa6xOgJuq2E3d+nrizAscLXqOLm7f7qHclbV8cODDf/gUD/3M17H/D96PDwOf+tcLbzl7+Us/+QvU2dkR137AmLvwgBzdxNG+siXA8G1XWH783Ux+8pfQrS0+9n2vZ//BK7d7aLekVJzDb7iO/OxX4b/4G+PPL377G7D775JE6ZdYUhyfVeSo3HU3KGQQbH+g31ne6E29qTuzrPLwP3yKZ7/tYeqWsPt0Zf8ff5j6eSCAHRxsvBg3BQRQPAkYgfB+39/DX0Q/Tn3s+HMU+Jt69VU5c4bV2x8HESafeI7hqac/7+Okn1AeuA/6juHTn70jgeT01z/D5PVvZLmf1+711o/xnoeMiFMmoZoxHxAXuh6QSumEyaMTqnioG3FKHWi0QAoUKqVAHQ7wy3OG5QI5dx+cOkOtc7rhIFqaKNl+vHYjTAKTL+fpfRdwrmT4yYhuRLKZ00cwmPiAsUVASmQj1AiqCGhqo0ItruijvdV97duIZjCGEwEp5PaNCskGXVgDFI+AlIAutAHFewrj+NNCDulCUenW2mrjdcNLMIM1tOCVUCHqKdg/xXB4GZ1fQ6RSNFRhETRTI1VZFO3K2H5pnn6DOCIVHQbmx0vs0Oge3WaiUOhGdae23XciwKURpKY7CxXeGtm4ZY52UzRqABk3qAMMJpgrxQXtJvRbPdoLtRrVFGRg58yU3YkxtQFxp+Z2iTjFlWpGJJRXikxYXK8s3SiD0dUlqqEu9FQJBpApubMDeGK6nmdmawWsh3pWFbxq/L2UOFbqqFu26Nqolh1PPanwK7aGuuB0Hu3QybAC6DUqJAB1DAyJalAw0rZdia7nVJVO1KAuqc9cQHZ2sa5Qrl+nLI4oMckjHTpbplubNtnOq00p3IwJ84C6+KjalISGLkSAUVJIk9yAnO9+Yt6LlAheskxMlrWqWMb13O5jSrRMjyt1XFDxLxUGlCvXKs8+eUx/ubJ6xHng0X1mOzvYUBmGY9ShLpRLl+YcXTf6mXBqX9idSbbDx+J193HujiJO4saBljyGKWvU5rnqnhBUA/1nCJR7wnEimbxKQnfL+V1m0O1C6fDVER1z0IHtXunPFWZTw6xntgdb4mgqJ0vu6xaoBKQdQ65Z1rYVvj7h0MKJNnV76q0PPMevLx9hdXAvu+a9cuWzytPfChe++qtZ3l9585ue/Ipfc386Z1E7FsPd9TXt1GzO8/9X4fEn38zi/C79W6+hrwIVY6ud2ZKPfPdp3vLRU9QrVynnzvLC196b2hfpDPYNHxQWigx3/knci8OpFf1k2Hzm3EU1fPLT3Pdjnw5vPaubVuhNfemSz+MdWyv13N4NN7V3njfKP//g3XavZFOvQNkbHmF1KlL8/E3nKc889zk+ht1rHuVT3/0Yxw9VxIQ3/bfb2K//5u0Y7het+sILPPDjxuqtj+GdMPn1T1FvoYoRXgWQUWnqHUfqAJSAFnkRPhXPpF+Jtmobo1MCcACq6ctXDbl2ARvm8MB5bP9+6sUBYZEqwhbaQjAPIxVQCX464nGmmX3SfNOgeeqJanwh9zrCsREyeh1bNhu4XGMNEq6U8f2jDzT99zTaKpsqMezdLLad8E2rNtDSrKW1PLcUYmn8wsdtjLEENPWxfbPBSF+nO2sqPc1QFO1mDHszmB+gh1dCDZowyd3HNO6T+y4FhxG8kS3wqspEV9G6eW1J9a24S9VUaJKqxAR1NDCb+16bV50zpi2P7KZBSAXxCKipNYGWKpQOVWd6trC126MLS//Pymxvyu5eR69LdOmhECuKagTOmA34YKE8U2HFwPFioPYaxwpSvZYQrMZbOpbt7AEsLdWzLW2aVK0FGwyQNn7GJpBrgttRISue2+ijBlJybkmCKJcSPNMbE0pFXaONQJHCkM920gfSHHyIFtoRlDZ/zoDF9eo16lPPU07twfUjyrBE2k6wmnPLUm3YGnk5sSbadmgARk9lr7b50kCoMobUyDqYJOwL24QBJ9cdLeQFXOu4z2LKnFBBjq3Ua8lwC4FSdXwF85Uj0wleDD86YnXFmZzepjt1imG5zfXDBdcuHnH1sjAgzKY92wRkNmlp04LVXASdj0rksGiQ9Gc8sVBzJrebAtVtbF1vMFI6yeAqb/gak56h28Imu2jnyHCVnhUlE3RcHC0w3W/qWI/zpAQodIv9pxqK5fD9zJMh6+RsyX957kNHNj0ot7EmWnnTQ8/zweVD+KJ86Sds6nNLQN9ynTefu3TToFonxt2ofzuzc8wnf8956razUw5v93Buee09fpVP/d/ezuN/5TdYve1xOHVvB4pIZ1ACNspRQaoEzLvDatMefQ/ULQ4u2NS9VV5r+7oPgA6w/76nGVZ3v1/upm5CnbjzZEXoNLMlTvz+yd/zGEevjc90B66+9TR7v35rh/lSq164iP7TSEG/HWfOex4ymlWoFS+Ca4Q3aHqziUrgRDsRvJJgUbQlzAJxHZ9hF4YcXWN4Dsr9DzPsn0WuvYB6JdlJMIvWejoq6NYQByw94EIB1AIamogwLsFLBnqkUAsf4UcLihgDHBIkiUTihhHBMUFISwJFp1Mdx9K2Lfz6EnhmOE5rYzTA1LNtc50IC+AmayCa0DG6kQMehbjOog2ZUN05gpQpPttGlyvs+iWKLwICnwCnLYoijSzBja70rKyGV6SGok0EOh+YTSo2UagWSbbV8a6EIlKbH2E7vo7m6+oJ0BRb0ahegtVUXZkFxCpdx2QGp+6fsOtCPy3s7PdMeqUeGZ0IOzsduzvC1Gu0FGvA0gY2G0S1YcgxKeYVGwzpS7aRJsBVGT0BiwaQUdJbVNriDRA1GheKZwvqjYFAZusAmQaoyLbitrvJkJi1KjDmk6Zv5Y2fzHJiPhNBMxYgtLUbZ1cyoi0BOuctwlBXMU8H8BcuhdJweTy2ATfvwzGwKCdYvBajIg7JtOvUEjbAh0RsTfgjBrSmdYQTULD5GCZnS0Cbs695dL6YfEluS4ooG4hsY431H8e15q5UMajGtBR29o0tjihHA7p7Cj21x2prl+FAmF+MBOndCZQuEpnjpsSQG+1QdEzJFm1gNdBuOz+4g3Sx8xva0wYe3fLmBvQoJhnUUsF9ylD2qP0U9wFZHKA2z53TtR0U55b0ypTxQOd/LAG0G43at/XXlJUnp9H4XG9HcFO3q/b7OY+ev8xnnzwHdXMsXm7p9sCjZ6/cVNWe3MUKwFPf8DzLoaSFw6urVJz+PZd55g+9g9UuIC/fm/OuKwHpQ9loVWGZysY7ZAr7dt20R29qU6+i8mEVIY0a4hRbrkJscfUIrXsAnHv/YbS7bmpTL6rJ5fnn+BdK18dneqsqnPrwtY1G4gvUPQ8ZEcaEW0gVTaY6W4ZIOE5JAOft916RNe6KLmQpkRLtA2V+ndXzF9D7z1JXC2R+meIJKRtoHOV0TXU1/k++bqS3anoptot/8cQobq3BEDJgw9NjTnWtWPJ8Qycehgpo85hrSqv03jv5hU9LLoz43+YX2fhkMpLkm02ZpGvFFKEA1TbOFpCSaqpQKXXBC01RnbKa7EQa98FllCWaSdIulh6Qkq3gzTEyQKZ7pajQskU0gUkRmGmF01v0RcCi7VxVkL4L9Vvb54PjtaJdFyox87FdlpJekTUhbh1AOlwL1QUfBsyNrQk8cLZgg1MmBZkoDE51Y7Kt9FvK9pbQEb6F6g2rBagLvql0XYd7BXV6EabbERwzLAasGtWMToUiawhMAsa2+y3h2vit2RnDjZzGHWXdruqMc6bBxQZXG3SkjTXb+NfwtcHyEyq0Nt9zLjdoHTSvtRHHyPPQ4jXmeNcVajXQyjA/xo4nlGFxYgwZQJPStwiiMUbvwwTbbVHnzI2RtHXnDYqvx9+UrRUfwZtYPlNAKTH3NBe9yxgg09rQvUG9PBB60vPFbNzPKoqqs72l9L2zf044vVPZVoAB5ktqPWRrssuZR7dgW1nOV+x0x2wVQz0NEnMtlYTLVvO9vWFeb3uMJhW0evJGQmxcrN016Jai4MowdLhu4ZMdVvQsjq8zlWNUF6gm2PZ1irXkuoe4AbLWva39ZN0sg4E852N7/no+r1Os87z8Yu+cTd3yenT3CvaI8NRTZzeg8WWUzgYee/ASvW5UNq06NaR35sv+dg/ltpSKc+3rj7Hj7tV1+0QIZWMXvsC+UlgJcrvOJwLsrZhs3do2sU1talO3udyx+ZxmI9SqfuyTPDhfghnD018k7GVTr74agoeUY0M+9MnPuUfmqyWv/bsX+Oj3nGPYq5z75YK//9YmNt9Nde9DRtYqo5bUKw7UUCq14GWvcRGsRfPC2FI5Fg3A5uBW01dPwA27cIn5ypk9eAY1KIsr6zAGCQ3R2M5JAoMRwiQgaCCIBgHSTzHh1Kg8THjZghPWlKkBmNxWCYBkkfMcsCPzTRqGCdBaMsPF0qtxrUsbL/UTfjYoOo43f+bYqJIkQZYm1Ivt1lSPhirOJhOYCH54HR2WRHNoQAwzGT0tLduD0daWmm3U1QJqFsXRFFgpvRmyFf51UgFLRV56w4lKthRHOEgDX2N3abaBuusIe0OsVWN3J4BkWEFRtqpj0xKBGmRATO9sTzr6HqYSwi8AhnXgUHtDy7mh2eI7EWM2E+qRMz+u9O5MDUppgDuDfkQRsxHmjgpUshsckMjiaNEt4cd3A4xKYNla+91xqQmCov1dhTEdGjcsW41J2NZgUIOAAcabl6imL2Qq3ZpCzUMdCFCqM4ijZhFD4oYMNQJPPFr2rc29VMJ5zreYepq/T6CYQH29RmIOuI4vQPWAijIqIzWhbBt/2z0JFkVizjpINUZZZrshMK6ZOA6Gpz1kQ7Bxi6KIceqUUqaF7Z0V26p0CT3xii6vo8Oc0m+xdd8ug29Rjgs6HFN9oIgRktga4Tm5D5vEWvJYxXlCqVYRKXHeGnvn2+MiFIfqqJQE8hPoZ/hkC9PC6vA6xa8z6Qb6VBFrpplbHouxjb+dHvJ/1An4GKfH8B2VfM44/WLGWoO/6xU+zs9N3d56bO8yPMIGNL7U6pzz919l1t38llgVR8XzvHL31avJi/HzVT8ZWNrdeexuSqkj0wpTbotvoxdH91Z0kw3839SmXrXl/jn/Hj77lfsmb+reK/nwJ9k+eBAuXKIeff7QvfrBj/Cmv/QgvruNffbpW5rWfLfVvQ8Z0zfQnIQHnr5hzWsw9VZ5ARx5FXlhrgHlqI5kYIVnVEs9rhweDBw+/yyr1cCpR+7DMbrFNUqGUYRyqvUyh4ItmxXXMGRUXbWTYLpINi/EZgagkqRzrWzixOV5u1PjJwCfOtFCXDzbJjMIpj0uN19OqN0awLoR7sTrB+AIWBNwMfVTOXZVvVFl5mCDgUzxyRQmHcyPkNUhHQPmNVpL217K59TEScUSZCKgEXYinunB5tFOXA3VFTrM0WkPJcHx2Ars+OAjI4JUxHn08Y7KtJZEIY4PqwCjKyJoRAQzqEMmgk81Ai4k/Oi8GqV3zCq9awhJhwpYhqEoLtGyazknPPrvUaBXYXdvwmKxwk2ZDxVZGtpH+E7pdExy1qbKwxFKKDAlAjdGNWuWS4C9UamYMHgd9hL7KCJBfA1kBcR0DWXFsg05lKwxhRphshNKtQJtZmZYzpiGnfA8AOEwwip1Y6IOHn6WYIyBLgkrb/CKHNWTLQQnH5tei7HhOU+rp/+jpfLXx5sMlm27YpLBSAEf163zMYfUPVufa3iY+hiRlO3aqd4bQ5qbLUAGnLiwXSpbe1BUo6W82aa6hEdpNYofYcNA382Qvqf2e7hVaq24G3W1QnxFEaMTQ9VSiR03MEKxqGgpAfmljGnbNdvFixa8ptKXnpVOsW4LnU7BB/z4On29FoDRLYC2ZeS0QhnPgYkKJda7twPTfDzD5DPWR7shMUZXS57ltOFx1nDxVXwxfofVBjS+xCrOg+evcGr6KmiH/TJKIM9VG5Xyq72autGrwKBQ5RVtp/be6faXG//FTW1qU5va1EsqOzqCj33ySz5uePa5WzCau79ekW9+Tz31FN/93d/NuXPn2N7e5p3vfCfve9/7xt+7O//Jf/Kf8PDDDzObzfiWb/kWPvCBD9zwGovFgu/7vu/jvvvuY2dnh9/5O38nTz755dx5sLVXGhkCo0BqFMUNN4vWVWkKxAY4UkSUCa9aJB5XB+pygWNo5/jBFZbPP0fd3mU13WcVG5l/wqMOWwc1tFCPICap/BHiPVPNJ0Uz3Tm2orVQh/pIcAquiml6NqbKzFOlSCq80EiyVk1zvASAAeJqwFDivaMZNbdbPPWV6ZHooexyEgKWIBgqJUJNPDwYw5POE74IUPBuim1NkNWSMj9CveLU3J54fLRuxr5XSux3acqoOG6FE+MS0gA6PQAztKZ0GezTxmNrfzhhrWQjt84SYEr6GoYQS4EpVZTBoYpiolQFSo+VnsGc1QDVnOKVYpXeZQxo0aKodriVhG6Z8pwtqyOcy3b26bQw2enpeqXrlDLrkbK+uNecL+bhMeia+ydbgFtfbOLxdevyCWAefeZrxZh4BPxES3qhSCpnAJPIvjYTWuDH6McZzx7/tgbLAZaU8CdtIUDeYKRBS7YpDlKICwCvhHci4+MlPSbXas307Yyr1mivT9KtMrpQAjXhVkvCTgBOQnKrAR3zwsbEED+hEGygM7cpWoMr2kl4k+YcRJrHqub+CSiv+ac5VopAp8qEkuFQrf24wdKKSLRG975AV9epw1GkW4vAdIrNdpC90/jOOerWOVb9GRa6R9UZpoUqE6pMMelAelQnmBZMOiodbhMGm7KoU6rswGQfn57Gdk7D9k4c6/khxa+zpSsmGMUs1I5dbKKgmUwdQDZF4Qhx/jHLTnGLY60lvUPzFoJSR9V482hswFHER0XpnVx31ufaK1+P7V3mkUcuQffqVqN9wRLYO3fI2dnnv9t9s+puVwMWvbvHv6mbW1JC3SjbA+wO+Kzi3fq77pddJvTXlOkLBd8y+tPzDWD8EvVq+0zb1KY2talN3bq66ZDx8uXLfNM3fRN93/MP/+E/5IMf/CB/4S/8BU6fPj0+5r/4L/4L/uJf/Iv81b/6V/nFX/xFzp8/z7d927dxcHAwPuYHfuAH+PEf/3H+h//hf+BnfuZnuH79Ot/xHd9BrS+v7SFCEUIlVbL9D8lQDVlfAhcpY8tkqKxkBE8l1Y5NeaYi9BNhsl3Z3h6YdXO64yvUSxexnV1qv4OZBoxQX0Ml0iuwJcQ0v0TPNmNx1EuACgeqBHDLxFqh4F0BLZkWHXClKfc8fdOqAFICYKlmk2lAQUkTQsNH8DWGNxjZdkqo7/K/uCLpXyiiY/tjU6k1A0rRiPdQk0gZdIF+C7anqA3Y/DrIEpEhxppgx92p7oxEI79tqodSMHwzLaAUAX5pzCyBasHRatlinWE3NVV1Xk6IpRQ0XeROQDM3x02oCRWrKtJPYv8tlqFirB3Dwrj2wjGXn1ly4YVjrh8aC+tihiXULJqhOZJehA0qWYZmWDsGAfFUhW4Ce6d7dvY7trYnbPUTJtqlmpRsa1+rGbV5F0pbxG0+tdCXprRb+ziqKjHTSoKe3Jmj2qypAmPeoaGStLqC5gM4VsLw5ltqoBYQd4RsGvBSHQrhT1gk1XxNSeik0lIpJnj1cW3EqxROzPJG41FjDO5xC19SSVmdi+KioSJVye0gFaA5rxOpj+EyozrYE1q3AJsI3gm2GSE46jKOya2pLAstlT5a19M+QENpKFIp5LnE1h6oa9Qd79dZpatHdPUItSNkOKSrh3TM6aeG7BR0Z4Zs7+PbZ6hb5xim51j2Zxj6s9StM9St09jkDEN3ilXZZ9g6w7B1lmF2irpzmjo7zTDZQrQLr9Hjy5R6QBkWFK/gNVrh3WCIc4rR5nFAVSXa6pEW1xLQVZoHQR4rafNwxMCh0h6hvzCqY+9kyHinfa7dqnps7zJvfd3T6GzTDnJDCezed8jD+9de+be6yyFjX+7Mub2pO6DUkYkhOwO+M+BbhvcngKODLuVz/mDh8ahLYfpC4cGfF978/7rGG/7L3+T1/93TSG939OfJnVCv1s+0Tb26qpw+RXni9fHnwQfium5Tm9rULSnxk+77N6H+9J/+0/zsz/4s//Sf/tPP+3t35+GHH+YHfuAH+FN/6k8BcSfswQcf5Ed+5Ef43u/9Xq5evcr999/P3/pbf4vv+q7vAuDpp5/mNa95Df/gH/wDfsfv+B1fchzXrl3j1KlTXPzj/yV7kxktrECpOEJ1hToQjcSjM2N2HafXoTaVUqiPcEloFDhiOThDdbpJD1IRnWLdPt2pPeToKjocUajpYaYYFURxD9hjNM+yVP15qhcbiLJUVQLePBPHVtJUZzbo0h4nJQCjD+HxeLIb0SRbHA2spj9h25rEBMlFWzt28IF4EfFQeooq1FBGWkgGx9ZqoYEihX4b29nDbYVfv0QZ5pTMm7amWhTFPFRTIkK1Gl6Fo1qxqUp9bDdure6C4lIyhKLDtk4ju1vosASUlrYdmxD7yUVwVdyGOBZWQ2elFZFJKE7NMEtvyaEymCN9j4lydFC58NlrDAtD+oDN2zsTZqc6pqUy7YWuy/AbjVAcqUOqVNO3zsMDsQUPlb7HJxOWS6FOe/zgALl2iWJLtC4QreOX7i5fB41QmuZ7mZo7oIHkBtTz+Ekod2tNr1GRgGgJcCN4RxEp+eU84Jib4VYRVUpJoOjZBp/QPFr0ZUwbNmxsR27tzg09BWSyUQZXUVxnsLNHWR6jtiSgnNKUtGZ2ArIG4DoZchLv4aNdaUD3gPaxP3Jumjep3QkfQ8lxtvVjue9CWtdCuz0h/rhiUt7s7qlezTZ4c6S0tuJQLRdRnIon3HWPxvDmozkqaWPLQcBcx32JKy2wxqXDRHDt42aD9HgX88cnW3jfj8DURcEGbLHEjpb4YkExkJLqWgZ8fkC/ukzPEKFI4tnIHBYTLg7aYS4U1ebKcOJmg2Ca2+6e93B8VCp6dFqHyURs7LgtjelK3uy5tjjm4b/yg1y9epX9/f0veY6/lXWnfa797p/6HvqdW/dl+dpqiw999jx2fO87rHzJSsD4mtNXbsnbLWrHYri79/vxqntVtkwfL3qW81dn8M1XVCZwveOh/1M4/fNPjjfmWq0eux9dDpRnL2OXr0SLW1b30Hk++J89ymR3eatH/XnLjuZ84g+99477XLtTPtNg/bn2LfwuOtmsl03dnCr7+/jrHw17qiw9XmEf/zS+WNzGkW1qU1+gRChnz2BXr92xXo+Dr/gn/MRL+ky76d/6/pf/5X/hPe95D7/39/5eHnjgAd71rnfx1//6Xx9//8lPfpJnn32Wb//2bx9/Np1O+a2/9bfycz/3cwC8733vY7Va3fCYhx9+mK/6qq8aH/NSS/P/8qqfmoovcYFSAghoqsUSfERrcSbOlgY1ok149JJTmPQwmwq9GhOUzgfKcBU/uIbP9rBuFyMgmCm0HtdoAW0KqbhqbyEbY2iJZJhEwpR1ezUjNGwAJFJyE3oALjWhhaVSLH8j4WEXCstQT4lH+2hLAx5PxaKZ5NygYfKZAbyG4m9VhcXcGWprZi4EXxRsMqPu7iNqcHwVHRYJmbJdfMRFGqq5fM+WbNygZlRr9412Y+lCiWrZ9irVEB+yDV3AszXVKo1ieB5vy8APM/AabexWDbWCqiOdo12haPjxaREm055uUtBJz2IQjofCwcI5GmAxwPUrS47njk97pOvQ0qH9BLSPiaJdzMJUx6rqOkAIsBpt64VIENatCUgJzpzt1wHeooUZ6WJCWMCepvPzseX3JDxOVWNCozaZ/ETLvsDoNRgKNRlVAJL/1hZ6Ms4PGcd/ovM1UoudbAtveDFeKSD2+idWo025ZNBRyBZiHnjOfktQKuOYDKPGtrkgldErVNbSSJpKsa0rbf6N0oKbch3ke4xeppbqyWx7DlPOtrYSe6YS0t3GcwVIBuGstzcNGRKrl1DkimAlPRuJ4xk3PRyTtd6vqKFK6DhlQHWg+ECpx3R1jgxH6OIAXVyhzK9Rlkd0vVNOz9BHzyKvfRB/zVkWe7u8cGHOC09f5/LzxxweLKjLVbTim6Orw7jxUoewI8gD6pogV2NEo2o5j3fbX7VZQTTbBW050gF+EQ+FtsoIJkmouFbZplL05t7vuql1p32u3era7+e847GnmZ5+lXsPegDGR05dvWVv2Yl96Qfd4dVv2lY39XJKHfZXPP3bjPrgaYannr7hj/z8r+Hv+wDDU0/fABgBVq8/T79zZwDGO7le7Z9pm7r3S86evgEwAtisRx9/9DaNaFOb+sJV3vYmPvtn/gU+9COv5+Pv/VqGf/ndt3tIX3HddMj4iU98gh/90R/liSee4Cd/8if5o3/0j/L93//9/M2/+TcBePbZZwF48MEHb3jegw8+OP7u2WefZTKZcObMmS/4mBfXYrHg2rVrN/wBwNLHjlT+jUpCEnhYwB082qnFx/CHoFHteaGGMzNqC3VJpZO4QYn2wuKGrg6x40N8MmPo9qimiNXRQ68BHs8mRKutDTF+l7ZmATkaALUaMMollEwObgFsqtdU/snYehkt4tlWkuqo6PqOVkiQEy2QAaPMLZVLsesUga61XAMl2rUdGAbl4IWBy88NXL4ycLzKlkpVvNuGnT1cHY6vI3WFSkCjmrCPbFevHknOgmC2QsQSbHUJdQJYeCbrulWa411JpaZoNrrWFe7rgJgGQwZzVsAQ/DGObSnQdUjXpcdfJkC7h0+feLS49krpS8A/FSbnZpSdgk6ilVlLgODVoiJakE6haLDF4uHzqAGMjfCelBIwWBKeuUOtFdVQzOqkx6Y9lC7mX7WcF2tg6pSA5B7+hpEyksqz5sMoLVwjQPEYcJPwB4855qLgJduPbVQGytiS3TE01eGoSkygm2pREbATHqLxvPRypCkFQ/UWuSDh0VfGCOoE5R6KzCKSFgDNBuDkazfvzAbhA1TFb8h53KBYAPRQZK5fJ16XUX3XVJAuMjoytpbu0aFRStgXSIdniFSbr5LALBf2OGZPUGlozDlztFkPpF2B4wEr83ninoniYXRQ2oJs0NaNzo1CRalInVOOD+DSRfzqVWxYIpOC9x0vXFzw5Eev8/Qnr3PpuTlHS6N2Be0UWy7xxRy1AdEIGaIpGD3bwyHDgXI1el173Oaazr0KJ/ZbwO28QaLpzjmqQWM3abZaSz7mThY63XGfa7ehtrslX/XQM/R7r+IL+M555NTVW+qTeLe3SwMUfXVCRr9LU8HvlJKdgSe/dR/d23uJTxCef/f2plX6JdTt+kyDO+tzbVP3cH2BE4HPJpu26U3dUVXuO8eHvv808q6rbJ85ZvKGa3ziu5RyB6nfv5y66T04ZsZ73vMe3vve9wLwrne9iw984AP86I/+KH/wD/7B8XHyosXfoN0Xqy/2mD//5/88f/bP/tnP/YWEaqyOz01FX5ixIZRQ95XWBtnG1/6bP3ACGBVp9oqhjMwU6PD989Q8DpTj6wwIzLZhrjAcht9Za7+0TK/2gBd2sh044Yk2OOj5ZVU9VIW5XRCgRVMh5GPbcvzePXzpzIdsj22qyFT2NVCUqcSaG30SGJAAsAFVlw6vznIOR9eNw0HgyLBSKad7VGfIbC8g29E1OD7CfZUtljJCNQiIEXCtZCu70tzf/AQoayq1aE1tECQBTq0RgGMW8neraIlWePJolC5ev/nFuVj46qmmD2G0TSf9AgwvobDUokjJ1mkqszN7nH3sFMurC4bDOSyGSDHcKpT2WDysAJsKTxskanCvKbgKLpES7NkWiw3IRNG+R1YFrV0A5nxNz8QNkfAHjG51j7t10pqmE163tvKT6yaH0FqIrc2VDDzKXZQhHzFHQk3YCFDOG2U9dzwVqu05Pr4JomUN9EbkGeOvBGQOJhXPkQSmayUmjRZnWEu22rb92daqy7j+mlYyfCljTo3+iPm4UB7m/NL1XF9LhWXdZq2SkNLXDxnPEb6ei6lS9VGZTIDeNudd8JLKvabcTM8CObFvra2VhvAs1MGukhavuX+RPE4B4G3uyIXwMZWuh9kM64SqHdr12KTS7W6h21McQ2xJKU4E78i47W33RqB4tq3Let452TbdAG8+q0FzpWBm6znC+mK7TS/x9b/aWdNOEuo7rO64z7XbVBOtvPM1T/KB585zdGl7XAv3fMmtbZE+WSpOp8ZwJ1P4L1HCqzNl2uqra3tfiTp665xP/Mmv4o3/7dMMn/z0F31s99rHuPrOJRt88KXrdn2mwZ33ubape7TmC2D3c39enWadtKlN3Qkl0ym6u7rhZ5PTC/x1j8Cv3b03YW76N6CHHnqIt73tbTf87K1vfSuf+cxnADh//jzA59zlev7558c7ZufPn2e5XHL58uUv+JgX1w//8A9z9erV8c9nP/tZIC+bHYqWzHrwUFeN6gCNzBOJvwtNmRVw0sc0mFRUtYtjJ4I+PC6wA1LJCHGwBeXwMnJ0HaZb1MkulX4dyIFjVmmSttYt3V4+POMyQbe1G9LCNNIvUhpY0swSaWqwglkqILFR/SYS4RRj7q+0EIwI6Ii3ilCNESyQyDPVm24DZsZgAyt3lgjzQZkvOhbdHrZ7Bpt22OIAltcRqQHb2mjG1tR4ZU0lm3siqBayk2Aodr8GpEg+FAndTTHm65RpGWC1DHAka89DPD0IzZFsR5ciYwuupvLRhyEAV7UI/OlLhqWsoVm/qpx+5DRnXneWvYdO0Z2ZMb1/h/0HdygdDMvK/PoyQmccrIbyNQ9rJpln6E7RDBNpC9FCtSiCzqbZ3h1KQ4mNHfebuOO1BgDUnFOWu0HDl5NRpepjGAneXPE0FZGxr6Klv6lmGdWyIuS+ynnglsA19723cbWJnyBucFgZUo0ux3Ei/yjWWIb0kB6cbrk9rJWADUQFmBJaHEwjjTFHs/k+IWvrmE4hcG5rwlj3cbv1RPCRtECm9o6ytkiwUdGZgDDPmid9IoGEie18sXaibM9zaSw1d2yeL8YTFTIet6Z4DsgZlg9ibZvamHVUdBoV6pIyv053+Qp68TI6VE7dv8vu686g9++w/dBpZvftUqY9bpUic0SGWEvm1FrxvBFyowI11q2181073m3obX87GYrDCcCYa3e8UaHj/kJkBLDm48G8I+tO+1y7ndWJ8TXnn+a+h66uAf29XJ2zf//12wIYW90LasbLnzjL1c+eut3D2NTdVgKrxxd85D94mO71r/2iD33uWx9msmmVfkl1uz7T4M78XNvUvVf10mV0/iJfOwN54dId63e3qVdn1eeeZ/b+2Q3dD6qOT+5uP+6bDhm/6Zu+iQ9/+MM3/OwjH/kIjz/+OACve93rOH/+PD/1Uz81/n65XPLTP/3TfOM3fiMA7373u+n7/obHPPPMM7z//e8fH/Pimk6n7O/v3/AHoHkQ3uDtRiKvbOfMq+rwR3OQahAhq1A9wVyDHT5CJ1WhBdhKKiRdKk4l/A6XyPwqdnQVJlPqzhmsbAW4SPlS6zh1HEoLKmmKu/V/g0EUTBMGiYxQMTBHGdt+RzUasaFusa0t680yhbql8noqtcQCapjG60oLv1BFSxcqziJQCmUyid9VpdvZYnrfGeT0aWRWkMMD5PgYGYZQFrqlGO2ER6SQyrNsmY1o6wAdEKy0SbykidlOtIqnGk2UCKRQIkpnuYzgFlpTdR7e9J1sJE6aGq9kQEyGo9RVzZb6VMzlvlZRepypr+iXR8x6Y+eUct9rtjn36A6zSXgLLg4rRxcHjo4qq6pwIsl5RFgnABKqiHbh+ynpf1mV0k1Q6aD01HICSJklkfE1pNEEdi2MY4RsbQ4xQkDReJ4l6GOcWzKK+MbkaVKhVwSyhfyEUWauoVwL3DjppEj68LX3TkA22qPG6zQPSFqL9giaToBET4xYFG8gUG9UxbqHIpSihMVnKE99TCd3jBYgk3MhyaxKmycyvq56eHKGOjP3C54nBQv/zlHZF+cEbSDV01sxFZjiY777qKZdl6+hYe7TOKmEb6ymyrgd15iXufbbqStbwtUqakt0mMPlq3TXDtnd7XnNb7mfR77hIU6/7UH6+7ZxFer8GFjQZQCOUCkS5ztqpEo3r0sf1Zo+nk8VQU0o2TrdBhMKhhOBQEmVAyJn6zpOFaNKmAiIkr6fdy5kvOM+1+6AeuLMCzz+2IV7Ws0o08r5hy7fUg/Gz1e3sj37larpReX0+zfKvk19eVXPL/jYv/vQF2yd1q0tLr/t7l8nt6pu12ca3Nmfa5u6d8oXC/zjn6ZcPECvHcd/P/5Z6nPP3+6hbWpTN5QPA4/9Pz/A6uPrz7f5xRn6iadu46i+8rrpiPRP/Ik/wTd+4zfy3ve+l+/8zu/kF37hF/ixH/sxfuzHfgyIi+Uf+IEf4L3vfS9PPPEETzzxBO9973vZ3t7m9//+3w/AqVOn+CN/5I/wgz/4g5w7d46zZ8/yQz/0Q7zjHe/gW7/1W1/WeCLJNpCTWcAYMsHYpLVMnpBYNZUgGu2mY49qXjDnl/3mWTa6kTl5AZ7qMY306E4qtrwOdYXvn2E4dQa53iOLA9Q8QmxrTZDhKCeSlRPSkGnXTXmoCYRcwiNSx3CQBBUOY9AEnkqxFpyRKsA2Vkh1p4ypzg1KGqCWCslsA2YIb7t+2rNz3zZFtth6cI+tUz1ddYZLVyjXD9AyhAchjnYnQh2ktVLoCC9qNZqnXmkAxRoka5AmwnPMndLalyWAm7tRVDFzpFYYBjCobqEStMCr0R7NiD1yJybEaYm7QtFuBLuRNqwNoSB2zJYJVoXiFTFgCE/P6oVhBasllJUznQXILCf8Nh1CkVZinrWk5aZu1JJ+f9rhpSeiP3rcAkFJmxMQMLQd8FRa6lpmF//BR37Y1HLNK1ATa51ssW4QVCwZnwSUVhJEN9VeUtOxJTs5saVqVNo+lpPDy/dRss03IOVanefgQ77DOqCltdBXAmiFDvOkkjH/Eiw0IPTo89hUdvE6o6ekxk0FtZxHJ6BdTL8cRc6D8Ki0BPQ+3gwIUWJTLqbyMCFv3Nho88jXe268QcB637WjlfuuXYq7RB67SjsX5dGqCf0SenqqCN0cFgv04Bp+YUJfzrE97Zg+cgpbDQzXDtE6wOKIyWpAsVBy5zmxzUktZVRztmPs1pSaZOJ53NwoJ4917kJs/d/Y9hfPstHlcVzf5Q5WMt5pn2t3Sj28c5WtJ1Z87Nn7qdfvrVTQfm/JI/ddYdatvvSDN/WS6vTHllx0uSeg6Zcq82ZLs6mbVavHFlz7V97O7t/+Z5/zO334PH5uo2J8qbX5TNvUq6FsPsc+9ZnbPYxNbepLVr1ylSf+m+f55L/9IMszxmt/slIvXrrdw/qK6qZDxq/92q/lx3/8x/nhH/5h/tyf+3O87nWv4y//5b/MH/gDf2B8zJ/8k3+S4+Nj/tgf+2NcvnyZr//6r+d/+9/+N/ZO3KH8S3/pL9F1Hd/5nd/J8fExv/23/3b+xt/4G5RSXtZ4VDrcSlixRT40zRuwKfc8YQCQLZOBSFpGhmc7b4iQAtC5JTlxxpZpPfEaLcBBUsWnw5x6+Tl87wy+dyoSlYdDhAFJZeHoAZdKPcGhtShqth2aZxRwqL80VVutgnXK2EIrHmMhVV6KgKYYLt+rgQ9LNSEafn/hBZdt3RbelaRCrbPC9rkdtk/vUTpHjo6ohwfI4piiGZzSAFbVgKkJ9gJgSoKmhEGiAUxGQBUAVVJpGm2VFqI6kWapGWm21dAVIAUZlthqSteVVI5FW/Ho7aiawExSghp0zCVAp3Yl24OTFLkEvExPzM41Xq9WZKipmExFoFe0M8rE6TpFWyuhNHyZnpPFQnVKzh/imCqOa/wREaQruPT4ylgsU21WoBehSAJQze0iZYLteBNzNdRwTcUX2+MJKtXSJiD3q40KQ0/FoFC8AaEGuRwxXQNg2oEgVJLWoG2CtliErDmeIiaIpKKQ9CCs0sLPMTFUSwxIDEExsRGj65qY0nxWvc21mBThUZjrzzGkeU4moPSEy5GgvgaRPlI/HaFsKJTbjYTEZR5t9g0H+rgbAoTWGyDoWqnYSFxb895ejzaIUPum8BpJpWVuPC3V+mTas0soetNgE7WBuriOXowBDGf2Kaf2ka6yNEGHSvEVDIsIUhLHpeQaJdueCUVwKl89Vc1NXV3NY+qR6dGk6lLW2zK2VI+Ky9i3Iu0GSRyfNllOtmDfaXWnfa7dSXV2esQ7X/MkH77wANcvbjfvkbu3irN9+pjXnLlyx8CwO2UcX2nNPvwc1w/uZ3//+HYP5RWv9WfCpm5aCTzzW523/tJrGT7xqRt+ZfvbdH39/M/b1OfU5jNtU5t6ZUq3ttAzp6kvXNi0ZW/qZVX9yMd57D/7VHCke2DuiN/JV3ZfQV27do1Tp05x+Y//ZXYmW0DomDTbLT09FlWUZqdIUy42vVbCxVC7rZV4N0A9z2vo0UvQE8qA10hilaIJ1ApeOobt08jOHnI8h8UhhWFM8s1XXSsYs223wQ+vNaFWPo4Mr0jvtLiAB5EuIB1EC24qNL21IacqLUBo6iBHyLRuH3WiQxcpmYvSMVCw2R66swMsqVcuw/wIFcdqpWsQEHDP5FqinXT0FZQu2ifFE7gJmkAodn22orY2zcBTkWQ7HpMEPbktQ1VMe9g5RdmaUKzBmIQjpVBKCQWgloZosFrxAQaFru8D4PmAeqhdSb/I6Op2htUqoBmOmWNmmHfQKYMLViv9VmFaACol1ZZKpEiDRDiNClZDGRrJxD1WemrXRbvplesMV69zfP06y8U8gJPCZFKYbildH8nYMTXi9dSbd2gqGz1gZJtfoyWiaO7vhFUnlHbQOIFQmog2L3LNKu6pMhRbX0k1IJeLQsUxrwkuA0BZUxvWdtHsmBes24Vuitbr4EuEAjTP0TrCXmnJ2dl2P0K2vFmguT8D6gU0rDS46An+hUL4XNq4VvP4korTnPuhdmx6O0vAWmMtZ3v9CCUbRM3dgocaMsK3JY9FWUPc3F3tOI2wtK3lbOcWKah2YxJ7WwctAfyGQBXLFHOBCmi3BbN9Fg8/RL3/DGYD9cIx/uTT9NeeZmt1iK5WIAHh0/Y0A55y2xox1nwX7cbzTbSwg4miLqMsU1wwD7/ZNQSO85jmvkUiHKad7xA4WC555C/+h1y9enXTPvUFqn2u/e6f+h76nTsv3uDTB2d4+qmzUO9O0Fh2Bs6fu8r+dH67h3JDVVMOV3fe8X45dfHnzvPYn/s5lr/jPTz1h1bsbC9u95Be0aouHF7fyi6ETd3Mmnxqyuv/2sdvaHs8+K5v4OLvPrqNo/r8ZUdzPvGH3rv5XPsi1T7XvoXfRSf3liJ+U6+u6h55mA/+R49y9tErXP7MGR75R7D7jz6IHRzc7qFtalM3pQZf8U/4iZf0mXbPG+SYEJBPEuwk5FpDhUjOdY8AilDrkK2IjfOlV5xL/Nviv3iLoWjwgPx5e2JcRGsqmZyBYkY5vgRXXmAQYPsUJrNQ7gmYhmqxKZ7w1n4pGYqdkiW3DIIoTfIU709rxawBSZvYqvnpSYOomuEOqSrEcGrsAwrmGvZ/liDIBPMZi34PO3UWmfX4wSW48Dzd8oCJrOi8osNoZonVZagCU1k2kkwBfMBtRVPNBchRkGnssWxhj92cCb1YBFMghNdhg5ABd1Qd8YqtBsKYrwFlG70VPdtyAxoFjBFVmAhl0oGEctMHGBoEEUGL4GK41vAo7DRSrTXa8OvgDIODr+gnkQYqPiAY1lKTU2lm6nGciVb90ZMzoS4122a3p9h0wkBhGJSVF+YrYT43jg4r8+UQHpZtvyZgaq2+4mt81kBW0WjIDwVZzBbTE22+KahTAjZll/aoOvQR7jXY62AJkKkxAzXnnXQByVIVWDzGpCUglnmHUTAJAFmFbFtP/9TYClpztFnAcRxMasxfq+s/zfszx+FSA1em96d7qHVNDPMac06HdchJIsWhKouVsho0w8CbFE/iPKHlxDnCsyWYtU5SCxQd7VUbh433JBPCE/TLCbQrEu3YZojVEdRXj0AWoSakazchZLzn4R7bJeJoKlOLVWRxjFy9hlw9gqMVZVnxoUJdRep8CQgaas843jVf1JsCVjx3e5wntO0PyDClmHee0NTccs61UB/yeK4vtoVsq8/ULaucgI6bulvr8b3LPPLIJWR6lymK1JmdPeZ15y/ccYDxXqvJT/4SOz+7c7uH8YrX0dH05P3oTd3EWr52wZPf/UakX4N33wjnNrWpTd3muvTNj/HmNz/Fg7vXecvbPsvW/+1pfvNH3kr3+Gtu99A2talbXvc8ZFQkfPvMoFr6EWooddojpAWRkKKaUKdVtzFZV4j2QHdnzIEZ+wA9WjitpSW3XymlnFA4Shf+ajjd6pDu+uWAD6fuo073cekIndU66EW1I5J4c5AKqiX800r6t+UXWVuPCBvpokIp6TNJBFkk79MEIpq/Fy/RQp4t2kMVqmf4yNYp6v5Z2NvBF8f4C88jh5fRekyXcAevdL1SSokghzCLjPeULtSDTRDaCdIXRCUUh6xDPwK2yhjyQYJbB5QSACMZhpaToLepGhNgdB1VPaBtUzAmhBXpMC2j6lREKe5IDY9IE8he9FTQVdQM9WhVFioqjnthNSjBbVbIwtDB6bxSxpZ5T+BraKd0RVECJLkZ6k4XdJFSYvIUAek76DtcOgY6hiFgePWO+UI4vg6LpTFYJP/SYHSTo6lGqnpTMabKURIWtRTuIpKBJbGPrUnyaq4FMUyUFMTS2viVSDrXomOr7ZojZdhSU7MlQC1tcWQ/sGpByoTqhbrsqelHKeqgzUHxpJgu14bpOC9EDE3w7l5Dm+jD6AcY0yJ8JrFCzcF6UwI2QE3cPDAcL9G2DmSSeYJVzcG0kCghlIDWkunjZgGS/piZJt8goguoOVQbVbni4SUq5glFaU4McTzzhkU7rax78PKGQbvJkGvGVOhMoFZYzZErl5FLV9DrC3y+RMUpXRzHYbWkZpiQWKSQNyhKlwncxFoe7w/coFyVE8c8w8Dl+zIAAQAASURBVJbyhNo0oC18KCdD/jUArd4QVrS5Ir8X6rG9y7zlsWeRrbsANEp6Lz56ideeu0Svd+aYRfyeaZkGeOh/v8Dx4t5WLPV93XgyvoJ18FULFr/9a273MDa1qU1taqwX3+zotfLmtzzFB/+j83SvefT2DGpTm7pNdc9DxqZYKprtjxptnJEE21Q2HgZ1kkoaP3GxTF50p6pPVEaVV4CKUA9Zu5jONkvy/VqFWoiEeYnFZIUeXoDja9SdPerWPlUalIgkXTvhgxZqtGjzHROmpWUlpyKvqfcyaKRtj7F+PJKBFk0tWMMfzkqEq1AUo8dlG+v2GLbP4nt7uKywK88ixxfoZEFxo7QgjAzyCAAXP5M0TmyaxBaoA4rXaPtse7gl97rX9KCLVmTVeD2lgJcRQyiAwbCsVJNQQTp4UbysT/RKqKmsqdlEMYTBK9WGaHW2SN52B8mUbUk4jTmeLc2eSroWAtJavVcDLOaV1bJS3ampsMMNGQysjttGrZlebni1SOc1w2xAbIBhiZYI3fFOkckEmUww6ZkvhVUtrKqAd6yqsFyS6tOEYZD+f/Gz2trPLcNV1qhqVI6FwDVgoo//x+hFijeuFV6dTQvbxJmW+8LTB3QAanqXVluNHpFiqXD0UeQba6gI1Y3FwhhqadnfOTPacctRG2Pb/Rq2ZbJ2m3PanlnH+YcoXjQUd57HEkbv1Pa4KoaUSt8NdLLK98pzgghlDH9P6NhWny5p/qrNXMERpAF+j1VhoQ0cbxiMgC7BJONejb9KGx+M888TKI8qyfG5bX+FT6KLhVp4cYhffB45PETNIojInGpD7lcLG4W8H1Ky9V6qp21B/NFMMfe8s+Gjz6dTUkEprJXFNHWnZ1r7eMhaRA5rIKty4nhu6m6v05Nj3vbYM0xO3bktsTKtnHnwGq9/8M5XL95LgBHAPvop6id2b/cwXtFS3SizX8mS4nz6X1X8m955g6JxU5va1KZuV53958/z3MHeDT9Tcd78hqf52B99zShe2dSmXg1104Nf7rRq6qrEL6hq+NQh4wW2pKJoLdWBFlAACRjlJBTLFN1Mex1DMYDRc9AydTff3C3bDD0UZGbxfsIKObqMr5b4qbOgPcyvoUQLKKqIEQnMqVz0lF021eLJ1knPJOoxZRhowSNt8NoAhWg8XglFX3Wsm4JPqKWnbm/DVPFhQK5dgvl1JlRUEgSmsrPBrbb/zFscSgRxaBJZtzG9AqQ7AWqaMqop7iS3Ya1tiv2oTUpFtYqbAl0q7zzVVmuoESaIqajUNVCOr/6WabayniTpZSiADTXUj/n+KorXIdqbc6juNd5PYbUyZFlhVtGtHq+Vqjeq4CJtJ457A8CWc6MpQQNeVpxQsJatKZPtGf1x5fioohZAZ1BDTBgGwwxkki3RYXiZKrnYo2qptEVCnecJuxOSmbV9H89tqdNrgtVCPjKsRiWThkctHWMaOI7aOkG5zcPmFepVUkE5ukHiKKqGbrUJTRzrDAYZIaUzrjPNjKK2HTaCOMbtABnvG+SipzTLAIk51MCeIphURJ2iGurC9ZsmBG/TXEaVoSeo1VQKW85TiUkT+8ZDcWveBi0nFIlt/cQ8kfRgNVkDeBoczrm/xg1tOxsobvPSobVW43Rm6BzqwQxme1j6nU5Kj/sQ7dht+zR+pyqjOjY8KL3Jn0fo2MSMsTtjTrcRtdbqGFyoPEUyndvXIT1Ca3FPb9lN3TO13895x8NP88FynuPLM05M3NtXDvTO/tlDHti7fscqF+/18tWS1/3EEZ98bIvdnTsb8H65NQybi8lXvE6v+Oi/V7j/je+mbt3uwWxqU5t6tVf92CeZ/J1vYPE9R0zLOrhDxZEnrlPOnLrrE4M3tamXWvf8Vd3YPjpCxWzjs4QtQdtG5Vdca/sIGMfHJJhoO0zScK2pq0bVW2sDbAqsEdYQKrYWzHECNKgPdIsD9MolvPT49AyDFQYVVqvKcm4Mg2IUvBRECqIlVFJd+L+NIRQZSLHeAQEoWhe3nBhO7IKAbzYIg09wZtR+G9/uETuCy8+jl5+lW1yhtzkMSyTMGsHqGmwmeRuBGaEAlFRUBsggWMoIZDPlebzhr0RWitPaUcFHeAVrQKUacM892o29OnUALL0SiXAPSX9MjQM5+h96DcVieMyFgrFRM2npyyrRml46GoyUVDtioYbtbMV0UumnHhC4aKhcaeq4hH21zcUGTtdhOOqp7/TAeE3dSFFk2jHd3WJ7f8bu6S1KFwBxZYGRTEvMpUxTNm/zmoSuynjwSa7VYHr78SjLlRsmR0vnDuUm4NGW7WSgScI5TkD7UYwHay9TI6BdiQnoow9qjFu9MpGBrenAVpfHpRHMNuhxX6by1m1MN/b0gHRfr2WRbKtPZEoCStU1AlORE+M3mt+geh2DaeKxNm7U2vO0/Yn3at6h7aex9NoOydbqPP44o89qU0hCa2dPb1Jaq3UqG30NZWlrblTV5nPzHBQLu2I+xDZ5pZMBWRwiqyFesybgV6huEQ7lsU9o84+mfiWVypKvn/szIb0TCeE+jjfG3MKCYh7kuSHBdWvBHs+h2uD2pu6lmmjlnQ89xdnzV7ndh1emlZ37jzj/0GUeOXX1rgOM5R5TxsnP/xqnf2qWNxXvvVpe3lCvW1FSnAvvMa6+6U64i7GpTW3qVV3unPnbv8xTP/so1W5ELGf2juDs6dszrk1t6jbUPa9ktBEOgFUPnzcBKQkTxza+gFgurc23/Ulok9DDW7KtNM1XhB4EyMh2UNacT7KNVzJ0Jr5QB3yrg1D6SFkWjLK4Fv56Z87h7MPxMavVimFZkU7pEPoO+tLUgZav2UR+Mra3SsKtUEZ5qpFknYbrkoDCMTpqN4Vpj/UFHwb08BCGeaoWIxmZhDmSkHaENw0+SFPkpQarnLiutAi10NL0VRm9oxKtzC1xxDwgXuloESYN1KmmGDAhl47SLQulosef+CXrRO6Ek5qAR81DAVhOJAgLqbqM41S6DMPgBDhWMsDG1/DaKtPO0P1IAC2dMFEoLul96Q3FhapOfT3mBsYSYGOOD4b0kRJdrVK6DtmeMhsGRAZWS2V5PIeVox1Mt5Sy5uPxGs2HsPUkB+dat8+3pOA8Okprsm2p4gmgkju2uUIee3Kut3b48RDn2lCVzPhJdR7rueoJ9iJ0RQLMFUErY2tywPCTkI9xPLHPhVxWEf5j6S+pCSMTUjaA6pSE0bJet82bsoUH5UwrmZwcguC1h+d4jMj5T1vnua8I+BwpzXkuGaV+Of4M/mm+hw1Zthsg7ad6YtvdBSxDZBJci1koRCXa4cfjlpA54KUCw3hUzVbocIyvFrg6porVmPeTrmdYDlQ31BPM580KC5qYN0QswWC0fYcPKvEcYbyh4S7r8w7NA3R9s2f02Rz/p51mNxeJ92q9+ewL/KYLl58+tTYRvlVlwu6D1zm/f3DXgcV7qYYdj1YxWx+D+//eb/LRt76F3Tdfvo0ju/llfvLm6aZe8dob8LLZ4Zva1KZuf/liwev+y9/gM/N3sPMvvsDZWaTeX/y1Bzj1yV+8zaPb1KZuXd3zkBFtOqTRBSx+nsomae3Okq2gkmqw6CJeI0ohfdxOgg8ACYuF7CykgRU/+Z4NepQEIqF6Ky6oW8A7A/OBrh5Sr4LvncH2TzNfzLl06SoqzvausHe2o/QF1Qi38AQFDYAw1GztDqgkDY6IJYwMeFDdoHZI6fF+Cl0BW8DxAbqco74CqbldDQ5ltrW1Fscmimtej42eNMVeJkGb3wAhtOEESXgoihM+ee6EOtOTnBKqtXg9Q7wlJQNeKdJSso3qQq0R3tG5oaXDS7RqevrQkR6dnZYExgWzOobjSFM/SswaM1vDm5ZuDJC5I0hAumkXLa5R2X6aCrYywqZQjok1NZzfAJeEBKDmGe0M9IJMC/3+DKPSL4XZTLBVJGZPOqPzZag5G9xt4E8Cimqq3dZE5wQbbECanBsj+Mr5rjLO5cYlY7qlelLjeQEIu1H5qB7ug9FaGy3gJJR3If37CqhQXfAhWunDF9ASvaVikXVrtgfVCnifu0ikrI+L3KhUjLWdKrnsKZa8uRBCwmix91yzo71BzvnRC5IWOtSO8eiEGm3zoxonAmbGxPMTEC3Gb+PrtfZ8lwY8G+TNueKREE96SK5xq9ACfNqyjyTwdpPhRBtytoSrO0UWWF2g0mHZwkyt1Dogna7tD9LDtKXNS7bKr2dqOw/memm2C55zvt1oofHqOO/6eFOgoUYd90UDkJu6d+tN517go+JcurAHq1vQRNE5s/05D+xfZ6tb3fW+hp0aq3r3tuAOpwd00mPzNWSsly/zxN+4yId+cJ/T912/jaO7+VVOrbC6OafdklJHyt29vje1qU3dO2UHBzz6n/885U1v4MK/8BjWwRN//5MMw/Cln7ypTd0jdc9DxtaGakIEatg6xTiuOQJ2hEDNxgAC8Wzz8whRCFme4tRULQVEECVbCw2RLtRSrbVTJIJnRFCT+CKk+R4Qr5WqH1xSAeZ0iwOGVcVP3Y/etwPX4OjKgjp3tpgwdAEXlIL2AWOsRrgIuhaKuBYqCTrS681dEFMoE2rX432HesWPr8LqiJKtxU4NxZU6biW81CzCcbQI6xSHaBUdtUka0CWUTwF1AlhYwCAn1WU+gsMRscna29IT9oh5BKVkqq8XpxB+cULBSh7HGjDKaoTlRKhPax0PpeTYTkyo7dzTd07XbcwNRItVnFB2pRVdACJbHz8RgS5oY1NzGrZOvPYIzzDxmHteA6hKaxcO4OJJz7y1468GvO+zNbW1TRcmu9sMh4IMBXTAGAKW1i6UaqN0oiEcMAZIxRlex1Z2yeRjQbASCyRQXB1BmLe10CByMLW1956QCtYEYHmcI0k7QSASASRtXUjbp3HszSKIqTShoHNCJeupVl3DqXEQ+fpxtGwcn1jeUtBAvaGmtBPCzfY6QSi9KUsJla/l+wYbS5BGKHA04bmdhGFGppckJM3xuzb/UEsloI83EyDndRtGA/AiuRriPCP5OM/zl+OjercFqZAw00QjoIh2PMJGYPSN1JhLOixwLeAdVpTOhMGhQ+lyDVQZzQnG9vc1UG3gOJPZvYULWYbEvCiop+2rk/Mu17et07Pi/HSPtk1uKkpx3nz2BT7ZVa78/IMsHqx4/wqoj07Axe1+efNf/zbVGsnfnaXbA5TPhaT1gx/hDX/rt/DkH+uZTVe3YWQ3v8xlHW62qVtSbgJ3L4Pf1KY2da+VO/XDH+PMhz8GRCjmpjb1aqp7HjK2LtwRLLaWx6ZeSvVdCyRwD481SHVSKp3WbX0aCq/mM5YSxqaMQiLBVvJiXCXSpK35qkmqsjJR1d2prc0yE2+lCB3H+PVLTPfPcfbt93E8d+qyYtOKS43WVBtwHyhuIbxLlmJeUS2IJiwxgIJL/DEtMJ0EdJof4ctDCisiY7kpkDwTiRUrdWzrdWtQNTz/3NdBMuaWvnSafo2hHEw8AkgqKRkDUZraLF64gZTwl7QaTnlSCoE5svF2/PbuqDfVowCFIcHisDJcavgqOpnUHeCoeb9F+A5oizrx3InWlGNh1qtF8cHiS6zG2EYUWgqtnRuRsd025go5f9pkDHCNlrHlvnEzi6if8LCsK7BpKDrb8dCCTHN+Hc4xP6RQIhjFGCGecqKlvSlAm0IUScVhhpRIBnrQxtjSggOatfCWUM3lPrJ1U2tTzcXUWKt743lN6km8fkLFBuoatmoK2FhiivmQ/NoiEb7tAAKmc8N7tPUrI6htADHWh6ZCswQY44THYVswCQFBUsWa1G7c7zF3Xdq4Ezqi6YmYENU09xn5ugm6RXItZdty7j2T8N5s5QkbpSWlE/u8zSXxNI5Oha95S5hOb0sCnIvncy0htlic0wzUa8ybaY/LFrCFy8Ck62M73cfnSinjPBq5bugjx/9rNwjifKmjZUM7njbu2wa1m1JSUv3YXme9PjZ175eKc/4XVkxfOOLCO/e5/HbHdupXfvzV6XeXPHz22j0FF1uJOCrNcuUurC8y7PLTv8LZB7+e67/vaoRz3eVVq25umtzq8hNfBTe1qU1talOb2tRtrXseMjJCrfW3j5YgHHDHxi/veX1MS7ON1OJQnmkzgXNFJMFZfqkJ779Mj5ZQ9KBlrVBrqj31Mcm3Xa6vgxICuzgV8YBHWhcMl65Q+gn7j55FdrfQpVEPj5HDY3y+QGsX7Y6qiK/SY1KpAkKfGSoJdHQCfYcWx4dj5OgI5ktEhmw1MVroRCj3BgarAWpLjk/XbZtmnt5wPu5TdRl9CEOdVBuSGdWI5qSaLyAmFiDEM/E5vP1ai3U8sbWeex1QNNhQKRg1WsZRqgtGBJ2sVqsASLMtRCpS47Udh+p4gdJl6zXgNjTdXR4OBanBeIbYB64WkLQpRdN3KWCrrUdZnVpjvolqtORWQ5sKThvIDTA9DD3X5ivmR5XCwGx3Qr/llNLa0OOx3gud9lT3mIPDIgI74uBQbbGeq6O+VPL/Y7JaqtJEEnyVLkdtIzAOcNaAW2tXbi3uo67wRDW1Y1O7BZKMPKTU/RkB3kqsqQCqjO2+odiLROTVCdVh1wxS2zqRWIOx3sASmAqpUk3gqY2lOiMItVQ0KhGUIhpzrrU6e3pmBhBbt457rmlPpV6o9tp7CpFwvkrlYOxTxhsQASbdBZdsExzPMyfcCb2dowjcbJ6gMJSZIuvtcF9BU6K6Z25Pu4nRwG+A+5au7dn+LHVBqccM0rE0ZUuVkuvRUqNZ8FGN6RI+pZ43CFq7e5sEgsQ8akpgGbnieh+2GzrIif1xYnqObPjuhwub+tL1+N5ljn74kMM/8xDn/puf54H7zjG86VGuvGmb4/uFw0cNn71E78TsEOh2Vzxw9hp7k8Vd3xb9hUrvdsj4xcqdUz/xq1x8x7vYfsfl2z2ar7g2gPHWVwt+23yObGpTm3qlSnd2OPy2t3N0f+H0xxaUn/61G3yGN7WpTa3rnoeMJhl24olCJJRZlle3cYHb/NXCcy0SZ8NRsApY7Vimr2AnkvgkQktaCu06dzr+NbZ+Jo5xST1Xhm60tu2TXmQtsVpcqA51cBYHh/jyItO9bWRngu5MKbvb+GrJcDSH+RBKtuUSPT5C5ou4di89lp6E2oZmhqwW2NEBujqmeAWpVINVrajmRUytEcrRoGwqLAOOhbLTzPFUbY6ZFG3bWSe+BEcwRLuAH15p3o2j+CxFWSpKTZWneSoa2+uOvm8l9p0KK6/ggURdBIqi0gU4GyJF16uF76TXNVj2CgNoX8b50NrjHcA8G4zTk7FhVUtVlurox5hSrtHb023dhi3iiNZEfp7p34zqWqSwcrh0ZcXzTx9ztBzY2RbOSs/+doT8UAlFozgl555PCz70MGTzvgh14VALXSdo3+Z0Q1gjFY3tlQBnMScqqhrttu3/LH0ZJQNxwhPghnUV418TxzjuXZvtCdajeym8HeNYm6UOrghihPIudnpC6ELBQwVYa4SRQF44tDSlIIhGDTVx7s+iAaxpNxHgBh9ATdg1+hkiFPoYbVPYrZ+ZSyaUp+MNCgJuRjtwCahnCXatIkVHpTKj4m/dPj56pOaYxjUj2Tbu3ThBTFJpSfOQzNFZycMZ+2RUA0qsT6QGc9Q8bjUhrgiFAXyBaYf7BMaZHgrJCGzJedp8JoQEsulvKazdEnJee5sPsAaKbU+2fzeomOeRtt+b+63Y5sL81VLb3ZKPfvM2j/5coV64iFy4yJmfgzNa6F7zMC/8tke58mYYtj8XGOgKZs8p2885ZeFc/N1HvO7+i/csXDxZcg9vo83nvPGvP8kH/6PznH7g4HYP5yuqOtwCz9FNfU6ZtziyTb1qSwvdIw/hyyX1uedv92g2dS+VFl74fV/N5bfH9dGVN09403NvoH7wI7d7ZJva1B1Z9zxkXEtmoMGPjB4O4GRD+gBK2t+lp5+F3GyowsGVgcvPLZmeFe4717HVN/XNibAImu5prfOK5OTUB0oJVZCxhj0NbuQ428V+NcWKMCwHlkcVX12jPHeZ6dkd8BW2FSc4E8G2e2Q6Rbd6jPRHG4ziAwwSKrzDY/zqNeTwAJkfQ10maDWkS+Va7fJujEewc/roWSRrBOAzIiyntbG6reFaC31o8rGEE9Eiq6lSSw/AbLcOdZg3rAuaYKy1jzdVXQOOZGtqSdhno0Nm4CcLVZe4I0OoMg2oHZShgbBAySYeyrbaAHOUJARxsxybZzuyU21AtQ8W1qZVqkQtqBA+1FCaiCRM8RHkhco1W3hTsblaKRcvzLl0dUWZEKE+fQe9RGKMp49fDBzRQkFhS8L36bgyDCuODmMvzraEUoyuAVPPFngJ1aaLJytNx0R3rA5ERLXmG9VUBKyXkPs6xCR+tm79d0v1X1MytvXglseSkSR7UTR9SE06TPp4LXXUFZeuaSCzhdcSbkX7r7QWbYATP2v7FNat62vonWEvyDhb2r+NE6BfkhnTXsdGD8ER2LYW8pzznmrV2EFQm7KwPTbNIksTQreU6/Es4SM07QibBvMytkDTHCClAcmx2Z+2hYaPvqDewpQI30Sjggx5gyCOa6kDZVpY1S1sOEaZhy2CZ1K1xlyQth/zToDnnlBv84RMrDfwVBe30BoJRTUesN3kBMRNn9O156ONa29Tr556/Ns+xeKfv5PuH79v/UOrDJ/+LGf+xmc5t7ODbE0/94m1Uq9eo7zpDTz5Iz1vOHX11g36NtfdHv7ypWr49Gd549+8j89+393rz2gbf9nbVm4RHrf5LHl1lvQTLnzPu7ny2+bUg563/acdw1NP3+5hbeoeKZ1tcfjIWi3tBeru5/mO8kVKuo4X/vDXUreE8//VL+CbIJhN3cN1z0PGABBrVZ66YtbARfOdI4ER0Q7soaQzEY4PhOc+PnCwdE7vwsph1lp5AVJ1Jw1nSIMdTemTgKJ51iWYyX7kUWvGiTZDKwHvtDjbpxyTSnf9Cnp5Bzt3lrIS6tUj6qXrAbm6Qjm1A2e3KQ+cigv/4xUsKnLlOhweUQ6uI8dHFJuHSnMZcCJav22EdJatlto1kEcEo3Q6jtdrHbdx3EIt678LqeiqCWcSVFjiuCLRTp4t7A2yNvBbEyQ1e0PVQgtuwf//7P15vG3ZVd+HfseYa+29T3P7qrrVqKrUlUqFkCWQaEMMpjONMTaJeYZPAol5LzgOBNxgO3ET7GdDwAl2DHYwbsBGBkyMMc+OsbHAEIQESEINQr1UKlXf3u50e+81x3h/jDHXPqW2Srp176lbe+hzdeues5u55ppr7b2+6zd+vwDBwTyVTgEKzUew+IDUimkZVZABDWuqSVdqrJrv72bjGLSUbC1NsDL2K0PRLtdUAEi04LVmmEyoZLXrUbexzdVHgJL7HUO1y3kMtdj2qQn7SyhiTCcdXV+CLxKelVYrTDTbwxOEugM9+AbLfdg5WAKCTpS+hdOMPok5700hmvvJiZAP9YTu6fk3+kUmXA2VWwKo0dtUxzUflgQJkHHGPnfXVLmG8tfFA7ClUs9dQDuclWK1KRZbynEcJg0ier5D8++EpuVrcSItjGn8ZTu2ad6fHPphw4ltrlZrWqRRXUbFcdt2e9JzYoeoFiyVrt4g/Ahfx6U9zjsj+Ex411LmSUiev/WWdu4JitFsic5U9eZ52EbvBMj2gJc+tnmHH6eJYLZAfIH0M4ZhQikVYUnJx8VYZeV7qSX3Z9s3EgE0ee5U17Q7YCTRTbkYZ8Ynq3piP0guuVR0jv6N63qu1MnJPm/8tsod77jhYypObHcXdnc/7vMvvewMZ489ty4gn+3hL0+l9DfeTveffR72+eeetepUW6uyr0q5Ccu9CZOta8+TdV2fvC5+w2ez91WX2ChGnS4Zbr0O1pBxXZepbG+PE+83Hn0VIDB9Qinvu4+n0yytd76I/+I7foUT3R7/+i1fjv76W56p4a5rXVe9rn3ISKH5ImoDit6CJmRM+g1IlAobFSiRgLu/7xxYZXLC2Tom9MVWeKKpEYHEDoEyRrolCQcikMEkfPkkU6QDELQAh0yxbpDOYDpTuhnQCW571IcewWZb+KZQ9+ZwsESqQykMUignt0DDj9F7hd09+kcepVw8hy72wOYByGqNFmqxbL/KLRjbyht+STVdqvQEg0zLtlGBGGpN3KG0VnIZw1mk9ZaikQSdIAjLYB0aTPT01LFDXoypRfNoW3WUZo3Z1JStxbx6pE6LG9TKop/CEK2z5k4xS9iTSqsGPYXwhCRasGttd5USdHomUaNBBTPsI9ZHzFMDLq0i5bpRsJWqzz3UjeEHKFAKHXDm9ITJTFnsLZnNeiazLiZgafjEwuPPHS8az3XHi2AlQNCyCgsKVjuWEr+rVIoMIzbztldFR0g3cnJaCFKCKR2X5KhMq54XuCLZPu+UUjI/JdrNLZOlVQtYrARXRWpAMIVUnrY5KEjRhP0xc577pAHGGOeQfouSbfPwpBAYWlMykPAzZHWWLbir3zutbToBlzgcDl0a96WPykgOAbWAqaliRMY2ZJG2FqH5rLbX93Z2cEeb9YAwqiHVM22clnUfx4kliPW8W6HSQB/5+FiBTdt4iJFCjksF6ArVoQXAiFVksYdMJtBNcF9Q60DR2E6z5pcq4zmsHeIijOrdFuZSqeMomhVAa5eHQxrSURlpIxZt5+URNK7rOVWfc/s9vPMb7+Lsjzy6WttPoeTVn8nFb7nIMX1ueSE968NfnkpZ5fbX3MO77jrLiZN7V3s0T7tsdUdpXVejnqVgel2fXkk/4ZFXCxslvkf4248jb3nz+lBc1+Urd07+y7dw8j13AKAfepB67tzTeolLLznJC6dxU/Xhz93gpl+/7KNc17qOTF3zkLH57UWSc17EtuCNBkzGq+eUGIaEC8WZbcH1L+jpN4VjmzA71Cu7UirGiwYoa2EoMKoWx6SEDMLwtPdTQVVRY5Xa29oTxSliEWBRjSqO7O+xPHcOn1yPz5fIYNHi2BekEyjCYr5Ajm3A/oLy4Dn04i6yGMKbEBnhUlPDRaurr9ocibG0dmfSl83qKhwnPPZ03D5t3nyQSjkJ5aB7QCQkPBnNxlAXaH6HAZO0ZLvmYUFTU29ZBpZkmIZphsoAXp1OAmD5GAYSqdLSRQvnMAwwVLQrgEcrdNsjMel4aaBtpTVrAMedBMAJRGihG8T2eYN4q79FS4Ce1u7bfOdCTpngKba3U2F7Kth0gnYd3aSNM4FNhm8IEvMkAbp1mOBTQcpAtQNceuq04GWZQHmgJUDH2CpaEkmNyr0M53FPiV6AqwYZSUWaSIDxMeTDnWGwWOsjmCZhYGxvBKxUPH0K2+FXa0W0J9J3lMJAyfG0tRGHY8NzsloTOSTPOGbNwzYg2EolyRiAsgrqadvhvvrbzIEA51IOvUH+3Y7xUXXr7fUTbmrDa5anDkn4x3gTIo65HEcC2iYexVtbMavU8nzTCHUJZWSDcKOaUkiVY65hXWmcFMfzzN4sCbTooflSihvulVpmMBwgpUKxDKbqxnGEslFCdVtWx7mTa+XQjgnImqrVbJOWNoZD+29UQecNBW9KYNb1XKxTf/h+5A0vw9/0jqf0+HLqFO/67za468T9z/DIjl49W5V9T7eG++7n9OtuY/m1+8+6bTZbt0tf1fL2GX+1B7KuK1m+XHDL/1O5d/MYJ36vcOvPvoc6n1/tYa3rGiufzyG/q3w6tzhVjPnJZ9dn27rW9XTrmnenHh3iEgCKQikNqDl4yfbE1BFZjWRnMRRje8u44ZRwahM2RFLFF+gjIEVTG7VWRk3g4anKWikeNUz1cJUIjYE4SzVgRgN+8TSrNUBgg0pW6R5/Aj2Yo2eO46UL8GQgXQcTpXSFerBA7nuY6fmL6GIOQ8VMMEr4BYlmW2mE1zTlkSXIcJEIvGmR0BLZF6RhoYsEU3Gl+eKZp7pCSirYQpkZqboWyjMNrz1SyRTzVZCmYLMGLxp01Uyi1gie0Bizm40ptqULdWPzwfOcf+kKnkZ4YoINqeAkgLOopFpL0aLpGxn7rHnQNRXcqKCTHpcOSlkl7SZ0akBORLGUYopnG3lT36Xq0s0C1mEIlV4qs2JMp4poYb6z5GDngGE5UIdMtFah1hrPzQWik44yLUxOzOimXazj0mHaB3D0km2sMTkRkmIJsSL0pylxVxw8oVSDqPkzcaF4+inGDg6wlcpHSo4pA0LMK57J5FaNWo3qFR9a4rEyJNcUM4rEPhT1bAnMeVUfgd4I6TyBohM+mB5K05FdOcT9k2zzFY0QFG1mTU2h6HEG1DxLeGs6bn6WfugFU5lILOGmwo3/Zft2OzaI80FrVU4yj5szNC9QSxArBfOC0VSGAcEDwNuqFby9zNis7ePNAk94qghFBDTWUYRttmfEPFqefxzHB8O1B52G+jQvzpzYZ5bgEwmYHirRhqYt/FGp6Dj3nq/R5iCnj6YUbknhbf5YzbWOZ+p1Pcfqps2LvOdPzihnTn/Sx8p0yr3fdhd3vvi52wL3bINun2qd/Xf3cOnCxtUextOuWq/5r9VHuqQz3Nb74LlYs3/7Ru78nt/lhr//eupjj1/t4azrGq9y9gbsi16JfNbLnvJdjWO/+wivv/RizJV+Z/2dd13Xdl3zn8SR7GoJdA613jrgipdQvEnjD+qri2KHiQh95/QkFGrqm4jGHf+IRFCFNrGPCC0hxPHwNSwe7zUqlhokCA9EETCzhALx0sNiCHhkoQCUvQPkgUfQXuluOgFbm/j2JnJiBie3YGvG9IFzTB94jHJph24xR32gUFGHakodwCvUYcBrDQBr4SMYYcoSczMoXkN9F0nJnkwmAiZqC58oniqnAFNeDaogFkDNqmO+QOqAWA3IIkTLc5Isdwkvu4QaWkqCGcN8AFIBaYaqBj6y2K+RKRJLOf5/QOuAecJEFUpXVnxJgpp6X8LO0Q33IVVeGfRiEQ5ThQBUogEt03vQJVV7NYARLRAnjyjXQtWegYKJMphQLVLDY04q6kbxBNtFcAr7O5VLTyy48Nic+cJZDgmd6oBUg+WQ86PB0LpCvz1j6/iEyRQ6N0w76Ddwn0TH+CFtZrSPR9s3rUX4sFG6j6xx3C8iJWBXjtWJY0BF0nMwQKZJAsCa68ksHBQloa2T4TUB47yb4VrQBLvuFuvSPYZXSajY/kii6TjeaICvQcnDXoYNTqcyNYJjAkxG0JOOwJ/SBUWPnRprgBXwJtW3nmDS873FfGy1r+Q6IjxOx3PD6miGEoAbXZ0DIjhmZIg44ZOZzpMZxtLUoW37Yp6VQzcmPI/FBvBE0h4itl2kINol6CvUpTDMl6DC0BdMSz4OtAiUBOvtXcd1n7E8o6AzzgXtPOeHQCh5E6atQPcaQVjm1KaYtaSkrJjuup579QV3fYB7v+2ln/Rxe1/9Cm786nufM6DtY9UTe88+8AYgD85CBfIUa7j/AU6/bvqsaw23NWS86rVWkj5Hyx3be/ZZLKzr2Vfd7bdy7ieO8Qf+/hu4/R98EHnlZzyl59X3381rf+Fz+A/nPpPn/fLFZ3iU61rX1a1r/ttQXN5rQLMEPId/p66t9zAu3F1RCupKkYBgBSGEPLZqtbZMKtZoQRyhAdB860ZVnDRwmd6PaLQfHqY7nr/xggwR9uGdI32JMXuo3twW2IVLyKUd/OQUbjpGeeEZuPUkenKTurtHeewJdP8Anx/gdTkq5twW0YLdOUil+cylsGxUzLUeVPearMQPpWjLqJryWrEhWrFxQyUeV+hQDw2Wk8pPi8RcM08Vp6Y/JaHQEoEBbEj4ZZWiQilCUc1hZZCGt9CaUNUZzpKKazpCBslj0gdU6VxQKVC6yOp1iZYmcv7dR5iqqqsEbUnwooIqAcMkwU56LjbVarSteq4jAQoVYbE0lgfCYu4c7A8sDpZ4NdQqWmu2rUYL+MG+sb87cP7xAy6dX7C/twx1ZzV8PiCLAXWQmuDJAFX6acfGyQ02NzukGlbBZjMW3Yxh6MOLT1aoMfZDqERDcehQ8/WKgpZV1zF5zHisWyXgMebIUGEIlW2bA0t1ZwA0H1Wb5M/UFYoirjCZJvBjbJmNF2l0KhWyCf1ZodJkoL5q1fcAzS4aQSQh28NNRqVy802MfRoK0+KT2KqEf66F5p/osrISEAnV71hWkVqRhPNjIE4bX1snhwasTZeoCaxHGBrC0JoUXHOuHaVm0IqkYjPOXn3DduP2pP4wWt8ZiPTw0TU1fR9tBccF3CpmRi0bWL4mI+TP48ALSsm1oOAFElgiXbSjj1tZEYY8dyTAHreJUDki1KZy9BA3S+7ndX/bc7uOf+lDdC98/sf9vR47xr1fZ/TPMR/Gj6yzx3au9hA+pTr5Xp52kuazTc1o+dm6rqtbY8PGuta1rnVd7tLCu7/rFv7YrW9hs8y5a+tBPvRHjz/lp9/+t36HR/7o1lO2iFnXup6t9Rz4NqSrdkEnA0lCe9T+hF1aQ4DZztsCMIIopipRV6hDdYRa7kK1FLVlIi9Fw69PSwA111FBZmHEl++bmiUDqjCmL4siXvIivYKFClCoKAf44xcoXUd/40n0+m3KxozlUNGHz6HndpDlHPUAWj5UrA4gFSk1Yd2IQ8Irr8aFfyCFUIqpJNxJCFerM+SsebJIydCG1uoJjonhRRJYCVI6vBTQgpaSsHLIOQ74G0EVLXk6AGALTxGXmJ9M71WJwA1LwKgpGo1vlrGttliE8rIv4SupjtcBaijx0IRjrAJhTAVXRbWgEi3RLZQiyse0Y0j4GlMYD0kgiINVZ9gfGPYqdVhSeqebCNoLJhWouA+4h5J0QJnvDuztDRzszvESsE3FoA7oEGvAa8UXAyyHgFtE6/3GqU22Tm7QFcGXlUVV9mSDHetYeMGlS1iVKcaF1KHqKqQnITsOJsYgoarFhcqASaQTN9VmTfTHYJRs5faa+yUtBOL4aJAwwmEC9E+h79Bacy9oqDylBNxLKIh2qPS0yGjP4230Pcx9Y5K+fx7KyRba4hLwLrcwnxKtyC34ScwTdiVkI5SBJf1VJc8BTW1pFoniHvcuRrWkpjzZxMcW5lD3Rqv5GH6SSslKqnkT4bmE6pX0kWyiW0+bhjjWlCEwXf4+oGC6NUR7eSp+zQWnQ+gQSoDdGuOcTI3pxoDIAqFQ6QhTyhLqQpM4D3q0bnuqd0WyCd1T1Uycs6I1vqlA4/wZ6yrG6tmqL24ZINT8VWMbSluA63rO1u3HzvHh//Lmjwubd778M3jJ8x+6wqM6elXk2ReQtKiF0+/cf9rPG+5/gNv+RWExlE/+4CNQKXxf11UuX8Peda1rXc9QlVMneNmrPhTXZ1nD1lP//moHBwwPrr7LlDOneeQ7vpDFH3z1ZR3nutZ1teua/xS2plz0bCk0h1qzi9Gb5Gi8vm0sq1m+haJNRy858VAp2vhNsimKUtVYBCkyKhy1+b4VB5TBFCOAT6jnok3TsGjdFU3wFPpDVAMQiKRiyig2UHbOw7mLSN/hDsvlAJf26R67hMznAaWa2k4SCHgq8GpLyI4EbUmvteKrVGg1UsGnq7kQoaQqSUbRnuOlUCUUSq05FMm5Sx82tfwhEl6Pork/lgk0nU4KQgHpwj+vKKb5+Ey09fQZLFqi1TpDaaIDu8S824AMCxiW8RiNIBWxAbHoFddU3oWPXkfRPn3iAvB6Bl6YBMisVnGHWi2TrtPbMdePZzttQEYf28u7idDPCpPemXROJ0bxBF2e4KoUWCqLhbG4NEdmwsbxGdNZh4ohwxKtFa2G7B8g+wdwsECW2ZhahLLR053YQrsJiDLfWXDpwpKdxYSDoWfwQiX9ImOjAlR5zTCb2PSWa+IeiehJ+hDpMqAk22Y92tZFCj76UKYPYs6faEnAlanLifK9drhOwqrAF6kI7gLouVPQDBwZl0woXbsIiqEImio6L+0Ggac3YOyTGHpTLeqoGm5t3q00CbHSWrElvFMpGAWnAM1fsc1P0LbDXohCKARdoEho/3Rsic5UbZek8Xn06So5uoXbSN7qaEE/kmnZ7VEuQhFdva8TALB5IXrzQmzKx7gtYZ7PKU19rUxV2PA5vQDdFJMOvCkjJfadlKZ/pckP27nA8oZJS9Nu59nwM7Vsr7dMfE/LBxxVa26ZgX3Dn2AtPVkX23/gYboX3P5RP9fZjPu+tlJ0TXA0E6afTbU3n9Df8+in9NzZL72Fya+cYHgWQKNhKOtW3SNSXmX9kbKuda3rspe98BZedfLD47+XXrjuLZ/66+193ov4V9/zg/y3/8e/przszsswwnWt62jUNZ8uLanUy7yIBA0ZkEDAvbDUy0RdVq2bDql+dEazvWwlbBHBQrRK2iFA06RtkqEo2ZGLu1Mr1OpMpjICjsZ63Z3BKkU10qAlficZimBjW+5AWR6wfOgx7LqTyOltZL6k7C6QvQN0vsSGGkCRhGk5jrzXPra2FnxMTTFq+OUhiPr4vmKEZ6DkYxtvCB1dNGRac5Ej1Ezm6auY6kZXigSAkpxzaHMT4EUgAViHy5AKuBSR0sBvKOG0FIoo1QZUJ1Qq7suAtoAMS2w5p2xMY0ga4FQJtV31CMJR7cPfT2rKwYTqtgrbSGlCU9Z50KRMGZZUaIGbU1SxVFWKOH0vVBVKCbCI1/Tzi5eOLuRo8bVUVEoRtk9usnVmStc5HOwzDAODW7TvI9B16GyK945Yh/Ud5oJuzLBlRXYP8L0F80tzFnVg0ndMB6OUUOBBW1uS6dCxX000Q12iVZzgRY0zjjaDzboQD7gXR4tiGo/XhGANuom3wJAm++uokz7UgVbjMbmoBEtVK6noa2pWG9eemmIMiGhzTx39B0efSdE0LRwRWSPlNFgHmgC+tS3nzzVUmCoJ+r0mDNNcEkPs+3a8J8iH3FZpr9eavD31fpZp15oWBCOSH9WL4emYSdGS7c+aYN+cIs3lUXIfhP+jixzaUaEk1Dx/eKZEx7oOQIw77ktUoZrhUhi8UGSJZrK85hyGfaLkOTDB+CEiHcC8zU1Lic5zlbSW9wS5bojH+ZL2bxUMXV8QrosXHH+Ct/+xu7jlBz70pJ/Xz7qTW29dG/m3KmpYfXao+3bnE7Z/7hh24cN0t9/K3kvPcvH2nmFTGDZh9phz4kNLZm/8APXcuY96vg8DZ3/sTXz49KvpXv3EVdiCp17DcPRB6HOl3AU3Rcr6xsS61rWuZ6b26pR/+Lov5q5/+65POW26zI09L3zF5of43j95kju+q6Qp/brW9eyuax4y0lr2JMFJ9NWm+qdd1a7UOXEXWhttDJCScKk1NoYozxMoxIV3qJvi1eL6OQMo4uVDRSSK9iB9pPe6BYhRzdcQpbT2yBItwUikC68UVAE91Qa4dAk7v0O3uUnfK3V/AYsFMESitDva2KgG3PMMkMEb2hyJUUDFBvYymRkDSwipQ6ggvRG/VFp6QiUyGCfUTWQ4TlNpCaZBqsp4p18zsfeQZE3IsBfP5OvEFh4wKMI6AppawrCAJQGEzI2+77NjtIVwROu6j3ZQse+8CoODqo5tn0VTTdaUVe4rT7/cXklCaKMKrYxtqw2sutcxzDheb9VaTaaOC9Hmay5oX9g8NkFnymSmTH1OmQ9QlzAsY3uc8BLseupigUwnsL1JEahS8AL99mZgKgM4YLlvLI5vcDAInYGWgxECok718BwdA1PkkFKPXALuAR8z1agB5tj4XPseytSulAwRai3PgmskiGORYG4tAXyo6dXn2RK8AvySZD4ao52WZC04aCiAm6JUcqyayePNH9UODTPfJrwVpYw/lFTuumZwjRw65mnb0IxDlVoHpMsjxw+lUGuDa9Eq36BjA3xkC3e0evtqbeXx7t5grTCYYAZdKTHlbYztnoTnGUvafmp+k22fpYK4Qel2g+XwOc8tuWYF24eyycKnaB2QZfi2TjpSkZkBMN7Oa6nWZaUChfTidF2NJ3+GrpTR7j6Oqx0PoR6P/bqudW38549RfuIG6sOPjD+7/w9scfvkU1PCXYvVqbF8lkDGvqs8/OVLFv+vm7l+a5cXb7+LjbJ40mP264Q33P98yq/fxS3/9iHqB+550kWWLxfc/m/Oc/fLezamyyu9CU+pLKHWuo5OWQ31/rrWta51Xa6Sd36Qn/7XX8L8tjmnfmvCS3/y7dTd3U/59aZvfj//7NwX8N1nXsdPfu3/yf/8S3+S2b/57cs44nU9Y9U6466CSkKPHUOvP4M98BB2cHDF3/+p1LUPGbUBorj4xmx1Yc5KvdigmWfytKRi0TOEpHm8wQqaNaWbe3jVrcJfgkpaXoiHOjBaaDvXEB+pIwXcC+aGpipLNNqWo7+5qXvi5yRorApSnXIwxx+/iB3bRrc2KItKN18iw4BqpDhLev7FwBOG2JhRGzAk52TlR9e2K34mqUwMxrBSM4lopmFH62zAG0V0oBkTCbrKQsESeDRQaIe85iKJOjN8o1267Td3KCXGN8SFh4lSPRReWlatrFoI/0CrMC9orXhXcFu9liGYCMuFYQJlWihulCK5L0JhKJaKL7MUsqbfYvPdbGosTV9LS8WWZuurj3Qn1tEop0vApAUpBbpon++nSj+FMt+l1AUFAxswG7LdF8JjD9wG/GCJLJb41jbdbMZAhLmUzSnFnen2Jvtz4WAQNo9tsLtjTCZO31U6HHWHzAiX1U7CgWpxZDS/THGjoHgpqS5NRSTRql5rgFUvnopDT0Wio9rFMzShmPSxNoZIMy8J0zJ5BorkUsy1OyaQpzohVcQB+n2FqC2BZ3p3uvKkYzxFjAHJDn0wtNZsJ0NixkM7TwSjGrkiUkdI3PKdkQSUCd0kg5MaYIdDxxY+HtOkd6OnBUOtsL/rPP74nGrCdTd2HNuSJuBMOJcYsSmv2xok1Jw2bk+2m483VGyEmI6nSDT2q+qCgU12hymXHtxnsT+weQyuv07Z6hnPAQ0wq7RX8Ry75fkpblg4dSXxhADUbUYlz4UGIhbjbPO+NjNbF/CiU4/x4Oe+mNm/CchYTp1i+fuenWEnz1Rp+2A54tWp8cITj/H5Zz/0CR+3URZ86W3vxb5Zue+PnOSdb/hc7vgHDzDcfc/4GHv7e9C3fR587tFUMzpgJp/0ceu6cuUuWFV0rWZc17rWdZnKdne5/X95/erfn+br1fMX+L9+97P57i95HS/s9tD/8WHk30/w5eKTP3ldV610NkNPnoBSsHPnr2iyfXfLzdzzXz2fYcvZePhmbvzxt17R93+qdc3fdm3iJMZAAkb1U/w+L4SbN6MI2oVfmqSqUUoZPd1aW2Jrf46L/6aMSh80axfjOoLHUGcly0vVk4qjDJTWD2ypQDNnGCo2RCjGqMyy1j4csKL3SnfxEnJxl4kLsj9Hl8uEW5pMMZKx1So6OOUQbGuQofkDUg01RVyoQ4Y+1Dq2WguexKABhwh1KA2YFInAFxG0lPTsk0wAllS9HdonojF3pshwyLOtK2iXadAmCIW69EiEVgW6aKmmx/uOSNLVVC/GdqmC2MByf76SgJVs17ZVYI9Zg0mWwSpDbLM1OtjmK2AqHiE4bp5gNYhUdrumUPbQBaCvdJojdEriZdrh3QTRnqJK3zt9ndMP+3T1AKlzVAa6Em3eRdLT0Rb0tqQb9tH5Llw8BzuXKMsIFhoWA6IwO9axfWaDyeYUKx11MmM5TFjWKYP1LJeKDQVXxYh9Ya54+oTigmqHaIeZsjRl8EyGHoy6HBgWxlCbN2RA5qB7JUJc0n8UVbzrkDJD+01K6ZES6wRtgUo63g5ooTaxtELVq+kHSVP6tvU1uiI2+B/grwBFIp1cGvjNYyfOB54Kxji2GlTm0AV8gPhQxbovgQq+8hRUUZL/rm5UZDS3yGiEkPwxzg+BByN0SlIl6sB8CY89XnnsfOXxiwsu7i3Tl1XGtTNC+VyTTTXalK6W/90UuJnC01hoKiwlQX+GMYXrKHtVufdh45EnhPM7sL9w8hSUMDOP/RrHAB7PUwk5YthSrG5oOD6qmuOmS1NxRqiUWYbK1DoeU+tal+Lc//sLMg2rC3v+zdx8+uJVHtXRKnkW+DKqODdvXeBk/9QDX1SM27ae4Cu/7HfY+zE49998AeXUqfilVW7/N0+wszt7hkb8qdf5h4+xc99xfN0ufeTK6hr8rmtd6zradef/vs8v7IQf4w+8+F9SP/9lV3lE6/pEJV2HnjoJXRfXpMe2r+j7733mzSxOONbB/llHb7juir7/U61r/htReJelB9ihL+WWSq12wW5N6aOawCAVUh5qHRFF0x3QU5lj5gFA0ABOGQSiWkbZVECB8JqL/BIfIWEDHSHHC+VkdcE6Ae2o6YXmFoEiWkqEZagiHRQZ0IN9dPeA+d4+zBd4XWAMAfSyzZkalKAWIh26wVGaBipVhQ5Wwz1NpUTadQK8lpzr1bD8ExByQCCUcXWAYRktlJJhNemap6mQFCGCXEifuRq7xVWzFbnB35h7TVWbdoJRI7VWLBWqFhAl91fXdaAJnSQuVuswJHhxbHCsRvt3QJlss/UhErVZqeLGVmdnBF4qYeoegThNERaAK9K2nwyBaEAxwy9CrZVqMulgsoFMZkinFJvT7V+kLPfAG+isUCuS0eXRClwRNaoPRHLvgrLcQS89Qbd3gTItVOnYeWTOwe4CVeg2CtJPkWNbDGWD/UXP/jBhzgYH1jNfdCyG+LNcFOZzWC6V5VBYDMrBoOzPC/tDoS4cG4wK1KoshsLBgbE8qHEsSIbBJPTVXO3miusM2zgBx44hWxtxkk7f0pbIPIbDjEeJEoEzZfR2fJKIR9IX85AiMI7z1b3FEWpzCFKSENCb/2E8KA0OaKnjQQCj7b7t1hC1xtrzpgz0DFYahyUJ91ZrivaeaZ0gUnIscW4wF+aDJ6g3Dg4Gqrd3bKE2CXDbMZoQX+SwWpbRw3GcA8hZBcnXU+9iPHVAhwXdRPHNCa5KNy10nQZkzqOjnVEdGdW84nXcrrb3RuLZ6Pt47s15kjL6QrZjA/dDdgbr+mRl4/64NuuVn/8+Hv5/vwqAnRdts9mv7+gfrqMe/qLiPG/7PNdNPzUFqorx8lMP8Or/4S3c849uZvFVn4P0E+zt7+bUL20cqRCYC/ed4DO+/xHu/PO/yw2v7VNJv66jUp7WROta17rWdVTL3vYu/unf+Doerj1bMlCnn9pnnG5toceOsfaJeIarlPiTtbruvzKly9X3P10IfuHSFX3/p1rXfLt083CL/26X+w0lSPi9cWiBZJACtMCXkBiOysT0SSRTece2Q5yWDDuCQ2vAMoIRGr4IUClj8rK5jNfh2gUEKerIbBIX49GhnN5nSiWShiuCDwewvw/782gldouAE7dMRQ5AdbgtefRTk3i85L9NYh7M6qj8Cg83TSBXohUSQYoCkdJsXhs5DGApq1ZTlUi/trws9gz18Jw7vLUY2QhpzJwiQvVUPTUgmdxGXUZ1pUmlxYqIW0CjEvOkQGdOaWDPm6KrxG/diUxshTqED2XzsmvAVRI+e6j0VCPkhvQVrDXmIyWqtGTw1pob68ESAqVarxPQKd71uDosF3Cwg9Y5XpeheusL6pa+VB4AtkR7+qjcc8ukb0N8H9s1dFLoT1zH8tyU/ccOwObUxRI5PqM/PqU/e5xhZ85yb8liWMYrWcUHC2XnHKpl63CJ9tth6dQh2rm7vjKbOtPZBO2Dv1kd0OJYEaCEL5WA9l2wOClImcDGMfzYNkwKVh32c+5yO2PNpL9l9J/jGhDfE/a2tuPVGubQ8dugs4xrcEwMx8f1HEFNsWYslcIxjtas30CkhMA1YWQDeDZGrzDaEYSzQPP2XKmOx0oRoEtrU5dUSEZ7PcBkKmweU+bLigMbWwXpg8WNoT/pGRk+rbJ64Wzdbm38ltvueR4Lrp8BUuOMpFJaoWPBRtnk1JktLi0XbGwJJQORGrdtCmH3UHSb5TGtNdStCYDNHdEGQQRcMTkEW53xRo20ICSXT7/n5DlUe8OE6ZNX2DVVE63s/f4d9Ce2eOKuwvGrPaAjWHKEIePp2R6nJ59+604nlS+7/b2c/182+PVveAUv/smB0//8zTzWv4pzv/+Aja0FRe2qAFdz4dJ9x7nrf39obOs+86v38vAX34xM16b9R6lqVTpd75N1rWtdR7eO/8wb+Y75/8juTYUbX/c7T/krsW5tcf4Pv5yLz1f2nxe2VbOHOvqLcPo9SzZ/64PUx9bBeZezfLHAl0ukD/sv33vqHRuXo2YfehwdbsI62LrfqefPX9H3f6p17UPGbGkdvf7yv8bWwQwqONzWGCnDIcJBQuEXF+sBG4MdharIG5PIK/cGf2QgFINlpWyTiNrFXaiWqbbJSVTae61ghtcACC4kbAGvNRioKiJOqQt8bw/d3UEW8xy74RbwT4SAbr7yOOyKUusy2h2T2WCBH0I8KSN0jPmJUBonkmFbAjXmeHFEumgrdY9wm0jzSNVfwjzTSO1tPn7Ns49QVrX9E62flp6ANaCFxH501VFZ2BJ71WKcmtCPXqH6GMrRIFCRDvNljEFj+8pUodZ4vWzpVk1lnUcrrWY7t1vMoSsRYOMZ3oOGqrJ50a00a4x+jqRXnrb04in0U1wVXyywnUvoch+oqMTcFOmy9VtWCs8Mzwj9nECGrEi2zdrg6IXHUKts3XCKSmH+0EWW+/vYch9bVk7esMH09DZ+2qmLIYKCBsMGY1jCQhYMy4SO1Rmqs1gMMZ+74a+o6sx6ZXNT2JgpUjpMjGFZcEoAaYEiHaUUaunDJ/L4NroxparjS4PlAaaCDKECbOnO3g6KcS41k5oDOIpXhPAylfF/qe6SaK0Oj8TmmZiQN5ONyX0Sj2iPUVzy+HYP24IRVPYRaGRDED/RANMpqozjQsebE6v9H4EmYdG4Wht+SDGJFEwEZ6CfCNff0HH8tIL0bEwKvRuaNwbQhPPZ2t48KcdzELJSUBbBYmlSkNHj1Nsxr1AouIUCVVmyKftcf2aLjW6TY1tz+iLg2dad3q0tuEY8U8B9hSyTWY6w1pBcr21qPM8wdTWHQCxsy/PGup5KbXdzYHK1h/GM1unju8jmJnWyXhcfq45q+MtWv+DmzQuoXL67Bif7fb7us9/Kbz/vdk5/962c+Ue/ydl/exY7e5pzLzvB+TuF+fWV6Zl9NqZLzIWiRjl0TjEHu0wKyGUt8NsnuOsnPsDw0MOX5TXX9cyVW3SvXGGxybrWta51PfWyytbP/RZbPI177iI8+Cdewd4X7oL4CHXqyfhK/sDnCPXL7+B5v/IiNv/j249sQMizrtyxx59ANjYiCPbTCP75VGr40L2cfueNXLpNuf43Hg5R1hGsax4yAiubtRbw0HwYx1AJWKU9pFKRgE3hsVchlWzRritjy6GZoSVaecU9/pZUnnnzZwuF0wihilBasAjZDp1Kw0rNdsJ4XgSRRBhMrTXeywNgilv4F873qQf7uEdntNJCZLoMU8jGU9Vs4Qx1YBXPdtY2B1BI+IqNEJQEBKKKS7ZHesyF4rhYtMimwtBtSLgoWM27x5LtnAQAidTcVH66JU/0FRTOjkvN1mbcQ3gpZBu4Yq7BWqxiGrtNzag4rqGWM5fwjtQEto0MqdGJUVNBSdoQ1mxp9kwIdk33PbEm1hzb5F0Vk5wnYimZZAq2R2t5otKx3V6KULsOLyXUqAd76LCItPCmpDPHZBnTXhJ/e1NrFJpAjPTEJJdx8YrVPfqLA1vLOf1NN3Hx2Gl27z1H3d1nfmGfHXGmG0vKrKObFLTrkA2hTCYMokwPjMXOnGG+BIPBQPcOqMOQ6egdmFNrZdcG3At9EPIAgN0E74BOGSYTulmHHtvCTm5Q+oLNB/xgASoULTSVrKSvaayrBg8TmFNHlBgBRF2sRyk09d5oASBCkYRybY+LpxISJNd1vlokoJOqQBkX+6j2iyZjG/9HKha9nS9S1Wo4nTlopQWZuKxCZCzH1DikN1WixP4uCa6ZOBudjCroEN82tR9IDfidYs4IcpIV9KMpjg+HqGhTPGYoUSp6I5QngqjUnUk54MTWhI3JFtPBKMxxMdSjdd8lgKFKjbWXbepGhAKtVJZxLosbCdFqPiB0mmjVHbyGchhNb1fWV4DrelLdsn2Bi3fcQpmv18XHqqMY/tKXyvO2ztPJM6Ma+9wb7uGXv/8l3Px3X8nwa2+BBx/ixFvhBFCOH+fuP/2Z3PWV76LTyna34Fi3uqCaW8el5YxH59sMplycz3ACGA41Pmdrfsh/PGWkuXDx3Ca3/kJh8xffzDCfPyPbua7LX1YLpVurGde1rnVdQyXK/AwfIXI4/Hun3LTHA9+kbL7os7n5R39nDRovU/kw4JeuUpuyVY797Bs5MZtSj2DgS6trHzIaAbsasBrbI2Eka57+hZJtjqnIC7Geh2oKHSGEIrReX1EyubiMUCG0QykjGnVW7XUiREJ8lQJrvgq6UGmqvhyjBxg0YoyDE2AAmlUculhiBxESIZH+EI3bLRVZJOhjU0YG/0PpY3tUI8E4wYcgeLXRb7AOhjFQNAJZXASrNoaeqDnOQFENmEKf8NUTnGWrt+oIe0JgYNE2ngA4I5yjhVIc9VTqweoE2toqNbwVS9fBHKxWNDezKQyrF6oJnQtVwzdTquEaLyBu9F2+tUeSeONM0nwjR5CiqGvALFHMHFvGlhmGdtki7KmaqxloARmAE0pMSoeUHqxi+3vIMEdsmcrLplAMxWf2V2fASVN8xrgCarYE8/iJCDA4IvtMa0UN+uvO0r/wDLv3nYOFocenlOObdMUoy32Y71Fc8WEXrQO9Czrp8I0+lJsDLLc3YyiWrcHuoB1Fp/QZ0iMZ1FNdqBhelDKdoCc26U5sUrY3qWbY7n6knw+x/0ycTjWgdAtekbZFAcPGOBgh1pA4ahqqvJaAHnsojp1cwyGk8ZU/IIDmLLfjF7LtmFXbNITiNeF6rKdU5RqEn2HcjHB13JomL1vO0yeyoVejtVbnEs793Lw8DW+skq55I+QNEZEGJBnBZrT+NyZ3aNuazUPKqpUGTllBv1yPnmur3f0SgYkb4ntM+uM4U8T2R3gp7Xxp0cZOU/sKlAShbUwFYu1Ky8KGTrvRrmCUah86Bzu5fetaV1ancR7RtVfnx6wW/mJ+dI6bs5uX2OqeWfD2Zbe/lzd8zwu44Z7bGD704dUvJj23f/E93Ln9sZWFm7rgVLfHbRuRTt18TQ+sZ27xVfjDu6fo8gbkQe24sNgAYD50XNifMbzlJJ/xz+5j+NCHjyDiXdcnKj/89WJd61rXuq6FssoL/vlDvPs7bqC7Yf/jwkZRY/dz91i+7TMo/+l3wrvx7PXUM8eQxYA+doH68CP4sP7C9awpq0cyUfpwXfOQsanyRFZqs6YSRBSz5Ri6INneFwq0VBkloxCyxVJao+OQJCpVd3nxz6GL9uaP1rzkTHwVBJMAM9RX0frcVE0khPCRZaZyL4McRKOFstLawCuyXKKlizvwGqE0KtnG60N+uQqvQLPEHBpwxK0GYBEIZVG0VJo7KpadlrFty6FSNJWIrW0Ti7Zky3CaFoDR5LttDnvJpOBQhbmng52Gn2X1UJf6EMom6SW7v218PUcwjZZQEWG5WFASKGmkvdBoo6NUE4qBddlKqqkqzH3hGCXhZ8ytjLBLR7mYEG2tUHKdDMCyhk5RJIBuJ4JLKNk8n6ca0Avt8G4aykgfsJ1dfH8PfIHVAdQSfAVsiSWjh4AU2f6eMNsDTml+Y/ZR9hY+jfgB/c5jiC05ecOtbN5xmmHuTDc7+jqHC+cp+xeRYYGT62Nwqg8BVpkgOsOkS0AU3plmQrW4QBMRbGfAO4XNjkWdc+mxgblVaoV+NmHr7B4nXnQdmyc26TZnzMXRvTn1YIlqg/cJzqQkkms7Q6AUzAbcDNUy3iComt6XFnPgluvXHVwTzlmq9vKYaLg7Ib2iVLc49px4HozMLjweGSFhG5Nn2Em0VEfbsmdwVJw2WoZ1AjRv9gwB/8RHHWWTI2JuFA8lbGPuTew47mNvoxrSl1PTHzHPP1LAPdqjxy8aPipAEZ40xlBWro7RWhcUd1z3qLoBNsV8GUDVAp7HcVtRizVZRDGv442Z1oaOrNr6C1C90lxlGxcd97XEVjXF5rrWdbhOfGCtPvpYpUcMMg6mXDe5Mi1Dn3f2Hv79d34Wd/zVx6NNSYRHvv4lfOWp33jKr9Fu2W3qgk2NYKFTJz72F/alF956/nlceMcxhnvu/fQ3YF1XvNxlrWZc17rWdc1Vfd8HuesHD7j4ubeyc0vh0vMNPzun9E8+1wkwbBb62YyD//wzWBw/ZLfykuNsPHYj/RveuVY6ruuy1bUPGVPZBHnx30wVlWyjbS2YK+Vg6oHSZ1FW1m4Z0BAttw0EEeo/GS+v82I84EGoHdsFfR7Qcghc1PAolCb50SCLTZ8U0IuALKIJk6J9tPRdhjJUZBgQ6QK8eFMiyghQ4mmW3oBxsS9DgwYtqKGMIKO1PdKgaPqvdSVUiiMJSaWVuEYoBJatnEE+HEdLCdBYQ1kZiqwO3LKFWkAF9S7bgjUUjCap+IyW1aCd6aUngpplAEaCQpXx346jvWImuFm0pyeAjVBeyRCLTKKWlceejO220aJeETxb3C3Vl8NgeCmpDk3QbIdazFPD5QZeCtZNkdkUhiV+aR8O9pE6j7VTJOFVvKc0/8aVqA+vhyDOIdXjqDDjyWMPH79Kv3sRP/cQ3Zmz+PEemc8pF87hu+eQYS/CdjyOE6yitUarsIZ3nkkJeWhqBasXTDsqAtpTSxcumweVvfMD5x+ZYzONfa6VvScO6E/uMrnpBGVjinQ9Q6csdyu2qGxQ6Dw1mlpyPWfGtLQAFc0gl1QgWoBE0sfR8oaArI6a8XgOhXAcP02ZG16gYV4w7inzANArTBZngXb4e3v/DPXx9MvMndTCaST9X1c/k9U5QnPf+bizEvrFtoaaLzfBm9pXxnb8BiWVVXp924AWguOaauBUKjok9G8p2nmMjgi0nSUFNUWsomUX046h26AsB1x2Ayi7jaE1bYwBgOMYLx7z7M17NMdoZtGmneeaWmPNV1/mOTJebASe61oXAYEuvGjG5qMD5nKk05SvVh2V8JdpN/Cy449eVh/GT1Qqxh/4ot/lnV/7crZ/9jdZ/MFX86pvfyvTZ0j22kvlc07dwwN/8Tzv3X0Vk//wpmfkfdb1zNZazbiuda3rWqzh/gfY/PkH2ATOTqfwmS9m97Ztzr+4sH/W0KVw6t2w8ctvAaBOP+IkKML+9RO6z3wxvOkdT/pVOXMauo768CNXaGvWda3UNQ8ZZUUUMbGW6ZJJxBFOgJOp0RG6ohopyu4NOqyUiZK+ZCE4jNbplQqQAGcwqtIaOYguS3kynEgoNmZcZwtotRptwKlQiFZpxWtL1NVUVAaqdKv4sIQywawFPeR7WR3hpYfhIl4DVlSxla+TaAbzrrztOKR8DBAno3+aoAkZHFUDF+pArCjVDEJJRVetSCkBvmpMSqIjnHiNkkqpUYyW2xeJyqFaU9EI5UBDhVl0BG5mjtBF4m2CZHWQ6gzLgTLtUwW38snj0DnWG1SmtdU3JWqhmiMe4Sue6bqlKKaKUyIUZHCqRAtp0UBy4BFx3/XIZIarUA/2sP0dyrAID0VWJDHUYJ7/bbmuMggoAXPg0wa1ZfyyHLsulXENfAmIVNi9gJQp9dgx5GCJ7O6gw/wQZJLxuStA5qhVChVXSRWnMaHLYBenmlC9Y2BKLVMmJzo2as/ezpJOlaKVjglWw5uzLCvmRtXCE0/M2Tu3w41nlF4K6kP6G8Z7uYWKsCn+RsCaydeOImIZfNJSl2L/uq8CUSRDZKQdxMQCG1XH+bsG51TiQgTN47T5OeY8Md4wkPx5W0+xZwzFPe0SZIUrpa2nBugOe42OJpsNsMaKjKCjlaozgKWP7d0RwNKUoO1Zh898K8Vy0swUuyYsZRVq1aCuu6HVUQ6wfhv3TVjOgVA0rs6BuVZcRvWv1RyztNcrCVAT4JuPydPjjZfV3RXW8dLr+sjav0647v/+EO996BZectP6C+5H1lEIf3Gg1/qMAb6PVxtlwaPfsMfk4ufgf+ZRbp5eeMbf8+bpBd71Zx5HH3wp9vZ3f9TvfbmEpcD0GR/Kuj6FWqsZ17WudV3r5fM5vPn32HwzbAI6m0WH5nIxfsvefv8FLnzGyY+64zK/boOJlvRTB+knvPuv38Hs7C4v+J4NhrvvuaLbsq5nd12eqL2jXBaKuqbwEmelDEpQE/CK8DAM7BUBJCUu8ptnoEu0IYcqSpCSXnnSZEggUlJ7lKJEEi6oQrZmRrtxjMesIiZopkKbOlJ0FZLiGajRYEHSMUt/PG0BMvNFcIRJPyYZU4Cuw0uAITznoQAdaEkEG6Rv9F4LPuoJBVbQ1EXG8Zg1yFBTCak5H6nWSmUhIriW8I9siivSh9ECAqV1Jc17LjiqpJdheMhp0QywYLwd3RKQHZCuhEcV6cmnAEMoK5ZDzKGWmEc/pHdbGeXl3wEsPVt1te/o+54yQkBFFEqn9Ap9J2g5fJ72CEZZROKwSkH6Hu8UWy6Q/T20zoFhVNk1dV1TKTZIqKnqM6vphRf7p5CQfFxf4ZMXCcyene2WKA6oc9i7gOzPYRhQWyK1wtBeN5FRrumiTa1Xx9co5vHHbYSPEypTWbAhe0yX59mq57nhTOXMdVM6d0quaToFE4aDObJYYsvK/nzg3BNzLh3AgglVJ4zKX2meiQ10S1sgURoEWSTWrbR15XGcKPnzVK56tkfT2vibt2F7bdFQ2qli7umbGm/VoH47/lMuOAJGT/BmljczeDLsVSL9W2hesL5SGI5jyZsNGi3Qscba3ZBIiW7DQApS0ibBLBPvPc9nAfNKO1/lAolAJRlBbRz04RMqomMqN+ppXVBRm6M+ULtNBt2gElDdRDMMKUNb8vUMGMTj2KyWvow5N63dO5rjYx+Y49WpQ6Y5WZ4Q1rWuj6j66KPc8b8e8PDO9tUeypGrcoWUg5+ojk3m3LR58aq89x944ft4/ve+my8++74r9p5fduN7ePef3kK3tj7qd/XRx5k9fM3fu39Wl9W0V1nXuta1rudA2cEBvlw86Wf1ne/l5Jseoiye/B3COkHK6salzKZs3XyJl97wMPd/3S1XZLzrunbqmoeMXiR9DcO7TUcdnlKH9O2DZnuYnn0BNMLHTEDKCCtBUS2oJKwSGdU9kbAcF97uktZ+0coZCdU2/hmDOkqhZhskmqnTHmBt1EeJUPEYgjteGU8CUmsAuyETaCcTtO8C0BnU+cBif6BatCZ79RFKiEsGacQEaFM/1pbuS8CvQwomIGBIV8ZgilBq1lEFGZ5zoXzrPfwjKV1sh1iIGQXoCzaGVZAK0RhH4hqyTzbATV1l/q60XC052FOhmUjWHWWgL5XehviddulFl3BNA2a1sB9P/aElGJWEMkWVUlrqtaMqlA5KcTqBTp3JpDCZKEVJT8n0GNQJlD7Uprs7sNgP1anXADpuiLXEY1uBXUsfvaGibiDZom4Jrxyo2W6ePyOh6QjSEkBhFZ0f4Ad7uA1ULFS9qe6tuTZbW20DQSpQxmTizFguHopYHIYlUgNaTnXJdnfACc5z3eQSp45Hi1nXCdPtGUKhKyX2o8V2qykXzw3M6anSg0a7vtdskRdNxBlJ7QGl87meStVckwHDa6xxi/Z3TVDsItD2pyTEaxCOVCRarPsxIX01pfmYBjk1biREz33ASfH8d7x+QLf0SvWKECDeUkHZuvFjzdZU9LWl3pq+Yx2rlDwS89xBKBnbzQBVH288xG4UBhp0lPEQiuMrXrtoNE9HAjaUUmhhMkgce51XOjtA+kLdOEXtN7FSkFLwcUXk/OX6kzwim5q0ec62ZZsHcrRMD9Yys6nLCI3RI+Itt66jV/aO93Hug6ev9jCOXDVfxqtVfancvv3Ek1Kcr2RNdbgq7/1Nr3wj9//JVyDdRwBFq5x+19UHv+v6xFWHa/7SZ13rWte6Pn65M9x9D9u/9j42HlkgNb5HzB47iGvUQ2UWdjW7n79HOXXqaox2Xc/SuuY/aQ+riBoAiDbiuGa3DGGJUJCWIE1CtgKUvFCWlCY2tVmocmgMMT3Qxiv6ppaTaDMuNHVYNjKOvcEZsqKrduB2kd6UWz6ChVDjeScsl5WhGksVhoQMUkkwVqiDs5gvGVyQXihFElwqIh2i3ehR56nmCg1X08ppsCtYqaqszVUsnEJAUVWijVjbFFgiqZiACIWptLbwmOHmV1cC9jUIoYyww7M1XURH4NN8MMe23mybLiJQQvVVraXrOr0MFAZkviTNGMGNmuCxASlS/Ye1EA+PJOkcrzhUj+2KNtVYR5RI5+4k1Fuqobgskw6Z9Ph0ivUdPl/A3j4+DNHmK6xafYUAVSPUUhBPpp3rIscoCZqrG2iAtViwaRyKNeFdiCotgmiwAV0sglURoUeiHVoTOKWvKONx4BFs3gAWGX/sTsmxer5JA1ZKZeLGVnfAya09Tp+sbB+rTMuSvgPtIiSkIEynhUnvHFxYcGnPWPYdJop4F2ugren4K2wOQqKYcF+fpDBcTZ0gWkDbumrKQRm3O+ZK4/g3w6gkeiWCfGxc5wH/2oqN9/dMHEc7XDvQPtqEE2SuxkU7OQTEbALHBoW9KVNDyafZkh/njVXYTIDPQL+xRoZRYRogUlKx2c56Op6XSKWhNwhNrm+32OIcT1PTUi1/X8EWuM1huolNjiM6iW3UOOZVjCJ5Y0QkoWLAW4cxjdysYj6Mv0cKooVOFS1xLsJH3e261jVWbW2nVun21xD6Y9XVhIxnZrtXvE36Y9XSrmzL+FQHvvSbf5u9P/TZH/W7k7/+IXx3rWY8yhVt01fm86ZeofdZ17rWta6nW/XcOcqvvYXjv/JeTr7+XuRN78yL8Ci7dInhnccxF152y4Pc/y13ffTNtXWt6+PUNf/ppzCCw1CsJcDoQKetffCQr6KMl+5jS7BbtuCOCqIACC2kIdorFXOh1lSENSlYQgN3b/kxVE8vR5dUV60eE5m3JS7IS1MGpUYoFYVqAbO8AQkXtAimDn1HLT1Gj2pPKYWuKJowwSThnvkYQCuiySUi3EEkdUpyCPAFqo12UqujGtIOt0OaRZuqJeCwgGGhGKtgA0UyDVgkIJ6QUCJVfGYrYEQA3QYNUcH7Al3BS4M40fyu5hTPPafdCCBFnc4dXS6xoljXZY5JqMMklatFIq+300InJUGPjCfbauE7OXptQrZnN+gZ60FV6Wc90nVIP8X7gi8W+O4etlzgEsE2ioIFmbVhwMypItDJ2NqKKnSK5xgNqEJul4Kt1F8rhFRCteo1lG50iMUHgtQDXCwVcAaSmjcPdR40r74MG/FsyxUPcNeUcfgYUBTrF4qmPyDQq7GhB2zrLluLi3T33Yd9+D78wkX6Gv6bm5sdG9sdtcK5B+fsDR2LskHVCSLTUAtqG0+8bsBfjW041MqPOWqB61wUikaLcYJrRSIN2VfHUHu9aNcX1J1CtqCLjErimoBQ8zzhopEAXTrQCa4TTEven1ipCgWhmCAWJwp38EGoFvDYfUC9xphSZWxEYI9mq3gdUf3qvCDiEZRzKNhllApmSz3uIXw1wC2P9ST0jXQ2v5XkNi0dO9S8JVWWc7qDHYovsY3jDN0Jqk9wCn7oBgxSQnGZ68F9pYr09EUQ87hpIwUpHUVLW7Rx40Q76rVvEbyup1kHLzkYv9CeeM9VHswRraJXRzlXXZiVqw8Yr1ad6va4+Xvez/yrP+dJPx8eepjrfvvq+mSu65OXVTl8Lf2MVP3wFi/+W/vP7Jusa13rWtenU+7Uc+cY7rsfHz76M/3FP3Yvb/3gbXRaueWPfIj9r/rom2vrWtfHqmseMjYK6NYUg+klWA2t6dvm0WaYV/95nd6AgaewT6K9NdVRLZDBRDCiDdiLI/1KUdeUfDbCyIAaw8FArZ6wKwCPuTBUGBIqlgxJ8VQXtTbL8GWsFDV6jbZZUbChRtrxtEc3espE0UmfPolNBhapzQ0SDjZgY/hMeLEVoBhgA+415yvVjemfqGNLcsyPJxhsyqxQYVm2lOf8J0wU91ERFvOcX/S0oKKx3ZZA1IYEahm4gqwCJlKBKNmS6uLRQh4SwQBuHgDWfAHDAq8DPpkA3diWS2sbbQo1DQVfNC9HGrfVGgE50hpZI1BH3LFh4JDkNKBOdUwLdTYJL7u9fXw5J8iPrLrms91UpUNKKMRENOcnlZ81AlDMHLzm7xJO5rpq35QDwAX8M5fgTjqgpSJSERsowwJSWTvKQpVslW7i2tjudizEPKe3YMJpgRGsSRJW1woazy0IxZf0tk+/8wR6z934uz8IDz5KNz9ge7tw5qYtTtywwaLCzo4z6JQ6mVG7HtMepIxQ3J3VHDngy2zJTSVjUyBLsxjI5HTJtVNSkXiorbwlkkdqdEDWAOUV9wFzw7wkSNex5VqlS9gK1RTzBI/SjhVQH3Br6r3Q1Zp6E6lmYEuMraq36Cesy+xnb63Y7eaD4em/ZmrR0i9NGxw3Boo66pa+pJXon2Y8nzUV7hhAk9JgoUVu5k0Di8eU6nR+gCz3UVXq7AQH5ThLOYbpJtZNwu8129pVulSRhrdns6fosr1cpUNkAtJRS8H6Husm1H6LRXeCebduh13Xk0t0RQGO37O44oq1Z0N1V8mX8dZj5zneX5026Y+spV+ddXHX9kPc+Jc/EKBxdceGG37xg+ijk6sypnU9tfKqHP/lLW7/u0r3tu3Lrmy0qtz62iX13e+/rK+7rnWta11XsoZ77+P5PyXsLKdsdgsee/laELCup1bX/Eqxml59RWkhq56+cEh4vbl4AkANZVjSFm2hDE2JND5/RE04hhYo3sUvWytlU18Bqwv6TJmeFGp2bmPNP82hj4ANoRLAwyldqopGz7wYi1q0ZVYMvMNLuE1aSRhYwQbHFwkSE75paRjEAj4mh6B5SsqhDRUfHfpEWhZ0ye1hbC8vEqoyScUkNJWnj63NMd6AsqT3XxFHJMBHa+Nt8NYz+CVCTRjfX5t6K+c+GHG2Ozd1FmRYRvraWYBCW1ZkEm3iugxoaDhS8v1THZpb0PZaC/lGtcT7pqdghODEXAVYjYVTAaZTKgrVqIslWiNNmsFYJNzu+/AHdIgQlhYAQkCm1ZbGXBYpq59I44s2huhEME6JVtemRk01IhpqPR8qah1Gl2s8AFOzH63eWrkZ/Q49E7ud4PCj6Fd0bAfGjM5SuZZt1K0V2MzCE/LcI/jFHXz7JNONGd2Jjtn0BBdOVWodWHY9XafIgoChuT9RRelw6ghcmx9jS10Hx71kYFJrd+5HVbG0/diOUcDVkCHbeyWVoBatyHGQx/YX6cY5XwG6CEyxVBaLxviaMqKthhD0CS5GJ3khLBnMlFJEgYTohlRN6KlAjTUtea4SyeNhleDc7BWaKtrHFaI5J+RaiZshbqFeDatNybWSEBdtHH0cvbrQz/dBJwyTTeTYKepyifsANqcs52hdYroM/0mvEURlq7zrKuEtWbXHpI8gKC2I9FB6bLqBTzZgeTSAxbqOTpX7ZuFbCsze8yAP7BznpuNXJ2TkqJakL6NdQU/TrX7BDdOd+Pw4AnW1ICMEaNz6Xxb85iu/gNt/7N3Ux59geOhhbv2Pt3HPHxc4BMrXdYTqQs8N/9c7qecvcOtvTzj4yldw35cWuOmAUj79dT19+ybTX3kTy0/+0HWta13rOtLVv/Yt7Bx7NR/4L+bc8vb1We25WN1NN7L3ilu5cGaA1/zCU3vOMzymq19qUAJsuQUI8wQIgmBJHqMl0rGmzkp45I0qaigLJcMiGGGMj6o3RaKdlWQvMAIX91AooU7nShHNds+Akuk+Fy3QTkDDGmo+YAUYayiaRFKNh6ZKSXGPhGfPsIuighfBqsNE6UtAL1vWbJGOEJMG8kYo5Qn1PFSPKgWwUApaTbiaARUeoKLBslBvpgdmm3OJYBhJZSHSgI83zpIwKICoWiM1miEgCa0SqGgm4nrOe2x9a3EGS8UgLTylKGIDLBawNQvFpw2BwWobg4dvpGi2BrPa/5bBHpqwmfD9c9FQUSbJCQjn0PWhRhPH6pKD/TndgdG3tuxCKjShKTojLTkCe8g2WotdQosraq2vTUo4AnP1XIfZrpremCM8pFAsFItiLXCjQ9P3z7xmCzu0dl88/PqUAEaWrcQx7yWgVngR5HpJpW/aDEQLeijtMk8IGSpuFyk7FeMU3m+wNSvMjk0YLB8nymAVGRaotb77FYwzq7mGCppp0J796itFXh5z6cNqeazGlKQyFKegEcWc0eeOY5Lt1RnFQ1vPbY0mhKtOrHuN80iltV4fApQf0bI9SKXpVA/d5xhheOyvsBMIFq9x3IiDRCgNh254NEWp5y5r+655xkLJmxgB5Md7AxBBUQlWMwOJ8KOM8dS0bhBzsAHpBnQq2NYGqluxL4bKcqgwDEhdIvMDdFjgdYmnX6OZYFLwUpDSQ+kwLeFhWQp4xyAFuj7A5brWdaimjzf1MdSHH+H8A89bQ8aPKBWnqGH1yoC26sKZ2e6RAYwQgXFG+Atfjbpt4wme98d/jf+rfDG3/a+/jQ8D0//0djZf8Sr2PuNgPPeu6+iUbw9w/Rk4fwFfLpj+32/kjl+eYa98CQ9+4TaXXjJQji+eNnBc7Ew4+TsTbvqZd1M/ItF1Xeta19Guct0Z6otvQd7yHnw+v9rDOTplla2f+y1e/EvHsJ2dqz2adV2hkn7C/MtewT1/SPm+r/hZvnzz55jsbXDqNU/t+dc8ZGwZzXRdXHebo3lh7x5AyyGhX4DGJl1zD2gUeSmhhDS39EJsAGLVpllhhBAu0ChRu8DX5mlXQwEVYCgBlYR6TbwbnyOd0eyWnBhfKCu7BGzC0oS6AJs5RYQehS5bE1kiHWhHwERLKFKUOlhsc60JBVsqMSM0UtXwo8zt1IQ7TS0Vjz/Uyu2xXZCKKos5lshCCb8851AYR8CeMWk7/21uaLZ2j+zEWjLvKgAjWlBTpUlI7NwcsQjDcAD10XtPlgdgW0jf4YvF6j099m8DSZpqvFHLWEr8vnlnFl1tf7umcceq4VIwCqYK5lx6eIfH77mEMHBsA45twayEq90YOJOBO57+fzEfJf7OUBzN1mDTlvztjejGorMMkNGSirdQGTYFYluJXoexNR0hg3Vi/pqCMX+ac5iAzjXUvqmRMzeosWO1gfuWBJ4zF4rVGseOaiiSlg62D3TotuKzDfpa6YsgsynDchnqNumpLFAPFSXS2rRHGSVWV1AxjjdDtd0gEFRLtJTbeCjGOUGNzhM4tmM5obfmvxv4l7xBQFtj49qvdIf8OUm/RJFIqaY1QAur4BhL9SEtJGWlFM4dNAL19lxpyuGVJLHR5QCRaVdqxBoY0+693STxhMht38beaYLtJvOMGyO5HmVMV4qUc1V80sNkgs6myKSP1vXWpl4rPlR8vqQulpE6jlPNcROs6+O4NVgOlcXuAgbLrKIB7Z3pRqHr1lfi63pyHc408WHgujcWeOnVG89RrU6N5RWCjA/dfYbP+pz7r8h7PZ2qrqjUq/b+ivO13/AGfvWBz+f0P3kDPp9z24+9iw99x10c3LFWaR+5anelD5UdHMBvvp2bfkt43smT+G03sn/zNvvXdezd9Ak+nxyOf6gye2Jg9s77GB56OG/UrWtd63q21M4f+zyO/ff38dVn/x9+/B99DTf+7ddf7SEdubJLl672ENZ1JUqE4Us/m90/e4H/38v/DjeUrfzFFhd56jfernnIOKad1iHVZwFKmkrNlVAMhslfKKq0rICTK6IRaBLX+XGxbxJQShMTeqodPVVcZDKvjF80mvRMxpFZXkGFd1lwItPmZRiti242Ph339KaDWmF3x3niCWN/bmzcopy5Rem6HqYzahFK1+E1Lv5FDTSVUl2EajC2X+eciIwgov3cRVLm1AAiK9iWKblVneIBEUbIQQRXjHAy1XbRZayp8pKRkeE10FbE9hIYrgaQqY4WHZPA8dQrNDBoAXWAEdpYjf0lGiEgWkCGATtYIJtTTAStNSAeoXbT9JNrwNIS6DSIuoI/K5Um6WFnZgFkpGCdQhEW8wWP33uBc4/N2T6lWIIXK9BroalrVYAu4Q8+Qr9RYUkEvoR8LNufBaRrsLGsVHua8y8llLlWYdyPBbziXkLJJ6F0bIAuN21UZgbE0vHn5JgBpKwUjL6KNc6gj0awUtAnHEpxDzUiB/vIZIpMJ4HdPFr1y2wSKHOoWB3ipkB6HWqidZcSScpuISnM+fAceywRDTVpjWT21PquVH+0Vv7VdYa3/ztE22Ps5DGSLct5w6CtTSQTntG0G2jejG1/tlb/OFdEME+s6ba+UgYbgE9LiKfzSGySyLZtbWIdj3bxMQQmgWie25pCU1ex5WGJkEdKEwzTzl/u1BYYpOH56QjSTWA6g2kPkx6bTGKdTXuYduGdOhD7YggrAXfD5gMypMejGYtLS/bPH+BV0ElPKU4n0G129NPC8mB9UbauVRnC6Xc/uS3nhl+8m7u/5jqef+aJqzSqo1nlCqkK95cdk8cL3VWEeR+vll7or/K4tsucF33be3jiXa9A3vA26rlzPP9nH+Hd33kGjq9bzI5UDYIsPs4+8QhC4Nw5pm+DKXDyqb7sZRreuq6tkumUcstN+MYU2TtguOe+lSXQuq56dc+7hZf82XfyB06+C4DNr3wY+eHuYwahrGtd13LJdMoHv/ez+Zff9Lf5fZMZsPVJn/Px6pqHjA2MIZmc7Izee9HCGeof6buAE0Pzn7OAAE0953HBPcZKR39hgCZNqJKBCdF6rQlFJFscfWy3bZDOLEFOA5VjW6aNsGP0Y1QdQy/MYH6gPPKI8fCjRtWO7W1nu/T00x5kk6pTdFiEYm3SJcwqmA3YUMNOMdvIpZnVeQCjgC0BSRRo/bcryyc5dBNYUFqCNJkim+BFS2zfUNNDTxm0KcFSsWfQ3NsEHSEJlj5zGfpi2arqeLS4qiYl9kYpCY/EAKmu2bZrAZOsEaTlEvcZeLR+q1SEIRSk7gmis82atr9SYag6pgyjEawSY6p4tnxSCtrFdgwHS/b3IlG6Lx19sWxR9hEUttASyWAeJH30WLX4tvkOqBvrQrOdvY3FJdq3AzQliEQID81QP5rF+w8OhuYcp/ouFZNuw5OhG4yvFf6OqXpM3OX5/619OtqlbfVc95jnlvgtoNUxX8J+gEabTqmDoXtLyqyHaQE2YrwHFsdV837M9TleSxZNVV/OjWt4YSaAjjmOuXIqjcDFcmjnBqIVeWxlDtVvSRipbbtL7PM2s6E0JT0dVynsK1Us4yx5C6hpM+ihTo33X/myirYnpXrRD8GD9t5tXaRFgXjuu4SxUmS8KdL2WTuluBktbKopKqUNiAyBkdjDAewL1k2QjU1sc4pMZ/h0AtOCTAplNqHWitZYtz5UxMAGQ5cDUiuyHKi7A4vlPt4p/UZP10OnoMWZzDqKaPicrmtdWefnG2zce+lJ90yHBx9i9toXYN94LuH5uiBapjs1BnvmsvyqC7tvOcP20RMxAlxRT8pPVHdtP8RPfOuLufONcYFa3/N+XvLjU977326vQeMRqv6Jjvrgw1d7GOt6DpR0HXLHCxg2+/jB1oRy8QT18fXNsqNSey+7iVcff/v47z7M1q/iiNa1rqtTu1/7Sl7/X/1vXFc+dbjY6po/gqSEcseytTQ6piXVNWPeanhzu9Opri7a2zWMBS70bAVusimzCNdIvQ+qAdbGMI9DScKr14uL+VC/BRizoaYCMpVhqQRrKa9NPTc+3ZX9uXDpwPBO8WnBJlPqbIpPFOs72Nqk6gSr4EuLv6VHtEsfxmzvtfbHsUw8tqb5Ek8wophI+NBJzGENKeHYCtoCN1wSOGpoEaU6tUjii0P6rlRBNtVTbYBGFO8yGThClEclWxVdKUjdcKvUNveWoLFkb3ZNMDaZAIoMOb7FItbCbIpJtmc3Lz9vyCxxi7U/CaGC6CQcberWGLN0PTrp0GkXid44vqy4Qek7HAOvdMUDQipjS657AOeR4q7EruMcQYK2hLlmlgnS0tDZSkXojtf0T/SAl9Fa3BR5kknJXb5/eoammrEB1qBUFaSuBJ7aYFY7FhpUruP6lrEDuCUus/IGTAVmV0CGOexeQvbnoYRbVOpiEQBuNkFPbOEbGzh9BjSRKsbmlyiN9pEOCEjJRCUH8bAQ6DTCiYRD7eOe3pslkpHJY14kYKKkV2nN4Cht25Bq4vDqrLGm3KjejpX4I5m0HOE4ZTz3jPR1XHt5HGRCtApIU1s3EA2pTpVDf1agc1y1FsrqPMmB+5iQHmxc829PxaSjRdAiAWnjiMEMqgliAjrFNzax2RQ2pnEs25CA3qm2xIcldbkIj8qiiELpBbpDN2MAUaFs9pSNwmSrZ3J8ysbxGZOJUmqF5foCfF2reu+9Z/H33P1RP7/pF+7mnsfXSeQfWUWfOUg/mLL8uRt4wQ+8nXJEbaqOCmQE+LpXv4Wdr3/V+G9/8+/xkh/fwXev+fv6z45yOPvbtvZcW9cVKT11CmuAkXZPd32T7CjVwZmOmay+g9579/X4sP5Ouq7nVpXrzvD5f/m3PyFg3LGnbv9yzUNGqkTUsGeLo6bKqURrrBQdFUaQoEYS+qC4wWAVq55qM0c12ii19YK6rkBRa3I8/PnRwGHCiQAShpSABEVl9CmUbMEFCRUiQh2a4o8MZHFqevDZAKWfsXnTMfpJh+8tIqBmexs7doKDfpNFmWKTCd6FQaOk0osSikP31LolEAj1IkC0DJsHUIvWXB+BW1OHNTzhKW+M1wkI2MBifKY22AiDV6oHADHzlSIQSyFX6v1qzfHGwCRVi054ZZbmHddUaDUSeV09GZjhXqGEkkytwsEc72LfR7hFUxBagLagjAnjAhIV7UYFKgk4k2ShUiha0FIic1gjMGUicOK6KadOT9jeFGYzoe+EIiMHS0grORc1YJelOg9fvZfX0b1PiPZcbT6RHuBJs51XtcSwW8/tiKFiXUUac2nMNDRrnv/tTTeZz3MZoXlDi+JA9dF6wAnVXOy/ilEx8REGqgb4DaieYF4dZQnzHWzvUgTxqOBV8YMlslyifUGPbWNbm9TSBRwWz7Z2bXgzQGkuEB8CbEvCcxuRMamCzOM627xrgv4IzCkRuKQlAWSH5nmiiX3bDYhyCEjHzQdbKRSJtWaiWEssb2nSbW1ne3U7d7RQlwYd1Y1mZ6AJd/MEwEoLm2sw1ZSioUwcwXPuxgZg0fT4bHCbhMtucZxny2VxzW0A7yf41ha2OY027/kCLu7DoxepD5zHPvwE9cFz+BM72LkdWCxz3fp48wILzjo7MWPr9CZb122wcWrGZLOnKwLDkjrfZbh04ROdydf1HCpDOP3r048JAYYHH2Ljl7evwqiOdlVTHnjk5DPy2o+/43qu/6m3Ybu77H4ib7qrWPUIfZ090+9y7pt30M3N8Wf+5t/jeb8k+HB0xvlcLd/tOPYbH30DY13ruhKlewvqhY8TYCZH8/x6rdfpX7+PX3jklTxRt/m3j72Cl/yT/UNKo6tXMp2u18S6rljd/1/fyd84+9sf9/dvXxzwlX/3O57y6137t1VLeqj1PS4rf7fw65OEbOllRjAly/ZYW9YIOEDjsdraELNFFRqVwXylEhoRjeeFfiOODeCkKklpbbmR8MuYadtgBKCFMiGUcFUDwjlsbsD1N07ptnv8zHFO336SThzfNWSrYCdnLHfm7NxfqJdgNoFjp5yNPsCkW6RuR0Bwl+Ah43BFcEr4xpnhUgPwIah0iRVlbOVUX22wCbhli3lrD8cQE5YDaKdICUCYWi0kVaURLNOgbbSIqzaFWYwrIGe8r+V7rt4rA3MoiBmike4sY7iFR4L0cgHMYLYJ+3sxjuohOKuBRaWUAKYNmFBRt/DgS4gUYSQaHpNdh2XLfXHDlsZUjbPXKT4YHc5EhS5DVcbAFSeVlIwemNYAYwshyZkSa+nDukoh96YujDWpHh6CaEnQlvsqFXbD4OzuLZls92z0SucCXnCp2YGevoMOUNBs31XXAFfN8w/wagEQIRWCuT615WHHvhQXqg0Jj0vmwwSUUzN8vo9tzOH4Fosl2M6SIpVus6CdINtbWFF8bw+WBxSrocgVcBsgYZm6ADXCmcwpsjq9jT6XmR0dNxKSfklDlTqqSEVChShpV2Dt5zXXXqon1T3Us+MTwVpbOIp7HaG5ulIzVCVEqZkMbwGmD/tbejv+M0a6pabG9B9Scea4mxLVzKAkcveGI40xjEmFWgWkohSM4dANEcFUkYhGpyZotY0NXBUOlnCwxA8WFAJkBvRVdNLhsy6S22d9HM/VkCG2VzemdJtTSjXUK1aXsKz4/hzfOY8d7OC7a8i4rqg33n07d/7r9/LxHKtu+oW7ufvL196Mh+tg6DjxmzP4w5f3dQdTbvnVAdvbo7v9Vm740qPZL321E6Y/sr7weR/i/ttvgXe9b/zZ5i+8iVvKq7n/q0H6tT3E1arTby3URx692sNY13Ok7Nw5yqnj2NYMqRUeeOSj/Bj12DG4/RZ8WihP7FDvexBfp5NfsRruvQ++5Vb+w6kvRB9+Ah783as9JLrn38b0n+3znn9/B8/7/jccCei5rmu3ypnT/A//3b9mKv3H/P3bFwd821/705z98V/nXU/xNS/7LdVhGPjLf/kv84IXvICNjQ1e+MIX8tf/+l8fWzEhQMX3fu/3cvPNN7OxscGXfMmX8Hu/93tPep35fM53fud3ct1117G1tcUf/sN/mPvuu+9pjyfaQwkA5q2FND3kvARwPGS0Zp7KG9FopRRJxZZBbSnAQh0GhBbQEkBLJYEBCctSfYcHIMp+xlAlWrZDi2AqmIRcyVJGHyosw33Al0P60hFgT4yN3jl1DG66ZcbNLz3N1qkJ1Bp+hA7e9eztOXvWc35PeejBOY8+ts/BUBPWZRutNyVjAiRJ7qKpOpMhk2lDIedSERkQDSDmVRIXBe1yNFRq0gJDQqVoDGiXiixv0S6CSMFEcPX0pAx4OYKPbEsOdWgqB2megCuFWKimCOWqkD6ZAXS1EonTEKrG5RBKvH6CS4+JUj3bovNpoRwLD8NgSE5Nrhpi02ijjSCXgs0m2KSwEGPZ9qMWZipsFGeqFkR/9E2EWi0CZ0QDaBGt0SIRplPFDilrIyhIi4xHrUm0lMMqJMjcaCEe4o7LQJWKpbr0YGGcO7fPxUtLBs91pzaGoKzkeNnm6gJESrM2VWNGFUd2kOCmCRgFly6PiSYHrbgPkfpcGuwNGBpw2nBfBPjtQCaF3cF4/JEDHr13hwuP7LG7PzCUHtvcok6PUWWanSbhLeiplPRMd1ac4mU8br0Oo/eklPT4JG4yjL5uuWZEcz/QbhIoVTS2syYEzAAXA0w0f0Zub0A3XAJopzJZEj42nWjzQRSJ9vKSNx0k3702uOlhpdAEoO08IkgetIKbpRI2HqTeVKt5WmshLm5oWjyIKyI1jkHXVHKmVYL2GB0uM3y6GZYTB0u4tIOfPw87O9juLr67i+/uI3t7yP4+/f4S3Z/DfA42xM0FDXsA6QkbgaJxEB3MkUsX4dLjdHsX6BYHdHZ0W1OO2ufatVwLK9z60x31scc/7mOGBx/i1M9us7ecXMGRHe06Np2ze5tfVsQ2HzqGn7+e2S+HV9UDX3srLzv50GV8h2u3bp6d5/zvO/PkH1pl61+9iRf/+AAXP/YX+XU9s6WPTDj78+9/xi7Yy3VnOPfffAG7/+XnBTg6orX+TLty5cNAfe8H4B3vw9/5/ggV+oiSW29iODmjbvQsbjmFvOzFSL/+fLuSNdxzL/bWdzI8eDQ+4x7+slv46Rf9O/7Ct/ws3c03Xe3hfNzqbroR+ayXUe58cQSErutZWYuXP58/sv2+j/p5dePb7/sCvutPfienf/zpwe7LDhl/4Ad+gB/90R/lR37kR3jXu97FD/7gD/K3/tbf4od/+IfHx/zgD/4gP/RDP8SP/MiP8MY3vpEbb7yRr/iKr+DSoWj07/7u7+bnf/7n+Zmf+Rle97rXsbOzwx/6Q3+IWp9uGlcCguZXlq2lLqkgg1AidUojLZJpsSKhYCwJq4DxeVrK+O9ovYyWTZWSUCBAkubreVMKZstnU+MFjwz4EEnAJHgMaFQsFW2lo0C2CQsUoesKmyc22Dwxo3QK1Smbk2yLVDi9Rd2cUacTlqXjwuNLLlxy5iZ4Kah0ed/fo8VYMhXWhhhf8RU8ovlaKkVKwhNL6NXRQnGkBeO4j7CGVClGPo4BQ/6eDB8JVZd4U3TmNnuCvQQTgxtebQWOBTAN5SIa+6wkpKwGNVRkUhIYN0izXMLBQXhTTnpqKaFYTT9HqxXJsBr1Q/BVOupkgk2nmHcR9tL3+HSC98pib8n5D+3w8AcucOH8gtr8AzPeuo7hINooUwAehNIppVfIFHBt3osJp0KpGO+ZToAJyqNtOSAxCcwrgmMar+8JY0U9fP9UWSxgWVcJwt6StVt7Lx+htKQNWUbE2wJ3QinaEpZT6eiZRu6a8cyHXjePzACrQieRRY473QQ2jm+wp8JDDy748N17PHj3LuceXXAwFGy6ybBxDOs28SBXiPagfaokS1ByFUqLeM81jHTZlmxoriElLARiX0V4zQioRahEEJS4jG3JbTtaOaHkZKh5g6BJPWOORMPvtCV5t7CfJ+GAUW0L4qF89bEVPSB7ez3NQJi4eRIt66qhEtYu41rSBzJsHPKQqpL+kp43DhSR2H6rIKYIHWiPdzOYbsHWJoOA7R7gF3aRvQP8YM5wcMCwt4ft7eF7B9TdA+reHr6/j86HUDGO9hSpmLYBXy4YLu1Sz1/ELjyGHFxAhgNgOfpCHsU6ep9r1269+b3PZ+OXP7mK4Ni/ehOP/urN1Gcw7OTZVL1Wbvysh5BP/tCnVIMpi9dex3U/8eaxbX1+BvQKJVl/KrW0o3OBozhPvOxj7A2r6Oveykv//jm2fneGL9br90qVHxTueM1F6qPPgIpRCxe/6fOZ/8wW3/w9v8jXf+9refif38wH/rdXX/73ugy1/ky78uXLxVNOKx62J5Sz1z/DI3oOlxbsi17J3h/9PLoXPp+j2JJ86v0HvH858PXb9/L4l9x2tYfzceueb30h/r9d4OL/Ydz9Nz4XedXL1rDxWViTR3Z45+LJN8Yu2D4vf8O3cO/XbjP592982q952dul3/CGN/D1X//1fO3Xfi0Az3/+8/npn/5p3vSmNwEBbP7O3/k7/KW/9Jf4hm/4BgD+6T/9p5w9e5af+qmf4tu//du5cOEC//gf/2N+8id/ki//8i8H4DWveQ233norr33ta/mDf/APPuXxRDtpQJ0x0ZcASaoavmYJRUQC4+HNu0xgsPD3i1eLRGHNdkMXsGhGbC2X7pkE3OJcE9CJZrtitq9aqvIEwasBRhHBfQgwMxDAR8MvzzN8xEVDUKkFlSk+m0EneAY4uIAdVGRibN56iv1LC5aX5hzsHbBYTtnZVTY2BRVDqaARjiF01JYS3fwfo08WRvWWUQ26ovFe1VJ5ZanYFIprtqFmL68q0pVoa5VMTLaApeKWUDKklKYNXqU6sSHQZlOpFSuhQGX0aUxgU5v6LKBb6Qq1DqGolIp4oRqjCs8Wc8Q3kWlP8QEbFgHQRBH1Edhppli7gXcC/QRvgR61wqSHaU9dVuYXFlx8dJeL5w/oerjhBSc4pkIvEuwwobWLrYB3oqSm3pQGorAMASLWafOKlHidWEqGpjI0VlpA2pjDbAn2BswDqU0nzvYxYeFO9Rakkw24WvDaAloMTXgeoSs1jfVXcH0lFg0I59nya7HbY2Ms1I8BRT3DrmUcX4PFIo4OS0CYbCgnbzrGzp6zd37B/InKpQuVUycHTpyaMNueMNlQumWPLud4Xca8qAREHlWYifFSKeweJ4SS60sQvAT8F/Ns8XbCUTPdG62B/ZLJ0um9KBrHWio2LdWpUiuIrnwUHTwhaxzv0dosqnkgGCXXgmtTWcd7lwYkc77b+Swb+hGP9VkbhGxnqVw/bs1FM57omq395Hmr+cnSfB7bsVdgsgXHjlNnU6pB3VsgBwu8xg0CT8U1CENXsWVl2FkwGWDW9xTVCOkxwCsmA3pg2M4ufuE8unueMizCt9UtwGk9uvDiqH2uXat1UDtu/1eCHXxyY2kfBm77O2/lA8dewYs//54rMLqjX9uTOfc8cYrhXcc59dmP0n2KYTAXD6aU/3iKm/7Rm8eWPek6FnftXc7hXvY6Sr6MAPObl3GxZR8NXOo738vN7/4A+vKXcN9XnmLnrgXSHd1z4LO+HG749Q5721Nt9Hrq1d10I+/7rhfwjV/1Om6anAegl8q3vfj17N848Bcu+zt++rX+TDta5fc+iJx4IV5W3+Xs9DG4fyVyWdflq3LnC7n3SzbxAk/cdTO3/uIW/pbf++RPvIJVfuN3+bpf/Q7e9RU/yrmXCieu9oA+Tt36i+f48Oec4EXXPc7p/+xuzr9qg4d+53O548ceYLh7/d3sqJV0HeXWW8Cd4UMfHn9e3/levv2nv51/8s1/jxvLHv/iwqv42X/4Zdz+D3+H+hS+k3+suuzfyL7oi76IX/7lX+a9730vAG9729t43etex9d8zdcAcPfdd/PQQw/xlV/5leNzptMpX/zFX8zrX/96AN785jezXC6f9Jibb76Zz/zMzxwf85RLoyU6wJlmOIaipQMUrYJaaz0MOmIIboINRtUGVhpkiJO9umLu1AxpEWm+jrYCEbQwlFAlyjICXMyBUgIUJDwqEiBJDGpefItLtquS7CODNKTHmFE3j+Gnj8NscsinzZFph4gy3ew4ced1bL3gNJPTJ9DTx+lPHoduimlH9QBPSGwzpohlMEQXakmhJBAKlVenOibXSqqrutZ6roqXkfGEb6A0BhZwIyBstmdKU9hlEI0EPLTcV5JQCB9CidUSmEduEjItWw657/L1UAzL1kzBqoxJvsEYhwBaB/NQME6mSDfFrSA1nwOM6rGW1lszgMdD+Welg34K2qO1w02oJiz2KucfOeD8pYGFZLhIEidLz043C7WirEJFWiNsKCfTS7Q0WOijcpBUuGmk5RBazvAH7GjzmW3JqRYMNR5MCpzYck5sO5NOES+otxZeGVNCVCL8pKkTdQw08lSmprchhDJXQ/GnFq3QmFESJsbxlODdEk7iqUpN9Wu1AHQYUxGOnZhwy52nOXHrMdjq2XflsfMDDz54wGMP77O3ryz6KcPWNnVjm1omGD3OBNcOkxLqxPzT1pxKCaCXvL/W8DKU3AFiT1Y/xg2EEtuQ69WL4CUUgEhBpcR8tH2kkbIe1eGpXNWmsmwHhUcKdbTQp2LR8rhiBQhD3egZuGOIHUocb9YC5vnzOFmklWYmSisqXezT0WfS86aFgwSsLlqi/byb4psb2NYsziv7c1gs4mZItm57pm7b4NgcLp1f8tCDO5x/Ype6v0SWsT/FKrI0yt6AXdrBzj2BXHqCblhQPL0mI0mHcoRvfB65z7VrtN7yey9g4z899S/6trfHHT96H++6++ZncFTPrpq+9jgv+P/+Dgf//gZ250+v3c6B+x84zY0/OOGGv/+GJwXveK10798I/+kjWkcpYRrguhsvIv0nuJdvFXvbu7jlb/82L3yN098zXSsbn6HqPzzl9L9+x2UHNt2NZ7n3/zzFd37dvxsB47Oh1p9pR6vs0iW69z+ALtYK0CtRPu1pH2XWw8NfcALpjlZMhQ8Dd/3lh/jMn/wfedFrjq6HrL3tXdz6/cI77rmZRS2cnO3z0i+8m7v/q1uOpEL0OV0i7H/1Z/O+b7+ZD37r8yhnTj/p18//K7/J9335N/CdX/Xf8rovfR5nf/j1T+mm/8ery35E/YW/8Be4cOECL33pSymlUGvlb/7Nv8k3fdM3AfDQQ+F1cPbs2Sc97+zZs9xzzz3jYyaTCadOnfqox7Tnf2TN53Pmh74QX7yYyV0SabGhZMwLc2uBJM7QEnqzJdpxpBTMLFVwsmpRri02IjzTmhqL5oGXEsBo59VUObGCYum/hkUQSmNG4cW3UqIVbaqrbM2WEmDADHHFSod1G3D8OD6bhtfbkO2JZtEarE4x2Nzu6X/fDWzesAH7B0z2d5HzT1CXA740pMvUZQjPNNIHEFJold57wpju28YX4s0AeE6qCTVBWvSHh+YqDArHYJOQiEn82yKUIklstmQnADFrXcUJgfTQCatm63nAMbdI1UULRo2E4ZTJxb6skdtT27w6Ot+AzU3o+vCdWw5YDdNFo+Z4fAyv8AyeQQUrBd2YRis3jk2gOz6h35giZS+CMMTRvkOHCe4Hq7AWCaWst/VWLRR1oqlorOChq2s+oGMrMgpmqZSLebNcOwFYJYM7IjWYDIoRcr7d6DunI7wEw3uTAKu5XuOtcr+038Vv2oJlFUiT6z8hbngx6nhYWPPZTAWrSc22WGmsGjBsWWGo6DTA9LQvnDpVmG0fY/d5M/Z3Bmx/wPeNea3sLpNJdyWOzUmJGwpaMFvGcSBGSeBGzjkIVi3VheFzKupxjsj9gUQojLtFknWCXfGVIrmtBUlV4Ep52oKbmhay5HzFWk6XgZEzmuQ6bR6acQSOukT3Ou6DhqHb/jR8tc8awA85Ii2Ap8HtOF49VbmeEU8Z4WSCEgDUpcf7KTLbwItiQ0UWywjoyf1lZniFOoANA9WFc+cX7BwY3bGBYVGZ5rhlEGQ5YAf72KULyO5FJsMSoUZgka/OfW05HcU6cp9r12B9+NIpXvqjl7C9p6eWG+65lzv/z5O870/fwB03PfIMje7ZU7oAn885+yO/xfCOV/LBb97m5uc9cUjr/LFrdz5h8Vun+Yx/dg/DfR8j3MWd2//dLhe/YsrJfv8ZGfunW0svRyr85amWDwPdr7yZF/z6BLnrhdz3VafZuWuOlGfXdhzVKg9OefE/+DDDoTbfy1G6ucl7/twL+K47fvFI2wh8rLpan2nw3PpcezpVH30UuXiRyamT0HXURx9bqxifoZIP3c/03EnmyVi8I78vH60a7rufF/7F+z9uCN5RKX/TO7jzv9/iiW/4fTzx1fucObnDEbvnty4i3OXhV3dYcZg5Mps9+QHuDB/80GV7v8sOGf/Fv/gXvOY1r+GnfuqneNnLXsZb3/pWvvu7v5ubb76Zb/3Wbx0fJx/pa+b+UT/7yPpEj/n+7/9+/tpf+2sf4zcFR/PCPtphG0hAleJ1BF+12tiKimgAJgmVIhZqrqYYlEzVdTO8evQ2pqrdTVaBJSIZ2NFlO7almihanyOhOMIpIghCIsF49K+LvwcD0Q50Ru1n+ImTyMlttC+oQV0OSJeefwpSB2x/H6mV6XRCf+Mx7ELBHtqno0B6sNVq6ecW2+Xpqej53+51HLOWghNhJy14RSWUXQ2YGgScrDUkayUSs4N8xbbEEDWh0wqQqYFrPk4T2rSzlIxPT6SZajgpsY/csDrgJtFerAmXLTwIA/cIAz76PzIMARtLwZcGXYf3ggyOzxfYUJNKasKsDK3AkdkE+g4zh2GJVUOnHSeefxybKvsXDtjcnAVM6nq0DpgNkSguAQpdMihEAOlwoDRYDbTbbE0Jas3XUhNkJfhpSlCrKyg7AsxGbyQuu3x8/Zotr4olcpIE8YeiT3LOUyGbsFAkPAbF2+qElpgciryaQJgVOArRZuwzN0QKwe095pCY2tGOgEox2C7CxrEpfnyDas6wrNTFgHqlKyXVdBWpleqK06dVQaYYW4W6BGqC6gD8COHB6Ifa7L0hQo+gGkpgvTqkchCELo7DRgpLU3dCCxoSr/nhmsnODmionkOBynjcc2iKQkx5aM7t0NjaAaABJqPdPvZBBAn5IdVwUzCWtA9YAXfG82B4dA6Jil0ULz3eb+BbW8hsEorFpcFyiQyLHKAwLJzl0lgunaEaS3N2d5csB2W5HKi1Zqq54UvH5wfYzi6yu0u3nCO2jD70PKPSzhlH2Fvv6H2uXVs1uHLwc2fZetsbPqXn+xt/lxf/wF3c/9dOcMuJC5d5dM/Sskr3K2/mrt+9nvu+5Q72X7XHdacuUfImogOLoePS3hT5vWM8/xfOY2//LYaP0drbSt78bn7rV17Fl33lW+jk6F32mAvVFT2CY3sq5csF/vZ3c8s7Oy78sVfz0JdUZOPZuS1HpXy340X/cudjg/NPp0T48He9ku/46n/3rAOMcPU+0+C587n2qZTP5wwPPXy1h3HNVz1/gZv/w8M8+JVnWRyHk+9b4sPRDR98NpTt7nLyJ9/AmX9zAtnY4NSFB1Ziq3VdnRKhnDwZVlXLBfuvegF148rtk8sOGb/ne76Hv/gX/yJ//I//cQBe/vKXc8899/D93//9fOu3fis33ngjEHfAbrpplZb0yCOPjHfMbrzxRhaLBefOnXvSHbJHHnmEL/zCL/yY7/s//U//E3/mz/yZ8d8XL17k1ltvxbyjWgE1SulDdWcJWLLNN1hIUBAJqWGqEQMa2GCrYJDBIhikyxZKQj0nFkqpUEB5KM9Us92ytU1Hi7QNQ4ARNbTTZJoBLtx8bHWVfC4Oqj21TKm6iW0cQ04cx7cnlKKRljzPdsZq+FDRUpCNKSwNny2RSUc37eC6k8hQ8WFAJBKyXWAYAo40P0KReH8tCbrEE4621s4GNCw8EsXShnEFTztpgRuhTGxyJSGUh64ZhKMgPoyQBhLeSgn1H5BaxjF92S1mSDtNT0vQEuouJAN3mvF0AtDmZenagKrhiyXVBqQCoshU0VmP9F20fFoEsXjxUP51HUw6SteNUMwchv2K9x2zrZ7rX3yauoxwEZ3PcV8AA2I1rCRHVW0bW7a6SvMFbeC1ZNp5QKIQ1rY29Qb8IlVYagtk8UOgafXvjIaJOa6xf1ws07Q1QHq20LZmf6jpUcnoiznCqkPKX2vp3wnrfWzrBhjIHHGQLqBtJPCMwymlj1Ac4hjStv/Ns108fAxtsUQWAz3Eept2SNehtYeFYfsdy/kcsfD4qxrHots0E6YNrRXp+ljrbihLmkqxrTGzSBQPZ0YN1jsGR5FrNObA3MZQp7xvkTca8l5FUwYnIA6ASfol5jSJtyVKdGTLuK887U0bzbV8fzv8w3EhjdLeQ54FyUNVkoNKBB41SKkBGGuZ4JNN2NjEN2ehYlxaeNIuB2RY4m7UCouFsVhYgsZIU18sAn421wmrDnVAFpW6fwB7e+jigGI1A4gYfcpkDEQ6uhdqR+1z7dx8gxu2rh348Du/dQd3/Mw7Pq0VYG97F7f8pTt5z//nNu74ffeukuOf41UffZSbfugxynXXcfCK29g7G6nG3YFz/bvPc8MTjzI8/M64IfNJypcLXvR3P8Cv3vlivvz573mmh/4p1dw6+vLsPjZ8GDj+M7/FqTe9gCc+7wYefRX4qWV8sVnXUy7fL9zxk3P8jZ88SOrpln3RK/mq//I36Z+lQPtqfabBx/9cW9e6rmTV936As/fcRzl1kuGRtWr0clU9fwHOr2/2Xu3SY8dYfO5LeOK2CZMd4+Tr72Vvq1xRhelll47s7e2F4utQlWw/BnjBC17AjTfeyH/8j/9x/P1iseDXfu3Xxg+lV73qVfR9/6THPPjgg7zjHe/4uB9c0+mU48ePP+kPAMdO4rMTDN1x9usG82HKwIQqHa49lG5Miy1aUkmkY2tkhMaESqwuQ7WzcBi8UAenzpf4UJ8ks9b0NkPL2GIoFkEpwxAtinSEWm+kEYJL+vyVbgz9GFCW1lGZMUy3GbaPUzdn+EYPIgzm1MWA78yRgwF2DpCLB/juHNk5QA4WcHGOX1qgS0MmHXr2NHL99fjmCXyyiZSYB0pHSjzBy5gMOybqOqEQE6LNVj0DL6KteGwPTbWUq+MeQNdTSScteALSe9ATKBGw0BMqqSbQCsoiGhnS7b9VBXXFlxVqzVbsADKaz2ktz4hHwLFHC2lYAyrmSl0YNhjeRWpzrTA4eD9Bt2bIyWP4iS3Y3sK3N7DZLNrp3fCug74g/QT6njoYph3TWc/2sRnb2zNmJzYo0ylmMoJbq6Gw8zqgLriHklClgRpdBZU0AahatNy657ZGS7f7SlGo2dIdQjmntJ57fPzwFPH4YzXasokgGm8Kw6b2c4gW93FVQ4ObqWg0Sa9IB7EcFwR0T/VotAlbdl7rCNOa72RwyWz1l/AvNAmVpppHa3M1Lj12icfe9ziPv+ccj7z3cR55z+M8+s5HuHTPE9Td/ThSpkLZmlA2JsgkfEkrwqAdtZ8xTDapsy1seow6PUYtm5j0VCsMppiH96pqF7YEhAqZFngj3ZMhI+F1KFICRHahNFztkwTz0tFazxuotHFdt/0SCfJNIc3ox1rGdeNGeEa6pso4lY8JSPGS4lNJ69KYY9XAp+IBPNux2LSZ2vXIZBPZ2EK2NqEreAWvsX99CP/FOhjzg7qCi4NRw0A1wp16ZTKbUEq0nrM0fD7H93fh4BK9LQK6JoUN31mSp8rIRY9iHbXPtQ++/XmXdfuuZv3O/c/jzr/3EHYZWhnr772HO7/3Xbzv/hsuw8iuoXKnPvoo/WvfzIl//puc+Oe/ydbP/Rb1997D8OBDHzOY5ONVffgRrnvNJvv16fk9Xqk6Sr6Mjz1yPL5UfILqnn8bB1/3uTzwPV/IA9/zhTz0p7+Q+iWfDZ/3cuqZbY5/YJ8bfjseK2vI+JTLdzte9DMVecPbL/tr6+YmD/7pBS/cOLo+aZ+srtZnGnyC67XLXSJHzmdvXUerRuXo0/gMXNe6jnpJ17H/+1/K+RdNsB4OTik7n/W8JwHG/qJg584/o+O47Gffr/u6r+Nv/s2/yW233cbLXvYy3vKWt/BDP/RD/Ik/8SeAuJj87u/+br7v+76PO+64gzvuuIPv+77vY3Nzk2/+5m8G4MSJE3zbt30bf/bP/lnOnDnD6dOn+XN/7s/x8pe/fEwwe8qlYBsTTDp29+aoCJ06xSpqlWJLsDlFaybJRhiCEcwgZP/R6lgNLl2C3b3K7KSxOVMmVrDifGRmQfNqUy3h95c7ViwDIhLYrXztUqmXAK66UiksrDDf7xikp9ue0d1wHN2YUCYlgIHD4I4vjTJksMZkCgLD0tDpBFGl65SSEMgmPXr2FL41w85fhL19ZHmA+CLDWmIk1SN8RjzaM6WBi5RrNZ2cmaTiMdGFeraNxqaZZHs04KOzRKi1mn8lNKjYIEuAJ3XBLJVdCni0gIKN6b0QQDjSkGt4bqqkEjJgiaujXfggDouK9D1MA6zaslLnAy5ChwSc3OopKtCB9pOAqIMhEmbBMhF82keYj9ZIBl+G8q9MkjTVBM6TGUsvuBVKSegm6Vto8d8u4eLXwnKSFsXayRRiR1pIcbbvt6lsqjoPaJm4l1TFVWvBLdFWqwmnzB0TpZMSsDtBcjCraJomA41a8rKlqrKFzoTdZrZG26G1nKnhLRxGxAkPwFAGjpdKLQ26K+Gr2Gdoz2JAhnjUsho2X6Cd4bNKX5zOHGwOu3MWdY/SFcpkQpl00Xo/UcQmiFm0veesLOuhtG0Fr5NI0tbmneioGyYGUsPfM5W5qbHN9eepwItzhaZXY+zO9FBNttue1+wBktHGMeUyguWmFEYypkgDvMfhFMdZs4HV8cyR8B/PNZSHijVl9qGbs7ndbcyUCZQpTDbxjQ1sNoW+j1cfAt5LHfA6YNWZH8DuzsBgzqI6tTpFYGOj4H3BJj0nTk6Z9kqpzlArdX4Ae5ciSdojBTzmUoiPn0zP9npIg3v06qh9rp18l2Bf8uzznvvIenDvODf/2OSyesDU8xe44+8OfOCP3I6+ZIftjTnXb+1cttdfF2z90jv4T3/sTr7mzndc7aF8VB0lX8aNuyf4MHzM35Uzp3noG+/kC//E7/C5x17PTFetens2pR4K2OllYM+m3D2/ng/sXMc9F05z/uImdalPFrSvC4Du/ikv+pnzz0iSNMDOV72c//qOX3lGXvtK1VH7TLvspYWDr3kVuzcWrnvrJXjruz/usbiuda3rMtWTLjrWdbWq3HITOzc9GfEdnCqc/t0LPPrKE1gP1799+bQ90J9uXXbI+MM//MP8lb/yV/hTf+pP8cgjj3DzzTfz7d/+7fzVv/pXx8f8+T//59nf3+dP/ak/xblz5/i8z/s8fumXfoljx46Nj/nbf/tv03Ud3/iN38j+/j5f9mVfxk/8xE+ESuZplO1cwmWC2YL5I3Pm+wOT4x3TYz1dN6HMZhQ5hi3maF0Sba0DJgHvQtEYEGa5VB67Z5+HnxjYOLXg5hdtc2IqzDLUpFp8tRXVCN3AcQpoh1ioiLT0cQFPKs/MqQ5aImCi4kjtGUyYW8fegXLpvLPsCtunOk5udtAZy4Mlw8JZ7A04Qt/NkB7Ua7Q/qgVI25ygfQdSYRjQwVFLALJRkP4Estym7h0wHCyQ5RJf7qN1kURkoPdIPQ6xZqiQrKmnCMVieN1F22jNNlfPllgMBqmorBpxG1AVSjKyaF9urasimdGdacyt9ZcS7ax489asuCk2DHRdiXZNjbGF76VhDKiX9N70nOsabdOl4EunLgfKbIprYcQ4RQM8D8HBXGObfDKBTlONV5EOOimUvsP6fF9zvBZsUNgwuu0T+J4BS0QcrZIK2ICL5NQ2GNb8DaWlUSeZkqRIQnrzeSg0DUsPylDeuUZKdihPW2tzKBFNEuz6AEwxSqgenRX8THUh3kDY0Pb2KI4M0FZGcGQaNgENojsCpaUkR1CPi6TvaT5GlCodTKbItAeM+cV95o/uUZdGNWe5v0R8SdcZm8UptVJK9G/LwpFFTJ9oYSgFJhPK5gY6KahOoBdogS0OYgVfgHiHyRSqYyXCocSjjVyt4nWZ8DVarYU6qp5jKmsoBYn1dPia1hMctzZgFUHURvCHFdrexsOfsgXAeP48AHze+IizGU3616Jb8GjqDuWz0zSWPh5T+XxvPqodTkFKx9D16Gwb29zCZxMooYSk+mpbzFCchQsHC2NvPuBWoAN3QQtsbQonpjPY3GTj+AxVD3/UxQI72KUbDgg31Mh9lwyQknG7VonaR7WO2ufa5KLxgXPXcdeZMNe/uNhgu58fKRXXJ6t3PXaW4//wOLPX/vZTfo4eO/YJlSkymzJ/6c1M736MF/3Q/cixbfbuvIFz3105s7H3rJqfo1y2t8fzfqrjsf95m+umRw/gHljPpi6u6hgM4bq3fzTU6F74fD74LTdz15e+jz979mfp5aMfs6nzj/rZTJec7nZ41dbd2A3KQ8MJPnRwHfftneTB3eM8cWmLxUG38rF+DpYfFE69pePGf/l+6qPPkMpQC/f9QWO7fOqJm0ehjtpn2uUu6TsuvKBjeQzu/Yrj3DL9TOQ33npVx7SudV1zpYXu5htZ3nYdBzdM2bu+sH3fwOZvvIe6DnS6arX30rMcuk8JQJ2CPvwEL/7HO9jJbfz33v+MX3WJ+7WJnC9evMiJEyd44Dv+PtubJ7DpjHmdsnPpACp0HZSuIH1Pd2xGf2ID84rOB+RggSznsNinkwyQcFiYc/+HB+6/fwFFOXlj4dZbe451QodFazEZLtNaAVMhZcMC0W5U/+GGaiZdZ+tua+EdamF/EHYXyt6usDco0zObnHzhNsdPdtjFA5Y7CxZLGOZGd2zGxo2bTDYLZQjghFmoK5vqrzpqoDVhiHhkmBSNFOfmMUcEaTBfwsECPZjDfBHBJrKgyOpue7RahFxrNJPP4JwmcDNLtWZTv+EJ4VZfhBtWiZ/oCBwl070tVY1pIEe1GhE8HrCq0tpOwx/Si46eeDFEGVVg0YbcYYPgx47DyVMwjLnjoIr2E2Tao9Muu1AF9UyvLhKds+mjSM6n11CS6WyKl1DkiTmyt0AXC/zSDvXxc8j8EqWEZ6APEbthGCVTjGMkhua2HTbPFhS0JoBqyteYf1cCEFp4ZmpKHsfUdPcRfAux700Ekx4pEzqPMJjRW7MBufzbrY5+kD4CxlSjpb8jQyYylxIQWAMqisWCEJXwHs0F4g1298fxU9dhJ7fYvTjn/IcvsHNhwbCo1MUCrwN9P3DyuHNsW+iL0ImiNUNmulQmWqj9KB1eShyHpadq7EdVofQ9Mu1jnQJqCjVDWXKfarbdiwTo02GJ+jIAvqePoEqCRo/E6WUAZzLopVZluRgoJUCcCHQeqs1Q18YaiIkx3DNMylcJ127hCeoe7cq0VnexoIg00N1gXSpRRXDNNmuVca5VO8wLtZti0xmysYlsbuJ9HyrhGmpdr041i1b4vX18Z4/F3sBjj+6xe3GASaHMhOV+ZWMq3HB2k9mxGWxt0m9OAvbXSt25BBefQJa7dBmrPeaUJw2N5Rn779L8gJv/XiRePmPtU8/yap9rL/2O7+Pv/ZmfiJ/ZjD2bcuA9C++YW8/Dy+Pcf3ByfN4yofZRgGw7w5R3vON2Xvp3H6e+5/2f8LHSdZRbbuKBP3Qru1+0y1fd8U5umZ7/uI/f1AWft/l+3npwO+eGLQCu6y5xx/QhPri4gV87fydbZcED+8dZWHck5uNy16M/eTun/8mnFqDzdEq6jvf94Kv56v/8LUcu+KKXyvHu6kKguXX85vd8Dv1r3wyAzmbc+12fzZf+sTfyRcffe9nf71Ld4Hf3nscjB8f48KVTPHphm2FRnhvQ0YTph6Y8/+fPYb/73me09dG/4BV81t9/G7dNH39Kj9/fGfgLn/Pr68+1T1Dtc+1L+Ho66S/b6+584+dz/sXxXa/fgZv+0VufceXOutb1XKnupht57CtewKXbBfuIe783/uaS6S++8eoM7FMpLddMy7xuRcr3sPHkn4vB2X/7wU87WGrwJb/KLzylz7Rr3qyi640JS+q+4WXJ8RMTnB6bBzg0MxYXDrhwYY+Dg0p3ouf4qW2m3Sb9cAxbDgyLBbqsiA2cuKnj4vwi87lHK2s3xXuobpSxTRcg7k4LkaArMsFEE0CGN2AVGERwibbdoUyYHxgHe3BQlf29Su35/7P35/GyneddJ/p93netmvbeZ9Q8WLIsyxMeYidxYpKQiYZ0QjqBdEhC0wEacoHL0E03NPSFXLhcutNpLoQ5TCHNDIkDIYRMzuBMthzbsWVbsuRJ86wz7amq1nqf5/7xPO+qLUuyZeno7HOO6snnRD777F21aq13rV31Xb+B2TVTjlwxZdpC98g2JkqetUwEStchsk9zdo/0VMGWS5L2DsEquBCHmqgrmlLNx9OVIsoiNy8lz4XTpkGmDWy0mCWsV3TZo/N96DukdGTroRaKaBRWRPutWEaLrvyiYVVGVzZrf14ZgIlGzzE4AEsBR5TIMxQw60nJFZAO2nqSmavn4nFNe1Ah4eUVadREY7H7SasNuDp1Zdz42deDLgs6gmaj9fbhsrIAi4Am8QvRskeXSln0lIWymHcUg8mxwuSKKUxaV3aaosnI4xGyMfVm3TLH+i4c1ToUwWTB256NKPYwMlGSEe0jbnPWlbJRwiNr5uDSjCSNgynxvZoIuDVsTyhuQ8HohSDZ7axhF/d8y1CdUVvA4xgiB46bQzBJhiVfc4DncUbhkaSw36aw4ltYeVNYZUctNhLmO0se//Qptk/vIJbpFwW0w2yf1goNmcYSWRNKTyNuH/emEYebOQmqPWW5RDKI1lZ3cRv/ImE7iUJCmtYB8mhCHk28yEm9yMbUVZqpSfRtS+rVVb79wgubqGuOUMx2YX8X+tJw9kzh1KNLZkeF41dMGI+ULvnPSIn/pgrYZZD3FuqxrNVP1SId4L2q/2TVVm/0rj6MwpdU/XMiGNnhswBphDVjdDpDNh0uimSs99buxWKBLv3a0YxHUW7lML8rwrKHfTXGTQrlpdGMGqRJ0LSknFzBbIZ0HbK3S+qXrg5FSXFjY1DK1pzKemquizqe9+x/0d4Ado7lPY7lz/rgNIX5pn9Y27Mxc215tD/K6X6DnX7MThmz1IZO8+cFbfPScHY5fcbX21Q4Mf78H9jqz8/7hkc/fhWv/PElt/3qb1K6Z1eapa0t+rfcyqNvn7L5dY/xXa94P185+1HGX0DBwldOnwkvr5zcy9uvuRdFeEqnPNCd5EO7r+BDp28gYeR0cYGyFzKdZo7fvX9Bnsv6ntf+rYf4lVtu4bfd8Llh8YWei8Eyff/+CaZ3PUKPfxi76/+8ju/90h99VpXi+ZitvM87tj4BW8CVcKrf5M6967jj1PU8cvrIZQkcrQjtIyOueV9h9lMfQBcvzb6tI+2Ie757zH/zPAHjel6CESEfPYIcOwpm6BNPPSc4PPb+Rzn3iuvQERe07GA967ncJx8/zoPffguLE8/8t9TD7L6zXCrILh8/jmxuQNd5AdAlDhvTFScok2d+vZkbeoELeS57yOgWXiUlpdWOtD8HaVAZ0bctfTvi7MPbPPXwOZZd52GZ12xw1XVT0ljQlJFR9kZhxmwcbXnFFZvs7XWMMozGGS9XFnqrBSOG9j1ZXPFkydVcRsJE0ORWXHLGcgujjLUN873C2U+fZbEoSJtpT4zYPDli60hDI0bZ7ciz7BF4811ksXDgZ4VkSjYbABoa6sEhsCe5qk+jrCNBEyUs1fbrjScO9pxrCUKDJi+HYDSCo1NUtrBeHEh0C6Sbu6JPvGjFuU+JX+qh0Iu8S0xITfbsQC0hGatxfk006+oAzyQlb3i2iB4SfzxXdUEKu6iGtTRJwlJGrQ/FZMI0DzDK1JWmksfQTkhtC63bjHW/o1sYzQYOIedLWBRS21KaxnMDDVhA2Te6/cL8XOdlGH2Um+SOvDmibbMDlXFLWgjWGHJkA1v26Lwg0jOU6MReklCgMeQmBrALMOxlGTD0bVu1kwelkRSKT39c88BE4kFC9ShI8dy/RKmiVkqTQRtScZXoUABtClHigsV6Ehkgp0Xbtwvy4sUMNmsHaUgi5ez5k1UBmVLY1xtoRyjC/tl9tk/tRJxAoZHCeNozm8HmtGFjnGjDgmzmWy/q4lJ1FO2t3yakPtTCBMwTV9JRegquarRujs0Fne9jzQgZT2imXnzS9144U5vl+5wRaUhNi2mPaUFLwcRoVMipQSmoKHv7whOPLzh1NjHpoZkZeZQYEbZ7OfjxN5raawkS+L7zAxhN3aF+lBzG4iiVSlFQVXpUiqtgzSKfNGE0mDRYarB2BOMpOpvCZIy1XtDULTr2t5fsnF6y3O+xUsiNMTuqjGZjPCZMKEBXXKmcm4zEcZQM0mRoEqlx1aSWgu7tIfM9pCyBQh7e4duwjYNKN1rJ08Xc/HKRzZ99y89+3u+pGW8TOshwXXsaALXE0jIdmaf6TfZ0zGPdUc71Ex7YP877P3wrswcyJ+7qEYXR6SWTe59pPbTZhIduux4+z2GrPz/pF2w+/hug5ZnoJ2Xsy9/IZ755ypd/1cf4Q1f931yZd18SSJQwrkx7XDne4y3jB3n06CYfX1xHK4VPza/ikfkRdrrxJal0vO/xE9x6z0MX7M19f98DXPm33sqDf+UYN8zOXKBnfX5zmJZpRXjPp1/JbU/dRb76Kj79t6/k+978zgu6DSeaHb7iyD18xZF7OHPjBh/dvf6yAY5WhMl9Y2541x75Nz7oBWMX4Hl3vvmL+KO/9dLOYrzUJ584DledHKJrUr4K/cx9PFsOXP/pe7nh56acecNRtj6zv1Yxrmc952nspmufARjFYHQarvvFU5S7PnE4G3ZwUiafPIFszigPP4p1/TMAYppMkCOb/jkkj8knT7x0URsXaj6r0Gv4cscFz6W97CFjEqO28iYjSjcUtQ6ZC6V0LHf2WG4v0JGSVFiePYse6aBPSOlCxebtr810ymZu2JgJ5EQaNWhKdDQwHmG5oYTCShPkVIFM2H3bjOVMMVc4pibswZ1RdE577RatKXmUmExb2jE0nSsUc7uEvR3y/h4Zt3FXS+3wEd7cSukW6PpLV0i5YSgmPJCtWNVpNTOy6oyslCjlWLqar0vIIlEAkRaaKdY0yMaYkja8abi4vZNSSN3C913fI9aD9QE8c6jyqpKtRLOsUFQonSJJkTaH+lFcbVWK231TbdCNHphaLpISaNVHuiLFxEgTbywuWmiSKzv7XihNS9rYwJpMt7dkd3uBLfzDQbvbUfqOtOgQhDSZkK9sSWO8MVdjmycN1hvFhEIhZYM2o+p28DxKpJS8kKVkYIwcPUrRnkb9jruEhbbE+lQrARtdRSlDmU0ZSj+oRThUVWAa8vyAobgnpeTma/MGb4yh+KM2EafkgK6I7+cU2YIpwK8G1Iw4S1dIVtuvOQRWXJ03VP/Edvgyi9bpsAYbOhTFmEJpWqzNWFFsv6MpPfPFkoQw2zROHDOOTDKthaqThOQmSmt8/5kWkggpC1gP5uDRsq8LaZJnApYCyWgk+TmpRpECuqQslpTlgr7bJ003yO3YdbWl+DFBkJyQZgRpTOlWjeZdUYr1WDF6LZzdW7KwhvHxTN/1nD1jbG61jHMfTnGhoGSEML+7lRxX2UpyZbC7212B6a3TrcP4uBZVdaqJr29Rz70sZEoSyBOsncJ4jDbZi4qmYz/HS6Hf79g517F9as7euSWl+Ok1QmiWSh4VXwD1uCOkRmhGycuTckM7akltG1b4+MXWd9hij1yWiPYOpcNm7iCx5mPGBSmgtF36QrILNje3TzLcQPgCJ4kyEWVCx9bILaWvnzyEWuIP/er38Lrvv5/+oYef9jPP9bZkcs+nntdzPtfPp9mMs7/rTTz5rXv89bf9e17dPvk8X8X5mYRxXd7mutndgCsgOxJndMK9yyv4yN6NnFpucHo5ZV7On43vpRg14ejPzi74G+T8S7/JJ3/8y7nuOz5wUdmmF9ocGmR892Ov5rXfe5p+PueB//mt/JU3/6tD2Y46x/LuABxP3bDJ7edu4TcefgXjnz7C6TcrbHWf/0EugrG9hiN3N1z9vj3S+z9wweAieDzAo79ncclnMV7qI7PZKhsbsCa7K+U5lPH64bs4coc8K4Rcz6U3Mh4z/7o38ejbWxAjz4Ubf+os9qE718f4Ao5HYTHk/qUlXHlHz+yX7kK3tw9344D0ptfyye86jr1in9lswfYTb6I53XD1+5Qj7/o4JRR9sjGDg7Fkk/Elb53urj32rMrt6ZPdGjKe9wnIkUIBV1TdbptwKKS7HDkm7J6BnV0l0zHbbGmbjtwJgue+WRLQBWl3F1s6KEjjMYwnSG6RyQyZtOgkk8YjLGesV0pxEJGmY8jV0pghJZrs1kZRQ1tlOs5MjrekKFtoipIWS8p8B93ZJi33abUnoRCWaHIOG3GUgag6YDRvv3WQ598/WGwPSE+imNg/4KsN0G+wclpV2RmUPgBWj3ULyIl+t8W0QcYNmh2iatsiow1//mKkZU9a7mO6gLKkVyOl5HZcEySvcKjmRM5h5bZqDcXbq80CmBF222rJdXiplFDcycpeKlWSJ14YI255SRsz0rSh65VT959j79zCoWnbwKldGnpmm5np1sQz/Pbn0CVsocjCCVCbYGsrMdkao/sdjDKjYzNSm8kZpO+xvrjluIkQ7OmI1G2h/T7JlKQa2y2IClnEGWKsgar09O2PD3DiFtbVr1ML1WJYunXQQA5lN7VZGKsgOfgkvsY9k7OB1Dm4Cpe7K84I1WKsqXgsDcXjEGK4SgZ0BCoSj3+gCTm5Ck7p0DRFJzNAsO1txrrP1VcnFqUhp8ykLWw0hZEQ+4KIG3CFcJMEtcgYNXOQlYQkXvKT2wYkU0yH5uuUEhJwD4wsCkSxi3XYcoF2CyyNSeOpv4Fl5K8xCTQpIJ+3mXvrvKI28gKYTrHdPdJGJvcdqWnpMyxoGWkmaxTL5ADHNTuTKAwCvFhJ8fDPuv8ynWUkJ3KFpJYgO/g3tRWkyxkbNeh0hk0m5ORN9Iw8q9KWhX6/Z/90x96ZBd3+Eik9mURuEqORMR0b42nCyoiinkdqYkwmmZwSnRYmo4bJtKVpMyk7zDVTynIBywWUjlogZCFyHUpqImsypXwAQK/fIB7mJFH+/jf+M/7zO97CT/7Gl/LKHyu0v/Chl+bNlgj6VW9h+RdP8Tde9fc4+hJZSF/ItKgrHSf38yWT+wfo+Gh/lM8sruK++Uk+de4Kxk1Pry8M9J7v6TRz/3tu4JYf/SgXHPOZccM//Rg/+eo387u++Dcv9LM/56gJnWXaL8Bmf77mgY9ew6sf+CAP/oV38F2/9+JSvp1odviGE3fwO45/lH978kt5ddPx6TMnefKpLY/UKQc+nQhIczjg2IrAMtGeatj6DJy4e87okw/SP/wItSDtQk561c1800XYpv5yG9vfh83pAAZksXxOwLj6ofV7i8thmmuu5q6/eDO/5x3vYzOv3jN84puu4sk/+XrsAx87xK17mc1H7uaaq97C2Vtapk8Zx97zIP1Dj6AXAZxrrrmau/7nGdddvcoePHLjAm6E8kbhrm94Nbf+80J69296Qe7BSeJReBfB63hBI8Letc/0SkuByScff84b/i/VXPaQsX7IrQ3G/oHXoVNKMMI4tiXo9Yn2bKYdGyePw7RVpFNSUYdwRbE+momtuCpv2ZNKh0lG9+fYcgFHtkhXHqdMMtJkkjXQeNt0zv61koR23EKTXRnYK7LsaTqAFul7UrdE9nbQ06dhd4emLMliJI1QM/EP5ckUFzRWS3FkLpqjnsqoMMWyawlruzTU39PVMi1eQGJ9KM3iTkXNXQzlmEgALjOk84Ibscj/y0JJGUiYjFg2I/Jkg7R5zK3r8yXSd5S+J5UFSZf+3DmKOCqKkICMYtHCHbZS4vklBH0llIBqYUFVing+pMgI7bwURZox4PZ0mW6Qjs5QSew+tsO5x/fpVFnqwiFUV2hESDKlnRmNFPLerr9GSagajRi67EgijNVzNo0Rtm/YIqEImQAo07HDYC2kJmGzCbY8gm5D0rkfQ5TahWNI2F1L/QIRnOj/mtIAAkVWa5yqmKU2DQeoIlRmChaqPKuwByGpqzOrBddUVusmBLieUegQWyxDctBnQYFdnGj+C0YZ1kiFi0kSkhNGNEvToqMN0niCzvfIi11macF0Fk8cRTJeYOMqRVND+yhoaRyQJkn+Esn+2iL/TyWhqmSRSPus6s4cADtKY0oJyGokMmYO0k2E0is6apAJ5HZE6QrdvKfJCW0yRRoWc2Ox6CEZk83MeGvM0ekI25yzODcnZWGy2ZI2El3p6JZ9RBt0pOQxB6bqNzEk+flFD0kjgzFRyPSM6FML2fdFbrK3QeeMVrW0+T6mEWgTaTTCUsBpA8kZ7YrHApzbZ7Ezp9/zvNlWhNQY7ciYtcK4dKQ93L6fBZqWZpQZNUKDMRkpW7PExrQht0Ieezajdg5qvSxHV4D6AGpW1VhbvrDNHP7mNWQ89JmlBd9+8na+/Xfezse/5jp+4F9/Czf9wEfO653pNJnwyB95K3/+T/wb3jx+6Lw97ks1A3Qc7fHG0SPolnDv8eNkjH/9xNt5fH+LWbM8NKXjg2ePMn3nMW5554ef2w6YMtI25OuuAUAfeQxdhnqt/jJ5EVPOnOV1f+s07/4/br2o8hkPCzKOn0psf+tb+aP/3U9ydXvmgj//85kkyndd+14A9IrEz1/xen7tJ97Mdb86R3olLwppb8nuLUfpNhKPfG1BJrU4MB5kZZb53PMsy8uWGVmsIP348Uwzh7wPRz/TM31sn/zIKcpjTwwQ6UJ/QDo4j331lXzj9PZD3IL1AJSnTtFMp9hs4k6Txy5xa+N6nt+IcN93v4rf/5XvfkaMyrF2n0e3xhxun/nLa6zvGf3M+7kyVH+HeW3+7Fm89nquvfrMs/5bFuP660/xyT9wnNffcw16bps0Hq3UjMsO6y8NZf+zTZrN6GbPvPl99N4l/f0PXvDtuewhY9Aw6sfcGvtV+t4tqdmz0o4fEzY2XGE3biFrofZFVImQqrjCrMIuDCsL/6ZuSZKC9T2lV+Sak6QrjgbUUHIxL4XBAVgpPWncIG1Ge2+mTmqIdKRS6E+dgcceJ/f7Lp6y4m205sUUqSoQS0FSjm6XEuQJVzkJDggEz2+LrEYLVVqW5EUtZvXzvo9Ur6xvs0hCtQyqxwTxcwIUchJSMlpTz+XzMEXUCs1igS126JsGG2+SZ1MYH3Gs0/Wee7jsvJmYHisdKdeSl0IprvMzC2iUErkRLKsXy4igJuSmdWhU3biWEFq0F/pi5GZMns5gYwazCdYmb+jeWaBmlE4pPeSNxOzKEdONhtmxEaNJIi33sIVbv6sCS1MKC3KP4XaNVCbYskEat83TxsaUJbIw6D3DD1PydEbpCrofQFmGKpzYh6E4NQs16qrcRlVChugqzVTBYpS9hAA1HsMQsoNaMSy5CjNZWFQpmAhJO7RJaPHjTcLXgGkA6wCcMmwlEr7qmg2psXZMzWMCQg2bwh7v8BSMhOUxMtsALchyjugSsd4fU2sGoZDbHMq+guRQ2BZB6D16FIkCFIZiILU+WqxlKAcaio9iaVMLSFJkWiq+LSm5rbrxGwhoQvuePk/pNNOpHw+1zFIzKkK/KG6JJ9EmYTwbcfLaKeXqMZIzTZNpzBArUAzpFet7+r4gvVuSTRIqQqaQrSClB4ycM0iDtROYTLBxgzUJzaGslFTTB1ZgOF5ghcPg54N1ii0L3c6cst+h+0uWe0tEEu04Md3KTMaZBoevOldMhL73t3TTzTHj7Ptj1GZmm5nRKGFNIrWNK0S7JXRzoGDJu3bqh1Kti5NQuVpaWabVsHLxWC3XA68dP8zf+O5/yp972+/m2v+9gd/46IuGUc2NN3DnX76GH/3aH2AmF9Pb0uc/CeOW5hQAf+Han+asjlmS+eD+zbzvzCtRhJ1u/JLnOZ7an3H2vVfxyh99knLne56pYBSh+7q38plvy7zxdfdzpJ3z31/1X8ii/Msn3sFTi2MA3PngteTPPP3Otyhc9YFCu11oz85Jn3iAsr39OY9/uesTXP//eg2/9Ndu5asvEtDYa3qhqQIveDrLzG+b8+ZvufOiBYyfPUmU3378o3z17/849337FSjCA/PjvO+Rm4BznHnMVY5X/cyIY3fvcO5Vm/QTYeOxnvETe+zdsMH82LN/xB/tKJv37jzzOc/tw2OreISys/s01bR7Zi6Okabh3G+7MIVK6/k8Y0b/4EPIaPSsGWvruTwnveE1vP1b73gaYOws89Gz1/HQD9/CiXe/9xC37mU8F+P59zzeel137Wm6W65Bfu1RbNmRphP/bL+7d1Eqn6VpyNdcDaMWO7dNefI5yscOFOHWGW0bo/fdM0SZXci57CFjLSytSqb6n5w9F03UwcUkwXjk7c9EkYX1QxVq7SYAIu+N2mzr0KYRsH4OfQHzzD8djWiObjiUUKX0PaUvjJZKWS6xk1vIia3YUPG4wn4fffhRmqeeQsqCBkgVjqghotE8G720jRc4WIDE9DTCNJiQA1zZkFtIqM4k8iYtN56pCEg0QJOrsVrdcomS4h27hg09ZxBxoFHboDELd6mRxJVKo6Lo/Ay6OIs1ExhN0c0NOH7EFWSq6LJD9hdY32NFKVLopdAtjfm+sr/fMWqFcZsYTYRRLg6lcqYjAYWUlIR4nl4aYUfHXu7SNNjGxFWEWWBR0KUiW2NGuz1tSpw4npiOlVHpyDannc9JCyVbj/RdxWp+AoftVbRgBXoabNeLNvJk5LkO4zEyapGlYJ3bupMVUjGMnjQeo/0UU28EJ4pS1HqEvLKP6oErZhKSClrtz0go8/yYutLR13mqjdwoKuKNz15pTXR2u0CSVbmOSSZR8zz9X51bp8GOXK2v9TlrTmNtko4nX0HPweosARhbdDJBmky/fQ5ZLLCug1RijbryNzW+LRZ2cVBSipxV87zJqiylWuNT8vPDgN7QJtZjbTUfGri9gEm0cRVwNlISVBKootaTcqKxJaUoi6WyN08YmdRkB4495Cg8SZKQvsN2O2y5TU5Czpk0bmlwm7KQsaxI0yC02KI47G/9hkBKycGv1LzC0Fi2CZmMaDcmMM4B/YnXom71LuqwslesFLeMmw2RBJh5E/reEp139POeve3C3m5BGmWShA1JNE2D9IpGYzlJUC2kBJsbDbnv6VBGk4Z2OkLbhia1DpdNscWC3HdIzYaV5I9RYh0k3/e1ET2yD/zSLOv70BfbzNKCv/umf8PtP/Qq/t3f/K84+UPvfcFvwJqbbmT7HzX859f8nUNt/T3fU63ev2PjTn77xl3sWsOnl1dxx96N3LNzFXv96Lw8T9HEqf0ZTz56hGMfHHHtu5/i6J3voTzL8UgbG3zye9/EP/1v/wGvb3ef8e9vvv6nV3+5BfiqZz7f3h/wx324n/Ire7fxLz75pczvOsY1txe2fvMR+gcefsYHjPKxu7nhz72Kn/rLb+Abbjt869phtEy3UvjzX/rTHMvP3O8X+7TSc+vkUQBumzzC1x270//hdfCPH/gq0g8dI33yAY58aNff60WJ0/QD8Mz++dU8296/CD+aPufkq6/i7Tffe9ibsZ46ZthL3CS+nhc+aTYjHT0CQDl1+rwcq+VVG1wx9psV5/oJP373mzjxs1Ou/Ln7OPHQe17046/n8pn29JzH52OOTJ573RV11yKAdUsXSFykI00Db3wN+ycnLuzpTzL+sFGeOvWM79X9fSanCv20wQSaOZz45fvpDykn87KHjNWu6Ygt1IdqFO1JKSPWIZqikFmiYMNz20humUSMLN6WW6hWTQcCSnLVlJNHrPSwv8f8fqWcXtDedh2TKzaRxuFF0xv99txbns/OyRtjb9rtFdvewe5/lPb0U2Sdky0EVtGcLFjk3Knn/IUd2gIkVcWlqSsJU/KCGcwz2bIkBylacNKKK+0MRBVBI7Myk0Vcem5lBbCMwTqdyIi43RMELd6eHZIlKs2teExTVWIK9K5co5tjqUWnE9jYgtkIbUdoX0iiSF9IPdiesWBJyca+FfqU6JLANPnzG0jrigUx6FGkbbHRCGlb8sQVo4Qqs6QMs4YGYTrZZzwTki1p9s/S7u15jmTCj7s5cHNHdz5gS2eAdH6UOqx0dCYUbcjL1i3SuXFba2pI47Fn5TWCLI1UCjKZUMRguUPqlw4Wh5w6wuLsSDBJjjsUFSRbZCNSZXyxraFwE8+9NBcHDtmbivmxwK3nmGIUB1VkJDmcMl2dQVqVjAZIjmOtAZNK7A3/PwKII4ZpDwfakM0E8hgbz+hLD90CswU5AKiQsGSeVYqff5KE1GaH7cXt+ZoLSBsZkRrW8RTWet8/FaNW+GhROOKZlhZYOqzdoZIVMxg1LuIcVKBGmwzRjm5ZSKMRfenpF4o1mWbckCfQ9kIqPZY6P0fEj3/fZGQ8QprWlYlt603RpZDHLalNftySoJEHUnNbSZ6rStigi4A1AWyLIJowLWGtFjS5ErYIJPPjrBh9r5SuR5c9qsL+vrG/NAp+7WFhLBWmGSB5zqy5GrHJxiSVKNMRRqNEM27J7RhpWrRJrlxdduRlH2pvP29c2OzbkOL4UDMYtR+KYBxev7TKr/W88Hn77FNc+Wffyd/l97wg0NjceAPb/6jhH7zmX19WgPGzJ2FsScebxw/x5vFDPHF0xvv3buEjO9fzqbNXsDn63G96zy3H7MzH7Dy+ASZs3NvQbkO7axz/+C5XPXaWkw9/BFssnhvSiHDPX30j7/62/4vJizinZvGzt7Zzbj16B3/wbXfA22D7u4zb5zfy1+78Buz2Y1zz3jnte+9E516IUe75FNf/qy9h9y+N2WgOHwR0mhmnC6OJ+7lHXstvvfrTlyRg/HzzR278ZR77Z8f4xadu45HdK9iZjykfPMZ1vzpndMe96NlzFzxU/kJN94oreePWrx/2ZqxnPRf95Fffwif/4NV013RgMH7wFl75Y6fRD9/1oh539Osf42d+6B2c/S0dr3ynceu7/ffg5XnFWc+LGf3wXdivfTn2dY8+p6jxsftOcPxjH7vwGdYvYPIrbmD/ytVtPGsErjoJzwIZMWP6Cx9h+ppXYqOG/MDj9I8+9szvu0Bz2UPGVHGIEllrDsly8g/23pirWEqhxslYEhI1HzBKNSS51bQYWELawEya0F5pUoOJIUldjbhnzPcT3eQMaXPKZGtG2d+B3QV0xdWNiw46JY8T7O6jDz5Cc+YcWTsavBFYsltdKSWgjZLN1WTE5/P6ob623UqCqnAbXh9QpHhTs0XOnqUQq3nenkMWBzGq/jOJtMqvkxWyVfWW7kGUa0C0XJtAUSOlqmTzghtEyJJRMxoErIduifb79PtzZOMITCcwTkhKJG0wFWiVpsmUeSEVHOpsZWyW/fsQUtu4WrU4zEyNJ7x5623GAqgqQJNJbWaUO5r5grLchp1dRmWPZD2WfB/RKSnszWRXyBlhNycglDRoU8jFy3ayQCkLsA7RTCp1n4IuGrQdkUYzUvJ2YOkMGc9ceclZrCyip8bVFyl5ezUkinpZkUSRC+bZlVIhuvmarSo4MLfhUlwp1gccJjRyUciBpeE1VeWuwy1zxVxiAIASalg1L1DSAJsOLvOwPqg5jhrt08nAOowpNhojowbb3oPS+fFLCeeShusXXRU5qHSKq4cheZP3YL0todSrxTa+NSKQUoNZ5+erJITk2Z0eIOp5jOLHNDVNQNroeMFzPg3IljDpGbfeZN0XpSwMWyql93zMRoQGoc0Fuh5Tc1UgiaWpW4pzC80IxiOXvksCa7DO8xJpW7+OmKHJvB1bGr+Z0RVSFlTCPi913wdwFM8jtabx16/+Gq0B7RRTh9Q0GUuFeVcovV8DNFlYrqNhfOKAnL4nLTuS9hGZqtAKMKIZjb3gapRJbYNRsK5Duj4aqUN9jSuvc6o3R/xX/mqNUqNGA6Cv52KdW0aP891/5r/wEx//GuTXPvS8f64qGC93wPhsc2Xa4xs2P8rZMuWBP/9qdree+y1Xs1s48YnHOD4/R3nK264/Wyn4fD5Qzb/xS/hX3/L3XhRg/FyzlYSvnz3I13/xP0a/GJ76o8If/8R3svzH13L0Fz9FefJJJu+6g/e+6W2841s+zDQfrkKgs8z4AnwU3dMR8o+u5PY/qrxt4zMv+fMdxlzdnuE7rnnf6gtvgDPftcEdOzfyrs+8jtkvbHL1e86QTm1THn3co0cuRjvdFzhPvHVj3Sq9nvV8npHxmE9999XozftDPmL/6p5P/7fHeOWdo89f0PM5Rudzrv47v87VcaP65fVOYj1f0Jjxin/5ae664Waufc3jzwCNe8uWm/6TobuXwM1AEfqrjjzzy4vnzo3U+RwC6h82hL/sIaMGdBFx2EEfVmCRgI4r1ZiFRTRpGhqWnTo4gFSE1DSeHVaMnN2e3FMwjKyNZ9OJkego+wv0iV32H9+lnU2wvsCic4tnCsXfzh7WJ+yhx+GpM9DNkb5Q4sjkUM5VFVk2QUTjk7kDDrdRC1hGiltKLRW3dkqiSC3mqN/rkMnbl9OgzDNLATZdTeaNxtHYXC/swmCllRrSCOQcirjIrCMF+jI8Py5+IziIi4oHDeBrBVnu02uPzMfYbBObTSiWWBbj9EN77OzMsTZz9KoZoxMto+mIFNbYlEAaIRtQEmZ9tCq7IsyBTZSEJFek2tmz8PiTyNlz5OUSdOl5gSpg9TXHWonj3xOt3QwJhSid/1eELIAVslkYjYVULOzPkLPSLHtsvo+RsfEEGY0dsE3GqG4hmoGlN06HzbVuguceDimLrk7UUvV5DlNDpeqKPc9cBLdIVz0vFjZ5GOCU0ERtTqInuSU5VGZmGkkD2YFhnFlFvDAgma8DFRv2m0O/HNZffItVoGlhOnI7cr90eK7m54O4bbvgGYWC0SRvh1YhGuIFLKHSQ80pjcqQYhaAq1qzCyINKYPnOhrFlCb725+UJMBn9rWcIssijnH2nY5KT5LMdJxos9Kb0Yoyp5DaxGQkTMbGJNmQUGBSQuGZyGbQJehDvbvwwpaUV4rTkjPWjilN4yrGnCEnz/5MmXx8CyL3UNW33UueVoFjJp7vKK14bIMmL4WSDG3A57lGyRP0SSFBk4XRONM0rkJNYd8uy4LqElTJ4hDYkucvMm5h3JDbhMTNF+1LXC/83qAUV05LAMpqVR/ukCRfuRKA0exSuKf48p43T+/jX/xvu1zxh66iPPb45/3+NJnwie87wX94zQ++7ADjwfk9Rz9A+YHEv3rvl/O6P/dxyrlzz/p9L/YNYb71lXz1X/s1bm0vDBBJwJXZ+JHX/mu2/y/jR7ffzD/4wG/D9hq+9ovu4A2bD3Hv/IqXPJ/yc01nL30MQ2eZ25+4mY3P7HDTscNTDRzGHMu7fNXRj/NVb/k4+ubEU39qk0/vX8kdZ67nvsdPIJ+ccdUHlenjC5qP3/+sFq+LelLm7JccviJ3Peu52CdtbrC8qn9GAUu/af658nz0aaxvRq/neUz/yKO87vvgE3/ylTS3bTMZde4WeXSLm37CGP/MBw97E5/XpM1NlrPPKhVUg8XFa+8+OJc9ZCSnsBgbomGYFiALzk4EpXgLrQSMUY3iieygpvjj1A/0SXIUFRhIoWksVG0FRGiyMR735J0ddH/mFsXkMCGpe3pl0npNeq/Io2eRx0+Ru45sYdVV9VIKqUZvBjjgbmQLgBQwT11d2DSZIoVEJhsU7Ydm4RUT9Df8KUypJjIoEpMElAViB1GViBbP63Zp338mbtMc7iuFihIVSijEkoFK2D5L2FVJ8brCVi1KYz2239Mve0yPYdMZvSW2zxV2T3eUfp9+p6Pbn7FxbMp0c0IauZqRCi8EtyhXZap6cYlmQTJI38G5Xeyx0+jZMyRd0LYJ+njdSQIiWrRZx0jdzw5GTRykJp6uKktF0BSdHOqvL4NDNDw3z0FRB7sdNl+QZlPyaEyZHcHaKbbcA11QdIlp54pGdbyXojyoqMM9RZDGVXqqDlYlhU259IEkZXBY+3LK1e/ta8IybqlvUcm+xeZwMiV1WK0B7ZIAxa38Fo+eEpqAIqhBblxVWLcHcwt3kTHMNtHxBN3ZReZLEmHZxtdWkWGjIk/ScxexaLdOXgLkJ0QJxWVaZa+KkpK6esKSQ2bE7btaIPIpE2lQOzqXjMRNWal4q141FVffZimkpjABGozpBJrUu1LP/GvOz8RvRJirp/0iq6vcSxUoCcnZ14IJRdwqnfPIEb5UspzJ4ylMW3Q2Jo2n0cnjilLqNSJnfxPX92jfk1IokEWgzYgllrsd+6eXlEVP3/XQ+3GcNJlpa4ySkfoOWRQUwxYd0kcEQ5OwLOTplDQekUZCapIXaZlfm7QI3VLJXo5NZlUUFBcwv774TjiQ5Rnn7TPuN67nYpzvf+07+cP/y/+DV/2vT31ulZII9/9Pb+XffNkPvKwBI3hD9e87djvf/jt/g+944M9w4/9x+/lXeKXM3X/ian74xL88v4/7PGcrCX/w6B38wa+9Y/iaAg/MWn5m+408unzm3fgLMRq/J1+qNagI/+HffSU3//Cn2f7Sq3jb1n0vyfNcCpNEubI5x5Vb53j71qfgRuBtoN+eeKpscvvZV/Kr97yV63+8ZfOnP/LcbegX0aQ33sa3vvE3D3sz1rOei35kcwNXezx9Jo+nyzZKYT0X7/SPPMot/+9TpBPHsKtPIPMl1z340Uvi904dF309/WvtueXzusl/McwF7t278CPFVh/uk2BZwmJapXWFHIoiTyFM0UBbyxeo4in/kAxIdtAiYRc2mmhxrepAYzzpOXbCmKRd2rGSR4KMMtoIlrLnQY4EWc5JT5yiWe6RFvuI9iCedZfNyxTUCpgbaquSLj7ZxzYpQk+KxmctidJ7461EQYjksA4Tai112KGaHBZVhV6oisQ0nkOJZEgHAUkcNLkskmRGzWk0s1BWqsPHJNHt4N8zqBwFDKVo8SzHKICwrieJ0lhH2j6HzHdoJ8Lxm47RzkaYZvbP9SzPFnRQJ4Qt9MBrrHBQq/KyEVKDK/h29rDHnoSd0zTWkcUt1qphKZYDN8qSQyKp/6BhGT/Q5jyUkQCIuG5QkisFteD1Qt7MjR6wM/cKpUNsjix30P1tKHN0ktBjx9HNEzDegjTByETFTai9UhR7QEoZJIUyl6Et3PAinqq6FALyhnKu/hGSl/6kBs0tTDYo4w06GaGSvQglchIlp1gj3tqsg4owk2gG36sWi31vrlZLUcIz2cKObLpCc7EMJR5I9v2I9b7uBMSUpN6cLZFLWPMwneb6OVThlb/YQlOzR5uMJdCup9ufuzoymzeXa/HzDEVNfW2i3iyNRAu8ghREjN56h8LiWaFYYdIoW2Nj1hiT1mhycfUnvu1CqDlNKsrH6D12IP5Y7y3OUnroO3LXkRa75OU+ebFHXuyR9s8hO2fg8SewJ57CFru+j3vPLC3LjrLsMBSNkpxEjv0uWOOqSBMoNOzuC3t7iuSWdjZmvDVidnTE5mZLE5092hVsWTCNFvWmgfGY5sgWeWsGG2OYjIiqcGqOqEqmV6FXV5KmeszUIvO1XkMt4HdVnAZwfPGX+/VcgEmifO/v+hH0HW/8nN9nX/Ym/ux3/+gl2yL9UkyL8gN/8B/z6J96uyuWz+PkW2/mb3/TD19Ub+oScFPT8d8f+wDXj89wpvtc9SDnf9QSP/eZ1/Lux17NU93GS/Icv/jobdz0Q5/Edvfgjz9xWeYxvtip8PGbTn6Y7/vyd/IH/tqP89C/vgn9yi867E37vLP96iPcMn3isDdjPeu56Kc8+jizTz296ExPj7npRx5dQ8b1HMpY50BO7/g45Z5PXVKAEcC6Pjo4fJrtjnzP/ZfM+XTZKxkNXUEDYMAu1S4KULzchRTKRcPVceIqKm/v1Vpr4q22bUb7DqriqEJJASsO2KazRC495dQpdOckeTKlHOmws3OKKqNi8OQZ8vY2tpwjyeGEg7tofxbPlXQRnfr2hNWbal0dCjriNSZImkJlFvApQCJDfl0KwWEi/qcDgCRoQMGVrZyAAA4RvVQ7IGdvoRY9sNOr6i/2v6Ikcth6/clMdVBppgDAKay8SQSxJf3ZbdqUOXb1Fnl8FduP7yKlZ3pszGwq5KzDMatgx+pzmzmcanOUZBR02cP2PrK3h3QLL//wHezy/lICmFQNG65WFLfPm0o0+0YeoQd9eu5QtOk2TXIw55TTX7OkoXCFJiHZdZz0xYFXqGel72Au6HgG4ynWHKMfzUjLfej2kb6LtRAZl42X2dhqt6/2bRwvV6PGug4lZELQAHY5ZYQGcgujMbq1iYxG6OkzsAuqS5L1oXBVV9GG1dVboF0dLCorVWyUl/g+8wVp4xkcOQrjMXr2HLkUh9Epk0KVWJupA6P6+tHipSfmcFOSW9MphWoltyErNNa/+NpvUM+TDLmdl9HkKpSkNsOnagUu6opkq6Z0P4a5CTXlIAf1r9dzPsUDWr2mxHYN1ul6bCJrcngOMYJkD6Uz9aokGBpWclGFUwbLjtL3yDVXIu3Yvx4qZIrnhiIJyeYiKS2huC6k3kgNjI6P6M8qI2k5MkqMmkQOOJvqtSx5nmtqwRrPO82TEYxbrM3QZt/GZYd1/ovOzI/LQhMjFUbDcamVQb7j6u4brsVxvpoHiq7nEpmbR0/yqT+cue29zbO+2cnHjnL6e3d4++TeC79xF/nc2Jzlb/zJf8hfvv8PM/ux28/b437mO6/mS8ZPnbfHO58zEeF/OPYhTh35EA/0R/jw/k3cOz/5kj/vR09fyy1/9ix2dpuPfOmbufZ7P8VrNs+fnflTe1dgP3gV5bHb0a94C9/9ip/+/D+0HrbyPn/x9f+F23/gVfzqD3w5x/7FxdsO+8QXrX8xrWc9z2dsseCmf3AXT3zLazn7Gmi2hZv/41OUT3z6sDdtPeu5JEd3d5l8+gn6a46RT+2in3ngom7C/ux5GUDGan3VUCviikWLfzXxvLeaD4ZGKYqDtOAzAQoiqxE7oNTxD9AppcjDg5SinEF7xinTnzqNPvgI6cZraK/YZDFtyd0Ce/IJ8lOnSMs5EhlzQrTjVkoREMm02kfV26MJmJFCfWkrFaX3X6Rgn+bW67C1hqM4vge8sMFIjVuKKjiR4XkDvAQ00mirtQAuB62uUvPhZJV/qAIpZ9/PFSKa25m19OScoiAkQI2CJ1MVxDo4lxm1Y45fucHm8ZZ+b4l0hUZdxWdtxnJama8bt86mAMeGOnvtC2mu6N4yyilKWDUtlHmumquLxtdCCutu4JEBHALqVmSiyCJEgr42us75Vw7AJQksOUwtipiQE2gOJZeVQY0qBuz32HKOtRO0HWNHjoEeJc2XsJwjukB1iZiDM1evyQoyWi38GfAjpDxAM2fWyTP/JCEywsZTbDJDjh5Bt6aeufdogv1tUu9ATSTyGZ1kOh+zCjlDjZYSKWzAHvjXYnmCbR5BtjZQMegLpr1vf6nZhTW70RuIRTTAXNiXjcH27TBNBniX6rkQjdclCpMcYBfanAJ11XPF6gp2Jac6YCulbof684dGXSIWINhhFEH5cxfzhuohZzAUezVaQZ+WM5hIyVCLPMUoFXLIqQyNTHGuZwSNoiq6PdK2q5BVEnbyGDQNmRy5st42bb2DSOYdZXuO9D3SFyg9LcqxDaM0rnRsm+Rt1wdfU0pIjuzSKKNJo4y1CUYZmgbLibLsIrc2Y8Xolx2LRc983+3aTVtIWSP3sh9UonHhDQVj7LOqtl5rGS+p+fNv/yn+w+u/Crvj40/7ujQN9/7JN/CvX/s3D2nLLv65Lm+z9ScfgF8+SXnyxYNBaRpe9TWfueg5/YkEJ0bneH17B3dOj/Lh/Zt4aHHsJclNfHR+hO6fXEN/73sBGP3sB3hw+iU88v88wldf9YkX/fgfOXcdp/7qzcx+zotQTr1hykZaZ/d9IfP2rU+x/6dG3PPAW8m/dHFmZOll/ylpPes5f1NOn+bEP3sPJxt3+JXLoPhpPes5zOnvvR/ue2BVhPoiR9oRp77rbXRbwjX/5INeFPMSzWX/6zNJDtWiK8+qQnHwxkpo7MIWLKHicXaWhljCJDX3zQbQYuDAo1ekcbWVYqScPK+RhNHRdnt0Dz8OsxFy8grG4wbdO+v148t9krkVkxTJcEbYiIHssEyQgC81G9BhkaoMMEaq3xm8HMaSwz3FbaIQZS3xOgRX8+F5apZzNLzWrDQYuEBtgy26sjkGkxsgLA4dLeSVVoSUM2p4KYisGord6ituBZcKfoLHYcMxyIs53dlt8sYM2ZrSbI4x92OS2jxkFWhZFU6AH9pkhnQF7Xuk4O23fedCNDVvzDV1JWLO/tOxNnK1IYeqDHPgjMmwJvyBiu9HSeFe91+o1Zpc19BwfNTBdoo2ZGX1YBJ2/MSSvu+xfoEsGmw0hnaKTSeUrU1XkO3twnKJ2ZKUStjdxUF15N75enF1Wy3y8X2cEXXlmeUJNtrAjh7Fjh/FtqbkSYulI76dTwB7hmryXEP6gLehxlRXviaJY9xkkjSu+JSMthN0cwu2ZtisRRde9tLE8S8V0lf4ZHFTIElkibqyt9QdHrEFJp5v6WxxpcyUaGuyYqtzZVAbug45VjgGlM7Xetu6ztCw2FehxIvGduolAwubsLnVGXHVoEnl/bFsLK4/da3HeWHiZTWDpO+g4pSwhAdwM4n9EXbuMkf3jPKkIAqyteFt0Tk70VZDuiW2vY/t7pBCsZtKGVS6bZMhNZBarPfrl+WEpAaakZe+ZPGs0/HIwWLbIqOGNGoBoV90KEZJmZShWM/+vLB7bkG335NFoQkbul8kvY08u81+pROuJVQHrsfruWTmttGjfOo7jvPKO57+9TPf8cX84B/8+4xl/eHic833vfLH+P3/3Z/hmh/49Rf9WGk24w1HHzkPW3VhphXhzaNzvHH0ER4tmY8vr+Zj+zdwupudl8d/z6OvZPYPj7H5nw8oRc2Y/sf3ke+5jR/+M+/g29/2fmbphSkCPnLuOk79lZto3/V+/4II21+953Ea6/mC5quP3kX5fuFTf+7iA43SjpjcdvawN2M967nk5lKxc65nPZfEnMfCo7Pf9lb+5f/nr3Mswe/s/xeu/MGXzklw2UNGES9/QIWUApzFsVI0rM0HVHihdAqEQi3OMLPIoJNQnzlscKhW4jFTcIywpIpDBrKS57voU9voaOqA5OHHSbu7ZO0cZFR7Zl1I2QED4hmSpq6ACkepqzExVPvIlKzZhrgNM5QBg+JSV3bECggT5pl08W+iDJmPoZX0huBkoa4Scs4rpWVypeCgnpCVArCE8o2AoF4CEcpAxK2eLg0c7MmorKzbImCJLilpOcfO7cHmBEktMhNSdouriz0F5gukq8qwaK+mIMuOtHDbKCWy+KR4s29tZk42QB3PdvQMO+2KZ2OmClQDVEUWnVjxFmnfPaGqi0w/cZBp1kOo8yDcqBbFILiZPDUOd01q7qB6ViQOznSpWLdP2W/Q6QZpukk5fhIrhnULcrcg9Uu312sBW8Q2+r7WWr4hAjSYNFhKSDtCp5vY0SNwxTF0OiK3DWRfu3bVEcq4oTw5Q7Z3sH6O2NzVqOJrB8kkcaCUJIG0WMpobulHLZw4Dkc3QskG2hvSK5qiIGapiPoaTxW2IcM+clu9pxqm7MC8WE9O/r0SvM/huSuNEynySBkAri/xyHv0FehlLjlhVvw4ScBkwxWJ4s8nfrJQs0clyo1MInMxxQ0Bi6Id5cBaj8DOFVcLRXDcBJADZ1v9nqrxk9W1h9psvlTyOcE0OWSdTKBpEKDsLbDtXdg/S57vkUsJxa5GxiqwFEiNr1esUl2kaaCdIHmE5ezZteOWNJ5gswnMpliZ+NneF5KzUIoZywK720uW+95uvbqhYXEFzQdUwYaZX2MTobgV80xN1pDxUpokyu/6Hbfz8b99Nf2jbkFtrr+Or/wzt3NlurRybw5jWpRrv/k+5AcnL/5O8g3X8HuO/fz52bALOAm4Lheumz7Ml00e4se330BnmVP9Bjtl/AW3Um/3E979/tfz2r/zFOXu9z3r95Q77+G1f2rGr37j2zn17Xt8860fed6wsbPMv7vzbdzyt5X2vR8Yvt5cfRVf86p7vqBtXc9qvu7YnTz0l4+h33Z+lL3nayQnTm6sr2XrWc961rOey2P2fu9ZrgsX2/K/Ogc/+NI912UPGQeiOBAHO1AioUgT2YO2yg5bZdvFf71Kl5QyqgGkxEGfkAbbKqFokySuRBRvAAYjlx47fQY2J97Me24byhIorlKSjIOLjvrI4DbGWpiiQFFXOuaUUe3d0mi1sTjakas8SMT5RbAOQulYrc6udsvQuiUVK656jIZlUxvaowmFpY8rP2Wwy9pq/9b22EENqUNmnQMTV9tZwJjAj/4TuQq6BFKimIImRDpk9yxld0q+4hi5bSnU8oiE9cVtuk0Gi8xENaxXbNGRO0WTkKygpl5Mokou5rAlJd/OHGrEol7uI+oQBMJCngbARBGHySJocas6yQtaSDnyHr0t2eGRkpoGwygDHHPI4w3YAbYjT5FcSNJSxBDpSVpIuqD0ezDfpoxn6GhCGo/R2SZKlGt0BRZLbLmMso3ij2fFHz+3aNvCeAIbm9iRLfpJIrUNpkqpBRyNkJoR0rTYkU3K7h7l9Dbp7A5psYdYh2mhCmZVzRvbmzEymdFvjNHjMzg+I2n2luICttgnaY9Ygb5HUFeIVv9+5J9WgOhqz9hPFCQnsmXQLtSiMsC7Cv5TStHA7GAtSSKZRbmPr9Oc4jaClAEeErZ5MyGpDOe4pbBAx3ll9fjFWkgm/tjULNNmiBzwfMNYJxTfjnruxLXJhjMMvDE+rRR/YkN5ip9mXpjDTsJGU6AB6SmLBf3pM+T5No0uSdqTK2i22HckKIb1C8/YzGFgVqB0sJhjKSMp+3UjeymQjGcw3UA3Z7Axc4Uj9RonLJcd83NLdNHT4OrRMly3Qlkq6hyxTr0ZIxK1UvXat55LaX770Y/xwTf/fkYBGR/+1pv5P0782CFv1aUzf+nmn+B//cY/xsY7z18246U6MxG+88idAMzNeLK03Nuf5N7lFZztZ5zuZ88JHZ9cbPLRJ67h+D/d4raf/RBl8blty7q3x+aP3M6R/7LBr/+Ot/PQ18BvedN9vOnoQ7TPosA93c/4lYdvof/5K3j1D32Ucu7c0/59/vobeNvWT73AV74egO+89n38/77t219SVcV61rOe9axnPS/XSVtbfP2Ndw9/PzbbZ1DrvARz2UPGwS6ZIvdMCajnFkrTxgFYZK65+qsBi2zF2PleHqGQvJbCPyDj8r9ULY4VsFVFX7U9KiI9zXxJ2dlDSkH6JQ3qkAW3mxYzb4ZlpUjUAzbShH+PSaaEDbpaQlOCnAOCRfGIkKJ8wZBBajfIu7ysI4dyzHWN0YAyGBiro3NQWJnJUFDj3yRADjmZDYUuKXk5jec+ColmWMMWtlbLMqjQBFYZgkk8eyA1JPElqosl5ew2cuwIfaNodrVaKxmywahBl24XTV1Bem/s9Ty6AKV9KE5FfH/U45TE1ZclWpmb5F1AKdZGvE6xNKyflGsJDFh2kKLg1nvxdScmq1ZyCbViFMlY2JolBVkFoB+Oj2cFejagEXmFkh3JdntIv4/tJchjrJmiTRt5eQ02myKbmyE1U6zvHJbmTGky0o5gY0LZmmCTkcPFroTVOaG9Nx8rYbnOQtqaorMx/bEt8t4+Zb5AekW63nMVE9BkZHPDlW8bLWnWUCzR7fXklMnWkZa95wRa8TzMUBDX0pOqmh1uDgznMAHM1c9lklveo/HaJDIOBUzKkO9osQ7Fd+CQC2rq+1hC5egdRIarkf0GgZpg0pMsyoqsngyCVp++VBVm9mtBwPgUYa5+jGMNkLxpvOYQ5BVc9NxJ/3mR2iS+anw3C2t9SmQzutJBtyQtRljXodtnSXtnaG0ZWY/1Twp1ZnFwKQ0kJSXFtALamlkbr6H05AKlT6Tcw3IJ+/vo3gyOHSUdO0ppWqQYqqBdYrkorj5F/LyhR1VISUF6JNSLA1KN/Vc3ISSqz30hX89FORPpuPfbhNf++haUwlXfej/tgdiK9XzuOZHmPPItS279sRf5Js9sdU26DGYiwg1Nzw3NY3zF5DE6Mx4uIx7qj/Fof4xHlsfYKWM6y+z2Y+7/m7dxw8/eRTl3ji9kL+ruLrMfu51X/wehXHM1v/rWL+P0q1v2rvVHafaEkx8tzB6ec9Udn0R37+HZQgAe/LrRulX6RU4rPfYNp5F/NsY+DyRez3rWs571rGc9X9h0b3s1f/yKv03Ff99z8y/zb696K+Wxx1+S57vsISNJo0shwJho/VztysTG1UtSeremRhvzUPRB2PkCTrn91K2bwKDIK8mcz0WwX81HtACclIJJR7O79JbgvkeKoqo0TY4Ga38uz5UrYAE8BnQZ7cAJ+mWBJpNQei0grpJLCFlCBYWrq8SKQxISlBTwA7c7h8ou+Miggqy25+FVhsLNHZ6BU0PdZ6hbyTWKtpNFEYkrF80iE5CV3dhif6q5wsl3sSvGtBipGQVXMiQ1SEq0XYcuFzBqkMYtyEXUQWRRoEM6pfRL6HrSskCvDnaWIH3n6qqCW93VyNHQXMIKn3LjtmOisVoSmoWkDostAJiEok2rPVsDZuX6AdvTFZPvllgWB5p0JUO1MZNdlWmRCWoOxAfIVB9SVu3DWcOWXTroOgfmKaPNiDzdxNoRNhqho4bM2FdQ68o02hZNbeQRLhzEmduYrXhpjS0VKYqNXJmpAakM0KNbyDUnISeHjWIwGUOLK2sVGoR5v4T5gtQbMm6jFbpzHGu1Z5lVVqVYWPkdXNdzp8J7C5WsmLq1OoXaMEkUFDmsRL3MpxBFKaaxqx3mS0B41XqzwaMQalQiAjlntFhAxCoH9nOoljDV0yxL8nSC+JDvWaOReYqR40UWQiELUXZUWNmHY3+EtDC0ww4AXUbpNwIqbESxfoHOM93eLrI4R2NzkhrJFM1eXCPRCK7a+9YLWDRbCQ7dTeuZHqAvZS+OKn69TKJY5zmo5ZygKWGzDdQyqtD1PUXDBk+iL0LJDmnR3rMYKyiP/atSDug3HfiyzjO75CaJ8g++5p/zj37yq/jNT97EP7n5nx32Jl1y842v+yifPHqEcubsC34Mu/9hfuT0l/AXrvqV87hlF8+0ItzUdNzUPAE8QWfGtgmdJf7Go7+drZ+5k7K9/cKfwIz+kUcZ/+SjXPMc3/KcV6eUKTe/dMHpL6f5jls+wC+95m3PKJM6rDEzFuX8lxKtZz3rWc961nOhx6qLL+ZrZ/fyw2/+FtqfXUPGFzRW7c1DCYErBga7qrrFUYcwQFYf7Cts0wofVvY/DAdrYkh2HSBhNbawKUp8qla8cCZZCYDljbo5mqAH9RMWKicNM3Co7cwfL3ldtNuiGwcDSYU0GhGElKHoJZheGlRQMFiZa15dvIbkvtCBmqxaiVcjNQySUOkhLujEnyMF8MEgWa4uTWpbrpWENKHONKj+7ZrV54TI38zlUGp6nqY33SZJrsqbL2A2wSxDgtw0lE4ppZCTg8HUFXLXIb1FDmMFWkpuM5oT0q8Uozbk7jk8TpKg7x3yNbH/QolmKZCXKoqRU8Y0rQprYg95K7gM+2vYjzXz8UBD+Mpu3niWp5nvN1Go7d2simMssjMx4gxWshlqxS3g5zrP3WsnWEp0JKRNpEkLbYv1LdI25KYJ5W72HpOusFh2JIG2cfBJn7G2RHFJtFkvC70tmV6xRXN8A20TNh1B36P7S1gWdNFjc4V9X4g5t2jpCZbvry3yD5MJllM0fXu2oy8RwVIFYr6WLKy/q7VIQPMUirxY+CjZGKzXDPB8pZi1AarV+wehAI7vk8aVoKJGNUJXi/ag8o3/JxrbH6dBirPYs1Trsa9fVdR6pJi3O1uFbBagLRSkVYxYofZw1yN7DEMxyt4Osr9HU+Yk7fzGgEATJ9WQ81jzTsVfsyUlhTV8yLUk1NkUquYwFSNl81IfXcJuoiu79HuJZcrIqKHJQjvN6Dl/vQVchZxX54XfLIlrkNVSHli1S61A7nourZmlBf/zDT/DiVesQcsLmf/62B387Wu/GV4EZNT9OT/3wGsuW8j42dOKcEIAlD9y1bv5zu//Y1z53swV7z+F3fsgunvhVIVpY8aX3nzvBXu+y3luGJ1i95VHmN7x+b/3QowtFjxx55Vw02FvyXrWs571HJiUyUePYPv7L2k78Hou/WmuvQbbmGIPPsL4/Z/gB099BX/pyvcCsCGJfpZoX6rnfoke96IZRSm14IMw66X4oBvqvCGDsDj4kcoRbQWfXA7kYMIqrQg7ZBgzB7WOuY95+HslWYrbErUUpPTkjOcIAmJul/TG54AmOASUVNVTEiAioEuoIPto1k2hkpPkxR2BDEHCUmrVjRuZlPFB39WCmapfdFAYACw9jT8CAT+QoU7dsgzQqzo0ffMMkQK4Oi0N/DwUaciB1+WgVkhgOTLvAiRJKDyLIV0HXe/AKeNlODk56OrMgY1q2MOrdMrVXV5wk9CmcUgZGY3gfDbl7E282sda8ebrFKBGk1Lbv3txxagqkbtXBpWauEwulHcydH9gVZ3W+6uXVfuxpTQUyJDM1Zckamu1Nzn3B9ZvACMlMgLFVZPaeUZg703a1ru9X8XQLEh2wGgIllvfHyQ6Ruyc6Zkv57Rtw+z4mOmRMc2oRVI0RluDZqFkQZIx39ujGWdkNqKbL2F/TuqUsrtAdjp0d0lBaGcjVwOXCrhDbYiDr7oWjQRD0RBQcPWjVGVsIie88EYCNCqu/KtZkmbDvq3K2MGGGATQG6GLr/HMsO4rsBxwsdaD5jVLjpdlAImgca4bIY+NLMX6PCtxnisUy9CeLfE8EurNUuoNhWjCPgCs40ri58YqwdCPbenIukC0j4ZuD8n06wDDdcRvLsgAUTEvBLKA536JC0BrguAq3kKUG0kC7Sllye58n91TRpcSo5NjJldsMr1ik+0z+3R7+0zHMBo3NCx9r8qAR4c8y9izrjCNs234pvVccpPXB+8Fzywt6K7aJN31Ih5EC/O7jsHbztdWXTpzazvnN77pbzL/RuPO5XF+eee1/Pu73kp5dMrJDwt5aRz/wJPwyOOUnV3Q89t6LtddzWs27zyvj/lynVZ6nnxTw40/fthbspqjdwt8w2FvxXrWs571+KTZjOWXvY6dK1pSZ2zdc4bysbs//w+u52U36S2v567/YQvbKEw/fS03/727eOe7vpz/7TvfQ0a4uxuz8Zntlyzk6LKHjIO1EvwDdnJd45DzJooWDQKXyMmz1LT0g3VzKIap0qWEKwulNr8SiiDxrD1CbSayAh5VHgVQSmQWElCkZhwGqKtgwbxsQsVICQQd8v48etHRZhIvfTHVKsYK4KQDxLDIpLMBcMigLJIKRPsAZebZj5KzA6GAX1UlZWqRD+egzWKbV+oyjdfgUM7LcAImkQ5AHyOnCkEcZBhu1STVTEsHrORQpHWKLQsmhUYaynJJX5RkhiyW0Be3dqpEaU4A0z5MywnSqEFLRiS5VTYKepJGOUiJJMycolXbs+OqBVyNQd1GZHWmHEBQD9ryDUvZIQ4S9mo801IqtqpFPRXeBvg2CZttABotg9BrVXjiDb0moX4MEFWtvwklqWfvScLXOUvosjeu58Yt1svCzjnl9BNGmmQWkpifXXD8qhlNY+RWGG2OyNMJTEbkPCI3rR/pvTml62HRkfqClELa7ynbS3RekElLzmBdhy07kjrcF/HCJJJDeQfDbqtPUcRj5lmiqanKzzKs2wpnPe9QV2C8wjlzU7EUjfPUsxxd1evwWc3cZh/734ZzwgLGRVERYFHoVO3ufuqaN0vTM6jxLK1uQnBAqIeDUqPCZx8vjOrijoBrIOPs8m1QQ1Kcu2FXTzjcNi2Y9gEY67qr4soA+gEtm1zlmrG/6sbFzRIlbhCov3614skG9a6Bes6l9omds/vslYK1mZKNyZVbbF45Q7dn2FNzNsaFUV6SS0QqBLtUs9W1Qfya6YUwaYCR67k0J62t7i94TqZ9Tr96wsl3f/7vTVtbyHVXM7/pGNNPPIGd8QISGY/8HIOnWWFeTjMR4a3jM7x1/F7+x6/wu/R8G3RmfLqf8Z/OvpWPnLmOT/zGTVzzXuXIBx+hv/+hFw0d+xMb3DA6dR5ewXoAuq2L6/fANe96hPf/oZv54iP3HvamrGc961kPcvMN7F3t2jPNwrnXHePIA0eeUUi2npf5iHDfNx1Dju0jwOI1++x81au57a9/iq95zR/gb7zhR/gffuJ7ePWdH3zJNuGyh4wgaB8NxynAkxD2RP93dzcHeDOoTRA2yBll+Hy++h8WtlaHDhbAUFIK66HnFKoZWTxfzsmgK9kk8viqfXl47CqTCsihg8rLAUYKmaVYGkCYHcg1JKCkBVyUUCQ6lgy7d1U0waBUJPIAa+lEkhRZdub5bOLN2rWwBHH7dm2XrpZOSd6QDLi9Ox4b7VCS26qpADbyKg0wwaShSI5CjICbGrmERWMfCGVnAeqvvywWSNdjfU9aLNHFkrxYYH2Plh5EKKVDeiU1CWkaZDzyYphSHKqkTFEd9hmSoVF/Gaq+zbEfTGJfxnFPsV8r6EXcekwpA/BJ2TMDD4JqiXWkRSunHKzTK0RLHCRbqW8H1acD7SGLMP6vlphkCd1bxhvCAzoqUYKChQLQFZ2NGYudjjIfgSRyu2AyUbZaWPZzOC00k5Y0ncBsRjpxhLw1RbSFc3uw33n+YV+QIqTOszW9QTojJSzoWoZ9o+pwOIVOD0lh19WAd67QpPgxMIIJpgQl+40As2hxZwD+KfbR0G5sRkpCKQWr6/qA5dwqmKuKZlspFVMKqC6JFGpfMfU1PsD1sPVbZB+KgTQOUv1SQVUmSlxLLGW01HXR+0akDCTUnG+vLgcyWPYr8ZYUNz5QmmTkIRZCqAWsFteQChrrNoQWO87dAJYhc66XIj9P/dpktfHc8POqF7r5ksYm9PsJ6wrjoxOOXjFDdMG420VKQStdjHUcZ8pwbsdV1G/kPEdr7HrWc7nPo2WTK37zuQtL8vHj7H7Fq7n/m4w/+o5f4ms2f4Hf0hrvW0x4vGwBXsDz26b/EY0s2vWsphXhNe0+f/aKX4MrgFth/h3Gr+7fyPff/V+xeM9Jbvy5c6R77n9BH9L2r5nQSn/+N/xlOsff8KT/LjzPitMXOv2n7+Xd7387X/y19x72pqxnPeuBQWTxsp3y9Ju6pRXkxDFYQ8b1HBhpWsrk6efJU69vmP7441zz3835/qu/hVs/9Rv+ufwlmsseMkpqkKZZQUCzQUEjkjwProKNGs6WiPZZ3D2otYghCh1CyaS1BMVYqYPEMwTpQ11V23MDGJTsH7KtKAUjt00ACx0Uc8TjJYmMR5e/oVVVWQtbALPiIMA0UI2R1AFEPEBIlGoOmgyZiCJuLTbRgDEwhCkmI4faCKDE4wkygLLBmgoBRRNWnNBUlWeFlr7ZriSrOW0pHkPxnaOWPRtTlRT5l0kytuy9VXviKjGZtJBBd+dI15H25shyDru7sFxAv0RLWFGBbrEELUhOyGhMGo+9+ARFadk92zM/t2R2MrE59XbpTCJLwaygxRWVmnSAg4EP4zUmV8clIYW91FJGiLZp4wAw1FBX1nbi5MecOP41fxAH1zWfUOt+q5DKVvb/AfJq6MQkGo0JsD5kEEJOobKsYEmF1pTZKDHdgO3dJanJTDdglnaYCV7+Yj15vg/LXcq5s3Q7ZylHj5I3j6CWsEWPdj229CZhTb5ec9Niy0Ia+d+p1voKvAVQh4TFHOxKMn/d6qrPVC3SZFLKrojNrvaUenIlPO+0dzhZRAJAhlhPfS0LQq7nBwzlMNTzpu4p82IWEbeye+aoAzfTCu3iBkMAOdE4J8zVhwm37VdFs0pkIMa1ROLGh1gO7Ne4cpDGb3Qkw0QHG/MQWSAVFfohrjcXxNJwLfObJ5HBGutFJIU6cbV+EAmVbwWgNXvVBtpa1yMoWZbMJrC3hOWiZ9wbabmkYYO0OUXP7WBdVWvmuA6UUFnGTY9QB5PiuhqFRi9XBdZ6Xt7TWUPa757ZWixC+W1fxNZfvZ9/88q/xdE0jX9wBcNXTxU4e+AHpnRW2NHFGjR+npmI8PWzB/n6L/oh+CJ48HsafvjUb+U//+KXceu/3YEP34N1y+f1WGdenZmk7iXe4pfPpItQ0X7LOzvOftWMo83eYW/Ketbzsh1pRyy+9k0sjzYc/dATlHs+ddibdChjDz7C6NUnWG75u+bJ6YI+/uQhb9V6LraxbskNv7jk09+VSSN/h7n5kP9+LefOXRAofdlDRroCzUqxp/FhF8PLDETAyvABt2aGmUZV8sAdxHPlwD9/J1fyWV/CEiluqTQDVc9EDKhnIp55qC0ptyTzwhAobrutCjW8QALUlV5ioVx0yFjUP/g3kjDzttgI8XNVXQ61UjKSZFBzyzQHsv/w/EULSiqpwscUMCJAWnCGRPJCkRw207qnbAVdXTgmgxpKyAcUmhLgUkLspbEvK6QLS3QU7yQzTBNKT27AeqVbGKnx11iWBSmGliVihby3B6fPwGKf3C3AuqEJWorbxbOG8kwN7eboYoS1GzDdYPvUkofv3WN/XzkyL7ziVTM2cnEIqnl4Ca4edWjk3T851kMcH6uW9Cp2dbBU3MM7IBQHXjaU5hBFMjVTsMZICkJlXr6GHI4PUjv3ta6UkcTxscqh/Hvd/h827mjGTsPhFeqWznLP1dcIs30jNYWNkbHRuJ1dUnb1oRaSFXLp6HeW2HxOObWLbG7BdFR5IWV/QS9CGo9gAbpt2JEUNu9EygmrcQRqUPzMFAIw1p0eYZiuZk3xvzu30pcDIMwc+LubuuZ9eus3Rug3WeUgZsE0gSk5zlmzsAinHAriPlSrAfEGJTFYcvVw3XsV9NkA8+Nao0oK4KfV9i9+zIdsypq/KkTxjdSTa5Aa+6XB1+8Qu1At99r4vrFUsaNfA8wY2sidTsf2DdLlYZ1ZqurleEUSik6J749AUTGjbYWjUpCU2O6U8RRmaqRu6b1N4wx7rnBO9UUMzxvHTEK9an6zwcxQabyNej3reZnNnfPrkXOfVVSSMo/+6bfzQ3/qB3jbeARMn/VnP3tayWym8Ro0foFzQ9PzF696N3/x976bu3/3lL923zdx38/ezE3vfIzyyXsvGlXdy2HeeuWD3Hf1lfSPPHrYmzJM+767+Xeffivfc9uvHvamrGc9L9tJt97EmVd5NEj3jqs4+eAj6N7LD/zr7i4bv/YJJq+5kTTv4Z57X5b7YT2ff5pf/BA3jd7Kw185odkVTv7MJ595Q/ulfP4L+FyHMxLFDPGBNol/AFaprb4rS6WgAWcC3gSEVPUsvSxCRvwAGd48S/CeHPF8oYz0z/FV5ecURJMXmkhVM+Xa9hyPB2hZknJ2aKRg2Q5sS1WkeemFBDBRvCDDAlCkgImJFThwtZUEeBHPGkzqJRvm27jKTlPPSlRC8Rnt1wOEdOBTlVTeML1SSlY3rnOZoeIF15gRQCeRIndxUNuJtyLTG/RKb4W+a9DkSjVygjZT5h3NuIG9feTJx2G+h2jxvL++g6aqPwOqJW/srSout1HvkXOi2Zygs02Wi332l4X5Qpklo5UAV57ESODbOJ5uWXempEhqBrjoJSY5IKo65E0tWWpqXgbrYk04kPbcRdc9xsJCRSG1DpnQihAxmlCEqatYJQ1q2WRRP6Q2qMW0qYZusJTjuWKxidvfjZ6cjKMT2BiBmJFzbRr2bTSLfMdSkKal6XvQfazr6Rb7pGPHSBszupx8LaixPLfPfFlI44atawqzIyPIeWhpVm8Ucbexgojb/osYQj84IlbAq/f1XFWYwz+6inDIs8TPMR2KXyKpTOoxjPPPhNrmbKqhSg6AG/bjREaTIFqwdGAlR1GTl9jE9sVr8RPZlcUaUDBSAoZT3QErsS4D9yUH+mJ+LlclYo07qPCvCIg1mAr7c88cmzReTgN+g8NEaUK5WbND62v001b89VQVdU5DoVAsDoYtjgZyRMhJmAi0rbGJoa2Suj1kv/XrVttAOyJFGY1W1aK4gjFevd+cCAWm35wocV1bz6U4o5csNvryn39//1s5+vBnhr+nrS3u+Stv4Be/7ft5RbP5BT/eGjS+uHlNu88/v/VH0FvhF/7ADXzvB7+Zm/8OyK9/+LA37WUxN02e4r7pNYe9GU8b3d2Fdx+H2w57S9aznpfxDA4loZsJ3PoKuOPjh71VhzLl9Gl47+nz9s5Lmob8ihuwyQgee5Ly1Dpn+HyONA3y+ltJ2/v0n7nvwj2xFsY/9Rvc8q4RmFL6Cxvtctk71GwAiPgFitWHbCQUZOqWRAv7on9rfEBXA80BmaAvBtKgoVwSgZTMLZhRBpLCduh4RyCLW6ubZmVzrco0xWGTKSJK0zSDGo2qqAwgVe3F1lvlIF7YG4BCkrjyjLBOhoLQrLJWiQw/g4zbxi3h/nAGWy2wslMW/DHwMhgN5SUMXNStzTi89S/GvwcsGeglDJmMCfMyCQJyaE8qPZSF57kVZbFd2D27ZDFXf52jBhm3pBZksUM69SR5b4fcLcjaI1HsQnELZsrijdFSVVSuUs0ZWiukxQ6TvMeVr9rg+Cu22DwxJjWe8aehyEwSBTEpk2kxsu+SUIqREkkkkuZqDmaU3cTPiRmqPVl8XzVSS3F0UDES+9WXRIArdbt2r+ot2vgvWBkyPAN2KpjWteS7XxIOKgO0ihnJvFFZJDN4byXAjimNGJPGmDXQUl9DdlFhHN8kgmmPlY7SzZFuj2axg505hZ3bpWkE2RghTcPe2SWnH9vj1AO7PPGZbfb2Oixlh2t1/9VioshDLFEWZFqzAn39STEyEnmLVRmaQunnikW3+0YyYfA+0xKQUV2JuqJ8IeoTVjmYxHmWyJLJuKox0eAnDJ7r2BePQyj+x/etIBSSZLfCmzfaS82cjL/7uaJDBsagGo415SpgGW5eiKbIW/Xt9xsF3gi+vVDue2TBI08Yu0sia1LIlmgsbiYQ0NQ0AKJfZ9yqHBmjGUSKg/pYizqc6/64Vi3uCE2TmLTCRqtMWdAsl8hy6WrXJiPtCJMG1BvP6wXN1dR+zCyuNxbPdfDas571vFxGEfbeddWglMuvvw378SN8/Pf+vRcEGOtU0JiHmwXr+UInAV8/e5Bf/4q/zx/+4f/I/rd86WFv0noOca7/2ae4a/e6w96M9aznZTt6z6fZevDAzWhZ/347X5NedTPzm0+yuGaL7vU3kY8cOexNuqxm+3d/MQ/85cydf/FKmmuuvuDPb90Su8CAEV4GkFFVhwzFWqxhEh/g+yhWMdxSSkILQ2GCWE286/xapkJRo1ssWJV4AGb0oTgy9Q/nFYaEMA3pQ+2k9cN6KNuEKFypJSoF8oFW60pF4sO/SMbaHMBRqHZdMR3y4lS9KEatUIq/oFo4Mbgwic/7FeJIirbq+pJCAZlxFVQokCSn4edXykXfxgGSZUJNJZBSQCQNdJIcpBTDiqG9+TYGf3DbcI8Z7O8V9s/1LPY7ul6wPEIzyHKOPP44eecc2i8cnnUdUrpoJe78uGktFAHEoZUGGDIriBXafpdjo12uvXnE8WumjHKLZLesawXGvidcwJjDTouXCSUxTApIwbWLUQIU+yu5Lx3Em6tFK+yRoa0YyaFENShlgIiesZgg+fHWAEamdc0O9NiVgVaVk57JmMxIYXM20dgPvi60mH+uVbdla24gNw6BE0jjQFhQcgSKllDLSig4vXm4kEpH2ttj8dQT9Dvb5ElGxg1Lg/n+kvneknNP7rN7tqPPvj4clipYbFvycyElQZNgWbyxm5qj6ipK60uoFEuoCeN8TopFyqZhlNoUbg6LXf3oDdWDltP8KCsJk0xv3lpeiCbvsAkbJcpJDMoStTkmS8jqNzFS42s9qt1DMxx/HCJKPV5WAhYG/POlENtjKAXES4ioOZB+Nsf3CJoTXYKzOx0PP7bP6bmyDFWkhTIzqKtf37Red+KaofXmw4HMSjvwWukxigPRHNcyUyi+T/2+SIrWaofX0vXQ+1qxpqHkEb02mCbE2uG6UpWUFkfLc0cDiNb9sZ5LatQSu9Ye9mZcktNZ4sTdnueXrzjJtT/0MD/92p+klfyiH3sNGs/ffO30Yf7E9/+7ZwWN6flFN67nEp9y1yf5yQ+8+bA3Yz3redmO9T2zn/8oxz+x5MTHF9idL89MxpdibLwytmqbsFdef4hbc5mNCI+/TZiOOo5fuc3y1msPe4su2Fz2kDFLOqCei4/fQnyAB0rC1NV+FUha/dQfsChV+CZKbqBtXaOYDIcgKbmCMFp/02ClrSKlKIxRXB0U1KG2xlYrYm2zFovHNi+jkKqIDNVkUsWS+GtTyBbQsaLA5FZpkrh1eIBeNsAcF5EFBBMHTYZbZFMoOA0jWfLvjTKRYA2IiCvzoqTDJYJRctP7YzRZXMXlsjoGe2typaGZK7oGy6ZFy6wYmkBVKAJdpyw6oZ+OSIslPP44cu4MdHuk0nuBxW30AABjiklEQVQxRpKASoSSVBBRRJfBacMKKnYAuBmt9Uz7bY7pDke2WkajKaopClgCGGbfDpE0ZGAiKXhxbHPQmmQVH5eARg5RhBQMalUcUlV3DmyTW28TICtrLESLN4RqT8I2fwAqB4RbtYMHYB0AU+T5qTrc7S32NXguYSbnZgDtFgpGEfGilVg+Iv5vRiKnhqYZk5sxkiHTM+7mcPpJOHuGyRjGRzLagy0VloX9nbmvdbKvZzVSZGdiAZq17sNQIYZVu9J6o6foEsGCwalDVA1LvFX5IyAZUoX5KYCzw8ADZ4urgEMVWq8BGurGenqqBQzX4turYVenDODQSnEUbFVBqg7l6nXFvzUUfkIix42F1boc1I8G9PGzsDquZojmKEaCjSMjtrYSbSaOX1jiJdYaVSFZIyAOqrn9WmNx7hM2dzFIxZWTWQ2xALoBy4nXKVLAOqQvvs463zfStPR5xFJGFGlR8WNuVAhbtZppOJeqNnU9l94sLbOtzy8zcD1PnzsW1zO75ylImfu+5zX8/Rt/8bw+/ho0nr+poLF89Vuf9vXUOWhfz2U+Wnjt3z23VjOuZz2HOLq3R/uz76f5hQ8873Ku9TyP+ax7/GU2gvTib3aux2d01kt3Tz9yhPajnznszblgc9m/M6p8zcIOrRhWHEQkSdAk988GdElZ4sO0K8wcTljAMFtBAJGINlzZ/bSUwfqooSqqajVpMjR5ACCSq2W2fjcBPg8WMzgE0QHOuTLJxPGI1u8KaWGqSjWgqA12aag5iTJYIAc9Y4ISVq2h2CaBNAEfU2ioQv0UqGEFxwLSmAhWomEXt+4OdktdQRvRAqUPKEbkMjoI035VptE0wvR4w3ijQUtDN5p4uch8h3ZvBymdw8/KKBLklFa7sFo/UVdIJnFlXqqwpiAUt6ij5H6HUb9PmoygnaG0sQaEqsFUwYEUIKqR31cBToq25PgwZ5VnuSrVTHDB5mobVIurF2NdJangWig4+F1l+fmxL9XaH8BsAFCxbaRos461qBIxAPXfxS311TabYv9VEG8iqxKelFypJ9kzPVOoYS12fPbXnFL27U8dbVnAubOws8PsyJg0arxgKYH2rpEjVVhJ2PEVK31Ym/08KqUErPNX7/tKQ11cMO1A+8F2T5yfgcvQYpSyWq3D8UcGoCkp+9qI15pT4/b6lIHkbmjEreoS15Ch0bpeXJSkq22tgLFmNoKvc/PulVAL26BeNPFzElWH7VqCT67ORVcSy1Bco/QklOOzzK03jrj6mDEZaax/T5KEeE3iQFcJ1aj1qHmma812TWpDUZBmQ0WHewIRaFBPhWj7dhWwFcWK0ncd2hXoevplRymGpRHWTtDcoJJWyuzVXQbf3liTKUDjei69maSOG5szh70Zl+ScLRtwZpuz3/kl/NQf/X7Gcv4VoWvQeP7ma6cP89ifniPtaPja5iOFpa0/jL0cRj92N+/+92+jWx/v9axnPZfRpDPbT/u7jRJptHaonJcx4+Z/cT8b//QYr/37O5QzZw97iy7YXPaQcVDpHCg8cDWPA6ikRjiCo+BE3JJsQukVNJFoSakJWAU5h+oHXBUVOYi5yUgO1WSATakf+lNGRq0r/7DIpYuMuQO6KqpNShyQSFoBiUH9Y66XJGVI2VtZ40N8tTxb2HqlWjXDyu2qOxtsnBDFEwG0VgWv1eDoeXyVKuZQMBJwtRa5WCgwB/WmHACg4NvnYjS8YTiTxDVtKR5DTFHtXV2HsTFpOHZyxOaVYyZXzmhESdt7SO/qKS1llWmpnnGI9gGb6itIg+29Kv1Syl5SUQs3RBy0LPZhuU9qp+jkOEVmIAHQam6lRitvKA2l7rPkGZhasyxDr2WhMhzWXrWlRr4eVIFngKRQRkpK0fq7KsiodveqKgx9ZN3B2IHjZ5FzqPV4BDiTlAcbLhUqF4O+R/qepK5CrQDTl7iv7SQB6rKQsv+3JKMkgSzklMlWGJV9mt1tJqIcOT5mtNHSzDLjaabRCgzBQg0qA0Dzs8CKhm1cwQJIe/OP53wq3squPVqWmPbRZpyGdAHPyXQVnmDDsbNqRw+rcKr7m6p+bZGoo1eVuvqjDCeTmtbPZVYKVoUhPzISVwfl4SBpjmtRbbePQzqspWEdBjC2yFgdyneo+aa1iigxboVjs8LWyBhLjXbQYc1IqFfrEnE4Hrcnai7EgfM8xJerrxgRZ1nLcmyw/5t2iIUVv3TosqdbKvOFsegMS4k8GSPtGKt4I9SaJXkhjgVsRFIAzTUEWc/La5Ioize9gj/+l370RWUwfr5Zg8bzN//iLf+M5de8afj79JE5nV3+HYrrAcy48R9+lL91+9cf9pasZz3rWc95G33yKVK3kjPm3Q5dLA5xiy6v6R94kOl/fB/64bsOe1Mu6Fz+kLGqAAM0CG4/rXGG3qgapQrRyou4gqvmD2LqltVoaNYAONViaEUHsFTC3ilqZJIXq1SQ2DaumgoyVW2oIauCgFKqOsSTVYCn6l+okKtChzIANVegiaVo0A7ElgNO1aIPT/nDFVZSxYwr8AKRP8cALtV0KNWQ4UNKtfz69w8lN+INtPFCfRtwVGMIvdmBghLxkhXro53aX2sKq3NDYZZ7jmwIs3Ei7e4ie3vQ9+EqdlDmkXGGmrc3u6M5lIUVkIaCsGZT1tfg0DmU4tKTux1svoO0I3TrJF2zRdERfQl4GDBRAj1qrA/TEihoOKix3mI3JAc6bhH3bE1Jmapc7XFbueWMZVek5XzgbnmsmxSqL6Fq9nzjRQMUlhJ9NFGugTgUi+eSsLa7kjZOC4nSkHo+1MePDFMNUulQzAZVG9qT1FV1kgOAZciipOUu7e45Thxr2To64ujJMUePtrQpzj91FZyq0fca3SQW/w3IqFGeIgWRaB8WJcX+dgWjQ2kC5qa6xpORZPX10IYO55Tg1mATB/UpZUQyKfmadHWngvSrDMU4NxQ/f8WqotihbU1gSDXfNCzaVW2a6v5nBSR9vR+4ESBDkuOBuIV6vkdxTOynLIVWCtnqa9Oh8MZt4qGojnVeW+3rPoFazBLbEmeqkB3YsrKfV3s6VUItbhd39CtYbxQSyyIBGYU0bqGdIjKm0IZCNtB4hcq2WoeyerHr+TzzUH/8sDdhmEw979bzhc5Xzj7B677vo/z3R558yZ+rlcyRNKGVy/5t30s6NzQ9+r88OQTjtw8+xUf2bjjkrbp85ur2LLuvvfKwN+M5p5w7x+u+7zT/6ZE3P6/v39fR5/+m9axnPes5xNH5nNGnHyPv9+T9nnTvI4NAYj3reaHzsni3KRUuRuYYA7hjKFhw1VmFJ+of9CWFqgi3qRahltUeSMx7Wp5bVUSZHLBEZqGoVGcoA6oTKBQ6Cr0Z6mGH/rxa8xqhwgMzz5hclWY4RtBadyEHmqQrjAwlkwleaCKE3drVfzX70fcJnhEXO0IsfrzSk+rgrNA0ni8QhMMsYbCv1nxJ7yapCkD8C4aDub4nBcQpVqLN223VVpZo6bGaGbizB7pAkkOtLGlQjlJtviljqVrcQwFGFe+tIGkFL/EXXxtFHZr1e8jeNqCU2QZlvIU2E3/NFbwFmJTslnoI62f4tQVBVEnqL7fmUYqsmsoVQVJ2xVrN4cMXSYVBQ4FQykhqqZ3lUEGYwzFVrQvd4XjKpHZEyg0miRLg1aoijhwg1lWIxM+opMjn5MD+0QF4mcQ2qaGlR9Rh4KrQqIK2QrvY4cio45rrNzlx1QbT3CA5gzSOscT8+OUaEeDrpBEji3jxTVeQPoCjaqxr8UzE3lvILdrZzbzUJw9H2EGaNzg7LKyQtEpzLda9Duu4ygt9f3lXk3lMAQHcRGIfe/EPYUUO3BjxCa42JTvEpCob46qxctWbW+8xxBKmFUyGyrBegGCA8PEQeIP1CvBowPQk1Zu9unlR163V5xxopGPFlARLcd6Th0gEv1cQry9uxvg1rgLyaB9vMzJqaEeZ3Aq5SUjboLmh1GKc+LmUcmTY4tex0uPtWOs3NM93rm9OH/YmDJPXgPEFz5Wp5+9ef/sFe74siU0Zr0Hji5x//tp/ydnf8ToA9NRpHtw7drgbdBnNLC2Yn7i47cjlnk+x+/ev53S/8Tm/7299+Gv5xT/95Rdoq9aznvWs54VP/9DDyO0fRW7/KOXJpw57c9ZzGcxl/05T6gd2I5pgA/FUeBNhaUbkzwU8sBKgJ3GgkGRl7bPwEooQWYS6Km4F/z5wi2BATcLiaaxgYwlYZRolEwEVvCHYbZkHVYImNtxccBhTcwxXY5iXjcjKYooltNSMRAZYsbL6cqCUYtWS682v9fkjnw8vBrESBSLRL2IpeQMxAVkpw2t3cBkt37qCJZZwu23s51o6o33vAEkSMhqBKXlnh9z3DLInl2DiyjVxMEUc1lB+OgiK0g9WCi6pNtfw12YxskcaIijS76NnTmG7u+h4hG4co4w30ewFLQ6QAjJXZZaILxjzNVfbc/278qroI6ysyWzVJB2W+wEFVaqnFjmN8cJqWQZ+XBwEWahSEykgjucorgC45MZBZc6uyhUZ1kjWgMsRI5BQV+oR1lwxpMRrTv44JinWFU87T5KEfk4Lwj5tf5aNtmeajFwUpHEsVjNQMZqc4o6Z/1w5mMloKzCmsU/c9l8z/GItiEZzdwXfkZkZ/NJfXz1BLLItNWzN3ipd80erw9mhX5ygWrDIMjxoORRziJlj3Za45gwpCLGvQyobNzfKChYO6NHXT7W/D+tpsJEPWJThC1IViH4AavFUvfJZbTY3XX2fMShhpZYDRTRDVXfXXasVah6wsjPcqIHB9p0bpM00s5bRrGE6GdG2mdRkZDyC8Rik9TU7nJ9xHGvMQWR+rmc9L6fZShfeZpslcTRNmZyHBuuX68xEePjr4ubO7i4f/M1XHfIWredCz+Z/+k1++Oe++jlLf+7avY5b/nqPvPcjF3jL1rOe9aznBU4VCa1nPedhLnvI6DKbjKaVdZEAd1ka17PVD91ROmEYlswLEswgG5LczlsVSxagsqp9XAC1smKrVPshBz6oBzAyL/XwvyeSNSRp8JxFgSbAEaBhO5aaI2mgyX9WVXGXqoOEJL6N7uqumZEBRvE8NFdYigOUWi1bBIq38KqtFIBONVbNvf6sNvBGl965/TqF5zMJq8cIcERoykpYwJNIhDEmSO2QF+gAxC3eKWcXgAE0ybP3Sgf0JHPLOgHrBBzglkLytg6HGAAmkftXVVeh3vMgSAcmygBHVX1tNChZ92mW26TdM9AvsckMRltYGnnhibQOpoHalK2qFPW6jJz9lRsaUKmaWmNtoX68kFVen4YdNmiwmVEq3BHDcrx2SbEufF27IjIKeAiwVsC093VRMZUGFayZixrPmw/+nK9zF7u5clCjkVuoLd2xtoa+Dl/TRQlQ6QrVXJbI/i5ZC6I9Qg9NA2RKcSt233WhSOwHC7zD+TLkpK5UvWGPT+brJKUoTFEK6qUlptAbDthXkNwjCC2s9gznTi3OqUUtYKE+9ddlYbN2oagCNS8ylI8FSo0niJzBobylfo/lUDcOyapDi7QN2YiGSl28Ad+Gf1Jqe7RRVdk111OolnGNJmjVjlK6uBEQl0GqitOzKCtU1PrazG+cJHPVaDLF0qCTjsZ5HW50mIlD65H/ydOWZtyS2kRuXNmbmhZGY6wdu8J4YKA1t9KvF+7iXyviLsXxGwvr+UIni70kRS/PdzbThJm0fLpf2zlfyHzj2z5M2toC4MafM/Z0fMhbtJ4LOdYtue37P83f+Yn/mp0yedq/LbTlF378bdgH7zykrVvPetaznvWs53Dn8k+r9lrYQRElkVWWIotxaI1llTt4gKK4jXEAVkbKmdoMTEBBabJbgjFXcElCFXIS1AoShrIsLWKdKw2zIL3Dvr4z0jiD9P6JP+BRBSPBF0J15IlpEg3YDgii0CWAmyVAI/dO4rUG6EhSVZ2exzY0aWe3YVt8YEzxOFV55y8/fjasl0EXSZbIplSzbIpG20o3DFdPVZGVIGTJaBKXVarbj1PODiECrkkS1BIlNbDssdKTVALMOfRIIsN+GXIt43nVFNXiNuYczyWRMclKIebqxRxKV98+LT1ZUsCyjr7rKbaFTTfoU0ve3wXpCK2YA6UsDuuKOqwUEAqJ7JZb0dgXDbEjGYpCakYleCs5brGVlEK5F8cgFGfmpM33c/L8PM8WdZWgiRM/IcBOtPL4ul/tI8tgVstxGGy+htFrrHdRUo4kT6uwHVfb1mxRiRzIPlR64gxai8FygfVLZNTSdL6f+iLQC9JEzqD0rmwUjddchuer212VnvXMrPvQ27p9pQUqjr83sc+sMv6BieagXaYynCOEwtWicCYTalQzkrqq2P3vRom1V9e56IHMUhsMzLGNyYnvAZWihYoSZIBt1POYVe6qSFVHAtpjeNajSiGrrCCpRa5miB9TiENrccy86/i3H/8Qe/0SM2NrNOXb3/BWPvTYg3zgkfu4Yes4X3/za9hoRrFnA5rW+IS4wUGsL6JlnSw0uaVLjdves5ByoXR+PMkZmYygm6FlSVYw8XKmofk7DN+yap1azyU0a7v0C5uZGO0hqwlnacSrmz38N9X6OH4h88eu/CX+1Jf8CZpf+ACbH3qYj+1dz5dsfvqwN2s9F3DKY49zy186w78489s59tWPcmQ858GzR8nvOs4r/vEHDjgW1rOe9axnPet54SPt6oaw9R2fnZkp7cjj/5bLZ/zbYc3lDxmrHVWi+CMJqmE5jg/0UIsyDEnNYNMkVHtWlX/mdkwRb5q1WogBbjmsz2WGFUMtxdcUiVZVxwpule0xz8prDEtKIvt/zYbiF0K1FZjQAQMWVlNXTJmGbRsCqkIRG7Ioo51k0HECg/JLWAFNlVAl2krtpgeKVagQllqOsYIbKeCsKybNIRkBUyIijmrPTBnqvqiqOiNUWP6/mzY7+MVboG0xR0rvz2Wh9DTPoZSUKaah/nTIhLqS1LJ/eKqgpBqqC66oFI18yBxqKrPYnzbkShqJZD223EGtoBtHILew2EP6ecCWHE3GDpmr3VjEn8t3YrQFD+Dbs/hUbVCpWigNNakDm1A7glCs/pxnAZZoEq/QTRAsiys2VYdoAMEf1wFj2PJXC9fhFinAD17G4zjUX3tKXrDii3v4GRA0BbgrntsoySMD3BqeMOuRfo4u5+TsgF+SUJpEKuINxdYH7I5sSes4QKSpZT0a5UxIiib0FLD1wNqO/SMIxZbUzFSruZg5Deo5X5+9Q/GahShui5aD6kUxRHT1GKuASiDUu2LRZK9w0LSsXlyTEhRRxIY+dRR1i7PluJMAStgUoknalZexfgOAWurI1lDMrfumJQBzwFRVSqz1yt93uwX/39t/jmXfM2tGXLd5lFuuvJI/8pP/imPTDR44c4rv+i1fzPd95e+KpSGuzlSlrqIkftOkiAyKbAFK8teu6qpPaTNmDUUtypZG6KRANyGrqzSLxfcmibWiQ3TAetbzcpitdHHYlY/nGY+XXR7uG25o+sPenEtmrs7KZ76l4dW/lCmPPMqvPPoqvuTWNWQ8H3Pq9cLR4Yb/xT3WLbn+/3wP8jdHMJ1w3f5T2GKxRvbrWc961rOeFzb1/WHlUSk/LVJKmhbrlsPf9bd9EQ9+zZQyNo7dDSf//YfRvT0A8skTyGRCeexxrL+w7/Euf8g4gBP/356httJe+Of4Ekq4A+BQQs0Tjc0mRmoyWkrICqmyL4eTATaqykxEA0qGDdIU6UpkrbnCSySTs5FTbXOO0gpxpaG3EIdSypRk/sE/hfwufoQUr8dfi4Vq0lV1FYgIiUFZhavFKK54q9CplspABSZWBXAHwBgD+BmUnqQhU9LMFYUWLcYhhyRhnn8YgfNSAuBSH8tcNZXitQuYZbQdk7Kg8z3QMgCnqqRLCH2QFDG3oFUWlAKiiaSwwgbYE1doCTZIvpKkgKA1J1FW4DaQUaZHun1knrDpJsomKY2xso/0nS8hjfbolNxmakYKVWK1AQ+5jCl5+U4F3BUWSzT2qmIpmqwtMhFFhjbzHJCX2jweyj7EwW4t9qn7eKWPYziOYN48Xb3s8W8Jib4SRWvHkCm5MsbYOc7cfO1oqEd9Gys4dWjLvMPGBbEAcPViWdQt0vG9xYpb5uO11TyHUuq5UFV/ofTMbqt2FWCF1oT6kEG9Ws+76KwJ/aifC942Ha8HC2XwgVO8nmgBWB2L+c/WJ0yRwxmSQ1+nzmkB8e1PEvDPG+DDsX+AV/bDeeVxB/5/qtWy7tuXDhQJFfPkgRWgs1Cb+kLy/xbuPneK3fk+v+9Nb+dbbnkDrz9+Df/4rvfx5N4e/+Qbvovvu/1d/OSnPspf+NKv58Roxk7p+B/f9SNcubHJ93757+TP/fJ/olfltmNX8q4HPsGxyZTT+3Nuu/pGHt/bZimZP/QN38HXvvbLvNynzaROsc7t1akdoaMxvS6RrvObBykCJQa7+HrW8/KYiShH0+Zhb8YwV+UNTiblnJa1ovELmJ/45h/ge371f2LzR38DXSuxz9t85dd+hEeuupLy2OOHvSnPb8ywxYKyWBz2lqxnPetZz3oul5GEx/R91qT4QGtGc+01fPxbx+imQ8cnrhCWW2/h+v/0AI98ww3YN5zmxMYe937ki3nNPzlN+djdF2zzL/tMRhEZLJKSXflULZjYqtjA1EJ+Gm3MCtUaWG191dpazP8gEsorV+ip1dZkc/UXxUsWKjQqBcsHS1XMc+FUB7Dh2rZQwlkAlHjTbwEz3J7pOXQmNmSdecmuS8meadPwDEAIEKgOGNNQLnNASZSiLITsiYsVquJd1nUsFKG1lMOLP5IrAhOhHA0lWUoOGU29KCZAJmFf11DYWdGwUPcoGTY2sW6J7c/BioM7cWWoR2R6E3gS/2NFERVSblclP+ZBtl58kxFrPIdTXFGGVBVlcYgcSi4EJLv6NUsC62nKgrS3je7sYAhzE3YXLdv7Dd1Sojgk1op4vuNw9JLvcyWAlRHgMNaZVnbkUNQViEqvPcUO2EtrEUeJbMCA2BUWi6m3PldgnBKa8gAZLRqZfWnF2hfxopg4Xn6oird7az80kntxSfy8KlZcdavq5Skg0Ptz9MvOKVgp2HKB9QW0Q0pxCJ+KrxOnlt7OHgVFfRdN43XV5jgeFeiFclBr+3LNXYyMyorTfY16zqFzV18LokrW2E+lj4bjPl6z52qqei6l++mzqyKtKnkJQB/PJQnEsyalHoeai6pxTGGl5vU7CQxlPxUISgWevjbNXJVYJM5/aYCGIv5Ha54kQyXMUIDkFnBfF5868ySLUnjnxz/Ed/zE/83/fvvPst8vMIypNGyMxhT1GyMajfcfeOwBPvrUIyjGh554kA8+/hCfOP0E73nwM7zlmhs5Nd/m33zoV/jSm17Db3zmo/yTn/9Rv9vWtFhu/RraF6zz9UM7ok9jJI88y1GauF7Ufbr+kL6ey38UoePiW+tZEkfSZN08/QXM1Vl56tt2kZzZW6yzLc/XvOPoJ+lfde1hb8Z61rOey2jSxgZ86RtpbrrxsDdlPet57vnsz0LP5XqJ92r7b7wB3TgAIsU4+6aOO//8tcy+5THeeNXDXL9xht/6ZXdy719paa65+iXa8GfOZf9uUsxWf4q5YsuijiNVxV58bzgUxaJQYoBt8SG4WjWRQVWVIpusrgk7YGCt/5ZSGvLbVBJ2QHnosPHAYVDzP+C5Zybe7Bsqsara0gCCpiswZwF/MPNcwN6BiVrFoxVi+camqvCrjcs4WPL9E85U8fbkCmUduhCKv1X7cHZaChRXz9UClFDmqfnzpSY7aI2swdL5Nlrv8DEjSA+iDbQbWDuG3T2argzHqBbvFFVKQKAhY1EIWMZQplJbiUW8qIJUlZYOp7SA9Yr2FRh7AVBI9lb72MAoCEvyYh/KknOnltz3iW1OnRXOLRuWZRwgk5WS9WmZdgeEW6FGMxEkJ8/2zE00Ekfjt1bbO6Rqqw61Z7WZg5BTjqxOGZ6gKj5dEev5obZqxIl1Fy3PEsekyjfDngv1aw5GTSrkrK3hq3VvpbiytQpYc7QpoxgFuh7peyidQ/fa+h2QMCp0EMmIuK24RBFO0qqb9WxLrJCsIFY8/qDCPmOAqVS7b9j+rdqtCXmiRNNyVSET+aFJBpDr4LPuUwng6O3eFi3sKdVzPdeziJoeaQHpayRBVfVi1V7u1vckq5sLFseHONe8eFlWqj+tUNO3+l333s0/uuPXWVoZ1KNemu63LMTg1Vsn+NNv+yr+47f+Ib7s+pv5D3d/mHu3zwwXoforLakXET3t6zWvMv7/pG34b175Wzg6nXH11nF+92u+jHHT+lpvE7Ruvacv2GJJ2d+nzJcUM37oQ+/i5x/4OJJabzePa6kEoF3Pel4O016kasHaPL0Gjc9/fuer7oIklA8fPexNuWwmr8uk1rOe9ZznOfPNb+QTv2+De/7YDaQ3vfawN2c963n2eZaYEDlglf7s6TYzQ3HD8AMGrfLQI8d5ZG/13uSt1z3Io998y3nb1M83l/07yRD2RaOwDU27UYa8UgBVO6kIkkOGWnMG44O/H3dXGFWVmIuWbGACVeg3KNQ85TAAiGApo0EPqlUSVh/yOZBr6P8NOGmKJaNEXp4hmLqVUkOtNGyzpAFCOSiwUPuFSs1JakAiHQBKVRaauZ04G0+zx0pVPsqqUVlNXe0X6q26dVXxVrFrSp6daLiqkbA9Z/FXL02E+zWubFJp0ckUm++TFrvkttCIQ5ChFldq2QYO0wIwKuYZjfGaUxJSTge2vVqoUwAtt8Sj/rqJw6yhHax/wPdDwmitI3UL9nd7Hnt4wYMP77Ddtyz6EUW9HCglB3cpSna0eLGPBAiuj28B8iwyQ6WCKbzkJ1lardVhfT2djlsowaSupsgrtBx5m32PqEazskNJZ4UV0EXWpRmWQHPGaDFNDvgkYHtiONaIDErduoYFdVVsHPskii2WsFi4pVyNVEqoPRv/E6pdkfpHDhynJhSCMkDDWhaEFm+wzinWZmLVzBz7Q7wAqTJeDbArccIaEnZwQyzyJ8XbzSX5mrFQoBp+7BJ+HqTkuaOrspgSUJdQBTJAxhqxmCRKm0LNqlab6uM6o4JSUOtRdTyLGMk8q9W3W92ubX79+ZUHP8k//9j7WNLHtSjgYm14N+VX7v8U7/zEHTy5v8u4aRnlhpOTGQI8Od/lqd1tTk43aHJGCNhvwlPzfc7M91n2feyvOv6XJL5GqeegtyChpcOWSw8gXi6xeU/XFf7lh9/FLz90N5ZGWMpDs7zfzFhDxvVc/jORwivbi8cq/WwzkWbdOv0FzuwRz+Fez4ufB5cnaE7vHfZmrGc967mMZnnEP/iXmfLYbz3+TMXYetZzCc7mJ8866Hq2KcL9Tx4f3pskUU59Wfe0EpmXci77d0QayjTTqmoyvNAxlExEcUVV+Fm1ftoAFSuMSykP5TEDXARUVn22EkpHKxq2yAAYEcuHeLGKBSiUgBokGUBWCJ6ojcEWWFIC/knQSUPw4uRAmQIp1+IVArz4a13lxVWomlxtRqgRVRikYKFGczAbFvMAppWliCQv0UiC2kqhSTQpm6nnQg629CinoNrXHTI6/Eu14jkyNBt0NIOmQeY7iC4QSpRoREbhoMYkcjarZTpabiVqSzQEloOCcGV+l8h+lJS8uCXlQd0Yck3ElIx6QQnFt9scWGftOHoyc+L6MWVpdDljkwk2akOhF7DM3GZuoezzoo6ygtLGUGIy2HGtLoSamVlJXlWqVd2dDWAabIDNdc2buDrSC4AsXlfY6d3nHFZxG1SsQ3t1DjVvCmgZ54fh+yxV2/aBY0G1UYd6sdcerJBRKF2A6VVDdKGCeI8xcIZYlbp+PCRFKUtVgCqDUraqBv38qCTRVb1oANwK2+KxJNaExfMM+7fusyFGoX45Hm9YcKvri+e7hqKx3hyod5wij9Kfw689q5Ub1yBz9bGW4vvtQOmUL9d6/tjwShUFXSKleOyBHni4ek2yeoXxzf3tN99GFuGP/9yP8sv3foJve8MX8cfe9OW86uhJ/uTPv5O7n3qcb7vtLczaEbC6QfDxJx7n9//kv+KTp56oOtm4jMjwBq1ea8xAlz39sqd0HbrskKoALspivwvRdENJE/rcUlZ8EksXp7prPes5n/NomdE9W8bORTRjaXnziLWi8QuYq3/xMT6we/Nhb8ZlMR86ewN2/8OHvRnrWc96LqO56tdOk/f8d9rudZCm00PeovW83MZ+61t49H98B2ky+cJ+rnzWe8YQ2vj//tw/W/r8tMzo6ZE5ki/Me7vLv/hF0qpMoYIKrfZEdeDCqhxCUbcRa9hNK/j5/7f35vGSVuW973et9VbVHrp790R30wxNg0zSODUGUa9zFCISr7kxTkhOvEk0DhBPnGLuR4+Jwsm5x3hzHZIQ4xD04seIQzSHCFFRAgICrQwySSuDNCDs3r2791D1rvXcP55nvbWbSaAb9q7N+vrpD3bVu2u/zzvU6vrV73l+OZk3iz+SBRabk0d/phqS8Jakm3LarFP3pI+xSRjOXamg4oC2mgrUSedH5p7lJmfGxD8qswypyGKygoZlRGt3VMWOnCbctM5Kv93VIlisitxGa8m3Tv/uJCAOvIluiTxrLphoCyQV0fNMtSx4NvZPa//st9o6opgbr04q1Lhk7eEJCR3SyFKkO4vr7qISa6lOSZ1tdqx8nuVGbmm30Btrf3VWa1Np/jn1GfZdWdZ2mrIoaqfX4XApqvjmMQFQrFVV8DLDct/FH9xiYqrD0JAjdFr4ug2zXU0PDs5ciXqOYm4nj6LzALPgnXJQiMlvoiFEjajjxK5VzOUa8eKbY+uc12sGmkTglN1hYu320VyqXnBiQrq5EfGOFK01OOm8UJHark00bKhpuxfLAbLr0alILXmeqQOfg2TsdzQp6KKCcvAqJtdSEy1VPEW9mKpgopUpmsnEst1ddHqeImIOP5PS84diS8OOLkASqiiWjmwzEOecY8J9QnHoi3Xeglsk3/9z9yOnWyM2P1VTqLM7Nm8nlhit4lyE5s6TvuPSXneuBNkPrbKwIAs10v2rCa4iptxmDS45Ox/2uhbW5ASevno9XznxFG7eMc6SVptnrjuQ4B1nn/gGbhz/FWtGlnDUyrUETPc21+Lhq/bhXc98EV4cSzsdlrU7vOqwp7DfkjH+8rhXMItjxfAon3ztexhevg+xFlwt0BXo6cgGJFHPwq6ds8Sk74jRd4CaILVW7uNu4m2hsBgJTviNjtByC9+123EtOq7FzjTDzAIXRRcC8aatfPMbx/G0195Cy5WU7j3h8usP4rBdP5rv3SgUCouIdPV1HPIvm7j1N5cxsk1IMyWoqfD44aqKG//Ic+JRl3HVT59K+98fYo1Lse+0zePxerWOfAPNEDF2Hjamn6EehKVLpqns+SSe+NOlpF8TUuaqip2/vZllF9xE/NU9D6e8B2TRi4zO5hM60A/d3ob1udzbl51g2j6r4kM0vx3WrqiuPCFRJ229TVlVTjbbziUTFXRGoVDTzBgzcSMFRyCS02xVh0vmXBTLf06m8yUciWDzD0WyUKhzGZNgjr5ogltogjKy3UvdRVmIs3JxjTiURT8RE5Vcvy287+yszRmpbjMPdgxtlqPLreCoe9BpEncOsxabIeecI2RXpDk4XfI4F9FoC/2lUSpia1hfY3YGX3f1WOTWSomNn0rwJqokG7XnzemY9Ni4vrMPm02Z50giySLhVYRKJgl7m3WXmgAga1e1+vU46kHyPlH5yJgThlYMIZXHhQSxRXIVzs2qaCrmRhRnYl2tzkn0700rrSWGi6q6KhC5/s828xTFQm4aZ6peH4FAo4KJ6LWDvhGJaAuvc+rCFBMT86zGJCra6nxGR0y5SdzjJJCcCWXJ7qk8RzJpi24yUcrb/ifn+w5i8dQ1OKkAIYSWGUYTNS1qEYJoSzBO3X8OcL5iToU0hVhrtZ4g37gNwWvSuojNe+ySE5sFb0nW6r51DoJooAr2xUPfNSnqnLUBpOItUMj1U6obYdhlQS4vBiaOemfCX76/AXTRULHc3JnkevQKi9g9m1OxJY9myO3/jphqfR/wLSTpOd/R7XLuz69nGLtvm+Mjzb2ZS7031vz71uss7V0fvH1ygtt2bNf9A3alyK5el6FWm16qCckxPl1z7/QuEMf3brkBIUCoOO+mK2F0OTsm7uH2H38fYsR1e8jULKnXQ1JitpuY3NVl58yUukW9Q9wQiYiXGr28iso4iMRy3h42yz0DITDOZYkfwqcuU9L79Rs/AZnoDQNdEGHjJ27kyy88htft+8P53q2BZsn1pVW/UCjsfeRHV3PAlsq6G8uXZ4XHGXEsq2a485ltDvi26zvNHnDb+zyXTU/3246+rnUfhm5rM9Eaobu6IonjRz9+Ekd8/GcaXvwQpGM3sf5Pb+KqTYdz4Icufuj9fAgWv8iIOeZsBqCYg06SCRqSHUz5g3xQ8QD07/YhPPWivUZ2OZqT0XmS6Dy7fJ5BBTXA/uuok7mymtbLvnAF4IIjmfvKZcchKub57NwSIUmNC16DMywRuklJnlt3bmX0WAto/2JuXGIp6Gs0f89+T3W15UAjn9B2T9EQmEZkw5lw5/tuRzC3mhWCQJDGCZfTkKijCazqFPU4XKyoq2FSu4N0Z6lmZqy9VMWW5NRp54K1ajeiap4JqS/vkt+tbdinRNSc7JxAs9vxwJx5OUW78b0mIcVkQSt9gTJ5RxQheE+Fp3JCqGeopSalYZJvEdrD0O3hXI23FGBqE4ulpanMPoubFZ48q9MELnOHJqfD+LW92xNT0jZlc78ljznhvLl0NUAn60w+ZcksEYKJc+ZYFWfCahZnk9hxFkgJHwI5+CW3cZutsjmWXnJrdjJdy2WNXI8dlpzdrnBVwPshJAAth4vqkOxFh8Q8n7NrTtTccm3HJN+8JuI7F6wtXqz1vNLr07k5c0VBUt18IxRtHmK+nsARRM9lFh4RIbhgkyUF7/U+jVa3iDlhTbAm6bXhxZHy3FK7G5PTlny9FQTn8ygBmjqQ3NRsvmS7Lp3vhzKJfSHgvV4hgTnp2dbyf8vkdt523pfZnRyV88Dc1x3q7G9zfaOT3Xv543O/dL/X6G9tf3dz06FNxLcvLAQasbMrXVIvIt2IdNpEp3M7PdMqGBcGjpTnbhQekuCEMT+Y4smIbxPEMZV6xAd9R3li8r0fH8Fhs5cBEO++m9v++TjufefVrKx2zvOeDS6jd5Tgl0Kh8NggdXGaFx5/pK7p3NyBp8GBL/oF/lMriPfcu8evu+xHt+Ofvz9piV3XPU/Y5Rm70bH2nBuhrrln00G4mDj8x1cTd+36ta85uWGIje0p0pN34qoW0us+qn1b/CKjUydayiKJYM6tHKaRO1RzGIj+XHYPqviQkEpDGYiC+KRilgva7pq8OqZwOs8xC4wmykhU11NKgu9m92QyEcM1TjSc1xZa3QF8DnlJgrqgQHwOkdBWb5Oa1FSYJcLcCk5fnJKcqmvigDOnWbYtepy28ppE0g8yUn+aE2eBLNnVqCEvjkC09szsvMOcd7i+oJlcnpGYQFQsc3ZsHebgAghtbZue6ZKkq9ujJ8m5CvG1ijZJ1L3oaObVqapksy6zoCp6TrA9VoGZrEDZ8bD5dc4Sf5OKLDpfz5TWpHP1gol7wTvdtyxuSqLq9YipR6pGILRJoYOLdqRqFd1C8ESvgpwze6oeSRVxtSRPcrWKLmLuVpeFsMraqPPP6HXjvcfFaEnaerxdFplsaJ9pkyTmpB5TI9QqzKoapK5ar0K8uhuFVEdz1aoQpuc7Em2uo7cM6aZDXvL8TG1pVudjm9gOzZxK19YAk24NvVpoJaHyEF0XJ3muYPYDO93f4HDiSd5pu7yoyKi/xb4ksHtAnanqqiSncYdssYVm7qaz18bRjCZtAm1MCM6uYOdNGBP7skEdyNGb+1mVv+bad3ODaHKAlMtXHY0wl6SvDLvm/vG2i3lmpjor8f32abUAOg5dsYrPnPAGRvOMV/JAAJ07mbCQnmxebEYfqBUz37eNmCtiYyL02KfmaxgdleBzPq4HcQE/vJR6xT6kkSV0uzUyuYve9l1Mj88wMVkzNdNjaqbHf7/uTOq6Zmp6mtFOC9rD1D1HSI4Uyj/8CouXISd0XGu+d+NR03EtvPfsTLNFaDQSMHb17ud01T9dyl8f/Qo+cvyX8CUluVAoFAqFwhxuvH0NTxr/yV55rfrW2zj8kx1mD1gOAkM/vwsZnyBu3944Fv0PxoFfO76xYfm1O9hVd/Z43x6xdeT73/8+r3jFK1i/fj3OOb72ta/t9ryI8MEPfpD169czPDzMC17wAq655prdtpmdneXtb387q1evZnR0lJNOOonbbrttt23Gx8c5+eSTGRsbY2xsjJNPPpnt27c/4gJBPzirWGLz5ySqcCLqCxIq+6+Kh41w5Jw636LDR09wHleFpsWYJCokAkLQ+XahwlUVzlW46EjdqHPJnMMnwQedSZeDShoHWEpI7Lt/MLcUWQDDPuqLOguZk8ibu76zBy+JU7dcchBVRGjiNcytRXbEYYENSUx0VUGFOTPodN6kihcBwPaz0WxxOm8ScyRmJ5SYFS+Cq8HVFhxj7slG1LA9T+KQTktDcnrThKTzC/NFqkJgyBqtjeVzjSCrwqK3VllIoq3YqqflGY42zzCq+8xZWrEGxDhzCebZghqa4kPLHGY0ScV2sQNmX5aEc4l26tLqTUJvJ95XRIaZma2YnoJuF+ooeF/hXCBFT8p9xqiAJAkLRgk0R9R5E7Bsip/p5d6ccyGhARtoqBDe4SwdOSdxe7xe+1kwRkyophEz9VToHEcnGmCk0csR752FgVhLPCrUS54DKGJOSEykzL/PUbXahFYHqgrwECrotJFWC9eqaA+1ca0WXdr0UkVKoRH79erI8UyBnDYdXFARTMTCZuyCzFKY1DQp3c2fYEndGu6jup6z5/R6DCbYBtMlJTmQoMfEhHlt+24jvrLAI/CibtTG+ensOLug16z9PZ/qJHPCe+aMT4Cccq/n1+dzIXpneWcJ1a4FrmVfcEDbBw4aW8nGsVVsXLaSQ5auZOOyFRy0dAWHjO3DoWOrOWRsFRuXr2LDshUcuGw5G5euYOPSlRyyYhUHL1/FIbbNwWOrOHj5SjauWM7GMX2dg8dWcvCyVRyyzF5nbCUbl69mw9hqNi5dxUHDYxwysoyDV+7Dk/bfwAEHHMI+o+tZEVaz78hqNixdx4axdbRCRa8nTE3WzEzNIEmoqw7d4RXU7WWP5G19rzGIa1ph8Fi6CEJUWi6wzA+VQBhjMgkrbrjPN/wpcuRHb+fzvzxufnaqUKCsa4VCobBQqX4+tFfb9eMNP6P6j8upvnM59c0/J46P03yQfhTUSzu0HmLO48PlEf9LcdeuXTz1qU/l4x//+AM+/9d//dd89KMf5eMf/ziXXXYZ69at4zd/8zeZnJxstjnttNP46le/ytlnn82FF17Izp07OfHEE4lz0nNe97rXsWXLFs4991zOPfdctmzZwsknn/yICxTJ8pqKDxrGoSEZPgRtU8ZEN0HniZkAGaNY22MiiYYYiM2qy8m+KafYSk6IFVxMpCjNt/3qXKpxKTUOM59bYMnSoLM2V5MaHOS5h00XYgIXBVI0h1R/VmPjQEpirdtRWz1dRMMmXNMS682ppA6pWgUmcissuwk3Ym4q9TBJ1qxsf/R3JixEJNebhVMTSbOQGFOijhZE4/WY6/zBoK25VZvUqkjdWZCIOEe0NvNGihTzaDl1WSWiur/IMl3fPZd9ilmRzG3eTpy57vT3e7L7S0VlNbba8coCFcGSttHjmaK1vvdDf7JM5KWmqqegniVRMTVVMTHhmJyCmVlHNyZ6SUjOE3xFsFmNIgJe5wo21wShcZfmVl2gGb2QXYMp2v7YnE0n6vZUo+Oc1lbRYxIQ/b2urYJ4TvqwFnfn7Ww6TSj3Lqpb0kJsJCVirPWeEk2FRhLeC8Gp+9B7Z3MmHb6qCK1AqEJzvTpvgl7lqdptkm8xGwOzyROlIjhP5U2wN/Ev3xxJVNyVmObM2WxuGfIMS/sOx86xNHMt3dxzPidRPLsv82v5LFR6218XQHIrt/4el+w1XDBRkDkzTR0iXtuvHXgf6McA5bCY/DtzGJVdrzHpeU02otFeV3DUsUWv9tS1b0Y+mO0VSRBTT+9fTPTNsx0lt3q75j6KKWmit81LNYulOS/7+yfeg6+Q5krH7rBEjLPUOyeQyUlwNWl0iEkJzJhDNngY6lR4p99RTO5K7JiYYXaqhyMgrVGmW6P3fft+XBi0NW2h0W1GPRQejLZLLPOPLE1woRKcZ4nrFKER+Pz2Yxm+9Gf3e7z+xa30/q81/Mtdx8zDXhUKZV0rFAqFhYJrtXFVxejtwu0zy1l7+cKeB9r61U4mex2Wjc7gly151K/ziNulTzjhBE444YQHfE5E+NjHPsb73/9+XvWqVwHwuc99jrVr1/LFL36RP/7jP2ZiYoJPf/rT/PM//zMveclLADjrrLM44IADOP/883nZy17GT3/6U84991x++MMfcuyxxwJw5plnctxxx3H99ddz+OGHP/wdFnUiWWKFCjfSF/h8UhFmbhuhQ2cNhhDQZFhzSDmdk+bn/OM6i25emuZUYh0JPujvyAEf9Ns+s4+smUFmzjUXPEjPAjrytiBRWzaDiSTeZjWKZB+hs93TusizC61Kl6RxbKVYq9PN+9wJ2Qh0/YLMv+jUCedDYE6sDSpCOROW9DiZddDERsOE0uyIVJ1HHYTYOUhJxZkQWvgwrK2wu6ahV6szrglh6Xsk8SYEpoiYMGQj9nSupXONDqoOSWnmGKpzM4uSzo6LHams3DnAO503aOKhzsizZG0TFwHwXq8Q13cIShZ7fY0PET/SoVd7pmdm8D4RWkKnXRFMhKURGK1l1q4YaRpUdbOAs/OfxdbszKQJGhH67a4CfYFIrBV8Tkq6ayTZXHMWhBx1Ss215az1HlTs0iRznQdKSnPSrk2Za64nHRHgWsHuAxW1JNZI3cPVESeO4ALtFqR2xXRs0xVHRaKSmH8rImnOa1pCu8z5g7W7k4OM1GGZ8v9v7r/+Ze6dhT3lcB8TMfst3+Cc2PxT19wBeqhlzk2TL/V8/YjOaXW+ubnEnL1ionnzU811GxHxjXosJoLj8wxYa68WR69usasXmK0TEnu0fcVYZ9icj1iQkrmkfR6d4Jr3O5pd0PNnNmSaEBr7b78w7D6xq1HyFwrSvF+SEq47RZrcjh8ewlcBGepQu0CQiqqCql0zNjxK27fpdlXqtjuSXnTsenQjP/aYgVvTCgPHmHeERSTKBedZQoedzNKTJ25L8Ocveg6HjV/6gM/5C7cw+dYj+ZdPHMP/saYkJT9c7q2XsPTWkvq6p5R1rVAoFOYX1+lw22mbOeiErVz3y7Uc/u5buP26Qxi5dEv/o+ACJN18C1fcfBQrVu6kCeh4FOzVf/Vu3bqVbdu28dKXvrR5rNPp8PznP5+LLroIgMsvv5xer7fbNuvXr2fTpk3NNhdffDFjY2PNogXwrGc9i7GxsWab+zI7O8uOHTt2+9OUKJDbHbPIRBaavDq+XJKmfVKFxEa7ICtkYm2g+qHagjKipkDjxVxcjnYITSCL8+qQSuJJUZ2B0XnEV7iqA+0OKbR0v5wj6xSSRB1SKYeymHjlhBh7ODGHokRSStbKajMcgeaFoBFDcmIy0jxgncFZgNCW7twe7YKmL6sG4yGiNSQh1ip++VDpfkZ9nUZ0yBqFVxeY95YCbLPskh1jdcl5Ei3i0Ii2nEuNszAdh+uLX05b1oOAk6garVNHVjKhTix9ySGWLN4XA52fE7TTKEmW/pvDVFwAa2/FBMYoOg8zi1B5vqe2DFuNrt9Gr7M6PcElQpql0+kxsrQitIeZmQnUsVJHW1BxJ6ZoIpoKyb6vTZnAZ9cx5gDFhNsYmwAjEJtb2Z8/6LJwmpIeTzsmOoLQaUs82uatRlgTdWsNO2na+HPysnOEkF2SEZdqJNWkWOtrkucU2ow/MeeezWBMKdKb2Ulv8h5k5zgyM0mc3Und3Qm9XbSYpeM9znXo0WFW2sQUIAcOiQbMeNHRB+q+7AuAYv9PXXvNqdVnsiifPxBn8Qxpjl+KqXH/oZeWyrxRHZOkhDMBMYu9WVzU9uxIc+GrTGjb2n9jX9wO3lrgs0CHXtP5F+fr14sOc6hMEK+jZ3o2MD4p3H13zT3jXd505PP43Mv+CyN4FRYTfWeqCdk5wz2H0eRr3jtHwPUFYtGxDTmcqvlqwV7LZWe1y1dlPubg6powMwWTk7TrmqXLR2iNDVMNtRgaCaxaOcrfvvRdnHzEiSxbPcKSsREqmyXq6i69qYX3wXI+1zR4qHVtYdEroT0PydIBDXx5KILzjLh2MzLmicZEEg781kNvk378U7Z++jCm0p7PNnqi8LVtT6O64qb53o1FTVnXCoVC4bFnxyufzv/9h5/mfQd+i3889vP88rc34P5zy6MOUnm8kNlZDv/oNPt8qEO8++5H/Tp79ZPBtm3bAFi7du1uj69du7Z5btu2bbTbbVasWPGQ26xZs+Z+r79mzZpmm/ty+umnNzNBxsbGOOCAA+yZLARo+IL31sap9i5zdanXp7E+Zdki1c3cv/xoSqlpUc5KkD6WHWwqYoTgyWEPqq55Ujea+yzoXDdXIa7ChQpCQHwOWPCNu06c4DHR0wJgnKiIFtS0qB/+HUi0lm90xqKOCnTgQvYm0jRqpkgyUUP1iL5olts5+wEQmOiAHgUTqbJDLM+Wy8egmVFori1JKqRoK3KwsAuHS9rGGlDRNQ11VIhJ4HpCXefAC2mE4SRigqJH4hwx0+xZzYxHoZkj2U8mxsQVEzztMRVQVDhK2VpmLkacaMiLz+7R7HBzhOAJwesxT5GUIsklJMfYSMKnHh26jAwLo2NtwugQtDp2DKzt29l8zZjwMWmAS6pVJIVmFqI6zUwoJ4eOWN04E9/sPHs9l6mOetxdjgGxepPOZMzhJvlyzi3xKqLlP5gAFdWFmKKNDjChO0WIaGt9cqToiIIqyM5BikicJU1thx13EyZ/Rdg5TpjZgZ8dx02P42Z3apt5JVStgBDoxopuqojS0nslR5mYUC6SW9q1hlygQ+tDkoli+mCKFk7j7F73+WDYuRALmxHdRgcjiF1D+RjlYJYsypknWuziSn0XYMrXnIBLjqz7JVJf8Hfmak42GiFFdejGfL+l5vdJEuro2DVdMzHRZWL7LD1xDPuK5e0hu+bNnypZJ1dXbHY/5/c8j17T+j8Lg0oJF82ZOmdWKtnhaOKls1Z0JOKx4+v19xJr3NQufG+aZcvarDxgOcv3H2Ns7RJcCCxxIyxbsoThpS1a7UByQh0FFyNDrYUnVsznmgYPta4tLLp7958Si4ohlwY68OWhaLnAEv/EFNA+v/1YRi/+9WLY6v/vSv7yspc/Dns0+CTx3PbvG0hzWnYLe5+yrhUKhcJjjHPccXyPpX4agJar2fXcXfRn4C1s0o9/ilx21R69xmPyycDd5wBmh9RDcd9tHmj7h3qd973vfUxMTDR/br31Vv0ZZx+gc7qsmJhgcxTFBLrc+oc4UjSBgsb3Zi5IsXRhcz6ZC0qlQWuDduqq00APrzPjEO1TjAkIJEKTBu3REIfkWggBqLQ112nSrdj8RWcf5vNsOBFIlj6d8uxGm4UokpqgE9cEoki/bdll8WHOcRbBeQ06aUQvmTtb0aQYcY07UdN/1UXZCIeCBcqo6ElMKkIJ/TZzZwE8oSK5iugCsd1BWi3qJDprLrRIOHq9SIwRERXxJKl4JsxJyzUBOc/6C+T5epXN0fNNa2/T7m3io89irl1bOWlaRaTsR9PWU5LOyfMm5GThJ896zIKsw2YVAviEl5oO0ywdFZbvt5TW0iU438aJCqzB+0asimKOOjFXqgk4OHLsMThtP9b2fZvlmUwIzmKsvYZzXhPJvV5tjcPPXIqIHtPcua0ieVQHpaijTVKt4qL90ahs/V0mmdvv7d8xMYrdR+bO7U3jdo1TzeygFWcIMourp6lil7araVeJVujRbkdaVaRVaYhSdBXJ6T2Dq7TmRuCX5r1amnml+UuDaDVl8du+LEh6Xbqe1pFNoNpun92u+fuDLKT3XYFOdMKrt9EJ+XpGgqU/98U5l68huy4Cgk/aVh7AHKbSd+wmFZjzXFhnEryeUn1fquualHraru41Bd4Hb6FGrnFNqrM0v1fm/dZ6vHP6MxYCJCZO912c+T6gOZ6NuzPZ+wI50duZG1rlUxdrXHcGt2uSEWZZvXaYlQetYGTFGImK6ZnEzExicrzLrskeEtokVyEIw8ML1+01H2saPPi6ttBIxcn4oIz6wfgH5aOl5QJD7tG30wwqn7/iWcR77v2126WZGQ4/Y4r/de9THoe9Gmz++3UvY8Pn7j/jsvDYUNa1wqPBdTr4pz2Z3ks2U+27br53p1BYsKxdO7Hb36trR/umpycAe/WTwbp1+mZz32+w7rrrruYbs3Xr1tHtdhkfH3/Ibe688877vf7dd999v2/eMp1Oh2XLlu32B7LIqInLyTkV5oSmHVFIzXw6zBnlg4kNogfIupTV8ZOyow4kOmINuJaJGSq85STbPA0tShbgehAj0bWIIRCDkLxDfEDweAkEr84iLwFvwSoE13cl2Uy1mPMhUkSoaZ7QeGLybLdmmU/mMLTWWBUPUGEoqvMQBHEajqOiYVCR0mURgabD1A6AtqE2zi6xWXj6XN9maMJe4/S00Bmb+5eqDjI8BEH1yOlpYWpKiGJuKx/wLpgrVF2TKUUN5vFAsPZPsECL7NnsizxCbpO3msXmNloLbiNE5f03cVpMtDUzKr5yuRxt+8Zcm3beIamrT7C5l56A0E417d4uqu4MYagidUaIXsUVnEPMyWqTQvsCY7JjFpMJPKaIJcBZSEwWb5MJPeY0dSHs5vTL8zLF2XZiLfm613r+hL6wlCO8LWxIRTF1wMbUQ6Ru7j89JMnOj+5KFE9yQeuLEd/raZq3ueHUTRcIPuA9BF/TdrO0QpfK11QBvAtEArU4RAIigZD0+CSnbcBiYr/eyzo+QLKTMfc8myDaJKOQ25hrRKI5FfV4Z4NjFgOds6mG2bHbSHEqDntzlebj5cnXl8cn8HYOJd8vKWnYSjTn4hw3anbX5otMbB5lEr3OApFWSxgecowu9YwOOzpDXlve7fj7RpG26zQH1eSwF3NjComUJwjkNnvv7FjZrFKfhXbJt40dTn0t7H2175KMuO4MYecu/M6ddFKPTqciLGnRGhmiagVaISBJmJ3u0Yt29di4gYXGfK5p8ODr2kIj4jh/15Hc2Fs937uyoPjqjqdzY294vnfjMWf4CdY2PZGE/b718Eeax2uu585T1vI/bnjZY7hXg81545vY972Retv93ycLe5eyrhUeNc5RP/so7t48xo4NbcafdxB+ZGS+96pQWHiIMHPuGiaT/hvwWxNP46Bz7pnnnXp82asi48aNG1m3bh3nnXde81i32+WCCy7g2c9+NgCbN2+m1Wrtts0dd9zB1Vdf3Wxz3HHHMTExwaWX9gdqX3LJJUxMTDTbPGxcgDz7LAI1OsvOPiS7WPWdXA5N9xUVB5w1THpnARZoq7UIEG3GnVNnlIY22GuoJw0haTttqw0hECy8xC8ZJbU6SNT2Ut1PZ4JMG6EDrk1CxUoIiLW7khJSO+puTTSXZCUOl9srmzZmD0R1K2X3kRNrpdT9U7EyNu4ndQqao5PcxuttvlturzVHoDkTnaiI4lzC6/i8xrUYQm5n9QTxjZCiJiiPCw5CIFVtZOkwUDG9M7J9e2Kq60nJEm6jEHuJWEdiSsTctoy1duf20sbNl5pZeN7lhuxAcJUl9Do8QedkNp41c0IKev7zVTOnhTWI03ZgEW2LlqjhJSYU6uGv8K6y40Z/BiKJVuoRpnfgJ3cioUVsjyC0tA5Bc6SdgPQI6Kw8R56H17+kVTS1AB3b52CCrEVu45K50+xapc7t6jSBNtFpO3AWy/TK1dmiedYe1hIttbojU9Rj4SSRaiHViSS55TerUI7gKoJr4Xyl80qTpoCHOa45u/DNCSh4SQR6dJih7WcJrodzeh5jdMRk8zcJ5hz0po3nZn9MwLS/i6OOkdi4lE0Y9MGuq4SPUcV/erDb1aDnLifG+yzipdgIrk07Nsmcignnahqhz9qu8d6ET9s/zIFqTtQ8bgGShkPkFHSwGZR5ZEGkCpElbWHlMmHdPi1WL6sYCjRuSO8tDV7yzEdLn26cZgmn41WZ63V0ydLGc+16odm1oqe1f4xT81z+LZoYr+8VPiZ8dwY3tQM3vQsXe1SVZ2jFEKP7jNJe0qLdqnA4ZmdqenVSp21YeC2lC3JNW4B86d5j+cabX8Rfnn4KP57db753Z8Hw2W+9iPfd+DvzvRuPOcF5Ou4R5wgOLD+YPpix//zFI/qZeP1N7PxREeEfiO9PHMHN7zuC+NMb53tXnhCUda3waKkO3J+Jg/ojMrpLHBx84DzuUaGwcFn3qR/xvo++iT/4wlv50dueQbzm+vnepceVR/yvwp07d3LTTf05NFu3bmXLli2sXLmSAw88kNNOO42PfOQjHHrooRx66KF85CMfYWRkhNe97nUAjI2N8aY3vYn/+l//K6tWrWLlypX82Z/9GUcffXSTYHbkkUdy/PHH84d/+If8/d//PQB/9Ed/xIknnviI08pSTEiwQAsTZZBkLb1edRH7oB9CHslo4RFOHTbJWehHVraIhEqFy5SsndWEL23n9CRxeK8zBJGE9y1qVyGuDUuW6AzGXRPgVNzzVQsnjhS7SE/U2RhaNutP2zpTFIIHvBAk4FwieA170BbfWsUkF0gkvK9IPjZCn4iQeknDazwkXyPSbzsm9Wx2n2sCRpJYsrBZGMVnISkn1jp1xokGezgXzM2Z3XyCF0/ySdurYyAlsbCiCnwLNzKCD216vS6zPehGR5iNxBFRV6nzxDribSZgfix4UzT1TDcCk6Y9B7xXmU5MUM3iqQOS15CbXB7ekSSa77DJzlVnn6gQJk6aOZPOa/CPZOElSzNi3rrGEWmuN5v5WOFIvR2kGXCdUVIagW7CSVfnMZrsp4ZUUYNZUsFLgjk3TcBMOTU4BBW88rGw3mfvbK6kaDiUasV5f1yzX6kJl9GfbfRMmxdJFISe1amCXXCOFKQRx7D7K7tZnKAist1vSE9rcOagRYVv8i1FniepMy2zfy6hsxiTtSaHLNLpS1iit10Bru+idOJJrgYJzRcGkQSSZxDqfZmc4MQjdcIFPYbNHFXUuZmPYbL3BdVTLeCICClgvex9Rx/ZdWt6JFCnSMsCdPTK1fs2j2xwhL6DVXJrf3bUZgemnsvRdoskQnAQbOZk0wYv/REAuaU8O2D1FgiQILkcXqO1zm2LJovPbk7TtdDU6ZKaH/Fag8/vEWIhWNKF2RlkZhZXDUFVMTTSJlSOmckuO7ZPMbOzZmb7FJOhYqjTIg7PUdIfRwZtTVto/Ly7mis/8gxGfnAJKy90fObqE7nlXfCPmz/HSj8z37s3LyQcH7vzJTzpn+7kFzv2pbcp0lrkLcUjvs1srInMz338eJGAv/xfr+JJd/zwEf/sqqsj99ZLWFnt3Ps7NqB8b+JIrn/PUVTfuXy+d2VRUda1wl7HB6YPX8t9p6PMrhuldfX87FKhsJCRXpc1n3zwEKxBwLXaTPzuM7jrmfr3ZVfMwue//rB+9hGLjD/60Y944Qtf2Pz9ne98JwCnnHIKn/3sZ3n3u9/N9PQ0f/Inf8L4+DjHHnss3/72t1m6dGnzM3/zN39DVVW8+tWvZnp6mhe/+MV89rOfJYT+P8K/8IUv8I53vKNJNjvppJP4+Mc//kh3F6FSt5gP1DGSelEFMGetsQRIPRU0XIBkbiKyyOjwEVNNwPxCpGTmNZuPqKqZCg8SdSZdaHkkCFGEOBuZZQhZ1aGzbATfaUFvFlf3dHaizWH0fhhcRR0jSMB1PaQpoMY7SKmnbYw55TUlDffwKlqomNojRo9vCSGE3E3ctDYmc15Vc9ohtbIsKnhcQoUd75o2TBHBpQA2C5IsplkPpTPXZBZiCCpy1SkSUm5pdrqvlQquKYyQhkeQKuC9o9VqI75i1pK4nRcCooHPoj+btS/rGdZzko+JdwShaZ3Nzj7seRETWRuXoYWqJBUUk9kGvckv4mx+o4i1R5tgU1twhw8kp+3skiI+RZJDW+Ylaiuz80hw4NQ5R4r47iTUPaTqkPwQITkkziJB3Yx58idNWnPWmPTC83Y+YxJS0FqDU28nzuZiOmvVtXbuZA5GwZ4XO8d2vWtidW55h5RsbiEJn4+dCegi2f2prbhIpa3+zgOB5AIhtOwcRQ2BkWTXmM4TzHP8VPoNJlSaQ9iu95bzxAg1ms6ekiO5RHBB3X62/87CdlTwQo+1AE7bjBMyp/Xf5FJHP8TEOYi1hdWExrGnTtZAFME1gprDJdtvZ18uiLd7Mg9JEOyiaEYTqOPVnK8myklu47br0fqYNXimEfj1SwLvVWytJIFMgxvVoKik11vC40VnPur8SXWPSp4r6fpuXb1GrHWaHDhl7wPekrxz2zQ0blP90sQ14r1+SZG/ZJFmHqtLCalrXLeLq7smxwYkeEK7oq4dE/fUzM50ST4wtnKYJfvNT4DEoK1pAGf8/AT+26bz8Pb+NXcmYn7s8eDamf048/85idVfNcFFBC69ioNOHuK9J7yF214RedMxF3LC0p8w4uqHfrFFQg/P71z0Zg77i+3Em29mwyd+xRFH/Z9c98J/XPRCY8t5ogXKLUYS8LIr3sThZ9zMo6lyydev5Mv3Hs+dz+ww9eQZnn3ozRyxRFtWR3yX/dtPnFaqa6b356zv/G8c9pntVD8pAuPeZhDXtYWCa7XxB+1PWjaMv/FWYkm/BiAc+STu3f/+HSe9ZRUtb/OuCoXComLH7zyDTadexWg1C8Adm9r85PMP72edNEPAFhc7duxgbGyM2/7sS4xZS+rOiWmmp6eBrrbvDbfptKDtIz54ckJwqLyGiahaQcpBJZJbIG32IpiooDhAoiA+aBSMC+Bh15RjcgKmwwjtA1axZMNKOiOe6u57qXbsJKSUtSGi93haOnMxzuJmduLqaVzahUtdJEbwGkCS259jTLhg8+G8J8VEBEIVcL6y8W4CrrLWSd1j7217nLmfPM4HDY7w1upMVihdY+R0TucYZrXPOQfJxDxJFoDs8MmTPLhso3MQk6OXhKo1RF2N0luyHLfPUp311+sxdecsv/rZPbi4gzUra5a2wJsLzgXd95ygKzY/TmzOYyMMW5uq85Xtd2oEFUSsD9rh6xyE42zmHRYqok7JLGiSZ2OircEu5Zl2WP35A36N1LXOV8zhMDl0JYFP0cYpJmzaH+I7pM5SJEVCbwZH10bjzZkhCfa7ox5L+6Ca1Cxn+rc0ApjOKDQvXkpNeImZdFU88vZY40Dtu9+0pT7pzEWpIdaazi4qTCUg5uCXLGi4AN4TXCBJhfgOBI/QI1CrNoq5LyWHEfVb/LOjVl2A0QyUHqiIUtGLjl4NvdpmYFJTkQgS8S4RvD7mXNJWdUfj4oOsSqsz1gac0heodTek+WIh0LSQS63CWnb02bFrnLF2HSRzCOYWe+Y4NjVQxQG1aXWm8uXWcfI8T2fJ0/nq6J+zfAtrQjYmho5o63iIeKnxqYe3j75JIi7qjFXvvabU+2D77MFbhrx4xKvVMkWZc6hc/03NOVweE0A+bvmLmiab2pLSrWVfHFQdZGQMt2QM2m2k6lBHYWoqcvddU9x5205mdnZpj1SsWT/C0CrP0X/120xMTJQZTQ9CXtdevOq/cNMHjuIZz7iJy685mNWXBKpZoTfqmHjhNH+5+eusb40/ZvsxlTq85btv5LAzZ+Cyq5s15X44R7V2DTuOO4htx3paT5rkdYf9iP992ZXZj7yoSDheedFbOPStvyDOmWXmly7l+g8/me+/8n+yf7VkHvfwsWNrbycvvuAdrPpeh3s3CU9/5k18bMPXGFqAs1YfKT0R/vyXJ3DJt47moL+7kXj33Xv+os4RVq3ELRkFIC0ZYeKo5ezY6Nl1SI9jn/wzfmvVTxjyvT3/XQuEe+slfPrm5zBzwWr2//d7SVdd/+DvHYuEWnp8j6+Xde0hyOvaC/htKje/Y1OqdWu54bSD6Ry2g3ZVM75tGUf+j3uIN948r/s134S1a7jnpYcQHyCfL3Rh5Vd+TJqaevx3rFAoPKZsPeM4fus3LwMgiePr/3kEt73jAw9rTVu0Q3Sydjq1ZhmyK1HvmOZX49NMjO8i1l1kNtKpPGv2G2J0qTDkHaHtiHWXUHtcTLiQRSdtuzVViRyKUKeIs5CYpAkGeHEkb4m+oh+4776zZvt0gJUtWjt2MT1RsawzwtCSIdzkLvxsV1u6gwMqJFSkYLP1Yo2ve/jYRXqzTXiLEC2UxlxRtYlN2bEH+DqYKGUCgTi8r5Bk8xW9U8EETcR2zuFCpe2a1kba1OyDCh5O1KUUkzqecnBECkg0d1oQFZ16QBUa+ct7rxkivgV1pOcStdS4yZ3U4xMQElQtWO1hpzArNbJzhhASwYNEFe6SWvB0/iSoKmKzJRt9xDm876Ez6RJZ5nHOmX6is+MaoVIS0ohcJsI0Y+wkK2Tk2Y0qMqnQKpiolXSuX+yhIhAmnCF4mx2oeS3eXJY1SaaoezV+yTJtr56ZUUecCFVV2b6pG1Gc4IMGvaio5SAlfMp+wMaiRl8OEm3pz/45tYQCOucvRrE6dTtp5hnq/EFtadawlywy5omjkqTfqkt2MTrEdUhB7xcvXbxEXNRdc5LboC1IJJ+Xxg0LoOcSr4nNkYpe8vR6wrTN5vSYw1V6BBKVT7RCxLkI4swd3E84zK3jOT0ac5jmyzfvR0Lsnqn00dRrjqlvxNx8/vvHXHzA+WhBPbVtNadtWUT3DUzQpxGQVdjVPalNjQ1C46QVyded09TuRtBzCJXNbeypyJiyyDhHeMeOvYmjeDsW9uWAeL2O1dWZj79vjp04B7WQaiFm0dEScx02p5a8PxYIY65wF1o6azFFqCK1wGxMpHaP9mqPW14x1A6EJT16s93d3rsL9ycfm9l77mTjqeNMDA9x8NR/7rbN8n+q+J9nP5cPHfb1x2w/Lp3al8PefxP1nXc99IYC9R230T7nNg48B/CB85/zDH7jE1ezYhG2Uu9IHfb/f2eZvfc+x2XHvRzyrsv4+OZn8Oerr5ufnXuMOf2Xz+PgP7gS6XVZBuw4eANXfHUJR7cn53vX9pjb64pb37yWfa+8gNm99aIC9d3bYI5eOXwVDAM4x/hBB3DVWas5avi2vfUb552zbnkKy1+/lbTrGrrzvTOPEzUqEpd17cHJx6amN+eL9flh/Dn7svyQ22x2PKxeMcVtL9yHfW5YPGL/o2Hm8H3oMsMD3rhdPXdJntjHqFBYjOz3tXG+sfpJrFu5gzuuX8OTPvYzbuPhrWmL1sl48803c8ghh8z3bhQKhULhEXDrrbey//77z/duLEjKulYoFAqDR1nXHpyyrhUKhcJg8XDWtEXrZFy5ciUAt9xyC2NjY/O8N3ufHTt2cMABB3Drrbcu2haMxV5jqW+wKfXtXUSEyclJ1q9f/5j/rkGlrGuDTalvsFns9cHir7GsawuPsq4NNqW+wabUN9gs5DVt0YqM3toix8bGFuVFlVm2bNmirg8Wf42lvsGm1Lf3WIwfMPYmZV1bHJT6BpvFXh8s/hrLurZwKOva4qDUN9iU+gabhbim+V+/SaFQKBQKhUKhUCgUCoVCoVAoPDhFZCwUCoVCoVAoFAqFQqFQKBQKe8SiFRk7nQ4f+MAH6HQ6870rjwmLvT5Y/DWW+gabUl/h8Waxn5NS32BT6ht8FnuNi72+QWSxn5NS32BT6htsSn3zx6JNly4UCoVCoVAoFAqFQqFQKBQKjw+L1slYKBQKhUKhUCgUCoVCoVAoFB4fishYKBQKhUKhUCgUCoVCoVAoFPaIIjIWCoVCoVAoFAqFQqFQKBQKhT2iiIyFQqFQKBQKhUKhUCgUCoVCYY9YtCLjJz/5STZu3MjQ0BCbN2/mBz/4wXzv0q/l9NNP55nPfCZLly5lzZo1vPKVr+T666/fbRsR4YMf/CDr169neHiYF7zgBVxzzTW7bTM7O8vb3/52Vq9ezejoKCeddBK33Xbb41nKw+L000/HOcdpp53WPDbo9d1+++284Q1vYNWqVYyMjPC0pz2Nyy+/vHl+kOur65q/+Iu/YOPGjQwPD3PwwQfzoQ99iJRSs82g1ff973+fV7ziFaxfvx7nHF/72td2e35v1TM+Ps7JJ5/M2NgYY2NjnHzyyWzfvv0xru6h6+v1erznPe/h6KOPZnR0lPXr1/PGN76RX/7ylwNT3xOJQVzToKxrMPj1lXVtcOora1pZ0waJQVzXypq2OOor69rg1FfWtQFd12QRcvbZZ0ur1ZIzzzxTrr32Wjn11FNldHRUfvGLX8z3rj0kL3vZy+Qzn/mMXH311bJlyxZ5+ctfLgceeKDs3Lmz2eaMM86QpUuXyle+8hW56qqr5Pd+7/dk3333lR07djTbvPnNb5b99ttPzjvvPLniiivkhS98oTz1qU+Vuq7no6wH5NJLL5WDDjpInvKUp8ipp57aPD7I9d17772yYcMG+f3f/3255JJLZOvWrXL++efLTTfd1GwzyPX91V/9laxatUq++c1vytatW+XLX/6yLFmyRD72sY812wxaff/2b/8m73//++UrX/mKAPLVr351t+f3Vj3HH3+8bNq0SS666CK56KKLZNOmTXLiiSfOa33bt2+Xl7zkJfKlL31JrrvuOrn44ovl2GOPlc2bN+/2Ggu5vicKg7qmiZR1TWSw6yvr2mDVV9a0sqYNCoO6rpU1bfDrK+vaYNVX1rXBXNcWpcj4G7/xG/LmN795t8eOOOIIee973ztPe/TouOuuuwSQCy64QEREUkqybt06OeOMM5ptZmZmZGxsTP7u7/5ORPRibLVacvbZZzfb3H777eK9l3PPPffxLeBBmJyclEMPPVTOO+88ef7zn98sXINe33ve8x557nOf+6DPD3p9L3/5y+UP/uAPdnvsVa96lbzhDW8QkcGv775v7HurnmuvvVYA+eEPf9hsc/HFFwsg11133WNcVZ8HWpjvy6WXXipA84/8QapvMbNY1jSRsq6JDFZ9ZV0b3PrKmlbWtIXMYlnXypo2ePWVdW1w6yvr2uCsa4uuXbrb7XL55Zfz0pe+dLfHX/rSl3LRRRfN0149OiYmJgBYuXIlAFu3bmXbtm271dbpdHj+85/f1Hb55ZfT6/V222b9+vVs2rRpwdT/1re+lZe//OW85CUv2e3xQa/vG9/4Bscccwy/+7u/y5o1a3j605/OmWee2Tw/6PU997nP5T/+4z+44YYbAPjxj3/MhRdeyG/91m8Bg1/ffdlb9Vx88cWMjY1x7LHHNts861nPYmxsbMHVPDExgXOO5cuXA4uvvkFkMa1pUNY1GKz6yro22PXNpaxpi6++QWUxrWtlTRu8+sq6Ntj1zaWsawu3vuoxedV55Fe/+hUxRtauXbvb42vXrmXbtm3ztFePHBHhne98J8997nPZtGkTQLP/D1TbL37xi2abdrvNihUr7rfNQqj/7LPP5oorruCyyy6733ODXt/NN9/Mpz71Kd75znfy53/+51x66aW84x3voNPp8MY3vnHg63vPe97DxMQERxxxBCEEYox8+MMf5rWvfS0w+OfvvuyterZt28aaNWvu9/pr1qxZUDXPzMzw3ve+l9e97nUsW7YMWFz1DSqLZU2Dsq7NZVDqK+vaYNc3l7KmLa76BpnFsq6VNa3PINVX1rXBrm8uZV1buPUtOpEx45zb7e8icr/HFjJve9vb+MlPfsKFF154v+ceTW0Lof5bb72VU089lW9/+9sMDQ096HaDWl9KiWOOOYaPfOQjADz96U/nmmuu4VOf+hRvfOMbm+0Gtb4vfelLnHXWWXzxi1/kqKOOYsuWLZx22mmsX7+eU045pdluUOt7MPZGPQ+0/UKqudfr8ZrXvIaUEp/85Cd/7faDVt9iYNDXNCjr2lwGpb6yrimDWt8DUda0+zNo9S0WBn1dK2tan0GpD8q6lhnU+h6Isq7dn/mub9G1S69evZoQwv1U2bvuuut+KvdC5e1vfzvf+MY3+O53v8v+++/fPL5u3TqAh6xt3bp1dLtdxsfHH3Sb+eLyyy/nrrvuYvPmzVRVRVVVXHDBBfzt3/4tVVU1+zeo9e277748+clP3u2xI488kltuuQUY/PP3rne9i/e+97285jWv4eijj+bkk0/mT//0Tzn99NOBwa/vvuytetatW8edd955v9e/++67F0TNvV6PV7/61WzdupXzzjuv+WYMFkd9g85iWNOgrGuDWl9Z1wa7vrmUNW1x1LcYWAzrWlnTBrM+KOvaoNc3l7KuLdz6Fp3I2G632bx5M+edd95uj5933nk8+9nPnqe9eniICG9729s455xz+M53vsPGjRt3e37jxo2sW7dut9q63S4XXHBBU9vmzZtptVq7bXPHHXdw9dVXz3v9L37xi7nqqqvYsmVL8+eYY47h9a9/PVu2bOHggw8e6Pqe85zncP311+/22A033MCGDRuAwT9/U1NTeL/7W0YIgZQSMPj13Ze9Vc9xxx3HxMQEl156abPNJZdcwsTExLzXnBetG2+8kfPPP59Vq1bt9vyg17cYGOQ1Dcq6Vta1hV3fE2ldK2va4Ne3WBjkda2saYO9pkFZ1wa9vrmUdW0B17f3s2Tmn7PPPltarZZ8+tOflmuvvVZOO+00GR0dlZ///OfzvWsPyVve8hYZGxuT733ve3LHHXc0f6amppptzjjjDBkbG5NzzjlHrrrqKnnta1/7gDHt+++/v5x//vlyxRVXyIte9KJ5iZx/OMxNLBMZ7PouvfRSqapKPvzhD8uNN94oX/jCF2RkZETOOuusZptBru+UU06R/fbbT775zW/K1q1b5ZxzzpHVq1fLu9/97mabQatvcnJSrrzySrnyyisFkI9+9KNy5ZVXNolde6ue448/Xp7ylKfIxRdfLBdffLEcffTRcuKJJ85rfb1eT0466STZf//9ZcuWLbu958zOzg5EfU8UBnVNEynrmshg11fWtcGqr6xpZU0bFAZ1XStr2uDXV9a1waqvrGuDua4tSpFRROQTn/iEbNiwQdrttjzjGc+QCy64YL536dcCPOCfz3zmM802KSX5wAc+IOvWrZNOpyPPe97z5Kqrrtrtdaanp+Vtb3ubrFy5UoaHh+XEE0+UW2655XGu5uFx34Vr0Ov713/9V9m0aZN0Oh054ogj5B/+4R92e36Q69uxY4eceuqpcuCBB8rQ0JAcfPDB8v73v3+3N7lBq++73/3uA95zp5xyiojsvXruueceef3rXy9Lly6VpUuXyutf/3oZHx+f1/q2bt36oO853/3udweivicSg7imiZR1TWTw6yvr2uDUV9a0sqYNEoO4rpU1bXHUV9a1wamvrGuDua45EZFH74MsFAqFQqFQKBQKhUKhUCgUCk90Ft1MxkKhUCgUCoVCoVAoFAqFQqHw+FJExkKhUCgUCoVCoVAoFAqFQqGwRxSRsVAoFAqFQqFQKBQKhUKhUCjsEUVkLBQKhUKhUCgUCoVCoVAoFAp7RBEZC4VCoVAoFAqFQqFQKBQKhcIeUUTGQqFQKBQKhUKhUCgUCoVCobBHFJGxUCgUCoVCoVAoFAqFQqFQKOwRRWQsFAqFQqFQKBQKhUKhUCgUCntEERkLhUKhUCgUCoVCoVAoFAqFwh5RRMZCoVAoFAqFQqFQKBQKhUKhsEcUkbFQKBQKhUKhUCgUCoVCoVAo7BFFZCwUCoVCoVAoFAqFQqFQKBQKe8T/D4bDqxOSjPdoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(16,9))\n",
    "ax1.imshow(img)\n",
    "ax2.imshow(mask_cellpose)\n",
    "ax3.imshow(mask_stardist)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>label</th>\n",
       "      <th>area</th>\n",
       "      <th>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>eccentricity</th>\n",
       "      <th>bbox-0</th>\n",
       "      <th>bbox-1</th>\n",
       "      <th>bbox-2</th>\n",
       "      <th>bbox-3</th>\n",
       "      <th>image</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>feret_diameter_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>42573</td>\n",
       "      <td>114.372584</td>\n",
       "      <td>134.664435</td>\n",
       "      <td>0.590280</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>240</td>\n",
       "      <td>253</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>832.315801</td>\n",
       "      <td>274.417565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>24120</td>\n",
       "      <td>63.611774</td>\n",
       "      <td>308.462935</td>\n",
       "      <td>0.641193</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>154</td>\n",
       "      <td>422</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>644.238636</td>\n",
       "      <td>214.200840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>49842</td>\n",
       "      <td>116.046547</td>\n",
       "      <td>510.143514</td>\n",
       "      <td>0.359108</td>\n",
       "      <td>0</td>\n",
       "      <td>388</td>\n",
       "      <td>249</td>\n",
       "      <td>631</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>866.748304</td>\n",
       "      <td>277.650860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>45595</td>\n",
       "      <td>80.636254</td>\n",
       "      <td>917.205220</td>\n",
       "      <td>0.705256</td>\n",
       "      <td>0</td>\n",
       "      <td>760</td>\n",
       "      <td>210</td>\n",
       "      <td>1102</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>951.595021</td>\n",
       "      <td>342.527371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17997</td>\n",
       "      <td>69.825138</td>\n",
       "      <td>1241.320720</td>\n",
       "      <td>0.798332</td>\n",
       "      <td>0</td>\n",
       "      <td>1143</td>\n",
       "      <td>180</td>\n",
       "      <td>1300</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>592.132034</td>\n",
       "      <td>223.600089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>61886</td>\n",
       "      <td>186.388682</td>\n",
       "      <td>1119.213877</td>\n",
       "      <td>0.568769</td>\n",
       "      <td>11</td>\n",
       "      <td>983</td>\n",
       "      <td>334</td>\n",
       "      <td>1255</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>983.986940</td>\n",
       "      <td>326.167135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>8656</td>\n",
       "      <td>132.348082</td>\n",
       "      <td>20.894177</td>\n",
       "      <td>0.976646</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>51</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>499.787843</td>\n",
       "      <td>222.324088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>40028</td>\n",
       "      <td>161.908689</td>\n",
       "      <td>702.735685</td>\n",
       "      <td>0.598231</td>\n",
       "      <td>29</td>\n",
       "      <td>602</td>\n",
       "      <td>291</td>\n",
       "      <td>819</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>802.358477</td>\n",
       "      <td>264.850901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>48578</td>\n",
       "      <td>276.923628</td>\n",
       "      <td>340.470563</td>\n",
       "      <td>0.698555</td>\n",
       "      <td>135</td>\n",
       "      <td>219</td>\n",
       "      <td>443</td>\n",
       "      <td>454</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>888.246825</td>\n",
       "      <td>309.657876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>17792</td>\n",
       "      <td>326.442952</td>\n",
       "      <td>1264.944357</td>\n",
       "      <td>0.949231</td>\n",
       "      <td>184</td>\n",
       "      <td>1209</td>\n",
       "      <td>468</td>\n",
       "      <td>1300</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>668.132034</td>\n",
       "      <td>284.112654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>43025</td>\n",
       "      <td>319.591331</td>\n",
       "      <td>906.064730</td>\n",
       "      <td>0.710462</td>\n",
       "      <td>194</td>\n",
       "      <td>789</td>\n",
       "      <td>467</td>\n",
       "      <td>1017</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>843.293506</td>\n",
       "      <td>293.552380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>47271</td>\n",
       "      <td>387.986355</td>\n",
       "      <td>226.346238</td>\n",
       "      <td>0.927667</td>\n",
       "      <td>233</td>\n",
       "      <td>89</td>\n",
       "      <td>557</td>\n",
       "      <td>398</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>1041.241774</td>\n",
       "      <td>413.175508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>23430</td>\n",
       "      <td>375.474904</td>\n",
       "      <td>40.805036</td>\n",
       "      <td>0.951484</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>534</td>\n",
       "      <td>92</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>722.735065</td>\n",
       "      <td>299.376018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>52692</td>\n",
       "      <td>385.633758</td>\n",
       "      <td>527.460241</td>\n",
       "      <td>0.713008</td>\n",
       "      <td>236</td>\n",
       "      <td>411</td>\n",
       "      <td>530</td>\n",
       "      <td>631</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>903.990945</td>\n",
       "      <td>307.358097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>53905</td>\n",
       "      <td>410.831593</td>\n",
       "      <td>745.444300</td>\n",
       "      <td>0.620854</td>\n",
       "      <td>262</td>\n",
       "      <td>622</td>\n",
       "      <td>540</td>\n",
       "      <td>890</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>938.536147</td>\n",
       "      <td>313.178863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>60554</td>\n",
       "      <td>453.870776</td>\n",
       "      <td>1116.579466</td>\n",
       "      <td>0.506663</td>\n",
       "      <td>314</td>\n",
       "      <td>976</td>\n",
       "      <td>593</td>\n",
       "      <td>1273</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>961.116882</td>\n",
       "      <td>308.768522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>45889</td>\n",
       "      <td>543.939136</td>\n",
       "      <td>195.414152</td>\n",
       "      <td>0.802051</td>\n",
       "      <td>405</td>\n",
       "      <td>64</td>\n",
       "      <td>663</td>\n",
       "      <td>372</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>907.993037</td>\n",
       "      <td>323.358934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>96953</td>\n",
       "      <td>684.202160</td>\n",
       "      <td>1000.089342</td>\n",
       "      <td>0.700270</td>\n",
       "      <td>487</td>\n",
       "      <td>822</td>\n",
       "      <td>844</td>\n",
       "      <td>1210</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>1256.283405</td>\n",
       "      <td>427.488012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>27706</td>\n",
       "      <td>640.809428</td>\n",
       "      <td>1242.168483</td>\n",
       "      <td>0.843306</td>\n",
       "      <td>499</td>\n",
       "      <td>1162</td>\n",
       "      <td>765</td>\n",
       "      <td>1300</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>703.730014</td>\n",
       "      <td>268.881015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>37523</td>\n",
       "      <td>614.828692</td>\n",
       "      <td>731.495030</td>\n",
       "      <td>0.621346</td>\n",
       "      <td>511</td>\n",
       "      <td>610</td>\n",
       "      <td>727</td>\n",
       "      <td>839</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>765.369624</td>\n",
       "      <td>261.137895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>38726</td>\n",
       "      <td>652.313045</td>\n",
       "      <td>530.632392</td>\n",
       "      <td>0.778509</td>\n",
       "      <td>527</td>\n",
       "      <td>439</td>\n",
       "      <td>807</td>\n",
       "      <td>623</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>801.062013</td>\n",
       "      <td>284.606746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>55983</td>\n",
       "      <td>717.541164</td>\n",
       "      <td>360.989229</td>\n",
       "      <td>0.463724</td>\n",
       "      <td>540</td>\n",
       "      <td>215</td>\n",
       "      <td>853</td>\n",
       "      <td>487</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>988.063059</td>\n",
       "      <td>313.996815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>46852</td>\n",
       "      <td>704.595023</td>\n",
       "      <td>90.721762</td>\n",
       "      <td>0.734250</td>\n",
       "      <td>545</td>\n",
       "      <td>0</td>\n",
       "      <td>830</td>\n",
       "      <td>213</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>886.191955</td>\n",
       "      <td>322.931881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>21303</td>\n",
       "      <td>793.924987</td>\n",
       "      <td>649.218326</td>\n",
       "      <td>0.722032</td>\n",
       "      <td>696</td>\n",
       "      <td>567</td>\n",
       "      <td>889</td>\n",
       "      <td>731</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>591.813275</td>\n",
       "      <td>207.337888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>17162</td>\n",
       "      <td>798.689430</td>\n",
       "      <td>770.005186</td>\n",
       "      <td>0.693853</td>\n",
       "      <td>720</td>\n",
       "      <td>701</td>\n",
       "      <td>886</td>\n",
       "      <td>840</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>522.617316</td>\n",
       "      <td>182.060430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>7606</td>\n",
       "      <td>870.608598</td>\n",
       "      <td>1274.302787</td>\n",
       "      <td>0.929803</td>\n",
       "      <td>786</td>\n",
       "      <td>1240</td>\n",
       "      <td>952</td>\n",
       "      <td>1300</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>395.279221</td>\n",
       "      <td>166.508258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>55287</td>\n",
       "      <td>929.110966</td>\n",
       "      <td>962.369544</td>\n",
       "      <td>0.751346</td>\n",
       "      <td>811</td>\n",
       "      <td>814</td>\n",
       "      <td>1030</td>\n",
       "      <td>1119</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>934.564538</td>\n",
       "      <td>325.098447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>25621</td>\n",
       "      <td>929.749541</td>\n",
       "      <td>63.038250</td>\n",
       "      <td>0.769402</td>\n",
       "      <td>818</td>\n",
       "      <td>0</td>\n",
       "      <td>1030</td>\n",
       "      <td>136</td>\n",
       "      <td>[[False, False, False, False, False, False, Tr...</td>\n",
       "      <td>663.693434</td>\n",
       "      <td>240.707291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>51991</td>\n",
       "      <td>929.556943</td>\n",
       "      <td>269.469274</td>\n",
       "      <td>0.674596</td>\n",
       "      <td>819</td>\n",
       "      <td>130</td>\n",
       "      <td>1030</td>\n",
       "      <td>418</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>892.943218</td>\n",
       "      <td>308.609786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>42666</td>\n",
       "      <td>941.917921</td>\n",
       "      <td>648.577486</td>\n",
       "      <td>0.905643</td>\n",
       "      <td>841</td>\n",
       "      <td>477</td>\n",
       "      <td>1030</td>\n",
       "      <td>815</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>907.488419</td>\n",
       "      <td>358.436884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>17846</td>\n",
       "      <td>979.530315</td>\n",
       "      <td>479.090104</td>\n",
       "      <td>0.771451</td>\n",
       "      <td>901</td>\n",
       "      <td>385</td>\n",
       "      <td>1030</td>\n",
       "      <td>597</td>\n",
       "      <td>[[False, False, False, False, False, False, Fa...</td>\n",
       "      <td>575.593975</td>\n",
       "      <td>212.461761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label   area  centroid-0   centroid-1  eccentricity  bbox-0  bbox-1  \\\n",
       "0       1  42573  114.372584   134.664435      0.590280       0      26   \n",
       "1       2  24120   63.611774   308.462935      0.641193       0     209   \n",
       "2       3  49842  116.046547   510.143514      0.359108       0     388   \n",
       "3       4  45595   80.636254   917.205220      0.705256       0     760   \n",
       "4       5  17997   69.825138  1241.320720      0.798332       0    1143   \n",
       "5       6  61886  186.388682  1119.213877      0.568769      11     983   \n",
       "6       7   8656  132.348082    20.894177      0.976646      13       0   \n",
       "7       8  40028  161.908689   702.735685      0.598231      29     602   \n",
       "8       9  48578  276.923628   340.470563      0.698555     135     219   \n",
       "9      10  17792  326.442952  1264.944357      0.949231     184    1209   \n",
       "10     11  43025  319.591331   906.064730      0.710462     194     789   \n",
       "11     12  47271  387.986355   226.346238      0.927667     233      89   \n",
       "12     13  23430  375.474904    40.805036      0.951484     235       0   \n",
       "13     14  52692  385.633758   527.460241      0.713008     236     411   \n",
       "14     15  53905  410.831593   745.444300      0.620854     262     622   \n",
       "15     16  60554  453.870776  1116.579466      0.506663     314     976   \n",
       "16     17  45889  543.939136   195.414152      0.802051     405      64   \n",
       "17     18  96953  684.202160  1000.089342      0.700270     487     822   \n",
       "18     19  27706  640.809428  1242.168483      0.843306     499    1162   \n",
       "19     20  37523  614.828692   731.495030      0.621346     511     610   \n",
       "20     21  38726  652.313045   530.632392      0.778509     527     439   \n",
       "21     22  55983  717.541164   360.989229      0.463724     540     215   \n",
       "22     23  46852  704.595023    90.721762      0.734250     545       0   \n",
       "23     24  21303  793.924987   649.218326      0.722032     696     567   \n",
       "24     25  17162  798.689430   770.005186      0.693853     720     701   \n",
       "25     26   7606  870.608598  1274.302787      0.929803     786    1240   \n",
       "26     27  55287  929.110966   962.369544      0.751346     811     814   \n",
       "27     28  25621  929.749541    63.038250      0.769402     818       0   \n",
       "28     29  51991  929.556943   269.469274      0.674596     819     130   \n",
       "29     30  42666  941.917921   648.577486      0.905643     841     477   \n",
       "30     31  17846  979.530315   479.090104      0.771451     901     385   \n",
       "\n",
       "    bbox-2  bbox-3                                              image  \\\n",
       "0      240     253  [[False, False, False, False, False, False, Fa...   \n",
       "1      154     422  [[False, False, False, False, False, False, Fa...   \n",
       "2      249     631  [[False, False, False, False, False, False, Fa...   \n",
       "3      210    1102  [[False, False, False, False, False, False, Fa...   \n",
       "4      180    1300  [[False, False, False, False, False, False, Fa...   \n",
       "5      334    1255  [[False, False, False, False, False, False, Fa...   \n",
       "6      235      51  [[False, False, False, False, False, False, Fa...   \n",
       "7      291     819  [[False, False, False, False, False, False, Fa...   \n",
       "8      443     454  [[False, False, False, False, False, False, Fa...   \n",
       "9      468    1300  [[False, False, False, False, False, False, Fa...   \n",
       "10     467    1017  [[False, False, False, False, False, False, Fa...   \n",
       "11     557     398  [[False, False, False, False, False, False, Fa...   \n",
       "12     534      92  [[False, False, False, False, False, False, Fa...   \n",
       "13     530     631  [[False, False, False, False, False, False, Fa...   \n",
       "14     540     890  [[False, False, False, False, False, False, Fa...   \n",
       "15     593    1273  [[False, False, False, False, False, False, Fa...   \n",
       "16     663     372  [[False, False, False, False, False, False, Fa...   \n",
       "17     844    1210  [[False, False, False, False, False, False, Fa...   \n",
       "18     765    1300  [[False, False, False, False, False, False, Fa...   \n",
       "19     727     839  [[False, False, False, False, False, False, Fa...   \n",
       "20     807     623  [[False, False, False, False, False, False, Fa...   \n",
       "21     853     487  [[False, False, False, False, False, False, Fa...   \n",
       "22     830     213  [[False, False, False, False, False, False, Fa...   \n",
       "23     889     731  [[False, False, False, False, False, False, Fa...   \n",
       "24     886     840  [[False, False, False, False, False, False, Fa...   \n",
       "25     952    1300  [[False, False, False, False, False, False, Fa...   \n",
       "26    1030    1119  [[False, False, False, False, False, False, Fa...   \n",
       "27    1030     136  [[False, False, False, False, False, False, Tr...   \n",
       "28    1030     418  [[False, False, False, False, False, False, Fa...   \n",
       "29    1030     815  [[False, False, False, False, False, False, Fa...   \n",
       "30    1030     597  [[False, False, False, False, False, False, Fa...   \n",
       "\n",
       "      perimeter  feret_diameter_max  \n",
       "0    832.315801          274.417565  \n",
       "1    644.238636          214.200840  \n",
       "2    866.748304          277.650860  \n",
       "3    951.595021          342.527371  \n",
       "4    592.132034          223.600089  \n",
       "5    983.986940          326.167135  \n",
       "6    499.787843          222.324088  \n",
       "7    802.358477          264.850901  \n",
       "8    888.246825          309.657876  \n",
       "9    668.132034          284.112654  \n",
       "10   843.293506          293.552380  \n",
       "11  1041.241774          413.175508  \n",
       "12   722.735065          299.376018  \n",
       "13   903.990945          307.358097  \n",
       "14   938.536147          313.178863  \n",
       "15   961.116882          308.768522  \n",
       "16   907.993037          323.358934  \n",
       "17  1256.283405          427.488012  \n",
       "18   703.730014          268.881015  \n",
       "19   765.369624          261.137895  \n",
       "20   801.062013          284.606746  \n",
       "21   988.063059          313.996815  \n",
       "22   886.191955          322.931881  \n",
       "23   591.813275          207.337888  \n",
       "24   522.617316          182.060430  \n",
       "25   395.279221          166.508258  \n",
       "26   934.564538          325.098447  \n",
       "27   663.693434          240.707291  \n",
       "28   892.943218          308.609786  \n",
       "29   907.488419          358.436884  \n",
       "30   575.593975          212.461761  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Based on the Cellpose Image and Stardist image we will extract the coordinates of the nuclei and muscle fibers as a table. \n",
    "# We will also extract their area in pixels, perimeter, eccentrictiy....\n",
    "from skimage.measure import regionprops_table\n",
    "import pandas as pd\n",
    "props_cellpose = regionprops_table(\n",
    "    mask_cellpose,\n",
    "    properties=[\n",
    "        \"label\",\n",
    "        \"area\",\n",
    "        \"centroid\",\n",
    "        \"eccentricity\",\n",
    "        \"bbox\",\n",
    "        \"image\",\n",
    "        \"perimeter\",\n",
    "        \"feret_diameter_max\"#ajout du diamètre\n",
    "    ],\n",
    ")\n",
    "df_cellpose = pd.DataFrame(props_cellpose)\n",
    "df_cellpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cellpose.to_csv(r'data_5_jpg.csv', index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2662827887.py (24): \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "2662827887.py (28): \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
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       "        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>label</th>\n",
       "      <th>area</th>\n",
       "      <th>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>eccentricity</th>\n",
       "      <th>bbox-0</th>\n",
       "      <th>bbox-1</th>\n",
       "      <th>bbox-2</th>\n",
       "      <th>bbox-3</th>\n",
       "      <th>image</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>feret_diameter_max</th>\n",
       "      <th>diameter_µm</th>\n",
       "      <th>State_cell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>42573</td>\n",
       "      <td>114.372584</td>\n",
       "      <td>134.664435</td>\n",
       "      <td>0.590280</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>240</td>\n",
       "      <td>253</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>832.315801</td>\n",
       "      <td>274.417565</td>\n",
       "      <td>85.618280</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>24120</td>\n",
       "      <td>63.611774</td>\n",
       "      <td>308.462935</td>\n",
       "      <td>0.641193</td>\n",
       "      <td>0</td>\n",
       "      <td>209</td>\n",
       "      <td>154</td>\n",
       "      <td>422</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>644.238636</td>\n",
       "      <td>214.200840</td>\n",
       "      <td>66.830662</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>49842</td>\n",
       "      <td>116.046547</td>\n",
       "      <td>510.143514</td>\n",
       "      <td>0.359108</td>\n",
       "      <td>0</td>\n",
       "      <td>388</td>\n",
       "      <td>249</td>\n",
       "      <td>631</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>866.748304</td>\n",
       "      <td>277.650860</td>\n",
       "      <td>86.627068</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>45595</td>\n",
       "      <td>80.636254</td>\n",
       "      <td>917.205220</td>\n",
       "      <td>0.705256</td>\n",
       "      <td>0</td>\n",
       "      <td>760</td>\n",
       "      <td>210</td>\n",
       "      <td>1102</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>951.595021</td>\n",
       "      <td>342.527371</td>\n",
       "      <td>106.868540</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>17997</td>\n",
       "      <td>69.825138</td>\n",
       "      <td>1241.320720</td>\n",
       "      <td>0.798332</td>\n",
       "      <td>0</td>\n",
       "      <td>1143</td>\n",
       "      <td>180</td>\n",
       "      <td>1300</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>592.132034</td>\n",
       "      <td>223.600089</td>\n",
       "      <td>69.763228</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>61886</td>\n",
       "      <td>186.388682</td>\n",
       "      <td>1119.213877</td>\n",
       "      <td>0.568769</td>\n",
       "      <td>11</td>\n",
       "      <td>983</td>\n",
       "      <td>334</td>\n",
       "      <td>1255</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>983.986940</td>\n",
       "      <td>326.167135</td>\n",
       "      <td>101.764146</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>8656</td>\n",
       "      <td>132.348082</td>\n",
       "      <td>20.894177</td>\n",
       "      <td>0.976646</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>51</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>499.787843</td>\n",
       "      <td>222.324088</td>\n",
       "      <td>69.365115</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>40028</td>\n",
       "      <td>161.908689</td>\n",
       "      <td>702.735685</td>\n",
       "      <td>0.598231</td>\n",
       "      <td>29</td>\n",
       "      <td>602</td>\n",
       "      <td>291</td>\n",
       "      <td>819</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>802.358477</td>\n",
       "      <td>264.850901</td>\n",
       "      <td>82.633481</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>48578</td>\n",
       "      <td>276.923628</td>\n",
       "      <td>340.470563</td>\n",
       "      <td>0.698555</td>\n",
       "      <td>135</td>\n",
       "      <td>219</td>\n",
       "      <td>443</td>\n",
       "      <td>454</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>888.246825</td>\n",
       "      <td>309.657876</td>\n",
       "      <td>96.613257</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>17792</td>\n",
       "      <td>326.442952</td>\n",
       "      <td>1264.944357</td>\n",
       "      <td>0.949231</td>\n",
       "      <td>184</td>\n",
       "      <td>1209</td>\n",
       "      <td>468</td>\n",
       "      <td>1300</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>668.132034</td>\n",
       "      <td>284.112654</td>\n",
       "      <td>88.643148</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>43025</td>\n",
       "      <td>319.591331</td>\n",
       "      <td>906.064730</td>\n",
       "      <td>0.710462</td>\n",
       "      <td>194</td>\n",
       "      <td>789</td>\n",
       "      <td>467</td>\n",
       "      <td>1017</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>843.293506</td>\n",
       "      <td>293.552380</td>\n",
       "      <td>91.588343</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>47271</td>\n",
       "      <td>387.986355</td>\n",
       "      <td>226.346238</td>\n",
       "      <td>0.927667</td>\n",
       "      <td>233</td>\n",
       "      <td>89</td>\n",
       "      <td>557</td>\n",
       "      <td>398</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>1041.241774</td>\n",
       "      <td>413.175508</td>\n",
       "      <td>128.910758</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>23430</td>\n",
       "      <td>375.474904</td>\n",
       "      <td>40.805036</td>\n",
       "      <td>0.951484</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>534</td>\n",
       "      <td>92</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>722.735065</td>\n",
       "      <td>299.376018</td>\n",
       "      <td>93.405318</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>52692</td>\n",
       "      <td>385.633758</td>\n",
       "      <td>527.460241</td>\n",
       "      <td>0.713008</td>\n",
       "      <td>236</td>\n",
       "      <td>411</td>\n",
       "      <td>530</td>\n",
       "      <td>631</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>903.990945</td>\n",
       "      <td>307.358097</td>\n",
       "      <td>95.895726</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>53905</td>\n",
       "      <td>410.831593</td>\n",
       "      <td>745.444300</td>\n",
       "      <td>0.620854</td>\n",
       "      <td>262</td>\n",
       "      <td>622</td>\n",
       "      <td>540</td>\n",
       "      <td>890</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>938.536147</td>\n",
       "      <td>313.178863</td>\n",
       "      <td>97.711805</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>60554</td>\n",
       "      <td>453.870776</td>\n",
       "      <td>1116.579466</td>\n",
       "      <td>0.506663</td>\n",
       "      <td>314</td>\n",
       "      <td>976</td>\n",
       "      <td>593</td>\n",
       "      <td>1273</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>961.116882</td>\n",
       "      <td>308.768522</td>\n",
       "      <td>96.335779</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>45889</td>\n",
       "      <td>543.939136</td>\n",
       "      <td>195.414152</td>\n",
       "      <td>0.802051</td>\n",
       "      <td>405</td>\n",
       "      <td>64</td>\n",
       "      <td>663</td>\n",
       "      <td>372</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>907.993037</td>\n",
       "      <td>323.358934</td>\n",
       "      <td>100.887987</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>96953</td>\n",
       "      <td>684.202160</td>\n",
       "      <td>1000.089342</td>\n",
       "      <td>0.700270</td>\n",
       "      <td>487</td>\n",
       "      <td>822</td>\n",
       "      <td>844</td>\n",
       "      <td>1210</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>1256.283405</td>\n",
       "      <td>427.488012</td>\n",
       "      <td>133.376260</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>27706</td>\n",
       "      <td>640.809428</td>\n",
       "      <td>1242.168483</td>\n",
       "      <td>0.843306</td>\n",
       "      <td>499</td>\n",
       "      <td>1162</td>\n",
       "      <td>765</td>\n",
       "      <td>1300</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>703.730014</td>\n",
       "      <td>268.881015</td>\n",
       "      <td>83.890877</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>37523</td>\n",
       "      <td>614.828692</td>\n",
       "      <td>731.495030</td>\n",
       "      <td>0.621346</td>\n",
       "      <td>511</td>\n",
       "      <td>610</td>\n",
       "      <td>727</td>\n",
       "      <td>839</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>765.369624</td>\n",
       "      <td>261.137895</td>\n",
       "      <td>81.475023</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>38726</td>\n",
       "      <td>652.313045</td>\n",
       "      <td>530.632392</td>\n",
       "      <td>0.778509</td>\n",
       "      <td>527</td>\n",
       "      <td>439</td>\n",
       "      <td>807</td>\n",
       "      <td>623</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>801.062013</td>\n",
       "      <td>284.606746</td>\n",
       "      <td>88.797305</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>55983</td>\n",
       "      <td>717.541164</td>\n",
       "      <td>360.989229</td>\n",
       "      <td>0.463724</td>\n",
       "      <td>540</td>\n",
       "      <td>215</td>\n",
       "      <td>853</td>\n",
       "      <td>487</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>988.063059</td>\n",
       "      <td>313.996815</td>\n",
       "      <td>97.967006</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>46852</td>\n",
       "      <td>704.595023</td>\n",
       "      <td>90.721762</td>\n",
       "      <td>0.734250</td>\n",
       "      <td>545</td>\n",
       "      <td>0</td>\n",
       "      <td>830</td>\n",
       "      <td>213</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>886.191955</td>\n",
       "      <td>322.931881</td>\n",
       "      <td>100.754747</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>21303</td>\n",
       "      <td>793.924987</td>\n",
       "      <td>649.218326</td>\n",
       "      <td>0.722032</td>\n",
       "      <td>696</td>\n",
       "      <td>567</td>\n",
       "      <td>889</td>\n",
       "      <td>731</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>591.813275</td>\n",
       "      <td>207.337888</td>\n",
       "      <td>64.689421</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>17162</td>\n",
       "      <td>798.689430</td>\n",
       "      <td>770.005186</td>\n",
       "      <td>0.693853</td>\n",
       "      <td>720</td>\n",
       "      <td>701</td>\n",
       "      <td>886</td>\n",
       "      <td>840</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>522.617316</td>\n",
       "      <td>182.060430</td>\n",
       "      <td>56.802854</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>7606</td>\n",
       "      <td>870.608598</td>\n",
       "      <td>1274.302787</td>\n",
       "      <td>0.929803</td>\n",
       "      <td>786</td>\n",
       "      <td>1240</td>\n",
       "      <td>952</td>\n",
       "      <td>1300</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>395.279221</td>\n",
       "      <td>166.508258</td>\n",
       "      <td>51.950577</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>55287</td>\n",
       "      <td>929.110966</td>\n",
       "      <td>962.369544</td>\n",
       "      <td>0.751346</td>\n",
       "      <td>811</td>\n",
       "      <td>814</td>\n",
       "      <td>1030</td>\n",
       "      <td>1119</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>934.564538</td>\n",
       "      <td>325.098447</td>\n",
       "      <td>101.430715</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>25621</td>\n",
       "      <td>929.749541</td>\n",
       "      <td>63.038250</td>\n",
       "      <td>0.769402</td>\n",
       "      <td>818</td>\n",
       "      <td>0</td>\n",
       "      <td>1030</td>\n",
       "      <td>136</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>663.693434</td>\n",
       "      <td>240.707291</td>\n",
       "      <td>75.100675</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>51991</td>\n",
       "      <td>929.556943</td>\n",
       "      <td>269.469274</td>\n",
       "      <td>0.674596</td>\n",
       "      <td>819</td>\n",
       "      <td>130</td>\n",
       "      <td>1030</td>\n",
       "      <td>418</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>892.943218</td>\n",
       "      <td>308.609786</td>\n",
       "      <td>96.286253</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>42666</td>\n",
       "      <td>941.917921</td>\n",
       "      <td>648.577486</td>\n",
       "      <td>0.905643</td>\n",
       "      <td>841</td>\n",
       "      <td>477</td>\n",
       "      <td>1030</td>\n",
       "      <td>815</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>907.488419</td>\n",
       "      <td>358.436884</td>\n",
       "      <td>111.832308</td>\n",
       "      <td>Hypertrophy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>17846</td>\n",
       "      <td>979.530315</td>\n",
       "      <td>479.090104</td>\n",
       "      <td>0.771451</td>\n",
       "      <td>901</td>\n",
       "      <td>385</td>\n",
       "      <td>1030</td>\n",
       "      <td>597</td>\n",
       "      <td>[[False False False ... False False False]\\n [...</td>\n",
       "      <td>575.593975</td>\n",
       "      <td>212.461761</td>\n",
       "      <td>66.288070</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label   area  centroid-0   centroid-1  eccentricity  bbox-0  bbox-1  \\\n",
       "0       1  42573  114.372584   134.664435      0.590280       0      26   \n",
       "1       2  24120   63.611774   308.462935      0.641193       0     209   \n",
       "2       3  49842  116.046547   510.143514      0.359108       0     388   \n",
       "3       4  45595   80.636254   917.205220      0.705256       0     760   \n",
       "4       5  17997   69.825138  1241.320720      0.798332       0    1143   \n",
       "5       6  61886  186.388682  1119.213877      0.568769      11     983   \n",
       "6       7   8656  132.348082    20.894177      0.976646      13       0   \n",
       "7       8  40028  161.908689   702.735685      0.598231      29     602   \n",
       "8       9  48578  276.923628   340.470563      0.698555     135     219   \n",
       "9      10  17792  326.442952  1264.944357      0.949231     184    1209   \n",
       "10     11  43025  319.591331   906.064730      0.710462     194     789   \n",
       "11     12  47271  387.986355   226.346238      0.927667     233      89   \n",
       "12     13  23430  375.474904    40.805036      0.951484     235       0   \n",
       "13     14  52692  385.633758   527.460241      0.713008     236     411   \n",
       "14     15  53905  410.831593   745.444300      0.620854     262     622   \n",
       "15     16  60554  453.870776  1116.579466      0.506663     314     976   \n",
       "16     17  45889  543.939136   195.414152      0.802051     405      64   \n",
       "17     18  96953  684.202160  1000.089342      0.700270     487     822   \n",
       "18     19  27706  640.809428  1242.168483      0.843306     499    1162   \n",
       "19     20  37523  614.828692   731.495030      0.621346     511     610   \n",
       "20     21  38726  652.313045   530.632392      0.778509     527     439   \n",
       "21     22  55983  717.541164   360.989229      0.463724     540     215   \n",
       "22     23  46852  704.595023    90.721762      0.734250     545       0   \n",
       "23     24  21303  793.924987   649.218326      0.722032     696     567   \n",
       "24     25  17162  798.689430   770.005186      0.693853     720     701   \n",
       "25     26   7606  870.608598  1274.302787      0.929803     786    1240   \n",
       "26     27  55287  929.110966   962.369544      0.751346     811     814   \n",
       "27     28  25621  929.749541    63.038250      0.769402     818       0   \n",
       "28     29  51991  929.556943   269.469274      0.674596     819     130   \n",
       "29     30  42666  941.917921   648.577486      0.905643     841     477   \n",
       "30     31  17846  979.530315   479.090104      0.771451     901     385   \n",
       "\n",
       "    bbox-2  bbox-3                                              image  \\\n",
       "0      240     253  [[False False False ... False False False]\\n [...   \n",
       "1      154     422  [[False False False ... False False False]\\n [...   \n",
       "2      249     631  [[False False False ... False False False]\\n [...   \n",
       "3      210    1102  [[False False False ... False False False]\\n [...   \n",
       "4      180    1300  [[False False False ... False False False]\\n [...   \n",
       "5      334    1255  [[False False False ... False False False]\\n [...   \n",
       "6      235      51  [[False False False ... False False False]\\n [...   \n",
       "7      291     819  [[False False False ... False False False]\\n [...   \n",
       "8      443     454  [[False False False ... False False False]\\n [...   \n",
       "9      468    1300  [[False False False ... False False False]\\n [...   \n",
       "10     467    1017  [[False False False ... False False False]\\n [...   \n",
       "11     557     398  [[False False False ... False False False]\\n [...   \n",
       "12     534      92  [[False False False ... False False False]\\n [...   \n",
       "13     530     631  [[False False False ... False False False]\\n [...   \n",
       "14     540     890  [[False False False ... False False False]\\n [...   \n",
       "15     593    1273  [[False False False ... False False False]\\n [...   \n",
       "16     663     372  [[False False False ... False False False]\\n [...   \n",
       "17     844    1210  [[False False False ... False False False]\\n [...   \n",
       "18     765    1300  [[False False False ... False False False]\\n [...   \n",
       "19     727     839  [[False False False ... False False False]\\n [...   \n",
       "20     807     623  [[False False False ... False False False]\\n [...   \n",
       "21     853     487  [[False False False ... False False False]\\n [...   \n",
       "22     830     213  [[False False False ... False False False]\\n [...   \n",
       "23     889     731  [[False False False ... False False False]\\n [...   \n",
       "24     886     840  [[False False False ... False False False]\\n [...   \n",
       "25     952    1300  [[False False False ... False False False]\\n [...   \n",
       "26    1030    1119  [[False False False ... False False False]\\n [...   \n",
       "27    1030     136  [[False False False ... False False False]\\n [...   \n",
       "28    1030     418  [[False False False ... False False False]\\n [...   \n",
       "29    1030     815  [[False False False ... False False False]\\n [...   \n",
       "30    1030     597  [[False False False ... False False False]\\n [...   \n",
       "\n",
       "      perimeter  feret_diameter_max  diameter_µm   State_cell  \n",
       "0    832.315801          274.417565    85.618280  Hypertrophy  \n",
       "1    644.238636          214.200840    66.830662       Normal  \n",
       "2    866.748304          277.650860    86.627068  Hypertrophy  \n",
       "3    951.595021          342.527371   106.868540  Hypertrophy  \n",
       "4    592.132034          223.600089    69.763228       Normal  \n",
       "5    983.986940          326.167135   101.764146  Hypertrophy  \n",
       "6    499.787843          222.324088    69.365115       Normal  \n",
       "7    802.358477          264.850901    82.633481  Hypertrophy  \n",
       "8    888.246825          309.657876    96.613257  Hypertrophy  \n",
       "9    668.132034          284.112654    88.643148  Hypertrophy  \n",
       "10   843.293506          293.552380    91.588343  Hypertrophy  \n",
       "11  1041.241774          413.175508   128.910758  Hypertrophy  \n",
       "12   722.735065          299.376018    93.405318  Hypertrophy  \n",
       "13   903.990945          307.358097    95.895726  Hypertrophy  \n",
       "14   938.536147          313.178863    97.711805  Hypertrophy  \n",
       "15   961.116882          308.768522    96.335779  Hypertrophy  \n",
       "16   907.993037          323.358934   100.887987  Hypertrophy  \n",
       "17  1256.283405          427.488012   133.376260  Hypertrophy  \n",
       "18   703.730014          268.881015    83.890877  Hypertrophy  \n",
       "19   765.369624          261.137895    81.475023  Hypertrophy  \n",
       "20   801.062013          284.606746    88.797305  Hypertrophy  \n",
       "21   988.063059          313.996815    97.967006  Hypertrophy  \n",
       "22   886.191955          322.931881   100.754747  Hypertrophy  \n",
       "23   591.813275          207.337888    64.689421       Normal  \n",
       "24   522.617316          182.060430    56.802854       Normal  \n",
       "25   395.279221          166.508258    51.950577       Normal  \n",
       "26   934.564538          325.098447   101.430715  Hypertrophy  \n",
       "27   663.693434          240.707291    75.100675       Normal  \n",
       "28   892.943218          308.609786    96.286253  Hypertrophy  \n",
       "29   907.488419          358.436884   111.832308  Hypertrophy  \n",
       "30   575.593975          212.461761    66.288070       Normal  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "#on importe les fichiers csv nécessaire \n",
    "df=pd.read_csv('muscle_atlas_clean.csv') #atlas du muscle\n",
    "df_cell=pd.read_csv('data_5_jpg.csv') # fichier info image 5 (taille, diamètre, centre...)\n",
    "\n",
    "\n",
    "\n",
    "def conversion_pix_µm(im, data_frame,muscle_atlas):\n",
    "    'Convertion du diamètre en µm'\n",
    "    barre_echelle= muscle_atlas.loc[(df['Number']== im), 'Calibration (um/px)']\n",
    "    \n",
    "    data_frame['diameter_µm']=(data_frame['feret_diameter_max'])*barre_echelle.values\n",
    "\n",
    "conversion_pix_µm('5.jpg',df_cell,df )\n",
    "\n",
    "def count_atrophic_cells (data_frame,min_size, max_size):\n",
    "    hyper='Hypertrophy'\n",
    "    hypo= 'Hypotrophy'\n",
    "    norm= 'Normal'\n",
    "    data_frame[\"State_cell\"] = ''\n",
    "    for index in data_frame.index:\n",
    "        a=data_frame['diameter_µm'][index]\n",
    "        if a > max_size:\n",
    "            data_frame[\"State_cell\"][index]=hyper\n",
    "        elif a < min_size:\n",
    "            data_frame[\"State_cell\"][index]=hypo\n",
    "        else: \n",
    "            data_frame[\"State_cell\"][index]= norm\n",
    "\n",
    "\n",
    "count_atrophic_cells (df_cell, 30, 80)\n",
    "\n",
    "df_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculer % de cellules normales, hypertrophiées et hypotrophiées et ensuite les rajouter dans l'atlas du muscle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hypertrophy    23\n",
      "Normal          8\n",
      "Name: State_cell, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#test\n",
    "#print(df_cell['State_cell'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "        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>Hypertrophy</th>\n",
       "      <th>Normal</th>\n",
       "      <th>Hypotrophy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>State_cell</th>\n",
       "      <td>74.193548</td>\n",
       "      <td>25.806452</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Hypertrophy     Normal  Hypotrophy\n",
       "State_cell    74.193548  25.806452           0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test \n",
    "####percentage= (df_cell['State_cell'].value_counts()/df_cell['State_cell'].count())*100\n",
    "###serie_to_df= pd.Series.to_frame(percentage).transpose()\n",
    "##serie_to_df['Hypotrophy']=0\n",
    "#serie_to_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pourcentage_state_cell (im,data_frame,muscle_atlas):\n",
    "     data = {'Number':[],'Hypertrophy': [],'Normal':[],'Hypotrophy': []}  # Ici on créer un dataframe pour mettre ensuite les valeurs des pourcentages\n",
    "     df_state_cell = pd.DataFrame(data)  \n",
    "     percentage= (data_frame['State_cell'].value_counts()/data_frame['State_cell'].count())*100\n",
    "     df_state_cell.loc[len(df_state_cell.index)] = percentage\n",
    "\n",
    "     \n",
    "     muscle_atlas.loc[(muscle_atlas['Number']== im),\"Hypertrophic_cell_%\"]=df_state_cell['Hypertrophy'].values\n",
    "     muscle_atlas.loc[(muscle_atlas['Number']== im),\"Normal_cell_%\"]=df_state_cell['Normal'].values\n",
    "     muscle_atlas.loc[(muscle_atlas['Number']== im),\"Hypotrophic_cell_%\"]=df_state_cell['Hypotrophy'].values\n",
    "\n",
    "\n",
    "pourcentage_state_cell ('5.jpg',df_cell,df)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ajout diamètre moyen des cellules, écart-type \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89.3582171790665\n"
     ]
    }
   ],
   "source": [
    "#print(df_cell['diameter_µm'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.69706987733848\n"
     ]
    }
   ],
   "source": [
    "#print(df_cell['diameter_µm'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>label</th>\n",
       "      <th>area</th>\n",
       "      <th>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>eccentricity</th>\n",
       "      <th>bbox-0</th>\n",
       "      <th>bbox-1</th>\n",
       "      <th>bbox-2</th>\n",
       "      <th>bbox-3</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>feret_diameter_max</th>\n",
       "      <th>diameter_µm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>39582.516129</td>\n",
       "      <td>503.006787</td>\n",
       "      <td>625.494737</td>\n",
       "      <td>0.725075</td>\n",
       "      <td>379.096774</td>\n",
       "      <td>517.806452</td>\n",
       "      <td>625.516129</td>\n",
       "      <td>738.322581</td>\n",
       "      <td>804.294927</td>\n",
       "      <td>286.404542</td>\n",
       "      <td>89.358217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.092121</td>\n",
       "      <td>18929.425332</td>\n",
       "      <td>307.216816</td>\n",
       "      <td>402.999155</td>\n",
       "      <td>0.147904</td>\n",
       "      <td>310.006597</td>\n",
       "      <td>399.628529</td>\n",
       "      <td>295.203305</td>\n",
       "      <td>402.716972</td>\n",
       "      <td>185.381443</td>\n",
       "      <td>59.926506</td>\n",
       "      <td>18.697070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>7606.000000</td>\n",
       "      <td>63.611774</td>\n",
       "      <td>20.894177</td>\n",
       "      <td>0.359108</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>395.279221</td>\n",
       "      <td>166.508258</td>\n",
       "      <td>51.950577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>23775.000000</td>\n",
       "      <td>231.656155</td>\n",
       "      <td>288.966104</td>\n",
       "      <td>0.631269</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>169.500000</td>\n",
       "      <td>388.500000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>665.912734</td>\n",
       "      <td>250.922593</td>\n",
       "      <td>78.287849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>42666.000000</td>\n",
       "      <td>453.870776</td>\n",
       "      <td>648.577486</td>\n",
       "      <td>0.713008</td>\n",
       "      <td>314.000000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>843.293506</td>\n",
       "      <td>293.552380</td>\n",
       "      <td>91.588343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.500000</td>\n",
       "      <td>50916.500000</td>\n",
       "      <td>755.733076</td>\n",
       "      <td>939.787382</td>\n",
       "      <td>0.800191</td>\n",
       "      <td>620.500000</td>\n",
       "      <td>801.500000</td>\n",
       "      <td>869.500000</td>\n",
       "      <td>1110.500000</td>\n",
       "      <td>921.278787</td>\n",
       "      <td>318.464348</td>\n",
       "      <td>99.360877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>96953.000000</td>\n",
       "      <td>979.530315</td>\n",
       "      <td>1274.302787</td>\n",
       "      <td>0.976646</td>\n",
       "      <td>901.000000</td>\n",
       "      <td>1240.000000</td>\n",
       "      <td>1030.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1256.283405</td>\n",
       "      <td>427.488012</td>\n",
       "      <td>133.376260</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           label          area  centroid-0   centroid-1  eccentricity  \\\n",
       "count  31.000000     31.000000   31.000000    31.000000     31.000000   \n",
       "mean   16.000000  39582.516129  503.006787   625.494737      0.725075   \n",
       "std     9.092121  18929.425332  307.216816   402.999155      0.147904   \n",
       "min     1.000000   7606.000000   63.611774    20.894177      0.359108   \n",
       "25%     8.500000  23775.000000  231.656155   288.966104      0.631269   \n",
       "50%    16.000000  42666.000000  453.870776   648.577486      0.713008   \n",
       "75%    23.500000  50916.500000  755.733076   939.787382      0.800191   \n",
       "max    31.000000  96953.000000  979.530315  1274.302787      0.976646   \n",
       "\n",
       "           bbox-0       bbox-1       bbox-2       bbox-3    perimeter  \\\n",
       "count   31.000000    31.000000    31.000000    31.000000    31.000000   \n",
       "mean   379.096774   517.806452   625.516129   738.322581   804.294927   \n",
       "std    310.006597   399.628529   295.203305   402.716972   185.381443   \n",
       "min      0.000000     0.000000   154.000000    51.000000   395.279221   \n",
       "25%     82.000000   169.500000   388.500000   420.000000   665.912734   \n",
       "50%    314.000000   477.000000   593.000000   731.000000   843.293506   \n",
       "75%    620.500000   801.500000   869.500000  1110.500000   921.278787   \n",
       "max    901.000000  1240.000000  1030.000000  1300.000000  1256.283405   \n",
       "\n",
       "       feret_diameter_max  diameter_µm  \n",
       "count           31.000000    31.000000  \n",
       "mean           286.404542    89.358217  \n",
       "std             59.926506    18.697070  \n",
       "min            166.508258    51.950577  \n",
       "25%            250.922593    78.287849  \n",
       "50%            293.552380    91.588343  \n",
       "75%            318.464348    99.360877  \n",
       "max            427.488012   133.376260  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cell.describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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>ordered</th>\n",
       "      <th>Number</th>\n",
       "      <th>File name</th>\n",
       "      <th>Organism</th>\n",
       "      <th>muscle</th>\n",
       "      <th>Disease Group</th>\n",
       "      <th>Diag.Final</th>\n",
       "      <th>GENE</th>\n",
       "      <th>Staining method</th>\n",
       "      <th>Age</th>\n",
       "      <th>...</th>\n",
       "      <th>OMIM</th>\n",
       "      <th>Mutated gene</th>\n",
       "      <th>Excluded Genes</th>\n",
       "      <th>Publication</th>\n",
       "      <th>Link</th>\n",
       "      <th>Hypertrophic_cell_%</th>\n",
       "      <th>Normal_cell_%</th>\n",
       "      <th>Hypotrophic_cell_%</th>\n",
       "      <th>diameter_mean</th>\n",
       "      <th>diameter_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>5.jpg</td>\n",
       "      <td>5 10199 HE-20.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>deltoid</td>\n",
       "      <td>Muscular Dystrophies</td>\n",
       "      <td>Muscular Dystrophy alpha dystroglycanopathy</td>\n",
       "      <td>FKRP</td>\n",
       "      <td>HE</td>\n",
       "      <td>38y</td>\n",
       "      <td>...</td>\n",
       "      <td>607155</td>\n",
       "      <td>FKRP mutation homo Leu276Ile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.193548</td>\n",
       "      <td>25.806452</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.358217</td>\n",
       "      <td>18.69707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>6.jpg</td>\n",
       "      <td>6 10199 SDH-20.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>deltoid</td>\n",
       "      <td>Muscular Dystrophies</td>\n",
       "      <td>Muscular Dystrophy alpha dystroglycanopathy</td>\n",
       "      <td>FKRP</td>\n",
       "      <td>SDH</td>\n",
       "      <td>38y</td>\n",
       "      <td>...</td>\n",
       "      <td>607155</td>\n",
       "      <td>FKRP mutation homo Leu276Ile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>13.jpg</td>\n",
       "      <td>13 10202 HE-20.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>deltoid</td>\n",
       "      <td>Muscular Dystrophies</td>\n",
       "      <td>Muscular Dystrophy alpha dystroglycanopathy</td>\n",
       "      <td>FKRP</td>\n",
       "      <td>HE</td>\n",
       "      <td>39y</td>\n",
       "      <td>...</td>\n",
       "      <td>607155</td>\n",
       "      <td>FKRP mutation homo Leu276Ile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>14.jpg</td>\n",
       "      <td>14 10202 SDH-20.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>deltoid</td>\n",
       "      <td>Muscular Dystrophies</td>\n",
       "      <td>Muscular Dystrophy alpha dystroglycanopathy</td>\n",
       "      <td>FKRP</td>\n",
       "      <td>SDH</td>\n",
       "      <td>39y</td>\n",
       "      <td>...</td>\n",
       "      <td>607155</td>\n",
       "      <td>FKRP mutation homo Leu276Ile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>21.jpg</td>\n",
       "      <td>21 10474 HE-20.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>quadriceps</td>\n",
       "      <td>Congenital Myopathies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ASCC1</td>\n",
       "      <td>HE</td>\n",
       "      <td>12 days</td>\n",
       "      <td>...</td>\n",
       "      <td>616867</td>\n",
       "      <td>ASCC1 mutation homo c.157dupG</td>\n",
       "      <td>ACTA1 CFL2 NEB RYR1 SMN1 TNNT1 TPM2 TPM3 RAS</td>\n",
       "      <td>J Med Genet. 2019; 56(9):617-621</td>\n",
       "      <td>https://jmg.bmj.com/content/56/9/617.long</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>5456</td>\n",
       "      <td>5456.jpg</td>\n",
       "      <td>9825 HE-40.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>deltoid</td>\n",
       "      <td>Congenital Myopathies</td>\n",
       "      <td>Congenital Myopathy Nemaline</td>\n",
       "      <td>NEB</td>\n",
       "      <td>HE</td>\n",
       "      <td>3y</td>\n",
       "      <td>...</td>\n",
       "      <td>OMIM: 256030</td>\n",
       "      <td>NEB mutations c.6937C&gt;T (p.Arg2313*) and c.377...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>5458</td>\n",
       "      <td>5458.jpg</td>\n",
       "      <td>9825 SDH-20.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>deltoid</td>\n",
       "      <td>Congenital Myopathies</td>\n",
       "      <td>Congenital Myopathy Nemaline</td>\n",
       "      <td>NEB</td>\n",
       "      <td>SDH</td>\n",
       "      <td>3y</td>\n",
       "      <td>...</td>\n",
       "      <td>OMIM: 256030</td>\n",
       "      <td>NEB mutations c.6937C&gt;T (p.Arg2313*) and c.377...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>5472</td>\n",
       "      <td>5472.jpg</td>\n",
       "      <td>9948 HE-20a.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>deltoid</td>\n",
       "      <td>Muscular Dystrophies</td>\n",
       "      <td>Calpainopathy (AR)</td>\n",
       "      <td>CAPN3</td>\n",
       "      <td>HE</td>\n",
       "      <td>16y</td>\n",
       "      <td>...</td>\n",
       "      <td>OMIM: 253600</td>\n",
       "      <td>CAPN3 mutations exon 19 c.2105C&gt;T (p.Ala702Val...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>5473</td>\n",
       "      <td>5473.jpg</td>\n",
       "      <td>9948 HE-20b.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>deltoid</td>\n",
       "      <td>Muscular Dystrophies</td>\n",
       "      <td>Calpainopathy (AR)</td>\n",
       "      <td>CAPN3</td>\n",
       "      <td>HE</td>\n",
       "      <td>16y</td>\n",
       "      <td>...</td>\n",
       "      <td>OMIM: 253600</td>\n",
       "      <td>CAPN3 mutations exon 19 c.2105C&gt;T (p.Ala702Val...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>5475</td>\n",
       "      <td>5475.jpg</td>\n",
       "      <td>9948 SDH-20.jpg</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>deltoid</td>\n",
       "      <td>Muscular Dystrophies</td>\n",
       "      <td>Calpainopathy (AR)</td>\n",
       "      <td>CAPN3</td>\n",
       "      <td>SDH</td>\n",
       "      <td>16y</td>\n",
       "      <td>...</td>\n",
       "      <td>OMIM: 253600</td>\n",
       "      <td>CAPN3 mutations exon 19 c.2105C&gt;T (p.Ala702Val...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1124 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ordered    Number            File name      Organism      muscle  \\\n",
       "0           5     5.jpg    5 10199 HE-20.jpg  Homo sapiens     deltoid   \n",
       "1           6     6.jpg   6 10199 SDH-20.jpg  Homo sapiens     deltoid   \n",
       "2          13    13.jpg   13 10202 HE-20.jpg  Homo sapiens     deltoid   \n",
       "3          14    14.jpg  14 10202 SDH-20.jpg  Homo sapiens     deltoid   \n",
       "4          21    21.jpg   21 10474 HE-20.jpg  Homo sapiens  quadriceps   \n",
       "...       ...       ...                  ...           ...         ...   \n",
       "1119     5456  5456.jpg       9825 HE-40.jpg  Homo sapiens     deltoid   \n",
       "1120     5458  5458.jpg      9825 SDH-20.jpg  Homo sapiens     deltoid   \n",
       "1121     5472  5472.jpg      9948 HE-20a.jpg  Homo sapiens     deltoid   \n",
       "1122     5473  5473.jpg      9948 HE-20b.jpg  Homo sapiens     deltoid   \n",
       "1123     5475  5475.jpg      9948 SDH-20.jpg  Homo sapiens     deltoid   \n",
       "\n",
       "              Disease Group                                   Diag.Final  \\\n",
       "0      Muscular Dystrophies  Muscular Dystrophy alpha dystroglycanopathy   \n",
       "1      Muscular Dystrophies  Muscular Dystrophy alpha dystroglycanopathy   \n",
       "2      Muscular Dystrophies  Muscular Dystrophy alpha dystroglycanopathy   \n",
       "3      Muscular Dystrophies  Muscular Dystrophy alpha dystroglycanopathy   \n",
       "4     Congenital Myopathies                                          NaN   \n",
       "...                     ...                                          ...   \n",
       "1119  Congenital Myopathies                 Congenital Myopathy Nemaline   \n",
       "1120  Congenital Myopathies                 Congenital Myopathy Nemaline   \n",
       "1121   Muscular Dystrophies                           Calpainopathy (AR)   \n",
       "1122   Muscular Dystrophies                           Calpainopathy (AR)   \n",
       "1123   Muscular Dystrophies                           Calpainopathy (AR)   \n",
       "\n",
       "       GENE Staining method      Age  ...           OMIM  \\\n",
       "0      FKRP              HE      38y  ...         607155   \n",
       "1      FKRP             SDH      38y  ...         607155   \n",
       "2      FKRP              HE      39y  ...         607155   \n",
       "3      FKRP             SDH      39y  ...         607155   \n",
       "4     ASCC1              HE  12 days  ...         616867   \n",
       "...     ...             ...      ...  ...            ...   \n",
       "1119    NEB              HE       3y  ...  OMIM: 256030    \n",
       "1120    NEB             SDH       3y  ...  OMIM: 256030    \n",
       "1121  CAPN3              HE      16y  ...  OMIM: 253600    \n",
       "1122  CAPN3              HE      16y  ...  OMIM: 253600    \n",
       "1123  CAPN3             SDH      16y  ...  OMIM: 253600    \n",
       "\n",
       "                                           Mutated gene  \\\n",
       "0                          FKRP mutation homo Leu276Ile   \n",
       "1                          FKRP mutation homo Leu276Ile   \n",
       "2                          FKRP mutation homo Leu276Ile   \n",
       "3                          FKRP mutation homo Leu276Ile   \n",
       "4                         ASCC1 mutation homo c.157dupG   \n",
       "...                                                 ...   \n",
       "1119  NEB mutations c.6937C>T (p.Arg2313*) and c.377...   \n",
       "1120  NEB mutations c.6937C>T (p.Arg2313*) and c.377...   \n",
       "1121  CAPN3 mutations exon 19 c.2105C>T (p.Ala702Val...   \n",
       "1122  CAPN3 mutations exon 19 c.2105C>T (p.Ala702Val...   \n",
       "1123  CAPN3 mutations exon 19 c.2105C>T (p.Ala702Val...   \n",
       "\n",
       "                                    Excluded Genes  \\\n",
       "0                                              NaN   \n",
       "1                                              NaN   \n",
       "2                                              NaN   \n",
       "3                                              NaN   \n",
       "4     ACTA1 CFL2 NEB RYR1 SMN1 TNNT1 TPM2 TPM3 RAS   \n",
       "...                                            ...   \n",
       "1119                                           NaN   \n",
       "1120                                           NaN   \n",
       "1121                                           NaN   \n",
       "1122                                           NaN   \n",
       "1123                                           NaN   \n",
       "\n",
       "                           Publication  \\\n",
       "0                                  NaN   \n",
       "1                                  NaN   \n",
       "2                                  NaN   \n",
       "3                                  NaN   \n",
       "4     J Med Genet. 2019; 56(9):617-621   \n",
       "...                                ...   \n",
       "1119                               NaN   \n",
       "1120                               NaN   \n",
       "1121                               NaN   \n",
       "1122                               NaN   \n",
       "1123                               NaN   \n",
       "\n",
       "                                           Link Hypertrophic_cell_%  \\\n",
       "0                                           NaN           74.193548   \n",
       "1                                           NaN                 NaN   \n",
       "2                                           NaN                 NaN   \n",
       "3                                           NaN                 NaN   \n",
       "4     https://jmg.bmj.com/content/56/9/617.long                 NaN   \n",
       "...                                         ...                 ...   \n",
       "1119                                        NaN                 NaN   \n",
       "1120                                        NaN                 NaN   \n",
       "1121                                        NaN                 NaN   \n",
       "1122                                        NaN                 NaN   \n",
       "1123                                        NaN                 NaN   \n",
       "\n",
       "     Normal_cell_% Hypotrophic_cell_% diameter_mean diameter_std  \n",
       "0        25.806452                NaN     89.358217     18.69707  \n",
       "1              NaN                NaN           NaN          NaN  \n",
       "2              NaN                NaN           NaN          NaN  \n",
       "3              NaN                NaN           NaN          NaN  \n",
       "4              NaN                NaN           NaN          NaN  \n",
       "...            ...                ...           ...          ...  \n",
       "1119           NaN                NaN           NaN          NaN  \n",
       "1120           NaN                NaN           NaN          NaN  \n",
       "1121           NaN                NaN           NaN          NaN  \n",
       "1122           NaN                NaN           NaN          NaN  \n",
       "1123           NaN                NaN           NaN          NaN  \n",
       "\n",
       "[1124 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def diameter_mean_std (im,data_frame,muscle_atlas):\n",
    "    cell_mean=data_frame['diameter_µm'].mean()\n",
    "    cell_std=data_frame['diameter_µm'].std()\n",
    "    muscle_atlas.loc[(muscle_atlas['Number']== im),\"diameter_mean\"]=cell_mean\n",
    "    muscle_atlas.loc[(muscle_atlas['Number']== im),\"diameter_std\"]=cell_std\n",
    "\n",
    "\n",
    "diameter_mean_std('5.jpg',df_cell,df)   \n",
    "df"
   ]
  },
  {
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