example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\_core\\anndata.py:522: FutureWarning: The dtype argument is deprecated and will be removed in late 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\pl\\_utils.py:556: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  row_labels = adata.obs[key][row_order]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import squidpy as sq\n",
    "from nhood_enrichment import nhood_enrichment\n",
    "# Load example data from Squidpy\n",
    "adata = sq.datasets.seqfish()\n",
    "\n",
    "# Create the graph\n",
    "sq.gr.spatial_neighbors(adata)\n",
    "\n",
    "# Run the enrichment\n",
    "nhood_enrichment(adata, cluster_key='celltype_mapped_refined')\n",
    "\n",
    "# Plot\n",
    "sq.pl.nhood_enrichment(adata, cluster_key='celltype_mapped_refined')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reproducing the correlation scores in the pre-print"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\gr\\_utils.py:198: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.\n",
      "  obj[key] = data\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\gr\\_nhood.py:188: RuntimeWarning: invalid value encountered in divide\n",
      "  zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\gr\\_utils.py:198: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.\n",
      "  obj[key] = data\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 106\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;66;03m# Compute zscores\u001b[39;00m\n\u001b[0;32m    105\u001b[0m zscore_analytical, _ \u001b[38;5;241m=\u001b[39m nhood_enrichment(adata, cluster_key\u001b[38;5;241m=\u001b[39mcluster_key, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 106\u001b[0m zscore_squidpy, _ \u001b[38;5;241m=\u001b[39m \u001b[43msq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnhood_enrichment\u001b[49m\u001b[43m(\u001b[49m\u001b[43madata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcluster_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcluster_key\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshow_progress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m    109\u001b[0m zscore_squidpy[np\u001b[38;5;241m.\u001b[39misnan(zscore_squidpy)] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m    110\u001b[0m p, s \u001b[38;5;241m=\u001b[39m pearson(zscore_analytical, zscore_squidpy), spearman(zscore_analytical, zscore_squidpy)\n",
      "File \u001b[1;32mc:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\gr\\_nhood.py:180\u001b[0m, in \u001b[0;36mnhood_enrichment\u001b[1;34m(adata, cluster_key, connectivity_key, n_perms, numba_parallel, seed, copy, n_jobs, backend, show_progress_bar)\u001b[0m\n\u001b[0;32m    177\u001b[0m n_jobs \u001b[38;5;241m=\u001b[39m _get_n_cores(n_jobs)\n\u001b[0;32m    178\u001b[0m start \u001b[38;5;241m=\u001b[39m logg\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCalculating neighborhood enrichment using `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_jobs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` core(s)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 180\u001b[0m perms \u001b[38;5;241m=\u001b[39m \u001b[43mparallelize\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    181\u001b[0m \u001b[43m    \u001b[49m\u001b[43m_nhood_enrichment_helper\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    182\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcollection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_perms\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtolist\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    183\u001b[0m \u001b[43m    \u001b[49m\u001b[43mextractor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvstack\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    184\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    185\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    186\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshow_progress_bar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_progress_bar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    187\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcallback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindptr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindptr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mint_clust\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mint_clust\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_cls\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    188\u001b[0m zscore \u001b[38;5;241m=\u001b[39m (count \u001b[38;5;241m-\u001b[39m perms\u001b[38;5;241m.\u001b[39mmean(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)) \u001b[38;5;241m/\u001b[39m perms\u001b[38;5;241m.\u001b[39mstd(axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m    190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m copy:\n",
      "File \u001b[1;32mc:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\_utils.py:178\u001b[0m, in \u001b[0;36mparallelize.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    176\u001b[0m     pbar, queue, thread \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m--> 178\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mjl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbackend\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    179\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrunner\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43muse_runner\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mcallback\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    180\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43muse_ixs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mcs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    181\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    182\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    183\u001b[0m \u001b[43m        \u001b[49m\u001b[43mqueue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mqueue\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    184\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    185\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcollections\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    186\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m thread \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    189\u001b[0m     thread\u001b[38;5;241m.\u001b[39mjoin()\n",
      "File \u001b[1;32mc:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\joblib\\parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1861\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[0;32m   1862\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[1;32m-> 1863\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1865\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[0;32m   1866\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[0;32m   1867\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[0;32m   1868\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[0;32m   1869\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[0;32m   1870\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n",
      "File \u001b[1;32mc:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\joblib\\parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1790\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1791\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m-> 1792\u001b[0m res \u001b[38;5;241m=\u001b[39m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1793\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   1794\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n",
      "File \u001b[1;32mc:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\gr\\_nhood.py:409\u001b[0m, in \u001b[0;36m_nhood_enrichment_helper\u001b[1;34m(ixs, callback, indices, indptr, int_clust, n_cls, seed, queue)\u001b[0m\n\u001b[0;32m    407\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(ixs)):\n\u001b[0;32m    408\u001b[0m     rs\u001b[38;5;241m.\u001b[39mshuffle(int_clust)\n\u001b[1;32m--> 409\u001b[0m     perms[i, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mcallback\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindptr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mint_clust\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    411\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m queue \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    412\u001b[0m         queue\u001b[38;5;241m.\u001b[39mput(Signal\u001b[38;5;241m.\u001b[39mUPDATE)\n",
      "File \u001b[1;32mc:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\numba\\core\\serialize.py:30\u001b[0m, in \u001b[0;36m_numba_unpickle\u001b[1;34m(address, bytedata, hashed)\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;66;03m# Keep unpickled object via `numba_unpickle` alive.\u001b[39;00m\n\u001b[0;32m     27\u001b[0m _unpickled_memo \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m---> 30\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_numba_unpickle\u001b[39m(address, bytedata, hashed):\n\u001b[0;32m     31\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Used by `numba_unpickle` from _helperlib.c\u001b[39;00m\n\u001b[0;32m     32\u001b[0m \n\u001b[0;32m     33\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[38;5;124;03m        unpickled object\u001b[39;00m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m     44\u001b[0m     key \u001b[38;5;241m=\u001b[39m (address, hashed)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import anndata\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "\n",
    "def load_osmFISH_data():\n",
    "    csv = pd.read_csv(r'C:\\Users\\axela\\Documents\\GitHub\\Points2Region_clean\\datasets\\osmFISH\\osmFISH_real.csv')\n",
    "    xy = csv[['X','Y']].to_numpy()\n",
    "    adata = anndata.AnnData(obs={'Gene' : csv['Gene'].to_list()}, obsm={'spatial' : xy})\n",
    "    adata.obs['Gene'] = adata.obs['Gene'].astype('category')\n",
    "    return adata\n",
    "\n",
    "def load_merfish_data():\n",
    "    csv = pd.read_csv(r'C:\\Users\\axela\\Documents\\GitHub\\Points2Region_clean\\datasets\\MERFISH\\data.csv')\n",
    "    xy = csv[['X','Y']].to_numpy()\n",
    "    adata = anndata.AnnData(obs={'Gene' : csv['Gene'].to_list()}, obsm={'spatial' : xy})\n",
    "    adata.obs['Gene'] = adata.obs['Gene'].astype('category')\n",
    "    return adata  \n",
    "\n",
    "datasets = [\n",
    "    {\n",
    "        'name' : 'Seqfish',\n",
    "        'adata' : sq.datasets.seqfish(),\n",
    "        'key' : 'celltype_mapped_refined',\n",
    "        'library_key' : None\n",
    "    },\n",
    "    {\n",
    "        'name' : 'IMC',\n",
    "        'adata' : sq.datasets.imc(),\n",
    "        'key' : 'cell type',\n",
    "        'library_key' : None\n",
    "\n",
    "    },\n",
    "    {\n",
    "        'name' : 'MERFISH',\n",
    "        'adata' : sq.datasets.merfish(),\n",
    "        'key' : 'Cell_class',\n",
    "        'library_key' : 'batch'\n",
    "    },\n",
    "    {\n",
    "        'name' : 'MIBI-TOF',\n",
    "        'adata' : sq.datasets.mibitof(),\n",
    "        'key' : 'Cluster',\n",
    "        'library_key' : 'batch'\n",
    "    },\n",
    "    {\n",
    "        'name' : 'Visium',\n",
    "        'adata' : sq.datasets.visium_fluo_adata(),\n",
    "        'key' : 'cluster',\n",
    "        'library_key' : None\n",
    "    },\n",
    "\n",
    "    {\n",
    "        'name' : '4i',\n",
    "        'adata' : sq.datasets.visium_fluo_adata(),\n",
    "        'key' : 'leiden',\n",
    "        'library_key' : None\n",
    "    },\n",
    "\n",
    "    {\n",
    "        'name' : 'osmFISH dataset (Genes)',\n",
    "        'adata' : load_osmFISH_data(),\n",
    "        'key' : 'Gene',\n",
    "        'library_key' : None,\n",
    "        'output_name' : 'osmFISH'\n",
    "    },\n",
    "    {\n",
    "        'name' : 'MERFISH dataset (Genes)',\n",
    "        'adata' : load_merfish_data(),\n",
    "        'key' : 'Gene',\n",
    "        'library_key' : None,\n",
    "        'output_name' : 'merfish_genes'\n",
    "    }\n",
    "]\n",
    "\n",
    "def pearson(a: np.ndarray, b: np.ndarray) -> float:\n",
    "    return pearsonr(a.flatten(), b.flatten()).statistic\n",
    "\n",
    "def spearman(a: np.ndarray, b: np.ndarray) -> float:\n",
    "    p, _ = spearmanr(a.flatten(), b.flatten())\n",
    "    return p\n",
    "\n",
    "results = {\n",
    "    'Dataset' : [],\n",
    "    'Pearson' : [],\n",
    "    'Spearman' : []\n",
    "} \n",
    "\n",
    "for dataset in datasets:\n",
    "    name = dataset['name']\n",
    "    adata = dataset['adata']\n",
    "    library_key = dataset['library_key']\n",
    "    cluster_key = dataset['key']\n",
    "\n",
    "    # Incase several replicates, select first\n",
    "    if library_key is not None:\n",
    "        ids = adata.obs[library_key]\n",
    "        uid = ids.unique()[0]\n",
    "        adata = adata[adata.obs[library_key] == uid]\n",
    "\n",
    "    # Compute neighbors\n",
    "    sq.gr.spatial_neighbors(adata)\n",
    "\n",
    "    # Compute zscores\n",
    "    zscore_analytical, _ = nhood_enrichment(adata, cluster_key=cluster_key, copy=True)\n",
    "    zscore_squidpy, _ = sq.gr.nhood_enrichment(adata, cluster_key=cluster_key, copy=True, show_progress_bar=False)\n",
    "    \n",
    "\n",
    "    zscore_squidpy[np.isnan(zscore_squidpy)] = 0\n",
    "    p, s = pearson(zscore_analytical, zscore_squidpy), spearman(zscore_analytical, zscore_squidpy)\n",
    "\n",
    "    results['Dataset'].append(name)\n",
    "    results['Spearman'].append(s)\n",
    "    results['Pearson'].append(p)\n",
    "\n",
    "results = pd.DataFrame(results)"
   ]
  },
  {
   "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>Dataset</th>\n",
       "      <th>Pearson</th>\n",
       "      <th>Spearman</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seqfish</td>\n",
       "      <td>0.986163</td>\n",
       "      <td>0.986770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IMC</td>\n",
       "      <td>0.964210</td>\n",
       "      <td>0.993883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MERFISH</td>\n",
       "      <td>0.980917</td>\n",
       "      <td>0.989529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MIBI-TOF</td>\n",
       "      <td>0.974419</td>\n",
       "      <td>0.975641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Visium</td>\n",
       "      <td>0.994463</td>\n",
       "      <td>0.989530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4i</td>\n",
       "      <td>0.994163</td>\n",
       "      <td>0.989860</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dataset   Pearson  Spearman\n",
       "0   Seqfish  0.986163  0.986770\n",
       "1       IMC  0.964210  0.993883\n",
       "2   MERFISH  0.980917  0.989529\n",
       "3  MIBI-TOF  0.974419  0.975641\n",
       "4    Visium  0.994463  0.989530\n",
       "5        4i  0.994163  0.989860"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\gr\\_utils.py:198: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.\n",
      "  obj[key] = data\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\gr\\_nhood.py:188: RuntimeWarning: invalid value encountered in divide\n",
      "  zscore = (count - perms.mean(axis=0)) / perms.std(axis=0)\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\squidpy\\gr\\_utils.py:198: ImplicitModificationWarning: Setting element `.obsp['spatial_connectivities']` of view, initializing view as actual.\n",
      "  obj[key] = data\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "def timeit(fun, n=5):\n",
    "    times = []\n",
    "    for _ in range(n):\n",
    "        start = time.time()\n",
    "        fun()\n",
    "        stop = time.time()\n",
    "        elapsed_time = stop - start\n",
    "        times.append(elapsed_time)\n",
    "    return np.mean(times), np.std(times)\n",
    "\n",
    "results_timing = {\n",
    "    'Dataset' : [],\n",
    "    'Avg wall clock time [s] (Analytical)' : [],\n",
    "    'Std wall clock time [s] (Analytical)' : [],\n",
    "    'Avg wall clock time [s] (Squidpy)' : [],\n",
    "    'Std wall clock time [s] (Squidpy)' : []\n",
    "} \n",
    "\n",
    "for dataset in datasets:\n",
    "    name = dataset['name']\n",
    "    adata = dataset['adata']\n",
    "    library_key = dataset['library_key']\n",
    "    cluster_key = dataset['key']\n",
    "\n",
    "    # Incase several replicates, select first\n",
    "    if library_key is not None:\n",
    "        ids = adata.obs[library_key]\n",
    "        uid = ids.unique()[0]\n",
    "        adata = adata[adata.obs[library_key] == uid]\n",
    "\n",
    "    # Compute neighbors\n",
    "    sq.gr.spatial_neighbors(adata)\n",
    "\n",
    "    # Compute zscores\n",
    "    avg_time_analytical, std_time_analytical = timeit(lambda  : nhood_enrichment(adata, cluster_key=cluster_key, copy=True))\n",
    "    avg_time_squidpy, std_time_squidpy = timeit(lambda : sq.gr.nhood_enrichment(adata, cluster_key=cluster_key, copy=True, show_progress_bar=False))\n",
    "\n",
    "    results_timing['Dataset'].append(name)\n",
    "    results_timing['Avg wall clock time [s] (Analytical)'].append(avg_time_analytical)\n",
    "    results_timing['Std wall clock time [s] (Analytical)'].append(std_time_analytical)\n",
    "    results_timing['Avg wall clock time [s] (Squidpy)'].append(avg_time_squidpy)\n",
    "    results_timing['Std wall clock time [s] (Squidpy)'].append(std_time_squidpy)\n",
    "\n",
    "\n",
    "results_timing = pd.DataFrame(results_timing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_timing['Speed-up'] = results_timing['Avg wall clock time [s] (Squidpy)'] / results_timing['Avg wall clock time [s] (Analytical)']"
   ]
  },
  {
   "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>Dataset</th>\n",
       "      <th>Avg wall clock time [s] (Analytical)</th>\n",
       "      <th>Std wall clock time [s] (Analytical)</th>\n",
       "      <th>Avg wall clock time [s] (Squidpy)</th>\n",
       "      <th>Std wall clock time [s] (Squidpy)</th>\n",
       "      <th>Speed-up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seqfish</td>\n",
       "      <td>0.005953</td>\n",
       "      <td>0.000855</td>\n",
       "      <td>1.086361</td>\n",
       "      <td>0.115733</td>\n",
       "      <td>182.477857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IMC</td>\n",
       "      <td>0.001360</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>0.140088</td>\n",
       "      <td>0.007217</td>\n",
       "      <td>102.995273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MERFISH</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>0.000522</td>\n",
       "      <td>0.215983</td>\n",
       "      <td>0.010593</td>\n",
       "      <td>120.294750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MIBI-TOF</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.039016</td>\n",
       "      <td>0.003178</td>\n",
       "      <td>49.788445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Visium</td>\n",
       "      <td>0.001098</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>0.098488</td>\n",
       "      <td>0.006106</td>\n",
       "      <td>89.681340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4i</td>\n",
       "      <td>0.001065</td>\n",
       "      <td>0.000483</td>\n",
       "      <td>0.098982</td>\n",
       "      <td>0.005607</td>\n",
       "      <td>92.929513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Dataset  Avg wall clock time [s] (Analytical)  \\\n",
       "0   Seqfish                              0.005953   \n",
       "1       IMC                              0.001360   \n",
       "2   MERFISH                              0.001795   \n",
       "3  MIBI-TOF                              0.000784   \n",
       "4    Visium                              0.001098   \n",
       "5        4i                              0.001065   \n",
       "\n",
       "   Std wall clock time [s] (Analytical)  Avg wall clock time [s] (Squidpy)  \\\n",
       "0                              0.000855                           1.086361   \n",
       "1                              0.000475                           0.140088   \n",
       "2                              0.000522                           0.215983   \n",
       "3                              0.000402                           0.039016   \n",
       "4                              0.000390                           0.098488   \n",
       "5                              0.000483                           0.098982   \n",
       "\n",
       "   Std wall clock time [s] (Squidpy)    Speed-up  \n",
       "0                           0.115733  182.477857  \n",
       "1                           0.007217  102.995273  \n",
       "2                           0.010593  120.294750  \n",
       "3                           0.003178   49.788445  \n",
       "4                           0.006106   89.681340  \n",
       "5                           0.005607   92.929513  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(results_timing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fd911WLp0Kd56662A/gKjxvyBH/3oR9i4cSPmzp2LlpYWvPrqqy73y3mjIaLoZuT3uczMTEe1gjOpMsrTfUREnhj5vQ4AfvKTn+Cdd97B5ZdfjgcffBAZGRl488038c477+Dee+/laAaiMHH69Gncc889sFgsuOaaa/p9PpOMHTvWa0FBe3s7hg0bhttuuw1WqxXJycl47733sHPnTvzqV78CIIYwL7zwAubOnYuLLroI//M//4PBgwfj4MGDqK6uRllZGQDgueeeww033IAZM2bgnnvuwfnz5/H73/8eqampPmflOa/zf//3f7F48WLU1tbilltuwcCBA1FTU4MNGzbge9/7Hn784x97ffyYMWOQl5eH9957D9/5znf8Hu9rX/saSktL8fWvfx033XQTampq8Ic//AGTJ0/GuXPnHNvde++9aGlpwdVXX41hw4bh2LFj+P3vf4+LLrrI8T5+0UUXwWKxYMWKFWhtbUV8fDyuvvpqZGdnAwA2bdqEESNGeK1ki2p6XvqPXD377LMCAKGysrLffT09PcLAgQOFq666qt993//+94Unn3wy5Ou74oorfF52mIjIH6O/z3lyxRVXCFOmTNHl2EQUnsLhvW7Hjh3CDTfcIOTm5gqxsbHChAkThKVLlwq9vb2aHJ+Igrd582afn8+kr7vuusvrPrq7u4Wf/OQngtVqFQYOHCgkJSUJVqtVWLVqVb9tt23bJsyZM8exXX5+vvD73//eZZv33ntPmDlzppCQkCCkpKQIc+fOFQ4cOOCyzZNPPikAEBobGz2u6fXXXxdmzZolJCUlCUlJScKkSZOEhQsXCp999pnfc/LrX/9aSE5OFjo7O11uHzlypHDTTTe53Ga324Vly5YJI0eOFOLj44WCggLhzTffFO666y5h5MiRju3Wr18vXHfddUJ2drYQFxcnjBgxQvj+978vnD592mV/f/7zn4UxY8YIFotFACBs3rxZEARBsNlswuDBg4Wf/exnftcfjUyCIAiapmCkuvvuuw+5ubmy0mcionDE9zkiigZ8ryMiCk5rayvGjBmDZ599Fvfcc4/eywEA/Otf/8Kdd96Jo0ePYvDgwXovx3DMei+AAtfX14euri7YbDaXPxMRRQq+zxFRNOB7HRGROlJTU/HTn/4Uzz33HOx2u97LAQCsWLECDz74IAMpL1gpFcaWLFmCp556yuW2v/71r7j77rv1WRARkcr4PkdE0YDvdUREFK0YShERERERERERkebYvkdERERERERERJpjKEVERERERERERJpjKEVERERERERERJqLkbOR3W7HqVOnMHDgQJhMplCviYg8EAQB7e3tGDJkCMxm5smhwPc6Iv3xvS60+D5HpD++z4Ue3+uI9Cf3vU5WKHXq1CkMHz5ctcURUeBOnDiBYcOG6b2MiMT3OiLj4HtdaPB9jsg4+D4XOnyvIzIOf+91skKpgQMHOnaWkpKizsqISJG2tjYMHz7c8ftI6uN7HZH++F4XWnyfI9If3+dCj+91RPqT+14nK5SSSh5TUlL4S02kM5Yghw7f64iMg+91ocH3OSLj4Ptc6PC9jsg4/L3XsYmZiIiIiIiIiIg0x1CKiIiIiIiIiIg0x1CKiIiIiIiIiIg0x1CKiIiIiIiIiIg0x1CKiIiIiIiIiIg0x1CKiIiIiIiIiIg0x1CKglbdVI1FWxehuqla76UQkYF19Hag9HApOno79F4KEZGubHYb9tTvgc1ui+o1EJE8PT2A3a73KohCg6EUBW31gdUoqy3D6gOr9V4KERlYWW0ZjrcdR1ltmd5L8YihGREFSmnAU9lYiayELFQ2VoZ4Zd7XpuUaiCgw3d1AfT1QWwscPw60tTGcosjDUIqCVjK5BEWjilAyuUTvpRCRgRWNKsKIlBEoGlWk91I8MnpoRkTGpTTgsWZZ0Xi+EdYsa4hX5n1tStfAyioi7fT0AE1NwLFjQGMjEBsr3nb8OMMpijwMpShoUzKnYMXsFZiSOUXvpRCRgSXFJqF4fDGSYpP8bqtH1ZKv0IxVVETki9KAx2K2wJplRWVjZdAhjxQW9fT1eAyNvK3NYrZgas5UWMwWWcdhZRVR6PX1Ac3NYvBUVwfExADp6UB8PJCcDAwaxHCKIg9DKSIiMhw9qpZ8hWasoiIib2x2GyobK2HNssoOeAD1Qh5pP6VHSj3uT2n45I2W1V1E0cZmA86eFSujTp8GTCYxjBowwHU7s/mrcKq7WwymTpxgOEXhjaEUEREZjtFa/Yy2HiIyjkDDJbVCHmk/xeOKfe4v2PY7tcItIvqK3Q60torh0smTgCAAaWlAQoLvx5nNwMCBYjjV1SUGUydOAO3tDKco/DCUIiIiw1HS6qcFo62HiEIjkODGOVxS8nhfIU8g+4mLifMZGskNz9SaHcUZVETeCYIYIElhUm+vGEYlJopVUnJJ4VRqqhhOSZVTDKconDCUIiIiVXH+EhGFK2/Bja+AxTlcUrslL9j9OK9bCs/yMvJ8hkVGew5EkUQQgI4OsSrqxAkxSBo0SGzJUxJGufMUTp08yXCKwgNDKQqZ6qZqLNq6CNVN1XovhYg0xPlL/jG4IzImby11lY2VSI9Px7pD63xW/qjdkudpP0oqkJyDISk8q2quctzmaV9aPAeiaNTZKc6LOnYMOHdODJEGDhQDJbU4h1Odna7hlCCodxwiNTGUopBZfWA1ymrLsPrAar2XQkQa0mP+UriFPAzuiPThL9Dx1lJnzbKivK4chYMLXSp/3Pen1twlX/tRUoHkKRhyvq2ioQINHQ2oaKiQdWy1ngNFjxdeeAH5+flISUlBSkoKZsyYgXfeecfnY86ePYuFCxdi8ODBiI+Px4QJE/D2229rtGL1dXWJV9I7flwcZp6cLIZGlhD+apjNQEqK+NXZKVZlMZwio2IoRSFTMrkERaOKUDK5RO+lEJGG9Ji/pFfIE2gYxsHpRPoItKXMYrZg/oT5aOlqcQl49GhRy8vIw/ZT25GXkedyu6fAzVMw5H6bAAF2wR7Q/CdfIR9nShEADBs2DM888wx2796NXbt24eqrr8bNN9+M6mrPnRQ9PT2YM2cOamtrsX79enz22Wf485//jKFDh2q88uD19AANDWJlVHOzeCW9QYOAmBjt1mCxiMHUwIFi26AUTp07x3CKjMMkCP5/HNva2pCamorW1lakpKRosS4icsPfw9DjOQ5fHb0dKKstQ9GoIk3DsNLDpTjedhwjUkageHyxZseNZPw9DK1oP782uw2VjZXIy8hDVXMVrFnWoCp5pP2578fb7cGy2W1Yd2gdCgcXoqWrBVNzpjru21O/B1kJWWg83+hyu5z12wU7chJzFD3W3zEDWU+0iPbfw/T0dDz33HO45557+t33hz/8Ac899xwOHjyI2NjYgI+h5znu7RWvqHfmDNDdDSQlAfHxmi7BK5tNDKfsdjGoSksT1xfMPCsib+T+HrJSiogoShmp5S3YtahRnRXIGljxRBRePM1VUmN/7sFTqCqoKhsrUZhbiPLT5f1mNQUyw0laf0F2gewZVu7D0+s762EX7LDZbR4Hq/vaZ09fD6upoojNZsPatWvR0dGBGTNmeNxm48aNmDFjBhYuXIicnBzk5eVh2bJlsNmM/zPS1ycGUcePi+16ZjOQnm6cQAroXzl17Bgrp0h/DKWIiKKUkeYaGWEtgaxBbhgWigDQSKEiUbgJ9RDuUO3fmmVFS3cL5k+Y3y8I8xSQyW2hc36szW7Drrpd2FW3y1FJ5RywVTRUoL6jHhUNFbCYLTCbzMhJzEFlY6XHwerSPp3XIW1XeqSUV+iLAvv370dycjLi4+Nx3333YcOGDZg8ebLHbT///HOsX78eNpsNb7/9Np544gn86le/wv/+7//6PEZ3dzfa2tpcvrRis4mVUceOAV98Id6Wng4kJGi2BMWkcColRQykjh8X197RwXCKtMdQiogoShmpyieUa5Eb3oRyDaEI3YwQ5BGFKzlDuIOZiaR0yLfc2UxK9+tpkLk/lY2VaOxsRFNnk6MF0T1gM+GrXh/n+31dvdA5fJK2Kx5XzCv0RYGJEydi79692LFjB+6//37cddddOHDggMdt7XY7srOz8ac//QnTpk3DN77xDTz++OP4wx/+4PMYy5cvR2pqquNr+PDhoXgqbmsF2trEOU0nToj/nZZm7DDKncUiDl0fOFAcgn7sGHDqFMMp0hZDKSKiKKXHQHI91iI3vAnlGkIReBkpVCSKRN5a8EIxwNvXsdYdWof0AekBVxMJUPbJ0pplRVZiFjITMx0zsZyDsILsAmQnZaMguwCAawDn6+qFnsInaXsAbOOLYHFxcRg3bhymTZuG5cuXw2q1YuXKlR63HTx4MCZMmACL06XpLrjgAtTV1aGnp8frMRYvXozW1lbH14kTJ1R/Hs56e8XKohMnxIHmgwaF92wmKZxKThaDNoZTpCWGUkREFNGMEN6EIvAyUqhIFI78hUtyq3487de5/U0O52M5r8vXDCk5CrILkJOU4wiQ5LCYLZieOx3Tc6d7rMhSWq3l6THOLYCAPlcxJP3Y7XZ0d3d7vG/mzJk4cuQI7Ha747ZDhw5h8ODBiIuL87rP+Ph4pKSkuHyF0rlzYsueFOSYI+RTdUzMV89JaklkOEWhFiG/PkRERJ5pEd40dDbg8W2Po6GzIWTHICJ1+QtCfFX9OA/39rRf5/Y3OZyP5bwu9xlSSqu0AgmQtOKtBZAiy+LFi7F161bU1tZi//79WLx4MbZs2YJvfvObAICSkhIsXrzYsf3999+PlpYWPPLIIzh06BDeeustLFu2DAsXLtTrKfQjCGI1UVxc5IRR7mJixOovhlOkhQj9NSIiIgqOkkHiK/esRG1rLVbu8dyOEMy+iSg0Ag1C3Id7e9qvc/ubUnkZedh+ajvyMvL6hUpGrShSGpb5agEMZr9kPA0NDSgpKcHEiRNxzTXXYOfOnSgrK8OcOXMAAMePH8fp06cd2w8fPhxlZWXYuXMn8vPz8fDDD+ORRx7B//t//0+vp9BPdzdw/jwwYIDeKwk993Dq+HHg9Gmgs5PhFKnHJAj+f5za2tqQmpqK1tbWkJdCEpFn/D0MPZ5jeTp6O1BWW4aiUUWqVx+Fct9KlR4uxfG2447WP1/rauhswMo9K/HI1EeQnZitaN/F44tDsfywxd/D0IqW8yu1vkkzkfTcv9K17KrbhabOJmQmZmJ67vSg9qWVPfV7kJWQhcbzjY4ZUUber96i5fdQT6E8x2fOiPOk0tNV3W1Y6OsTWxdNJrHNb9AgcbB7uM7SotCS+3sYEZVS1U3VWLR1EaqbqvVeChFRxAvlVd+MdEW5WUNnofF8I2YNneV3XdmJ2Vg6a6msQAowxpwrokgWTEWRnOocf5U9PX09LnOh5KzF+bjehpOHqh0v2IqkYNvv3I8v/XdeRh7b+shQBEGsGIqP13sl+pAqpxITgbNnxbY+qXKKKFAREUqtPrAaZbVlWH1gtd5LUQ2DNiIyqlAGKkYKa7Z9sQ1ZCVnY9sU21dfFIeVEoRVMSOIpRJIb2kiPLT1S6jIXynkt3vYlPdZitigeTh6sYNsCgw3L3I8v/XdVc5VhZ2JRdOrqip7WPV9iY13DKamt7/x5vVdG4SgiQqmSySUoGlWEksklei9FNZEYtBFRZAhloGKksMY5iDLSuoiiSaAVPMGEJM6DzKWKp4qGClmhjRRAFY8rdgRRcudCSY8tyC4IOohxPm9yzqHeg8bdj6/3eoi86ewE7HbAwpwUwFfhVEKC2NYoVU4xnCIlIiKUmpI5BStmr8CUzCl6L0U1kRi0ERGFE09BFAeUE2lLj8HezoPM1x9ej/oOMaCSE5JIAVRcTJzXYMlb4BJIkOav6qqysVLWOVSzLdBmt2FX3S7sqtsV0FUC3edmcdg5GYXd/tVV98gVwykKRkSEUpEoEoM2IiIjCSRgMtLMK6JoILdiRu3gQjruuEHjYIIJZpNZtdBGTgCktF3QW9WVNcuqedVRZWMlGjsb0dTZpChMlJ6ze1Wa+3NkSEV6YeuefwynKBAMpYiIyDC0rEQKJGBSOlvK1/Nh1RWRf3IreKTgoqKhQpXAQjrutJxpyE7K1nS+EyC/QkzNqiu1WLOsyErMQmZipqIgTHrOAHy28ulRPUcEsHVPCSmcGjAAaGkRw6m6OoZT5BlDKSIiMgwtK5ECGV6udLaUr+fDqisi9UjBBQBFgYW/qptQhzveji+3uknO+rQOcSxmC6bnTsf03OmKzpu3mVruz5HzpkgPUuseq6SUiYsD0tLE89bcLA5Er68Xq86IJAyliIhId1LV0KyhszS7+p4Ww8t9BV9aXWmQFVkUCeSGRwXZBWg834i8jLyg2t/UWpc/3o6vZhjmPLhdzjrVao9TOmxd7nPWswqMoldXl/jFUCowUjgVHw80NYmVUwynSMJQKkDVTdVYtHURqpuq9V4KEVHYcQ9KpKqhbV9sM+RV7gINdnwFX1pd0Y8VWRTOvM0Z8kYKLKqaq4Jqf5Mr2FArLyMP209tR15GXkCPl/gaLu48uF3OOtWqrFI6bJ3IyDo7AUEAzPz0HBRv4dSZM+LX2bPiV2ur+NXWBrS3i1/nzolfHR3iV2en2A54/vxXoWF3t/jV0wP09opffX3il80mftnt4mspCHqfDZLE6L2AcLX6wGrHX/BXzF4h6zHVTdVYfWA1SiaXcIC5AfD1oEjU0duBstoyFI0qMlyw48w5KCkeX4yiUUWOdRuR+3rDidHPLZEvUphR31kve+B5ZWMl8jLyUNVcJbv9TQ73q8IBYqhV0VDhuF9p9U5VcxVmDpmJquYq2evwpKKhAp/UfYKRKSNR2VjZb1/WLKtj7Z6eh7dtg+F8bvIz82W9Hu78rZVIC3a7GJCwSko9cXHiV3e32NbnKSASBMBk8nyb9F2639N3X/dJpD+bzV89Rgoenb97u0/u8ZSuT859kYShVIBKJpe4fJcjkCCLQoevB0Ui5/DEOYwINKAKVcjlHpRIVUNGFc7BjtHPLZEvUkBSkF0gK5SQQqxgQx5f+3YOfaQqJOl250BHznrVCoAAYHjScFQ1VWHOiDn97nMO3/bU7+n3PLxtGwznc+Pr9fAVPDkPsDebzAynSBdSJU5qqt4riTzx8eJXoKQwy/m73Nuc75Oqp5Q+zl9o5k2goZTzn+WGZ+63qRWQxcSIX2pgKBWgKZlTFAcZgQRZeovkaqJwfD2I/Jk1dBZW1q/ELeNuwbIdyzAoflBQ1T2hqhDSKihRK1RjsEOkLeegQklAEmzI4ysg8bRvm90Gu2BHfWc9CrILPAZXavK2vvzMfGw+sRm3TbjNbyCnZhDmj5xj+Tpn0uMBhPS8EvnS0eH6oZ6Mw1NoY3T+gi659/X1BbYv6c/ewjNvgZp7UJWSAgwZIu85+2MSBP/dlG1tbUhNTUVraytSUlLUOXIIRHKAopdFWxc5PtCxmkhf4fJ7GM4i4RyXHi7F8bbjaDzfiNS4VLT2tOKxSx8zXKWUVqTzMSJlRNSHSuHyWkbC76GRGf38SqGLXbAjJzEHjecbAw4hAmn9kqqI5B7XfXulxwzmeM6BT2VjJdLj01FeV475E+brUk0UaKudnMdFWhuf0X8PI4Fa59hmA2prxT8nJKizNqJw5Bx0dXSIvw8jRvh+jNzfQ0PmvYEOEZfasVYfWB2ilUWfksklKBpVxGoiIo0Ee6U06Ypuj0x9BOPSxvkMpOQcS6th3KGi1RXuwgEHnpORuQ80BxDUAHLAtQJH7hXl/A0+d9+P+/ZKrwyndNC68/bOz8+aZUVLd4tugRSgfEC6dC4B+D1nvOIe6UVq3eM8KYp2UrWg9KUmQ4ZSgYZLDFDUJ7UpsvKMSBvBBgdSiJSdmO03TIqGkCLcQzU1MaAjI/IWRhVkF/gMIZzDIW+Bk7cAxxd/4YfzjCO5gUowx/O0vfR88jLyvAZackM4f5TsR2nA5u818XRsuetR6/kTdXz573bh1B5GFG4MGUoFGi4xQCGicKdlcFA0qgg5STnotnUHXJlF4YMBHRmRFEwA8sIo98dVNlZ6DTecAx9rlhX1nfWwC3avQYWcIEMKXgD0O6acx6sRlrgPc7eYLf3OgdKqJW9rU7If94DN33P1F2J5Orbc9QTy/Inc9fUB586xSooo1AwZSjFcMpZA2ymJSDktg4Ok2CTEW+JR31Ef0dVS7oJtkSQidbgPCQ+07U1OhY50JbicxByvQYWcIEMKXgqyC/odU87j1QhLPD1f99uUVi15W1sg+/G1P2f+qsSkY+dl5DnCLbnrCWbdRJKuLrbuEWnBkKEUGQtndRFFrmhs6TJK2yLDMYp2lY2VyEnMgdlkVtz+5hxoyG2B8xdUKAkyPB1TzuOdtwm0asrTsd1vC2QGk6f1BzPLKdhgSDp2VXOVI9ySux7OoCI1SFfdY+seUWgxlCK/OKuLKHJFY0tXoEGc2iGSUcIxIr1oXc3iHFR4CoSCDTI8ta99cvoTrPl0Dc73nO83gyrQqqlQzUtSO8jxd77lYtUT6aGvD2hvZ5UUkRYYSpFfbKckUl8oqmRYeSNPoEGc2iGSEavU+DNEWvIVgoR6ULUabXT+1ljZWImdp3fi+Nnj+N3e3/U7Xl5GHraf2o68jDzN1661YNYcyqonDkQnb6Sr7sXH670SosjHUIqISAehqJJh5U1oqR0iGbFKTfoZ2nh0I8Mp0pXcECPQUEGN6ht/a7RmWZE6IBUD4wdi9tDZ/Y5X1VyFmUNmoqq5KmRrN0roEsz5DuVzCMeAj7Rx7hxgsbB1j0gLDKWIiHQQiioZLStvorGixoghktqknyEADDhJV95CDPeAItBQQemV4jzxV+lkMVuwYNICzBg6A5cMviSgGVRy1u6L8/mx2W3YVbcLu+p2aR5SBVPtpPQ1dn4tg70CIEWn3l5edY9ISwyliIgihJahibeqrEgKqyLpucgl/QzNGzvPcK2FFF28hRjuAYVaoUIg4ZacSidfYYwWw7idz09lYyUaOxvR1NkUVpVBSofDO7+WwV4BkKLT+fNAdzcQF6f3Soiig+ahVHVTNRZtXYTqpmqtD01EZBjh3GrX0duBbls3cpJy+oUWRn9ezkGTt9BJun3j0Y2Gfi6hFA1VYRSeQtUGJne/zvsIRZWNmq1qNrsNlY2VsGZZYTFbYM2yIisxC5mJmapXBilZt9Ln6BwcyQkPnV8XVkJRINrb2bpHpCXNQ6nVB1ajrLYMqw+s1vrQRESGYcQh13KV1ZahvqMe8Zb4fqGFkZ9XR28Hlu1YhqNnj6KstsxrgCbdDsCwz4Uo0ngKKuRcIU9JhZOvbeVWzDjvIxRVNmrOOKpoqMDpjtP4x2f/gM1ug8VswfTc6ZieO131yiC1Xgd/5IRMzq+L9GcAhpitRcbX0wN0drJ1j0hLmodSJZNLUDSqCCWTS7Q+NBGRYYRzJYqv4MnIz6ustgypcak4230WRaOKvD4P6fZ5Y+cZ9rloLRpbGUlbnoIKpVUxWswPCnXljZz9K6k0OtZ2DKlxqSFv15PWnZeR53dtgZxD6TkDCCgI5EBzkqurS2zd41X3iLRjEgRB8LdRW1sbUlNT0draipSUFC3WJUt1UzVWH1iNksklmJI5Re/lEIWUUX8PIwnPcWTr6O1AWW0ZikYVMWhSqPRwKY63HceIlBEoHl8c0mPx9zC0jHp+3VvNvN3my576PchKyELj+UZHdUw48/b85T5Pm92GioYK2AU7zCYzCrILQj47ScnatHxtlR4v1Iz6exhJAj3HX3whtu/xZSHyrqNDDG5HjPC9ndzfw7AedB5NrYCcxUVEFBwjV3EZnZHbMikyeGqFk+YgSVeO88dfBY6a85q02Ld7dY90jLyMPFnVYRazBQXZBfi89XNkJmRqUiUktwrKW+WStzZOu2BHfWe936sxesOB5iRHT4/4YZute0TaCutQSo9WQL3CoWgK4IiIokk4tMYx0CO9KGm78hc8KAlC3Dlv42l7pe1hvo7pKXxyPkZVc5Xswd+VjZUozC1E+elyTYZ9yw1/vIVX3to4cxJzYDaZ/V6NkYznhRdeQH5+PlJSUpCSkoIZM2bgnXfekfXYtWvXwmQy4ZZbbgntIr90/rwYTPGqe0TaCutQakrmFKyYvULT1j29wiG5ARwrqoiIwovRr1hIpITaFUNqznBSEoS4c97G0/ZK1+nrmM7hk1Qp1tPXA7tgx+mO07ALdsf59Xdca5YVLd0tmD9hvqGqhLyFV56ej6/n6H6fv5+/UFbLkWfDhg3DM888g927d2PXrl24+uqrcfPNN6O62vdnldraWvz4xz/G5ZdfrtFKgbY2IDZWs8MR0ZfCOpTSg16D2uUGcKyoIiIKL86tceFQNUXki9qVK2q2XSkJQtw5bxPKgenurWrS+Sw9UoqcxBx83vo5chJzHOfX3/lRev7kXgUxVDy1bPp6DnKvxig9h4qGClZWaWzu3Lm48cYbMX78eEyYMAFLly5FcnIyysvLvT7GZrPhm9/8Jp566imMGTNGk3V2dxv7qnsyxkAThS2GUgrpUZ2lhNfQ7Is9wOv3it+JKCwxsIhMzq1xvqqmtHz9+bNGgVL76nFakBPcOIclQP8rwCkN49yPabPbsKtuF/7x2T+QmZDpaFWTzmfxuGKX76FqxQv0KoihWENFQ4XinxNPP382uw3rDq1Denw6AIT0/JFvNpsNa9euRUdHB2bMmOF1u6effhrZ2dm45557NFvb+fNAb6/xKqV6bXY8VbYXxX/9ALtONOm9HKKQCOtQiq1q/XkNzcpXAdWl4nciCqlQfaBnm1d4c/658PYz4mugeLCvv5KfS/6sUaCksAWA10ChsrES6fHpWHdonezAwQhBlq9wJtjqqcrGSjR1NiE1LtUx/8n5inFxMXGYmjPV8T1UrXh5GXnYfmo78jLyHLep2UIph3Q8AIrDME8BY2VjJQoHF6K8rhwF2QVez58RfsYi1f79+5GcnIz4+Hjcd9992LBhAyZPnuxx223btuGll17Cn//8Z0XH6O7uRltbm8uXXIJgzNa9PrsdS8oqUPbZF6g/14UfvvEJNh06pfeyiFQX1qGUUVvVDBmWFT4ATCkWvxNRSIXqAz2vgBbepJ+LjUc3YtmOZTh69mi/nxFfA8WDff3Lastw9OxRLNuxzG8wxZ81CoT0ob6nr8dRmeItwCmvK0fh4ELZgYOnQEjrEMFXy50UHgUaFlmzrMhMzEROUo5j/pMez7mquQozh8xEVXOV4zY5QaOapOMVZBd4rHryNXDeE2uWFS1d/udq+QodGVgFZ+LEidi7dy927NiB+++/H3fddRcOHDjQb7v29nZ8+9vfxp///GdkZmYqOsby5cuRmprq+Bo+fLjsx3Z3i5VSRmrds9kF/O+mSmw+UodYsxlTh2Wgzy7gyf9U4LU9n+u9PCJVhXUopdd8J39UC8vUbLkbOhW49UXxe5gyZNhH5AE/0JMn0s8FAKTGpeJs91lFPyPBXgGvaFQRznafRWpcqt/AlFfbo0A4zz+SKlM8VddYzBbMnzAfLV0tsqtvPAVClY2VSB+grOLKnZKwwVubn9yr+vk6lsVswfTc6ZieO92xf2/PWY1WOm9r8VUV5XxsNQaK+9vGW9WTtIaKhgrUd9SjoqHC53P11Z7pvAa5z52Ui4uLw7hx4zBt2jQsX74cVqsVK1eu7Lfd0aNHUVtbi7lz5yImJgYxMTFYvXo1Nm7ciJiYGBw9etTrMRYvXozW1lbH14kTJ2Svr6vLWK17dkHAsx/sx7ufnYLFbMIvbijAylsuxW3WUQCA32/7FCu3HoCdc6YoQoR1KGXU+U6qhWUR2HIXTLBk1Mo4Ineh+kBvlJYqzhsKjPRzMW/sPIxLG4fHLn0MSbFJmp3PpNgkPHbpYxiXNo6BKalK+mCfl5HnmHvkrzJF6QBuT9tbs6woP12Owlz5FVfu/IUNcsIVuVf1kxteSbw952Bb6Rwzlgb0r2Tz9bo4H9vfefN0f09fD9YeXIuevh6v2/jj/vztsOPQmUMBh5LOwZbc507Bs9vt6O7u7nf7pEmTsH//fuzdu9fxNW/ePFx11VXYu3evz+qn+Ph4pKSkuHzJIQhAaysQFxfw01GVIAj49ZZq/PvACZhNwFNFBZg9NhcWswk/mD0ZC2dOAgD8Y28NnvxPBXr6WL1H4S+sQymjUi0si8CWu2CCJaNWxhFpxSgVWEYJx8KVe2ip5fkMNjBlIEmeSOFCVXOVJnOPJI6Kq+4W5GXkBdRe5S9skBOcyL2qn7djya348XUsZ/6CtMrGShTmFjpmV8nlfGxvA8V9VR2VHinF6JTRKD1SCsD1fMitWHNeQ0F2Adp72nHZ4MuCqmAywdRv/c7UaM+MZosXL8bWrVtRW1uL/fv3Y/HixdiyZQu++c1vAgBKSkqwePFiAMCAAQOQl5fn8jVo0CAMHDgQeXl5iAtBciQIQF8fEBOj+q4DWIuA3/33U5TuPwYTgJ/NseLq8YMd95tMJnxz2lg8ed1FiDGb8P7h0/jBG5+gvbtXv0UTqcAwoRRbszyIgJY7d8EES0atjCPSilFaqowSjkWKcDqfDCTJE0/DsbWawSOFFFXNVQG1V/kLefyFVv5a8pz37etYUjCiBn9BmjXLipZu/zOWfPHXWufp/uJxxahpq0HxuGLHPqSqq4qGClmvn/P5dg4lA61gKsguQHZSNgqyC7yeN7buBaehoQElJSWYOHEirrnmGuzcuRNlZWWYM2cOAOD48eM4ffq0zqs0hj9+/Bn+sbcGAPD/rrkQ108a5nG7oklD8at5lyAxNgYVX7Tg/vUfo6H9vJZLJVKVSRD8N6O2tbUhNTUVra2tskshlVq0dRHKastQNKoIK2avCMkxiMKZFr+H0Y7nWB0dvR2O93O9AzRSlxavLX8PQysU53dP/R5kJWSh8Xyjy0Bs99sC4a9KRbo/LyMPVc1Vju/S9qGucpHzPOU+BzlrdN4WgMfHBfuc3R8vd3+BHFc6f/Wd9TCbzH4fq9bPlSc9fT0oPVKK4nHFiIv5qiInFD9DfJ8LPbnn2G4HamoAk0nfQed//eQw/lx+CADwoyum4NYv50f5cqixFT96YyeaO7uRnTwAv7r5EozNGBjilRIBHR1AfDwwYoTv7eT+HhqmUoqtWcqwsoyIghHKNixW00Quo1Trkb7cq4M8tWFJ86XkVrB4u6qa3NlFUuuge8WUnJlRu+p2YVfdroCquuTMGvK3Bk9VRc5XMnQ+18778rZfpfO6/K03kIHiSq6M13i+EQXZBbLWHMrZTp6uPAgEfz6J/Pn77qOOQOqhWRfICqQAYEJWKv50+2UYmZaEhnNdeGD9R6g42RzClRKFhmFCKbZmKcOh30QUjFAGR+HUjkbq4byp6FHRUIGGjgZHSOH8od09JPL0Qd594DXgPWzxFkJ4C798zXHyFJRUNlaisbMRTZ1NAbVnyQksAglSnK9k6BwQOe8rVAGNp/0qbS+U2/Lm7/y5v2bBBkS+wjLpebvPJ9OqFZWi07rKGvzf9oMAgO/PmIgFU8coevzglET8cf5luHBwGtq7+/Dovz7BB4fZDknhxTChVNT4Yg/w+r3i9yB4rCxTad9EFPlCGRxpVU3DEMRYWCEXXQR4nv4gJyhxH3jt/jjnGVXeQgg54RfgOTBzDkqsWVZkJWYhPSEddsEekuAhkCBFOh/F44pdzqfzvuTuV2moIu0XENvl8jPzHXOXlK4/VFcJDJSvsMzbfDLOlKJQ+VfVcfzmwwMAgP+5ZBzuunhcQPtJGRCH3339Uswek4Neux1PvLPHMZuKKBwwlNJa+SqgulT8HgSPlWUq7TtU9p08i0fWVmDfybN6L4Uo6mkRHIU6NGIIYiyskIseBdkFyEnK8RhSOA+v9haCuA+8lh4nBSze2qiceQs9fAUInh5jMVswPXc6YswxyEnM6fc4f4FOqKpopPMRyJUM3dcUaKhS2ViJ9Ph0lB4pVTxPyVdLotxzJfcqgUr2Kycsk3vVRKJgvPPpSTz3wX4AwJ1Tx+DeSycEtb/4GAuW3jgNxReOhABg5dYDeH7bp7D7Hx9NpDuGUlorfACYUix+D6d9q+ClbTV4c99pvLSNyT1RNNh4dCM21W7CxqMb+92nRmDFEMRYOG8qevir0PEXgsTFxGH+hPmoaq7y2UblKwTwtgZfj/W1bn8hV0VDhcfgw4hVNO5r8nR1RDmsWVaU15WjcHChz+cnNxSSM9/LfVaZnKsEKnkN5FSXebtqIgCPc8+IlHrv0Cksfa8SAoDb8kdi4cxJMJmCvwKnxWzCj66cgvtmTAQArNnzOZ5+dy96bfag900USgyltDZ0KnDri+L3cNq3Cu6ZNRpfyx+Me2aN1nspRBHHsK1sXv6OpUaVkxohiJbnzbCvEZHK5IRKnoZnSx/0AQQ8NyjQmUPeHicFOnbB7ginnAejG7GKxn1N/irPvAUsFrMF8yfMR0tXi9eZXnKG0XtbV6D7kbPfUJAzZJ7Inw+P1uGpsr2wC8C8KcPx6BVTVAmkJCaTCSUXj8PP5lhhMZvw7men8KONn+Bcd69qxyBSG0MpnSlqaQtyZpTe7XP5wwZh5R0FyB82SJfjE0UyI7ayzRs7D3NGzsG8sfP63WeUKictz1swx2KgReHEU2WJJ9LwbCmYqGioCOqDfiiqV6RAx2wyo/F8IwCgqbMJjZ2NqGysNOSV2dzX5C+0cQ5Y5A4WlzOM3pkUOjm3AbrPjHLfj1rD0oPhfD6sWVbUd9bDLtgVX12SCAA+qm3AE+/sgU0QcP2kofjp1RfCrGIg5ezGC4bhubkXIyHWgl0nmvHA6x+j8VxXSI5FFCyGUjpT1NIW5Mwots8RRS6jhDzOfFUyGaXVS8vzFsyxjBg6EvniCBzi0z2GHQXZBY7h2VL4AED2B31vV9JTu3pFCkoKsgswNWcqCrILkJmYiazELK8VP0bjL7Rxvuqc82vmi3OAJCcU8vTauM+M8hemaX2ObXYb/vHZP3D63GlUNFTAYrbAbDIjJzHH74B9Inc7jzfhsbd2o88u4Jrxg/HYtfkhC6QkhSOz8H+3zkB6YjyONLXje+s+Qm1Le0iPSRQIhlI6U9TSFuTMKLbPEUUuo4Q84UbL8+Z8LKWVT0YMHYl8qWysROHgQpTXlTsGnzuHEs4D0aWqk4LsAr9D0p337+lKet7aw3r6egIKNDzNF5qeOx3Tc6e7BBLh3M7lfNU559dMzmPkhjKeXhu5M6MkWp/jysZKpMan4lj7McdtRmzXJOOr+KIZP31zJ3psdswek4Mnr7sIMWZtPoZPyk7FH+dfhuGDklDffh7fX/cxKk+1aHJsIrkYSulMUUtbkDOj2D5HROSbVm1ySiuf9Aod2TZIgbJmWdHS9VXg4OnDvBQyOFedBDOfyFNQUtFQgYaOBqw/vN7jftWqvglVWKFVdZDNboNdsKPpfJPskEjufr3NCVM6MF/JOVbjqonWLCtyEnNwSe4ljitNGrFdk4yt6vQZ/GTjTnT32TFjZBaevr4AMRZtP4IPTU3EH+dfhik5g9De3YtHNuzAh0frNF0DkS8MpUhbQc7FIiIKxrnuPvxj53Gc6+7zeL9WbXKBVD45B0RGDc+IJN6uYOb8Yd5b9Uwg84l8ESBgbOpYR4uacxjhLQRTGgapHVbImbOlZmBV2ViJnMQcmE3mfs8hmOP4u3qhL+4/C0rOsb9wU86VACsbK1GQXdCvKo5IroMNrfjhG5+gs9eG6cMzsOymaYiL0ednaVBCHH5fXIhZo7PRY7Pjsbd24/XKWl3WQuSOoVQYq26qxqKti1DdVK33UuQLci4WUTTiFeKCJ4VRpbtPoLapA2/tO+VxO63a5AKpfHIOiIwcnhHJ5SlkkDMkXUkbV0F2AXKScjA9d7qjRU1O9U0oW8XktBRWNlYiPT4dh84cQn1nvceQrrKxEukD0rHu0LqQVnrJORfegitpvwAUh1NKgz73oeS+wk0lw9+JAnGkqQ2P/msHzvX0wTokHSu+Nh3xOgVSkgGxFiy7aRpuzhsBAcCvPqzGC9sPQhAEXddFxFAqjK0+sBpltWVYfWC13kuRL8i5WETRKFyuEGdkb+07hdqmDggwYVRmEm7KH+JxOyPP5nIOiIwcnlFkU6Mtytf2ciqElLRx+Rue7S34COXsICnwKD1S6vM5lteV47Ihl3msXnJsc7ochbmFAQVGzvcB/dvrnI/j71x4C3Gk81uQXeASToUi7HFeg79Ay9/9eRl52H5qO/Iy8gAYe5A9GU9tSzse3rADbV29mJI7CL+cdzESYmP0XhYAIMZsxk+vysN3CycAAF7ZfRS/2FSJXptd55VRNGMoFcZKJpegaFQRSiaX6L0U+YKci0UUjcLlCnFGdlP+EIzKTMKt04bhGxePQHK8Mf5yqIRzQBTM0HSiYATbFuVve/cr8bm32wHBtcrJqcQK9hj+SCFP8bhir2GPxWzB/Anz0dLV4jUMspgtKB5XjPK6ckd44omv10TO6yXnXPgLrtzDKU/bBRv8qBkkVjVXYeaQmahqrgLAyimS7+TZDjy8YQfOnu/BxKwU/PrmS5AUZ6y/c5hMJvzPJePx2DX5sJhM+M/BL/DjjTvR0eN5tAFRqDGUCmNTMqdgxewVmJI5Re+lEFEIBVKtEmhQEamVMcnxMWEbRvkTqdVtZEzBtkX5217674LsAo/tdmpRGjLIqTZSOn8qLiYuqGoeoH944omv10StIMf5aoo2u83rOfH1nJxfk0ACqkCDRE/Hcj8vrJwiOU63deKh0nI0dXRjbMZA/PaWSzEwPlbvZXn1tSnD8ezc6RgQY8HOE01Y+PrHaO7o0ntZFIXCNpQKy3lKREQq8xY+OQcV4VJJ428IeTCMfA6CXVukVreRMclpi8rLyMO6Q+vQ09fjcRtfH+j9tdupRel+g602ChV/z8PfUHg1K8Kcz0MgA86dn4uW59TTsdzPCyunyJ+G9vN4qLQc9ee6MDItCSu/filSE+L0XpZfM0Zl4/lbCzEoIQ6HGtvwvXUf4diZc3ovi6JM2IZSYTlPiYhIZd6qZJyDinCppJHmPnkaQh5scGPkc+Bvbf6eezDVbUYO6yh8lR4pxeiU0Sg9Uurxfk/hhXt1jJxZR8Fwr+zxR4tqI1+UVB45b6tVeGKz22AX7I6h7NYsK+o763HozCGkD0iXdXzn5xLoOQ2kgknOsbxV9IXyNafw0dzRhYc37MCptvMYmpqI3329EOmJ8XovS7bJOYPwp/mXYWhqIk63ncd96z7C/tNn9F4WRZGwDaXCcp4ShQSr5iiaeauScQ4qAqmkURpWqBFuSHOfPA0hDypU6m7HTWebMSYhy5DVRP5en1AGakYO6yh83TzmZmz9YituHnOzy+1SYJCXkef4QO+tOkaLMEXJMXxVFKlVbeQrUFGyVudt3cOTQEIbOa2LFQ0VyEnMcQxlt5gtMJvMuGzwZSg/Xa44vPF2Tv2tP5CfG3/BnqdtQjlzjMLLmc5uPLxhB46f7UDuwAT8vrgQWckD9F6WYsMGJeGP8y/DpOxUtHb14qHScvz383q9l0VRImxDKc5TIgmr5kgvRqgykVMlE0gljdKwQo1ww33uk3M7n9xgzWMLYPUGxJ/9AvO6bIacleXv9Qlle55WrX9G+F0h7Xx65lPcMfEOfHrmU5fbpcCgqrnK8YHeW3WMFpUoagQ2avIVqCg5H87buocnlY2VSB+QjnWH1sl+nnJaFwH0W581y4qW7hbMnzBftfDGX+ik1s9NsPOtKDq0dfXg0X99gpqWc8hKGoDfFxcid2CC3ssKWHpiPJ4vLsSMUVnosdmx+K1d+Nf+Y3ovi6KApqEUK1ooFFg1R3qJ5CoTpWFFKMIN53Y+qeJLmpEl5zEOU74OZIwRv4ehUA6f12qwfST/rkQjfx/SvQUDcq/Q5h5Wqble5z87H8Nmt2HdoXWyW80CPX5PXw/WHlzrcd6Wr/Oj5Hz42taaZUX56XIU5hbKfp5yWhelwfTOxwxFNZGSnyEg8KDRWwUfAyqSnOvuxQ/e+ASHm9qQnhiP3xVfiqGpiXovK2iJcTFY8bXp+NrkYbALwLObq/Cnjz+DIAh6L40imKahFCtaKBRYNUd6ieQB00rDilCEG+7tfO7BhnP1jfTnqy5I7d8CGD8QmFoifvekux3Ys1r8Tv2oUeUUyb8r0chftYoUDADw2QIlhxohgJy2wMrGShTmFgbUauZvnc7HdJ+35S0kCxWL2YL5E+ajpbtF9vNUs3VR61CnsrES6fHKKsMA7/OtOOCcAKDXZsePN+7Ep/WtGDQgDr/7+qUYmZas97JUE2M2Y/E1+fjOJeMBAC/vPIJl7+9Dn82u88ooUmkaSrGihQL2xR7g9XvF70QGoVWVSbRyb+dzDzacQyrpz9tOv49v5KchuXqN/JCpegPQ/Ln4nfpRo8qJvyuRRW6LlBof4NXYR15GHraf2o68jDyfVVzBtJp5W6f7APDiccWoaatB8bhi1Z6fUkqHvKsp2Oer9PF5GXlYd3gdLs65OOBjqjGAnSLLfz+vx77TZ5AcF4Pffv0SjMnw8o9eYcxkMuHewglYdPWFMJuAtw6cxE/f3IXOHvWvkEykaShlxIoWthSGifJVQHWp+J2ICK4hlUtgpTRkUtDeF42zkVjlRO7kVseo8QFejX1UNVdh5pCZqGqu8rp2JRU/nqp9vK2zsrHSZQB4XEwc7ph0B+Ji4lR7foHQMgxzPl/BPl+lj69qrsL8CfOxs36nKrPDOOCcAGDL0ToAwM0XjsCErFSdVxNaN+eNwDM3TUd8jBnlxxrxYGk5Wjq79V4WRZiwHXSuFrYUhphaFU6FDwBTisXvRAYRjQGFntwrdpyrb1wqcZTOkPLX3udjDdGAVU4UKLkf4H2FBGqEAGoGP95mT3lbp9IZSPLWIGBnbQtsdsGxJqUhi5xzolarnXMAFuzPhNLzZc2yoqXLtQKOLXgUjO4+Gz6qEa9Kd+XYXJ1Xo41ZY3LwfHEhUgfE4mBDK773z49w4iz/7kvqifpQii2FIeahwimg6rShU4FbXxS/ExlENAYUepJdsaMgZHIhY7aUGlVDDDMpUgUaYoQ6JFCzukXp7Ck5x1Z63vYcP4Os5HjsOX7GsSal50/Ouny1JCpZbyChoFo/E56ep7f1cIg5ybHrRBM6e23IShqAC3IG6b0czUzJTcMf51+GISkJONXWie//8yMcqDur97IoQkR9KGXElsJw5xI6eahwYnUaRQr3gIJhgzLnuvvwj53Hca5b3nyCUFbsdPR2YPcHT6Cn8TOfbX9qrCGYMJM/Y2QUnj7ABxokhNOcnmBnT3ni77y5V0ZZhw3C1sONsA4b5FhTKM6fr5ZEb+v19HMRSCgYyp8Jb+thBRXJ8eGXrXtXjM2B2WTSeTXaGpGWjD/dPhMTs1JwtqsHD5aWY/uXVWNEwYj6UEov+06exSNrK7Dv5Fm9l+JCjRlbLqGThwonVqdRpHAPKCK1cipUQchb+06htqkDb+07pep+A1FWW4by9MHYb+/w3/YX5NX6gqm2CtXPGMMuUsrTB/hAg4RwmtPjvlYl1TXetnU/bza7DbvqdmFX3S7xMe6VUSfPYvbYdBzdvQm2vh5UNlbCmmXV7Pz5ep2ln4uKhoqgqo68XcHRmdqVTeEUjmqls7NT7yUYSp/djv9+LoYwV0RJ65679MR4PH/rDFw6IgtdfTb8vzd3Y2PVcb2XRWGOoZROXtpWgzf3ncZL22r0XooLNaqY/IVOkVCdxgH55EkkDIT2FE6EKgi5KX8IRmUm4ab8Iaru10FBeFQ0qgiD08dj0rVL/bf9BXm1vmCqrUL1MxapgSqFjqcP8GqES76CBqO1V3mbL+WNe5AnPR8ALuetsrESTZ1NaOxsRGVjJaaOSEN923kgvgY9fT1AfA06jn6ECWPH4UjVa67hoN0GHPtY/O5lzXLPobfKIV+vs/RzAUCVqiNf1UtKK7b8CadwVAsPPvggsrOzcdFFF+HIkSN44IEHcNttt+GFF17Qe2m6qfyiBa1dvUgdEAvr0HS9l6ObpLgYPDd3Om6YNBQ2QcAzH+zHSzsOQRAEvZdGYYqhlE7umTUaX8sfjHtmjdZ7KS7UqGKKhNDJH7YgkieRMBDaUzgRqiAkOT4G37h4BJLjY1Tdr4OC8EjRa6d0kLqKQvUzpkWgymqsyBKqD/CBhhBKqDnAW8l8Kfcgz9vzycvIQ0t3C9IHpH9ZAWVCbNJx5CZlo/RIKXKTstE9KgOWjgaMy1vgGg6e+ARIzhG/e3i+Ss6htN68jDzZ50v6uSjILlCl6shX9ZKv+yoaKtDQ0YCKhoqgjh/N3nnnHTQ1NeGFF17ArFmzMGzYMJSUlGDr1q144oknZO/nhRdeQH5+PlJSUpCSkoIZM2bgnXfe8br9n//8Z1x++eVIS0tDWloarr32WnzyySdet9eS1Lp3+ZgcxJij+2N0jMWMn82xomT6WADASzsO45kP9qPPbtd5ZRSOovu3SUf5wwZh5R0FyP9yHoBR2LqGouvUHbB1DdV7KYamZgsiq67ISDyFEyEJQpS2wAXSMheq8CjQQeoGpkWgymos8sQ9IAo0hJCzb4la4Zav+VLOx/ZWEeXt+VQ1V+HyoZcj1hLbb9viccXiY3KmAiNnwBIT5xoODr8EOFcPDL/EMYtqT/1ex/NVcg6lgKmquUrR+ZLCr0BbCp3Pna/w018wKkBe1YbRKvCMIjU1FQMGDMCMGTOQmpqKxx57DPPmzcOrr77qM1RyN2zYMDzzzDPYvXs3du3ahauvvho333wzqqs9/713y5YtWLBgATZv3oyPP/4Yw4cPx3XXXYcvvvhCracWELsg4MOj0d26585kMuG+yybhx1fmwWwC/l19Aovf3I3zvfJmhRJJGEqRC73aCsMtmFGzGoxVV+Et0qo/NKv2+rKKqa1iNR7f9jhqzpz2PfS88jXgwEbxu1xhGB5F2s+Ts0hobyVlpA/7PX09Xj/0uwdEvmY2Ka3O8hY+qTU7yNd6nI+ttA3OV2tknHsI5c5sAUbOAMwW7Dl+BhlJcdh/NBX/PbkdeRl5Ho/pL5TxNPPK1/ZyQj9f+1AjNCzILkBOUg4Ksgv8bhvo8Tw9h0gKuBobG/Gvf/0LNTU1SEr66u8EFotFUZvW3LlzceONN2L8+PGYMGECli5diuTkZJSXl3vc/u9//zseeOABXHTRRZg0aRJefPFF2O12vP/++0E/p2B8Wn8WjR1dSIyNwfThmbquxWiK80di6Y3TEGcxY3ttAx4q3YEznd16L4vCCEOpaPDFHuD1e8XvfujVVhjNwQwHv4c3ras/Iia0+LKK6TddtahtrcX/e+efhhl6rqdAfp7C5WciEtpbSRnpw37pkVKvH/r9BUTBBBTe9q3F7CDnYysNwSxmC6xZBdhzvNVxxT1v3K/M52zqiDRsO9KErPQmpAoXoaq5yuM+/J1j9/Plb3s5z9fXPjw9XmnYo+Q1DjSk9PQcIukKfj/84Q/x73//G3fccQc+//xzXHbZZfif//kfPPPMM2hubg5onzabDWvXrkVHRwdmzJgh6zGdnZ3o7e1Ferq+M5y2fNm6d9nobMTHcO6YuyvG5uL3xZciZUAsDtSfxffXfYSTZ4399xIyjogMpcKt6iYUXM5B+SqgulT87odebYXRHMxEwwyuSKZ19UfEtEB9WcV0/yU/wajUUXjmhtt9Dz23LgAmzxO/RwBvQVIgP08R8zNBEcE5POjXcubhQ7+/8CCYqia1wic5FV/O27lXdcldh3PAtKe2CcPa9mJPbZPP7f++4xjSE+McV+ZzZjGb8M1LRyJ7wDhkp3d4PYdKz3FeRh62nxIrrzyR83x9HdPT40M5T0z+6+P6WE/nIZKu4PeDH/wAL730Enbs2IGzZ8/i73//O4qLiyEIAmbOnKloX/v370dycjLi4+Nx3333YcOGDZg8ebKsxy5atAhDhgzBtdde63O77u5utLW1uXypRRAEfHhEDKWuZOueVxcOTscfbrsMuQMTcLK1E99f9xE+rT+r97IoDJgEGfWXbW1tSE1NRWtrK1JSUrRYV1AWbV2EstoyFI0qworZK/Reji5czsHYb4iBVOEDwNCpei+NAhRuv4fhKBzOcUdvh+N3mxUnOutuF9sQp3xdcYtg6eFSHG87jhEpI1A8vjioZUTaz0Q4/B6Gs1Cf3z31e5CVkIXG842YmvPV3zmCnTOkhPOxAAR9XOk5bT+1HTOHzOz33Ny383a/+9rc17OztgVZyfFoPNeNqTiIqrYE5KWch2XUZR73tbO2BRlJcdh2pAnfvHQkLGZTQM/PG29r9fc8Q/Faq7VPOa+R3McGs69oe5/r6enB8ePH0draivXr1+PFF1/Ehx9+6DeYeuaZZ/Dss89iy5YtyM/P97ntkiVL8NRTT/W73d85ttuBmhrAZAIGDPC8zdGmNnx7zX8RZzHj7e/OQWJciC7QEiGaOrrwozd24nBTGxJiLVh6w1QUjsrWe1mkoo4OID4eGDHC93Zy3+sislIqmqtuJC7nYOhU4NYXAw+kFLT/EUUrrVqoPLVAhUv7Vkj5GYQeknOk4Op+7tSssGNbHIWS0rYpb1U0UqVLRUNFyGfuyJnnpOR5yan4ct4u0La1qSPSUN92HoivAYZNhzW1C5YRl3rd19QRaWhuP49vDj4FC9S/4lWgM7mCqWryVpUmtjRaUdlY2e81C+S1DKSSyf2xkVQV5Ut9fT02b96M//u//8PChQtx5ZVXIicnR9E+4uLiMG7cOEybNg3Lly+H1WrFypUrfT7ml7/8JZ555hm8++67fgMpAFi8eDFaW1sdXydOnFC0Rl+k1r1LRmQxkJIhM2kAVt1aiIuHZ+J8rw0/+fcuvHVAvdeDIk9EhlJy2qFC0eK37+RZPLK2AvtOnlVtn4FyPgdBr0tB+x9RtNKzhSrS27fOdff1G4Le7zY/AVFIzlEQV/djkEThQmnAUNVchZlDZvabXyR9gAcQdGDhL3yQM89JyfOSO2TcVxuYtPa8jDwfbWsmxCYdR25SNiqbq2AbXoidPuZKWcwmXGw5AsvAXODEJ18ex/uMKaUCnckVTFhT2ViJ1NhUPLb9MaTGp8qa1xTIa6lGBZcWs8n0NGvWLOTk5GD27Nn4zW9+g2PHjmHdunV49tlncfDgwaD2bbfb0d3tfRD2s88+i1/84hf4z3/+g+nTp8vaZ3x8PFJSUly+1PLhl6HUlePYuidXUnwsfjnvYlw3cQhsgoCl7+3DyzsPKxqST9EjIkMpOUIxWFuvK9f5E/S6Ch8AphSL34nIIzUrX5RW9RSNKkJOUg7aetrw2sHXIq5i6q19p/oNQe93m5+AKCSzv8Lw6n5ESikNGPwFGQXZBYqu5ObMV/igdJ5TqKpcevp6sPbgWpzvOe/yvKS1VzVXeVyPp9Bqz/EzyEqOx67aZuw5vRO22u2A+3kafglwrl78Drg+Rjq+3QYc+7j/Y30Ipl0umLDGmmVF6dFSfH3M11F6uNTl9fH2mmlVsRRJQ8zlGDJkCMaOHYs//elP2LhxI5599lkkJSXhkksuQVpamuz9LF68GFu3bkVtbS3279+PxYsXY8uWLfjmN78JACgpKcHixYsd269YsQJPPPEE/vKXv2DUqFGoq6tDXV0dzp07p/pzlOPk2Q4caWqHxWTCrNFsQVMi1mLGz6+7CN+aNhYA8KePD+GXW6pUCc0pskRtKBWKFj+9rlznT9DrCrb9jygKqFn5orSqJyk2CfGWeOw4tQNbT24NWcWUXm2CN+UP6TcEvd9tfgIiViYRBUZpwOBve/c2rMrGSqQPSMe6Q+sUVUBJpDCnoqFCUWCg9HnJDc9Kj5RidMpoPF/5vMt6/F1RrrKxEulxqfjv9mdg7TgHC8T2vMZz3TAnHMPQs6fwaU+LoyLKwWwBRn55FbNjH2PqsBTHY9Ljvzyvx8uB5Jz+j/VBbgCjtL3T32MtZgseKXgEJzpO4JGCR1xeH28tfIGEYJ6O7e+5+Aq/gjkPRvXPf/4Tf/zjH/Hb3/4W1113HXbs2AGTSfncsoaGBpSUlGDixIm45pprsHPnTpSVlWHOnDkAgOPHj+P06dOO7V944QX09PTgtttuw+DBgx1fv/zlL1V7bkpIVVJTh2UgZUCcLmsIZ2aTCQ/MnIQfXDEZJgAb9h/HY2/vRldv5PyuUPAictA5qaO6qRqrD6xGyeQSXhnOAPh7GHpan2NvA6oDGVzd0duBjUc3AgDmjZ0XkvBFzeHcpL5IGXjO97rQ0vP8SuGLXbAjJzHH8QF/3aF1KMwtREt3iyN08Feh476v+s56x30F2QWwmC2qDtyWO9S6p68HpUdKcfOYm/HpmU99Htt5n9YsKzZvX478uAyc6+3AmOEzHWFTT18f1lfuwPw0AbGjZohBlN0mhkzDL4ENwJF9r2L04EtQV/Uahl7xM8BsEc/r4EK0dDZiak+fWE2lIISTc+721O9Benw6yuvKMX/CfI8VYBUNFQC+el0ku+p2obGzEVmJWZieK69FK5jh4v72436b9FoWjytGXIxrGOF+fpSuK9ze53bt2oWf//zn+PDDD/HBBx/g0ku9zzozCrnn2N+g8+/+czuq687ix1fmoTh/ZAhXHPk2HzmNp8r2osdmR17uIDw392KkJjDoC0ccdK6RUMycCjehaHEkoq94q4gKpKonKTYJCyYtwIJJC0IWSISkBS6MOFeKeZpzpbdIny1G4ce9ekSqvgHgCGIsZgvmT5jvEkjJqdBx31dBdgHMJjNyEnMcj/W0L38VLd7ul9siFhcThzsm3YGEuAS/1TvOQ+EtZguumvFTnImNx8ghlzja8Wx2G9bt24XCERdir3nyV6HSiU8c1U+VjZVIHHUFKj9ZiZgx1+JI1WtfndeuFlhzpooBVwhmH1mzrCivK0fh4EJHBZP7a97Y2YimzibH6yBtYxfsMEFZ5Y3c1yGQqifptryMPOyp34P1h9djdMpolB4p7bdf98q8SBh6vmDBAlRVVXm8b/r06Xj77bfx7rvv4rHHHsO1116r8er00XiuC9V1Z2ECMHussuHu1N9V4wbjt7dcioHxMaiqO4vvr/8Ip9s69V4WGQBDKS8YyPAqhkSh5h7yGP0qerq1wPm5sp5W+3cOfTzNuVJK7dc72kNDUpca7UjuoZD0wb0gu8AlsHFuv5L74d7Tvtyv/OdpX/5CL2/3h2KotftQeEtMHCZO/Q4so2aKodOX1TgzxwzBxycOYOoIpzk+TrOkrFlWNHa3wFr0G7S31mBc3gLY7AL2HG+FNavA75o9vdZyw0GX8MtDqGjNsiIrMQuZiZmO10HaxmwyIzspGwXZBbLPmZxh8lIVk69A0n0/zpVP+5r2oaGjAWNSx6CmrQbF476qDPYUrPpbV7j4xz/+gWuuucZrMCUIAqxWK95//308/vjjGq9OH1u/bN3LG5yGzCQPZVSk2EVD0/HCbZchJ3kAjp/pwPf++RF2nWjC8TPncLqtE43nunD2fA86unvR3Wfj/KkowfY9L9i6Zhz7Tp7FS9tqcM+s0cgfNkjv5egmGn8Ptab3OWZ7nKhfG9qe1eKV9TLGiHOj1CZz/87rEuzxeGvfKdyUPwTJ8YFdHpqvt2d6/x5GOrnnV2pHqu+sh9lkdlQ1KWmJU7N9Ts6+5bRQ+VtTKNcs+1jHPharoM7Vwzb8koDWs7O2BRlJMfj4xAHccdHFflvw3M+b0vMk/XdeRh6qmqt8rlfuOVb6Wri3Qyr5+XC+zy7YvbYVqvXzYcT3ObPZjDlz5qCiogIffPAB8vLyXO6vr6/H0KFD0ddnnAphX9Ro33uotBy7TzbjwVkX4M6pY0K84ujSeK4LP3zjExxt9v8PjmYTEGM2I9ZiRqzZjBiLCbEWM2LMZsSYTU63mxFrMSHWbIbF/OWfv9xO3Mb05e3ife77lP7bfZ+u/y3uP9bidJ90bLMJMRYzzAHMXgs3arfvMZQiw3tkbQXe3HcaX8sfjJV3yP/XtEjD38PQ0/scR8pMoGD1C2u624HqDeKV9UJxpbtQ798Lvt6e6f17GOnknl9P85+m5kxVbaZPoLyty/k+LQKlkJLmRQ2dBnyxW9YsKLEy6gymjkiDxWyCzS5g7d4dmDlmCFq6m3y+Vs7nDYDHc+hvhlIofi6U7tPT/Cpfz83bfd7OgZqM+D5nsVhw8uRJ/OAHP8AHH3yA999/HxdeeKHj/vr6egwePBh2u13HVcoXbCh19nwP5r74HmyCgHV3XYWhqYkarDq6nOvuxTPv78eek83os9vRa7ejzybA5j+eMDSLFFR9GVLFfhmQSX92vd016LK4h2xmpwDO4hyAfRWsuW8nhW4Ws+t24u2uYV6M2RTQBQwYSlHUYaWUiL+HoReO5zjcgw1P69frOZ3r7gu6+omCF46/h+FE6fl1r4CRUwkTSt4quNzXG/bBFOBSMeW4wp4zp2HnO4+3Iis5Ho3nunHxqHQAgZ0Lb0GQ++3eKqWUnndfj1Nj/XKro7QOWI34Pmc2m1FXV4fMzEx885vfxHvvvYcPPvjAEUzV19djyJAhsNnC46ppwYZSbx44gWXv7cP4zBT87c7LNVgxSWx2AX12O/rsAvpsYljVaxP/W/wuhleO22129DrfJ/3Z8Vhpf+Kfpe2kfUp/lrbzeAwvx+612RHeERqcAiqzy5/dK81izF+FWybBhDGZA/GL+RN97puhVIRgIEMS/h6GXjie43BtAZMCIFviLjR0GWP9/9h5HLVNHRiVmYRvXOznn37Ir0DDxXD8PQwngZ5frT7AB9teF8p1qhl4ydqXU+jksVLKpc2vEHtqmzDVfBiWEZcGPNTc27pCFfbJfb0CbflTO/QK5nHOjPg+J4VS2dnZsNvtjmDq/fffR35+ftSFUj/ZuBPbaxvw3cIJ+J9LxmuwYu/sduDsWSAhQfwiY7HZBZeQyzn06v0yWOuzu4Ze/baVAjjbVxVjztVjX93ufgzBcbvL45yO7Xx7j029Ssf8wenY+IiHfzBxIvf3kP8U7MSIc6Re2laDN/edBoCobl0jIs+KRhU5PviHE2lQ+OC0fIzIQEjXLzccuSl/iKNSioLnPBhe78CRguc8oycU3NvyKhsrPYYU0kBp58c4hwPO61QrSJG7NiWBiPMgbq9hjNniuUJKMvwSR2hlMZsw1XwIn/a04ILj5eKwdD/r88T5/Mq5PVjS6yVd8c7b2mSdLw/r9LXuQJ+T3LWEG+cWHrPZjL///e/41re+hWuuuQbvv/8+cnKi5+pzHT19+OR4EwDgirG5Oq8GaG8HkpOBzk7AYgHi4vReETmzmE2wmC2IjzF+da4giO2RztVeNrtr6NXrsyJNDMU6ztuRNTBetXWpevW96qZqLNq6CNVN1WruVjNGvOLePbNG42v5g3HPrNF6L4WIDEi3K+IF6ab8IRiVmYTiglEhX79zOOJLcnwMvpGfhuTqNaG70l8U4dUAI0uwVxfzdzU/b1c188XTFdac1yn3CnJyjwMA9Z31sAt2j8/D/Xi+ji/3KoOA+K/wO2tb+l+FSgqtvnxNKuPjkGWzozLe8ydWtc6HEv5ed+n1qmqu8rk2JedL7TVquRY9uTfPmM1mvPrqq5gzZw6uueYa7N27V5+F6eDj2gb02u0YMSgJo9OTdV1Ld7dYxZWVJX6dOydWThEFwmQSW/AGxFowMD4W6YnxyEoegCGpiRiZloyxmSmYlJ2KCwenoWBoBi4ekYnLRmXjirG5uGbCEFw/aRi+Nnk4vjZpJGaPUS+wVTWUMmKoo0TJ5BIUjSpCyeQQXN0pQPnDBmHlHQWatO6Fe6hIROEjOT4G37h4hCazm4pGFSEnKQfdtm509Hb43rh6g3glvuoNIV+Xu47eDpQeLvW/xjARroEpBc7Xh3t/gYj0Qb8gu0B2+OUpHHBeg1rhgfPazCazo1rK/Zh5GXkux/N1fCUh357jZ5CVHI89x8/4XmfOVHyRPhxWL9U7eoQpcoMwf2vzdL6UhkneHqc0rAs2oDWqt956C6mpqS63ScHUddddh1tvvVWnlWnvw6N1AMQqqUCGQKtFEMSB0pmZQFISkJEBpKWJrXxhPgucyIWqoZQRQx0lpmROwYrZKwzTuqe1cA8VibTiKzyItGAhEiTFJiHeEo/6jnq/1VKY8nUgY4z4XWNyK7qIjEr6cF/RUNEvLAgkdPDH02OcAwal+/QWcjjvx9PzqGysRHp8OkqPlLq0n6kVXkwdkYbGc92YOiLN53b+jud8f6CBjlJyg7BAzpWnMEl6Xj19PbID0kitfFLqhhtuQHx8/3Ycs9mMV155BTfffLMOq9Jed58NH9U2AACuGKdv657UtjdokPjfZrNYLZWYKN5HFClUDaWiPdQJd+EeKhJpxVd4wGDBmDy1knkMEOMHAlNLxO9BUhpQKm13YwBKRiN9uAfgs60u1Gvw1WLniRRkVDRU+K2Y8fQ8rFlWlNeVo3BwYUha4yxmEy4elQ6L2X/FhtywSW51UDDhVaivhOgeJtnsNqw7tM4REMppnYyoqzWGkFQx9fHHH+u9lJDbebwJ53ttyE4egAuyU/0/IER6esRqqMxMIMapqDwuDsjJEVv6zp/XbXlEqlI1lCJt7Dt5Fo+srcC+k2dV3W80h4psXSQlfIUHnKNjTJ5ayUIdICrdv9J2NwagZDRSYFOQXeC18iTYkMPfYy1mi0uLnZzHBDLPyv2Y8yfMR0tXS7/HalWRJFGrXU7p/tR+rBzuAWFlYyUKBxeivK4cxeOKZbVO6jFnK1yZTCZccsklei8j5LYYoHVPEMTZURkZYqWUu6QkIDsb6OoC+vq0Xx+R2hhKGYjcYES6It9L22o0WlnkY+siKeErPAjFHB1/FTHhVjFjlPWGOkAM9/2rxSivN2nHV1WUrxY/fyoaKtDQ0YCKhgqf2zkHLnKOF8g8K3fenrOc0EPN4EpO2KSkOiiY1jZ/M7/UZs2yoqWrBfMnzEdcTJys15Kte+Ssz2bHtpp6AMCVOl51r6NDDJ7S071vM2iQWEXV1sbB5xT+GEoZiNxgJORX5PtiD/D6veJ3H0JVsaUHti6SkfmriAm3ihm56w00zDjX3Yd/7DyOc92+//kw1IO4w33/atl4dCM2HduEjUc36r0UMgBfLX5yCPA/3dfTDChfx1Nr9pQnckIP9+AqmOBGznNRUh3kvj8la/M380ttas0lo+i191QL2rp6MSghDvlDfCRCIdTbK1Y/ubftuTOZxEqqlBQxmCIKZwylDERuMBLyK/KVrwKqS8XvPkRSxVY0ty6S8fmriAmkYkbP6hW56/UbXnW3A3tWi9+dvLXvFGqbOvDWvlMAWKmjF+m8d9u6ISNHoCghp8XPm4LsAuQk5aAgu0DR8aRj1HfWq1IRU9lYifQB6Vh3aJ3fcEZO6OEeXIUiuPF2VUKlAViwa2NlEhnZliNi697lY3JkzXALhfZ272177mJixDa+uDixuoooXDGUMhBvwYjm844KHwCmFIvffQh5xVYY44wqUpO/iphAKmb0rK6Su16/4VX1BqD5c/G7k5vyh2BUZhJuyh8CIPwqySKFdN7jLfGYM2oO5o2dp/eSyECCqWoBoDhIyUnMgdlkln3lOU/bSLflZeSh/HQ5CnPVGWzufi4CCW78PSdvVyVU2l4YbKikZlWa1vO6KLLZBQFbPxdDKb1a9zo6gIQEsW1P7jirAQPEwed9fUB3d2jXRxQqDKXCgObzjoZOBW59UfzuQ8grtsIYZ1SR0YXDPCK/4dWUrwMZY8TvTpLjY/CNi0cgOV6se5d95b0wZOTnIZ33eWPnhUWbIfmnRwjg6ZhKq3XyMvKw/dR25GXkyX68p22k26qaq8TB5t39B5urwVNwoyR08rS9tzBJaXuh3FBJrZ8VX6+V1kPKGYJFtk8bzqKpoxtJcTGYNixD8+P39Ymte1lZQGyssscOHCg+rqMDsPHHk8IQQ6kwwHlH4YevGRmd0eYRBRSuxA8EppaI378kzZOqb+tyzJXS48p7WjHy8zDazxgFT48rlXk6ptJqnarmKswcMhNVzVWyHy9tk5eR5wginMOtYGYteaI0dPK2Xm8tgN7CJKm9UbpSoZx9y3ku6w6tQ/qA9KB/VqxZVtR31sMu2NHT1yMraAsVXqkvsv23RqySmjk6G3Ex2s8Ya2sD0tLEgCkQ6eniV2urePU+onDCUCoMcN6RdtRqu+NrRqFk5OqYQKkVrkjzpJ79z0GXuVLuwqFSTI5IeR4UHrQIAeRU+ChtAXPfh5zHS9vsa9qH+o56VDRU9Au3nMltg9tVtwu76nY5np/0fCsaKhSFTu77db+antyr8Mk5ttLzXdlYicLcQpSfLnccP9DQzmK2wGwyIycxB6VHSmUFbaHCeViRSxAERyh1hQ6te52dYhteRob8tj13ZrNYLZWczMHnFH4YSmkgkq5SF+nYdkfhwMjVMYFSK1yR5kn99PpJLnOl3EVKFU+kPA8yPk/Bh3S70rDB12N8VfjIPZb7dsGGFyaInxJ9hRJy2+CaOpvQ2NnoeH7S8wXg8/HenoPNbsM/PvsHTp87jYqGCr/bu69HzrGVsmZZ0dLdgvkT5juOr3R2lfv+Gs83onhcsc9gLtjWOn/74JX6ItfnLe043d6JOIsZhSOzND22zQZ0dYmBUlxccPuKjRUHn1sswPnz6qyPSAsMpTQQSVepi3Rsu6NwEInVMWqFK9I8qZyUAS5zpYgoON5ChcrGSqTHy7sSnb99Ab7DHbntU87bBRtWFGQXIDspGwXZBT5DCblX2ctMzERWYpbj+UnPtyC7IKDAo7KxEqlxqTjedlzZE1Ph2IDnIMfTufBX6eWrYkvaX1xMnNd1qtFax/a86PXfWrFKqnBkFhJitf17g9S2l5Kizv4SE8XB593d4owqonDAUEoDvEpd+GDbHYUDVscQkdbch4VLrFlWlNeV4+Lci2UHU74CCl/hjtz2Keftgg0a1KyOsZgtmJ47HdNzpzv2F+z+rVlW5CTl4OLBF6Mgu0DxeoJ9bnIroNyr7JzDLDUqttRorWN7XvTaplPr3vnzYnVUZmbgbXuepKSI+2xvB+x29fZLFCoMpTTAq9QRERFROPM2T8litqB4XDFKD5fi4uyLZYU/gYYhch/nvF2kBw2egi4g9FeKk/afl5Gn6Op9nm5To2LL38+Gt/PhfDvb86LT503nUHOmHRaTCTNH52h2XKltLzMTiI9Xd98mkzifKjVVHHxOZHQMpYiIKGxE4pB3onDgK9ypaq7C/AnzsbNhp9dwQu2QRO7+1A4a3I8b6vBHKbnDy4MlhUpVzVWy2hbdf3acb9MiDPLVfsqWvej2bnU9AKBgaAZSBsRqdtz2djE0Sk0Nzf4tFnG+1IAB4rGIjIyhlIY48JyIKDiROOSdKBz4a6tr6XIdbO1O7Q//eoUJ7sdVax1qhVtqDy/3N3xczv6lnx0AAVclOa8jkHPlbb3OtxstYCRtlFWLrXuXj9Kude/8eSAmRqxmMofw03h8vDhfShDEqiwio2IopSEOPCcigNU+wYjEIe9E4c5TwOD+AT/YNjq19xco9xDDLthR31kvex3egg8l4Zav8ESNVjhnFQ0VqO+od7myHxBYBVowAZ7zYwPZj7f1Ot/Oqqnoc6ajBwdOt8IEYOYobVr37HYxlMrIABISQn+85GTxyn6dnUBfX+iPRxQIhlIa4sBzIgKMU+0TjuEYh7wThQf3D/juoYDSqhR/+9OKe4iRk5gDs8ksex3egg8lIZuv8ETJeZH7Gpjw1QTmYKqJggkSnR8bqkDS236DrdIi40pLisOOxdfif4umIz1xgCbHbGsTW/bS0jQ5HAAgPV0MwdraxKopIqNhKKUhDjwnIkDfah/nIMoo4RgRRQ65A7CVVqVYs6yo76yHXbBrFgj4CyCUhiO+KquUhElqhTJyXoOC7AJkJ2U7ruzn6zH+zlcwQaLzY0MVSHrbr3O1GKupIs+gxDgUjtCmSqqrS5z1FOq2PXcmk1gtNXAgB5+TMTGUIlnc52FVN1Vj0dZFqG6q1ndhRGFIrWqfQCqdnIMotsIRkdqkD+37mvb53E5usCIFHQBgNpmRk5ijWiDgL0TxF0AoDUcCqayS9PT1YO3Btejp61EtlMnLyMP2U9uRl5HndRv3Y/l63YwY2KhV2SRVi0X61RxD4YUXXkB+fj5SUlKQkpKCGTNm4J133vH5mHXr1mHSpEkYMGAALrzwQrz99tsarTZ0BEFsocvIABITtT9+TIw4+DwuDugInwJ5ihIMpdwwbPHMfR7W6gOrUVZbhtUHVuu8MqLoFUilk3MQJScc6+jtwGsHX8NrB18LqzY/0l84toeSf3KrhwCoEug4Bx1qBwL+QhS1jxfM/kqPlGJ0ymiUHikNag3Or19VcxVmDpmJquYq2Y/3N/DeX/ubvzWpTXqNKxoqFB9DWld+Zr6jWsxitiAvIw/rDq1DT1+P6uuNRMOGDcMzzzyD3bt3Y9euXbj66qtx8803o7ra82etjz76CAsWLMA999yDiooK3HLLLbjllltQVSX/59SI2tvFSiUt2/bcJSSIwVRvL9DDH18yEIZSbhi2eOY+D6tkcgmKRhWhZHKJzisjil6BVDoprdIqqy3D1pNbsfXEVrb5acCoQU6wVXkUOeRWDxVkF6gS6DgHHWq3bfkLidQ+XjD7Kx5XjJq2GhSPKw5qDZ5CvryMPFVCIW/Pz9/PjPv9aoZUckNSX+uqaq5yuXrg+sPrVQkIo8XcuXNx4403Yvz48ZgwYQKWLl2K5ORklJeXe9x+5cqVuP766/GTn/wEF1xwAX7xi19g6tSpeP755zVeuXq6u8XvmZli+56eUlLEYOrcOcDG0WhkEAyl3ERU2PLFHuD1e8XvQXKfhzUlcwpWzF6BKZlTgt43EQVGi6HfRaOKMHvYbMwePpttfgFSEuhsPLoRm45twsajGzVYmXzBVuVR5JBb7aNWoCNnP3JCDE/b6DUsXS7nNcfFxOGOSXcgLiYuqH16Cvn2Ne1zzEwKRdWSr3lgnuZsBTO3yp3SkNR5/+4/69K6xg0ap0pAGI1sNhvWrl2Ljo4OzJgxw+M2H3/8Ma699lqX24qKivDxxx9rsUTVCYLYLpeRASQZ5Bot6elixRYHn5NRMJRyE1FhS/kqoLpU/E5EFICk2CQsmLQACyYt4BXvAqQ40DHgXxCVBkzSIH2pTZQihxGDHDmzjLSadxRoqOPpcXLXLPeYNrvNUSHl/vpJM5NCdZ6OnD2CzAGZ/fbrac6WrzbAdYfWIX1AOiobKxWd60DaRb3N05qWM02VgDCa7N+/H8nJyYiPj8d9992HDRs2YPLkyR63raurQ06O6+DxnJwc1NXV+TxGd3c32traXL6M4Nw5IDlZ37Y9d2azOPg8MVFsKyTSG0OpSFb4ADClWPxORES6UBLozBs7D3NGzcG8sfM0WJl8gbR9snWPtCKnesvXNmpWB1U2ViI9Ph3rDq1TtD9PYZCc5+Ue1HjbZk/9HlQ0VHgMnJyvsBeKQd6VjZUoHFyIj05/1K9aytPxfLUBFuYWovx0OaxZVvFcD/B/rt1fX1+vt6/nb8RANlxMnDgRe/fuxY4dO3D//ffjrrvuwoEDB1Q9xvLly5Gamur4Gj58uKr7D0Rvr9gil5kpDho3krg4sY3PZALOn9d7NRTtGEpFsqFTgVtfFL8TUcQw6twho9L7fCkJdLRoydQCW/eii9JQR8n2craVwgIAXrf1FSioWR1kzbKivK4chYMLFe1PSTjjzD2okTifN+n5AfAYuDgfJxTBizXLipauFkxIm9Dv6on+jufeTtfS3YL5E+bDYraI5/p0OQpzfZ9r99fXV3DI4Ck04uLiMG7cOEybNg3Lly+H1WrFypUrPW6bm5uL+vp6l9vq6+uRm5vr8xiLFy9Ga2ur4+vEiROqrT8QgiBWIWVkiJVSRpSUBOTkAF1dYoBGpBeGUkREYYZVKMrwfGkvUsI1kkdpdZCStrR1h9YhPd5/FZBz+KI0XFKzOshitmD+hPlo6WpRbYC4L9YsK5q6mjBu0DiX2z0NNC/ILtAlcJE718lfC6N7YOQ4192u59p9P+4zrfwFh6G8GiCJ7HY7uqXp325mzJiB999/3+W2TZs2eZ1BJYmPj0dKSorLl546OsSr3aWn67oMv1JTxUqu9nbAbtd7NRStGEoZ1L6TZ/HI2grsO3lW2wOrOBydiEJDqyqUjt4OvHbwNbx28LWwrsoyYtVOoNVboaz60ruijMKX3Oog6cN+XkaerBBIavsqryv3uq2n8EVpuBSqq+xVNVfJDskCDdQsZgvMJnO/CqRArloY6jDG3zoCaWH0dK7d9+N+jpyDQ0/71Wr+WLRYvHgxtm7ditraWuzfvx+LFy/Gli1b8M1vfhMAUFJSgsWLFzu2f+SRR/Cf//wHv/rVr3Dw4EEsWbIEu3btwoMPPqjXU1Cstxfo6xPb42Jj9V6NbyaTGEqlpoqDz4n0wFDKoF7aVoM3953GS9tqtD0wh6MTGZ5WVShltWXYenIrtp7YGtZVRkas2gm0eiuUVV+sKKNAuX/Idw43PFUyVTVXyQpJpLYvqV3L2zZKwxetKAnJgqnWkh7rXC0UyFUL9Q5j8jLysP3UduRl5Dluk/uaOp8/T+fS/TZf+w3FXK1o1tDQgJKSEkycOBHXXHMNdu7cibKyMsyZMwcAcPz4cZw+fdqx/WWXXYY1a9bgT3/6E6xWK9avX49//etfyMvL83YIw2lvFyukjNq2585iEQO0+HhxMDuR1kyC4P9CkG1tbUhNTUVra6vupZDRYt/Js3hpWw3umTUa+cMGaXfgL/aIgVThA4HPolJjH9QPfw9DL1LOsVpXPuvo7cDGoxsBiAO4jRTqhLtAX6NQXtXOKFfMi5TfQ6PS4vzuqd+DrIQsNJ5vBADHn6Xh1J6u/kbBcz7v0owtJds7X50PgOavlfN6QvWz4usKhMFsqxTf50JP7jm224GaGrFiaMCA4I/b0SFe3W74cHGYeDhpbwdOnhTPQ3y83qshI+voEH9GRozwvZ3c30NWShlU/rBBWHlHgbaBFKDOcHRWWxHpSq2Kl6TYJCyYtAALJi1gIKWyQKu3QlH1JbXtATBcRRmFJ+cZPs6tempVMoWqzSzcZwkprfDxVT3kXjWl9rnxtD/n9YSqakvJfvWuHKPw09cH9PQAWVnhF0gBwMCBYsVUR4d41UAirTCUIvUVPgBMKRa/E5ELLeb2GHGGEhmDp58/tu2R2pxn+Mht1XPmLwBREhYoCVPCPYSQrkhX2Vgp6/n6a2FzHg4unZuKhgpVwilP59p5PaFqodOqpZKiU1sbkJYmhjvhKi1NvGJga6t4BUEiLTCUIvWpUW1FFKF8BQAcNE2h5unnjyEmhYKSD/TuwVFFQwUaOhpQ0VDht6LGHyVBk5z96l1N5e/4FQ0VqO+oR0VDRVDHcR8OLp0bAEEFd3KH3YdqPpjR5o5R5OjsFNveMjLEVsBwZTaLg8+Tkzn4nLTDUCqc8Mp4RGHPVwAgt2LFX3gVCZUvDOhCw9PPnxEHwVN4UzqLx1NwJEDwep+SYEFJgCVnv2pXDCklJ2QzQZ1PxJ6GyBdkFwRVPaR02L03WoSD4V45R9qx2YCuLjHMiYRZTLGxQE4OEBMjhm1EocZQKpxwVpOL6qZqLNq6CNVN1XovhUg2XwGAt8DKPaDxFjpJ280aOivsK18iIVgzIgZQpAV/H+bdAwX34Cg/Mx+tPa3Iz8wPquIKUL8yRq2KoWCP7+18FGQXIDspGwXZBUEfy9O5k24D0O9cywmK1KpGkxMYBRtcsX2P5JLa9iJpZn1CgjhfqqdH/CIKJYZS4YSzmlysPrAaZbVlWH1gtd5LIVLMUyWQt8DAPaDxFl5J2237YlvYBw9sKSMKX/4+zLsHCu7hR1VzFWYOmYmq5ipFx61srER6fDrWHVoXdAWNe6Ah/TcAvxVDalbxuO/LV8gWyqvFuXN+DaU1VjRU+A2KlFSj+dqPnMCosrES6QMC/3lgqx/Jcf68ONQ8I0NsfYskKSni0PZz58SrFBKFSoT96kQ4zmpyUTK5BEWjilAyuUTvpRAppqQSyD2g8RZeRVKQw4oeovDl7cO83HlCgV6FzZplRXldOQoHFwZdweR+XH9Bmq/HBrsOucGKlu1mnl4jAKpUFskJnOQERtYsK8pPl6MwN/ifByJP7HYxlMrMFOdJRRqTCUhPBwYNEgefE4UKQykdsf0sOFMyp2DF7BWYkjlF76UQKaYkQJIb0IRrkMP5UUTRQe48IaVXYXOuYpo/YT6azjc5rhoXCJvdBrtgR31nPaxZ1n7/7enYngax52XkBV0xpSRY8XWu1J7B5Ok1KsguUKWySOlVBH3tZ/6E+WjpbmELHoVEezuQmip+RSqLRWzjGzBAfL5EocBQSkdsPyOKXqEIkMI13OH8KKLoEMiMHjkVMc5XnHO/apw/nsKaioYKNHY2Oo5f2ViJnMQcmE3mfuvwNYi9qrkq6Mol52DFX8ilVfWWkuMGylsrptxwzb3d0t/a9L6qIoWf7u6vrlQXaW177uLigNxc8c/nz+u7FopMEf4rZGxsPyMiNYOkcA13Iqnt0KjCNbCkyKA0IAiE8xXnlIRf3sIaufsL9D4l5IRc/kKVcBva7a0VU264pjSE45X2SKmuLnHmUkKC3ivRRlKSWDF1/jzQ16f3aijSMJTSEdvPiEjNIClcw51wbTs0MvcQauPRjdh0bBM2Ht2o88oo2tjsNqw7tA7pA9JD9oHf/YpzSip3PIU1cvbnKWhTMpQ8EL6CJX+hSrgN7XZUiHW5tt7JDdfyMvKw/dR25GXkAYi80I70Z7OJQU00GTRIrAxrawMEQe/VUCRhKEVEpCM1gySGO5Ej2Momj2En/wJJOqhsrERhbiHKT5erMmPJk2ACF+mxABxrC/QKcXKqbfyFI77u9zVA3tvMq3Dm/HyVVtu5X8FRem0qGio8nt9wC+1IXz09YktbJA4398VkEkOpgQM5+JzUxVCKiEhHDJLIk2Ar6NzDznlj52HOqDmYN3aemssk8isvIw/ldeUoHlesyowld2rNAlLavuWpskZOtY2/4wTSRlbZWInMhEwcOXtE9mPCTaCvjxSESld8BMA2PQpad7fYthcXp/dKtBcTA+TkiM+9gxMBSCUMpYiIiAwm0Ao6qcIKgEvYyfCT9OJcsRKKFim1ZgEpXZunyho51Tb+jhPIOVJyhb5wpfTKgu5zuKQrPhZkF7BNj4LW2ytWC0WrAQPE+VK9vWJARxQshlIBqG6qxqKti1DdVK33UoiIvHJuAYvWQdfh+rwDDZHCddg9RS7nMEFpi5ScKii1h4mHun3L33ECWYfzFfoiNWwJ5MqCntoa2aZHwbLZxGqhaGvdc5eSIgZTHR3iOSEKBkOpAKw+sBpltWVYfWC13kshIvLKOaCI1rAi2p73rKGz0Hi+EbOGztJ7KUQA5IUAPX09WHtwLXr6elxu91cFZbPbUNlY6Qi8opnaYYuvQFC6r6evR7UZYcG0YXoLJisbK5GTmAOzyRz1Px+knu5uID5e/Ip26elAWhoHn1PwGEoFoGRyCYpGFaFkconeSyEi8sq5BaxoVBFyknLQbesOu6qhYITrFQkDte2LbchKyMK2L7bpvRQi2UqPlGJ0ymiUHil1ud1fFZRarXvUPxRyPrfe7lt/eD1OnzuNf3z2D8Vhkq/jKX28t0DO38+PWvPIKLp0d4uteyaT3ivRn9ksVkslJgLt7XqvhsIZQ6kATMmcghWzV2BK5hS9l0JE5JVzC1hSbBLiLfGo76jXvWpIy5a6QNvgwrXtL9pCOIoMxeOKUdNWg+JxxY7b5FRBhWJGVbgKNmBxD4Wcz623+8amjsWx9mNIjU9VHAz6Op4cFQ0VqDtX5zMQ81c9xlCTlLLbxe8JCfquw0hiY8XB52YzcP683quhcMVQiogoSvgKLLQMYcKhpS4c1ugJB5pTOIqLicMdk+5AXMxXl7KSExj4Ch1sdht21e3CrrpdsoOacKic8bZGOa2Ovp6beyjkfG7dr2QHAFNzpmJ67nRcknsJchJzFAeDvo4nV21bLVLj5AVinp4/Q01SqrtbnCUV7fOk3CUmihVTXV3i8HMipRhKERFFCV+BhZYhTDhU84TDGokimZzAwFfQUtlYicbORjR1NsmuhFGjcsZ5TaEIubytMdhWR1+hkPuV7KR9WMwWTM+djum5072GSd7OQbAzsAqyC3DJ4EuQkyQvEPP0/Dn0nJTq7gaSkwELf2T6SU0FMjPFNj6pooxILoZSRETkCGFmDZ0V8oqpcKjmMeIaw7WlkMgfT8GFFBgA8Bhq2Ow2rDu0Dunx6R6DFmuWFVmJWchMzJRdCaNG5Yxz+BGK9jBva/QXsKjx3ALZR0VDBRo6GlDRUOF1m0DCOzmBmDNWRZEaBEGsCqL+TCYxlEpNBVpb9V4NhRuGUkQKVDdVY9HWRahuqtZ7KUSqkkKYbV9sC8u2tWgQri2FRP74G6qdHp+OdYfWuYQWlY2VKBxciPK6co9Bg9LQQnpMoJUz0rrzMvIc4UcoghA9q3sCPbYA35flCia8kxtoSW2I0s8YkVI9PeL8JF51zzuLRWzjGzAAOHdO79VQOGEoRaTA6gOrUVZbhtUHVuu9FKKQ0KptjVU/yrGlkCKVv6Ha5XXlKBxc6BJa5WXkoaWrBfMnzNcsoPHXLpiVkIWq5ipHcCMnxNFqjpWv4CeULYcF2QXIScpBQXaBx+MBwVUxKQm0ONicgtHdLQ44j4vzv200i48XB5/b7eKMKSI5GEoRKVAyuQRFo4pQMrlE76UQhUSo29akMGrj0Y2s+nHiHNJ5C+yM2FJIpAZPQ7WdB2DPnzAfLV0tLqGVc/gjCTRQkTsU3VeoEWiwolVQ4mt9wbQc+jvnnoI592MEU/2l5LyzhY+C0dsLDByo9yrCQ3IykJUFdHYCfX16r4bCAUMpIgWmZE7BitkrMCVzit5LIQpLUgsaAJeqn2ivnHJuzWObHkUzTwGFr9DKWaABj9yh6O5XofM0A0tpsKIkKPEWAMkJ43ytz3kNSoMbuefceY2ejhFooKjkvHOwOQWqrw+IieFV95RISwMyMoC2NnEWF5EvDKWIiEgzUgvavLHzHFU/Hb0dWLZjGY6cORK1QYxza57cNr1oD/IoOskNV5SQOxTd21XogqEkKPEWAAVbbeW8BqXBjdxz7rxGf9VTWrU0EsnV3S0GUpwnJZ/ZLFZLJSdz8Dn5x1CKiIg046kFray2DIPiB6G1pzVq5yU5nxe5bXr+KqoYWlG0CbQSJlyu5ObtuHq2pck95/7W6GmuWEVDhUs4xbCK9NLTI7bumUx6ryS8xMSI86ViY8VWPiJvGEoREZGuikYVYeygsXjs0sc4L0mmjt4OdNu6kZOU4zXIYxsgUWj4CmJCGZx4O244tKX5W6OnFk0ALhVgHFROerDbxe9s3QtMQoIYTPX0iF9EnjCUIiIiXXGAt3JltWWo76hHvCXe63nj1fqItMfgJDDOYZ4UUBVkF7hUV3FQeXhavnw5Lr74YgwcOBDZ2dm45ZZb8Nlnn/l93G9/+1tMnDgRCQkJGD58OH7wgx+gS4fLuUmtewylAjdwoNjKd+4cYGOhI3kQlaFUdVM1Fm1dhOqmar2XQkSkuUhv6zLK8wvlOqTAadbQWV6PwbCPSB1Kqp/cgxO2nMnjKcxzr64Kh4ow6u/DDz/EwoULUV5ejk2bNqG3txfXXXcdOjq8/79xzZo1+H//7//hySefxKeffoqXXnoJ//jHP/DYY49puHJRd7c4F8nCH7uAmUzi0PO0NHG+FAefk7uoDKVWH1iNstoyrD6wWu+lEBFpzrmtyygBjpqM0rYWyDrkvh5S4LTti22GeK5EkcxTYOItbHIPTiobK5E+IB3rDq1TPZgKNPAKRVAW7D5ZBRW5/vOf/+Duu+/GlClTYLVa8fLLL+P48ePYvXu318d89NFHmDlzJu68806MGjUK1113HRYsWIBPPvlEw5WL4YkgAImJmh42IkmDz5OSgPZ2vVdDRhOVoVTJ5BIUjSpCyeQSvZdCRKQ557YuowQ4ajJK25r7OuQETkpfj0COQUS+uQcsngITuW161iwryk+XozC3UPWWvkBbBSsaKlDfUY+Khgrd1yKRZklJV9+jyNX65aXY0tPTvW5z2WWXYffu3Y4Q6vPPP8fbb7+NG2+80etjuru70dbW5vIVrJ4eIC6OrXtqiYsDsrPFyqnz5/VeDRlJVIZSUzKnYMXsFZiSOaXffWztI6JI59zWZZQAR01GaVtzX4ecwEnp6xHIMYjIN/eAxVNgIreyx2K2YP6E+WjpblG9CigvIw/bT21HXkaeosfZBTtqW2thF+w+t/NX/eR8v3Q+8jLyAq6Y8hZssQUyctjtdjz66KOYOXMm8vK8/9zeeeedePrppzFr1izExsZi7NixuPLKK3227y1fvhypqamOr+HDhwe93p4esUoqNjboXdGXkpLEYKqrC+jt1Xs1ZBRRGUr5wtY+IoomUqgBgBU2ISYncAo2UIvEkJFISza7DXbBjvrOep+VUUquwBeqWUhVzVWYOWQmqpqrFD3ObDJjVOoomE2+Pwa4P2f35+V8v/Qcq5qrAg6WvAV9HB4fORYuXIiqqiqsXbvW53ZbtmzBsmXLsGrVKuzZswelpaV466238Itf/MLrYxYvXozW1lbH14kTJ4Jeb2+vGKKQugYNAjIzxTY+u+9snKIEQyk3gbT2sbqKiMKdUStsIqkdTYsKLqNUiRGFq8rGSuQk5sBsMruESEqqkrQKUQKdw1SQXYCcpBwUZBco2r/78/J0/GCCJefwzlMVFudNhbcHH3wQb775JjZv3oxhw4b53PaJJ57At7/9bdx777248MIL8fWvfx3Lli3D8uXLYfeSYsTHxyMlJcXlKxh9fUBMDFv3QkEafJ6SAqjQZUkRgKGUG1+tfd6wuoqIwp1RK2y0DMu8BWCRFIwRRTo1Bm7Xd9bDLthd9uGtKsnT8bQKUQKtwAr0ce4tegD67cfbvpWeE09VWLzqXngSBAEPPvggNmzYgA8++ACjR4/2+5jOzk6Yza4fUy1fXv5O0OjSbd3dYiAVH6/J4aJOTIzYxhcXB5w7p/dqSG8MpVTAwelEFO6MWmETSFgWaIjkLQAzahUZEfWnxsBts8mMnMQcl30oqQCSG6IYfVaSt5bFQFr0lAZLrI6KHAsXLsSrr76KNWvWYODAgairq0NdXR3OO026LikpweLFix3/PXfuXLzwwgtYu3YtampqsGnTJjzxxBOYO3euI5wKte5uYOBAsaqHQmPAACAnR7zC4Zkz4ldLy1d/9vQl3X/2rPjV2ipWW7W1ie2A7e1iyNXRIX51dopf589/9dXVJX51d4tzw3p6xFbNvj7xy2YTv+z2r67ASKFlEmTEzW1tbUhNTUVra2vQpZBEFBj+HoYez7G+Ono7UFZbhqJRRUGFY6WHS3G87ThGpIxwzMsK5viebldrrdQffw9DK9LPr81uQ2VjJaxZ1oAra5TsI5jj7anfg6yELDSeb8TUnKkBrVWOQNfo7XHeblfyfNR4ncJZpP8eOjN5SXX++te/4u677wYAXHnllRg1ahRefvllAEBfXx+WLl2KV155BV988QWysrIwd+5cLF26FIMGDZJ1XLnn2G4HamrE8Elq1bPbxZBj1Chx0DmF1vnz/QMg9z+73yZ1cdrtrn+WtvF0m3Pq4Wvf3m4ThP4hpafb3JlMX23j/N3Xbe63y92PViFqR4dYRThihO/t5P4eMpQih+qmaqw+sBolk0sUtS+SNvh7GHo8x/oKNExyF0xgJPexaq2V+uPvYWjx/PqmZVii9rHUCIsCPYa/+9xpFcgZFX8PQy+YUEoq4ho1CtCoMIs0oDSAUvoYb/vxFpJ5C9Gc9xfosX2dA2/BmpLwS6okVCuUivG9G4om0mwsAFgxe4XOqyGiaFM0qsgRCAXD+YqCSjm36vnah1prlYuVWUShJQUqdsHuaN0LdVgitbSpxbndznm/1iyrIyzyRU6o5H4M98fIfT5y10Skh+5ucRA3A6nI4h60GJWagZiS+/1VmjnfZjarexEAhlLkIM3E4mwsIgoVf61welcdyQ2bggm+AiE3LCOiwEhhS31nPeo76wGIIU04tZZ5C3p8hUU2uw0VDRWO//YXyLkfw1sQ5o/agRyRWqQP32zbI72ES3imJg46J4dArjwYCtVN1Vi0dRGqm6p1XQcRqc/T0PBQDRIPZOB5JA18JyKRnIHi0mDtguwCj4POjS7QVsDKxko0dTahsbMRAFyuridnYLmcgeRGH+hO5KynR5yVo2YVCBH5xlCKDEdqI1x9YLXeSyEiBH41O088hSuhClwi6ap5Rg3LiMKBtyvyOYclzmFLoFd+0zN8CfSqg9YsKzITM5GVmIWC7AKfV9fzRM5V9YK9IiKRlrq7xSqp2Fi9V0IUPRhKkeGUTC5B0agithESGYSa4Y6ncCVUgQuri4gIcK3mcQ6OvIUlcoIWT4INX5zXpjTgCjRIs5gtmJ47HdNzpyuqflJC7f0RhVJfH5CcrPcqiKILQ6kIoVXLmxbHMUobIRGJlIY7alZWBSNcqouMcr6IIpVzyOQcHKndehZs+OK8NqUBl/NzDKZiKxRXHww05CPSWl8fEBPD1j0irTGUihBatbyxtY4o+igNd/Rsm1Mr4NEyKIqkNkMio3MOjtRuPVMSvngKjpzXJjfg8rQf9zUrCak8PV9fj+e8KIok3d1iIBUXp/dKiKILQ6kIEWjLm9LKJ7bWEZE/erbNqRXwKN1PMCEW2wyJtKO0aidUrWeewh/ntcldZ0VDBRo6GlyuoOe+Zk/buJPCpbyMvH7P11cwx3lRFEl6eoCUlOi66hmRETCUihCBtrwprXxiax0R+aNn25y3gEdpaKQ0KAomDAuXNkOiaBSq1jP34CjQaiSb3YbP2z53uc/TmgUIPtcjhUtVzVX9HusrmOO8KIoUgiCGUWzdI9IeQ6kox8onIook3gIe59BITkClNChitRNRdFCrXc09OPLVNlfRUOG1GslitmBMyhiPoZn0+PzMfOQk5aAgu8DrenyFS76COc6LokjR3Q3ExzOUItIDQ6kox8onIooGzqFRKGY4sdqJKPLZ7DasO7QO6fHpitvV/IVZnkIhKagC4DUwKsguQHZStsfAyVf1kztP4RLnRVE06e4GBg4EzPx0TKQ5/toREZEhhHK4uHNoxKomIgpEZWMlCgcXoryuXHG7mr/ZS55CISmoKsgukF2p5BwkqXk1QKJIFxsLJCbqvQqi6MRQioiIDEGrq9CxqomIAmHNsqKlqwXzJ8xX3K7mHBBJwVFPX4/PSiS5rXHOQZRzkOTv8d4qoXwNPSeKVPHx4hcRaY+hFBERGQIrmIjIyIKZn+T8WCk4Kj1SqkolknMQpaQ6yttV+ZS0/RFFisREsVqKiLTHUEoj1U3VWLR1EaqbqhXdR0QULZwrmELZykdEpCcpOCoeVxxUJZKniialwZmnq/LxinoUbSwWIDlZ71UQRS+GUhpZfWA1ymrLsPrAakX3ERFFI61a+YiItCYFR3ExcUFVIgVb0VSQXeDxqny8oh5FE5MJSEgQv4hIHzF6LyBalEwucfku9z4iomgkXSWPrXxERJ5Zs6yOlr1ASOETUTQzmYCcHL1XQRTdGEppZErmFKyYvULxfURE0Uhq5SMiIs8YKhERUSRg+x4REREREREREWmOoRQREREREREREWmOoRQREREREREREWmOoRQREREREREREWmOoRQREREREREREWlO1tX3BEEAALS1tYV0MUTknfT7J/0+kvr4XkekP77XhRbf54j0x/e50ON7HZH+5L7XyQql2tvbAQDDhw8PcllEFKz29nakpqbqvYyIxPc6IuPge11o8H2OyDj4Phc6fK8jMg5/73UmQUZEb7fbcerUKQwcOBAmk0nVBRKRPIIgoL29HUOGDIHZzM7bUOB7HZH++F4XWnyfI9If3+dCj+91YpXK8OHDceLECaSkpOi9HN3xfPQX6nMi971OVihFREREREREROGhra0NqampaG1tZQgDng9PjHJOGM0TEREREREREZHmGEoREREREREREZHmGEoRERERERERRZD4+Hg8+eSTiI+P13sphsDz0Z9RzglnShERERERERERkeZYKUVERERERERERJpjKEVERERERERERJpjKEVERERERERERJpjKEVERERERERERJpjKEVEREREREQUhrZu3Yq5c+diyJAhMJlM+Ne//uVyvyAI+PnPf47BgwcjISEB1157LQ4fPqzPYjWwfPlyXHzxxRg4cCCys7Nxyy234LPPPnPZpqurCwsXLkRGRgaSk5Nx6623or6+XqcVh9YLL7yA/Px8pKSkICUlBTNmzMA777zjuN8I54KhFBEREREREVEY6ujogNVqxf/93/95vP/ZZ5/F7373O/zhD3/Ajh07kJSUhKKiInR1dWm8Um18+OGHWLhwIcrLy7Fp0yb09vbiuuuuQ0dHh2ObH/zgB/j3v/+NdevW4cMPP8SpU6dQXFys46pDZ9iwYXjmmWewe/du7Nq1C1dffTVuvvlmVFdXAzDGuTAJgiBoekQiIiIiIiIiUpXJZMKGDRtwyy23ABCrpIYMGYIf/ehH+PGPfwwAaG1tRU5ODl5++WXccccdOq5WG42NjcjOzsaHH36I2bNno7W1FVlZWVizZg1uu+02AMDBgwdxwQUX4OOPP0ZhYaHOKw699PR0PPfcc7jtttsMcS5YKUVEREREREQUYWpqalBXV4drr73WcVtqaiouvfRSfPzxxzquTDutra0AxCAGAHbv3o3e3l6XczJp0iSMGDEi4s+JzWbD2rVr0dHRgRkzZhjmXMRodiQiIiIiIiIi0kRdXR0AICcnx+X2nJwcx32RzG6349FHH8XMmTORl5cHQDwncXFxGDRokMu2kXxO9u/fjxkzZqCrqwvJycnYsGEDJk+ejL179xriXDCUIiIiIiIiIqKIsnDhQlRVVWHbtm16L0VXEydOxN69e9Ha2or169fjrrvuwocffqj3shzYvkdEREREREQUYXJzcwGg39XU6uvrHfdFqgcffBBvvvkmNm/ejGHDhjluz83NRU9PD86ePeuyfSSfk7i4OIwbNw7Tpk3D8uXLYbVasXLlSsOcC4ZSRERERERERBFm9OjRyM3Nxfvvv++4ra2tDTt27MCMGTN0XFnoCIKABx98EBs2bMAHH3yA0aNHu9w/bdo0xMbGupyTzz77DMePH4/Yc+LObreju7vbMOeC7XtEREREREREYejcuXM4cuSI479ramqwd+9epKenY8SIEXj00Ufxv//7vxg/fjxGjx6NJ554AkOGDHFcoS/SLFy4EGvWrMEbb7yBgQMHOmYjpaamIiEhAampqbjnnnvwwx/+EOnp6UhJScFDDz2EGTNmROSV9xYvXowbbrgBI0aMQHt7O9asWYMtW7agrKzMMOfCJAiCoNnRiIiIiIiIiEgVW7ZswVVXXdXv9rvuugsvv/wyBEHAk08+iT/96U84e/YsZs2ahVWrVmHChAk6rDb0TCaTx9v/+te/4u677wYAdHV14Uc/+hFee+01dHd3o6ioCKtWrYrI9r177rkH77//Pk6fPo3U1FTk5+dj0aJFmDNnDgBjnAuGUkREREREREREpDnOlCIiIiIiIiIiIs0xlCIiIiIiIiIiIs0xlCIiIiIiIiIiIs0xlCIiIiIiIiIiIs0xlCIiIiIiIiIiIs0xlCIiIiIiIiIiIs0xlCIiIiIiIiIiIs0xlCIiIiIiIiIiIs0xlCIiIiIiIiIiIs0xlCIiIiIiIiIiXVxxxRUwmUwwmUyIi4vDBRdcgDVr1ui9LNIIQykiIiIiIiIi0pwgCKioqMAvf/lLnD59Gp999hmuv/56lJSUoKamRu/lkQYYShERERERERGR5g4fPoz29nZcf/31yM3NxejRo3HPPffAZrPhs88+03t5pAGGUkRERERERESkud27dyMtLQ2TJ08GAJw8eRKPP/444uPjkZ+fr/PqSAsMpYiIiIiIiIhIc3v27EFraysGDhyIhIQEDB8+HJs2bcIf/vAHDBkyBADw9a9/HWlpabjtttt0Xi2FAkMpIiIiIiIiItLcnj17sHDhQuzduxf//e9/ccUVV+AHP/gB7r77bsc2jzzyCFavXq3fIimkGEoRERERERERkeb27NmDyy67DOPGjcP06dOxatUqrFixArW1tY5trrzySgwcOFC/RVJIMZQiIiIiIiIiIk19/vnnOHv2LPLy8hy3TZ48GWPHjsWaNWt0XBlpiaEUEREREREREWlq9+7diI2NxYQJE1xuv+aaa7BhwwadVkVaYyhFRERERERERJras2cPxo8fj7i4OJfbr732WuzevRsnT57UaWWkJZMgCILeiyAiIiIiIiIi8mTLli14/vnnsX79er2XQipjKEVEREREREREhnTttdeisrISHR0dSE9Px7p16zBjxgy9l0UqYShFRERERERERESa40wpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIiLSHEMpIiIiIiIiIoo4zz77LCZNmgS73a7pcQ8cOICYmBhUVVVpetxwxFBKR2VlZTCZTI6v2NhYjBs3DkuWLEFPT49u6zp37hyefPJJXH/99UhPT4fJZMLLL7/s8zF79uzBvHnzkJ6ejsTEROTl5eF3v/udNgsmIkMz6ntddXU15s+fjzFjxiAxMRGZmZmYPXs2/v3vf/fbdufOnXjwwQcxZcoUJCUlYcSIEbj99ttx6NAhHVZORERE1J/z37e8fS1ZskTvZWqmra0NK1aswKJFi2A2fxV9SOfi3nvv9fi4xx9/3LFNU1NTv/u3bNmC4uJi5ObmIi4uDtnZ2Zg7dy5KS0sd20yePBk33XQTfv7zn6v/xCJMjN4LiGaVlZUAgF//+tfIyspCZ2cn1q1bh6eeegrd3d1Yvny5LutqamrC008/jREjRsBqtWLLli0+t3/33Xcxd+5cFBQU4IknnkBycjKOHj2KkydParNgIjI0o77XHTt2DO3t7bjrrrswZMgQdHZ24vXXX8e8efPwxz/+Ed/73vcc265YsQLbt2/H/PnzkZ+fj7q6Ojz//POYOnUqysvLkZeXp8tzICIiIpK88sorXu9bsmQJjh49iksvvVTDFenrL3/5C/r6+rBgwYJ+9w0YMACvv/46Vq1ahbi4OJf7XnvtNQwYMABdXV39Hvfkk0/i6aefxvjx4/H9738fI0eORHNzM95++23ceuut+Pvf/44777wTAHDffffhxhtvxNGjRzF27NjQPMkIYBIEQdB7EdHqW9/6Fl5//XWcO3cOFosFANDT04ORI0ciISEBn3/+uS7r6u7uxpkzZ5Cbm4tdu3bh4osvxl//+lfcfffd/bZta2vDhAkTcNlll2H9+vUuCTQREWDc9zpPbDYbpk2bhq6uLhw8eNBx+0cffYTp06e7/KXl8OHDuPDCC3Hbbbfh1Vdf1WO5RERERH69+OKL+O53v4uHHnooLLpZOjo6kJSUFPR+rFYr8vPz+4V1JpMJt9xyCzZu3IjS0lLcfPPNjvs++ugjzJw5E7feeitef/11NDY2IjMzEwCwfv16zJ8/H7fddhvWrFmD2NhYl/2WlZWht7cXX/va1wAAvb29yMnJwYMPPoinn3466OcTqZgg6KiyshJTpkxxfEgDgLi4OAwZMgStra26rSs+Ph65ubmytl2zZg3q6+uxdOlSmM1mdHR0aN6vS0TGZtT3Ok8sFguGDx+Os2fPutx+2WWX9ftXtPHjx2PKlCn49NNPNVwhERERkXzV1dV4+OGHUVBQgOeee87v9u3t7Xj00UcxatQoxMfHIzs7G3PmzMGePXtcttuxYwduvPFGpKWlISkpCfn5+Vi5cqXLNh988AEuv/xyJCUlYdCgQbj55pv7/b1pyZIlMJlMOHDgAO68806kpaVh1qxZjvtfffVVTJs2DQkJCUhPT8cdd9yBEydO+H0eNTU12LdvH6699lqP9w8dOhSzZ8/GmjVrXG7/+9//jgsvvNBjFfwTTzyB9PR0/OUvf+kXSAFAUVGRI5ACgNjYWFx55ZV44403/K43mjGU0klPTw8+++wzWK1Wl9tPnTqFAwcO4OKLLw5ov729vWhqapL1pUZ49N577yElJQVffPEFJk6ciOTkZKSkpOD+++/3WO5IRNElHN7rOjo60NTUhKNHj+I3v/kN3nnnHVxzzTV+1yAIAurr6x3/ekZERERkJJ2dnbj99tthsViwdu1axMfH+33MfffdhxdeeAG33norVq1ahR//+MdISEhwCZM2bdqE2bNn48CBA3jkkUfwq1/9CldddRXefPNNxzbvvfceioqK0NDQgCVLluCHP/yhowqptra233Hnz5+Pzs5OLFu2DN/97ncBAEuXLkVJSQnGjx+PX//613j00Ufx/vvvY/bs2f3+AdHdRx99BACYOnWq123uvPNO/Pvf/8a5c+cAAH19fVi3bp2j/c7Z4cOHcfDgQdxyyy0YOHCgz2M7mzZtGqqqqtDW1ib7MVFHIF1UVFQIAIRf/OIXQmNjo3Dq1CnhP//5j2C1WoWkpCRh586dAe138+bNAgBZXzU1NX73t3PnTgGA8Ne//tXj/fn5+UJiYqKQmJgoPPTQQ8Lrr78uPPTQQwIA4Y477gjoORBR5AiH97rvf//7jm3NZrNw2223CS0tLX7X8MorrwgAhJdeeimg50BEREQUSt/5zncEAMLf/vY32Y9JTU0VFi5c6PX+vr4+YfTo0cLIkSOFM2fOuNxnt9sdf77ooouE7Oxsobm52XFbZWWlYDabhZKSEsdtTz75pABAWLBggcu+amtrBYvFIixdutTl9v379wsxMTH9bnf3s5/9TAAgtLe397sPgLBw4UKhpaVFiIuLE1555RVBEAThrbfeEkwmk1BbW+tYV2NjoyAIgvDGG28IAITf/OY3Po/rbs2aNQIAYceOHYoeF0046Fwn+/btAyCWAD7xxBOO26+88kps27YNF110UUD7tVqt2LRpk6xt5bbo+XLu3Dl0dnbivvvuc/QnFxcXo6enB3/84x8dQ+CIKDqFw3vdo48+ittuuw2nTp3CP//5T9hsNr9XBTx48CAWLlyIGTNm4K677pK9biIiIiItrFmzBn/5y1/w7W9/GyUlJbIfN2jQIOzYsQOnTp3CkCFD+t1fUVGBmpoa/OY3v8GgQYNc7jOZTACA06dPY+/evfjpT3+K9PR0x/35+fmYM2cO3n777X77ve+++1z+u7S0FHa7HbfffrvLFfByc3Mxfvx4bN68GY899pjX59Hc3IyYmBgkJyd73SYtLQ3XX389XnvtNXzrW9/CmjVrcNlll2HkyJH9tpUqnZRUSUnHAODxKn4kYiilE+lqVG+99Rbi4uJQX1+P5cuXY/fu3UhNTXXZ9oUXXsCf//xn7N+/H48//rjPy3impaV57ZsNhYSEBADod0WDO++8E3/84x/x8ccfM5QiimLh8F43adIkTJo0CQBQUlKC6667DnPnzsWOHTscf7lyVldXh5tuugmpqalYv369y6wsIiIiIr0dPnwY9913HyZMmIBVq1b1u99ms6GxsdHltvT0dMTFxeHZZ5/FXXfdheHDh2PatGm48cYbUVJSgjFjxgAAjh49CgA+rzx87NgxAMDEiRP73XfBBRegrKys3zDz0aNH93sOgiB4/SzpaaZTIO688058+9vfxvHjx/Gvf/0Lzz77rMftUlJSAIgzt5QQvryunKe/U5KIoZRO9u3bh5EjR+LGG2903DZ16lRMnjwZq1atchlCN3jwYCxZsqTfEDZPenp60NLSImsNWVlZQX+YGjJkCKqrq5GTk+Nye3Z2NgDgzJkzQe2fiMJbOL7X3Xbbbfj+97+PQ4cO9fvLVGtrK2644QacPXsW//3vfz3+CyIRERGRXrq7u/GNb3wDPT09WLt2rcdKoRMnTvQLgTZv3owrr7wSt99+Oy6//HJs2LAB7777Lp577jmsWLECpaWluOGGG0K2bqnYQWK322EymfDOO+94/HucrwooAMjIyEBfXx/a29t9VjfNmzcP8fHxuOuuu9Dd3Y3bb7/d43bSP2Du37/f31NxIX0e5gxS7xhK6WTfvn245JJLXG674IILMH36dLz++usuH9RuueUWAPBY5ujuo48+wlVXXSVrDTU1NRg1apTsNXsybdo0bNq0yTHoXHLq1CkA4odBIope4fhed/78eQDod2XArq4uzJ07F4cOHcJ7772HyZMny94nERERkRZ+/OMfo6KiAitXrkRBQYHHbXJzc/uNQXC+KM3gwYPxwAMP4IEHHkBDQwOmTp2KpUuX4oYbbsDYsWMBAFVVVV6r1qX2t88++6zffQcPHkRmZqZLlZQnY8eOhSAIGD16NCZMmOBzW0+kEKmmpgb5+flet0tISMAtt9yCV199FTfccIPX8GjChAmYOHEi3njjDaxcudJvKCapqamB2WwO6DlEC4ZSOqirq0NDQ4PHkseioiIsXboUn376KS644ALF+9Z6ptTtt9+OZ555Bi+99BKuvvpqx+0vvvgiYmJicOWVVwZ9DCIKT0Z/r2toaHBUdUp6e3vx/9n79zg57rvMF39X37urqnuufZ2RdZnRyLpY0shOfAmxwyZRQtaQhd0FspAAYSGcwB4CC4v3xXLLhuwvOcvCAsk5PxZIdkNOAuyaLAmxc8F2EsdOrBnZsiRrNKOLNdO3uXdXVd+76vzxVZV7RqNLwPIt33deE1k91VXfqp4uzTzzPM/nv//3/040Gt0gOnW7XX74h3+YJ554gs997nPcdddd3/GaJRKJRCKRSG4mDz74IH/0R3/E93//9/Nv/s2/uep2kUhkS0Gp2+1imuaGioVkMkk2m6XZbALC8b5jxw5+//d/n5/4iZ/Y0CvlOA6KopDJZDh06BCf/OQneeCBB7xtTp48yZe+9CV+7Md+7Lrn8oM/+IM88MAD/PZv/zaf+tSnNsTfHMdhdXWVwcHBqz7f/V7t2LFj1xSlQAh5u3bt4ujRo9fc7rd/+7f5kR/5EX76p3+aT33qUwQCG+WUL33pS7RaLf7pP/2n3mNTU1Ps27fvitoKyQtIUeplwO1YOXDgwBWfe+tb38qHPvQhvvCFL/yDflB7sXpW/uiP/oj19XXP8fS3f/u3LCwsAPALv/AL3pvq8OHD/NRP/RR/9md/RqfT4d577+XRRx/lr/7qr3jggQdktEUi+S7mlX6v+9mf/Vmq1SpvfOMbyeVylEol/uIv/oIzZ87wn//zf97wG7Bf/uVf5n//7//N/fffz+rqKp/61Kc27OtGvrmSSCQSiUQiuVkUi0Xe+9734vf7+Sf/5J9c8b2Ky65du676yzXDMBgZGeGf//N/zsGDB9E0ja985Ss89dRT/Of//J8B8Pl8fPzjH+f+++/n0KFD/ORP/iSZTIYzZ85w6tQpHn74YQA++tGP8va3v5277rqL9773vdTrdf7wD/+QRCJxzd7Q3nX+x//4H3nggQe4ePEi73znO9F1nQsXLvDggw/yMz/zM/zbf/tvr/r8nTt3sn//fr7yla/wUz/1U9c81sGDBzc4xa7GD//wD/Pss8/yoQ99iOPHj/OjP/qj3HLLLaysrPDQQw/x1a9+dUMNRbvd5rHHHuP/+D/+j+vu+7ual3X233cpH/nIRxzAeeaZZ674XKvVcnRdd970pjdd8bmf/dmfdX7zN3/zJVih49xyyy03PF691Wo5v/Vbv+XccsstTjAYdMbGxr7jUZkSieS1xyv9Xvf//r//r/PmN7/ZSaVSTiAQcPr7+503v/nNzuc+97krtr333nuvek+U/5RKJBKJRCJ5uXnkkUeu+b2K+/Ge97znqvtoNpvOr/zKrzgHDx50dF13VFV1Dh486HzsYx+7YttvfOMbzlve8hZvu9tuu835wz/8ww3bfOUrX3HuueceJxqNOvF43Ln//vud06dPb9jmN3/zNx3AWVpa2nJN//N//k/nDW94g6OqqqOqqrNnzx7n/e9/vzMzM3Pda/J7v/d7jqZpTq1W2/A44Lz//e+/5nOvta6vfvWrzg/8wA84yWTSCQQCzvDwsHP//fdf8T3kF7/4RQdwZmdnr7vW72YUx7lcBy95xfO+972PdDp9Q8qyRCKRvFqR9zqJRCKRSCQSyT+WSqXCzp07+chHPsJ73/vel/z473znO1EUhQcffPAlP/arCd/LvQDJ9el0OjQaDbrd7ob/lkgkktcS8l4nkUgkEolEInmxSCQS/Oqv/iof/ehHsW37JT32c889x+c//3k++MEPvqTHfTUinVKvAn7rt36L3/7t397w2J//+Z/zEz/xEy/PgiQSieQmIO91EolEIpFIJBLJdxdSlJJIJBKJRCKRSCQSiUQikbzkyPieRCKRSCQSycvA1772Ne6//36y2SyKovA3f/M3133Oo48+yuTkJOFwmLGxMT7xiU/c9HVKJBKJRCKR3CykKCWRSCQSiUTyMmBZFgcPHuSP//iPb2j7Cxcu8I53vIM3velNPP300/ziL/4iP/3TP+2N35ZIJBKJRCJ5tSHjexKJRCKRSCQvM+50nne+851X3ebf/bt/xxe+8AVOnjzpPfYjP/IjrK+v89BDD70Eq5RIJBKJRCJ5cQncyEa2bVMoFNB1HUVRbvaaJBLJFjiOg2EYZLNZfD5pcrwZyHudRPLyI+91V+eJJ57gzW9+84bHjh49yi/+4i9e9TnNZpNms+n93bZtVldXGRwclPc5ieRlQt7nbj7yezqJ5OXnRu91NyRKFQoFRkdHX7TFSSSSfzjz8/OMjIy83Mt4TSLvdRLJKwd5r7uSUqlEKpXa8FgqlaJarVKv14lGo1c858Mf/vAVUy0lEskrA3mfu3nI7+kkklcO17vX3ZAopeu6t7N4PP7irEwikXxHVKtVRkdHvfej5MVH3uskkpcfea97cXnggQf4pV/6Je/vlUqFbdu2yfucRPIy8lq+z3384x/n4x//OBcvXgRg3759/MZv/AZvf/vbAWg0GvzyL/8yn/nMZ2g2mxw9epSPfexjGwT3S5cu8XM/93M88sgjaJrGe97zHj784Q8TCNzQj66A/J5OInklcKP3uht6Z7uWx3g8Lt/UEsnLjLQg3zzkvU4ieeUg73VXkk6nKZfLGx4rl8vE4/EtXVIA4XCYcDh8xePyPieRvPy8Fu9zIyMj/Kf/9J8YHx/HcRw++clP8gM/8AMcP36cffv28YEPfIAvfOEL/NVf/RWJRIKf//mf5wd/8Ad5/PHHAeh2u7zjHe8gnU7zzW9+k2KxyLvf/W6CwSC/+7u/e8PrkN/TSSSvHK53r7txuVkikUgkEolE8rJx11138Xd/93cbHvvyl7/MXXfd9TKtSCKRSDZy//33b/j7hz70IT7+8Y/z5JNPMjIywp/+6Z/y6U9/mu/93u8F4M///M+59dZbefLJJ7nzzjv50pe+xOnTp/nKV75CKpXi0KFDfPCDH+Tf/bt/x2/91m8RCoVejtOSSCQ3EdmsJ5FIJBKJRPIyYJomTz/9NE8//TQAFy5c4Omnn+bSpUuAiN69+93v9rZ/3/vex/nz5/nVX/1Vzpw5w8c+9jH+8i//kg984AMvx/IlEonkmnS7XT7zmc9gWRZ33XUXU1NTtNvtDQMb9uzZw7Zt23jiiScAMdDhwIEDG+J8R48epVqtcurUqZf8HCQSyc1HOqUkEolEIpFIXgaOHTvGm970Ju/vbvfTe97zHj7xiU9QLBY9gQpgx44dfOELX+ADH/gAf/AHf8DIyAj/7b/9N44ePfqSr10ikUiuxrPPPstdd91Fo9FA0zQefPBB9u7dy9NPP00oFKKvr2/D9qlUilKpBFx9oIP7uauxedJotVp9kc5GIpHcbKQoJZFIJBKJRPIycN999+E4zlU//4lPfGLL5xw/fvwmrkoikUj+cUxMTPD0009TqVT467/+a97znvfw2GOP3dRjykmjEsmrFxnfk0gkEolEIpFIJBLJi0IoFGJsbIwjR47w4Q9/mIMHD/IHf/AHpNNpWq0W6+vrG7Yvl8uk02ng6gMd3M9djQceeIBKpeJ9zM/Pv7gnJZFIbhpSlJJIJBKJRCKRSCQSyU3Btm2azSZHjhwhGAzy1a9+1fvczMwMly5d8gY23HXXXTz77LMsLi5623z5y18mHo+zd+/eqx4jHA57k/bkxD2J5NWFjO+9CnEch7yZJ6tmKVgFclrummMW3e2vt51EIpG8krFtm7yVJ6fm8PnE71Qcx6HaqqIHdYy2QTwU9+5z7ud6H5NIJBKJRHLzeOCBB3j729/Otm3bMAyDT3/60zz66KM8/PDDJBIJ3vve9/JLv/RLDAwMEI/H+YVf+AXuuusu7rzzTgDe+ta3snfvXn78x3+cj3zkI5RKJX7913+d97///YTD4Zf57CQSCYBtQ7cLweCLsz8pSr3KcByHY6Vj9If7mSpPcWDoAHkzz4g+ctXn5M08Q5Ehb7vriVRSxJJIJC81juNQaVZQUIiHhYjkOA7VZhUHh0Q4Qd7Kk46lObN6hoyawacIYUoP6949y91eQTzfwWG+Oo8e1DE7JplYhmK9SDaWxeyY1xSspKglkUgkEsl3xuLiIu9+97spFoskEgluu+02Hn74Yd7ylrcA8F/+y3/B5/PxQz/0QzSbTY4ePcrHPvYx7/l+v5/Pf/7z/NzP/Rx33XUXqqrynve8h9/5nd95uU5JIpH00OmAa2TMZl+cfSrOtRo2L1OtVkkkElQqFWmFfBlxHIep8hR9wT4WG4vk1BxLjSUmk5Oea8DdbsFYAPDEql6RacFYYCgyxHJjeUsxy/38Un0JRVFuSJySQtbNR74Pbz7yGr88uPcs27GJh+L4fD70oM6ZtTOkoil8io9qqwo2lOoldiV2Ue/W0UIaCgqKoqAFNApWAduxhWuqY6A4Cg4ORtPA7JiMJcY4sXyCHYkd1Nt19gztwWgZJMIJIYo1KlTbVXJqDrNjYndtqp0qiWCCvmjfy32ZvmuQ78Obi7y+EsnLj3wf3nzkNZZIXnyaTSiVYG0NBgZg27Zrb3+j70PZKfUy4/4wdgPaIHkzz76Bfay11hjVR/H5fOwf3E/BKmzY31R5ika3QavbIm/mURSFEX3EE4uyapaTKyfJqltLmzktx3JjGYChyBALxgLz1Xnmq/NXrNNdvytk5c38P/RSSCSS1zi2bXOpeom12hrr9XXW6uLPeWOeqD+K0TQoGAUR0zPyDEeGmVubI2/mcWyHaqdKOpam3q2j+lWKZhE9qJMIJzBaBpVWxROnFEdBDaiYLRPHcRgODfNk6Um2qduwWhZdu0veyKMHdQCqrSqVdgUtqDGzPuP9aXdt8mYe27apNCqsN9Zv6H4tkUgkEolEIpG8VqjVYGEBTBOi0Rd331KUegnZLEC5AtJgePC6Yo7jODiOw3JjmdvTtzMaH2VEH2G5sUxWzTJfnef59ef54vkvsiexh9nVWYK+IDktd8W+ClaB/YP7yZt5bz22bXOsdAzbtj0Ra0QfYam+RMkq0Wg3mDfmPQeWixsNBFhuLF9xvN79SiSS7w4cx2G9sU6lUaHb7TJvzHt9UFpQo1ArUGlXMFoGC9YCWkBjsb6IHtbRwhoAlVaFslnGxiYdTVOql9ADuufeLNVLJKNJZtZm6Ha7zKzNkIqmKNVLjGgjaCGNcq2MFtSIh+Mst5a5M30nTbuJz+cjp+fQQpoQo5oVokqUc2vnqNarTPRNULAKjCfGWWwsisjg2hk63Q55I39Vgb7SrEjBSiKRSCQSiUTymqJahXwe2m3o7wffi6wiSVHqJaS328kVpPb27+XU6ilPzNlKuOp1IpVrL4xIdcWjglWg0W3wyMIjjGgjfPLMJ7lv5D58Pt+WUbqtnFAPX3yYXfouphenN+xfURT2D+5ndn2W4cgwJau0QcTKxDJeDLDXjeUyvTjNWHyM6cVpHMe5quNKIpG8+rFtm3ljnlVrlTOrZ1irrzGzNuOJOplohqJZRPNraH5N3Os6DmbLZKJ/AgUFHKg2q6Sjac5XzhNVojy99DRj8TGsjoUe1MkbeWL+GLNrs9hdm6fKT3Hb4G18q/gtVL+KHtKx2hZRf5SCWcBsmYzFx4RIFdLYM7AHRVEwmybVZhUtqPF46XHuzt3NYnMRv9/PiDZCwB9gPDFOuV5mPDHOueo51KCK7dgsmAveLwvWG+tcqlxivX75T+mmkkgkEolEIpG8ynEcEdXLX/bP3KwkrBSlbhJbCTCuGJTTcuTNPHv79/JY/jEmk5OemNMrXPX+HeDU6in2Dey7wlWV03KsN9a5O303J5ZP8BO3/gSn105vELq2EoNyWs5zQr0x+0aeLD/J4eHDG0Qxd837h/az3lr3HFau2HR86bjXS7XVMSaTk8xV55hMTpI38zQ7zS0dVxKJ5NWPW0T+zdI3iSgRzq6dpdvtcnb1LLv0XXy98HVS0RSKX6FUKxEPxXF8Do7iUKgVUIMqiqJgtAzmqnNs07ZxybpExBfhxMoJYoGY6HrCxufz4fP5UEMqml/jifIT7B3ci+JTKNQK6EGdUqOEFtSIBWOcq56j63TRQ2JKn0/xYWNjOzYFs8Ddqbv56vNf5dDgoQ0F636fn939uzm7fpax+BgKClbLIuaPUWlUWDAWqDarGC2DaruK0TYAqDQr0jklkUgkEolEInlVYtuwtASFAoTDoKo371hSlLpJuD1MjW7Dc0a5ReAghKJTq6e4b+S+DZ1QWTXLiaUTXq+KKwqN6CMcSR1hpblCTsttcFQpisLt6duJhWO869Z3sdZe40jqyAahq9Vt0eg2mCpPeetaMBYo18rsG9jHc+vP8bYdb6NYK24QxVy3VDKWJBVLcWr1FFk1u0Fsco/RtJtej5WLz+fj9vTtIi6j5VhrrpGMJl+ql0EikdwEeruhVqwVTpZPcmrxFKlwiuOLx3l96vUsNZbw4UMLaeghnadXnmbvwF6eKD6BFtBQ/SrVVpV6q048GCerZinXyjg46CGddCxNw2mwXd9Oo9tgIDxArV1DDagojri3TQ5PstRYIq2m2RXfhR8/RssgG8uiKAq7E7uxuha1Vo3ByCC1dg2jaVBtVul2u0JMaoi/l+ol7sreRbFW5LnV5+h0OxgtA9uxKVpFtIBGpV0RHVWXy9MXjAWqjSpdW4hdiWACPaBTbVbBAS2oeY4qiUQikUgkEonk1UCnIwrNFxeFGBWJ3NzjSVHqJjKsDrPeWCen5VgwFmh2mlyqXOKhCw8xGB4kraY9kcmlYBVoO23UkMr04vSGknL3v4ENXVS2bTNVniKrZvH5fN42C8YCtm3jOA4BJcDs6ix7+/dSrpVRHMUTpE6vnfZErF43l4tbjA54xeq9YpMbYQkpIUL+0JY9ViAEriOpI6y31q+6jUQieeXjdkPla3lm12Y5sXKCdrfN8aXjxPwxFq1FsrEsyVgSq20RD8XZndjNBeMCd2bupFQvofgUslqWWDBG3sxTMApM9E/QF+7zJvBNDk+y0lphVBtltblKKpbC6lpoYdETVWqUOJI6wmJzkZyew7Itbh28FbNjood0SvUS44lxunRZqi+RjqUxuyYZLUOxXiQeilNulknFUpgtk4JVwGgaJCNJnll+RghgKNi2TcEqYDUtuk4XB8eLUseCMWqdGn2RPuLhOIpfQQ/poEDBLAgxq1nxrp3snpJIJBKJRCKRvFJpNqFYhNVVSCQgFLr5x5Si1E1iRB8hGohye/p2z7HkKA6nVk5xZ+pOHss/tqUwk9NyjGqj1No1JpOTL4xKt23PGeVO4XO7qNwo3VR5ytvWFa2mF6cZigxxevU0943c5wlQIX+IVCzFcmN5g6uqVwRzj5038+wf3I+iKFuWmefNPMPRYU8Qc4WyraYKFqwC+wb2eY4t+YOZRPLKxRVQbNv2hBTHcdADOkbTQPcLV9BEYoIzq2fo2l1iwRiGbaCGVfx+P/FwnEQkQV+0jz19e1hsLDKeGEdBRPhs28Zsm3SdLmbHJBFJoPgU4qE4x5ePc3f2bmars+xI7KBcL5OJZbxuu5yaw2yb7OnfQ9Eqko6mRTTYgfXGOtgwsz5DPBQno2Xw+/1M9E0wszKD6hOT+Q4NHmKxvkhGy5BTczg4zFXmODBwgFK9hO2I9SWjScyuSSKUIB6Oe47PWquGFtLQQhpG20AP6hgtAxxx/WzHxmgZXuF7pVFBD+lUW1UpUEkkEolEIpFIXjHUaiKuV61CXx/4/S/NcaUo9R3QKxBdr7C7V9wBIVJF/BGObj/KOeMcR7cfJW/mvRidu/+8mWc0PsodmTvw+Xxep5QrLuXNPFk1y6nVU14XlRulS8VS3rZ7+/fy6MKjHB4+zKnVU9ybu9cTpNwC9OHo8Ibi9M24x3Ych5MrJ8lpuS3LzHvdVQvGAvVOnYcuPMRAaICp8pT3g+yCseCtvT/U73VLbSW8SSSSlw/Hcag0Kswb80R9UR5beIxms8lTpadYr68Tj8Tx+X0kogkOJw9z3jjPHak7SKtpzq+fR/fpYIMe0tGCmte95DgOmViG6cVptJDGeGKcS9YlhsJiiIPnKnLAwRHl4pVz3Jm+E6tjkYllhPgUS3vdTSDut/FQHMWn4FN86GGdklWibbepNWoiUmyDGlApWkUcHGxsqu0q5VqZVCRFySphNA1yeo6smhVT96JpimaRWCDGUn2J3X27SUQSJMIJ6t06KTWFZVtk1SwzazNkYhnMjim6shQHLagxtz5HzB/j2OIxb91uvK/SqGA7tvi7RCKRSCQSiUTyMmEYotC80RCC1LUm7L3YP69LUeo7oFcg2qo/6Vq4IpXf7+dI6ggFqyD6oJwXBJ7NJefwguAzmZz0hJ+CVfBidPBCb9NofJTlxjKHhw/zWP4x7s3dS7FW5EjqCKut1Q2OqJyW84rT3el+veKR4zjesd0JfHkzf0Oi0ZK1xK6+XTx47kH29u8lb+a9cytYBY6kjrDWeqFbaivhTSKRvDy494D15jpaQOMbxW8wqo7yxee/SH+wn+nyNKeXThNVoiwYCyzWFrk9dTuLjUXK9bJ4X/ugaBWptEQHU7VdJRaIMVuZpdquMt43jtk2mavO8ebRNzNTmWEyOSkKykM6Do7X3aQHdeHC1Ebw+X3EQ3GMjnAkuTHCBWMB27HxIbrrKg3hPmp0GkTDURLhBNVOlVKtJMrOgzoXjAukYilQYLY6SzgQpmAVMNsmelCIaQVLlK+j8IIYf/m/dw/s5kL1AoeHDzNbmWWif4KCWaDarGLbYi1G2yAVTXF2/SzDoWGOLx4nq2ZFJBCbauuyONWseGuW7imJRCKRSCQSyUvF5gl7iQRs8qBsoFJv8WsPfZvHzhVftDVIUeo7wBVpDg8fZq2+RtAXvOFupF6xxxVhFEUhHAiTVbMcKx0jHU1zcuUkWTXrbQ/CZeVG49zep6X6kvcDjIsrfBVrRS+ql9NyW0byAK843XEcz7HVK4y5z8tpOU6unNywXS95M89geJCp8hQ5LUdOy/FU6Sl+YMcPeGtwe6kysQx5M8+R1BEiwYi3/17hLatmpWNKInmJccWQSrMihBgHirUiBwcOstJc4dDgIWbWZxiKDGF0xHS8dCxNuSHclnpQlJNfqFyg0qh44o6Dg921eWbpGQ4OHsSv+PH5hLg0nhjnxOoJ3nrLWynVSugBXTy3ZWI7NiWrRMfpYDQNCpboZwJIBBMoikI2JhxKYSXM7PosXbsLwOzaLJFABC2oMaKO4OAQD8RJxVL48IECh4YOUWvXiPljqIrK+bXz2LYt3FS1orj/OOK+Wu/UuW3wNr5Z/CZ210YLaELwTx6h1q2xp38PZtvEaBnEAjGKVhHbsbFaFg4OakBlsb5I1B/FaBk4OCgId5fZMdGCGmv1Nb5d+jbr9XUv3ieRSCQSiUQikdwsbBuWl0WHVDB4/Ql751YM3vvZx5kuLPNfvn6KRrv7oqxDilLXoFdIgo2iz4GhA14MznEcLlUu8VTxKWzb3nJfvWJP70S9EX2E40vHGYuP8aXnv0R/uJ8FY4Gp8tSG+Fsvbun5cHTYm+zXu053/6lY6prrcBzH62ZxHVtbFZ27ziy3IH0zva6rglVgqbHE993yfXzuwue8iGHezNMf7vfcUAWrcEWBe++0wF7H1Obzu9rrI5FI/mG476VOt0O1UcVoGphtk/HEOMWamDw3Eh/hwOAB6t066UiaVCTFiZUT3JW+i6XGEhk1w/nqefYN7qNu10V5eFsUjte6NQ4MHeCccY4RfUQ4l1pVqq0qmWhGuKlaVbSQRrVZJW/mqdRFtM2Pn2q7SjqWZmZ9Bj2so/gU9KDO1OIUg8FBHlt4jP5QPyWrRN7KMxgZ5HnjefSwTl+sj33D+0hEEtS6NdFxFU5gdkwUn4Lf70eP6ehhHTWkMrs+S8wfo1QroQU1UCATy/Bk+Ulel3odxXqRQq1ATst5UT2jZeDYIq5ntkwAjJZBUk1idSxxDq0qUV+Us2tn6Xa6ogQ9qKEHdcyWyVxljsHQIGfXz1JtVkUEULqmJBKJRCKRSCQ3gU4HymXxEYtBNHrt7b92rsTP/uXjFKo10nqU37v/9USCL07plBSlrkGvA8j9ocB1Jy3VlzzhJm/mWTAXUANiYt5W9Io9m0UYtxNq/9B+HMXxpuI9ln+MfQP7roizOY6Dbds8u/wsWTV7hYizWbTaah1ZNcvDFx9ml76Lxfoi4UDYW9Pm3qheJ9Nac+0Kd5jba3Vq9RSZWIZkNMmT5Sd55853big0dxTHK1ffymHmngewwQnW+zpsLnyXcT+J5B9Gr+BRbVVRgyrlWhkbG7NjklEzHF88TtEq0rW7FKwCpUaJsb4xAsEAfZE+duo7+WbpmxwcPMgzK8+wf2A/F42LqH6VeDjOiDaC3+cnFUuxVFsiHUkzX53n+fXnqTaqlKwSUX+Ux4uPMxQa4lj5GEWzSMQfod6tk9NzKD6FnJqj2qqiB3RPsJlZn+EW/Ra+VvgauxK7uGReIh1Lowd0Gu0Gw9Fh4qG4JxqhiHuZg+PF5jKxDI7j4HN8xAIxTwiqNkQJudEyUFBwFIdDQ4dYbiyTiWbQ/CIyqAU0FkwRHay2q8QjcfyKHzWk0u10OV85TyaWoUMHNaDy9cLX6Q/280TpCYYjw0wvTWM7NiiwU9/J1OIUQ+EhHFuIhM+vP0+lURGl7RKJRCKRSCQSyYtAqyXcUSsrEI9fe8Ke4zj8+bdn+bUvTFFrdzkyMsgff/8b2Dmov2jrkaLUJjZ3Kp1aPcXe/r2eMOVOmnOFHxA/6IxoI1gdi8nk5A3v36W3Eyrij3ixuqPbj7LSXNly2l3LaTEQGaBgFbZ0N/WKT70l4iDigAWrwBuzb+Tvnv87JpOTVwhRvevsdYjtG9jH9OL0BpGud0Lf8aXjJGNJDgwf4Ln15+gP99O0myiKQsQv4nqb999bgu46yFxRzV37I/OPcGvfrTx04SEabREh3Oq8JRLJtXHFqGqzKmJiTSHAYIPt2CiOQiaWwepYqEGVZDRJuVGm1qmxM76Tc9Vz3oQ5o22QDCd5vPg4+/r38e3yt0nFUqS1NFbXAgXi4TgBf4CMlqFYF7G2cr2MrdjoQZ2Taye5I3kH3176NqlICtu2eb7yPFF/FJ/i84T8xZqYkOfeeyf6JnjeeJ7Dw4cJB8KMJcawuhZ6WCceiROPxAGotqqeOJU38+CIAvZEOIHP58Pn86GGVBLhBOfWzxHzxzhfPU9aTaMoCrZjUzSLJCIJ9g7txe/3U+1U0QIaM+uXy83bJvFg3OuK0kM65WaZXYldzK7P4nN8LDWXODx0mNNrp+kL9vFE4QkGQ4MUDREVrNt1jiSP8Lz5PHkzL0TCuhAJjabBvDFPt9uVzimJRCKRSCQSyT+Yel30R1UqotA8ELjGtu0Ov/7Faf7kybMA/POD2/kvP/A64pFrqFj/AKQotQnXfeOWfyejSS+a5k6+c3ufXBRFYVtimzcx71r7vVaZtyv+uP1RgBevcxzHm/iXVbOEfWFC/tCWzqvefRWswpbHzWk5nlt/jh8c+0GKtStLyraK0LlT+Nxr4TgOU+UpBsODAJ6baqm+BAgHWMgfYrW26ol8m9fjRhUHw4OewOZe5+XGMgAtp8V43zh/c/5vGEuMsVhf3HCOm6cBSiSSq+OKGl27S97Ii0LzoMZcdY7d/buxuhaKongdUcuNZSaHJplMTnK+ch7Vr6IGVKqtKlbLwmgbDIWHuGBe4MDgAfSQzmJ9kXQkTbVZpdKoEA/F8Sk+0tE05VqZndpOGp0GaTXN92S/h5XmCvek76HWqeEoDmpI9USlvJGnYBU4NHyI2cos8ZB43O/3c3vqdsKBMFpIQ1EUMmqGglkQwlBAp2AV6Npdqs0qRtsgq2Yp1Arg4BWWZ7WsmLzXMlD9KqV6iVQshdWxxP3Vp5COpXlu9TnW6+vYjk08GKdgFUhGkkyVp8jEhFhWbVVFrHH5BGPxMQpmATWgoqAwFh8j4A8wlhgjEUkwFBnionFRFK+HdTJqhnlrnr39e7HaFkbTYDwx7hWmD4eH+eKFL9LpdFhvrEtxSiKRSCQSiUTyHeFO2KvXob//2hP2itUa7/urJ3hkrkTAp/Br33uAX7p3HwH/iy8hSVFqE677BqBpN2nbbdKq+MHMdUptdgttZis31FZT9DZvb9u2Jzw5jsP04jRj8TGmF6eFO6rbomk3KVgFRuOjjMZHryvIXO24vZE7t1i999i9Tqv56jxPFZ+i0W2QiqU895Z7LU6tnvL6sVxRrmk3yZt5fD4fg9FBWk5rQ5+Wux7A24e7T7dzyl1rSAmx3lrnn+36Z1TaFUb1UU+0c69f79olEslGXHeUbdsYLQPbtkUZt23j2A5Ti1McGjrE00tPM5YY4+zaWRaMBVF0PnyQc8Y5zI5JVsuS1tNML4rYWe/7TQtown3kF87PUqOEg0OlVRHRu5BOqV5ivG+cxeYiO/t2Uq6X8fv93Dp4K/6An4yaEXE528Fsim4mLaihBlXm1ufY078HYEPs0I3j2Y44Jz2oEw/FKdaKZNQMRtMQ8Wq/yszajNeTpQd1cET3U07LoYd1tLBGIpRAC2tkY1mq7SqKrVC0isT8MRaMBaqNKgWrQDaWpVgrkowmmVqawnZsbMfm2dVnuUW/Batr4VOEC8vsmuTiOUYSI9w6fKuYIhiOsyOxg4AvgA8fZtvktsHbeG79OW4buo1zlXMYbUMUwveN85ezf8mR4SOcrZylaBXRgposQ5dIJBKJRCKRXBfHgfV1IUjZtnBIXUtGOJ5f4b2feZzZ5Sr90RB/9IN38v37t9209UlRahOu+2ZEH/HcSL1xMmCDc8qlV1hynT+9JeRw5RQ9F1eIcfuXGt0GeTPPZHKS2cosyWiSrJoloAQ4u3KWTCxz3fO4keO6BeYFq8CCscC8MU+z09wwea9gFWjaTVrdFqeWT1GySp47K6flWGmuMJmc9M7VPXbZKnviVsgfIuwLb3B1uesZ0UdYaa5wJHXEe67b1+VGJd1442pr1Ys5uufhurUa3Qatbkv2S0kkW1BtVdGCGmdWzxDzx7z7yuzaLDOrM0SJMrM6QyqSYnpxmogSodqq4nN8zK7PMhQeYqEiBBmjbmC0DKL+KIuNRfSgjt/vJ6fn8Pl9jOqjGC0DPaCL+C/CdXWpcomyWWa9IZxZi7VFktEkC+YC6411bNsmb+apd+pEQ1HUoMqCuUDBKBAPxYXA1BIxNi2oUWlUyFfz5I08TtfxJvYB3mQ+qyOcXLv7d3O2cpaJ/gnmqnNM9E9QqBVEabqikIgkSIQTjMZHyegZsmqWp5efJuaPUWlWKNfKGE0Dx3Eo1oukY2nMrklfqE84rHwqRtNAcRSi/ihW08KHj4yaod6uEw1ExbVriX1k1SzxcJygL8jEwASVVgWjaVBtVzk0dIgnSk8wEB4QZfDtKnPVOf7l+L/k9Oppaq0aO/WdzKzNCGFNIpFIJBKJRCK5CrYtuqMKBTFhT9Ouvf2Dzz7Pv3nwW6w3WkwMx/mzH3kDt2UHbuoav+tEKdu2OVY6tuWUvM09SjktR7lW3uAcGtFHvM6nXrfTgrFAo9tgqjzFvoF9nFw56e1vc1n6Znqn5Q2rw6w31slpOXw+HxktQzKW9Kba3ZW+iy89/6UN+9rKmbVVSfvVjuueRzKavKLIPKflPHFuKDpEf7T/CuGnN/I4X52nZJVIRVNe98u1XF2uSAUwVZ5iKDLkPW+rcnj3WrsdWfPVeRLhBGv1NS/OKJFIxL3uUuUSl9YvYXeF4JOOpSnVS4zFxzixcoJYIIZP8WHaJrVuja7SxbEdLhoX0fwaekQnG8tSapSwOhYpLcXp9dPcnbmbb5a+yZ3pO7G6Flk1i0/xedG6alM4mHyKj9H4KEbboFwr0x/p56JxkdH4KFktS71bJ6tmMZoGZ9fOYjZNwv4wzU6TUr1EOpamS5dSrYSCQtfpgiPuOUbbIOKPULJKnK+cF/d0BxzxfxRqBdKxNIlQgnw1j92xyVfzTPRNYLZNcmrOE5oA+iJ9JCIJ+sJ9lGol9g/u59mVZzHbJrfot7DcXCYRTjCeGKdUKzGijTASHxF9WbqI75ldk52JnZxZP8NwZJhSrSTieUGdEysnSMVSzKzPiNcHUY7ulq536WI1LYyOwW2Dt1Hv1sGGeDDORN8ES80l0mqaA0MH+MrCV9jdtxujZVBpVuh2u8wb81ed/iqRSCQSiUQi+e6j24XFRSiVrj9hr921+egjz/LRR07StR3evDvLx//53aT064zlexH4rhOl3EjcVHlqg5DT24/k9klNlae8+Fxvf9FWXUauOygZTbLSXCGtpq/rrJqvznOpcskr7R6NjxINRLk9ffuGEnVXmJlMTvJk+Unuzd3rrXHzFDp3v91ul0fmH2Fv/96rOrrcOJ17PpFgZMOxXRRF4fb07dwSv4WIP7Jh6uBQZMjrmnIch6bdZCAywHprfUPEbvOxN1/7p4pPUe/UeXb52S2jje62m7u5yrWycCRomSuEr95Y3+ZjSiSvVdyoXt7Mi/hcu4LRMYgH45htEz2gY3ZMbhu4jVq7RrfbRbEVduo7abQbqCGVA8MHWGoueRG9uD9O1+4ytzrHHcN38GTpSd5+y9s5uXKSdCyNc/l/lUblhQEGigOX346O7RDzx2h2mxwaOoTP5yMRTqAFNJ5bfk68r8ND1Lo1/D4/O/t2klNzlGolEqGEVzpuNk1vCl8mlmFqaYrhyDDDsWGWmqLLzqf4MDrCHTW9NI0aVDlvnCelpTDaBnlLlJ0riuIVsVeb1RfK38M6akDlQuUCQ+Eh0tE0F82L3DZ4G/FInEAgwN7BvV5UOatmsdoWDg7jiXG+Vvga96Tv4bNnP0tICVFr1ThfPc+exB6+kf8G4/FxCmaBolUkFU1htAxRlB6KgwIxf4xnVp5hon+CRCyBHtYp1Ark1BxaSONC5QJ7+/YyszpDpVkRDrg14YA7s3pG3uMkEolEIpFIJN6EveXl60/YW6s1+T8f/BYPPnsJBXjf3RP89tFDRIL+l2St33WilBuJcxzHi9gBG/qRAIYiQ6RiKeaqc9edqAfiB5x0LI3P5/NElaX60lWdVW5H1Lw57/UvXW2/rgDm8/l42463sdoSxeHNTvOKKXR5M0+j2+CZpWcY6x/j9NppbwJfr4C1ufj8aqXhC8aCF+nrdTzZtnAdfPXSV3Ech/2D+1EUhbAvTDgQ5kjqCIqibBlr3HzsvJmnZbfED8c9Uw3zZt6LDvYWtPd2Uh1JHSEcCF8hgPVe46bdvGbBvETyWsBxHNYb6yxUF+jaXbSAJsSXroLREI6geDiOHtHpC/ex1FxiWB0mGAyS1tIstZaYGJigP9JPrV0jGUlitAysjoXiV8jFc2hBjfPGeWL+GPVunVQsBT4oWkWqzSp5M08sEAOg1q6RjYl7T7VVJRvPktNz+P1+rw+q2qmCAmk1zSXrEgeTB8lpOerdOhktQyKcQA/pWG0LLahhdAzSsTSFWoFCrcAP7PoBGk4Dn8/HbYO3YXZM9JDuCVrjcdEfdaD/ABeqF4j5YxgNg9XGKpVGBcd2RPzPcUQk8HKMzu/zEwvF0MM6i61FJocnRQE8iucGqzQrVJoVUciugBpQ+Xrh6xweOMw3St9gf/9+IRYFY6RjaVbaKxwaPkSpUSKjZcjEMsxV5sBBFMx3LPSgzlxljgMDB5irzKEFNApmgUwsw5m1M+T0HGpIpd6t03W6aEGNglUgExX7SkVTsmdKIpFIJBKJ5LucRkPE9dbXrz9h7+xShZ/67OM8XVglFgzwkftv5923j72kw8S+a0QpVxxRFIWMluHA0AGv5HvBWCCrZr1uoxF9hOXGMqPxUW5P337ViXq95LScF31zu5B6BZattg/5Q4xqo17fkhsBdLugtorl9YpHjuJseMx9zlp9jT2De6g0KxxJHfEm3vUWjR8ePnzFFMGtjgd4x+llenGaeqeOGlRZrC960UZXuAJeKEhvvxBrPLV66opj57Qc2/RtqCHV65ZyH++dMth7rm4n1VZdWe55ZNWs12e1VcG8RPJaotqqCmGquU7BKGC0DBSfgh7RiYVjVJtVIRDVxXYT/RMECBD1RylZJcYT45xZPUO726ZslbEdGwWFtJpGD+qigPzy/5KxpOfuURzRGdW1u9iOjdWy8Pl8Xo+TGlBxcKh1amhBja7d5dTyKeyuTTwQRw/q+BQfh4YOEfQHiUfiTAxMMLs+SywQo1QrkdWyFKwC6WiaEysn2N23Gz0gBJzxxDgTAxMsNhbZM7BHiF2X3VRWxyIVSXHBvMDk8CR1u04kGKHWrlFtV7GxPYEsb+S9qYIZNYMPH1bbYnJ4ErNj4lN8aCHN278W1DBaBtVmlXgw7pWhLzWW2NO3h7XmGn2hPnz4yKpZL+KYU3Ni312L8cQ4NjbTS9Oko2kcxUHza1w0LrIzvtP7BcBUeYrh0DAzqzPEg3ExnTB4ucw9mqFgFERnVdNAC2gyyieRSCQSiUTyXYppikLzWu36E/b+frbI+/7qCcpGnZFEjP/2w3dzz47US7fYy7zmRSlXoFgwFjYIM64AVbAKnnvHjbJdzTW0FW5H1YKx4JWGb+5q6o3XubhdS9sS2zz3keM4lM0yRbPouZo290K5kTTHcQj7NjqEXDEso2WIBWNeFC+rZq8QoPJmnr5I34Y1bbXOnJbzOq56r+fh4cPEgjFigRhHUkc8Mc6NyS0YC970wsX6oje170jqCMVa0btWvdcio2U2XPPr9VH1rmerGGbvlMKtxCuJ5LWAG9fTgzrVZhWzbeI4DrZiowU0irWimCBXEwXdxXoRBwezYxKPxIWDSct5bqdnl5+lP9LPUmMJs2OKovKwTiKcoC/SRyaWod6tk9NyZPUs5XqZjJah1q2J+5EPMmqG6cVpxhPjmG3T64hTUDi7LoSqaruK2TXF+9PvQw/rnrBmtkxSkRTPLD/DcHiYglVAC2iUaiW2a9uZXZul0hLnbCs2VsciGxPClWO/EF90o3uHk4c5WznLeN84K40V0rE0ekCnaBaxHRs9JArPa50aalClWC9y69CtjOgjlGolcMQ5FawCelDHsR1mVmfEtEBFQQ/plGtlYv4YjuNQs2sk1SR9kT4Uv4LRNsQ9HtFXaLQNMrGM2DeQjqYp1orggNk12aHv4HzlPPPVecyWSTKa5JxxDgcHLaThV/xYXYvdfSKiWOlUSMaSlOolphanSEVT5M28N6VQIpFIJBKJRPLap1IRglSnc+0Je7bj8CdPzvDrX5ym0enyum1D/LcffgPbB16eITqveVHKFVqAK0qz3R+Uejuftuo96hU+Nn/e7Z0q18osN5bJqtkNXU1wZaE4bOxXcnuPQAgxA5EB8maerJrl7+f/nnq77n3ejbS17fYVTqzeMvZeAaZ3yp4rdJ1cPgn2xrVk1ewV6+x9bu/1LNaKvC77Ol6XfR2KolwRzQMI+8KM6qPiIz66wc3lTtjb/Dr1dmVdzbnVew2nylMMhAaYKk95joK9/Xs5tXrqCiFN/nAmeS1SbVXRQ0LQsToW433jorMpmKBklRhLjFHr1jiSOsJcZY5kOMnZtbNoAQ01qGK1RB8S4HXhrTXXSMfSjPeJ+FulUWFmbYa0msYf8LNnYA9G2+C5lefYGd/J2dWz6AEhirmRsx36Dr6e/zpaQBOitSXur5lYBqstonCZWIbnVp8jqkR5bOEx2u02BbMg+quMOfYP7Oe8eZ4RTbgitZBGrVOj63QxWyZdp4vP8XmRtUwsw9nKWRzbEefWttgV38Xs6iypSIpSrcSR1BFqXeGUUgOqEM0Q4r2Dg+IoaAENo2mAAlpIQw/pFGtFcqoQ39db66SiKc6unaXb7TKzNsNtQ7fRoIEe0tECGgqisyqjivONBWMYLYMFc4GoL8psZZZMLIPf50cP64xoI9Q6Ijb5ZOlJdiR24Ff8dJ0uJauE7hc9WmbHJB6Os2dgD7OVWQ4PHybuj3OpeolUNMWh4UMcXzqO4zhElAiPzj9Kp9N5Gb9CJRKJRCKRSCQ3E8cR3VH5vIjq6dfQlqxWhwe+MMWff3sOgB89vIP/6/vvIB4J3tCxul1oNsUkvxeL17wolVWzPLv8rDdBb6sS797Op606l3odRL2fHwwPYts2jxcfJxlNktNynvOq1920lfOqdz9ub5KiKIzqo4QDYW9fiXCCrt2lXCsDl2N/Sogla4lnFp+h2+1uOBc3Bui6qdznuGKTK8LdN3Ifa801AM9FVrAKV6xzs6C2lcDW28flxuTcKF+vE8zddqtoo7tfx3G8COP1JgjmzTx7+/fy4LkH2du/l+nFafYN7OP02mmv06r3WveKjlKgkrzasW2bS9VLdDtd8kbe6yZabCxy6+CtoIhJdFbbIqNmMNsmWkBjsbHIzsROClYBq20BoId04qE4fp+fkfgI943ex0h8xOtysloWw5FhpspTYEPeyBMLxrwYGgrEQjGKtSJGy8BoGZyrnuPA0AGKZpGvLXyNZDhJoVYgHo6T03MkwgkKpnBAnaueY7u2namlKTFRr1bi8NBhcU8ZmvTicoqjYLZNLxLo57JjqH83ZsdkZm2GnfpOSrUSfsUv1t61UEMqPp9POMRaJqpf9ZxhWkAjb+UxWgYj+ghm1yQRSXixPqfrYLQNsjHxC4eu3QUb5ipzxAJCaIoFYizWFxmPj1Pr1LAdm0Qkwa0Dt1KqlRiODFM2y9i2TcwX48TKCbHOekkUpXcs4uG4cJx1amzXt1O2ymghjb5IH1ktixpRqXVqJEIJr0tqom+CWrdGIpbgnpF78Pl8Iu4Yy5LVsnxl/ivcmbmTqaUp6ZqSSCQSyUvChz/8Ye644w50XSeZTPLOd76TmZmZDdvcd9993s8C7sf73ve+DdtcunSJd7zjHcRiMZLJJL/yK78if8kikWxB74S9aPTaE/YW1i1+5i8f5+vny4T8Pv7DWw7yC9+zl8AN1BWB6KqqVGBgAIaGXqQT4DUsSvXG9lp2a0NZ9mZ6RSM36nZ4+PAGIWepvoTjOJ6baDI5yanVUwxGB+mP9NNxOp5Daqtpe5tx95mMJgkpIa83abOLa3PXklt4vlhfJKNnePjiw1dMp2t1W9Q7dR668BC2bV8xOfBI6girrdUNEwK36lvayjXmXiPXmeSeixvNu1bHkysMbnWsDWt0xHOzapZH5h+hL9jn9Wz17i+rZjm1eoo70nd4gpi7jt6Sdfc1y8QyfPH8F6m1a8xX52/YkSWRvBJxRdZirYgW0lAUBatjsadfuJiqjarnKEqEEyK+p6aJBWJcqF5AC2hCVGkbYptIgkQkQU4XgjgKJMIJRuIjOIpDvVtHC2rEQjGq7Sq1do20mhbTL2MZymYZzS86l9KxNKpf5XzlPF263JW5i6/Mf4Xdfbu9fiY9LCJzWlCj2+1yevk0BwcPslxfJh1LU66XeePIGynVS3Q6HR7LP0bX7pJSUyw1xBAJxaeI7qrLzi8toIk4oZpBDakUagXPtWS0DXJaDtuxma3Osiu+i7pdx2ybxPwxCqboZXJdUtV2layWpVQviQl3q2eEIOVAuV5mV3wXVsuiYBQwW6YQl2yLXf1iv+7UvJSaYqm5hBoWwlipXuLg0EEuGBcY7xtnZn2GdDTNcyvPsV5b5/zaebpOl3gozrbENuKhOJVGhZIprsN6a528kUcLalSbot9K9atetFBBQQ2qFMwCd6bu5MTyCcb0MWzHptqUJegSiUQiubk89thjvP/97+fJJ5/ky1/+Mu12m7e+9a1YlrVhu3/9r/81xWLR+/jIRz7ifa7b7fKOd7yDVqvFN7/5TT75yU/yiU98gt/4jd94qU9HInlF024LMWppSUzYC4evvu2x+WV++i8f58KqyZAa5o9/6E7efuuVw8KuhmkKh1QmA+m0dErdEK47plwrk4wlvRLy6+HG1Yq14gYhR1EUhqPDG6bVeZPftJENopLrvOqdegcbRR53P8PRYRbri4zoIxsKyd1jbEts447MHeK3/D0iy8Hhg5SMEvsG9214TlbNslJf4VuFb/H65OuZXpy+4hxdASin5Ti5cpKclttSSJoqT1Hv1Pnqpa/SbL8wIXB6cZqx+BjTi9Ne99VWLrTNr0XezF+3r6u3ML5gFTznx1b7K1gFESdS/OKH402C2OaOrGOlYyxUFzi9dJpyrXyF+603OiiRvJJxHAcc8acW0DBbYupcIpzAaBvoIR2jY5AIJ1CDKmfWzrBT28mJ5RMkQglPQC7Xy4z1jaH4Lr8fHeGCsh1RAG47tngfJnYLh5Jfpdaqsad/DwBWx0INqvj8PtFPpWfxK37MjklOz5GOpVFQOLF0grdsewuleomcmsO2bZ5bfg7Vr3qdVP2xfhZqCwxHhzHbJt1u13NSzVVFlO9c5Rxls0zMJ7qb9KAuzj2gs2AtoAZVFEfBaBmYTZOJ/gmvzymn5jBaBgWzwE59J+eq59jdtxstqFGulYkEIhhtQ7ikcIgH46L83K9SNIpChHegVBORyFKthI0tXGLBGIVagWwsS71bZ6Jvgr5IHzk1R61VIxaIiVL3y5MB/T4/44lxzq6dJRVJMb00TdQf5cnyk4R8IS6Zl4SIdDmWWLfrJGNJLpgXUP0qs+uz2F2bs+tnScfSlBriuhZrRTrdDkWrCEC/2s9E/wQ1u0a30+X0ymkurV+SJegSiUQiuWk89NBD/MRP/AT79u3j4MGDfOITn+DSpUtMTU1t2C4Wi5FOp72PeDzufe5LX/oSp0+f5lOf+hSHDh3i7W9/Ox/84Af54z/+Y1qt1kt9ShLJK5JGQ8T11tauPWHPcRz+8ukLfOBvvk210WZvqo8//eE3sC/df0PHsW1xDL8fRkdhcPDa5en/EF6zopTryDmSOkLEHyGtpq/YZrMTyLZtbNvm2eVnr5hK5/YgAQyEBjwXElwZz3P/3hvlc/uONvcuPbv8LI1Og/nq/AZH1mYhyxV/XDHmlr5b+L5d38e2xLYNzqOCVWAgMsD2+Hb+YuYvGAoPbSmyuKJTIpzY0tHlRvK+Xfw2dyTv4Oz6WU9kOzx8mLnqHJPJyS3L0a/2WtxIx1Nvh1VOyxEOiF6q3j6q3tdoRBeCILDB0dV7XMdxaHaazKzOcEv8FqqtKqlYyuu1cq970SxS79Q3uLIkklci1VbVKxePR0Qczmgb2LZNpVFhobrARN8EVtvCbJuMJcZ4ovwEB4cPgiLeZ2pAFbEza5GsmqXaFNPoAPSgTt7IUzDFxLuvF75Ox+5gdkz0sOhX6p22B+L9ZnUtJgYn6Av3YXZMEbsLaUT9UeYqc2RjWYy2QaUtomQFq0DMLybsxYNxBiODLNWWWGuuYbZMbMfGaBuM6WNcNC4ylhgjGoxitAweX3ictcYalUaFeXMes2Fydu2s6HHSMig+4RxznUN5M4/RNkjH0izWF9EDujcVMKWmCPgCYqJeU8QP9bCOzyfENkdxiIfjWB0RhXQFv3g4TlpNs1RbQg2oGC3hxjI6Bo7jeM/fltiG4hMu10QkIUTAyzqg2TEZj49zoXqBUXWUerfOcGQYs2XS6QoH7g5tB6dXTnN36m5OLJ/gwNABZtdnGYoMcbx8nIm+CcyOKES/UL3gFa7njTyO7aAFNU6snCAVS1FpV8hbV79fSyQSiUTyYlKpVAAYGBjY8Phf/MVfMDQ0xP79+3nggQeo1Wre55544gkOHDhAKvXCFLCjR49SrVY5derUS7NwieQVjGXd2IS9VqfLh796gt//2mm6jsPb9uT44x+6k2EtckPHabVgfV24sEZGQNNevHPo5TUhSm0lcrjxN7egu9fltHkinysSTS9O07bb9Ef7veeB6FxqdBuUrBI5Lcdj+ce4M3UnX3r+S14f1FaijOtESoQTTJWnvN6lw8OHPYeSoij0R/opWaUN7in3v91juw6pzVP0NgtirtNopbnCvqF9Xrn5ZvJmnkQ4wenl0960v80xu5XmCj+650dpOA3etuNtnpOrWCtye/p2fD7fVUvc3fJ2N/LnrtFxHI6Vjm0Qf3qP23uOW03fm6/O8/TS06KPxipscLH19oBtvjaO4vDGkTdiKzb3jt5LMpa8IsveH+1nyVr6Tr/8JJKXjG5XuF2iSpTpxWmSkaRwN5l5ISSZeU9YKtVLxIIx1IDKsdIxIk6EmdUZcISjcnppWkTnnC5nVs5wqXJJxNcu9zBpQQ3bsTlWPkbMH8O2bRbri+BAzB/jXOUcOHiT+dzepYJZEF13DugBnXK9jGML19WCsQAOKLaI0+EIMbnWqaEg7oXD6jB+x0+pVhKF5QGVc8Y5diV2kYgkaHQbmF2T/kg/i/VFClYBHz66SpdEKEE6lmauMkc6mhaiWEATPVdNg5g/htUR5+bgsFBZYL25jmKLe0AinAAFspo4D9WvUm1WiQfjovMplhGF8k2LrJYlHoxT69ZIxVLEw3GMtkHeyNPpdjizcgbHcYiH4hity+d6OUIXD8Ux26aY3ucoFOtF9vfvF7HqWBo1pGLbNk8vPU0qmuKCeYE7M3fyzOozHBw6KF4HwO/zk1ST3sTFxdoiB4YOsFhfxGgbQihrG1RbVXbqO7HalnBsBXTpCH0F8cd//Mds376dSCTC61//er797W9fc/vf//3fZ2Jigmg0yujoKB/4wAdoNBov0WolEonkxrFtm1/8xV/knnvuYf/+/d7j73rXu/jUpz7FI488wgMPPMD/+B//gx/7sR/zPl8qlTYIUoD391KptOWxms0m1Wp1w4dE8lrkRifsrVgNfuHBb/H50wv4FPiFN9zKf3jLQcIB/w0dp1YTH8kkZLPXjgb+Y3lNiFJXi19tNXlvq8cnk5OUrTJls0xACRD2iaLxXrFkyVqiL9JHwSpwdPtRzhnneOstbyXsC3vRvauhOAqpWMrrO3ILzKfKU0wmJ6l36qTVNEMR4Wq6QnhyFGzb5uGLD7O3f+8GweyKYykKt6dvZzI5iRbUGNFHtowROo7DemOdoeiQN+2v9zq6kTy/38/t6ds9QWnz1Lyrlbi3ui2advMKQSxv5umL9HnijytSNdpCeNs87a8Xx3E4uXySnfpO5tbmvHVs1QPWy4g+QtgXxu/387Ydb7vCXebuY72x7k0ulEheaTiOw9TiFDvjO/ny/JfZrm/nXOUceVNEzBbMBdSACjYUjAKrtVXMhonRMfD7/EQjUfw+P4pPoWAVhOOxMofVtmjbbeFsala9omxXvFEchWQsSaVZ4bah23Ach7nKHDv0HVgdi0Q4QaVZwbZtzqydQQ2qFOui58rqCmdRrSsibAWjwKXqJcyWyXhiHKNjcHLlJHv791Kul6k2q/jwoSgKB5MHsdoWs2uztNotZldnQYHbU7eTU3MkggligRjjfeOoIZX+UD8TAxOUG2UmhyeZXp4m5o9RbYlzCvvCzK3PoQU1fD4faS3NReMiWlDzermMlvGCiIS4vyk+BS0ktjHawkGlhTQKtQJGx2AsMcb56nmMpnBJOTjU2jViwRjr9XWeWxUxxWq7KiKXvCDU+/1+8ImC+mPLx9g7uBejY7BUW6JYKzIcHqZULzGeGGeuMif+XisRD8U5PHyY85XzxANxr/9KC2s0ug0i/gh21+bMqhDGFBT0iE5ftE/EExWHSqMiy89fAXz2s5/ll37pl/jN3/xNpqenOXjwIEePHmVxcXHL7T/96U/za7/2a/zmb/4mzz33HH/6p3/KZz/7Wf79v//3L/HKJRKJ5Pq8//3v5+TJk3zmM5/Z8PjP/MzPcPToUQ4cOMC/+lf/iv/+3/87Dz74IOfOnfsHH+vDH/4wiUTC+xgdHf3HLl8ieUXhOLCyIgQpn+/aE/aeK6/z3s8+zrPFNfRwgP/r+1/Hj07uvGqFzubjrK+LP3M5GB4W0b2byatalNpcZA1scMq4Lh63P8mdSudu7wpPeTPPqZVTHEkd4fTqac+Z44o0rmMn4o+Q03L4fD6OpI54vVO9Tp5e3Ajc7PqsJ3a42y3WFukP91OsFb0S8xNLJyhZJfYN7PNEmRF9hHAgzGJ9kdcnX8+D5x4Uo8t7BLPNTjG3i+p12dexLbHtiq6qDefVM+3PvV6br6P7nM1T8662hpyWI+QPeeJeLzktR8QfYTQuInkLxgLNdpOvL3z9mkXo7hruG7mPeWueo9uPekXPW/WA9bJVJ9jmr6PpxWn2D+7H5/NtKEmXP7BJXm4cx6HSrFBtVjk8fJgnik/w5tE3c37tvFd+fWb1DEbDQEGhZtdIaSn8Pj+2YhNQAuyK7yKoBElFU+hBHc2vUaqVmOibYKxvjNX6KjFfTHTwRZPkrTy3Dt5K3a4z1jfGcmuZvYN7WWosiThdYozF5iLpaJoFcwHHdqi2q2RiGay2JUStlhC14qE4EwMT1Dt1jI5Bp9uhUCtwYuUE433j7BvYx5fnv8wOfQdqSMVqW1TbVRELVCDij7DaWKXjdPApPirNCiig+BX2Du3F6lr0hfvQwzpG2yATy3Bm9QyqT5R9Wx2LHfoOvlH8BmHClCwx8a7erXPb8G3gE3FFHKg0hVCjoJBRMxStIqpPZXpxmogSEa6utogXZGLC5fSN/De8WLDRMsT1DWhYbYu8mWdXYhdfL3ydTCxDwSpgdy9PTex2qTarxHwxZtdmecvoW1hprJCOpgn7wzTaDUr1EqlIitnKLMOhYc4b54n5YwDMVmY5MHiA2eos2ViWolVkVB9FD+n4FB/nq+fRA7roF2sZlKwSmVjGc6i5Ec4FY4FKQ4pTLxe/93u/x7/+1/+an/zJn2Tv3r383//3/00sFuPP/uzPttz+m9/8Jvfccw/vete72L59O29961v50R/90eu6qyQSieSl5ud//uf5/Oc/zyOPPMLIyLV/4fv6178egLk5MaI+nU5TLpc3bOP+PZ2+spIF4IEHHqBSqXgf8/Pz/9hTkEheMbgT9opFMV0vFrv6tl+ayfNzf/0Ei2aDW/pV/uRf3sOdtwzf0HE6HdEfpaoirpdIXN2J9WLyqhalejuWXNGnV9DodfHkzbwXtXO3z5t55o15mp0m+wb3cXzpOG+95a3e/ntFLVdEcYWQG+1SOr12mvtG7hM/jNg2x0rHyKpZtunbPDEob+Zp2206ToeByACnVk9tOIecliMVS/FE+Qlel3rdBlfT5v/ejG3bFM0ii7VFb5/uhEBXhHFFtc0F6K5byxWdFmuLG/quNq/B7c8CrojdufRG8gBKVoml+hLb49sp1zb+4+Ou/1jpGLZte5HC/UP7Nwht1+us2txDtfl6ueJh73XvjU1KJC8n1ZaI1FWaFYq1Im/MvZGG0xCizOBeZtZnsJoWlVYFo2Uw0TdBvV1HDar0h/vZPbCbml0jpwtB3WgbVJoV1pvrGC2DWqfGjr4dNJwGOxM7KdVFv5NbDl7r1hhPjFPv1IkGouCIHqSJvgmK9SJqQKXSquDD55WvO44j4nxaFrNj0hftIx6Oc2joEM8sP0MykmSHvoOSVeJ89Ty39d/GYn2RWquGjc1Y3xgXjAvs7t9N026yLbGN/nA/ekhnrjJH2B/GbJmcXj6N2RTdU3pIFzE2HOqdOsPRYS4ZlxhPjPPsyrMcHDzI89bzpGIpCrUCI9qI2G+kn1uHbsXsmN51Xq+vM1WeYld8FydWT4i11kuifyqWwmyZPLf8HLZjk4yI+FytXaPdbVOwCt6kQy2o8czSM9yTuYfZyixZNetds68Xvk7YF+bEygligRhmW5TVx8NxAv4Ag+ogWTUrjh/fwfTyNDviOyg3yughHT2g82TpSXboOyjWhSDl8/lQEHHlnfGdmF0Ts2WihlQhstWK5LQcZtskHUtzdvUstmNvKFWXvHS0Wi2mpqZ485vf7D3m8/l485vfzBNPPLHlc+6++26mpqY8Eer8+fP83d/9Hd/3fd931ePISItEInkpcRyHn//5n+fBBx/k7//+79mxY8d1n/P0008DkMlkALjrrrt49tlnN7hGv/zlLxOPx9m7d++W+wiHw8Tj8Q0fEslrgd4Je7p+9Rhd13b42ONn+K2Hn6bVtbl7e5I/+Zf3sK3/xoqg6nWoVmFoSDikotEX8SSuw6talNosRrgCjiscbd52q6hdMiom821LbONtO95GsVb0CrOBq05zu5qjp1cQURSFyeQkJ1dOekXn7tS6XsHMdRaNaCOsNdeYTE5eEYdLxpKiB8n/QneUe/xriTLupLxTKy+UAvZOCNwcU3SP1x/u3yDauFMCe91Gm9dwavUU+wb2XVOo21zcvn9wPzk9hxbWmExOemXw7mv48MWH2aXv8ib9nVw+yWB4EHghkulew/nqPN8ufJunik95+3DX39sV5q7bjTVm1awXrey97orzEsjCEslVcB1SWkBjZm3Gcx2ZHZNEOMGR5BHmzXkm+idQFMXrDzLaBhODE/j9fvSQTsESE+zyZt6L2Z2rnkMLaHTpYrZNfIqPZDRJvVMnERQF3tVW1euL8vv9ZNQMi7VFuo7oblIUhXhQbOdX/OT0HMV6EQeHs5WzJGNJzq6fRfNrzFfnifliXDQv8n23fB8Np+GVfqfVNIutRbSgBj7IqTkW64uekHMkdYSBsChHXagucGjoEM9XnheOHxvSWhqrZVG0ip5Ta0d8B1/Lf43dfbuZXZ9lPDFOwB9gl76LolUk5osxX5lnwVhAD+jeuRgtA7NlUm1X2ZXYxVfmv8Jd6btYai4xqo2S0TKUa2ViwRiLjUVsbOKROEF/kMnUJOer50WPl19lbl38tnc8MU6pViIdSVNtVHG6DiWzxN3pu3mq/BTbtG2YbZO56hxj/WPUujUyaob+kBDhDg0e4kL1ApPDkzTaDfSAcHXhgzvTd/LsyrOoPpW1+hrr9XU63Q4+28diY5HJ4UmMjoEe0ClaRbSAhqIo6CFxzg5iemPBLODYjnRLvcQsLy/T7Xa37E25WmfKu971Ln7nd36HN7zhDQSDQXbt2sV99913zfiejLRIJJKXkve///186lOf4tOf/jS6rlMqlSiVStTrdQDOnTvHBz/4Qaamprh48SL/+3//b9797nfzxje+kdtuuw2At771rezdu5cf//Ef55lnnuHhhx/m13/913n/+99P+GYW20gkrzCaTSgUhHspkYBgcOvtzGabX/38U3xqSkRgf/zILv5///R2tPBVntCD4wgxqtMRYlQqdfVJfjeLV7UodbU+o61cQ1uVZrvROHcyn/vczYXZbnG3bdtep9JWx958fFcU6g/1s2AukIqlmKvOkYqlNrhw3LX5fD4ODB24ok/J7UyaTE4S8Uc2dB654o67pt41LBgLJKNJnig9wb25ezdck80uss3XzMamZJU2ROomk5NXdaJtFrquxmZhb6W5wh2ZO7gjcwfFWnGDYylv5rk3dy9Plp9kMjnJ9OI0d6bu5LH8Y1cUq0+Vp5g35oVbwW5vcD1tjhO6Itb04jSD4UHPOdf7WrpfG7JfSvJyUW1VPVEpHU1TtIRgbndt5qvCkp5SU/RF+tjZv5Om02R3/26MpiHia5d7jFxBwrZt9KCO1bV4Y+6N1Lo1/PhJxVL4FB8+hIvKnfimB0XsKx6OkwgnMDsmMb+I+NmOjdEyUEOqJ0gZLYNMNIPZMklH0tQ7dRErqxXpOl1KjRJjiTEumhdJxpIkwglyWk64gwJCbBvvG8fqWoxoIyJKGElydu0sBatAUk1itA3K9TJqWOVg8iBmx6RUK6GHdMb7xzm7ftYrZB+LjzGzNkPX7pLVs9TtOpl4hng4TqlWomAVRCyyVfUK4n2Kj4yWIR6Mc65yTsQkq+eZ6J8Qrq+2ScwXo2SURBTQEP1Oe/r3UOvUGEuMEVACxMNxz/3kOtiKtSLVdhU1rOLgMFed4y3b3kLDbpAIJTg4cJDH848T8UeIh+NkE1n6In34A0IQ7I/2Y3ZNUloKs2Myoo1wwbjAoaFDVDtVCmaBBXNBFMT7Hcb6xnh65WkysQyleomMJn77vGAueOKm28GVUTNUWhUZWX4V8Oijj/K7v/u7fOxjH2N6epr/9b/+F1/4whf44Ac/eNXnyEiLRCJ5Kfn4xz9OpVLhvvvuI5PJeB+f/exnAQiFQnzlK1/hrW99K3v27OGXf/mX+aEf+iH+9m//1tuH3+/n85//PH6/n7vuuosf+7Ef493vfje/8zu/83KdlkTykmNZsLAg/uzvv3qv06U1k5/+y8d54uISIb+P3zp6iJ+7Zw9+3/UNFt2uELzCYRHX6+9/aeJ6m1GcG/gOtFqtkkgkqFQqL5sVsld8Aa4QYtxt5qvzlGtlz/WSN/Nk1aznkNm8/bHSMfrD/Z4AsdX2C8YCzU4TG5tKs7LBUbNZFOr9+4KxQKPd4Mnik9yVuYtwMMxofJT56jytbouQP+TF2Lbal4s7JXCpvrTBDTYUGeLkykn2D+73Yoa9+7Ftm5bTIqSE8Pl8V5yXu50baQO8fRfNIgORgQ3XZatrf63z36pny7ZtphenOTx82IuS3Mi1BLzXdjI5yfGl4+zt38vptdOkYimGIkM8u/wsAGk1zWh81BMFJ5OT+DbNyVwwFhgMD3Jq9RSTyUnv/N1reK1zeLl4JbwPX+u83NfYcRwhRl0WhGzHxmyZXsRqqbFEKppCDak8u/ws+wf3s1hbZEQfQQtqzKzO0HW6ZNUspVoJLahhtkxioZgoEPcpjGgjorA7qLNgLqAFNFFy3qhiO0LQmUxN4vf5SYQTYk3NKuv1dQq1AplYBr/fTywQo2SV0EIao/ooRtsQbhvFwYdwQa3V1pg35rFaFhktg4JCtS2mzyUiCfoifZ57q9KsUOsIl5Ae0pkqT6H6Vbp08Tk+at2aF4mrNqucN85zaPAQPr+ParOK1bFIRpOUa2W0oEbJKpFUk/jxo/gV0tE0s+uzpKNpCkaBQk2IfYmocH1pQQ2zbRIPx1EQ5eYFU/yCIKtlObN6Rhy7UeWicZGR2AgL1gIHhg/QH+kHBa+/ybEdr3i+3hFRSgcHs2lidcU6E+EEpboolY9H4pxZPSPWb12O5wV1irUi433CaQVi6qErwiXCiRfW6EDX6YronuNgtSwcRUwzRYGiVSSrZjE6hrjfV/NegXpftI8FU0xF1EKa97q/nLzc78OXilarRSwW46//+q955zvf6T3+nve8h/X1dT73uc9d8Zzv+Z7v4c477+SjH/2o99inPvUpfuZnfgbTNK/4t24rvluur0TySka+D28+8hpLXs1UqyKyZ9twrS/fJy8u8hsPHcdsdUhqEf7TP72dPckb+z6u2XxB8BoehlDoRVp8Dzf6PnzVOKV6O4tckWaraXutbou23WbBWPCmurmRsKny1IbfAruT4Mp10WXkOn98Pp/nnHH7UYK+IOuNdfb2792wn7yZZyA0wEMXHsK27SscVOV6me1921lrrXmCx9VcOFeLH25VQL6Ve8mNxrnHUBQFxXmhK6pgFTZch95OLrcIfKo8RavbEge+/NXR26+0VR/Ttf6+GbeU/PjS8Su26712mwWq+eo88+Y8qVjKc1Q9ln+MfQP7ADzHlVvuDvDwxYfZqe3k4YsPX/Hb/6ya9QSpglXw+sY292NJ14DkpaTarGLbNs+tPEfX7lIwC0L8qZWotWts17dTqpUomSW2qds4XzlPRhXun0KtQCwYQw/plOolIeYqYtqbK0iN6pedog7MG/OoftXrOoqH4vgVP+loGsVRvC62BWOBteYatmODAj5FCNwlqwSOKAl3RfBKs4LZEt1I3W6X48vH0YIaekQnEUlQapRIx9KUaiXsrs3z68+zZq0xszqDHtRJx9KYbZPnVp5jZ2InRatIrVUDRcT0cnqOEysncBRHuKo6logIBjUvXhgLxLwppH6fX7iOYlnOrp8V+++aKH6Fsf4x6tTpi/SR03NCINKynhhoNA0cx/HKwDW/6JsyGyYDoQFm1mbY07+Hcq1M1+6CI14/PahTbVdJx9Is1hfZ3b+bRDiBT/ERD8cZ6xsj4AsIp1L/BA7iXrc7sZvF2iKpWAocOLt+lp3xnXyj8A1PqAv4A+hhnZwupvyZbVP0AF7uC4uH4vj9ftJaGhQw2ybFWpHdA7sxuyaj+ihFq8hcdY7t8e3MVedQFIVEKCFEuaZJpSFEQsnNJxQKceTIEb761a96j9m2zVe/+lXuuuuuLZ9Tq9WuEJ78l391Kv+9kkgkEonk1c3mCXtX03Ecx+HT0+f4t3/7FGarw4FMP3/2I2+4YUHKsqDRgHQaMpmbI0h9J7xqRKneziLgqtP21ppr3m/L+8P9LNbFN/lb9R15PVO+jT1Tvbh9Su7Evcfyj7G3f68niGXVLI/lH+PO1J1ML05veO6IPkLYHxY/ZPSwWXzZLKxtjg664kxvkftWAtrm524Wv9wC80Q4wYKxsGUvVSqWwlEc0mqa9cb6C1E95wWBzo0wuvvsjfRda3pe7+c3RwHdc10wFrz+rd5eqHKtTF+4j3Kt7MX+jm4/ykpz5YrJhu51vDd3L1+89EXemH3jBoHJdVDt7d/L9OI0WTW7oW/sRvuxJJIXG9u2mVmfIeqLMluZRQ2oojQ7libqj/Lt0rfZqe9EC2hcNC8yFB7C6lhUm1Wyahaf4sPv83PrwK3Ew3GstkVGzXjCA+CVnJtNk0KtwFh8jLPrZ8npOeLROHuG9lCsF9FDOnkrjxpU8eGj3CgznhjH6IjeqoyawUYMUrBtUZStKApqUCVv5jlWPsadqTs5vXoaPaBTbVYZi49x3jgv3D/1EkbL4GzlrDhPsyhK0KNJ/Iqfcr3MeP84elgHBXDgzMoZbtFuYbG2SDwk/pUejgwzV5lDQSGtpQn4A2hB4SCqNqp07S4zazMiAlkrovk1tKBGvV1nPDEutrUK7E7s5uz6WSK+CAWzQN7Me9ffhw89ogtHVACy8Sz7hvbRps1Y35hXsO44DnkrTyaW8c4lb+YxWgbrzXW6dpdSrUROzzGiiV8UuF1ORku4mMp1EY8cjgzz1fmvck/mHhHpdoTAOKKNYLZMQAiC1VaVaqtKTstRqImJerPrs+J+jUNOzQlHm184zLSgxsHBg5xaPcXhocPCLRfSMNoGxXqRWCBG3pL3vZeKX/qlX+JP/uRP+OQnP8lzzz3Hz/3cz2FZFj/5kz8JwLvf/W4eeOABb/v777+fj3/843zmM5/hwoULfPnLX+Y//If/wP333++JUxKJRCKRSF592LYoMy+VRJzuahP2mp0uv/OlZ/ijb5zBduD+vaP84T97PQOx6/et2Tasr4uI3siIKDW/AZP1TedVE98DrnACwZUxq60iaSP6yBVRLnc7x3EYjg5viL9tPqZ7DNdBc2r1FMlokrbdJugT5WFuZND9DWbvOqYXp9k3sM8TUHo/v/n4m8+x95jXig1e7bHNbI4OXi0y17uuXvfWta7VtV63G1mr64A7uXLS65Zyo3W2bVOulUX/TY8Qd71jbnX93ejeI/OPsLt/N+FA+IoY5VZF9y83r5T34WuZl/saX6peQg2IkuzxvnGMloHRMDA6BrV2jQPDB7hgXMCxHYZjw1ysXGQyNQngOWXcr9dKs4IaUDm7fpZMNAM+xIS8y5+zHRu/4vccUHpA9E05jkNWE11QOVV0RTk46EGd51afQw/qqAGVEytiKp3f7xdxuZpwQVlti67TxbEdLpgXODBwgIbdwHEc9LDuuTddJ5hjO+CDWqfGweGDPFF8gu/Jfg/FmphS5xaPZ9WsKCkPxqi1a+wd2st6fZ2zlbOMxcfw+/wYbQMtiBkRXQABAABJREFUoFGsFYkGolgtC7Nrsjuxm1KtRCqWolwrMzEwQd7MEw+Jonb3vqAFNJ5eeprt8e34FT9WxyIVFe7MbCyL2RV9WcdXjjOmjwkR4LJg5vbw7Yrv4svzX2Y0NoriU8TUwmAUxVEwOyZ7BvbgU3zea1AyS6S1tHCYhUWJudE0MLsmY/Ex6nbdE5jcTrB4MI4e1r17qNE0qLarxHwxinUhvLn/Fo3GR6m2qnS7XYpWET2oo4d1fIpPvPbglcZ3nA7L9WWOpI5gdswNX08vJS/3+/Cl5o/+6I/46Ec/SqlU4tChQ/zX//pfvfHo9913H9u3b+cTn/gEAJ1Ohw996EP8j//xP8jn8wwPD3P//ffzoQ99iL6+vhs63nfb9ZVIXonI9+HNR15jyauJTgcWF2F1VUzYu1qh+ZLZ4Ne+cIznyhX8isL/+ca9/NBtt9zQ92vtNhiGKEwfHoZI5EU+iS240ffhK16U6hUXClbhhkSkrXqlNj9vc0+Tu/+r9U/1rsXti2raTdbqaxwYOnBFp9NUecoTQlxRZysBprcnyn28ZJXYP7jfe27vvlzhavNj3+n17O3L2ryOzQ4u15UFXLWj61qCWG9/05HUEYAt1+86pFKxlHfMvJmn0W2gOAqrjVVadovh6DDVVtXb1/WEuM1fQ+6fbudW2LdRlLpa59f1zvNmI/9xvfm8nNfYtm0WqgtUW5ddTz6fV8KtBlQcx6HhNBiPj1Npif6lmF9E1cr1MkeSR4RgcVlIcBzRa5SJZUQf0uXuqFsHbqXaqmK2Te/epAZVzq6dZbxvnKJVZFQfFd1Kl7/Gbdsmb+WxuzZ6WOfpxae5O3s3TxSf4ODQQay2hRpUhWPIcag2qhhdg4mEKNJOxVL4FT+KT/HcPQpiCly1VRWijm0L8UfNovgUMQGwWRWF6gGxby2gMVuZZVd8FzW7RjwYF8JOrURGzZCIJDxxqWgKAcuv+FGDKlbHAgfUkEqtXRPxRGwhkvkVNL9GoS56phbriyIWGYoztTjFbYO38c3iN9mZ2Ilf8aOHdSrNiudAi4fj3rX+ev7r7BvYx7nKOXyKj+HIsDieIlxOiu8F0dBsmXTtrpiUGBCOMNuxqbVFf5bVscR0PEs4oKyuxZ6BPUIodBwRMcTxXGPua+mu0Y0Mdjod/ub833Bo4BD1bp2V1gr3ZO5hrjJHRs2AI56roJDRMkI0a1fQ/TqWbZFTczfUVfRiIe91Nxd5fSWSlx/5Prz5yGssebXQbEK5LHqkEomrF5qfLK7x7/9uimWrSTwS5D++fZLbR4du6Bj1uojrDQ2Jj5fKXP2a6JRyxZeB0AAPX3yYwfAgwDXjYXkzzyXjEmbbZKo85UXaNj/PjZG50a+CVWAoMsRUeWrDZLxeejuf3OhfMprk2eVnRaFszxr29u/l0YVHycQyXiTtUuUSTxWfotPpUDSLXkGxu09X6OoP93vT47aaate7/97j3sj13Cyo5LQcS/UlSlaJwfCgF0t0z7fZaTJdnuap4lMAG66VK/a5z9kcO3RFLTc22BfqY746z1R5ir39ezm5ctKLBLqvdavTYt6Y33C93WhdKpYiFU0xtzbndXtdrV9s8+vWu2532t5ofNSbZtgbHSxZJWzsK87DvfbX6sySSL5TbNtm3pgnb+RRwyqKomB0DPJWnlggJhxMHYtRfZQ9/Xvw+YVT0GybWG2LUq3E9vh2Hrr4EJ1Oh+crz/P82vOcXj5NOpIWTqlYRjhoghp5M4/ZNlGDKkbLIKflsNoW6YgoAk9GksyszbBqrXpTR/OmWIuCgtWx+J7s93Bi+QRvyL4BRRHl5ZVmhWqjSsEocK5yzpsY2LE7lExRzt3rxLQdIXQlwgkUn0JfrI94JE48EsdoGVSbQqzSghpWxxIimU/EA0v1EnpQx8ERhd194xhNg+dWnkPza6LQPT7CQGyAXDzHueo5MloGo2PgU3ysN9epNquUzBKVdgXVr3K2chbNr3mdWO7EwcnkJN8sfpOB0ABnV8+yUF2g0qjgdB0q9QoLVXF/yMaynF07y4H+AyzVl5jomyCjCoHHVmz6wn30RfuEO6tZxemK+15OzaEHdM8J5ff52T2wG6trkVVFF1bUHxUi4mXnWiKcwGyb2IqN2Ta9SCWKcJxpYSFA6mEh+h1fPs47dryD48vHWWoscWv/rXxt4WvYXdEZZrZNMZ3PB36fn/XmOkbDYHpxmnQsLeN8EolEIpFIJDeBWk1M2DOMa0/Y+7vnFnj//3ySZavJjgGNP/3hN9yQIOU4UKmIKXu5HCSTL50g9Z3wihal8mbeK7S+N3cvp1ZPbdkfBOIHu2OlY2RiGUK+EEElKMan2zaLtUWADWLM5kLy3k4lxbm648btOnLLwTtOh/5ov+j8uExOy3F67TT3jdzH8aXj1Dt1/v7S3zO1OIXVtvjMzGcYT4xTrpU9McoVzsK+MOFA2Iut9TqV3LW6PVb35u7dcNyrrflqPU3udVAUhf2D+zm1egrAE3kcx+Hs+ln0kE7bbm/o7lqqL3n73iz6uVMNG20h7imKQlpNgw9KVolEOOFFIOeNeear8+TNPP3hfpbqSxvWrygKo/FR7yMSjHB0+1EeXXiURFBMBtuqX2wrNvdd9V5XV2yaXpxm/+B+Ks2K51LrvWbX68ySSL4Tut0ujy08RjKSpOt0KVtlYv4YZtMk5o+xWFskraU94UYP6ygozK7PEvaFMdoGql/l5MpJbu2/lbPrZ7HaFoV6gWgwyvHl4+zu2y1KvKNCwDZaBjF/THQuNUUPVDwcJxFNkI6l+Xb529yi3cIT5SeIBqLkrbwQhlqWcOUE4wQCAW5P3U6pXsJsmezu381SfYlKu4LZNtkR38FSQ7yXHduhYBU4s3KGSqNCt9slb+QpmGKSX6VZ8UrCs7EsM6szRP1RLzboOI6IunWFOJaOpr0pc2bbZDwxzvGl41SaFWKBmBBpLjuJ9KBOwSpwePgws+uzjMfHeXrpaWK+GEbHwMYmEUowV5ljLDGG1bWodWtktAx5Ky+ebxZIRVJYbQurbWG0DCrNCuV6GbNt0nW6LJgLFCxRNF+za4zER/D5feghISjGg3HWm+vevU4LaRhdg3g4TqleYr21joPjnbPZEgLUzPoMO/QdXDQvEg8JwU5BodqqogU0fPjQghrxYNw7Xy2giS6xaEYcK6CxS9/Fk6Un+d7R7+Xg8EEumZdIR9N0lS4+fF4PlepTsR0bH0JI25XYxez6LDlV3u8kEolEIpFIXkyqVVFo3mpBX5/oedpMx7b5g6+d5j9++Rnats0bd6b4///Le8glrlI41fvcDqytQTQq+qOudoxXAq9YUcp1zyw3ljm6/SirrdUNnUq92y0YC0yVpxiLj3F86Th3ZO4gEowwEBugbbdZrC8yHB2+pmDhChSj8VGvHHyzS2bBWKAv1MfJlZNeIXbIHyLsC28QKXrdTZPJSebW5phMT2I2TYK+IG/IvoG56hypWGqDoOMef0QfYXpx+goByaVgFbhv5D5Or53eMHVvK5dQr9ji9jRtFlTc4vAjqSNemTrAcHSYA0MH0EM6o/roBjHHndbnOI53PXoFHrdkvve1XKuviU4ofKRVEZHpC71QXh4OhJlMTbItvm3LSKL7GhVrRcb6xzi9dhrAEyqvJxZdq2C+t4DdvRYFq3DFNdssEEok/xjOrJ1hb/9evvT8l/DhY2JggkAggBpW8fv8pGNpnl15ll2JXZgtU4gMQQ01qFJv14WoEklwJHlEiCB+Hcd2SIfT1Ft1dum7mFmdIRMT0Ta/38/u/t2iGNxRiAVjVFtVL1JntSxen369ELkSt3Ju/RzZWJa+SJ/3vrcd2yvX1oO6cC7VSoz1jTGijZBW0+I4fbvRQhpLjSVS0RS2Y1OoFbySbjWoUrSKIrrn2MId1aqiBlXKlij7VlAo1UVX1dnKWbJqlsXmInsG92C0DbrdLl/Lf40d2g7MtknJLLHeXPc6noy2wYg2Qq1b49aBW5kz5rg7ezcXrYvimlyeOpeKpJhdnyUVTolyc+Nyn1bbQFEUklqSptMkFUuhhlQvdldtVrHaFppfw7ZtrKbo5IqHhHiEAplYhnOVcxhNsV631DweiAuxzbbxOT6MtnA7ZbUsRkcIX6pf5VzlHDv1ncJZZuSxHRs9pIvXwyfcpEbHIB6Ki8mLDRFlLNZFFPLM2hnMjsmuxC4C/gB+xU/MF6PSqqB0FWzb5uz6WVLRFEbX8ETK/nA/fdE+bh24FUVRqDQrcrqbRCKRSCQSyT8SxxFiUf7yj/mJxNZiUbXR5pc/9xSfffoCAD/1unF+9x1HUEOB6x6j0RDuq8FB4ZC6Wmn6K4VXrCjlTr0DvB4gt/B6qzhVKpZitjJLMpoE4EjqCBF/hJA/xOHhw5xcOXlDUbet3DMbhCEfpGIp7zHXwXO1Hiafz8fbdryNVrfFu259F+FAmG2Jbdyevp3R+OiGCGFv31Sv+NVLr1h3JHUEx3F46MJDDIQGrhCweqfl9YotmwWV3nPu7bdaqi8xGh/ljswdjMZHPecWvOCWKtfK7BvYd4VTLOQPEfQFvf6tBXOB/nA/Pp/PE/2OpI6ICIpt81TxKXJajm2Jbd71vBo5LUelWWHv4N4NzrfeQvbrsfm1dZ/f25XV657bimuJgRLJ9XAcB82v8ezKs9yi3kLX6TKzOkNWzdIf7hcCQ73E3Zm7eWb5GfSQTlbNMrM+Q07LEY/Eyak5qm0xUa3eraOFNdSIit/vR1EUCrUCMX+MUq1E1+6CA0WryHjfOGr4hW4l1zGlBlXK9TKpaIpat4YaUKk2q54gEfPHmFmbwe7anphkdV7oHIqH4vRF+8hpOaqdKlpIEw6fcJx4KE4mmhHv+cuCkyuuGG0DLSiiczhQbVdR/SoFq8B4fJy59TmSkSQFq8BE34SI3hklzqydYSg0xHnjvFeybjQN1mvrzK7NogU0IbJcLjTfndjNuco57s3di9/vJ6NnKNVKGF0xTfCccQ4tpFFpVTBahnAeBTWWakvcNnQbI/oI2+LbPAHIPTezY4r1d01P4NNDotPJ6lrsTOxE8YnuL7e8HQWxj7AoLU+EEmRiGQpmgZyaEx1aIbF+s21itsR1c/ufXGfUzPoMGTWD0TJ4evlptmvbOVY+RkyJcax0jJgv5nV4oUCxXsRWRDfYSnuFeCSOGhDCYjwgrlM8HGc0Pkp/tF/EM5vVK/7tlUgkEolEIpF8Z9g2LC9DoSAm7Knq1ttdXDX46c9+g6fml4kE/PzHt0/y03fuxncDxgjDEO6rdBpSqauXpr+SeMWKUq4gACKWdax0jEvVSxwrHdsgJrjbjcZHyWgZkjExgrs39lWsFdk/uN8TTq4mJlzNPeOKNCP6CGFfmHKtvKWLacFY2LKPyufzcXv6doq1In2Rvg29TVd13fgQkbfL+3V/IFgwFhiODnvPefjiw7w++Xoeyz/mrbO358nd9noT69z4o9sf4z7P3b5XxOnthekP928pni3WF9k3sI+p8hQFo0DX6bLaWN0gwPl8Pnw+H/VunVq7xvTi9IbX4Gqvk6Io3J6+nWggusHR1ttrdT3BqDeCuFVfVLfb5aELD3kF+Vs51mS/lOQfQ7VVRQtr6CFdTJ9r14gFRfxMD4nJakEnyOfmPsehwUMkIgkKZoFkOMn04jS7+3ZTrpdJx9LMrM+ImF6thGIrFKwC4UAYHDhXOYfdtZldnyWtiuib2TFF/BXnBbHBdrDaFnpQFG6bbeFocu+bekjnXPWcEKpaVQBv4t6ZtTNoQU24c7QsRtvAsR1OLJ/g4PBBUUoejmN1LeGWVHziPIM6tm2/0B8VEn1QE/0TnDfOM94/ztnKWVKxFCdXTpKOpTHbJsVakcHoIK1OS0zX69tN3a6TjCXx+XwsNhdJxVIUzAKVRoUFY0E4oC4Xqfv9fk80cuNvVttiLD7GXGUOLaDRsTucWT0jxMOQht8nysP1sO6JaXpIJ6tliYfilOtlxhJjFKwC6/V1Tq+cRg/q5LQcfp+fnJZjon+CYq2IbQtnmE/xkYgk6Iv0EQ/HwYd3z9WCGuVamfG+cRxHdGc5tkO1VcWxHeaNeSqNCqpP5czyGeHs7NvHt8vfJhVJMWfMkYwmsboWfeE+TxhMRVIsWUvYXZud+k4hbl0W2PSwjupXear0FCvWCpWGcHJVmhXmK6L3bK2+9vK8YSQSiUQikUhexXQ6otC8XBZi1NWm3z1+ocxPf/abLFRqpPUo/8+/uJvvHc9cd/+2LRxYgYCI6w0Owks4p+YfxSt2mb2OncXaIku1JRLBBMCWvUCA5wraKp7WK0DkzTyD4UGmylMbfvvb+/hWXU6A5wzaKgYHXLWPqvfz5Vp5Qzn35t9Aj+gjXgF3b/xuMDxIySp555g389ybu5dvLX6Lo9uPXiEg9V6r64k004vTjMXHmF6c3rI3qfcxd//lWtkTz3qdYgvGgtcFlggnKNVK+PCR0TJXiGKTyUligRjRQJRkNLlBWFowFmh2mp6A1yuc9Tqjtuq12iyibT733gjiZoEzq2b5zMxnODR0iJPLJ7eMBfa60GS/lOQ7xXEcHNuhZJUYS4zRF+7DURxvmtyZ1TPYts308jSHk4d5vPQ4IIShcr3MUHRIiEyxNMVakUw0w9n1sxwePswF4wKHhw/T7DRRFIVUNEW1UyUVTXF29awXCyvWRPF50SqKRSmQ1bLCkWOLqXRaSBMOU0eIVKloCj2kY7ZNFEVhd99uji8dJxlOismZEeEidQWf7fHtPLP8DOOJcQq1y5PtGotoQQ0tJKbKVdvCVeRG5eLBOD6fjyPJI2KqXlTE324duJUTyydwHIfxxDiXjEsi6qsKR+pE/wT9kX6yWpZd+i5P/LGx0YIaZ9fPeoJapVHBscV9QQ/q9EX62Du0F7/fL5yRDjy9+DTtTpuiefk61UTU0GgZ2IqITebiOXyK+Gc0HUtT69SIh+JeUfhTJTEgwnOA+sT5KYq4tolwgkQ4ge3YVBoVikaRqBLlWPmY54yzuhY+xceh4UNYXQsFhfXWOj58GB0DR3FERD0yzMm1k9yduVt0U3UdzKbJiDZCVsuKSY5GnkqzghbWaDgNMnqGcqOM3+dnYmCCklXi6eWnGQwP8szSM1wyLpE3xOu52Fgk4o8wuz4r3VISiUQikUgk3wGtFhSLsLIi4nqh0JXbOI7DJ5+a41f/9hi1dofDuQH+9IfvYXz4+tMjWy1YXxf7Hh0FTXvxz+Fm8ooTpXoFBNexs1hf5E2jb+KSeYnb07dv6fjZyt3j4j7mTtfLqllOrZ5i38A+phenPfEip+W8x7dyvywYC8QDcf7X3P/i8PDhK44zoo8QDoTJqllPPNnq85PJSW/ynCs4bY6SuefY23V0avUU+wf3b4jZrbZWeduOt20Y1715suBWUcTNQs1kcpK56hyTyckNQt/mOKL73KX6kheRdB/vFcNWmisc3X5UxOwG9hLyh7bsifL5fLwu+zqyepaW0/Kiia7QYyOm4TmOs0E4c9fW7DS99bjn3nvdXIGqV2x0zz0Ty2yIdfZO6Xvnznfy9wt/z9HtRzd8vW3lQpP9UpLvlEqzQrVdJaNmqHVr4IOJ/glK9RI4EPFHqLVrTCQmOLN6hn39+6g0RCF4p9thyVoi6o+ih3T8it9zFz2z8gz3ZO9hrjpHTs+R00XMT/cLMctxHNJqmrnKHLFADKttoQZV4fy5HO/i8pdzly4lqyT+okBGzWB1LBRHIeqLcmblDPPr80R8EebW58SEuI5JIiSEFqNtkAglSEaTzK7PCmdXo8yR1BGsrnBkOY6D3bGZWZnh7MpZYn5RQJ4Ii/6rUX0Un8/HTn0n0+VpDgweoGt3Ob50nANDBxiIDmB2RKyt2qyCT0Sq/UE/E/0TKD4FHz4URWGib4JSrYTqV1kwFphZnyHmj1GsF3EQ9zgU6Av3Ydkicmd1LKqtKmbbZKJ/QhSb212shkW1ebmLq13FxhbxxXCckfgIjW4DW7FJRpPkrcvCuLkgHEkRsU1/REQ0F8zL0059CmpI5cTqCbZr24VzFDG9VA+LwvRsLIvt2PgdP1pIIxfLsVhfZFQd5YJxgbH4mOiN8vkZUofE1xaiuyzqj7JgLnCxcpGh6BB6QOf44nF26DvIm3mKZpGUmmJXfBeXzEsMhAYwmybrjXUUFMbiYyw3l9kV3+U55SQSiUQikUgk16ZeF/1R1aooG99q+l2j3eU3HjrO//PEDA7wzw5s4w/e+Xr6Y+Hr7t+yxBS/VAoyma0Fr1c6rxhRynEc5qvzHCsd86Jxm8un9w/tv6oAkFWzGwSGraJ4ruBUsAobishdd0zezHvHupq76Mz6GQ4lD3F86fgVa3BFjeNLxzeIJ+5+AK+o2xWX3PO7kZJud81ZNXvN+J+iKGTVrOcEc8+/9xhbdSqlYikWjAUuVS5xqSKikv3Bfh668JC3nwVjgVa35T2nl14xzO1nOpI6QjQU5fb07d7zXWFoviom77nXV3GUDYLciD5CpVlh/+B+77WZq85xePiwtx8bWzi2YIPrqddJlYlleHThUfb27yVv5r1zP750fEOss/c81tprvOvWd+H3+68QSq/mQpOdK5Lr4U6SwwEtINw3akBFC2gcXxKT8tYbIvo1FB1iUBvk7tzdLDWWRKzU6dLoNogGoygozFXmGO8bR0FE9g4NHuL44nHsjk3X7qKgiILzgJ+x/jHheKmLaFs8FMdRHBKhhBCiHDBbJulYmqXGkjfhrtKqUG1WmV6cZrx/nHKjzFJzif5IP18rfg01oGK0RdG2G2nLW3l29+1mrjqHgkI6lqZULzHRJ+Jre/r3YHUtcnqOxeYiKEKIe/jiw6h+1euwqjQrANTtOrcO3kqpVmJufY7hyLA3VVUNqOTNPAWrQEYVE+fcON6IPuLF44y26I0q1YTQNhwe5tTKKVKRFNVGlUuVS9hdEaub6JtAURT8it+7vkbL8GJ8jiLuPTOrM+L8G4Y36c5xHLSARiKQwK/4Re9Xq0pWzVKwCp47CkW4rtweKafrYDQNbondwrdK3+LQ0CEUFGbWZshqWfGLBx/eFEafX8Sf3X8X1IBK1xaTANOxNCv1FSKBCJVWhUwswzNLz6AFNG6J30Kj2yCtptECGrNrs+K53S7lWpl4KM6h4UM0nSaxQMwT6/pj/UwmJ6nbdeKh6//GTiKRSCQSieS7HcMQglSjIQSpreJ0JaPO+/76m3x1tojfp/Crb9rPr7zpAAH/taUax4FKRZSkj4zA0NDWgtergVeMKJU38zTtJv3hfi8a11s+DXhOp61+6C9YhQ0Cw1aii/vNu7tvt4jcdccMRYYoWIUN7qJeh82IPsKh4UPUO3Umk5Pesa/lOtpqLZudTD6f76ol3b3PddecN/Mbuqu2Eng2u4o2s5VI1eq2WDAXNhST/835v+HO1J0b9uMoV3YwbTWpsNFuMFWe8q537/bu693qtsibec9F1uum2vyaud1cBatAo9sAhONk38A+imaRZ5ef3VBm7zqpphenuTd3L48uPIpt22TV7DXFwM2xzd51bxbe3PjgYHiQhy8+fNWJiRKJ+x7pdDrkzTxW22LPwB6vI2lXfBdFq8gF4wK3p25n3ponp+WodWqoIZVkJMmTxScZDA/iV/zeRLWz62dBEe/pOWOOpJpE8SkUrSLVdpVEOMGegT3U23UURWHPwB62xbdRqpfIxDIilgaggBpSqXVrjPWNEQ/FUYOq6JkK6exK7GJufY5d+i6ivihnV8/yL8b+BRfNi4z3jePz+cjGshwrHyMZTnriPApeGXrBEsXrBUuUeRstg3Q0jebXWGmusHdgrxeTW2+sU21WvYJ2AB8+krEkVsdCD4uScMWnCCGrUWFmWbifHBxRmN6qep1Iju14pfAZNcPz5vPcmbmTxcaimNTXMijUCughHatr0R/tZ7x/nHPGOdSgiqIoJMIJHEd0b2kBDQdH9GN1TDJqhoJVYGZ9hqSaxOgYwoV1uZDcPWdFUcSUwcBlx9bqDKloilKjhBbUuGhd5PWZ13N8+TiVRkW4uUwxDdCHj0RIdIHpQd0rms+oGbSQxnnjvLevjJphVB8VDjhHXLuQL8Ql8xKpcIrjK8c9R57ZNplZnaFrdylaRXw+H7v6drHaWvV6yNyop+oX5fdSfJdIJBKJRCLZmt4Je7Z99Ql7zxRWee9nvsHZpSp9kRD/9Z+9nnceuOW6++90xP5jMSFIxeNb7//VwitGlMppOcK+MCF/aMO0s15niut0cuNTvd8UbxZZNv/ddbm4AslWx9+qR6k3zqcoCtsS27gjcwfAhmLw3o4ot4jbjdRtdnFt1VV1tdLsrdYFG7urNgs8AIeHD/NE6QkODx++Yv9bXQt3Yt6IJoSyEU2IRD8y8SOcM855Aptb9u44Dt1u1xOCXFGr0W14It5ifZH+cP8VYlxOy3mvd9AX3NClBRuL3d3Hr3jNbNHvNZmc5PTaaQbCA7Tt9pUTCBWHZDTJowuPEg/GadpNb8Je76Q992tkK8dTb9dU73Xb/LV5b+7eq3aNSSTVZhXbsZmtzJKKpai2q5gtk3goLpxENRFTvSdzD6dXT7ND3wGIr//+cD8XzAv8k23/hNX2qojmacKBgy361oymcPgECKCHdK+TaMEU9yRXxKk2q56TabYyy1hiTERkbce7r+T0HPFonFsSt3Dr4K0oKJ4QY3ZNRhOjvGHkDSy3lhmLj3nvoZm1GW7Rb+Er81/h8PBhap0aiqNg2zbz1Xm6togEqkHV65AaSYyQiCX4npHvodapgYMnjkQDUcr1Mhk9QzwU9xxZ8XDce5/Fg3HOV8+TVJNUWhVKVomCWaBjd6g2qzy38hxdu0u1VcWHmBBYs2scHDrIYn1RRAYbBtVGFacrur5s28bu2hStImPxMcyW6U0hjIfjjPWPsdRYYnffbgpmgZgvRsEsoPpVrwMLYIe+g2PlY+TNPFlVFMADxENxzq6fJaWKjq5zxjl2xXex2Fjk0NAhzlfPk46mvehgt9vlzNoZ1ICK4lM8Ycoti1cUBTWgovpVLyoZD8dFBDI+SqlR4u7c3Xxr8VvcmbmTRwqPsF3bzkXzIlk1ixpWhQPssvrn3r/fkH0DdbuOHhJT/lLRFGcrZ1mtr3J65fQVEXWJRCKRSCSS73bcCXvFoph8d7V+p8+dvMQv/K8nWau3GB+K86c/cg+Hc4PX3X+9LhxYw8OQy129MP3VxCtGlHKn5fl8vivKp92+INfZUrJKniNlczTO/eFocy/SfHWeeqfOsdKxG/4N72anTq8baKticGBL59BmF9dWscCrik9bCFibXUVZNctqbZWVuoj2OY7D8aXj3Ju7l+NLx3EcZ0PZu1sM3ivuudd/W2Kb9zEaH8Xv928Q2Nx1LJjCTdUX6WN6cZqsmiXkD7HeWGffwD4Agr4gIX/oilL63teot2zccRymylMMhge9YvetnHEj+gjrrXXvmh5JHWGttUYymrxiu7AvzGJ9kbH+MfAJ94Xb+dVob5yU2Fsqv1XH12aHWu9rloqlWGmucCR1RHZMSa7AccTUND2ok41lKdfL7O7b7ZV8u1P0HBwWG4uM9Y2hhTSeKj3Fen0dB4fJ4UnOV89zJHmERDjB/8fenwdJcubnmeDjHrcfEZFXXJlZV14FoApVlVXoBvpAg83uBpekKI1IqWU2Em013JWJAjXUcEwmUhIpW+0sOTOSRrO70pAjGsekNRqHPaKoi+pzSAJkAwUClXVXofKqKlRmxpVXRLh73O6+f3zljsjIyALY6hvxmqUBiMPD/XOPSMSb7/v8JEliu7FNWk1jdkxqnZoAhUfiJCIJtJDG8t4yiqywUdug1hSmil8lM/PoQZ2VygoziRlM28RoG9iuTcEsMK1PE48IIwgJ4pE4juT4VTYPSo6MPzFQC2m8a7zLc6nnfCB2pVUhb+UxWoZf75NciVqrJkyujslTo08hSzJ1u05aSYv1GZmn3Cgzn5zH6lgUG0XOTZzj5u5NJtVJtqwtHMchX89zbuIcD2sPiYfiqGEVNaRSrpepNCpUW1X/tbWQhtk1mVQnqdt11JCK2TYpN8soYQUtonF957qYCNitkdNy1O06RscgFoxhtsVEwoYt0rLXd68zEZ3gvnkfLahRbBTRIzrZWBYceGPrDcYj43RtMclPD+mA+Ew5PXKaRlcYPhcnLlJulrmYukgwGOT0yGlR0ZNktKDGjZ0bBJ0gv7v6u3S6HQCfB+aByHdaO8yPzZNTc6zur/owd4CF5AL3a/f5S3N/iVu7t/iRYz/Cvco9zo2eI6NlCLgBTsZP0ug2WBgV1cWMkmF5fxk9qFNr15hLzHFj5waKpLC6v4pjO2wZw1ToUEMNNdRQQw01lKfeCXuKArHYgMfYDv/k1dv8D39wi67j8unZLL/2F14gG1eeuG3XFVyqbleYUamUmLT3/aDvGlPKU78502tc5K08kiRxZuyMn0h5vylrvZPitq1tktHkwMcelVSCg3Br7zGDwOBT+tTA6W/9SalB0HEvgQMcquL1q5eX5JleI7EROm7Hr8Z50+96U17eOgK+iTbI+PkgjKRULEVIDvkmVN7KMx2f5lLmErutXSRJ4uz4WWRZHmjS9KaMyvUyBbPARm3Dn2zogd37ofPeWl2YuMCdvTvk1JyfTIuGogfqf/DetMRqq8rx+HEuZS75qS6PLeMd7/vV+uBgQq0XjD6Eng/1JO3X99kytsgbeabiUzw1+hSr1VWySpYta8uv0cXDcbSQ5vOiJqIT3Nu/x5ax5Zs3Ztek2qri4IiqllX02USb5ibVdpVquyoqbmGdQr2A0TJ8c8Z1xHsIF2LBGFbLEpP8YhlqnZpgTSFMtFpLmGZeFU4P6H46RwtrbFlb6CFdJJ9aBriQjqVpOS2yahbbtXlQeSBSSK7gV8UjcYyuQVbL+ka9JEnUOjVmEjOsV9eZT84fYk8tJBd4s/gmL2ReYKm8hGM7LO8vowZU6t06l9KXmIxPkowkCcgB5kbmsGxRbTO7pjBrKqvE5JjPmis1Sjiuw8n4SZp2k1K9xAvZF1irron3+uOPvlQsxXplHcd1RFIrHCdv5jmpn+RPin/C2dGzvpG2UlnB6BooEUVU6mr3xRRBJUutXaPSqPCo+kiYja6LhITZNTk9Ko5VC4p1dW1XAM47FsfUY/zew98jHUlzY/sGtfZ7hp73+ZyKpbDaFmbbxHZtHFek094qvgUIY2rNWGNxYpEH1gOezzzPurEuJqdKLg23wfzIvD/hz2gbGB0DxxVGZLFR5Pz4eepuHTWgUmqU6Npdn/811FBDDTXUUEMN9WFW74S9eHwwcLzSaPO3/v1b/Jub7wLw116Y5//5f7lALPRkd8m2RV0vEhHT9ZLJ7+26Xr++o6bUIBNpUDKov/bVm0jpn7LWbyx5919MX2Q6Pk00EB342KOSSr3VOHgPbu3xjXqn3g2aUOfVxLxUjwf59u7zXuMAa6nbYsPYOJDi6V+vfs5RpVkhHUv7x+JNv/NSXv3H6O3H7d3bvvHTb74tlZZodVuH9mNSm6TSrnApc8k3ofrh670G3aBz7Rl1k5qYHjWXmKNUL/nnthfg23tOjoKUH1WJfGb0Ge7u3+VS5pIYyf74/rSSJhKI+Amo/mTUpDbpJ+x664X93CtvPbwU2vDL2VD9cl2XazvXiMgRCvUCm+amnzxZKi3hdB2RXlJymF1TQNDDGplohgfGA1RZRQkpVNtVNs1N37Sy2paYGudKKCGFzdomeTNPTI5htkTly3VdPz3kVbIchLHi4HBz5yaRYEQYNM2SMIEem1laSPONKe89sW6sMzcyh9k1yVt55pPzFBtFkpEkkiyJiX0NwUaSJZlGt8GJxAni0TjJmHhMPBInHopTsApoIQ09rFNpVjBbpj/lzrItJjXBnHId8dmRiCb41NSnuG/c55R+ymdPOa6D2TGJR+KMxEZIxpLiswCR5JIlmdMjp1mprnBSP8nNvZsoQYWV6goXJi7QsBvUbWFqXUpf4ubOTeYSc0zqkxQaBTJKhvs1kYTSQpqo0YVUzI4pDCP9mDCzJxZZ2V8hE82QU3I0ug0mohNoIY1UJCUSWLZDtSMMxdXaqjD/OjVwxeeVHhI1ubmROW7u3SSniz9k7HZ2eW78OfINcX69aYNG2/DTX9PxaaSARFbLIiFRskoUG0VO6ie5t3+PpfKSYJBVV5hUJyk2iswmZsUfe1zB5cqbeVzHRQ/rmF2TucQceTNPtVVFC2jUu3Xmk/MEAgFSsRTletlndg011FBDDTXUUEN9WNVoQD4vwOPJ5OAE0/pOjZ/6wte5urmLEgrw3//IRf6vz829b6ih1RLbHR0V/CjlyYGq70l9R02pJ6WTPPXzjwZVwLz/7mX/9NbSPJj4dHzaNyYGJbK87fQaKL2sq0ltEsdxfJbUUfu7VFpiPDru72/va20am7Sclv+acNh0228drqL1r1fvc/qTQr2AeI+D1LsWeStPy2nRcTpk1Aw7zZ0DtT4Q5lsqlqJULx1KTnlmjwc/7wXF95pbvUmrfmh8r1Hn8a8WU4sHKpfetgcZlL1TE49Kc/UbmN75AYgEI1zKXDpwfoAD5mDTbvqptaP2xzs2ODj9b6ihPNXaNZ4dfZaV6gqpSApccB2XpfISSkCh1qnh4lJsFMmpj40pCR9kLskSQTmIHtQFW2j3Hnr4MeRbkgRHySxya+cW58bP8VbpLTJqBtMWRk08GmckOkJADnBMP0bJKlFr1YiH4ozHxpFdmbXKGqlIinxdwLhlSSZviml2RkcYH/f27nFCO8Efb/0xWSXLpDrJalXwiyRJmBp3d+9yQj1Bvp4nHomzMLpAMBAkG8vi4qIGVLZqW361TAuK7Rotg9mRWcqNMnpYx7Ed7u3ew3EcXMlFCSi8tvkae/U9coqo1GUVwZkqN8rMJGa4UrrCrrXLo+ojqs0qRtdgYVxU0ZYry8zGZ7lfu89x9TjrlXXm4nOsVdeYHZllSp+i1q6xUl0R/DwJlkpLpCIpViurzCXnUMIK67V15hJzrOyvkFbSFOvCgKu2q6xWVknFUhQaBQKBAB/NfZQRdYSF0QUeWg85O3aWfD1PIpRAdmUy0Qy4gomFhP9ZnYlmWN1b5dnRZymYBTJqhtnELC23xfH4cZSQAJ+vVdZIx9LCwHsMkfeMzUl9koyWYS4xx/3qffSgzvlxMZlRCwhzLRFK+AD6rJal3CqTUURazptAeL92Hy2koYU1/zo1OwLqHpADTMREks9Lwg011FBDDTXUUEN92GSaAmher8PIyOAJe6+tF/lr//oNCrUGkwmFf/EXP86LM5kPtO1mEzIZ8RMKfQsO4LtA31FT6qh0kqdextAH+bLfa7o8yezqN7o8E8W2ba4Ur7BR2zgw8c4zs/JWng1zAyWkHDnVzjNsvHphfzWvaBWhz8/qN9oyaubIRE6vETUQAN6zH4P4SN52PMi4p95aX+9UwLSSplQvHdgWcKha159Yazkt34Dzjr/3Ob2Pz1t55pJzXCle4a38W7xdePvIc95ruHn/7OdjHXXOveRT026K89C3/lP6lJ94yqk5Ks0KE+oERas4MDHWu87e2g0h50P1Sw/plFtlPjP9GYLBIC4uRkfU7RzJQXZlSlaJVDRF3hTXtSwJILcruSSjIvnjTXKD96DpRstAQsJxHabVaX7v/u/xg1M/SLFRJBPLsLK/ghbUQBLpwEfGI5SgghpSKVgFJFei3CxzIn6C1/OvY9s2d3buUGlWsG2b5d1lbNsmb+bJxDL8SelPODN6hpXqCrVWTYCv91fExDrH4rPHP8vb22+TiWVEXbBtoEU0LNsip+dY3l9my9piy9qi0+3w7+7/O2biMwBc377OhYkL5Ot5jK5BWkmzZW6Rr+V5Y+sNnhp5ipu7N6m0K0hIYtqfnmNhZIHr29eJyBHWKms4iDSSHtRZ2V/BdV3mknOs1daYH5mn5bY4lTxFoSEm2gWkAK7rUjALTEQmWCovYbZNxmPjArKuiPpfUAqymFoUUwtVkUYCKNaLqAGVrt3lxvYNFFmh0qxQaVaIh4RxOKOLyYV6UCcRTZCIJZhOTpOMJUnGkiQiCfJmXgDrO4ZIwMli+wE5gByQSWtpcnrOr0efiJ/gfu2+SJR1DCa1SZGY0gWbMRFOgCyg6jk9R6lZIqfk6NIVfxzARQtpGF0Dq2MxE58RHDDHxXZslivLzCRmxHTBtonZMnFc8ctLj+roEZ2G0/Cv41q79h14dw011FBDDTXUUEN9Z+S6UKm8N2FvUKXOcV1+409W+IX/tESjY3Npeozf+PzHOTWmP3HbjiPqeoGASEeNjw82u75f9B09tEHGSn9Frdfg6deg+h8cnJY26P7+hJb3319996vMxmcp1UsDnz+pTTKlTnG1eJXxyPgHSuf0JoQ2jU3OjJ2h0q4wpU8dycA6ik/0pPXq5V31VgR7+Uj9UPhyo3zAUPGMGW/7Xl3tYvrigXTSlD7l3+a9Vm910XEc9up7vvnlmWi9dbz+Yyk3yhTqBayuRcfpHDjn/eu0aWz6JlFvyqmfj9V7njeNTd+M6mWL9a5jb+Ipb+W5lLlELBgjraS5vXP7yPM9aO2GGsqT0TGYT86zVltDC2mCGxXUMNsmATcAMswkZig1S+gh3Z8wN6lP0ug2yKpZNo1N0tE0D2oPRJrl8VQ2NaSiRTSCUpCG2+ClqZe4b97nqdGnKDfLXEhdYKm8hG3brFfXGYuMYXUtimYRBwdJkkjFUtzZvcNEbIJCvYCMTLUjmFQODlbHout0Wauu8dTIU9zevc18ct5PaNmOTbVVZVKdZL22zguZF3wuUbVd9afZFUxR1xuPjLNT3+HO7h1++PgP87VHX8NxHd8UycayOLYjDJ6AgiM5nBk7w929u4yFxzBbJkpIQQtpFCxh1CkBBRmZWCAGNuL4XIfTo6dJRBIUrAI5JUcikiCrZgVgPKjjPP4Lgdk1mYhOcLlwmVQkhYNDgADw3ueuHtGxuhbZWJaCVWDL2GImMUOj08DoGFhdi1PxU5RbZWzHZrWyiu3aAhQvgyzJqGGVTXMTRVa4UrxCTIqxUdtgv7EvEnSPDUs1qHJt+xpK4HE+2wU9qGO2TNLRNKlYipbT4mLqooDT2zZbtS0/wauHdLasLayWRUoRZqdfdWyaFKwC3W6X1coqWlDDdm3KDZGUQhJ/PIlKUa5vXxepqkgCJaxgtS1sW4Dw9aDOfHKeUr1EPCg4W0MNNdRQQw011FAfBjmOYEfl86KqN2jCXr3d5e9/8Sq/8SerAPzF8yf4n/7sR4hHB8CmetRuC7MrHheGlP5k/+r7Qt8VftuTWElHTTR7Uorq/RJTvabVRm0Dx3HYbmzz2WOf9Wtkk9okS6UlGt2Gb+RIksR2c5sL2QtsWVsDAeG9qSgPnu0lhIBDhlVvysdxHBzH4dbOLX+Knnf7UfDzQUmdXmNLlmXfLOlnV3mGn2eoePvs1e4APzHVm07yDLPeyXm9+9N224zFxvxx5N52ve31VySn9Cmm9WkB0W3Xmdan/TVyHIcvP/gyo+HRA2B4r1bYm3LqT2/1A+YlVyKjZg6wxXr3eywyJkyrx7wvb9uleolEOEHRKh7Y7/7E3aDreagPr1zXpdqsYts217avMROfwZVEdbVYL5LW0kiyRFSO8gcbf0A6msZFXFf7jX2WK8sC+N0QZk6pWeL8xHnq3TpG2yAqRblfuY/ruKgh1TcItKD4rXh65DRr1TVmEjMsV5ZRAgoNu0FOy6FFNB/0rYd1zk2cQw2pSLaYiqfLOoqsUDIFzDogBZhJzLDX3uPc+DmsrkUinHjP1GmbyLLsT9GrtWo4joMe1P1kEZL47H3XeJdj+jFOaCe4XLjMcfU4juNwffc6qWiKlcoKZsfkuH6c+4aongWDQS5MXCAQDKCGBNjc6lqk1bQw0iQZJEjGktSdOhfSFyjVS4LTFNbF1L+IMPyS0SR6WKfWqrFV22LL2CIdTXO5eJnxyLiAvYfilOollKCC67oYbYNEJEEikmClusLsyCxW1+J+7T5jkTGadpOJyAS3925zSjtF2SrT6XZY3VtFD4rX91KnWlDj6s5VzqfO83rxdZSQItJWYRUZGQmJWrPGRGSClf0Vqu0qelhHDsoQgIyeoVwvU2vVuFO6w92du2zVtqi0KtRaNb9inVVFgqnerWN2TFHHbpQoN8u8kH2Bh9ZDwZSqP2ZKBQUnS0YmrabZae1wKnEKs2tSaVYoW2ViwRilRgnbsdk0N1mtrnIxcxHLsYaG/FBDDTXUUEMN9aGQbUO5DMWimK43aMJevlrnr//OG7y6XiQky/zdzzzL33rxGYLvE3eq18VPKgW5nACbfxj0XWFK9RtRg6DRg6blPSlFBYfB3v2cqV62kiRJFOoF5kbnyFt5tswtkuEkd3fvHtiPxdQijXaDkBzi6ZGnDxhTXhpno7bBleIVmp0mW+aWnxDqT9P0s4yulq/SdtuMRkf9ffBu92Dr/ekex3G4uX3T518N4mUNWo9Bhp9nzHzl4VcYi4wdqMR593n1RsdxDgHbvdfoZXD1n99B59yrSAaDQX7w+A8emNh3tXyV59PP89rWa/55LNVLpGKpA1+CvCRWb3qrl1vlpb68KqbHFnNd1z+e27u3OTN25lBKLaNmCAfDZNSMn9LaqG0caYr2m41Dc+rDqWpLTMEzOyYzcTFVzmyZqAGVaCDKemUdJaDwzv47nJ84z3ptHVmSBV/KKjIRnWCpKKbMuY6LHtLF/ZLLbHKWV/OvMhodpW7X/YqcJEkoQYV3dt8R9bpomvXKOposuEB6WEcP6yTCCUoNYXIFAgGSsSShYAgtoqFEFLbb29TdOidHTtJ0mjg4lOtlTsVPUbfr6CHxJxsJSSS/wsIIkyQJoysYVHkrL/hDStY3Ywr1Aif1k7yz/44PEY+FYvxx/o85M3KGtdoaalBA2beb274RNalNcm/vHtVGVSSughpqUKXeFvuSVtM07AYZJYNt21wtXkUJKoLTZG2hBlTu7d1DD+l+zazarlJqlIgFY1zbvsYJ/QSu7BKSQ5hdk4gcYb26Tq0lHu+6LpVmhUw0w9r+GnPJOWLBGDutHY5rx3mj/Aafnvo0t/ZuYXQN1JCKK4nPIqNpULSKgttl5pkITXC9fJ2PZz5OvVMno2SQXPE7KK2kaTgNpICEHtKFSdUWRlkulmO9uo7runTocKdyBxeXQr1AqS5YYVpIMKOsrsWUKn7fzCXneKPwBjOJGeYSc+y2d3kx96KoVSrvccwm9Un0iE5QDjKXnBMss8em3oQywbvmu2LKX9cS0xulGK8+ehW7Yx/JWRxqqKGGGmqooYb6flGnIybs7eyIJNMg02hpY4ef+sLXWdsxGFUi/LMff54ffXr6idv1qoCuC5OTMDEhqnsfFn1XmFL9rCQvgdPLQxo0Lc8zVYCBX/57K2JHTebz2Ep+Dc19zzCqtCs8Pfb0AYNClmWeyz7Hc9nnuLt/90AyB0Qap2gVadktSvXSof0YtH/epLrF1CIROUJQClIwC2SV7IHbe40eEOZHx+nQdbt0nM6B6XFPer1BwHjvmG/v3mYmOcPt3dsAB8zCO3t3SIaTbBjCdPMmEvabQ/2mz1HmVb9xNqh2uZhaZN1Y53PHP+fX8HorkP3y+FeDYPCD1mTL3KJtt334/CAzbzo+zbH4MT/15UrCGOs3RT3jM6fmfHPsKBbVUN//kpB886berZNW0+gRMdWsbteZS85R79YZi4zxbvVd0rE0jusgI5NRM5StMrV2DduxWautMalPIskSU9oUN3Zu8GL2Rfaae9i2jdWycB2XVCzF5cJlH6BerBeJBWMYXUPwkdSsmK4XEftltsW0P4CFEQEFDxJkJj5DNpZlp7FDOpYmEUmQUcUUuoyS4d7+PWzXFomjdg2zLab9VVuC5WS23+MPJSIJtIgmDDNZ4XL+MjvmDm7XxcFhw9rgL8z/Be7u32U+MQ8SxCNxTo+cJhAIMKVNka/nUYIK281t6t26eM2uKYDv0Tj1Tp2J6ASv518no2b8z7jN2iZmy6TaqqIGVX/Cneu61Dt1xiPjNLoN5pPzYkJgJMnpsdPEw3EadoNULIXZNf2pe47jYNomOS0nzLxIktnkLBv1DX5i5if44rtf5IR2gpnEDBvmhp90Q4JYKMatvVt03S7brW1mk7M0nIY/NdC0BdjeY0/pAXGtKAEFWZJJRBMEggEWU4tYXYuAHeC4ehw9pJNTc5yfOI8kSZgdk0QkwbH4MWRZRg2pFK0iM/EZkXCLJnh67GmCwSBT2hTFRpG55BxWx8JxHGrNGvv1ffJWnqyS9ZllzW6TT+Q+QTAQZFKbJBFNUHfqpJQURtfg3v69oQE/1FBDDTUUv/Irv8Jzzz2HruukUin+3J/7cywvLx94TLPZ5JVXXmFsbAxN0/jxH/9xSqXSgcc8evSIH/mRH0FRFFKpFH/7b/9tut3ut/NQhhrqgJpNwY86asKe67r8m5sP+Vv/7i2qzQ6nUwn+t89/nLPZkSdut9sV/ChFEXW9ROIwm+r7Xd82U+pJlaZ+w6B/wpqX7OmfludV1Xp5SoNeyzNGvPHVXg3OM1BkWfZB3/ut/YMT7QJRP03jbcczGXoZSd5rhQMiUZNRMoQD4YHGyVHH700I3G5uM5eY49r2tYGTAz1NapPiNbSpQ4bVUWv+fucho2YISAEyasY3y3qZUJV2xZ8M6Erv/wXEqxJ6//6k2tug2qUsy1zKXKJQL/g1xaMqnf3pufeDwfeuYaVZGZiS8vbLu9YmtUmigah/7p9UMwRwcPza31AfHrmui4uLLIn6rCu5xENxXFzBi1InMdoGRscgpaYIBoMiXWTmqTQr4EKpUeKEdoI7e3c4qZ1keW8Z1xGfW4sTi9zZFxyoRDSBElWQJIn12jpnxs5gdSxwBN9NQiIRTpBW0uStPK7jsmWIepfZMbGxqTarbFlbPD3+NJPxSYKBIHJA9idU5o08eVNMylytrJKOpSlZJVxc6t06sWBMsKscG9d1sbqWn9gxOoY43o5BsVFkPDZOLBSj0q1wLH6MFydf5KH5kBcnXwQJrI4lzDxJGHuO42B3bbbr24yHx1GCCrVmDVwx2TAeiQumU8diNDLKenWdmByj0qhwc+cmpxKnKDfKWB2LWCDGlrWFhPisq9t1tLBGIppgJDoijD9JIh6Jk1NzyJJMVsmyZW4Jg8kqCBPPFWabHtKxbItPZD/BQ+shf27mz9FwGgTkAOfGzyHLMtlYllq7huzIjIRH2K3v+lVh27H9unROydHoNnBxSWtpSq0Si+lFbu7eRA2qVFtV4uE4q9VVXpx+kUA4wFOpp5hOTHN67DTr1XVwICbHeGf3Hfbr+1SaFarNKst7yyghBQkJF1Er9WqJmViGa9vXyCgZivUitU6N5eqyqMiXl3zYfk4XUw+n9CkS0QRT+hRZJUuj0wAH0rG0nyobaqihhhrqw6vXXnuNV155hTfffJOvfe1rdDodPve5z2FZlv+Y/+a/+W/4j//xP/Kv//W/5rXXXiOfz/Pn//yf9++3bZsf+ZEfod1u88Ybb/Cv/tW/4l/+y3/JL/3SL30nDmmooQ5M2EsmD0PHO7bD//iHt/knr97Bdl0+t5DjV3/iBVL6gG5fjxoNqNVgbEwYUoOqgB8GSe4H+LZcq9VIJBJUq1Xi8W8MZuoZRx4U2tNRBsVR6n28N90tLIV97pEkSYdey/vv27u3SUaTSK5EOBD2zQZ4zzCZiE0c2Eevkie5EpGgyOcNOo7e1+w1MLxj+iDH2cuQurN7h5dPvEwgEMBxHJZKS6SV9AFj6v22OWjNjzoPnhzH4Wr5KoupReQBnVfv/gsTFyjUC+973rx9dF2X8eg4d/bukFbSh9b5/fbhg67fN/qYQRMZe/97o7ZB224TDoT9xFTvc7/84Mt8NPVR3iy9yYQywZmxM+w0dyjVSzw98jR39u74Rt83yl75ZrwPh3qyvhlr7LouG8YGakCl2CiSU3PEI3HyVp54KC7YRm2DWqtGVI5yOX+Zs+NnRaopEGPD3OBE/ARpNc3bpbf5SPojbDe3SccEPykeFhPdIlKEN4tvcnb0rDC1whq4YHQNYV7JElpQEwkYHFzbpdQsMROfodwsk1EzmC2TWruG0TFYGFkgIAcEw+hxbczblouLhCS2GdAwuyZqUPUh6OVGmZnEDPVunYJVYCI2IcwKCXJaDrNjMqlPsmvu8tWHX6XZbfLS1EucGj914PPsnd13mIhN8KD6gLSaJh6OU6gXUIMqtmvzbu1dnh1/lpJVIqOJ/UcWNbI3Cm9wMn6SRDSBi8utnVs8l3qOdyrvcH78PC4ua9U1FlOL5M28qPG5orLmfc54rCkPNm51LLSwhuQ+riUGNAEtRySXNowNsqoAn2djWa5uX+XCuJgg6BlAekgnb+V9M6vrdtlt7nJ+4rwwrHHRwzrJaBIk6HQ63Ni9wbmxc5SaJWJyjFKzxEJiQTC1Aior1RUy0QxSQEJyJfL1PLFADKNl8K7xrjAmuwI2X6gXOBk/6adVtZAmTDJJRgtq4rlSjHKrjCoLcH7JLKFEFKbUKfE7yHXERMhInGqr6l/rEhIODmbLFOk7FyRZ8q/R7+T7cKijNVzfoYb6zuvD9D7c3t4mlUrx2muv8eKLL1KtVpmYmOC3fuu3+Imf+AkA7t27x1NPPcXly5d5/vnn+dKXvsSP/uiPks/nSafTAPzar/0af+fv/B22t7cJh58MioYP1xoP9a1VtSr4UTAYOr5Xb/H3vrjEjfw+EvDTHz/Nf7l46n2+IwujCwQ/atDkvu8HfdD34bctKXUU52hQrW6Q+qfMbRqbFK0iOO8lbIAD9SnvtXJqjls7txiPjLNX3yMoBSlaRZ8H1F+j662P9TOS3o/XlFWyvF14m63a1gEw+Qc5zi1zi5bTYtPaZGF0gUK9AMBSaQmrbfHIeHQkl2mQ+kHfTzoPnno5TP1r38tpKtQLA6HlRymn5nh181WeHnkaYCDrq3cfnhl9hqXS0oFU25MmNcLBxN1R+3TUmvU/dxAv6qjU05a5xacmP8WflP+EM+NnODN2xq8/LqYWubt/l0QkwSPj0bDK9yFQtVUFF1YqKygBhbyRZ8vYQg/qaCGNLXMLLaihh3TKjTIn4yepdWrg4MPNrY7Fg9oDPpIShtTCyALFurj2HNdBD+nc3r/N89nnqbt1phPTjMRGSEQTwjxCQg2oFKwCjusQD8cxbZPZ5Cz3jfvMj8xjdS3iEcGDUmUx7a3b7eI6Lpu1TTHNzXZQAypm20QP6UxpIrmphTWfdSRLMnPJOUKBEAATkQkeVh9iuzZZVaSEHNvhnd13KNfLnBg5wenx0+x2d0Vy1RBMt1qrxkR0grcKbzEWGQMXzI7JQnIBgJAU4pO5T1K368yPzFMwC1TbVbSgxn3zPmcnzmI5FvFwHKtj8VTyKe7s3eHMiPg8M9oG58fPc7V8FTWgood1YcpYedSAKpJXrRoSElklS9EqogU1AR+XJHKqYC9JSOhhnWqrSjaWZXlvGS2gsby/zKnEKVYqK1hti67bFZwoqyAg8q6E7djc27vHWHiMlcqKSNQ5ggnlOOI8FRtFZhIzWLbFpDZJ3a6TVbI+8H65skwmlqHWqbFv7vPVd7/KsegxHlQfgIuYfti1iIfiZJQM6XCaB9UHzCXmmNREQs9si1pgvi4qeuVmGSWg+EnRnJ4jGUmSjAkofKFeQAkqbBqbVJtVqs0qRssQFc3HU/2Mtvhv13UPGFdDDTXUUEN9uFWtit8Jo6OjACwtLdHpdPjMZz7jP+b06dMcO3aMy5cvA3D58mXOnj3rG1IAL7/8MrVajTt37nwb936oD7NcV7CjtrZEVW+QIbVcrvJTv/11buT3UcNB/tGPPcdfvjjzREPKtkVdLxQS6aiRke9PQ+pPo2+bKXWUqTCIN9SvXpPAdV1u797GdUWlYL+17xtSRxkOeSvPSGyEvJVnVBllu7nNmbEzhyDp3j5umVs07SabxuYhRtL78ZqubV/DtE0e1R5xbfuab0IcBV3vVU7Nsd/YZ0qdOsC5SitpkWBoVb9hg8mrIG4am0cmiY46H+8Hon+SOeYB0r/88Mvii/Rjs8bbh/7nevD2P9z4QxKRxAG4e//ku6XSEqPh0UNTEJ+0T4O4VYP2ubcG6D3Gq/htGpsHpiHm1Bx39+/y8omXmdKn/OtzLDLmJ76qrSrpWPrQaw31fSgXkX4J6iKl5H1hxyFv5dFCGpvGJkZHAMG1sMa7tXdxXZfTydNsmBtoYY3ZxCzlVpmMkvHNA0mSkBGJlQvjF9hubjOfmOdR7RGVRgWjLbZpOzaXi5dJRVOYbZNNc9Oftnd+7DwrlRVySo5ENIEsyZTbZc6MnuHG3g1RybPr2JIt0kXGGjlNTAN9Z+cdduu7mC0Ts2OKtFXX9AHtEhJW12JCmSAZSSLLspiMJ0NWzeJKLolQgobd4Pz4eZbKS6RjacGocmzu1+6TVbNsN7ZF7U+bJBAIcCx+jHgkjtkxfWB6TI5Rskos7y4zo81QqpfIxXIgQVpJs1Hf4FTiFOvGOmk1jdW1WK2uMpOYodAooId07pv3mU3McnX7qjDaHlf3CvUCC6MLyAGRiNLDOsv7y2SUDEbL4M7OHSqNij/dz3Edau0aJbOEFtJIq2ma3SZ6RBf8KSmAK4l1nVQneafyDlpAQ4+I+p83Bc9xBFPMcUT6yHEcJtVJkbJyYLO6iRJQWK2sIiFxZecKH8t9jN945zc4O3aWgBwQFTttiqn4FHWnjh7TmU3OUqwXqTar1Fo1lKCY+DefnCdvit8PuIhpfZ06alil1qlRa9W4t3+P2cQs17avYbs2IMxCB1EN9dhetmv7lUSjbQwry0MNNdRQQ+E4Dn/rb/0tPv7xj3PmzBkAisUi4XCYZDJ54LHpdJri4zhKsVg8YEh593v3DVKr1aJWqx34GWqob1QfZMLe/7mS56//zhuUzCbHkiq//hc/zsdOpJ643VZLJK9GRoQhparfogP4HtO3FXQ+aILeRGxiIMenV70mgSRJnBk7Q6leEqOrlbRfD/MMB9d1/Sl4XnIqIkfEBDY5wmJq8UgukS+HA6mYD5IGApGM0QIasVCMp0af8m/vTXMdNbUtb+U5O36WQCCALMuMR8dZKi0xpU8RC8X49PSnDySY3o+Z1GtaeUabtyaDuFtLpSWf2+Staf92ekH0vdD57cb2AV5X7z7c3r1Nx+7QsTvsNHYOPbfXWNsyt2i7beZH5qm2qgdYWV6V0ht5/szoM7y29ZoPm+89pqMMu0HG46B1670+vOs0o2bYbe0CHJiG6Jl/W+YWV8tXGYmMMBIb8fctb+UFnywU/UCMsaG+N+W6rjAqDHEt5rQcsiyjh3XU0OMqWEgkjMy2KZI0SNSdOmfGz2A5FoFAgKyWZW5kjus711FllYJRoGt3MTsmU/oU8ahgKMWjceLhOCvVFWJyjOX9Zfab+5htk2KjyOnkad4svokSUkiEE1i2xXxynteLr/smiCSJ1M9sfJabOzc5oZ7AbJmkYiniwTileolT2inyVp6V6grhYJiHxkNsbNSQSqleIqtk4fFHkBpWkWVhmsUjccEiUnOYbZNaoyZ4WvFJPn3s05QaJU7pp3g9/zqz8VmKVpGIHGGvsYfjOmhBjU1TvJ9r7Rq2a7NpbpI3RVXt9u5tHNfBcRzWjDX0kI4W0TDbJuvVdZ4eeZqSVUKTNYpmkYWRBXJKjkK94P/z7OhZXs+/zkntJFeKV7C7NpVGBddxMVoG8XBcfK6Ym2RiGVYqK9jY4MKWteUnyNZqa+LzIiA+X6y2hesKflgymkSSJRZGF5CQ2G3ucjpxGsu2SEQSXEpf4n7tPvPJeZYry8yOzPLO/juk1MdT7iR4YDzgZOIkt3ZvgQtKUAEXLo1f4nL+Mn9x9i9yc++mgNq3DSptYVBOaVPIkuyD5/NWHjWkUm6Wyak5io0ikiQxOzLLTmuHaX2ajJrxU2K1do355DxrlTVxTYTiIOED/AGmtClhTCKTUlKsV9fJKBn/3A011FBDDfXh1SuvvMLt27f57d/+7W/5a/3Kr/wKiUTC/5mefvK0s6GGOkqdjjCjtrcHT9hzXJdfe+Mev/Tla7S6Ds8fn+DXP/9xToxqT9yuZQmGVCYD2Sx8gBbqh0bfVlOqP70yqF42SDk1x529OyymFpnSp9hubJNW0j4XaiwyxlJpCRCGQqleAue9SWx5K/+B0k6epvQpKu2KbzQM2vejTCpvOt+51DliwdghE6I/hdOr3hSPlwjzTI1eqLqn9zPKeo0wx3FY2VthXHnPdOo9nt79gvem7g1iMA2CzkuSRNttH0g2efdl1AxPjz9Nw27w8omXDz23H3IfkSNEghEuZS4dgrv3Tkfcbe3yueOf487eHTH6veeYPgjk3NvWUevWOz2wXC/7KS7XdQlLYUJyCMcRX4q3G9sAPDP6DPutfaKBKC+feNk/Zx9kf4b63la1JWDhsWCMUqOE1bWYS85RbpbRg7pf1TLaBhklQ6PTQAtrnB45zXZzmwsTF4hH4mSVLK/nX+fZsWexHAtbsql36iKZIkkkIgniYcGoUgIKOHBj+wbRQFRUyyI62ViWu3t3OZ08TaPbQA+L6t1KZYWPZT/G9e3r5JScXzWUAhI5JYdpmxgdg4AcQA7KLKYWubV/i0wsw3xi3od0YyOSNiPzBAIBEpEEAAEpwFNjT5GIJPwBAPf275FRM5RaJXJxUYGTZRk9pFNqljgzcoav579Ox+4QlIJMKBM07Aa1Vg3XEXwuLahRtIqoARXHdShZJU7ET6CHdZp2U6yDKz7z1bDKXHKOd813SatptKiGJEuCGyWBHhR1tGwsS92uk4qlKDfLpBXB7Lq3f4+O3WGptMSetcfdnbs4tkOtUyOn5JBdGaNtEA/GyapZMYwhdYmiVUQP9jCpQqqfUsWBlf0V5kbmmB2ZpS21mUvOYXQMCvUCi6lFf59WK6t8ZvozPKg9QAkoFKwCz6ef53fWfodPT32a9dq6SFO5DoFggKdGn6LltEhFUzQ6DZSQgszj5FrP544e0n1OViaWAUkYSl4y7ZO5T2J2TWEkajkCckCA3B9ztaZ0YT7FQ8IU9WH0ksS0Pk08IiYWLqYWWamsoAW1YY1vqKGGGupDrJ/5mZ/h937v9/jDP/xDpqbe+z6UyWRot9tUKpUDjy+VSmQyGf8x/dP4vP/2HtOvX/iFX6Barfo/Gxsb38SjGerDomYT8nlRr0skDk/Ys1odfv73rvD/u7IOwH+5eIp/9GeeQ4+Ejtym40ClIip6U1MwPn4YlP5h17d1OXrNDMdx+MrDr/D0yNMH0j+D1FtD8wyQlJLy/4f79u5tEpGEz5waiYyw39rnYvrioRTOB+FXeZPmdlu7fs2rn1P1fpW13olz3nQlL8HjpXCAgUykvJU/kMzxDJf+2t1R+9BvVm0am/548lq7xpQ+daiG17tfvVP3nnScjuOIdIFt+0bNoCmAU/oUSkjhh07+0PvC0fvrkv3biQQj5NQcS6UlcmqOvJUnGU76/z0oHeVVF3tTXN623i+55J3LcqNM222zYW7QcTpIkkS5UabltOg4Hf/xO80d30yTZfkA62yYGvj+loREJpZhvbqOKqsoQYXV6irnJ85zc+8mSlBhpbaCFtKwbAslpGB1LMyOyaX0JRpuw2c/PTv2LOuVdarNKlpAw0GwnZb3lrFtUaHSgzqrlVViwRhqSCUZSfoVOlmWOZk4yW57l4yW8UHeWkBjtbLKxdRFjLZBtVWl1q6RCCfQIhoBSRgRXv3w2vY1Xki/wM3dm8Iw07Iko0mKTcE9WiovCRD6Y+5bPBJHlmUfgL1lbpGJZVitrnJKO8XV0lVS4RR3tu+wVdsiHU1ze+82oxHBmSg1SgQIcDJxkobdwJUeg8LreeZH5v2ET+9n5rmJc8JAieicHj1NQAoIzlViDrNtUjALqAGVWkvwr8yuSVpJ+ymhhdEFXNfFbJnYto0W0Li1fYvx6DhXt6/iOA4Fq+BXFpWQQiAQQItoot4XSRAIBJAkCSWksLK/guM4GC0Bib+7e5eO3aHarLJWWSOn5nyml+u6xAIxYeIENGqdGnpQsMZmE6Jyl46lubV3i8/PfZ4/Kf8JM/EZlIjCdnObWqeGFtYoNUtM6pOoEZWgHEQP68TDcSpNUTFMx9KUGiX/d0w8GkdCotKsCJPq8eQ9JaCwZWxhtAxsxxY1vccMsy1TTC00OoZIUzWKJCIJnyElIQaHWF1LVA4fT08caqihhhrqwyXXdfmZn/kZ/u2//bf8wR/8ASdPnjxw/8WLFwmFQvz+7/++f9vy8jKPHj3ihRdeAOCFF17g1q1blMtl/zFf+9rXiMfjPP300wNfNxKJEI/HD/wMNdSfRpYl+FGWJep1gcDB+zcrFn/tX7/B1x+UCQdkfulz53jlE08RkI/+/51ORxhSui4MqeFlOVjfNlOq3yi5Wr7K8+nneW3rtYEmQi87qJ9z1F8ny6gZZN6b0laql3AcZ2DCp5+HdJS8yXxXy1cZi4yRt/JPhKn3ykuAeTVCrxbXm+CBwTU+L41za+eWD1a/Wr46kJ3Un1jqh8H3bjelpKi0K34lrb+G5x2v9xzvr+uDanCeUfXVd7/KbHyWrzz8Ci2n5RtKRxlOgwyuQYZR/zXQe048bteMPsNXHn5FpJgaZUYiI/456n/9TWOTDWODVrf1voZkv7zjX0wtinSUFCIkCyf8mdFnqDQrhANhXNel5bT8/Xy/8zHU95+0kMZ6bZ3ZxCxqVKVu1zk9cpq16hovpF/gcuEyE6EJ8mYePaQTkAKoYRUXV1TztPdSjeu1dZH4GZnjofmQeDjOrb1bPDvxLFe3rwp4OMIsKDfLzI/MI8syATkgpvABsiSTjqWxOhZ6SGfT3MSRBEzb6BiiyhVUD6RH9YjOpD6JHtRZrixzUj/JG4U3ODt2luXKMlpIE+BuWeVq+SrpaJpKq8K9vXuoQZVHtUc8qjxio7aBYzsC4v14Ml25UWY2OcvXC1/Hdmwq7Qq39m5xZuwMQTnITnOHT059Eld2ReInqJAIicRVTsmxUlnhwsQF1mprwnyTJTJKhnVT1MX8SZ241Fo1Nq1NtJCGi0uxXqRrd3FxySk5yg2xZvFQnNXqKjktRzwaJxAIEI/GmU3OIssymZhgZmkhjXgkjhJSxNCM6Dj1Tp14WEyjs22baqtK0SwyEZ3A7JiU6iVsx8Z1XO7X7ovPW2WCYrNIMpokEUmghTXKjTILIwvkrTx5M0+1VcVxHFarqygBBbMr6pRr1TXOjp7F6loECLCYWiQRSmB1LBYnFrG6FslI0p8mWGvXMNqGmPBYvExEFtlz13UFJL5ZZcvYYr+1L163XRVMKGzBv+oKtpTZNsWxWUUyivjrdL1bZyG5QKVZ4e7OXSqNCrv1Xd4uvY0W1FCDKtfL10WCbaihhhpqqA+VXnnlFX7zN3+T3/qt30LXdYrFIsVikUajAUAikeCnfuqn+Lmf+zn+8A//kKWlJf7qX/2rvPDCCzz//PMAfO5zn+Ppp5/mr/yVv8KNGzf4yle+wt//+3+fV155hUh/l2qoob4JqlaFIdXtDp6E99ajbX7qC6/zYM9kXI3wv/zEC/zQ6SeHGxoNMWFvYgJyOYhGv3X7/72ub4sp5fGKWp0WG4ZgGi2mFlk31nn5xMuHpqX1mheDuFP9Vaj+1MtuY5e206bltA5VyXqNmPervw2q2nn7dpQBAu8lu0r1EpIrkVbSA42dQTU+j6k0Gh0lb+UHspN6j6d3H7x9Aw683pQ+RSwY41Lm0oH97TfpPCh5r/k1qHbmmW6fPfZZ1mprPDP2zJFVuKP2y0tZbdQ2aNuHz1XvNdB/ni5MXOCL736RF3Mv+tWRSDDyRFh+KpZiv7V/6Dx6ptGga6D3dSVJpE+enXjWX4veVJQkSYfW4KjzMdT3p/L1PM+OPcvt3dtItiT4UZLE6ZHT3Ny7yXOp51jaEWBvSZJwcZGR/TperV3DcR3MtkksEEMP6tyv3uekfhJcOK4d543NNxgPj+PYDkbTwMUlHUn7BkhWzZK38r65FAgGmNQnqbXFdD+jaWC0DHBhLjnHenWdSW2SRCSB2TX99/qNnRtEpAhr1TXmknMCZv24mlZr1QRwPCCYQ2t7a3Q6Ha6UroALlXYF13W5sXtDTPYLq1hdCzWkYnZMTumnKNVL1Dt1zo6epWk3iUfjfGrqUxTrReKhuJ8AqnUEpDRv5ZlLCM7WbHwWLaSx3dim1q5xYeIC67V1cppIlNbaNX8q33p1HQmRrC01SuhhHaNrCM5VxwQJ5pPzFC0BAVeDgjY5FZ9iOj7NVHyKnJYTBtLj7Z0ZPUOz2/TNLzWo8kdbf8RsYhbLtmjYDdSgStfpcmvnFlpIYz45T07L0eg0xOS8Vg09rGN1LRZGFjA7ps9pcl1hoo2GRrm1c4taowYudLtd3ii+QUbJkIgIRlg8Guf02GkKjQLxkOB4eQkto2WgBBUeGA94LvUc9/buUW1UyVt5UrEUhXoBo2VgtS1wBCdKC2kkQglwhDkquYI5ZnUsJmITrO6vEg/HUYMqy/vLVJtVYXJZBe7s3eGEfoKl8hJL5SWmtWn+OP/Hw4ToUEMNNdSHTL/6q79KtVrlpZdeIpvN+j9f+MIX/Mf803/6T/nRH/1RfvzHf5wXX3yRTCbD7/7u7/r3BwIBfu/3fo9AIMALL7zAX/7Lf5mf/Mmf5B/+w3/4nTikob6P5bqwuysMKVk+PGHPdV2+cO0BP/fv38JodXgmk+R/+0uf4Ol08onbrFYFLH1yElKpw6mroQ5Kcj/A/zHWajUSiQTVavUbikJuGpuMhkf5N6v/hhPJExzTjhEIBPz6lZfQGYuM+ewo73bgQOLJMwqAQ2aJ67pcKV4hEUlQaVbIatmBj/G255kG241tPxXUn3Lpf51BjKV+eY/pPb7+xx61nf7X9Y7f29b7bfOotTnqdb0Uj2eaLJWWeGb0GXaaO4fWpPd8es+Z0qfed00cx/Gn0HlphivFK8zoM1wuXuaZsWcoN8pcTF98L+0w4Dx5r7dpbPrXipf8etI+DLrPu811XSZiE/62++9rOS1wxBdt77rsfY63f73nxVsjz8z6ZrGk/nPfh0O9v/5z1thxHF7bfI0Xsi9wY+cGc8k5AbzGRQ2qvL71Ok+PPS24cXqOSW0Ss2OSiCSotqroYZ0tY4tKswJAPCyqcGpQFXDuxzVeAD2sY3ZMsqpgEM0mZ7HaFpIsMalOiulnuL7ZVWvWqLQq1Dt1slqWeFhMmMsoGQJygHgkLhhOCOZVs9Pknd13GFfG+Wj2o36qyqumOa5Do9PA6ljYjk0gEODs2Fm229uokorpmMzEZ3hoPCQqRTFtk5ySY0QZEXWv9mOWlSwRDwszpdqsUrAKROUoJauEg0NACpBRBf/IM3A2jU0e1B7wfPp5bu3eIq2mmVQnKTQKKLKC1bVwHAfLFuwko21QapRYnFhktbZKOpr2Jwbm1BzLlWVS0RRFS9T5PBi70TZEDe/xWsqSTEbNsLyzzAPjAR9Jf0QM1sBlPDLOlx58iYWRBeZH5lmrrlG0ihzXjtOW2iyMLBCPxNkyt3yzUpKEcZm38kxpU1SaFWqtGsV6kRPaCb7y6Cs8M/IM++19Yey1LU7ET/DIeuTzx/KmqL5rIQ0kkY4DcFwHXERVMZLmf1/+3zkzdoa91h6LqUXWamtMhCe4X7tPWkmTiCTQo7pIHEuw39j3wfzJaJJKs+J/hukRnYJZYCI2wY3tG6QiKUrNEs+OPctbpbd4Pvs8q3urbDe3Oamf5PjIcRLRxLftfTjU+2u4vkMN9Z3X8H34rddwjYd6P9k27OwIoLmiHE4ytbs2/+Mf3uaL74jv1z/81BR/+wfOEAke7TDZtjCkNE2YUcqHPDT+Qd+H35ak1KQ2yd39uzyffR5ZkinVS7S6rQO1tkltkjt7d3h65GmWSks+hwk4lAbqTdb0plk8nlS1VeW57HNHVsm8x3oVPOBQveqoyYAfCKD9n2FEeBU4z3zZNDaZ1EQdo7dOOKgK5r1ey2n5azdo4mFvQqi/huilugatiaf+hNX7HW/vdDpvXxZTi7xZepOXpl5iu7nN2fGzR04W7Afi90/H6z2u/mpgv8HXbxb18rM89SacInKE/da+D5z3HufVSfuTcyBqmY1u40Dyr9f79VJijuMMXK+hvjclyzKfzH2Smzs3mdVnRe2pLapYXjqlbtfJKBm0oMa9vXsCBt2s4jou1WYVPaT7Jo0kiXra6v6qX4WLh+LCgHAhHU1zvXyd86PnqXfqaCENPaizYWxgOzZG2wDA6BjY2MiSjCMJWLrRMYTZZRX8z1rbtZEQ77f91j7HE8c5PXpapKTCul8jkySJveYeSkhBCSm03TZpJc1D8yFzyTnkoMzCyAK3925zQj/Bvaqo9pm2SCYlogmSkSSJaIIpbQoJyU9fpZQU281twUYKBJmITXBz5ya1Vo1sLEuhXkCWZD6e+zh/sPkHnE+dR5ZlrK4wUMy2Sa1TIxFJ8PTY0/4k07nkHCuVFdLRNCuVFcyWSVSOslReYjw8zq2dW8TkGLWOSD4ZLUN8TiD5XCtcyBt5ys0yl1KXeHXzVdSgSjwU587+HXJaDldyubl3k6yW5dmJZ9mqb5GKpXDcx8B3JUOhXhDcLtdly9hCDahsGBvEw3GsrsVsYpYHtQec0k+xvL9MTI6hBlRS0RTvmu/yiewnkCVZ/JFCy6GHxZ/0zLaJFtR8sLrRNtCCGmu1NX7o5A+xVl3jlH6Kteoas/os261tTiVO+deI5AomWDwcx+oI5pnRMqi2q8TDcXG+9Ckx9TaWFik+7SQPzYecmzjHu9a7fCz3MVE9DakoIQVJkqi2qsO01FBDDTXUUEMN9V2l3gl7un7YkNqxmrzyu2/yxXc2kSX42U8+zd/7zLNPNKSaTajVYHRUJKQ+7IbUn0bfFlPKA4dHQ1GO6cfIqBlcyT1Qa/Mec2fvDm27LRhAzmEG0KQ2STgQJiK/B7wei4yxaWxStIq4kktGzRxpRi2VlmjaTdp22zcSBhkTvSyl96v5HaUnGSWDOEP9FcaW06Jtt/3HbxqbR07uO7DePTUybx82ahu8XXgb27b9SXH9NcRNY9PnIg1aE3/7fTXI95NnYhXMAs1Ok01jE1mW+aGTP8ReW/zlvtfk6l/vXtD9Uaknjzvmgen7a6BHnY9Bhpp37qd0UeG5lLnkT9HrNyv7mVte3XJtf40JdYKCWeBR9RGPqo/YNDZ9wP+MPsPV8tX3XbuhvnfkOA4rlRVmE7PIAfHRqod0zLZJvVvnVPIUjW4Ds22yZW6RVbLk63kcV0x2M9smriTSTbIko4U0VvdX6bpd1LBKsVlkOjHNSGyERCTBWm2NM+NnuLF3QwCmbQvbtTHbJnkrjxbS2LLE6zQ6YgLf6eRpJCRs26ZgFvwkkFcbdHExOgZzI3OMRkf9KXm4gIMwv8Iaz4w+Q71bB+C51HPU7TontBO+MVKoF/hE7hPc3rvN4vgiO40dtKCGYzt+EiwRSYgpbhHdfy/VO8K0m9ammU/Os93a5szYGQJywDee9JBOqV7i+fTzrFXWyMayfvKq1qmhh3SMtiEA7pGEX01UQyrFRpGcmkMJK9zcvkm70+Zy/jLH9GM8NB8ym5hltbpKtVVFC4l6omu7rFZWyagZrK7F2bGzXCld4bnUcxgdYV7NxmfFOtsNPpH7BIlIgqbb5IXcC8iSzGp1lbnEHKvVVcGPqud947HarmI0Dd7ZfQclqGB2TB8QHpACKCGF7dY2WkRjJj7Dte1raCGNSXVSJNgCKqv7q3TsDlvG44mp1hYu4vMzE8vw0HjIZ6Y/Q4sWM4kZ1ow1ZhOzNJ0mAGpIpdauHeAvevB4NSimCOLiw+zrTl1MlmyVOa4f59bOLS6MX6DYKDIRmWCntUMqlkIOyOhhXdRHhzpS//yf/3NOnDhBNBrlox/9KG+99dYTH1+pVHjllVfIZrNEIhHm5+f54he/+G3a26GGGmqooYb63lardXDCXqhvcN7dUoX/6re/zp1iBT0S4n/6sx/h8xdOPjFwYhjQbkMmI376tznUk/VtY0ptmVu+ASRJEhE54ieCellRGTVDWkkTkkNE5AhZJeunSvqTL3kr75s0ruuSDCdZ3Vs90rDxDAMPTu09bpAx0Xtbv5lxlEnVf7tnWOTUnA/z9swoOMwZ6n2dSW2SiBx5D6LdbeE4jl9vPOpN0c/X8vahVC+hBlW2rPdg64Og756h9X7pp0EA9KPkGTgjsRFKjdKB23Nqjqvlq7iuy3h0nKXS0iHDrve1BhlLvUZR3sofWMPeffyg+9wLfe8H5vdvo3+dcmqOO3t3ePnEy0QDUXaaO+hRnTu7d8Q0r/JVPjX5Kd4svcliavF9126o7w25rss7e+8QlaOsVFaEieOKBIoe1pmJz/BW6S1icgwlrIALq9VV0hGR3FEDqqi1NausVFZQgiJlYrs28VCcklViPiGmZ8bDcZBgNjHLW4W3OD9+ni1zCzWoUjSLxIIxJEei1qzR7XZZ2VthfnSeZCxJICimxtXtOrFQDKtjsWlu4jriMyARSYh0kCvMqXg4Lqb0daogieqWLMlst7aZHZ3l9Php9rp7PJt6lqbTpNQooYWFcVRsFHl29Fnetd5lIjqB0Ta4t3+PWktA2mutGo7t8M7OO9hd2zeRphPTyAGZZCzJQnKBht0gp+YEwN0Fo2ugh3QsxyKrZik0RLWxUC8wqT42qyVRcfQqca7jkggnyCk5JEkiKAVJKSlkScZFDEs4P3FeJJQep4dW9ldwcbG6FjPJGVYrq6hBAbD/xOQnCIaCAnTeqSLJEtPJaZ7LCIMuEU3w1OhT/lS+mbgwoefic+StPJlohnt790hH0zyoPqDrdHEdAbw32yYpNcVua5eF0QU2zA3OjZ9ju7mN0TWYTc6Sr+cxOgaT2iTXdq4xEh3hYe2hqGo+NuYAzI5JIBDgucxzBIKiMl+sF5nRZyg1SiyMLCBLskjVPf41UGvXiEfjJCIiGbVeFSB5B0dsv13zJypmohnqTp1z4+dYrQnDbbe9y7MTzxIKhMhpOWRJFtfsUAP1hS98gZ/7uZ/jH/yDf8DVq1c5d+4cL7/88oGJU71qt9t89rOf5eHDh/zO7/wOy8vL/Pqv/zqTk0Nm4VBDDTXUUEO9n+p12Nw8esLel+9t8jd+5zI7VosTIxq/8fmP85FjE0duz3GEuRUMiul6Y2OCTTXUn07fVqbUq5uvMh4b5+z42QPJk15e1EZtg1K95Fezvvzgyzyffp51Y520kmbD2CAVSxENRQ/wfJZKS7RtUSOJhqK+KdOrJ3GcnsQj8oywSW3yEFOo93UGsY6821vdFq7kEpEjR7KajmJCbdREamyvvsdYbIxwIMx0fHrgsfWzjbzj8lJiaSV9YLKgx4/ytvFBqodPWq9B/KgnHVsvW2pCmeDM2Jkn8qyexIc6ikF21H97jx20Zt45vr17mzNjZwZyxwbJuwa86YvxcJwrpSv8pYW/5Kc0jmKCvZ+G3fhvvb7RNa42q+w19ljfF1PzskqWQrNAJpZBQuKB8YCnk0/zwHiAFtSwbIsLqQtc377OqcQpylbZn3Q2EZvgofGQtJIWqZ+uQTKc9I2WWlMYA0bLIK2kKTaK6EEdJLEfDbtBLBDzuUkTsQlabovTI6eRJFGVsx1bTFpzbbSQMKk84Hm1VSVv5plJzHBj5wbjkXEaXbHNcqvMXGIOSZIEKByRxLm2cw01oLIwtsDK/gpaWCMRTpC38kQDUW7v3GY8Nu6nrhpug9n4LGbXFGkk1yUREdyhRCQh+FrmFh27Q6PbIKtmCcgBKq0Kekgnb4okmNk1eWr0KfJ1wdszWgZGR9TWkCBv5lFCCjIykiyJeqKWw2ybfrJtIjpBMBBEC2lYHcGjkmRJ7OvjNbW6lqjqRUTFzmPp3d29i+u4uJLLSGSErJZleX+ZSXVSQMc7Bo4tqntaUPOrhP/+/r/nz5z8M9zcvclJ/SQ3d2+SiqaYik9RawtTaWVvBSWkCAPOtnxDzegY/r7JkowSUPh6/usc04/56TbHdZCl91JKXvJKj+h+JU8NqKxWxIQ/WZaZ0qdIxpLYts29/XtkY1lM2ySrZFnZX0EP6z4HbdPYJK2kWdtfQwtrSEjMj86zWl1lPjFPofGeQegZqX+az7sP02fdRz/6UZ577jn+2T/7Z4D4HTo9Pc3f/Jt/k5//+Z8/9Phf+7Vf4x/9o3/EvXv3CH2Df4b9MK3vUEN9t2r4PvzWa7jGQ/WrVhOVPceB/kvCdlx+9Y17/NbV+wB84mSKf/C586iRo3/Xtttiul4yKSbsDQdDHtZ3HVPqta3XOBY/Rttpc2fvzoHqXW+9qm236Tgdf/LepyY/xeXiZVKxFK7rHpqi5j3vzNgZUesLRg5UuHrlGS7AgfsHpW96t92fwoGDKSfPTHMch9u7tw9NyeutHPamxQapVC8dSmWBYBt5tUfv9t5j8Iwvb02vFK9wpXjFX19Zlnku+xyyLL6keobUnb07ADTtJhvmhr9OvTqKSzVova6WrzIbn+VK8QpvF97mUfWRb3ZNx6cPcL6883m5eJmXpl4io2bYbe0eWqP3q0/2JpvgMIOsv8LnTRjcqG0cYpv5BqLrstPc8auFRat44Fo9ap88NloymqTttFneW+Z4/DjXtq/5bLBvFvh8qO8Oua7LfnOfklWi63bRQqK+loqmuF+9j9WxeCHzAn9S/BOUgEhALaYWWausMRGZoGSViAVj6CEdx3XYaexwbuycPxFOciQc10EPiel3W+YWjuvg4FBqloiH47iSS76ex+paRINRrK5FvV2n63TZaewwn5xny3oPkm7ZFgujC0zr08K8CAqzqNIQySIloHCtfE1Ax+sllIjCTnuHi+mLFOtFrK6FHtLRIyIRldNyZPUsxXoRPaz7aa6FkQW2G9u8kH3B5xJJAYlzE+dYqa5QbVbBAWxR01UCCrVODaNtoIZUthvbxEIxsd3HKS2ja/iMq3hYGD+T6iR5M0+tLZhQntmlBlVKVkkYcV2bTWOTrdoW8UicEWWEj2Q/wmhsVIDUOxYZLUOpWRKGV8f0E56T+iRI+Dwozxya1MTtASmAFtJY3lvGdVwqrQpb5pZv8OAI+LjruvzR1h9xdvQsv33vtzmlnRIprfHzJKNJ8TtKm6LYKHIxc1EwsQKPk0YymLZJPCL+3UuEWV2Lk4mTNDtNAWV3BQNry9xio7ohoOcgTLK2WDsvqReTY1i25bO+gPeg9tUVtKAmEmIhMZVw09ik2hKctFK9hBJS0MIaruRStIpklSxm1xTXlSwLg21Y3ztS7XabpaUlPvOZz/i3ybLMZz7zGS5fvjzwOf/hP/wHXnjhBV555RXS6TRnzpzhl3/5l7Ft+9u120MNNdRQQw31PSXXhb09MWFPkg4bUkarw9/+j2/7htRPXprhv//RS080pOp18ZNOQzY7NKT+c/UtMaV6v7B7yZTPHf8cjW6DY/oxLqYvHqje9dar9lv7pGIpQFSh7u7f5cz4GVKKAOxGQ1Eupi/6BkKvSZRRM36t7yjTBA4bFU+qdfXe18sa6jW3No1NYerUNnwzo5dFBRwwZDwTy3t87371r0mvKTYdnyYaECmwgbwqSZg8r26+SiKSYCQycog/5R3DYmrRh4VP6WLyUyqaomAWfIPtKDbTk3hbFyYucLl4GdcVE8c2zc0nnoeJ2AQTysQBM8pb10Em2FGGmHcOvPXuPV6vpuhVQD3jsFQXE748HpVvMkrvJcZkWUaSJM6MnTmwlke9ns9PC7zHT9tt7pKIJD4Qf2uo7z3V2jVkSSYaihKSQ7iSS07N0XJanIifAOD69nVOJk6KaXphlaJV9M0CFxcZ2Z+wp0U0kVCKjVNsFDE6hqjQtaoYHYOMksFoGWKSXiyLHtYJSAFySo6MmiEkh5jUJpkZmWEkOsJcco7l/WXUgEq1WfWB2/l6nmQsSTKWJB59bGxZed9AkCTJr+IFCTIbn6VoCdMpo2ZYqa7g2A4Fq4ASUKh3H8PWwzpGxyARSRAIBLiUvsR2a5unxp7iWOIY84l5/sP6f+CUdop4NI4e05EDMhklw1p1Texnq4qMSBUFCaIGVaoNwXlKhBLEI3ES4YSf0jHaBg4OWkijWC8KY6otklxaWCMeiVNqlkjFUhQaBVGVbFX99ZeQiAViXCtf44R2gpX9FSrNCtVGVYC6HZeAFCCrZH2OlJf+SUQSZJSMMP0ciAai3K/eR5EVVqorpGNptIhGy20xPzKP7Mrc3b3LXHKOUrNETs35wz9W9laotWosjCwIs08Rwx1c5z3TBxeRdnoMyPdYUIZt0HW6XN2+ylJxiVgwRqFewGgZfvrT43ipQRXXdnlYe0gmlmFSnfQTa2pAZb26znhoXLC0Yhm/5ujB+/WwMCSntClK9RLzyXmRCnMd3xgDwVTbNEQ9dAg7P6ydnR1s2yadTh+4PZ1OUywWBz7n/v37/M7v/A62bfPFL36RX/zFX+Sf/JN/wn/33/13R75Oq9WiVqsd+BlqqKGGGmqoD4McR8DMCwVhHKnqwfvf3Tf5v3/hdd58d5tIUOYf/tAF/vrHTiMfESBwXahUxD+npmB8/HAFcKg/vb4lptQgE6FQL/Bc9jmOJY75yZb+CWqSJHEpc8mv33mAa4Dbu7eZ1CYPmE6eweAZBR4UvH86nKf+qXsflIfUm8Lx/r3XrAGoNCs4OLSdtv/c3iTYURDz3vu8/Uor6QPpK88w6a3W9RtpU/oU0UAUWZZ5aeolKs0K+619nz/lba93mp+3LW/dq50qo9FRWk7Ln/K3VFo6tF5P4m0V6gV+YPoHyGpZUXPRpg6ZV73He2fvjm8QDbp+vDXvNwYHnjsHAbvvZWT1QNmvlq8yEZvwE1mLqUXW9tc4M3bGf9yUPkVEjvjXi/ez09w5cK2C4G8dlZjqNbWeHnuaaqt6ZIJvqO9teZPUWt0WZ8fPEpSDTMWnyGk5mt0msXAMLSiMEcdxqHfq2I6ozRUbRd/c8AyVnJojFowJOHlQRwkobFlbGC2DrJKl1CghSzIzIzNYXct/LhKMREc4PXaaeDTOSHRE1K1sk1Q0xdXtq2xWN0lFUlwtXxWA8FbV5w/JyML0QKSGFlOLNO0mk/qkMG4eV9ri4Tgr+yssTiyyXlvnVFxMdNNDuqiJSZAIJ/z3Sr6eZ35knryVp9qqslZb48+c+jPc2ruF2TKJB+PkNGG+eAaTGlSFaRZNIgUkJuMiCbVlbfnmcq0lKoxaSBNGSVDH7Jhk1SxWx/L/6SW35hPzvGu+y4XxC/7nbrVVxeyYKEGF9do6s4lZdpo7qCEVx3W4sXMD27ExuyZT8SlRo9NyVJtVNs1N4uG44C7VBHdJiSg8sh5xZvQM13evM6PNUG6WmdKmuJS+xFptjY9PfVwkxiQJxxGQ+2gwyrXtayTCCVYqK/5UPr96J8PC6AKFekGcC0nCwfGrikZT1ATX99c5rh3HdmxK9RKZmICzz43M+Um5eDhOsV4EGc5OnKXhNpBkcQ1tmptYXYvzE+d5VH/EbHJWTOaTJMHk0nIkwglkWSan5FitrjKTmBEg9+Qc69V1smrWNzY9LpkrucO01DdJjuOQSqX4F//iX3Dx4kU+//nP8/f+3t/j137t1458zq/8yq+QSCT8n+np6SMfO9RQQw011FDfL+p2RV2vXAZNOzxh742HZf5vX3idRxWLtBbl137iY3xmPvfE7e3vC2Nrakokrobll2+OviWmVK8plFNzB5IqXkLIe5z35cBTb8XOS7B4SZW8lQdEgspj9iTDSZZKS8I8eFxtO2o63FEpmyfV0frv703IZJUsf7jxh+TUnDDTglHSatp/jpd68qqKo+FR3+TxIOa98O68lT9goHjGVa9h079OvYaelzTabe2S1bKcHT/rr5lXjWzaTa4UrxxKaXkJn3AgzF59j4nohJ8o8ib0AYdMlaNg4tPx6QMmZO+69R7vxfRF7u7fPVB5fJLxdBTzakqfotKucGbszMCJjTvNHS5MXDhgbhbqBV6aeom7+3cPGG69105vUq33+D2gvCRJA8HrHtT8zNgZau0alzKX3jfBN9T3nrxJZ1pYI6NmeGA+YC45508rU8MqQUnwioyuQUAOMD82j9WxMDsm6UiaQr2AElRIRBLk1Bz39u6hh3S0sMakPkmxUSQdTVNtVylYBeZH5kESk/Bs16bWqrFpCYNEkiUfrm50DZDE9X9r9xapaAqja1BqimRLoV7Admy2zC0xpS6aQA7I5NQcruT6k/Q2LTE10ugIXlM8GmdKm6LerTMRnuBy4TJRKUqlXcF1BaxbD+v+GmVjWa6WrhKTY9i2Tafb4XL+Mif0EyghkSaKR+LU7TrnU+cp1osU60XmknNsmpvoQcGQcnHJKAKwXawXiYVjGB2D5coymVjGN8bikTi1do1qs0qxXkQLakiyRCAY4MXJF1mrrFGpV9isiQTPpDZJ0SoSk2PCDAqq6CGd7cY2x7RjItXmOFSbVbSgJtJCrSq2I+qARttgNjFLsV5kJDLCudFzvFl8k+Pace6b93ku85xfZctEM6xV1phJzJBVRdXNtV3KVplT+ilubt9kJj4DkoCUmx0xETERTmC2TWGAOsJMs1oWakjFsi1c2SWrZ1HDKjutHbJaltOjp0lEE2LqoFkkG8uyUdug0qyQU3Pk1BwNu8HpkdMkIoL/lVNzqCGVUqPEubFzvuk5qU8iyZJI1UXixMNxlspLnIif4MbODXLKY4B6fIbl/WXf6PQA5zJD2PkgjY+PEwgEKJUO/o4vlUpkMpmBz8lms8zPzxPo+bPsU089RbFYpN1uD3zOL/zCL1CtVv2fjY2Nb95BDDXUUEMNNdR3obwJe3t7hyfsua7Lby6t87f/w9tY7S7nciP8xl/6BAupxJHbazTEhL3xcZichFjs23AQHyJ9S0yp3i/2/UZL227TclqHDB4vceIZVx4PqVQvHTIpvARVuVGm3CgzEhkR1b7Ae4Bzz7jKqblDSSTgA9X3jkxWPU7kXC1f5YXMC1zbvuanjWLBGFP61IEkmFdVfG3rNd/k6a3z9b6+t98XJi4QkSPst/YPGDa96/SkhM6UPnXILAoHwlSaFUYiI/456D3WLVNM5htVRum6XTJqhp3mzoEaprduR4HLnwRK9yb79R6vZ4Z54PtB2zjKNOxNKQ3aTv+a5K08I5GRQ+c9rRysTvQnszxTdaO24V+vR61z7/N7K5Le/vbWBIf63letXSOrZFmvrmM7toBH76/66R1ZEvVPSZZEZcp1yZt5clqOSX2Snc4OF1IXKDfK1No1YeiEdFzZpd6tI8kSWSXLfeM+WlDDcRyW95eZS85htA2stkXH7iA5Enkz7yeZ8lZeVM1aBpvGJqf0U0iIpFM2lsXsmKhBFatj+TVbj0V0Y+eGMNbqeaodYcQYHQMJiUxMTI3zkll1p87x+HG2W9vIyFTbVRzb4e72Xd6tvovjOBQaBc5PnOfW7i3u7d0jHolzInGCnZaYCjoTnxFpqsQ8bxTe4FT8lD8lTgtoVDsiBZXTcpTrZS6MX8CxHUpGCRyYS8xxfec6F1IXqLVrXCldIRaIsV5b50T8BKvVVf/YC/UCsWCMUqMkYPFtwzfx9KhO3a7jyi6BQIDZkVkCcoCG3UAJKtzbvydMPEuwovJGnryRx3ZsZFn2P8+KjaIYjtDa9qftOY7jT8bzgPUPzAekY2lKjRJqSKUrdfn0sU/TcERCzmiLqqYsyYL3JAkuVKFRIKNkMG0TyRUJp3gwjtEykCWZ2cSsXxferG5itEStrtAQx54389Q6Aj4ej8RF6qxdY1IVEPOAHOD06GmCwaCoJsYyvLbxGoqs+L8Hqq0qM4kZ7uze4fz4eWRZmE7eZMRhKuqDKRwOc/HiRX7/93/fv81xHH7/93+fF154YeBzPv7xj7O2tobjOP5tKysrZLNZwuHwwOdEIhHi8fiBn6GGGmqooYb6flW9LvhRhiEg5L31ulbX5v/x1ev8L6/fwwV+7Jlp/j//xfOMKoOhUK4rAOndLuRygiEVDH5bDuNDpW+6KTXIyOn9ku4Bv30Dwj1oPHgJoZHIyAETxzMpHMehYBYo18uklTRTmkis9MOxt8wt34DoN1P6zYGjjJTeRI93f28iJ62kWautsZha9LczqU36plpOzR2oKr584uVDpkn/dDjPcCvUC0zHp7mUuXTgOb3r9EFTN95rTOlTXMpcIhKMHDgHvdsF/BSXd8xeaqjXrOk3it7PLPOSRYOmIvYD6Hu3Meh89e+ztw/953FQtc6V3AOGGuCnx3rNN+989BqspXrpQEXwqCmEvdW9SW2STWOTK8UrjEfH/e0N9f2heDhOoV5gMbXoJ1RigRjLe8vkawLcDcKEaHQbZDQxjU+SxCS4C+MXuF6+jhJUhOnkCvMiSJCFkQVwRZUuFU5xv3bfT5+uVldxXRclpLDT3EGP6qRjaa6UrrBf30cLaoL1FFQEYykkEjTzo2IymhbS/Mlt17evE5NjVFtVtiwxNOL1/OtkY1km1UmsriX4TSFNpJKUDHkrTzwcJ6tkqXfqpCNput0u9/fvs1nbFDWzxymybCzL1979Gk+NPCUSPx2TervOTGIGNaTS6DaYVCcpNAqcGTvDA+MBLq5vImlBzedf5bScYFnh4EouekSkqDKxDHkzj4TERHSCcr2MGhDbziki/XNv/x45JUej22AiOkG9U6fWrglTJxwnQEBM2AvGBbsqnKBu1zk7dpZbu7eYSczwZuFNZhOzwoSTZVJKimJdfB6YbZP95j5qUBUJpORpTNsUUwG7ombnVeqKjSKfzH2S7dY2KSVFPBIno2So23UxubEhEnF1R0xFrLVraEGNLXOLheQC92v3WUwtUmgUSEQTJGIJgsEgF9IXWK+uY3QNsmqWYquIg4PVsZhUJynXyyghxTcuHdfh7s5daq0a1VZVVO1wMVrCqHMdl69ufJWPpD/C1wtfZ8PYQA2KWmHdrvPS9EsEg0GRnorEBfD9MecL8M0pB2doVB2hn/u5n+PXf/3X+Vf/6l/xzjvv8NM//dNYlsVf/at/FYCf/Mmf5Bd+4Rf8x//0T/80e3t7/OzP/iwrKyv8p//0n/jlX/5lXnnlle/UIQw11FBDDTXUd40MQxhSrZYwpHqGwVM2Gvz071zmq8t5ApLEf/upZ/g7nz5LKDDYErFtwY+KRERdb2RkWNf7Vumbbkr1Gjn99bz+CWy9RkV/QigSjAw0ca6WrzKXmOPO7h1SSgpZlpmOCz7CIAMC3kuugOBOtd22bw4cxQQ6ygzxEjk7zR3/3716mmdSbJgbKCGFq+Wr/nM8k6Lf/PKmvXkmSX9qq9+wySpZf52exMXq53qNhkf58oMv47ruoSl4va87pU8NTHHl1NwBA6Z/P3vNMo9F1WsuHcWh6mdt9W9j09j063P9Zs4T+VIcNq087lZv5c51Xe7s3KHVafnQ+ka34ZtU8F7qbjG1eKAieNRx9O9D226TjCYPQeeH+t6XJEnklBzLe8tMhCcomkWKZpFquyo4Rca6bwDlNGGIdO2uuCZdkTSaTcxidSzu7d3DaIuJblpYGDGu66JGVOpOnZPxk5QaJXARBpbtsF5ZJxURgyHWKmuE5TDL+8uYHRMtpFFqlDiln+Kt8lt8LPsxrm1fQwuKFBfAWnWNY/oxrm9fF0ZEUBfVrXFR3fK4cHpEF+maUNxPcwFYjmAsEYSHxkNGoiNsmpukoinMjumbTT84/YMsbS+RUTMcix8jp+co1AskIqJetmVtkYlmeFB7wEn9pG/0pJU069V1FpILPkMKR7CKJFcSFbe2iRJSKFkl2t02d7bv0O62yagZJFkip+XEVNDELPl6ntNjpwmGgswkZ9DDwtTypgKeHj+N5Vgko0nMrkkqlqLcKPPs2LM8qD3gs8c+y1pljdnELDklR71bJx1Li4l1IRWrbQnzxRX7mFNz4nehOknBKpCOpbG6FqdHTouKYmKOkegIiWiCZDRJRsnw5YdfZjw8zsr+ClpA453dd+h0OlwpXUEJKJgdk/nkPKV6iYXkAkbb8A3CtcoaM/EZXNtldX+V82PnRWV0RIDIT4+eZjQ6iiSJ5JiLSORF5SjXt69TaVTEdD9gy9qi2q7ykdRH+OLDL/KxzMew2hYuLslIkmn94CTVWrsmeIWIqmK326XarFJtVAXz6jELbKiD+vznP88//sf/mF/6pV/i/PnzXL9+nS9/+cs+/PzRo0cUCgX/8dPT03zlK1/h7bff5tlnn+W//q//a372Z3+Wn//5n/9OHcJQQw011FBDfcfluoL3tPU4r5FIHDSQbhX2+a++8Dr3ylUS0RD/7//io/z4uRNHhgVaLahWhRE1NXUYkD7UN1eS+wH+L7FWq5FIJKhWq+8b++5Nj3hf2j020Z29O4dg0Z68SXoeQ8r/n/nHJoi33Uwsw9cefY3PHvssxUbRv997vmesDEqxOI7DUmmJtJL2TZf+1+3d3mh4lFc3X+WZsWcOGUobtQ1a3Rb7rX0yaoaJ2IRvkGzUNijVS1xMX0SW5SMTNf52nBYROeKba4PkVcdW91b5gekf8KfVfZDzAPDlB1/m+fTzrBvrXMpceuI5HKT+9T3q9XKq+ALone8zY2cOPKe/9uddI70JrN5teObfoLUbdKy9j7Ftm6+++1U+d/xzB/gbvY/fNDZ5VHuEi8vx+HFc1+V6+Tqnx06jhBSm9KkDx+7tY68Z13ut96/PUTXHb0R/mvfhUN+YvpE13jA2sB3b/8KeN/OoAZW6XWdhZAEtrAlzR5+i2qyyvL/MeGScndYOc8k5CkaBSqeC7Mpk9SyyJGN2TGaTs1zbvsZcYo5aq0apWWJxYpG8madgFTBbJilVTCWNh+Os7K2ghBRkZBbGF0RqJaRxpXyFU/opSo0SelAYEVbXIqfmqLQqrFfXScfSTMWn/ARXtV31zTRPEhJ6WBdw8LZgHeU0YcipQdVPKilBhWAwyOnR02Iim+1wfec6H899nHKj7CdwClaBlJJibX8NNayK449nKZpFMmoGs2OK3wVaTrCVHJdHxiMk5z2DWg2LKXt1u854dJx39t4hq2YpmAUmtUnabpuMkiGn5bi2fY2LqYsEAgH/famGVOTHf5uptWq4ksu0Pi2qdq0ajuMIuLkiOFs+OD0Sx2gb4MJ+a18kfiXAgbXaGid1MWmxbtc5PSLWodKs+JMWJVkiJsVYq62RjWXF9EPH5Y3iG1wcv8jd6l1m47OYXRMloHC/dp/x6DgBKYAkS2JtH79+tV3FtV3MrqjzqRHV5zs9MB5wKX3JryjWWmJSnxYUUHMZMfXwq4++ysXURXabu0zFp+h2uxTrRaJSlO3WNulYmoAcEHyqRpHTI6cxu6YwSMMa93bvoYVFusxLaV0tXWUmMcNadY20kiYZSfrTCr8V78OhPriG6zvUUN95Dd+H33oN1/jDI8eB3V0BNI9GD/Oefu/uBv/oD27TcRxmxnT+hx+9RC6hHLk90xQpqYkJGB09mLYa6k+nD/o+/KYv8aAJcYupRW7v3iYZSnKleGVgxas3zdRbT/PSK54xcH3nOj8w/QMUG8UDX/J7GVL9++Fpy9zy/0rs3e69ruu6Pi/Iu/21rdc4Fj/Gte1rbBgbBypfgM+zAg4wko4ljvFc9jnf3OplEfXKO/6IPLjW1q9ta5uZ5Ayvbr56gJXV7ys6juOvs7cWL594mXVjnQsTF76hCXDvl0ry1Mt2Wkwtst3Y9jlhnino1QQ9A6pcL1MwRd1nUpskb+V9HtOg1+s/bs8Q8q4T7/Zr29d85tdRazWlT3Esfozj8eP+9fLU+FNUW9VD8Pb+CYhHrU8/62pQMm2o7x9NqpMEEAaQHtJZnFgkGAxyKX2JybgAlWfVLJumeP/PJmZ5p/IOJ+MnsboWjuQQkALEgjGslkXeyjMTn+Fa+RrpaBqjbRAIBLgwfoFr29cwWgYpJYUaVgnIAb8SN5OcIRgIktNyWB0LLSTSVnOJORpOg0QkQTwa9yfIGV2DRCRBVs3617QHPJ/WpwlIwshNRBIkIgnfzDXbJrFQjFqzxlJpyZ/wtjC6QDwaJxFLiATS4yELtXaNWCDG9fJ11ID4M5PRNVgYW6DcKPspm1pHTJpzeMzKcR8ncJo1/zHxcBwCkIglyMVFUkkP66hBlWanyUJygYJZ4COZj1BpVzgRP+GD0y+mLwojhfd+PwTkAJIkUWvXcCTHZ4AZbUMknx6bd/6+PQaeb9Y20UM6elin3q7j4IipdAGZZ8ee5X7tPlbbYi4hpt7pIZ14KC5MrZBOVsnyZulNJqIT5Bt5jLbBam2Vj6Q+wjvVdzg/el6A8KNprI5FOpIGB0r1ErPxWZb3l8GFSrtCrVnjcuEyBbOA7drUu3Vm9Ble3XqVZ8ee9V/fS+fpYZ3lyrIw7+oFjLbBtDot0sfRFLVWjdX9VSLBCI+sR6JyKksUGgXMrun/wchxHZ9xpgQV/1yrssrb+beJulGWd0WC0GpaByY9DjXUUEMNNdRQQ30z1O1CqSR+VPWgIdV1HP7nP7rDL/+fN+k4Dp+aSfO//oWPHWlIOY5IWwUCIh01Pj40pL5d+pYuc29tLaNmKDfLjIRH+MrDrzAWGTvAQ+oHRy+mFg+YVMCB2/vNCo/F5H0ROkpepa//dSVJ8vlW3u0vn3iZRrdBVsmSiqUOPG9Kn2JKm2K/te9PczuKSVWqlw5se9D9T6oRbhqbTGqTTMfFX/BfmnqJvJU/0oy5Wr5KLBzzJ90ByLLMxfRFrm1fO7D2R71uv3rXadBzNmobPKo9YqO24Sfdrpav4rouHadDy2mxVFoiEUlwe/c2gF/zLNVLKCGFpdLSIY6XN7Gu9xj7a3m90xh7b78wcYHLxcucHz/PUmmJscgYm8am/+8e2L2/UhoLxriUufREeLvrugcmKnrXRH89cajvf0mSRDwa51jiGFP6FOVWmUvpS8QjcZb3l5lPiEl3OTXnw8FfPv4yZatM3syTjqZxJZcAAQrNAif0E7xeeB0tqKFFRMoqo4iE6Lnxc8QjcZp2k4WRBfE5FBeDFUKBEBk1gx4RaShvmprVtYiHBCdJQtQNjZZIMOUNwb3aNDf9qW6emRqPxP1r3jON1JCKg0O5XsZ2bGrtGgWrgBoWybCclvMTNa7r4jouq5VVHMfBcR0xbe7x7d6xI4HsisROwSqwMLJAsVHElVy0sCb4VI8ZU7IsM6VOiWPxPlMlmNQn0WM60XCU53PPs9Pe4WOZj3GldIXz4+fRghrL+8toQe3Q+dPDumBCNU2qjSobtQ1/X1zH5d3qu5hNE6NlUG1VyZt5qq0qtVaNLWuLlJLy+VQ5NUe5USarZEkraa5tXxPA+baBaZucHj2NJEmsVFb47PRn2W5uk41mqbVqzOgzbDe3uTB+gWKzyNzIHOVWman4FLlEjhYtzo6f5Y3iG6RjaVzXpd6uU+vUOKYfE9fQY0j5dnubHzv1Y9zcvcmkOonRMQ6kO2f1Wb768Kuc0AUIPh6Ni9/RjTLVVpWoHOVR7RHHlGMUjSIFo8BJ7SQFqyCmSgZFQq3WqTGffDxN8vF0QMM2RKrMFZP7yq0yDg4S0pArNdRQQw011FBDfdPUbkOhIFJSiQT0zvuoNdv8t//+bf6P6w8B+KmPzvH/+uGLKOHBlPJOR/Cj4nFhSOnDv6N9W/UtMaVc1+VR9RFvF9720zpT+hTT+jT77X0+Nfkp30Q4wIDqqVV5dTnPpPL+vb9Gd+SEvL798YydaCDq17Z6E1uDQNyyLPNc9jk+kvsI0VD0wH2SJCHLMmfHzx4ywvr36WL64kDI96Q2yZ29Ozwz+syR4HJvQuGWueWDz71kV07Nsd3YpmgVfZPFdV1SsRT1Vt1ndfWaOV6trp8F1W9s9R+Ld656JyP27mupXiIZTgoYOMIYm9FnuL1zm5AcIiJHSCtp/4tkb7ItraSptCqklfShRFZ/gs5LV/Wm4jxDUpKkA88t1Av8wPQPcG372gEzrHcN+g02L+HXy0IbJG8tX918lUQkcSAF18vh+kZSaUN9b6nWrolqVFuYFOlYmnv793wo9Ja1xZQ2hdkxSUQSTGlT1Lt1LMfi3MQ53iq/Jb7Yuxbnxs9xpXSFZ8efRQ2rlOtlFkYWWK2s8pH0R7ixc4NJXUzpLNQLwhh6XMnSIzpT2hSFegE1IEwhD1jtuA61Vo2N2gZ5K4/t2pgdk3w9z1ptDddxWa4u+4mk/uOKh+PoQZ2iVWRKm+KpsadouA3Bf2oUSYQS77Gz1BylRomsmmXL2kINiDqZ2TbRghpm10QP63TtLq/nXycmx6jbdeKRuKiHWUUysYxgRrVNMspjOLwsMa1PY9om8Ugc0zaZH5n301ZTmjCryvUy6WiaG3s3+Myxz3Bj9wZGx2B+ZJ5NY5O7u3exbds/PqMjEmOmbYp1atZYKi+BI6p5y3vLdNwORtvwzSfXdlneXyYTE0aOFtZEMs0SFcaUmmK9ti44Vo9TRWpA5d7ePbp2168vZpUsZlfwv7w1cHFRAyrXytdYSC6QiCQISAFm4jM8NB7ydPJp1iprbJlbzI/Oc0w/xlR8itMjp5nURZrz9Mhpyk1hjnoDQraM90z867vXeXr0ae7X7jOXmCMgB8iqWd+YbNgNZpOzNNwGWkRDj+jcN+4zl5zDtE1kScbBIR4SoP9JfZKslhWJMkciHoijBTU/FSZJEi6uD0AfaqihhhpqqKGG+s9RoyH4UbXa4Ql793cNfuoLr/P2xg6xUIBf/uFFfuqj88hHtFbqdbAsSKXEhL3I4EF8Q30L9S0xpbbMLTZqGxhdw0+ReImUS5lL7LX3fA5Tr7ExKGFyVF2q/zm9E/KOqnf1p296jSDPjNg0NnlUFYmfXqPiKCOst57W/3pHTYXrVVpJs93YJhVLDZwwB2JCYS+byJsG55lhyWiS27u3fTMlpaTI6TmOJY75x+qZfbut3QNcr0GmT696QexPgnZfTF/E6lqkFfEX/MXUIm+W3uSlqZd8GP10fNoHhV/bvuYn26bj0xyPHx/I1OpP0Hn1Pu+5vUB673Gu63KleIWskmWnuXPADOtfg0HX3AdJOnnbOTN+BsmVKJgF/xrw9rl37Yf6/pPjOMJ4CqhsGVu4jjATrm9fF0DrtqgsmS1RGUtEEgeqwye1k/zH+/+RH5z6QYr1IvOJeRp2gx868UMUjSJrlTXS0TSyLKMGVcqNMqf0UyzvL/Oo+ohKo8K10jU2qhuCa2XbLFeWWRhZoNQsMamLhIzjOqxV1qi2q5hdEzWkUrJKwnjtumiySGPNxecwO6aoyCEmC3oQbQDTNlkYXcDsmFSbVTLRDO+a7/Li5IvIsizMlceA9oWRBVarq+ghnVgohtEyUIIC0j2lTWF1LYr1IqORUZary5zUT7JeWxf1Lkn8QUCP6EiyRDKa9LlZm8YmSkAhb+bJxrL88dYfc0I7QaFe8P9QkNUEU6prd7levs5ialFMmzNFKnMmMePX32qtGmpAZXl/mZgUE3VK1wEXbNem1W2RCCfYbmyjhTQmlAkCUoDt5jbnJ86zWl3l9OhpEpEEVtdCDaqoIWEmZpWsSIe1BXvJq77dN+6TUlOUGiXMjjDCKs0Kb+bfpNVqsbq/iuM6aCGNWqvmm2d1p85iapF3zXeJBWPE5Bh/vPXHZNUs0/FpzI5JpVWh0qj4PKxqs8qGsQGAGlIxOgZ5U9RDG7aYejiijDAdn8Z1XG7t3gIHslqWeEQYS0bDIG/kSUfS/hQ/j40FiHPmiqmkWlADWTy/4TRYGF0gEU2QU3M+fH+ooYYaaqihhhrqP0fehL1m8/CEvT9aL/LX/o/X2arWycZj/K9/4WO8NJsduB3XFeko14XJScGQ6jW3hvr26VtiSnlVkogk0jG9GmQy9D5vu7F9KEE1SF5qx3GcQ2ZOv6kwKH3jGUHletl/Pc902TAFfPwoM6HX5JIkibbbpm23D70eDDZ6PHmGz63tW7Sclr8+/Wu539o/sK3+45ER9UhvXXrXo3dN4TBs+0nnw19ryfXXKCSHiAaih4D1XkWz5bS4UryCJEn80MkfYq+9d2CSYD9vytsvz1DzAPKeATcIFN57/FvmFhOxCf+8uK4rJm3FBSTaO7bVyirPjD5z6FwMYmV9EH6Wt27T8Wn2W/t0nI6faPPUn+jy17MvfTZMU31vatMUBslSeQnbtXFxKdQLnIqfYq2yhhpUaXQbZNTModqSi8tOa4eXpl7igfWAnCK4RUbLwOyYGI7BifgJru9eZ9PYRA/raBGNcrNMShET4bYb23SdLqVGCTWoslZbYzY+y1JpCUVSyBt5YnKMtcoax7Xjooocy/qTALNKlobbQA2p6GEdsy0MEtu22TA2/JQrCPNGkUXF1rZttowtUfmzXVb2Vqi0KmSVLHkzjxbQyJt5FpIL6GGd/fY+5ybOIQdkkN5LI2pBTdQQkws0nSaLqUXMjjBwHMfBaBtiUmCr6u9D1+lSMAqiRmfleXb8WW7t3UINqOL3gS2eZ3YFKN6VXGEKSRAPxTmVOMWN7RtkFFFzxIU/zv8xJ/QTNJwG8UgcWZKZiE1Q79bJqllyeo7ZxCyVRoXXt17nmHpMANorAlJea9dIRBJMapMU6gW0kCbSTiGVmzs3mU3OYnQElNxoGZzST7FtbZOJZUjH0jTsBhvmBs9OPMvV3atMxCZ4Z/8dUrEUhXoBPayzaW6iBTXxeRedoNFp8KD2gGfGnmFlf4V39t6h63Qx2yL9Vu2INdu0NoX51q5htQVnTAtq1Lt11KCKy3u/a2/s3mAxs8iGtYEsiQEddbuOIztoYY1Ss4QXdDLaYppe3sqLa+exYelNTVyrrXFu4hxXt68iIWHZgjmWrz+5Xj/UUEMNNdR3TvV6/Tu9C0MN9UR5JtLWluA/9U7Yc12Xf/n2Kj//n5aod2wWJ0f5jc9/gtnxwXDtblfwoxRF1PX6p/UN9e3Vt8SUkiSJ57LPcTxxOP3ypGlkXoqgP0HV+7zeNFLbbtNyWn5lzHtcrzHTP4WuN23kfbnzXm9SmyQcCDOtTROWwocSUJ56TYtJbZKIHCEcCB8wX/qNnqPMh3KjTCKSoFwvDzzOvJXnmdFnKNVL/jH1Jq96a4cbtQ3erb1LwXxvfPST1rT/nA3iJgGEpTCleomxyBjlRnlg6st1XYpWkbIljqefszTodUr1km+0HeBr9XC/3i89N8gAXEwtslZbYzG16K/hpyY/xWtbrx3iTvXW9bw656D9fj9NKBNUmpUDRtZRnLPeYxryp7435bouuFCwCkxEJzCbpphCJ6uUGiVROZMllJCC1bF8aLfrulSbVXBhIjLB7d3bzOqz5OsCdq2EFKqtKvFAnIe1h5wbO4caUjG7JolQglQ0xY3tG5zSTzGTnCEeiTOTmKHerTMXnxM8OTkmjIl21X+P3dkXk0+TSpJ4JI4e1rFsi7NjZ3loPkQNqWzVxTW4VF4iHUvz6sarPKo84lHlEZVWhWK9yKn4KVarq3SdLpvGpjCJ6oIz5BkUkiyhhUVNLxAI8OLUi4RDYf/zstKsiMl3QRVkGImOcHrstDDjOgZpNU2xUfQTWnkzj23bbBqbWG2LWqeGFtJwHIdSo8RMfAYkqDVrLO8vYzs2WSXLA+OBmFZYF/tV69QIBoLMj8wzEhuh1qqRt/K8kHmBO3t30EKan+xpOA0ySsaH0Fsdi3uVe8wn5/nSu18SUPuwjiu5GG2DarPKlrXlp9Ryao5Co8Cz489yffs6alAVNTrJIRgM+pW49do6i6lFzo6dJW/mWRwXzMRPT32a23u3ycQyGC2DeEjU+qyOhSzLTCgTSJLEtrWNElLIKuIvgFbXEpW/kADTS7ZEpVUhHor7Vcy8mcfB8VNz9/bv4TgO58bOsWls8pGJj2B0DGqtGmk1TYAAVtciJsfAFWasFhKsLyWg8M7OO7i2qIpm1ayoL0ZTAtQfS2O7NmpA5WrpKtnY4L9UDjXUUEMN9Z3Vz/zMz5BKpTh//jxra2v8jb/xN/iJn/gJfvVXf/U7vWtDDQW8N2Evn4dQCLSe8HWj0+UXv3SNf3F5BYAff/Y4//Of+yjJWHjgtppNUfsbGxOGVP+0vqG+/fqWMaU8k2cQHLqfR9R7f3/Sx5PHVuqdjhcOhKk0Kzwz+syhKX2eUbVlbjEWGWOptCSm4LWbfOn+l8jEMtzevc2FiQuHUjGekdNyWmzUDk7dG8QgGjRdrX8NBrGbPFi6Z4RN6VMDU1539u74zKR+o6TXoClaRYpm8UBqy9vGB5mcN8j4m4hNUG6UOTN2hte2XhuYNvIee2bsDOFAmP3GPs1uk0fVR4eMOO81No3NA2wnbx8vpi8SDbzH7zoqPddvbnoGoDcZ6mL6IvLjLOekNsnd/bu8NPXSIe6Ud221uiLh1bSb/jnvTTINmhjZe9y1du0AHP1J695van6QczPUd5dq7RqT+iTxcJx6V8CmY4EYxabgIXlA70Q4gYuL44rrp9aq4bgO1VaVy4XLjIRHuLp9lfnkPGbHpN6uIyExPz5PTsuJWlzLxLXF58mWtcUJ/QT3jfvEI3FOj55mJDYioOSyREYRjCOjZeDaLrZjs93Y5tmxZ0XFzYu6yPDU6FO0aPF85nnWq+vosgCkn9RO8tv3fpuQG8LoGBQaBfSQuK9cL7OYWiQUCPH02NMEA0FS0ZQwfoI6iUgCeK9yrAWFeXF69DSJqJgS6HGr1mvrpGIpjJbBvb17VJtV5pPzXN++znxyHiRRecsoGVYqK/6EN8kV0/EkJHJajng47nOllKAijG3ZZT4xz1ptjbnEnKhua+9NGASR9klFU9zYucFsYpbTY6d9PtK0Nk3drvsJNUmWmE3M8mbxTT6Z+yRW1wLAaluoIZVau4YWFEypheSCmNoXE2ytCxMXKDVKKEGFACITrgUEeH0iMsHV7atMxieZHZ0lHAozk5jh5t5Nzo2fw5VctqwttJBGsV5kLjkHEuKcTjwLMhyLH0OWZUZiI1zKXMLsmqL2GEniyA4yskhqSfgTBPO1PGbTpGiI63XL2EIOyMyPzhMKhUgraQpWgXqnzsLoApcylwgGgiIR+PhaVGVVAOlxUcMq+brgaXmmYaMrUnhWx8K0TRbTixQaBYYaaqihhvru05e+9CV2dnb41V/9VT7xiU8wNTXFT/7kT/JHf/RH/OIv/uJ3eveG+pDLtsV0vWJRJJt6TaRCrc5f/9eX+YO1AkFZ4u98+iz/7UtnCAYO2xyuK6p/7bZgR2UyEBzMPR/q26xvGVNqUPpjy9zi6ZGneXXz1UOVJu/+idjEQPMFAEdwK3oNIa8OBiIp4zgOv7v2uzyVfMo3hTyYeKle4s7eHY7rx/nao68d4BJ5Rkmr2+KR8QjXdak0K0yoE4f2seW0Dhk/nnq3NahC6O2nx7GSJIlRZfQAr6rflEsr6ScaF95rppU0GS1zILV1lEE4SIOMP6/muNPc4eUTL7Pb2iWn5tiobRxIkXmMpUuZS+ILUnSEO7t3juQ1AQfYTr2TGntTSkclvY7iduWtPKPhUb784Mt+/aj3OvEMrN71LlrF98bQO3B757ZvnHqvc7V81Tc3e42pnJrjzt4dFlOLTzQMj7r9/ZhpQ313Kh6OY7QMjI5IhyTCCRp2g0wsQ6lZ4vToaQKBgOAiIZG3xJS7arPKvb175M08I+ERHhmPBAi8UWQ+OY8ru2ghjdXKKvMj81hdi2q7ykplBdu2abQb3Nm9w8nESVEXbIjJdFk1S8ESXKXZkVlh6gYgEU2QVtKCMaVOEo+I9JHkCvB0ThHT4maTs8RjcUYiI9zav8XzmefZa+8RkALMJ+aptWrgIoy3epGF0QWOJY9xMX2RulMnE8tQ69QEXL1jiGl5YZ17+/dE7eyxsVJr16i1hGmRjWUJyAFM22RCmcDsmBQbRS6mLlKoF4iH40yqkxQbReYSczS6DZAErygZTYIsTJZCo8CF1AWkgMRIdISMmiERTmB2TTHJ7/HvDO996zgOd3fv0u60BWhdimFjU7AK/qQ6PayLz0wkZGQWRhaIRWL82Zk/SygUEglLfRI9ohOQAyLVJYEe1FmuLJNVsyJdJMX43ZXfZTw8Tr1bZyo+RSKSEDD6oADAK7KogGpBDdcV59Tje61V1phNzLJSXfGnEnpA93q3zqQqhoLEwyJJVWvWRO3x8Vp7Bp7riGSfiyvWTZZI62kIiM+jWluwq7SQhhbWKDfKpGNpau2aPzUxq2UJSAFqnZrgScni8y8RTlCql5hLzIkpk0rOv4Zu791GC2lMqpMCtO5woBY61FBDDTXUd4cSiQTRaJQXXniBRCLB3/27f5cf+7Ef4zd/8zf50pe+9J3evaE+xGq3RTpqd1dMxuudsHd9a4+f+sLrrO7USMbC/H///PP82TPHBm7HtkVdLxiE6WkYHR3W9b6b9C0xpY5i6fQmVrbMrUNplCdN0JvSp3xIdq9Z4MHLvaRMuVHmh4//sF/VAnxD4mL6IhdSF6jbdT53/HO+SeSZTCCmLaWUFCDMoP3G/oEaX29dr3+6Wm8SDHjflIwnD8K9aWzS6Da4tX3LT+ZsGptPNup4z6CRZZkTiRM8l33Of6xv3hibsP9QWMR96k1IDYKqp5SUDxH2jJ8nGXMX0xdpdBu8fOLlI3lNg8yhoxJJ/WvmOA4Fs0C5Xh64vq9tvcbz6ee5Wr763hofMVXPSzpVW1UuZS5RaVd4aeqlQwmuxdSib2562/Dg74MqekN9f0uSJIyumOhWtIq4kst8ch5ZlklH09zbFWaMjEw8Evcn0rmSMJC0kEbLaTEeHRf3KzlWK6uiBtUxeGr0KayuhRJQKNfLxOQYlm2R1bN8YvITNLoNMXUzPMbV4lXeyr9FTI4hIRIyWS1LMpxED+sEpICYCLh3D9d1BThcFp8nDg4S7yWPHMdhIjLBI+MRF1IXeC77HLIsc792n1gwRqlRwnEFSygefjwBLzmPZVvEQ3Hu7d0jFohhtk22zC2fCSW5ElvGFmpQxWybSJJETs8Rj8SZS8xRMgUXS5VFnSwTy7BlbVFtVcmpOSxbDFEw2ya1Zo2u3UVyJRzbQQ/qFMwCT40+RTwSF0B5JJFcMvJUWhXUoMo7e+/guq7YvpLhzt4dTsRPUGqVCEgBfxKfGlS5UrqCFtaQAzKT8Uks2+KpsadIxpIkI0lyak6k0VwX13F9oLnRMZhLzrFSWcHFZd1cZ35knlv7t3x+06a5KSakdusiTRRWySpZjI7giWVjWZFOC+vMJed8dpXRNsgpOZbKS8z+/9n78yBH8vu8E/5k4kZmAqgLV1V1T3ddPX1MnxxyhkMOLw0pyZRkaW1JZtiWgyF5HZbfXeu1Y0N2hLzy+rVsrV+Hwrt8X4kKKRiyKZvS7itaos1LpIYUZ4Zz9N09011X93RV4aoDQF64M98/fp3ZKFTVcEhxLhJPREV3VwGJXx5AFx48z+ebmhVpLenBBw9KUGGpvkROyVGwCmIaoiyjhlWQxP9tZsskEUqQjWcJSkEWUgsU7aKYqtcyKRgFcMX/4XbPJh6KU26UmVQnCcgBYbyFNdSwSiIs0nmT2iQ5JcdSbYm51BxW1+LM2BmubV3jkbFHcByHi5WLSK5EThMTGYcaaqihhnp7aXNzk89//vPcuXMHRVH87wcCgSH3dKi3TI2GMKTqdQE07081ff76q/yjP/k2tUab+YkEv/9zT3A6P7rvdtptsY2REWFI9V3iQ71N9IaYUgexdCRJ8t/c97N9Xg9Xpz/t4pkFFbtCwSj4NbG8kicTz7CsL3Ny/KSfrulP4RxKHuJC9oKfkJpUJwlLYXaaAsh9PnPeB+t2nA5dt+uno7xteXW9welqG+aGX0kbNF36TTRvyt+aviZMrvtMKIBbO7c4rB3mK69+hfHoOK7rfkdYtmcCTqqTDx7TdXF37uB6IPhuF9QM1F4VxlSfQeVVHMt2mXBA2M+tbss34vr5XN5xHuRo9Z9DWZa5kL1AIBBgSpvCcRy+uPpFer3egckgbw1fvvvlPfXOwftcLF8kFo5Rtsv7GnUnxk7sYkr1P8bgddY/jU+WZf8a8wD9/ddO//XXn/jaz3z0QPz7McmGcPMfDHlGkhbWODZ2DMsRvB/vVdXsmn5iqGAVyEQzuI5g78iSzNnMWZKxJFpU/DwejGN2TR9MrYU0yo0yJ0ZPUO/UWUgtMKVNYfUsJlVhBDxfep7x2Dh2z6bUKKG3deqtOkWrSDwYx+pafvJlIjbBKzuvULWr1Bt1qo0qJbNERskgSzIODrdrt8kqgoeViCREgqajc2rsFK+arwrOkuOgt3TW9DXUoIrZMUmGRSUuGoiyUltBCSkkw0m0iCaA4aowXYyOgRJW/ISTElSwHEsYSbLEYm2RqBTli3e+SK/bEwktCZLhpIC8K2ksx2KzuUk8HGdZXyYdT3N96zr1Zp1Or8NSbYlas4bZNenRE5P3rIIwsRwHNahidSzem3svATnAXGLOn7inhTQWa4ucmTjDYm1RJKfuTyCUJEkcJ8kBCYyuAQiYuNkxfaOmZJc4NnKMKW2KOW2OreYW6UiavJJnw9wgHoizWl9FCSmcnjhNs9ckEUmQiqQ4NnaMki2m2OktnYAcIK/kfXZVwSpwevw0zxafJRfP+edIC2mUrBLjkXEWq4vkYjlKVol4MC7OjQur1VUxNEPCr3wW7SLzKVFzjAfjFBtF//8/NawSIEA2fp9rFUkwrU0zEh0hGUkiyzJ5Je8n/9KxtA86R4b51Dx2z2apvsQj449QsAoUTZFGG2qooYYa6u2lX/mVX+HP/uzP+Lmf+zlWV1d5/PHH+Xt/7+/xb/7Nv2F7e/utXt5QP4QyTQE0t21hJnkT9ro9h//9L67zm39xg57j8uG5HL/9PzxOVtsfDGVZYhvZLORyu5NWQ7199IZN3zsI8O0ZVoBvtvRX2/arSHnyDAIQBkPZFp+uf/3e12l1W1yqXCIdT5NTxYjszcbmvrDyfkh6wSogyzKnxk9RsAr++iRJIhwIM6UKcykiRw5MPXmJqbyS31VJ61d/emzD3KDVaXGpfGkXr2pKm+LMxBmsrsXx0eNsNjYBSEVT+1YhPYPFW7OfPnMcePUZNtwu6XZDrCV1mML6q7jJQ8KY8gyq+/txY/sGqWgK13Up22UcHMp2eVdKq/8xB5NHr8VG+sqrX+HsxFm+8upXDjRkvJrlk5NP+iklT4P3ycQz6A19z2RH77h414As7768902pDRhe3p8t5/VNABw0H/vX4V1j/eeuP03Xzxcb6p0l13Up2AXmknMYHYOCXkAJKCgBBatpobd0er2eAIDbBVGvaxSxezaJaIKcmqPUKJGP55GRcdwHZolXv9qwNphLzXFx8yIfPvRhSo0SBatALp7zp7KdHj9NQA4wo82AizC0Wjo9t8dyfRklpCBLMjklx2ZjU0zIswv06LHZ3CSjZEQt7v4+ZWNZbuzc4PT4aTENsG2gBTUavQZnxs4gSzIVWxhcJbsE4KdljI5Bo9tgIjaB1bVESksOMJ+a51LlEnPJOSRX8tNO2ViWovWAV+W6Lk5PpGoOKYcwusK8kyUZLayRi+ew2hZaQGM8Mk7ZLDObmOVra1/jA1Mf4PLWZa5uXmU0Mur/32C1LbSQSIvNj86L+ltiimQ0yZg6xnRymmQ8SUAO+MZPLpZjqbpELpbzGVSu63Kvfo81fQ0loFCwCuTjeVHfjOUwmoafBHMcR1TVgBFlhPPZ82gRTZjpUozl+jITsQmSEQFUf3jsYd8kMtsmgIDbt03qrTq1Vg2jZdDpdlg31lnaWeLU2CnMjimqkEFR1XNdF1mSiYfi3K7eJhqIcke/gxpWfei+7MoUrSKu6wo+1P2UVT6ep9QocUQ7QrkhpuxNJ6aRAhKpWMqfmihJEsmoMBDzap7F2iJKUKHn9ASXLKSJxFZtCaNtMKlNspBa4NrWNfEhgbR3wuxQQw011FBvvf7xP/7H/N7v/R7PP/88tVqNz372s/z0T/80ruvy3ve+961e3lA/RBqcsJdKPajZVe0W/9Pnn+dPrt9DAv7Hxxb4lx87SzQU2LMdxxHbkSQBMx8ff2BsDfX20xs2fc97o74fuHuruYUkSX6aqn9aXX9Faj+5riuA1J0mE9EJLm9e5rHsYyxWF3dBy71foDtOZ48xkFfy7DR3CEvhXRWtvJL3k0FT2hTTiWkOJQ/tgZgP7qeXmPKqhP2VNC8t01/zmlQnWawtMpOY4Zvr3/STQV4KS5ZlvzInSZJf7+s/Bv0JJm/9ruvS7DS5tvQFnOwZgmuvst3YZlLJs1ZtYCdjrFUbkDoMZln8eX8/skoWGZmyXfbrbOcz5/eYbv1crEuVS69rctxTh5/i8uZlnjr81IGpOC8Jt9Pe2WPqDULip7Qp/7wMGlavZY59N/ym/mM+uObXw+nyQPyemdnPGvO4aoOTAId650hv6+SVPJc3LzM3MkepUfIrfUbPEMmgrkgG5eN5zI6oTXlg76JZFBPTJLEtXCg3ymRiGVF3Kl8UaZdGiR859CM8W3qWWCBGPBDnpfJLxANxzI7pM4rMnilqgRFVGAfhJNl4loAUIBFJMBIbYWFEVLUy0QwVu8JR7Sh210aLaCyMLrDZ3EQLaxxNHBWDIhxhjkiShCRLYiJbWwDdb+yIIRFm1xS8IHMDLaSRVbJsNbfIKaJuBlC0BfNpqb5EvS1SOK7rUmqUyMVzYhvqJAEpgCRLzKfmafaayMjk1TzJSFJsS4ZUJIUWE2YXMiSiCU6MnuD58vM8MvoIE5EJXt5+maOJoxStImpITAGcH5lnqb7EQmrBZyzVmjW0kOYnqMyWieM4GF3BlEISk+Z6vR4vlV+i5/RQwyqr+ip5RZzTaW0a27FRogp5Nc/lrcsAdOn6Ez1LZgmjYzCbmmXVXGU2OevzsQZfQ/SOzmxyludLzxMPxnFwkCUZq2tRaQr2l+VYNHqi9idJEgW7gBJSBBi+YyIjowZVNhubjEXGKFkltJBGTslhOzazyVmRpjPF8ZERKbl8PE/TaXIhc4FEOMGGuUEulrv/QYfr/79Ta9ZwHRe9pZOL5TDbJlbHIh0TtfeSXSIdTVNv1VnT1wCRmiraRb/OOdRQQw011NtbR44c4eMf/zi/+qu/ymc/+9nv6r7f/OY3+fjHP04+n0eSJD7/+c/v+vkv/MIvPHiPc//rYx/72K7b7Ozs8IlPfIJEIkEqleKTn/wkpmn+VXdrqLe5vAl7Gxuiqtc/YW9pU+eTn3uGyxs7xENB/u1fu8Dfedfsvu/FOh1hSKmqMKQSiTdvH4b63vR9NaX2S8EMmgReygTYA/T2jAnPYNkvQbJhbpCKpig3ymw2N/mZuZ9h1Vjlg9MfFJOT+tI7g+aTp4JV4NT4KWRZ3gWbLliFfflN36leuB9Dy0vENHtN2r02juPw9XtfF+kJ4GNHPsaatcZfn/nru5JBXgXwxvaNBwyrvnpf/3q8CYP9ZkulUUEZf5hrq5dIZh9io5uE6l2C9jXoaBDYonD3Nl11ki+ufpHu5grrfTXCfv5WP0NqP37XufQ5/9x6kPj+5JenQCDAjx79UQKBwL6m0X5JpP5raRAS760D2DXN8buBuvdvfz9jq9qq+mscXHP/9XBQ8mtwKqNXTyxZJW7u3DxwEuBQ7wwlwiLVMpecY6m2xNmJs9SbdVZqKzykPMRqfZVEMCH4PnaBSUVckyW7RF7No4ZVv87kui4uIuUiSRK2YzOTnKFgF1ADKpc2L3FEPSLu3xDT0rzqWMEu0HE6lK0yG8YGFbvCwsgCsizSRa7rUm/V/WTXwugClVaFC9kLNNwGU5owtQpmgYnIBFc2rwACJi4HZCQk1s11lIAizBzXxepaHNWOUmqUmFQmKdgF8oqYFGj3bM5OnGWxtojjOsQDcZHm1AskQgm0kEbX7bLZFKktsycMKaNtUG/XUYMqQTmIFhWVSKNtsGas0ev1MNoGjuuIqtp9+PmlyiWmElOcGTvDlfIiq/U7HB85zmqthOP0oFkD1xXcpOQct6u3qbfq/mADo2P4VUVXcjE7JlpIEz9rGTiOw62dWzwy9gg3tm8guWIKn/capbeFMWO3bfSmzlH1KEWjiNkwfaB3Opam0qhgtkzOjp8VKa6wIva7Y/jnqNao4TgOlzcv82jmUcqNMrIrDCYlqJCNZsX0w/Fz4Ir1uY5LLpZjubaMElAe/L8pQTaepek0xQccskQgGODsxFmW9WVA/HyptkTP6YltyS5T6hSBQACjY/j/l5asEt1e16+ce0m8ol3ElVwS0QRqWMXu2L5Za3ZNcT0joXd1XFysjoWL+2AC5FBDDTXUUG87lctl/uIv/oJPfepT/MN/+A/5wAc+QCazt5nwWrIsi9OnT/OpT33qwNt87GMfo1gs+l//+T//510//8QnPsHNmzf56le/yhe+8AW++c1v8ku/9Evf0z4N9c5QrweVipiyNzhh7y+Wi/z9P36WktFgMhnnd//m4zxxdP/rstEQ1b+JCTFhLxp9k3ZgqL+SJPd1dId0XSeZTFKv10m8htXoTZzzak2eBs2C73Q713WZiE3s+bl3m/7pcAVLvCHyEkieYdBvYuz3OIMmyEHb7jciDjI7+ven3xQbj45zY/sGWSVLySoRC8eoN+qEA2GySta/bf86vGPgOA7tXptqq8qF7IVdj+sZXidGT/gT5UAAwC+WXiLfbkNvhPVAmNPxHtVWlWzmNKXKNZx2ktHMNF+68n/xxOETfG3nBn899zibUWXfY71hbviVw9c6LuvGOi2nRVgKI8uyf6z6t+Hdb/AY7nc9rOlrtJwWETnCdGL6wOtoNDzK0+tPc3L8pD+lz6s9Dp7ffq3pazR7TWrNGpl4hrbb9h/roOtzv+NyqXJpz3n4Trf1koKDx+g7mWmv93k41Peu13uMPWB1Xsmjt3TMtslibZHHc4/z9bWv8yOHf4Sl6hIAOSUn0iPxNGWr7JtFZsf0DQuA+ZF5iqZg+siSSAnpXR2n51CySsiyzLmJc9zeuY3RNVhILZCKpXix9CIRKcJd/S4zqRlkWSan5oThhfvgunJFEmdSmcTsCvNFb+tISNRbdZFichCJpPuQ9Hq7jtNzxDS8UAItIgwbx3V8SPekOik+EFAmqTVqXNm6whP5J7C7tkiy5h7j2tY1zmfOs2FsYHZM5lJzPjvJ7AoDx8XFaBpstjZ5ZOwRGk5DTAhU89zauoUkS+RiOSzHIhfPsVhbZC4poOI1o82d6qskzQ5WMsxH8ido6DXqSoikJJMfP86t6i0iUoQ7tTu4ksvCyALTSWEar+lrOI6D1bHIq3mQoGAWyCpZzJbJqrHK47nHfY6dGhIf3XmG81xqjmcKz3BEOyJqh4hzYHUsnwN1dfMqeTUv0meIiXcyMq7rokU0NowNtIhG3a5TaVeY0WYYiY1QsArEAjGsjkUymkQLaziuGPaghBUCUoB4IM7V7as8pD6E1bNIhkXl0HEdKlaFrJplSpsSUxDvc8CMtkEsGMPu2EiSRE4RqbVpbRrHEXwxp+MQjUR5tfYqGSXjr9Xu2GTiGWGy3k+N9dweEvc/9UYSFcSOSSIsuFdROcrN6k3el38fwe8wf3n4WvfGanh8hxrqrdfb7Xn4xBNPsLS0RCqVYmFhgWPHjvGZz3yGL3zhC8zNzTEyMvI9bVeSJP7kT/6En/qpn/K/9wu/8AvUarU9CSpPr7zyCsePH+fFF1/kwoULAHzpS1/ix37sx1hfXyef3zvBfT+93Y7xUAer0xFmVK0mUk3erwmO6/L7zy/x+y+I36nfNT3O//ajZ0lE94KhXBd0HQIBSKchmRxO13s76PU+D7+vSamDqlMHVfgG0zJe6mU/uHe/YeQlULwkj2eCeG/+NxubflLqoCRK2S7vy68q2+U91bTvVPvq35/+RMxWc4sL2QtMJ6bFRLp2g5AcYiQy4vOsYDe/qP+xyo0yqWhqD3uoH/rePwGwYBV4JJRkS7cZi0pMOV22YirZyXeDWaYbO0Xu8DzVyjp/7V1/iy9tVviZ3PvYjMT3PUb9cHbP8LlUucRYZIyXSi/xUuklP6U0pU0RDUSRJGnXcfW2cbF88cAk1UHXQ8Ws7Do3/Rwrx3H86yQRSdByWgAPJip2W6wZa/s+nqeKXWEkMuJXlUpWiV6vd+Bkv8F1XKpc4vjI8T0MrP2O4bohakBfv/d1Udvq28/+tNdQ7wzpLR0loHBr+xZ6U2fD2uB46jhfuPMFPjz5YS5vXiYWiOG6YppeNi5SLlklSzKSJBVNkY1nub5znfmReaa0KeyeLfhMiUnUqJiclgwlsbs2SkghHUtzsXIRvauTjWcxu2La24XMBUKhEEcTRzG7pmApdSyUkIIW0sRUuLaBFtFIhBJsWBs4PYd1Xbyu9JweWkhDdoVBkggmcHFRwypm26TUEOwoJSLMlHqzzkullxgJjVA0i9zeuS2A4B2DQkOkUC9vXqbeqvOQ9hBXKlc4M3aG29XbTKqTPDz2MHbPRgtrSLIweIy2gdk2MTsmE9EJCmbB5zvd3rmN67hicl/XZEqdwuoIY6pkiwpg2SrhbqxzR6pz1g5iN7roAZvq+ibh6AQvlF6AHtzR79B1uzS6DcHM6hhIkoQW0ljRV4gGoizVxS8+mXiG5foyrutyRDvCYnURs20Ko+p+EshFJJW+VfgWJ0ZOULJLaEENXJAR/y/NJeZ4tvgsRxNHcVwHq2350PR4KE7RLqK3hPm4XF3G7JjMJGYwe6Zfl1ypr4CLb2iZbRPHdZBcCSWosFxf5rBymE17E9mVfZaY3bOJhWPCZLML4tjdB+WrIZWKXSEejKOGVFF3vF+vK9gF5kfmcXG5Z9xjPDLuT2oMyAHyat6vjJo9k0ltUjD87k/nU0IKRaso4PFhYWTe2LnBwyMPc2nz0pCjN9RQQw31NlM+n2dmZoZPf/rT/Omf/im/+Zu/iaIoPProo9+zIfVaevrpp0mn0ywsLPAP/sE/2AVTf+6550ilUr4hBfCRj3wEWZZ5/vnnD9xmq9VC1/VdX0O9/dVqibre4IQ9q93ln//3i74h9bNnjvD//sl37WtI9XpQrYp01dTUbg7VUO8MfV9NqYPMm/0qfF6qxDNU+ifX9fOmPHlmT7+J1F+d2s9AGWQ8gUgTfenOl3g49TDf2PjGLn5Vf3UuHUvvqRe+nv32gN0eLN17bFmWeVfuXbwr9y4iwchucPp9Q6T/F/UpbYpD2iEickSMft9nIp1njHjQbNd1KQcjpB14pdIgd1i82XWB/75ZYVQJs15r0kseomh0+B+Of5RNs3ug4dZ/3ryK4tmJs9zYvkG71yYZSfqGzEFcMO9+6VgaV9p/Cp23v/1rkCSJtJreFyDumYYTsYn7G4Lb27f9qt+UNkW1VfUZJ/tpUp0kLIcJB8Kcz5yn1q5xcuwkX3n1K8wl56g0Kvsek3421InRE7xcfXlfsP3gMSzbZRrdBrFIjK/e++quyZPedT+s8b1z5CKSTl4tLh1Nc3HrIj9x9Ce4Vr3GXHIOuyfMJKtjkQgnhAlzv7qkhUUF7ZGxR1isLYo37i4Com2K2l6tJepcakhMQTM6BkpAQQ2KaXpaWPMZU7lYjlQsRV7Nk4qmmNKmxJS0qJiAl1fzFMwCG5aY/vZK9RUWa4vEA3GKVhGja5DX8ozER3Bl8Ty9tXMLJSjg7ZIrYbdtek6PV41XeUh7iM+vfh6n59Dr9cRUOTmO2TC5Ur4iTJWOCZKoN1/ZvsJR7SgvlV+i1qiBKyqQIDhIeTWPGlH9KYBbzS1mU7Nc3rpMXsmTiCYwO6Z//BzXERPvvOl/DlijGY5vN4nkJ2jVVsBQMLUwX1tdRAko9KQe+XiekBxiNDLqQ9ABMa2vZbJSXWEiMuFXIeeSc1g9UTvz1rRUW2JSmUQLaxhtA6tr8cjoI9wx7/i3z6mCF2a0DYyuwUxiRqSskNDCGslIUtQQzRIT0QnKdhlZlhmNjYoPJOwyyZC4TUAOMJeaQw2rlKwSakglGUn6H8jIkkwmluFV81XSihjyUG6Uycaz4pqSFZaqS8SIsa4LPtS6uS7SXhI0Og9A90bHQEIiH8+zWF1Ei2icGhWTF9WA6qfEEpEEqXAKs2uSi+fYsDZERRVJGIwdEyWkUO/UeXn7ZfSmTlgK8/L2y5ydOCs4akMNNdRQQ71t9Ed/9Ef8zu/8Dr/1W7/FU089xfPPP/+GDab42Mc+xh/8wR/wta99jX/7b/8t3/jGN/jRH/1RH3FSKpVIp3f/Dh8MBhkdHaVUKh243d/4jd8gmUz6X9PT02/I+of6/qnZhEJBTMdLpR6AyDfqNn//j5/lGytlQrLMP//II/xP7z9OcB9SebMpDK3RUZicFNW/od55elMY9AeZVf0Jqkl10mcZTWlTeybneWbPidETvimwbqz7RlV/Kqqf8TTI/LlUucS70+/m86uf56nDT+3iV3lryCrZXaDxg7hBBykTz7wmbLufNTSlTfmGSH8iqh96fnLspG9aDBpxnqEBMBGbYKd4D3P8DEklwouv1nAdhxvL/43HJ+d4+tVLAIwpYQq1Bjvri+QUoHrXX1//9vuTSR6ovWgXxfFR0j4Mvb/+uGFucC59ju3Wtl/1Ozl2ElmWiQai+7Kx9ptC5yWvBitxeSXP9a3rTERFTS+rZJFlmZHoCG237SfbLmQvEA3tvb8nb12e+eQlz546/BTL+rKYFLWPvDUDuyYtDl4jg4bb+cx54qE4akD1rzsPUn/QxMah3r5KhBM+Yykby9JwGnz00Ee5unWVM+Nn/ERPQA6QU0TVLBPPcHXrKkpIEcweZRKrZ5GP50V6yXX85E7ZLqOGVC5vXRappajKSHQELSJMitnkLLjwpTtfYiw6xrq1jiSLSXFqSOWVnVdQQyqpaOoBKBwBnF6pryC5Eg+pD3HXvEteyfv1WjUoDDAlJIwovaOLJGpikpyWo9KokAwnuaPf4fHc47iSy139Lt1ul28Vv8WEMkEimhDQ9nhGvI7jcHr8NH925884mjzKhrVBvVVn3VwnGUn6TKmyVeZo8ijBQJDT46e5vHmZh9SHuLV9SyRuQhpqWKXWrFE0i7R7bQqm+OBifPQoj8gOudkFQkaHw9o4S9uLaC2Vw0lNTAMMaqgRldmRWYKBoDBN7kO3K80Kx8eP0+w1WTVWBcNJzWJ1RSJrVV/lxOgJlupLPDz6MLIs+9XHWDDGirHiG1JKQBHVt55DvVXHaIljr4QEl6vWqqG3dBJRUYdsOk2/9tfutMmqWXJKDheXglnAdV2SETHxLqtkWTPWfFNnSpsSE+1kiZnkDBISt6u3ictxFmuLzI/Oc6t+i9MTp1kxVzA6gstltkyWaksCrI+47tSQioSEi+uzoZKRJDd2bvAjD/0Id827fn3TaBsC1K6IoR3eVEcPEK+FNGHGhhI+SD0gB0jH09hd2zckhxpqqKGGevvo1KlT/Mmf/An/+l//a37913+dcrn8msmk71U/93M/x0/8xE9w6tQpfuqnfoovfOELvPjiizz99NN/pe3+6q/+KvV63f9aW1v7/ix4qDdEti0SUo3G7qrdS2tbfPJz32J122AsHuFTP/Mefvz4/gajaUK7DbkcZLMQCr2JOzDU91Vv6mDE15qU1m9ceW/O1411mr2mbzR4xgHgGwM3tm+QjCT9CpkkSb7hULAKe6DU6Via58rPcSFzgUsVYdJMxCZ21fQ8U8wzqw4Cne9nVq0b67R7bYDvaGh5U7a8qYFeIqrfpBk0Lb6TkTeaP0y8vUVFznBqMknx1UVOHv4Qa4VneOroOa5VrvGVmwVGErpgLw2sa3Bf+02YfiNl0JDqv21/Qs07x1Pa1C7eVv/5987nYBJsPyOzYBUYiYywYYnbTiemmdbEV1gK++dsMI3nOM6ea68/0eXdPhAIcCF7QVRR9lH//vSvb91Yp9Ft8FLpJf+a6U/2ybLMo/lHeTT/KIFAYNe1/nonAg719pHRMcRkuGiSYDDI8bHj2D0x2WypLpI0kixqYUZbJJwuVy7zeO5xFmuLJMIJktEkyVCSglUgSpRbO7coGAXigTixQIzl2jKnRk8J9lJAFdeRJFhFds9G7+qcGDvBXf0uATfgV7sW64vMpma5Xb1NtVEV5qgLeTUv6m5KjvmRebY727x/8v0giVSUGhTJmbyaZ6W+wkR0QnCj7kPOzZbJTGqGsdgYD48/zJQ2RdNpcmLsBOVmWSRq9FeJBqLk4jkaToNz6XPIyFzZvMKT+Sep2BUSoQSO64jEjrGO3tL9mlqlUfFh27l4jhVjhZyaw5Vcyo0yjuNQtIpMxCZ4VX+VrtPFcRxCzjbJzAyJSBI17nDHdjg7nafeqTM5MkJey2M7NolIAkkW0wTViKgN1pt1ZhOzVFtVDmuHmYhOULJLBCTxPPUA4ZVGBTWgck+/R9WuorfEmlf1Vc6On6Vkl3xWWDaW5XbtNvVGnVqzRskqISHRdboUjaIAhjd11KBKz+2xqq9yIXuBXCLHSHTEZz4pYcVfbyKUoGgVMVumb2xKkkQinECWBNje7tlkYhmKjaJ/zX146sOs6qvMarOoQVXwzWJpMQHRLpEIJ5hUJ5EDMlpIE/U9vUC1WcXsmLwv/z7uGHc4PXZacL/uA+e9yvh8ap7F2qI/ZVIJKBTsArOJWVbqK6RjaWRXxuyaIr0lMXy9G2qooYZ6i/XzP//z3LhxY9+fXbhwgf/+3/87X/nKV/hn/+yf8ZGPfOQNXcvRo0cZHx9nefn+II5slkqlsus23W6XnZ0dstnsgduJRCIkEoldX0O9PWVZIiHVbj+o2rmuy/919S7/+PMvoDc7PJxJ8vs/9wQnc3vro44j6nqBgKjrjY09SFkN9c7Um3r6Bg2P7/SGfEKZoNas+Qkhz4iZVCe5sX2DSXVSJGXuw2IHp7V5SSmvhrdhbpCOp0nHRTWv1WlRNIv7TgH0KoDeY1bsCkWz6E9U8vZnPy5Vf03ttSb3XapcYjYxy+XNy35l0Us9eduF3dW2SXXSN8z6f+Z9pZUM7kSCd8+Ms221yR2eRzLLjAdn+fLyS2Sj06zYz3Bjw0DJ5yh16jDykL+mwarlfiZMv+nXf9z3m0LYbw4NTsobPF+vp77mTcbzqnleouxQ8hCyLPvnbPD4e4wwz/ADfJPz+1Wb27Q2SUVT/jXjmV5r+hovlV7yrx3XdVnT1/wU4FDvPGkhzYc4e88LxxVAcM+QUILizTmAGlZ9hs+xkWMA1Ft1UXsLxsVrAKIaZfUsgoEgF7IX/Cl1S/oS8WAcvaWzVF1CCShoAY1Gt8FschYtIqqBjuuQjWa5XLlMJpbBaBt0HTE5rWAWUIIKakil1ChxIX0Bq2shSzKJcAJHEpW4DXODdCzN9Z3rzCRnWDVXySrCTAgFQiSiCY6PH8d2bZ6cepKW22IhtUAwGORM+gyjsVGQEJMDK5dQQgpZJUvLbZFXRRUvIAVAgnggzu3qbe4PwxOT6Np1f9peLpoTEwk7NqfHT3Nt+5pfrT6SPEJQDmJ1LeKJHIlYmGKgSjU5SVd1MJng5JEjyLLYP880SYQSzCfnsToWuNDpdSg3y8yl5khGk4Ix54IaUtHbgh22YW5gtkx2GjssV5dZM9bQwhqVZoVz6XMs15eJh+KCe9UxRc0uoGJ0DZq9JvFQHKMj/u5KLkpYQe/oFGxhQuptnaJRZEqdQpIlFkYXfGC4FtRwHWEGqSHVn9yoBe+nlu4bpKVGibmUqB4rAQWra3EufY7N9iaPZh8lGBJTDfNKnmV9GSWoICOjRTT/GtDbujAJOzpWy6LWqlG0ijw8+jCBYIBkJEkikvB5WlpIo2gXmU/NiymT6iTlZpn5kXm+uv5VDicOs2KsIAUlzoyf4drONWF+DjXUUEMN9Zbqc5/7HB/+8IcPNKZc1+X06dN87Wtf45//83/+hq5lfX2d7e1tcjnBXH3ssceo1WpcvHjRv83Xv/51HMfh3e9+9xu6lqHeeJmmMKR6PZGQAmh3e/ybr13n33/jJj3X5aMLk/x/fuYxJtS9o/Pa7QdA9KkpUIe/VvxA6A0xpQ5KBx0EtN7vtl59K6sIR7zf3Ok3Raa0KSLBCJIk+aaDZwoUrAITsQnfsPEe/3zmPGE5zFZri1Q0tSudddBab27fZEab4ct3v3xg2qZ/3Z4R07+dwX09lz7nV8X62Uue6ZaMJH0Dpb8O1m+YHbRmSZKYGomxXmtSqDexFZnTIY2btaucy5wgoqwTCUTJTr4bF3ZV9voTRt4+9R+fQbD7fudlUBvmBsdHjvP0+tP+NL+W0xIVnH3YXwddU8CB1bx+M3LQ8DqXPrcnlfV6U0qDlcn9jMgpbYrpxDTRQNQ//p7pVbbLu66dDXODdq9Ny2kN4ebvUBkdg0lNpKFAGEwSEnklT8kqEZfjXCxdJBMT42pLVol4KC64PW1DJGVaOvFAnHKjjCIL46OgF5AcMQnNaAk2T9fposgKVzevond00rE09XadK9tXiAXFVLZOr8PF8kU26hvU23Vy8RwBOSDSMPcB/l4qqWAJI6RgFXymkhpSCRIU/3YBCWYSM6zqqzwy9oiYvnc/3TWlTQkOVDxP0S5ybPQYqVjKN1G8yXBr9TXqjTqLO4uoAVWYHS0dJShSPlpIo2SXRO1MlggGgmgRDSWocHXnKpl4BsuxmNamyak5VuorYipfr0FOzTEaHSWn5sS6wilqAYmYkqfYLJObeJhEWmM6dQgtrPl8K++cWY4lPrSQXFZqK0QCEQpGQRhcwbifHtPCGot1wd66W7/L5fJlUuEUjW4DSZKYT4qUUCaWIRlOsqwvk1WyaGFRFQxIATKxjDAv1SkWRhaYVCcJSAESoQTZWJZvFb7FybGTlBolH7q+WF0kG88iSzJmx6TWronaXdv0q59IUG+K6Yi3dm4xl5yj1ChxbPQYI7ERMYHRElMTvQSp2TJxcMjH8z6/Cxfq7br/eiZLMjklh9WzUEMqtXaNWqMmKnsuvjHl8b3UoICkZ2NZblVvMZeYY7G6yMOJh7lTu4MiKyykFrhj3OGJ/BO+UTvUUEMNNdRbqzNnzvChD31oX2OqUqmQSqUA+OAHP/hdbdc0Ta5cucKVK1cAuHPnDleuXOHevXuYpsk//af/lG9/+9vcvXuXr33ta/zkT/4ks7OzfPSjHwXg4Ycf5mMf+xi/+Iu/yAsvvMAzzzzDL//yL/NzP/dzr3vy3lBvT+m6qOy5LmjiV1B27Bb/6E+e589eXkOW4JefOMavPXWaSDCw5/62LVJWmQzk8xCJvMk7MNQbpjfElPLeuL9UemlXGmQ/E2AwSTRovniT2/qZUfvV/qa0qT2mwyBbyjOrvJHpJ8ZPUG/V9zWM+te6YW7w5OSTfPHeF3ly8sldSa/BtM1rmTqDVcKCVeBc+pxfMxyJjdBxOhQsMYpcciUfgN5/X4+r5E2g89TPgHJdl/Vqg3E1wnogRNBowUSCD808QjhmMBJ7YMbtl+Z6rcqitwbvz/3Oy+B9XNfl5s5NPjD1AX99YUmAxl9PUql/PQeZSd73C1Zhz229SY3edfJ601HedLzR8Kh/Hd7cucnxkeO7jKlBVlj/es5nzvPt8rd5cvJJv0q43dgmLIX3vfaGevtLC2lsmBuoQZU1Y41ut8u6Kc5t2S5Tb9WJBqI+kNxLMhWtIrVWTTwncLF7NgsjCzRokFWyNGkSj8QpNgR8/Gz6LPeMe7iSqB7n4gJiXbbKHFYPs1pfpd6pc0e/I4ymRkHUyBAGczKaJK/k/Z8X7SJaWPB+HNdh3VwXSRlZBlkkupAgQAA5IHMufY6G02BKEeyi/mvU7JiCB9Ux0Fs6PafnT82LB+Pcqt0iEohgdS2W68t03S6u63J58zJzI3OYXZE0M9umMHhS88iSTNEq8t7se7m2dY1MNMPLWy9jtA0ysQx2z37AyoomkWWR9JEkiWQ4KeqH8bzgYCmTfs1XCYkEk97SffOwaBYx2yYTsQlavRZKWCS6Nu1NjLbhVypn1Bm+VfwWmXiGqcQU9U6dC9kLpKIpio0iM4kZrmxeoWAUmEvM+ccnKAc5M3GGSrOC0TRwcUlGkxxOHWZKm8LoGBStIo9nH+fLd7/MEe0ItWaNDUsk1Vb0FXpuj67bxWpbAuweVplUJ/3pfw4ORtcgE83wrcK3yMVyJCJieiIuIukVUvzXXKNjkAjdrzBKwvwsWkVcV3CkkpEkk9okI7ERzqfPU2lUyMcFb8yrDHqvV3pLp+f2/DrfUm2J2eQDOH0gFECLakwmBDvtfPo8y/Vl8vHhG4qhhhpqqLdakiTxmc98hg996EN86EMf4vr163tu098O+W700ksvcfbsWc6ePQvAr/zKr3D27Fl+7dd+jUAgwLVr1/iJn/gJ5ufn+eQnP8n58+f5y7/8SyJ9DsNnP/tZjh07xoc//GF+7Md+jCeeeIJPf/rT39vODvW2UL0uElKy/CDdtLpt8Mn/8i2uF6uo4SD/7uPv4m+dm9nzHs91RTrKdUU6anxcVPeG+sHRG2JKeQmiVDRFu9feU9fq16DBMmi+PL3+NMdHjlOwCr7hAvtPa/O4RZ4RdRBbyrt/LBjjQvYCkiSxpq9xr36PNV1A8QbrZTvtHX7+2M+z0975rk0Ub3uDVULPcGv2mriuS0SO+CbNIADdu29eyXOpcomRyAOwd/961411/3GnRmJsmS3eNT1DID0C902rkBzCxd2X7dV/Dvu5WoPb9+pwXsoJONBY2TA3mIhNkFWybLe2cV2Xdq/tGzmvhy/yWhW/12KV7afXYwINTtnzJjUWrALnM+d5ufqyn5BzHGdXPW9QkiRxcvykv+/r5jqj0VFkWT7QFBzq7S2jY5BX8ryy8wp6Q+dS5RLZeJZr29c4OX6SreYWzV6TTDwjKlghjZJVIhaI4TrCMEpFUiyMLFC0i7wv9z4CcoBTo6ewWzZKQCEXy7FcX+b0+GkBukai3ChzIXOBnJoTvKeQwnxyHiWgULbKKJKCFtRIhkWaJRlJIkkSRxNHqTQrzCfnSUVSJCIJtJCohW0YG7iOSzwQ51LlEnk1D7JIFRXtIpPKJOvmOtVGlZubN3m1/iq9Xs+HeCfCwgQx2yKFowZVKs0KH5j8APVOnaOJo3Sdrs8VOp8+T9EqIrkSmXiGG9s3iAVi3K7eJq/l0UIaV7evioRWo4jVseg6XVb0FfJq3n/eeKkzx3HYsDZQwyqJqEhzSYjnVSaaYbG6iNE0UEIKwqsRa9XCGrFQjEa3QU7JkYqkBBcsNUtGzXDXuksmluGZ0jM8NS2GE8yNzPHk1JOYXTF5cVKZ5NniszyUfIie1MN2bGGIaZOoYZVnS8+SCqe4Ub0hIOeNGje3bvLK5ivEgjFkZO5Z93hi8glWjBWMloESVKjYFY4mjmJ1LAIEyCpZEmGRTpIlYbi5uKL26MDVras8lnuMxfqif21e3b7KbFKwxXrdHpfKl4jJMYp2kbyax8XF6txPhrn4x8ST3tJRQ6r4/n02mhbSxATHkBhtU7bKYkpkTzDClqpLnBk/49cdo1KUoln06645JSdSa0MNNdRQQ72lcl2XQCDAH/7hH/LhD394X2Pqe+X/feADH9j1/sH7+sxnPkMsFuPLX/4ylUqFdrvN3bt3+fSnP00mk9m1jdHRUf7wD/8QwzCo1+v8/u//Puqwp/WOVbUqDKlQCBRFfO+ltS3+xz9+lrLZ5FBK4Xd/9r2856G9k9O7XXF/RRGGVD8UfagfHL0hppSXIIrIEXaaO36qZr833gWrwEhshHavvScRVbAKfGDqA7xcfXlPVczTIJ9nw9zYxfXpT/H0c6H6E01r+hrXt66jRTXBE+H1JXO8FI3HSerXoDHiGTODVcJMPIPkSnuSNoNg94nYBCC4TMlQku3GNjv2zh4zD9iVIpsejftv4vqB7tl49jX/sxnkag1u36vD9e/fQZW0Qdh52S6L2sh3oe/mHAwmxvrVXxs8CFzvAejHImOAmLL30Yc+ynZr27+W+ycMemwwD5zfvy3vz2a7yTfWvuHD9qut6h5u1/eLbTXUGy8tpHG7eluAqumhhcRrx3uz7+WOfodMLMO5zDnsro3e0lm31lGDKmpEpdwoM6VNkYwmKdgFsvEs5VaZ9+Tfw6g6ihYVyZ9io8jCyAJWV6RktIjGXGqOxdqiuFYkyMazLOvLZJQMWkSj4TbIa3kkSaLWrLGui+tZDsjMpeYIBoMkIgkS4YSfmlLDKmbXxOpYjEfHub19G7fnord0ptQpCnYBNaSyXF/2jQyPJ1dvi+l1nvk1qU5i92zOZ87Tklr8+NEfJxgKcmz0GEiQCAluUTKcJK/lubJ1heOjxyk3BMepYBQwOyZHE0cpN8oECDCTnGGzucmZ8TPcrt5GCSisGWvUm/UHLKXkHEu1JbSAht4RCR5veuFsatZPFultXZg5gBbW2Gxsko6l/WOhhTVySo5mt8lDykNc2bzCIeUQa/YaHzr8IUbjoxRsweZaN9eRJIn3T74fu2MTcALEA3FwRB1OQuKRsUcoWAUWkgsYbYPF2iJROUrBLggTMawwk5jhrn6XGXWGDWuDml1Db+vYHVuwpWQJu2ujhMVvcXpbZ8PcELXErokru4K3tXWNbCwr6n+1RR7PPc7Vrav+lMfx2DiVVoVJRfzfqoZUHNfB7tq4kktezfvVOgeHpfoS8VCcUqNEXstjdS0SkQSTyiR2x0YLaShhhWQ4SalZIqNkQMKHp/fo0XSaxMNxf9qi67j+8R9qqKGGGuqtlyzLfPazn+UjH/kIH/rQh7h27dpbvaShfoDkurC9LQypcBhiMfH9L91a51f+6wuY7S6n8yP8zt94nMMje03HRkNU/sbHYXLywf2H+sHT99WU6jeIQLzQnRo/5aec9nvjPalOEpEjVFtVjo8c51Llkm+oDE6eG+QzeQDpZqfJmrHmmwAVs7KLj+QZLJVGhbnk3C7zwDMp5lPzrOvrnM+c99c1uN5BntCGucGJ0RPc3Lnp366/fjgIKO/fXn/tsN+g2E/efb2EUaVRQZZlRmIjfppnEEgO7JseAjg5dpJau+bf7iCzz3Ecrm9dJ6/kD1y/t395Jc9Oc8evpHnpIY9l402/WzfWOTl2knqrvocJ1X/8BqflHaSDzsFBZmFeyVNtVAnJoV3H3HEcvrj6ReyOzcXyRX+b3vH06n9eNbCfg9XPButfV/8x/VbhW4zHxrmxdYNoKOon9PY7lkO9/WV0DOZT85gdk2a3KbhIkiSmj4UVsmqWxdqieB65gtvjuA5bzS3OjJ/hVvUW3W4XsyUqZR73x2gbqEHVZzbdrt4mHoqTV4XRVGqUmE3M8kLxBe5U71BtVlGDKpvNTTHE4b7B0nN6FK0iHadD2S6LOp8robd1qnaVDWuDbCwrzBpkcnFR45KRcSUXNSKMKkmSyMfz6E0dumA2TCJEKFtiEp5XX9RbumAlBQIcGz0mTLSght7WhYnh2CQjSZSImORXb9epN+rEA3EB2w5rzI/OU2/VRQXSKpNX8yyMLdBwG5yfOM/lrcukI2kuli5Staus1dcoGAUBdt+8zExyhlKjxKQ6idWxfOaT1bFQg4Jp5bqun5aSZFEZXDVXmUvNcbt2Gy2sUW4JUHeTJkeTR2m7beZScwSkAHpbJxfPiWlzSh69rRMIBDiUPEQ+KYybWqtGySxRb9VJRVOcTp9mTBljUp1kIjLBC6UXmIhMgHvf/JclzmXOcce8w0xihm8WvslYZIyiXaRoiVSTFtaw2hYblkhmqiEBO1cCCmbLxOyazKXmkGWZerOOFhQm6YXMBb/CGSCAKqvUW3XUgDi/uKLaaLdtAJHAckWSbDYxi921mdFmuFS5xFxyDqNjIMsy04lpMUUPCcdx0AIaS9UlsjHBgFRCCs1Ok4gUoWyUWdxZZDY5S6lRIhlJvpVP3aGGGmqoodidgvKMqR/5kR/hwx/+8NCYGur7Is+QKpWEmRSLifdon3lxiX/5lat0HZcPz+X4rZ96N8lYeM99dV2kpCYnBUMqGHyLdmSoN0XfV1NqMC3TD572/u3VLvrB2tOJaS5kL+yqRMHuN+v9TCiv8tRyWoxERliqLe2axpZW03seB3aDxT3TJBfPEZEjRENRPnbkY4Ktwv5GwSDYfNA06zdDPAPGM1dgb+UQ+I5w8EEDJK2kCckhMvEM1UaVCWVi3/UeNOlwSpvas+b+WmH/Y3ecDqloikuVS/4xPMjAuli+yEhkxDcAvfTQV179Ci2nxbqxTstpAex6fO/+/SDx/nrgfpW2wWrl4P68VHqJRDjhT2gcPN6nxk/56TFPlyqXOJQ4xCs7r5COpbm5c5Nz6XN71tif5PMkyzIXshf8a8e7VrzbTWlTHB87TrPX5MTYiT37M9Q7T4lwQkCkIwlmUjPcqN5gNjXLUl28Mb+yeQXXcVGCClZHTLhLRpOcGT/Dn975U45qR7m0eYnZkVlhGt032eOBOCWrJCb3WQVmtBmubl4V6ZmmTkyK8aU7X8JsC77Qcm0ZJawwl5pjJDZCKiYqgSWrRDQQZdPaJB4UU+FW9BWigShLtSWUoELJLjGtTZOMJinaRT9RNKlMYrQMXEcY02bXJBAMkE1kcYMut/RbnBw7yaq+KrYVUHBcRzxX73vIRsfw05AFq4AaVHFdF6tl+cbO1e2rzKRmuGPe4eHRhyk1xH4bXQMQ6SuzbaIEFC5tXuKodpRlfRm9o2O2TSrNCj23R9EuEpEiPoPKq5n5Vb37LCa9rQOilubfBshGsyzuLDKXmON29TaziVkKeoG6XWdlZ4VMLCMGYsgSeVXA3RdSCz4o3nsNlVwJF5eAFBBVQQkKdoGpxJTPv7J6FocTh2m5LZSI4k+yk5DIxrKs6qs8kXuCW7VbzCZnxUS8++t1cHyYudNzBES+ukg6lsbu2gJY3qpza+cWXaeL3tb91+NULIUclImHBeD+5e2XMdumb4zFQ3HqzTqvbL+C3hQcqkAgwJQ2RcNtcGbiDJc3L/spNccR0PWe2+OOcYecJlhW3rmzuzazo7O0aKFGVCbiE1zdusqxkWND832ooYYa6m2gwQ99ZVnmP/2n/+QbUx6kfKihvhc5DmxtCUMqHodoFLqOw7/9+nU+/dwiAH/r3FF+/WNn9wDNez3Bj4pERF1vZGRY1/th0PfVlJpUJwkHwkTkyC4IeT94GvY3NvaDhvdrMKXkJazCgTAnx08SCUZ808Wbfjf4OJ55IEkSX777ZWa0GS5vXvYrZf0v0IO1wP3WuJ8R5CVsAMYiY3z57pcZDY/ugb73H7PXgoP3m0UeByun5kjH0+TUHLFgjEl1cpcB5lX6+u97UIKrv1YI7DJ7woEw1UaVZCTpm20lq7SvgTUSHuHG1g0KRoE1fY2zE2dZ1pd56vBThAhRbVUJEfIrfOvGOmv62q6qXD87a7Ae2H9c+m/fb1R6629322Kd9ubrPt7n0udodBucnTiLLMucGD2xx4wbi4ztSvINrmsQlN/PQHs0/ygXshcIBAJDftQPgLwpaS4uFbvCe3Pv5XL5MnE5TtkqM5OYwcGhZJfIq3nUiEoinODK1hXen38/z5We49zEOZZry6SjaW7VbuE44vYOjkhchRSeLT3LqfFTrBgrmF2TFWOFhxIP4SISWAujC6QiKeSASPIlwgkWq4tk4hmCchA1ojKpiWv+kHqIa5vXiEpRynaZXDznX8daSMPqWkypU6RiKTGFLhRnw9wgEU6Qj+dxHRe7ZTOnzPHFu19kPDzOkcQRlupLFExRaXMQfKd0JM0zG89Qtao4PYeCKepiZldwhZZqSzyefZwv3PkCj2UfEwZPUBPGlKygd3SqjSq3q7fRWzpHtCPc2LlBLpbj9Php1q11TqROsFpfJSpH2WpuMRGdwOqJhJS3Do9jVLSKGC1DTDRsm8RDcQpmgaJdRIkoaGGNpfoSmWiGy5uX0bs6BCAainJ15yrVRlUk2VoiybZuiCpavVVHb+skIglxXSChhlS0kADbp8NpvrH2DbqdLmpIRZZk8kqeTDxDADEdMRFOYLQNelKPXDxHOBTmkfFHkCXZN9WySha7Y1O2yn5lUwkrRINRni89TywQQ28Jsw4HlmpLuI7LmiH+z6m36mRjWR94v93YJh4U1W7PoC9ZJaqtKku1JcEHu///SVyOs1Rb4uzEWS5uXiQeiLNhbODgYHdsTo2eYrm+LCY+SgHBq7p/rOLEMVoGAQJcyOw27ocaaqihhnrr9N/+238jmdydXPWMqaeeeoqf+ZmfeYtWNtQ7XY4Dm5tQLgugeSQCVrvL//JnL/GnN8WEvf/nkyf45SceRh54P9VqCSB6KiUSUh5/aqgffH1ff0PcbwIZ7IVmD/67//4HcYNc1+XG9g0/peQ9lizLpONp/xPhfjNgv1QL4E/T+3b525ydOOsbVIO1Pi/15aWeBiuBg/Km/aVjaR+4PpOa4en1pxmJjOzhLXmGTF7J70l2DTKo+o+PN0HO+3vBKvgGmGfctZyWv51BgLx3TAeTP4McremEGMUuI5OJZ/yUmMfd6j+/1XaV8fg4XadLs9fk8uZlzmfOEwgE2Gxu8t7ce9lsbu5KubV7bS5VLu0y8rx99upy+x2X/tvvZ/Ck1TT39Hs8ln3MN5ZeTxWw//gOTtgbTMkNat1Yp9Vt+cafdw2NR8d3mWf90/+GU/fe2ZIkiUQkQU7JsawvEwvFyCVyIInJdI1OQxg/suSzlOJynM3GJqfHTmP1LLKxLCvGCulIGrNrogZVcKHb7VKySjyeeZyVuvh5VI6iBQT36OGxh5kZmfGf/4mw2L6DgxJUKJpFCmZBVKbsEulYmlavJcyQQIB4KI7VtXzDwuNMgbiWM7GMX9HTWzqpWIoRZYQzmTNcrV3l9Phpyq0yd+p3yMayqCEVoy0Mn3w8z7OlZ3k0+yir1iqSLKGGVYpm0Yej5+I5VowVfvLoT7Kqr+I6LtVWlVw8x1Zri7nUHFe3rnJq7BTXt65TsSu8b/J9JKNJ7ph3+ODUB7lZvcl4dJxNe5OJ6ASr+irxQJxup8sz688QJUqvJ2qMWkgjq2QxegJ4brZM1ow1YlKMkikmnHrptLgcx+25GE2DHXuHQ/FDLNeW0cIaRtfwa5qO61C0iqhBlQ1zA6N9f9s9U6SMElM8W36WE2MnWK4vc7FykfmReeSAMILiQWH2KCGFeqtOxarQ7XV9I9PsmSQiCdSQylJtifnReZAhEUmISYQdm6AUZFqZZrm+jN4U518NC1PM6BgoAcG+8hJs2XiWAAEmYhMiDee6qEGVRCgh+FItm57Tw3VdCnaBeFBMdJxSp/zqaMku4bgOWlgjEU7417He1nEch8WdRZSQIlhojjBXS43Sg/rkUEMNNdRQb7l+9Ed/dNe0O0+yLPMf/+N/5Cd/8iffglUN9U5XryfMqM1N0DTBkdqymvzD//s5nnt1k0hQ5l//2Hl+5vRDe+5rmtBsQjYLuZy471A/PHpTPrb0TJV2r826sb4Hor1fKqlfg9PbBqfE7cd+Gqy99Suv5Hm5+jIffeijFO2ib1D1M4H6U1+u69LsNXcZDvupYBUYjY7SclpcqlwSbwClgJ/kCkvhXUbcICC73xTaj5/lHZ9+SLtnLN3cucmTk09yY/uG2L4DZbvsmyL9yan+tFH/MdrvWE5pU0SCEaYT05xLn+MbG9/g+MjxXfXEDXODsxNnfa6Ml67yjtcgc6k/5eYBw72fDaaw+o+Ltx9bzS3OZ87vMXi84xMNRPn5Yz/PirHCufQ51o11Gt0GL5Ve2jWdEB6YcxfLF/1z4SXi+uuk3vc2G5s+LH9QruTuMpr6OV79Zp9nAA6uZah3lrzqlSzLzCXnsNs2SzuiGpeIJMioGayeJWpoXTFxTI2oSLIwXR3HEfWrSJpKs8LxseO4uOhtnetb10nH0iwbyyyMLCAFJULBEHOjc2y2NsmpOeyujdERNTS9rQuDoGPhuA7lZpkjiSP8+dqfk46lRZJHEqwfbxqgGlJZN9eFodQRZouLS8/pUbJLqCFVpI5cB72tk4/nWdVXOT8u0qLzqXmyalakXzy+EMLEOj12mjv6Hc6MniERTpCKiPrbbGqWcrPs85yWdZGwcRAcKQeHudQcds/midwTfH3t6zyafRQtrFG0hKmViWYoN8rEQ3GUkELZLvOq8SonRoX5c3nrMslQkhc3XxSpqGAcvaVTtstMhCco22X0li4mwnVNktGkD2qXkEAGLaoxlZzikcwjbHe2OZI4IqYDxvPgghpSsToW8yPzbFgbAvodEpXIXDznp6lOjpzkeuU6elPnsHqYxeoiEhJKUOHK1hWOJo+yWFskGUmSjqe5uX2ToBvkiytfpNvpsq6vk4wkOTZyzK8NWl2LQ8lDnJg4wVRiikqzghIQtcdEKIErifTVXFIYe5loBr2ts1hbJKNkMHsmOS1HtV0lq2apd+oCqj+6gBQQFUUkyMfzlKwSiWACJahQb9QpGKKKqYU1ZElGDavYXVucP7tMvV3HcR3B8QqpqAGVu8ZdxiPjbFgb6C39LX7WDjXUUEMN9Z3kJaaee+65t3opQ72D1O0KQ2p7GxIJMWnvzrbBL/7Rsyxu6qRiYT7104/x/pnsrvs5jqjrBQKirjc+DsNg9Q+f3tRT7uBQskq73rB7b9Ab3QaXy5d9SPp+b+696lf/VLWD2E+vNc3M4zh52/Gmq3kTpfbbruQ+2L5ngNyr32NNX6PX6/l8qnAgTK1Z48ToCSRJ8g0dL9U1Hh330zdnJ87yXOk5zk6c3bPu/sc/aLJdv/nWP6nPA5mfS5/zTar+it7F8kWOjxzfBQc/6FgOHjdvGiKwi/301Xtf5fHc42y1tvx0ladB5lJ/ok6WZX+C3Xh0fFcybPB8eo/pVS09887bp0a34RthsiyTVR5MGKzYFUYiIwC7rg3vGGbimV3G2X510v1g+d51OqlOEg1EkSRpz9TGKW2KzcYmjuOwpgsg/0FVyKHeOZIkyU8XLVYXyagZtLCGJEvggt2x/RTklPrgeeUBwOutuoBKd0zoIXg+LR0kcZ17hsJidZFsLIuLS8ku+Xyf+ZF57K6NFhb8JL0tTGGra3EkcYTni89zIX2Bq5tXhZkQUgnIAXKJHAE5gN4Rya1bO7dQgypOz+F29Ta4IlFTskrs2Dsi+RJQKNgFzk6cpek2mUvNkYqmBJMqIkwdV3LRQhoFu0AylhTg82BAmD7RJAsjC9zR73B+4jzJiOBYLYwsYHQNZEk8XxvdBmpYRUKi4TR4T+49wgi2iyhBwWmSZAk1IG6zWlvlocRDHNIOsWaukYlliEpRFuuLHEsdw+paFO0iAGpY5Z51j8OJw7xqvOrX6TxT2+gY5JW8v085JUcynCQby1K2ysRlUWd0cQU8XBVgcy2oUW/VMduCx+VV4hLhBLZrk1WzpBXBqVNDqqgv1pc4qh2lbJdZSC2QiCSwuzaHtEMs1heJBCNYPQskwefyAPhFu+hfS/VWHReX2eQsDadBTsmBjA+UXzVWOTV2im8VvoXjOMSlOFe2rgjuVsdgWpnmyuYVJEcYhFbHEtML2zpaUFzHU9oUakjly3e/TLVRpdQoiSSrJAsjE4n5kXka3QYTsQnsjk08GPdf41PxFO/Nv5et5pbPFRtqqKGGGurtL0mSePTRR9/qZQz1DpFnSO3sQDIpoOSX1rf5+3/8LGWjwaGUwu/+zfdyPJvac79aTaSqpqbEn0P9cEpyX8dvibquk0wmqdfrJBKJ7+mBHMfhS3e+xHxqnkhIGDWeXNfli6tfZDo5TbPT5EL2gj8Bbbu1vWtKm5cu8UyqQQ0C0fdTf61tIjaxy/Do3673WJuNTT/hIkmSX9Uq2WIM9tLOEo9lH2NZX+ZC9sKBa/ASSt5+eY85uP391nuvfo+b2zf56EMfRZZlv57oTTb01jUWGePmzk3OZ877hkv/9r217zR3fNOmfwrf4Br6j7dnCHomSn+lMa/kubx52YeDf6dz0K/BdR+0lv7jd3PnJifHTvrnbiwyxl+s/QVzo3O+OTQWGePG9g0y8Yy/De84eMfNe3xgV13woPU7jsOlyiXOjJ/hytYV0rE0E7GJ11y7B19v99pk4hkiwQhlu7zv9f1a+n48D4d6bX23x7jerAvYtlFEDasYLYNENIGLy3RiGqNt+NPGXNdl3VwXqZaO4ad4VvVVYoEYWTVLo93ARfB4skqWcqPMucw5rmxeYTY1i+RKfs1PlmUxhc0W6ZVEJCGg4gFVMJF6sGFvcJ83Lgyz+9e/3tFxHZeCVSAZTqJGVJBADaqU7BIA0UCUu/W7TMQnfPNmw9zwa1uyLO+apOY4guOUi+W4XbuNElRIRpL+7erNOg4Okis9GLKAK9JJABLCXOuIFJOXXNowN5hNzXJ757bPZFqsLXI6fZpn1p4hHo6jBTUmE8Jcur1zG9d1fWPJ6BoYDQNkiAfivGq9ynsy7+H6znWfc3Svfs9fWyqawkWkjbzqW71VB0CWxKRAJaRQskrklTxGx/D3Qw2rWF2LSWXSB5T3nJ5gNOGQiqRAxjf5UuEUWkRUMgtmgV6vh97SsToWOTWHFtIoN8rEAjHsni1qlc0yiVCCvJbHbJvobR01JGqfZlewoBIRAUg3Oyadbsef3jqTmsHu2ThdhxfLL4qpgnKAYDBINpalJ/VodIQxKCPjICqKo+FRni08y2xylpH4CJPqJIloAqNlgIQPZO+5PUpWiYXRBcyOSSKcENytlu5ff6lo6vv+PBzqu9Pw+A411Fuv4fPwjdfwGL856nQE0NxjQckyfOX2Bv/qq2LC3qncCL/51y7smbDXbIJti2TU+Phwut4Pql7v8/ANT0p5SZINc4P5kXkqzcqe20iSxMeOfIxWt8X5zHnWDVFZuL51fQ936rXA4F6lbL/pcIMQai91dWP7Bnklv+92PfaV93fPZMgreXaaO0wpU0TkCE8dfmpPyma/xNFg+ma/etd+a5Ykic3mJo/nHudS5dK+1Ttvjf2T6A5MmMliLR2nQ7PX5GL5on/c1vQ1Xiq95NfTDkpvgagHegmsQCCwZwLdQdfCfvDx/nX3Q+374e3rxjrpWJqt5tYuELq3xpPjJ33IvceASkVTdJyOn16YiE34qSzvugT8KinsBfF763Ycx0+lXdm6IvgqVomn15/m+MjxXemofhbWurHOSGQEB1HXmlQnXxPqP9Q7R44r0p/pWJpyo0xezaNFNJLhpEg9uQ+YeF4FrmgLxlFOyREMBDk7fpa8kqfRaTA/Mo8kS+TUHFbXYiIywVdf/SpH1aNiSpss+WksF9ev73nmjRoU5lIqkmI6OU0yJEwjs2OK2lhY8R/f6BrIyPToIbkSiWCCkl3i2MgxHh59mLAcZiI6gYyMFtSE+RBKICH5++Y4DvVWHcdxMDoGU+oUZtf0p995k/dqzZowdu4numrNmuAwtQxcafeACS+VmggLzpEaVClZJZLhJBklQ8EsMBGe4Kt3vsp4ZJzZ5CxWz8J1XIpWkaySZSoxRV7LEwgESIaTSLKYMNdwGrx/8v3cMe4wk5hh3Vin1qyhBlWstoXRMqi2qr6B6PQc1s11tKCoq2XjWSQk8YFEPOObb1bHIh6Is1xbJhvLihSRLPlrUEIKW80tYsEYyXASq2shIxMLxtCbOi+WXiQWiCHLMql4igu5C9iOTblRZkKZoNKsoIU1VowVsvEsXbfL7Z3b9Jyef/7NjjA5JSSMtkEiLHhULafFRGyCjJJhs7XJsdFj3DXuklfzvFh6ka3GFhOxCcyuSUgKCSZVUKPeFudrPjlPtVXl9MRpQsEQC6MLgHidVEMi/VRvimvA7JgsjCygN3XW9XW2rW0KZoGckhNVyT4Tc6ihhhpqqKGGemer3YZCAXRdGFKS5PIHLy7zv375Cl3H5YOzWf7DX3/3HkPKNMV9cznIZIaG1FBvginlvcEHxIQ8dWoX/8eTJEm76lYy8h7u1H7T4/Z7nIMYU/3bcV3Xr/EVrMIeTpO3No/L1G8YFawCp8ZP+SOzi3aR85nzBxoyjuPwQuEFXii8gOu6/voHweUHrRkecJky8YyfFBo0NAb3of9YeduTJIloIMr5zHl/ul4qnPJZTWW7zIw2w5fvftl/M+1VLgeP937r8BJxo+HRPaykfjZUv9E0eE77oeKegXSxfJE1Y412t03ZLu8yf/oZZf0Q9kw847Or+s0rz9AC9r1mBg3KdWPdN++8c9J/PuZSc3uOQz+3C8S1Hw1E+eD0B3ddb68nSTbU21eyJIYAXN+5ztHEUcyuMH8kSVT4PMaS3hZJkYJVIBfPUTALlKwSOSVHIBjgoZGHOD5+nMX6IjE5RtEqYnZMKs0Kx0aPCQ6TJIk39S7U23Vcx/WTOEbHQA2obFgb4OInlCa1SbSoxvyIgGQHpACZeEakeeKTIhEUSqBFNOSAzMNjD2N2TWRZZjo5zaHUISa1SSRZpJa0sIbrCvOn5/RY19epNWq8WHoRJaj46SAlqFBpVlgYWaBgFwQwvS1+poU0SnaJWCBGwSr4xwdA7+piLbKoh5kdk3g4LoyusEbZKlPv1LEdm8PJw8iyzNPrT3NEO0KxUSQTz7DZ3EQLCRNJCSks1haZiE3wqvEq6VhaTPq7z5Ny7xPAra6FK7l+QsjoCD6T0TbAFT/3zkNOzZGP530A/XJ9mdnkLNd3rnN6/DRLtSV63R5rtTVeLL5IOprm5erLHB89zh39jp/Cyqk5Ko0KruSKiXr1ZcHxQmKptsRcag4lpLBpbzKjzWC0DWa0GYpWEdkV+2Z2RAKrYBVo99pcqVwRJqHrUG/XKdkljiaPEgwEOZQ4xIX0BYp2kcdyj9F22rwn9x6OJI6w2RBm1XRymsPJw4I7dd9IGomPsDC2wLgyzkxqhts7t1k318V1bBfEeevo4tyFxDW+WFtkIj7B9R3BRru8eVkYm0Ptq0996lM89NBDRKNR3v3ud/PCCy+8rvv9l//yX5AkiZ/6qZ96Yxc41FBDDTXUUANqNoUhZZrCkHJw+M2/uMFvP3cbgJ8/e4T/7UfPEQkG/Pu4rqjrybKYrjc2BsO3QkPBm2BKDSZtvITOQXykDXPDh2ufz5zfVRvz0jP7NQ69yXd5Jf+aazgIJr7fOg4yXvbbXv+EvkFdqlzC7JnYHdtPJXm386pieSW/i5U1uDaPyzSdmN6VKtpPnonSD2bvT01NaVPiTef96Xr9V8G59Dm+Xf42T04+yYa5sQfGPri9/mqct6/vybyHp9ef9hlKnrnlui4Vu0LJKjEWGfMNp0Hzqj9Rdi59zp/UOBGdYLG26MPHB7lj/dPxPDi+t5/9JmC/+bVfkmw/w0hyJTLxjH9OdnGyZHxD1VvTmr5GMpLkxvYNprQpphPTXMheGKajfsCkhYXBckQ7QsEoULSKuI6LFtF85o5nQhhtw59SV7JLRANRbldvo4UEk8hL7JQbZbSwxmxqFrtr02g3yCt5f8IeiMRSwSqgBBVKZklA02uL5OKiruYpERGQ8WQ0yfGx42hhDbtrC/aUY3Euc447xh3UoCrMoaYApjuOw5qxhhpUxUS6QJx1fZ11fZ2l2hIT0QmKdlHwjjoG6Viaxeoi9XadTCzDlc0rTETFBwqTyiRFq4gSUvzK4Hh4nJvbN0lH07iOi97SqTfq0BNVOS2k+Umfil1hPiUSZFpEYyElkjoBAiDDk1NPcmPnBnE5zmp9lUfGHmGpvoQaUlmsLnIkcYS7xl1OjZ3ijn6HTq+D4zrCZAtqbJgbxINxnweViCSY1qYF56lnowZVem6P29XbzKfmxWRDW0w2bLgNZpOzPFN8hvdk3sOzxWfJxrMU7AJG1yAWiPHVe1/lQvoCL1dfZiY54x9bs2WSi+dIhAQwfz45T9EuklNyZGIZLlUuoYU0jo0eo9KuCO6dsUw2nmVFXyEeEAD3olEkLIV5ZfsVUqEUJbtE0ShSNIuMRca4Y9zh4bGHScVSmF2TSXWSUCjEBw59gBPpExwaOcSjuUcJBAL+NMZ8PE+5UebYyDEkSfJh+nbHRg2pxINxLlUukYvl0Ns6iqxgtSz0lk48GCcdTXNz6yYnR05SaVSYScygd3S/BjnUA33uc5/jV37lV/gX/+JfcOnSJU6fPs1HP/pRKpW9qfJ+3b17l3/yT/4J73vf+96klQ411FBDDTWUUKMhDCnbFoZUo9Plf/nCS/zXG/eQgH/85HH+0fuOI/e9l+p2oVoFRRnyo4baqzfclPLe4Besgp9K8abaHWT0ePeRZdlP/riuy43tG77pMCgv9XSpcsmftLYftPw7QdC9yp7ruuSV/L4GUH8iyZtqBxxomp1Ln0MNqMRDcTLxzC4jxjO1Bg2ag5Jb/dPbXgsH1g9mHzwGg/tba9YAkRoq2kU+duRj7LR3mFQnd03OG6wUArSc1i7z61z6HCvGCifHT9JyWv7UQC9lVGlUODl2kps7N3dV8A6qWMqySMydGj9FrV3jxNgJ33wcTJP1m0avdZ77AfEeZ+ul0kuMRcb2VPa8ZJsHrB88hlPaFNFA1N/Omr5Gs9ekZJWQ2Q1bf73nbqh3joyOQS6ew+pZbLY2OT1xmsX6IkbLeADIv89MSkaSpKIpzI5JOirqfpPKpKjghTXMjokW0ZhUJlFDKte2rvF47nFsxyYZTfrsIL2jPzC4OqL+5kou6Wiaq1tXUWRFsK56PV7ZfsWv4noV1ryaZ8PaIB/Pc3XrKu/Nv5dLW5eIB+Osm+voLV0kYZQci/VF8kqeZX3ZN6CUoMJmYxMtqJGNZcVrjQuu4xKX43yr+C2OJI9gdA0kV2LD2iAbzxKUgyLZ5fa4a97l5PhJ7J7NqrFKRslQbBRxJRdZkn0OEYgPHIy2MPgmlUmsnsWkOkleyzOpTHJHv8Mj448QDAaZSc3wbPFZjmhHuLVzi1ggRsWuMJOcodFrMB4bZ9PexMEhGUli9Sxyiphk2A+u9+p7M8kZNpubyMhkYhmKdhEHh0wsw1J9iXxcwM5PjZ7i2+Vv83j2cTG5MKCiBlUaPQFrrzTFgARZklmsLYpz1xWcKzkgcyh5iEAgQC6eY7G6iNWxyMQFkPxW9RZzyTlW6itkIhmeKz7HY7nHuLF9QySimnVe3n6Z+dQ8q8YqM8kZ1IiYkLdpb9JzetSbAqqPC3pLGI8urp++kyQJx3F4ZecVlKAipvGNLLBhbVBv1lk31lFCCo4rKnqNToOjiaMs1kUdudwqMzc6h9kVH77IAZkz6TOEQiHySl4ck6D6gB82lK9//+//Pb/4i7/I3/t7f4/jx4/z27/928TjcX7/93//wPv0ej0+8YlP8Ou//uscPXr0TVztUEMNNdRQP+yybWFINZsCar5jN/mH//dzPHd3k3BA5l//+Hn+xukju+7TaomK39iYSEhFo2/R4od62+pNm77nJZkm1Ul/8lp/sgTY14QB/NRLVskemDTpT+54k9b2M68Gp9p5KSfPPPFuMxGb2MVs2o+H5CWSSlZpT1Kn//ayLPNo/lEezT/KlDa1K9HlHZezE2f3NVEGjZeDvtcvz0SZ0qYO5Dj1s75OjJ6gbJf9SXD9x6g/EbTf4w6aX/2Jrmqj6k9c9BJn59LnfKPPMx09SPtguqv/3G63tskqgqUyEhvxDaX+xJrHtxpMRQ0eg8H7NXtN2r22f30OHuPXMpP6TdexyBg3tm6AIwyyaqu653yuG+s0O81dpulQ71ypQZWl2hK5eI4zY2d4rvgcZ8fP+rwn13GpNqu8svUK3W6XdXOdbCwLkqgoe9Bwo2X4HCazawJwJHGE50rPcS59TtTJwgkKpmA0mW3Tn1KXi+comSXKdpmH1IdYrC9Sa9W4tXNLJF/adYy24SeQClbBB3TPp+Yp22VmtVkWq4vgQM/toQU1FquLzCXmhAEWUIUBJUnklJwwPSIaVs9CjarYPRslrLBcX+aIdgSrbREkiIMjTJ+eTSIiaoKJcIJMLENADpCIJHgi9wTXt65zVDvKSnWFaqOK0TJwEAaIK7kC7h5WuV0VEHO9oyNJkkh7pc+xWl+l2+sSkAKcnThLpVGh5/SwO2JdwUCQeCDO3fpdInKEoBQUVUTHpWgWiQfiFMwCtVbNT6/iQskuMZOaQYtovomYDIvJgbl4TkwDvM+YOqId4er2VeZG5pCDwmhaGF0gKAdJRVLktTxGVxxzs2OKOpsjXos9NlfRKpKNi0mLVlskjzKxDCW7xPzIPHJA5sTICZ4rPsdYZEzwo7oGY7Ex1sw1zk+cp9KoMKlMClM/IDObmqVgF0SFNCJg43pbR2/p1Fo1fwrkrZ1bIo1aXSQfz3O7ehs1qNJ1uqzUV3AdF6trCaaUDFbbIipHub19m1ltlm9tfIt0NI0W1kR9VJLBFcZtVsli9SwSkSFotl/tdpuLFy/ykY98xP+eLMt85CMfec1R7P/yX/5L0uk0n/zkJ9+MZQ411FBDDTUUAJYlDKlORySkXq0a/OIfPcvtTZ1UNMz/+dPv4cmZ7J77NBqQzQ75UUMdrDfNlOrnN8FeMLn3d28SW3/iKBfP8Rdrf0FeyX9HDs+UNsUh7RCRYGRf82qw8uWxhVpOi3av7f+sbJUpmkU/ZbCfIeO6Lq9svUIykvQB2J5pdpBxNHgcvH8X7eK+JspBAPat5hZ5Jb+nCjjIaPLMnjV9bddt+xlc3iQ7z8zZ71j1P663ln7za1CSJGDNWTXrm4n9RhTsNR8HDa7+bXkVu53mDmEpvOfc7necPHbZYAqq32TKK3lqzRppJU1Wyfrryit5PzHnvUk9yAj0bnNj+wYfmPoA1VaVSqPCidETfgrLcRyfz1W2yweapkO9s1SwC5zLnKPcLCPLMkeTRylZJZ+rtFhbpGgW6TpdLlYukolluLpzFQmJifgEt6q3fJNFi2gUrSLxYFwk7SSZmeQMZtvEdUStSg2pgrMUimN1LCRJAK6VkIKLy6q+ihoQ8GnXcek5YpqaElD89NGUOoXZMUW6SXLRQhqWY5FX8igRAe2WZImFkQUW64u4rmAt2Y4tal7BAMdGjwnD6D6TSg2pyJKYBuglb5SQIpJSujBztZDGdGLaT2shiedtqVHizMQZVvVVJuITgIChGy1DTBlE9k2SiegERsvAaBroLQGOX6ov8VDiITabm7i4JKNJv3Lm4CA5EvVWneXaMtPaNNvNbZEGM9aJB+MYHYOl+v1KolVkw9jA6Tm+OScjo7d0lKA4holIAhlZGI9tAyWsiNdOWWI2KYYfqEEVva0LE+b+VMNb27dQA2Jq4qHEIaaSU5g9wcq6tXMLx3FwHDHtTgtpYruu5NczE+EEVtvC6lkcTR4lr+WpNCpkI1kCcvdY7boAANFESURBVIBMLIMcFMc2EAyQjCaZS82xaW+SjWapN+t+PRMXjLYwTeuNOi8UXyAiRajYFTGx8P71a7QMVuorPJZ9jLvGXeZT8yzWFlkYWcDsmQTkAPFwnGV9mcOJw1QaFZBEKtBxHTaMDbq9LnbHZkodMvQGtbW1Ra/XI5PZXYPPZDKUSqV97/Otb32L3/u93+N3f/d3X/fjtFotdF3f9TXUUEMNNdRQ340MQxhSvR4kEnB5fZu//8fPUjIaTKcUPv03H+dkbsS/vcePAlHXGx8XLKmhhtpPb9ql0W+k9BsF3pt513UZi4xRskpc37pOMpL0DZZLlUscShzawzXqV38lqz+JddDtvJTQpCo+UfaA2K7r0nJa3Ny+yVxyzn/M/YwgSZI4NnaM5eoyeSW/b01w0DgaNE++E9dqX77R/e95KR/vOB1knJWtMiWrtAv27tUOp7Qp30zz1uoByAcnGQ6uZfDf+5lYtWbN51j178PgWl/L4PLkGXiVRuU1j1M/18ljOw1ywfqvl/OZ89SaNf9+3vclSfIh63klf2DVcN1Yp91rk4ln2GnvkFWyfkWxZJWIhqJcLF9kw9zg5NhJUV89wDQd6p2lSWWSklUiG8v6jKeiLZI3lyqXmEnMiFSUJHNUO8p/XfmvPJx8mEqjQqPTIBfPsWFuoAQUbu3cQgkIeHVOySEHhMGwWFvEce9X8CTZr5kZHYOckkNv6ZStMr1eDzUoEkwBOUBeyxMKhpgdmWXFWBEmQ0fwplxXcJz0lu5PTjNaYhqfFtLQQhq3d24/4BZZRXCEWTSlTglQujpJKpJ68NyTJR+Ujgsb1gau5GL1RBXtG+vfoNasicmEXR3HcbhVvSUm+smS4GZFEjS6DZSQIg6wJL70lo4SUFiuihqhGhb7aXZMAR2vrRCVowK03jFYGF3w01lm1yQWiGG0DF7ZfoXx2DjxcBzXdbm6fRUloJCOprm+fR01oPqPG5SDyAEZLSrSRUbHwGiLCmNezfswe6ttkY1nkZF9Y87qWsSDcfE6cj/NFAvEuLx1GacnzmW9WUcLahTMAplYhnKjDBLklBzFRlEYX7KLI4nb367dJqNkhDnVsShaRc6kz9CQGsyPzqNFNWRXGGiddocXSi+ghTW0qIbpiKSZ3tF9GH0mLh6zYIuU513zrp/c86YKGl2D2eQs13au8b7J91FsFMnGstzauSWmDLYNkqEkmViGRqfhT//TWzpmxyQWjtHoNfyE1lB/NRmGwd/+23+b3/3d32V8fPx13+83fuM3SCaT/tf09PQbuMqhhhpqqKF+0KTrwpByXcGC+srtDf7nz7+A0epyMpvid/7G40ylFP/2vZ7gR8XjwpBKDIPSQ30HvWmmVH/NqeW0GImMcHPnJgCpaAqAG9s3/L/LfUvLxDPUWrU9UG1P/SbL4CQ4L6XiqT8ddZDhIrkSJ8ZO+CylwfX3A9n1tu5PVOtfj2daDALa9zN2BqthrwVt33Nc+9JF+xlckiSRjYu0kpf8WTfWmYiJREK/KeTtm8e3gr2TDAePu7ef/dPmvG32p8K+U+rq9UyjG6xI7nec+tdRtsuMx8dZrorz2L/t/scvWAVGo6O03TbALqbYzZ2bHB85zqXKJb/auK/51zfW3qsbns+cJ6tk0Rs6mXjG/75Xbxy+SXvnS5aFiVO0i2RiGZary9SbdRZ3FplJzNBwGhwbOUYymqTUKHFu4hzPl58nHU2LDdxn/OhtXRjbsoQsyWgRjSl1iqX6EmcmzlC0i4AAl09pwhTKxXMUrSLJSJJ0PE21XSUdT2P3bPKKYB1lYhnKdpnTY6dZqa+Qi+dYN8XzsEcPs21SsMXz0+yZwtSVXDasDZSQ4ptgkiQRDUYpmAWfSWR0DLSIoFQWbFEJNDsmOFDv1FFlFatjMZ+c55niMxxSDwkQvBhLyLXNa0SJord1JFdAzEeiI8yNzGF3bXpuT9Tc7k8trLfqbDY2ycTE9EAZ2Z88qAU1f0JgIpzA7AigdzKSFK8TOzd4KPWQmEQnB7HbNgBnxs/gSi6bzU2OJo7iIY+8SYN5JY/ZNkVyKaQgIaEEFGE2uRJqWMWVXAFEj6g4rsNSbYlYIIbdFemgRCSBFtREInX0JIu1Rb/emYiKlFm5UeaodlSYTWaR+dQ8SDClTpEKC+NvPjWP3bExuyYLowukIilRjYtmKVpFJtVJMTVPzXFl+wpROcql8iW0oEYiKI5JIpTAwUENq1QaFdKxNGpA5Z5xj0fGHsHoGKwb68wkZljVV5lUJkFCsLM6ljDRbFEhLTbEY8oBmWAgKP6PaW76qT01qGK3BSjeY3UNtVvj4+MEAgHK5fKu75fLZbLZ7J7br6yscPfuXT7+8Y8TDAYJBoP8wR/8AX/6p39KMBhkZWVl38f51V/9Ver1uv+1trb2huzPUEMNNdRQP3iq1YQhFQiAorj8x5dW+F+/fIWO4/CBmSz/x0+/h1Qs7N++3YZ6HUZHBT8qFnvr1j7UO0dveohuUp0kIkf86XqeCSRJks+DSsfS7DR3fLNjOjHN4cRhphMPPt0bNH7Go+OU7d2/2O03Oc4zgQDfxNpv8t90YnrXRDUvddOfmOmfEtefiFo31lkz1mh1WwC7jJT9NDjBz+M8Xapc8lNL/TU37zEm1cld6aL94OiT6iTVVtU3fDwzaqu5JVJh3ZZf7fMqaxPRCa5vXfdTZAeZJ4OTCo+PHOfp9ad9k6gfGj+Yuvpu5Z1nD47u/ds7Tv21xBOjAoaeiWeot+p7TMP+Y+VdDyE5RLVR9c+rxxQ7nznPy9WXd52//WqM0UDUT1b1s8imE9McSh7yTajvZLwN9c6T0TFIR9Nc27mGi8uR1BGsrkWpUfLftE9qkyRCCTFZL6ihRBTUsMqyvkw6nsbqWCJlc7/2pTdFgikTzbBcW+bYyDFS0ZR4LTLWcB1XmDuICYABOcDJsZPcNe8yl5qjYBV8s2N+ZJ4VY4XzmfMUG0XfPJJd2Z+WV7bLZGNZwcBDJhFKoIVEEikRTjCXmmOrtUU2nqXWqrGmr+H0HNZ1AUbPxXPCfEYke3LxHHeNuyhBBb2tM6PNCMC4V0PrWTw8+jAXty4SC8Qo2AU/XeY6LlklS6Pb8CHn2ViWu/pdppVpNpubPDz2MMloEqtj0XN7gDDo9baO67q+CVJv1jE6Bk/knqDttNEiGhklgyRLqEGVUqNEKpxidmQWgEpT1NcS0QTJSJINa0NUEqNJgoEgeS3PYn2RhdEFXMn1Ad4e56tgFjiiHWGlvoIW0vxEmtExeCz7GK+arzKfmhcfULgiUSRLMgujC9wx75CJZwSHzy6JupssoYZUXMdlcWeReCCOElIomkXyqjCKkEXF/Xb1NjEpxuLOIjE5huRKOK4woMyuYFjpbf0Bx6rnULbKqBGVs5mzbDY3ScfSlKwSZsckG8+SjCZFfRTXT+vhINhX0fvpwPvV0MWdRcbCY7xUfklcO13TB/N70PqhdiscDnP+/Hm+9rWv+d9zHIevfe1rPPbYY3tuf+zYMa5fv86VK1f8r5/4iZ/ggx/8IFeuXDkwARWJREgkEru+hhpqqKGGGuq15Loi7VQoQCgE4ajDv3v6Bv/fZ28B8LNnjvCvfuwckWDAv49ti69sVnyFQm/V6od6p0lyXwdtWdd1kskk9Xr9e/5lZnByW3/VyqvReTWoneYOI7ERJFfaZbr039ZLIHnw7EuVS5wYPcF2a9u/veM4XKpc4uzEWYp20Tcc1vQ1Wk6LiCzMp/7teoaBZ6L4zKluC1dy/Ulrg7f3pq7VmiLR1e61qbaqXMheANh1+8HH8/7tui4TsQk2G5uU7bJvrozERvy1Dq5tv7rb4NrHo+O+OVS2y74Z+GLxRdq9NvV2nQ9OfZCXqy/7a/f21YOlD+5v//H1UkgvFl+k43SY1oQR461lLDLGje0bZJXsLtbVa+3D690n7zh5532/Y+kZT4Nm0OD15G3T037X6GsZSq/3dt+rvh/Pw6FeW9/LMXYchxdKL5COprFaFrZrk46mhWlyv+bmQc/1jqihlRpiQlutXSMoCbMjGUny9L2nCcthGt0GOS0n4Of3YdrJSJJ6W3Clas0arxqv8kT+Cayu5dfl5pJzLNeXickxKs0KR7WjbLY3mU/OU7SLKMEH6adasya4QveHMeSUnL9eva0/4Aq1DcyuybGRY2I6X1MXKay4SCxpIc2vFLqICW+LtUWiUhS7ZyNLYgql0TZEaigsgO6XK5eZ1qbZae4wNzLHM4VneFf2XdzYukFOzbEwsoDVtUhGktyr36NqV7lr3OWRsUeQAzIS4rkpyeJPj60lIWH1LNLRNCv1FdKxNI1eg4WRBQpmgQ1zg5nUDHf0O5xNn2VxWySXrI7FmfQZluvLTKniNann9CjYBaYUkXjyqpZWT+y3FhEpLi2koYU1Xt58mc3mJg+pDxEMBgVc/P7+SrI4Rl41PSbHWDVWOTdxDqtn4fQcH7CuhgU8PhsXxo/ZMUnH02w2NplLzWF2TJKRJN1ul8WdRTYbmxwfPc6quYoW1JhLzXFt5xqPjD7iG20FqyDOccdCb+t0u13KjTK5aA4pIAmzCpGga/VazKZmsXoWruPi4GC3bVzJFSaZ61JpVphNzLJUXyITy7BSW0EJK2RjWSqtCpmomFaYU3I+R+uNfB6+U/W5z32Ov/t3/y6/8zu/w6OPPspv/dZv8Ud/9EfcunWLTCbD3/k7f4fJyUl+4zd+Y9/7/8Iv/AK1Wo3Pf/7zr/sxf5iO71BDvV01fB6+8Roe4+9drgs7O1AuQyQCbqDLr33pMs/erSAB/4/3H+dnzxzZdXtdF2mqTEbU9YafwQ8Fr/95+KYlpTz+0ZXKFZq95p7anAeBDskhzqXPUW1UCcmhA1lL/VWuQci4v3P3J8EV7eJrTo3bL8HSn4aZVCcFB0iOvCbEfNPa9OuHtXbtQRJsYPuD9+9Pb1VswUvyptRl4pk9APD+tXlmn+M43Kvf48Xii+TiuV1r32xsUrJKtHttf3LdhrnBSGzEf0P4jY1vcGL0BMCufd2vltc/uc+r53n7mYwldyXWvPPUD1L3EnAVu+L//Ttp8Hx4Zlb/efdMIS+p5TGz9uNe9Xo9vnTnS4yGR3dVAV3X3ZO6e70Jp4Om9B00AXGoHwxJkoQWFNPZtKjGwsgCtmOjt3XUkOpPzgPAhYAc4PjYcaSA4CiVG2US4QQb1gaHE4fZamxhd2zismASmS1TJFtwSIREDavRaXBq7JSYmAcgwbHRYyzry2SVLHbPJh1Ps6wvowQUbu/cxnEdwUbqGighhZJdIhaIsdkURkfJLhEPxrm1c8tP5yzVlnBcR6SruqZgSMkiEXhz+yZKQMF1XYpWkVqzht7WkWWZ+ZF5ggExXiUWfJDbdnEx2ybJWJLZkVmioSiZeAajY/CQ8hA3tm4wk5ghr+Qp2kX/uCXCCeyeTTwUp9QoYXQM9I4uJvO1TR/s7roivTSbnGVVX2U8ItY5EZngYuUiXaeLElTYbGxybuIcS7UllLCCFtFIx9Nc2byC03WoNWv0ej2KdpF8XEDZ9bbgYG1Y4vmdiIhzMaVOCQ5T9TZKSGE8Os6qvkq9VUcJKL7xh4swJlviulgxVjiSOMLt2m0AktEkiYjga1kdi3QszXJ9GS2oCWZTq0E2msVoGX6SrWSXMHoGx8eP80r1FXIxYWRWmhVOj52m0qyQjYvEbz4uqoiO4whj1CqhhTTKzTJKWMGRHTbb4v+wWDDGkr4kQPOSjN2xcXDIxrPitdER+1IwRFKqZJc4mjiKLMmYbZOYFPMnRQakgG/yDbVXP/uzP8u/+3f/jl/7tV/jzJkzXLlyhS996Us+/PzevXsUi8W3eJVDDTXUUEP9sMh1YXsbSiVRvbOdJr/8//s2z96tEA7I/L9+7NwuQ8rjR0Wjgh+VTA4NqaG+e73hppT3htybcnZs7Bi1Zm0Pp+hS5RJzyTkqjQpFu8ip8VPIsryvUZRX8myYG5ydOMvNnZt7IOP7PfbrnRrnaRdj6n4Ny0sq7WeoTGlTTCem/RpX/4S9wePhOA7XNq/5E9lAGFUTsQkqjQoTsYnXDQD3DK6L5YtcrlwmHohzefPyrrV766m2qr7ZNKlOEg1EOZM+QywY46nDT/kw8H5QvFeH6weFrxvrtLotHMfZxXQ6nzlPo90gE8/4+9VfcfTuf9C+HnTtDKbrDvq7dywuVS4xEZvYlXobhLd/5dWv8J7Me/jGxjd8rpRnnO1Xt9yPT/Za56P/vq81uW+od770tk5WyfpJxGqjihJUsNqWPwVu3VzHccXzfd1cp9aooQU1VvQVTo2f4lb1Fvl4nlAgRE7N8Z7ce3i5+jJzyTkfEi25km9uKCEFu2sLMHUkgeu43Nq+hSKLiW05JUdQDpKP56l36nR7IlHzcuVl6ladF4sv8sjYI1zausTjucdZqi8Rl+Nc2bzCeHicF0svslhb5LBymLvGXb8Sl4gkmFKn2G5t82j2UeyuLRJZiaOUGiUkRGVMlkTySpIl8moex3WwuzZKUEELa8JYCYvUVDKSxGgbaFGN+ZF5UtGUYEIpDxKHnmEzm5oVfKeAOL5eusdxHayehdWxmEnOsFxb5nz6PC23xaOZR3m+8jxH1CNUGhUSkQR5NY8si7okDj7kPRaMYXZNMa1PXxZprZ5Ia7mOy1J9yZ9qeKt6SyTZEM/xTFTUz62uWEMinKDcLJNX8sgBmUREcLhqrRpm22QuMUfFrqDIijD0WmJaXzaepd6us1Jb4cz4GZAFdN2VXfKJPFbPIhG9D7qP59ACGndqd3gs9xjJaJK8lveZYllFTOZLRVOMxEf84+hKLqloioySYTY5i9kyCbgBHhl5BLNtEiDAXGIOs2syqU2iRTQm1UnKjTIXsheotCscTR7lrn4XF5dcLIft2Myl5pBkCSSwe7aorUYTyMNxO6+pX/7lX+bVV1+l1Wrx/PPP8+53v9v/2dNPP81nPvOZA+/7mc985rtKSQ011FBDDTXUQXIc2NwUhpSiQNEy+aU/fpZblTqpaJj/46ffwwdmc/7tPX7UyIjgR8Xjb+Hih3pH6w3/TbF/ytmF7AXioTgXshd2TUlzXZdz6XMs1ZdIx9J7TCRPg7Dxy5uXDzR/4IF54rGiDpoaBw8MkH6G034JF29/bm7f3JN28sycKW1qV5LJ41E5jsPF8kXavTYdt0Oz1+RLd76E4zi+4eYxkwDGImO7ANuDa/ASY1vNLTLxDCdGT3BXv0s6lt615v0A24O8o4JVIBVN7TFiimaRzcamn/pyXZeiWaRoFSnb5V0g84JVIKtkScfTu1NpA8d7cF/3A6kPJrT2Oxf7wdM3G4KLMsgLu1i+KCqYrstWc4unDj/FirHCU4ef8s/74DTC/nXtxycbXO9+Bmj//g6n7f1gymMkJSNJUQsz7ohrUkIYLvfNg6X6EnpbRwkq3K7fFjDogMqVyhV6nR7r5jrTiWnelXsXd627PJp5VCRlQsJ4kiSJervuP9/MroB5e/W6eCiO3tEp2kVGYiPCyJYhGUr6vKA1e42u1CURTvDt8rf58cM/zp/f+3PUgEh0jYXHeLrwNGpA5UjiCH9Z+ksezz1OqVGi1+vxys4raGFhHjV7TVxcZhIzVOwKuZj4JUVCQMtLjZKfwNpsbHJYO8y1rWuCs9TWUcPiMZHEMZRkUVGUZRk1rFJv1lkzxOumJInJfsFgkOMTxwmGgpzLnqPcLBMPxLm5fZMjmmB5yZKMElSEURTLcMe4w6OZR1kxVphNzPqVww1rg3gwzqqxSjwUx+wKELiXpDqqHRUAceV+nbFjcGb8DHbX5o5+h6PaUW5Vb4nJgCFRycwqWZ/xJcsy5ybOsVxfJhfL+YkpCQklpGD2BK/L7Ir00rq5Di6UGiUWRhfIqlmW9WUS4QQ9emIi4vZtlICC3tRRgyrL9WUx8U6+/wGELPnHz+gYwsjkwYcnWkjDaBvYHZtzmXM0nSbTqWlSsRRqTMV2bBLRBFktS7lZZlIR1dNJZRKrZ7GQWmDD3GBWm+WucZfj48dFTdW5X1PtGkyqkwTkADPJGZ9JNtRQQw011FBDvb3lGVKVipiwd3Nzm7//x89S1BtMJeN8+m8+zqnciH/7RkPwo9JpwY8Kh19j40MN9R30hptSr/WGvN9ckWWZnJojHU/vAkW/1jZfy9Tw1D8V7bW0bqzT7DW5WL7oQ8cHa2veY9/cucmTk0/uSg/BA2MC2JXeaffatJyWz72qtqpMq9MsVZc4pB7iYvmib9zIsuxXzg4CpPcfU+9+04lpouEoj6Qf2WUK7cc5Oij1M1gTvFi+SCwc86tsXtJoJPbgBWnQ/AH2rRb2m0eD+7rfee4Hp+fiuX3PxX4JpLJd9lNSXp1uq7klAL52yT83siw4N940yI7T2VWvHDQCz6XP7ZrG2K9+A22/a3cIOP/BliRJTGvT5JQcy7VlHlIfomyVUUIK9WadRDjBurGOElBQgypW12JWm/WnsClBhVKrhOu4rOvrrJvrIkXTqjCbFLfr9XrUmjXKVpl0NE2lWWE+NU/BFjwjNahStsq4PZdOr8O92j1e3n7ZT+k8OfUkm81NjiWO0eq0yCpZzoyf4dr2NQ5ph3AQ6abN1ibnJ4QB3eg2OD9+niuVK6Qjab658U0mohPcrt72zbaYHOPK5hWRpIom/FSX0RZGnNW1SIaTHE0c5YXKCxzRjvj1wcXaolifJKOGxKQ+NaTiIoybgi0YSBuWeH7Lssy0Nu3XJRe3F4kFYlgdi1Pjp7hj3uHsxFmW68sAgoPUs8kpOf94lZtl0vE0t6u3ycfzrNRWCBLkeuU6Tk+YX6lYipnkDCW7RKfb4aXyS8TkGLhQaVTIqTnem38vf77252SiGRzHERXLnjDbsrEsRsdACShc3rrMmYkzLNYXUUOqn5wr2kXUoJjWhwur+irZWJZ6p04ulqNkl9CCmqgOArIr40oiMTqZmETv6Nyu3mYsOsar5qscTRxlWV8WqVUXLlYukollKNgFEpEEtWaNNWMNvaWjRTSy8SyXNy+TiWYw2wJIrgU16s06davO5dJlolKUDXNDTD+0C0yqkxQbolIpBcRU15AcEpNK768zGRKmYl7NMxIb8c3UoYYaaqihhhrq7ateT/CjNjcFD+rpOwX+5z95AaPV4UQ2xe/8jceZSinAA35Utwv5PExMCJbUUEP9VfSGm1KD9aqxyBgXyxf9yXD9ptLrTZS8HlMDHkxFez0gbcCfSuTBrvtra/2G0/nMeXbaO356yNN+Jkk/j8oz0bJKlunENKfGT1Hv1MnEM/vu436Jnf79B/bU2/pTWv1r8gwl13X3Tf3sVxPMxDPoDZ1MPLPLdIoGokQCEU6Nn9pj/kxpU75J5VXlLlUu7VrDfhMFBzWpTvJy9WU+MPUBLm9e3lMh9G4zuK+D58wzmGRZFlO3BrheABE5QjgQ3gXQ965RTx6fbL8aSv/EP8dxWNPXDty3IV/qB1OSJBEIBMireaSAJODjQL1dB4SZkVYEcHtSmWTFWGFGmwEJZElmLjEnqn8tUf0zeyZTyhSrxipHk0dZqi9hdkxmkjNstjaZS85RMAvE5Tgvll7ExWVhVLCsZGRuV2/T6/bEJLSghtW1mBuZY0QZIasJ5lQimvBNMqttcWzsGPm4qH2pIdWvialhlXKzzEPqQ1zfvk42lkVviSl3d4w7jEZGWa2vsl5fx2gafi1ZC4vpfTklR8Np8N7ce0WNLZQgIAVYSC34UwJLjRJzI3MU7IJflXO7Li+sv0DNqlG1qziOQ71Vp96s40ouruSSV/OU7BJm2yQbzfo8KQAZYXZNqpM+iysTzbBSX2EiMsGtnVt0nS6tbktM8JPwX8+QxHP15Z2XOawe5pniM2gRjZyaIxlJslxf5sTICYp2EaNtCKMtopCKplg1VskqWYqNInPJOZZqS8LwuQ+pLzaKzKcEeB5Ai2i+CSa5EmbX5OGxhwVkXHKRJRktoiEjEmBFowguZGIZmr0mp8dPEwwEmU/OY7REPVCRFQpWgXxcTC/0pultWBu4PZcrm1cYi4xhdS0fTr9UWwLA7Jn06LGqrwqGlrlBLpbz/9RbOkVTQPMlWRLgfPlBVdzBQeI+YzCSfAuejUMNNdRQQ72V+uY3v8nHP/5x8nmBVxmsGLuuy6/92q+Ry+WIxWJ85CMfYWlpaddtdnZ2+MQnPkEikSCVSvHJT34S0zTfxL344VG3K+p629uQSLh87uoK/+JLl+k4Dk/OZPg/f/o9jMQjgEhT1Wpiqt7UFKRSQ37UUN8fvamgh344+Ya5sSdB8lq1uoNqW4M/f60pf69liExpU9TaNU6Nn/K/t9nY9A2jfsPpoOTLQaZav4kmSZLPUZpOTHMocchnVQ3qOyVs9jOc9qvKbTY2KZpF7I7NS6WXRJpgIPWzH6S7v97nbcc7VheyF/Y1zPpTQ8C+lcQv3/3yntTT4DY2zA0f9u792V8hHEyk9Xo9rlWuUbEr/u28+uaavgZAWAr72+830foZWoPX6EHrG6wNbre2ySpZOk6HltM68L5DvtQPrhJhAZ+uNCqcGjslJuwFVV7ZeYXTY6e5q98lExfplaPaUa5tX0NCYjY1S8NtMJuaJRlOUrJLojbVNTg7cVakc+I5svEsK8YKR9WjLNYXMZoGBbvwwNCQJBZGFnz+VNkuC7g2Aq6tt3Qfbq0EFW7t3BKvoYiJagW74EPSC3aB8cg4VzavoAZV8vE8ATngpxD1jk5OyZGOpVmqL3Fs5BiVVgVXEhMG9bbOYm2R+RFhvqghYX5Na6IqlowmqbfqGC2DdWOd+dQ8i9VF1IBKvVWnaBeJR+Ks2Ws4ONyu3fYB7HpHBxeRltpZxGpbdJ2uDz3XOzrJSJJDyUOMxEZELS/ywFyb0WZ4vvw8nU4Hq2PhuA5KUKFsl8W+3udNybLMu9Lv4sXyizwy+oioTLbEL8STyiRSQEzTc1wH13Gxmhau63JUPcpybZlMREwnzMfzKGEFs2lyuXKZGW2GxdoiuXgOXPzUlCRJqGGVRDiB0RLmnt4ScHWjbaC3BXMKGWHGNUrklTyJaEJMIpRcjI6BLMnEI3ECkqgQJsIJJtVJlmpLzCXnuLp9lVPjp6g0KqJS6LiiyhiIIyGm8DXaDaJSFKtjCeh8Q0yvLdpFem7PH0yiBEWVcF0XUxl7Tg+9qbNmrOE6Q+N9qKGGGuqHUZZlcfr0aT71qU/t+/Pf/M3f5D/8h//Ab//2b/P888+jKAof/ehHaTab/m0+8YlPcPPmTb761a/yhS98gW9+85v80i/90pu1Cz806nSEIVWtgqI5/NZf3uRTz9wC4G+eeYh/9aPniQQD/m1rNZGkmp4WzKmhhvp+6U01pfrTP3klf6CZ1K/vBI4eNGa8dE7/7fuNEi+1M2gK9K/NdV3avbZfBfPMrOtb132o90H7N2gi9a91cOoc8FeqdfVP1hs0ebyKnmdUjcRGeGXnFUYiIxTtIucz59kwN3alevabCuglp7zveYbaQRMFvYrizZ2bfnJrsJL4/vz7eXr96QOPZT+HbL9E3H7XxJfvfpmsluXm9s3dpqbk+uexH6x+kAHab4YNVjPv1e/xQuEFXiy+uOt496fU+icXHnTOhnypH0x5CZtcPEelUWE+Oc+yvsxscpYVY4V3Zd9FMBAkF89xx7jDTHIGNaL6ppOXaMnFcqyb6/5Us4dHHyYZSVKySqSjaa5vXycdS9OTeqTCKeyezbHRY6SiKVKxFFpE81OB3mQ7Lw3TdboUzSJ6Sxg7XbeLjEzRLpKL5wQXKZZFRqZsl5lLzZGICVC12TPJK3mMrsGUOoXdszmUPMRPzfwUi/oip0ZOiUl4jusneQpmwX99UUKKP7nOdV2W68uMR8dF0uk+2FzvCPNsIjLBza2bzGvzVOwKcTlOLCAmuk2pU5gdE2SwuzbTiWnW9DXxPI9OYLUsAZa/n9hyHZd6q47VtpgfmWfVWOXRzKMs1hfJxDIEggHyWp6MIkwks2MyrU2zMLLAy7WXeU/2PchBGatr4UiOqBdKEiMRUU/zqudmTxhi5UaZ2ZFZKu0KeTVPwS5gtk1KjRInx0/yTPEZFFnhdu02eltnLjXHirFCNp6lZJWE8SSJBJUsyRhdAzWkAmC2TbT/P3v/HSZHnp93gp+I9OEyy6UtoBsoB9MAulDo6e7xFEdjKO2JQ8rsrPbh8/D2kU6G0kqzOul4t6J0EvdI6eEjaY9yS1IUV/dwVxS15IrUkj1D09Nj2sM0GrYMXFW6KqAqM0z6jLg/fojorEIB3cMlG93T8T5PPTNdmRn5C5NZyDff9/ON6lScCgtjC0FSa+gNgymEekwnJsXQ4zpaXAsmIh4dP8pKc4VjY8d4rfoahVSBnJLj+s513KGL2TfR47qYRKhlkWUZNa5SsSvCJO1ZqFGVTWeTgTvA9VxqrRpaXBPJNcTkQ387vjkZKlSoUKE+WvrSl77ET/7kT/LlL3/5gds8z+Of/bN/xn//3//3/Kk/9ac4efIk/+7f/TsqlUqQqLp69SovvPACv/ALv8Czzz7LJz/5SX72Z3+Wf//v/z2Vyv4c4VDfvXo9YUg1GpBQB/zdF87ya2/fRgL+208d4298+jgRWXxOarfBtkVVr1gM+VGh/vD1vo/EGYWVj9akHpYgGZ2455tWox/s/f8PPMA08jVar5pKTj1gLvkw8g1rI2AJeZJHTskFpk9n2KE/7O9iNb2XGtboWh81de4PUusanay3t9o2WtErKAVWtlc4NXGKRDQRrKXrdukNe8E6HmaYlO0y4/Fx3t56m83WZnD76JpHOV+jqaa9613KLXG1cZXPTn/2oYD6/c75KDB+P5j48YnjbDQ3ODZ+LHiMX99cyi0Fa9tqb+2amrh3P/czNEcNz9agRX/YD0y10QSeD9T3U1cPO2chX+p7V3bPxvVEfcnpO8wYYhLckbEjAAEvKafkMOJGMKXOn1ZW0ApU2hUkJKyBmFDnm0rmwESSJJ7Un+Rm8yZG1KCklzhoHBQQ9G4TzxN1r3QyzezYLLFojAP6AYy4wWZnU0Cy+yIxlYqmBPxf8pgbmwug3rIkk9fylNQSrudSMStst7aFSWaL9JfVE8ZUOpmm1q7x8cLH2exsvsOGimtE5AiSJNhDyzvLmG2TwXDA7Z3bvF57nZPjJ7m8fZnFqcVgHyUEmHuzs8mJqRNE41Hmx+ZxBg41p4YiK5SdMmpEfD2XTWTZ6e1w0DjITHqGt7ffZsgwYFGZPTPgZaUiKc5tnuPU+CluWjc5kz1DJBLh9NRpcGGztSnYUfdVbVf5RPETrJlrWF1hyPiGkBpVRTXTI5hKl1Ny6DFdmDVWDUVSuH7vOnNpAXs/bBzmYv0ig+GAil0hn8ojIRJts/qsMAQVwaMy4qLiqEbFNEWzZwbvd81+Ez0quFS5VA6za+L0ROJLi2nCxIprggVlC+ZYs9Ok2W2SS+Z4qfwSR8eOYvdt3r73NpPJSZyhSLEhgT2wUSNiuiMIOLokSeCB3beZSk2x3d1Gj+tiwiISrX4LPMT0xHgGWRI1zRByHipUqFChRnXz5k1qtRqf+9zngt+l02meffZZXnnlFQBeeeUVMpkMZ86cCe7zuc99DlmWee211x667W63i2mau35C7a9uF6pVMTnPjXf56//7q3z75ibxiMxP/sBp/tzioeC+liVSUsWigJqH/KhQfxR630yp/SpPozWpR1XfSlqJc5vngnTK6Af7vSwl///7H/pHa10AfbfPWGosYB75E/G6g25Qu5rWp0nIojtbtatk4hlWdlaYUqeCdb3XGtboWkeNm1FzZO+kue9GfnVsb7VttKJ3fus8Hy98nLvdu7tqaqMspb1r9c+ZPzXwGxvf4Pn882y2N3cllnxjEXgg1bRfzRJ4gJW1t1rpb8ufsugn4nwTzU9QwTtMrYPpgyzmFtnqbD1wnYyuqd6qP/S87TU4R5N4/pqUqEIsEuOz05+lbJffNYEX6qMlPa7j9B3UuIqHRzQSFemiviVYSHhBeqjSqnBAP0A6mcbu28ylhTGUT+ZFVStqYCSMwJiaT89j923awzaH04exhzZWzwLA7Il0StkpC66VJBGRI8FrzB7YPD35NNv9bfJKHjWmiil0cZ18SiR08ESaqdISphgyRCNRlITCZmdT/LWQBV/NxX0nBXPfrMAV7wnNdpOVnRUBwFaLXLh7gdnMLLVODQmJi/cu8qT2JK/UX+HTpU9Ta9XE0AXv/hQ+JFELu38MrL7YRzWmsmqtUlALVFtVASVXMpzOnWYsNUatXePkxEkiUkTUzpRiULkbDAb83vrv8YT2BG9tv8X82DyRWEQknSIyjutwKHOINWsNLaaxYW9QSBW4sHWBbCqLFtOw+za1Vo2FsQWu71zH7JrsdHaotCosZBaISBHsnk06kUZP6BCBVCwl9j89SzQaRY7IZJUs9kCcx2ljmoXxhaDqafXeqe3pcTHBMKtkqbfrSAheE55glekxndagJQyo+8ypilPBc0WN0Z9qaHZN1m3xPr7SXKGklLiyfQUQ3MQ79h3mM/OCqXW/Tnqvf4/F7CIROQISYl+7guu11dni5MRJ7IEt2FGyxNzYHJIkUW1VhVGaPkgmlQnN91ChQoUKtUu1mhg6lMvt5unmcrngtlqtRjab3XV7NBplfHw8uM9++qmf+inS6XTwc+DA/niUj7o6HWFI2TaY2Pyl//gdrtabpJMxfvaHnuX7ZsU0ZdcVtb5oVPCjxsZCflSoPzq9b6aUP93OTyQBQYLl3apsewHW+2k/JhLA129/ncWpRX7zxm8yFh9ju7MdGE6jdbOd7s6u2lW9VQ9MkCvbV5hMTpKMJClpJTasjX3TOu+mh7GlRvdvbzro3dJTkiRRVIucrZ8N0jqTyUmqrWoA5vYnxy1OLe5iT42ylHz5NbU3qm+wbq7TG/boeT2emnyKNWttF4dq1Fgc5VqNnje/WvlG9Q1uNW+xbq4/svo3auyMJqY8zyMuxYNpT/6x6Q66bFgbj0yNja7nUdfRfrD4UfbUwfRBnik8Q0ErBObVwxJ4IdD8o6l0Mo2RMIQxEdcBYRi5rjBxJKTAHEhJKa5uX8XsihRMrV2joBa4Yd1gYXwBe2gH5i4QpE8GwwEX6hew2haNdoOd9g6eK7hRRaVIxakIjpAuKlcb9gYFtUBr2OLpyadpDVoUlALtfpuYHOPyvcukIimGDDm/dZ7Z9Cxmz8Tu2Oy0d5Bdmdn0LBEigdEjI1hFvhnmSZ5Iew1aWEOLbCpL2SlTsSvMGrMCtH0fRP5c7jkub1/mxMQJYWbdl9W3kGQp4CaNJcfw8HD6DkpEISJHWJxY5GztLMPBEHfoCpPGBSQoKaVg2p4kSdh9Gz2uY/Utblg3ODVxihduv8Ah9RBW3yIdS4sUUbeJKgum1Iwxw/Wd6xSUAtV2lUP6IW42bwY8K39iJx6k5BSX710mn8pTtsuYAxMtpomKn+vh9BwiRHh68mlWzVX0qM7Tk0/THraZH5unqBUxEgYVpyISUgPBgwJodBtc3b7KXHqO71S+w2HjMGWnjBETJp2MjJEQ0w4jcgQtfn+yX1zH80Rd0e6K/W92m4FhWFTENMZD6UOU9BJ3u3f59PSnabmtICXcGrb4TOkzRCNR9LguwPP3zb2qI9JZ9U6dhbEFKq0KWlQYdh4eWlR74G9wqFChQoUK9X7ox3/8x2k2m8HP+vr6417SB07tNpTL0GrB7dY2/7dffZmK2aaUVvi5P/sJThTGAQE/9/lR09OgaY933aG+9/X+MqW83UkV30h4tyrb3jTQw7Q3veR5HkfHjvKrK78q0gCtGgWtwAHjQGA8jE7EG50SeHz8OI1Og3gkzmRqkonUBJIk7Urv7JXPcXJdN3h+v3K2F9TuT/nzjQ9///amg/buj58oGt3u2fpZUtEUZ+tndyXO/PUAnMmfodqqvmuap2yXWbfWcXqiLuMzkg4YBwKTa5RX5aeegAe2PZo86rk9BsMBNaf2AHB+NEH2sPrgZHIySGmNXi+e5O16vkddJw+7fe/1tl8Sb9RAm0q9k5jzr52+26fvvlPv3A9CH+p7X6PXiyzJSAijwDekAIyEQSFV4KZ9k7nMHGbPpGwL0HTVrjIeH+dc7Rzu0GU4HHJ1+yopOcX5rfM0Og02O5soMYV6p47ZEwymsl3G7JrY/fvcp54AiLueS0EpsNxYRo/pyBGZol6k3q0znhjn99d/n3wqz43mDZyuw2RikvagjYzMgAGyJONJHi23hRbTkGRhkpk9k2anGVS70jFhxh1OH2ZanRaAb7UoanyR+8DtpEFBKbDV2+LpqaeJRqJ4eCIdJQlouF/3ysQzQepsUpmk63U5OnGUVWuVnJJDiohUjizJVFoVirpgXUmSFJiAjU6D69vXyaVyZJMCyP4DT/4Am71NcY5kKZimKUWEud8etskr+aDKeNO6yUHjIBEpgtWzcPoORsxgYWKB285tns0/G0yty6fyrDXXcIcutXaNhfEFjKQRfCFQbVfJpDIi5Xl/up/ZMymppYDlZXZNNpobVCwxWbHm1JgxZlhrrpFPCeMqHU+jxYT540PMnYFDUSuSSWRwhg4FrYAruSJl5ifZPJg2pgPjHUCNqFSsClpEo2JVUGPv1AU9T7DBml2R8NPjerBuPFjeXmYyPslbd99CkRSqTpUNcyNIuYUKFSpUqFB7lc/nAajX67t+X6/Xg9vy+Tybm5u7bh8MBmxvbwf32U+JRALDMHb9hHpHrRZUKqK6d3azwt/431/D6vY5nsvwP/2Zj3MgI9AInQ6YJkxOQqEAicRjXnioj4TeN1NqWp8mEU0E6SjfeCiqRS7du7SL8bQfcLugFHjh5gsMh8OHPsfeCuCGtUHFqXBAO8C4Mh5wOfykVkkrBVyjyeRkwLfyzYsz+TM8U3iGg8bBgMW0t+I1ajqMcpz8/egNew9MY/ONjVGjw/+Wem86yDeuRo/Luc1zu7abU3I0uo1gUqCvs/WzKBElqNf5YPSHMZX8+8TkGLFIjLyaD9JUruvy2zd+m+FwyLnNc8zoM3zt1teCc+ubfAWlsMuY87d5UD+IGlfJq/kHJvDtV7Mb3V+AS/cuBd/Yj6aYGp3GQ+uHe/Ww278bA2n0/PvncFqfZqe7QzaVfeB+nufRGXaCay7U9758M1NP6AEnye7bFDSR4Ekn06hRFVy4dvcaakTF8+4ngmIKdzt3cT2XoTfk7OZZ8qk8F+9eJCknsfs2uVSOdr9NNplF8sS2lagigNfukOvb1xl6Q2EYIYkpePfrWYBIFrlws3mTo+NHuWHeIJfK0eg1qDt1PDyKWpGoJEyjVr8luFDNZTF1rVVDjQnjoeyUSSfSweS4zrDD0amjHB0/SrVVBVdUGlVZ5WztLMv3lknJKWHYSRLphDCzQJgdzU4zSHKmE2kKWoHusMtsRqS3soksq9urWF2LQqqAFtfIJ/Ocq59Djagk5SSvV1+nP+iLY6XkqLUFPPypiacEVD5VoNFuYHZFsklCrF3ypOB86XEdWZZZyi6x3d1mJjODLMuU9JLgLvVtDmuHWTOFWVRUi7y19RaTCWGez6TFhL10Is2RsSOsbK/gDl3KVllUJ/sOzW4zMBTnM/NcvHuRVCyFPRDpsVq7JvhkskQ+lQ+O59AdBmB6n5tV0kQqzuybzKfnqbVq6BE9OJZKXMFIiJSVkTCQZMEsQ4Jmp8m5rXMMh0PsgY2LmPZXtsus2+sBp0ySxXTHeqdOXs3j4nJ55zLHJo7x2uZrzGRmcIYOzsAJWVKhQoUKFWpfHTp0iHw+z+/93u8FvzNNk9dee43nn38egOeff55Go8HZs2eD+/z+7/8+ruvy7LPPvu9r/l6QbQtDqtfz+K0ba/zdF87TG7p8+nCOn/2h5xhXEsH9ul1hRuVyoroXKtT7offNlHqU8TCWGNs3YTOamPFreF+//fVd2/XTSHead3aByn1NqVOU9BJ6XOeLh764bxIJ2MW32puUGa257a14eZ4XVMj21uSKanHfaWx74eejhgi8w1PyTQ9/f/zHLU4tst3ZJkYsAHo/YTzBAePALs5TNpVlpbESmFXvJZkmSRLPFJ7hibTY3n7Hf3Fqkd+6/Vt8uvjpBybQnd86Hxhzo+aiLMucyZ/hgHGAy9uX+UzpM0Fdcb802ej+TuvTAjrvbO2a1nhu8xzHxo5xbvPcA0m0/cylveyq0STeXpbUXo0ysfxz6RuGkiRxJn+GZCwZJBB2XUNeWMD+qEmP6UGSRZIl5jPznNs8hxbVSCfT3LJvcSJ7AmfosNJcodkWIOqoFCWn5ATs29lkKjmF1beC6pgRFwykw+nDEAEjaXB04ijtYVsYMC0By5YkSaRkJIIpbelEmkwygz20mRufQ5ZlEtEEZ3JncFwH2ZMD9pXVtyjppeC19831b3JYPyzYVpl5KnYFq2OhRBSanSaNbkPwsBSR9nmz/ibZVJZKq0Kz0+T8vfMoUQVXFkyigHfUM2l2m0hINHtNyk6ZZqfJhr1Bo90gIkcoakXGUmPgwZq1RlbNUtSLOK4jEkwDi5ySo2yV+f313+fk5EnO3z1PTsnRGrSY1qYDkLwW07CHtoCmxxRRdUwYZJIZJFkS0/Lu1/M8z0OWZRbGFgI2WNkqMxwMxcQ6XApqAXtgs9xc5nD6MJIsgOCbrU0WMgvBe649tMkqotJYNasMBgNWd1YZDoeB0f+k9iR1px4Y+gWlEJh9raGo1yELQ0yNqSzvLIsvczwoW2U27A1yqRznts5RVIs4QydIN43FxaRA33CTkNCjupgK2KrxhP5EwBIzYgae5zF0h0iuAOgDTGvi73chVaDm1CgqRQ4bh7nbusuzuWeDfQ4HOYQKFSrUR1u2bXPhwgUuXLgACLj5hQsXuHPnDpIk8Tf+xt/gJ3/yJ/mN3/gN3n77bX7kR36EYrHID/7gDwJw9OhRvvjFL/IX/sJf4PXXX+c73/kOP/ZjP8Z/+V/+lxSLD5+CHmp/WdZ9Q6rv8W/OX+aff/saAH/61JP8Dz+wRDIWwfNEXU+WRV1vYiLkR4V6f/W+T9/bT6MVLHjnAz28A7L+/BOf5/zWeT7/xOd33ddPI63b6w8kkqb1aVLRFM8UnuGZwjMBn2W0NrfV3gIIpsa9V0bUqOngr983XvyaXMWp7MttGuVf+WwqYF+e0igM3L//+a3zPDXxFFudLaZSUwH42+dLvbj+IulYmnqrzvGJ49Rb9QcMmL3TCfcm0/zj7xtGf/zgH+f81vngf78882WuNq4+cLxOZ0+z0lwhm8o+sG8b1gZlu8zi1CJXdq5wOnuailPZN0229xhLkkRWy7LZ3tzFAnup/NIDTKuHQehHk2ajkx/3Y0nt1eg2R5Nu7/bhy08I+scz1EdDVt8SJoZdxnM9rm1fI5vKsmEL/tmnip/ilnWLfDKPElGotWvocT1gBBXVIkjC3Npsb3Jq8hS37dsU1AJG3MCTBNcJxOvk6MRR2m6bucycMKDiaQFRT6SxezZ6VKfRadDsNAOA96dKn6JklIhFYkGSSo/pgenh19vsgU02lWXVXEWLaFxvXEeP6aTiKeqtOo1OI4CJrzRWMLsmM+kZLt69SD6VZ7mxzBPqE+J9N1ViLjNHOpEOeFT+87X6gnVVdaq4Q5frO9cDVpL/OsulcsieTL1VD9YckSIMvSF3O3dZmlrirbtv8dnSZ2m77Xe+pPBAiSpU7IqoRbpDnJ6DFhWQBHco4OLrzXXwYOgNg2qkDxf3jZ+VxgqHjcNsdbYE6BuJufQcTt+hPRDmoBpXsfs2rutydfsqJ8dO8tLGS+RSOVzJpeW2UGMqtXYNZ+CQV/K0hq3ArPOnF07r08iSTC6ZY3lnGVVWwRNA8lxKGHEb1gZDd0hBKXB+6zzZZFa8F7oeZascXFdjKWFMWT0rOO5aXOOwfpgr21dIkaJsl4N0lhEXbC8ZmaE3DN5P9YTOwB1w8d5FiloRPaFjD22MuBHCzUOFChUqFG+++SaLi4ssLi4C8NWvfpXFxUV+4id+AoC//bf/Nn/tr/01/uJf/Is888wz2LbNCy+8QDKZDLbxy7/8yxw5coTv//7v5wd+4Af45Cc/yc/93M89lv35MKvZvF/ZGw75/3zzLP/x4m0A/tonj/I3P32MiCwxGAiguaoKQ0rXH/OiQ30k9dhMKT95UtJKJCPvJExGUy6jRkAkEuFLh79EZM8cypJWIh6Jc0A7sCuR5D/e/1Dis5cmk5PBh5zR5NCosbM3UbM3WTOqaX16FwB9lJE0am7sTe/4++Y/b0kr7aox7gW3+1Dvs/WzAax7PwZTxakwOzYbTFfa6e5wfPw4b9beZN1cD47ZKIx2tDK4d40+7L3WrvHFQ1/kwt0LHBs7xpWdKyzllgB2PUaWZQpagaySpeJUdqWKACYSE3z99teDhJOfJotL8V21wtHj5bqu4FtJ8aD+6ZuIX3jyCw8wrfarhI7u5+LUYmDcjdbq9jNDfRXVIm/ffRvXdfeF3D/MCHu3SmGo700ZcQGwVmOqqM25sNpcxR24IhkkiVSOkTRIJ9MUlAKyLKPHdMyuSdWq0h/2uXj3IqcmT/FK/RVOTJ6g3q6jxlS6bpe8mkdP6AG/R4/p1Fo1jowfwfM8rt67SqPdwMPD9VyWd5bFtDi7QlbJUnNqqFEVq2cFhrYUEWZHzaoJPlBEfYeFFTWotCpkU1mWd5bZcrbIKTmcnkOj1+C2dZuZ9AweHvV2ncXJRZyhw2x6lq3OFjOZGYyk4Ec12g1hkrUFl0qWZI5MHCEiRwJjLCWnuHb3Go1Og+FwiNk3SSfSpJW0MP+dKmbXJK/kudu+yyeKn6BHj88/8Xli0VhwLF3cgPElSzJm36SoFbEHAs4NsNJcYW58jlv2LbJKlrXGGp7rMRgOqFgV1s11yk6ZqcQUZs9krbHGjD4jAO6q4BXqMVHXtHoWEuLvyLWdaxzWD/Ofb/9nPlX8FDfMG+hRHS2qocU0ikpRHPf7kPKiWgwMR3/dkiRRa9VIRBPcsG5Q1IqYfVN82YCHGhOQdmfgsJBZQI6I/dQSwtDzp+q5rsuGvYEeE/B3F5HIutu/y4mJE1zYucBEYoK37r5FQRHA/YJSAAkxUTImDLFqq0pn0OH4xHHObp6lYlWoWBUG7iBkSYUKFSpUKD772c/umjDu//zSL/0SIP5t/A/+wT+gVqvR6XT43d/9Xebn53dtY3x8nP/lf/lfsCyLZrPJL/7iL6KFtO3vSo2GmLJn9rr8nRde5ds368QjMj/5pdN85fRhJEmi2xX8qIkJKJVgxBcMFep91WMzpfaaMqN1vvH4OC/cfIGCUnjXCXfvxiMaBU+PTl7za3+u6+7iNo0+1p8Gt3cq3H7P79cC1811ztbPBh80Rrc5ms7Za1xVnApPTTwVMK722wdP8sgpuQDWvXefQZgnqzurTCQnGEuNkVfzXN6+TG/QozPs7AtQ37sPo2mthJwIJt5tWBscHz8eGFKSJO2agOdr1OTyjUBfl+5d4jOlzwQJJ//8+wko/7lHt3u2fhYlJqC/vtEly3LABCtppV0pp/2O5eh+VltV5sfm2eps7Vv1248xVXEqjKXG6A17nNs890BN9L3wukJ9dCRJEtPaNBFJmOiyLHNIP8Sl7UuoUZXrO9dJRVJU7SpI73DZrjeuC5OhUyciRUhEErx19y1OjJ+g1RfpGmfgsJhdxBk4om51vwZnJAyRgOnZLDeWSUaSlO0yVs+iYlXEBMCOSaVVEYZN36RqV+m5PWpODRcXNaJy07qJi4sSU1huLFPSSrTdNlklix7Vubh1kUP6IQbegNXtVRq9BlpEYyI1Qd2pI0kSR8aPEIlGOKAfIBKJYMTFpLhmv4nVs7i6cxWzYwoAe0LUyayeRbPXJK/msfs2rUGLRreB67q8UX2DndYOy9vL5FN5UR/sWeTVPCvNFWYzs4IXpRQCJpORMGj2miIpdt/oyipZIkRwhg4LYwuC7dRrMpueZbWxyifyn+CmeZOp1BSu57JmrpGKpkSqKKJx27rN6exp9IROvVMnp4gEk9WzMHsmU6kp7nbvBrW5+cw8r9Re4b849F/wRv0NJhIT1Ft1AXVPGhgJA3toU1ALOH0HD490PI3rudhdm+3WNmbXZOAOiHgRUnJKVA5jBlkli92zcXoOOSWHERcGpxETPCfJE19syJKMFtN4s/6mSFfZZfBAi2jUW8I83Oxs8qWDX+LazjVOTpzk3OY5PlH4BPVOPfgbJSML3phaJJvKcsu6RTaRZae7w5azhd2zQ5ZUqFChQoUK9ZjleSL5VKlAzbH5b3/zZa7UGxjJGP/fLz/LH5srAOA4YhpfPh/yo0I9fj02U2q/NJH/+5fKL/Fc7jnOb51/aHJlVPulVPZuf+/kNT8F1Hf7D9SwRhM1l+5dYnFqcV/DYTRR5dcC6636Lti5f5+iWtzFrdrv8ZutzV3PMWruAMEUvEclbypOhe878H3EI/EAAp5X82S1LI1OY1+A+n7HzHXdYJqfLMvBxDn/GI6ek4fVLytOJTACQYDBc0qOKztX+PwTn+de915gXB0bO8bbd9+malffmV54f7s+yB14wGgcrQXuhcU/zMwsaSVRqdOmqbfqAXB973EYfT7foPOTZ/slogB6w14INQ8FEEDNM8kM82PznNs6x/cf+H6WG8vklTybrU2UuCJA4bKYeJZP5XF6DjPGTHAtzxgzbHbEFJppYxo9qnN+6zzzmflg2pwaUbm2fS1I6+RSuaC2q0ZFlUyJKNxqCiPBZwI1+01kT6RyIlIEZDhsHMYZONhdGzWqUnEqLGYXuWndxJM9np56mjutO+gxYTJpMY079h2OjB9BT+jocZ2KLYyvRkcAxX0+UkkRQG49omMNLKYSU5ytn8V1XVxctKjGmrnGbHoWV3KRkak6VayexWZrk77b5+yWMPazySzfLn+bbCKL03dwPZdKq0JSTnJ957qo4t0Hg/tTCeWIgJUf0A+I2mBUxe7ZyJLM0fGjwmhK5ZAl8byH9EPcNm+TV/IYKYOl3BLO0BFwelfA6bWYJiYCIrHV3uKwcRg8wBPvx58sfJIb1g1OTJyg5/VIxBLYAxsJiUqrQj6V51uVb5GMJpElGWTBv0pEErxae5Wp5BRdt4ssi8mJWkzD7tu0B20WxheCJJNfRbaHNgsTC9hDW0w9jBtcb1zn5MRJvl35tqgvekPqnTqL2UVWzBXm0/P06HFq6hSRSIS5zBxr5hpHxo5gD2xKunhvtfs2g+GAW/YtTk2c4vL2ZfSYTjKWFFyuMBEaKlSoUKFCPTZ5Hmxvi4TUamObv/6bL1MxWxQNhZ/7Mx/nZHE84EeBqOtNTgqWVKhQj1OP7RJ8WK1JkiS+8OQXWLPWOJ09DTy8GuVrPxNi7/Z3MaDuGz9xKU48Et9V+RuFjVdbVZ6aeIpqq7ovIHw0UeUzhpZyS6yaqw+sveJUWMotBdyq0cl8/uM325u7nmPU3HmvDCP/WEiSFKSF/PSUz3B6L9vyJwn6ya6t9laQ5vBNPb8KOVq/3LsW38Tyk0ySJHF8/Djnt84HqSY/feV5Hnbf5s3am0Etclqf5oBxgIP6QfJqPjDT/HPo1wL962NvbXM/+fB6WZZ5auKpB4Dr/vkfTV/5jzmTPxOcw70pK8/zuHT3UpiUCvWAap0af2rmT3Fp5xLz6XkxaS+qkIlnAqi253pYAwsjaXAseyxIgNZbdWYzs1hdi2v3rlG2y5yaPMX5rfMM3SGe53F95zp5RSSIzK4p0leaeO3YAzGFbr21zrQ6zdvbb+MO3aDu1fbaLIwJcyMdTxONRFmcWuSOfQclqqBFNWqtGrPGLBISzsDhM6XPIEcE9NoZOjxffJ56p44W1Wh0GlTsCo1OQ6Sfuk3KrTKZRIZINMKRiSOkU2lKaomtzhaHjEPYfRvJk6i2qpzOnqY1bJGOp8lredKJNLOZWTzJI0IERVIo22VumDf4RPETrFlruJ7gPqkRlVdrr3LYOCzqebJHa9BCiSlYfQtvKDhL245IIJk9MYHP6oukk2/gea6H1bdw+g4np04SjYivDz1PTOeTZImSUSKdTAtuV98ODDk9oVNpVXA9McWu2qqyMLaAi4vds0nH0hRUYdLNZ+Y5v3Wek+MnafVbWD0LLaKhRBSu3LvCmakz/O6d3+Vw+jB6XKc1bCFLMnk1Lyrpm2dJRVKA4JgBFFIFztbOoshKwBHLp/K8de8tTk2cwhyY2D0xxfH85nlyqRxm3xQcNK2I3Rdm1tHxo4GpZfWsYCrjpe1LPF94nt9Z/x2ezT9Lx+1wdPyomEwYKlSoUKFChXos8jy4exdqNXi9WuVv/R+vYXb6HM2l+bk/+3EOjmkMhyJFpSjCkDLCgHOoD4geqy/6sClpkiSRV/O7ps49qhr13XJ7NqwNesNeYDQ8rPI3anbtZ3wVlAIvrr/I05NPB7f5sHP5vuU8+rjRdfosrIScCNhQ+zGi9m7j3eSbTb7ZUtJKnNs8x4H0gaB29l5g3v4kwZySC0wsz/N4s/ZmwFW6dO9SUJ3b79iPcrHgHaNnNDG2N8E2ajSNPlaSJLJKNnieUT7YqHk0ug8Pm7Y3elz95644lYCftdcUHOWM+fsxasz5z3Xp7iVm07PUW/V3PU+hPjpyXRd36LKys8JSdolqu4oSV5AlGXtoC8B4XBgZWkTD7Ji8UX2DVDSFHtdRogrfrnybVDRFMpKk3qpzvn6ew8ZhUbvz3KCe5uGJ6WqyhJG4X+eKG0QiEZ7LPceKucIT2hPYA5uhO2S1ucqsMctyY5miViSdSKMndN669xZncme4ad1EkkUV0YdZGwmxveOTxzmQPkBJLQXgcWfoiCpuapKqLRJOnueJiW54GHEDu28HhttkcpK3776NElWQZFH7q7VrFNQCelzHGTgcGT/CmDLGx/Ifo6gXabktsqksakxltbHKbHo2SBhJssSJiRPUW8IgS8fS5FN5Vhti0t0N8wbJWJK37r1F3+0Hr1U9poMnJtsVtSL1Tp2Z9Ay1do2oHEVP6ALOPjBJykleq75Gv9+n0WkECSUjYZBL5bh49yLz6fl3JiEiUXEqtIdtTmZP0vbajCvjTOvT1No1TmdP0x62xaS/8QVqnRp6UueQcYhbzi2Ojx2nZtcoW2XUiCrMqUELT/KYTYvaotW3UKMqjXaDs1tnmUnPUG1XATD7pth2ZgFn6FBSSyBBNBJlKbdErVXD6lqoETWoQ/vvs57niWpo3MCIG5TtMifGTnDx7kWeyz5Hx+twJneGg5mDwd+8UKFChQoVKtT7K9eFzU2o1Tz+j9Ub/L9/5xy9ocsnD+X45z/0HONKgl5PgM/HxwU/KpV63KsOFeodPdZ/Rb7blDSf6QPsm1T6PyNPEgbXG9U3uNO8s4vz5AO/4R0DYj/j6/zWeZ7PP8/5rfMPfZ69j/NZVhvWRpAC8tlQo4yoUUPlYcmfUVNv9P/vNXpySg6zbYqKySMMvFGYtyRJnMmfYVqfDqDhfnrq3Oa5gNtUtsvfVbXST5P51b3RfTuTP0N32OX7pr/vAfbVqJm2lw+23zTDUYPKn7bnw979te41CRNyYldybvQ4n62f3VXz84+5X7lcN9eZz8xz27od1BtDhQIoO2X0hI4W16i0KhSVIq1+K+Dz4MH1nevMZ+ZZM9dQEgp5NU/dqQteUdfikH6IVr/F1e2rHJ84DhLUnToDd4DTc5g2pkkn0pS0EnbfxogKE8h1Xay+JbhQXpsvz32ZLl2KajGor1VaFQpKgeXGsqgS43FIPcSb9Td5Un0SPLhj3glMCz2m0+w2AWECpZNpClohuC2XytEZdNDjOkcmjjCWGgsSRZIkkU6kaXabNDtNbpo3ecJ4QpgsnofdswWLS44I0DdSYIpYAwsjbjA3Nse97j2OTByhqBWJRqLocZ0D+gHwwBmIqXqSJGH2zSApVmvVGIuPcWvnFgkpQavb4nBaVBUlSXpn0h8S+ZQ4/gVFmGMSEsPhELtj8+LGi3zpyS/xwvoLaDGNZr8pYPAerFlrfLzwcVbMFRbGFoLzXEgVUGMqW84W8+l5mp0mza6YhLjcWEaLaSyML1A2yzTbTapmFQ+PGX2Gze4mZt9EiSlUWpWAp4cnroGiWmR+bJ6KU+F64zqHtEOsNdcoKeJ9rKgUKVtlmp0mWvT+VD9tGo93jrce17EHYkpj2SmjxTTMnkmz22QwGPB69XUa7QYSEvbARokpRCIRAZ5Pph/jqytUqFChQoX6aGs4vG9I1T3+7cUr/MtXruIBP3zyCX7qTyyRikVptQRDKpcTDKlY7HGvOlSo3XqsptTDUjt+CsfzvAAOvt/Esz+o/GpYvVVHjaps2IJJ5E+g+tqtrzEeH3/AANub7PLTRNlUls6ws4sl9DDDaLS29yiDbS9sfb/7jpp3b1Tf4HbjdlA/G9UB4wAH0wc5YBzYdz98U+Vs/SxjiTF63jtpoVFouL+/p7Ong3Pned4DoPNRPapaWbbLu46bLMt88dAX2e5tP5BO22sg7U1X+abRKDjfv44Wpxa5vH2ZsfgY69b6vmv1U3OjyTlfG9YG6ViaF9dfDPheZbu8q3JZb9XxZI8pZSrkqoTapZJawukLKHlRLWL1LZHMAayuBRIsjC1Qa9dYyi4Rk2JiGltCZyYteFJO38FIGDxfeJ5b1i0KSgEXFyNhiGlt92tWlVaFglqgYldYt9a5du8aeSXPtZ1raFGN9rDNx/If40BaGDg+38ke2CyMLWANxOS4NWuNoxNHuWndpNlrYnUtylYZu2ezYW2gx/XALAKISBH0qE6z10SWhGliJETta3QCarPbDGq6WlywmLrDLlpMCwx4z/MwEgatfgslqlCxKly5d4WUnKJsl3EGDkvZJZyBgxEX5puE+OJAlmXyWp56p87AG2B3bcyuGQDhx1PjKAmF+Yl57KFN3a4zHA4ZDodcu3cN1xXTEfWEqMqV9BIb9gYeHrV2jbye55nsM7xw+wX+ePGPc2HrArigRBWqrSqLk4t8p/IdcsmcgJ/3TZSowtnNsxwwDlDUi1TbVYbeENd1+VblW8ymZ7EHNnbfptapkdNy2EObdDLN29tvc9g4jORJtHotrK6FFhV1QyNhoCZUjIQhjkXMYCoxxdvbb3Mmd4YxZQwjbnBu6xypaApzYOIMBEw9MJ66Jo1uQ2w3pmH1RGLq+vZ19Jgw41aaKxxKH+LC3QsM3SFVp4oaUSk7ZYy4Eb7fhQoVKlSoUI9JviG1URvyM6+c5dfevgXAj33yKF/9zHFkSaLZFEmqkB8V6oOsx3pZPixF9Eb1DbqDLkBQ9fLNBngQev6wGuDe2/0aFwhjaiknPthMa9OBaXJu8xzP5Z7jpfJLDxhgeyfNjdYMJW/3P8xHUz57Ez8xOcZOeyeAjj+sWvawSp//mIJSCMy7vtunS5d6q/7Q6XqPmkzYG/bIJDPsdHdIyIkgLTZqBo5WE3dV2/aAzkf3aW8F7k7zDm9U3whg5nuP2+j997s+/GNf0kpsWBtB6mw0OeXf99zmOY6PH6faqrKUW2Knt0M2lX3X62W/6+fKzhVmx2aDiX57Yfins6dpdptBeixUKF+yLAeGp9WzsHpWAO63+lbAJCqppYBNdEA/gBE3qDt1DumHBMjbdQV0Wi0xnZ4WjKb7qRcAs2dSVIssby9TaVVQIgqu53J+8zz5VB6AZrfJurlO2SqjxTW0uIYzFO+B/hrMnokqq9TsGu7QxWybNDtNrm1f4wn9icCY8lwvYCx5nhfwrAbugOs71wPO04Z9/73XFmZWs9tEj+nIkkiG+kadPyHQf43r8XcqiWpE5cLWBWFquaJSBgRcKNdzMXsmJU0YgHPpOTr9DqlYCi2miRrgxBEkWXCcbjRuoEZVrIGFFtdYtVbR4zouLhWrwvmt8xzSDlFtVYPqYVEp4vQcopEoH8t9jLu9u+RSwnyqtWrkUjlWGitkU1nsvo3ZF4D31eYqh/RDfHPjmwz6A3EeuzZVp8rxseN8u/xttKiGFtXIJ/PUrBqaLGqcB9WD3DBvUNSK6CmdubE5MaExorK8s4wW0ZBkiQP6AdLJNC23xanJU1xvXBcmnyP4YzebN5FdGTWqBubocmOZoTcU15bkEpEjeJIwDJWYgtk18fCYT89zo3mDJ/UnkSWZolbEGToU1WJwHkKFChUqVKhQ768GA8GPulHu8hMvvsq3btaJR2T+4RcX+a9OH8bzJHZ2IJkUhlQ6DeH3SKE+qPpAeaUb1gZ3rDtU7Sodt8Olu+LDvg+WhgcNlf2qVaMavX1v6kiWZZ4pPMPB9MHANMmmsqw0Vzg2fiwAX+8ywEYMmFHYdyKaCEyzUQj3fokfWZY5MXmCilN5aIXRN2RGK317n/fc5jkyyQwAB/QD6FGdpdzSrhre3qQW7D+ZMB6Jk4wkd00n9AHt+9X9/G36tTf/dwDr5jp3mnd2pbb887BurpOKpzi3eY5pfXrXcXuURs/jhrXB2fpZ2oN2cH5Gk1P+Mdpb8TuTP0MylkSSpEeC8/dKkiSOTRwL+DG+2VbSSpzfOr/L+Bq9VkOFGpVvtBY1YW5YPQtFVrhj3qHZbrJurQsjB2HkIIEaV9nsbpKKprAHYkqcGlOp2BW0uCbMY3uDRrsRcKWUmMJMeoabzZsYcYOZ9AxO36HiVIIkUKPbwO7ZOH0HNapi9kzSiTRWXySl1IRKx+2Q1/I4roMsy8xmZvlW5VtiEl1cJLTKdpmd7g5lqxyYULesWxwyDrHcWBYg8ahGxRG8rA1zg43GBuvWujCmImLyH4A9sElFU1StKlfuXiGv5GkNWoGplFNy6DHB3rJ7Nnpcx+qL5NDo1E5JkjCSBlpCIybHkGSJglrAHtgc0A8QjUbJq3m0mIbnigru4sQiAO1+Gy2mMZmcZM1cw4gZ4nl6VlA91BIanudxq3mLoTsEj2AfcSEVSbHWXKOoFMX7hFripnWTjxc/zltbbwU1yaJW5G73LofSh0CCs5tnyWt5dno7pOIpnIFDe9BGiQhIeyFVYK25xlx6jus718kq2YDbNRwOQYKSUmKlsUJKTokvFZQib919i0PGIfSkHvydK9tl5tPzQRotE8+8U4GUQI/qXN+5jhJRqHVqzKfnicgRzJ4paufagSCdFypUqFChQoV6f9Xviwl7l27b/O2vv8yVegMjGeN//PKzfP98kX5fTNgbGxP8KEV53CsOFerR+kCZUgBZJUtBK9Dtd/ns9GcfMEX2Gip7zQd4J3G1N0VzOnv6kcD0sl0mq2SRZZk+fdatde5Yd3alq0YnzflrGZ1K52/Hh4P7/z3KhBplV42aV+9Fnic4MW/ffZtsKhskjUZTTBWnwnhyPKjh7Zec8plLvsHiJzkqToXJ5CTAI+uSo6bVXt5XvVXHSBm7gN/+eYpFYrS6AlTsH1N4MP223/P55xHg+PhxVrZXgg+0e42zvRU/f79Hp+r5abDRGuN+65jWp0lFUxS0QrCfvkl2bOzYLuPruwHuh/royYgbwXS8olqk3q6LFNLONayOSFEBQTonIkXIp4Q5o8fEdLeIHAn4R9cb14MK8npznQ1zA1yIyBHmMnOYAxOrawWpHbtvB1U2I2GgxTTsvg3+Je+B67m0B21OTZ4iGomykFlAj+v0vB7fN/19OK4DgIxI3jg9h6E3xEgYyBGZT5U+xU3zJvOZeYy4qJYVlAJVp0rVqdLsCx5V2SmLCuH9/Z5Nz3LHvsOkMokaU7lw9wK5VE6kw7QS09o0rWGLfDLPcDjk6r2rYp8GNmpcxfM8ru1cC/hYJa0UHCc8AZxvdgQjqWbXqNgV9LhOXsmzYq5Q0ksUtSJI0Bl0yCv5YJKeXxNsdpvU7Bp23+b45HHaXlvUFO/zrpS4wpXGFU5MnKDaqmIPbApagWwyy7c3vs3AHfCk9iRr1hrpRJqiWiQiRbB7Nk9PPs3v3vldikqRu+27KDEBfxenxaPWqnEmf4Zqq4rnethdm1q7RjaV5aXySwyHQ6qtKvlUnlqrhtkxqTgVZtIztIdtXM+lbIm/B1pUE/dVxbVlJAz0mOBJldQS9U6dp6ee5nfu/A4zxgzLTWEwqhGVazvX0OM6mWQmfK8LFSpUqFCh3mf1esKQenV1m7/99Zcpmy2KRor/6c98nFPFcdptsG3IZgU/Kh5/3CsOFerd9YEypXzTp6gX+cKTX+DKzhUKSmGXUbD3g/9+5oNfR+sMO9ScGnc7d1nKLQW1s4cB00crWTvtHaJSlKySfeh6R82d0Qlve6Hco9D20UqbbwD55s2ombafQeMbIV23y3hyHFmWSUQTQfLHr7P5fJQYMTzP25fH9bCE1n5G234MKh/y7bouruvuMtaWckt0+p1dwO+iWuTy9mXO5M9Q0Ar03X5g9u03Me9Rk/J8U2lKmSKn5t7TB6P9KoX+8R+tMfoMs9Hn32tmjcLWr+xc2XXthQr1KEmSAEXPjc2x2hTTLZ2+A54wHvSYHkysk5Ao6SXkiMz82DySLCDcelxH8iSa3Sb5ZJ6qXUXyJDacDVzPxRpYYhKaLF53tXYNLapRdsQgACNhIEuySArdd6M8xGva7AtOVFEr0hq2KGpFMX1Pnxbg7mGLkloKps35ya2IFMGIG4H5NJ+ZFxPxJPHadYYOalwlm8oiI4uElqzS6DSoOBWUqMKqucrH8x/ntnUbz/WCyqF/3OSIzPz4PK1hK6gnXm9cF2mmmM5Kc4W59BwVR8DkrZ4VVATNvknFrnDbvM1bW28Ri8boDrtIshQYdhW7IpJdMS0wu62uRUktBfVEz/PIqTm0uOBzzRvz4jgrwsyKSBFOjJ9gs72JGlXRohrXt6/jDB0Opw9TUAtc2LrAyfGTmF0Ta2BR0kuoMZXzd89zJHOEeCTOVGqKqBxlYWwBSZJwhy5Vs8rVu1fRozpFXVToTk2e4nfv/C5PjT0lpqWmcmx2NtGiGgDD4ZCaI6YZRuSImOYYU6i1auJa64trrdFtcLZ+llQkRcWpsJBZ4MLdCzyTfYbvVL/DjD5Da9Ci2q6SV/LCUAwVKlSoUKFCva/qdqFSgd++VOX/9buv0ez0OZpL83N/9hM8MaZhmqLWVyrB1BREIo97xaFCvTd9oEwpv94ylZri/NZ5npp4ivNb5/c1Ckan2PlmyKgpFI/EaXQaPDXxVLBdeDhcfVQVp8KJyRMU9SKpaCpI8+xn5Oyd8Ha2fhZ4J7njP5/rutxp3uFO886udfrMLD/R5EPQR00s13V5s/Ymd5p3SMfSXL93nbutu8E2fLMEoDfscWHrAofSh7iyfYWp1NS+PK73chx87WVpjUK++26fntfbdYxHU1ujxykTzwTncbQGWVAKvLj+IgWlsO8x9usm/rQ+/1o5MXmCZre5q/7nXxdv1t4Mqn7+z97t7ldjHD0fe/Uo2HqoUO9VJVVUrGbTswGge25sDiNpiNRPTMXsm6JKJwlekDN00GIajW6DDWuDZq8pUkb3mUhaXEN2ZZHCGgKuMErq7TrzaWGc6FFh3Ay9IUpUYcVcEeaFRDDlrqSVsAc2kiyu8UpLMNQ8yUOOyBzQD1BpVcTUuW6TZq+J03coaAXsoS1e8xIgifdRz/OwehbTmmBgRSNR5sfmcV2X12uvs93exnM9Lt69SDaR5fzWeQ6nD9MetsmreS7duySg4fdrek7PwfVcCmqBWqtGQSng4VFtVTmsHebc1jn0qI6e0AVvqmtidkwkT2LgDWj324wnx+kP+zxpPCkSYzFDcLHwUKMqa+aaMIGcqoCk369cF7UinuTh9B0iUoSiVsQe2BhxQ0w8vF9l849dRI7gei523w7SS5FIhNNTp7lh38DsCgZYxa5QdapMJabY7GxiJA3GlXHmJ+aptCqkE2m2ultMqpO7JgouZBZou23++ME/zi37FjPaDGvmGotTi0QiEbSEqD0W1ALO0BHJsZjGVmuLXCqHkTACHljNqTGZnGSztSnScwOb2fQsN6wbHNIOsWquMpWYQvIk7J5gj4UKFSpUqFCh3j91OsKQ+uU3b/LT3zxHb+jyyUNZ/vkPPUcmmWBnR0zVm56GTCbkR4X6cOkDZUrBgwBpf3LaXqPATwV1hp2HVtR8xk9RLQYJJHiw6uXL3wawKy3kP99+iaNRIPl+6/SNjM32JkbK4NK9S8HEudE1+uypeCQeMJr8/Tm3eY5ZY5bL9y6z2dkkk8hQa9eCCX6jSZ6YHCOv5LH7Np9/4vO71jtqLj2savawtNCoiTS6z6PQ9r0aTX65rsuV7SvBWPjRGuT5rfM8n3+e81vn9zXLNqwNOsMOZ+tndwHjR00h37hbN9fpul3GEmNB1W8yOYnneVy6d4mCUnjoteCfD3+q4N601n6Jse8GmB4qFAjTdlqbRpIlMXEvv0Rr2CIdT4vJal0Lq2PRH/RFHQ+Y1qbF5DTPEwmbjkXFrtDsNHF6Dp7kUTAKZJIZHM9BiSksN5eD9wIZGWfgCL5U1+GV2iucmjjFVncrAI37XKSiIl7LZsdEj+p4noeMjBbVuHL3CsPBkA1zQ0zui+kClj5wKCpFASIfepStMik5RcWu4HpukMJSIyIRVHfq5NQcq81VtITGYeMwLVfwoyRJCup7H8t/jIt3L9JoN3ij9gZqTGVanyYSiXBk7AhWTxyHfCrPmrUmwON9KzDEhgyFMdU3ScfS5LU8T6afZGFigQllAi2hIcuySCxpJSp2BYZws3kT13UZekPMnhnwutIx8f6ViqS4vnM9gMBXHcG0avYFiH3dWqfZaVJzaixmF9nsbnJ0/CgFrcBN8yaKrDB0h0H6Kq+IiYF+kq2klag6VbSIxtAbkkvmiBLFdV0kT4DK7aEwh/yJqJvdTWbSM6w0Vsgr+aBeWXWqFJQCVt+i5baYGZuhNWyJCX5xQ0wsVPLEIjGKWpFMMoMe06k5NQG8bwvzb7O3yfz4PHJE3vWFQ6hQoUKFChXqj1adDqxvePyzb17m59+8ggf80Ikn+Kk/cYYoURoNMAxhSKnq415tqFDfvT5w/7L8bgDSU+oUjU7jAZj4Xkh3xakECaR3q4g9jBE1uj0gmObnGySyLD8SdO1X2p6aeGrXxLm90+Z8ttNoVex09jQrzRWOTxzngHaAeCTOsbFjwb6PTqWTZZlT2VMU9SKRSOQB42nvpLy92i8ttJelNQphH4W2B89xfz0b1kZw3DfbmxwbPxYkm0bXtTi1yCu1V1icWnyoWSZ5EjkltwsYP3o/37irt+ok5ASJaCKo+m21t6i36hwfP875rfN03S69YW9XGm30fDys4jlq6pXtcrCdcNpeqO9WfvVtYWyBWqsmkicSIIMckdGSGu1Bm96wxxs1Ma0yn8pz+d5lklISs2+CB61Bi6ySpdW/b2rFNWaNWdaaaxRShWCymh7X0WM69VYdCYlnss/wcvVlZvQZKnaF4XDIla0rNNtNrt67ihYTBpWLiySiVIHBZPUtnIEjWEyNFYyYEaSqjLgBEdBiGjetmwLS3bO4eu8qrutyvXGdQ/ohJEkkl35w5gc5aBwknUxTVIpBNdFIGMwas2y1twJjaOgOqdpVmt0muGD3bVxP1IeXd5bRZA2rJ8w1I2aAB3bXpuJUmB+bp+W2OGgcDN4ztJhGzamhRBWMmBGYdwNpgBpVaQ1byMjoMZ0NeyNIEBXUAhfvXSSbzGIPbDFdzzjEha0L6FGdWrsWTPIDwdg7MnaEdDKNM3SYzcziyR6tfotmVzCulneWKaQKIBEYRdPaNNV2VdQ6EzqO66DFReXy+s51kbJqVVgYW6DaEsaTM3DQ4zqyLOPJHpFIhIImKpV4UEgVqFk1mp0makQVAHW1IP5WGNPB4IyyXUaNqtgDGz0qAOkL6QVag1aYkgoVKlSoUKHeR3W7cGt9yN/7+ln+09VbAPzVTxzhv/vscXpdCdsWVb1iERKJx7vWUKH+oPrAmVKw/+S0vUaFD58+kz8TGAl7K3OjtazRRM+7Tbzba4iMbu9R0/z2Pn7U/PIrbQfTB4OJc6O37zXKRrclyzIFrUBOzQUTA1Pxd/Z9dH/2rnV0m9P6dJDCGmVg7T0Ge9NCjwJ47/d8flXO8zy2O9vEpThLuSWSMTHdz0+5+c9dbVX5vgPfR7VV3fd68Kf0+Wad53m8WXszmLYFcDp7mlVzlaXcUmDs+deFJEk8NfEUl+5dIpvKst3aJibHAB7K1dpqb+G67kOnL/pTB+OReDhtL9R3LX+Smz/BzEiI6peMTEktkYln0OIa9zr3ODV1ims71/hW9VssZZe4snMFWZJRogpaTGOztUlBLYjHSzKtYStg6Hl4pGNpzJ6JizCn1ITK3c5dJlOT2AMbu29TbpWpt+sM3AF1p84btTeEodS1KDtlwVnyhEEleRL5VJ6zm2eZSc9g9k3KTplcMsfyzjJ6RKc1aHFi8gRb3S3BMYoKjlEhVaDjdvj0gU9zOnua5eYynucxrQsDRo2pAuSNeN+RZImnp57mXudeMAnP6Ts0u0122jtcunuJ/qDP3fZdUrEUsiQ/wO/LprKsNFZEsqpvMfSGWD2Lsl0W1baBQzqZJp1Io8ZVolIUNa4yl5kD4Or2VdSISrMnUlDXt69zcuIk9VZdTGxNZLlh3uDpqaeRIhLz6Xnsnk3NrpFTcoHJ5HkenuuBB1HEc2gxjZv2zaD+aMQNyo744sTsmcwas1y5e4UNc4OknGR1Z5VmpwlDYZIXUgUqVgXP9TASRgBP3zA3sNs2SkRBRlwHSlTh7OZZASyPqSw3lsUkyPtGFtz/m2VvBBMZW4MWalwlnUwzpoxxwDgQpqRChQoVKlSo90m9Hqze7vPf/eZrvHynTkyW+QdfXOTPL81g2xL9vjCjstmQHxXqw60P5L8ufaMjp+T+QBWpvSaKb+z4iZ5H8ZT2e77R7Y0aZqezp991Qt3eKpy/LSAwt/arHz7smOxn0hXVIpfuXaKoFh9Y617TzE8Bna2fDWqE+6nequ9b43vUsd5bf/S5T/5t/rH1a5f+h0e/Frl3Gt5+zwEiFTWjz/C1W18L7rsfx8qXD1nPKTl6Xo+J1IRIItxPUfncrtEPspIk0Xf7QboOdifGRlNtIVMq1B9UZs9Ejwu4uc81kiSJTCrDE+knWMotsdZYQ4tqnBg7weWdyxzSD5FTc7SGLZHI8VzMrslyY5m8mgdJpIgAUTsb2EESyPVcMokMelzHiBlU7SqKrCC7Mtlklnvte0ykJkQ6RhZQ9oWxBSqOSEH5dTdJFsbUanMVq2OJWt7WeRazi1TbVQpqgc6ww+mp01h9i4gUYX5snjFljGOTx4hEIlTbVXJKjg1nIzB+VporZJNZak6NRreBFtWot+t8vPBxWl4LJaJg9kysnkhrHZs4xpq1xjP5Z7hj30GJKWx1tkhFU9Q7dbS4RjQSFRXFnsWgP+CtzbdIykk816PslFFkhavbVxkOh2SSGebG5gRgXpZxcVEiCsuNZZyumDLoeZ7gbkmSYHlFZBYyAgKfjqWRIzLTxjQ5LcdmexPXE+Z52SkL061dxcUNzKqnJ5/G7tvCYHIqFJSCqEG6Ht+pfodn8s/Q9brYA1sA1t02qYQw4KrtKp4kDKc3N9/EHYo0mhpTcWWX1rAVMK7ObZ4jKSexehY1R8Dvza4ZfPnQ6DTYsDeCKY3tYZuTkydpD9ukE+nH+TIJFSpUqFChPnLq92H5Vp+/+Z9e4/LmDlo8yj/78sf4Y7NFGg2IRkVdb2ws5EeF+vDrA2lK7Wee+FBz34R4mInzbtU839Txa3577/coc8g3Ly7du8Tp7OnA2HiYKeFX4Y6NHXvA3NmwNkgn0ly6d4mSVtplLO0nH0a7n0FUcSo8NfFUwFF5FMzch4pPJad21Qg9z+NO8w5vVN9g3Vzn+PhxXiq/tKvGt/fY+hwnP7G0t/44CmD3q271Vj2oXQJB4sw/H48yynydzp7m1fqrfKb0mfdUnSvbZTLJDJ7nsdPeISbHguvAv872pt5G+V7+8Rs1FEOWVKg/DBlxA6tniVSeLdhrzW6TZqcZvEaQoKgV6XgdlrJLTKdFdbagFgSI3BMm1Gx6lpXtFfCEGYWEYEXFtHf+t2sJI8IVxkpezSNHZYykQSldYio5xR3rDqlIikwiw5GxI6IalhLPJXkSOTWH2TNx+g6KrGD3bZrdJgWlwPL2Mp7rCSPmvjmmx3W0mBZwlwDSiTQLmQVWG6vIriwm7UlgRA1agxY5JUd70EaJKUSkCJudTU5nBSB8fmyedCKNHhOJrM+WPssd+w4fz3+czfYmJ8ZPUHfqqBEVNaIiIWH1REJq1VrlqcmnuGndxJM8tKjGy9WXmUpOcX3nOkbCCI4LiOSaPbApqkW0uDDIlKjCZnuTGUOwmbS4MOpKeimYVmp2TFq9FrlUDrNrsm6ui+qcUwMPlIgiKnWIdFtJL+H0Bcy+bJWD9R7SD/HW1ltMJaaYVsUExFwqR7vXDvYPF9Yaa0wlp6jYYirhWmMNhjB0hwEPay49R2vYIq/khcEYF+fEvz7snkhWLe8so8d0ATo3bwQTAEOFChUqVKhQ74/6fbh6s8tf//VXWb7XJJ2M8bM/9BwnchM0GqBpwpDStMe90lCh/nD0gTSlYHdaSvIk6q36A6bBfiml91LNG63g7Vfdelj6qWyX6bt9xlJj+zKUHlaFu7JzhePjx4NkkOu61JyaqMGoeSRJ2mUs7SefmXRu89yu3/vfcm+1t3bxtHwA+l7zzYeKb3W2dtUIz9bPsm6to0ZVak6Ny9uX+fwTn9/FyNp7bM9tnuOwdpj/9dr/KgC8+yTUfHPKr7ot5ZZIRpLk1fwuftWGtSE+sLk8IP/4+mkmSZL44qEvst3bfmR1zgetV+0quCL9NZYa23ca497Um5+EmtanHzAv3y3VFirUe5Vf47MGFgWlQLVVRULC9VwanQYvrr9IUk5yfec60/q0MLEGFkZUmFne0MPu22gxkQjSEhpFvciGvYGEhB4VKSzP85CQaA1buLhial7PwepZFFIFYXrbNeyBzRPGE9zt3SWdTGP1Bcz86tZVKlaFsllGRkaWZHJqjrtdUQO8Zd2iqBeD9NBqc5WiWsTsm0hIVNtV8kqeSqsimFCAM3SYH5tHT+o4AwdZlskpAlRetavMZea40bzBbGYWNaZSs2vM6DNUrSpWz0KJKRhxg83uJku5JVatVQ4bh3l7521kSWZ2fJa3t99Gi2tB7W/OmGOztclceo50PI0zcHhq/CnWmiKN1uw20aIay41lXNfFwyMTF+Bvq2cxlZjilnWLUxOnuGHfYC4zx1hyDCNhYHUtMRUR8GSPglZgzVwDQIkpVFtV9ITO3NgcN82buJ6LGlWptET9bnlnGbMjJgZqMY26U0eKCKi5oRhIkkStU0OL3edKNa4LhlZCI6/maQ/auJ6LLMlklSyO62DEDcyeKa41WeLI+BEySoaiXqTZaQpYfltMcvQ8MVkwrwo4frUtzoE9sB/PiyNUqFChQoX6CGowgCs3OvzY//YqN3ZMxpUE//yHn+cJI41pwsREyI8K9b2nD6wp5RsaBwwB9s4pucB4Gb39Ufynh8mv4F26d+mBKWvvxk/ab9rco0yKUUZTzakFyaDj48dZaawElbWCUnhkUsqHnWdTWYbDYZBQKttlplJTgdGyd//9tfmG2OLUYsBeGq0RHhs7RkyOCYCxmuepiaeotqr71gQLSiHY1m/f+W2+ePCLu6p0o/I8L2BdjTKeJpOTnNs8F0xX9DyPpyaeYqe7A+xmXvmA8dHpe4/id42el96wx1hqjEavIQxAb/d5HYW2j5qW/rb2O7fv5RoLFeq70bQ2TWvY4sjYETw87J6N03eYSkyx1lzDiBm4rstLGy+RS+UC/lpr8M7EOoCSKqa2GTEDPaFjD0VSSZIk7J5NLpXD6TlEpAiTyiSyJLNirqDFNVLRFE7fYau1xYw+Q7PbxB2K95ib5k0mUhO03Tb20KaoFdlsb3Jq8hTbvW0+Wfwk1VZVVIhlibn0nKhKq4I1p0d1qnaVfCqP1bVodBroMWEUaXGNdCLNtDbNxXsXScgJYYq0qsymZ4OpnMjCWLlp3WQiOcEt8xZVp8p8Zj6YGrjaWGUqMUUqkuLl6ss8n3ue5cYyeOJ1G4mK4Q/plJiip8d1MskMWkxDi9+ffHh/EqHdt7F7NgN3wKq5Sl7Nc693jxMTJ3i59jKnp07jDAWPqdltCjMpKmDr6ZgwvE5nTwMEcPGCUqDerjObmUWPi2OiR3WqTpXD6cNYQwtZksXEwvs8MC2uISNj9S1m07O8Wn8V13PFc/UtnL4TVCun9WmQRPoqm8wKPpcrEr5WT2z7gH6ATDLDDesGTxpPcsO+gZ4QUxStvoXkCVNUj4qElRE3HtvrIlSoUKFChfooaTiEt9fa/JX/+Cp3mjaTaoJ/8cPPUUjpdLtQKEA+L6p7oUJ9L+kDa0r58k2MrJINPng9qjr1KLPCr5sVlAL3uvfIq/l9p6ztfdzotvebNueDsfcaXHv34amJp7i8fZnFqUVeKr/EZ0qf4fzWeSaTk5zfOv/IpJQPO88qWb5+++tBaqqoFnn77tviW/37PKTRdJS/Nt8Qq7aqu9hLG9YGmXiGy9uXeabwDM8UnuGAcWBf08VPc/lrrraqfOXIV3h98/WHVul8rtbXbn0tSKaNJqT8/ZYkiXvde+SUHF23GyTZ/NqjJ3nB9L2iWmTdXN8FIX+YeeRX8LKpLEAAJn8YYH7vtvYzoB5lXIYK9QeRLAuzwB7YASeK+5dkUSmixTTObp7lSPoI/2ntP5GSU+hxXdTvIjJ6XGfoDam0KsEkP7NjokZUKlYF13VxcUkn05SMEkcnj9IddlHjKvlUXphR7S1mxmbIKTlWzBW2nW3ObZ0jl8oJk9qp8tTYU3iuh9W1ODJ+hGgkyowxIwx2pUgmlSGTyCDLsphW17dFPRGxP/WWgKmbXZNGp4GHBy40e00a7QaTyUnutu+iRlXwRP0wp+So2lXUiMryzjJHx45ydecqh9KHmB+bp9oSJpyRMAQ7a2BjJAwWJxe5uH2RxalF7KGN2RP8JNcTBg0gElRDh4JS4PrOdTasDTG9L2GgxTSanSavVV4jE8uw2dpkNj3LVmeLTxQ+wfKOSFM1Oo2gbuc/dyaVYVqfDiDqelKnoBVYaa5QUksYCYNIJIKe0EnFUlSdKjIyB9QDHJk4Qr1dZ25sjmqrSjqRxogbFNQCKzsrPJd7DlmSMRIG09r9SaayRDqeRgxKFGm19qBNQS0IqPt9WD3SO3+PTo2f4ur2VU6On0RGxuk7FNRCsA+SLKYghu9zf/T6F//iX/Dkk0+STCZ59tlnef311x9635//+Z/nU5/6FGNjY4yNjfG5z33ukfcPFSpUqFAfDg2HcGGlxV/61VcoWw45PcW//OHnGY9quC6USiIlFf5ZDvW9qA+8KQW7kynfbXVq9P5n62dJRpJ87dbXKGmlXaDrR7GlRo2LvWmhUTD2qMG11+woaSXude+xlFui2qry2enPcmXnSlAbG62P7X2s/98+EPzzT3yeVXOV09nTVJwKY6kx+m4/eO7RdJT/O3/63L7GmUxQI4R3T6GNrlWW5V1Vuv32+/L2ZT5T+syuaYpLuSXude/tC4uXPImckguMK4BGpxFUAct2mXVrne6g+wAsfa955E+L6rrdgFW1YW1wx7zDurn+UDPLvy6A0IAK9b7JiBtU7Ap23yav5YlFY2hJkWCZSkyJCXxTS0QiESKRCAeMAxydOIokSTg9BzWicvXeVVzXpeJURJ0uoeEMHYpqkaojkjNm1xS1sJiBMxC3zY3NCX5Te1NAx80VDhuHuWHeIJPKcHzyOLfsW6gxFbMrJu4Nh0PWzDWmUlOCOXW/juibI959Z83qiQSS67rUWjVc1w1MneuN62gxjeXmMiW9hJE0mDam0eM6ds9GQmLoDXEGDtlUlrvdu3yi+AmikSiSJIkUGWJ/C1ohmGooSZLgbDWEEeSvR5bkYI1+ba3aEmwlPa4HU/DqrTqb7U2xth0BkR9LjVFQC6w2V4P711o11JiKjCwMoajGuiUM83RCTPUzYoKVtZARfCYJKYDNX7x7kZNTJ0XdLilMoHwqz4WtC+RSOTzJw+ybyLIwH+WITDqZFkMWZPG+pMd08X7fLNNsi9TWYDjgwuYF+oO+ANFvnhcMqvtqe22eLzzPTfumqARKouKpx0SyLh1Ph4Dz90G/8iu/wle/+lX+3t/7e5w7d45Tp07xhS98gc3NzX3v/41vfIOvfOUrvPjii7zyyiscOHCAz3/+85TLYZ08VKhQoT6scl04e93hL/2HV6jbbUpphX/5w8+joRKNirqeEQaXQ30P60NhSo2aJL4x8rBJbfuZIputTSpWhcFgwBu1N5jJzOwybHwz6WGQ8NEpehvWBplkZl8w9n61udHJd6P74BtUfm1stD6297H+f1ecCtP6NJFIhKXckqisqMWA1+Q/t78WIJiGN5oMGx2ZPjpRbhR27gOJ3+187NV++72UW2K7t81SbukB48vf74pTYSIxQc2psd3ZZlqfDmqP9VadY2PHOLd5Ljiv2VQ2qPqNwtL3S8iVtBKNToMpdQoQaY1MPEO9VX9oEmqvyRgq1Pshv1JWUAvUW/WAqWf1LCRZ4pnsM9zt3A0McqR3HqfFNeyBjRp9Z4pdxalgxAxKagmzJ3hFG+YGG/YGHp5gPamC9ZROpHGGDqcmTyHLMsVUkWgkypncGeyhDZ4AZ680VgSLShXJn8nkJDeaN4KqXrPbFAymvoURN8TrXJIFs6kvKmzO0CGv5DG7Jk7XoWyVmTMEiHspuxQkrPx0T0SKkIqkuGXeEhXEgUNRK2IPbDzPE/eNajgDkfbxj4ssy8H7guu52D2RRAMBW7cHoopoxA3S8TRKRGHT3mTD3GAqNYUSU3A9l4PGQfAIpvT55r7dt5nPzOMMRI1Pj+tcb1ynoBYoO++8B/rJqUqrghbXMPvm/RMHp6dOs9XaQotqqFGVq9tXsfs2M+kZWoMWds9Gi2g0e+K4+vvup582bHE+8aDcKmMNLNrDNpudTTLxDGe3zlJ1qhxKH+L6znUx2e/+++IN8wZPTz3N9cZ1SloJe2CTTqbJJDOkk+nQjH8f9E/+yT/hL/yFv8CP/uiPcuzYMf71v/7XKIrCL/7iL+57/1/+5V/mr/yVv8LTTz/NkSNH+IVf+AVc1+X3fu/33ueVhwoVKlSoPwy5Lrx2zeIv/eorbLU6PDGm8i9+6HmSwxSplDCkQqB5qO91fShMqVH5ZkbFqTAeH+eFmy8EBsp+AHNJkthsb5KKp7jWuMZcZi4Av/qGzcNSWKMJJT+1U2/VgyTPXjD2qFHzKO7Qu9W/9tYB925rdD8rTmUXkNvnTPlJsM3WJjWnRkEpALDV3gr2fdRw2Qs7P1s/u29FbrRS5+thdbeHAeBHn9O/vaAU+LXVXyOdSDOeHKdsl4P1HR8/zq+t/hp6TOfN2psU1SKNXiPgtVzcuki332XdXN+1Jn+tAGfyZ0hFU4HZ5QwcckruoRW+kBsV6nEpnUgTiUQ4OnGUaCSKJ3miVtW3aQ1bzGRmkCQpMEgubV1iw9zAcz3S8XTAdNpsbwqDBlGDq1gVrK5FxamgRcXktUKqQK1VI5/Kc71xXTyus0lJL3E8e1y8rzhlikox4M3pMWGaVZ0qc+k5opEoC2MLyLKM2TPR47owTKLaroELyJCOiRTTkbEjANw2b/OE/gStQQs58k6F0fO8ABouIaHFNG40b3BAO8CN5g0GwwHX711HiSgBjL3Za+K5HhVb7J9vVuHBTnuHil15pzrYbgiWX6rAcmNZGFNJg9vObQ7oB6i36zg9h3Q8zaH0IUpaiZXmCrPpWS7eu0gqkqLerguGV6uKGlWx+zYeHkVFJNLyyTxX7l1hMBgEExW1qEbFFkahh0gmSZIEEhTUAmc3z6JGVAbugHqrjhbTUGOqgKRHdayBRUktCXMtqlOxKqhRVXCgcCkqwmCby8wxl55jw97g06VP0+63qTk11KgaGFmeJyrRNbtGLpmjYovqZ2hEvX/q9XqcPXuWz33uc8HvZFnmc5/7HK+88sp72kar1aLf7zM+Pv5HtcxQoUKFCvVHJM+Dl6+a/KVfeZXtdpeZCZ2f/cHnifaT6LowpFKpx73KUKH+6PWhM6V8FdUiv7726zybfTYwUN6ovkE6lg6A4f7Etmwqy53mHT5e/DiJaIIz+TNM69Pc7dwNKmF7IeFlu0xn2OGOeYez9bOczp4O0k2JaIIDxoEHJtuN6lHG07uZNXuTOnu35YPa/TqcbyiNx8f52q2vMR4fD8yYzbaYNPX1218PgOijtcUNayOAjh8fPx7Azn22U2/YC8yholrkGxvf4NjYsV3JstHq3CjTat0UFbs3a2/SGXaC+pyvUTPr/NZ5vnTwS7xRe4OYHAPeMQ2v7Fzh2fyzXNu5RiaZCThU5zbP0XW7bLW36Lk93r77dnBMfZPraOboA8dwlM91bvMcvWGPrtsNU1GhPhDyK3B+akpGxhk4lLRSABgfekNw4frOdZy+Q9/rU2vVSCfTHDQOCqZTXAMPlhvLpCIp7nXukYqlxNS5oSMmqw1tikqRWrvGrCGg4rlUDrtvCwZf/U0BVm9XOTp+lI7bIaeIpJIe08mkMqKeljBodBoMB0Ou3ruKKquUbVHvu2PeodFpYPeEaaPHBUDbHtocnzjOndYdkQhDEime+/VCXAR4vGfhei7JaJI79h0mk5NsdbZIxVK0Bi30uEhlOT2Hnd4OEhLWwMIeiKmEZt+kalcZeANavRZmT1QPlajCcmOZfCrP8s4ynutxSD/E1e2rzKRniEQjzE3MoSd11sw1DhuHWW2s8vTk09Q7dbKpLNd2rqHG1ADYLiNqg3pMFyZWZpZzW+fQ4zpmz3wHiH6ft+W6Ltd2rpFNZblw9wKH9cOsNldp9Vrk1TxWz6JiC+OpbJfxXE9UA/HQEwKwXrWqDIYDIkQwkgbHJo8xlhojo2T4ZPGT3HZuc3LypEg/JdKi5qgUeLMuhmVUW9XgerH61uO+/D9Sunv3LsPhkFwut+v3uVyOWq32nrbxd/7O36FYLO4ytvaq2+1imuaun1ChQoV6nPr7f//v7xqAJEkSR44cCW7vdDr81b/6V5mYmEDTNH74h3+Yer3+GFf8hy/Pg29eavCXfuVVmt0eC1MG/+xPPYfcTzA2Fk7YC/XR0ofWlKo4FX7w8A/y23d+m2wqS2/Yo+/22exsijqKUwnMlqyS5eTUSdS4ypn8meDNby8QfXRKnF/5AhhLjAXVOVmWKarFwAj7bvhWvh6WOBrVw5I6frLHZzuV7TIb1gbHx48H8PQXN14MkkOns6dZNVf5/BOf32Uc+aYXiKSYPwXvTP5MADvfWwss22Vmx2YDM2xvrXDUNJtMTlJv1Rl6Q65vX4fho/fxdPY0r22+xg/N/lBQ6fNNw6XcEslYksWpRZKRZMCh8qtNU6kpzL7JfGY+MMu+fvvrfOngl/hm5ZsPZV1ttbfIprLE5BgJObHrWH+37LJQof4oZPUtwVm6D5yWkCgoBVr9FmpcpaAU8DyPbr9LLpWj2RGJHKtnUdSKwQS7teYazxeeJypH0RIa85l5Ad3WSlgDi6JaZGVnhWQkSc2poUQVzm6e5dTkKc7VzwkYeadJXs1Tb9Vxcd9JIgFmVxguK40VUnKKt+69hRpRqbaruEOXNXONlCzSRVbXEunIVIGtzhZPTzxNRI7sMqyQAAnsgU0qmmK1ucq0Ns1sZpaIFCEpJ3G6DlpMI5PMYMQMhgyJeBHUuEomnqGklWh2hGkzHA5Jx9J4kocaUQXbyq6RV/LYfRslqoiqY8JgJjNDJpkhn8pTMSvUnBqHtENsdjYpqgLmbkQNrK4FQ8HA0+M66UQas28ydIeU7TLZRJYLmxdYnFzE6okqY2vQEmb41jkKiqg/zqRnuNG8wVJ2iRvmDU5NncIe2Nh9m6E7xOk7uJ5La9AKgPFWz6Jsl9HiggW12d1ES2hIsqh6+kwre2hzJnfmHSMTMemx6lRRIoo41xFFTOZDDiftfcj00z/90/z7f//v+fVf/3WSyeRD7/dTP/VTpNPp4OfAgQPv4ypDhQoVan8dP36carUa/Hz7298Obvubf/Nv8pu/+Zv86q/+Ki+99BKVSoUf+qEfeoyr/cOV58GLb+/wl//Da9i9PsfzGX7mTz6H1IszNRVO2Av10dOH1pQqaSWuNq4GJkZMjhGTYxzQRJVtL5R7NBH1bhPX4B0WUjwS32XMAJzbPMesMUu9VX8k38rX3ucbTRyN8p1G7wv7A7bLdjlIPPnGGwhm1Bee/ALbvW2mUlOklTT1Vh1ZljmTP0MkEtm3Xugfl2qrylMTTwUmlw9w37sGGZmckqNsl/eFi/um2VZ7i6XcEmuNNf7Ek3+CleaKGFc+In/7IExGf/2+ceY/tw8sP5g+GIDLp/VpYZxFEzxTeIbT2dMkY8nALPt08dP81u3f4vjE8YeeX3+qoyzLAho8sp9hfS/UB0FG3MDqWQEw2+pbgsukFohGosgRmYWJBYp6kZYrTAuzL5I4VUdUvuyB4B7F43FKeomFiQVWmiscGTuC3bcxYsKIsAYWBaWANRCpoxljhtXmKiWtRF7Ls9xcRkJCjapYPQuzawZGUrPXxO7aDNwBN82bnJg4Qb1Tp5ASUPAn1Ce4tnMNJaIwdIeYPZN6u87i1CJr1hoFtYAsyYINFdfIK3luNm+SklK0eq0gvWXExVS4dDKNlhDJHrMrUh8ltYTZN7E7No12g0anwWZ7kwllAjki0xq2KGoiFVbSS+hJnbHUGB4eTt8hl8qxZq6xML6APbSRZAl7aHM4fZjX6q8JM8tzKTtlClqBltcir+exhzaZZAazZ6JFNVYbqyhRhc3OJku5JWqdWgAPz6Vy3DRv8vTk01zfuc5sepbN9ibP5J9BlmXm0nPcaN5gNj2LERcQ+qySZauzxeLUInWnTtWukpSTSN59yLvrokgKZauMFtHw8ILqpOd6ouqX0Clpgiu2YW0wdIcUtSKSLAWmZ8iQev81OTlJJBJ54Nv/er1OPp9/5GN/5md+hp/+6Z/m61//OidPnnzkfX/8x3+cZrMZ/Kyvrz/y/qFChQr1figajZLP54OfyUnxmabZbPJv/s2/4Z/8k3/CH/tjf4ylpSX+7b/9t7z88su8+uqrj3nV/+flefA7b93jr/yH12j1BzxdHOcfffFZIsMY+TxMTUEk8rhXGSrU+6sPrSk1OsHNNy/GFcFU8I2HvQBxX/ulYPypekW1CAhz5dzmOZ6aeCqAj/vGkZ8+WsotBXyr/VI1vsHkV+T82ytOJZi+B+wLNd8LXvc1apbsNU58I+dM/gydfoel3NIjj99+JpW/nr0gd/94JqIJJEnaNyHlr983zWRZ5gtPfoE1a41j48ceatz5ybG9sHL/GK6b69xp3tnFtxrdB99U8o0l37B8rvAcPa/3AOtq77Hcz1R8N+5XqFDvh0arfGbPDKa82QObklbCiBvIyLieixbVAqPCr5C5nphKB4JVVVSLrDRWmE/PBywmJLB7tjBEzBvMZ+aFUXO/opdL5fj6ra9zcuykqHpJsjA0EKkcs2Oiyipr5hrZZBYlqtDqt0SKSxK8uK3uFk8YT6DEFG5aN1FjKnklz3eq3+HU5CmWG8vCGEkIyPZac43jU8exXZuW28JIGHiShz2wKagFnL4jKnNRTaSHJKh36sIUslZZt9dZba5ySD/EurVOQSkIaLonJtVZPSsw4zKJDEWtSL1dZzY9S9WpUlSKVOwKuWSOi3cvcmz8GM7AwepamG2TdXOdQlJwvopKkWa3GUzycz2XVq9FNpXl+vZ11Igq3l/wcIYOS7kl6p06RUWA2vWoWE+z2xTTCiWQJfGn2YgbQTqu1q5RUAuk4iluNG8wZIgzdBhLjUEUSnpJmGlI4tzG9YAfpUVFXVCWZJSYgizJtNwWH8t/jEgkEk7Ze0yKx+MsLS3tgpT70PLnn3/+oY/7x//4H/MP/+E/5IUXXuDMmTPv+jyJRALDMHb9hAoVKtTj1srKCsVikcOHD/Pn//yf586dOwCcPXuWfr+/q5Z85MgRDh48+Eje3oelqvzb57f4a7/6Op3BkGcOTPKTn/8YES9KoQATEyB/aD+dhwr1B9eH+rLfaxxInkS9Vf8DVeMqTmVXUsivxF3evgzsNo789JF8/11jv+2NwsiBXbePTt/za2p7J+eN1uNG01R7E0T7mWJ71zeqUaNr9P8HMGIEDH1xanGXSfdux8/XXnPP5zcNvAFdt/tAMszfng+S32vsle0yXbfLur2+i2/1KI2m3HbaOxTV4q5U1rul5EKF+iDKiBtEpAhGwuCAfkBMt0sY2AMxGW/D3hBmy/2qH4j6m59+8v/7yPgRqi3BIXqjJiZtDr0hraFIJFkDCwkJTxIpqAv3LvC5Jz7Hq5uvsjC2gBpTWW2s0uyKOp81sFiz1/hY7mO8Un+FpfwS9lCkc9r9NgVNpKDmxuboul1OTpykalVZ3lnmqbGnWGuucWTsSGBANzvNIEFkRAymklMCDh410GIaETlCSS9xZOII9sBG8sS+LmQWcIYOT6pPstPe4ZB+iK3uFp+e/jQtr0VJLwljRxOTDVORFG/U3kCNqu9M6ZMljLgRTOWLRCPMZ+aJRqJoMY1aq0Z32GXdWkdLiOqgPbg/0U+CWrvG3NgcQ4bcsG6QTWaptqqCk9Uz0SIaFecdoLjdszG7JjudHayuxWpzlcPGYTbMDb5T/g7ZVJZWv4UruehRHQ+PKFFSUcEG02PCeMrEMuCJv4FqTEWWZNJxwY/yXAGNlySJolak1RccrmlNGPq+6Rnq8eirX/0qP//zP8///D//z1y9epW//Jf/Mo7j8KM/+qMA/MiP/Ag//uM/Htz/H/2jf8Tf/bt/l1/8xV/kySefpFarUavVsG37ce1CqFChQn3XevbZZ/mlX/olXnjhBf7Vv/pX3Lx5k0996lNYlkWtViMej5PJZHY95t14ex+GqvJvvFnnr//HN+kOXZ5/coqf+OwZ4nKEUgnGxiD8cxzqo6oPtSk1Kj/Fs5RbesDg2Js42o8n5VfOgF2VuFHj6GE1vf1SNRvWBumEgK7vNatG2VUPGGsj/z2aXppITPBm7c1daaH9QOOj8m93XTdY96gBs5dttWFt0Bv28DyP81vnOT5+PEhZwDvmzX4JKV++uec/zj+2USnKTnsHz/MemHBYtstBzdK//+i+JeQEB7QDD9Qo92qvySbLMicmT+y7D/7+753AGCrUB1WSJGpr6URaTLm7n/jRYhpWz8Lu2TQ7Ta5uXxWpIISRJUsyRaUoJmtGVMpWmaE7pObUmEoJw6dm12h0GkhIwuxI6GTiGSRJ4vTUadYaa5wYP0HFrtAaiAmARsJgpbFCSS1xevI0ZzfP8qdn/jQXti6IGpnr4SJYSEu5Jdpem4WxBRzXQZKEeVJr19BiGs1Ok9err9Mf9Fk3RcppPj2P4zn3d14Y3GOpMYyEqKxd375OUSli9kzMronZM3m28CzxeJyPlz5Oe9gmn8pzbfsahVQBuydSTcvby0wkJviNG7/BycmTLDeXRT0ymUaWZIyEIY5t18LqCjaXb9xMJCdYa6wxa8zySuUVhoMhekwXFUIPCqkCzsAhQoTDxmHWTFFNlCQJ13OpOmJSn9k1sfoWSlTBHtg4PQclpgT1vnq7zmJ2kd+4+RsoMUXUOAcWY8kxClqBiBwR5yqZZiw1xrQxjTMUAyrObZ5DiSpIkkQmkUGNq2y2BQ/LNzIzyUxoRH1A9Of+3J/jZ37mZ/iJn/gJnn76aS5cuMALL7wQwM/v3LlDtVoN7v+v/tW/otfr8af/9J+mUCgEPz/zMz/zuHYhVKhQob5rfelLX+LP/Jk/w8mTJ/nCF77Ab/3Wb9FoNPgP/+E//IG3+UGvKv9vr1f56q+dZeC6fPpwjv/HJ86gJIUhFQZYQ33U9T1jSo1WuU5nT3N5+3KQ1tmwNugOug9Mf4N3Ek2Tycldk+lApH5Gq4APq+k9TDJyAF33zZ8Na+Ndk1x792lan+by9mUyycyutNC7mUT+fvv7t7fGVlSLvLj+IkbcCI6NJ3nUW/Vd0/18Parutvc+o1MB/WlZJyZPIEnSLgNodB8AesMeG9bGrt+P8qRGmWCjZtvotkanBe5Ne+1XfxzljYUK9UGX2RPMqLJTxogLg8E3Upq9Jt7Qo2yVQYJpY5p0Mk21XaWgFlhuLgdJGjWmEpWiOH2HVDSFhASS2L6EgHf75q6e0IlEImhxDT2u0+qLSt3R8aPIsqiCfeHJL/B2422mklMgwaq5ysL4AkiI6YFqCddzsdqCA6VGVSJSBD2uU3bKqFFRAbT7NtlUltXmKrPGLDfNm6IKyH2z2t7A7ttMpab4ZvmbmB2ToTcU9TT5frVQkqi2qph9k7wieFj+xDqrb4k06OQiF+9eZCGzgOeJJJMRFykzs2tSaVUEAL1VAQm0uEa738ZIGFy8d5Fncs9QaVUo22Wa3aaYrNc3SSfSlPQSrWFLcLkaqygRUWlMRVKsNFYwuyZFrUh72GYuMwdAzalhJAzmxuaYG5vjzdqbLE0tISESVUbMQI/rQRqr0qrgeR7NblMMoUjP8u3Kt0lKSSpmhXVzPXje2fSseF/0CCYBhvrg6Md+7Me4ffs23W6X1157jWeffTa47Rvf+Aa/9Eu/FPz3rVu3dn154//8/b//99//hYcKFSrUH5IymQzz8/Osrq6Sz+fp9Xo0Go1d93k33t4Huar8v75S5v/+6+cZuB7fP1fkv3v+NIYmUyqBqj7u1YUK9fj1PWNKjWpvFc/zPDxpd13NV9ku7zJgRifT7eUqfTfwaz+55UPXL29f5tjYMWpOjbfvvk06kd7XJNtPfh0tGUnuSgvtx8FaN9d3p6kkj5yS2zV5zzdgKk6F+bH5wISb1qdJRpIBq2spt7TLqPEfu2FtBIyn/dbqb3/02C5OLfL23bfxPG8XN2rvMfUkb9/fj8o3n97t/Ox3HexXfwzNqFAfJhlxg4pToagWA3PBB4AbMSN4DaUTacyuiTt08YYeFavCfHqeml1j6A3JJDKkU2nmxuaIylEkTzCi/IRT2SkHhpH/XHbPFvVB40AA93ZdF3fo8kb1DVKkqDpVXM8ll8hxrn4ONaKiRTWu7VwT25c9CnqBjtfhyMQRJCRRO4urLGQWKKpFnIFDLpljs7PJqclTtIYtABqdBlpUYzAc8Hr1dQ6oB/DwaPfbaFEtuP2tu2/xpPYkTs+h1qoxl57D7Jpcu3eNycQkds+m3W+TV8Q/btfNdRrtBuvNdRqdhjDNIirVVhUtIpJcdtfGHtrMjc0xn5nn9frrMAQ1qgZ8LT9dZSQM0vE0a+YaJydPcnHnInklz2Z3k9n0LJ7kYXVFyq01aFHQCmjx+9wnZA6mD7KYXaTttlGjKjWnxnZnmw1zg1wyx7c2vkU2kaVsCzOv2W2yfG+ZVCQFCL6W53msNFcAUds04gZIBJMAQ4UKFSpUqA+KbNtmbW2NQqHA0tISsVhsF2/v+vXr3Llz55G8vQ+q/n/fXuf/+Z8u4HoeXzoyzV9/5mnGM8KQesTg1FChPlL6njSl9kK7JUkiGUkGgO7RpNMo32mvUeJXyvzUz2jtbj+Da1R+/c5/rqXcEld2rnB8/Dh323cDDsp71aiB4roub9beZMPaCKpyfiqp0+9wx7rDhrVBSSvR6DQCY2wU1u5/uxqPxDmoHwy2/TA4vP+YdXOdi5sXabttak7tkcfBP445JUfFqZBJZgKm1Og2a06N4VDUieJSfNda9jOM3u38ALtqjcB7TqeFCvVBlyRJTGvTwTS6ZrcpmEQ9C8/zkCUZLaph9kw812O5sczAGyBJEs7QIa/licpRkN4BaSODGlfR4hpm16Tm1EQ1riO20ew0kSWZglqg7JSDZJEe14Xp64jXtDkwkTyJG+YNXM8NpgFe37lONpml4gg2lORJAlLuulRaFebHBbfJSAhzraSVkGUB5Xb6jpgod3+ioIdHx+1wJneGa9vXsPs2eTUf/DW73rjOE/oT3DRvosU1SmqJmiMYFEpU4Y5zh6ySpeW1UGMqZzfPBqnLC1sX6A/6wXuqETFYbi6jRBU8yaOoFmn1Wtzr3eO5/HMYKTEhT4/rIq0W1xl6QzasDcyeyeLUIq9UX+ET+U9Qa9dYmlqi3q5TUkuUnTJKVKHZa+L0HYy4EUzBk2VRIyyqRS5uX2QiOcFWewuzb1JtVzmcPsyatYYiK3yr/C3mMnM4rsNsZpbN1qYwFl0XNaoGcHwgmOIYGvGhQoUKFepx6m/9rb/FSy+9xK1bt3j55Zf58pe/TCQS4Stf+QrpdJr/5r/5b/jqV7/Kiy++yNmzZ/nRH/1Rnn/+eZ577rnHvfTvSr/4zdv83f98EQ/4vxw7yF9eOsnUpEShAPH4415dqFAfHH1PmlK+oVHSSgHTya9obbW3xISk+x9CgMCwGf3dXnNmbzXsYXDsUZNm9D5+2unKzhU+O/1ZGr1GAN7e+7i9v3Ndl3VznTdrbzKRmODrt7/OrDFLvVXfBUU/Pn6clcYKWSUL7E4K+dB13xTasDaYSk0Fk+v2m3i3d9pd2S7TGXYYekPMtkho7J0suPc8+KkzECmHKXVq133ObZ5j1pjlV5Z/hVljlsv3Lr8nw+9RExX31hr9+uPx8ePBcXRdd99thwr1YdDoVD4JiXq7zpQyRdtrU1ALrJgrKBGF5cYyCTnBprOJ67noMT2ozBlxQ/CY+iYFVUyS8+t682PzVFoVcZtWYKWxwtAdcv3edWF4dUXVzeyYNDoNGArDJypF8fCYSkyx1lxDjag4PYeCUqDqVNFjOnpCx3EF/+hrt7/GjDHDcmMZCYmhNwQIpsf5k+QKSoE362+iRBTsroC337JucTB9kIImpvHZPRvP9dCj9/cxJuqHQ2+I1bdQYyrtQZsTEydoD9vMGrPUnBozxgxWz2K7vU1JKfHixovMpGdoeS2IwGx6ltXmKlbHCo79ickTXLp3CS2mUVSL2H07eD5/cp4aU6m1asFEPy2qUWlVWBhb4HrjOik5xXcq30GRFVxckRhDCr4w8I/DyYmT3G3fJZvKUlJLFFIFbpg3mNFnqLVrnJg4wYWtCyxNLbHZ2URP6JzKnqJLl2ljmrGkYE6FHKlQoUKFCvVB0cbGBl/5yldYWFjgz/7ZP8vExASvvvoqU1Pic8I//af/lD/5J/8kP/zDP8ynP/1p8vk8v/Zrv/aYV/3d6V///k3+wW9dAuCHTzzJXzz9FLmsRC4H0ehjXlyoUB8wSd57iI6Ypkk6nabZbH6g+rnvJt8wGTVuPM9jKjXFpXuXeGriqV2JqtHfjRpG8CCgfO9/jz7nRGKCy9uXOZ09vYtJtd/j/P/21zX63P76L927RCaZARcavQaLU4uc3zrP4tRiwGIqaaWg0jNaZ/O/8Z9MTnJ5+zI5JcdUaoqt9laQ5tqPRdUZdpA8Kagg+sfgzdqbpBNpVnZWmB+fJyEndiXMKk5l1zb3JpjKtqgC+WvdsDaot+osTi3yO3d+h8+UPsOVnSsPPQ/vdn791NS5zXOczp4OJhD666jaVebSc6yaq5zJv/so7Q+SPqyvww+TPozH2PM8Gu2GMDwyCyw3l5kxZni5+jInxk9Qb9fx8Dg6cRS7Zws+UlTjeuM6c+k5VnZWUOIK6VgaSZbAg2a3CRKossrFnYtMxCYwkgb1dp2FsQVhn8gSjU5DpJ3sCtFIVDCUmqukIqmgliYjmFRVu8qUMsVN8yaLU4t8c+ObPKk/yVZ3iyNjR9DiGuc2zzGTniEqC3PL6lmoUVGjwwUlrtAatDg6cRSzY9LsNrH7NgW1QCaV2fX+okU0mr0mNbtGXs0HXzKUbZFQag1aaFENa2DhDlwqToV77Xs8m3+Wze4mRsxAiSis2WvMGXNYA0usqWPRGraYSk0xlhhjpbnCYeMwzsBBlmT0mJiSJ0syZk+YemdrZ8mpOSRXpNWScpLVxiqSJ6YczmXm2Oxscjp7mlqrBh4oMYWaXcOVXEpqiUgkghE3uLp9NTDK8sk85VYZPaIjR2X0qI7ruayZayxll4hEIo/78vwD6cP4OvwwKTy+oUI9foWvwz96Pc5j/D9+fZV/+vvXAfjKqRn+65ML5HISExPhhL1QHy2919fh92RSytcoY2h00tpWe4up5BRv331bAHTvT95bnFrk0r1LFJTCA0mdR03JG1VRLfKNjW9wbOwYFacSVAb96thorW80TQUPToDz1784tchOe4d4JM5STnzQOJM/Q7VVpTMUdb2yXQ7SXX46aTQptHeS4KO4WCWtREJOBPwqP7kEcCZ/BiWmcGLyBDLyIxlc/nEarTH6Hwo7ww5v1t6kN+ix6WxScSp84ckvsN3b3lXLe6/ndy8ra+8EQN+wWsotsWqucjp7+tEXT6hQHxJJksSYMsbxyeNEo1GOjB1hzVzjk8VP0nbblPQSxyaOYXUtNuwN1KgqDKnMHOe3zjM/Po8sidey2TPR4lowFW/NWuOQfoj2sE29VWcpu4Q9EKPnXc8VHKq+RWfQYSo1Rb1VJ6/maQ/bzI/N4/QdXEmkEvNqnhvmDQ7rh6k6VcFzkgmMlOWdZRanFmkNBURdi2rCVHc9jJhBTs3R7rfJJXNc3bpKs9sknUhzZOIIdt9mw9xAi2jYPZtpbRpJlojIEdSYijV4J+XkV4ZTkRQVp4LrutRaNbS4xpPGk2x1tpCQKGgFbtg3WJxapNquYsQM8ECWZCaTk7QHbcpOmaennmbNXEPyJJSIEgDFPUTdr+pUmdFnqLeEOegfHz2uo8ZFtW6luRK8h7qey9ATdeYhQ4yYgd2zaXQabJgbzBqznN88jxbRggl71lCYd2W7LGqCH2JDKlSoUKFChfqwyvM8/vFvLweG1I8szvEjTy9QLIaGVKhQj9L3tCk1alSMTlqTJImBN2AsNcb5rfNMpaaCaU1PTTzF+a3z39WUvdG6W9ku89npz3Jl50ow4c2HnPvG1GjVrKSVdk3727A2gtqcv/6KU2E8OR4YQP5zep7HTnuHXCq3az17Aej7GUOPmiQoSRIHjANBrW9vDXFan+aAcWAXyN03z/ZOu4P9q44+U+vy9mWUqMK6tc65zXMBS+a9AMj3Gny+9gLP/eTXhrWBLMucyZ8JElShQn2vSZZljowdodauUdJKImUJLDeWySazLO8ss5BZoNaqcXrqNFWniuRJuJ6L53ksb4ukVb1VZ0af4YZ5g5ySY2FsQWxTLSEG9UlocY2oHOVQ+hDtfhsQ3KKSVkKOyOhxnXQ8jdW3BJNKETXBaqvKbGaW2+ZtMZGvZ5FX89TagmW1YW7wjfVvcEg/xIq5ElTh9LhOa9jClVw8xJTBN6tv0uw0xXumLOFxn3d1P7FkxA2MmIE7dHm9+jpKREGLa2y2NymoBZyeI+pznoTjOqhxlbya5+zmWbLJLOc2z4l0mCyJiXqZGa7tXCOn5CgqRVabq5zJnsFIGlRbVWHi32ds2QObA/oBWm6LvJJHlmQiUoSiVuTI+BHSyTQzYzMUUgXqrTq5VA4jbiAj47ruLvag2TMZukNWzVVySg4Xke5SIgqyJ1O1qgDklbyYGBgqVKhQoUKFet/keR7/w3++xr98SQwZ+b8uHeG/XpynVJIYGwsNqVChHqWPzCfzvQZVPBInISd2pXIeBtB+FN8IhOnSG/boul2AAO7tP+8o5Nyvl/iJLN+o8b8l77pduoPuA1DuvdMDy3aZqdQUBa1AMpbcVXPbmxTy17h3Ut1WeyvgVT1q//abhLd3il1RLfL1218PEmKPerw/mfBM/gyL2UXUuEpMjgXH570c84ft1961Bb/7LsHyoUJ9mGX1LUpaCasvEkJlu8xUcoob5g1hDA1sSmqJqlPF7JooUUWYPjEB6m4NhIlSa9eYTExSb9cBgjSOETcCflJBLQhwugzz4/PYA5uiVsTu2WgxDVmSOaALg1uNqdwwb/Ck9iQvV19mMjmJElNYbayCC0bMYMPeoNFriDTrvbfJJ4XJosd0UtEUVbsKQ3B6DkpUweyLCYBVp0qz08TsCPNmw9oAD2GgyRL20OZw+jA3rBsYMQM1qgoOFR6zmVlu2beYNWaRkWkNWhSVIlpCQ4/pOH0HPMilcrxSfYVnss+wZq4J/lZmnqv3rrLR3MAbeqgxlXQsLd6/PPGP1GDanQRGwmAsNcaYMsbC2AL1Vh2rZ5FNZqk6VayuhZ7QMZIGRtJAlmU8yRPrGDgsZBZw+g5WzyKXynHx3kXmxueQIhJFrUitdd84DBUqVKhQoUK9L/I8j7/3n67wC9+5AcBfPHOMP396hlIJwnZmqFDvro+MKTWqh1Xv9ps+t5/psdc0KapFtjvbuybHTaWmgsf4xtS97r2AuzSayAICMywhJ9jp7gQGDQgTZ+/0wNHk12hayP/Zam/tWwUc/V29VafrdukNe49MhT1qEp6vc5vneC73HC+VXwoSX/7xeVj1UZbF6POPFT/GM4VnuNe9R1EtcrZ+lvH4+LtOy3vYvu6Vb4I9ik8VKtT3koy4gdWzAjNEj+l4nhfUwFzXZcPeYOgNcV2X1eYq85l5KnYFp+cwGA6oOlVc16Xu1LG6FmWrjN2zUaIKG/ZGYLhYfQskODIu0lnT2jT2wEaNqTgDMTlPkiSMhEFr0OJJ40luWbd4wniCdCLNbes2nyx9klVzFS2uISNjxAwikQhL2SXSqTRFpYjVt6jbdVKxFEbSoGSURHLJmOFu7y7zY/MgCR7W9bvXeav+FtvtbZGg8iT0qM6ms8lsehazb4pEVd+kpJW4ad7k2NgxVporIIn3dCTYdDZZGF8ACfHjidpirVVjLj3H0B3ytVtfo9Fp0B/2uWXfIiJFOJA+gCwLlpbZM4OJekWtyIb9znvjcnNZsKYiEpd3LqPGVMy+iSzJpONpZEmmpJU4oB8gEokILljfJqfkaA/aVJ0qx8eP8+3yt1nILBCJRDg2cSxMgoYKFSpUqFDvk1zX4//xv13i3716C4C/+rGn+K/OHKJUAlV9vGsLFerDoo/sv1x9s2k/DtKo/ETR6ESkvUZVxalwYvJEYGIV1eJDk0VAYKSczp4Oqnt+be2AcYAz+TOBgTXKQxrlQY0aPRvWBt1Blw1rI0hQjVb9Rp/f/92GtYERNXi18ipRKfqu/KZR7ZdkOp09zZq1xhee/MKuauB7TT356yvbZTLxDN/Y+AbHx48HkwL9yYj+djzP42z9LJPJyQf29WHbDidPhfqoaHQ6H4h0zm37NicmT7DZ2aTZayJ5EjIyyDCXmaPSquAMHCZTk1zduUpeySMhUWmJ17OHh9W1WG2sokZUXFzsgY0e1wU4fec6RaWIJEloUY3VhgCe40Gz06TRaeB5HplEhryaZzw5TjqZ5tOlT3Nh6wKH9ENc275GQS0gyzIlvYSRMJAlGXtgc2T8CObAxBt6wRrUiMpN8yanJk6J5NZ9zp09tDloHGS7u43ds2n2miJ9lDJIJ9M4PQezayJ54r1jLj3HDfMGTtcJmFt5NY81sLC6lpjW6cFKY4WnJsUQBnfo8vsbv8/JsZNsWBu8XH6ZhfQCVt8SJtweY1CLalzfvo7y/2/vzqPbqs+8gX+vdkv3SvKqxXJWO3b2xSHBLA0DGcKUlyFdDpT2ANPSaTsk78vSaRtoIZ3SKZmW4dCFlre003bO+0JoeE9op6UUBkgokAJxnJJA4sRxEjuyJMdOLF0t1nZ/7x8XCdtxyNJYcsL3c47PwfKV9OjGura/PL/nZ9CXKwsh0OxuRjKThGJUMN0xHRIkOM1OfedBg96BGkzo11HFpOBIXL8W9qf6McM9A0aDEf3D/VhUuwiJfGLUvzkRERFNrFxew5d//Rc8tb0HBgm4s20hPnXRVPj9gM1W7uqIzh8f2lDqZEv1CrOhRnb5AEBaSxeHfY/tOip8Dui7+BUGjI/3x8HI0GjkUPKTLT8bb57TeI8rJFGsZWyINuq4ESHRnqE9WORZpA/2fW8A8NjXP57xusdGzmoqnI9C11O1tboYLo193PFCKyEJ1FTUjDqnhfCwMHNre3g75lTOwTvH3jmjQI3ow0jNqrjMfxm6h7qRz+eRGE7AJ/vgsrkwRZmiDzoXEmY6Z6In3oPlnuVI5pIwGA346LSP4lj2GJwWJ4QkMMM5A13RLsSGY3AY9SVw8VwcTe4m7D22Fz2xHhyJHUGtrRYHogcA6N1FfXF9J9B4Tg+M3BVuuG1uxHP67nnhVBg+uw+dxzsRTeszooKJIGSLDAig87i+W+BgdhALaxeiY6AD8VwcdrMdoVRI73B16hs7NDmbMJwfxgznDMhm/f598T747X79cSqbYDQa4ZN9ULMq1Jw+06rOUYdIKoJZrll4re81LKxeiP3R/YgOR3FEPVIMwRZUL8Ab/W+gzdOGzQc2w+fwYZFnEXoSPfA7/BDi/blWsYy+SyAAQOizvRxGB47Ej0CSJCg2BUqFgnpXPVw2FyABQTUI2Sxj7/G9sEk2/OHQH3Bs+FhxJ0Kv3YtULoXmqmY0yHpXVnGJIBEREU24bF7DHRt3YvPOIAyShC9fshg3XBSAzwdYLOWujuj88qENpU62VK8wG2psiFKYSTSyc2nscrST7Ww3MngZL9AqhEhju4HGfr3wUTim8N/1cj1sRtuoGU+1FbXjdhkF40FUW6sRSUawsHoheqO9WFy7GIAeNp3Ocr7xlgKOfJ3A+8PU51bNxTvH3gGAYqg0sqYj6pETZl11He/CvOp5xflfuwd3Y3Ht4mJIlcln4La58e7xd9HqaWVnANEpOC1OJPNJKDYFs6pnQZM0PRwxORDLxBDLxOB1eNGf6ketrRaJXAKKRUFzZTMOxQ/hI/6PQDJK8Nl96Fa70ehu1He6TOpBk2yWsX9IH0gum2WEkiFEUvrgbgGBUCIEh8kBSIDL4iqG4NF0FPlcXg+fjDKC8SDUtAq70Y6+eB+gAcFYEAICPrtPX66nzEQkEcEMZQYkoXdlucyuYigjSRIMJgOaq5vhsrkQSUWQ1/KIZ+OIpqPwVnjReawTslFGX6wPx5LHoKZUOM1OuG1utFS1QM2qmK5Mx2uh1zBDmYFwKqwPTbc5US/rywZbXC14PfQ6LvdfDqvJCofFAb/DD5fNVewMe/fouziWPIZgNIigGkSFqQKKSUFXtKs4U1DL64FdLB2DmlGR1/KIpqN6163dj12Du9DibsHR5FGEk2HMqpwFo9GIBqe+pM9lc8Ftc/M6SEREVCKZnIbb/88O/H5XCCaDhHWXL8Enl/nh9QImU7mrIzr/fGhDqfEUBqBbJAvCiTCqrdUIxoMIKAFYjHrkXQhRxoYrmqadEFYVnKrbKZKMoMpShSf3PokqS9UJgVgkGSnef+RjjdexVHgdI7uMtoe343DsMHqiPdA0DS/3voy5VXMxkB7AlVOuLO4YJYSARbLAYrR8YPfRyTq2xhumPpgeRKuntRjYARj1WgGMCt36En24InAF3jn2TjFEm1c9D6FkqBj8mQ1mDA0PYUndEv4hRnQaCsv5WipbEElG4LK40FzZjH3RfagwVUDNqHpIZXHAYXagK9qFaDqKUDKEJZ4l2Bfdpw/azifQWtuKSDKCfD4PaPoMJwh9eZrIC+w/vh+Nzkb47O91IaVVfec5gwFuqxuKRUE0HUUsHYNiUdCldqHR1YhutRsSJNRU1OBw/DAUswK7xQ41q0IxKwglQ1DMCmDQuymNRiPqXfWY4p6CBleD3v15bA+yuSzUtIpjiWN4NfgqqsxV6FF7UFtRi5AaQufxTsQzcWiShkg6gmQ+ibyUR0JLoMGpdx0VlvzZjXbEs3F4K7wwwADZJAPQZ3SZTCYE5ADSIg2vw4vqimp4HB4E40HEMjEIIRBOhRFOhqFJGpxWJ4a1YchWGR67B6GE/noKnWKJfELvGEu+F4CZ9aWGdRV1GM4Po6myCXOq5yCRS7ArioiIqEyyeQ1rn9iBF/ZEYDYY8I0rluLjy7yoqwM40pHo7PCtM4IkScU/SuZVzysuDRvZfQRg3GVlI5eXnarbaaRgPIi5VXPxTPcz+Lspf4etwa3Fxy4ET3Or5mL34G5omgZN04qDvceGT4UZToVwrF6ux66BXehP9MNlceGdwXeQ1bJoqmrCu8ffHbV0sbCssDDXqhD2nGwm1Hi3j+2gGrtDXyFU2hrcWnytYwfDj31NhfNdGJ4O6EsF59fMP2GXPyL6YEajEXNr5qLB1YB4No5mdzMicb2TSDbKUEwKDsYOYqo8FfFsHHajvdhVJCDgsrgQSoXQ6G7EwfhB2E12JLIJdA51wm6242jmKBorG5ESKRiM+lLeWCaGvoS+ex4kIJaOQROaPiQ9raLJ2aQviataAEB/v8+v0t/ffdE+CE3oAZZZQTQTLe5qFx2OQs2oxSHub0XeQq21Fm8Pvg2Pw4O9Q3vRUtWCPdE9mOachuH8MDRJg9fuRR55JNNJzJBnAAIwwogmVxN61V4MpYaK11mP3YNELlFc6ndAPQCP3YO+ZF9xeLtH9sBoMEJI+iD5vMgXd8pbWLMQgL6roGyS9d0IoS+1E5reKSab9NsK87gK8wML50q2yJAkqdgNxblRRERE5ZHLa7hz4048/64eSH3zqqVYvawO1dUAfzQTnT2GUuMY2eFT+OXf7/Bj9+BufVemEZ8XlpUVhpaP7LAqGBlqjR3+XXiuTzV/CgfUA5hbPXfUTCYhBAaGB+B1eJERGWS1bPHxCkFPIXwqdG0VAqq+RB/cNjcqbZXoifVg1bRVsBgtsBltWFK3BH2JvmLo5rP78HLvy/DZfaPOxcjuJyEEeqI9eCv0FnpjvR84IP5kJEnCqmmr0BXrQrWtGpqmjdpBr/C6CqFgIcgaOTz9ZMsHiej0FMINo9EIxabAaXUiloshkUtgcd1iDKYH4XV4kcwn9euQJBBOhKFYFNQ76rHz6E5c6rsUhxOH4bF7IBtlHBg6gBnKDCSzST1YkQNI5BJwmB2QICGW1Tuj1KwKCfp1NZqOQs2qqLPVoT/VD9kiw+/wI5wKQzbLiAxH4LA4cCB6AHktj0QmAYNkgE/2oX+4Hx67B3uO7cHeY3sxQ5mBbrUbC6sXoj/Vj4vqLsKx4WOYVzVPD4GgDxbvT/WjubIZMELf2U/xw2l1Yt/QPsgmGdF0FH2pPsx0zkRKS8Hv8MNhdiCcCGOGMgOv9b2GJlcTJEmCX/bDJJmgQYPf4Uc8G4ckJKhZFbOrZmNYDGOZbxncdjcSmj7HS82piGfiOJI4og9bf28wu9PmLP5PAYPBAJfVhVhW/7pskfVdDomIiKgs8prAlzf9pbhk7/4rW3HdRbVwuxlIEf21GEqNY7zlaX2JPsyrnoeOox2osdWg42jHqGVlhaHlIzusRhoZpJxsULhP9qHOXlcclN6X6CsORQ8oAVgN1nGX1vUl+lBZUYmslgWA4vPUy/UYGh5CnVyH+bXzi3NIAkoAO/p3jArPOo52oM3bho6jHQDe74QauZNgMB7EkfgROEwORJKRE4KhsQHWyXbdK5wr2Szjj4f+iBpbDcKJ8AlB3chQcOT54256ROdOITyqd9RDQEAySGiubIbJaILf4Udfog8SJH03uqyqb2rgWYr+4X5c5rsMkWQE8WwcngqPvqRNCejDvYejkM0yjJIRDosDsknWd9ez+yBJUnH4eTAeRDwbh2yV9YHdVidcFhcUiwLFpEDN6KFVJBXRh7NbXUjmk1hUswiv9b0Gb4UXHrsH/al++Ow+GI1GtFS1wGqxoqW6BZUVlQgN68PBu9QutHpa0R3rxiz3LISSIUiQoEGDbJIRSoQQz8TR5GpCUktiTvUcuCvcMBr0TqlkPonpzun67n9mB4wGIxxmB9RhFW8E34DI6wPOZbMeItU76otdYvWOeuw/tr84x8tb4UUkFUEsE0OjsxHtkXbIJnlUN1RADsAgGeCyuLhkj4iIqEw0TeBr/+9t/GZnH4yShK9fsQTXL6uDkz+aic4JhlKnaexufSOXvo08phCmABg9KH1EkDJyOd/IzqaT7epXCGIanA3FpXVju62sBivMBnPxfoXAxuvwwma0oV6uHzXsvDCAvPBcS+qWoCvWhSV1SwC8HzCN3ElwZPdDYU7UyFpGBlhH1CMYzg8Xl9yN5bF7cDx9HNW2auwe3I1KayXSWvqkc7cYRBFNDIPBgDnVc2A0GvWAXTLAZdNDkL54X3GHPQMM+hK693aSU8wK+uJ9ULOqvowvl9A7ojIxAEA0G9XfrwbAbXUjnNKHdPcl+wABhBIhVFurMZAagMPogMgLhNQQ8vk8ZIsMo9GIVl+rHo45/ah31MNoMMJpdSIgBxAZjuBS36XYN7QP4XgYfocf9Uo91KyKWDqmh1tWF+K5OBqdjTgQPQBvhRd9qh72h+Ih+O1+CAgYYAAkwOfwQbEqSOVTmF01e9RAdghA0zQksgk4TA4YJAME9A6yoewQBlODOJ45jv5kP+KZOBSzgmA8CNkkI5bRO56EpA+FF5pAd6wbM50zEZAD6Ip1YXHdYv3cjPm3aXA2wF3BQeZERETloGkC927ehafbj8AgSbjnisX4xMVeBlJE5xBDqdM0dre+kbv2jT0GANoj7ai2Vo+aMVUIbzRNw+6B3ai2VgPAaXcAjQyiCqHREfVIcRi7wWAoLhEEUJwTJUnSCcvfBtODWFK3RN/96b0h7R6754TOpMIsp8IxDc4GXOS7CIYRk/zGC7AAfcfCk3VMNTgbYDVZsaB2QTGgskgfPGCdiCZGoTunsGxMkvRwKY88ErkEZKsMyaAvS1MsCoKJ964xySDqbHU4mj4Kv8MPTWjI5/I4EjsCkRMYGh6CYlIAAC2VLQjFQ5CE3iXlc/jQm+jFxb6LcTB+UA+3zHbsj+0v1qVmVPgdfhgNRgScAbhsLmiahj3H9sBX4UNXrAteuxeapEHNquhL9MEv+4thUq/aC2+FF/3D/WhyN8Fl02diOS1OqDkVMAAuq0vfwc7q1jvGlHo4rU4IIfDOwDvYM7BH727KxhAZjsBj9yCeiwPvXdI8FR4M54ZRZatCJp/BdNd0QAKCiSAcJkdxuWIsE0NzZTO6Y91QbAoa3Y1IaklIBgktlS0IJ8Ood/D6R0RENFkIIXD/b9/Bxrd6YZCAr35kIW68xMdAiugcYyh1DhUCmN5YL1xWF3YP7gaAE3bLe/7w87jYc3Fx0PfJgqiRwVPhY2xX1cjHHzv3amSn1XjL3wpB1Y7+HcjkMxjOD6M90g4hxAnHtEfakc6lx+18GtvhVQjgjg0fG1XfSJIkodXTisH0ICRJwvya+cVlfURUfk6LE26Lvluey+IqDhSPpqIQeYHjyeMwaAYYJANa61phMOo/TvZF9wEA1LwKg2RALBtDNBXF3uN7IZtk+JX3Zi9JEhbWLMSb4TdxsedifRlgJg6vzYt4Ng6/rB+nQYOaVjGUGkJvrBedxzrR6G7EvqF98Nq8OBA9ALvBjlgmBr/dDzWtd0r1qr3w2r3oGOhAc2UzJEhoj7RjUfUiRJIRfRnhe8E5AH25oEVBPBOH0+JE51AnPHYP8iKPrqEuQACLaxZj1+AuNLr057cb9RCt0dWIgDOAxspGmA1mKBYFfocfiWzi/cd+bwfBJbVLYJSMkCCh3lFfDAMblIZRYT8RERGVjxAC3/rdu/g/fz4MCcCXL1uIz1xWD0Upd2VEFx7+BnwOFUKkSDICAwzwOrwIKIHiUr3C8rarp16NA+oBrJq26gNDmELIJIQYtRPdyGApoASKtxXmXo23K914XVgjlyRajBYcTx2Hy+pCb6x31NLAQv0atNM+DxmRQZWtCpIk4WjqKDRNQ0+0B72x3mJoVQjXRr4GIpocJEmCu8KNKa4pcFe4oWZVOK1OhFIh2C12HFQPok6ug8FggMFggMPowL6hfagx1yCUCEHk9Pe40AT6kn2oMFUUN2Col+thMBigZlVMVaZiW2QbFtctRr2zHi6bCwAQTUUxNDyEkBpCTsuhc6gTeS0Pu8mOrqEueCu8CCVDmOGagZ5ED5qrmhFM6I9vM9gQUkNoD7Wj1lqLIzG9o7TGWoOdAzvhc/j0JXEGqTjfas/gHmiahmg6img6iiZnE7qOd8EII1o9rXBZXdgf3Y8F1QvQcbQDtbZavBJ8BXW2OoRSIRgNRjS4GuC0OiFBQjAWxFBqCLFUDD2xHqgZFfVyPUKpEOqd9XBXuLksj4iIaBISQmDDH/biF68dAgDcedl83PKRAAMpognCUOocKoQ8rZ5WWIyW4u2FnfcKy9uMRv2PnGA8OCqkGbvMrRAySZKEgeGB4k57hUHihccuhE1jO6XGDlQfuXyw0PFUWPbX4GyAT/bBAAMiyciopYEAML9mPoaGh4qPM9LY5ynMuCoMZY8kI8jkM+hVe4uB18j7cF4U0eTntDihZlR9B7tkP6Yp05DKpaCYFWjQ0Hm8EzOdM7E3uhfTndNxKH6oOKDcW+FFKpvSd++T6xHLxCCEQCKjLw1sdDYikU9ANsrYN7QPdRV12Dm4E3ajHX3JPiTSCTiMDqgZFS6rqxhqOcwOpHIpLKxeiHAyDKfZCa/di7ePvQ2PwwNhEDAYDUjkElAsCiLDEdTZ6hBUg9hzdA/yuTzUtAo1o0I264POo+koYukYwqkwWqpb4FW82De0D5rQoJgVxHNxyCYZyVwSXtt7OxTa/foOeRkVAgKa0LAvug+hZAjRbBSJbAICQg+mHPVQMyoHlxMREU1SD7+wD//7lW4AwP+6ZB4+t2IKAymiCcRQ6hwaOXeqEESNN8Ac0IOctJZGOpdGe6R91NK8Ar/Dj10Du4odS5IkjbtzXyFsCsaDmFc9T98lb8zg8cJzFpbrjbekLqAEYDVZ0eppLS4NrLZWI5wIY2B4AB67B2nt/SV84w04L5yHwlD2vkQf5lbNxfH0cZgNZtQ6agGcuOSPiCa3wtwpo9GI2dWzUWWvQkAJQLEqkKDvphlJRXCx92Icjh/GVHkqDqoHMdM5EyktBb/DD5fVhVg6VpzH5JN9MBlNcNlcCCgBhIfDWFy3GK+HXseCqgU4GD+IRlcjJKOEgDMAt80NSPq1RxMajAYj/LIflfZKBOQABARi6RgcBgcggGZXMyRJ303QaXFCMSkIp8JwmB3IiRx2Du5EhbFCD5M0PTSSTTI0oUFo+oDzZDaJ5spmJHIJOMwOJDJ6wCVbZNQ79c0fJIN+bpwWvUsqlAhhujwdEiQYYYTPoe8aOHZuFxEREU0uP3hxP374UhcA4PaL5+ALV05lIEU0wRhKTZDxZjiNXTpnNVhxPH0cc6vmAsAJIU1fok8fnisyxZlSYwMgQA+bCuHR0dRRABh38PjYHQRPFggV6q2X67HlyBbMrZoLSdKXuUhCGvW8Y59nbMdXYaj6Uu9SXOS7CBWmiuKx7I4iOj9JkgS3za0PCLe5i0F0QA7AZDJhSe0SuO1uNLmbEElFUFdRh33RfXqA9d57PprWO4gCsj7AXM2oaHI2YefRnbjUeykSuQS8Ni8k6F2gEiQoFj0AK3QdCaGHUIXuJgA4EDuA6e7pOKgehCRJUMwKYukY+pJ98Dq88Nl9CCfCGM4OY17lPLx97G34HD4ktASaKpv04eNCQm+8F+qwCodZ331QNuvdUT7ZB8Wmv47CDCjZLCMYDyKajkKxKvDLfqRFGq3eVrTUtKDKXsVrHRER0ST34y1dePgFfTbmF5fNxu0rp0OWy1wU0YcAQ6kJcqrQpfBH3FLvUgymB8cdeO53+HFs+Bgskr4UsNpajR39O4ph0si5T7sHd8NtcxfDoKOpoyeETmN3EBxb29jB6sF4EFcErsC7x98tzn6ymqzFHQZHBm+FMGpsx9fI88AgiujCM2rnvvdCKneFG26bPo+qXq5HJBVBo7MRfYk+OC1OaEJDOBFGTsshmo7qA9TTUewb2odFtYvQMdiBeDYOr+JFIp+AZJAQzUQB6F1S8WwcEiTkkdeHoWsajsSPYCg1hDpbHd7oewNzq+Zix9EdiKVjCCaCsJvsODB0APFcHM1VzZhVPQsDmQFc5rsMoUQIslFGIpvA7KrZiOfjUMwKorkoEjl96R0A5PN59MX7AA16d5hFhppVkcjqXVQSJKgZFW6bG7OrZ8NoNMJldZXzn4eIiIhOw8/+1I3vPtcJALhtaTP+59UzGEgRlQhDqTL7oKCmL9FX3JUuoASwe3A3XGYXtoe3ozfWi+H8MI6oRyBJErwOLyQhYffAbtTYaooh0JkYu6MfgOKyvfFqHfl5IdAq3IfL8og+vApBlSRJ+tI8WZ+lF5D160Uil0CdvQ7JXFIfCp4IQjbLsJvseK3vNcxUZsJutqM/2Q9fhQ/RdBTxdBya0KBmVGiapi/fE0bIZhl98T547V5EkhHAACyoWYADQwdgN9pxPHUc0AB1WO+s8tl9iGfjgABko4z9x/fDbrJDMkj6kHJJgq/Ch2QuiYBd7xiVICGWjendW7IX4eEw/LK/OLRdsSgwSkY4rc7i6x55DoiIiGjy+tXrh/Dt3+8BANyyZBbuuqaRgRRRCTGUmsTGLgH0OrzoH+6Hy+rC7oHdKGyGV+iOGkwNQrEo2DWw65Sh0HiD1Ucu29s9uLv4vIXZWKdT63gdX0T04VUIpgrL/AD9emEymFAv18NpdaLeUY94Ng6TwYTL6y9HSkuh0loJv6xv2hBKhGAz2hBOhKEJDbF0DGpWBSTAaXVC0zR0DXXBbrTDaXHCYDTAbXOjTq5DWktDtsg4pB5Cja0GB2IHIJtk7IvuQ17kISSBZC4JAaGHT5kYDEYDmqub4ba7YTAY4LQ64ba44XP49BlT7mbEs3EEZL3ztLCUkdc9IiKi88v/feMw1v/2HQDApxc14qsfZSBFVGoMpSaxsZ1JASWAKcoURNNRXBG4AkOZIQSUAILxIGorajE4PIhKWyUGUgOnfOzxBqYXFHb9K3QBnE7nE5fmEdHpKuz46a5wF2czTXFOQcAZQCKX0K8l73UuhVIheOweDKQH4HV4ixtJFHavi2VjMBqMqHPUwWl1IpKMwO/wo7mqGel8Go3uRiRyCUxVpuJw4jAaXY0IpUKY6ZyJ/lQ/6h318Mt+uK1uOK3O4uMaYCj+dyFYq7JXocHZUFyWx+sdERHR+evQQAL3/0YPpG6YPwP3/o9ZkGX+bCcqNYZS55GRc6iOZY6h1dMKSZKKwdGqaavQq/biisAVp93ZNF7YdKoh7UREE2HkkjenxQk1o6KlsgUmgwlN7iYYJSMCSgANSkNxxz63xa3v4ieZYDAasMSzBKFUCEajEXOq56DKXoV6uR6VFZVYUrsEBoMBLZUtSGkpXOS9CJX2SlRWVBY7nUYOced1j4iIqHweffRRTJs2DTabDcuXL8ebb755Th9/Wo0D3/3EAnxq8XR847oWBlJEZWIqdwF05gpB0XifXzP9GgTjwdPubDrTrxERlUIhoAKABmfDqK+5bK4T/rsKVdA0DcFEEPWO+lGP467Qh6+P1KCMfkwiIiKaPJ566incfffdeOyxx7B8+XI88sgjWLVqFTo7O1FXV3fOnucTrQF8ovWcPRwRnQV2Sl1g2NlERB9WBoMBDUoDDAb+aCMiIjqfPfzww/jHf/xHfPazn8WcOXPw2GOPwW634z/+4z/KXRoRnWP8zZ2IiIiIiIgmhUwmg/b2dqxcubJ4m8FgwMqVK7Ft27Zx75NOpxGLxUZ9ENH5gaEUERERERERTQoDAwPI5/PweDyjbvd4PAiHw+Pe58EHH4TL5Sp+NDRwmT7R+YKhFBEREVGZnOkg302bNqGlpQU2mw3z58/Hs88+W6JKiYgmr3vuuQfRaLT40dvbW+6SiOg0MZQiIiIiKoPCIN/169djx44dWLhwIVatWoX+/v5xj3/99ddx00034bbbbkNHRwdWr16N1atXY/fu3SWunIho4tTU1MBoNCISiYy6PRKJwOv1jnsfq9UKp9M56oOIzg8MpYiIiIjK4EwH+X7/+9/HNddcg6985SuYPXs2HnjgASxZsgQ/+tGPSlw5EdHEsVgsaG1txYsvvli8TdM0vPjii2hraytjZUQ0EUync5AQAgA4MI6ojArvv8L7kc49XuuIyu/Dcq0rDPK95557iredapDvtm3bcPfdd4+6bdWqVXjmmWdO+jzpdBrpdLr4eTQaBcDrHFE5fViuc3+Nu+++G7feeiuWLl2KZcuW4ZFHHkEikcBnP/vZ07o/f6cjKr/TvdadViilqioAcGAc0SSgqipcLle5y7gg8VpHNHlc6Ne6Dxrku3fv3nHvEw6Hz2jwL6AP//2Xf/mXE27ndY6o/AYHBy/o69xf48Ybb8TRo0dx//33IxwOY9GiRXjuuedOuAaeDH+nI5o8TvU73WmFUn6/H729vVAUBZIknbPiiOj0CSGgqir8fn+5S7lg8VpHVH681p1b99xzz6juqqGhIUydOhU9PT3nzR/DsVgMDQ0N6O3tPa/mxJyPdbPm0ohGo5gyZQqqqqrKXcqktnbtWqxdu/as7svf6c7P98ZE4vk40USfk9P9ne60QimDwYBAIHBOCiOis3e+/AFxvuK1jmhy+DBc685mkK/X6z2j4wF9+K/Vaj3hdpfLdd79Un6+Di8+H+tmzaVhMHC870Th73TvOx/fGxOJ5+NEE3lOTud3Ol4JiYiIiErsbAb5trW1jToeAF544QUO/iUiIqLz1ml1ShERERHRuXWqQb633HIL6uvr8eCDDwIA7rjjDqxYsQL//u//jmuvvRYbN27E9u3b8dOf/rScL4OIiIjorDGUIiIiIiqDUw3y7enpGbW855JLLsETTzyBb3zjG7j33nvR1NSEZ555BvPmzTvt57RarVi/fv24S/omq/OxZuD8rJs1l8b5WDOdf/h9NhrPx4kmyzmRBPciJSIiIiIiIiKiEuNMKSIiIiIiIiIiKjmGUkREREREREREVHIMpYiIiIiIiIiIqOQYShERERERERERUckxlCIiIiK6gDz66KOYNm0abDYbli9fjjfffPMDj9+0aRNaWlpgs9kwf/58PPvssyWq9H1nUvPjjz+Oyy+/HJWVlaisrMTKlStP+Ronypme64KNGzdCkiSsXr16Ygscx5nWPDQ0hDVr1sDn88FqtWLWrFkl/x4505ofeeQRNDc3o6KiAg0NDbjrrrswPDxcomqBV155Bddddx38fj8kScIzzzxzyvts2bIFS5YsgdVqRWNjI375y19OeJ10YTjV95sQAvfffz98Ph8qKiqwcuVK7N+/vzzFlsCDDz6Iiy66CIqioK6uDqtXr0ZnZ+eoY4aHh7FmzRpUV1dDlmV84hOfQCQSKVPFE+snP/kJFixYAKfTCafTiba2NvzhD38ofn0ynAuGUkREREQXiKeeegp333031q9fjx07dmDhwoVYtWoV+vv7xz3+9ddfx0033YTbbrsNHR0dWL16NVavXo3du3dP2pq3bNmCm266CS+//DK2bduGhoYGXH311QgGgyWr+WzqLjh06BD++Z//GZdffnmJKn3fmdacyWTwt3/7tzh06BCefvppdHZ24vHHH0d9ff2krfmJJ57AunXrsH79euzZswc///nP8dRTT+Hee+8tWc2JRAILFy7Eo48+elrHHzx4ENdeey3+5m/+Bjt37sSdd96Jz3/+8/jjH/84wZXSheBU32/f/e538YMf/ACPPfYY3njjDTgcDqxataqkQW0pbd26FWvWrMGf//xnvPDCC8hms7j66quRSCSKx9x11134r//6L2zatAlbt25FX18fPv7xj5ex6okTCASwYcMGtLe3Y/v27bjyyitx/fXX45133gEwSc6FICIiIqILwrJly8SaNWuKn+fzeeH3+8WDDz447vE33HCDuPbaa0fdtnz5cvHFL35xQusc6UxrHiuXywlFUcSvfvWriSpxXGdTdy6XE5dccon42c9+Jm699VZx/fXXl6DS951pzT/5yU/EjBkzRCaTKVWJJzjTmtesWSOuvPLKUbfdfffd4tJLL53QOk8GgNi8efMHHvPVr35VzJ07d9RtN954o1i1atUEVkYXorHfb5qmCa/XK773ve8VbxsaGhJWq1U8+eSTZaiw9Pr7+wUAsXXrViGE/vrNZrPYtGlT8Zg9e/YIAGLbtm3lKrOkKisrxc9+9rNJcy7YKUVERER0AchkMmhvb8fKlSuLtxkMBqxcuRLbtm0b9z7btm0bdTwArFq16qTHn2tnU/NYyWQS2WwWVVVVE1XmCc627m9961uoq6vDbbfdVooyRzmbmn/729+ira0Na9asgcfjwbx58/Cd73wH+Xx+0tZ8ySWXoL29vbjEr7u7G88++yw++tGPlqTms1Hu9yFduA4ePIhwODzq+8vlcmH58uUfmu+vaDQKAMWfEe3t7chms6POSUtLC6ZMmXLBn5N8Po+NGzcikUigra1t0pwLU8meiYiIiIgmzMDAAPL5PDwez6jbPR4P9u7dO+59wuHwuMeHw+EJq3Oks6l5rK997Wvw+/0n/FE/kc6m7ldffRU///nPsXPnzhJUeKKzqbm7uxsvvfQSPvOZz+DZZ59FV1cXbr/9dmSzWaxfv35S1vzpT38aAwMDuOyyyyCEQC6Xw5e+9KWSLt87Uyd7H8ZiMaRSKVRUVJSpMjrfFa7l5bzOl5Omabjzzjtx6aWXYt68eQD0c2KxWOB2u0cdeyGfk127dqGtrQ3Dw8OQZRmbN2/GnDlzsHPnzklxLhhKEREREdF5acOGDdi4cSO2bNkCm81W7nJOSlVV3HzzzXj88cdRU1NT7nJOm6ZpqKurw09/+lMYjUa0trYiGAzie9/7XklCqbOxZcsWfOc738GPf/xjLF++HF1dXbjjjjvwwAMP4L777it3eURUQmvWrMHu3bvx6quvlruUsmpubsbOnTsRjUbx9NNP49Zbb8XWrVvLXVYRQykiIiKiC0BNTQ2MRuMJu+ZEIhF4vd5x7+P1es/o+HPtbGoueOihh7Bhwwb893//NxYsWDCRZZ7gTOs+cOAADh06hOuuu654m6ZpAACTyYTOzk7MnDlzUtUMAD6fD2azGUajsXjb7NmzEQ6HkclkYLFYJl3N9913H26++WZ8/vOfBwDMnz8fiUQCX/jCF/D1r38dBsPkm15ysveh0+lklxT9VQrvk0gkAp/PV7w9Eolg0aJFZaqqNNauXYvf/e53eOWVVxAIBIq3e71eZDIZDA0NjeoQKuXPvlKzWCxobGwEALS2tuKtt97C97//fdx4442T4lxMvqsyEREREZ0xi8WC1tZWvPjii8XbNE3Diy++iLa2tnHv09bWNup4AHjhhRdOevy5djY1A/puUg888ACee+45LF26tBSljnKmdbe0tGDXrl3YuXNn8ePv//7vi7utNTQ0TLqaAeDSSy9FV1dXMUADgH379sHn8014IHW2NSeTyROCp0KoJoSYuGL/CuV+H9KFa/r06fB6vaO+v2KxGN54440L9vtLCIG1a9di8+bNeOmllzB9+vRRX29tbYXZbB51Tjo7O9HT03PBnpOxNE1DOp2ePOeiZCPViYiIiGhCbdy4UVitVvHLX/5SvPvuu+ILX/iCcLvdIhwOCyGEuPnmm8W6deuKx7/22mvCZDKJhx56SOzZs0esX79emM1msWvXrklb84YNG4TFYhFPP/20CIVCxQ9VVUtW89nUPVY5dt8705p7enqEoihi7dq1orOzU/zud78TdXV14tvf/vakrXn9+vVCURTx5JNPiu7ubvH888+LmTNnihtuuKFkNauqKjo6OkRHR4cAIB5++GHR0dEhDh8+LIQQYt26deLmm28uHt/d3S3sdrv4yle+Ivbs2SMeffRRYTQaxXPPPVeymun8darvtw0bNgi32y1+85vfiLfffltcf/31Yvr06SKVSpW58onxT//0T8LlcoktW7aM+hmRTCaLx3zpS18SU6ZMES+99JLYvn27aGtrE21tbWWseuKsW7dObN26VRw8eFC8/fbbYt26dUKSJPH8888LISbHuWAoRURERHQB+eEPfyimTJkiLBaLWLZsmfjzn/9c/NqKFSvErbfeOur4X//612LWrFnCYrGIuXPnit///vclrvjMap46daoAcMLH+vXrJ3XdY5UjlBLizGt+/fXXxfLly4XVahUzZswQ//qv/ypyudykrTmbzYpvfvObYubMmcJms4mGhgZx++23i+PHj5es3pdffnnc79FCnbfeeqtYsWLFCfdZtGiRsFgsYsaMGeIXv/hFyeql89upvt80TRP33Xef8Hg8wmq1iquuukp0dnaWt+gJNN65ADDqPZVKpcTtt98uKisrhd1uFx/72MdEKBQqX9ET6HOf+5yYOnWqsFgsora2Vlx11VXFQEqIyXEuJCEmaR8rERERERERERFdsDhTioiIiIiIiIiISo6hFBERERERERERlRxDKSIiIiIiIiIiKjmGUkREREREREREVHIMpYiIiIiIiIiIqOQYShERERERERERUckxlCIiIiIiIiIiopJjKEVERERERERERCXHUIqIiIiIiIiIiEqOoRQRERERERERlcWKFSsgSRIkSYLFYsHs2bPxxBNPlLssKhGGUkRERERERERUckIIdHR04KGHHkIoFEJnZyeuueYa3HLLLTh48GC5y6MSYChFRERERERERCW3f/9+qKqKa665Bl6vF9OnT8dtt92GfD6Pzs7OcpdHJcBQioiIiIiIiIhKrr29HZWVlZgzZw4A4MiRI/j6178Oq9WKBQsWlLk6KgWGUkRERERERERUcjt27EA0GoWiKKioqEBDQwNeeOEFPPbYY/D7/QCAj33sY6isrMQnP/nJMldLE4GhFBERERERERGV3I4dO7BmzRrs3LkTf/rTn7BixQrcdddd+Id/+IfiMXfccQf+8z//s3xF0oRiKEVEREREREREJbdjxw5ccsklaGxsxNKlS/HjH/8Y//Zv/4ZDhw4Vj7niiiugKEr5iqQJxVCKiIiIiIiIiEqqu7sbQ0NDmDdvXvG2OXPmYObMmXjiiSfKWBmVEkMpIiIiIiIiIiqp9vZ2mM1mzJo1a9TtV111FTZv3lymqqjUGEoRERERERERUUnt2LEDTU1NsFgso25fuXIl2tvbceTIkTJVRqUkCSFEuYsgIiIiIiIiIhrPli1b8KMf/QhPP/10uUuhc4yhFBERERERERFNSitXrsRf/vIXJBIJVFVVYdOmTWhrayt3WXSOMJQiIiIiIiIiIqKS40wpIiIiIiIiIiIqOYZSRERERERERERUcgyliIiIiIiIiIio5BhKERERERERERFRyTGUIiIiIiIiIiKikmMoRUREREREREREJcdQioiIiIiIiIiISo6hFBERERERERERlRxDKSIiIiIiIiIiKjmGUkREREREREREVHIMpYiIiIiIiIiIqOQYShERERERERERUcn9fyOlplLCAz5tAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import anndata\n",
    "import squidpy as sq\n",
    "import matplotlib.pyplot as plt\n",
    "from nhood_enrichment import nhood_enrichment\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "points_per_area = 5\n",
    "def sample(R):\n",
    "    points = int(points_per_area * R**2 * np.pi)\n",
    "    theta = np.random.rand(points) * 2 * np.pi\n",
    "    radius = np.sqrt(np.random.rand(points)) * R\n",
    "    x = np.cos(theta) * radius\n",
    "    y = np.sin(theta) * radius\n",
    "    return np.column_stack((x,y))\n",
    "\n",
    "\n",
    "R0 = 1\n",
    "R1 = [2,4,8,16,32]\n",
    "\n",
    "zscore_analytical_list = []\n",
    "zscore_squidpy_list = []\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(2,4, figsize=(3*4,6))\n",
    "\n",
    "for i,R1i in enumerate(R1):\n",
    "    z_an = []\n",
    "    z_sq = []\n",
    "    for _ in range(15):\n",
    "        xy_A = sample(R0)\n",
    "        xy_B = sample(R0)\n",
    "        xy_C = sample(R1i)\n",
    "        xy = np.vstack((xy_A,xy_B,xy_C))\n",
    "        labels = ['A'] * len(xy_A) + ['B'] * len(xy_B) + ['C'] * len(xy_C)\n",
    "        adata = anndata.AnnData(obs={'labels' : labels}, obsm={'spatial' : xy})\n",
    "        adata.obs['labels'] = adata.obs['labels'].astype('category')\n",
    "        sq.gr.spatial_neighbors(adata,coord_type = 'generic', radius = R0)\n",
    "        zscore_analytical, _ = nhood_enrichment(adata, cluster_key='labels', copy=True, radius_test=(R0, 1.25*R0))\n",
    "        zscore_squidpy, _ = sq.gr.nhood_enrichment(adata, cluster_key='labels', copy=True, show_progress_bar=False)\n",
    "        z_an.append(zscore_analytical[0,1])\n",
    "        z_sq.append(zscore_squidpy[0,1])\n",
    "\n",
    "    zscore_analytical_list.append(np.array(z_an))\n",
    "    zscore_squidpy_list.append(np.array(z_sq))\n",
    "    a,b = np.unravel_index(i,(2,3))\n",
    "    for label in ['A','B','C']:\n",
    "        ind = adata.obs.labels == label\n",
    "        axs[a,b].scatter(xy[ind,0],xy[ind,1],label=label,s=2/R1i**2,zorder=-1 if label == 'C' else None)\n",
    "        axs[a,b].set_title(f'$R_1 = {R1i}$')\n",
    "        axs[a,b].set_xticks([])\n",
    "        axs[a,b].set_yticks([])\n",
    "\n",
    "zscore_squidpy_list = np.array(zscore_squidpy_list)\n",
    "zscore_analytical_list = np.array(zscore_analytical_list)\n",
    "\n",
    "z_score_analytical_mean = np.mean(zscore_analytical_list, axis=1)\n",
    "z_score_analytical_ci = 1.96 * np.std(zscore_analytical_list,axis=1)/np.sqrt(zscore_analytical_list.shape[1])\n",
    "\n",
    "z_score_squidpy_mean = np.mean(zscore_squidpy_list, axis=1)\n",
    "z_score_squidpy_ci = 1.96 * np.std(zscore_squidpy_list,axis=1)/np.sqrt(zscore_squidpy_list.shape[1])\n",
    "\n",
    "axs[0,3].plot(R1,z_score_analytical_mean)\n",
    "axs[0,3].fill_between(R1,(z_score_analytical_mean-z_score_analytical_ci), (z_score_analytical_mean+z_score_analytical_ci), color='b', alpha=.1)\n",
    "\n",
    "axs[0,3].set_xlabel('$R_1$')\n",
    "axs[0,3].set_ylabel('$Z_{AB}$')\n",
    "axs[0,3].set_title('Z-score (fast)')\n",
    "\n",
    "axs[1,3].plot(R1,z_score_squidpy_mean)\n",
    "axs[1,3].fill_between(R1,(z_score_squidpy_mean-z_score_squidpy_ci), (z_score_squidpy_mean+z_score_squidpy_ci), color='b', alpha=.1)\n",
    "\n",
    "axs[1,3].set_xlabel('$R_1$')\n",
    "axs[1,3].set_ylabel('$Z_{AB}$')\n",
    "axs[1,3].set_title('Z-score (MC)')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "# Assuming R1, zscore_squidpy_list, and zscore_analytical_list are defined\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import anndata\n",
    "import squidpy as sq\n",
    "import matplotlib.pyplot as plt\n",
    "from nhood_enrichment import nhood_enrichment\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "points_per_area = 5\n",
    "def sample(R):\n",
    "    points = int(points_per_area * R**2 * np.pi)\n",
    "    theta = np.random.rand(points) * 2 * np.pi\n",
    "    radius = np.sqrt(np.random.rand(points)) * R\n",
    "    x = np.cos(theta) * radius\n",
    "    y = np.sin(theta) * radius\n",
    "    return np.column_stack((x,y))\n",
    "\n",
    "\n",
    "R0 = 1\n",
    "R1 = 4\n",
    "\n",
    "zscore_analytical_list = []\n",
    "zscore_squidpy_list = []\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1,1, figsize=(3,3))\n",
    "\n",
    "xy_A = sample(R0)\n",
    "xy_B = sample(R0)\n",
    "xy_C = sample(R1)\n",
    "\n",
    "\n",
    "# Scatter plot for points with labels A, B, and C\n",
    "ax.scatter(xy_A[:, 0], xy_A[:, 1], label='Label A', marker='o', color='orange')\n",
    "ax.scatter(xy_B[:, 0], xy_B[:, 1], label='Label B', marker='s', color='magenta')\n",
    "ax.scatter(xy_C[:, 0], xy_C[:, 1], label='Label C', marker='^', color='gray')\n",
    "\n",
    "# Draw circles\n",
    "circle_0 = plt.Circle((0, 0), R0, color='black', fill=False,  linewidth=2)\n",
    "circle_1 = plt.Circle((0, 0), R1, color='red', fill=False, linewidth=2)\n",
    "\n",
    "# Add circles to the plot\n",
    "ax.add_patch(circle_0)\n",
    "ax.add_patch(circle_1)\n",
    "\n",
    "# Draw lines indicating the radius with LaTeX annotation\n",
    "ax.annotate(fr'$R_0$', xy=(R0 / 2, 0), xytext=(R0 / 2 + 0.65, -1), ha='center', va='center', fontsize=12, color='black', zorder=5)\n",
    "ax.annotate(fr'$R_1$', xy=(R1 / 2, 0.5), xytext=(R1 / 2, -0.5), ha='center', va='center', fontsize=12, color='red', zorder=5)\n",
    "\n",
    "# Draw lines indicating the radius\n",
    "ax.plot([0, np.cos(-np.deg2rad(45)) * R0], [0, np.sin(-np.deg2rad(45))*R0], color='black', linewidth=2, zorder=5)\n",
    "ax.plot([0, R1], [0, 0], color='red', linewidth=2, zorder=5)\n",
    "\n",
    "# Add legend outside the figure\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), frameon=False, ncol=3, columnspacing=0.25)\n",
    "\n",
    "# Set axis equal for better visualization\n",
    "ax.set_aspect('equal', adjustable='box')\n",
    "\n",
    "# Show the plot\n",
    "plt.title('Points sampled in a circle')\n",
    "#plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import anndata\n",
    "import squidpy as sq\n",
    "import matplotlib.pyplot as plt\n",
    "from nhood_enrichment import nhood_enrichment\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "points_per_area = 10\n",
    "def sample(R):\n",
    "    points = int(points_per_area * R**2 * np.pi)\n",
    "    theta = np.random.rand(points) * 2 * np.pi\n",
    "    radius = np.sqrt(np.random.rand(points)) * R\n",
    "    x = np.cos(theta) * radius\n",
    "    y = np.sin(theta) * radius\n",
    "    return np.column_stack((x,y))\n",
    "\n",
    "\n",
    "R0 = 1\n",
    "R1 = [2,4,8,16,32,64]\n",
    "\n",
    "zscore_analytical_list = []\n",
    "zscore_squidpy_list = []\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(2,1, figsize=(3,3))\n",
    "\n",
    "for i,R1i in enumerate(R1):\n",
    "    z_an = []\n",
    "    z_sq = []\n",
    "    for _ in range(10):\n",
    "        xy_A = sample(R0)\n",
    "        xy_B = sample(R0)\n",
    "        xy_C = sample(R1i)\n",
    "        xy = np.vstack((xy_A,xy_B,xy_C))\n",
    "        labels = ['A'] * len(xy_A) + ['B'] * len(xy_B) + ['C'] * len(xy_C)\n",
    "        adata = anndata.AnnData(obs={'labels' : labels}, obsm={'spatial' : xy})\n",
    "        adata.obs['labels'] = adata.obs['labels'].astype('category')\n",
    "        sq.gr.spatial_neighbors(adata,coord_type = 'generic', radius = R0)\n",
    "        zscore_analytical, _ = nhood_enrichment(adata, cluster_key='labels', copy=True, radius_test=(R0, 2*R0))\n",
    "        zscore_squidpy, _ = sq.gr.nhood_enrichment(adata, cluster_key='labels', copy=True, show_progress_bar=False)\n",
    "        z_an.append(zscore_analytical[0,1])\n",
    "        z_sq.append(zscore_squidpy[0,1])\n",
    "\n",
    "    zscore_analytical_list.append(np.array(z_an))\n",
    "    zscore_squidpy_list.append(np.array(z_sq))\n",
    "\n",
    "\n",
    "zscore_squidpy_list = np.array(zscore_squidpy_list)\n",
    "zscore_analytical_list = np.array(zscore_analytical_list)\n",
    "\n",
    "z_score_analytical_mean = np.mean(zscore_analytical_list, axis=1)\n",
    "z_score_analytical_ci = 1.96 * np.std(zscore_analytical_list,axis=1)/np.sqrt(zscore_analytical_list.shape[1])\n",
    "\n",
    "z_score_squidpy_mean = np.mean(zscore_squidpy_list, axis=1)\n",
    "z_score_squidpy_ci = 1.96 * np.std(zscore_squidpy_list,axis=1)/np.sqrt(zscore_squidpy_list.shape[1])\n",
    "\n",
    "fig, axs = plt.subplots(2,1, figsize=(3,3))\n",
    "\n",
    "axs[0].plot(R1,z_score_analytical_mean)\n",
    "axs[0].fill_between(R1,(z_score_analytical_mean-z_score_analytical_ci), (z_score_analytical_mean+z_score_analytical_ci), color='b', alpha=.1)\n",
    "axs[0].set_xlabel('$R_1$', color='red')\n",
    "axs[0].set_ylabel('$Z_{AB}$')\n",
    "axs[0].set_title('Z-score (local.)')\n",
    "\n",
    "axs[1].plot(R1,z_score_squidpy_mean)\n",
    "axs[1].fill_between(R1,(z_score_squidpy_mean-z_score_squidpy_ci), (z_score_squidpy_mean+z_score_squidpy_ci), color='b', alpha=.1)\n",
    "axs[1].set_xlabel('$R_1$', color='red')\n",
    "axs[1].set_ylabel('$Z_{AB}$')\n",
    "axs[1].set_title('Z-score (MC)')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "# Assuming R1, zscore_squidpy_list, and zscore_analytical_list are defined\n",
    "\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import ScalarFormatter\n",
    "\n",
    "fig, axs = plt.subplots(2,1, figsize=(3,3), sharex=True)\n",
    "\n",
    "axs[0].plot(R1,z_score_analytical_mean)\n",
    "axs[0].fill_between(R1,(z_score_analytical_mean-z_score_analytical_ci), (z_score_analytical_mean+z_score_analytical_ci), color='b', alpha=.1)\n",
    "axs[0].set_xlabel('$R_1$', color='red')\n",
    "axs[0].set_ylabel('$Z_{AB}$')\n",
    "axs[0].set_title('Z-score (anal.)')\n",
    "\n",
    "axs[1].plot(R1,z_score_squidpy_mean)\n",
    "axs[1].fill_between(R1,(z_score_squidpy_mean-z_score_squidpy_ci), (z_score_squidpy_mean+z_score_squidpy_ci), color='b', alpha=.1)\n",
    "axs[1].set_xlabel('$R_1$', color='red')\n",
    "axs[1].set_ylabel('$Z_{AB}$')\n",
    "axs[1].set_title('Z-score (MC)')\n",
    "axs[1].yaxis.set_major_formatter(ScalarFormatter(useMathText=True, useOffset=False))\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "# Assuming R1, zscore_squidpy_list, and zscore_analytical_list are defined\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n",
      "c:\\Users\\axela\\anaconda3\\envs\\iss_decoding\\lib\\site-packages\\anndata\\__init__.py:51: FutureWarning: `anndata.read` is deprecated, use `anndata.read_h5ad` instead. `ad.read` will be removed in mid 2024.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "osmFISH dataset (Genes)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import anndata\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "from nhood_enrichment import nhood_enrichment\n",
    "import squidpy as sq\n",
    "\n",
    "\n",
    "def load_osmFISH_data():\n",
    "    csv = pd.read_csv(r'C:\\Users\\axela\\Documents\\GitHub\\Points2Region_clean\\datasets\\osmFISH\\osmFISH_real.csv')\n",
    "    xy = csv[['X','Y']].to_numpy()\n",
    "    adata = anndata.AnnData(obs={'Gene' : csv['Gene'].to_list()}, obsm={'spatial' : xy})\n",
    "    adata.obs['Gene'] = adata.obs['Gene'].astype('category')\n",
    "    return adata\n",
    "\n",
    "def load_merfish_data():\n",
    "    csv = pd.read_csv(r'C:\\Users\\axela\\Documents\\GitHub\\Points2Region_clean\\datasets\\MERFISH\\data.csv')\n",
    "    xy = csv[['X','Y']].to_numpy()\n",
    "    adata = anndata.AnnData(obs={'Gene' : csv['Gene'].to_list()}, obsm={'spatial' : xy})\n",
    "    adata.obs['Gene'] = adata.obs['Gene'].astype('category')\n",
    "    return adata  \n",
    "\n",
    "datasets = [\n",
    "    {\n",
    "        'name' : 'Seqfish',\n",
    "        'adata' : sq.datasets.seqfish(),\n",
    "        'key' : 'celltype_mapped_refined',\n",
    "        'library_key' : None\n",
    "    },\n",
    "    {\n",
    "        'name' : 'IMC',\n",
    "        'adata' : sq.datasets.imc(),\n",
    "        'key' : 'cell type',\n",
    "        'library_key' : None\n",
    "\n",
    "    },\n",
    "    {\n",
    "        'name' : 'MERFISH',\n",
    "        'adata' : sq.datasets.merfish(),\n",
    "        'key' : 'Cell_class',\n",
    "        'library_key' : 'batch'\n",
    "    },\n",
    "    {\n",
    "        'name' : 'MIBI-TOF',\n",
    "        'adata' : sq.datasets.mibitof(),\n",
    "        'key' : 'Cluster',\n",
    "        'library_key' : 'batch'\n",
    "    },\n",
    "    {\n",
    "        'name' : 'Visium',\n",
    "        'adata' : sq.datasets.visium_fluo_adata(),\n",
    "        'key' : 'cluster',\n",
    "        'library_key' : None\n",
    "    },\n",
    "\n",
    "    {\n",
    "        'name' : '4i',\n",
    "        'adata' : sq.datasets.visium_fluo_adata(),\n",
    "        'key' : 'leiden',\n",
    "        'library_key' : None\n",
    "    },\n",
    "\n",
    "    {\n",
    "        'name' : 'osmFISH dataset (Genes)',\n",
    "        'adata' : load_osmFISH_data(),\n",
    "        'key' : 'Gene',\n",
    "        'library_key' : None,\n",
    "        'output_name' : 'osmFISH'\n",
    "    },\n",
    "    {\n",
    "        'name' : 'MERFISH dataset (Genes)',\n",
    "        'adata' : load_merfish_data(),\n",
    "        'key' : 'Gene',\n",
    "        'library_key' : None,\n",
    "        'output_name' : 'merfish_genes'\n",
    "    }\n",
    "]\n",
    "\n",
    "def pearson(a: np.ndarray, b: np.ndarray) -> float:\n",
    "    return pearsonr(a.flatten(), b.flatten()).statistic\n",
    "\n",
    "def spearman(a: np.ndarray, b: np.ndarray) -> float:\n",
    "    p, _ = spearmanr(a.flatten(), b.flatten())\n",
    "    return p\n",
    "\n",
    "results = {\n",
    "    'Dataset' : [],\n",
    "    'Pearson' : [],\n",
    "    'Spearman' : []\n",
    "} \n",
    "\n",
    "\n",
    "R0 = 6\n",
    "m = 0.065 # um per pixel\n",
    "R0 = 5 / m\n",
    "R1 = 2 * R0\n",
    "dataset = datasets[6]\n",
    "name = dataset['name']\n",
    "print(name)\n",
    "adata = dataset['adata']\n",
    "library_key = dataset['library_key']\n",
    "cluster_key = dataset['key']\n",
    "\n",
    "# Incase several replicates, select first\n",
    "if library_key is not None:\n",
    "    ids = adata.obs[library_key]\n",
    "    uid = ids.unique()[0]\n",
    "    adata = adata[adata.obs[library_key] == uid]\n",
    "\n",
    "# Compute neighbors\n",
    "sq.gr.spatial_neighbors(adata, coord_type='generic', radius=R0)\n",
    "\n",
    "# Compute zscores\n",
    "zscore_analytical, _ = nhood_enrichment(adata, cluster_key=cluster_key, copy=True,  radius_test=(R0, R1))\n",
    "zscore_squidpy, _ = sq.gr.nhood_enrichment(adata, cluster_key=cluster_key, copy=True, show_progress_bar=False)\n",
    "\n",
    "\n",
    "zscore_squidpy[np.isnan(zscore_squidpy)] = 0\n",
    "p, s = pearson(zscore_analytical, zscore_squidpy), spearman(zscore_analytical, zscore_squidpy)\n",
    "\n",
    "results['Dataset'].append(name)\n",
    "results['Spearman'].append(s)\n",
    "results['Pearson'].append(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Dataset': ['osmFISH dataset (Genes)'],\n",
       " 'Pearson': [0.8816351759388912],\n",
       " 'Spearman': [0.939316918760808]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  }
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