run_cshmm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, './CSHMM-for-time-series-scRNA-Seq/')\n",
    "import pandas as pd\n",
    "import os\n",
    "import subprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scdiff_init\n",
    "import CSHMM_train\n",
    "import CSHMM_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CSHMM Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_file = 'data/tcell_data.E'\n",
    "\n",
    "tf_dna_path = '/net/dali/home/mscbio/aar126/genomics/jubilant-barnacle/scdiff/tf_dna/Mouse_TF_targets.txt'\n",
    "\n",
    "#initial clusters - number of time points? \n",
    "kc = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start clustering...\n",
      "calculating affinity matrix ...\n",
      "cell:0\n",
      "cell:1\n",
      "cell:2\n",
      "cell:3\n",
      "cell:4\n",
      "cell:5\n",
      "cell:6\n",
      "cell:7\n",
      "cell:8\n",
      "cell:9\n",
      "cell:10\n",
      "cell:11\n",
      "cell:12\n",
      "cell:13\n",
      "cell:14\n",
      "cell:15\n",
      "cell:16\n",
      "cell:17\n",
      "cell:18\n",
      "cell:19\n",
      "cell:20\n",
      "cell:21\n",
      "cell:22\n",
      "cell:23\n",
      "cell:24\n",
      "cell:25\n",
      "cell:26\n",
      "cell:27\n",
      "cell:28\n",
      "cell:29\n",
      "cell:30\n",
      "cell:31\n",
      "cell:32\n",
      "cell:33\n",
      "cell:34\n",
      "cell:35\n",
      "cell:36\n",
      "cell:37\n",
      "cell:38\n",
      "cell:39\n",
      "cell:40\n",
      "cell:41\n",
      "cell:42\n",
      "cell:43\n",
      "cell:44\n",
      "cell:45\n",
      "cell:46\n",
      "cell:47\n",
      "cell:48\n",
      "cell:49\n",
      "cell:50\n",
      "cell:51\n",
      "cell:52\n",
      "cell:53\n",
      "cell:54\n",
      "cell:55\n",
      "cell:56\n",
      "cell:57\n",
      "cell:58\n",
      "cell:59\n",
      "cell:60\n",
      "cell:61\n",
      "cell:62\n",
      "cell:63\n",
      "cell:64\n",
      "cell:65\n",
      "cell:66\n",
      "cell:67\n",
      "cell:68\n",
      "cell:69\n",
      "cell:70\n",
      "cell:71\n",
      "cell:72\n",
      "cell:73\n",
      "cell:74\n",
      "cell:75\n",
      "cell:76\n",
      "cell:77\n",
      "cell:0\n",
      "cell:1\n",
      "cell:2\n",
      "cell:3\n",
      "cell:4\n",
      "cell:5\n",
      "cell:6\n",
      "cell:7\n",
      "cell:8\n",
      "cell:9\n",
      "cell:10\n",
      "cell:11\n",
      "cell:12\n",
      "cell:13\n",
      "cell:14\n",
      "cell:15\n",
      "cell:16\n",
      "cell:17\n",
      "cell:18\n",
      "cell:19\n",
      "cell:20\n",
      "cell:21\n",
      "cell:22\n",
      "cell:23\n",
      "cell:24\n",
      "cell:25\n",
      "cell:26\n",
      "cell:27\n",
      "cell:28\n",
      "cell:29\n",
      "cell:30\n",
      "cell:31\n",
      "cell:32\n",
      "cell:33\n",
      "cell:34\n",
      "cell:35\n",
      "cell:36\n",
      "cell:37\n",
      "cell:38\n",
      "cell:39\n",
      "cell:40\n",
      "cell:41\n",
      "cell:42\n",
      "cell:43\n",
      "cell:44\n",
      "cell:45\n",
      "cell:46\n",
      "cell:47\n",
      "cell:48\n",
      "cell:49\n",
      "cell:50\n",
      "cell:51\n",
      "cell:52\n",
      "cell:53\n",
      "cell:54\n",
      "cell:55\n",
      "cell:56\n",
      "cell:57\n",
      "cell:58\n",
      "cell:59\n",
      "cell:60\n",
      "cell:61\n",
      "cell:62\n",
      "cell:63\n",
      "cell:64\n",
      "cell:65\n",
      "cell:66\n",
      "cell:67\n",
      "cell:68\n",
      "cell:69\n",
      "cell:70\n",
      "cell:71\n",
      "cell:72\n",
      "cell:73\n",
      "cell:74\n",
      "cell:75\n",
      "cell:76\n",
      "cell:77\n",
      "cell:78\n",
      "cell:79\n",
      "cell:80\n",
      "cell:81\n",
      "cell:82\n",
      "cell:83\n",
      "cell:84\n",
      "cell:85\n",
      "cell:86\n",
      "cell:87\n",
      "cell:88\n",
      "cell:89\n",
      "cell:90\n",
      "cell:91\n",
      "cell:92\n",
      "cell:93\n",
      "cell:94\n",
      "cell:95\n",
      "cell:96\n",
      "cell:97\n",
      "cell:98\n",
      "cell:99\n",
      "cell:100\n",
      "cell:101\n",
      "cell:102\n",
      "cell:103\n",
      "cell:104\n",
      "cell:105\n",
      "cell:106\n",
      "cell:107\n",
      "cell:108\n",
      "cell:109\n",
      "cell:110\n",
      "cell:111\n",
      "cell:112\n",
      "cell:113\n",
      "cell:114\n",
      "cell:115\n",
      "cell:116\n",
      "cell:117\n",
      "cell:118\n",
      "cell:119\n",
      "cell:120\n",
      "cell:121\n",
      "cell:122\n",
      "cell:123\n",
      "cell:124\n",
      "cell:125\n",
      "cell:126\n",
      "cell:127\n",
      "cell:128\n",
      "cell:129\n",
      "cell:130\n",
      "cell:131\n",
      "cell:132\n",
      "cell:133\n",
      "cell:134\n",
      "cell:135\n",
      "cell:136\n",
      "cell:137\n",
      "cell:138\n",
      "cell:139\n",
      "cell:140\n",
      "cell:141\n",
      "cell:142\n",
      "cell:143\n",
      "cell:144\n",
      "cell:145\n",
      "cell:146\n",
      "cell:147\n",
      "cell:148\n",
      "cell:149\n",
      "cell:150\n",
      "cell:151\n",
      "cell:152\n",
      "cell:153\n",
      "cell:154\n",
      "cell:155\n",
      "cell:156\n",
      "cell:0\n",
      "cell:1\n",
      "cell:2\n",
      "cell:3\n",
      "cell:4\n",
      "cell:5\n",
      "cell:6\n",
      "cell:7\n",
      "cell:8\n",
      "cell:9\n",
      "cell:10\n",
      "cell:11\n",
      "cell:12\n",
      "cell:13\n",
      "cell:14\n",
      "cell:15\n",
      "cell:16\n",
      "cell:17\n",
      "cell:18\n",
      "cell:19\n",
      "cell:20\n",
      "cell:21\n",
      "cell:22\n",
      "cell:23\n",
      "cell:24\n",
      "cell:25\n",
      "cell:26\n",
      "cell:27\n",
      "cell:28\n",
      "cell:29\n",
      "cell:30\n",
      "cell:31\n",
      "cell:32\n",
      "cell:33\n",
      "cell:34\n",
      "cell:35\n",
      "cell:36\n",
      "cell:37\n",
      "cell:38\n",
      "cell:39\n",
      "cell:40\n",
      "cell:41\n",
      "cell:42\n",
      "cell:43\n",
      "cell:44\n",
      "cell:45\n",
      "cell:46\n",
      "cell:47\n",
      "cell:48\n",
      "cell:49\n",
      "cell:50\n",
      "cell:51\n",
      "cell:52\n",
      "cell:53\n",
      "cell:54\n",
      "cell:55\n",
      "cell:56\n",
      "cell:57\n",
      "cell:58\n",
      "cell:59\n",
      "cell:60\n",
      "cell:61\n",
      "cell:62\n",
      "cell:63\n",
      "cell:64\n",
      "cell:65\n",
      "cell:66\n",
      "cell:67\n",
      "cell:68\n",
      "cell:69\n",
      "cell:70\n",
      "cell:71\n",
      "cell:72\n",
      "cell:73\n",
      "cell:74\n",
      "cell:75\n",
      "cell:76\n",
      "cell:77\n",
      "cell:78\n",
      "cell:79\n",
      "cell:80\n",
      "cell:81\n",
      "cell:82\n",
      "cell:83\n",
      "cell:84\n",
      "cell:85\n",
      "cell:86\n",
      "cell:87\n",
      "cell:88\n",
      "cell:89\n",
      "cell:90\n",
      "cell:91\n",
      "cell:92\n",
      "cell:93\n",
      "cell:94\n",
      "cell:95\n",
      "cell:96\n",
      "cell:97\n",
      "cell:98\n",
      "cell:99\n",
      "cell:100\n",
      "cell:101\n",
      "cell:102\n",
      "cell:103\n",
      "cell:104\n",
      "cell:105\n",
      "cell:106\n",
      "cell:107\n",
      "cell:108\n",
      "cell:109\n",
      "cell:110\n",
      "cell:111\n",
      "cell:112\n",
      "cell:113\n",
      "cell:114\n",
      "cell:115\n",
      "cell:116\n",
      "cell:117\n",
      "cell:118\n",
      "cell:119\n",
      "cell:120\n",
      "cell:121\n",
      "cell:122\n",
      "cell:123\n",
      "cell:124\n",
      "cell:125\n",
      "cell:126\n",
      "cell:127\n",
      "cell:128\n",
      "cell:129\n",
      "cell:130\n",
      "cell:131\n",
      "cell:132\n",
      "cell:133\n",
      "cell:134\n",
      "cell:135\n",
      "cell:136\n",
      "cell:137\n",
      "cell:138\n",
      "cell:139\n",
      "cell:140\n",
      "cell:141\n",
      "cell:142\n",
      "cell:143\n",
      "cell:144\n",
      "cell:145\n",
      "cell:146\n",
      "cell:147\n",
      "cell:148\n",
      "cell:149\n",
      "cell:150\n",
      "cell:151\n",
      "cell:152\n",
      "cell:153\n",
      "cell:154\n",
      "cell:155\n",
      "cell:156\n",
      "cell:157\n",
      "cell:158\n",
      "cell:159\n",
      "cell:160\n",
      "cell:161\n",
      "cell:162\n",
      "cell:163\n",
      "cell:164\n",
      "cell:165\n",
      "cell:166\n",
      "cell:167\n",
      "cell:168\n",
      "cell:169\n",
      "cell:170\n",
      "cell:171\n",
      "cell:172\n",
      "cell:173\n",
      "cell:174\n",
      "cell:175\n",
      "cell:176\n",
      "cell:177\n",
      "cell:178\n",
      "cell:179\n",
      "cell:180\n",
      "cell:181\n",
      "cell:182\n",
      "cell:183\n",
      "cell:184\n",
      "cell:185\n",
      "cell:186\n",
      "cell:187\n",
      "cell:188\n",
      "cell:189\n",
      "cell:190\n",
      "cell:191\n",
      "cell:192\n",
      "cell:193\n",
      "cell:194\n",
      "cell:195\n",
      "cell:196\n",
      "cell:197\n",
      "cell:198\n",
      "cell:199\n",
      "cell:200\n",
      "cell:201\n",
      "cell:202\n",
      "cell:203\n",
      "cell:204\n",
      "cell:205\n",
      "cell:206\n",
      "cell:207\n",
      "cell:208\n",
      "cell:209\n",
      "cell:210\n",
      "cell:211\n",
      "cell:212\n",
      "cell:213\n",
      "cell:214\n",
      "cell:215\n",
      "cell:216\n",
      "cell:217\n",
      "cell:218\n",
      "cell:219\n",
      "cell:220\n",
      "cell:221\n",
      "cell:222\n",
      "cell:223\n",
      "cell:224\n",
      "cell:225\n",
      "cell:226\n",
      "cell:227\n",
      "cell:228\n",
      "cell:229\n",
      "cell:230\n",
      "cell:231\n",
      "cell:232\n",
      "cell:233\n",
      "cell:234\n",
      "cell:235\n",
      "cell:236\n",
      "cell:237\n",
      "cell:238\n",
      "cell:239\n",
      "cell:240\n",
      "cell:241\n",
      "cell:242\n",
      "cell:243\n",
      "cell:244\n",
      "cell:245\n",
      "cell:246\n",
      "cell:247\n",
      "cell:248\n",
      "cell:249\n",
      "cell:250\n",
      "cell:251\n",
      "cell:252\n",
      "cell:253\n",
      "cell:254\n",
      "cell:255\n",
      "cell:256\n",
      "cell:257\n",
      "cell:258\n",
      "cell:259\n",
      "cell:260\n",
      "cell:261\n",
      "cell:262\n",
      "cell:263\n",
      "cell:264\n",
      "cell:265\n",
      "cell:266\n",
      "cell:267\n",
      "cell:268\n",
      "cell:269\n",
      "cell:270\n",
      "cell:271\n",
      "cell:272\n",
      "cell:273\n",
      "cell:274\n",
      "cell:275\n",
      "cell:276\n",
      "cell:277\n",
      "cell:278\n",
      "cell:279\n",
      "cell:280\n",
      "cell:281\n",
      "cell:282\n",
      "cell:283\n",
      "cell:284\n",
      "cell:285\n",
      "cell:286\n",
      "cell:287\n",
      "cell:288\n",
      "cell:289\n",
      "cell:290\n",
      "cell:291\n",
      "cell:292\n",
      "cell:293\n",
      "cell:294\n",
      "cell:295\n",
      "cell:296\n",
      "cell:297\n",
      "cell:298\n",
      "cell:299\n",
      "cell:300\n",
      "cell:301\n",
      "cell:302\n",
      "cell:303\n",
      "cell:304\n",
      "cell:305\n",
      "cell:306\n",
      "cell:307\n",
      "cell:308\n",
      "cell:309\n",
      "cell:310\n",
      "cell:311\n",
      "cell:312\n",
      "cell:313\n",
      "cell:314\n",
      "cell:315\n",
      "cell:316\n",
      "cell:317\n",
      "cell:318\n",
      "cell:319\n",
      "cell:320\n",
      "cell:321\n",
      "cell:322\n",
      "cell:323\n",
      "cell:324\n",
      "cell:325\n",
      "cell:326\n",
      "cell:327\n",
      "cell:328\n",
      "cell:329\n",
      "cell:330\n",
      "cell:331\n",
      "cell:332\n",
      "cell:333\n",
      "cell:334\n",
      "cell:335\n",
      "cell:336\n",
      "cell:337\n",
      "cell:338\n",
      "cell:339\n",
      "cell:340\n",
      "cell:341\n",
      "cell:342\n",
      "cell:343\n",
      "cell:344\n",
      "cell:345\n",
      "cell:346\n",
      "cell:347\n",
      "cell:348\n",
      "cell:349\n",
      "cell:350\n",
      "cell:351\n",
      "cell:352\n",
      "cell:353\n",
      "cell:354\n",
      "cell:355\n",
      "cell:356\n",
      "cell:357\n",
      "cell:358\n",
      "cell:359\n",
      "cell:360\n",
      "cell:361\n",
      "cell:362\n",
      "cell:363\n",
      "cell:364\n",
      "cell:365\n",
      "cell:366\n",
      "cell:367\n",
      "cell:368\n",
      "cell:369\n",
      "cell:370\n",
      "cell:371\n",
      "cell:372\n",
      "cell:373\n",
      "cell:374\n",
      "cell:375\n",
      "cell:376\n",
      "cell:377\n",
      "cell:378\n",
      "cell:379\n",
      "cell:380\n",
      "cell:381\n",
      "cell:382\n",
      "cell:383\n",
      "cell:384\n",
      "cell:385\n",
      "cell:386\n",
      "cell:387\n",
      "cell:388\n",
      "cell:389\n",
      "cell:390\n",
      "cell:391\n",
      "cell:392\n",
      "cell:393\n",
      "cell:394\n",
      "cell:395\n",
      "cell:396\n",
      "cell:397\n",
      "cell:398\n",
      "cell:399\n",
      "cell:400\n",
      "cell:401\n",
      "cell:402\n",
      "cell:403\n",
      "cell:404\n",
      "cell:405\n",
      "cell:406\n",
      "cell:407\n",
      "cell:408\n",
      "cell:409\n",
      "cell:410\n",
      "cell:411\n",
      "cell:412\n",
      "cell:413\n",
      "cell:414\n",
      "cell:415\n",
      "cell:416\n",
      "cell:417\n",
      "cell:418\n",
      "cell:419\n",
      "cell:420\n",
      "cell:421\n",
      "cell:422\n",
      "cell:423\n",
      "cell:424\n",
      "cell:425\n",
      "cell:426\n",
      "cell:427\n",
      "cell:428\n",
      "cell:429\n",
      "cell:430\n",
      "cell:431\n",
      "cell:432\n",
      "cell:433\n",
      "cell:434\n",
      "cell:435\n",
      "cell:436\n",
      "cell:437\n",
      "cell:438\n",
      "cell:439\n",
      "cell:440\n",
      "cell:441\n",
      "cell:442\n",
      "cell:443\n",
      "cell:444\n",
      "cell:445\n",
      "cell:446\n",
      "cell:447\n",
      "cell:448\n",
      "cell:449\n",
      "cell:450\n",
      "cell:451\n",
      "cell:452\n",
      "cell:453\n",
      "cell:454\n",
      "cell:455\n",
      "cell:456\n",
      "cell:457\n",
      "cell:458\n",
      "cell:459\n",
      "cell:460\n",
      "cell:461\n",
      "cell:462\n",
      "cell:463\n",
      "cell:464\n",
      "cell:465\n",
      "cell:466\n",
      "cell:467\n",
      "cell:468\n",
      "cell:469\n",
      "cell:470\n",
      "cell:471\n",
      "cell:472\n",
      "cell:473\n",
      "cell:474\n",
      "cell:475\n",
      "cell:476\n",
      "cell:477\n",
      "cell:478\n",
      "cell:479\n",
      "cell:480\n",
      "cell:481\n",
      "cell:482\n",
      "cell:483\n",
      "cell:484\n",
      "cell:485\n",
      "cell:486\n",
      "cell:487\n",
      "cell:488\n",
      "cell:489\n",
      "cell:490\n",
      "cell:491\n",
      "cell:492\n",
      "cell:493\n",
      "cell:494\n",
      "cell:495\n",
      "cell:496\n",
      "cell:497\n",
      "cell:498\n",
      "cell:499\n",
      "cell:500\n",
      "cell:501\n",
      "cell:502\n",
      "cell:503\n",
      "cell:0\n",
      "cell:1\n",
      "cell:2\n",
      "cell:3\n",
      "cell:4\n",
      "cell:5\n",
      "cell:6\n",
      "cell:7\n",
      "cell:8\n",
      "cell:9\n",
      "cell:10\n",
      "cell:11\n",
      "cell:12\n",
      "cell:13\n",
      "cell:14\n",
      "cell:15\n",
      "cell:16\n",
      "cell:17\n",
      "cell:18\n",
      "cell:19\n",
      "cell:20\n",
      "cell:21\n",
      "cell:22\n",
      "cell:23\n",
      "cell:24\n",
      "cell:25\n",
      "cell:26\n",
      "cell:27\n",
      "cell:28\n",
      "cell:29\n",
      "cell:30\n",
      "cell:31\n",
      "cell:32\n",
      "cell:33\n",
      "cell:34\n",
      "cell:35\n",
      "cell:36\n",
      "cell:37\n",
      "cell:38\n",
      "cell:39\n",
      "cell:40\n",
      "cell:41\n",
      "cell:42\n",
      "cell:43\n",
      "cell:44\n",
      "cell:45\n",
      "cell:46\n",
      "cell:47\n",
      "cell:48\n",
      "cell:49\n",
      "cell:50\n",
      "cell:51\n",
      "cell:52\n",
      "cell:53\n",
      "cell:54\n",
      "cell:55\n",
      "cell:56\n",
      "cell:57\n",
      "cell:58\n",
      "cell:59\n",
      "cell:60\n",
      "cell:61\n",
      "cell:62\n",
      "cell:63\n",
      "cell:64\n",
      "cell:65\n",
      "cell:66\n",
      "cell:67\n",
      "cell:68\n",
      "cell:69\n",
      "cell:70\n",
      "cell:71\n",
      "cell:72\n",
      "cell:73\n",
      "cell:74\n",
      "cell:75\n",
      "cell:76\n",
      "cell:77\n",
      "cell:78\n",
      "cell:79\n",
      "cell:80\n",
      "cell:81\n",
      "cell:82\n",
      "cell:83\n",
      "cell:84\n",
      "cell:85\n",
      "cell:86\n",
      "cell:87\n",
      "cell:88\n",
      "cell:89\n",
      "cell:90\n",
      "cell:91\n",
      "cell:92\n",
      "cell:93\n",
      "cell:94\n",
      "cell:95\n",
      "cell:96\n",
      "cell:97\n",
      "cell:98\n",
      "cell:99\n",
      "cell:100\n",
      "cell:101\n",
      "cell:102\n",
      "cell:103\n",
      "cell:104\n",
      "cell:105\n",
      "cell:106\n",
      "cell:107\n",
      "cell:108\n",
      "cell:109\n",
      "cell:110\n",
      "cell:111\n",
      "cell:112\n",
      "cell:113\n",
      "cell:114\n",
      "cell:115\n",
      "cell:116\n",
      "cell:117\n",
      "cell:118\n",
      "cell:119\n",
      "cell:120\n",
      "cell:121\n",
      "cell:122\n",
      "cell:123\n",
      "cell:124\n",
      "cell:125\n",
      "cell:126\n",
      "cell:127\n",
      "cell:128\n",
      "cell:129\n",
      "cell:130\n",
      "cell:131\n",
      "cell:132\n",
      "cell:133\n",
      "cell:134\n",
      "cell:135\n",
      "cell:136\n",
      "cell:137\n",
      "cell:138\n",
      "cell:139\n",
      "cell:140\n",
      "cell:141\n",
      "cell:142\n",
      "cell:143\n",
      "cell:144\n",
      "cell:145\n",
      "cell:146\n",
      "cell:147\n",
      "cell:148\n",
      "cell:149\n",
      "cell:150\n",
      "cell:151\n",
      "cell:152\n",
      "cell:153\n",
      "cell:154\n",
      "cell:155\n",
      "cell:156\n",
      "cell:157\n",
      "cell:158\n",
      "cell:159\n",
      "cell:160\n",
      "cell:161\n",
      "cell:162\n",
      "cell:163\n",
      "cell:164\n",
      "cell:165\n",
      "cell:166\n",
      "cell:167\n",
      "cell:168\n",
      "cell:169\n",
      "cell:170\n",
      "cell:171\n",
      "cell:172\n",
      "cell:173\n",
      "cell:174\n",
      "cell:175\n",
      "cell:176\n",
      "cell:177\n",
      "cell:178\n",
      "cell:179\n",
      "cell:180\n",
      "cell:181\n",
      "cell:182\n",
      "cell:183\n",
      "cell:184\n",
      "cell:185\n",
      "cell:186\n",
      "cell:187\n",
      "cell:188\n",
      "cell:189\n",
      "cell:190\n",
      "cell:191\n",
      "cell:192\n",
      "cell:193\n",
      "cell:194\n",
      "cell:195\n",
      "cell:196\n",
      "cell:197\n",
      "cell:198\n",
      "cell:199\n",
      "cell:200\n",
      "cell:201\n",
      "cell:202\n",
      "cell:203\n",
      "cell:204\n",
      "cell:205\n",
      "cell:206\n",
      "cell:207\n",
      "cell:208\n",
      "cell:209\n",
      "cell:210\n",
      "cell:211\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell:212\n",
      "cell:213\n",
      "cell:214\n",
      "cell:215\n",
      "cell:216\n",
      "cell:217\n",
      "cell:218\n",
      "cell:219\n",
      "cell:220\n",
      "cell:221\n",
      "cell:222\n",
      "cell:223\n",
      "cell:224\n",
      "cell:225\n",
      "cell:226\n",
      "cell:227\n",
      "cell:228\n",
      "cell:229\n",
      "cell:230\n",
      "cell:231\n",
      "cell:232\n",
      "cell:233\n",
      "cell:234\n",
      "cell:235\n",
      "cell:236\n",
      "cell:237\n",
      "cell:238\n",
      "cell:239\n",
      "cell:240\n",
      "cell:241\n",
      "cell:242\n",
      "cell:243\n",
      "cell:244\n",
      "cell:245\n",
      "cell:246\n",
      "cell:247\n",
      "cell:248\n",
      "cell:249\n",
      "cell:250\n",
      "cell:251\n",
      "cell:252\n",
      "cell:253\n",
      "cell:254\n",
      "cell:255\n",
      "cell:256\n",
      "cell:257\n",
      "cell:258\n",
      "cell:259\n",
      "cell:260\n",
      "cell:261\n",
      "cell:262\n",
      "cell:263\n",
      "cell:264\n",
      "cell:265\n",
      "cell:266\n",
      "cell:267\n",
      "cell:268\n",
      "cell:269\n",
      "cell:270\n",
      "cell:271\n",
      "cell:272\n",
      "cell:273\n",
      "cell:274\n",
      "cell:275\n",
      "cell:276\n",
      "cell:277\n",
      "cell:278\n",
      "cell:279\n",
      "cell:280\n",
      "cell:281\n",
      "cell:282\n",
      "cell:283\n",
      "cell:284\n",
      "cell:285\n",
      "cell:286\n",
      "cell:287\n",
      "cell:288\n",
      "cell:289\n",
      "cell:290\n",
      "cell:291\n",
      "cell:292\n",
      "cell:293\n",
      "cell:294\n",
      "cell:295\n",
      "cell:296\n",
      "cell:297\n",
      "cell:298\n",
      "cell:299\n",
      "cell:300\n",
      "cell:301\n",
      "cell:302\n",
      "cell:303\n",
      "cell:304\n",
      "cell:305\n",
      "cell:306\n",
      "cell:307\n",
      "cell:308\n",
      "cell:309\n",
      "cell:310\n",
      "cell:311\n",
      "cell:312\n",
      "cell:313\n",
      "cell:314\n",
      "cell:315\n",
      "cell:316\n",
      "cell:317\n",
      "cell:318\n",
      "cell:319\n",
      "cell:320\n",
      "cell:321\n",
      "cell:322\n",
      "cell:323\n",
      "cell:324\n",
      "cell:325\n",
      "cell:326\n",
      "cell:327\n",
      "cell:328\n",
      "cell:329\n",
      "cell:330\n",
      "cell:331\n",
      "cell:332\n",
      "cell:333\n",
      "cell:334\n",
      "cell:335\n",
      "cell:336\n",
      "cell:337\n",
      "cell:338\n",
      "cell:339\n",
      "cell:340\n",
      "cell:341\n",
      "cell:342\n",
      "cell:343\n",
      "cell:344\n",
      "cell:345\n",
      "cell:346\n",
      "cell:347\n",
      "cell:348\n",
      "cell:349\n",
      "cell:350\n",
      "cell:351\n",
      "cell:352\n",
      "cell:353\n",
      "cell:354\n",
      "cell:355\n",
      "cell:356\n",
      "cell:357\n",
      "cell:358\n",
      "cell:359\n",
      "cell:360\n",
      "cell:361\n",
      "cell:362\n",
      "cell:363\n",
      "cell:364\n",
      "cell:365\n",
      "cell:366\n",
      "cell:367\n",
      "cell:368\n",
      "cell:369\n",
      "cell:370\n",
      "cell:371\n",
      "cell:372\n",
      "cell:373\n",
      "cell:374\n",
      "cell:375\n",
      "cell:376\n",
      "cell:377\n",
      "cell:378\n",
      "cell:379\n",
      "cell:380\n",
      "cell:381\n",
      "cell:382\n",
      "cell:383\n",
      "cell:384\n",
      "cell:385\n",
      "cell:386\n",
      "cell:387\n",
      "cell:388\n",
      "cell:389\n",
      "cell:390\n",
      "cell:391\n",
      "cell:392\n",
      "cell:393\n",
      "cell:394\n",
      "cell:395\n",
      "cell:396\n",
      "cell:397\n",
      "cell:398\n",
      "cell:399\n",
      "cell:400\n",
      "cell:401\n",
      "cell:402\n",
      "cell:403\n",
      "cell:404\n",
      "cell:405\n",
      "cell:406\n",
      "cell:407\n",
      "cell:408\n",
      "cell:409\n",
      "cell:410\n",
      "cell:411\n",
      "cell:412\n",
      "cell:413\n",
      "cell:414\n",
      "cell:415\n",
      "cell:416\n",
      "cell:417\n",
      "cell:418\n",
      "cell:419\n",
      "cell:420\n",
      "cell:421\n",
      "cell:422\n",
      "cell:423\n",
      "cell:424\n",
      "cell:425\n",
      "cell:426\n",
      "cell:427\n",
      "cell:428\n",
      "cell:429\n",
      "cell:430\n",
      "cell:431\n",
      "cell:432\n",
      "cell:433\n",
      "cell:434\n",
      "cell:435\n",
      "cell:436\n",
      "cell:437\n",
      "cell:438\n",
      "cell:439\n",
      "cell:440\n",
      "cell:441\n",
      "cell:442\n",
      "cell:443\n",
      "cell:444\n",
      "cell:445\n",
      "cell:446\n",
      "cell:447\n",
      "cell:448\n",
      "cell:449\n",
      "cell:450\n",
      "cell:451\n",
      "cell:452\n",
      "cell:453\n",
      "cell:454\n",
      "cell:455\n",
      "cell:456\n",
      "cell:457\n",
      "cell:458\n",
      "cell:459\n",
      "cell:460\n",
      "cell:461\n",
      "cell:462\n",
      "cell:463\n",
      "cell:464\n",
      "cell:465\n",
      "cell:466\n",
      "cell:467\n",
      "cell:468\n",
      "cell:469\n",
      "cell:470\n",
      "cell:471\n",
      "cell:472\n",
      "cell:473\n",
      "cell:474\n",
      "cell:475\n",
      "cell:476\n",
      "cell:477\n",
      "cell:478\n",
      "cell:479\n",
      "cell:480\n",
      "cell:481\n",
      "cell:482\n",
      "cell:483\n",
      "cell:484\n",
      "cell:485\n",
      "cell:486\n",
      "cell:487\n",
      "cell:488\n",
      "cell:489\n",
      "cell:490\n",
      "cell:491\n",
      "cell:492\n",
      "cell:493\n",
      "cell:494\n",
      "cell:495\n",
      "cell:496\n",
      "cell:497\n",
      "cell:498\n",
      "cell:499\n",
      "cell:500\n",
      "cell:0\n",
      "cell:1\n",
      "cell:2\n",
      "cell:3\n",
      "cell:4\n",
      "cell:5\n",
      "cell:6\n",
      "cell:7\n",
      "cell:8\n",
      "cell:9\n",
      "cell:10\n",
      "cell:11\n",
      "cell:12\n",
      "cell:13\n",
      "cell:14\n",
      "cell:15\n",
      "cell:16\n",
      "cell:17\n",
      "cell:18\n",
      "cell:19\n",
      "cell:20\n",
      "cell:21\n",
      "cell:22\n",
      "cell:23\n",
      "cell:24\n",
      "cell:25\n",
      "cell:26\n",
      "cell:27\n",
      "cell:28\n",
      "cell:29\n",
      "cell:30\n",
      "cell:31\n",
      "cell:32\n",
      "cell:33\n",
      "cell:34\n",
      "cell:35\n",
      "cell:36\n",
      "cell:37\n",
      "cell:38\n",
      "cell:39\n",
      "cell:40\n",
      "cell:41\n",
      "cell:42\n",
      "cell:43\n",
      "cell:44\n",
      "cell:45\n",
      "cell:46\n",
      "cell:47\n",
      "cell:48\n",
      "cell:49\n",
      "cell:50\n",
      "cell:51\n",
      "cell:52\n",
      "cell:53\n",
      "cell:54\n",
      "cell:55\n",
      "cell:56\n",
      "cell:57\n",
      "cell:58\n",
      "cell:59\n",
      "cell:60\n",
      "cell:61\n",
      "cell:62\n",
      "cell:63\n",
      "cell:64\n",
      "cell:65\n",
      "cell:66\n",
      "cell:67\n",
      "cell:68\n",
      "cell:69\n",
      "cell:70\n",
      "cell:71\n",
      "cell:72\n",
      "cell:73\n",
      "cell:74\n",
      "cell:75\n",
      "cell:76\n",
      "cell:77\n",
      "cell:78\n",
      "cell:79\n",
      "cell:80\n",
      "cell:81\n",
      "cell:82\n",
      "cell:83\n",
      "cell:84\n",
      "cell:85\n",
      "cell:86\n",
      "cell:87\n",
      "cell:88\n",
      "cell:89\n",
      "cell:90\n",
      "cell:91\n",
      "cell:92\n",
      "cell:93\n",
      "cell:94\n",
      "cell:95\n",
      "cell:96\n",
      "cell:97\n",
      "cell:98\n",
      "cell:99\n",
      "cell:100\n",
      "cell:101\n",
      "cell:102\n",
      "cell:103\n",
      "cell:104\n",
      "cell:105\n",
      "cell:106\n",
      "cell:107\n",
      "cell:108\n",
      "cell:109\n",
      "cell:110\n",
      "cell:111\n",
      "cell:112\n",
      "cell:113\n",
      "cell:114\n",
      "cell:115\n",
      "cell:116\n",
      "cell:117\n",
      "cell:118\n",
      "cell:119\n",
      "cell:120\n",
      "cell:121\n",
      "cell:122\n",
      "cell:123\n",
      "cell:124\n",
      "cell:125\n",
      "cell:126\n",
      "cell:127\n",
      "cell:128\n",
      "cell:129\n",
      "cell:130\n",
      "cell:131\n",
      "cell:132\n",
      "cell:133\n",
      "cell:134\n",
      "cell:135\n",
      "cell:136\n",
      "cell:137\n",
      "cell:138\n",
      "cell:139\n",
      "cell:140\n",
      "cell:141\n",
      "cell:142\n",
      "cell:143\n",
      "cell:144\n",
      "cell:145\n",
      "cell:146\n",
      "cell:147\n",
      "cell:148\n",
      "cell:149\n",
      "cell:150\n",
      "cell:151\n",
      "cell:152\n",
      "cell:153\n",
      "cell:154\n",
      "cell:155\n",
      "cell:156\n",
      "cell:157\n",
      "cell:158\n",
      "cell:159\n",
      "cell:160\n",
      "cell:161\n",
      "cell:162\n",
      "cell:163\n",
      "cell:164\n",
      "cell:165\n",
      "cell:166\n",
      "cell:167\n",
      "cell:168\n",
      "cell:169\n",
      "cell:170\n",
      "cell:171\n",
      "cell:172\n",
      "cell:173\n",
      "cell:174\n",
      "cell:175\n",
      "cell:176\n",
      "cell:177\n",
      "cell:178\n",
      "cell:179\n",
      "cell:180\n",
      "cell:181\n",
      "cell:182\n",
      "cell:183\n",
      "cell:184\n",
      "cell:185\n",
      "cell:186\n",
      "cell:187\n",
      "cell:188\n",
      "cell:189\n",
      "cell:190\n",
      "cell:191\n",
      "cell:192\n",
      "cell:193\n",
      "cell:194\n",
      "cell:195\n",
      "cell:196\n",
      "cell:197\n",
      "cell:198\n",
      "cell:199\n",
      "cell:200\n",
      "cell:201\n",
      "cell:202\n",
      "cell:203\n",
      "cell:204\n",
      "cell:205\n",
      "cell:206\n",
      "cell:207\n",
      "cell:208\n",
      "cell:209\n",
      "cell:210\n",
      "cell:211\n",
      "cell:212\n",
      "cell:213\n",
      "cell:214\n",
      "cell:215\n",
      "cell:216\n",
      "cell:217\n",
      "cell:218\n",
      "cell:219\n",
      "cell:220\n",
      "cell:221\n",
      "cell:222\n",
      "cell:223\n",
      "cell:224\n",
      "cell:225\n",
      "cell:226\n",
      "cell:227\n",
      "cell:228\n",
      "cell:229\n",
      "cell:230\n",
      "cell:231\n",
      "cell:232\n",
      "cell:233\n",
      "cell:234\n",
      "cell:235\n",
      "cell:236\n",
      "cell:237\n",
      "cell:238\n",
      "cell:239\n",
      "cell:240\n",
      "cell:241\n",
      "cell:242\n",
      "cell:243\n",
      "cell:244\n",
      "cell:245\n",
      "cell:246\n",
      "cell:247\n",
      "cell:248\n",
      "cell:249\n",
      "cell:250\n",
      "cell:251\n",
      "cell:252\n",
      "cell:253\n",
      "cell:254\n",
      "cell:255\n",
      "cell:256\n",
      "cell:257\n",
      "cell:258\n",
      "cell:259\n",
      "cell:260\n",
      "cell:261\n",
      "cell:262\n",
      "cell:263\n",
      "cell:264\n",
      "cell:265\n",
      "cell:266\n",
      "cell:267\n",
      "cell:268\n",
      "cell:269\n",
      "cell:270\n",
      "cell:271\n",
      "cell:272\n",
      "cell:273\n",
      "cell:274\n",
      "cell:275\n",
      "cell:276\n",
      "cell:277\n",
      "cell:278\n",
      "cell:279\n",
      "cell:280\n",
      "cell:281\n",
      "cell:282\n",
      "cell:283\n",
      "cell:284\n",
      "cell:285\n",
      "cell:286\n",
      "cell:287\n",
      "cell:288\n",
      "cell:289\n",
      "cell:290\n",
      "cell:291\n",
      "cell:292\n",
      "cell:293\n",
      "cell:294\n",
      "cell:295\n",
      "cell:296\n",
      "cell:297\n",
      "cell:298\n",
      "cell:299\n",
      "cell:300\n",
      "cell:301\n",
      "cell:302\n",
      "cell:303\n",
      "cell:304\n",
      "cell:305\n",
      "cell:306\n",
      "cell:307\n",
      "cell:308\n",
      "cell:309\n",
      "cell:310\n",
      "cell:311\n",
      "cell:312\n",
      "cell:313\n",
      "cell:314\n",
      "cell:315\n",
      "cell:316\n",
      "cell:317\n",
      "cell:318\n",
      "cell:319\n",
      "cell:320\n",
      "cell:321\n",
      "cell:322\n",
      "cell:323\n",
      "cell:324\n",
      "cell:325\n",
      "cell:326\n",
      "cell:327\n",
      "cell:328\n",
      "cell:329\n",
      "cell:330\n",
      "cell:331\n",
      "cell:332\n",
      "cell:333\n",
      "cell:334\n",
      "cell:335\n",
      "cell:336\n",
      "cell:337\n",
      "cell:338\n",
      "cell:339\n",
      "cell:340\n",
      "cell:341\n",
      "cell:342\n",
      "cell:343\n",
      "cell:344\n",
      "cell:0\n",
      "cell:1\n",
      "cell:2\n",
      "cell:3\n",
      "cell:4\n",
      "cell:5\n",
      "cell:6\n",
      "cell:7\n",
      "cell:8\n",
      "cell:9\n",
      "cell:10\n",
      "cell:11\n",
      "cell:12\n",
      "cell:13\n",
      "cell:14\n",
      "cell:15\n",
      "cell:16\n",
      "cell:17\n",
      "cell:18\n",
      "cell:19\n",
      "cell:20\n",
      "cell:21\n",
      "cell:22\n",
      "cell:23\n",
      "cell:24\n",
      "cell:25\n",
      "cell:26\n",
      "cell:27\n",
      "cell:28\n",
      "cell:29\n",
      "cell:30\n",
      "cell:31\n",
      "cell:32\n",
      "cell:33\n",
      "cell:34\n",
      "cell:35\n",
      "cell:36\n",
      "cell:37\n",
      "cell:38\n",
      "cell:39\n",
      "cell:40\n",
      "cell:41\n",
      "cell:42\n",
      "cell:43\n",
      "cell:44\n",
      "cell:45\n",
      "cell:46\n",
      "cell:47\n",
      "cell:48\n",
      "cell:49\n",
      "cell:50\n",
      "cell:51\n",
      "cell:52\n",
      "cell:53\n",
      "cell:54\n",
      "cell:55\n",
      "cell:56\n",
      "cell:57\n",
      "cell:58\n",
      "cell:59\n",
      "cell:60\n",
      "cell:61\n",
      "cell:62\n",
      "cell:63\n",
      "cell:64\n",
      "cell:65\n",
      "cell:66\n",
      "cell:67\n",
      "cell:68\n",
      "cell:69\n",
      "cell:70\n",
      "cell:71\n",
      "cell:72\n",
      "cell:73\n",
      "cell:74\n",
      "cell:75\n",
      "cell:76\n",
      "cell:77\n",
      "cell:78\n",
      "cell:79\n",
      "cell:80\n",
      "cell:81\n",
      "cell:82\n",
      "cell:83\n",
      "cell:84\n",
      "cell:85\n",
      "cell:86\n",
      "cell:87\n",
      "cell:88\n",
      "cell:89\n",
      "cell:90\n",
      "cell:91\n",
      "cell:92\n",
      "cell:93\n",
      "cell:94\n",
      "cell:95\n",
      "cell:96\n",
      "cell:97\n",
      "cell:98\n",
      "cell:99\n",
      "cell:100\n",
      "cell:101\n",
      "cell:102\n",
      "cell:103\n",
      "cell:104\n",
      "cell:105\n",
      "cell:106\n",
      "cell:107\n",
      "cell:108\n",
      "cell:109\n",
      "cell:110\n",
      "cell:111\n",
      "cell:112\n",
      "cell:113\n",
      "cell:114\n",
      "cell:115\n",
      "cell:116\n",
      "cell:117\n",
      "cell:118\n",
      "cell:119\n",
      "cell:120\n",
      "cell:121\n",
      "cell:122\n",
      "cell:123\n",
      "cell:124\n",
      "cell:125\n",
      "cell:126\n",
      "cell:127\n",
      "cell:128\n",
      "cell:129\n",
      "cell:130\n",
      "cell:131\n",
      "cell:132\n",
      "cell:133\n",
      "cell:134\n",
      "cell:135\n",
      "cell:136\n",
      "cell:137\n",
      "cell:138\n",
      "cell:139\n",
      "cell:140\n",
      "cell:141\n",
      "cell:142\n",
      "cell:143\n",
      "cell:144\n",
      "cell:145\n",
      "cell:146\n",
      "cell:147\n",
      "cell:148\n",
      "cell:149\n",
      "cell:150\n",
      "cell:151\n",
      "cell:152\n",
      "cell:153\n",
      "cell:154\n",
      "cell:155\n",
      "cell:156\n",
      "cell:157\n",
      "cell:158\n",
      "cell:159\n",
      "cell:160\n",
      "cell:161\n",
      "cell:162\n",
      "cell:163\n",
      "cell:164\n",
      "cell:165\n",
      "cell:166\n",
      "cell:167\n",
      "cell:168\n",
      "cell:169\n",
      "cell:170\n",
      "cell:171\n",
      "cell:172\n",
      "cell:173\n",
      "cell:174\n",
      "cell:175\n",
      "cell:176\n",
      "cell:177\n",
      "cell:178\n",
      "cell:179\n",
      "cell:180\n",
      "cell:181\n",
      "cell:182\n",
      "cell:183\n",
      "cell:184\n",
      "cell:185\n",
      "cell:186\n",
      "cell:187\n",
      "cell:188\n",
      "cell:189\n",
      "cell:190\n",
      "cell:191\n",
      "cell:192\n",
      "cell:193\n",
      "cell:194\n",
      "cell:195\n",
      "cell:196\n",
      "cell:197\n",
      "cell:198\n",
      "cell:199\n",
      "cell:200\n",
      "cell:201\n",
      "cell:202\n",
      "cell:203\n",
      "cell:204\n",
      "cell:205\n",
      "cell:206\n",
      "cell:207\n",
      "cell:208\n",
      "cell:209\n",
      "cell:210\n",
      "cell:211\n",
      "cell:212\n",
      "cell:213\n",
      "cell:214\n",
      "cell:215\n",
      "cell:216\n",
      "cell:217\n",
      "cell:218\n",
      "cell:219\n",
      "cell:220\n",
      "cell:221\n",
      "cell:222\n",
      "cell:223\n",
      "cell:224\n",
      "cell:225\n",
      "cell:226\n",
      "cell:227\n",
      "cell:228\n",
      "cell:229\n",
      "cell:230\n",
      "cell:231\n",
      "cell:232\n",
      "cell:233\n",
      "cell:234\n",
      "cell:235\n",
      "cell:236\n",
      "cell:237\n",
      "cell:238\n",
      "cell:239\n",
      "cell:240\n",
      "cell:241\n",
      "cell:242\n",
      "cell:243\n",
      "cell:244\n",
      "cell:245\n",
      "cell:246\n",
      "cell:247\n",
      "cell:248\n",
      "cell:249\n",
      "cell:250\n",
      "cell:251\n",
      "cell:252\n",
      "cell:253\n",
      "cell:254\n",
      "cell:255\n",
      "cell:256\n",
      "cell:257\n",
      "cell:258\n",
      "cell:259\n",
      "cell:260\n",
      "cell:261\n",
      "cell:262\n",
      "cell:263\n",
      "cell:264\n",
      "cell:265\n",
      "cell:266\n",
      "cell:267\n",
      "cell:268\n",
      "cell:269\n",
      "cell:270\n",
      "cell:271\n",
      "cell:272\n",
      "cell:273\n",
      "cell:274\n",
      "cell:275\n",
      "cell:276\n",
      "cell:277\n",
      "cell:278\n",
      "cell:279\n",
      "cell:280\n",
      "cell:281\n",
      "cell:282\n",
      "cell:283\n",
      "cell:284\n",
      "cell:285\n",
      "cell:286\n",
      "cell:287\n",
      "cell:288\n",
      "cell:289\n",
      "cell:290\n",
      "cell:291\n",
      "cell:292\n",
      "cell:293\n",
      "cell:294\n",
      "cell:295\n",
      "cell:296\n",
      "cell:297\n",
      "cell:298\n",
      "cell:299\n",
      "cell:300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell:301\n",
      "cell:302\n",
      "cell:303\n",
      "cell:304\n",
      "cell:305\n",
      "cell:306\n",
      "cell:307\n",
      "cell:308\n",
      "cell:309\n",
      "cell:310\n",
      "cell:311\n",
      "cell:312\n",
      "cell:313\n",
      "cell:314\n",
      "cell:315\n",
      "cell:316\n",
      "cell:317\n",
      "cell:318\n",
      "cell:319\n",
      "cell:320\n",
      "cell:321\n",
      "cell:322\n",
      "cell:323\n",
      "cell:324\n",
      "cell:325\n",
      "cell:326\n",
      "cell:327\n",
      "cell:328\n",
      "cell:329\n",
      "cell:330\n",
      "cell:331\n",
      "cell:332\n",
      "cell:333\n",
      "cell:334\n",
      "cell:335\n",
      "cell:336\n",
      "learning K...\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "4.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "6.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "8.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "12.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "14.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "16.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "18.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "20.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "22.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "24.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "26.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "28.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "30.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "32.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "34.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "36.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "38.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "40.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "42.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "44.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "46.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "48.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "50.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "52.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "54.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "56.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "58.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "60.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "62.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "64.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "66.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "68.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "70.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "72.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "74.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "76.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "78.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "80.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "82.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "84.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "86.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "88.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "90.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "92.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "94.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "96.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "98.0%\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "100.0%\n",
      "learning clustering seeds...\n",
      "seeds:0\n",
      "seeds:1\n",
      "seeds:2\n",
      "seeds:3\n",
      "seeds:4\n",
      "seeds:5\n",
      "seeds:6\n",
      "seeds:7\n",
      "seeds:8\n",
      "seeds:9\n",
      "seeds:10\n",
      "seeds:11\n",
      "seeds:12\n",
      "seeds:13\n",
      "seeds:14\n",
      "seeds:15\n",
      "seeds:16\n",
      "seeds:17\n",
      "seeds:18\n",
      "seeds:19\n",
      "seeds:20\n",
      "seeds:21\n",
      "seeds:22\n",
      "seeds:23\n",
      "seeds:24\n",
      "seeds:25\n",
      "seeds:26\n",
      "seeds:27\n",
      "seeds:28\n",
      "seeds:29\n",
      "seeds:30\n",
      "seeds:31\n",
      "seeds:32\n",
      "seeds:33\n",
      "seeds:34\n",
      "seeds:35\n",
      "seeds:36\n",
      "seeds:37\n",
      "seeds:38\n",
      "seeds:39\n",
      "seeds:40\n",
      "seeds:41\n",
      "seeds:42\n",
      "seeds:43\n",
      "seeds:44\n",
      "seeds:45\n",
      "seeds:46\n",
      "seeds:47\n",
      "seeds:48\n",
      "seeds:49\n",
      "seeds:50\n",
      "seeds:51\n",
      "seeds:52\n",
      "seeds:53\n",
      "seeds:54\n",
      "seeds:55\n",
      "seeds:56\n",
      "seeds:57\n",
      "seeds:58\n",
      "seeds:59\n",
      "seeds:60\n",
      "seeds:61\n",
      "seeds:62\n",
      "seeds:63\n",
      "seeds:64\n",
      "seeds:65\n",
      "seeds:66\n",
      "seeds:67\n",
      "seeds:68\n",
      "seeds:69\n",
      "seeds:70\n",
      "seeds:71\n",
      "seeds:72\n",
      "seeds:73\n",
      "seeds:74\n",
      "seeds:75\n",
      "seeds:76\n",
      "seeds:77\n",
      "seeds:78\n",
      "seeds:79\n",
      "seeds:80\n",
      "seeds:81\n",
      "seeds:82\n",
      "seeds:83\n",
      "seeds:84\n",
      "seeds:85\n",
      "seeds:86\n",
      "seeds:87\n",
      "seeds:88\n",
      "seeds:89\n",
      "seeds:90\n",
      "seeds:91\n",
      "seeds:92\n",
      "seeds:93\n",
      "seeds:94\n",
      "seeds:95\n",
      "seeds:96\n",
      "seeds:97\n",
      "seeds:98\n",
      "seeds:99\n",
      "seeds:0\n",
      "seeds:1\n",
      "seeds:2\n",
      "seeds:3\n",
      "seeds:4\n",
      "seeds:5\n",
      "seeds:6\n",
      "seeds:7\n",
      "seeds:8\n",
      "seeds:9\n",
      "seeds:10\n",
      "seeds:11\n",
      "seeds:12\n",
      "seeds:13\n",
      "seeds:14\n",
      "seeds:15\n",
      "seeds:16\n",
      "seeds:17\n",
      "seeds:18\n",
      "seeds:19\n",
      "seeds:20\n",
      "seeds:21\n",
      "seeds:22\n",
      "seeds:23\n",
      "seeds:24\n",
      "seeds:25\n",
      "seeds:26\n",
      "seeds:27\n",
      "seeds:28\n",
      "seeds:29\n",
      "seeds:30\n",
      "seeds:31\n",
      "seeds:32\n",
      "seeds:33\n",
      "seeds:34\n",
      "seeds:35\n",
      "seeds:36\n",
      "seeds:37\n",
      "seeds:38\n",
      "seeds:39\n",
      "seeds:40\n",
      "seeds:41\n",
      "seeds:42\n",
      "seeds:43\n",
      "seeds:44\n",
      "seeds:45\n",
      "seeds:46\n",
      "seeds:47\n",
      "seeds:48\n",
      "seeds:49\n",
      "seeds:50\n",
      "seeds:51\n",
      "seeds:52\n",
      "seeds:53\n",
      "seeds:54\n",
      "seeds:55\n",
      "seeds:56\n",
      "seeds:57\n",
      "seeds:58\n",
      "seeds:59\n",
      "seeds:60\n",
      "seeds:61\n",
      "seeds:62\n",
      "seeds:63\n",
      "seeds:64\n",
      "seeds:65\n",
      "seeds:66\n",
      "seeds:67\n",
      "seeds:68\n",
      "seeds:69\n",
      "seeds:70\n",
      "seeds:71\n",
      "seeds:72\n",
      "seeds:73\n",
      "seeds:74\n",
      "seeds:75\n",
      "seeds:76\n",
      "seeds:77\n",
      "seeds:78\n",
      "seeds:79\n",
      "seeds:80\n",
      "seeds:81\n",
      "seeds:82\n",
      "seeds:83\n",
      "seeds:84\n",
      "seeds:85\n",
      "seeds:86\n",
      "seeds:87\n",
      "seeds:88\n",
      "seeds:89\n",
      "seeds:90\n",
      "seeds:91\n",
      "seeds:92\n",
      "seeds:93\n",
      "seeds:94\n",
      "seeds:95\n",
      "seeds:96\n",
      "seeds:97\n",
      "seeds:98\n",
      "seeds:99\n",
      "seeds:0\n",
      "seeds:1\n",
      "seeds:2\n",
      "seeds:3\n",
      "seeds:4\n",
      "seeds:5\n",
      "seeds:6\n",
      "seeds:7\n",
      "seeds:8\n",
      "seeds:9\n",
      "seeds:10\n",
      "seeds:11\n",
      "seeds:12\n",
      "seeds:13\n",
      "seeds:14\n",
      "seeds:15\n",
      "seeds:16\n",
      "seeds:17\n",
      "seeds:18\n",
      "seeds:19\n",
      "seeds:20\n",
      "seeds:21\n",
      "seeds:22\n",
      "seeds:23\n",
      "seeds:24\n",
      "seeds:25\n",
      "seeds:26\n",
      "seeds:27\n",
      "seeds:28\n",
      "seeds:29\n",
      "seeds:30\n",
      "seeds:31\n",
      "seeds:32\n",
      "seeds:33\n",
      "seeds:34\n",
      "seeds:35\n",
      "seeds:36\n",
      "seeds:37\n",
      "seeds:38\n",
      "seeds:39\n",
      "seeds:40\n",
      "seeds:41\n",
      "seeds:42\n",
      "seeds:43\n",
      "seeds:44\n",
      "seeds:45\n",
      "seeds:46\n",
      "seeds:47\n",
      "seeds:48\n",
      "seeds:49\n",
      "seeds:50\n",
      "seeds:51\n",
      "seeds:52\n",
      "seeds:53\n",
      "seeds:54\n",
      "seeds:55\n",
      "seeds:56\n",
      "seeds:57\n",
      "seeds:58\n",
      "seeds:59\n",
      "seeds:60\n",
      "seeds:61\n",
      "seeds:62\n",
      "seeds:63\n",
      "seeds:64\n",
      "seeds:65\n",
      "seeds:66\n",
      "seeds:67\n",
      "seeds:68\n",
      "seeds:69\n",
      "seeds:70\n",
      "seeds:71\n",
      "seeds:72\n",
      "seeds:73\n",
      "seeds:74\n",
      "seeds:75\n",
      "seeds:76\n",
      "seeds:77\n",
      "seeds:78\n",
      "seeds:79\n",
      "seeds:80\n",
      "seeds:81\n",
      "seeds:82\n",
      "seeds:83\n",
      "seeds:84\n",
      "seeds:85\n",
      "seeds:86\n",
      "seeds:87\n",
      "seeds:88\n",
      "seeds:89\n",
      "seeds:90\n",
      "seeds:91\n",
      "seeds:92\n",
      "seeds:93\n",
      "seeds:94\n",
      "seeds:95\n",
      "seeds:96\n",
      "seeds:97\n",
      "seeds:98\n",
      "seeds:99\n",
      "seeds:0\n",
      "seeds:1\n",
      "seeds:2\n",
      "seeds:3\n",
      "seeds:4\n",
      "seeds:5\n",
      "seeds:6\n",
      "seeds:7\n",
      "seeds:8\n",
      "seeds:9\n",
      "seeds:10\n",
      "seeds:11\n",
      "seeds:12\n",
      "seeds:13\n",
      "seeds:14\n",
      "seeds:15\n",
      "seeds:16\n",
      "seeds:17\n",
      "seeds:18\n",
      "seeds:19\n",
      "seeds:20\n",
      "seeds:21\n",
      "seeds:22\n",
      "seeds:23\n",
      "seeds:24\n",
      "seeds:25\n",
      "seeds:26\n",
      "seeds:27\n",
      "seeds:28\n",
      "seeds:29\n",
      "seeds:30\n",
      "seeds:31\n",
      "seeds:32\n",
      "seeds:33\n",
      "seeds:34\n",
      "seeds:35\n",
      "seeds:36\n",
      "seeds:37\n",
      "seeds:38\n",
      "seeds:39\n",
      "seeds:40\n",
      "seeds:41\n",
      "seeds:42\n",
      "seeds:43\n",
      "seeds:44\n",
      "seeds:45\n",
      "seeds:46\n",
      "seeds:47\n",
      "seeds:48\n",
      "seeds:49\n",
      "seeds:50\n",
      "seeds:51\n",
      "seeds:52\n",
      "seeds:53\n",
      "seeds:54\n",
      "seeds:55\n",
      "seeds:56\n",
      "seeds:57\n",
      "seeds:58\n",
      "seeds:59\n",
      "seeds:60\n",
      "seeds:61\n",
      "seeds:62\n",
      "seeds:63\n",
      "seeds:64\n",
      "seeds:65\n",
      "seeds:66\n",
      "seeds:67\n",
      "seeds:68\n",
      "seeds:69\n",
      "seeds:70\n",
      "seeds:71\n",
      "seeds:72\n",
      "seeds:73\n",
      "seeds:74\n",
      "seeds:75\n",
      "seeds:76\n",
      "seeds:77\n",
      "seeds:78\n",
      "seeds:79\n",
      "seeds:80\n",
      "seeds:81\n",
      "seeds:82\n",
      "seeds:83\n",
      "seeds:84\n",
      "seeds:85\n",
      "seeds:86\n",
      "seeds:87\n",
      "seeds:88\n",
      "seeds:89\n",
      "seeds:90\n",
      "seeds:91\n",
      "seeds:92\n",
      "seeds:93\n",
      "seeds:94\n",
      "seeds:95\n",
      "seeds:96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seeds:97\n",
      "seeds:98\n",
      "seeds:99\n",
      "seeds:0\n",
      "seeds:1\n",
      "seeds:2\n",
      "seeds:3\n",
      "seeds:4\n",
      "seeds:5\n",
      "seeds:6\n",
      "seeds:7\n",
      "seeds:8\n",
      "seeds:9\n",
      "seeds:10\n",
      "seeds:11\n",
      "seeds:12\n",
      "seeds:13\n",
      "seeds:14\n",
      "seeds:15\n",
      "seeds:16\n",
      "seeds:17\n",
      "seeds:18\n",
      "seeds:19\n",
      "seeds:20\n",
      "seeds:21\n",
      "seeds:22\n",
      "seeds:23\n",
      "seeds:24\n",
      "seeds:25\n",
      "seeds:26\n",
      "seeds:27\n",
      "seeds:28\n",
      "seeds:29\n",
      "seeds:30\n",
      "seeds:31\n",
      "seeds:32\n",
      "seeds:33\n",
      "seeds:34\n",
      "seeds:35\n",
      "seeds:36\n",
      "seeds:37\n",
      "seeds:38\n",
      "seeds:39\n",
      "seeds:40\n",
      "seeds:41\n",
      "seeds:42\n",
      "seeds:43\n",
      "seeds:44\n",
      "seeds:45\n",
      "seeds:46\n",
      "seeds:47\n",
      "seeds:48\n",
      "seeds:49\n",
      "seeds:50\n",
      "seeds:51\n",
      "seeds:52\n",
      "seeds:53\n",
      "seeds:54\n",
      "seeds:55\n",
      "seeds:56\n",
      "seeds:57\n",
      "seeds:58\n",
      "seeds:59\n",
      "seeds:60\n",
      "seeds:61\n",
      "seeds:62\n",
      "seeds:63\n",
      "seeds:64\n",
      "seeds:65\n",
      "seeds:66\n",
      "seeds:67\n",
      "seeds:68\n",
      "seeds:69\n",
      "seeds:70\n",
      "seeds:71\n",
      "seeds:72\n",
      "seeds:73\n",
      "seeds:74\n",
      "seeds:75\n",
      "seeds:76\n",
      "seeds:77\n",
      "seeds:78\n",
      "seeds:79\n",
      "seeds:80\n",
      "seeds:81\n",
      "seeds:82\n",
      "seeds:83\n",
      "seeds:84\n",
      "seeds:85\n",
      "seeds:86\n",
      "seeds:87\n",
      "seeds:88\n",
      "seeds:89\n",
      "seeds:90\n",
      "seeds:91\n",
      "seeds:92\n",
      "seeds:93\n",
      "seeds:94\n",
      "seeds:95\n",
      "seeds:96\n",
      "seeds:97\n",
      "seeds:98\n",
      "seeds:99\n",
      "clustering for time: 1.0\n",
      "clustering for time: 2.0\n",
      "clustering for time: 3.0\n",
      "clustering for time: 4.0\n",
      "clustering for time: 5.0\n",
      "clustering for time: 6.0\n",
      "building graph...\n",
      "time adjustment...\n",
      "connecting nodes ....\n",
      "G1\n",
      "ID:  1.0_0\n",
      "index:  0\n",
      "P index:  None\n",
      "ST:  0\n",
      "T:  0\n",
      "cell.ID:  AAAGATGTCCAAGTAC-1\n",
      "cell.ID:  AAAGTAGCATACAGCT-1\n",
      "cell.ID:  AACACGTGTCCTGCTT-1\n",
      "cell.ID:  AACGTTGGTCGGCACT-1\n",
      "cell.ID:  AACTCCCAGCTGAAAT-1\n",
      "cell.ID:  AACTCTTTCCGAACGC-1\n",
      "cell.ID:  ACATCAGCACCATGTA-1\n",
      "cell.ID:  ACATGGTGTTAAGATG-1\n",
      "cell.ID:  ACCGTAAAGCTCCCAG-1\n",
      "cell.ID:  ACGCAGCTCCCGACTT-1\n",
      "cell.ID:  ACTGTCCTCATGGTCA-1\n",
      "cell.ID:  AGGCCACCAAGGTGTG-1\n",
      "cell.ID:  AGGTCCGTCAAGAAGT-1\n",
      "cell.ID:  AGTAGTCAGCCATCGC-1\n",
      "cell.ID:  AGTAGTCTCCTCGCAT-1\n",
      "cell.ID:  AGTGAGGCAGGGATTG-1\n",
      "cell.ID:  ATTGGTGCATCGGAAG-1\n",
      "cell.ID:  CAACCTCAGAAGATTC-1\n",
      "cell.ID:  CAACCTCAGTTAACGA-1\n",
      "cell.ID:  CAAGATCTCTTCCTTC-1\n",
      "cell.ID:  CAAGGCCGTATATGAG-1\n",
      "cell.ID:  CACACAACATCTATGG-1\n",
      "cell.ID:  CACAGTATCTTGTTTG-1\n",
      "cell.ID:  CAGCGACTCTGTACGA-1\n",
      "cell.ID:  CAGTCCTCATGAAGTA-1\n",
      "cell.ID:  CATGCCTAGTTTAGGA-1\n",
      "cell.ID:  CCACTACAGCCCAGCT-1\n",
      "cell.ID:  CCCAGTTGTGATGCCC-1\n",
      "cell.ID:  CGGAGCTAGGCAGGTT-1\n",
      "cell.ID:  CGTGTAAAGGAACTGC-1\n",
      "cell.ID:  CGTTCTGGTAGAAAGG-1\n",
      "cell.ID:  CTAATGGGTGTTTGTG-1\n",
      "cell.ID:  CTCACACGTCCTGCTT-1\n",
      "cell.ID:  CTCAGAATCTCACATT-1\n",
      "cell.ID:  CTCTGGTCATTGTGCA-1\n",
      "cell.ID:  GAAATGAAGCCCAGCT-1\n",
      "cell.ID:  GAATAAGAGCACAGGT-1\n",
      "cell.ID:  GACGGCTCACGGTGTC-1\n",
      "cell.ID:  GACGGCTCATAACCTG-1\n",
      "cell.ID:  GAGGTGACAAGCCCAC-1\n",
      "cell.ID:  GATGAGGAGAAGGGTA-1\n",
      "cell.ID:  GCAATCACAGACGTAG-1\n",
      "cell.ID:  GCACATAGTCCTGCTT-1\n",
      "cell.ID:  GCAGTTAAGCTGCGAA-1\n",
      "cell.ID:  GCAGTTATCTGGTGTA-1\n",
      "cell.ID:  GCATGTATCCTTCAAT-1\n",
      "cell.ID:  GGAAAGCTCGCAAGCC-1\n",
      "cell.ID:  GGACATTCAAACCTAC-1\n",
      "cell.ID:  GGCAATTCACGAAGCA-1\n",
      "cell.ID:  GGCCGATGTAAGGGCT-1\n",
      "cell.ID:  GGGAGATGTGCAGTAG-1\n",
      "cell.ID:  GGGCATCGTCGGCATC-1\n",
      "cell.ID:  GTATTCTGTTGTTTGG-1\n",
      "cell.ID:  GTCACAAAGATCGATA-1\n",
      "cell.ID:  GTGGGTCTCTTGAGAC-1\n",
      "cell.ID:  TAAACCGTCAAGATCC-1\n",
      "cell.ID:  TACGGTATCAGTTAGC-1\n",
      "cell.ID:  TACTCATGTGATAAGT-1\n",
      "cell.ID:  TACTTACTCCACGCAG-1\n",
      "cell.ID:  TACTTGTAGGAATTAC-1\n",
      "cell.ID:  TAGGCATTCTTTACGT-1\n",
      "cell.ID:  TCAGATGCAATACGCT-1\n",
      "cell.ID:  TCAGCTCGTCATGCCG-1\n",
      "cell.ID:  TCGGTAAAGGCTCAGA-1\n",
      "cell.ID:  TGACTTTTCTATGTGG-1\n",
      "cell.ID:  TGAGAGGGTACGAAAT-1\n",
      "cell.ID:  TGAGGGACATGCAATC-1\n",
      "cell.ID:  TGCCCATAGGTACTCT-1\n",
      "cell.ID:  TGCGTGGCAAGTACCT-1\n",
      "cell.ID:  TGTCCCATCAGATAAG-1\n",
      "cell.ID:  TGTGGTACATCTCGCT-1\n",
      "cell.ID:  TTAGGCATCCTATGTT-1\n",
      "cell.ID:  TTCCCAGAGTCGATAA-1\n",
      "cell.ID:  TTCCCAGCAATGTAAG-1\n",
      "cell.ID:  TTCGAAGAGAAGCCCA-1\n",
      "cell.ID:  TTCTCAATCATCTGCC-1\n",
      "cell.ID:  TTGTAGGGTAAACCTC-1\n",
      "cell.ID:  TTTATGCCAGTGGGAT-1\n",
      "ID:  2.0_0\n",
      "index:  1\n",
      "P index:  0\n",
      "ST:  2.0\n",
      "T:  1\n",
      "cell.ID:  AAACGGGGTATCGCAT-1\n",
      "cell.ID:  AAAGTAGGTCAGGACA-1\n",
      "cell.ID:  AACTGGTCAAGCCGCT-1\n",
      "cell.ID:  AACTTTCAGGTGTTAA-1\n",
      "cell.ID:  AACTTTCCAATTCCTT-1\n",
      "cell.ID:  AAGACCTGTCGAGATG-1\n",
      "cell.ID:  AAGGCAGGTCAGGACA-1\n",
      "cell.ID:  AAGGCAGGTGGTCCGT-1\n",
      "cell.ID:  ACATCAGTCACCACCT-1\n",
      "cell.ID:  ACCCACTAGGCGCTCT-1\n",
      "cell.ID:  ACGAGGAAGCTGAACG-1\n",
      "cell.ID:  ACGAGGAGTTAGAACA-1\n",
      "cell.ID:  ACGATACGTGAGCGAT-1\n",
      "cell.ID:  ACGCAGCGTCTGATTG-1\n",
      "cell.ID:  ACGGCCAAGCCGATTT-1\n",
      "cell.ID:  ACGGGCTTCTGAGGGA-1\n",
      "cell.ID:  ACTGAACAGAGCTATA-1\n",
      "cell.ID:  AGAGCGAAGCACCGTC-1\n",
      "cell.ID:  AGAGCGAGTGATGATA-1\n",
      "cell.ID:  AGAGCGATCTACCTGC-1\n",
      "cell.ID:  AGAGCTTTCATCGGAT-1\n",
      "cell.ID:  AGCCTAATCTGTTTGT-1\n",
      "cell.ID:  AGCGTCGTCTACCAGA-1\n",
      "cell.ID:  AGCTCCTAGCACGCCT-1\n",
      "cell.ID:  AGGCCACAGGACACCA-1\n",
      "cell.ID:  AGGCCGTTCCAGATCA-1\n",
      "cell.ID:  AGTTGGTTCTTGAGGT-1\n",
      "cell.ID:  ATAGACCGTTAAAGAC-1\n",
      "cell.ID:  ATCGAGTAGAGGTAGA-1\n",
      "cell.ID:  ATGAGGGCACGCCAGT-1\n",
      "cell.ID:  ATGTGTGCAGTCCTTC-1\n",
      "cell.ID:  ATTGGTGAGCTGTTCA-1\n",
      "cell.ID:  CAACCTCGTTAGAACA-1\n",
      "cell.ID:  CAACTAGAGAGACGAA-1\n",
      "cell.ID:  CAACTAGCACTTAACG-1\n",
      "cell.ID:  CAAGAAACACTCGACG-1\n",
      "cell.ID:  CAAGTTGAGGCGATAC-1\n",
      "cell.ID:  CACACCTGTAACGCGA-1\n",
      "cell.ID:  CACACTCGTCAGATAA-1\n",
      "cell.ID:  CACAGTATCGTGTAGT-1\n",
      "cell.ID:  CACATAGTCTTCGAGA-1\n",
      "cell.ID:  CACCTTGCAGATTGCT-1\n",
      "cell.ID:  CAGCAGCCACGCGAAA-1\n",
      "cell.ID:  CAGCTAACACCCTATC-1\n",
      "cell.ID:  CAGCTGGGTCACCCAG-1\n",
      "cell.ID:  CAGTAACTCGGCTACG-1\n",
      "cell.ID:  CAGTCCTAGGACCACA-1\n",
      "cell.ID:  CATCAGATCAGAGCTT-1\n",
      "cell.ID:  CATCGAAGTTCGCTAA-1\n",
      "cell.ID:  CATGGCGCAAGTAGTA-1\n",
      "cell.ID:  CCAGCGAAGTGGTAAT-1\n",
      "cell.ID:  CCGGTAGCATAAGACA-1\n",
      "cell.ID:  CCTACCACATGACATC-1\n",
      "cell.ID:  CCTACCAGTCGAGTTT-1\n",
      "cell.ID:  CCTAGCTCAAGACACG-1\n",
      "cell.ID:  CCTAGCTTCCATTCTA-1\n",
      "cell.ID:  CGAACATGTAGCTGCC-1\n",
      "cell.ID:  CGACCTTCATTTCAGG-1\n",
      "cell.ID:  CGACCTTTCACCCGAG-1\n",
      "cell.ID:  CGATTGATCCCAGGTG-1\n",
      "cell.ID:  CGCTGGATCGAACTGT-1\n",
      "cell.ID:  CGCTTCACAGGATCGA-1\n",
      "cell.ID:  CGGACTGAGGCATGGT-1\n",
      "cell.ID:  CGGAGTCCATCGGTTA-1\n",
      "cell.ID:  CGGGTCACACCGGAAA-1\n",
      "cell.ID:  CGTAGGCGTTTGACAC-1\n",
      "cell.ID:  CGTCACTAGACGACGT-1\n",
      "cell.ID:  CGTCAGGTCAGCCTAA-1\n",
      "cell.ID:  CGTGAGCAGGAGTAGA-1\n",
      "cell.ID:  CGTTCTGCAGCCTGTG-1\n",
      "cell.ID:  CTACATTCACAGGCCT-1\n",
      "cell.ID:  CTACATTGTTCCGTCT-1\n",
      "cell.ID:  CTCGGAGCAGTGGGAT-1\n",
      "cell.ID:  CTCGTCATCAGGCAAG-1\n",
      "cell.ID:  CTCGTCATCTGCAGTA-1\n",
      "cell.ID:  CTCTAATGTGACCAAG-1\n",
      "cell.ID:  CTGATCCCACCGCTAG-1\n",
      "cell.ID:  CTGATCCTCGGCTTGG-1\n",
      "cell.ID:  CTGCCTAGTACTTGAC-1\n",
      "cell.ID:  CTTACCGCAACGATGG-1\n",
      "cell.ID:  CTTACCGGTCATTAGC-1\n",
      "cell.ID:  GAACGGATCTGCAGTA-1\n",
      "cell.ID:  GAAGCAGAGAACAATC-1\n",
      "cell.ID:  GAAGCAGTCACTTATC-1\n",
      "cell.ID:  GAATAAGGTACCCAAT-1\n",
      "cell.ID:  GACCAATGTCTGGAGA-1\n",
      "cell.ID:  GACTAACAGCGTAGTG-1\n",
      "cell.ID:  GACTACAGTCCGTCAG-1\n",
      "cell.ID:  GAGCAGACACCCTATC-1\n",
      "cell.ID:  GAGGTGAAGGATGGTC-1\n",
      "cell.ID:  GATCAGTTCACTCTTA-1\n",
      "cell.ID:  GATCGATTCATAAAGG-1\n",
      "cell.ID:  GCAATCATCAGTTAGC-1\n",
      "cell.ID:  GCAGTTAGTTACTGAC-1\n",
      "cell.ID:  GCCAAATGTTCCACAA-1\n",
      "cell.ID:  GCCTCTACAAATACAG-1\n",
      "cell.ID:  GCTGCGAGTCGACTAT-1\n",
      "cell.ID:  GGCGTGTCATTGTGCA-1\n",
      "cell.ID:  GGGACCTCATAGACTC-1\n",
      "cell.ID:  GGGAGATGTAAAGTCA-1\n",
      "cell.ID:  GGTGCGTGTTGTACAC-1\n",
      "cell.ID:  GTAACGTGTTCCACAA-1\n",
      "cell.ID:  GTACTCCTCCCGACTT-1\n",
      "cell.ID:  GTACTTTGTGCACTTA-1\n",
      "cell.ID:  GTAGGCCAGATCGGGT-1\n",
      "cell.ID:  GTAGGCCTCACCCTCA-1\n",
      "cell.ID:  GTCGGGTCAAGTAGTA-1\n",
      "cell.ID:  GTCGGGTTCTGGTGTA-1\n",
      "cell.ID:  GTCTTCGGTCTAGAGG-1\n",
      "cell.ID:  GTCTTCGGTGTCGCTG-1\n",
      "cell.ID:  GTGCGGTGTATTCGTG-1\n",
      "cell.ID:  GTTTCTATCACAAACC-1\n",
      "cell.ID:  TACAGTGGTAAGGGAA-1\n",
      "cell.ID:  TACTCATAGTACCGGA-1\n",
      "cell.ID:  TAGACCAAGAGTCTGG-1\n",
      "cell.ID:  TAGCCGGCAAGTACCT-1\n",
      "cell.ID:  TAGGCATTCCACGTTC-1\n",
      "cell.ID:  TATTACCAGGCTAGAC-1\n",
      "cell.ID:  TCAGCTCTCGTCTGAA-1\n",
      "cell.ID:  TCGCGAGAGAAACGAG-1\n",
      "cell.ID:  TCGGGACAGTCCCACG-1\n",
      "cell.ID:  TCTATTGCAGAGTGTG-1\n",
      "cell.ID:  TCTATTGCAGCTGTTA-1\n",
      "cell.ID:  TCTTTCCAGGTCGGAT-1\n",
      "cell.ID:  TGAGAGGCAAGGGTCA-1\n",
      "cell.ID:  TGAGAGGGTCAACTGT-1\n",
      "cell.ID:  TGAGCCGGTACGCACC-1\n",
      "cell.ID:  TGAGGGAAGCACCGTC-1\n",
      "cell.ID:  TGCCAAAAGACTAAGT-1\n",
      "cell.ID:  TGCTGCTCACAGGTTT-1\n",
      "cell.ID:  TGCTGCTCACGGATAG-1\n",
      "cell.ID:  TGGCGCATCCACGAAT-1\n",
      "cell.ID:  TGGCTGGGTGTGGTTT-1\n",
      "cell.ID:  TGTGGTAAGTTAAGTG-1\n",
      "cell.ID:  TGTGGTAGTACTCAAC-1\n",
      "cell.ID:  TTCGGTCGTTTGGGCC-1\n",
      "cell.ID:  TTCTCAATCCCATTTA-1\n",
      "cell.ID:  TTGCGTCAGCGAAGGG-1\n",
      "cell.ID:  TTTATGCGTTAAGATG-1\n",
      "cell.ID:  TTTGGTTGTGTTTGTG-1\n",
      "ID:  3.0_0\n",
      "index:  2\n",
      "P index:  1\n",
      "ST:  3.0\n",
      "T:  2\n",
      "cell.ID:  AAAGATGAGGACCACA-1\n",
      "cell.ID:  AAAGATGCAATCTGCA-1\n",
      "cell.ID:  AAAGATGGTGCAGGTA-1\n",
      "cell.ID:  AAAGATGGTTTGTTTC-1\n",
      "cell.ID:  AAAGTAGAGCTGTCTA-1\n",
      "cell.ID:  AAATGCCAGGGATACC-1\n",
      "cell.ID:  AACACGTAGTGTTGAA-1\n",
      "cell.ID:  AACCATGGTGGACGAT-1\n",
      "cell.ID:  AACGTTGAGTTCCACA-1\n",
      "cell.ID:  AACGTTGCATTCTCAT-1\n",
      "cell.ID:  AACTCAGGTTACGCGC-1\n",
      "cell.ID:  AACTCCCCAGTTCATG-1\n",
      "cell.ID:  AACTCTTTCTGCTGTC-1\n",
      "cell.ID:  AACTGGTTCGATCCCT-1\n",
      "cell.ID:  AACTGGTTCGTCCAGG-1\n",
      "cell.ID:  AACTTTCAGGTACTCT-1\n",
      "cell.ID:  AACTTTCTCCACTCCA-1\n",
      "cell.ID:  AAGACCTGTGCCTGTG-1\n",
      "cell.ID:  AAGCCGCCAGCTTCGG-1\n",
      "cell.ID:  AAGCCGCGTTCTGTTT-1\n",
      "cell.ID:  AAGGAGCTCTTCATGT-1\n",
      "cell.ID:  AAGGTTCTCTGCGTAA-1\n",
      "cell.ID:  ACACCGGGTGTGCCTG-1\n",
      "cell.ID:  ACAGCCGGTGAGTATA-1\n",
      "cell.ID:  ACATACGAGGCTAGAC-1\n",
      "cell.ID:  ACATACGAGTAGCCGA-1\n",
      "cell.ID:  ACATACGTCCTGTACC-1\n",
      "cell.ID:  ACATCAGCAATGAATG-1\n",
      "cell.ID:  ACATCAGCAGTGGAGT-1\n",
      "cell.ID:  ACCCACTAGCTAAACA-1\n",
      "cell.ID:  ACCGTAACAATACGCT-1\n",
      "cell.ID:  ACCTTTATCTACTATC-1\n",
      "cell.ID:  ACCTTTATCTCTGTCG-1\n",
      "cell.ID:  ACGAGCCAGTTCGATC-1\n",
      "cell.ID:  ACGAGCCGTGCTTCTC-1\n",
      "cell.ID:  ACGAGCCGTTGTCGCG-1\n",
      "cell.ID:  ACGATACGTTAAAGAC-1\n",
      "cell.ID:  ACGCCAGCAGGCGATA-1\n",
      "cell.ID:  ACGCCGAAGGAGTACC-1\n",
      "cell.ID:  ACGCCGATCACGCATA-1\n",
      "cell.ID:  ACGCCGATCGCCTGAG-1\n",
      "cell.ID:  ACGGCCATCAATAAGG-1\n",
      "cell.ID:  ACGGGCTCATCCGTGG-1\n",
      "cell.ID:  ACGGGTCCATGGTCTA-1\n",
      "cell.ID:  ACGGGTCGTGCGATAG-1\n",
      "cell.ID:  ACGGGTCGTTCTGTTT-1\n",
      "cell.ID:  ACTGAGTAGCCCAACC-1\n",
      "cell.ID:  ACTGAGTAGCCGCCTA-1\n",
      "cell.ID:  ACTGAGTCAAGGACTG-1\n",
      "cell.ID:  ACTGATGAGCAGGTCA-1\n",
      "cell.ID:  ACTGATGCACTAAGTC-1\n",
      "cell.ID:  ACTGTCCAGGGCATGT-1\n",
      "cell.ID:  ACTGTCCTCCTCAACC-1\n",
      "cell.ID:  ACTTTCATCACGGTTA-1\n",
      "cell.ID:  AGAATAGTCCCATTTA-1\n",
      "cell.ID:  AGACGTTAGTCTCGGC-1\n",
      "cell.ID:  AGAGCGAAGCGCTCCA-1\n",
      "cell.ID:  AGAGCTTAGACTTTCG-1\n",
      "cell.ID:  AGAGCTTTCAATAAGG-1\n",
      "cell.ID:  AGAGCTTTCGATGAGG-1\n",
      "cell.ID:  AGAGTGGAGGGCTTCC-1\n",
      "cell.ID:  AGAGTGGAGTGCGTGA-1\n",
      "cell.ID:  AGAGTGGCAGACGTAG-1\n",
      "cell.ID:  AGAGTGGGTCTCCATC-1\n",
      "cell.ID:  AGAGTGGGTGGACGAT-1\n",
      "cell.ID:  AGCAGCCAGCGATGAC-1\n",
      "cell.ID:  AGCAGCCGTAAGTAGT-1\n",
      "cell.ID:  AGCGGTCTCGCGGATC-1\n",
      "cell.ID:  AGCGTATCAATAGCAA-1\n",
      "cell.ID:  AGCGTCGGTCGGATCC-1\n",
      "cell.ID:  AGCGTCGGTCTTCAAG-1\n",
      "cell.ID:  AGGCCACAGTTGAGAT-1\n",
      "cell.ID:  AGGCCACCACAGTCGC-1\n",
      "cell.ID:  AGGCCACTCGTGACAT-1\n",
      "cell.ID:  AGGGATGAGTGGACGT-1\n",
      "cell.ID:  AGGGTGAAGAGGGCTT-1\n",
      "cell.ID:  AGGGTGAGTGAGTATA-1\n",
      "cell.ID:  AGGTCATCAGTGAGTG-1\n",
      "cell.ID:  AGGTCATGTTTAAGCC-1\n",
      "cell.ID:  AGTAGTCCACGTCTCT-1\n",
      "cell.ID:  AGTAGTCTCGTACCGG-1\n",
      "cell.ID:  AGTCTTTTCACAATGC-1\n",
      "cell.ID:  AGTGTCAGTAAAGGAG-1\n",
      "cell.ID:  AGTTGGTGTGAGCGAT-1\n",
      "cell.ID:  ATAAGAGTCCAATGGT-1\n",
      "cell.ID:  ATAGACCAGTAGTGCG-1\n",
      "cell.ID:  ATCACGACATGCCCGA-1\n",
      "cell.ID:  ATCATCTAGATCACGG-1\n",
      "cell.ID:  ATCATCTCAGCTTAAC-1\n",
      "cell.ID:  ATCATGGAGTGTCTCA-1\n",
      "cell.ID:  ATCATGGGTCCTCTTG-1\n",
      "cell.ID:  ATCCACCCATTCTCAT-1\n",
      "cell.ID:  ATCGAGTAGGCGCTCT-1\n",
      "cell.ID:  ATCGAGTCACAAGACG-1\n",
      "cell.ID:  ATCGAGTCACAGCGTC-1\n",
      "cell.ID:  ATCGAGTGTCTACCTC-1\n",
      "cell.ID:  ATCTACTCAGATAATG-1\n",
      "cell.ID:  ATCTACTGTAGAGCTG-1\n",
      "cell.ID:  ATCTGCCAGGTCGGAT-1\n",
      "cell.ID:  ATCTGCCGTCTCTCGT-1\n",
      "cell.ID:  ATCTGCCTCCAAACAC-1\n",
      "cell.ID:  ATGCGATAGATCACGG-1\n",
      "cell.ID:  ATGGGAGGTCTCTTTA-1\n",
      "cell.ID:  ATGTGTGCATTATCTC-1\n",
      "cell.ID:  ATTACTCGTGTGAAAT-1\n",
      "cell.ID:  ATTCTACTCTGGAGCC-1\n",
      "cell.ID:  ATTGGACTCTACTATC-1\n",
      "cell.ID:  ATTGGTGTCGGTGTCG-1\n",
      "cell.ID:  CAACCAACAATGGAGC-1\n",
      "cell.ID:  CAACCAATCACCACCT-1\n",
      "cell.ID:  CAACCAATCACGATGT-1\n",
      "cell.ID:  CAACTAGAGGCTATCT-1\n",
      "cell.ID:  CAACTAGTCACATACG-1\n",
      "cell.ID:  CAAGAAACACCACCAG-1\n",
      "cell.ID:  CAAGATCGTATGGTTC-1\n",
      "cell.ID:  CAAGGCCCAAGTTGTC-1\n",
      "cell.ID:  CAAGGCCGTCTAAAGA-1\n",
      "cell.ID:  CAAGGCCTCCTTTCGG-1\n",
      "cell.ID:  CAAGTTGAGCCACTAT-1\n",
      "cell.ID:  CAAGTTGCAGGTGCCT-1\n",
      "cell.ID:  CAAGTTGTCCGTCATC-1\n",
      "cell.ID:  CACAAACAGTGGGCTA-1\n",
      "cell.ID:  CACAAACCATGGATGG-1\n",
      "cell.ID:  CACACCTAGTGAAGTT-1\n",
      "cell.ID:  CACACCTCACCTCGGA-1\n",
      "cell.ID:  CACACCTCACGGACAA-1\n",
      "cell.ID:  CACACCTTCAGTCAGT-1\n",
      "cell.ID:  CACACTCAGAAGGGTA-1\n",
      "cell.ID:  CACACTCAGTTCCACA-1\n",
      "cell.ID:  CACAGGCGTAAGGGAA-1\n",
      "cell.ID:  CACAGGCTCAGTCCCT-1\n",
      "cell.ID:  CACAGGCTCCACTCCA-1\n",
      "cell.ID:  CACATTTCATGCAATC-1\n",
      "cell.ID:  CACATTTGTAATCACC-1\n",
      "cell.ID:  CACATTTTCCGTCATC-1\n",
      "cell.ID:  CACCACTGTGCTTCTC-1\n",
      "cell.ID:  CACCAGGCACACCGCA-1\n",
      "cell.ID:  CACCAGGCATGTAAGA-1\n",
      "cell.ID:  CACCTTGAGTACGTTC-1\n",
      "cell.ID:  CACCTTGGTCGAAAGC-1\n",
      "cell.ID:  CAGAATCAGCTAGTTC-1\n",
      "cell.ID:  CAGAATCGTTGTTTGG-1\n",
      "cell.ID:  CAGATCAGTCTAGAGG-1\n",
      "cell.ID:  CAGCAGCAGACCGGAT-1\n",
      "cell.ID:  CAGCAGCCATTGGGCC-1\n",
      "cell.ID:  CAGCTAAAGTTGCAGG-1\n",
      "cell.ID:  CAGCTGGTCCACGCAG-1\n",
      "cell.ID:  CAGGTGCCAACGATGG-1\n",
      "cell.ID:  CAGGTGCCATGCGCAC-1\n",
      "cell.ID:  CAGTAACTCGAACGGA-1\n",
      "cell.ID:  CATATGGGTTGCTCCT-1\n",
      "cell.ID:  CATATTCCATGCTAGT-1\n",
      "cell.ID:  CATATTCGTCCGCTGA-1\n",
      "cell.ID:  CATATTCTCCGCGTTT-1\n",
      "cell.ID:  CATCAAGCATCTCCCA-1\n",
      "cell.ID:  CATCAAGGTGGACGAT-1\n",
      "cell.ID:  CATCAAGTCGCATGGC-1\n",
      "cell.ID:  CATCAGATCCGAACGC-1\n",
      "cell.ID:  CATCCACAGCTAACAA-1\n",
      "cell.ID:  CATCGGGGTTGAACTC-1\n",
      "cell.ID:  CATGACAAGAGCCTAG-1\n",
      "cell.ID:  CATGCCTGTCTAGCGC-1\n",
      "cell.ID:  CATGGCGGTTCGCGAC-1\n",
      "cell.ID:  CATTATCCACGAAATA-1\n",
      "cell.ID:  CATTATCGTAGCGCTC-1\n",
      "cell.ID:  CCACCTAAGAAGATTC-1\n",
      "cell.ID:  CCACCTAAGGTGACCA-1\n",
      "cell.ID:  CCACCTACAGTGACAG-1\n",
      "cell.ID:  CCACCTAGTGGCCCTA-1\n",
      "cell.ID:  CCACCTATCCGCATAA-1\n",
      "cell.ID:  CCACGGAGTTCGTCTC-1\n",
      "cell.ID:  CCACGGATCCAACCAA-1\n",
      "cell.ID:  CCACTACAGGAATTAC-1\n",
      "cell.ID:  CCAGCGAGTAGATTAG-1\n",
      "cell.ID:  CCATGTCGTCAGAGGT-1\n",
      "cell.ID:  CCCAATCGTGCTAGCC-1\n",
      "cell.ID:  CCCATACAGTCCGTAT-1\n",
      "cell.ID:  CCGGTAGCATCACGTA-1\n",
      "cell.ID:  CCGGTAGCATTACGAC-1\n",
      "cell.ID:  CCGGTAGTCATTCACT-1\n",
      "cell.ID:  CCGTACTCAGCAGTTT-1\n",
      "cell.ID:  CCGTGGAAGTTATCGC-1\n",
      "cell.ID:  CCGTGGAGTGGTAACG-1\n",
      "cell.ID:  CCGTGGAGTTCAGGCC-1\n",
      "cell.ID:  CCGTTCAAGATGTTAG-1\n",
      "cell.ID:  CCTACCACAGCGTTCG-1\n",
      "cell.ID:  CCTAGCTCAAACGTGG-1\n",
      "cell.ID:  CCTAGCTTCCTTCAAT-1\n",
      "cell.ID:  CCTCAGTTCGGATGGA-1\n",
      "cell.ID:  CCTCTGACAGGAATGC-1\n",
      "cell.ID:  CCTTACGCAAAGTGCG-1\n",
      "cell.ID:  CCTTCCCTCATGCAAC-1\n",
      "cell.ID:  CCTTCGAGTACCCAAT-1\n",
      "cell.ID:  CCTTTCTTCTCCAACC-1\n",
      "cell.ID:  CGAATGTAGAGTCGGT-1\n",
      "cell.ID:  CGAATGTGTACAGACG-1\n",
      "cell.ID:  CGACCTTAGTTGAGAT-1\n",
      "cell.ID:  CGAGAAGAGCGATGAC-1\n",
      "cell.ID:  CGAGCACCACACCGAC-1\n",
      "cell.ID:  CGAGCACCATAAGACA-1\n",
      "cell.ID:  CGAGCACGTCAAGCGA-1\n",
      "cell.ID:  CGAGCACGTTCAGTAC-1\n",
      "cell.ID:  CGATCGGAGTGAATTG-1\n",
      "cell.ID:  CGATGGCGTACGACCC-1\n",
      "cell.ID:  CGATGGCGTATTACCG-1\n",
      "cell.ID:  CGCGGTAAGGGATACC-1\n",
      "cell.ID:  CGCGGTATCACCACCT-1\n",
      "cell.ID:  CGCTGGATCTGCCAGG-1\n",
      "cell.ID:  CGGACACGTCTGGAGA-1\n",
      "cell.ID:  CGGACGTGTGTTGGGA-1\n",
      "cell.ID:  CGGACTGAGCCAGGAT-1\n",
      "cell.ID:  CGGCTAGCATATGGTC-1\n",
      "cell.ID:  CGGCTAGCATGTCGAT-1\n",
      "cell.ID:  CGGCTAGGTGAGTGAC-1\n",
      "cell.ID:  CGGCTAGGTGGTACAG-1\n",
      "cell.ID:  CGTAGCGGTGTGCCTG-1\n",
      "cell.ID:  CGTCACTTCTGAAAGA-1\n",
      "cell.ID:  CGTCCATTCAAACGGG-1\n",
      "cell.ID:  CGTCTACAGATGGGTC-1\n",
      "cell.ID:  CGTGTAATCGATAGAA-1\n",
      "cell.ID:  CGTGTCTGTCATACTG-1\n",
      "cell.ID:  CGTGTCTTCGCGTAGC-1\n",
      "cell.ID:  CGTTAGACAGTGACAG-1\n",
      "cell.ID:  CGTTAGATCAGCGACC-1\n",
      "cell.ID:  CGTTGGGGTCAACTGT-1\n",
      "cell.ID:  CTAAGACAGATAGTCA-1\n",
      "cell.ID:  CTAAGACTCAACGAAA-1\n",
      "cell.ID:  CTAATGGCAGTCAGAG-1\n",
      "cell.ID:  CTAATGGTCGTCTGCT-1\n",
      "cell.ID:  CTACACCGTCGCATAT-1\n",
      "cell.ID:  CTACACCGTCGTGGCT-1\n",
      "cell.ID:  CTACACCTCTTGAGAC-1\n",
      "cell.ID:  CTACCCATCGCATGGC-1\n",
      "cell.ID:  CTAGAGTCAAACTGTC-1\n",
      "cell.ID:  CTAGAGTGTGTTAAGA-1\n",
      "cell.ID:  CTAGCCTAGCCACCTG-1\n",
      "cell.ID:  CTAGCCTGTACAGACG-1\n",
      "cell.ID:  CTCAGAAAGCCAGTAG-1\n",
      "cell.ID:  CTCATTAAGGATCGCA-1\n",
      "cell.ID:  CTCATTATCTTTAGGG-1\n",
      "cell.ID:  CTCCTAGAGTACTTGC-1\n",
      "cell.ID:  CTCCTAGAGTGCCAGA-1\n",
      "cell.ID:  CTCGAAAGTACCGTTA-1\n",
      "cell.ID:  CTCGAGGGTAGCTGCC-1\n",
      "cell.ID:  CTCGAGGTCAGTGTTG-1\n",
      "cell.ID:  CTCGGAGAGTGACTCT-1\n",
      "cell.ID:  CTCGGAGCACCGGAAA-1\n",
      "cell.ID:  CTCGTCAAGGCACATG-1\n",
      "cell.ID:  CTCGTCAGTATATCCG-1\n",
      "cell.ID:  CTCGTCATCCACGCAG-1\n",
      "cell.ID:  CTCGTCATCGACGGAA-1\n",
      "cell.ID:  CTCTAATAGCTAAGAT-1\n",
      "cell.ID:  CTCTACGAGTGAACAT-1\n",
      "cell.ID:  CTCTGGTAGAGTTGGC-1\n",
      "cell.ID:  CTGAAACTCTCTTATG-1\n",
      "cell.ID:  CTGAAACTCTTTAGGG-1\n",
      "cell.ID:  CTGAAGTGTGGCAAAC-1\n",
      "cell.ID:  CTGATCCGTAGCCTAT-1\n",
      "cell.ID:  CTGGTCTGTCTAGGTT-1\n",
      "cell.ID:  CTGTGCTCATGTTCCC-1\n",
      "cell.ID:  CTGTGCTGTTGTACAC-1\n",
      "cell.ID:  CTGTGCTTCAAGAAGT-1\n",
      "cell.ID:  CTGTGCTTCATGCATG-1\n",
      "cell.ID:  CTGTTTACACGAAGCA-1\n",
      "cell.ID:  CTGTTTATCCTGCTTG-1\n",
      "cell.ID:  CTTACCGAGGGTCTCC-1\n",
      "cell.ID:  CTTACCGCAAGAGGCT-1\n",
      "cell.ID:  CTTACCGTCGGACAAG-1\n",
      "cell.ID:  CTTAGGATCTCCGGTT-1\n",
      "cell.ID:  CTTGGCTCACATTAGC-1\n",
      "cell.ID:  CTTGGCTGTCGAGATG-1\n",
      "cell.ID:  CTTTGCGAGGGAGTAA-1\n",
      "cell.ID:  GAAACTCGTAGCCTAT-1\n",
      "cell.ID:  GAAATGACAGGTTTCA-1\n",
      "cell.ID:  GAAATGAGTTCATGGT-1\n",
      "cell.ID:  GAACGGAAGAAGAAGC-1\n",
      "cell.ID:  GAAGCAGCATTATCTC-1\n",
      "cell.ID:  GACGGCTCAGGCTGAA-1\n",
      "cell.ID:  GACGGCTTCTTGGGTA-1\n",
      "cell.ID:  GACTACACATCTCCCA-1\n",
      "cell.ID:  GAGCAGATCAACACTG-1\n",
      "cell.ID:  GAGCAGATCGGAGCAA-1\n",
      "cell.ID:  GAGGTGATCGTGGACC-1\n",
      "cell.ID:  GAGGTGATCGTGGTCG-1\n",
      "cell.ID:  GAGTCCGCATAGGATA-1\n",
      "cell.ID:  GATCAGTTCCATGCTC-1\n",
      "cell.ID:  GATGAGGTCCGTCAAA-1\n",
      "cell.ID:  GCAAACTAGTATCTCG-1\n",
      "cell.ID:  GCAAACTTCAACGCTA-1\n",
      "cell.ID:  GCAAACTTCACTGGGC-1\n",
      "cell.ID:  GCAATCACAATCTGCA-1\n",
      "cell.ID:  GCAATCATCCAGATCA-1\n",
      "cell.ID:  GCAATCATCGTGGACC-1\n",
      "cell.ID:  GCACTCTAGGTACTCT-1\n",
      "cell.ID:  GCACTCTTCGGCGCAT-1\n",
      "cell.ID:  GCACTCTTCTGTCTCG-1\n",
      "cell.ID:  GCAGTTACAATAACGA-1\n",
      "cell.ID:  GCAGTTATCTCCGGTT-1\n",
      "cell.ID:  GCATACAAGTGGACGT-1\n",
      "cell.ID:  GCCTCTAAGCACAGGT-1\n",
      "cell.ID:  GCCTCTAGTTCGCGAC-1\n",
      "cell.ID:  GCGAGAAAGACTAGGC-1\n",
      "cell.ID:  GCGAGAATCTCGATGA-1\n",
      "cell.ID:  GCGCCAAGTCTTCGTC-1\n",
      "cell.ID:  GCGCGATCAACTTGAC-1\n",
      "cell.ID:  GCGGGTTCACCGAATT-1\n",
      "cell.ID:  GCGGGTTTCAGTACGT-1\n",
      "cell.ID:  GCTGCAGCAGACACTT-1\n",
      "cell.ID:  GCTGCAGGTCGCCATG-1\n",
      "cell.ID:  GCTGCTTAGTGCCATT-1\n",
      "cell.ID:  GCTGCTTTCACATACG-1\n",
      "cell.ID:  GCTTGAAAGTGTTTGC-1\n",
      "cell.ID:  GCTTGAACAATAACGA-1\n",
      "cell.ID:  GGAAAGCCAAGCCCAC-1\n",
      "cell.ID:  GGAAAGCCAAGCGCTC-1\n",
      "cell.ID:  GGAACTTAGGATCGCA-1\n",
      "cell.ID:  GGAACTTTCCAAACAC-1\n",
      "cell.ID:  GGAATAAAGCGTTCCG-1\n",
      "cell.ID:  GGACAAGGTTCTCATT-1\n",
      "cell.ID:  GGACAGAAGTGCAAGC-1\n",
      "cell.ID:  GGACAGATCCGTAGTA-1\n",
      "cell.ID:  GGACATTAGAAGGACA-1\n",
      "cell.ID:  GGACGTCGTGTAATGA-1\n",
      "cell.ID:  GGAGCAACATCTACGA-1\n",
      "cell.ID:  GGAGCAATCGTGGTCG-1\n",
      "cell.ID:  GGCAATTTCCCTAATT-1\n",
      "cell.ID:  GGCGTGTGTAGCACGA-1\n",
      "cell.ID:  GGCTCGAAGAACAATC-1\n",
      "cell.ID:  GGCTCGATCTACGAGT-1\n",
      "cell.ID:  GGCTGGTCAAGGTGTG-1\n",
      "cell.ID:  GGGCACTAGGGAACGG-1\n",
      "cell.ID:  GGGCACTAGGGAGTAA-1\n",
      "cell.ID:  GGGCACTTCGGCGCTA-1\n",
      "cell.ID:  GGGTCTGCACGGCTAC-1\n",
      "cell.ID:  GGGTCTGGTTCAACCA-1\n",
      "cell.ID:  GGGTCTGTCTCACATT-1\n",
      "cell.ID:  GGGTTGCCAAGTAATG-1\n",
      "cell.ID:  GGTATTGAGTCACGCC-1\n",
      "cell.ID:  GGTATTGGTTATTCTC-1\n",
      "cell.ID:  GGTATTGTCCTGTAGA-1\n",
      "cell.ID:  GGTGAAGAGCTATGCT-1\n",
      "cell.ID:  GGTGTTAAGCGTTTAC-1\n",
      "cell.ID:  GTACTCCTCTACTCAT-1\n",
      "cell.ID:  GTACTTTAGTTAAGTG-1\n",
      "cell.ID:  GTAGTCATCAGAAATG-1\n",
      "cell.ID:  GTATTCTGTCGCATAT-1\n",
      "cell.ID:  GTATTCTTCGGAATCT-1\n",
      "cell.ID:  GTCAAGTGTGTATGGG-1\n",
      "cell.ID:  GTCACGGCAGACACTT-1\n",
      "cell.ID:  GTCACGGGTCGCGGTT-1\n",
      "cell.ID:  GTCATTTCACAGGTTT-1\n",
      "cell.ID:  GTCATTTGTACAGTTC-1\n",
      "cell.ID:  GTCATTTTCAACCAAC-1\n",
      "cell.ID:  GTCCTCAGTAGCAAAT-1\n",
      "cell.ID:  GTCGGGTGTCAAAGCG-1\n",
      "cell.ID:  GTCGTAAGTACTCGCG-1\n",
      "cell.ID:  GTCTCGTAGCCACGCT-1\n",
      "cell.ID:  GTCTTCGTCAGCACAT-1\n",
      "cell.ID:  GTCTTCGTCGATAGAA-1\n",
      "cell.ID:  GTGCAGCAGGAATGGA-1\n",
      "cell.ID:  GTGCATATCCTGTACC-1\n",
      "cell.ID:  GTGCGGTAGACGCTTT-1\n",
      "cell.ID:  GTGCGGTTCACCCTCA-1\n",
      "cell.ID:  GTGCTTCAGTGTACCT-1\n",
      "cell.ID:  GTGCTTCTCATATCGG-1\n",
      "cell.ID:  GTGGGTCTCGTTGCCT-1\n",
      "cell.ID:  GTGTGCGTCGAATGGG-1\n",
      "cell.ID:  GTTAAGCGTCAACTGT-1\n",
      "cell.ID:  GTTACAGGTCAGAGGT-1\n",
      "cell.ID:  GTTCATTCAATTCCTT-1\n",
      "cell.ID:  GTTCTCGCAAGTAGTA-1\n",
      "cell.ID:  GTTCTCGTCATTTGGG-1\n",
      "cell.ID:  GTTTCTACAAATACAG-1\n",
      "cell.ID:  GTTTCTAGTCAATACC-1\n",
      "cell.ID:  TAAACCGCAGGATCGA-1\n",
      "cell.ID:  TAAACCGTCCCGGATG-1\n",
      "cell.ID:  TAAACCGTCGTGGGAA-1\n",
      "cell.ID:  TAAGAGACATTCCTCG-1\n",
      "cell.ID:  TAAGCGTCAAACCCAT-1\n",
      "cell.ID:  TAAGCGTGTTCAGGCC-1\n",
      "cell.ID:  TACACGATCCTTAATC-1\n",
      "cell.ID:  TACAGTGGTGGGTCAA-1\n",
      "cell.ID:  TACAGTGTCAGTGTTG-1\n",
      "cell.ID:  TACAGTGTCTATCCCG-1\n",
      "cell.ID:  TACCTATCATTTCACT-1\n",
      "cell.ID:  TACCTATGTATTAGCC-1\n",
      "cell.ID:  TACCTATTCACGATGT-1\n",
      "cell.ID:  TACCTTACAAGTAGTA-1\n",
      "cell.ID:  TACCTTATCGAACTGT-1\n",
      "cell.ID:  TACGGATCATTAACCG-1\n",
      "cell.ID:  TACGGATGTGTCCTCT-1\n",
      "cell.ID:  TACGGTACACTTAAGC-1\n",
      "cell.ID:  TACGGTATCCACGACG-1\n",
      "cell.ID:  TAGTGGTAGAATTCCC-1\n",
      "cell.ID:  TAGTGGTAGACCTTTG-1\n",
      "cell.ID:  TAGTGGTTCAAACCGT-1\n",
      "cell.ID:  TAGTGGTTCCAACCAA-1\n",
      "cell.ID:  TAGTGGTTCCACTCCA-1\n",
      "cell.ID:  TAGTTGGTCTCCTATA-1\n",
      "cell.ID:  TATCTCAGTGTTAAGA-1\n",
      "cell.ID:  TATTACCCATGCAACT-1\n",
      "cell.ID:  TCAACGACAGATGGGT-1\n",
      "cell.ID:  TCAACGATCTTGAGAC-1\n",
      "cell.ID:  TCACAAGGTTGAACTC-1\n",
      "cell.ID:  TCACAAGTCCCAAGAT-1\n",
      "cell.ID:  TCAGATGCAGTAACGG-1\n",
      "cell.ID:  TCAGATGGTATAGGGC-1\n",
      "cell.ID:  TCAGATGGTTATCGGT-1\n",
      "cell.ID:  TCAGATGTCCATGAGT-1\n",
      "cell.ID:  TCAGCAAGTGGTTTCA-1\n",
      "cell.ID:  TCAGGATGTACGCACC-1\n",
      "cell.ID:  TCAGGTAAGCCAACAG-1\n",
      "cell.ID:  TCAGGTACATGTCCTC-1\n",
      "cell.ID:  TCAGGTAGTCCGTTAA-1\n",
      "cell.ID:  TCCACACTCTCCGGTT-1\n",
      "cell.ID:  TCCCGATTCGATGAGG-1\n",
      "cell.ID:  TCGAGGCCATCCGCGA-1\n",
      "cell.ID:  TCGCGAGAGAGTAATC-1\n",
      "cell.ID:  TCGCGAGTCTGTCTCG-1\n",
      "cell.ID:  TCGCGTTGTATTCTCT-1\n",
      "cell.ID:  TCGCGTTTCTCGCATC-1\n",
      "cell.ID:  TCGGGACCACGAGGTA-1\n",
      "cell.ID:  TCGTACCAGCCAGAAC-1\n",
      "cell.ID:  TCGTACCCAGTGAGTG-1\n",
      "cell.ID:  TCGTACCTCATTGCCC-1\n",
      "cell.ID:  TCTATTGAGATGTCGG-1\n",
      "cell.ID:  TCTCATAGTAATAGCA-1\n",
      "cell.ID:  TCTCATAGTACCCAAT-1\n",
      "cell.ID:  TCTCTAAAGTCGTACT-1\n",
      "cell.ID:  TCTCTAAAGTTAAGTG-1\n",
      "cell.ID:  TCTGAGAGTTCGCTAA-1\n",
      "cell.ID:  TCTGAGATCGCCATAA-1\n",
      "cell.ID:  TCTGGAACAAGACGTG-1\n",
      "cell.ID:  TCTTTCCGTCTGCCAG-1\n",
      "cell.ID:  TGACAACAGTACGACG-1\n",
      "cell.ID:  TGACGGCGTTTGTGTG-1\n",
      "cell.ID:  TGACTTTAGGTGATTA-1\n",
      "cell.ID:  TGAGAGGAGGCATGTG-1\n",
      "cell.ID:  TGAGAGGCAATAGAGT-1\n",
      "cell.ID:  TGAGCATAGCCGGTAA-1\n",
      "cell.ID:  TGAGCCGCAACAACCT-1\n",
      "cell.ID:  TGAGCCGGTAAATACG-1\n",
      "cell.ID:  TGATTTCGTCTCCACT-1\n",
      "cell.ID:  TGCACCTTCCCATTAT-1\n",
      "cell.ID:  TGCACCTTCTGCTTGC-1\n",
      "cell.ID:  TGCCAAAGTGCCTTGG-1\n",
      "cell.ID:  TGCCCATAGAGGTTAT-1\n",
      "cell.ID:  TGCCCTAGTCTCGTTC-1\n",
      "cell.ID:  TGCGCAGGTCTAGGTT-1\n",
      "cell.ID:  TGCGTGGTCTTGTACT-1\n",
      "cell.ID:  TGCTGCTGTGTTCGAT-1\n",
      "cell.ID:  TGGACGCCACAGACAG-1\n",
      "cell.ID:  TGGACGCTCCCATTAT-1\n",
      "cell.ID:  TGGACGCTCTTGAGAC-1\n",
      "cell.ID:  TGGCCAGGTACGACCC-1\n",
      "cell.ID:  TGGCGCAAGGAGCGAG-1\n",
      "cell.ID:  TGGCGCACATTCGACA-1\n",
      "cell.ID:  TGGCGCAGTCCGCTGA-1\n",
      "cell.ID:  TGGCGCAGTGCCTGCA-1\n",
      "cell.ID:  TGGTTAGAGATCTGAA-1\n",
      "cell.ID:  TGTCCCAAGACTGTAA-1\n",
      "cell.ID:  TGTCCCAGTTCGCGAC-1\n",
      "cell.ID:  TGTGGTAAGCACACAG-1\n",
      "cell.ID:  TGTGGTAAGGGTATCG-1\n",
      "cell.ID:  TGTGGTAGTGGGTCAA-1\n",
      "cell.ID:  TGTGGTATCAACGAAA-1\n",
      "cell.ID:  TGTGTTTCATAGAAAC-1\n",
      "cell.ID:  TGTGTTTTCTGGGCCA-1\n",
      "cell.ID:  TGTTCCGCACCAGTTA-1\n",
      "cell.ID:  TTAGGCACACCTGGTG-1\n",
      "cell.ID:  TTAGGCAGTAGCTCCG-1\n",
      "cell.ID:  TTAGTTCAGATGGCGT-1\n",
      "cell.ID:  TTAGTTCAGCTAGTCT-1\n",
      "cell.ID:  TTAGTTCAGGCAAAGA-1\n",
      "cell.ID:  TTAGTTCCATTCTTAC-1\n",
      "cell.ID:  TTATGCTAGCATGGCA-1\n",
      "cell.ID:  TTATGCTCACGTGAGA-1\n",
      "cell.ID:  TTCTCAACACAAGCCC-1\n",
      "cell.ID:  TTCTCAATCGTATCAG-1\n",
      "cell.ID:  TTCTCAATCGTGGACC-1\n",
      "cell.ID:  TTCTTAGGTAAACGCG-1\n",
      "cell.ID:  TTGAACGGTTGCGCAC-1\n",
      "cell.ID:  TTGAACGTCCTCAACC-1\n",
      "cell.ID:  TTGAACGTCCTGCAGG-1\n",
      "cell.ID:  TTGACTTTCACAGTAC-1\n",
      "cell.ID:  TTGGAACCATGTCTCC-1\n",
      "cell.ID:  TTGGCAAAGCGATGAC-1\n",
      "cell.ID:  TTGGCAAGTAGTAGTA-1\n",
      "cell.ID:  TTGGCAAGTGTGGCTC-1\n",
      "cell.ID:  TTTCCTCTCACATACG-1\n",
      "cell.ID:  TTTGCGCCACTCTGTC-1\n",
      "cell.ID:  TTTGCGCCAGGGAGAG-1\n",
      "cell.ID:  TTTGGTTCAATGGAGC-1\n",
      "cell.ID:  TTTGTCACAAGTCATC-1\n",
      "cell.ID:  TTTGTCACACTTGGAT-1\n",
      "ID:  4.0_1\n",
      "index:  3\n",
      "P index:  2\n",
      "ST:  4.0\n",
      "T:  3\n",
      "cell.ID:  AACCATGCATATGCTG-1\n",
      "cell.ID:  AACCGCGTCCTTGCCA-1\n",
      "cell.ID:  AAGCCGCGTTTAAGCC-1\n",
      "cell.ID:  AAGTCTGGTTAGATGA-1\n",
      "cell.ID:  ACAGCCGCACCGCTAG-1\n",
      "cell.ID:  ACAGCTATCAACACGT-1\n",
      "cell.ID:  ACGAGCCAGCACCGCT-1\n",
      "cell.ID:  ACGCCGAGTACCGTTA-1\n",
      "cell.ID:  ACGGCCAAGGCGTACA-1\n",
      "cell.ID:  ACGGGTCAGATATGGT-1\n",
      "cell.ID:  ACGTCAAAGTTCGCGC-1\n",
      "cell.ID:  ACTGAGTGTATATGGA-1\n",
      "cell.ID:  ACTTACTCACCAGGTC-1\n",
      "cell.ID:  AGAATAGAGGCTACGA-1\n",
      "cell.ID:  AGAGCGAGTCTACCTC-1\n",
      "cell.ID:  AGCATACCAAGCCGCT-1\n",
      "cell.ID:  AGGGATGTCTGTCCGT-1\n",
      "cell.ID:  AGTGGGATCGGATGGA-1\n",
      "cell.ID:  AGTTGGTCAGGTCGTC-1\n",
      "cell.ID:  AGTTGGTCAGTCGATT-1\n",
      "cell.ID:  ATCATGGTCCGAACGC-1\n",
      "cell.ID:  ATCCACCCAGTTAACC-1\n",
      "cell.ID:  ATCCGAACATAGTAAG-1\n",
      "cell.ID:  ATCGAGTAGCTGATAA-1\n",
      "cell.ID:  ATCTGCCCATCAGTCA-1\n",
      "cell.ID:  ATGAGGGTCAGGTAAA-1\n",
      "cell.ID:  CACACAAAGCTGCAAG-1\n",
      "cell.ID:  CACACCTGTGTGCCTG-1\n",
      "cell.ID:  CACTCCATCAGTGTTG-1\n",
      "cell.ID:  CAGAATCAGCCGGTAA-1\n",
      "cell.ID:  CAGCAGCAGTATCTCG-1\n",
      "cell.ID:  CAGCCGATCTCTGTCG-1\n",
      "cell.ID:  CAGCTGGAGCTCCTTC-1\n",
      "cell.ID:  CATGACAGTCTTGTCC-1\n",
      "cell.ID:  CATTATCTCCGCGTTT-1\n",
      "cell.ID:  CCATTCGAGACGCTTT-1\n",
      "cell.ID:  CCGTACTGTGTAATGA-1\n",
      "cell.ID:  CCTACACTCCGTAGTA-1\n",
      "cell.ID:  CCTATTAGTCTTCTCG-1\n",
      "cell.ID:  CCTATTATCGCTGATA-1\n",
      "cell.ID:  CCTTACGGTATATGAG-1\n",
      "cell.ID:  CGACTTCTCCGAATGT-1\n",
      "cell.ID:  CGCCAAGTCCAAAGTC-1\n",
      "cell.ID:  CGCTGGAAGCCTCGTG-1\n",
      "cell.ID:  CGGACTGGTAAATACG-1\n",
      "cell.ID:  CGTTGGGGTCGAATCT-1\n",
      "cell.ID:  CTAACTTAGTGGTAGC-1\n",
      "cell.ID:  CTAACTTGTCGTCTTC-1\n",
      "cell.ID:  CTAATGGTCGGTTAAC-1\n",
      "cell.ID:  CTAGTGAAGTAGCGGT-1\n",
      "cell.ID:  CTCGAAAGTGGTACAG-1\n",
      "cell.ID:  CTCGGGAAGTCACGCC-1\n",
      "cell.ID:  CTCGGGAGTAAGGGCT-1\n",
      "cell.ID:  CTGAAGTTCGAATGCT-1\n",
      "cell.ID:  CTTACCGTCAACACCA-1\n",
      "cell.ID:  CTTCTCTGTGACGCCT-1\n",
      "cell.ID:  GACGGCTAGACACGAC-1\n",
      "cell.ID:  GACGTTAGTATTAGCC-1\n",
      "cell.ID:  GAGGTGAGTGGTGTAG-1\n",
      "cell.ID:  GATCAGTCAAGCCCAC-1\n",
      "cell.ID:  GATCGATTCGCCTGAG-1\n",
      "cell.ID:  GATCGTACAACAACCT-1\n",
      "cell.ID:  GATGAGGCACGAAAGC-1\n",
      "cell.ID:  GATTCAGAGAGTAATC-1\n",
      "cell.ID:  GCAAACTCAGGGTTAG-1\n",
      "cell.ID:  GCACATACAGACACTT-1\n",
      "cell.ID:  GCAGCCACAAACTGTC-1\n",
      "cell.ID:  GCGAGAAAGTGGGTTG-1\n",
      "cell.ID:  GCGCGATGTTCCACAA-1\n",
      "cell.ID:  GCTGCTTAGTATCGAA-1\n",
      "cell.ID:  GCTGCTTGTGCGCTTG-1\n",
      "cell.ID:  GGACAGATCCCAGGTG-1\n",
      "cell.ID:  GGGAGATGTGATGCCC-1\n",
      "cell.ID:  GGGCATCTCCATGCTC-1\n",
      "cell.ID:  GGTGTTACACACGCTG-1\n",
      "cell.ID:  GTACGTAAGTCAAGGC-1\n",
      "cell.ID:  GTACTTTTCCGAAGAG-1\n",
      "cell.ID:  GTCAAGTGTTGCCTCT-1\n",
      "cell.ID:  GTCGTAACAATGGAAT-1\n",
      "cell.ID:  GTCGTAATCCTTGACC-1\n",
      "cell.ID:  GTTACAGAGAAACGAG-1\n",
      "cell.ID:  TACTCATCAGTCGATT-1\n",
      "cell.ID:  TAGGCATTCGTGGGAA-1\n",
      "cell.ID:  TAGTGGTTCATGTCCC-1\n",
      "cell.ID:  TCAATCTAGGATGGTC-1\n",
      "cell.ID:  TCACGAAAGTCGCCGT-1\n",
      "cell.ID:  TCATTACTCTGTACGA-1\n",
      "cell.ID:  TGACTAGAGCGTCTAT-1\n",
      "cell.ID:  TGAGAGGTCTGTTGAG-1\n",
      "cell.ID:  TGAGCCGAGCTGCAAG-1\n",
      "cell.ID:  TGCCAAATCTACCTGC-1\n",
      "cell.ID:  TGCGGGTGTTGATTGC-1\n",
      "cell.ID:  TGGACGCCACATGACT-1\n",
      "cell.ID:  TGGACGCTCTGAGGGA-1\n",
      "cell.ID:  TGTATTCAGTCATGCT-1\n",
      "cell.ID:  TGTATTCGTAGATTAG-1\n",
      "cell.ID:  TTAGGACGTCGAACAG-1\n",
      "cell.ID:  TTATGCTGTAGTGAAT-1\n",
      "cell.ID:  TTCCCAGTCGCCAAAT-1\n",
      "cell.ID:  TTGACTTGTTCGGCAC-1\n",
      "cell.ID:  TTGCCGTCAGTCGATT-1\n",
      "cell.ID:  TTTATGCTCATGCTCC-1\n",
      "cell.ID:  TTTATGCTCTCAAACG-1\n",
      "cell.ID:  TTTCCTCTCTCGTTTA-1\n",
      "ID:  6.0_0\n",
      "index:  4\n",
      "P index:  3\n",
      "ST:  6.0\n",
      "T:  4\n",
      "cell.ID:  AACACGTTCGTCCGTT-1\n",
      "cell.ID:  AACTCAGGTGCTGTAT-1\n",
      "cell.ID:  AACTCCCCAACGCACC-1\n",
      "cell.ID:  AACTCTTGTATGAATG-1\n",
      "cell.ID:  AACTTTCGTTAAAGAC-1\n",
      "cell.ID:  AAGACCTAGAAGGCCT-1\n",
      "cell.ID:  AAGACCTAGTGGACGT-1\n",
      "cell.ID:  AAGGCAGGTCAATACC-1\n",
      "cell.ID:  AATCCAGGTCTGATTG-1\n",
      "cell.ID:  AATCGGTCATGAACCT-1\n",
      "cell.ID:  ACACCAATCTGCTGCT-1\n",
      "cell.ID:  ACACCGGCAAACCTAC-1\n",
      "cell.ID:  ACATCAGTCATCGATG-1\n",
      "cell.ID:  ACCCACTCAGTGACAG-1\n",
      "cell.ID:  ACCCACTTCTAACGGT-1\n",
      "cell.ID:  ACGAGCCTCCTTTCTC-1\n",
      "cell.ID:  ACGATACGTCATCCCT-1\n",
      "cell.ID:  ACGCCAGGTCTTTCAT-1\n",
      "cell.ID:  ACGGAGAGTCTCAACA-1\n",
      "cell.ID:  ACGGCCAAGTAGGCCA-1\n",
      "cell.ID:  ACGGGTCAGCTAGTCT-1\n",
      "cell.ID:  ACTGATGAGTGACATA-1\n",
      "cell.ID:  ACTGCTCAGCGTTTAC-1\n",
      "cell.ID:  ACTTGTTAGGGTTTCT-1\n",
      "cell.ID:  ACTTGTTCAGGGCATA-1\n",
      "cell.ID:  ACTTGTTCATCCCACT-1\n",
      "cell.ID:  AGAGCTTTCCGATATG-1\n",
      "cell.ID:  AGCGTCGGTACATGTC-1\n",
      "cell.ID:  AGCTTGAGTACCGTAT-1\n",
      "cell.ID:  AGGCCACGTACCGTTA-1\n",
      "cell.ID:  AGGGATGAGCTGATAA-1\n",
      "cell.ID:  ATAGACCGTGGGTATG-1\n",
      "cell.ID:  ATCATCTAGTCACGCC-1\n",
      "cell.ID:  ATCATCTGTAAGTTCC-1\n",
      "cell.ID:  ATCCGAACATCCTAGA-1\n",
      "cell.ID:  ATCGAGTAGTGGTAGC-1\n",
      "cell.ID:  ATGAGGGAGGCAGTCA-1\n",
      "cell.ID:  ATGGGAGGTGAGGGAG-1\n",
      "cell.ID:  ATGTGTGTCTTGACGA-1\n",
      "cell.ID:  ATTCTACTCCTGCTTG-1\n",
      "cell.ID:  CAACCAATCAGTCAGT-1\n",
      "cell.ID:  CAACCTCAGAAACCGC-1\n",
      "cell.ID:  CAAGATCGTTTGGGCC-1\n",
      "cell.ID:  CAAGATCTCGTCACGG-1\n",
      "cell.ID:  CACACCTAGAAACCAT-1\n",
      "cell.ID:  CACATTTAGGATGGTC-1\n",
      "cell.ID:  CACCAGGAGCAAATCA-1\n",
      "cell.ID:  CAGCTAATCAGGCAAG-1\n",
      "cell.ID:  CAGTAACGTCTCTTTA-1\n",
      "cell.ID:  CAGTCCTCAGCCACCA-1\n",
      "cell.ID:  CATCAAGCAATAGAGT-1\n",
      "cell.ID:  CATCAGACACAGCCCA-1\n",
      "cell.ID:  CATCGAACATTCTCAT-1\n",
      "cell.ID:  CATCGAAGTTACAGAA-1\n",
      "cell.ID:  CATGACATCTACGAGT-1\n",
      "cell.ID:  CATTATCAGACACTAA-1\n",
      "cell.ID:  CCAATCCTCATGTCTT-1\n",
      "cell.ID:  CCAGCGACATCTATGG-1\n",
      "cell.ID:  CCCAATCCATTAACCG-1\n",
      "cell.ID:  CCCAGTTCACAAGTAA-1\n",
      "cell.ID:  CCGGTAGAGAGCTATA-1\n",
      "cell.ID:  CCTAGCTTCACCACCT-1\n",
      "cell.ID:  CCTTTCTAGTACACCT-1\n",
      "cell.ID:  CGAGCACGTTTGGGCC-1\n",
      "cell.ID:  CGAGCCACAGTCGATT-1\n",
      "cell.ID:  CGATGGCCACAACTGT-1\n",
      "cell.ID:  CGATGTACAGAGCCAA-1\n",
      "cell.ID:  CGCGGTAGTCAGCTAT-1\n",
      "cell.ID:  CGCTATCGTGCAACTT-1\n",
      "cell.ID:  CGCTTCAGTCTGCAAT-1\n",
      "cell.ID:  CGCTTCAGTTACGCGC-1\n",
      "cell.ID:  CGGACGTTCCCATTTA-1\n",
      "cell.ID:  CGGACTGTCTGATACG-1\n",
      "cell.ID:  CGGGTCAAGGCGTACA-1\n",
      "cell.ID:  CGGGTCAGTCTAGCGC-1\n",
      "cell.ID:  CGTCACTAGTGCTGCC-1\n",
      "cell.ID:  CGTCCATGTCTCGTTC-1\n",
      "cell.ID:  CGTCTACAGCAATCTC-1\n",
      "cell.ID:  CGTGAGCGTTTCCACC-1\n",
      "cell.ID:  CGTGTCTTCCTCAACC-1\n",
      "cell.ID:  CGTTAGAGTGTCAATC-1\n",
      "cell.ID:  CGTTAGATCTAGCACA-1\n",
      "cell.ID:  CTAATGGCAAGCTGAG-1\n",
      "cell.ID:  CTAATGGGTTACGACT-1\n",
      "cell.ID:  CTACACCGTTACGTCA-1\n",
      "cell.ID:  CTAGAGTAGCCAGTAG-1\n",
      "cell.ID:  CTAGAGTCACTGAAGG-1\n",
      "cell.ID:  CTAGTGAGTCATTAGC-1\n",
      "cell.ID:  CTCACACCACATCCGG-1\n",
      "cell.ID:  CTCATTATCAGGTAAA-1\n",
      "cell.ID:  CTCGAAAGTACAGTGG-1\n",
      "cell.ID:  CTCGAAATCAAACCAC-1\n",
      "cell.ID:  CTCGAGGGTCTCCATC-1\n",
      "cell.ID:  CTCTAATAGGAGTTTA-1\n",
      "cell.ID:  CTGCCTAAGGGTGTGT-1\n",
      "cell.ID:  CTGCTGTCAGCTCCGA-1\n",
      "cell.ID:  CTGGTCTGTACCGAGA-1\n",
      "cell.ID:  CTGTTTACACTGTCGG-1\n",
      "cell.ID:  CTTCTCTAGGTGGGTT-1\n",
      "cell.ID:  GAAATGATCAACGCTA-1\n",
      "cell.ID:  GAACATCTCGTTACAG-1\n",
      "cell.ID:  GAACCTAAGAGACTTA-1\n",
      "cell.ID:  GAAGCAGGTTATCACG-1\n",
      "cell.ID:  GAATAAGGTTCCGTCT-1\n",
      "cell.ID:  GACAGAGGTCTAGGTT-1\n",
      "cell.ID:  GACCAATAGAGCTTCT-1\n",
      "cell.ID:  GACGTGCAGATGCCAG-1\n",
      "cell.ID:  GACGTGCCATGGTCTA-1\n",
      "cell.ID:  GACGTTAAGGCTATCT-1\n",
      "cell.ID:  GAGGTGATCCGTAGGC-1\n",
      "cell.ID:  GATCAGTTCAACACAC-1\n",
      "cell.ID:  GATGAAAGTTCGCTAA-1\n",
      "cell.ID:  GCATGATCAGCGTAAG-1\n",
      "cell.ID:  GCGACCAAGTAATCCC-1\n",
      "cell.ID:  GGACAAGAGATGCCTT-1\n",
      "cell.ID:  GGACAAGTCCTACAGA-1\n",
      "cell.ID:  GGACAGAGTAGCCTCG-1\n",
      "cell.ID:  GGACATTAGAACTCGG-1\n",
      "cell.ID:  GGCCGATGTCGGGTCT-1\n",
      "cell.ID:  GGCTGGTAGTACGCGA-1\n",
      "cell.ID:  GGCTGGTGTAAACACA-1\n",
      "cell.ID:  GGGAGATTCTTTAGTC-1\n",
      "cell.ID:  GGGCACTGTTAAAGAC-1\n",
      "cell.ID:  GGGCACTTCAACACAC-1\n",
      "cell.ID:  GGGCATCGTGGTTTCA-1\n",
      "cell.ID:  GGGTCTGTCCCTAATT-1\n",
      "cell.ID:  GGTATTGTCTGGCGAC-1\n",
      "cell.ID:  GTAACGTCATCGGACC-1\n",
      "cell.ID:  GTACGTACAGACAGGT-1\n",
      "cell.ID:  GTACTCCTCGTTTAGG-1\n",
      "cell.ID:  GTAGTCAGTATTAGCC-1\n",
      "cell.ID:  GTATTCTTCTCACATT-1\n",
      "cell.ID:  GTGAAGGGTGGAAAGA-1\n",
      "cell.ID:  GTGCATAGTGACAAAT-1\n",
      "cell.ID:  GTGGGTCCAGCCTTTC-1\n",
      "cell.ID:  GTGTTAGAGCCACGCT-1\n",
      "cell.ID:  TAAACCGAGAAGATTC-1\n",
      "cell.ID:  TACACGAAGGTGTTAA-1\n",
      "cell.ID:  TACAGTGGTGTCCTCT-1\n",
      "cell.ID:  TACCTATAGGATGCGT-1\n",
      "cell.ID:  TACCTTATCGGTGTCG-1\n",
      "cell.ID:  TACGGTACAATAGAGT-1\n",
      "cell.ID:  TACTTGTCAAACGTGG-1\n",
      "cell.ID:  TAGGCATAGGGATGGG-1\n",
      "cell.ID:  TAGTTGGTCCAGATCA-1\n",
      "cell.ID:  TATTACCAGACTTTCG-1\n",
      "cell.ID:  TCAACGAAGACATAAC-1\n",
      "cell.ID:  TCAACGACACCATGTA-1\n",
      "cell.ID:  TCACGAAGTTACGCGC-1\n",
      "cell.ID:  TCAGATGTCGGATGGA-1\n",
      "cell.ID:  TCAGGATCATTTGCTT-1\n",
      "cell.ID:  TCCCGATCACTGAAGG-1\n",
      "cell.ID:  TCCCGATTCAAGAAGT-1\n",
      "cell.ID:  TCGAGGCGTACCGCTG-1\n",
      "cell.ID:  TCGCGTTGTTTGGCGC-1\n",
      "cell.ID:  TCGGGACTCAGTGTTG-1\n",
      "cell.ID:  TCTCATAGTAGGACAC-1\n",
      "cell.ID:  TCTCTAATCTGTCCGT-1\n",
      "cell.ID:  TCTTTCCGTGCACGAA-1\n",
      "cell.ID:  TGACTAGCAGCATGAG-1\n",
      "cell.ID:  TGCACCTAGTGTGAAT-1\n",
      "cell.ID:  TGCACCTGTTTAAGCC-1\n",
      "cell.ID:  TGCGGGTCACCCATTC-1\n",
      "cell.ID:  TGCGTGGGTGGTTTCA-1\n",
      "cell.ID:  TGGCTGGCATGAGCGA-1\n",
      "cell.ID:  TGGTTCCTCCTGTAGA-1\n",
      "cell.ID:  TGGTTCCTCTTGTACT-1\n",
      "cell.ID:  TGTCCCAAGTTCCACA-1\n",
      "cell.ID:  TTAGGACAGTGCTGCC-1\n",
      "cell.ID:  TTCGGTCCACGAAATA-1\n",
      "cell.ID:  TTCTCAATCTGTCCGT-1\n",
      "cell.ID:  TTCTCCTCAGACGTAG-1\n",
      "cell.ID:  TTCTTAGAGGGTTCCC-1\n",
      "cell.ID:  TTGGAACCATGCGCAC-1\n",
      "cell.ID:  TTTGGTTTCTACCAGA-1\n",
      "cell.ID:  TTTGTCACAGACGCAA-1\n",
      "ID:  5.0_0\n",
      "index:  5\n",
      "P index:  3\n",
      "ST:  5.0\n",
      "T:  5\n",
      "cell.ID:  AAAGCAACACTCAGGC-1\n",
      "cell.ID:  AAAGCAATCATTGCCC-1\n",
      "cell.ID:  AAAGTAGCAAACAACA-1\n",
      "cell.ID:  AACCGCGAGTCGATAA-1\n",
      "cell.ID:  AACGTTGGTTTGGCGC-1\n",
      "cell.ID:  AAGTCTGGTTGAACTC-1\n",
      "cell.ID:  AATCCAGGTGATGTCT-1\n",
      "cell.ID:  ACAGCCGTCACGGTTA-1\n",
      "cell.ID:  ACCCACTGTTACTGAC-1\n",
      "cell.ID:  ACCTTTATCTGAGGGA-1\n",
      "cell.ID:  ACGATGTCATGATCCA-1\n",
      "cell.ID:  ACGCCGAGTGGCCCTA-1\n",
      "cell.ID:  ACTGAACCAGACAAAT-1\n",
      "cell.ID:  ACTGAACGTCTGCAAT-1\n",
      "cell.ID:  ACTGAGTTCGCACTCT-1\n",
      "cell.ID:  ACTGCTCCAAGGTTTC-1\n",
      "cell.ID:  AGAATAGCACACTGCG-1\n",
      "cell.ID:  AGAATAGGTCATCGGC-1\n",
      "cell.ID:  AGTCTTTCAAGAAGAG-1\n",
      "cell.ID:  AGTGAGGTCCGGCACA-1\n",
      "cell.ID:  AGTTGGTCACGAAGCA-1\n",
      "cell.ID:  ATAGACCAGTCGATAA-1\n",
      "cell.ID:  ATCACGACATCACGAT-1\n",
      "cell.ID:  ATCACGAGTCATGCAT-1\n",
      "cell.ID:  ATCTACTAGCTCCTCT-1\n",
      "cell.ID:  CAACCAATCCACGTTC-1\n",
      "cell.ID:  CAACCTCCATTGGTAC-1\n",
      "cell.ID:  CAACTAGCAAATTGCC-1\n",
      "cell.ID:  CAAGAAAAGTCCTCCT-1\n",
      "cell.ID:  CAAGATCGTACGACCC-1\n",
      "cell.ID:  CAAGATCTCGCGATCG-1\n",
      "cell.ID:  CACAAACGTGAACCTT-1\n",
      "cell.ID:  CACACCTAGCTAACAA-1\n",
      "cell.ID:  CACACCTCAGCGTTCG-1\n",
      "cell.ID:  CACCAGGAGTGTGGCA-1\n",
      "cell.ID:  CACCAGGTCGGCGCAT-1\n",
      "cell.ID:  CACTCCAGTTCAGTAC-1\n",
      "cell.ID:  CAGTAACAGGTGACCA-1\n",
      "cell.ID:  CATCGAATCACCGTAA-1\n",
      "cell.ID:  CATGACACACAGATTC-1\n",
      "cell.ID:  CATGGCGCAGACAGGT-1\n",
      "cell.ID:  CATTATCAGTGAATTG-1\n",
      "cell.ID:  CCAATCCAGGACATTA-1\n",
      "cell.ID:  CCCTCCTCAATAGAGT-1\n",
      "cell.ID:  CCGGTAGGTTCAGGCC-1\n",
      "cell.ID:  CCTACACAGGGCTTGA-1\n",
      "cell.ID:  CCTTCCCAGACTGTAA-1\n",
      "cell.ID:  CCTTCCCGTCTTGATG-1\n",
      "cell.ID:  CCTTCCCTCAACCAAC-1\n",
      "cell.ID:  CCTTCGAAGCGAGAAA-1\n",
      "cell.ID:  CGAGCCAAGGGATCTG-1\n",
      "cell.ID:  CGATCGGAGTATCTCG-1\n",
      "cell.ID:  CGATGGCAGCGCCTTG-1\n",
      "cell.ID:  CGCCAAGCAATCAGAA-1\n",
      "cell.ID:  CGGACACAGTACGACG-1\n",
      "cell.ID:  CGGCTAGAGAAGCCCA-1\n",
      "cell.ID:  CGGGTCATCGTCCAGG-1\n",
      "cell.ID:  CGGTTAAGTCCGCTGA-1\n",
      "cell.ID:  CGTGAGCCAATCTACG-1\n",
      "cell.ID:  CTAGAGTCACAACGTT-1\n",
      "cell.ID:  CTAGAGTTCCGCTGTT-1\n",
      "cell.ID:  CTAGCCTCATTACGAC-1\n",
      "cell.ID:  CTCACACAGTCCAGGA-1\n",
      "cell.ID:  CTCCTAGAGGTGCACA-1\n",
      "cell.ID:  CTCGTACAGGGTGTTG-1\n",
      "cell.ID:  CTGCGGAAGTAGATGT-1\n",
      "cell.ID:  CTGGTCTAGGTGACCA-1\n",
      "cell.ID:  CTGTTTACACAAGCCC-1\n",
      "cell.ID:  CTTAGGACACCCATTC-1\n",
      "cell.ID:  CTTAGGACATCGTCGG-1\n",
      "cell.ID:  GAACCTATCACATACG-1\n",
      "cell.ID:  GAATAAGGTCGACTGC-1\n",
      "cell.ID:  GACAGAGAGCAGCGTA-1\n",
      "cell.ID:  GACGGCTAGCGGCTTC-1\n",
      "cell.ID:  GACGGCTCACTTCTGC-1\n",
      "cell.ID:  GACTGCGTCCACGCAG-1\n",
      "cell.ID:  GAGGTGAGTCAAGCGA-1\n",
      "cell.ID:  GAGTCCGTCATACGGT-1\n",
      "cell.ID:  GATCGATGTCGCGGTT-1\n",
      "cell.ID:  GATCGATTCAGCCTAA-1\n",
      "cell.ID:  GATTCAGCACCTCGGA-1\n",
      "cell.ID:  GCAAACTAGCCAGGAT-1\n",
      "cell.ID:  GCAATCAGTACAGTGG-1\n",
      "cell.ID:  GCACATAGTTCCACAA-1\n",
      "cell.ID:  GCACTCTCAGACGTAG-1\n",
      "cell.ID:  GCAGTTACAGTTTACG-1\n",
      "cell.ID:  GCATGCGCACCCAGTG-1\n",
      "cell.ID:  GCATGTACACAGCCCA-1\n",
      "cell.ID:  GCATGTACAGTTAACC-1\n",
      "cell.ID:  GCCAAATAGGCAGTCA-1\n",
      "cell.ID:  GCGAGAAGTCAGTGGA-1\n",
      "cell.ID:  GCGCAACGTTCCCGAG-1\n",
      "cell.ID:  GGACGTCAGGCGATAC-1\n",
      "cell.ID:  GGATTACAGCACCGTC-1\n",
      "cell.ID:  GGCTCGAAGTAGCGGT-1\n",
      "cell.ID:  GGGAGATCAGCCACCA-1\n",
      "cell.ID:  GGGAGATTCATGTCTT-1\n",
      "cell.ID:  GGGCACTAGCGAAGGG-1\n",
      "cell.ID:  GGGTTGCAGGAATTAC-1\n",
      "cell.ID:  GTAACGTGTAAAGTCA-1\n",
      "cell.ID:  GTAACGTGTTAAGGGC-1\n",
      "cell.ID:  GTACGTACAGTCACTA-1\n",
      "cell.ID:  GTACGTAGTACTTCTT-1\n",
      "cell.ID:  GTAGGCCGTACCATCA-1\n",
      "cell.ID:  GTCTCGTAGGAGCGAG-1\n",
      "cell.ID:  GTCTTCGCAGCTGCAC-1\n",
      "cell.ID:  GTCTTCGTCTCCTATA-1\n",
      "cell.ID:  GTGCATAAGGAGCGAG-1\n",
      "cell.ID:  GTGCTTCGTCTGCGGT-1\n",
      "cell.ID:  GTTAAGCGTAGCTGCC-1\n",
      "cell.ID:  GTTCATTAGGCTCAGA-1\n",
      "cell.ID:  GTTCGGGAGGTTACCT-1\n",
      "cell.ID:  TACCTATCATGCATGT-1\n",
      "cell.ID:  TACGGTAGTAAATGAC-1\n",
      "cell.ID:  TACTTACAGAATTGTG-1\n",
      "cell.ID:  TAGAGCTGTACTCTCC-1\n",
      "cell.ID:  TATGCCCGTGCCTGCA-1\n",
      "cell.ID:  TCACAAGCATTAACCG-1\n",
      "cell.ID:  TCATTTGGTGTAACGG-1\n",
      "cell.ID:  TCCACACCATGGTCTA-1\n",
      "cell.ID:  TCGCGTTGTAGAAGGA-1\n",
      "cell.ID:  TCGTAGACACAGATTC-1\n",
      "cell.ID:  TCGTAGACAGCGTCCA-1\n",
      "cell.ID:  TCTCATAAGCCGCCTA-1\n",
      "cell.ID:  TCTCATACAGACTCGC-1\n",
      "cell.ID:  TCTTCGGTCCTTCAAT-1\n",
      "cell.ID:  TCTTTCCTCAAGATCC-1\n",
      "cell.ID:  TGAAAGAGTCAGGACA-1\n",
      "cell.ID:  TGACAACCATGGTCAT-1\n",
      "cell.ID:  TGACGGCTCTGAGTGT-1\n",
      "cell.ID:  TGATTTCGTTGGACCC-1\n",
      "cell.ID:  TGCCCATAGAGTCTGG-1\n",
      "cell.ID:  TGCGGGTTCCTCCTAG-1\n",
      "cell.ID:  TGCTGCTAGTCTCAAC-1\n",
      "cell.ID:  TGCTGCTTCCAGTAGT-1\n",
      "cell.ID:  TGGACGCTCAGTGCAT-1\n",
      "cell.ID:  TGGACGCTCGCAAGCC-1\n",
      "cell.ID:  TGGCTGGAGACAATAC-1\n",
      "cell.ID:  TGGGAAGAGCTGTCTA-1\n",
      "cell.ID:  TGGGAAGTCAAACCAC-1\n",
      "cell.ID:  TGGGAAGTCGTATCAG-1\n",
      "cell.ID:  TGGGCGTTCACCGGGT-1\n",
      "cell.ID:  TGTGGTAGTAACGCGA-1\n",
      "cell.ID:  TGTTCCGAGGCCCGTT-1\n",
      "cell.ID:  TTCTCAACATGGATGG-1\n",
      "cell.ID:  TTCTTAGGTTCCACGG-1\n",
      "cell.ID:  TTCTTAGTCCGCAGTG-1\n",
      "cell.ID:  TTGCCGTAGGAGTTTA-1\n",
      "cell.ID:  TTTGCGCTCGTAGATC-1\n",
      "ID:  2.0_1\n",
      "index:  6\n",
      "P index:  1\n",
      "ST:  2.0\n",
      "T:  5\n",
      "cell.ID:  AAAGTAGAGGCCATAG-1\n",
      "cell.ID:  ATTCTACTCAATCTCT-1\n",
      "cell.ID:  CAAGTTGTCACATACG-1\n",
      "cell.ID:  CACATTTGTTCGTTGA-1\n",
      "cell.ID:  CAGAATCGTGAGGGAG-1\n",
      "cell.ID:  CAGCTGGAGTGGCACA-1\n",
      "cell.ID:  CTAATGGCATAGACTC-1\n",
      "cell.ID:  CTCGGAGCAGCCAATT-1\n",
      "cell.ID:  CTCGTCAGTGCAACGA-1\n",
      "cell.ID:  GATCTAGTCTCTGCTG-1\n",
      "cell.ID:  GATTCAGAGACTCGGA-1\n",
      "cell.ID:  GGAGCAAAGATGGGTC-1\n",
      "cell.ID:  GGTATTGAGCCAGGAT-1\n",
      "cell.ID:  GTAACGTGTCTAGAGG-1\n",
      "cell.ID:  TACTTACCACTGTCGG-1\n",
      "cell.ID:  TCTCATATCCCATTAT-1\n",
      "cell.ID:  TTGGAACCACCTTGTC-1\n",
      "ID:  4.0_0\n",
      "index:  7\n",
      "P index:  3\n",
      "ST:  4.0\n",
      "T:  5\n",
      "cell.ID:  AAACGGGGTAGCGTGA-1\n",
      "cell.ID:  AAAGATGTCTGATACG-1\n",
      "cell.ID:  AAATGCCTCCTGCTTG-1\n",
      "cell.ID:  AACACGTGTCGCGTGT-1\n",
      "cell.ID:  AACCATGGTACCTACA-1\n",
      "cell.ID:  AACCGCGCAGTGGAGT-1\n",
      "cell.ID:  AACCGCGCATCACGAT-1\n",
      "cell.ID:  AACCGCGGTCCATCCT-1\n",
      "cell.ID:  AACCGCGTCTACGAGT-1\n",
      "cell.ID:  AACTCAGTCAATCACG-1\n",
      "cell.ID:  AACTCTTCATACGCTA-1\n",
      "cell.ID:  AACTCTTTCTCAAGTG-1\n",
      "cell.ID:  AACTGGTGTTAAGATG-1\n",
      "cell.ID:  AAGACCTGTCAAACTC-1\n",
      "cell.ID:  ACACCCTCATCGATGT-1\n",
      "cell.ID:  ACACCCTTCGATAGAA-1\n",
      "cell.ID:  ACACCGGAGCGCCTCA-1\n",
      "cell.ID:  ACACTGACAGCTTCGG-1\n",
      "cell.ID:  ACAGCCGAGATGCCAG-1\n",
      "cell.ID:  ACAGCCGGTCTGATTG-1\n",
      "cell.ID:  ACAGCTATCCACGACG-1\n",
      "cell.ID:  ACAGCTATCGGTTAAC-1\n",
      "cell.ID:  ACATACGAGAGCTATA-1\n",
      "cell.ID:  ACATACGCACTGAAGG-1\n",
      "cell.ID:  ACATACGTCTTAACCT-1\n",
      "cell.ID:  ACATCAGTCCAAAGTC-1\n",
      "cell.ID:  ACCAGTAAGTTCCACA-1\n",
      "cell.ID:  ACCGTAATCACTTACT-1\n",
      "cell.ID:  ACCTTTAGTCATGCAT-1\n",
      "cell.ID:  ACGAGCCCATGCATGT-1\n",
      "cell.ID:  ACGATACGTGGACGAT-1\n",
      "cell.ID:  ACGATGTAGATGGCGT-1\n",
      "cell.ID:  ACGCAGCAGATGCCAG-1\n",
      "cell.ID:  ACGCCGACAATGAAAC-1\n",
      "cell.ID:  ACGCCGATCTGTCAAG-1\n",
      "cell.ID:  ACGGCCAAGTCATGCT-1\n",
      "cell.ID:  ACGGCCATCTGATTCT-1\n",
      "cell.ID:  ACGGGTCAGCTTATCG-1\n",
      "cell.ID:  ACGTCAAAGATAGTCA-1\n",
      "cell.ID:  ACGTCAAGTAAAGTCA-1\n",
      "cell.ID:  ACTATCTAGCTAACTC-1\n",
      "cell.ID:  ACTATCTCAAGACGTG-1\n",
      "cell.ID:  ACTATCTCACCCATTC-1\n",
      "cell.ID:  ACTGAACAGGTGACCA-1\n",
      "cell.ID:  ACTGAGTAGAACTCGG-1\n",
      "cell.ID:  ACTGAGTCACGGTAAG-1\n",
      "cell.ID:  ACTGATGCACGAGGTA-1\n",
      "cell.ID:  ACTGTCCAGTACGCCC-1\n",
      "cell.ID:  ACTGTCCCAGATTGCT-1\n",
      "cell.ID:  ACTGTCCCAGCTCGAC-1\n",
      "cell.ID:  ACTGTCCCAGCTGTGC-1\n",
      "cell.ID:  ACTGTCCGTAGAGGAA-1\n",
      "cell.ID:  ACTGTCCGTCGTCTTC-1\n",
      "cell.ID:  ACTTACTCAGTAAGAT-1\n",
      "cell.ID:  ACTTTCAGTTCCACGG-1\n",
      "cell.ID:  AGAGCGAAGCCCAATT-1\n",
      "cell.ID:  AGAGCTTGTATATCCG-1\n",
      "cell.ID:  AGAGTGGGTTCAACCA-1\n",
      "cell.ID:  AGAGTGGTCACCTTAT-1\n",
      "cell.ID:  AGATCTGAGAATCTCC-1\n",
      "cell.ID:  AGATTGCTCATGTCCC-1\n",
      "cell.ID:  AGCAGCCAGTGTCCCG-1\n",
      "cell.ID:  AGCAGCCCACGGATAG-1\n",
      "cell.ID:  AGCGTATGTATATGGA-1\n",
      "cell.ID:  AGCTTGAGTTATCCGA-1\n",
      "cell.ID:  AGCTTGATCTCTAGGA-1\n",
      "cell.ID:  AGGCCACCAGCTCGCA-1\n",
      "cell.ID:  AGGCCACGTTCTGGTA-1\n",
      "cell.ID:  AGGGATGCATGAAGTA-1\n",
      "cell.ID:  AGTAGTCCATGAAGTA-1\n",
      "cell.ID:  AGTGAGGAGGCTCATT-1\n",
      "cell.ID:  AGTGTCAGTTTCCACC-1\n",
      "cell.ID:  AGTGTCATCCCTCAGT-1\n",
      "cell.ID:  AGTGTCATCGGCGCAT-1\n",
      "cell.ID:  AGTTGGTAGCACAGGT-1\n",
      "cell.ID:  AGTTGGTGTTGATTCG-1\n",
      "cell.ID:  ATAACGCGTATATGGA-1\n",
      "cell.ID:  ATCACGAAGTGTACGG-1\n",
      "cell.ID:  ATCACGAGTCGACTAT-1\n",
      "cell.ID:  ATCATGGGTCTCTTAT-1\n",
      "cell.ID:  ATCATGGTCAACCATG-1\n",
      "cell.ID:  ATCCACCTCCCTTGTG-1\n",
      "cell.ID:  ATCGAGTGTCCAACTA-1\n",
      "cell.ID:  ATCTACTGTTTGTTTC-1\n",
      "cell.ID:  ATCTGCCAGAGTGAGA-1\n",
      "cell.ID:  ATGAGGGAGATGTTAG-1\n",
      "cell.ID:  ATGAGGGGTTGGGACA-1\n",
      "cell.ID:  ATGGGAGCATACTACG-1\n",
      "cell.ID:  ATTACTCGTGTTGGGA-1\n",
      "cell.ID:  ATTACTCTCACCATAG-1\n",
      "cell.ID:  ATTATCCCATCACCCT-1\n",
      "cell.ID:  ATTGGTGTCAGTGCAT-1\n",
      "cell.ID:  ATTTCTGTCGACCAGC-1\n",
      "cell.ID:  CAAGATCTCCATGAGT-1\n",
      "cell.ID:  CACAAACTCACGAAGG-1\n",
      "cell.ID:  CACACAATCGGATGTT-1\n",
      "cell.ID:  CACACCTCAGTCAGAG-1\n",
      "cell.ID:  CACACCTTCTCGATGA-1\n",
      "cell.ID:  CACATAGCAGCTCGAC-1\n",
      "cell.ID:  CACCTTGCAAAGAATC-1\n",
      "cell.ID:  CACTCCAGTGACTCAT-1\n",
      "cell.ID:  CAGCATACACGCATCG-1\n",
      "cell.ID:  CAGCATATCATCTGCC-1\n",
      "cell.ID:  CAGCATATCCATGAGT-1\n",
      "cell.ID:  CAGCATATCGCCAGCA-1\n",
      "cell.ID:  CAGCTAATCGGCCGAT-1\n",
      "cell.ID:  CAGCTGGCATCGACGC-1\n",
      "cell.ID:  CAGTCCTAGCCACCTG-1\n",
      "cell.ID:  CATATTCAGAGTACCG-1\n",
      "cell.ID:  CATATTCTCCGTAGGC-1\n",
      "cell.ID:  CATATTCTCGTCCGTT-1\n",
      "cell.ID:  CATCAAGCACCCATGG-1\n",
      "cell.ID:  CATCAAGCATAAGACA-1\n",
      "cell.ID:  CATCAGATCGTCCAGG-1\n",
      "cell.ID:  CATCCACTCCGCAGTG-1\n",
      "cell.ID:  CATCGGGCAGACAGGT-1\n",
      "cell.ID:  CATGCCTCAGCGAACA-1\n",
      "cell.ID:  CCAATCCAGCTGCAAG-1\n",
      "cell.ID:  CCAATCCTCCTACAGA-1\n",
      "cell.ID:  CCACCTAAGTAACCCT-1\n",
      "cell.ID:  CCACCTAGTGTGGTTT-1\n",
      "cell.ID:  CCACCTATCAACACAC-1\n",
      "cell.ID:  CCCAATCGTAGAGCTG-1\n",
      "cell.ID:  CCCTCCTTCTCCGGTT-1\n",
      "cell.ID:  CCCTCCTTCTGGTGTA-1\n",
      "cell.ID:  CCCTCCTTCTTCAACT-1\n",
      "cell.ID:  CCGGTAGCAACACGCC-1\n",
      "cell.ID:  CCGTGGATCATAACCG-1\n",
      "cell.ID:  CCGTTCAAGCTGTTCA-1\n",
      "cell.ID:  CCTACACAGATGTTAG-1\n",
      "cell.ID:  CCTACACCACTCTGTC-1\n",
      "cell.ID:  CCTACCAAGACAAGCC-1\n",
      "cell.ID:  CCTAGCTAGGGATGGG-1\n",
      "cell.ID:  CCTATTAAGCGATATA-1\n",
      "cell.ID:  CCTATTAAGTAGCCGA-1\n",
      "cell.ID:  CCTCTGACATAGAAAC-1\n",
      "cell.ID:  CCTTACGGTCGCATCG-1\n",
      "cell.ID:  CCTTACGGTTATGTGC-1\n",
      "cell.ID:  CCTTCCCGTCAATACC-1\n",
      "cell.ID:  CCTTCCCGTTTAAGCC-1\n",
      "cell.ID:  CCTTCGAAGCACGCCT-1\n",
      "cell.ID:  CCTTCGACAAATCCGT-1\n",
      "cell.ID:  CCTTCGAGTATTAGCC-1\n",
      "cell.ID:  CGAACATCAGCTGCTG-1\n",
      "cell.ID:  CGAATGTTCATCGCTC-1\n",
      "cell.ID:  CGAGAAGAGTGTGGCA-1\n",
      "cell.ID:  CGAGAAGTCGATCCCT-1\n",
      "cell.ID:  CGAGCACTCAGTTAGC-1\n",
      "cell.ID:  CGAGCACTCCATGAAC-1\n",
      "cell.ID:  CGAGCCAAGTCTCAAC-1\n",
      "cell.ID:  CGAGCCAGTCACTGGC-1\n",
      "cell.ID:  CGATGGCAGGTAAACT-1\n",
      "cell.ID:  CGATGGCCAAGTTAAG-1\n",
      "cell.ID:  CGATGGCCAGCATACT-1\n",
      "cell.ID:  CGCGGTATCAACACTG-1\n",
      "cell.ID:  CGCGGTATCCGTACAA-1\n",
      "cell.ID:  CGCTTCAAGCGGCTTC-1\n",
      "cell.ID:  CGCTTCACAAGCCGCT-1\n",
      "cell.ID:  CGCTTCAGTTCCACAA-1\n",
      "cell.ID:  CGGACGTAGTTGCAGG-1\n",
      "cell.ID:  CGGACTGAGGTAGCCA-1\n",
      "cell.ID:  CGGAGCTAGCTAGTTC-1\n",
      "cell.ID:  CGGAGCTTCTTTAGGG-1\n",
      "cell.ID:  CGGGTCATCCGAACGC-1\n",
      "cell.ID:  CGGTTAAAGCTACCGC-1\n",
      "cell.ID:  CGTAGCGGTTGATTCG-1\n",
      "cell.ID:  CGTAGGCCAACGATCT-1\n",
      "cell.ID:  CGTCACTCAGCTGCAC-1\n",
      "cell.ID:  CGTCTACGTTCAGACT-1\n",
      "cell.ID:  CGTGTAAGTTGCTCCT-1\n",
      "cell.ID:  CGTGTAATCCGTAGTA-1\n",
      "cell.ID:  CGTTAGATCAGTCCCT-1\n",
      "cell.ID:  CGTTCTGCAGCGTAAG-1\n",
      "cell.ID:  CGTTCTGTCCGTCATC-1\n",
      "cell.ID:  CGTTCTGTCCTAGAAC-1\n",
      "cell.ID:  CGTTGGGCAAGGTTTC-1\n",
      "cell.ID:  CTAACTTGTCGCGTGT-1\n",
      "cell.ID:  CTAAGACCATCCCATC-1\n",
      "cell.ID:  CTAAGACGTTAGAACA-1\n",
      "cell.ID:  CTACACCCAGTATAAG-1\n",
      "cell.ID:  CTACACCGTGATGTCT-1\n",
      "cell.ID:  CTACACCTCAGAGCTT-1\n",
      "cell.ID:  CTACACCTCGAGGTAG-1\n",
      "cell.ID:  CTACACCTCTCTTATG-1\n",
      "cell.ID:  CTACATTGTGTGCGTC-1\n",
      "cell.ID:  CTACATTTCTGGCGTG-1\n",
      "cell.ID:  CTACCCAAGAAACGCC-1\n",
      "cell.ID:  CTACCCAAGGGTGTTG-1\n",
      "cell.ID:  CTACCCAGTCCGTTAA-1\n",
      "cell.ID:  CTACGTCCAGACGCAA-1\n",
      "cell.ID:  CTAGAGTCACCCATTC-1\n",
      "cell.ID:  CTCATTACATTCTCAT-1\n",
      "cell.ID:  CTCCTAGCACAGGAGT-1\n",
      "cell.ID:  CTCCTAGTCACTTACT-1\n",
      "cell.ID:  CTCGAGGCACGTCAGC-1\n",
      "cell.ID:  CTCGGGATCTTCTGGC-1\n",
      "cell.ID:  CTCGTACAGTCGTTTG-1\n",
      "cell.ID:  CTCTAATCACGTTGGC-1\n",
      "cell.ID:  CTGAAACAGTACGCGA-1\n",
      "cell.ID:  CTGCCTAGTCTAGCCG-1\n",
      "cell.ID:  CTGCGGAAGCGGCTTC-1\n",
      "cell.ID:  CTGCGGAGTACCGGCT-1\n",
      "cell.ID:  CTGCTGTAGTCCAGGA-1\n",
      "cell.ID:  CTGCTGTCACCAGGCT-1\n",
      "cell.ID:  CTGGTCTTCTCTGTCG-1\n",
      "cell.ID:  CTGTGCTTCACCTCGT-1\n",
      "cell.ID:  CTTAACTGTGTGACGA-1\n",
      "cell.ID:  CTTAACTGTTCTGTTT-1\n",
      "cell.ID:  CTTACCGGTCTACCTC-1\n",
      "cell.ID:  CTTAGGAAGGATGGAA-1\n",
      "cell.ID:  CTTGGCTGTCCCTACT-1\n",
      "cell.ID:  GAAATGAAGTTACCCA-1\n",
      "cell.ID:  GAACCTAAGACGCTTT-1\n",
      "cell.ID:  GAAGCAGTCGTGGACC-1\n",
      "cell.ID:  GAATGAATCACTCCTG-1\n",
      "cell.ID:  GAATGAATCGGAATCT-1\n",
      "cell.ID:  GACACGCAGATCACGG-1\n",
      "cell.ID:  GACCAATAGTCTCAAC-1\n",
      "cell.ID:  GACCAATTCATGTCTT-1\n",
      "cell.ID:  GACCTGGTCCGCATCT-1\n",
      "cell.ID:  GACGTGCAGCGTGAAC-1\n",
      "cell.ID:  GACGTGCCAATGTAAG-1\n",
      "cell.ID:  GACGTGCGTAAATACG-1\n",
      "cell.ID:  GACGTGCTCTGGCGTG-1\n",
      "cell.ID:  GACTAACCAAGACACG-1\n",
      "cell.ID:  GACTACAAGGCATGGT-1\n",
      "cell.ID:  GACTGCGCATGTCCTC-1\n",
      "cell.ID:  GATCGATCAGACGCTC-1\n",
      "cell.ID:  GATCGCGGTTTGTGTG-1\n",
      "cell.ID:  GATCGCGTCTCGATGA-1\n",
      "cell.ID:  GATCGTACATACTACG-1\n",
      "cell.ID:  GATCGTAGTCCGTCAG-1\n",
      "cell.ID:  GATCGTATCGCATGAT-1\n",
      "cell.ID:  GATCTAGAGACAGACC-1\n",
      "cell.ID:  GATCTAGAGTCAATAG-1\n",
      "cell.ID:  GATCTAGGTCATGCAT-1\n",
      "cell.ID:  GATGAAATCATAAAGG-1\n",
      "cell.ID:  GATGAGGAGTCCTCCT-1\n",
      "cell.ID:  GATGCTACACTTGGAT-1\n",
      "cell.ID:  GATGCTACATCTATGG-1\n",
      "cell.ID:  GCAAACTCACACGCTG-1\n",
      "cell.ID:  GCACTCTTCCGTCATC-1\n",
      "cell.ID:  GCATACAGTCTTCAAG-1\n",
      "cell.ID:  GCATACATCAGCTCGG-1\n",
      "cell.ID:  GCATACATCCCATTAT-1\n",
      "cell.ID:  GCATGCGGTTACGTCA-1\n",
      "cell.ID:  GCATGTAAGCGCCTTG-1\n",
      "cell.ID:  GCCTCTACACTCAGGC-1\n",
      "cell.ID:  GCCTCTAGTCTGCAAT-1\n",
      "cell.ID:  GCGAGAATCGAACTGT-1\n",
      "cell.ID:  GCTCCTACAGACGCTC-1\n",
      "cell.ID:  GCTCCTATCTTCGAGA-1\n",
      "cell.ID:  GCTCTGTGTGTGTGCC-1\n",
      "cell.ID:  GCTGCAGTCATTCACT-1\n",
      "cell.ID:  GCTGGGTAGGCTCTTA-1\n",
      "cell.ID:  GCTTGAAAGGTTACCT-1\n",
      "cell.ID:  GCTTGAACAAGCCATT-1\n",
      "cell.ID:  GGAAAGCGTCAATACC-1\n",
      "cell.ID:  GGAAAGCGTTCAGACT-1\n",
      "cell.ID:  GGAACTTAGATGGGTC-1\n",
      "cell.ID:  GGAATAACACGACTCG-1\n",
      "cell.ID:  GGACAAGTCATGTCTT-1\n",
      "cell.ID:  GGACATTAGCAATCTC-1\n",
      "cell.ID:  GGATGTTCAAGGGTCA-1\n",
      "cell.ID:  GGATTACAGTCCCACG-1\n",
      "cell.ID:  GGATTACCACAGGAGT-1\n",
      "cell.ID:  GGATTACCACTATCTT-1\n",
      "cell.ID:  GGATTACCAGCTATTG-1\n",
      "cell.ID:  GGCAATTTCCCGGATG-1\n",
      "cell.ID:  GGCGACTCATCGATTG-1\n",
      "cell.ID:  GGCTGGTAGCCTTGAT-1\n",
      "cell.ID:  GGCTGGTTCTTTCCTC-1\n",
      "cell.ID:  GGGAATGAGCTCCTTC-1\n",
      "cell.ID:  GGGACCTCATTCCTGC-1\n",
      "cell.ID:  GGGATGACAGCTGTAT-1\n",
      "cell.ID:  GGGCACTTCGTGGGAA-1\n",
      "cell.ID:  GGGTTGCAGTGTGGCA-1\n",
      "cell.ID:  GGTATTGGTCGACTGC-1\n",
      "cell.ID:  GGTGAAGAGCCCAATT-1\n",
      "cell.ID:  GGTGCGTAGTACGCGA-1\n",
      "cell.ID:  GGTGCGTTCAGTACGT-1\n",
      "cell.ID:  GGTGTTACACGGCTAC-1\n",
      "cell.ID:  GTACGTAAGTTGCAGG-1\n",
      "cell.ID:  GTACGTATCAACACCA-1\n",
      "cell.ID:  GTACTCCAGACAAAGG-1\n",
      "cell.ID:  GTACTCCAGTGTCCAT-1\n",
      "cell.ID:  GTATTCTCAAGCTGGA-1\n",
      "cell.ID:  GTCAAGTCACACGCTG-1\n",
      "cell.ID:  GTCAAGTTCAACTCTT-1\n",
      "cell.ID:  GTCACAAAGGCAGTCA-1\n",
      "cell.ID:  GTCACGGAGCGGCTTC-1\n",
      "cell.ID:  GTCGGGTCATGACATC-1\n",
      "cell.ID:  GTCTTCGAGAGCTGGT-1\n",
      "cell.ID:  GTGCATAAGCCACGCT-1\n",
      "cell.ID:  GTGCGGTTCTCAACTT-1\n",
      "cell.ID:  GTGCTTCCATAAGACA-1\n",
      "cell.ID:  GTGGGTCAGGTGATAT-1\n",
      "cell.ID:  GTGGGTCTCAATCTCT-1\n",
      "cell.ID:  GTGTTAGTCCTCCTAG-1\n",
      "cell.ID:  GTTAAGCCATTCGACA-1\n",
      "cell.ID:  GTTCATTGTTGCCTCT-1\n",
      "cell.ID:  GTTCATTGTTGCTCCT-1\n",
      "cell.ID:  GTTCATTTCTTGTCAT-1\n",
      "cell.ID:  GTTCGGGTCCGTTGTC-1\n",
      "cell.ID:  TAAACCGTCTTTAGGG-1\n",
      "cell.ID:  TAAGCGTAGACAGACC-1\n",
      "cell.ID:  TAAGCGTCACTCGACG-1\n",
      "cell.ID:  TAAGCGTGTACGCACC-1\n",
      "cell.ID:  TAAGCGTGTTGGTGGA-1\n",
      "cell.ID:  TAAGTGCGTGATGATA-1\n",
      "cell.ID:  TACACGATCAGTTCGA-1\n",
      "cell.ID:  TACACGATCCGAATGT-1\n",
      "cell.ID:  TACACGATCTACTCAT-1\n",
      "cell.ID:  TACAGTGCATTAGGCT-1\n",
      "cell.ID:  TACGGATTCGGCGGTT-1\n",
      "cell.ID:  TACGGGCCACAAGACG-1\n",
      "cell.ID:  TACTCATTCTACTATC-1\n",
      "cell.ID:  TACTTACGTTATCACG-1\n",
      "cell.ID:  TACTTACGTTTAGGAA-1\n",
      "cell.ID:  TACTTGTCACGAAAGC-1\n",
      "cell.ID:  TAGACCAAGGGTCTCC-1\n",
      "cell.ID:  TAGACCATCACATACG-1\n",
      "cell.ID:  TAGACCATCGGCATCG-1\n",
      "cell.ID:  TAGGCATAGACCACGA-1\n",
      "cell.ID:  TAGGCATGTCTAAACC-1\n",
      "cell.ID:  TAGTGGTAGTACGTTC-1\n",
      "cell.ID:  TAGTGGTGTACGAAAT-1\n",
      "cell.ID:  TAGTTGGAGATGCGAC-1\n",
      "cell.ID:  TATCTCAAGGAGTAGA-1\n",
      "cell.ID:  TCAACGACAATCGAAA-1\n",
      "cell.ID:  TCAACGAGTCCGACGT-1\n",
      "cell.ID:  TCAATCTCAACTGCTA-1\n",
      "cell.ID:  TCACAAGAGAGGGCTT-1\n",
      "cell.ID:  TCACGAAGTAATCACC-1\n",
      "cell.ID:  TCAGATGTCTTGCATT-1\n",
      "cell.ID:  TCAGCAAAGGACATTA-1\n",
      "cell.ID:  TCAGCTCAGTAGATGT-1\n",
      "cell.ID:  TCAGCTCTCGGTCCGA-1\n",
      "cell.ID:  TCAGGATGTGGACGAT-1\n",
      "cell.ID:  TCATTACAGGCATGGT-1\n",
      "cell.ID:  TCATTACCAGGTTTCA-1\n",
      "cell.ID:  TCCACACAGAATCTCC-1\n",
      "cell.ID:  TCCCGATAGTGAATTG-1\n",
      "cell.ID:  TCCCGATCACAGAGGT-1\n",
      "cell.ID:  TCGAGGCCAACTGCTA-1\n",
      "cell.ID:  TCGCGAGAGAAACCAT-1\n",
      "cell.ID:  TCGCGTTCAATCTGCA-1\n",
      "cell.ID:  TCGGGACTCAGTGCAT-1\n",
      "cell.ID:  TCGGTAAGTTACTGAC-1\n",
      "cell.ID:  TCGTAGACAGTAACGG-1\n",
      "cell.ID:  TCGTAGATCCCGGATG-1\n",
      "cell.ID:  TCTATTGAGTGTACCT-1\n",
      "cell.ID:  TCTCATATCAGTGTTG-1\n",
      "cell.ID:  TCTCATATCGTAGATC-1\n",
      "cell.ID:  TCTGGAAGTCGAATCT-1\n",
      "cell.ID:  TCTTCGGTCGCGTTTC-1\n",
      "cell.ID:  TGACGGCCAAATTGCC-1\n",
      "cell.ID:  TGACGGCGTGCTAGCC-1\n",
      "cell.ID:  TGACTTTCATTCTTAC-1\n",
      "cell.ID:  TGAGCATCATCCCACT-1\n",
      "cell.ID:  TGATTTCGTGACTACT-1\n",
      "cell.ID:  TGCCAAAAGGCTAGGT-1\n",
      "cell.ID:  TGCCAAAAGTCTCCTC-1\n",
      "cell.ID:  TGCCAAACATCGACGC-1\n",
      "cell.ID:  TGCGGGTTCAGCTCTC-1\n",
      "cell.ID:  TGCGTGGCACAGTCGC-1\n",
      "cell.ID:  TGGCCAGAGGCTCTTA-1\n",
      "cell.ID:  TGGCCAGCACCTATCC-1\n",
      "cell.ID:  TGGCCAGGTGCACCAC-1\n",
      "cell.ID:  TGGCGCAAGTCAAGCG-1\n",
      "cell.ID:  TGGCGCATCATCGGAT-1\n",
      "cell.ID:  TGGCTGGGTAGAGCTG-1\n",
      "cell.ID:  TGGGAAGCACTCGACG-1\n",
      "cell.ID:  TGGTTAGGTACAAGTA-1\n",
      "cell.ID:  TGGTTCCGTGTTAAGA-1\n",
      "cell.ID:  TGTCCCAGTGCAGGTA-1\n",
      "cell.ID:  TGTGGTAGTCCAGTAT-1\n",
      "cell.ID:  TGTGTTTCAATGGATA-1\n",
      "cell.ID:  TGTGTTTCAGGGTTAG-1\n",
      "cell.ID:  TGTGTTTTCTAGCACA-1\n",
      "cell.ID:  TTAGGACCAGTAACGG-1\n",
      "cell.ID:  TTAGTTCTCACTTACT-1\n",
      "cell.ID:  TTATGCTAGACTAGGC-1\n",
      "cell.ID:  TTATGCTGTCACTGGC-1\n",
      "cell.ID:  TTCCCAGCATACTACG-1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cell.ID:  TTCGAAGTCAAAGTAG-1\n",
      "cell.ID:  TTCTACACATATGCTG-1\n",
      "cell.ID:  TTCTCAAAGCTCCTCT-1\n",
      "cell.ID:  TTGAACGGTGTGCGTC-1\n",
      "cell.ID:  TTGACTTAGAGCAATT-1\n",
      "cell.ID:  TTGACTTGTAGCCTCG-1\n",
      "cell.ID:  TTGCCGTTCAAGAAGT-1\n",
      "cell.ID:  TTGTAGGTCCAGGGCT-1\n",
      "cell.ID:  TTTACTGTCGACAGCC-1\n",
      "cell.ID:  TTTATGCCAATTGCTG-1\n",
      "cell.ID:  TTTGTCACACCACCAG-1\n",
      "cell.ID:  TTTGTCAGTTAGATGA-1\n",
      "ID:  5.0_1\n",
      "index:  8\n",
      "P index:  5\n",
      "ST:  5.0\n",
      "T:  6\n",
      "cell.ID:  AAACGGGCACATGGGA-1\n",
      "cell.ID:  AAAGATGTCCAAATGC-1\n",
      "cell.ID:  AAAGCAATCTGTGCAA-1\n",
      "cell.ID:  AACTCCCGTTCCAACA-1\n",
      "cell.ID:  AACTGGTAGTTCCACA-1\n",
      "cell.ID:  AACTTTCGTTTGGCGC-1\n",
      "cell.ID:  AAGACCTTCTAAGCCA-1\n",
      "cell.ID:  AAGCCGCTCGTTACAG-1\n",
      "cell.ID:  ACATACGAGGGCTTCC-1\n",
      "cell.ID:  ACATGGTAGGGATGGG-1\n",
      "cell.ID:  ACGATGTTCCCGGATG-1\n",
      "cell.ID:  ACGGGCTTCCGGCACA-1\n",
      "cell.ID:  ACTGAGTTCGTAGATC-1\n",
      "cell.ID:  AGATTGCCACGGTGTC-1\n",
      "cell.ID:  AGATTGCGTCAGAATA-1\n",
      "cell.ID:  AGCTCCTAGCTCCTTC-1\n",
      "cell.ID:  AGCTCCTCACCTTGTC-1\n",
      "cell.ID:  AGCTCCTTCGGAGCAA-1\n",
      "cell.ID:  AGCTCTCGTTAAAGTG-1\n",
      "cell.ID:  AGGGAGTTCTTATCTG-1\n",
      "cell.ID:  AGGGATGGTGTAACGG-1\n",
      "cell.ID:  AGGGTGAGTGTCAATC-1\n",
      "cell.ID:  AGTCTTTCAAATACAG-1\n",
      "cell.ID:  AGTGAGGCACCGATAT-1\n",
      "cell.ID:  AGTGTCATCCGCGGTA-1\n",
      "cell.ID:  ATAACGCCATGGAATA-1\n",
      "cell.ID:  ATAGACCTCAGTCAGT-1\n",
      "cell.ID:  ATCACGAAGTCTCGGC-1\n",
      "cell.ID:  ATCACGACAAGTTCTG-1\n",
      "cell.ID:  ATCACGACATTCACTT-1\n",
      "cell.ID:  ATCCGAATCTTACCGC-1\n",
      "cell.ID:  ATCTGCCGTTAAGACA-1\n",
      "cell.ID:  ATGGGAGAGCACACAG-1\n",
      "cell.ID:  ATTACTCTCCTCGCAT-1\n",
      "cell.ID:  ATTATCCTCATAACCG-1\n",
      "cell.ID:  ATTCTACAGGCTCAGA-1\n",
      "cell.ID:  CAACTAGTCCACGAAT-1\n",
      "cell.ID:  CAAGAAAGTCTCAACA-1\n",
      "cell.ID:  CAAGAAATCCCAAGTA-1\n",
      "cell.ID:  CAAGATCGTATAATGG-1\n",
      "cell.ID:  CACATAGTCACCTCGT-1\n",
      "cell.ID:  CAGAATCGTATCTGCA-1\n",
      "cell.ID:  CAGAGAGCACAACGTT-1\n",
      "cell.ID:  CAGAGAGCAGGGATTG-1\n",
      "cell.ID:  CAGCTAACACTTACGA-1\n",
      "cell.ID:  CAGCTAATCACGATGT-1\n",
      "cell.ID:  CATATGGGTTATGCGT-1\n",
      "cell.ID:  CATCGGGGTCTAGCCG-1\n",
      "cell.ID:  CATGACAGTGGTCCGT-1\n",
      "cell.ID:  CATGACATCGTTGCCT-1\n",
      "cell.ID:  CATGCCTGTAAGCACG-1\n",
      "cell.ID:  CATGGCGCACAACGTT-1\n",
      "cell.ID:  CCACCTACATCGTCGG-1\n",
      "cell.ID:  CCACTACAGGCCATAG-1\n",
      "cell.ID:  CCACTACGTCGGCATC-1\n",
      "cell.ID:  CCACTACTCATTTGGG-1\n",
      "cell.ID:  CCACTACTCCCAAGAT-1\n",
      "cell.ID:  CCGTGGAGTTGGTAAA-1\n",
      "cell.ID:  CCTAGCTCATGATCCA-1\n",
      "cell.ID:  CCTAGCTTCCGAGCCA-1\n",
      "cell.ID:  CCTTCCCTCAAGGTAA-1\n",
      "cell.ID:  CCTTCGAGTTAAGTAG-1\n",
      "cell.ID:  CGAACATTCGTGGGAA-1\n",
      "cell.ID:  CGAATGTTCAGCCTAA-1\n",
      "cell.ID:  CGAGAAGGTTAAGAAC-1\n",
      "cell.ID:  CGAGCACAGCCCTAAT-1\n",
      "cell.ID:  CGAGCCATCGACCAGC-1\n",
      "cell.ID:  CGATGGCAGCGCCTCA-1\n",
      "cell.ID:  CGGACGTAGGAGCGTT-1\n",
      "cell.ID:  CGGACGTTCCAAACTG-1\n",
      "cell.ID:  CGGACTGGTCACTTCC-1\n",
      "cell.ID:  CGGTTAAGTCTCCATC-1\n",
      "cell.ID:  CGGTTAATCCCTCTTT-1\n",
      "cell.ID:  CGTAGCGGTAGGGTAC-1\n",
      "cell.ID:  CGTAGCGGTCAGCTAT-1\n",
      "cell.ID:  CGTGAGCGTGGCAAAC-1\n",
      "cell.ID:  CGTTGGGTCTCGTTTA-1\n",
      "cell.ID:  CTCGAAACACACGCTG-1\n",
      "cell.ID:  CTCGAAACACGGTTTA-1\n",
      "cell.ID:  CTCGAAAGTAGGCTGA-1\n",
      "cell.ID:  CTCGAAAGTCTAAAGA-1\n",
      "cell.ID:  CTCGGAGCACGGCCAT-1\n",
      "cell.ID:  CTCGGAGGTAGCGTCC-1\n",
      "cell.ID:  CTCGGGAAGTTGAGTA-1\n",
      "cell.ID:  CTCGGGATCATGTCCC-1\n",
      "cell.ID:  CTCGTACGTTGGTAAA-1\n",
      "cell.ID:  CTCGTCACACGGATAG-1\n",
      "cell.ID:  CTCGTCAGTACCTACA-1\n",
      "cell.ID:  CTCTAATCACTCTGTC-1\n",
      "cell.ID:  CTCTAATTCATAACCG-1\n",
      "cell.ID:  CTGAAACGTCTAAAGA-1\n",
      "cell.ID:  CTGAAGTCAAATTGCC-1\n",
      "cell.ID:  CTGATCCCATCTATGG-1\n",
      "cell.ID:  CTGATCCGTTGAGTTC-1\n",
      "cell.ID:  CTGCTGTGTCCTCTTG-1\n",
      "cell.ID:  CTGTTTAAGGAGTACC-1\n",
      "cell.ID:  CTTGGCTTCTAACTTC-1\n",
      "cell.ID:  GAATGAACAAGGTTCT-1\n",
      "cell.ID:  GACAGAGAGACGACGT-1\n",
      "cell.ID:  GACGGCTGTATCAGTC-1\n",
      "cell.ID:  GAGTCCGGTTGTACAC-1\n",
      "cell.ID:  GATCAGTAGACGACGT-1\n",
      "cell.ID:  GATCGATAGAGTACAT-1\n",
      "cell.ID:  GATCGTACACCTGGTG-1\n",
      "cell.ID:  GATCGTATCATGGTCA-1\n",
      "cell.ID:  GCAAACTAGGCTACGA-1\n",
      "cell.ID:  GCACATAAGTGGACGT-1\n",
      "cell.ID:  GCATACACACCATCCT-1\n",
      "cell.ID:  GCATACAGTAGCTGCC-1\n",
      "cell.ID:  GCATGATGTCCAAGTT-1\n",
      "cell.ID:  GCCAAATCACTTAAGC-1\n",
      "cell.ID:  GCCTCTATCTGAAAGA-1\n",
      "cell.ID:  GCGCCAACACGGATAG-1\n",
      "cell.ID:  GCTCTGTAGTCATCCA-1\n",
      "cell.ID:  GCTGCAGCATTCACTT-1\n",
      "cell.ID:  GCTGCGATCCAAATGC-1\n",
      "cell.ID:  GCTTCCAAGGTACTCT-1\n",
      "cell.ID:  GGAGCAAGTTTGGCGC-1\n",
      "cell.ID:  GGCAATTAGTGGACGT-1\n",
      "cell.ID:  GGCTCGATCAACTCTT-1\n",
      "cell.ID:  GGCTGGTAGTGCGATG-1\n",
      "cell.ID:  GGGAATGTCCACTCCA-1\n",
      "cell.ID:  GGGATGAAGCGACGTA-1\n",
      "cell.ID:  GGGCACTGTCAGATAA-1\n",
      "cell.ID:  GGGCACTTCTGGCGAC-1\n",
      "cell.ID:  GGGCATCCAAGCTGTT-1\n",
      "cell.ID:  GGGCATCTCGCACTCT-1\n",
      "cell.ID:  GGGTCTGAGCTGCAAG-1\n",
      "cell.ID:  GGGTCTGAGGGATCTG-1\n",
      "cell.ID:  GGGTTGCTCTCACATT-1\n",
      "cell.ID:  GGTGAAGGTCTAGTCA-1\n",
      "cell.ID:  GTAACGTTCCACGACG-1\n",
      "cell.ID:  GTACGTACAGGAATGC-1\n",
      "cell.ID:  GTACTTTCACACCGAC-1\n",
      "cell.ID:  GTAGTCAAGCAATCTC-1\n",
      "cell.ID:  GTCCTCAAGCCGCCTA-1\n",
      "cell.ID:  GTCCTCACAAGCTGGA-1\n",
      "cell.ID:  GTCGGGTCACCATCCT-1\n",
      "cell.ID:  GTCGTAAGTGTGTGCC-1\n",
      "cell.ID:  GTGCAGCCAATCTGCA-1\n",
      "cell.ID:  GTGCTTCTCCGCTGTT-1\n",
      "cell.ID:  GTGGGTCAGAAACGAG-1\n",
      "cell.ID:  GTTACAGCATGGATGG-1\n",
      "cell.ID:  GTTCTCGCACCGAATT-1\n",
      "cell.ID:  GTTTCTACACTTCTGC-1\n",
      "cell.ID:  GTTTCTAGTCTCACCT-1\n",
      "cell.ID:  TAAGTGCCATTGCGGC-1\n",
      "cell.ID:  TACACGATCTGGTTCC-1\n",
      "cell.ID:  TACAGTGAGCGTTGCC-1\n",
      "cell.ID:  TACAGTGCATAGTAAG-1\n",
      "cell.ID:  TACCTTACATCGGAAG-1\n",
      "cell.ID:  TACGGTAAGAGTACAT-1\n",
      "cell.ID:  TAGCCGGGTCCGCTGA-1\n",
      "cell.ID:  TAGTGGTTCACTTCAT-1\n",
      "cell.ID:  TATCTCAGTGGCGAAT-1\n",
      "cell.ID:  TATCTCAGTTCAGACT-1\n",
      "cell.ID:  TATGCCCAGGTTACCT-1\n",
      "cell.ID:  TCAACGATCATGTAGC-1\n",
      "cell.ID:  TCAATCTGTAAGTGTA-1\n",
      "cell.ID:  TCAGCAACACCACCAG-1\n",
      "cell.ID:  TCAGCAAGTATTCGTG-1\n",
      "cell.ID:  TCGGTAAAGTACGTAA-1\n",
      "cell.ID:  TCGTACCGTGTCAATC-1\n",
      "cell.ID:  TCGTAGATCGTTGCCT-1\n",
      "cell.ID:  TCTCATATCTACTATC-1\n",
      "cell.ID:  TCTCTAAGTAGCTAAA-1\n",
      "cell.ID:  TCTGAGAGTCTAGTCA-1\n",
      "cell.ID:  TCTTTCCGTTGACGTT-1\n",
      "cell.ID:  TGACTAGGTAGAAGGA-1\n",
      "cell.ID:  TGAGCATAGGTGCAAC-1\n",
      "cell.ID:  TGAGCATGTAAAGGAG-1\n",
      "cell.ID:  TGAGCCGAGCTCAACT-1\n",
      "cell.ID:  TGAGGGAAGTGCCATT-1\n",
      "cell.ID:  TGAGGGAGTGTTTGTG-1\n",
      "cell.ID:  TGATTTCTCAACTCTT-1\n",
      "cell.ID:  TGCGCAGCATGAGCGA-1\n",
      "cell.ID:  TGCGTGGTCCAACCAA-1\n",
      "cell.ID:  TGGACGCCAGACGCCT-1\n",
      "cell.ID:  TGGGAAGAGCTCCTTC-1\n",
      "cell.ID:  TGGGCGTGTCAGTGGA-1\n",
      "cell.ID:  TGTATTCCAAAGGAAG-1\n",
      "cell.ID:  TGTGTTTAGAGTGACC-1\n",
      "cell.ID:  TGTTCCGTCGCAAGCC-1\n",
      "cell.ID:  TTAGGCAAGCCGTCGT-1\n",
      "cell.ID:  TTAGGCAAGTCTTGCA-1\n",
      "cell.ID:  TTAGTTCTCATGCAAC-1\n",
      "cell.ID:  TTATGCTCAACACCTA-1\n",
      "cell.ID:  TTCCCAGAGTGGTCCC-1\n",
      "cell.ID:  TTCGAAGAGGCTAGAC-1\n",
      "cell.ID:  TTCGGTCAGAAGGACA-1\n",
      "cell.ID:  TTCTCCTCACCAGTTA-1\n",
      "cell.ID:  TTGACTTCAGATAATG-1\n",
      "cell.ID:  TTGCCGTCAAAGTGCG-1\n",
      "cell.ID:  TTGCGTCAGGAGTACC-1\n",
      "cell.ID:  TTGGAACCAGCTCGAC-1\n",
      "cell.ID:  TTTGGTTAGAGGACGG-1\n",
      "ID:  3.0_1\n",
      "index:  9\n",
      "P index:  2\n",
      "ST:  3.0\n",
      "T:  6\n",
      "cell.ID:  ACACCGGCACCCAGTG-1\n",
      "cell.ID:  ACGATACTCTTACCTA-1\n",
      "cell.ID:  CACTCCAAGGCATTGG-1\n",
      "cell.ID:  CCACCTAAGTCGCCGT-1\n",
      "cell.ID:  CGAATGTCAAGACGTG-1\n",
      "cell.ID:  CTAAGACCACATGGGA-1\n",
      "cell.ID:  GGAGCAACATCGATTG-1\n",
      "cell.ID:  GTAACGTAGGCTAGGT-1\n",
      "cell.ID:  GTGTTAGCAGCTTAAC-1\n",
      "ID:  6.0_1\n",
      "index:  10\n",
      "P index:  5\n",
      "ST:  6.0\n",
      "T:  6\n",
      "cell.ID:  AAACCTGGTCGACTGC-1\n",
      "cell.ID:  AACCATGTCGATAGAA-1\n",
      "cell.ID:  AACTGGTTCGCATGAT-1\n",
      "cell.ID:  AACTTTCTCCATGAGT-1\n",
      "cell.ID:  AAGCCGCAGCGCCTCA-1\n",
      "cell.ID:  ACACCGGCAGGCTCAC-1\n",
      "cell.ID:  ACACTGAAGCAACGGT-1\n",
      "cell.ID:  ACGAGGAGTCGCATCG-1\n",
      "cell.ID:  ACGGAGATCAATAAGG-1\n",
      "cell.ID:  ACGGCCATCCCTTGCA-1\n",
      "cell.ID:  ACGTCAAAGATGCCTT-1\n",
      "cell.ID:  ACTGTCCCATGTAGTC-1\n",
      "cell.ID:  ACTTTCAGTGGTCCGT-1\n",
      "cell.ID:  AGATCTGTCTGGTATG-1\n",
      "cell.ID:  AGATTGCTCCGCGGTA-1\n",
      "cell.ID:  AGCCTAAAGGCTAGGT-1\n",
      "cell.ID:  AGCGGTCAGAGCTGCA-1\n",
      "cell.ID:  AGCGTATGTCGTTGTA-1\n",
      "cell.ID:  AGCGTCGCATGATCCA-1\n",
      "cell.ID:  AGCTCCTGTCGAGATG-1\n",
      "cell.ID:  AGCTCCTTCACTTATC-1\n",
      "cell.ID:  AGCTCTCGTGACTCAT-1\n",
      "cell.ID:  AGCTTGAAGGTTACCT-1\n",
      "cell.ID:  AGCTTGACATGTCCTC-1\n",
      "cell.ID:  AGGCCGTGTGGCAAAC-1\n",
      "cell.ID:  AGGGATGAGTAACCCT-1\n",
      "cell.ID:  AGTCTTTAGTGAACAT-1\n",
      "cell.ID:  AGTCTTTTCCTCAACC-1\n",
      "cell.ID:  ATAACGCAGTCGATAA-1\n",
      "cell.ID:  ATAAGAGAGACTAAGT-1\n",
      "cell.ID:  ATAAGAGAGGATGGAA-1\n",
      "cell.ID:  ATCCGAAAGATGCCAG-1\n",
      "cell.ID:  ATCGAGTCATCCCATC-1\n",
      "cell.ID:  ATTCTACCAAACGTGG-1\n",
      "cell.ID:  ATTGGACCATCCTAGA-1\n",
      "cell.ID:  CAACCAAGTAGTACCT-1\n",
      "cell.ID:  CAAGTTGGTAGCGCTC-1\n",
      "cell.ID:  CACAAACCACTTGGAT-1\n",
      "cell.ID:  CACAAACGTCAGAAGC-1\n",
      "cell.ID:  CACCACTAGTTTGCGT-1\n",
      "cell.ID:  CACCAGGCATGCGCAC-1\n",
      "cell.ID:  CACCTTGCAGACACTT-1\n",
      "cell.ID:  CACCTTGGTTGGTTTG-1\n",
      "cell.ID:  CAGCCGAGTCTAAAGA-1\n",
      "cell.ID:  CAGCGACGTACATCCA-1\n",
      "cell.ID:  CAGCTGGAGGTGCTAG-1\n",
      "cell.ID:  CAGCTGGGTACCAGTT-1\n",
      "cell.ID:  CATTCGCGTGCAACGA-1\n",
      "cell.ID:  CCACCTATCAACCATG-1\n",
      "cell.ID:  CCCAATCAGGATGCGT-1\n",
      "cell.ID:  CCGGGATTCATGGTCA-1\n",
      "cell.ID:  CCTACCACACCATCCT-1\n",
      "cell.ID:  CCTTACGAGGCACATG-1\n",
      "cell.ID:  CCTTACGAGTCTCGGC-1\n",
      "cell.ID:  CCTTTCTTCACGCGGT-1\n",
      "cell.ID:  CGAACATAGTTGCAGG-1\n",
      "cell.ID:  CGAGAAGTCACATGCA-1\n",
      "cell.ID:  CGAGCACAGCCAGTTT-1\n",
      "cell.ID:  CGAGCCATCAACGCTA-1\n",
      "cell.ID:  CGATGGCTCAATCTCT-1\n",
      "cell.ID:  CGATGTAGTTAAGATG-1\n",
      "cell.ID:  CGCCAAGAGGTTCCTA-1\n",
      "cell.ID:  CGCCAAGTCTAACTCT-1\n",
      "cell.ID:  CGCGTTTCAGACAGGT-1\n",
      "cell.ID:  CGCTATCGTCTGATTG-1\n",
      "cell.ID:  CGTCCATAGAGAGCTC-1\n",
      "cell.ID:  CGTGAGCAGAAAGTGG-1\n",
      "cell.ID:  CTAACTTAGATCCCGC-1\n",
      "cell.ID:  CTACCCATCTTCGAGA-1\n",
      "cell.ID:  CTAGAGTGTAGCCTCG-1\n",
      "cell.ID:  CTAGCCTTCGTCGTTC-1\n",
      "cell.ID:  CTAGTGACAGGACGTA-1\n",
      "cell.ID:  CTCAGAATCGTTGCCT-1\n",
      "cell.ID:  CTCGGGATCTTGTACT-1\n",
      "cell.ID:  CTCGTACGTTGCGTTA-1\n",
      "cell.ID:  CTCGTACTCTAACTCT-1\n",
      "cell.ID:  CTCGTCACAAGTTCTG-1\n",
      "cell.ID:  CTCTAATGTGTTAAGA-1\n",
      "cell.ID:  CTGGTCTCATCTACGA-1\n",
      "cell.ID:  CTTAACTCATAGACTC-1\n",
      "cell.ID:  CTTAACTGTCCGTCAG-1\n",
      "cell.ID:  CTTACCGCAAACCTAC-1\n",
      "cell.ID:  CTTCTCTGTGTAATGA-1\n",
      "cell.ID:  GAAACTCGTTATGTGC-1\n",
      "cell.ID:  GAACATCCAGTAAGCG-1\n",
      "cell.ID:  GACTACACATGTCTCC-1\n",
      "cell.ID:  GATCGTATCTCTTGAT-1\n",
      "cell.ID:  GATCGTATCTTAACCT-1\n",
      "cell.ID:  GATGAGGGTTTGTTGG-1\n",
      "cell.ID:  GCAAACTCACGTGAGA-1\n",
      "cell.ID:  GCCTCTAAGAAACGAG-1\n",
      "cell.ID:  GCGCAGTAGCGTGAGT-1\n",
      "cell.ID:  GCGCGATCAACTGGCC-1\n",
      "cell.ID:  GCGCGATGTCCGACGT-1\n",
      "cell.ID:  GCGGGTTCACCAGGCT-1\n",
      "cell.ID:  GCTCTGTGTGTTTGTG-1\n",
      "cell.ID:  GCTGCGATCTTACCGC-1\n",
      "cell.ID:  GGAACTTCAATCTACG-1\n",
      "cell.ID:  GGACAGAGTGTCCTCT-1\n",
      "cell.ID:  GGACATTGTCACCTAA-1\n",
      "cell.ID:  GGAGCAACAAACGTGG-1\n",
      "cell.ID:  GGATTACAGGCAGTCA-1\n",
      "cell.ID:  GGCCGATAGGTAGCTG-1\n",
      "cell.ID:  GGCCGATCAATGGACG-1\n",
      "cell.ID:  GGCGACTCATGTTCCC-1\n",
      "cell.ID:  GGCTCGAGTTCTCATT-1\n",
      "cell.ID:  GGGTTGCAGGTACTCT-1\n",
      "cell.ID:  GGTGAAGGTACTTGAC-1\n",
      "cell.ID:  GGTGTTACATCACCCT-1\n",
      "cell.ID:  GTAACTGGTCGCTTCT-1\n",
      "cell.ID:  GTACGTAAGACTAGAT-1\n",
      "cell.ID:  GTCACAAGTCTTTCAT-1\n",
      "cell.ID:  GTCACGGAGGCAATTA-1\n",
      "cell.ID:  GTCATTTCAATGGAGC-1\n",
      "cell.ID:  GTCCTCAGTAGGCATG-1\n",
      "cell.ID:  GTCGTAAAGATCGATA-1\n",
      "cell.ID:  GTCGTAAAGCTGATAA-1\n",
      "cell.ID:  GTGCGGTAGGGTGTTG-1\n",
      "cell.ID:  GTGCGGTTCAGCTCGG-1\n",
      "cell.ID:  GTGCTTCGTAGTGAAT-1\n",
      "cell.ID:  GTGGGTCCAAACGTGG-1\n",
      "cell.ID:  GTTACAGAGCACAGGT-1\n",
      "cell.ID:  GTTCATTCAGTAACGG-1\n",
      "cell.ID:  TAAGAGACACCCAGTG-1\n",
      "cell.ID:  TACAGTGAGATCCTGT-1\n",
      "cell.ID:  TACTCATAGTTAGGTA-1\n",
      "cell.ID:  TAGTGGTAGATGCCAG-1\n",
      "cell.ID:  TATTACCGTCGGATCC-1\n",
      "cell.ID:  TCAACGACAGATGGCA-1\n",
      "cell.ID:  TCAGCTCAGGGCTTGA-1\n",
      "cell.ID:  TCAGGATAGAGAACAG-1\n",
      "cell.ID:  TCAGGTAGTTGTGGCC-1\n",
      "cell.ID:  TCATTACGTAGTACCT-1\n",
      "cell.ID:  TCATTTGTCTTCAACT-1\n",
      "cell.ID:  TCCACACCAAATACAG-1\n",
      "cell.ID:  TCCACACCATATGGTC-1\n",
      "cell.ID:  TCGCGAGAGTGTACGG-1\n",
      "cell.ID:  TCGCGAGTCCGCGTTT-1\n",
      "cell.ID:  TCGGGACGTCTCAACA-1\n",
      "cell.ID:  TCGGGACTCAAGATCC-1\n",
      "cell.ID:  TCGTAGAAGAGTGAGA-1\n",
      "cell.ID:  TCTCATAAGATCACGG-1\n",
      "cell.ID:  TCTCATAAGGAATGGA-1\n",
      "cell.ID:  TCTCTAAAGTGTCCAT-1\n",
      "cell.ID:  TCTGGAATCTGGCGTG-1\n",
      "cell.ID:  TGACAACTCAGCATGT-1\n",
      "cell.ID:  TGCACCTAGCGTTGCC-1\n",
      "cell.ID:  TGCCCATCAAGGTGTG-1\n",
      "cell.ID:  TGCCCTAGTGACGCCT-1\n",
      "cell.ID:  TGCGTGGCAAGCGTAG-1\n",
      "cell.ID:  TGGACGCGTCCGAAGA-1\n",
      "cell.ID:  TGTGGTATCTACGAGT-1\n",
      "cell.ID:  TTAGTTCTCAGCCTAA-1\n",
      "cell.ID:  TTCCCAGTCGAATGCT-1\n",
      "cell.ID:  TTCCCAGTCGTCGTTC-1\n",
      "cell.ID:  TTCTCAAGTTGTTTGG-1\n",
      "cell.ID:  TTCTCCTCACACCGAC-1\n",
      "cell.ID:  TTCTTAGCACTTGGAT-1\n",
      "cell.ID:  TTGGAACTCCATGCTC-1\n",
      "cell.ID:  TTTACTGCATTATCTC-1\n",
      "cell.ID:  TTTGCGCGTTCGTCTC-1\n",
      "['0 1', '1 2', '2 3', '3 4', '3 5', '1 6', '3 7', '5 8', '2 9', '5 10']\n",
      "['AAAGATGTCCAAGTAC-1 0', 'AAAGTAGCATACAGCT-1 0', 'AACACGTGTCCTGCTT-1 0', 'AACGTTGGTCGGCACT-1 0', 'AACTCCCAGCTGAAAT-1 0', 'AACTCTTTCCGAACGC-1 0', 'ACATCAGCACCATGTA-1 0', 'ACATGGTGTTAAGATG-1 0', 'ACCGTAAAGCTCCCAG-1 0', 'ACGCAGCTCCCGACTT-1 0', 'ACTGTCCTCATGGTCA-1 0', 'AGGCCACCAAGGTGTG-1 0', 'AGGTCCGTCAAGAAGT-1 0', 'AGTAGTCAGCCATCGC-1 0', 'AGTAGTCTCCTCGCAT-1 0', 'AGTGAGGCAGGGATTG-1 0', 'ATTGGTGCATCGGAAG-1 0', 'CAACCTCAGAAGATTC-1 0', 'CAACCTCAGTTAACGA-1 0', 'CAAGATCTCTTCCTTC-1 0', 'CAAGGCCGTATATGAG-1 0', 'CACACAACATCTATGG-1 0', 'CACAGTATCTTGTTTG-1 0', 'CAGCGACTCTGTACGA-1 0', 'CAGTCCTCATGAAGTA-1 0', 'CATGCCTAGTTTAGGA-1 0', 'CCACTACAGCCCAGCT-1 0', 'CCCAGTTGTGATGCCC-1 0', 'CGGAGCTAGGCAGGTT-1 0', 'CGTGTAAAGGAACTGC-1 0', 'CGTTCTGGTAGAAAGG-1 0', 'CTAATGGGTGTTTGTG-1 0', 'CTCACACGTCCTGCTT-1 0', 'CTCAGAATCTCACATT-1 0', 'CTCTGGTCATTGTGCA-1 0', 'GAAATGAAGCCCAGCT-1 0', 'GAATAAGAGCACAGGT-1 0', 'GACGGCTCACGGTGTC-1 0', 'GACGGCTCATAACCTG-1 0', 'GAGGTGACAAGCCCAC-1 0', 'GATGAGGAGAAGGGTA-1 0', 'GCAATCACAGACGTAG-1 0', 'GCACATAGTCCTGCTT-1 0', 'GCAGTTAAGCTGCGAA-1 0', 'GCAGTTATCTGGTGTA-1 0', 'GCATGTATCCTTCAAT-1 0', 'GGAAAGCTCGCAAGCC-1 0', 'GGACATTCAAACCTAC-1 0', 'GGCAATTCACGAAGCA-1 0', 'GGCCGATGTAAGGGCT-1 0', 'GGGAGATGTGCAGTAG-1 0', 'GGGCATCGTCGGCATC-1 0', 'GTATTCTGTTGTTTGG-1 0', 'GTCACAAAGATCGATA-1 0', 'GTGGGTCTCTTGAGAC-1 0', 'TAAACCGTCAAGATCC-1 0', 'TACGGTATCAGTTAGC-1 0', 'TACTCATGTGATAAGT-1 0', 'TACTTACTCCACGCAG-1 0', 'TACTTGTAGGAATTAC-1 0', 'TAGGCATTCTTTACGT-1 0', 'TCAGATGCAATACGCT-1 0', 'TCAGCTCGTCATGCCG-1 0', 'TCGGTAAAGGCTCAGA-1 0', 'TGACTTTTCTATGTGG-1 0', 'TGAGAGGGTACGAAAT-1 0', 'TGAGGGACATGCAATC-1 0', 'TGCCCATAGGTACTCT-1 0', 'TGCGTGGCAAGTACCT-1 0', 'TGTCCCATCAGATAAG-1 0', 'TGTGGTACATCTCGCT-1 0', 'TTAGGCATCCTATGTT-1 0', 'TTCCCAGAGTCGATAA-1 0', 'TTCCCAGCAATGTAAG-1 0', 'TTCGAAGAGAAGCCCA-1 0', 'TTCTCAATCATCTGCC-1 0', 'TTGTAGGGTAAACCTC-1 0', 'TTTATGCCAGTGGGAT-1 0', 'AAACGGGGTATCGCAT-1 1', 'AAAGTAGGTCAGGACA-1 1', 'AACTGGTCAAGCCGCT-1 1', 'AACTTTCAGGTGTTAA-1 1', 'AACTTTCCAATTCCTT-1 1', 'AAGACCTGTCGAGATG-1 1', 'AAGGCAGGTCAGGACA-1 1', 'AAGGCAGGTGGTCCGT-1 1', 'ACATCAGTCACCACCT-1 1', 'ACCCACTAGGCGCTCT-1 1', 'ACGAGGAAGCTGAACG-1 1', 'ACGAGGAGTTAGAACA-1 1', 'ACGATACGTGAGCGAT-1 1', 'ACGCAGCGTCTGATTG-1 1', 'ACGGCCAAGCCGATTT-1 1', 'ACGGGCTTCTGAGGGA-1 1', 'ACTGAACAGAGCTATA-1 1', 'AGAGCGAAGCACCGTC-1 1', 'AGAGCGAGTGATGATA-1 1', 'AGAGCGATCTACCTGC-1 1', 'AGAGCTTTCATCGGAT-1 1', 'AGCCTAATCTGTTTGT-1 1', 'AGCGTCGTCTACCAGA-1 1', 'AGCTCCTAGCACGCCT-1 1', 'AGGCCACAGGACACCA-1 1', 'AGGCCGTTCCAGATCA-1 1', 'AGTTGGTTCTTGAGGT-1 1', 'ATAGACCGTTAAAGAC-1 1', 'ATCGAGTAGAGGTAGA-1 1', 'ATGAGGGCACGCCAGT-1 1', 'ATGTGTGCAGTCCTTC-1 1', 'ATTGGTGAGCTGTTCA-1 1', 'CAACCTCGTTAGAACA-1 1', 'CAACTAGAGAGACGAA-1 1', 'CAACTAGCACTTAACG-1 1', 'CAAGAAACACTCGACG-1 1', 'CAAGTTGAGGCGATAC-1 1', 'CACACCTGTAACGCGA-1 1', 'CACACTCGTCAGATAA-1 1', 'CACAGTATCGTGTAGT-1 1', 'CACATAGTCTTCGAGA-1 1', 'CACCTTGCAGATTGCT-1 1', 'CAGCAGCCACGCGAAA-1 1', 'CAGCTAACACCCTATC-1 1', 'CAGCTGGGTCACCCAG-1 1', 'CAGTAACTCGGCTACG-1 1', 'CAGTCCTAGGACCACA-1 1', 'CATCAGATCAGAGCTT-1 1', 'CATCGAAGTTCGCTAA-1 1', 'CATGGCGCAAGTAGTA-1 1', 'CCAGCGAAGTGGTAAT-1 1', 'CCGGTAGCATAAGACA-1 1', 'CCTACCACATGACATC-1 1', 'CCTACCAGTCGAGTTT-1 1', 'CCTAGCTCAAGACACG-1 1', 'CCTAGCTTCCATTCTA-1 1', 'CGAACATGTAGCTGCC-1 1', 'CGACCTTCATTTCAGG-1 1', 'CGACCTTTCACCCGAG-1 1', 'CGATTGATCCCAGGTG-1 1', 'CGCTGGATCGAACTGT-1 1', 'CGCTTCACAGGATCGA-1 1', 'CGGACTGAGGCATGGT-1 1', 'CGGAGTCCATCGGTTA-1 1', 'CGGGTCACACCGGAAA-1 1', 'CGTAGGCGTTTGACAC-1 1', 'CGTCACTAGACGACGT-1 1', 'CGTCAGGTCAGCCTAA-1 1', 'CGTGAGCAGGAGTAGA-1 1', 'CGTTCTGCAGCCTGTG-1 1', 'CTACATTCACAGGCCT-1 1', 'CTACATTGTTCCGTCT-1 1', 'CTCGGAGCAGTGGGAT-1 1', 'CTCGTCATCAGGCAAG-1 1', 'CTCGTCATCTGCAGTA-1 1', 'CTCTAATGTGACCAAG-1 1', 'CTGATCCCACCGCTAG-1 1', 'CTGATCCTCGGCTTGG-1 1', 'CTGCCTAGTACTTGAC-1 1', 'CTTACCGCAACGATGG-1 1', 'CTTACCGGTCATTAGC-1 1', 'GAACGGATCTGCAGTA-1 1', 'GAAGCAGAGAACAATC-1 1', 'GAAGCAGTCACTTATC-1 1', 'GAATAAGGTACCCAAT-1 1', 'GACCAATGTCTGGAGA-1 1', 'GACTAACAGCGTAGTG-1 1', 'GACTACAGTCCGTCAG-1 1', 'GAGCAGACACCCTATC-1 1', 'GAGGTGAAGGATGGTC-1 1', 'GATCAGTTCACTCTTA-1 1', 'GATCGATTCATAAAGG-1 1', 'GCAATCATCAGTTAGC-1 1', 'GCAGTTAGTTACTGAC-1 1', 'GCCAAATGTTCCACAA-1 1', 'GCCTCTACAAATACAG-1 1', 'GCTGCGAGTCGACTAT-1 1', 'GGCGTGTCATTGTGCA-1 1', 'GGGACCTCATAGACTC-1 1', 'GGGAGATGTAAAGTCA-1 1', 'GGTGCGTGTTGTACAC-1 1', 'GTAACGTGTTCCACAA-1 1', 'GTACTCCTCCCGACTT-1 1', 'GTACTTTGTGCACTTA-1 1', 'GTAGGCCAGATCGGGT-1 1', 'GTAGGCCTCACCCTCA-1 1', 'GTCGGGTCAAGTAGTA-1 1', 'GTCGGGTTCTGGTGTA-1 1', 'GTCTTCGGTCTAGAGG-1 1', 'GTCTTCGGTGTCGCTG-1 1', 'GTGCGGTGTATTCGTG-1 1', 'GTTTCTATCACAAACC-1 1', 'TACAGTGGTAAGGGAA-1 1', 'TACTCATAGTACCGGA-1 1', 'TAGACCAAGAGTCTGG-1 1', 'TAGCCGGCAAGTACCT-1 1', 'TAGGCATTCCACGTTC-1 1', 'TATTACCAGGCTAGAC-1 1', 'TCAGCTCTCGTCTGAA-1 1', 'TCGCGAGAGAAACGAG-1 1', 'TCGGGACAGTCCCACG-1 1', 'TCTATTGCAGAGTGTG-1 1', 'TCTATTGCAGCTGTTA-1 1', 'TCTTTCCAGGTCGGAT-1 1', 'TGAGAGGCAAGGGTCA-1 1', 'TGAGAGGGTCAACTGT-1 1', 'TGAGCCGGTACGCACC-1 1', 'TGAGGGAAGCACCGTC-1 1', 'TGCCAAAAGACTAAGT-1 1', 'TGCTGCTCACAGGTTT-1 1', 'TGCTGCTCACGGATAG-1 1', 'TGGCGCATCCACGAAT-1 1', 'TGGCTGGGTGTGGTTT-1 1', 'TGTGGTAAGTTAAGTG-1 1', 'TGTGGTAGTACTCAAC-1 1', 'TTCGGTCGTTTGGGCC-1 1', 'TTCTCAATCCCATTTA-1 1', 'TTGCGTCAGCGAAGGG-1 1', 'TTTATGCGTTAAGATG-1 1', 'TTTGGTTGTGTTTGTG-1 1', 'AAAGATGAGGACCACA-1 2', 'AAAGATGCAATCTGCA-1 2', 'AAAGATGGTGCAGGTA-1 2', 'AAAGATGGTTTGTTTC-1 2', 'AAAGTAGAGCTGTCTA-1 2', 'AAATGCCAGGGATACC-1 2', 'AACACGTAGTGTTGAA-1 2', 'AACCATGGTGGACGAT-1 2', 'AACGTTGAGTTCCACA-1 2', 'AACGTTGCATTCTCAT-1 2', 'AACTCAGGTTACGCGC-1 2', 'AACTCCCCAGTTCATG-1 2', 'AACTCTTTCTGCTGTC-1 2', 'AACTGGTTCGATCCCT-1 2', 'AACTGGTTCGTCCAGG-1 2', 'AACTTTCAGGTACTCT-1 2', 'AACTTTCTCCACTCCA-1 2', 'AAGACCTGTGCCTGTG-1 2', 'AAGCCGCCAGCTTCGG-1 2', 'AAGCCGCGTTCTGTTT-1 2', 'AAGGAGCTCTTCATGT-1 2', 'AAGGTTCTCTGCGTAA-1 2', 'ACACCGGGTGTGCCTG-1 2', 'ACAGCCGGTGAGTATA-1 2', 'ACATACGAGGCTAGAC-1 2', 'ACATACGAGTAGCCGA-1 2', 'ACATACGTCCTGTACC-1 2', 'ACATCAGCAATGAATG-1 2', 'ACATCAGCAGTGGAGT-1 2', 'ACCCACTAGCTAAACA-1 2', 'ACCGTAACAATACGCT-1 2', 'ACCTTTATCTACTATC-1 2', 'ACCTTTATCTCTGTCG-1 2', 'ACGAGCCAGTTCGATC-1 2', 'ACGAGCCGTGCTTCTC-1 2', 'ACGAGCCGTTGTCGCG-1 2', 'ACGATACGTTAAAGAC-1 2', 'ACGCCAGCAGGCGATA-1 2', 'ACGCCGAAGGAGTACC-1 2', 'ACGCCGATCACGCATA-1 2', 'ACGCCGATCGCCTGAG-1 2', 'ACGGCCATCAATAAGG-1 2', 'ACGGGCTCATCCGTGG-1 2', 'ACGGGTCCATGGTCTA-1 2', 'ACGGGTCGTGCGATAG-1 2', 'ACGGGTCGTTCTGTTT-1 2', 'ACTGAGTAGCCCAACC-1 2', 'ACTGAGTAGCCGCCTA-1 2', 'ACTGAGTCAAGGACTG-1 2', 'ACTGATGAGCAGGTCA-1 2', 'ACTGATGCACTAAGTC-1 2', 'ACTGTCCAGGGCATGT-1 2', 'ACTGTCCTCCTCAACC-1 2', 'ACTTTCATCACGGTTA-1 2', 'AGAATAGTCCCATTTA-1 2', 'AGACGTTAGTCTCGGC-1 2', 'AGAGCGAAGCGCTCCA-1 2', 'AGAGCTTAGACTTTCG-1 2', 'AGAGCTTTCAATAAGG-1 2', 'AGAGCTTTCGATGAGG-1 2', 'AGAGTGGAGGGCTTCC-1 2', 'AGAGTGGAGTGCGTGA-1 2', 'AGAGTGGCAGACGTAG-1 2', 'AGAGTGGGTCTCCATC-1 2', 'AGAGTGGGTGGACGAT-1 2', 'AGCAGCCAGCGATGAC-1 2', 'AGCAGCCGTAAGTAGT-1 2', 'AGCGGTCTCGCGGATC-1 2', 'AGCGTATCAATAGCAA-1 2', 'AGCGTCGGTCGGATCC-1 2', 'AGCGTCGGTCTTCAAG-1 2', 'AGGCCACAGTTGAGAT-1 2', 'AGGCCACCACAGTCGC-1 2', 'AGGCCACTCGTGACAT-1 2', 'AGGGATGAGTGGACGT-1 2', 'AGGGTGAAGAGGGCTT-1 2', 'AGGGTGAGTGAGTATA-1 2', 'AGGTCATCAGTGAGTG-1 2', 'AGGTCATGTTTAAGCC-1 2', 'AGTAGTCCACGTCTCT-1 2', 'AGTAGTCTCGTACCGG-1 2', 'AGTCTTTTCACAATGC-1 2', 'AGTGTCAGTAAAGGAG-1 2', 'AGTTGGTGTGAGCGAT-1 2', 'ATAAGAGTCCAATGGT-1 2', 'ATAGACCAGTAGTGCG-1 2', 'ATCACGACATGCCCGA-1 2', 'ATCATCTAGATCACGG-1 2', 'ATCATCTCAGCTTAAC-1 2', 'ATCATGGAGTGTCTCA-1 2', 'ATCATGGGTCCTCTTG-1 2', 'ATCCACCCATTCTCAT-1 2', 'ATCGAGTAGGCGCTCT-1 2', 'ATCGAGTCACAAGACG-1 2', 'ATCGAGTCACAGCGTC-1 2', 'ATCGAGTGTCTACCTC-1 2', 'ATCTACTCAGATAATG-1 2', 'ATCTACTGTAGAGCTG-1 2', 'ATCTGCCAGGTCGGAT-1 2', 'ATCTGCCGTCTCTCGT-1 2', 'ATCTGCCTCCAAACAC-1 2', 'ATGCGATAGATCACGG-1 2', 'ATGGGAGGTCTCTTTA-1 2', 'ATGTGTGCATTATCTC-1 2', 'ATTACTCGTGTGAAAT-1 2', 'ATTCTACTCTGGAGCC-1 2', 'ATTGGACTCTACTATC-1 2', 'ATTGGTGTCGGTGTCG-1 2', 'CAACCAACAATGGAGC-1 2', 'CAACCAATCACCACCT-1 2', 'CAACCAATCACGATGT-1 2', 'CAACTAGAGGCTATCT-1 2', 'CAACTAGTCACATACG-1 2', 'CAAGAAACACCACCAG-1 2', 'CAAGATCGTATGGTTC-1 2', 'CAAGGCCCAAGTTGTC-1 2', 'CAAGGCCGTCTAAAGA-1 2', 'CAAGGCCTCCTTTCGG-1 2', 'CAAGTTGAGCCACTAT-1 2', 'CAAGTTGCAGGTGCCT-1 2', 'CAAGTTGTCCGTCATC-1 2', 'CACAAACAGTGGGCTA-1 2', 'CACAAACCATGGATGG-1 2', 'CACACCTAGTGAAGTT-1 2', 'CACACCTCACCTCGGA-1 2', 'CACACCTCACGGACAA-1 2', 'CACACCTTCAGTCAGT-1 2', 'CACACTCAGAAGGGTA-1 2', 'CACACTCAGTTCCACA-1 2', 'CACAGGCGTAAGGGAA-1 2', 'CACAGGCTCAGTCCCT-1 2', 'CACAGGCTCCACTCCA-1 2', 'CACATTTCATGCAATC-1 2', 'CACATTTGTAATCACC-1 2', 'CACATTTTCCGTCATC-1 2', 'CACCACTGTGCTTCTC-1 2', 'CACCAGGCACACCGCA-1 2', 'CACCAGGCATGTAAGA-1 2', 'CACCTTGAGTACGTTC-1 2', 'CACCTTGGTCGAAAGC-1 2', 'CAGAATCAGCTAGTTC-1 2', 'CAGAATCGTTGTTTGG-1 2', 'CAGATCAGTCTAGAGG-1 2', 'CAGCAGCAGACCGGAT-1 2', 'CAGCAGCCATTGGGCC-1 2', 'CAGCTAAAGTTGCAGG-1 2', 'CAGCTGGTCCACGCAG-1 2', 'CAGGTGCCAACGATGG-1 2', 'CAGGTGCCATGCGCAC-1 2', 'CAGTAACTCGAACGGA-1 2', 'CATATGGGTTGCTCCT-1 2', 'CATATTCCATGCTAGT-1 2', 'CATATTCGTCCGCTGA-1 2', 'CATATTCTCCGCGTTT-1 2', 'CATCAAGCATCTCCCA-1 2', 'CATCAAGGTGGACGAT-1 2', 'CATCAAGTCGCATGGC-1 2', 'CATCAGATCCGAACGC-1 2', 'CATCCACAGCTAACAA-1 2', 'CATCGGGGTTGAACTC-1 2', 'CATGACAAGAGCCTAG-1 2', 'CATGCCTGTCTAGCGC-1 2', 'CATGGCGGTTCGCGAC-1 2', 'CATTATCCACGAAATA-1 2', 'CATTATCGTAGCGCTC-1 2', 'CCACCTAAGAAGATTC-1 2', 'CCACCTAAGGTGACCA-1 2', 'CCACCTACAGTGACAG-1 2', 'CCACCTAGTGGCCCTA-1 2', 'CCACCTATCCGCATAA-1 2', 'CCACGGAGTTCGTCTC-1 2', 'CCACGGATCCAACCAA-1 2', 'CCACTACAGGAATTAC-1 2', 'CCAGCGAGTAGATTAG-1 2', 'CCATGTCGTCAGAGGT-1 2', 'CCCAATCGTGCTAGCC-1 2', 'CCCATACAGTCCGTAT-1 2', 'CCGGTAGCATCACGTA-1 2', 'CCGGTAGCATTACGAC-1 2', 'CCGGTAGTCATTCACT-1 2', 'CCGTACTCAGCAGTTT-1 2', 'CCGTGGAAGTTATCGC-1 2', 'CCGTGGAGTGGTAACG-1 2', 'CCGTGGAGTTCAGGCC-1 2', 'CCGTTCAAGATGTTAG-1 2', 'CCTACCACAGCGTTCG-1 2', 'CCTAGCTCAAACGTGG-1 2', 'CCTAGCTTCCTTCAAT-1 2', 'CCTCAGTTCGGATGGA-1 2', 'CCTCTGACAGGAATGC-1 2', 'CCTTACGCAAAGTGCG-1 2', 'CCTTCCCTCATGCAAC-1 2', 'CCTTCGAGTACCCAAT-1 2', 'CCTTTCTTCTCCAACC-1 2', 'CGAATGTAGAGTCGGT-1 2', 'CGAATGTGTACAGACG-1 2', 'CGACCTTAGTTGAGAT-1 2', 'CGAGAAGAGCGATGAC-1 2', 'CGAGCACCACACCGAC-1 2', 'CGAGCACCATAAGACA-1 2', 'CGAGCACGTCAAGCGA-1 2', 'CGAGCACGTTCAGTAC-1 2', 'CGATCGGAGTGAATTG-1 2', 'CGATGGCGTACGACCC-1 2', 'CGATGGCGTATTACCG-1 2', 'CGCGGTAAGGGATACC-1 2', 'CGCGGTATCACCACCT-1 2', 'CGCTGGATCTGCCAGG-1 2', 'CGGACACGTCTGGAGA-1 2', 'CGGACGTGTGTTGGGA-1 2', 'CGGACTGAGCCAGGAT-1 2', 'CGGCTAGCATATGGTC-1 2', 'CGGCTAGCATGTCGAT-1 2', 'CGGCTAGGTGAGTGAC-1 2', 'CGGCTAGGTGGTACAG-1 2', 'CGTAGCGGTGTGCCTG-1 2', 'CGTCACTTCTGAAAGA-1 2', 'CGTCCATTCAAACGGG-1 2', 'CGTCTACAGATGGGTC-1 2', 'CGTGTAATCGATAGAA-1 2', 'CGTGTCTGTCATACTG-1 2', 'CGTGTCTTCGCGTAGC-1 2', 'CGTTAGACAGTGACAG-1 2', 'CGTTAGATCAGCGACC-1 2', 'CGTTGGGGTCAACTGT-1 2', 'CTAAGACAGATAGTCA-1 2', 'CTAAGACTCAACGAAA-1 2', 'CTAATGGCAGTCAGAG-1 2', 'CTAATGGTCGTCTGCT-1 2', 'CTACACCGTCGCATAT-1 2', 'CTACACCGTCGTGGCT-1 2', 'CTACACCTCTTGAGAC-1 2', 'CTACCCATCGCATGGC-1 2', 'CTAGAGTCAAACTGTC-1 2', 'CTAGAGTGTGTTAAGA-1 2', 'CTAGCCTAGCCACCTG-1 2', 'CTAGCCTGTACAGACG-1 2', 'CTCAGAAAGCCAGTAG-1 2', 'CTCATTAAGGATCGCA-1 2', 'CTCATTATCTTTAGGG-1 2', 'CTCCTAGAGTACTTGC-1 2', 'CTCCTAGAGTGCCAGA-1 2', 'CTCGAAAGTACCGTTA-1 2', 'CTCGAGGGTAGCTGCC-1 2', 'CTCGAGGTCAGTGTTG-1 2', 'CTCGGAGAGTGACTCT-1 2', 'CTCGGAGCACCGGAAA-1 2', 'CTCGTCAAGGCACATG-1 2', 'CTCGTCAGTATATCCG-1 2', 'CTCGTCATCCACGCAG-1 2', 'CTCGTCATCGACGGAA-1 2', 'CTCTAATAGCTAAGAT-1 2', 'CTCTACGAGTGAACAT-1 2', 'CTCTGGTAGAGTTGGC-1 2', 'CTGAAACTCTCTTATG-1 2', 'CTGAAACTCTTTAGGG-1 2', 'CTGAAGTGTGGCAAAC-1 2', 'CTGATCCGTAGCCTAT-1 2', 'CTGGTCTGTCTAGGTT-1 2', 'CTGTGCTCATGTTCCC-1 2', 'CTGTGCTGTTGTACAC-1 2', 'CTGTGCTTCAAGAAGT-1 2', 'CTGTGCTTCATGCATG-1 2', 'CTGTTTACACGAAGCA-1 2', 'CTGTTTATCCTGCTTG-1 2', 'CTTACCGAGGGTCTCC-1 2', 'CTTACCGCAAGAGGCT-1 2', 'CTTACCGTCGGACAAG-1 2', 'CTTAGGATCTCCGGTT-1 2', 'CTTGGCTCACATTAGC-1 2', 'CTTGGCTGTCGAGATG-1 2', 'CTTTGCGAGGGAGTAA-1 2', 'GAAACTCGTAGCCTAT-1 2', 'GAAATGACAGGTTTCA-1 2', 'GAAATGAGTTCATGGT-1 2', 'GAACGGAAGAAGAAGC-1 2', 'GAAGCAGCATTATCTC-1 2', 'GACGGCTCAGGCTGAA-1 2', 'GACGGCTTCTTGGGTA-1 2', 'GACTACACATCTCCCA-1 2', 'GAGCAGATCAACACTG-1 2', 'GAGCAGATCGGAGCAA-1 2', 'GAGGTGATCGTGGACC-1 2', 'GAGGTGATCGTGGTCG-1 2', 'GAGTCCGCATAGGATA-1 2', 'GATCAGTTCCATGCTC-1 2', 'GATGAGGTCCGTCAAA-1 2', 'GCAAACTAGTATCTCG-1 2', 'GCAAACTTCAACGCTA-1 2', 'GCAAACTTCACTGGGC-1 2', 'GCAATCACAATCTGCA-1 2', 'GCAATCATCCAGATCA-1 2', 'GCAATCATCGTGGACC-1 2', 'GCACTCTAGGTACTCT-1 2', 'GCACTCTTCGGCGCAT-1 2', 'GCACTCTTCTGTCTCG-1 2', 'GCAGTTACAATAACGA-1 2', 'GCAGTTATCTCCGGTT-1 2', 'GCATACAAGTGGACGT-1 2', 'GCCTCTAAGCACAGGT-1 2', 'GCCTCTAGTTCGCGAC-1 2', 'GCGAGAAAGACTAGGC-1 2', 'GCGAGAATCTCGATGA-1 2', 'GCGCCAAGTCTTCGTC-1 2', 'GCGCGATCAACTTGAC-1 2', 'GCGGGTTCACCGAATT-1 2', 'GCGGGTTTCAGTACGT-1 2', 'GCTGCAGCAGACACTT-1 2', 'GCTGCAGGTCGCCATG-1 2', 'GCTGCTTAGTGCCATT-1 2', 'GCTGCTTTCACATACG-1 2', 'GCTTGAAAGTGTTTGC-1 2', 'GCTTGAACAATAACGA-1 2', 'GGAAAGCCAAGCCCAC-1 2', 'GGAAAGCCAAGCGCTC-1 2', 'GGAACTTAGGATCGCA-1 2', 'GGAACTTTCCAAACAC-1 2', 'GGAATAAAGCGTTCCG-1 2', 'GGACAAGGTTCTCATT-1 2', 'GGACAGAAGTGCAAGC-1 2', 'GGACAGATCCGTAGTA-1 2', 'GGACATTAGAAGGACA-1 2', 'GGACGTCGTGTAATGA-1 2', 'GGAGCAACATCTACGA-1 2', 'GGAGCAATCGTGGTCG-1 2', 'GGCAATTTCCCTAATT-1 2', 'GGCGTGTGTAGCACGA-1 2', 'GGCTCGAAGAACAATC-1 2', 'GGCTCGATCTACGAGT-1 2', 'GGCTGGTCAAGGTGTG-1 2', 'GGGCACTAGGGAACGG-1 2', 'GGGCACTAGGGAGTAA-1 2', 'GGGCACTTCGGCGCTA-1 2', 'GGGTCTGCACGGCTAC-1 2', 'GGGTCTGGTTCAACCA-1 2', 'GGGTCTGTCTCACATT-1 2', 'GGGTTGCCAAGTAATG-1 2', 'GGTATTGAGTCACGCC-1 2', 'GGTATTGGTTATTCTC-1 2', 'GGTATTGTCCTGTAGA-1 2', 'GGTGAAGAGCTATGCT-1 2', 'GGTGTTAAGCGTTTAC-1 2', 'GTACTCCTCTACTCAT-1 2', 'GTACTTTAGTTAAGTG-1 2', 'GTAGTCATCAGAAATG-1 2', 'GTATTCTGTCGCATAT-1 2', 'GTATTCTTCGGAATCT-1 2', 'GTCAAGTGTGTATGGG-1 2', 'GTCACGGCAGACACTT-1 2', 'GTCACGGGTCGCGGTT-1 2', 'GTCATTTCACAGGTTT-1 2', 'GTCATTTGTACAGTTC-1 2', 'GTCATTTTCAACCAAC-1 2', 'GTCCTCAGTAGCAAAT-1 2', 'GTCGGGTGTCAAAGCG-1 2', 'GTCGTAAGTACTCGCG-1 2', 'GTCTCGTAGCCACGCT-1 2', 'GTCTTCGTCAGCACAT-1 2', 'GTCTTCGTCGATAGAA-1 2', 'GTGCAGCAGGAATGGA-1 2', 'GTGCATATCCTGTACC-1 2', 'GTGCGGTAGACGCTTT-1 2', 'GTGCGGTTCACCCTCA-1 2', 'GTGCTTCAGTGTACCT-1 2', 'GTGCTTCTCATATCGG-1 2', 'GTGGGTCTCGTTGCCT-1 2', 'GTGTGCGTCGAATGGG-1 2', 'GTTAAGCGTCAACTGT-1 2', 'GTTACAGGTCAGAGGT-1 2', 'GTTCATTCAATTCCTT-1 2', 'GTTCTCGCAAGTAGTA-1 2', 'GTTCTCGTCATTTGGG-1 2', 'GTTTCTACAAATACAG-1 2', 'GTTTCTAGTCAATACC-1 2', 'TAAACCGCAGGATCGA-1 2', 'TAAACCGTCCCGGATG-1 2', 'TAAACCGTCGTGGGAA-1 2', 'TAAGAGACATTCCTCG-1 2', 'TAAGCGTCAAACCCAT-1 2', 'TAAGCGTGTTCAGGCC-1 2', 'TACACGATCCTTAATC-1 2', 'TACAGTGGTGGGTCAA-1 2', 'TACAGTGTCAGTGTTG-1 2', 'TACAGTGTCTATCCCG-1 2', 'TACCTATCATTTCACT-1 2', 'TACCTATGTATTAGCC-1 2', 'TACCTATTCACGATGT-1 2', 'TACCTTACAAGTAGTA-1 2', 'TACCTTATCGAACTGT-1 2', 'TACGGATCATTAACCG-1 2', 'TACGGATGTGTCCTCT-1 2', 'TACGGTACACTTAAGC-1 2', 'TACGGTATCCACGACG-1 2', 'TAGTGGTAGAATTCCC-1 2', 'TAGTGGTAGACCTTTG-1 2', 'TAGTGGTTCAAACCGT-1 2', 'TAGTGGTTCCAACCAA-1 2', 'TAGTGGTTCCACTCCA-1 2', 'TAGTTGGTCTCCTATA-1 2', 'TATCTCAGTGTTAAGA-1 2', 'TATTACCCATGCAACT-1 2', 'TCAACGACAGATGGGT-1 2', 'TCAACGATCTTGAGAC-1 2', 'TCACAAGGTTGAACTC-1 2', 'TCACAAGTCCCAAGAT-1 2', 'TCAGATGCAGTAACGG-1 2', 'TCAGATGGTATAGGGC-1 2', 'TCAGATGGTTATCGGT-1 2', 'TCAGATGTCCATGAGT-1 2', 'TCAGCAAGTGGTTTCA-1 2', 'TCAGGATGTACGCACC-1 2', 'TCAGGTAAGCCAACAG-1 2', 'TCAGGTACATGTCCTC-1 2', 'TCAGGTAGTCCGTTAA-1 2', 'TCCACACTCTCCGGTT-1 2', 'TCCCGATTCGATGAGG-1 2', 'TCGAGGCCATCCGCGA-1 2', 'TCGCGAGAGAGTAATC-1 2', 'TCGCGAGTCTGTCTCG-1 2', 'TCGCGTTGTATTCTCT-1 2', 'TCGCGTTTCTCGCATC-1 2', 'TCGGGACCACGAGGTA-1 2', 'TCGTACCAGCCAGAAC-1 2', 'TCGTACCCAGTGAGTG-1 2', 'TCGTACCTCATTGCCC-1 2', 'TCTATTGAGATGTCGG-1 2', 'TCTCATAGTAATAGCA-1 2', 'TCTCATAGTACCCAAT-1 2', 'TCTCTAAAGTCGTACT-1 2', 'TCTCTAAAGTTAAGTG-1 2', 'TCTGAGAGTTCGCTAA-1 2', 'TCTGAGATCGCCATAA-1 2', 'TCTGGAACAAGACGTG-1 2', 'TCTTTCCGTCTGCCAG-1 2', 'TGACAACAGTACGACG-1 2', 'TGACGGCGTTTGTGTG-1 2', 'TGACTTTAGGTGATTA-1 2', 'TGAGAGGAGGCATGTG-1 2', 'TGAGAGGCAATAGAGT-1 2', 'TGAGCATAGCCGGTAA-1 2', 'TGAGCCGCAACAACCT-1 2', 'TGAGCCGGTAAATACG-1 2', 'TGATTTCGTCTCCACT-1 2', 'TGCACCTTCCCATTAT-1 2', 'TGCACCTTCTGCTTGC-1 2', 'TGCCAAAGTGCCTTGG-1 2', 'TGCCCATAGAGGTTAT-1 2', 'TGCCCTAGTCTCGTTC-1 2', 'TGCGCAGGTCTAGGTT-1 2', 'TGCGTGGTCTTGTACT-1 2', 'TGCTGCTGTGTTCGAT-1 2', 'TGGACGCCACAGACAG-1 2', 'TGGACGCTCCCATTAT-1 2', 'TGGACGCTCTTGAGAC-1 2', 'TGGCCAGGTACGACCC-1 2', 'TGGCGCAAGGAGCGAG-1 2', 'TGGCGCACATTCGACA-1 2', 'TGGCGCAGTCCGCTGA-1 2', 'TGGCGCAGTGCCTGCA-1 2', 'TGGTTAGAGATCTGAA-1 2', 'TGTCCCAAGACTGTAA-1 2', 'TGTCCCAGTTCGCGAC-1 2', 'TGTGGTAAGCACACAG-1 2', 'TGTGGTAAGGGTATCG-1 2', 'TGTGGTAGTGGGTCAA-1 2', 'TGTGGTATCAACGAAA-1 2', 'TGTGTTTCATAGAAAC-1 2', 'TGTGTTTTCTGGGCCA-1 2', 'TGTTCCGCACCAGTTA-1 2', 'TTAGGCACACCTGGTG-1 2', 'TTAGGCAGTAGCTCCG-1 2', 'TTAGTTCAGATGGCGT-1 2', 'TTAGTTCAGCTAGTCT-1 2', 'TTAGTTCAGGCAAAGA-1 2', 'TTAGTTCCATTCTTAC-1 2', 'TTATGCTAGCATGGCA-1 2', 'TTATGCTCACGTGAGA-1 2', 'TTCTCAACACAAGCCC-1 2', 'TTCTCAATCGTATCAG-1 2', 'TTCTCAATCGTGGACC-1 2', 'TTCTTAGGTAAACGCG-1 2', 'TTGAACGGTTGCGCAC-1 2', 'TTGAACGTCCTCAACC-1 2', 'TTGAACGTCCTGCAGG-1 2', 'TTGACTTTCACAGTAC-1 2', 'TTGGAACCATGTCTCC-1 2', 'TTGGCAAAGCGATGAC-1 2', 'TTGGCAAGTAGTAGTA-1 2', 'TTGGCAAGTGTGGCTC-1 2', 'TTTCCTCTCACATACG-1 2', 'TTTGCGCCACTCTGTC-1 2', 'TTTGCGCCAGGGAGAG-1 2', 'TTTGGTTCAATGGAGC-1 2', 'TTTGTCACAAGTCATC-1 2', 'TTTGTCACACTTGGAT-1 2', 'AACCATGCATATGCTG-1 3', 'AACCGCGTCCTTGCCA-1 3', 'AAGCCGCGTTTAAGCC-1 3', 'AAGTCTGGTTAGATGA-1 3', 'ACAGCCGCACCGCTAG-1 3', 'ACAGCTATCAACACGT-1 3', 'ACGAGCCAGCACCGCT-1 3', 'ACGCCGAGTACCGTTA-1 3', 'ACGGCCAAGGCGTACA-1 3', 'ACGGGTCAGATATGGT-1 3', 'ACGTCAAAGTTCGCGC-1 3', 'ACTGAGTGTATATGGA-1 3', 'ACTTACTCACCAGGTC-1 3', 'AGAATAGAGGCTACGA-1 3', 'AGAGCGAGTCTACCTC-1 3', 'AGCATACCAAGCCGCT-1 3', 'AGGGATGTCTGTCCGT-1 3', 'AGTGGGATCGGATGGA-1 3', 'AGTTGGTCAGGTCGTC-1 3', 'AGTTGGTCAGTCGATT-1 3', 'ATCATGGTCCGAACGC-1 3', 'ATCCACCCAGTTAACC-1 3', 'ATCCGAACATAGTAAG-1 3', 'ATCGAGTAGCTGATAA-1 3', 'ATCTGCCCATCAGTCA-1 3', 'ATGAGGGTCAGGTAAA-1 3', 'CACACAAAGCTGCAAG-1 3', 'CACACCTGTGTGCCTG-1 3', 'CACTCCATCAGTGTTG-1 3', 'CAGAATCAGCCGGTAA-1 3', 'CAGCAGCAGTATCTCG-1 3', 'CAGCCGATCTCTGTCG-1 3', 'CAGCTGGAGCTCCTTC-1 3', 'CATGACAGTCTTGTCC-1 3', 'CATTATCTCCGCGTTT-1 3', 'CCATTCGAGACGCTTT-1 3', 'CCGTACTGTGTAATGA-1 3', 'CCTACACTCCGTAGTA-1 3', 'CCTATTAGTCTTCTCG-1 3', 'CCTATTATCGCTGATA-1 3', 'CCTTACGGTATATGAG-1 3', 'CGACTTCTCCGAATGT-1 3', 'CGCCAAGTCCAAAGTC-1 3', 'CGCTGGAAGCCTCGTG-1 3', 'CGGACTGGTAAATACG-1 3', 'CGTTGGGGTCGAATCT-1 3', 'CTAACTTAGTGGTAGC-1 3', 'CTAACTTGTCGTCTTC-1 3', 'CTAATGGTCGGTTAAC-1 3', 'CTAGTGAAGTAGCGGT-1 3', 'CTCGAAAGTGGTACAG-1 3', 'CTCGGGAAGTCACGCC-1 3', 'CTCGGGAGTAAGGGCT-1 3', 'CTGAAGTTCGAATGCT-1 3', 'CTTACCGTCAACACCA-1 3', 'CTTCTCTGTGACGCCT-1 3', 'GACGGCTAGACACGAC-1 3', 'GACGTTAGTATTAGCC-1 3', 'GAGGTGAGTGGTGTAG-1 3', 'GATCAGTCAAGCCCAC-1 3', 'GATCGATTCGCCTGAG-1 3', 'GATCGTACAACAACCT-1 3', 'GATGAGGCACGAAAGC-1 3', 'GATTCAGAGAGTAATC-1 3', 'GCAAACTCAGGGTTAG-1 3', 'GCACATACAGACACTT-1 3', 'GCAGCCACAAACTGTC-1 3', 'GCGAGAAAGTGGGTTG-1 3', 'GCGCGATGTTCCACAA-1 3', 'GCTGCTTAGTATCGAA-1 3', 'GCTGCTTGTGCGCTTG-1 3', 'GGACAGATCCCAGGTG-1 3', 'GGGAGATGTGATGCCC-1 3', 'GGGCATCTCCATGCTC-1 3', 'GGTGTTACACACGCTG-1 3', 'GTACGTAAGTCAAGGC-1 3', 'GTACTTTTCCGAAGAG-1 3', 'GTCAAGTGTTGCCTCT-1 3', 'GTCGTAACAATGGAAT-1 3', 'GTCGTAATCCTTGACC-1 3', 'GTTACAGAGAAACGAG-1 3', 'TACTCATCAGTCGATT-1 3', 'TAGGCATTCGTGGGAA-1 3', 'TAGTGGTTCATGTCCC-1 3', 'TCAATCTAGGATGGTC-1 3', 'TCACGAAAGTCGCCGT-1 3', 'TCATTACTCTGTACGA-1 3', 'TGACTAGAGCGTCTAT-1 3', 'TGAGAGGTCTGTTGAG-1 3', 'TGAGCCGAGCTGCAAG-1 3', 'TGCCAAATCTACCTGC-1 3', 'TGCGGGTGTTGATTGC-1 3', 'TGGACGCCACATGACT-1 3', 'TGGACGCTCTGAGGGA-1 3', 'TGTATTCAGTCATGCT-1 3', 'TGTATTCGTAGATTAG-1 3', 'TTAGGACGTCGAACAG-1 3', 'TTATGCTGTAGTGAAT-1 3', 'TTCCCAGTCGCCAAAT-1 3', 'TTGACTTGTTCGGCAC-1 3', 'TTGCCGTCAGTCGATT-1 3', 'TTTATGCTCATGCTCC-1 3', 'TTTATGCTCTCAAACG-1 3', 'TTTCCTCTCTCGTTTA-1 3', 'AACACGTTCGTCCGTT-1 4', 'AACTCAGGTGCTGTAT-1 4', 'AACTCCCCAACGCACC-1 4', 'AACTCTTGTATGAATG-1 4', 'AACTTTCGTTAAAGAC-1 4', 'AAGACCTAGAAGGCCT-1 4', 'AAGACCTAGTGGACGT-1 4', 'AAGGCAGGTCAATACC-1 4', 'AATCCAGGTCTGATTG-1 4', 'AATCGGTCATGAACCT-1 4', 'ACACCAATCTGCTGCT-1 4', 'ACACCGGCAAACCTAC-1 4', 'ACATCAGTCATCGATG-1 4', 'ACCCACTCAGTGACAG-1 4', 'ACCCACTTCTAACGGT-1 4', 'ACGAGCCTCCTTTCTC-1 4', 'ACGATACGTCATCCCT-1 4', 'ACGCCAGGTCTTTCAT-1 4', 'ACGGAGAGTCTCAACA-1 4', 'ACGGCCAAGTAGGCCA-1 4', 'ACGGGTCAGCTAGTCT-1 4', 'ACTGATGAGTGACATA-1 4', 'ACTGCTCAGCGTTTAC-1 4', 'ACTTGTTAGGGTTTCT-1 4', 'ACTTGTTCAGGGCATA-1 4', 'ACTTGTTCATCCCACT-1 4', 'AGAGCTTTCCGATATG-1 4', 'AGCGTCGGTACATGTC-1 4', 'AGCTTGAGTACCGTAT-1 4', 'AGGCCACGTACCGTTA-1 4', 'AGGGATGAGCTGATAA-1 4', 'ATAGACCGTGGGTATG-1 4', 'ATCATCTAGTCACGCC-1 4', 'ATCATCTGTAAGTTCC-1 4', 'ATCCGAACATCCTAGA-1 4', 'ATCGAGTAGTGGTAGC-1 4', 'ATGAGGGAGGCAGTCA-1 4', 'ATGGGAGGTGAGGGAG-1 4', 'ATGTGTGTCTTGACGA-1 4', 'ATTCTACTCCTGCTTG-1 4', 'CAACCAATCAGTCAGT-1 4', 'CAACCTCAGAAACCGC-1 4', 'CAAGATCGTTTGGGCC-1 4', 'CAAGATCTCGTCACGG-1 4', 'CACACCTAGAAACCAT-1 4', 'CACATTTAGGATGGTC-1 4', 'CACCAGGAGCAAATCA-1 4', 'CAGCTAATCAGGCAAG-1 4', 'CAGTAACGTCTCTTTA-1 4', 'CAGTCCTCAGCCACCA-1 4', 'CATCAAGCAATAGAGT-1 4', 'CATCAGACACAGCCCA-1 4', 'CATCGAACATTCTCAT-1 4', 'CATCGAAGTTACAGAA-1 4', 'CATGACATCTACGAGT-1 4', 'CATTATCAGACACTAA-1 4', 'CCAATCCTCATGTCTT-1 4', 'CCAGCGACATCTATGG-1 4', 'CCCAATCCATTAACCG-1 4', 'CCCAGTTCACAAGTAA-1 4', 'CCGGTAGAGAGCTATA-1 4', 'CCTAGCTTCACCACCT-1 4', 'CCTTTCTAGTACACCT-1 4', 'CGAGCACGTTTGGGCC-1 4', 'CGAGCCACAGTCGATT-1 4', 'CGATGGCCACAACTGT-1 4', 'CGATGTACAGAGCCAA-1 4', 'CGCGGTAGTCAGCTAT-1 4', 'CGCTATCGTGCAACTT-1 4', 'CGCTTCAGTCTGCAAT-1 4', 'CGCTTCAGTTACGCGC-1 4', 'CGGACGTTCCCATTTA-1 4', 'CGGACTGTCTGATACG-1 4', 'CGGGTCAAGGCGTACA-1 4', 'CGGGTCAGTCTAGCGC-1 4', 'CGTCACTAGTGCTGCC-1 4', 'CGTCCATGTCTCGTTC-1 4', 'CGTCTACAGCAATCTC-1 4', 'CGTGAGCGTTTCCACC-1 4', 'CGTGTCTTCCTCAACC-1 4', 'CGTTAGAGTGTCAATC-1 4', 'CGTTAGATCTAGCACA-1 4', 'CTAATGGCAAGCTGAG-1 4', 'CTAATGGGTTACGACT-1 4', 'CTACACCGTTACGTCA-1 4', 'CTAGAGTAGCCAGTAG-1 4', 'CTAGAGTCACTGAAGG-1 4', 'CTAGTGAGTCATTAGC-1 4', 'CTCACACCACATCCGG-1 4', 'CTCATTATCAGGTAAA-1 4', 'CTCGAAAGTACAGTGG-1 4', 'CTCGAAATCAAACCAC-1 4', 'CTCGAGGGTCTCCATC-1 4', 'CTCTAATAGGAGTTTA-1 4', 'CTGCCTAAGGGTGTGT-1 4', 'CTGCTGTCAGCTCCGA-1 4', 'CTGGTCTGTACCGAGA-1 4', 'CTGTTTACACTGTCGG-1 4', 'CTTCTCTAGGTGGGTT-1 4', 'GAAATGATCAACGCTA-1 4', 'GAACATCTCGTTACAG-1 4', 'GAACCTAAGAGACTTA-1 4', 'GAAGCAGGTTATCACG-1 4', 'GAATAAGGTTCCGTCT-1 4', 'GACAGAGGTCTAGGTT-1 4', 'GACCAATAGAGCTTCT-1 4', 'GACGTGCAGATGCCAG-1 4', 'GACGTGCCATGGTCTA-1 4', 'GACGTTAAGGCTATCT-1 4', 'GAGGTGATCCGTAGGC-1 4', 'GATCAGTTCAACACAC-1 4', 'GATGAAAGTTCGCTAA-1 4', 'GCATGATCAGCGTAAG-1 4', 'GCGACCAAGTAATCCC-1 4', 'GGACAAGAGATGCCTT-1 4', 'GGACAAGTCCTACAGA-1 4', 'GGACAGAGTAGCCTCG-1 4', 'GGACATTAGAACTCGG-1 4', 'GGCCGATGTCGGGTCT-1 4', 'GGCTGGTAGTACGCGA-1 4', 'GGCTGGTGTAAACACA-1 4', 'GGGAGATTCTTTAGTC-1 4', 'GGGCACTGTTAAAGAC-1 4', 'GGGCACTTCAACACAC-1 4', 'GGGCATCGTGGTTTCA-1 4', 'GGGTCTGTCCCTAATT-1 4', 'GGTATTGTCTGGCGAC-1 4', 'GTAACGTCATCGGACC-1 4', 'GTACGTACAGACAGGT-1 4', 'GTACTCCTCGTTTAGG-1 4', 'GTAGTCAGTATTAGCC-1 4', 'GTATTCTTCTCACATT-1 4', 'GTGAAGGGTGGAAAGA-1 4', 'GTGCATAGTGACAAAT-1 4', 'GTGGGTCCAGCCTTTC-1 4', 'GTGTTAGAGCCACGCT-1 4', 'TAAACCGAGAAGATTC-1 4', 'TACACGAAGGTGTTAA-1 4', 'TACAGTGGTGTCCTCT-1 4', 'TACCTATAGGATGCGT-1 4', 'TACCTTATCGGTGTCG-1 4', 'TACGGTACAATAGAGT-1 4', 'TACTTGTCAAACGTGG-1 4', 'TAGGCATAGGGATGGG-1 4', 'TAGTTGGTCCAGATCA-1 4', 'TATTACCAGACTTTCG-1 4', 'TCAACGAAGACATAAC-1 4', 'TCAACGACACCATGTA-1 4', 'TCACGAAGTTACGCGC-1 4', 'TCAGATGTCGGATGGA-1 4', 'TCAGGATCATTTGCTT-1 4', 'TCCCGATCACTGAAGG-1 4', 'TCCCGATTCAAGAAGT-1 4', 'TCGAGGCGTACCGCTG-1 4', 'TCGCGTTGTTTGGCGC-1 4', 'TCGGGACTCAGTGTTG-1 4', 'TCTCATAGTAGGACAC-1 4', 'TCTCTAATCTGTCCGT-1 4', 'TCTTTCCGTGCACGAA-1 4', 'TGACTAGCAGCATGAG-1 4', 'TGCACCTAGTGTGAAT-1 4', 'TGCACCTGTTTAAGCC-1 4', 'TGCGGGTCACCCATTC-1 4', 'TGCGTGGGTGGTTTCA-1 4', 'TGGCTGGCATGAGCGA-1 4', 'TGGTTCCTCCTGTAGA-1 4', 'TGGTTCCTCTTGTACT-1 4', 'TGTCCCAAGTTCCACA-1 4', 'TTAGGACAGTGCTGCC-1 4', 'TTCGGTCCACGAAATA-1 4', 'TTCTCAATCTGTCCGT-1 4', 'TTCTCCTCAGACGTAG-1 4', 'TTCTTAGAGGGTTCCC-1 4', 'TTGGAACCATGCGCAC-1 4', 'TTTGGTTTCTACCAGA-1 4', 'TTTGTCACAGACGCAA-1 4', 'AAAGCAACACTCAGGC-1 5', 'AAAGCAATCATTGCCC-1 5', 'AAAGTAGCAAACAACA-1 5', 'AACCGCGAGTCGATAA-1 5', 'AACGTTGGTTTGGCGC-1 5', 'AAGTCTGGTTGAACTC-1 5', 'AATCCAGGTGATGTCT-1 5', 'ACAGCCGTCACGGTTA-1 5', 'ACCCACTGTTACTGAC-1 5', 'ACCTTTATCTGAGGGA-1 5', 'ACGATGTCATGATCCA-1 5', 'ACGCCGAGTGGCCCTA-1 5', 'ACTGAACCAGACAAAT-1 5', 'ACTGAACGTCTGCAAT-1 5', 'ACTGAGTTCGCACTCT-1 5', 'ACTGCTCCAAGGTTTC-1 5', 'AGAATAGCACACTGCG-1 5', 'AGAATAGGTCATCGGC-1 5', 'AGTCTTTCAAGAAGAG-1 5', 'AGTGAGGTCCGGCACA-1 5', 'AGTTGGTCACGAAGCA-1 5', 'ATAGACCAGTCGATAA-1 5', 'ATCACGACATCACGAT-1 5', 'ATCACGAGTCATGCAT-1 5', 'ATCTACTAGCTCCTCT-1 5', 'CAACCAATCCACGTTC-1 5', 'CAACCTCCATTGGTAC-1 5', 'CAACTAGCAAATTGCC-1 5', 'CAAGAAAAGTCCTCCT-1 5', 'CAAGATCGTACGACCC-1 5', 'CAAGATCTCGCGATCG-1 5', 'CACAAACGTGAACCTT-1 5', 'CACACCTAGCTAACAA-1 5', 'CACACCTCAGCGTTCG-1 5', 'CACCAGGAGTGTGGCA-1 5', 'CACCAGGTCGGCGCAT-1 5', 'CACTCCAGTTCAGTAC-1 5', 'CAGTAACAGGTGACCA-1 5', 'CATCGAATCACCGTAA-1 5', 'CATGACACACAGATTC-1 5', 'CATGGCGCAGACAGGT-1 5', 'CATTATCAGTGAATTG-1 5', 'CCAATCCAGGACATTA-1 5', 'CCCTCCTCAATAGAGT-1 5', 'CCGGTAGGTTCAGGCC-1 5', 'CCTACACAGGGCTTGA-1 5', 'CCTTCCCAGACTGTAA-1 5', 'CCTTCCCGTCTTGATG-1 5', 'CCTTCCCTCAACCAAC-1 5', 'CCTTCGAAGCGAGAAA-1 5', 'CGAGCCAAGGGATCTG-1 5', 'CGATCGGAGTATCTCG-1 5', 'CGATGGCAGCGCCTTG-1 5', 'CGCCAAGCAATCAGAA-1 5', 'CGGACACAGTACGACG-1 5', 'CGGCTAGAGAAGCCCA-1 5', 'CGGGTCATCGTCCAGG-1 5', 'CGGTTAAGTCCGCTGA-1 5', 'CGTGAGCCAATCTACG-1 5', 'CTAGAGTCACAACGTT-1 5', 'CTAGAGTTCCGCTGTT-1 5', 'CTAGCCTCATTACGAC-1 5', 'CTCACACAGTCCAGGA-1 5', 'CTCCTAGAGGTGCACA-1 5', 'CTCGTACAGGGTGTTG-1 5', 'CTGCGGAAGTAGATGT-1 5', 'CTGGTCTAGGTGACCA-1 5', 'CTGTTTACACAAGCCC-1 5', 'CTTAGGACACCCATTC-1 5', 'CTTAGGACATCGTCGG-1 5', 'GAACCTATCACATACG-1 5', 'GAATAAGGTCGACTGC-1 5', 'GACAGAGAGCAGCGTA-1 5', 'GACGGCTAGCGGCTTC-1 5', 'GACGGCTCACTTCTGC-1 5', 'GACTGCGTCCACGCAG-1 5', 'GAGGTGAGTCAAGCGA-1 5', 'GAGTCCGTCATACGGT-1 5', 'GATCGATGTCGCGGTT-1 5', 'GATCGATTCAGCCTAA-1 5', 'GATTCAGCACCTCGGA-1 5', 'GCAAACTAGCCAGGAT-1 5', 'GCAATCAGTACAGTGG-1 5', 'GCACATAGTTCCACAA-1 5', 'GCACTCTCAGACGTAG-1 5', 'GCAGTTACAGTTTACG-1 5', 'GCATGCGCACCCAGTG-1 5', 'GCATGTACACAGCCCA-1 5', 'GCATGTACAGTTAACC-1 5', 'GCCAAATAGGCAGTCA-1 5', 'GCGAGAAGTCAGTGGA-1 5', 'GCGCAACGTTCCCGAG-1 5', 'GGACGTCAGGCGATAC-1 5', 'GGATTACAGCACCGTC-1 5', 'GGCTCGAAGTAGCGGT-1 5', 'GGGAGATCAGCCACCA-1 5', 'GGGAGATTCATGTCTT-1 5', 'GGGCACTAGCGAAGGG-1 5', 'GGGTTGCAGGAATTAC-1 5', 'GTAACGTGTAAAGTCA-1 5', 'GTAACGTGTTAAGGGC-1 5', 'GTACGTACAGTCACTA-1 5', 'GTACGTAGTACTTCTT-1 5', 'GTAGGCCGTACCATCA-1 5', 'GTCTCGTAGGAGCGAG-1 5', 'GTCTTCGCAGCTGCAC-1 5', 'GTCTTCGTCTCCTATA-1 5', 'GTGCATAAGGAGCGAG-1 5', 'GTGCTTCGTCTGCGGT-1 5', 'GTTAAGCGTAGCTGCC-1 5', 'GTTCATTAGGCTCAGA-1 5', 'GTTCGGGAGGTTACCT-1 5', 'TACCTATCATGCATGT-1 5', 'TACGGTAGTAAATGAC-1 5', 'TACTTACAGAATTGTG-1 5', 'TAGAGCTGTACTCTCC-1 5', 'TATGCCCGTGCCTGCA-1 5', 'TCACAAGCATTAACCG-1 5', 'TCATTTGGTGTAACGG-1 5', 'TCCACACCATGGTCTA-1 5', 'TCGCGTTGTAGAAGGA-1 5', 'TCGTAGACACAGATTC-1 5', 'TCGTAGACAGCGTCCA-1 5', 'TCTCATAAGCCGCCTA-1 5', 'TCTCATACAGACTCGC-1 5', 'TCTTCGGTCCTTCAAT-1 5', 'TCTTTCCTCAAGATCC-1 5', 'TGAAAGAGTCAGGACA-1 5', 'TGACAACCATGGTCAT-1 5', 'TGACGGCTCTGAGTGT-1 5', 'TGATTTCGTTGGACCC-1 5', 'TGCCCATAGAGTCTGG-1 5', 'TGCGGGTTCCTCCTAG-1 5', 'TGCTGCTAGTCTCAAC-1 5', 'TGCTGCTTCCAGTAGT-1 5', 'TGGACGCTCAGTGCAT-1 5', 'TGGACGCTCGCAAGCC-1 5', 'TGGCTGGAGACAATAC-1 5', 'TGGGAAGAGCTGTCTA-1 5', 'TGGGAAGTCAAACCAC-1 5', 'TGGGAAGTCGTATCAG-1 5', 'TGGGCGTTCACCGGGT-1 5', 'TGTGGTAGTAACGCGA-1 5', 'TGTTCCGAGGCCCGTT-1 5', 'TTCTCAACATGGATGG-1 5', 'TTCTTAGGTTCCACGG-1 5', 'TTCTTAGTCCGCAGTG-1 5', 'TTGCCGTAGGAGTTTA-1 5', 'TTTGCGCTCGTAGATC-1 5', 'AAAGTAGAGGCCATAG-1 6', 'ATTCTACTCAATCTCT-1 6', 'CAAGTTGTCACATACG-1 6', 'CACATTTGTTCGTTGA-1 6', 'CAGAATCGTGAGGGAG-1 6', 'CAGCTGGAGTGGCACA-1 6', 'CTAATGGCATAGACTC-1 6', 'CTCGGAGCAGCCAATT-1 6', 'CTCGTCAGTGCAACGA-1 6', 'GATCTAGTCTCTGCTG-1 6', 'GATTCAGAGACTCGGA-1 6', 'GGAGCAAAGATGGGTC-1 6', 'GGTATTGAGCCAGGAT-1 6', 'GTAACGTGTCTAGAGG-1 6', 'TACTTACCACTGTCGG-1 6', 'TCTCATATCCCATTAT-1 6', 'TTGGAACCACCTTGTC-1 6', 'AAACGGGGTAGCGTGA-1 7', 'AAAGATGTCTGATACG-1 7', 'AAATGCCTCCTGCTTG-1 7', 'AACACGTGTCGCGTGT-1 7', 'AACCATGGTACCTACA-1 7', 'AACCGCGCAGTGGAGT-1 7', 'AACCGCGCATCACGAT-1 7', 'AACCGCGGTCCATCCT-1 7', 'AACCGCGTCTACGAGT-1 7', 'AACTCAGTCAATCACG-1 7', 'AACTCTTCATACGCTA-1 7', 'AACTCTTTCTCAAGTG-1 7', 'AACTGGTGTTAAGATG-1 7', 'AAGACCTGTCAAACTC-1 7', 'ACACCCTCATCGATGT-1 7', 'ACACCCTTCGATAGAA-1 7', 'ACACCGGAGCGCCTCA-1 7', 'ACACTGACAGCTTCGG-1 7', 'ACAGCCGAGATGCCAG-1 7', 'ACAGCCGGTCTGATTG-1 7', 'ACAGCTATCCACGACG-1 7', 'ACAGCTATCGGTTAAC-1 7', 'ACATACGAGAGCTATA-1 7', 'ACATACGCACTGAAGG-1 7', 'ACATACGTCTTAACCT-1 7', 'ACATCAGTCCAAAGTC-1 7', 'ACCAGTAAGTTCCACA-1 7', 'ACCGTAATCACTTACT-1 7', 'ACCTTTAGTCATGCAT-1 7', 'ACGAGCCCATGCATGT-1 7', 'ACGATACGTGGACGAT-1 7', 'ACGATGTAGATGGCGT-1 7', 'ACGCAGCAGATGCCAG-1 7', 'ACGCCGACAATGAAAC-1 7', 'ACGCCGATCTGTCAAG-1 7', 'ACGGCCAAGTCATGCT-1 7', 'ACGGCCATCTGATTCT-1 7', 'ACGGGTCAGCTTATCG-1 7', 'ACGTCAAAGATAGTCA-1 7', 'ACGTCAAGTAAAGTCA-1 7', 'ACTATCTAGCTAACTC-1 7', 'ACTATCTCAAGACGTG-1 7', 'ACTATCTCACCCATTC-1 7', 'ACTGAACAGGTGACCA-1 7', 'ACTGAGTAGAACTCGG-1 7', 'ACTGAGTCACGGTAAG-1 7', 'ACTGATGCACGAGGTA-1 7', 'ACTGTCCAGTACGCCC-1 7', 'ACTGTCCCAGATTGCT-1 7', 'ACTGTCCCAGCTCGAC-1 7', 'ACTGTCCCAGCTGTGC-1 7', 'ACTGTCCGTAGAGGAA-1 7', 'ACTGTCCGTCGTCTTC-1 7', 'ACTTACTCAGTAAGAT-1 7', 'ACTTTCAGTTCCACGG-1 7', 'AGAGCGAAGCCCAATT-1 7', 'AGAGCTTGTATATCCG-1 7', 'AGAGTGGGTTCAACCA-1 7', 'AGAGTGGTCACCTTAT-1 7', 'AGATCTGAGAATCTCC-1 7', 'AGATTGCTCATGTCCC-1 7', 'AGCAGCCAGTGTCCCG-1 7', 'AGCAGCCCACGGATAG-1 7', 'AGCGTATGTATATGGA-1 7', 'AGCTTGAGTTATCCGA-1 7', 'AGCTTGATCTCTAGGA-1 7', 'AGGCCACCAGCTCGCA-1 7', 'AGGCCACGTTCTGGTA-1 7', 'AGGGATGCATGAAGTA-1 7', 'AGTAGTCCATGAAGTA-1 7', 'AGTGAGGAGGCTCATT-1 7', 'AGTGTCAGTTTCCACC-1 7', 'AGTGTCATCCCTCAGT-1 7', 'AGTGTCATCGGCGCAT-1 7', 'AGTTGGTAGCACAGGT-1 7', 'AGTTGGTGTTGATTCG-1 7', 'ATAACGCGTATATGGA-1 7', 'ATCACGAAGTGTACGG-1 7', 'ATCACGAGTCGACTAT-1 7', 'ATCATGGGTCTCTTAT-1 7', 'ATCATGGTCAACCATG-1 7', 'ATCCACCTCCCTTGTG-1 7', 'ATCGAGTGTCCAACTA-1 7', 'ATCTACTGTTTGTTTC-1 7', 'ATCTGCCAGAGTGAGA-1 7', 'ATGAGGGAGATGTTAG-1 7', 'ATGAGGGGTTGGGACA-1 7', 'ATGGGAGCATACTACG-1 7', 'ATTACTCGTGTTGGGA-1 7', 'ATTACTCTCACCATAG-1 7', 'ATTATCCCATCACCCT-1 7', 'ATTGGTGTCAGTGCAT-1 7', 'ATTTCTGTCGACCAGC-1 7', 'CAAGATCTCCATGAGT-1 7', 'CACAAACTCACGAAGG-1 7', 'CACACAATCGGATGTT-1 7', 'CACACCTCAGTCAGAG-1 7', 'CACACCTTCTCGATGA-1 7', 'CACATAGCAGCTCGAC-1 7', 'CACCTTGCAAAGAATC-1 7', 'CACTCCAGTGACTCAT-1 7', 'CAGCATACACGCATCG-1 7', 'CAGCATATCATCTGCC-1 7', 'CAGCATATCCATGAGT-1 7', 'CAGCATATCGCCAGCA-1 7', 'CAGCTAATCGGCCGAT-1 7', 'CAGCTGGCATCGACGC-1 7', 'CAGTCCTAGCCACCTG-1 7', 'CATATTCAGAGTACCG-1 7', 'CATATTCTCCGTAGGC-1 7', 'CATATTCTCGTCCGTT-1 7', 'CATCAAGCACCCATGG-1 7', 'CATCAAGCATAAGACA-1 7', 'CATCAGATCGTCCAGG-1 7', 'CATCCACTCCGCAGTG-1 7', 'CATCGGGCAGACAGGT-1 7', 'CATGCCTCAGCGAACA-1 7', 'CCAATCCAGCTGCAAG-1 7', 'CCAATCCTCCTACAGA-1 7', 'CCACCTAAGTAACCCT-1 7', 'CCACCTAGTGTGGTTT-1 7', 'CCACCTATCAACACAC-1 7', 'CCCAATCGTAGAGCTG-1 7', 'CCCTCCTTCTCCGGTT-1 7', 'CCCTCCTTCTGGTGTA-1 7', 'CCCTCCTTCTTCAACT-1 7', 'CCGGTAGCAACACGCC-1 7', 'CCGTGGATCATAACCG-1 7', 'CCGTTCAAGCTGTTCA-1 7', 'CCTACACAGATGTTAG-1 7', 'CCTACACCACTCTGTC-1 7', 'CCTACCAAGACAAGCC-1 7', 'CCTAGCTAGGGATGGG-1 7', 'CCTATTAAGCGATATA-1 7', 'CCTATTAAGTAGCCGA-1 7', 'CCTCTGACATAGAAAC-1 7', 'CCTTACGGTCGCATCG-1 7', 'CCTTACGGTTATGTGC-1 7', 'CCTTCCCGTCAATACC-1 7', 'CCTTCCCGTTTAAGCC-1 7', 'CCTTCGAAGCACGCCT-1 7', 'CCTTCGACAAATCCGT-1 7', 'CCTTCGAGTATTAGCC-1 7', 'CGAACATCAGCTGCTG-1 7', 'CGAATGTTCATCGCTC-1 7', 'CGAGAAGAGTGTGGCA-1 7', 'CGAGAAGTCGATCCCT-1 7', 'CGAGCACTCAGTTAGC-1 7', 'CGAGCACTCCATGAAC-1 7', 'CGAGCCAAGTCTCAAC-1 7', 'CGAGCCAGTCACTGGC-1 7', 'CGATGGCAGGTAAACT-1 7', 'CGATGGCCAAGTTAAG-1 7', 'CGATGGCCAGCATACT-1 7', 'CGCGGTATCAACACTG-1 7', 'CGCGGTATCCGTACAA-1 7', 'CGCTTCAAGCGGCTTC-1 7', 'CGCTTCACAAGCCGCT-1 7', 'CGCTTCAGTTCCACAA-1 7', 'CGGACGTAGTTGCAGG-1 7', 'CGGACTGAGGTAGCCA-1 7', 'CGGAGCTAGCTAGTTC-1 7', 'CGGAGCTTCTTTAGGG-1 7', 'CGGGTCATCCGAACGC-1 7', 'CGGTTAAAGCTACCGC-1 7', 'CGTAGCGGTTGATTCG-1 7', 'CGTAGGCCAACGATCT-1 7', 'CGTCACTCAGCTGCAC-1 7', 'CGTCTACGTTCAGACT-1 7', 'CGTGTAAGTTGCTCCT-1 7', 'CGTGTAATCCGTAGTA-1 7', 'CGTTAGATCAGTCCCT-1 7', 'CGTTCTGCAGCGTAAG-1 7', 'CGTTCTGTCCGTCATC-1 7', 'CGTTCTGTCCTAGAAC-1 7', 'CGTTGGGCAAGGTTTC-1 7', 'CTAACTTGTCGCGTGT-1 7', 'CTAAGACCATCCCATC-1 7', 'CTAAGACGTTAGAACA-1 7', 'CTACACCCAGTATAAG-1 7', 'CTACACCGTGATGTCT-1 7', 'CTACACCTCAGAGCTT-1 7', 'CTACACCTCGAGGTAG-1 7', 'CTACACCTCTCTTATG-1 7', 'CTACATTGTGTGCGTC-1 7', 'CTACATTTCTGGCGTG-1 7', 'CTACCCAAGAAACGCC-1 7', 'CTACCCAAGGGTGTTG-1 7', 'CTACCCAGTCCGTTAA-1 7', 'CTACGTCCAGACGCAA-1 7', 'CTAGAGTCACCCATTC-1 7', 'CTCATTACATTCTCAT-1 7', 'CTCCTAGCACAGGAGT-1 7', 'CTCCTAGTCACTTACT-1 7', 'CTCGAGGCACGTCAGC-1 7', 'CTCGGGATCTTCTGGC-1 7', 'CTCGTACAGTCGTTTG-1 7', 'CTCTAATCACGTTGGC-1 7', 'CTGAAACAGTACGCGA-1 7', 'CTGCCTAGTCTAGCCG-1 7', 'CTGCGGAAGCGGCTTC-1 7', 'CTGCGGAGTACCGGCT-1 7', 'CTGCTGTAGTCCAGGA-1 7', 'CTGCTGTCACCAGGCT-1 7', 'CTGGTCTTCTCTGTCG-1 7', 'CTGTGCTTCACCTCGT-1 7', 'CTTAACTGTGTGACGA-1 7', 'CTTAACTGTTCTGTTT-1 7', 'CTTACCGGTCTACCTC-1 7', 'CTTAGGAAGGATGGAA-1 7', 'CTTGGCTGTCCCTACT-1 7', 'GAAATGAAGTTACCCA-1 7', 'GAACCTAAGACGCTTT-1 7', 'GAAGCAGTCGTGGACC-1 7', 'GAATGAATCACTCCTG-1 7', 'GAATGAATCGGAATCT-1 7', 'GACACGCAGATCACGG-1 7', 'GACCAATAGTCTCAAC-1 7', 'GACCAATTCATGTCTT-1 7', 'GACCTGGTCCGCATCT-1 7', 'GACGTGCAGCGTGAAC-1 7', 'GACGTGCCAATGTAAG-1 7', 'GACGTGCGTAAATACG-1 7', 'GACGTGCTCTGGCGTG-1 7', 'GACTAACCAAGACACG-1 7', 'GACTACAAGGCATGGT-1 7', 'GACTGCGCATGTCCTC-1 7', 'GATCGATCAGACGCTC-1 7', 'GATCGCGGTTTGTGTG-1 7', 'GATCGCGTCTCGATGA-1 7', 'GATCGTACATACTACG-1 7', 'GATCGTAGTCCGTCAG-1 7', 'GATCGTATCGCATGAT-1 7', 'GATCTAGAGACAGACC-1 7', 'GATCTAGAGTCAATAG-1 7', 'GATCTAGGTCATGCAT-1 7', 'GATGAAATCATAAAGG-1 7', 'GATGAGGAGTCCTCCT-1 7', 'GATGCTACACTTGGAT-1 7', 'GATGCTACATCTATGG-1 7', 'GCAAACTCACACGCTG-1 7', 'GCACTCTTCCGTCATC-1 7', 'GCATACAGTCTTCAAG-1 7', 'GCATACATCAGCTCGG-1 7', 'GCATACATCCCATTAT-1 7', 'GCATGCGGTTACGTCA-1 7', 'GCATGTAAGCGCCTTG-1 7', 'GCCTCTACACTCAGGC-1 7', 'GCCTCTAGTCTGCAAT-1 7', 'GCGAGAATCGAACTGT-1 7', 'GCTCCTACAGACGCTC-1 7', 'GCTCCTATCTTCGAGA-1 7', 'GCTCTGTGTGTGTGCC-1 7', 'GCTGCAGTCATTCACT-1 7', 'GCTGGGTAGGCTCTTA-1 7', 'GCTTGAAAGGTTACCT-1 7', 'GCTTGAACAAGCCATT-1 7', 'GGAAAGCGTCAATACC-1 7', 'GGAAAGCGTTCAGACT-1 7', 'GGAACTTAGATGGGTC-1 7', 'GGAATAACACGACTCG-1 7', 'GGACAAGTCATGTCTT-1 7', 'GGACATTAGCAATCTC-1 7', 'GGATGTTCAAGGGTCA-1 7', 'GGATTACAGTCCCACG-1 7', 'GGATTACCACAGGAGT-1 7', 'GGATTACCACTATCTT-1 7', 'GGATTACCAGCTATTG-1 7', 'GGCAATTTCCCGGATG-1 7', 'GGCGACTCATCGATTG-1 7', 'GGCTGGTAGCCTTGAT-1 7', 'GGCTGGTTCTTTCCTC-1 7', 'GGGAATGAGCTCCTTC-1 7', 'GGGACCTCATTCCTGC-1 7', 'GGGATGACAGCTGTAT-1 7', 'GGGCACTTCGTGGGAA-1 7', 'GGGTTGCAGTGTGGCA-1 7', 'GGTATTGGTCGACTGC-1 7', 'GGTGAAGAGCCCAATT-1 7', 'GGTGCGTAGTACGCGA-1 7', 'GGTGCGTTCAGTACGT-1 7', 'GGTGTTACACGGCTAC-1 7', 'GTACGTAAGTTGCAGG-1 7', 'GTACGTATCAACACCA-1 7', 'GTACTCCAGACAAAGG-1 7', 'GTACTCCAGTGTCCAT-1 7', 'GTATTCTCAAGCTGGA-1 7', 'GTCAAGTCACACGCTG-1 7', 'GTCAAGTTCAACTCTT-1 7', 'GTCACAAAGGCAGTCA-1 7', 'GTCACGGAGCGGCTTC-1 7', 'GTCGGGTCATGACATC-1 7', 'GTCTTCGAGAGCTGGT-1 7', 'GTGCATAAGCCACGCT-1 7', 'GTGCGGTTCTCAACTT-1 7', 'GTGCTTCCATAAGACA-1 7', 'GTGGGTCAGGTGATAT-1 7', 'GTGGGTCTCAATCTCT-1 7', 'GTGTTAGTCCTCCTAG-1 7', 'GTTAAGCCATTCGACA-1 7', 'GTTCATTGTTGCCTCT-1 7', 'GTTCATTGTTGCTCCT-1 7', 'GTTCATTTCTTGTCAT-1 7', 'GTTCGGGTCCGTTGTC-1 7', 'TAAACCGTCTTTAGGG-1 7', 'TAAGCGTAGACAGACC-1 7', 'TAAGCGTCACTCGACG-1 7', 'TAAGCGTGTACGCACC-1 7', 'TAAGCGTGTTGGTGGA-1 7', 'TAAGTGCGTGATGATA-1 7', 'TACACGATCAGTTCGA-1 7', 'TACACGATCCGAATGT-1 7', 'TACACGATCTACTCAT-1 7', 'TACAGTGCATTAGGCT-1 7', 'TACGGATTCGGCGGTT-1 7', 'TACGGGCCACAAGACG-1 7', 'TACTCATTCTACTATC-1 7', 'TACTTACGTTATCACG-1 7', 'TACTTACGTTTAGGAA-1 7', 'TACTTGTCACGAAAGC-1 7', 'TAGACCAAGGGTCTCC-1 7', 'TAGACCATCACATACG-1 7', 'TAGACCATCGGCATCG-1 7', 'TAGGCATAGACCACGA-1 7', 'TAGGCATGTCTAAACC-1 7', 'TAGTGGTAGTACGTTC-1 7', 'TAGTGGTGTACGAAAT-1 7', 'TAGTTGGAGATGCGAC-1 7', 'TATCTCAAGGAGTAGA-1 7', 'TCAACGACAATCGAAA-1 7', 'TCAACGAGTCCGACGT-1 7', 'TCAATCTCAACTGCTA-1 7', 'TCACAAGAGAGGGCTT-1 7', 'TCACGAAGTAATCACC-1 7', 'TCAGATGTCTTGCATT-1 7', 'TCAGCAAAGGACATTA-1 7', 'TCAGCTCAGTAGATGT-1 7', 'TCAGCTCTCGGTCCGA-1 7', 'TCAGGATGTGGACGAT-1 7', 'TCATTACAGGCATGGT-1 7', 'TCATTACCAGGTTTCA-1 7', 'TCCACACAGAATCTCC-1 7', 'TCCCGATAGTGAATTG-1 7', 'TCCCGATCACAGAGGT-1 7', 'TCGAGGCCAACTGCTA-1 7', 'TCGCGAGAGAAACCAT-1 7', 'TCGCGTTCAATCTGCA-1 7', 'TCGGGACTCAGTGCAT-1 7', 'TCGGTAAGTTACTGAC-1 7', 'TCGTAGACAGTAACGG-1 7', 'TCGTAGATCCCGGATG-1 7', 'TCTATTGAGTGTACCT-1 7', 'TCTCATATCAGTGTTG-1 7', 'TCTCATATCGTAGATC-1 7', 'TCTGGAAGTCGAATCT-1 7', 'TCTTCGGTCGCGTTTC-1 7', 'TGACGGCCAAATTGCC-1 7', 'TGACGGCGTGCTAGCC-1 7', 'TGACTTTCATTCTTAC-1 7', 'TGAGCATCATCCCACT-1 7', 'TGATTTCGTGACTACT-1 7', 'TGCCAAAAGGCTAGGT-1 7', 'TGCCAAAAGTCTCCTC-1 7', 'TGCCAAACATCGACGC-1 7', 'TGCGGGTTCAGCTCTC-1 7', 'TGCGTGGCACAGTCGC-1 7', 'TGGCCAGAGGCTCTTA-1 7', 'TGGCCAGCACCTATCC-1 7', 'TGGCCAGGTGCACCAC-1 7', 'TGGCGCAAGTCAAGCG-1 7', 'TGGCGCATCATCGGAT-1 7', 'TGGCTGGGTAGAGCTG-1 7', 'TGGGAAGCACTCGACG-1 7', 'TGGTTAGGTACAAGTA-1 7', 'TGGTTCCGTGTTAAGA-1 7', 'TGTCCCAGTGCAGGTA-1 7', 'TGTGGTAGTCCAGTAT-1 7', 'TGTGTTTCAATGGATA-1 7', 'TGTGTTTCAGGGTTAG-1 7', 'TGTGTTTTCTAGCACA-1 7', 'TTAGGACCAGTAACGG-1 7', 'TTAGTTCTCACTTACT-1 7', 'TTATGCTAGACTAGGC-1 7', 'TTATGCTGTCACTGGC-1 7', 'TTCCCAGCATACTACG-1 7', 'TTCGAAGTCAAAGTAG-1 7', 'TTCTACACATATGCTG-1 7', 'TTCTCAAAGCTCCTCT-1 7', 'TTGAACGGTGTGCGTC-1 7', 'TTGACTTAGAGCAATT-1 7', 'TTGACTTGTAGCCTCG-1 7', 'TTGCCGTTCAAGAAGT-1 7', 'TTGTAGGTCCAGGGCT-1 7', 'TTTACTGTCGACAGCC-1 7', 'TTTATGCCAATTGCTG-1 7', 'TTTGTCACACCACCAG-1 7', 'TTTGTCAGTTAGATGA-1 7', 'AAACGGGCACATGGGA-1 8', 'AAAGATGTCCAAATGC-1 8', 'AAAGCAATCTGTGCAA-1 8', 'AACTCCCGTTCCAACA-1 8', 'AACTGGTAGTTCCACA-1 8', 'AACTTTCGTTTGGCGC-1 8', 'AAGACCTTCTAAGCCA-1 8', 'AAGCCGCTCGTTACAG-1 8', 'ACATACGAGGGCTTCC-1 8', 'ACATGGTAGGGATGGG-1 8', 'ACGATGTTCCCGGATG-1 8', 'ACGGGCTTCCGGCACA-1 8', 'ACTGAGTTCGTAGATC-1 8', 'AGATTGCCACGGTGTC-1 8', 'AGATTGCGTCAGAATA-1 8', 'AGCTCCTAGCTCCTTC-1 8', 'AGCTCCTCACCTTGTC-1 8', 'AGCTCCTTCGGAGCAA-1 8', 'AGCTCTCGTTAAAGTG-1 8', 'AGGGAGTTCTTATCTG-1 8', 'AGGGATGGTGTAACGG-1 8', 'AGGGTGAGTGTCAATC-1 8', 'AGTCTTTCAAATACAG-1 8', 'AGTGAGGCACCGATAT-1 8', 'AGTGTCATCCGCGGTA-1 8', 'ATAACGCCATGGAATA-1 8', 'ATAGACCTCAGTCAGT-1 8', 'ATCACGAAGTCTCGGC-1 8', 'ATCACGACAAGTTCTG-1 8', 'ATCACGACATTCACTT-1 8', 'ATCCGAATCTTACCGC-1 8', 'ATCTGCCGTTAAGACA-1 8', 'ATGGGAGAGCACACAG-1 8', 'ATTACTCTCCTCGCAT-1 8', 'ATTATCCTCATAACCG-1 8', 'ATTCTACAGGCTCAGA-1 8', 'CAACTAGTCCACGAAT-1 8', 'CAAGAAAGTCTCAACA-1 8', 'CAAGAAATCCCAAGTA-1 8', 'CAAGATCGTATAATGG-1 8', 'CACATAGTCACCTCGT-1 8', 'CAGAATCGTATCTGCA-1 8', 'CAGAGAGCACAACGTT-1 8', 'CAGAGAGCAGGGATTG-1 8', 'CAGCTAACACTTACGA-1 8', 'CAGCTAATCACGATGT-1 8', 'CATATGGGTTATGCGT-1 8', 'CATCGGGGTCTAGCCG-1 8', 'CATGACAGTGGTCCGT-1 8', 'CATGACATCGTTGCCT-1 8', 'CATGCCTGTAAGCACG-1 8', 'CATGGCGCACAACGTT-1 8', 'CCACCTACATCGTCGG-1 8', 'CCACTACAGGCCATAG-1 8', 'CCACTACGTCGGCATC-1 8', 'CCACTACTCATTTGGG-1 8', 'CCACTACTCCCAAGAT-1 8', 'CCGTGGAGTTGGTAAA-1 8', 'CCTAGCTCATGATCCA-1 8', 'CCTAGCTTCCGAGCCA-1 8', 'CCTTCCCTCAAGGTAA-1 8', 'CCTTCGAGTTAAGTAG-1 8', 'CGAACATTCGTGGGAA-1 8', 'CGAATGTTCAGCCTAA-1 8', 'CGAGAAGGTTAAGAAC-1 8', 'CGAGCACAGCCCTAAT-1 8', 'CGAGCCATCGACCAGC-1 8', 'CGATGGCAGCGCCTCA-1 8', 'CGGACGTAGGAGCGTT-1 8', 'CGGACGTTCCAAACTG-1 8', 'CGGACTGGTCACTTCC-1 8', 'CGGTTAAGTCTCCATC-1 8', 'CGGTTAATCCCTCTTT-1 8', 'CGTAGCGGTAGGGTAC-1 8', 'CGTAGCGGTCAGCTAT-1 8', 'CGTGAGCGTGGCAAAC-1 8', 'CGTTGGGTCTCGTTTA-1 8', 'CTCGAAACACACGCTG-1 8', 'CTCGAAACACGGTTTA-1 8', 'CTCGAAAGTAGGCTGA-1 8', 'CTCGAAAGTCTAAAGA-1 8', 'CTCGGAGCACGGCCAT-1 8', 'CTCGGAGGTAGCGTCC-1 8', 'CTCGGGAAGTTGAGTA-1 8', 'CTCGGGATCATGTCCC-1 8', 'CTCGTACGTTGGTAAA-1 8', 'CTCGTCACACGGATAG-1 8', 'CTCGTCAGTACCTACA-1 8', 'CTCTAATCACTCTGTC-1 8', 'CTCTAATTCATAACCG-1 8', 'CTGAAACGTCTAAAGA-1 8', 'CTGAAGTCAAATTGCC-1 8', 'CTGATCCCATCTATGG-1 8', 'CTGATCCGTTGAGTTC-1 8', 'CTGCTGTGTCCTCTTG-1 8', 'CTGTTTAAGGAGTACC-1 8', 'CTTGGCTTCTAACTTC-1 8', 'GAATGAACAAGGTTCT-1 8', 'GACAGAGAGACGACGT-1 8', 'GACGGCTGTATCAGTC-1 8', 'GAGTCCGGTTGTACAC-1 8', 'GATCAGTAGACGACGT-1 8', 'GATCGATAGAGTACAT-1 8', 'GATCGTACACCTGGTG-1 8', 'GATCGTATCATGGTCA-1 8', 'GCAAACTAGGCTACGA-1 8', 'GCACATAAGTGGACGT-1 8', 'GCATACACACCATCCT-1 8', 'GCATACAGTAGCTGCC-1 8', 'GCATGATGTCCAAGTT-1 8', 'GCCAAATCACTTAAGC-1 8', 'GCCTCTATCTGAAAGA-1 8', 'GCGCCAACACGGATAG-1 8', 'GCTCTGTAGTCATCCA-1 8', 'GCTGCAGCATTCACTT-1 8', 'GCTGCGATCCAAATGC-1 8', 'GCTTCCAAGGTACTCT-1 8', 'GGAGCAAGTTTGGCGC-1 8', 'GGCAATTAGTGGACGT-1 8', 'GGCTCGATCAACTCTT-1 8', 'GGCTGGTAGTGCGATG-1 8', 'GGGAATGTCCACTCCA-1 8', 'GGGATGAAGCGACGTA-1 8', 'GGGCACTGTCAGATAA-1 8', 'GGGCACTTCTGGCGAC-1 8', 'GGGCATCCAAGCTGTT-1 8', 'GGGCATCTCGCACTCT-1 8', 'GGGTCTGAGCTGCAAG-1 8', 'GGGTCTGAGGGATCTG-1 8', 'GGGTTGCTCTCACATT-1 8', 'GGTGAAGGTCTAGTCA-1 8', 'GTAACGTTCCACGACG-1 8', 'GTACGTACAGGAATGC-1 8', 'GTACTTTCACACCGAC-1 8', 'GTAGTCAAGCAATCTC-1 8', 'GTCCTCAAGCCGCCTA-1 8', 'GTCCTCACAAGCTGGA-1 8', 'GTCGGGTCACCATCCT-1 8', 'GTCGTAAGTGTGTGCC-1 8', 'GTGCAGCCAATCTGCA-1 8', 'GTGCTTCTCCGCTGTT-1 8', 'GTGGGTCAGAAACGAG-1 8', 'GTTACAGCATGGATGG-1 8', 'GTTCTCGCACCGAATT-1 8', 'GTTTCTACACTTCTGC-1 8', 'GTTTCTAGTCTCACCT-1 8', 'TAAGTGCCATTGCGGC-1 8', 'TACACGATCTGGTTCC-1 8', 'TACAGTGAGCGTTGCC-1 8', 'TACAGTGCATAGTAAG-1 8', 'TACCTTACATCGGAAG-1 8', 'TACGGTAAGAGTACAT-1 8', 'TAGCCGGGTCCGCTGA-1 8', 'TAGTGGTTCACTTCAT-1 8', 'TATCTCAGTGGCGAAT-1 8', 'TATCTCAGTTCAGACT-1 8', 'TATGCCCAGGTTACCT-1 8', 'TCAACGATCATGTAGC-1 8', 'TCAATCTGTAAGTGTA-1 8', 'TCAGCAACACCACCAG-1 8', 'TCAGCAAGTATTCGTG-1 8', 'TCGGTAAAGTACGTAA-1 8', 'TCGTACCGTGTCAATC-1 8', 'TCGTAGATCGTTGCCT-1 8', 'TCTCATATCTACTATC-1 8', 'TCTCTAAGTAGCTAAA-1 8', 'TCTGAGAGTCTAGTCA-1 8', 'TCTTTCCGTTGACGTT-1 8', 'TGACTAGGTAGAAGGA-1 8', 'TGAGCATAGGTGCAAC-1 8', 'TGAGCATGTAAAGGAG-1 8', 'TGAGCCGAGCTCAACT-1 8', 'TGAGGGAAGTGCCATT-1 8', 'TGAGGGAGTGTTTGTG-1 8', 'TGATTTCTCAACTCTT-1 8', 'TGCGCAGCATGAGCGA-1 8', 'TGCGTGGTCCAACCAA-1 8', 'TGGACGCCAGACGCCT-1 8', 'TGGGAAGAGCTCCTTC-1 8', 'TGGGCGTGTCAGTGGA-1 8', 'TGTATTCCAAAGGAAG-1 8', 'TGTGTTTAGAGTGACC-1 8', 'TGTTCCGTCGCAAGCC-1 8', 'TTAGGCAAGCCGTCGT-1 8', 'TTAGGCAAGTCTTGCA-1 8', 'TTAGTTCTCATGCAAC-1 8', 'TTATGCTCAACACCTA-1 8', 'TTCCCAGAGTGGTCCC-1 8', 'TTCGAAGAGGCTAGAC-1 8', 'TTCGGTCAGAAGGACA-1 8', 'TTCTCCTCACCAGTTA-1 8', 'TTGACTTCAGATAATG-1 8', 'TTGCCGTCAAAGTGCG-1 8', 'TTGCGTCAGGAGTACC-1 8', 'TTGGAACCAGCTCGAC-1 8', 'TTTGGTTAGAGGACGG-1 8', 'ACACCGGCACCCAGTG-1 9', 'ACGATACTCTTACCTA-1 9', 'CACTCCAAGGCATTGG-1 9', 'CCACCTAAGTCGCCGT-1 9', 'CGAATGTCAAGACGTG-1 9', 'CTAAGACCACATGGGA-1 9', 'GGAGCAACATCGATTG-1 9', 'GTAACGTAGGCTAGGT-1 9', 'GTGTTAGCAGCTTAAC-1 9', 'AAACCTGGTCGACTGC-1 10', 'AACCATGTCGATAGAA-1 10', 'AACTGGTTCGCATGAT-1 10', 'AACTTTCTCCATGAGT-1 10', 'AAGCCGCAGCGCCTCA-1 10', 'ACACCGGCAGGCTCAC-1 10', 'ACACTGAAGCAACGGT-1 10', 'ACGAGGAGTCGCATCG-1 10', 'ACGGAGATCAATAAGG-1 10', 'ACGGCCATCCCTTGCA-1 10', 'ACGTCAAAGATGCCTT-1 10', 'ACTGTCCCATGTAGTC-1 10', 'ACTTTCAGTGGTCCGT-1 10', 'AGATCTGTCTGGTATG-1 10', 'AGATTGCTCCGCGGTA-1 10', 'AGCCTAAAGGCTAGGT-1 10', 'AGCGGTCAGAGCTGCA-1 10', 'AGCGTATGTCGTTGTA-1 10', 'AGCGTCGCATGATCCA-1 10', 'AGCTCCTGTCGAGATG-1 10', 'AGCTCCTTCACTTATC-1 10', 'AGCTCTCGTGACTCAT-1 10', 'AGCTTGAAGGTTACCT-1 10', 'AGCTTGACATGTCCTC-1 10', 'AGGCCGTGTGGCAAAC-1 10', 'AGGGATGAGTAACCCT-1 10', 'AGTCTTTAGTGAACAT-1 10', 'AGTCTTTTCCTCAACC-1 10', 'ATAACGCAGTCGATAA-1 10', 'ATAAGAGAGACTAAGT-1 10', 'ATAAGAGAGGATGGAA-1 10', 'ATCCGAAAGATGCCAG-1 10', 'ATCGAGTCATCCCATC-1 10', 'ATTCTACCAAACGTGG-1 10', 'ATTGGACCATCCTAGA-1 10', 'CAACCAAGTAGTACCT-1 10', 'CAAGTTGGTAGCGCTC-1 10', 'CACAAACCACTTGGAT-1 10', 'CACAAACGTCAGAAGC-1 10', 'CACCACTAGTTTGCGT-1 10', 'CACCAGGCATGCGCAC-1 10', 'CACCTTGCAGACACTT-1 10', 'CACCTTGGTTGGTTTG-1 10', 'CAGCCGAGTCTAAAGA-1 10', 'CAGCGACGTACATCCA-1 10', 'CAGCTGGAGGTGCTAG-1 10', 'CAGCTGGGTACCAGTT-1 10', 'CATTCGCGTGCAACGA-1 10', 'CCACCTATCAACCATG-1 10', 'CCCAATCAGGATGCGT-1 10', 'CCGGGATTCATGGTCA-1 10', 'CCTACCACACCATCCT-1 10', 'CCTTACGAGGCACATG-1 10', 'CCTTACGAGTCTCGGC-1 10', 'CCTTTCTTCACGCGGT-1 10', 'CGAACATAGTTGCAGG-1 10', 'CGAGAAGTCACATGCA-1 10', 'CGAGCACAGCCAGTTT-1 10', 'CGAGCCATCAACGCTA-1 10', 'CGATGGCTCAATCTCT-1 10', 'CGATGTAGTTAAGATG-1 10', 'CGCCAAGAGGTTCCTA-1 10', 'CGCCAAGTCTAACTCT-1 10', 'CGCGTTTCAGACAGGT-1 10', 'CGCTATCGTCTGATTG-1 10', 'CGTCCATAGAGAGCTC-1 10', 'CGTGAGCAGAAAGTGG-1 10', 'CTAACTTAGATCCCGC-1 10', 'CTACCCATCTTCGAGA-1 10', 'CTAGAGTGTAGCCTCG-1 10', 'CTAGCCTTCGTCGTTC-1 10', 'CTAGTGACAGGACGTA-1 10', 'CTCAGAATCGTTGCCT-1 10', 'CTCGGGATCTTGTACT-1 10', 'CTCGTACGTTGCGTTA-1 10', 'CTCGTACTCTAACTCT-1 10', 'CTCGTCACAAGTTCTG-1 10', 'CTCTAATGTGTTAAGA-1 10', 'CTGGTCTCATCTACGA-1 10', 'CTTAACTCATAGACTC-1 10', 'CTTAACTGTCCGTCAG-1 10', 'CTTACCGCAAACCTAC-1 10', 'CTTCTCTGTGTAATGA-1 10', 'GAAACTCGTTATGTGC-1 10', 'GAACATCCAGTAAGCG-1 10', 'GACTACACATGTCTCC-1 10', 'GATCGTATCTCTTGAT-1 10', 'GATCGTATCTTAACCT-1 10', 'GATGAGGGTTTGTTGG-1 10', 'GCAAACTCACGTGAGA-1 10', 'GCCTCTAAGAAACGAG-1 10', 'GCGCAGTAGCGTGAGT-1 10', 'GCGCGATCAACTGGCC-1 10', 'GCGCGATGTCCGACGT-1 10', 'GCGGGTTCACCAGGCT-1 10', 'GCTCTGTGTGTTTGTG-1 10', 'GCTGCGATCTTACCGC-1 10', 'GGAACTTCAATCTACG-1 10', 'GGACAGAGTGTCCTCT-1 10', 'GGACATTGTCACCTAA-1 10', 'GGAGCAACAAACGTGG-1 10', 'GGATTACAGGCAGTCA-1 10', 'GGCCGATAGGTAGCTG-1 10', 'GGCCGATCAATGGACG-1 10', 'GGCGACTCATGTTCCC-1 10', 'GGCTCGAGTTCTCATT-1 10', 'GGGTTGCAGGTACTCT-1 10', 'GGTGAAGGTACTTGAC-1 10', 'GGTGTTACATCACCCT-1 10', 'GTAACTGGTCGCTTCT-1 10', 'GTACGTAAGACTAGAT-1 10', 'GTCACAAGTCTTTCAT-1 10', 'GTCACGGAGGCAATTA-1 10', 'GTCATTTCAATGGAGC-1 10', 'GTCCTCAGTAGGCATG-1 10', 'GTCGTAAAGATCGATA-1 10', 'GTCGTAAAGCTGATAA-1 10', 'GTGCGGTAGGGTGTTG-1 10', 'GTGCGGTTCAGCTCGG-1 10', 'GTGCTTCGTAGTGAAT-1 10', 'GTGGGTCCAAACGTGG-1 10', 'GTTACAGAGCACAGGT-1 10', 'GTTCATTCAGTAACGG-1 10', 'TAAGAGACACCCAGTG-1 10', 'TACAGTGAGATCCTGT-1 10', 'TACTCATAGTTAGGTA-1 10', 'TAGTGGTAGATGCCAG-1 10', 'TATTACCGTCGGATCC-1 10', 'TCAACGACAGATGGCA-1 10', 'TCAGCTCAGGGCTTGA-1 10', 'TCAGGATAGAGAACAG-1 10', 'TCAGGTAGTTGTGGCC-1 10', 'TCATTACGTAGTACCT-1 10', 'TCATTTGTCTTCAACT-1 10', 'TCCACACCAAATACAG-1 10', 'TCCACACCATATGGTC-1 10', 'TCGCGAGAGTGTACGG-1 10', 'TCGCGAGTCCGCGTTT-1 10', 'TCGGGACGTCTCAACA-1 10', 'TCGGGACTCAAGATCC-1 10', 'TCGTAGAAGAGTGAGA-1 10', 'TCTCATAAGATCACGG-1 10', 'TCTCATAAGGAATGGA-1 10', 'TCTCTAAAGTGTCCAT-1 10', 'TCTGGAATCTGGCGTG-1 10', 'TGACAACTCAGCATGT-1 10', 'TGCACCTAGCGTTGCC-1 10', 'TGCCCATCAAGGTGTG-1 10', 'TGCCCTAGTGACGCCT-1 10', 'TGCGTGGCAAGCGTAG-1 10', 'TGGACGCGTCCGAAGA-1 10', 'TGTGGTATCTACGAGT-1 10', 'TTAGTTCTCAGCCTAA-1 10', 'TTCCCAGTCGAATGCT-1 10', 'TTCCCAGTCGTCGTTC-1 10', 'TTCTCAAGTTGTTTGG-1 10', 'TTCTCCTCACACCGAC-1 10', 'TTCTTAGCACTTGGAT-1 10', 'TTGGAACTCCATGCTC-1 10', 'TTTACTGCATTATCTC-1 10', 'TTTGCGCGTTCGTCTC-1 10']\n"
     ]
    }
   ],
   "source": [
    "# scdiff_init.run_scdiff_init(data_file, tf_dna_path, kc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CSHMM Training without cross validation\n",
    "Don't use cross validation. It is taking too long."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmd = \"python CSHMM-for-time-series-scRNA-Seq/CSHMM_train.py \\\n",
    "--data_file data/tcell_data.E \\\n",
    "--structure_file data/tcell_data.Einit_cluster.txt \\\n",
    "--n_split 100 \\\n",
    "-ng 16000 \\\n",
    "--n_iteration 10 \\\n",
    "--cross_validation 0 \\\n",
    "--random_seed 0 \\\n",
    "--model_name tcell_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#i just ran this with slurm\n",
    "# result_stdout = subprocess.check_output(cmd.split(\" \"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load this before loading the pickled because otherwise it don't load correctly\n",
    "def dictlambda():\n",
    "    return []\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure you have a conda env named 'cshmm' with gprofiler installed. \n",
    "\n",
    "There are models 1-10 for each iteration (it1-it10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "plotting path figure for model file:  TRAIN_OUTPUT/nocv/tcell_data_nocv_it1.pickle\n",
      "loading model from file:  TRAIN_OUTPUT/nocv/tcell_data_nocv_it1.pickle\n",
      "loading data......\n",
      "1922  cell loaded with  8846  genes selected\n",
      "structure.png\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_file = 'TRAIN_OUTPUT/nocv/tcell_data_nocv_it1.pickle'\n",
    "CSHMM_analysis.plot_path_fig(model_file, data_file, circle_size = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THIS WILL OVERWRITE WHATEVER IS IN THE OUTPUT FOLDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "analyzing for model file:  TRAIN_OUTPUT/nocv/tcell_data_nocv_it1.pickle\n",
      "loading model from file:  TRAIN_OUTPUT/nocv/tcell_data_nocv_it1.pickle\n",
      "loading data......\n",
      "1922  cell loaded with  8846  genes selected\n",
      "rm -rf ANALYSIS_OUTPUT/nocv_it1\n",
      "mkdir ANALYSIS_OUTPUT/nocv_it1\n",
      "path:  0_1_2_3_4\n",
      "path:  0_1_6\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-625b5c24980d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# generate go analysis for each path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel_temp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCSHMM_analysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0manalyze_gene\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_folder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'ANALYSIS_OUTPUT/nocv_it1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/genomics/jubilant-barnacle/CSHMM-for-time-series-scRNA-Seq/CSHMM_analysis.py\u001b[0m in \u001b[0;36manalyze_gene\u001b[0;34m(model_file, data_file, ng, model_ana_temp, out_folder)\u001b[0m\n\u001b[1;32m    482\u001b[0m         \u001b[0mes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_gene\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    483\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 484\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_gene\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    485\u001b[0m                 \u001b[0mts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0mp_gt_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m                 \u001b[0mes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0mp_ge_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/genomics/jubilant-barnacle/CSHMM-for-time-series-scRNA-Seq/CSHMM_analysis.py\u001b[0m in \u001b[0;36mcalculate_scor\u001b[0;34m(fullpath, level)\u001b[0m\n\u001b[1;32m    461\u001b[0m     \u001b[0;31m#print ori_to_leaf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m     \u001b[0mori_to_leaf_2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mori_to_leaf\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m     \u001b[0;31m#print ori_to_leaf_2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    464\u001b[0m     \u001b[0mcmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'rm -rf '\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mout_folder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m     \u001b[0;32mprint\u001b[0m \u001b[0mcmd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# generate go analysis for each path\n",
    "\n",
    "model_temp = CSHMM_analysis.analyze_gene(model_file, data_file, out_folder = 'ANALYSIS_OUTPUT/nocv_it1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('6CD4sp', (4, 3, 2, 1, 0))\n",
      "('7CD8sp', (4, 3, 2, 1, 0))\n",
      "0.0 Mrpl15 ('', '')\n",
      "6CD4sp_7CD8sp__top0_scor_diff\n",
      "0.0 Med7 ('', '')\n",
      "6CD4sp_7CD8sp__top1_scor_diff\n",
      "0.0 Itk ('', '')\n",
      "6CD4sp_7CD8sp__top2_scor_diff\n",
      "0.0 Cyfip2 ('', '')\n",
      "6CD4sp_7CD8sp__top3_scor_diff\n",
      "0.0 Adam19 ('', '')\n",
      "6CD4sp_7CD8sp__top4_scor_diff\n",
      "0.0 Gm12166 ('', '')\n",
      "6CD4sp_7CD8sp__top5_scor_diff\n",
      "0.0 Lsm11 ('', '')\n",
      "6CD4sp_7CD8sp__top6_scor_diff\n",
      "0.0 Clint1 ('', '')\n",
      "6CD4sp_7CD8sp__top7_scor_diff\n",
      "0.0 Ebf1 ('', '')\n",
      "6CD4sp_7CD8sp__top8_scor_diff\n",
      "0.0 Rnf145 ('', '')\n",
      "6CD4sp_7CD8sp__top9_scor_diff\n",
      "0.0 Ublcp1 ('', '')\n",
      "6CD4sp_7CD8sp__top10_scor_diff\n",
      "0.0 Ttc1 ('', '')\n",
      "6CD4sp_7CD8sp__top11_scor_diff\n",
      "0.0 Pwwp2a ('', '')\n",
      "6CD4sp_7CD8sp__top12_scor_diff\n",
      "0.0 Slu7 ('', '')\n",
      "6CD4sp_7CD8sp__top13_scor_diff\n",
      "0.0 Pttg1 ('', '')\n",
      "6CD4sp_7CD8sp__top14_scor_diff\n",
      "0.0 Rack1 ('', '')\n",
      "6CD4sp_7CD8sp__top15_scor_diff\n",
      "0.0 Ccng1 ('', '')\n",
      "6CD4sp_7CD8sp__top16_scor_diff\n",
      "0.0 Hmmr ('', '')\n",
      "6CD4sp_7CD8sp__top17_scor_diff\n",
      "0.0 Mat2b ('', '')\n",
      "6CD4sp_7CD8sp__top18_scor_diff\n",
      "0.0 Rars ('', '')\n",
      "6CD4sp_7CD8sp__top19_scor_diff\n"
     ]
    }
   ],
   "source": [
    "# analyze differences between two paths\n",
    "# path 1: 6CD4sp (4,3,2,1,0)\n",
    "# path 2: 7CD8sp (4,3,2,1,0)\n",
    "\n",
    "p1 = (\"6CD4sp\", (4, 3, 2, 1 ,0))\n",
    "p2 = (\"7CD8sp\", (4, 3, 2, 1, 0))\n",
    "\n",
    "\n",
    "model_path_diff1 = CSHMM_analysis.analyze_path_difference(model_temp, p1, p2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bex1', 'Foxa2', 'Snca', 'Top2a', 'Itgb4', 'Syt4', 'Ccng1', 'Tnnc2', 'Sept4', 'Homer2', 'Sept3', 'Begfa', 'Clint1', 'Sox9', 'Dlk1', 'Pdpn', 'Sox2', 'Coro2b', 'Hmmr', 'Cebpa', 'Ascl1', 'Tubb3', 'Nestin', 'Gabpb1', 'Slu7', 'Cdc6', 'Atoh8', 'Dcn', 'Stmn3', 'Mat2b', 'Tcf12', 'Med7', 'Sftpd', 'Sftpb', 'Sftpc', 'Muc1', 'Stxbp1', 'Abca3', 'Dmpk', 'Foxj1', 'Gm12166', 'Sox11', 'Ager', 'Cdk4', 'Sv2a', 'Ppp3ca', 'St18', 'Rack1', 'Snap25', 'Eln', 'Mtap1a', 'Scd1', 'Ecm1', 'Mapt', 'Mrpl15', 'Ppp3r1', 'Cyfip2', 'Jund', 'Pwwp2a', 'Rnf145', 'Nasp', 'Vamp2', 'Nrxn3', 'Map2', 'Rars', 'Pax6', 'Cox8b', 'Ebf1', 'Hes1', 'Pttg1', 'Klhl24', 'Ublcp1', 'Gria2', 'Naa50', 'Fabp7', 'Ank2', 'Hes6', 'Camta1', 'Tcf4', 'Ttc1', 'Adamfs10', 'Ube2c', 'Id2', 'Id3', 'Insm1', 'Scgb1a1', 'Cadm1', 'Acta1', 'Hmga2', 'Syp', 'Rab3c', 'Rars2', 'Adam19', 'Dner', 'Aqp5', 'Akap9', 'Myt1l', 'Zfp238', 'Cftr', 'Kyz2', 'Myh3', 'Itk', 'Hmgb2', 'Birc5', 'Myo1b', 'Lsm11', 'Gli3', 'Myo18b', 'Col1a2', 'Peg3', 'Dlx3']\n",
      "0 ['1DN']\n",
      "1 ['1DN' '2preDP' '3DPbla' '4DPre' '6CD4sp' '7CD8sp']\n",
      "2 ['2preDP' '3DPbla' '4DPre' '5DPsel' '6CD4sp']\n",
      "3 ['3DPbla' '4DPre']\n",
      "4 ['2preDP' '5DPsel' '6CD4sp' '7CD8sp']\n",
      "5 ['4DPre' '5DPsel' '6CD4sp']\n",
      "6 ['2preDP' '3DPbla' '4DPre' '5DPsel' '6CD4sp' '7CD8sp']\n",
      "7 ['2preDP' '3DPbla' '4DPre' '5DPsel' '6CD4sp']\n",
      "8 ['1DN' '2preDP' '3DPbla' '5DPsel' '6CD4sp']\n",
      "9 ['3DPbla']\n",
      "10 ['3DPbla' '5DPsel' '6CD4sp' '7CD8sp']\n",
      "mk_gene_list: ['Bex1', 'Foxa2', 'Snca', 'Top2a', 'Itgb4', 'Syt4', 'Ccng1', 'Tnnc2', 'Sept4', 'Homer2', 'Sept3', 'Begfa', 'Clint1', 'Sox9', 'Dlk1', 'Pdpn', 'Sox2', 'Coro2b', 'Hmmr', 'Cebpa', 'Ascl1', 'Tubb3', 'Nestin', 'Gabpb1', 'Slu7', 'Cdc6', 'Atoh8', 'Dcn', 'Stmn3', 'Mat2b', 'Tcf12', 'Med7', 'Sftpd', 'Sftpb', 'Sftpc', 'Muc1', 'Stxbp1', 'Abca3', 'Dmpk', 'Foxj1', 'Gm12166', 'Sox11', 'Ager', 'Cdk4', 'Sv2a', 'Ppp3ca', 'St18', 'Rack1', 'Snap25', 'Eln', 'Mtap1a', 'Scd1', 'Ecm1', 'Mapt', 'Mrpl15', 'Ppp3r1', 'Cyfip2', 'Jund', 'Pwwp2a', 'Rnf145', 'Nasp', 'Vamp2', 'Nrxn3', 'Map2', 'Rars', 'Pax6', 'Cox8b', 'Ebf1', 'Hes1', 'Pttg1', 'Klhl24', 'Ublcp1', 'Gria2', 'Naa50', 'Fabp7', 'Ank2', 'Hes6', 'Camta1', 'Tcf4', 'Ttc1', 'Adamfs10', 'Ube2c', 'Id2', 'Id3', 'Insm1', 'Scgb1a1', 'Cadm1', 'Acta1', 'Hmga2', 'Syp', 'Rab3c', 'Rars2', 'Adam19', 'Dner', 'Aqp5', 'Akap9', 'Myt1l', 'Zfp238', 'Cftr', 'Kyz2', 'Myh3', 'Itk', 'Hmgb2', 'Birc5', 'Myo1b', 'Lsm11', 'Gli3', 'Myo18b', 'Col1a2', 'Peg3', 'Dlx3']\n",
      "Top2a ('ciliated', 'treutlein2014: novel')\n",
      "OUTPUT/nocv/Top2a_ciliated_continuous_all.png\n"
     ]
    },
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'OUTPUT/nocv/Top2a_ciliated_continuous_all.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mIOError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-3f6fbb93c22f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mCSHMM_analysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_cont_marker_gexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path_diff1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/genomics/jubilant-barnacle/CSHMM-for-time-series-scRNA-Seq/CSHMM_analysis.py\u001b[0m in \u001b[0;36mplot_cont_marker_gexp\u001b[0;34m(model_ana_temp, remove_path, rescale)\u001b[0m\n\u001b[1;32m    763\u001b[0m         \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout_folder\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmk_gene\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmk_gene_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmk_gene\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"|\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_continuous_all.png'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    764\u001b[0m         \u001b[0;31m#filename=out_folder+'/'+mk_gene+'_continuous_all.png'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 765\u001b[0;31m         \u001b[0;31m#filename=filename.replace(\"|\",\"_\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    766\u001b[0m         \u001b[0;32mprint\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, **kwargs)\u001b[0m\n\u001b[1;32m   2060\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_frameon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2261\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2262\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2263\u001b[0;31m                 **kwargs)\n\u001b[0m\u001b[1;32m   2264\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2265\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    531\u001b[0m                 _png.write_png(renderer._renderer, fh,\n\u001b[1;32m    532\u001b[0m                                self.figure.dpi, metadata=metadata)\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/contextlib.pyc\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/cbook/__init__.pyc\u001b[0m in \u001b[0;36mopen_file_cm\u001b[0;34m(path_or_file, mode, encoding)\u001b[0m\n\u001b[1;32m    629\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m     \u001b[0;34mr\"\"\"Pass through file objects and context-manage `.PathLike`\\s.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m     \u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_filehandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopened\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/cbook/__init__.pyc\u001b[0m in \u001b[0;36mto_filehandle\u001b[0;34m(fname, flag, return_opened, encoding)\u001b[0m\n\u001b[1;32m    614\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbz2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    615\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 616\u001b[0;31m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    617\u001b[0m         \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    618\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'seek'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'OUTPUT/nocv/Top2a_ciliated_continuous_all.png'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CSHMM_analysis.plot_cont_marker_gexp(model_path_diff1, None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Bex1', 'Foxa2', 'Snca', 'Top2a', 'Itgb4', 'Mtap1a', 'Tnnc2', 'Sept4', 'Homer2', 'Sept3', 'Begfa', 'Sox9', 'Dlk1', 'Ecm1', 'Sox2', 'Coro2b', 'Cebpa', 'Ascl1', 'Tubb3', 'Nestin', 'Gabpb1', 'Cdc6', 'Atoh8', 'Dcn', 'Stmn3', 'Tcf12', 'Sftpd', 'Sftpb', 'Sftpc', 'Muc1', 'Stxbp1', 'Abca3', 'Dmpk', 'Foxj1', 'Sox11', 'Ager', 'Cdk4', 'Sv2a', 'Ppp3ca', 'St18', 'Snap25', 'Eln', 'Syt4', 'Scd1', 'Pdpn', 'Mapt', 'Ppp3r1', 'Jund', 'Nasp', 'Vamp2', 'Nrxn3', 'Map2', 'Pax6', 'Cox8b', 'Hes1', 'Klhl24', 'Gria2', 'Naa50', 'Fabp7', 'Ank2', 'Hes6', 'Camta1', 'Tcf4', 'Adamfs10', 'Ube2c', 'Id2', 'Id3', 'Insm1', 'Scgb1a1', 'Cadm1', 'Acta1', 'Hmga2', 'Syp', 'Rab3c', 'Rars2', 'Dner', 'Aqp5', 'Akap9', 'Myt1l', 'Zfp238', 'Cftr', 'Kyz2', 'Myh3', 'Hmgb2', 'Birc5', 'Myo1b', 'Gli3', 'Myo18b', 'Col1a2', 'Dlx3', 'Peg3']\n",
      "0 ['1DN']\n",
      "1 ['1DN' '2preDP' '3DPbla' '4DPre' '6CD4sp' '7CD8sp']\n",
      "2 ['2preDP' '3DPbla' '4DPre' '5DPsel' '6CD4sp']\n",
      "3 ['3DPbla' '4DPre']\n",
      "4 ['2preDP' '5DPsel' '6CD4sp' '7CD8sp']\n",
      "5 ['4DPre' '5DPsel' '6CD4sp']\n",
      "6 ['2preDP' '3DPbla' '4DPre' '5DPsel' '6CD4sp' '7CD8sp']\n",
      "7 ['2preDP' '3DPbla' '4DPre' '5DPsel' '6CD4sp']\n",
      "8 ['1DN' '2preDP' '3DPbla' '5DPsel' '6CD4sp']\n",
      "9 ['3DPbla']\n",
      "10 ['3DPbla' '5DPsel' '6CD4sp' '7CD8sp']\n",
      "mk_gene_list: ['Bex1', 'Foxa2', 'Snca', 'Top2a', 'Itgb4', 'Mtap1a', 'Tnnc2', 'Sept4', 'Homer2', 'Sept3', 'Begfa', 'Sox9', 'Dlk1', 'Ecm1', 'Sox2', 'Coro2b', 'Cebpa', 'Ascl1', 'Tubb3', 'Nestin', 'Gabpb1', 'Cdc6', 'Atoh8', 'Dcn', 'Stmn3', 'Tcf12', 'Sftpd', 'Sftpb', 'Sftpc', 'Muc1', 'Stxbp1', 'Abca3', 'Dmpk', 'Foxj1', 'Sox11', 'Ager', 'Cdk4', 'Sv2a', 'Ppp3ca', 'St18', 'Snap25', 'Eln', 'Syt4', 'Scd1', 'Pdpn', 'Mapt', 'Ppp3r1', 'Jund', 'Nasp', 'Vamp2', 'Nrxn3', 'Map2', 'Pax6', 'Cox8b', 'Hes1', 'Klhl24', 'Gria2', 'Naa50', 'Fabp7', 'Ank2', 'Hes6', 'Camta1', 'Tcf4', 'Adamfs10', 'Ube2c', 'Id2', 'Id3', 'Insm1', 'Scgb1a1', 'Cadm1', 'Acta1', 'Hmga2', 'Syp', 'Rab3c', 'Rars2', 'Dner', 'Aqp5', 'Akap9', 'Myt1l', 'Zfp238', 'Cftr', 'Kyz2', 'Myh3', 'Hmgb2', 'Birc5', 'Myo1b', 'Gli3', 'Myo18b', 'Col1a2', 'Dlx3', 'Peg3']\n",
      "Top2a ('ciliated', 'treutlein2014: novel')\n",
      "OUTPUT/nocv/Top2a_ciliated_continuous_all.png\n"
     ]
    },
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'OUTPUT/nocv/Top2a_ciliated_continuous_all.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mIOError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-a356d6cf20b3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mCSHMM_analysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_cont_marker_gexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_temp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremove_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/genomics/jubilant-barnacle/CSHMM-for-time-series-scRNA-Seq/CSHMM_analysis.py\u001b[0m in \u001b[0;36mplot_cont_marker_gexp\u001b[0;34m(model_ana_temp, remove_path, rescale)\u001b[0m\n\u001b[1;32m    763\u001b[0m         \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout_folder\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmk_gene\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mmk_gene_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmk_gene\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"|\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'_continuous_all.png'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    764\u001b[0m         \u001b[0;31m#filename=out_folder+'/'+mk_gene+'_continuous_all.png'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 765\u001b[0;31m         \u001b[0;31m#filename=filename.replace(\"|\",\"_\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    766\u001b[0m         \u001b[0;32mprint\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, **kwargs)\u001b[0m\n\u001b[1;32m   2060\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_frameon\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframeon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2062\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2063\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2064\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mframeon\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2261\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2262\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2263\u001b[0;31m                 **kwargs)\n\u001b[0m\u001b[1;32m   2264\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2265\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 530\u001b[0;31m             \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    531\u001b[0m                 _png.write_png(renderer._renderer, fh,\n\u001b[1;32m    532\u001b[0m                                self.figure.dpi, metadata=metadata)\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/contextlib.pyc\u001b[0m in \u001b[0;36m__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"generator didn't yield\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/cbook/__init__.pyc\u001b[0m in \u001b[0;36mopen_file_cm\u001b[0;34m(path_or_file, mode, encoding)\u001b[0m\n\u001b[1;32m    629\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_file_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    630\u001b[0m     \u001b[0;34mr\"\"\"Pass through file objects and context-manage `.PathLike`\\s.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m     \u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_filehandle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath_or_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mopened\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/net/dali/home/mscbio/aar126/.conda/envs/cshmm/lib/python2.7/site-packages/matplotlib/cbook/__init__.pyc\u001b[0m in \u001b[0;36mto_filehandle\u001b[0;34m(fname, flag, return_opened, encoding)\u001b[0m\n\u001b[1;32m    614\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbz2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    615\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 616\u001b[0;31m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    617\u001b[0m         \u001b[0mopened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    618\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'seek'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'OUTPUT/nocv/Top2a_ciliated_continuous_all.png'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABAgAAAJTCAYAAACFACQUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi41LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvSM8oowAAIABJREFUeJzsnXt4VNW5/7/vzCSZJJAJCQkJoFCutgoIooAXRKPBltLYKqCiQmtrrb/TRjw9R/GA5lQqtvaIeNoe661gxcpNTTFWKRSKcpOLEi8VEApySQgkJEAuk8zM+v2x9h5m35KZyVyT9/M88wxZs/Za757Z7Hftd70XEkKAYRiGYRiGYRiGYZjujS3eAjAMwzAMwzAMwzAME3/YQMAwDMMwDMMwDMMwDBsIGIZhGIZhGIZhGIZhAwHDMAzDMAzDMAzDMGADAcMwDMMwDMMwDMMwYAMBwzAMwzAMwzAMwzBgAwETY4iojIgEEU3q5DiTlHHKIiNZSHMvUeYeGIGxIvJ9MAzDMB2j3G836tos78NEdDsRfUREZ5U+zyjth4joUJRl3UhEEalFHUm91R0gogIiWkpER4nIq3x32TGWYbYy7+xYzpssxGr9FMn/hwyTLLCBgGG6AMm0+EsmWRmG6b4Q0QQAywD0BPB/AP4bwLtxFSrOJNPDUidlXQLgLgD/ALAA8rdviZBoMYWIBio6d0m8ZUlEeE3CMEYc8RaA6Xb8FsDrAL7q5DgfAvg6gFOdlohhGIbpzljppSkACMDdQogtus+KYiEYE3uIKBXAjQDWCSFmxlGUNwFsA1AVRxkY4G4AGfEWgmFiCRsImJgihDiFCDzUCyGaAHzReYkYhmGY7kw7eqmv8n7c5JgDURWKiScFkB62ht89lgghGgA0xFMGBhBCdHZDi2GSDg4xYDqEiKYT0SYiaiCiZiL6hIjmElGaSd9DyiuLiJ5W/t2m5groINZzJhHtVuaoIaI/EVFfMzdBqxwEal8ichDRI0S0n4jcRHSEiH6l7Azo572ZiF4lon1E1Ki8dhHRz4goIv9HiOgyInpXiWM9Q0TrFPdVq/5By6R8N7OUP/+lnL8IjI9V5l9MRHuIqI6IWpTv5n+IqJfJ/KnKXLuJ6DQRNSm/ZTkR3WDS/yLFTe8IEbUS0Qkieo2IhocqK8MwyUugOzMRDSaiVURUq9z71hLRJUq/PCJ6noiqlPvRDiK6zmJMFxEtJKK9St/TRPSe2b1I6Z9KRPOJ6IBy//8XES0w01lKf41eIiX2G8D3lS6B96qBSh/LHAQk8xZsIKJ6Rd5/EtG8dua/Tbm/a3Sf1XfcHkR0AxG9r+iMOiJ6i4guaqf/bCJaTUQHlfnPENFmIrpT12+g8p1cq/wtAl4bA/pdp/yunytjNRPRp0T0GBE5TebvqfxWnyr9zyq/23Iiusyk/zjlmqpWdM0RIvpD4PcVrKwW38chAIeVP2cFHLckoE8aET1Mci3UpMj9PhFNNxkv8P/DMOW8aojIRx3EzpNFDgI6v87KJKKniOgr5Tr/kogeIiIK6FsG4F8m52M27mQieoeITinjHVDGN+ReoBDWekQ0i2QeD/X6fpmICizOeSgRvUJEx5Tf97jy99D2vivdGJFeP5mGqhCRjYjuI3nvOqfMs4OIfqKfR51LGas3nb/3uYnoMyL6vr4/w8QT9iBg2oWIngAwF3J35TUA5wB8E8ATACYTUbEQolV3WCqAvwPIAbAWwBmcV1BW8/wngF8BOA1gKaTV/EYAmxGeBf01ANcA+Ksy/7cA/CeAfJxf9Kk8CcAHYDuAYwBcAK4HsBjA5ZBxiGFDRFcCWAf5vbwB4EsAlwLYCPk9mRGKTP8N4GYAo5TP65X2+oA+PwLwXch4ynWQxsHLADwI4JtENE4IcTag/xIAtwP4FMArAJohd9OuBnCTMoZ6fjcp55UCYI1yfv0BfA/AFCK6TgixOwRZGYZJfgZC3r/+CXk/GQh5D9pI0jj6LuS9eTmkrrgNwF+JaFjgjp3ycLIZwDcA7ADwDIDeAKYDWEtEPxFC/CGgPwFYAaAEwAHI8IFUAD8AMCJI2T9GmPcqInoZUsccBbBa6T8ewOMAiojoRiGEJ6D/HABPK/1eUd4nA9iCEHUfEd0K+X22Ku9VkPfsrQAqLQ77PwCfAdik9M+F1Jd/IqLhQoj5Aef93wBmAxig/FvlUMC/HwJwkSJ/BQAngKsAlAGYREQ3CCG8irwEeR1cqcj4IgAPpP64DsD7AHYFnN8PADwPwA3gLwCOABgK4IcAphLReOXaCVZWM56BvFZLAewB8JbS/rEiQyqA9yCND18A+B2k+/mtAJYT0aVCiEdMxh0M+f9hH2Rei3TI6z9cUhQ5+kKuczyQ1+uTkN+5es4bAWSbnI//nJTzegzyN6oD8DaAGgAjAfwcwLeIaIIQQi9vMGu9OQCKIa/HdyGvx+9DXgvjhBAnA2S4HHJt0RPy9/0c8lq6E0CJcu3sCOK7ifT6yYo/AbgD8jp8EYCAvMf9XjlPs/AU9X7WCmAVgDQA0wC8TEQ+IcTSIOZlmOgjhOAXv0xfACZA3vC+AlAQ0O6AfBAUAB7RHXNIaV8HINNkzDLl80kBbYMAtAE4CeCCgHYC8Gelv9CNM0lpL9O1b1TadwHICWjPhHxw9Qaei/LZYBM5bZCGCgFgnO6zJUr7wCC+Q4JcRAgAJbrPStVzC/w+oiET5CLJbtJ+j3LcQwFtLkjlutPimNyAf/eCNOqcAvANXb9LIA1Ku8P9/vjFL34l1wvy4Uq9r/2X7rP5SnsdgOcA2AI+u0v5bJHumD8o7X8AQAHtQyEfoN2B9xLIBbuAfOB0BrTnQBoMBICNujnKLO7DlvcqSF13SNc2W+n/BoB0izlKdd9Vq/J9BJ6DDdK4YNB97XzvPQDUQurSsbrPFgX8JgN1n5npmlQA65Wx+uk+29ieTJD6nEzaH1fmnxHQNkJpe9Okvw1Ar4C/hynf1ZcmMhVB6vY3Q5E1iGt4iclnc5XP3gHgCGjPx/n1z5UW/x+eCFEO9XqabXLtqTKk62SoV14pwZyP8vl1yudbAGRbyKD/f6nK0NFarxXAaIvr8aWANoI0JgoAM3X9ZyjtX0B7z1Dn0P+/jfT6yXAdQW6gCAC7AfQIaM+EXDsJAHfojlGvgxcRsLaCNH56AHwe6rXKL35F68UhBkx7/EB5XyCEqFYbhdz9+HfIh8gfWhz770KIxiDnuQPS6PC/QogjAfMIAA9DKv5QeUgIURcwViOk1d4GYGxgR2ESSyqE8EFakwG5mxMuVwIYDmCTEKJc99lvIResBiItkxDisFB2bXS8DGn1DxxPQCprN+RvrB+rNuDPuyEt4o8JIT7X9fsUwAsARhPRN0KRl2GYpOcQ5E5eIOruWBqA/1DuaSqvQS6SL1UblN3aOyENjXMVnQAAEELsB/As5MPs3QHjqB5ijwghWgL610E+pEaTUshz+IEQoln32eOQD/CBu4ozIXeC/1cIcUhtVL6X/4DJ/bcdSiCNIK8JIXbqPiuDhTeCha5phdwZdyDEZIxCiIOBv1MAi5R3M92l/64ghPAJIU4HNP0E8rsqFUIc0/VdD7njPJWIeoYibxj8AFJHPigCPEGEEDU4f32ZrYtOQOvJEAl+FnidKTKUQxr5h1seZTKO8v4jIYRm51wIsQTS08AqWWNHa70/CSE+0rWVQV6Pd9D5sJsrIb0FtgohlulkWA7gA8hzurr9U4n6mk5FXR8/LIQ4FzBPI6QXDWB+HTRBXjvegGM+h/Qq+DoR9YiAbAzTaTjEgGmPMcq7wQ1eCLGPiI4C+BoRuYRMpqPSAmt3RjNGK+8fmMxzmIiOQFrAQ0G/QAKkGxggd739EFEu5GLsW5C7H5m64/qFOHcg6nf4D/0HQggvEX0A6XqoIdIyEVEKgB9DuvF+A3IBEWgg9I8nhDhDRGsATAXwMRGthnT13C5kcshA1DwKo0iXD0JhmPL+dUh3QYZhugcfmxgl1aRv+4Q2pEm9H56AdC9XGQ7pvr050OAbwN8BzMN5HQLIe64PJvoEcicwKhBRBqSb8ikADwSEgQfihrwXqrSnHw4qum9AkCK0N1YDEX0MJSZfJ/eFkA80RQAuhHR9DyRUXZMJaSj5LuT9vyekwdlsvM8hHz5vJ6IBkA+3HwDYKYyhi6quuVZxRdeTD8CuzLnL5PNOoxgfhgA4JoQwS5KsrpVGm3y2RwjhjqA4DUKIL03aTdc5HTAB0ltkGhFNM/k8FUAeEeXqNgiCWet1dD1+HfIasFxvBrRfDfndbmpvwiiv6VTU+8xGk8/+AbmxZXYd7BfGUA1A+7udM/mcYWIKGwiY9nAp71YldqogFxTZ0O5O1FjsIHQ0zwmLz08gRAOB3gquoFr77WqDEt+6A8DXIEsnvgLp7unB+bg908RSQdLRuVXrG6Ik03LIBdtByEVYNeRiFQAeMBlvBuSi8Q6c3/VoIaJVAH4uhFDPJ1d5/1EH87NVnGG6F4YdayGER3lwtoqt90DuEqsEo4MAeV8MPKZOCNFm0t9wv40gvSAfhPMAPBbkMcHoh2ANBOHomkGQOqYXpBF4LeRv44XUubMQgq5RDNF/B3AFZP6a5ZChg+pv8VjgeIpR6HoAj0LG8P9K+egsES2F9BpRH5ZUXfMfHYgRTV0TzvWoEulrzypG3rDOCYJcyOeBjq5bNYxFJZi1XkfXo0v3Hs536ycGazoV9T6jN2Sp97lTkEYrPZH83RgmarCBgGkPdRFXAHNX+EJdP5VQjAPA+UQ9fSCTJenpE+J4ofBDSEXy30KIssAPlERapZ0cX/1urM7BLJNvRGUiorGQxoF1AL4ptAmybJDJGzUobotlAMqI6AIAEyFjEe+EXDheo3RVz2+UECIUrxGGYZiOCNRBZpjpoAYAOUSUYmIksBonEqgyfCSEGNNuT+MxVrovFHnD0TUPQj4cfl9xJfdDRLfjfHb3YCmBNA4sEUJokgETUSFMHkCVMII5AOYQ0RDIXeUfA/g3yAc6NaGcen4uix3YWBDO9agS6rooljRAxvbnhHhcMOfU0fXYoHsP57sNJNprOhXL+wwROSATqcbrOmWYTsM5CJj2UOPGJuk/UBR5fwD/stitD2ceQ2yZ4nZ4QSfHb48hyvtqk88M7phhoGbvN3PttMM8ni4cmVRXXjPrszreXwKNAwpXwOhSqkEIcUSJCZwMmSDqasWFDwC2Ke/XmB4cuqwMwzAqeyFjdkeRSak1yORqwPn7rPpvG8zvrZMiKl0Ayk73ZwAuJqJgH7Ta0w+DEJrua28sFwJyOwQQtq5R9JfVeG+EMJ4fIcSXQoiXlL7nIA0OKmHrGgtZQ0YJizkAoJ9F2T2z6zER6EjnbgPQi4gujsLc7V2PLZCJCYF21psKwX63kV4/WfER5H1moslnE5WxEu06YJigYQMB0x4vK+/ziChPbVSU7W8gr5+XIjCPmpzqp8putToPAViI6D5IHlLeJwU2EtFoyGzFnWUL5CJ3IhGV6D77N5jkHwhTJtXt78IQxsuHTEQFXXseEZmVA8uEdDH0QGYmBoA/QrrMPUZEV5iMZSNjref2ZGUYhgHgT5a3DDKOXZNgkIgGQyZXa4MsN6byR+X9l0TkDOifA5mvIJo8DRmv/bKZQYOIehFRoHfBMkj5f0pEAwP62QA8hdDWaOWQFWXuULzGAinDeRfuQA4p75N0ck6GdQLicHTNIJwPHwhs/5rymZ5ekG7ggckLfwv5XS0iomH6A4golYj0xoNo6JqXIUNJngo0PBBRb8gqHWqfROI05G6/1fegJpB8gYj66j8kokwiGh/m3Hcpa5dAyiCvxz8H5GXYDLlWulop1xk4/62QhqF9MM8tEsgh5X2Sboxw109WqL/xQiX/iDpPBs4nZ43E+phh4gKHGDCWCCG2ENGvIV3QP1XizxsBfBOyhN0HkIuYzs5zgIgeBfAEgD1EtBzSfetGyKzMeyDr8UaDVyBjGp8housA7Icsn/VtyF2QGZ0ZXAghiOgeAH8DsJqI3oDchb8UMinUuwBuioBM65VjXlCSCp4FUC+E+C1kPN5mAN8joi2Qv1sfyN9xL84nDlPpB+AjIvoEMgHREQBZyvwFAJ5VE4wJIWoV5f0mgG1EtB5yF01A7n5NgHRhdQaM356sDMMwgTwM+XDwb0pyug2Q7rvTIQ0H/yaE+FdA/z9D3iO/A6m3yiHzGtwKeS80M8pGBCHEy0R0GYD7ARwgovcgywTnQLo9T4Q0YNyn9D9ERA8D+B/Ie66q+yZDutdXIkjdJ4Q4R0T3Qsb9v6+MVQXpSXEJZGI3/W7n7yGrPqxU9Ptxpe9NAFbAWtdMA/AGEb0D+RB/WAjxJ8jyx18CeFAxMn8E+dD1bQAVMD6AjVLG2QG5k3wcModDCeRv5jcqCCG+IKIfQD6YfUZE70I+MKYo414Dme/goiBlDZffQOrOEsj1yjuQiTSnQcac/1oI0dFDbExRro3tAK4homWQ35sX0quwUgixXrkOFwLYr5zTvyA3BAZA7rx/AONaJRj+CmAzEa3A+evxasgH+YcDZBRENAtyrbRc+X/7BWSi0psh1wl36yqfmBHp9ZMpQojXlE2f6ZDX41uQ656bIf+vL9dXY2CYpEIkQK1FfiX2CzLz/QeQN80WyAfA/0JAjemAvoegqw2t+7wMJnVrlc/uglxQtEAq+lcB9IVMdlSv6ztJGadM174RFnWPYV1T+BuQJZJqIA0guyB3TwbCpHYwOqiZazH3ZZDGgLPKax3kw7Pp9xGqTMoxD0IustxKn0MBn+VALgYPKd/vAUiDTIb+N4NcmD4KmWzqmDJelfLd3g7zGtcDIXd49ivjn4FU7n8CcHMosvKLX/xK3ld79yjlcwFgo8VnpvpDuSf9Srm/uCG9lv4GoNhinFTlHnZQ6X8IwC8hd6UN87dzH7a811vJqnz2bQBvK/fvVsiEbB8CWADgIpP+t0O6I+t130ZY6LN2vv8bIfV1E+TOcTnkQ7PpuUCWl/u70vescuzNsNaxdkV3HITc0dd8n5CG4WWK7miGXC/8J+SGlL5vf2WszTifOPco5EPlNy3Ob4RyLoeV/nWQa4Q/ALg+FFk7cQ07ATyizNsc8L3dHupYHcgxG+ZrlvauPatreQikAacWMvu+2bhXQxqGjivX7UnICgNPAxgbrAx6OZTz+Fj5rk5CGskKLY4bDrluqFJ+syrI/w/DQzjXSK+fNsLk/yGkh8/9kFWzmpTXLgD/DzKng75/e/e+JQhxXckvfkXzRUIkct4UprtDRFmQWXA/FkJM6Kg/wzAMwzAMEz9Ilj1+DMB1QoiN8ZWGYZhQ4RwETEKgxL2n6NockK6XTkgXdoZhGIZhGIZhGCZKcA4CJlG4BcAviGgdZMx7DmS85DBI17T/jaNsDMMwDMMwDMMwXR42EDCJwnbIGL6JkEntAJkk55cAfiWEaLY6kGEYhmEYhmEYhuk8nIOAYRiGYRiGYRiGYZjE9SDo3bu3GDhwYLzFYBiG6Rbs2rXrlBAiL95yMMkB62iGYZjYwPqZiTUJayAYOHAgdu7cGW8xGIZhugVEdDjeMjDJA+tohmGY2MD6mYk1XMWAYRiGYRiGYRiGYRg2EDAMwzAMwzAMwzAMwwYChmEYhmEYhmEYhmHABgKGYRiGYRiGYRiGYcAGAoZhGIZhGIZhGIZhwAYChmEYhmEYhmEYhmHABgKGiRgVBytQvKoYI5eORPGqYlQcrIi3SAzDMAzT7WH9zDAMEzyOeAvAMF2BioMVKNtShhZvCwCgqrEKZVvKAABTBk2Jo2QMwzAM031h/cwwDBMa7EHAMBFg8e7F/sWHSou3BYt3L46TRAzDMAzDsH5mGIYJDTYQMEwEqG6sDqmdYRiGYZjow/qZYRgmNNhAwDARoCCzIKR2hmEYhmGiD+tnhmGY0GADAcNEgNIxpXDanZo2p92J0jGlcZKIYRiGYRjWzwzDMKHBSQqZLkPFwQos3r0Y1Y3VKMgs8Ct/fVs0khKpY8ZiLoZhGIZJJsz085RBUyzbIwnrZ4ZhmNAgIUS8ZTBl7NixYufOnfEWg0kS9FmKAcBBDhAR2nxt/jan3YmyK8t4YcAwOoholxBibLzlYJID1tFMsJjpZ6fdiZIhJSj/stzQzjqaYbSwfmZiDXsQMF0CsyzFHuEBdPYvNXMxLz4YhmEYJvpYVRFYuW8lfMJnaA9FRy/YtsA/jo1smDZsGkbnj46bNyHDMExXgA0ETJcglGzEofQ1W3zMGz8vruEMDBNrdu/ePdnhcDwmhChA18td4yOiao/H899jxox5L97CMExXw0rn6o0DHfXXs2DbAizfu1wz3vK9y7Fq3yp4hRcAUNVYhXkfzNN4E1Y1VqFsSxkAsI5mkh7Wz0w0YAMB0yUoyCxAVWNV0H2DwWrxcbjhMD4++bF/R4QXIExXZvfu3ZPT0tJ+O3DgwNb09PTTNpstMePSwsTn81Fzc7Pr0KFDv929e/e/8SKEYSKLlX62kc3USBCsjl65b6Vpu2ocUGFvQqarwvqZiRZdzdLEdFPMshQ7yIEUW4qmLZTMxVaLj23V20zDGQJzHQDnFyAMk8w4HI7HBg4c2JqZmdnc1RYfAGCz2URmZmbzwIEDWx0Ox2PxlodhuhpWVQSmDZvWqeoCVh4IwRLspgLDJCqsn5lowR4ETJfAKkuxWVuwOwadXXwAoYUzMEwiIoQoSE9PPx1vOaJNenp6i+KiyTBMBGmvioBZvoBgdbSVB0Kw2Ij3yJjkhvUzEy3YQMB0GaYMmmK6sAjXhbCziw8AyErN6tTxDJMA2LrizoQe5Rz5iYFhokB7+jlcHT1t2DRNGKCKneyGMAMzIrEJwDBxhvUzExX4y2YYC6YNm2baPr5gvMEt0goiiqRIDMMwDMMAmDd+HmYMn+H3BLCRDTOGz8Avr/4lCjMLQSAUZhYiOy3b9PjCzMJYisswDJM0sAcBw1gwb/w8AAiqioFVLGODuyGWIjMMw4QNEQ0F8AmAVUKIO+MtD8N0xLzx8/y6OpBAr4SKgxV4+P2HDX2CzXXAMAzT3WAPAoZph3nj52HP3XvwyaxPsOfuPf6FyJRBU7D21rWonFWJtbeutdyJCDYbM8Mw4XHixAn7jTfeODg9PX103759Rzz33HM58ZYpifkdgB3xFoJhIslb+98KqZ1hmMjBOjo5ibkHARHZAewEcEwI8e1Yz88w0aB0TCnvUDCMjle3Hc55dv3+fifPulPzeqa1/qxo6LE7xw+oi+QcP/zhDy9MTU0V1dXVe7Zt25Zx6623Dhk7dmzT2LFjWzo+mlEhotsA1APYAmBInMVhkhi9h117iQdD6Rsu26q3hdTOMN2BWOhngHV0shIPD4JSAP+Mw7wMEzV4h4JhtLy67XDO429/PqDmrDtVAKg56059/O3PB7y67XDEdg/OnDlje/fdd3stXLjwmMvl8k2ePPlcUVFRw8svv5wbqTm6A0SUBeAXAB7soN+9RLSTiHaePHkyNsIxSUXFwQqUbSlDVWMVBASqGqtQtqUMFQcrOtWXYZjIEQv9DLCOTmZiaiAgov4ApgB4MZbzMky04R0KhtHy7Pr9/dwen0bHuD0+27Pr9/eL1ByffPJJmsPhECNHjnSrbSNHjmz64osv0iM1RzfhcQAvCSGOttdJCPG8EGKsEGJsXl5ejERjkonFuxejxavdGGzxtmDx7sWd6sswTOSIhX4GWEcnM7H2IHgGwH8CMK0tw7sTTLBUHKxA8apijFw6EsWrii13HILtxzBMZDl51p0aSns4nD171p6ZmanRJy6Xy3vu3Dl7pObo6hDRpQBuALAo3rIwyU91Y3XQ7aH07Qz5zvyQ2hmmqxML/Qywjk5mYmYgIKJvA6gRQuyy6sO7E0wwBOuWyO6LDBM/8nqmtYbSHg49e/b0NjY2avTYmTNn7D169Oi4CDqjMgnAQABfEVE1gJ8DuIWIdsdTKCY5sUrMa9YeSt/OcMp9KqR2hunqxEI/A6yjk5lYehBcBeA7RHQIwOsArieiV2M4P9NFCNYtMZbui+MLxofUzjBdnZ8VDT2W5rBpdg7SHDbfz4qGHovUHCNGjHB7PB765JNP0tS2ysrK9Isuuqg5UnN0A54HMBjApcrrOQAVACbHUygmOSkdUwqn3alpc9qdpgl7Q+lrRTBegj7hQ0vGBNT2XYSTF7yC2r6L0JIxAT5h6szKMF2eWOhngHV0MhOzKgZCiLkA5gIAEU0C8HOus8yEQ7BuibFyXwSAAa4BpvkGBrgGRHwuhkkG1GzI0cySnJWV5Zs8eXL9I4880nfZsmWHt23blr5u3brsDRs2fBGpObo6QogmAE3q30R0DkCLEILj/JiQUSsQBFOZIJS+ZqhegupGgOolGDg2ALRkTMDZnHsAm3xG8Tl6y78ZppsSC/0MsI5OZmJe5pBhOktBZgGqGqtM28PpFwlW7ltp2T5v/LyIz8cwycCd4wfURaNsUiAvvfTS4ZkzZw7s06fPqOzsbM9TTz31FZdPCh8hRFm8ZWCSmymDpgT9kB9KXz3teQkGjtmYPd1vHPBjS5PtDNNNiYV+BlhHJytxMRAIITYC2BiPuZnkp3RMqWbXADB3Swy2XySwclVkF0aGiS59+vTxrlu37kC85WAYJrYE6yXos5tXVLNqZxgmcrCOTk7Yg4BJOoJ1S+ys+yIgXRiDOd5GNjSlj0Nj9nT47LmweWuRWb8CGc3bO3GmDMMwDJO4bH7vPWRubkVOqwt1qQ1ovCoV9UM9QevdYHWsGcF6Cdq8tfA5ehv62by1Qc3DMAzT3WADAZOUBOuW2Bn3xWDjGwHAkXUjzvacZohxzDqbFdbcDMMwDJNI6B/mZ4lbMG73EDhFBgCgd2s2emxsxcp9q1CVJR/c29ObVjr2o5qPsOnopg6NBqVjSvHw+w+btgeSWb9Ck4MAAOBzI7N+BWSFz45ZXV2HhQercMzdhn5pKZg7qBC3FOQEdSzDMEyyEcsqBgyTVIRSBaEq8ybTGMeqzJuiKSLDMAzDRB2zssEX7ekDp9CWTXeKVNx1Qvswb6U3rXTs8r3LDeWJF2xbYKhW8Nb+t0xl1bc7m7bissoX0PvGjm3oAAAgAElEQVT0SZDwoffpk7is8gU4m7YGde6rq+vw871HcNTdBgHgqLsNP997BKurox6+3aFcY7d8hsINH2Psls/iLg/DMF0H9iBgGAtCqYLAMY4MwzBMV8XsYT7PY76DbtZupjfNwgPMUI0Ggcfp8wsFoq8odNVnXvz4nc1wejafH9MB/OFbFNT8Cw9WodknNG3NPoGFB6vi5kWgGi1UuVSjBQD2bGAYptOwgYBJeDoT4xiL+EaAYxwZhmGYroGZ3jR7wD/pqEMfj9EIftJh3Mk21ZtkCzuRr5VxwIw7Ngo4Pdo2pwf4/lqB/TuL4KmqgqOwEPlzHoBr6lTD8cfcbabjWrXHgkQ0WgDAvu3V2Fp+AOfq3OiRk4YJJYMxbFzkK0cxDBNd2EDAJAxmi5Ls/Q702egIK8axvRwCQMfJC4ONbwQ4xpFhGIZJfqz0pivNhXp3vabvkvxylFbNhFOc13st5MYr+Ws0/Zx2Jyb2n4jiVcUanRurKj+5Z8zbe7YAnuPHAcj3qvmPAoDBSNAvLQVHTYwB/dJSIitoCCSi0WLf9mpsWPYFPK3ydz1X58aGZbLcPRsJGCa54BwETEJgFt9YtqUMGR+4w45xtIpvXLh9oelcFQcrNH2DjW8EZIxjz7qXYPOcAoSAzXMKPeteSvoYR4ZhGKb7YKU3hRBw2p2a9o2unVhcuAwnHLXwQeCEoxaLC5dhV95eFGYWgkAozCxEyZASlH9ZbtC52WnZnZI1hcwf0AdnDdbkK6i1yBWsDzAQLS2oWfSMod/cQYVIt2l7p9sIcwcVhiN2RLAyTsTTaLG1/IDfOKDiafVhazlXuGOYZIM9CJiEwGpRkttmvoAIJsbRKodAQ2uDoU01HAR6FVjFR+rjG1WKdnyAOza+j9wzQG0W8NokwuaL7aZ99SSquyB7NTAMw3QfrPTmmdYzWHiNUUdudO3ERtdOTV9qJXxw+wf+v4tXFZvqdzI8okvGF4zH4bOH/fMM6DnAVO9+b9j3cLjhsOazwVmDcazxmMYD4rVJhB+/ow0zEDAaCADAU2XU+6rOSyRdOHdQoSYHARB/o8W5OndI7QzDJC5sIGASAqtFSWdiHLNSs0yNAVY0tDb4+webPElFJkE6vwDJOwP8+B0BwBvU8YnoLshJkBiGYboX7eXe0ZcNLl5VHFSeHiv93uxtNm3/Z90/kZGS4f/7i9NfmPbbdHQT1t66VtNmZozYfLEdRwrH4fCAGajNzkVufS3urngdUzdvMYzpKDR/wL6lICeh9F4iGi165KSZGgN65KSZ9GYYJpFhAwETF/T5BsziGwFgReHf8KOj39OEGbSQG3/qow0HcNqdhtwAROa7EwSCgDD9LFw6mwQpEWMcE9WrgZMgMYE88cQTea+99lrvffv2pU+dOrVu9erVh+ItE8MkK6VjSg0VAlT9qtfbA3oOMDUQDOg5QJNvwEq/WxGssb66sdogk1n/lowJ+LjvPfA65IPqqV55eHbGvbB7gG9tP28kIKcT+XMeCFrOeJNoRosJJYM1OQgAwJFqw4SSwXGUioknrJ+TFzYQMDHHLAmSgxxIsaWgzXf+Idlpd6LnZYX4rffPuKvm28jz5OCkow5/yn8bvS8fiMKjhzUJjwBoFiVWCxIBGUsZShbkQMYXjDe0dTYJUiK6CyaiVwMnQUoydryUg3/8qh/O1aSiR34rrn3oGC6/J6KJNfr169f20EMPVb377rtZLS0tnFeHYTqB6iGgT+ILwKC3gwnDq2qssgwlyHBkoMnTFLasTrvTIJMZnsxpfuOAvy0lDX+45TZ858ghgwG/Yc0a1Cx6pkPDPqNF1cFswE8SWD8z7cAGAibmmOUb8AgPXCkuZKRkaBYli3cvRpWrCutd2zX9C49+pXEtNDM6WFGYWegfW52r2dNsalBIoRS0ifMPxOMLxuOFyS8Y+tVmybACPVZJkPSLjUR0F0xEr4b2kiDxIiTB2PFSDt6bOwAet1wUnDuRivfmDgCASC5CZs2aVQ8AO3bsyDh27FhqR/0ZhmmfKecaMeXIcaDhKODyAcMaUbzvxbCN6lYee2riw3DHbfG2BOUN2OI0hikCwNkeuRj69/WatoY1a1A1/1GIFilTe4Z9xsiwcQWsi5MB1s9MB7CBgIk57SVBCkxsBABz358b1BhmRgczHOTwlzTUl0Scv3m+xoMhxZaCx6963FD+UO0faGAY1MkkSEDiuQsmolcDJ0FKIv7xq37+xYeKx23DP37VL9K7FAzDRIjKFahY+yAWZ/dEda/+KPB4Ubr2QVTn9Yr4VM3eZswYPgMr962ET/hgI1tIpQ+DDRXMq6tFTW6eob3HuXMYu+UzjVF+5KJn/MYB/zwWhn2GSVpYPzMdwAYCJua0lwQp3L5WRgc9al4C/QP+xP4TIYR2saH/W0VvTKhqrEJVBJIgJRqJ6NXASZCSiHM15rsFVu0M09WpXAGs/4WyM98fKHoUGDk93lJpqNjwXyjLdaHFJp8dqlIcKMt1weX1od4eeQ/h8i/L/UaBUIwDoTBj7ev4v1vvgyflvPcbeTxoTk/HWcVLTk3C+2C/r+EGJSwwECvDPsMkJayfmQ7geBAm5pSOKTXUUzZLMggAE/tPNB1D325mXDCjzdeGhdsXomxLmaYm8/K9y+ER2iyDHuHB4t2LDWM8+eGTGk8DQCZB2jXyRzjVKw+CbDjVKw+Lb7sXfx1/paZfMiZB2nnlxai67lLsvPLiuHs4TCgZDEeq9rbFSZASlB75rSG1M0xXpnIFsOZnQMMRAEK+r/mZbE8gFmc6/MYBlRabDVY+WuMLxqMwsxAEQmFm6MbvcMMLOiJQpi1fJ4OvgbDb4XFow+WafQIvfu8O0/GSxbDPMEHB+pnpAPYgYGJOe0mQApMMlo4pxaajm0zHePdf72LT0U0aD4DV+1YbHvLNCKX0YVVjlUEms1wFjdnTAZt2F9vrSMP/Tr8NU78yJkFiwoOTICUR1z50TBPjCACONB+ufehYHKVimPiw/hdAm66sX1uzbE8gL4Jqh920vdlmnmhwz8k9yHZm+/+ORpUgK6xCEgozCzU5ivr+bR18OmMALKoc1bh6gZxOTZhBshn2GaZDWD8zHcAGAiYumOUA0CcZ1JdaCkRfBmn1vtVRc09UQxxUmczw2c131pudvU2TIHGG5PDhJEhJghrHGOUsyW1tbWhrayOv10ter5eampooJSVFpKTEL5kmwxhoOBpae5zI8gk02M0fns1o9jajuVEaPtpLDhxpnHYnSoaUoPzLctOSjIH47OZJCs3o50xF4eO/YB3NdG1YPzMdwAYCJj7oYjEX98k2GANavC1BJy0KxnMAkIsHt9cd9g6HlcGid30tTvUyJkHqXX9K8zdnSGa6FZffUxfthEcPPfRQ30WLFvn9fzMzM3PmzJlT9fTTTxsDiRkmXrj6K+EFJu0JRPCmgdihGgMCvQbVZMOj80cbvBENiYV9ZwF7lnFgITSeBGmtbjzQUgvX1Kmsj5muD+tnph04BwETe0xiMatbjW77gExapM9XECqBsYhlV5ZFxP3RQVrb2o/efB1pbm2UZprbjR+9+bqmraadDMkMw4TO008/fVwIsSvwxYsPJuEoehRISde2paTL9lhRuQJYdAlQli3fTfIfNFiEEsQSG2ywkVye2siGkiElGJ0/2rTvlEFTsPbWtaicVYm1t67FlEFTUHGwAsWrijFy6UgUryq2NHo43c3oU3sSJHzoU3sSP3/1eYx76okonRXDdD9YPycv7EHAxJ71v0BFKmFxn76odthR4PHC5fOh3m6MfSzMLETpmFLNDkF1Y3WnHvILMws75QppIxsWXL1AI9PovVvw82XAiyW3oSYnF/l1tfhh+es4kw5NGaVZnCGZYRim+6HmGQi2ikGkKx6ohnk1D4KaJDFQNgAFPqDKPA1BzPDBB1XF+4QPq/etxsq9K2U7ZCjDvA/mAYDBW8AsXFHk9DSdx53mxOtz7tG0eSxyEzAMw3Qn2EDQ1UiGMkqeWpT1ztGUUXL4fLAJAV+AcnaQw+8uGLgIGLF0REjz6XMIXJp3aacMBD7hM8h034ffwNAT2n4fDx6Kd6+6Dt6AMkq/ufNeAAI37NCWP+QMyQzDMF2ckdOD08dBPsyHRJBJEkuzRqDsbKWmkoHT5zNUNoglZiGEHuHBwu0LAWgTHjd7mg2hgDZvLXyO3oYx8utqDW2sixmGYTjEoGuRLGWUcrINiw2PzQZ9pgGr3APZadmm7cHQ4m3Bhyc+DPt4AKalnNZffjWevvNenMiVZQ5P5OahYuJkeB3aygbu1FS8ePPtmjbOkMwwDMP4ae9hXk8QYQMAgk6SOOXIJyg7VYfCNg9ICBS2eVB2qg42X2wqE4RCQ2uDoWSxWZWhzPoVgE8bAmj3uDG7XBsCCIeDdTHDMAzYg6BrkSxllExCCQAYyg754DPdIWhsbezU/MFWO3CQA0SENl+bv80sQzIgyxz6dMYAyzJKvXLh6NuXMyQzDMMwRoKteBCKp0GwSRIbjmIKBKY0NmmaH84LvhIAcL4EoY1ssMOONtHW8UFhYJU4OBBn01bknhuMry64AT6bDTafDyP2bkDRbq0nH3F4AcMwDAA2EHQtumAZJXWHIDCeMFq4Ul3ISMnQZEMG0HGGZIReRklf+pBhGIZhAAT/MB/KpkDRo1pjAmCeJNFqbgCTGsZidk0J8jw5OOmow5L8cmx07TTtu+fuPf5/j1w60rRPrLiwbhz2XHI9fMrmhM9ux6fDivCPMfs14X6irQ01i55hgz3DMN0eNhB0JbpoGaVgdghCxWl3Gmonzx031/Th36zNQIhllICLwxGbYRiG6eoE+zAfyqZAsEkSix5FxdoHsTi7pz+JcGn9WUw6czlKq2bCKaSnXB9PLkqrZgKApZFAJSs1Cw2tDe32iSb7ht4Nj0Nbd92TkoL/nT7LkA8o2RMGV1ZWYv369WhoaIDL5UJRURFGjoyvgYZhmOSDDQRdiWAXFdEiyASJ8S6jlOHIwNTBU7Fy30q/C2TJkBIAQPGq4g69BQCZKTnQs4Cy55nWVXC6m+FqbNRUNhh37BDwzaLonSDDMAyTvAT7MB/qpkAQSRIr6j5BWa5Lk0S4LNeF5w6U+I0DKk6Rhtk1JR0aCALD9OLB2QzzKgZnMo3tyZyksLKyEmvWrEFbm/y+GxoasGbNGgBgIwHDMCHBBoKuRChllOJUQgmIfxmlJk8Tyr8s9+ci4DJKDMMwTEIRTMWDSGwK6NYCi7MEWhzaJMItNhvyPDmmh1u1BxrbmzxNpn0SkR7XToy3CEHTsGYNahY9489n9LcbivzGAZW2tjasX7+eDQQMw4QEVzHoaoycDsz5FCirl+9WxoFIVzsIIetyadYIOH26RIHCOkOy0+4MXy4L9GELHuHxGwcC29QkiYEs3r3YtIySGVxGiWEYhokKI6cDU58FXBcAIPk+9dngjf0ma4Fqi/xAZ+lcSO2BlQXij/n6gkzWHef+sSnawkSEhjVrUDX/UXiOHweEgOf4cZx1u837NsQvvINhmOSEDQTdkVBKKAVLCLGQZmWUbBYGghRKQb/Mfpq2ng7z3fpo0NDagOJVxRi5dCSKVxWj4mCF6YLHqozS3Wu0ZZS4pCHDRI7m5maaPn36gL59+47IzMwcfdFFF31jxYoVJslAGCZKBFtmMFoEsylghclaoMDjxaSGsViy/3FU/PN3WLL/cUxqGGtZlQdxDhkMDnMZhck5JUsOgppFz0C0aDcqMprMPTVcLlcsRGIYA6yjkxcOMeiOhFrtIJhwhFBiIU3KKI0YeIHp1G2iDQfOHNC0nfWcRb4zH6fcp2JSRkk1CFQ1VqFsSxkIBKHbkXA2bQWglDu058LmrUVG/QpQ2xbAbge8XsBuh+u7N3OGZKbbsHzv8pzn9jzXr7a5NjU3Pbf1vlH3HZsxfEZdpMZva2uj/v37t23YsGHvkCFDWlesWOH6/ve/P3jUqFGfDR8+vDVS8zCMKaGUGUxETHT2Y1UXo0+TMRlhmkg1HaKnNzOqIrZHhiMjqPAFm7cWPkdvQ3ufulOGtmTx8DMzZIzcU4kdV1wOr+P80j4lJQVFRcHnPNq3vRpbyw/gXJ0bPXLSMKFkMIaNK4iIzExiEW39DLCOTmbYg6A7YpXAyKw92HCEokdl7GMgVrGQEaiqUNNSgz1378Ensz7Bnrv3wCM8nR4zGFq8LQbjgMqwoz7knBUgCOScFbi20ovrKiGNA5DvDW++hQYlaRDDdGWW712e8+sdvx5wqvlUqoDAqeZTqb/e8esBy/cuNw9aDoOsrCzf008/fXz48OGtdrsdt99+e0O/fv3cW7duzYjUHAxjSTS88WIJGZMBDWq83TQZoSBzvXfSEdHnCUtSSFuFYHzB+HaNA4WZhSAQCjMLkX3mLUC3gUC+Nsx6O3k9/MwMGQO++grj9n/p9xhwuVyYOnVq0PkH9m2vxoZlX+BcnfSGPFfnxoZlX2Df9urICc4kBLHQzwDr6GSGDQTdkVAe5oNdAIUSC2k2P2Du1hgkBZnxtXDLOss/xqleeRBkw6leeXj3mvvwwegrNf1ESwtqFj0TJykZJnY8t+e5fq3eVo2OafW22p7b81w/q2M6y5EjRxyHDx92jho1KvK1URlGT6jeeLEi2LAH4TU0eWHcaQcAEoQW0obRtZAbS/LLOy1uRzjIYYgS+Pjkx+0es/bWtaicVYm1t65Fm6/VkIZAAFg/iuDo2xcg+V74+C+SxsMvf84DIKc2PxM5nRh350zMmTMHZWVlmDNnTkjJCbeWH4CnVZeLqdWHreUHLI5gkpV46GeAdXQywSEG3ZFQqh2EWmc5GLfKkdNRse9NLG74GNV2Owq83k7VWAaAif0nYvne5R3PHSEc5NB4LXCdZc6QzGipba419Um2au8sbrebZsyYMeiWW26pHT16NC8+mOgTapnBWBBK2IPrAnP5TRAQWFy4DLNrSpDnycFJRx2W5Jeb6mdXqgsZKRn+KgahJipMt6cj25ntP77Z04x6d72mjz5RcHs0Zk8HbFr9DFsKPrrkNgydc0NIsiUKqiEjsIpB/pwHOmXgUD0Hgm1nkpdY62eAdXSywQaC7kqwD/NRWABVbJyPsrOfoEWJk6tyOLCwJvwaywDw3qH3wpYnHIhIsyPBdZa5zjKjJTc9t/VU8ynDYiM3PTficYderxe33HLL11JSUnx//OMfv4r0+F0dInoVQBGATADVAH4thHgxvlIlAZEoMxhp2vP60+t8E/kFyDSlH4Gw0bWzQ33stDsxd9xcTXngEUtHWPZPsaWgzdem+XtU3ih8eOJDCAicaDrhL0kcLj57bkjtyYJr6tROGQT0xv6U3heAThk9zHvkpJkczSQzsdTPAOvoZIRDDJj2CSUcwQqdu+PiA2+gRZf5ONQay/oljH53IdoELmhCJZnqLOtZv369ZZ1lhgnkvlH3HUu1p2pW9qn2VN99o+47Fsl5fD4fZsyYMfDkyZOOioqKA2lpadY1UxkrFgIYKITIAvAdAAuI6LI4y5T4dLbMYDQI1etPL38Y2Mjmfy8ZUgIAmuo/7fG9od/THH9hjwuxrXqb3yjQnnHA3JQhPRAC57cqQ2y3aO8OqMZ+tQRiQ0MDTqd+gdYeJzX9HKk2TCgZbDrGvu3VWPrIZvzuvr9j6SObOVdBEhEr/Qywjk5W2IOAaZ9QwhHMMHF3rO5lXIScpXNwCeNuu1WNZQGB4lXFfhfE+CNgVkopmessA7LWcqALY8PEa8z7cZ1lRoeaDTnaWZLvvPPOC/fv3+/ctGnTvh49evDCIwyEEJ8F/qm8BgPYFR+JkohgvfFiRahefzr5z8x9x1QXn7HQxQA0D/Or963GG/vf8BvROwovKP+yXHO8vmqRFU67EyVDSjRzAYANNniER1N9KFOswNmcewBbwE64z42M+hUAkjPEoLOYGfu9Pg/aeh9BTmr/DqsY7NtejXWvfO5PY3Guzo11r3wOAFz1IAmIlX4GWEcnK2wgSAaCKTMYTTqzALKoszy8abwmljEFKebHt1NjOXABECvsZEfP1J4mHgtdr85yw5o1qJr/qL/Wsuf4cWQ0NaEpw5h8lussM2bMGD6jLhoLDpV9+/al/vnPf85LTU0VhYWFo9T2//mf/zn8k5/8JDbp1bsIRPR7ALMBpAP4CMA7Jn3uBXAvAFx44YWxFI8Jlk6GPTxXsBJzqu5CaoBObkUbnitYGdTxHuExJAS0gkAh5RKwkc1f2rhkSAnmjZ+H0fmjsXj34nbzFZiVIc6sX4GiHR9g//8VRSyGP5mwMuo3tZzDrCeu6vD4TSv2GnJcCq9sZwNBchBt/Qywjk5m2ECQ6CR9nWWjW6NZnWWr0oHxrLEMyAVMoGwEwuSBk1H+ZblmYdMV6yzXLHrGbxxQGfnxHuwYdwW89vPlsUKts8wwkWLYsGGtQgje5Y4AQoj7ieinACYAmATAkJlMCPE8gOcBYOzYsbwTlIh00uvv457bsYhgTEaYtQNWhvBwsdL7VgR6GpR/WY7R+aMxZdAUTb6DkUvNc+E4m7b6DQUAcNVnXvz4HQGP5zgAaQCvmi+NKN3BSOByuUyNBMEa+92NxgoY7bUz3RPW0ckLGwgSnVASDiUi6b2AZq2RcFDj7fBCm/TGKpbwVMrpqImmJ92eDrfP7d+hcNqdhjrLHuHBpqObUHZlmWbXwtb4Nk70vF3jwmj3uDH7bW1lhWSqs2zm6TDgq68AAv55001hVzHYt70aW8sPdOjCyDBMbBFCeAF8QER3AvgJgGfjLFLXIhregFZjhjnuw7WnMT9vhyYZYYpJqFy8afG2YPHuxRrjAADLqglXfebFHRsFcs8AtVlAWhvg9Gj7qGWIu4OBoKioSJNwGGBjP8Mw52EDQaKTqHWWO4FVnWWhy5/cavNgSV50aiw77U6NB4DT7sR3hnwHm45uQnVjNfpk9LEMXahqrDLsWoxYOgJp1BfunkWQuT99cDT9A3vzt+DbffsmpQujo7AQnuPHDe2D2zy4ac6csMbct70aG5Z94a+1fK7OjQ3LvgDAcYsMk0A4IHMQMJEiGt6AURhzSmMTsluuQqb7TuR4clDnqENj2qt4uN/nqA/wHGsPveddtKhuNCbFMyt5rHoLqAaBvDPWURBmOq8rohr1wy1Z7Mx0oKXRY9rOMEzyw/+TE51ErLMMBL8T0mz0ACCchYDRja0Zbpx1NPrdGpcV/BUf9d5v4ujaefQeABP7T9SEDYSa16AlYwLcPa4FSF1A2eHucS3WX/4lnvv18xGWPjbkz3lAk4MA6LwHxNbyA37jgIqn1Yet5QfYQMAwcYCI8gFcD+BtAM2QWdtuV15MpIiGN2AUxmz0TMKAlvsBJQdBb08uenvux621L+PF/D1BjREL4wAA0wTFm44akwDfsVEYvAUiGyyRnIwcOTLs8sTXTB+G9a/8Ez7v+d/aZidcM31YpMRjGCaOsIEg0UnEOsuh7FqYGjgsEhISMHvofO3hwmW6218ypMS/22/lUmhFdlq2wQOgeFVxSMmS9DRmT9dmSAYAW5psT1JUT4fAKgad9YA4V2du7bFqZxgm6gjIcILnIN2fDgN4QAjxl7hK1dWIhjdgFMZs8PwIRh2dghtOzwzaQBAJCjMLNQb81ftWywSICg5yoHRMqeE4M6+C3DPAusuvxIslt6EmJxf5dbX4YfnruGHHlqieQ1dGNehzuCDDdE3YQJDodLbMYDQIZdfCxMAhkG46bLpIM7Q1tDbgyWue1Oz2l44pNcQdjlg6IijRU2wpePiKh7Fg2wKs3LfSn2+gvVrLweCz54bUniy4pk7tlEGgsrJS48KY0vsC0KkcQ78eOcbfnmGY6COEOAng2njL0eUxycfjbw+XKHgY+pBl2p7li2zCYL3hX8/aW9f6/11xsAJv7H9DExdAJhWCAPMcBG9NvBLPf+9euNOknjmRm4ffzLwXnw4aim0jLmOjQZgMG1fABgGG6aLEzEBARE4AmwCkKfOuEkI8Fqv5k5pEq7Mcyq6FiYFDtITm3qff7TfDDju8MGbPJRAKMgs0xoWPaj7SxCiGahzITss2zu+tg9dhNAbYvd23iktlZaUmCVJDQwPsqY3o0WMoUs/l+fs5Um2YUMLhzgzDMCGRiB6G7RDoFVA6phQPv/+wZd/iVcWa0oVtvjbN522+NtMkhaVjSlG2pUxjfHjx5tv8xgEVd1oayq+dDCiGBtVogNRUfL2zJ8owDJPkxNKDwA3geiHEOSJKgcyU/FchxLYYysBEglB3LXQGjjNz34FL9DR0O0PnTA+vOFhh8CAAoGlLtaei2dtsONZpdxraVu4Lrp6zgxwQEPAGFPtVPRD0MvXEYNT3mq0NM/C54TrzJoDumRV4/fr1mgzJAOD1edDW+whyUvt36JbI1Q4YhukymOTjabc9GKLiYShgZsIXEJjUMNZY/jCg2kGopNvTTfU2cD4PUHvhg2bhBKrBIFA/VzrNEyND54XgTkvDH++4Bz8NRniGYZguTMwMBEIIAUB9AkxRXolXO4fpmE7uWjxXuBJzjt+F1IA4x1a04blC8wf3wN2AqsYqzPtgHojIv6PQ3gKi2duM5sZmf7+yLWXtegzodzgAmBon9DKloAo9hReN2dPhs+fC5q1FZv0K2APqLnc3zGosA0BTyznMeuKqdo/dt70a6175HKpt5lydG+te+RwAVztgGCYJiVbC4Zh5GBJKq2bCqYQC9vHkorRqJgAEbSQIfOgv21KGkiElWLF3RdhJDQsyCyw3EAKxeWvhc1gYCfQyUnCVGhKBhjVrIpojiGEYRiWmOQiIyA5gF4AhAH4nhNiu+/xeAPcCwIUXXhhL0ZhQ6OSuxaasHQAQ9E6EPk7RIzxhm5Y6SkQYGPeoondhtEpo6GzaCqfOIHDVZ17sv76oWypwl8tlaiRwuYwVLO8ha8wAACAASURBVPRsWrEXQhcxIryynQ0EDMMkHYkaDvD2g8CuJfIGS3bY8QK8yDd0E/D5jQMqTpGG2TUlYXkRtHhbsOnoJiy8ZqHmAT/YhMMpthRM7D8xqA2ETLECZ3Pu0Xr4CWHwIACA/PrkCAtsWLNGU2XIc/w4qubLa6m7rDEYhoketlhOJoTwCiEuBdAfwBVEdInu8+eFEGOFEGPz8vLMB2ESg5HTgTmfAmX18j2EHYxpZ85iY9YOzB46H1O+/v8we+h8bFSMBvGmeFUxRi4dieJVxag4WGHax8yt0Qy19rLn+HFACL8Cb1izJpIiJyxFRUVISdFmw05JSUFRUcchF+5GYz6J9tqZ7ktJScnX8vLyRvbo0WP0wIEDL3n66aeD2ypkmFgycjow9VnAdQEAku9Tn41vfqG3HwR2vgS/NVZ4kUbbYbTAC9gslot5HmPS2WCpbqzGlEFTsPbWtaicVYm1t65FYWZhUMcKIfDeofdMNxD0+QqcTVtxWeUL6H36JEj40Pv0SXznH+8hza2tnpPmduOHb74W9vnEkppFz2hKEAOAaGlBzaJn4iQRw5jDOjo5iUsVAyFEPRFtAHATgE/jIUOXpXJFYlU8MGFeXT0AYGVWT/ggrVSdqyEQOfQukIDRgyDYXQ6z2suqAu8OFn61vnJgFYOioqKw6y4zyUfdn1/Pqf397/t5Tp1KdfTu3Zp7//3Hcm6/LaJbdPPnz6+6+OKLD6Wnp4uPPvrIeeONNw6//PLLm6655pqmSM7DMJ0m0RIO71piaHKLcTDmICD44IUdRvf7k47w/zsXZBYYKgpd0ecKnG453aG3n0d4UO+uD2oeaazfDKdns79NABhxcH/Slj70VJmvQazaGUZPLPQzwDo6WYllFYM8AG2KcSAdwI0AfhWr+bsFlSu0LowNR+TfQOcWJVEwOsypGYV7js+CF71hxyk8U/AXvNNrt6FfCqWgTbQZ2m2wwRemWaG9xEiBtHhbTLMkT+w/UVMFQeWqz7y4Y6NA7hmgNgvofcZ8XM/x42HJnYyMHDkyLIOAM9OBlkaPaTuTHNT9+fWcmiefHCDcbhsAeE6eTK158skBABDJRcjYsWP9TxI2m00Qkdi7d28aLz4YpgP0cVwAvDDf3LOB0EKtcIpUf1sLubEkvzzs6b1er6Gi0LbqbRhfMB6Hzx4OOezAijs2CvTocwXSLv4uKD0HorkO7s/exA07thgMApRtrFKUiDgKC03XEo7C4DwwmO5NrPQzwDo6WYlliEEhgA1EVAlgB4C/CSHejuH8XZ/1v9DGNwLy7/W/CH9M1ejQcASAOG90qFwR9pCNnkmo9/xUiXO0wYt8/OTEXZhUP9bQ16rWcbjGAQDom9kXDgruQdMsnGDT0U2GNjWcIO+M/E+VZ2EcAGAa98houWb6MNjs2u/JZidcM31YnCRiQqX297/vpy4+VITbbav9/e/7RXquO++888L09PTRl1566SV5eXlt06ZNM8+QyTDdgcoVwKJLgLJs+W6lr00S8tlxyrRrm60Ozxa+hhOOWvggcMJRi8WFyyzzDxRmFoJA/nczalpqTNu3VW/ThB3YyHqpqq9U5CAHUmza0LaCnlfAOfou2DJyQUSwZeTCOfou2PtdoR3M4UDhfz1iOVcikT/nAZBTe+7kdCJ/zgNxkohJJmKpnwHW0clILKsYVAIYHav5uiUNR0NrD4b2jA5hehGc8dwNAZ1SF2mYfbIEG7O1i41WX2tYc7THgTMH8OQ1T2oSIzV7mk3dFQsyjQnxzIwGZuEElmYAwcU7OkJNRMhlDpMXz6lTqaG0d4ZXX331qyVLlny1fv36HuvXr+/pdDr5PxmTHETaQy8UT8LLZsscBAFkOZai1vNT2AJ0tA8t+EPeW9jg2o0Nrg+DEiMw4e+IpSNCPo1A2qs8VHZlWcdlkC/5LsihTbBIjjSkXfxdNFXvArxewG5H9rRbkyb8zzV1KtY4MvBUC6HGlY38hnr8h1Pgzm92z7LKTGjEUj8DrKOTEfbX7UpEo4xSFIwOXpgnoMzvRLKjUJkyaIomdKDiYAXmfTBPVkhQcJDDtGSSmctj7hlg3eVXJm08YyIybFwBGwSSGEfv3q2ekycNiw1H796Rt/oBcDgcmDx58rmlS5fmPPXUU3nz5s0z355kmEQhGmGBoRj1v/20fA+oYrDRtQPrU5Zh5qmb/VWGlvV+C3/L3oV2zN4GilcV+x/QCRRyKcPA47PTsk0N+IWZhQZdrhLYdmSn0esPAGzpOdI4AABeL+pXrkLGmDFJYSRYXV2H+Rm90aw8Z53IzsF8GyG9ug63FMRuLcUkJ7HWzwDr6GQjplUMmCgztDi09mCwMi50qnaz+W5AZ8IGIoE+nMEqvKF0TKnBrfGtiVfiNzPvxYncPAiy4URuHn4z8148M2MWblvwLK7//TLctuBZrLv8yqjJzzCJRO799x+jtDTNf2pKS/Pl3n//sWjO6/V66cCBA2kd92SYOBONsMBQjfoXjgey+gIgIKsvFvfKxt967dJUGfpbr10hi1HVWAUB4X/vzPENbnNv5In9J6LiYEWH1YfO4rTp8aJZF2rt8aDql0+ELGs8WHiwCs0+7ffa7BNYeJCTFDIdEy/9DLCOThbYQNCV2L82tPZgKHpU1moOpNO1m80vO6sySsHmC0ihFE3cY3voFxVPfvikoTRSm68Ni3cvNhw7ZdAUlF1ZppnrxZtvgztNe79zp6Wh/NrJBqPBumtvDOp8GCaZybn9trr8hx8+7MjLawURHHl5rfkPP3w4kgmQjh075nj++ed7NTQ02DweD1avXp31l7/8JeeGG25oLwsIw0SOYOP9zQj1YT6YuUIx6pvkGKpyGPMSRIIUSvHnErCRDTOGzwj6WCsDw7v/ehdlW8o0xoSyLWUGI0HzZ29AeLQlDYXHDfdnbxrnqg+uMkK8OeY2Jm9ur51hAomFfgZYRyczHGLQlYhGDgLVJTGiVQx8gEm5JAGBJfsf97s1Lskvx0bXTtwy7BZNGSQ77KaVDTJSMjRxj8Wrik2zH7tSXSjbUuYvo9RehmSzfAOAMUSh4O8fmQ+g80Jwp6Xhj9Pvxk8tZ2SYrkPO7bfVRaNskgoR4YUXXsj/93//9wFCCOrbt697wYIFR2bOnMkJkJjo09kQgfRegH4XW20Pd66iR7X9AGujvpkHgxWdTK7bJtrwyaxPNG3v/utdNLSG/1/V7NgWbwsWbl+oyUGwaP8R/OVrw/HCdcU4kW5Hn2YvfvTe+7j+WHD5FBKRfmkpOGpiDMgnG5Y+splz9zAdEm39DLCOTmbYQNCViEYOAiAKtZvNFxoEQh9PLgCgjycXpVUzAQDlX5b7kxT5hM8yFKGhtUETtzix/0SUf1muqafstDtBRB3WWFbJSs3SjFk6ptQ03tHmrYXPYV4eSk+VSeZohmFCp2/fvp4dO3bsjbccTDeloxCBzhjW9ckLWxuDyy3QnlFfP6bZeqEdJjWMxeyaEoMRP1zmjptryP0TCRpaG/zGg6rGKqy7/Eo8/a0iuNPkkrc6w4Fff6sIvtMHjGUO09MN4yUicwcV4ud7j2jCDNIAXPXhWZyrk94S5+rc2LDsCwBgIwETF1hHJy8cYtCViEo4QOSxKqOkL4XkFGmYXVMS9MM8oI1bLP+yHCVDSjShAGVXllnGM+pxkANNniZT90V9iEJm/QrAp3VhtKpWkN9gHg+ZiDSsWYP91xfhn1//BvZfX4SGNWviLRLDMExiYOm1dyS48sDNFrqguc54vJmngTpXMJiVLDYx1lstCic1jEVp1Uz08eTCphjzS6tmYlKDsTyxGa5Ul6FtyqApWHD1gqBDA8PlpRLzEMAXS24z9LWlpSWF3rulIAe/GX4B+qelgAD0T0vBzZ+04OKD2nWIp9WHreUH4iMkwzBJC3sQdCWiEg4QAd5+UJMlOctxNU57SgGcT6AqIExrJed1orJBi7cFm45u0oQdALL8kVlYQbo9HW6f2x/KkGpPRZOnyTDmwu0L4fa6NSEKTsjxGrOnw2fPhc1bi29/sBPvTbhOszBJc7vxwzeWAd+9PuzzihUNa9agav6jEC3yPD3Hj6NqvjQ2JUOWZ4ZhmKhitQtP9uB2+0M53gq9R5pVKIIj3WRMAWkkOG/MvqK5GdvS07UhBULg+zUlcArtQ7ZqxA/Gi+Cmr90U1OlYVSwww5Xq0uhiK07m5Jq215i0e+vrk0bv3VKQo6lY8LtX/m7aT/UoiDT7tldzKWKG6aKwgaCrEfFwgE7y9oPaOstCKSkUZLmkk47OhUeZ5RAoHVOqyUEASG8Bj/BoQhn0xgEVq5jJoh0f4I6N7yP3DFCbBeSeBUYc3G8sfbh7e6fOKVbULHrGv0hSES0tqFn0TMItlBiGYWKOVby/1cO93uNgaLFWP6r49WQQ6PtahT1YGhwE4LrAv6lwOMVrzDdAhN4WxvpgjfibjhpLDVYcrDDkA3KQAym2FE3SYAc5QESmbS3eFtjI5jfsqzo8kN6na3Eyx1heOb+u1igoUdLqvR45aabGgB45kU8Yv297NTYs+wKeVvl9czgDw3Qt2EDARJddSwxNZzyzAKRo2ggEHwRsAYaDFnJjSX55p6Z32p0Y9coo/+Jh2rBpmDd+HgBokhg1e5qD3rUw46rPvPjxOwJOJZQy74zck7lhxxZDjGOy4KkyT95o1c4wDNOtsPLaW/+L4PIBWVUYInvwRgLXBdq/Q8wrANcFwJxP/X9WL7nEtNtJR50/R5C+PRiqGqsM+XwW715s2P33CA/Sbenwktevt28ZdgtG54/26+ys1Cw0eZr8OtsnfHDanZaeBJlnduNkr2KDV8T4T0zKN1qEBiaD3ptQMljz0A4AjlQbJpQMjvhcW8sPaOYBzoczsIGAYZIfNhAw0cVkkeOFdTK/E45afwKkP+W/3akESPj/7J17eFTVuf+/e+6TBCaZQEjCRQQjKBoLKKAcRYklaoxBq1TFCh6t7dFTAZUKXgNYxSO/Ip7a9lhrQYsi16QxKmhQoHIRJBJFboIIJBMCCZOQSWYyl/37Y7Mns/dea2bvzGSSCevzPH3iLNa+JIW8a73rfb9fAK3+9lOTAB/ABwc+AAA8O+5Zidjg5csuV3U/i94Ci8GiSCbc+wWPlH5jYB5xOzirHXxrAzx718FHUEk2ZGd35FuJO4asLPhqaojjDAaDwQC9ak+NkwBNw4D3EyoRdABJoDdnkvQzNbnAAUZLxHfK9PnhMCqXhkv7lmJG7VRJm4HWJL7Y2ifq+dA29KS4/cGBD5CVnIWXrxUcCuSVfKGVBHKODRhNrIrYfvlo4INlqt49EeKeuDGPR9k/rW2hs9oZGAxGfGEihYyOodb7maDYTxMpPG1owPSc51BwyaOYnvMcNqftJuoSiIQKG4WbJ0dMEoQi+jNHek7xNcWYM2YOLHqLZE5mrzGwjPwVdEnp4DgOuqR0WEb+Cvr+YyTzOIsFGbNmqn7XriRj1kxwFun3mUjvz2AwGF1C7hSg8PVzp/uc8LXwdWUigeYwJM4Pvd6aSp4rr0KgVh7wqt5pxhknLAHpJtsSCOCbXjuwJGs5ThrqEQCPk4Z6LMla3uEkvrih14KYWKBZE5OSAwAQ0KvXIOCs1oSOexePzcS0l8bj0b9OxLSXxnfaaT6tbaEz2hkYDEb8YRUE5wNya6NohQu1eD+Pnq7osextWAan73fg0R6EPZwH/5CdRIT2G5IIFR9UWwFAg7awkD8nlNAWBdNlt4MzSAMjZzDDPOJ2tNR+Dfj9gF4P2+2Tu30fo4itsBBlhiS86uZQZ0tFRqMTsy087rs5r6tfjcFgMLo3avSAaBoGYowOvb6YkiCQVyHYBlLaGwaqeqcCl6C9syQtFbUGPTJ9fsw4I1TMzc34WpEQkOsFaCFSawCJcJUCWclZxOQBzYaYqEFgNiPrmadRt/g1+BwOGLKykDFrZsLE7XgRz3YGBoMRf1iCoKejZTOvlnDez/J73vpH4WuIi0GyYRO+MZuQ7LkPdp8dDYYG/L1vdH7KtAWDWmgLC5rtUsGQAkmLwvFdSgEmANBZ7UJyAAD8fjSuK0HSqFEJsdhYU9uA55L6oNUi9GSeTLXjOR0Ha22DRDmZwWAwGB1Ai/MQzfFAXoWQ9zxQ8ggQumnXGYVxlYcFBa6WYKJA5EV7Kngo+/NHZ4zGT2d/CibLaaf7NCwGC8x6M1X8lwQpsWDRW4K6BvJ3SHauxFn7g4BO5ihUukJxb97phK2wMCFidFcSz3YGBoMRf1iCoKejZTOvFqr3M2X81j+2JwoAlL+aheI+++DWPdc+hxftltQTKng0pt8YbK/drvra8iPlkgqA6wZch7WH1kpOQow6I2aMmqHqft4UHqZm5fvzMu/qRFFDBoCXjzjQGpAuCFsDPF4+4mAJAgaDwYgFtFN9+WbePoScILAPEdr8xHk5k4j99ji2HdjzXocPC1b17kUc/+rkV9hz/57g50mrJ2lKEjg9Tlj0Fk32hlnJWcFkQKjooZi0l+sbWFq2AZDaED+x/P2EFRDuLlw8NpMlBBiMHgrTIOjpaN3Mq4HaN0kZl+kVLLGnwa2T/dWTL2hU4HA5wIOHw+XA13VfY1zmuGBPY6TeRrGPUbx+zcE18AekvZs8z6OyrhKTVk9C7rJcTFo9CeVHyon3+7R5OXifVJyH93ng2btOMZck/NcdqfaQy0Zp4wxGV/Ltt9+azWbzqKKiogu7+l0YjKgQK/8ajwPgha8/kqvU8ONm6bxdbwP+Nukcf5tQxUc7LFABrT5PXrk3Y9QMhUYPACQZkmAz2Yj3cPvdqpMDYqVAwZACbLhzA6qmVWHDnRuCyYGCIQUovqZYoh0ECEmC9JpZ6Hv8fqTXzGLJAQYjjrD4nHiwCoKejtqyRC2E65uUQ2hxcKQNVM6LEm/Ai/1n9ktOMv7j/f8gli1y4IjWSnJ8vE8iaCgKJAGQtBcAwCUbt8Ddy6PKxQB6pXBjd6S/2YgThGRAf7ORMJvBUPLtphP2XR8d7d/S2GZKspnarrxlcPXlEwao80XTyG9/+9tBl112masz7s1gxBVS5R+hvJ88TplHEy8kHBa4fBPQ5JsGP/pAj9PobVgGHY4QkwTyZLwYG1/e8bIk/rb4WoiJg0jYTDYkGZOIlQI05C2AJI2iAAfoST+qBInPDEa0sPjMCAdLECQC0YgMhutH7OiztPRNEhc6nYP8BGLu2Ll49t/PSjb/Bs5ATAaoxe13Y8nuJQCkIoWLmwBf01fkhIAcv0p/6y5m7pAsPHnguKTNwKrj8EDAimVPf8n6Dhlh+XbTCfuXq364wO8L6ACgpbHN9OWqHy4AgFgvQt588800m83mHzt2rOvw4cNMRpuR2ERT4acV2WGByzdBIiLsRwacvt/h97V/x8KsvYrL77r4LkXL3oxRM5BkTNJkRUjCordg7ti5ERMCHWHDSOCm3UDFVdfgraK7UWdPR0ZDPf5r7y5cEvOnMRjdCxafGZFgCYLuDk1k8Nh2wd5ITdKA1o8o3+ADQOmj7eWJjceFzwA5SaAmSRHPhY4McVEhX7gs/Gqh6nJGEnIPZ4fLAZ4D/YBHhiE7u8PPjieizsDLRxyo9njR32zEAwErDCuPofmccnFzgwefL98PACxJwJCw66Oj/cXFh4jfF9Dt+uho/1guQBoaGnQvvfRS/4qKigNvvPFG31jdl8GIOR8+LhHsxejpEn2eILTKP8gDjYbAAwiVfhEq/5p80yQOQwDAw4KJzrvw4/WXYdXBVQjwAeg4He66+C4AwJwtc4Jz5fFRjlrnAlFnoDOSAwDwj3wDqjPGYPVND8NjFvYsJ9P74uVxE2H881u4bvX7zMWA0WNh8ZkRCZYg6O7QRAZ3vY3gwiCc2FDFfHI/Iul6Tkee+/FTyvuSKg3E54WOERY6OpD7Ga9vvBLT64rQ12fHKUMDlmZoczYg9TfKSw0BofQxGnScTrG40dGqOqGUXkyZcF1Uz48nv8i0SwQJlz39ZTA5IOJrC2Bb6WGWIGBIaGlsM2kZ7yhPPPFE/6lTp54eOnQoE8dgdF8+fFxq+cv72z/LkwS0Nr4BY4Cj/25PMAz+D+DEV+qr9ApfJ8ftEJFDP94gXupHH/Q6wOHtQ/OCMfpjz3Z8YFLq8kSyIqS5DYhw4Kj2wh2BJoC44dq7g8kBEY/JjNczLsA157SCfDU1cDwn/JxYkoDRU2DxmREJliDo7lBP4GU7UpozgZbraciU+IlVDSWPCJUJodUHZY8BV9wrVU4GMKa1FdutVkllw/XOKzGjdiosvBCs+/nSMcMxFUn6JGy0fSXZkHPgiHZLN114k2KMVPrY1NZE/14jQDv5ON0b6Eu4LUl6sfHjT9C8aXNCnk40N3g0jTPOX5JspjbSYiPJZmojze8IW7dutW7ZsqX3d999932s7slgdApfL6WPyxMEpDa+nEnA7nfatQR4P/DTVmDU/dJqQmLlQch9Q9cIhFiux2n4kaG41MU1o2D/WEmMvvvIjTiZdZKYyCclB0IFBguGFFAdDzKT1SebSTFefiiQPzgfJ9a+h3u/4JHeBNT3BnZdBLz6UDrxnnV26XgiuQ8xGGpg8ZkRCeZi0N3RIiZISgZEI0ZIg1TVEPAqqw+8rcLCpfB1wDYQAAfYBuIno0nR9jD9VFFw4SFi4c144PRkFF1UJHEnsBqsxNfafEKq8lx+pFzhVlC8tRg2M1lJWY5Fb8Evh/1SooYsqiPLee96Dm5Zuo0qKeV0Ck4GPB88nWgsK1P1Tl1Nip3cPkYbj4aDO2qx7Okv8cZvN2LZ01/i4I7amD+D0Xlcecvgar1BJ9kl6A26wJW3DK6O1TM+++yzXtXV1aYBAwbk9unT54q//vWv/davX5926aWXsjZiRveCJhJIG8+dAsz6Dih2Cl/3rpNqCQHC573rpPO0QIjlvQ3L4If0OX544eN0xBg9va5I1aNSzakovqZYsnm/bgC5mo42LocW4+VuQ64PP8ZvPuLRt0lY9PZtAvJ3AxkN9cT7ksZ9DmUio7GsDIcm5mHfJZfi0MS8hInjDAaLz4xIsAqC7g6p1JDWd0hKBuRMkpY1hoVyX6vM816LrkDjCcWpRe3SyxTT+vrsijEASGo1o/SH0uBpRIAPoMXXQpxb65JuIJfsXqI47Xf73TDrzYpKAIvegqKLirD5xGZVasnyHssvR+hxPGssfrrgl6hPTUe6sx4P/GsFbtke2UopkU4nri4ais+X74cvpM3AYNLh6qKhMX3OwR21kucwrYPEQ+xj7EyV5FmzZp2ePn168H4vvvhi5rFjx0xvv/32sVg9g8GICZyenAzgVKrmyyv5aOPGZMBLEAs3JivHCLG8JCUJ1ziB0LcKAOgVIFwPeuyWYzVYFfFUntSPNC6HFuOX7F4iedbNGxpgkWkT6wA8VLoCi6Y+LGkzMHs8eKh0heJZhizpwUBjWRkczz0P3i08n7UidB4lldV4df0B1DhbkZ1qxez8YZg8sn9Xv1ZCw+IzIxIsQdDdoZUaysr2qTaDh2h9fLJkgNEqtAPsfkfpeHDzK9JLrWn0xYocQtIi0+eHwyj9q3fK0IB+PmW53ylDQ0QxIxEDZ8Ck1ZOCG3xaf2NTWxNevvbliGWJNAqGFKCyrlIi1tRiHYuvBzwI6ISFxum0vnj9nodh8kOV3zLpdKI7Im7Ot5Ue7lQXg22lhyVJCIBpHSQil08Y0NBZtkkA0KtXr0CvXr2Cf1FSUlICZrOZz87O7rhVCYPRGYyeTk7Wp+cA8+yRhQvDEaIhQMVAqPIitCQMb74bRkitbI0wIoAASE1zXnix9NCCiNpB8gQ+bSzceEev70PpKhRjc6iLwUOlK/Dzb3dLpSAtFmTMmim5tm7xa8HkgEgiJfsThZLKasxd+y1avUJyrdrZirlrvwUAliSIEhafGeFgCYJEgOQYMGicOpvBcBoEtoHk60NVlkfdr95SUQ4laTHjjBPFfexw69o7XJb2LZVoEACAm/NgaUap6sd5eW8wKUBLDgDa+htJlB8pV1Q1uFKnBJMDIh6TGW8V3a0qQSA/nejOXDw2s9M36UzrgNER/vjHP9Z09TswGETETX9ofE3PAU7vb58TKlwoj/HhEDf54fQHWs8oxwgVirSKAI6oqAOYYAwm9/v50jHTcR8AKJIEpLhLS+SrjdFqr29JT0JyPbny8MadWxUxOuvV/0Hd4tfC6gTRkvqJkuxPFF5dfyCYHBBp9frx6voDLEGQYLD4nFgwDYJERd6fSNvE0xYWtoHK66tWCpUJoSJIe94TxkMhLTRC73tOawCFrxPfq8DVguLTDcjy+sDxPLK8PvTjNuLP/f6Jk4Z6BMDjpKEef+6/Ct9k/BD5Z6EBi96C6wZcp6pvkQaprDGgVyd2BEChv0A6nTjfiafWAYPBYMSFQeOA3tkAOOFr/UHyvF1vCxv3xuMA+PAbf7WQ1gK5U4TKQbHNgdPjlEHbgaI8cWDmTURdApKuwIxRM2DRSy0VRSFDNai9fs0NFtUaQWqhJfUTKdmfCNQ4yQLatHEGgxEbWIKgp5P3vHCSHwqtHYFmqVgxXzqmJelAocDVgg0nalB19Dg2nKjBsw1O2HWf4z8vfgEFlzyK/7z4BdivHIg5Y+YoFgBakYsMbj6xmdq3qAZSWaPOTxY76ksQO7KOGwtDdjbAcTBkZyNrwXxWkijj6qKhMJikv546Q+uAwWAw4oLoGBC66Sco/Qvw6q0L1UCL+YRDgff6lMDNSSu13JyH6BxEg1SFQNIVKBhSEBT+DY3RWtr91FxfnnMW/3cLh1O9BT2FU70Bt5F8T85qheO55yMKCWfMmgnOIl2bsGR/7MlOJYtS08a7IyWV1Ri/cCMunFOO8Qs3oqQyZjqADEanwVoMejokDQOt7QjycZpHM2kBopLy5CSs7d1LUra/9tBajMwYieJriiV6mG8+rQAAIABJREFUAfWt9WgLqHNiyUrOUvgpz90ylzhXbd8jqawx2bkSZ+0PStoMzG0e/JogduTetx/Dt29T9azzlXhpHTAY5zscx5kB/BnAjQDsAA4DmMvz/Mdd+mI9DVICvrOw2gFTcuSYT3inT1N3oU3HYXpdkURX4FLXEBQ2Xq/q8aQqBFp8FS0PO4qa6zOTM/HlCAe+HNE+Nn6vH4+W8zCEVK9zRiN0ZjP8TqfkepK2gPjfkVoRGILocEdj+ez8YRINAgCwGvWYnT+ss143pjANBUaiwhIE5wMkDQMSNPFBa5ryfse2S3spr7hX+LNQsSTaooTAwvQ0eGWl996AFwu/Wogtd2+RLAByl+WquietVDHavscZo2YoXAwsLcKG35U6BQF9OnT+ejzxz/eJ+gO8bPHBIBMPrQMGgwEDgOMAJgA4BuAWACs5jruc5/mjXfliPQot7j96M+DvoN6K0SoIC6uJvYTWhUyfH1/Ydik0BA4kbccvL7kbLTtqhfp8DmjK8sLk4GHh2+3UadpB0Wr/RAMpZn+dm4z6YYXov3yTZINf8/uniPcgaQvYCgvP64RAVVUVKioq0NjYCJvNhry8POTmStdn0ToSiZvoRHUxYBoKjESFJQjOV6pWKqsKaPg80o2/6KIQqlWw+x2g8l3Af+5kv/G4UGUAqFqoOHXkbhenR7mZpm3wbSYbkoxJEZ0JiBt8DX2P4j1DqxocLgcsLduCiQIAuHEnE2plMBjdG57nXQCKQ4Y+5DjuRwCjARztinfqkRAcAwAI9oM+t9TFYO86oFVlgkBttYCcqpUgWRuTRIQtgQBmnHHC/lAO7JNzguPXrrgWP8NFimoDeXJBS3ztDEjOQ0UXFeH6cc8CD0rn1i1+TWgvkMHZbHF628SgqqoKZWVl8HoF16vGxkaUnWvDCE0SxMKRaPLI/gm7mWYaCoxEhSUIzkfEXkixtFDczNPKH70uoNHVPnfX21BI/IRaIwavO6df0FEXBAq0Df7csXNVlSqSNvhabA7Fe8irGrT0aDIYDEZ3hOO4fgAuBrCX8GcPA3gYAAYNGhTnN0twaK15ha8pY+Sut9XdU0u1gJyK+SBJ9RW4BLX/JWmpqDXokenzY8YZJwpcLSg/Ui6Jm06Pk1htAAjtfR2Nr7Gm/Eg5Vh5YGYzRAT6AlQdWYmTGSMV7pUy4Ds73V+Czq66R2h+WrURWWdl5XTEQSkVFRTA5IOL1elFRUSFJEJzvjkTZqVZUE5IBiaShwDg/YQmC8xGaGCGnb68KCIuGjTClrNLlm4Am3zT40Qd6nMYtZ/6Fj+y7FfNsJmXWvjM2+NFCSg6ctQC93cq5XGpqzJ7LYDAYsYLjOCOA5QCW8Ty/X/7nPM+/CeBNALjyyitZRlQLWvSAaNUGpGoBoGOtfWFaHgpcLcFEgUh5WoYkMR/OShiAQvvHVVmHpvVH4Xd6oE81o3f+YCSPzIj8njFg3tZ5ihjNg8e8rfMU64DmTZvx2VXXYNHUh+ExC5pCJ9P7YtE9D0FXvgqP9qAEgZoWAdq8xsZG4j3l4yl2MzEZcL44Es3OH4Y5ayrh9rW30FoMfMJoKDDOX1iC4HyEtjDg/cKJRCyFlAiOBy7fBDh9vwMPQQHYjww8evI+eDgOFWlfB+cZOAPmjiULCqrd4MtPPOJ5kvGPSRz+q4yHKXRdYjAg65mn4/J8BoPBUAvHcToA7wJoA/DfXfw6PRO1ekC0agN5tQCtGlB8VjjCJSFanRD0/kV0WNK3H9xe8qZQcQu9FZNWTwrG3Rd6PYHB/04B7xXu6Xd64Fx7CADikiRo9ZPXNKRxn8OBtx6ZE0wOiHjMZrw5IR+PxvC9GsvKukzkUG2LAG2e1WpFa6vy52eTtWJcXTRUokEAaHckctSW4sjhRXB7HLCYszBk6JPIylRaaXZHxmbtwv2XrsKag/mod6ch3XIGv7h4PcZm+QEkZtsE4/yAJQjOR2gLA9tAYWESesLR5iILF6qB4mzQ5J8WTA6I6GDBY3W34fsBNTHbzJcfKVeceBRvLQaAuCQJvhyhB+DHE7uymMoxg8HotnAcxwH4O4B+AG7heZ7QM8aIG2qrDcJZE0dKEJCSEOCAzMuBn/4tyw9wcKhMDnDg4ON9wQoDh8sB6x43eG+SZB7vDaBp/dG4VRGQGL/Xj0MT8yTxmbPZUGdPJ86vs/eJ2bMby8rgeO558G5hfSLaKQKIyxpBbYsAbZ7BYIDRaJT8mdFoRF5enmRutI5EjtpS7N//DAIB4e+p21OD/fufAQBikqC7JROOHF6EsZk1GJu5TTb+U8IkORjnJyxB0NP48HGpu8Do6cCtf5TOCWdTKD/hkJ9QRMI2MGKpo58nB1ljgByUO1oFsGT3EolOAQC4/W4s2b0k5gmCVHMqUVBx76h05PxPhWSsK08NGIyexpgxY4bt2bMnWa/X8wCQkZHhPXr06Hdd/V4Jxl8AXALgRp7nmXpWd4BUbSCP77SWQDWOCaIbkURTiAd+3KScG/BDx/MIyJyGREL1Blp9rYpY2MebRrzO74xPHzoHTtFiMH6vH7/5iIfPJwgSBjfoOh0yGupxMr2v4j5Zqlow1VG3+LVgckCEZKfYWahtEaDNa21txR133KGqRSEaR6IjhxcFkwMigUArjhxepNhga00mxAO3h9yKQxvvibAYnZiwBEFP4sPHgV1/b//M+9s/hyYJwp1OkNwNCl+XjpGqD0RmEf7Ny+6pxzz4oTw1aOJcePmbR4JqyO86SlB5VSVKfyjtUBUAzXeZNh4Nc8bMwdNbnkYg5NhFBx0WuH4uOaFImXAdGteVdNmpAYMRT7759CP79tXv93c5z5iSU9Paxt15T/XPfn5LB0uS6Lz88svHHn/88dOxvu/5AMdxFwD4DQAPgFqufRP4G57nl3fZizGkkOI7DXlrXzAGH29PLNgGChWCKjWFAgD+yzEFBc5roYMOAQRQnroFf8laKZlHSpSfMjSgn095AKBPjU8f+pRhU/DBgQ8kY/d+wcMiMxoS4/JDpSskGgQAYPZ48MzPchArSLaJ4cZjjc1mI27+5S0C4ebl5uYSEwKxRMsGW2syIR6VBhZzFtwepSuGxZwV82dpJV7xGWAxOhFhCYKexNdL6ePyKgLS6UTVSqDkkXZHgsbjwufJf5Zu/OelAbzUtgYAwBGsCgk9kr0NyyQaBADghRdJsMDm6wUA6OdLx3/X/BKvf/Ue3DZlFcDLO16OWFVAs0PU4sespXrh2u95TPnch/QmoL43UHmRDn33robPI/w8fTU1cL6/QnFdPE8NGIx48c2nH9m/WPa3C/xerw4AXM4zpi+W/e0CAOisRQhDOzzP/wTB747RnaHFdzny1j55DBYTC+ES/QSeqL4LeWcngDv3V0UPPQqdEwAgmCSgCRcuzSjFzNr7YA6YgmOcUYfe+YM1vUNHeXbcswAgsTns00Sff+POrQAgcTF4eNN6/OKmRTF7J0NWFtFO0ZAVn41jXl6eRFsAILcIqJ3XWWjZYLs9DmyrGY11PxQG+/1vv6gMV2dLBbDjWWkwZOiTkmcBgE5nxZChT8b0OVph8ZkRCbL5PCMxoZ0oqC2L+/gppV1hwCuMS+5HSA7Qxgk9ksmGTTia/H84bahHADxOG+rRovPAyBsl8yy8GdPqbiM+qrGtEQ6XAzz4YFVB+ZFyyZzrBlxHvJY2LkfUMIj0HAD499sv4cFyL/o2Cf+o+jYBN+4OQOdR18pLOzVoLCvDoYl52HfJpTg0MQ+N50SEGIzuzvbV7/cXFx8ifq9Xt331+zFXZlqwYEH/tLS0K0aNGjX8ww8/7BXr+zMYXU7YioGBADjha+Hr0uQ/SaegA4QmB0Q4cLjVeR2WHlqA8n1vYOmhBbi+8UrFtdvt36F2gi9YMaBPNSP1jpy46g88O+5Z7Ll/D76d9i323L8Hxuxs4jwuNRUwGHDjzq1Y8exj2PjIVKwofhz3TZoQ0/fJmDUTnEWqxcRZLMiYNTOmz6GRm5uLwsLCYMWAzWZDYWGhoiJA7bzOYsjQJ6HTSS0BaRvsr09NxDvf34N6tx0Ah3q3He98fw++PjVRMi9cpUGsycoswvDhf4DFnA2Ag8WcjeHD/9Dl+gPxjM8Ai9GJCKsg6EnQehI5vbrraWKE8nGrnTzXaleOEXohy5OTUNxnH9y659rH9r1BfHRfH+GeBEjaAptPbCbOpY3L0aJhcPOGBkW5opbsG+nUoKtFjM4nSiqr8er6A6hxtiI71YrZ+cMweSRTGI4Gl/OMSct4R1m4cOGJkSNHtlosFv6tt96y//KXv7zoq6+++n7EiBHnh9E24/wgXHwntfaJqNEjCEVnBAI+SFoPOD04SkTjwAXbB/r50jHDMRUAsDl1d/C0vuiiIowflw/ka3uVziRj1kxJfAWEDbrt5pvQuHqNpPGCo2gvRIMYwxNBj8jSmgH7qTEwNXiQ0maGpTV+iR1xI62mHWDtD7ehLSD9/6otYMLaH27D70PG4q0LkJVZ1OUJATnxis8Ai9GJCksQ9CRGT5f2KIoM/o+O+STHAoJmwZK0VLh10sWGm/MgiZdm08Xxn5+9BlNrbw5qEyzNKMUXtl2KuXJtgWg1CLRcH65cMRK0U4OuFjE6XyiprMbctd+i1SssvqudrZi79lsAYEmCKEhOTWsjLTaSU9PaYvmciRMnusT//t3vfle/cuVKe0lJiW3EiBF1sXwOg9Gl0OL76Onhr4ukGwRIxYVzJgG7/gFJgoDnhSYUglyBvKrAwpsxva4oGKMDfAClP5QCEJLzXWE5TIK2Qa9b/Bp4mWo/7/V2Sty1FRbG/J4Hd9SqcgxQa3N4cEetxKawucGDz5fvB4AOCw9qRe0G++RZciJHPt6ddQHiRbziM8BidKLCEgQ9CVFnIFTlePB/AD9tVeoKHNsOHNogTRpwOnr7QGiCgVppcEY5RnBMqDUoKxosPDlpaeXNmFk7FTqf8Au+ny8dMx33AYAiSSDXFohWg0DL9S3pSUiub1F1XwAwZGdHPDXoahGj84VX1x8IJgdEWr1+vLr+AEsQRMG4O++pDu1xBAC90RgYd+c91Z35XI7jwPPqhNcYjISBFN9HTwcGjQt/AEC0M5QRWoHwyoWQehwCQABJhs/Q4v25ZJQHr0gQAMrKP7ffLREJjLflMA3SBr3m908R5yZC3NWymVdrc7it9DCadQ64+hxFQO+Bzm9GcvNgbCs1wm2tU+ViEC+yU62odir/nmenSlsUuqsuQDzpqvgMsBidKDANgp7GrX8EXmgAihuFr7XfknUFdv393KkCL3wte4yeHACkc2l6VnLlZEBYqFxxb3ubA6dHpl9ZJklaZIjjYnJAxMyb8EDdZMmYgTNgxqgZkrEZo2bAopdWJVj0FsU8GlquX3ODBW5Zuo3660+vR87GClyy73vkbKygniDQxIriJWJ0vlBDWFCEG2eo42c/v6Xh+mm//kk8kUhOTWu7ftqvf4qlANLp06f1a9as6d3S0sJ5vV785S9/se/cuTPltttuU2fazmB0V6pWChv/4lTha9VKZXwfNE6I3fJY/uHj7ddWzD8XgynLPWOy9DmUAwC7fgmSxmW2h38OaOXIFcJnueaIugRiu160lB8px6TVk5C7LBeTVk8iagRpIVLc7c66QNtKDweTAyK+tgC2lR5WzFVrc3i65RjO2g4iYPAAHBAweHDWdhA13m9RVlYWnC9WIFRVVcXou9HO7PxhsBqlB1BWox6z84dJxrqrLkA8iUd8BliMTmRYBUFPQ25TSDvtl+NtBbWGUAGvnCtXTg59nz3vtfdO8n5c52rBB717ASF9fQEEoIdKrQQAfXxSX2VSj6B4MqHWhSCa68tzzuLMLRzu/YIPuhhQ2w78fon1Ia2CgNYjGS8Ro/MFtacODO387Oe3NHSmInJbWxv3wgsv9L///vstOp2OHzJkiPu99947nJuby3obGYkLwf0HZY8J/x1JgNDbCux6G8H43HhciMGjHwAq3wX8IRXEnF44MBBbECK0Itgn58A+ud3q77n/m4VpRwtg4dvtANsIjkSiLoG86i9ay2FRSLgjNsg0wsXd7q4L1NxA/rVHGldrc9jc+zDAydaFHA9PUi0gO3siVSDEE7HiT42eUHfUBYg3nR2fARajExmWIOhJkBYVmtBS8sNL+xZpugaEBczmpCRJcgAAylO3oNBJUEk26cC3KSsbThmkv9O8AS9RPLBgSEFUJYxqr89MzsSXIxz4ckT72Btv+NCXlCTguKC9UbgFRiKJGHU1avsuSczOHybRIADIpw7dlfNZYDE7O9v33Xff7evq92AwOsyHjyvbBg5tIG/8K+ZL4yxVgFAWy72twj2L3pAeILS51B8imJIVQ+Yr0rHEsxzT64qCGkEW3gybP0UyT65LIGIzSzejWtEiJEyisayMGl9J44cm5uHTy0dJ7A8fKl2Bm0L0CcLds7NJsZuJyYAUu1kxpta+kOd88kvDQqtMiBeTR/Y/b+JfIsBidOIStwQBx3EDAbwDoB+E6PUmz/PR15cx2omRnZEqbAPDKyeLEBYwJA2Cv2StBMcDhU3XBwsUksZmwnyBDc61h8B725MEbs6DpRmlyvtGeRoRDTNGzZCcZADA6olm/OZjgtWhrPcqnPBgZ4gY9TSiFVHScurQ3WACiwxGAiGv8LMPAX7c1P7nvJ8sRCgij6dqBAhDr82dIk0wFKeqf3fC+cHmE5sxDNLfM739ykQCQHYkirYPORoh4kjVAPK421hWhk/6D8aiqQ/DYxY23CfT+2LR1IeB5X9Djop7xhp5MuKKKU9gR3OSpM3AYNLh6qKhimvFU/5YawjIKxAYDEZiEs8KAh+AJ3ie381xXC8AX3Mc9ynP89/H8R16NqrtjCitBKZk4UQh0nxaOwEJwgIm0+eHw6j8q7eu73v4r7nPKcZLfliH4Xv6BU8o3sv8BF/0UroYqBUf7AxI7Qj5D8/AgPEBSQAXKwfkJIIAUrypqqpStXgJ13eptoogUU8dmMAig9FNkScDciYJpf6hFX5aq/zkOj9qBAhp14pjat/Bq1wbDK8ZgJk198EEIwChnYCnVCLKq/4AoLEtutPmaISItbgEiRv/t579n2ByQMRjNuOtO+7F7zTeM1pIyQjLX5/B2N/+AXuO2VRV0+Xm5kZMCHC8HjxHsNfkORhNhogVCAwGIzGJW4KA53kHAMe5/z7Lcdw+AP0BsARBrKAFe6td2PxHKivUmwFjQLrYMFoFgSO544Fam0TCAmbGGSeK+9glVoeWQAAzzjgVl7+4/UV80PIBkCMd13N6+EM8obWID3YWxHaEIdKTg0MT84hJAiY8KEWtBROgre+yp8EEFhmMbgip3S9UF0ANRqsyFssT82IcFhMR4e6fM0k5piXBQOC3J6cEkwMiHDiFuwGt6k9HE05UyYxRM/Dcl8/BGyLEbNQZVa0FtLgEiRv/Ons68Zo6W5rme0YLLRmRvPL/YdrGipg9J6XpIpztfUAqaR4AejVdjJ//54hu5WLAYDBiR5doEHAcNxjASAA7ZOMPA3gYAAYNGhT390p4SMHeaAVufkVdWWHrGeCON6WnHlqSASTkCxjbABScS2IsSUtFrUGPTJ8/mByYtHqSRBBw1cFVxNv6eT+ykrO6jacyIAgmRRI0zJg1E46nn5H4LHNGIxMelKHWggnQ1nfZ02ACiwxGN4TY7qchOcDpgMLXtcdiTt8uCCzn0AblGCE+o7WBXEloVbYI9PInUV/ltMkJe5sNDaZG/L3PWoX+AAAEwjknqUTepqC2bUFvs8HvVB5K6Akl8uIGP6OhHifT+yr+vL9FsGmmVQl2xgEALelw2GjAh4sXx2zT3idpENAEuFKkNod9kgapqkBgMBiJSdwTBBzHpQBYA2Amz/MSCTee598E8CYAXHnllcwkUyukYE9aVNAqDWwDlD2KsXqv0Hu+ciEKXA0ocLUEh8qTk1DcJx3uc+WCohpxuAXEhjsJC54uovxIOeZumRssr3S4HJi7ZS4ApZpyRxc0iYjaNgE5ai2YAODqoqESDQKA3nfZ05idPwxz1lTCHWIFajHwCSOwyGD0SFS3+1EwqEzwySsVaMmBcO8kj89VK4GS3wKBkHvp9MJBg4xw9sQ/my9Uzg0CcGD1/wKEnENWcnQb5yW7l8DHS0X0fLxPlUghbWVBGhc3/g+VrpBoEACAVcdh7hDh+4in8xApGfHToEHYOWYM/DLrQUBZeacWIb56YTndr/3Z50l8ZTDOZ6Kr79IIx3FGCMmB5TzPr43ns88bcqcI4oHFTuErabOf97xQWRBKOF0Bkh9zNNz8CqCTliUusafCrZMuNuTqxKGQShNdlXVwLPwKJ+ZsgWPhV3BV1kX3nhqYt3WeoveSB495W+dJxuoWvwb4ZKrAPp8w3sMQ2wTU+CRXVVVh8eLFKC4uxuLFi2G1khfIJAGki8dm4oapw4MVAyl2M26YOly1/kAiMzZrF+6/dAXSLQ0AeKRbGnD/pSswNkt5WsdgMOIEqd8fACgbagVel7DxbzwOgG+3OZTHXi3CxNR3IhDgw38+hy6JcsZklH6fM0bNgEVvkYzFoi0wGpFCnpKEJo1nzJoJGAy4cedWPLn8TfSrPwWO55Ed8GHRsIH4RaZQXWErLETWgvkwZGcDHAdDdjayFszvFIHCjFkzwVmkP9Oqn10Bv14qAi1W3nWU8zm+MhjnM/F0MeAA/B3APp7n/xiv5zIIqK00ANT7MWt9/rHtEmunWoO2v4pj+o2RtCO80OsJDP53StDtwO/0wLn2EAAgeWRGx95TA61+8iJNPh7PHkWgay2X1LYJkPQGdDod9Ho9/P72U6xwAkgXj82MasHiqC3FkcOL4PY4YDFnYcjQJxPCI/nI4UUYm1mDsZnbZOM/JcT7Mxg9Elq7n1zPp6kaoFXJRbI5rFqpTeSQpEEg3id0LdDSAOU5egD4+Cm4/Nejaf1R+J0e6FPN0GclIXCY4OXr5+GqrAvGXpKIbyzaAqMRKQzXDiCPmykTrgPHceAB3LhzK27cuRWc0Yisl/4AW+aVkuvj5TxEsmNsSSK3fERrPRhtfGUwGIlHPFsMxgP4FYBvOY775tzY0zzPfxTHd2CIqG0lIJ1QkPyYw0FSc658t70ckvejt9+PRr3S/tBmsuGmC2/CqoOrEOAD0HE6jOk3Bt+c+iZYYeBwOWDd4wbvlQZH3htA0/qjcUkQ0Bi/1y8IE54L4JzFAr5VmUzgOsEaKN6WS4rnq2wTICUSAoEArFYrTCZTpwsgOWpLsX//MwgEhP9f3J4a7N//DAB0+02220NOLNHGGYxEpqSyOjHsSNUm4Ys1/t4XEwJi4l4Le9cBt8rOZkgHABRcZy+XWA77nR74nRQh2AAUsZco4hslJHthtZUJtHaAlAnXKeKm8/0Viut5r7dT3Am0IE9G2M5pDyjmMetBBoOhkXi6GPwbquvrGF2GfDNPWzCo7bEkqjkrfZ5pfzE4jsOz457Fs+OeDY5NWj1J0X7Qx5tGvJ66gIkxonJzKOP3+vGbj3j4fMIpBc3iEOicXp94Wi6RsNlsqhYrtERCa2srnnrqqU55t1COHF4UTA6IBAKtOHJ4kSJB0N0qDSzmLLg9yr9XFjNzxWD0LEoqqzF37bdBW89qZyvmrv0WALpvkkCeEJDHV6ud7CgUjsWXabdHBMjP0dCi0OSbBp7aua8kHrE3msoEW2EhWnbvhnPlKsDvB/R62G6fjOZNmxVxk0Z3syfOy8uTVOMBsbEePLijFttKD6uyTmQwGD2DuGoQMLo5VSuBkkekfY801PYzqlyANOp0uL7xSiw9tADl+97A0kMLcH3jlWj0KDePpP5CkscyAOhT46NkP2WYspri3i94WHyEyQT8UZYAkoh3O4OcvLw8GI1SrQnSYoV2uhGvUw+1p/BipYGwIeeDlQaOWqV9V7wYMvRJ6HRSvQadzoohQ5/sojfqGt588820IUOGjLBarSMHDhx42SeffJLS1e/EiC2vrj8QTA6ItHr9eHX9gS56I42IyfLQ+Oo5C+hN0nlyfSA5HUkOUO+lXkzRD6V6fzjiFXsLhhRgw50bUDWtChvu3KC6SqGxrAyNq9cIyQEA8PvRuHpN2ES+HJLjQVeSm5uLwsLCYOy02WwoLCyMqvLu4I5afL58f9ApqLnBg8+X78fBHZF1HhgMERajE48usTlkdFM+fgoIeCPPowkayk9H8p5XvQApahiNaaemwsILi4p+vnTMcExFqjlVYR9oM9vg9EjtiZZmlGJm7X0wB9oXW5xRh975g1U9P1rECofQVog+hNZMGp1hgxRPyyUS4qIkkotBZ516qEXtKbyWSgMgPtUG4v26U1VDKM3ba+xNFcf7B862mXS9TG298wZWp4zL1nhkGp5169b1Li4uHvDuu+8euf76613Hjh0zRr6KkWjUEOw8w413OfJ42OZSJssDXsCYLDgGnNPjwRX3EqvsVGEbqC2BEK5KUAEP1UWgei5usbejnPzDSxK7YQCKz5EIrafoSr2fUGJtPbit9LDEIQgAfG0BbCs9zKoIEpx4xGeAxehEhSUIGO2EK3W0DQzfSylWH4gJhsbjwmdrmqoSygdOF8HES08cLLwZU2tvwa+2Pi3RGzBwBhh1RnhDkhnb7d+hdpgPQyp7BQWUeucPjqv+gLwV4tDSPPJpBMcBIdaGnWWDFE/LJRpqFitqEwmdxZChT0o0CADyKbzb48C2mtFY90Mh6t1pSLecwe0XleHq7N2Ke8ZT1yArs6jbJARCad5eY3d++OMF8AV0ABA422ZyfvjjBQAQy0XI/Pnzs2fPnu3Iy8tzAcCFF16obZXPSAiyU62oJiQDslNVWgLGEw29/fCG+P/xfmDPe4ApWUgoaEFM3K/9tfpr8p6Xxu2w0JMDnFUPvlU4idclGWArHNql2j9q8DudkSc+4tIXAAAgAElEQVRFQHQ86Gq9n85ErBxQO85IDOIVnwEWoxMVliBgqGPWd+H/nFR9EPACPo+wcAk9OdEZBeXmEM9mUyCdeNvkVjPGNVyG6XVF6Ouz45ShAUszSlHZ9xCSjEmSvsPxQ/KB/I5+g7GHtkG3jPwZWr/aKel77IxFBEnlON6nGlVVVao2/rE+9dCC2lP4r09NxDvf34y2c1Uq9W473vn+HpiMqZDXOmitNuiJNFUc7y8uPoL4ArqmiuP9Y7UA8fl8+O6775JOnTplGDRo0GUej0eXn5/v/POf/3w8JSWF7M3GSEhm5w/D7FV74A2x3DPqOMzOH9aFb0VBi/2gHG+roE2g9wL+NnXX2Aa2J+61JAgAIWGtilYABJV8I4f+L1yj7Zk9BLEar27xa/j08lF4q+hu1NnTkdFQj4dKV+CmLhYxjAWWZAPcLmWvpCWZbR8SmXjEZ4DF6ESG/QtntEMTTLLaI19LqxLwuoA7/qZsPZDZHOrNbvg9ysVHE9eMGQ5l68ESLMeB7GoN31z8IW3QUyZch8Z1JdK+x3UlSBo1qtOSBF21QCHZF5aVlQGAIhnQ1SJIak7h1/5wG9oC0sV0W8CEtT/cht/L5jJ3AeFEQst4Rzhx4oTR5/Nx//rXv9K2bNlywGQy8QUFBRfNnTs3+3//93+79y8Ihnbke9nuKnscrU5A6xngjjfPxc3jQusB76fPD03ga4njFfPVJyFgIQ/7EnSNb7UCBEchtYRW433S/0IsmvpreMzCOuVkel8smvowsPxvyInJy0ZHNPFVLr4caZyRGMQjPgMsRicyTKSQ0c7NrygFk/QmYTwacqcIC5hiZ/tCZvc7UpvDwF/B6aV9bpxRB06nCyYHRCy8GdPriuBwOcCDh8PlQPHWYry4/UVMWj0JuctyMWn1JJQfKY/uvWOArbAQORsrcMm+75GzsYKokCw6CyQKB3fUYtnTX+KN327Esqe/pIoVkewLvV4vKioqFPdLBBGkk2fJuxHSOM1F4HxyF9D1MhF3HrTxjpCcnBwAgN/85jd1F1xwgTcrK8v32GOP1X722WfdSz2METWvrj8Ar1+6KfH6+e4pUsgpLXvP/YFw2i9+pSXfbQOEuJn3vFCBFy45AE5wNihOFb5mXk6eNuJ25ZgGkUJqNiZB94mcP9zPlIwhOxvgOBiys5G1YH4w+f7WHfcGkwMiHrMZb91xb0zeNRqija8eF/nnRBtnJAbxiM8Ai9GJDKsgYLRD824GzlkrhdEg0AKhHSFZvxEwWtFkminREPB/sJ94i74+6cLK7XfjgwMfBD+LSQMAMfdejoaudhaIFnGxIYoWiYsNAIoTCZp9oXx8W+lhNOsccPU5ioDeA53fjOTmwdhQdhrlW493iS4BCS090Gp1DXoyvfMGVof2OAIADLpA77yBMTs16Nu3r79fv35eLqRMmlNdMs1IJMKJFJZUVuPV9QdQ42xFdqoVs/OHda31IXVDz0tP++VaBUC7lkDVSmDdbyMkB87dU6xYaDxOr144tIFgs6hOIyj4nG5bsiFFLmxMsj7k27Tvg3I2SpPbojBh3bOvEufX2cj2y/EkWpHBFLuZqDeQYo+PSwWjc4hHfAZYjE5kWIKAIUXu3UwSWyp7TGgROLShfaFBE1UinZBQFiTJgXIkz3lPMrZ/zVb08yn1CWi2hqG4/W4s2b0kqgSBmoWGFrraWSBatCw2bDYbMUkgty883XIMZ20HAU44igoYPDhrO4Cz4IBGYSxce0K8mJ0/TOLDDgBWo57YA93d3QXigdjH2NkqyXfffffpv/71rxm33357o8lk4l9//fV+kyZNil6BjNGtoCXobFaj5N9ltbMVc9d+CwCqkwQxTzCoLfMPl5QvfVRFckADYuwOjeU6o1AlqKLNgIMbPEGDgDN1r0LU8iPlKN5aLBE27ozDglBhwoyGepxMV9pA9rfEtFq7Q0QrMnh10VDJoQAAGEw6XF00NCbvx+ga4hWfARajExWWIDhfIVkSkqoCSGJL3lZg19sI1haKCw2dXrBrEhHbE+TP0kDJwE2YdrRA0mbg5jzYkfItlh5aIBEu/MK2S3F9ravjZeqdsdDoDs4C0aBlsaHWvrC59+FgciAIB8hrV8X2hK5KEIgbBrUbie7qLhBPUsZlN3TGgiOUV155xfHggw8ahg8ffpnZbOZvvfXWhpdffjkxSnIYqqEl6DgOkjFA+Pzq+gOqNvklldVRJxiiQp6UB4BXLtSgDaASTk+2WbTahQS/GJ+bqgURYRk8RYOAbwvAsfCrLnMPkrNk95JgzBaJxWEBl5oq+Vy3+LVgHH+odAUWTX1Y0mZg1XGYO6TrE//RVgCIif+u1AhidA7xiM8Ai9GJCksQnI/QqgIA5UKF2qMo29CRFhriSYhauydCtYH5inQs8SyXuBjsSPkWkxqvVggXAlAkCTKTOx7EOmOh0R2cBaJBy2JDrX0hzykVkmnQ2hbixeSR/bu2dJmhwGw28//85z+PATjW1e/C6DxoCbpZH3xDnE9rSZDz6voDUSUYiLSe0TaumBduzc4B4M9ZD6sUQ5Q7Ccnf6akf2z8Xk1uD9TgNP8gbf7/TE/zqXHsIALosSUA7FNB0WGAwAD6f5HPWM09LpoS2Bd64cysABF0M+lvMmDskC7/IVCHw3MnEogLg4rGZLCHA6DAsRicmLEFwPvDh4xLHABgt5KqAivnKBIFtgPpFiHyhAQjaBWrtnggiSptPbMYwSBdpE85eSRUulCcIrhtwnbpnE4jFQkPsUZQnA6JJCNDuGQ+0LjZibV8ob09gMBjnD6QE3avrD1BbD8Yv3Bix2iectkGHocVNjRV0RKxp6hINtoHSZL3oiBDpnSiJh969P8QZ14OAP7wqIe8NoGn9USSPzICrsg5N64+GrS4gzQEQ8ToamcmZcLiUh5NaDguyX34pYoyVtwveuHMrbty5FYbsbIVWAdB1cZtVADAYjI7AEgQ9DXk5v30I8OOm9j/n/WStAIBcLZD3vFJESTzBkENa/GhRSd67Drj1j5Kh4TUD8JjjXkm1AM1eRy5cCACf/PgJnh33rPp3CCHahUZojyIA+Gpq4HhOqKogLQzULCC03jNa5O/Ub9ZM3DD1qpguNvQ6I/y8N+I8UnsCg8E4v5mdPwyzV+2BN9AeF3QAXG0+OFuF3yvh2ga0iI+qJu95oOQRqRivztheVUciNHZzOmKZP4D26oJI1XihYogipY9KWxf0JuU75T1PnjfidmCHOssCv9OD6nlbwbf6JWNnPjiAMx8ckCQCnGsPgfcG2uesOgBwXDARobUqYcaoGZLWQACw6C2YMWqGqncH1NkDa2kXjHfclnM+VQB0O8FSBiNBYQmCRIWkIQAoy/m1+DGTNvgkEaWcSYJNoZrFj6YKBGVZ5X+evl1RLcBRlJRJwoWNbR0vSY92oRHaoygiWhp2dOOv5Z7RQnunrAXzMe2l2D0rzTsMp3XfSU1XA0AKPwB6+9lu42LAYDC6KbKQEAAQkJ1009oGtIiPansnTvn52HZy3P74KWn846PwDtQZ6dbE8vvSnkOY17Snl/CDVQEPHmilCyyKm37OqAsmB4IEhDtI7hdSlRAJsf0vluLCJLS0C9Ytfg2fXj4q2IKQ0VCPh0pX4KZOiNuJDmmDPzZrlyrB35LKasxevSdoh1rtbMXs1XsAxElPhMLSL/6FP21qxunW3uhjbcJ/T0jB9Otv67L3YTDUwBIEiQhNQ8AQps8wEqK1EgmSs0Hlu9I5NNsSYgWCetLbyCXlPHhJosDNebA0o7RDz6BRMKQAlXWVWHVwFQJ8ADpOh6KLilQvNLRYGqrd+MfTJjFeyQjutB29LMPgSpHaHFrc/fDogokxew6Dweh5vLr+QHBDEAlS24BW8VFVVMxXigz625TiviWPnDstpwgScnqhkiBSol3eTkATHJbZCyPgVbYWUub53Xrio+WxOAAeOhV2iLw3oEwOhEHUOVBDwZCCuNgbR6o0ECvwPuk/WCJieDK9LxZNfRhY/jfkdPpbJg4kwdA5aypx/6WrMDZTaOdwe2qwf/8zAKBIEswr26v4XeD185hXtlfVv2dHbalq5yG1lQpLv/gXXtrgR1tAWMuebrXhpQ1tAP7FkgSMbg1LECQiNGcBTZtw7tyiI8KigvZ80uKHpGFAqkBobVBtichxHLGbAQBOm5ywt9nQYGrEuxnl+KKX0sUg1ZxKuFId5UfKUfpDKQLnSj0DfAClP5RiZMZIVYsPLZaGajf+8bRJjFcyIsVuBhr6weLupxxnMBiMMGjRCqC1DcRcfFSLuG84+ABQfM4NbPFlFA2BgeR2ArXvJB+nzNPjFFGksIlrhlvfFhQRziC0+sUCfWpixYPQCry3HpkjcTgAAI/ZjLfuuBe/i/Fz9235HFtWvIOz9afRK70Prr37flxy7Q1R3bOqqoooNvzhhx/i66+/Bs/z4DgOo0ePxq233trh55AEQ90+DmsO5mNs5rbgWCDQiiOHFyk272dayP+eaOOhOGpLsX//MwgEhN8n4RIRWpxP/rSpOZgcEGkLmPCnTY2Yfn3E12IwugyWIEhEtPT10+gzDPBStAjkyNsZaCcZtPciVSCQehxJZZGU5AAHDj+bL2TuBwGoPqLD519+BW/IgsuoM2LOmDlhvrHwROtikDLhOjjfX0Ecl6N24x9Pm0TaOx3PzcUnixfHrPSf+SwzGIyOQtMQkCvlxKRtQC1aWusi3UeEVI0XrvJP7TspRArJ83r3/hDO1ockp/4eXRv+mrlKIg689NAC9POlq3olzqoHfLy0kkAHiQYBAHBGXVCzIFEIrcCrs5N/HnW2tJg+c9+Wz7HhzT/B1yZUW5w9fQob3vwTAKhOEsiTATk5OdizZ0/QrrixsRFlZWWorKzEjz+2i1LzPI9du3bhTE01Wvds71CCgpbsq3crf06f/5iN32+RipBGw5HDi/DliUux7odC1LvTkG45g9svKoPJqExEaHE+Od3am/g82jiD0V3QRZ7C6HbQlJCtdmHBIIFS6nf6wLlFAN/eolC1UjlPbGcInUu7p1qF5twpwMhfCeWTgPB15K+IFQy0UwP5eMGQAiwYvwBZyVngwCErOQsLxi+IqswwWheD5k2bVY9nzJopWCuFYjAoNv62wkJkLZgPQ3Y2wHEwZGcja8H8TuljzJg1E5xF6n3909Ch+OrSS4J2g+JioaqqqsPPuXhsJm6YOjxYMZBiN+OGqcPPG1ElBoPRcWbnD4PVKC1/txr1mDpuEPqnWsEB6J9qxct3XB6/PuS859XHYhryzX/uFKDwdaFiAJzwtfB19ZV/pHciJRhyJhEvT85NQ+odOcHYq081o3aCD9vt0uqF5ZkfI2CI3PLBGXVIve0ixT3T7hqGtDsvloyl3pETc9tEfSq5upA2rhWfw4HPrroGd7/4OnjK//f9LaaYPEtky4p3gsmB4Hu0ebBlxTuqrq+qqkJZWZkkvu/atSuYHBDxer2S5EAoh6trcPb0KYDngwmKfVs+V/V8WoVPssGF328uxoMbluD3m4vx7vd34p1996Da2Qoe7Sf4SUbylibVaoz47M9/zMI739+DercdAId6tx3vfH8PPv8xCyWV1Ri/cCMunFOO8Qs3EhOSADnBkWIkz6WNMxjdBVZBkIjQThLEE3g1p/3yo3mazSGpnYF0rE9SQ6ZRtVIQOeTPZWB5v/B50DjF883D09CyXbkhNw9XZpRj3XcYrYuB1hJ9juMkP1mOousQrU2iWkgiTHvHXwNfm7S9xOv1oqKiIqoqgvNJZZnBYMSOTtEQiBaauO+e96TxVGckaxBY7YJrQMV8YO3D0jZAeYwmCRaTkgakdyLN3buO/D3tXYfkW/8o2ahnASg+4pMIAuZdexvSG4drsi4kbf5jnRCQ0++Zp+F4+hnwIZtfzmhEv2eejsn9N954CxYV3KVoLRCx6jjMHRLb1sCz9afR1tuOtr79wRtN4LxtMJ2qxtn606qur6ioUCQDOkLz0Mslz9+y4h1VVQSz84dhzppKuH3tax89F4DHb4HLlwIAqHfb8cWJayFPuLV6/Ui1GuH2BRBiaAIdB9x6RZbC8lQufLjuh4fRFpAmbNoCJry//xcIHJC2E1B8vIgJDr3eChB+pHp9FC4pDEYcYAmCRCRSoA8N+PPs7RvxSJBaBNS2MwTCPEO+gGlpIIoguUpK0PTRYMkCwrOf7PdMG48l0boY0Er0OZsNhybmSZSP6xa/JlmoAADv9XaKO4EW5MmIs8XFxHniiQODwWDEm5hrCMQC0mZ+0Diyi4EaR6KyxwQXhEMb6EkHcZ74fDXvJIfgJhRunJaY74pNvxa0uBB0hL9PvhseHWGJzfMYYDFh7pAs/CIztnoN+uxB8KTYAZ1QUcObzPBkXQBdWh8sVtEWGKs4zpvMkufXO35Sdd3YrF24/9JVWHMwP1jm7/Gb0exNls0kH544W70w6jgEQl04eOD9r47DHwhxNlhViQcukwofktoYAMDlSwYgXd/yUN/C1ORWDIUdZzC6CyxBICNhPFTVBHpAfXIAAKyEX5Bqeyl5v2DVRDrdkC90CLh8E+D0PQC+RSiPE22QaCrHWhSNO0q0dkkkvQAYDIDLBZ9TEJ4SrQPlbgEineFOEA02m424iLDZyG4TDEZnk5SUNDL0s8fj0d133311y5Yti0ETeM+H47j/BjAdwOUA3ud5fnqXvlBPRp7cr5gvJATkIoOLLyMLEctdEEI/h84jVQMyFHRmNZ6DlBwAwIHH+88+JrQPxvjZbRkDALdsbaTTozWpN1plbYEAFEkCq9WK1lZ1pe8cx4En2WTKKx91evgyBymmkcQUG3SLMDazRiJI+OCGJareBwD0HAdvQPpOAQCQjXkDHN7bVyh5TrrlzLn2AnXwEFqXIu0V0q1NON2qXB+lW5tUPyuRYfE5cWEJghC0KJMmDLaB6sWSfIRNtxabQtIJA7FFQUmTbxp4SPvdeW9AmaY9R7wUjaNpW7AVFqJl9244V64C/H5ArwdnMoFvaZHM491uQK8X5sjoDHeCaMjLy0NZWZmkDNFoNCIvLy+q+x7cUYttpYfR3OBBit2Mq4uGspaDHsDOnTvtmzZt6t/c3GxKSUlpmzBhQvVVV11FOaLsGC0tLZXifzc2NuqysrKuuPvuuzu/xKjnUAPgRQD5AFjda2dCsygGpBt6tS4INBXfaISMrXZyLCe4DDGUiNaGGY/Mwcn0voo/z2ioh6+mBjVzhVaGWCYoWuTJAQq0tkA/YQ1Cg5ogIODXS7caNDHFyx5QVlzSNu6kE3y5cGA45FUJt19Uhne+v0fSZmDStcGk9xIqGITkwJdzItswTx5aQrzv5KElAO5V/b6dAYvPjHAwkcIQwimTJgxVK4XTh+JU4WvOJIJYEgWvS3k9oBRG0oLKhYofykAKAOAFMaNQEkXRuLGsDI3rSto3/n6/IjkQxO8HZ5QK6XBGY6e4E0RDbm4uCgsLgxUDNpsNhYWFUekPHNxRi8+X70dzg7BYaG7w4PPl+3FwhzoxSEb3ZOfOnfb169df0NzcbAKA5uZm0/r16y/YuXNnp+003nnnnTS73e7Lz89v7qxn9DR4nl/L83wJgPqufpceD82iuGK+dEyt4C+NaK6/+RVBGyEUnZHsMsSQIFob+mpq8FDpCpg90g272ePBQ6XnnI18Pjj+8FJMn6+lko9UCdgm0xcKRyAQgF6vjzyR8F40MUWvS3nwc/tFZTDppe2XNBHSzF7qEhYkrs7+Gvdf+j7SLQ0AeKRbGnD/pe/jyn5fE+ffMJyyZlXc9xtck70dOvgB8NDBj2uyt+Pq7G86/K6xgMVnRiRYBUEINIsVLT7LcYMkTAQoTyf2vAdcca+0bzFcRQHpdKPwdWkJZLGGcnLVdk8BAIRgwwGpd+RQxY26M6E2RxHR6RTZeLXZ+XiTm5sbVUJAzrbSwxKLQwDwtQWwrfQwqyJIYDZt2tTf5/NJsns+n0+3adOm/rE+pRBZvnx5+pQpU+p1Opb77gw4jnsYwMMAMGiQsmyYEQFawlw+bh+iwSZRdpaqxfqQhFoxQ4aC0Jh/486tAIC3iu5GnT0dGQ31eKh0RXAcAPhzrYaxglThR8NojKzsHwm1FQc5OTmSzzTRxOrt6Rjy8zMIBNrX3OMHfI8GnR6l33Lw8zz0HIdfjO6PFydfrrj+4MF5eGvPzTKxQVExQI58fcXh6uyvcXW2NCFQcngy8V0/33+KOC5nW83PsLVmHALn1rcB6LG1ZhwuSv0R0dVdRgeLz4xIsARBCDQ/ZZr1SpdBK1M0WMmnE3vXAaaQEilTMtDmUt6X09FPNzq6OMh7Hih5RClKqHw4eZgXhI0SISEghyRQSCUQEP4nuYGvy0UK44FYOaB2nJEYiCcTasej5eDBg6adO3f2Wrp06dHOuD8D4Hn+TQBvAsCVV17ZPTOY3Rlawlx+4n/03+ruZ7QqDwBisZlXq3HEkCDXDLpx51ZJQqCzERP3FRUVQUFCmvCgz+dTjGnRINDCnj17cOjQoeA76bMHwV+tFC70Oy/C8OGTJe4Ch7yz8fE+I/zn9LT8PI81X1cDEDbpoRoAo/tuRNulTqz7oTAockgTHwQ4WMzZwefY029Abe1aSXJCp7OivrU38Wq1B4clhycT3RFKDk/Gs6ru0Dmw+MyIBEsQhDA7f5hEgwCgK5N2KbQyRVqvf2tDe09h43GhXFCnlzoP6E1KqyUR1ScZFCh2fZIpOAseysoEzqquhK1bQtEV0EJ3EynsDCzJBrhdysWKJZn9ekpkUlJS2kiLjZSUFPV1rBp466230keNGtU8fPjwTrk/gxE1NIti+Yl/OHFh28CEP9l3VdYlZFVgJGjOReA4gFARqE9Njfk7yCv8iinOQ6QKxREjRmDXrl2qnsPxevCcuvWN1+sNJioaGxuhT+0Hc2sr9A11wTkGkxnX3n0/sjJvQFZmUXD89ws3olW2tm31+rF8+7FgDYCoF/bAZRNxdXaFpArgoQ2vgSccQHEch/Hjt0jGUlNHS5ITQ4Y+iaSNBrjalN9nkknd2pSWYKCNxwsWnxmRYCvwECaP7I9dPzXg/R3HJaVM3U6gMNoNe8ArCA6ZkqULjXW/JS9MONkvQi0iRhXz6YmHEHiQy914H9nFICGIMjkAdD+Rws6Apwht0cYZicGECROq169ff0FoGaPBYAhMmDChujOet3LlyvSZM2f2/IwaI3FRW77P6emxWO54kGC4KuskDkWiYxHQvWwQO0LGrJlwPP2MxLKYMxphu/MXaFy9RjHe75mnFfcQRQ7VWC865s2TiCCnTrkLWS+8oPp9P1m5Aju//Q5+nR76gB96s4U8UV6lzwOmlr7wWOsAXYA+j4I/EAAuyEEvHS9xMbjk2hsUc0lVveKjQmn1+rH2h9twVb+tkioAUnIAIOZrkJVZJElOAEBLWznx+hZC0oBEdmoSpTI5SdX1nQWLz4xIsARBCCWV1VjzdTX8535ziKVMV15g715JAtriARxgtKh0HDgDPPWjdGztr8lz5c8acTuw6+/KeSNuV46pVlOmtHF4E3eTyKWmauox5CwWiWYBZ7F0O5FCGtG4EHhc5EBLG2ckBmIfY2erJAPAp59+mnzy5EnjtGnTmDqyRjiOM0BYC+gB6DmOswDw8TyvLOthqIekEySW7kc69R89nRxjR0/vjDeNK03rjyrsi3lvAE3rj3brBIHajTtJSyhp1CgkjRoVvJ6z2aADUPP7pwTHg3P3EkUOxXWAaIMMKN0OHPPmwfn+ivYBvz/4OTRJoNfrqVoB27/7HjjnMODXG+D3eskVn/IhDvBYT6FXUw5cKUcR0Hug85sR0KtvC2xxe/D7N/4RcZ6OU7gUUjl5lsNPeAl/2tKM06290ScGVoK0R6tdmc7OH4bZq/fA62+/wqjnurwymcVnRiRYgiCEcC4G3SpBQC0/5AVBwdBFSZuLfNpPUjkOl3hYfJn0niQObSA/J9qKh0SF5lhAIWvBfNUnB90J0YVAFBoUXQgAqEoSpNjNRL2BFHt8rCwZncdVV13V0FmCR6H84x//6JOfn+9MS0tL4JKjLuNZAKHHjvcBmAeguEvepieg1s6QxqBx5ATBoHGxe8cuwu8kbyJp490BtRv3usWvAfLe/nNaQjkbKyRJAD/hXiRhY97tJmoROVd8QHxX54oPJAkCqpAgzwNysTgV7aDtc/1ICWTBcrpfcOhUxpeAyrYDtY4LapMDAGAx6vBKhR6tXuHep1vpz0i1qhNp1HNc8NBQPq4ale6k8YbFZ0Y4WIIghIRxMbANpAgdDVSeTsgXKoDQ85gzSbrpz3s+fOJBfF64zX7jceU9ST2XCgdbAGgFQCi5Mmr4JRxHyo+UY8nuJah11SIzORMzRs1AwZACyRxeg2UQICw0EiEhICdaF4Kri4ZKEgwAYDDpcHXR0Ji/K6Nn8t577ykVrxiq4Hm+GCwZEFvC2RmqSRCUUSrHSh5NeHcBfaqZmAzQp3bfhLDajTtNMyh0PNy91FzffhF5l/nTwIH4ZPHioCCgZnhemigI0zYw+Ho9vtqzHX64oYcFFw0ahiPV+xGQCS7LqxiMRiPy8sga/o7aUokOAPAU/QVkeHwBVQkFo45D8W0jVN3znrED8c/tx4jjanh1/QF4ZS/lDfDd7+CxE2HxOTFhXhMh0NwKbFYjxi/ciAvnlGP8wo0oqdTWolNSWR3V9QpyJqkfz50iVBXYBgLghK9X3AtUvntus39u81/6KFlDQBOc9J7iiYn8+YT0qQ5klwOdsfuJFJYfKUfx1mI4XA7w4OFwOVC8tRjlR8i9atHQWFaGQxPzsO+SS3FoYh4ay8pi/oxoidaF4OKxmbhh6vBgxUCK3Ywbpg5nFocMBiMxUWtnSMNLqdILtCljbNXKDr1iWKpWCsn+4lThawyf0Tt/MDijdKxjRg4AACAASURBVOnJGXXonT84Zs+INWo37jTNoNBxn8OBz666Bne/+Dom/nk57n7xdXx21TXBysFI14fjp0GDsHPMVRJBQM0EAtD5zAAP4SsNDti1bzP8nBvgAD/nxk+1hzBq1KhgYsJms+GOO+5AUVGRZKywsJBoleyoLcW+7+fA7akBwMPtqUGygfJvgfTqYZID/VOt4M59ffWuK1Rvzl+cfDnGD5WujccPtRNtFkkkzMEjgyGDVRCEQHIxMOo4uNp8cLYKG1hRLRWAql8wJZXVkntqvZ4IqZQ/3Li8quCVC5XCgf42wOcRqgvUaBgoIFQFiCcms76TPn+eXVGtEEAv4l0DLd2vDXbJ7iVw+6UnAG6/G0t2L1FUEajFkJ2tGNPSj9iVxKJF4OKxmSwhwGAwegZq7QyjhVaVQNM/UEO07REREHUGEsnFgOZOwNlsODQxL7i5T5lwHRrXlYTVEtp44y1YVHAXPGYhPp5M74tFUx+GLjUN902aIIn5pOuDWK2AzJKw6opc+A0ql/XUEnk90k+PDX461W8z9RZer1fx+dChQ5g1a5ZiLikhIOfA9y+Ch3Rtes/wNfj7d/eBR+TDIlo7QP9UK76cMzHi9SRKKqux+5g00bL7WCNKKqtVreF7W3g0upU/696WbtJnwGBQYBUEIUwe2R8v33G5JNOYYjFIxEWAdl0CNYTTNegw0Z5OkDQJAOHUQnHaH4YIVQHUdyK0Muhxmni5PtUMV2UdHAu/wok5W+BY+BVclXXEufGi1lWraTwStAVAuFLE7sTVRUNhMEl/lbAWAQaDcd6S97yQbA+FZGdIg9OwNJPHWHGD39FKg3DtETEieWQGsuaMwYCF1yJrzphunRwAgJT/z96bx0dRpW3/16neyUogIQubYBBckE0BFRVxdEZFUEfFZdR5XoffjPobdMYFVyLuj/OIzOtsPuqgMy6DGzGD68R9AQERlH2RLYsBQvb0ft4/Kt3pqjrVqUpv6c799cOH9OFU1WmTzjnnPvd9XWecLmznra1y4IBz+Gtr0fzmCuRdNFcO+DMGa2kpSh5YrAjoPzt3Xjg4EMLjcODZufOQN3s2Sh5YHPX6EKWL79ds8jsGxEEZP8ZdQa+yFrrwc/HaVGJc9VoW+ovEZbNg2qiBwutnji3s9ZhiXcO7dUpN9doJoq9AGQQq5k5U2hoetVCcNm40PSgh6UWJPJ1QZxvcPxDgAl0RJintlpYcb3xMAg2FXOvzaPL/FhzdEyezSXCMHdjnLJGKs4pR165NOSzOMn4Cbi0t7VGM0FQ9YgoJnfz31sWAIAgiozBqZ6jH5F+KRQpFqOfYWPUPYj2AyEDaPtE5RVcJAHK3G22ffIryD6t171UniZfdoXajWkShPpHCxjkOB1pFG88gFGX8UsAFZ7YFHe42TVcpoMr8M2hdGMJmdWBJhAbCrFmzDGUPAICvzQp7jjJr9M2dsxHgyv9nQQ7k2q3IclhR29SJ0nwXbjv3GN1N+0dbDxp/AyrMrOFXrK/B4+9tU4zJE7QLr9drJ4i+AgUIeqA036XjYeoS/jJQpxxFu77XiIT/zJxOuArEWQSuAm1qoig4AGjbzYxJ0DfL9TU8Q3zo2OUIT0iuyUXwbD0SkyVS+/oGYSqjXrsRFkxagIovKxRlBk6LEwsmLTB0PYCoC4gQemmNevWIZvyT4w2VCBAEYQQj82Y6seatv2HYN4+jiB9EAyvE/km34aQL/z9jdoZ6DJ8GrP075J1dBBa7sjxQNMfGusF3DdRZH4hPZ2OZS9MFM0F5f22touxAPQ+XOWw44NFqLpU5jKnqR6IOJvxk40ZUVVUpU/9DG/yITX7Q0glfWz5gkQAW8TMWlJDVNlL5EB4AROn9wSAAKyBFXg/4fF40N8slh83Nzajq0k0yEiQ4snk0Cidth2Trzhg47Bb/3DV3+vDtIqXu1i3/+lbYN5YDOaNr+GjlxASRjlCAoAduO/cY3PbqBoUKqU1imDm20JC2gEjXwGWzxOaBOv4yYN8qYN0y+Zc3s8jCg0YXIz97DFhxAxCMmEQkG3DcRdraQ6HjALTlB2ZOTATjby+5HZ0/5HYHHjjQua5BExwIEWjyoO7RrxWLEkBZ1+gYO1Bxj1D2gWdvs7AdMJaVENIZ6MnFIFaKbrnZcD1iuugVpDuZtrkhiGSSEE2eJKL+/C8oWo/Zex+Fi3kBBhTjIPLW3YM1gBwk6C3Vi6EJDgCAPRuwZynnWEDpHqS3wc8bGps2gYD29Q04/PpWSH559xlo8uDw67LFbSYFCfSC9XqE+orm4TtHleDWbfvRGbGmdEkMd44yJkQYjfHjx2Pfvn1Yt24dOOdgjIFzrs0AYIDP0gSLJxcBR0u42eLNhtM9RNlXbYUYvgdDTnM52rP3IGjxQAo4wFkAXFJmAPh8PlRXVxsKEBQVXoC9n7yM0qkNsGX74WuzIg8taIbWjSEkHh45FyfiQM7oGl6vFEFnBY08Z6+HRBBJgTQIjCD45bpyY52huiSRrsEjF58Q22Jo43Jgw0vdtfw8IL/WqzFUKxIDwNw/KzUE5v5ZFjnUCBQK8svMZCsYHH/LjpHCTAHOxNoGHDxslRRo8uDIq9tw5LXtiraOVfXCe3asFre3vLfH8Fs4f9T5eP/n72PjtRvx/s/fj3twAICpesR00StIZ0Kbm5qmTnB0b25idiWJkbr6SnzxxQxUf3g0vvhiBurqK1M6HoLQIyGaPElC9Pk/Zc+f5eBABC7mxbBvHhffxKg7gN5pv3rjv2+VVm/A0ypnGkQSsjY2ok3QeUTn2dr2H1duCQcHQkh+hh9XbhHfI03R0yAwgnoevqS4AH84ZhiGOmxgAIY6bPjDMcNwSXGsLlLAxo0bsWHDBjkoAIT/1iPgaOnOLmDy65ac7YafZ21uRNaujcjZuhZZuzZqggMhjOoS7F6/Bk278rD5pXJseHocNr9UjqkNX8PKlfcNiYer5+KZYwvhUjlfxXogZ3QNr5elwMFgk5TfB5vEcf+cib0eE0EkA8og6IHH39umESn0BTiOdIht+US/JNS6BjHTk4hQ5AlB+TnyZlytSDz7j0oNAQB4Y77OA3mXbkCUUwczyseC8Qf4IN1Hu5kHTt5dFxcEh6QOWgS7OhtBp5vImznVGK1HDFknPTNnHhoKBqGo8TCur3wFZ6/9KgmjTC9EWQBTS9YqvJdHjb4VJcVzFNdF29yk6vSzrr4Sf//wVby+fT4OuwdikPMILtn7Kn55FjTjJ4hUky6WX6LfEaLPfykTi+sWcUG73hy5b5UcnI+cX/V0hsJWwl3Xr30Omgkt6JPLBdWZBka1CaJoHK3cvVKROfdM2z3C929rM1G0ngY0v/NuTNerSxQuKS6IS0BATXV1tcZZQF8/gGudDBjgGVAPtI4x9Dx/xwcAujbvwVYwnxfcrq2tH+B04Okbf4nWw4eQM2gwZsy7BgDw2SsvKNpaD2s/N8e07wQYw6by2eHPY4fXr1mDd/oC+GjrQTxy8QmGsvzMZAMaWcPrZS+URfz+oMxDIp2gAEEPmF24xKQtYBTdGsP92gWIaAGhJ1ikuzAYpg0mqDEjjCQYvwWHEIA2JbHB2ohlRZW4rmEOCv0FOGhtRJE/xolVJ+fLkm/clq+vEc06aVycn7Xls480E/u4GTNjuufGjRtRXV2tEDYCoGkzKnakhyjFeeHr63HNsa9iarGcFur21GLr1rsBKDfZosk/WruIuvrKHgMRkWPtaVHxj09WYtn3l8DbJXh02F2AZd9fAqtlJW6/nAIERN8iIZo8MSD6jAEQlkGogwMAUMsHY6ggSNDABqNYnc7vbRfPkZFihM37gcobgeHTdQIE6olLJ9rdeQS44wdlm94BgHo+1tETWjnxIoX2Tl17HTiCENWnB0XlEWkMb2qK6XprSUlSNIJicRCIJGRdnF3gwEE9O0TOEQ4OdGE/eACekhGAFPEzEQyA/7ANrU3y56T10EG885cnwRhD0O8Pt73/9FNwZmfD3dqqedQUZxOejrApjCYebmQzn4hSp2ilCHE/JCSIJEABgh7QW9Dku2zw+IPx1RYwit5Gnll0SgQEiIIMsYgfmhFGEoxfz8VgxdBP8LFzLT7OWxtuX7bjAQzx62Qc9ACzSXBNLtLoGzCbFNYxSEeenTsPHpU6csg66cY4PmfLZx/h/aefgt8rZ1uEJnYAhoME6mBAeXk5NmzYED75aG5uxooVKwAAwWAw3FZZKafOxxIkEJ0Cuv0Mr28/F1OLu7MtgsFO7N71B8XmXc9j2aLrJ63EzGm/0QXMv7bMCAcHQniDdvxrywzcbmhU6c+2bdvs8+fPH75+/fpsu93OzzvvvCPPPvvsPpvNvOgXkVgSosljADOBAKdNEmYKiT7//+2/DI/ansGAiDKDTm5H+8hZAj0fgwS8wJ7PevEuIxC6Bxl0P9LRE1q6/RmFMC8ASDpVqnrt6YJ6M28Ki0XhbsCcTmSfcbohjaBYgwh5eXlxCRJc+/Cp4a8rKj4QdxLMe/YWuQTGW1gGbrOD+bywH6yBtUVZGsMDAc3K1O/1gOtMpaMmnqR4HWugMRHZgKHrKFNAC83R6QkFCHpAb0FTceFxAFL0y0BvI68JDkRBtICIxZrJjPXirPs0IolZ9i+Bk65Hy5ZchfDgtLyz8e6XnysWJi8Wv4Ob669S1j5KkCesiHKQUDDAs/WI1sVgRF7ClZct+fkICE4eLPn5cX0O0LN1Urz47JUXwsGBEH6vB5+98oKhAMFGlcpyc3Mz1q5dq+kXCgxEEggE8M4778DWfLjXGQx6GUEipeSPfijF7Z91iyCJggMAdNvVmDntN7qA0VN41mtPNgcOvFjww56nyrzeg3a7vdB71MibaoYOvUpsdt1L5s+fP3zw4MH++vr6DYcOHbKcffbZYx577LGie+65pyGezyFiJ9GL6HgEAkSZAoD8OXfZLIp//8ByBk4bMRin7/sLivghNLDB2D/5Npy06/+am4/V6LkHCVGlxJlwD9LtK3BgqF//oKZbg7VRGKw/ZGvCME1rzyTbEUG0IQeA2jvvArpOt3sUJwwFBCwW5F92KQZMmqS5ZzSNoFAAIB5Cw7NmzULlircQCIq1AHpFFJtDb26BJhhgb2kMBwrMEvCIyzx3r1+jeB1roDFRpU7plimQjPkZoDk6XaEAQQ/0tKBJyS8DvY189WL9ukUjC4jQvXujamw2+0BT+8aQdZQbWXNOVjSfD61jwKwZF2JQ81jNQgKA4cVF1sSihKssD7n7LtTddTd4RE0gs9kw5O674v6seFonRUNUIxitXY2wRtIEnZ2dMWUw6J08ZFnbcfunFeGT/RMGf48v66bDG5D71jR16qoRlxk8tdA77X/+u1mo/EGpxmx0AZPrAFoEa6rcPlAtc+DAiwU7dj40Ihj0SADg9TbYd+x8aAQAxHMRsn//fscNN9xwcMCAAXz48OH+mTNntmzevDk1OetEj8SyiI5WdqOXdWM2EKCHfi3xTwH8HgBQ3PUHFXf06v2ZxuaSHYzUGgZ67kFAr10MirOKUdeurKVfVlSJBXVXKTSC3MyDvxeuwERcaOqttK9vQNMbO3rtLmSW5qoqxfzsr61F3V13A1ZrODhghHGbtOWX6g197e3in4dIbQIjQQTRe4gMRgQv+z2yW8rR6twddhaQWuvhz8vVpP0DkLNOVUjM6JqBwVMyMuxywO0OubwA6HWAQA/1+mLuxDI0Na/DU5+04VBnLga7WnDTGdmGf6/0tVKnVJCs+RmgOTpdoQCBAfpkVFBvIy/apIsWEIDSGinaQsGINZKZxUf1YqWXMyC/FukVQHYMELkEiBYNfclaKTSpJ7ruEEisdVIkOYMGo/XQQWG7EWJOf+QcHc4seIeNUZxaGM1gEJ08WBmHJ+BEuz8bgHyy//GBGVAfm4iCAzYLw23nHmNI+PCwW7xIbPdnob2pOxBx5xvfIX+ATSiEql7AWCx2ANp+FrWKeQr4Yc9TZaHFR4hg0CP9sOepsnguQH7zm9/8+Morrww877zzWg8dOmT58MMP8+69997UWksQcUcUALj5X9/i/qpNWDT7ON2sG7OBgGjlg4bXAnoZdWrxwI5GwNduanwawWBAnt+N0NsDAAALJi1QaBAACJf+RWoELSuqxLZS8x+/lvf26LoLJWJe//GhhxXBe/l5PsBEAJu5jO1x9GwSWV4edpw1Sw4U6GSiqQUOQ4gyDtat7oDdUYhBbYXhfu4jT8DboT3pBwBPySill1kQyGoZhefv+iKsQWAJ5na7HYTgADjTWiBKFngLh6oCBCGbhKCijUkSeDBiHrY7YHXYhRoE6vVFXX0lRuBuPDaje60rwYW6em5InDdVpU59iWTNzwDN0ekKBQgyCaOb9GiOA+rr9VwQIp8X+Xwjiw+9ekwzdZppglEXglgJKSI/srsONR4fyhw23DmqJO5KyTPmXYOql/6BzoFDwosN15EfMWPeLwxdH48ayUgRpNCpxeG6vYauFZ08+Hgumt3qX4UGVbg5sHZvI/61Zn/Y7aSmqRO3vboevzxeKXw4yHkEh92i74fyWZ2+ABxWSZPOLFrANOm4qei1JxOv96AwSqHX3ltmzZrVtmzZssKBAwdODAQCuPjiiw9fffXVsamKEX2CyMCbpKMBcqTDpysiGA29QEBcygf1Mup+9phyjty4XFNuB8mmfK0mUjDYjHtQjISC9JHZfCNyRuBjrFJoBAHA5UMvN31/PRehRLkLicr/zKI+8dej6JabFZt5AHKmQns7/BHjEDkR/bRmj/CeoowDt11QWibliNP+pRzYW45Be/aecLZBVttION1D0OaW/5+3NXpQgAlozP9WDhJ0YfHkKl5Hwm12QMoBgq2AlAOr8zQAgN/9uaLNmW0DC36lcTaIzBAE5MBB6N9C7N71BwSDygwAkW6QHqQXkLz5GaA5Ol1JWoCAMfYcgAsANHDOj0/Wc/sdRjbpeo4D79wB+Dt774JgFGYBuGAxJ0h3I4xz9povMF5dTxnn4IQvbxA8JSPBuzQC5A36SOxqbse7S5b06DhQXl4u1BxwOBzwRNQfMsb0PZwli+a1v3i4ofGLTh7+z/tLDV0rwhfkeHH1Ps3hjy/I8NKW2Qrhw4uOrsILm69QlRmICzybO31YcvmEHhcwQ3I46lu11w/JMWj5mUDs9kKv19ugWWzY7YVeUf/eEAgEcMEFF5T/4he/OLh27dqtzc3N0lVXXTXyhhtuGPrXv/5VRzmV6GsY0Q6IpvWhJyKohzoQUNPUCQtjYZ2P2849Bl9EqKabxmiwXq/fO3cAnYJDPJcqwGjGPSgOqLP5znntHGG/Tw98avrelnyHMBjQp92FDP68ibIJeUeHIkjxn5NOwR+umq90Irp6PgYG21AuuKcos8DhaYTHqdSEsDpPg7/zA4B3l01Y7Q7Aehqs7iFwuof0OP6CpgmK15KFoTV/HTrcbZq+UtABZ96vNO1Wh9JPye8Dbvyr1lmjblczNnywHEF/CyRrLk6YdZkmO9DtEWdV6LWL6JOZwUkkGfMzQHN0OpPMDIJlAJ4C8EISn0mI0HMcEC1IzLggGEUUHIjWTigwKqxUe6esdRDPDIbq6moEVAKCgWBQselvbm5GVVUVAK3jwI4dO4T39ajEiUTBAYvFgkBAR0DMov1VJrJjbJS0Jw96J/t6mgNq9NaIbb4sxevppesAAG/unB3WOvAE7GjzZWuuLc13GVrAzBn1Ov7+3YWKoINd8mLOqLcgx2NTx1Ejb6qJrHEEAElyBI8aeVPcUgsbGhqsdXV19ttvv/2gy+XiLpcrcN111x1evHhxGQBafPRB1MGAmWML8fq6Go12gMRgKitAJCIookwQbLvttQ3KDKDXNgCIUWPIaEadXr/KG5WleBa7nIEQiRn3oARQ315vqj0aueeOVGgQAGngLmQxfqihzibcMu5Yxb8/M2deODgQwmN34ElHNq4W3E9UtjDo0HeoLTtDofFkdYzDsLH5OLTvP4q58LPXJPg8xj9fkdaH0+eMxg81Vny14SOARawHuIRjyibCHeju62n3C5/jzNLO2dtX12PHN/mw51wfbtvxjYSh4+oxZmpx97WOErg92pINpyO+JZXxwoy9cbJIxvwM0BydziQtQMA5/5QxNjJZzyOioFcfafYevb52mI7jQW90j/sXukrHjGmFlfx+1D30cFwDBEbLA3w+H6qrqzUBArPlBaFMAsYYJk6ciB07dgjvkZeXp3itZ8d4/C+1iwrRyb7LZsElk8vw0daD4Y1Mm7sDzW5R6UEUmWcV00vXhQMFAPBV7WT8/furEIj4VWyTmOFayJOHfIpth4vw6YFTEYQECUGcUroKJw8xf4IXb0J1jIlUSS4pKfGXlZV5/+d//qfw/vvvr29ubra88MILg8aOHdsRr2cQ8UOkI/Diqn2aQJzZcgFAKSIoEiALoc4MuL9qUzg4EMIX4Li/alPqThj1MgsApXaQa6A4sB/L/BwFtcvAnIGzsML5H02/4qxiwdXRCekMJNPFIFbyL7u019eqN/gNBWLr5hqB+DAgLltoLBwvtB9sbR6J+X/6u6LNYq/Hf57fAh40lgURaX0IAF/dtQs5HeWaEgW3L0fR95nffwIIqkR8voBC62D6nNH4qnIX/F7lAYTfG8RXlbsUAYJRo2/F1q13K4L9kuTCqNG3GnovyaSuvlIxVrenFlu33g1Aa2+cTJIxPwM0R6czfUqDgDE2H8B8ABg+3FjKMNELZt0HrPh1t5ItIKdtO/J0sghMuCAYfb4ZxwMijJ7SsR48DnWWkZjREBD1i1o6ICDUl3OODRs24MQTT8Q333yjsEGUJAmzZs1SXKdnx+hrd8CWpWyfXroOdls+Kn+YFzWd/7//NR/PbPiZ5rQ+EGQIQKv8bGfqVZE4J4Gpmw3KHwBygOHL2mkIQj7JCsKCL2un4ej8HzCrh2uTwdChVzUmwjYpkuXLl++8+eabhz/11FPFFouFT58+vfXPf/5z5gmaZAAiIUGzxTCiT5FaRHDkwpWG7ycSA43WnjTUmQUivQHJJs/dkXO5xZ6QuVTkMvCrtovQXtyBD3K+DPdzWpxYMGlBr56RDHehmFBZGpYsWtTrW6k3+EWNh/HjoEJNPz0nIlHZgtshtrdta9Tu0EMb7q8qd4U36e42L/xe7SfSkaXNlGhr9MAJbYlCSL8ghKddJ+vPy8Pjamv04KMXt2qCA3rjD22s+9qpvIhY9RISSTLmZ4Dm6HSlTwUIOOdPA3gaAKZMmZL6ItpMZd8q5YICkF8XnwAc+NqYC0Is9Y0x2i31Z/QUjZPFrFmzUFVV1WurQjPBATU+nw+bNm3S3EN0Tz3bxZpVgzDqJ0c0Jw9FQy4Afoj+/MmFH8J7bJOiROCio6vwyrZL0ObTLuJcDgecjtLwAqZg0EzU17+hePabOy+Enyt/DfsCHI+/t83Q6eWbO+cIrRPf3DkH9/R4dWZwyimndH799dfbUj0Oomdi9RkHgCWXy/XQ0fQ5Buq4gAwcEF/b16Qi0hsQiRnG8Ds2GiKXAcnPcFPTlfi++IewcOGCSQuErkN9DWtpqdBZQBfGNJaGonI/oxl76g3+/E/ew+MXXQk36xaW78mJSF228OVdX6Cp7luNIGB+yQTh9WOmFitO5revrsd/XtisqPZkFuD0y7QZbaGSA1F7JI4si26QIBK/NwgmAVwQI1DfE5CDBKneYBshHnoJ6Q7N0elJnwoQEEli3TJx+57PgYv+mpyNewx2S/0ZPbskMCZcGFry8+P6/FDJQHV1dViQ0EzZQKwuBp2d2g0G5xxVVVWKMVlKhyNQo3U2CDQdjbFj5ypOHnb4bsNj1XZ0+pRWg4CyBtnpKNGUCADAM98rFZZDNLsZTj31M0Vbfv5kxbMb3eLvj9GNVKM7z1Q7QaQSPf9xURKNaJub77KFP5ORn80V62tw6qMfhgMG548vUTiLALIl6aLZx2nu6bJJ6PRpdyUum6RpEyESWExIaYJRXYGgLyEihXpuAvY2Ce//3KDNooCVu1cqnBGSFWDIPuN0NL38ivELVPOrbrkfjOv+RG7wxwEorm+MyYlo2JiDOLTrAwBd5YbBVvg7PsCwMcZ+HkVZBdPnjFYEEUJMnzNac+pvtUuYPmd0+PX21fXwucVZASJ4UL5HtHumG+mml0AQIShA0B+JJhJIG/c+jajukDmdyLtoLppfe13h68xsNgy5+664j2H8+PEKbYGKigrD1+q6GNhd8HgiNw4MYIItgk65v8/nCwcempubYckfAkdnJyyNDeE+IbukkuKZipOH2x/9MBwcCNHpC6DirU2Khf+vpt2GEdJdmuwDiQGiMk6LoBZUfeox4L130e7Vfh4H2I2JX5XmDxBuuErzBxi6niCSiZ7/uFrvQy1cGOoXch6IRKRr8Pq6Glx+0jDFPW879xis3duI3y/fgADnsDCGK6YOg9NmEQYIGKAIOog2/qJni4KLYTYu730A3ox2UAJEChPhMrBy90pUfFkBd0Cez+ra61DxZQUAJDxI0PzOuzFdr1fu17DkyV7r/lxSXBCTNfHONZUIBwfC+LFzTSXOum6upv/21fXCYIAoIKDGSDDhq8pdCAYEk6NOBDBSi6CnAEW6kE56CQQRSTJtDl8GcCaAwYyxAwAWcc6fTdbziQjIZjBtEdUdhtIaB0yaZCjd0UxapJG+NptNWHJgs2nTeXVdDNxeSEFHWOzI5hkIj6sBkCIW7kFJqZgchUAwCIwoR47EFcrNarskALqCZk2dPjR1+sJ9Hqu2445ZD6Pc8bii7jH4rlg0wIjlWocgOBCtXY3ehsuoyCFBJBMz/uNTRhQY6ifSNej0BfDR1oMKQcJ7VnyHf67aF34d4FzxWk2HL4iOpuhZRXrPFpYIiTQEqn4rf20kSFB+DrDW4JIpASKFiXAZWPrN0nBwIIQ74MbSR49GqQAAIABJREFUb5YmPEBgVp9HnY2nV+6XyjJAvdI6Ufv21fWKDICQDgAAwxvyyGBCKNjwwd83hzf2ohIEAADXzxQwGqBIF9JJL4EgIkmmi8EVyXoW0QOTrxMvNCZfl+yREBHEUs8IaOsR9Z5hNC2yuaoKtQvvlEWZuvrWLrxT05cJTspD7e8ufwVrvvseAckCSzAgtCOUOwcQtMrPCVo98Ej1cHQWw+c4olBIbs0zXsbW4fbgdpVyswi9DAA1nb4A/ndVNr5YqCwbsLC3hcEAUQaBGr3HGq0iNrPhIoi+gFH/caP99Mpx1O0vr45NE0u08Tf6bABiDQFfp/FygB16afwxiggbzGrQcxn4OG8Nlr7WuxKBeNokxhNms/WYjadX7mctiX/quNG1Qc6gwWg9dFDYrsaoY4AR9IINziwr3O3qjIbMzBSIRrroJRBEJFRi0B8ZPk3WIVAr0QyflrIh9XeMbtxjrXs0kxZZt6giHBwIEwigblGFoq/X64UIr9eLVd9vBrqCAgGLVa7jFG2c1U0S4HEeROHBbrskq12C27YHPr/OqYQKtfWhHgZdngDIJ4nqtGO9TAEjGQQWxnodXCAIQl/XoDTfpXgd7fPoslkMWSuqN/5Gnw1AP+3faDmAbj/eZR3ci7IFk1kNapeBWEsEirOKUdeuPXHvjU2iWdiAAeAdWqc1NmAASu6v6HFDrlfuV3TLzXEdp5k5f8a8axT2vkB3aZ0avdN93VP/LkRlCXrBBouN9ZtMAYLINIyp8BCZRfVibYkBD8jtREqItnHvTT89zKRFihZP0dq1HTkgqX7FiMQU9dbtUiCsXpxd4MDMq8Zi9oXnQ1LfE4DFoiyPsdlsGuvDeFHT1AmO7rRjPWX0MtEmQcUVU4eZalcTqoFWj2nF+hpD1xNEunPbucfAZVN+/kVlNnpBNwtjeOTiE1CW7wKD/LnV+0yrN/5Gnw1AP+3faDmAS2xhB1cBcMv3QEWT/LcZDSG9rIaqm4H7C4CKPPnvf/9OeHm0EgEjLJi0AE6LU9GmtklsX9+Auke/xoGFn6Hu0a/Rvr5BfZveYbfrtufNno3yD6sxbstmlH9YLQy+582ejZIHFsNaWgowBmtpKUoeWNxr/QE9zMz542bMxDnzb0LO4EKAMeQMLsQ5828SltaJnAGitQPdmQJqe0K9oIKnPYCZV43VzOMUGCCIvg9lEPRH9ISOmvfHJqJE9BqjG/dY6x7jkRa5d/hwvLtkSdgxoDdI/m69gaBF58SCAY2FX6PZ3gxvXh7crhyhi0IoEKBuixRSjKSuvlJRD5jruAMtnt6d2Hf6AnBYJc0JpFEdgAfnngBATn+OFE4LtfeEqRpogshAjJbZXDF1mFBz4IqpwzTlDGrxQUD8mTZV4jPrPuVpPWC+HCDe6GUl+Nq7v+aB7pLEC55QdBOd/kdrVxPKMtBzMWhf36DQPQg0edD0hqxjE5nJ0Bu4jpuOXrsII2V9sRJtzheVHoybPVsYEFAzfc5oVL+wRSEiKFlY2DHATKZANHtCyhQgiPSEAgSEklhElMxAgQgFRjfusW7wTaVFCk779w4fjjUnn4RAhGNAbxh0aGr464NDPtXtF+lMUFVVBUDrohBCLyAQSV19JbZsXggOuSzC7anF5eUv4e+broSf9y5I0Nzpw5LLJ/RaB2DKiIKw2npxnhNTRhhXsTZVA00Q/RgzwTgzG3+jWgnh+U097wHAkuN7ngs7j4jvq9duBDPOCOuWaQIEEpMQFOwMJWY8OfX8UefrliO0vLdHIYoIANwXRMt7e2IOECRTQyAWrCUleLdsJJ6ZMw8NBYNQ1HgY11e+gnN3bI7ZZpGr0vdCr/U0BdTBgfB1cbQn3Lhxo+FgP0EQiYMCBIQSoyJKsWzwY1VzzkCKbrkZdXfdrRFGyj7jdOw4a1b4hCD7jNPR/OaKXtc9RnNBUJM/73KNT/TGE8cjYDX4a0O3jr53G3Gfz4fq6uqYFgvbNj8YDg6EmFa6GjubjsanNdPCG4dpowbim33NihNEPW/20nyX8U2CCtM2aSpynRzNbu3/z1ynCWEFgkhjzHyGHpx7guHsnN5+pqOithGONhcCyjnWNRDobNTeMxbHAlFWgx4C5yNRcCBau1lEtorR2s2QLA2BWFl92134g5QNj11O0/9xUCH+cPV84I0XcfYnHyj6mrFZ/Kpyl7DS9D/Pb4bDZTWdKWBWdFAUCACAp999FV+0H4MjnnIM5E3Y9e6rmA/gm8Y9eOqTNhzqzMVgVwtuOiMbADRt1515IZZ9/FZatBNEX4YCBP0RPZtDPdRpiLFu8GNVc85QuOq0nvv9aH7t9XDQwF9bi+Y3VyDvorlo++TThLodAEDJokUAgKblr8pihRYLOrKyDD1DkiQ4HA50dmoXnlKg977Zvc1YCOHnjZq4xVe1k/FF7cSwiFmAc3yzr1nozf6vNfvhi0jJtFlYTJaCsZYIuL1eANr/n24d4chM5JtvvnHecMMNwzdt2jRg4MCB/gcffPDANddcY87DjEhb0rrMRm8ufOcOwNMKBLsCxs37AUiAxQ4EIj7bNpdsf2gkA0GEKKuhpUa8CxTYIJdklQjLCUqy4nMKb8l3CIMBlvzezyEhzATLU8mTecXweJQ2wh67A8/8ZLYmQAAYLzfU0w3gQQidB0L/ZlZ0UC8QULlihWxHDHler1yxAgcH+PF+0yR4g7I+xBHPQLzvm4SGd9/Gt03HwBuUSxoPdebhgXf9YAzw8+62h9/3Ys0Pz+KDHQWKvn2xHXir3wQJaI5OTyhA0B/Rszm0ZwHedm27+oQi1g1+rGrOGUjDkicBv2pS5lyRUQDIJwRtn3yK8g+rkzKukkWLwoECAMjr0h5QIzELgsHuRfqosrEYf9JYVFVVwReZFQELstpGKq5lQSu4RbwgUTPAmR1TCqKvzQp7jvJZb+6cHV6QhBD5qK9YX4N/fa1Kx43xoD7WEgFPUCy0pdeebJ6vOVTwxJ76sgav315kt3p/N7K45tqywYJj0N7h8/lw8cUXH33dddcd/Pzzz7e//fbbOZdffvnREyZM2Dx+/PjYjxmJPk9aldmoM+/00vtFmQIIyr9vQgF+ZgGGngxseCm2bDx1VsPzFwI/fKLtN/I0TdOCSQsULgaAVmQwFnLPHanQIAAAZpOQe+7IuNw/GRoCsVKjCg6EaCjQWhcCxksksgscPToWiK7RyxRQWxqfdMLxKB17rDAQYJGkcFuIQDCIz1rHaOZib9COtY3HIghlgCoAq2b+9QbteGdboaZvX2x/6pNmXHcmUkqi52eA5uh0hgIE/ZELngAO71QuAo46A5h4tTERpVg3+Hqpkjoqze3rGzTey7HWH/Y1jEb9ATmTILLsIJmnHrNmzRJs+hmCwaCicmDnvi0oHFiCKeNOx9cbvkAAbljgRJ57FCxu5cKGQyc4wKGsRghKkNrzFc9XaxP0xJHNo1E4aTskW/fK4rBb/HOn3mA8/t42+FSeiL4gj+mk0pRNGuQgRWRddF/m+ZpDBfftrBnhCXIJAH70+u337awZAQDxWoR8++23zoaGBtt99933oyRJuPDCC1snTZrU9uyzzw5aunSptsCYyDjMfoZSxsblwIobVFkBJglGZA/wAPDDp9DskmLNxmvcbbi9J5HBWAnN85k+/0ejzGHDAUGQoIQHwJxOUyUSkcKDzixr1GRSt/NHtGfvCYsJ57hHYfqcM7B778fYZ/kegSEWNAYDGLL3eOzeC42l8arvN8O6dRsCqjkzEAwiEAgISxCPePKFYwmaMFzT69vX2g915grbk0Uy5meA5uh0JuMCBOoFtBmxsH7DxuXAga+VbQe+lgMEs//Ys7aA3slHLLWQOiRSxbgvoSeYJISxcN/eCBPFgshFoKWpDWCqVQbjWP3tJxjSdBoKvCdHv6mkjgSE4JD8zvACJattpLxgUWVVmNEmmHjabVj1fgWKp9TDlu2Hr82KPLSgGVo3BvUGIxEnlbede4whtXRAXGutR55T95+SxhN76stCi48QniCXnthTXxbvU4pIOOfYvHlzH9sdEonCzGcoIRjV43nnju7gQNzQSWGKJRvP5AFANJHBeJA1sSij5nqz3DmqBLdu24/OiI22S2K4e9wolDyw2HCJhFp40N3uh2Rh4IzD7VAGA2yegfC46sNG6EGrB605W7F6QxN2HajRBAIYD4bbwkgS/IFgFC0iLQMdTTji0QbsJQQ1p/B66PXta+2DXS267yEZpGp+BmiOThcyKkAQq+BXvyFaiYARH2U9uyajtZAm1JgTqWLclxCJFOqi1iowIUyUCDjERxBB5kejYys8A+vDbY6OYuS2jjF870i3AwBozdsm7GdGm+DIrnw0bs8Jv56WsxYfD5kFT8TbcNksmDm2EKc++mE42Jg/wIYjHdrvTywnlWbU0kW11t3Sid2LMJvEcf+cib0eU7xo8PqFdQ567b1h/PjxnoKCAv9999035N57721YuXJlzpo1a3KmTp3aGq9nEH0bU1aD8caMHo+wbCBBxBKsT+IBANEzlxTLrjaP7K5DjceHMocNd44qkdt1SiT0LArbpDq0D96jCLpbHRJaHdsUwQCPpU6zsQ9yjl01tYCkOhGXJHCzDkABv3wfKWLjHAzgDOs6vC2drigzsEtenF66D5/WDle0WxDSILAq+v6kvLGr5t/ep9tDIoupIhnzM0BzdDqTUQGCtBYrihdGTjPMlgiI7qnONCg/x3gtpIkFiL/JDSY4XfY3uTVt6Y5apNAMZkoUYmHjxo2aFH9tLUAIDs+AesU/eQbUowUwHCRwN/8vEGwFpBxYnafBYRsAj79D0y8vT5sBIOKzV14ADyh/R4xp3Q5HVjbWlZypECR8fV2NIthokxhsFqYQKYzHSaVRtXT9TAWGsnxXn8uaKrJbvT8KFhtFdmvcFBQdDgd//fXXd950003Dn3rqqZLjjz++/bzzzjvicDjiI6NOpAUJcRwwQl8U3BWVBZpB7wAglnsSMXFJcUE4UNAT21fX460XX0R7TiP4ECsO+f04+GIBmFSK1lxlIKA1dxsYZ9BkwZs49Y+KTiAgu/kggn4/OgcOAbfZwXxeuI78iMuv/AVG7N+Kf2zLQaMnHwWOJvzimFbccuWCLieAZoGLQbPKHeD/CPv2vfbUChQmY34GaI5OZzIqQJBWYkWJQO80Y98qYMf7xuyS1MEAvY3/7D/K2QYhlhxvfKGkswBZOfEiLH3tHEUt41EWC3IDWuX8Vot2k5jOCEUKTZAs7+bq6mqF/gCA6IsJ9T8xOUiQbTshfLpxMNoDg63hv/0dH8B55Ez489wKgSOLJIWVkSPZ8tlH+OyVF9B6+BByBg3GjHnXoPXwIeFjRvy4Hn9cujj8+tRHP9QEG31BjnyXDVkOa0o243q11mX5LoWYYl/hdyOLayJrHAHAIbHg70YW18TzOVOnTu1cs2ZNOLVk4sSJY6+88krxN5oQwhgrAPAsgHMAHAJwJ+f8pdSOKg0wE2x3FRjPInAVKF0MomHLAgYU9M7FQITI2SDWexIJ4R8Vj2G3rxncagPz+zDKlodDrQ605TcDkg0AwG02tOU3g/EWQFJNyBLAeRC9tR4OEwwATFJmFwSDGFNYgANbN2kCAedf+QsAUM7PV16DcTNmYhxm4hbBI64780KhqJ+4Ta9v32pPJcmanwGao9OVjAoQpI1YUTwQnerrnWasfQ7hWsWQXZKIglHaAEPktZH3VG/8zSyUBAuQlRMvQsWBd8NqyHXtdaj4sgLLsFh7PQAeq3x8H8Ow/oCAZHo3x2ozGOLah08Nf11RobVqAqAppQD8CB74DI6OPMWCw3HkRzR8/Tme/tuS8GJj1MSTsOmTavi9skhu66GDeP/pp+DMzoa7VZvZljNIKZyoF1Rs7vTh20XnGHqP8dZDSXmttUlCdYyJVklevXq164QTTnAHAgH2+OOPFzY0NNhuuummw/F8Rj/gTwC8AIYAmABgJWNsA+d8U2qH1ccxk47/s8eAyhuVNoXMIp+uqq0Lf/aY/LURxwNfB3BLnLW+1M4GRJ/jHxWPYVewHbDJh8DcZpdfu1oBSasHYDpDkXNl8D8YgKO9BZ6sPE0gYNoJxwOAxsXgp5fN0wbquwIBAMJ/E8knWfMzQHN0upJRAYJ0W0D3Gr1MAXVwIIx6YtDJ7NnzuUDS1qAIktm6RdUCZOlr5yiskgDAHXAjR5A9AECYVZDWMCbYEItxTZ8G3959GmGi5qqqhHs65+XlmQgSxHgqIcxM8MDS2IDsxgZF64YP3g5/3XrooOJ1CL/XA71SyVETT1K8jjXYmAg9lJTWWveSa8sGNyZa8Oi5554b9PLLLw/2+/1sypQpbe+99952l8uVWRHEBMIYywJwCYDjOedtAD5njL0F4BcAFqZ0cH0dM+n4eifzorZQ38hN+pLjSRuACLPb1xwODoSRJPPlATplAJamQwhm5ytO/mdf+QvsrftRGAgAgJ8KYkrjZsykQEAfJRnzM0BzdLqSUQGCdFxA9wq9TIFonjVGMHOtelESY91ifXu9sP2gtRFD/IM07e2u9LBPFW3aAWjbogUHLBYgEAAsFuRfdilKFi0SPqfu3vvClkeJcjcQ2RymEwGP+Odm9/o1itexBhsTpYeSslrrPszf/va3A3/7299ikG3v94wB4Oecb49o2wDgDHVHxth8APMBYPjw4ckZXV/GbDq+3sm8kdN60gYgIuBWm7kLTOoBTJo8BbvXrxGUAIgDAQShB83R6UlGBQiAfrKA1kvn5wF5waCbSdBbQkrpXYgWJTHWLRZnFaOuXSu093xRFX5bdwWc3BFuczMPXix+Gw/gJ2bfSFJprqpSOBP4a2tRe8dCIKJ+3l9bi9rb74h6n3Gbvo/674AccIj0QwYS424wfvx41O1sxtcbvkAAbljgRGHOANS3HNKkHWrUjkOoDzj0NA4BtI0+IbxosR+sg721DeDxF6hUaxPEGmzs93ooRDqRDUDtudUMIEfdkXP+NICnAWDKlCl0AgQkLx2ftAEUtK9vQMt7exBo8sCS70DuuSMzytmoJ5jfB67OIADkwwQpPnoABEH0XzIuQBAr8a4bTgi66fzDurUIeqpb1EMdYLC5gBOvVIoc6i1KYlgoLZi0ABVfVijKDJwWJz7K+xocQVzXMAeF/gIctDZiWVElPnGuwwO9elLy+PGhh7W2hUFBeUeU7AGWn2/oWXouBrG6G6gzINov+z32bB6AAu/J4T4d25+BI9sKb2FZxGa+Bt6iYeA27SmHJehQvvbkIuBoUQYJOAAwcLvcl9sd8JSMhMWRA8vBzwCYF3S02h2wOuyGNAgAYGrJWvz3jD/A7amD01GCUSW3AjD2u6Bf6aEQ6U4bgFxVWy4AsqHqa5A2AAA5OND0xo6wBXKgyYOmN3YAQL8JEoyy5eHdwQOxevTxaHO4kO3pxNRd3+Onh46g8NgRpAdAEERMUIAggkTUDSeEaKmG6gWEXt2iCGbR2heGMgV2vB+/8Qs4f9T5AICl3yxVuBgs/WYpPsZafJy3VtG/JCs5qv2xEGhqivkeznFjDfWzlpQIhQ5ZXh52nDWrV7oEorKFdas74Hc4Ff14sAX2FsDeoi1l85QcDUgRQZGghAEtI/H8XV+EXQwKmiagMf9bOUgQvikDJFXgRALc+V7ktf8EfvfnYftDZjkK3LcZyqCBFSPGn4bG2k0KFwMAeP/pp8LihYAcOAj9W4i6+kps3Xo3gkH5M+b21GLr1rsBACXFc3r8f9dv9FCITGA7ACtjrJxzvqOr7UQAJFBI9Ela3tsTDg6E4L4gWt7b028CBAN+/St8+v0P8FnkkoE25wB8OnYyLj7+KPy0uID0AAiCiAkKEERgtm44ZdkGZlINRcEEPXhAG2DQE0SMHEecOH/U+eFAQYj1Devxr23/0vQ9fejpcX12X6Xz6zU9dwJQdMvNis08AMBqBdrb4e8KVJjVJRCVLbjtA7UdpZxuO8II7G0+2FvK0Z69B0GLB1LAgay2kXC6h6DNLW/Q2xrlvwuaJiiuPTjkU+GYghYPrI5xsDrGKdr9njJF0MDqPA3t7RMw/0+3Cu+jtj9UL5p27/pDODgQfnawE7t3/cFQgKDf6KEQaQ/nvJ0x9gaAxYyx6yG7GMwBcEpqR0YQYgJNYi0ZvfZM5JHddeHgQAifxYJHdtfhkuKCFI2KIIhMgQIEEZipG16xvga3vboBvqB8ylnT1InbXt0AIEnZBkZTDUXBBG+72I85b5i2TU8QUW1zmCA+PSDeKOq1ZxwBY8KRoQ1/ZDkA7+jQZDGY0SUQlSc4PI3wOJWikVbnafB3fgDw7hN8q90BWE+D1T0ETvcQQ+8hEinoQNCiXewNcGXDapfg9ypPj0RBg1DwQY2RUxS3R1yaodcuol/ooRCZwg0AngPQAOAwgN+QxSHRV7HkO4TBAEu+Q9A7M6nxiIWC9doJgiDM0K8DBOoMgPwBNhzp0P5yFdUNV7y1KRwcCOELclS8tUmzKbhnxXd4efV+BDiHhTFcMXUYHpx7QnzfTDR6ygoA9NWQ9QQR9drjjJ67gV57xqE6IYhG3uzZio3/lnHHCvsZ1SUQlS2M3v0Wtoz9BXiEz7J9wLE44Ywy7FxTqTiV/3KFBe5241oB2QWOcNnB0RNPw9fff4JAsPt6i2TFT887B87OInxVuSvc1+8JCJ+TXdD7xaLTUQK3R1uy4XT0/dIWgjAL57wRwNxUj4MgjJB77kiFBgEAMJuE3HNHpm5QSabMYcMBQTCgzGHS3YAgCEJAvw0QiPQGbBKDzcLgC3Rv/PXqhps6xVFadfs9K77DP1ftC78OcB5+ndQgQSRmShR0BRGT472s525QnFWclOenmvzLLu31tXq6BNYSY5tcYdmCzSYHLSJiYxwcQ8dNw1nXKfcXFns9ql/YgmDAmNj5tQ+fGv56++p6bP6yFq3O3eHyhGz3KDg7izBmajHGTO3+/n/y0lZ8/6n2fY48XmuPaZRRo29VaBAAgCS5MGq0uGQhldTVV2L3rggxxdG3GiqDIAiCSEdCOgP92cXgzlEluHXbfnRGHFS5JIY7R1EQmyCI2Om3AQKR3oAvyJHvsiHLYY1b3fDLq8UCgS+v3p+6AAGgX6KwcbkycFB+DrDhpaR4L4tsi/TcDRZMWhD356ea/CvmoWn5q3JZgcWC/MsuRcmiRb2+n2iDz5xOFN1ys6HrRWULe8dfCd6p9CTkAeCryl2KTTuA8OvI0353mxd+rzZg4MhSZkp8VbkL9rZCDGor1LSrn7Pn+8PC8e/5/rDWyN0goQ12X994xyqmSBAEkY5kTSzqVwEBNSGdgUd216HG40OZw4Y7R5WQ/gBBEHGh3wYI9PQGmjt9+HbROT1eP1CnHGHgAGV6V0DHwk6vPaWIBAk3vAQMPRnY87m8E2QW2fYwzvoDerZFZ158EipOqdC4G6jFDPsi1tJS4Qm+CEt+PkoWLYopIKBGtME342IQukdk//d//aGwn169v/q0f/vqevznhc3gEbE5ZgFOv0yZpaN3P1G7mb5mKCme0+c32bGKKRIEQRDpySXFBRQQIAgiIUipHkCq0PMjN+pTvmj2cbBZlCepNgvDotnHKdosTNmnp/aUoidI+MOnCO/oeEAOGmxcHtdHR7MtOn/U+Xj/5+9j47Ub8f7P30+L4AAgn+Azm7F6wIBHvJltrqrCjrNmYcu4Y7HjrFlorqoyNYa82bNR/mE1xm3ZjPIPq00FB0TINf9b4G7+X7iPPAF38//C79liuN5/zNRinH3NseH+2QUOnH3NsZqsAL37idrN9M004iGmmO48/PDDhccff/w4u90+6ZJLLhkZ+W+VlZU5Rx111HEul2vi1KlTx2zfvt2eomESBEEQRL+C5uf0pd8GCG479xi4bMq0Zj29gRXra3Dqox/iqIUrceqjH2LF+hrMnViGx39+IsryXWAAyvJdePznJ2rKEaaNEtjCRWkXIXp+QtAVHlRlO4RcDOJIomyLVu5eiXNeOwfjnx+Pc147Byt3r4zpfmbhRjNFOrUZLc1VVai79z45C4HzsE2h2SBBPBk25iD8HR902xoGW+Hv+ADDxhw0fI8xU4tx7cOn4sa/noVrHz5VExwAgOlzRsNqV/56stolTJ8zOqa+mYaeaGJfEVP856q9BSc/9J8Tjlq4cvLJD/3nhH+u2hv3466ysjLfHXfcUXfppZceimyvq6uzXn311aPvvffe2sOHD387YcKEjksvvXRUvJ9PEARBEOkGzc9ENPptiYFRn3KRmOGdb3wXvoeof+Q9O7xiFfc9h8UlDmp6er4GtYaAnvigCD1BQhFxdjFIhG3Ryt0rFfoFde11qPiyAgCSkoXQsORJwG9cxV90vUIgEOZsChPBzjWVANTvyY+dayo1IoWAXFIQqUEwfc5oYUBAjUi/QO9aM30zjb4spvjPVXsLHvj35hEef1ACgIZWj/2Bf28eAQBXTxsh8FntHddee20TAKxZs2ZATU1N+ATixRdfzD/66KPd//Vf/3UEAB5//PHaoqKiCevXr3dOnDjRrXc/giAIgshkaH4meqLfBggAYz7lIjHDTl8Aj7+3TRgcuO21DWEXhBodnQNA1kBQBxNEAQozzxdqCFT9Vv7aSJBg1n1A5Y1AwNtz3zi7GCTCtmjpN0sV4oYA4A64sfSbpUkJEBjVHwBkDQLN9Tp2hEZtChNB6+FDhtu3r67HRy9uhd8rf0/bGj346MWtAGA4SGB0k2+mbybRl8UU/1i9oyy0+Ajh8QelP1bvKIvnAkSPTZs2uY499tiO0Ovc3NzgsGHDPBs2bKAFCEEQBNFvofmZ6Il+HSAwgp6Yoaj9/qpNCovEaOS5bIYyA8w8X1dDoHqx8SwCTUq8BFisyqBBAlwMEmFbVN9eb6o97lgssiNBDzCbDUPuvkvTHqtNYSLIGTStPNcGAAAgAElEQVQYrYe05QQ5gwZr2r6q3BUODoTwe4NCJwKi9/RVMcWDrR5hPaFee7xpb2+XBg8erEh3ycnJCbS0tFj0riEIgiCITIfmZ6In+q0GgVHMiBmKXA1EuGwWMAbdzIDePl837d9oOUD1YiCofg9BwJ4N5A0DwOS/Z//RnIvBxuXAkuOBinz5bx2Bw6yJRShZeDKGPjoDJQtPDgcHeqsjUJwl3oTqtcedKMEBa2kpwBispaUoefghYclA0S03gzmdijYzNoWJYMa8a2C1K8s+rHYHZsy7RtM3Ue4CRHpQmOMQpiLptcebrKysoHqx0dbWJuXm5vYctSMIIi1pX9+Auke/xoGFn6Hu0a/Rvr4h1UMiiD4Hzc9ET1CAoAfMiBlGI1LM8JGLT0CTTjBBnRlg6vl6af9GywH0AgmdR4BbvgcqmuS/zQYHqn7bpW3Au8seDLoghHQE6trrwMHDOgJGggQLJi2A06LcYDstTiyYtMD4+GPBohMItVgMOQvkzZ6NkgcWK4MJDyxOmf4AAIybMRPnzL8JOYMLAcaQM7gQ58y/CeNmzNT07c/uAgTw21nlNQ6rpEghcVil4G9nlSdIZVXJcccd17l58+YBodctLS3S/v37HSeeeCKlLxJEBhKySw7pGYXskilIQBBKaH4meoJKDHrAqJghAOS7bGjq1G788102fLHwLEXb4+9tE2oUqDMDzDwfs+5TahAA5soBXAOBTkHpkcu444IGvbKHqpuBN38t2yYyCzD5OuCCJzSXx6IjEPr3pd8sRX17PYqzirFg0oLk2STqZRAYKDsIkTd7dkoDAiLGzZgpDAiomT5ntEKDAOg/7gJEt9DRH6t3lB1s9dgLcxze384qr4l3faPP54PP52OBQIAFAgHW0dHBbDYbv/LKK5vuv//+ocuWLcu/7LLLmu+4446SMWPGdFJ9I0FkJtHskmMpVySITIPmZ6InKEBgACNihgBQceFxuO3VDfAFu+v4bRJDxYXHafredu4xCg0CQD8zwOjzwyf7vXUxSAR6WQm+9u6veQBY+6z8tSpIUNcuFuTTa1dz/qjzkxcQUGEtLRVrCJSWpmA0vaO5qgoNS56Ev64O1pISFN1ys+GARX92FyBkrp42ojHRgkd33HFH6ZIlS8LCHFlZWQW33HJL3RNPPFH7j3/8Y9ctt9wy/Ne//vWo8ePHty9fvnx3IsdCEETqSJRdMkFkIjQ/E9GgAIEBjLgNAOZO+01lBphh/GXagIBR68POI+J76rUbwYx14rplmgCBxCQEeVDTVWJ9vzqm6JabUXfvfQqrwlRrCJihuapKMX5/bS3q7pWzUfpaVgPRf3niiSdqn3jiCaFlyNy5c1vnzp27KdljIggi+STCLpkgiN5D83P6QgGCHlixvsaQ20AIw6f9Jvv2GjPWh3qb+VgsDUVlD3pwbeq9KDgQrb0vEdpE9/YEPtU0LHlSEdwAAO52o2HJk4beQ6w2hwRBEARhlETYJRMEQfRHKEDQA4+/t03XbSDhm/t4YEYDIFYNAxGisoeWGkC0wWdaUb+SrBJhOUFJVuqs/szQFzUEjOKvE5dx6LWrIZtDgiAIIlkkwi6ZIAiiP0IBgh5Quwr01N7nMKsBMPuP2nIEQLYn7K2ugbrs4d+/635eJJOv0zQtmLQAFV9WKIQKk+pE0I+xlpSINRRKjAVnyOaQIAiCSCZZE4soIEAQBBEjfb+QO8WoXQV6ak85G5fLm/mKfPlvMw4E65Zp2/atismmUMjwadD+6Eld7UrOH3U+Kk6pQElWCRgYSrJKUHFKRcqEB/sTRbfcDOZU2kSa0VAgm0OCIAiCIAiCSC8og6AHzLgNpJyNy4EVNwDBLqvF5v0AJDl1X1Dfr4EHtHoFa58DwJX9fJ1ylkFv3RGqFwNQlxgEde+ZSieC/kysGgpkc5gxBILBIJMkiffcNX0JBoMMgHEPUoIgCIJILTQ/EwmBAgQ9kDC3ATMYdSF4547u4ECYoGZ/HxWNmKDOxXqlC0bQuzaWexIJIRYNBbI5zBg+37t372mlpaVtdrvdxxhL9XjiCuccXq/XVltbmw3g81SPhyAIgiAMQvMzkRAoQGCApLgN6GHGhaAzoXamSmJxNkiEWwLRJxkztZgCAmmO3+//VVNT029aW1uv45wXIPNK04KMseZAIPDHYDD4l1QPhiAIgiCMQPMzkSgoQNDX0XMhiCXFXw/dUgQGRSZBrM4GiXBLIAgiIUyePNkLYGnXH4IgCIIg+gA0PxOJItMiTZmHmXR8V4GJG6vSkGwuYORp4q5HnQ7kDZOvyRsmOx3EEpwYf5l8j3jekyAIgiAIgiAIgoiJpGYQMMZ+CjnKZQHwDOf80Xg/Y+zdb8Md6D7tdloYtj50HkYuXKnpOyTHjh9bvYrXnd4AWjzdp+i5Dgu8/qDwnj954mPsaOi2CywvysKNM8s1egV/+miHpt8HvztTOP57VnyHl1fvR4BzWBjD2uwiDPT9qO3oGqi1HvzZY8CKXwPByCwA1el/qM2RC3iau5sc+UDjbuGYUP8dYM9SthnVRdBj3yqgpVYeW0stsG8Vtr9dCFeTJdylMz+AMQtnon19g8bXGNB6HTd/tBfBhm47RKnIibyZI4SeyEbvacYuSXRP0fVbxo7TtI3bugU7L7gAvp27wm22o0fjnYFD8MyceWgoGISixsO4vvIV/OzIj5p+R//738LrrYWF6PxqVbjNNX0a/AcPavr59h8APBH2gw4Hxm34Fo/+Zj7cRd22hs6GOrgLi4HIOjfe9fOlaqtYvBgV991nqK/RNjP3tEmAj3e32RjH3RWLcfTC1+FHt5OCFR74YYcyaBb6zPTctufR2Ri5sKrX15u5p8vSgc5A92fRZWlHZ2CA8HqCIAhCxuj8TBAEQQCM8+QIXzLGLAC2A/gJgAMA1gC4gnO+WdR/ypQpfO3ataaeoQ4OpALRdlyEKEhwz4rv8M9V+xRtF0qf41HbMxjAugMZHm6BRZJg5RGChDYXcOKVwDcvCIQK44xkkzdjge4xweYyngXw798Ba59VNG33PAUXHwEWsdHh4PC6gnD6beC+CNcDCV3PN/+9ZjYJrslF6FzX0OM9mU1C/sXlhhYR7esb0PTGDsU9RdeLggN6/OekU/CHq+bD4+jezDo8Htz64tM4e82Xys4WCxCIr8Br5cwZcnCgtxt8zuW2OAYIYr3nPz3ju4ID8dvMy39YH7un+SABY2wd53yKqYuIfktv5miCSAVG52eC6KvQ/Ewkm2SWGJwMYCfnfDfn3AvgFQBz4vmAVAcHAOOGAZEZBSFeXq0V7nsreBoW+q7HgeBgBDnDgeBgtMOlDA4Acj3/umWJDw4A8jMigwOh51cvNnb9umWaJnVwAAAYGOydknIjD8gOib38XnNfEB2r6w3dk/uCaHlvj6H7try3R3NPM9eLeGbOPEVwAAA8DgeemTNP2znOwQEA2uAAoN2c99QW6/Vxvqc2OABoN+K9aevr9yQIguifJGJ+JgiCyGSSWWJQBiByB3wAwNTIDoyx+QDmA8Dw4cOTN7I+QkAnm+Ot4Gl4y9utD7DbcaX4BkKBwSRi1KYw1eM0EVsINHl67hSln9HrRTQUDDLVThAEQRCEkkTMzwRBEJlMnxIp5Jw/zTmfwjmfUlhYmOrhJB2LQf/SWj5Y/A/MIm5PFkZtClM9ThOHq5Z8R8+dovQzer2IosbDptoJgiAIglCSiPmZIAgik0lmgKAGwLCI10O72uKG05L6tFqjIygvytK0XTF1mKCnlv/2XwYvU01sNhcw+Tr5795i0bmWqX5MJBtgsWufb9SmcPJ1mqZOthdcdbQf0iBgNvXzAfTye81sEgZMLTZ0T2aTwuKFPZF77kjNPc1cL+L6ylfg8ChPOBweD66vfEXb2RL/oIuzoa67lj9ESAfAaFus18f5nlZ4oE0h4XFo6+v3JAiC6J8kYn4mCILIZJIZIFgDoJwxdhRjzA5gHoC34vmArQ+dpwkSOC0Mex49X9h/SI5d8zrXodxo5TosuvdUb/LLi7Kw5PIJKMt3gQEoy3fhycsnCPuJXAwenHsCrp42PJxJYGEMV08brmnLPflK2C96SmsTeMETWvvAi/8XGDxW+aDBY8Vt99YDR52hbD/qDOCivynvOffPwJw/9d6m8IIngCn/pzuTgFkw5tT16MwPgEf815kfwOhFZyL/4vJwpN+S78DAS4/BwJ+PUbZdfgykIqfiMVKREwMvP0bRL//ichTMLTd0TzMCRlkTizT3FF0/busW4fXjtm6B7ejRirafHfkRt774NIYcPgjGgxhy+CBuffFp/OyI0tXCdvRojNv0veZ629Gj4Zo+TdHmmj5N2A8qrQM4HFj4l6e7gwRdf9Svo/2pWLzYcN9E3NPGtK93PnpJRJBA/qN+bfaPLAbY++vN3NNlaY/6Wnk9QRAEYXR+JgiCIGSS5mIAAIyx8wA8Cdnm8DnO+UN6fUkhmSAIInmQSjJhBpqjCYIgkgPNz0SySaZIITjnbwN4O5nPJAiCIAiCIAiCIAiiZ/qUSCFBEARBEARBEARBEKmBAgQEQRAEQRAEQRAEQVCAgCAIgiAIgiAIgiAIChAQBEEQBEEQBEEQBAEKEBAEQRAEQRAEQRAEgSTbHJqBMXYQwN4YbjEYwKE4DaevQO8pPaD3lB5k2nuK9f2M4JwXxmswRGZDc7SGTHs/AL2ndIHeU3oQy3ui+ZlIKn02QBArjLG1meYZSu8pPaD3lB5k2nvKtPdDZDaZ9vOaae8HoPeULtB7Sg8y8T0RmQuVGBAEQRAEQRAEQRAEQQECgiAIgiAIgiAIgiAyO0DwdKoHkADoPaUH9J7Sg0x7T5n2fojMJtN+XjPt/QD0ntIFek/pQSa+JyJDyVgNAoIgCIIgCIIgCIIgjJPJGQQEQRAEQRAEQRAEQRiEAgQEQRAEQRAEQRAEQWRmgIAx9lPG2DbG2E7G2MJUjydWGGPPMcYaGGPfp3os8YIxNowx9hFjbDNjbBNjbEGqxxQLjDEnY+xrxtiGrvdzf6rHFC8YYxbG2HrG2L9TPZZ4wBjbwxj7jjH2LWNsbarHEw8YY/mMsdcYY1sZY1sYY9NTPSaCIAiCIAgi/ci4AAFjzALgTwB+BuBYAFcwxo5N7ahiZhmAn6Z6EHHGD+D3nPNjAUwDcGOaf588AM7inJ8IYAKAnzLGpqV4TPFiAYAtqR5EnJnJOZ+QQZ7ESwG8yzkfC+BEZN73i8gQGGMFjLE3GWPtjLG9jLErUz2mWGCM3cQYW8sY8zDGlqV6PLHCGHMwxp7t+t60dgVSf5bqccUKY+yfjLE6xlgLY2w7Y+z6VI8pXjDGyhljbsbYP1M9llhhjH3c9V7auv5sS/WY4gFjbF5X8L6dMbaLMTYj1WMiiGhkXIAAwMkAdnLOd3POvQBeATAnxWOKCc75pwAaUz2OeMI5r+Ocf9P1dSvkDU1ZakfVe7hMW9dLW9eftFcAZYwNBXA+gGdSPRZCDGMsD8DpAJ4FAM65l3PelNpREYQufwLgBTAEwFUA/sIYOy61Q4qJWgAPAngu1QOJE1YA+wGcASAPwD0AljPGRqZwTPHgEQAjOee5AC4E8CBjbHKKxxQv/gRgTaoHEUdu4pxnd/05JtWDiRXG2E8APAbglwByIM/Xu1M6KILogUwMEJRBntxCHEAabzz7A10Lj4kAVqd2JLHRlYr/LYAGAB9wztP6/XTxJIDbAQRTPZA4wgG8zxhbxxibn+rBxIGjABwE8PeuUpBnGGNZqR4UQajp+rm8BMC9nPM2zvnnAN4C8IvUjqz3cM7f4JyvAHA41WOJB5zzds55Bed8D+c8yDn/N4AfAKT1Zppzvolz7gm97PozOoVDiguMsXkAmgBUp3oshC73A1jMOV/V9Zmq4ZzXpHpQBBGNTAwQEGkEYywbwOsAbuact6R6PLHAOQ9wzicAGArgZMbY8akeUywwxi4A0MA5X5fqscSZ0zjnkyCXId3IGDs91QOKESuASQD+wjmfCKAdQNprrxAZyRgAfs759oi2DQDSOYMgo2GMDYH8fduU6rHECmPsz4yxDgBbAdQBeDvFQ4oJxlgugMUAfpfqscSZRxhjhxhjXzDGzkz1YGKhq+x5CoDCLl20A4yxpxhjrlSPjSCikYkBghoAwyJeD+1qI/oYjDEb5ODAi5zzN1I9nnjRld79EdJfN+JUABcyxvZALtU5KxNqHEORe855A4A3IZclpTMHAByIyFh5DXLAgCD6GtkA1IHgZshpt0Qfo2uOfhHA85zzrakeT6xwzm+A/LM2A8AbkLWD0pkHADzLOT+Q6oHEkTsAjIKc+fs0gCrGWDpnegyBXHL6c8g/dxMgZ8zek8pBEURPZGKAYA2AcsbYUYwxO4B5kFMYiT4EY4xBrpnewjl/ItXjiRXGWCFjLL/raxeAn0A+pUhbOOd3cs6Hcs5HQv4cfcg5vzrFw4oJxlgWYywn9DWAcwCktTsI57wewH7GWKhWcxaAzSkcEkHo0QYgV9WWC6A1BWMhosAYkwD8A7JexE0pHk7c6Mr0+xzy4dFvUj2e3sIYmwDgbABLUj2WeMI5X805b+WcezjnzwP4AsB5qR5XDHR2/f1/u7S3DgF4Aun9noh+gDXVA4g3nHM/Y+wmAO8BsAB4jnOe1qlxjLGXAZwJYDBj7ACARZzzZ1M7qpg5FXLd6XdddfsAcBfnPF1T/koAPN+VTiYBWN5Vu0n0LYYAeFOOT8EK4CXO+bupHVJc+P8BvNgVFN0NWQyJIPoa2wFYGWPlnPMdXW0nIgPS1zOJiAD+EADncc59KR5SIrAivTUIzgQwEsC+rvksG4CFMXZsVwldpsABsFQPordwzo90rdsjRavTXsCayHwY5/RzShAEQRBE4mGMvQJ5gXw95HTbtwGckq6BfMaYFfJmcxHkU+lfQdZZ8Kd0YDHAGPsr5O/N2RHuPGkLY6wIwP9j777Do6jWB45/303ZNEJCCp1QQu9Kld7E3gV7L9i9v3u92EWxXfWq16uCKPaG14K9oAJSBOkgvfdeUgjp5/fHmei6bBrZzSbk/TzPPoHZmXPOzs7OnHnnlMHAV9gnukOxXQwuNsZUyxamIhLFX1vj/AMbMLjJGLM3KIWqIKcVZk9gOnYq7JHYbgZdvcYtqVZE5BHsmEenA3nYVs3TjDEPBLVgSpXguGtBoJRSSqkq62bslIB7sCP/31RdgwOO+7HBgSKXYUctHxOU0lSQiKQAN2L75+9ynk4D3GiMeS9oBasYg+1OMB7bwm8zdmDkahkcADDGZAFZRf8XkUwgu7oGBxxh2ClD2wAF2G6a51Tn4IBjLJCIbUGVDXwEPBbUEilVCm1BoJRSSimllFJKqeNykEKllFJKKaWUUkqVkwYIlFJKKaWUUkoppQECpZRSSimllFJKaYBAKaWUUkoppZRSaIBAKaWUUkoppZRSaIBAKaWUUkoppZRSaIBAKaWUUkoppZRSaIBAKaWUUkoppZRSaIBAKaWUUkoppZRSaIBAKaWUUkoppZRSaIBAKaWUUkoppZRSaIAg6ERkk4hs8lp2lYgYEbkqOKVSqvpzfkPTgl0OpVT1IyIDnXPImGCXpSYTkTHO9zDweMxbr1PFE5Gmzv55M8D5aJ1bKS8aIFB+JyLTRMQEuxxKKaWUL5V186GOfyLSX0QKnOPp0SCVQetdxdBgn1LlFxrsAiifPgPmADuDXRClqrG2QFawC6GUqpZ+w55D9gW7IKrqEpFawFvYa03MMSSh16ng0zq3Ul40QFAFGWPSgLRgl0Op6swYsyrYZVBKVU/GmCxAzyGqNP8BagNPAI+Vd2O9TgWf1rmVOpp2MagEYt0qIstFJFtEtovIiyJSu5j1ffaHEpFOIvKBM25BjojsFZGFIvK8iIR5rRsiIqNEZJaIpInIERFZJyKviUhLj/X+6GMnIpeIyFwRyfQxLkJPEflYRHaJSK6IbBWRV0Skgcc6TZ0mbgOc/xuP1zSv9Bo5+2CD81n2i8gXItL9GPbtHSKywnvf+hrfwWO7i0VkqogccrZbKSL3i4jbx7rGab6XKCITRGSnU+blInJ1CWUbLiLfiMg+Z/31IvK0iMSV8zOGisjNIjJHRNJFJEtEFjnHlMtr3clOeW/3kc5Y572JHsv+aHonIr1F5EfneMkQke9FpJuPdPx2zHis29zZt+ucY/WAiCwTkfEikuCxXriI3O4c9wedfbFJRD4XkaFeafrs2+kcG0+IyGrnuz/ofNahPtb13D9dRORr55jJEpHpInKS9zZKqeKJSA8RmeScq3Oc8+kPIjLCx7ojROQX+fMatkxE7inmPL3JeUU759ktTvrrRGS0iIjHumOAjc5/r5S/Xquuctbx2SzZuRYY57x8r4isdfLZKiL/EpFwr/VL7MogxTQNFxGX2Gv4POf8etj5900+zvvlzkOsK0Vktti6RLbzGb4XkZG+0vGRrue14Eqx16UjIrJHRF4XkXrFbNdSRN52joFcEdnh/L+lr/W9to13zr/rPb9Tr3W+dMp11PWrmPVPFJHvxF730sVeB3uXYbuzgauB24EdZcnLRxq+6kee+/UCEfnN+cwHRORDEWnosW5A6l1SynXe85gTkTZi6x4HnON0poicXMzndYvI3WJ/y1nO/p4hPn7/JeyzViLypIjMd47dHBHZLLYO0chr3TeBqc5/H/LaPwOddYodg8A5Nj5xjumifF4Wkfo+1n3TSaepiNzofMZsEdntlM1nnV+pqkhbEFSO57EXkJ3ABCAPOBvoCYQDuaUlICKdgLmAAb7AVm5igVTgZuB+J13EVlC+AoYBW4H3gXSgKXAuMBNY65XF3531v8SeTP84kYnINU65c5y8twItgeuAM0WklzFmC3AIeBi4Ckhx/l1kk0d6JwA/AHWA74FPgUTgHGCmiJxrjPmmtH3ieAm4CXtxnoDdl2cBPYCwon3iSURex17UtwGfOOXuBYwFhojIMGNMvtdmccAsJ/2PATdwIfC6iBQaY97yyuMhYAxwAPtd7AE6Af8AThOR3saY9NI+nNjAz5fAcGA19rvMBgYB/8UeQ5d7bHINsAh4SkRmGGMWOekMAe4FVgC3+ciqJ3AP8CN2n6YC5wH9ReRkY8wMH9v445jBudDOwx7P32C/kwigmfPZXgT2O0m/CVwM/A68DRwBGgB9gVOc8hdLbHBmFtDOyfN57LE3AvhBRG4yxrziY9NuwD+BX4HXgCbA+cBPItLFGLO6pHyVUiAi1wPjgALseWEtkIz9fd0MfOSx7uPYc9I+7HkvEzgVeBwY7pyXvK+dYdhrSgPgWyAfe115EntOKbomTcOe0+8AlgCTPdJYXMaP8z7Qz8knHTgNe45Ixl5fKuod4BLsufM17LX/XOBl7Pnu0gqm/xh2/27E7vc0oD7QHXttm1SOtP4GnOxs851TvquBgSLS0xizt2hF52b0R6AW9hhYAbQBLgPOFpGhxph5xWVkjDkoIh866Q8Fpni+LyKNscfJAmPM/NIKLjbI+yO2LvYpsA7ogj1Gfi5hu2TgVWCyMeZdXzeXfnAztj7zBTAde50eCXR2rjs5BL7eVex13tEMe11cBryCPYZGAt+KyCXGmD+OI6du+j02mLEKW9eIAi4AJjmf6d4y7JfzgFFOeWZj62Xt+bN+0c0Ys91Zt+i3fSV2H07zSGdTSZmIyBnY+ohg632bgROxdc6zRaSvMWajj02fwtbZvsTu80HA9dh61eAyfD6lgs8Yo68AvoCTsBf2dUAdj+UR2JOqATZ5bXOVs/wqj2X/dpad7SOPeMDl8f/H+TOQ4PZa1w0kefx/jLPuYaCrj7RbYU++64CGXu8NwVb0PvNaPs0eWj73R6iTVjYwwOu9BsB2bCDF7Wt7r/X7OWVfDcR5LA8Hfill334KRHq9V7Qv7vBabpzXa0CIx/J22AroCq/1Bznrz/Ysl1f+z5Xx+Ckq03+98g4BJvo6JpxjLg9Yg+0TWdfZp1lAe691B3p8vlu93jvbWb7W6/jy6zGDDVgctd+d96KLvidsxaQQmO+5LzzWTfDxvU3zWvaKs/wVQDyWt8RWkHOApsXsn6u80rrRWf5yWc8H+tJXTX0558s8bNC0vY/3G3n8u7fz29oC1PNYHoqtdBvgXq/tNznLv8Hj3I69YT/kvMI8ljd11n+zmPIW/fbHeC2f5ixfwF+v6dHOOa/Aq8yl5TMNr+slNghqgIVAjFce8533LqlgHvuxQfIoH+snlvE7HePkm4vXtQB4znlvoscyAVY6yy/1Wn+ks3wVvq83Az2WdXOWfVxCma4vQ/nFyc/XdfQO/jz3D/Sx7efY4FVd5/9XOes+Ws7fha/rVNFnSAc6er33vvPeiNK+Y6/fTbnqXZR+nS865gzwtNd73bC/9YNArMfye/jzNxrq9Rvd5Lx3UmnHNdAQH3VEbJCqABjntXwgPn7LHu8XfXdXeSyLwf5GCoB+XuuPdtb/wWv5m/x53mritf+L6qQ9ynN86EtfwXppF4PAK3qS8Jgx5kDRQmNMNvZkWV5HvBcYYw4aYwrBdi3ARp2PAKOMjTB7rptjPKL5HiYY52mzl5uwT2XuMH9GZIvS+gkbhDhT7EA9ZXE60AL4rzFmuld6O7CR13rYG8nSXOn8fcwYc8gjnVyK37d3YG/qrzHGeO/LsdgLgq8nM1nA/xljCjzyWYF9Gt1WRDwHJypq3n+9Z7mcbd7EPqEq9emP2GaktwG7gL955V2Ajewb77SMMbOBB7A3va9gn0TVA243xiwvJrt12CdTnul8jo24p2KDMd78fcz4OrYPe3xPBluhy8EGCrzX3e+9zJPz9OIy7JPIe4wxxmPbtcAL2ODSFT42n+V8d55exx5LPUrKVykF2PNCKDDW13nIGLPN47/XOH8fNcbs8lgnH3veK8Q+LfTlds9zuzFmD/Zmrm7hUpsAACAASURBVDbQukKf4K9Ge13TDwPvYbtulqlpewmKPv/dxphMrzxGO/8t7vOXRx72BugvjDHlHZjxHR/XgjHYoOsl8meXkJOwrQV+Nca855XnJGzrxtbYFgjFMrZlwHzsU9w/ujE49Z9rgQzggzKU+yQnv1+c652nF4H1vjZyWsidBdxsjNldhnyO1QvGmGVey151/pbnulOReldx1/kiacAjXmnOx/4W4rCtXopcg72O/5/xaKXp/EbHOv8t9bg2xmz3rts6y38AlmOf3lfU2djWFpPM0S0o/40NaAwTkSY+tn3EOC0knXLlA284/9X6gqoWtItB4J3g/J3u472Z+Lg4F2MS9uZ2soh8jG0SN8sY430Ba4OtCM11Tvxl9Vsxy4v64Q3w1U8NG/kNwT41XlCGfIrSSxHfU84U9UFsi40yl6Sr83emj/fmYG/e/iAiUUBnbNT/TvHdfTHHydvbWuO7S8BW52889sYT7GfMAy4UkQt9bBMOJIlIQik3ta2wF6i1wP3FlPdIMeX9F7YlwyXO/z8wxrxWQl4zioJMXqZhmwN25ehj2F/HzBfYVi8vichwbBPEWdiWGZ438eki8iVwJrBYRD4BZmCP9bKMAt0a25xxlmfF3sPP2K46XX28d1RTVWNMnojsxn73SqmS9XL+fluGdYuum0c18TbGrBGRbUAzEalt7ABjRdKMMet8pOd5nvYXX83X/ZXPCdggyDQf703H1ht8nafK4z1sAHqFiHzkpPur1/4sq6PqN8aYNBFZjL1+tMUGxov9Xj2W98V+tl9KyfNlbJD2Guz1A2w3j0bYJ8iZxW3oodj6mTGmQERmYm+s/yAiTbFd0/5njPnIezs/89cxVpF6V3HX+SILjTEZPpZPwz7E6Qq85TwQSAW2G98DMxYdE6Ue12IrQ5din/x3xu6LEI9VSu22WwYlnYPyReQXbAuHrtgWA54CeW5QqlJogCDwivprHRVldk4yZYrUG2N+E5F+wH3Y/lqXA4jIauBhY0xRtLxoALztR6dSol3FLC8aIO6uUrYv6/Q+Ren5unEub3ol7dsCEfG++Y7HPoFOAh4qQ/qeDhWzvCgI4XlxSsD+tkrLo6gJW3GK9lXLUtI6al8ZY4yIfIptcge2QlOS4p6CFB0XvgbX8csxY4zZLCI9sE+cTsH2LwTYKiLPGGNe8NhmJPYJ2iX82dcy2wma/aOUpzlFn6G4qYyKlvsaRLKk7z+kmPeUUn8qz7WpLL/VJk6anje05TlPV4h36zA/51MbOGCOHmPBs96QXME8/gZswLZyvNt55YvIN8Dfiwm0FKes14+KnIO9fYh9knu9iDzpBLhvcN7zNY6ML8XWIRy+rnGvYwPzN5cxj4rw1zFWkXpXcdf5IsH47p8F7nS2+R57TilqNXQVdiyGivJ3fcHv5yClAkm7GAReUeWlrvcbIhKKHSSmTIwxvxpjzsDe6PbBNsmqC7wvf47AXnRiaugjiRKTL2Z5UflrG2OkhJevFhIlpXd2Kek9XGIqVtETfV/7NoQ/L4reeS8qJW+fj+rLIQ04WFoexpjNZUgHbH/9ktJp5r2h2NGgn8H2ASwEXhORiBLyOmofOoqab/p6quS3Y8YYs9IYMxL7nXXDVlZdwH9E5FqP9Y4YY8YYY1phbxAuw7YguQw7iFBJisrlc2Rt7OBKxX1WpVTFlOfadDz9VotaZhX3QMbXDUYaUEe8ZieCv9QbPFu0lTsPY0yBMeZ5Y0xn7Pn/fOx88GcB34mPmSJKUNbrh9++V6cbyZvYp7gnewxOONcYs6S07b3yKa38nk7ABmf2eo6Iz59NyO9zlk32sW2wVKTeVdx1vkilfvfO4JC3Ywcqbm2MucwYM9qpF4zBtgL1h+PpHKRUuWmAIPAWOn8H+HivL8cQTTR2HIHZxpgH+bO/+9nO31XYilgn8TGd3DGY4/z11Qe9OAXwx026P9IrTlG/OF/9FXvhVVlymhwuB9qLSB0/5F+cOUC8iLSvYDpF32UvXxXF4jgVu0nYAa1GYudn7kjJrQj6itfUWY6Bzt+S+iB6O+bv2BiTb4xZYIz5F3agLrCjLPtad6ux/ViHY8dQ6CseUyL6sBo7lkRn8T3V5CDn70If7ymlKqbovHBqGdYtOt8M9H5DRFKxzcg3FvMUv6yKuvcF+oneQedvY+83RCQW29XK2yJs/ay/j/f6Y8vseZ46ljz+YIzZY4z51BgzAtukugXQoaRtvBxVv3GmdOuCHRhvpbO42O/VUd5z8DjsDeyN2LEHQih76wHPfHyVPwTfdYu3sQMEe7+KukQsdv4/xce2gVRZ9S5vJxQzBtVA5+8iAKcbwnqgofiezrKs331z7G/jB++uDWKnOGzuY5tj+a2XdA4K5c99qfUFdVzSAEHgven8vc/zptR5mvtEWRMRkZNEJNLHW0XR2yz4Y/C6l4FIYLz3UwCx88gnlb34vIjtT/+ciBxVyXDS877oFDWb9zV4y+fYi8QtInKarwxFpLczXkBp3nb+3ice88s6g9E97nsTnsWOAfC6r5tEsXMsn3D0ZuXynPP3VV9BGrHzdPfyXu7NGdjmv9hI9Qu+vn8RqS8i7bwWP4PtF/eUMWYKtnvCLODGYsZEANuN4S9NJsXO8TwAe/Pta5rD4pTrmBE7z7CvLgx/ObZFJElEOvpYLxrbNDKfEvoeOs1138NOrzXW8z0RaYENtuVhB3VUSvnXOOxv9AEf56yiyn2R152/93ter5ybn2ewdZeJFSzPQezNpa/rlN84NzGrgD6en9v5LM9ir9Xeij7/E57XQuffTzr//ePzlzcPsXPR9/HO1AlEF9VTyjKuS5HLRcS77/gYbDPtD8yfA8rNwgZq+4rIBV55X4C96VqD73GFjmLs4LI/AWdgp707hO16UFaznfL0d653nm7Fa/wBJ8/bjTHXeb/4swXB186yl8pRDn+orHqXt9rAg15pdcOOEZCGbZVS5HVsN8+nPQMZIpKIHVi5aJ2SbHL+9vVKIwY7gKOvVjQl7ZviTMbOuHKxj/randjpHX80HoMRKnU80TEIAswYM0tE/osdDOh3p690HvaJ/0GK79/k7Z/AYBGZgZ23OBM77+upTjoTPNZ9GDtf7pnAGhH5Cjuqb2Nsn/S7+DNwUVr5V4kdsfd1YLmIfIe9gIdhT7b9gL3YwRGL/ITt6/ap05/xCLDZGPOOsQO7nYftN/a1iMzGRtyznPJ1x0aA61NKBcUYM11EJmD7HS4XO2hdnvO504AdeI12b4x5XUROxN4MrxeR77EDzNTBnvD7Yy/0o8qyf4op108icjc2ALTW2QcbsTexKdib7pnY/valGYsdhGcUduT/n7H97ZKxN/V9sONSrAAQkXOxFZu52AH3isZjuBi7n18TkQXGmA1e+XwH/FtETsXOC56KHQsgGzvjg68BDIv7/OU9Zi7HBi9mYisxB7EVszOxzQWLWj40BBaJyDJgKXbQn1hs5bAedsRnX4Mlebrbyf9WsQMoTsU21x2BDRzcanzPa6yUqgBjzAoRuRkYj/0df44dgDUBe95Px3mKaIyZLSJPYa97RdfNw9jrXQfs+fPpCpYnU0TmAv1E5D3sOaoA+MIYs7QiafvwNPaGfpaI/A97Xh2EPScuwZ7jPcv2vnPDOgJ7Dp2MDWacg71OTTJeswCUM49IYKaIrMMOFLsZO/XyMOxAdV8YY1ZSdt86+X6ErdP0dV6bsOfcos9lRORK7NP1Sc4xsAo7gOw52HrKFeW53mAfiAzFBpT/a46enahYTnmudcrzidhxe9ZhWz4MwV4Xy3Kdrgoqpd7lwy/AdSLSExsAqo9tuegCbjR/Hdz5Gexv+GxgiVPOKKfcydiHGiUGh4wxu0TkQ+Ai7GDFP2CDFMOwx/xi7PfnaTW23nSRiORhj3eDnX3DZ1dP5/xwDfA/YLrzm9oCnIitR+/CtlxR6vhkqsBci8f7CxsxvRXbzC4He+P6EvaktgnY5LX+VRw9J+vJ2BvXFdib38PYk94LQIqPPEOdPH/DBhMOYytjE4BUj/XGUMw8v17pdcQGFTY7n+EAtg/YK8Bgr3VDsE/wN2Bv2H3N85uMfRLyO/aClOmU72Nsf/LQksrjkY4LO9jSKh/7NgNYXMx2ZwBfAXuwT513OfvqUaCN17pHld/jvTed95v6eK8v8JFTplzsTfFi7BOdbuU8fi7HVgAOOGltx1aS7wUaO+s1cd4/VEx5znbK+hsQ7iwb6Cwbgx3p+EdsRT0D+AHo7iMdvx4z2GDWOGwl9gC2YrMOe7x38FgvDvukoihIkoOtjE7DdkeQsnxvTjr/co63HGd/TQFO9rHuH/unmM+4Ca/fr770pa/iX8555hOPc+8O7I3YBT7Wvcg5z2VgK//LsQHRCB/rFvtbLO6chQ2Efol9wliIx3W3uN8+Jc83fxVe126P9651yp+Dvd68gg2O+EwPe227GTsiepbzWgDcAriKyb9MeWCDBv/E3thvcfbtXmxT9FE414cyfJd/7Ffnsy/Gnr/3Ys/f9YvZrjW2pdZObB1hJ/Autk95mb47j/dDnPwM0P4Yj8kTnWMww3n96BynJeZdzHf/aDnz9lU/KjZf7JgLBnjTx37wW72rDPv9j3Jgg0qfY4P7WdhAwfBitovA1lt+d46VDOxv/OJyfNYo4DFsPSEb+7DgJV/Husc23bF1qDT+/K0PLMPvtju2FcRe7PlqC7a+0sDHum9SfH1wICXUJfSlr6r2EmNKG39EqerH6eO2BvjQGHNxaevXVCIyEPsU/WFjB/hRSimlSiV2yryHgEHGmGlBKkNz7I3iLGNMIPrYKx/ETve4EXjLGHNVUAujlPI7HYNAVWsiUk+8Btdz+tEVNUv/7OitlFJKKXUc+Ae2ld2LwS6IUkodL3QMAlXd3YkdRGYatpliPWzfwUbY5pP/C17RlFJKKeVPItIEuAQ7Ds/V2O5peq1XSik/0QCBqu6mYAdfOhk70GA+tmvBC8DzRvvQKKWUUseT5thBgLOwdYCbTPkGNlRKKVUCHYNAKaWUUkoppZRSVbcFQWJiomnatGmwi6GUUjXCggUL9hljkkpfUym9RiulVGXR67OqbFU2QNC0aVPmz58f7GIopVSNICI+54NWyhe9RiulVOXQ67OqbDqLgVJKKaWUUkoppTRAoJRSSimllFJKKQ0QKKWUUkoppZRSCg0QKKWUUkoppZRSCg0QKKWUUkoppZRSCg0QKKWUUkoppZRSiio8zaFS1c2jH07kw/Am7I9LIOHQfi7K3cL9F10b7GIppZRSSimlVJlUuAWBiDQWkakiskJElovIHT7WGSgiaSKy2Hk9WNF8lapKHv1wIq/Gd2BffBJGXOyLT+LV+A48+uHEYBdNKaWUUkoppcrEHy0I8oG/G2MWikgtYIGITDHGrPBab4Yx5gw/5KdUlfNheBNywt1/WZYT7ubD8CbcH6QyKaWUUjWVyc0le/VqjixeQvaqlRSkpVGYnkHh4cO4YmsRmpREWHIy7latiOrZi7C6ycEuslJKVQkVDhAYY3YCO51/Z4jISqAh4B0gUOq4Y4xh3q557I9L8Pl+ccuVUkop5V8mL4/MGTNJ++xTMn+ZQWFODtkRCWQ07ERu7frkRLQhNzEGyctFtmbgWpVGxLffUitzAnUSQ4nrfxJxIy7E3axZsD+KUkoFjV/HIBCRpkBXYK6Pt3uLyBJgB/APY8xyH9vfANwA0KRJE38WTSm/MsYwd9dcxi0ex8I9C0mo9Sz74pOOWi/h0P4glE4ppZSqOQoyMznw5lsc/PBD8vft51BKTw4MuZt9JonDR+SP9cIjQ4muHY4xkJedT252AXk5BfZNY6i1bBvJPz5L08bQ8LLziBk0EBEpJlellDo++S1AICIxwCfAncaYdK+3FwIpxphMETkNmAy09E7DGDMBmADQrVs346+yKeUvxhh+3fkr45eMZ9GeRSRHJnN3j7tZv3gTE3Nj/9LNwJ2bw0W5W4JYWqWUUur4VZiby6EPP2TfuPEcycxjf5/L2BrdkczD4CaUhm3iadQ6ngYt44hNjCTMHXJUGofTcti7OYM9WzLYvDiK9TGNWQ/ETVhNqze/o/1dlxPVsUPlfzillAoSvwQIRCQMGxx4zxjzqff7ngEDY8w3IvKyiCQaY/b5I3+lAs0Yw+wdsxm3ZBxL9i6hblRd7u15L+e1PA93iBvaguvDiXwYnsK+uATcuTlcf2i5zmKglFJKBUDWokXsvPsejmzdwa7eV7Ih6gTy8gwNG8VxUr+GNO+SREho6WNxR9d2E93JTdNOifQ4oxlpe7NYM3cXv08p4LecVqx6Yi7tG3xPx3uvJqxOnUr4ZEopFVxiTMUe1Itte/UWcMAYc2cx69QDdhtjjIj0AD7GtigoNvNu3bqZ+fPnV6hsSlWUMYaZ22cyfsl4lu5bSr3oelzX4TrObXku4SHhPre57sMv+SU6juXDuhMWEVHJJVbq2IjIAmNMt2CXQ1UPeo1WwWJyc9n78svsm/Aq+1sOZl2Lc8nKgqadEul1TnMSGsT4JZ+CvEJWTNvI/M/XkJXvJiljNQOv6ULykN5+SV+pstLrs6ps/mhB0Ae4HFgmIoudZfcCTQCMMeOBC4CbRCQfOAJcVFJwQKlgM8YwY/sMxi0ex+/7f6d+dH0e6PUA56SeU2xgoMjQ+ol8VRjJr3Pm039g30oqsVJKKXV8y9u+nW233U7Gmk1sGHo/23PrkpRYi5PPT6Vh63i/5hUS5qLjsBa0G9SMBR/MZ8GM5nz6wQE6TX+bXvddhMtdcl1AKaWqK3/MYjATKHEEF2PMi8CLFc1LqUAzxjB923TGLxnP8v3LaRjTkDG9x3BWi7MICwkrUxond++C69eV/LB1O/0DXF6lAm3BggXhLpfrppCQkKuNMbUp5XxfDRkRSSsoKHijsLBw3Iknnpgb7AIppY52ZPFitt5yK/vdjVk59Clycl30OqcZXU9OweUK3GkpJNRFj8t70GrAfqY8/TOL9jRi+22TOG3sGUTX9W9QQimlqgK/zmKgVHVljGHq1qmMXzKelQdW0iimEY+c9AhntDiDMFfZAgNF6kRF0unAHqaHR2GM0RGQVbUWGhr6amxsbJ8GDRocDg8P33+8Hc/GGHJzc8N27NhxW3p6+gnAlcEuk1Lqr9K+/pod99zLtlZnsiZxMPFxUZx5TXuSmtSqtDLENUngghcuYN5/vmb+irpMum8ap97ShfpddUpEpdTxpfTRW5Q6jhWaQn7a/BMjvhrBHVPvIDMvk7F9xvLFuV9wbstzyx0cKDI4Kpy19RuzedlRs3kqVd30TUlJSXO73XnHW3AAQERwu915KSkpaYD2CVKqijnwzrts+8c/WdttFGsSB9PihCQuvLd7pQYHiogIPe48g9NOj6KwECaPW8PyyQsrvRxKKRVI2oJA1UiFppCftvzE+CXjWXNwDSmxKTzW9zFOa3Yaoa6K/yxO6dKOZ9fs4vulK7mxk06PpKq1EJfLddyPGeN8xqPnQFNKVQ5jIGs/ZOyyr5x0Dnw3n+1vfMuK/veyT+pz4qkp9DyzORLALgVl0fTsvpyfspJvn53DtO/CyM6ax4mXdA9qmZRSyl80QKBqlEJTyJTNUxi/ZDzrDq2jaWxTHu/7OKc2O9UvgYEiHRvUJXnhGqYeyedGv6WqlFJKHUf2rYX1P8OmmbB5NmT9Ofv1/tXRbF9Wj2XdbyFdkhgS9yJtdu2Dr7tCy5MhdSiEuoNW9PgubTnnkRi+vu9L5vzShuyc3zjpqu7arVApVe1pgEDVCAWFBUzZPIVXlr7CukPraFa7GU/2e5JTmp5CiMv/Dw1FhAEF2XzdMIXDu3YRXa+e3/NQSimlqp3cw7B8Mix8C7bOBaAgtjG7kvqyPrQF2/Nrw2/raL9sFr/1uItsdzJhKes4FN+APXmHSVj2KSEL3oSI2tD2TDjxGmh0YlA+SlTTxpz5+Bl8N/p/LJ7bldyc3xh4Yw8NEiilqjUNEKjjWkFhAd9t+o4JSyewIW0DLWq34Kn+T3FyyskBCQx4Gt6iCf/bl80vs+Zx6vlnBjQvpZRSqkrLzYK542HW85CdRm5cC2Y0vo23D3Vm+p4o2GNXG7Z/Jbf8+itzeowmx53E4iahbAprzTNbGpNXYAgln+GRq7jKPZ8uyz4jbNG70GIIDLwbGveo9I8VkdKEU588nyl3vceKxb0JfWsxfa/sokECpVS1pQECdVzKL8zn243fMmHpBDalbyI1LpWnBzzNySkn45LKGZtzYJtUwqYtZsqe/ZxaKTkqVfPs3r075LLLLms6c+bM2Pj4+PwHH3xw+6hRow4Eu1xKKUdhASx6B6Y9CRk72Z48gBfdp/PBroaE7XXRo1kd/t4lgV4tEkjdu4kdN4xhca//o8CdzJmjOnJbx0QA8goK2bz/MCt2ZjBtdQo3rOpKTtZFXB/xIzdu/oaoicOg5XA4/RmIa1KpH9HdtClDnxjJ9/+cxNI5vXHXXk2Pc9tUahmUUspfNECgjiv5hfl8s/EbJiydwOb0zbSMb8m/B/yboSlDKy0wUCQmLJRumQeZGZdMYVYWrqioSs1fqWB7d87mOi/8tLbh3oyc8KRa7tzbh7TcflmvFL/evF933XVNwsPDza5du5bMmTMn6oILLkjt1q1bVrdu3bL9mY9S6hjsXw+Tb4Ktc9kX15kHw2/lmy3NaF23Fg+c0ZhzujQgIcaOI5C7eTMbb7+NFR2u5VBYPU65tj1NneAAQFiIi9TkWqQm1+Kszg0oKDTM23SAd+Y0o+fvw7jM9QN3rptM2Es9cQ15CHpcDwFuKegpIjWVIXcN4funpzHv+x6ER7npMlynQFRKVT8aIFDHhfzCfL7a8BWvLn2VLRlbaB3fmucGPsfgJoMrPTDgaUhSHI/mhbFy9hzaDx0ctHIoVdnenbO5ztivVqTk5Be6APZk5ISP/WpFCoC/ggTp6emu7777Ln7BggXLa9euXTh8+PDMIUOGpL3++usJ3bp12+6PPJRSx6CwEOa9BlMeJE/CeNp9JxN2dadz43jeubAVfVMT/9IEvyDzMFtuuplVDU5nT3Qr+l7YkhYnJJeYRYhL6NU8gV7NE9h+qC1vzW7LKbNP4uHC1+j/3WgKln1MyIVvQFzjQH/aP8T07sXgK/Yw5Z1FzPoMYhKjSD2xbqXlr5RS/qABAlWt5RXm8dX6r5iwdALbMrfRtk5bnh/0PIMaDwpqYKDIaZ3a8uiCdXy/dhPthwa7NEpV3F0fL2m8ZldGqc1hVuxMj84rMH/phJuTX+h6+MvlTf83f2tSSdu2qlcr6+kLOm8tLY9ly5a5Q0NDTadOnXKKlnXq1Clr5syZlT9BulLKyk6HT2+ANd+yMroHV+2/gpikxrx6RVuGtk0+qm++MYad993HuoJUtiX1ouuwJnQeXL6b+oZxkdx7Wluu6J3CE9+059PlH/PY9jcIH9efsJFvQvMBfvyAJYs/5yz6b32ZH2ev58eJhdRKiKRu09hKy18ppSoq+HdQSh2DvMI8PlnzCWd+diYPzn6QWuG1eGHQC0w6YxJDmgypEsEBgOaxMTTJTGe6y40pLAx2cZSqNN7BgdKWH4uMjIyQ6Ojov/ywateuXZCZmVl57YqVUn/avx5eG0rh2h942nU1Zx68kxGDe/D17f0Y1q6uz4H7DrzxJpvmbWV9s7NIPTGZ3ue2OObsG8VH8dKlJ3Lxdf/gpqhn2HQkksK3zyF/xvNgTEU+WbnUvfUmesWvIizrIF//ZwEZB7THk1Kq+tAWBKpaySvIY/L6yby29DV2HN5B+4T23NPjHvo36l9lRwweFC6836wlB5cspU7XLsEujlIVUpYn+wA9Hvux456MnHDv5cm13Lmf39p3tT/KUqtWrYLDhw//JRqYnp4eEhMTU+CP9JVS5bBhGuajK8nON1yTczeH6vbmi+s6065B8U/PD8/9jU0vvcWK7neT0DCGwVe0RVwVv5b3bJ5Ahzsu4qkvUumx9AFO/+khMvesJ+bc5ytlXAIRodm/xpB58SjmhF3K1y8s5Ly7exAeodVupVTVVzUesypVityCXD5a/RGnf3Y6j/z6CImRibw85GU+OP0DBjQeUGWDAwCntGtJXlg4Py1cFuyiKFVpbh/Scrs71PWXp/vuUFfh7UNa+m1sgI4dO+bk5+fLsmXL3EXLli5dGtmmTZsj/spDKVUGK7/CvHch2wriGHb4YRp1Hc5nN59UYnAg/8ABtvzjbn7vfBOuyEhOHdWJMLf/bt6j3aE8fGEvQka8zUTOJmbZ2+x/50rIz/VbHiUJqVWLNv9+iI5r3mb/ziNMe3clphJbMSil1LHSAIGq0nILcvlw1Yec9ulpjJ0zluSoZMYPHc+7p71Lv0b9qnRgoMhJDZOJzMvl53S9Z1E1x2W9Ug48cEa7zcm13LmCbTnwwBntNvtzFoPY2NjC4cOHH7r33nsbpKenu3744YfoH3/8Me6aa67Z7688lFKlWPoR5qMrWGGacU7WA9xy7hCeuqATEWHF3+wbY9hx3wMsTz6VzPAkhl3XgdpJkQEp3ikd6zPwlpd5JewKEjZ+ya4J50FuVkDy8hbRuhUd/nEpzTZ9xdr5e1k+Y0el5KuUUhWhbZ1UlZRTkMMnaz5h4u8T2ZO1h67JXXmkzyP0rt+7WgQFPLldLnrnHWF242bkbN2Gu3GjYBdJqUpxWa+UA/6e1tDbxIkTN1966aVN69at2zkuLi7/6aef3qJTHCpVSRa8ifnyTuZLe243/2TCDf04MaVOqZsd+uh/rF15hN1tTqTnWc1IaZ8Q0GK2SIqhzp3/ZsKEeK7b/QKbx51Hyi1fQOhRvaD8rvaZZ9J55mzSNy5nxoeQnFKL5BQdtFApVXVpCwJVpWTnZ/Peyvc47ZPTeOK3J2gU04hXT36Vt055i5ManFTtggNFhjWux+6E1k6hYQAAIABJREFUJBbP/DXYRVHquFK3bt2CH3/8cf2RI0cW7dy5c9moUaMCGpBQSjmWfgRf3sEM04W7wu7jnZsGlyk4kLNxIxuen8ja1hfRsFUcJ5zSNPBlBeKjw7nqtjFMqn8XKQd/ZfW4izAF+ZWSd70H7qNT2hTCc9L47pVlZB/Oq5R8lVLqWGgLAhUUs95/iegVDamTX4cDoQdIa7OVLSfE8frvr7PvyD661e3GE/2eoHu97tU2KOBpeKtm3PPrCn7cvpuewS6MUqpGEJHGwNtAXcAAE4wx//FaZyDwObDRWfSpMeaRyiynqobWfI/5bBRzCtvzZO37+OC6vtSvXXoXAZOXx7a77mZ5y8sIjXIz9Or2uPwwKGFZhYe6GHHDvXw14TBn7HqJxeOvovNNbyOuwD4vC4mJoemTD5N+0/0sDPsb095bzfDr2x8X9Rul1PFHWxCoSjfr/Zeou6w1ifkJuBAS8xNouLwtv/34M81rN+f14a/zxilv0KN+j+Pm4tkgIpxWWRn8EptAQUZGsIujlKoZ8oG/G2PaAb2AW0SknY/1ZhhjujgvDQ6okm2eTeGky1lemMJTcQ/y7qgBZQoOAOx/401WZDUnI6ohg69sR0y8u/SN/CzEJZx2w2NMq3slXfZ+yewJd1TK4IFRXbvS4rLTaLbhC9Yv3MOaubsCnqdSSh0LDRCoShe9oiER5q+Vggjj5po9ZzNx+ES61+sepJIF1pC4aJY1b8WumbOCXRSlVA1gjNlpjFno/DsDWAk0DG6pVLW2dzX5741gU34CD8aM4dUbBlEnumz9+HM2bGTt29+ypckw2vdvSPMuSQEubPFcLmHAjc8zL+Fs+ux6m58n/af0jfwgcdSNtI7bQ1zmJqZ/sJr0fTp4sVKq6tEAgap0dfJ991FMKGb58WJ421QKQ0KYsnJtsIuilKphRKQp0BWY6+Pt3iKyRES+FZH2JaRxg4jMF5H5e/fuDVBJVZWVdYDcdy7kUK6Le6LGMO7G4STGlK0FgCksZOsDD7My9WJi48Ppc35qgAtbOnG5OHHUa6yJ6krflWOZ8v0Xgc8zNJSGj42l7aq3MTk5/PjmCgoLdepDpVTVogECVekOhPoeQ6y45ceLbnG1iM3NYXpBCCa/cgZGUkopEYkBPgHuNMake729EEgxxnQG/gtMLi4dY8wEY0w3Y0y3pKTgPf1VQVCQR84Hl0H6dkaHjubZG8+ibmxEmTc/NGkSy9MacSQigcFXtyfMXfwUiJXJFRZO01Efcygsma6zb2HabwsDnqe7ZUsaXzOClivfZ+e6NBZP2RLwPJVSqjw0QKAq3eGW68mWnL8sy5YcDrfbHqQSVY5Ql9AvxDCnVXsOL1wU7OIopWoAEQnDBgfeM8Z86v2+MSbdGJPp/PsbIExEEiu5mKqKy/3qLtxbZzHG3Mjfr7mchnFlG3MAIG/nTtaM+5htDQfRYUBDGraKD2BJyy88NpHYaz4m0pVP0tdX8/um3QHPM/H660mJSyM5bQW/fbmBg7sOBzxPpZQqKw0QqErXJ34Tu6MnsC90PwZDjuSwu+Nq+lxyS7CLFnDDU1M4GFub3yrhKYVSqmYTO8rrRGClMebZYtap56yHiPTA1gv2V14pVVWXP/9Nwhe9wSsFZzL80r/RrkFsubbf/sQzLG82gpi4MHqf2yJApayYyAbtKTjnFdrLJla9fTt70rMDmp+EhdHgscdotfI9XAW5/Pz2Ku1qoJSqMjRAoCrX/vWw6D36nNCULo+eQ3TXukRGxXDSRTcHu2SVYnCDJMQYfjqoMxkopQKuD3A5MFhEFjuv00RklIiMcta5APhdRJYALwAXmcoY0l1VD7t+x3x9F78UdCTujLEMaFW+riWZs2bx+9pQjkQmMeTqDoRHVN3ZtWM7n8W+TjdwQeF3vPHaf8jOKwhofpEd2lPvkvNIXfE+uzaksWzatoDmp5RSZaUBAlW5pj0BIeHQ7+8AuFPjKDycR14NaV6XGB5Kp7wjzGrYlJyNG0vfQCmljpExZqYxRowxnTymMfzGGDPeGDPeWedFY0x7Y0xnY0wvY8zsYJdbVRE5GWS8cykHCqP4reuTjOzZrFybF+bmsu7JcWxpMpTWPZJp1KbqD0ScePbjHKrTmZvSnuPfk74L+PSHSbfcTCPZQmLOZuZMXq+zGiilqgQNEKjKs3sFLPsYet4IteoCEJEaB0DOukPBLFmlGlo/kdUpzdk8fWawi6JUtff4448ndejQoW14ePgJ559/ftNgl0ep44IxHProZqIyNzMu4V7uOPukciex/423WB7djzB3CH0ubBWAQgZASBhxl79DeGgoZ665jw/nrA9odq7oaOrdew+tFr4K+flMe29VwIMSSilVmgoHCESksYhMFZEVIrJcRO7wsY6IyAsisk5ElorICRXNV1VDUx8Ddy3o8+chElLbTWhyJNk1KEAwvFkjjMvFT5u0OaE6zs2bWIdnWnVkTNyJPNOqI/Mm+v0RYsOGDfNGjx6988ILL9zn77SVqqkO/zqRuPVfMCH0Ym695irCQspXXczbuZPfP57HobiW9BnRhsha4QEqaQDEpxB+/st0cm3kwLePsnxHWkCzqzVsGAk9O9Bi4xdsXXmQdQv2BDQ/pZQqjT9aEOQDfzfGtAN6AbeISDuvdU4FWjqvG4BxfshXVSfbF8Cqr6D3rRD113uEiNR4cjemYfILg1S4ytUhJpLEvBymx8STf/BgsIujVGDMm1iH7+9JIXN3OBjI3B3O9/ek+DtIcOWVVx66/PLLDyUkJOjcoUr5gdm3jtAp9zGrsCO9r3yMxBh3udPY9vQLrG1yBsmNImh7Uv0AlDKwXO3OIrvdCG50fc6L70wiIzsvYHmJCPXuv5+G23+htqQx839ryT2ipzOlVPBUeLQYY8xOYKfz7wwRWQk0BFZ4rHY28LYz8NEcEYkTkfrOtqom+PlRiKwDvW466i13ahyZs3eQszmdiBZxQShc5XKJMDjGzddtO5I2YwYJZ50V7CIpVXaTb2nMnhVRpa63a1k0hXnyl2X5OS6+Hd2URe+WPNJZcrssznlpa4XKqZQqv4J89r5zNe5CF9sHPMOIJuWP5x1ZvJglayPIbxjNoKs6IS4pfaMqKOLMp8nZNIO/H36OBz/uyLOX9sKZ8MPvwps0IfH662j59njmnzia377cSN8RLQOSl1JKlcavYxCISFOgKzDX662GgGdlb5uzzHv7G0RkvojM37t3rz+LpoJp0yxY/zP0/RtEHD09krt5bXDVrHEIhrVI4XBUNLOWrCh9ZaWqI+/gQGnLlVJBt/ubJ0hOW8qHyX/jwsE9y729MYa1T73K9gZ96NCvHomNYgJQykoSGYf7/HGkyg46rPoP/5sf2G6BCdddS53oXJoc+Z2lU7eyd6vOdqSUCg6/zTcjIjHAJ8Cdxpj0Y0nDGDMBmADQrVs3HaXleGAM/DwWYupBj+t9ruKKCCW8cawNEAyv5PIFycCEWEILC5mWazgzNxcJr0b9M1XNVtYn+8+06mi7F3iJqZvLDVNX+7tYSqmKObJpPgkLnuN7Vz9GXHXnMT0tT//ue5YVdMAdDj3OqSYDE5akxSBM9+u5dt6rXP5lT3q3uJHGdUpvQHUsXJGRJP/97xy550F2D3qK6e+v5vy7Tqy2LTCUUtWXX1oQiEgYNjjwnjHmUx+rbAcae/y/kbNMHe/W/QRbfoX+/4CwyGJXc6fGkbstg8Ia0u+uVmgI3aSAOa3akzV/frCLo5T/DRi9nVD3XwcWCXUXMmC0nvuVqmryc0n74Dr2mtrUufAF4qPLH7QuzM3l9/Ffcii+NT3Pa0VEdFgAClr5ZNjD5NdqxBh5ldGT5lFQGLjnV7FnnE5s+1akbpzM7o3prP5tV8DyUkqp4vhjFgMBJgIrjTHPFrPaF8AVzmwGvYA0HX+gBihqPRDXBE64ssRVI1LjwEDOhprTzeDklAZsaNSENbPmBLsoSvlf92sPMPyJzcTUzQWxLQeGP7GZ7tce8Gc2eXl5ZGVlSUFBgRQUFEhWVpbk5QVuQDGljkfrP3uEejkb+bXt/XRv2/yY0tj31nusihtAXJzQvn8jP5cwiMKjCT3reVrIdrpte5vXZmwIWFYiQt377iV53c/UcR/m18/Wk5tdMx6cKKWqDn+0IOgDXA4MFpHFzus0ERklIqOcdb4BNgDrgFeBm/2Qr6rqVn4JOxfDgLshtOSnEeGNayHhLrLX1pwAwdB6CQD8tOeAznusjk/drz3AP9YsY8yhBfxjzTJ/BwcARo8e3SA6OvqEl19+ud7nn39eJzo6+oTRo0c38Hc+Sh2v0jYvpcnycUwNH8AZF1x9TGkUpKWx8IuVZEcm0v+qzrjKOS1ilddyGKb9edwW9jmf/DCVlTuPqSdtmUR26kTc2WfS/LcJZKXlsvC7zQHLSymlfPHHLAYzgRI7SDmzF9xS0bxUNVJYAFMfg4SW0GlkqatLqAt3s9o1aqDCllFuGhXkMrtRM25du5aIVsdBf02lKtmzzz6749lnn90R7HIoVS0VFnDggxsoMBE0GPkfwkOP7cZ++ytvs6nuAJo0j6BxG7/OZFplyClPErLuJx6X1/nn/1rz2S19CA1QICTpb38j/btTaBy2ncU/umjbpwG1k4rvpqmUUv50nIV4VZWx7GPYuwoG3QshZYtDuVPjyd93hPxD2QEuXNUgIgxJjGNh6/bsnzY92MVRSilVw/z+2dM0y17J/LZ307pFs2NKI2/PHhbNPkhhqJu+V3T2cwmrkFp1cQ17mG5mOa13f8nrszYGLKuwevWoc8XlNPnlZUQMsz9dF7C8lFLKmwYIlP8V5MG0x6FuR2h3Tpk3i2gZB9Sw6Q4b1yPbHcGM1YHr06iUUkp5O7hjA82XPcv8sG4MvuDYe35ufvFNtif3pk3XOOLrRfuxhFXQCVdiGvXggYiPeOWHxWzadzhgWSVcfz1REYYW2YvZsGgv21cfDFheSinlSQMEyv8WvwcHN8Hg+8FV9kMstG4UrpgwsmtQgKBPXAzuwgJ+ia5N/r59wS6OUkqpGmLzB3cgxlBnxH8JDQ05pjRyt25l0fIQXCHQ86KOfi5hFeRyIaf+i9iCQ9we8in3fLosYGMIhcTGknDTKOrNeoPoKJj1yTpMAGdQUEqpIhogUP6Vlw3Tn4JG3aHV8HJtKiJEpMaRs+5QjRm0LzLExUmRYcxp34WM6drNQCmlVOAtnfYJXTJ+YUHKNTRv2e6Y01n93NvsSepK5wH1iK7t9mMJq7CGJyAnXM7lru/Ys3Epk+ZtDVhW8ZdcQkT9ZFrsmsLeLRmsnb87YHkppVQRDRAo/1rwBqRvh8EPgJQ4dqVP7tR4CjPzyNuVFYDCVU0nN6nPjuR6LJ+3INhFUUopdZzLysokfvp9bJUGdLv4oWNOJ3vtWpbuSMQdkseJ57T1YwmrgcEPIuFR/LvWBzz57UoOHM4NSDau8HCS7ryDhIWfEx9byJzJGyjIKwxIXiow8vftY8N555E5Y0awi6JUmWmAQPlP7mGY8W9o2g+aDzimJNypReMQ1Jy+dkMSYgGYeqSAwpycIJdGKaXU8Wzeuw/R2Ozk8NAniYiMOuZ0Vrz4MYfiWtLt9GaER1R4UqzqJSYJGXQfXXIX0it3Dv/6dlXAsoo9/XQiWrWkxer/kXEgm6XTtgUsL+V/OevWk7NiZbm63CoVbHq0Kv+ZOx4O74UhDx5zEqFxbkKTImvUQIVNIt2kUsCc1h3ImjMn2MVRSil1nFq96nd6bn+LpbUH06bP2cecTvaaNfy+tx5RoTl0PDnVjyWsRrpfC0lteDzmIz6dv5EFmwPzYENcLpLuvIPY1b9QPyGXBd9uIvtwXkDyUv6Xs2E9AO7UGvo7UdWSBgiUfxw5BLP+Ay2HQ+MeFUrKnRpHzoY0TH7NaUY3tEEyS1q2Zdcv2gRNKaWU/xUWGvZ/NhojLppe8nyF0lr638/IiE2hxzktCQmtoVXJkDAYNpY6OdsYFT2dByb/Tn5BYOotMYMGEdGpE03nv0HukXwWfLc5IPko/8tdtx5XTAyhycnBLopSZVZDz+rK7359EbLT7MwFFRSRGo/JKyR3S4YfClY9DEuOIz80lOnbdteYARqVUkpVnuk/fMZJOTPZ0Po6YuumHHM6WStXs+JQQ2qFZ9N2UHM/lrAaajkMmvXntpBP2LpzF+/OCcyNu4iQfOcdRG5eSkpiFsumbSPzYHZA8lL+lbN+Pe4WLZBjGJdLqWDRAIGquMP7YM44aHcO1O9U4eTcLWqDQHYNGoegR+0YYkwhsxs1I3vFimAXR6lq4ciRIzJixIiUBg0adIyOju7apk2bdh999FFssMulVFVzKPMI9ec8zF5XEm3Pu69CaS158QsORzeg5/ltcIXU8GqkCAwbS3juIR5PmsK/p6xhf2ZgxhKK6t2bqJ49aTRjPKbQMO/rTQHJR/lXzvr1hKe2CHYxlCqXGn5mV34x8znIy4JBFat0FHFFhBLeuFaNGocgzCUMiItmbocuZEydFuziKOUXk1ZPqjPoo0EdO73V6cRBHw3qOGn1pDr+TD8vL08aNWqUN3Xq1NXp6emLHnrooe1XX311i9WrV4f7Mx+lqrupk56jDZvIGfgQLnf0MaeTtXI1KzObUNt9hFb9mvmxhNVYgy7Q6SJOz5pM7dzdPPfjmoBkIyIk3XkH4bvW06LOQVbO3smh3TVnxqfqqODQIQr27cPdXAMEqnrRAIGqmPQd8Nur0OkiSGrlt2TdqXHkbs2gMDvfb2lWdUPrJ7Ivrg6Lli4PdlGUqrBJqyfVeWreUyn7juwLNxj2HdkX/tS8p1L8GSSIjY0tfPbZZ3e0bt06NyQkhIsvvjitYcOGOb/++uuxD82u1HFmxcZt9Nsyji3RnWjU77IKpbV43LcciUyi98j2iEubTP9h8P24gJfqfc37c7ewald6QLKJ6tqV6P79qD/1ZUJChblfbghIPso/cjbY78etLQhUNVPD5qVRfvfL02AKYeBovyYbkRpHxs9byVmfRmT7BL+mXVUNqWNbRv8SFcvgXbsIq1cvyCVS6mgPzHqg8bqD60q9AV91cFV0fmH+X+4gcgtyXU/+9mTTyWsnJ5W0bWp8atbYPmO3lrdsW7duDd28eXNE586dtXOuUoAxhpUfP0I7SSfzgn/bJvHHKHv9Rlam1Seu9mGa9z72MQyOS3GNodcoOs16ga4Rwxj71QrevbZnQPqdJ91yC4dHXkSrOvtZMb+QE07OIKlJLb/noyouZ906AMJb6AwGqnrRFgTq2B3YCAvfhhOugPimfk06vEksEuaqUeMQJLvD6BjmYm77LmROmxbs4ihVId7BgdKWV1ROTo6MHDmy+fnnn7+/a9euGiBQCvhp7iJOz/yUTQ1OJ6ZZxWYYWvTy12RHJtHzgnY64Jovfe5E3LV4NulrZq3bz48r9wQkm8jOnYnu34/kH/6LOzKEuV9oK4KqKnf9eiQykrAG9YNdFKXKRVsQqGM3/V/gCoX+d/k9aQl14W5eu0aNQwAwrEESz+e0YuvkN4m/6KJgF0epo5T1yf6gjwZ13Hdk31FjASRGJuZ+cMYHq/1ZpoKCAs4///xmYWFhhW+88cYWf6atVHWVnVfAkSmPEiKGxhc8XqG0crZtZ+W+JOJiM2nRr4bPXFCcqDpw0m2kTH2M0xNO57GvVzCgVRLhAZgGMunWWzk8YiSt43az9PcCdm1Io17z2n7PR1VMzrr1uJs3R1z6PFZVL3rEqmOzdzUsnQTdr4PYwERG3alx5O89Qn5aYEYEroqGJsRS6HIxIyOHwiwdfEhVX6M6j9oeHhL+l0nBw0PCC0d1HrXdn/kUFhYycuTIpnv37g39+uuv17vdbp0nVCng029/4LT8n9nb7ipC6jStUFqLX/ySI5FJ9DivtbYeKEmvmyAqgbG1PmPT/izenxuYaQ8jO3UiekB/Er57kYjoUH7TsQiqpJwNGwhvoQE1Vf1ogEAdm6mPQVgU9P2/gGXhTo0HIGdtzWlF0CU2ijoYfm3TkcOzZwe7OEods5GtRx74Z/d/bk6MTMwVhMTIxNx/dv/n5pGtRx7wZz6XXXZZk7Vr10b88MMP62JiYjQ4oBSwKy2bhgv+RXZINA3OqNgMQ7m79rB8Zzy1QzJIHei/wYiPS+5a0Pdv1Nk1k2sabeeFn9eRnp0XkKySbrkFObiX1rV3snXlQXbUoLpSdVCQmUn+zp24dfwBVQ1pgECV384lsOJz6HUzRAduAMGwulG4YsLIqUHjELhEGJwUx7wOXTg0dWqwi6NUhYxsPfLA1BFTly29cumCqSOmLvN3cGDNmjXhH3zwQdKqVaui6tev3zkqKqprVFRU13Hjxvl1OkWlqptPP/2AAbKI3N7/Z5u+V8CScV/Z1gNnp2rrgbLofh3Uqs/fQyZx4HAOr0xfH5BsIjt1Irp/PxK+fYmoWmHaiqCKydUZDFQ1pgECVX4/PwoRcfD/7N13fFPl/gfwz5M9O5IWundL6YLSUihTGYLAFZSCgAIyZCiooF5R8V69COJEqIgyHAhXBVHkh8gsZZVRugfdLd17JU2TNMn5/RHgKhbpOE1act6vV18vSXKe59vaJud8z/N8vyNW9eg0hEXA97GBOq8RFGU5NwYn2NugSSxBfH4xKIPh/gcwGBbKz89PS1FUgkajSVSpVEm3v1auXElrIqIvI4S4EkLOEkIyCSEZhJAX23kNIYRsI4TkEUJSCSFDzBErgx6ZZU0YURiNJl5/2D70fLfG0jU1I6NICCvSDN8JATRF+IDjCoExr0JcdR3/9C3HnouFqGzqmbqpditXAg018LetQllOI0qzmLe+3kKTZ0wM8b2ZBAGj72ESBIzOKb4K5J4ERr4ICHq+II7AxwYGZRt0VZazH/8hmRRsisJlV0+oU1PNHQ6DwejbdABepigqAMBwAM8TQu6+0nsUgO+tr2UAdpg2RAadTv68G4NZ+eCOewPgCro1VvrO39Ai7I8hE11BWMzqgQ4LnQ9YuWCJ7gAMBgpbTuX0yDSi0FCIIofD5vh2iK15uHqk0KJuqPRmmvw8EB4PXBcXc4fCYHQakyBgdBxFATEbAHE/YNhyk0zJ97EBAKgtqJuBDZeDcKkAV4KHQHE21tzhMBiMPoyiqAqKohJv/bcCwA0Azne9bDqAvZTRFQA2hBCmL1cfdD6rElNq9qBR5AHR0Ke7NZZeo0FqugFiQzMGzmAWlXQKhweMXgN+RTz+FViNgwklyKlS9MhUditWAtWVGGhXg8qCJpRmWc62zN5Mm18AnocHCIdpGMfoe5jfWkbHFcQCRReAye8DPLFJpuTYCMCxE0KT2wDpqLvPaR9cE/rJsFHpgcJjB9HP3MEwGIwHAiHEA0AogKt3PeUM4I/tK0tvPVZx1/HLYFxhADc3t54Kk9FFegOFa0e+wCusMrRN/gpgd+8UL+vr41AIHTEikgMWs3qg80LnAxc+wZMt/8X7vLX4+GQ2vpwf3qWhKIpClaoKBU0FKGwqRLWqGrWttahrrYNSq8DTniJID70D9bB38N13vyNn9ClIeBL0E/WDg9gBjmJH+Nr6wtvaG1w2l+ZvlNEeTX4+hMFBPTpHQkICj8VirWSz2YsoirIGwPyhMjrCACBdp9MtDQsLq27vBUyCgNExt1cPWLkA4YtMOjXfxwaqxCpQOgNID/QT7o3Gy62wsaACF0RWCC0tBY9ZosZgMLqBECIBcAjASxRFNXdlDIqidgLYCQDh4eHMOuZe5teEQjyp3IdGWQBsgh7v1lgGvR7JV5og4ALB86bRFKGF4fCBUWvAPfYK3g6ux8vX9UgpacQgV5v7HqrQKpBYlYiUmhSk1qQioy4Dyjbl/4ZmcWAntINcIIeUJ0XKFF88tj0FImEyBPWjYVXriHLrXKTUpKBR0/in47ytvRHaLxTDHYcj3CEc1vye3y5qaQytrWgrLYX1jOk9Og+Hw9llZWU10snJqYXH49UxRUQZHWEwGEhNTU1AZWXlbgCPtfcaJkHA6Jjs34GyBOAf24wfeiYk8LFBy5UKaEsU4HtaxgfZQLEAjmyCq0GDoYw5C9mC+eYOicFg9FGEEC6MyYH9FEX93M5LygC4/uHfLrceY/QR6jY9Ck98jidYNTBM2QGwupdMz/8xBo0CFwwN0oLDZdMUpQUKnQ9c+Bgzmr7DRvFafHQyG98tGfaXl1EUhZyGHMSWxCKuPA4pNSnQU3qwCRsDZAMw1WsqfG184WXjBU9rT8gF8j91lKAmUig6/yTGJV7B5dDxeLh6Fh6fb9wW0qprRYWyAjkNOciqz8KN+hv4Nf9X/JD9A1iEhcH2g/Go56OY6D4RcmHPdaayJNrCQoCiTNHicJS7u3sji8ViEraMDmOxWJS9vX1TZWXlPZe4MAkCxv0ZDMDZjYDMCxg8z+TT871tAGKsQ2ApCQJCCCb0l+FQwCA0/LSbSRAwGIwuIcariD0AblAU9ck9XnYEwCpCyA8AhgFooiiq4h6vZfRCP8TlYH7bQTT1Hwpr3wndHi/pTDl4RIbQxY/QEJ0F4wqAUWvA/v2feCd0EVZf1uJyfh0ivY0X4vmN+ThWeAwni06iqLkIABAgD8DioMWIdIpEkF0QhBzhfachhMBu+TKUrlqNgdOUuJ6pRVl2A5wH2ELIEcLLxgteNl6Y7DkZANCmb0NqbSoul1/G6ZunsfHqRrx37T2McBqBuf5zMcp5FFjEMlZs9gRNXh4AgO/t1dNTsZnkAKMrbv3e3POPnJYEASHkKwDTAFRTFPWXbAQh5CEAvwIovPXQzxRF/YeOuRkmkPEzUJUOPLEbMMPeNZaQA56LFJq8RmCiu8nnN5cJcit8V16HK/UKeCgUYEul5g6JwWD0PSMBzAeQRghJvvXYGwDcAICiqC8AHAMwBUAeABUA0+4jY3SLUqNDXewO9COWPgOwAAAgAElEQVSNwJS3gW4uMy45FY8aritC3JrAFZl2xeADacgC4PxHmNL0X7xrtQofnEzBU+Pr8UvuL0itTQWLsBDePxzzA+ZjnNs42AntujSNZNw48Hy8ITv1JUQBLyP+t0I4D7Bt97VcNhdh/cMQ1j8Mq0JXIbchF78X/o5f837F82eeh6vUFfP852Gm38wOJSgYf6bOzgbhcsHz8DB3KAxGl9C1guAbAJ8B2Ps3r7lAURSzka2v0euA2PeAfgFA0EyzhcH3sYHiXAkMah1YAstY+DLKRgIeKFwZGIJ/XLwIq0cfNXdIDAajj6Eo6iLuU7iKMvZFe940ETHo9k1sBhYYfoHCaQSkHqO6PV7CLzfA1tshfNk4GqJjgCsEIp9HeewGhA5wwEVFPN65rIWXtRdeCX8FU72mdjkp8EeExYLds8+i/LV1CJyqRXyyFuW5jXDyvX/NA19bX/ja+mLl4JU4c/MM/pv1X7wf/z72pO/B0uCliPKLAp/NJIs6SpOVDZ6vDwiXKQjJ6JtoWT9EUdR5APV0jMXoZVJ/AOrygIff7Paexu7g+9gABkBT0GS2GExNzGEj0laKqyFDoDh71tzhMBgMBqOXqVNqoL70JexJM6ST/9Xt8WrTClCmd4K3vBlCO8vY0tfTUmtS8aI6F1NdHHFVFQe+NgSOLa/il8d+wcLAhbQkB26zmjIFXGdnyM/ugVDKxfXfizp1PJfFxWTPydj76F58M/kbeFh5YPO1zZj681T8VvAbjLlExv2os7MhGOBv7jDMjhASJhQKQ1evXm05bch6EVdX1yAulztk+vTpnp091pRXfJGEkBRCyO+EkMD2XkAIWUYIuU4IuV5TU2PC0Bjt0mmA2PcBp1DAf6pZQ+G7W4FwWcZtBhZkgtwaxf0ckZOWCUqnM3c4DEavM336dE97e/sQiUQS6uHhEfTJJ5/Qd7bNYPRyu8+kYjH5FSrXsYB7ZLfHi//mCghlwNAlo2mIzrIlVCVg2clleOrYU0ioTcFS60CcKC7FuwMWIqdYjpgs+s9zCZcL2ZLF0KYkImAAQUlmPaoKu9S0BGH9w/DVpK+w+5HdsBPaYd2FdVh8YjFyG3JpjvrBoquthb62FgL/AQCMBSj1eoOZozKf+Pj4zOjo6DtFb+Pi4oSBgYEDhUJhaGBg4MC4uLgO72F58cUXnfz8/AI4HE7Y2rVrnToTx7Jly1zc3d2DxGJxqKenZ+Bnn33W4YqcO3futPXw8AiSSqWDZTLZoCeeeMKjvr6+Q9fQ2dnZPEJImEgkCr399eqrrzp2dO65c+e6e3h4BLFYrLBt27b9JeZ33nmnn52d3SCJRBI6a9Ysj9bW1jsrBktKStJXr15d2dG5/shUCYJEAO4URQ0CEA3gcHsvoihqJ0VR4RRFhdvb25soNMY9Je4FmoqBceu7vaexuwiHBZ6nNdR5DWaNw9QmyK0AAJfdvNGalGTmaBiMzqn//gdZ7ugxwTcGBoTljh4TXP/9DzK653jrrbcqiouL05RKZdIvv/ySt2nTJucLFy6I6J6Hwehtyhtbwbm+GzKihGhS91cPKEtrUKSQw41fCRtf5oZfV2XUZWDZyWV45vgzyG7IxpqwNTgx8wReeORz9GPxManhB7jJRPj0TE6P3JG3eeIJsO3s0O/yPvDFnE6vIvgjQgiGOQ7D/in78a/IfyG3MRez/m8WtiZuRZu+jb6gHyDqrGwAAP/WCoKWRg12vXgeuderzBlWr6BWq0lUVJTP7Nmz6+rr65Pnzp1bFxUV5aNWqzt0keHj46N59913S8eOHdvpu4Visdhw+PDhvObm5qQ9e/YUvfHGG66nTp0Sd+TYcePGKS9fvpylUCiSCwsL03Q6HVm7dm2n3iSbmpqSVCpVkkqlSvrwww87XAQ4JCREtXXr1psBAQGqu587dOiQ1bZt2xyPHz+eXVhYmHrz5k3+yy+/3KnEyb2YJEFAUVQzRVHKW/99DACXEMLc5enNtCrg/IeA2wjAe7y5owFgbHeoq26Fvklj7lBMxlPEhyefi6vBQ6CIYbYZMPqO+u9/kFVv3uyuq6nhgaKgq6nhVW/e7E53kiA8PFwtFAopwFiVlxBCZWdnM5tlGQ+8nadTsZh1FK0e4wGX8G6PF7/rHAyEjfB5Q2iIzvIUNxfjlXOvYM7RObhRfwOvhL+C4zOPY3HQYoi5YkAsB8KeASvtAF4bLkR6WTNOZdJ/0cgSCCBbuACaS+cQGMRHUWotakoU3RqTzWJjlt8sHJ1xFP/w/gd2p+3G3N/mMqsJ2qHJzgKAOysIaooV0OsMkMoE5gyrVzh27JhUp9ORt956q1ooFFLr16+vpigKR48e7VAV7tWrV9fNnj27WSKRdHpJxpYtW8pDQ0PVbDYb48aNawkPD1devHhR0pFjfXx82hwdHe8s42Wz2VRhYaFJzjNef/31munTpyv4fP5fvudvvvlGPnfu3Nrw8HC1vb29/s033yw/cOAALdfXJqn2RghxAFBFURRFCImAMTFRZ4q5GV0UvwtQVgGzvjH76oHb+D7GQjvqvEaIw/qbORrTmWhvg28HBKJ26yH0f+2f5g6HYeHK33jTVZObe9879OqsLDHa2v705kFpNKyqTZs8mn7++W+XiPF9fVVOmzaWdDSmp59+2u3QoUNytVrNGjhwoGrWrFmWU6yEYZFK6lUQJ38FW44SmPhmt8fTKlqRUyaEA6sUDpHdb5NoSZRaJXam7sR3N74Dl8XF8pDlWBi4EFJeO9c8kauAa7vwqOIneMin4tPTuZgY0B+E5vMs2zlzUPflTjgk/wSeYBoSfi/C5GXB3R7XRmCDDSM34GHXh/HO5Xfw5NEnsSZsDZ4e+DTt30Nfpc7KBsfBAWwb4zlrTbEChABy5w5di3bLqz+luOZUKnp0BZ2fg1T1YdSgDn8+/1FaWprA39+/lfWHmmb+/v6taWlpwqioqK7thekCpVJJ0tLSRM8991x1R485ceKEJCoqykepVLIFAoFh//79+Z2Z09nZOYQQgtGjRzdHR0eX/jHh0FU5OTnCxx577M5qioiIiNa6ujpOZWUl28HBQd+dsWlZQUAI+R7AZQADCCGlhJAlhJAVhJAVt14SBSCdEJICYBuAORRT6aT3UjcDF7cYVw64jzB3NHdwHcRgibkWV4dgvNwKGg4HVwUSaAoK738Ag9Eb3JUcuO/j3bBv375ihUKRdPz48exp06Y1CgQC5vOF8UDbcTIFS9m/QeMxHnAO6/Z4SV/HQscRInRSp2tZWSwDZcAvub9g6i9T8XXG15jmNQ2/Pf4bVoWuaj85AADWzkDIbLCS9+PlUfbIrGjGyR5YRcCWSmE7dy7Up44hYIgU+Uk1qC9voW38cW7j8PNjP2Ok80h8EP8BXj3/KlRtf1kBbZE0WVkQDBhw5981JUrYOIjB5bPNGFXvoFQq2VZWVn+6cJVKpXqFQmHSH86CBQvc/f39W5944okOJyUmTZqkVCgUyQUFBakrV66s8vHx6dByZgcHB925c+dulJWVpV6/fj1TqVSyZs2aRcsbrUqlYtna2t75ecpkMj0ANDU1dfvnScsKAoqi5t7n+c9gbIPI6AuufA60NhhrD/QihEXA97GBOq8RFEVZTLZ6uI0YIgJcCQrF9LNnwfdiTuAY5tPRO/u5o8cE62pqeHc/zrG313oePJBNd1wcDgeTJk1Sfvvtt7IPP/zQfv369R2+M8Bg9CUFNUpI07+FLUcBjF/X7fH0Oj0y0tSwaauF5+NP0RDhgy+3IRfvXnkXidWJGGQ/CNvHb0eQXVDHDh6xGkjejynq3/CJ3TBsPZ2LR3pgFYFswXzUf/stnHOPIZ07CoknbmLCogDaxpcL5dj28DZ8lf4VtiVtQ15DHrY8vAWe1pZ7jmLQaqEpLIRk3P9ahNYUK+A84P6tJunQ1Tv7piKRSPQKheJPN6eVSiVbKpV26253ZyxfvtwlOztbeOHChWxWF7qzeXp6tk2dOrVpzpw5XpmZmTfu93pra2vDmDFjVADg6uqq27lzZ7G7u/ughoYGlq2tbbeqV4pEIkNjY+OdZEBDQwPr1pzd/nmar28do3dS1QNxnwH+0wDn3rcPUeBjA4NCC1215WSq+SwWxsitcC10KNPukNFnyJ97rozctWeO8PkG+XPPld3rGDro9XqSn5/P1CBgPLC2n0zDMvZv0LqPBVwjuj1e1s/X0Mq2RnCYGMSM7Yz7ArVOjS0JWzD7/2ajoKkA/xnxH+x9dG/HkwMA0G8g4DsJ7PidWDXaGZkVzYjJoj+fybG3h/WMGVD/ehADw22RE1+F5tpWWucghGBJ8BJ8OfFL1Kvr8dRvT+FaxTVa5+hLtHl5gE53p/6AqlmLlkYN7F07tMX+gRccHKzOysoSGQz/OzXIysoSBgcH0/uLeQ9r1qxxiomJsT5z5kyOTCbr8sW5TqcjJSUlXTrPuJ0INBgM3c4I+vn5taakpNzZUnLt2jWRXC7XdXd7AcAkCBh3u/QpoFX2utUDt92pQ5BrWdsMJsitUWllg8yKKugaLKuTA6Nvks2dU99v3bqbHHt7LQgBx95e22/dupuyuXPq6ZqjrKyMs3PnTtumpiaWTqfDoUOHrI4cOSKbMGGCyfYyMhimlFOlgM2NfZCTZvDGv9Ht8SiKQvLZcojUtQhYPJmGCB9ciVWJiPq/KHyV/hWmek3FkRlH8Ljv42CRLpxKj3wBUNViBjkHF1shtsXk9UhHA/mSxaB0OrhXnAdhAYkni2mfAwCGOw7HD9N+QD9RPyw/vRy/FfzWI/P0dv/rYHCrQOGt4pBMgsBoypQpCjabTW3cuLFfa2sr2bRpkz0ATJs2TQEA27Ztkzs7O9+zWIZGoyEqlYoYDAbodDqoVCqiu9UC/HY7wezs7L+sXASA119/3eHnn3+WnTlzJru9C2hnZ+fg9toIAsCOHTtkubm5PADIycnhrV+/3jkyMvJO5c+ZM2d6zJw506O9Y2NiYsQpKSl8vV6PyspK9ooVK9wiIiIUcrlcDwBr1651ioiIGNDesYCx84NKpSIURZG2tjaiUqmIXm8Mf+HChXXff/+9XUJCgqC2tpa9adMmx9mzZ9fea6zOYBIEjP9RVAJXdwLBs4wZ7l6IYysAx05ocXUIxsmMHy5XAwah5cIFM0fDYHSMbO6cet8L59MG3shM8L1wPo3O5ABgzMTv2rWrn4uLS4iNjU3o66+/7vLuu++WPPXUU0yRQsYD6fNT6VjBPoo2t9GA2/Buj1ccl4tGyhb+bq3giJnuoO1Rtanw3tX38MzxZ6Az6LDrkV14d9S7sBXYdn1Q95GAcxjYV7bj+bGeSClpxIVcWs7r/4Tn7g7pI49AfXAvBoTZ4UZcOVp6qBOUk8QJ3z76LQbbD8a6C+uwO213jyQ9ejNNdhaIQACeuzsAoPZWgsDOtecLFPYFAoGAOnjwYN4PP/wgt7W1Dd23b5/dwYMH827XDSopKeGFhYUp73X8vHnz3MVi8ZCjR4/KoqOjHcVi8ZDPP/9cDgCFhYU8JycnrYeHR7v9Nzdv3uxcUVHB8/f3DxaJRKEikSh03bp1DoDxIryxsZEzevTodgt1ZGZmCkeNGuUvFApDR48e7e/j46Peu3dv0e3ny8vLeSNGjGg37tzcXP6UKVP8JBJJaHBwcCCPxzMcPHiw4PbzpaWl3GHDht3zex4zZoyfWCwekpSUJH7llVfcxWLxkN9//10KAFFRUc2rVq2qfOSRRwZ4eHiEuLi4aD/++OPye43VGSbpYsDoIy58DOi1wEPd39PYk/g+NlAlVoPSG0DYlpHjchLwECgW4GpoBBRnz8L6scfMHRKDYXZOTk66+Ph42usZMBi9UXalAtY3voc9txF4+DVaxrz+Uzq4WjYGL3uElvEeNCk1KXjjwhsoUZRg3sB5eCH0BYi4NCRSCAFGvAAcXIgocTK2WVshOiYXo33taK9FIF+6FIrjx+GlSkSW3hXJp4oxMsqX1jlus+Zb48uJX2L9pfXYmrgVLW0teCH0BYupGaXOygbf1xeEbdwWXlOsgJW9EHwR18yRmQePx6MiIyMHLl68uHrr1q3lADBy5MjWjIyMdvfux8XFSaKjo+9ZR+HQoUNFAIraey4mJkb60ksvVfD5/HazUhRFJdxr3HPnzonHjRvXGBoaqm7v+ejo6LLo6Oh2t0eq1WpSVVXFXbVqVbvd+ZYvX16/fPnye94cSU1NFcfGxt7zPObatWt/e47z9ttvV7399tvtVjr18PAIqq6u5k6dOrXTS4+ZBAHDqLEYuP41EPo0IPc2dzR/S+Bjg5YrFdCWKMD3sDZ3OCYzXm6F7e7eqNqzDc5aLQiv3VVUDAaDwXgAbT+didc5R6FzjgDHY1S3x6vNr0Vlqw38xTkQujjSEOGDo03fhh0pO7AnfQ8cRA7YM2kPhjoMpXeSgf8AbD3BvbodK8buxr+PZOBqYT2Ge7W7yrnLhEGBEEUOh/bHr+Az/zOkXyhH2GQPCCQ9c9HKY/OwefRmSLgS7E7bjTZ9G14Of/mBTxJQFAVNVhakj0y881hNsQJWTmKMej8G//5HICYGWE6LbgDQaDSJnXn9pUuXcrs61wcffFDR1WMnTZqknDRp0j3v4v8dgUBAFRQUZHR17qysrMyuHns/RUVF6V091jJuvzLu79z7xoz22H+aO5L74ntZA8QS6xBYQc9i4Zq7N1TXr5s7HAaDwWCYSHalAoIbB+FI6sB56DXj53U3Xf/uMlh6LUKf7v5WhQdJcXMx5v8+H7vSduEx78dw6LFD9CcHAIDFBoY/B5TGY45jBewkfETHdPn66G/JlyyFrroafrx86DR6pJzt2WL3LMLCW8Pfwjz/efg281tsvrb5gd9uoKuqgr6pCfwB/gAAdUsbmmvV0FlxUdrQCjGPaXPI6DuYBAEDqM0Dkr8HwpcA1i7mjua+WCIuuM4Si6tDMMRKDBs2C1dDwqCIYboZMBgMhqX47HQWVnGOQNd/EOAzvtvjqZrUKCznwVmXB9mwwTRE2PdRFIUj+Ucw6/9moURRgi0PbcGGkRsg4fXg/vHB8wCBNfjxX2D5GC9cyqtDUjH9hYjFI0eA7+8P/YHd8AyRI+1sKbRqHe3z/BEhBOsi1mFBwAL8N+u/+Oj6Rw90kkCTbVwJfruDQW2p8YZ0E1uNlzg/wZ/dow18GAxaMQkCBhC7CeDwgdFrzR1Jhwl8baEtaYahhz/gehMOi+BhuRWuDQpD89nYB/qDlsFgMBhG2ZUKkMxf4EaqwHnoVVpWDyTtuwIDi4vBj3jREGHfp2pT4Y2Lb+DNi29ioHwgDj12CBPcJ/T8xHwJEPYMcOMI5g0ArIVcfB6bT/s0hBDIlyyBNj8f/vZ10Kh0yLhASy2z+877SvgrmOc/D3sz92J32u4en9Nc/tLBoNhYoLBFXYCXOD/DtrXIXKExGJ3GJAgsXWU6kH4IGLYCkPQzdzQdxvexAQyAptCyipWPl1uhXiBCJpsHTW7PLEVkMBiMnqIpLETZK69CnZNj7lD6jOgz2XiB+yv0dv7AgKndHk/Xpkdmqgp2ihy4zjTBRXAvl9uQizm/zcGxwmN4bvBz2PPIHjiIHUwXQMRygLAgTt6DZ0Z44FRmFXKqFPc/rpOsJk8C18kJrMNfwXmALZJPF0Pf1uVW8B1GCMFrEa9hmtc0bEvahh+zfuzxOc1BnZEBrqsr2FJj16maYgUktnzoGozvdcTOz5zhMRidwiQILN3ZjQDf2tiTtw/hu1mBcFkWt83gIZkVCIArwaFQMtsMGAxGH0MIQfPRo1CnpZk7lD4hr1qBtsyj8CGlYI95BWB1/7Qt87d0aIkAQYOEFl/s9nDeYcz7bR6aNc3YNXEXVg5aCTbLxHvFrZ2BwMeBhG/xTJgcIh4bO3piFQGXC9kzz6A1IQFB3m1QNWmRdaXLdd06hUVY+M/I/2Csy1hsvLoRJ4pOmGReU2pNT4MwOOjOv2tLFLBzlYLfnA8DCGDracboGIzOYRIElqz0OpB9DBixGhB2o5+vGRAuCzwPK4srVGjH42CIlQjXhkZCeZZJEDAYjL6F6+oKIhRCw6wg6JDPzuTiec4R6K09jBeR3URRFFJOF0OiLIPf4u6vRuirtHot3rn8Dt669BZC7EPw02M/IcIxwnwBDX8O0Cpgm/0D5kW44UhKOUrqVbRPYxM1E2xra/BO7EU/dykSTxbDoO/5VQQAwGVx8dHYjzC432C8efFNpNakmmReU9DV1kJXXgFBUDAAoE2jR0OVCsJ+Qjjry9EidAK4AjNHyWB0HJMgsGQxGwCRHBi+wtyRdInAxxa6ahX0zRpzh2JS4+VWuNHPCeUFRdDV1po7HAaDwegwwmaD7+MDdTaTILifwtoW1KWdRAjJB3v0SwC7+52pbyaUoVknxoB+DeDa2dEQZd9ToazAwt8X4qecn7AkaAm+nPgl7IRm/lk4DwHcRgBXvsDSke5gEeDL8/SvImCJRLB9ah5aYmIQEipEc00r8hKraZ/nXgQcAT59+FPYC+2xOmY1ypU9XwfBFFpvrYi6vYKgtlQJUIBKzIIXKYfetne3D2cw7sYkCCxV4QWgIBYYtRbgS80dTZfwfWwAAGoL22YwQW4FihBcCwiB8tw5c4fDYDAYncL382VWEHTA9rN5eI57BHpxf2O1exokHEwBT9OEoGfG0TJeX3Ot4hqePPokCpsL8elDn+KlsJfAYXU/8UKL4SuBpmI4VMYgKswFB66Xolqhpn0a26eeAuHxIL14ALYOIiQev2nSoscygQzbx29Hm74Nq2JWQantUvv5XkWdlg6wWBAEBAAAqouaAQAVRAdPUgm+wwBzhmc2hJAwoVAYunr1amdzx/IgOnz4sFQkEoWyWKyww4cP03oxxyQILBFFGVcPSB2BoUvMHU2XcR3FYIk5FleHIEgiRD8eB9fCI6FgthkwGACAtLQ0Pp/PHzJ9+nRmo2cvJxgwAPr6emYF1N8orlOhIPkcIkkG2CNXGzsNdVNdmQKVTUJ4ULmQhATd/4AHCEVR2H9jP5adWgZbgS2+n/o9xrt3v10krQZMAaxdgStfYNkYb7TpDfjmUhHt03Dkclg/PgPNvx7GoBEy1JW14GZ6He3z/B0vGy98/NDHKGgswOsXXoeBMs02h57Smp4Gvrc3WGIxAKCysAkSWz6qGsogIWoILTRBAADx8fGZ0dHRd3o8xsXFCQMDAwcKhcLQwMDAgXFxccKOjvXiiy86+fn5BXA4nLC1a9c6dSaOZcuWubi7uweJxeJQT0/PwM8++0ze0WN37txp6+HhESSVSgfLZLJBTzzxhEd9fX2HrqGzs7N5hJAwkUgUevvr1Vdfdezo3EeOHJEGBAQMlEgkoS4uLsEfffTRneVOM2bMUKhUqiRHR0dtR8frKCZBYIlyTwElV4ExrwLcDv9d9jqERcD3toE6r9GiWv6xCMF4uRXiBwSi6fIVGDSWtcWC0XeknSuVff3axeDtK2LCvn7tYnDauVJZT821YsUKt6CgoJaeGp9BH76fsZq3+lbfcMZf7TiXh+fYv8LAtwHCFtEyZsL+eLD0WgyablnJAY1eg7cuvYXN1zZjtMto7J+yH57WvTCPyOYAEc8CNy/CU1eAR4Mc8N2Vm1Co22ifSv7MM6B0OsjTfoNExkfC76ZdRQAAkU6ReHXoq4gtjcWetD0mnZtOFEVBnZYOQXDwnceqCprR39Ma6qpb73FyHzNF17uo1WoSFRXlM3v27Lr6+vrkuXPn1kVFRfmo1eoO9W718fHRvPvuu6Vjx47t9J1BsVhsOHz4cF5zc3PSnj17it544w3XU6dOiTty7Lhx45SXL1/OUigUyYWFhWk6nY6sXbu2U6simpqaklQqVZJKpUr68MMPO1QdVKPRkKeeesp70aJFNc3NzUn79+/P/9e//uV6+fLlHr94YxIElub26gEbdyB0vrmj6TaBjy0MzVroalrNHYpJjZdZQcHlIcPRBaorV8wdDoPxF2nnSmWXDua5q5q0PABQNWl5lw7mufdEkmDnzp221tbW+rFjx9LfG4xBu9sJAk0O06q1PeWNrUhOuIwJrOtgDV8B8CXdHrNVqUV+vg5OzWmwnzaRhij7htrWWiw+sRi/5v+KFYNWYOvDWyHhdf/n2WOGLAC4IuDqF1g+xhsKtQ7fXyumfRqehwekEyei6ccfMHiMAyoLmlBhhtWY8/zn4VHPR/FZ8me4XH7Z5PPToa2sHPqGhjv1B1qaNFDUq+HgZQVew606Ena+Zoyw9zh27JhUp9ORt956q1ooFFLr16+vpigKR48e7dDy+NWrV9fNnj27WSKRdHrJyZYtW8pDQ0PVbDYb48aNawkPD1devHixQ28GPj4+bY6Ojrrb/2az2VRhYWH3l3XdR3V1NVupVLJXrFhRz2KxMHbsWJWXl5c6NTW1xxMEvWTjFcNkbhwBKlOBGV8AnL7f3uhOHYLcBnD7icwcjemMlUnBIcDV0AiMiTkLydix5g6JYSHO7L3hWl+mvO8fW22pUmzQU3+6K6DXGVgXD+R6ZMVV2P/dsTJniWr8goElHYmnvr6etWnTJuczZ85kb9++/W/HZfQOHJkMbHs7pg7BPew8X4BlrP+DgSsCa9hyWsZMOZwOA+EgaJgtCJdLy5i93Y26G3jh7AtoVDfi47Ef4xGPR8wd0v0JbYFBc4Ck/Rg04R1Eesmx52IhFo7wAJ9Db/tF+ZLFUJw8CYeyixBKvZBw/CacfE3b0YoQgrcj30ZuQy5eO/8aDvzjABzEDiaNobvU6cYChbc7GFQVGOsPsO0FcNKXQscTgCPt1Gr4rjv8vCuqM3v2ZLhfgAoztnfo8/luaWlpAn9//1bWH9q1+vv7t6alpQmjoqKaaYvxPpRKJUlLSxM999xzHa7QeeLECUlUVJSPUqlkCwQCw/79+ztVRdTZ2TmEEILRo1Fw4YoAACAASURBVEc3R0dHl/4x4XAvrq6uumnTptV/9tln8ldffbUmNjZWXF5ezhs/fnyP3wxhVhBYEoMeiNkI2PkBIbPNHQ0tODIB2HKBxdUhkHLYGGYtwbWw4VDGxlrUFgtG33B3cuB+j3fVyy+/7PzUU0/Vent7078Ol0G7VoUWmZfKAd8QaJgtBn9Ro9Dg3LUEPMaOAytsESDq/oIbfZsBGXHVkDfcgPuCGTRE2fuduXkGC48vBEVR2Pvo3r6RHLgtYjmg1wAJX2PFQ96oatbg1yT6q/0LBw2CKDwczfu+RchYJxRn1KOm2PSLsERcEbY8tAVagxYvx76MNkPfeitvTU0D4XIhGGBcGVVZ2AQWh6CGY4AnqYTG2hNgMZdbAKBUKtlWVlb6Pz4mlUr1CoWC3uzXfSxYsMDd39+/9YknnuhwUmLSpElKhUKRXFBQkLpy5coqHx+fDu3vdXBw0J07d+5GWVlZ6vXr1zOVSiVr1qxZHd7jNHfu3PqPPvrISSAQhE2ePNn/zTffLPPx8enxPxJmBYElSTsI1GYDs74BWCb9W+xRAh8bqJJrQOkNIGzLeROeILfCO40ylGl1cMnMhDAw0NwhMSxAR+/sf/3axeDb2wv+SGTN0856fSgtV4ZxcXHCCxcuWKWnp2fSMR6j5ykbNDj7XRaGOQ+GJGE7KL0ehP3gfB511+6LBViIo2CxWEDk87SMmRNXDLWBhyHOSnAe8NaGFEXh64yvsSVhC0LsQrB13FbztzDsrH7+gNfDQPwejHnxRQQ4WuGL8/mICnMBi0VrfhWypUtQumIl3DUZSBRYI/HETUx61vQ1KjysPfDOiHfwyrlXsCN5B14Y8oLJY+gqdVoa+P7+IDzjx11lQRPsXaXIr1NhAikHp98w0wXTxTv7piKRSPQKheJPJ+pKpZItlUr19zqGbsuXL3fJzs4WXrhwIZvVhcSNp6dn29SpU5vmzJnjlZmZeeN+r7e2tjaMGTNGBRhXBOzcubPY3d19UENDA8vW1vZvt0okJSUJlixZ4rVv3778GTNmNKenp/Mfe+wxX2dn57Y5c+Y0dTr4TrCcqylLp28DYt8DHIKBgdPNHQ2t+D62oDR6aEv7fquczhgvtwIAXA0aDGUM082A0buET/EoY3NYf/rwY3NYhvApHmX3OqazTp8+LS0rK+O5uLiE2NnZDfriiy/6nzhxwjYgIGAgXXMw6CVzEoPFJlCIXUBpNNDepH9/dV/V0KLFb5fTMI8bCxLyJGDd/c5gFEUh6Ug2xC0VGLBwMg1R9l5t+jb8O+7f2JKwBZM9JmPPpD19Lzlw27AVgKICJOsolo/1QkFNC07fqKJ9GsmYMeD7+kDx7R4EjXVGXmI1GqtUtM/TEZM8JuFxn8exO2034ivjzRJDZ1F6PdQZGRDeKlCo1xtQc1MBB09r5FfUw5VVA35/y+1gcLfg4GB1VlaWyGD436lBVlaWMDg42CSFxNasWeMUExNjfebMmRyZTNbl1hk6nY6UlJR0qQYBIcYkn8FguG+2Lzk5Wejh4aGZOXNmM5vNxqBBgzTjx49vOnbsmHVX5u4MJkFgKZK+AxqKgHFvPXBLnQTe1gABNLkN5g7FpHxFfLgJeIgf+RCUTLtDRi8TPNalfuQsn5sia54WMK4cGDnL52bwWJd6uuZYs2ZNbXZ2dlpiYmJGYmJixtNPP13z0EMPNZ4+fZqpftdLsTksyJ0laNQba1JpcphtBrd9HVeEWYZj4FJaYCQ9d1DLcxvR0MKFJ3IgGjSIljF7o2ZtM1aeXolf8n7BspBleH/M+xBwBOYOq+t8JwK2HsC1XZga7AgXWyF2ni+gfRrCYkG2eAk0OTnwFZeDzWEh8eRN2ufpqHUR6+Bm5YbXL7yOJk2P3iClhbawEAaV6k4Hg7pSJXRtBvT3skJzZR44MDAdDP5gypQpCjabTW3cuLFfa2sr2bRpkz0ATJs2TQEA27Ztkzs7Owff63iNRkNUKhUxGAzQ6XRQqVREpzNu5b/dTjA7O7vdAmuvv/66w88//yw7c+ZMtoODw19WLDg7Owdv27at3daHO3bskOXm5vIAICcnh7d+/XrnyMjIO/txZs6c6TFz5kyP9o6NiYkRp6Sk8PV6PSorK9krVqxwi4iIUMjlcj0ArF271ikiIqLdLFJERITq5s2b/CNHjkgNBgMyMjL4p0+ftg4JCenxLN6DdaXIaF+bGjj3IeASAfj2oX14HcQSccF1lkBtYXUIyK12hwmunmjOyUVbZaW5Q2Iw/iR4rEv9ovdHpT3/xbiERe+PSqMzOQAAUqnU4Obmprv9JZFIDHw+n3Jycrpv8R9LQQj5ihBSTQhJv8fzDxFCmgghybe+/tXTMdm7SlBXZwDFYjGFCm9RqNtw4FImlvJOg/hPBezpueuY+FMKuG1KBD0+hJbxeqMyZRkWHFuAhOoEbBy1EatDV4NF+vjpLYsNDF0KFMeBU5OBJaM8cf1mAxJu0n8jxHrqFHD694dq/1cIGOGI7CuVUDaoaZ+nI0RcEd4f/T7qWuvwzuV3en19pdY049vq7Q4GVYXGLe39Pa3AqsszvkjOdDC4TSAQUAcPHsz74Ycf5La2tqH79u2zO3jwYJ5AIKAAoKSkhBcWFnbP5cDz5s1zF4vFQ44ePSqLjo52FIvFQz7//HM5ABQWFvKcnJy0Hh4e7e7P37x5s3NFRQXP398/WCQShYpEotB169Y5AMb2i42NjZzRo0e32yo5MzNTOGrUKH+hUBg6evRofx8fH/XevXuLbj9fXl7OGzFiRLtx5+bm8qdMmeInkUhCg4ODA3k8nuHgwYN3sn2lpaXcYcOGtXtsYGCgZtu2bUUvv/yym1QqDR03btyAqVOnNqxZs6b2Xj8jujA1CCzB9T2Aohx44kuA0Lt/rbcQ+NhAcb4MBo0OLL7l/FpPkFvh67JaJPsOhFtsLGznzDF3SAyG2XzyySf0V/Lq+74B8BmAvX/zmgsURU0zTTiAvZsUmZcqYPAOhppJEAAA9l0pxtS2kxBzFcCoNbSM2VTTiuJiPTzrE2A77U1axuxtMmoz8PyZ56E1aLFz4k4MdRhq7pDoE/q0sbD0tZ2YPWkLPj2di53n8/Hl/HBapyE8HmQLF6L6gw/gv6wV6RSQfKoEo2ab58I20C4Qq0JX4dPET3Gs8Bimek01SxwdoU5LA0skAs/TWHOusqAJImsemlkUnPSlxtuwcm/zBmlGPB6PioyMHLh48eLqrVu3lgPAyJEjWzMyMtrdux8XFyeJjo6+Zx2FQ4cOFQEoau+5mJgY6UsvvVTB5/PbzSpRFJVwr3HPnTsnHjduXGNoaGi7mbHo6Oiy6OjodrdHqtVqUlVVxV21alVde88vX768fvny5fe8OZKamiqOjY2951K6pUuXNixdurTdzOCvv/4qffrpp73b2tpYbJpr+VjOlZSl0iiBC58AnmMBzzHmjqbH8H1soIgthaawGUJ/2tus91ojbCQQsAjiI0fj4bNnmQQBg8H4E4qizhNCPMwdxx/ZuRm3F6g8QiHIOmPmaMxP3abH3gvZ+E1wHHAdDbjQcwGYfDQLxGBA4Ah7sHh9v63x3c6VnMOr51+FTCDDV+O/gpeNl7lDopfQ1thxKvUAxBPewfzh7tgem4eCGiW87DvUvr3DbGbPQu2OHdAe+AZ+EcuQcbEMYY+6Qyg1z+/NM4HPIKYkBu9dew/DHIf12loSrampEAQG3im0WlnYDAdPa+RWK+FFKtAmkIMrtDFzlOaj0WgSO/P6S5cudXl74AcffFDR1WMnTZqknDRpUpcKmQkEAqqgoCCjq3NnZWV1ucjy9OnTFQqFIrmrx/+dPr4Gi3FfV3cAqlpgfI+vGjUrvrs1wGFZXB0CIZuFUbZSXA0ORcvlKzCozFNciMFg9GmRhJAUQsjvhJB7tkMhhCwjhFwnhFyvqanp8mR2zhIQFkGLrSfaSkpgaGl3VafF+DG+BKPUZyHT1wGjXqJlTG2rDlnXatGvNglOT8+kZcze5ED2Abxw9gV4Wnti35R9D15y4LaIZYCuFUjah4UjPMBls7DnYiHt07AlEtjOeRKKU6cQHMyFrs2A1LOltM/T4XhYbGwYsQGtba3YeGVjr9xqYGhpgfrGDQjDjNt3VM1aNNe0or+XFbIqFPBkVYLYMdsLGH0TkyB4kLU2AJeiAb9Habsj0VsRLgt8TyuLq0MAGLsZlAjEKLGRoSUuztzhMBiMviURgDtFUYMARAM4fK8XUhS1k6KocIqiwu3t7bs8IYfHhsxRhCaWsR6UJi+vy2P1dVqdATtjc/GS4Jixy5D3eFrGzTxfDB3FxgCnFnAdHWkZszegKApbE7diw5UNGOk0El9P+rrX3l2mhUMQ4D4SiN8FezEHM4c446eEUtQqO9SCvVNs588HYbNhOLIfXoPskRZbCm2r+cq5eNl44fnQ53G6+DRO3DxhtjjupTUlBdDrIQoznl9XFRnrDzh4WiOzohm+rApw7JkEAaNvoiVB0IEiSIQQso0QkkcISSWEPLjVcnqTuGhA0wSMezD3Ht5N4GMDXZUKeoXW3KGY1HiZcbnu1fBIKJh2hwwGoxMoimqmKEp567+PAeASQnr8isveVYp6hXGXozrbcjsZHE4qQ5DyEpz1pcDIl2ipE2QwUEg5ng+rpgJ4Lei9+7c7q83QhvWX1mN32m7M9J2JbeO2QcQVmTusnhexDGgsBnJPYuloL2j1Buy9TH+nAW6/frCeMQNNv/yCwcOtoFHpkHbOfKsIAGBBwAIEyYOw6com1KtprXHbbarrCQCLBWHoYADG+gMsFoG9uxTF5eWQoQlgVhAw+ii6VhB8A+DvGuw+CsD31tcyADtompdxL8oa4MoXQOATxrsSFoDvYwsAFreKwE3Ih59IgOvDR0N57hwoQ5dbuzIYDAtDCHEgtxozE0IiYDwvaLfYEp3s3KRobdGjzcYBmqysnp6uV9IbKOyIzcMa0TFQth5AwAxaxi1KrYGylQ0v3Q2Ihj4YRftUbSqsPrMaR/KP4PnBz+Pfkf8Gh2UhZbT8pwJSRyB+N7ztJRjv3x/7rtxEq/Yvndq6TbZ4Eai2NrBjfoZrgAwpZ0rQ1gPzdBSHxcGGkRugaFPg4+sfmy2O9qgSEsD3HwC2xFgPojynEfbuUugAkDsdDJgWh4y+iZYEAUVR5wH8XWpvOoC9lNEVADaEkAdnzVtvdPET4761h98wdyQmw3UUgyXiWFwdAsDYzSDRzgEKZQvUqanmDofBYPQShJDvAVwGMIAQUkoIWUIIWUEIWXHrJVEA0gkhKQC2AZhDmWDDr/2tQoVq/0i0ZnS5vlOf9nt6BfrVJ8Bflw0yYjXApueCN+nXLPDV9fB/PALkAehcVNdah8UnFuNKxRW8Hfk2Vgxa8UB8Xx3G5gJhzwB5p4G6fCwb44X6Fi0OJdJ/d5/v6QnpxIlo+P57hI7tj1ZFG25cMm9zGB9bHywKXIQj+UcQXxlv1lhuo7RatKak3NleoFXrUF3UDGc/W2RXKeCJW/XymBaHjD7KVDUInAH8sW1F6a3H/oSuAkgWr6kMiN8DDJpnUcubCIuA720DTV5jryxo05PGy6VoIwSJASHMNgMGg3EHRVFzKYpypCiKS1GUC0VReyiK+oKiqC9uPf8ZRVGBFEUNoihqOEVRJilkYuciAQjQ4uAPTVY2qLZ2W1c/sCiKwvaz+XhZ9BsosT0w+Claxq0pUaCyQgfX2iuwnW6yzpU9pkRRggW/L0B+Yz62PrwVM/0evIKLHTJkIcDiANe/wlAPWwxyscaei4XQG+g/15E/uxQGhQKihONw9LFG0sli6HXmXZn4bMizcJY4Y8OVDWjTm/+9Qn3jBii1GqKwMADG7QUGAwXnATbILG+GH6sUFIsLyDzNHCmD0TW9qkghXQWQLN75DwDKAIz9p7kjMTm+rw30zVroalrNHYpJRVhLIGWzcH3sBCjPMgkCBoPRu/EEHNj0E0HBdwCl0UCTn2/ukEzqbHY1UJmGCH0iyLAVAFdIy7jJx3LB0msQMLw/WGIxLWOay426G5h/bD6atE3YPWk3xrqONXdI5mPlCPhPA5L2gbS14tkxXiisbcHpG1W0TyUMDoZo2DDUf/MNhkxwgbJBg+yrlbTP06mYOEK8MewNFDYV4puMb8waC3Cr/gAA0a0OBmXZjWCxCBy8rHGjohkD2WXGG3RsrjnDZDC6zFQJgjIArn/4t8utxxh0qy8AkvYZl6PZups7GpMT3KpDoLGwOgRcFsFYmRRXvAdAnZsLbal5CwsxGAzG/di7SdHQygcAqC1omwFFUfgsJg9rhMdA8aTA0KW0jNvSpEFecj0cK6+g//zZtIxpLlcrrmLRiUXgsXnYO3kvBtkPMndI5hfxLKBuBNIPYXKgA5xthNh9oaBHppIvXQpddTWscy/A3k2KxOM3YdCbdxXBGJcxmOg+EV+mfokSRcn9D+hBqoQEcN3dwLl1M7MspwH9PKzAE3CQWdGMgZxyEPsBZo2xNyCEhAmFwtDVq1f/ZdV4b9La2kpEIlEoh8MZ8sILLziZO57ewFQJgiMAFtzqZjAcQBNFURUmmtuyxG4GWFxgzCvmjsQsODIB2DKBxRUqBIztDqs4POQ7u0HJbDNgWIiIiIgBfD5/iEgkChWJRKEeHh5B5o6J0TH2rlIom/XQWdtbVILgamE9akqyMcFwCST8GUBoQ8u46WeLYaBY8HNUgufed28QnCw6iZWnV8JR7IjvHv0OXjZe5g6pd3AfCdgPBOJ3gcMiWDLKE/FFDUgspr/uknjUSPADBqJ+924MecQVTTWtyEuopn2ezvrn0H+CTdj4MP5Ds8VAGQxoTUz8c/2Bmwo4+9nAYKBQVFGNfvoq4/8rBuLj4zOjo6Pv3BSOi4sTBgYGDhQKhaGBgYED4+LiOrx86sUXX3Ty8/ML4HA4YWvXru30hfzhw4elAQEBA4VCYWj//v1Ddu/ebQsAQqGQUqlUSdOnT+9drTLMiK42h/crgnQMQAGAPAC7ADxHx7yMu1TfAFIPGLPMUgdzR2M2Ah8baPIbQektrA6BzAoAED9mPJSxTIKAYX7Jp47Jvlg+P/jjJ6eFfbF8fnDyqWOynpjnvffeK1apVEkqlSqpqKio3Xa7jN7H3s1Y/VtjYYUKt5/Nw2rBCRA2Bxj+PC1j6tr0SI8phrwuHW5PT6dlTHP4MetHvHLuFQTZBeGbyd+gv7i/uUPqPQgBhi4BKlKAsgTMHuoKqYDTI6sICCGwW7YM2ps3YV+TDJmTGNd/vwmqB2oedIaD2AHPhjyLsyVncbn8slli0BYUQN/YeKf+QEV+EygDBecBtiiuV8GxrQQEFNDP3yzx9WZqtZpERUX5zJ49u66+vj557ty5dVFRUT5qtbpDVUd9fHw07777bunYsWM7fRcwISFBsHjxYq+33367rLm5OSklJSUjMjJS1fnvwjLQ1cXgfkWQKIqinqcoypuiqGCKoq7TMS/jLmc3AjwJMGqNuSMxK76PDSiNHtpShblDMal+fC5CpELED4lAy7V46BWW9f0zepfkU8dksd/ucm9pbOABQEtjAy/2213uPZUkYPQ9dq7GTgYtjgHQ3MiyiEKFqaWNyMzNxxPkLEjIk8a95TTIja+CWkvgqU2HeNQoWsY0JYqisCN5B969+i7GuIzBlxO/hDXf2txh9T6D5hjP8+J3Q8LnYN4wNxxPr0RJPf3XOdKJE8Hz8EDdrp0Im+yOhooW5CeZv4D4/ID5cJY444P4D6Az6Ew+/1/rDzSAxSZw8LZGZkUz/MitLZ7MCoK/OHbsmFSn05G33nqrWigUUuvXr6+mKApHjx6VduT41atX182ePbtZIpF0er/L22+/7Th//vya2bNnN3O5XDg4OOgDAwM1nf8uLIOFNJG1AOVJwI3/A8auA0SWff7N97YBiLEOAd/dytzhmNQEuRU+VajQxBeg5eJFWD36qLlDYjxgTuz41LW25Kbofq+rLioUG/S6P90V0Le1sc5+vdMj4+ypv61Ca+fqrpq08qUObzLdsGGD84YNG5w9PT01//nPf8qmTZvGZMf6AIGYC2t7IZpYTuin1UKTnw+B/4N912372TwsF5wC26AFRr5Iy5gURSHptzyIlWXwnjEChNWr6k/fl96gx3vX3sOP2T9iuvd0vD3ibXBYzOlpu/hSIORJY62pSZvwzAgP7LlQiK8uFeLf/wikdSrCZkP+7FJUvLkezq25sOkvwvVjRfAOtQdhma/NJJ/Nx8vhL2Nt7Fr8lPMT5vjPMen8qoQEsO3swL21jacspxH9Pa3A5bGRWd6MAewyUGweiMz0W2PeuvSWa15D3n0/n7vDx9ZHtWHkhi4VgUhLSxP4+/u3sv7wHuXv79+alpYmjIqKaqYtyHYkJSVJvLy8NH5+fgENDQ2cESNGKHbu3Fncv39/fU/O21f1rU8Rxr3FvAsIbYFIepYr9mVsMRdcJwnUefTvy+vtJsisYABBYvhwpt0hw6zuTg7c7/Gu2rx5c2lBQUFaeXl56qJFi2qefPJJn4yMDD6dczB6joOXNWqVPFB48AsV5lQpcDGjCAvYp0AGTqOtDXFZdgMa6nRwq74I25lP0DKmqbTp27Duwjr8mP0jFgUuwoaRG5jkwP0MXQLoNUDyfjhaC/GPQU44EF+Cplb6V+BY/+Mf4Dg4oGH3LoQ96o66MiUKU2tpn6ezJrhNwFCHodievB1NmiaTzq1KuA5RWBgIIdC26lBTrICzn7FA9o2KZgziV4DIfQE283t8N6VSybaysvrTBblUKtUrFAp2T89dVVXF/emnn+Q//fRTfl5eXrparWY9++yzbj09b1/F/PY+CG5eBvJOAxPeAQSWdcf8XgQ+NlBcLINBoweL3+PvO73GYCsR5FwO4seMx/htm0HpdCAc5s+cQZ+O3tn/Yvn84NvbC/5IbGOrfWrTlmy64hk3blzL7f9evXp13YEDB2SHDx+2DgwMNH9FLcZ9OXhZIftqJbS2LmhNT4fNzAe3z/2O2Hws5J2FQK8ARtK3FTDpeAG4bQr4RTqBbdV3zgFUbSq8dPYlXK64jLVha7EoaJG5Q+ob+gcCbpFA/B5g+PNYMsoTvySV4ftrxVgx1pvWqQiPB/miZ1D13ma4cipgZSfA9WNF8BxkB0LMt4qAEILXhr6G2Udn44uUL/BaxGsmmVd78yZ05RUQLVkCACjPawRloODkZyw0mlnRDF9SCvQbaZJ47tbVO/umIpFI9AqF4k83p5VKJVsqlfb4XXyBQGCYM2dObUhIiAYA1q9fXzF16lS/np63r2JWEPR1FAXEbAAk/YGIZeaOptfg+9gAegqaItNmls2NRQgelklx2dENbc3NaE1KMndIDAs1PGpuGZvL/dM+QTaXaxgeNbdHW9wSQkBRllWgtC/r72ncZ64aMALqjEwzR9NziutUOJ5yEyv5xwHPMYBLGC3jNlapUJzVDOeyC7B/eh4tY5pCg7oBS04swbXKa9gwcgOTHOis8CVAQyFQcBZBztaI9JLjm0tF0Orob0X4/+ydeVxUdffH33dmmIWdGTZBWQQRF1QEd83SyizLMjO1cqss27O97KnnKftVT08+aWllZlm2aJblUmYuqaCGIoggCLKIgCD7DDAzzMz9/YFZPmGCzjAg9/168Sru3O85B2Tm3nu+53yO9223IffxoXL5B8SND+P0CT0FRyrs7qe19NT25JbIW/gq6ytO6ttmtLMhIQEA9zM6H0XHqpEpBAK7e1FZZ6amphpd4ylJf+A8xMTEGDMzM11ttj/+TjMzMzUxMTENjvYdFRXV8OekljMTXB0BKUHQ0cndAQUJMOpJUDq07ahDoQrzBIWAKbvzjTu8WudJtSAjKzJaajOQcBoDrrm+8sqZ9xa4efuYoaly4MqZ9xYMuOZ6u40RKi8vl69bt86zvr5eaGxsZNmyZdqkpCT3m266qXNlBjswumA3FCo5er9oTJmXr1Dh+7uOM0m2B4/GcrsKCaduO4Fgs9AjsA5VD/u0LDiaEkMJM3+aSXZ1NouuXMTNkTc7O6SOR++bwNUXDnwMwL1XhHOq1simtGK7u5K5uqKdNYu6X3cR4lGJh1ZN0qb8dpGIndd/HnJBzrsp77aJv7qERFy6dsUlpKky/WRmJQFhTfoDR0tqiRDO/P79erZJPB2N66+/Xi+Xy8WFCxf6NzQ0CK+99pofwO+6QYsXL9YFBwfHnG+9yWQS6uvrBZvNhsViob6+XrBYmoQqs7KylIIgxGVlZf2lchHgrrvuKv/yyy99MzIylHq9Xvbaa68FjhkzRrpXOA9SgqAjI4qw7RXw6gZxM50dTbtCcJGjCvPC1Al1CK7UeiAX4OA14zHskBIEEs5jwDXXV97/wWdpT3y98eD9H3yWZs/kAIDZbBZeeumlYD8/vwFarXbA+++/7//FF18c/72EUKL9I5PLCAj1oFLmh3hGqPByo7TWyLcHTvC462bo0h+6X2UXu8a6Ro4mFBFQmkSXOztGa0ZudS53/XgXp+tP8/7V73NViH1+F50OhQoG3gVZm6HmJFdG+RPh58byXXkOeXD3uWM6Mk9Pqpd/QNz4UMrya9tFFUGAWwB39rqTzbmbyazMdKgvsbGR+n37cBsxAkEQqKs2UV5oILSvDmjSHzg7wcBfqiBoDrVaLa5duzbnq6++0vn4+MR+/vnnvmvXrs1Rq9UiQGFhoTIuLs5wvvXTp08PdXNzG7hx40btkiVLuri5uQ1cunSpDiAvL08ZFBRkDgsLazbL/Nhjj1VMmTKlYsSIEb1CQkJilEqluHz58hOO+Uk7PlKCoCOTtRmKk2H0M00XC4lzUEV603iqHqve7OxQ2hRvFwWDPN3Y27Mv5vx8TLl5zg5JQsIhBAUFWY4cOXK0rq7ukF6vT0lNTc285ZZbHKqELGF/AsK9qNLLsMpczU8MxgAAIABJREFUMB454uxw7M7yXbmM5Td8zSdh5PymefZ2IH13EVarQHfTEdyvav8P2mmn05j500wsNgsrr1tJfGC8s0Pq2MTNbtooOvgpMpnAPaO6k1FSy95c+z+4y93d0d55J/qtvxDua8BDpyZpo2OSEa1lTswcPJQe/Df5vw7105Caiq2uDrcRwwEoSG/6PYf29QUgraiGWPUpkCvBJ9yhsXQUlEqlOGzYsF6PPvpo0O/HRowY0ZCenn7UaDQmZ2RkHB0xYsTZ9oLExET3l19+ueR89tatW5cviuLBP3898sgjFQDbt2/3eOyxx0pUKtV5/ygXLVpUXFVVlVpVVZW6fv36PD8/PytAQ0OD4OHhMWDz5s0+Li4uzv+jbgdICYKOis0G2xeCNgL6T3N2NO0SdWSTaIzpeOdrMxir8+SoUkO5l49URSAhIdGuCezuiWiDOr8oGi6zSQaVdWZW7y/gWY8fm67XvW60i12rxcbhrfn4VB4lZPJYBHn7FuNNLE7k7p/vxt3Fnc/Gf0a09vIeZ9km+IRCj2sgeRVYG7klNhidm5IVux2zKaCdcRcyV1eqPlpO/PVhlBW0Dy0CT6Un98bcS0JRAr+V/OYwP4aEBJDLcRs6FICCIxW4+6jQBbsBcPhkDf1VJeAbJU0wOIPJZErW6/Up77zzTot6XxISErIHDhxovBhfb775ZslTTz11USM2NBqNqNfrUxoaGg795z//OW+CojMhJQg6KunfQlk6XPW89EF0HlyC3JG5KjB2Uh0CgOSx46QEgYSERLvmd6HCusghGFMPOzka+/Lxnjziral0M2bBiEdBZp8H+ZyDZdTX2Qgp24P35Ml2sekofsr7iQe3PUg3j26sGr+Kbp7dnB3S5UP83WA4BVmbUbvIuXNoKNsyy8gpO2+V9kUj9/bG547p1P74I+EBRjx9208VwbRe0wh0C+S/yf91WDx1CYlo+vVD7umJ1WKj8GglIX11CIJATX0jeeV1hNpOgJ+U/JLo+EgJgo6I1QI7XgP/PtCnY808bksEmYAqwhtTTnW7uIC1JdFuaoJULvw2eDj1yclYqjqfFoOEhETHwNVTiaefhlqfSIxZWdjq6i68qANQa2zk0735LPD6ETy6QP+pdrEriiIpW/Jwqz9F91FRyL297WLXEXyV+RVP73qafr79WHndSvxc/Zwd0uVFj2uadKiSVgBw17BQlAoZHyc4qIpg1iwEpZKq5R8SN76piiA/zflVBCq5inn955FWnsbuot12t2+pqsKYlobbiKbxhSU51TQarYSd0R84XFSNK0Y8TSXgLyUIJDo+UoKgI5L6BVQehzEvgEz6J/w7VJHeWGtMWModPkGlXSEIAlfrPNnv7UejIFC32/4XTAkJCQl7ERjuSaXFC9FqpSEtzdnh2IXP9hbQw5RBz4YUGP6w3bSCio9VU17cQLfCbehm3GkXm/ZGFEWWpixl4f6FjO46mg+u+QBPpaezw7r8kMmbRKrzfoXyHHzdVUyKDWbdwZNU1tlff0mh0+EzdSo1GzacrSL4bUMuos35mzA3RtxIV/euvJfynt03her37QNRxH1kU4Ig/0gFMoVAcE8foKm9oMfvAoXSiEOJywDp6bKjYTHBr29CcBz0vN7Z0bR7zuoQ5HS+NoOxOk8MIqTHDZHGHUpISLRrArt70WAEo0pLw6FDzg7nkqk3W1ixJ48XvX8CjRYG2m/SUMrWAlwsdYSHCe1ytKHVZmXh/oUsS13GxIiJLLpqEWqF2tlhXb7EzgCZAg6uBODukeGYLDY+31fgEHe6e+4+U0XwAYMnhFNeaOD4odMO8dUaXGQuzO03l4yKDHYW7rSrbUNCAjJPT9R9+wJw4kgFwVE+KNVNLb4phdUM9zzzO5BaDCQuA6QEQUfj4CdQUwhjFthNCflyRq5VI/dRdUodgpE+7igFgeSx46jbvRvR3LmmOUhISHQcArs36RDU9xxKfXLHTxB8+Vsh/vU5xDbsg6HzQOVuF7tVp+rIP1JJ8Mlf8bvrDrvYtCdmq5mndj3F11lfM7vvbF4Z8QoKmaST5FA8AprELw99Do0N9Ajw4Mqefqzam4+x0Wp3dwpfX3ymTaNmw0ZCfBvw6eLGbxtysVltdvfVWm6MuJFuHt1YlrrMblUEoihSl5CI29ChCAoFNacbqDpVT2gf3dlzDp+sZpBbKchVoJUmGEh0fKQEQUfCXAe73oLQkXabo3y5IwgC6h4+mHKrEa3OL4FrS9zkcoZ7u5PQLQJbXR31Bw44OyQJCQmJZtEGu6FwkWEI7k9DSgqizfkPGxeLsdHKB78eb6oeULrD4HvtZjtlWyEy0UIYObiPvsJudu2BwWxg3i/z2FqwlSfjn2R+3HwEaSOjbYi/G4zVkP4dAPeO6k65wcwPKS0Sj281unvuRlCpqPzgfYbcFE7VqXqy9pc6xFdrUMgU3NfvPo5WHmV74Xa72DTn5GApKTmrP/D75IbQmKYEwakaI6W1JnpSAP697CZEKiHhTKQEQQcg6YcPOPVyJOLCIKgr46gqRqoeaAWqSG9EoxVzkd7ZobQ5V/t6kivIKQnqKrUZSEhItFvkchkB4Z5UuXTBptdjyslxdkgXzdoDhWgMBQw37oL4OaDxsYvd+lozWYnFBJbsJXDqze1qtGF5Qzmzt8wmuTSZ10a+xsw+9mupkGgBYSObxuudESscHqGjVxdPlu/OdYhIs0Knw2f6NGo3bSLYU49fiAdJG/OwNjo/sXdD9xsI9QxlWcoybOKlx6P/5RcQBNyvuhKAgiPleAe44u3vCkDqyWpAxL8uGwJjLtmfhER7QEoQtHOSfviAvgcXEMjpszmBsKwVJP3wgXMD60CoIs7oEHTCNoOx2iZRqEPXT8SwY0enm+YgISHRcQiK8qGyVk6jQkPDoRRnh3NRmC02lu08zgLvn0HmAsMetJvttJ0nsVpFQsv34n3rrXaze6kU1BZw5+Y7KagtYPGYxdwYcaOzQ+p8CEJTMqroAJQcRhAE7h0VTnaZgZ3HHKMPoJszB0GlouK9pQyd2B19pZGMBMdULLSG36sIsqqy+LXw10u2V/vzVjQDBuDi74+5wUJRVvVf2guCZNW4mCohsN8l+7ucEAQhTqPRxD788MPBzo6lvXLrrbeGqdXqgQEBAe3qj0dKELRzuib/G41wbu+4RjDTLfnfToqo4yF3c8ElyA1jJxQqDHdV0V2jYl/fWBqLijAdy3Z2SBISdufDDz/06d69ex+NRhPbrVu3vj/99JN9Gr4l2pSuPZuSubVdY2lITnZyNBfHuuSTUHOSsaZtCANngEegXew2mq2k7SjEtyKNoAlXIPfwsIvdSyW9PJ0ZP86gvrGeFdeuYFTXUc4OqfPSfyooNHDgYwAm9AsiwFPFR7tzHeJOodOhvfNOajdvxk9WRlAPb5I252M2WhzirzWMDx9PsHswH6V9dEkbI+bCQkxHj+Jx7bUA5KWexmqxERHnf/acwydruFpb1vRNYN9LivtyJCkpKWPJkiVFv3+fmJio6dOnTy+NRhPbp0+fXomJiZqW2nr00UeDoqKieisUirj58+cHtSaOuXPndg0NDe3r5uYWGx4e3ufdd9/VXXhVEx9++KFPWFhYXw8PjwFarbb/pEmTwiorK1v9DP3kk092EQQhbv369Wc/wNetW5f/zTfftLubcylB0A6pM1n4IbWY+z47QIDYfObXXyxv46g6NqpIH8wnarGZ7S/Y0965WufJb2o3jC5KDDukNgOJtsOwr1hbvHB/zMlnd8cVL9wfY9hXrLW3j++++87z5Zdf7rp8+fJ8g8FwaNeuXVk9e/Y02duPhOMJCPNC7iJDHz6E+g44yaDRamPpzhye9/4ZARFGPGo325mJJZgarISc+AXtne1jtOHuk7uZvWU2GoWGVeNXEeMnlVc7FY0P9L0VDq8BYy1KhYxZw8NJyKkgvbjGIS5199yNzNOT8ncWM+yWCBpqzaRuK3SIr9agkCmY3Wc2h8sPk3Qq6aLt6H/eCoDHNVcDkH2wDHetisDwpupMURRJLaxmhHtJ04KAPpcW+GWO0WgUJk+eHDllypSKysrKlGnTplVMnjw50mg0tqhvOjIy0vTqq6+eHD16dKt3/Nzc3Gzr16/Pqa2tPbRixYr8559/vtvWrVvdWrJ2zJgxhr1792bq9fqUvLy8NIvFIsyfP79VVRHp6emqH374wcfPz6+xtbE7AylB0E6oN1vYeLiYeZ8fZOArW3nky0McOlFNCb7Nnl8mNH9connUPbzBKmLOc8xFsj0zVueJSYT0a6+XEgQSbYZhX7G2emNeqE1vVgLY9GZl9ca8UHsnCf71r38FPfXUUyVjx46tk8vlhIeHN4aHh3eIC7DEuchdZHSJ8KJS3Y3GEyewlHesRPj3KcUYK4sZb/4Zof808O5mF7s2m0jKLyfwqjtBcHwYypAQu9i9FNbnrOfh7Q8T6hnKZ+M/I8wrzNkhSUBTm0FjHaStAWD64BBclXJW7M5ziDu5lxe6u+/GsHMnntXHiYj1I/nnE9TXOn9q0s09bkan1vFR2kcXbUO/dSvq3r1Rdu2Ksa6RwvRKIuMCEGRNz7P5FfXUGi30Ih98wkDtZZ/gL1M2b97sYbFYhBdffLFMo9GICxYsKBNFkY0bN7aoJOrhhx+umDJlSq27u3urxSUWLVpUHBsba5TL5YwZM6YuPj7esGfPnhZVG0ZGRjZ26dLlbGmMXC4X8/LyVK3xf//994e88sorRS4uLh2i11eaPeNEGsxWdmaVsTGthO1Hy2hotOLrruL2Qd2Y0C+I+FAfDm58Gu3BBee0GTSISgrjnsI+hYudA1WYJygEjDnVqHvafROzXTPU2w1XuYykEaOJe+F7LOXlKHylBJPExVH5zbFujafqXC90XmNJnRtW8dxdAYtNVr0hN6zuQKnf3611CXSr106OuuA2lMVi4ciRI66nT59WhISE9DWZTLJx48ZVL126tNDd3b1DXIQlziU4yof9mVWYXdyoP3QIz2uucXZILcJitfHejhye9foFmdkCIx+3m+3cQ6epLTfSN28Lvovm283uxSCKIsvTlrPk0BKGdhnKoisX4a6UOnraDcEDm/rgkz6G+LvxcnVhSnw3Pt9XwNPXRRPopba7S+1dd1L52WecfnsRQ956n7zUcpI25jF6ek+7+2oNKrmKGX1msOjgItLL0+nj27rd/cbSUhpSUvB7rKkSKDflNDabSI/4P7cXNG1kBzRkQ5DzK2iKn3+hmyk7+4LX50tB1aNHfdBrCy+qTCQtLU0dHR3dIJP9sT8dHR3dkJaWppk8eXKt3YK8AAaDQUhLS3N94IEHylq6ZsuWLe6TJ0+ONBgMcrVabVu9evXxlq79+OOPfZRKpe3222+vmT/fuZ/hLUWqIGhjjI1Wfjpyioe/PETcq1uZtzqZfccruDUumC/vHcr+58fyr4l9GRyuRSYTGHTTfRyJe5VT+GETBU7hx5G4Vxl0033O/lE6FIKLHFWoJ6ZOqEOgksm4wsedPbpARFHE8Ouli/ZISFyQ/00OXOj4RXDy5EkXi8Ui/PDDDz67d+/OSklJyThy5Ijrc88916reRIn2Q3DPJsX/Gl10hxIq3HC4mJryEiZafkKIuQ10EXaxK4oiyT8X4NpYRVd/M5r4eLvYvRgsNgv/2vcvlhxawg3db2Dp2KVScqC9IQgw6G4oS4fC3wC4e2Q4NlHkk8R8h7iUubrie//91B84gEv2IfqMCiJ9TzFVp+oc4q81TImagofS46KqCPS//AKAx5kkZXZSKV5+GvxC/tjsTimsRudiQlmTLwkUtgCDwSD39PQ8p9fXw8PDqtfr23Qky4wZM0Kjo6MbJk2a1OKkxLhx4wx6vT4lNzf38Lx580ojIyNb1MpYVVUl++c//xm8dOlS5/fetAKpgqANMDZa2Z1dzqbDxWzNKKXObMXH1YWJA4K5sV8XBodrUcjPn6sZdNN9cCYhEHjmS6L1qCJ9qN2Sj1VvRu6hdHY4bcrVOi9+Kq/lZL8BeGzf0a4UsCU6Fi3Z2QcoXrg/5vf2gj8j81CaAx6KzbJHLG5ubjaA++67ryw0NLQR4JFHHjn1xhtvBAFFf7tYol3iH+aBQiWnNnwIDcmJzg6nRVhtIku25fCU1zbkJiOMtN8OUVFWFacL9PTM/RHfx2YgOGnEcX1jPU/++iS7i3ZzT8w9PBz7MDJB2mNql/SdDFsWwIEVEDKEblpXxvftwur9BTw0JhJ3lf1v/X2m3EblypWULXqbuJVfkLnvFPu+z2X8fc7dVXdXujO151Q+SvuI3Opcunt3b/Fa/c9bUUZEoIqIoL7WTFFWFQOvCz3nPZhaWM11vpUIVWK7GHF4sTv7bYW7u7tVr9ef88FhMBjkHh4ebSYQdt9993XNysrS7N69O+vPlQwtJTw8vPGGG26omTp1aveMjIyjFzr/6aefDrrtttsqevbs6fy+m1Ygfbo7CJPFyrajpcz/OoVBr/7CvasOsPPYaW7sH8Rndw8m6YWr+b9JMQyP9P3b5ICE/VBHnhl3eLzzVRGM0TZlvJPHTaAuMRGbSdJwk3AsnmO7FaGQndsnqJDZPMd2s9uDu5+fnzUgIKDxzzdsznqAkrAPcrmMoAgvqjy605Cejq2+3tkhXZANqcVUlJdym3UzQu+bwD/abraTtxSgEhvoas3FY/x4u9ltDeUN5czeMpuE4gReHPoijw58VEoOtGdU7k0TDdLXQ10FAPeMCkdvtPB1kmOeHwWlEr/HHsWUcRTLzi0MHBdC7qHTFGdXOcRfa7iz950o5UpWZaxq8RpLVRX1SUlnxQmPJ5chitAjPuDsOcZGK2lFNYzyOtV0IECaYHAhYmJijJmZma422x+3BpmZmZqYmJiGtvD/+OOPB23fvt1r27Ztx7Rabat1DH7HYrEIhYWFLdIg2L17t+eKFSv8fX19+/v6+vY/deqUcubMmREvvPBCu97vlT7h7YjZYmNHVhlPrEkl/tVfuPvTA/xytJTxMYF8OqcpKfD6rf0Y1cNPSgo4AZdgdwSNolOOOwxSK+njriaxe0/Ehgbq9+1zdkgSlznuQ4MqvSeEF8g8lGZoqhzwnhBe4D40qNKefqZOnVr+/vvv+xcVFSlOnz4tX7x4ccC1117b+d7klxHBPX2oNWswo6b+4EFnh/O3WG0ii7dn84zXNhSWOhj9jN1snz6hp/BoFV1zt+B71zRkyravfMupymH6punk1eSx+KrFTOk5pc1jkLgI4ueA1QQpqwGIDfFhcJiWj/fkYbFe9HPR3+J5ww2o+/albNF/iRnhh7uPil1fZ2OzOVcORqvWcmPEjWw4voGKhooWrdH/vBVsNjzPjDfMPlCKTxc3dMF/tNSkFlbTaBXpKzsBam/w6uqQ+C8nrr/+er1cLhcXLlzo39DQILz22mt+ABMmTNADLF68WBccHHzeUgyTySTU19cLNpsNi8VCfX29YLE0aQdmZWUpBUGIy8rKavaD8rnnngv89ttvtdu2bcsKDAz8S8VCcHBwzOLFi5sdfbhs2TJtdna2EuDYsWPKBQsWBA8bNkz/++u33npr2K233hrW3NqdO3dmpaampicnJ6cnJyen+/n5NS5atKjg6aefbrH+gTOQnlIvkUarjV+Pnebpb1IZtPAXZq9M4ueMU1zbO5CVswZxYME1vDm5P6Oj/HCRkgJORZAJqCO8MGVXX9Jc3I7KWK0nB1FQr9Wh3y5NM5BwPO5DgyqDXhiS1vX1UQeDXhiSZu/kAMAbb7xRMmDAgLro6Oi+vXr16hsTE1P/f//3fyX29tNREQThY0EQygRBOHKe1wVBEBYLgpAjCMJhQRAGtnWM/0twVJMOQbVvL+oS9zo5mr9n4+FiKk6Xcpt1I/SeaNcxZ8lbClDQSLfqg/jcfrvd7LaUvcV7uevHu2i0NbLyupWM7ja6zWOQuEgCekPIMDjwMZzZrb33iu4UVTfw45FTDnEpyGQEPPM0llOn0H/5OcNvjaTipIGjCcUO8dca7up9F2abmTVZa1p0fvW361D1iETVqxf6SiMlx2vOEScEOFDQVB0R2JDd1F4gVa9dELVaLa5duzbnq6++0vn4+MR+/vnnvmvXrs1Rq9UiQGFhoTIuLs5wvvXTp08PdXNzG7hx40btkiVLuri5uQ1cunSpDiAvL08ZFBRkDgsLa3aK0euvvx5cUlKijI6OjnF1dY11dXWNffbZZwOhafxidXW1YtSoUc0KZ2RkZGhGjhwZrdFoYkeNGhUdGRlpXLVqVf7vrxcXFyuHDx/ebNyBgYHWkJAQy+9fcrlc1Gq1Fi8vL8dk6uyEpEFwEVisNvbmVrDpcAk/pZ+iur4Rd5WCa3sHcEO/Lozs4YtK0aZ6GxItRBXpQ8ORCiwVRlx8Nc4Op025WufJ4hNlpN00Cc/N3yO+/JJUji3R4VGpVOLnn39+Ajjh7FjaKZ8A7wLnq68dD/Q48zUEWHbmv07DL8QdF7Wc2oih1O392Zmh/C1Wm8jibdk87bUNF5N9qweqy+o5nlxGt8Jf8Z00Abmnp91st4Rvs7/llb2vEOYVxtKxS+ni3qVN/UvYgfi74dt7IHcHRI5lbLQ/3X3d+HBXLhP6dXHI9d910CDcrx5LxYfL6T5pEkE9vNn3fS4RA/1Ru7nY3V9L6e7VnSu6XsFXWV8xu+9s1IrzT3MwZWdjTD2M/7PPIAgCGWcSHD2HnFsRnpRfSU8/DYrTGU2/a4m/oFQqxWHDhvWaM2dO2TvvvFMMMGLEiIb09PRme/cTExPdlyxZct4+mHXr1uUD+c29tn37do/HHnusRKVSNbsDKIriecvRfv31V7cxY8ZUx8bGGpt7fcmSJUVLlixptj3SaDQKpaWlLg899FCLylOKiorS/vz9lClTQjdv3qzVarXtajyzXRIEgiBcB7wDyIGPRFF8/X9enwX8mz9Eo94VRfHiB5M6AatNZH9uBRvTSvjpyCkq68y4KeVc3TuAG2K6cEWUH2oXKSnQ3jmrQ5BT1ekSBAM93fBWyNk/YBBDPlmOMT0DTV/77XZJSEi0P0RR3CUIQtjfnDIRWCU2lVXtEwTBWxCELqIoOq0KQyaXEdTDm4pj4Zj2ZGKpqECha7by06n8kFpE+elSprjbv3rg0NYTCKKNbkU78LnrG7vZvRBWm5V3kt9hZfpKhgcN563Rb+GhbNGIcon2Ru+b4CddUxVB5FhkMoF7RnXn+e/S2JdbybAIx7yn/J94gtydN1H+7nuMvPdJ1r6WxIFN+Yyc0sMh/lrKjN4zuOfne9iUu4lbo84v1Fy97ltwccHrppuwWm1k7CkmpLcOzz/dM1ptIgcLqpgdZYZjxnYhUNgeMZlMya05PyEhIftifb355psXfc0aN26cYdy4ceetXPg71Gq1mJubm36xvtesWVMAFFzsekdxyTXvgiDIgfdo2oXoDUwTBKF3M6d+LYrigDNfHSI5YLWJ7MutYMH6NIa89gvTP9rP+kNFjIj05f074zj44jW8MzWWa/sESsmBDoJcp0burcKY3flalBUygau0Hux288Imk2HYIbUZSEhIEAz8ecfm5Jljf0EQhLmCIBwQBOHA6dOnHRpUt15aDCYl9Wpf6vfvd6ivi8FitfHOL9k8470dFztrDxiqjGQmlhBUth+/McNQdm32n8Pu1DfW8/jOx1mZvpLbe97Oe2Pfk5IDHRmFCmLvhKzNUNO0PzdpYDA6NyXLd+c6zK0qPByfadOo/uYbPAwn6T0yiLSdJ6ksdu7Yw8GBg4nWRrMqYxU2sfnqbtFspub77/G46ioUWi35h8uprzHTd/S578FjpXr0Rgsj3c+0TwRKAoUSlxf2aIofDOSIopgriqIZ+IqmHYkOic0m8lteJS99f4Sh/7eNqR/u45uDJxkSrmPpHQM5uOAalkyL5bq+UlKgIyIIAqpIb0zHqxGdLJzjDMbqPCm32DgxdpyUIJCQkGgVoih+KIpivCiK8X5+fg71FRbTtLtZGRRH3d72p0PwbXIR1RVntAd63WTf6oGfTyDabHTL3Yx29hy72f07SgwlzPxpJr+e/JVnBz/LgqELUMikLtQOT9xsEEVI/hQAtYucWcPD2J5ZRtYp/QUWXzx+Dz6A3MuLU6+8yuAJYbho5Oz8ItOp912CIDCj9wxya3LZU7Sn2XP0O3dirarC+9ZJABz5tQh3HxWhfc+ttjiQ3ySnEy2cAJkL+PZ0bPASEm2MPRIELd19uPWMANI3giB0a85QW+5O/BmbTeRAfiUv/5DOsNe3MeWDvXyVVEh8qA/vTo8l+cVreO+OgVwf0wWNUkoKdHTUPbwRjVYaiy6qmqhDc5XWEwFIGj0WY0YGjaccI1bUnjm2/xSfPp/Ae/dv59PnEzi2v/P9DiQk/kQR8Odrclf+aAd0Gl5+rvgEulIVOqTdCRWaLTbe2ZbNiz5bUTTWwVXP2812fa2ZjD3FdKlMQdcvEk2M43cmD5UdYuqmqZzUn+S9se9xR687HO5Too3QhkPkWDj4KVibWpzvGhaKxkXOh7scV0Ug9/bG/8knaEhOxrz9J4ZPiqQkp4aje52rH3td2HX4afz44ugXzb5evW4dioAA3EaOpLq0npOZVfQZFYRMdq5ew2/5VQR6qvGozgC/aFC0/YQRCQlH0lay+huAMFEU+wFbgU+bO6ktdydEUST5RBWvbMxgxBvbmfz+Xr747QT9u3rzztQBJL94DcvujGNCvyBclVIW/XJCFdGkQ2DMcf583rZGp1Qw0NOVxC5NzwOGnTudG1Abc2z/KXaszsRQaQLAUGlix+pMKUkg0Zn5AZhxZprBUKDGmfoDfya0r44K/Gk4VY650DHz2y+GNQcKMVeXcLN5I0LMbeDfy262U7edwNpoJeTYD+jmzrWb3fPxXfZ3zNkyB3cXd1bfsJqRwSMd7lOijYm/GwwunHebAAAgAElEQVSnmloNAG9XJVMHd+P7lCJKahw3ft7rllvQDBhA2b//TVRfN4J6eJO4Lof6WrPDfF4IF7kLt/W8jYTiBPJr8s95rbG0lLrde/C65WYEuZz03UXIZAK9RgSdc54oiiTlVRIf6o1QfAiCBrThTyAh0TbYI0Fwwd0HURQrRFE0nfn2IyDODn5bjSiKpBRWs3BTBiPf2MGkpYl8treAPkFe/Pf2ARxccDUfzohn4oBg3FRSUuByRe6uxKWLG6ZOqEMATdMMUs02DNG90G/f7uxw2pS93x/HYj6399BitrH3++NOikhCwrEIgvAlsBfoKQjCSUEQ7hYE4X5BEO4/c8pmIBfIAZYDDzgp1L8QFuOLTRSo8unZbqoIjI1W3t2ew8s+PyGzNcKVz9rPdl0jaTuLCDBk4hOuw23EcLvZ/l8abY28tv81/pH4D+ID4vnihi/o7tXdYf4knEjUOPDqBkkrzh66e2Q4IvDxnjyHuRVkMgL/8SLW6mrKFy9h9PSeNJqsJHxz0Tp0duG2qNtQyBR8nfX1Ocdrvv0WbDa8J03CYrZydG8J4QN8cfNSnXNeUXUDp2qNjAmoh4YqCHbKI42EhEOxx1NwEtBDEIRwmhIDU4Hpfz7hfxSRbwKaHW9hD9YfKuLfW7Iorm4gyFvDk9dGEenvwca0YjYdLuFkVQMucoFRPfx44tooru4dgKfaeaNXJJyDqoc3hoRibGYrsk7WNjJW58kbeadIvWEiI5e8ja2+Hpmrq7PDcjj1teazlQP/y/mOS0h0dERRnHaB10XgwTYKp1UERnqh1Cio7DqIur178bl9irND4vN9BchqTzLe9SeE2DtAF2E324e3F9JoshJy9Ft8X33SYWNoKxoqeOLXJzhYepAZvWfweNzjkt7A5YxMDnGzYPsrcPoY+EXR1ceVG/t14Yv9J3hoTA+8NI65D1b37o3PtGlUffklYbfcwsDrQjmwKZ+eQwMJ6e2cySS+Gl+uDb2W9TnreTj2YVxdXLEZjVR+vhq3kSNRhoSQkVCMqc5C3yv+2jF9IL+p+nSQ8kxyRUoQSFyGXHIFgSiKFuAhYAtND/5rRFFMFwThX4Ig3HTmtEcEQUgXBCEVeASYdal+m2P9oSKe+zaNouoGRJqyfPPXpHLju3tYsTuPSH93/j25HwdeuIaPZw1i0sCuUnKgk6KO9AGriDm/1tmhtDkx7hoClAr29uyLaDZTl5jo7JAcis1q4/COQla/tO+857hrVed9TUJCwjnI5TJCemup8OlD3b79iLbmlcfbCr2xkfd25PCabjMyAbjiabvZNtU3krr9JAGmXHx8FXhce63dbP+Zw6cPc/vG2zlSfoTXR73OU4OekpIDnYGBM5rE9A58fPbQ3CsiqDNb+XyfYyes+T36CAqdjpIXXmDgmCC8A1zZ8VkmpnrnjX2fFj0NQ6OBjbkbAaj+9lusFRXo5t6L1Wrj4I/5+IV4ENzT5y9rk/IrcVcpCK7LAIXGri1GEhLtBbtoEIiiuFkUxShRFCNEUVx45tg/RFH84cz/PyeKYh9RFPuLoniVKIqZ9vD7v/x7SxYNjdZzYwO8NS4cWHA1n8wezG3x3fBylZICnR1lmCfIhU6pQyAIAmN0nuzBBZuXF/rtl+80g5Kcatb83wF2f51NQJgHwyZFoFCe+7GnUMoYNtF+u4ASEhL2IzRGh1FUUW1xx5iW5tRYlu/Ow7vhBKPrtzapw3s3q7d8UaRsK8TcYCHk8Ffo7r4bQW7fyjZRFPk682tm/jQThUzBqvGruKH7DXb1IdGOcfeH3hMh5QswN40b7B3kyagevqxMyMf4P/fO9kTu6UngP/+JKSuL6pUruHpWb+pqzOxe47xWg/5+/eml7cWXmV9ia2ykcsXHaPr3x3XQILL2naK23MjgCeHNVvEcyK9iYKgPsuJk6NIf5NIzxfkQBCFOo9HEPvzww20zq7UD8vjjjwdpNJpYQRDiGhudlzT7X9pKpLBNKK5uXmylpqERb1dJYVTiD2RKOapQz06tQ1BjtZF34y0Ydu5EtDru5sAZ1Nea+eWTDL59KxlTXSPXze3LjY8MYOC1oVx1R/TZigF3rYqr7ogmakigkyOWuFhcXV1j//wll8vjZs6cab8nNwmnEtpHBwJU+PVDv815minlBhMf7c7lbd33CAo1XPGk3Wwb6xpJ3VZIoLUAH1czXjffbDfbAA2WBl7Y8wKv7n+VoV2G8vWEr+mt621XHxIdgEH3gKkGjqw7e+iBKyMpN5j45uBJh7r2GHMVnjfeSPn77+NlKiZufChZ+06Re6jtJpb9GUEQmBY9jZzqHFK/WkpjURG6++Zis4lnqwdCY/7aAlFuMJFVqmdoiAeUpErtBS0gKSkpY8mSJWe16RITEzV9+vTppdFoYvv06dMrMTFR01Jbjz76aFBUVFRvhUIRN3/+/KALr/iDuXPndg0NDe3r5uYWGx4e3ufdd99tcY/Lhx9+6BMWFtbXw8NjgFar7T9p0qSwysrKFj1DL1u2TPvne5TfkwG7d+92BVi0aFFxSkpKemt+lrbgskoQBHk3/zd2vuMSnRtVD28aS+qwGpynqOssrvDxQCHA/kHDsVZW0nD4sLNDsgs2q43U7YWs/sdespNKibsulOkvDyVioP/ZnYCoIYHMfG0ED74/hpmvjZCSAw4kKSlJ+9Zbb8W8/PLLcW+99VZMUlKS1t4+6uvrD/3+VVJSkqpSqWxTp07tfKVBHQijxdjiczUeSgLCPKnsNhj99m0OjOrveXd7Dr2sWcQadsGIR5p2ZO1Eyi8naDRaCUn+DO2cOciU9tvQOF59nOmbprMxdyMPDHiA98a+h5fKy272JToQIUPBvw/8thxEEYCh3bUM6ObNh7tysVgd28IT8PxzyL29KX7+eeKuCcYvxIOdX2Q6barB+PDx+Lh4Ub/yc1Q9InG/8soLVg8kHq8AYKyuAixGCB7Y1mF3aIxGozB58uTIKVOmVFRWVqZMmzatYvLkyZFGo7FFgiuRkZGmV1999eTo0aNbvbvn5uZmW79+fU5tbe2hFStW5D///PPdtm7d6taStWPGjDHs3bs3U6/Xp+Tl5aVZLBZh/vz5LaqKmDdvXuWf71PeeOONE127djWNGDGivrU/Q1tyWSUInhrXE43LuWV5Ghc5T43r6aSIJNoz6sim3jLT8RonR9L2eCjkDPFyZ4+nFuRyDDt2OjukS6Y4u5o1rx1gz5psArp7Me0fQxh6cwQuqs4lQtleSEpK0m7ZsiXUYDAoAQwGg3LLli2hjkgS/M6qVat8tFqtZdy4cQZH+ZC4NDIrMxn/7Xj2Frd8KkFYjI4amS/6E6cx5+c7LrjzUFhZzxf7C3jLax24+cOwh+xmu8Fg5vD2kwRZC/BSm/GZervdbK/PWc+0TdOoNFbywTUfMK//PGTCZXXbJ9EaBAEGzYFTh6Ho4JlDAvOujOBEZT2bjzh23K/Cx4fAf7yIKeMoVcuXc/Ws3pgbrGxfdRTRJjrUd3OoFWruNQxEW2xAOXMqNhEO/piPf2jz1QMACdnleKgV9LAcazogVRC0is2bN3tYLBbhxRdfLNNoNOKCBQvKRFFk48aNHi1Z//DDD1dMmTKl1t3dvdXZrEWLFhXHxsYa5XI5Y8aMqYuPjzfs2bPHvSVrIyMjG7t06WL5/Xu5XC7m5eVdlHjV6tWrdVOmTKmQydr3Z/FlpUxzc2xTMufPUwyeGtfz7HEJiT/jEuyOoJZjzK7Ctb+fs8Npc67WefLP48XUXnElqh078J//uLNDuijqakzs/fY4WftP4a5Vcd19fek+wM9hCuCdnfXr13crKyu74NiLU6dOudlstnP+ESwWi+zHH38MO3To0N++4fz9/etvvvnmVg++7ygX3s5MoGsgWrWWR7Y/wtKrlzIocNAF13SP9Wf/D3mU+Q2k27bt6O6e0waR/sF/fs5ijCyZ8PpUuOE/oGrRPWWLSNl6gkaTlW4HV6F7cDYyzaVXPNY11rFw30I25G5gUOAg3hj1Bn6une8aJ9EM/W6HrS9D0kfQNR6Aa3oFEOHnxrKdx7mxXxeHXjs9r70Ww8SbKF+2jJDBgxkxOZJdXx3j4E8FxF8f5jC/zSHabAz6MZ9SL0iIrGNYYgm15UZG3R7V7O9AFEX25JQzPEKHrPgH0GjBp21jvhDbVh3tVllkcOhYKm2we/3YGb1afX0GSEtLU0dHRzf8+RodHR3dkJaWppk8eXKbqYYbDAYhLS3N9YEHHihr6ZotW7a4T548OdJgMMjVarVt9erVrZ6PfezYMeWBAwc8Pv300/zWrm1rLru7qJtjg0l4dgx5r99AwrNjpOSAxHkRZAKqCG9MOdWIYttnr53NWJ0nAMljx2HKzsZ80rE9iPbGZrWRuq2QL17aR/bBM+0ELw0lItZfSg60A/43OXCh45fKsWPHlElJSR5z584td4R9CfvgrfZm+bXL6erRlQe3PcjB0oMXXKPt4oauqzunQ0eh3962OgSphdVsSCnkVfdvQBcJA2fazXZdjYnDO04SLBbgqTLhM3XqJdtMO53G5B8msylvE/P6z2P5Ncul5IDEH6g8oP/UJh2CuqaPSplM4P7RERwtqWXnMcdrAgT+4x8oQ0MpfvJJovuqiRocwP4NuRRmVDrc95+p/uYbxKPZJN3Ugx+O/si+9ccJ7O5FaN/mqwfyK+opqm5gZA8/KDrUVD0g3Wu0CoPBIPf09DxH9MrDw8Oq1+vbtNRzxowZodHR0Q2TJk1qcVJi3LhxBr1en5Kbm3t43rx5pZGRka2ej718+XJdXFycITo6ut33Nl9WFQQSEq1FHemNMb0Ca4URhW/n0qro4aoiRK0koVt3rgQM23egnXGXs8NqEcXZ1ez6KouKojpCemsZdXsU3gEOTZpLnKGlO/tvvfVWzO/tBX/G3d3dPHfu3Cx7x/XRRx/pBg4c2CEuvJ0drVrL8muXM2fLHB745QHev+Z9Yv1j/3ZN1KAA9p40UHmogK4VFSh0jp+hLooiCzcfZbZmD74NeXDjKrsqlh/YlI/VYqPbbyvRzpuNzK1F7bDNYrFZ+PjIxyxLWYa/qz+fXPfJBX+nEp2UwfdC0nJIXgWj5gMwcUAwb289xrIdx7mqp/30NZpD5uZG8H8XkX/bFEqeeZbR7y6j/KSBnz9OZ8rzg/DQqh3qH8BSVcXptxfhGh9Pn2nTKPw4DVODhSvv7HneDYY9OU0JlVEhGthyFHrd6PA4W8vF7uy3Fe7u7la9Xn/O5rTBYJB7eHi0mVL2fffd1zUrK0uze/furIupNgwPD2+84YYbaqZOndo9IyPjaGvWrlmzRjd//vySVjt1ApddBYGERGtQ9WjSITDmdL5pBoIgMFbnSYLRihjVE/0O5ymEt5S6GhNbV6bz3X+SMTVYGH9fDBMe7i8lB9oho0ePLlIoFOf0CSoUCtvo0aOLzrfmUlizZo3ujjvukKoHOgi+Gl8+uvYj/F39mfvzXPYU7fnb8yPjmh5aSv0GYti5sw0ihK0ZpRzNO8mTLmsgZBj0uslutqtL68nYU0yoLQcPpRmf6dMv2lZ+TT4zf5zJkkNLuCb0GtbetFZKDkicH7+eEH4FHPgYbE3PZUqFjHtHdee3/Er251Y4PAR1z54EPP88dQkJ1H7yEdfN7YvVYuOnD9JoNDn+WfH0f9/BqtcT8OKLRNXFEVU+iIpex9AFnb99KCG7nGBvDaHmYyDaJIHCiyAmJsaYmZnparP9cWuQmZmpiYmJaX4MnZ15/PHHg7Zv3+61bdu2Y1qt9qJVOS0Wi1BYWNgqDYKff/7ZrayszGXGjBkdQkRZShBIdGoUOjVybxWmnA7xfrU7V+s8abDZyJpwE/VJB7Dq9c4OqVl+bydY/dI+cg6WETe+aTpB91hJa6C9MmjQoMpx48YVuLu7m6GpcmDcuHEFgwYNsnsd6datW91KS0tdZs6c2TnfyB2U33e6w73CeXjbw/yY9+N5z/X01RDQ3ZOy4KFtMu6w0Wrj9R8zedFjIypzNVz3ul3Liff/kItMJhKcuALt7FnI3VtfPWATbaw+uprbNtxGfm0+b17xJm+OfhNPpafd4pS4TBk8F2oK4dhPZw9NGxyCzk3Juzty2iQE79un4DlhAqffWYz80C6untWb0yf0bFl+BKsDJyo0pB2hes0afO6YjjwsgoSvjmPzMvK9xwrKG5rPMVttIonHyxkZ6YtQlNx0MEhKELSW66+/Xi+Xy8WFCxf6NzQ0CK+99pofwIQJE/QAixcv1gUHB8ecb73JZBLq6+sFm82GxWKhvr5esFiatAOzsrKUgiDEZWVlNTsG5rnnngv89ttvtdu2bcsKDAz8SxYqODg4ZvHixc2Wpi1btkybnZ2thKZ2xgULFgQPGzbs7A3zrbfeGnbrrbeG/d3PvnLlSt/rrruuysfHx7HjQuyElCCQ6NQIgoAq0hvj8RqnqOg6m+He7mhkAvv7DACLhbo9f7+L5wyKs6v4emESe9Zm0yXCm2kvDmHoxAhclNJ0gvbOoEGDKp988sm0l19++eCTTz6Z5ojkADRdeMeNG1fdUS68En+g0+hYMW4F/f3788yuZ1h9dPV5z40aFIBBFUBpcg62esdOiPpi/wnEihwmWzchxN4JQQPsZrusoJacg2WEGw/j6q5Ae1frW7vyavKY9dMsXv/tdeID4/lu4neMDx9vtxglLnOixoNn16aRh2fQKOXcM6o7u7PLSS10fFWlIAh0WfgqmoEDKX7mWQJthVwxrScFRyrY+VmmQ7ShxMZGTv3rX8h1Ovwefph93x9HX2FkxLRwTIKR9Tnrm12XVlRDrdHCiB6+TRMgvEPAXdL2aC1qtVpcu3ZtzldffaXz8fGJ/fzzz33Xrl2bo1arRYDCwkJlXFzceacQTZ8+PdTNzW3gxo0btUuWLOni5uY2cOnSpTqAvLw8ZVBQkDksLKyxubWvv/56cElJiTI6OjrG1dU11tXVNfbZZ58NhKbxi9XV1YpRo0bVNbc2IyNDM3LkyGiNRhM7atSo6MjISOOqVavyf3+9uLhYOXz48PPGXV9fL2zcuNFn1qxZji/PsROSBoFEp0cd6U39gVIaiwwou7Vo0splg0YuY4SPBzvrjMz29ka/fQee49vHTWZdjYnEdTkc+60UD62a8ffHEN7fV6oYkPgLX3zxRYGzY5C4eDyUHrx/9fs8vetpXv/tdfJq8nhm8DO4yM7t94+MC2DPmmxO+fTDsHsPnuOudUg8lXVm3t56jJVeaxBsahj7D7va3/vdcVQq6LL7U3yfebxV2gONtkY+Tf+UZSnLUCvUvDriVW6KuEn6XJRoHXIFxM+C7a9CeTb49gDgzqEhvP/rcd7dkcPyGfEOD0OmUtH1vXcpmDqNkw88SNRXX1I/IZykjXm4eikZdkukXf2VvvEmxrQ0ghe9TfYRA4e3nyTmyq4MHBDFoNJBfHPsG+b0nfOXcaAJZ/QHRkToYHsydJXGG7YEpVIpDhs2rNecOXPK3nnnnWKAESNGNKSnpzfbu5+YmOi+ZMmS8+oorFu3Lh/Ib+617du3ezz22GMlKpWq2cySKIrnVcT99ddf3caMGVMdGxtrbO71JUuWFC1ZsqTZ9kij0SiUlpa6PPTQQ+d9+Hd1dRX1en1Kc6898cQTXT788MMApVIptqfPcamCQKLTo4rwBjqnDgE0TTPIN5qpHD8Bw65diBbLhRc5EKvVRsovJ5raCZLLiL8+jGkvD5FGF0pIXMaoFWoWXbmI2X1m83XW18z7ZR41pppzznH1VBLc05uywCFUr//eYbH85+csYs0HGWjcjzD6KXC3n2hbwZEKTmZW0b0yEbW/D963397itQdLDzJlwxTeSX6H0d1G8/3N3zMxcqL0uShxcQycBXLlOVUEHmoXZo8Ia9LfKGmbqXMKHx+6ffgBCAIn7rmX/v0V9LkimOQtJ9jzTbbdqjurv1tP1eefo505E33EUHZ8nklwTx9G3NaUhJjUYxJFhiIOnDrwl7W7s0/Tu4snOlsF1JyArhcezyoBJpMpWa/Xp/yeHLgQCQkJ2QMHDmz2If1CvPnmmyVPPfXURekQjRs3zrBhw4a8i1mrVqvF3Nzc9PMlJi7Ef/7znxK9Xp9iMpmSFYr2s28vJQgkOj1yDyUugW6dVodgrLapauK34Vdgq6mhPjnZabEUHatizcIkEr7JaWon+McQhtzUXWonkJDoBMhlcubHz+eVEa9wsPQg0zZNI6Mi45xzegwKpEGlpeRQPpZK+3esHCmq4ZvfcviP+2rQdoch99vNttVqY8/abDzcRQIOfIXfQw8iU11Y56qioYIFexYw66dZ1DfWs/iqxbx95dv4anztFptEJ8TdD/rcAilfgPGPZMCs4WG4qxS810ZaBADK0FC6ffghVr2eE3fcydBBMmKu6krqL4VsXZmB1XJp3WMNaUc49dJLuA4dinLmg/z4QRpefhqum9sXubzpUWhsyFjcXdz/0mbQYLaSXFDNqB6+UJDYdDBk2CXFIyHR3pESBBISgKqHN6b8WmzmNpu00m4I0aiIclWzR+uP4OKCYcfONo+hrtrEzyvSWf/2IRpNVsbfH8OEh/rh7S9NJ5CQ6GzcHHkzH4/7GJPVxJ2b72T10dVn+5EjBvqjUAgUBQyjduNGu/oVRZF/bkjnCfUmdKZCuOFtULRKqPpvObKziOrSeqIKvkcd2g2viRP/9nyz1czKIyuZ8N0ENuVu4u6+d/PdxO+4KuQqu8Uk0ckZch+Y9ZD65dlD3q5K7hoWyqa0EnLK2k64WBPTl9DPViGKNk7cNYP4XiaG3tyd7KRSNr2Xiqm+2dbyC2I+eZKTjzyC3FeH67ML2bg0DUEQuOHBfqjd/mhj0ig0XBd+HVsLtmIw/9FOnpBTjtlqY2QPXzixF5TuENjvkn9eCYn2jJQgkJCgSYcAq4i5oG1K6tobV+s82ac3wvARGHbsaDO/VquNQ1ub2glyD50m/oYwpr/k2HaCw4cPs2jRIl5++WUWLVrE4cOHHeJHQkLi4on1j+WbG79hWNAwXv/tdR7f+TiVxkpUGgVRw7pQGjiYsu/PP/XgYvghtZiKgnTuFtZDzG0QYb8H8Qa9md825tFFZ8YzbSt+jz6CcJ5yUlEU+Tn/Z27+/mbePvg2cQFxfDfxOx6LewxXFylpKmFHguMgOB72fwB/Gj13z8hw1Ao5i7e1XRUBgDoqirDVq5G5uVE4axYRDSlcdVc0J7Oq+fJfv1GQ3jqNt4bUVPKn3I6tvh7b/Lf47v1sGk1WbniwH15+f30v3Rx5M0arkS35W84e25pRiodKwZBwXVMFQbfBTRoO7QObzWaTeowkWs2ZpPt5S3OkBIGEBKAM9wK50Il1CDxoFEUOXzsec34+ptyLasVqFUVZVXz9ahKJ63II6uHNtJcGM+TG7igc2E5w+PBhNmzYQE1NU29zTU0NGzZskJIEEhLtEB+1D++OeZcn459k18ldTFw/kU25m+h7RTA2QUFBjQ5j1jG7+Ko1NrJwYwaL3D5D5qKBaxfaxe7v7N+QR6PJQti+D3EdMACP6677yzmiKJJYnMi0TdN44tcnUMlVfHD1B7w79l3CvMLsGo+ExFmG3A+Vx+H4H+NDde4qZgwPZcPh4jatIgBQhoQQ+sVq1L17U/Lcc3h+/Qa3zItCqVGwcUkqOz7PxFh34WqC2i0/UzBjJrh5UPnQUn7ZVI23vyu3PTeIwHCvZtf08+1HuFf42TYDm01kW2Ypo3v6oTRXQ1kGhAy36897KQiCcKqhoUHt7DgkOh5ms9lFEISa870uJQgkJACZUo4yxBNTdufUIRjs5Y6HXMa+8CgAh1YRnG0nWHQIi9nK9fNizpvNtxeiKFJZWcmPP/5IsUsD34uRfGKM53sxkmKXBrZt2+Yw347mk50/EP/PLwh7diPx//yCT3b+4OyQJCTshiAIzOwzk7U3riXEI4Rndz/Lv449j7abC0XBo6j+rvmxZK3lzZ8yGdGwnf6WVISrXwaPALvYBSg/qSdjdxERnuVoio4S8Nyz51RIiaLIvpJ9zNkyh/u23kelsZJXR7zKNzd+w/Dg9vMwInGZ0nsiuAfA/vfPOTx3VHc0Lm1fRQDgEhBAyKef4PfEfPQ7dmB4YBrXhOcw4KouHE0o5tPnEtj5RRYVxX+dLGcuKKD42ecomP8MRQOmkBj7AskJ1UQNDmDSkwPx0J7/eVoQBG6OvJmU0ynk1eRxqLCacoOZa3oHQOH+ppNC24/+gMVi+Wd+fr6yrq5OI1USSLQUm80mFBcXu1ut1k/Od067qZGRkHA26khvarcWYK1rRO7mcuEFlxEuMoHRWg921NbzYHQ0+h3b0f1/e/cdHlWVPnD8e2YmM+k9EEghlQ6hS+8gIGsvuIKwa/lZWFddV3RX3dVd3HVtay9rR1ysK6AI0osoUgPSk5BAEkIS0nsmc35/zIj0BDKZSXk/z8OTyb137nkPJNw77z3nPbf81qlt1NXZ2Lkyk81fH8JWpxlwWQz9L+3UJCMGbDYb+fn5ZGRknPhTWlpKnreV5UX9qbGZASisDmJ5bX90wFasde5dveFifLD2a/65wkaNzf40JL8ygCe/rQEWMWv05e4NTggnig+M54PJHzB/73xe2v4SR73KGeM1ndTV39D+Aes5h+s3xNaMQpZt2sla7/nQYSD0/43T4rbZNGvm78fiZaTjt8/jP3UqXklJ9n3axvrM9by560125u0kzCuMOQPncH2X6zEbzU6LQYjzMplhwC2w5knIT4FQe1X/EF8LNw+J4Y11qdwzLoGEdq5dBloZjYTedhu+w4aR8/gT5D/5d0L9/Bh3+c1kePdm38Zsdq/LIqiDN4EBBnyNFdhS91F4KI9K7xhKR/4LqzYS0cGXMTOjie4e3KCpi7+K+xUvbnuRhSkLqc6bhMmgGN2lHazfaDpNCBQAACAASURBVF/1IaL5LHHYr1+/Zdu2bZudmpr6F611OPLgVzRMHbDBZrO9dq4DJEEghIMlMRCWZ1CdWoR37zB3h+Ny40P8+SqvmOzJlxH6wvNYCwsxBQU55dyZ+wtZt+AAhUfLiekVwvDrE506YqCuro5jx46dkhCorKwEwM/Pl/YhocSHhfB4qulEcuBnNTYzSwqHsOTPy8526mbuzP/Ca2xmXl5bzKzRro9GiKZkNBi5ucfNTIyZyIubX6L2YAlH/Pvy1YK5TJg2B0/ThY+0ra2z8afPd/Kc17t4UQVXvAIG591j/7Q2i2OHSuhv3o5HXSXt7r+PitoKFqUuYv7e+aSXpNPRpyOPDn6UKxKuwGJ0XlFEIRqs/yxY9zT8+CZM+deJzbePjOOD79N5YWUKL93Y1y2heXbvTszHC6jcsYPj779P6YLXiaqro72HDznRoygq7MQxSxjpXqFo1RlzRCyBEf4kRgfSc2QE7Tr5X1B7Yd5hDIsYxuLUxZDZm0viggnw8rDXH+jYDzy8mqinF6dfv37LgJZ4AyOaMUkQCOFgjvBDeRqpTmmbCYKxwfaL6KakgVxms1G+fj0BlzfuKXRZYTUbPz/IwS25+Id6MuWu3sT2bvzSXFarlezs7BPJgMOHD1NTUwNAgL8f7QP8MXt5UHMsi8LtyRyrreUYUBh7riXLNL8Z0PKml7y7JQg484lIfuWF3RAJ0ZKE+4Tz5Oi5LM7eyOG1vtR8+jiXeqxiZo+Z3NDlBnw8fBp8rjfXpdEjfwkjzJvtdQfCujgtzrLCKn5YmErHSBP+H76Fbea1PHX4HZasWUJpbSm9Qnvx1IinmBAzAQ9D2xq1JpoZv/bQ82rYMR/G/Am8AgEI9jEzc2gMr69N5XdjE+jc3rWjCE7m1acPkX36YD1+nOr9+6k+dIj2h9JRxnLMceGYYgIxxSXiFRbY6LauTLiS+zPvp6I8mRmXXAk15XB0Bwz9nRN6IkTzJwkCIRyUUWGJC2yzhQrbWTxI8vNirVJcHhZK6arVF50gqLPaSF51hC1fp2Or0wycGku/idEXPZ2gpqaGzMzMEwmBzMxMrFb7lIAAXx9CPc2omnLKj6RRV1bKccDD4kn7+ARiJ/2K8PhEwuMTmf/KCgqqzxwVEWwp4i/Xzrio2Nxp8e6PyK88s9hSqFfbWY1j//795ttvvz16+/btvmazWU+ZMqXw7bffPuzhIR+4Wrsxk/vxwbrvCKgbzZCadJ7f+jxvJL/BZXGXcV3n6+gW0u2870/JLeWTld/zjec8iBwKg+90anzrFhzAVmcjbOO/KQkwMTv0f5DiyfhO45nWZRp92vVxantCNMrgO2Hnx7B93ikfhG8bEce87zN47tsDvD7D/cPrTSEhmIYOxWdo09XnGBk5EovBh1r/7Yzv/n+QuRls1mZVoFCIpiQJAiFO4pkYSNWe41iPV2IKaV7DyFxhXIg//04/Rt34iZQvWoiuqUGZL2wu7JF9BaxfcIDCnApieocy/LpEAsIu7O+yqqqKw4cPn0gIZGdnY7PZUICfp4VAbaX2eDbW47nY6qxUmEyEdYoldujIE8mA4IhIDIZTExIzupTy2k8+p0wzMBtqmNHFtVWanWX2KF+e/LbmjP7MHuXrxqh+kZk5P/hQ+ssRNTV5ZrM5rCY2ZnZWZORNBc5s4/bbb48ODQ215uTkJOfn5xvHjx/f+amnnmr3yCOP5DqzHdH8+AZ50rl/CAdtw7ltj5npf5jDf/f9l0Wpi/j0wKd0C+7GxJiJjIseR2xA7Cnvra2z8YePt/NP05t4GWxw5StgaHw9FJu2sbdgLxvWJ2NNDqeYLwlJO8DHs2J4YMRMpsRNwd8sI3xEM9Sxr/0D8KY34JI7TyzlF+xj5tYRsfx7xUGSjxSRFNX4J/TNncVowaumHzUBPxLsCyR/DyiIvsTdoQnhEpIgEOIklgT7ha8qpQjfNpggGB/iz3Ppx9g+Ygx9//sR5Zs34ztsWIPeW1ZYxXefpZCy1T6d4LK7ehPTwOkE5eXlp9QPyMnJAewVhb0N4F1WRF3+MYyVZWit8Y2IIrxbd8LjryA8oTOh0TGYGvDE+L5f3wkfvca8/X4UVAcSbCliRpdS+/YWyF6IcBEvry0mv9KfUK8SZo/ybRYFCjMz5wcfTJnbyWarNgDU1OSaD6bM7QTgzCTBkSNHLHfddVeet7e3jo6Oto4ZM6Zkz549be+Xt40adFUXDmzNZ9cemKrbM3f4XB4c+CBfpX3FkrQlvLDtBV7Y9gJxAXEMCh/EgPABDGg/gHnfHWdYzocM8dgJk56H4LiLar+2rpZ9BftIzksmOS+ZH3N+pLKkluuTHwKfPKauWItx1BD++tA7Tu65EE1gyN3w8U2wbzH0uOrE5ltHxPHB9xk88+1+5t3S+j8kF5TXcDSrG16d1rPqyCqmZmyE8J7gefblEYVobSRBIMRJTKFeGAPMVKcU4XtJB3eH43J9/LwJ8TCxIaAD/SwWylavqTdBUGe1kbzyCJuXpKNtmkG/iqXvxGhMHud+GldcXPxLQiA9nfzjxwF7+V2ztRpz4XGMFaUYK8sJDAujfXxnOowYQXh8Z9rFxWP2vPjPf/f9+k7uu+h3Nz+zRl/u0oKEe/bOiSovO1BvhcnSsr0+WteeUiDBZqs2HDj4t5ijRz87b5EPH9/OFd27PXWkIfHceeedxxYsWBA0ZcqU0vz8fOOqVasCHn300ayGvFe0fP6hXnTrF8jeLYM58u4nxD54NwGWAG7qdhM3dbuJnPIcVh5eydoja1mYupAF+xcAYLJ6MTCykKf9B9LR20K7jOWEeYXhb/HHYrRgMVpQKKrrqqmyVlFeW05eZR75lfnklOeQXpJOalEqh0sOY9X26U7hPuEMDh9M54xx1OLBiLL1GI0mYv86151/RUI0XJfJEBQD3796SoLA12LirtHx/P3rvWxMzWdofONrCTVnK/Yew1oRQ6hnOF+lLmZq5mbo2/KmIQpxsSRBIMRJlFJYEoKo2nscbdMoQ9taVtagFGOC/VhVUMKDQ4dStno1+s9/OufSQKdPJxhxfSL+oad+eNdaU1BQcCIZcCgtjZIy+9rFBq0xlJdgrijFVFGKr9mDDgmdCe87hvD4zrSPS8DbXzL2LdHpyYH6tl+scePGlb333nthQUFBfevq6rj66quPT58+vW0WEmmjBl3fi33b1rF9cwWdKioweP+Svwr3CT+RLKi11bLj2E/M+WI+/fRiDnt78bEupHrzPy+oPYMyEOUXRVxAHGOixtAtpBtJYUmE+4Sza00m69IOMLB3LYYXvyJszhw8OrS9ZLNooQxG+/SCpXMgcwtEDjixa/rgTry94RBPL9vPF3eGNGjJwJZq4Y4sOoX4cmXiVN7Z9Tb5dVWEdpL6A6LtkASBEKfxTAykYusxarPLMEe6r2Kvu4wP8eezY4WkTZhEh9WrqT5wEM8unU85pqywig2fppC6zTGd4O7exPSyP1Gw2Wzk5eWRkZFByv59HD6SSZVjhQFVZ8VYXoqlohQvm5WOkVF06JdkTwbEJ+IXEtqqbzpag4Y+2V+/YUivmprcMwpYmM3tagYO/N9+Z8RSV1fH1KlTE2fMmJG3ZcuWfcXFxYabbrop5q677op8/fXXM53RhnAyrWH3F9DtihNznBvLJ9BCt97e7E7uy+F5i4j5v2lnPc7D4MEXGzRzc75jkLkI43WrsbXrSmFVIfmV+eRW5FJWW0Z1XTU1dTXU6To8jZ5YjBa8PbwJ8wojzDuMYM9gTIYzYy84Ws53n6cQleBLwAe/x9yjB8Ezpjulj0K4TN+bYPVc+OFVuPaXqTGeHkbuGZfIw1/sYsXeXCZ0b+/GIJtOTnEVG1OPc8/YRKbGRfPWrrdY6uvL9NiR7g5NCJeRBIEQp7HE/1KHoC0mCEYH+2FU8ENid64CylavPpEgONt0gqTxkeTl57JmxQpSDh7gWF4+tTYbAKq2BmNFKd7VlYSFBBEdF0+HhC6ExycSFN4R5cT1xkXzEhszO+vkGgQABoPFFhsz22nD/3Nzc01Hjx41P/jgg3leXl7ay8urbtasWcefeOKJCEASBM3RobXw2W9h9MMw+iGnnfaSGQPZl7yazWuPEz29HIPPmUsdfrblCD2T/84Q0x741WvQvjsGIMQrhBCvELoEX/wShzVVVr596yc8LEa67v8QW1UVHZ/+F8okt1mihbH4Qb+b4YfXYPzjEBh1Yte1/SP5z7o0nlq6jzFdwjAZW981fFFyFlrDlX0jiA30oZs28VVQKNO9g90dmhAu45Qrl1JqEvACYATe0lr/87T9FuADoD9wHLhBa53ujLaFcDajnxmPcG+qU4pgdFT9b2hlAj1MDPT3YXVVHe2HjiPzSAb6L39BWa34lgZhqelDSGItHu0K2LR3M99sLsfmeK+qqcJUUUaYlydRkRF06tKVDgldCImMxig3ym3Kz4UIm3IVgw4dOlgjIiJqnn322bDHH388p7i42PjBBx+EdO3atcJZbbR0Dbg+zwKeBn5O3LystX6ryQKKGw29p8Hap+yvowc75bRefmb6DPZn66ZubH/2E/o/9ptT9u/LKeHwor9zv2kltmH3Yejza6e0C6BtmlXv76Ugu5xRPY5T9/Jy2j/2KJa4iyt8KITbXXIHbHrdniSY9OSJzR5GAw9N7srt87ayYPMRpg/u5MYgm8aX27NJigokNtQHKgv5VWE+/woOJK04jbgA+Z0WbUOj79iVUkbgFWAC9ic2m5VSi7TWe0467BagUGudoJSaBjwF3NDYtoVoKpaEIMp+yEbX1qHOU2yvtRoX4s/ctKP0ieuIj9U+PUB7eFAaVEqpXkd+qQFKwVBVgbfStA8JJj4hkU7detAuJhYPi6ebeyCag8jImwqcvazh6T755JOUe++9N/rll18ONxqNesiQIaWvvvpqg6ZBtHYNvD4DfKy1nu2ywKY8DUd+gM9vhTs2gJdzlk0bdPMlpG5eyNaMEBL2pxPQJQaAsmorn7/3HH82LKCq69V4jnvMKe39bMs36aRuz+OS0UEY/3E/3iNHEHTjjU5tQwiXCoyCntfA1vdg1B/BK+jErgnd2zMoNph/rzjAFX064udZ/wpCLcX+nFL2HC3hr7/qbt9waB2Ty8p4JjiIbw59w9197nZvgEK4iDPGBg0CUrTWaVrrGmABcMVpx1wBvO94/RkwTslEY9GMWRICwaqpTi9xdyhuMT7Evk734dDTimspBVrTLyaKGy+bwoOPPsYD/3iaGQ88zNArryWiSzdJDgiXGjp0aOWPP/64v6SkZEdhYWHykiVL0qKioqzujquZaMj12fU8/eGat6EkG766116XwAkMRgPj/68vVqMnq15YD4C1zsZ/3n6DP1a+SHH7wXhe+zo4cWpT2o48flx8iM79Qwn66K8YvL3pOHeu1FIRLd/Q30FtOWw5dYlOpRR/ntKN/LIa3lib5qbgmsaXO7IwGhRTkzraN6SuItTky8DwASw9tBTtpP+rhGjunHGVjABOflqT6dh21mO01lagGAg5/URKqduVUluUUlvy8vKcEJoQF8cSGwBGZZ9m0AZ19fHEp6qCw8FnKUJkMHD5rFvoMnAQnj6+rg9OCNFQDbk+A1yjlNqplPpMKXXOeVVOvUZHDoAxf4Ld/4PNzpvR0D4phh4dCsm2RbD74418Ou8l7j72KGX+iQTM+hhMFqe1lX2wiOXv7KZdjB+dk9+mJiWFjk8/jSnsvKt4CtEyhPeC+LGw6Q2orTplV1JUIFf06ch/1qdxtLjSTQE6l82mWbg9ixGJoYT6WuyJy5RVEDuSSbFTSC9JZ1/BPneHKYRLNKvqIlrrN7XWA7TWA8LkAivcyGAxYo72o6qNJgiUUnTKP0pmUBh1pz0JU9ZaN0UlhGgCi4EYrXVvYDm/jPY7g9Ov0cPvg8RLYckDsP3Dxp/PYeiDv8Kv6igblhcwft8b5Pp1J/iuZU6bygBwNKWIxS8n4xfsyWDzZipWfEv7OQ/iO3yY09oQwu2G/R7KjsHOj8/Y9cDELmjgX0udsiiN221OLyC7uIqr+jpyqMdTofgwxI9lfPR4TMrE0vSl7g1SCBdxRoIgCzj5iUMkvxQ7OuMYpZQJCMBerFCIZsszIYja7DLqytvmB+JBRcXUmjzICThpsI/NRpxHgPuCEkJciHqvz1rr41rrase3b2EvJuwaBiNc/4H9KeXC2ZC8wCmn9fCy0KPTZpTNysL8v+E/fZFTkwM5acUsfjkZ30ALY/sUUfbmSwRcfTVBN9/stDaEaBZiR0F4b9j4Ethsp+yKCvbm1uGx/G97FlvSm7TUjEt8ujUTb7Pxl+UbU1fav8aPJdAzkMEdB8s0A9FmOCNBsBlIVErFKqXMwDRg0WnHLAJmOl5fC6zS8hsmmjlLQiBoqE5tm6MIHr1rJkabjcOBYaA1qraGeIMPM/46x92hCSEapt7rs1Lq5EIjlwN7XRgfeHjCtI8gdgR8eSdsm9e4mgQlR8l9dTL91bt0LZ+HFS8WP7OV6grnJHozfjrO4hd34OVnZvwwK0V/eQivvn0J/+tfpO6AaH2Uso8iOH4Q9n99xu7ZYxPoEODJowt3U2drubf1BeU1LErO5qq+EXibHfXbU1dBUCwExwIwOXYy2eXZ7Mzf6cZIhXCNRicIHDUFZgPLsN9YfKK13q2UekIpdbnjsLeBEKVUCnA/4LzFj4VoIuZIP5TF2GbrEPgYjXT29WJ3TBfeGH0Viy+7Ce87bnN3WEKIBmrg9fkepdRupVQycA8wy+WBenjBjR9DzAhYNBs+mQFlF1jjwGaD3f+j+qXB+Obt4I3A++n/zocMqF1LcYlm8bObqGrEaDBt02z++hBfvZKMX6gXE4fVUvjA3ZhjY4l89RUMZvNFn1uIZq37lRAUA+ufPSN552028chl3dl7tIT5mzLcE58TLNh8mBqrjZlDY+wbrDVwaD0kjDtxzJioMZgNZpYekmkGovVzSg0CrfUSrXVnrXW81nquY9tjWutFjtdVWuvrtNYJWutBWuvWVfZUtErKqLDEBbTZOgSf5xSQUlmDVYMGMqtreWD/ET7PaflDCYVoKxpwfX5Ya91Da52ktR6jtW7yKlxb0gvOHKZr9oYZ/4MJf4MD38Krl8D2+VBTcf6TORID+o3h8OksUqoD+FvEq8y8+xF8fLzo88wD9Mz6gtzMShY8/gPZBwsvON6KkhqWvLbTvlrBoPZMGlJB4R9nY46PJ/rddzAFBdV/EiFaKqPJXi8kezukrT5j95Re4QxLCOGZZfs5XlZ9lhM0b9Y6Gx9+n8GQuBA6t/ezbzyyyb6CQ/zYE8f5mf0YHjGcZenLqLPVuSlaIVyjWRUpFKK58UwMoq6gCuvx1lGl90L8I+0otafdxFfaNP9IO+qmiIQQLd3G1Hyuff17Hv5iFzXWU+c0YzDCsHvg/9ZBYCdYeBc8nQCf32pf7SB9g/1DytGd9qkIX94NLybBp7PILSzl3pq7eDnhP/z1t1fh6WEEwBQWxoDHb2XA7pfQ+cf48rnt/Lg4DWtN/Tf41ZVWNi1KY96j33N4dwEjbkikn8cOcu69B0tiIp0kOSDaiqQbwa8jrHv2jF1KKR6/vAcVNXUtsmDhir3HyC6u+mX0AMD+b8BohtiRpxw7OXYyeZV5bMvd5toghXAxk7sDEKI5syTYC1tVpRbhG+Ll5mhcK6v67MNxz7VdCCHqMzg2hNljEnh5dQqH8st5bXp/gn1OG57frivcuhIyvoNdn8Kehfavp/MKprx9f56vvoH3ipK4d0JX7hqdgMFwai0A7wED6PXK3/C98x72RV3B5q8heVUmiQPa0XVIB0KjfDGaDCilqCqvJftgEVn7C9n/Yw7V5VYSBrRjwOgwKl/4O7nLV+AzaiQR//oXxgAp2CraCJMFhv4Olj0Mh3+A6MGn7E5o58ctw2N5Y10aV/WLYHDcGSuZN1vvbUwnItCL8d3a2TdoDXsX20cPWPxOOXZk5Ei8TF4sS1/GwPCBbohWCNeQBIEQ52EK88Lob6Y6pQjfQR3qf0MrEmHxIPMsyYAIi4cbohFCtAYGg+KBS7uQ0M6XBz/fyZWvfMe/p/WhX3TQ6QfaCxfGjoApz8CxXVBdBjXlYK2iPKgrr+5SvLUhHV+Lifdv6cuwhNBztuuVlET8/Pcw33obHY5+T8GI6ez/wcbu9dkn4vLwNFJdaQUNJg8Dkd2CGTg5Gs9935N7+++pzc2l3YMPEjxrJsogAzBFG9N/Jqx/BtY9A9M/O2P3veM7881POTz0+U6W3jvyxCie5mxfTgk/pBUwZ1JXTEbH7/TRZPvyhqPPLMjs7eHN8IjhrDy8kocHPYzR0Pz7KMTFkCucEOehlMKSEEh1ShG6BVfovRgPx3XA67QncV4GxcNxbStRIpq3bdu2eQ4ePLizn59fn+jo6J4ffPCB89azE03myr4RLLh9MFW1dVz96kZufX8zu7OLz36wyQwR/SFuFEXR43m3uC8j3snilTVpTOoZztf3jDhvcuBnlthYYhb8l4guQcR9/hAjdv6dwZ2LGHRpB/pMjKbzoHAGTY3lqj/0ZdYjPRgatJuS268n6777UZ6exHw0n5Df/kaSA6JtMvvA4LsgZTlk7zhjt5fZyD+v7kX68QqeX3HADQFeuPc3ZmAxGZg28KTVYPcuBmWEzpPP+p6JnSaSX5nPjrwz/w6EaC1kBIEQ9bAkBlGxLZfao+WYI3zdHY7LXBMeDNhrEWRV1xJh8eDhuA4ntgtRn/ez8oOfS8+JyK2xmtuZTTX3x4RnzYwIdVqVy9raWq6++uqEWbNm5W3YsOHAkiVL/G644YaEPn367Ondu3fLq5bVxvSLDmLVA6N577tDvLkujcte3MCgmGD6dQqiT1Qg0cHelNdYKauyknG8nOV7j/FDWgF1Ns3guGD+NKUbvSMvLB9kCgsj+j9vUrFlC3n/fgH15p8BMAYHY4mLQ6MpS0mluMhenNYzqTftHvwjfmPHoozytFC0cYNug+9ehHVPw7T5Z+wemhDKtIFR/GddGlN7daRXZPOdhpNVVMnnWzO5pn8kQSdPc9q7GGKGgc/Zp0mMiByB2WBmRcYK+rfv76JohXAtSRAIUQ/PePsNaHVKYZtKEIA9SSAJAXEx3s/KD34sJatTtU0bAI7VWM2PpWR1AnBWkmDHjh2eubm5Ho899tgxg8HA5ZdfXtqvX7+yt99+O+SFF17IdkYbomn5WkzMHpvIjCExvPvdIVbvz+PtDWnU1p05Yis+zIf/GxnHxB7hJEUGoJQ6yxkbxnvAAKLnfUDl9u1UJu+kJi2V6pRUMBjwmzgRS0I8Xr1745mU1Kh2hGhVPANg6GxYPddeMLRj3zMOeXhKN1bty+XBz3ey8O5hmE3Nc8TNSysPAjB7bMIvG/P2Q/5+eyLkHHw8fBgWMYzlGcv548A/YlDNs39CNIYkCISoh9HfjKm9N1UpRfiNiqr/DUK0YvfuPRy1r7zKu77jdpdV+tRqfconq2qbNjxyMCvmv0cLws733q4+nhX/7hZ95GLi01qzZ8+etlVRtBUI8PLg3vGduXd8Z6pq69idXcKxkip8LSb8PE2E+lqICq73x+6CKKXw7tcP7379nHpeIVq1S+6A71+B1f+Amz45Y3eAlwdzr+rFbR9s4dnl+3l4cjc3BHl+6fnlfLo1kxmDOxEReNLlYu9i+9eul533/RM6TWD1kdX8lP8TvcN6N2GkQriHpL2EaADPhECqD5Wga231HyyE4PTkQH3bL0bv3r2rg4ODrY899lj76upq9cUXX/hv3rzZr7KyUq5tLZinh5H+nYKY0qsDIzuH0Tc6yOnJASHERfL0ty9HenAZZG456yETurfnxkHRvLE2jQ0H810cYP1eWHkQD6PirjHxp+7YuxgiB4J/x/O+f1TUKEwGE8szljdhlEK4j4wgEKIBLIlBlH2XTXVGCZ4JUgNNtF0NfbKf9N1PvY7VWM2nb29vNtUsHdDZKYtlWywW/fnnn6fMnj07+uWXX+7Qs2fP8ilTphRaLBbJ5AkhRFMZdLtjFMGTMOOLsx7y6NRu/HjoOPd/soOl9448czlTNzlwrJQvd2Rx+8g42vl5/rKj6DAc3QETnqj3HP5mf4Z0GMLyjOXc3/9+mYYkWh15yiJEA1hi/cGgqE4pdHcoQrQI98eEZ1kM6pQP6haDst0fE57lzHYuueSSys2bN+8vKirasWHDhoOHDx+2DBw4sNyZbQghhDiJxQ+G/R5SV8LhH856iLfZxIs39qWoopYHP9uJ1s1jJajnlx/Ax2zijpGnjx74yv6169QGnWdCpwlklWWxt2CvkyMUwv0kQSBEAxgsJszRflSlFLk7FCFahJkRoQVPJERktDebahT2kQNPJERkOHMVA4BNmzZ5VVRUqNLSUsNjjz3WPjc312P27NnHndmGEEKI0wy8FXzCYNXf4Rwf/nt0DGDO5K6s2HuMt9YfcnGAZ1p/MI9vfsrh1hGxp65cALBzAYT3hpD4s7/5NGOixmBURlZkrGiCSIVwL0kQCNFAngmB1GaVYauodXcoQrQIMyNCC5KH9dx1dEyfrcnDeu5ydnIA4J133gkJDw9Pat++fdKaNWv8ly1bdsDLy6t5PKoSQojWyuwDIx+E9PVw8Nxz8X8zNIbJPcP5xzd7WbM/14UBnqq82srDX+wiLtSHO0adlgTI2QVHk6Hv9AafL9AzkIHhA6UOgWiVJEEgRANZEoNAQ1VqsbtDEUI4vPHGG5klJSU7Kioqtq9bt+5gz549q90dkxBCtAn9Z0FwHKz4C9jqznqIwaB49vokuoT787v/bic1r8y1MTo88+1+Mgsreera3nh6GE/duX0+GM3Q67oLOue46HGkl6STVpzmxEiFcD9JEAjRQOZIX5TFKHUIhBBCCCFMZhj3GOTugeT/nvMwz+v5LgAAFtNJREFUb7OJ/9zcH7PRwG3vb6G40rUjMbdmFPLexnRuHtKJgTHBp+601sCuT6DLFPAOPvsJzmF01GgAVh9e7aRIhWgeJEEgRAMpowFLXIDUIRBCCCGEAOh+JUT0h1VzoabinIdFBnnz2vT+HC6o4Lb3t1BRY3VJeFW1dcz5fCcd/D15cFLXMw84sBQqjl/Q9IKfhfuE0yOkB6uOrHJCpEI0H5IgEOICWBICqTtehbWgyt2hCCGEEEK4l1Iw4W9Qmg2bXj/voYNig3n+hj5sySjg1ve3UFV79mkJzmKzaR74NJmU3DKevLoXvpazrO6+Yz74dYD4sRfVxtjosezK20VeRV4joxWi+ZAEgRAXwDMhEIBqGUUghBBCCAExw6DzZFj/HJQeO++hv0rqyDPXJfF92nHu+HAr1damSxI8v+IAX+08ypxJXRndpd2ZB5TmwMFvIWkaGIxn7m+AsVFj0WjWZK5pXLBCNCOSIBDiApjaeWPwN1MldQiEEEIIIewunQt11faChfW4ul8kT17VizX787jtg62UVDm/JsFnWzN5aVUKNwyI4o5RcWc/KHkBaBv0ufDpBT+LD4wnyi+KVYdlmoFoPSRBIMQFUErhmRBIdWoR2iYrqQkhhBBCEBIPQ2bbixUe3lTv4TcOiuapa3qxMSWfq1/dSHp+udNCWb7nGA9/sZOh8SH8/aqeKKXOPMhWB9veh6jBEJpw0W0ppRgbNZZNRzdRVuOeFRqEcDZJEAhxgSwJgdjKrdTmOO9iJoQQQgjRoo18APwjYMkD51z28GQ3DIxm3i2XkF9WzRWvfMf6g42bx6+15tU1Kdw+bwtdw/157ab+eBjP8VFn31dQkAaD72xUm2CvQ1Brq2VD9oZGn0uI5kASBEJcoBN1CA5KHQIhhBBCCADMPjDx75CzE7a+16C3DIkPYdHdw2nvb2HG2z/ywKfJ5JdVX3DTVbV13PfxDv61dD9Te3fkk/8bQoC3x9kP1ho2/BuCYqHbry64rdMlhSUR7Bks0wxEqyEJAiEukNHfgqmdt9QhEEIIIYQ4WY+rIGYErHwCyho2IiA6xJsv7x7GnaPjWbgji7HPrOGdDYcobUBtgmprHR/+kMHYZ9bw5Y5sHpjYmRen9cHLfJ6igxnfQfY2GPq7iy5OeDKjwcioyFFsyNxArc359RSEcDVJEAhxETwTAqlJL0HX2twdihBt2pNPPhnWs2fPbmazud8111wTc/K+hQsX+sXGxvbw8vLqe8kll3Q+cOCA2U1hCiFE26AUXPYs1FbYpxo0kLfZxJxJXfnm9yPpHRnIE1/tYcDfV3D3R9tY+lMOqXllFFfWorUmt6SK1ftyeWHFQUY/vYZHvvyJ8ABPPrr1EmaPTTx7zYGTffcieIdCn183srO/GBU1itLaUrYf2+60cwrhLmdZEFQIUR9LYiBlG7OpPlyCZ3ygu8MRoln68IeM4BdXHozIK602h/lZau4Zl5g1fXCnAme2ERERUTtnzpyjS5cu9a+qqjqR9D569Khp+vTp8S+88ELGtGnTiu67776I6667Li45OXmfM9sXQghxmrAuMGoOrPob7FkI3a9o8FsT2vky75ZBbD9SxMLtWSzeeZSvdx49sd/DqKit+6VI9MCYIP51bW+GJ4TWnxgAOLYHDi6DMY+Ah9cFdet8hnQYgtlgZm3mWgZ1GOS08wrhDpIgEOIiWOICwADVKUWSIBDiLD78ISP4b1/t6VRttRkAckurzX/7ak8nAGcmCWbOnFkEsHnzZu+srKwTIwTmz58fmJCQUPXb3/62EODpp5/ObteuXZ/t27d79u3bt8pZ7QshhDiLYb+HvYvg6z9Ap+HgE9Lgtyql6BcdRL/oIB6Z2p1tGYUcLa4iv6yavLJq2vt50qOjP906+uPveY46A+ey8SXw8IaBt1xgh87P28ObgR0GsjZzLX8c+EennlsIV5MEgRAXwWAxYY7ypyqliIBL3R2NEK7zx8+Sow7klHrXd9yeoyU+tXX6lMc51Vab4fHFu2M+3XIk7Hzv7RzuV/H0tUlHGhPn7t27vbp3717x8/f+/v62qKio6uTkZEkQCCFEUzN6wBWvwpujYekcuOatizqNh9HAJXENTy6cV94B2PUJDLwNvIOdc86TjIocxZObniS9OJ2YgBinn18IV2lUDQKlVLBSarlS6qDja9A5jqtTSu1w/FnUmDaFaC4sCYHUZpZiq5CCNEKc7vTkQH3bna28vNwQEBBwyjpbfn5+dSUlJY2vSCWEEKJ+4T3tSx/u+hR2/8/d0cCyP9lHD4z4Q5OcfmTkSADWZq5tkvML4SqNHUHwELBSa/1PpdRDju/nnOW4Sq11n0a2JUSz4pkYSOnKw1SnFePVM9Td4QjhEg19sj9o7opeuaXVZxQFbOdnqVk4e/h+50d2Kh8fH9vpyYCysjKDv79//YtzCyGEcI4Rf4CDy2HRPRDeG0Li3RPHgW8hZTlMnAu+5x3EdtEifCNICExgXeY6ZvaY2SRtCOEKjV3F4Argfcfr94ErG3k+IVoMc5QfymykKqXI3aEI0ezcMy4xy2IynLLMh8VksN0zLjHLFe336NGjcs+ePSemQpSUlBiOHDliSUpKkukFQgjhKkYPuO5dUAb4dCbUuuG/YGuNffRASAIMur1JmxoVOYptx7ZRUlPSpO0I0ZQamyBor7X+ubRoDtD+HMd5KqW2KKV+UEqdM4mglLrdcdyWvLyGrZ0qhLsoowFLXADVkiAQ4gzTB3cqeHRq94x2fpYahX3kwKNTu2c4exWD2tpaKioqVF1dnaqrq1MVFRWqtraWX//610UHDx70fO+99wIrKirUnDlzOnTu3LmyLdYfUEpNUkrtV0qlOEb7nb7fopT62LF/k1IqxvVRCiFarcBouOoNyNkFyx52ffub/wPHD8KlT4KpaVe7HR01Gqu2sjFrY5O2I0RTqneKgVJqBRB+ll1/PvkbrbVWSumzHAfQSWudpZSKA1YppXZprVNPP0hr/SbwJsCAAQPOdS4hmg1LQiBV+wqwFlZhCvJ0dzhCNCvTB3cqcHZC4HRz5szp+Pzzz3f4+XsfH5/g++677+hzzz2XPW/evNT77rsv+o477ojr3bt3+SeffJLWlLE0R0opI/AKMAHIBDYrpRZprfecdNgtQKHWOkEpNQ14CrjB9dEKIVqtLpPsKxt89wJEDoI+N7qm3dIcWPMUxI+DxIlN3lyv0F4EWgJZm7mWSbGTmrw9IZpCvQkCrfX4c+1TSh1TSnXQWh9VSnUAcs9xjizH1zSl1BqgL3BGgkCIlsYzMZBi7MsdmgaeLY8mhGhKzz33XPZzzz2XfbZ9V155ZemVV16529UxNTODgBStdRqAUmoB9umBJycIrgD+6nj9GfCyUkpprSVRL4RwnrGPQtY2WPQ78GsP8WObtr06K3x2C9TVwOSnQDV9jVyjwciIiBGsy1pHna0Oo0Hq4oqWp7FTDBYBP1fhmAksPP0ApVSQUsrieB0KDOPUGxMhWixTO28Mfh5Sh0AI0VxFACcXlsx0bDvrMVprK1AMnLGumEwDFEI0itEDps2H0M7w8QzI3tG07a35B2RsgKnPQWhi07Z1kpFRIymuLmZn/k6XtSmEMzU2QfBPYIJS6iAw3vE9SqkBSqmfFzztBmxRSiUDq4F/nja0UYgWSymFZ0IQ1SlFaJs8bBNCtF5a6ze11gO01gPCwpqmCrgQopXzDIDpn4FXEMy/DgoONU07KStg/bPQZzr0+XXTtHEOQzoMwaAMbMja4NJ2hXCWRiUItNbHtdbjtNaJWuvxWusCx/YtWutbHa83aq17aa2THF/fdkbgQjQXloRAbOW11OaUuzsUIYQ4XRYQddL3kY5tZz1GKWUCAoDjLolOCNH2+HeE6Z/bh/6/NxVy9zn3/EVH4IvboV03mPK0c8/dAAGWAJLCkiRBIFqsxo4gEKLN80wIBJDVDERrZbPZbE0/cdPNHH201Xtgy7MZSFRKxSqlzMA07NMDT3bydMFrgVVSf0AI0aTCusDMRfYkwTsTIcNJVf+Pp8K7k6GuFq57H8ze9b+nCQzrOIw9x/eQX5nvlvaFaAxJEAjRSMYAC6Z2XlKHQLRKSqmcysrKVr9ER2VlpadSKsfdcTibo6bAbGAZsBf4RGu9Wyn1hFLqcsdhbwMhSqkU4H7gjKUQhRDC6Tokwa3LwScMPrgSfvqicec7thvemQS1FTBzMYR1dk6cF2F45HAANmbLcoei5ZEEgRBO4JkQRM2hYrS1NT6AFG2Z1Wp9PD093VxeXu7VGkcS2Gw2VV5e7pWenm62Wq2PuzuepqC1XqK17qy1jtdaz3Vse0xrvcjxukprfZ3WOkFrPejnFQ+EEKLJBcXAb7+1Jws++4191YGyiyiCmrYG3p0CBhP85hvo2MfZkV6QbsHdCPYMlmkGokWqd5lDIUT9LAmBlG3MpjqjBM/4QHeHI4TT9OvXb9m2bdtmp6am/kVrHU7rSyzblFI5Vqv18X79+i1zdzBCCNHm+ITArK9gw79h3dOQuhImPAG9bwCT5fzvLcyA5Y/CnoUQHA8z/gdBnVwT93kYlIHhEcNZm7lWljsULY4kCIRwAktcABjsdQgkQSBaG8cHZ/nwLIQQommYLDB6DnS/AhbfA4t+B8sfg57XQK/r7csUegaCwQClOfaaBYfWwY6PwGCEMY/A0Nng4eXunpwwPGI4i1IX8dPxn0gKS3J3OEI0mCQIhHACg6cJc5S/vVDhpe6ORgghhBCiBWrXFX6z1D6KIPm/sP1D2OxYOV0ZwOIHVcX27z18oMeVMO4vEBDhvpjPYWjHoSeWO5QEgWhJJEEghJMoHxPVewrIfGg9xkAL/pfG4NO3nbvDEkIIIYRoOQwGSJxg/1NVDCkroewYVBRAZQEExUKnIRDeG4we7o72nAIsAfQK7cWGzA3c3edud4cjRINJgkAIJyjfnkv1gcIT39cVVVP0xUEASRIIIYQQQlwMzwDoebW7o7howyOG8+qOVymoKiDYM9jd4QjRIK2t2JQQblGyLB2spy4brmtt9u1CCCGEEKLNGR4xHI2W5Q5FiyIJAiGcoK6o+oK2CyGEEEKI1q17SHfmDp/LkA5D3B2KEA0mUwyEcAJjoOWsyQBjYD3L8wghhBBCiFbJoAxcHn+5u8MQ4oLICAIhnMD/0hiUx6m/TsrDgP+lMe4JSAghhBBCCCEukIwgEMIJfi5EWLIsnbqialnFQAghhBBCCNHiSIJACCfx6dtOEgJCCCGEEEKIFkumGAghhBBCCCGEEEISBEIIIYQQQgghhJAEgRBCCCGEEEIIIZAEgRBCCCGEEEIIIZAEgRBCCCGEEEIIIZAEgRBCCCGEEEIIIQCltXZ3DGellMoDMhpxilAg30nhNBfSp5ZB+tQytLY+NbY/nbTWYc4KRrRuco1uFOl729WW+9+W+w6N679cn4VLNdsEQWMppbZorQe4Ow5nkj61DNKnlqG19am19Ue0bm3551X63jb7Dm27/2257yD9Fy2LTDEQQgghhBBCCCGEJAiEEEIIIYQQQgjRuhMEb7o7gCYgfWoZpE8tQ2vrU2vrj2jd2vLPq/S97WrL/W/LfQfpv2hBWm0NAiGEEEIIIYQQQjRcax5BIIQQQgghhBBCiAaSBIEQQgghhBBCCCFaZ4JAKTVJKbVfKZWilHrI3fE0llLqHaVUrlLqJ3fH4ixKqSil1Gql1B6l1G6l1O/dHVNjKKU8lVI/KqWSHf153N0xOYtSyqiU2q6U+srdsTiDUipdKbVLKbVDKbXF3fE4g1IqUCn1mVJqn1Jqr1JqiLtjEqK+a7FSyqKU+tixf5NSKsb1UTadBvT/fsc1cKdSaqVSqpM74mwKDb0PU0pdo5TSSqlWs/xbQ/qulLr+pPufj1wdY1NqwM99tOP+b7vjZ3+KO+JsCvXdryu7Fx1/NzuVUv1cHaMQDdHqEgRKKSPwCjAZ6A7cqJTq7t6oGu09YJK7g3AyK/AHrXV3YDBwdwv/d6oGxmqtk4A+wCSl1GA3x+Qsvwf2ujsIJxujte7TitYkfgFYqrXuCiTR+v69RAvTwGvxLUCh1joBeB54yrVRNp0G9n87MEBr3Rv4DPiXa6NsGg29D1NK+WG/vmxybYRNpyF9V0olAg8Dw7TWPYB7XR5oE2ngv/0jwCda677ANOBV10bZpN7j/Pfrk4FEx5/bgddcEJMQF6zVJQiAQUCK1jpNa10DLACucHNMjaK1XgcUuDsOZ9JaH9Vab3O8LsX+gSbCvVFdPG1X5vjWw/GnxVcAVUpFApcBb7k7FnF2SqkAYCTwNoDWukZrXeTeqIRo0LX4CuB9x+vPgHFKKeXCGJtSvf3XWq/WWlc4vv0BiHRxjE2lofdhf8OeFKpyZXBNrCF9vw14RWtdCKC1znVxjE2pIf3XgL/jdQCQ7cL4mlQD7tevAD5w3DP+AAQqpTq4JjohGq41JggigCMnfZ9JC/7g2RY4hpX2pYU/RXAMxd8B5ALLtdYtuj8O/wYeBGzuDsSJNPCtUmqrUup2dwfjBLFAHvCuY8jmW0opH3cHJdq8hlyLTxyjtbYCxUCIS6Jrehd6L3IL8E2TRuQ69fbdMbQ6Smv9tSsDc4GG/Lt3Bjorpb5TSv2glGpNI0Qb0v+/AtOVUpnAEuB3rgmtWZDPKKJFaI0JAtGCKKV8gc+Be7XWJe6OpzG01nVa6z7YnwINUkr1dHdMjaGUmgrkaq23ujsWJxuute6Hfajf3Uqpke4OqJFMQD/gNceQzXKgxddeEaKtUEpNBwYAT7s7FldQShmA54A/uDsWNzFhH2I+GrgR+I9SKtCtEbnWjcB7WutIYAowz/EzIYRoJlrjL2QWEHXS95GObaKZUUp5YE8OzNdaf+HueJzFMbx7NS2/bsQw4HKlVDr2YYJjlVIfujekxtNaZzm+5gL/wz4ksiXLBDJPGrHyGfaEgRDu1JBr8YljlFIm7MONj7skuqbXoHsRpdR44M/A5VrrahfF1tTq67sf0BNY47i+DAYWtZJChQ35d88EFmmta7XWh4AD2BMGrUFD+n8L8AmA1vp7wBMIdUl07iefUUSL0BoTBJuBRKVUrFLKjL0AyiI3xyRO45hn+jawV2v9nLvjaSylVNjPTwCUUl7ABGCfe6NqHK31w1rrSK11DPbfo1Va6+luDqtRlFI+jsJYOIbhTwRa9OogWusc4IhSqotj0zhgjxtDEgIadi1eBMx0vL4W+/8xLb52i0O9/VdK9QXewJ4caE3z0M/bd611sdY6VGsd47i+/ID976A1rCrTkJ/7L7GPHkApFYp9ykGaK4NsQg3p/2Hs1ymUUt2wJwjyXBql+ywCbnasZjAYKNZaH3V3UEKczuTuAJxNa21VSs0GlgFG4B2t9W43h9UoSqn/Yr+YhDrmbP1Fa/22e6NqtGHADGCXY94+wJ+01kvcGFNjdADed1TwNWCv0NsqlgVsZdoD/3PUQTMBH2mtl7o3JKf4HTDfcUOWBvzGzfGINu5c12Kl1BPAFq31IuxJ4nlKqRTshb2muS9i52pg/58GfIFPHf8nHdZaX+62oJ2kgX1vlRrY92XARKXUHqAO+KPWulWMnGlg//+AfVrFfdhrAs1qLYnBs92vYy9ajdb6dew1F6YAKUAFcq0WzZRqJb+TQgghhBBCCCGEaITWOMVACCGEEEIIIYQQF0gSBEIIIYQQQgghhJAEgRBCCCGEEEIIISRBIIQQQgghhBBCCCRBIIQQQgghhBBCCCRBIIQQQgghhBBCCCRBIIQQQgghhBBCCOD/AVqSX2F1r2v4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAFgCAYAAABEyiulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi41LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvSM8oowAAIABJREFUeJzs3Xd4VFX6wPHvnZbeeyWEQAKE3kKTjnRBRRGxIEWxYMO266I/+6prQWyAFVFAEQHpAtJraAFSSS+E9EzKTDIz9/dH0FVXJcDM3JTzeZ558uxk5pw3K5k35973vEeSZRlBEARBEJoXldIBCIIgCIJw5UQCFwRBEIRmSCRwQRAEQWiGRAIXBEEQhGZIJHBBEARBaIZEAhcEQRCEZkgkcEEQBEFohkQCFwRBEIRmSCRwQRAEQWiGNEoHcDm+vr5yRESE0mEIgiAIgl3Ex8cXy7Lsd7nXNfkEHhERwbFjx5QOQxAEQRDsQpKkrMa8TlxCFwRBEIRmSCRwQRAEQWiGRAIXBEEQhGZIJHBBEARBaIZEAhcEQRCEZkgkcEEQBEFohkQCFwRBEIRmSCRwQRAEQWiGRAIXBEEQhGZIJHBBEARBaIZEAhcEQRCEZqjJ90IXBME+6i9coPbUaUwXL2LWV2LRV2Gp0iNptWj8/H596CIj0YaGIkmS0iELQqsmErggtFL1Fy+i37yZmmPx1J46henixV+/JwNmFy/qvQKxmCxI5SVozEbUZiMqSx3aoCBc+vbFOS4OlwED0Ab4K/eDCEIrJRK4ILQiFqORql27KF+7lup9+8FsRh0egbH3aCr8OlMs+6CvkqjRmzDVW/50DK3agrulFJfUJFwPfo/Hi2/h178rXtOm4TJwAJJK3JkTBHsQCVwQWgGLwUDZiq8pWboUc3k5qsAgDLc+Rq5zJwpyjNTXmCELvIN1BES64OLp0PDwcECtUVFvNFFvNFNnMKMvMVCU40mexh+z/yAA3PW5+P/fSoK17xB86wS8brsNlYODwj+1ILRsIoELQgsmm0yUr11L8eL3MRUWoho0ipKeN5GWraY6vw5nDxPR/QIJ7uBJSAcvnN11jR7bYrZQdqGG7HOlpB51Jc05lDTAc3MK7b57kI7zpuA+bqy4Vy4INiLJsqx0DH+rd+/e8rFjx5QOQxCanZpjxyj410LqMjJQde9HbtwsUlJMWGSZ8E7edB4cQkQXH1Rq61zyLr9YQ9qxQhJ+yqSmRsa9MpMOqiS6PH4bzj16WGUOQWgNJEmKl2W592VfJxK4ILQsFqORonfepfTzz1GFRVAy6XHOpGkw1VnoNCiYnqPDcfd1stn85noLiQfyOLY2iWqDGjd9Nn1iqol5ag6SrvErfEForUQCF4RWqPbMWfKffoq6tPMYptzHKUsvqivqaNvNl/5T2uEV6GK3WMxmC8l7sjn4bSIGs5Y2tQkMeWYibh2j7BaDIDRHIoELQitTumIFha++Bj4B5E16luRUC97BLgy5rQPB7b0Ui6uu1sSexbtJTpNxqKukXy+J2Acmi3vjgvAXRAIXhFZCrq+n8NVXKfv6G+qH3shp33FUFBvpNjKMuBsi0WjVSocIQO6xTHYuOY5e5UmkUx6jXrkFjZOoVBeEP2psAhcbNgWhGTNXVJA9dy6lX39D2c1Ps081ArMZbnikO4Nubt9kkjdAaO8Ipi+eRAevItJrQ1j98A9U5JQoHZYgNFsigQtCM1WXk0PmrdOoij9B7oy3OFEcRptYX6b9qy+hMd5Kh/enNDoNo169lcFdq6i0uLHqhYNk7ktVOixBaJZEAheEZqguM5OsGXdgqKwlZeoiUnMd6DEqnLH3dcHBWat0eJfV9f5JTLrZA12dno3LszixOl7pkASh2RGNXAShKTPVQVVhw0N/AYx6jEXVZD+3lGqNF2cGP4H+gpnhd8bQcUCw0tFekeAxA7k53I9NL+/gwM5oamuO0v+u3qK4TRAaSSRwQWhKKnIh6wBk7YfM/VDy+8vLxgoNWbt8qHbw52TXWZgrq7jB9x2Cz2mgvDsE94QO14Nz07yE/keunTow6SUdm59Zw4lDPTDUHmHovX1RqUQSF4TLEVXogqC0mlI4vRpOLIfCMw3POXhAeByW4B5U6fwokbwozKnF9c1F1Dj6c7LHfGRU9BmcTbTuHM4lZ5AuJEBdFag0EDkMYm+CmHHg6KHsz9cIxsxMflqwnEzfgbRt78T18/uh1oo7fELrJLaRCUJTl3UQjiyBpB/BXAfBPamOnsJJVWd2lPlxKKOClEI9JouMb205b+9+D4OTP6e6PYRFUrHK1UiJuuH3181RQ3s/Z4Z5XGCc6jBtL2xBVZkDagfoeQcMfAQ8wxT+gf9eXVYWux/5kJSA0YS3c2LcY/1QW6nNqyA0JyKBC0JTVXAKdrwAaT+BoyfGzrew3XE0y1KcOZlTDoCjVkXPcC+6hnoSrjMR++8nMVRLnOq9AEmnoeed0Wi9HKg01HO+qIrUwipSL+o5l19JpcGEVg3Tg4u4Xfcz7Qt+RALoPh0GPwZeEQr+8H+vLiuLvQ8tIil4PJGxHlx/f09xOV1odUQCF4SmpuQ87HoZzqwBR09yYufxbuVQNpwrw2iyEB3gxsRuQfRv50OXEE90GhWWujpyZs+h5FwmJwc/h8rBgcmP9sAzwPlPpzCZLRzPLmdHYiE/JRZyvqiaCE0prwbspF/5RlQWE8TNg2H/AJ392qpeCUNiInseX0pq+ASi+/gxYmYskkjiQisiErggNBUWMxx8H3a+BCo1BR1nsrB4ONvTjbg7arihewhTe4fSJcTjdxXYssVC/oInKNm2m1PXv4ZR1jFlQU98gl0bPXXShUq+OJDJ98fz8DCV8KrPRkZUbwLPcJjwNkSNtMVPfM2q9u5j9yvryGwzltjBQVw3PUZUpwuthkjggtAUFKfBD/Mg9wiVbUaz0DSTH87L+LrquG9IO2bEtcHxL7qlXXzrbS4u+4wzY1+j3OjMpIe7E9ze86rCKK2u45sj2XxxIJPwqlMsdvucwLps6DIVxvwbXHyu5ae0ibLv1rDv03iyw0fSe1wb+k1qp3RIgmAXjU3gYhuZINiCLMPhj+Gn55A1jvzQ9nkeT2qPu5OGp8a0464BbXDW/fWvX+XWbRQtXUbyqOcorXFkzNzOV528AbxddDwwLIqZAyP4eHc4o/a0Zw7reODMWqSsA6huWQ6hva56fFvwuvkm+uXlU79zP8c2gYe/MzFxQUqHJQhNhliBC4K11dXA+gfhzBpKgodxT+kMTpU7MSMunCfHxODu+Ped0oxpaWTeciupXe8my7EzQ27rQOyQUKuGmF9eyxtbk0k5uY9lju8SIJWjGvsa9L4HmtClalmWyX38SfbmRVLhE80Nj/ZQ9GQ1QbAHcZiJICihLAs+HY185ns2+t9Lr/TZ6LW+rL63Py9N7nLZ5G3W68l98CHygweS5diZHqPCrZ68AYI9nXj71u4snHMbM3VvssfUETY+hmXtvIY/QJoISZIIeen/6GXYhVPNRTZ9cIrywqYTnyAoSSRwQbCWjL2wdBjm0iz+4bSQh3KG8MCwKDbNH0zftpfvjCZbLOQ//QxF5WqSwicT1smbuCm2ve/bL9KH1Y+OY0Pnd3jHdCOcXonxk/ENzWWaCJWzM20XvUG31M+xVFfz4+KTGKrqlQ5LEBQnErggWEPCd7B8Mnq1JxMM/8e2ui4sn9WPJ66P+csitT8qWbqMkr1HOdfnYVy9HRk9q7Nd9kC7O2r5z609aX/LKzzO41CYQM3Ho6Ey3+ZzN5auTRuiXnqKLic/QF9Uw9alZ7BYmvbtP0GwNZHABeFaHfsMec1sMp27MKDoGZyDYtg4fzADo3wbPUTtyZMUvvcBiYOeoF7SMm5eVxxd7Huq2PiuQTw2/3GedXkOc3kuVR+MaKiibyLchg0j6o6xdEj6mtzkMo7+mKF0SIKgKJHABeFa7H8XfnyE0059ub74YW4e2ImVc+MI9HBs9BDmqirynniS87G3U2rxZsRdnfAJafxeb2sK83Zm4UP38VbwWxhqq6j6aAT1uScUieXP+D5wP+2j1ARdPMyxTZlkJhQrHZIgKOayCVySpJslSVojSVKWJEm1kiQlS5L0qiRJbo14r/wXj+7WCV8QFCLLDY1Zti9kr8N1TC1/gGdv6MFzEzujvcL+3YUvvUye0Zccz150HxVOVC9/GwXdOG6OWp6dcxtrui2jvF6D8dOJGHITFI3pF5JKRfBrr9KxcAtu9UVs//QslcW1SoclCIpozCfNAsAM/AMYA3wIzAO2S5LUmPd/DvT/wyPlaoIVhCbj59dgzxts1I5iTvV9LLq9L3f0j7jiYSo3beLC5j0kx87Ev40bcTdEWj/Wq6BWSdx74/WcGLacKrMGw6cTqS5IVjosADS+voS98gKx8e8hG4xsWXIGU71Z6bAEwe4ak4AnyrJ8iyzLK2RZ3i3L8jvAfKAfMLQR78+TZfnQHx5iH4jQfB38AHa/xnppOM/Uz+bLWf0ZE3vlDUbq8/LIe+4Fkvo8ADodo2d3Rq1pWne1Jg4dwNmRX2I2m6heOp7KC5lKhwSA65AhBN88lo6nl1KUrWff6tTLv0kQWpjLflrIslz0J08fvfQ1xLrhCEITd+Ir2PoMO6Q4XlLdx3fzBjZqi9gf/bJlLCNwGGWaQIZOj8bD788PKFHaiMHXkTzqSxzN1VQuGUdpYY7SIQHg/8QCQryNRBTt5ezefNJP/tlHlSC0XFf75/6QS18TG/HaeZIkGSVJqpEkaackSYOvck5BUNa59cjrH+KQ1I1nVQ+zYu4AOgRcthTkT5WvWkVeajkZISOJiQukQ99AKwdrXQMGDef86M/wNhdT+vEkysuV3yeucnAg+D9v0jZtHR6UsWt5EtUVRqXDEgS7ueIELklSCPAC8JMsy5frcfoVcD8wEpgL+AA7JUkaepk55kqSdEySpGNFReKvaqEJyNyH/N0sztCex6Qn+HzOYNpfZfKuz8sj/63FJHabg4e/M4OndbBysLbRY+AYMoe/T1tzBskfTKfGWKd0SDh26EDAo/OJOfIe9bV17PgiEVnsDxdaiStK4JIkuQLrABMw83Kvl2X5DlmWV8myvFeW5a+AQUA+8NJl3rdEluXesiz39vPzu5IQBcH6Ss5jWTmDLNmP+3maZbOHEB14dclblmUKnnuelLAJGFUujJrZGZ1j8zlTqNOQqaR0f4Z+dQfZsfhB6kwWpUPC+4478I0Opn3mOnLOlXJ6V67SIQmCXTQ6gUuS5ARsACKB62VZvuLfElmW9cBGoM+VvlcQFFFbjnnFreiNJuZZnuLD2SPoFOx+1cNVrP2B7HOl5Pv3o8focALaXv1YSuk4+UnOh09lon4V3yz7N2aFV7ySWk3Qyy8TnLeHAKmAg2vPU5JXpWhMgmAPjUrgkiRpge+A3sA4WZavdVOouMYlNH1mE5Zv70YuzWBe/aP8647xxIZ4XPVw9RcvkvvGOyTH3o1XkDN9JrS1YrB2JEm0u+tD8jz7MK3gTT79egVKn2roENkW//kP0X7fO2jVZrZ/ehZzE7g6IAi21JhGLipgBTAcmCzL8qGrnUySJHdgAnDkascQBHuRtzyNKn0X/6ifyS03TWPAFbRG/TOFL75IStBYjGoXRtzVCU0je6Q3SWotIXNXU+0UxJTUZ1i5U/lfae+778a9YyQxiV9RkldN/JYspUMSBJtqzAr8fWAq8B+gWpKkuN88QgEkSWojSZJJkqSFv7xJkqQFkiQtlSRpuiRJQyVJugvYDwQC/7TBzyII1hP/BdLRpSwxjafNyPuY3OPadkzqd+0iI76AAv++9Ly+DQERze/S+f9w9sZz5ne4qeqI3D2fnxOVPfxEUqsJfuVlfAriCVXnEr8pk+JccSldaLkak8DHXvr6T+DgHx6zL31PAtR/GC8Z6AQsArYDbwEZwCBZlvdec+SCYCsFpzBvXMAecxeyez7J/UOv7UhPi8FA3itvktz5TryDnOkzvpleOv8TqoAY5Alv00+VRMrKf5B2Ua9oPA5RUfjeP4+2uxeh08ns/DIRi1lcShdapsY0comQZVn6i8fzl16T+dv/fem5DbIsD5Rl2VeWZa0syz6yLE+SZVn5a22C8FcMFRi+voMisysrw/7F8zd0RZKu7UjPkiVLSHbohVHtyrA7O6LWNq1ua9fKsdd0qmNvZ660liWffExZtbLby7xnzcIlzJ/ozLUUZes5sT1b0XgEwVZa1ieJIFwLWcbw3X1o9Dm84PgEr8wYhuYKDyb5o7rMTDK+2Upu6BBih4QQ2Pbqi+CaMpcb/kONVwzPGN7h2S+3YFJw1avS6Qh87jl8kn4i1K2CIz9mUFpQrVg8gmArIoELwiWmA4txTNvEW5bpzJ85A09n3TWNJ8sy+S++TFLUrTi7aombfG2X4ps0rRPOt6/AVWNmZsGL/GfrOUXDcYnrh/ukiUTseAOtRmLnl6LBi9DyiAQuCICcfRhp+3NsNfcm9uZ/EhN47UVm+q3bSMnUoHcJZdC0aBycmk/DlqviG4V28nv0VqWg3v8OOxILFQ0n4MkncdSY6Vi5h8KMSs7uU7bIThCsTSRwQTBUUv3NTPIt3iT1e41xXYOveUhLTQ1Zb75PersbCO/kpfgZ33bT5WbMnW7kEe33LFm1ltwy5Q4e1Pj64v/YY3gd+IYAbxOHfjhPTaXy7V8FwVpEAhdavfK1j+NUk89ngf/gwXG9rTJmyaefkegxBLQ6rrst5poL4ZoT9YT/gIsfL8vv8ciKw4q2W/W8ZSpO3brS7tAH1BvN7F8jjh0VWg6RwIVWzXh6LZ7Jq/lcdSPz7rgdteraE219YSEp3+2lyK8HvSdE4uHnZIVImxFnbzRT3idKymXMhY95ZVNjDi20DUmlIvBfC3EsSKGDWz4phwvJTVL+JDVBsAaRwIXWS38B07qHOW1pS8y0l/Fzc7DKsIX/eYeU8Btw99LSY2S4VcZsdqJGQp/ZzNZsJvnQRn46p9z9cKfYznjcOIWAzW/h5qlh9zcpmOvF3nCh+RMJXGidZJnC5bNQmWo52vM1BkYHWWXY2oQEzh6voMY5gEG3tbw931dk1AtYvNvxjuMSXvjuEEV65c7q9n/0UbQaic5lOykvrOHEdtFmVWj+WvGni9Calex6n4CL+1juPoe7Jo62ypiyLJP92jtktp1AWLQHEV18rDJus6VzQTXlI/wtxcwxreDpNacVO/RE4+uL7/3347x7FRHhEsc2ZVFZXKtILIJgLSKBC62OqSQT5z0vsp9ujL/n2Wtu1vIL/datnNVHYNY4MLiVFa79pbC+SH3nMEO1jbLkfXxzJEexULzvmIGuTRsiDnyIpIL9a9IUi0UQrEEkcKF1kWXyl8/BLEPt9W8R4uVslWEtdXWkLvqKgqABdB0Whlegi1XGbRFGLAT3YN51+YzXfjxNRrEyXdEknQ7/Z55GlZZAp4BS0k8UiYI2oVkTCVxoVXJ3LSW8/AgbA+Yxsr91towBlH39DYkeQ3B0kugzMdJq47YIDm5I4/9DmCmLueoNPLLqpGKtVl2HDMFl8GB81r2Jm5eOvatTxWEnQrMlErjQahhKc/Hc8zzHpU5cf9fTVhvXrNdzduU+Kjza0X9qTMvvuHY1osdCp8nMU61Bn3uOJXvTFQlDkiQCnnoSqaqCzuoESvOrObMnT5FYBOFaiQQutA6yTPaX96GR66gb/y6eLo5WG/riss9IDRiJl4+GmP7WqWZvkca+jlrnzEceX/LuT8mcL1LmrG6HqCg8b7oJp3UfEhLhxJENGdRWiQ5tQvMjErjQKqTtWk6H8r3sDJ5DXO++VhvXVFREwrbzGJz8GDS9MyorNIJpsdwCYNSLdDCc5hbNPp767jQWhQ4Y8X3oQVQaDR3yN1FnMHN4fYYicQjCtRAJXGjxDPpSvPc8S6IqiiF3PmfVsfPfX0JG8EhCIpwI79zKt401Ro87ILQP/9StIjkrjy8PZioShtbfH5+ZM5G2fUfHzjrO7s2jOFeZKwKCcLVEAhdavLPLF+AhV1I35j+4OFnv0nldVhanjtVg0jozaEas1cZt0VQqGPcGDnWlvOm3ide3JpNTqsyBJz6z7kHt60vo/mU4OGk48L3YViY0LyKBCy3a+ZN76FH4PYd8b6Rb36FWHTvz7aXkBl9Hh+5e+Ia6WXXsFi24B1KvuxhdtY72ZPPM9wmKNHhRubjg9+CDmI4fokv7OnLOlZJ9tsTucQjC1RIJXGix6uvrsWx4lFLJg9gZb1h1bENiIqez3ECtpv+tnaw6dqswfCGSgxsf+65mX1oRa44rUwnuefNN6CIj8V73Nu6+juxfk6bYfXmheTIVFSFblNmKKBK40GIdXP0G7c1p5Pb7Fx5e1r0/nbroKwoD+tBtaDCuXta7LN9quPjAiH8RWHqUB/0TeHVTIuU19q8ElzQa/B9/DFNmOl0DL1KaX03SgQK7xyE0T7Iskz5hIhdefFGR+UUCF1qk7KwMeqQsItGpF93HzLLq2LUJZzhTHIhObabXpA5WHbtV6TUTArvwsPlz6mr1vL41WZEwXIcPx6lbN5y+fYfAtm4cXp9OncGkSCxC82IuLcVcUYFD27aKzC8SuNDiyLJM1spHcZDq8Z/2Hli5J3nS4pWU+nSm55gI0bTlWqjUMO5NtNUXeL/NXr45ks2J7DK7hyFJEn6PPoq5sJBYpxRqKus4sT3b7nEIzY8xtaHwUdeunSLziwQutDh7d/zI4NpdpLSbiU+bzlYdu+bECc5WtsFJa6Lb9aJl6jULj4POUxhc9A2dXat49oczirRZdYnrh8uAAUhfL6ZdN29Obsumuly540+F5sF4viGBO0S1V2R+kcCFFqWixojfvoUUq3zoNPV5q49/dvH3VHi0o8+kKDQ6tdXHb5VGPo8km/kweAtn8ytZfkiZs7r9Hn0Uc3k50TWHsZhljm4UzV2Ev1d3/jwqNzc0/n6KzC8SuNCi/LzyLTqSTu2Q51A5ulp17OqjRzlnbI+LQz2dhkVYdexWzSsC+s4lNGstMyIq+M+2FAorDXYPw6lLLG6jRlG3Ygkd+/pwbn8B5YXK7FEXmgdj2nkc2rVT7OhgkcCFFuNseg4Dsz4gy6UrYdfdafXxT3+4kSrXUOKmdkStEb86VnXdAiQnT/6pWUGd2cy/NycpEobfIw9jMRhom7cdtVbF4fXKHLoiNA/GtDR0Ucrc/waRwIUWwmKRSfn2ObwlPd43v2X1wjX9wcMkmqLxcK6nw4Awq44tAE5eMORpnHL38UpsAd+fyOO4AgVtDu3a4XHDDdSu+pIu/bxIi7/IxaxKu8chNH2m0lLMpaU4REUpFoNI4EKLsPHnvYyv+YHs8Btxa9vH6uOfXPYTtc4B9L+tiziwxFZ63wPekdxY9BFBrhr+b8M5RZqq+D7wALLFQmjyehxdtBz64bzdYxCavrrzDf8uHNqJBC4IV628pg6PPc9hUjnQZuqrVh+/6thxUkzt8XYxEtlbHBdqMxodjHoBVUkK73c6y6mccr4/Yf8ObbrQEDxvuonq71fTfaAXOYll5CSV2j0OoWkz/pLAxSV0Qbh6P3z/NddxnKp+jyK5BVh9/BNLtmFw8iXutq6KFau0GjETIKwfPTKW0DfUiX9vSaLKaP+mKr73zkUCAk58h6uXA4fWnlekX7vQdBlT01C5uKAJDFQsBpHAhWYtqaCcPilvUaYLwn/EfKuPX3UygZS6dng7G4joFWz18YU/kCQYsRBJX8DbbY9SpDfy/i77nxKmDQ7Gc+pU9GvX0GuQJxez9KSfKLJ7HELTZTx/Hl2UchXoIBK40IzJssyu1e/RWZWFdvTzoLV+T/KTS7ZicPQRq297ihgE7UYQcuZDbu/mySd7M8gqqbZ7GD73zkVSqfA68A1egc4c3pAhDjoRfmU8n6bo/W8QCVxoxrafzuKG0k8pdu+Ma89brD5+9bkkkmoi8HaqIaJ3iNXHF/7GiH9BbRn/8PwJjVriNQW2lWkDAvC89VYqf/iBHnFulBVUk3q00O5xCE2Pubwcc1ExDgq1UP2FSOBCs2SoN5Px4xsES6V4Tv43qKz/T/nEh1swOnoTN62LWH3bW3AP6HQDLsc/5pE4LzafuUB8lv0LyXzmzEbSaHD/eQU+oa4c/TEDswKtXoWmpSkUsIFI4EIz9fXOeKbXraEkdCSayMFWH78mNZ0kfSjejtVE9A23+vhCIwx7Fky1zJTX4O/mwMsbE+1eSKb198dr2jQq16+jVz8XKopqST50wa4xCE2PMe2XBN7EL6FLknSzJElrJEnKkiSpVpKkZEmSXpUkya0R73WUJOkNSZIKLr33oCRJ11kndKG1ulhpwOHAmzhLRnwmv2aTOU58uLlh9X2bWH0rxq8DdJ+O9vhn/GuQK8ezy9l8xv7J02fObCSdDucdK/CPcOfoxgzM9WIV3poZz6chOTujCVJ2W2ljVuALADPwD2AM8CEwD9guSdLl3v8JMAdYCEwACoCtkiR1v+qIhVbvy407uIWfqO5yB/ha/xQgY24+SaX+eOv0RPRtY/XxhSsw5GkAxpevIDrAjX9vSaLOZN/kqfH1xevWW6jcsJ5e/V2pKjVybn++XWMQmpa6tDQcIiORbHDr7ko0ZvaJsizfIsvyClmWd8uy/A4wH+gHDP2rN0mS1A2YDjwqy/JSWZZ3ALcA2cAL1x660BolX9ATfW4RslqH+/XP2mSOkx9uxODoQ98bY8TqW2meYdDrblSnvub561zIKqnhKwVOK/O+ZxaSWo3j9hUEt/fk2KZM6uvMdo9DaBqMaecVv3wOjUjgsiz/2ebHo5e+/l1p7iSgHlj1m7FMwErgekmSHK4gTkEA4Osf1jNRfQhzv3ng6m/18etKSjmb646HqpLIIR2sPr5wFQY9CpKauNzPGBjlw6KdqVTU1ts1BG2AP55Tp1Kxbh29BrhRU1nHmd327xInKM9cWYnp4kXFC9jg6ovYhlz6mvg3r+kMZMiy/Mfz+M4COkD5P1+EZmVvahHD8z7CoPHAacgjNpkj4cON1Dr50Xt8W7H6bircg6HX3UinvmHhQGcqauv54Gf7N3fxmTMbCdBu+5oDYswWAAAgAElEQVTQGC9ObMui3ihW4a3NLxXoOoW3kAForvQNkiSF0HAJ/CdZlo/9zUu9gT87Tqj0N9//qznmAnMBwsNFBbAAZovMhh9W8br6NKahL4Cjh/Xn0FeRkKrBzamS6LHDrD6+cA0GPQrxnxOd8jGTu8/h8/2Z3DOwLQHu1m/eY7KYKKgqoNhQTFFNEcW1xZQaSjGajXQc1Jbwb1eTuEDCLWkQ7634Ck33Cpw0Tvg4+RDoHEiASwCBLoG4aF2sHpugPGNawx+PTeES+hUlcEmSXIF1gAmYaZOIAFmWlwBLAHr37i1aHwmsic/hNv1n1LoE4tRvrk3mOLN0E9VOAVw33AVJnDjWtLgHQe+ZcGQpC+54kA2nZBbtSOXlKV2uadiLNRc5XXSacyXnyKjIIKMigyx9FibL7/uvS0g4qB0I7Kri1d0WPH/eQF5QEB4nAvhCWkydyvA/Y/s5+dHBuwPRXtHEeMfQ1a8rIa6iIVBzV5d2HsnREW2I8v8tG53AJUlyAjYAkcAQWZZzL/OWMuDPSnh/WXmL432ERqmtM3N063JuUaUhj1wEWierz2ExGjl1uh5nXSWdbxpq9fEFKxj4CMR/TsjpxdzWdx7fHMlm7nWRtPFp/Eo3uzKbA/kHOHrhKKeLT3OhumFbmlpSE+YWRluPtgwNG0ob9zYEOAfg4+SDr5Mvng6eqFVqAPLznmXohh9xeXAU65elszTkOzqPCKS4tpgL1RcorC6koLqA9Ip0kkuTOVxw+Nc/CEJcQ4gLiqNfUD/iguLwcvSy/v9Pgk0Zz59vEhXo0MgELkmSFvgO6A2MkmU5oRFvOwtMkSTJ+Q/3wTsBdYD9b2IJzdJn+9KYU7eCWs9InLrfbpM5Ej/fht4xiAFxalRq5X8xhT/hHgS9ZsKRJTwy8yG+jZd4a3sK707r8ZdvqTfXc6jgELtzd7M/bz+5VQ3rjiCXIHr49aBLpy509etKjHcMDurG1dX6zp1Lxdof0O1YTVin0Zzcnk2XIaGEuIb86Qq73lzP+YrzxBfGc7jgMNsyt7EmdQ1qSU2/oH6MiRjD8PDheDhY/7aQYH3G8+dx7tNb6TCARiTwS3u9VwDDgQmyLB9q5NgbgP8DpgJfXBpLA9wKbJNl2XhVEQutSnlNHTl7vqSDKg+u/wLUV1y2cVmyLHPyUDmOajNdZky0+viCFQ16BOI/w+f4e9wz8CE+3H2e+4a0o2OQ+68vqTfXc7DgINsyt7EzZyf6Oj1OGif6Bfbjzs53MiB4AOFu4VddpKgLD8d9/DjKVq2i56e3se6jUs7szqPH6D+v19GqtcR4xxDjHcPtHW/HZDFxruQcO7N3siVzCwsPLOSFQy8wOGQw02KmERcUh+qyLTYEJZj1ekwFBYofYvKLxnwavk9DEn4ZqJYkKe4338uVZTlXkqQ2wHngBVmWXwCQZfmEJEmrgHcureAzaGgA0xawzTJKaHE+2pnMvfK3GHw749hxkk3mSF+zm3KHEHp3NKDRqG0yh2AlboHQ8y449gn3zXmcrw5peHNrMp/c3YeUshTWpq5lY/pGyoxluGndGBY+jNFtRtM/uD86tc5qYfjOnUvl+g047PmBsE6DObE9i9ghIWgdLv/vR6PS0NWvK139uvJwz4c5W3KWzRmb+TH9R3bl7CLCPYJpMdO4od0NuOpcrRazcO2MyckAOMZEKxxJg8Yk8LGXvv7z0uO3/g94HpAANf+7LW0mDYn/JcATOAWMkWX5+FXGK7QieeW16A9/SYS6EEYvssmBJQDHt2ajlb3oMXuUTcYXrGzgfDj2Ke7HP2DWdTNZfGQ1k75/nQx9EhqVhuFhw5nUbpLVk/ZvOURF4TZqJKVffUWvT2/mh8WlJOzOpefoK+vcJ0kSsb6xxPrG8nDPh9mauZWVSSt57chrLD6xmBmdZnBHpztw17lffjDB5gxJDQncISZG4UgaXDaBy7Ic0YjXZNKQxP/4fC3w2KWHIFyR97ae4SHV9xgDe+LQ4XqbzJG76wQX1aF0CSlD52L9LUmCDXiEcqHLFFad/57VvgdwDNKTXxHCk/2eZELkBLsVhvncex/67T/hcPBHwjr1abgXPjQUre7qruLo1DomtpvIxHYTOVN8hk8SPuGjUx+x4twK7uh8BzM6zsBNd9kjKAQbMiQlovbyQuNv/SZSV0PcaBGapNRCPbrTKwiRinEY+SzYqKlK/HdnUJuN9L53hE3GF6wrpSyFJ/c8yRj9ET51d6av2pVbQ1+lOPlB2unG2rWq2ym2My6DBlH6+Rf0GhlMrb6ec3ut0yM91jeWt4e9zbcTv6V3YG8+OPkBY78fy6qkVZgtonmMUoxJyTjERDeZJk8igQtN0tubT/Og5gfqQ+Og3XCbzFF8OoNcUxDtvMtw9ve0yRyCdZwpPsP8nfO5af1N7M7Zze0dZ7DJuTtvp51hQe9+BHk48db2FLsfN+p7372YS0pwit9GSLQnx7dmYbJij/QY7xgWDV/EygkrifaK5qXDL3Hbxts4efGk1eYQGkc2mTCmpOAY01HpUH4lErjQ5BzPLiMo9Wv8pTK0I/9ls9X3kS8OIckW+syy/nnignWcLT7LfT/dx20bbyO+MJ77u93Ptpu38USfJwgZ8g+or8bx+FIeGBbFsawy9qQW2zU+5969cerVi5JPP6X36FBqKus4t7/A6vN09unMstHLeGPIG5QaSrlj8x38c98/KTeUW30u4c/VZWYi19X9TwHbzyuSSDmizBnxIoELTc57m0/xgHY95ojrIGKQTebQ5xSRpfemjWMBnh3CbDKHcPXSK9J57OfHmLZxGmeLz/JIz0fYdvM25nWf99/90gGdIGYCHP6IW7p4EOKp3CrcVFCAS+JegqI8OL41yybnhUuSxJiIMayfvJ7ZXWazKWMTU9ZPYU/uHqvPJfwvQ2ISAA6/WYHXG82c3ZdPeeEfj/ywD5HAhSblQFox7bNX4k0l6hH/stk8R5buxiKp6XN702jIIDS4WHORhfsXMmXdFPbn7Wdet3lsvnEzs7rM+vPe4oMfB0MFuuOfMn9EFKdyytmVfNGuMbsMGoRjp06ULltG7zHhVJcbSTxo/VX4L5y1zjzc82FWjl+Jl6MXD+x4gIX7F1JVV2WzOQUwJichabU4RLb99bniHD3I4NdGmV0CIoELTYYsyyzaeop52o2YI4dBWF+bzGOoqCGtwJlgOQf/fp1tModwZQwmA0tOL2HC2gn8mP4j02Oms/mmzdzf/f6/3wsd0hMih8GhD7mxqy/h3s52X4VLkoTP3DnUZWbikXOcgLbuxG/JxGyy/ir8t6K9o1k5fiWzu8xm3fl13LT+Jk4VnbLpnK2ZITEJXfsoJK321+eKcvQA+IUpsztAJHChydiZdJGu+d/iRSXqoc/YbJ7jn/6MSe1Ij3HKHwfY2smyzJaMLUz6YRLvnXiPgcEDWXfDOp7q+xTejn95YOHvDXoEqi+iTVjF/BHtOZNXybZzhbYN/A/cRo1CFxFB6bKl9B4XQVWpkeTDtr8vqlPreLjnw3wx5gskSeLuLXfzdeLXdr+N0BoYkpNxjP79/u+ibD1OblpcPG3Tb+ByRAIXmgSLRWbx1tPM027CEjkcwvvZZB5TvZlz50x4G7Jpc4MoXlNSekU6s7fN5ok9T+Cuc+fT6z/l7WFvE+Z+hTUJbYdAUHc4sIjJXQOI9HXh7e0pWCx2XIWr1fjMnoXh7Fl8KpLwC3cjfksWFrNtV+G/6O7fnVUTVjEoeBCvHnmVp/c+TU29MvdlWyJTURHm4mIcO/5vAvcLd1dsW5lI4EKTsPnMBXoXfY8XFaiGPm2zec6uOohR7Uq3/l5NZi9na1NrqmXR8UXctP4mEksTebbfs6yasIo+gX2ubkBJaliFl6ajSd3Eg8OjSLqg56dE+67C3SdNQuPvT+nSZfQeF0FlUS2px+x3P97DwYN3h7/L/B7z2ZK5hekbp5NdmW23+VuyXzuw/WYFbqozU1pQg1+4cu1uRQIXFGe2yHyw7RT36zYiRw6z2epbtsic2leEa+0Fou+yTWc34e/ty9vHlHVTWJqwlHFtx7Fh8gZujbn116M6r1rHSeDVFva9w6SuQUT4OLNoZ6pdLyWrdDq8Z86k5vBhAsnDO9iF+M2ZyHa8EqCSVMzpOoePR31MiaGE2zfdLvaMW4EhKRH4fQ/04rwqZIuMf7hybW5FAhcU98OJPPqXrcdLrkCy4eo7bcc59HjQOcqM2lG0TbWnckM5/9z3T+b9NA+dWsdn13/Gy4NexsfJxzoTqNQw4CHIP44m5wAPDIviTF4lO5PsW5HudctUVB4elCxbRu+xEZRdqCH9ZJFdYwCIC4pjxbgVuOvcmbV1Flszt9o9hpbEmJSMJjgItcd/j3wtzm4oYPMVK3ChtTKZLSzZkcADuo3IkUMhPO5yb7lqxzck42Aso8vcMTabQ/g9WZbZmrmVG9bdwKb0Tdzb9V6+m/gdvQNtsH2v+3Rw8YP97zC5Rwhh3k4s2mHnVbiLC963307VTzsI9ajEw9+JY5szFSkqC3cP56txX9HZtzMLdi/gszOfieK2q2RISvrTAjZHFy1u3sotBkQCFxT1/Yk8BlX8iJdcjjTEdqvvvFO5FNd50sG7CIcAP5vNI/xXqaGUx3c/zoLdCwh0CWTlhJU82ONBm50QhtYJ+t0HaT+hLTrHA0OjOJVbwe4U+66Ave6YgeTkRNknn9BrTATFOVVknSmxawy/xuLoxdLRSxndZjRvxb/F60dfF0n8ClkMBuoyMv6ngO1ith6/cFdFa2lEAhcUU2+28NFPZ7nfYRNyxGBo099mcx37Oh5NfTU97xlmszmE/9qVvYsp66bwc87PPNzzYVaMW0G0tx3OUO4zC3SusP9dbuwZSoinE+/aeRWu8fLCc+rNVGzcSGSYBTdvR45tUmYVDuCgduCNIW8wo+MMvkr8ipcOvYRFtk91fEtgTE0Di+V3BWzmegul+dX4hbuRV16LyU67Df5IJHBBMd/F5zJQvxkfSynSkCdtNk95XgW55S5EqDJx7WyHJNKK6ev0PLvvWebvmo+fkx8rJzQ0GtGoLntysXU4eUHPu+DMGnTV+cwb2o4T2eXsT7PvCtjn7rsBKP/qS3peH05hRiV5yWV2jeG3VJKKJ/s8yazYWaxOWc3C/QvFqWaN9GsB229W4CX5VVjMMr5hboxftJeF688qEptI4IIi6kwWPtqRyHyHjchhcRBhuz3Zx75sOLSkx81dbTaHACcvnmTqhqlsSN/AnC5z+Gb8N3Tw6mD/QOLua/h6+COm9g4l0N2Rd3ek2DUEbXAwHuPHU/btd0R1dMLZQ8exzZl2jeGPJEni4Z4Pc3/3+1l3fh3P7HuGeku9ojE1B8akZFTOzmhDQ399ruhSAZvax4Hymno6+CtTyCYSuKCI1cdy6F+1DT9LEdKQJ2x24lhtVR1pmRBck4jfKNG4xRZMFhMfnvqQu7fcDcAXY75gfs/5aNXav3+jrXiGQ6cbIP4LHEzV3DckkqOZZRxOt/MqfPYs5Joa9N+upMeocPKSy7mQXmHXGP5IkiTmdZvHIz0fYXPGZv6x9x9iJX4ZhqQkHGJikFT/TZdF2Xp0Thry6usJoYhoXwdFYhMJXLA7Q72Zj3Ym8ajjj8jBPaDdCJvNdXLVMcySlq5Dgn73CyhYR35VPrO2zuKDkx8wtu1Yvpv4Hd39uysdFgx4EIyVcGI50/qG4+uqY/GuNLuG4NC+Pa5Dh1K2/Cs69vLC0UVLvMKr8F/M6jKLx3o9xpbMLbx46EVR2PYXZIsFY1LS/xwhWnSpgC2lUM9Gh3/Q/dxrisQnPtEEu1t9LIe+VTsJMF9Auu5Jm62+zfUWzh4tx6cimYjp420yR2u2I3sHN2+4meSyZF4d/CqvDn717w8esaeQXhA+AA59hKNKZtagSPamFnMqx77nZ/vMnYO5vJzqH9fRdXgomQklFOfq7RrDX5kZO5M5XeawJnUNb8W/JZL4n6jPzcVSXf37AjazhZK8avzC3MjPz8FTqsYxUJnamlaVwAuef54LL76kdBitmtFk5uNdKTzutAE5IBaix9psrnPbkzHiSKcYCZXLnxxFKVyVOnMd/z7ybx7Z9QhhbmF8O+FbJkROUDqs/zXgQajIhsR1zIgLx91Rw/t2XoU79+yJU8+elH72GbGDAtA6qonfnGXXGP7OQz0eYlr0ND4/+zlLE5YqHU6TU5uQAIBTl9hfnysrqMZssuDXxo3agoYWq5KvArUetLIEbi4pofrAAaXDaNVWH8ulZ9VuQsx5SNfZ7t63LMuc3JKOa1UuMbOaYHJppnL0Odyx+Q6+SvyK2zvezvKxy6/88BF76TAWvNvBgcW4OWi4e2Bbtp0rJPmCfVfAPrNnU5+fT92eHXQZEkra8YuUFzaNg0YkSeKZfs8wMXIi7514j5VJK5UOqUkxJJxBcnDAoX37X5/7pYDNN9QNTdn5hid9opQIr3UlcIcO0dRlZWGprVU6lFbJaDLz0c4UHnf6Edk3uqF/tY1kHs+nss6JDp4X0f2melS4eruyd3HrhlvJ0efwzrB3eLrv07ZrymINKhX0vx/yj0P2QWYOiMBZp+aDn+27CncdOgSH9lGULFlK1+GhqDUq4rc2nVW4SlLxwsAXGBo2lFePvMqe3D1Kh9Rk1CYk4Nix4+/PAM/So3VQU+sgEWzOxazSNRROKqB1JfDoDmCxYEw7r3QordJ38bnEVB0kwpyJNPixhg9YG4n/NqGhberdonHLtTJZTLx7/F3m75pPqFsoqyesZkS47QoPrarb9Ia94QcW4+WiY0ZcGzacyiezuNpuIUgqFd6zZmFMTUU+dZhOg4JJOXSBypKms5DQqDT8e/C/ifaKZsHuBSSVJikdkuJkkwnD2bM4du3yu+eLcvT4hrmSUlRFO6kAo3tEQy9+BbSqBO7YoeE+hTElWeFIWp86k4UPdqbxpPOPyJ7hEHuTzeYqyq6ksFxHRH0iLr162mye1qCktoT7tt/HsoRl3NT+JpaPW06oWzO6oqFzht6zIHkTlKYze1BbNGoVH+227x/xHuPGoQkMpGTpMnqMCgcJTm5rWkd9OmudWTxiMe46dx7Y8QCF1fY9jrWpMZ4/j2ww4NTlv/0jLGYLxTlV+Ie7k1qoJ1LKR+Pf/m9Gsa1WlcC1YWFITk4YkkUCt7c1x3Npoz9GtCkZaeAjYMM9wvErj6M2G+k2uYs48/saJBQlcMuPt3Cy6CQvDHiB5wc8j4Namf2u16TPbFBp4PAS/N0dubV3GGuO53KhwmC3ECSdDu+776Lm6FE0OclExwVy7kABNZV1douhMfyd/Xl/xPtU1VXx0M6HqKlvGvfqlVB7+jQATr9ZgZfkV2OqbyhgSy0op43qIjp/5bo7tqoELqnVOLRvjzHZvl2ZWrs6k4X3d6XxtMuPyK6B0P12m81VXW4kPd1EcNlxfCbZrsK9pVubupa7ttyFVqVl+djlTGk/RemQrp57EMTeCCe+AkMlc6+LxCLDsr3pdg3Da+p/jxrtOboNFpOFUzua1iocINo7mjeGvEFyWTJP73261fZNN5xOQOXhgTb8v/e3Cy814glq54H+QhoazOArVuB24xjdAWNystjzaEdrT+TiX36KrvWnkQY8BFrbHb93ct1ZZBm6xPmg0jXhAqsmqt5cz0uHXmLhgYX0CujFyvEr6ejTUemwrl3cPKjTw4mvCPN2ZmLXIL4+kk15jf1WwCoXF7ym34b+px04VRcS1cufhN15GKqbXjvT60KvY0HvBezK2cUnCZ8oHY4iahMScIqN/d1VvAvplTi563DxckAqvlQM6SMSuN04dIjGXF6Oqci+Rwy2Viazhfd3nedp103ITl7Q626bzVVvNHP2cDF+JQmE3nmjzeZpqUpqS5i9bTarklcxs/NMPhz5IZ6OnkqHZR3BPSC8Pxz+CCxm7hvajpo6M18etG81uPeMGUg6HaWffUrPMRHUG8wk/Jxr1xgaa0bHGYxrO473TrzHgbzWtf3WUluLMTX1fwrYLqRXENjWnbxyA6GWS//dfJXZQgatMoFfKmQTl9HtYt3JfFzKEulbfwQp7n5wsF2nrnM/Z1Fv0RATVovW399m87RESaVJTNs4jXMl53j9utd5rPdj9jtBzF7i5kF5FqRsISbQneEx/ny2P4OaOpPdQtD4+OBx4xQqfliHh66GiC4+nNqZQ53BfjE0liRJPNf/OaK8onhy75PkVeUpHZLdGBITwWz+XQFbrb6OiqJaAiM9SCnUEykVUO/o07DLQSGtMIE3XO4Qlei2Z7bIvL8rjadcNyHr3KDvHJvNJVtkTm5Ow70yg6i7RdvUK7E1cyt3br4TWZb5YuwXjG3bQmsHoseDRzgc+hCAeUPbUVZTz+qjOXYNw+eee5DNZsq+/JJeYyMwVps4ty/frjE0lrPWmXeGvoPFYuHRXY9iMNmv8E9Jvxaw/aYD24WMSgACIz1ILtQTqSpAUvD+N7SyBG6xyJi0rmgCAkQluh1sTCjAXHKeIfX7kfrcY9O/VDNOFVFl0NBOlYZzt242m6clscgW3j/5Pgt2LyDaK5qVE1bSyaeT0mHZjloD/eZC5l4oOE2fCG/6RHixdG8G9Wb7FWrpwsJwH3M9ZStX4eenIiTakxPbszHXN81isXD3cF4Z/AqJpYm8cvgVpcOxC0PCGTRBQWj8/H597kJ6BSqVhH8bN1IL9bRXFaDxEwncbta8Hs/2z87iEN1BXEK3MYtFZvHOVJ503dKwZSzuAZvOd3ztWRwMpXS8ZZBN52kpak21LNi9gI9OfcTkqMl8cv0n+Dr5Kh2W7fW4A7QuDffCaViF55XXsv6kfVfAPrNnY6mqomzlSnqNiaCmoo7EgwV2jeFKDA0bypwuc1ibtpZN6ZuUDsfmahMScOry+/vfhekV+Ia5otGpyS24gDcVilagQytL4N5BzhRl63HoEI0xPR25vulVf7YU285doKIwm7GmnUg9ZoBbgM3muphVSeFFmTblR/EcM8pm87QUF2suMnPLTH7K+onHez3OCwNeaNotUa3JyRO6T4eEb6GqiGHR/kQHuPHR7vNYLPbbmeLYqRMuAwZQ+uWXBLd1xj/CnRPbsrDY8UrAlbq/+/109+vOi4deJFffNAvvrMFUVkZ9dvbv9n9bzBYKMysJiPTAbJGhJLXhGwpWoEMrS+B+4e7U6usxh0dDfT3GjAylQ2qRZFlm0Y40HnfbjoQMA+fbdL7j6xJRmwx0Gh6BJLaO/a1zJee4beNtpFek8+6wd7k79u7W1+ym371groP4z5EkiXlD25F6sYqdSRftGobPnNmYi4qpXLeeXmPaUFlsIPVo0+1+plFpeO2615CQeGrvU9RbWuYCyHDmDACOsb9p4JJXjanOQmCkO1kl1YRbLhX0NYcVuCRJoZIkvSdJ0kFJkmokSZIlSYpo5HszL73+j4/J1xL41fALdwNA79pwepK4jG4bOxIvkl+Qx42WbUhdbgavCJvNVVVmJP2cnuCLhwiYPtVm87QEO7N3cveWu1FJKpaPXc6w8FbaJ963PbQbAcc+AXM947sGEeLpxMd77Nte1TkuDsfYWEo+/YSIzl54B7sQvyUL2Y5XAq5UiGsIC/sv5HTRaT48+aHS4dhEbUICSBKOsZ1/fe7CpQYugW0vVaCrCpAljU0/2xqjsSvwKOAWoAzYexXzbAX6/+Gx+yrGuSa+oa5IEpTXOYNWKyrRbUCWZd7blcZ8151ozLUw6FGbznd6ezqyDB2jVWh8W8E93KsgyzJfnv2SR3Y9QpRnFN+M/4Zob+XaPzYJ/e4DfQEkrkerVjFrUFuOZpYRn1VqtxAkSWo4ajQrm6odO+g1tg1lF2pIP9W0e1SMaTuGyVGTWZawjCMFR5QOx+oMpxPQtYtE7frfLa8XMipwdtfh5uNISmEVkVIBslcbm7aEbozGJvA9siwHyLI8Dvj2KuYplmX50B8eZVcxzjXROqjxDHShKK8Gh8hIUYluA/vSiknNucDt0maIHgf+tuviVW80c3ZPHn7Fpwi903aHozRnJouJlw+/zBvH3mBkm5Gtp1jtcqJGgnckHP4YgGl9w/B01vLxbvu2V3UbNRJdmzaULFtGu57+ePg5Eb85q8l3inym7zO0cW/DM/ueocJYoXQ4ViPL8qUCtq6/e/5CeiWBkR5IkkRyoZ4YzQVUvh0UivK/GpXAZbnlNMP1D3drKGQTleg28d7ONO512YNDfSUMesymcyUdzKfOpCLKIft/KkYFqK6v5qGdDzV0VoudyZtD3sRJ46R0WE2DSgV950LOYcg/gbNOw51xbdieWEjaxSq7hSGp1Xjfcw+GM2cwHD1CzzFtKMrWk33OflcCroaz1pnXBr9GSW0Jrx99XelwrMZUUIC5pATH3+z/rqmso7KoloBIdwDOXygnjAJFO7D9wl5FbBMv3Ts3SpJ0SIn737/wC3ejpqIOOaIjpsJCzOXlSoXS4hxOL+FkRiGzNRshYjCE9bHZXLJF5uTGVNwqM4m8bbTN5mmuCqsLuWvzXRzMP8jC/gt5rNdjqKRWVbN6ed2nX9pStgSAOwdEoFOr7H7IicfkG1D7+lKyZCnR/QJx9XIgfnOmXWO4Gp19O3NP7D2sP7+en3N+Vjocq6g5cQIAp67/7SVRmHHpAJNID+pMFowl2WjlesUr0ME+CXwD8BBwPXA7/D975x0eVZn98c+dyUySSZn0XkgghIReAiJVUBBUFBVRmqJg11VX3XX1t7uW3dWVtayoC4hKEbsgIr13SAgEQoD03ttkMr3c3x8TgQhKy5TAfJ4nD0/eufeeA5qce973nO9BD6wUBGHGb90gCMLDgiBkCIKQUdfBmuW/FLK1BiUCoM91Z+Edxfxt+cxU7MPHUAcj7Jt9l2Q30KKGLqp0lOPdrWNnk9uUy/S10ylTl1d2Ka4AACAASURBVDF/7HymdHcX950XL6UtiGd/B611hPh6MmVQDD9kVlDb4jjFMYmnJ0H3z0Kzdy/GUyfoPy6OqnwVlXkOP2W8ZB7t+yhJgUm8tu+1q2IrXZuRgcTHB68eZ2pEqgtbkEgFQuP9yK9tJU5s0wxwcgU6OCCAi6L4lCiKS0VR3CWK4nfAWCAD+Nfv3LNQFMVBoigOCj1LCacjCIn1BQGahWDAXYneURwpa2ZPXi1Pea6FyH6QaN8K58M/5+KpbyJ5Qi9369hZ7K3c204WdXi0W9jmdxn8sK2lLPNzAOaOSMRstfLpnmKHuhF4771IfH1pWPQJqcOi8PaTkbHOsYNWLge5VM4bw96gUd/IWwffcrY7V4wu4xDe/fsjeJyZA1BdqCIk1g8PmZScqhYShbYAfo1k4O0QRdGCrRAuRhCESEfbl3t5EBCmoLHRijQgwF2J3kHM35rPXV6HCNCV2irP7dhbXFemprJET0z1LoKn3mM3O52NH/N/5InNTxDpE8kXt3xBj6AeznbJ9QntDl3HQPqnYDERH+zDhN6RfLG/BLXecX3OUj8/AqdNQ71hA9bKMvrdGEdZTiM1xS0O8+FySQ1OZW6fufxU+BPbSrc5253LxtLcjCEvD8WgQWfWLFZqi1uIaDv/zqlsIUlajeilBB/nF4M6+1DMKaWWoacL2ZLRn3QH8Cslp7KFzSeqecFnLQR3g5Tb7GrvyIYipBYDyb0U7bSKr1VEUWRB1gJe2fMKAyMGsnTCUiJ8IpztVudh8COgroSTPwPwyMhE1AYzXx107JCToFkzEWQyGhZ/Sq9R0XgqPMhYW+xQHy6Xh3s/THJgMq/t77xb6drMTAAUgwaeXqsva8VsshKRqAQgp0pFL89ahOAkuyYpF4vDA7ggCB7AVKBUFMVqR9sHWwBvbTJAUi8MubluSdUr5MPt+YzzPE6Y5hQMewYkUrvZ0qgM5B+qI7JqHxGz7rWbnc6C2Wrm1X2vMv/IfG5NvJWPx36Mn9zP2W51LpJugoB4OLgIgD4xAVyXGMSnexw75MQjJATlXXeiWrUKQdVI37GxFB+tp75c7TAfLheZVMbrw16nUd/I+5nvO9udy0KbcQhBLsfrrI6WilO2OoTo7oGIokhOZQvxVLrE+TdcQgAXBOFuQRDuBn55PZnQtjbqrGvMgiAsPuv7+wRB+EoQhFmCINwgCMK9wDZgAPCnDvo7XDJhbYVs2shURIMBQ4FjFZiuJvJrW1l7rIpX/NeDfzT0mWpXe8e2l2O1QlffKrz79LnwDVcxWpOWZ7Y9w/d53zOn9xz+OfyfyJwsLNEpkUghbQ6U7Iaa4wA8MrIrVSo9P2U5eMhJ26jRxiVL6D06BpmXlEOd4CwcICU4hekp0/k291uO1B5xtjuXjDYjA68+vZF4ep5eqzjVRGCkDwp/OeVNOix6NUpTnW2n0QW4lAz827avR9u+/6jt+1fPukba9vULRUAY8DawEfgfYABuFkXxq8v0+YoJaQvgKi/bEfwv2rduLp2PtxcwxCOfOHUmDH0SPOxXUGY2WsjeWkJIwzFip0+ym53OQKO+kTkb57CrYhevDHmFPwz4w7Wnad6R9J8BHl6ns/DRyaF0D/dl4c5Ch4qqyGNj8Z84keavvkJm1tJ7dAz5mbU0Vmkc5sOV8GS/J4nwieDVfa92Kq10q1aLPicHxcD259+VBSpiugcAkFPVQpLQpoFuR4GqS+GiA7goisJvfI3+1TUPnPX9flEUx7SpuMlEUQwQRfFGURQ3dOxf49Lw9PZAGepNk1qKxNcXnTuAXxZljVpWHang1aCNtlnfA2bZ1d6pA9UYDBDfkoH/+PF2teXKlKnLmLVuFrlNubw7+l2m9rDvrsc1gSIIet8NR78GXTOCIDB3RCInq9XszKt3qCvBc+dg1WppWrGCfmNj8ZBJyFzfObJwhUzBS4NfIr85n2U5y5ztzkWjy8oCs7ldAVttsRqzwUJ0j0DAVuuTLGmbwhbqGgWizi5icxq2QrZWvHr2RH88x9nudEr+t6OAHkI5yardNm1pT98L33SZiKLIkfWF+KrLSLz1umu2dSynIYeZa2fSbGjmk3GfMCZujLNdunpImwsmLWR9CcDt/aIJ9/dkoYOHnHglJ+MzaiSNS5fh6WGh58hoctNrUNVpHerH5TImbgxjYsfw8ZGPqWitcLY7F4U2PQMkErz79zu9VnGqCQSITrIF8OOVLQxS1Nh2apw8xOQXrukArm7UI6T0wXDyJKLR6GyXOhXVKj3fZpTzRthmm5rV4Iftaq80p5HmBhOxldsJvPfabB3bW7mX2etnI5fKWXrzUvqF9bvwTW4unqh+EDvEto1utSL3kDB7WAJ78hvIrnBsZXXII49gaWqi+dvv6H9THBKJ0GmycICXhryERJDwj/3/cHlddwDtoUN4paS0G2BSfqqJ4GhfvHxtdSUnqlroJauE0GS7FupeCtd0AIe2QjaTCX1enpM96lws2lVIpFhDv+bNMGi2bQvSjmRtKEZubCGpfwiysDC72nJF1hSu4YnNTxDtF83yictJDEh0tktXJ2lzobEACrcCMG1IHL6eHixysLyqYsAAFIMG0fDppyi8BVKHRXJyfzXqRscpxF0JET4RPNHvCXZV7GJbmWv3hotGI7ojR9q1j1lMVqoLVcR0t2XfzVojFc06Yi0lEOoa59/gDuC0nC5kO+5MdzoVDa0GvjhQwj/CtyMIErjucfvaq2ylLFdFTPl2QmZNt6stV2TJ8SW8tOsl+of3Z8nNSwhTXHsvMA4j9XbwCTtdzObvJeO+wbGsOVpFeZNjt7CDH3kEc3U1qtWr6T8+HqBTZeHTUqbRLaAbb6e/jcFicLY7v4nu+HFEgwHvgWcCeHWRCovJeub8u6oFfzT4GmohzDXOv+EaDuBePjL8Q7xoVEmQ+Pu7K9EvgU/3FOFnbuL6lnXQ915QRtvVXtbmMiRWE4nKerz69r3wDVcJVtHKvPR5zMuYx03xN/Hxje4eb7vjIYeBD0DuBmgsAmD2sAQE4DMHy6v6DB+GV8+e1C9ahK9SRsr1keTsraS1qXNk4R4SD15Me5Hy1nKXLmjTZmQAtCtgqzjVhCBAVFJbBXplC91+qUB3Z+CuQWicH7Ularx79UR/3J2BXwwqnYmle0t4I2IXEovBJtxiR7QtRk7tryKyah/h0+++ZlqlTFYTL+9+mSU5S7g3+V7eHvk2nlLPC9/o5soZNBsECRz6DICoAG9u6xvFVwdLUekc1xolCALBjzyMqaSUlvXrGTA+HqyQuaHUYT5cKUOjhjImdgwLjy6kVlvrbHfOiy7jEPLERDyCzhwDlp9qIjTOD09vmyZ6TmULaYoq24cu0kIG13gAD09Qom7QQ3I/9Hl5WA2uu83jKizdWwyGFm5s/QlSJ9l9Jm72DptwS3zrYfwnTrSrLVdBa9Ly1JanWFO4hqf6P8VfhvwFqYsUzVwT+EdBj1sgcxmYbNnunBEJaIwWvjzo2ODpd+ONyBMTaViwEL9gL3oMjSBndyWa5s7zu+r5tOcxW828d+g9Z7tyDqLFgjYzs132bTJaqClqIbrt/BtsW+iDFDW2gl1lrDNcPS/XdAD/Rd+2JSwFTCYM7tGiv4vGYGbxniL+FrkfqUkNw+07MtRstHBsWykh9ceImXwjkmugdaxJ38ScjXPYV7WPvw/9Ow/3efia2XVwKdLmgK4Rjq8EoGeUkuHdQvhsTxFGs+PkVQWJhOCH52LIzaV1+3YG3NwFq1Ukc0PnOQuP9Yvl/p7381PhT2TVZTnbnXYY8vKwqtXtCtiqC1RYLSLRybYArjdZyK9tpbukoq0C3XXCput44gRC43yRSAWaJLapMu5z8N/niwMl6LQabtevsk1wirJvG1PuwRr0WiuxVdsJuPfqFyupbK08LdDy3uj3uKv7Xc526dolYSSEdIf0RaeX5o5MpKbF4HB5VeUttyCLjqbhfwvwD/Ei+boIju+uRKPqPFn43N5zCfMO480Db2IVHfcCdCE0+/cD555/SyQCkd1sCV5+bStmq0i4vgjCUp3i529xTQdwD5nUJuhSa0UaGOhWZPsd9CYLC3cW8VJEBjJdvd2zb1EUObKxGD9NBXHXdbvqW8dym3KZuXYmDfoGFt60kBvi7DtP3c0FEARbFl5xCCpsU6pGJoWQHO7Hol2OlVcVZDKC5zyELisL7YEDDJoQj9Uicnhj5zkLV8gUPDPwGbIbsvm58Gdnu3Maze49yBMTkUVFnV4rP9VEWBc/5F628+/jlSoCUONlqHepCnS4xgM42LbRa0vVyHv1cbeS/Q7fZJTR3KrhXvMqiBkMXYbb1V5pTiNNtXpiSzcRfP9Mu9pyNodqDvHAugcAWHLzEgaED3CuQ25s9L3XduaZbpvPJAgCc0fa5FV3OVheVXnnnXiEhVH/4UcoQxV0HxzO8Z0VnSoLvyXxFlKDU5l/eL5LtJVZ9Xq06en4DB92es2oN1Nbom5//l3ZQh95266LC1WggzuAE5GoxGKyok/sjyE/H6u+c7RoOBKj2cr/thfwdNhRvFrLYcRzdp+Fe2RTKZ5mNXERpqt66tjW0q08sukRgr2DWTZxGUmBrjGm0A3gpYQ+90D2d6BtBGBS36g2eVXHCrtIPD0JnjMHbXo62vR0Bk3ogqWTZeESQcKzA5+lUlPJVyedNsvqNNqMQ4gGA77DzyQjlbnNiFbxdP832ArYhivbXtjcGbhrEZHoD0BLQFewWDCcPOlkj1yPlYfLqVJpeYhVENYTkuw7SKShopXyk01El24lZMbVK9zyQ94PPLv9WboHdmfphKVE+UZd+CY3jiVtDpj1cOQLAOQeEh64PoHd+fUcr3SsvGrAPVOQhoRQ//HHBIQrSB4STvbOik5VkX5d5HUMix7GwqMLURkc++/3azS7dyPI5SjS0k6vFWc3IPOUEtXV1v9ttYqcqFLT17MKPP1tI5NdiGs+gPsGeuEb6EmD2fYfTOfeRm+H2WLlo+0FzAk9iU9Lvi37tnMV5pFNpUhFM3HGE/iPH2dXW85AFEUWHV3E3/b+jaFRQ/lk3CcEegVe+EY3jieiF8QNtW2jW23FV9MGx6GQS/lkV5FDXZF4eRH84INo9u5De/gwgyYmIFpEDnWiinSAZwc8i9qoZnH2Yqf6odmzG8WggUi8vQHbz2XJsXpiU4KQymy/40oatbQazCRYS20TyFysI+SaD+DQdg5eaUAaEuKuRP8Va45WUdKg4QmP1bYJPKl32NWeptlA7sFqIit2Ezbl9qtu6phVtPLmwTf57+H/cmvirXww5gMUMoWz3XLze6TNgaYiKNgCgFIhY2paLD9lVVKl0jnUlcB7pyINDKT+449RhnrT4/pIju+q6DQa6QDJQcnc1vU2vsj5gmpNtVN8MFVXY8jLx2fYme3zhgoNrU0G4nsHn147Wt4MiARpClxu+xzcARywBfDWJgP0HIQu+5iz3XEZrFaR+dvymRpcREDTUZvqmtTDrjaPbi/HahWJq95F4NSrq3XMaDHy4s4XWXFyBbNSZ/GP4f9AJpE52y03FyJlEviEni5mA3hwWAJWUeRzB8urShQKgmbPRrNzF7pjxxg4IR5EONSJNNIBnuz3JADzD893in3Nnj0A+Jx1/l2SbTvnju91JoAfKWsmWqbGw9Dkci1k4A7gwBlBl9a4/hgLCrGo1U72yDVYl11Nfm0rzyvWgG8E9JtmV3tGvZnsHeWENh4j/Mbr8AgJsas9R6IxaXhiyxNsKN7AcwOf44W0F5AI7h+/ToGHHAbMgrwN0GwrGosNUjCxdyQrDpSi1jtOXhUgcNo0JEol9R99jH+wN6nDojixp5KWesfuBlwJkb6RTEuZxuqC1eQ2OV5Aq3X3bjzCwvDsfqZotPhoA6Fxfvgoz0gWZ5U1My6kyfZNqDsDd0lCYn2RyiSofONBFNEdcS21IGcgiiIfbM3jlqByQuv2w/VPgod9tbhP7qvCqLMQV7yBoFlXT+tYva6e2etnk16dzhvD3mB2r9nOdsnNpTLwAdufhz4/vfTwyETUBjNfp5c51BWprw9B98+idds2dMePM3BCPIIgcGhdsUP9uFLm9J6Dr8yXj4585FC7osWCZu8+fIYNO61yqG81UVOkostZ2+cmi5XjlS0M8a2zLbiQBvovuAM4IPWQEBbvR4PWG6RSdIczne2S09l8opaT1Wpe8fsZvANhoH2DjtUqkrW5jABdGZHJoXilut521eVQ1lLGrHWzKG4p5r9j/svt3W53tktuLoeAOOh+M2QuBbMRgD4xAQxOCOKzPcWYLI5VFwuaOROJvz/18z/EN9CLniOiOLGvGlWdY0eeXglKTyUze85kS+kWchpyHGZXn52NVaVq1/9dcrwBUYT43md2/U5VqzGYraRIy8ErAHzDHebjxeIO4G1EJCipq9AgS05Fm3nY2e44lV+y79EB1UTW7LDN+/b0tavNwsN1tDToiSlYR+BVkn2faDjBjHUzaDG2sGjcIkbGjHS2S26uhEEPgaYOTqw+vfTwiEQqmnWsPVblUFekfn4EPzjbloUfO8aAm+ORSgUO/uTYyvgrZUbKDPzl/g7Nwlt37wZBwOf660+vlRyrx9tfTljcmXG9WeXNAEQY2iRUXawCHdwB/DQRXZVYzSKGnsPQZWUhmhx7ruVK7Myr52i5ilcDN9h6Hwc/bHebRzaXorC2EOXZgN+YMXa3Z2/2V+1n9obZeEo9WTphKX1Dr5055lctXcfYOjHOKmYb0yOMxFAfh8urAgTOmIlUqaTugw/wUXrSZ0wMuek1NFS0OtSPK8FP7sfsXrPZUb6Do3VHHWJTs3sPXr164RFoa920WqyU5jQS3ysYQXImSGeVNROkkCFvynXJCnRwB/DT/FLIpg7tgajToT95yskeOQdRFPlgSx7X+dUTV7XR1kLjHWBXm1X5zdQUtRCTv46gGdMRpJ17dOa6onU8tvkxIn0iWTZhGYnKRGe75KYjkEhsWXjpXqg53rYkMHdEItkVLewrbHCoO1JfH4LmPIRm5y60hw/Tf1w8ci8P9v/oWJW4K2Vaj2kEegY6JAu3qFTosrLabZ9XF6owaM10Oav6HCCrTMWoSDOCXuVyEqq/4A7gbSj85fiHeNEo2oa6X6vn4PsKG8goaeKN0M0IHl4w9Am72zy8qRSZYCS6JYuAuzv3BK5lOct4ceeL9A3ty5IJSwj3cb1zMzdXQP8ZIPWEjE9PL03uH02Ir5xFDpZXBQiaNg1pUBD1H8zHy0dG/3FxFB+tp6rAuSpnl4JCpuDBXg+yp3IPh2vte3yp2bcfrNZ28qnFxxqQSAViU4JOr7UazOTWqhmlrLEtRPSyq1+XizuAn0VEopKaSgMeUVHX7Dn4+5vz6OuromvVz7bKWx/7tnI1VWsoyqonpmQrQXfchtTP78I3uSBW0co7h97h3+n/5sa4G1lw0wL85f7OdstNR6MIgl53QtZXYLC1m3rJpMwa2oVtp+rIrXFsC6rEx4fguXPR7N2LNiODvmNi8faXs39VgcO39K+EqT2mEuwVzIeHP7SrHfXWLUiUynbzFUqyG4hKCkDufUbjIrtChShCH482rfnwnnb163JxB/CziEoKQKsyYu03HF1mZqf6AegI9hc2cKCokTcjtiIIErj+KbvbPLKpFKlgJbp8O0GdVPfcZDHxl91/4bPsz5iaPJV5o+bhKbVvy50bJ5I2B4ytcPTr00szrovHSybhk12Oz8ID752KNDSEuv9+gMxTyqAJXajMa6Ysp9Hhvlwu3h7ePNT7IQ5UHyC9Ot0uNqxGI63btuM3diyCzCag1FKvo7FSQ5fe7ROVrDJbAVu0Id9W9+CltItPV4o7gJ/FLyPkmqP6Ya6txVRR4WSPHMsHW/Po4aOhR9Vqm2iL0r7C/RqVgZP7q4msSydoeBryLl3sas8etBpbeXzL4/xc+DNP93+al4e8jFTSuc/w3VyA6IEQ2RfSP4W2l/wgHzlTBsay6nAltS2OlTWVeHsTMvdhtAcPotm3j54jovAL9mL/j4WI1s6ThNyTfA+h3qEsyFpgl+dr9+3DqlbjN+6m02tFWW3qa71/df5d3kxskDeeddkQ4brTEN0B/CyUYd62wSaC7dxSl3ntnINnFDeyJ7+BeVHbEKxm29ASO3N0WzmixUpMwTqCHrjf7vY6mjptHbM32ARaXh/2OnP7zD0tDOHmKkYQbMVstcehdP/p5TkjEjBZrSzZV+xwlwKm3oNHZCS177yLRCow+LYE6krV5GfWOtyXy8VT6sn9Pe/nQPUBsuo6XkyrZeNGJL6+7drHctNrCI3zIyCs/TyCrDIVg6Pk0FjoDuCdBUEQiE4OpLrKguDrh/YaCuDvb8mju0JDz6ofoO+9tm0jO/KLbGqYNo+ghNB2I/06A4WqQmaum0lJSwnzx87njm72HfLixsXofTd4KiH9k9NL8cE+3NwzguX7S9EYzA51R+LpSeiTT6I/dgz1pk10HxxBcLQP+1cVYDE7VmTmSpjSfQpKTyWfHP3kwhdfAqLJROvmLfjecAOStgFJqjodtcUtdBsU1u7aOrWBimYdo5VtLz+R7gDeaYhJDkTXasLcbyS6a6SQLbO0iV159bwdvRPBYoQRf7S7zZzdlRh1FmJPrCRo9gOdKnPNrMlk5tqZ6Mw6Phv/GcOjh1/4JjdXF3If2zFTzo/QWnd6ee7IRFQ6E99kOFZeFUB5+yTkXbtS9977CFYLQ+/sRku9nuwdnecoUCFTMD1lOtvLt3OqseNaebXp6VhUqnbb53kZtgrzpEHtO0WOtgm49PFoGxAT0bvD/Oho3AH8V0Qn287BVXEDMeTlYWlpcbJH9ue/W/JI9NbSp/p76D0Fgrva1Z7FYiVrSxnB1hqCFAb8x4+3q72OZGPxRuZunEuQVxDLJy6nZ4hrVqe6cQCDHgSrCQ4vPb00IC6QQfGBLN5dhNnB8qqChwehz/wBY2Ehqh9/JC41iNiUQNLXFqHXdB5hqmk9pqHwULD4WMfNC2/ZuBHB27td+1h+Rg2RXZX4BXm1uzarrBmpRCBanw+KYPCL7DA/Ohp3AP8VfkFe+Id60yCNahtscsTZLtmVI2XNbD9Vx9sxexBMOhjxvN1t5qfX0NpkIDr7ewKnT+80M7+X5Szj+R3PkxqcyrIJy4j1i3W2S26cSWh3SBgJGZ+B1XJ6ee7IRMqbdKzLdvysa78bb8SrTx/qPpiPaDRy/V3dMGjNnWrcqNJTydQeU9lQsoGSliv3W7RYUG/egu+oUUi8vQFoqGyloUJDt0Hn6jQcKVfRPdwPj9pjtvNvF94ddAfw8xCTHEhNHYhSj6v+HPy9zbnEe+sZUPMN9Jxs+6VkR0SryKENpfhLWgnRFRF4zxS72usILFYLbx18i3+n/5uxcWNZNG4RAV72Vadz00lImwOqMsjbdHrpppRwEkN8WLjT8fKqgiAQ9txzmKuraVrxJSExfvS4LoKj28o61bjRWamzkElkHZKF6zIzsdTX43/W9nl+Ri2CAN0Gtj//FkWRrLJmBsT4QO0Jl94+B3cAPy/RyQEY9RYMvUdc1efgmaVNbD9Vx7y4vQhGDYx8we42i47W01SlIfbkSgIn34E0wLUDoc6s47ntz7H8xHJmpMxg3qh5eHl4XfhGN9cGyRPBN6JdMZtEIjBnRCLHKlQOl1cF8LluCD7DhtGwYAEWtZohkxKRCEKnklgN8Q7hzqQ7+angJ6par2xQTMvGTQhyOT4jRwG2IJ2bXkN0ciAK//a7fyUNWlQ6E8OVjWAxunQFOrgD+Hn5pR+8pUsauqNHEY1GJ3tkH97bnEecwsSg6m8g5TYIt+8IT1EUydxQgo/MQGhVusvP/K7X1fPQhofYVraNPw/+M38a/Cd3j7eb9khlNsXC/M3QeGYS2J0DbPKqC50grwoQ+tyzWJqbaVi4CN9AL/qOjSUvvYaa4s5T0zO7p22E8ZKcJZf9DNFqRb1pEz4jRiD19QGgrlRNS52OpLRzt88PlTQB0FfWpsDmwhXo4A7g58VH6UlgpA+NnnGIej26o46ZkuNIDpU0sTO3jnfidiMYWmDki3a3WZlrG1oSW7ge5Y03uLRwS6GqkBlrZ5DXlMd7N7zH9JTOqRLnxgEMvB8ESTt9dC+ZlPuHdmH7qTpOVTtWXhXAu2dP/CfdRuOSJRjLKxgwPh5vPxm7v8ntNAqTkb6RTEycyA95P6AyXJ62u/7oUczV1e22z/PSa5BIBRL7hZ5zfXpxI0pvGZHaPPDwhuBul+2/I7ioAC4IQowgCB8IgrBPEAStIAiiIAhdLvJeiSAILwmCUCwIgl4QhCxBEFx+YkVM9wBqm6RYpR5o9u5ztjsdznubc+miMDCw6itb9u2AN81DG0rwlFkIL9pG0IMP2t3e5XKg6gAz1s6wtYnd/Blj4jr/eFM3dsQ/CnrcAoeXg+mMCtvMofF4y6ROy8LDnn0WJBLq3nkHubcHQyd3pbqwhdyDNU7x53KYlToLnVnHt7nfXtb9LevWg0yG7w03ALYanPxDtcT1DMbLR3bO9QeLGxkUH4hQc8y2I+niO24Xm4F3A+4BmoBdl2jjdeDvwHxgArAf+FYQhImX+ByHEt0jELPRiqHvGDR79zrbnQ7lUEkju/LqeSd2N4JBDaP+bHebtSUtlOU0Ele1E99+vVH07293m5fDyryVPLrpUcIV4ay4ZQW9QlxzCpEbFyNtDugaIWfV6aUAhZypabH8eKSCKpXjC8hkkZEEPziblrVr0R4+TI/rIgmL92PvD/kY9Y4VmrlckoOSGRo5lBUnVmCyXFornGg0olq9Gr8bbkDqbxssVFWgorXJQFJa2DnX17caKKzTkNYlEKqPuvz5N1x8AN8pimK4KIoTgYt+FRIEIQx4HnhTFMV5oihuE0XxEWAb8Oalu+s4opMCQYCWrkPQHNjs/QAAIABJREFUHTuGRe34bTB78e6mPLr6GOhf9RWk3uGQUXmZG0qReYhEnlhD8EOul31bRSvvHnqXv+79K4MjB7N0wlKife2rBe/mKiJhJAQntStmA3hoeAIi8NmeYqe4FfzQQ3iEhlLz5puAyIh7u6NVGTm0zjn+XA4P9HyAOl0da4vWXtJ96q3bsDQ1ETDl7tNrJ/ZUIvOUnjO8BGxy0gDDQnWgV7l8BTpcZAAXRfFyFQnGA3Jg+a/WlwO9BUFIuMzn2h0vXxkhMb40eESDxYL24EFnu9QhHCxqZHd+PfNidiEYtTDa/tl3c42WgsO1xKkyUcRFnt7OchW0Ji1/3P5HPs3+lHu638OHYz/ET945x5q6cRKCAGkPQXk6VJ7RjogNUnBL70hWHCilRe94MRWJjw+hzzyDPusoLWvXEZGgpMfQCI5sLqO5Rutwfy6HoVFDSQpMYknOkks6v2/+7js8IiNPa5/rW03kHaoleUgEci+Pc65PL27C00NCD9p6zyP7doj/9sTeRWw9AQOQ/6v1421/2rfs+QqJTg6krs6K1cf/qjgHF0WReRtP0c3XQL/Kr21932Epdrd7aEMJUglEHvnWJpsqcZ3ayWpNNQ+sf4CtZVt5YdALvHLdK3hIzv3hduPmgvS9z1b4lNG+d/nhkYm0GsysOFDqFLeUk+/AMzWF2v/8B6tez3V3dEUqk7D7uzyn+HOpCILA/an3k9eUx77Ki/s9bKqoQLNnDwGTJyNIbefYJ/ZVYTFZ6TXq/Dtr6cWN9IsNQFZ7zFaUGObS4QmwfwAPAprFc1+bGs/6/BwEQXhYEIQMQRAy6urqzneJQ4hPDcZiFtEOvPmqOAffnV/PwaJG3o3ZgWDSwqg/2d1mS72O3P3VxJlO4a30RHn77Xa3ebEcqzvGfT/fR6m6lA/GfMCsnrM6lSa7GxfDOwD6TIGj34Ku+fRyr2glw7uF8OnuIgxmy+88wD4IEgnhf/oz5qoqGj79FB+lJ2m3JFByrIHio/UO9+dymJgwkTDvMD4//vlFXd+80laLoLzzTsBWvJa9s4LIbkqCo33PuV5jMHO8soXBCUFQfcxWfS5XnHOdq+E6qdBZiKK4UBTFQaIoDgoNPbfU31FEJQUg85TSEDEAY1ERpqorExRwJqIoMm/DKXoqDfSq+MY2TSmsh93tZm4oAUEk8sBSgmbMQOLpaXebF8PawrXM3jAbT6knyycsZ2TMSGe75OZqYNBDYNZB1pftlh8ZlUit2sCqw84ZLOIzZDB+N99Mw4KFGMvL6XNDDIERCnZ+nYvJ4PiXiktFJpUxLWUa+6r2XXDIiWix0PzD9/gMHYo8xpZtl51opKVOR+9RMee9J7O0CYtVZFCXtgDeCQrYwP4BvAkIEM5Na37JvBtxYaQyCbGpQVRpAxChU2+jbz5RS1a5inejtiKY9Q7Jvlub9JzYV0WcpBRvqZHAe6fa3eaFsFgtvHfoPf6060/0DO7JiltW0C3QtXs93XQiovpBTJqtmO2sjcfh3ULoGeXPgp2FWK3O6cMO//OfQCql5vU3kEgFRk/vgbpBz8E1RRe+2QW4u/vdeHt4szRn6e9ep9m7D3NlVbvitWM7KvD2k5HY//wJYXpRIxIBBoSKoCrtFAVsYP8AfhzwBH493uqXw4UcO9u/Yrr0DkbbakEX0wvNvs4ZwK1Wkf9sPMWQIC1JpV/bxiCGJNndbubGUkSrSOSexQROnep02VS1Uc3T255mcfZi7u5+N5+M+4Qgr/Oe4rhxc/mkzYGGfCjacXpJEAQeHdWVwjoNG3Oc04cti4gg9Mknad2xg9atW4lKCiB1RBRZm0upK3X9Lhulp5I7ut3BuqJ1NOh+W6K2+fvvkQYE4Dt2LAAtDTpKjtWTOiwKqcf5Q156cROpUf74NZ2wLbgDOADrARPwaxmrGUC2KIou/+oX3ysEBFCljkWzbx+i1bEjAjuCn49VcbJazVuh6xHAIX3fGpWBnF2VxHlWoTCpCHrgAbvb/D2KVcVMXzudvRV7eWXIK/xt6N+QSc8VcnDj5opJvQO8g+DgonbLE3pFEBek4H87CpymhhY0cwaeSUnU/OOfWLVarp/cFW8/OduWn8Tq4PGnl8N9Pe7DZDXxXe535/3c3NiIessWlLdPQtI25TBnVyUAqSOiznuP0WzlcFkTaV2CoCLDthjlmjoVv+aiA7ggCHcLgnA3MLBtaULb2qizrjELgnC6BFMUxVrgHeAlQRCeEwRhtCAIHwNjgJc65q9gXxT+csK7+FPrnYiloQFDXueo3PwFs8XKu5tzGRPSQnzpStsZXYD9x2Ae3lSK1WolavcnKCdPRhZ+rnCCo9hRtoP7fr6PZn0zC8ctZGoP52/lu7mKkXnBgJlwai2oyk8ve0glzB2ZyJGyZg4UOef0UJDJiPjbXzFVVlL/vwV4KmSMmNqdulI1R7eVX/gBTiZBmcCwqGF8c+obTNZz2/JUP64GkwnlXTaxT4vJSs6eSuJ7h+Af7H3eZ2ZXqtCbrG0BPBOCuoKic+zMXUoG/m3b16Nt33/U9v2rZ10jbfs6m5eBN4A/ABuAYcA9oiiuuRyHnUGX3sE0qDwwyvzQ7Olc1eg/ZFZQWKfhHwGrETy8YMRzdrepUxs5vrOCWEUD3ppaguc8ZHeb58MqWvnoyEc8ufVJYv1i+fLWL0mLSHOKL26uMQY9aDsDz/is3fKUgTGE+Mr5344CJzkGikGDUN5xBw2ffYahsJCuA0Lp0juYA6sLO8XI0Wkp06jV1bKldEu7ddFkomnZMrwHDMCru20scsGRWnRqE71/o3UMbOffAGnxgVCeATGD7Od8B3PRAVwUReE3vkb/6poHfnWfRRTFN0RRjBdF0VMUxT6iKJ5//8NFiW9T7WnuMbpTnYPrTRbe3ZzL5MgGIsvXwnWPga/9M+Ejm0sxm6xE712M/8SJyOPi7G7z16iNap7e+jQfZ33MpK6T3MpqbhxLYBfoPh4yl4DZcHrZSyblgettQ05OVDlvMljYC88j8fam6v/+CqLIyPuSQRDYvuKUyw87GR49nFi/WFacWNFuXfXzz5gqKwmeOwewtY5lri8hIFxBbMpvZ9TpxY0khPgQKjZAazVED/zNa10Nl2wjczVCYnzxDfSkMSYNbXo61k4yXnTpvmKqVHr+qvgBvJRw/VN2t6ltMXJ0Wzlx/iq8G0sInjvX7jZ/zanGU9y75l72VOzhpcEv8cawN9wzvN04nrS5oKmDnNXtlmde1wUfudSpWbhHcDDhL72E7tAhmpYvxy/Ii2F3dqUsp5HsHc5pdbtYJIKEe5Pv5XDtYU402IrORKuVhoWL8ExOxnf0aAAKDtfRUKEh7ZYuCJLz6ztYrSIZJU0Mig88c/4dfRVm4NcygiAQ3zuEWlMwFoMZ7cF0Z7t0QVQ6Ex9uK2BufA2BFdtg2DM2oQk7k7mhBIvJSsy+T/C94Qa8krvb3ebZrMpfxfS109GZdSwev5hpKdPc4ixunEPXMRCUCOnti9mUChn3DY5jzdEqShucJ2eqvON2fEeNovaddzEWF9NzZDRxPYPY+30+TdUap/l1MdyRdAfeHt6sOGnLwtWbN2MsLCTkkYcRBAGrVeTgmiICIxR0G3Tu3O9fyK9rpVlrIi0hCCoOgVTukNkQHYU7gF8kXXoHYzZDc3gv1Fs2O9udC/K/HQW06I08Ky4D3wgY8ojdbWqaDWTvrKBLcCtetbYfJkehN+v5+96/8397/o++oX355rZvGBA+wGH23bg5B4nEVjRadgCqjrb7aM6IRKSCwIKdzsvCBUEg4rXXEORyKl9+BUSRMbNSkMolbP4sB4sLV6X7y/2Z1HUSawvX0qBroGHBQmTxcfiNHw9AwaFamqo0pN2agOQ3sm+A/YW2drQhCUFQfsjWPubhGmJTF4M7gF8kMcmBeMgkqFLH0rp5i0u3k1Wr9Hy2p4i/JuShqM2EMS+D3Mfudg+tK0a0iETvXYTiuuvw7tfP7jbB1iI2c91Mvs/7njm957DgpgWEeJ87bciNG4fTf7pNH/1XU8oilF7cNTCGbzPKqW3R/8bN9kcWHkb4X9q20pctw0fpyQ3Te1BboubQ2mKn+XUx3NfjPoxWI9u+fxf98eOEzJ2LIJVitYqk/1xEUJQP3Qb8fs3Prrx6YgK9iQvwhMrDner8G66xAK42qmk1tl7WvR5yKTE9Aqnz7IKprg79sWMd7F3H8f6WXCRWEzM0SyA0Bfr9ug2/41E36jm+p5KEEDXyynxCn3jc7jYB1hSuYeqaqVRpqpg/Zj5/GPAH9zASN66Dd6BNtvhYe310gEdHJWK2Wlm827lyGMrbb8d39Ghq330PY3ExXQeEkXxdBBnrSqguUjnVt9+ja0BXhkQOQb78JzwiwlFOmgRAXnoNTdVa0m5J+M2zb7C12O4vaGBEUghC/SkwaTrV+TdcQwHcKlp5YssTPLr5UTSmyzvf6dInhFadBI1/HOrNWy58gxMoqGvlm4xy5iVkIlMVwU2vgeTXnX0dT8baYhAhatcCFEOGoEizb7uW1qTlr3v+yku7XqJHUA++u+07RsWOuvCNbtw4msFzwaSFI+2rpuODfbi1TxTL95fQrHVeYawgCES8+iqCXE7Fi39CNBoZMbU7PgFyNi0+jl7j+DGoF8tMcxpJxUaa7xyFIJdjtVhJ/7mI4Ghfuv6GbOovZJU3ozaYGd4t1Hb+De4M3FWRCBJmpc4iuz6bxzY/dllBPLFfKIJEoLHvLai3uGYA/9fak4TKDNzcsAQSRkLSTXa3qarTcmJvFV1D1ciqCgmxc/Z9svEk9/18H6vyV/Fwn4dZPH4xET4RdrXpxs1lE9kXYofYitl+dfT22OiuaIwWluwtcZJzNmThYUS+8Tr6o0ep/c9/8PT2YPycXrQ2Gdj8eQ6ik/Tbfw9RFIn/dj9qhcAXSbUA5B6sQVWrY/Btv599g237XBDg+q7Btv5vL6Wt6LATcc0EcIAb42/k3yP/zdG6ozy++XG0pkurAPX2kxObEkSVogeGwiIMhYV28vTy2FtQz+YTNXwYvwOJrhFueh0cUIGdvqYYiVQgcsfHKAYPxmfwYLvYsYpWlhxfwrSfp6E2qllw0wKe6v+Ue8vcjesz+GFoLISC9i/+KZH+3JgSxmd7i9AYzE5yzob/uHEEzpxJ45KltGzaRESikuFTkig51kDGumKn+nY+1Bs2otu3n4opw9hWt5eS2nL2rSwgLN6PhL4XroHZnVdP72glgT5ymwJb9EBb4WEnonN52wGM6zKON0e+SVZdFo9vufQg3n1wOBqDByr/BJfaRrdYRd5Yc4J+Sg0DKr+E3vfYJiPZmfpyNacOVpMUqsKjqpiQJ56wi51abS2PbHqEeRnzGB49nO8nfc/QqKF2seXGTYeTMgl8w+HgwnM+emx0N5q1Jr48WOoEx9oT9sLzePXuTdVfXsZYXk6vUdEkD4ng4JoiSrJ/e4CIo7FqNNT86194pqQw+LG/IooiP31xAF2ridHTe1ywdVStN3G4rJnh3ULAqIHa451u+xyuwQAOcHOXm/nXiH9xuPYwj295/JIK2xL6hiCVSahPGe9S7WQ/ZJaTU9XCB6GrEEQRxv6fQ+zuW1mA3EtK5NaPbNn3kI7PvtcXr+fO1XeSVZfF34b+jfdveJ9Ar8AOt+PGjd3wkMPA2ZC3CRrat44NjA/kusQgFu0qRG9y7mxuiVxO9LvvAFDxzLOIJhOjpicTHO3Lpk+Pu4zUat1HH2GuqSHir/9HdEAs4z3uRDgVSJ8bowmN87vg/fsLG7FYRYYnhUBVFojWTlfABtdoAAeYkDCBN0e8SVZtFg9ueJB6Xf1F3Sf38iChTwg1fj3QHs3GVFNrZ08vjNZoZt7GU9wbUUFs+c8w7GkIsL98afnJRkqPN5Ia1ohQXdrh2XeTvonndzzPCzteIM4vjq9v/Zq7u9/tFmZx0zkZNNtWUPqrljKAJ29IoqbFwLeHnD9QRB4TQ+Q//4E+O5vaN99EJpcy4RGbuMnaj49h0Dl3q9+Ql0fjkqUo77oTRf/+mIwWko6NROVVh65P2UU9Y3deHd4yKQN/0T8Hdwbe2ZiQMIEPxn5AcUsx96+7n3L1xf3wJKWFYzB70BTYg9ZtW+3s5YVZuLOQuhYd/yddAv7RMPxZu9sUrSJ7fyjAN0BO0Nr/okhL69Dse1vpNib/OJktpVt4uv/TLJ2wlARlQoc9340bh+MXAam3w+EvwNB+129Yt2D6xwXwv+0FGM3O15jwv+kmgh58kKYVX9Lw+ecoQxWMm9OTpioN6z4+itlJOwWiKFL9+htIfHwI++MfATj4UxGmZjiauoHvi769qOfsyq9ncEIQnh5SWwV6QBz4/n7VuityTQdwsAnjLxq3CJVRxax1s8htyr3gPfE9g/H09qAuYZTTz8GrVXoW7Cjk9bgj+DRk29rGHCDakp9ZS12pmlT/UqirJvS5jnlpaNQ38uLOF3l629OEeIfw1S1fMbfPXHehmpurg8GPgEEFR79utywIAk+PTaKiWcfKw87PwgHCnv8jfuPGUfvWv2lZv4G41GDGPpBCRW4zmxbnYHVCZXrLmp/RHjxI2LPP4hEURG1JC1mbS0kdHsWowWnsqdxDmfr3s/CKZh2FdRpGJLUVulUc6pTZN7gDOAB9Q/uy5OYlCILA/evuZ1f5rt+9XiqTkDgglFr/FFrSM7Go1Q7y9Fz+vf4kCmsr96o/g7ih0Osuu9u0mK3sX1VAUIQXfj+8h++YMSj697+iZ4qiyE8FP3H7qtvZVLKJx/s+zpe3fElyUHIHee3GjQsQOxgi+sDBRbZxo2cxunsofWKUfLitALMLyJgKEglR/34L7379qHzxRbSZmXQfHMHwe5IoPFLHDgdPLjOWV1Dzj3/g1asXAVPuRq8xsenTHLz95Vx/Z1cmJ01GIkj4Pvf7333O7rw6AEYkhYK6BlRlnfL8G9wB/DRdA7ryxcQviPGL4cmtT7Lk+JLf/Z+ze1o4ZlFKvX8P1Bs3OdDTM6QXN/LD4QoWxG1CqmuECW85pG3s+K4KWur1pHAMUdNK6DN/uKLnlanLeGzLY/xl91+I94/nu9u+47F+jyGTyjrIYzduXARBsM0lqDsBxbt+9ZHAkzd0o7RRy+qsSic52B6JlxcxH32ILDKS8scex1BYRN8xsQy8OZ6c3ZUc+LHQIUHcqtVS/uSTiBYL0fPexioKrF9wjJZ6HePn9MRTISPCJ4JRMaNYmb8Sk/W3xWd25dUT5udJ93DfTivg8gvuAH4WET4RLLl5CWPjxjIvYx6v7HkFg8Vw3mujugeiUMqp6zIS1apVDvbU1jb2tx+Pc51fPQNrvoMBs2yCEXZGrzGRvqaYqC4KPFd+jHLSJLy6X97EMYPFwMdZHzP5x8kcrjnMnwf/mSU3L6FrQNcO9tqNGxei113gHQQHFpzz0U2p4fSI8GP+tnwsLiKe4hEYSOyihSCVUjrnIYzFxQy5PZHU4VEcWl/Crq/z7LqdLooiVa+8guHUKaLf+Q+y+Hi2Lz9JRW4zY2alEJV0piPlrqS7aNQ3/uYuqtUqsreggeHdQmzFsKV7bRPIHNByaw/cAfxXKGQK5o2ax+N9H2d1wWoeXP8gVa1V51wnkQgkDQqn3rcrqsPZGMsurvqxo1hxsJScKhUfKpcjyH1g7F8dYvfgmiIMWhMpqh1gtRLy1OXNGN9dsZvJP07moyMfMTp2NKvvWM30lOlIHSD76saNU5F5w8D74dRaaGqvwCYIAk+NSaKwTsPaY+f+3nEW8rg44j5ZhKg3UDx9BoaTJxk9LZl+N8ZybHs5Gxdl262wrWHRJ7SsXUfoc8/iO2IEh9YVc3J/NYNvSyB5SHsFxmHRwwjxDmFl/srzPiunqoVGjdHWPgZQsteWfcu87eK7vXEH8PMgESQ81u8x3hn9DgWqAu766S62lJxbrJY8JAKrKKE6YgiqVT86zL9GjZF5G07x54hMgusPwo2vgo/9p281VLSSvaOCHv38EFcvI3DqVOQx0Zf0jGJVMU9teYrHNj+GVJCy8KaFzBs1j3Cf357Z68bNVUfaHEA4r7DLhF4RdAvzZf7WfKcUiv0WXqmpxC9fjiCXUzJzFrrMQwy7O4lhd3ej4HAdq98/0uG66a07d1L37rv4T5xA8Jw5nDpQzYHVRSQPiWDQxC7nXO8h8WBS10nsKt913tbgHbm28+/h3UJsnQCVRyD++g712ZG4A/jvcFP8TXx767fE+cXxzPZneGP/G+221EPj/AhP8Key63iaV61y2IjRtzecQmZoZK7+U4i9Dgbcb3eboiiy65s85F5SuuR8i+DpScijFz9jXGVQ8dbBt5j842TSa9L5w4A/uNXU3Fy7KGMgdRJkLjunpUwiEXhqTDdO1ahZl13tJAfPj2diAl1WfIFHWBilD81BvX07/W6MY9xDPakpbuGHtw9RV9YxRb3qbdsof+ZZPJOTiXjtdTLWFrP58xyiuwdww4zfVlu7o9sdWEQLPxX8dM5nm3Jq6BOjJMzfC8oPgmhxB/CrmVj/WJZNWMb9qffz9amvue/n+zjRcOL0571Hx6AR/KnR+qFNz7C7P0fLm/kqvZTFkT8iNarhtvccot9beKSOilNN9O/ngWHDTwTPno1HyIWzfoPFwNLjS7ll5S2sOLmC27vdzprJa5jTew5yqdzufrtx47IMeaytpeyrcz66tU8U3cJ8eW9zrsuchf+CLDKS+OXL8OzWjfLHHqd23jy69g3ktqf6YdCa+e5fGRxcU4TlMivpRVGkcelSyp94Es8uXQh//yM2LCvg4E9FdB8czq1P9kUq++3feQnKBPqH9Wdl/sp2BXY1LXqOlDUzLrVtt69kLwgS26CZToo7gF8EMqmM59Oe58OxH9Koa+S+n+/jvUPvoTfr6TYgDG9fGRXxY1GtPP+5S0dhtlh5eWU247xz6duwFob9AcJS7GoTwGy0sOe7fIKifAj48V08IiIInvPQ795jspr4NvdbJv4wkbcz3iY1KJVvbv2Gv1//d0K87b/d78aNyxM7GKL624rZfrV7J5UIPHNjEnm1raw56hoV6WfjERRE/NIlBEyZQsMniymecg8h1HLf34bQbVAY6WuK+O7NDOrLLy0bF81mal5/nZp//gvfMTfg/5+F/PhZCcXHGhh+TxI3PpCKh/zCdTKTu02mSFVEVl3W6bXNJ2oAuCm17dy8ZJ+t8NfzwtKrroo7gF8CI2NG8uMdPzKp6yQWZy/m7p/u5nBDJj1HRlMfkELN9nSsmsubNX4xfL63mNyKOv6j+AwCE2DkC3azdTaHN5WibtDTP7IG4/Fswv74HBLv8xd9mKwmVhes5o5Vd/DavteI9Ilk8bjFLBy30N3T7cbN2QiCLQuvz4XCcxUdJ/aKJDncj/e35LlcFg4g8fEh8rVXifn4I8wNDRTfPYXW5Z8xdmoXJjzaG02zgW/+mcH6hceoyG26YLuZPjeXsocfpmnFl3jMfJzctMf55j9H0WtM3P6HfvQdE3vRMsrjuozD28O7XTHbppwa4oMVtvYxswHK0yF+2BX9GzgbdwC/RJSeSl4b9hoLb1qI2Wpm9obZ/Cj/HEGA8qA0WuzUE17WqOU/G3P5T/hGfFtL4NZ3HVI5qarTcmh9CYm9A5EsfRvvvn3xv/XWc64zWAx8c+obblt5Gy/vfhkvDy/mj5nPsgnLGBxpn/Gibtx0enpOtk0p2/+/cz6StGXhhXUaVmdVOMG5i8PvhhtIXP0jvqNHU/fuu+TdMAafjZ8z5eF4+o2NpfxkE6veOcxXrx/k2PZy6srUmIxnKtb1OTmUPfkUJ6bcT1mxkZN3vcvGsp7kZ9TSc1gU9/xlMNHJlza8yEfmw/gu41lftB6tSUurwcze/AZuSgm3vQRUZILF0KnPvwHc+pSXydCoofww6Qc+OfYJS3OWMjooEIl5GHU/fEvA5Ds61JYoiryyKpu+Qh63tHwN/aZD1xs61MZv2d3+xSmkUoHU1t1o6+oJnz+/3VuwyqDi+7zvWZ6znDpdHX1C+vDnwX9mZMxIJIL7/dCNm9/FQw6DHoLt/4T6PAhJavfx+J4RpET68/7mPG7rE4WH1DV/pjyCgoj+7/voDh+hcdlSGpcsofHzz4kdNYqEnr2p8kjkVIWBnV+dkapWeBjxNjah11nRed+KdehkALyNMgbfFkPvUTF4+V6+mNPkbpNZlb+KjSUbkWmHYLRYGdfzl+3zPbY/4zp3Ea07gF8BCpmCpwc8zZTuU/if1zIsmxWUlknJ2Po+t4+Yi0Km6BA7q7MqOZBbzoGgRQiyKLj5Xx3y3Atxcl815SebGDYhDN3/fYL/pNvw7msTi8lvyueLk1+wpmANeoueIRFD+OeIfzIkYoh7WpgbN5fCoNmwa57tLPyWee0+kkgEnr0xiYeXHWLl4QqmDIp1kpMXRhAEFAP6oxjQH1NVFU0rvqTl559p3bYNb6AvoFVEoPGJRKMIR6sIx6CMIiBSSbd+MQTEBKAM9Sayq/KizrkvRP+w/nTx78LKvJUEqOIJ8pHbpo+BrYAtNAUUQVdsx5m4A3gHEOkbyd/veoElh7dRHjOKms/fYlztt0xPmc60HtNQeiov+9lNGiOv/ZTD24ErUWpLYNZq8Lr8510s2hYje77PI7KrkuAtn6CRSlE+/QRrCtewMm8lB6sPIpfIubXrrUzrMc19vu3GzeXiGwa97oYjK2DMy+Ddfrv4ptRwekX789+tedzRPxqZi2bhZyOLjCTsj88R9sfnsOp0GEtLMRYVYaqsQhYViTwhEXmXeCSennbzQRAEbu92O+9nvg9lOdzcoxdSiQAWM5QdgD5T7WbbUbj+/wmdBEEQ/r+9+w6PqkofOP49k5k0EkhIARJSIIHQQQhSA0joKqAIVmyroqwVXctvVVBXZS3rCuqKbVnFXQuCFKXXgIAcOtykAAAgAElEQVSE3iEQSAghBALpfe7vjxsUQ0ImmZnMTHg/zzNP4M495773PMm8c8+95xxih8eQ5xNG/+Pt6eXTiY92fsSwucN4ffPrHMo6VKd6X1u8nw5FO7ixcCH0ehhaD7Rx5FXb8P0RSovL6dk2h7zly9g7Ipph62/nhYQXSMtL4/FrHmfl+JW80vcVSd5CWKvPZCjNh23/uewtpRRThrYlNauQb7fW74yPtmDw8sIzJobGI0YQcP99NB4xAs+YtnZN3hfd0PoGFIpiz0SGXhw+dno3lOS5/P1vkARuUzG9muPpZSC5xVCeP9WDuTfOZXD4YOYdmccti27hjp/uYN6ReeSV5NVcGbBkTzordhzhw0afQUAbiJ9q5zPQHd9zliNbMyhpn8KJNx/jZAC8E3OUgWED+XzY5/x888882OVB/D1r92CJEKIazTtDq4F6N3pZyWVvXxcTTM9If95fdYTCEsesxe2KmjdqTqCxPe5Nduizr4HefQ6SwMUfmTzciL0xigv+bTk6byNtfFrxZtybrJ6wmud6PkdBaQFTf5nKgG8HMHnlZOYdmUdWUVaVdZ3JKeL/5u1mZpM5+JaehZtmgbtt7qlXJbs4m6XHlzJ1zat8/1kCWV6nKf31PQIulKP9ZRIr7ljLm3Fvcm2La+XhNCHsoe9jkHsK9l++OJJSimdHtCMzt5h//5LsgOBck6Zp5GZ2Rbmf43D2Xn3jiV/0YbiNQxwbnA3IPXAb6xQXyo7FRzjSdAAxCxfhf8s4mng04a4Od3Fn+zvZlbmLFSdWsCplFVN/mYpBGegU0IkezXoQ2zyWbsHd8DX58twPuxldtoxB2lq47kVoabvl7jRNIz0/nd2Zu9mVuYudZ3ayP2s/ZrOZEcfuJ6jEl7YDs2n7Whl+48fTfuyTNju2EKIaUfEQGAObPoDO4y9bGrhnZFPi2wXz8dqj3HltBE28Zbndmuw7lUPmmRj8m3qw+NhiugV20Vcgi7ne0aHZhCRwG3MzGeh5cwxr58CB/y2gz803oSqmOlVK0S24G92Cu/FM7DMcOn+IlSdW8uvpX/nqwFf8e9+/USiaurekMM9EbMAuVgT0JrzTjQQXncfPw69WT3gXlBaQWZjJ6fzTJGcnc/TCUZKzk0m6kMS5onMAeLp50jGwI5O6TCI6swdHNudz7Q2RBM6eRam/P8HPPG2XdhJCVGIw6PfCFz0BxzdAq7jLdnlmeAyjZiTw8fqjPDeinQOCdC1L957GoHkysOUglh5fyrMRo3EvPN8gus9BErhdtO/Tgm3zD3A4L5aOq9fSZMjgy/ZRStGuaTvaNdX/CIvKitiduZtVyZv4dsd6mnkdYpbRB818ChaPB/SVdoK8gvDz8MPDzQMPNw9MbiaMykhReRHF5cUUlRVRUFZAZkEmBWUFfzhmI1MjWjdpTb/QfnQK7ETXoK608W+DyWAi52wh38z+lRbRTWh1bgOZe/cS8u47uDWx/xPvQogKXW6FVa/Cpg+rTODtWzRmTNcQ/r0xmXv7RtKssacDgnQNmqaxYFca/aIDGdd2DKtSl5Gw77/EA0S49vjviySB24HBzUDv8e1ZMdvA3jlr6VdFAq/M0+hJt6BY3vihmJln59PfcJqiexZyzKcJJ3NPcrbwLGcKznC28CwXii9QUl5CSXkJeaV5lJnL8DB64Onmia+3L15GL+JC4wj0CiTYO5gg7yAiG0fSzLtZlVfw5nIzK77YhwIGDm1C5n3v0ygujsajRtmhdYQQ1TJ56UuNrvs7nE2CwOjLdpkyNIbFu9OZseoIr9/U2QFBuoZtJ86TmlXIU0Pa0iekOQGeASw6lUC8X7h+D7wBuLoSeHIClBZC22F2P1Sba0PY+t0eDhV2pGvidnxiu9dY5vWf9hOXPps40w4Y8Q5eEX3pCHQM6GjXWBOXnOD0sRyG3hNDzutTUEYjLV59RSZkEcIRej4AG/4Jmz/Up0yuJDzAmzt6hfP1lhT+1L8VrYN8HBCk85u3Iw0vkxvDOzbHaDAyMnI43xz4muxWA2nSQD7bLHqcWCkVppSaq5TKVkrlKKXmKaXCLSyrVfPqZl3otaRpsHIqzHsQsk/a/XDKoOhzWycKvYLYOnNJjRP5L9x1iuwtX/O0aa7ejdbzAbvHCJCy/xyJPyUT06s5/lt/oGj3blq8+gqmFi3q5fhCiEp8gqHLBH1il/yzVe7y2OA2eBoNTF9ysJ6Dcw3FZeX8tDud4R2b0chDv0690TuCMqVY5tdwhr/WmMCVUt7AaqAdcA8wEWgDrFFKNbLwOLOBPpVeh69UwOaUgps/BXMZzHsIzPYfS9mqZygtmpZw0L07ad8vqXa/Ixm5LPhhDu+4z8Ic0R9Gz7zsCVR7uJBRwPLP9tE0pBE92xVw9uNZNLnpJhqPGGH3YwshrqDvY1BWBL9+UuXbQb4eTL4umuX7M9h09Fw9B+f81hw8Q3ZhKTd1b/nbtvanDxNVUsqi3GMOjMy2LLkCfxBoDYzVNO1HTdMWAKOBCGCShcdJ0zRtc6VXQc3FbCwgCq5/V5/IPuFdux9OKcWQKXEYFKxfnEF53uVLjeYXl/HOl3N53/APtIA2GG77Goz2n6GouKCUnz7ajTIoht8dReZfn8MUGkqzv/7V7scWQtQgKAZiRukJvKTqJYr/1L8VIU08ef3n/ZidcLlRR5q/I41AHw/6RQX8tk0dW80NxgB2nttDaq7rzWhXFUsS+Ghgs6ZpSRc3aJqWDGwExtgrMLvpepveRb32TUjZbPfDNQ5sRK9Bfpz3jmDLO/P/8J6maUz/Zjmv5k3D5N0E093zwMvP7jGZy80s/2wfOZmFjHioEwUfvEVpRgahb7+Fm4+lnSpCCLvq9yQUnoftX1X5tqfJjedGtmNvWg7zdzjvcqP17UJBCasPnmFMt0tWb8vLhPRdjAofAsCy48scGKHtWJLAOwJ7q9i+D+hg4XEeUUoVK6UKlFKrlVKXj4+oT6PeAb8I+OEB/Q/Ezrre3ptmbmfYlRZI5u7fZ1H6eOF67j06BT9TGR73zIcmoXaPBeCXeUdJ2Z/FgNvb4rXlJ3IWLSJw8iN4davfxxKEEFcQ3gvCeusTu5SXVrnLjV1C6Brmx9vLDlFQUlbPATqnxbvTKS3XuOmaSz5Pj60BIKTdGLoFdePn5J8dFJ1tWZLAmwJVZbkswJKnAeYAk4EhwENAALBaKTWougJKqYeUUolKqcTMzEwLDlFLno3hls8hNx3mP6KvTmNHSimGPNEPg1bOyn8lopk1Fqxax+jt9xFqzMF94lxoZul3IetsX36CXatS6XxdSyLUcTLefBOfwYMJfOSRejm+EKIW+j8J2amwb36VbxsMipeub8/pnCI+XS9TrILefd4m2IeOIY1/35i0ErwDoEU3RrYayZHzR0g6n1R9JS7C7pNaa5o2UdO0bzVNS9A0bQ7QHzgF/O0KZT7RNC1W07TYoKAg+wQW2gNGTIfDS2D+Q3ZP4n5tw+je6gJZWgAL//Yd/dbfha9bOcb7f0bV06QCO5ansGneUaJjg+nZw0jaU0/hER1NyFtv/TZbnBDCibQZDkHtYOP7+kiaKsRGNmVU5+Z8vO4oGTlF9Rygc0k5V8C2E+e5qXvo78NgzWY4uhpaXwcGA8Mih2FQBpYcr/7BYldhyaf2eaq+0q7uyvyKNE3LBX4Ceta2rM1d+yAMfRX2/gDzJ9k9iXefcjPNsndz8lQQSXmDMT6wDGPL+um23rkyhV/mJRHVPZjBN7fk1J8no0wmWn70kdz3FsJZGQzQ93HI2AtJq6rd7fkR7SnXNN74+UA9Bud85u9IQykY2+2S7vOMPZCfCdHxAAR6BXJt82tZklzz8F5nZ0kC34d+H7yyDsB+K47tHC3X7wkYMg32zoUfH7Hf8DJNI3P9xwwJeoumWfvZljeR9HT7P7AGevLeODeJqO5BDJkYTfozUyg5dYqWM2fg3rJ+7rsLIeqo83hoHAob/1ntLuEB3jw8MIoFO09dtcPKSsvNfLM1hX5RgYT4ef3+xsUvPlG/z4g5qtUoUnNT2XduXz1HaVuWJPCFQG+lVOuLG5RSkUC/ivdqRSnVGLgB+LW2Ze2m/1MQ/zLs+U6f6KU417b1550h5/OxNN80jd2BHeg+xJvGuSdY/tle0g7b7yE6c7mZX35I0pP3NUHET2xD+tNPk//LJlpMm4Z3D9utcCaEsBOjO/SeDMcTILX6j83Jg6Jo6e/Fywv2UlpurscAncPyfRmkZxdxb9/IP75xdDU06wS+zX/bFB8Rj9FgZEmya3ejW5LAPwWOAwuUUmOUUqOBBUAqMOviTkqpCKVUmVLq5Uu2PaOU+lQpdYdSapBS6h704WfNAecacBz3NMRPhb3z4F994dg629R7eDmlH/TGPXUj75om0erPC4h56H7iWp/CMy+Dn2Zs58yJHNsc6xL52cUs+OdOdqxIodOAUOInRpP+5BPkrV5Ns5dexG/czTY/phDCTnrcC15NYf071e7iaXJj2o0dOXImj9kbj9dbaM5i9i/JhDf15rp2wb9vLM7ThwtXdJ9f1Ni9Mf1D+7P0+FLMmut+2akxgWualg8MRp857SvgayAZGKxpWt4luyrArVKdh9C72mcAK4B/VJTtr2lagi1OwKbipsD9S8Fggi9Hw+Kn6nY1rmlwdA38+3r473iOFTZicqN/cOejrxIWoN9vDn/pWXoVLcct/wLz3trG7jUnbXY/Jj3pAt+9sZUzx3MYcm974m6OIP2xx8hft57mr7xC0zvvtMlxhBD1xMNHX2r0yDJI31XtbkM6NCO+XTD/XHmY09lXzwNte9Oy2Xr8PHf3icDNcMkslscTwFyqr7VeyahWozhTcIbtGdvrMVLbUs5+Ez82NlZLTEy0SV2fJRzDw2hgYp/IK+9YUgBrXteX9PNpps9L3GkctOh65SlOS/L1xL3hPUhLpMAjiH8UjGRL0zH8+8E4An3+OMNaaVoahybczb5WEzjr04aIzgHE390eL1/3Op1fYW4J25adYM/qk/gGeDJiUmf8/RQnH3uUgs1baPG31/AbN65OdQshHKwoG97rDK0Hwq1VT+4C+pPYQ99bx7COzZl5+zX1GKDjPP3dLpbsTWfTC/E08TL9/saiJ2H3d/Bc8mUzXBaUFjDou0Hc2PpGXurzUj1HfGVKqW2apsXWtN9VsxqZ2ayx+VgWKw9kcPB0LtNGd8TkVk0HhLs3DH8d2o+GhHdg80fwywxoGgXtRkGjYHBvBO4VqwCd2q5305zeA1o55ibhfBv8FFNTutEvJoQ5t15DE2/TZYcxhYYS/dkHuD80iRO+3UlyG83/XvuV/uOjieoWjJvJsqFdJYVl7FiZwq6VqZSVlNOuTwv63RKN+eghku9/mtKTJ2nx5hv4jR1b1+YTQjiaZxPo9RCsfxvOHIDg9lXuFh7gzeRB0by38jC3xobRv01gPQdav87mFbNo1ylu7Rn2x+RtLoeDi6HN0Cqnp/Y2eTOo5SCWn1jO872ex2S4/DPa2V1VV+DlZo23lh1k1rpj9G7dlH/d2QP/RhZc7RZkwYFF+nCz4wlQ+Z6J0QtaxkLYtaT6duX+dY04mlXMM8NjeHhAFAbDlRcmKU1LI+WhSWSdLeXQgL+QnWvAw9tIm57NaNe7BcGRvpct7ZmfXcypwxdIO3yeo9szKcovJap7EL1Gt8Yv2Ius2f/hzHvvYQwMJPSdt+WBNSEagoIseK+TfiEx7rNqdysqLWfk+wmUmc0sfWLAbytyNUQzVx3h3RWHWTllANHBvr+/cXwjzB4Ft3yh96BWYU3KGh5f8zgfD/mYfqH96inimll6BX5VJfCL5m0/yfPz9tCssQef3h1Lu+aNay50kbkcSgv07vKSfH2Kw4Aockph1rqjfJaQTGMvEzNvv4berQNqrq9C+YULpD76KAWJ2ym7awrpQT1J3nue8lIzHt5G3D2NmDzdMHm4UZRfSvaZQgDcPd1o2b4pPUZEEBzRmJLUVE6/9hr56xPwHTqEFq+9hptf/QxXE0LUg+Uv6dOrPpqoL9BUja3Hs5gwaxP39Ilk2uiqRgK7vtJyM/2mryamuS9f/anXH99c8jwkfgHPHgUP3yrLF5cXM/DbgYyIHMG0vtPsH7CFJIHXYEfKeR76ahvn80sYH9uSP18XTUt/71rXU1Jm5ustJ5i5Ooms/BJGdw3hxRvaE+zrWeu6zMXFpL/0EjkLF+Hm54fvvQ9wrm085zKKKS0u119FZbiZ3AiJ9iM0xo/Alj4Y3AwU7tnLuS8+J3fZcpTRSLMXnsfvttsuu3IXQri43Ax4vwt0vgXGfHjFXacu2MuXm0/w/aQ+xEY2racA68/CXad4/H87+OLeWAa3a/b7G5qm91S06AK3/++KdTy3/jl+OfULqyesdppudEngFjiTW8SHq5P436+paGjc1jOcSQNb15jINU1jf3oOy/dlMH9HGilZBfSNCuCFke3p3LKJ1XEV7tlD5oyZ5Cck4BYQgN+4cXjEtMUjKgr3yEiUuzulp9IpOZpE8dFj5K1bR8GWLRh8fPC//Tb875qIqVlwzQcSQrimn5+FxM/hse3gH1HtbvnFZQx7bz0eJgM/Px6Hp8mtHoO0L03TGP3BRnKKSlnz9KA/3qo8uQ0+GwxjP4Zut1+xnlUpq3hyzZN8MvQT+oTUz7TWNZEEXgtpFwr5cE0S321NpcysEernRbdwP64J8yMq2IfCknJyi0rJLSojNauAlQfOkHahEKWgZ0RTJl8XxcC2QTa/2i3YvoOzH8wkf/MWfT5fAKVQ7u5oxcW/7WcKDcX/zjvxmzAeNx8fm8YghHBC2Wkw4xroMr7Gq/CEI5lM/PxXHhkUxXMj2tVTgPa3dG86D8/Zzlu3dGFCbNgf31zxsj6K6C9J4HXlNbeKyooY+O1ARrUexdQ+U+0YseUkgddBalYBy/adZkfqBXamXCDtQuFl+3gYDfSPDmRYx2bEt2922dAwezAXF1Ny/MRvV9zm/HzcW7fCIzoaj9at5R63EFejJc/Dr5/Ao1uveC8c4Lm5u5m7/STzJ/elS0vX/7woN2uM+Od6yjWN5U8O+H3db9C7z2dcA01bw8R5FtX37Lpn2XJ6C6vGr8JocPwDfzKMrA7CmnrzQNxvM8ZyJreIlHMFNPIw4utpxNfThI+H8Y8TBdQDg4cHnjFt8YxpW6/HFUI4sf5PwbbZsHY6jPv0irv+3/XtWXc4kye/2cmix/q7/FPpC3amceRMHh/e0f2PyRv0hV/OJ+tLsVpoaORQlhxfwraMbfRq0avmAk5C1pC8gmBfT2Ijm9K+RWNa+nvTxMtU78lbCCGq5NtMHxe+53t9XPgVNPEy8c/bupF8Lp9pC117AY+SMjPvrTxMx5DGjOzU/PId9i8EZYCY6y2us39of7yMXqw4scKGkdqfJHAhhHBVfZ/QJ5Va+2aNu/ZuHcBj10Xz/baTLNiZVg/B2cd3iamkZhXyzPCYqufYOLAQwvuCT5DFdXoZvYgLjWPliZWU22tFSjuQBC6EEK6qUYC+Utn+BZC+u8bdH49vQ48If16cv5eUcwX1EKBtFZWWM2PVEWIj/BnUtooEnXkYMg9Ch9G1rnto5FDOFZ1j+xnXmRtdErgQQriyPn/Wp1ld80aNuxrdDLx/WzdQ8Pg3O1xu2dEvNx3nTG4xfxkeU/WonwML9J/tb6x13QNCB+Dp5sny48utC7IeSQIXQghX5uUHfR+Dw0sgdWuNu7f092b6zV3YmXqBt5YerIcAbSMjp4iZq5MY0DaIXlXNcqlp+sIlYb2hcUit6/c2edM/tD8rU1ynG10SuBBCuLpej+iLLK14SU9kNbi+Swvu6RPBpwnJzN12sh4CtI6mafx1/h5Kysy8Ut20sCe3wtnDcM1ddT7OsMhhnC08y87MnXWuoz5JAhdCCFfn4QODnoeUTXDwJ4uKvHhDB/pFB/B/8/aw7USWnQO0zsJdp1h54AzPDIuhVWCjqnfa/iWYGkHHuq+6GBcah8lgYlXKqjrXUZ8kgQshREPQ/R4IbAsrp+qLLNXA5Gbgwzu6E+LnyaSvtnHyvHM+1HYur5hXFu2na5gf9/dvVfVOxXmwbz50vKnahUss4ePuQ68WvVidshpnn+QMJIELIUTD4GaEIa/AuSR9ghcL+Hm789k9PSkuM/Pgl9vILy6zb4x1MHXhPvKKynj7li7Vz8OxfwGU5EH3iVYfLz48nrS8NA6fP2x1XfYmCVwIIRqKmJEQ0U+fna0ox6Ii0cE+zLz9Gg6dzuHR/26npMx5nkxfuvc0i3en89jgaNo2u8KV9Y45EBANYdbPojYobBAKxeqU1VbXZW+SwIUQoqFQCoa9BgVnYeP7FhcbFBPMa2M7seZQJo/9b7tTDC9LzSrgxR/30KFFYx4edIW53s8mQcov+sNrNlhQKtArkG7B3VziPrgkcCGEaEhCe0CncfpqXDmnLC52Z68Ipt3YgWX7Mnjym52UOTCJZxeWcv/srRSXmZlxezdMlec7v9TOOaDcoOuVlw2tjfjweA6dP8TJXOd+Ql8SuBBCNDTxL4Nm1pfVrIV7+7Xixevb89OedJ7+fhfl5vp/kKukzMzkr7eRfDafWXf1IDr4Cl3n5WWw83/QZij4VjEveh0NDhsM4PTd6JLAhRCiofGP1Ffj2vM9JCfUqugDca15bkQ7Fuw8xZPf7qSotP4mNbk43ntj0jmmj+tC3+jAKxc4ugryTls19rsqYY3DaOPfxum70SWBCyFEQ9T/KfALh5//YtGwsks9MiiK50e2Y9GuU9z6yWYycorsFOQffbgmie+3neTxwdHc0qNlzQW2/QcaBUHbETaPJT48np2ZOzlXeM7mdduKJHAhhGiITF4w4u+QeQC2zKp18YcHRvHxXT04kpHLjTM3sDP1gh2C1JWVm3lt8X7eWX6Ysd1CeGpo25oLnT0Ch37Wx7+7mWweU3x4PGbNzLqT62xet61IAhdCiIYqZiS0Ga4vN5qTXuviIzo1Z97kvniYDEyYtYlvt6bYfIKTCwUl3Dd7K59vSObevpG8M75r1QuVVLbxfTB6QK+HbRrPRTH+MYQ0CnHqbnRJ4EII0VApBSOn613oK16qUxXtmjdmwZ/70yPcn+d+2MOtn2zm4GnLxpjX5HBGLmM+3MjmY+d4a1wXpo3uiPFKT5xflJMOu7+FbnfWat3v2lBKMTh8MJtPbSa/NN8ux7CWJHAhhGjImra+5IG29XWropE7Xz/Qizdv7syRjFyun7GBaQv3kV1Yu3vrF2Xll/DW0oOM/XAj+cXlfPNQbyb0DLO8gs0fgblMX4XNjuLD4ykxl7AhbYNdj1NXytnne42NjdUSExMdHYYQQriu0kL4qLf+70d+AfdqFgSxwIWCEt5dfpivt5ygkYeRUZ1aMKZbCL1aB1Q/1WmFzNxiPks4xlebT1BYWs6ozi148fr2tGjiZXkAhRfgvU7Qdhjc8kWdz8MSZeYyBn03iAGhA3gjrub11m1FKbVN07TYmvYz1kcwQgghHMjkBWM+hNnXw8pXYNRbda7Kz9ud18Z24taeYXyxMZnFu0/xbWIqzRp7MLRDM0L9vAn0cSfI1wMvkxuHM3LZn57DvlM5HEzPpcxsZnTXEB4dHH3lMd7VSfwcSnKh3xN1PgdLGQ1G4kLjSEhLoNxcjpvBze7HrA1J4EIIcTWI7A/XToJfZ0GH0fr/rdAptAn/mNCNwrHlrDqYwYKdp5i/PY38ksvHjft5m+gY0pj7+kdya2wYrYN86nbQ0iLY/DFEDYYWXa2K31IDwway+NhidmXuonuz7vVyTEtJAhdCiKvFkKlwZDks+LPVXekXebm7cUOXEG7oEgJAQUkZZ3NLyMwrIr+4nOhgH1o08bTsyfKa7Pov5J+Bfk9aX5eF+oX0w6iMrD251ukSuDzEJoQQVwv3RnpX+vnjele6HXi7GwkP8KZHRFMGtA0ixM/LNsm7tAg2vAch10CrAdbXZyFfd196NO/BulTnGw8uCVwIIa4mkf30sdO/zqr1NKsOtWkmXEiB+Kk2WXWsNga1HMSx7GOk5qTW63FrIglcCCGuNvEv68PL5j0E+WcdHU3NstMg4R/Q7gaIuq7eDz8wbCAAa0+urfdjX4kkcCGEuNq4N4Lx/4GCc/DDA2CuvwVL6mTlVD3G4a875PBhvmFENYlyum50ixK4UipMKTVXKZWtlMpRSs1TSoVbWNZTKfW2UipdKVWolNqklKq/GxhCCCEu16ILjHobjq2BdXUfVmZ3KZv1SWj6PqavsuYgA8MGsi1jG7kluQ6LobIaE7hSyhtYDbQD7gEmAm2ANUopSx5h/Bx4EHgZuAFIB5YppbrVNWghhBA20P1u6Ho7rPs7JDnhnN/mcljyLPiGQNwUh4YyKGwQZVoZG9M2OjSOS1lyBf4g0BoYq2naj5qmLQBGAxHApCsVVEp1Be4AntI07VNN01YBE4AU4FWrIhdCCGEdpeD6dyGoHcx7UL/X7Ex2zIH0XTD0VZsMebNGl8Au+Hv4O9V9cEsS+Ghgs6ZpSRc3aJqWDGwExlhQthT49pKyZcA3wHCllEetIxZCCGE77o1gwpdQVgzf3AHFeY6OSJedBiunQVhv6HyLo6PBzeBGXMs4Ek4mUGYuc3Q4gGUJvCOwt4rt+4AOFpRN1jStoIqy7kC0BccXQghhT0FtYdxncHoPfDcRykocG095Kcy9T/9SMXpmvQ8bq86gsEHklOSw88xOR4cCWJbAmwLnq9ieBfhbUfbi+5dRSj2klEpUSiVmZmZaEKIQQgirxIyEG9+Ho6thwWQwmx0Xy8ppkLoFRs/Qv1w4ib4hfTEajKw/WbdV3WzNKYeRaZr2iaZpsZqmxQYF2WetVyGEEJV0n6iPEd/zfZ3XD7fagcWw6QPo+YBTdJ1fqpGpET2a9SAhzTkmwLEkgZ+n6ivt6q6uLXGqdTMAAAp9SURBVC0Lv1+JCyGEcAb9p+iLnmz6ABLerd9jZx2DHyfr06UOr7/lO2sjLjSOpAtJpOelOzoUixL4PvR72ZV1APZbULZVxVC0ymVLgKTLiwghhHAYpWDEdOg8Hla9CktfqJ/u9MLz8N3doIDxs8HonM84x4XGATjFVbglCXwh0Fsp1friBqVUJNCv4r0rWQSYgPGXlDUCtwLLNU0rrmW8Qggh7M1ggJtmQe/JsPkj+P4eKC203/FyM2D2DXDmIIz73KETttSkVZNWhPqEsiFtg6NDsSiBfwocBxYopcYopUYDC4BUYNbFnZRSEUqpMqXUyxe3aZq2A30I2T+VUg8opeLRh5C1Aqba7jSEEELYlMENRrypd2UfWARfjoECO9z1PH8cvhgOWclw53fQZqjtj2FDSin6h/Znc/pmSsod+7R+jQlc07R8YDBwGPgK+BpIBgZrmnbpgEEFuFVR533Av4G/AT8BYcAITdO2Wx29EEII++rzZ71L+9RO+GSgbWdsO3MAvhihd5/fvQCiBtuubjuKC42jsKyQbRnbHBqH0ZKdNE1LAcbVsM9x9CReeXshMKXiJYQQwtV0HAuNQ+HHR2DOzdB5gn5l7lPHUULmctj+pT5czOgJ9y2BZjVNK+I8ejbvibvBnQ1pG+gT0sdhcTjlMDIhhBBOJqwnPLwBBj4H++bDhz0h8Yvaz9x2bB3MGgCLn4TgDnD/UpdK3gDeJm9im8c6/EE2SeBCCCEsY/KE6/5PT+SBMbD4KXinLcybBEfXVL0sqabB+ROw6xv4723w5WgoztGXM73vZ2jaqv7PwwbiQuNIzk7mZO5Jh8VgURe6EEII8ZvgdvqVc8omPTHv+xF2fwOeTcCnOXgHgHdTUAY4mQi5p/Rynn4QP1V/ut3k6dhzsFJcyzj+vvXvbEjbwG3tbnNIDJLAhRBC1J5SENFXf418Cw4vhWNroeCc/rR61jEoK4Lw3vo+4X30LnNDw+j4jWgcQZhvGAlpCZLAhRBCuCiTp/6gW8exjo6kXsWFxjHvyDyKyorwNNZ/j0LD+CokhBBC1LO4lnEUlReRmJHokONLAhdCCCHqILZZLB5uHg6blU260IUQQog68DR6Mj1uOm382zjk+JLAhRBCiDoaEjHEYceWLnQhhBDCBUkCF0IIIVyQJHAhhBDCBUkCF0IIIVyQJHAhhBDCBUkCF0IIIVyQJHAhhBDCBUkCF0IIIVyQJHAhhBDCBUkCF0IIIVyQJHAhhBDCBUkCF0IIIVyQ0jTN0TFckVIqEzhhwyoDgbM2rO9qJG1oPWlD60kb2oa0o/Vs3YYRmqYF1bST0ydwW1NKJWqaFuvoOFyZtKH1pA2tJ21oG9KO1nNUG0oXuhBCCOGCJIELIYQQLuhqTOCfODqABkDa0HrShtaTNrQNaUfrOaQNr7p74EIIIURDcDVegQshhBAuTxK4EEII4YIaRAJXSoUppeYqpbKVUjlKqXlKqXALy3oqpd5WSqUrpQqVUpuUUgPsHbOzqWsbKqVilVKfKKUOKqUKlFIpSqmvlVKt6iNuZ2PN72Klep5XSmlKqQ32iNOZWduGSqn2SqnvlVJnK/6mDymlnrBnzM7Gys/EcKXUfyr+lguVUoeVUn9TSjWyd9zORCnVUik1syInFFT8PUZaWNaglHpBKXVcKVWklNqllBpn6xhdPoErpbyB1UA74B5gItAGWGPhL9znwIPAy8ANQDqwTCnVzT4ROx8r2/A2oCMwAxgJPA90BxKVUmF2C9oJ2eB38WI9rYEXgTP2iNOZWduGSqlYYAvgATwAjALeBdzsFbOzsaYNK95fCQwAXkJvv8+Ap4Ev7Bi2M4oGJgDngYRaln0NmAZ8gP65uBn4Xik1ypYBommaS7+AJ4ByIPqSba2AMmBKDWW7Ahpw3yXbjMAhYKGjz81F2jCoim0RgBl41dHn5irtWKmeZcAsYC2wwdHn5SptiH5Bsh+Y7+jzcOE2HFbxmTis0vbpFeW9HX1+9diOhkv+/UBFu0RaUC4YKAZeqbR9FbDbljG6/BU4MBrYrGla0sUNmqYlAxuBMRaULQW+vaRsGfANMFwp5WH7cJ1SndtQ07TMKradADKBUBvH6eys+V0EQCl1B3oPxgt2idD5WdOGg4D2wD/sFp1rsKYN3St+5lTafgH9C5KyVZDOTtM0cx2LDkdvxzmVts8BOtvy9mJDSOAdgb1VbN8HdLCgbLKmaQVVlHVH70K5GljThpdRSrVH/xZ6wMq4XI1V7aiU8gfeA57VNC3LxrG5CmvasH/FT0+l1GalVKlS6oxSaoZSysumUTo3a9pwJXAE+LtSqoNSykcpNRj9qv5jTdPybRtqg9QR/Qo8qdL2fRU/a/2ZWp2GkMCbot+jqCwL8Lei7MX3rwbWtOEfKKWMwMfoV+CfWx+aS7G2Hd8GDgOzbRiTq7GmDUMqfn4LLAeGAm+hd3/+11YBuoA6t6GmaUXoX4QM6AknF73rdzHwqG3DbLCaAhe0in7zS9g8rxhtVZEQFT4A+gLXa5pW1YeIqIJSKg64G+hexR++sMzFC5I5mqa9XPHvtUopN2C6Uqq9pmlXW69QrSilPNG/AAWjP/yWAlyL/pBvGfCI46ITlTWEBH6eqr9VVvcttHLZiGrKwu/fmBo6a9rwN0qp6cBDwD2api23UWyuxJp2nIXeY3FSKeVXsc0IuFX8v1DTtGKbReq8rGnDcxU/V1Tavhz9IaxruDpu61jThn9Cf5YgWtO0oxXb1iulsoFPlFIfa5q2y2aRNkznAT+llKr0ZdzmeaUhdKHvQ7/nUFkH9CdSayrbqmLYReWyJVx+D6OhsqYNAVBK/RV4Dnhc07SvbBibK7GmHdsDD6P/8V989QN6V/z7arnysfbv+Urq+lCSq7GmDTsD5y9J3hf9WvGzvZWxXQ32oQ9jjKq0/eK9b4s+Uy3REBL4QqB3xdhZACoG2/ereO9KFgEmYPwlZY3ArcDyq+SKB6xrQ5RSjwN/A/6qadoHdorRFVjTjtdV8dqF/jDSdcBc24frlKxpwyXoDw8Nr7R9RMXPRNuE6PSsacPTgL9SqvIDvL0qfqbZKMaGbCn66KY7K22/C9hbMSLANhw91s4GY/UaoV8p70EfIjEa/YPvGOBzyX4R6PdwXq5U/hv0K5wHgHj0D8oi9HuRDj8/Z29D9IlczOgfnr0rvTo4+txcpR2rqW8tV984cGv/nqdWbH8DGII+sVAhMNvR5+YKbQhEog8hO4w+Ccx1wF8qtiVyydjoq+EF3FLx+hf6OPBHKv4/8JJ9yoDPK5WbXpFHpqDfkvhXxefkDTaNz9ENZKNGDgd+qPglywV+pNKA+4pfTA2YVmm7F/q40dMVDb4FGOToc3KVNkR/Ylqr5rXW0eflKu1YTV1XXQK3tg3RxylPqUhgJcAJ4FXA5OjzcqE27AB8B6Sif/k5DLwD+Dv6vBzQjjV+tlX8f3alcm7osymeQO8V2g3cYuv4ZDlRIYQQwgU1hHvgQgghxFVHErgQQgjhgiSBCyGEEC5IErgQQgjhgiSBCyGEEC5IErgQQgjhgiSBCyGEEC5IErgQQgjhgv4fvQoHHQq775oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "CSHMM_analysis.plot_cont_marker_gexp(model_temp, remove_path=tuple([9,2,1,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}