fig3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79397f4d-a0e8-4d80-91f4-d55817eed0b9",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Data Preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d754c62-7a2b-4992-89db-cf990b407c77",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '9'\n",
    "from deepsramp import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c062fe6d-f0fe-47b2-bc93-82e8d2a68b8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import Parallel, delayed\n",
    "import random\n",
    "\n",
    "def task_transform(name, position=0):\n",
    "    idf = df\n",
    "    indf = ndf\n",
    "    \n",
    "    tdf = []\n",
    "    for _, i in tqdm(idf.iterrows(), total=idf.shape[0], position=position):\n",
    "        exist_grp = set()\n",
    "\n",
    "        pospos = list(utils.notnaor(i[name], []))\n",
    "        negpos = list(utils.notnaor(i[f'{name}_neg'], []))\n",
    "\n",
    "        for pos, label in [(pospos, 1), (negpos, 0)]:\n",
    "            for j in pos:\n",
    "                genome_pos = get_genome_pos(j, i)\n",
    "                if i[0] + i[6] + str(genome_pos) in exist_grp: continue\n",
    "                else: exist_grp.add(i[0] + i[6] + str(genome_pos))\n",
    "                for trans, k in indf.loc[i.id].iterrows():\n",
    "                    mature_pos = get_mature_pos(genome_pos, k)\n",
    "                    if mature_pos != -1:\n",
    "                        tdf += [(i.id, trans, mature_pos, i[0] + i[6] + str(genome_pos), label)]\n",
    "\n",
    "    tdf = pd.DataFrame(tdf, columns=['id', 'trans', 'pos', 'grp', 'label'])\n",
    "    tdf = tdf.merge(indf.reset_index(), left_on=['id', 'trans'], right_on=['id', 'trans'])\n",
    "\n",
    "    return name, tdf\n",
    "\n",
    "def task_atlas(m6adf):\n",
    "    tqdm.pandas()\n",
    "    m6adf['pos'] = m6adf.progress_apply(lambda x: [\n",
    "        [\n",
    "            gene, \n",
    "            trans, \n",
    "            (lambda y: y if y != -1 and len(re.findall(RRACH_PAT, i.seq[y-2:y+3])) else -1)(get_mature_pos(x['start'], i)),\n",
    "            # x['seqnames'] + x['strand'] + str(x['start'])\n",
    "            i.length # for max length\n",
    "        ] for (gene, trans), i in get_trans(x['seqnames'], x['strand'], x['start'], ndf).iterrows()\n",
    "    ], axis=1)\n",
    "    m6adf['pos'] = m6adf.pos.apply(lambda x: [i for i in x if i[2] != -1]) # drop -1\n",
    "    m6adf['pos'] = m6adf.pos.apply(lambda x: [max(x, key=lambda y: y[-1])[:-1]] if x else []) # keep max length\n",
    "    return m6adf\n",
    "\n",
    "def get_m6a_neg_ratio4(osdf, label='m6a_pos', test_ratio=10, pat=RRACH_PAT):\n",
    "    sdf = osdf\n",
    "    \n",
    "    sdf['_test_len'] = (sdf[label].apply(lambda x: len(utils.notnaor(x, []))) * test_ratio).apply(round)\n",
    "    sdf['_neg'] = sdf.apply(lambda x: (random.choices(list(x.m6a_neg), k=x._test_len)) if len(x.m6a_neg) else [], axis=1)\n",
    "    sdf[f'{label}_neg'] = sdf.apply(lambda x: set(x._neg[:x._test_len]), axis=1)\n",
    "    \n",
    "    return sdf\n",
    "\n",
    "def atlas(m6adf, file):\n",
    "    m6adf['start'] -= 1\n",
    "    m6adf['seqnames'] = m6adf['seqnames'].str.slice(3).replace({'M': 'MT'})\n",
    "    \n",
    "    npar = 40\n",
    "    nperpar = m6adf.shape[0] // npar + 1\n",
    "    m6adf = Parallel(n_jobs=npar)(delayed(task_atlas)(m6adf.iloc[i*nperpar:(i+1)*nperpar]) for i in range(npar))\n",
    "    m6adf = pd.concat(m6adf)\n",
    "        \n",
    "    m6adf['Cell_Line'] = m6adf.Cell_Line.apply(lambda x: list(map(lambda y: y.strip(), x.split(';'))))\n",
    "    m6adf = m6adf.explode('Cell_Line')\n",
    "    \n",
    "    ress = []\n",
    "    for cl, i in tqdm(m6adf.groupby(['Cell_Line', 'Technique'])):\n",
    "        cl = ('_'.join(cl))\n",
    "        # if cl not in ['A549_m6A-CLIP-seq', 'CD8T_m6A-CLIP-seq', 'HEK293T_miCLIP ', 'MOLM13_miCLIP ']: continue\n",
    "        res = pd.DataFrame(sum(i.pos, []), columns=ndf.index.names+[cl])\n",
    "        res = res[res[cl] != -1]\n",
    "        res = res.groupby(ndf.index.names).agg(lambda x: set(x))\n",
    "        ress += [res]\n",
    "    \n",
    "    res = pd.concat(ress, axis=1)\n",
    "    res['m6a_pos'] = res.apply(lambda x: set(sum([list(i) for i in x if i==i], [])), axis=1)\n",
    "    res = pd.concat([ndf, res], axis=1)\n",
    "    res = res.dropna(subset=['m6a_pos'])\n",
    "    res = get_m6a_neg(res)\n",
    "    \n",
    "    cls = res.columns[9:-2].tolist()\n",
    "    df = res.reset_index().set_index('trans')\n",
    "    \n",
    "    for i in tqdm(cls):\n",
    "        df = get_m6a_neg_ratio4(df, label=i, test_ratio=10)\n",
    "    \n",
    "    utils.save([df, cls, ndf], file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f483d010-7e67-4c2e-bdcb-9db4ffbea93d",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Human"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4cd80a47-afea-439a-80be-39d22ee6ae25",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfs/fanrui/work/deepsramp/deepsramp/preprocess.py:30: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  gtf = pd.read_csv(filename, sep='\\t', comment='#', header=None)\n",
      "51662809it [02:30, 342889.52it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 38s, sys: 1min 32s, total: 3min 11s\n",
      "Wall time: 3min 9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "gtf = read_gtf('data/Homo_sapiens.GRCh38.79.gtf.gz')\n",
    "seqs = read_seq('data/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f9bec22a-b4ba-43d6-a206-fc6591fd86c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.2 s, sys: 1.96 s, total: 18.2 s\n",
      "Wall time: 18.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sdf = get_mature_splice(gtf, seqs, grp=['id', 'trans'], keepmax=False)\n",
    "ndf = get_mature_cds(gtf, sdf, grp=['id', 'trans'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbb7c8e3-1e7a-470e-a4ed-c8a2da07dad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# atlas2 human\n",
    "m6adf = pd.concat([\n",
    "    pd.read_csv('data/m6aatlas/m6A-Atlas2_Human_Site_last7method.txt', sep='\\t'), \n",
    "    pd.read_csv('data/m6aatlas/m6A-Atlas2_Human_Site_top5Method.txt', sep='\\t')\n",
    "])\n",
    "atlas(m6adf, 'data/m6aatlas/atlas2_human.data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6758a1aa-4320-4677-a548-0f9330502a5e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Mouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "af19e895-9c9e-4ad6-b962-cd6d371861a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfs/fanrui/work/deepsramp/deepsramp/preprocess.py:30: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  gtf = pd.read_csv(filename, sep='\\t', comment='#', header=None)\n",
      "45514629it [01:22, 549924.18it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 25s, sys: 29.4 s, total: 1min 55s\n",
      "Wall time: 1min 56s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "gtf = read_gtf('data/Mus_musculus.GRCm38.102.gtf.gz')\n",
    "seqs = read_seq('data/Mus_musculus.GRCm38.dna.primary_assembly.fa.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2f43c7de-37a6-441b-bb64-b216def4bc50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.1 s, sys: 1.07 s, total: 13.1 s\n",
      "Wall time: 13.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sdf = get_mature_splice(gtf, seqs, grp=['id', 'trans'], keepmax=False)\n",
    "ndf = get_mature_cds(gtf, sdf, grp=['id', 'trans'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "868a6011-6c96-4cd2-ba30-bf2137ff2048",
   "metadata": {},
   "outputs": [],
   "source": [
    "# atlas2 mouse\n",
    "m6adf = pd.concat([\n",
    "    pd.read_csv('data/m6aatlas/m6A-Atlas2_Mouse_Site_1.txt', sep='\\t'),\n",
    "    pd.read_csv('data/m6aatlas/m6A-Atlas2_Mouse_Site_2.txt', sep='\\t')\n",
    "])\n",
    "atlas(m6adf, 'data/m6aatlas/atlas2_mouse.data')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ee91df5-10ae-4295-bb02-b149ef3551d7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Rat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d98bde22-69a0-40c6-ab4b-1a15e20e2dbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/nfs/fanrui/work/deepsramp/deepsramp/preprocess.py:30: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  gtf = pd.read_csv(filename, sep='\\t', comment='#', header=None)\n",
      "47837831it [01:57, 407265.28it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 17s, sys: 51 s, total: 2min 8s\n",
      "Wall time: 2min 8s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "gtf = read_gtf('data/Rattus_norvegicus.Rnor_6.0.104.gtf.gz')\n",
    "seqs = read_seq('data/Rattus_norvegicus.Rnor_6.0.dna.toplevel.fa.gz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2829050d-2c2d-4819-99a0-7a839e94bbe8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.97 s, sys: 460 ms, total: 7.43 s\n",
      "Wall time: 7.43 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "sdf = get_mature_splice(gtf, seqs, grp=['id', 'trans'], keepmax=False)\n",
    "ndf = get_mature_cds(gtf, sdf, grp=['id', 'trans'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7014c840-26c9-4291-827d-506ae23f128d",
   "metadata": {},
   "outputs": [],
   "source": [
    "m6adf = pd.read_csv('data/m6aatlas/m6A-Atlas2_Rat_Site.txt', sep='\\t')\n",
    "atlas(m6adf, 'data/m6aatlas/atlas2_rat.data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce4c53db-4328-4d31-8e34-2688f688093e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Test on Single Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "280dc205-078f-4772-9a30-08c445001998",
   "metadata": {},
   "outputs": [],
   "source": [
    "df, cls, ndf = utils.load('data/m6aatlas/atlas2_human.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4b0a4d56-bc27-40c7-b618-3d3c4e28b240",
   "metadata": {},
   "outputs": [],
   "source": [
    "df, cls, ndf = utils.load('data/m6aatlas/atlas2_mouse.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e445cf75-975a-4dab-8323-76be59177b21",
   "metadata": {},
   "outputs": [],
   "source": [
    "df, cls, ndf = utils.load('data/m6aatlas/atlas2_rat.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3ddd2039-72fc-4f64-95d9-e291121858b2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2878/2878 [00:00<00:00, 7057.88it/s]\n",
      "100%|██████████| 14120/14120 [00:06<00:00, 2059.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test] ACC: 88.5%, Loss: 0.269515, AUROC: 0.9271, AUPRC: 0.6070, Size: 14120, Ratio: 0.10064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2878/2878 [00:00<00:00, 3750.66it/s]\n",
      "100%|██████████| 19555/19555 [00:09<00:00, 2083.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test] ACC: 87.7%, Loss: 0.285613, AUROC: 0.9189, AUPRC: 0.5763, Size: 19555, Ratio: 0.10263\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2878/2878 [00:00<00:00, 8628.29it/s]\n",
      "100%|██████████| 10165/10165 [00:04<00:00, 2129.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test] ACC: 87.2%, Loss: 0.290454, AUROC: 0.9184, AUPRC: 0.6069, Size: 10165, Ratio: 0.10408\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "model = SRAMP().to(device)\n",
    "model.load_state_dict(torch.load('model/full_400_ythdf_single.model', weights_only=True))\n",
    "loss_fn = TriLoss()\n",
    "\n",
    "result = []\n",
    "for i in cls:\n",
    "    testds = df2ds(df, pos_label=i, neg_label=f'{i}_neg')\n",
    "    testdl = ds2dl(testds, drop_last=False, num_workers=4)\n",
    "    result += [test_loop(testdl, model, device, loss_fn)]\n",
    "\n",
    "utils.save(result, 'model/atlas2_rat_full_400_ythdf_single.metrics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "17213be3-4b82-4647-bc8f-d8cd7f772ddb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2878/2878 [00:00<00:00, 4784.84it/s]\n",
      "100%|██████████| 14120/14120 [00:06<00:00, 2072.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test] ACC: 59.2%, Loss: 0.852105, AUROC: 0.5940, AUPRC: 0.1459, Size: 14120, Ratio: 0.10064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2878/2878 [00:00<00:00, 3937.94it/s]\n",
      "100%|██████████| 19555/19555 [00:09<00:00, 2094.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test] ACC: 58.4%, Loss: 0.871867, AUROC: 0.5693, AUPRC: 0.1336, Size: 19555, Ratio: 0.10263\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2878/2878 [00:00<00:00, 8464.14it/s]\n",
      "100%|██████████| 10165/10165 [00:04<00:00, 2136.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test] ACC: 57.4%, Loss: 0.880474, AUROC: 0.5565, AUPRC: 0.1264, Size: 10165, Ratio: 0.10408\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "params = torch.load('model/full_400_ythdf_dp.model', weights_only=True)\n",
    "dps = [DeepPromise(i).to(device) for i in ['onehot', 'enac', 'embedding']]\n",
    "[dp.load_state_dict(param) for dp, param in zip(dps, params)]\n",
    "model = DeepPromiseEnsemble(*dps).to(device)\n",
    "model.load_state_dict(params[-1])\n",
    "loss_fn = DPLoss()\n",
    "\n",
    "result = []\n",
    "for i in cls:\n",
    "    testds = df2ds(df, pos_label=i, neg_label=f'{i}_neg')\n",
    "    testdl = ds2dl(testds, drop_last=False, num_workers=4)\n",
    "    result += [dp_test_loop(testdl, model, device, loss_fn)]\n",
    "\n",
    "utils.save(result, 'model/atlas2_rat_full_400_ythdf_dp.metrics')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5958eaa9-9b37-493b-8819-0b635d1312eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f98e40e6-c257-4240-adf5-35b23fd49462",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### WHISTLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e790a0a4-13c0-4869-8c14-4873fa68a5c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def df2bed(testdf, pos_label, neg_label, name=\"\", mode=\"mature\"):\n",
    "    if name == \"\": name = pos_label\n",
    "    whi_test = []\n",
    "    for _, i in tqdm(testdf.iterrows(), total=testdf.shape[0], desc=pos_label):\n",
    "        for j in utils.notnaor(i[pos_label], []):\n",
    "            genome = get_genome_pos(j, i, mode)\n",
    "            whi_test += [[f'chr{i[0] if i[0] != \"MT\" else \"M\"}', genome, genome+1, i[6], 1]]\n",
    "        for j in utils.notnaor(i[neg_label], []):\n",
    "            genome = get_genome_pos(j, i, mode)\n",
    "            whi_test += [[f'chr{i[0] if i[0] != \"MT\" else \"M\"}', genome, genome+1, i[6], 0]]\n",
    "\n",
    "    pd.DataFrame(whi_test, columns=['#chromsome', 'modStart', 'modEnd', 'strand', 'label']).to_csv(f'whistle/data/{name.replace(' ', '.')}_whistle.bed', sep='\\t', index=None, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bce890eb-e55c-4ac8-ac9c-5f52f44fc4d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "A549_m6A-CLIP-seq: 100%|██████████| 16707/16707 [00:01<00:00, 13964.35it/s]\n",
      "A549_m6A-seq with improved protocol: 100%|██████████| 16707/16707 [00:01<00:00, 15366.25it/s]\n",
      "Brain_m6A-REF-seq: 100%|██████████| 16707/16707 [00:01<00:00, 16569.59it/s]\n",
      "CD8T_m6A-CLIP-seq: 100%|██████████| 16707/16707 [00:01<00:00, 11846.58it/s]\n",
      "CM2005.1_miCLIP : 100%|██████████| 16707/16707 [00:00<00:00, 19144.22it/s]\n",
      "CRMM1_miCLIP : 100%|██████████| 16707/16707 [00:00<00:00, 21209.22it/s]\n",
      "ESC_MAZTER-seq: 100%|██████████| 16707/16707 [00:02<00:00, 8102.34it/s] \n",
      "HCT116_miCLIP : 100%|██████████| 16707/16707 [00:01<00:00, 14272.44it/s]\n",
      "HEK293_m6A-REF-seq: 100%|██████████| 16707/16707 [00:00<00:00, 19653.80it/s]\n",
      "HEK293_m6A-SAC-seq: 100%|██████████| 16707/16707 [00:01<00:00, 9610.07it/s] \n",
      "HEK293_meCLIP: 100%|██████████| 16707/16707 [00:00<00:00, 20431.84it/s]\n",
      "HEK293_miCLIP : 100%|██████████| 16707/16707 [00:01<00:00, 15282.12it/s]\n",
      "HEK293E_miCLIP : 100%|██████████| 16707/16707 [00:06<00:00, 2551.88it/s]\n",
      "HEK293T_DART-seq: 100%|██████████| 16707/16707 [00:01<00:00, 15390.61it/s]\n",
      "HEK293T_MAZTER-seq: 100%|██████████| 16707/16707 [00:01<00:00, 8645.26it/s] \n",
      "HEK293T_m6A-REF-seq: 100%|██████████| 16707/16707 [00:00<00:00, 20707.71it/s]\n",
      "HEK293T_m6A-label-seq : 100%|██████████| 16707/16707 [00:00<00:00, 20868.89it/s]\n",
      "HEK293T_m6ACE: 100%|██████████| 16707/16707 [00:00<00:00, 21890.93it/s]\n",
      "HEK293T_miCLIP : 100%|██████████| 16707/16707 [00:03<00:00, 5409.10it/s]\n",
      "HEK293T_miCLIP2: 100%|██████████| 16707/16707 [00:01<00:00, 15903.93it/s]\n",
      "HSPC_m6A-SAC-seq: 100%|██████████| 16707/16707 [00:03<00:00, 4564.60it/s]\n",
      "HeLa_PA-m6A-seq: 100%|██████████| 16707/16707 [00:01<00:00, 11988.47it/s]\n",
      "HeLa_m6A-CLIP-seq: 100%|██████████| 16707/16707 [00:00<00:00, 17141.46it/s]\n",
      "HeLa_m6A-SAC-seq: 100%|██████████| 16707/16707 [00:01<00:00, 14108.55it/s]\n",
      "HeLa_miCLIP : 100%|██████████| 16707/16707 [00:02<00:00, 5908.96it/s]\n",
      "HepG2_m6A-SAC-seq: 100%|██████████| 16707/16707 [00:01<00:00, 12653.27it/s]\n",
      "HepG2_miCLIP : 100%|██████████| 16707/16707 [00:01<00:00, 11162.59it/s]\n",
      "Kidney_m6A-REF-seq: 100%|██████████| 16707/16707 [00:00<00:00, 19289.37it/s]\n",
      "LNCaP_miCLIP : 100%|██████████| 16707/16707 [00:00<00:00, 18280.15it/s]\n",
      "Liver_m6A-REF-seq: 100%|██████████| 16707/16707 [00:00<00:00, 19177.54it/s]\n",
      "MCF7_meCLIP: 100%|██████████| 16707/16707 [00:00<00:00, 20682.90it/s]\n",
      "MDAMB231_meCLIP: 100%|██████████| 16707/16707 [00:00<00:00, 21601.74it/s]\n",
      "MOLM13_miCLIP : 100%|██████████| 16707/16707 [00:01<00:00, 13332.13it/s]\n",
      "OCM1_miCLIP : 100%|██████████| 16707/16707 [00:00<00:00, 20613.44it/s]\n",
      "OCM1a_miCLIP : 100%|██████████| 16707/16707 [00:00<00:00, 23228.07it/s]\n",
      "OM431_miCLIP : 100%|██████████| 16707/16707 [00:00<00:00, 19784.82it/s]\n",
      "PIG1_miCLIP : 100%|██████████| 16707/16707 [00:00<00:00, 20084.94it/s]\n",
      "RWPE_miCLIP : 100%|██████████| 16707/16707 [00:01<00:00, 14707.21it/s]\n"
     ]
    }
   ],
   "source": [
    "for i in cls:\n",
    "    df2bed(df, i, f'{i}_neg')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "541c721e-1f1a-42de-ab87-78c8b6f1a9ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a53b46-948a-4300-8299-47b0522a1d45",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Test on Multiple Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a9070d16-fd75-4a88-8f91-af979a8274c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df, cls, ndf = utils.load('data/m6aatlas/atlas2_human.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d5f2d07-767f-4103-a123-d96d24792aa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df, cls, ndf = utils.load('data/m6aatlas/atlas2_mouse.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72b5711d-10c6-4a3d-8d19-ea279a208204",
   "metadata": {},
   "outputs": [],
   "source": [
    "df, cls, ndf = utils.load('data/m6aatlas/atlas2_rat.data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "605eebd6-340f-407b-b71b-43587e68e731",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2878/2878 [00:02<00:00, 1027.99it/s]\n",
      "100%|██████████| 2878/2878 [00:04<00:00, 713.33it/s]\n",
      "100%|██████████| 2878/2878 [00:04<00:00, 584.65it/s]\n"
     ]
    }
   ],
   "source": [
    "data_dfs = {}\n",
    "dfs = Parallel(n_jobs=20)(delayed(task_transform)(name) for idx, name in enumerate(cls))\n",
    "data_dfs = dict(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "354bfedf-3c09-41a0-bfbf-59169a791452",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 19619/19619 [00:11<00:00, 1716.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test] ACC: 88.2%, Loss: 0.300866, AUROC: 0.9364, AUPRC: 0.6450, Size: 14118, Ratio: 0.10058\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25983/25983 [00:13<00:00, 1944.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test] ACC: 87.1%, Loss: 0.322018, AUROC: 0.9272, AUPRC: 0.6024, Size: 19550, Ratio: 0.10256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13482/13482 [00:06<00:00, 1990.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Test] ACC: 86.5%, Loss: 0.329232, AUROC: 0.9270, AUPRC: 0.6252, Size: 10160, Ratio: 0.10413\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "model = MultiSRAMP().to(device)\n",
    "model.load_state_dict(torch.load('model/full_400_ythdf.model', weights_only=True))\n",
    "# model.load_state_dict(torch.load('model/full_400_mature.model', weights_only=True))\n",
    "loss_fn = TriLoss()\n",
    "\n",
    "result = []\n",
    "for i in cls:\n",
    "    testds = df2ds_multi(data_dfs[i])\n",
    "    testdl = ds2dl(testds, drop_last=False, num_workers=4)\n",
    "    result += [test_loop(testdl, model, device, loss_fn)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3157c55-c709-473a-893a-a4d5946bd6a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.save(result, 'data/m6aatlas/atlas2_human.metrics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77b4f93a-714c-4dba-9dc8-31e86d6c23d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.save(result, 'data/m6aatlas/atlas2_mouse.metrics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad9c278d-aa99-4046-a241-02e576d0ee49",
   "metadata": {},
   "outputs": [],
   "source": [
    "utils.save(result, 'data/m6aatlas/atlas2_rat.metrics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5ddc3c1-8d3f-483d-8fb3-fef2ac9fe17a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66c08c87-2e33-42c6-bbf4-211c2b698ed7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "43a79a84-aeba-415a-94ad-a77ebb4cd2a4",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b8822d00-f34a-409b-ad8e-31d758ca7e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib import font_manager\n",
    "font_manager.fontManager.addfont('/home/fanrui/fonts/ARIAL.TTF')\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "plt.rcParams['font.size'] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36d7ce03-f0b5-4d98-a17b-2d66be63836c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f491d166-d49f-45bb-a0f6-6d8a1d1c7a49",
   "metadata": {},
   "outputs": [],
   "source": [
    "_, cls, _ = utils.load('data/m6aatlas/atlas2_human.data')\n",
    "result = utils.load('data/m6aatlas/atlas2_human.metrics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf1ddd76-0b44-47dd-bfb6-9b14756335b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = []\n",
    "for name, i in zip(cls, result):\n",
    "    name = name.split('_')\n",
    "    # df += [[name[0], name[1], i[-1]['1.0']['precision']]]\n",
    "    df += [[name[0], name[1], i[2]]]\n",
    "df = pd.DataFrame(df).replace({'m6A-seq with improved protocol': 'm6A-seq\\n(improved)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52f3f234-8efe-4c10-b735-d0dfdda9298f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.6585631844146544)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "678bf925-e36f-477e-bd48-0b1b1bb78b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('fig/atlas2_human.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f8960ff-719e-4b2e-846d-a7c1fcd5ddc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['meCLIP', 'm6A-seq\\n(improved)', 'miCLIP2', 'm6ACE', 'm6A-CLIP-seq',\n",
       "       'm6A-REF-seq', 'm6A-label-seq ', 'miCLIP ', 'PA-m6A-seq', 'm6A-SAC-seq',\n",
       "       'DART-seq', 'MAZTER-seq'],\n",
       "      dtype='object', name=1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(1).agg({2: 'mean'}).sort_values(2).index[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7425aea-77cd-4ed0-9e53-93ae24f571e6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6d68f449-d302-48df-8f26-02105309cd15",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmaps = []\n",
    "cmaps += list(zip(['OCM1', 'OCM1a', 'OM431', 'PIG1', 'CM2005.1', 'CRMM1'], sns.color_palette('Blues', 6*2)[:5:-1]))\n",
    "cmaps += list(zip(['HEK293', 'HEK293E', 'HEK293T'], sns.color_palette('autumn', 3*2)[:2:-1]))\n",
    "cmaps += list(zip(['LNCaP', 'RWPE'], sns.color_palette('Greens', 2*2)[:1:-1]))\n",
    "cmaps += list(zip(['MCF7', 'MDAMB231'], sns.color_palette('Reds', 2*2)[:1:-1]))\n",
    "cmaps += list(zip(['ESC', 'HSPC', 'MOLM13'], sns.color_palette('Purples', 3*2)[:2:-1]))\n",
    "cmaps += list(zip(['A549', 'HepG2', 'CD8T', 'HCT116', 'HeLa'], sns.color_palette('tab10')[5:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2574611d-8c42-4271-ae74-a3cd32c30194",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fanrui/miniforge3/envs/sramp/lib/python3.12/site-packages/seaborn/categorical.py:3399: UserWarning: 6.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/home/fanrui/miniforge3/envs/sramp/lib/python3.12/site-packages/seaborn/categorical.py:3399: UserWarning: 6.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAJOCAYAAAAqFJGJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxUVf8H8M+dfYaBAVkEBVlVUMkFNBU1RFxIEzXLnrQ0fSrNsuzRrJ7KbDNLTc20xdIss1XNFjfEFfcdt0gRBBQ3ZB1mv78//MEjgToz3Aszx+/79eL1ipk7d87Hc4j5cu45l+N5ngchhBBCCCGE1IOksRtACCGEEEIIcX9UWBBCCCGEEELqjQoLQgghhBBCSL1RYUEIIYQQQgipNyosCCGEEEIIIfVGhQUhhBBCCCGk3qiwIIQQQgghhNQbFRaEEEIIIYSQeqPCghBCCCGEEFJvVFgQQgi5rTFjxiAsLOyWz4eFhWHMmDEN1h5CCCGuiQoLQgghhBBCSL1RYUEIIYQQQgipNyosCCGECIbjOLz55ps1HnvzzTfBcVz192PGjMGAAQPw+eefIzIyEmq1GgkJCcjKysLvv/+O2NhYaDQa3HvvvThy5EiNcy1ZsgTx8fHw8PCAWq1Ghw4d8NNPP1U/v2zZMshkMuzduxfdunWDSqVCaGgoZs+eLWZsQgghoMKCEEKInSwWS51fzti1axcWLlyIuXPnYunSpTh58iTuv/9+TJ48Ga+++iq+//57nD9/HiNHjqx+zSeffIKnn34aQ4YMwR9//IEVK1ZAqVTi0UcfRX5+fvVxNpsNDz/8MB555BH8+eef6NGjB6ZOnYoNGzbU+9+AEELIrckauwGEEEJcX25uLuRyuWDnKysrw48//ojo6GgAwLZt2/Dpp59i8+bNSEpKAgD8/fffmDJlCoqLi+Ht7Y3s7GxMnToVr732WvV5wsLCEBcXh507d+KRRx4BAPA8jzfeeAPjxo0DACQkJGDVqlX4/fff0b9/f8EyEEIIqYkKC0IIIXcUFBSEtWvX1vnc4MGDHT6fj49PdVEBAE2bNgUA3HvvvdWP+fr6AkB1YTFnzpzq70+fPo0zZ85gy5YtAACj0Vjj/N26dav+b6VSCX9/f1RUVDjcTkIIIfajwoIQQsgdKRQKxMfH3/I5R3l5edX5uIeHxy1fc/bsWTz99NPYvHkzFAoFoqOj0b59ewA3ZiluptFoanwvkUhgs9kcbichhBD7UWFBCCFEUFartcb35eXl9T6nzWbDwIEDoVAosH//fnTo0AEymQwnT57EN998U+/zE0IIqT9avE0IIUQwXl5eNRZSA0BGRka9z3v16lX89ddfGDduHOLj4yGT3fi72Lp16wCAZiMIIcQF0IwFIYQQwQwaNAjff/89unbtiqioKCxbtgxnzpyp93kDAgIQFhaGhQsXIjg4GD4+Pli/fj3mzZsHALR+ghBCXADNWBBCCBHM3Llz8cADD2DKlCkYPnw4tFot3n//fUHOvWbNGjRv3hxjxozBww8/jD179uC3335DdHQ0duzYIch7EEIIcR7H/3PFGyGEEEIIIYQ4iGYsCCGEEEIIIfVGhQUhhBBCCCGk3qiwIIQQQgghhNQbFRaEEEIIIYSQeqPCghBCCCGEEFJvVFgQQgghhBBC6u2uKSx4nkdpaSlod11CCCGEEEKEd9cUFmVlZdDpdCgrK2vsphBCCCGEEMKcu6awIIQQQgghhIiHCgtCCCGEEEJIvVFhQQghhBBCCKk3KiwIIYQQQggh9UaFBSGEEEIIIaTeqLAghBBCCCGE1BsVFoQQQgghhJB6o8KCEEIIIYQQUm9UWBBCCCGEEELqjQoLQgghhBBCSL1RYUEIIYQQQgipNyosCCGEEEIIIfVGhQUhhBBCCCGk3mSN3QDW8TyPv//+G/n5+TCbzdBoNGjbti2aNGnS2E0jhBBCCCFEMFRYiMRisWD79u1IW7cOuSdPwVZZCTkHmMFB7q1D5/vuQ/8BA9CqVavGbiohhBBCCCH1xvE8zzd2IxpCaWkpdDodSkpK4OXlJep7GY1GfDx/Pg6lbUaE0YSOag+EKpXgOA5Gmw0n9BU4bDahrIkPnnjuWSQlJYnaHkIIIYQQQsRGMxYCs9lsWLRwIY6t34AHFWqE+XjWeF4pkaCT1hMdeR6brxfjq3nzoFKp0L1790ZqMSGEEEIIIfVHi7cFdvDgQezflIZBChXCVKpbHsdxHProvBFVUobln38Ok8nUgK0khBBCCCFEWFRYCGzzpk0IrDQgUqW+47Ecx6Gnpw6lBRewd+/eBmgdIYQQQggh4qDCQkCXL1/G8b370EF565mKf/KWydDCZMaWtDQRW0YIIYQQQoi4qLAQ0KVLl2AzGBCiUDr0umYyGQrP54nUKkIIIYQQQsRHhYWArFYreJ6HhOMcep0UHCwWs+DtKSkpQUlJieDndSWUkQ2UkQ2UkQ2UkQ2UkQ3ulpF2hRKQp6cnJHIZii0WaKVSu19XYrXAy8dH8PbI5XLBz+lqKCMbKCMbKCMbKCMbKCMb3C0jzVgIKDw8HAFhYTimr7D7NSabDac54N6ePQVvj9VqhdVqFfy8roQysoEysoEysoEysoEyssHdMlJhISCJRII+KSn4SyZBuZ2D4Li+Ary3NxITEwVvj7sNRmdQRjZQRjZQRjZQRjZQRja4W0YqLATWq1cveEdGYFXpdRhtttsee95owHbeil4pA+Dv7y94W+RyudtNoTmKMrKBMrKBMrKBMrKBMrLB3TJSYSEwLy8vTHn1VRjDw/BtSRH+qtTDxvM1jqmwWrGrrAQ/mwxo2ycJY554QpS28DwP/h/vzRrKyAbKyAbKyAbKyAbKyAZ3y0iLt0UQFhaGN957D1998QX+PHgQypLrCLHxkHFAhc2G8wo5lP7+GDDwfjzyyCOiVaJms/A7TbkaysgGysgGysgGysgGysgGd8tIhYVImjVrhtemT8f58+exbds25J49C5PRCG8vL9zXsSN69OgBrVYrahuUSsfup+GOKCMbKCMbKCMbKCMbKCMb3C0jFRYia9GiBR577LFGeW93mjpzFmVkA2VkA2VkA2VkA2Vkg7tlpDUWDDMajTAajY3dDFFRRjZQRjZQRjZQRjZQRja4W0aasWCYSqVq7CaIjjKygTKygTKygTKygTKywd0y0owFIYQQQgghpN6osGCYwWCAwWBo7GaIijKygTKygTKygTKygTKywd0y0qVQDHO36TNnUEY2UEY2UEY2UEY2UEY2uFtGmrEghBBCCCGE1BsVFg2A53mUlpbi2rVrqKysbLD3dbfpM2dQRjZQRjZQRjZQRjZQRja4W0a6FEpE169fx7Zt27BtyzoUXy8EYAM4GVq26oCkPv0RFxcn2l23Afe7qYozKCMbKCMbKCMbKCMbKCMb3C0jFRYiSU9Px3ffLoYMl9G9owFtW8qgUHAoKeWx69A6fPbJDvg3bYMXXnwZzZo1a+zmEkIIIYQQUi9UWIhg06ZN+O6beehz71U8OEANtUpT4/kenYELl0xYtOIAZr77Gv77+nsIDAwUvB0mkwkA4OHhIfi5XQVlZANlZANlZANlZANlZIO7ZaQ1FgI7f/48Vn67CP27X8OoIRqoVVydxzVrKsUr4+XwUpzGJws/EuWW7TKZDDIZ27UjZWQDZWQDZWQDZWQDZWSDu2WkwkJgmzdvho/2Ch66/87bg3loJBg1RIL888fw119/Cd4WqVQKqVQq+HldCWVkA2VkA2VkA2VkA2Vkg7tldJ8SyA3o9Xrs2bUBA3tYIZXatyg7OlKGwCalSE/fjOjoaEHbYzabBT2fK6KMbKCMbKCMbKCMbKCMbHC3jFRYCCg3NxcmQxE632P/Tk8cxyE+1oqdx44I3h6Oq/syLJZQRjZQRjZQRjZQRjZQRja4W0YqLAR0Y59hGzRqxwaBh5qDwaAXvD3udE2esygjGygjGygjGygjGygjG9wto3u11sXduO26BPpKHjpP+1+nN/BQ/WPnKCFYLBbBz+lqKCMbKCMbKCMbKCMbKCMb3C0jLd4WUIsWLaBQNcHB4/ZfD8fzPA5mShDVqr2ILSOEEEIIIURcVFgIyMPDA/d27Yste6Ww2ezbPvavbCsuXNMhKamP4O1xty3KnEEZ2UAZ2UAZ2UAZ2UAZ2eBuGamwEFif5GRcL/PHz+sMdzxWX8nj2zVWNA+JFXxHKODG9Jm7TaE5ijKygTKygTKygTKygTKywd0yUmEhsNDQUIx4dDzW7WyClWsrUWmoe+bi4mUr3v/UhGJDa0x8drIoq/55nhflxnuuhDKygTKygTKygTKygTKywd0yus/cihvp378/pFIpVn77KbYfuILuHSvRrpUcCgVQXMpj9yErTpzVool/LF5+9RUEBQWJ0g653P5tb90VZWQDZWQDZWQDZWQDZWSDu2WkwkIkycnJ6NixI7Zt24ZtW9chfd8lADaAkyEyqj3+Pb4funTpIuqAsVqtop3bVVBGNlBGNlBGNlBGNlBGNrhbRiosROTr64thw4YhNTUV5eXlMJlM0Gg08PDwaJD3d6dr8pxFGdlAGdlAGdlAGdlAGdngbhmpsGgAUqkUOp2uwd9XoVA02HvxPI/y8nIYjUYolUpotdoGuVtkQ2ZsLJSRDZSRDZSRDZSRDZTR9VBhQepFr9cjIyMDGzdsQH5uDnibDZxEguDQMPTr3x8JCQnQaIS/+R8hhBBCCHEttCsUw4xGI4xGo2jnz8zMxKRnJ+KzBfNQmZ+DjgE+6BrSFB0DfFCZn4PPFszDpGcnIjMzU7Q2iJ3RFVBGNlBGNlBGNlBGNlBG10MzFgxTqVSinTszMxOz3nsXWosJD9wTDfU/purCAvxQaTJhd9Y5zHrvXUx79b+IjY0VvB1iZnQVlJENlJENlJENlJENlNH10IwFcZher8f8j+ZCazEhsW2rWkVFFbVCgcS2raC1mDD/o7nQ6/UN3FJCCCGEENJQqLBgmMFggMFw5zuAOyojIwPFVy6jW6twSO6wQFvCcejaMhzFVy4jIyND8LaIldGVUEY2UEY2UEY2UEY2UEbXQ5dCMUyM6TOe57FxwwYEeqhuOVPxTxqlAk01KmzcsAHJycmC7hblblOEzqCMbKCMbKCMbKCMbKCMrodmLIhDysvLkZ+bgxa+TRx6XahfE+Tn5qCiokKklhFCCCGEkMZEhQXDxJg+MxqN4G02KOSOTXYpZDLwNpvg7XG3KUJnUEY2UEY2UEY2UEY2UEbXQ5dCMUypVIpyTk4igcns2J0gTRYLOIlE8Ck9MTK6GsrIBsrIBsrIBsrIBsroemjGgmEcxwl+92utVovg0DCcv3bdodflXi1CcGgYPDw8BG2PGBldDWVkA2VkA2VkA2VkA2V0PVRYMEyMm6pwHId+/fujsKISlSaTXa/RG024pDegX//+gv9wuNuNY5xBGdlAGdlAGdlAGdlAGV0PFRYMk8vlkMvlgp83ISEB3v4B2J11Djaev+2xNp7Hnr/Pwds/AAkJCYK3RayMroQysoEysoEysoEysoEyuh4qLBgm1vSZRqPB85NfRLlMga0nsqA31j1zoTeasPVEFsplCrzw4n+g0WgEb4u7TRE6gzKygTKygTKygTKygTK6Hlq8zTCz2SzauWNjYzHt1f9i/kdz8XvmaTTVqBDq1wQKmQwmiwW5V4twSW+At38AXn7xP2jXrp0o7RAzo6ugjGygjGygjGygjGygjK6HCguRZWdnY8uWLcg/lw2TwQgPnQ73dOyIXr16wcvLS9T3lkqlop4/NjYWCxZ+goyMDGzcsAGHc3PA22zgJBIEh4bj6f79kZCQIMpMRRWxM7oCysgGysgGysgGysgGyuh6OJ6/w0XyjCgtLYVOp0NJSYnoH+gBIC8vD199/jnOHT+GJiYDWqtkUEgkKDVbkGmygdf5oGe//nh05Ego7LyDtaP0ej0AiPrBvgrP86ioqIDBYIBKpYKHh0eDTN01ZMbGQhnZQBnZQBnZQBnZQBldD81YiODs2bOY/fZb8L5SiPFBTdBWFwDJTR+yKyxW7L5yHb//9D0u5OdjyrRpohQXDTl9xnEctFottFptg70n4H5ThM6gjGygjGygjGygjGygjK6HFm8LrKSkBPM/mIXAa4WY0ioYsd6eNYoKAPCQSZEc5IdJLfxxbvdOfPXll6K0xd0W/DiDMrKBMrKBMrKBMrKBMrLB3TLSjIXAtm7disq8XDzdsjlUd7guLkKrwXA/T6zcvAnDHnwQAQEBgrZFJmO/eykjGyijcAoKCpCeno6zf51AZWUF1GoPtIyJRe/evdGsWTNR35v6kQ2UkQ2UkQ3ultG9WuvirFYrtm7YgM4aOTzl9v3TdvHzxprTediyZQtGjBghaHssFoug53NFlJENlLH+ioqKsOTzT3Hq6G54ccXoEGiCh5JDuZ7H3t8zkPb7D2jbMQH/fuppeHt7i9IG6kc2UEY2UEY2uFtGKiwElJOTg+sFeegW5G33axQSCeI0chzIyBC8sLgb1uVTRjZQxvq5cuUK3n/nDeDqMUyIAzqFyiGT/m+hn9nK42DONfx45De882YeXnl9Bnx9fQVvB/UjGygjGygjG9wtI62xEFBZWRl4qxW+CsfukOinVKC8tETw9rjb3RqdQRnZQBmdZ7FYMG/2LMiKjuH1FBm6RCggk9a8Hlcu5dA1UoHXBkiBq4cxb+4HsNlsgreF+pENlJENlJEN7paRCgsByWQygONgdbC6tPA8ZCIMGqvVCqvVKvh5XQllZANldN6hQ4dwIfsonuklgbfm9v9Lb+IhwYSeEuT/fRhHjx4VvC3Uj2ygjGygjGxwt4yNWlgYDAaMGzcO3t7eCAoKwpw5c2557OrVqxETEwOtVosePXrg0KFDDdhS+zRt2hScQom/y/QOve5MhQFNg4MFb4+7DUZnUEY2UEbnbdm8Ca28yxHqa9+VrZH+MoR7liF98ybB20L9yAbKyAbKyAZ3y9iohcXUqVNx4MABpKenY9GiRZgxYwZ+/vnnWsedOHECjz76KF555RUcPXoUHTp0wMCBA6tvGuIq/P390aZzF+y4Vmr3ay4bjDhlARL7JAveHnebPnMGZWQDZXSOXq/H6cwD6BHp2FaEPSM5HD+0ByaTSdD2UD+ygTKygTKywd0yNlphUVFRgSVLlmD+/Pno1KkThg4dipdeegkLFy6sdezGjRvRtm1bPP7444iMjMTMmTNRWFiIkydPNkLLb69P3744x8lw7Pqdiwue57E2/wq0gc3QpUsXwdvC87zbLfpxFGVkA2V0TkVFBWAzw1fr2P/KfT0kAG++8XoBUT+ygTKygTKywd0yNlphcfToUZjNZnTv3r36sR49emDv3r21FhX6+vrixIkTyMjIgM1mw9KlS+Hl5YXIyMiGbvYdderUCZ369MVXF6/jRHHZLY+z8Ty+zy3EYYkSo59+WrQ7b7vbHRsdRRnZQBmdU7Wuy+LgLLnFBgASwfdHp35kA2VkA2Vkg7tlbLTtZi9evAg/P78aH6ibNm0Kg8GAa9euwd/fv/rxESNGYO3atejRowekUikkEgn++OMP+Pj4NEbTb4vjOIyfMAGLrFYs2rIZba4U4z5/b8TotJByHMotFuy9Wowd1ytwzcMLY56dKMpsBQAolUpRzutKKCMbKKNzPD09ofFsgr8uXcE9IfZPlf9VaIGnty88PDwEbQ/1IxsoIxsoIxvcLWOjFRZ6vb7WP1bV90ajscbj165dQ2FhIRYuXIiuXbti8eLFeOKJJ3Do0KFb3q3aaDTWOE9pqf3rHupLoVDgueefR0Z8PDZv2IDFJ46DL7gGKThYOQ4yLx3iBw3BhP79ERERIVo73GnqzFmUkQ2U0TkymQw9k1Kw49dzGNKBh1x257UWRjOPHTkK9B6eAolE2Elr6kc2UEY2UEY2uFvGRissVCpVrQKi6nuNRlPj8WnTpiE2NhYTJ04EAHz++eeIiYnB0qVLMW3atDrPP3PmTMyYMUOElttHKpWiV69e6NmzJ86dO4eCggKYTCZoNBq0adMGOp1O9DZU/XsK/VdJV0IZ2UAZnZeYmIgNa7/HuuOXMLiD6o7H/5lphEEWhMTEREHbAVA/soIysoEyssHdMjZaYdG8eXNcvXoVFoul+jrfwsJCqNVqeHt71zj24MGDmDRpUvX3EokE7du3R25u7i3P/8orr+DFF1+s/r60tBQhISHChrADx3GIiIgQdWbiVlSqO3/IcHeUkQ2U0XmBgYEY/NBorP5uMVTycvRtowDH1Z654Hke644bsTbLC8Mee6LG5aZCoX5kA2VkA2Vkg7tlbLTCokOHDpDL5dizZw969OgBANi5cyc6d+5ca3q+WbNmtXaA+uuvv9C5c+dbnl+pVLrddWmEEOKMIUOGwGw2Y+Wqr7HrXBGSWnGID1VArQD0Jh77c8xIzwLyKn3xwCNjMWjQoMZuMiGEEAY1WmGh0WgwevRojB8/HkuXLkVBQQFmz56NpUuXArgxe6HT6aBWq/Hkk09izJgx6Ny5M7p164YlS5YgNzcXo0ePbqzmuwWDwQCg9qVlLKGMbKCM9cNxHB5++GHExMQgbeMGLDu4E0sPlAK8DeCk4BRN0L5LL4zo2w9t27YV/P2rUD+ygTKygTKywd0yNlphAQBz587FhAkT0Lt3b+h0OsyYMQPDhg0DAAQFBWHp0qUYM2YMRowYgfLycrz33nvIz89Hhw4dkJ6efsuF2+QGd5s+cwZlZANlFEZsbCxiY2Nx5cponD17FgaDAWq1GpGRkfDz8xP9/akf2UAZ2UAZ2eBuGTne3ZabO6m0tBQ6nQ4lJSXw8vJq7OY0iKo7k7tLlesMysgGysgGyiisK1euICsrC3q9HiqVChEREWjevLno70v9yAbKyAZ3y9ioMxZEXO42feYMysgGysgGyiiM48ePY9PG9cg8uhO8rQRSiQ1WKwdIPNEquguS+w5AfHx8nYv0hUD9yAbKyAZ3y0iFBcPuhsXrlJENlJENlLF+eJ7HmjVrsHb1lwgNvIYnhnLofI8CKqUEZjOPwyeLkL77Tyz6eBfuSxqBxx9/XPB7kQDUj6ygjGxwt4xUWBBCCCEuYN26dVi76jM81L8EKYnKGjMScjmHLu0V6NIe2Lm/FF/98g3kMhlGjhrViC0mhJCahP9TB3EZJpMJJpOpsZshKsrIBsrIBsrovOvXr+OXH7/EwF7FuL+36raXOfXorMCjD1QibeMPt72fk7OoH9lAGdngbhmpsGCYTCarvvkgqygjGygjGyij87Zt2waF9CoGJtm3A0zvrgo08bqOzZs3C94W6kc2UEY2uFtGKiwYJpVKIZVKG7sZoqKMbKCMbKCMzuF5Htu2rEO3DgaoVfYtyJZKOSR2sWLv7k3VizuFQv3IBsrIBnfL6D4lEHGY2Wxu7CaIjjKygTKygTI6x2Qyobj4ElqFO/YruWWYDKa0UhQXFyMwMFCw9lA/soEyssHdMlJhwTCxtiJ0JZSRDZSRDZTRORaLBQAPRzd4kssAgP//1wuH+pENlJEN7paRCguGudM1ec6ijGygjGygjM5Rq9WQSpW4dt3m0OuuFNkAyKHVagVtD/UjGygjG9wtI62xYJjFYhH8L1muhjKygTKygTI6RyKRoGNcL+w8wIHnebtft+OAFVGtOsLb21vQ9lA/soEyssHdMlJhQQghhDSypKQ+uHBNhxN/2/cBoqDQipNnteid1E/klhFCiP2osGCYu21R5gzKyAbKyAbK6Lzo6Gi0bN0VX/wAFF6x3vbYklIbPl5uQdNmsejcubPgbaF+ZANlZIO7ZaTCgmHuNn3mDMrIBsrIBsroPI7j8Oxzk+Hhcy/eXcRj6x4jDMaal0WZLTz2HDbhrYUWGLl78OJ/XoFcLhe8LdSPbKCMbHC3jO5TAhGHOXKtrruijGygjGygjPXj5eWF/772FpYu/RLL127BD39eQ4doA7QeElQaeBz7S4GySl+0adcd4/79FJo0aSJKO6gf2UAZ2eBuGamwYJgYf8lyNZSRDZSRDZSx/jw8PPDss5Nw9eqj2Lp1K/46nYm86+VQqjS4t1c0evfujWbNmonaBupHNlBGNrhbRiosGGa13v46XRZQRjZQRjZQRuH4+flh+PDhAIY3yPvdjPqRDZSRDe6WkQoLhrnTNXnOooxsoIxsoIxsoIxsoIxscLeMVFgwTKFQNHYTREcZ2UAZ2UAZ2UAZ2UAZ2eBuGWlXKEIIIYQQQki9UWHBMKPRCKPR2NjNEBVlZANlZANlZANlZANlZIO7ZaRLoRimUqkauwmio4xsoIxsoIxsoIxsoIxscLeMNGNBCCGEEEIIqTcqLBhmMBhgMBgauxmiooxsoIxsoIxsoIxsoIxscLeMdCkUw9xt+swZlJENlJENlJENlJENlJEN7paRZiwIIYQQQggh9UaFBcPcbfrMGZSRDZSRDZSRDZSRDZSRDe6WkS6FYphSqWzsJoiOMrKBMrKBMrKBMrKBMrLB3TJSYcEwjuMauwmio4xsoIxsoIxsoIxsoIxscLeMdCkUw9ztpirOoIxsoIxsoIxsoIxsoIxscLeMNGPBMLlc3thNEB1lZANlZANlZANlZANlZIO7ZaTCgmHuNn3mDMrIBsrIBsrIBsrIBsrIBnfLSIUFw8xmc2M3QXSUkQ2UkQ2UkQ2UkQ2UkQ3ulpEKC4ZJpdLGboLoKCMbKCMbKCMbKCMbKCMb3C0jFRYMc7fB6AzKyAbKyAbKyAbKyAbKyAZ3y0iFBcPcbfrMGZSRDZSRDZSRDZSRDZSRDe6WkQoLhrnbgh9nUEY2UEY2UEY2UEY2UEY2uFtGKiwYJpOx370NmZHneZSXl8NoNEKpVEKr1TbIDzz1IxsoIxsoIxsoIxsoo+txr9YSh1gslsZugugaIqNer0dGRgbSN61DYd5ZgLcCnBSBIZFI6puChIQEaDQa0d6f+pENlJENlJENlJENlNH1UGHBMJ7nG7sJohM7Y2ZmJhbN/xDmkjzEBVViaLwUHkoOFUYeB3Mv4YfPD2PVDyF45vmpiI2NFaUN1I9soIxsoIxsoIxsoIyuhwoLhrnb3RqdIWbGzMxMzJ/1Jtp6FWBsqhI6jbrG813CgRK9DV/tysL8D2bg+Zemi1JcUD+ygTKygTKygTKygTK6HkljN4CIx2q1wmq1NnYzRCVWRr1ej0XzP0RbrwJMSlJBp6n7R0WnkWBSkgptPfOxaP6H0Ov1grelIfuR53mUlZXh6tWrKCsra7C/lNBYZQNlZANlZANlZIO7ZaQZC4a500B0llgZMzIyYC7Jw9hUJaSS2y/Qlko4PNFdiSm/5iEjIwN9+/YVtC0N0Y+NvY6ExiobKCMbKCMbKCMb3C0jFRYMc7fpM2eIkZHneaRvWoe4ID10dn6Y9tZI0CmwEumb1iE5OVnQ3aLE7kdXWEdCY5UNlJENlJENlJEN7paRCguGuduCH2eIkbG8vByFeWcxNN6xH4/4MCn2HziLiooKaLVawdojZj+6yjoSGqtsoIxsoIxsoIxscLeMtMaCYWaz2e3u2OgoMTIajUaAt8JD6disg0bBAbwVBoNB0PaI1Y+utI6ExiobKCMbKCMbKCMb3C0jzVgwTKlUNnYTRCdGRqVSCXBSVBgd+yuB3sQDnBQqlUr49ojAldaR0FhlA2UUVmFhIU6fPg2DwQCVSoWIiAi0aNFC9PelfmQDZWSDu2WkwoJh7jZ95gwxMmq1WgSGROJg7iV0Cbf/dQdyrAgMiYSHh4eg7REjo6utI6GxygbKKIwjR45gw4aNOHL4GEwmG6RSGSxWMxRyKWLatEb//n1x7733CvozeDPqRzZQRja4W0YqLBhmNBoBQPAPuq5EjIwcxyGpbwp++PwwSvS2W14idLNivQ2HCtUY8VSK4L/sxcjoautIaKyygTLWD8/z+OWXX/DTT2ug1fjjntj+CAuNgVQqg81mQ37B38j6+xDmzvkY/fofxxNPPAGpVCp4O6gf2UAZ2eBuGamwYJjQl+S4IrEyJiQkYNUPIfhqVxYmJalue6mQ1cZj6S4j5LpWSEhIELwtYmQUYh2JkIUFjVU2UMb6+f333/HDD6vQNuY+tIm5t8ZzEokELUJao0VIa2SfO47169dBLpdj9OjRgreD+pENlJEN7paRFm8TUgeNRoNnnp+KE2XBWJBuQLHeVudxxXobFqQbcKIsGBNfeEnUez0IydXWkRByt7t27Rq+//4nREXcW6uo+KeI8HZoH5uEdes2ITs7u4FaSAghd0aFBcMMBoPgOxS5GjEzxsbG4vmXpiPL1ApTfpVg0ZZK7DtnwvECM/adM2HRlkpM+VWCLFMrvDDtTbRr106UdoiR8X/rSBy78Y5Y60horLKBMjpv27ZtMJs5tGvb1a7jW7XsCAmnxpYtWwRvC/UjGygjG9wtI10KxbC74a/KYmeMjY3FnAWfVt+Vev+BmnelHvGU+HelFiOjq60jobHKBsroHJ7nkZ6+FcHNYyCX27cDDMdJEBnRAdu378LIkSMFbRf1IxsoIxvcLSMVFoTcgUajQd++fZGcnIyKiorqrR89PDxE25WlIbjSOhJC7mYmkwlF167jntguDr3O3z8YWWd2o7i4GIGBgSK1jhBC7EeXQjHM3abPnNGQGTmOg1arhZ+fH7RabYMVFWJldKV1JDRW2UAZnWO1WsHjxgJtR0gkUoDnYbFYBG0P9SMbKCMb3C0jzVgwzN1uquIMylg/VetIFs3/EFN+zUOnwErEh0mhUXDQm3gcyLHiUKEacl0rvDDtJdHWkTT0Tceys7NhNBqhVqvRsmVL+Pr6iv6+NFbZIEZGtVoNuVyG8vISh15XXl4MiVQCT09PQdtD/cgGysgGd8tIhQUhdzlXWEciNp7nb9x0bNMm7D6WiQqrDTYAUgCeMhl6xnVC/3590aZNm8ZuKrkLcRyHrl07Y/euY2jbpqvds6Fns4+iTZvW0Ol0IreQEELsQ4UFw0wmEwD3uamKMyijMBp7HYmYGXmex4oVK/D9ug0oDwiCsl8qtK1jwMkV4I0GlJ86jl8P7cOWAzMx7qHhGDx4sCiZaayyQayMffr0wbatGSi4cBbBzaPuePz165dxvbgA4/79oKDtAKgfWUEZ2eBuGamwYJhMxn73UkZhVa0jEfLmd/YQM+MPP/yAb/5cDz55IJp07FzjOU6lhrZjZ/Ad4lG2azs++/EnKBQKpKSkCN4OGqtsECtjq1at0L5DO+zb/ye02kfhrfO75bF6fRm27/wFkZGhiIuLE7wt1I9soIxscLeMtHibYVKpFFKptLGbISrKyAaxMubl5WHl73/A1qsvtP8oKm7GcRy8Eu6DoVM3fPX9D7h+/brgbaF+ZINYGTmOw6RJzyE8Igib07/F6dP7YTLVXLBpsZhx5uxRbEz7Gn7+Gkyd+h/I5XLB20L9yAbKyAZ3y+heZRBxiNlsbuwmiI4yskGsjOnp6ShVauAdb99Nx7wSElF0ZD+2bduGIUOGCNoW6kc2iJlRq9Xi9df/i+XLl2PHjp04fnIH/PxCoVSqYTabcOVqDiQSK+LjO2LcuLHw9vYWpR3Uj2ygjGxwt4xUWDDMne+xYC/KyAax1jRs3LETkvadwdn51x6JUglbzD1Yl74FqampgrarofqxqKgI27Ztw99Zp2EwlEOt1qJlqxjcd9998PHxEfW9aazWn0ajwfjx4zFixAhs27YNp06dRkWFHmq1Cj17pSAxMRFNmzYVtQ3Uj2ygjGxwt4xUWDDM3a7LcwZlZIMYGa9fv45SgwHKkDCHXqdsEYorp47AaDQKesdTsfuxrKwMy5cvw8F96VDKitA2qhJ+Og56PY/1a9fjtzXL0fneZDz2+GjRFgHSWBWOj48PhgwZAoEnzuxC/cgGysgGd8voXq0lDhH6pkmuiDKyQYyMFosF4GH3bEU1mQw2Xvg2idmPxcXFeH/mDOiLD+CxB6zo2lEBtep/xUOlgceuQ4VYs2kl3svLxrSX34CXl5fg7aCxygbKyAbKyAZ3y0iLtwkhTNJqtZByHKylxQ69zlpSAoVUArVaLU7DBGaz2TB/3ocwle3HaxM59O6mhFpVc+pcreLQp7sS/32GQ3nRHiyYPwc8zzdSiwkhhLCKCguGyWQyt5tCcxRlZIMYGXU6Hdq3agnDscN2v4bneZgzDyMhrpPgu3CI1Y9Hjx5Fztn9mDCSQ4Dv7dsc6C/F+H8BZ7P24vjx44K3hcYqGygjGygjG9wtIxUWDLNYLG43heYoysgGsTL2T+4DRX4OzFcu2XW8MfccNNevoE9SkuBtESvjlvQ0hDcvQ8sw+37xREfKENK0FOnpaYK3hcYqGygjGygjG9wto/uUQMRhd8OlDpSRDWJl7Ny5M9oGN8fhVSuhG/VvSD1ufeM/S3ER9L//gp6tWqJt27aCt0WMjJWVlcg8tgujB9u/awjHcejVBfjuz50wmSZCoVAI1h4aq86zWq04evQo9u/fj+KSEshkMgT4+6NXr14IDQ0V5T1vhfqRDZSRDe6WkQoLholx4yRXQxnZIFZGuVyOl//zIqa/+x5OL/8cqsS+ULdqU2NBt81sRuWpTJi2bkIHPx9MeeEFUbb3EyNjeXk5wJvh7+vY5HOArwS8zYSKigpBCwsaq87Ztm0bfvxlNbLzLsGg8AGUXuB5GyT64/hp7Xp0bNsKjz82CpGRkYK/d12oH9lAGdngbhmpsGCY1Wpt7CaIjjKyQcyMTZs2xbvT38DiL77A/j9XoXjzOvDhLW/cs6KyEpLsLHhZzOgX1xFPjhsnym5JgDgZJRIJAA6OntpiBQBO8HUkNFYd9/PPP2PpylWo9AyHV8eH4OX9v3tU8DYb9Jezsf3UPmTNeAevTn0RsbGxgr5/Xagf2UAZ2eBuGamwYJg7XZPnLMrIBrEz+vr64rWXX0Z+fj62bNmCk2fOQF9yBVqNBrED+qJ3796i33RMjIxeXl5QqXU4m3sV90Tb/1ets7kWqDXegt/PgsaqY7Zu3YqlK1fBGtIV/hFxtZ7nJBJ4BEZB7R+GwkO/Y9ac+Zj17gw0b95csDbUhfqRDZSRDe6WkQoLhgl5iYOrooxsEDKjzWZDZWUlpFIplEpljcuagoOD8dhjjwn2Xo4Qox/lcjm69+iPbbs+xwPJPGTSO1/CZTbz2HFAgZ6JKYLPWNBYtZ/FYsH3P61CpVdEnUXFzSRSGfw6DcSFjG/w559/4sknnxSkDbdC/cgGysgGd8tIhQUhxO3xPI9Tp04hLS0N+/btg8ViAcdx8PX1RZ8+fZCYmAgfH5/GbqYoevfujfRNP2HLrqvo21N5x+M3ZRhRVhmAxMRE8RtHbunIkSPIuXAFujj7diCTSOVQNIvF5u278Mgjj8DT01PkFhJCiONou1mGGY1GGI3Gxm6GqCgjG+qTsaSkBG+//TamT5+OvXv3IiQkBG3btkV0dDQAYMWKFXj22Wfx559/NuruGmL1Y3BwMPoOeAQr/1Aj44Dptsdu22vEzxs8kDJoJIKCggRvC41V++3btw9GpS8UXv52v0bboh2ulVbi8GH7783iDOpHNlBGNrhbRpqxYJhKpWrsJoiOMrLB2Yzl5eV45513kJubi/j4eAQEBNS49Ck0NBRmsxknT57EV199BZPJhCFDhgjUaseI2Y+PPPIIjEYjlvz8A3YdLkZSVyk6tJFDKuVgsfI4fMKMLbutOJXjg6Tkf+Ghhx4SpR00Vu137fp1cCqdQ6+RKtTgpUqUlpYK0oZboX5kA2Vkg7tlpMKCEOK2vvzyS+Tk5KBXr163vDRELpejffv2kMvlWLlyJaKiotCuXbsGbqm4JBIJxowZg5iYGGxOW4+F3x2AFGVQqXgYDBys8EHL6C6Y8OwAdO7cWZTtdIljpBIpeDgzg8b//25gN1y5cgVbtmzB4aOZKKvQQ61SIrplJHr37o2IiAjhGkwIIXagwoJhBoMBAKDRaBq5JeKhjGxwJuOVK1ewZ88exMTE2HW9eUxMDC5evIhNmzY1SmEhdj9yHIeuXbuia9euyM3NxdmzZ2EwGKBSqdCyZUuEhISI8r43o7Fqv8CmAeAOngXP83YXeuby65DaTPDz80NFRQWWfPkVtu7cg+uVgNUzGBK5J2xWM/ad2o7Vf6ShY2xrPDP+aYcve6N+ZANlZIO7ZaTCgmHuNn3mDMrIBmcybtmyBWazGS1atLDreI7jEBYWhn379uHq1avw8/Nz+D3royH7MTQ0tMHv1gzQWHVEjx49sOrPNBiu5kLtH2bXa8pyj6JFgA9atmyJN996B/tPnockuDM8mrYCJ/3fr3Oet8Fw7Ty2HtuL/NdnYMYbr9r9cwJQP7KCMrLB3TI26uJtg8GAcePGwdvbG0FBQZgzZ06dxyUmJoLjuFpfY8eObeAWE0JcxcmTJ+Hn5weZzP6/j7Ro0QIGgwFZWVkitoyQO2vZsiXatgxH2Zl94G13vgGWRV8K2+XT6NcnEYs+/Qz7TuVB1XYwNM3a1CgqAIDjJFD5hcEzdgjOFNkwc9Zs6PV6saIQQki1Ri0spk6digMHDiA9PR2LFi3CjBkz8PPPP9c6btWqVbh48WL115o1a6BQKPDMM880Qqvdh8FgqJ5CYxVlZIMzGfV6vcP7e8vlN24iV1lZ6dDrhED9yAahMnIchzGPj4IfV4yrR9bftriw6EtRdHA12oYFIDo6Grv3H4U8vCdkHrffQlkiV0Ib0x9Z5y9h165ddreN+pENlJEN7pax0S6FqqiowJIlS7Bu3Tp06tQJnTp1wokTJ7Bw4UIMHz68xrFNmjSp/m+r1YpXX30VL730EuLj4xu62W5FqbzznvbujjKywZmMarUa5eXlDr2m6g6mjTG1TP3IBiEzxsTEYNqLz+HDeQtxOeNbqILvgTa4DSTyG+9h1pegLOcobJdOoV2oH16dNhW//fYbSq1KePqG2fUeUpUnjB7BWL8xDX369LFrPQf1IxsoIxvcLWOjFRZHjx6F2WxG9+7dqx/r0aMH3n33Xdhsthq7Xtxs2bJlKCoqwrRp0xqqqW7rbtj5hTKywZmMLVu2xMmTJ2/7/4t/KigogEKhaJTdcqgf2eBIxitXrmDr1q3YtXc/iktKIZPJ0CK4Gfr0TkTnzp0hl8sRHx+PWe+8id9//wNbM/bi+rkMQKYGeBukVgOa++kwYMT9uP/+++Hl5YXtu/ZC4tvSoXaoA6Px19l0XLp0CYGBgYJmdFeUkQ2U0fU0WmFx8eJF+Pn51biUoWnTpjAYDLh27Rr8/WvfNIjnecyaNQsvvPACtFptQzbXLVXdUMVddhJwBmVkgzMZe/fujd9++w35+fl2L0zNzs5Ghw4dRLk53J1QP7LBnowmkwlLly3Dxi07UGaWQB7UGvImLQCbDXln87Hr4McIDvDBk2PHoEuXLggLC8Ozz07EyJGP4tChQygtLYVUKoW/vz/i4uKqf09aLBZU6Csh8XXs959EqYXFyts9w0f9yAbKyAZ3y9hohYVer681vVP1/a3uMLh161bk5+fjySefvOP5/3mnQrFvKOSKqq4nZxllZIMzGZs3b44OHTrgyJEj8Pf3h1qtvu3x2dnZ0Ov16Nevn7PNrJeG7kee55GTk4OwsLAG+4sXjVXAbDZj1oezsfPQKWjb3IeQsFhIZDVfYywrQn7mNsycuwAvTnwaPXv2BAD4+PigT58+tzy3VCqFhJMAdiz2roG3guPs7x/qRzZQRja4W8ZGW7ytUqlqFRB3qsp+/vlnpKSk1FhzcSszZ86ETqer/mqIPdxdTdXuWSyjjGxwNuNTTz0Ff39/bN++HSUlJXUeY7PZkJWVhRMnTuCBBx5AbGws8vPzcfbsWVy4cKF63YXYGrIfeZ7Hd999hzdnvYkffvgBPO/MjdgcR2MVWLFiBXYeOgm/bg+hSVSnWkUFACg9m6B5tyEw+8VgweIvcO7cObvfO7RFc5iKLzrUZlPxBWjVSru3WKZ+ZANlZIO7ZWy0GYvmzZvj6tWrsFgs1dtFFhYWQq1Ww9vbu87XrF+/Hm+++aZd53/llVfw4osvVn9fWlp61xUXZrO5sZsgOsrIBmcz+vr64vXXX8eHH36Ibdu2QafTISwsDB4eHrDZbLhy5QrOnz8PAOjfvz8UCgWefWYCSoqKwNts4CQS+DUNRHK/frjvvvvs+qOFsxqqH6uKil+3/ApziBmr0lYBAEaMGCH6L6e7fayWlpZi/eat0LTsDo1/8G3Pw3EcAuP6I29THjZs2IDx48fb9f79k5Nw5OMvYTVWQKr0uOPxPM/DevkUkgZ0g4fHnY8HqB9ZQRnZ4G4ZG62w6NChA+RyOfbs2YMePXoAAHbu3InOnTvXuRDz6tWryM7ORkJCgl3nVyqVbreSXmhSqbSxmyA6ysiG+mRs2rQpZs6cif379yMtLa16QTdwY/YzJSUFHh4e+HXVLzCXlyHUxwuxYc2gkEphtFiQc/kaVn61BL+u+gXPvTAZcXFxQsWqoSH68eaiQhIvgU+4Dyr8KxqsuLjbx+r27dtxXW9B88j2dp2Lk0jgEdYBW3fuwaOPPgovL687viYhIQFNv/0eeef2w6v1fXfsz8qLJ6GTGpCUlGRXmwDqR1ZQRja4W8ZGKyw0Gg1Gjx6N8ePHY+nSpSgoKMDs2bOxdOlSADdmL3Q6XfV108ePH4dKpUJ4eHhjNdntuNtgdAZlZEN9M8rlcnTv3h3du3dHSUkJysvLIZPJ4OPjgz179mDR/HloppYjvmNbyP7xXoHeOpgtFuz++xzmzJqFqa+8go4dO9arPXURux//WVRowm9cUuoR6YEKNExxcbeP1cNHjoLzDYNUcfv1PjfThbXDxVPbcPLkSXTt2hXAjb78+++/kZ6ejiMnjqKyshIqlQptWsYgKSkJT48bjQ8XfIrSs3J4Rna/ZX9WFv4FacEePDgsBS1bthQkIysoIxsoo+tptMICAObOnYsJEyagd+/e0Ol0mDFjBoYNGwYACAoKwtKlSzFmzBgAwKVLl+Dt7e1W15k1NnebPnMGZWSDkBmr1lUBwLVr1/DFp4sRpJLj3qjwW/7/Qy6ToWd0FLad+huffLwACxZ+IvgOHGL2462KiioNVVzc7WO1pKwcMrXOofNJFWrYwKGiogLAjS1qFyz8GMf/PgGLhodnS1/IVRpUmCzYfHoH0vdsRVRIJEYOH4wfV/+Oa4fyIA2IgappK0jkKvBWM4zXcmC6eBKetiI8mNoPjz32mEP9fbf3IysoIxvcLWOjFhYajQZff/01vv7661rP/XOx4YgRIzBixIiGahoT7oYijDKyQayMW7duhbGsFPEd297xPTiOQ5fIMPyeeRq7du1CcnKyoG0RK+OdiooqDVFc3O1jValQgC93cDMA3gbwPBQKBS5fvow33pqOfMMlhAyMgi7Ut8b78d14lF8swd9bsnAtvQgvTHwKJ06cQPqOPSi+uB9WGw8px0GrkuHe+HvQv984dOrUyeF+udv7kRWUkQ3ulrFRCwsirqpF8SyjjGwQI6PFYkHaxo0I0Wkht3MqWaNUoKlGhU0bNwpeWIiR0d6ioorYxcXdPlbDQ0Nw4Mx+8LwNHGffpovlhTlQKyQICgrC7I9mI994CdEjOkGuUdQ6luM4eDbzRvRDnfDXL4fx/c8/YO4HczBy5Ej89ddfqKyshFKpRGhoaL3u1XK39yMrKCMb3C2je7WWOKShttFsTJSRDWJkvH79Oq5fvYJWwU0del1wE29k5uTAZDLVuIFnfQmd0dGiooqYxcXdPlZ79+6N3zZuQfnFc/BsFmnX+UrOHkKn1pEoKyvD6ZwshA1vW2dRcTOpQobIge3w19cHsXv3biQlJaFLly4O5bidu70fWUEZ2eBuGRvtPhZEfDzPN9j+9Y2FMrJBjIxGoxE8z9s9W1FFJpWC520wGAyCtkfIjM4WFVU8Ij3AdeKwKm2VoPe5uNvHakREBNq1jkLRie2wmk13PFd5YTa467nol9wHaZvTwPnK4Rlo3xoNpZcaqlBPbEjbKPi/+d3ej6ygjGxwt4w0Y8Ewd7tbozMoIxvEyKhWq8FJJDA4uPDNaLaAk0gEX7wtZMYffvjB6aKiys0zF1KpFA899FC923W3j1WO4/D0k+OQP+MdFOz8Ec26PwiZsu4dosounMX1g2vRL6EzunXrhsVLPoXPvY7NrvnGBOLMprMoKiqCr6+v3a8zmUzYv38/cnNzYTQaodFo0LZtW7Rte2Mt0t3ej6ygjGxwt4xUWDDMarU2dhNERxnZIEbGJk2aICQ0DDkFOWjhZ/+N73KvFaFN+zjBr2sVKiPP8zhx+gSMciO8m3rX61zKpkqUyEpw8q+T4Hm+3pdE0VgFWrRogddfeQnvfzgXeRs+hSwoGj4R7SH30IG3WaG/koeS7MOQlRdiQI8ueGbCBJjNZlh4K+Qejl16p/BQwsbboNfr7SosKisr8euvv2J9+kZcuFoIi5oDL+XAmWxQ/SRDq9BIDB74AOLj491uwaijaKyygTK6HiosGOZug9EZlJENYmTkOA59+/fHFx/PR6XJBLUd6yWKK/S4brJiXN++grdHqIwcx+H5Z5/H7Hmz8dfmv+DVxwtSjeP7nFvKLShLL0O7gHaYNHGSIB8kaazeEBUVhTkfzMTWrVuxbtNmFOxeCYvVBo7joJRLcF/7dkju8wji4+MhkUjAcRwk4GCz2Bxqi81iBQeuei2Q2WzGvn37kL51G/IvXITZbIHOyxMJXbsgPj4eX3z5BfacOgQ+RA2Pe4Lhob1xE1me52G6WoHDZ87i9KK5eKB3Ch599FHH/3HcSEONVZ7nkZWVhRMnTqCyshIKhQKhoaHo1KmT6Ity6eeRDe6WkQoLhrnb9JkzKCMbxMqYkJCAX376ETv/Oouktq0hldx6WZnZYsGuv8+heViYKHffFjKjn58fprwwxenioqqoaOPbBlMmT4GPj48g7aKx+j86nQ6pqakYOHAgsrOzq2/aGBAQgMDAwBrHKhQKNA9qjrycKwho08zuthTnXINO64UmTZpg3759+GLp17hwtRgyvxbwCGgDTiJFXnkxvvzpN3y0cCGMagsC+rWG0qfm5XMcx0Hpr4XSX4uKnCKsTvsdOp0Oo0aNsrst7kbsscrzPHbu3Im169bhZG4eDEo1eI0GMJsgLytFiybeSElKwqBBgwTdJOJm9PPIBnfLSIUFw9xpsY+zKCMbxMro4eGByf+Zgplvv43NJ/7CvZFh0GlqX/NeVF6BPWdyIPP2xX+mTBXlf+RCZ3S2uBCrqABorNZFJpOhVatWdzyuf59++PjrRTDrTXfcFQq4MVtRevoqRg4YgT179mDeos9h9YtE+KBHoNbVvCzqsl8zXNzwOcpDpbCVXkWIdwgkt5ih8ghrguIyA1b9vgaDBg2Ct7e3XTndjZhjled5LF++HD9s3AR9WEtoho+CV2hE9ayg+colnDm0H4tW/YpjJ07gpf/8R/A1XVXtYB1ldD20KxTDzGaz292x0VGUkQ1iZoyOjsZ/p0+H0j8QG06dwabMUzhdcBFnCy/jZP4FbDh2EpuzctAkNALT33oLLVq0EKUdYmSsKi5ae7dG6eZSWPW3nzIXs6gAaKzWR48ePdBE44Pc7Vl2fZDI35MNjU2JiIgILPxsCbjgWEQlPVSrqACAy1kHYfNXQdEiGCXlFbh86dJtz62O8sV1Yxm2bdvmdB5XJ+ZYXb16Nb7bsAmW5EFoMnwkVGGRNS41lPs3hU//QVD96wlsP5uDeQs+FuVyF/p5ZIO7ZaTCgmFKpRJKpbKxmyEqysgGsTO2atUK8xZ8jP+88l+0iO2IrDIjMq+VIVtvQavO3fDyG9Px4Zw5CAkJEa0NYmW0t7gQu6gAaKzWh1arxYRxT8OWXYlzm0/BZq17vQVv45G3+yzKj1zDmH+NxqFDh1Au0SKs2/11rpMxlF1H0YUs8MFekCmUkHnocO16MSy3+SArU8hhDVIibetmwfK5GrH6sbi4GN/9uhaWrr2g7RB/22MVzYKhSh2B7ccyceTIEcHbQj+PbHC3jHQpFMPcbfrMGZSRDQ2RUS6Xo1u3bujWrRt4nofFYoFMJmuw3W/EzHiny6IaoqgAaKzWV/fu3WGxWPDJksU4cW4PdG0C4Nc6EDK1HFaTBUV/X0bR8UtQGWT496NPoHv37vhm5U/wad0T3C3WD5kqSmHlLYDnjQ8mco0nKitKUFJcfJudpHjIdSpcyb8qyG5hrkisfty2bRuu2wDPzt3tOl4VFoGips2wMW2z4Gu76OeRDe6WkQoLhhmNRgA3rjNnFWVkQ0NnbIy9+sXOeKvioqGKCoDG6s0qKiqwc+dOHMvMRFlZBdQqJcLDw5CYmFhr8fbNevXqhaioKGzevBlp2zbj3OFM2HgbJBwHT6UWqQkp6NOnDyIiIrB7926UVBrRKuqeOzf8/2sDiUQCTq5CWXn5LQsLm42HjefBw70+0DhCrLGatn0HrNHtIFGp7H6NsmMX7N24FteuXXPofiR3Qj+PbHC3jFRYMEzlwP/Y3BVlZANlFMY/iwtNVw30u/UNUlQA1I/Ajeuhv//+e/yxPg2Xr1egUtYEnEwF3maGYsdhrPxpDbp36YRx48bCz8+vznM0a9YMjz32GIYPH47c3FxUVlZCpVIhJCQEWq22+riKigrwnARy1a0X/io0npBwUqDMCHjcmLXgpDJYrbe+ZlsikcBWZoJfk0AmZysAccYqz/O4dPUqFDEdHXqdIjAIJt6G69evC1pY0M8jG9wtIxUWhBDCkJuLi9ObT6NtSNsGKSrIjTtaz/rgQ6TvOgyLdyuoWkbBS/6/Xch4mxXFRbn4bdsRnDn3Bt6a/jqCgoLqPNf58+eRnp6OzBOnoDcYoFIqEdMqCklJSYiMvLEYWKFQADYbeJvtlpdCqbyaoElgFAryTwOBXv/fEB6S22y9bDNbISk0IOnRRKf/Le5WPM8DjhZjHAeed79LXgipCxUWDDMYDAAgyjZ2roIysoEyCququNi0aRMGDBjQYEXF3d6PS5Z8ic27DkMS0gsaT/9az3MSKVR+EbDpmuHUmXS8O/MDfDjrPajV/ys+iouLsWjxpziUeQIWmQbeYTGQ+2hQbDJi3Z5jWL9lB9pERWDiM+PRvHlzKGUSlFzIhndw1C3bHNimG66knYKppBLwUsNmNkDl4XnL48vPXIFOqkFiYqID/zLuRYyxynEcfH28cfXaFYdeZ756BUoJB51OJ1hbAPp5ZIW7ZaTCgmHuNn3mDMrIBsooPD8/P/zrX/9q0Pe8m/vxwoULWL95G3j/9pDXUVTcTCJXQRXeEyfPbEBGRgaSk5MBANeuXcOMt9/F+et6hN83HP7hMZBI/7cIn+dTcO383zi5az3emPE2Xn/1ZcREhePkqf23LSx8I9rCL7A1Co+cgqmdNzibFd7edReb+rzrkJ2twNDURwS9LMfViDVWkxISkPXnBtgS+0Fi5zouw5H9uLdlSwQEBAjalrv555El7paRtpslhBBC6mnLli0oqeSh9A2363ipyhMGRQDWb9wEnufB8zzmzluAvGIDOgx7Ck2j2tUoKoAbfxH3C22FjkP/jSJehdlz5yEpsResl7NRUnD2lu8lkcoQ3f9x+HlGQLonH5rLFij4mr/+Tdf1uL7vPHDkOh7onYLhw4c7/o9AkJiYCJ3VjIojB+w63nTpIhR5OeiX3EfklhHSMKiwYJjBYKieQmMVZWQDZRQez/MoLCxs0Ou27+Z+3LZzNyzaYHCSO9/9vIrSPwqn/87BhQsXcPz4cZz8+yxa9XkQKu3tL4mRqzRo2/8R5Fy8DJlMhsSuccjf/jOK88/c8jWcRAK1WosQTQBamQOgTzuP65uzcX1rNoo3nYU14xLaykIw+cnnMGrkKJhMJrtzuCOxxmpAQABS+ySB37YJlWf+uu2x5qJrqPh5BeIiw9GlSxfB23I3/zyyxN0y0qVQDHOnG6o4izKygTIKi+d5rFq1Cts3/oak+4di8ODBDbK7z93cj8XFJZAqIx06l1SphdliQ2lpKdI2p0Pq3RTeQaF2vVaj84VHsyikpW/B6/99FRJuMbZs/wGXvJvDLzoenk1bgJNIYSwvxpWsw9CfP4FmPh54ef5HaN68OXbv3o3z58/DaDRCo9Ggbdu2aN++PaRSKSoqKhzK4Y7EHKujRo1CcUkJ/lz9Pa7fEwdtXBfI/f53mZO1ohwVRw/Ctn83Ojb1xUsvvijK9td3888jS9wtIxUWhBDCkOqi4reV6KArQfqabwGgwYqLu5VEKgEcnB26cfO5G5c47Tt4CAEdkh3qo8Dojsja9gsqKyvxwgvPo8eBA0jbnI6D+3/DZYsNPACZhEMzf1/0/9dQJCYmwtvbGwCQlJTkUFuJ/WQyGSY99xzCw37D75s24fyxAyj3awqrWg2J2QzZpYvwU8rRt2d3jBgxosYWwoS4OyosGFY1le0uN1VxBmVkA2UUxs1FxUMRZejRQoltOaVY1UDFxd3cj8HNgnD+76sOnctSfgVeSjk8PT1htfFQar0cer1S6wUrz6O8vBw+Pj7o0qULunTpgsLCQly+fBkWiwVarRYRERGQyez/dX8396NQJBIJUlNTMXDgQBw4cAAnT56EXq+HQqFAaP8k9OjRQ/R/X+pHNrhbRiosGObILxJ3RRnZQBnrr3ZRcePSivvC5AAapri4m/uxb5/eOHT8C9jMlZDcdO+K27EWnUXCfXHw9/cHB8BmsTjUFpvFAgmHWpfRBAYG3vbu3ndyN/ejGO/TtWtXdO3atUHe75/vzTrK6Hpo8TbDpFIppFL7FxK6I8rIBspYP7cqKqrcFybHsNBSpK/5FmvXrhVtQffd3I89evRAU19P6Asy7TqP8Xo+PLkKJCf3gVwuR2BTf1wvyHaoLUX52fDUaNCkSROHXncnd3M/soQyssHdMlJhwTCz2Qyz2dzYzRAVZWQDZXTenYqKKg1RXNzN/ejh4YFxo0dCZylARcGx2/77mkougLu4FwP69ET79u3BcRz6JvVG8bnjMBv0drXDZrXi6umDSLqvx407cAvobu5HllBGNrhbRiosGMZxHPOLNSkjGyijc+wtKqqIXVzc7f2YnJyMZ/79OHyM56DP2gjDlbPgbTcub+J5HqbSQpSf3Q7Zxd0YmNQNEyaMrz5Xr1694KWQ4uyeNLv6JffwDihsBlEWYd/t/cgKysgGd8voXhduEYe423V5zqCMbKCMjnO0qKgi5pqLhu5H/v8XLhuNRiiVSmi1WtF/Ad8p46BBgxAWFoY//vgTu/YfQunlQ+AlcnC8FWqFBHGtwnH/gIdw33331bi8wcvLC/9+4nF8/NmX+FuuQMvu/evMwvM8zh/ZhUuHt+Dxh4ehefPmDZ6RBZSRDZTR9bhXa4lDLA4uBHRHlJENlNExzhYVVcQqLhqqH/V6PTIyMpC+aR0K884CvBXgpAgMiURS3xQkJCRAo9GI8t72ZGzXrh3atWuHwsLC6t2AlEolWrRogVatWt3y3zoxMRFmsxlLvv4W+86dRNM28QhseQ/kKg0sJgMuZ59C4Yl94CquYeSDqRg6dKjQ8QDQzyMrKCMb3C0jFRaEEOJm1qxZ43RRUeXm4kIikeCBBx4QtpEiyczMxKL5H8Jckoe4oEoMjZfCQ8mhwsjjYO4l/PD5Yaz6IQTPPD8VsbGxjdpWZ3Zn6tu3LyIjI5GWloZtGTtw8cBm2HgeEo6DSi7FfffGo2/y04iJiRGp1YQQ4jwqLBjmbtNnzqCMbKCM9uN5HkcO7oM3V4b2Tet3zo5BMqTlluLIwX0YNGhQvWctxO7HzMxMzJ/1Jtp6FWBsqhI6Tc1tXbuEAyV6G77alYX5H8zA8y9NF7y4aIixGhERgaeeegqPPPIIsrOzUVlZCaVSibCwMMF3gKoL/TyygTKywd0y0uJthlksFrebQnMUZWQDZbQfx3GY8OzzsPi3xScHbSgzOrcAu9TIY+FBG9A0Fk8/85xgl0KJ1Y96vR6L5n+Itl4FmJSkgk5T968vnUaCSUkqtPXMx6L5H0Kvt2+XJXs15Fj18vJChw4d0K1bN3Tq1KlBigqAfh5ZQRnZ4G4ZqbBgGM/zou1X7yooIxsoo2MCAwPx3Isvocw7xqnioqqoqGzSFs+9+BICAgIEaZeY/ZiRkQFzSR7GdldCKrl9ESSVcHiiuxLmkjxkZGQI2g4aq2ygjGygjK6HCguGyeXyWndkZQ1lZANldJyzxYVYRQUgXj/yPI/0TesQF6S/5UzFP3lrJOgUWIn0TesE/aVMY5UNlJENlNH1UGHBMKvVCqvV2tjNEBVlZANldI6jxYWYRQUgXj+Wl5ejMO8s4kIdu9Y4PkyKwryzqKioEKwtNFbZQBnZQBldDxUWDHO36/KcQRnZQBmdZ29xIXZRAYiX0Wg0ArwVHkrH1oFoFBzAW2EwGARrC41VNlBGNlBG10OFBcMUCgUUCkVjN0NUlJENlLF+7lRcNERRAYiXUalUApwUFQ6uJdGbeICTQqVSCdYWGqtsoIxsoIyuhwoLQghhwK2Ki4YqKsSk1WoRGBKJg7mOXQ5wIMeKwJBIeHh4iNQyQgghN6PCgmFGo/HGJQQMo4xsoIzC+GdxkVdsatCiQqyMHMchqW8KDl5Uo0Rvs+s1xXobDhWqkdQ3RZCtdKvQWGUDZWQDZXQ9VFgwTKVSCXoJgCuijGygjMKpKi7OWfzxWlo58vjABpupEDNjQkIC5LoQfLXLCKvt9pdEWW08lu4yQq4LQUJCgqDtoLHKBsrIBsroehwqLOrasu/ChQtutb8uIYSw7ujRoygyy5CvCMc1owSZmZmN3aR602g0eOb5qThRFowF6QYU32Lmolhvw4J0A06UBWPiCy9Bo9E0cEsJIeTuZXdh8csvvyAiIgKHDx+u8fi///1vhISE4NdffxW8caR+DAaDoLuhuCLKyAbKKJwNGzZg2Q+rYWzeCX79J8IQ1B5frvgJmzdvFv29xc4YGxuL51+ajixTK0z5VYJFWyqx75wJxwvM2HfOhEVbKjHlVwmyTK3wwrQ30a5dO8HbQGOVDZSRDZTR9di1Kfi6devwr3/9C5MmTUKLFi1qPPfJJ59g4cKFeOihh/Dnn38iOTlZlIYSx7nT1JmzKCMbKKMwqoqKioB20ETEAQDUEfEoB/Dlip8AAH369BHt/RsiY2xsLOYs+BQZGRlI37QO+w+cBXgrwEkRGBKJEU+lICEhQbSZChqrbKCMbKCMrofj7biOqUePHnjggQcwbdq0Wx7z2muvYfv27di+fbugDRRKaWkpdDodSkpK4OXl1djNaRB6vR4AmL4UgDKygTLWX11FRRWe51GZfQDaKycxbuRDohUXDd2PPM+joqICBoMBKpUKHh4egi7UrguNVTZQRjZQRtdj16VQx44dw/Dhw297zMiRI3Hs2DFBGkWE4W7TZ86gjGygjPVzu6ICuLGrkjoiHuX+bUS9LKqh+5HjOGi1Wvj5+UGr1YpeVAA0VllBGdlAGV2PXZdCaTQalJWV3fYYs9l84yZGdzGe51FeXg6j0QilUtlgv+hu5W7oD1fNKORYcNWMQqKMzrtTUVGluriAeJdFUT+ygTKygTKywd0y2lVY9O7dG59//jkWLVp0y2M+++wzxMfHC9Ywd6LX65GRkYF1GzbibE4+bDYbJBIJIsOCkdK/n6jX+95OYxY1DcXVMlaPhY3rkZ2XAxtvg4STICIkDCn9Bjg1Flwtoxgoo3PsLSpuboOYxQX1IxsoIxsoIxvcLaNdl0K9/vrr+Pbbb/H000/j77//rvFcVlYWxo8fj6VLl+L1118XpZGuLDMzE+MnTsLMjz7F3rMVKPFsj3Lfe1Hi2R57z1Zg5kefYvzESaJu93jx4kU89dRTCAoKglqtRtu2bTF79mxUVFTUuKnKyZMn8cgjj6Bp06bw9PRE9+7d8eeff1Y/n5OTA47j4OHhUee028svvwyO47Bs2bJaz7377rsYM2aMGPFuy5VuHJOZmYkJkyZi1uK5OFiahco2apg76lDZRo2DpVmYtXguJkya6PBYsCcjx3HYunVrnc8tW7YMHMdh06ZNtZ5LTEzEm2++WeOxXbt2YdCgQfD19YWPjw/69u2L3bt3293eqver+pJIJPDy8sLw4cNx+vTpOl/jSv0oFqEzOlpUVBHzsijqRzZQRjZQRja4W0a7Cos2bdpg/fr12LZtG6Kjo+Ht7Y0WLVrAy8sLMTEx2LFjB/7880907dpV7Pa6lMzMTMx4dxbOFkkgjRoIbWRPKH1DodAFQekbCm1kT0ijBuJskQQz3p0lSnGRl5eHLl264Ny5c/jxxx9x8uRJvPHGG1i4cCFGjRoFqVQK4MaHxXvvvRc6nQ7r1q3DoUOHMHjwYKSmpuKnn36qcU6z2Yy0tLRa77VmzZo6K+eVK1di+vTpgmezh1wuh1wub5T3vllmZibeev8d5PBXoOkbiibdwqBp4QNVoCc0LXxufN83FDn8Fbz1/jsOjQWhMk6cOPGO/3P65ZdfkJSUhA4dOmDr1q3YtWsXYmNj0bt3b2RkZNj9XsHBwbh48SIuXryIgoIC7Nq1C9euXcMDDzwAm632/QdcpR/FJGTGjRs3OlVUVPlncZGeni5Iu6gf2UAZ2UAZ2eBuGe26FAoAunfvjtOnTyMjIwPHjh1DcXExfH19ERcXh7g4x3+xuTu9Xo8P587HxUoNPKLuA8fVXaNJFGp4RN2Hi2e24cO58/HpJwsEvSzqueeeQ0REBNavX19dRISHh6Nbt25o27YtlixZgsmTJ+OJJ57AiBEj8Nlnn1W/9uWXX8aVK1cwZcoUDBs2rPrxXr16Ye3atRg0aFD1Y6dOnUJZWRlCQkKqH7NYLHjuueewbNkyREZGCpbJEa4wRajX6zF7/lxcVlXAJyEMnKTuNknVcvgkhOFyRg5mz5+LxQs+sWssCJHRx8cHly5dwqxZs/DGG2/UeUxpaSmeeuopvPbaa3jttdeqH587dy5yc3Px0ksv2V1cSKVSBAYGVn8fFBSEmTNnolu3bjh27Bg6dOhQ43hX6EexCZWR53lsSt+CYoscni1i69UeVWh7FF/MQlr6FvTu3bvebaR+ZANlZANlZIO7ZXToztsAkJCQgAkTJuCVV17BU089VaOoEOqvXu4gIyMD+ZeuQxPa9ZZFRRWOk0Adei/yL1136K++d3Lp0iWsXbsW06ZNqy4qqrRo0QL/+te/sHTpUuzatQtZWVmYMmVKrXO8/PLLWLVqFSSS/2VITU3F77//XuOO6mvWrMGQIUNqDPDy8nIcO3YMe/fuRbdu3QTL5Qiz2Qyz2dwo710lIyMDBUWF0MUH37KoqMJJOOjig1FQVGj3WBAio5eXF95++23MnDkTZ8+erfOY3377DaWlpXj++edrPTdnzhx88cUX1d8vWbIE0dHRUCgU8PPzw8SJE2G1Wm/bBpnsxt8xFApFredcoR/FJlRGjuMwcfzTiPCRQ390PWwWk1PnsVlMqDyyDpG+Kkx4+ilBfnlRP7KBMrKBMrLB3TLaXVh8++23ePDBBzFixAj8/PPPNZ7Ly8vD8OHD0bdvX8Eb6Ip4nse6DRthUDSFRKG26zVShQZGZVOs27ARdtw6xC6HDh0Cz/Po3Llznc93794dx48fx4EDB+Dp6Yno6Ohax/j7+yMuLq7Gh4rk5GSUlJRg//791Y+tXr0aQ4YMqfFab29vZGRk4J577hEkjzOkUmmtoqoh8TyPdRvXw+wvg1Rt31SlVC2H2U+GdRvX2zUWhMo4ceJExMTE4Nlnn63z+aNHjyI6Ohqenp61ngsLC0ObNm0AANu2bcOkSZPw3nvvISsrC59++im+/PJL/Prrr7d874KCArz++uuIjo5G69ataz3f2P3YEITMGBYWhqmTJyFUZXSquKgqKsI8LHjpxedrzETWB/UjGygjGygjG9wto12FxYwZMzB69GiUlZVBr9dj1KhR+PTTTwEA8+bNq15ncfNfNFlWXl6Oszn5UPi0uPPBN5F7t0B2Tj4qKioEaUdRURGAG5e51KVJkyYAbrTXkZsCqtVq9OvXD2vXrgVw40PhmTNnkJiYWL8Gi6Cxf+DKy8uRnZcDVXOdQ69TBeuQnZdj11gQKqNUKsXixYuxcePGWn8cAIDi4mLodHfOodVq8eWXX2LYsGEICwvD8OHD0bFjR5w4caL6mPPnz0Or1UKr1UKtViM4OBiFhYVYuXJlnVkaux8bgtAZnS0uxCoqAOpHVlBGNlBGNrhbRrsKi2XLlmHOnDnYuHEjfvvtN6xYsQJz5szBlClTMGXKFIwdOxZZWVkYO3as2O11CUajETabDZys9iUdt8PJ5LDabILd6KSqcCgsLKzz+fz8fACAn58fiouLHTp3ampqdWHx66+/YuDAgS65eKixpwiNRuONLWUVdi9XAgBIFFLYePvGgpAZ7733Xjz55JOYPHkyysvLazzn6+uL69ev3/EccXFxaN++PaZPn47hw4ejdevW2Lt3b41LoZo1a4YjR47gyJEjOHHiBK5fv47Dhw/XWltRpbH7sSGIkdHR4kLMogKgfmQFZWQDZWSDu2W0q7C4cOECUlNTq78fOnQocnJy8MMPP2DHjh1YsGCBXX/pZIVSqYREIgHv4OUHvMUMqUQClUolSDvi4+MhlUpx8ODBOp8/cuQI2rVrh7i4OFRUVNS51Wd2djZSUlKqi5AqgwYNwsmTJ5GTk4M1a9Zg6NChgrRZaFVbmjYWpVIJCSeBzWRx6HU2kxUSzr6xIHTGmTNnwmg01trJKy4uDllZWXXeDHPHjh0YNmwY9Ho9NmzYgLi4OBQWFiIlJQU///wzEhISahwvk8kQFRWFqKgoREREwNvb+7Ztaux+bAhiZbS3uBC7qACoH1lBGdlAGdngbhntKizMZjM8PDz+96L//3D86aefNtqi3cak1WoRGRYMc/F5h15nLj6PiLDgGv+W9eHv74+hQ4fi7bffrrVwNi8vD9988w2eeOIJxMXFISYmBnPnzq11jk8++QRHjx5FUFBQjcf9/PzQvXt3rFixAvv27cOAAQMEabPQZDJZ9aLgxqDVahEREgZDQYlDrzPklyAiJMyusSB0Rh8fH3z44YdYsGBBjYXcAwYMgLe3Nz7++ONar5k3bx7y8/Oh0WjwxRdfYOzYsfjss88wbtw4xMTE4OzZs/VaO9TY/dgQxMx4p+KiIYoKgPqRFZSRDZSRDe6WsV4tjYmJEaodboXjOKT074cjH30Km6nSrgXcVpMeSuMlpPQfL2jlOX/+fPTo0QMpKSl444030KJFCxw8eBBTp05Fjx49MHbsWHAch08++QQpKSmQSqUYP3485HI5vvvuO8yfPx8//vhjndfvpaamYsaMGUhKSmqUO4fbw2JxbKZAaBzHIaXfABxbfBLWSrNdC7itlWbIr1qQ8tAAu8aCvRn37dtX69KqXr161Xns6NGj8eWXX2LHjh3Vj2m1WsybNw9jxoxBZWUlHn30URiNRixatAh//PFH9Q34fH19sWvXLmRmZkIikWDmzJm4ePFivW7g09j92BDEzlhVXHz40QLkHl0PTfsBkMgUDVZUANSPrKCMbKCMbHC3jHbNWNQ1DeNuUzNCS0hIQHBTH+hz94Dna9/w62Y8b0Nl7l4EN/WpdclIfTVr1gx79uxB69at8eijj6J169Z47bXXMH78eHz//ffVfdS7d2+kp6cjJycHycnJ6Nq1K7Zu3Yo//vijxj0sbpaamoqysrJau0G5Ep7nBdtly1kJCQlo3iQQJQfywdtu3xbexqPkYAGaNwm0eyzYm3HatGlISUmp8XXhwoVbHr948eJa62ZGjhyJNWvWYNu2bejevTuSkpKQm5uL7du3V98A880330RAQAC6du2Kvn37QqVSYcKECTh8+LBdeeqT0Z01RMZ/zlxYDRUNVlQA1I+soIxsoIxscLeMHG9HayUSSa0iguf5OguLO+1l31hKS0uh0+lQUlLi0A5Jt1N15+2LlRqoQ++FVFH7r/pWkx6VuXsRpNbjzddeRrt27QR5b3vo9XoAcNnZBiG4SsaqO29fVlVAFx9c58yFtdKMkgP5CDB4YPorr9s9Flwlo5goo7BycnLw4UcLcO5yKSIDvRukqACoH1lBGdlAGdngbhntKiy2bdtm9wnvu+++ejVILGIUFsCND5Qfzp2P/EvXYVQ2hdy7BTiZHLzFDHPxeSiNlxDc1Acv/eeFBi0qAFQvwq3rvgSscKWMmZmZmD1/LgqKCmH2k0EVrLux+5PJCkN+CeRXLWjeJBBTX/iPQ2PBlTKKhTIKLycnB2t+/RUPDhvWIEUFQP3ICsrIBsrIBnfLaFdhwQKxCgvgRjWZkZGBdRs2IjsnH1abDVKJBBFhwUjp3w8JCQmNUmlWbTF7px153JmrZaweCxvXIzsv58ZWtJwEESFhSOk3wKmx4GoZxUAZ2UAZ2UAZ2UAZ2eBuGe0qLMrLy/HCCy9g1apVUCgUGDJkCGbNmuVWW8yKWVhU4XkeFRUVMBgMUKlU8PDwaNR1KFU3XxNqFypX5KoZhRwLrppRSJSRDZSRDZSRDZSRDe6W0a5dof773/9i/fr1mDZtGqRSKRYtWoQrV67gl19+Ebt9boXjuOq7DbuCu2EyylUzCjkWXDWjkCgjGygjGyijsIxGI86dOwe9Xg+FQoGQkJAG+cMs9SMb3C2jXYXFTz/9hB9++AE9e/YEAPTp0wf33nsvjEYjlEqlqA0kznOnOzU6izKygTKygTKygTIKo7CwEJs3b8aOzdugL6kAbDzAAVKVHPHdu6BPch9ER0eL9v7Uj2xwt4x2FRaXL19Gy5Ytq7/v0KEDOI7D5cuXG2xRIHHc3VD0UUY2UEY2UEY2UMb627dvHz77eDEUlRy6NmuPuDbtoFVqYLSYcLLwDHbtPISZ23cjZdggjBgxQpTLpqkf2eBuGe0qLGw2GySS/93yguM4KBQKt6ui7jbuNn3mDMrIBsrIBsrIBspYP4cOHcKiuQvR3jMKI7oPglz6v49aWqUHekZ2Ro+IeOw4ux9rf/wNHMdhxIgRgreD+pEN7pbR6RvkVT1O/ofneZSVleHq1asoKytr9MFgNBrrdTdkd+CqGYUcC66aUUiUkQ2UkQ2U0XkmkwlLFn+OGI8wjIxPrVFU3IzjOPSK6oJBkffhz19+Q3Z2tuBtoX5kg7tltGvGgud5TJo0CWq1uvoxo9GIl156qda+ul999ZWwLXQD/9tudhOyc/Nh5QEpB0SEBiOlf99G225WpVI1+Hs2NFfLWDUWNqRtQE7+edhggwQShAW3QP/k/k6NBVfLKAbKyAbKyAbK6Lw9e/ag/GopUnv9y64/vvaK6oKd5w9h8+bNiIiIELQt1I9scLeMds1YPP7441CpVNW3Fed5HiNHjoRWq63xWGP/hb4xZGZmYsKzz+P9BUuwv8CGssAeMIT0QVlgD+wvsOH9BUsw4dnnkZmZKVobLl68iKeeegpBQUFQq9Vo27YtZs+eDYvFUuO4kydP4pFHHkHTpk3h6emJ7t27488//6x+PicnBxzHwcPDAwaDodb7vPzyy+A4DsuWLav13LvvvosxY8YIHc2tZGZmYuILz2LeVx8jR1IInz7BaDowEj59gpEjKcS8rz7GxBeeFWUshIWFVc8schwHiUSCJk2aIDU1FXl5eWjbti3eeuutGq+ZOXNmnf3573//G0OGDKnzvHK5HNHR0Zg3b1718W+++WaNY27+utvHBCHk7rJl8xZEe4fB18PHruMlnATdQzpgz/Zd1duKEuLO7JqxqOuDJLnxQXLGex+i0OoDz/b3Q62sucewOiAKVmMFzmZtxVvvfYg3Xp2K2NhYQduQl5eH7t27Izo6Gj/++COCg4Oxb98+TJs2DRs2bMDKlSuh0Wiwa9cu9O/fH48++ijWrVsHT09P/PLLL0hNTcV3332Hhx56qPqcZrMZaWlpGDRoUI33WrNmTZ1/gVm5ciWmT5+OUaNGCZrNHlUFUGPf6j4zMxPvzp4Jkz+P6OGdofCoudjKr3UgTBVGnNt4Eu/Onon/TnnF7rFgb8Z58+ZVX6drtVpx8uRJjB8/HqNHj0bPnj2xb9++Gsdv2bIFzZo1w5YtW2oUALt378aTTz5Z53nNZjPS09Mxbtw4NGnSBI8//jgAoFu3bli1alWtNt08yylERndGGdlAGdkgVsa8c+eREtjNode0DojAHzk7cOnSJUFnLagf2eBuGe0qLM6fP1/n43K5HN7e3nZ/eGCJXq/H7I8WoNDqA13bFHCSuid/pEoP6Nqm4OKJdZj90QIsXjhf0MHx3HPPISIiAuvXr4dUKgUAhIeHo1u3bmjbti2++eYbvPDCC3jiiScwYsQIfPbZZ9Wvffnll3HlyhVMmTIFw4YNq368V69eWLt2bY3C4tSpUygrK6uxC5jFYsFzzz2HZcuWITIyUrBMjnCFKUK9Xo+5H38Ekz+PVqntbzkWFB5KtEptj6xfj2Luxx/hk3kL7RoL9mbU6XQIDAys/r558+Z46623MGrUKIwePRovvfRS9XMmkwkZGRl455138NFHH1U/XlJSglOnTuG+++675XlHjx6NlStXYtWqVdWFhUKhqHGMo1yhH8VGGdnQEBl5nkd2djbS09Nx4sQhVFbqoVSq0LJlW/Tpk4yYmBhR1zhSPzqH53mYzeZbrqu4FblUDvDCbytK/cgGd8to16VQYWFhCA8Pr/UVHBwMrVaL6OhofPHFF2K31aVkZGQg/0opPFsl3vKDZBVOIoFnq0TkXylFRkaGYG24dOkS1q5dW33jwpu1aNECo0aNwtKlS7Fr1y5kZWVhypQptc7x8ssvY9WqVTV2/UpNTcXvv/9e49K2NWvWYMiQITV+mZWXl+PYsWPYu3cvunWr/ReakydPon///vD09IRKpULPnj1x6tQpIaK7lIyMDFwuu4rwfm3sGgvhfWNwueyqoGPhVqq2qUtKSsLly5eRm5sL4MZWiN7e3hg7diwuXLiAM2fOAAD27t0LnU6H9u3b3/a8MpkMCoVC3MYTche6evUq3n77TbzxxgvYf+B7BAdnI/aeywgLz8Wp07/i3XdfwquvTkV+fn5jN5X8w41LiTUoNZQ79LpSQxk4Cec2f5Em5HbsKizOnTuH7OzsWl9nzpzBgQMH8Pzzz+PVV1+9axZu8zyPdRs2wegRAqnSvlusS5UeMHkEY92GTYKtRTl06BB4nkfnzp3rfD4+Ph6ZmZk4cOAAPD0967wRj7+/P+Li4moUDMnJySgpKcH+/furH1u9enX1dfdVvL29kZGRgXvuuafWeW02Gx544AGEh4fjyJEj2LVrFywWC6ZNm+Zk2roZDIY614M0FJ7nsSFtA1RhXrUuf7oVhVYFVagXNqRtsGssOJvx7NmzmDlzJgYMGICQkBCEhoZWXw61ZcsW3HfffdDpdOjQoQO2bNkC4MZlUD179qxRaN7MbDZj1apV2LhxI1JTUx1u0600dj82BMrIBjEzXrlyBW+99Rry83cidYgUTz0VgN5JvujWzQf33dcEY8cGYPhDcpSVHcJbb/23+g8FQqN+dN498R1w8OIJh37P7z+fCd8gfzRv3lzQtlA/ssHdMtpVWISGhtb5FR4ejo4dO2LChAmYM2dOjQWdLCsvL0d2bj6U/o5dC6nwj0R2boFgC7SKiooAAD4+dS8S8/f3B3CjvV5eXnafV61Wo1+/fli7di0AoKCgAGfOnEFiYqLd56isrMT48eMxZ84cREZGolOnThgzZgxOnDhh9znsoVQqG/XmMeXl5cjJP48mLQMcel2TVgHIKThv11iwN+P48eOh1Wqh1WqhUqnQsWNHtGnTBt9++y2AG5e43VxY9O7dGwCQmJhYXVjs2bOnxmVQdZ139OjRmDx5MkaOHFl9zI4dO6qPuflrx44ddv17NHY/NgTKyAaxMtpsNsyb9yGMxr8xcpQvIiM1tS534jgOLVqo8ejIAKjU+ZgzZ6YoHzioH52XlJSEa9ZSnL501q7jy416HLlyGkn9+tzyDzrOon5kg7tlFGwUJyQk4OxZ+36Q3J3RaISVByQyxzpaIlPCyvOC/SJo0qQJAKCwsLDO5y9evAgA8PPzQ3FxsUPnTk1NrS4sfv31VwwcOBByudzu13t4eGDChAlYvnw5xo0bh4SEBLzwwguwWq0OtcPVGY1G2GCDTGX/vw0AyJQy2HiboB8K3nrrLRw5cgQ7duxA//79ER4ejpkzZ8LX1xcAqhdwG41G7N69u7pQTExMREZGBniex969e2sVFlXnPXLkCHJzc1FcXIxZs2bVOCY+Pr76mJu/4uPjBctHCMuOHTuGc+eOY9AgHbTa21+jr1RKMGRIE1y9mo09e/Y0UAuJPVq2bIlWsdH44difKNKX3PZYk8WMr/evgsrXA7169WqgFhIiLsEKi4qKCmi1WqFO59KUSiWkHGCzOHbDEpvFCCnHCbYQJz4+HlKpFAcPHqzz+QMHDqBt27aIi4tDRUUFTp8+XeuY7OxspKSk1Lped9CgQTh58iRycnKwZs0aDB061KG2lZeXo3Pnzvjuu+8QHR2NGTNm4MMPP3ToHPYwmUwwmUyCn9deSqUSEkhgMTi26M5itEDCSewaC/ZmDAgIQFRUFDp27IiffvoJwI0CsWpBYK9evXDo0CHs3r0bvr6+aNmyJYAbBUdBQQG2bt0Kq9WKjh071nneqKgoBAcH11rPA9yY5ao65uYvezd2aOx+bAiUkQ1iZUxPT4O/vxHNmtu7WYMc4eE2bNq0XvCt3qkfncdxHJ6d9Bw0Id5YkPE1jhWchtVmq3EMz/M4X1SAxbtWoIArwqT/vODQVQX2on5kg7tlFKSwsFqteP/99++ailur1SIiNBimq47dKdN05SwiQpvDw8O+dRl34u/vj6FDh+Ltt9+uNROQl5eHb7/9FmPGjEFcXBxiYmIwd+7cWuf45JNPcPToUQQFBdV43M/PD927d8eKFSuwb98+DBgwwKG2bd26FRcuXMCWLVswdepUJCcn4/z584L/ApTJZJDJHNuBQ0harRZhwS1w/e/LDr2uKOsywpq3sGssOJNRoVBgyZIlOHLkSPWuT61bt4ZGo8GKFStqXNZWtc7iiy++QI8ePeosHMTW2P3YECgjG8TIaLVaceTIPrRp49gfndq280Bu7unqy2KFQv1YP97e3njtzdcR0jEKy0+txbubP8GvxzYhPWsX1p3cio+2fYX5+7+F0V+Cl994pc71j0KgfmSDu2W0q6Vjx46t83GbzVa9yJfjOGzfvl3QxrkqjuOQ0r8vji5YAquxwq4F3FZjBRQV+Ujp/29BtwmcP38+evTogZSUFLzxxhto0aIFDh48iKlTp6Jnz54YP348OI7DJ598gpSUFEilUowfPx5yuRzfffcd5s+fjx9//LHOD5OpqamYMWMGkpKSHN6twtfXF+Xl5VizZg3i4+ORlpaGhQsXCv5Xmcb4EHwzjuPQP7k/5n31MUwVRrsWcJvKDTDklqL/2NF2jQVnM3bu3Bnjxo3D22+/jVGjRqFZs2bo2bMnVq5cWWs9VGJiIhYvXozp06c79V4mk6nOS/JkMhn8/Pzu+PrG7seGQBnZIEZGg8EAm80Mradj59ZqpeB5M/R6ffUlj0Kgfqw/nU6Hl199Gbm5udi8eTNOHMlExXU9lEolwuLD8UifsYiNjRV8XcXNqB/Z4G4Z7RrR/7y7dtWXQqFAy5Yt8f777+P06dNut9dufSQkJCDY3wtlWVvB/2Oa8594mw3lWVsR7O+FhIQEQdvRrFkz7NmzB61bt8ajjz6K1q1b47XXXsP48eOxcuXK6pmM3r17Iz09HTk5OUhOTkbXrl2xdetW/PHHHzXuYXGz1NRUlJWV1doNyh7dunXDG2+8gWeeeQb33HMPli1bhk8++QSXL19GQUFBfSLXYDabBd/721EJCQkI8PTDuY0n7RoL5zadRoCnn91joT4Z33vvPcjl8up7WPTs2RMVFRW1FuInJiZCr9fXWl9hr927dyMoKKjWV9euXe16vSv0o9goIxvEyCiXy8FxEpjNjs3oWsw8wHGCb/1M/Sic0NBQjB07FnMWfIRPv/wM8xctwOQXJ6N9+/aiFhUA9SMr3C0jx9fz2hSj0YjVq1fj66+/RlpamkPhDQYDJk6ciF9++QVqtRpTpkzBf/7znzqPzczMxIQJE3Dw4EFERUVhwYIF1bva2KO0tBQ6nQ4lJSWC/dU8MzMTb733IS5afeDZKrHOmQursQJlWVsRJL2O6f99Ce3atRPkve1RWloKAKJcu+kqXCXjzXfeDu8bA4W2dpFtKjfg3KZTUFzh8NrUV+0eC66SUUyUkQ2U0XlTpjwPT88TGPSA/TvMbd1yDVlZgVi4cIlDm2vcCfUjGygjG9wto9MXbWVkZODrr7/GTz/9hNLSUsTExDi83ezUqVNx4MABpKenIzc3F6NHj0ZoaCiGDx9e47iSkhL07dsXgwcPxrJly/DNN99g6NChyMrKQkCAY9t8Cik2NhZvvDoVsz9agPyj38PkEQyFfyQkMiVsFiNMV85CUZGPSH8vTH2xYYsKAG51TZ6zXCVjbGws/jvlFcz9+COcXnYAqlAvNGkVAJlSBovRgqKsyzDkliLA0w//mfqiQ2PBVTKKiTKygTI6LympP1asOAm93gqN5s6XPlgsPI4ft6Bv3/6CFhUA9SMrKCMb3C2jQzMWubm5WL58OZYvX47s7Gx4e3ujuLgYK1euxMMPP+zQG1dUVMDPzw/r1q2rvizjnXfeQVpaGrZu3Vrj2AULFmDBggX466+/qq8169y5M2bMmIH777/frvcTY8aiil6vR0ZGBtZt2ITs3AJYeR5SjkNEaHOk9O+LhISERrmjprtVuc5wtYxVY2FD2gbkFJyHjbdBwkkQ1rwF+if3d2osuFpGMVBGNlBG55WVleGFF8YjLOwyUu73u+P6q+3bi3DooAYffPBJrc036ov6kQ2UkQ3ultGuMmjp0qVYvnw5tm/fjmbNmmHw4MEYNmwY7rvvPqjVaqf+En/06FGYzWZ07969+rEePXrg3Xffhc1mq3Ht4datW5GamlpjAcvNd4VubBqNBn379kVycjIqKipgMBigUqng4eEh6EJt4vpoLBBCnOHp6YmxY5/B4sUfQCa7huS+vpBIav8/g+d57NpVjP37pPjXv54QvKgghJD6sKuwGDduHKKiorB8+fIad9utj4sXL8LPz6/GorOmTZvCYDDg2rVr1XeNBm7ca6FLly546qmnsHbtWoSFhWHOnDmCL4SuL47jqu847ArcbfrMGa6aUcix4KoZhUQZ2UAZ6ychIQFm82R89dVCnDl7Gffco0BMjAfUailMJhuysipw5IgRFeWeGDHiCQwaNEiUdlA/soEyssHdMtrV2q+++grff/89xowZg8mTJ2PgwIEYOnQo+vXr5/Qb6/X6Wrcor/reaKx547ny8nK8//77eP7557Fu3Tp8//336NevH06fPo2QkJA6z280Gmucp2oq6W5isVgauwmio4xsoIxsoIz1l5iYiMjISGzevBk7dmzC3j3XwfNWcJwECoUXunUbjD59khEVFSVaG6gf2UAZ2eBuGe0qLMaMGYMxY8bgypUr+PHHH/HDDz9g6NChUKvVsNls2Lp1K1q2bOnQAjKVSlWrgKj6/p/XoMtkMnTs2BEzZswAAHTs2BEbN27EN998g1dffbXO88+cObP6+LuV0Dejc0WUkQ2UkQ2UURghISEYM2YMHn74YeTk5KCyshJKpRItWrRokOusqR/ZQBnZ4G4ZHdpE2d/fHxMnTsT27duRm5uL6dOno0OHDnj22WfRrFkzvPjii3afq3nz5rh69WqNSqywsBBqtRre3t41jg0KCqp1Z8pWrVohLy/vlud/5ZVXUFJSUv11u2NZJZfLBd8txNVQRjZQRjZQRmFpNBq0adMGcXFxaNeuXYMt3qR+ZANlZIO7ZXT67izBwcGYOnUqDh48iL/++gvPPvss1q9fb/frO3ToALlcjj179lQ/tnPnTnTu3LnWTWO6du2Ko0eP1njs9OnTCAsLu+X5lUolvLy8anyJjed5lJWV4erVqygrK2v0KtNqtVbfII9VrppRyLHgqhmFRBnZQBnZQBnZQBnZ4G4ZBVkR0rJlS0yfPh3Tp0+3+zUajQajR4/G+PHjsXTpUhQUFGD27NlYunQpgBuzFzqdDmq1GuPHj8fHH3+MN998E6NGjare7nbUqFFCNL/eqrYYXb8xDdnnC2Cz8ZBIOES0aI4B/ZIbbbtZd7suzxmulrFqLKStW4+C7GzAZgUkUjSPiEByygCnxoKrZRQDZWQDZWQDZWQDZWSDu2UU937ydzB37lzExcWhd+/emDhxImbMmIFhw4YBuHH50w8//AAACA0NxYYNG/Dbb7+hXbt2+O233/DHH3+gefPmjdl8AP9/R/DnXsAHn3yFw1ekMIcngY8eCHN4Eg5fkeKDT77ChOdeQGZmpmhtuHjxIp566ikEBQVBrVajbdu2mD17NiQSCRQKBbZu3QqO4xAREVHn6x955BFwHFfr/iEA8OGHH9aaGTpw4AC6d+8OjUaD1q1bY/ny5XWed+fOnbd8T6EoFIoaO4s1pszMTLwwYQKWzvoAyoOHMEBvwBCTDQP0BigPHsLSWR/ghQkTHB4L9mTkOA4cx+H8+fO1nvv000/BcRzefPPNGo//8ccf6N27N3Q6Hfz9/TF06FCcPHmy+vmqcVPX15IlS7Bs2bI6n/vnjKNQGd0dZWQDZWQDZWQDZXQ9jbqHlUajwddff42vv/661nP/vHQkISEBBw8ebKim2SUzMxNvzZyNa9IA+HQfDpnKo8bzHs1aw2KoQF7mJrw1czbeeGUKYmNjBW1DXl4eunfvjujoaPz4448IDg7Gvn37MG3aNGzatAk///xz9bH5+fk4fvx4jfuOmEymW17Clp2djTfffLPG1r8lJSVISUnBmDFj8O2332L37t144oknEBkZWWP738zMTAwfPhwqlUrQvK4qMzMTs996C0GXr2Kklze02pp3zo3ReKDcasW6nPOY/dZbmPLGG4KPBblcjrVr1+LZZ5+t8fjq1atr3UNj/vz5ePXVV/HWW29h8eLFMBqN+OCDD9CzZ0/s3r0brVq1qj724sWLtd5Lp9MBAAYMGFD9mNlsRlJSkmhbYBJCCCHEtTXqjIU70+v1mD3vY1yTBsAvbnCtoqKKTOUBv7jBuCYNwOx5H0Ov1wvajueeew4RERFYv349evbsifDwcIwYMQLbt29HRkYGFi9eXH1sr169sHbt2hqv37x5M9q0aVPnucePH4+OHTvWeCwvLw8pKSn44IMPEBERgZEjR6Jdu3bIyMioPuazzz5D9+7d0bRpUwGT1u2f2wo3Br1ej49nz0bQ5at40LsJtFJpncdppVI86N0EQZev4uPZs+0eC/ZmrKt/S0tLsXv37hr9mJ2djZdeegmfffYZ/vOf/yA6Ohrt27fHN998g8jIyFq7qQUGBtb6UqvVUKvVNR779ttvwfM83n//fbtyOZPRnVFGNlBGNlBGNlBG10OFhZMyMjJw4VoZfGL7grvDpR+cRAKf2GRcuFZW4wN4fV26dAlr167FtGnTatyVHABatGiBxx57DN9++231Y6mpqbU+eK5ZswZDhw6tde7ly5dDr9dj3LhxNR5v164dli9fDo7jYLPZ8Ntvv+Gvv/5Cr169qo9Zt24dvv76a0yePFmImLelUqkafVYkIyMD5QUXkOLlDckd7q4t4TikeHmjvOCC3WPB3oypqanYtm1bjXu2/PHHH+jZsyc8PT2rH1u5ciV8fX3x6KOP1mybRIKvv/4a77zzjl3tullRURFmzZqF999/v9b9aezhCv0oNsrIBsrIBsrIBsroeqiwcALP81i/MQ1W7/BbzlT8k0ylhVUXhvUb0wTbLerQoUPgeR6dO3eu8/lu3bohMzOzutIdPHgw9u/fj0uXLgEAbDYb1q5diyFDhtR43ZUrVzBt2jR89tlntS6hqWIymaBSqTB48GA8/vjj6Nq1a/Vza9asqV4rwzqe55G2bj2izJZbzlT8k1YqRaTZgrR16wXdOSw2NhbNmzevcWnb6tWra/Xv0aNHERcXV+daiJiYGISHhzv83osXL0azZs0wfPhwh19LCCGEEDZQYeGE8vJyZJ8vgCbQsTufaoJaIvt8ASoqKgRpR1FREQDAx8enzuc9PDxqHBcaGop77rkHv/32GwBgz5498PX1RcuWLWu8bvLkyRgzZgzatm172/ffs2cPVqxYge+//x5z586tVxZnGQwGGAyGRnlv4MZYKMjORiulY39NaK1UoSA7266x4EjGm2eljEYjNm7ciNTU1BrHFBcXV6+RsIdWq63x9dhjj9V4nud5LFmyBM8995zd5/ynxu7HhkAZ2UAZ2UAZ2UAZXU+jLt52V0aj8caWsnLHPkxK5ErYbDwMBgO0Wm2929GkSRMAN7bmDQ4OrvX81atXaxwH/O+D57///e86L4PasGEDdu/ejS+++OK2761QKNCpUyd06tQJFy5cwIIFCxy6QaJQGnt60Gg0AjYrVBL7ZiuqqCQSwGa1ayw4kjE1NRUPPvggLBYLNm/ejNjYWAQEBNQ4xtfXF9evX7f7nEeOHKnx/T/be+DAAeTn5+ORRx6x+5z/1Nj92BAoIxsoIxsoIxsoo+uhGQsnKJVKSCQcbGbHKkib2QiJhBNskMTHx0Mqld5yt6zDhw+jXbt2Na55T01NRVpaGvR6fZ2Fxffff4+8vDz4+/tDq9Vi/PjxOH/+PLRaLXbs2IFz585hw4YNNV7Tpk2b6iLmbqNUKgGJFAabYzevMdhsgEQq+P8wevToAeDGVr+3Wj8TFxdXfRndP/3444944oknajwWFRVV4yswMLDG8+vXr0evXr1uOXNGCCGEkLsDFRZO0Gq1iGjRHPrCMw69Tn/xb0S0aF59iVJ9Vd174O233651V8a8vDx8/fXXtW4i2LFjR/j7++OTTz6ByWRCp06dajw/a9YsnDx5EkeOHMGRI0fw1ltvoVmzZjhy5Aji4+Oxd+9ejBgxApWVldWvOXjwIGJiYgTJ5KjGniLUarVoHhGBLKNjbfjLaEDziAi7xoIjGWUyGQYOHIi1a9fit99+q7OweOihh1BUVISVK1fWeNxqtWLOnDkOX6q3d+/eGlsNO6Ox+7EhUEY2UEY2UEY2UEbXQ4WFEziOw4B+yZAWn4PFYN+HMIuhHNKSHAzol3zLBdHOmD9/PoqKipCSkoKdO3fi/PnzWL16NXr37o2ePXvimWeeqfWawYMH4+233661qBcAAgICavx1OiAgADKZDFFRUVCr1Rg0aBB0Oh2efvppZGVl4bvvvsMHH3yA1157TbBMjlAqlU7tQiQUjuOQnDIAZ+QylFvtm7Uot1pxVi5DcsoAu8aCoxlTU1OxZMkSNG3atM6F2KGhoZg+fTrGjRuHjz76CH///Tf27duHBx98EGfOnMHMmTPtfi8AOH78+C23LLZXY/djQ6CMbKCMbKCMbKCMrocKCyclJCSgma8nrmduAm+z3fZY3mbD9cw0NPP1rPdfdv+pWbNm2LNnD1q3bo1HH30UrVu3xmuvvYbx48fj559/rrUNLXDjg2dZWVmdhcWdaLVabNiwARcuXECnTp3w3//+F/Pmzau1QLihVN3tuTElJCRA27wZ1pUWw3aHXZ5sPI91pcXQNm9m91hwNGP//v1hNptv27+vvvoqPv/8c3z33XeIi4vDAw88AKlUil27diEyMtLu9wJubHtc38ugXKEfxUYZ2UAZ2UAZ2UAZXQ/HC7nfpQsrLS2FTqdDSUkJvLy8BDlnjTtvxyZDpqq9CNdiKMf1zDT4Wi9j+qtTa9z1WmxVC3RZvvbdVTLefOftFC/vOreeLbdasa60GBcD/DB1+nS7x4KrZBQTZWQDZWQDZRQOz/P4+++/cfLkSej1eigUCoSGhqJjx46QycTdP4f6kQ3ulpEKi3rKzMzE7Hkf48K1Mlh1YdAEtbyx+5PZCP3FvyEtyUEzX09MnTypQYsK4MZWqEDtXXxY4koZMzMz8fHs2SgvuIBIswWtlSqoJBIYbDb8ZTTgrFwGbfNmmDTVsQLTlTKKhTKygTKygTLWH8/z2LVrF9avW4v885nwVJfD04OHwcShqEQJnU84EpPux/333w+FQiFKG6gf2eBuGamwEIBer0dGRgbWb0xD9vmCG1vRSjhEtGiOAf2SkZCQAI1GI+h72qNqEa5Qi8VdkatlrBoLaevWoyA7G7BZAYkUzSMikJwywKmx4GoZxUAZ2UAZ68dsNmPfvn3Yt3cPSoquQCKVwa9pEHr27IV27do12OUQ1I/1w/M8vv32W6RvXIEOra+jT4IcbVvKqvsv76IVW3cbseOQBmFRiXhh8lRRPiNQP7LB3TJSYSEgnudRUVEBg8EAlUoFDw+PRr0urri4GADg7e3daG0Qm6tmFHIsuGpGIVFGNlBG5/A8j7S0NKz95TuUX8tDa59yBHrysPHAuSIp8vVeCAhpjUcfH4v27dsL9r63Qv1YP6tXr8baVYswekgFErveetHt2fMWzP2SQ2jLFPxnykt1romsD+pHNrhbRrpBnoA4jqu+O7ErEPp/Uq7IVTMKORZcNaOQKCMbKKPjeJ7HD99/jw2rl+G+kGL0v1eJIJ26xvNnr1Rg7dE9mD8rF+MmThV8E5B/on50XnFxMf5Y+y0G9y5HYtfb36cosoUME0eZ8eGXW3DkSD/ExcUJ2hbqRza4W0baFYphUqnU7QakoygjGygjGyij47Zs2YINq7/GqPZlGJOgQZCu5rk5jkNUgAwv9FGjZ1Ahli6ei7///luw968L9aPztm3bBimuon8v+7YHbdNSjqiQMqSnbxK8LdSPbHC3jFRYMMxsNsNsNjd2M0RFGdlAGdlAGR1jtVrxx5qf0L3ZdfSJuf0HUYmEw+PdVGgmu4g/fv9NkPe/FepH52XsSEPX9gZo1PZf+prYVYKTmbtx7do1QdtC/cgGd8tIhQXD3G3vY2dQRjZQRjZQRsccPXoURRfPol9b+3YFkko49GkNHNu/HVevXhWkDXWhfnQOz/O4du0iQps79tfl0OZSgDdWbysqFOpHNrhbRiosBMTzPMrKynD16lWUlZWhsdfFy2Qy0ffJbmyumlHIseCqGYVEGdlAGR1z4MABhHqWIdTX/vN1jVBAZinGoUOHBGlDXRq6Hxvjd6dYGXkecPQzoIQDAB62O9xs11H088gGd8voPi11YTdvN3surwA2/sb/KMJDGne7WYvF0uDv2dBcLWPVWNi8YT0Kc84BNhsgkSAwLBx9+ju33ayrZRQDZWQDZXRMWWkJ/NRWh16jlHPwVFpRWloqWDv+qaH6ser/l+vSNiO74AJs4CEBh4jmzZCS3EfU351iZOQ4DjpdExReyXfodRcvWwFOIfiuP/TzyAZ3y0gzFvWUmZmJZya9gDmLl+FkqRKytilQdxoKWdsUnCxVYs7iZXhm0gvIzMwU/L3DwsKqp8g4joNcLkd0dDTmzZsHAPj2229r3YjNYrFg9uzZaN++PTw8PODt7Y2UlBRkZGTU+R5FRUVo2rQpcnJyBG+/EHieb/SZoSqZmZn4z8RnsPKjOQg6ewpjPeV41leDsZ5yBJ09hZUfzcF/Jj7j8FiwJ2PVGDh//nyt5z799FNwHIc333wTALBs2bJa4yY4OBiTJk1CWVlZnedPTEyEh4dHredzcnLAcRw8PDxgMBhqve7ll18Gx3FYtmwZAGDMmDE13luj0aB79+44ePBgjYx//PEHOnToAK1Wi3vuuQdr166tfs5qteLll19GYGAgPD098fDDD+PSpUt1/rv169ev+r0bmyuNVbFQRsfI5ApYnDiVzXbj51YsDdGPmZmZmDB5MmYtW45DKk8Y7h8Ky7CRMNw/FIdUnpi1bDkmTJ4syu9OQLyMXbv3QcYhBUwm+8+9da8V4ZEdEBAQIGhb6OeRDe6WkQqLesjMzMTb789GIZogsN/TCO4+BLoWMdAGhkPXIgbB3YcgsN/TKEQTvDNrtij/g5w3bx4uXryIixcvIjs7G6+88gqmTJmC5cuXQyqV1rguz2azYeDAgfjwww8xdepUHD9+HDt27EBsbCz69OmD3bt31zj39evX8cADD+Dy5cuCt1socrlc1F+w9srMzMS8d99BeNElvBMVhH9HBiPOV4cYnRZxvjr8OzIY70QFIbzoEua9+45DY8HejHK5vMYH8CqrV6+udX1mcHBw9bg5c+YMPv/8c6xbtw6pqam1puMLCgqwa9cuBAQE4Oeff67zvc1mM9LS0mo9vmbNmlrv/fDDD1e/9+HDh9G1a1c8/PDDMBqNAIBjx45h2LBhGDt2LI4cOYKnn34aw4cPx9GjRwEA77//Pr7//nv8+OOP2Lt3L4qKivDYY4/VeA+bzYZJkyZh0ybhd1pxlquMVTFRRsc0bdoU54qVsFjt/9BwudSKEpNc8A+hNxO7HzMzM/HW7DnIbRIIj/EvosmQEdDExEIVHgVNTCyaDBkBj/EvIrdJIN6aPUeU351iZezduzcqjD7Yvt9k1/HnL1hx/IwH+iQPELwt9PPIBnfLSIWFk/R6PebM/xil6iAEJwyHXF33/Qrkai2CE4ajRBWEOfM/hl6vF7QdOp0OgYGBCAwMREhICEaPHo3k5GSsWrWqVoX76aefYufOndizZw9GjRqF8PBwxMbG4oMPPsCoUaPw3nvvVR+7c+dOxMXFVd9K3lVZrVZYrY5dSiA0vV6PRXPnoHVlKcZHBUOnqPt/ADqFHOOjgtG6shSL5s6xeyzYm7FXr161CovS0lLs3r0bHTt2rPG4VCqtHjehoaG4//778fvvv2PHjh1YvXp1jWN/+OEH3HPPPRg8eDC+/vpru9/71KlTKCsrQ0hISI3H1Wp19Xu3bt0aH3zwAQwGA7Zs2QIA+O6775CUlIRJkyYhKioKEydORO/evfHjjz8CuDHr9tFHH6FXr15o06YNJk2ahJ07d1afv6CgAH369MHatWtd6oZCrjBWxUYZHdOzZ0+U8t44lGv/ji9b/zJB4xMs+D0PbiZmP+r1esxeuBBXmoXCZ/ijkGo96zxOqvWEz/BHcaVZKGYvXCj4706xMgYEBKBX71R8/4cSmadv36+Xr1kxf5kVwaHx6NKli+BtoZ9HNrhbRiosnJSRkYHCogoExt8PTnL7f0ZOIkFgfAoKiypuecmRkGQyGRQKBWw2W43i4ssvv8QTTzyB8PDwWq95//33sWLFiurvN2zYgLFjx+KXX34Rvb314Qo/cBkZGTBeKsSo0EBI77BqT8pxGBUaCOOlQrvHgr0ZU1NTsW3bthrXXv/xxx/o2bMnPD3r/uV9s9atW6NXr161CouVK1eiV69eGDRoELZv317nZXGpqan4/fffa4y3NWvWYMiQIXfczaJqvFZlHD16NN5///1ax5WUlAAApk+fjqFDhwIALl++jCVLliAxMbH6uEOHDiEkJAQHDx6ETqe7Y+6G4gpjVWyU0THNmzdH69h78dtxGwzmO89aXCmzYts5JXokDYBCYd9OUs4Qsx8zMjJQUK6H7v4h4CS33z2Jk0ihS0lFQble8N+dYmZ87LHHEdtpMOZ9rcCKXytvrKG4SVmFDX9uNeCdT3jItV0w+cVpovxFmn4e2eBuGamwcALP81i/MQ2cf+QtZyr+Sa72BOcfgfUb00S7Vs5sNmPVqlXYuHEjUlNTa1wKZTKZcPjwYfTs2bPO1/r5+cHLy6v6+7fffhuvvfaay+9E0NhThDzPY/OG9WivkNxypuKfdAo52isl2LxhvV1jwd6MsbGxaN68OdavX1/92OrVqzFkyBC72gUAbdq0wcmTJ6u/P3v2LA4cOIAHHngAiYmJ8PLywvLly2u9Ljk5GSUlJdi/f79D722xWPDJJ59AoVCgT58+AICYmBi0b9+++pgTJ05g8+bN1c9XmT59Opo2bYqdO3dizpw51Y8/8MADWL58Ofz8/OzO3RAae6w2BMrouEdHPY6r0ggsSDdAf5vr8i+XWjE7zQptcEcMHDhQsPevi1j9yPM81qVthiUy+pYzFf8k9fSCJbI11qVtFvR3p5hjVSaT4dlnJ2Hwg89j78k2eHWuAjMWGPHRlxWYudiAF9/jsDo9BLHxj+O/r72FJk2aiNIO+nlkg7tlpMLCCeXl5TiXVwDP4NYOvc4zOBrn8gpQUVEhWFvGjx8PrVYLrVYLlUqF0aNHY/LkyRg5cmSN/wlfu3YNPM/X+B9YVlZW9WurvtxNYy9qKi8vR2HOOXT0se+XZJWO3p4ozDln11hwJGNqamr1JUlGo7G6yLSXTqersUB75cqVaNKkCXr16gW5XI5BgwbVWVio1Wr069ev+r0LCgpw5syZGjMJVVasWFFjzD777LN4/vnn4eHhUevYq1ev4sEHH0RCQkKtHI899hj279+P5ORk9OvXT9RdcoTQ2GO1IVBGx7Vo0QIvvPQGcvnWeGWtFasOVeJaua36vfKKrFi+S483/pSAC+yCKdP+a9cMZH2I1Y/l5eXILrgAVXQbh16nim6LcwUXBP3dKfZYlUqlSE1NxZyPPsOTE95C85ZjIfUehSYhYzBsxDTMnfclnnzySVH7kn4e2eBuGV37z9Euymg0wsYDUoXKoddJFUqYeMBgMAj2If6tt97CsGHDAAAqlQpBQUHVt363Wq3Vg9HHxwcAUFxcXP3a8PBwHDlyBACwd+9ejBo1SpA2NaTGvhul0WgEbDZoZI7dEEkjkwI2o11jwZGMqampePDBB2GxWLB582bExsY6tMiztLS0xszVypUrMWjQoOoxNWzYMKxYsQI7d+5Ejx49ar333Llz8c477+DXX3/FwIED6/wry+DBgzFr1iwAN34Wdu7cicmTJ8PT0xMTJ06sPu7SpUvo27cvbDYbfv75Z0j+cclhVFQUAGD58uUIDg7GqlWrMGbMGLuzNrTGHqsNgTI6p3Xr1pj+zofYsGEDNm1dj99OF0IuscDGA1ao4N00BgMeTUHfvn3rLMCFJlY/Go3GG1vKqtQOvU6iVOP/2DvzsKjK/nHfZ1hmgAEUFQHZXHLHfcfMBUWUV9Q0LS0ty7Tc98p+opmluWumZYmmpma55272Gm5pLuNuriiiIQqyDcuc3x9+mVcCdYAZZjg993XNdck5zznnc8/zIPOZZ8tGNuvfzuJqqw4ODrRo0YIWLVoUy/OeRPw+KoOS5igSi0KgVqtRSZCdkXd5zWeRnaFHJT1OAMyFp6en8QPWP7G3tzcOhdJoNNSpU4eDBw/Ss2dP4PF/eDnX3rpVsHW3bQW1Wm3956tUpGYVbPxjalY2qFQmtYWCOOZ82P/999/ZuHGjcS6CqZw+fdq4RPHp06c5d+4cFy5cyDX/BmD58uV5Eovw8HDefvttrl+/zsaNGxk0aFC+z3B1dc3VZmvXrs0ff/zBt99+a0wsbt++Tdu2bQHYv38/5cqVM5bfunUr9evXp0KFCsDjtl2pUiWL7kRsDqzdVosD4Vh4PD09ef311+nRowc6nY67d+9y6dIlEu7fR6t15caNG+zbt49WrVpZfO6QpRzVajUqJAzpaQW6zqBPww7JrH87RVtVBsLR9hBDoQqBVqulol8FHt26WKDrHt26QEW/CsXyjVN+vPvuuyxbtoyYmJg8527fvm2FiIqOtbsItVotXoEVOfEw//0fnsaJh4/wCqxoUlsoiKO9vT2dO3dm8+bNbNmypUCJxeXLlzlw4IAx8VyzZg2lSpXixIkTnDx50vjq3bs369atIy0t94eDsmXL0qJFC1atWsXRo0fp2NH05RMNBoNxclpKSgodO3ZEpVLx22+/4ePjk6tsznLKOTx69IhLly5Ro0YNk59nDazdVosD4Vh07OzsuHDhApvXb0T3+wnUt7JxuKIn+c84Nixbx6j3h7N48WKLrthnKUetVkulCj6kXzz3/MJPkH7hLBUr+Jj1b6doq8pAONoeoseiEEiSRMcOIZz9KorMtGSTJnBnpj1C/vsqHXv0f+4qOeYiMzMzV2McNGgQu3fvpkWLFkydOpXg4GBSUlJYvXo1c+fOzfMNdEkgZ+8DayVrkiTRLrQjP8w5S2JGpkkTuBMzMjmlN/BqaEeT2kJBHSMiInjzzTepVKlSviuAweNhcnFxccD/JvaPHj2adu3aER4eDjxOLPr06UOdOnVyXTtq1CjWrFnDxo0bad68eZ5nT548mbZt2z51x9y0tDTjs7Ozs4mOjmbdunWMGjUKgGnTpnHlyhX2798PYCzr5OSEu7s777//PpMmTaJu3boEBATw4YcfUqVKFcLCwkx6f6yFtdtqcSAci0ZaWhpfTP+CG6f/om1AU5o1rI+b5n9/X1Iz0vjjxmn27DnEtb+u8cHEDyyypLKlHCVJIiykHaejVpCd/MikCdzZj5Kwv3KRsP5vmPVvp2irykA42h6ix6KQBAcH4+XhQtyxX5D/saHYP5ENBu4e24GXhwvBwcHFFOHjoU5P/kesUqn4+eef+fjjj1m0aBH169fnxRdf5NChQ3z99dfGD3IlCY1GY9bu8cIQHByMurwXK2/Ekf2cbxWyZZmVN+JQl/cyuS0U1DE0NJTMzMxnrsh069YtvL298fb2plq1aowfP54+ffoYl5o9fPgw165dY8CAAXmubdy4MQ0bNsx3T4uIiAgePXr0zGevW7fO+OzAwEAmTJjA2LFjGTduHAA//fQTaWlpNG3a1FjO29ub4cOHA/D+++8zbtw4Bg8eTOPGjZEkic2bN+eZg2Fr2EJbtTTCsfDIssxXi74iRneV95v2oUONF3MlFQDOjk689EJThrV4g5QbCcyZNZusrCyzx2LJegwODqaC1pnEXzYiG549hFQ2ZJO4fRMVtM5m/9sp2qoyEI62hySXpP6VIpCUlIS7uzuJiYm5JqcWBZ1Ox9TpM0nUeOPVKAwHp7zfvmSmPSLu2Hbc0+/w8YSxxvHrxUHOhkJP++ZYCdiKY87O29XSkugb4JVvz0ViRiYrb8Rx0cmNkRM/Nrkt2IqjJRGOykA4Fp4rV64wZcIk3qjZhToVqj+3/I2E28w/+j3DPhxFo0aNzBqLpesxZ+ftv30CcA+LwM4179/k7EdJJG7fRLnYG0waO8bsfztFW1UGwtH2EIlFEdHpdMyat4C4hBSkcpVw9a2OnaOa7Aw9j25dQP77Kl4eLowZMaxYkwqAhIQEAIutkW0L2JKjTqdj0exZ6O/GUVeton4pV5zt7UjNyubEw0ec0htQl/fi/dEF+yNpS46WQjgqA+FYeL755hvO7DnOR20HmzzkZ/5/l6Ot5cn4D8abNZbiqEedTsfMhQu5nZxKVuVqaKrXQqV2wqBPI/3CWeyvXKSC1pmxQ4da5G+naKvKQDjaHiKxMAOpqY93Bd2xaw/XYm5jkEElQUW/CnTsEEJwcLBVMs2SluUWBltzzGkLe3fuIO76NTAYQKXCK7Ai7UI7Fqot2JqjJRCOykA4Fg5Zlnmn/9u0LdOQ9tVNn+t29MYp1l3ZxYKvvzTr37Xiqsec/y+379nLtduxZCNjh0TFCj6EhbSz6N9O0VaVgXC0PcTkbTPg7OxM+/btCQkJISUlhfT0dDQaDS4uLsU2UVtgG4i2IBAICkpaWhqZ+gzKaQv2jWQ5bRnIlnn06JHZvzArDsT/lwKB8hCJhRmRJMmmdrBOT3+8z0ZJyXILg606mrMt2KqjORGOykA4Fo6chQey5WcvBPJPDP9X3twLFxR3PVrjb6doq8pAONoeIrFQMCVtU5XCIByVgXBUBsKx8Pd0LeVGzIM7NPQzfT7BzYRY7NUOZl9yVtSjMhCOyqCkOdr2+owCgUAgECgcSZJo1fYljt05Q0ZWpknXyLLMoZgTNG7ZFCcnJwtHKBAIBKYhEgsFk5GRQUZGhrXDsCjCURkIR2UgHAtPmzZt0Dtk898rR00qfzzmDAmGR7Rr187ssYh6VAbCURmUNEcxFMqMyLJMcnIyer0etVqNVqu16gQ0e3vlV6+tOpqzLdiqozkRjspAOBaecuXK0fnl/7D1h424ql1oGljvqWXP3LnEunM7aNHhRapUqWL2WEQ9KgPhqAxKmmPJitZGyVkyb+fuPVyPiTUuNxvo50Noe+stN2tnZ1fszyxubM0xpy3s2rmTWzeuIxsMSCoVvgGBdAgNLVRbsDVHSyAclYFwLBo9evQgNTWVdVt2cjL2PC0rNqSGVxVUkgpZlvnr7xtEXzvOmQd/0ah1M94a8JZFvrwS9agMhKMyKGmOYihUEdHpdLw/bCRzvl7OVb0Lbo27UPbFXrg17sJVvQtzvl7O+8NGotPpLBbDgwcPGD16NBUrVsTZ2ZkaNWowd+5c9Ho9mZmZBAYGIkkS//3vf/Ncu2PHDiRJon///sZj27Zto169emi1WurUqcPmzZtzXfPDDz9QuXJlnJ2d6datG/Hx8cZzsiwzYcIEypUrh4eHB+PGjcNg+N9KJ3PmzEGSpFyvMWPGPNfx008/zRVjDpmZmWRmmjYm2dLodDqGDXmfJfPnknbrOvU9S9PMrzz1PUuTdus6S+bPZdiQ9wvcFgrqGBUVhSRJfPvtt7mOP3jwIM97X7Zs2TzXZ2VlUa9ePSIjI3Md//7776lWrRpubm5069aNuLi4Ank8C1uqR0shHJWBJR0lSeKNN95g8OghZPurWXZmIx/tnMOUvQuZuGsOi0+u5UHZDPoNGcD7Q97HwcHBInGIelQGwlEZlDRH0WNRBHQ6HZ/OmEWamy8vdBuAo3PupfLKVKpFRmoy16M38+mMWXw0bjRBQUFmjeH+/fs0a9YMHx8fvv32WypWrMjRo0cZOnQo58+f54svvgDAwcGBzZs306pVq1zXb9iwIdc3XqdPn6Z79+588cUXdOrUiZ07d9KjRw/++OMP6taty9GjRxkwYACLFy+mXr16DBs2jP79+7N161YAZs+ezerVq9mwYQOZmZn07dsXT09PY/Jw7tw53nvvPT7++GPjM11cXJ7p+MMPPzBp0iT69u2b55ytrHWu0+mYPu1TtFkZ/KdOdZwcHXOdD/QsS1pGBocuXWP6tE8Z/+FHJreFgjrmJH4rVqxgwIABxuPnzp2jTJkynDlzxngsv2UqZ86cyalTp+jatavx2M6dO3nzzTeZO3cuISEhfPrpp4SFhXH8+HGzLHVpK/VoSYSjMrC0oyRJNG/enObNm3P16lUuXrxIWloaGo2GSpUqUa1atWKJQekIR2UgHG0PsfN2IUlNTeX9YSNJUJfjhXa9kZ7x4Uo2GLi8dw0e+r/5cv4csw6Leueddzh06BDHjh1Do9EYj2/ZsoWIiAhOnDhBREQEVapUISYmhosXL/4vLlnGx8eHihUrUrVqVaKiopgwYQKnTp1i+/btxnKhoaE0atSITz/9lDfeeAOVSkVUVBQAMTExBAQEcOXKFSpWrIi/vz9Tpkwx9i6sXLmSiRMncv36dQBatmzJG2+8wcCBA5/rlpWVxdChQ4mKisLf35/mzZsbn5uDLexImZqayrAh72P/KJHWtaqiesZ/AgZZZv/ZS2S5ujN/4ZcmxV0Qx3v37uHj48OyZcvo16+fsV4Ali5dynfffcfBgwefev1ff/1Fq1atKF26ND179jT2WoSHh1OmTBmWL18OPN7Qy9fXl9WrVxMaGvrcuJ6HLdSjpRGOykA4KgPhqAyEo+0hhkIVkujoaO4mphAY3OWZSQWApFIRGPwf7iamEB0dbbYY9Ho9a9asYciQIbmSCnj8QXDz5s34+PgA0LlzZ65evcqFCxeMZQ4fPoyHhwdVq1Y1HuvXrx+ff/55nmclJiYar3my18PPzw9/f38OHz5MbGwsMTExuc63bNmSGzducOfOHQDOnz+f63nPIjk5mdOnT3PkyBGaN2+eb5msrCyysrJMup+liI6O5uHf92heteIzkwoAlSTR7IWKPPz7nsltoSCOP/74I6VKlaJPnz74+PiwYsUK47lz5849971/9913iYyMpFy5crmOX716laZNmxp/dnJyokqVKhw6dMikuJ6HLdSjpRGOykA4KgPhqAyEo+0hEotCIMsyO3fvQe1dNc/wp6fh6OyK2vsFdu7eg7k6ia5cuUJycjKNGzfOc06SJFq1amXcWKV06dK8+OKLueZLbNiwIddwF4AaNWpQt25d489nz55l7969xiUN79y5Y0xWcihfvjy3bt0yJg9Pni9fvjwAt27d4u7duyQkJBAVFUVgYCA1atRg5syZT30/SpUqRXR0NHXq1DH1LSl2ZFlm186deLlo8gx/ehrOakfKO2vYtXOn2dpCDmvWrKFz586oVCq6dOnCihUrjM84f/48t27dokmTJlSoUIHevXsb6wxg2bJlpKen88477+S5b/ny5bl9+7bxZ4PBwO3bt3PNrxEIBAKBQPDvRiQWhSA5OZnrMbGUDqxRoOtKB9bk+q1YUlJSzBLHw4cPAXB3d8/3vL29fa5lyiIiInIlFps2baJbt25PvX98fDwvv/wywcHBREREAI+75P65C6RarUav1xu76548n/NvvV5v7C0pX748W7Zs4YMPPmDq1KnMnTvXROPnOxY3ycnJ3LpxHf8yHgW6LqCsB7duXDepLZjqGBMTQ3R0tDFZ7N69O1evXuX3338H4MKFCyQlJTFnzhzWrl1LbGws4eHhZGdnc+/ePT744AOWLFmS73jOXr168dVXX3Ho0CEyMzOZNm0ad+/eNdva2taux+JAOCoD4agMhKMyEI62R8mJ1IbQ6/UYZLBXF2y3U3u1BoMB0tPT0WpN6+l4FmXKlAEer/aTH//sOouIiGD06NHEx8dz79490tLSaNSoUb7X3r17l/bt22MwGFi/fr1xgq5Go0Gv1+cqq9frcXZ2Ng7H0uv1uf4Nj8cGNmjQgPj4eGPcQUFB/P3333z11VeMHDmyMG+B1bsH9Xo9ssGAo0PBfpUc7e2RDQaT2oKpjmvWrEGj0RjnPLRu3ZrSpUuzfPlyXnzxRc6ePYskScZdetevX4+3tzdHjhxhwYIFvPnmm9SuXTvfe7/zzjvodDpefPFF4PGymJ06dTLLfCWwfj0WB8JRGQhHZSAclYFwtD1Ej0UhUKvVqCTI0qcV6LosfToqFXnmQxSWypUr4+7uzvHjx/M937t3b3799Vfjz4GBgdSqVYutW7eycePGPMOgcrh9+zatWrVCr9ezf//+XOPtK1SokGeZ0bi4OLy9valQoYLx5yfPAXh7ewP/S4ZyqFGjRq4hNgVFlmWzDycqCGq1GkmlIiOzYL/4GVlZSCqVSW3BVMcffviBtLQ03NzcsLe3R6PR8ODBA3788UfS0tJwdnY2JhUAnp6elClThtu3b7NmzRrmzZuHVqtFq9Vy4MABpk2bRq1atYDH62h/+eWXJCUlce/ePdasWcOdO3cIDAwskHdRHUsywlEZCEdlIByVgXC0PURiUQi0Wi2Bfj48vHG+QNc9uH6OQF+f5y6vair29vb07t2bhQsX5hmSsmXLFrZv346vr2+u4xEREWzduvWpw6BSUlLo2LEjKpWK3377Lc98imbNmhmH1sDj4TcxMTHGJW/9/f1znf/999/x9/fH29ubpUuXUq1atVy/ICdPnqR69eqFfg8cHBwstpa7KWi1WnwDArl5P/9eo6dxIz4B34BAk9qCKY6XLl3ixIkTzJ8/n5MnTxpfa9asISkpiR9//JHSpUvnSjRz5khUr16dy5cvc/r0aeN1jRo1YtCgQfzyyy/A4/1HPv/8c5ydnfHw8ODOnTucOHGC1q1bF8i7KI4lHeGoDISjMhCOykA42h5iKFQhkCSJ0PYhzPl6ORmpySZN4M5IfYT+zmVCB/Yz65rEkZGRNGnShNDQUCIjI/H19WX//v2MHTuWwYMH51kFKCIigtmzZ+Pk5JRnTwuAadOmceXKFfbv3w/8r8fByckJd3d3Bg8eTOvWrWnevDmNGzdm+PDhhIeHG5c0HTx4MOPHjzcmNBMmTGD06NEAtG/fnlGjRjFmzBgGDx7MsWPHmD59Ot98883j9ygjg4SEBMqVK2fyTpPZ2dkFf9PMiCRJdAgNfbwpXkaGSRO4U/UZ3E1N593QUJPagimOP/zwAx4eHgwcODDXHJfatWszZcoUVq1axYsvvsjIkSP55ptvsLOzY/jw4XTs2DHf/TScnJzw8PAgICAAgIoVK/Lmm2/StGlTPD09GThwIJ07d37q0KmCYu16LA6EozIQjspAOCoD4Wh7iB6LQhIcHEx5dxeuR29GfmJn6fyQDQZuRG+hvLsLwcHBZo3Dy8uL6OhoKlWqRJ8+fahduzZz5sxhypQpTJkyJc/YvIYNG1K6dGk6d+6c74f3n376ibS0NJo2bYq3t7fxNXz4cACaN2/OkiVLmDx5Mi1atKB06dIsW7bMeP3YsWPp1asX3bp1o2fPnrz++uvG+RMBAQH88ssvHDx4kDp16vDBBx8wffp0XnnlFQAOHjyIt7c3MTExJvvbwjJswcHBlCrnyaFL1zA8p7vSIMscvnyNUuU8TW4LpjiuWbOGvn375plYD4+TvT179vDVV1/RoEEDOnXqROvWrQkMDGTVqlUmxdC1a1fGjRtHnz59aNmyJVWrVuX777836VpTsIV6tDTCURkIR2UgHJWBcLQ9xAZ5ReDJnbcDg/+Do7NrnjIZqY+4Hr0Fp6RbTBw/xmzf8JpCzopD5hp6ZYvYiuOTO283e6Eizuq8PRep+gwOX75Gsr0jEz6aaHJbsBVHSyIclYFwVAbCURkIR2VQ0hxFYlFEdDods+ct5G5iCmrvFygdWBN7tYYsfToPrp9Df+cy5d1dGD1iaLEmFVDyGmNhsCVHnU7HvDmzefj3Pco7awgo64GjvT0ZWVnciE/gbmo6pcp5MmLU6AK1BVtytBTCURkIR2UgHJWBcFQGJc1RJBZmIDU1lejoaHbu3sP1W7EYDKBSQaCvD6HtQwgODrbKVuwJCQkAeHgUbI+FkoStOea0hV07d3LrxnVkgwFJpcI3IJAOoaGFagu25mgJhKMyEI7KQDgqg+J2lGWZ5ORk9Ho9arUarVZr1jml+SHq0fYQiYUZkWWZlJQU0tPT0Wg0uLi4WPyX6lnkbFhnjaSmuLBVR3O2BVt1NCfCURkIR2UgHJVBcTnmfKG2b/d24mKugJwNkh1efpVp2z7Mol+uinq0PcSqUGZEkiTjPgCCfzeiLQgEAoFA6eh0OhbN+4LMxBgaeqfRrZEdLmqJFL3M8Rt3Wfv1CX5e68d7w8fmuwKhQHmIxELBpKenAyUnyy0MwlEZCEdlIByVgXBUBpZ21Ol0zJseSS2327wVocbd2SnX+SYVITHVwHcHLzFvxmSGj5tk9uRC1KPtIZabVTAajcZsu3zbKsJRGQhHZSAclYFwVAaWdExNTWXRvC+o5XabYW01uDvn/3HS3VnFsLYaarneYtG8L4zDesyFqEfbQyQWAoFAIBAIBAKTiY6OJjMxhrdaqLFTPXv+oJ1K4s0WajITY4iOji6mCAXWQiQWZkSWZR49ekR8fDyPHj3C2vPi09PTjV1oSsVWHc3ZFmzV0ZwIR2UgHJWBcFQGlnKUZZl9u7fT0Dv1qT0V/6SUs4oGXmns273drJ+NRD3aHmKOhRkwLje7Zy83b8VikGVUkoS/rw+hIe2sttxsfrswKw1bc8xpC3t37eHOzViQZZAkvP19aNehcEsP25qjJRCOykA4KgPhqAws5ZicnExczBW6NSrYR8hGgXb8cewKKSkpZlvYRNSj7SF6LIqITqdjyPCRLFi6gjsGN3xe7E7FDn3xebE7dwxuLFi6giHDR6LT6SwWw4MHDxg9ejQVK1bE2dmZGjVqMHfuXGRZRpIkWrdujSRJuV5ubm60bduWM2fOGO+TU27FihV5nnHhwgXjvQD279+PJElUqlQp35h69+6NJEns378/z7l33nmHyMhIc6gbfWwBnU7HqKEjWbUoirIPnOhbrTPv1OlB32qdKfvAiVWLohg1tOBtwRTHwMBAoqKi8hyPiooiMDCwQM97Fhs2bKBNmzZ4eHjg7OxM48aNWbZsWa4yDx8+5O2336Z8+fKUK1eO/v378/Dhw2fe15bq0VIIR2UgHJWBcCw8er0e5Gxc1AW7t7OjBHK2Wb99F/Voe4jEogjodDqmfTGbVK0XDfuMJqhjL7xeCKKMXxW8XggiqGMvGvYZTarWi8++mG2R5OL+/fs0adKEY8eO8e2333L27FkiIyOZNm0ao0aNevwfADB69Gju3LnDnTt3iI2NZfv27SQlJdGtWzcMBoPxfg4ODmzevDnPczZs2JBvw75161au5AQgIyODHTt25BvvjBkzWLp0aVGUc6HX642O1kSn0zHn81n4G8rwcZv36dekO/V8a1LNsxL1fGvSr0l3Pm7zPv6GMsz5fFaB2oKtOE6dOpVevXrRrl07Dh48yJkzZxgwYAAjR45k1qxZxnKDBg3i1KlT/PLLL+zcuZPz58/zzjvvPPPetuJoSYSjMhCOykA4Fh61Wg2SHSn6gg1pSs2QQbIz60RkUY+2hxgKVUhSU1OZM38hchl/6nTqg0pll285tYsrdTr14fQvq5gzfyEL580x67CoCRMmoFar2blzp/GXNafnIiIigsGDBwOg1Wrx8vIyXuft7c28efNo2bIlOp2OunXrAtCqVSt27dpFRkYGjo6OxvIbN26kWbNmeZ7fqlUrNm/eTO3atY3H9u7dS82aNTl06JDxWFJSEm+99Rb79u3Dz8/PbP4ODg5mu1dhSU1N5cu5C6ni6MNbTV/BTpV/vu6m0fJW01f47sg6vpy7kNkLTGsLtuCo0+mIjIxk1apV9OrVy3h80KBBaLVahg0bxvDhw9Hr9axfv57o6GgaNmwIwNy5c3nxxReNmwXmhy04WhrhqAyEozIQjoVHq9Xi5VeZ4zfu0qSi6dcdu56Nl19lXFxczBaLqEfbQ/RYFJLo6Gjik1Kp0abbU5OKHFQqO2q06Up8UqpZV0TQ6/WsWbOGIUOG5PnAFh4eztatWwkICHjq9Tnj9uzt/5dftmjRAo1Gw759+4zHYmNjuXz5Mm3atMlzj4iIiDw9HBs3bqRbt265jl27do309HT+/PPPpw6fKgy20EUYHR1NekIyveqHPzWpyMFOpeKVep1JT0g2uS2YyzEmJoYuXbrg7OxMYGAgkydPJjs7G3g8ZKply5ZMmDABV1dX/P39c/UsRUVFUaNGjVxJRQ6vvPIKp0+fxt7eHpVKxdatW6lXr16uMtnZ2SQnJ1vc0ZYRjspAOCoD4Vi0+7ZtH8bxO04kphqefwHwMNXAn3FOtG0fZtaYRD3aHiKxKASyLLNzz15c/WugdnE16Rq1ixuu/tXZuWev2VZEuHLlCsnJyTRu3DjPOUmSaNGiBaqnfNC9c+cOEydOpFatWlSvXt14XKVSER4enitZ2LhxI2FhYflmzV26dOGPP/7g7t27ABgMBjZv3kzXrl1zlatbty5bt24163h/gMzMTDIzM816z4IgyzJ7d+2hdpkXcNOYNhnN3cmV2mWqsHfXHpPagjkcZVmme/fueHp6cuLECaKioli9ejXTpk0zljl69CgnT57k0KFDREZG8t5777Fr1y4ADh8+TMuWLfO9t6OjI76+vgA4OTnRsWPHXJPN5s2bR506dShbtqxFHW0d4agMhKMyEI5FIzg4GAd3P747qCfb8Oy/Y9kGmWUH9Ti4+xEcHGzWOEQ92h4isSgEycnJ3LwVS7nKNQt0XbnKtbh5K5aUlBSzxJEzIdbd3T3f83Z2dtjZPe5NmTZtGlqtFq1Wi5OTE5UqVcLR0ZFt27YZy+QQERHBli1bjD/n1wORQ0BAAHXq1DGWP3z4MGXKlOGFF14oqp5JPOloDZKTk7lzM5a6PtWfX/gJ6vrU4E6MaW3BVMecYUlPvgYNGgTAvn37uHHjBl9//TXVqlWjdevWzJw5k7lz5xqvV6lUrFixgtq1a/PWW2/x6quv8vXXXwMQHx+Ph4dHrudVrlw517MOHDiQJ6aFCxeybt06vvjiC7M4lmSEozIQjspAOBYNZ2dn3hs+lrOPfJm/L52HT+m5eJhqYP6+dM4+8uX9EePMvkKmqEfbQyQWhUCv12OQZRw0Ts8v/AQOag0GWTbbighlypQBHq8KlR9PNsZBgwZx8uRJjhw5Qt++fSlfvjyffvppvkOl2rdvz/379/nzzz95+PAhhw8fpmPHjk+N48nhUM9KQiyBtX/hHq+OIePkWLDJaE4OGjCY1hZMdZwyZQonT57M9ZoyZQoA58+f5/79+7i5uRkTgV69epGQkMD9+/cBqFKlCp6ensb7NWrUiPPnzwNQunTpPCs77d271/iclJQU47CqHBYtWsSwYcOYM2cOHTp0MItjSUY4KgPhqAyEY9EJCgpi+LhJXMqoyphNKhb9msbRaxmcuZ3J0WsZLPo1jTGbVFzKqMqI8ZG55mKaC1GPtoeYvF0I1Go1KkkiMz2tQNdl6tNRSZLZVkSoXLky7u7uHD9+PN/hUD169ODdd98FwMPDgypVqgDw9ddf06lTJzp37oxOp8vT4+Hs7Ez79u3ZvHkzVatWpXXr1s9cczoiIoIZM2aQmprKxo0bWbNmjVn8TMHa3YOPV8eQSMsoWLKYlpkOKtPagqmOnp6exjp+8hhAVlYW1atXZ9OmTXmuy6n/fw51y87ONg6la9q0Kf/9739znX/WsLaZM2cyduxYvvjiC4YPH/7c2K1dj8WBcFQGwlEZCEfzEBQUxKz5i4mOjmbf7u38cewKyNkg2eHlV5leA8MsupeXqEfbQ/RYFAKtVou/rw9/XzlXoOv+vnIWf18fs62IYG9vT+/evVm4cCEZGRm5zm3ZsoXt27fj7e2d5zpJkliyZAkJCQl88MEH+d47IiKCrVu3smnTpuf2QNSvX59y5crx5ZdfkpGRQYMGDQovVUCsPalJq9Xi7e/DqdgLBbruVOx5vP1MawvmcKxWrRo3b96kXLlyVKlShSpVqnDt2jUmTZpkvPdff/2Va4L1sWPHqFOnDgADBgzgzJkz+S5FfPv27Vw/L1++nLFjxzJnzhzGjBljUnzWrsfiQDgqA+GoDISj+cj5MnLa9Fks+GYVXyyMYsE3q5g2fRbt27e36AbBoh5tD5FYFAJJkggNacejm+fRpzwy6Rp9ShKPbl4gNKSdWRtIZGQkSUlJhIaG8ttvv3HlyhW+/fZb+vXrx3vvvffUrkd/f38+/PBDlixZwsmTJ/OcDw8P59SpU+zcuZP//Oc/z42jS5cufPLJJ3kmbVsae3v7XKtaFTeSJNGuQwhn7l8mKf3pqx49SWLaI87c/4t2HUJMagvmcOzQoQMBAQH07dsXnU7HgQMHGDhwIM7OzsYu1uTkZAYNGsSFCxf45ptv+PHHH3nvvfcAqFOnDp9//jm9e/dm6tSpnDlzhr/++otFixbRsGFDfH19CQwMJCEhgSFDhtCvXz969+5NXFyc8fXPoVLmdrR1hKMyEI7KQDiaH0mS0Gq1lC1bFq1WWywfhkU92h4isSgkwcHBlHVz5vyvGzAYnv6BCcBgyObCrxsp6+Zs9hURvLy8iI6OplKlSvTp04fatWszZ84cpkyZwtSpU8nKynrqtTm7dQ8dOjTPOU9PT5o2bUq9evWeuZpPDhERETx69KjYE4usrKxnOhYHwcHBaDy0rD2xlWzDs5feyzYYWHdyGxoPrcltwRyOdnZ2bN68GYPBQNOmTXn55Zfp1KkT8+fPN5bx8/PD29ubRo0aMWPGDFauXJkrxtGjR7N582YOHjxImzZtCAoKYvHixbz//vucOXOGwMBAdu3aRXJyMsuXL8fb2zvXKyYmxqKOto5wVAbCURkIR2UgHG0PSTbX2qc2TlJSEu7u7iQmJuLm5maWe+p0Oj77YjaGMv7UaNMVtUve++pTkjj/60ZU92/y4bjRFpm89DQSExOBp68apQRsxTFn5+0qjj68Uq8z7k55lyFOTHvEupPb+CsjllEfjDG5LRSHY1RUFJGRkVy/ft1iz3gWtlKPlkQ4KgPhqAyEozIQjrZHyelbsUGCgoL4YOwo5sxfyPFVs3H1r065yrVwUGvI1Kfz95WzPLp5gbJuzowq5qQCSt5ujYXBVhyDgoIYOWE0X85dyNT9i6hdpgp1fWrg5KAhLTOdU7HnOXP/LzQe2gIlFWA7jpZEOCoD4WgZDAbDU/cksgSiHpWBcFQGJc1RJBZFJCgoiIXz5hAdHc3OPXu5eeBnDLKMSpLw9/Wh/9tvWHRFhGfxrDHtSsGWHIOCgpi94HFb2LtrDysvbQODDCoJbz8f+vTqX6i2YEuOlkI4KgPhaB4MBgOnT59mz759nDh7Dn1GBg6ODtSqUoUO7drRoEEDi465FvWoDISjMihpjmIolBmRZZmUlBTS09PRaDS4uLhYdSZ/zr4DpUqVsloMlsZWHc3ZFmzV0ZwIR2UgHItObGwsX8ydx4Vbt0kr64l99SBUTk7Iej2Zly+gvhNDxbJlGDNsaJ7lpc2FqEdlIByVQUlzFD0WZiRnRYRn7flQnJS07rPCYKuO5mwLtupoToSjMhCOReP27dtMnDqVayo1Lq8NoLSPb+4vJJoGk3nvLhd2beXjaZ8ROX4c1apVM3scoh6VgXBUBiXNUawKpWBkWUbpHVLCURkIR2UgHAtPVlYW0+fM4ZqdhlJ9B6Cu4JdvL6eDZ3lK936D2DLlmT53HikpKWaPRdSjMhCOyqCkOVo1sUhPT2fAgAGUKlUKb29vZs2a9dSyERERxk1Ccl5bt24txmhLHpmZmSVux8aCIhyVgXBUBsKx8Jw4cYJLsXFo//MyKo3TM8tK9g6U6tKTm0nJREdHmz0WUY/KQDgqg5LmaNWhUGPHjuXYsWPs27ePGzdu0K9fPwICAujRo0eesufOnWPlypW0a9fOeKx06dLFGW6JQ61WWzsEiyMclYFwVAbCsfDs3ruPdG8/PDy9TCpvp3Ulq0o1tu/ZS/v27c06n0/UozIQjsqgpDlaLbFISUlh6dKlbN++nQYNGtCgQQPOnj3LwoUL8yQWer2ea9eu0bhxY7y8TPtP1xrIskxycjJ6vR61Wl1sO08+Kx6lY6uO5mwLtupoToSjMhCOhSMrK4s/z57F8aXQAl3nVLMuV7es5f79+yZtZGoqoh6VgXBUBiXN0WqJxalTp8jMzKRFixbGYy1btuTTTz/Ns2b3xYsXkSSJSpUqWSPU55Kamkp0dDT79m4nLvYKkA3Y4eVTmbbtwqy23KxerwfAxcWl2J9dXNiaY05b2LVrFzExMciyjCRJ+Pn50aFDh0K1BVtztATCURkIx8LfM1sGlXPB7qlycSFbhrS0NLPFkhMPiHos6QhHZVDSHK02x+LOnTuULVsWR0dH47Hy5cuTnp7O/fv3c5U9f/487u7uvP7663h7e9OkSRO2b9/+zPvr9XqSkpJyvSyBTqdj9KhBrF05Df/SBxncO54xbz1kcO94/EsfZO3KaYweNQidTmf2ZwcGBhIVFZXneFRUFIGBgWg0GurXr59nbkrOa//+/QC0bt2ayMjIXPe4efMmvr6+hIaGkpGRgSzLTJ8+nYoVK+Lm5ka7du04d+6csXxycjLvvPMO5cqVw9fXl+nTp+e637Fjx2jRogXOzs5Uq1aNFStWmOU90Gg0aDQas9yrqOh0OoYNG8ZXX31FUlISNWvWpH79+tSsWZOkpCS++uorhg0bVuC2YKrjgwcPGD16NBUrVsTZ2ZkaNWowd+5cDAYD8Lien6x/Z2dnGjRowKpVq3LdJz09naFDh+Lp6YmnpyfvvvuucYLoP+/xz1dhd+62pXq0FMJRGVjC0dHREUkCuYDjqOXMDFSS+YdKiHpUBsJRGZQ0R6v1WKSmpub5zzDn55zsLIcLFy6QmppKaGgoEyZMYMOGDfznP//h8OHDNGrUKN/7f/bZZ0yePNkywf8fOp2OebMjqVXpNm/1UOPulnvCXZO6kJhk4Lv1l5g3ezLDR00iKCjIojHlx9y5c+nVq1ee4x4eHvmWv3//PqGhoQQEBLBhwwYcHR1ZvHgxM2fOZNmyZVStWpUZM2YQFhbG+fPncXZ25p133uH48eNs3LgRg8HA66+/joODA6NGjSIxMZGwsDD69+/PypUrOXToEG+++SaVK1cmODjY0vrFgk6n4/PPP0ej0dC+fXucnHK3BX9/f9LS0jh27Biff/45EyZMMGtbuH//Ps2aNcPHx4dvv/2WihUrcvToUYYOHcqVK1dYsGABAKNHj2bMmDHIskxiYiKbNm2if//+ZGZm0r9/fwAmT57Mb7/9xi+//IIsy/Tr148PP/yQefPm8fPPP5ORkQHAzJkzOXjwID///LMxjnLlypnNSSD4t+Dg4EAl3wocu3IRlzr1Tb4u/fJFAt3cnvp/uUAgMB9ZWVncuHGDlJQUHB0d8fb2xt3d3dph2RxWSyw0Gk2eBCLn538OFfn4448ZNmyYcbJ23bp1OX78OF9//fVTE4sPPviAUaNGGX9OSkrCz8/PbPGnpqay6MsvqFXpNsP6abCzy3/8vLubimH9NMxffotFX37BrNmLi21YVHp6OrIs4+7ubvLclJSUFDp37oxGo2Hbtm3GWKOiohgzZgzh4eEAfPXVV5QuXZro6Gjq16/PmjVr2LdvnzFRmD59OiNHjmTUqFHExMQQFhbGjBkzjEPaZs2aRXR0dJETi/T0dCBvmylOUlNTmTdvHhqNhuDg4FzD+J7EycmJ4OBgoqOjmTdvHvPnzzcpblMcJ0yYgFqtZufOncZvNnJ6LiIiIhg6dCgAWq3W2Ba8vb2pXr06ycnJjBs3jt69e6PRaPjll18YOHCg8Xdr8ODBLFmyBMidjGq1WhwdHc0y78kW6tHSCEdlYCnH0LZtORm1guxHSdi5Pn8TV0NGBpw9SVh4mNl34Rb1qAyEo3lISEjg119/ZdeOvdyLu092tgFJknBydiT4xaaEtA+hevXqFptTW9Lq0WpDoSpUqEB8fDxZWVnGY3FxcTg5OeXZXVClUuVZAapGjRrcvn37qfdXq9W4ubnlepmT6OhoMtNieKuH+qlJRQ52dhJv9lCTmRZjkaUBn4ZGoylQQ8/KyqJnz548ePCAnTt35qqHmTNn0qdPH+PPkiQZv/W+evUqAE2bNjWer1OnDnfu3OH69evUrl2bFStWIEkSBoOBLVu2cPHiRVq1amUWR2t3EUZHR5OQkECjRo2emlTkoFKpaNSoEQkJCSa3hec56vV61qxZw5AhQ/KUCw8PZ+/evQQEBDz1+oEDB/L333/z+++/A1CmTBnWr1/PgwcPePDgAT///DP165v+LWphsIV6tDTCURlYyjE4OBhvZycSd28zabJm4v5dlMFA69atzR6LqEdlIByLzpkzZxg9chxRS9aRcltLZW1bapfpQo3SYbhn1WDv1mN8NGEy33//vXHYsbkpafVotcSiXr16ODg4cPjwYeOx33//ncaNG+f5cNa/f3/eeuutXMdOnjxJ9erViyXWfyLLMvv2bqdhzVTc3Ux7C0u5qWhQM419e7fb5Ax/WZZ566232L59O0uXLsXT0zPX+ZYtW+Lr62v8eenSpWRlZdGyZUvKly8PkCvRi4mJASA+Pt54LCMjA41GQ5cuXXjjjTdo1qyZJZWKBVmW2bVrF+XKlcsz/OlpODk5UbZsWXbt2mWWtnDlyhWSk5Np3LhxnnOSJNGmTZtnjsH28/NDq9Ua58x88cUXXLt2jTJlylCmTBkSEhJYtGhRkeMUCARPx9nZmZGDB+Fx/TIPNv2IIUOfbzk5K4uHe35Be/o4g/u9IYYfCgQW4uLFi3w2dQbJcQ7U9+5GVa9muDmVQ+PggrOjOxVK16B+hS54quqy/octrFy50iY/3xU3VkssnJ2d6devH4MGDeKPP/5g48aNzJw5k+HDhwOPey9yVrro0qULK1euZMWKFfz1119MmTKF33//3Ti8o7hJTk4mLvYKDYMK1v3cKMiOuNgrZt0pddCgQWi12lyvQYMGAf8bCpVfmVq1auW6z5IlS9ixYwc1atRg8uTJz/zlOHLkCKNHj2bs2LF4eXkREBBAs2bNGD58OAkJCcTFxRkng+eMx8/h8OHDrFq1ijVr1jB79uwi+6enpxu7Ca1BcnIyMTExVKhQoUDX+fr6EhMTY1JbeJ7jw4cPAYo01tPd3Z1Hjx4B8Ndff+Hv78++ffvYuXMn6enpuYYVWgJr12NxIByVgSUdGzRowAfDhuIdc4WkL2fyYNdW9DE3yIz/m4zYWyTu303iV7MorTvOiLf607ZtW4vEIepRGQjHwpOdnc2XC74iPUFDLZ+22Ns55ltOkiR8SlfDR9OAjet/4ezZs2aPpaTVo1U3yJs9ezaDBw+mTZs2uLu7M3nyZLp37w48Hv+9bNky+vfvT/fu3Vm0aBFTp07l5s2b1KpVix07dhAYGGiVuB/PBcnGxblg4+mcnSQgm/T0dLRarVlimTJlivE9y+Hnn39m0aJFqNVqJEnKt4yDg0Oun9PS0ti/fz+ZmZk0b96cJUuWGBOUJzl06BBhYWGEhYUxZcoU4/Hvv/+eHj16ULZsWdzd3fnss884dOhQriFojo6Oxj1LYmNjmT9/fpE/sFp74xi9Xo8sy7lWNzMFR0dHZFk2qS08z7FMmTLA41WhCsujR49wc3MjKSmJAQMGsHfvXuPQtu+++45WrVoxZcoUvL29C/2MZ2HteiwOhKMysLRj48aNWVCxIvv372fH/v3EnT5OliyjkiQquDgT2q41bdu2tdjvIoh6VArCsfCcOnWKG9diqVq2PSrJ7rnlvUtV5e7ti+zdu4/atWubNZaSVo9WTSycnZ1Zvnw5y5cvz3Pun9+Yv/3227z99tvFFdozeVzJdqSkFqzLKzVNBuzMOlbO09OTKlWq5Dn2vDL/ZOjQocZx9MOGDWPs2LGEhYXlGpu/f/9+wsPD6dChAz/88EOuIWtVqlTh5MmT3Lt3D3d3d65cuYJKpcLf359r165x6dIlQkP/t/lTzZo1cw2TKqnkJG//7Jl5HhkZGUiSZJa2ULlyZdzd3Tl+/Hi+w6GenLydH9evXycpKYnatWtz4cIFUlJSqFu3rvF8/fr1MRgMxMTEWPTDjEAgeEzZsmXp0aMHERER3Llzh7S0NDQaDV5eXiXuQ4ZAUBLZt3cfdpmuuGrKmFRekiQ8XaoSfeAIb7zxIM+84H8TVhsKVZLRarV4+VTmuC67QNcd02Xj5VO52DY5ydl/whSeXFVk6tSplClThgEDBhivP3PmDF26dCEsLIx169bl6vEwGAx06NABnU6Hp6cnarWabdu20aBBA9zc3Dhy5Ai9evXKtYnT8ePHqVGjhlkcC/qh3pxotVr8/PyeuZBAfty6dQs/Pz+T2sLzHO3t7enduzcLFy7MU27Lli1s3rwZHx+fp17/3Xff4eXlxYsvvmgs9+QeJRcuXAAerzJlKaxdj8WBcFQGxeno4OCAv78/1apVIyAgoNiSClGPykA4Fp6/Ll/DXf30v5v54eFSAX1qRoE/DzyPklaPIrEoBJIk0bZdGMfPOZGYZNoqAA+TDPx5zom27cIstiTZP7G3t0eSJBITE4mLi8vzetr4fhcXF7766iv27t1rXGb03Xffxc/Pj9mzZxMfH2+8R1paGiqVCmdnZyZMmMDly5fZuHEjkydP5sMPPwQer0zk7u7Ou+++y6VLl1i9ejUzZsxg4sSJZnE091KLBUGSJDp06MDff/9t8u63aWlpxMfH06FDB5PagimOkZGRJCUlERoaym+//caVK1f49ttv6devH8OHD6dmzZrA/80P+r+6u3DhAp988gnTp0/niy++wN7eHl9fXzp27MjAgQM5fvw4x44dY+DAgfTu3duik0StXY/FgXBUBsJRGQhHZWApR71ej52qYPe1U9ljMMhmTwJKWj2KxKKQBAcH4+Dkx3fr9WRnP7tXIDtbZtl6PQ5OfsW6IZyd3eNxgSNGjMDb2zvPa86cOU+9NiwsjFdffZVx48Zx48YNDh48yLlz5/D39891j7Vr1wKwePFi7OzsaNCgAaNHj2bBggV069YNePyt/s6dO4mNjaVBgwZ89NFHzJ07l4iICLM45nhai+DgYDw8PDh27Nhzl5szGAwcP34cDw8Pk9uCKY5eXl5ER0dTqVIl+vTpQ+3atZkzZw5Tpkxh1qxZxnKzZs0y1l1wcDB79uxh/fr19O3b11hm9erV1KlTh06dOhEeHk6jRo345ptvTIq1sNhCPVoa4agMhKMyEI7KwFKOrq5aMrJM+7Iwh4ysNOzsVGbfb6Kk1aMk/0vWxkpKSsLd3Z3ExESz7WnxeOftydSqdIs3e6gplc/Ssw+TDCxbr+fsVV9GjI40+6SeZ5GYmAgUbbUgW8dWHJ/cebtRo0b5Lj2bs/N2eno6H3zwgcltwVYcLYlwVAbCURkIR2UgHAvPihUrWP/9Dhr4djNp8jbAlXt/4OB5n0WLFxZ4QZdnUdLqseT0rdggQUFBDB81iUVffsGYz2NoUDONRkF2ODtJpKbJHNNl8+c5JxycqjJi9LhiTSqAYhtyZU1sxTEoKIgJEyYwb948du/eTdmyZfH19cXR0ZGMjAxu3bpFfHw8Hh4eBUoqwHYcLYlwVAbCURkIR2UgHAtPmzZt2PzzL8Q/uomn2/PnF2YbskjQX6NPh65mTSqg5NWjSCyKSFBQELNmLyY6Opp9e7fzx5orQDZgh5dPZXr1DSM4ONgqW7GXpDF5hcWWHIOCgpg/fz7R0dHs2rWLc+fOIcsykiTh5+dHz549C9UWbMnRUghHZSAclYFwVAbCsfD4+fnRuHkDovccQ6spg7Pj00e6GGQDF+MOUNrTySJ7y5S0eixZ0doozs7OtG/fnpCQEFJSUkhPT0ej0eDi4mLVTDMrK8tqzy4ubM3REm3B1hwtgXBUBsJRGQhHZSAci8Z77w3m73ufcO70TgJKNaaM1h+VlHvIe4r+IVfj/8DO7REjRo/Ks9y/OShp9SgSCzMiSZJxZ2vBvxvRFgQCgUAgKLm4uroyKfJjFn35FUcPHeXGreOUVvvjaO+MwZBFoj6ODFUCPgHleH/oeIKCgqwdsk0gEgsFU9K6zwqDcFQGwlEZCEdlIByVgXAsOq6uroyf8Hh1zF9//ZUjh46RkhyHg6MD9SsHEtL+LRo2bGjROEpaPZasaAUFoqR1nxUG4agMhKMyEI7KQDgqA+FoPgICAujfvz/9+/cvluc9SUmrR5FYKJh/w0rCwlEZCEdlIByVgXBUBsJRGZQ0R5FYmBFZlklOTkav16NWq9FqtVadvO3g4GC1ZxcXtupozrZgq47mRDgqA+GoDISjMhCOyqCkOYrEwgykpqY+Xm52z3bibl8BORskO7wqVKZtiPWWm83Ozi72ZxY3tuaY0xb27NnB7dtXAQOgokKFSoSEdCxUW7A1R0sgHJWBcFQGwlEZCEdlUNIc824VLSgQOp2O0SMGsXbZNPwdDjK4fTxjujxkcPt4/B0OsnbZNEaPGIROpzP7swMDA4mKispzPCoqisDAQLKysqhVqxaSJOX72r9/PwCtW7cmMjIy1z1u3ryJr68voaGhZGRkIMsy06dPp2LFiri5udGuXTvOnTtnLJ+cnMw777xDuXLl8PX1Zfr06bnud+zYMVq0aIGzszPVqlVjxYoVxnP79+9/aoyBgYHPfA+ysrJsZvyhTqdjxIj3WBb1BWr1KTqGZdO1m0THsGzU6lMsi/qCESPeK3BbMMXxeW0hhwcPHjB69GgqVqyIs7MzNWrUYO7cuRgMBgAiIyOfWheSJOV5xu+//06lSpXyjenTTz/NMx5VlmUmTZpE+fLl8fDwYODAgaSnp9tUPVoK4agMhKMyEI7KQDjaHqLHogjodDrmzYykVvnbvNVLjbvWKdf5JtUhMdnAdzsvMW/mZIaPmVSsy5E5OjoiSRJz586lV69eec57eHjke939+/cJDQ0lICCADRs24OjoyOLFi5k5cybLli2jatWqzJgxg7CwMM6fP4+zszPvvPMOx48fZ+PGjRgMBl5//XUcHBwYNWoUiYmJhIWF0b9/f1auXMmhQ4d48803qVy5MsHBwcbn3rlzJ08sdnZ2z3W0BXQ6HTNnfoK3dwJ9+nqg1eb+1apRQ0tychbbf7nFzJmfMGbMxya3BXM53r9/n2bNmuHj48O3335LxYoVOXr0KEOHDuXKlSssWLCAMWPGMGjQIAAOHjzIyy+/nKte3N3djf/W6XT06NEDjUaT51k//PADkyZNom/fvrmOT58+nUWLFrF27Vq0Wi2vvfYakydPZuLEiWZxtGVspa1aEuGoDISjMhCOyqCkOYrEopCkpqayaMEX1Cp/m2HdNNip8h8/765VMaybhvkbbrFowRfMmru42IdFubu74+XlZVLZlJQUOnfujEajYdu2bcZYo6KiGDNmDOHh4QB89dVXlC5dmujoaOrXr8+aNWvYt2+fMVGYPn06I0eOZNSoUcTExBAWFsaMGTOQJIlKlSoxa9YsoqOjcyUWpsZoa6SmprJgwSy8vRN4uUc5VE9pC1qtPS/3KMdP6/9mwYJZzJ27qFjbwoQJE1Cr1ezcudOYDOT0XERERDB06FCqVq1q3HsjJ/HMr16WLFnCmDFjqFSpEomJicbjWVlZDB06lKioKCpXrpzrmuzsbGbPns3MmTONu5NOnjyZ5cuXW8RXIBAIBAJB8SKGQhWS6OhoMpNjeCtU/dSkIgc7lcSboWoyk2OIjo4upghBr9cXaDWBrKwsevbsyYMHD9i5cyelSpUynps5cyZ9+vQx/ixJErIsk5iYyNWrVwFo2rSp8XydOnW4c+cO169fp3bt2qxYsQJJkjAYDGzZsoWLFy/SqlUrszjq9foi36coREdHk5wSS1gnj6cmFTmoVBJhYR4kp8Sa3BbM4ajX61mzZg1DhgzJ08MQHh7O3r17CQgIMPl+27dvZ/ny5YwcOTLX8eTkZE6fPs2RI0do3rx5rnNnz54lPj6erl27Go/16dOHXbt22UQ9WhrhqAyEozIQjspAONoeIrEoBLIss2/PdhpWSsVda9pbWEqrokHFNPbt2V5sS4dpNBqTVyKSZZm33nqL7du3s3Tp0jzb0rds2RJfX1/jz0uXLiUrK4uWLVtSvnx5AG7fvm08HxMTA0B8fLzxWEZGBhqNhi5duvDGG2/QrFmzQrvloNFo8h2KU1zIssyePTuoUtmQZ/jT09C62lO5koE9e3aY1BbM4XjlyhWSk5Np3LhxnnOSJNGmTRvUarXJ99u4cSPdu3fPc7xUqVJER0dTp06dPOeuXr2Kh4cHBw8epH79+vj5+TFixAj0er3V67E4EI7KQDgqA+GoDISj7SESi0KQnJxM3O0rNHyhYCPJGlW1I+72FVJSUswWy6BBg9BqtbleOWPkn1WmVq1aucosWbKEHTt2UKNGDSZPnvzMD7xHjhxh9OjRjB07Fi8vLwICAmjWrBnDhw8nISGBuLg442TwjIyMXNcePnyYVatWsWbNGmbPnp3r3D9j1Gq1TJs2rQjvjuVJTk7m9u2rVK1WsCFN1ao7c/v21WJrCw8fPgRyz5EobpKTk0lNTWXChAnMmjWL7777ji1btjBmzBirxSQQCAQCgcB8iDkWhUCv14OcjYumYPsSOGskkLNJT083jmMvKlOmTMnzzfHPP//MokWLSE9PR5blfMv8c13ktLQ09u/fT2ZmJs2bN2fJkiV5EhSAQ4cOERYWRlhYGFOmTDEe//777+nRowdly5bF3d2dzz77jEOHDuHm5mYs4+joSIMGDWjQoAGxsbHMnz+fUaNGGc+fPHkyz/OeNsE8h/T0dACrLOcL/9cWMKDRFCxH16hVgMGktmCq47PaQpkyZYDHq0JZC3t7e9LS0pg/fz4vvfQSALNmzeLVV19l0qRJqFQqq9VjcWDttlocCEdlIByVgXBUBiXNUSQWhUCtVoNkR0p6wYY0pabLINmZtUvL09OTKlWq5DkG/xsKlV+ZfzJ06FDq168PwLBhwxg7dixhYWG5xt3v37+f8PBwOnTowA8//IBK9b8P01WqVOHkyZPcu3cPd3d3rly5gkqlwt/fn2vXrnHp0iVCQ0ON5WvWrJlrmFTOPQqKtbsHHw8fUpGeXrB1ptP1BsC0tmCq47PaQuXKlXF3d+f48eP5DofKmbwdEhJi0rMKg7e3NwDVq1c3HqtWrRrp6ek8evTIOKROqVi7rRYHwlEZCEdlIByVQUlzFEOhCoFWq8WrQmWOXy7Yh8ljl7LxqlAZFxcXC0VWeOzt/5djTp06lTJlyjBgwADjkKgzZ87QpUsXwsLCWLduXa4eD4PBQIcOHdDpdHh6eqJWq9m2bRsNGjTAzc2NI0eO0KtXL9LS0ozXHD9+nBo1ahSfoIXQarVUqFCJSxdTC3TdxQupVKhQqdjagr29Pb1792bhwoV5hqdt2bKFzZs34+PjY9EY6tevj6OjI6dOnTIeO3/+PK6ursYeFYFAIBAIBCUXkVgUAkmSaBsSxvGrTiQmG0y65mGygT+vOdE2JMzkCdVFJWcoVGJiInFxcXleTxvf7+LiwldffcXevXtZsmQJAO+++y5+fn7Mnj2b+Ph44z3S0tKMQ1gmTJjA5cuX2bhxI5MnT+bDDz8EHq865O7uzrvvvsulS5dYvXo1M2bMyLN3QX4xxsXFGTdve5pjTjehNZAkiZCQjvx1RUVysmkb2CQ/yuLKVRUhIR1NagvmcoyMjCQpKYnQ0FB+++03rly5wrfffku/fv0YPnw4NWvWLPIznoWbmxvvvPMOQ4cO5fDhwxw6dIjx48fz9ttvk5WVZdV6LA6s3VaLA+GoDISjMhCOyqCkOYrEopAEBwfjoPXju516sg3PHhKVbZBZtlOPg9Yv174NlkatViNJEiNGjMDb2zvPa86cOU+9NiwsjFdffZVx48Zx48YNDh48yLlz5/D39891j7Vr1wKwePFi7OzsaNCgAaNHj2bBggV069YNePyt/s6dO4mNjaVBgwZ89NFHzJ07l4iIiFzPzC9Gb29vYmNjn+lYkNWMLEFwcDBaFx+2/5KA4TltwWCQ2b4jAa2Lj8ltwVyOXl5eREdHU6lSJfr06UPt2rWZM2cOU6ZMYdasWUW+vynMnj2bsLAwOnXqRKdOnejYsSOfffaZTdSjpRGOykA4KgPhqAyEo+0hycW19qmVSUpKwt3dncTExFwTiovC4523J1Or/C3eDFVTKp+lZx8mG1i2U8/Zu76MGBtJ7dq1zfJsU0hNfTw8p6RM+CkMtuL45M7bYWEeaF3zTl9KfpTF9u0J3Lnjwdix/8/ktmArjpZEOCoD4agMhKMyEI7KoKQ5isSiiOh0OhYt+ILM5BgaVEyjUVU7nDUSqekyxy5l8+c1Jxy0frw/bFyxJhXwvxWASpcuXazPLU5syVGn07FgwSySU2KpXMlAterOaNQq0vUGLl5I5cpVFVoXH4YNG1OgtmBLjpZCOCoD4agMhKMyEI7KoKQ5isTCDKSmphIdHc2+PduJu30F5GyQ7PCqUJm2IWEEBwdbJdNMTk4GMNvStraIrTnmtIU9e3Zw+/ZVwACoqFChEiEhHQvVFmzN0RIIR2UgHJWBcFQGwlEZlDRHsdysGXB2dqZ9+/aEhISQkpJCeno6Go0GFxeXYpuonR/WfHZxYWuOlmgLtuZoCYSjMhCOykA4KgPhqAxKmqNILMyIJEnGHY9tgczMTGuHYHFs1dGcbcFWHc2JcFQGwlEZCEdlIByVQUlzFImFgrGzs7N2CBZHOCoD4agMhKMyEI7KQDgqg5LmKBILBVPSGmNhEI7KQDgqA+GoDISjMhCOyqCkOYrEQsGUtO6zwiAclYFwVAbCURkIR2UgHJVBSXMUiYUZkWWZ5ORk9Ho9arUarVYrJm9bGFt1NGdbsFVHcyIclYFwVAbCURkIR2VQ0hxFYmEGjMvN7t5OXMwTy836VaZte+stN2tvr/zqtTXHnLawfc9ert6OxYCMColKFXwIC2lXqLZga46WQDgqA+GoDISjMhCOyqCkOebdKlpQIHQ6HaOHDWLt19PwTz/I4EbxjGn5kMGN4vFPP8jar6cxetggdDqd2Z8dGBhIVFRUnuNRUVEEBgaSlZVFjRo1kCQp39f+/fsBaN26NZGRkbnucfPmTXx9fQkNDSUjIwNZlpk+fToVK1bEzc2Ndu3ace7cOWP55ORk3nnnHcqVK4evry/Tp0/Pdb9jx47RokULnJ2dqVatGitWrDCee1p8kiQRGBj4zPcgKyuLrKysAr1vlkKn0zF45EimR63gT40r6Z26kdW9D+mduvGnxpXpUSsYPHJkgduCKY7PawvPY//+/Vb9VsSW6tFSCEdlIByVgXBUBsLR9ihZaZCNodPpmDc9klput3krQo27s1Ou800qQmKqge8OXmLejMkMHzeJoKCgYotPlmVkWWbu3Ln06tUrz3kPD498r7t//z6hoaEEBASwYcMGHB0dWbx4MTNnzmTZsmVUrVqVGTNmEBYWxvnz53F2duadd97h+PHjbNy4EYPBwOuvv46DgwOjRo0iMTGRsLAw+vfvz8qVKzl06BBvvvkmlStXJjg4mDt37hif3b17d1q0aMGYMWOA509aspX9HXU6HVNmzuJvnwDc+3bFTuua67xzjSCykx9x45eNTJk5i/83ZrTJbcFWHC2JcFQGwlEZCEdlIByVQUlzFD0WhSQ1NZVF876glttthrXV4O6c/1vp7qxiWFsNtVxvsWjeF6SmphZbjA4ODkiShLu7O15eXnlejo6Oea5JSUmhc+fOaDQatm3bZhy2ExUVxZgxYwgPD6dq1ap89dVX3L9/n+joaOLj41mzZg1LliwhODiYF198kenTpzNz5kwAYmJiCAsLY8aMGVSqVIk+ffpQu3ZtoqOjAfLEpNVqjT+XK1fuuY4ODg5mfucKRmpqKjMXLuRvnwBK93gtT1KRg53WldI9XuNvnwBmLlxocluwBUdLIxyVgXBUBsJRGQhHZVDSHEViUUiio6PJTIzhrRZq7FTPHkJip5J4s4WazMQY44fp4iA7O7tA5bOysujZsycPHjxg586dlCpVynhu5syZ9OnTx/izJEnIskxiYiJXr14FoGnTpsbzderU4c6dO1y/fp3atWuzYsUKJEnCYDCwZcsWLl68SKtWrYomyGPHgnqam+joaG4np+LeqSuS6tk9LJLKDvewCG4np5rcFszlGBMTQ5cuXXB2diYwMJDJkyebfN+kpCTeeustPD09cXR0pHr16mzcuLHIMeVgC/VoaYSjMhCOykA4KgPhaHuIxKIQyLLMvt3baeid+tSein9SyllFA6809u3eXmzdWtnZ2SY/S5Zl3nrrLbZv387SpUvx9PTMdb5ly5b4+voaf166dClZWVm0bNmS8uXLA3D79m3j+ZiYGADi4+ONxzIyMtBoNHTp0oU33niDZs2aFdotB2v/wsmyzPY9e8mqXP2pPRX/xM7VjazK1di+Z69J9WMOR1mW6d69O56enpw4cYKoqChWr17NtGnTTLp++PDhXLx4kV27dnH27FlefPFF3n77bTIyMooUVw7WrsfiQDgqA+GoDISjMhCOtodILApBcnIycTFXaBhQsCkqjQLtiIu5QkpKitliGTRoEFqtNtdr0KBBwP+GQuVXplatWrnus2TJEnbs2EGNGjWYPHnyMz/wHjlyhNGjRzN27Fi8vLwICAigWbNmDB8+nISEBOLi4oyTwf/5wfPw4cOsWrWKNWvWMHv27CL7W7uLMDk5mau3Y9FUr1mg6zTVa3HtdqxJbcFUx2e1hX379nHjxg2+/vprqlWrRuvWrZk5cyZz5841Kd6XXnqJJUuWUK9ePV544QXGjBnD/fv3uXv3rknXPw9r12NxIByVgXBUBsJRGQhH20NM3i4Eer0e5Gxc1AVbRcfZUQI5m/T0dLRarVlimTJlCt27d8917Oeff2bRokXG5CC/Mv9spGlpaezfv5/MzEyaN2/OkiVLjB9Kn+TQoUOEhYURFhbGlClTjMe///57evToQdmyZXF3d+ezzz7j0KFDuLm5Gcs4OjrSoEEDGjRoQGxsLPPnz2fUqFFF8rf2pCa9Xv94SVmN0/MLP4FK7UQ2skltwVTHZ7WF8+fPc//+/Vz1YTAYSEtL4/79+8+99xtvvMHGjRv5+uuvuXDhAsePHwcKPtzuaVi7HosD4agMisvRYDBw6tQpLl68SHp6Omq1mkqVKtGwYUOLLz8p6lEZCEdlUNIcRWJRCNRqNUh2pOgLVtmpGTJIdmg0GrPF4unpSZUqVfIcg8e7NcqynG+ZfzJ06FDq168PwLBhwxg7dixhYWEEBAQYy+zfv5/w8HA6dOjADz/8gEr1vw6vKlWqcPLkSe7du4e7uztXrlxBpVLh7+/PtWvXuHTpEqGhocbyNWvWzDVMqrBYe0dKtVqNCglDelqBrjPo07BDMqktmOr4rLaQlZVF9erV2bRpU57r3N3dn3vvN954g4MHD/L6668zePBgvL29ad68uUlxmYK167E4EI7KwNKOBoOBXbt2sXvnFhLi/qKcNhUXjYG0DBW7Nqtx9QikTUhnwsPDLfYtpqhHZSAclUFJcxSJRSHQarV4+VXm+I27NKlo+nXHrmfj5VcZFxcXywX3BGq12uS9CZ78Bmzq1Kls2LCBAQMGsHv3biRJ4syZM3Tp0oWwsDB++OGHXOUNBgMdO3Zk1qxZxiVUt23bRoMGDXBzc+OXX35h0KBB3LlzByenx9/sHz9+nBo1apjF0ZpotVoqVfDhz4vncK5h+lLC6RfOUrOCj0ltwRyO1apV4+bNm5QrV86YSOzevZuoqKhce4rkR1JSEqtXr+bIkSM0btwYgF9++QUw3zcp1q7H4kA4KgNLOmZlZbH4q0X8eXAzwVUf0eYlRyp5/+//2tj4DH49dYZf1l/hwvkzjBg5xvh/qjkR9agMhKMyKGmOYo5FIZAkibbtwzh+x4nEVINJ1zxMNfBnnBNt24cV20ZkOR/6EhMTiYuLy/N62vh+FxcXvvrqK/bu3cuSJUsAePfdd/Hz82P27NnEx8cb75GWloZKpcLZ2ZkJEyZw+fJlNm7cyOTJk/nwww8BCA8Px93dnXfffZdLly6xevVqZsyYwcSJE83iaM1uQkmSCAtph/1fF8hOfmTSNdmPkrC/cpGwkHYmtQVzOHbo0IGAgAD69u2LTqfjwIEDDBw4EGdn51x7hezYsSPXa//+/Wg0GlxcXPjpp5+4fv06O3fuZMiQIcD/DQs0A9aux+JAOCoDSzp+//33nDy0kaHh6Qzo5JwrqQDwKWtHn3ZOjH85i5vnd7P4qy8tEouoR2UgHJVBSXMUiUUhCQ4OxsHdj+8O6sk2PLvCsw0yyw7qcXD3Izg4uJgifPyhT5ZlRowYgbe3d57XnDlznnptWFgYr776KuPGjePGjRscPHiQc+fO4e/vn+sea9euBWDx4sXY2dnRoEEDRo8ezYIFC+jWrRvw+Fv9nTt3EhsbS4MGDfjoo4+YO3cuERERZnE014fbwhIcHEwFrTOJv2xENjx7zoFsyCZx+yYqaJ1NbgvmcLSzs2Pz5s0YDAaaNm3Kyy+/TKdOnZg/f36ucjnzZ3Jeffv2xdHRkZUrV7J+/Xpq1qzJqFGjmDhxIt7e3pw4caJIceVgC/VoaYSjMrCUY2xsLP/ds4E+L6VTv8qzhzhVqWDPwI4GTh/by7lz58wei6hHZSAclUFJc5TkkpQGFYGkpCTc3d1JTEzMNYG1KOh0OubNmEwt11u82UJNqXyWnn2YamDZQT1nH/kyYnwktWvXNsuzTSFnA7acTe6UiK045tp5OywCO9e8bSz7URKJ2zdRLvYGk8aOMbkt2IqjJRGOykA4Fp6VK1dydM8SZg+0w97OtJ7Mj5dn4FWzN0OGDjNrLKIelYFwVAYlzVHMsSgCQUFBDB83iUXzvmDMphgaeKXRKNAOZ0eJ1AyZY9ez+TPOCQf3qowYP65YkwpB8RIUFMT/GzOamQsXcnvJHLIqV0NTvRYqtRMGfRrpF85if+UiAVpnxhYgqRAIBMpHlmUOHthF21qZ2NuZ9mdZkiTa1IFVB/eTkjKg2ObuCQQCwbMQiUURCQoKYtb8xURHR7Nv93b+OHYF5GyQ7PDyq0yvgWEEBwdbJdNMT08HSk6WWxhsyTEoKIiv5swhOjqa7Xv2cu2XDWQjY4dEzQo+hPV/o1BtwZYcLYVwVAbCsfD3TEtJwt/T7vmFn8DP0w7ZoCcxMdGsiYWoR2UgHJVBSXMUiYUZcHZ2pn379oSEhJCSkkJ6erpxwmtxTdTOD3Mua2ur2JqjJdqCrTlaAuGoDIRj4SjsiGRL/XUR9agMhKMyKGmOIrEwI5IkGXc8Fvy7EW1BIBCYipOTExonLbfjC7aT/e372UgqtdnmDQoEAkFREatCKZj09HRjF5pSEY7KQDgqA+FYOCRJolnL9vz3rD1Z2ab1XsiyzK+noE6Dlmb/AkPUozIQjsqgpDmKxELBqNXqErexSkGxZccdO3bw9uAh7Nixo0j3sWVHcyEclYFwLDxt2rThod6dQ+cyTCp/9kYWNx+40rZdiNljEfWoDISjMihpjiKxEAgswI4dO1i+dgO3De4sX7uhyMmFQCBQNv7+/jRrFc73vzpy9nrmM8veuJvF4l8katR5iaCgoGKKUCAQCJ6PSCzMiCzLPHr0iPj4eB49emT1nRIzMjLIyDDt26+Sii065iQVKeXr4N4wnJTydYqUXNiio7kRjspAOBaNt94aQPUGnZmzyZGVe1KJjc+94WZ8ooH1v6Xx2To7PCu1ZsiwERZZIETUozIQjsqgpDmKydtmIDU1lejoaHbu2M2NazHIBhlJJRFQ0Y/Qju2tttysvb3yq9fWHJ9MKpwrNQDAuVIDUoDlazcA0LFjxwLd09YcLYFwVAbCsWg4ODgwbPhItm2ryt5dW9h75ga+pdPQarJJy1Bx874GjWsArcM70a1bN4sNjxD1qAyEozIoaY5i5+0iotPpmDNrHn/fScRF8qasNgB7OzVZ2Xrik2+QIt+hnLc7I0cPN3uXdWBgIDdu3Mj33K+//kqTJk1Yt24d8+fP59y5c7i4uNCuXTumTZtGlSpVcpXftm0bM2fO5M8//8TR0ZGWLVvy6aefUrNmTbPGbG5saUfK/JKKJ0m9+icud0/Tr1e3AiUXpjj+81vLsmXL0rVrV+bMmVPoiZ2RkZHs37+f/fv3F+r6gmBL9WgphKMyKC7HrKwsjh07xsWLF0lPT0etVlOpUiWaNm1q8fHWoh6VgXBUBiXNUSQWRUCn0zHtk+lkJbpRtXwL1PZ5K12flcqluwexd3/Ehx+PM2tyERgYyMiRI+nVq1eecx4eHqxfv57BgwezePFimjZtysOHD5k6dSpHjhzhwoULuLq6AjBv3jw+/PBDpkyZQufOndHr9cyYMYMdO3Zw6NAhqlataraYzU1iYiIA7u7uVo3jeUlFDoVJLkxxlCSJn376iRYtWpCdnU1MTAzvvvsuLVq04KuvviqYzP+RnJxMRkYGHh4ehbq+INhKPVoS4agMhKMyEI7KQDjaHmKORSFJTU1lzqx5ZCW6Ucunbb5JBYDa3plaPm3JSnRlzqx5xszTXLi7u+Pl5ZXn5ejoyA8//ECfPn149dVXqVSpEg0aNGD16tUkJSWxbds2AK5evcq4ceNYsmQJo0ePpnr16tStW5fvv/+eypUrM3nyZLPGa24kSbLqJoRgelIB/zcsqoBzLkx19PDwwMvLiwoVKtCsWTM++OAD1q5da9Iz8kOr1RZLUgG2UY+WRjgqA+GoDISjMhCOtodILApJdHQ0f99JpGr5FqikZ7+NKklFVc8W/H0nkejo6GKK8PG4vGPHjpGcnGw8ptFoOHHiBJ06dQLghx9+oEyZMrz22mu5Y1apWL58OVOnTi22eAuDvb29VccfFiSpyKGgyUVhHV1cXHL9HBgYyPjx4/H29qZ+/frIsszmzZupX78+Go2GUqVK8eqrrxrbS2RkJK1btwYgKiqK1q1bM2nSJMqWLUupUqUYNWqU2RYosHY9FgfCURkIR2UgHJWBcLQ9Sk6kNoQsy+zcsRsXyfupPRX/RO3gjIvkxc4duwkJCSmW7HPAgAF07dqVChUqEBYWRkhICJ06dco1v+LUqVM0bNgQlSpvclSjRg2Lx1hUsrKyrPbswiQVORRkQndhHOPj45k/fz59+/bNdXzVqlXs2rULg8HA1atX6dGjB19++SXt27fn0qVL9OnTh6+//ppRo0bluefBgwfx8vIiOjqaP/74g379+hEWFkb79u0LHN8/sWY9FhfCURkIR2UgHJWBcLQ9RI9FIUhOTubGtRjKagMKdF1ZbSA3rsWQkpJitlgGDRqEVqvN9apVqxYArVq1YseOHbRt25bNmzfzzjvv4O/vz5AhQzAYDAA8fPiwxIzbsyWKklTkUJhhUc8iLCwMrVaLi4sL5cqV48SJEwwbNixXmb59+xIUFETdunUxGAwsWLCAd955h8DAQDp06EBISAhnz57N9/7Z2dl8/fXXVKtWjb59+1K3bl3++OOPIsctEAgEAoFAGYgei0Kg1+uRDTL2DgVbmcNe5YicJZOenl7olXr+yZQpU+jevXuuYw4ODo+fZ29PixYtCAkJIT09nd9++43ly5fz5ZdfUrlyZUaOHEmZMmV48OCBWWKxBtbqHly/aSsPHcviXsikIgfnSg14+CCW9Zu2PrXXwlTHpUuX0rRpU2RZJj4+noULFxIcHIxOp8PT0xN4PBwqhxdeeAG1Ws2nn37KmTNnOHv2LGfPnuX111/P9/7ly5fPtfCBm5sbmZnP3sjLVEpSN29hEY7KQDgqA+GoDISj7SF6LAqBWq1GUklkZesLdF2WIQNJJaHRaMwWi6enJ1WqVMn1CggIIDk5mREjRhiXo9VoNISGhrJ69Wp69erFnj17AGjYsCF//vlnvmPl161bx5tvvmm2WC1BVlaWVboJe0SEUyojntSrfxbpPqlX/6RURjw9IsKfWsZUxwoVKlClShVeeOEFmjdvzrJly0hNTWXdunXGMk+2vVOnTlGrVi3OnTtHq1at+Pbbb+ndu/dT7+/o6JjnmLnmWFirHosT4agMhKMyEI7KQDjaHiKxKARarZaAin7EJ+e/h8TTiE++TkBFvzyTai2Bk5MT69atY+PGjXnOlSpVinLlygHQs2dPEhIS+OGHH3KVyc7OZtasWWYdtmUJZFm2yg7nHTt2pF+vbrjcPV3o5MLUpWcL66hSqTAYDGRnZ+d7/vvvv6dVq1asWrWKwYMH07hxYy5fvmyV99Na9VicCEdlIByVgXBUBsLR9ihZ/Ss2giRJhHZsz/wzX6PPSjVpArc+M5UUOY7QjgPNOnE7MTGRuLi4PMddXV0ZN24cU6ZMQaVS0bVrVzIzM9mzZw+rVq0ybnoWEBDApEmTGDBgAHfv3iU8PJwHDx4wbdo0/vrrL1avXm22WC1BzrAva5CTDCxfu4EUKNBci4LsZ2GqY0JCgrEtPHr0iJkzZ5KdnU2XLl3yLV+mTBlOnz7N0aNHKVWqFEuWLOGPP/6gUqVKJnuYC2vWY3EhHJWBcFQGwlEZCEfbQyQWhSQ4OJgfVq3l0p2D1PJp+8wlZw2ygcv3DlLO253g4GCzxjFixAhGjBiR5/gnn3zC8OHDKVWqFMuWLeOTTz4BoHHjxvzyyy80bNjQWPbDDz/Ez8+P+fPnM2nSJJycnGjZsiUHDx6kcuXKZo3X3Dzt2/jiojDJRUE3yTPV8eWXXzb+29nZmUaNGrF9+3YqVqyYb/lhw4Zx4sQJQkJC0Gg0tGrViv/3//4fa9asMel55sTa9VgcCEdlIByVgXBUBsLR9hA7bxeBxztvzyAr0ZWqni1QO+Sz83ZmKpfuPd55+6P/N57atWub5dmmkDMpu3Tp0sX2zOLGVhwtufO2rThaEuGoDISjMhCOykA4KoOS5ih6LIpAUFAQH348jjmz5nH6ziZcJC/KagOxVzmSZcggPvk6KXIc5bzdGTWmeJMKyH+yrdKwFUdTei4Kk1SA7ThaEuGoDISjMhCOykA4KoOS5igSiyISFBTEwkXziY6OZueO3dy4dgw5S0ZSSQTU8iO040CCg4NxdjZtIz1ByeVZyUVhkwqBQCAQCASCkoJILMyAs7Mz7du3JyQkhJSUFNLT09FoNLi4uBTLDttPQ69/vBxucaxCZS1szTG/5KKoSYWtOVoC4agMhKMyEI7KQDgqg5LmKBILMyJJknH3a1vAnPtl2Cq26PhkcvHwQSylMuKL1FNhi47mRjgqA+GoDISjMhCOyqCkOYrEQiCwADlJxPpNW+khhj8JBAKBQCD4FyA2yFMw6enppKenWzsMi2LrjpKqYLuz54etO5oD4agMhKMyEI7KQDgqg5LmKHosFExJ6z4rDLbquGPHDlb8vIJk92RW/LwCQAyFegbCURkIR2UgHJWBcDQ/qamppKSk4OjoiKurKyqV5b+fL2n1KBILMyLLMsnJyej1etRqNVqt1qqTtwXWISepyKiaQdmgsiTpkoqcXAgEAoFAICh+MjMz+eOPP/h1zy7+unASDFkgqXAt7clL7cJo3bo1ZcqUsXaYNoMYCmUGUlNT2b17N+PGTuDtAYN4b9Aw3h4wiHFjJ7B7925SU1Mt8tzAwECioqLyHI+KiiIwMJD09HQePXpEZGQklSpVQq1W4+/vz6hRo3j06JGxfP/+/ZEkyfiys7PDy8uLYcOG5SoHcPDgQcLDwylTpgylS5emffv2HDp0yCJ+pmBrXYRPJhVuQY83YnQLciOjagYrfl7Bjh07CnxPUxwlSWL//v35nouMjKR169YFfm5xYmv1aAmEozIQjspAOCoDSzvGxsby4bhRfDPn/+EYu4O36/3NmJYPGdY0gSZaHXvWzWPc8HfZvXu3xWIoafUoeiyKiE6nY+6cBdyPT6K0awCVfV7C0V5DRlY6d+5c4csF37Lmhx8ZMXIoQUFBxRqbWq1m0qRJ7N+/n2+++YbKlStz5coVhg8fzuXLl9myZYux7CuvvMK8efMAMBgMXL58mddee43k5GS+++47AH766Sf69OnDmDFj+Oyzz7C3t+ebb76hTZs27N27l+Dg4GL1y3G0FfJLKnJwC3IjicL1XBTVccyYMQwbNqxI97A0tlSPlkI4KgPhqAyEozKwpGNcXByfTZlIqfQLfBpmj08pp1zn6/s70KOhzMYTt1i9dA4Gg4HQ0FCzx1HS6tGqPRbp6ekMGDCAUqVK4e3tzaxZs557zfXr19FqtU/9drY40el0fDbtCzJTtbzY4HUa1Q6jQvmqlCvjT4XyVWlUO4wXG7xOZqqWzz/7Ap1OV6zxSZLEqlWr+OSTT2jXrh2BgYG0a9eOxYsXs3XrVu7cuWMs6+TkhJeXF15eXvj4+PDSSy8xdOhQfv75ZwCSkpIYOHAgEydOZOrUqQQFBVGjRg1mz55N586dGTduXLG6PeloC8PNnpVU5FDYnouiOmq1Wjw8PAp9fXFgK/VoSYSjMhCOykA4KgNLOcqyzKIFc3FNu8CEUAd8StnlW07jING7iROdKyewZvkirl+/bvZYSlo9WjWxGDt2LMeOHWPfvn0sWrSIyZMns379+mdeM3jwYFJSUoopwqeTmprK3DkLsJfL0jgoHI06/41LNGoXGgeFY2coy9w5Cyw2LCo/9Ho9kiSxb98+DAaD8Xjz5s05e/YsZcuWfeb19vb2xq3kt2zZQlJSEsOHD89TbtasWXzzzTfmDd5E9Hq9cfMYa2FKUpFDYZKLojrmDIUyGAxUqFCBZcuWGc/Jsoyvry8rV64E4MCBAzRq1AgnJyeCgoL46aefjGX79+9P//79qVu3Lp6enly+fLnQMf0TW6hHSyMclYFwVAbCURlYyvHixYvE/HWKvo1VuKif/1G5ewMNHlI8e/fsMXssJa0erZZYpKSksHTpUubNm0eDBg3o1q0b48aNY+HChU+9ZtWqVXnG/FuL6Oho7scnUbd6u+euCqBSqahbvS3345OIjo4upgjBwcGB9957jwULFhAYGMjgwYP56aefSEtLo2bNmjg4OOR7ncFg4MSJE3z55ZdEREQAcOrUKapXr46rq2ue8oGBgdSsWdOiLk/DwcHhqR7FQUGSihwKmlyYy1GlUtGzZ09jLxTA4cOHuX//PhEREcTFxREeHk7//v3R6XSMHz+e/v37c+DAAWP577//nqlTp7Jt2zZeeOGFIseUg7XrsTgQjspAOCoD4agMLOX46769eKmTqOFt2owBlUqiTRUDRw7sNvuX3yWtHq2WWJw6dYrMzExatGhhPNayZUuOHDmS69v1HO7fv8+4ceNYsmRJcYaZL7Iss2vnHkq7Bjy1p+KfaNRaSmv92bVzD7Ismy2WQYMGGXf7znkNGjQIeNx9NmHCBFauXImfnx9ff/01PXr0wMfHJ9e31vA4acu5XqPR0LhxY+rWrcuMGTMAePjwIe7u7maL21xYs4uwMElFDgVJLszp2Lt3b3bv3m1M0NevX0+nTp1wdXXlyy+/JCQkhCFDhlClShX69u3LwIEDmTt3rvH6xo0b85///IfGjRubJZ4cSlpXb2EQjspAOCoD4agMLOX414XTNPTLLtC9GwU6kJn6wOzDoUpaPVpt8vadO3coW7ascagNQPny5UlPT+f+/fuUK1cuV/lRo0bRr18/atWqZdL9/9l1lJSUZJ7AgeTkZG7evEVln5cKdJ23ZxWu3PyNlJQUtFqtWWKZMmUK3bt3z3Xs559/ZtGiRWRmZgLQp08f+vTpw/3799m5cycLFixgwIAB1KlTh4YNGwLQpUsXpk+fDjzOjj09PXFy+t9EpTJlyvDgwQOzxGxOchyLm6IkFTmYOqHbnI7NmjXD29ubbdu20bt3b37++Wdj8nj+/Hm2bNmSq21mZmZStWpV48+BgYFmi+VJrFWPxYlwVAbCURkIR2VgKce01FScyxTsw7yzowRyttlXcCpp9Wi1HovU1NQ8M91zfv7nWLI9e/bw+++/8/HHH5t8/88++wx3d3fjy8/Pr+hB/x96vR7ZIONoX7BNSxzs1cgG2ayNztPTkypVquR6eXp6Ao8/KD75npUpU4bXXnuN3377DV9fX/bt22c85+rqarw+ICAgV1IB0LBhQy5dupTvULQDBw7QvXv3Yp0/koOdnR12dvlPqrIkP235iWT35EInFTm4BbmR7J7MT1t+emoZczv26tWLn376iePHj/P333/TuXNnALKysujbty8nT540vs6ePcvWrVuN11pqox5r1WNxIhyVgXBUBsJRGVjK0cnZmdSMgo0uSc2QQbIz+9/JklaPVkssNBpNngQi52dnZ2fjsbS0NN59910WLVqU58Pus/jggw9ITEw0vmJiYswTOI8TIEklkZFVsAQhM0uPpJKKbRdFg8HAwoULOXHiRK7jjo6OODs75+kVehYdO3akVKlSLFiwIM+5uXPncuvWrVz1VlxY6xfu5f+8jDZRS5KuaD1hSboktIlaXv7Py08tY27H3r17s2vXLtavX0+XLl2M9VatWjUuX76cK0ndtGkTq1atMtuzn0ZJ+4+zMAhHZSAclYFwVAaWcqxSvS7HYuwKNHT92PVMHFxKm71nv6TVo9WGQlWoUIH4+HiysrKwt38cRlxcHE5OTpQqVcpY7ujRo1y9epWXX879wSssLIx+/fqxePHifO+vVqsttvavVqvF39+XO3euUKF81edf8H/cufcX/v6+uLiYNi+jqNSuXZvQ0FAiIiL4/PPPadGiBXFxcSxfvpz09PQ87+mz0Gq1zJ07l/79+5OWlsZrr72GXq9n0aJFbNu2zWrL/1qrizBn2NKKn1eQRFKhei6SdEk4XnLkje5vPHNfC1Mdjx49mqc3rFWrVnnK1atXDx8fHxYuXGhcDQrgvffeY/78+UycOJF+/frxxx9/8OGHHxr3MbEkJa2rtzAIR2UgHJWBcFQGlnJs264dn/+2hXN3HlHL5/kTpw0GmV+v2NGsTQezf8YrafVotcSiXr16ODg4cPjwYVq2bAnA77//TuPGjXOtstSkSZM8y1q+8MILLF26lPbt2xdrzDlIkkSH0BC+XPAt6foUkyZwp+uTeZB8k9dCBxTbJBxJkoiKimLBggVERkZy8+ZNXFxcCA0N5b///W++Kzw9iz59+lCqVCmmT5/OwoULkSSJxo0b89///pcmTZpYyOLZWHNCU1GSC1OTCjDdcfz48XmOPW1J2F69ejF37txczw4ICGDLli2MHz+eL774ggoVKjBr1iz69Olj0vOLQkmamFZYhKMyEI7KQDgqA0s5Vq1aFf8X6rHy6G9MDDM8d8nZn/5M54HBi3YhIWaPpaTVoySbc4miAjJo0CB+//13li1bxu3bt+nXrx/Lli2je/fuxMXF4e7unu/wJ0mS+PXXX2ndurXJz0pKSsLd3Z3ExETc3Io2Lh4ezxEZOmQEmalaGgeFP3PJWYPBwB+6rTg4J7Ng4dxiGzKUM+fBGkOUigtbcCzoRO6CJBVgG46WRjgqA+GoDISjMhCORSMuLo5pkz/CLfU8g1vZU6F03uFIaRkyP/+Zzp4bHrw6YAQdOnQwexwlrR6tukHe7NmzadiwIW3atOH9999n8uTJxhWOvL29Wbt2rTXDeybOzs6MGDmUbFU8f+i2kq5Pzrdcuj6ZP3RbyVbFM3LUsGJtGFlZWWRlZRXb86yBLTh27NiRN7q/geMlx+fOuShoUgG24WhphKMyEI7KQDgqA+FYNLy8vJjw8SdklGnExB2OzNiZzoFLenS3Mjl+PYPvD6YyaoPMr3f86DtwtEWSCih59WjVHovixNw9FjnodDrmzlnA/fgkSmv98fasgoO9mswsPXfu/cWD5JuUKevGyFHDqF27ttmeawqJiYkANrn/hLmwJcfn9VwUJqkA23K0FMJRGQhHZSAclYFwNA9ZWVkcP36cfXt2censn2DIAklFqTJevBQSxksvvUTp0qUt9vySVo8isTADqampREdHs2vnHm7evIVskJFUEv7+vnQIDSE4ONgqXVglrfusMNia49OSi8ImFWB7jpZAOCoD4agMhKMyEI7mJy0tjdTUVBwcHNBqtc8cBm8uSlo9Wm3ytpJwdnamffv2hISEkJKSQnp6OhqNBhcXF6tOusnOzrbas4sLW3PMb0J3UZIKsD1HSyAclYFwVAbCURkIR/Pj5ORUoK0PzEFJq0eRWJgRSZLQarVm21W7qJS0xlgYbNHxyeQi/m482kRtoZMKsE1HcyMclYFwVAbCURkIR2VQ0hxFYqFgHByev/ZyScdWHXOSiJ+2/MTL3V8udFIBtutoToSjMhCOykA4KgPhqAxKmqNILBTMv2H6jK07OhuKHp+tO5oD4agMhKMyEI7m5dq1a5w9e5bU1FTUajX+/v7UrVvX4uPzRT0qg5LmKBILBVPSdmssDLbquGPHDravi6KqJp7t66IACt1rYauO5kQ4KgPhqAyEo3k4cuQI27b9wvmzl8nOlLC312CQszDIaVUgsQAAardJREFUenz9vOgQGkJoaKjFvpEW9agMSpqjSCzMiCzLJCcno9frUavVaLVaq07eVqvVVnt2cWGLjjlJRSefBEKraNj5VwK/FCG5sEVHcyMclYFwVAbCsWjIsszatWtZv24TTg7lqR4QQvmygUjS4x6Kh0l3uXZLx7ffrOL0KR0jR42wyIRgUY/KoKQ5WnWDPKWQmprK7t27GT9uAgMHvseQISMYOPA9xo+bwO7du41LhZkbSZKQJImbN2/mObd48WJcXFyYOnVqruNr1qyhadOmODs74+npycsvv8ypU6dylWndujWRkZH5PvP69etIkoSLiwvp6el5zk+YMAFJkoiKispz7tNPP6V///65jt27d4+ePXvi7u6Ol5cX48ePL9BGMLIs21Q3Ye6kwhGA0CqOdPJJYPu6KHbs2FHge5riKEkS+/fvzxOLg4ODsS7zK5PD/v37rZoE21o9WgLhqAyEozKwpOOWLVtYu2YjgV7NaFavK17lKhmTCoBSbuWpXzOE+tXC+ePIGRYu/BKDwWD2OEQ9KoOS5igSiyKi0+kYNmwkSxZHkZqqoX7dMJo1eZn6dcNITdWwZHEUw4aNRKfTWeT5Dg4ObN68Oc/xDRs2IElSrtUEIiMjefvtt3nttdfQ6XTs3LmTsmXL0qJFC/bu3Vug52ZmZrJnz548xzdu3JjvB9QffviBSZMm5Tnep08fEhMTOXToED/++CM//PADM2bMMDkOvV6PXq8vUOyWIr+kIoeiJBeFcTxy5Ag9evTg/fffNyYWd+7coUWLFgW6T3FhS/VoKYSjMhCOysBSjomJiaxb+xM+ZepQ0a/uM8uWKV2BOlU7cCj6WJ4v+MyBqEdlUNIcxVCoIqDT6Zg+fSZalwr8p/PrODnlXmY2MKAmaWnJHDryC9Onz2L8+NEEBQWZNYZWrVqxefNmhgwZYjyWlJTEoUOHqFu3Lvb2j6v4zz//5JNPPmHnzp2EhIQYyy5ZsgS1Ws2bb77JpUuX0Gg0BXpueHi48dj58+d59OgRfn5+xmNZWVkMHTqUqKgoKleunOseer2e8uXLExkZSZUqVQDo0aMHv//+u8n+psZraZ6VVOTw+HjBh0UV1PHixYt07tyZV155hTlz5hiPe3l5Feg+xYmt1KMlEY7KQDgqA0s5/ve//yXlUSYNqzUwqbxnmQDU1z3Ys2cv9evXN2ssoh6VQUlzFD0WhSQ1NZV58xaidalA61Y98iQVOTg5aWndqgdaFx/mzVto9mFRERER/PbbbyQlJRmPbdu2jRdffBFXV1fjsW+//ZaGDRvmSipy+Pjjj7l9+3aBvkmPiIhg69atubrnNm7cSNeuXXP1WCQnJ3P69GmOHDlC8+bNc91DrVazcuVKY1Jx9uxZNm/eTOvWrU2OwxYwJanIoajDop5HbGwsoaGhtG7dmm+++SZXXTw5FCopKYlXX30VV1dXqlatyh9//GEslzPc7eeff6Zy5cpoNBrCw8NJSEgwljlw4ACNGjXCycmJoKAgfvrpJwBiYmJQqVT8+eefxrL37t3D3t6ev/76y+y+AoFAYEvs27efMu4VcXQw/cOgv3dtjv1xItf/sQJBSUUkFoUkOjqahw+Sad6003OXjFOpVDRrEsbDB8lER0ebNY6goCAqVKiQ60Pqhg0b6Nq1KwaDwThf4dixYzRu3Djfe5QrV46qVaty9OhRk58bEhJCYmJirg+kOc99klKlShEdHU2dOnWeeb+XXnqJ2rVrU6pUKd5//32T40hPT893rkdxUZCkIoeCJhemOj58+JDQ0FASEhJYvnw5dnZ2Ty07aNAgLly4wG+//caCBQuYNWtWnjLTpk3jhx9+4LfffuOPP/4wlomLiyM8PJz+/fuj0+kYP348/fv358CBA/j5+dGyZUvWr19vvM9PP/1E/fr1jQlkURxLMsJRGQhHZWAJR1mWuXc3nlKu5Qt0XSk3T7IyDWZPLEQ9KoOS5igSi0IgyzK7d+3Bq3yVp/ZU/BNnZ1e8yldm9649Zp+EExERYZxnodfr2bVrFxEREdjb2xtXmkhISKB06dJPvUfp0qW5f/++yc90cnKiQ4cOxufevn2bv/76q9C9DfPnz+fXX39Fr9fz6quvmnydh4cHHh4ehXpmUSlMUpFDQZILUx0HDRqERqMhOzs730Qhh8TERNatW8f8+fNp0KABoaGh/L//9//ylJs8eTJNmjShadOm9OnTx5hEfvnll4SEhDBkyBCqVKlC3759GThwIHPnzgXg1Vdf5ccffzTeZ926dfTu3dssjiUZ4agMhKMysJSjLBsKvBCGhAoZ2ewTuEU9KoOS5igSi0KQnJxMzK1Y/P2rFeg6f//qxNyKJSUlxazxREREsH37drKysti7dy9BQUF4enrmKuPh4UFcXNxT7xEbG0uZMmUK/NycxGLTpk107ty50Otx161bl9atW7Ns2TK2bNnC9evXC3Wf4qIoSUUO5h4W5enpya5du5gyZQpTp07l9OnT+Za7dOkS2dnZ1KtXz3gsv96sF154wfhvNzc341ra58+fZ8uWLWi1WuNr4cKFXLp0CYCePXty/fp1Tp48yd27d/n999/p1atXkf0EAoHAlpEkiVKlS5Gc+qBA1yWnPsDeToW7u7uFIhMIig+RWBQCvV6PLMs4OhZs3WlHBw2yLJu9S6tly5YA/P7772zcuJFu3brlKdO0adNc496fJC4ujlu3bj11qNTTCA8P59y5c1y/fv2pz30WSUlJrF27Nte3NDVr1gQgPj6+QPcqbrZvWk9VTXyhk4ocQqs4Pt5Eb9P65xd+DrNnz6Z06dKMGDGCOnXq0L9//2cu3ftkz5mjY16Pfx7LKZ+VlUXfvn05efKk8XX27Fm2bt0KQNmyZQkJCeGnn37i559/plmzZvj6+hbZTyAQCGydVq2CuZtwmexs05dNv3nnLNVrvkD58gUbQiUQ2CIisSgEarUaSZLIyEgr0HUZmelIkmT2Gf729vZ07tyZzZs3s2XLlnw/4A8YMIDTp0+zcePGPOemTp2Kl5cXYWFhBXpuzlK1q1at4ujRowXe/C01NZXevXtz5MgR47Hjx49jZ2dH1apVC3Sv4iYsogeX0suy86+MIt1n518ZXEovS1hEjyLHlLMCmJ2dHUuXLkWn0/HZZ5/lKVetWjUcHBxyzY85ceKEyc+pVq0aly9fpkqVKsbXpk2bWLVqlbHMa6+9xpYtW9i2bdtzh0EJBAKBUmjdujUOjgZu3jlnUvmk5Hgepd6mffu8C6sIBCURkVgUAq1Wi5+vDzdjLhboups3L+Dn64OLi4vZY4qIiGDp0qWUL1+eihUr5jlft25dPvnkE15//XUWLFjA1atX/28PjmF89913REVF5Up4/vrrL3bs2JHrdevWrXyfO336dFq3bo2zs3OBYvby8qJ79+4MGTKEEydOcODAAd5++22GDh2Km5tbwd+EYqRjx46EvdKfX2I9Cp1c7Pwrg19iPQh7pX+hduR+FvXq1WPUqFF88sknefZQcXNz44033mDo0KEcOXKE/fv3P3VDxPx47733OHbsGBMnTuTy5cusXr2aDz/8kICAAGOZrl27cunSJfbv30/Pnj3NpSUQCAQ2jZeXF+1D2/BXzCH+Toh5ZtnUtCSOn/2FqjUq0bRp02KKUCCwLCKxKASSJNG+QwhxcX+RlpZs0jWpqY+Iu3uF9h1CLLLDcWhoKJmZmXlWZXqSDz74gBUrVrB27Vrq1atH27ZtiY2N5dChQ3mWoV21ahVhYWG5XvnNA4iIiODRo0fPfO6z+O6776hbty7t27enW7dudO7cmenTpxfqXsVNUZILSyYVOURGRuLv75/vkKgFCxbQokUL2rdvT79+/Rg6dKjJ9w0ICGDLli1s376d2rVrM3HiRGbNmkWfPn2MZVxdXQkLC6N58+Z55vsIBAKBkunXrx8tWzXk1MVfOHv5d1LSEnOdz8zUc+XmCQ6dWo9fxdKMHTs63+GoAkFJRJJL0j7hRSApKQl3d3cSExPN8m14amoqw4aNxN6uDK1b9XjmkrMGg4H9/11PVvZ95s+fU+Bv9gW2TUEnchdHUmELBAcH8/bbb/Pmm29aOxSBQCAoVrKysti4cSM7tu/m/t8PcXIsi4O9E9mGTFLS/8bJxZ4XWzXntddeE5O2BYpCJBZF4PHO27PQuvjQrEkYzs6uecqkpj7i8NHtJKfEMmHCGGrXrm2WZwtsC1OTi39DUvHrr78SHR3NrFmziImJQas1bUlmgUAgUBoZGRkcPnyYc+fOkZKSglqtJiAggFatWomEQqBIRGJRRHQ6HfPmLeThg2S8ylfG3786jg4aMjLTuXnzAnF3r1CqtJYRI4aKpELhPC+5+DckFQBvvvkmmzZtYsmSJWJ+hUAgEAgE/yJEYmEGUlNTiY6OZveuPcTcikWWZSRJws/Xh/YdQggODhbDn/4lPC25+LckFQKBQCAQCP69iMTCjMiyTEpKCunp6Wg0GlxcXCwyUVtg2/wzuRBJhUAgEAgEgn8D9tYOQElIkmTciVjw7yUnefhlXRR/JcQ/3qdCJBUCgUAgEAgUjkgsBAILkJNEbN+0nrBXeoikQiAQCAQCgeIRQ6EEAoFAIBAIBAJBkREb5AkEAoFAIBAIBIIiIxILgUAgEAgEAoFAUGREYiEQCAQCgUAgEAiKjEgsBAKBQCAQCAQCQZERiYVAIBAIBAKBQCAoMiKxEAgEAoFAIBAIBEVGJBYCgUAgEAgEAoGgyPxrNsjL2a4jKSnJypEIBAKBQCAQCAQlC1dXVyRJemaZf01i8ejRIwD8/PysHIlAIBAIBAKBQFCyMGWT6X/NztsGg4HY2FiTsi1LkJSUhJ+fHzExMYrd+Vs4KgPhqAz+DY7w7/AUjspAOCqDf4Pj0xA9Fk+gUqnw9fW1dhi4ubkpviEKR2UgHJXBv8ER/h2ewlEZCEdl8G9wLAxi8rZAIBAIBAKBQCAoMiKxEAgEAoFAIBAIBEVGJBbFhFqtZtKkSajVamuHYjGEozIQjsrg3+AI/w5P4agMhKMy+Dc4FoV/zeRtgUAgEAgEAoFAYDlEj4VAIBAIBAKBQCAoMiKxEAgEAoFAIBAIBEVGJBYCgUAgEAgEAoGgyIjEQiAQmJ2rV69aOwSBQCAQCATFjEgsSgCHDh1i7NixzJw5kwsXLlg7HIEZuH79OikpKdYOw+zExcXRsWNHwsLCuHHjhrXDKREYDAZrhyBQIGJdFoFAYA1EYmHDxMXF0alTJ0JCQrhx4wYLFy6kY8eObNmyxdqhCQrJt99+i7+/P6+99hqNGzcmOjqazMxMa4dlFsaOHYufnx9OTk7s3buXgIAAa4dk88iyjEql7P+GReJU/GRkZCBJkrXDsHn+jclXSf59zM7OtnYINoGtvw/K/otWghk3bhyBgYGo1WouX77MunXruHz5Mq6urvz444+kp6dbO0RBAdm+fTuzZ8/mk08+YcGCBdSpU4dx48bx66+/Wju0IrFq1SrKli3Lzp072bt3Lxs2bMDX19fm//OzJjnvjSRJyLLM559/ztatW4GS/Yf/SXIccxKnK1euWDMci2Fr9RUZGUmPHj0YNGgQmzdvtnY4Nossy/+q5MtgMOT6IuO///0va9assXJUppHzO2ZnZ5fn2L+JrKws4PH7EBMTw927d60cUf6IxMIGWb9+PTNnzmT16tVs2LABHx8fUlJScHBw4JVXXmHv3r25fsGUgtL/o9i0aRMBAQH069ePhg0bsmbNGu7fv49Op7N2aIVm8+bNvP766wwePJjTp0/TqlUrDAYDmZmZimyj5iLnvZk7dy4TJ05k5cqVzJo1C0ARPRiyLBsdL1++zDfffMNrr71GRkaGlSMzHzl/5FUqFVlZWWzatMmqydPy5cvx8PBg48aNtG3blt9//53Ro0ezadMmq8Vki+TUmyRJpKWlsWTJEm7evAkouwdDpVIhSRJ79+4lMjKSTz75hHXr1pGQkGDt0J5Jdna28f/EtLQ0unTpwp07d1CpVIqur/ywt7fHYDBw+fJlIiIiGDRokLVDypeS/xdMIVy7do1169aRlZVFjx49CAoKYu/evcDjbm0XFxcAbt26RZMmTRQzfAYe/2f+5Dcpjx49snJE5mHHjh38+eefPHjwAHhcj76+vsD//oBVq1aNQ4cOWS3GolK5cmXCw8O5du0a8NhRpVLh4OBAUlISX3zxBefOnbNylLZHeno6PXv2ZNGiRWi1WlxdXTl16hRffvklUPKTbEmSiIuLo0OHDvTs2ZOvvvqKc+fOsWLFCmuHZjbs7e0B+Omnn/joo4+IjIy0Su/j5cuXadasGSNHjmTmzJmcPHmSESNGsHHjRoKCgli1alWxx2TL5NTb4sWLmThxIvPnzzcOL1ZyD4bBYGD06NF07dqVu3fvcu/ePQ4fPmzzvRZ2dnakp6fz/vvvs2TJErZu3crEiRMBZSeCT5LjuWXLFrRaLePHj+fvv/9m27Zt7N+/37rB5YcssAnee+89uUmTJvJvv/0my7Isb9++XVapVPLx48dlWZblhw8fyn369JHt7e3ln376yZqhmpXs7Gzjv5OTk+XPPvtMjoyMlBMSEqwYVdGIioqSvby85Hr16smlS5eWw8PD5bi4OHnLli3yli1b5NTUVFmWH7vXq1dPHjdunPFnW2ft2rXyjz/+KO/bt894bP369bK/v7+8ZcsW47HPPvtMdnV1lTt27Cjfu3fPGqHaDFlZWbIsy7LBYDAeu3nzplyrVi15165dsizL8t9//y1PnjxZrlKlivzw4cM85W2d/GIdN26c3KpVK/ny5cvy5s2b5c6dO8vBwcHy9evXrRCh+blz547coEEDuXr16nLPnj1lrVYrt2/fXtbpdMUax8iRI+WyZcvKO3bskGX5cXvLzMyUZVmWe/bsKXfu3FnW6/Ulqj1ZkuvXr8vVqlWTmzRpIr/55ptyqVKl5NatW8snT56UZblk/d49jfz+z4mNjZWbNGki//zzz7Isy3JaWprcv39/uWvXrkZ3WyQuLk5u2rSp3KpVK3nBggVy06ZNZUmS5AMHDsiyXDL+bhYUg8GQpx1mZ2fLHTt2lCdOnCjLsiwfOnRIfvnll+W6devaXJsViYWVyfmluHr1qvzSSy/J48ePlx88eCDLsix36tRJbtOmjTx9+nS5VKlScseOHeWLFy9aMVrLMWjQIPnVV1+VAwMD5Zo1axoTrJLEn3/+KQcFBckeHh7ykiVL5JSUFHnp0qVykyZN5Ndff12W5f/9h5+dnS3//fffctmyZeXvvvvOmmGbxM6dO+WKFSvKdevWlZs1ayZLkiR37NhRvn79uhwfHy+/8847csuWLeWtW7fKVapUkWvWrClv2LDB2mFblSf/4BkMBjkpKcn48+7du2UnJyc5MTHReOzGjRty+/bt5SFDhuS53lbJzs7O80fNYDDICQkJcpUqVeTFixcbjx8+fFh+5ZVX5OHDhxdzlEUnpy6edF27dq3crFkz+f79+7Isy/KBAwfkhg0bytOmTZP1er1F41mzZo0cGRkpy7IsX758WX7ppZfkCRMmyHfv3s1V7sUXX5SHDh1q0VhsFYPBkG+9ff/993KLFi2Mf2f37dsnd+zYUR49erQ1wjQr//xA+ujRI+O/L126JEuSJJ8/f9547PDhw/Krr74qT5gwoVjjzI8n6yvnZ1l+HGPlypXlCxcuyLIsyw8ePJDffvttuVq1alaJ09LkfCnwTy5evCi/8MIL8po1a4zHUlNT5VKlSslffvllcYVnEmIolJWQ/69rK2ecYMWKFenSpQvR0dHs27cPgDlz5nDixAlmzJjB6tWr2b59O1WrVi3xwySeRJZlPv74Y6Kjo3nttdd49913SUhIICoqijt37lg7PJP5/vvvadiwITVr1uT+/fsMHDgQZ2dnBgwYQJs2bbhy5QqxsbHGcecqlYozZ86QkpJChw4drBz9szl9+jSRkZG8/fbbHDlyhH379vHbb7/RsWNHypcvT5kyZXjttddISEjgP//5D2PHjuXQoUN07doVyNtdraT2+yxyhvYtWrSImjVr0q1bN8LCwrh58yaNGjWibNmyLFu2DHj8nvj7+xMSEsK6deu4ePFiiRhDnDNu++jRo3z99df8+eefpKenU7p0aVxdXYmJiTGWbdCgAe3atSM6OpqjR49aMWrT+ecE9CeHypw9e5YHDx7g4eEBQMuWLXnllVc4evQov//+u8ViSk9P58KFCyxatIhr165RpUoVOnXqxIEDBzh9+jTweD6Xn58fiYmJjB492mKx2CrZ2dlIkoRKpcqzQtaJEyd49OgRpUqVAqBNmzZERERw4cIFduzYAZTc/6MkSUKSJH777Tc6dOjAq6++ykcffcT58+fx9PSkWbNmbNiwwVi+adOm+Pr6smfPHqKjo60Wt8FgMNbX/fv3SUlJMf7uHT9+HIDAwEAASpUqxbRp04iPj2fJkiWA7a+SVBByhupNmzaNadOm8e233wLg6enJgwcPjOczMjJwcnIiMjKSKVOmcP/+favF/E9EYlGM3L17l/nz55OUlGRcDeZJBg8ejFar5ZdffuH69etUrVqVIUOG4OTklOvDZ0mc3CnLcr6//Onp6axfv55+/foRHh7OhAkT+P777zl16hQHDhyw+Q9WOfuKeHt7U69ePV566SVjzDkTVXM+IPr4+OS69scff6Ry5cpUqFABgJSUFDZu3EhiYmIxGjydnD+uK1euRKVSMXToUNRqNU5OTrz44ou8//77aDQa4PGHxldffZWAgAD69OmDm5ubcR6QJEncuHGDfv36kZmZWSLbb2FZvHgxs2bNYvz48UyaNAlXV1defvllDh06xMCBA5kzZw6pqanG9yQuLo6///6bCRMmALY/5js1NZXXX3+djh07smrVKl5//XV69uxJXFwcXbt2ZdeuXdy6dQsABwcHAgMD0el0LF682MqRm0bOFwGLFi0iPDyct99+m9WrVwPg7++Pl5cXJ06cMJYfOHAgFy9eZMuWLTx8+NAiMWk0Gnr37k2DBg346KOPABgyZAharZYlS5bQrFkzBg0axPvvv8+pU6cICAiw+f9HzU1OvU2ZMoX27dvz+uuvM2fOHADq1KmDvb09J0+eNJb/z3/+Q0JCAuvXr8/1+1gSWbt2LT179qRZs2aEhoYSGxtLWFgY9+/fp1GjRhw+fJizZ88ay7u7u3Py5ElWrFhBWlqaVWLOeb+HDh1K/fr16dy5M126dOHevXt07dqVq1evcvjwYeDx36Vy5crRs2dPJk2aBDyub6W08QsXLlClShXWrVvHvXv3mDx5MgMGDODRo0e89tprfPrpp6Snp+Po6AhAuXLluHfvHvPnz7dy5P+j5P72lBCebOwbNmzgu+++48cffwT+96FBkiQMBgNOTk4MGDCA/9/emcfVnP1//HxaUFq1SZsSKe3aV5WktIisEUIIYxn7lskwYSwjsiZrJkW27CZjyZ4la3Y1EqHSovX1++P+7vneT90MY+lWn+fjMY/R7X5u53PP55z3ee8ZGRnk+PHjhBBCpk+fTioqKsiCBQt+/OC/Efj/sn7i4uLk3bt35Pz58+TVq1cEAKmoqCBqamp0kRBCSNeuXYmFhQXZunWryHZwrqysJIsXLyaurq6ksrKSdO3alTg7O5N9+/bRZKpmzZqRxMREEhMTQzw8PGi5P0J4ClVqairx9/cnhBCyZs0a0qpVK7J9+/b6uqVa8K19qampJCQkhMjKyrKeZ77lhBBC5OTkSGBgIFFXVydTp04lhPAOkmVlZWTmzJnE0NCQPHr0iJSXlzcaASAIv9JMTU6dOkWGDBlChg4dSiwsLEh5eTnJysoiysrKJDg4mKirqxNvb2/y559/kvPnz5ObN2+SyMhIkpGRQfbu3UsIER3rqTDDwJkzZ0hGRga5ceMG+fvvv8m5c+fI4cOHydatW4m1tTVRUlIis2bNou8vLi4mRkZG5P79+w0iqbiyspKMHDmSLFq0iHh5eRFpaWnyxx9/kGnTphFnZ2dSUVFBUlJS6PsVFBRI8+bNyZEjR8ixY8e+2ThqPgP6+vpk8ODB5Ny5c+TEiRNEWlqajBw5kly6dImIi4uTa9euUeWUbw1uSnz8+JEMGjSI7Nq1i4SGhhJjY2OyevVqMmfOHKKnp0c0NDRYe62Ghgb58OEDOX36NElISKjHkX8+dVnp09LSyMCBA0lkZCQZN24c0dTUJC9evCB5eXmkT58+hGEYMnbsWJKenk4ePXpE7t69S3r16kWys7NJUlLSDxm7MC/2nDlzyKVLl8imTZvImjVrCADSr18/8vz5czJixAgyfvx4Qsj/lBB+AvrkyZOFfqaoU5exNSUlhVhZWZEbN26QlStXkoiICLJ9+3ayY8cOEhERQR49ekSioqLIvXv3CCGEPHr0iPj6+pKoqCjRKZTyI+OumiLFxcX032/fvsXw4cPRu3dvPHnyBIDwOOrBgwcjMDAQ6enpAICNGzdCVla2wSc9zp8/H/Ly8rCzs0P79u2xYMECAEDPnj0xbNgwZGVl0femp6dDXFwc0dHRKC0tra8hf5KzZ8/CysqKJlM9fPgQjo6OWLhwIS5duoSuXbuiVatWWLVqVa1rS0pKYGRkhCFDhsDW1hbKysrYsWPHj74FFoI5AMD/4nXt7Oxo3L+wJLEXL17gyJEjAID169dDR0cHd+7cQXJyMlRUVNCxY0ecO3fu+9+ACJCYmIi0tDS8f/8eJSUl8PT0xOHDhzFnzhzIysrC398f9+7dw8mTJ5GdnY3s7GwMGDAANjY2UFZWxs8//4xXr17hp59+gqWlZX3fDoDasc+C+QORkZE0ZyI2Nhbt27eHlZUVHjx4gIKCAhw7dgwqKipwdHREcHAwNDQ08Msvv2DMmDHo3r07Kwa8vhEW25yTkwNHR0ecP38eAC8B1tTUFCoqKvj48SMWLlwIJycnzJ8/H3l5eTh58iR8fX3h7u6O0NBQPH78+D+P5+DBgxg6dCgA4XIiOzsbQ4YMgaOjI31t8ODBCAoKws2bNwE0jkTkf0PYvPETlfky9Pnz59DR0YG1tTXy8vIQHR0NY2NjzJs3D0+ePMH+/fvh6emJ4OBg9OnTh8paUf3+BMd15coVmjdRVVUFS0tLnDhxAocOHUKbNm3QuXNnnDp1CteuXUN+fj4ePXoER0dHmJubQ1ZWFn379sWDBw8wePBg9O/fH8+fP/+u4+bnGQpSUlICExMT7Nu3DwBw//59tG3bFi4uLrh37x5yc3PRunVrBAUFITY2FvHx8XB3d8fSpUvBMAyePn1a63sRZQS/g9LSUnz8+JGOfciQIZgzZw7Ky8sxcOBAyMrKIiIigt7joUOH4OTkBG1tbdjZ2UFRURG3b99G165d4efnVx+3UwtOsfhOnDhxAt7e3vDz80NQUBBNZD127Bjc3d2xcOHCWtfwH7ZLly7BxsaGlQRoYGAAb2/vHzb+r0HY4j579ixMTExw6NAhAMCBAwcgJSWF1atX48yZMzA2NmYleu7YsQNKSkro0qULLl269MPG/jnw76+kpATLli2Djo4OPUAsXrwY7dq1Q7NmzTBq1Ci8ePGCXid4OMjOzgbDMGjWrFm9J86Vl5cjODgYXl5e9EDCp7S0FMHBwfD398c///wDgD2/FRUV+O2339C1a1fk5eXh+fPn6NmzJxiGgZycHNauXftD76W+2Lt3L1RUVNCpUycoKioiOjoaAODv7w+GYWBvb49jx47R93fo0AE7d+4EwPs+37x5w6qedfLkSWhra4tUAvzly5fh7++PoKAgbNmyBQAwefJkmJiYwNXVFerq6li9ejUAnoK5cuVKAMC1a9ewbt06DBkyBCkpKQB4Ve+sra0xe/bs+rkZAWruVxkZGXQuLl++DC0tLQCgymH//v1x+/ZtZGRkoKKiAtu2bYOCggKsra3BMAw2bdqEAwcOwM7O7quSKpOTk8EwDN3/hB3Ijh07hvbt29O/c/78edjY2GDhwoVUdjSUw9aXInhfHz9+xMWLF6lx6q+//kKHDh0AACNHjoSMjAzCwsLw8OFDXLlyBeXl5di1axe0tLRgZmaGFi1aYMeOHTh48CA8PT2FymdR49atW7C0tISOjg5kZGTw+++/AwBGjRoFhmGgpaWF1atX0+fA29ubvqekpARZWVm4fv06/bz4+HiYmZkhLi7uu489NzcXa9euxYULF/Dq1Svk5+ejR48eSEpKQt++fSEvL49JkyYhOzsb+/btQ1FREW7cuIHw8HBYWVlBS0sL69atw8ePH+Ht7Q1XV1cADe9ZnzdvHvT19eHm5kYNlKNHj4a9vT3k5OTg6+tL52j69Ol0383NzcW+ffuwevVqary+efMmGIahyll9wikW35Dq6mp8/PgREydOhKKiIiIiIrB48WL06NEDOjo6yMzMBMATxt27d8eFCxcACK84MmPGDHh5edHDyIULF5CQkPCD7+jLqauiwciRIzF8+HAAwI0bN+Dg4AAtLS3s378fADBr1ixYW1vDx8cHMTExsLKyQnR0NAwNDTFz5kxawaM+4Fu9hN3bvXv30K1bN/Tv3x8Ab8P28fFhHcRrWhv5P2/ZsgV5eXnfc+ifRUFBAZydnaGoqIi2bdvi4sWLrEPM2rVr0aFDB5ZHRbD6yOrVq6Gnp0d/l5ycjOXLl/+4G6hnCgoK4OTkhF9//RUAzzDAn/vbt2+DYRha4hHgVffQ19fH4cOH6Wv877K8vBwA7zkSVErrmzVr1kBOTg4TJ07E8OHDMXXqVFRWVuLBgweQlZVFz549WZ6M4OBgeHp61vl57969Q2RkpEgIQT7btm2Duro6jI2NoaysjCdPnuDVq1cwNDSEpKQkbG1tqWfu9u3bsLGxoZVqsrKycOHCBZa1t0+fPnB3d/9sr8W7d+/w9u1bWo66oqIC/fv3h6mpaa338p+X9+/fY+bMmTAwMEBRUREAYOrUqejevTstZdzY+eOPP6CgoEAr8j1+/BgfPnyAiooKGIaBt7c39Zjevn0bHTt2pPP09u1bpKens8qbjxgxAl26dKEWYlGkqKgIvr6+GD58OAoLC5GamkrlVGpqKnR0dFhGnefPn6Ndu3aIj48HwJZJ/L2+srKSeua+FcIO+osXL4a0tDQsLCzQrl07bN++HQBgY2MDhmEQEBCAhw8fAuCVmlVXV8eVK1fo9Tk5OSz5dPLkSTAM06AqZpaVlWH48OEwNjZGYmIifvvtN3q+S0hIgJKSUi2ji76+PmbOnCn08/hyIzo6WiSeW06x+MZcuHAB9vb2uHjxIuv1mzdv0oPplStX4O3tjQkTJgitVQzwFpSRkREmTZpEBU1DoaCgADNmzMDy5ctpb4OFCxfCy8sL4eHhkJWVxejRo/Hq1SscP36cbnZnz57FoEGD4OLigoiICAA8Yd+uXTuqgAjyI6wTiYmJYBiGKgA1lYTKykrs3LkTWlpaOHHiBABg165dsLW1xYYNG4R+pqhZVcrLy9GjRw8sXLgQPj4+MDMzo5YtPg4ODvD29sbly5fpa1VVVSgrK8OAAQPg5+fXKC2kxcXFePHiBT20CQtJSUtLg7GxMfbs2UMtoevXr8emTZvw7t07zJkzB3p6ehgxYgQOHDgAd3d3ODg40DKlokBCQoJQizjAez6GDRtGQxcBnnAvLS1FcXExxo8fD21tbTx+/BhlZWXIzc2FhYUFPTAIIlieVpSek7S0NBgZGSEmJgYvXrzA1q1bkZubi8LCQsyYMQPt27dnlXLlK9s5OTm1Puvjx48AeAok/7D3b0yePBnt27eHg4MDzM3NsWzZMgA870nLli2pFVmYcePKlSuwsbHBpEmTAPBCE42MjDB58uQGJzu+lLNnz8LIyAg7duxASUkJEhISkJ+fj/LyckREREBVVZX1nEVHR8PExAS5ubm11jJ/3u7du0dDlUWVO3fuQFlZmZZlT0tLw7Fjx3DixAk8evQIv/76K6SlpREdHY3z589j7NixMDEx+SH39eeff7KiDwR58OABTExMcODAAQA8rwu/5HZycjJatmyJU6dO0fefPn0a7du3x61bt2p9Fv8wXVFRIVJ7aU2E7XOlpaVwdnam6/rVq1fIzMykntLevXvD398f8fHxKC4uxt69e2Fqaio0ekOU9lE+nGLxjeBPbkhICPz9/esU0nyioqLg7u7OsmSWl5cjPT0da9aswcePH3HmzBmRsGh/CcnJyZCVlYWjoyO8vLzg5+eHiooKbN++HZqamqzNraqqCn369EFYWBjd1IXFYNra2tY6pDx58oRlIf1ei+v169ews7ND37596Zhrkp2djZCQEFasc3BwMHr37i1yTZdevHjBylnhH1SmTZsGBwcHlJSUYOzYsWAYBkuXLqWhBceOHYOzszOsrKzw4sULvH37FmVlZYiPj2+0PSvmzJkDbW1tWFlZwdzcHLdv32b9ni/YiouL0blzZ+jo6IBhGBgZGUFLSwvt2rWDl5cXAGDDhg3o1q0bLCwsMHToUJHJG9q9ezeUlZXRtm1bvHnzps73tW/fHgEBAVi5ciX69u2LHj16wNDQEJGRkSgoKICbmxt0dHTg5uYGFRUVeHt7i0xjxNLSUqxbtw5Xr14FULcV1cTEBADPmnjhwgWkpaXh0aNHOHPmDNzd3eHp6Yljx47hypUrcHFxwciRI796XWdlZcHFxQUODg44fPgwUlJSMHjwYPTu3RsvX75EVVUV5s6dC0VFRXpNTcWstLQUa9asQdu2bel+c/To0QYnO/4Lc+fOhbW1NSorK/HhwwccPXoUR44cwfXr13H9+nV07twZnp6e2LFjB1JTU2FpaUnzxUSRw4cPY9q0aVi1ahUuX75cy6BRVVVF/62np4eOHTtCVVUV7dq1Q5s2bSAtLY2RI0fizZs3mDVrFuzt7aGnpwd7e3tW/4rvwdWrV2FqagopKSka8lyT/fv3o1OnTkhNTUVWVhZiYmLw22+/4Y8//gDAyxGytrZGSEgINm3aBH19fQwaNOi794X5VpSWluK3337DihUrkJqaWuf7nj17Bnd3dzg5OWHw4MGwsbFBhw4dIC0tjVmzZuHp06eYMGECmjVrBmtra0hLSyMqKurH3chXwikW3wj+Ju/q6orw8HAAn25wlZubi969eyMkJARv3rxBbm4u5s+fj+bNm8Pe3h4vX778IeP+lpSVlaFv375YsmQJfY1v5cvOzkb//v3Ro0cP+r2Ul5fDzs6OxqMLwreGAxBqdRszZgwGDRpEY7a/FcuXL8eqVauwdetW6h5PSUkBwzDUC/U5sc5paWmwsbHBrFmzRGJTzM/PR+/evdGmTRs4OzvXyus4cuQI1NTUaNw439oXFBRElb5r167BwcGBhos4OzujVatWdXpmGioJCQlQVVWFgYEBDh8+jO3bt8PW1hYWFhYoKipCVVUV6xnIy8vDkydPsHPnThw4cABpaWl48+YNEhMT0aFDB9y9excA73kXDLn4N+PD9yQjIwPW1taQk5MTuv4A9vhSUlJgbW0NBQUFBAYGYsKECRg7dizMzMyQkpKCDx8+IDU1FYsWLarzUFFfpKenQ1dXFwsWLGAZMID/KYfnzp0DwzBwd3eHuro6OnbsCElJSbRv3x5xcXG4f/8+7O3tYWZmBhUVFYSEhNDP+hrWrVuHHj160NA5gGc553dfB3jGAF1dXUyePJk1ZuB/c5SVlQUbGxtMmTLlq8ckCjx69AhA3TKUv6f+/fffYBgGlpaWUFRUhKGhISQkJGBqaoq1a9fi2bNn8PPzg4WFBdq0aUO/Q1EjOzsb3bp1g7q6OgYNGgRjY2PIycnRuHuA7a0qLi5GTk4OIiIisGTJEhw8eBCPHz9GXFwc2rdvT8PvysvLaVgR8H2abhYUFNC8jilTpggtyMCfr9u3b8PV1RVycnKQkJCAlZUVdHV1oampiZCQEADA5s2b4ePjA1tbWyxatOibj/d7wQ/xsrS0RNu2bdGyZUvs2rULAKjMEPz+9+/fj169esHZ2RkRERFYs2YNjZDgh3XduHEDBw8eZIWCi4qR8lNwisVXIjjJ/ETXAQMG1Kqww+f48eOYPXs2ysvLaWWD4OBgGBsbQ0ZGhiY8iip1PdR8T4OpqSkGDRqE27dvY/bs2Rg1ahT8/f2RmpqKvXv3okuXLtDW1kZ4eDiMjY1hbm5Ohci/kZycjKlTpwLgbbKbN2+mnW75lp3/uuiSkpKgrq4OW1tb9OnTB82bN4eBgQFu3bqFyspK9O/fn1o0BeFvFPxYZyMjI7qxjh07FgsXLmQdBOqLWbNmoUuXLrhw4QJWrVoFbW1tlnv5zJkz0NHRwe7du+lr7du3B8Mw8PPzo0rVhw8fcOLECcTFxWH9+vUicW/fig8fPqB///5gGAZbtmxhHawfP34MCQkJlhXqzp07sLW1Rbt27fDu3TsUFxfjyZMn1FIcFRWF7t271wpfEbQ6/miqq6uxcOFCeggQpugIrqGSkhIMHz6cejMEPS35+fno0KEDKyldkPpUnGoyZ84ceHp60nDFiooK1vjevHmDAwcOYOjQoVi5ciUSExPx+vVreHh4IDg4GAAvrj0rK4tVve5L5/HDhw/0OywrK4ObmxvGjh3Leg//+xfMvduyZQvExMSQnZ0NgPfdvn37FnFxcRgxYgQAfNdqPj+K3NxcKCkpQV1dHbdv36ZzJMxiD/Cez2PHjmH69OnYvn07Dh8+jDdv3sDPzw9DhgwBwPuucnJyWM+6qHW1nzZtGgYMGIBXr17R13bv3l0rP6e0tBQjRoyAmpoaDcMTNELu27cPNjY2QnMSv8d6nDFjBlq0aAFTU1MwDEOLfwjOlyAPHz5EZmYmkpOTcfHiRWRkZKC4uBixsbHQ19enhsiPHz+y9k1R2ktqcuzYMWhpaaFt27Y0AuX+/fsIDw+HsrJyrfcfPHgQ/fr1Q0ZGBn2Nf6+3bt2Cubk59TwKUllZ2SCUCoBTLP4zdT3o/EQ6wVh04H/CYuPGjdDW1sa7d+/w4cMH9O3bFxISEggLCxMJy3ZNKioqaJwmIHxDFtwANm7cCHl5eTAMAxsbG3h5ecHd3R26urq4c+cO8vPzsWDBAgwfPpwmu34OVVVViIiIgImJCU6fPk1fP3XqFAwMDDB37ly6mX7p4nvy5Am6dOmCFStW0Nfu3LmDPXv2UCXhxo0brFhnYQfq9PR0GBkZ0SR1UZnPgoICaGlpsTwLwmK11dXVERMTg6SkJLRp0wampqZYunQplJWV0aFDB+zZs+dHDvuHwV/L//zzD3r37g07Ozv6O/4h8N69e9DU1ERSUhIAXuJ9q1atEBYWRpOsMzIy4OjoCBcXF3h6eqJly5a0iocowFd+hg0bBnNzc/psl5aWCl0zK1euhKqqKszMzPDw4UMUFhbil19+wY4dO3Dv3j1MnToVJiYmtCgFH1ERfoJlcnNycuDi4oKJEyeyQoTu3r2LLl26YNiwYfQawRLhw4YNo+V0BfkvyuHFixfh7u5Ok6qLi4uhrq5OwzzrUtLfvn2L8vJyODk50cqAp06dgqenJ8TExBAaGioy3/m3wMPDA5KSknB1da3T0Hbu3DkYGhrSefvw4QPLg9S7d2/MmjWr1nWieDi7e/cuFBQU6P7K35v5/+eP9+DBg5CTk0PXrl2poefp06cwNjZGv379MHToULRs2RKRkZGsXKbvxbZt22BsbEw9lAMHDoSNjY3Q9yYnJ0NVVRW9evVCcXExXr9+jTt37tC1OHToUIwaNarWdaI4X4IsWbKEGmlqcvz4cSgoKODs2bMAeOvY3d0drVq1wrx581BeXo7MzEzMnTsX4eHhOHLkCMzMzNCjRw/WHtQQ4RSLr+TgwYPYunUrTTgqKCiArKwsJk2aRK18gosjPj4eqqqq1Npw+/ZtaoUSReLi4sAwDNq2bUurWAHCK1nt2bMHDx8+RHZ2Np49e4Z3797Rw3WLFi1YFWAEr/s3awT/95mZmejXrx/69OnD+v3WrVvh5eWFadOm/ad7nD59OszMzPDhw4c6x1JZWVkr1pnPmzdvcO3aNQC8WHp+dQdR2BCrq6tRUlICBwcHbNq0CQDPKjJlyhTExsayvBZ9+vQBwzBQVFTEb7/9RhW1kydPwsnJCQ4ODnV64hoqDx48gIaGBk2y5ZeprBke9PPPP0NTU5NaENPT02vlXAC82PZly5Zh1qxZIpNQeOjQIWhpaSEmJgYA7x67detWSxjy47tzcnJw9epVdOnShbry+cycOROmpqZo27YtLCwsaN6CKFHXGl69ejXs7e1pmd+wsDBISkoiLCyMVprZvXs39PT08Ouvv8Lb2xuqqqosQ8bXUFBQUOuZcXZ2RlBQkND3V1dXY8OGDTS/6/Tp05CSkoKbmxukpKTg4uLSoCrhfA55eXkICwvDnDlzMHr0aKipqWHRokVUCS4rK8OQIUMgLy+PGTNmUC9EbGwsdHV18dNPP8HR0RGampq1CqiIKgkJCejQocO/en8fPnyIxMREoddPnz4d/fr1++6l2V+8eIHz588jLy+v1nifPHkCKSkpanyrrq5GRUUFoqKi0KpVKyxevJgemI8ePQoXFxfY2NjA3NwcWlpa9AAu6lRXV+Po0aMAeKF4fINTRUUFq1pifHw8bGxsaDTFmTNnMGPGDJYXqrS0FDExMXB2doaJiYnIhup9KZxi8R95/fo13NzcoKqqioCAAMjJyWHatGkoLi7Gpk2boKGhgaVLl7KuKS8vR69evTB+/Ph6GvWXk5SUhK5du8Lb2xuamppC67IfPnwYhoaGMDY2xvXr1/Hhwwfs378fqampqKqqQnR0NOzs7GpVpBBchJ9DdXU1Vq5cCUdHR1bIzsePH7FmzRq0b98eaWlpAD7t6t6wYQPdGMrKyhAQEPBZ9fT/+ecf6Ojo0ANZQUEBUlNT4enpCUlJSZFqYCjokSgsLES3bt3w66+/IikpCfr6+vD19YW5uTlUVVWpwjd58mS0bduWeqcE54ZfuaMxoqenh0GDBqGqqgqFhYWYNWsWDA0NAfAEh4GBAdq2bftJwVfX81afFrfHjx/DxcUFsrKymD9/Ph1jWVkZFi1aBFtbWzx//hz5+fnw9vZGy5YtERUVRZ8dQQuwYGhDQUEBq9+JKCjQwoiJicHcuXOpklhdXY3AwEAMGDAARUVFSE5OplV1+FRVVWHq1KkICQnBsGHDvplyKPgdPXv2DH/++ScA4Ndff4WBgQE1TNRUipYsWQIzMzPk5+ejuLgYI0aMgLq6ukiV6f2vnDhxguYfCTJo0CD06tUL7969w2+//QYxMTGEhYVRQ93OnTtx586dWtdFRkZi5MiRmDp1ap1lz+uTd+/e0X1UcF9ITU2FpKQk9XzW3EvKysqwdu1amishzKgnyPcItSwrK0NoaCjk5eWhra0NDQ0NHD9+vFao2ty5c6Gurs6SF+np6awQL/7Yz58/j+XLl1ODR0MgJiYG4uLicHR0pIrV8OHD4erqytoT4+Pjoa2tzfJ21oxgqBlyKpibIsqhX58Dp1h8BsI2qc2bN8PDw4NacA8ePAhJSUlaknHChAnQ0tJCWFgYtm7dShvmdejQoUF0Iebf8/379yEvL4/79+9j3LhxUFNTw8yZM+mh486dO1BQUMCcOXNowuH79+8xe/ZsyMnJwdzcHMrKytRK+F+Jjo6Gnp4eunXrBoZh4Onpydq8njx5ggEDBtRp/ePz6NEjjB07liXQHB0dMWjQIADCF/SDBw+otT82NhaSkpI4ceIEJk6cCAkJCbi6un73ihv/lYSEBDx79gzTpk2Do6MjwsLCsHbtWnqfgwcPhpeXF168eIFDhw5BQkLik9WBGhP8Z/zChQtgGIYqm+np6bC1tYWqqirU1NQwf/58amn7kkN0feZRhIeHQ1xcHIMGDWLNJ3/eb9y4gV69eqFdu3aQlZXF4MGD/1MpSlEUgLm5ubCzs4Ouri4mTZoEXV1dDBgwAFeuXMGlS5dgbm7OMpAIK3/7reK7hcmOsWPHwtraGnfv3sXNmzdha2uLAQMGCL1uyJAhsLKyoq+Lihfsazh58iRat24NIyMjtGrVCj///DOrZ8uBAwfQvHlzGm//xx9/QEtLC127dqV9Q4D/zZfg/AiuOVFSLvjNDkNDQ2v97ty5czA1Na0zUfnx48dwcnL6rKZ932PPWbp0KVq0aAEXFxecPXsWp0+fhouLC8zMzFgN9gBeHpK2tjamT59e5+fVtYeK0nzVhF/2tnXr1rQ8Pp+cnBzo6upizZo1OH78OCwsLCAjIwMpKSm4u7vj559/xuPHj+n9CQtTE8yrElVDzZfAKRZfQFJSEi5cuICqqipMnz6daqNRUVFQUVFBnz59kJmZicrKSpSVlWHfvn2wtbWFqakpzM3NERISIrKhJHPmzMHkyZOxefNm+hr/ATcyMkJkZCQA3r3yN0h+IxZhZSXLy8tx6dKlL7asCVtU/IZicXFxePjwIWbOnInOnTuz6uoDvLh3foLy53w2340eERGBtm3b0iQ4wc25qqoKCQkJ0NLSQnZ2NoqLi+Hh4QGGYaCtrS0ylsOaZXqzsrJgYGAAJSUlaGlpYfbs2TT3RTDZ9vHjx2jTpg1SUlLw+PFjSEtL19o4Gwv8tSf4HPDnOjAwELa2tigsLER5eTliYmKgoKBAlQ1RPEALIz8/H87OzpCXl2cZMMrLy2sdOjZs2ABdXV3MmzfvRw/zq+DPn2ACtuCcJiYmwt3dnQryv/76C9LS0jTJOSwsDL6+vjRs5FOC/Fsd1JKSkqg39ebNm+jevTv1fK5ZswZqampYvHgx65oHDx7AyclJaD+QhkxQUBAmTJiAsrIybN26lXZY5nP69Gno6enRvfXs2bNQUFAAwzCws7MT2lhSkC/1hP8ITp06RctRDx8+nBWyVFxcjF69esHLy4t6YmqGGamrq7Nk84/gyZMnMDMzg6qqKus7B3jKu4SEBA0BF/y+d+7ciebNm7OqUTVU3r9/Dy8vL1oGlj8v/DAv/v4QFRUFWVlZyMrKYtasWfjnn3+QmZmJmTNnQkNDA2pqapg0aVKjMAx8DpxiIYS6DmmqqqpQVVXF3LlzERQUhODgYOjr68PAwIDGIgt2UAR4G0R+fn4tV6CokJubC0tLS9jY2CA0NBSSkpKYNm0avf/i4mIMHjwYAQEB1COxevVqMAwDNzc3VkWnT23mn2ONqOs9y5cvR8eOHemiLi8vx++//w5HR0eW5+HmzZvo0qWL0GZ6NT9727Zt8PX1xcuXL/H333/D1NSUJvvVvI+4uDjo6uqisLAQ1dXVOHPmDPVgiAKC433x4gXOnDmDw4cPY+bMmSgtLcXUqVMxaNAguLu7Q0FBoVZ5WF1dXURHR+PRo0fw8fERWpGiIZObm4vOnTsjNja2zi7ob968gYSEBO1W+/jxY/j5+SEwMPCHj/drmTRpEry9vWlIm6AL/q+//qJJos+ePcOIESPg7e1NPTKiVi2nJhs2bEDPnj0BsJ97wX8vXLiQegAmTJgAOTk5jB49miagP3r0CA4ODpg3bx6Nf/5WfEp2KCsrIyIiAtXV1Vi+fDmcnZ1pIvfy5cshISEBb29vzJ49GwsWLICmpib8/PxEph/It+Dhw4dQVlb+ZN7Ky5cv0bp1a/z666+0uMmUKVOwbds2WFlZoUWLFli/fr3INv8TJgePHDmCnj170jwfExMTVr7N7t27YWVlRZPRBTl16lStHMcfwdGjR1lykW8wBXjPtZaWVp2lpZ2cnNCrV68fNtbvwT///IMdO3awql0BdRdlsbW1xcCBA1mlogGebJk4cSIYhkG7du2EhvE1NjjFogb/dkjjl4Xz8fGBtLQ0wsPDWRuchYUFLR8oih1ma7J//35YWlpSTfrZs2c0kZU/7smTJ6Nz584AgMuXL6NTp07Q19eHlJQUzM3NaaWcb0F1dTU2btyIY8eO0QWYlJQEJSUllsC+desWfH19azU78vT0xNy5cwEIPyQlJibiwYMH2LJlC7p164Zly5ahuroa48ePR7t27Vhx9HxlJDw8HJ6ent+kbv33ZO7cuWAYBnp6emAYhla5evfuHYYMGYLhw4fDyMgIvXv3porw5cuXYWpqKrSzaWMiICAAPj4+rFAKPvznavbs2dDQ0KBhGXv27IG2tjbtHi+Krvrc3FxERERg7dq1OHPmDACe0Hd2dsZPP/1EwwVfvnyJPn36QFxcHPPnz6fX7927F05OTrTTuijvVQAvTIZhGFp1b+XKlXB3d8ewYcNoKeCoqCjY2tpCWVkZ9vb2+OuvvwAAy5Yto+Em06ZNg5WV1TdVov9NdsyaNQuurq5ITEzE+/fvERgYiJCQELr3pqSkYMSIEQgMDISnp2etxPnGQFFRETQ0NKgFfOHChfDz80NISAjS0tKogtu9e3cwDANfX1+W5+3ly5fo0aMHZs+eLZJKcHFxsdAGpGVlZVBUVMTevXtx584d2Nvbo02bNjTRGQBWrFgBNTU1jBo1Cunp6cjJycHFixfh5OSE/v37/zD5w98PP378SHOxaibCT5kyBQ4ODnVWYrx8+TLExMRErqfN57B582a0bt0a0dHRKC4uRseOHWlXe0EZsGPHDgQFBbHOKbq6uqwoBsH337lzp1GUhP4cOMWiDv7tkBYaGorWrVtj5MiRNAHw+vXrMDc3x8mTJ+tx5F9GTEwM9PT06M9v375FVlYWqw52amoqdUM3b94co0aNQn5+Ps6dOwdnZ2cwDCPUS/Bv1Kx1ffjwYaioqFAPiqamJlJSUnD58mU4OzvXKk/r6ekJbW1tVmjP5s2bERQUVKuJGd9yqKysjDZt2mDu3LkICwuDv78/MjIykJ2djZ49e0JFRQUpKSl4+vQp8vLysHbtWujp6QmtxiEqJCYmYt68eZg5cybS0tJw5MgRyMvLIyoqilpkExIS0KdPHwwbNgyzZs2CpKQkunbtCmlpaQwfPhxlZWUif6j8XN68eYM9e/bg0qVLdCN/8uQJtLW1sWzZslqWTsH7VlNTw88//0w/Z8yYMTA3N/9xg/8CpkyZgmbNmsHW1hYaGhqQl5ene9Eff/wBFxcXJCYmIiIiAnJycggMDKSKFf+e8/PzMWvWLDg6OjaI0IXS0lIMGjQIRkZGOH78ONq1a4eoqCi4urrC0tISq1evRlZWFtTU1DBixAjWHuDh4UGtqPn5+UITh78FdcmO/Px8hISEoFevXnj//j2SkpLg4uJCvWR8GlNvmJqJxK9fv8bAgQMxdepUREdHw97ennqfra2t6R4/ceJEGBkZ0YIYdXmnRInZs2fD2toa3bp1Q0hICK38yH8Ge/XqhYCAAAC8sMyAgAAoKirSrtMAT3E2MzODlJQUnJycoKysjLFjx373UMzExERWpAV/zq5du4aePXti9OjRAHjyVUtLCwzDwMfHB8eOHWMpUsD/5qdv377/uVJjfXDnzh107twZqqqqWL16NX09OTkZEhIStJoTv1GsiooK1q9fz1IeevTogd69e9N8NWHPbUMJq/0aOMWiBp97SOvfvz/69OmDAQMGQF5eHl5eXmjZsiWGDBlSa6GJGoIP9tq1a+Hu7o7MzEysX78eampqMDMzg6amJg4cOICKigqkp6dDQ0MDjo6OtSzbWVlZiI2N/WprbkVFBfz8/Ki3AeBtxJqamjhz5gzmzZuH9u3bs2JQfX194eDgQA+CAK+PBj92ubKysk6vU+/evTFq1Cj069cPEydOpGPo27cvdHR0oKOjAwsLC+jo6LA2XFGE767t1q0bffZmzJgBa2trlqVp4sSJCAwMRFpaGh4+fIj4+PjvXp7wRzN37lzIy8vD1dUVysrKUFVVpRXE5syZA0tLS6FlKPmHuV27dqF58+bUa3fixAl06NBBpEoh7ty5E4qKijA1NaVW+lu3biEgIACdOnUCwHuW/f39IScnB0NDQ1boSc2D2dmzZ2Fra4vY2Ngfdg9fQs295ebNm1BVVUXr1q2xfv16ALyD2rp16yAjI4M3b95g6tSp6NKlC9auXYuCggKcO3cO5ubmSE5OZn3Wtzykfo7s4DcJXbFiBaqqqjBy5Ei4ubkJ9aQ1Jl69ekVDRBYsWIDu3bvD0dGRNhQDeLluDg4OePbsGdavXw8NDQ1WR3JBBHuU1De3b9+GqakpzMzMkJiYiAULFkBPT48ll/hecScnJ2rYmD9/PhiGQbNmzVj9VXJzc3HlyhUcPnyYldT+vQ6kz549g5iYGJo3by40QXzt2rUwMzODhoYG2rVrh+XLl2PTpk0ICQkBwzAwNTXFjh07qOzhz0tDUZDz8/NpqfVmzZqxwiP5+4O7uzvc3d0RGhoKWVlZjBs3jhXezr/XGzduQF1dHStWrGgw9/894BSLGnzJIa1Pnz5ISUnBxYsXsWzZsh8eA/lfqNkp/M6dO9DX10diYiL69u2LvXv3Ij09HRMmTIChoSH27duH8vJyauUH6o7D/lIhXVZWhp9++gnp6el4/fo1bG1t8erVK+Tk5KBv376Qk5PDihUrUFpaiocPHyI4OBgKCgoYOHAg7O3t4eDgUKsu/OHDh2lcfF2Ww/fv32PIkCEYPHgwRo4ciYCAAHr4LCkpQWZmJk6fPv2fvDD1RUBAAFxcXOjPfBfu9OnTaVWg8+fPw8rKChEREfU0yu9LREQEbG1tcfLkSRQVFeH27dv45Zdf6NxWVFTA2NgYP//8Mw0/EfYs29vbw93dHeXl5fj48WOtmNn6ZPz48WAYhoYuCbJ161aoqKhQS/yJEydgbGzMajImeDg5evQoNm7cCACsA4yoUHNuBMe4ePFiMAzDMnS8fv0a3bt3x6hRo/Dx40dERESgRYsWsLGxQYsWLTB+/Pjvau3+XNkxefJkeHt74969ezhz5gzGjRvH6p7c0KlZpSk8PByysrJwdXVFXFwcCgoKYGlpCWlpadZ9X7t2DdbW1vj7779x9+5dVsibKLNgwQL07duXFXv//v37WpXk+GXo9+zZA0tLS8jLyyMuLo52aLa2thYaf19ZWfldlainT5+iW7duGDFiBJo1a4YpU6bQwiz834eEhEBHR6eWonfy5En06tWL5g8I8wKKigIojEOHDlHvy19//QU5OTls3boVALsIQEZGBqSlpWFsbMyqACm4n/AVidGjR2P58uUi61n7EXCKhRA+55B27tw5WFtbs+KVRZWaCYV3796Fi4sLDAwMEBsbCzMzM8jJycHb25u1GHx8fBASEgKAV8nD3d39k3/j38ZQk4cPH8LGxga7du1CXl4epKSkMHToUCgqKiIgIAD37t3Du3fvMGjQIFrRZ/PmzZg6dSqmTZvGsmTyN6/S0lIkJCT8q+UwMTERAQEBGDlyJIYOHYqhQ4c26DKr6enpEBcXR0pKCn1t06ZNMDQ0xJEjR+hr/Dj8xkR1dTWysrJgbGwstNO1YFjc1q1b0a5dO9b3xP8MvgUqPT0dxsbGrOehvoUjf/3s2rULurq6OHXqVK11HRUVBS8vL9ZYhw8fDn9/f9bB9sGDB/D09ATDMLW8FPV5n0ePHmXVcufDnzNTU1OMGjUKhYWFePfuHfT19TF16lQA/xv3qlWr0L17d7rO7969iyNHjrAOSt9T4H+O7EhLS0Pnzp1ppb3GSmFhIa5cuQJvb28cOHAAoaGhaNu2LR48eIDExERoaGiwLOTv3r2DlJQUDh8+jHv37iEyMhJFRUUifUArKChA9+7dERUVRV/jy6XS0lJWSPG9e/cgKysLhmEwduxYGnpYXl6OgwcPssJ6f0R+puBnm5ubIzo6GgkJCTAyMoKdnR3LIp+QkAAnJycatlXTexIbG4t58+Y1mDCfU6dOYejQodi7dy8rh0dYHw4+kydPRocOHWhJWMHvb8WKFfDy8kJ+fr5IP68/Ck6xEEJjOqTVrM1eXV2N0aNHIzw8HNOmTYO9vT1CQkIgISGBoKAgVjncLVu2QENDAwCvnrqHhwdro/wvCHaKrayshI2NDfUkhIaGgmEY1kK/ceMGZGVl66wiImwj+xKvU3BwMEJCQuDm5vZNk9Drg7CwMBgZGbEsZ+bm5ggMDGzQStPncOLECSgqKn7WwdjT0xP9+/en1rd3797hzz//hJGRkUhV+6oLb29vBAYGsjq4rl69GioqKrU8Gfw+CUuWLEFubi4mTpwIMTEx9O/fX6RKX5eWlkJKSoqW8OaHukycOBFt2rTBH3/8gSVLlsDa2pomUm7duhUKCgqsUqU///wznJychP6N7235BRqX7PgSBL/X1NRUdOrUCUZGRjA1NcWqVasA8Cqt9evXD35+fgBAe4ysX78ez549w5o1a2BjY0NzExoKLi4u6NmzJy5fvoyjR4/i119/Ra9evaCkpAQLCwssW7YMhYWFePbsGQwNDTFmzBh6raB8/hF5FIMHD8akSZOQnJxMQ68AXpgoP4zy5MmTUFVVhYODA5WJeXl5mDJlChwdHWne2o9YT9+aO3fuwMbGBgzDQEFBgVa15M9DUVERtLS0hPbhePPmDVq1asVSIk+dOgVjY2MoKSnVMmo1tO/mW8IpFnXQ2A5pkZGRsLW1ha+vLzw8PKhlcN68eRg6dCgsLCxgbW1NO8ICvHCDrl27AuC5qfmu3c+hpjW1uroa27Ztg7i4OPbt20cP/FOnToWzszMA4OrVq5CUlMSGDRuocPn111/RpUsXofGKn7IMfI7l8OzZs3B1dcXkyZOFxt03NF6/fo1WrVpRQQ7w5q2x5VEIY8OGDejYsSNLceXDf07Onj2LiooKXL58GZqamti0aRNOnz4Nf39/SEhIoFevXqzEblGrAsVfT+np6dDS0kJCQgLtpC4jIwNZWVn06NEDM2bMwKtXr+j7IyIioK2tDXFxcZibm7OaWonSPcbHx6Nly5bUkvvPP//AxsYGBw4cAMBTAJ2cnNCqVSsaMuLm5gZra2ts27YNly5dgq2tLX755Zd6uweg8cmOz+X169dIT0+Hr68vIiMjMWPGDMjIyLASeA8ePAgdHR2a6xIZGQltbW106tQJKioqtRqpirL1l7++Ll26hLZt20JcXBwMw4BhGBgaGiIwMBBubm5QVFSkCr+1tTVtkidMkfgeykV6ejosLCygrKyM8PBwODk5QVtbG/b29vQcEBsbC0NDQxrOzS+AIC8vTyt4XblyBZ6enhg/fnydf0tU56usrAzDhg0DwzAIDw/Hpk2boKWlJfRM86k+HKtWrYKKigouX76MwMBAiIuLN6gE9R8Fp1jUQWM5pF2/fh1paWkwNTXF/Pnz0aZNG5iamtLQgNu3b6N///4YOnQoevToASsrK4SEhGDJkiVQUVHBkiVLWJ/3ORufoKZeUlKCFy9e0Ndmz54NQ0NDutHu3bsX3bp1o+OJiYlB27ZtYWhoCEdHR8jLy/+nrt2fazn8VD31hsjSpUvBMEyjPsAIwhdkly9fhri4OK2KVJP3799DV1eXlj8MCwuDjIwMxMXFYWNjw6pTLgrCsa4x8NfR2LFjISMjAyUlJaxYsQJZWVm4d+8exowZA0VFRejq6mL+/PkoLy/Hhw8fEBwcTPtX8D9HFC1qTk5OtE/F/fv3YWpqiry8PJw7dw49e/aEj48P/Q/gWf6VlJSgr68PHx8f9OnTp96LZzQW2fGljBs3DtLS0vD29gbAU1onTpwIb29vmivB95pZWFhQpfbt27e4evVqvY37c/i39Xjjxg0kJiYiNjaWFnrgh+NNnDgR/v7+AHjfkba29g8YMS/EKjQ0FOLi4pg4cWKtSo9t27ZFYGAgSktLcf36dbRo0QIvXrxAUVERRo4cCRkZGejp6UFOTo7m5c2dOxeurq4Nyqt09OhRMAwDV1dXmpM1c+ZMGBgYABBuXHFychLaw6iyshIGBgZgGAb+/v6sHjOiZKSpbzjF4hM09EPa27dvwTAMOnToQBORjx8/Di0tLfz55590IaxatQqBgYFYvnw5UlJSMGTIELi6un51x9dFixZBVVUV1tbWMDMzo9aQVatWwcTEBIsWLcLp06dhYWHBiue8efMm4uLisHTpUqEx159LU7QcVlRUICoqqsHEun4J/ybcbW1tP9nQzsjIiMZ05+TkwNXVVWQO2xUVFYiOjqZN7YSNQ1Bw5efno3379pg8eXKt+vaPHj3CyJEjwTAMOnfuTDvM8xHlZ+PSpUsQExOjyv+tW7fw/PlzDBgwABEREXj58iXi4uIgIyODEydOAABGjRqFkSNHsuKi61tpauiyQxh1rT/BMBJTU1P4+vrS+/7777/h5eXFqpCUlpYGXV1dmh8jiKgczr50PdakqqqKetlXrlwJQ0NDALwy0PwGpd/bgFFcXAyGYVh5LIKe/9OnT4NhGERHR6OoqAi6urowMzODjIwMHB0dcfHiRXz48AFDhw4FwzAYP348njx5IvL9nGqSmpqKo0ePsl4bO3YsLC0t65xD/j7E32MEuXHjBq5du0Z/5oeYc/wPTrH4BI3hkPbHH3+AYRiWRSgoKAg+Pj60gsPbt29pnoGw8n6fI6RrvicuLg4mJiZISkrCuXPn0KtXL9jZ2SE+Ph4ALyaeXy+6efPmtAnZl27gn6KpWg4bC18q3Pft2wdxcXHs3LmTKpP8tZuWlgZFRcU6e8zU9xqPi4sDwzC1Ouzy71lQcO3atQuvX7/GunXrYGRkREvOAuz7uHz5MkupqM/Ddn5+Pj2QCPuuq6ur6VyGhITA1NSUvm/69OlwcnKiVaEiIyNpyElOTk6t8pD1rVQAjUN2ADzPb1hYGDIzM4X+vmbI68aNG6Grq8vqLTR//nx4enpS73FBQQG2b98u0l6KL1mPiYmJuHnzJl6+fFnLGJednY2AgACsWbOGdf2PIjY2Fh4eHiyPLPC/8YeEhMDIyAh3796Fr68vNDQ0EBcXxxpnYWEh9uzZw6rA2BCea2HnBv59+/j4UC9SXXMybNgwdO7cuc7fi6rXVxTgFItGTmlpKfT09GivBgDIzMyElpYWtVQAwO7du7F48WKUlpZ+USMXwYV1//59xMfHo6SkBF5eXrQDOcATJj/99BM8PT1p8teOHTto3GNdcdFfawlojJbDpsKXCPc9e/bg5s2bmDp1KjQ0NBAdHQ0AtFxsWFgYAgMDa5WOFRUBmZSUhK5du8Lb2xuampr0ICLI4cOHYWRkBGNjY2RkZADgxWyPHj2aevyErZf6vscNGzZAWVmZdr2uSc0DQHZ2Nqtx2OjRo+Hm5gaAV/zBx8cH8fHxtQ5x9X2fjZGwsDAwDEPLwPKpqcDl5uZixowZAHjJzKGhoVQRzMzMhL+/PwIDA0WqYMCn+JL1aGpqimvXriE9PR1KSkoICQnB0qVLsXLlSrRp0wbu7u61yjn/KAt3VVUV9dTyvf+Cc/f06VNaMGXy5MkwNDREeXn5v3qnRA3+uObOnSvUEyb4vvLycnh6emLgwIGf/Mx//vkHDMOwmuVxfB6cYtEE2L17NyQlJVn1l2fOnAlDQ8Nv0viroqICycnJMDIyQmRkJO7duwc3Nzda7YnPwYMHYWtrS70WAK9xko2NDatR0reksVgOmyKfK9wNDQ1hYmJCD9ujRo2CpqYm9PT00KtXL+jq6sLCwoL+XpTgH6rv378PeXl53L9/H+PGjYOamhpmzpxJrfx37tyBgoIC5syZwwr5OXToENTV1bF9+3aRFfrLly+HhoYGmjdvjnnz5iErKwtAbUvhihUr4OjoiIyMDCxduhRKSkrIy8vDqVOnICsrC2tra0hJSSEkJOSLCklwfBmCOSo7duxAYGAgbG1tYWtry+qJwmfOnDlQUFCguRXHjh2DtrY2EhIS6L4bExOD9evXi+wzyue/rEdBY0VCQgI8PT3h6OgIBwcHkagyd/78eRgbG9eqRFZVVYWioiI4Ojpi2rRpOHnyJJo3b47c3FwAoqtE1EVeXh71ZM6ePZs2Oa1ZRAYAOnToQJOuhd0nf286cOAAK0yb4/PgFIsmQpcuXWiZP4CXVO3s7IwbN26w3vdvm0nNA3ppaSltRCdYLcLDwwNBQUGshLGqqipW5Q/+Z3GHfg5Bvla4V1RU4ObNm1i9ejXmz5/PKiNc38Jyzpw5mDx5MjZv3kxf44+Jr5gDvJ4UDMMgNDSUFjYQTBQUJDAwELt27fq+A/8K1q5di27dumHDhg3Q1taGn58fbVAI8HpNtG7dGvr6+tToUFZWhg4dOtDY/OvXr2Pt2rW4cuUKva6+57KxER8fD09PTwQGBmLVqlUoKCjA/v374e7ujoMHD2LmzJkQFxfH77//Tp/FrVu3wszMjFYO4jNw4ECqJAKiO1ffYz0CYD3fQP3LOB8fH4SHh1OlQVCpNzExwbJly2hvEcFy7w2FyspKVFdXIywsDPb29ujVqxeMjY1Zod38eX379i3LUCX4XfBDxmo+r6L6/IoqnGLRRLh+/TrExcW/yjMguAAFtfjr169DXV0d4eHh9LXz589DRkYG8fHx9LrMzEwYGhri1KlTQj+bW7xNk+8l3GtSn8I9NzcXlpaWsLGxQWhoKCQlJTFt2jQ6puLiYgwePBgBAQFUSVq9ejUYhoGbmxuttw6gVqhifR9a+Lx+/RqPHj2i4ZX8RFF+SEFmZiYOHjwIPT09WFlZUW/pixcvEBUVVStMbc+ePWAYhuVpBbjY5m8Nvxxpq1atsHDhQgwcOBAaGhqYOHEiSkpK0Lx5c5ofMWbMGLRu3ZplpBKcC/6zeP/+/XoNAfo3vsd6FPz3l4QT/wiys7PRtm1bJCcns8Z7/fp1GBgY4O+//0ZhYaHQztmiyPv377F8+XJWp/Li4mIMHz4c48ePx19//QVLS0t06tSJFSEB8MpWKykpYe3atfS1W7duwdnZGaqqql8kUziEwykWTYjg4GDaXIrP52x8ghtRTk4OevTogY4dO8Lf3582hZk/fz7k5eVZ8dJjx46FlZUVAgICsH37dnTu3Bldu3atdYDgaJp8L+FeE1E4zOzfvx+WlpbUkvns2TPqquePb/LkyejcuTMAXuJ1p06doK+vDykpKZibm3+ygWPNTrA/mmnTpkFeXh6WlpYwNzdnNe/Ly8uDra0t5s6dC4BX4trExATt27en1eoA4fNUM49CFOaysVCzHKlg/sOcOXNgbW2Nq1ev0oaSfObNmweGYRAcHEzLdQvKkYag9H3v9SiKzJgxAz179qQ5jpcuXYKzszN69+7N6hhd33vJv1FSUkIrVUVFRbHOHPPmzYORkREA4MmTJ3BycoKKigq2bdtGexTdvHkTzZs3x9WrV1FYWIjQ0FCIiYlh6NChrAqSHP8dTrFoQnxtKb/z589jzJgx6Nu3L5KTkzF27FiIiYlh9+7dePjwIQwMDFhJ4qWlpUhOTkZAQAAcHR1Zv+PgaErCPSYmBnp6evTnt2/fIisrq1ZteYZhYGdnh+bNm2PUqFHIz8/HuXPn4OzsDIZhWAdxUeHvv/+GsbExUlNTcfHiRXTv3h2rVq2ic1hWVgYPDw8MHjyYXhMeHg6GYdCyZUvExMRQgS7KB5rGhrBypPwE36SkJBqGyO9zVFJSghUrVkBKSgp2dnZQVVWFvr4+rl69WqdsERWLfU0a83qsi5KSErRr1w5Lly7FgAEDwDAM7XTf0Jg+fTqaNWuGfv36sUrCXrp0CZKSkrSSYE5ODgwMDKCkpIQJEyagoqICDx48gJKSEgYMGAAFBQXY2dmxvKKiUvK4IcMpFk2Qf9vshYUaHDx4EAzDQFNTk1XDefz48bC0tMTNmzexbds2iImJITs7m34O/+8JdjQWVWHD8WNp7MJd8Dlfu3Yt3N3dkZmZifXr10NNTQ1mZmbQ1NTEgQMHUFFRgfT0dGhoaMDR0ZE2cuKTlZWF2NhYkRR6o0ePhoODwyffM23aNJiZmeHMmTMwNjaGkpISYmNj4evrCyUlJfTr1++retZw/Df45UgFu7EDPA+0qakpqqur8fvvv0NMTAyqqqrQ1tZGXFwcAODIkSOws7Ors96/qNFU1uOn2LFjBxiGQdeuXVleRVGWyYLGBv44jx49CklJSXh4eGD69OnIy8sD8D/jU1JSEu7evQtra2u0bt0abm5ukJGRQVhYGDZv3gyGYWBgYMAyTFVWVjYIb1tDgFMsOOrk5s2b2L9/P60SMnz4cLRu3ZolhD5+/AglJSVs2LABFRUV6NKlC7p06QKgtvWRy6PgaCrCXfA5Ly0txZ07d6Cvr4/ExET07dsXe/fuRXp6OiZMmABDQ0Ps27cP5eXlaNOmDQ0ZqkvIidoaWrBgAa0JX1BQgDFjxmDWrFmsGGZ+voSkpCQmTJhAwzEKCwsRFRUFNTW1Wodbju8PvxzpkiVLAPDKj7q7u0NGRgbJyckAeI3UWrdujREjRrAarAG8Z7shKBVNaT1+iurqatY64yc9iyLl5eXo06cPxo4dW6uc9osXLzBy5Ej069cPHh4eSEhIoNdpa2tDUVERUlJSCA4OxuvXr1FQUIBt27aBYRgMGzaMKsd8RFmxaohwigUHALb7r6qqChMnToSEhATU1NTg6emJoqIiPHz4EJqamti5cydLwISFhdFSg0ePHoWKigpevnz5w++BQ7Rp7MK9ZrOwu3fvwsXFBQYGBoiNjYWZmRnk5OTg7e3NGq+Pjw9CQkIA8JpXuru7f/Jv1Dc1hfCiRYvg6emJc+fOoUuXLujRowcGDx6M5s2bY9y4ccjNzcWlS5fQqlUrLFu2jF4nmODKWQrrj3PnzqFTp04IDAyErKwshg0bxtq/L1y4AB0dnVr1/GvOmSg8m4I0lfX4X6j53YgiRUVFtHxsaGgoK1H70aNHGDRoEM6cOQNfX18MGTKENnEcN24c5OXlazUFBHjh3IKlqkXRKNUYECMcTZbq6mpSXV1NCCFEQkKCEELI5cuXydWrV0lRURHJyMgge/fuJRkZGWTZsmVEX1+f9O/fn2zcuJFkZmbSz3n16hUxMzMjhBDi5uZGXr16RdTV1X/8DXGIHABIVVUVIYQQhmHIvXv3iKurKzE3NycXL14kLVu2JMOHDycfPnwgPXv2JBYWFmTlypVEV1eX7N27l0hKShIHBwdy/vx5QgghYmK1tywAhGGYH3pfdY1BXFycVFZWEgBk1apVxNjYmAQEBJBNmzYRMzMzUlJSQlq2bEmKiorotX379iWnTp0ihBDSsmVLwjAMyc/PF/p3ROE+xcXFCSGEvHz5khBCyODBg8n169dJbGwsMTAwIImJiWTbtm0kJSWF/P333+TgwYPExsaGSEhI0P2mqqqK3ou4uDgRExOjzwnHj8XR0ZEYGBiQ8+fPk127dpHNmzcTdXV1Old2dnZEXFycZGdnE0IInaeaa7G+n01Bmsp6/K/wvxtRpmXLlmTt2rVETEyMvH79mgwYMIAUFBQQQghp164dycrKIrdu3SKzZ88mGRkZ5K+//iKEEKKmpkZUVVWJlJQU/SwAhBBCHBwciLS0dK1zD8e3hVMsmhhXr14l/fr1I5WVlURMTIwKh/T0dNKqVSvSu3dvEhQURJ4+fUp0dHSIg4MDmTJlCtm1axe5du0a+eWXX0hOTg4JDQ0l0dHRZNmyZeTy5cvE2dmZEEJIs2bNiJiYGKmsrKzP2+QQAZqScOePYcGCBcTJyYn4+/uThw8fksWLF5PFixcTT09PIiYmRkxMTMjz58/J4cOH6bW5ubnE0NCQEELITz/9RA4cOEAUFBTq4zaEUpdy6OHhQRYsWEA0NDTIlClTyJYtW4iEhAQV6B4eHqRjx47kxIkThBBCTExMqPAXdqgR9YNOYyY6OppIS0sT8KIYCCE8xYFvfDI2NiY5OTmEkIYxT415PTYlRowYQaSkpIiXlxdp0aIFGTVqFDly5AghhJBRo0aRkydPEjs7O2JhYUFSUlLI8+fPiYODA3n69CmRkZGhn1NTRggzUHF8O7hvtwkxceJEsmPHDjJ06FCqqb97947MmzePbN26lfzyyy8kMTGRGBoakoKCAtK8eXNCCCE///wzadmyJVm3bh2RlpYmkZGR5MqVKyQ1NZUcOnSIrF+/nvj4+LD+FmcJ4GhKwv3GjRvkwoULJCkpiXh7e5P09HTy5s0bkpeXRwjhKUofP34kZmZmRFVVlfz+++9kyJAhZOnSpeT3338n3bp1I4QQYmlpSaSlpUXGev8p5bB3795kx44dZNGiRWT69OlEW1ub5OTkkBcvXtDrDQwMqHezY8eOZNiwYfV1KxyfoE2bNqR///5k8+bN5OnTp/R1vvFpxYoVZMuWLfU3wC+ksa7Hpoa4uDhJSkoiW7duJUuXLiVKSkpkxowZ5P79+6R169akVatWpKKigoSHh5O8vDyyfft2oqioSFRVVcmrV6/qe/hNl3oIv+L4wfBjYWfMmEGbUpWWluLDhw/IyMiAjY0NlJSUaDOj58+fo0WLFti2bRv9jH379kFPT48m9Dk7O2PEiBG0mQyXmM1Rk+vXryMtLQ2mpqaYP38+2rRpA1NTU9rc7vbt27SUZY8ePWBlZYWQkBAsWbIEKioqNKGUj6jGBL99+xYMw6BDhw60QtXx48ehpaWFP//8k8bxrlq1CoGBgVi+fDlSUlIwZMgQuLq61urVIIpERkbC1tYWvr6+8PDwoBWcIiMj4erqilu3buHYsWO0W/azZ8+Ql5cHNzc3LF68GEDD6G/QlCkpKYGenh7WrVtHu9vX3NNFdQ0K0hTWY1PD2NgYCxcuxMuXLxEWFgZLS0ts2bIFurq6yMrKAsDrm+Xs7IwLFy6w8ig4fjycYtGEsLOzo9V2vL29MW7cOAC86jxiYmK4f/8+fe/s2bOhoaHBaprk4uKCrl27AuBt1Hp6ekhISOCaynDUoqkJ9z/++AMMw+Dq1av0taCgIPj4+NButm/fvkVISAjc3Nzwzz//1PoMUTx4/5tyeO/ePQQEBGDEiBGorq7Gn3/+iU6dOqFTp05o3bo17O3taQUooOEmuzYVtmzZAmNjYzq/DZXGuh6bKk+ePIGMjAxu374NABgyZAhtqhoTEwOA14hT8AzTEJTgxgqnWDQB+MJ8ypQpGDNmDACepdHOzg7Xr19HYWEhunXrBj8/P3pNYWEhtLW1aTUegFeK8MGDB/RnPz8/2NnZUYsBB4cgTUm4l5aWQk9Pj9UEMjMzE1paWoiOjkZRUREAYPfu3Vi8eDFKS0tZVZFEkc9VDmNiYuDq6oqdO3cC4HVUv3r1KlJTU+tr6Bz/kerqatqksiHTGNdjU2fYsGEICgoCALx69Qpr1qwBwzBYsGABq7oTZ7yofzjFogkxb948DB06lJb6DAwMpN1w9+/fD21tbRrqBABxcXFgGIZlcQRAPRTPnz/HsWPHftj4ORoWTU247969G5KSkqwurjNnzoShoSENQWxofI5y+O7dO4SEhMDd3V2opbshzmVTpzEczhrjemzKFBUVoVmzZjh8+DB97dChQ/U4Io664BSLJsShQ4egrq5OvQ4bN26ElZUV9u3bh+rqaowcORK2trasa3bs2FEfQ+VoJDQ14d6lSxeW56+kpATOzs64ceMG630N5eD2JcrhkiVLaDNNDg5RoLGtx6bOihUr0LFjRxQUFLBebyie7aYCA/x/bTmOJkHHjh2Jh4cHWbNmDcnKyiK//PILyc7OJvv37ycZGRmkR48e5KeffiKzZ8+u76FyNBLc3NyIrKwsOXDgACGEkNLSUuLl5UWio6Np/xNCRKMfxddy48YNYmVlRRITE0lgYGB9D+ebkJiYSIKDg8mtW7dIx44dCSGEzJo1i+zbt49s2LCBODk51fMIOTiE0xjXY1MGALl48SKxt7ev76FwfAJOsWhiHDhwgAQFBZG0tDRiZWVFDh48SJYuXUr8/PzI1KlTyebNm4m1tTUxMTGp76FyNBKamnAfNGgQUVVVJcuXL6evVVVVNYj6/3XRlJRDjsZFY1yPTR1unxFtOMWiCeLn50fevn1L4uPjibq6Opk0aRLJysoiu3fvZnWr5OD4VjQl4V5ZWdno+rg0NeWQo/HQGNcjB4cowykWTZDCwkLi5eVFjI2NycyZM4mGhgaRkJBolIc8DtGgKQr3xqY4NSXlkKPxwT2rHBw/Bk6xaKKcO3eOxMfHk9OnT5MzZ84QZWVlQgjnYuT4vnDCveHSFJVDDg4ODo4vg1MsmjgjRowglZWVRFlZmfz+++/1PRwODg4Rh1MOOTg4ODjqglMsmih8z0RVVRV5/fo1OXPmDOnXr199D4uDg4ODg4ODg6OBwikWTZzq6moiJiZW38Pg4ODg4ODg4OBo4HCKBQcHBwcHBwcHBwfHV8OZqjk4ODg4ODg4ODg4vhpOseDg4ODg4ODg4ODg+Go4xYKDg4ODg4ODg4OD46vhFAsODg4ODg4ODg4Ojq+GUyw4ODg4ODg4ODg4OL4aTrHg4ODg4ODg4ODg4PhqOMWCg4ODg4ODg4ODg+Or4RQLDg4ODg4ODg4ODo6vhlMsODg4ODg4ODg4ODi+mv8DcSoOxhbFDB4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6))\n",
    "ax = fig.subplots()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "order = ['meCLIP', 'm6A-seq\\n(improved)', 'miCLIP2', 'm6ACE', 'm6A-CLIP-seq',\n",
    "       'm6A-REF-seq', 'm6A-label-seq ', 'miCLIP ', 'PA-m6A-seq', 'm6A-SAC-seq',\n",
    "       'DART-seq', 'MAZTER-seq']\n",
    "# sns.stripplot(df[~df[0].isin(['Brain', 'Liver', 'Kidney'])], x=1, y=2, hue=0, s=10, alpha=.75, palette=dict(cmaps), jitter=.2, linewidth=1, order=order)\n",
    "# sns.stripplot(df[df[0].isin(['Brain', 'Liver', 'Kidney'])], x=1, y=2, hue=0, order=order, s=10, alpha=.75, palette='tab10', jitter=.2, linewidth=1, marker='D')\n",
    "sns.swarmplot(df[~df[0].isin(['Brain', 'Liver', 'Kidney'])], x=1, y=2, hue=0, s=10, alpha=.75, palette=dict(cmaps), linewidth=1, order=order)\n",
    "sns.swarmplot(df[df[0].isin(['Brain', 'Liver', 'Kidney'])], x=1, y=2, hue=0, order=order, s=10, alpha=.75, palette='tab10', linewidth=1, marker='D')\n",
    "\n",
    "for idx, i in enumerate(order):\n",
    "    plt.vlines(idx+0.5, df[2].min(), df[2].max(), linestyles='dashed', color='grey', lw=.2)\n",
    "\n",
    "plt.ylabel('AUPRC')\n",
    "plt.xlabel('')\n",
    "plt.xticks(rotation=30)\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "should = ['OCM1', 'OCM1a', 'OM431', 'PIG1', 'CM2005.1', 'CRMM1', 'HEK293', 'HEK293E', 'HEK293T', 'ESC', 'HSPC', 'MOLM13', 'LNCaP', 'RWPE', 'MCF7', 'MDAMB231', 'A549', 'HepG2', 'CD8T', 'HCT116', 'HeLa', 'Brain', 'Liver', 'Kidney']\n",
    "order = [labels.index(i) for i in should]\n",
    "plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order], loc='lower left', ncol=2, frameon=False)\n",
    "plt.title('Human')\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/atlas2_human_prc.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37fbf033-6f51-4907-8fe8-7f41d5745448",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2f6e5e81-1873-4bff-8512-0143464b4991",
   "metadata": {},
   "outputs": [],
   "source": [
    "_, cls, _ = utils.load('data/m6aatlas/atlas2_mouse.data')\n",
    "result = utils.load('data/m6aatlas/atlas2_mouse.metrics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da9c4ea4-db55-42eb-8853-a7a58ef65ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = []\n",
    "for name, i in zip(cls, result):\n",
    "    name = name.split('_')\n",
    "    # df += [[name[0], name[1], i[-1]['1.0']['precision']]]\n",
    "    df += [[name[0], name[1], i[2]]]\n",
    "df = pd.DataFrame(df).replace({'m6A-seq with improved protocol': 'm6A-seq\\n(improved)'})\n",
    "df = df.replace({'Mouse Embryonic Fibroblasts (MEFs)': 'MEFs', 'dorsal root ganglion?(DRG)': 'DRG', 'macrophages': 'Macrophages'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "821facab-ffb3-48b2-8b8a-39d4641f146f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.658177311538554)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[2].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9bfaa0e5-0aef-4412-a66a-0e58c5064449",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('fig/atlas2_mouse.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a5b5dd37-2496-439e-9f2c-9220cfcf154b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MAZTER-seq', 'm6A-seq2', 'm6A-seq\\n(improved)', 'miCLIP2',\n",
       "       'm6A-CLIP-seq', 'm6A-REF-seq', 'miCLIP '],\n",
       "      dtype='object', name=1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(1).agg({2: 'mean'}).sort_values(2).index[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f03def5d-de0a-4920-95c4-e34efc8a1ca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAGGCAYAAAC0W8IbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACpBUlEQVR4nOydd1wT9//HX5cACTOoyJCpoqJWxQFucO896h5Va921dVWrVVutbb+u1lG1KtZqrXvg3tZRxa1VVFwICg4QEEJCSN6/P/jlSgSUQC6B8/N8PPJQLpe79yvvy73uszkiIjAYDAaDwSiSSCwdAIPBYDAYjLxhRs1gMBgMRhGGGTWDwWAwGEUYZtQMBoPBYBRhmFEzGAwGg1GEYUbNYDAYDEYRhhk1g8FgMBhFGGbUDAaDwWAUYZhRMxgMBoNRhGFGzWCIkCZNmoDjODRo0CDPfXr37g2O4zB48GDzBcZgMIyGGTWDIVIkEgnOnz+P2NjYHO+lpaUhPDzcAlExGAxjYUbNYIiUWrVqQS6XY+vWrTneCw8Ph729PTw9PS0QGYPBMAZm1AyGSLG3t0f79u1zNerNmzejR48esLKy4repVCp89913CAgIgFwuR4UKFfDjjz9Cp9Px+/j5+eWoKl+3bh04jsPjx48BAOnp6Rg1ahS8vLwgk8kQEBCA+fPnG3wmMTERn332Gdzc3CCXy1GvXj0cO3bMdOIZDBHBjJrBEDG9evXKUf2dkpKCAwcOoE+fPvw2IkLHjh3x008/YdiwYQgPD0fPnj3x9ddfY8SIEUadc/z48Thw4ADmz5+PQ4cOoXPnzpg0aRLCwsIAZD0QNGvWDLt378bcuXOxY8cOeHl5oU2bNjh+/LhphDMYIsLq/bswGIziSvv27WFvb4+tW7fiiy++AADs3LkTrq6uaNSoEb/fwYMHcfToUWzatAm9e/cGALRs2RJ2dnaYMWMGPv/8c1StWjVf5zx16hRatmzJH6dJkyZwcHCAq6srAOCPP/7A9evXcf78edStWxcA0LZtWzRp0gRTpkzBxYsXTaafwRADrETNYIgYW1tbdOzY0aD6+6+//kKvXr3AcRy/7eTJk7CyskLPnj0NPt+/f38AWeabX5o2bYrffvsN7dq1w9KlS/Ho0SPMmDED7du3BwAcO3YM7u7uqF27NjIzM5GZmQmtVouOHTvi0qVLeP36dWEkMxiigxk1gyFysld/JyQk4OjRo3xpV09iYiJcXFwglUoNtru7uwMAkpKS8n2+xYsXY86cOXj06BHGjh2LcuXKoUGDBrh+/ToAICEhAfHx8bC2tjZ4TZo0CQAQFxdXCLUMhvhgVd8Mhshp06YNHB0dsW3bNtjb26Ns2bKoXbu2wT4lS5bEq1evoNVqDcxab5ouLi4AAI7joNVqDT6bmppq8LdMJsPXX3+Nr7/+Gk+ePEF4eDi+++479O3bF7du3YKzszMqVKiAP//8M9d4y5YtW2jNDIaYYCVqBkPkyGQydOnSBdu2bcOWLVsMOpHpCQ0NRWZmZo4e4hs2bAAAvj3byckpx7jsM2fO8P9PT09HxYoVsWDBAgCAj48PRo8ejT59+iA6Opo/V0xMDFxdXVGnTh3+dfjwYfz0008GPdEZDAYrUTMYHwS9evVChw4dIJFIsGTJkhzvt23bFk2bNsWnn36Kp0+fokaNGjh16hR++OEHDBo0CFWqVAEAdOjQAfPmzcO8efNQr1497Nmzx6Cntq2tLWrXro3Zs2fDxsYG1atXx927d7Fu3Tr06NEDAPDJJ59g6dKlaNmyJaZNmwYfHx8cOXIEP/74I8aOHQtra2vzfCkMRnGBGAyG6AgNDaXQ0FD+74yMDCpRogTVqFHDYD9fX18aNGgQERGlpaXRhAkTyNPTk2xsbKhSpUr0v//9j7RaLb9/amoqffrpp1SiRAlycHCgXr160Z49ewgAPXr0iIiIUlJSaNy4ceTj40M2Njbk5eVFEydOJKVSyR/n+fPnNGTIEHJ1dSWZTEaVKlWin376yeBcDAYjC46IyNIPCwwGg8FgMHKHtVEzGAwGg1GEYUbNYDAYDEYRhhk1g8FgMBhFGGbUDAaDwWAUYZhRMxgMBoNRhGFGzWAwGAxGEeaDMWoiQkpKCthoNAaDwWAUJz4Yo37z5g0UCgXevHlj6VAYDAaDwcg3H4xRMxgMBoNRHGFGzWAwGAxGEYYZNYPBYDAYRRhm1AwGg8FgFGHYMpf5JC4uDrdu3UJaWhqsra3h7u6O6tWrs7VzGQwGgyEozGXew+3bt3H08EE8+PcKbDJT4GhD0GiBFK0NHEp7o15Ic7Rs2RJyudzSoTIYDAZDhHwwy1ympKRAoVAgOTkZTk5O+frMsWPHsGdTGMpav0SID4fqblJYSTgAQHyqDmefZOL8S1uU8q+NUWPH5/u4DAaDwWDkF2bUeXD+/Hls+u1ntHRNQPuK1uA4Ltf94t7osPwqB6cKDfD5hEmwsbExdegMBoPB+IBhnclyISMjA7u2/Ikgp1fvNGkA8HCUYEQg4endS7hw4YIZo2QwGAzGhwAz6ly4du0a0hNi0dr/3Satx9NJguqKNJw+cZRNUcpgMBgMk8KMOhfOnzuDSo5pKG2f/6+nobcUz6Pv4cmTJwJGxmAwGIwPDWbUufD6ZTx8HI0rGXsrJECmGomJiQJFxWAwGIwPETY8Kxd0Oh3yUeNtQFZncIJOpzPY/vr1a7x48QJarRZ2dnbw9vaGVCo1Wax5oVQqAQB2dnaCn8tSMI3igGkUB0yjcDCjzgWnEqXw4qlxJeoXaTpAag0nJycQEW7fvo2Tp/5GxLWbUGVooSOClYSDl5sLmjdpjAYNGgg6nCszM1OwYxcVmEZxwDSKA6ZROJhR50KtoHoIv30Bb9QaOMryV7Q+9yQTCjcf+Pj4YN3vv+PwqX+QaqWAtEwQrEuUASRSZKjeIPLZPdzftAsHDh/D52NGoly5cgKrYTAYDEZxhrVR50LdunXBOZTGqWhNvvZPVulwKcEW9UOaY+PGPxF+4h+oyjaGfVBXyD0DILVzglRuD2tndzhUCYF1UC88SLPG/MVLEBMTI4gGKysr0U9vyjSKA6ZRHDCNwsGMOhfs7OzQrG0XHHnmiMvP3l3VkZpBWHVVBzuPSnB0dMShv8+DKoRC5l4+z89IbOSwD2yDmHRrrPv9D0GGdGVmZoq+KoppFAdMozhgGoWDGXUetGvXDkHNu2D9PQf8dVONZ28MO4mpMwnnnmiwMEKHJKcAjBj7Bc5fuIg0eWnIXMu+9/ic1BrW5YIQ+fAJHj58aPL4iUj0Y7qZRnHANIoDplE4xF1PUQg4jkO//v3hUaYMjh/ah38uP4GXLBXONpnI1HF4rJRDZe2GqnXqo1uPnsjIyMC123dh7dc43+ewLumJVM4Op0+fQfnyeZfAC4K1tbVJj1cUYRrFAdMoDphG4WBG/Q44jkPz5s0RGhqKmzdv4ubNm1CmpcHGxgaN3dxQv359lCpVCgBw+fJlpGu0sCnlbdTxdYoyeBQTa/LYtVqtyY9Z1GAaxQHTKA6YRuFgRp0PrKysULNmTdSsWTPPfTIyMqAjAEaOkeYkUqjVGYWMMCdibysCmEaxwDSKA6ZROJhRmwg7OztIJRx06nRI5fb5/pwuIx2OLqYfPP8hrOLFNIoDplEcMI3CwTqTmQh/f3+UsJdDHReV78/oMjNgkxyLmtWrCRgZg8FgMIozzKhNhL29PUIbBAPP74DemkY0L9RxUVDIgPr165s8HrVaDbVabfLjFiWYRnHANIoDplE4mFGbkCZNmqC0jRZpd868twt/ZsorSJ5cQWiDYJQoUcLkscjlcsjlcpMftyjBNIoDplEcMI3CwYzahHh5eeGzIQNRKi0aaTePQqtMzrEP6bRQPbuHzJv7EVylLPr26WOBSBkMBoNRXGCdyUxMcHAwbGxsELZ+I55e3YFUe3dIFO7gJFLoVKmQJDyCwkqLRo1qY+CA/pDJZILEoVKpAIh7JRumURwwjeKAaRQOZtQCEBgYiP9VqYJr167h1N+nEfPsETI0Gjg5OKBux6Zo1KgR3NzcBI1B7FVQANMoFphGccA0CgczaoGwsbFBcHAwgoODLR0Kg8FgMIoxrI1apKhUKr6aRqwwjeKAaRQHTKNwsBK1SBGq7bsowTSKA6ZRHDCNwsGMWqRwHGfpEASHaRQHTKM4YBqFg1V9ixQ2+YA4YBrFAdMoDiylkZWoRQpbck4cMI3igGkUB2yZS4ZJYdVQ4oBpFAdMoziwlEZm1CJFo9FYOgTBYRrFAdMoDphG4WBGLVKkRq6LXRxhGsUB0ygOmEbhYEYtUtiPRhwwjeKAaRQHzKgZJoVVQ4kDplEcMI3igFV9M0wK69ghDphGccA0igM2jpphUqysrGBlJe7nMEtofN8646aG5VEcMI2mJykpCWfPnoVOpzPbOS2VR3FfOR8wmZmZZjsXESEtLQ1qtRoymQz29vZmefI0p0YAuHbtGjZu2ox+fXohMDDQLOc0t0ZLwDSKA3NqfP36NX6ePx8vHjzAgwcP0L9/f0gkwpc7LZVHZtQixRwlP6VSiYiICJw9dRwvYh8BpAM4CVy9yqJhaDMEBwcLum6rOUu3165dw8+//oY4JYeff/0Nn4/81Cxmbe4SvCVgGsWBuTTqTTr91i00k1rhRHg4AJjFrC2VR44+hCsIQEpKChQKBZKTk+Hk5GTpcARHqVQCEG6B88jISIStXArN66eoUUKJGm4S2FlzUGoI15/rcP21HaxLeOKTz8agcuXKgsQgtEY9epN+KSsD+yqhSLt9CqXVz8xi1ubSaEmYRnFgDo3ZTbqfvQMUUilup6djLwh1O3YU3KwtlUfWRi1StFottFqtIMeOjIzEql/+h/LaKMxuoMHgQBvU9LBCJRcpanpYYXCgDWY30KC8NgqrfvkfIiMjBYlDSI16DE26CTiJFPZVmuClrAx+/vU3XLt2TdDzm0OjpWEaxYHQGnMzaQCoYmuLDuBwITwcGzZsELTN2lJ5ZEYtUoS6oJRKJcJWLkWAVSyG1bSGkyz3tmgnGYdhNa0RYBWLsJVL+SdRUyL0jyanSWf9XDiJxGxmzW7w4oBpLBx5mbQec5n1B2nUKpUKQ4cOhbOzMzw8PLBgwYI89925cycqV64MBwcHNGrUCFeuXDFjpMUPa2trQSaQj4iIgOb1U/T5yBpSybs7jEklHHpXtYLm9VNcvHjR5LEIpRHI26T1mMushdRYVGAaxYFQGt9n0nrMYdaWyqNFjXrSpEm4dOkSjh8/juXLl2P27NnYtm1bjv1u3bqFvn37YurUqbh+/ToCAwPRvn17QUppYoGITN7xgYhw9tRx1CiRlmdJ+m0UcgmqOytx5uQxQeIRoovF+0xajznMWiiNRQmmURwIoTG/Jq1HaLO2VB4tZtRpaWlYvXo1fv75Z9SqVQtdu3bF5MmTsXTp0hz7Hj58GFWrVsXAgQNRvnx5zJs3D/Hx8bh9+7YFIi8eaDQak8+ik5aWhhexj1DDzbhp9ALdJXjx9JHJH6yE0Jhfk9YjtFkLobGowTSKA1NrNNak9Qhp1pbKo8WM+vr169BoNGjQoAG/rVGjRrhw4UKOL7ZUqVK4desWP7g9LCwMTk5OKF++vLnDLjbIZDLIZDKTHlOtVgOkg521cWOkba05QKeDSqUyaTym1misSesR0qyFyGNRg2kUB6bUWFCT1iOUWVsqjxYbRx0XFwcXFxfY2Njw29zc3KBSqZCQkIDSpUvz23v16oU9e/agUaNGkEqlkEgk2LdvH0qUKJHn8dVqdZax/D8pKSnCCCmiCFE9I5PJAE4Cpca4Y6drCJBIIJfLTRqPKTUW1KT18GZ9+6RJx1mLvboUYBrFgqk0qtXqQpm0niq2tkB6OvaGh8PW1hY9e/YsdGyWyqPFStRKpTLHk4n+7+wGCwAJCQmIj4/H0qVLceHCBQwcOBCffPIJXrx4kefx582bB4VCwb+8vb1NL6II8/aDiimwt7eHq1dZXH9u3NPptXgdXD3Lmnzsoak0EhE2btqMOCUH+yqhRpu0niyzDkWcksPGTZtN8qMWIo9FDaZRHJhKI8dxkHAciApvjPT/L1OtemWpPFrMqOVyeQ7B+r/fvqFPmTIF1apVw+jRo1G7dm2sWrUK9vb2CAsLy/P4U6dORXJyMv+KiYkxvYgijFwuN3kJluM4NAxthuuv7ZCizt8PKFmlw40kOzRq0tzk04qaSiPHcejXpxc87Ahpt0+BClhNRjod0m6fgocdoV+fXibRK0QeixpMozgwlUYbGxuMmzgRDtU+wsa0NCQVcNrOW/8/EUrDLl3QtWvXQscFWC6PFjNqT09PvHr1ymDu1Pj4eNja2sLZ2dlg38uXL6NGjRr83xKJBDVq1EB0dHSex5fJZHBycjJ4MQpPcHAwrEt4YtO/Gmh17zZrrY7w161MWJfwRFBQkJkiLBiBgYH4fOSnKK1+hrTbJ4026yyTPmm2GcsYDDHj7OxcKLPObtJ9+/Yt9it7WcyoAwMDYW1tjfPnz/Pbzpw5g6CgoBxTwJUpUyZHD++7d++ibNmyZom1OKJSqUzeeQvIqu345LMxuJPphdVXNUhW5W5oySodVl/V4E6mF4aMGCvIlHum1lhQsxbSpIXKY1GCaRQHptZYULMW0qQtlUeLzvU9YsQInDlzBmFhYXj69CkGDRqEsLAwdOvWDfHx8VAoFLC1tcXmzZsxePBgrFq1CvXr18fq1auxYsUK3Lt3D66urvk6F5vr27Rkn+u7urMSge4S2FpzSNcQrsVnVXdbl/DEkBFjERAQIEgMQmk0pmOZ0CVpNke0OGAaC05SUhJ+mT8fqTf/RT97ezi/Y5lJoUvSlsqjRY1aqVRi5MiR2L59OxQKBSZNmoTx48dnBcZxCAsLw+DBgwEAa9aswfz58xEbG4vAwEB+/HV+YUYtzDkuXryIMyeP4cXTR4BOB0gkcPUsi0ZNmiM4OBi2traCnh8QRmN+zNoc1d3sBi8OmMbCkR+zNkd19wdp1ObkQzPqxMREAEDJkiUFPxcRQalUQqVSQS6Xw87OzixtQkJrfJdZm6tN2px5tBRMo2mx1PrwQmt8l1mbq03aUtcqM2qRkpaWBiBrSJVYMYfG3MzanB3HWB7FgTk0Wnp9eHNozM2szdlxzFLXKjNqkcJufqbDkutRszyKA6E1FoX14c2Vx+xmXcdKipMSidl6dzOjFpgPzahfv34NAO+cva24Y06NerOOU3LwsCOzDcFieRQHQmrUrw8fYBWLPh/lvvRsipqw6d+sURjDx00SxKzNmUe9Wcfff4CGHTuYbQiWpa5VZtQi5c2bNwAAR0dHC0ciHObWeO3aNWzctBn9+vQy2zhplkdxIJRGpVKJWdMmobw2CsNqvnvpWa2OsPqqBg+kFTDr+/+ZvBrc3HlMTk5GZGQk6tata7Zx0pa6Vi021zdDWEw1ZV5RxtwaAwMDUaNGDbNOnsDyKA6E0sivD98g/+vDz/ona3340NBQk8Zi7jwqFArUq1fPrOe01LVq0fWoGcLBltUTBnPPcMTyKA6E0FjU1odneRQOZtQiheO4Yj9t3vtgGsUB01gwitr68CyPwsGqvkWK1Ttm7xELTKM4YBoLhinWhzdl72WWRwHPa5GzMgQns4ArzhQnmEZxwDQWjKK2PjzLo3Cwqm8Gg8EohhS19eEZwsGMWqRYWVmJviqKaRQHTGPBKGrrw7M8CgczapGSmZkp+qooplEcMI0FpyitD8/yKBzMqEUKEZl8+EVRg2kUB0xjwSlK68OzPAoHm5lMpLBl9cQB0ygO2Prw4sBSGsXdoPABo9VqLR2C4DCN4oBpLDyVK1fGrO//x68Pf+2R4frwXboJvz48y6NwMKMWKWJvKwKYRrHANJoGOzs7hIaGIiQkxCLrw7M8CgczapFiY2Nj6RAEh2kUB+bWmJKSggcPHiAwMNBss0yZUyPHcbC3tzf7UozsWhUOZtQMBuODISkpCb8sWIDnDx+iRY8e6NKli+invWQUf1ivb5GiVquzphgUMUyjODCXRr1Jp968iXrKdBz56y/s2rXLLL14WR7FgaU0shK1SDH19IBFEaZRHJhDY3aT7mdnD2crK9ilpeHIX38BgOAla5ZHcWApjcyoGQyGqMnNpAGgjr09YEazZjAKCjNqkaJSqQCIe0wj0ygOhNSYl0nrMZdZszyKA0tpZEYtUlg1lDhgGgvO+0xajznMmuVRHLCqbwaDwTAR+TVpPawanFGUYUYtUlg1lDgwp0YiQlpaGtRqNWQyGezt7c1iVqbWaKxJ6xHSrNm1Kg5Y1TfDpMhkMkuHIDhMo2lQKpWIiIjA6ePH8Tw6GqTTgZNI4Obri8bNmiE4OFjQG5MpNRbUpPUIZdbsWhUHltLIjFqkfAjVdkxj4YmMjMSaZcuRHh+HChoNgmxkkEskUOl0uHP9Orbevo297h4YOnoUKleuLEgMptKoVqsLZdJ6spu1XC5H27ZtCx0bu1bFgaU0sglPRAqbfEAcCKkxMjISv86fD7cnTzBKJkdXhTMq29qirEyGyra26KpwxiiZHG5PnuDX+fMRGRkpSBym0qjVaqFOT4cNEawLeUO1l0gArRZpaWmFjgtg16pYsJRGZtQixdraGtbW1pYOQ1CYxoKjVCqxZtlyeL98hR4KBRyk0lz3c5BK0UOhgPfLV1izbDm/zJ8pMZVGOzs7jJs0CVShAv5MfYO0Aq50FJmejnDSoW6HDujWrVuh4wLYtSoWLKWRGbVI4ThO9FVRTGPBiYiIQHp8HNo5OkLynuNLOA5tHR2RHh+HixcvmjwWU2p0c3MrlFnrTTq4QwcMGDgQEolpbpHsWhUHltLIjFqkaDQaaDQaS4chKExjwSAinD5+HBU0mjxL0m/jKJXCX6PB38eOmXxubFNrLKhZC2XSALtWxYKlNDKjFilSqRTSfN6EiytMY8FIS0vD8+hoBNgY14O1so0Mz6OjTV79LYRGY81aSJMG2LUqFiylkRm1SGE/GnEghEa1Wg3S6SA30oxkEglIp+PHkpoKofKYX7MW2qQBdq2KBWbUDJPCqqHEgRAaZTIZuP8fgmUM6v8fX23qaRSFzOP7zNocJg2wa1UssKpvhklhHTvEgRAa7e3t4ebrizsZxg0zicxQw83X1+STnwidx7zM2lwmDbBrVSywzmQMk2JlZQWrAk74UFxgGgsGx3Fo3KwZoqytkZrPjlZvtFrct7ZGSPPmJr9RmSOPb5v1FWWa2UwaYNeqWLCURmbUIiUzMxOZmZmWDkNQmMaCExwcDFt3D+x/8wa69/Ti1hHhQOob2Lp7ICgoyOSxmCuP2c36iFRqNpMG2LUqFiylkRm1SCEikw+jKWowjQXHzs4OQ0ePQkxpF2xLTsabPErWb7RabEtORoyLC4aNGS3InN/mzKObmxvGT5mCXqNGmc2kAXatigVLaeRI7N/s/5OSkgKFQoHk5GQ4OTlZOhzB0Q+hEfNKNkxj4ck+17e/RoPKNjLIJBKodTpEZqhx39oatu4eGDZmNAICAgSJgeVRHDCNwiHuBoUPGG0Bp08sTjCNhady5cr49qcfcfHiRfx97Bj2Zl89q3IAPm7ePKua3NZWsBhYHsUB0ygczKhFCvvRiANzaLSzs0NoaChCQkKgVCqhUqkgl8thZ2dnlh6uLI/igGkUDmbUIkXsk+MDTKOp4TgO9vb2sLe3N9s5AZZHscA0CgczapHyIXQ9YBrFAdMoDphG4WBGLVLEPkMQwDSKBaZRHDCNwmHR4VkqlQpDhw6Fs7MzPDw8sGDBglz3a9KkCT8jTPbXkCFDzBxx8UEmk0EmM27RheIG0ygOmEZxwDQKh0VL1JMmTcKlS5dw/PhxREdHY9CgQfD19UWPHj0M9tuxYwcyMjL4vy9cuICPP/4Yo0aNMnfIxQZWDSUOmEZxwDSKgw+u6jstLQ2rV6/GgQMHUKtWLdSqVQu3bt3C0qVLcxh1yZIl+f9rtVpMmzYNkydPRp06dcwddrFBrc6ax9ncHYPMCdMoDphGccA0CofFqr6vX78OjUaDBg0a8NsaNWqECxcuQPeOVX3WrVuHxMRETJkyxRxhFlvkcrnJVzkqajCN4oBpFAdMo3BYrEQdFxcHFxcX2NjY8Nvc3NygUqmQkJCA0qVL5/gMEeHHH3/E+PHj4eDg8M7jq9Vq/ukHyJqZjMFgMBiM4obFStRKpTJHo7z+7+wGm52TJ08iNjYWn3766XuPP2/ePCgUCv7l7e1d+KCLESqVCiqVytJhCArTKA6YRnHANAqHxYxaLpfnMGT933nNo7pt2za0bdvWoM06L6ZOnYrk5GT+FRMTU/igixGsGkocMI3igGkUB5bSaDGj9vT0xKtXrwyWDIuPj4etrS2cnZ1z/czBgwfRpUuXfB1fJpPBycnJ4MVgMBgMRnHDYkYdGBgIa2trnD9/nt925swZBAUF5br03KtXr/Dw4UM0bNjQnGEWW1g1lDhgGsUB0ygOLKXRYp3J7OzsMGjQIIwYMQJhYWF4+vQp5s+fj7CwMABZpWuFQsGv2vPvv/9CLpejbNmylgq5WCH2iQcAplEsMI3igGkUDovOTLZw4ULUrl0bTZs2xejRozF79mx069YNAODh4YHNmzfz+z5//hzOzs5mWc2HwWAwGIyiAkcfwnQyyBqepVAokJyc/EG0V79+/RoAUKJECQtHIhxMozhgGsUB0ygcbFEOkWJlJf7UMo3igGkUB0yjgOe1yFkZgiOVSi0dguAwjeKAaRQHTKNwGGXURJSjjfjZs2fw8PBgbcdFDLbknDhgGsUB0ygOivwyl9u3b0e5cuVw9epVg+3Dhg2Dt7c3du/ebfLgGAVHvxSomGEaxQHTKA6YRuHIl1EfOHAAffr0Qffu3eHj42Pw3rJly9CrVy/07NkTR48eFSRIhvFYWVmJvs2IaRQHTKM4YBoFPG9+dpo7dy6+++67XFesKlu2LBYsWABbW1t8++23aNGihcmDZBhP9hnfxArTKA6YRnHANApHvkrUN27cyLFG9Nv069cPN27cMElQDAaDwWAwsshXidrOzg5v3rx55z4ajeaDmJmmuCD2KiiAaRQLTKM4YBqFI18l6qZNm2LVqlXv3GflypWoU6eOSYJiFJ7MzEzRV0UxjeKAaRQHTKNw5OvxYMaMGahXrx60Wi0mTpyIChUq8O/du3cPCxcuxPr163H8+HHBAmUYx4cw4RzTKA6YRnHANApHvoy6SpUqOHjwIIYMGYLVq1fD0dERTk5OSEpKQlpaGgICArB//37Uq1dP6HgZ+cTa2trSIQgO0ygOmEZxwDQKR74r3Bs0aIA7d+7g7NmzuHHjBpKSklCqVCnUrl0btWvXFjJGRgHQarWWDkFwmEZxwDSKA6ZROIxuGW/YsGGea0IfP34czZo1K3RQjMIj9rYigGkUC0yjODCnRiJCWloa1Go1ZDIZ7O3tzTIRiaXymG+j3rBhA3bu3AkrKyv07NnTYLhWTEwMvvjiC+zcufODeKoqDtjY2Fg6BMFhGsUB0ygOzKFRqVQiIiICx/8+juhn0dCRDhJOAt8yvmgW0gzBwcGws7MT7PyWymO+jHr27Nn49ttv0bx5c8hkMvTv3x+vXr3CiBEjsHjxYkyfPh329vb47bffhI6XwWAwGB8gkZGRWP7bcsSlxEHrroWslgwSGwk0GRrcfHITtzfchsceD4z6dBQqV65s6XBNSr6Met26dViwYAHGjx8PIGve76+++gr379/H4sWLMWrUKHz33XdQKBRCxsowArVaDQCwt7e3cCTCwTSKA6ZRHAipMTIyEvOXzkeCYwIUHRSQ2hquYmXnawdtuhYx52OwYOkCTBgzQRCztlQeOcpHf3OZTIY7d+6gbNmyAACdTgeZTAZ3d3ds2bIF9evXFzzQwpKSkgKFQoHk5GQ4OTlZOhzBUSqVACBoNZClYRrFAdMoDoTSqFQqMWX6FMTYxKBEaAlwkrzboklHeH3qNbwzvPHjnB8FiQUwfx7zNeGJRqMxeIKQSCSQy+VYsWJFsTBpBoPBYBRPIiIiEJcSB0U9xTtNGgA4CQdFXQXiUuJw8eJFM0UoPPle5jI3xNYOICZUKhVUKpWlwxAUplEcMI3iQAiNRITjfx+H1l2bo7o7L6R2UmjdtTh26pjJJyixVB7z1Uad2xqcH8Lao8UZuVxu6RAEh2kUB0yjOBBCY1paGqKfRUNWy7h1JGQ+Mjy58gRKpdKk7cmWymO+jJqI4O7unmObv79/jn3Z8CwGg8FgmAK1Wp01BMvGuMpfibUEWtJCpVKJogNfvoz6xIkTQsfBMDH66hkxd15hGsUB0ygOhNAok8kg4bKGYBmDTqODDWdj8hKwpfKYL6MODQ0VOg6GifkQlhxlGsWBuTUeP34cf585ixHDP81RUygULI8Fw97eHr5lfHHzyU3Y+ebfHNVP1KhYpqLJDdVSecxXfUJqaiqGDRuGkiVLwt3dHSNGjEBycrLQsTEKwYfQh4BpFAfm1HjkyBGs+n0Tzt95gnk/zUdcXJxZzsvyWPBjNgtpBmm8FNr0/DWrapVaSOOlaB7aXJB4LJHHfBn1119/jYMHD2LKlCmYOHEiDh8+jCFDhggdG6MQqNVqfnC+WGEaxYE5NCqVSixZsgS/LF4EZUIsbFUv8CjyGsaNHoFdu3bx42OFguWx4AQHB8PDyQPJ55NBunf34iYdIeVCCjycPBAUFGTyWCyVx3xNeFKmTBls3rwZjRs3BgBcvXoVdevWxZs3b4pNlc6HNuFJamoqAMDBwcHCkQgH0ygOhNYYGRmJhT98B+WrJ/iopAYBrjaQWQGqTB3uxqsQ+cYBJb0q4rOxXwo25JTlsXBERkZiwdIFeOX4Coq6Ckjtcg7V0iq1SL6QDJc3Lpg4diICAgJMHoel8pgvo7ayskJsbCzfnkNEkMvluH//Pry9vQUP0hR8aEadlpYGQNxTFjKN4kBIjZGRkZg/Zwb8tI/Qwk8LJydHg/eJdHiTmIiT8Q54ahuA0ROmCWLWLI+FJ8dc3z4ySKwl0Gl0UD9RQxovhYeTB0YPHy2ISQOWy2O+OpPpdDpIJP/VknMcBxsbG2g0xvXEY5iPDyE3TKM4EEqjUqnEwh++g5/2ETr6a2Ft55hjH46TwLFkSbS1SsCB2DtYuWQhvp//s8k7IbE8Fp7KlSvjxzk/4uLFizh26hieXHkCLWlhw9mgYpmKaD6gOYKDg2FraytYDJbKY4EnPNFvZxRNpNL8zeJTnGEaxYFQGtesWQPlqydoUS13k9bDcRJYOZVCqHsiVkTdw5EjR9C5c2eTxsLyaBrs7OwQGhqKkJAQKJVKqFQqyOVy2NnZmcWPLJXHfE94Mm7cOIMnFbVajcmTJ8PR0fAHsHbtWtNGyCgQ7MYgDpjGgnHkyBEcPngADUtqclR35wbHSeBUsiQqO6Ziw+9rszoweXiYLB6WR9PCcRzs7e3NXgVtqTzmq9f3wIEDIZfLQUT8q1+/fnBwcDDYZup5VRkFR6PRiL66jWkUB6bWePToUazZsAUcR6jkapPvz3GcBJXc5VCnp+HH/y1AfHy8yWJieRQHltKY7/WoGfnj4MGD2LZ7L3p07oA2bdpYLI4PoVnCVBoPHjyI/bv3op2Fc5YbLI/GcfToUaz9cxtSS1aAzZtXkFmlGvV5uZUEUlt7RCVm4H8LF2PSl+NNMikKy6M4sJTGfBn1kydPct1ubW0NZ2dnQRvvixMHDx7E75t3IsnGBb9v3gkAFrvxW1nlK7XFGlNoPHjwIPZu3g0/Gzfs3bwbgOVylhssj/nn9evX2LRtJ5JsSsO+XB1onlyAOtO4Y6gzAeKsIK/aAneu7sHOnTsxcuTIQsfG8igOLKUxX2f18/N755NEhQoVMGHCBHz66acmC6y4oTfpNLfqUJSrhbSHVyxq1pmZRt6hiiGF1ag36VC32mhSLhgnH0YUObNmecw/zs7O6NimJTbtOoD0+PvQ2bvh3uuXqFgq/01yd19z0NiVhubeWfiVdkK7du1MEhvLoziwlMZ8GfWjR49y3a7T6ZCUlITz589j2rRpkEqlH+SMZdlN2q5cLQCAXblaSAMsZtYfQn+Bwmh826QB8P8WJbNmecw/HMfxvbU37TqAFNvSuPHSHk0zUmGfj6bq1AzgZpIdMmVa+NtpMPnLz+Hr62uS2IpDHlUqFeLi4lC2bNkCfb44aCwsltKYr85kvr6+ub7Kli2LmjVrYuTIkViwYAEWL14scLhFj9xMWo9duVpIc6uO3zfvxMGDB01+bv2wOf2rdOnS+PTTT5Gamgpra2tYW1sbfcxZs2ahSZMmJo9VCAqqMTeT1tOkXDBC3Wpj7+bdguRMXzvFcRwkEgkcHBzQsGFDHDp06L8YmjTh93F2doazszOcnJzQrFkz/PvvvzmOuWbNGtStWxdOTk5wdHRESEgIwsPDTR67UBQ0j7mhN+s+XdrCIf05XutsceixBO+ZeRI6Ag494pCo4lC2lJ1JTRowrUYhUCqVWLxoEX6Y8x1OnTpVoGMUdY2mwFIajVvk8x00bNgQDx48MNXhigXvMmk9Qpv19u3bERcXh9jYWISHhyMiIgKTJk2CVqst0NrgEydOxI4dO0wepxAUROO7TFqP0Ga9ePFiPmfnz59Hw4YN0b59exw9epTfZ8KECYiLi0NUVBTu3buHAwcOICUlBV27doVOp+P3GzZsGMaPH4+BAwfiypUruHTpEtq1a4eePXti27ZtJo9dCAp6reaF3qz7dWsPmdwBEYlO2BUlQWpG7vunZgC77nGIeCmHu6cPpk760qQmDRRc47Nnz3D9+nWTxvI2SqUSv/z8M+7fvAZnnQYbwtYWyKxNnceiiKU0mqxlPC0tTdTz2L5Nfkxaj5DV4PoVzQDA09MTU6dOxahRozBv3rwCHa845VAIk9YjZDW4QqHgc1amTBn89NNPiIuLwxdffIGbN28CyMqDu7s7kpKSAGT1A/n555/RqFEj3Lx5EzVq1MD+/fuxdu1anD17FvXr1+eP/9VXXyEzMxOzZ89Gjx49TBa3UAhx48teDf77XzvwT5I17txIRzXnNFQqQZBZZXUcu/uaw80kOySqOLh7+mDW9GkmN2mgYBofP36M+T/PR7IyGZ/0+gTNmjUzeVzZTbquTxk4yWW49TQeG8Ky5sMwZoljsZs0YDmNJilRa7Va/PDDDwgJCTHF4Yo8xpi0HqFL1nr0EwDoq2j8/PwwZcoUeHh4oGbNmiAi7NmzBzVr1oRcLoezszP69OnDTzafvep73bp1aNKkCWbOnAkXFxc4Ozvjyy+/LDJtUcZUQxlj0nqELllnZ/jw4fj3339x//59g+3ZNeoXwNH3PF2zZg3atWtnYNJ6Pv/8cxw/flzQmE2FUNWJerMe1Lsb7J1KIdG1Ds5kfoTfH7ti9X0X/P7YFac1VfBK5g2/chUFM2nAeI16k46VxELpq8Tav9aaPJ9vm7TCVg6O41DV0x0e1pzRJWtW9S0c+SpR59VBTKfTITk5GRcvXgTHcfj7779NGlxRpCAmrUfoDmavXr3CL7/8gv79+xuY6caNG3H48GHodDo8fPgQPXr0wLJly9CyZUvcu3cP/fr1w6pVq/Dll1/mOOa5c+fg7u6Os2fP4uLFixg0aBDatm2Lli1bmjT2gpDfB4aCmLQec3Uwq1KlCgDg9u3bBtv1GuPi4jB9+nRUrVqVX3Dg/PnzGDduXK7Hc3R0zDFrYFFFyAe/tzuYpXvXho1HBZBWAwkImn+Pm7zjWG4YozG7STs3dQZnxSFFkoK1f2WVck1Rss7NpPXozRpGlqyLygO8kFhKY76nEM0NGxsbVKhQAd27d0fXrl2RkpJi0uCKGoUxaT2mNuu2bdtCKpWCiKBUKlGqVCn8+uuvBrPn9O/fH9WqVQMAREVFYcmSJfxQOj8/P7Ro0QK3bt3K9fharRarVq2Ck5MTKlWqhIULF+LixYtFwqjzM0NQYUxajznMWqFQAADevHkDAPj+++8xf/58AP9Vt7Vs2RL79u3jpzF89eoVSpYsyR9DrVajVKlSBse9ffs2fHx8TB6vKRF6pqe3zVrFcZCVqYj0awfgZwaTBvKv8W2TllhnVXo6BTohBaYx63eZtJ6CmLXYZyUDiviiHGFhYXm+p1arsXPnTvTo0QNHjx4VdbK27d6LJBsXKApo0nrsytVC0utn2LZ7b6Fv+qtXr0bdunVBRHj16hWWLl2Khg0b4sKFC3B1dQWQZcZ6KlSoAJlMhrlz5+Lff//FrVu3cOvWLQwYMCDX47u5uRksC+rk5FRkcpyftdD3794LPxu3Apu0niblghH9Ohb7TZCz3NA/5Oq/6xEjRmDcuHFISkrCsmXLcOLECcydO9fAUEqUKMG3YQNZD87Xrl0DADx9+hRNmjQx6HhWVDHHmvZvm/Wb2H9RViE1i0kD+dOYl0kDWfGbwqzzY9LZz2mMWZsjj5bGUhoL3EZ99uxZDB8+HO7u7ujXrx9iYmJEPzyrR+cOcM54BeXDK4U6jvLhFThnvEKPzh0KHZOnpyf8/f1RoUIF1K9fH2FhYVAqldi+fTtfEyKX//djvH79OqpWrYrbt28jJCQEa9asQe/evfM8vo1NzgGoRaWKKz/zy7fr3AGPM57j5MOIQp3r5MMIPM54jnYmyFlu3LhxAwDw0UcfAcjqJOjv74/KlStjyZIlqFy5Mtq3b4/k5GT+M3Xr1sW5c+f4vzmOg7+/P/z9/c1iPqbCXOsE6M26b9d2CCgtN5tJA+/X+C6T1qM36/Ry6QVqszbGpLOfM79t1h/Ceg+W0mhUr+/o6GisX78e69evx8OHD+Hs7IyUlBRs2rQJH3/8sVAxFhn0JanfN+9EGlCg6m/lwyuwf34Dg3p1FaRkJpFIoNPpkJGRAbVaneP9P/74AyEhIdi4cSO/LSoqCpUrVzZ5LEKj1/euFXT037G+2rogJeuTDyNw6vlldOjVWbA26rVr16J27do5JpvQa1y5ciWqVKmCqVOnYvny5QCAzz77DJ07d8aVK1dQq5bhtfj06VNB4hSC/OTRVOjNumPHjpBITDY69b28S2N+TFpPfkvWarUaz54946+ngph09nPmp2RtzjxaCktpzNeVGhYWhqZNm6JcuXJYtWoVWrVqhcOHD+P58+eQSCR8KcBYVCoVhg4dCmdnZ3h4eGDBggV57nvz5k00atQItra2qFatGk6cOFGgcxaWNm3aYFCvrrB/fsPokrUQJp2YmIj4+HjEx8cjKioKo0ePhlarRefOnQ1K0npKlSqFGzduICIiAvfu3cOECRNw8eLFXE29qCOXy3PV+DZt2rRBh16dcer5ZaNL1kKYdHJyMuLj4xEXF4ebN29i/Pjx+Ouvv3K9/vUafXx8MG3aNKxcuZKv3m7Xrh1GjRqFFi1aYMmSJbh79y4iIyMxb948tG3bFlWqVDFowy6q5DePpsScJg3krdEYk9bzvpK1SqXCsl9+wYJZs3Ds2DFkZGQU2KSznzN7yTp7Tc77NIoJS2nMV4l66NCh8Pf3x/r169GvXz+TnXzSpEm4dOkSjh8/jujoaAwaNAi+vr45xn4mJyejZcuW6NSpE9atW4c//vgDXbt2xb179/h2WHNSkJK1UCXp7t278/+3s7NDnTp1cODAAYN26eyMGzcOV69eRYsWLSCXyxESEoJvvvkGf/31l8liKooUpGQtVEl6/PjxGD9+PD+bXK1atXD8+HE0atTonZ+bMGEC1q5di7Fjx+L06dMAwI+tXrZsGb755htkZGSgatWqmDNnDoYPHy76G2dxpiAmrSevkrVKpcLyJUvw5Nw5VNHpsGPtWiiVSsQ9ewpbqQQOsvwv+5nbOV2dHBH99DkeP36MBg0aFPhYDOPgKB8V7uvWrcNff/2FY8eOoUSJEmjfvj26du2KVq1awdHREdevX+eHl+SXtLQ0uLi44MCBA/y43Tlz5uDo0aM4efKkwb6//PILfvnlF9y9e5fv8RoUFITZs2fne9L8lJQUKBQKJCcnG3SOKgz57QUudHV3biQmJgJAsShRFZSCaMxvL3BzVHfnB5ZHcfC2xhcvXuDbed8WyKSzQ0RIuZYC24e2GN5/OC5duIAn586hl0wOD2trnE5NxXk7WzRs3x7nz50Fl/Ia9cp6Q1qAGoUXKam4EvcSdRo1xqefDs8xnvhDzKO5yFeJevDgwRg8eDBevnyJLVu2YPPmzejatStsbW2h0+lw8uRJVKhQwaiB4NevX4dGozF4KmvUqBHmzp0LnU5nUDV18uRJdO7cmTdpALh48WK+zyUU+SlZW8KkAXwQJamCaMxPybqomDTA8igW8tRoguWNOY6DVqfFvj17oH7wAL1kcpT5/06gjR0cgNRUnN23jzfr849ijDbr95k08IHnUWCMeqwqXbo0Ro8ejb///hvR0dGYOXMmAgMDMWbMGJQpUybXCTPyIi4uDi4uLga9it3c3KBSqZCQkGCw78OHD1G6dGm+l3m9evVw9uzZdx5frVYjJSXF4CUE72qztpRJM97Nu9qsi5JJM8SLq6srJn4+EV5aLySdTIJOY/wwOiLCm+tvYB1ljTIObjlMGsgy8cYODqinTMfZfftQr0FDkFMJnH8UA20+h+7lx6QZwlLgHhVeXl6YNGkSLl++jLt372LMmDFGTbOoVCpzjEnT//12x6bU1FT88MMP8PDwwIEDBxAaGopWrVohJiYmz+PPmzcPCoWCf3l7exuhzjhyM2tLm7RKpYJKpTL7ec1JYTTmZtZF0aRZHsVBbhr9/PwKbNZvm7Q2OjqHSespjFkbY9Ifah7NQb7aqIVg69atGDt2LOLj4/ltkZGRqFKlChISEgzaAAICAuDh4WHQ07tmzZro2bMnpk2bluvx1Wq1geGnpKTA29vbpG3Ub6Nvs06ycYFzxiuLlqTT0tIAiHuohCk06tus/Wzc8DjjeZEyaYDlUSy8SyPfqUwaC+cm72+vNsak3/6cMW3WxpakP/Q8Col5xyhkw9PTE69evUJmZia/LT4+Hra2tnB2djbY18PDg5/fWE/FihXfWaKWyWRwcnIyeAmNvmTtKUlm1d3FBH3J+onkVZEzacaHgTEl64KaNGBcyZpVdxctLGbUgYGBsLa2xvnz5/ltZ86cQVBQUI4xjvXq1cuxJuudO3fyHIJkaexh+eqfjIwMZGTksQCvSDCVxjZt2uCXX5cWSZNmeRQH79OYH7MujEnryY9ZF9SkWR6Fw2JGbWdnh0GDBmHEiBG4ePEidu3ahfnz5+Pzzz8HkFW6Tk9PB5A17/GNGzcwa9Ys3L9/H9988w0ePnyI/v37Wyr8XDl48CAObFmHitxjHNiyTvClEd+FlZUVvxyiWGEaxQHTmMW7zFpv0jb3bQps0nreNuuGjUN4s45LSilwSZrlUTgsZtQAsHDhQtSuXRtNmzbF6NGjMXv2bHTr1g1AVnX35s2bAQC+vr44dOgQwsPD8dFHHyE8PBz79u2Dp6enJcM3QG/S7cokYnSwHO3KJFrUrKVSqcFwNjFiCo1EhNTUVCQkJCA1NbXIzVXM8igO8qsxN7PWm7TsgQz9uvQDp9XCWatFqUIYBsdxKCuTAUolVOnp+PyLL0FOJXA1/lWBq7tZHoXDYp3JzI0QE57oyW7Srf3/e8I9dD8D+5+VRNuPB5u9WlW/eIN++UQxUhiNSqUSEREROH7qbzyOjYNOR5BIOPh5eaBZaAiCg4NhZ2dn6pCNhuVRHBirMXsHM2kpKeSP5BjSawiaN2+O6OhoLPnpJyhiY9HL0QmyAkxeEpORga0ZapQPCcFnI0dCJpPh0aNHuHHjBtq1a1egNmmWR+GwaIlaDORl0gDQ2t9G0JK1n58f1q1bl2P7unXrUL16dXCcCWZTeIuHDx/iwIEDJj9uQeA4rkAaIyMjMXnaDCxZuwlXXnF4490QSv/meOPdEFdecViydhMmT5uByMhIk8fs5+fHx/326+TJk2jSpInBNi8vL3Tr1g337983eSxFhYLmsThhrMbsJWvbx7a8SQNZNYxjJ09GspcXNr9JgdrIpUxzM2kAKFu2LDp37lzgjmMsj8LBjLoQvMuk9Qht1u9CiLaUoUOH4sKFCyY/bkEoSHtRZGQk5v+8DI8zHCAN+hgO1VpA5l4eNqW8IHMvD4dqLSAN+hiPMxww/+dlgpj14sWLERcXl+Oln6VvwoQJiIuLw7Nnz3DixAmUKlUKnTt3LnLV8qaCtW3mjp+fH6ZOnIqJIyfyJq2noGadl0mbApZH4WBGXUDyY9J6LGXW2Ye+mYqiZBaZmZlGaVQqlVi2cjWeS0rBvnorSGS5V21LZHawr94KzyWlsGzlaiiVSlOFDCCr2szd3T3HSz9Ln4ODA9zd3eHh4YGKFSti7ty5uH37Nm7evGnSOIoKxuaxOFJQjZ6enggMDMz1PWPNWkiTBlgehYQZdQEwxqT1WMqsY2Ji0KlTJ9jZ2cHPzw+zZ8+GVqvl31+9ejUCAgJgY2MDFxcXfplM4L853mvUqAFXV1f069cPp06dwuzZs/mFVIoTERERiEtMhV3lEHDvadfjJBLYVW6MuMRUi88rXxTayhlFk/yatdAmzRAWcddTCEBBTFpP1v6J2L9lHQAI3sFMKpWiW7duqFGjBq5evYq4uDh89tlnkEgkmDFjBk6dOoVx48Zhw4YNqFWrFi5duoT+/fujefPmfO/7P/74A7t27YK7uzsqVqyIR48eoUGDBnnOCGdOjKmCIiIcP/U31AovOORRkn4bicweSoUXjp08hZCQEIu0TWm1WixcuBDVq1dHtWrVzH5+cyD26lJAWI16s17y00/YnEsHM3OZNMujcLAStREUxqT1mLpkPWLECDg4OBi8RowYAQA4duwYoqOjsWrVKlSqVAlNmjTB/PnzsXjxYgBZVaxr1qxBt27d4Ofnhx49eqBmzZq4desWf/ygoCB07NgRQUFBUCgUsLGxgYODQ5FYys6Yaqi0tDQ8jo2DdemyRp3DunRZRMfGmbT6O7ecVa1alX//+++/57e7uLhg0aJFmDlzpmg76rAq08KTV8nanCVplkfhEP8jkAk5sHsbKspfobV/4ZY6a+1vg/uJr3Bg97ZCl6q//fZbvvSrZ8eOHVi6dCnu3r2LhIQEg+FoOp0O6enpSEhIQO3atWFra4uZM2fi1q1buHnzJqKiotC6dWt+/6I6+xtgXHu5Wq2GTkfgrI27UXHWMmh1BJVKZbL5fXPLWfaetiNGjMC4ceMAZK0yd+TIEfTu3RsHDhzI0alIDBSlfg9CYQ6Nb5esG8ptsVuTYbbqbpZH4WBGbQRtO/fAgS3rcOh+wUvUQNb46nsqF7T9uEehY3J1dYW/v3+ObRzHgYgQEBCA3bt35/icQqHAoUOH0KVLFwwcOBBt27bFzJkzMWrUKIP9ivIas8YMI5HJZJBIOJBG/f6ds0EaNaQSzqTfQ245y07JkiX598uUKYPatWvjwoUL+PXXX0Vp1B/CPNLm0pjdrLfGxaGyGdukWR6Fgxm1EehLv1ltzAUza3NOglK+fHk8efIEpUuX5gfoHzlyBOvWrcP69evx22+/YciQIVi2bBmArGqdBw8eoFmzZnkesyhVv2bvFPc+7O3t4eflgSsvH0HmXj7fn9O8fARfLw+LdejSayQio/QWJ8SqKzvm1Ojr64txU6bg8uXLaNeundk6jrE8CgczaiMpjFmb06SJCKGhofD19UX//v3x/fffIykpCcOHD0eLFi0glUpRqlQpnDt3Djdv3oREIsG8efMQFxeXYz3w7Njb2yMqKgovXryAq6uroBrehzFtRRzHoVloCG6t3QSdWpnn0Kzs6NRpkCXHonmPviZ9QElOTjZY3lWPo6MjgKz11/Xvv3z5EocOHcKxY8ewYcMGk8VQlBB7uyZgfo0+Pj7w8fEx6zlZHoWDdSYrAG3atEHbjwdj/7OSOHQ/fyupmHs6UY7jYGtriz179kCn06Fu3bro3r072rVrh19++QUAMGvWLLi6uqJevXpo2bIl5HI5Ro4ciatXr+Z53GHDhuHAgQNFYqUpGxsbfuxxfggODoZHSQcoI/8GvWfMKel0UEaehkdJBwQFBRU2VAPGjx8PDw+PHK9FixYBABYsWMBvq1OnDtasWYNly5ahT58+Jo2jqGBsHosjTKM4sJRGNtd3IchvL3BLzPnNFnHPHf3MZM8lpWBXuTEkspyf1anToIw8DTddAiaNH5NjLXRzwvIoDphGcWApjazquxDkpxrcUgtz6KuvxfyjKYjGypUrY+Lno7Fs5WrEXdwKpcIL1qXLgrOWgTRqaF4+giw5Fn4lHTBmhGVNGmB5FAtMoziwlEZWojYBRXH1LP24XzHPalUYjUqlEhcvXsSxk6cQHRsHrY4glXDw9fJA8yahCA4Ohq2tralDLlCcAMtjccfcGtVqNZ49e4ayZY2bN6AwsDwKBytRm4DcStaWNGnG+7Gzs0NoaChCQkKgVCqhUqkgl8thZ2dXpHq2MxjGolKpsGTZcvx7JwoDe/cQ5ZC+Dw1m1CYiu1nfT3z1/+OkLWfSKpUKgLifbk2hkeM42NvbF9nqOpZHcWAujXqTPnvjPjKdfbFm41YAMItZszwKBzNqE6I35QO7t6Htxz0sWpIuyhOVmAqmURwwjaYhu0lLP2oNmVNppD60M5tZszwKBzNqAVBKWNUpg8EwH2+btLUia44D23J1kAqYtWTNMD3MqE3IwYMHsX7HeqQqUrF+x3oAwq+QlResGkocMI3iQEiNeZk08P/zKZjJrFkehYMZtYnQm3RGxQy4VHNBys0Ui5r1h7DerCk0EhHS0tKgVqshk8lgb29fpDqTsTyKA6E0vsuk9ZjLrFkehYMZtQnIbtJO1bKGfjlVc0IKLGfWRclshKIwGpVKJSIiInD21HG8iH0EkA7gJHD1KouGoc0QHBxcJEoGLI/iQAiN+THp7OcX2qxZHoWDTSFaSHIzaT1O1ZyQUTED63esN8na02/DcRxOnjyZIx5ra2vMnj0barU61330nDx5slj/uNRq9TvnJc+LyMhIzJo2CbvDFsPz9XkMLvcSowISMbjcS3i+Po/dYYsxa9okREZGmjxmPz8/cByX60ufp02bNqFWrVqQy+Xw8vJC3759cf/+/RzH2rdvH5o2bQqFQoHSpUuja9euuH37tsljFpqC5rE4YWqNxpi0Ht6sS1fBmo1bcezYMZPFA7A8Cgkz6kLwLpPWI7RZZ+fChQvo0aMHRo8ejRkzZsDa2hpxcXFo0KCBoOe1FNbW1kYvOxcZGYlVv/wP5bVRmN1Ag8GBNqjpYYVKLlLU9LDC4EAbzG6gQXltFFb98j9BzHrx4sWIi4vL8WrQoAF2796NESNGYNKkSbh9+zY/V3toaCjevHnDH+Pnn3/Gxx9/jA4dOuDChQs4evQo7Ozs0LhxY9y7d8/kMQtJQfJY3DClxoKYtB4hzZrlUTiYUReQ/Ji0HnOY9d27d9G+fXt8/PHHWLRoEV9Kc3d3F+1E+XqN+UWpVCJs5VIEWMViWE1rOMly/6yTjMOwmtYIsIpF2Mql/GxEpkKhUMDd3T3Hy8bGBr///js++eQT9OnTB+XKlUPNmjURFhaGlJQU7Nu3DwDw8OFDTJ48GStXrsSECRMQEBCAGjVq4I8//kD58uUxe/Zsk8YrNMbmsThiKo2ZmZkFNunssWQ361OnThU6Lv1xWR6FgRl1ATDGpPUIadbPnj1D69at0aRJE/z222/gOA4ajQYajcagSjUlJQV9+vSBo6MjKlasiIsXL/LHePz4MTiOw44dO1C+fHnI5XJ06NABiYmJ/D6nT59GnTp1YGtri2rVqmH79u0AgJiYGEgkEly5coXf98WLF7Cyssq1ytZU6DXml4iICGheP0Wfj6whfc8QOqmEQ++qVtC8fmrwPQmNRCLB+fPnkZqaCiBLo1QqxdWrV9GuXTsAWVXjpUqVQt++fXN89vfff8ecOXPMFq8pMDaPxRFTaVSpVHgS8xSZNg6Q2jsX+Dgcx8G6pBfSNDo8evS40HEBLI9CwozaSApi0nqEMOukpCS0bt0aiYmJ+P333yGVSgEAUqmU/7+eESNG4M6dOzh16hSWLFmCBQsW5Dje999/j02bNuHUqVO4ePEiv098fDw6dOiAwYMH4+bNm5gyZQoGDx6M06dPw9vbG40aNcK2bdv442zfvh01a9aEv7+/SXTmRm4a84KIcPbUcdQokZZnSfptFHIJqjsrcebkMZhrSvzRo0fj0qVL8PT0RO/evbFhwwa8ePEC/v7+/Bz1169fR+3atSGR5Pz5Vq5c2azzO5sCY/JYXDGVRgcHB0z8Yhx87TKhvH4Qusz8LbP7NprXcdDdPoImdT5Cnz69Cx0XwPIoJMyojaAwJq3H1GY9YsQIyOVyaLVaA+N9+4JKTk7Gli1b8Msvv6BWrVpo3bo1vvnmmxzHmz17NoKDg1G3bl3069ePL00uW7YMLVq0wJgxY+Dv74/+/ftj+PDhWLx4MQCgT58+2Lp1K3+cLVu2oHdv09wA8sKYH01aWhpexD5CDTfjfmSB7hK8ePrIpNXfI0aMgIODg8GratWqAICmTZvi9OnTaNasGfbs2YOxY8eiSpUqGDNmDHT/v4Z2UlISFAqFyeKxNOwGbxy+vr6Y/OXn8JWrC2TWepMOqVUZo0Z8ZrIhRyyPwsGM2gi2h29HqiK1wCatx6maE1IVqdgevr3QMbm6uuLw4cP49ttvMWfOHNy4cQNAziqae/fuQavVIjAwkN8WFBSU43gVKlT4L04nJ/4YkZGRCA8PNzCXpUuX8h2XevbsicePH+PatWt4/vw5zpw5g169ehVa37swphpKrVYDpIOdtXHtS7bWHKDT8RMdmIJvv/0W165dM3jt37+ff79+/frYuXMnEhMTsX37dnTt2hXLli3Dzz//DAAoVaoUXr9+bbJ4LA2rMjWegpq1UCYNsDwKCTNqI+jesTsckh2QcjOlUMdJuZkCh2QHdO/YvdAxLVy4ECVKlMD48eNRvXp1DB48GJmZmXl2eshehZtbJ7O3t+n3z8zMRP/+/Q3M5datW9i7dy8AwMXFBS1atMD27duxY8cO1KtXD15eXoXW9y6M6dghk8kATgKlxrgq7HQNARKJSef4dXV1hb+/v8HL19cXqampGDNmDGJjYwFkzSvcokULrFmzBr169cLRo0cBALVr18aVK1dyrY7fsmULPvnkE5PFag5YJ6SCYaxZC2nSAMujkDCjNoI2bdpgYLeBsLlnU2CzTrmZApt7NhjYbaBJJkGxssqas0YqlWL16tW4efMm5s2bBysrK/49AKhUqRKsra0NOkZdvXo13+epVKkSoqKiDMxl9+7d2LhxI79P3759ER4ejn379gle7Q0gh8Z3YW9vD1evsrj+XGfUOa7F6+DqWdYsk5/Y2tpi48aNBm39eo3Ozs4oXbo0gKzai8TERGzatMng8/rmj7S0NMFjNSXG5LG4IpTG/Jq10CYNmD+PKSkpiIiIMFv/EcBy1yozaiMpjFmb2qTfJjAwEF9++SW+++47XL9+HZmZmfx7Tk5OGDhwIMaOHYsLFy7g5MmTmDVrVr6PPWrUKFy6dAnTp09HVFQU/vzzT0ybNg2+vr78Pl26dMG9e/dw8uRJ9OzZ05TSciUzM9NA47vgOA4NQ5vh+ms7pKjz98NOVulwI8kOjZo0N+lTdHJyMuLj43O8VCoVvv76a0ydOhU//vgj7t69ixs3buCXX37Bxo0bMXbsWABZN+eZM2di6NChWLRoEaKiohAREYHu3bvj/v37mDdvnsliNQfG5LG4IqTG95m1OUwaMG8ek5OTsXDBfKxcugSbN282m1lb6lplRl0ACmLWQpu0nlmzZsHHxwcjR47M0ZayZMkSNGjQAC1btsSgQYP4G39+8PX1RXh4OA4cOICPPvoI06dPx4IFC9CvXz9+H0dHR7Rt2xb169eHq6vx4zuNhYiM+oEGBwfDuoQnNv2rgVb37s9pdYS/bmXCuoRnrm35hWH8+PHw8PDI8Vq0aBEmTpyIZcuWYdu2bahduzZatGiBffv2Yf/+/ahduzZ/jGnTpmHVqlX4888/Ubt2bXTs2BFSqRTnzp1D+fLlTRqv0Bibx+KI0BrzMmtzmTRgvjwmJydj0cIFeBZ1D372MhwK32M2s7bUtcqR2H8h/09KSgoUCgWSk5P5YS6FJb+9wM1l0tnR91I293zVDRs2xLBhw8zSTloQjfqZyQKsYtG7qhUU8pzPqskqHf66lYk7mV747PPJCAgIMFnMxmKpPJoTptF0REdH46eFPyNaJYONb03Q3RNmMWnAPBqzm3Q93zJwkMvwJOE1biWkoHXHTujVq5egbciWulaZUReS95m1JUwaAD/dpKOjo1nOd+LECZw9exYLFixATEwMHBwcBD9nQTVGRkYibOVSaF4/RXVnJQLdJbC15pCuIVyLz6ruti7hiSEjxlrUpAHz59ESMI2mRW/WzxJSEBJU3SwmDQivMTeT1mMus7bUtSruHhxmQG++63esRwpSDMzaUiYNZHUsMifr16/H7t27sWrVKrOYNFBwjZUrV8as7/+Hixcv4szJY7j26BGg0wESCVw9y6JLt+YIDg6Gra2tiSM2HnPn0RIwjabF19cXUyaMx+XLl9GuXTuzLc0opMZ3mTQA+JQqAQA4FL4HAAQza0tdq6xEbSLeLllb0qQB8D1/7e3tzX5uc2EKjUQEpVIJlUoFuVwOOzu7IjXEhOVRHDCNBed9Jp0doUvWlsojK1GbiOwl61fPX8Eh2cFiJg1A9J1zANNo5DgO9vb2RfYGyvIoDsytMTExEbdu3ULDhg1znWpWCITQaIxJA8KXrC11rTKjNiF6U94evh3du3W3mEkDEP0MQQDTKBaYRtPy6tUrLFn0PyQ9e4jHjx6hX//+ZjFrU2s01qT1CGnWlrpWmVELgLxg8+SbFHO1S1kSplEcMI2mQ2/S1i9uoLuvDjuO7wIAs5i1KTUW1KT1CGXWlrpWmVGbkIMHDyJ8/Xp4paQgfP16AGBV3wLCNIoDptE0ZDfpMbU5OMut4SBTYr2ZzNpUGjMyMrB44cICm7Se7GYtk8nQtWvXQsfGqr6LOXqTbqBSoVHJUjiT+saiZq1WqwGIu/OKKTQSEdLS0qBWqyGTyWBvb1+kOpOxPIoDoTXmNOksQ67lYQXAPGZtKo1EBI1GAw6A5D3rxr8PqUQCECEjwzTVnJa6VplRmwADk3bIGl/XyMERsKBZm3IRiaJKYTQqlUpERETg+N/HEf0sGjrSQcJJ4FvGF81CmiE4OLhITMDB8igOhNSYl0nrMZdZm0qjTCbDFxMmYNGCBfjn8UPU9/OEXS4LCL2Pp6+TcfPFa4S2aoUePXqYJDZLXatsCtFCkptJ62nk4IgGKhXC15tm7em34TgOJ0+ezPW9uXPnWrQzW1EmMjISU6ZPwdINS3Ez4ybUtdTQ1tdCXUuNmxk3sXTDUkyZPgWRkZEmP7d+9Z0nT57keG/FihXgOI6fg33dunV8j3T95/SvJk2aAAAeP36c4z39a/r06SaPn1G0eJ9J66nlYYWBlZS4fHwXNm7YwK9tXlQpVaoUvpgwAaV9y+Gfx0+hNLJErDfpkFatMGDAwGK/TjYrUReCd5m0HkuVrD/77DMMGTLELOeyFPo1oo2dQnT+0vlIcEyAooMCUlvDH7Cdrx206VrEnI/BgqULMGHMBFSuXNmkcVtbW2PPnj0YM2aMwfadO3fmqHYvU6YMjh49ihIlShhsf3s50oiICHh7extsM9fEM4WlIHksbgihMb8mrUfokrWpNerN2tiStZAmbalr1aIlapVKhaFDh8LZ2RkeHh5YsGBBnvt27tw5R4lBvxayJciPSesRumSdGy4uLvDw8DDLuSyFXC43qipKqVRi+W/LkeCYgBKhJXKYtB6prRQlQkvgleMrLP9tOT+/r6kICQnBnj17DLalpKTgn3/+Qc2aNQ22W1lZwdfXF+7u7gavkiVLGuxXunTpHPsUF6M2No/FEVNrNNak9QhZshYij8aWrIUuSVvqWrWoUU+aNAmXLl3C8ePHsXz5csyePdtgLd7s3L59Gxs2bEBcXBz/atmypZkjzsIYk9ZjbrPWV33rdDp4enoiLCyMf4+I4OXlhQ0bNgAATp8+jTp16sDW1hbVqlXD9u3b+X0HDx6MwYMHo0aNGnB1dUVUVJTgsQtFREQE4lLioKinAPeeTiqchIOirgJxKXEGa3ibgs6dO+PUqVNISflv5bV9+/ahcePGgswhvHnzZlSqVAlyuRxVqlTBrl27TH4OhvkoqEnrEWs1uNiqu7NjMaNOS0vD6tWr8fPPP6NWrVro2rUrJk+ejKVLl+bYV61W49GjRwgKCjIoMVhiTFtBTFqPOc06MzMTOp0OEokEPXv2xI4dO/j3zp8/j4SEBHTu3Bnx8fHo0KEDBg8ejJs3b2LKlCkYPHgwTp8+ze//xx9/YM6cOdi3bx8qVKggaNzGoFKp+Kqo90FEOP73cWjdtXmWpN9GaieF1l2LY6eOmXRYRrVq1eDp6WlwDezcuRNdunTJsS8R5Vtjbrx48QIDBgzA1KlTcffuXQwZMgR9+vRBYmJigY9paozJY3HFVBpVKhWWLPofrJ4XzKT16M360rGd2Jnt3lDY2ITK4/vM2lwmbalr1WJGff36dWg0GjRo0IDf1qhRI1y4cCHHE97du3fBcRzKlStn7jANKIxJ6zGXWUulUr69s3fv3jhy5Ai/8su2bdvQrl07ODo6YtmyZWjRogXGjBkDf39/9O/fH8OHD8fixYv5YwUFBaFjx44mX5e5sMhksnw/rKWlpSH6WTRkPsY93Ml8ZHjy7InJq787d+7MV3+r1WocPnwYnTt3zrFfbGwsfHx84ODgYPDauHGjwX5Vq1Y1eD8kJAQA8PTpU2g0Gnh5ecHX1xcTJkzA7t27i1RVszF5LK6YSiPHcbCysoaWOGgLWRDOyCSAk8Da2rrQcQHC5zEvszZnSdpS16rFjDouLg4uLi4GnWLc3NygUqmQkJBgsG9kZCQUCgUGDBgADw8PBAcH48CBA+YOGfu2b4dXSkqBTVpPIwdHeKWkYF+2KmYhqVevHjw8PLBv3z4AwI4dO9C7d28AWd9teHi4wU1+6dKluHfvHv95Pz8/s8QpJGq1OmsIlo1xl7zEWgItaU3+FN25c2ccOHAAmZmZOHbsGKpVqwZXV9cc+3l4eODcuXO4du2awatTp04G++3fv9/g/T///BMAEBgYiPbt26Nly5YICAjAV199hbJly4q645aYkclkGPvFREi9amHJZSBBWTC3Ph+jwaYHDmjY9mN0fOtaKsq8bdYPXyaItro7OxYzaqVSmePJRP+3flC5njt37kCpVKJ169Y4ePAg2rVrh44dO+LSpUt5Hl+tViMlJcXgVVjad++OWCcnnEl9U6jjnEl9g1gnJ7Tv3r3QMeWFVqs1qK7t1asXtm/fjsuXL+Ply5do3749gKwq8v79+xvc5G/dumXQUa8olb6yk5GRke+JDGQyGSScBLoM425sOo0OUk5q8u+gUaNGAIAzZ85g165dec6aJJVK4e3tDX9/f4PX223Zvr6+Bu97eXkBAN/p8sKFC+jRowfCw8NRq1YtXLt2zaR6CoMxeSyumFKjs7Nzocw6u0n3/Phjk03wY648Zjfre0lpZjVpS12rFjNquVyew5D1f7/9tD9jxgw8ffqU79Q0a9YstG3bFqtWrcrz+PPmzYNCoeBfbw9dKQht2rRBx4EDcU4uL7BZn0l9g3NyOToOFHZlLYlEYvAD7N27Nw4fPoxt27ahU6dO/HdcqVIlREVFGdzkd+/enaNqtShiZWUFK6v8jTC0t7eHbxlfqJ+o379zNtRP1PAp42PyEqiVlRXat2+PPXv2IDw8PE+jzqrqLPgoyjt37mDixIkIDg7GnDlzcOvWLXh7e+PQoUMFPqapMSaPxRVTayyoWQtl0oB581iqVClMmDQJg4d/ZtaStKWuVYsZtaenJ169eoXMzEx+W3x8PGxtbeHs7Gywr0QiyTGOtHLlynj69Gmex586dSqSk5P5V0xMjEniLoxZC2HSEREROHjwoMFLqVTmMOrAwECUKVMGS5cuRa9evfjto0aNwqVLlzB9+nRERUXhzz//xLRp0+Dr62uS+IREKpXm+wfKcRyahTSDNF4KbXr+Fn/XKrWQxkvRPLS5INOKdu7cGatXr4abmxvKli2bewxaLV6+fIn4+HiD1/Pnz/N1DmdnZ/z666+YM2cOHj16hH379uHx48c5hoFZEmPyWFwRQqOxZi2kSQPmz2OJEiUQEhJi1nNa6lq12GNsYGAgrK2tcf78eYNqwKCgoByD8AcPHgyJRIK1a9fy265du4Zq1arleXwhG/31Jhu+fj2Q+iZfbdZClaSnTJmSY1tUVBS0Wm2OTnm9evXC4sWLDc7v6+uL8PBwTJkyBf/73//g6emJBQsWoF+/fiaLUSiMXXIuODgYHns8EHM+BiVCS7xziBbpCCkXUuDl5CVYJ7rWrVtDo9Hk2ttbz9OnT1G+fPkc26VSqcFDbl64u7tjx44dmDJlCubOnQtXV1fMmzcPrVq1KkzoJoUtc1lw9Ga9ZNF8LLl8BWNr61DKLmf5S2iTBlgehYQjCy5dM2LECJw5cwZhYWF4+vQpBg0ahLCwMHTr1g3x8fFQKBSwtbXlOz+tXr0aDRo0wJ9//okffvgBt2/fzndHp5SUFCgUCiQnJ8PJyckk8ee3F7i5qruzo2+TN5XWokhBNEZGRmLB0gV45fgKiroKSO1yPh1rlVokX0iGyxsXTBw7EQEBASaL2VhYHsWB0BqTkpKwZNF8aGOvYGxtGJi1OUwaYHkUEosatVKpxMiRI7F9+3YoFApMmjQJ48ePzwqM4xAWFobBgwcDAFavXo2ffvoJT548QdWqVbFo0SJ+CEp+EMKogfebtSVMGgA/nEjMvXsLqjEyMhLLf1uOuJQ4aN21kPnIILGWQKfRQf1EDWm8FB5OHhg9fLRFTRpgeRQL5tCYm1mby6QBlkchsahRmxOhjBrI26wtZdIAe7p9H0qlEhcvXsSxU8fw5NkTaEkLKSeFTxkfNA9tjuDgYNja2po6ZKNheRQH5tKY3awbltFib4x5TBpgeRQScXe1NBO5tVlb0qQZ78fOzg6hoaEICQmBUqmESqWCXC6HnZ1dkVqPmsEwhuxt1ntjotCwbTezmDRDWJhRm4jsZv0kMQGxTk4WNWmxD3cBTKOR4zh+KcmiCMujODCnRmdnZ4z7chLu3r2LoKAgs5k0y6OA57XIWUWK3pT3bd+Ojt27W7QknZ8ewcUdplEcMI2mR6FQIDg42KznZHkUDmbUAqCVWn7e4g+h6wHTKA6YRnHANAoHM2oTcvDgQWz7axccJW7Y9tcuALBYqdpUE+0XZZhGccA0igOmUTiYUZsIvUmXc66JKj5BuP3kokXNWqvN3+xbxRlTaCQipKWlQa1WQyaTwd7evkh1vGF5FAdMoziwlEaLTSEqJt42aQCo4hOEcs41se2vXYIuZ6nRaDBr1iyUK1cOMpkMPj4++PLLL/H69WtkZmbCz88P69aty/WzHMfh5MmTgsUmNJmZmQVuM1IqlTh58iTmzJyJr0aPxjfjx+Or0aMxZ+ZMnDx50uTLWurhOA4cx+HJkyc53luxYgU4jsOsWbP4bYXRaEqEvFaKikYhYRrFgaU0shJ1IcnNpPXo/xayZD1lyhQcOXIEv/32G8qXL48HDx7g888/x507d7B169Z3fjYuLg4lS5Y0eUzmIvsSqcYQGRmJNcuWIz0+DhU0GgTZyCCXSKDS6XDn+nVsvX0be909MHT0KFSuXNnEUWdVn+3Zswdjxowx2L5z584cpfmCaixOMI3igGkUDmbUheBdJq1HaLNet24d1q5di+bNmwPIWjt6xYoVaNy4MeLj49/5WXd3d5PGUhyIjIzEr/Pnw/vlK7RzdISDneGwrMq2tkjVarH/yRP8On8+Rk6caHKzDgkJyWHUKSkp+Oeff4rUYhkMBqNowKq+C0h+TFqPkNXgEokEx48fN1iAo379+jh79iwcHBwM9r1w4QIcHBz4xU2yV2f6+flh+fLlqFevHuRyOQIDA3H58mWTxmpq1Gp1jqVS34VSqcSaZcvh/fIVeigUcMhjFRwHqRQ9FAp4v3yFNcuWm7wavHPnzjh16pTBGun79u1D48aNDdaZzsjIwKRJk+Dv7w9ra2v4+fkZLO2alpaGzz77DKVKlUKpUqUwfPhwqFQqAFm5/eabb+Di4oJOnToBAP755x80atQI9vb2KFu2LFasWMEfa/DgwRg3bhw6duwIW1tb1KxZE+fOnTOI+/Tp06hWrRrkcjlCQ0MRHR3Nv7dnzx7UrFkTcrkczs7O6NOnD1JTU/n3N27ciPLly8POzg59+/ZFnz59+Cp+lUqFuXPnokyZMnB2dkbHjh0NmgY2b96MSpUqQS6Xo0qVKti1a1chvn3LYOy1WhxhGoWDGXUBMMak9Qhl1p9//jmWLFkCPz8/ft709PR0BAYGGtz07927hw4dOmD27NkYMmRIrseaOXMmvvrqK9y4cQMKhQLjxo0zWZxCIJfLIZfL871/REQE0uPj0M7REZL3dBiTcBzaOjoiPT4OFy9eLGyoBlSrVg2enp4G18HOnTtzrKI1b948HD16FJs2bcLdu3cxaNAgjBkzhl/ictiwYThz5gz27NmDI0eO4MyZM5g+fTr/+fDwcJw9exY//PADIiMj0axZM4SEhODKlSuYNWsWJkyYgJ07d/L7r1ixAlWrVsXVq1cRGhqKdu3a4dWrV/z7v/32G5YsWYKLFy/i9evX/MptDx48QI8ePTBq1CjcuXMHW7ZswdGjR/mHijNnzmDIkCGYPHkyrly5Ant7e2zevJk/7rp167B9+3b8+eefOH/+PNzc3NCqVStoNBq8ePECAwYMwNSpU3H37l0MGTIEffr0QWJioukSYgaMvVaLI0yjcDCjNpKCmLQeIcx6xowZ2LBhA7y9vbFq1Sr06NEDZcqUwfr16/l9nj9/jjZt2uDTTz/FhAkT8jzW4MGD0aVLF1SsWBETJkwwuUFZEiLC6ePHUUGjybMk/TaOUin8NRr8feyYycdPdu7cGXv27AGQ9ZR++PBhdO7c2WCfGjVqYPny5QgODka5cuUwbdo0aDQa3Lt3D69fv8bWrVuxbNkyNGzYELVq1cLKlSsN1hH/7LPPUKlSJVSpUgW//fYbatasie+//x6VKlXCoEGDMHbsWPz000/8/lWrVsUPP/yAgIAALFy4ECVLlsRff/3Fvz99+nQ0adIE1apVw9ChQ3H9+nUAgE6nw5IlS/Dpp5/Cz88PrVq1QosWLXDr1i0AwPLly9GrVy989tlnCAgIwK+//govLy/+uIsWLcLcuXPRpEkTBAQEYOXKlUhMTMTBgwfx9OlTaDQaeHl5wdfXFxMmTMDu3btFbwgMRnaYURtBYUxajxBm3a9fP5w9exYvXrzAxo0bUbVqVYwaNQrnz58HAHzzzTd4/PgxvL2933mcChUq8P93cnIq8uvLqlQqvqr3faSlpeF5dDQCbIybjKayjQzPo6MFqf4+cOAAMjMzcezYMVSrVg2urq4G+3Tp0gXJycmYNGkS2rdvzy/pqtVqcf/+fWi1WtSuXZvfv3Hjxhg7diz/d/YlYCMjI1G3bl2D4zdo0ACRkZH83w0bNuT/L5FIULNmTYP3s6+LrVAo+O++QoUKaNu2LebOnYs+ffqgevXq2LJlCz+U5caNGwZreltZWaFOnToAgNTUVDx9+hQDBw6Eg4MDHBwcoFAokJCQgHv37iEwMBDt27dHy5YtERAQgK+++gply5Ytdis0GXOtFleYRuFgRm0Ee3bthaPErcAmraeKTxAcJW7Ys2tvoY5z48YNgxJyqVKl0LdvX5w6dQqenp74559/AADt27fH4sWL8fXXX+Ply5d5Hq+49do0phpKrVaDdDrIJcZd8jKJBKTTmfzH2ahRIwBZ1cK7du1C165dc+wzffp0jBw5EnK5HAMHDuQfvID8TbyQ/bvJ7XvSarUG40LfPqZWq4Uk2/clfasmQl/LcP36dVStWhW3b99GSEgI1qxZg969e/P7WVlZ5aiR0P+tH+qyYcMGXLt2jX/dvXsXn3zyCTiOw969e3HhwgX06NED4eHhqFWrFq5du/Ze/UUJVi0sDljVdzGgU5cOeKN7jttPClclfPvJRbzRPUenLh0KdZzMzEwsXLgQV69eNdhuY2MDOzs7uLi4AAA6deqE0aNHw8vLi29X/NCQyWTg/n8IljGodTpwEonJf5xWVlZo37499uzZg/Dw8FyNesWKFVi4cCG+++479OrVC2lpaQCyTK5cuXKQSqV89TMA7N69G7Vq1cr1fJUqVTIweiCrc1mlSpX4v7Obn1arxbVr11C9evX3avnjjz8QEhKCjRs3YuTIkQgKCkJUVBRvxlWrVjXomKg/NpC1gETp0qXx/Plz+Pv7w9/fHz4+Ppg8eTLu3r2LO3fuYOLEiQgODsacOXNw69YteHt749ChQ++Ni8EQC8yojaBNmzbo0bsLHiZdLbBZ335yEQ+TrqJH7y6FHqpVq1YttG/fHp07d8aff/6Jx48f4/z58xg5ciTS09MNji+VSvHLL79g3bp1fEm7uGNMNZS9vT3cfH1xJ8O4HpuRGWq4+foKUtXauXNnrF69Gm5ubihbtmyO90uVKoXw8HDcuXMHZ86cwYABAwBk1Q44OTlh0KBBGDduHCIiInDp0iVMmzaNH6b3NqNGjcK1a9cwbdo03Lt3D7///juWLVuG0aNH8/ucPHkSCxYswN27d/H5559DqVSiZ8+e79VRqlQp3LhxAxEREbh37x7fv0HfO3bMmDH466+/sGbNGty9exfjx4/H48eP+THjI0eOxKxZsxAeHo6oqCgMGzYMZ8+eRUBAAJydnfHrr79izpw5ePToEfbt24fHjx8Xu2FsrFpYHLCq72JCYczalCatZ8uWLRgwYABmzZqFgIAAtG/fHsnJyTh8+DBfotbTpEkTdO/eHaNHjxbFdH8ymQwyWf7anDmOQ+NmzRBlbY3UfGp/o9XivrU1Qpo3F2Ra0datW0Oj0eTo7a1n7dq1uHXrFho2bIjBgwfj448/RnBwMF+DsnjxYtSoUQMtW7ZE27Zt0bRpU8yZMyfXY/n4+GDv3r04ePAgqlWrhjlz5mDhwoX45JNP+H06deqE48ePIzAwEFevXsWRI0fg7Oz8Xh3jxo1D/fr10aJFCzRq1AjR0dH45ptv+Djr16+PZcuWYfbs2ahZsyZSUlJQv359vqllwoQJGDRoEIYPH47AwEBER0fj0KFDKFGiBNzd3bFjxw5s27YNVapUwejRozFv3jy0atXKiG/a8hhzrRZXmEbh4OhDWPIEWRNKKBQKJCcnw8nJqdDHM7ZjmRAm/S70nZ+KW6cbYzBWo1KpxDeTp8DtyRP0UCjeOURLR4RtKcl47u2Db3/60WLfo7nyOHjwYADIc7rZwhAREQGFQmFQzV61alVMmjQJgwcPZteqSGAahYOVqAuIMSVrc5s0wCYfyA07OzsMHT0KMaVdsC05GW/yKFm/0WqxLTkZMS4uGDZmtEVvPGLI4z///IP27dvj3LlzePToEb7//nvExMTwvwUxaHwfTKM4sJRGNoVoIdDfaPTTg+ZWsraESQNsybm8qFy5MkZOnIg1y5bj1/g4+Gs0qGwjg0wigVqnQ2SGGvetrWHr44NRY0YjICBAgMjzjxjyOHr0aDx69AjdunVDcnIyAgMDceDAAX4KWzFofB9MoziwlEZW9W0C8qoGt5RJA+B7CNvb279nz+JLYTQqlUpcvHgRfx87hufR0aD/793t5uuLkObNERwcDFtbW1OHbDQsj+KAaRQHltLIStQmILeStSVNGkCRn6zEFBRGo52dHUJDQxESEgKlUgmVSgW5XA47O7sitR41y6M4YBrFgaU0MqM2EdnN+mXKM7zRPbeYSQM5J6cQI6bQyHEc7O3ti2wpgOVRHDCN4sBSGplRmxC9Ke/ZtRc9uljOpAH2oxELTKMwEJFZa05YHsWBpTSyXt9CQJavAtJoNKKvimIaxYE5NRIR9u7di+lTv0ZMTIxZzgmwPIoFS2lkRm1CDh48iK1/boRVWjK2/rnR5GtPGwPHcUWqrVUImEZxYC6NepM+uG0vKE6FJQt/MZtZszyKA0tpZEZtIvQm7Wtng0YVysLXzsaiZm1lZQUrK3G3bDCN4sAcGvUmfWj7PrT0rIeRdftAkSYzm1mzPIoDS2lkRm0Cspt0gEfWUoUBHq6CmvXgwYP5p7vcXidOnOBXJsovRITly5cbnEM/Y1VRJDMz02iNxQ2msfC8bdINfGtBbiXDwMDOZjNrlkdxYCmNbBx1IcnNpLNzJ+4FopUZ6Nm3n0k7lyUnJyM9PR0AsHnzZsyfPx8XL/43Q5pUKoWNjQ0UCkW+j3nq1Ck0adKEX/UoOTkZAIw6hjkp6vGZAqaxcORm0tlRZaqx/tpuJNurMfbLce9ds72gsDyKA0tpZCXqQvA+kwaEK1krFAq4u7vD3d0dCoUCUqmU/9vd3R329vZGz6Lz9jObQqEo0j86a2tr0c+GxDQWnPeZNACzlaxZHsWBpTQyoy4g+TFpPUJXg79NTEwMevToARcXF/j5+WH27Nn8alkajQaffvopXFxc4ODggE6dOuHp06d4/PgxmjZtCiCrw8TJkycNqr6TkpLQvXt3ODs7o0SJEujfvz9SUlIE1/IutFqtKFYBexdMY8HIj0nrMYdZszyanoyMDERHR5vtfIDl8siMugAYY9J6zGXWRIRu3brBxcUFp06dwrp16/Dnn3/i+++/BwAsXboUp06dwuHDh3Hp0iW8efMGX3zxBby9vbF9+3YAQFxcHBo0aGBw3JkzZyI+Ph5nz57FiRMncO3atTyXVDQX7OYnDkyt0RiT1iO0WbM8mha1Wo1fl6/A99/9iGPHjpnlnIDl8ijuLnoCUBCT1hPg4QrEvcDWPzcCgCATohw/fhzR0dE4fvw4JBIJ7O3tMX/+fAwePBgzZszA48ePYWtrCz8/P5QsWRLr1q1DQkICpFIpSpYsCQD8YgnZefz4MRwcHFC2bFnY2dlh27ZtOarKzY3Yq9kAptFYCmLSevRmvf7abixZ+ItJ26xZHk2HWq3Gil9X4ualO3B18MNfG7YBAJo3by74uS2VR1aiNoLCmLQeoUvWkZGRSEhIgIeHB9zc3ODg4IBevXohMTERCQkJGD58OOLi4uDu7o5WrVph//79qFy58nuP+/nnn+PcuXMoXbo0OnfujIsXL6JixYomj98YiMjiDwtCwzQad5yCmrQeoUrWLI+mIbtJ1/Nvj6AKzeHrVB1/bdhmlpK1pfLIjNoI9uzcCXudpsAmrSfAwxX2Og327Nxposj+IzMzEwEBATh16hROnTqFa9eu4caNG4iKioJCoUDVqlXx+PFjbNy4ER4eHpg6dSpatWr13ouvWbNmiImJwfLlyyGTyTB8+HAMGjTI5PEbA5sJSRyYSmNSUhIO7zuIcvIyBTJpPXIrGXpWaQ3l82Ts3bu30HEBLI+m4G2TdnHyAMdxqOoTbDazZjOTFQM6de2KNIk17sS9KNRx7sS9QJrEGp26djVRZP9RqVIlPHnyBGXKlEHlypXh7++PR48eYebMmeA4DuvXr0d4eDh69uyJ33//HQcPHsSZM2fw4sWLd864s2jRIly+fBmDBg3Cli1bEBYWxrdpWwqZTAaZTGbRGISGacw/JUqUQMdunfEwIw4nH14o8HHSMtKx6d/9cPQsiS5duhQ6LoDlsbDkZtJ6zGnWlsojM2ojaNOmDXr27YdoZUaBzVqocdV6WrVqBV9fXwwdOhQ3b97E6dOnMXz4cNjZ2UEqlSI5ORmff/45jh07hkePHmHjxo3w8vKCi4sLv4LU5cuXoVKpDI4bGxuLMWPG4Pz584iKisK2bdtQs2ZNk8dvDKw6URyYUmOrVq3QqXdXnHpxpUBmnZaRjt+v7YKqBGHcl5/Dw8Pj/R/KByyPBeddJq3HXGZtqTyyzmRGojfXrX9uBOJeGFUNLrRJA1kTnezZswcjRoxA06ZN4eDggJ49e2L+/PkAgNGjRyM2NhYDBgxAYmIi6tSpgz179kAqlaJatWpo2bIlGjRogE2bNhkc97vvvkNycjI6deqE1NRUhIaGYuPGjYJoyC9qtRqAuBeqZxqNp1WrVgCAPX9lNS01KVc3X58TyqQBlsfCHPN9Jq1Hb9Z4AsE6mFkqj2xmsgJibMcyc5h0dpRKJQDAzs5O8HNZCqZRHAil8fDhw9jz106EutZ6r1kLadIAy2NBMMaks0NEuPUkAtEpN9C7fw+TmrWl8shK1AXEmJK1uU2awWDkv2QttEkzjKegJg2Yp2RtbphRF4L8mLWlTFrfxizmJ3imURwIqfF9Zm0uk2Z5zD9arbbAJq3nbbO2srJCaGhooeICLJdHZtSF5F1mbcmStH7yEjHDNIoDoTXmZdbmLEmzPOYftVqN6MfRkFs5QWFXqsDH4TgOZUr64cHLa7gfdd8kRm2pPLJe3yYgt97grLqbwSg6vN0bnFV3F13s7Ozw+RfjYK3Q4MydcGgyMwp0nMQ3z3Euai8+ql0J/fr3M3GU5oV1JjMh+g5m9joN0iTWzKQZjCKGvoOZrcYaVu52zKSLMNHR0Vi88Bfo3sjRKKAjrK1s8v1ZvUlXrlkeo0ePglwuFzBS4bFoiVqlUmHo0KFwdnaGh4cHFixY8N7P6OecPnnypPABGom+ZJ1pr2AmzWAUQVq1aoUufbvDsbwLM+kijq+vL8Z/OQ4SR5VRJWuxmTRg4RL12LFj8ffffyMsLAzR0dEYNGgQ1q5dix49euT5mbZt2+LgwYM4ceIEmjRpku9zmaNEzWAwGAzTYkzJWowmDViwRJ2WlobVq1fj559/Rq1atdC1a1dMnjwZS5cuzfMzGzduxJs3b8wYJYPBYDAsSX5L1mI1acCCRn39+nVoNBqDdY8bNWqECxcuQKfT5dg/ISEBkydPxsqVK80ZJoPBYDAszPvMWswmDVjQqOPi4uDi4gIbm/+qMdzc3KBSqZCQkJBj/y+//BKDBg1C1apVzRkmg8FgMIoAeZm12E0asKBRK5XKHKuQ6P/Wz6eq5+jRozhz5gxmzJiR7+Or1WqkpKQYvBgMBoNRfHnbrJ8nxYjepAELGrVcLs9hyPq/s8/6kp6ejs8++wzLly+Hra1tvo8/b948KBQK/uXt7W2awBkMBoNhMbKb9T/394nepAEL9vo+d+4cQkJCoFKpYGWVNUHaiRMn0L59e6SmpkIiyXqGOHXqFJo0aWKwWklaWhrkcjkGDRqEFStW5Hp8tVpt8CCQkpICb29v1uubwWAwRMCTJ08QERGBDh06iNqkAQtOIRoYGAhra2ucP38ejRo1AgCcOXMGQUFBvEkDQHBwMKKiogw+W6FCBaxevRotW7bM8/gfwkLtDAaD8aHi4+MDHx8fS4dhFixm1HZ2dhg0aBBGjBiBsLAwPH36FPPnz0dYWBgAID4+HgqFAra2tvD398/xeU9PT7i65n8taH3FAWurZjAYDEZRwdHRERzHvXsnsiBpaWk0cOBAsre3pzJlytCiRYv49wBQWFhYrp8DQCdOnDDqXDExMQSAvdiLvdiLvdiryLySk5Pf618fzFzfOp0Oz549y9/Ti4nRt4/HxMSItn2caRQHTKM4YBqLD/nxpA9mmUuJRAIvLy+LxuDk5FSsL6j8wDSKA6ZRHDCN4oAtc8lgMBgMRhGGGTWDwWAwGEUYZtRmQCaTYebMmaIeLsY0igOmURwwjeLig+lMxmAwGAxGcYSVqBkMBoPBKMIwo2YwGAwGowjDjJrBYDAYjCIMM2pGsYF1p2AwGB8iH8yEJ4zix6JFixAXF4cKFSpgyJAhkEqllg6JkU8eP36M0qVLG6x6x8iJTqczWISIITxEZPbZKQsLu0I+ALRaraVDMIpHjx4hKCgI69atQ0JCAr7++mv06tULsbGxlg6N8R7WrFkDHx8f9O3bF0FBQTh79iw0Go2lwyqSEJHoTVqn01k6BAMyMjKKnUkDzKhFTWZmJgBAKpUiJiYGz58/t3BE+ePw4cNwcHDA5cuXsWbNGty4cQNHjhzB4cOHLR0a4x0cOHAACxcuxHfffYclS5agevXqmDx5Mk6cOGHp0IoU+gdnjuNARPjhhx+wd+9eAEXP2AqKXqP+QeTBgweWDAcAMGvWLPTo0QMjRozAnj17LB2OUTCjFjFWVlbQ6XSIiopC586dMWLECEuH9E70bdC3b9+Go6MjJBIJtFot3N3d0bhxY4SHh1s4wsIhlptwXuzevRu+vr4YNGgQateujb/++gsJCQm4efOmpUMrUuibcBYvXozp06djw4YNWLBgAQCIooRNRLzGqKgo/Pbbb+jbty8yMjIsEs/vv/+OkiVLYteuXWjWrBnOnDmDCRMmYPfu3RaJpyAU/6uCkQO94YWHh8PBwQFTpkzBy5cvsW/fPpw8edKyweWBTqfjq6QyMzMRGBgIpVIJqVQKrVYLKysr2NvbQ6fTFbtOZURkUM355s0bC0dkGg4ePIgrV67g9evXALKqFfUL3+hzVKlSJfzzzz8Wi7EoolKp0LNnTyxfvhwODg5wdHTE9evXsWzZMgDF/4GO4zjEx8ejVatW6NmzJ3799Vfcvn0b69evN2scUVFRqFevHr744gvMnz8f165dw/jx47Fr1y5Uq1YNGzduNGs8hcKoRZ0ZRRKdTkc6nc5gm1arpTZt2tD06dOJiOiff/6h7t27U40aNXLsa0lyiz0tLY2ePHlisL1WrVo0bdo0c4dXaLRaLf//1NRUmjdvHs2aNYsSExMtGFXhWLduHbm7u1NgYCCVKFGCOnToQPHx8RQeHk7h4eGkVCqJKEt7YGAgTZ48mf/7QyMzM5OIyOBafvLkCVWtWpUOHz5MREQvX76k2bNnk7+/PyUlJeXYv6iTW6yTJ0+mkJAQioqKoj179lD79u2pYcOG9PjxY7PF9cUXX5CLiwsdPHiQiLJyodFoiIioZ8+e1L59e1Kr1cXiu2ZGXczRX3hvc/fuXapQoQL99ddf/DalUknOzs60bNkyc4X3TvIysdevXxvsFxUVRTY2NnTo0CEi+u/mV5wYMWIE9enTh/z8/KhKlSp06tQpS4dkNFeuXKFq1apRyZIlaeXKlZSWlkarV6+m4OBgGjBgABH9lxutVksvX74kFxcXWrt2rSXDtgjZr22dTkcpKSn830eOHCFbW1tKTk7mt0VHR1PLli1pzJgxOT5fVNFqtTlMTqfTUWJiIvn7+9OKFSv47efPn6ePP/6YPv/8c0Fj+uuvv2jWrFlElHXfCA0Npa+++oqeP39usF/jxo1p7NixgsZiSljVdzHHyiprhN3333+P77//HmvWrAEAuLq64vXr1/z7GRkZsLW1xaxZs/Dtt98iISHBYjHr0VcFjxw5Ep9++ilWrlyJLVu24MaNGwb7HTlyBA4ODqhevTrf/pWenl4serMTEWbMmIGzZ8+ib9+++Oyzz5CYmIh169YhLi7O0uHlmz/++AO1a9dGlSpVkJCQgOHDh8POzg5Dhw5F06ZN8eDBAzx79oxvm5RIJPj333+RlpaGVq1aWTh686O/tpcvX44qVaqga9euaNu2LZ48eYI6derAxcUFYWFhALKqun18fNCiRQts2bIFd+/ehUQiKfJNPBKJBBzHISIiAqtWrcKVK1egUqlQokQJODo6IiYmht+3Vq1aaN68Oc6ePYuIiAhB4lGpVLhz5w6WL1+OR48ewd/fH+3atcPp06f5e8ru3bvh7e2N5ORkTJgwQZA4hIAZdTHnzp078Pf3x5YtW/DixQvMnj0bQ4cOxZs3b9C3b1/MnTsXKpUKNjY2AIDSpUvjxYsX+OWXXywcuaGJ9evXL08TO3fuHAIDA+Hu7g6dTodp06ahR48eiI6OtmD0hhBRrg8OKpUK27Ztw6BBg9ChQwd89dVX+OOPP3D9+nWcPn26yN+M79y5AwDw8PBAYGAgQkND+Zj1nYP0plKmTBmDz27duhXly5eHp6cnACAtLQ27du1CcnKyGRVYjhUrVmDBggWYMmUKZs6cCUdHR3Tv3h3//PMPhg8fjkWLFkGpVPKmHh8fj5cvX+Krr74CgCI/jEipVGLAgAFo06YNNm7ciAEDBqBnz56Ij49Hly5dcPjwYX5IpbW1Nfz8/HDz5k2sWLFCkHjkcjl69+6NWrVq4euvvwYAjBkzBg4ODli5ciXq1auHESNGYPTo0bh+/Tp8fX2L/O+Px2JleYZR6HS6XKt858+fT7169eL/Xr16NVlbW9P3339PCQkJ5OjoSDNnzqTbt28TEdHs2bOpY8eOZGNjQ7du3bJo7EqlkgICAmj+/Pn8tiNHjlCtWrVoy5YtfPVf9erV6dtvv6Xjx49TmTJlyMfHh44cOWKW2PND9uq/hIQEOnPmDMXFxZFOp6Pk5GQKDQ2lX375xeAzQ4cOpXbt2tH9+/fNHW6+0Gg09MMPP5CrqyvfvDJu3Dhq1aoVHT9+nN9vy5YtpFAoaPr06QZVoenp6VS5cmW+X8HSpUvJxsaGunXrxrfDioW8mp969OhBs2fPJiKiN2/eUOfOncnNzY0iIiLo4cOHVK9ePQoJCaFNmzbRmTNnqFmzZvTdd99R+fLlafv27URUdKrAc/v9HjhwgGrUqEHR0dFERJSYmEgcx9EPP/xA+/fvpzZt2vBNIkREO3bsoJo1a1L9+vVpw4YNJonr7e8nMzOTNm7cSN7e3nwfgG3btpG3tzc1aNCAnj59mudnizLMqIsB2X8k6enppFKp+BvioEGDaPr06ZSRkUF9+/bljfnRo0dERLR3715q1KgR+fj4UL169ahEiRL077//UosWLahjx46Cx15QE2vbti09fPiQXr9+TSVLliSO48jOzo4WLlyY67GLArNmzSKFQkH16tWjChUq0HfffUdERF26dKFPPvmEYmJi+H2vXLlCUqmUlixZQunp6ZYK+Z2cPn2a6tSpw3dIjIqKooYNG9LcuXPpwoUL1KJFCypZsmSO/BFlPYRVqVKFBg0aRHXr1iUXFxeT3ZyLKlu3bqVz587R69evSalUUsuWLWn//v00ffp0cnR0pE6dOlFkZCQdPXqUYmNjKTY2lvr06UPBwcHk4uJCEyZMoPj4eBo3bhzVqlXL0nKIKOs3lt3Q1Go1//9vv/2Wb3Nes2YNVahQgerUqUN3796l5ORkOnToEJUuXZoaNmxI/fr1I09PT5o9ezaNHDmS2rRpQ2/evClQTOHh4TR48GAiyt1sY2NjadCgQdSwYUN+24ABA6hHjx50/fp1Xldxghl1MeKbb74hf39/atq0KX/zHDFiBNWvX5+cnJyoQ4cOdPXqVSIimjJlCq1bt46IiJ4/f067du2ipUuXUlpaGhERXb9+nTiOo127dpkldmNM7PLlyySRSHgDaN++PY0aNcqg5JJXKcYc5PYjP336NFWrVo327t1LRER79uwhW1tbWrp0Kf3999/00UcfGXSu2bBhA5UqVYqaNGlCFy5cMFvs+UGvT6lU0oIFC8jX15cePHhAREQ//vgjlS9fnmxsbOizzz6jJ0+e8J/LftOMjY0ljuPIxsaGvvrqK/MKMDM7duyg0qVLU9WqValEiRK0ZMkSIiLq1KkTcRxH9evX5ztCEhFVrFiRNm7cSERZ3/XLly/pxYsX/PtHjx4lHx8f2rlzp1l1vIuIiAjq1KkT9ejRg7+vfPnll1StWjUKDQ0lDw8PWrp0KRERrVy5khYvXkxEWb/lFStW0KBBg2jfvn1ElFUSDwoKoq+//rpAsezcuZM4juN/N7mV9g8dOkQVKlTgO86ePXuWgoODae7cufzDRnEya2bUxQC1Wk1Dhw6ljz76iLZu3Urz5s2jzZs3ExHR5s2bqVSpUjkuen9/f5o6dWqux8vIyCAioiVLlvAlb1NhjIktW7YsVxP7448/qFSpUhQSEkJXrlwxOKYlDfpd5//0009p6NChRER07do1atCgAXl7e9Pu3buJiGjatGkUFBRE7dq1o+XLl1OdOnVoyZIlVLlyZZo6dWqOnu7m5MqVK0SUu7bIyEhq1aoV9e7dm4iyzLtdu3bUqVMnvhrx7VKN/u9169bRq1evhAzd4iQnJ1OjRo1ozpw5RER04cIF/nv5999/ieM42rFjB7//3bt3yd/fn/bv389v01/f+t+lUqk0eACyNMuWLSMnJycaP348DR06lCZNmkSZmZl09+5dcnR0pC5duhiUtPv160ctW7bM83iJiYn07bff5ruQkJiYSAkJCfywP41GQ71796bq1avn2Ff/Xb5+/ZqmTp1KlSpVotTUVCIimjRpErVp04avEi9OMKMuYuRmdOnp6dS4cWMKCwsjIqL4+Hi6d+8e/xTevXt36tSpE/3555+UlpZGO3bsoOrVq+daUhPyKVJoE7O0SetJTk6mr776ihYuXEjh4eFERDR37lxq3bo1jRo1ihwdHWnEiBEUHx9Phw8fpj///JOIsh5Y+vfvTyEhITRz5kwiIlq/fj2VL1+e/y6yY44n/q1btxLHcbyhvqvNT98vYNOmTVS3bl1atWpVrscsTiWVd6Efz6+/0edWzXru3Dn66KOPaPv27ZSRkUGbNm2ilStX0urVqykxMZGmT59O5cqVo2HDhtGePXuoWbNm1KBBA0pISDC3nDzZvHlznkMeMzIy6JNPPuFrwIiI4uLiKD09ndLS0mjs2LHk4+NDDx48ILVaTc+fP6eaNWvSH3/8keNY2fsw5Pca+fLLL6lChQrUoEEDCgwMpAULFhAR0c2bN8ne3p6/J+Z2b7h48SIFBwfTF198QURZ49erVKlCX375JW/6xQVm1BYkPT2d5s2bR4sWLaITJ07kud/jx4+pWbNm1KhRIxowYAAFBwdTxYoVyc7OjqZNm0aPHj2izz//nGxsbCgoKIjs7Ozohx9+MJ+QbCQnJ9PUqVMLZGL9+vUzMLHff/+dypcvT3v27LGIltzYuXMnOTo6UsOGDal169bUsWNH0mg09Mcff5CXlxdVq1aNHj58SERZN6aePXvS8OHDSaVSEVHuHevq1q2b48b28OFDg1KKUOb34sULqlevHn388cd8zG8TGxtLAwcONGjz69evH3Xv3p2uXbsmaHyWYvr06eTj40N16tShwMBA+vfffw3e15d+09LSqHbt2uTr60scx1GVKlXI29ubypcvT61btyYiolWrVlGrVq2oZs2aNHjw4CLTJ2HLli3k4uJCfn5+9PLlyzz3q1ChAnXu3JkWL15MH3/8MbVv354qV65M3377LSUnJ1PTpk3J19eXmjZtSqVLl6a2bdsaVOUXhJiYGAoJCaEGDRrQ/v37ad++fTRgwADq3r07PXv2jLRaLc2YMYNKlCjBf+bth4D09HRatmwZ+fn58dfpwYMHi2UtDzNqC/Hjjz+SnZ0d1apVi/z8/Mje3p42bdpERFk3y8zMTIOb5u7du6lbt27UuHFjmjlzJi1btowvDd29e5eIskqr4eHhBtWo5ryBmsvELIVaraaPP/6YfvrpJ36bfiKF2NhY6t27N7Vv357PW0ZGBtWrV49vs8yOVqvljTi3p/uRI0dS//79+XY9U7Fw4UL65Zdf6Pfff+dnR9u3bx9xHEfnz58novy1+Z07d46Cg4Np2rRpBg8UxZ3NmzeTq6srVapUifbv309//PEH1a1bl2rWrEmpqan8b1PPq1ev6OHDh7Rx40bas2cPnTt3jl6+fElbt26lihUr8qMtMjIyDGajs+SkPTdv3qSgoCBycnLK9dokMoxv3759FBQURM7OztS1a1f6/PPPafTo0VSjRg3at28fvXnzhk6cOEHff/8937xVWFasWEHt27c36KWtUqkMRgw8efKEypYtS19++SUR/ffwlD3+mJgYCg4OpokTJ5okLkvBjNrMHDp0iLy9vcnPz49vu7pz5w6NGjWKXFxccuwfHh5OvXr1ops3b/Lb9NU8N27coMDAQP5pMTuZmZlmNWlzmpiQ5PWd6R8iqlevTv3796d///2Xvv76a/rss8+oU6dOdOLECdqxYwc1adKEfHx8aNSoUfTRRx9RYGBgvodg7dy5kyZNmkREWTleu3Yt1a5dm77//nu++rWgOd22bRt5eHhQ3bp1qWfPniSTyahSpUp048YNyszMpN69e1O1atVyfE6fL32bX5UqVfjeuqNHj6a5c+ca3CCLK2/evKHevXsTx3G0bt06A6N68OABWVlZGdR63bp1i+rWrUvly5enxMRESktLo4cPH/KltR9++IHatGmTo0pWq9VabFiQTqejuXPnEsdxNHHixFwfHLJfX0qlkoYOHcqXtrPXBCQlJVHFihUNOsllx9gHkTdv3vDHV6vV1LRpUxo9enSO+In+uyZ1Oh2tW7eOJBIJxcbG8udNSEigsLAwGjZsGBERP3ysOMOM2oz89NNP/I/kbQ4fPkzOzs50+vRpIsoaytSsWTMqWbIkffPNN5SRkUH37t2jGTNm0KhRo/gxjO3bt+d7cguNJU1MKDQaDS1ZsoTOnj1LRLlX/Wa/2f7222+kUCiI4zgKDg6m1q1bU7Nmzahs2bJ069YtSkpKou+++46GDh3KdzDKD1qtlmbOnEnVqlWjkydP8tuPHTtGlSpVohkzZvA1Jcaa9cOHD6lJkya0aNEiftutW7do+/btvOleu3bNoM0vN/O9cuUKValShe9vIIaStN5Qnj59St27d6d69erx7+mNIzIykry8vGjbtm1ElNVJrmTJkjR8+HC+09fNmzepYcOGFBISQi1btiR7e3u+d3RRQP8w8cknn1BgYCCf9/T09Fyvp8WLF5OrqyvVqFGDoqKiKCUlhWbPnk0bNmygyMhImjRpElWrVo3u3btn8LmCPEieP3+emjVrxnfySktLIw8PD74mLa8HwYSEBMrIyKBGjRpR27ZtiSjr99KyZUuSSCQ0ZMgQ0TTJMKMWGJ1Ox08Kf+rUKf4Hr9FoDBak+PPPPyk4OJgvOf3999/01Vdf8cNiiLJ+VMuXL6fGjRtTtWrV+CofoSgqJiYkYWFhxHEc+fn50T///MNvz/7Urmf79u0UFRVFsbGx9PjxY0pMTOTNSi6XG/Rizf6595Uu9O/fu3ePevXqRT179jR4//fff6fWrVvzi1sYy5QpU6hGjRr05s2bPGPJzMzM0ean5+XLl3T58mUiympv1Y84KO43wbt375Knpyff6/348eNUsWLFHLU8EyZMIC8vL/63eOXKlRxt1kRZ7Z8LFiygadOmFZnOYnv37iVvb29avnw5EWVpbNWqVY7Cwv79+2ny5MkUFxdHly5doiZNmvBNcXqmTp1K1atXJz8/P6pZsyZdunTJJDEmJyfn+D4bN25MPXr0yHV/nU5Hq1at4vtVnDx5kmxtbalp06Zka2tLISEhfHOgWGBGLSDLly8nqVRKDRs25J8Khw4dSqGhofzAe6Isk/bx8TGYsP7t0srbVVLZJwsQqr2rKJiY0Gzbto1atGhBbdu2JS8vr1wXLNm/fz9VrlyZPvroI7p69Sq9efOGdu/eTSdOnCCtVktLliyhevXq8e3venJbGexd6HQ6Wrx4MTVs2JC2bNnCb1epVLRs2TKqUKECnTt3jojePavSqlWr+IdDtVpNnTt3zteY1adPn5Kvry9/E09OTqYTJ05Qy5Ytydra2qwrH5mLcuXKUf/+/Umr1VJKSgpNmzaNKleuTERZD9aVKlUiPz8/vqYrN/LKhbmbn7Lz4MEDCgkJIUdHR5o1axYfo1qtpu+//57q1q1L0dHRlJSURG3btiV7e3v64Ycf+Advfb8Rov/0abVaSk5ONrh3FVZf9s8/fvyYX0Rozpw5VKlSJf4B8e37xE8//UQ1atSgpKQkSktLo2HDhpGHh4fZ5oUwN8yoBeDkyZNUoUIFcnd353s164mLi6OyZcvSsmXL6PDhw1SzZk1ycHAgW1tbatasGU2YMIEePHjA/2DyWqEmr/dMSVEyMVOj/37v3LlDCoWC7ty5Q2PGjCE3NzeaOnUqf6O6desWOTs70/Tp0/mOLK9fv6avv/6anJycKDAwkFxcXPgJLArKkiVLqFy5ctSqVSviOI5atmxpsLrSw4cPqU+fPnmWMvTcv3+fRo8ezXdiIiJq2LAh9e/fn4hyfzC6e/curV69moiyZpiytramI0eO0Pjx48nKyopCQ0MpMjKyUPqKGvr8//PPP8RxHP9gc+XKFapbty65urqSm5sbzZo1i29aMuZ6tWQ79KhRo0gqlVL//v0NenPrc3/t2jXq1q0blS9fnhwdHWnAgAE5fp/5oTAP2bkNpxo9ejQFBQXR7du36fr161S3bl3q06dPrp8bNGgQ1alTh99eVGowhIIZtQl5/fo1tW7dmh82pS9F63Q60mg0/I/3hx9+IEdHR3J0dKRp06bR06dP6d69ezR16lTy9PQkNzc3+uKLLyx28RU1EzMF06dPpy+//NJgyUX9jbdKlSr07bffElFWbjiOoyFDhvCTweQ21CQjI4MuXLhg9BN8bjd7/SQYYWFhFBUVRVOnTqXatWsbjF0lymobbdKkiUHtxruOre8sNHPmTPLz86Nnz54RkaGJaLVa2rx5M3l7e1NsbCylpaVR8+bNieM48vHxEU0JRb/MZPbvSP89dO3alerWrUspKSmUkZFBy5cvJ2dnZ4N1jIsDSUlJ1LhxY1IoFHTmzBl+e0ZGRo4Hh1WrVlHZsmXpm2++MXeYBmzbto2vJbp+/Tq1adOGr9FZtmwZubm50Y8//mjwmbt371KjRo2KzGgQc8CM2kQ8ffqUNmzYQBzHGVQN5dXhpm7dutS3b98cCxS8fPmSxo8fTxzHUfny5c2ycEZRMTEheP78OdWqVYuCg4NpyJAhZG1tTZMnT+ZvvmlpaTRgwADq3Lkzn4ulS5cSx3HUtGlTg85u7ypR5Wcylrz2WbhwIQUEBPAPdhkZGTR//nxq2LChQcn4+vXr1KRJk1wnR3n72OvXr6cOHTrQs2fP6NSpU1S9enV+gYy3dYSFhVHZsmUpJSWFdDod/f3333wJu7jz/Plzql27Nq1ZsybPGdRevnxJVlZW9OuvvxJRVrVxx44dqWvXrmaPt7B88cUX1LZtW75fSfb7z/Hjx/nFPh4/fkzDhg2jtm3b8jUGQtYCvD30MiYmhipVqkSurq7k4uJCM2fOJJ1ORwsXLqTGjRvzHcsWLlxIVlZW1LZtW/r666/pu+++Iy8vL+rYsWOhx2oXJ5hRF5K1a9eSu7s7LVmyhNLS0iggIICfCSf7zXPDhg3Uo0cP3ni3bdtGZcuWNTCz7PvfunVL8GEFRcnEhGL37t1Uq1Ytvnbi8ePHfOchfcxffvkl1a5dm4iy5jSuWrUq+fv7k62tLQUGBvK9fU2BTqej3377jQ4dOmRwLZQqVcrgRnbjxg3q0KEDjRkzxuDzLVu2pBkzZhBR7jfWrVu30t27d2ndunXUqlUrWrBgAel0Oho7diyVL1/eoK1Vn5dRo0ZRy5YtDdolxUTnzp2pXbt2dOfOnRzv6b/zr7/+mjw9Pfle3Nu3bycfHx9+0p6iMitedp4/f04zZ86kX3/9lf7++28iyjLAxo0b07hx4/imk2fPnlHPnj1JKpXSrFmz+M/v2LGDGjVqxK9eJ1RTVPbjPnnyhP7++2/av38/TZ06ldLT02natGkUGhpKW7dupdevX1PXrl1p4MCB/G923759NGzYMOratSu1bNkyRye3DwFm1AXk1q1bVLt2bXJ1deUnoyfKGgtrZWXF9xC9fPkyNWjQgEqXLk0rV640+MG3b9+eunfvzrcPZb+g9f8XstqtqJmYECxfvpzKlSvH/52QkEAxMTEGk8KcOHGCOI6jevXqkUwmo88++4ySkpLozJkz1LhxY+I4LtdS7PvI3gmHKKs9v3Tp0vzDkZeXF+3bt48iIiKocePGOXrCt2zZknx8fAzGqq5du5Z69OiRY+INfQnFxcWFypQpQzNmzKDhw4dTp06d6ObNmxQbG0tdunSh0qVL0759++jRo0f06tUr+vXXX6lcuXK0detWo/UVRV6+fEnbt2+nCxcu8A+6Dx8+JB8fH1qwYEGOcfnZf3Nubm40YcIE/jgjR46kwMBA8wVvBBMnTiQbGxuqW7cueXp6kkKhoFOnThER0c8//0whISG0detWmjlzJjk5OVHXrl35BxW95qSkJJo2bRo1bNiQoqKiBI95xowZxHEclStXjjiO44cLJiUl0cCBA6lbt270+vVr2rZtG4WEhPA1HHrEMF6/oDCjNpKkpCTq2bMnvzKQfjgV0X8/gGbNmlGzZs1oyJAh5OjoSGPGjKH4+Hh+P/0Fd+3aNfLw8KBFixZZ5CK0pIkJSXYD+/XXX6lZs2Z07949WrlyJbm5uVGNGjXIy8uL9uzZQxqNhq5cuUKenp7UsGFDunHjhsGxYmJiaM2aNYUuUWk0Gvq/9s49Kqpqj+O/MwOSiiASJFfQMBOHBhHknYhEyAIFLsQFC5PxAT5Q41r4WFxJdNXSMCnsqaaJD1JYSGpqIebSpOQaD7FQaV2XUkGEL+RCwcD3/sE9Ow6gQQIzzOzPf3Nm5sw+c87Z3733+f1+3+DgYDYbBoDw8HBYW1vjzJkzSE5OxpNPPslm2U1NTZg5cya8vLyYeABtKXDiMzy1Wt3lDGXlypV47rnnsHDhQkRFRSEhIYG1ITIyEmPGjMGYMWPg5OSEMWPGsHSrgc7atWthamoKHx8fPProo7C0tGTR8//617/g7OzMqq+1R7z3MjMzYWRkxAareXl5GD9+/AMjvvubffv2wczMDBMnTmQFWC5evIjQ0FA89dRTANrOc0hICExMTKBQKCR5+R1nzWfPnoW7uzs++uijPmtzVlYWkpOTsWbNGhQUFOD48eMwNTXFxo0bWf8p1llIS0tDS0sLYmNj4evr2+UqiD7ChboHHD16FIIgICgoCKdOnYKJiQl2794NQBrFXFZWhiFDhkCpVEqiZdvfJGLnsGjRImzZsqXfIqC1UcR6k/b/Y2NjI7777juMGzcOWVlZiIyMRE5ODoqKivDSSy9BoVAgNzcXTU1NbBYK3P9ZXU/P0e+//47ly5ejqKgINTU1cHd3R3V1NaqqqhAZGQkTExOkpaWhsbERFRUViI6OxvDhw/HCCy/A09MTXl5enfJLjx07xp6d3m+Gcvv2bcTExODFF19EbGwsQkNDmWA1NDTg6tWrOH36tNYNsB6GV199Fe7u7jh58iTq6+tx6dIlpKSksONubm6GUqnEyy+/zFaQujrPnp6eeOaZZ9DU1NSpZKWmWbZsGQRBYEvV7dm9ezcsLCxYTENeXh6USiWzmwSk9/6JEyewfft2AOhzpy4xdmf69OmsiMzq1avh6uoqGTitWLECgYGBKC8vx5kzZ7B06VIWAKnvcKHuBvn5+VCpVMjJyZFEU65duxZWVlaSNBqRFStWYPz48SyFqn0nn5aWhoCAANy5c6dfU5S0ScR6k46BKt9//z2mTp0KOzs7fPTRR3B0dISJiQkCAwMl7QwKCsKcOXMAABEREXjmmWce+Bt/1oaOVFRUwM3NDZmZmaitrcXgwYOhUqlgZmaG0NBQlJeX49atW5g9ezaLSt65cycSExOxcuVKyQBI/N8bGxtx4MCBP52hZGVlITQ0FLGxsVCpVFCpVA80XhiotLa2orKyEkqlsstKYO0fEYgmLx3rp7e2trIVr6KiIiiVSsl/palUKxHx2srMzIStrS3y8/M7XfMbN25EQECApK3z589HSEiIRAyvXLkCf39/CILQaRbdl8cZGhqKqVOnstdiPM+qVavYf11QUIDJkyez4FXOH3ChfgDfffcd3NzcIAgChg8fzoKnxBunvr4eNjY2WLVqVafv/vrrrxgxYoTExSo/Px9KpRLm5uadOpW+ukm0QcT6ko5e1a2trVi0aBGWLFmClStXwtPTE3PmzIGBgQEiIiKYIAJt6U6jRo0C0JaX6efn99C+0O0rIqnVari5ubGZ7rx58yAIgmSwV1JSgmHDhkmWJ9vTVYxCd2coCQkJiI6Oxpw5c+Dr66v18QR/lby8PJiZmXXrHvL398esWbOY2cOtW7fwySefwN7efkBEugcGBiIsLExSsfCdd96BhYVFp5m2mIv8xhtv4JdffkFCQgJkMhlmzZoluQ/6g6KiIsjlcskgaceOHVAoFDh+/DjbJgbFcaRwoe6C33//HXPnzoUgCFiyZAl27NgBGxubLmtq79u3D0ZGRl0GY6Snp8PCwgKFhYUICwuDXC7/y2Ug/wraJmJ9yfr16+Hu7o6ZM2fCz8+PVW5LTk6GSqWCk5MTXF1dWeUjoM3B7NlnnwXQFvTXk5rpHQdAra2tyMjIgFwuR25uLhPQxMREeHt7AwAuXLgAQ0NDbNu2DVVVVQDaKjBNmzatyxiFBw2AujNDOXv2LHx8fLBixYoun83qCtu2bcOECRO6LBsp/odnz55Fc3MzCgsLYW1tjR07duD06dMICQmBgYEBwsPDJYFm2vQ4B/hjwFZUVAQbGxscOHAA2dnZGDduHIyNjTFs2DDMmDEDq1evRnV1Nfv8q6++itGjR0Mul2PSpEkoLi5m++zvY4yLi4O9vb0kZWzSpEkICwvTydWe3oQLdQdOnDgBQRDg4+PDnsmuWbMGdnZ2ALq+uKdMmdJlzqVarYadnR0EQUBISIgk768/b5L+FrH+pLi4GAUFBZg4cSLWrVuHv/3tb5g4cSLL87506RJmzZoFlUqFGTNmwMXFBXPmzMEbb7wBCwsLidsX0L0o+/Yzt4aGBty4cYNtS0pKgkKhYLObnJwcTJ8+nbXnvffew+OPPw6FQoGnn34apqamf6kgTHdnKPebqesCoggXFhZCLpezqOeO3L59G7a2tsyCMS4uDsbGxpDL5XBzc+vVkpi9wf3aIF5j8fHxMDY2hrm5OdLS0lBZWYny8nIsXrwYZmZmsLW1xbp169DU1IR79+4hOjqa5U+L+9HEcn5NTQ1GjBiB9PR0tu3bb7/F+fPn+70tAw0u1B348ssvWUUikfj4eDg7O99XXM+fPw+ZTIa8vLxO75WUlLB6tcAfM9v+QBMi1p/cvHkTgiBg/PjxLDDqiy++gI2NDT755BN2vtLT0xEWFoYtW7bgs88+Q0xMDHx8fB66stHrr78OS0tLuLq6wtHRkVUMS09Ph4ODA15//XWcPn0aTk5Okqj/0tJS7Nq1C6mpqZKa7T1Fn2YofyZe7u7uDyxQYm9vj9deew1AWxlfHx8frRAvoOfmN3fu3MGTTz6JFStWdMp9/+GHHxAbGwtBEDB58mSJlSWg+Xs4NTUVgiDo3PXZ13Ch/j9dibDYOQQFBSEkJATA/Z8lz507F5MnT77v+/3dEWhaxPqLt99+G4IgSJx8IiIiEBQUxCJgb968yZ7TtjeiF+nOeen4mV27dsHBwQHZ2dn46quvEB4eDg8PD1bbPS8vj+XYGxkZscIZf9YJ9wRdnqH0VLxyc3Mhl8uxb98+NnARRamgoABmZmY4efJkl7+lafHqiflNZmYmampq8MEHH8De3l7ikd3+OAoLCyUiremAOJHm5mZs3LhR4//5QEMvhVq88NeuXYvExMQHfq6pqQn+/v544YUXHrjPn376CYIgSIqfaJr+EjFN0tjYiLFjx7JcYaDNLtLGxgZbt25lUdAHDx7Epk2bJP67PV3mvnz5Mvbv34+GhgYEBARIjO3v3r2L5cuXw9/fnxXa2Lt3L4t1SElJ6XL/D7u6oqszlJ46t5WWliIxMRGjRo1iNpVielVcXBzCwsI6pVppi1h01/zG3t4eSqUSZWVlAABXV1csWrSIrdZ0dS1pyzFyHg69FGoAqK2thSAIEAQBSUlJrMhBxwAhABg/fjwLAuvqZhA7j8OHD0uWODVNX4uYtnDw4EEYGhpKctbXrFkDhULRK8UqmpubcejQIVb3vLy8HL6+viyaW+TIkSNwd3eXOKZVV1fDzc0NOTk5D92O+7VNF2coPXFuc3BwYOK1cOFCWFtbY+zYsQgPD4etrS2cnJzY+9rEXzG/aZ8KevToUVhZWWHPnj1a8Wyd03fopVCLPrFxcXHw9PREeHg4lEqlZEYpXvg3b96UdBTtZ1hiEMr9bCi1gb4WMW1h2rRpCA4OZq8bGhrg7e2NkpISyef+7Nx0FLzGxkZWWGTZsmVsu5+fHyIiIiSR8C0tLbCwsGDBYeK+dE1E+5KHcW4Tv19aWop33nkH69atk6Skafq+7G3zG6DN+Usfa1/rG3oh1Ldv38aWLVskTlT//e9/MX/+fCxbtgynTp2Cs7MznnrqqU7+0bdu3YK5ubmk7uzFixfh7e0NS0vLAeHg0lsips0UFxdDLpc/1My1/SCs/cpIcXExrKyssGTJErbt3LlzMDY2xv79+9n3rl69CoVCgfz8/C73PZD/376iL8SrKzQ5WOoL8xs+CNQvdF6oGxoaoFKpIAgCNm7cKAlASU5Ohr29PYC2wv1TpkyBhYUFMjIyWE5laWkpjIyMcOHCBdTV1WHevHmQyWRQqVT3tbDUNnpDxAYC0dHRzLlMpDsdWXsBraqqwowZMzBhwgSEhISwwjTr1q2Dqamp5PqJj4+Hi4sLQkNDsWfPHkyePBnPPvusVpWd1Fb6y7lNGwZHfW1+07HyIUf30HmhBoBVq1Zh0KBBiIqKkqRQnT9/HoaGhiyytKqqCnZ2djA3N8dLL72E5uZmXLlyBebm5nj++ecxfPhweHh4SJaRta0wwv34qyI2kHjYc3Hu3DksXrwYkZGROHToEOLj4yGTyXDw4EFUVFTAzs5O8ry/sbERhw4dQmhoKJ5++mnJe5wHow/ObSK6an7D6T90TqjbjyxFITpx4gQMDQ3h5+eHVatWoba2FsAfN392dja+//57uLq6YuTIkfD19YWxsTHi4uKwc+dOCIIAOzs7ScegVqu1PiK6PQNlQNEb/NkApKtUuSNHjkAQBFhbW0vy3pctWwZnZ2eUlpYiIyMDMpkMP/74I9uP+Hvtq1rp2gCoL9B18dJ18xtO/6IzQt3U1IR//OMfiI+P75SucOPGDcTGxiIqKgp+fn4SW7/Ro0fDzMwMgwcPRnR0NGpqanD37l1kZGRAEATMnTsXu3btkvzWQO6IB3Lbe5vS0lJ8+umnrNzn/PnzMXLkSEmZxd9++w3m5ubYtm0bmpubMW3aNEybNg1A52VV/hz6weiLeOmq+Q1Hc+iMUNfX17N0q3nz5kkCx3744QfMnj0bZ86cwcyZMxETE4OrV68CAJYuXQpTU1NJGUGRc+fOSUpnamOnwOkeHZ2oEhISYGBggMceewz+/v6or69HRUUFrK2tsW/fPknt7bi4OAQGBgJoW52xsLDg9ns9RNfFS9fNbziaRUY6wtChQ+n9998nmUxGNTU19Pzzz9Pdu3eJiOiJJ56gyspKunjxIiUlJVFZWRmdOnWKiIgee+wxsrS0pMGDB7N9ASAiIi8vLxoyZAi1trYSEZGBgUE/HxXnYWhtbe107goLC+nChQtUX19PZWVllJOTQ2VlZfTmm2/SuHHjaNasWbR9+3a6evUq2091dTU5OjoSEZGvry9VV1eTlZVV/x/QAAMAtbS0EBGRIAhUXl5OPj4+NGnSJPrmm29o6NChNH/+fLp37x79/e9/JycnJ3rrrbfI1taWcnJyyNDQkLy8vOjcuXNERCSTde6uAJAgCP16XPdrg1wuJ7VaTQAoPT2dlEolhYaG0o4dO8jR0ZEaGhpo6NChVF9fz74bGRlJ+fn5RNTWhwmCQHfu3OnydzR9nBzNoTNCTUS0YMECGjx4MAUEBNAjjzxCCxcupOPHjxMR0cKFC+nkyZPk4eFBTk5O9Nlnn9H169fJy8uLrl27RsbGxmw/HW+IrjoIjnZy4cIFioqKIrVaTTKZjJ27oqIiGjFiBD333HMUERFB165dozFjxpCXlxe98sorlJmZSd9++y2lpKRQVVUVzZs3j7Zu3UpvvvkmFRYWkre3NxERDRo0iGQyGanVak0eptajT+IltmHDhg00ZcoUCgkJoYqKCtq0aRNt2rSJ/P39SSaTkYODA12/fp2OHTvGvvvLL7+QQqEgIqLly5fT4cOHafjw4Zo4DI4Wo1MKJJfLKTs7m3bv3k2pqalkbm5Oq1evpsuXL9PIkSNpxIgR1NzcTEuWLKHa2lras2cPmZmZkaWlJVVXV2u6+ZyHJCEhgfbu3UsqlYrNoG/dukXJycm0e/duSklJoaysLFIoFHT37l0yMjIiIqKXX36Zhg4dSh988AENGTKE1q9fT//+97/pyy+/pKNHj9KHH35IQUFBkt/iqysPRp/Eq6SkhL7++mvKzs6mwMBAKioqol9//ZVqa2uJqG3g8dtvv5GjoyNZWlrS5s2bKSYmhlJTU2nz5s00ffp0IiJydnamIUOGsFUIDoehwWX3PkOpVOK1117Dzz//jLi4ODg7O+Pjjz+Gra0tKisrAbTlwHp7e+Prr7/WWgtHTvcQn12uXr2aVVtrbGzEvXv3UFZWBjc3N5ibm+PGjRsAgOvXr+ORRx5BRkYG20dubi7Gjh2LQ4cOAQC8vb2xYMECVlSDB4r1DF13bhPRF/MbjmbRSaH+z3/+A2NjY1y6dAkAEBMTw4oqvPfeewDaan1fvnyZfUdbOwJO9/Hw8GARw4GBgVi6dCmAtghjmUwmOd9JSUkYNWoU6urq2LapU6cyD+4vvvgCY8eOxYEDBwZMYRttQd/ESx/MbziaRSeFGmiznYyIiADQVg7y3XffhSAI2LBhgyQCmM+SBj7iOXzllVewePFiAMD69evh4eGB4uJi1NXVYfr06ZIyqnV1dRg9ejSLKAaAa9eu4cqVK+x1cHAwPDw82CoMp/vok3jpi/kNR3PorFDX19dj0KBBOHbsGNt29OhRDbaI09ckJydDpVKx9J+wsDC8+OKLANoqYY0ePZotbQN/WCmKtpQi4gz6+vXr+Pzzz/ut/bqEvomXvpjfcDSDzgo1AKSlpWHChAkSazhg4IzUOT1DtP0TZ8Xbt2+Hi4sLcnNz0draitjYWLi7u0u+s3fvXk00VS/QN/HSB/MbjmYQgP8nDesgAOibb74hT09PTTeF009MmDCB/Pz86N1336XKykpKSUmhH3/8kT799FMqKyujGTNm0PLlyykpKUnTTdULfH19adiwYXT48GEiImpsbKSAgADaunUry00n0o586IelpKSEXFxcKCsri8LCwjTdHI4OodNCLaILnQCnexw+fJgiIiKooKCAXFxc6MiRI5SamkrBwcGUmJhIO3fuJFdXV3JwcNB0U/UCfROv2bNnk6WlJW3ZsoVta2lpIblcrsFWcQY6eiHUHP0iODiYbt68Sfv37ycrKyv65z//SZWVlXTw4EFJBTpO/6BP4qVWq3mOPafX4ULN0Tnq6uooICCAlEolrVmzhkaNGkUGBgY6KQwDAX0UL10diHA0g05VJuNwiIhMTEwoNTWVDA0NaebMmXTv3j3WafJxaf8jirQ+VdziIs3pTfiMmqPTLFiwgNRqNT366KO0efNmTTeHw+FwegwXao5OIgYQtrS0UE1NDZ05c4aioqI03SwOh8PpMVyoOTpNa2srdz/jcDgDGi7UHA6Hw+FoMXyqweFwOByOFsOFmsPhcDgcLYYLNYfD4XA4WgwXag6Hw+FwtBgu1BwOh8PhaDFcqDkcDofD0WK4UHM4HA6Ho8VwoeZwOBwOR4vhQs3hcDgcjhbzPydUAX+zwQduAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 4))\n",
    "ax = fig.subplots()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "order = ['MAZTER-seq', 'm6A-seq2', 'm6A-seq\\n(improved)', 'miCLIP2',\n",
    "       'm6A-CLIP-seq', 'm6A-REF-seq', 'miCLIP ']\n",
    "# sns.stripplot(df[~df[0].isin(['Brain', 'Liver', 'Kidney', 'Heart', 'Skin', 'Testis'])], x=1, y=2, hue=0, s=10, alpha=.75, palette='tab10', jitter=.1, linewidth=1, order=order)\n",
    "# sns.stripplot(df[df[0].isin(['Brain', 'Liver', 'Kidney', 'Heart', 'Skin', 'Testis'])], x=1, y=2, hue=0, order=order, s=10, alpha=.75, palette='tab10', jitter=.1, linewidth=1, marker='D')\n",
    "\n",
    "sns.swarmplot(df[df[0].isin(['Brain', 'Liver', 'Kidney', 'Heart', 'Skin', 'Testis', 'DRG'])], x=1, y=2, hue=0, s=10, alpha=.75, palette='tab10', linewidth=1, order=order, marker='D')\n",
    "sns.swarmplot(df[~df[0].isin(['Brain', 'Liver', 'Kidney', 'Heart', 'Skin', 'Testis', 'DRG'])], x=1, y=2, hue=0, s=10, alpha=.75, palette='tab10', linewidth=1, order=order)\n",
    "\n",
    "for idx, i in enumerate(order):\n",
    "    plt.vlines(idx+0.5, df[2].min(), df[2].max(), linestyles='dashed', color='grey', lw=.2)\n",
    "    \n",
    "plt.ylabel('AUPRC')\n",
    "plt.xlabel('')\n",
    "plt.xticks(rotation=30)\n",
    "\n",
    "# handles, labels = plt.gca().get_legend_handles_labels()\n",
    "# should = ['OCM1', 'OCM1a', 'OM431', 'PIG1', 'CM2005.1', 'CRMM1', 'HEK293', 'HEK293E', 'HEK293T', 'ESC', 'HSPC', 'MOLM13', 'LNCaP', 'RWPE', 'MCF7', 'MDAMB231', 'A549', 'HepG2', 'CD8T', 'HCT116', 'HeLa', 'Brain', 'Liver', 'Kidney']\n",
    "# order = [labels.index(i) for i in should]\n",
    "# plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order], bbox_to_anchor=(1, 1), loc=2)\n",
    "# plt.legend(bbox_to_anchor=(1, 1), loc=2)\n",
    "plt.legend(loc='lower left', ncol=2, frameon=False)\n",
    "plt.title('Mouse')\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/atlas2_mouse_prc.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77c963e5-38eb-428c-9004-8a865e8cb90b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54266745-f360-4a4a-af35-bfaee2ad260d",
   "metadata": {},
   "outputs": [],
   "source": [
    "_, cls, _ = utils.load('data/m6aatlas/atlas2_rat.data')\n",
    "result = utils.load('data/m6aatlas/atlas2_rat.metrics')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e6eb13d-8b15-479a-90d6-a6a266650b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = []\n",
    "for name, i in zip(cls, result):\n",
    "    name = name.split('_')\n",
    "    # df += [[name[0], name[1], i[-1]['1.0']['precision']]]\n",
    "    df += [[name[0], name[1], i[2]]]\n",
    "df = pd.DataFrame(df).replace({'m6A-seq with improved protocol': 'm6A-seq\\n(improved)'})\n",
    "df = df.replace({'Mouse Embryonic Fibroblasts (MEFs)': 'MEFs', 'dorsal root ganglion?(DRG)': 'DRG', 'macrophages': 'Macrophages'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eaa0b962-8eb8-45c6-88df-1530ed23fecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brain</td>\n",
       "      <td>m6A-REF-seq</td>\n",
       "      <td>0.645015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kidney</td>\n",
       "      <td>m6A-REF-seq</td>\n",
       "      <td>0.602383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Liver</td>\n",
       "      <td>m6A-REF-seq</td>\n",
       "      <td>0.625162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0            1         2\n",
       "0   Brain  m6A-REF-seq  0.645015\n",
       "1  Kidney  m6A-REF-seq  0.602383\n",
       "2   Liver  m6A-REF-seq  0.625162"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df#[2].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2a362c73-c7f9-43e9-9e08-2279aecc4203",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('fig/atlas2_rat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a91e1de1-2418-4fed-b00c-e9057736853a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['m6A-REF-seq'], dtype='object', name=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(1).agg({2: 'mean'}).sort_values(2).index[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b47c559c-87dc-4130-83b6-ec8a4124a00f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 300x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(3, 4))\n",
    "ax = fig.subplots()\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "order = ['m6A-REF-seq']\n",
    "sns.swarmplot(df, x=1, y=2, hue=0, s=10, alpha=.75, palette='tab10', linewidth=1, order=order, marker='D')\n",
    "\n",
    "plt.ylabel('AUPRC')\n",
    "plt.xlabel('')\n",
    "plt.xticks(rotation=30)\n",
    "\n",
    "# handles, labels = plt.gca().get_legend_handles_labels()\n",
    "# should = ['OCM1', 'OCM1a', 'OM431', 'PIG1', 'CM2005.1', 'CRMM1', 'HEK293', 'HEK293E', 'HEK293T', 'ESC', 'HSPC', 'MOLM13', 'LNCaP', 'RWPE', 'MCF7', 'MDAMB231', 'A549', 'HepG2', 'CD8T', 'HCT116', 'HeLa', 'Brain', 'Liver', 'Kidney']\n",
    "# order = [labels.index(i) for i in should]\n",
    "# plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order], bbox_to_anchor=(1, 1), loc=2)\n",
    "# plt.legend(bbox_to_anchor=(1, 1), loc=2)\n",
    "plt.legend(bbox_to_anchor=(1, 1), loc=2, frameon=False)\n",
    "plt.title('Rat')\n",
    "plt.tight_layout()\n",
    "plt.savefig('fig/atlas2_rat_prc.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e3ca26a-7d8b-40b2-be35-249cc7e15b35",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3cc2e97b-c08f-44b4-b895-b1a96622dd12",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d7012d84-ddc0-4ce2-99c0-2a47b91069cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "_, cls, _ = utils.load('data/m6aatlas/atlas2_human.data')\n",
    "whistle = utils.load('whistle/model/atlas2.metrics')\n",
    "whistle = dict(whistle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "07a26277-6c14-4e6e-b69e-917db5bed797",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = []\n",
    "for j in cls:\n",
    "    key = f'data/{j.replace(' ', '.')}_whistle.bed.hg19.bed.out.csv'\n",
    "    k = whistle[key]\n",
    "    j = j.split('_')\n",
    "    result += [('human', 'WHISTLE', j[0], j[1], k[2])]\n",
    "\n",
    "for method in ['single', 'dp']:\n",
    "    for i in ['human', 'mouse', 'rat']:\n",
    "        _, cls, _ = utils.load(f'data/m6aatlas/atlas2_{i}.data')\n",
    "        test_res = utils.load(f'model/atlas2_{i}_full_400_ythdf_{method}.metrics')\n",
    "        for j, k in zip(cls, test_res):\n",
    "            j = j.split('_')\n",
    "            result += [(i, method, j[0], j[1], k[2])]\n",
    "\n",
    "for i in ['human', 'mouse', 'rat']:\n",
    "    _, cls, _ = utils.load(f'data/m6aatlas/atlas2_{i}.data')\n",
    "    r = utils.load(f'data/m6aatlas/atlas2_{i}.metrics')\n",
    "    for j, k in zip(cls, r):\n",
    "        j = j.split('_')\n",
    "        result += [(i, 'deepSRAMP', j[0], j[1], k[2])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "59e43e68-10f0-4aee-a6a6-70e3ef699fb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(result).replace({\n",
    "    'human': 'Human', \n",
    "    'mouse': 'Mouse',\n",
    "    'rat': 'Rat',\n",
    "    'dp': 'DeepPromise',\n",
    "    'single': 'deepSRAMP_single',\n",
    "}).set_index([0, 2, 3, 1]).unstack()[4][['deepSRAMP', 'deepSRAMP_single', 'WHISTLE', 'DeepPromise']].to_csv('fig/atlas2_performance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d126771d-66ff-48c2-b635-8d47a9d1c364",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4836f88-990f-4791-8b63-7948d4360279",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:sramp]",
   "language": "python",
   "name": "conda-env-sramp-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}