From a306f04c5c5d3e289e33879142af0fdade3d0525 Mon Sep 17 00:00:00 2001 From: Daniel Bojar Date: Mon, 15 Apr 2024 13:14:52 +0200 Subject: [PATCH] robust min_samples; ALR/CLR warnings; LFC capping - min_samples now operates on a fraction (default: 10% of samples, rounded down) - centralize multiple testing correction in correct_multiple_testing to avoid boilerplate - shout-out ALR + CLR fails and re-rout e failed ALR to CLR - raise all-zero impute from 1e-6 to 1e-5 to cap log2FC further --- 03_motif.ipynb | 6945 +++++++++++++------------ build/lib/glycowork/motif/analysis.py | 55 +- glycowork/glycan_data/stats.py | 40 +- glycowork/motif/analysis.py | 55 +- 4 files changed, 3578 insertions(+), 3517 deletions(-) diff --git a/03_motif.ipynb b/03_motif.ipynb index ea6e748..e58f85a 100644 --- a/03_motif.ipynb +++ b/03_motif.ipynb @@ -156,171 +156,171 @@ "\n", "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", "\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 3\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 2\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 3\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 2\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 6\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 3\n", "\n", - "\n", - "\n", + "\n", + "\n", "β 4\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 6\n", "\n", - "\n", - "\n", + "\n", + "\n", "α 6\n", "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", "" ], @@ -594,93 +594,93 @@ "data": { "text/html": [ "\n", - "\n", + "
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 motifpvalcorr_pvaleffect_sizemotifpvalcorr_pvaleffect_size
4GlcNAc0.0381200.2058491.5309054GlcNAc0.0381200.2058491.530905
8Man0.0543560.2349901.3902538Man0.0543560.2349901.390253
25Man(a1-?)Man0.0609230.2349901.30833325Man(a1-?)Man0.0609230.2349901.308333
10Man(a1-3)Man0.0342120.2058491.19658610Man(a1-3)Man0.0342120.2058491.196586
11Man(a1-6)Man0.0195430.1758851.16881511Man(a1-6)Man0.0195430.1758851.168815
13Man(b1-4)GlcNAc0.0195430.1758851.16881513Man(b1-4)GlcNAc0.0195430.1758851.168815
14GlcNAc(b1-4)GlcNAc0.0195430.1758851.16881514GlcNAc(b1-4)GlcNAc0.0195430.1758851.168815
7Kdo0.3287900.479672-0.8116797Kdo0.3287900.479672-0.811679
2Glc0.6441800.668956-0.8116792Glc0.6441800.668956-0.811679
16Man(a1-2)Man0.1774610.4796720.77232016Man(a1-2)Man0.1774610.4796720.772320
\n" @@ -1050,8 +1050,9 @@ "> feature_set=['exhaustive', 'known'],\n", "> paired=False, impute=True, sets=False,\n", "> set_thresh=0.9, effect_size_variance=False,\n", - "> min_samples=0, grouped_BH=False,\n", - "> custom_motifs=[], gamma=0.1)\n", + "> min_samples=0.1, grouped_BH=False,\n", + "> custom_motifs=[], transform='CLR',\n", + "> gamma=0.1)\n", "\n", "Calculates differentially expressed glycans or motifs from glycomics data\n", "\n", @@ -1067,9 +1068,10 @@ "| sets (bool): whether to identify clusters of highly correlated glycans/motifs to test for differential expression; default:False\n", "| set_thresh (float): correlation value used as a threshold for clusters; only used when sets=True; default:0.9\n", "| effect_size_variance (bool): whether effect size variance should also be calculated/estimated; default:False\n", - "| min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero\n", + "| min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10%\n", "| grouped_BH (bool): whether to perform two-stage adaptive Benjamini-Hochberg as a grouped multiple testing correction; will SIGNIFICANTLY increase runtime; default:False\n", "| custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty\n", + "| transform (str): transformation to escape Aitchison space; options are CLR and ALR (use ALR if you have many glycans (>100) with low values); default:CLR\n", "| gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n", "\n", "| Returns:\n", @@ -1095,8 +1097,9 @@ "> feature_set=['exhaustive', 'known'],\n", "> paired=False, impute=True, sets=False,\n", "> set_thresh=0.9, effect_size_variance=False,\n", - "> min_samples=0, grouped_BH=False,\n", - "> custom_motifs=[], gamma=0.1)\n", + "> min_samples=0.1, grouped_BH=False,\n", + "> custom_motifs=[], transform='CLR',\n", + "> gamma=0.1)\n", "\n", "Calculates differentially expressed glycans or motifs from glycomics data\n", "\n", @@ -1112,9 +1115,10 @@ "| sets (bool): whether to identify clusters of highly correlated glycans/motifs to test for differential expression; default:False\n", "| set_thresh (float): correlation value used as a threshold for clusters; only used when sets=True; default:0.9\n", "| effect_size_variance (bool): whether effect size variance should also be calculated/estimated; default:False\n", - "| min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero\n", + "| min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10%\n", "| grouped_BH (bool): whether to perform two-stage adaptive Benjamini-Hochberg as a grouped multiple testing correction; will SIGNIFICANTLY increase runtime; default:False\n", "| custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty\n", + "| transform (str): transformation to escape Aitchison space; options are CLR and ALR (use ALR if you have many glycans (>100) with low values); default:CLR\n", "| gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n", "\n", "| Returns:\n", @@ -1151,7 +1155,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "You're working with an alpha of 0.07862467893233027 that has been adjusted for your sample size of 6.\n" + "You're working with an alpha of 0.07862467893233027 that has been adjusted for your sample size of 6.\n", + "Significance inflation detected. The CLR/ALR transformation cannot seem to handle this dataset. Proceed with caution; for now switching to Bonferroni correction for being conservative about this.\n" ] }, { @@ -1190,157 +1195,157 @@ " \n", " 1\n", " Man(a1-?)Man\n", - " 4.801684\n", - " 1.828249\n", - " 0.000630\n", - " 0.005480\n", + " 15.368202\n", + " 22.923220\n", + " 0.000931\n", + " 0.012109\n", " True\n", - " 0.712118\n", - " 11.162594\n", + " 0.754083\n", + " 10.476591\n", " 1.0\n", " \n", " \n", - " 0\n", - " Man\n", - " 1.742252\n", - " 2.040883\n", - " 0.001124\n", - " 0.005480\n", + " 5\n", + " Man(a1-2)Man\n", + " 4.412133\n", + " 6.346114\n", + " 0.001307\n", + " 0.016991\n", " True\n", - " 0.712118\n", - " 11.722248\n", + " 0.786241\n", + " 7.340269\n", " 1.0\n", " \n", " \n", - " 5\n", - " Man(a1-2)Man\n", - " 9.603368\n", - " 1.828249\n", - " 0.001384\n", - " 0.005480\n", + " 0\n", + " Man\n", + " 20.169886\n", + " 30.453754\n", + " 0.001533\n", + " 0.019932\n", " True\n", - " 0.712118\n", - " 7.706198\n", + " 0.748979\n", + " 11.008766\n", " 1.0\n", " \n", " \n", " 3\n", " Man(a1-3)Man\n", " 6.154385\n", - " 1.577755\n", - " 0.001686\n", - " 0.005480\n", + " 9.046573\n", + " 0.002202\n", + " 0.028632\n", " True\n", - " 0.712118\n", - " 10.816363\n", + " 0.748979\n", + " 10.176535\n", " 1.0\n", " \n", " \n", " 2\n", " GlcNAc\n", - " 4.412133\n", - " 1.414310\n", - " 0.003992\n", - " 0.008649\n", + " 9.603368\n", + " 15.061067\n", + " 0.004448\n", + " 0.057825\n", " True\n", - " 0.646326\n", - " 12.565934\n", + " 0.697982\n", + " 11.930557\n", " 1.0\n", " \n", " \n", " 4\n", " GlcNAc(b1-4)GlcNAc\n", - " 2.425508\n", - " -2.942876\n", - " 0.003992\n", - " 0.008649\n", + " 4.801684\n", + " 7.530534\n", + " 0.004448\n", + " 0.057825\n", " True\n", - " 0.646326\n", - " 12.565934\n", + " 0.697982\n", + " 11.930557\n", " 1.0\n", " \n", " \n", " 6\n", " core_fucose(a1-3)\n", - " 4.851017\n", - " -2.942876\n", - " 0.005616\n", - " 0.010430\n", + " 1.742252\n", + " 2.700459\n", + " 0.005577\n", + " 0.072505\n", " True\n", - " 0.880637\n", - " 4.478167\n", + " 0.845431\n", + " 4.525408\n", " 1.0\n", " \n", " \n", " 10\n", " Kdo\n", " 7.276525\n", - " -2.942876\n", - " 0.026566\n", - " 0.033288\n", - " True\n", - " 0.646326\n", - " -3.853011\n", + " -7.260513\n", + " 0.024312\n", + " 0.316058\n", + " False\n", + " 0.697982\n", + " -3.961309\n", " 1.0\n", " \n", " \n", " 7\n", " GalNAc(a1-4)GlcNAcA\n", - " 15.368202\n", - " 1.604485\n", - " 0.026566\n", - " 0.033288\n", - " True\n", - " 0.646326\n", - " -3.853011\n", + " 2.425508\n", + " -2.420171\n", + " 0.024312\n", + " 0.316058\n", + " False\n", + " 0.697982\n", + " -3.961309\n", " 1.0\n", " \n", " \n", " 8\n", " Kdo(a2-?)Kdo\n", - " 3.865840\n", - " -4.489319\n", - " 0.026566\n", - " 0.033288\n", - " True\n", - " 0.646326\n", - " -3.853011\n", + " 4.851017\n", + " -4.840342\n", + " 0.024312\n", + " 0.316058\n", + " False\n", + " 0.697982\n", + " -3.961309\n", " 1.0\n", " \n", " \n", " 9\n", " betaGlucan\n", - " 20.169886\n", - " 1.656116\n", - " 0.033288\n", - " 0.033288\n", - " True\n", - " 0.646326\n", - " -4.156264\n", + " 3.865840\n", + " -4.939233\n", + " 0.034353\n", + " 0.446589\n", + " False\n", + " 0.697982\n", + " -4.095340\n", " 1.0\n", " \n", " \n", " 11\n", " Glc(b1-3)Glc\n", " 7.731680\n", - " -4.489319\n", - " 0.033288\n", - " 0.033288\n", - " True\n", - " 0.646326\n", - " -4.156264\n", + " -9.878466\n", + " 0.034353\n", + " 0.446589\n", + " False\n", + " 0.697982\n", + " -4.095340\n", " 1.0\n", " \n", " \n", " 12\n", " Glc\n", " 11.597520\n", - " -4.489319\n", - " 0.033288\n", - " 0.033288\n", - " True\n", - " 0.646326\n", - " -4.156264\n", + " -14.817699\n", + " 0.034353\n", + " 0.446589\n", + " False\n", + " 0.697982\n", + " -4.095340\n", " 1.0\n", " \n", " \n", @@ -1348,35 +1353,35 @@ "" ], "text/plain": [ - " Glycan Mean abundance Log2FC p-val corr p-val \\\n", - "1 Man(a1-?)Man 4.801684 1.828249 0.000630 0.005480 \n", - "0 Man 1.742252 2.040883 0.001124 0.005480 \n", - "5 Man(a1-2)Man 9.603368 1.828249 0.001384 0.005480 \n", - "3 Man(a1-3)Man 6.154385 1.577755 0.001686 0.005480 \n", - "2 GlcNAc 4.412133 1.414310 0.003992 0.008649 \n", - "4 GlcNAc(b1-4)GlcNAc 2.425508 -2.942876 0.003992 0.008649 \n", - "6 core_fucose(a1-3) 4.851017 -2.942876 0.005616 0.010430 \n", - "10 Kdo 7.276525 -2.942876 0.026566 0.033288 \n", - "7 GalNAc(a1-4)GlcNAcA 15.368202 1.604485 0.026566 0.033288 \n", - "8 Kdo(a2-?)Kdo 3.865840 -4.489319 0.026566 0.033288 \n", - "9 betaGlucan 20.169886 1.656116 0.033288 0.033288 \n", - "11 Glc(b1-3)Glc 7.731680 -4.489319 0.033288 0.033288 \n", - "12 Glc 11.597520 -4.489319 0.033288 0.033288 \n", + " Glycan Mean abundance Log2FC p-val corr p-val \\\n", + "1 Man(a1-?)Man 15.368202 22.923220 0.000931 0.012109 \n", + "5 Man(a1-2)Man 4.412133 6.346114 0.001307 0.016991 \n", + "0 Man 20.169886 30.453754 0.001533 0.019932 \n", + "3 Man(a1-3)Man 6.154385 9.046573 0.002202 0.028632 \n", + "2 GlcNAc 9.603368 15.061067 0.004448 0.057825 \n", + "4 GlcNAc(b1-4)GlcNAc 4.801684 7.530534 0.004448 0.057825 \n", + "6 core_fucose(a1-3) 1.742252 2.700459 0.005577 0.072505 \n", + "10 Kdo 7.276525 -7.260513 0.024312 0.316058 \n", + "7 GalNAc(a1-4)GlcNAcA 2.425508 -2.420171 0.024312 0.316058 \n", + "8 Kdo(a2-?)Kdo 4.851017 -4.840342 0.024312 0.316058 \n", + "9 betaGlucan 3.865840 -4.939233 0.034353 0.446589 \n", + "11 Glc(b1-3)Glc 7.731680 -9.878466 0.034353 0.446589 \n", + "12 Glc 11.597520 -14.817699 0.034353 0.446589 \n", "\n", " significant corr Levene p-val Effect size Equivalence p-val \n", - "1 True 0.712118 11.162594 1.0 \n", - "0 True 0.712118 11.722248 1.0 \n", - "5 True 0.712118 7.706198 1.0 \n", - "3 True 0.712118 10.816363 1.0 \n", - "2 True 0.646326 12.565934 1.0 \n", - "4 True 0.646326 12.565934 1.0 \n", - "6 True 0.880637 4.478167 1.0 \n", - "10 True 0.646326 -3.853011 1.0 \n", - "7 True 0.646326 -3.853011 1.0 \n", - "8 True 0.646326 -3.853011 1.0 \n", - "9 True 0.646326 -4.156264 1.0 \n", - "11 True 0.646326 -4.156264 1.0 \n", - "12 True 0.646326 -4.156264 1.0 " + "1 True 0.754083 10.476591 1.0 \n", + "5 True 0.786241 7.340269 1.0 \n", + "0 True 0.748979 11.008766 1.0 \n", + "3 True 0.748979 10.176535 1.0 \n", + "2 True 0.697982 11.930557 1.0 \n", + "4 True 0.697982 11.930557 1.0 \n", + "6 True 0.845431 4.525408 1.0 \n", + "10 False 0.697982 -3.961309 1.0 \n", + "7 False 0.697982 -3.961309 1.0 \n", + "8 False 0.697982 -3.961309 1.0 \n", + "9 False 0.697982 -4.095340 1.0 \n", + "11 False 0.697982 -4.095340 1.0 \n", + "12 False 0.697982 -4.095340 1.0 " ] }, "execution_count": null, @@ -1476,7 +1481,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1725,7 +1730,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", 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XXtrge1osFt1///26//7727AqdBT+0tI6Q48kVWzfIX9pKcEHAKIcd2cH/l/VkaMtagcAtH8hb/FZs2aNHA6HJKm6uloFBQX6/PPPg9a56qqrWqc6oA11juvWonYAQPsXcvDJyckJen7zzTcHPbdYLKqqqmpZVUAEWBMTFZ+RrortdR/jY01MjEBVAIDWFNKururq6kYfhB5Eq5j4eKXkzlZ8RnrQ8pNndeVyfA8AdAAtOrgZ6GhsLpf63zGP6/gAQAfVrOCzf/9+OZ1OxcXFBS33+/0qLCzUJZdc0irFAZEQEx9P0AGADiqkXV0HDx7UsGHD1K9fPzmdTk2fPl1HjhwJtJeWltZ5nR0AAID2IKTgc9ddd6lTp07atGmT3n77bX3xxRcaMWKEfvzxx8A6bX1tH3QsPp9PJSUl2r9/vw4dOiSfzxfpkgAAHUhIu7rWrVun1atX64ILLpAkffDBB5o8ebIuu+wyFRQUSDp5VhfQHB6PR263W16vN7DMbrcrNTU1cJsUAABaIqQtPmVlZTrttNMCz202m1atWqX+/ftrxIgROnToUKsXCHPw+Xy1Qo8keb1eud1utvwAAFpFSMFn4MCB2nHKJf1jYmK0cuVKDRw4UFdccUWrFgfz8Hg8tUJPDa/XK4/H07YFAQA6pJCCz7hx4/Tcc8/VWl4TfjIzM1urLpiM3+9vUTsAAE0R0jE+Dz74oI4dO1Z3RzEx+vvf/67vvvuuVQqDuVit1ha1AwDQFCFt8YmJiWn4Vu8xMerXr1+Li4L5OJ1O2e32OtvsdjsHNwMAWkWzLmA4d+7cOpdbLBZ16dJFZ555prKzs5XIvY3QRDabTampqfWe1WWz2SJYHQCgo7AYzbjwzogRI7R161ZVVVUpNTVVkvT111+rc+fOSktLk9vtlsVi0fvvv68hQ4a0etHhVF5eLofDobKysga3biE8fD6fPB6P/H6/rFarnE4noQcA0Kim/n6HtKurRnZ2tkaNGqUDBw5oy5Yt2rJli/bv36/LL79c06ZN03fffadLLrlEc+bMafYA0LGcqKiQd+9eHdn5hbx79+pERUWd69lsNrlcLvXp00cul4vQAwBoVc3a4nP66adr7dq1tbbm7Ny5U6NHj9Z3332nrVu3avTo0fr+++9brdi2wBaf1ucrKdG+J59SxU8uhXDyjuezZXO5IlgZAKCjCOsWn7KysjovVnj48GGVl5dLOnmwamVlZXO6RwdyoqKiVuiRpIrtO7Rv8VP1bvkBACAcmr2r66abbtLq1au1f/9+7d+/X6tXr9bMmTM1YcIESdLmzZs1aNCg1qwVUchfWlor9NSo2L5D/tLSNq4IAGBmzTqr69lnn9WcOXM0depUnThx4mRHMTHKycnR448/LklKS0vTX/7yl9arFFGp6sjRFrUDANCamhV84uLi9Oc//1mPP/64/vd//1fSydtZxMXFBdbhKs6QpM5x3VrUDgBAa2rWrq4acXFxSkxMVGJiYlDoAWpYExMVn5FeZ1t8RrqsXOsJANCGmhV8qqurdf/998vhcKhfv37q16+fnE6nHnjgAVVXV7d2jYhiMfHxSsmdXSv8nDyrK1cx8fERqgwAYEbN2tX129/+VkuWLNFDDz2kiy66SJL0/vvv695779Xx48f14IMPtmqRiG42l0v975gnf2mpqo4cVee4brImJhJ6AABtrlnX8UlOTtYzzzyjq666Kmj5a6+9ptmzZ0f1jUq5jg8AANEnrNfxKS0tVVpaWq3laWlpKuX0ZAAA0E41K/hkZGToySefrLX8ySefVHp63QeyAgAARFqzjvFZtGiRxo8fr3Xr1ikrK0uSVFhYqH379ukf//hHqxYIAADQWpq1xecXv/iFvv76a1199dXyeDzyeDyaOHGidu7cqRdeeKG1awQAAGgVzTq4uT7bt2/X+eefr6qqqtbqss1xcDMAANEnrAc3AwAARCOCDwAAMA2CDwAAMI2QzuqaOHFig+0ej6cltQAAAIRVSMHH4XA02j59+vQWFQQAABAuIQWfZcuWhasOAACAsOMYHwAAYBoEHwAAYBoEHwAAYBoEHwAAYBoEHwAAYBoEHwAAYBohnc6O5vH5fPJ4PPL7/YqNjZXD4ZDNZot0WQAAmA7BJ8w8Ho/cbre8Xm9gmd1uV2pqqpxOZ+QKAwDAhNjVFUY+n69W6JEkr9crt9stn88XocoAADAngk8YeTyeWqGnhtfr5d5mAAC0MYJPGPn9/ha1AwCA1kXwCSOr1dqidgAA0LoIPmHkdDplt9vrbLPb7RzcDABAGyP4hJHNZlNqamqt8FNzVhentAMA0LY4nT3MnE6nMjMzA9fxsVqtcjqdhB4AACKA4NMGbDabXC5XpMsAAMD02NUFAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMo0MGn8WLF6t///7q0qWLhg8frs2bN0e6JAAA0A50uODz17/+VXPnztXChQu1detWZWRkaMyYMTp06FCkSwMAABHW4YLPY489pl/+8pe68cYbNWTIED3zzDPq2rWrli5dGunSAABAhHWo4FNZWaktW7Zo1KhRgWWdOnXSqFGjVFhYWOdrfD6fysvLgx4AAKBj6lDB5/vvv1dVVZVcLlfQcpfLpeLi4jpfk5eXJ4fDEXikpKS0RakAACACOlTwaY758+errKws8Ni3b1+kSwIAAGESE+kCWlOPHj3UuXNnlZSUBC0vKSlRUlJSna+x2Wyy2WxtUR4AAIiwDrXFJzY2VkOHDlVBQUFgWXV1tQoKCpSVlRXBygAAQHvQobb4SNLcuXOVk5OjCy64QMOGDdMf/vAHHT16VDfeeGOkSwMAABHW4YLPlClTdPjwYS1YsEDFxcXKzMzU22+/XeuAZwAAYD4WwzCMSBfRnpSXl8vhcKisrEwJCQmRLgcAADRBU3+/O9QxPgAAAA0h+AAAANMg+AAAANMg+AAAANMg+AAAANMg+AAAANMg+AAAANPocBcw7Kh8Pp88Ho/8fr9iY2PlcDi4xxgAACEi+EQBj8cjt9str9cbWGa325Wamiqn0xm5wgAAiDLs6mrnfD5frdAjSV6vV263Wz6fL0KVAQAQfQg+7ZzH46kVemp4vV55PJ62LQgAgChG8Gnn/H5/i9oBAMC/EHzaOavV2qJ2AADwLwSfds7pdMput9fZZrfbObgZAIAQEHzaOZvNptTU1Frhp+asLk5pBwCg6TidPQo4nU5lZmYGruNjtVrldDoJPQAAhIjgEyVsNptcLlekywAAIKqxqwsAAJgGwQcAAJgGwQcAAJgGwQcAAJgGwQcAAJgGwQcAAJgGwQcAAJgGwQcAAJgGFzBEu+Pz+QJXqY6NjZXD4eAq1QCAVkHwQcjCGUw8Ho/cbre8Xm9gWc19ybghKwCgpQg+CEk4g4nP56vVtyR5vV653W5lZmay5QcA0CIc44MmayyY+Hy+FvXv8Xhq9f3T9/B4PC3qHwAAgg+aLNzBxO/3t6gdAIDGEHzQZOEOJlartUXtAAA0huCDJgt3MHE6nbLb7XW22e12Dm4GALQYwQdNFu5gYrPZlJqaWus9ag6e5sBmAEBLcVYXmqwmmNR3VldrBBOn06nMzMzA6fJWq1VOp5PQAwBoFQQfhKQtgonNZpPL5Wq1/gAAqEHwQcgIJgCAaMUxPgAAwDQIPgAAwDQIPgAAwDQIPgAAwDQIPgAAwDQIPgAAwDQIPgAAwDS4jg8QQT6fL3AxyNjYWDkcDq5SDQBhRPABIsTj8dR7+w9uyAoA4cGuLiACfD5frdAjSV6vV263Wz6fL0KVAUDHRvABIsDj8dQKPTW8Xq88Hk/bFgQAJkHwASLA7/e3qB0A0DwEHyACrFZri9oBAM1D8AEiwOl0ym6319lmt9s5uBkAwoTgA0SAzWZTampqrfBTc1YXp7QDQHhwOjsQIU6nU5mZmYHr+FitVjmdTkIPAIQRwQeIIJvNJpfLFekyAMA02NUFAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMgys3A1HE5/MFbnERGxsrh8PBLS4AIAQEHyBKeDweud1ueb3ewLKam5pyN3cAaJqo2dX14IMP6sILL1TXrl3r/ZIvKirS+PHj1bVrV/Xq1Ut33HGHTpw40baFAmHg8/lqhR5J8nq9crvd8vl8EaoMAKJL1ASfyspKTZ48WbNmzaqzvaqqSuPHj1dlZaU+/PBDPf/888rPz9eCBQvauFKg9Xk8nlqhp4bX65XH42nbggAgSkXNrq777rtPkpSfn19n+zvvvKMvvvhC69atk8vlUmZmph544AHdeeeduvfeexUbG1vn63w+X9C/lsvLy1u9dqCl/H5/i9oBACdFzRafxhQWFurcc8+Vy+UKLBszZozKy8u1c+fOel+Xl5cnh8MReKSkpLRFuUBIrFZri9oBACd1mOBTXFwcFHokBZ4XFxfX+7r58+errKws8Ni3b19Y6wSaw+l0ym6319lmt9s5uBkAmiiiweeuu+6SxWJp8PHVV1+FtQabzaaEhISgB9De2Gw2paam1go/NWd1cUo7ADRNRI/xuf322zVjxowG1xk4cGCT+kpKStLmzZuDlpWUlATagGjndDqVmZkZuI6P1WqV0+kk9ABACCIafHr27KmePXu2Sl9ZWVl68MEHdejQIfXq1UuStHbtWiUkJGjIkCGt8h5ApNlstlq7dAEATRc1Z3UVFRWptLRURUVFqqqq0rZt2yRJZ555puLi4jR69GgNGTJEN9xwgxYtWqTi4mLdfffdys3N5V/EAABAkmQxDMOIdBFNMWPGDD3//PO1lq9fv16XXnqpJGnv3r2aNWuWNmzYoG7duiknJ0cPPfSQYmKanu/Ky8vlcDhUVlbG8T4AAESJpv5+R03waSsEHwAAok9Tf787zOnsAAAAjSH4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA0yD4AAAA04iJdAEA0N75fD55PB75/X7FxsbK4XDIZrNFuiwAzUDwAYAGeDweud1ueb3ewDK73a7U1FQ5nc7IFQagWdjVBQD18Pl8tUKPJHm9Xrndbvl8vghVBqC5CD4AUA+Px1Mr9NTwer3yeDxtWxCAFiP4AEA9/H5/i9oBtD8EHwCoh9VqbVE7gPaH4AMA9XA6nbLb7XW22e12Dm4GohDBBwDqYbPZlJqaWiv81JzVxSntQPThdHYAaIDT6VRmZmbgOj5Wq1VOp5PQA0Qpgg8ANMJms8nlckW6DACtgF1dAADANAg+AADANAg+AADANAg+AADANAg+AADANAg+AADANAg+AADANAg+AADANAg+AADANAg+AADANLhlxSkMw5AklZeXR7gSAADQVDW/2zW/4/Uh+JyioqJCkpSSkhLhSgAAQKgqKirkcDjqbbcYjUUjk6murtaBAwcUHx8vi8US6XLqVF5erpSUFO3bt08JCQmRLqdNMGbG3FExZnOMWTLnuNtyzIZhqKKiQsnJyerUqf4jedjic4pOnTqpT58+kS6jSRISEkzz4anBmM2BMZuDGccsmXPcbTXmhrb01ODgZgAAYBoEHwAAYBoEnyhks9m0cOFC2Wy2SJfSZhizOTBmczDjmCVzjrs9jpmDmwEAgGmwxQcAAJgGwQcAAJgGwQcAAJgGwQcAAJgGwaedycvL089+9jPFx8erV69emjBhgtxud4Ovyc/Pl8ViCXp06dKljSpuuXvvvbdW/WlpaQ2+ZuXKlUpLS1OXLl107rnn6h//+EcbVds6+vfvX2vMFotFubm5da4fjXP83nvv6corr1RycrIsFoteffXVoHbDMLRgwQL17t1bdrtdo0aN0jfffNNov4sXL1b//v3VpUsXDR8+XJs3bw7TCELX0Jj9fr/uvPNOnXvuuerWrZuSk5M1ffp0HThwoME+m/P5aGuNzfWMGTNqjWHs2LGN9hutcy2pzs+3xWLRww8/XG+f7X2um/L7dPz4ceXm5qp79+6Ki4vTpEmTVFJS0mC/zf0uaC6CTzuzceNG5ebm6qOPPtLatWvl9/s1evRoHT16tMHXJSQk6ODBg4HH3r1726ji1nH22WcH1f/+++/Xu+6HH36oadOmaebMmfr00081YcIETZgwQZ9//nkbVtwyH3/8cdB4165dK0maPHlyva+Jtjk+evSoMjIytHjx4jrbFy1apD/96U965plntGnTJnXr1k1jxozR8ePH6+3zr3/9q+bOnauFCxdq69atysjI0JgxY3To0KFwDSMkDY352LFj2rp1q+655x5t3bpVq1atktvt1lVXXdVov6F8PiKhsbmWpLFjxwaN4eWXX26wz2iea0lBYz148KCWLl0qi8WiSZMmNdhve57rpvw+zZkzR2+88YZWrlypjRs36sCBA5o4cWKD/Tbnu6BFDLRrhw4dMiQZGzdurHedZcuWGQ6Ho+2KamULFy40MjIymrz+tddea4wfPz5o2fDhw42bb765lStrO7/+9a+NM844w6iurq6zPdrnWJKxevXqwPPq6mojKSnJePjhhwPLPB6PYbPZjJdffrnefoYNG2bk5uYGnldVVRnJyclGXl5eWOpuiVPHXJfNmzcbkoy9e/fWu06on49Iq2vcOTk5RnZ2dkj9dLS5zs7ONi677LIG14m2uT7198nj8RhWq9VYuXJlYJ0vv/zSkGQUFhbW2Udzvwtagi0+7VxZWZkkKTExscH1jhw5on79+iklJUXZ2dnauXNnW5TXar755hslJydr4MCBuu6661RUVFTvuoWFhRo1alTQsjFjxqiwsDDcZYZFZWWlXnzxRd10000N3hg32uf4p3bv3q3i4uKgeXQ4HBo+fHi981hZWaktW7YEvaZTp04aNWpU1M59WVmZLBaLnE5ng+uF8vlorzZs2KBevXopNTVVs2bN0g8//FDvuh1trktKSvTWW29p5syZja4bTXN96u/Tli1b5Pf7g+YtLS1Nffv2rXfemvNd0FIEn3asurpat912my666CKdc8459a6XmpqqpUuX6rXXXtOLL76o6upqXXjhhdq/f38bVtt8w4cPV35+vt5++209/fTT2r17t/7t3/5NFRUVda5fXFwsl8sVtMzlcqm4uLgtym11r776qjwej2bMmFHvOtE+x6eqmatQ5vH7779XVVVVh5n748eP684779S0adMavHljqJ+P9mjs2LFavny5CgoK9Pvf/14bN27UuHHjVFVVVef6HW2un3/+ecXHxze6yyea5rqu36fi4mLFxsbWCvINzVtzvgtairuzt2O5ubn6/PPPG93Hm5WVpaysrMDzCy+8UIMHD9azzz6rBx54INxltti4ceMC/52enq7hw4erX79+euWVV5r0L6Rot2TJEo0bN07Jycn1rhPtc4xgfr9f1157rQzD0NNPP93guh3h8zF16tTAf5977rlKT0/XGWecoQ0bNmjkyJERrKxtLF26VNddd12jJyRE01w39fepPWKLTzt166236s0339T69evVp0+fkF5rtVp13nnnadeuXWGqLrycTqcGDRpUb/1JSUm1zhIoKSlRUlJSW5TXqvbu3at169bpP/7jP0J6XbTPcc1chTKPPXr0UOfOnaN+7mtCz969e7V27doGt/bUpbHPRzQYOHCgevToUe8YOspcS9I///lPud3ukD/jUvud6/p+n5KSklRZWSmPxxO0fkPz1pzvgpYi+LQzhmHo1ltv1erVq/Xuu+9qwIABIfdRVVWlzz77TL179w5DheF35MgRffvtt/XWn5WVpYKCgqBla9euDdoiEi2WLVumXr16afz48SG9LtrneMCAAUpKSgqax/Lycm3atKneeYyNjdXQoUODXlNdXa2CgoKomfua0PPNN99o3bp16t69e8h9NPb5iAb79+/XDz/8UO8YOsJc11iyZImGDh2qjIyMkF/b3ua6sd+noUOHymq1Bs2b2+1WUVFRvfPWnO+C1hgI2pFZs2YZDofD2LBhg3Hw4MHA49ixY4F1brjhBuOuu+4KPL/vvvuMNWvWGN9++62xZcsWY+rUqUaXLl2MnTt3RmIIIbv99tuNDRs2GLt37zY++OADY9SoUUaPHj2MQ4cOGYZRe7wffPCBERMTYzzyyCPGl19+aSxcuNCwWq3GZ599FqkhNEtVVZXRt29f484776zV1hHmuKKiwvj000+NTz/91JBkPPbYY8ann34aOIPpoYceMpxOp/Haa68ZO3bsMLKzs40BAwYYXq830Mdll11mPPHEE4HnK1asMGw2m5Gfn2988cUXxn/+538aTqfTKC4ubvPx1aWhMVdWVhpXXXWV0adPH2Pbtm1Bn2+fzxfo49QxN/b5aA8aGndFRYUxb948o7Cw0Ni9e7exbt064/zzzzfOOuss4/jx44E+OtJc1ygrKzO6du1qPP3003X2EW1z3ZTfp1tuucXo27ev8e677xqffPKJkZWVZWRlZQX1k5qaaqxatSrwvCnfBa2J4NPOSKrzsWzZssA6v/jFL4ycnJzA89tuu83o27evERsba7hcLuPf//3fja1bt7Z98c00ZcoUo3fv3kZsbKxx+umnG1OmTDF27doVaD91vIZhGK+88ooxaNAgIzY21jj77LONt956q42rbrk1a9YYkgy3212rrSPM8fr16+v8f7lmXNXV1cY999xjuFwuw2azGSNHjqz1t+jXr5+xcOHCoGVPPPFE4G8xbNgw46OPPmqjETWuoTHv3r273s/3+vXrA32cOubGPh/tQUPjPnbsmDF69GijZ8+ehtVqNfr162f88pe/rBVgOtJc13j22WcNu91ueDyeOvuItrluyu+T1+s1Zs+ebZx22mlG165djauvvto4ePBgrX5++pqmfBe0Jsv/FwEAANDhcYwPAAAwDYIPAAAwDYIPAAAwDYIPAAAwDYIPAAAwDYIPAAAwDYIPAAAwDYIPAAAwDYIPgA7FYrHo1Vdfjch7z5gxQxMmTIjIewNoGoIPYHIzZsyQxWLRLbfcUqstNzdXFotFM2bMaPvC6uH1epWYmKgePXrI5/NFuhwAUYbgA0ApKSlasWKFvF5vYNnx48f10ksvqW/fvhGsrLa///3vOvvss5WWlhaxLTsAohfBB4DOP/98paSkaNWqVYFlq1atUt++fXXeeecFrVtdXa28vDwNGDBAdrtdGRkZ+tvf/hZor6qq0syZMwPtqamp+uMf/xjUR80uoUceeUS9e/dW9+7dlZubK7/f32itS5Ys0fXXX6/rr79eS5YsqXOdgwcPaty4cbLb7Ro4cGBQfRs2bJDFYpHH4wks27ZtmywWi/bs2SNJys/Pl9Pp1Jo1azR48GDFxcVp7NixOnjwYNA4586dK6fTqe7du+u//uu/dOqtD99++21dfPHFgXWuuOIKffvtt4H2PXv2yGKxaNWqVRoxYoS6du2qjIwMFRYWBvXzwQcf6NJLL1XXrl112mmnacyYMfrxxx+bNB8AghF8AEiSbrrpJi1btizwfOnSpbrxxhtrrZeXl6fly5frmWee0c6dOzVnzhxdf/312rhxo6STP8R9+vTRypUr9cUXX2jBggX6zW9+o1deeSWon/Xr1+vbb7/V+vXr9fzzzys/P1/5+fkN1vjtt9+qsLBQ1157ra699lr985//1N69e2utd88992jSpEnavn27rrvuOk2dOlVffvllSH+PY8eO6ZFHHtELL7yg9957T0VFRZo3b16g/dFHH1V+fr6WLl2q999/X6WlpVq9enVQH0ePHtXcuXP1ySefqKCgQJ06ddLVV1+t6urqoPV++9vfat68edq2bZsGDRqkadOm6cSJE5JOhrKRI0dqyJAhKiws1Pvvv68rr7xSVVVVkhqfDwCnCNt93wFEhZycHCM7O9s4dOiQYbPZjD179hh79uwxunTpYhw+fNjIzs42cnJyDMMwjOPHjxtdu3Y1Pvzww6A+Zs6caUybNq3e98jNzTUmTZoU9J79+vUzTpw4EVg2efJkY8qUKQ3W+pvf/MaYMGFC4Hl2draxcOHCoHUkGbfcckvQsuHDhxuzZs0yDMMw1q9fb0gyfvzxx0D7p59+akgydu/ebRiGYSxbtsyQZOzatSuwzuLFiw2XyxV43rt3b2PRokWB536/3+jTp4+RnZ1db/2HDx82JBmfffaZYRiGsXv3bkOS8Ze//CWwzs6dOw1JxpdffmkYhmFMmzbNuOiii+rsr7nzAZhZTAQzF4B2pGfPnho/frzy8/NlGIbGjx+vHj16BK2za9cuHTt2TJdffnnQ8srKyqBdYosXL9bSpUtVVFQkr9eryspKZWZmBr3m7LPPVufOnQPPe/furc8++6ze+qqqqvT8888H7Ta7/vrrNW/ePC1YsECdOv1rA3ZWVlbQa7OysrRt27ZG/wY/1bVrV51xxhlB9R06dEiSVFZWpoMHD2r48OGB9piYGF1wwQVBu7u++eYbLViwQJs2bdL3338f2NJTVFSkc845J7Beenp60PtI0qFDh5SWlqZt27Zp8uTJddbY1PkA8C8EHwABN910k2699VZJJ8PLqY4cOSJJeuutt3T66acHtdlsNknSihUrNG/ePD366KPKyspSfHy8Hn74YW3atClofavVGvTcYrHU2gX0U2vWrNF3332nKVOmBC2vqqpSQUFBrR//+tQEpJ8GlLqOLaqrPuOUY3gac+WVV6pfv37685//rOTkZFVXV+ucc85RZWVlve9lsVgkKfC3sNvt9fbflPkAEIxjfAAEjB07VpWVlfL7/RozZkyt9iFDhshms6moqEhnnnlm0CMlJUXSyQNxL7zwQs2ePVvnnXeezjzzzKADeptryZIlmjp1qrZt2xb0mDp1aq2DnD/66KNazwcPHizp5JYtSUEHKoe6NcjhcKh3795BYe7EiRPasmVL4PkPP/wgt9utu+++WyNHjtTgwYMDBySHIj09XQUFBXW2NWU+AARjiw+AgM6dOwcOAv7pbqga8fHxmjdvnubMmaPq6mpdfPHFKisr0wcffKCEhATl5OTorLPO0vLly7VmzRoNGDBAL7zwgj7++GMNGDCg2XUdPnxYb7zxhl5//fWgXUSSNH36dF199dUqLS1VYmKiJGnlypW64IILdPHFF+u///u/tXnz5kA4qgkF9957rx588EF9/fXXevTRR0Ou6de//rUeeughnXXWWUpLS9Njjz0WdKbYaaedpu7du+u5555T7969VVRUpLvuuivk95k/f77OPfdczZ49W7fccotiY2O1fv16TZ48WT169Gh0PgAEY4sPgCAJCQlKSEiot/2BBx7QPffco7y8PA0ePFhjx47VW2+9FQg2N998syZOnKgpU6Zo+PDh+uGHHzR79uwW1bR8+XJ169ZNI0eOrNU2cuRI2e12vfjii4Fl9913n1asWKH09HQtX75cL7/8soYMGSLp5G6ll19+WV999ZXS09P1+9//Xr/73e9Crun222/XDTfcoJycnMAuvauvvjrQ3qlTJ61YsUJbtmzROeecozlz5ujhhx8O+X0GDRqkd955R9u3b9ewYcOUlZWl1157TTExJ//d2th8AAhmMULdaQ0AABCl2OIDAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABMg+ADAABM4/8AKN75GZpKcl4AAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -1831,7 +1836,7 @@ "### get_glycanova\n", "\n", "> get_glycanova (df, groups, impute=True, motifs=False,\n", - "> feature_set=['exhaustive', 'known'], min_samples=0,\n", + "> feature_set=['exhaustive', 'known'], min_samples=0.1,\n", "> posthoc=True, custom_motifs=[], gamma=0.1)\n", "\n", "Calculate an ANOVA for each glycan (or motif) in the DataFrame\n", @@ -1843,7 +1848,7 @@ "| impute (bool): replaces zeroes with with a Random Forest based model; default:True\n", "| motifs (bool): whether to analyze full sequences (False) or motifs (True); default:False\n", "| feature_set (list): which feature set to use for annotations, add more to list to expand; default is 'known'; options are: 'known' (hand-crafted glycan features), | 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), | and 'chemical' (molecular properties of glycan)\n", - "| min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero\n", + "| min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10%\n", "| posthoc (bool): whether to do Tukey's HSD test post-hoc to find out which differences were significant; default:True\n", "| custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty\n", "| gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n", @@ -1859,7 +1864,7 @@ "### get_glycanova\n", "\n", "> get_glycanova (df, groups, impute=True, motifs=False,\n", - "> feature_set=['exhaustive', 'known'], min_samples=0,\n", + "> feature_set=['exhaustive', 'known'], min_samples=0.1,\n", "> posthoc=True, custom_motifs=[], gamma=0.1)\n", "\n", "Calculate an ANOVA for each glycan (or motif) in the DataFrame\n", @@ -1871,7 +1876,7 @@ "| impute (bool): replaces zeroes with with a Random Forest based model; default:True\n", "| motifs (bool): whether to analyze full sequences (False) or motifs (True); default:False\n", "| feature_set (list): which feature set to use for annotations, add more to list to expand; default is 'known'; options are: 'known' (hand-crafted glycan features), | 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), | and 'chemical' (molecular properties of glycan)\n", - "| min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero\n", + "| min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10%\n", "| posthoc (bool): whether to do Tukey's HSD test post-hoc to find out which differences were significant; default:True\n", "| custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty\n", "| gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n", @@ -1901,7 +1906,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "You're working with an alpha of 0.0694557066556809 that has been adjusted for your sample size of 9.\n" + "You're working with an alpha of 0.0694557066556809 that has been adjusted for your sample size of 9.\n", + "Significance inflation detected. The CLR/ALR transformation cannot seem to handle this dataset. Proceed with caution; for now switching to Bonferroni correction for being conservative about this.\n" ] }, { @@ -1927,121 +1933,135 @@ " \n", " Glycan\n", " F statistic\n", + " p-val\n", " corr p-val\n", " significant\n", " \n", " \n", " \n", " \n", - " 8\n", - " GlcNAc(b1-4)GlcNAc\n", - " 82.235752\n", - " 0.000194\n", + " 9\n", + " Man(a1-3)Man\n", + " 86.040072\n", + " 0.000038\n", + " 0.000497\n", " True\n", " \n", " \n", - " 9\n", - " Man(a1-3)Man\n", - " 79.916469\n", - " 0.000194\n", + " 8\n", + " GlcNAc(b1-4)GlcNAc\n", + " 83.915951\n", + " 0.000041\n", + " 0.000535\n", " True\n", " \n", " \n", " 10\n", " GlcNAc\n", - " 82.235752\n", - " 0.000194\n", + " 83.915951\n", + " 0.000041\n", + " 0.000535\n", " True\n", " \n", " \n", - " 11\n", - " Man(a1-?)Man\n", - " 68.236278\n", - " 0.000194\n", + " 12\n", + " Man\n", + " 76.691977\n", + " 0.000053\n", + " 0.000694\n", " True\n", " \n", " \n", - " 12\n", - " Man\n", - " 71.277353\n", - " 0.000194\n", + " 11\n", + " Man(a1-?)Man\n", + " 74.086792\n", + " 0.000059\n", + " 0.000766\n", " True\n", " \n", " \n", - " 3\n", - " Kdo\n", - " 40.864365\n", - " 0.000462\n", + " 7\n", + " Man(a1-2)Man\n", + " 49.366261\n", + " 0.000188\n", + " 0.002444\n", " True\n", " \n", " \n", " 4\n", " Kdo(a2-?)Kdo\n", - " 40.864365\n", - " 0.000462\n", + " 42.964593\n", + " 0.000278\n", + " 0.003614\n", " True\n", " \n", " \n", " 5\n", " GalNAc(a1-4)GlcNAcA\n", - " 40.864365\n", - " 0.000462\n", + " 42.964593\n", + " 0.000278\n", + " 0.003614\n", " True\n", " \n", " \n", - " 7\n", - " Man(a1-2)Man\n", - " 45.032728\n", - " 0.000462\n", + " 3\n", + " Kdo\n", + " 42.964593\n", + " 0.000278\n", + " 0.003614\n", " True\n", " \n", " \n", " 6\n", " core_fucose(a1-3)\n", - " 21.120818\n", - " 0.002501\n", + " 19.566967\n", + " 0.002349\n", + " 0.030541\n", " True\n", " \n", " \n", " 0\n", " Glc\n", - " 12.185651\n", - " 0.007710\n", - " True\n", + " 12.128481\n", + " 0.007798\n", + " 0.101373\n", + " False\n", " \n", " \n", " 1\n", " Glc(b1-3)Glc\n", - " 12.185651\n", - " 0.007710\n", - " True\n", + " 12.128481\n", + " 0.007798\n", + " 0.101373\n", + " False\n", " \n", " \n", " 2\n", " betaGlucan\n", - " 12.185651\n", - " 0.007710\n", - " True\n", + " 12.128481\n", + " 0.007798\n", + " 0.101373\n", + " False\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Glycan F statistic corr p-val significant\n", - "8 GlcNAc(b1-4)GlcNAc 82.235752 0.000194 True\n", - "9 Man(a1-3)Man 79.916469 0.000194 True\n", - "10 GlcNAc 82.235752 0.000194 True\n", - "11 Man(a1-?)Man 68.236278 0.000194 True\n", - "12 Man 71.277353 0.000194 True\n", - "3 Kdo 40.864365 0.000462 True\n", - "4 Kdo(a2-?)Kdo 40.864365 0.000462 True\n", - "5 GalNAc(a1-4)GlcNAcA 40.864365 0.000462 True\n", - "7 Man(a1-2)Man 45.032728 0.000462 True\n", - "6 core_fucose(a1-3) 21.120818 0.002501 True\n", - "0 Glc 12.185651 0.007710 True\n", - "1 Glc(b1-3)Glc 12.185651 0.007710 True\n", - "2 betaGlucan 12.185651 0.007710 True" + " Glycan F statistic p-val corr p-val significant\n", + "9 Man(a1-3)Man 86.040072 0.000038 0.000497 True\n", + "8 GlcNAc(b1-4)GlcNAc 83.915951 0.000041 0.000535 True\n", + "10 GlcNAc 83.915951 0.000041 0.000535 True\n", + "12 Man 76.691977 0.000053 0.000694 True\n", + "11 Man(a1-?)Man 74.086792 0.000059 0.000766 True\n", + "7 Man(a1-2)Man 49.366261 0.000188 0.002444 True\n", + "4 Kdo(a2-?)Kdo 42.964593 0.000278 0.003614 True\n", + "5 GalNAc(a1-4)GlcNAcA 42.964593 0.000278 0.003614 True\n", + "3 Kdo 42.964593 0.000278 0.003614 True\n", + "6 core_fucose(a1-3) 19.566967 0.002349 0.030541 True\n", + "0 Glc 12.128481 0.007798 0.101373 False\n", + "1 Glc(b1-3)Glc 12.128481 0.007798 0.101373 False\n", + "2 betaGlucan 12.128481 0.007798 0.101373 False" ] }, "execution_count": null, @@ -2158,7 +2178,7 @@ "### get_time_series\n", "\n", "> get_time_series (df, impute=True, motifs=False, feature_set=['known',\n", - "> 'exhaustive'], degree=1, min_samples=0,\n", + "> 'exhaustive'], degree=1, min_samples=0.1,\n", "> custom_motifs=[], gamma=0.1)\n", "\n", "Analyzes time series data of glycans using an OLS model\n", @@ -2170,7 +2190,7 @@ "| motifs (bool): whether to analyze full sequences (False) or motifs (True); default:False\n", "| feature_set (list): which feature set to use for annotations, add more to list to expand; default is 'known'; options are: 'known' (hand-crafted glycan features), | 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), | and 'chemical' (molecular properties of glycan)\n", "| degree (int): degree of the polynomial for regression, default:1 for linear regression\n", - "| min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero\n", + "| min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10%\n", "| custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty\n", "| gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n", "\n", @@ -2189,7 +2209,7 @@ "### get_time_series\n", "\n", "> get_time_series (df, impute=True, motifs=False, feature_set=['known',\n", - "> 'exhaustive'], degree=1, min_samples=0,\n", + "> 'exhaustive'], degree=1, min_samples=0.1,\n", "> custom_motifs=[], gamma=0.1)\n", "\n", "Analyzes time series data of glycans using an OLS model\n", @@ -2201,7 +2221,7 @@ "| motifs (bool): whether to analyze full sequences (False) or motifs (True); default:False\n", "| feature_set (list): which feature set to use for annotations, add more to list to expand; default is 'known'; options are: 'known' (hand-crafted glycan features), | 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), | and 'chemical' (molecular properties of glycan)\n", "| degree (int): degree of the polynomial for regression, default:1 for linear regression\n", - "| min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero\n", + "| min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10%\n", "| custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty\n", "| gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n", "\n", @@ -2234,7 +2254,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "You're working with an alpha of 0.0694557066556809 that has been adjusted for your sample size of 9.\n" + "You're working with an alpha of 0.0694557066556809 that has been adjusted for your sample size of 9.\n", + "Significance inflation detected. The CLR/ALR transformation cannot seem to handle this dataset. Proceed with caution; for now switching to Bonferroni correction for being conservative about this.\n" ] }, { @@ -2269,17 +2290,17 @@ " \n", " 0\n", " Fuc(a1-2)Gal(b1-3)GalNAc\n", - " -0.087399\n", - " 0.006474\n", - " 0.012042\n", + " -0.129345\n", + " 0.003815\n", + " 0.007630\n", " True\n", " \n", " \n", " 1\n", " Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga...\n", - " 0.085752\n", - " 0.012042\n", - " 0.012042\n", + " 0.127698\n", + " 0.006367\n", + " 0.012734\n", " True\n", " \n", " \n", @@ -2288,12 +2309,12 @@ ], "text/plain": [ " Glycan Change p-val \\\n", - "0 Fuc(a1-2)Gal(b1-3)GalNAc -0.087399 0.006474 \n", - "1 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga... 0.085752 0.012042 \n", + "0 Fuc(a1-2)Gal(b1-3)GalNAc -0.129345 0.003815 \n", + "1 Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-6)[Gal(b1-3)]Ga... 0.127698 0.006367 \n", "\n", " corr p-val significant \n", - "0 0.012042 True \n", - "1 0.012042 True " + "0 0.007630 True \n", + "1 0.012734 True " ] }, "execution_count": null, @@ -2428,23 +2449,23 @@ " \n", " \n", " \n", - " 1\n", - " Gal(b1-3)GalNAc\n", - " 0.055556\n", + " 0\n", + " Neu5Ac(a2-3)Gal(b1-3)GalNAc\n", + " 0.013889\n", " 0.013889\n", " 24.0\n", - " 1.5\n", - " 0.021117\n", + " 16.5\n", + " 0.633554\n", " True\n", " \n", " \n", - " 0\n", - " Neu5Ac(a2-3)Gal(b1-3)GalNAc\n", - " 0.088095\n", - " 0.044048\n", + " 1\n", + " Gal(b1-3)GalNAc\n", + " 0.013889\n", + " 0.013889\n", " 24.0\n", - " 16.5\n", - " 0.496296\n", + " 1.5\n", + " 0.080996\n", " True\n", " \n", " \n", @@ -2454,18 +2475,18 @@ " 0.113095\n", " 24.0\n", " 13.5\n", - " 0.298503\n", + " 0.381456\n", " True\n", " \n", " \n", " 3\n", " Fuc(a1-2)Gal(b1-3)GalNAc\n", - " 0.113095\n", - " 0.113095\n", + " 0.244048\n", + " 0.244048\n", " 24.0\n", " 4.5\n", - " 0.332329\n", - " True\n", + " 0.464148\n", + " False\n", " \n", " \n", "\n", @@ -2473,16 +2494,16 @@ ], "text/plain": [ " Molecule_Name BH_Q_Value Adjusted_P_value Period_Length \\\n", - "1 Gal(b1-3)GalNAc 0.055556 0.013889 24.0 \n", - "0 Neu5Ac(a2-3)Gal(b1-3)GalNAc 0.088095 0.044048 24.0 \n", + "0 Neu5Ac(a2-3)Gal(b1-3)GalNAc 0.013889 0.013889 24.0 \n", + "1 Gal(b1-3)GalNAc 0.013889 0.013889 24.0 \n", "2 Gal(b1-3)[Neu5Ac(a2-6)]GalNAc 0.113095 0.113095 24.0 \n", - "3 Fuc(a1-2)Gal(b1-3)GalNAc 0.113095 0.113095 24.0 \n", + "3 Fuc(a1-2)Gal(b1-3)GalNAc 0.244048 0.244048 24.0 \n", "\n", " Lag_Phase Amplitude significant \n", - "1 1.5 0.021117 True \n", - "0 16.5 0.496296 True \n", - "2 13.5 0.298503 True \n", - "3 4.5 0.332329 True " + "0 16.5 0.633554 True \n", + "1 1.5 0.080996 True \n", + "2 13.5 0.381456 True \n", + "3 4.5 0.464148 False " ] }, "execution_count": null, @@ -2555,47 +2576,47 @@ " 0.013889\n", " 24.0\n", " 15.0\n", - " 2.168404e-17\n", + " 6.938894e-17\n", + " True\n", + " \n", + " \n", + " 0\n", + " Neu5Ac(a2-?)\n", + " 0.110119\n", + " 0.044048\n", + " 24.0\n", + " 13.5\n", + " 2.716547e-01\n", " True\n", " \n", " \n", " 2\n", " Gal(b1-3)\n", " 0.141369\n", - " 0.056548\n", + " 0.113095\n", " 24.0\n", " 6.0\n", - " 2.623302e-01\n", + " 3.016949e-01\n", " True\n", " \n", " \n", " 4\n", " Fuc(a1-2)\n", - " 0.188492\n", + " 0.141369\n", " 0.113095\n", " 24.0\n", " 4.5\n", - " 2.291082e-01\n", + " 3.695613e-01\n", " True\n", " \n", " \n", - " 0\n", - " Neu5Ac(a2-?)\n", - " 0.244048\n", - " 0.244048\n", - " 24.0\n", - " 12.0\n", - " 5.598415e-01\n", - " False\n", - " \n", - " \n", " 3\n", " Neu5Ac(a2-6)\n", " 0.244048\n", " 0.244048\n", " 24.0\n", " 13.5\n", - " 3.470592e-01\n", + " 4.264049e-01\n", " False\n", " \n", " \n", @@ -2605,17 +2626,17 @@ "text/plain": [ " Molecule_Name BH_Q_Value Adjusted_P_value Period_Length Lag_Phase \\\n", "1 Neu5Ac(a2-3) 0.069444 0.013889 24.0 15.0 \n", - "2 Gal(b1-3) 0.141369 0.056548 24.0 6.0 \n", - "4 Fuc(a1-2) 0.188492 0.113095 24.0 4.5 \n", - "0 Neu5Ac(a2-?) 0.244048 0.244048 24.0 12.0 \n", + "0 Neu5Ac(a2-?) 0.110119 0.044048 24.0 13.5 \n", + "2 Gal(b1-3) 0.141369 0.113095 24.0 6.0 \n", + "4 Fuc(a1-2) 0.141369 0.113095 24.0 4.5 \n", "3 Neu5Ac(a2-6) 0.244048 0.244048 24.0 13.5 \n", "\n", " Amplitude significant \n", - "1 2.168404e-17 True \n", - "2 2.623302e-01 True \n", - "4 2.291082e-01 True \n", - "0 5.598415e-01 False \n", - "3 3.470592e-01 False " + "1 6.938894e-17 True \n", + "0 2.716547e-01 True \n", + "2 3.016949e-01 True \n", + "4 3.695613e-01 True \n", + "3 4.264049e-01 False " ] }, "execution_count": null, @@ -2657,7 +2678,7 @@ "| feature_set (list): which feature set to use for annotations, add more to list to expand; default is 'known'; options are: 'known' (hand-crafted glycan features), | 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), | and 'chemical' (molecular properties of glycan)\n", "| custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty\n", "| paired (bool): whether samples are paired or not (e.g., tumor & tumor-adjacent tissue from same patient); default:False\n", - "| permutations (int): number of permutations to perform in ANOSIM statistical test; default:999\n", + "| permutations (int): number of permutations to perform in ANOSIM and PERMANOVA statistical test; default:999\n", "| gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n", "\n", "| Returns:\n", @@ -2669,7 +2690,7 @@ "| (iv) Uncorrected p-values (Welch's t-test) for difference in mean\n", "| (v) Corrected p-values (Welch's t-test with two-stage Benjamini-Hochberg correction) for difference in mean\n", "| (vi) Significance: True/False of whether the corrected p-value lies below the sample size-appropriate significance threshold\n", - "| (vii) Effect size as Cohen's d (ANOSIM R for beta; F statistics for Shannon/Simpson (ANOVA))" + "| (vii) Effect size as Cohen's d (ANOSIM R for beta; F statistics for PERMANOVA and Shannon/Simpson (ANOVA))" ], "text/plain": [ "---\n", @@ -2693,7 +2714,7 @@ "| feature_set (list): which feature set to use for annotations, add more to list to expand; default is 'known'; options are: 'known' (hand-crafted glycan features), | 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), | and 'chemical' (molecular properties of glycan)\n", "| custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty\n", "| paired (bool): whether samples are paired or not (e.g., tumor & tumor-adjacent tissue from same patient); default:False\n", - "| permutations (int): number of permutations to perform in ANOSIM statistical test; default:999\n", + "| permutations (int): number of permutations to perform in ANOSIM and PERMANOVA statistical test; default:999\n", "| gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n", "\n", "| Returns:\n", @@ -2705,7 +2726,7 @@ "| (iv) Uncorrected p-values (Welch's t-test) for difference in mean\n", "| (v) Corrected p-values (Welch's t-test with two-stage Benjamini-Hochberg correction) for difference in mean\n", "| (vi) Significance: True/False of whether the corrected p-value lies below the sample size-appropriate significance threshold\n", - "| (vii) Effect size as Cohen's d (ANOSIM R for beta; F statistics for Shannon/Simpson (ANOVA))" + "| (vii) Effect size as Cohen's d (ANOSIM R for beta; F statistics for PERMANOVA and Shannon/Simpson (ANOVA))" ] }, "execution_count": null, @@ -2768,7 +2789,7 @@ " 1.855369\n", " 0.000420\n", " -8.941867\n", - " 0.000840\n", + " 0.001261\n", " True\n", " \n", " \n", @@ -2778,18 +2799,18 @@ " 0.804112\n", " 0.002471\n", " -8.011404\n", - " 0.002471\n", + " 0.003706\n", " True\n", " \n", " \n", " 2\n", - " Beta diversity\n", + " Beta diversity (ANOSIM)\n", " NaN\n", " NaN\n", " 0.108108\n", " 0.777778\n", - " 0.072072\n", - " True\n", + " 0.108108\n", + " False\n", " \n", " \n", " 3\n", @@ -2798,7 +2819,17 @@ " 12.000000\n", " 0.422650\n", " -0.816497\n", - " 0.211325\n", + " 0.422650\n", + " False\n", + " \n", + " \n", + " 4\n", + " Beta diversity (PERMANOVA)\n", + " NaN\n", + " NaN\n", + " 0.506507\n", + " 4.639672\n", + " 0.506507\n", " False\n", " \n", " \n", @@ -2806,17 +2837,19 @@ "" ], "text/plain": [ - " Metric Group1 mean Group2 mean p-val Effect size \\\n", - "0 shannon_diversity 2.278677 1.855369 0.000420 -8.941867 \n", - "1 simpson_diversity 0.876248 0.804112 0.002471 -8.011404 \n", - "2 Beta diversity NaN NaN 0.108108 0.777778 \n", - "3 species_richness 13.000000 12.000000 0.422650 -0.816497 \n", - "\n", - " corr p-val significant \n", - "0 0.000840 True \n", - "1 0.002471 True \n", - "2 0.072072 True \n", - "3 0.211325 False " + " Metric Group1 mean Group2 mean p-val \\\n", + "0 shannon_diversity 2.278677 1.855369 0.000420 \n", + "1 simpson_diversity 0.876248 0.804112 0.002471 \n", + "2 Beta diversity (ANOSIM) NaN NaN 0.108108 \n", + "3 species_richness 13.000000 12.000000 0.422650 \n", + "4 Beta diversity (PERMANOVA) NaN NaN 0.506507 \n", + "\n", + " Effect size corr p-val significant \n", + "0 -8.941867 0.001261 True \n", + "1 -8.011404 0.003706 True \n", + "2 0.777778 0.108108 False \n", + "3 -0.816497 0.422650 False \n", + "4 4.639672 0.506507 False " ] }, "execution_count": null, @@ -2920,7 +2953,7 @@ }, { "data": { - "image/png": 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", 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CDQCOjo7Q0NDAqVOnKu2bkJAAIyMjdO/eHWKxGAUFBUqJkYiIiIiIiJRLsY3UlLgeui4VFRWhqKhIpkwkEkEkEr3xObOzs2FsbCxTpqWlBQMDA2RnZ1fYb+zYsWjfvj1MTU1x4cIFBAUFITMzEzt37nzjWIiIiIiIiEg1FNtIrYH8FxoaipYtW8ocoaGh5d7znDlz5DY6++dx5cqVN36mkydPhpOTE6ytreHh4YH/+7//w08//YQbN2688TmJiIiIiIhINRQc6ZbUVhwqJRaL4e/vL1NW0Sj3F198gfHjx1d6PgsLC5iYmCAnJ0emvKSkBLm5uTVar/3uu+8CAK5fv44OHTpUux8RERERERGpnkJJt7LWQ9e1mkwlb926NVq3bl1lu759++Lp06c4d+4cevXqBQA4dOgQJBKJNJGujvT0dABA27Ztq92HiIiIiIiI6geOdCuJlZUVnJ2d4ePjg7Vr16K4uBgzZszAmDFjpDuX37t3D4MGDcL//d//oXfv3rhx4wY2b94MFxcXGBoa4sKFC/Dz88P7778PGxsbFd8RERERERER1ZSCrwyjyiQkJGDGjBkYNGgQNDQ0MGrUKERGRkrri4uLkZmZKd2dvGnTpjhw4ABWrlyJ/Px8mJmZYdSoUZg3b56qboGIiIiIiIgUwOnlSmRgYIDNmzdXWG9ubo6yv+0Ab2ZmhiNHjtRFaERERERERFQHOL2ciIiIiIiISEmEsrI3f9l2b1OH2oxFZU7/ydFlIiIiIiIiqn2KTS/nSDcp2aUOQ1UdgsJ0m79UdQgKa6pbouoQFKKhpf5LYbT01P8eip9rqDoEhey+85aqQ1BYE/X/bYTDmgWqDkEh3aCr6hAUdkMoVHUIChv0snpvramvPux0T9UhKKxNcrKqQyCqM1zTTURERERERKQkiu1e/uYz04mIiIiIiIgaPMU2UgOnlxMRERERERFVRKEFdpKysgZxEBERERFR/TRw4EDMnj27wnpzc3OsXLmyzuL5p+TkZAiCgKdPn6osBkEQsGvXLpVdnyqnUNJdWiZpEAcREREREamnM2fOYPLkyXVyrfK+AOjXrx/u37+Pli1b1vr1srOz4evrC0tLS2hra6NNmzbo378/oqOjUVDwZhtLxsXFQRAEODs7y5Q/ffoUgiAguZxN7qZMmQJNTU1s3779ja7Z2Cm2ppsbqRERERERkQq1bt1apddv2rQpTExMav28N2/eRP/+/aGvr4+lS5fC2toaIpEIFy9exPr16/HWW2/h448/fqNza2lp4cCBAzh8+DA++OCDStsWFBQgMTERgYGBiI2NxaeffvpG12zMONLNkW4iIiIionqtpKQEM2bMQMuWLWFkZIT58+dLN3X++/TysrIyBAcHo127dhCJRDA1NcWsWbOk5ykqKkJAQADeeust6Onp4d1335Ub2T1+/DgGDhwIXV1dtGrVCk5OTnjy5AnGjx+PI0eOYNWqVRAEAYIg4Pbt23LTy+Pi4qCvr4///ve/sLKyQrNmzeDs7Iz79+/L3M+sWbOgr68PQ0NDBAUFYdy4cRgxYoS0zbRp06ClpYWzZ8/Czc0NVlZWsLCwwPDhw7F37164urpW+Lzu3r0Ld3d3GBgYQE9PD/b29jh16pS0Xk9PD97e3pgzZ06Vz3779u3o2rUr5syZg6NHj+KPP/6QqS8qKkJQUBDMzMwgEolgaWmJmJiYKs/bmHBNN9d0ExERERHVa/Hx8dDS0sLp06exatUqhIeHY8OGDXLtduzYgYiICKxbtw7Xrl3Drl27YG1tLa2fMWMGUlNTkZiYiAsXLuDTTz+Fs7Mzrl27BgBIT0/HoEGD0LVrV6SmpiIlJQWurq4oLS3FqlWr0LdvX/j4+OD+/fu4f/8+zMzMyo23oKAAy5cvx6ZNm3D06FFkZWUhICBAWr9s2TIkJCRg48aNOH78OJ49eyazJvvx48dISkrC9OnToaenV+41BEEotzwvLw8ODg64d+8e9uzZg4yMDAQGBkIikR1sDA4OxsWLF/Hjjz+W/9D/v5iYGHh6eqJly5YYMmQI4uLiZOq9vLywZcsWREZG4vLly1i3bh2aNWtW6TkbG04vJyIiIiKiOlVUVISioiKZMpFIBJFIVG57MzMzREREQBAEdO7cGRcvXkRERAR8fHxk2mVlZcHExASOjo5o0qQJ2rVrh969e0vrNm7ciKysLJiamgIAAgIC8Ouvv2Ljxo1YunQpvvnmG9jb22PNmjXSc3br1k3666ZNm0JXV7fK6eTFxcVYu3YtOnToAOBVsr9o0SJpfVRUFMRiMUaOHAkAWL16Nfbt2yetv379OsrKytC5c2eZ8xoZGaGwsBAAMH36dCxbtkzu2ps3b8bDhw9x5swZGBgYAAAsLS3l2pmamsLX1xdz586VGWH/u2vXruHkyZPYuXMnAMDT0xP+/v6YN28eBEHA1atXsW3bNuzfvx+Ojo4AAAsLi0qfTWOk2CvDykprKw6VEovF0t+8VL/4VN2EiIiIiNRMaGgoQkJCZMoWLlyI4ODgctv36dNHZmS3b9++WLFiBUpLZfORTz/9FCtXroSFhQWcnZ3h4uICV1dXaGlp4eLFiygtLUWnTp1k+hQVFcHQ0BDAq5Hu2lizrKurK024AaBt27bIyckBAPz111948OCB9MsAANDU1ESvXr3kRqP/6fTp05BIJPDw8JD70uK19PR02NnZSRPuygQFBWHdunWIjY2Fm5ubXH1sbCycnJxgZGQEAHBxccHEiRNx6NAhDBo0COnp6dDU1ISDg0OV12rMFBvpbiBTswsLCxEREaHqMKgcl/YMVXUIRERERFTLxGIx/P39ZcoqGuWuCTMzM2RmZuLAgQPYv38/pk2bhm+//RZHjhxBXl4eNDU1ce7cOWhqasr0ez0dWkdHR+EYAKBJkyYynwVBqFHuZGlpCUEQkJmZKVP+ehS5sjhrcg/6+voQi8UICQnBsGHDZOpKS0sRHx+P7OxsaGlpyZTHxsZi0KBBtfa8GjpupMaN1IiIiIiI6pRIJEKLFi1kjsqS7r9vAgYAJ0+eRMeOHeWSZ+BV0unq6orIyEgkJycjNTUVFy9ehJ2dHUpLS5GTkwNLS0uZ4/V0cRsbGxw8eLDCOJo2bSo3ul5TLVu2RJs2bXDmzBlpWWlpKc6fPy/9bGhoiMGDB2P16tXIz8+v0fltbGyQnp6O3NzcarWfOXMmNDQ0sGrVKpnyffv24fnz50hLS0N6err02LJlC3bu3ImnT5/C2toaEokER44cqVGMjQ03Umsgo/VERERERA1VVlYW/P39kZmZiS1btiAqKgq+vr5y7eLi4hATE4PffvsNN2/exA8//AAdHR20b98enTp1goeHB7y8vLBz507cunULp0+fRmhoKPbu3Qvg1Qj8mTNnMG3aNFy4cAFXrlxBdHQ0Hj16BODVTumnTp3C7du38ejRoyqng1dk5syZCA0Nxe7du5GZmQlfX188efJEZgr9mjVrUFJSAnt7e2zduhWXL19GZmYmfvjhB1y5cqXcLxwAwN3dHSYmJhgxYgSOHz+OmzdvYseOHUhNTS23vba2NkJCQhAZGSlTHhMTg6FDh8LW1hbdu3eXHm5ubtDX10dCQgLMzc0xbtw4eHt7Y9euXbh16xaSk5Oxbdu2N3ouDZWCSbekQRxERERERFR/eXl54cWLF+jduzemT58OX19fTJ48Wa6dvr4+vv/+e/Tv3x82NjY4cOAA/vOf/0jXbG/cuBFeXl744osv0LlzZ4wYMQJnzpxBu3btAACdOnVCUlISMjIy0Lt3b/Tt2xe7d++WTq8OCAiApqYmunbtitatWyMrK+uN7icoKAju7u7w8vJC37590axZMzg5OUFbW1vapkOHDkhLS4OjoyPEYjFsbW1hb2+PqKgoBAQEYPHixeWeu2nTpkhKSoKxsTFcXFxgbW2NsLCwCpN0ABg3bpzMBmgPHjzA3r17MWrUKLm2GhoaGDlypPS1YNHR0Rg9ejSmTZuGLl26wMfHp8aj8w2dUKbAwuzWLTtX3UgNeHq7cE13PXWpg/qv6dZt/lLVISisqW6JqkNQiIaW+s9o0dJT/3sofq7Q97wqt/vOW6oOQWFN1P+3EQ5rFqg6BIV0g66qQ1DYDUH9N58d9FLxtcOq9GGne6oOQWFt/vFu7MZOIpHAysoKbm5uFSbTpL64kRoREREREVEdunPnDpKSkuDg4ICioiKsXr0at27dwtixY1UdGimBYq8Me8M1DERERERERI2VhoYG4uLiEBAQgLKyMnTv3h0HDhyAlZWVqkMjJVAo6ZaAI91EREREREQ1YWZmhuPHj6s6DKojHOkmIiIiIiIiUhLF1nRzpJuU7FZeC1WHoLAuOtV7R2J9Jqj3/ldYnWWq6hAU1r5EzX8IAFqUqvffGR6j1P//5Sae41QdgsLc01JUHYJCJNfuqDoEhe1OVP+/m4uEqtvUZ0tut1F1CAqLrLoJUYPBkW4iIiIiIiIiJeFINxEREREREZGS8JVhREREREREREqi2O7lTLqJiIiIiIiIKqRQ0l3y8l5txaFSfn5+qg6BiIiIiIgakffffx9Tp07F2LFjVRrHy5cv0alTJ/z444+wt7dXyjWSk5PxwQcf4MmTJ9DX11fKNeoz9d8Ol4iIiIiIGqzx48dDEASEhYXJlO/atQuCULtb0Zubm0MQBJnjn9d9rUuXLhCJRMjOzq7xdfbs2YMHDx5gzJgx1e6TnJyM4cOHo23bttDT00OPHj2QkJBQZb/o6GjY2NigRYsWaNGiBfr27YtffvlFWt+0aVMEBAQgKChIru+zZ88wf/58dOvWDTo6OjA0NMQ777yDb775Bk+ePKl27I0dk24iIiIiIqrXtLW1sWzZsjpJ9BYtWoT79+9Lj5kzZ8q1SUlJwYsXLzB69GjEx8fX+BqRkZGYMGECNDSqn46dOHECNjY22LFjBy5cuIAJEybAy8sLP//8c6X9/vWvfyEsLAznzp3D2bNn8eGHH2L48OH4/fffpW08PDyQkpIiU5abm4s+ffpg48aNCAgIwKlTp3D+/HksWbIEaWlp2Lx5c43vu7Fi0k1ERERERPWao6MjTExMEBoaWmGblJQUDBgwADo6OjAzM8OsWbOQn58vrRcEAbt27ZLpo6+vj7i4OJmy5s2bw8TERHro6enJXSsmJgZjx47F559/jtjYWLn6u3fvwt3dHQYGBtDT04O9vT1OnToFAHj48CEOHToEV1dXmT7h4eGwtraGnp4ezMzMMG3aNOTl5Unrv/rqKyxevBj9+vVDhw4d4OvrC2dnZ+zcubPCZwIArq6ucHFxQceOHdGpUycsWbIEzZo1w8mTJ6VtWrVqhf79+yMxMVHmellZWTh9+jQmTJgAGxsbtG/fHh999BG2bNmCadOmSdtu2rQJ9vb20mc3duxY5OTkVBpXY8Kkm4iIiIiI6lRRURGePXsmcxQVFVXYXlNTE0uXLkVUVBTu3r0rV3/jxg04Oztj1KhRuHDhArZu3YqUlBTMmDGjxrGFhYXB0NAQdnZ2+Pbbb1FSUiJT//z5c2zfvh2enp4YPHgw/vrrLxw7dkxan5eXBwcHB9y7dw979uxBRkYGAgMDIZFIALz6ckBXVxdWVlYy59XQ0EBkZCR+//13xMfH49ChQwgMDKw01r/++gsGBgbVvrfS0lIkJiYiPz8fffv2lanr3bu39D4kEgm2bt0KT09PmJqalnuuv0/tLy4uxuLFi5GRkYFdu3bh9u3bGD9+fLXjaugU2kitoROLxSgsLFR1GI2ao6oDICIiIqJaFxoaipCQEJmyhQsXIjg4uMI+I0eORI8ePbBw4ULExMTInc/DwwOzZ88GAHTs2BGRkZFwcHBAdHQ0tLW1qxXXrFmz0LNnTxgYGODEiRMQi8W4f/8+wsPDpW0SExPRsWNHdOvWDQAwZswYxMTEYMCAAQCAzZs34+HDhzhz5ow0Iba0tJT2v3PnDtq0aSM3tfx17MCrteVff/01pk6dijVr1pQb67Zt23DmzBmsW7euyvu6ePEi+vbti8LCQjRr1gw//fQTunbtKtPG1NQUd+7cAfBqNP7p06fo3LmzTJtevXohMzMTwKsR9C1btgAAvL29pW0sLCwQGRmJd955B3l5eWjWrFmV8TV0TLorUVhYiIiICFWH0ajt3eyu6hCIiIiIqJaJxWL4+/vLlIlEoir7LVu2DB9++CECAgJkyjMyMnDhwgWZjcXKysogkUhw69YtuVHlivw9JhsbGzRt2hRTpkxBaGioNL7Y2Fh4enpK23l6esLBwQFRUVFo3rw50tPTYWdnV+EI9IsXL8r9EuDAgQMIDQ3FlStX8OzZM5SUlKCwsBAFBQXQ1dWVaXv48GFMmDAB33//vTT5P3bsGIYMGSJts27dOnh4eAAAOnfujPT0dPz111/48ccfMW7cOBw5ckQm8dbR0UFBQUGlz+enn37Cy5cvERQUhBcvXkjLz507h+DgYGRkZODJkyfSUf2srCy55L4x4vRyIiIiIiKqUyKRSLqb9uujOkn3+++/DycnJ4jFYpnyvLw8TJkyBenp6dIjIyMD165dQ4cOHQC8mg5dVlYm06+4uLjS67377rsoKSnB7du3AQCXLl3CyZMnERgYCC0tLWhpaaFPnz4oKCiQrofW0dGp9JxGRkZyG8Ldvn0bw4YNk26Udu7cOXz33XcAXr3S6++OHDkCV1dXREREwMvLS1pub28vc/8ff/yxtK5p06awtLREr169EBoaCltbW6xatUrmvLm5uWjdujUAoHXr1tDX15eOar/Wrl07WFpaonnz5tKy/Px8ODk5oUWLFkhISMCZM2fw008/lRt7Y8WRbiIiIiIiUhthYWHo0aOHzNTnnj174tKlSzLTuP+pdevWuH//vvTztWvXqhzZTU9Ph4aGBoyNjQG82kDt/ffflybEr23cuBExMTHw8fGBjY0NNmzYgNzc3HJHu+3s7JCdnY0nT56gVatWAF6NFEskEqxYsUI67Xzbtm1yfZOTkzFs2DAsW7YMkydPlqnT0dGp9P7/TiKRyK2h/+2332BnZwfg1fpyNzc3/PDDD1iwYEGF67oB4MqVK3j8+DHCwsJgZmYGADh79my14mgsONJNRERERERqw9raGh4eHoiMjJSWBQUF4cSJE5gxYwbS09Nx7do17N69W2YjtQ8//BCrV69GWloazp49i6lTp6JJkybS+tTUVKxcuRIZGRm4efMmEhIS4OfnB09PT7Rq1QrFxcXYtGkT3N3d0b17d5lj0qRJOHXqFH7//Xe4u7vDxMQEI0aMwPHjx3Hz5k3s2LEDqampAF4l3UZGRjh+/Lj02paWliguLkZUVBRu3ryJTZs2Ye3atTL3ffjwYQwdOhSzZs3CqFGjkJ2djezsbOTm5lb6vMRiMY4ePYrbt2/j4sWLEIvFSE5Olk49f+3YsWP46KOPpJ+XLl2Kt956C71790ZsbCwuXLiAGzdu4KeffkJqaio0NTUBvBr9btq0qTT2PXv2YPHixdX9cTYKTLqJiIiIiEitLFq0SLpuGHi1/vrIkSO4evUqBgwYADs7O7kR2hUrVsDMzAwDBgzA2LFjERAQILNWWiQSITExEQ4ODujWrRuWLFkCPz8/rF+/HgCwZ88ePH78GCNHjpSLx8rKClZWVoiJiUHTpk2RlJQEY2NjuLi4wNraGmFhYdIkVVNTExMmTJBZf25ra4vw8HAsW7YM3bt3R0JCgtzr0eLj41FQUIDQ0FC0bdtWenzyySeVPqucnBx4eXmhc+fOGDRoEM6cOYP//ve/GDx4sLRNamoq/vrrL4wePVpaZmhoiNOnT8PLywvffvstevfuDWtrawQHB+Ozzz7D999/D+DVDIK4uDhs374dXbt2RVhYGJYvX15pTI2NUPbPhQ2NkJ+fX7kbplVUTnVnbxv130itS+vKv31UB7r66r0e57u7bVUdgsLal6j/d6QtStX7rxuXUU+qblTPNfEcp+oQFCZJS1F1CAqRXLuj6hAUtjuxhapDUFixUHWb+ux8k8rXIauDyNtbVR2CSmVnZ6Nbt244f/482rdvr+pw8Nlnn8HW1hZfffWVqkNpkNT/X3FERERERERqxMTEBDExMcjKylJ1KHj58iWsra3h5+en6lAaLG6kRkREREREVMdGjBih6hAAvNrZfN68eaoOo0HjSDcRERERERGRkjDpJiIiIiIiIlISTi+neu3DpW+pOgTF6XZSdQQKu/7VeVWHoBB9ifp/v6jum/4AgLaa79v55wH1jh8Arm/fpeoQFKYvqPcGUi11JVU3IqXLVvN/AWtz3IxIrfD/WCIiIiIiIiIlYdJNREREREREpCRMuomIiIiIiIiUpNZWtIjFYhQWFtbW6epUTk6OqkMgIiIiIiKiBqjWku7CwkJERETU1unqFF8ET0RERETUsAUHB2PXrl1IT09XdShqz9zcHLNnz8bs2bNVHYpa4PRyIiIiIiKqt8aPHw9BEBAWFiZTvmvXLghCA3i9BzV4TLqJiIiIiKhe09bWxrJly/DkyRNVh0JUY0y6iYiIiIioXnN0dISJiQlCQ0MrbPP999/DzMwMurq6GDlyJMLDw6Gvr19u26NHj6JJkybIzs6WKZ89ezYGDBgg/Xz8+HEMHDgQurq6aNWqFZycnKSJ/6+//or33nsP+vr6MDQ0xLBhw3Djxg1p39u3b0MQBOzcuRMffPABdHV1YWtri9TU1Grfd2XXLyoqwqxZs2BsbAxtbW289957OHPmjLRvcnIyBEHAf//7X9jZ2UFHRwcffvghcnJy8Msvv8DKygotWrTA2LFjUVBQIO03cOBAzJgxAzNmzEDLli1hZGSE+fPno6ysrMI4nz59ikmTJqF169Zo0aIFPvzwQ2RkZAAAHj58CBMTEyxdulTa/sSJE2jatCkOHjxY7Wehzph0ExERERFRnSoqKsKzZ89kjqKiogrba2pqYunSpYiKisLdu3fl6o8fP46pU6fC19cX6enpGDx4MJYsWVLh+d5//31YWFhg06ZN0rLi4mIkJCTA29sbAJCeno5Bgwaha9euSE1NRUpKClxdXVFaWgoAyM/Ph7+/P86ePYuDBw9CQ0MDI0eOhEQikbnW3LlzERAQgPT0dHTq1Anu7u4oKSmp8hlVdf3AwEDs2LED8fHxOH/+PCwtLeHk5ITc3FyZ8wQHB2P16tU4ceIE/vjjD7i5uWHlypXYvHkz9u7di6SkJERFRcn0iY+Ph5aWFk6fPo1Vq1YhPDwcGzZsqDDWTz/9VJrMnzt3Dj179sSgQYOQm5uL1q1bIzY2FsHBwTh79iyeP3+Ozz//HDNmzMCgQYOqfA4NQa1tpEaNk7J3rV/aXVNp5yYiIiIi1QgNDUVISIhM2cKFCxEcHFxhn5EjR6JHjx5YuHAhYmJiZOqioqIwZMgQBAQEAAA6deqEEydO4Oeff67wfBMnTsTGjRvx5ZdfAgD+85//oLCwEG5ubgCAb775Bvb29lizZo20T7du3aS/HjVqlMz5YmNj0bp1a1y6dAndu3eXlgcEBGDo0KEAgJCQEHTr1g3Xr19Hly5dKoytquvn5+cjOjoacXFxGDJkCIBXI/379+9HTEyM9J4A4Ouvv0b//v2l9ywWi3Hjxg1YWFgAAEaPHo3Dhw8jKChI2sfMzAwREREQBAGdO3fGxYsXERERAR8fH7k4U1JScPr0aeTk5EAkEgEAli9fjl27duHHH3/E5MmT4eLiAh8fH3h4eMDe3h56enqVzlpoaJh0k0KUvWv9i5gApZ2biIiIiFRDLBbD399fpux1wlaZZcuW4cMPP5Qm169lZmZi5MiRMmW9e/euNOkeP3485s2bh5MnT6JPnz6Ii4uDm5sb9PT0ALwaaf70008r7H/t2jUsWLAAp06dwqNHj6Qj3FlZWTJJt42NjfTXbdu2BfDqlcVVJd2VXf/GjRsoLi6WJtMA0KRJE/Tu3RuXL1+Wafv367dp0wa6urrShPt12enTp2X69OnTR2aTur59+2LFihUoLS2FpqbsoFhGRgby8vJgaGgoU/7ixQuZ6fbLly9H9+7dsX37dpw7d65aP++Ggkk3ERERERHVKZFI9EZJ1/vvvw8nJyeIxWKMHz9eoRiMjY3h6uqKjRs34u2338Yvv/yC5ORkab2Ojk6l/V1dXdG+fXt8//33MDU1hUQiQffu3fHy5UuZdk2aNJH++nUi+88p6OWp6vrV9c/r//3z67LqxFORvLw8tG3bVubZvfb3NfU3btzAn3/+CYlEgtu3b8Pa2vqNr6luuKabiIiIiIjURlhYGP7zn//IbEjWuXNnmU3EAMh9Ls+kSZOwdetWrF+/Hh06dJAZObaxsalwo6/Hjx8jMzMT8+bNw6BBg2BlZVXrO6tXdv0OHTqgadOmOH78uLSsuLgYZ86cQdeuXRW+9qlTp2Q+nzx5Eh07dpQb5QaAnj17Ijs7G1paWrC0tJQ5jIyMAAAvX76Ep6cnPvvsMyxevBiTJk1CTk6OwnGqCybdRERERESkNqytreHh4YHIyEhp2cyZM7Fv3z6Eh4fj2rVrWLduHX755Zcq3+Pt5OSEFi1a4Ouvv8aECRNk6sRiMc6cOYNp06bhwoULuHLlCqKjo/Ho0SO0atUKhoaGWL9+Pa5fv45Dhw7JTZdXVGXX19PTw7///W98+eWX+PXXX3Hp0iX4+PigoKAAEydOVPjaWVlZ8Pf3R2ZmJrZs2YKoqCj4+vqW29bR0RF9+/bFiBEjkJSUhNu3b+PEiROYO3cuzp49C+DVZnJ//fUXIiMjERQUhE6dOkk3rGsMmHQTEREREZFaWbRokcyU6P79+2Pt2rUIDw+Hra0tfv31V/j5+UFbW7vS82hoaGD8+PEoLS2Fl5eXTF2nTp2QlJSEjIwM9O7dG3379sXu3buhpaUFDQ0NJCYm4ty5c+jevTv8/Pzw7bff1uo9VnZ94NWI/6hRo/D555+jZ8+euH79Ov773/+iVatWCl/by8sLL168QO/evTF9+nT4+vpi8uTJ5bYVBAH79u3D+++/jwkTJqBTp04YM2YM7ty5gzZt2iA5ORkrV67Epk2b0KJFC2hoaGDTpk04duwYoqOjFY5VHQhllb1wrQb8/PyUuqGWMlUUuzrfU11R9jNqEBup6eqpOgKFXf/qvKpDUMh/S1uqOgSF6dbKn9Sq9Vbxm68Xqw+66NfutEFVuP5EX9UhKExfKFZ1CAppqau8N37UlYxCfVWHoLA/mlTdpj7LFUpVHYLCvrm9RenX8PHxwZUrV3Ds2LFK202cOBEPHz7Enj17lB6TOhg4cCB69OiBlStXqjqUBoMbqRERERERkdpbvnw5Bg8eDD09Pfzyyy+Ij4+Xed3WP/3111+4ePEiNm/ezISblIrTy4mIiIiISO2dPn0agwcPhrW1NdauXYvIyEhMmjSpwvbDhw/HRx99hKlTp2Lw4MF1GOkrQ4YMQbNmzco9li5dWufxkPJwpJuIiIiIiNTetm3batS+vFdc1aUNGzbgxYsX5dYZGBjUcTT/o+rn0hAx6aZ6rSw3V9UhKK5Q/dfvPXtR8/do1idFIvVfEK2JyndfVQd5Guo9uUpLS73XpAOANtT/HpprF6k6BIU0aar+a3FN8tV7XT0APNJS70XdVWzITWrirbfeUnUIVEfU+19ARERERERERPUYk24iIiIiIiIiJWHSTURERERERKQkTLqJiIiIiIiIlIRJNxERERERqT1BELBr1y5Vh0Ekh0k3ERERERHVa9nZ2fD19YWlpSW0tbXRpk0b9O/fH9HR0SgoKKjx+eLi4iAIApydnWXKnz59CkEQyn1t1pQpU6CpqYnt27e/6W1QI8Wkm4iIiIiI6q2bN2/Czs4OSUlJWLp0KdLS0pCamorAwED8/PPPOHDgwBudV0tLCwcOHMDhw4erbFtQUIDExEQEBgYiNjb2ja5HjReTbiIiIiIiqremTZsGLS0tnD17Fm5ubrCysoKFhQWGDx+OvXv3wtXVtdx+d+/ehbu7OwwMDKCnpwd7e3ucOnVKWq+npwdvb2/MmTOnyhi2b9+Orl27Ys6cOTh69Cj++OMPmfqioiIEBQXBzMwMIpEIlpaWiImJUezGqcHQUnUARERERETUuBQVFaGoqEimTCQSQSQSyZQ9fvxYOsKtp6dX7rkEQZAry8vLg4ODA9566y3s2bMHJiYmOH/+PCQSiUy74OBgWFpa4scff8To0aMrjDcmJgaenp5o2bIlhgwZgri4OMyfP19a7+XlhdTUVERGRsLW1ha3bt3Co0ePqnwO1Dgw6aY6JRaLUVhYWO32S0yVGAwRERERqURoaChCQkJkyhYuXIjg4GCZsuvXr6OsrAydO3eWKTcyMpL+m3L69OlYtmyZTP3mzZvx8OFDnDlzBgYGBgAAS0tLuThMTU3h6+uLuXPnYsSIEeXGeu3aNZw8eRI7d+4EAHh6esLf3x/z5s2DIAi4evUqtm3bhv3798PR0REAYGFhUb0HQY0Ck26qU4WFhYiIiKh2+4JvvZUYDRERERGpglgshr+/v0zZP0e5K3P69GlIJBJ4eHjIjZgDQHp6Ouzs7KQJd2WCgoKwbt06xMbGws3NTa4+NjYWTk5OMDIyAgC4uLhg4sSJOHToEAYNGoT09HRoamrCwcGh2vFT48Kkm4iIiIiI6lR5U8nLY2lpCUEQkJmZKVP+eiRZR0en3H4VlZdHX18fYrEYISEhGDZsmExdaWkp4uPjkZ2dDS0tLZny2NhYDBo0qEbXosaJG6kREREREVG9ZGhoiMGDB2P16tXIz8+vdj8bGxukp6cjNze3Wu1nzpwJDQ0NrFq1SqZ83759eP78OdLS0pCeni49tmzZgp07d+Lp06ewtraGRCLBkSNHanRv1Hgw6SYiIiIionprzZo1KCkpgb29PbZu3YrLly8jMzMTP/zwA65cuQJNTU25Pu7u7jAxMcGIESNw/Phx3Lx5Ezt27EBqamq519DW1kZISAgiIyNlymNiYjB06FDY2tqie/fu0sPNzQ36+vpISEiAubk5xo0bB29vb+zatQu3bt1CcnIytm3bppTnQeqHSTcREREREdVbHTp0QFpaGhwdHSEWi2Frawt7e3tERUUhICAAixcvluvTtGlTJCUlwdjYGC4uLrC2tkZYWFi5Cfpr48aNk9kA7cGDB9i7dy9GjRol11ZDQwMjR46UvhYsOjoao0ePxrRp09ClSxf4+PjUaGSeGjau6VZzNd0NvLbl5OSo7NpERERE1Di0bdsWUVFRiIqKqrBNWVmZzOf27dvjxx9/LLft+PHjMX78eJkyTU1N/P777zJlxcXFFV5vzZo10l9ra2sjPDwc4eHhFbanxotJt5qr6W7gtc3Pz09l1yYiIiIiIqrvOL2ciIiIiIiISEk40o1X00HKG7Hl1GkiIiIiIiJSBJNuAKGhoeWWc+o0ERERERERKYJJNylFRRu81XT2QNmLotoKSWUEgas4VK1U1QHUgiJB1REoTgNlVTeqx0R6FW+moy5a5xWoOgSFNWuu3n8vNNFW/z+RdP4qUXUIChOVNVF1CApR7z9NiRofJt2kFBVt8MbZA0RERERE1JhwCI6IiIiIiIhISZh0ExERERERESkJk24iIiIiIiIiJWHSTUREREREak8QBOzatUvVYVRo/vz5mDx5svTzwIEDMXv2bJXE8uuvv6JHjx6QSCQquX5jw6SbiIiIiIjqtezsbPj6+sLS0hLa2tpo06YN+vfvj+joaBQU1PzNDHFxcRAEAc7OzjLlT58+hSAISE5OluszZcoUaGpqYvv27W8U/6pVqzB37twa9du5cyc++ugjGBoaQhAEpKenV9nn9u3bmDhxIt5++23o6OigQ4cOWLhwIV6+fClt4+zsjCZNmiAhIUGmr7m5OQRBQGJiotx5u3XrBkEQEBcXV6N7ICbdRERERERUj928eRN2dnZISkrC0qVLkZaWhtTUVAQGBuLnn3/GgQMH3ui8WlpaOHDgAA4fPlxl24KCAiQmJiIwMBCxsbE1vtaGDRvQr18/tG/fvkb98vPz8d5772HZsmXV7nPlyhVIJBKsW7cOv//+OyIiIrB27Vp89dVXMu3Gjx+PyMhIuf5mZmbYuHGjTNnJkyeRnZ0NPT29GsVPrzDpJiIiIiKiemvatGnQ0tLC2bNn4ebmBisrK1hYWGD48OHYu3cvXF1dy+139+5duLu7w8DAAHp6erC3t8epU6ek9Xp6evD29sacOXOqjGH79u3o2rUr5syZg6NHj+KPP/6QqS8qKkJQUBDMzMwgEolgaWmJmJgYaX1iYmK5cZaUlGDGjBlo2bIljIyMMH/+fJSV/e9N7J9//jkWLFgAR0fHKmN8zdnZGRs3bsRHH30ECwsLfPzxxwgICMDOnTtl2rm6uuLs2bO4ceOGTLmHhweOHDkic4+xsbHw8PCAlpbsG6fDw8NhbW0NPT09mJmZYdq0acjLy5PWx8XFQV9fH//9739hZWWFZs2awdnZGffv36/2/TQETLqJiIiIiKhOFRUV4dmzZzJHUVGRXLvHjx8jKSkJ06dPr3CUVRAEubK8vDw4ODjg3r172LNnDzIyMhAYGCi3hjk4OBgXL17Ejz/+WGm8MTEx8PT0RMuWLTFkyBC5KdZeXl7YsmULIiMjcfnyZaxbtw7NmjUDAOTm5uLSpUuwt7eXO298fDy0tLRw+vRprFq1CuHh4diwYUOlsbyJv/76CwYGBjJl7dq1Q5s2bXDs2DGZ8jZt2sDJyQnx8fEAXo3yb926Fd7e3nLn1dDQQGRkJH7//XfEx8fj0KFDCAwMlGlTUFCA5cuXY9OmTTh69CiysrIQEBBQy3dYv2lV3YSo9mhra8PPz6/a7b9upcRgiIiIiEglQkNDERISIlO2cOFCBAcHy5Rdv34dZWVl6Ny5s0y5kZERCgsLAQDTp0+Xm369efNmPHz4EGfOnJEmm5aWlnJxmJqawtfXF3PnzsWIESPKjfXatWs4efKkdKTY09MT/v7+mDdvHgRBwNWrV7Ft2zbs379fOiJtYWEh7Z+VlYWysjKYmprKndvMzAwREREQBAGdO3fGxYsXERERAR8fn3JjeRPXr19HVFQUli9fLldnamqKO3fuyJV7e3vjiy++wNy5c/Hjjz+iQ4cO6NGjh1y7v28EZ25ujq+//hpTp07FmjVrpOXFxcVYu3YtOnToAACYMWMGFi1apPiNqREm3VSnQkNDa9Q+f5GHkiIhIiIiIlURi8Xw9/eXKROJRNXuf/r0aUgkEnh4eJQ7Qp6eng47Ozu50d3yBAUFYd26dYiNjYWbm5tcfWxsLJycnGBkZAQAcHFxwcSJE3Ho0CEMGjQI6enp0NTUhIODQ7nnf/HiBYBXg0//1KdPH5mR+r59+2LFihUoLS2FpqZmlbFPnToVP/zwg/Tz36d2A8C9e/fg7OyMTz/9tNxEXkdHp9yN6IYOHYopU6bg6NGjiI2NLXeUGwAOHDiA0NBQXLlyBc+ePUNJSQkKCwtRUFAAXV1dAICurq404QaAtm3bIicnp8p7a0g4vZyIiIiIiOqUSCRCixYtZI7ykm5LS0sIgoDMzEyZcgsLC1haWkJHR6fc81dUXh59fX2IxWKEhITIJaClpaWIj4/H3r17oaWlBS0tLejq6iI3N1e6oVpV13qdrD958qTaMVXXokWLkJ6eLj3+7s8//8QHH3yAfv36Yf369eX2z83NRevWreXKtbS08Pnnn2PhwoU4deoUPDzkB8Ju376NYcOGwcbGBjt27MC5c+fw3XffAYDMTulNmjSR6ScIgsy69caASTcREREREdVLhoaGGDx4MFavXo38/Pxq97OxsUF6ejpyc3Or1X7mzJnQ0NDAqlWrZMr37duH58+fIy0tTSa53bJlC3bu3ImnT5/C2toaEokER44cKffcHTp0QIsWLXDp0iW5ur9v7Aa82iW8Y8eO1RrlBgBjY2NYWlpKj9fu3buHgQMHolevXti4cSM0NOTTvsLCQty4cQN2dnblntvb2xtHjhzB8OHD0aqV/JrPc+fOQSKRYMWKFejTpw86deqEP//8s1pxNzZMuomIiIiIqN5as2YNSkpKYG9vj61bt+Ly5cvIzMzEDz/8gCtXrpSboLq7u8PExAQjRozA8ePHcfPmTezYsQOpqanlXkNbWxshISFyr9CKiYnB0KFDYWtri+7du0sPNzc36OvrIyEhAebm5hg3bhy8vb2xa9cu3Lp1C8nJydi2bRuAV5uNOTo6IiUlRe66WVlZ8Pf3R2ZmJrZs2YKoqCj4+vpK63Nzc5Geni5N2DMzM5Geno7s7OwKn9frhLtdu3ZYvnw5Hj58iOzsbLk+J0+ehEgkQt++fcs9j5WVFR49eiT3+rDXLC0tUVxcjKioKNy8eRObNm3C2rVrK4yrMWPSTURERERE9VaHDh2QlpYGR0dHiMVi2Nrawt7eHlFRUQgICMDixYvl+jRt2hRJSUkwNjaGi4sLrK2tERYWVukI8rhx42Q2QHvw4AH27t2LUaNGybXV0NDAyJEjpa8Fi46OxujRozFt2jR06dIFPj4+MiPzkyZNQmJiotzu6V5eXnjx4gV69+6N6dOnw9fXF5MnT5bW79mzB3Z2dhg6dCgAYMyYMbCzs6s0ud2/fz+uX7+OgwcP4l//+hfatm0rPf5uy5Yt8PDwkK69Lo+hoWGF0+dtbW0RHh6OZcuWoXv37khISKjx/k2NhVBWSxPq/fz8EBERURunqjfU4Z5UHWNF16+tuBrCRmqCbvXXFNVXacufqjoEhRyswcYs9VVTyL8ORd20L1bv9VsDzNT/naK5D8p/3Y46adZcfsMkddJEu1TVISjsj7v6qg5BYRe01Pvv5pua6v/76JvbW1QdQp0qKyvDu+++Cz8/P7i7u6s6HDx69AidO3fG2bNn8fbbb6s6nAaPI91ERERERERKJAgC1q9fj5KSElWHAuDVJmhr1qxhwl1H+MowIiIiIiIiJevRo0e577pWBXt7e9jb26s6jEaDI91ERERERERESsKRblKItrY2/Pz85Mob2wvvK1OWV/3XW9RXgqDea3GNJOq/HrohfEOqK1Hv30eSUvX/fXT3hfqv6W6nod6/j1qIXqg6BIXpNi1WdQgKa/VSW9UhKMRAo3qvkyKi+oFJNymkoh0Ky0vEiYiIiIiIGpuGMHhCREREREREVC8x6SYiIiIiIiJSEibdRERERERERErCpJuIiIiIiIhISZh0ExERERGR2hMEAbt27VJ1GBWaP38+Jk+eLP08cOBAzJ49u87j6NOnD3bs2FHn123MmHQTEREREVG9lp2dDV9fX1haWkJbWxtt2rRB//79ER0djYKCghqfLy4uDoIgwNnZWab86dOnEAQBycnJcn2mTJkCTU1NbN++/Y3iX7VqFebOnVujfjt37sRHH30EQ0NDCIKA9PT0KvvcuXMHXl5eeOutt9C8eXN89NFHuHnzprR+3rx5mDNnDiQSibTs9fOwsrKSO9/27dshCALMzc1rFDv9D5NuIiIiIiKqt27evAk7OzskJSVh6dKlSEtLQ2pqKgIDA/Hzzz/jwIEDb3ReLS0tHDhwAIcPH66ybUFBARITExEYGIjY2NgaX2vDhg3o168f2rdvX6N++fn5eO+997Bs2bJq9zl79izat2+Pn3/+GcePH0d+fj4mTpworR8yZAieP3+OX375Raafnp4ecnJykJqaKlMeExODdu3a1ShuksWkm4iIiIiI6q1p06ZBS0sLZ8+ehZubG6ysrGBhYYHhw4dj7969cHV1Lbff3bt34e7uDgMDA+jp6cHe3h6nTp2S1uvp6cHb2xtz5sypMobt27eja9eumDNnDo4ePYo//vhDpr6oqAhBQUEwMzODSCSCpaUlYmJipPWJiYnlxllSUoIZM2agZcuWMDIywvz581FWViat//zzz7FgwQI4OjpWGeNro0aNwuLFi2FnZwcbGxuMGjVKJl5NTU24uLggMTFRpp+WlhbGjh0r86XC3bt3kZycjLFjx8q0vXHjBoYPH442bdqgWbNmeOedd+S+/DA3N8fSpUvh7e2N5s2bo127dli/fn2176MhYdJNRERERER1qqioCM+ePZM5ioqK5No9fvwYSUlJmD59OvT09Mo9lyAIcmV5eXlwcHDAvXv3sGfPHmRkZCAwMFBmSjUABAcH4+LFi/jxxx8rjTcmJgaenp5o2bIlhgwZgri4OJl6Ly8vbNmyBZGRkbh8+TLWrVuHZs2aAQByc3Nx6dIl2Nvby503Pj4eWlpaOH36NFatWoXw8HBs2LCh0lhq4o8//kBERAS8vb1lynv37o1jx47Jtff29sa2bdukU/bj4uLg7OyMNm3ayLTLy8uDi4sLDh48iLS0NDg7O8PV1RVZWVky7VasWAF7e3ukpaVh2rRp+Pe//43MzMxauz91oaXqAKhxEIvFKCwsrHG/r1spIRgiIiIiUqnQ0FCEhITIlC1cuBDBwcEyZdevX0dZWRk6d+4sU25kZCT9t+X06dPlpl9v3rwZDx8+xJkzZ2BgYAAAsLS0lIvD1NQUvr6+mDt3LkaMGFFurNeuXcPJkyexc+dOAICnpyf8/f0xb948CIKAq1evYtu2bdi/f790RNrCwkLaPysrC2VlZTA1NZU7t5mZGSIiIiAIAjp37oyLFy8iIiICPj4+5cZSE3fv3sV7772HESNG4KuvvpK77z/++AMSiQQaGv8bh7Wzs4OFhQV+/PFHfP7554iLi0N4eLjMmnAAsLW1ha2trfTz4sWL8dNPP2HPnj2YMWOGtNzFxQXTpk0DAAQFBSEiIgKHDx+W+3k2dEy6qU4UFhYiIiKixv3yF3koIRoiIiIiUiWxWAx/f3+ZMpFIVO3+p0+fhkQigYeHR7kj5Onp6bCzs5Mm3JUJCgrCunXrEBsbCzc3N7n62NhYODk5wcjICMCrRHLixIk4dOgQBg0ahPT0dGhqasLBwaHc87948QIAoK2tLVfXp08fmZH6vn37YsWKFSgtLYWmpmaVsU+dOhU//PCD9HNeXp7016GhoXjrrbcQGRkp109HRwcSiQRFRUXQ0dGRqfP29sbGjRvRrl075Ofnw8XFBatXr5Zpk5eXh+DgYOzduxf3799HSUkJXrx4ITfSbWNjI/21IAgwMTFBTk5OlffV0DDpJiIiIiKiOiUSiaqVZFtaWkIQBLkpya9Hkv+ZML5WUXl59PX1IRaLERISgmHDhsnUlZaWIj4+HtnZ2dDS0pIpj42NxaBBg6q81utk/cmTJ2jdunW146qORYsWISAgoNy6P//8E506dSp3+n1ubi709PTKjd3DwwOBgYEIDg7G559/LnPfrwUEBGD//v1Yvnw5LC0toaOjg9GjR+Ply5cy7Zo0aSLzWRAEuSn+jQGTbiIiIiIiqpcMDQ0xePBgrF69GjNnzqxwXfc/2djYYMOGDcjNza3WaPfMmTMRGRmJVatWyZTv27cPz58/R1pamszI82+//YYJEybg6dOnsLa2hkQiwZEjR8rd8KxDhw5o0aIFLl26hE6dOsnU/X1jNwA4efIkOnbsWK1RbgAwNjaGsbFxuXXLly+v8Dy//fYb7Ozsyq0zMDDAxx9/jG3btmHt2rXltjl+/DjGjx+PkSNHAng18n379u1qxdwYcSM1IiIiIiKqt9asWYOSkhLY29tj69atuHz5MjIzM/HDDz/gypUr5SaW7u7uMDExwYgRI3D8+HHcvHkTO3bskHsd1mva2toICQmRm4odExODoUOHwtbWFt27d5cebm5u0NfXR0JCAszNzTFu3Dh4e3tj165duHXrFpKTk7Ft2zYAgIaGBhwdHZGSkiJ33aysLPj7+yMzMxNbtmxBVFQUfH19pfW5ublIT0/HpUuXAACZmZlIT09HdnZ2lc8tJCQE69atK7fu2LFj+OijjyrsGxcXh0ePHqFLly7l1nfs2BE7d+5Eeno6MjIyMHbs2EY5gl1dTLqJiIiIiKje6tChA9LS0uDo6AixWAxbW1vY29sjKioKAQEBWLx4sVyfpk2bIikpCcbGxnBxcYG1tTXCwsIqHUEeN26czAZoDx48wN69ezFq1Ci5thoaGhg5cqT0tWDR0dEYPXo0pk2bhi5dusDHxwf5+fnS9pMmTUJiYqJcYurl5YUXL16gd+/emD59Onx9fTF58mRp/Z49e2BnZ4ehQ4cCAMaMGQM7O7sKR6D/LisrC/fv35crv3fvHk6cOIEJEyZU2FdHRweGhoYV1oeHh6NVq1bo168fXF1d4eTkhJ49e1YZU2MllP39RXAK8PPze6ONsuozdbin+hrjP+N60zgbxEZqDeBbv4x1L6tuVI+la1V/XVd91RC+IW1brN7/L9j964GqQ1DYb3/U7lpCVWinl1d1o3qshcELVYegsNyH1ZveW59lvmyu6hAUcqup/BpddTPnzg9VN2pAysrK8O6778LPzw/u7u4qjSUoKAhPnjxptO/MVoWG8O84IiIiIiKieksQBKxfvx4lJSWqDgXGxsblzg4g5eFGakRERERERErWo0cP9OjRQ9Vh4IsvvlB1CI0OR7qJiIiIiIiIlIQj3aQU2tra8PPzk37Oycl5o/NIcp/VVkikgKcS9V6/V6D+S9/QAG4BuZrq/T1vabF6xw8AzYRSVYegsGYtilQdgkLKeV2u2mmpr/7r0ps8aKbqEBSi/v8nEzUuTLpJKUJDQ2U+/z0BJyIiIiIiaizU/2t7IiIiIiIionqKSTcRERERERGRkjDpJiIiIiIiIlISJt1ERERERNTgxcXFQV9fX678/fffx+bNm6WfBUHArl276i6wv5kzZw5mzpz5xv2Dg4PrxWvJSBaTbiIiIiIiqteys7Ph6+sLS0tLaGtro02bNujfvz+io6NRUFDwxufds2cPHjx4gDFjxtSo35IlS9CvXz/o6uqWm8hX5fr162jevLlc34CAAMTHx+PmzZtyfXbs2IEPP/wQrVq1go6ODjp37gxvb2+kpaXV+PqvmZubQxAEnDx5UqZ89uzZGDhwoFz7u3fvomnTpujevfsbX7Mx4u7llfjna6/qozd9FRcRERERkTq4efMm+vfvD319fSxduhTW1tYQiUS4ePEi1q9fj7feegsff/zxG507MjISEyZMgIZGzcYiX758iU8//RR9+/ZFTExMjfoWFxfD3d0dAwYMwIkTJ2TqjIyM4OTkhOjoaHz77bfS8qCgIKxYsQKzZs1CSEgI2rdvj4cPH+KXX36BWCzGr7/+WqMY/k5bWxtBQUE4cuRIlW3j4uLg5uaGo0eP4tSpU3j33Xff+LqNCZPuSvzztVf1UX3/UoCIiIiISBHTpk2DlpYWzp49Cz09PWm5hYUFhg8fjrKyMgBAeHg4Nm7ciJs3b8LAwACurq745ptv0KxZ+e9lf/jwIQ4dOoRVq1bJ1d2/fx9DhgxBcnIy2rZti2+++QajR4+W1oeEhAB4lYTW1Lx589ClSxcMGjRILukGAFdXV8ydO1eadJ88eRLffPMNVq1ahVmzZknbtWvXDr169ZLef0ViY2OxYsUKXL9+HQYGBhg1ahRWr14trZ88eTLWrl2Lffv2wcXFpcLzlJWVYePGjVizZg3+9a9/ISYmRi7pPn78OObOnYvTp09DJBKhd+/eSExMRKtWrar1bBoqTi8nIiIiIqI6VVRUhGfPnskcRUVFcu0eP36MpKQkTJ8+XSbh/jtBEAAAGhoaiIyMxO+//474+HgcOnQIgYGBFcaQkpICXV1dWFlZydXNnz8fo0aNQkZGBjw8PDBmzBhcvnz5De/2fw4dOoTt27fju+++q7BN7969cffuXdy+fRsAsGXLFjRr1gzTpk0rt/3r+y9PdHQ0pk+fjsmTJ+PixYvYs2cPLC0tZdq8/fbbmDp1KsRiMSQSSYXnOnz4MAoKCuDo6AhPT08kJiYiPz9fWp+eno5Bgwaha9euSE1NRUpKClxdXVFaWlrhORsLjnRTnXjTqfqLlBALEREREalWaGiodLT4tYULFyI4OFim7Pr16ygrK0Pnzp1lyo2MjFBYWAgAmD59OpYtW4bZs2dL683NzfH1119j6tSpWLNmTbkx3LlzB23atCl3avmnn36KSZMmAQAWL16M/fv3IyoqqsJzVcfjx48xfvx4/PDDD2jRokWF7UxNTaXxmZub4+rVq7CwsICW1v9St/DwcCxYsED6+d69e2jZsqXcub7++mt88cUX8PX1lZa98847cu3mzZuHjRs3IiEhAZ9//nm5ccXExGDMmDHQ1NRE9+7dYWFhge3bt2P8+PEAgG+++Qb29vYyz6hbt24V3mdjwqSb6sSbTtV/Ptu1liMhIiIiIlUTi8Xw9/eXKROJRNXuf/r0aUgkEnh4eEhHyA8cOIDQ0FBcuXIFz549Q0lJCQoLC1FQUABdXV25c7x48QLa2trlnr9v375yn9PT06sdX7du3XDnzh0AwIABA/DLL7/Ax8cHY8eOxfvvv19pXx0dHQCodIM4b29vfPzxxzh16hQ8PT3LnWKek5ODP//8E4MGDaoy3tatWyMgIAALFizAZ599Jlf/9OlT7Ny5EykpKdIyT09PxMTESJPu9PR0fPrpp1VeqzFi0k1ERERERHVKJBJVK8m2tLSEIAjIzMyUKbewsADwvwT19u3bGDZsGP79739jyZIlMDAwQEpKCiZOnIiXL1+Wm3QbGRnhyZMntXA38vbt24fi4mKZGA8dOoQ9e/Zg+fLlAF6tkZZIJNDS0sL69evh7e0NAMjNzQXwKhEGgI4dOyIlJQXFxcVo0qQJAEBfXx/6+vq4e/duhTG8vm51+fv7Y82aNeWO5m/evBmFhYUya7hfx3/16lV06tSpxtdrTLimm4iIiIiI6iVDQ0MMHjwYq1evllk//E/nzp2DRCLBihUr0KdPH3Tq1Al//vlnpee2s7NDdnZ2uYn3P1+hdfLkyXLXflekffv2sLS0hKWlJd566y0AQGpqKtLT06XHokWL0Lx5c6Snp2PkyJHSvr/99huaNGkinZrt7u6OvLy8Gk9tb968OczNzXHw4MFqtW/WrBnmz5+PJUuW4Pnz5zJ1MTEx+OKLL2Tiz8jIwIABAxAbGwsAsLGxqfa1Ghsm3UREREREVG+tWbMGJSUlsLe3x9atW3H58mVkZmbihx9+wJUrV6CpqQlLS0sUFxcjKioKN2/exKZNm7B27dpKz2tnZwcjIyMcP35crm779u2IjY3F1atXsXDhQpw+fRozZsyQ1mdlZSE9PR1ZWVkoLS2VJqJ5eXkVXs/Kygrdu3eXHm+99RY0NDTQvXt3md29jx07hgEDBkhHjvv27YsvvvgCX3zxBfz9/ZGSkoI7d+7g5MmTiImJgSAIFb7yLDg4GCtWrEBkZCSuXbuG8+fPIyoqqsIYJ0+ejJYtW2Lz5s3SsvT0dJw/fx6TJk2Sib979+5wd3dHfHw8SkpKIBaLcebMGUybNg0XLlzAlStXEB0djUePHlX8Q2gkmHQTEREREVG91aFDB6SlpcHR0RFisRi2trawt7dHVFQUAgICsHjxYtja2iI8PBzLli1D9+7dkZCQUOWeQpqampgwYQISEhLk6kJCQpCYmAgbGxv83//9H7Zs2YKuXbtK6xcsWAA7OzssXLgQeXl5sLOzg52dHc6ePavw/SYmJsLHx0embPny5di8eTPS0tIwbNgwdOzYEZ9++ikkEglSU1Mr3Jht3LhxWLlyJdasWYNu3bph2LBhuHbtWoXXbtKkCRYvXizdpA54NcrdtWtXdOnSRa79yJEjkZOTg3379qFTp05ISkpCRkYGevfujb59+2L37t0yG8A1VkJZVS92qyY/Pz9ERETUxqmoBhr6c+dGavXDsS3lv6JDXVwSqf8f9hW/DER9GKj5G0MGGj1QdQgK++OBvqpDUFg7k6eqDkEhmk0qfh2Puih5qf5jNmkPWqs6BIVcFqn/3wpz78gnuqqQnZ2Nbt264fz582jfvr2qw8Evv/yCL774AhcuXGCy2oCo/5+aREREREREb8DExAQxMTHIyspSdSgAgPz8fGzcuJEJdwPDnyYRERERETVaI0aMUHUIUqNHj1Z1CKQEHOkmIiIiIiIiUhKOdFO9puWq/mu6hRaGqg5BYd2ORqs6BIWcfdxG1SEozECi/uv3Wpaq91pWkV6JqkNQmCZqZRsXlbp6X73/TDVq+kLVIShss6b8+47VzUtt9d5kIg/qHT9RY8ORbiIiIiIiIiIlYdJNREREREREpCRMuomIiIiIiIiUhEk3ERERERERkZIw6SYiIiIiIiJSEibdRERERERUb40fPx6CICAsLEymfNeuXRCE2n27h7m5OQRBkDn+ed3XunTpApFIhOzs7FqNgRoeJt1ERERERFSvaWtrY9myZXjy5InSr7Vo0SLcv39fesycOVOuTUpKCl68eIHRo0cjPj5e6TGRemPSTURERERE9ZqjoyNMTEwQGhpaYZuUlBQMGDAAOjo6MDMzw6xZs5Cfny+tFwQBu3btkumjr6+PuLg4mbLmzZvDxMREeujp6cldKyYmBmPHjsXnn3+O2NhYufq7d+/C3d0dBgYG0NPTg729PU6dOlWzm6YGg0k3ERERERHVa5qamli6dCmioqJw9+5dufobN27A2dkZo0aNwoULF7B161akpKRgxowZNb5WWFgYDA0NYWdnh2+//RYlJSUy9c+fP8f27dvh6emJwYMH46+//sKxY8ek9Xl5eXBwcMC9e/ewZ88eZGRkIDAwEBKJpOY3Tg2ClqoDICIiIiKixqWoqAhFRUUyZSKRCCKRqMI+I0eORI8ePbBw4ULExMTI1IWGhsLDwwOzZ88GAHTs2BGRkZFwcHBAdHQ0tLW1qxXXrFmz0LNnTxgYGODEiRMQi8W4f/8+wsPDpW0SExPRsWNHdOvWDQAwZswYxMTEYMCAAQCAzZs34+HDhzhz5gwMDAwAAJaWltW6PjVMTLpJqcRiMQoLC9+4/9JhVrUYDRERERHVB6GhoQgJCZEpW7hwIYKDgyvtt2zZMnz44YcICAiQKc/IyMCFCxeQkJAgLSsrK4NEIsGtW7dgZVW9f1P6+/tLf21jY4OmTZtiypQpCA0NlX4hEBsbC09PT2k7T09PODg4ICoqCs2bN0d6ejrs7OykCTcRk25SqsLCQkRERLxx/xcH19diNERERERUH4jFYpkEF0Clo9yvvf/++3BycoJYLMb48eOl5Xl5eZgyZQpmzZol16ddu3YAXq3pLisrk6krLi6u9HrvvvsuSkpKcPv2bXTu3BmXLl3CyZMncfr0aQQFBUnblZaWIjExET4+PtDR0anyPqhxYdJNRERERER1qqqp5JUJCwtDjx490LlzZ2lZz549cenSpUqncbdu3Rr379+Xfr527RoKCgoqvVZ6ejo0NDRgbGwM4NUGau+//z6+++47mXYbN25ETEwMfHx8YGNjgw0bNiA3N5ej3QSAG6kREREREZEasba2hoeHByIjI6VlQUFBOHHiBGbMmIH09HRcu3YNu3fvltlI7cMPP8Tq1auRlpaGs2fPYurUqWjSpIm0PjU1FStXrkRGRgZu3ryJhIQE+Pn5wdPTE61atUJxcTE2bdoEd3d3dO/eXeaYNGkSTp06hd9//x3u7u4wMTHBiBEjcPz4cdy8eRM7duxAampqnT4nqj+YdBMRERERkVpZtGiRzG7gNjY2OHLkCK5evYoBAwbAzs4OCxYsgKmpqbTNihUrYGZmhgEDBmDs2LEICAiArq6utF4kEiExMREODg7o1q0blixZAj8/P6xf/2q54549e/D48WOMHDlSLh4rKytYWVkhJiYGTZs2RVJSEoyNjeHi4gJra2uEhYVBU1NTiU+E6jNOLyciIiIionrrn+/RBgBzc3O53c/feecdJCUlVXgeU1NT/Pe//5Upe/r0qfTXPXv2xMmTJyvsP2rUKJSWllZYf+nSJemv27dvjx9//LHCttS4cKSbiIiIiIiISEmYdBMREREREREpCZNuIiIiIiIiIiVh0k1ERERERESkJNxIjSokFotRWFio0DlycnIUC6JJU8X61wclL1UdgcLKJIKqQ1BI8zL1jh8A9CRVt6nvdMvU+yYKnzepulE9d1tDW9UhKKxM1QEoSLtY/f9e09ZU/z9Tm0C9d5HW5rgZkVph0k0VKiwsREREhELn8PPzq6VoiIiIiIiI1A+/JiMiIiIiIiJSEibdRERERERERErCpJuIiIiIiIhISZh0ExERERGR2hMEAbt27VJ1GBWaP38+Jk+eLP08cOBAzJ49WyWx/Prrr+jRowckEtlNTgVBgCAI0NfXf6PzqvKe6jMm3UREREREVK9lZ2fD19cXlpaW0NbWRps2bdC/f39ER0ejoKCgxueLi4uDIAhwdnaWKX/69CkEQUBycrJcnylTpkBTUxPbt29/o/hXrVqFuXPn1qjfzp078dFHH8HQ0BCCICA9Pb1G/YuKitCjRw+5vs7OzmjSpAkSEhLk+mzcuBFXr16VKXv58iW+/fZb9OzZE3p6emjZsiVsbW0xb948/PnnnzWK6bXbt29DEAQYGxvj+fPnMnU9evRAcHCwXJ8tW7ZAU1MT06dPf6NrqgqTbiIiIiIiqrdu3rwJOzs7JCUlYenSpUhLS0NqaioCAwPx888/48CBA290Xi0tLRw4cACHDx+usm1BQQESExMRGBiI2NjYGl9rw4YN6NevH9q3b1+jfvn5+XjvvfewbNmyGl8TAAIDA2Fqalpu3fjx4xEZGSlXrq+vD2NjY+nnoqIiDB48GEuXLsX48eNx9OhRXLx4EZGRkXj06BGioqLeKLbXnj9/juXLl1erbUxMDAIDA7FlyxaFX21cl5h0ExERERFRvTVt2jRoaWnh7NmzcHNzg5WVFSwsLDB8+HDs3bsXrq6u5fa7e/cu3N3dYWBgAD09Pdjb2+PUqVPSej09PXh7e2POnDlVxrB9+3Z07doVc+bMwdGjR/HHH3/I1BcVFSEoKAhmZmYQiUSwtLRETEyMtD4xMbHcOEtKSjBjxgy0bNkSRkZGmD9/PsrKyqT1n3/+ORYsWABHR8cqY/ynX375BUlJSRUmtK6urjh79ixu3LhR6XkiIiKQkpKCQ4cOYdasWejVqxfatWsHBwcHrF27FkuXLq2wb1XPBQBmzpyJ8PBw5OTkVBrHrVu3cOLECcyZMwedOnXCzp075drExsaiW7duEIlEaNu2LWbMmFHpOesKk24iIiIiIqpTRUVFePbsmcxRVFQk1+7x48dISkrC9OnToaenV+65BEGQK8vLy4ODgwPu3buHPXv2ICMjA4GBgXJrmIODg3Hx4kX8+OOPlcYbExMDT09PtGzZEkOGDEFcXJxMvZeXF7Zs2YLIyEhcvnwZ69atQ7NmzQAAubm5uHTpEuzt7eXOGx8fDy0tLZw+fRqrVq1CeHg4NmzYUGks1fHgwQP4+Phg06ZN0NXVLbdNu3bt0KZNGxw7dqzSc23ZsgWDBw+GnZ1dufXlPf/XKnsur7m7u8PS0hKLFi2qNI6NGzdi6NChaNmyJTw9PeWS9+joaEyfPh2TJ0/GxYsXsWfPHlhaWlZ6zrqipeoAqGHT1taGn5/fG/dfOtK2FqMhIiIiovogNDQUISEhMmULFy6UW8d7/fp1lJWVoXPnzjLlRkZG0unF06dPl5t+vXnzZjx8+BBnzpyBgYEBAJSbgJmamsLX1xdz587FiBEjyo312rVrOHnypHRk1dPTE/7+/pg3bx4EQcDVq1exbds27N+/XzoibWFhIe2flZWFsrKycqd5m5mZISIiAoIgoHPnzrh48SIiIiLg4+NTbizVUVZWhvHjx2Pq1Kmwt7fH7du3K2xramqKO3fuVHq+q1evYuDAgTJlI0eOxP79+wEANjY2OHHiRLn9KnsurwmCgLCwMLi6usLPzw8dOnSQayORSBAXFyedyj5mzBh88cUXuHXrFt5++20AwNdff40vvvgCvr6+0n7vvPNOpfdWV5h0k1KFhoYq1P/F0bjaCYSIiIiI6g2xWAx/f3+ZMpFIVO3+p0+fhkQigYeHR7kj5Onp6bCzs5Mm3JUJCgrCunXrEBsbCzc3N7n62NhYODk5wcjICADg4uKCiRMn4tChQxg0aBDS09OhqakJBweHcs//4sULAK8Go/6pT58+MiPFffv2xYoVK1BaWgpNTc0qY586dSp++OEH6ee8vDxERUXh+fPnEIvFVfbX0dF5o43o1qxZg/z8fERGRuLo0aPltqnqufydk5MT3nvvPcyfPx+bN2+Wq9+/fz/y8/Ph4uIC4NWXLoMHD0ZsbCwWL16MnJwc/Pnnnxg0aFCN76UucHo5ERERERHVKZFIhBYtWsgc5SXdlpaWEAQBmZmZMuUWFhawtLSEjo5OueevqLw8+vr6EIvFCAkJkUtAS0tLER8fj71790JLSwtaWlrQ1dVFbm6udEO1qq71Oll/8uRJtWOqrkWLFiE9PV16AMChQ4eQmpoKkUgELS0t6Qi/vb09xo0bJ9M/NzcXrVu3rvQaHTt2lHv+bdu2haWlZaVfatTkZwAAYWFh2Lp1K9LS0uTqYmJikJubCx0dHenPYd++fYiPj4dEIqnxteoak24iIiIiIqqXDA0NMXjwYKxevRr5+fnV7mdjY4P09HTk5uZWq/3MmTOhoaGBVatWyZTv27cPz58/R1pamkxyu2XLFuzcuRNPnz6FtbU1JBIJjhw5Uu65O3TogBYtWuDSpUtydX/f2A0ATp48iY4dO1ZrlBsAjI2NYWlpKT0AIDIyEhkZGdJY9+3bBwDYunUrlixZIu1bWFiIGzduVLhW+zV3d3fs37+/3GS4MlU9l3/q3bs3PvnkE7mN7R4/fozdu3cjMTFR5meQlpaGJ0+eICkpCc2bN4e5uTkOHjxYoxjrCqeXExERERFRvbVmzRr0798f9vb2CA4Oho2NDTQ0NHDmzBlcuXIFvXr1kuvj7u6OpUuXYsSIEQgNDUXbtm2RlpYGU1NT9O3bV669trY2QkJC5N7/HBMTg6FDh8LWVnafoa5du8LPzw8JCQmYPn06xo0bB29vb0RGRsLW1hZ37txBTk4O3NzcoKGhAUdHR6SkpMitG8/KyoK/vz+mTJmC8+fPIyoqCitWrJDW5+bmIisrS/ou7NcjziYmJjAxMSn3ebVr107m8+uNyzp06IB//etf0vKTJ09CJBKV+zz+zs/PD3v37sWgQYOwcOFCDBgwAK1atcLVq1fxyy+/VPgFgbm5eaXPpTxLlixBt27doKX1vzR106ZNMDQ0hJubm9ymbS4uLoiJiYGzszOCg4MxdepUGBsbY8iQIXj+/DmOHz+OmTNnVnp/dYEj3UREREREVG916NABaWlpcHR0hFgshq2tLezt7REVFYWAgAAsXrxYrk/Tpk2RlJQEY2NjuLi4wNraGmFhYZWOII8bN05mo68HDx5g7969GDVqlFxbDQ0NjBw5UrqDdnR0NEaPHo1p06ahS5cu8PHxkRmZnzRpEhITE+V2T/fy8sKLFy/Qu3dvTJ8+Hb6+vpg8ebK0fs+ePbCzs8PQoUMBvNpAzM7ODmvXrq3m06vYli1b4OHhUeHu5q9pa2vj4MGDCAoKwsaNG/Hee+/BysoKs2fPRv/+/bFr164K+1b1XP6pU6dO8Pb2lnkHd2xsLEaOHFnuLumjRo3Cnj178OjRI4wbNw4rV67EmjVr0K1bNwwbNgzXrl2r+kHUAaHs7y+CU4Cfnx8iIiJq41RUA8p87vXhZ9oQNlITmlR/U5D6KvvfsaoOQSE/5bZRdQgK0y9VdQSKa1tSouoQFNLJuHpTFOuz1MfGqg5BYbXyjxYVaieR3/BJ3SRpN1F1CApT9z9SS9X+/wTgm9tbVB1CnSorK8O7774LPz8/uLu7qzocPHr0CJ07d8bZs2elu38Dr3YS/+mnnyrcyZ3eDEe6iYiIiIiIlEgQBKxfvx4l9eQL6Nu3b2PNmjUyCfdr7u7uMtPQSXFc001ERERERKRkPXr0QI8ePVQdBoBXO5nb29vLlb+ejl3djdyoeph0ExERERERkXQHdKpdTLrVnLa2Nvz8/JRy7pycHKWctyYETfVfNyYYtVd1CAprZSupulE9VpSs6ggU11T9l+9BV1DvVZQlJeq/IstG9JeqQ1DYlcIWqg5BIcVl6v/76MDLP1UdgsIMtCrfOKq+0xPU/99HRI0Jk241FxoaqrRzKyuZJyIiIiIiaizU/+tWIiIiIiIionqKSTcRERERERGRkjDpJiIiIiIiIlISJt1ERERERERKcvv2bQiCgPT0dFWHQirCpJuIiIiIiOqt8ePHY8SIEdVuLwgCdu3apbR4lEnZCfrr8xsbG+P58+cydT169EBwcLBcny1btkBTUxPTp0+v1VhU9XMqLi5GUFAQrK2toaenB1NTU3h5eeHPP5X3ZgYm3URERERERP9QXFys6hCU5vnz51i+fHm12sbExCAwMBBbtmxBYWGhkiNTvoKCApw/fx7z58/H+fPnsXPnTmRmZuLjjz9W2jWZdBMRERERkVoYOHAgZs2ahcDAQBgYGMDExERmdNbc3BwAMHLkSAiCIP0MALt370bPnj2hra0NCwsLhISEoKSkRFovCAKio6Px8ccfQ09PD0uWLEFwcDB69OiBTZs2wdzcHC1btsSYMWNkRol//fVXvPfee9DX14ehoSGGDRuGGzduKOX+b9y4geHDh6NNmzZo1qwZ3nnnHRw4cECmTVFREYKCgmBmZgaRSARLS0vExMTItJk5cybCw8ORk5NT6fVu3bqFEydOYM6cOejUqRN27twp1yY2NhbdunWDSCRC27ZtMWPGjCrvo7yf0+3bt6GhoYGzZ8/KtF25ciXat28PiUSC5ORkCIKAvXv3wsbGBtra2ujTpw9+++03mT4pKSkYMGAAdHR0YGZmhlmzZiE/Px8A0LJlS+zfvx9ubm7o3Lkz+vTpg9WrV+PcuXPIysqqMvY3waSbiIiIiIjURnx8PPT09HDq1Cl88803WLRoEfbv3w8AOHPmDABg48aNuH//vvTzsWPH4OXlBV9fX1y6dAnr1q1DXFwclixZInPu4OBgjBw5EhcvXoS3tzeAV4nurl278PPPP+Pnn3/GkSNHEBYWJu2Tn58Pf39/nD17FgcPHoSGhgZGjhwJiURS6/eel5cHFxcXHDx4EGlpaXB2doarq6tMsujl5YUtW7YgMjISly9fxrp169CsWTOZ87i7u8PS0hKLFi2q9HobN27E0KFD0bJlS3h6esol79HR0Zg+fTomT56MixcvYs+ePbC0tKzyPsr7OZmbm8PR0REbN26Ui2H8+PHQ0Phf6vrll19ixYoVOHPmDFq3bg1XV1fpzIQbN27A2dkZo0aNwoULF7B161akpKRU+mXAX3/9BUEQoK+vX2Xsb0JLKWclIiIiIiKqQFFREYqKimTKRCIRRCJRlX1tbGywcOFCAEDHjh2xevVqHDx4EIMHD0br1q0BAPr6+jAxMZH2CQkJwZw5czBu3DgAgIWFBRYvXozAwEDpuQBg7NixmDBhgsz1JBIJ4uLi0Lx5cwDA559/joMHD0oT9lGjRsm0j42NRevWrXHp0iV07969Ws+jumxtbWFrayv9vHjxYvz000/Ys2cPZsyYgatXr2Lbtm3Yv38/HB0dpff6T4IgICwsDK6urvDz80OHDh3k2ry+76ioKADAmDFj8MUXX+DWrVt4++23AQBff/01vvjiC/j6+kr7vfPOO1XeR0U/p0mTJmHq1KkIDw+HSCTC+fPncfHiRezevVum/8KFCzF48GAAr76E+de//oWffvoJbm5uCA0NhYeHB2bPng3g1e+RyMhIODg4IDo6Gtra2jLnKiwsRFBQENzd3dGiRYsqY38TTLpJIWKxWKlrO0JH2yvt3ERERESkGqGhoQgJCZEpW7hwYbkbef2TjY2NzOe2bdtWOU06IyMDx48flxnZLi0tRWFhIQoKCqCrqwsAsLeX/7enubm5NOEu73rXrl3DggULcOrUKTx69Eg6wp2VlVXrSXdeXh6Cg4Oxd+9e3L9/HyUlJXjx4oV0pDs9PR2amppwcHCo8lxOTk547733MH/+fGzevFmufv/+/cjPz4eLiwsAwMjICIMHD0ZsbCwWL16MnJwc/Pnnnxg0aFCt3d+IESMwffp0/PTTTxgzZgzi4uLwwQcfyCwTAIC+fftKf21gYIDOnTvj8uXLAF79rC9cuICEhARpm7KyMkgkEty6dQtWVlbS8uLiYri5uaGsrAzR0dG1dh//xKSbFFJYWIiIiAjlnf94QtWNiIiIiEitiMVi+Pv7y5RVZ5QbAJo0aSLzWRCEKqdy5+XlISQkBJ988olc3d9HPvX09Gp8PVdXV7Rv3x7ff/89TE1NIZFI0L17d7x8+bJa91MTAQEB2L9/P5YvXw5LS0vo6Ohg9OjR0mvp6OjU6HxhYWHo27cvvvzyS7m6mJgY5ObmypxTIpHgwoULCAkJqfG1qqNp06bw8vLCxo0b8cknn2Dz5s1YtWpVjc6Rl5eHKVOmYNasWXJ17dq1k/76dcJ9584dHDp0SGmj3ACTbiIiIiIiqmPVnUr+Jpo0aYLS0lKZsp49eyIzM7Na641r4vHjx8jMzMT333+PAQMGAHi1iZeyHD9+HOPHj8fIkSMBvEowb9++La23traGRCLBkSNHpNPLK9O7d2988sknmDNnjkz548ePsXv3biQmJqJbt27S8tLSUrz33ntISkqCs7MzzM3NcfDgQXzwwQc1vpfyfk7Aqynm3bt3x5o1a1BSUlLuFyUnT56UJtBPnjzB1atXpSPYPXv2xKVLlyr9Wb9OuK9du4bDhw/D0NCwxvHXBJNuIiIiIiJqMF4ngv3794dIJEKrVq2wYMECDBs2DO3atcPo0aOhoaGBjIwM/Pbbb/j666/f+FqtWrWCoaEh1q9fj7Zt2yIrK0sugX0TmZmZcmXdunVDx44dsXPnTri6ukIQBMyfP19m1N3c3Bzjxo2Dt7c3IiMjYWtrizt37iAnJwdubm7lXmvJkiXo1q0btLT+lxpu2rQJhoaGcHNzgyAIMu1dXFwQExMDZ2dnBAcHY+rUqTA2NsaQIUPw/PlzHD9+HDNnzqzyHsv7OQGAlZUV+vTpg6CgIHh7e5c7or5o0SIYGhqiTZs2mDt3LoyMjKTvcg8KCkKfPn0wY8YMTJo0CXp6erh06RL279+P1atXo7i4GKNHj8b58+fx888/o7S0FNnZ2QBeTVVv2rRplbHXFHcvJyIiIiKiBmPFihXYv38/zMzMYGdnB+DV+uWff/4ZSUlJeOedd9CnTx9ERESgffv2Cl1LQ0MDiYmJOHfuHLp37w4/Pz98++23Ct/DmDFjYGdnJ3M8ePAA4eHhaNWqFfr16wdXV1c4OTmhZ8+eMn2jo6MxevRoTJs2DV26dIGPj4/0dVnl6dSpE7y9vWX2aYqNjZW+zuufRo0ahT179uDRo0cYN24cVq5ciTVr1qBbt24YNmwYrl27Vq17LO/n9NrEiRPx8uVL6Q7y/xQWFgZfX1/06tUL2dnZ+M9//iNNlm1sbHDkyBFcvXoVAwYMgJ2dHRYsWABTU1MAwL1797Bnzx7cvXsXPXr0QNu2baXHiRMnqhV7TQllZWVltXEiPz8/pa7tpbpXnZ+psn/uDWFNt2D8tqpDUFjh1/NVHYJCopPbqjoEhZkVqzoCxZlJiqpuVI+ZtHpedaN6rqiwSdWN6rkrhcpbc1cXDCQlVTeq5+ZqPlB1CAoz0NJVdQgK0RPU///l7Xd2V92IGq3Fixdj+/btuHDhgkx5cnIyPvjgAzx58kRpr/dSBo50ExERERERkcrl5eXht99+w+rVq6s1RV1dMOkmIiIiIiKqA1OnTkWzZs3KPaZOnarq8GpNQkJChff5943Z/mnGjBno1asXBg4cWOHUcnXEjdSIiIiIiIjqwKJFixAQEFBunTJfWVXXPv74Y7z77rvl1v3zFWx/FxcXh7i4uArrBw4ciFpaHV2nmHQTERERERHVAWNjYxgbG6s6DKVr3rw5mjdvruow6g0m3VQhbW1t+Pn5VdomJydHuUE0qf0t++uaRisTVYegsEfn1PuPij8F9d+FrJmm+m+ak6ehnPex1pV2oqeqDkFheXnq/TNoCO5pqf//yzoNYBOvvFL13tixSFD/DfmIGhP1/pc0KVVoaGiVbapKyomIiIiIiBozbqRGREREREREpCRMuomIiIiIiIiUhEk3ERERERERkZIw6SYiIiIiIqpj77//PjZv3qzqMAAAffr0wY4dO1QdRoPFpJuIiIiIiOqt8ePHQxAEhIWFyZTv2rULgiDU6rXMzc0hCILM8c/rvtalSxeIRCJkZ2fX+Dp79uzBgwcPMGbMmGr3SU5OxvDhw9G2bVvo6emhR48eSEhIqLJfaWkp5s+fj7fffhs6Ojro0KEDFi9eLPO+63nz5mHOnDmQSCTSsri4OAiCAGdnZ5nzPX36FIIgIDk5udqxV8frn/Pfj39e+7UpU6ZAU1MT27dvr9UYlIVJNxERERER1Wva2tpYtmwZnjx5ovRrLVq0CPfv35ceM2fOlGuTkpKCFy9eYPTo0YiPj6/xNSIjIzFhwgRoaFQ/HTtx4gRsbGywY8cOXLhwARMmTICXlxd+/vnnSvstW7YM0dHRWL16NS5fvoxly5bhm2++QVRUlLTNkCFD8Pz5c/zyyy8yfbW0tHDgwAEcPny4Zjf4hpydnWWe/ZYtW+TaFBQUIDExEYGBgYiNja2TuBTFpJuIiIiIiOo1R0dHmJiYVPpK25SUFAwYMAA6OjowMzPDrFmzkJ+fL60XBAG7du2S6aOvr4+4uDiZsubNm8PExER66OnpyV0rJiYGY8eOxeeff15u4nf37l24u7vDwMAAenp6sLe3x6lTpwAADx8+xKFDh+Dq6irTJzw8HNbW1tDT04OZmRmmTZuGvLw8af1XX32FxYsXo1+/fujQoQN8fX3h7OyMnTt3VvhMgFfJ+vDhwzF06FCYm5tj9OjR+Oijj3D69GlpG01NTbi4uCAxMVGmr56eHry9vTFnzpxKr/HHH3/Azc0N+vr6MDAwwPDhw3H79m1p/cCBAzF79myZPiNGjMD48eNlykQikcyzb9Wqldy1tm/fjq5du2LOnDk4evQo/vjjD5n6oqIiBAUFwczMDCKRCJaWloiJiak0fmVj0k1ERERERHWqqKgIz549kzmKiooqbK+pqYmlS5ciKioKd+/elau/ceMGnJ2dMWrUKFy4cAFbt25FSkoKZsyYUePYwsLCYGhoCDs7O3z77bcoKSmRqX/+/Dm2b98OT09PDB48GH/99ReOHTsmrc/Ly4ODgwPu3buHPXv2ICMjA4GBgdKp2ykpKdDV1YWVlZXMeTU0NBAZGYnff/8d8fHxOHToEAIDAyuN9a+//oKBgUGlbfr164eDBw/i6tWrAICMjAykpKRgyJAhMu169+4tcx+vBQcH4+LFi/jxxx/LPX9xcTGcnJzQvHlzHDt2DMePH0ezZs3g7OyMly9fVhrbPyUnJ8PY2BidO3fGv//9bzx+/FiuTUxMDDw9PdGyZUsMGTJE7ksTLy8vbNmyBZGRkbh8+TLWrVuHZs2a1SiO2qal0quT2tPW1oafn5/Szh/q3k9p5yYiIiIi1QgNDUVISIhM2cKFCxEcHFxhn5EjR6JHjx5YuHCh3MhlaGgoPDw8pKOpHTt2RGRkJBwcHBAdHQ1tbe1qxTVr1iz07NkTBgYGOHHiBMRiMe7fv4/w8HBpm8TERHTs2BHdunUDAIwZMwYxMTEYMGAAAGDz5s14+PAhzpw5I02ILS0tpf3v3LmDNm3ayE0t//tIsLm5Ob7++mtMnToVa9asKTfWbdu24cyZM1i3bl2l9zRnzhw8e/YMXbp0gaamJkpLS7FkyRJ4eHjItDM1NcUff/wBiUQiE5upqSl8fX0xd+5cjBgxQu78W7duhUQiwYYNG6Rr7Ddu3Ah9fX0kJyfjo48+qjS+15ydnfHJJ5/g7bffxo0bN/DVV19hyJAhSE1NhaamJgDg2rVrOHnypHR039PTE/7+/pg3bx4EQcDVq1exbds27N+/H46OjgAACwuLal1fmZh0k0Iqm+JTGwpPq8fmCERERERUfWKxGP7+/jJlIpGoyn7Lli3Dhx9+iICAAJnyjIwMXLhwQWZjsbKyMkgkEty6dUtuVLkif4/JxsYGTZs2xZQpUxAaGiqNLzY2Fp6entJ2np6ecHBwQFRUFJo3b4709HTY2dlVOAL94sWLcr8EOHDgAEJDQ3HlyhU8e/YMJSUlKCwsREFBAXR1dWXaHj58GBMmTMD3338vTf6PHTsmM3q9bt06eHh4YNu2bUhISMDmzZvRrVs3pKenY/bs2TA1NcW4ceOk7XV0dCCRSFBUVAQdHR2Z6wUFBWHdunWIjY2Fm5ubTF1GRgauX7+O5s2by5QXFhbixo0b5T6D8vx9Uzlra2vY2NigQ4cOSE5OxqBBgwC8evZOTk4wMjICALi4uGDixIk4dOgQBg0ahPT0dGhqasLBwaHa160LTLqJiIiIiKhOiUSiaiXZ//T+++/DyckJYrFYZj1wXl4epkyZglmzZsn1adeuHYBXa7r/vmM38GpqdGXeffddlJSU4Pbt2+jcuTMuXbqEkydP4vTp0wgKCpK2Ky0tRWJiInx8fOQS1n8yMjKS2xDu9u3bGDZsGP79739jyZIlMDAwQEpKCiZOnIiXL1/KJN1HjhyBq6srIiIi4OXlJS23t7dHenq69HObNm0AAF9++SXmzJkjTWqtra1x584dhIaGyiTdubm50NPTKzd+fX19iMVihISEYNiwYTJ1eXl56NWrV7k7qbdu3RrAq6nzNX32FhYWMDIywvXr1zFo0CCUlpYiPj4e2dnZ0NL6XxpbWlqK2NhYDBo0qMpnrypMuomIiIiISG2EhYWhR48e6Ny5s7SsZ8+euHTpksw07n9q3bo17t+/L/187do1FBQUVHqt9PR0aGhowNjYGMCr9cTvv/8+vvvuO5l2GzduRExMDHx8fGBjY4MNGzYgNze33NFuOzs7ZGdn48mTJ9KNws6dOweJRIIVK1ZIp3Zv27ZNrm9ycjKGDRuGZcuWYfLkyTJ1Ojo65d5/QUGB3FR2TU1NmdeDAcBvv/0GOzu7Cp/FzJkzERkZiVWrVsmU9+zZE1u3boWxsTFatGhRbt9/PvvS0lL89ttv+OCDDyq83t27d/H48WO0bdsWALBv3z48f/4caWlp0unmr+OeMGECnj59Cmtra0gkEhw5ckQ6vbw+4EZqRERERESkNqytreHh4YHIyEhpWVBQEE6cOIEZM2YgPT0d165dw+7du2U2Uvvwww+xevVqpKWl4ezZs5g6dSqaNGkirU9NTcXKlSuRkZGBmzdvIiEhAX5+fvD09ESrVq1QXFyMTZs2wd3dHd27d5c5Jk2ahFOnTuH333+Hu7s7TExMMGLECBw/fhw3b97Ejh07kJqaCuBV0m1kZITjx49Lr21paYni4mJERUXh5s2b2LRpE9auXStz34cPH8bQoUMxa9YsjBo1CtnZ2cjOzkZubm6lz8vV1RVLlizB3r17cfv2bfz0008IDw/HyJEjZdodO3as0vXX2traCAkJkXnuAODh4QEjIyMMHz4cx44dw61bt5CcnIxZs2ZJN7378MMPsXfvXuzduxdXrlzBv//9bzx9+lR6jry8PHz55Zc4efIkbt++jYMHD2L48OGwtLSEk5MTgFdfeAwdOhS2trYyz/71rukJCQkwNzfHuHHj4O3tjV27dkljKe8LjLrEpJuIiIiIiNTKokWLZEZqbWxscOTIEVy9ehUDBgyAnZ0dFixYAFNTU2mbFStWwMzMDAMGDMDYsWMREBAgM21bJBIhMTERDg4O6NatG5YsWQI/Pz+sX78eALBnzx48fvxYLlkFACsrK1hZWSEmJgZNmzZFUlISjI2N4eLiAmtra4SFhUlHZzU1NTFhwgSZ6di2trYIDw/HsmXL0L17dyQkJMjtnRQfH4+CggKEhoaibdu20uOTTz6p9FlFRUVh9OjRmDZtGqysrBAQEIApU6Zg8eLF0jb37t3DiRMnMGHChErPNW7cOLmNyXR1dXH06FG0a9cOn3zyCaysrDBx4kQUFhZKR769vb0xbtw4eHl5wcHBARYWFjKj3Jqamrhw4QI+/vhjdOrUCRMnTkSvXr1w7NgxiEQiPHjwAHv37sWoUaPkYtLQ0MDIkSOlm+tFR0dL77dLly7w8fGReXWcKghl/5xc/4b8/PwQERFRG6cikmoIG6lpWvRSdQgKyxr4b1WHoJBVefqqDkFhnUqbVN2onmtSK3/bqI6T0QNVh6Cw3Me6VTeq526UqPa1L4p6oSGoOgSFbRTU//+F0jJJ1Y3qsSaCZtWN6rmDd5NUHYJKZWdno1u3bjh//jzat2+v6nAQFBSEJ0+eSL9goNrFkW4iIiIiIqI6ZGJigpiYGGRlZak6FACAsbGxzMg31S5upEZERERERFTHynvntap88cUXqg6hQeNINxEREREREZGScKSb6jW7Yd+qOgQCEKZR8es31EHIgD9VHYLCJAVqviAawNM72qoOQSG/3zdSdQgKs++q/v8v/EvjqapDUMij2+q9Jh0ARg5R/z0moKHe406CTlNVh0BENaDef+IQERERERER1WNMuomIiIiIiIiUhEk3ERERERERkZIw6SYiIiIiIiJSEibdRERERERERErCpJuIiIiIiNSeIAjYtWuXqsOo0Pz58zF58mTp54EDB2L27NkqieXXX39Fjx49IJFI3qh/cnIyBEHA06dPazewBopJNxERERER1WvZ2dnw9fWFpaUltLW10aZNG/Tv3x/R0dEoKCio8fni4uIgCAKcnZ1lyp8+fQpBEJCcnCzXZ8qUKdDU1MT27dvfKP5Vq1Zh7ty5Neq3c+dOfPTRRzA0NIQgCEhPT69R/6KiIvTo0UOur7OzM5o0aYKEhAS5Pmlpafjss8/Qtm1biEQitG/fHsOGDcN//vMflJW92StMx48fD0EQEBYWJlO+a9cuCIJQbp8uXbpAJBIhOzv7ja5ZnzDpJiIiIiKieuvmzZuws7NDUlISli5dirS0NKSmpiIwMBA///wzDhw48Ebn1dLSwoEDB3D48OEq2xYUFCAxMRGBgYGIjY2t8bU2bNiAfv36oX379jXql5+fj/feew/Lli2r8TUBIDAwEKampuXWjR8/HpGRkTJlu3fvRp8+fZCXl4f4+HhcvnwZv/76K0aOHIl58+bhr7/+eqM4AEBbWxvLli3DkydPqmybkpKCFy9eYPTo0YiPj3/ja9YXTLqJiIiIiKjemjZtGrS0tHD27Fm4ubnBysoKFhYWGD58OPbu3QtXV9dy+929exfu7u4wMDCAnp4e7O3tcerUKWm9np4evL29MWfOnCpj2L59O7p27Yo5c+bg6NGj+OOPP2Tqi4qK8P/Yu/O4GvP+f+CvU+m0SUlkqSyFtBAxjCUMQrqHsYxGSBi+9pomGkvFTdkpg0EJExmmMWbsk7UkMk6WEjI0DBq7tC+/P/xct8tpdcrp8Ho+Hudxn/PZrvd1dWt6n8/n+lwzZ86EqakppFIpLCwsEBoaKtRHRkYWG2d+fj6mTJmCWrVqoU6dOpg7d65oNnnkyJGYN28eevXqVWaMbztw4AAOHz6MZcuWFVvv4uKChIQEpKamAniV4I8dOxbOzs7Yt28f+vTpg6ZNm8LKygpjx45FYmIiatWqVeLxYmNj0b17d+jo6MDQ0BBOTk6iBLtXr14wMTFBYGBgmbGHhobiq6++wsiRI4v9kqOsn211w6SbiIiIiIjeq5ycHDx//lz0ysnJkWv36NEjHD58GJMnT4aurm6xYxW3PDkjIwOOjo64e/cu9u7di8TERPj4+Mjdw+zv749Lly5h9+7dpcYbGhoKNzc31KpVC/369UN4eLioftSoUdixYweCg4ORnJyMH374AXp6egCAx48fIykpCQ4ODnLjbtmyBRoaGjh79ixWr16NFStWYNOmTaXGUh4PHjzA+PHjsW3bNujo6BTbxszMDPXq1cOpU6cAAIcPH8ajR4/g4+NT4rglLQWXyWT47LPP0KpVK8TFxSEmJgYuLi4oKCgQ2qirq2PRokUICQnBnTt3SjzGixcvsGvXLri5uaF379549uyZECNQ/p9tdaKh7ABIcb6+vsjOzlZ2GERERERE5RIYGIiAgABRmZ+fH/z9/UVlN27cQFFREVq0aCEqr1OnjvD37+TJk+WWX2/fvh3//vsvzp07h9q1awMALCws5OJo0KABpk+fjtmzZ2PgwIHFxnr9+nWcOXMGUVFRAAA3Nzd4eXlhzpw5kEgkuHbtGn766SccOXJEmJFu2rSp0D8tLQ1FRUXFLvM2NTXFypUrIZFI0KJFC1y6dAkrV67E+PHji42lPIqKiuDu7o6JEyfCwcEBt27dKrFtgwYNcPv2bQDAtWvXAEB0rc+dO4cePXoInyMjIzFgwAC5cZYsWQIHBwesXbtWKLO2tpZrN2jQILRp0wZ+fn6ilQBvioyMhKWlpdB/+PDhCA0NRdeuXQGU/2dbnTDp/gBkZ2dj5cqVyg6jShyM6KDsEIiIiIiokvn6+sLLy0tUJpVKy93/7NmzKCwsxIgRI4qdIZfJZLC3txeSstLMnDkTP/zwA8LCwjBs2DC5+rCwMDg5OaFOnToAgP79+2Ps2LE4evQoPvvsM8hkMqirq8PR0bHY8bOysgC8uqf5bR07dhTNHnfq1AnLly9HQUEB1NXVy4x94sSJ+PHHH4XPGRkZCAkJwYsXL+Dr61tmf21t7VI3orOzsxM2YLO0tER+fn6x7WQyGYYOHVrm8QBg8eLF6NmzJ7y9vYutDwsLg5ubm/DZzc0Njo6OCAkJQc2aNSv0s60uuLyciIiIiIjeK6lUCn19fdGruKTbwsICEokEKSkpovKmTZvCwsIC2traxY5fUnlxDAwM4Ovri4CAALkEtKCgAFu2bMG+ffugoaEBDQ0N6Ojo4PHjx8K9xmUd63WyXp4NxCpq/vz5kMlkwgsAjh49iri4OEilUmhoaAizwA4ODhg9erSo/+PHj2FsbAzgVVINQHStX9+fXtZMckWud7du3eDk5FTslwJJSUk4c+YMfHx8hOvdsWNHYSO7ih6rumDSTURERERE1ZKRkRF69+6NNWvW4OXLl+Xu93qG9vHjx+VqP3XqVKipqWH16tWi8v379+PFixe4cOGCKLndsWMHoqKi8PTpU9ja2qKwsBAnTpwoduxmzZpBX18fSUlJcnVvb/515swZWFpalmuWGwDq1q0rJMWvE+Pg4GAkJiYKse7fvx8AsHPnTixcuFDom52djdTUVNjb2wMA+vTpg9q1a7/TTul2dnaIjo4ud/ugoCD89ttviIuLE5WHhoaiW7duovhlMhm8vLyE5egV/dlWB0y6iYiIiIio2lq7di3y8/Ph4OCAnTt3Ijk5GSkpKfjxxx9x9erVYhNUV1dXmJiYYODAgYiNjcXNmzfx888/yyV5r2lpaSEgIEDuEVqhoaFwdnZG69atYWNjI7yGDRsGAwMDREREoHHjxhg9ejQ8PDywZ88e/PXXXzh+/Dh++uknAICamhp69eqFmJgYueOmpaXBy8sLKSkp2LFjB0JCQjB9+nSh/vHjx5DJZELCnpKSAplMVuqzq83MzESxNm/eHMCr5L9Ro0ZCuzNnzkAqlaJTp04AAD09PWzatAn79u2Ds7MzDh06hJs3b+LixYtYsmQJAJT4ZYCvry/OnTuHSZMm4eLFi7h69SrWrVuHhw8fFtve1tYWI0aMEF3vvLw8bNu2Da6urqL4bWxsMG7cOMTHx+PKlSsV/tlWB0y6iYiIiIio2mrWrBkuXLiAXr16wdfXF61bt4aDgwNCQkLg7e2NBQsWyPXR1NTE4cOHUbduXfTv3x+2trYICgoqdQZ59OjRog3QHjx4gH379mHw4MFybdXU1DBo0CBh9nXdunUYMmQIJk2ahJYtW2L8+PGimflx48YhMjJSboftUaNGISsrCx06dMDkyZMxffp0fP3110L93r17YW9vD2dnZwCvNhWzt7fH+vXry3n1SrZjxw6MGDFCtLv5oEGDcPr0aejo6GDUqFFo0aIFevbsiaNHj5a4iRoANG/eHIcPH0ZiYiI6dOiATp064ddff4WGRslbiM2fP190Pfbu3YtHjx5h0KBBcm2trKxgZWWF0NDQd/rZKpuk6M0HwSnA09Pzg93Mq7r7kK+9VV1upFYdBKlV7x0hy9Kt6z/KDkFhhZmV8qtaqZ7elt9ARpVc/Vd1NmwpiUMr1f+3IFHx6YKHt/SUHYLCTPup/u8jqKn2/5Ek2prKDkFheoujlB3Ce1VUVIRPPvkEnp6ecHV1VXY4ePjwIVq0aIGEhAQ0adJE2eF88FT7Nw4REREREVE1J5FIsGHDhhJ3/37fbt26hbVr1zLhfk/4yDAiIiIiIqIq1qZNG7Rp00bZYQB4tZO5g4ODssP4aHCmm4iIiIiIiKiKcKabqrWhOpbKDkFhl4teKDsEhcVKVPv+vU8eKDsCxaVeM1Z2CArTrpGn7BAUoovqsSRQEbtumCo7BIW5GJe8Y68qqNcyQ9khKExrtuIbOClbfuzPyg5BIYVXryo7BCKqAM50ExEREREREVURJt1EREREREREVYRJNxEREREREVEVYdJNREREREREVEW4kVo15evri+zs7HK1TU9Pr+JoiIiIiIiqN4lEgl9++QUDBw5UdijFmjt3Lh48eIANGzYAALp37442bdpg1apV7z2WgwcPYtasWfjzzz+hpsZ52KrGK1xNZWdnY+XKleV61a1bV9nhEhERERFVmfv372P69OmwsLCAlpYW6tWrh86dO2PdunXIzMys8Hjh4eGQSCTo27evqPzp06eQSCQ4fvy4XJ8JEyZAXV0du3bteqf4V69ejdmzZ1eoX1RUFPr06QMjIyNIJBLIZLIK9c/JyUGbNm3k+vbt2xc1atRARESEqH3jxo0hkUgQGRkpN5a1tTUkEgnCw8MrFAMx6SYiIiIiomrs5s2bsLe3x+HDh7Fo0SJcuHABcXFx8PHxwe+//44//vjjncbV0NDAH3/8gWPHjpXZNjMzE5GRkfDx8UFYWFiFj7Vp0yZ8+umnMDc3r1C/ly9fokuXLli8eHGFjwkAPj4+aNCgQbF17u7uCA4Olis3NTXF5s2bRWVnzpzB/fv3oaur+05xfOyYdBMRERERUbU1adIkaGhoICEhAcOGDYOVlRWaNm2Kzz//HPv27YOLi0ux/e7cuQNXV1fUrl0burq6cHBwQHx8vFCvq6sLDw8PzJo1q8wYdu3ahVatWmHWrFk4efIk/v77b1F9Tk4OZs6cCVNTU0ilUlhYWCA0NFSoj4yMLDbO/Px8TJkyBbVq1UKdOnUwd+5cFBUVCfUjR47EvHnz0KtXrzJjfNuBAwdw+PBhLFu2rNh6FxcXJCQkIDU1VVQ+YsQInDhxQnSOYWFhGDFiBDQ0xHcnr1ixAra2ttDV1YWpqSkmTZqEjIwMoT48PBwGBgY4dOgQrKysoKenh759++LevXsVPh9VxqSbiIiIiIjeq5ycHDx//lz0ysnJkWv36NEjHD58GJMnTy5xllUikciVZWRkwNHREXfv3sXevXuRmJgIHx8fFBYWitr5+/vj0qVL2L17d6nxhoaGws3NDbVq1UK/fv3klliPGjUKO3bsQHBwMJKTk/HDDz9AT08PAPD48WMkJSXBwcFBbtwtW7ZAQ0MDZ8+exerVq7FixQps2rSp1FjK48GDBxg/fjy2bdsGHR2dYtuYmZmhXr16OHXqlKi8Xr16cHJywpYtWwC8muXfuXMnPDw85MZQU1NDcHAwrly5gi1btuDo0aPw8fERtcnMzMSyZcuwbds2nDx5EmlpafD29lb4HFUJN1KjMlVkU7fKVlMpRyUiIiKiqhQYGIiAgABRmZ+fH/z9/UVlN27cQFFREVq0aCEqr1OnjvD36eTJk+WWX2/fvh3//vsvzp07h9q1awMALCws5OJo0KABpk+fjtmzZ5e4Adv169dx5swZREVFAQDc3Nzg5eWFOXPmQCKR4Nq1a/jpp59w5MgRYUa6adOmQv+0tDQUFRUVu8zb1NQUK1euhEQiQYsWLXDp0iWsXLkS48ePLzaW8igqKoK7uzsmTpwIBwcH3Lp1q8S2DRo0wO3bt+XKPTw88M0332D27NnYvXs3mjVrhjZt2si1mzFjhvC+cePG+O9//4uJEydi7dq1QnleXh7Wr1+PZs2aAQCmTJmC+fPnv/P5qSIm3VSm15u6KcO8X0Yo5bhEREREVHV8fX3h5eUlKpNKpeXuf/bsWRQWFmLEiBHFzpDLZDLY29sLCXdpZs6ciR9++AFhYWEYNmyYXH1YWBicnJxQp04dAED//v0xduxYHD16FJ999hlkMhnU1dXh6OhY7PhZWVkAAC0tLbm6jh07imbqO3XqhOXLl6OgoADq6uplxj5x4kT8+OOPwueMjAyEhITgxYsX8PX1LbO/trZ2sRvROTs7Y8KECTh58iTCwsKKneUGgD/++AOBgYG4evUqnj9/jvz8fGRnZyMzM1OYYdfR0RESbgCoX7/+R/f0JS4vJyIiIiKi90oqlUJfX1/0Ki7ptrCwgEQiQUpKiqi8adOmsLCwgLa2drHjl1ReHAMDA/j6+iIgIEAuAS0oKMCWLVuwb98+aGhoQENDAzo6Onj8+LGwoVpZx3qdrD958qTcMZXX/PnzIZPJhBcAHD16FHFxcZBKpdDQ0BBm+B0cHDB69GhR/8ePH8PY2FhuXA0NDYwcORJ+fn6Ij4/HiBHyE2G3bt3CgAEDYGdnh59//hnnz5/H999/DwDIzc0V2tWoUUPUTyKRiO5b/xgw6SYiIiIiomrJyMgIvXv3xpo1a/Dy5cty97Ozs4NMJsPjx4/L1X7q1KlQU1PD6tWrReX79+/HixcvcOHCBVFyu2PHDkRFReHp06ewtbVFYWEhTpw4UezYzZo1g76+PpKSkuTq3tzYDXi1S7ilpWW5ZrkBoG7durCwsBBeABAcHIzExEQh1v379wMAdu7ciYULFwp9s7OzkZqaCnt7+2LH9vDwwIkTJ/D555/D0NBQrv78+fMoLCzE8uXL0bFjRzRv3hz//PNPueL+2DDpJiIiIiKiamvt2rXIz8+Hg4MDdu7cieTkZKSkpODHH3/E1atXi01QXV1dYWJigoEDByI2NhY3b97Ezz//jLi4uGKPoaWlhYCAALlHaIWGhsLZ2RmtW7eGjY2N8Bo2bBgMDAwQERGBxo0bY/To0fDw8MCePXvw119/4fjx4/jpp58AvNpsrFevXoiJiZE7blpaGry8vJCSkoIdO3YgJCQE06dPF+ofP34MmUwmJOwpKSmQyWS4f/9+idfLzMxMFGvz5s0BvEr+GzVqJLQ7c+YMpFIpOnXqVOw4VlZWePjwodzjw16zsLBAXl4eQkJCcPPmTWzbtg3r168vMa6PGZNuIiIiIiKqtpo1a4YLFy6gV69e8PX1RevWreHg4ICQkBB4e3tjwYIFcn00NTVx+PBh1K1bF/3794etrS2CgoJKnUEePXq0aAO0Bw8eYN++fRg8eLBcWzU1NQwaNEh4LNi6deswZMgQTJo0CS1btsT48eNFM/Pjxo1DZGSk3O7po0aNQlZWFjp06IDJkydj+vTp+Prrr4X6vXv3wt7eHs7OzgCA4cOHw97evlKS2x07dmDEiBEl7m4OvFppUNLy+datW2PFihVYvHgxbGxsEBERgcDAQIXj+hBJiippQb2np6fSNtv6EFXkelb1tVfmz3ZeY9XfSO1y0Qtlh6AwC0nxj+hQFV5mqv8syNRrdZQdgsK0a+QpOwSFPM/VVHYICrusWf5NiqorF+OSZ3dUgV591f53AAD6m1R/Jis/9mdlh6CQwqtXlR2CwnTn/Fh2ow9IUVERPvnkE3h6esLV1VXZ4eDhw4do0aIFEhIS0KRJE2WH88HjTDcREREREVEVkkgk2LBhA/Lz85UdCoBXm6CtXbuWCfd7wkeGERERERERVbE2bdoU+6xrZXBwcICDg4Oyw/hocKabiIiIiIiIqIow6SYiIiIiIiKqIlxe/gHQ0tKCp6dnlY2fnp5eZWOX5W9kK+3YleVevupvpOYs0Vd2CArJyyrfsy6rM1XfhAwADAyylB2CYp4qOwDF9dIu/zNuqytdY9X+t6Cur/rzHQW3Lyo7BMXl5Cg7AoVItLSUHQIRVQCT7g9AVW/NX5UJPRERERER0YdM9b9uJSIiIiIiIqqmmHQTERERERERVREm3URERERERERVhEk3ERERERHRW0aOHIlFixYpOwwAwPDhw7F8+XJlh0HviEk3ERERERFVW+7u7pBIJJg4caJc3eTJkyGRSODu7l6px0xMTMT+/fsxbdq0CvWbNm0a2rVrB6lUijZt2pSrT1RUFHr37g1jY2Po6+ujU6dOOHTokKjNnDlzsHDhQjx79kwoO378OCQSCQwNDZGdLX7iz7lz5yCRSCCRSCoUP1UNJt1ERERERFStmZqaIjIyEllZ/3v8ZHZ2NrZv3w4zM7NKP15ISAiGDh0KPT29Cvf18PDAl19+We72J0+eRO/evbF//36cP38ePXr0gIuLCy5cuCC0sbGxQbNmzfDjjz/K9a9ZsyZ++eUXUVloaGiVXBd6N0y6iYiIiIioWmvbti1MTU0RFRUllEVFRcHMzAz29vZC2cGDB9GlSxcYGBjAyMgIAwYMQGpqqlB/69YtSCQSREVFoUePHtDR0UHr1q0RFxcntCkoKMDu3bvh4uIiimHbtm1wcHBAzZo1YWJigq+++grp6emiNsHBwZg8eTKaNm1a7nNbtWoVfHx80L59e1haWmLRokWwtLTEb7/9Jmrn4uKCyMhIuf6jR49GWFiY8DkrKwuRkZEYPXq0qN2jR4/g6uqKhg0bQkdHB7a2ttixY4eoTffu3TFt2jT4+Pigdu3aMDExgb+/f7nPhYrHpJuIiIiIiN6rnJwcPH/+XPTKyckptY+Hhwc2b94sfA4LC8OYMWNEbV6+fAkvLy8kJCQgOjoaampqGDRoEAoLC0XtZs+eDW9vb8hkMjRv3hyurq7Iz88HAFy8eBHPnj2Dg4ODqE9eXh4WLFiAxMRE7NmzB7du3ar0Ze0AUFhYiBcvXqB27dqi8g4dOuDs2bNy12nkyJE4deoU0tLSAAA///wzGjdujLZt24raZWdno127dti3bx8uX76Mr7/+GiNHjsTZs2dF7bZs2QJdXV3Ex8djyZIlmD9/Po4cOVLp5/kx0VB2AFT9aWlpwdPTU9lhEBEREdEHIjAwEAEBAaIyPz+/UmdV3dzc4Ovri9u3bwMAYmNjERkZiePHjwttBg8eLOoTFhYGY2NjJCUlwcbGRij39vaGs7MzACAgIADW1ta4ceMGWrZsidu3b0NdXR1169YVjeXh4SG8b9q0KYKDg9G+fXtkZGS80zL0kixbtgwZGRkYNmyYqLxBgwbIzc3F/fv3YW5uLpTXrVsX/fr1Q3h4OObNm4ewsDBRrK81bNgQ3t7ewuepU6fi0KFD+Omnn9ChQweh3M7ODn5+fgAAS0tLrFmzBtHR0ejdu3elnePHhkk3lSkwMFBpxx7zy+CyGxERERGRSvH19YWXl5eoTCqVltrH2NgYzs7OCA8PR1FREZydnVGnTh1Rm+vXr2PevHmIj4/Hw4cPhRnutLQ0UdJtZ2cnvK9fvz4AID09HS1btkRWVhakUqncJmTnz5+Hv78/EhMT8eTJE9HYrVq1Ktd5v5mcu7m5Yf369aL67du3IyAgAL/++qtc0q+trQ0AyMzMlBvXw8MD06dPh5ubG+Li4rBr1y6cOnVK1KagoACLFi3CTz/9hLt37yI3Nxc5OTnQ0dERtXvz2gCvrs/by+ipYph0ExERERHReyWVSstMsovj4eGBKVOmAAC+//57uXoXFxeYm5tj48aNaNCgAQoLC2FjY4Pc3FxRuxo1agjvXyfXr5PoOnXqIDMzE7m5udDU1ATwatm6k5MTnJycEBERAWNjY6SlpcHJyUlu7NLIZDLhvb6+vqguMjIS48aNw65du9CrVy+5vo8fPwbw6suHt/Xr1w9ff/01xo4dCxcXFxgZGcm1Wbp0KVavXo1Vq1bB1tYWurq6mDFjRqnXBnh1fd5enk8Vw6SbiIiIiIhUQt++fZGbmwuJRAInJydR3aNHj5CSkoKNGzeia9euAICYmJgKH+P1o76SkpKE91evXsWjR48QFBQEU1NTAEBCQkKFx7awsCi2fMeOHfDw8EBkZKSw7P1tly9fRqNGjeRm9wFAQ0MDo0aNwpIlS3DgwIFi+8fGxuLzzz+Hm5sbgFdfMly7dq3cs/T07riRGhERERERqQR1dXUkJycjKSkJ6urqojpDQ0MYGRlhw4YNuHHjBo4ePSq3hL08jI2N0bZtW1HCbmZmBk1NTYSEhODmzZvYu3cvFixYINf3xo0bkMlkuH//PrKysiCTySCTyUqdDd++fTtGjRqF5cuX45NPPsH9+/dx//590TO5AeDUqVPo06dPieMsWLAA//77r9yXEa9ZWlriyJEjOH36NJKTkzFhwgQ8ePCgrMtBlYBJNxERERERqQx9fX25pdkAoKamhsjISJw/fx42Njbw9PTE0qVL3+kY48aNQ0REhPDZ2NgY4eHh2LVrF1q1aoWgoCAsW7as2H729vb44YcfcO3aNdjb28Pe3h7//PNPicfasGED8vPzMXnyZNSvX194TZ8+XWiTnZ2NPXv2YPz48SWOo6mpiTp16sjdi/7anDlz0LZtWzg5OaF79+4wMTHBwIEDy3E1SFGSoqKiosoYyNPTEytXrqyMoaqcr68vsrOzlR1GqdLT00X/0D9WYxqr/kZqV/MeKzsEhY2TNFR2CArpU/+eskNQ2L/3K29XVGUxMMhSdggKefpUW9khKExHO0/ZISisTpOXyg5BIRq1VX++Q2v6RGWHoLCi1MvKDkEhRfdU/79rOt6blB1CqbKystCiRQvs3LkTnTp1UnY4WLduHX755RccPnxY2aHQO/go7+nOzs6u9l8Q8BFdRERERETKoa2tja1bt+Lhw4fKDgXAq83NQkJClB0GvaOPMukmIiIiIiIqTffu3ZUdgmDcuHHKDoEUoPprnIiIiIiIiIiqKc50U7XmnKOl7BAUlqlZS9khKKyX0X1lh6CQOhNaKzsEhT1acEPZISis3mD5Z4aqkl/D1MtuVM01f1ag7BAUpvG3ap+DSRtdZYegsKLnj5QdguIaNlF2BAqR1DJUdghEVAGc6SYiIiIiIiKqIky6iYiIiIiIiKoIk24iIiIiIiKiKsKkm4iIiIiIiKiKMOkmIiIiIiJ6z7p164bt27crOwwAQMeOHfHzzz9X2fi3bt2CRCKBTCarsmNUZ0y6iYiIiIio2nJ3d4dEIkFQUJCofM+ePZBIJJV6rMaNG0MikYhebx/3tZYtW0IqleL+/Yo/5WXv3r148OABhg8fXu4+x48fx+eff4769etDV1cXbdq0QURERLn63r17F25ubjAyMoK2tjZsbW2RkJAg1M+ZMwezZs1CYWGhqF9ubi6WLl2Ktm3bQldXF7Vq1ULr1q0xZ84c/PPPP+WO/WPHpJuIiIiIiKo1LS0tLF68GE+ePKnyY82fPx/37t0TXlOnTpVrExMTg6ysLAwZMgRbtmyp8DGCg4MxZswYqKmVPx07ffo07Ozs8PPPP+PixYsYM2YMRo0ahd9//73Ufk+ePEHnzp1Ro0YNHDhwAElJSVi+fDkMDf/36Ll+/frhxYsXOHDggFCWk5OD3r17Y9GiRXB3d8fJkydx6dIlBAcH4+HDhwgJCanweX+smHQTEREREVG11qtXL5iYmCAwMLDENjExMejatSu0tbVhamqKadOm4eXLl0K9RCLBnj17RH0MDAwQHh4uKqtZsyZMTEyEl66urtyxQkND8dVXX2HkyJEICwuTq79z5w5cXV1Ru3Zt6OrqwsHBAfHx8QCAf//9F0ePHoWLi4uoz4oVK2BrawtdXV2Ymppi0qRJyMjIEOq/++47LFiwAJ9++imaNWuG6dOno2/fvoiKiirxmgDA4sWLYWpqis2bN6NDhw5o0qQJ+vTpg2bNmglt1NXV0b9/f0RGRgplK1euRExMDI4ePYpp06ahXbt2MDMzg6OjI9avX49FixYJbQ8ePIguXbrAwMAARkZGGDBgAFJTU0uN62PCpJuIiIiIiN6rnJwcPH/+XPTKyckpsb26ujoWLVqEkJAQ3LlzR64+NTUVffv2xeDBg3Hx4kXs3LkTMTExmDJlSoVjCwoKgpGREezt7bF06VLk5+eL6l+8eIFdu3bBzc0NvXv3xrNnz3Dq1CmhPiMjA46Ojrh79y727t2LxMRE+Pj4CEu3Y2JioKOjAysrK9G4ampqCA4OxpUrV7BlyxYcPXoUPj4+pcb67Nkz1K5du9Q2e/fuhYODA4YOHYq6devC3t4eGzdulGvXoUMH0Xns2LEDvXv3hr29fbHjvrm0/+XLl/Dy8kJCQgKio6OhpqaGQYMGyS1X/1hpKDsAeje+vr7Izs5WdhhVrrOyAyAiIiKiShcYGIiAgABRmZ+fH/z9/UvsM2jQILRp0wZ+fn4IDQ2VG2/EiBGYMWMGAMDS0hLBwcFwdHTEunXroKWlVa64pk2bhrZt26J27do4ffo0fH19ce/ePaxYsUJoExkZCUtLS1hbWwMAhg8fjtDQUHTt2hUAsH37dvz77784d+6ckBBbWFgI/W/fvo169erJLS1/HTvw6t7y//73v5g4cSLWrl1bbKw//fQTzp07hx9++KHUc7p58ybWrVsHLy8vfPfddzh37hymTZsGTU1NjB49WmjXoEED/P333ygsLISamhquXbuG7t27i8YaNGgQjhw5AgCws7PD6dOnAQCDBw8WtQsLC4OxsTGSkpJgY2NTanwfAybdKio7OxsrV65UdhhVbnfkCGWHQERERESVzNfXF15eXqIyqVRaZr/FixejZ8+e8Pb2FpUnJibi4sWLoo3FioqKUFhYiL/++ktuVrkkb8ZkZ2cHTU1NTJgwAYGBgUJ8YWFhcHNzE9q5ubnB0dERISEhqFmzJmQyGezt7Uucgc7Kyir2S4A//vgDgYGBuHr1Kp4/f478/HxkZ2cjMzMTOjo6orbHjh3DmDFjsHHjRiH5P3XqFPr16ye0+eGHHzBixAgUFhbCwcFBWA5ub2+Py5cvY/369aKkW1tbG4WFhcjJyYG2tnaxsa9duxYvX75EcHAwTp48KZRfv34d8+bNQ3x8PB4+fCjMcKelpTHpBpeXExERERHReyaVSqGvry96lSfp7tatG5ycnODr6ysqz8jIwIQJEyCTyYRXYmIirl+/Lty7LJFIUFRUJOqXl5dX6vE++eQT5Ofn49atWwCApKQknDlzBj4+PtDQ0ICGhgY6duyIzMxM4X7okhLW1+rUqSO3IdytW7cwYMAAYaO08+fP4/vvvwfwagfxN504cQIuLi5YuXIlRo0aJZQ7ODiIzv8///kPAKB+/fpo1aqVaAwrKyukpaWJyh4/fgxdXV0hfktLS6SkpIja1K9fHxYWFnJfKLi4uODx48fYuHEj4uPjhfvX3479Y8WZbiIiIiIiUhlBQUFo06YNWrRoIZS1bdsWSUlJomXcbzM2Nsa9e/eEz9evX0dmZmapx5LJZFBTU0PdunUBvNpArVu3bkJC/NrmzZsRGhqK8ePHw87ODps2bcLjx4+Lne22t7fH/fv38eTJE2EH8fPnz6OwsBDLly8Xlp3/9NNPcn2PHz+OAQMGYPHixfj6669Fddra2sWef+fOneWS52vXrsHc3FxUdvnyZdH9266urpgzZw4uXLhQ4n3dAPDo0SOkpKRg48aNwhL7mJiYEtt/jDjTTUREREREKsPW1hYjRoxAcHCwUDZz5kycPn0aU6ZMgUwmw/Xr1/Hrr7+KNlLr2bMn1qxZgwsXLiAhIQETJ05EjRo1hPq4uDisWrUKiYmJuHnzJiIiIuDp6Qk3NzcYGhoiLy8P27Ztg6urK2xsbESvcePGIT4+HleuXIGrqytMTEwwcOBAxMbG4ubNm/j5558RFxcH4FXSXadOHcTGxgrHtrCwQF5eHkJCQnDz5k1s27YN69evF533sWPH4OzsjGnTpmHw4MG4f/8+7t+/j8ePH5d6vTw9PXHmzBksWrQIN27cwPbt27FhwwZMnjxZ1O7UqVPo06ePqF+nTp3w2WefYfXq1fjzzz/x119/4dChQzhw4ADU1dUBAIaGhjAyMsKGDRtw48YNHD16VO7WgY8dk24iIiIiIlIp8+fPF+2MbWdnhxMnTuDatWvo2rUr7O3tMW/ePDRo0EBos3z5cpiamqJr16746quv4O3tLbpXWiqVIjIyEo6OjrC2tsbChQvh6emJDRs2AHi1C/ijR48waNAguXisrKxgZWWF0NBQaGpq4vDhw6hbty769+8PW1tbBAUFCUmquro6xowZI7r/vHXr1lixYgUWL14MGxsbREREyD0ebcuWLcjMzERgYCDq168vvL744otSr1X79u3xyy+/YMeOHbCxscGCBQuwatUqjBjxv72T7t69i9OnT2PMmDFCmZaWFqKjozFz5kxs3rwZXbp0gZWVFWbMmIHOnTsLj19TU1NDZGQkzp8/DxsbG3h6emLp0qWlxvSxkRS9fWPDO/L09FSZjb1UIdayYlSFc6gMu+ur/kZquzRflt2omltilFF2o2qs7iQ7ZYegsJQFN5QdgsIsx8g/51SVbAhTV3YICmueU6DsEBTWwqj0GZ3qzsRFtf8dAID6Z72UHYLiNMu3k3W1lfFU2REoTHvAxz0Tev/+fVhbW+PPP/+UW+atDDNnzsSTJ0+ELxiocnGmm4iIiIiI6D0yMTFBaGio3GZmylK3bl0sWLBA2WF8sLiRGhERERER0Xs2cOBAZYcg+Oabb5QdwgeNM91EREREREREVYRJNxEREREREVEV4fJyqtbWqf+r7BAUdup+krJDUNhc9TbKDkEhdY1NlB2Cwpp7qv6GfHhjl1lVZJ3zQtkhKOyZmupvBvf8hWpvgFW4t/RnAqsC01bV4x5URUhatVd2CAqRNGiu7BCIqAI4001ERERERERURZh0ExEREREREVURJt1EREREREREVYRJNxEREREREVEVYdJNREREREQfrdDQUPTp00fZYQAADh48iDZt2qDwHTcfPX78OCQSCZ4+fVq5gZFCmHQTEREREVG15e7uDolEIve6ceOGwmNnZ2dj7ty58PPzq1C/DRs2oHv37tDX169wkhseHg47OztoaWmhbt26mDx5slDXt29f1KhRAxEREXL9Lly4gC+//BL169eHVCqFubk5BgwYgN9++w1FRUUViv+119c2KChIVL5nzx5IJJJi+7Rs2RJSqRT3799/p2N+jPjIsGpKS0sLnp6eJdanp6e/x2iIiIiIiJSnb9++2Lx5s6jM2NhY4XF3794NfX19dO7cuUL9MjMz0bdvX/Tt2xe+vr7l7rdixQosX74cS5cuxSeffIKXL1/i1q1bojbu7u4IDg7GyJEjhbJff/0Vw4YNQ69evbBlyxZYWFggJycHp0+fxpw5c9C1a1cYGBhU6Bxe09LSwuLFizFhwgQYGhqW2jYmJgZZWVkYMmQItmzZgpkzZ77TMT82TLqrqcDAwFLrS0vIiYiIiIg+JFKpFCYmJqIyd3d3PH36FHv27BHKZsyYAZlMhuPHjwMACgsLsWzZMmzYsAF///036tWrhwkTJmD27NkAgMjISLi4uIjGPXfuHL777jtcuHABeXl5aNOmDVauXIm2bduKjgNAOE55PHnyBHPmzMFvv/2Gzz77TCi3s7MTtXNxccGUKVOQmpqKZs2a4eXLlxg7diycnZ0RFRUlamtlZYWxY8eWOtMdGxuL2bNn4+zZs5BKpejQoQMiIyOFBLtXr164ceMGAgMDsWTJklLPITQ0FF999RUcHR0xffp0uaT7zp07+Pbbb3Ho0CHk5OTAysoK33//PT755JNyXaMPFZeXExERERHRe5WTk4Pnz5+LXjk5OZV+HF9fXwQFBWHu3LlISkrC9u3bUa9ePaE+JiYGDg4Ooj4vXrzA6NGjERMTgzNnzsDS0hL9+/fHixcvFIrlyJEjKCwsxN27d2FlZYVGjRph2LBh+Pvvv0XtzMzMUK9ePZw6dQoAcPjwYTx69Ag+Pj4ljl3SUnCZTIbPPvsMrVq1QlxcHGJiYuDi4oKCggKhjbq6OhYtWoSQkBDcuXOnxGO8ePECu3btgpubG3r37o1nz54JMQJARkYGHB0dcffuXezduxeJiYnw8fF55/vTPySc6SaF+Pr6Ijs7W9lhEBEREZEKCQwMREBAgKjMz88P/v7+xbb//fffoaenJ3zu168fdHV1Sz3GixcvsHr1aqxZswajR48GADRr1gxdunQBADx9+hTPnj1DgwYNRP169uwp+rxhwwYYGBjgxIkTGDBgQLnOrzg3b95EYWEhFi1ahNWrV6NWrVqYM2cOevfujYsXL0JTU1No26BBA9y+fRsAcO3aNQBAixYthPpz586hR48ewufIyMhiY1uyZAkcHBywdu1aocza2lqu3aBBg9CmTRv4+fkhNDS02PgjIyNhaWkp9B8+fDhCQ0PRtWtXAMD27dvx77//4ty5c6hduzYAwMLConwX5wPHpJsUkp2djZUrV1bZ+J/tqh47SRIRERFR5fH19YWXl5eoTCqVlti+R48eWLdunfBZV1e3zHupk5OTkZOTI1rK/aasrCwAr+5pftODBw8wZ84cHD9+HOnp6SgoKEBmZibS0tJKPd6b+vXrJ8wCm5ub48qVKygsLEReXh6Cg4OF3dJ37NgBExMTHDt2DE5OTkJ/bW1tZGZmlji+nZ0dZDIZAMDS0hL5+fnFtpPJZBg6dGi5Yl68eDF69uwJb2/vYuvDwsLg5uYmfHZzc4OjoyNCQkJQs2ZNyGQy2NvbCwk3/Q+TbiIiIiIieq+kUmmpSfbbdHV15WZN1dTU5O5lzsvLE95ra2uXOqaRkREkEgmePHkiKh89ejQePXqE1atXw9zcHFKpFJ06dUJubm654920aZOQ1NeoUQMAUL9+fQBAq1athHbGxsaoU6eOXEL/+PFjYaM4S0tLAEBKSgo6duwI4NX1K88sclnX4E3dunWDk5MTfH194e7uLqpLSkrCmTNncPbsWdF93AUFBYiMjMT48eMrdKyPDe/pJiIiIiIilWNsbIx79+6Jyl7P/gKvklVtbW1ER0cX219TUxOtWrVCUlKSqDw2NhbTpk1D//79YW1tDalUiocPH1YotoYNG8LCwgIWFhYwNzcHAGGH9JSUFKHd48eP8fDhQ6EN8GolaWpqKuzt7QEAffr0Qe3atbF48eIKxQC8mhEv6fyLExQUhN9++w1xcXGi8tDQUHTr1g2JiYmQyWTCy8vLS1iO/nr2/fHjxxWO80PHpJuIiIiIiFROz549kZCQgK1bt+L69evw8/PD5cuXhXotLS3MnDkTPj4+2Lp1K1JTU3HmzBnRPctOTk6IiYkRjWtpaYlt27YhOTkZ8fHxGDFihNws7v379yGTyYRnhV+6dKnMhLN58+b4/PPPMX36dJw+fRqXL1/G6NGj0bJlS9H92WfOnBFm1wFAT08PmzZtwr59++Ds7IxDhw7h5s2buHjxorDbuLq6erHH9PX1xblz5zBp0iRcvHgRV69exbp160r8EsHW1hYjRoxAcHCwUJaXl4dt27bB1dUVNjY2ote4ceMQHx+PK1euwNXVFSYmJhg4cCBiY2Nx8+ZN/Pzzz3IJ/MeISTcREREREakcJycnzJ07Fz4+Pmjfvj1evHiBUaNGidrMnTsX33zzDebNmwcrKyt8+eWXSE9PF+rHjh2L/fv349mzZ0JZaGgonjx5grZt22LkyJGYNm0a6tatKxp3/fr1sLe3x/jx4wG8Wpptb2+PvXv3lhrz1q1b8cknn8DZ2RmOjo6oUaMGDh48KCxBB17d5z1ixAjo6OgIZYMGDcLp06eho6ODUaNGoUWLFujZsyeOHj1a4iZqwKtE//Dhw0hMTESHDh3QqVMn/Prrr9DQKPku4/nz54t2HN+7dy8ePXqEQYMGybW1srKClZUVQkNDoampicOHD6Nu3bro378/bG1tERQUVOIXAh8TSVFpD3WrAE9PzyrdUKsyqVKsJaku51DVcXzWSPU3Ujv1b1LZjaq5Pxu2UXYICmm2spuyQ1BY0V+pyg5BcSr+yJCYxYo9KqY6eKam+n/4NNPIUHYICtHTq/xHIr1vprPaKDsEhUlatVd2CAqR1KpXdqNqTtq8i7JDEAwdOhRt27Ytc2O29+Hhw4do0aIFEhIS0KRJE2WHQ5WEM91ERERERPTRWrp0qehxZMp069YtrF27lgn3B4a7lxMRERER0UercePGmDp1qrLDAAA4ODjAwcFB2WFQJeNMNxEREREREVEV4Uw3VWsFUO17QAGgsHK2TVCq51nlf45mtfTsSdltqrn8K38pOwSFqdevrewQFJIhUf3vqWsWFig7BIUZm6j4Pd1mqv8zUPX7oYEP4J7oPNXfG4DoY6L6f0EQERERERERVVNMuomIiIiIiIiqCJNuIiIiIiIioirCpJuIiIiIiIioinAjNRWlpaUFT09PZYeB9PR0ZYdARERERERUbTHpVlGBgYHKDgEAqkXiT0REREQfLnd3d2zZskWu/Pr167CwsFBCREQVw6SbiIiIiIiqtb59+2Lz5s2iMmNjYyVFQ1QxvKebiIiIiIiqNalUChMTE9Fr7NixGDhwoKjdjBkz0L17d+FzYWEhlixZAgsLC0ilUpiZmWHhwoXvN3j66HGmm4iIiIiI3qucnBzk5OSIyqRSKaRSaaUex9fXFxs3bsTKlSvRpUsX3Lt3D1evXq3UYxCVhUk3VRlfX19kZ2crOwwiIiIiqmYCAwMREBAgKvPz84O/v3+x7X///Xfo6ekJn/v16wddXd1Sj/HixQusXr0aa9aswejRowEAzZo1Q5cuXRQLnqiCmHRTlcnOzsbKlSsVGqP7rl6VFA0RERERVRe+vr7w8vISlZU2y92jRw+sW7dO+KyrqwtfX99Sj5GcnIycnBx89tlnigVLpCAm3URERERE9F5VdCm5rq6u3E7lampqKCoqEpXl5eUJ77W1tRULkqiScCM1IiIiIiJSOcbGxrh3756oTCaTCe8tLS2hra2N6Ojo9xwZkRiTbiIiIiIiUjk9e/ZEQkICtm7diuvXr8PPzw+XL18W6rW0tDBz5kz4+Phg69atSE1NxZkzZxAaGqrEqOljxOXlRERERESkcpycnDB37lz4+PggOzsbHh4eGDVqFC5duiS0mTt3LjQ0NDBv3jz8888/qF+/PiZOnKjEqOljxKSbiIiIiIiqrfDw8BLrAgIC5HZBf5Oamhpmz56N2bNnV0FkROXD5eVEREREREREVYRJNxEREREREVEVYdJNREREREREVEWYdBMRERERERFVEW6kRgrR0tKCp6dnsXXp6ekKj59RkKPwGKS43ELV/n6u6GWGskNQmFpNLWWHoLDCp6r9czCRSJUdgsKKIFF2CArLz1Xt30dZ9wqVHYLCtO79pewQFFeQr+wIFKPOP+GJVAn/xZJCAgMDS6wrKRknIiIiIiL6WKj218VERERERERE1RiTbiIiIiIiIqIqwqSbiIiIiIiIqIow6SYiIiIioo9WaGgo+vTpo9QYcnNz0bhxYyQkJLzzGI0bN8aqVasqLyiqNEy6iYiIiIio2nJ3d4dEIpF73bhxQ+Gxs7OzMXfuXPj5+VWo34YNG9C9e3fo6+tDIpHg6dOnZfa5ePEiBg0ahLp168LAwABDhgzBw4cPAQCamprw9vbGzJkz5fo9f/4cc+fOhbW1NbS1tWFkZIT27dtjyZIlePLkSYXifu348eOQSCSwtrZGQUGBqM7AwADh4eFyfQIDA6Guro6lS5e+0zE/Zky6iYiIiIioWuvbty/u3bsnejVp0kThcXfv3g19fX107ty5Qv0yMzPRt29ffPfdd+Xuc+rUKXTu3BnHjh3DoUOHcOnSJXz77bdC/YgRIxATE4MrV64IZY8fP0bHjh2xefNmeHt7Iz4+Hn/++ScWLlyICxcuYPv27RWK+203b97E1q1by9U2LCwMPj4+CAsLU+iYHyMm3UREREREVK1JpVKYmJiIXmPHjsXAgQNF7WbMmIHu3bsLnwsLC7FkyRJYWFhAKpXCzMwMCxcuFOojIyPh4uIiGuPcuXPo3bs36tSpg1q1asHR0RF//vmn3HFmzZqFjh07lvscJk+eDG9vb1hbW+OTTz6Bk5MT/v77b6He0NAQnTt3RmRkpFD23XffIS0tDWfPnsWYMWNgZ2cHc3Nz9OnTBzt27MCkSZNKPN7Tp08xYcIE1KtXD1paWrCxscHvv/8uajN16lT4+fkhJyen1NhPnDiBrKwszJ8/H8+fP8fp06dF9WVd548dk24iIiIiInqvcnJy8Pz5c9GrrMTvXfj6+iIoKAhz585FUlIStm/fjnr16gn1MTExcHBwEPV58eIFRo8ejZiYGJw5cwaWlpbo378/Xrx4UWlxJSYmYuvWrfDw8BCVd+jQAadOnQLwKpHduXMn3Nzc0KBBg2LHkUgkxZYXFhaiX79+iI2NxY8//oikpCQEBQVBXV1d1G7GjBnIz89HSEhIqfGGhobC1dUVNWrUgKurK0JDQ0X1ZV3nj52GsgOgD5eWlhY8PT2VHQYRERERVTOBgYEICAgQlfn5+cHf37/Y9r///jv09PSEz/369YOurm6px3jx4gVWr16NNWvWYPTo0QCAZs2aoUuXLgBezQQ/e/ZMLqHt2bOn6POGDRtgYGCAEydOYMCAAeU6v9JcvHgRPXr0wJw5c/DVV1+J6ho0aIDbt28DAP799188ffoULVq0ELVp164dUlJSAAAuLi7YsWOH3DH++OMPnD17FsnJyWjevDkAoGnTpnLtdHR04Ofnh++++w7jx49HrVq15No8f/4cu3fvRlxcHADAzc0NXbt2xerVq6Gnp1fmdSYm3VSFAgMDFR7DIbJrJURCRERERNWJr68vvLy8RGVSqbTE9j169MC6deuEz7q6uvD19S31GMnJycjJycFnn31WbH1WVhaAVxNFb3rw4AHmzJmD48ePIz09HQUFBcjMzERaWlqpx3tTv379hBlrc3Nz0X3avr6+cHJygre3t1w/bW1tZGZmljr2L7/8gtzcXMycOVM4h7fJZDI0atRISLhLM3bsWCxfvhyLFy/GokWL5Op37NiBZs2aoXXr1gCANm3awNzcHDt37sTYsWPLvM7EpJuIiIiIiN4zqVRaapL9Nl1dXVhYWIjK1NTUUFRUJCrLy8sT3mtra5c6ppGRESQSidwO4KNHj8ajR4+wevVqmJubQyqVolOnTsjNzS13vJs2bRIS4ho1aojq/vnnH7Rv377Yfo8fP4axsTEAwNjYGAYGBsKs9mtmZmYAgJo1a5a4a3pZ5/4mDQ0NLFy4EO7u7pgyZYpcfWhoKK5cuQINjf+ljoWFhQgLC8PYsWMrdKyPFe/pJiIiIiIilWNsbIx79+6JymQymfDe0tIS2traiI6OLra/pqYmWrVqhaSkJFF5bGwspk2bhv79+8Pa2hpSqVR4tFd5NWzYEBYWFrCwsIC5ubmoLiIiAl9//XWx/S5fvgx7e3sAr75UGDZsGH788Uf8888/FTq+nZ0d7ty5g2vXrpWr/dChQ2FtbS235P/SpUtISEjA8ePHIZPJhNfx48cRFxeHq1evlnmdiUk3ERERERGpoJ49eyIhIQFbt27F9evX4efnh8uXLwv1WlpamDlzJnx8fLB161akpqbizJkzok3AnJycEBMTIxrX0tIS27ZtQ3JyMuLj4zFixAi52dz79+9DJpMJzwq/dOkSZDIZHj9+XGbcU6dORVRUVLF1p06dQp8+fYTPixYtQsOGDdGhQweEhYXh4sWLSE1NxS+//IK4uDi5jdFec3R0RLdu3TB48GAcOXIEf/31Fw4cOICDBw+WGFdQUBDCwsLw8uVLoSw0NBQdOnRAt27dYGNjI7y6deuG9u3bIzQ0tFzX+WPHpJuIiIiIiFSOk5MT5s6dCx8fH7Rv3x4vXrzAqFGjRG3mzp2Lb775BvPmzYOVlRW+/PJLpKenC/Vjx47F/v378ezZM6EsNDQUT548Qdu2bTFy5EhMmzYNdevWFY27fv162NvbY/z48QCAbt26wd7eHnv37i0z7tTU1GJnzuPi4vDs2TMMGTJEKDMyMsLZs2cxatQoLF26FB06dICtrS38/f3x5ZdfYuPGjSUe5+eff0b79u3h6uqKVq1awcfHBwUFBSW279mzJ3r27In8/HwAQG5uLn788UcMHjy42PaDBw/G1q1bkZeXV+Z1/thJit6+EeIdeXp6YuXKlZUxVJVTpVg/dg71VX8jNdmjm8oOQWF/GH6q7BAU0sHfRNkhKKzw+m1lh6CwovxCZYegkEu7yn/vYXVVVFT8o2VUSYN6z8puVI1JdfOVHYLCDGc5KzsEhUnqmio7BMWoq/62TFrti0/klGHo0KFo27ZtmRuzVbUvv/wSrVu3xnfffafUOKjycaabiIiIiIg+WkuXLhU9jkwZcnNzYWtry8ftfqBU/2syIiIiIiKid9S4cWNMnTpVqTFoampizpw5So2Bqg5nuomIiIiIiIiqCGe6qVorRKVsOaBUMxp0U3YICvuhKEvZIShk8+KKPWajOvq3UPXvA60l0VR2CAqZUKj631N3OjpR2SEorPBKrLJDUEjBhURlh6CwouQryg5BYRcnnlZ2CApJK1L95yIPvVd97ukmqmqq/xcEERERERERUTXFpJuIiIiIiIioijDpJiIiIiIiIqoiTLqJiIiIiIiIqgiTbiIiIiIiolLcunULEokEMpmsUsdt3LgxVq1aValjVgZ3d3cMHDhQ2WF8MJh0ExERERFRtSSRSEp9+fv7v5c4TE1Nce/ePdjY2LyX4xXnfSboq1evRnh4eLnafvnll+jQoQMKCgqEsry8PLRr1w4jRoyooghVCx8ZRkRERERE1dK9e/eE9zt37sS8efOQkpIilOnp6VVovLy8PNSoUaPCcairq8PExKTC/VRVrVq1yt127dq1sLa2RlBQEGbPng0AWLBgAe7du4c//vijqkJUKZzpJiIiIiKiasnExER41apVCxKJRFQWGRkJKysraGlpoWXLlli7dq3Q9/WS8J07d8LR0RFaWlqIiIgQlk4vWrQI9erVg4GBAebPn4/8/Hx8++23qF27Nho1aoTNmzfLjfV6efnx48chkUgQHR0NBwcH6Ojo4NNPPxV9IZCamorPP/8c9erVg56eHtq3b1+lSeivv/6Ktm3bQktLC02bNkVAQADy8/MBAN7e3hgwYIDQdtWqVZBIJDh48KBQZmFhgU2bNgGQX16+e/du2NraQltbG0ZGRujVqxdevnwJADAyMsKGDRswf/58XLx4EQkJCQgMDMSmTZtgaGhYZeerSph0ExERERGRyomIiMC8efOwcOFCJCcnY9GiRZg7dy62bNkiajdr1ixMnz4dycnJcHJyAgAcPXoU//zzD06ePIkVK1bAz88PAwYMgKGhIeLj4zFx4kRMmDABd+7cKTWG2bNnY/ny5UhISICGhgY8PDyEuoyMDPTv3x/R0dG4cOEC+vbtCxcXF6SlpVX6tTh16hRGjRqF6dOnIykpCT/88APCw8OxcOFCAICjoyNiYmKEJeAnTpxAnTp1cPz4cQDA3bt3kZqaiu7du8uNfe/ePbi6usLDwwPJyck4fvw4vvjiCxQVFQlt/vOf/2D48OEYNWoURo8ejdGjR6N///6Vfp6qisvLiYiIiIjovcrJyUFOTo6oTCqVQiqVlnsMPz8/LF++HF988QUAoEmTJkLCOXr0aKHdjBkzhDav1a5dG8HBwVBTU0OLFi2wZMkSZGZm4rvvvgMA+Pr6IigoCDExMRg+fHiJMSxcuBCOjo4AXiX3zs7OyM7OhpaWFlq3bo3WrVsLbRcsWIBffvkFe/fuxZQpU8p9nuUREBCAWbNmCefdtGlTLFiwAD4+PvDz80PXrl3x4sULXLhwAe3atcPJkyfx7bffYs+ePQBezdw3bNgQFhYWcmPfu3cP+fn5+OKLL2Bubg4AsLW1lWu3atUqNGzYEPr6+lixYkWlnp+qY9Ktonx9fZGdna3sMIiIiIiIKiwwMBABAQGiMj8/v3JvjPby5UukpqZi7NixGD9+vFCen58vdz+yg4ODXH9ra2uoqf1v0W+9evVEm6Spq6vDyMgI6enppcZhZ2cnvK9fvz4AID09HWZmZsjIyIC/vz/27dsnJK5ZWVlVMtOdmJiI2NhYYWYbAAoKCpCdnY3MzEwYGBigdevWOH78ODQ1NaGpqYmvv/4afn5+yMjIwIkTJ4QvD97WunVrfPbZZ7C1tYWTkxP69OmDIUOGyC0d37FjByQSCR4+fIirV6+iQ4cOlX6eqopJt4rKzs7GypUrlR1GlWsb2UXZIRARERFRJfP19YWXl5eorCKz3BkZGQCAjRs34pNPPhHVqauriz7r6urK9X97MzWJRFJsWWFhYalxvNlHIpEAgNDH29sbR44cwbJly2BhYQFtbW0MGTIEubm5pY75LjIyMhAQECA3ow8AWlpaAIDu3bvj+PHjkEqlcHR0RO3atWFlZYWYmBicOHEC33zzTbFjq6ur48iRIzh9+jQOHz6MkJAQzJ49G/Hx8WjSpAkA4ObNm/Dx8cG6detw7NgxuLu748KFCxX6mX7ImHQTEREREdF7VdGl5G+rV68eGjRogJs3b1bbx1LFxsbC3d0dgwYNAvAqMb5161aVHKtt27ZISUkpdnn4a46OjggLC4OGhgb69u0L4FUivmPHDly7dq3Y+7lfk0gk6Ny5Mzp37ox58+bB3Nwcv/zyC7y8vFBYWAh3d3d89tlnGDVqFD7//HPY2Nhg3rx5WLx4cWWfqkpi0k1ERERERConICAA06ZNQ61atdC3b1/k5OQgISEBT548kZtFVwZLS0tERUXBxcUFEokEc+fOLXPmvCx3794VdlB/zdzcHPPmzcOAAQNgZmaGIUOGQE1NDYmJibh8+TL++9//AgC6deuGFy9e4Pfff0dQUBCAV0n3kCFDUL9+fTRv3rzYY8bHxyM6Ohp9+vRB3bp1ER8fj3///RdWVlYAXj3T+8qVK7hy5QqAV48b27RpEwYMGIDBgwdzmTm4ezkREREREamgcePGYdOmTdi8eTNsbW3h6OiI8PBwYcmzsq1YsQKGhob49NNP4eLiAicnJ7Rt21ahMZctWwZ7e3vRa9++fXBycsLvv/+Ow4cPo3379ujYsSNWrlwpbHwGAIaGhrC1tYWxsTFatmwJ4FUiXlhYWOL93ACgr6+PkydPon///mjevDnmzJmD5cuXo1+/frh27Rpmz56NkJAQ0XPMnZycMGbMGLi7u8ttmPcxkhS9ude7AlRpY6/09HREREQoOwyFeHp6fhz3dNdX/Xu6e0pNlR2Cwu4WZSk7BIVoStTLblTN/Vuo2j8DAKgl0VR2CAqZkF2j7EbVXKejE5UdgsIKr8QqOwSFFFxIVHYIClMz0Fd2CAq7tPqZskNQSFqRtrJDUNjQe6r9tzhRRVTa8vLAwMDKGqrKeXp6KjsEIiIiIiIi+ghweTkREREREZESRUREQE9Pr9iXtbW1ssMjBXEjNSIiIiIiIiX6z3/+I/fos9fefpQZqR4m3UREREREREpUs2ZN1KxZU9lhUBVh0k3VWlPN2soOQWG3il4qOwSFtYZq/0fALrtS9otUqjNa7/4s0+rCJle172iqJX2h7BAUVrBf9TcuKsrNV3YIClEzMVZ2CAqT2DkoOwSF2Yw9rOwQFNIs4Y6yQyCiClDtv4CIiIiIiIiIqjEm3URERERERERVhEk3ERERERERURVh0k1ERERERERURZh0ExERERERvWXkyJFYtGiRUmNISkpCo0aN8PKl6m/M+zFj0k1ERERERNWWu7s7JBIJJk6cKFc3efJkSCQSuLu7V+oxExMTsX//fkybNq1C/aZNm4Z27dpBKpWiTZs25erz66+/okuXLtDX14eJiQlmzZqFoqJXT15p1aoVOnbsiBUrVoj6dO/eHRKJBEFBQXLjOTs7QyKRwN/fv0KxU9Vh0k1ERERERNWaqakpIiMjkZWVJZRlZ2dj+/btMDMzq/TjhYSEYOjQodDT06twXw8PD3z55Zflbn/kyBH83//9H/7880+sW7cOq1evxtatW4X6MWPGYN26dcjPFz8y0dTUFOHh4aKyu3fvIjo6GvXr169w3FR1mHQTEREREVG11rZtW5iamiIqKkooi4qKgpmZGezt7YWygwcPokuXLjAwMICRkREGDBiA1NRUof7WrVuQSCSIiopCjx49oKOjg9atWyMuLk5oU1BQgN27d8PFxUUUw7Zt2+Dg4ICaNWvCxMQEX331FdLT00VtgoODMXnyZDRt2rTc57ZmzRqMGDECFhYWGDRoECwtLfH3338L9b1798bjx49x4sQJUb8BAwbg4cOHiI2NFcq2bNmCPn36oG7duhWK/fjx45BIJIiOjoaDgwN0dHTw6aefIiUlpdznQSVj0k1ERERERO9VTk4Onj9/Lnrl5OSU2sfDwwObN28WPoeFhWHMmDGiNi9fvoSXlxcSEhIQHR0NNTU1DBo0CIWFhaJ2s2fPhre3N2QyGZo3bw5XV1dhJvnixYt49uwZHBwcRH3y8vKwYMECJCYmYs+ePbh161alL2vfvHkz/vrrLwwbNkwo09TURJs2bXDq1ClRW01NTYwYMUJ0TcLDw+Hh4SE3bnljnz17NpYvX46EhARoaGgUOxZVnIayAyDy9fVFdna2ssMgIiIiovckMDAQAQEBojI/P79S70N2c3ODr68vbt++DQCIjY1FZGQkjh8/LrQZPHiwqE9YWBiMjY2RlJQEGxsbodzb2xvOzs4AgICAAFhbW+PGjRto2bIlbt++DXV1dbnZ4jcT0KZNmyI4OBjt27dHRkbGOy1Df9uWLVswffp0/P7772jevLmorkGDBsJ5vx1T165dsXr1apw/fx7Pnj3DgAED5K5jeWNfuHAhHB0dAQCzZs2Cs7MzsrOzoaWlpfD5fcyYdJPSZWdnY+XKlcXWDYn6z3uOhoiIiIiqmq+vL7y8vERlUqm01D7GxsZwdnZGeHg4ioqK4OzsjDp16ojaXL9+HfPmzUN8fDwePnwozHCnpaWJkm47Ozvh/ev7n9PT09GyZUtkZWVBKpVCIpGIxj5//jz8/f2RmJiIJ0+eiMZu1apVuc77zQTXzc0N69evB/BqSfvUqVOxdOlSIel9k7a2NjIzM+XKW7duDUtLS+zevRvHjh3DyJEjoaEhn+KVN/aSrktV3Df/MWHSTURERERE75VUKi0zyS6Oh4cHpkyZAgD4/vvv5epdXFxgbm6OjRs3okGDBigsLISNjQ1yc3NF7WrUqCG8f51cv05E69Spg8zMTOTm5kJTUxPAq2XrTk5OcHJyQkREBIyNjZGWlgYnJye5sUsjk8mE9/r6+sL7jIwMvHjxAi1atCi23+PHj9GsWbNi6zw8PPD9998jKSkJZ8+elauvSOylXRd6d0y6iYiIiIhIJfTt2xe5ubmQSCRwcnIS1T169AgpKSnYuHEjunbtCgCIiYmp8DFeP+orKSlJeH/16lU8evQIQUFBMDU1BQAkJCRUeGwLC4tiy/X09HDu3LkSk+7Lly9jyJAhxdZ99dVX8Pb2RuvWrYudca+s2OndcSM1IiIiIiJSCerq6khOTkZSUhLU1dVFdYaGhjAyMsKGDRtw48YNHD16VG4Je3kYGxujbdu2ooTdzMwMmpqaCAkJwc2bN7F3714sWLBAru+NGzcgk8lw//59ZGVlQSaTQSaTlTkbfv/+fbi5uSE5OVmu7tatW7h79y569epVbF9DQ0Pcu3cP0dHRxdaXN3aqOky6iYiIiIhIZejr64uWZr+mpqaGyMhInD9/HjY2NvD09MTSpUvf6Rjjxo1DRESE8NnY2Bjh4eHYtWsXWrVqhaCgICxbtqzYfvb29vjhhx9w7do12Nvbw97eHv/880+px8vLy0NKSkqx923v2LEDffr0gbm5eYn9DQwMoKurW2xdeWOnqiMpKioqUnYQ75unp2eJG3epig/hHF4r7VyGmHMjteqgNWoqOwSF2OWo/q+5Mx/ApqE2uar9PW8rjRfKDkFhLaYaKTsEhRXl5is7BIVI9HSUHYLCJHYOZTeq5gqPHlZ2CArJSnig7BAUVufAibIbKVFWVhZatGiBnTt3olOnTkqLIzc3F5aWlti+fTs6d+6stDhIMar9FxAREREREVEl09bWxtatW/Hw4UOlxpGWlobvvvuOCbeK40ZqREREREREb+nevbuyQ4CFhUWJm6+R6uBMNxEREREREVEV4Uy3kvn6+iI7O7vC/dLT06sgmrK9a7ylKe1c8opU/7mAapAoOwSFFan4KahB9e/plhap/nekUhXfQqSgUMX/IQAoepml7BAUV1Cg7AgUo6FedpvqTv0D+PNRXbV/DhoGqv/fBKKPyQfwW1O1ZWdnv9OGaJ6enlUQTdneNd7SKOtciIiIiIiIqhq/JiMiIiIiIiKqIky6iYiIiIiIiKoIk24iIiIiIiKiKsKkm4iIiIiIiKiKMOkmIiIiIqJqy93dHRKJBBMnTpSrmzx5MiQSCdzd3d9/YETlxKSbiIiIiIiqNVNTU0RGRiIr63+PPszOzsb27dthZmamxMiIysakm4iIiIiIqrW2bdvC1NQUUVFRQllUVBTMzMxgb28vlB08eBBdunSBgYEBjIyMMGDAAKSmpgr1t27dgkQiQVRUFHr06AEdHR20bt0acXFx7/V86OPCpJuIiIiIiKo9Dw8PbN68WfgcFhaGMWPGiNq8fPkSXl5eSEhIQHR0NNTU1DBo0CAUFhaK2s2ePRve3t6QyWRo3rw5XF1dkZ+f/17Ogz4+GsoOgIiIiIiIPi45OTnIyckRlUmlUkil0hL7uLm5wdfXF7dv3wYAxMbGIjIyEsePHxfaDB48WNQnLCwMxsbGSEpKgo2NjVDu7e0NZ2dnAEBAQACsra1x48YNtGzZUtFTI5LDpJuUTktLC56ensoOg4iIiIjek8DAQAQEBIjK/Pz84O/vX2IfY2NjODs7Izw8HEVFRXB2dkadOnVEba5fv4558+YhPj4eDx8+FGa409LSREm3nZ2d8L5+/foAgPT0dCbdVCWYdJPSBQYGllj3+c8D3mMkRERERPQ++Pr6wsvLS1RW2iz3ax4eHpgyZQoA4Pvvv5erd3Fxgbm5OTZu3IgGDRqgsLAQNjY2yM3NFbWrUaOG8F4ikQCA3BJ0osrCpJuIiIiIiN6rspaSl6Rv377Izc2FRCKBk5OTqO7Ro0dISUnBxo0b0bVrVwBATExMpcRLpAgm3UREREREpBLU1dWRnJwsvH+ToaEhjIyMsGHDBtSvXx9paWmYNWuWMsIkEuHu5UREREREpDL09fWhr68vV66mpobIyEicP38eNjY28PT0xNKlS5UQIZEYZ7qJiIiIiKjaCg8PL7V+z549wvtevXohKSlJVF9UVCS8b9y4segzABgYGMiVEVUmznQTERERERERVREm3URERERERERVhEk3ERERERERURVh0k1ERERERERURbiRGlVrF1/+rewQFGaubazsEBR2S6Piz9GsTurU0FJ2CApTg+pv8JKiqewIFNO0UPV/Bmotmis7BIUV3ftH2SEopPDOA2WHoDD1R/eVHYLCcq/cU3YICsm8o/rzZgbKDoDoPVL9f7FERERERERE1RSTbiIiIiIiIqIqwqSbiIiIiIiIqIow6SYiIiIiIiKqIky6iYiIiIiI3jJy5EgsWrRI2WEAAIYPH47ly5crOwx6R0y6iYiIiIio2nJ3d4dEIsHEiRPl6iZPngyJRAJ3d/dKPWZiYiL279+PadOmVajftGnT0K5dO0ilUrRp06ZcfWJiYtC5c2cYGRlBW1sbLVu2xMqVK0Vt5syZg4ULF+LZs2dC2fHjxyGRSGBoaIjs7GxR+3PnzkEikUAikVQofqoaTLqJiIiIiKhaMzU1RWRkJLKysoSy7OxsbN++HWZmZpV+vJCQEAwdOhR6enoV7uvh4YEvv/yy3O11dXUxZcoUnDx5EsnJyZgzZw7mzJmDDRs2CG1sbGzQrFkz/Pjjj3L9a9asiV9++UVUFhoaWiXXhd4Nk24iIiIiIqrW2rZtC1NTU0RFRQllUVFRMDMzg729vVB28OBBdOnSBQYGBjAyMsKAAQOQmpoq1N+6dQsSiQRRUVHo0aMHdHR00Lp1a8TFxQltCgoKsHv3bri4uIhi2LZtGxwcHFCzZk2YmJjgq6++Qnp6uqhNcHAwJk+ejKZNm5b73Ozt7eHq6gpra2s0btwYbm5ucHJywqlTp0TtXFxcEBkZKdd/9OjRCAsLEz5nZWUhMjISo0ePFrV79OgRXF1d0bBhQ+jo6MDW1hY7duwQtenevTumTZsGHx8f1K5dGyYmJvD39y/3uVDxmHQTEREREdF7lZOTg+fPn4teOTk5pfbx8PDA5s2bhc9hYWEYM2aMqM3Lly/h5eWFhIQEREdHQ01NDYMGDUJhYaGo3ezZs+Ht7Q2ZTIbmzZvD1dUV+fn5AICLFy/i2bNncHBwEPXJy8vDggULkJiYiD179uDWrVuVvqwdAC5cuIDTp0/D0dFRVN6hQwecPXtW7jqNHDkSp06dQlpaGgDg559/RuPGjdG2bVtRu+zsbLRr1w779u3D5cuX8fXXX2PkyJE4e/asqN2WLVugq6uL+Ph4LFmyBPPnz8eRI0cq/Tw/JhrKDoDejZaWFjw9Pd/7cd/+Nk8Rvr6+cvefEBEREdGHLzAwEAEBAaIyPz+/UmdV3dzc4Ovri9u3bwMAYmNjERkZiePHjwttBg8eLOoTFhYGY2NjJCUlwcbGRij39vaGs7MzACAgIADW1ta4ceMGWrZsidu3b0NdXR1169YVjeXh4SG8b9q0KYKDg9G+fXtkZGS80zL0tzVq1Aj//vsv8vPz4e/vj3HjxonqGzRogNzcXNy/fx/m5uZCed26ddGvXz+Eh4dj3rx5CAsLE8X6WsOGDeHt7S18njp1Kg4dOoSffvoJHTp0EMrt7Ozg5+cHALC0tMSaNWsQHR2N3r17K3yOHysm3SoqMDBQKcetzEQ/OztbbpOIt+3Z2rrSjkdERERE1YOvry+8vLxEZVKptNQ+xsbGcHZ2Rnh4OIqKiuDs7Iw6deqI2ly/fh3z5s1DfHw8Hj58KMxwp6WliZJuOzs74X39+vUBvJpcatmyJbKysiCVSuU2ITt//jz8/f2RmJiIJ0+eiMZu1apVuc77zeTczc0N69evFz6fOnUKGRkZOHPmDGbNmgULCwu4uroK9dra2gCAzMxMuXE9PDwwffp0uLm5IS4uDrt27ZJbnl5QUIBFixbhp59+wt27d5Gbm4ucnBzo6OiI2r15bV5fn8qcePsYMekmIiIiIqL3SiqVlplkF8fDwwNTpkwBAHz//fdy9S4uLjA3N8fGjRvRoEEDFBYWwsbGBrm5uaJ2NWrUEN6/Tq5fJ9F16tRBZmYmcnNzoampCeDVsnUnJyc4OTkhIiICxsbGSEtLg5OTk9zYpZHJZMJ7fX19UV2TJk0AALa2tnjw4AH8/f1FSffjx48BvPry4W39+vXD119/jbFjx8LFxQVGRkZybZYuXYrVq1dj1apVsLW1ha6uLmbMmFHqtQFeXZ+3l+dTxTDpJiIiIiIildC3b1/k5uZCIpHAyclJVPfo0SOkpKRg48aN6Nq1K4BXj+OqqNeP+kpKShLeX716FY8ePUJQUBBMTU0BAAkJCRUe28LColztCgsL5e7dvnz5Mho1aiQ3uw8AGhoaGDVqFJYsWYIDBw4UO2ZsbCw+//xzuLm5Cce4du1auWfp6d0x6SYiIiIiIpWgrq6O5ORk4f2bDA0NYWRkhA0bNqB+/fpIS0vDrFmzKnwMY2NjtG3bFjExMULSbWZmBk1NTYSEhGDixIm4fPkyFixYINf3xo0byMjIwP3795GVlSXMbLdq1UqYNX/b999/DzMzM7Rs2RIAcPLkSSxbtkzuGeGnTp1Cnz59Sox7wYIF+Pbbb4ud5QZe3Z+9e/dunD59GoaGhlixYgUePHjApPs9YNJNREREREQq4+1l2a+pqakhMjIS06ZNg42NDVq0aIHg4GB07969wscYN24ctm7dKixlNzY2Rnh4OL777jsEBwejbdu2WLZsGf7zn//I9Ttx4oTw+fXjzP766y80bty42GMVFhbC19cXf/31FzQ0NNCsWTMsXrwYEyZMENpkZ2djz549OHjwYIkxa2pqFjsL/tqcOXNw8+ZNODk5QUdHB19//TUGDhyIZ8+elXk9SDFMuomIiIiIqNoKDw8vtX7Pnj3C+169eiEpKUlUX1RUJLxv3Lix6DMAGBgYyJW5u7sjMDAQcXFx6NSpEwDA1dVVdI/122MDEO2kXl5Tp07F1KlTS22zefNmdOjQAR07dhTKunfvLnf8Nw0cOFBUX7t2bdG1Kk5x8ZfVh8rG53QTERERERG9QVtbG1u3bsXDhw+VHQqAV5ubhYSEKDsMekec6SYiIiIiInrLuyxLrypvP7ObVAtnuomIiIiIiIiqCGe6qVL4+voiOzu7Qn3S09PLbJORX7Exq6OXheV/diNVDb0P4NGSWf//GaKqrOS7zlRDYZHq/wyQnaXsCBSnptrzBUX5H8AvJE0tZUegMDU91f4TWEOar+wQiKgCVPs3DlUb2dnZWLlyZYX6eHp6VlE0RERERERE1YNqf11MREREREREVI0x6SYiIiIiIiKqIky6iYiIiIiIiKoIk24iIiIiIiKiKsKkm4iIiIiI6C0jR47EokWLlB0GAGDWrFmYOnWqssOgd8Skm4iIiIiIqi13d3dIJBJMnDhRrm7y5MmQSCRwd3ev1GMmJiZi//79mDZtWoX6TZs2De3atYNUKkWbNm3K3S8nJwezZ8+Gubk5pFIpGjdujLCwMKHe29sbW7Zswc2bN4WyW7duQSKRQF1dHXfv3hWNd+/ePWhoaEAikeDWrVsVOgeqfEy6iYiIiIioWjM1NUVkZCSysrKEsuzsbGzfvh1mZmaVfryQkBAMHToUenp6Fe7r4eGBL7/8skJ9hg0bhujoaISGhiIlJQU7duxAixYthPo6derAyckJ69atk+vbsGFDbN26VVS2ZcsWNGzYsMKxU9Vg0k1ERERERNVa27ZtYWpqiqioKKEsKioKZmZmsLe3F8oOHjyILl26wMDAAEZGRhgwYABSU1OF+tezw1FRUejRowd0dHTQunVrxMXFCW0KCgqwe/duuLi4iGLYtm0bHBwcULNmTZiYmOCrr75Cenq6qE1wcDAmT56Mpk2blvvcDh48iBMnTmD//v3o1asXGjdujE6dOqFz586idi4uLoiMjJTrP3r0aGzevFlUtnnzZowePVpUVlBQgLFjx6JJkybQ1tZGixYtsHr1alEbd3d3DBw4EMuWLUP9+vVhZGSEyZMnIy8vr9znQ/KYdBMRERER0XuVk5OD58+fi145OTml9vHw8BAll2FhYRgzZoyozcuXL+Hl5YWEhARER0dDTU0NgwYNQmFhoajd7Nmz4e3tDZlMhubNm8PV1RX5+fkAgIsXL+LZs2dwcHAQ9cnLy8OCBQuQmJiIPXv24NatW5WyrH3v3r1wcHDAkiVL0LBhQzRv3hze3t6iWX0A6NChA+7cuSO3XPw///kPnjx5gpiYGABATEwMnjx5IvelQWFhIRo1aoRdu3YhKSkJ8+bNw3fffYeffvpJ1O7YsWNITU3FsWPHsGXLFoSHhyM8PFzh8/yYaSg7APp4aWlpwdPTU9lhEBEREdF7FhgYiICAAFGZn58f/P39S+zj5uYGX19f3L59GwAQGxuLyMhIHD9+XGgzePBgUZ+wsDAYGxsjKSkJNjY2Qrm3tzecnZ0BAAEBAbC2tsaNGzfQsmVL3L59G+rq6qhbt65oLA8PD+F906ZNERwcjPbt2yMjI+OdlqG/dvPmTcTExEBLSwu//PILHj58iEmTJuHRo0eiLxkaNGgAALh9+zYaN24slNeoUQNubm4ICwtDly5dEBYWBjc3N9SoUUN0nBo1aoiueZMmTRAXF4effvoJw4YNE8oNDQ2xZs0aqKuro2XLlnB2dkZ0dDTGjx//zuf4sWPSTUoTGBhYZpsfw/a/h0iIiIiI6H3y9fWFl5eXqEwqlZbax9jYGM7OzggPD0dRURGcnZ1Rp04dUZvr169j3rx5iI+Px8OHD4UZ7rS0NFHSbWdnJ7yvX78+ACA9PR0tW7ZEVlYWpFIpJBKJaOzz58/D398fiYmJePLkiWjsVq1aleu830zO3dzcsH79ehQWFkIikSAiIgK1atUCAKxYsQJDhgzB2rVroa2tDQDC/2ZmZsqN6+HhgU8//RSLFi3Crl27EBcXJ8zcv+n7779HWFgY0tLSkJWVhdzcXLkN36ytraGuri66PpcuXSrX+VHxmHQTEREREdF7JZVKy0yyi+Ph4YEpU6YAeJVAvs3FxQXm5ubYuHEjGjRogMLCQtjY2CA3N1fU7s1Z4NfJ9eskuk6dOsjMzERubi40NTUBvFq27uTkBCcnJ0RERMDY2BhpaWlwcnKSG7s0MplMeK+vrw/gVVLbsGFDIeEGACsrKxQVFeHOnTuwtLQEADx+/BjAqy8f3mZra4uWLVvC1dUVVlZWsLGxER0LACIjI+Ht7Y3ly5ejU6dOqFmzJpYuXYr4+PgSr83r6/P28nyqGCbdRERERESkEvr27Yvc3FxIJBI4OTmJ6h49eoSUlBRs3LgRXbt2BQDhPueKeD3zm5SUJLy/evUqHj16hKCgIJiamgIAEhISKjy2hYWFXFnnzp2xa9cu0TL1a9euQU1NDY0aNRLaXb58GTVq1IC1tXWxY3t4eGDSpEnF7nAOvFqO/+mnn2LSpElC2ZubzFHV4UZqRERERESkEtTV1ZGcnIykpCTREmjg1b3IRkZG2LBhA27cuIGjR4/KLWEvD2NjY7Rt21aUsJuZmUFTUxMhISG4efMm9u7diwULFsj1vXHjBmQyGe7fv4+srCzIZDLIZLJSZ8O/+uorGBkZYcyYMUhKSsLJkyfx7bffwsPDQ1hSDgCnTp1C165dRWVvGj9+PP7991+MGzeu2HpLS0skJCTg0KFDuHbtGubOnYtz586V97KQAph0ExERERGRytDX1xeWZr9JTU0NkZGROH/+PGxsbODp6YmlS5e+0zHGjRuHiIgI4bOxsTHCw8Oxa9cutGrVCkFBQVi2bFmx/ezt7fHDDz/g2rVrsLe3h729Pf75558Sj6Wnp4cjR47g6dOncHBwwIgRI+Di4oLg4GBRu8jIyFI3M9PQ0ECdOnWgoVH8YuYJEybgiy++wJdffolPPvkEjx49Es16U9WRFBUVFSk7iPfN09MTK1euVHYYAKpXLOVRUrxVdR7GtVpU+pjvW2PdesoOQWG2mnXLblSNdc/TUnYICvtXvew21Z2q/8eme2GGskNQmPWC5soOQWFFz54pOwSFFNy8q+wQFKbh1EvZISgsd/fvyg5BIdl/yW+QpWrqvbHjeHWUlZWFFi1aYOfOnejUqZOyw8GBAwfwzTff4OLFiyUm1VR9caabiIiIiIjoDdra2ti6dSsePnyo7FAAvNrIbfPmzUy4VRR/akRERERERG/p3r27skMQDBkyRNkhkAI4001ERERERERURTjTTRWipaUFT09PufL09PQqOV4j7TpVMu77ZKyhp+wQFGZbqNr3RGsXqvrdxMCzGmW3qe7M8yXKDkEhejrlfw5rtdXATNkRKEyi+UDZIShEo2ZNZYeguPSSN4RSFWpS1f4TOPOJav8+JfrYqPZvHHrvAgMDiy0vLhEnIiIiIiL62HF5OREREREREVEVYdJNREREREREVEWYdBMRERERERFVESbdRERERET0wfP390ebNm2UHUa5hYeHw8DAQGljdO/eHTNmzCi1TePGjbFq1ap3Gv9jwqSbiIiIiIiqLXd3d0gkEgQFBYnK9+zZA4mEO7lXlaioKCxYsEDZYXwQmHQTEREREVG1pqWlhcWLF+PJkyfKDuWDl5v76hGZtWvXRs0P4TGH1QCTbiIiIiIiqtZ69eoFExOTEh9fCwAbN26EqakpdHR0MGjQIKxYsaLEpdUnT55EjRo1cP/+fVH5jBkz0LVrV+FzbGwsunfvDh0dHRgaGsLJyUlI/A8ePIguXbrAwMAARkZGGDBgAFJTU4W+t27dgkQiQVRUFHr06AEdHR20bt0acXFxFTr3Q4cOwcrKCnp6eujbty/u3btXoXMAXq0KsLS0hJaWFpycnPD3338Lda+X3W/atAlNmjSBlpYWAPnl5enp6XBxcYG2tjaaNGmCiIiICp3Hx4xJNxERERERvVc5OTl4/vy56JWTk1Nie3V1dSxatAghISG4c+eOXH1sbCwmTpyI6dOnQyaToXfv3li4cGGJ43Xr1g1NmzbFtm3bhLK8vDxERETAw8MDACCTyfDZZ5+hVatWiIuLQ0xMDFxcXFBQUAAAePnyJby8vJCQkIDo6Gioqalh0KBBKCwsFB1r9uzZ8Pb2hkwmQ/PmzeHq6or8/PxyXafMzEwsW7YM27Ztw8mTJ5GWlgZvb+9yn8PrMRYuXIitW7ciNjYWT58+xfDhw0XHuXHjBn7++WdERUVBJpMVG4u7uzv+/vtvHDt2DLt378batWuRnp5ervP42GkoOwD6OPj6+iI7O1vZYRARERFRNRAYGIiAgABRmZ+fH/z9/UvsM2jQILRp0wZ+fn4IDQ0V1YWEhKBfv35CQtq8eXOcPn0av//+e4njjR07Fps3b8a3334LAPjtt9+QnZ2NYcOGAQCWLFkCBwcHrF27VuhjbW0tvB88eLBovLCwMBgbGyMpKQk2NjZCube3N5ydnQEAAQEBsLa2xo0bN9CyZcsSY3stLy8P69evR7NmzQAAU6ZMwfz588t9Dq/HWLNmDT755BMAwJYtW2BlZYWzZ8+iQ4cOAF4tKd+6dSuMjY2LjePatWs4cOAAzp49i/bt2wMAQkNDYWVlVeY5EJNuek+ys7OxcuXKCvez39G5CqIhIiIiImXy9fWFl5eXqEwqlZbZb/HixejZs6eQXL+WkpKCQYMGico6dOhQatLt7u6OOXPm4MyZM+jYsSPCw8MxbNgw6OrqAng10z106NAS+1+/fh3z5s1DfHw8Hj58KMxwp6WliZJuOzs74X39+vUBvFqqXZ6kW0dHR0i4X/d/c3a5rHMAAA0NDSFRBoCWLVvCwMAAycnJQtJtbm5eYsINAMnJydDQ0EC7du3kxqGyMekmIiIiIqL3SiqVlivJflu3bt3g5OQEX19fuLu7KxRD3bp14eLigs2bN6NJkyY4cOAAjh8/LtRra2uX2t/FxQXm5ubYuHEjGjRogMLCQtjY2Agbkb1Wo0YN4f3r3dbfXoJekjf7vu5fVFRU7nMorzeTdKp8vKebiIiIiIhURlBQEH777TfRhmQtWrTAuXPnRO3e/lyccePGYefOndiwYQOaNWuGzp3/t8rSzs4O0dHRxfZ79OgRUlJSMGfOHHz22WewsrJS2s7qpZ0DAOTn5yMhIUH4nJKSgqdPn1ZoaXjLli2Rn5+P8+fPy41DZWPSTUREREREKsPW1hYjRoxAcHCwUDZ16lTs378fK1aswPXr1/HDDz/gwIEDZT7H28nJCfr6+vjvf/+LMWPGiOp8fX1x7tw5TJo0CRcvXsTVq1exbt06PHz4EIaGhjAyMsKGDRtw48YNHD16VG65/PtS2jkAr2bLp06divj4eJw/fx7u7u7o2LGjsLS8PFq0aIG+fftiwoQJwjjjxo0rczUAvfJRLi/X0tKCp6enssMAAO74R0RERERUQfPnz8fOnTuFz507d8b69esREBCAOXPmwMnJCZ6enlizZk2p46ipqcHd3R2LFi3CqFGjRHXNmzfH4cOH8d1336FDhw7Q1tbGJ598AldXV6ipqSEyMhLTpk2DjY0NWrRogeDgYHTv3r0qTvedzwF4dV/4zJkz8dVXX+Hu3bvo2rWr3EZ05bF582aMGzcOjo6OqFevHv773/9i7ty5lXEKHzxJ0Zs3BdB75+np+U4bjFU3ZZ3Hu56nvYnqb6RWX7OWskNQWG/UVnYICmmUp/q/5i5V/La3asc8v/TZhuqus85jZYegMPOQ/soOQXEPHyg7AsVkZyk7AsWpqf5Cyfz4i8oOQSEP4wqUHYLCmiQeqfJjjB8/HlevXsWpU6dKbTd27Fj8+++/2Lt3b5XHVFU+hHP4kH2UM91ERERERPRhWbZsGXr37g1dXV0cOHAAW7ZsET3u623Pnj3DpUuXsH37dpVNVj+Ec/gYqP5XlURERERE9NE7e/YsevfuDVtbW6xfvx7BwcEYN25cie0///xz9OnTBxMnTkTv3r3fY6Sv9OvXD3p6esW+Fi1aVK4xlH0OVD6c6SYiIiIiIpX3008/Vaj9uzxaqzJt2rQJWVnF33JSu3b5bu1T9jlQ+TDpJiIiIiIies8aNmyo7BDoPWHSTZWirB3h33WX9qzC3HcNqdp4VpCt7BAUlqGh2huRqfj+XQAAnSLVPwk11f6/EfLzP4A7sv69p+wIFFaUkaHsEBSTrfr/TZA0tVR2CIrTqqHsCBSiW+cD2JCP6CPCpJsqRWBgYKn11eURbURERERERO/TB/C1PREREREREVH1xKSbiIiIiIiIqIow6SYiIiIiomqre/fumDFjRon1EokEe/bsKfd4x48fh0QiwdOnTxWOjag8eE83ERERERGprHv37sHQ0FDZYRCViEk3ERERERGpLBMTE2WHQFQqLi8nIiIiIqJqrbCwED4+PqhduzZMTEzg7+8v1L29vPz06dNo06YNtLS04ODggD179kAikUAmk4nGPH/+PBwcHKCjo4NPP/0UKSkp5YrF398fbdq0QVhYGMzMzKCnp4dJkyahoKAAS5YsgYmJCerWrYuFCxeK+q1YsQK2trbQ1dWFqakpJk2ahIw3HoMYHh4OAwMDHDp0CFZWVtDT00Pfvn1x797/Hvfo7u6OgQMHYtmyZahfvz6MjIwwefJk5OXlCW2ePHmCUaNGwdDQEDo6OujXrx+uX78u1N++fRsuLi4wNDSErq4urK2tsX//fqH+8uXL6NevH/T09FCvXj2MHDkSDx8+LNe1oeIx6SYiIiIiompty5Yt0NXVRXx8PJYsWYL58+fjyJEjcu2eP38OFxcX2Nra4s8//8SCBQswc+bMYsecPXs2li9fjoSEBGhoaMDDw6Pc8aSmpuLAgQM4ePAgduzYgdDQUDg7O+POnTs4ceIEFi9ejDlz5iA+Pl7oo6amhuDgYFy5cgVbtmzB0aNH4ePjIxo3MzMTy5Ytw7Zt23Dy5EmkpaXB29tb1ObYsWNITU3FsWPHsGXLFoSHhyM8PFyod3d3R0JCAvbu3Yu4uDgUFRWhf//+QmI+efJk5OTk4OTJk7h06RIWL14MPT09AMDTp0/Rs2dP2NvbIyEhAQcPHsSDBw8wbNiwcl8bksfl5URERERE9F7l5OQgJydHVCaVSiGVSottb2dnBz8/PwCApaUl1qxZg+joaPTu3VvUbvv27ZBIJNi4cSO0tLTQqlUr3L17F+PHj5cbc+HChXB0dAQAzJo1C87OzsjOzoaWllaZ8RcWFiIsLAw1a9ZEq1at0KNHD6SkpGD//v1QU1NDixYtsHjxYhw7dgyffPIJAIg2g2vcuDH++9//YuLEiVi7dq1QnpeXh/Xr16NZs2YAgClTpmD+/PmiYxsaGmLNmjVQV1dHy5Yt4ezsjOjoaIwfPx7Xr1/H3r17ERsbi08//RQAEBERAVNTU+zZswdDhw5FWloaBg8eDFtbWwBA06ZNhbHXrFkDe3t7LFq0SCgLCwuDqakprl27hubNm5d5bUgek256L7S0tODp6ansMIiIiIioGggMDERAQICozM/PT7Rs/E12dnaiz/Xr10d6erpcu5SUFNjZ2YkS5w4dOpQ5Zv369QEA6enpMDMzKzP+xo0bo2bNmsLnevXqQV1dHWpqaqKyN2P8448/EBgYiKtXr+L58+fIz89HdnY2MjMzoaOjAwDQ0dEREu6SztPa2hrq6uqiNpcuXQIAJCcnQ0NDQ0j0AcDIyAgtWrRAcnIyAGDatGn4v//7Pxw+fBi9evXC4MGDhWuRmJiIY8eOCTPfb0pNTWXS/Y6YdNN7ERgY+E79DkS0r+RIiIiIiEjZfH194eXlJSoraZYbAGrUqCH6LJFIUFhYqFAMb44pkUgAoNxjFhdPaTHeunULAwYMwP/93/9h4cKFqF27NmJiYjB27Fjk5uYKSXdxYxQVFZV57Ipci3HjxsHJyQn79u3D4cOHERgYiOXLl2Pq1KnIyMiAi4sLFi9eLNfv9RcTVHG8p5uIiIiIiN4rqVQKfX190au0pLu8WrRogUuXLomWrp87d07hcRV1/vx5FBYWYvny5ejYsSOaN2+Of/75p9KPY2Vlhfz8fNG95I8ePUJKSgpatWollJmammLixImIiorCN998g40bNwIA2rZtiytXrqBx48awsLAQvXR1dSs93o8Fk24iIiIiIvogfPXVVygsLMTXX3+N5ORkHDp0CMuWLQPwv9lsZbCwsEBeXh5CQkJw8+ZNbNu2DevXr6/041haWuLzzz/H+PHjERMTg8TERLi5uaFhw4b4/PPPAby6t/zQoUP466+/8Oeff+LYsWOwsrIC8GqTtcePH8PV1RXnzp1DamoqDh06hDFjxqCgoKDS4/1YMOkmIiIiIqIPgr6+Pn777TfIZDK0adMGs2fPxrx58wCgXBukVZXWrVtjxYoVWLx4MWxsbBAREfHOt1+WZfPmzWjXrh0GDBiATp06oaioCPv37xeWpRcUFGDy5MmwsrJC37590bx5c2EztwYNGiA2NhYFBQXo06cPbG1tMWPGDBgYGIjuV6eKkRS9fZMAvVeenp5YuXKlssOotlrWVf17uo1q1Cy7UTXXV0O17+GxyCu7TXV3u4byvp2vLPXylR2BYtpLnyo7BIU1m99a2SEorOiNZ9qqpOxsZUegMElTS2WHoLD8oyeUHYJCsi49V3YICqsb/f5+BhERERgzZgyePXsGbW3t93Zcote4kRoREREREX0wtm7diqZNm6Jhw4ZITEzEzJkzMWzYMCbcpDRcI0BERERERB+M+/fvw83NDVZWVvD09MTQoUOxYcOGcve3traGnp5esa+IiIgqjJw+VJzpJiIiIiKiD4aPjw98fHzeuf/+/fuRl1f8vWn16tV753Hp48Wkm4iIiIiI6P8zNzdXdgj0gWHSTdXaTzomyg5BYVItFd89CsD+TNXexOuiZqGyQ1DY7aJMZYegMDsN1X6+ZwPbF8oOQWGFydeUHYLCirJyym5UjUn0VfvfAQAg+YqyI1DYw2OqvaHd2fRGyg5BYV8qOwCi94j3dBMRERERERFVESbdRERERERERFWESTcRERERERFRFWHSTURERERERFRFmHQTEREREdEHw9/fH23atFF2GNXCrVu3IJFIIJPJlB3KR41JNxERERERVUsSiaTUl7+/v1wfb29vREdHv/9giUrAR4YREREREVG1dO/ePeH9zp07MW/ePKSkpAhlenp6wvuioiIUFBRAT09PVE5VKy8vDzVq1FB2GNUaZ7qJiIiIiKhaMjExEV61atWCRCIRPl+9ehU1a9bEgQMH0K5dO0ilUsTExMgtL3d3d8fAgQOxaNEi1KtXDwYGBpg/fz7y8/Px7bffonbt2mjUqBE2b94sOvbMmTPRvHlz6OjooGnTppg7dy7y8vKE+tfH2bZtGxo3boxatWph+PDhePHihdCme/fumDZtGnx8fFC7dm2YmJjIzc6npaXh888/h56eHvT19TFs2DA8ePBAqE9MTESPHj1Qs2ZN6Ovro127dkhISMDLly+hr6+P3bt3i8bbs2cPdHV1RXG86cqVKxgwYAD09fVRs2ZNdO3aFampqQCAwsJCzJ8/H40aNYJUKkWbNm1w8OBBoe/r5eo7d+6Eo6MjtLS0EBERAQDYtGkTrKysoKWlhZYtW2Lt2rXl+Al/HJh0ExERERHRe5WTk4Pnz5+LXjk5Oe801qxZsxAUFITk5GTY2dkV2+bo0aP4559/cPLkSaxYsQJ+fn4YMGAADA0NER8fj4kTJ2LChAm4c+eO0KdmzZoIDw9HUlISVq9ejY0bN2LlypWicVNTU7Fnzx78/vvv+P3333HixAkEBQWJ2mzZsgW6urqIj4/HkiVLMH/+fBw5cgTAqyT3888/x+PHj3HixAkcOXIEN2/exJdffin0HzFiBBo1aoRz587h/PnzmDVrFmrUqAFdXV0MHz5c7suCzZs3Y8iQIahZs6bcdbh79y66desGqVSKo0eP4vz58/Dw8EB+fj4AYPXq1Vi+fDmWLVuGixcvwsnJCf/5z39w/fp1uWs+ffp0JCcnw8nJCREREZg3bx4WLlyI5ORkLFq0CHPnzsWWLVvK+vF9FLi8nMrk6+uL7OxspRx7jFKOSkRERERVKTAwEAEBAaIyPz+/Yu/RLsv8+fPRu3fvUtvUrl0bwcHBUFNTQ4sWLbBkyRJkZmbiu+++A/Dq792goCDExMRg+PDhAIA5c+YI/Rs3bgxvb29ERkbCx8dHKC8sLER4eLiQ4I4cORLR0dFYuHCh0MbOzg5+fn4AAEtLS6xZswbR0dHo3bs3oqOjcenSJfz1118wNTUFAGzduhXW1tY4d+4c2rdvj7S0NHz77bdo2bKlMMZr48aNw6effop79+6hfv36SE9Px/79+/HHH38Uex2+//571KpVC5GRkcKS8ObNmwv1y5Ytw8yZM4VrsHjxYhw7dgyrVq3C999/L7SbMWMGvvjiC+Gzn58fli9fLpQ1adIESUlJ+OGHHzB69OhSfzYfAybdVKbs7Gy5b/Xel4u/uCjluERERERUdXx9feHl5SUqk0ql7zSWg4NDmW2sra2hpva/Rb716tWDjY2N8FldXR1GRkZIT08Xynbu3Ing4GCkpqYiIyMD+fn50NfXF43buHFj0Yzy68T3TW/Pvr/ZJjk5GaampkLCDQCtWrWCgYEBkpOT0b59e3h5eWHcuHHYtm0bevXqhaFDh6JZs2YAgA4dOsDa2hpbtmzBrFmz8OOPP8Lc3BzdunUr9jrIZDJ07dq12Huwnz9/jn/++QedO3cWlXfu3BmJiYmisjev+cuXL5GamoqxY8di/PjxQnl+fj5q1apVbBwfGy4vJyIiIiKi90oqlUJfX1/0etekW1dXt8w2byeZEomk2LLCwkIAQFxcHEaMGIH+/fvj999/x4ULFzB79mzk5uaWOe7rMSrSpjT+/v64cuUKnJ2dcfToUbRq1Qq//PKLUD9u3DiEh4cDeLW0fMyYMZBIJMWOpa2tXe7jlubNa56RkQEA2LhxI2QymfC6fPkyzpw5UynHU3VMuomIiIiIiN5w+vRpmJubY/bs2XBwcIClpSVu375d6cexsrLC33//jb///lsoS0pKwtOnT9GqVSuhrHnz5vD09MThw4fxxRdfiO7jdnNzw+3btxEcHIykpKRSl3Pb2dnh1KlTog3hXtPX10eDBg0QGxsrKo+NjRXF8rZ69eqhQYMGuHnzJiwsLESvJk2alOs6fOi4vJyIiIiIiOgNlpaWSEtLQ2RkJNq3b499+/aJZpcrS69evWBra4sRI0Zg1apVyM/Px6RJk+Do6AgHBwdkZWXh22+/xZAhQ9CkSRPcuXMH586dw+DBg4UxDA0N8cUXX+Dbb79Fnz590KhRoxKPN2XKFISEhGD48OHw9fVFrVq1cObMGXTo0AEtWrTAt99+Cz8/PzRr1gxt2rTB5s2bIZPJhB3KSxIQEIBp06ahVq1a6Nu3L3JycpCQkIAnT57I3UbwMeJMNxERERER0Rv+85//wNPTE1OmTEGbNm1w+vRpzJ07t9KPI5FI8Ouvv8LQ0BDdunVDr1690LRpU+zcuRPAq3vNHz16hFGjRqF58+YYNmwY+vXrJ7cJ3dixY5GbmwsPD49Sj2dkZISjR48iIyMDjo6OaNeuHTZu3CgsgZ82bRq8vLzwzTffwNbWFgcPHsTevXtFm7cVZ9y4cdi0aRM2b94MW1tbODo6Ijw8nDPd/5+kqKioSNlBfMw8PT2VtklZeSkzxouNVX8jNalWvrJDUNj+TCNlh6CQdLXy3zdVXd0uylJ2CAqzQ9n33FVnX7f5u+xG1ZzUpq6yQ1BYUda7PVKoupDoq/a/AwCQaGspOwSFPdh+p+xG1djZdNX/t/zlvdJnTqlitm3bBk9PT/zzzz/Q1NRUdjj0Fi4vJyIiIiIiUkGZYiwtswAApVxJREFUmZm4d+8egoKCMGHCBCbc1RSXlxMREREREamgJUuWoGXLljAxMYGvr6+yw6ESMOkmIiIiIiJSQf7+/sjLy0N0dDT09PSUHQ6VgEk3ERERERERURXhPd0qyNfXF9nZ2e/teOnp6e/tWG97mq36m7U0qvlM2SEo7G811d4M7gUKlB2CwnQlqv/rOhOqvW/ni79rKDsEhUnbqf69frlJyvtvUmXIe/Jc2SEoTMtcXdkhKOzig3rKDkEhUhX/fUr0sVH9v+I+QtnZ2e91N3FPT8/3diwiIiIiIqIPCZeXExEREREREVURJt1EREREREREVYRJNxEREREREVEVYdJNRERERET0lpEjR2LRokXC58aNG2PVqlVKiWX9+vVwcXF55/7h4eEwMDCovICoQph0ExERERFRteXu7g6JRIKJEyfK1U2ePBkSiQTu7u6VeszExETs378f06ZNq1C/DRs2oHv37tDX14dEIsHTp08r1P/Ro0do1KiRXF8PDw/8+eefOHXqlFyfY8eOYcCAATA2NoaWlhaaNWuGL7/8EidPnqzQsd/UvXt3SCQSREZGispXrVqFxo0by7XPyspC7dq1UadOHeTk5LzzcT9UTLqJiIiIiKhaMzU1RWRkJLKysoSy7OxsbN++HWZmZpV+vJCQEAwdOhR6enoV6peZmYm+ffviu+++e6fjjh07FnZ2dnLlmpqa+OqrrxAcHCwqX7t2LT777DMYGRlh586dSElJwS+//IJPP/1U4ScQaWlpYc6cOcjLyyuz7c8//wxra2u0bNkSe/bsUei4HyIm3UREREREVK21bdsWpqamiIqKEsqioqJgZmYGe3t7oezgwYPo0qULDAwMYGRkhAEDBiA1NVWov3XrFiQSCaKiotCjRw/o6OigdevWiIuLE9oUFBRg9+7dxS7nfvHiBVxdXaGrq4uGDRvi+++/F9XPmDEDs2bNQseOHSt8juvWrcPTp0/h7e1dbL2Liwv27t0rfPGQlpaGGTNmYMaMGdiyZQt69uwJc3Nz2NnZYfr06UhISCj1eL/99hvat28PLS0t1KlTB4MGDRLVu7q64unTp9i4cWOZsYeGhsLNzQ1ubm4IDQ2Vq79y5QoGDBgAfX191KxZE127dhX9XD50TLqJiIiIiOi9ysnJwfPnz0WvspYle3h4YPPmzcLnsLAwjBkzRtTm5cuX8PLyQkJCAqKjo6GmpoZBgwahsLBQ1G727Nnw9vaGTCZD8+bN4erqivz8fADAxYsX8ezZMzg4OMjFsHTpUrRu3RoXLlzArFmzMH36dBw5cuRdL4MgKSkJ8+fPx9atW6GmVnyK5uDggPz8fMTHxwN4Nbucl5cHHx+fYttLJJISj7dv3z4MGjQI/fv3x4ULFxAdHY0OHTqI2ujr62P27NmYP38+Xr58WeJYqampiIuLw7BhwzBs2DCcOnUKt2/fFurv3r2Lbt26QSqV4ujRozh//jw8PDyE6/0x0FB2AFT9aWlpKbw85V0NKrsJEREREamYwMBABAQEiMr8/Pzg7+9fYh83Nzf4+voKCV1sbCwiIyNx/Phxoc3gwYNFfcLCwmBsbIykpCTY2NgI5d7e3nB2dgYABAQEwNraGjdu3EDLli1x+/ZtqKuro27dunIxdO7cGbNmzQIANG/eHLGxsVi5ciV69+5dofN/U05ODlxdXbF06VKYmZnh5s2bxbbT0dFBrVq1hPO/du0a9PX1YWJiIrT5+eefMXr0aOFzXFwcbG1t5cZauHAhhg8fLvoZtG7dWq7dpEmTsHr1aqxYsQJz584tNq6wsDD069cPhoaGAAAnJyds3rxZ+Fl+//33qFWrFiIjI1GjRg38P/buOiqq9X0b+DVDtyIooNIoICAg9lexsevYCdgtFmCAcVQsVOxAMFDEPHhsMUGwAZUQAcEAUbHo2u8fvszPOdQgwp6N92etWct59p6Zawa2zL33E8CPz+5PQkU3qdDatWtZe+3bx4ay9tqEEEIIIaR6uLq6Yt68eUJtMjIy5T5GXV0dffr0ga+vLxiGQZ8+faCmpia0T1xcHNzc3HDv3j18/PhRcIU7OTlZqOj+edy0pqYmACAtLQ3GxsbIzs6GjIxMqVeK27ZtW+J+ZWY079Wrl2AyNB0dHTx//hyurq4wMTHBmDFjKny8nJwcsrKyBPf/m9HOzg7h4eF4+/YtOnXqhMLCwlKfJzw8HJMmTarw9WRkZLBy5UrMmjUL06ZNK7G9sLAQBw8exNatWwVtY8aMwYIFC+Dm5gY+n4/w8HB06NBBUHD/iajoJoQQQgghhNQoGRmZCovs0jg6OmLmzJkAUGI8NfBj3LOOjg727dsHLS0tFBUVwczMDHl5eUL7/VwAFheuxQW6mpoasrKykJeXB2lp6UpnLM/+/fsFY7KLM1y/fh1Pnz7FyZMnAQAMwwhyLFmyROhqdHp6OtTV1QEARkZG+Pr1K1JTUwVXuxUVFWFoaAhJyfLLPDk5OZEzjxkzBhs3bsTff/9dYubyy5cv4+3btxg+fLhQe2FhIYKCgtC9e/dKvVZtRWO6CSGEEEIIIZzQs2dP5OXlIT8/H3Z2dkLbPn36hNjYWCxduhRdu3aFiYkJPn/+XOnXsLS0BPBjnPV/hYWFlbhvYmIi8nM3bNgQhoaGMDQ0hI6ODoAfXcIjIiIQHh6O8PBw7N+/HwBw584dzJgxQ/DY+Ph45OTkCCaOGzJkCKSkpLBu3bpKvT/gx5X+oKAgkfbl8/lYu3Ytdu3ahVevXglt8/b2xogRIwTZi28jRowQTKhmYWGBO3fuiDQLem1FV7oJIYQQQgghnCAhIYHo6GjBv39Wt25d1KtXD3v37oWmpiaSk5MF468rQ11dHdbW1ggODhYU4MVCQkKwfv16DBw4EFevXsWJEydw/vx5wfbU1FSkpqbi5cuXAICnT59CSUkJ2traUFVVLfX1DAwMhO5//PgRAGBiYoI6deoI2u/cuQN9fX3B/tra2ti0aRPmzJmD9PR02NvbQ09PD+np6Thy5Eipn1Exd3d3dO3aFQYGBhgxYgQKCgpw4cIFODs7l7p/nz590Lp1a+zZswcNGjQAAHz48AHnzp1DYGCgUNd9ABg3bhwGDRqE9PR0zJw5E9u2bcOIESPg6uoKFRUVhIWFoVWrVmjatGmpr1fb0JVuQgghhBBCCGcoKytDWVm5RDufz4e/vz8ePXoEMzMzODk5YcOGDb/0GhMnToSfn1+J9vnz5+Phw4ewsrLC33//DU9PT6Er7rt374aVlZVgvHTHjh1hZWWFwMDAX8rxs2PHjpUYhz1r1ixcuXIFHz58wJAhQ2BkZITevXsjMTERly5dKnUSNQDo1KkTTpw4gcDAQFhaWqJLly64f/9+ua+/bt065OTkCO4fOnQICgoK6Nq1a4l9u3btCjk5ORw5cgT16tXD9evXkZGRAVtbW7Ro0QL79u37o8Z485jiQQOEFU5OTti8eXO1P4arbmtwfyK1Rupf2Y5QZdu/l35mliu+o/RJREjN0sTvHRdX0yaovmc7QpWpDWzAdoQqy33yhu0IVZL/uewlfLhCVqf0K2dccvMyt4+F2vDlfWDqUbYjlCs7OxtNmzbF8ePHS0yexobnz5+jS5cuePHiBVRUVNiOQyqJrnQTQgghhBBCyE/k5ORw6NAhQVdvtqWkpODQoUNUcHMUjekmhBBCCCGEkP/o1KkT2xEEunXrxnYEUgV0pZsQQgghhBBCCKkmdKWbiLVchvvnhYLT67MdocrUOf4/Rc+civcRdy+lOf5DAFCvgNujEAvyuD+ONXxnLtsRqsx8gCzbEapEkvt/EiA1uB/bEaqss+Q/bEeokldB3D4OCPnTcL+iIYQQQgghhBBCxBQV3YQQQgghhBBCSDWhopsQQgghhBBCCKkmVHQTQgghhBBCCCHVhIpuQgghhBBCCCnHq1evwOPxEB4e/lufV1dXF1u2bPmtz0nEDxXdhBBCCCGEELHE4/HKvS1fvrxGcjRu3BgpKSkwMzOrkdcrTXUX6Lq6uuDxeAgLCxNqnzt3bqlrlr958wbS0tK//TPp1KkT5s6d+1ufU1T79u1Dhw4dULduXdStWxfdunXD/fv3q/y8VHQTQgghhBBCxFJKSorgtmXLFigrKwu1LViwoFLPl5+f/0s5JCQkoKGhAUlJ7i/hWR5ZWVk4OzuLtK+vry+GDRuGb9++4d69e9WcrGbcvHkTI0eOxI0bNxAaGorGjRujR48eePv2bZWel4puQgghhBBCiFjS0NAQ3FRUVMDj8YTa/P39YWJiAllZWRgbG2Pnzp2CxxZ3CT9+/DhsbW0hKysLPz8/2NvbY+DAgVizZg0aNGiAOnXqYOXKlSgoKMDChQuhqqqKRo0awcfHp8RzFXcvv3nzJng8HoKCgmBjYwN5eXm0a9cOsbGxgsfEx8djwIABaNCgARQVFdGyZUtcu3atWj6nwsJCTJgwAXp6epCTk0PTpk2xdevWEvsdOHAAzZo1g4yMDDQ1NTFz5kyh7ZMnT0ZYWBguXLhQ7usxDAMfHx+MHTsWo0aNgre3d4l9QkJC0KlTJ8jLy6Nu3bqws7PD58+fy31ee3t73Lp1C1u3bhX0ZkhMTIShoSE2btwotG94eDh4PB5evnwJ4EeviF27dqFXr16Qk5ODvr4+Tp48KfSY169fY9iwYahTpw5UVVUxYMAAvHr1SrDdz88P06dPh6WlJYyNjbF//34UFRUhKCio3NwVoaKbEEIIIYQQUqNyc3Px7ds3oVtubm6lnsPPzw9ubm5YvXo1oqOjsWbNGixbtgwHDx4U2s/FxQVz5sxBdHQ07OzsAADXr1/Hu3fvcPv2bXh6esLd3R19+/ZF3bp1ce/ePUydOhVTpkzBmzdvys2wZMkSbNq0CQ8fPoSkpCQcHR0F2zIyMtC7d28EBQXhyZMn6NmzJ/r164fk5ORKvU9RFBUVoVGjRjhx4gSioqLg5uaGxYsXIyAgQLDPrl27MGPGDEyePBlPnz5FYGAgDA0NhZ5HT08PU6dOhaurK4qKisp8vRs3biArKwvdunXDmDFj4O/vj8zMTMH28PBwdO3aFaampggNDUVwcDD69euHwsLCct/H1q1b0bZtW0yaNEnQm0FbWxuOjo5CJ0EAwMfHBx07dhR6D8uWLcNff/2FiIgIjB49GiNGjEB0dDSAH70c7OzsoKSkhDt37iAkJASKioro2bMn8vLySs2TlZWF/Px8qKqqlpu7IrW7fwQHyMrKwsnJqVKPSUtLq6Y05XN1dUVOTk6NvmbvGn01QgghhBBSE9auXYsVK1YItbm7u1dqjLa7uzs2bdqEwYMHA/hRMEZFRWHPnj0YP368YL+5c+cK9immqqoKLy8v8Pl8NG3aFOvXr0dWVhYWL14M4Mf3Xg8PDwQHB2PEiBFlZli9ejVsbW0B/Cju+/Tpg5ycHMjKyqJ58+Zo3ry5YN9Vq1bhzJkzCAwMLHGFuaqkpKSEPk89PT2EhoYiICAAw4YNAwD8/fffmD9/PubMmSPYr2XLliWea+nSpfDx8YGfnx/Gjh1b6ut5e3tjxIgRkJCQgJmZGfT19XHixAnY29sDANavXw8bGxuhngfNmjWr8H2oqKhAWloa8vLy0NDQELTb29vDzc0N9+/fR6tWrZCfn4+jR4+WuPo9dOhQTJw4EcCPz/vq1avYtm0bdu7ciePHj6OoqAj79+8Hj8cD8KNwr1OnDm7evIkePXqUyOPs7AwtLS1069atwuzloaKbZWvXrq30YypbpP8uOTk52Lx5c42+5tWjw2v09QghhBBCSPVzdXXFvHnzhNpkZGREfnxmZibi4+MxYcIETJo0SdBeUFAAFRUVoX1tbGxKPL5Zs2bg8/+v02+DBg2EJgSTkJBAvXr1KrzYZWFhIfi3pqYmgB8XyLS1tZGRkYHly5fj/PnzSElJQUFBAbKzs6vlSjcA7NixAwcOHEBycjKys7ORl5cHS0tLQaZ3796ha9euFT6Puro6FixYADc3NwwfXvK7+JcvX3D69GkEBwcL2saMGQNvb29B0R0eHo6hQ4f+lvcFAFpaWujTpw8OHDiAVq1a4dy5c8jNzS3xGm3bti1xv3hIQEREBF6+fAklJSWhfXJychAfH1/iNT08PODv74+bN29CVla2Svmp6CaEEEIIIYTUKBkZmUoV2f+VkZEB4Mds061btxbaJiEhIXRfQUGhxOOlpKSE7vN4vFLbyuti/d/nKb56WvyYBQsW4OrVq9i4cSMMDQ0hJyeHIUOGlNmVuSr8/f2xYMECbNq0CW3btoWSkhI2bNggmOBMTk6uUs83b9487Ny5U+hKdbGjR48iJydH6HNnGAZFRUV48eIFmjRpUunXE8XEiRMxduxYbN68GT4+Phg+fDjk5eVFfnxGRgZatGgBPz+/EtvU1dWF7m/cuBEeHh64du2a0ImVX0VjugkhhBBCCCGc0qBBA2hpaSEhIQGGhoZCNz09PbbjAfgxkZi9vT0GDRoEc3NzaGhoCE3a9btfq127dpg+fTqsrKxgaGgodPVWSUkJurq6Ik8IpqioiGXLlmH16tX4/v270DZvb2/Mnz8f4eHhgltERAQ6dOiAAwcOAPjRA+BXJx+TlpYudex37969oaCggF27duHSpUtC4+eL/Xe5s7CwMJiYmAAArK2tERcXh/r165f4nfm5d8T69euxatUqXLp0qdReEr+Cim5CCCGEEEII56xYsQJr166Fl5cXXrx4gadPn8LHxweenp5sRwMAGBkZ4fTp04KidNSoURVeOa/I27dvhYrd8PBwfP78GUZGRnj48CEuX76MFy9eYNmyZXjw4IHQY5cvX45NmzbBy8sLcXFxePz4MbZt21bma02ePBkqKio4evSooC08PByPHz/GxIkTYWZmJnQbOXIkDh48iIKCAri6uuLBgweYPn06IiMjERMTg127duHjx48VvkddXV3cu3cPr169wsePHwWfmYSEBOzt7eHq6gojI6MSXckB4MSJEzhw4ABevHgBd3d33L9/XzB+fvTo0VBTU8OAAQNw584dJCYm4ubNm5g9e7Zgwrx169Zh2bJlOHDgAHR1dZGamorU1FRBz4pfRUU3IYQQQgghhHMmTpyI/fv3w8fHB+bm5rC1tYWvr6/YXOn29PRE3bp10a5dO/Tr1w92dnawtrau0nNu3LgRVlZWQrfz589jypQpGDx4MIYPH47WrVvj06dPmD59utBjx48fjy1btmDnzp1o1qwZ+vbti7i4uDJfS0pKCqtWrRKaSNnb2xumpqYwNjYusf+gQYOQlpaGCxcuoEmTJrhy5QoiIiLQqlUrtG3bFv/8849I65wvWLAAEhISMDU1hbq6utAY+AkTJiAvLw8ODg6lPnbFihXw9/eHhYUFDh06hGPHjsHU1BQAIC8vj9u3b0NbWxuDBw+GiYkJJkyYgJycHCgrKwP4McN7Xl4ehgwZAk1NTcHtvxO2VRaPYRimSs9AapyTk1ONT2jG1utebcD9idRSJKUq3knMveX47A8tcgrYjlBlL6U5/kMAUK+A239ubOpVfHZe3KV+VKp4JzFnPuB7xTuRaiU1uB/bEaosP+AftiNUyaugqk3qJA4sXp1jOwLhoDt37qBr1654/fo1GjRoILSNx+PhzJkzGDhwIDvhysH9b3GEEEIIIYQQQmqt3NxcfPjwAcuXL8fQoUNLFNzijrqXE0IIIYQQQgiL/Pz8oKioWOpNlPWtuSI5ObnM96moqFjmcmrHjh2Djo4Ovnz5gvXr19dw6qqjK92EEEIIIYQQwqL+/fuXWPqs2H+XMuMyLS0twbrZZW0vjb29vWAN8LKI86hpKroJIYQQQgghhEVKSkpQUuL+vBsVkZSUhKGhIdsxahwV3bWcq6ur0IyDVZGWlvZbnqcyQmW5/yv6icf9SbyyUHKtRC5pz2M7QdXVLZRgO0KVpUpx+wchKc3t4wAAFGTy2I5QZRJqymxHqJK8Fx/YjlBlMtrc7+rKV7nCdoQqkZHNZzsCIaQSuF/RkHLl5OT8thnHnZycfsvzEEIIIYQQQsifgiZSI4QQQgghhBBCqgkV3YQQQgghhBBCSDWhopsQQgghhBBCCKkmVHQTQgghhBBCCOGE5cuXw9LSku0YlUJFNyGEEEIIIUQs8Xi8cm/Lly8v87GvXr0Cj8crd11oNujq6mLLli1sx6hR9vb24PF48PDwEGo/e/YseDxur24iCiq6CSGEEEIIIWIpJSVFcNuyZQuUlZWF2hYsWFDl18jL4/5yitXpd30+srKyWLduHT5//vxbno9LqOgmhBBCCCGEiCUNDQ3BTUVFBTweT6jN398fJiYmkJWVhbGxMXbu3Cl4rJ6eHgDAysoKPB4PnTp1AvDjquvAgQOxevVqaGlpoWnTpgCAw4cPw8bGBkpKStDQ0MCoUaOQlpYmeL7CwkJMmDABenp6kJOTQ9OmTbF161ahvMXPvXHjRmhqaqJevXqYMWMG8vN/rK3eqVMnJCUlwcnJSXC1vlhwcDA6dOgAOTk5NG7cGLNnz0ZmZqZgu66uLtasWQNHR0coKSlBW1sbe/fuFWwvvrJ/+vRpdO7cGfLy8mjevDlCQ0MF+3z69AkjR45Ew4YNIS8vD3Nzcxw7dkzoPXTq1AkzZ87E3LlzoaamBjs7Ozg6OqJv375C++Xn56N+/frw9vYW6WfZrVs3aGhoYO3ateXut2/fPjRu3Bjy8vIYNGgQPD09UadOnVL3vX37NqSkpJCamirUPnfuXHTo0EFwPyQkBJ06dYK8vDzq1q0LOzs7QfF/6dIl/O9//0OdOnVQr1499O3bF/Hx8YLHivK5VoSKbkIIIYQQQkiNys3Nxbdv34Ruubm5lXoOPz8/uLm5YfXq1YiOjsaaNWuwbNkyHDx4EABw//59AMC1a9eQkpKC06dPCx4bFBSE2NhYXL16Ff/++y+AH0XkqlWrEBERgbNnz+LVq1ewt7cXPKaoqAiNGjXCiRMnEBUVBTc3NyxevBgBAQFCuW7cuIH4+HjcuHEDBw8ehK+vL3x9fQEAp0+fRqNGjbBy5UrB1XoAiI+PR8+ePfHXX38hMjISx48fR3BwMGbOnCn03Js2bYKNjQ2ePHmC6dOnY9q0aYiNjRXaZ8mSJViwYAHCw8PRpEkTjBw5EgUFBQCAnJwctGjRAufPn8ezZ88wefJkjB07VvBZFTt48CCkpaUREhKC3bt3Y+LEibh06ZIgLwD8+++/yMrKwvDhw0X6eUlISGDNmjXYtm0b3rx5U+o+ISEhmDp1KubMmYPw8HB0794dq1evLvM5O3bsCH19fRw+fFjQlp+fDz8/Pzg6OgIAwsPD0bVrV5iamiI0NBTBwcHo168fCgsLAQCZmZmYN28eHj58iKCgIPD5fAwaNAhFRUUif64VkRRpL0Lwo0uIk5NTjb5m3Rp9NUIIIYQQUhPWrl2LFStWCLW5u7uXO0b7v9zd3bFp0yYMHjwYwI8r21FRUdizZw/Gjx8PdXV1AEC9evWgoaEh9FgFBQXs378f0tLSgrbiIg0A9PX14eXlhZYtWyIjIwOKioqQkpISyqynp4fQ0FAEBARg2LBhgva6deti+/btkJCQgLGxMfr06YOgoCBMmjQJqqqqkJCQEFxN//nzGD16NObOnQsAMDIygpeXF2xtbbFr1y7IysoCAHr37o3p06cDAJydnbF582bcuHFDcLUeABYsWIA+ffoAAFasWIFmzZrh5cuXMDY2RsOGDYW65M+aNQuXL19GQEAAWrVqJWg3MjLC+vXrhT6zpk2b4vDhw1i0aBEAwMfHB0OHDoWiomL5P6ifDBo0CJaWlnB3dy/1Cvm2bdvQq1cvQcYmTZrg7t27ghMjpZkwYQJ8fHywcOFCAMC5c+eQk5Mj+JmsX78eNjY2Qr0gmjVrJvj3X3/9JfR8Bw4cgLq6OqKiomBmZiZoL+9zrQgV3URkFXUFqQ4rT4+u8dckhBBCCCHVy9XVFfPmzRNqk5GREfnxmZmZiI+Px4QJEzBp0iRBe0FBAVRUVCp8vLm5uVDBDQCPHj3C8uXLERERgc+fPwuudCYnJ8PU1BQAsGPHDhw4cADJycnIzs5GXl5eiZm0mzVrBgkJCcF9TU1NPH36tNw8ERERiIyMhJ+fn6CNYRgUFRUhMTERJiYmAAALCwvB9uKu9j93gf/vPpqamgCAtLQ0GBsbo7CwEGvWrEFAQADevn2LvLw85ObmQl5eXug5WrRoUSLjxIkTsXfvXixatAjv37/HxYsXcf369XLfV2nWrVuHLl26lDoePzY2FoMGDRJqa9WqVblFt729PZYuXYqwsDC0adMGvr6+GDZsGBQUFAD8uNI9dOjQMh8fFxcHNzc33Lt3Dx8/fhT6uf9cdJf3uVaEim5CCCGEEEJIjZKRkalUkf1fGRkZAH6M/23durXQtp8L3rIUF2TFMjMzYWdnBzs7O/j5+UFdXR3Jycmws7MTTCTm7++PBQsWYNOmTWjbti2UlJSwYcMG3Lt3T+i5pKSkhO7zeLwSXZVLez9TpkzB7NmzS2zT1tau1HP/vE/xmPHifTZs2ICtW7diy5YtMDc3h4KCAubOnVtisrT/fj4AMG7cOLi4uCA0NBR3796Fnp6e0LhpUXXs2BF2dnZwdXUV6r7/q+rXr49+/frBx8cHenp6uHjxIm7evCnYLicnV+7j+/XrBx0dHezbtw9aWlooKiqCmZlZic+kvM+1IlR0E0IIIYQQQjilQYMG0NLSQkJCAkaPLr1nZPGV7OKxu+WJiYnBp0+f4OHhgcaNGwMAHj58KLRPSEgI2rVrJ+jeDUBowi1RSUtLl8hkbW2NqKgoGBoaVvr5KiMkJAQDBgzAmDFjAPwoGl+8eCG4kl+eevXqYeDAgfDx8UFoaCgcHBx+OYeHhwcsLS2FusUDP7qwP3jwQKjtv/dLM3HiRIwcORKNGjWCgYEB2rdvL9hmYWGBoKCgEsMZgB8Ty8XGxmLfvn2CEwjBwcG/8pbKRROpEUIIIYQQQjhnxYoVWLt2Lby8vPDixQs8ffoUPj4+8PT0BPDjCqicnBwuXbqE9+/f4+vXr2U+l7a2NqSlpbFt2zYkJCQgMDAQq1atEtrHyMgIDx8+xOXLl/HixQssW7ZMpILwv3R1dXH79m28ffsWHz9+BPBjfPbdu3cxc+ZMhIeHIy4uDv/880+JidSqysjICFevXsXdu3cRHR2NKVOm4P379yI/fuLEiTh48CCio6Mxfvz4X85hbm6O0aNHw8vLS6h91qxZuHDhAjw9PREXF4c9e/bg4sWLFa7lbWdnB2VlZfz9998lTga4urriwYMHmD59OiIjIxETE4Ndu3bh48ePqFu3LurVq4e9e/fi5cuXuH79eolhD78DFd2EEEIIIYQQzpk4cSL2798PHx8fmJubw9bWFr6+voKlwiQlJeHl5YU9e/ZAS0sLAwYMKPO51NXV4evrixMnTsDU1BQeHh7YuHGj0D5TpkzB4MGDMXz4cLRu3RqfPn0SuuotqpUrV+LVq1cwMDAQTPZmYWGBW7du4cWLF+jQoQOsrKzg5uYGLS2tSj9/eZYuXQpra2vY2dmhU6dO0NDQwMCBA0V+fLdu3aCpqQk7O7sqZ1u5cmWJ7tnt27fH7t274enpiebNm+PSpUtwcnISTCRXFj6fD3t7exQWFmLcuHFC25o0aYIrV64gIiICrVq1Qtu2bfHPP/9AUlISfD4f/v7+ePToEczMzODk5IQNGzZU6X2VhscwDPPbn5VUKycnJ2zevPm37yuOVupwfyK1T7yKuzSJuyxw+z2MzWE7QdW95UtXvJOYS5Ms/yy1uBuollrxTmLu8yf5incSc0Zjfn0MqDjIe/GB7QhVJr/Sne0IVZa/j7vfjQDg7YV8tiNUWdOYi2xHIL8gIyMDDRs2hI+Pj2DW+Oo2adIkxMTE4M6dO+XuN2HCBHz48AGBgYE1kqsyaEw3IYQQQgghhJAyFRUV4ePHj9i0aRPq1KmD/v37V9trbdy4Ed27d4eCggIuXryIgwcPCi339V9fv37F06dPcfToUbEsuAEqugkhhBBCCCGElCM5ORl6enpo1KgRfH19ISkpKbStvInYoqKihGZgr8j9+/exfv16fP/+XbBe+sSJE8vcf8CAAbh//z6mTp2K7t27i/w6NYmKbkIIIYQQQgghZdLV1UVZo5K1tLQQHh5e5mMrO/Y7ICCgUvv/vDyYuKKim4NkZWXh5OQk0r5paWnVnKZ6aRZyewwoADQv4P58hc9kKl7vUpzFS3D/90iqFsy+0TS3gO0IVZKaqsx2hCpravuZ7QhVlvpPLtsRqiQzQ4ntCFXW2K3ksjtc8yJEle0IVdJIJ4PtCIQISEpKVvtSZ1xHRTcHrV27VuR9RS3OCSGEEEIIIYT8fty/BEcIIYQQQgghhIgpKroJIYQQQgghhJBqQkU3IYQQQgghhBBSTajoJoQQQgghhHAaj8fD2bNny9x+8+ZN8Hg8fPnypUqv4+vrizp16pS7z/Lly2FpaVml1yG1CxXdhBBCCCGEELGWmpqKWbNmQV9fHzIyMmjcuDH69euHoKAgkR7frl07pKSkQEVFBYBoxfOvWrBggci5yJ+BZi8nhBBCCCGEiK1Xr16hffv2qFOnDjZs2ABzc3Pk5+fj8uXLmDFjBmJiYip8DmlpaWhoaNRAWkBRURGKioo18lqEG+hKNyGEEEIIIURsTZ8+HTweD/fv38dff/2FJk2aoFmzZpg3bx7CwsIE+338+BGDBg2CvLw8jIyMEBgYKNj2c/fymzdvwsHBAV+/fgWPxwOPx8Py5csBAJ8/f8a4ceNQt25dyMvLo1evXoiLiyuR6ezZszAyMoKsrCzs7Ozw+vVrwbb/di8vKirCypUr0ahRI8jIyMDS0hKXLl0SbM/Ly8PMmTOhqakJWVlZ6OjoCC0R/OXLF0ycOBHq6upQVlZGly5dEBER8Ts+WlJDqOgmhBBCCCGEiKX09HRcunQJM2bMgIKCQontP3cRX7FiBYYNG4bIyEj07t0bo0ePRnp6eonHtGvXDlu2bIGysjJSUlKQkpKCBQsWAADs7e3x8OFDBAYGIjQ0FAzDoHfv3sjPzxc8PisrC6tXr8ahQ4cQEhKCL1++YMSIEWW+h61bt2LTpk3YuHEjIiMjYWdnh/79+wuKeS8vLwQGBiIgIACxsbHw8/ODrq6u4PFDhw5FWloaLl68iEePHsHa2hpdu3Yt9b0R8UTdywkhhBBCCCE1Kjc3F7m5uUJtMjIykJGREWp7+fIlGIaBsbFxhc9pb2+PkSNHAgDWrFkDLy8v3L9/Hz179hTaT1paGioqKuDxeEJdzuPi4hAYGIiQkBC0a9cOAODn54fGjRvj7NmzGDp0KAAgPz8f27dvR+vWrQEABw8ehImJCe7fv49WrVqVyLVx40Y4OzsLCvN169bhxo0b2LJlC3bs2IHk5GQYGRnhf//7H3g8HnR0dASPDQ4Oxv3795GWlib4bDZu3IizZ8/i5MmTmDx5coWfC2EfFd2kTK6ursjJyWE1gymrr04IIYQQQqrD2rVrsWLFCqE2d3d3QTfvYgzDiPycFhYWgn8rKChAWVkZaWlpIj8+OjoakpKSgmIaAOrVq4emTZsiOjpa0CYpKYmWLVsK7hsbG6NOnTqIjo4uUXR/+/YN7969Q/v27YXa27dvL+gibm9vj+7du6Np06bo2bMn+vbtix49egAAIiIikJGRgXr16gk9Pjs7G/Hx8SK/N8IuKrpJmXJycrB582ZWM+w7MYbV1yeEEEIIIb+fq6sr5s2bJ9T236vcAGBkZAQejyfSZGlSUlJC93k8HoqKiqoWtAZYW1sjMTERFy9exLVr1zBs2DB069YNJ0+eREZGBjQ1NXHz5s0Sj6uu2dfJ70djugkhhBBCCCE1SkZGBsrKykK30opuVVVV2NnZYceOHcjMzCyx/VfX3ZaWlkZhYaFQm4mJCQoKCnDv3j1B26dPnxAbGwtT0//rf1lQUICHDx8K7sfGxuLLly8wMTEp8TrKysrQ0tJCSEiIUHtISIjQcyorK2P48OHYt28fjh8/jlOnTiE9PR3W1tZITU2FpKQkDA0NhW5qamq/9N5JzaOimxBCCCGEECK2duzYgcLCQrRq1QqnTp1CXFwcoqOj4eXlhbZt2/7Sc+rq6iIjIwNBQUH4+PEjsrKyYGRkhAEDBmDSpEkIDg5GREQExowZg4YNG2LAgAGCx0pJSWHWrFm4d+8eHj16BHt7e7Rp06bU8dwAsHDhQqxbtw7Hjx9HbGwsXFxcEB4ejjlz5gAAPD09cezYMcTExODFixc4ceIENDQ0UKdOHXTr1g1t27bFwIEDceXKFbx69Qp3797FkiVLhAp/It6o6CaEEEIIIYSILX19fTx+/BidO3fG/PnzYWZmhu7duyMoKAi7du36peds164dpk6diuHDh0NdXR3r168HAPj4+KBFixbo27cv2rZtC4ZhcOHCBaGu6/Ly8nB2dsaoUaPQvn17KCoq4vjx42W+1uzZszFv3jzMnz8f5ubmuHTpEgIDA2FkZAQAUFJSwvr162FjY4OWLVvi1atXuHDhAvh8Png8Hi5cuICOHTvCwcEBTZo0wYgRI5CUlIQGDRr80nsnNY/HVGZ2AsI5Tk5OvzwuuyqP/V32NeL+mO76BeI/lqgiz2R4bEeoEq0CbucHAKla8D+1WmEB2xGqpK5EHtsRqqyp7We2I1TZp6fSbEeokswMbucHgMYtv7MdocpehKiyHaFKGul8YTtClTUMvc52BEJqDF3pJoQQQgghhBBCqgkV3YQQQgghhBBCSDWhopsQQgghhBBCCKkmVHQTQgghhBBCCCHVRJLtAKR6ycrKwsnJ6Zcem5aW9pvTVF5WLTgtlCLF/Tehn8/tWby4P5UdoFjE/XfxnS/BdoQqMWv8he0IVcf9XyOotSyseCcxpvLuG9sRqkzKkPtrAxtmfWQ7QpW8fa7MdoQqa8h2AEJqEBXdtdzatWt/+bG/WqwTQgghhBBCCPmB+5fgCCGEEEIIIYQQMUVFNyGEEEIIIYQQUk2o6CaEEEIIIYQQQqoJFd2EEEIIIYQQzuPxeDh79mylHtOxY0ccPXq0egJVUps2bXDq1Cm2Y5BqQEU3IYQQQgghRKylpqZizpw5MDQ0hKysLBo0aID27dtj165dyMrK+qXnDAwMxPv37zFixAiRH5OTkwN7e3uYm5tDUlISAwcOFOlxp0+fho2NDerUqQMFBQVYWlri8OHDQvssXboULi4uKPppxRJfX1/weDyYmJiUeM4TJ06Ax+NBV1dX5PyEHVR0E0IIIYQQQsRWQkICrKyscOXKFaxZswZPnjxBaGgoFi1ahH///RfXrl37pef18vKCg4MD+HzRS6LCwkLIyclh9uzZ6Natm8iPU1VVxZIlSxAaGorIyEg4ODjAwcEBly9fFuzTq1cvfP/+HRcvXhR6rIKCAtLS0hAaGirU7u3tDW1tbZEzEPZQ0U0IIYQQQggRW9OnT4ekpCQePnyIYcOGwcTEBPr6+hgwYADOnz+Pfv36lfq4N2/eYOTIkVBVVYWCggJsbGxw7949AMCHDx9w/fr1Eo/19PSEubk5FBQU0LhxY0yfPh0ZGRmC7QoKCti1axcmTZoEDQ0Nkd9Dp06dMGjQIJiYmMDAwABz5syBhYUFgoODBftISEigd+/e8Pf3F3qspKQkRo0ahQMHDgi9t5s3b2LUqFFC+8bHx2PAgAFo0KABFBUV0bJlyxInJXR1dbFmzRo4OjpCSUkJ2tra2Lt3r8jvhVQeFd2EEEIIIYSQGpWbm4tv374J3XJzc0vs9+nTJ1y5cgUzZsyAgoJCqc/F4/FKtGVkZMDW1hZv375FYGAgIiIisGjRIkHX7eDgYMjLy5fots3n8+Hl5YXnz5/j4MGDuH79OhYtWvQb3vH/YRgGQUFBiI2NRceOHYW2tWrVCnfu3CnxGEdHRwQEBAi60vv6+qJnz55o0KCB0H4ZGRno3bs3goKC8OTJE/Ts2RP9+vVDcnKy0H6bNm2CjY0Nnjx5gunTp2PatGmIjY39re+T/B9JtgMQ8SUrKwsnJydWM+iy+uqEEEIIIaQ6rF27FitWrBBqc3d3x/Lly4XaXr58CYZh0LRpU6F2NTU15OTkAABmzJiBdevWCW0/evQoPnz4gAcPHkBVVRUAYGhoKNielJSEBg0alOhaPnfuXMG/dXV18ffff2Pq1KnYuXPnL73Pn339+hUNGzZEbm4uJCQksHPnTnTv3l1oHy0tLbx+/RpFRUVC2aysrKCvr4+TJ09i7Nix8PX1haenJxISEoQe37x5czRv3lxwf9WqVThz5gwCAwMxc+ZMQXvv3r0xffp0AICzszM2b96MGzdulPicye9BRTcp09q1a9mOgK2nxrAdgRBCCCGE/Gaurq6YN2+eUJuMjIzIj79//z6KioowevToUq+Qh4eHw8rKSlBw/1d2djZkZWVLtF+7dg1r165FTEwMvn37hoKCAuTk5CArKwvy8vIV5kpOToapqang/uLFi7F48WIAgJKSEsLDw5GRkYGgoCDMmzcP+vr66NSpk2B/OTk5FBUVITc3F3JyckLP7ejoCB8fH2hrayMzMxO9e/fG9u3bhfbJyMjA8uXLcf78eaSkpKCgoADZ2dklrnRbWFgI/s3j8aChoYG0tLQK3x/5NVR0E0IIIYQQQmqUjIyMSEW2oaEheDxeia7P+vr6AFCiMC1WVnsxNTU1fP78Wajt1atX6Nu3L6ZNm4bVq1dDVVUVwcHBmDBhAvLy8kQqurW0tBAeHi64/3PRz+fzBVfbLS0tER0djbVr1woV3enp6VBQUCg1/+jRo7Fo0SIsX74cY8eOhaRkyVJuwYIFuHr1KjZu3AhDQ0PIyclhyJAhyMvLE9pPSkpK6D6PxxOaNZ38XjSmmxBCCCGEECKW6tWrh+7du2P79u3IzMwU+XEWFhYIDw9Henp6qdutrKyQmpoqVHg/evQIRUVF2LRpE9q0aYMmTZrg3bt3lcorKSkJQ0NDwa2sK+0ABFe0f/bs2TNYWVmVur+qqir69++PW7duwdHRsdR9QkJCYG9vj0GDBsHc3BwaGhp49epVpd4D+f2o6CaEEEIIIYSIrZ07d6KgoAA2NjY4fvw4oqOjERsbiyNHjiAmJgYSEhIlHjNy5EhoaGhg4MCBCAkJQUJCAk6dOiVYdsvKygpqamoICQkRPMbQ0BD5+fnYtm0bEhIScPjwYezevbvEc0dFRQkK+q9fvyI8PFzo6nZp1q5di6tXryIhIQHR0dHYtGkTDh8+jDFjhIdS3rlzBz169CjzeXx9ffHx40cYGxuXut3IyAinT59GeHg4IiIiMGrUKLqCLQaoezkhhBBCCCFEbBkYGODJkydYs2YNXF1d8ebNG8jIyMDU1BQLFiwQTAj2M2lpaVy5cgXz589H7969UVBQAFNTU+zYsQPAj+W5HBwc4Ofnh759+wL4MQmZp6cn1q1bB1dXV3Ts2BFr167FuHHjhJ67d+/eSEpKEtwvvjLNMEyZ7yEzMxPTp0/HmzdvICcnB2NjYxw5cgTDhw8X7PP27VvcvXsXR44cKfN55OTkyu067+npCUdHR7Rr1w5qampwdnbGt2/fytyf1AweU95vByEs26rN/YnUZGrBEaZSyO03UYSSS4lwjWItOEudV8qSLlzSVjuF7QhVpqjL7WMZAHjyJa9ocUn+u3y2I1SZrLUa2xGqLDfyI9sRquTtc2W2I1SZxatzbEdAamoqmjVrhsePH0NHR4ftOHB2dsbnz59pzexaiLqXE0IIIYQQQv44Ghoa8Pb2LjGzN1vq16+PVatWsR2DVAPqXk4IIYQQQgj5Iw0cOJDtCALz589nOwKpJnSlmxBCCCGEEEIIqS4MIX+wnJwcxt3dncnJyWE7yi/hen6GofcgDrien2HoPYgDrudnGHoP4oDr+RmG3gMhpCSaSI380b59+wYVFRV8/foVysrcm5SE6/kBeg/igOv5AXoP4oDr+QF6D+KA6/kBeg+EkJKoezkhhBBCCCGEEFJNqOgmhBBCCCGEEEKqCRXdhBBCCCGEEEJINaGim/zRZGRk4O7uDhkZGbaj/BKu5wfoPYgDrucH6D2IA67nB+g9iAOu5wfoPRBCSqKJ1AghhBBCCCGEkGpCV7oJIYQQQgghhJBqQkU3IYQQQgghhBBSTajoJoQQQgghhBBCqgkV3YQQQgghhBBCSDWhopsQQgghhBBCCKkmVHQTQsgf6sKFC7h8+XKJ9suXL+PixYssJPrzfP36Fenp6SXa09PT8e3bNxYSEcIOOhbYk5KSgiNHjuDChQvIy8sT2paZmYmVK1eylIyQ2oOKbkIIqQRvb+9yt3///h0TJ06soTRV4+LigsLCwhLtDMPAxcWFhUR/nhEjRsDf379Ee0BAAEaMGMFCoj/PgwcPcO/evRLt9+7dw8OHD1lIJJpv376JfOMCLh4LdevWhaqqqkg3cfXgwQOYmppixowZGDJkCJo1a4bnz58LtmdkZGDFihUsJiSkdqB1uskfZ+3atWjQoAEcHR2F2g8cOIAPHz7A2dmZpWTlmzdvnsj7enp6VmOSqnvz5g3q1KkDRUVFofb8/HyEhoaiY8eOLCWrmIqKCjp06ID9+/dDQ0NDaNvly5cxadIk1K1bFxERESwlFJ2cnByio6Ohq6sr1P7q1Ss0a9YMmZmZ7AQTQUpKCoKCgqCqqopu3bpBWlpasC0zMxObNm2Cm5sbiwlFo6qqipCQEJiYmAi1x8TEoH379vj06RNLySonLi4ON27cQFpaGoqKioS2ifvPoVWrVli0aBGGDBki1H769GmsW7eu1IJcHPD5fPB4vHL3YRgGPB6v1JNr4oaLx8LBgwdF3nf8+PHVmOTXde/eHY0bN8b+/fuRmZkJZ2dnBAQE4OrVq7CyssL79++hpaXFid8hQsSZJNsBCKlpe/bswdGjR0u0N2vWDCNGjBDbovvJkyci7VfRlzA2paSkYMCAAXj06BF4PB5GjRqFnTt3Corv9PR0dO7cWaz/uEdERMDBwQHNmjXD9u3bMXLkSHz//h1z587F4cOHsWDBAs5cFVBRUUFCQkKJovvly5dQUFBgJ5QIHjx4gB49eqCoqAj5+flo2LAhzp49i2bNmgH4vysz4l7sAUBubi4KCgpKtOfn5yM7O5uFRJW3b98+TJs2DWpqatDQ0BD6P4jH44n9zyEqKgrW1tYl2q2srBAVFcVCItHcuHGD7Qi/FRePBXEtpCvj0aNH2LFjB/h8PpSUlLBz505oa2uja9euuHz5MrS1tdmOSEitQEU3+eOkpqZCU1OzRLu6ujpSUlJYSCSa2vAFy8XFBXw+H/fu3cOXL1/g4uKCzp0748qVK6hbty6AH1dmxJmuri5u3LiBLVu2YNKkSfDz88PTp0+hqKiIkJAQtGzZku2IIhswYADmzp2LM2fOwMDAAMCPgnv+/Pno378/y+nKtnjxYgwaNEjoyoytra3gygyXtGrVCnv37sW2bduE2nfv3o0WLVqwlKpy/v77b6xevVpsT1hWREZGBu/fv4e+vr5Qe0pKCiQlxfdrkq2tLdsRfqvacCwUy8nJKTE2WllZmaU0FcvJyRG67+LiAklJSfTo0QMHDhxgKRUhtYv4/jUhpJo0btwYISEh0NPTE2oPCQmBlpYWS6n+DNeuXcOZM2dgY2MD4MdnPnToUHTp0gVBQUEAxPtK/c+mTJmC27dv4+zZs1BQUMC///4Lc3NztmNVyvr169GzZ08YGxujUaNGAH50/e/QoQM2bNjAcrqy1aYrM3///Te6deuGiIgIdO3aFQAQFBSEBw8e4MqVKyynE83nz58xdOhQtmP8sh49esDV1RX//PMPVFRUAABfvnzB4sWL0b17d5bTVU5WVhaSk5NLFHwWFhYsJRId14+Fn7tml9YVXlx7cJmZmeHu3bslfkcWLFiAoqIijBw5kqVkhNQuVHSTP86kSZMwd+5c5Ofno0uXLgB+/GFftGgR5s+fz3I60T18+BABAQGlfsE6ffo0S6nK9/XrV8EVbeDHFabTp09j6NCh6Ny5M44cOcJiOtGFhITAwcEBkpKSuHTpEvbv34+2bdti9erVmDNnDtvxRKaiooK7d+/i6tWriIiIgJycHCwsLMR6TH2x2nJlpn379ggNDcWGDRsQEBAg+Bl4e3vDyMiI7XgiGTp0KK5cuYKpU6eyHeWXbNy4ER07doSOjo6gp0R4eDgaNGiAw4cPs5xONB8+fICDg0OZqw6Ia8H3M64fC4sWLcKNGzewa9cujB07Fjt27MDbt2+xZ88eeHh4sB2vTOPGjcOtW7dKPX4XLVoEhmGwe/duFpIRUsswhPxhioqKmEWLFjGysrIMn89n+Hw+Iy8vz6xYsYLtaCI7duwYIyUlxfTt25eRlpZm+vbtyzRp0oRRUVFh7O3t2Y5XJnNzc+bkyZMl2vPz85mBAwcy2traDJ/PZyGZ6ObNm8dIS0sz8+bNY7KzswXt/v7+jJqaGmNra8skJCSwmLBqioqKmAsXLjB//fUX21HK1KFDB2bXrl2lblu3bh0jIyMj9r9HFSksLGTOnTvHdgyRrFmzhlFTU2PGjx/PbNy4kdm6davQjQsyMjKYPXv2MNOnT2fmz5/PHDx4kMnLy2M7lshGjRrFtG/fnnnw4AGjoKDAXLlyhTl8+DDTtGlT5t9//2U7XpVw5Vho3Lgxc+PGDYZhGEZJSYmJi4tjGIZhDh06xPTq1YvFZIQQcUBFN/ljff/+nbl//z7z9OlTJicnh+04lWJubs5s376dYRiGUVRUZOLj45mioiJm0qRJjJubG8vpyrZo0SKmR48epW7Lz89n+vfvL/bFkoGBAXP79u1St6WmpjL9+/dnFBUVazhV1SUkJDBLly5lGjVqxMjIyDB9+vRhO1KZ9u3bx4wZM6bM7R4eHoyurm4NJvp94uLiGFdXV0ZTU5ORlJRkO45IdHV1y7zp6emxHe+XRUVFMfPnz2c7hkg0NDSYe/fuMQzzo+CLjY1lGIZh/vnnH6Z9+/ZsRvtlXDsWFBQUmKSkJIZhGKZhw4aCn0dCQgKjoKDAZjSR5efnM1evXmV2797NfPv2jWEYhnn79i3z/ft3lpMRwn1UdBPCQfLy8kxiYiLDMAyjqqrKREZGMgzz40uihoYGi8nKl5+fz3z9+rXc7a9evarBRJWXmZlZ4T6HDh2qgSRVl5OTwxw5coTp3LkzIyUlxfD5fMbT07PcnxH5/bKyspiDBw8yHTp0YPh8PmNra8vs2rWLSU1NZTvaHycjI4PZv38/07ZtW4bH4zHNmjVjO5JIlJSUBH8TtLW1meDgYIZhfhR8cnJyLCarHC4fC+bm5szNmzcZhmGYrl27Ck7YbN26lWnYsCGb0UTy6tUrxtjYmJGXl2ckJCSY+Ph4hmEYZvbs2cyUKVNYTkcI99GYbvLH6dy5c7mTdV2/fr0G0/yaunXr4vv37wCAhg0b4tmzZzA3N8eXL1+QlZXFcrqySUpKCs3gmpeXh8TERBgYGEBSUhKSkpLQ0dFhMWHF5OXlS20vLCzE06dPoaOjg7Fjx9Zwqsp59OgRvL29cezYMRgaGmLs2LE4duwYGjVqBDs7O7GeZfe/CgoKcPPmTcTHx2PUqFFQUlLCu3fvoKysXGIdeHHz4MED7N+/H/7+/jAwMMDo0aNx9+5d7Ny5E6ampmzHq7T/Hs9cEhISAm9vbwQEBCA7OxtOTk44cOAAjI2N2Y4mkqZNmyI2Nha6urpo3rw59uzZA11dXezevbvU1TrETW04FhwcHBAREQFbW1u4uLigX79+2L59O/Lz8+Hp6cl2vArNmTMHNjY2iIiIQL169QTtgwYNwqRJk1hMRkjtwK2/ioT8BpaWlkL38/PzER4ejmfPnnFmzc2OHTvi6tWrMDc3x9ChQzFnzhxcv34dV69eFcz6Ks6ysrIwa9YsHDx4EADw4sUL6OvrY9asWWjYsCFcXFxYTlixuXPnwtzcHBMmTEBhYSFsbW1x9+5dyMvL499//0WnTp3Yjlim1q1bY9asWQgLC0PTpk3ZjvPLkpKS0LNnTyQnJyM3Nxfdu3eHkpIS1q1bh9zcXLGe/MfCwgLfvn3DqFGjcPfuXcEa41z43f8vrh7PaWlp8PX1xYEDB/D161eMHDkSN2/eRNu2beHo6MiZghv4UTAVL3np7u6Onj17ws/PD9LS0vD19WU3XAVqy7Hg5OQk+He3bt0QExODR48ewdDQkBOzx9+5cwd3796FtLS0ULuuri7evn3LUipCag8quskfZ/PmzaW2L1++HBkZGTWc5tds375dMHvzkiVLICUlhbt37+Kvv/7C0qVLWU5XMVdXV0RERODmzZvo2bOnoL1bt25Yvnw5J75snTx5EmPGjAEAnDt3DomJiYiJicHhw4exZMkShISEsJywbF27doW3tzfS0tIwduxY2NnZcWaptp9x+cpMbGwshg8fjs6dO3PmSl5ZuHo86+joYMiQIdi6dSu6d+8OPp/PdqRfVvx/EQC0aNECSUlJiImJgba2NtTU1FhMVjGuHwvZ2dkICgpC3759Afw4HnJzcwXbw8LCsHLlSsjKyrIVUSRFRUWlznL/5s0bKCkpsZCIkNqFim5C/r8xY8agVatW2LhxI9tRKqSqqir4N5/PF9svtWU5e/Ysjh8/jjZt2ggVe82aNUN8fDyLyUT38eNHaGhoAAAuXLiAoUOHokmTJnB0dMTWrVtZTle+y5cv4/Xr1/Dx8cG0adOQnZ2N4cOHA+DOOukAt6/MJCQkwNfXV/D5jxw5EqNHj+bU51+Mq8ezjo4OgoODoa2tDR0dHU5d2a6IvLw8rK2t2Y4hEq4fCwcPHsT58+cFRff27dvRrFkzyMnJAQBiYmKgqakpdCVcHPXo0QNbtmzB3r17Afz4W5CRkQF3d3f07t2b5XSEcB93T+sS8puFhoaK/Zno/3r+/DkiIyMFt+fPn7MdSSQfPnxA/fr1S7RnZmZy5otWgwYNEBUVhcLCQly6dAndu3cH8KOrrYSEBMvpKta4cWO4ubkhMTERhw8fxocPHyApKYkBAwZg8eLFePz4MdsRK8TlKzMNGzbEkiVL8PLlSxw+fBipqalo3749CgoK4OvrixcvXrAdUWRcPZ5jYmJw5MgRpKSkoGXLlmjRooWgJ5Q45/7Zly9fsGvXLsH90aNHY/DgwYLb0KFD8eXLF/YCioDrx4Kfnx8mT54s1Hb06FHcuHEDN27cEKw7Lu42bdqEkJAQmJqaIicnB6NGjRKcwFy3bh3b8QjhPCq6yR/n5y8kgwcPxqBBg9CmTRs4ODhgypQpbMcr1507d9CyZUvB/TZt2sDKygqWlpawtLSEhYUFrl27xmJC0djY2OD8+fOC+8VfcPfv34+2bduyFatSHBwcMGzYMJiZmYHH46Fbt24AgHv37nHuiln37t1x9OhRvHv3DrNmzcLFixeFfs/EVfGVmWJcvTLTpUsXQfG3fft2XL9+HcbGxpwYBwpw+3hu3749Dhw4gJSUFEydOhUnTpxAYWEhpk+fjn379uHDhw9sRyzXvn37EBwcLLgfGBgIPp8PFRUVqKio4OnTp0LHiLjj4rHw8uVLmJubC+7LysoKDVVo1aoVoqKi2IhWKY0aNUJERAQWL14MJycnWFlZwcPDA0+ePCn1pBohpHJ4DMMwbIcgpCYkJCRAV1cXEyZMEGrn8/lQV1dHly5d0KNHD5bSiWbkyJFo27YtZs+eDQBQUlLC+fPnoaOjA4Zh4OXlhaSkJJw6dYrlpOULDg5Gr169MGbMGPj6+mLKlCmIiorC3bt3cevWLbRo0YLtiCI5efIkXr9+jaFDh6JRo0YAfnQ1rFOnDgYMGMByuqp5/Pix2HdPff36NXr27AmGYRAXFwcbGxvExcVBTU0Nt2/f5vQXxfDwcBw4cABeXl5sR6lQbTmei0VHR8Pb2xuHDx9Geno68vPz2Y5UptatW2P16tWCk35KSkqIiIiAvr4+AODMmTNYuXIlnjx5wmbMKhH3Y0FOTg7h4eFlTkoZExMDS0tLwTwshJA/ExXd5I8hISGBlJQUwRfx4cOHw8vLCw0aNGA5meiMjIxw5swZmJmZASj5BevJkyfo06cP3r17x2ZMkcTHx8PDwwMRERHIyMiAtbU1nJ2dha4YkOqRnJws0n7a2trVnKTqCgoKcPz4caHfo9GjRwvGU5KaURuP5/z8fJw7dw6DBw9mO0qZ1NXV8fjxYzRu3BjAj14HZ8+eFZwETEhIgIWFBWcmCeUiIyMjeHh44K+//ip1e0BAABYvXoyXL1/WcLLKi42NxbZt2xAdHQ0AMDExwcyZMznXe4sQcURFN/lj8Pl8pKamCopuZWVlhIeHCwpWLpCTk8OLFy8EX7BOnz6Nnj17CtaOTkpKQpMmTYRmTiW/j5eXFyZPngxZWdkKr7oU90YQR3w+v9QxqwzDCNp5PB4KCgpqOprI8vPzYWxsjH///RcmJiZsx6m0zp07VzhumMfjISgoqIYSEeDH79WrV69Qv359qKiosB2nQvLy8rh//77gROx/PX36FK1bt0ZWVlYNJxMd14+FOXPm4Nq1a3j06FGJeWGys7NhY2ODbt26if0Em6dOncKIESNgY2MjGBYSFhaGBw8ewN/fv8yTCoQQ0dDs5eSPxcXzTUpKSoiPjxcU3f+9ApOYmAhlZWU2olVaYWEhzpw5IzijbmpqigEDBkBSUnz/W9q8eTNGjx4NWVnZMpeeA358QRTnorusrqYMw8Df3x9eXl5QVFSs4VSVIyUlxenumpaWlmVu+/79O44ePcqZk2ffvn0rtZ3H40FGRqbE7PLiYv369Zg1axbk5ORQWFgIZ2dnbNu2DQUFBeDz+Rg7diz27NkDKSkptqOWSV9fH48fPy6z6H748CH09PRqOFXlcP1YWLx4MQICAtC0aVPMnDkTTZo0AfDjqvH27dtRUFCAxYsXs5yyYosWLYKrqytWrlwp1O7u7o5FixZR0U1IVTGE/CH4fD6TlpYmuK+oqMgkJCSwmKjy+vbtyzg4OJS5ffz48UyfPn1qMNGvefbsGaOvr8/Iy8szVlZWjJWVFaOgoMDo6uoyT58+ZTveH+nq1atMixYtGCUlJcbd3Z359u0b25EqtHr1amb8+PFMfn4+21F+i/z8fGbLli2Muro6Y2hoyBw7doztSCLh8XgMn88v86atrc24ubkxhYWFbEcVwufzmffv3zMMwzAbNmxg6tatyxw4cIB5/vw5c+TIEaZ+/frMunXrWE5ZvqVLlzKNGzdmUlNTS2xLSUlhGjduzCxZsoSFZFXDtWMhISGBsbOzY/h8PsPj8QTHhJ2dHRMfH892PJHIyckxcXFxJdpfvHjByMnJsZCIkNpFfC8pEfKbMQwDe3t7yMjIAABycnIwdepUKCgoCO13+vRpNuKJZN68eejWrRvq1auHhQsXCrrKp6WlYd26dThy5AiuXLnCcsqKTZw4Ec2aNcPDhw9Rt25dAMDnz59hb2+PyZMn4+7duywnrFhCQgKnhiaU5fHjx3B2dsadO3cwceJEXLhwgTMTkD148ABBQUG4cuUKzM3NOXUs/5efnx/c3NyQnZ2N5cuXY/LkyWLd6+Nnvr6+WLJkCezt7dGqVSsAwP3793Hw4EEsXboUHz58wMaNGyEjIyNWV/yYn3o7HT16FB4eHnBwcADwo+cNAKxduxaLFi1iJZ8oFi1ahFOnTsHIyAhjx44Vusp65MgRNGzYEM7OziynrBwuHgt6enq4dOkS0tPTBWO3DQ0NoaqqynIy0XXq1Al37tyBoaGhUHtwcDA6dOjAUipCag8a003+GMVfpiri4+NTzUmqZufOnXByckJBQQGUlZXB4/Hw9etXSEpKYtOmTZg5cybbESskJyeHhw8folmzZkLtz549Q8uWLZGdnc1SMtHx+Xw0atQItra26NSpE2xtbUt8WRFn8fHxWLx4MU6dOoVhw4bh77//5txJhIqOaXE/lgHg0qVLcHFxQWJiIhYsWIB58+aVOHkg7rp27YopU6Zg2LBhQu0BAQHYs2cPgoKCcPjwYaxevRoxMTEspSyJz+fj/fv3UFdXh5qaGm7evCnUTTsxMRFmZmbIzMxkMWXFPn/+DFdXVwQEBAjW5K5Tpw6GDRuGNWvWcKbwqw3HApft3r0bbm5uGDZsGNq0aQPgx5juEydOYMWKFdDS0hLs279/f7ZiEsJZVHQTwkGvX7/GyZMnERcXB+DH7KlDhgwRjPUWd82bN8fmzZvRpUsXofbr169jzpw5ePr0KUvJRPf27VvcvHkTt27dwq1btxAXFwctLS3Y2tqic+fOmDhxItsRyzR9+nR4e3ujc+fO8PDwKHdMJake9+/fh7OzM8LCwjB16lQsWbIEampqbMf6JXJycoiMjISRkZFQe1xcHJo3b46srCwkJiaiWbNmYjWhF5/Px99//w1FRUV4eHjA398fHTt2FGyPjIxEp06dkJ6ezmJK0TEMI1hXXF1dvcLJycRFbToWuOzntcXLw+PxUFhYWM1pCKl9qOgmhNS4CxcuYNGiRVi+fLnQGfWVK1fCw8MD//vf/wT7cmViuLi4OKxevRp+fn4oKioS6y8lfD4fsrKyFS4D8/jx4xpKVDUfPnxAbGwsAKBp06ZQV1dnOVHF+Hw+5OTkMHny5HInuhLnCfmKNWnSBIMHD4aHh4dQu4uLC86cOYPY2Fg8fPgQAwYMwNu3b1lKWZKurq5QYTpnzhzMnTtXcH/r1q3w9/dHaGgoC+n+HLXpWCCEkLJQ0U0IB+Xm5uLevXtISkpCVlYW1NXVYWVlJfaz1Bb7+Yx68Zfe4v+Kfr4vzmfUs7KyEBwcjJs3b+LmzZt48uQJjI2N0alTJ3Tq1AkDBgxgO2KZVqxYIdJ+7u7u1ZykajIzMzFr1iwcOnQIRUVFAAAJCQmMGzcO27ZtEyylJ47+W/CVhsfjISEhoYYS/brAwEAMHToUxsbGaNmyJYAfs2bHxMTg5MmT6Nu3L3bt2oW4uDh4enqynFZ0YWFhkJGRgZWVFdtRyhUdHQ1/f3/cuXOnxN+EHj16YMiQIYK5TMRRbToWuKy2zFNCiLiiopsQDgkJCcHWrVtx7tw55OfnQ0VFBXJyckhPT0dubi709fUxefJkTJ06FUpKSmzHLdPNmzdF7vpoa2tbzWl+jbS0NOrWrYvRo0ejU6dO6NChg2BSOFIzpkyZgmvXrmH79u1o3749gB+T/syePRvdu3fHrl27WE7450hMTMTevXuFehxMmTIFurq67AarxR4/foxFixYhODgY7du3R6tWraClpSX4m/Ds2TPcuXMH3759w6JFizB37lyxLr4Ju/h8PmxtbTFhwgQMGTKkxJrjhJCqoaKbEI7o378/Hj9+jFGjRqFfv36wsbGBnJycYHtCQgLu3LmDY8eOISIiAocOHUL37t1ZTFy7DRw4EMHBwZCWlhZc3e7UqZNg9mBS/dTU1HDy5El06tRJqP3GjRsYNmyYYHwrIeU5dOhQudvHjRtXQ0kqR09PDwsXLsSoUaNQp06dMvcLDQ3F1q1bYWFhIVazxxPxEh4eDh8fHxw7dgx5eXkYPnw4JkyYIFiRgBBSNVR0E8IRe/bsgaOjI6SkpCrcNyoqCikpKejatWsNJKs8PT09ODg4wN7eHtra2mzHqZLIyEjBZGp37tyBpKQkOnXqBD8/P7ajlcrKykrkXgbiPqZbXl4ejx49gomJiVD78+fP0apVK7GdddrLy0vkfcV1HGtkZCTMzMzA5/MRGRlZ7r4WFhY1lOrX/LeHSn5+PrKysiAtLQ15eXmxnUgtPz9fpL8Hv7p/TagNx0JtU1BQgMDAQPj6+uLSpUto0qQJHB0dMXbsWE7Ml0GIuKKimxBS47Zs2QJfX188e/YMnTt3xoQJEzBo0CBOdn1kGAZPnjzBjRs3cOPGDVy+fBkMw6CgoIDtaKUSdTw3IP5jurt27Yp69erh0KFDgq6Q2dnZGD9+PNLT03Ht2jWWE5ZO1LkXxHkcK5/PR2pqKurXrw8+nw8ej4fSvk6I87wM5YmLi8O0adOwcOFC2NnZsR2n1qoNx0JtlZubi507d8LV1RV5eXmQlpbGsGHDsG7dOmhqarIdjxDOoaKbEMKax48fw9fXF8eOHUNhYSFGjRoFR0dHWFtbsx2tQp6enrh58yaCg4Px/ft3NG/eHB07dqTx3TXo2bNnsLOzQ25uLpo3bw4AiIiIgKysLC5fvlxiHXjy+yQlJUFbWxs8Hg9JSUnl7qujo1NDqX6vhw8fYsyYMWK1tnhpoqOjERYWhrZt28LY2BgxMTHYunUrcnNzMWbMmBJLMxJSnocPH+LAgQPw9/eHgoICxo8fjwkTJuDNmzdYsWIFvn37hvv377MdkxDOoaKbEI7ZuXMnTp8+DVVVVUyZMkWoC/nHjx/RqlUrzl0RyM/Px86dO+Hs7Iz8/HyYm5tj9uzZcHBwENu1Zlu2bAlbW1tBka2iosJ2pD9SVlYW/Pz8BIWRiYkJRo8eLTTfAalet2/fRrt27SApKSnUXlBQgLt37wqtfc0l4eHh6NixI759+8Z2lDJdunQJAwYMgKKiIrKysnDmzBmMGzcOzZs3R1FREW7duoUrV65Q4U0q5OnpCR8fH8TGxqJ3796YOHEievfuLbTayJs3b6Crqyu2PbkIEWdUdBPCIV5eXnB1dYWDgwO+fv2KgIAALF++HK6urgCA9+/fQ0tLizPdOfPz83HmzBn4+Pjg6tWraNOmjeCM+o4dO9ClSxccPXqU7Zi1VmFhITZv3oyAgAAkJycjLy9PaLu4jmWtbd68eYPAwMBSfwZcWGJLQkICKSkpqF+/vlD7p0+fUL9+fbH//ygwMFDoPsMwSElJwfbt29G4cWNcvHiRpWQVa9euHbp06YK///4b/v7+mD59OqZNm4bVq1cDAFxdXfHo0SNcuXKF5aSi4fqxwGVGRkZwdHSEvb19md3H8/LycOzYMYwfP76G0xFSCzCEEM4wNTVl/Pz8BPdDQkIYdXV1ZtmyZQzDMExqairD5/PZiieyR48eMTNnzmTq1avHqKurM/Pnz2eio6OF9nn69CkjKyvLUkLR3L59mxk9ejTTpk0b5s2bNwzDMMyhQ4eYO3fusJxMNMuWLWM0NTWZjRs3MrKyssyqVauYCRMmMPXq1WO2bt3KdrwKrVmzhvH29i7R7u3tzXh4eLCQqPKuXbvGyMvLM2ZmZoykpCRjaWnJ1KlTh1FRUWE6d+7MdjyR8Hg8Ji0trUR7bGwso6SkxEKiyuHxeEI3Pp/PNGjQgBk5ciTz7t07tuOVS1lZmYmLi2MYhmEKCwsZSUlJ5vHjx4LtT58+ZRo0aMBWvEqpDccClyUmJjKFhYUl2ouKipikpCQWEhFSu0hWXJYTQsRFYmIi2rVrJ7jfrl07XL9+Hd26dUN+fj7mzp3LXrhKaNmypWAd5YEDB5Y6o66enh5GjBjBQjrRnDp1CmPHjsXo0aPx5MkT5ObmAgC+fv2KNWvW4MKFCywnrJifnx/27duHPn36YPny5Rg5ciQMDAxgYWGBsLAwsZ8teM+ePaX2hGjWrBlGjBgBZ2dnFlJVjqurKxYsWIAVK1ZASUkJp06dQv369TF69Gj07NmT7XjlGjx4MIAfk1zZ29sLTYRYWFiIyMhIof+vxFVRURHbEaqkeAgOn8+HrKys0FAXJSUlfP36la1olcLlY6E2MDAwKLXHSnp6OvT09MS+xwoh4o5f8S6EEHGhpqaG169fC7WZmZnh+vXr8PHxwaJFi1hKVjkJCQm4dOkShg4dWuYSNgoKCvDx8anhZKL7+++/sXv3buzbt0/oPbRv317sl9oqlpqaCnNzcwCAoqKi4Mt53759cf78eTajiSQ1NbXUbpDq6upISUlhIVHlRUdHC9aBlpSURHZ2NhQVFbFy5UqsW7eO5XTlU1FRgYqKChiGgZKSkuC+iooKNDQ0MHnyZBw5coTtmCLLy8tDbGwsp8ar6urqIi4uTnA/NDRUaBnG5ORkzsw0zeVjoTZgyhhtmpGRIVgdghDy6+hKNyEc8r///Q+nT59Ghw4dhNpNTU0RFBSEzp07s5Sscjp37owHDx6gXr16Qu1fvnyBtbU1JyaCi42NLXWCKBUVFXz58qXmA/2CRo0aISUlBdra2jAwMMCVK1dgbW2NBw8ecGL5tsaNGyMkJKTEskMhISHQ0tJiKVXlKCgoCMauampqIj4+XjDr+sePH9mMVqHik2K6urpYsGABFBQUWE70a7KysjBz5kwcOnQIAPDixQvo6+tj1qxZaNiwIVxcXFhOWLZp06YJXYE0MzMT2n7x4kXOTKLG5WOBy+bNmwfgR48JNzc3yMvLC7YVFhbi3r17sLS0ZCkdIbUHFd2EcIiLiwsePXpU6rZmzZrh+vXrOHXqVA2nqrxXr16V2lUtNzcXb9++ZSFR5WloaODly5fQ1dUVag8ODoa+vj47oSpp0KBBCAoKQuvWrTFr1iyMGTMG3t7eSE5OhpOTE9vxKjRp0iTMnTsX+fn5gsIiKCgIixYtwvz581lOJ5o2bdogODgYJiYm6N27N+bPn4+nT5/i9OnTaNOmDdvxRCLu67lXxNXVFZGRkbh586ZQN+Zu3bph+fLlYl10T506tdzta9asqaEkVVcbjgUuevLkCYAfV7qfPn0KaWlpwTZpaWk0b94cCxYsYCseIbUGzV5OCKkxxbMEDxw4EAcPHhQae1hYWIigoCBcvXoVsbGxbEUU2dq1a3HkyBEcOHAA3bt3x4ULF5CUlAQnJycsW7YMs2bNYjtipYWGhiI0NBRGRkbo168f23EqxDAMXFxc4OXlJbhCJisrC2dnZ7i5ubGcTjQJCQnIyMiAhYUFMjMzMX/+fNy9exdGRkbw9PTkxBrX79+/x4IFCxAUFIS0tLQS3VTFfSyojo4Ojh8/jjZt2kBJSQkRERHQ19fHy5cvYW1tLdZLhpXm2LFj6N+/P+d6HtSGY4HLHBwcsHXrVigrK5e735s3b6ClpSW0lBghpGJUdBPCcebm5rhw4QIaN27MdpQKFf+R5vF4Jb6YS0lJQVdXF5s2bULfvn3ZiFcpDMNgzZo1WLt2LbKysgAAMjIyWLBgAVatWsVyuj9LRkYGoqOjIScnByMjoxJd4+lLYvXq1asXkpOTMXPmTGhqagom9io2YMAAlpKJRl5eHs+ePYO+vr5Q0R0REYGOHTtyZiKyYsrKyggPD+dMjxvCLfT7Rcivoe7lhHDcq1evkJ+fz3YMkRTPEqynp4cHDx5ATU2N5US/jsfjYcmSJVi4cCFevnyJjIwMmJqaQlZWFu/evRPrMcVFRUV4/vy5YBK13bt3C62JKyEhgWnTpnGmSFVUVETLli3L3G5qair2XxK/f/8udCKKz+dDUVGRxUSiCw4Oxp07dzg77tPGxgbnz58X9E4pPmmwf/9+tG3bls1ov4Tr11K4fCz8Cbj++0UIW6joJoTUuMTERJH248JVfGlpaZiamgruR0REwNraWqy71Pr7+2P37t24ffs2AGDhwoWoU6cOJCV//En4+PEjZGVlMWHCBDZj/jbi+CUxPDwcixcvFiwtp6WlJegxAfwo/EJDQ8s9mSAuGjduLJafsajWrFmDXr16ISoqCgUFBdi6dSuioqJw9+5d3Lp1i+14tV5tOhYIIaQs3LiMQQgpU4cOHSAnJ8d2jGrBpav4XOLj44MZM2YItd26dQuJiYlITEzEhg0bOLXUExdt27YN//vf/4TaDh8+jOvXryMoKAijRo2Cl5cXS+kqZ8uWLXBxccGrV6/YjvJL/ve//yE8PBwFBQUwNzfHlStXUL9+fYSGhqJFixZsx6u0ixcvomHDhmzHEFltOhYIIaQsdKWbEI4rvjpAiKhiYmJgY2NT5nZbW1ssXry4BhP9ee7evYuZM2cKtbVp00bQBV5OTg7Dhg1jI1qlDR8+HFlZWTAwMIC8vLzQuvUAkJ6ezlIy0RkYGGDfvn1sx/gt/lvAirvadCwQQkhZqOgmpBb5/Pkzzp07h3HjxrEdhYixDx8+CN1PSEgQWjNdSkoKmZmZNR3rj5KUlAR1dXXB/ZUrVwrNcaCpqYn379+zEa3StmzZwnaEX8Ln80tM+vZfPB4PBQUFNZTo11y4cAGnT5+GqqoqHB0dYWxsLNj2+fNn/PXXX7h+/TqLCctXm46FP0FFxwwhpHRUdBNSiyQnJ8PBwYGK7moUGRlZ7nYuLHfWoEEDxMbGwsDAAACEvvACQHR0NDQ0NNiIVi3E8UuirKwskpKS0KhRIwAosS7669evIS8vz0a0Shs/fjzbEX7JmTNnytwWGhoKLy8vweSP4uro0aMYN24cevbsidjYWGzbtg379+/H6NGjAQB5eXliPy69Nh0LfwIuz99ACJuo6CaEQypaL/b79+81lOTPZWlpWeqSZ8D/LYUmjkXez7p27YrVq1ejd+/eJbYxDIO1a9eia9euLCSrHuL4JdHKygpnz55F+/btS91++vRpWFlZ1XCqqsvJyRGaCR9Ahev+sqW0pcxiY2Ph4uKCc+fOYfTo0Vi5ciULyUS3YcMGeHp6Yvbs2QCAgIAAODo6IicnhzMTIdbWY4Frvn79isLCQqiqqgq1p6enQ1JSUnAcR0VFifXqHISIKyq6CeGQOnXqlFvQcaHg4zpRZ14XZ0uWLIG1tTVat26NBQsWoEmTJgB+FBwbN25EbGwsDh06xHLK30ccvyROnz4dI0aMgK6urtDybIWFhdi5cye2bduGo0ePspxSNJmZmXB2dkZAQAA+ffpUYrs4z+Rf7N27d3B3d8fBgwdhZ2eH8PBwmJmZsR2rQnFxcejXr5/g/rBhw6Curo7+/fsjPz8fgwYNYjGdaGrTscBlI0aMQL9+/TB9+nSh9oCAAAQGBgrmjxHn1UQIEWc8RhwvARBCSqWiooIlS5agdevWpW6Pi4vDlClTOPElVxRHjx7FgAEDoKCgwHaUXzZ9+vQSYxTFwf3792Fvb4+YmBjBiRqGYWBsbAwfH58yf8fYNnjwYJH3PX36dDUmqTpnZ2ds2LABSkpKgkmjEhISkJGRgXnz5mHDhg0sJxTNjBkzcOPGDaxatQpjx47Fjh078PbtW+zZswceHh6Crs7i6OvXr1izZg22bdsGS0tLrFu3Dh06dGA7lsi0tLRw+vRptGnTRqj91q1b6Nu3L+bMmYO1a9eK/d+E2nIscJmqqipCQkJgYmIi1B4TE4P27duXekKNECI6KroJ4ZDOnTujV69eWLRoUanbIyIiYGVlJfbjEGfPng1DQ0NBl8hi27dvx8uXLzk7MVNplJWVER4eLvgiKW6ePHmCuLg4AICRkZHYd+N0cHAQ/JthGJw5cwYqKiqC2dgfPXqEL1++YPDgwfDx8WErpsjCwsJw7NgxoZ/ByJEjSxRR4kxbWxuHDh1Cp06doKysjMePH8PQ0BCHDx/GsWPHxHaFhfXr12PdunXQ0NDAmjVrSu1uLu4GDhyI5s2bY8WKFSW23bx5E3379kV2drbYF91A7TgWuExBQQFhYWEwNzcXan/69Clat24ttHY6IaTyqOgmhEP27duH7OzsEsVqsffv32P37t1wd3ev4WSV07BhQwQGBpZYA/fx48fo378/3rx5w1Ky309JSQkRERFiW3RzmbOzM9LT07F7925ISEgA+NEldfr06VBWVqarYzVEUVERUVFR0NbWRqNGjXD69Gm0atUKiYmJMDc3R0ZGBtsRS8Xn8yEnJ4du3boJfn9KI849Jm7duoW7d+/C1dW11O03btzAoUOHOHECirCrc+fOMDMzw7Zt24TaZ8yYgcjISNy5c4elZITUDlR0E0JqnKysLJ49ewZDQ0Oh9pcvX8LMzAw5OTksJfv9xLXoLioqwq1bt3Dnzh0kJSUhKysL6urqsLKyQrdu3Tgxbk9dXR3BwcFo2rSpUHtsbCzatWvHme6QycnJQj+DZs2aQUZGhu1YIrOwsMC2bdtga2uLbt26wdLSEhs3boSXlxfWr18vtifR7O3tRZoDgwrWmsP1Y4HLQkJC0K1bN7Rs2VIwkWZQUBAePHiAK1eucGrYBSHiiCZSI4TUOENDQ1y6dAkzZ84Uar948aLYFae1TXZ2NjZt2oRdu3YhPT0dlpaW0NLSgpycHF6+fImzZ89i0qRJ6NGjB9zc3MS6a2dBQQFiYmJKFN0xMTFiP8Ti1atX2LVrF/z9/fHmzRuhGdalpaXRoUMHTJ48GX/99ZdgYilx5eDggIiICNja2sLFxQX9+vXD9u3bkZ+fD09PT7bjlcnX15ftCNWCSzPIA7XrWOCy9u3bIzQ0FBs2bEBAQADk5ORgYWEBb29vGBkZsR2PEM6jK92EcFRmZiZu3bqF5OTkEl+wyup+Li4OHDiAmTNnYuHChejSpQuAH2fUN23ahC1btmDSpEksJ/x9xO1Kd+PGjdG2bVvY29uje/fukJKSKrFPUlISjh49ij179mDJkiVi+/OYN28eDh06hMWLF6NVq1YAgHv37sHDwwNjx44V24Jv9uzZglmy+/Xrh1atWglOfKSnp+PZs2e4c+cO/P39ISEhAR8fH7Rs2ZLt2CJLSkrCo0ePYGhoCAsLC7bj/BGysrKwaNEizs0gX9uPhdqgqKgIFy5cQN++fdmOQgi3MYQQznn8+DGjoaHBKCsrMxISEoy6ujrD4/EYBQUFRk9Pj+14Itm5cyfTsGFDhsfjMTwej9HT02MOHjzIdqzfTlFRkYmPj2c7hkBUVJTI++bl5TEvX76sxjRVU1hYyKxbt47R0tIS/B5paWkx69atYwoKCtiOVyYXFxfm48ePIu178eJF5tSpU9WcqGaYmZkxycnJbMeolaZPn86YmJgwJ0+eZOTk5JgDBw4wq1atYho1asQcOXKE7Xhl+lOPBS6Ii4tjXF1dGU1NTUZSUpLtOIRwHl3pJoSDOnXqhCZNmmD37t1QUVFBREQEpKSkMGbMGMyZM6dSyyqx7cOHD5CTk4OioiIAID09Haqqqiyn+n2mTZuGVatWid2SYbXNt2/fAIh3N9o/nbj1+qhNuDqDPBEv2dnZOHHiBPbv34+QkBB06NABI0aMwKBBg9CgQQO24xHCaVR0E8JBderUwb1799C0aVPUqVMHoaGhMDExwb179zB+/HjExMSwHbHSrly5gv379+PcuXPIzs5mO06pIiMjRd6XS91qv3z5ghMnTiA5ORk6OjoYOnQoVFRU2I4lkoKCAty8eRPx8fEYNWoUlJSU8O7dOygrKwtO5HAJwzAoKioqdzZtrqKiu/pwdQb5suTm5uLNmzdo1KgRTaRWAx48eID9+/fD398fBgYGGD16NJydnREZGQlTU1O24xFSK9CMFIRwkJSUlGBCmfr16yM5ORkAoKKigtevX7MZrVKSkpLg7u4OXV1dDB06FHw+H4cOHWI7VpksLS1hZWUFS0vLUm/F28R9revBgwfj5MmTAIDnz5/DyMgIS5YswdWrV7F06VIYGxsjOjqa5ZQVS0pKgrm5OQYMGIAZM2bgw4cPAIB169ZhwYIFLKcrX0FBAZYuXQpbW1vBEn8bNmyAoqIi5OXlMX78+BJzNRBSFn19fSQmJgIAjI2NERAQAAA4d+4c6tSpw2Kyivn6+iI0NBTAj0ngJkyYAAUFBTRp0gSKioqYOnUqcnNzWU5Ze1lYWGDo0KGoV68e7t69i8ePH2P+/PkizexPCBEdFd2EcJCVlRUePHgAALC1tYWbmxv8/Pwwd+5cmJmZsZyufHl5efD390e3bt1gbGyMx48f482bNwgODoa/vz+GDh3KdsQyJSYmIiEhAYmJiaXeirclJCSwHbVcN2/eFPyeLFy4ED169MCbN28QFhaG169fo0+fPpg7dy67IUUwZ84c2NjY4PPnz5CTkxO0Dxo0CEFBQSwmq9iKFSuwf/9+2NjY4OTJk5g2bRq2bduGvXv3Yt++fQgKCsKWLVvYjkk4ongGeQBwcXHBjh07ICsrCycnJyxcuJDldOVbuXKl4CTysmXLcP36dZw4cQLPnz/HyZMncePGDSxbtozllLVXbGwsOnbsiM6dO9NVbUKqE6sjygkhv+TBgwfM9evXGYZhmPfv3zN2dnaMkpISY21tzYSHh7OcrmwzZ85k6tWrx7Rp04bZvn27YAIdSUlJ5vnz5yyn+3PIyckJJkjT1NRkHj9+LLQ9NjaWUVFRYSFZ5aiqqjIxMTEMwwhPWJeYmMjIycmxGa1C+vr6zLlz5xiG+TFhEZ/PZ/z9/QXbjx8/zpiZmbEVr1qI26SCtdmrV6+YU6dOMREREWxHqZCMjAyTlJTEMAzDNGnShLl48aLQ9lu3bjHa2tpsRPsjvHnzhvn7778ZAwMDRktLi5k/fz7z+PFjRkpKiv4uE/Ib0TrdhHCQjY2N4N/169fHpUuXWEwjul27dsHZ2RkuLi5QUlJiO85vERUVVeqybf3792cpUcUsLCxw/fp1GBgYQENDA0lJSUJd4pOSkoSuHIuroqKiUpdCevPmjdj/fr179w7NmzcH8GPdemlpacF9AGjZsiWSkpLYikc4TkdHBzo6OmzHEImGhgbi4+Ohra2NzMzMEpNOqqurl7oMGvk9GjZsiCVLlmDJkiW4fv06Dhw4gPbt26OgoAC+vr6YOHEimjRpwnZMQjiPupcTQmrM4cOHcf/+fWhqamL48OH4999/xXb92IokJCSgefPmMDMzQ58+fTBw4EAMHDgQgwYNwqBBg9iOV65ly5bBxcUFvr6+mD17NpycnODt7Y27d+/Cx8cHEyZMwNixY9mOWaEePXoIdcHm8XjIyMiAu7s7evfuzV4wEaioqODLly+C+9bW1kInCnJzc8V+TGWXLl2E3kNF9uzZQzMg/0b+/v4i7/v69WuEhIRUY5pfN3r0aCxZsgRfvnzB2LFjsXLlSsHEb1lZWVi+fDnat2/Pcso/Q5cuXXDkyBGkpKRg+/btuH79OoyNjTk1MSghYovtS+2EENHY2dkxoaGhFe737ds3xsPDg9m+fXsNpPo1CQkJjJubG6Otrc2oqakxfD6fOXHiBNuxKqVv377MgAEDmA8fPjCKiopMVFQUc+fOHaZVq1bM7du32Y5XoZMnTzKNGjVi+Hy+YI1rHo/HyMrKMnPnzhXrda6LvX79mjE1NWVMTEwYSUlJpk2bNky9evWYpk2bMu/fv2c7Xrk6d+7M+Pr6lrk9ICCAadGiRQ0mqjwejyf2n3Nt1rFjR8bY2JhZt24dExUVVWL7ly9fmPPnzzMjR45k1NTUmH/++YeFlBXLzc1l+vfvz9StW5fp3r07Iysry8jLyzNGRkaMgoICo62tzcTGxrId84/15MkTZtasWWzHIITzaMkwQjjC29sbbm5uUFFRQb9+/WBjYwMtLS3Iysri8+fPiIqKQnBwMC5cuIA+ffpgw4YN0NbWZjt2uRiGwZUrV+Dt7Y3AwECoqalh8ODB8PLyYjtahdTU1HD9+nVYWFhARUUF9+/fR9OmTXH9+nXMnz8fT548YTtihQoLC/H48WMkJCSgqKgImpqaaNGihdh3zf5ZQUEBjh8/joiICGRkZMDa2hqjR48W++7xL168gJSUFPT09ErdfvToUUhKSmLYsGE1nEx0fD4fqampqF+/PttR/liBgYHYtm0brl+/DgUFBTRo0EDwNyE1NRVqamqwt7eHk5OT2PcyuHTpEs6dOyf0/1H79u0xatQoKCgosB2PEEKqhIpuQjgkNzcXJ06cwPHjxxEcHIyvX78C+NGt1tTUFHZ2dpgwYQJMTExYTlp56enpOHToEHx8fASz8IqzunXr4vHjx9DT04OBgQH279+Pzp07Iz4+Hubm5sjKymI7Yq13+/ZttGvXDpKSwtOTFBQU4O7du+jYsSNLyf4MfD4f169fh6qqarn7UdfU6vfx40cEBwcjKSkJ2dnZUFNTg5WVFaysrAQzgxNSms6dO1c4lIXH44n9ihCEiDsqugnhsK9fvyI7Oxv16tWDlJQU23H+KB06dMD8+fMxcOBAjBo1Cp8/f8bSpUuxd+9ePHr0CM+ePWM7YqUoKysjPDwc+vr6bEcRmYSEBFJSUkpcaf306RPq16/PufkC+vTpg/3790NTU5PtKCLh8/ng8Xgo7WtEcTuPx+Pcz4Gwb/r06Vi5cmWJSdXI7+fk5FTmtu/fv+Po0aPIzc2l45iQKqLZywnhMBUVFaioqLAdQ2QrV66scB8ej8eJNVmXLl2KzMxMAD/eV9++fdGhQwfUq1cPx48fZzld5XHx/GtxUfdfnz594mR31Nu3byM7O5vtGJVy7949qKursx3jj/fgwQMUFRWhdevWQu337t2DhISE0IoXXHDkyBEsWLCAiu4asHnz5hJtBQUF2LFjB1avXo2GDRti1apVLCQjpHahopsQDlq7di0aNGgAR0dHofYDBw7gw4cPcHZ2ZilZ+c6cOVPmNh6Ph9jYWOTk5HCi6LazsxP829DQEDExMUhPT0fdunXFftZprhs8eDCAH78z9vb2kJGREWwrLCxEZGQk2rVrx1a8P4q2tjaN6RYDM2bMwKJFi0oU3W/fvsW6detw7949lpL9Gi6eBKwt/Pz84ObmhuzsbCxfvhyTJ08uMYSHEFJ5dBQRwkF79uzB0aNHS7Q3a9YMI0aMENuiu6zJxcLDw+Hi4oJnz55h0qRJNZzq96lobKs4GzNmDJSVldmOIZLi3h0Mw0BJSUlo0jRpaWm0adOGk79HOjo6NEyE/JKoqChYW1uXaLeyskJUVBQLiQjXXLp0CS4uLkhMTMSCBQswb948TvYYIkRcUdFNCAelpqaWOu5TXV0dKSkpLCT6NYmJiVi2bBmOHz+OwYMH4/nz5zAyMmI7lkgyMzPh4eGBoKAgpKWloaioSGh7QkICS8l+za5du9iOIDIfHx8AgK6uLhYsWFBrvhhybR4AW1tbSEtLi7z/sWPH0L9//1rz8xInMjIyeP/+fYk5GVJSUjh5lfL79+9sR/hj3L9/H87OzggLC8PUqVNx7do16tZPSDWgidQI4SAjIyO4u7tjzJgxQu2HDx+Gu7u72Bd8Hz9+xIoVK7B3717873//g4eHB1q2bMl2rEoZOXIkbt26hbFjx0JTU7NEl/I5c+awlKzq3r9/jz179sDNzY3tKH+szMxMPHr0qFbNwM7Fyfq4YuTIkUhJScE///wj6Any5csXDBw4EPXr10dAQADLCX9dQUEB3r17J/ZLYHIVn8+HnJwcJk+eXOYShgAwe/bsGkxFSO1DRTchHLR+/XqsX78eGzZsQJcuXQAAQUFBWLRoEebPnw9XV1eWE5YuMzMTGzduhKenJwwNDbF27Vr06NGD7Vi/pE6dOjh//jzat2/PdpTfLiIiAtbW1pyYrfbkyZMICAhAcnIy8vLyhLY9fvyYpVRVx6WfgaiUlJQQERFBRXc1ePv2LTp27IhPnz7BysoKwI9hOw0aNMDVq1fRuHFjlhP+utp4LIgTXV1dkZYME/eT+YSIO+71OSKEYOHChfj06ROmT58uKDRkZWXh7OwstgU3ABgYGOD79++YNWsWRo4cCR6Ph8jIyBL7cWFd37p163J2DHdpn/nPYmNjayhJ1Xh5eWHJkiWwt7fHP//8AwcHB8THx+PBgweYMWMG2/EIqTENGzZEZGQk/Pz8EBERATk5OTg4OGDkyJE0TwAp16tXr9iOQMgfga50E8JhGRkZiI6OhpycHIyMjIRmcRZHfD5f8O//ru/LtXV9jxw5gn/++QcHDx6EvLw823Eqpbasr2xsbAx3d3eMHDlS6Cqqm5sb0tPTsX37drYjlqmiEzaFhYXIyMgQ+59BZdCV7poXHR0Nb29vbNy4ke0oZSptArifZWdn48WLF7XqWCCE/Hmo6CaE1JikpCSR9tPR0anmJFVnZWWF+Ph4MAwDXV3dEleTxLlrs5qaGtavX4+uXbuWuv358+fo16+f2H/JlZeXR3R0NHR0dFC/fn1cvXoVzZs3R1xcHNq0aYNPnz6xHbFMCgoKmDZtGszNzUvdnpSUhBUrVoj9z6AyqOiuGZmZmfD394e3tzfCwsJgamoq1pP0ycrKYsSIEWWOJ05JScG+fftq1bEgjmbPng1DQ8MSY7e3b9+Oly9fYsuWLewEI6SWoO7lhHBQ586dyx2Ddf369RpMIzouFNOiGjhwINsRflmLFi3w7t27Mn8eX7584cQ6uRoaGkhPT4eOjg60tbURFhaG5s2bIzExUezzW1paonHjxhg/fnyp2yMiIrBixYoaTkW4LCQkBN7e3ggICEB2djacnJxw4MABGBsbsx2tXGZmZmjdujWmTZtW6vbw8HDs27evhlP9eU6dOoXAwMAS7e3atYOHhwcV3YRUERXdhHCQpaWl0P38/HyEh4fj2bNnZX6JFyc+Pj5QVFTE0KFDhdpPnDiBrKwssX8PBQUF4PF4cHR0RKNGjdiOU2lTp05FZmZmmdu1tbUFy3KJsy5duiAwMBBWVlZwcHCAk5MTTp48iYcPH2Lw4MFsxytXnz598OXLlzK3q6qqYty4cTUXqAbQOuS/X1paGnx9fXHgwAF8/foVI0eOxM2bN9G2bVs4OjqKfcENAO3bty93HgklJaVaNYu/uPr06ZNg5vufKSsr4+PHjywkIqR2oe7lhNQiy5cvR0ZGhliP3wOAJk2aYM+ePejcubNQ+61btzB58mROTOSlpKSEp0+fQldXl+0of6yioiIUFRUJ1iH29/fH3bt3YWRkhClTplRqDWlCuEhOTg5DhgzBmDFj0L17d8G8GVJSUoiIiICpqSnLCQlXmJmZYerUqZg5c6ZQ+7Zt27Br1y5ERUWxlIyQ2oGudBNSi4wZMwatWrUS+6I7OTm51PF7Ojo6SE5OZiFR5XXp0gW3bt2qNUV38fnXipaOESd8Pl9ocr4RI0ZgxIgRLCb6c9StW1fk35X09PRqTvPn0tHRQXBwMLS1taGjo8OJK9tEPM2bNw8zZ87Ehw8fhJYi3bRpE3UtJ+Q3oKKbkFokNDQUsrKybMeoUP369REZGVmiYI2IiEC9evXYCVVJvXr1gouLC54+fYoWLVpAQUFBaHv//v1ZSlY53t7e2Lx5M+Li4gAARkZGmDt3LiZOnMhysoqVtfQZj8eDrKwstLW1xX5Gf+DHEm3btm1DdHQ0AMDExASzZs1C06ZNWU5WNvoSLh5iYmIEY7lbtmyJJk2aYMyYMQC4dQKt2OfPn+Ht7S10LDg6OnJ2eUYucXR0RG5uLlavXo1Vq1YB+LGG965du2rdUBdC2EDdywnhoP+OV2UYBikpKXj48CGWLVsGd3d3lpKJxtnZGcePH4ePj49grN6tW7fg6OiIIUOGiP2VekB4+bP/4sJyWwDg5uYGT09PzJo1C23btgXw48TN9u3b4eTkhJUrV7KcsHzFS58VK17qrJiUlBSGDx+OPXv2iO3JqFOnTmHEiBGwsbER/AzCwsLw4MED+Pv746+//mI5IeGKjIwMHDt2DD4+PggLC4OtrS1GjRqFgQMHQl1dne14Fbp9+zb69+8PZWVl2NjYAAAePXqEL1++4Ny5czSuuwZ9+PABcnJyUFRUZDsKIbUGFd2EcEhCQgJ0dXUxYcIEoXY+nw91dXV06dIFPXr0YCmd6PLy8jB27FicOHFCMB63qKgI48aNw+7du2ksbg1RV1eHl5cXRo4cKdR+7NgxzJo1S+wnz/nnn3/g7OyMhQsXolWrVgCA+/fvY9OmTXB3d0dBQQFcXFwwfPhwsT2RY2BggNGjR5c4weHu7o4jR44gPj6epWSis7W1xYQJEzB06FDIycmxHYfg/9bnPnz4MNLT05Gfn892pAqZm5ujbdu22LVrFyQkJAD8WK9++vTpuHv3Lp4+fcpyQkII+XVUdBPCIRISEkhJSUH9+vUBAMOHD4eXlxcaNGjAcrJf8+LFC0REREBOTg7m5uacXVIsJydHbK+klqdOnTp48OABjIyMhNpfvHiBVq1alTu7tjho1aoVVq1aBTs7O6H2y5cvY9myZbh//z7Onj2L+fPni23xKi8vj8jISBgaGgq1x8XFoXnz5sjKymIpmejmzp2Lo0ePIjc3F8OGDcOECRPQpk0btmMR/FjZ4ty5c2I/mz/wY1K48PDwEsMqYmNjYWlpiezsbJaS/TlOnjyJgIAAJCcnIy8vT2jb48ePWUpFSO1Qdv9IQojY+e85sosXL5a79JO4a9KkCYYOHYq+fftyruAuLCzEqlWr0LBhQygqKiIhIQEAsGzZMnh7e7OcTjRjx47Frl27SrTv3bsXo0ePZiFR5Tx9+rTU3xsdHR3BVTFLS0ukpKTUdDSRderUCXfu3CnRHhwcjA4dOrCQqPK2bNmCd+/ewcfHB2lpaejYsSNMTU2xceNGvH//nu14f5z8/HzExcXh69evkJKS4kTBDQDW1taCsdw/i46ORvPmzVlI9Gfx8vKCg4MDGjRogCdPnqBVq1aoV68eEhIS0KtXL7bjEcJ5NJEaIRzG5Y4qb968QWBgYKln1D09PVlKJbrVq1fj4MGDWL9+PSZNmiRoNzMzw5YtW0oMARAX8+bNE/ybx+Nh//79uHLliuDK5L1795CcnMyJiXOMjY3h4eGBvXv3CoYk5Ofnw8PDQzCL89u3b8WuJ0hgYKDg3/3794ezszMePXok+BmEhYXhxIkTWLFiBVsRK01SUhKDBw/G4MGDkZaWhr1792LZsmVYvHgxevfujdmzZwtmRCa/z/r16zFr1izIycmhsLAQzs7O2LZtGwoKCsDn8zF27Fjs2bNHbNdH/3kyxNmzZ2POnDl4+fKl0LGwY8cOeHh4sBXxj7Fz507s3bsXI0eOhK+vLxYtWgR9fX24ubnRCgSE/AbUvZwQDpGQkEBqaqpgUhwlJSVERkaWuvyWOAsKCkL//v2hr6+PmJgYmJmZ4dWrV2AYBtbW1rh+/TrbEStkaGiIPXv2oGvXrlBSUkJERITg/bRt2xafP39mO2Kp/rs2ell4PJ7Y/xzu3r2L/v37g8/nw8LCAsCPq9+FhYX4999/0aZNGxw+fBipqalYuHAhy2n/T3mT8P2MKxPy/ez+/fvw8fGBv78/lJWVYW9vj7dv3+Lo0aOYPn262I6t56qfhxxt3LgRa9aswaZNm9C6dWs8efIE8+bNw/z587Fo0SK2o5aqeDLEir6KcvFY4Bp5eXlER0dDR0cH9evXx9WrV9G8eXPExcWhTZs2+PTpE9sRCeE0utJNCIcwDAN7e3vBMkg5OTmYOnVqieWqTp8+zUY8kbm6umLBggVYsWIFlJSUcOrUKdSvXx+jR49Gz5492Y4nkrdv35YYhwv8mBBOnCctunHjBtsRfpt27dohMTERfn5+ePHiBQBg6NChGDVqFJSUlAD86EIvboqKitiO8FulpaXh8OHD8PHxQVxcHPr164djx47Bzs5OMJu8vb09evbsSUX3b/ZzsXr06FF4eHjAwcEBAGBqagoAWLt2rdgW3YmJiWxHIP+fhoYG0tPToaOjA21tbYSFhaF58+ZITEzkdK86QsQFFd2EcMj48eOF7hevx8o10dHROHbsGIAf3VKzs7OhqKiIlStXYsCAAZg2bRrLCStmamqKO3fulBhTfPLkSVhZWbGU6s+jpKSEqVOnsh3jj9aoUSMYGBjA0dER9vb2pS5PZWFhgZYtW7KQrvYrPrGRnJyMdu3aCW0rPjElrrg2l0dt1qVLFwQGBsLKygoODg5wcnLCyZMn8fDhQ87MC0CIOKOimxAO8fHxYTvCb6GgoCAYx62pqYn4+Hg0a9YMAMR+mapibm5uGD9+PN6+fYuioiKcPn0asbGxOHToEP7991+245Vp6tSpWLp0KRo1alThvsePH0dBQYFYT6oWFxeHGzduIC0trcQVZDc3N5ZSlc/f3x8jRowQad/Xr18jOTkZ7du3r+ZUvy4oKKjCSd+UlZVrVS8LcbJv3z4oKipCWlq6xNjb79+/C3pGiaOwsDCRZ7rPyspCYmKi4G8F+b327t0r+D90xowZqFevnmAID53YJKTqqOgmhNS4Nm3aIDg4GCYmJujduzfmz5+Pp0+f4vTp05xZamjAgAE4d+4cVq5cCQUFBbi5ucHa2hrnzp1D9+7d2Y5XJnV1dTRr1gzt27dHv379YGNjAy0tLcjKyuLz58+IiopCcHAw/P39oaWlhb1797IduUz79u3DtGnToKamBg0NDcEVP+DH1T9xLbp37dqFFStWwMHBAf369YOJiYnQ9q9fvyIkJARHjhzB1atXxX42fK7Msl4baWtrY9++fQAAGRkZPH78GB07dhRsv3HjRokluMTJ2LFjoa+vj4kTJ6J3794lhkoBQFRUFI4cOQIfHx+sW7eOiu5qwufzheabGDFiBEaMGIEvX77gxIkTGDVqFIvpCOE+mkiNEFLjEhISkJGRAQsLC2RmZmL+/Pm4e/cujIyM4OnpSV0Oq9n79++xf/9++Pv7IyoqSmibkpISunXrhokTJ4r9+HodHR1Mnz4dzs7ObEeptMDAQGzbtg3Xr1+HgoICGjRoIDjxkZqaCjU1Ndjb28PJyUnsZl8HACsrK6GTHOWh9X3ZExYWBhkZGbEd8pKfn49du3Zhx44dSEhIQJMmTYROAsbExCAjIwODBg3C4sWLYW5uznbkP05ERASsra1pIjtCqoiKbkKI2CgoKEBaWhq0tLTYjlIhfX19PHjwAPXq1RNq//LlC6ytrQXrdou7z58/Izk5GdnZ2VBTU4OBgYHIxRTblJWVER4eDn19fbaj/LKPHz8iODgYSUlJgp+BlZUVrKysRJ7lnA2VWc7M3d29GpOQ2uLhw4elHgudO3eGqqoq2/H+WFR0E/J7UNFNCBEbXPrjzufzkZqaivr16wu1v3//Htra2sjNzWUp2Z9jwoQJaNmyJY03JH+8Q4cOlbt93LhxNZSE1DZc+rtMiDijMd2EEFIJgYGBgn9fvnwZKioqgvuFhYUICgqCrq4uC8kqz8fHB4qKihg6dKhQ+4kTJ5CVlVVitnxxY2hoiGXLliEsLAzm5uaQkpIS2j579myWkhFSs+bMmSN0Pz8/H1lZWZCWloa8vDwni+5bt24hMzMTbdu2Rd26ddmOQwghVUJXugkhYoMLZ9SLu/zyeLwSa5dKSUlBV1cXmzZtQt++fdmIVylNmjTBnj170LlzZ6H2W7duYfLkyYiNjWUpmWj09PTK3Mbj8cS2i3/dunVF7sL/39moxVFhYSE2b96MgIAAJCcnC1YmKMaF91AbxcXFYdq0aVi4cCHs7OzYjlOmdevWISMjA6tWrQLwY+3xXr164cqVKwCA+vXrIygoiCZQqyZeXl7lbn/79i02btwo1n+XCeECutJNCCGVULykip6eHh48eAA1NTWWE/265OTkUgtXHR0dJCcns5CocsR5/eHybNmyhe0Iv9WKFSuwf/9+zJ8/H0uXLsWSJUvw6tUrnD17VmxnkP8TGBkZwcPDA2PGjEFMTAzbccp0/PhxockQT548idu3b+POnTswMTHBuHHjsGLFCgQEBLCYsvbavHlzhftoa2vXQBJCajcqugkhNSYyMrLc7eJ+ZfVnXC34fla/fn1ERkaW6A4fERFRYoI4cVfc64ALk8CJe7f9yvLz88O+ffvQp08fLF++HCNHjoSBgQEsLCwQFhZG3fxZJCkpiXfv3rEdo1yJiYmwsLAQ3L9w4QKGDBkiWJt+6dKlJYbAkN+nNvwtI4QLqOgmhNQYS0vLUrtlA//XXZsLRVOxoKAgBAUFIS0tTXAFvNiBAwdYSiW6kSNHYvbs2VBSUhKs7Xvr1i3MmTMHI0aMYDmdaA4dOoQNGzYgLi4OwI8u8wsXLsTYsWNZTlZ5OTk5JbpmKysrs5RGdKmpqYKlnBQVFfH161cAQN++fbFs2TI2o/0xfp5rAvhxEiolJQXbt28XFK/iqqCgADIyMoL7oaGhmDt3ruC+lpYWPn78yEIyUhpzc3NcuHABjRs3ZjsKIZxCRTchpMbUpjPqK1aswMqVK2FjYwNNTU1OnSwotmrVKrx69Qpdu3aFpOSPPwdFRUUYN24cVq9ezXK6inl6emLZsmWYOXOmoLAIDg7G1KlT8fHjRzg5ObGcsGKZmZlwdnZGQEAAPn36VGI7F8ZRNmrUCCkpKdDW1oaBgQGuXLkCa2trPHjwQKiYItVn4MCBQvd5PB7U1dXRpUsXbNq0iZ1QIjIwMMDt27ehr6+P5ORkvHjxQnASEADevHnDuZ43tdmrV6+Qn5/PdgxCOIcmUiOEiK3p06dj5cqVYjluWlNTE+vXr+fkFdX/iouLQ3h4OOTk5GBubg4dHR22I4lET08PK1asKDEz88GDB7F8+XJOnOSZMWMGbty4gVWrVmHs2LHYsWMH3r59iz179sDDwwOjR49mO2KFXFxcoKysjMWLF+P48eMYM2YMdHV1kZycDCcnJ3h4eLAdkYixffv2wcnJCcOHD0dYWBjq1KmDkJAQwfa///4b9+7dw7lz51hMSYopKSkhIiIC+vr6bEchhFOo6CaEiC1lZWWEh4eL5R/3evXq4f79+zAwMGA7ym/17ds3+Pn5wdvbGw8fPmQ7TrlkZWXx7NkzGBoaCrXHxcXB3NwcOTk5LCUTnba2Ng4dOoROnTpBWVkZjx8/hqGhIQ4fPoxjx47hwoULbEestNDQUISGhsLIyAj9+vVjO84fJS8vD4mJiTAwMBD0XuGCAwcO4Ny5c9DQ0IC7uzs0NDQE26ZPn47u3btj0KBBLCYkxajoJuTX8NkOQAghZRHnc4ITJ07E0aNH2Y7x29y4cQNjx46FpqYmVq1ahdatW7MdqUKGhoalzmh8/PhxGBkZsZCo8tLT0wVfXpWVlQXLa/3vf//D7du32Yz2y9q2bYt58+ZRwV2DsrKy4OjoCHl5eTRr1kyw+sCsWbM40dPA0dERZ86cwa5du4QKbgDYuXMnFdyEEM7jzmlQQggRIzk5Odi7dy+uXbsGCwsLSElJCW339PRkKZno3r59C19fX/j4+ODLly/4/Pkzjh49imHDhnFijPqKFSswfPhw3L59WzCmOyQkBEFBQZxZXkhfXx+JiYnQ1taGsbExAgIC0KpVK5w7dw516tRhO16ZAgMD0atXL0hJSZWYxOu/+vfvX0Op/lyurq6IjIzEzZs30bNnT0F7t27dsHz5cri4uLCYrnIKCgpw48YNJCcnQ0dHB507d4aEhATbsQghpEqo6CaEkF8QGRkJS0tLAMCzZ8/YDVNJp06dgre3N27fvo1evXph06ZN6NWrFxQUFGBubs6JghsA/vrrL9y7dw+bN2/G2bNnAQAmJia4f/8+rKys2A0nIgcHB0RERMDW1hYuLi7o168ftm/fjvz8fLE+cTNw4ECkpqaifv36JSbx+hmPx+PEZHBcd/bsWRw/fhxt2rQROn6bNWuG+Ph4FpNVbNasWbCzs0Pfvn3x5s0bdO/eHXFxcVBTU8PHjx9hamqKixcvomHDhmxHJYSQX0ZjugkhYovGjlUPSUlJODs7w8XFBUpKSoJ2KSkpREREwNTUlMV0f7akpCQ8evQIhoaGQmsXE1IeeXl5PHv2DPr6+kL/b0ZERKBjx46CZdzEkYaGBq5duwYzMzMMHz4c6enpOHbsGNTU1JCeno7x48dDVlYWJ06cYDsqAXD06FEMGDAACgoKbEchhFPoSjchhFTC4MGDK9yHx+Ph1KlTNZDm10yYMAE7duzAzZs3MXbsWAwfPhx169ZlO5ZIvn37JvK+4rzGdXZ2NoKCgtC3b18AP7oH5+bmCraHhYVh5cqVkJWVZStilXz58kWsu8fXNjY2Njh//jxmzZoFAIKr3fv370fbtm3ZjFahr1+/Cgq4u3fv4tSpU4IVK1RVVbF27Vp07tyZzYi1lpeXl8j7zp49GwAwatSo6opDSK1GRTchRGyNGTNG7AonFRUVtiNU2Z49e7BlyxYEBATgwIEDmDt3Luzs7MAwDIqKitiOV646depU2P2dYRix79Z88OBBnD9/XlB0b9++Hc2aNYOcnBwAICYmBpqampxYa3zdunXQ1dXF8OHDAQBDhw7FqVOnoKmpiQsXLqB58+YsJ6z91qxZg169eiEqKgoFBQXYunUroqKicPfuXdy6dYvteOVq0qQJ7t+/Dz09PSgpKZU4sfb9+3ex/3+JqzZv3izSfjweT1B0E0J+DXUvJ4TUiMjISJH3pW61NSsuLg4+Pj44ePAgMjIy0KdPHwwZMkSkq/o1rTIFhK2tbTUmqZoOHTpg0aJFghm+/zuU4siRI9ixYwdCQ0PZjCkSPT09+Pn5oV27drh69SqGDRuG48ePIyAgAMnJybhy5QrbEf8I8fHx8PDwQEREBDIyMmBtbQ1nZ2eYm5uzHa1cvr6+WLZsGQ4fPozXr19j7dq12LZtG0xMTBAbG4s5c+agdevW2LdvH9tRCSHkl1HRTQipEXw+Hzwer8xlwIq3ifsVytqsqKgI58+fh7e3Ny5evCjU3Zn8XpqamggNDYWuri4AQF1dHQ8ePBDcf/HiBVq2bCnWY3GLycnJ4cWLF2jcuDHmzJmDnJwc7NmzBy9evEDr1q3x+fNntiMSMefp6Ylly5aBYRgUFhaioKBAsK1///44fPgwFBUVWUxICCFVQ93LCSE1IjExke0IpAJ8Ph/9+vVDv379kJaWxnYckdy5cwd79uxBQkICTpw4gYYNG+Lw4cPQ09PD//73P7bjlenLly9CJzU+fPggtL2oqIgzJz3q1q2L169fo3Hjxrh06RL+/vtvABAUUKT6FJ/MLA+PxxMqYsXRvHnz4OjoiCtXriAxMRFFRUXQ1NRE+/btYWRkxHa8P8abN28QGBiI5ORk5OXlCW0T59UUCOECKroJITVCR0eH7Qjk/7t9+7ZI+9WvX7+ak1TNqVOnMHbsWIwePRqPHz8WFKlfv37FmjVrcOHCBZYTlq1Ro0Z49uwZmjZtWur2yMhINGrUqIZT/ZrBgwdj1KhRMDIywqdPn9CrVy8AwJMnT2BoaMhyutrtzJkzZW4LDQ2Fl5cXZ8ZD16lTB8OGDWM7xh8rKCgI/fv3h76+PmJiYmBmZoZXr16BYRhY/7/27j0oyut+A/izC4qIRAVd0KAIohgQAsYhzqTQjvdVFKSXSCwIYtJYoiRqFZhKhE61arVCvNWpXEzAqs22mJBoAS2GkcFLuJZgBDEXBLwApgiosPz+yLi/bEBEcd/Du/t8Zpxhz3n/eEYH5Puec75n2jTR8Yhkj9vLiUiYioqKHt+oL168WFAi06BUKh8593DVTA6rY97e3njnnXcQGhqqdya6qKgIarUa9fX1oiM+UlRUFHJycnDp0qVuHcrb2towffp0zJ49G4mJiYIS9t2DBw+QmJiIb775BmFhYbo70v/yl7/A2toaK1euFJzQtFy+fBnR0dH46KOPsGzZMiQkJMjipWdKSgqGDRuGX/7yl3rjx48fR2trK5YvXy4omWnw8fGBWq1GfHy87uepSqXCsmXLMH/+fKxatUp0RCJZY9FNRJK7evUqlixZgrKyMr1z3g8LPm5JNaxHnRNubW1FYmIikpKS4OzsjPLycomTPZmhQ4eioqICEyZM0Cu6r169Cjc3N7S3t4uO+EgNDQ3w8vLC4MGD8dZbb2Hy5MkAvi+Y9uzZg46ODhQVFcHOzk5wUpKL69ev491330VaWhrmzZuHrVu3YurUqaJj9dnkyZPx17/+tdv1YHl5eXjjjTdw+fJlQclMg7W1NYqLizFx4kSMHDkS+fn5cHd3R0lJCQICAnDt2jXREYlkjdvLiUhyUVFRcHJyQm5uLpycnHD+/Hncvn0b69atw5///GfR8Yzej68902q1SE5ORnx8PJRKJfbu3SuLVSV7e3tUVVXpmo89lJ+fr+sCPlDZ2dnh3LlzWLVqFaKjo/VePM2ZMwf79u0b0AX3iRMnoFarMWjQIJw4caLXZ7lzxbAeHqd477334OXlhdzcXPj6+oqO9cS+/vprODk5dRt3dHTE119/LSCRabGystLtOhszZgyqq6vh7u4OALh165bIaERGgUU3EUmuoKAAp0+fxqhRo6BUKqFUKvGTn/wEW7duxZo1a1BUVCQ6osnQaDSIjY3FzZs3ERMTg9WrV8PCwkJ0rD55/fXXERUVheTkZCgUCly/fh0FBQVYv349Nm3aJDreYzk5OeHkyZNobGxEVVUVAMDFxQU2NjaCkz1eYGAg6uvroVKpEBgY+MjneBuBYW3fvh3btm2Dvb09jhw5goCAANGRnppKpUJpaWm3l2glJSWwtbUVE8qEzJgxA/n5+XjhhRewYMECrFu3DmVlZdBoNJgxY4boeESyx6KbiCTX2dkJa2trAMCoUaNw/fp1uLq6wtHRkVsIJZKXl4eNGzeirKwMUVFR2LhxY7cV8IEuOjoaWq0Ws2bNQmtrK/z8/GBhYYH169dj9erVouP1mY2NDXx8fETHeCI/bM4ll0Zdxig6OhqWlpZwcXFBWloa0tLSenxOo9FInOzJBQcHY82aNbC2toafnx+A739ORUVFYenSpYLTGb9du3ahpaUFABAfH4+WlhYcPXoUkyZNYudyomeAZ7qJSHK+vr5Yt24dAgMD8dprr6GpqQm///3vcfDgQVy6dGnAnyWWuwULFiAnJwcrVqzA5s2bYW9vLzpSv9y/fx9VVVVoaWmBm5tbt/t8v/32W4wdO7bXBnL0dLRaLVJTU6HRaHDt2jUoFAo4Ozvj5z//OUJCQh57nRX1T1hYWJ/+jlNSUiRI0z/3799HSEgIjh8/DnPz79eEtFotQkNDsX//ftnswCEi6gmLbiKS3KlTp3D37l0EBQWhqqoK/v7++PLLL2Fra4ujR49i5syZoiMaNaVSCXNzc1hZWfX6C3tjY6OEqQznueeeQ3Fx8YA/5y03XV1dWLRoET755BO8+OKLmDJlCrq6uvDFF1+grKwMixcvxr/+9S/RMUlmrly5guLiYlhaWsLDw0MWndeNyf3793Hjxo1uO1jGjx8vKBGRceD2ciKS3Lx583Rfu7i4oLKyEo2NjRg5ciRXxiQgh1WvZ4nvlg0jNTUVZ8+eRW5ubreO06dPn0ZgYCAOHz6M0NBQQQlJjiZNmoRJkyYBAL777jvs378fhw4dwsWLFwUnM25ffvklIiIicO7cOb3xrq4u9mYgegZYdBPRgCCH5lHGQg6dyWngO3LkCGJjY7sV3AAwc+ZMREdHIz09nUU3PbEzZ84gOTkZGo0Gw4cPx5IlS0RHMnrh4eEwNzfHxx9/jDFjxvAFONEzxqKbiCR39+5d/OlPf0Jubm6P29iuXr0qKJlpuXDhArRaLV5++WW98cLCQpiZmWH69OmCkpEclJaWYvv27Y+cV6vVSEpKkjARyVltbS1SU1ORkpKC5uZmNDU1ISMjA7/61a9YAEqguLgYly5dwpQpU0RHITJKLLqJSHIrV65EXl4eQkJC+EZdoMjISGzYsKFb0V1bW4tt27ahsLBQUDKSg8bGxl7vErezs0NTU5OEiUiOPvzwQxw6dAhnz56FWq3Gzp07oVarYWVlBQ8PD/7/IBE3Nzfex01kQCy6iUhyn376KbKysvDKK6+IjmLSKioqMG3atG7j3t7eqKioEJDIMPhLu2F0dnbqukz3xMzMDB0dHRImIjl69dVXsXHjRhw9elR3lSRJb9u2bdiwYQO2bNkCDw8PDBo0SG/+ueeeE5SMyDiw6CYiyY0cOZJnuAcACwsLNDQ0dOvqXVdX12sxJTdspGYYXV1dCAsLe+RVTvfu3ZM4EclRREQE9u7di//85z8ICQnBq6++ipEjR4qOZXJmz54NAJg1a5beOBupET0bvDKMiCT3wQcfIDMzE2lpaRg6dKjoOCYrODgYdXV1yMzMxPDhwwEAzc3NCAwMhEqlwrFjxwQnfDa++eYbjB07FmZmZqKjGJXw8PA+PWdq3fLpybW1teHYsWNITk5GYWEh5s2bh6ysLBQXF2Pq1Kmi45mEvLy8Xud/+tOfSpSEyDix6CYiyXl7e6O6uhpdXV2YMGFCt21sn3/+uaBkpqW2thZ+fn64ffs2vL29AXzfTMfOzg7Z2dkYN26c4IS9Y0M+IuNz5coVpKSkIC0tDS0tLVi4cCF+8YtfICgoSHQ0IqKnxqKbiCQXHx/f6/y7774rURK6e/cu0tPTUVJSAktLS3h6eiI4OLjbi5CBKDg4uNeGfFFRUYKSEVF/abVaZGVl4dChQ/j00095XMEASktLMXXqVCiVSpSWlvb6rKenp0SpiIwTi24iklRHRwe2bNmCFStWwMHBQXQckrERI0awIR+RCbhx4wZUKpXoGEZHqVSivr4eKpUKSqUSCoWixx4YPNNN1H/G0ymHiGTB3NwcO3bsQGhoqOgoJunEiRN9fnbx4sUGTNJ/bMhHJH9nz57t03Msup+9mpoajB49Wvc1ERkOV7qJSHIBAQE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glycanApif(a1-2)Xyl(b1-2)[Glc6Ac(b1-4)]GlcAra(a1-2)Ara(a1-6)GlcNAcAra(a1-2)Glc(b1-2)AraAra(a1-2)GlcAAra(a1-2)[Glc(b1-6)]GlcAra(a1-6)GlcAraf(a1-3)Araf(a1-5)[Araf(a1-6)Gal(b1-6)Glc(b1-6)Man(a1-3)]Araf(a1-5)Araf(a1-3)Araf(a1-3)ArafAraf(a1-3)Gal(b1-6)GalD-Apif(b1-2)GlcD-Apif(b1-2)GlcAD-Apif(b1-3)Xyl(b1-2)[Glc6Ac(b1-4)]GlcD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)AraD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)D-FucD-Apif(b1-3)Xyl(b1-4)[Glc(b1-3)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)[Rha(a1-3)]D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-3)D-FucD-Apif(b1-6)GlcD-ApifOMe(b1-3)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeD-ApifOMe(b1-3)XylOMe(b1-4)[GlcOMe(b1-3)]RhaOMe(a1-2)D-FucOMeFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]Glc6AcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)Glc3Ac6AcFruf(b2-1)Glc4Ac6AcFruf(b2-1)Glc6AcFruf(b2-1)[Glc(b1-2)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc3Ac(b1-2)]GlcFruf(b2-1)[Glc6Ac(b1-2)]GlcFruf1Ac(b2-1)Glc2Ac4Ac6AcFuc(a1-2)Gal(b1-2)Xyl(a1-6)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-4)XylFuc(a1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcFuc(a1-6)GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Man(a1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)[Gal(?1-?)]GlcNAc(?1-?)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(?1-?)Man(a1-3)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(a1-4)GalGal(a1-6)GalGal(a1-6)Gal(a1-6)GalGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)[Fruf(b2-1)]GlcGal(a1-6)Gal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Glc(a1-2)FrufGal(a1-6)ManGal(a1-6)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)[Gal(a1-6)]ManGal(b1-2)GlcAGal(b1-2)GlcA6MeGal(b1-2)Xyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)[Xyl(b1-3)]GlcAGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Gal(b1-3)GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[GlcNAc(b1-4)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(a1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Gal(b1-3)GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-6)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-4)Gal(b1-4)ManGal(b1-4)Gal(b1-4)ManOMeGal(b1-4)GlcAGal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-4)]Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGal(b1-4)Man(b1-4)ManGal(b1-4)Man(b1-4)Man(b1-4)GalGal(b1-4)Xyl(b1-4)Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1FerOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucOMeOSinGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGalA(a1-2)[Araf(a1-5)Araf(a1-4)]Rha(b1-4)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalAGalOMe(b1-2)[XylOMe(b1-3)]GlcAOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalOMe(b1-4)XylOMe(b1-4)[D-ApifOMe(b1-3)]RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalf(b1-2)[Galf(b1-4)]ManGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNGlc(a1-2)Rha(a1-6)GlcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-4)Glc(a1-2)Rha(a1-6)GlcGlc(a1-4)Glc(a1-4)Glc(a1-6)GlcGlc(a1-4)Glc(a1-4)GlcAGlc(a1-4)GlcA(b1-2)GlcAGlc(b1-2)AraGlc(b1-2)Ara(a1-2)GlcAGlc(b1-2)Gal(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcA(b1-3)[Glc(b1-3)]AraGlc(b1-2)GlcGlc(b1-2)Glc(a1-2)FrufOBzOCinGlc(b1-2)Glc(b1-2)GlcGlc(b1-2)GlcAGlc(b1-2)[Ara(a1-3)]GlcA6MeGlc(b1-2)[Ara(a1-3)]GlcAOMeGlc(b1-2)[Ara(a1-6)]GlcGlc(b1-2)[Glc(b1-3)]Glc(a1-2)FrufGlc(b1-2)[Glc(b1-3)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-2)[Glc6Ac(b1-3)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-2)[Rha(a1-3)]GlcAGlc(b1-2)[Xyl(b1-2)Ara(a1-6)]GlcGlc(b1-2)[Xyl(b1-2)D-Fuc(b1-6)]GlcGlc(b1-3)AraGlc(b1-3)GlcGlc(b1-3)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Coum6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Rha1Fer(a1-4)Fruf(b2-1)GlcOBzGlc(b1-3)[Araf(a1-4)]Rha(a1-2)GlcGlc(b1-3)[Xyl(b1-4)]Rha(a1-2)D-FucOMeGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-4)Glc(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc(b1-4)GlcGlc(b1-4)Glc(b1-4)Glc(b1-4)ManGlc(b1-4)Glc6Ac(b1-3)Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-4)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-4)Man(b1-4)GlcGlc(b1-4)RhaGlc(b1-4)Rha1Fer(a1-4)Fruf(b2-1)GlcOBzGlc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGlc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcGlc(b1-6)Glc(b1-3)GlcGlc1CerGlc2Ac(b1-4)[D-Apif(b1-3)Xyl(b1-2)]GlcGlc2Ac3Ac4Ac6Ac(b1-3)AraGlc6Ac(b1-2)Glc(a1-2)FrufOBzOCinGlc6Ac(b1-3)Glc6Ac(b1-3)[Glc6Ac(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOAcOBzGlc6Ac(b1-3)Glc6Ac(b1-3)[Glc6Ac(b1-2)][RhaOAc(a1-4)]Glc1Fer6Ac(a1-2)Fruf1CoumOAcOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Coum(a1-2)Fruf1CoumOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1CoumOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlcA(b1-2)GlcGlcA(b1-2)GlcAGlcA(b1-2)GlcA(b1-2)RhaGlcA4Me(a1-2)[Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)]XylGlcA4Me(a1-2)[Xyl(b1-4)Xyl(b1-4)Xyl(b1-4)]XylGlcA4Me(a1-2)[Xyl(b1-4)]XylGlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc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)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)GlcApif(a1-2)Xyl(b1-2)[Glc6Ac(b1-4)]GlcAra(a1-2)Ara(a1-6)GlcNAcAra(a1-2)Glc(b1-2)AraAra(a1-2)GlcAAra(a1-2)[Glc(b1-6)]GlcAra(a1-6)GlcAraf(a1-3)Araf(a1-5)[Araf(a1-6)Gal(b1-6)Glc(b1-6)Man(a1-3)]Araf(a1-5)Araf(a1-3)Araf(a1-3)ArafAraf(a1-3)Gal(b1-6)GalD-Apif(b1-2)GlcD-Apif(b1-2)GlcAD-Apif(b1-3)Xyl(b1-2)[Glc6Ac(b1-4)]GlcD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)AraD-Apif(b1-3)Xyl(b1-4)Rha(a1-2)D-FucD-Apif(b1-3)Xyl(b1-4)[Glc(b1-3)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-2)[Rha(a1-3)]D-FucD-Apif(b1-3)[Gal(b1-4)Xyl(b1-4)]Rha(a1-3)D-FucD-Apif(b1-6)GlcD-ApifOMe(b1-3)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeD-ApifOMe(b1-3)XylOMe(b1-4)[GlcOMe(b1-3)]RhaOMe(a1-2)D-FucOMeFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc4Ac6Ac(b1-3)]Glc6AcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]GlcFruf(a2-1)[Glc(b1-2)][Glc(b1-3)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)Glc3Ac6AcFruf(b2-1)Glc4Ac6AcFruf(b2-1)Glc6AcFruf(b2-1)[Glc(b1-2)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]GlcFruf(b2-1)[Glc(b1-2)][Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc(b1-4)Glc6Ac(b1-3)]Glc6AcFruf(b2-1)[Glc3Ac(b1-2)]GlcFruf(b2-1)[Glc6Ac(b1-2)]GlcFruf1Ac(b2-1)Glc2Ac4Ac6AcFuc(a1-2)Gal(b1-2)Xyl(a1-6)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)GlcFuc(a1-2)Gal(b1-4)XylFuc(a1-4)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcFuc(a1-6)GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)Gal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Man(a1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(?1-?)[Gal(?1-?)]GlcNAc(?1-?)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-3)[Gal(?1-?)Man(a1-3)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(a1-4)GalGal(a1-6)GalGal(a1-6)Gal(a1-6)GalGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)Gal(a1-6)Gal(a1-6)[Fruf(b2-1)]GlcGal(a1-6)Gal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Gal(a1-6)GlcGal(a1-6)Gal(a1-6)Glc(a1-2)FrufGal(a1-6)Glc(a1-2)FrufGal(a1-6)ManGal(a1-6)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)ManGal(a1-6)Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)Man(b1-4)[Gal(a1-6)]ManGal(a1-6)Man(b1-4)[Gal(a1-6)]ManGal(b1-2)GlcAGal(b1-2)GlcA6MeGal(b1-2)Xyl(a1-6)Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)Xyl(a1-6)[Glc(b1-4)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)GlcGal(b1-2)[Xyl(b1-3)]GlcAGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Gal(b1-3)GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[GlcNAc(b1-4)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[GlcNAc(b1-4)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)GlcNAc(b1-4)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(a1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Gal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[GlcNAc(b1-2)Man(a1-6)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-3)[Xyl(b1-2)][Man(a1-6)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Gal(b1-3)GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[GlcNAc(b1-2)Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)Man(a1-?)[Man(a1-?)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-4)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-3)[Fuc(a1-6)]GlcNAc(b1-2)[Man(a1-6)]Man(a1-6)[Xyl(b1-2)][Man(a1-3)]Man(b1-4)GlcNAc(b1-4)[Fuc(a1-3)]GlcNAcGal(b1-4)Gal(b1-4)ManGal(b1-4)Gal(b1-4)ManOMeGal(b1-4)GlcAGal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-4)]Man(a1-3)[Gal(b1-4)GlcNAc(b1-2)[Gal(b1-4)GlcNAc(b1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGal(b1-4)Man(b1-4)ManGal(b1-4)Man(b1-4)Man(b1-4)GalGal(b1-4)Xyl(b1-4)Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)D-Fuc1FerOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)Rha(a1-2)[Rha(a1-3)]D-FucOMeOSinGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)D-Fuc1CoumOMeGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-FucGal(b1-4)Xyl(b1-4)[D-Apif(b1-3)]Rha(a1-2)[Rha(a1-3)]D-Fuc1CoumOMeGal(b1-4)[Fuc(a1-3)]GlcNAc(b1-2)Man(a1-6)[GlcNAc(b1-2)Man(a1-3)][Xyl(b1-2)]Man(b1-4)GlcNAc(b1-4)GlcNAcGalA(a1-2)[Araf(a1-5)Araf(a1-4)]Rha(b1-4)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)Rha(a1-4)GalA(a1-2)GalAGalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalA(a1-4)GalAGalOMe(b1-2)[XylOMe(b1-3)]GlcAOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)D-FucOMeGalOMe(b1-4)XylOMe(b1-4)RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalOMe(b1-4)XylOMe(b1-4)[D-ApifOMe(b1-3)]RhaOMe(a1-2)[RhaOMe(a1-3)]D-FucOMeGalf(b1-2)[Galf(b1-4)]ManGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-2)Glc(a1-3)Glc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcNGlc(a1-2)Rha(a1-6)GlcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-3)Man(a1-2)Man(a1-2)Man(a1-3)[Man(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAcGlc(a1-4)Glc(a1-2)Rha(a1-6)GlcGlc(a1-4)Glc(a1-4)Glc(a1-6)GlcGlc(a1-4)Glc(a1-4)GlcAGlc(a1-4)GlcA(b1-2)GlcAGlc(b1-2)AraGlc(b1-2)Ara(a1-2)GlcAGlc(b1-2)Gal(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcAGlc(b1-2)Gal(b1-2)GlcA(b1-3)[Glc(b1-3)]AraGlc(b1-2)GlcGlc(b1-2)Glc(a1-2)FrufOBzOCinGlc(b1-2)Glc(b1-2)GlcGlc(b1-2)GlcAGlc(b1-2)[Ara(a1-3)]GlcA6MeGlc(b1-2)[Ara(a1-3)]GlcAOMeGlc(b1-2)[Ara(a1-6)]GlcGlc(b1-2)[Glc(b1-3)]Glc(a1-2)FrufGlc(b1-2)[Glc(b1-3)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-2)[Glc6Ac(b1-3)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-2)[Rha(a1-3)]GlcAGlc(b1-2)[Xyl(b1-2)Ara(a1-6)]GlcGlc(b1-2)[Xyl(b1-2)D-Fuc(b1-6)]GlcGlc(b1-3)AraGlc(b1-3)GlcGlc(b1-3)Glc(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc(a1-2)FrufGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Coum6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer(a1-2)Fruf1FerOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)]Glc1Fer6Ac(a1-2)Fruf1FerOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Coum6Ac(a1-2)Fruf1CoumOBzGlc(b1-3)Glc6Ac(b1-3)[Glc(b1-2)][Rha(a1-4)]Glc1Fer6Ac(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[Man(b1-4)Man(b1-4)Man(b1-4)Gal(a1-6)]Man(b1-2)[Gal(a1-6)]Man(b1-2)[Gal(a1-4)Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)Man(b1-4)Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man(b1-4)Man[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)[Gal(a1-6)]Man(b1-4)Man[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Gal(b1-3)Gal(b1-6)[Araf(a1-3)]Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-3)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)Gal(b1-6)]Gal(b1-3)Gal[Gal(b1-6)]Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal(b1-3)Gal[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Araf(a1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Fuc(a1-2)Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Gal(b1-2)Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)[Gal(b1-5)Araf(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)[Xyl(a1-6)]Glc(b1-4)Glc(b1-4)Glc
Family
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\n" @@ -8996,15 +9029,15 @@ "\n", "### get_possible_linkages\n", "\n", - "> get_possible_linkages (wildcard, linkage_list={'b2-4', 'b1-2', 'a1-11',\n", - "> '?1-?', 'b1-4', 'a1-?', 'a2-4', 'a2-5', '?1-6',\n", - "> '?2-8', 'a1-6', 'a1-5', 'a2-9', '1-6', 'b1-?',\n", - "> 'a2-?', 'a2-3', 'a1-4', 'a1-9', '?1-4', '?1-3',\n", - "> 'b2-6', '?2-?', '?1-2', 'a1-8', 'b2-8', 'a1-1',\n", - "> 'b1-8', 'a2-7', 'b1-5', 'b2-3', 'a2-11', 'b2-7',\n", - "> 'b1-7', 'a1-7', 'b1-9', '1-4', 'b2-5', 'a1-3',\n", - "> 'b1-1', 'a2-2', '?2-3', 'a2-6', 'b1-3', 'b2-2',\n", - "> 'a1-2', 'a2-1', '?2-6', 'b1-6', 'a2-8', 'b2-1'})\n", + "> get_possible_linkages (wildcard, linkage_list={'a1-?', 'b1-7', 'b2-1',\n", + "> 'a1-2', '?1-?', 'a1-9', 'a2-2', '?2-6', '?1-2',\n", + "> 'b1-1', '1-6', '?1-4', '?2-8', 'a2-4', 'a1-6',\n", + "> 'b1-3', 'a1-8', 'a2-11', 'a2-9', 'a1-1', 'a1-5',\n", + "> 'b2-5', 'b2-6', '?2-?', '1-4', 'a1-7', 'b1-5',\n", + "> 'b1-9', 'b2-2', 'a2-?', 'a1-4', 'b2-4', 'b1-6',\n", + "> 'b2-7', 'a2-5', 'a2-6', '?2-3', 'a1-3', '?1-3',\n", + "> 'a2-1', 'b1-4', '?1-6', 'a1-11', 'b1-?', 'b1-2',\n", + "> 'b1-8', 'b2-8', 'b2-3', 'a2-8', 'a2-7', 'a2-3'})\n", "\n", "Retrieves all linkages that match a given wildcard pattern from a list of linkages\n", "\n", @@ -9022,15 +9055,15 @@ "\n", "### get_possible_linkages\n", "\n", - "> get_possible_linkages (wildcard, linkage_list={'b2-4', 'b1-2', 'a1-11',\n", - "> '?1-?', 'b1-4', 'a1-?', 'a2-4', 'a2-5', '?1-6',\n", - "> '?2-8', 'a1-6', 'a1-5', 'a2-9', '1-6', 'b1-?',\n", - "> 'a2-?', 'a2-3', 'a1-4', 'a1-9', '?1-4', '?1-3',\n", - "> 'b2-6', '?2-?', '?1-2', 'a1-8', 'b2-8', 'a1-1',\n", - "> 'b1-8', 'a2-7', 'b1-5', 'b2-3', 'a2-11', 'b2-7',\n", - "> 'b1-7', 'a1-7', 'b1-9', '1-4', 'b2-5', 'a1-3',\n", - "> 'b1-1', 'a2-2', '?2-3', 'a2-6', 'b1-3', 'b2-2',\n", - "> 'a1-2', 'a2-1', '?2-6', 'b1-6', 'a2-8', 'b2-1'})\n", + "> get_possible_linkages (wildcard, linkage_list={'a1-?', 'b1-7', 'b2-1',\n", + "> 'a1-2', '?1-?', 'a1-9', 'a2-2', '?2-6', '?1-2',\n", + "> 'b1-1', '1-6', '?1-4', '?2-8', 'a2-4', 'a1-6',\n", + "> 'b1-3', 'a1-8', 'a2-11', 'a2-9', 'a1-1', 'a1-5',\n", + "> 'b2-5', 'b2-6', '?2-?', '1-4', 'a1-7', 'b1-5',\n", + "> 'b1-9', 'b2-2', 'a2-?', 'a1-4', 'b2-4', 'b1-6',\n", + "> 'b2-7', 'a2-5', 'a2-6', '?2-3', 'a1-3', '?1-3',\n", + "> 'a2-1', 'b1-4', '?1-6', 'a1-11', 'b1-?', 'b1-2',\n", + "> 'b1-8', 'b2-8', 'b2-3', 'a2-8', 'a2-7', 'a2-3'})\n", "\n", "Retrieves all linkages that match a given wildcard pattern from a list of linkages\n", "\n", @@ -9063,15 +9096,15 @@ "data": { "text/plain": [ "['a1-?',\n", - " 'a1-6',\n", - " 'a1-5',\n", - " 'a1-4',\n", + " 'a1-2',\n", " 'a1-9',\n", + " 'a1-6',\n", " 'a1-8',\n", " 'a1-1',\n", + " 'a1-5',\n", " 'a1-7',\n", - " 'a1-3',\n", - " 'a1-2']" + " 'a1-4',\n", + " 'a1-3']" ] }, "execution_count": null, @@ -10094,7 +10127,7 @@ { "data": { "text/plain": [ - "[{'dHex': 1, 'HexNAc': 2, 'Hex': 1, 'Neu5Ac': 1, 'Neu5Gc': 1}]" + "[{'HexNAc': 2, 'Hex': 2, 'Neu5Ac': 2}]" ] }, "execution_count": null, diff --git a/build/lib/glycowork/motif/analysis.py b/build/lib/glycowork/motif/analysis.py index 034caef..a5fc0f1 100644 --- a/build/lib/glycowork/motif/analysis.py +++ b/build/lib/glycowork/motif/analysis.py @@ -22,7 +22,7 @@ test_inter_vs_intra_group, replace_outliers_winsorization, hotellings_t2, sequence_richness, shannon_diversity_index, simpson_diversity_index, get_equivalence_test, clr_transformation, anosim, permanova_with_permutation, - alpha_biodiversity_stats, get_additive_logratio_transformation) + alpha_biodiversity_stats, get_additive_logratio_transformation, correct_multiple_testing) from glycowork.motif.annotate import (annotate_dataset, quantify_motifs, link_find, create_correlation_network, group_glycans_core, group_glycans_sia_fuc, group_glycans_N_glycan_type) from glycowork.motif.graph import subgraph_isomorphism @@ -509,7 +509,7 @@ def select_grouping(cohort_b, cohort_a, glycans, p_values, paired = False, group def get_differential_expression(df, group1, group2, motifs = False, feature_set = ['exhaustive', 'known'], paired = False, impute = True, sets = False, set_thresh = 0.9, effect_size_variance = False, - min_samples = 0, grouped_BH = False, custom_motifs = [], transform = "CLR", gamma = 0.1): + min_samples = 0.1, grouped_BH = False, custom_motifs = [], transform = "CLR", gamma = 0.1): """Calculates differentially expressed glycans or motifs from glycomics data\n | Arguments: | :- @@ -526,7 +526,7 @@ def get_differential_expression(df, group1, group2, | sets (bool): whether to identify clusters of highly correlated glycans/motifs to test for differential expression; default:False | set_thresh (float): correlation value used as a threshold for clusters; only used when sets=True; default:0.9 | effect_size_variance (bool): whether effect size variance should also be calculated/estimated; default:False - | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero + | min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10% | grouped_BH (bool): whether to perform two-stage adaptive Benjamini-Hochberg as a grouped multiple testing correction; will SIGNIFICANTLY increase runtime; default:False | custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty | transform (str): transformation to escape Aitchison space; options are CLR and ALR (use ALR if you have many glycans (>100) with low values); default:CLR @@ -628,9 +628,7 @@ def get_differential_expression(df, group1, group2, corrpvals = [p if p >= pvals[i] else pvals[i] for i, p in enumerate(corrpvals)] significance = [significance_dict[g] for g in glycans] else: - corrpvals = multipletests(pvals, method = 'fdr_tsbh')[1] - corrpvals = [p if p >= pvals[i] else pvals[i] for i, p in enumerate(corrpvals)] - significance = [p < alpha for p in corrpvals] + corrpvals, significance = correct_multiple_testing(pvals, alpha) levene_pvals = multipletests(levene_pvals, method = 'fdr_tsbh')[1] else: corrpvals, significance = [1]*len(glycans), [False]*len(glycans) @@ -739,7 +737,7 @@ def get_volcano(df_res, y_thresh = 0.05, x_thresh = 1.0, def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exhaustive', 'known'], - min_samples = 0, posthoc = True, custom_motifs = [], gamma = 0.1): + min_samples = 0.1, posthoc = True, custom_motifs = [], gamma = 0.1): """Calculate an ANOVA for each glycan (or motif) in the DataFrame\n | Arguments: | :- @@ -751,7 +749,7 @@ def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exh | 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), \ | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), \ | and 'chemical' (molecular properties of glycan) - | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero + | min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10% | posthoc (bool): whether to do Tukey's HSD test post-hoc to find out which differences were significant; default:True | custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty | gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n @@ -793,10 +791,11 @@ def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exh if p_value < alpha and posthoc: posthoc = pairwise_tukeyhsd(endog = data['Abundance'], groups = data['Group'], alpha = alpha) posthoc_results[glycan] = pd.DataFrame(data = posthoc._results_table.data[1:], columns = posthoc._results_table.data[0]) - results_df = pd.DataFrame(results, columns = ["Glycan", "F statistic", "corr p-val"]) - results_df['corr p-val'] = multipletests(results_df['corr p-val'].values.tolist(), method = 'fdr_tsbh')[1] - results_df['significant'] = [p < alpha for p in results_df['corr p-val']] - return results_df.sort_values(by = 'corr p-val'), posthoc_results + df_out = pd.DataFrame(results, columns = ["Glycan", "F statistic", "p-val"]) + corrpvals, significance = correct_multiple_testing(df_out['p-val'], alpha) + df_out['corr p-val'] = corrpvals + df_out['significant'] = significance + return df_out.sort_values(by = 'corr p-val'), posthoc_results def get_meta_analysis(effect_sizes, variances, model = 'fixed', filepath = '', @@ -904,7 +903,7 @@ def get_glycan_change_over_time(data, degree = 1): def get_time_series(df, impute = True, motifs = False, feature_set = ['known', 'exhaustive'], degree = 1, - min_samples = 0, custom_motifs = [], gamma = 0.1): + min_samples = 0.1, custom_motifs = [], gamma = 0.1): """Analyzes time series data of glycans using an OLS model\n | Arguments: | :- @@ -916,7 +915,7 @@ def get_time_series(df, impute = True, motifs = False, feature_set = ['known', ' | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), \ | and 'chemical' (molecular properties of glycan) | degree (int): degree of the polynomial for regression, default:1 for linear regression - | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero + | min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10% | custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty | gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n | Returns: @@ -950,11 +949,12 @@ def get_time_series(df, impute = True, motifs = False, feature_set = ['known', ' df[df.columns[0]] = df.iloc[:, 0].apply(lambda x: float(x.split('_')[1][1:])) df = df.sort_values(by = df.columns[0]) time = df.iloc[:, 0].to_numpy() # Time points - res = [(c, *get_glycan_change_over_time(np.column_stack((time, df[c].to_numpy())), degree = degree)) for c in df.columns[1:]] - res = pd.DataFrame(res, columns = ['Glycan', 'Change', 'p-val']) - res['corr p-val'] = multipletests(res['p-val'], method = 'fdr_tsbh')[1] - res['significant'] = [p < alpha for p in res['corr p-val']] - return res.sort_values(by = 'corr p-val') + df_out = [(c, *get_glycan_change_over_time(np.column_stack((time, df[c].to_numpy())), degree = degree)) for c in df.columns[1:]] + df_out = pd.DataFrame(df_out, columns = ['Glycan', 'Change', 'p-val']) + corrpvals, significance = correct_multiple_testing(df_out['p-val'], alpha) + df_out['corr p-val'] = corrpvals + df_out['significant'] = significance + return df_out.sort_values(by = 'corr p-val') def get_jtk(df_in, timepoints, periods, interval, motifs = False, feature_set = ['known', 'exhaustive', 'terminal'], @@ -1000,11 +1000,11 @@ def get_jtk(df_in, timepoints, periods, interval, motifs = False, feature_set = df = quantify_motifs(df.iloc[:, 1:], df.iloc[:, 0].values.tolist(), feature_set, custom_motifs = custom_motifs).T df = clean_up_heatmap(df).reset_index() res = df.iloc[:, 1:].apply(jtkx, param_dic = param_dic, axis = 1) - JTK_BHQ = pd.DataFrame(multipletests(res[0], method = 'fdr_tsbh')[1]) - Results = pd.concat([df.iloc[:, 0], JTK_BHQ, res], axis = 1) - Results.columns = ['Molecule_Name', 'BH_Q_Value', 'Adjusted_P_value', 'Period_Length', 'Lag_Phase', 'Amplitude'] - Results['significant'] = [p < alpha for p in Results['Adjusted_P_value']] - return Results.sort_values("Adjusted_P_value") + corrpvals, significance = correct_multiple_testing(res[0], alpha) + df_out = pd.concat([df.iloc[:, 0], pd.DataFrame(corrpvals), res], axis = 1) + df_out.columns = ['Molecule_Name', 'BH_Q_Value', 'Adjusted_P_value', 'Period_Length', 'Lag_Phase', 'Amplitude'] + df_out['significant'] = significance + return df_out.sort_values("Adjusted_P_value") def get_biodiversity(df, group1, group2, metrics = ['alpha','beta'], motifs = False, feature_set = ['exhaustive', 'known'], @@ -1114,9 +1114,10 @@ def get_biodiversity(df, group1, group2, metrics = ['alpha','beta'], motifs = Fa f, p = permanova_with_permutation(beta_df_out, group_sizes, permutations) b_test_stats = pd.DataFrame({'Metric': 'Beta diversity (PERMANOVA)', 'p-val': p, 'Effect size': f}, index = [0]) shopping_cart.append(b_test_stats) - df_out = pd.concat(shopping_cart, axis = 0) - df_out["corr p-val"] = multipletests(df_out['p-val'].values.tolist(), method = 'fdr_tsbh')[1] - df_out["significant"] = [p < alpha for p in df_out["corr p-val"]] + df_out = pd.concat(shopping_cart, axis = 0).reset_index(drop = True) + corrpvals, significance = correct_multiple_testing(df_out['p-val'], alpha) + df_out["corr p-val"] = corrpvals + df_out["significant"] = significance return df_out.sort_values(by = 'p-val').sort_values(by = 'corr p-val').reset_index(drop = True) diff --git a/glycowork/glycan_data/stats.py b/glycowork/glycan_data/stats.py index ae938d4..70bbcc9 100644 --- a/glycowork/glycan_data/stats.py +++ b/glycowork/glycan_data/stats.py @@ -185,7 +185,7 @@ class MissForest: Determines the number of iterations for the imputation process. """ - def __init__(self, regressor = RandomForestRegressor(n_jobs = -1), max_iter = 5, tol = 1e-6): + def __init__(self, regressor = RandomForestRegressor(n_jobs = -1), max_iter = 5, tol = 1e-5): self.regressor = regressor self.max_iter = max_iter self.tol = tol @@ -221,29 +221,29 @@ def fit_transform(self, X: pd.DataFrame) -> pd.DataFrame: return X_transform -def impute_and_normalize(df, groups, impute = True, min_samples = 0): +def impute_and_normalize(df, groups, impute = True, min_samples = 0.1): """given a dataframe, discards rows with too many missings, imputes the rest, and normalizes\n | Arguments: | :- | df (dataframe): dataframe containing glycan sequences in first column and relative abundances in subsequent columns | groups (list): nested list of column name lists, one list per group | impute (bool): replaces zeroes with predictions from MissForest; default:True - | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero\n + | min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10%\n | Returns: | :- | Returns a dataframe in the same style as the input """ if min_samples: - min_samples = [min_samples] * len(groups) - masks = [(df[group_cols] != 0).sum(axis = 1) >= thresh for group_cols, thresh in zip(groups, min_samples)] - df = df[np.all(masks, axis = 0)] + min_count = np.floor(df.shape[1] * min_samples) + mask = (df != 0).sum(axis = 1) >= min_count + df = df[mask] colname = df.columns[0] glycans = df[colname] df = df.iloc[:, 1:] for group in groups: group_data = df[group] all_zero_mask = (group_data == 0).all(axis = 1) - df.loc[all_zero_mask, group] = 1e-6 + df.loc[all_zero_mask, group] = 1e-5 old_cols = [] if isinstance(colname, int): old_cols = df.columns @@ -965,8 +965,34 @@ def get_additive_logratio_transformation(df, group1, group2, paired = False): ref_component = np.argmax(scores) ref_component_string = df.iloc[:, 0].values[ref_component] print(f"Reference component for ALR is {ref_component_string}, with Procrustes correlation of {procrustes_corr[ref_component]} and variance of {variances[ref_component]}") + if procrustes_corr[ref_component] < 0.9 or variances[ref_component] > 0.1: + print("Metrics of chosen reference component not good enough for ALR; switching to CLR instead.") + df.iloc[:, 1:] = clr_transformation(df.iloc[:, 1:], group1, group2, gamma = 0.1) + return df glycans = df.iloc[:, 0].values.tolist() glycans = glycans[:ref_component] + glycans[ref_component+1:] alr = alr_transformation(np.log2(df.iloc[:, 1:]), ref_component) alr.insert(loc = 0, column = 'glycan', value = glycans) return alr + + +def correct_multiple_testing(pvals, alpha): + """Corrects p-values for multiple testing, by default with the two-stage Benjamini-Hochberg procedure\n + | Arguments: + | :- + | pvals (list): list of raw p-values + | alpha (float): p-value threshold for statistical significance\n + | Returns: + | :- + | (i) list of corrected p-values + | (ii) list of True/False of whether corrected p-value reaches statistical significance + """ + corrpvals = multipletests(pvals, method = 'fdr_tsbh')[1] + corrpvals = [p if p >= pvals[i] else pvals[i] for i, p in enumerate(corrpvals)] + significance = [p < alpha for p in corrpvals] + if sum(significance) > 0.9*len(significance): + print("Significance inflation detected. The CLR/ALR transformation cannot seem to handle this dataset. \ + Proceed with caution; for now switching to Bonferroni correction for being conservative about this.") + corrpvals = multipletests(pvals, method = 'bonferroni')[1] + significance = [p < alpha for p in corrpvals] + return corrpvals, significance diff --git a/glycowork/motif/analysis.py b/glycowork/motif/analysis.py index 034caef..a5fc0f1 100644 --- a/glycowork/motif/analysis.py +++ b/glycowork/motif/analysis.py @@ -22,7 +22,7 @@ test_inter_vs_intra_group, replace_outliers_winsorization, hotellings_t2, sequence_richness, shannon_diversity_index, simpson_diversity_index, get_equivalence_test, clr_transformation, anosim, permanova_with_permutation, - alpha_biodiversity_stats, get_additive_logratio_transformation) + alpha_biodiversity_stats, get_additive_logratio_transformation, correct_multiple_testing) from glycowork.motif.annotate import (annotate_dataset, quantify_motifs, link_find, create_correlation_network, group_glycans_core, group_glycans_sia_fuc, group_glycans_N_glycan_type) from glycowork.motif.graph import subgraph_isomorphism @@ -509,7 +509,7 @@ def select_grouping(cohort_b, cohort_a, glycans, p_values, paired = False, group def get_differential_expression(df, group1, group2, motifs = False, feature_set = ['exhaustive', 'known'], paired = False, impute = True, sets = False, set_thresh = 0.9, effect_size_variance = False, - min_samples = 0, grouped_BH = False, custom_motifs = [], transform = "CLR", gamma = 0.1): + min_samples = 0.1, grouped_BH = False, custom_motifs = [], transform = "CLR", gamma = 0.1): """Calculates differentially expressed glycans or motifs from glycomics data\n | Arguments: | :- @@ -526,7 +526,7 @@ def get_differential_expression(df, group1, group2, | sets (bool): whether to identify clusters of highly correlated glycans/motifs to test for differential expression; default:False | set_thresh (float): correlation value used as a threshold for clusters; only used when sets=True; default:0.9 | effect_size_variance (bool): whether effect size variance should also be calculated/estimated; default:False - | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero + | min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10% | grouped_BH (bool): whether to perform two-stage adaptive Benjamini-Hochberg as a grouped multiple testing correction; will SIGNIFICANTLY increase runtime; default:False | custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty | transform (str): transformation to escape Aitchison space; options are CLR and ALR (use ALR if you have many glycans (>100) with low values); default:CLR @@ -628,9 +628,7 @@ def get_differential_expression(df, group1, group2, corrpvals = [p if p >= pvals[i] else pvals[i] for i, p in enumerate(corrpvals)] significance = [significance_dict[g] for g in glycans] else: - corrpvals = multipletests(pvals, method = 'fdr_tsbh')[1] - corrpvals = [p if p >= pvals[i] else pvals[i] for i, p in enumerate(corrpvals)] - significance = [p < alpha for p in corrpvals] + corrpvals, significance = correct_multiple_testing(pvals, alpha) levene_pvals = multipletests(levene_pvals, method = 'fdr_tsbh')[1] else: corrpvals, significance = [1]*len(glycans), [False]*len(glycans) @@ -739,7 +737,7 @@ def get_volcano(df_res, y_thresh = 0.05, x_thresh = 1.0, def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exhaustive', 'known'], - min_samples = 0, posthoc = True, custom_motifs = [], gamma = 0.1): + min_samples = 0.1, posthoc = True, custom_motifs = [], gamma = 0.1): """Calculate an ANOVA for each glycan (or motif) in the DataFrame\n | Arguments: | :- @@ -751,7 +749,7 @@ def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exh | 'graph' (structural graph features of glycans), 'exhaustive' (all mono- and disaccharide features), 'terminal' (non-reducing end motifs), \ | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), \ | and 'chemical' (molecular properties of glycan) - | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero + | min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10% | posthoc (bool): whether to do Tukey's HSD test post-hoc to find out which differences were significant; default:True | custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty | gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n @@ -793,10 +791,11 @@ def get_glycanova(df, groups, impute = True, motifs = False, feature_set = ['exh if p_value < alpha and posthoc: posthoc = pairwise_tukeyhsd(endog = data['Abundance'], groups = data['Group'], alpha = alpha) posthoc_results[glycan] = pd.DataFrame(data = posthoc._results_table.data[1:], columns = posthoc._results_table.data[0]) - results_df = pd.DataFrame(results, columns = ["Glycan", "F statistic", "corr p-val"]) - results_df['corr p-val'] = multipletests(results_df['corr p-val'].values.tolist(), method = 'fdr_tsbh')[1] - results_df['significant'] = [p < alpha for p in results_df['corr p-val']] - return results_df.sort_values(by = 'corr p-val'), posthoc_results + df_out = pd.DataFrame(results, columns = ["Glycan", "F statistic", "p-val"]) + corrpvals, significance = correct_multiple_testing(df_out['p-val'], alpha) + df_out['corr p-val'] = corrpvals + df_out['significant'] = significance + return df_out.sort_values(by = 'corr p-val'), posthoc_results def get_meta_analysis(effect_sizes, variances, model = 'fixed', filepath = '', @@ -904,7 +903,7 @@ def get_glycan_change_over_time(data, degree = 1): def get_time_series(df, impute = True, motifs = False, feature_set = ['known', 'exhaustive'], degree = 1, - min_samples = 0, custom_motifs = [], gamma = 0.1): + min_samples = 0.1, custom_motifs = [], gamma = 0.1): """Analyzes time series data of glycans using an OLS model\n | Arguments: | :- @@ -916,7 +915,7 @@ def get_time_series(df, impute = True, motifs = False, feature_set = ['known', ' | 'terminal2' (non-reducing end motifs of size 2), 'terminal3' (non-reducing end motifs of size 3), 'custom' (specify your own motifs in custom_motifs), \ | and 'chemical' (molecular properties of glycan) | degree (int): degree of the polynomial for regression, default:1 for linear regression - | min_samples (int): How many samples per group need to have non-zero values for glycan to be kept; default: zero + | min_samples (float): Percent of the samples that need to have non-zero values for glycan to be kept; default: 10% | custom_motifs (list): list of glycan motifs, used if feature_set includes 'custom'; default:empty | gamma (float): uncertainty parameter to estimate scale uncertainty for CLR transformation; default: 0.1\n | Returns: @@ -950,11 +949,12 @@ def get_time_series(df, impute = True, motifs = False, feature_set = ['known', ' df[df.columns[0]] = df.iloc[:, 0].apply(lambda x: float(x.split('_')[1][1:])) df = df.sort_values(by = df.columns[0]) time = df.iloc[:, 0].to_numpy() # Time points - res = [(c, *get_glycan_change_over_time(np.column_stack((time, df[c].to_numpy())), degree = degree)) for c in df.columns[1:]] - res = pd.DataFrame(res, columns = ['Glycan', 'Change', 'p-val']) - res['corr p-val'] = multipletests(res['p-val'], method = 'fdr_tsbh')[1] - res['significant'] = [p < alpha for p in res['corr p-val']] - return res.sort_values(by = 'corr p-val') + df_out = [(c, *get_glycan_change_over_time(np.column_stack((time, df[c].to_numpy())), degree = degree)) for c in df.columns[1:]] + df_out = pd.DataFrame(df_out, columns = ['Glycan', 'Change', 'p-val']) + corrpvals, significance = correct_multiple_testing(df_out['p-val'], alpha) + df_out['corr p-val'] = corrpvals + df_out['significant'] = significance + return df_out.sort_values(by = 'corr p-val') def get_jtk(df_in, timepoints, periods, interval, motifs = False, feature_set = ['known', 'exhaustive', 'terminal'], @@ -1000,11 +1000,11 @@ def get_jtk(df_in, timepoints, periods, interval, motifs = False, feature_set = df = quantify_motifs(df.iloc[:, 1:], df.iloc[:, 0].values.tolist(), feature_set, custom_motifs = custom_motifs).T df = clean_up_heatmap(df).reset_index() res = df.iloc[:, 1:].apply(jtkx, param_dic = param_dic, axis = 1) - JTK_BHQ = pd.DataFrame(multipletests(res[0], method = 'fdr_tsbh')[1]) - Results = pd.concat([df.iloc[:, 0], JTK_BHQ, res], axis = 1) - Results.columns = ['Molecule_Name', 'BH_Q_Value', 'Adjusted_P_value', 'Period_Length', 'Lag_Phase', 'Amplitude'] - Results['significant'] = [p < alpha for p in Results['Adjusted_P_value']] - return Results.sort_values("Adjusted_P_value") + corrpvals, significance = correct_multiple_testing(res[0], alpha) + df_out = pd.concat([df.iloc[:, 0], pd.DataFrame(corrpvals), res], axis = 1) + df_out.columns = ['Molecule_Name', 'BH_Q_Value', 'Adjusted_P_value', 'Period_Length', 'Lag_Phase', 'Amplitude'] + df_out['significant'] = significance + return df_out.sort_values("Adjusted_P_value") def get_biodiversity(df, group1, group2, metrics = ['alpha','beta'], motifs = False, feature_set = ['exhaustive', 'known'], @@ -1114,9 +1114,10 @@ def get_biodiversity(df, group1, group2, metrics = ['alpha','beta'], motifs = Fa f, p = permanova_with_permutation(beta_df_out, group_sizes, permutations) b_test_stats = pd.DataFrame({'Metric': 'Beta diversity (PERMANOVA)', 'p-val': p, 'Effect size': f}, index = [0]) shopping_cart.append(b_test_stats) - df_out = pd.concat(shopping_cart, axis = 0) - df_out["corr p-val"] = multipletests(df_out['p-val'].values.tolist(), method = 'fdr_tsbh')[1] - df_out["significant"] = [p < alpha for p in df_out["corr p-val"]] + df_out = pd.concat(shopping_cart, axis = 0).reset_index(drop = True) + corrpvals, significance = correct_multiple_testing(df_out['p-val'], alpha) + df_out["corr p-val"] = corrpvals + df_out["significant"] = significance return df_out.sort_values(by = 'p-val').sort_values(by = 'corr p-val').reset_index(drop = True)