{
"cells": [
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"cell_type": "markdown",
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"source": [
"# Introduction\n",
"\n",
"Active _cis_-regulatory sequences in the genome are characterized by accessible chromatin and specific histone modifications, which reflect the action of DNA-binding transcription factors (TFs) that recognize specific sequence motifs and recruit chromatin-modifying enzymes [@bib44]. These epigenetic hallmarks of active chromatin are routinely used to train machine learning models that predict _cis_-regulatory sequences, based on the assumption that such epigenetic marks are reliable predictors of genuine _cis_-regulatory sequences [@bib13; @bib19; @bib27; @bib41; @bib50; @bib77; @bib90]. However, results from functional assays show that many predicted _cis_-regulatory sequences exhibit little or no _cis_-regulatory activity. Typically, 50% or more of predicted _cis_-regulatory sequences fail to drive expression in massively parallel reporter assays (MPRAs) [@bib58; @bib48], indicating that an active chromatin state is not sufficient to reliably identify _cis_-regulatory sequences.\n",
"\n",
"Another challenge is that enhancers and silencers are difficult to distinguish by chromatin accessibility or epigenetic state [@bib11; @bib20; @bib62; @bib66; @bib76], and thus computational predictions of _cis-_regulatory sequences often do not differentiate between enhancers and silencers. Silencers are often enhancers in other cell types [@bib5; @bib11; @bib20; @bib30; @bib37; @bib61; @bib62], reside in open chromatin [@bib11; @bib29; @bib30; @bib62], sometimes bear epigenetic marks of active enhancers [@bib14; @bib30], and can be bound by TFs that also act on enhancers in the same cell type [@bib1; @bib21; @bib30; @bib35; @bib37; @bib52; @bib53; @bib65; @bib69; @bib70; @bib80; @bib85]. As a result, enhancers and silencers share similar sequence features, and understanding how they are distinguished in a particular cell type remains an important challenge [@bib76].\n",
"\n",
"The TF cone-rod homeobox (CRX) controls selective gene expression in a number of different photoreceptor and bipolar cell types in the retina [@bib6; @bib17; @bib18; @bib60]. These cell types derive from the same progenitor cell population [@bib45; @bib83], but they exhibit divergent, CRX-directed transcriptional programs [@bib9; @bib25; @bib31; @bib60]. CRX cooperates with cell type-specific co-factors to selectively activate and repress different genes in different cell types and is required for differentiation of rod and cone photoreceptors [@bib7; @bib23; @bib25; @bib28; @bib34; @bib43; @bib51; @bib55; @bib56; @bib60; @bib65; @bib75; @bib79]. However, the sequence features that define CRX-targeted enhancers vs. silencers in the retina are largely unknown.\n",
"\n",
"We previously found that a significant minority of CRX-bound sequences act as silencers in an MPRA conducted in live mouse retinas [@bib85], and that silencer activity requires CRX [@bib86]. Here, we extend our analysis by testing thousands of additional candidate _cis_-regulatory sequences. We show that while regions of accessible chromatin and CRX binding exhibit a range of _cis_-regulatory activity, enhancers and silencers contain more TF motifs than inactive sequences, and that enhancers are distinguished from silencers by a higher diversity of TF motifs. We capture the differences between these sequence classes with a new metric, motif information content (Boltzmann entropy), that considers only the number and diversity of TF motifs in a candidate _cis_-regulatory sequence. Our results suggest that CRX-targeted enhancers are defined by a flexible regulatory grammar and demonstrate how differences in motif information content encode functional differences between genomic loci with similar chromatin states."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Setup imports for analysis\n",
"import os\n",
"import sys\n",
"import itertools\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as mpatches\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"from scipy import stats\n",
"from sklearn.feature_selection import RFE, RFECV\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import StratifiedKFold\n",
"from pybedtools import BedTool\n",
"from IPython.display import display\n",
"import logomaker\n",
"\n",
"sys.path.insert(0, \"utils\")\n",
"from utils import fasta_seq_parse_manip, gkmsvm, modeling, plot_utils, predicted_occupancy, quality_control, sequence_annotation_processing\n",
"\n",
"data_dir = os.path.join(\"Data\")\n",
"figures_dir = os.path.join(\"Figures\")\n",
"\n",
"# Load in all sequences\n",
"all_seqs = fasta_seq_parse_manip.read_fasta(os.path.join(data_dir, \"library1And2.fasta\"))\n",
"# Drop scrambled sequences -- we don't need them for any analysis\n",
"all_seqs = all_seqs[~(all_seqs.index.str.contains(\"scr\"))]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"plot_utils.set_manuscript_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Results\n",
"\n",
"We tested the activities of 4844 putative CRX-targeted _cis_-regulatory sequences (CRX-targeted sequences) by MPRA in live retinas. The MPRA libraries consist of 164 bp genomic sequences centered on the best match to the CRX position weight matrix (PWM) [@bib49] whenever a CRX motif is present, and matched sequences in which all CRX motifs were abolished by point mutation (Materials and methods). The MPRA libraries include 3299 CRX-bound sequences identified by ChIP-seq in the adult retina [@bib9] and 1545 sequences that do not have measurable CRX binding in the adult retina but reside in accessible chromatin in adult photoreceptors [@bib31] and have the H3K27ac enhancer mark in postnatal day 14 (P14) retina [@bib72] (‘ATAC-seq peaks’). We split the sequences across two plasmid libraries, each of which contained the same 150 scrambled sequences as internal controls ([Supplementary files 1 and 2](#supp1)). We cloned sequences upstream of the rod photoreceptor-specific _Rhodopsin_ (_Rho_) promoter and a _DsRed_ reporter gene, electroporated libraries into explanted mouse retinas at P0 in triplicate, harvested the retinas at P8, and then sequenced the RNA and input DNA plasmid pool. The data is highly reproducible across replicates (R^2^ > 0.96, [Figure 1—figure supplement 1](#fig1s1)). After activity scores were calculated and normalized to the basal _Rho_ promoter, the two libraries were well calibrated and merged together (two-sample Kolmogorov-Smirnov test p = 0.09, [Figure 1—figure supplement 2](#fig1s2), [Supplementary file 3](#supp3), and Materials and methods)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing data for library1 with the Rho promoter...\n",
"Reading in barcode counts.\n"
]
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" <th>DNA</th>\n",
" <th>RNA1</th>\n",
" <th>RNA2</th>\n",
" <th>RNA3</th>\n",
" </tr>\n",
" <tr>\n",
" <th>barcode</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>AACAACAAG</th>\n",
" <td>chr16-87432635-87432799_CPPQ_scrambled</td>\n",
" <td>3019</td>\n",
" <td>148</td>\n",
" <td>325</td>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACCGC</th>\n",
" <td>chr4-119112319-119112483_CPPE_WT</td>\n",
" <td>4117</td>\n",
" <td>24493</td>\n",
" <td>25950</td>\n",
" <td>23406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACGGG</th>\n",
" <td>chr7-128854234-128854398_UPCE_WT</td>\n",
" <td>86</td>\n",
" <td>76</td>\n",
" <td>39</td>\n",
" <td>233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTAC</th>\n",
" <td>chr4-138107597-138107761_UPPE_WT</td>\n",
" <td>827</td>\n",
" <td>926</td>\n",
" <td>857</td>\n",
" <td>659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTGT</th>\n",
" <td>chr5-31298508-31298672_CPPE_WT</td>\n",
" <td>7170</td>\n",
" <td>492</td>\n",
" <td>392</td>\n",
" <td>149</td>\n",
" </tr>\n",
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"text/plain": [
" label DNA RNA1 RNA2 RNA3\n",
"barcode \n",
"AACAACAAG chr16-87432635-87432799_CPPQ_scrambled 3019 148 325 97\n",
"AACAACCGC chr4-119112319-119112483_CPPE_WT 4117 24493 25950 23406\n",
"AACAACGGG chr7-128854234-128854398_UPCE_WT 86 76 39 233\n",
"AACAACTAC chr4-138107597-138107761_UPPE_WT 827 926 857 659\n",
"AACAACTGT chr5-31298508-31298672_CPPE_WT 7170 492 392 149"
]
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"text": [
"Removing detection-limited barcodes and normalizing to counts per million.\n",
"Barcodes missing in DNA:\n",
"Sample DNA: 1090 barcodes\n",
"1090 barcodes are missing from more than 0 DNA samples.\n",
"Barcodes off in RNA:\n",
"Sample RNA1: 1744 barcodes\n",
"Sample RNA2: 1913 barcodes\n",
"Sample RNA3: 1491 barcodes\n",
"2215 barcodes are off in more than 0 RNA samples.\n",
"There are a total of 157.151 million barcode counts.\n"
]
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" <th>AACAACAAG</th>\n",
" <td>chr16-87432635-87432799_CPPQ_scrambled</td>\n",
" <td>73.436588</td>\n",
" <td>4.307406</td>\n",
" <td>7.418047</td>\n",
" <td>2.561422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACCGC</th>\n",
" <td>chr4-119112319-119112483_CPPE_WT</td>\n",
" <td>100.145224</td>\n",
" <td>712.846538</td>\n",
" <td>592.302519</td>\n",
" <td>618.068596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACGGG</th>\n",
" <td>chr7-128854234-128854398_UPCE_WT</td>\n",
" <td>2.091933</td>\n",
" <td>2.211911</td>\n",
" <td>0.890166</td>\n",
" <td>6.152695</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTAC</th>\n",
" <td>chr4-138107597-138107761_UPPE_WT</td>\n",
" <td>20.116614</td>\n",
" <td>26.950390</td>\n",
" <td>19.560819</td>\n",
" <td>17.401829</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTGT</th>\n",
" <td>chr5-31298508-31298672_CPPE_WT</td>\n",
" <td>174.408855</td>\n",
" <td>14.319214</td>\n",
" <td>8.947306</td>\n",
" <td>3.934556</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" label DNA RNA1 \\\n",
"barcode \n",
"AACAACAAG chr16-87432635-87432799_CPPQ_scrambled 73.436588 4.307406 \n",
"AACAACCGC chr4-119112319-119112483_CPPE_WT 100.145224 712.846538 \n",
"AACAACGGG chr7-128854234-128854398_UPCE_WT 2.091933 2.211911 \n",
"AACAACTAC chr4-138107597-138107761_UPPE_WT 20.116614 26.950390 \n",
"AACAACTGT chr5-31298508-31298672_CPPE_WT 174.408855 14.319214 \n",
"\n",
" RNA2 RNA3 \n",
"barcode \n",
"AACAACAAG 7.418047 2.561422 \n",
"AACAACCGC 592.302519 618.068596 \n",
"AACAACGGG 0.890166 6.152695 \n",
"AACAACTAC 19.560819 17.401829 \n",
"AACAACTGT 8.947306 3.934556 "
]
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"name": "stdout",
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"text": [
"Normalizing RNA to DNA.\n",
"Averaging across barcodes within a replicate.\n"
]
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" <th>BASAL</th>\n",
" <td>0.331679</td>\n",
" <td>0.306512</td>\n",
" <td>0.277308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-104768570-104768734_UPCQ_MUT-allCrxSites</th>\n",
" <td>1.005172</td>\n",
" <td>0.826315</td>\n",
" <td>0.930872</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-104768570-104768734_UPCQ_WT</th>\n",
" <td>1.114088</td>\n",
" <td>1.080287</td>\n",
" <td>1.091619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-106008207-106008371_CPPE_MUT-allCrxSites</th>\n",
" <td>1.180305</td>\n",
" <td>1.094909</td>\n",
" <td>0.798394</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-106008207-106008371_CPPE_WT</th>\n",
" <td>0.441799</td>\n",
" <td>0.533383</td>\n",
" <td>0.868990</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" RNA1 RNA2 RNA3\n",
"label \n",
"BASAL 0.331679 0.306512 0.277308\n",
"chr1-104768570-104768734_UPCQ_MUT-allCrxSites 1.005172 0.826315 0.930872\n",
"chr1-104768570-104768734_UPCQ_WT 1.114088 1.080287 1.091619\n",
"chr1-106008207-106008371_CPPE_MUT-allCrxSites 1.180305 1.094909 0.798394\n",
"chr1-106008207-106008371_CPPE_WT 0.441799 0.533383 0.868990"
]
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"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing to the basal Rho promoter.\n",
"Computing p-values for the null hypothesis that a sequence is no different than the basal promoter alone.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ryan/Documents/DBBS/CohenLab/Manuscripts/CRX-Information-Content/utils/quality_control.py:408: RuntimeWarning: invalid value encountered in double_scalars\n",
" cov = std / mean\n"
]
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"name": "stdout",
"output_type": "stream",
"text": [
"Done processing data!\n"
]
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" <td>0.000749</td>\n",
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" <td>3.606621</td>\n",
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" <td>0.001206</td>\n",
" <td>0.003548</td>\n",
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" <td>0.003039</td>\n",
" <td>0.007388</td>\n",
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" <tr>\n",
" <th>chr1-106008207-106008371_CPPE_WT</th>\n",
" <td>2.068611</td>\n",
" <td>0.944664</td>\n",
" <td>3.0</td>\n",
" <td>0.080583</td>\n",
" <td>0.103242</td>\n",
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" <tr>\n",
" <th>chr1-106171416-106171580_CSPE_scrambled</th>\n",
" <td>1.439587</td>\n",
" <td>0.579277</td>\n",
" <td>3.0</td>\n",
" <td>0.279730</td>\n",
" <td>0.312931</td>\n",
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"text/plain": [
" expression expression_std \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ_MUT-allCrxSites 3.027744 0.330482 \n",
"chr1-104768570-104768734_UPCQ_WT 3.606621 0.297412 \n",
"chr1-106008207-106008371_CPPE_MUT-allCrxSites 3.336604 0.396284 \n",
"chr1-106008207-106008371_CPPE_WT 2.068611 0.944664 \n",
"chr1-106171416-106171580_CSPE_scrambled 1.439587 0.579277 \n",
"\n",
" expression_reps \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ_MUT-allCrxSites 3.0 \n",
"chr1-104768570-104768734_UPCQ_WT 3.0 \n",
"chr1-106008207-106008371_CPPE_MUT-allCrxSites 3.0 \n",
"chr1-106008207-106008371_CPPE_WT 3.0 \n",
"chr1-106171416-106171580_CSPE_scrambled 3.0 \n",
"\n",
" expression_pvalue \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ_MUT-allCrxSites 0.000139 \n",
"chr1-104768570-104768734_UPCQ_WT 0.001206 \n",
"chr1-106008207-106008371_CPPE_MUT-allCrxSites 0.003039 \n",
"chr1-106008207-106008371_CPPE_WT 0.080583 \n",
"chr1-106171416-106171580_CSPE_scrambled 0.279730 \n",
"\n",
" expression_qvalue \n",
"label \n",
"chr1-104768570-104768734_UPCQ_MUT-allCrxSites 0.000749 \n",
"chr1-104768570-104768734_UPCQ_WT 0.003548 \n",
"chr1-106008207-106008371_CPPE_MUT-allCrxSites 0.007388 \n",
"chr1-106008207-106008371_CPPE_WT 0.103242 \n",
"chr1-106171416-106171580_CSPE_scrambled 0.312931 "
]
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"name": "stdout",
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"text": [
"Processing data for library2 with the Rho promoter...\n",
"Reading in barcode counts.\n"
]
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" <tr>\n",
" <th>AACAACAAG</th>\n",
" <td>chr7-141291911-141292075_UPPP_MUT-allCrxSites</td>\n",
" <td>132</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACGTT</th>\n",
" <td>chr19-16380352-16380516_CPPN_MUT-allCrxSites</td>\n",
" <td>1779</td>\n",
" <td>36</td>\n",
" <td>17</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTAC</th>\n",
" <td>chr1-44147572-44147736_UPPP_MUT-allCrxSites</td>\n",
" <td>2928</td>\n",
" <td>433</td>\n",
" <td>802</td>\n",
" <td>510</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTCG</th>\n",
" <td>chr12-116230818-116230982_CPPE_WT</td>\n",
" <td>2822</td>\n",
" <td>3043</td>\n",
" <td>2967</td>\n",
" <td>3013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTGT</th>\n",
" <td>chr5-65391346-65391510_CPPP_MUT-allCrxSites</td>\n",
" <td>1810</td>\n",
" <td>1572</td>\n",
" <td>2281</td>\n",
" <td>1559</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label DNA RNA1 RNA2 \\\n",
"barcode \n",
"AACAACAAG chr7-141291911-141292075_UPPP_MUT-allCrxSites 132 0 1 \n",
"AACAACGTT chr19-16380352-16380516_CPPN_MUT-allCrxSites 1779 36 17 \n",
"AACAACTAC chr1-44147572-44147736_UPPP_MUT-allCrxSites 2928 433 802 \n",
"AACAACTCG chr12-116230818-116230982_CPPE_WT 2822 3043 2967 \n",
"AACAACTGT chr5-65391346-65391510_CPPP_MUT-allCrxSites 1810 1572 2281 \n",
"\n",
" RNA3 \n",
"barcode \n",
"AACAACAAG 1 \n",
"AACAACGTT 46 \n",
"AACAACTAC 510 \n",
"AACAACTCG 3013 \n",
"AACAACTGT 1559 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removing detection-limited barcodes and normalizing to counts per million.\n",
"Barcodes missing in DNA:\n",
"Sample DNA: 277 barcodes\n",
"277 barcodes are missing from more than 0 DNA samples.\n",
"Barcodes off in RNA:\n",
"Sample RNA1: 875 barcodes\n",
"Sample RNA2: 678 barcodes\n",
"Sample RNA3: 774 barcodes\n",
"1180 barcodes are off in more than 0 RNA samples.\n",
"There are a total of 157.724 million barcode counts.\n"
]
},
{
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" <th></th>\n",
" <th>label</th>\n",
" <th>DNA</th>\n",
" <th>RNA1</th>\n",
" <th>RNA2</th>\n",
" <th>RNA3</th>\n",
" </tr>\n",
" <tr>\n",
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" <th>AACAACAAG</th>\n",
" <td>chr7-141291911-141292075_UPPP_MUT-allCrxSites</td>\n",
" <td>3.144868</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACGTT</th>\n",
" <td>chr19-16380352-16380516_CPPN_MUT-allCrxSites</td>\n",
" <td>42.384243</td>\n",
" <td>0.933407</td>\n",
" <td>0.406204</td>\n",
" <td>1.301935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTAC</th>\n",
" <td>chr1-44147572-44147736_UPPP_MUT-allCrxSites</td>\n",
" <td>69.758888</td>\n",
" <td>11.226812</td>\n",
" <td>19.163280</td>\n",
" <td>14.434499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTCG</th>\n",
" <td>chr12-116230818-116230982_CPPE_WT</td>\n",
" <td>67.233464</td>\n",
" <td>78.898818</td>\n",
" <td>70.894577</td>\n",
" <td>85.276757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTGT</th>\n",
" <td>chr5-65391346-65391510_CPPP_MUT-allCrxSites</td>\n",
" <td>43.122810</td>\n",
" <td>40.758772</td>\n",
" <td>54.503043</td>\n",
" <td>44.124283</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label DNA \\\n",
"barcode \n",
"AACAACAAG chr7-141291911-141292075_UPPP_MUT-allCrxSites 3.144868 \n",
"AACAACGTT chr19-16380352-16380516_CPPN_MUT-allCrxSites 42.384243 \n",
"AACAACTAC chr1-44147572-44147736_UPPP_MUT-allCrxSites 69.758888 \n",
"AACAACTCG chr12-116230818-116230982_CPPE_WT 67.233464 \n",
"AACAACTGT chr5-65391346-65391510_CPPP_MUT-allCrxSites 43.122810 \n",
"\n",
" RNA1 RNA2 RNA3 \n",
"barcode \n",
"AACAACAAG 0.000000 0.000000 0.000000 \n",
"AACAACGTT 0.933407 0.406204 1.301935 \n",
"AACAACTAC 11.226812 19.163280 14.434499 \n",
"AACAACTCG 78.898818 70.894577 85.276757 \n",
"AACAACTGT 40.758772 54.503043 44.124283 "
]
},
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing RNA to DNA.\n",
"Averaging across barcodes within a replicate.\n"
]
},
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RNA1</th>\n",
" <th>RNA2</th>\n",
" <th>RNA3</th>\n",
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" <th>BASAL</th>\n",
" <td>0.196778</td>\n",
" <td>0.218638</td>\n",
" <td>0.236666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-10229074-10229238_CPPE_MUT-allCrxSites</th>\n",
" <td>7.325586</td>\n",
" <td>5.922791</td>\n",
" <td>6.286389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-10229074-10229238_CPPE_WT</th>\n",
" <td>6.418129</td>\n",
" <td>5.188716</td>\n",
" <td>4.976230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-106171416-106171580_CSPE_MUT-shape</th>\n",
" <td>0.282047</td>\n",
" <td>0.264416</td>\n",
" <td>0.290612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-106171416-106171580_CSPE_WT</th>\n",
" <td>0.260469</td>\n",
" <td>0.276250</td>\n",
" <td>0.212923</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" RNA1 RNA2 RNA3\n",
"label \n",
"BASAL 0.196778 0.218638 0.236666\n",
"chr1-10229074-10229238_CPPE_MUT-allCrxSites 7.325586 5.922791 6.286389\n",
"chr1-10229074-10229238_CPPE_WT 6.418129 5.188716 4.976230\n",
"chr1-106171416-106171580_CSPE_MUT-shape 0.282047 0.264416 0.290612\n",
"chr1-106171416-106171580_CSPE_WT 0.260469 0.276250 0.212923"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing to the basal Rho promoter.\n",
"Computing p-values for the null hypothesis that a sequence is no different than the basal promoter alone.\n",
"Done processing data!\n"
]
},
{
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>expression</th>\n",
" <th>expression_std</th>\n",
" <th>expression_reps</th>\n",
" <th>expression_pvalue</th>\n",
" <th>expression_qvalue</th>\n",
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" <th>chr1-10229074-10229238_CPPE_MUT-allCrxSites</th>\n",
" <td>30.293101</td>\n",
" <td>6.011230</td>\n",
" <td>3.0</td>\n",
" <td>0.000003</td>\n",
" <td>0.000128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-10229074-10229238_CPPE_WT</th>\n",
" <td>25.791454</td>\n",
" <td>6.063103</td>\n",
" <td>3.0</td>\n",
" <td>0.000019</td>\n",
" <td>0.000167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-106171416-106171580_CSPE_MUT-shape</th>\n",
" <td>1.290214</td>\n",
" <td>0.124284</td>\n",
" <td>3.0</td>\n",
" <td>0.023905</td>\n",
" <td>0.031469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-106171416-106171580_CSPE_WT</th>\n",
" <td>1.162281</td>\n",
" <td>0.229405</td>\n",
" <td>3.0</td>\n",
" <td>0.226254</td>\n",
" <td>0.246199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-106171416-106171580_CSPE_scrambled</th>\n",
" <td>1.995027</td>\n",
" <td>0.380942</td>\n",
" <td>3.0</td>\n",
" <td>0.012703</td>\n",
" <td>0.018175</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" expression expression_std \\\n",
"label \n",
"chr1-10229074-10229238_CPPE_MUT-allCrxSites 30.293101 6.011230 \n",
"chr1-10229074-10229238_CPPE_WT 25.791454 6.063103 \n",
"chr1-106171416-106171580_CSPE_MUT-shape 1.290214 0.124284 \n",
"chr1-106171416-106171580_CSPE_WT 1.162281 0.229405 \n",
"chr1-106171416-106171580_CSPE_scrambled 1.995027 0.380942 \n",
"\n",
" expression_reps \\\n",
"label \n",
"chr1-10229074-10229238_CPPE_MUT-allCrxSites 3.0 \n",
"chr1-10229074-10229238_CPPE_WT 3.0 \n",
"chr1-106171416-106171580_CSPE_MUT-shape 3.0 \n",
"chr1-106171416-106171580_CSPE_WT 3.0 \n",
"chr1-106171416-106171580_CSPE_scrambled 3.0 \n",
"\n",
" expression_pvalue \\\n",
"label \n",
"chr1-10229074-10229238_CPPE_MUT-allCrxSites 0.000003 \n",
"chr1-10229074-10229238_CPPE_WT 0.000019 \n",
"chr1-106171416-106171580_CSPE_MUT-shape 0.023905 \n",
"chr1-106171416-106171580_CSPE_WT 0.226254 \n",
"chr1-106171416-106171580_CSPE_scrambled 0.012703 \n",
"\n",
" expression_qvalue \n",
"label \n",
"chr1-10229074-10229238_CPPE_MUT-allCrxSites 0.000128 \n",
"chr1-10229074-10229238_CPPE_WT 0.000167 \n",
"chr1-106171416-106171580_CSPE_MUT-shape 0.031469 \n",
"chr1-106171416-106171580_CSPE_WT 0.246199 \n",
"chr1-106171416-106171580_CSPE_scrambled 0.018175 "
]
},
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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Process data for the Rho promoter: convert counts into activity scores for each sequence\n",
"library_names = [\"library1\", \"library2\"]\n",
"rho_activity_data = {} # {library name: pd.DataFrame}\n",
"barcode_count_dir = os.path.join(data_dir, \"Rhodopsin\")\n",
"\n",
"for library in library_names:\n",
" print(f\"Processing data for {library} with the Rho promoter...\")\n",
" # File names\n",
" barcode_count_files = [\n",
" os.path.join(barcode_count_dir, f\"{library}{sample}.counts\")\n",
" for sample in [\"Plasmid\", \"Rna1\", \"Rna2\", \"Rna3\"]\n",
" ]\n",
" \n",
" # Masks and metadata for downstream functions\n",
" sample_labels = np.array([\"DNA\", \"RNA1\", \"RNA2\", \"RNA3\"])\n",
" sample_rna_mask = np.array([False, True, True, True])\n",
" rna_labels = sample_labels[sample_rna_mask]\n",
" dna_labels = sample_labels[np.logical_not(sample_rna_mask)]\n",
" n_samples = len(sample_labels)\n",
" n_rna_samples = len(rna_labels)\n",
" n_dna_samples = len(dna_labels)\n",
" n_barcodes_per_sequence = 3\n",
" \n",
" # Read in the barcode counts\n",
" print(\"Reading in barcode counts.\")\n",
" all_sample_counts_df = quality_control.read_bc_count_files(barcode_count_files, sample_labels)\n",
" display(all_sample_counts_df.head())\n",
" \n",
" # Remove barcodes that are detection-limited.\n",
" # Barcodes below the DNA cutoff are NaN (because they are missing from the input plasmid pool)\n",
" # Barcodes below any of the RNA cutoffs are zero in all replicates\n",
" print(\"Removing detection-limited barcodes and normalizing to counts per million.\")\n",
" cutoffs = [10, 5, 5, 5]\n",
" threshold_sample_counts_df = quality_control.filter_low_counts(all_sample_counts_df, sample_labels, cutoffs,\n",
" dna_labels=dna_labels, bc_per_seq=n_barcodes_per_sequence)\n",
" display(threshold_sample_counts_df.head())\n",
"\n",
" # Normalize RNA barcode counts by plasmid barcode counts\n",
" print(\"Normalizing RNA to DNA.\")\n",
" normalized_sample_counts_df = quality_control.normalize_rna_by_dna(threshold_sample_counts_df, rna_labels, dna_labels)\n",
" # Drop DNA\n",
" barcode_sample_counts_df = normalized_sample_counts_df.drop(columns=dna_labels)\n",
" \n",
" # Average across barcodes\n",
" print(\"Averaging across barcodes within a replicate.\")\n",
" activity_replicate_df = quality_control.average_barcodes(barcode_sample_counts_df)\n",
" display(activity_replicate_df.head())\n",
" \n",
" # Basal-normalize, average across replicates, do statistics\n",
" print(\"Normalizing to the basal Rho promoter.\")\n",
" sequence_expression_df = quality_control.basal_normalize(activity_replicate_df, \"BASAL\")\n",
" print(\"Computing p-values for the null hypothesis that a sequence is no different than the basal promoter alone.\")\n",
" sequence_expression_df[\"expression_pvalue\"] = quality_control.log_ttest_vs_basal(activity_replicate_df, \"BASAL\")\n",
" sequence_expression_df[\"expression_qvalue\"] = modeling.fdr(sequence_expression_df[\"expression_pvalue\"])\n",
" print(f\"Done processing data!\")\n",
" display(sequence_expression_df.head())\n",
" \n",
" rho_activity_data[library] = sequence_expression_df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing data for library1 with the Polylinker...\n",
"Reading in barcode counts.\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>DNA</th>\n",
" <th>RNA1</th>\n",
" <th>RNA2</th>\n",
" <th>RNA3</th>\n",
" </tr>\n",
" <tr>\n",
" <th>barcode</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AACAACAAG</th>\n",
" <td>chr16-87432635-87432799_CPPQ_scrambled</td>\n",
" <td>987</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACCGC</th>\n",
" <td>chr4-119112319-119112483_CPPE_WT</td>\n",
" <td>1326</td>\n",
" <td>4963</td>\n",
" <td>4554</td>\n",
" <td>17827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACGGG</th>\n",
" <td>chr7-128854234-128854398_UPCE_WT</td>\n",
" <td>35</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTAC</th>\n",
" <td>chr4-138107597-138107761_UPPE_WT</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTGT</th>\n",
" <td>chr5-31298508-31298672_CPPE_WT</td>\n",
" <td>5007</td>\n",
" <td>934</td>\n",
" <td>993</td>\n",
" <td>575</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label DNA RNA1 RNA2 RNA3\n",
"barcode \n",
"AACAACAAG chr16-87432635-87432799_CPPQ_scrambled 987 2 3 10\n",
"AACAACCGC chr4-119112319-119112483_CPPE_WT 1326 4963 4554 17827\n",
"AACAACGGG chr7-128854234-128854398_UPCE_WT 35 0 0 2\n",
"AACAACTAC chr4-138107597-138107761_UPPE_WT 5 8 6 4\n",
"AACAACTGT chr5-31298508-31298672_CPPE_WT 5007 934 993 575"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removing barcodes missing from the DNA pool and normalizing to counts per million.\n",
"Removing detection-limited barcodes and normalizing to counts per million.\n",
"Barcodes missing in DNA:\n",
"Sample DNA: 1722 barcodes\n",
"1722 barcodes are missing from more than 0 DNA samples.\n",
"Barcodes off in RNA:\n",
"Sample RNA1: 0 barcodes\n",
"Sample RNA2: 0 barcodes\n",
"Sample RNA3: 0 barcodes\n",
"0 barcodes are off in more than 0 RNA samples.\n",
"There are a total of 92.122 million barcode counts.\n",
"Now removing RNA barcodes missing from any replicate.\n",
"Barcodes missing in DNA:\n",
"Sample DNA: 0 barcodes\n",
"0 barcodes are missing from more than 0 DNA samples.\n",
"Barcodes off in RNA:\n",
"Sample RNA1: 5842 barcodes\n",
"Sample RNA2: 11412 barcodes\n",
"Sample RNA3: 9805 barcodes\n",
"12991 barcodes are off in more than 0 RNA samples.\n"
]
},
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>DNA</th>\n",
" <th>RNA1</th>\n",
" <th>RNA2</th>\n",
" <th>RNA3</th>\n",
" </tr>\n",
" <tr>\n",
" <th>barcode</th>\n",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>AACAACAAG</th>\n",
" <td>chr16-87432635-87432799_CPPQ_scrambled</td>\n",
" <td>48.214705</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACCGC</th>\n",
" <td>chr4-119112319-119112483_CPPE_WT</td>\n",
" <td>64.774771</td>\n",
" <td>238.306557</td>\n",
" <td>198.604223</td>\n",
" <td>639.087016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACGGG</th>\n",
" <td>chr7-128854234-128854398_UPCE_WT</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTAC</th>\n",
" <td>chr4-138107597-138107761_UPPE_WT</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTGT</th>\n",
" <td>chr5-31298508-31298672_CPPE_WT</td>\n",
" <td>244.590708</td>\n",
" <td>44.847537</td>\n",
" <td>43.305664</td>\n",
" <td>20.613397</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label DNA RNA1 \\\n",
"barcode \n",
"AACAACAAG chr16-87432635-87432799_CPPQ_scrambled 48.214705 0.000000 \n",
"AACAACCGC chr4-119112319-119112483_CPPE_WT 64.774771 238.306557 \n",
"AACAACGGG chr7-128854234-128854398_UPCE_WT NaN 0.000000 \n",
"AACAACTAC chr4-138107597-138107761_UPPE_WT NaN 0.000000 \n",
"AACAACTGT chr5-31298508-31298672_CPPE_WT 244.590708 44.847537 \n",
"\n",
" RNA2 RNA3 \n",
"barcode \n",
"AACAACAAG 0.000000 0.000000 \n",
"AACAACCGC 198.604223 639.087016 \n",
"AACAACGGG 0.000000 0.000000 \n",
"AACAACTAC 0.000000 0.000000 \n",
"AACAACTGT 43.305664 20.613397 "
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Normalizing RNA to DNA.\n",
"Averaging across barcodes within a replicate.\n"
]
},
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" <td>0.742818</td>\n",
" <td>0.983263</td>\n",
" <td>1.267636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-104768570-104768734_UPCQ_MUT-allCrxSites</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-104768570-104768734_UPCQ_WT</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" </tr>\n",
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" <th>chr1-106008207-106008371_CPPE_MUT-allCrxSites</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>chr1-106008207-106008371_CPPE_WT</th>\n",
" <td>0.000000</td>\n",
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" </tr>\n",
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"</table>\n",
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],
"text/plain": [
" RNA1 RNA2 RNA3\n",
"label \n",
"BASAL 0.742818 0.983263 1.267636\n",
"chr1-104768570-104768734_UPCQ_MUT-allCrxSites 0.000000 0.000000 0.000000\n",
"chr1-104768570-104768734_UPCQ_WT 0.000000 0.000000 0.000000\n",
"chr1-106008207-106008371_CPPE_MUT-allCrxSites 0.000000 0.000000 0.000000\n",
"chr1-106008207-106008371_CPPE_WT 0.000000 0.000000 0.000000"
]
},
"metadata": {},
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"name": "stdout",
"output_type": "stream",
"text": [
"Removing the 'basal' promoter (Polylinker) and averaging across replicates. No statistical analysis is performed here.\n",
"Done processing data!\n"
]
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"text/plain": [
" expression expression_SEM\n",
"label \n",
"chr1-104768570-104768734_UPCQ_MUT-allCrxSites 0.0 0.0\n",
"chr1-104768570-104768734_UPCQ_WT 0.0 0.0\n",
"chr1-106008207-106008371_CPPE_MUT-allCrxSites 0.0 0.0\n",
"chr1-106008207-106008371_CPPE_WT 0.0 0.0\n",
"chr1-106171416-106171580_CSPE_scrambled 0.0 0.0"
]
},
"metadata": {},
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"name": "stdout",
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"text": [
"Processing data for library2 with the Polylinker...\n",
"Reading in barcode counts.\n"
]
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" <td>3</td>\n",
" <td>20</td>\n",
" <td>15</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACGTT</th>\n",
" <td>chr19-16380352-16380516_CPPN_MUT-allCrxSites</td>\n",
" <td>990</td>\n",
" <td>10</td>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTAC</th>\n",
" <td>chr1-44147572-44147736_UPPP_MUT-allCrxSites</td>\n",
" <td>1056</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTCG</th>\n",
" <td>chr12-116230818-116230982_CPPE_WT</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AACAACTGT</th>\n",
" <td>chr5-65391346-65391510_CPPP_MUT-allCrxSites</td>\n",
" <td>1653</td>\n",
" <td>1441</td>\n",
" <td>9</td>\n",
" <td>4695</td>\n",
" </tr>\n",
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"</table>\n",
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],
"text/plain": [
" label DNA RNA1 RNA2 \\\n",
"barcode \n",
"AACAACAAG chr7-141291911-141292075_UPPP_MUT-allCrxSites 3 20 15 \n",
"AACAACGTT chr19-16380352-16380516_CPPN_MUT-allCrxSites 990 10 9 \n",
"AACAACTAC chr1-44147572-44147736_UPPP_MUT-allCrxSites 1056 2 4 \n",
"AACAACTCG chr12-116230818-116230982_CPPE_WT 7 4 6 \n",
"AACAACTGT chr5-65391346-65391510_CPPP_MUT-allCrxSites 1653 1441 9 \n",
"\n",
" RNA3 \n",
"barcode \n",
"AACAACAAG 21 \n",
"AACAACGTT 10 \n",
"AACAACTAC 3 \n",
"AACAACTCG 0 \n",
"AACAACTGT 4695 "
]
},
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"name": "stdout",
"output_type": "stream",
"text": [
"Removing barcodes missing from the DNA pool and normalizing to counts per million.\n",
"Removing detection-limited barcodes and normalizing to counts per million.\n",
"Barcodes missing in DNA:\n",
"Sample DNA: 2107 barcodes\n",
"2107 barcodes are missing from more than 0 DNA samples.\n",
"Barcodes off in RNA:\n",
"Sample RNA1: 0 barcodes\n",
"Sample RNA2: 0 barcodes\n",
"Sample RNA3: 0 barcodes\n",
"0 barcodes are off in more than 0 RNA samples.\n",
"There are a total of 89.662 million barcode counts.\n",
"Now removing RNA barcodes missing from any replicate.\n",
"Barcodes missing in DNA:\n",
"Sample DNA: 0 barcodes\n",
"0 barcodes are missing from more than 0 DNA samples.\n",
"Barcodes off in RNA:\n",
"Sample RNA1: 12647 barcodes\n",
"Sample RNA2: 12055 barcodes\n",
"Sample RNA3: 10999 barcodes\n",
"13873 barcodes are off in more than 0 RNA samples.\n"
]
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"text/plain": [
" label DNA RNA1 \\\n",
"barcode \n",
"AACAACAAG chr7-141291911-141292075_UPPP_MUT-allCrxSites NaN 0.0 \n",
"AACAACGTT chr19-16380352-16380516_CPPN_MUT-allCrxSites 38.377926 0.0 \n",
"AACAACTAC chr1-44147572-44147736_UPPP_MUT-allCrxSites 40.936454 0.0 \n",
"AACAACTCG chr12-116230818-116230982_CPPE_WT NaN 0.0 \n",
"AACAACTGT chr5-65391346-65391510_CPPP_MUT-allCrxSites 64.079506 0.0 \n",
"\n",
" RNA2 RNA3 \n",
"barcode \n",
"AACAACAAG 0.0 0.0 \n",
"AACAACGTT 0.0 0.0 \n",
"AACAACTAC 0.0 0.0 \n",
"AACAACTCG 0.0 0.0 \n",
"AACAACTGT 0.0 0.0 "
]
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"Normalizing RNA to DNA.\n",
"Averaging across barcodes within a replicate.\n"
]
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"text/plain": [
" RNA1 RNA2 RNA3\n",
"label \n",
"BASAL 0.000000 0.000000 0.000000\n",
"chr1-10229074-10229238_CPPE_MUT-allCrxSites 1.486824 0.405204 1.305344\n",
"chr1-10229074-10229238_CPPE_WT 0.000000 0.000000 0.000000\n",
"chr1-106171416-106171580_CSPE_MUT-shape 0.000000 0.000000 0.000000\n",
"chr1-106171416-106171580_CSPE_WT 0.000000 0.000000 0.000000"
]
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"name": "stdout",
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"text": [
"Removing the 'basal' promoter (Polylinker) and averaging across replicates. No statistical analysis is performed here.\n",
"Done processing data!\n"
]
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"text/plain": [
" expression expression_SEM\n",
"label \n",
"chr1-10229074-10229238_CPPE_MUT-allCrxSites 1.06579 0.334422\n",
"chr1-10229074-10229238_CPPE_WT 0.00000 0.000000\n",
"chr1-106171416-106171580_CSPE_MUT-shape 0.00000 0.000000\n",
"chr1-106171416-106171580_CSPE_WT 0.00000 0.000000\n",
"chr1-106171416-106171580_CSPE_scrambled 0.00000 0.000000"
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"data": {
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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Now process data for the Polylinker (experiment is in Fig 4, but it is easier to process the data here)\n",
"# Process data for the Rho promoter: convert counts into activity scores for each sequence\n",
"library_names = [\"library1\", \"library2\"]\n",
"polylinker_activity_data = {} # {library name: pd.DataFrame}\n",
"barcode_count_dir = os.path.join(data_dir, \"Polylinker\")\n",
"\n",
"for library in library_names:\n",
" print(f\"Processing data for {library} with the Polylinker...\")\n",
" # File names\n",
" barcode_count_files = [\n",
" os.path.join(barcode_count_dir, f\"{library}{sample}.counts\")\n",
" for sample in [\"Plasmid\", \"Rna1\", \"Rna2\", \"Rna3\"]\n",
" ]\n",
" \n",
" # Masks and metadata for downstream functions\n",
" sample_labels = np.array([\"DNA\", \"RNA1\", \"RNA2\", \"RNA3\"])\n",
" sample_rna_mask = np.array([False, True, True, True])\n",
" rna_labels = sample_labels[sample_rna_mask]\n",
" dna_labels = sample_labels[np.logical_not(sample_rna_mask)]\n",
" n_samples = len(sample_labels)\n",
" n_rna_samples = len(rna_labels)\n",
" n_dna_samples = len(dna_labels)\n",
" n_barcodes_per_sequence = 3\n",
" \n",
" # Read in the barcode counts\n",
" print(\"Reading in barcode counts.\")\n",
" all_sample_counts_df = quality_control.read_bc_count_files(barcode_count_files, sample_labels)\n",
" display(all_sample_counts_df.head())\n",
" \n",
" # Remove barcodes that are detection-limited.\n",
" print(\"Removing barcodes missing from the DNA pool and normalizing to counts per million.\")\n",
" cutoffs_dna_only = [50, 0, 0, 0]\n",
" # Barcodes below the DNA cutoff are NaN (because they are missing from the input plasmid pool)\n",
" # Barcodes below any of the RNA cutoffs are zero in all replicates\n",
" print(\"Removing detection-limited barcodes and normalizing to counts per million.\")\n",
" threshold_sample_counts_df = quality_control.filter_low_counts(all_sample_counts_df, sample_labels, cutoffs_dna_only,\n",
" dna_labels=dna_labels, bc_per_seq=n_barcodes_per_sequence)\n",
" print(\"Now removing RNA barcodes missing from any replicate.\")\n",
" cutoffs_rna_cpm = [0, 8, 8, 8]\n",
" threshold_sample_counts_df = quality_control.filter_low_counts(threshold_sample_counts_df, sample_labels, cutoffs_rna_cpm,\n",
" dna_labels=dna_labels, bc_per_seq=n_barcodes_per_sequence, cpm_normalize=False)\n",
" display(threshold_sample_counts_df.head())\n",
"\n",
" # Normalize RNA barcode counts by plasmid barcode counts\n",
" print(\"Normalizing RNA to DNA.\")\n",
" normalized_sample_counts_df = quality_control.normalize_rna_by_dna(threshold_sample_counts_df, rna_labels, dna_labels)\n",
" # Drop DNA\n",
" barcode_sample_counts_df = normalized_sample_counts_df.drop(columns=dna_labels)\n",
" \n",
" # Average across barcodes\n",
" print(\"Averaging across barcodes within a replicate.\")\n",
" activity_replicate_df = quality_control.average_barcodes(barcode_sample_counts_df)\n",
" display(activity_replicate_df.head())\n",
" \n",
" # Drop \"basal\" and average across replicates\n",
" print(\"Removing the 'basal' promoter (Polylinker) and averaging across replicates. No statistical analysis is performed here.\")\n",
" activity_replicate_df = activity_replicate_df.drop(index=\"BASAL\")\n",
" sequence_expression_df = activity_replicate_df.apply(lambda x: pd.Series({\"expression\": x.mean(), \"expression_SEM\": x.sem()}), axis=1)\n",
" print(f\"Done processing data!\")\n",
" display(sequence_expression_df.head())\n",
" \n",
" polylinker_activity_data[library] = sequence_expression_df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"caption": "### Reproducibility of massively parallel reporter assay (MPRA) measurements.\n\nEach row represents a different library and experiment. For each column, the first replicate in the title is the x-axis and the second replicate is the y-axis.",
"id": "fig1s1",
"label": "Figure 1—figure supplement 1."
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 16 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# File names of the raw barcode counts\n",
"raw_data_files = [os.path.join(data_dir, dirname, filename) for dirname, filename in itertools.product([\"Rhodopsin\", \"Polylinker\"], [\"library1RawBarcodeCounts.txt\", \"library2RawBarcodeCounts.txt\"])]\n",
"raw_data_names = [\"Library 1\\n+Rho\", \"Library 2\\n+Rho\", \"Library 1\\n+Polylinker\", \"Library 2\\n+Polylinker\"]\n",
"comparison_columns = [\"Rep 1 vs 2\", \"Rep 1 vs 3\", \"Rep 2 vs 3\"]\n",
"fig, ax_list = plt.subplots(nrows=4, ncols=3, figsize=(8, 8))\n",
"\n",
"# Read in each dataset\n",
"for row, filename in enumerate(raw_data_files):\n",
" row_df = pd.read_csv(filename, sep=\"\\t\")\n",
" # Get all 3 pairs of combinations and plot them\n",
" for col, (x, y) in enumerate(itertools.combinations([\"RNA1\", \"RNA2\", \"RNA3\"], 2)):\n",
" rsquared = stats.pearsonr(row_df[x], row_df[y])[0] ** 2\n",
" ax = ax_list[row, col]\n",
" ax.scatter(row_df[x] / 1000, row_df[y] / 1000, color=\"k\")\n",
" ax.text(0.02, 0.98, fr\"$r^2$={rsquared:.2f}\", transform=ax.transAxes, ha=\"left\", va=\"top\")\n",
" max_value = max(ax.get_xlim()[1], ax.get_ylim()[1])\n",
" ax.set_xlim(right=max_value)\n",
" ax.set_ylim(top=max_value)\n",
" \n",
"# Add \"axis\" labels\n",
"fig.text(0.5, 0.025, \"Raw barcode counts (thousands)\", ha=\"center\", va=\"top\", fontsize=14)\n",
"fig.text(0.025, 0.5, \"Raw barcode counts (thousands)\", rotation=90, ha=\"right\", va=\"center\", fontsize=14)\n",
"\n",
"# Add column labels at the top\n",
"for col, text in enumerate(comparison_columns):\n",
" ax_list[0, col].set_title(text)\n",
" \n",
"# Add row labels on the right\n",
"for row, text in enumerate(raw_data_names):\n",
" twinax = ax_list[row, 2].twinx()\n",
" twinax.set_ylabel(text)\n",
" twinax.set_yticks([])\n",
" \n",
"display(fig)\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"caption": "### Calibration of massively parallel reporter assay (MPRA) libraries with the _Rho_ promoter.\n\nProbability density histogram of the same 150 scrambled sequences in two libraries after normalizing to the basal _Rho_ promoter.",
"id": "fig1s2",
"label": "Figure 1—figure supplement 2."
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Scrambled sequences from L1 and L2 are drawn from the same distribution, KS test p = 0.087, D = 0.14\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"library1_rho_df = rho_activity_data[\"library1\"]\n",
"library1_rho_df[\"library\"] = 1\n",
"library2_rho_df = rho_activity_data[\"library2\"]\n",
"library2_rho_df[\"library\"] = 2\n",
"\n",
"# Get scrambled sequences from each library with RNA barcodes measured\n",
"scrambled_library1_df = library1_rho_df[library1_rho_df.index.str.contains(\"scrambled\") & (library1_rho_df[\"expression\"] > 0)]\n",
"scrambled_library2_df = library2_rho_df[library2_rho_df.index.str.contains(\"scrambled\") & (library2_rho_df[\"expression\"] > 0)]\n",
"\n",
"# Compare distributions of log2 expression\n",
"scrambled_library1_expr = np.log2(scrambled_library1_df[\"expression\"])\n",
"scrambled_library2_expr = np.log2(scrambled_library2_df[\"expression\"])\n",
"ks_stat, pval = stats.ks_2samp(scrambled_library1_expr, scrambled_library2_expr)\n",
"print(f\"Scrambled sequences from L1 and L2 are drawn from the same distribution, KS test p = {pval:.3f}, D = {ks_stat:.2f}\")\n",
"\n",
"# Show the two histograms\n",
"fig, ax = plt.subplots()\n",
"ax.hist([scrambled_library2_expr, scrambled_library1_expr], bins=\"auto\", histtype=\"stepfilled\", density=True, label=[\"library 2\", \"library 1\"], color=plot_utils.set_color([0.75, 0.25]), alpha=0.5)\n",
"ax.set_xlabel(\"log2 Scrambled Activity/Rho\")\n",
"ax.set_ylabel(\"Density\")\n",
"ax.legend(loc=\"upper left\", frameon=False)\n",
"display(fig)\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Joining together data from the two libraries with the Rho promoter.\n",
"Annotating sequences as strong enhancer, weak enhancer, inactive, silencer, or ambiguous.\n",
"Cutoff to call something a strong enhancer: activity is above 2.84\n",
"Joining together data from the two libraries with the Polylinker promoter and annotate for autonomous activity.\n",
"Computing the effect size upon mutating CRX motifs in the presence of the Rho promoter.\n",
"This is for Figure 5, but it is easier to do it here.\n",
"Joining Rho and Polylinker data together.\n",
"Annotating sequences for binding patterns.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ryan/miniconda/envs/bclab/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
" return (self.a < x) & (x < self.b)\n",
"/home/ryan/miniconda/envs/bclab/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
" return (self.a < x) & (x < self.b)\n",
"/home/ryan/miniconda/envs/bclab/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
" cond2 = cond0 & (x <= self.a)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done processing and annotating data. This table corresponds to Supplementary file 3.\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>expression_WT</th>\n",
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" </tr>\n",
" <tr>\n",
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" <th>chr1-104768570-104768734_UPCQ</th>\n",
" <td>3.606621</td>\n",
" <td>0.297412</td>\n",
" <td>3.0</td>\n",
" <td>0.001206</td>\n",
" <td>0.003548</td>\n",
" <td>1</td>\n",
" <td>1.851048</td>\n",
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" <td>0.147455</td>\n",
" <td>0.000000</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>chr1-106008207-106008371_CPPE</th>\n",
" <td>2.068611</td>\n",
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" <td>3.0</td>\n",
" <td>0.000008</td>\n",
" <td>0.000217</td>\n",
" <td>1</td>\n",
" <td>3.046526</td>\n",
" <td>Strong enhancer</td>\n",
" <td>#1f78b4</td>\n",
" <td>WT</td>\n",
" <td>...</td>\n",
" <td>0.003104</td>\n",
" <td>0.013211</td>\n",
" <td>0.795621</td>\n",
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" <td>0.196017</td>\n",
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" <td>0.453279</td>\n",
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" <td>0.019478</td>\n",
" <td>0.031968</td>\n",
" <td>1</td>\n",
" <td>-2.426678</td>\n",
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" <td>...</td>\n",
" <td>0.005790</td>\n",
" <td>0.019789</td>\n",
" <td>0.308888</td>\n",
" <td>0.138871</td>\n",
" <td>-1.648877</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>No binding</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" expression_WT expression_std_WT \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ 3.606621 0.297412 \n",
"chr1-106008207-106008371_CPPE 2.068611 0.944664 \n",
"chr1-106696554-106696718_CPPE 8.261201 1.317719 \n",
"chr1-118321635-118321799_CPPP 1.368148 0.397835 \n",
"chr1-118589610-118589774_UPCE 0.184993 0.077742 \n",
"\n",
" expression_reps_WT expression_pvalue_WT \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ 3.0 0.001206 \n",
"chr1-106008207-106008371_CPPE 3.0 0.080583 \n",
"chr1-106696554-106696718_CPPE 3.0 0.000008 \n",
"chr1-118321635-118321799_CPPP 3.0 0.166861 \n",
"chr1-118589610-118589774_UPCE 3.0 0.019478 \n",
"\n",
" expression_qvalue_WT library_WT \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ 0.003548 1 \n",
"chr1-106008207-106008371_CPPE 0.103242 1 \n",
"chr1-106696554-106696718_CPPE 0.000217 1 \n",
"chr1-118321635-118321799_CPPP 0.196017 1 \n",
"chr1-118589610-118589774_UPCE 0.031968 1 \n",
"\n",
" expression_log2_WT group_name_WT \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ 1.851048 Weak enhancer \n",
"chr1-106008207-106008371_CPPE 1.049360 NaN \n",
"chr1-106696554-106696718_CPPE 3.046526 Strong enhancer \n",
"chr1-118321635-118321799_CPPP 0.453279 Inactive \n",
"chr1-118589610-118589774_UPCE -2.426678 Silencer \n",
"\n",
" plot_color_WT variant_WT ... \\\n",
"label ... \n",
"chr1-104768570-104768734_UPCQ #a6cee3 WT ... \n",
"chr1-106008207-106008371_CPPE grey WT ... \n",
"chr1-106696554-106696718_CPPE #1f78b4 WT ... \n",
"chr1-118321635-118321799_CPPP #33a02c WT ... \n",
"chr1-118589610-118589774_UPCE #e31a1c WT ... \n",
"\n",
" wt_vs_mut_pvalue wt_vs_mut_qvalue \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ 0.092328 0.147455 \n",
"chr1-106008207-106008371_CPPE 0.145377 0.212937 \n",
"chr1-106696554-106696718_CPPE 0.003104 0.013211 \n",
"chr1-118321635-118321799_CPPP 0.080966 0.132766 \n",
"chr1-118589610-118589774_UPCE 0.005790 0.019789 \n",
"\n",
" expression_POLY expression_SEM_POLY \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ 0.000000 0.000000 \n",
"chr1-106008207-106008371_CPPE 0.000000 0.000000 \n",
"chr1-106696554-106696718_CPPE 0.795621 0.058574 \n",
"chr1-118321635-118321799_CPPP 0.000000 0.000000 \n",
"chr1-118589610-118589774_UPCE 0.308888 0.138871 \n",
"\n",
" expression_log2_POLY autonomous_activity \\\n",
"label \n",
"chr1-104768570-104768734_UPCQ -6.643856 False \n",
"chr1-106008207-106008371_CPPE -6.643856 False \n",
"chr1-106696554-106696718_CPPE -0.311827 False \n",
"chr1-118321635-118321799_CPPP -6.643856 False \n",
"chr1-118589610-118589774_UPCE -1.648877 False \n",
"\n",
" crx_bound nrl_bound mef2d_bound binding_group \n",
"label \n",
"chr1-104768570-104768734_UPCQ False False False No binding \n",
"chr1-106008207-106008371_CPPE True False False CRX only \n",
"chr1-106696554-106696718_CPPE True False False CRX only \n",
"chr1-118321635-118321799_CPPP True False False CRX only \n",
"chr1-118589610-118589774_UPCE False False False No binding \n",
"\n",
"[5 rows x 31 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Join and annotate all data\n",
"print(\"Joining together data from the two libraries with the Rho promoter.\")\n",
"color_mapping = {\n",
" \"Strong enhancer\": \"#1f78b4\",\n",
" \"Weak enhancer\": \"#a6cee3\",\n",
" \"Inactive\": \"#33a02c\",\n",
" \"Silencer\": \"#e31a1c\",\n",
" np.nan: \"grey\"\n",
"}\n",
"\n",
"# Join the libraries and add a pseudocount to take log2\n",
"rho_df = library1_rho_df.append(library2_rho_df)\n",
"rho_pseudocount = 1e-3\n",
"rho_df[\"expression_log2\"] = np.log2(rho_df[\"expression\"] + rho_pseudocount)\n",
"\n",
"# Define cutoff for a strong enhancer based on scrambled sequences\n",
"print(\"Annotating sequences as strong enhancer, weak enhancer, inactive, silencer, or ambiguous.\")\n",
"scrambled_mask = rho_df.index.str.contains(\"scrambled\")\n",
"scrambled_df = rho_df[scrambled_mask]\n",
"scrambled_df = scrambled_df[scrambled_df[\"expression\"].notna()]\n",
"strong_cutoff = scrambled_df[\"expression_log2\"].quantile(0.95)\n",
"print(f\"Cutoff to call something a strong enhancer: activity is above {strong_cutoff:.2f}\")\n",
"\n",
"# Drop scrambled sequences\n",
"rho_df = rho_df[~scrambled_mask]\n",
"\n",
"# Helper function to label and color a sequence\n",
"def label_color_sequence(row, alpha=0.05, strong_cutoff=strong_cutoff, inactive_cutoff=1, color_mapping=color_mapping):\n",
" expr_log2 = row[\"expression_log2\"]\n",
" qval = row[\"expression_qvalue\"]\n",
" # Inactive\n",
" if (np.abs(expr_log2) <= inactive_cutoff) & (qval >= alpha):\n",
" group = \"Inactive\"\n",
" # Silencer\n",
" elif (expr_log2 < -inactive_cutoff) & ((qval < alpha) | (row[\"expression\"] == 0)):\n",
" group = \"Silencer\"\n",
" # Enhancer\n",
" elif (expr_log2 > inactive_cutoff) & (qval < alpha):\n",
" # Strong\n",
" if expr_log2 > strong_cutoff:\n",
" group = \"Strong enhancer\"\n",
" # Weak\n",
" else:\n",
" group = \"Weak enhancer\"\n",
" # Ambiguous\n",
" else:\n",
" group = np.nan\n",
" \n",
" color = color_mapping[group]\n",
" return pd.Series({\"group_name\": group, \"plot_color\": color})\n",
"\n",
"# Annotate both WT and MUT sequences\n",
"rho_df = rho_df.join(rho_df.apply(label_color_sequence, axis=1))\n",
"rho_df[\"group_name\"] = sequence_annotation_processing.to_categorical(rho_df[\"group_name\"])\n",
"\n",
"# Now do Polylinker data\n",
"library1_poly_df = polylinker_activity_data[\"library1\"]\n",
"library2_poly_df = polylinker_activity_data[\"library2\"]\n",
"print(\"Joining together data from the two libraries with the Polylinker promoter and annotate for autonomous activity.\")\n",
"poly_df = library1_poly_df.append(library2_poly_df)\n",
"poly_pseudocount = 1e-2\n",
"poly_df[\"expression_log2\"] = np.log2(poly_df[\"expression\"] + poly_pseudocount)\n",
"poly_df[\"autonomous_activity\"] = (poly_df[\"expression_log2\"] > 0)\n",
"\n",
"# Compute effect of mutating CRX motifs in the presence of the Rho promoter.\n",
"print(\"Computing the effect size upon mutating CRX motifs in the presence of the Rho promoter.\")\n",
"print(\"This is for Figure 5, but it is easier to do it here.\")\n",
"wt_mask = rho_df.index.str.contains(\"_WT$\")\n",
"mut_mask = rho_df.index.str.contains(\"_MUT-allCrxSites$\")\n",
"\n",
"# Add variant info as a column, then trim it off the index\n",
"rho_df_no_variant_df = rho_df.copy()\n",
"rho_df_no_variant_df[\"variant\"] = rho_df_no_variant_df.index.str.split(\"_\").str[2:].str.join(\"_\")\n",
"rho_df_no_variant_df = sequence_annotation_processing.remove_mutations_from_seq_id(rho_df_no_variant_df)\n",
"\n",
"# Separate out WT and MUT, then join them together on the same row\n",
"wt_df = rho_df_no_variant_df[wt_mask]\n",
"mut_df = rho_df_no_variant_df[mut_mask]\n",
"wt_vs_mut_rho_df = wt_df.join(mut_df, lsuffix=\"_WT\", rsuffix=\"_MUT\")\n",
"wt_vs_mut_rho_df[\"wt_vs_mut_log2\"] = wt_vs_mut_rho_df[\"expression_log2_WT\"] - wt_vs_mut_rho_df[\"expression_log2_MUT\"]\n",
"\n",
"# Compute parameters for lognormal distribution to do stats\n",
"wt_cov = wt_vs_mut_rho_df[\"expression_std_WT\"] / wt_vs_mut_rho_df[\"expression_WT\"]\n",
"wt_log_mean = np.log(wt_vs_mut_rho_df[\"expression_WT\"] / np.sqrt(wt_cov**2 + 1))\n",
"wt_log_std = np.sqrt(np.log(wt_cov**2 + 1))\n",
"mut_cov = wt_vs_mut_rho_df[\"expression_std_MUT\"] / wt_vs_mut_rho_df[\"expression_MUT\"]\n",
"mut_log_mean = np.log(wt_vs_mut_rho_df[\"expression_MUT\"] / np.sqrt(mut_cov**2 + 1))\n",
"mut_log_std = np.sqrt(np.log(mut_cov**2 + 1))\n",
"\n",
"# Do t-tests and FDR\n",
"wt_vs_mut_rho_df[\"wt_vs_mut_pvalue\"] = stats.ttest_ind_from_stats(wt_log_mean, wt_log_std, wt_vs_mut_rho_df[\"expression_reps_WT\"], mut_log_mean, mut_log_std, wt_vs_mut_rho_df[\"expression_reps_MUT\"], equal_var=False)[1]\n",
"wt_vs_mut_rho_df[\"wt_vs_mut_qvalue\"] = modeling.fdr(wt_vs_mut_rho_df[\"wt_vs_mut_pvalue\"])\n",
"\n",
"# Pull out WT polylinker measurements\n",
"print(\"Joining Rho and Polylinker data together.\")\n",
"poly_wt_df = poly_df.copy()\n",
"poly_wt_df = poly_wt_df[poly_wt_df.index.str.contains(\"WT\")]\n",
"\n",
"# Drop the variant ID\n",
"poly_wt_df = poly_wt_df.rename(index=lambda x: x[:-3], columns={\"expression\": \"expression_POLY\", \"expression_SEM\": \"expression_SEM_POLY\", \"expression_log2\": \"expression_log2_POLY\"})\n",
"\n",
"# Join with Rho\n",
"activity_df = wt_vs_mut_rho_df.join(poly_wt_df)\n",
"\n",
"print(\"Annotating sequences for binding patterns.\")\n",
"# Get info on CRX binding from the seq ID strings\n",
"activity_df[\"crx_bound\"] = activity_df.index.str.contains(\"_C...$\")\n",
"\n",
"# Read in BED files\n",
"library_bed = BedTool(os.path.join(data_dir, \"library1And2.bed\"))\n",
"nrl_chip_bed = BedTool(os.path.join(\"Data\", \"Downloaded\", \"ChIP\", \"nrlPeaksMm10.bed\"))\n",
"mef2d_chip_bed = BedTool(os.path.join(\"Data\", \"Downloaded\", \"ChIP\", \"mef2dPeaksMm10.bed\"))\n",
"\n",
"# Get binding patterns for NRL and MEF2D\n",
"library_nrl_bound_df = library_bed.intersect(nrl_chip_bed, wa=True).to_dataframe()\n",
"activity_df[\"nrl_bound\"] = activity_df.index.isin(library_nrl_bound_df[\"name\"])\n",
"\n",
"library_mef2d_bound_df = library_bed.intersect(mef2d_chip_bed, wa=True).to_dataframe()\n",
"activity_df[\"mef2d_bound\"] = activity_df.index.isin(library_mef2d_bound_df[\"name\"])\n",
"\n",
"# Helper function to \"reverse one hot encode\" binding patterns\n",
"def annotate_binding(row):\n",
" crx, nrl, mef2d = row[[\"crx_bound\", \"nrl_bound\", \"mef2d_bound\"]]\n",
" if crx:\n",
" if nrl:\n",
" if mef2d:\n",
" return \"All three\"\n",
" else:\n",
" return \"CRX+NRL\"\n",
" elif mef2d:\n",
" return \"CRX+MEF2D\"\n",
" else:\n",
" return \"CRX only\"\n",
" elif nrl:\n",
" if mef2d:\n",
" return \"NRL+MEF2D\"\n",
" else:\n",
" return \"NRL only\"\n",
" elif mef2d:\n",
" return \"MEF2D only\"\n",
" else:\n",
" return \"No binding\"\n",
"\n",
"activity_df[\"binding_group\"] = activity_df.apply(annotate_binding, axis=1)\n",
"print(\"Done processing and annotating data. This table corresponds to Supplementary file 3.\")\n",
"display(activity_df.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Strong enhancers and silencers have high CRX motif content\n",
"\n",
"The _cis_-regulatory activities of CRX-targeted sequences vary widely ([Figure 1a](#fig1)). We defined enhancers and silencers as those sequences that have statistically significant activity that is at least twofold above or below the activity of the basal _Rho_ promoter (Welch’s t-test, Benjamini-Hochberg false discovery rate (FDR) q < 0.05, [Supplementary file 3](#supp3)). We defined inactive sequences as those whose activity is both within a twofold change of basal activity and not significantly different from the basal _Rho_ promoter. We further stratified enhancers into strong and weak enhancers based on whether or not they fell above the 95th percentile of scrambled sequences. Using these criteria, 22% of CRX-targeted sequences are strong enhancers, 28% are weak enhancers, 19% are inactive, and 17% are silencers; the remaining 13% were considered ambiguous and removed from further analysis. To test whether these sequences function as CRX-dependent enhancers and silencers in the genome, we examined genes differentially expressed in _Crx^-/-^_ retina [@bib71]. Genes that are de-repressed are more likely to be near silencers (Fisher’s exact test p = 0.001, odds ratio = 2.1, n = 206) and genes that are down-regulated are more likely to be near enhancers (Fisher’s exact test p = 0.02, odds ratio = 1.5, n = 344, Materials and methods), suggesting that our reporter assay identified sequences that act as enhancers and silencers in the genome. We sought to identify features that would accurately classify these different classes of sequences."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing predicted occupancy of 8 TFs on every WT and mutant sequence. This might take 2-3 minutes.\n",
"Done computing predicted occupancies. This corresponds to Supplementary table 4.\n"
]
},
{
"data": {
"text/html": [
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"<style scoped>\n",
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" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CRX</th>\n",
" <th>GFI1</th>\n",
" <th>MAZ</th>\n",
" <th>MEF2D</th>\n",
" <th>NDF1</th>\n",
" <th>NRL</th>\n",
" <th>RORB</th>\n",
" <th>RAX</th>\n",
" </tr>\n",
" <tr>\n",
" <th>label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>chr1-4357766-4357930_CPPP_WT</th>\n",
" <td>2.297972</td>\n",
" <td>1.871720e-01</td>\n",
" <td>2.204502e-08</td>\n",
" <td>1.421229e-06</td>\n",
" <td>3.064604e-07</td>\n",
" <td>1.001505</td>\n",
" <td>2.370847e-02</td>\n",
" <td>0.005755</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-4357766-4357930_CPPP_MUT-allCrxSites</th>\n",
" <td>0.239708</td>\n",
" <td>3.783122e-11</td>\n",
" <td>2.204502e-08</td>\n",
" <td>1.421229e-06</td>\n",
" <td>3.064606e-07</td>\n",
" <td>1.411916</td>\n",
" <td>2.340304e-02</td>\n",
" <td>0.004416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-73826292-73826456_CPPE_WT</th>\n",
" <td>2.290427</td>\n",
" <td>6.397380e-03</td>\n",
" <td>5.577725e-03</td>\n",
" <td>1.815852e-09</td>\n",
" <td>6.713635e-07</td>\n",
" <td>0.993418</td>\n",
" <td>2.922269e-04</td>\n",
" <td>0.000004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-73826292-73826456_CPPE_MUT-allCrxSites</th>\n",
" <td>0.293410</td>\n",
" <td>1.203730e-08</td>\n",
" <td>5.577725e-03</td>\n",
" <td>6.339047e-11</td>\n",
" <td>6.713632e-07</td>\n",
" <td>0.993414</td>\n",
" <td>1.239630e-07</td>\n",
" <td>0.000002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr11-87108697-87108861_CPPP_WT</th>\n",
" <td>2.718470</td>\n",
" <td>6.025624e-01</td>\n",
" <td>2.744230e-12</td>\n",
" <td>2.986062e-06</td>\n",
" <td>6.477337e-07</td>\n",
" <td>0.040965</td>\n",
" <td>4.672926e-05</td>\n",
" <td>0.190641</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CRX GFI1 \\\n",
"label \n",
"chr1-4357766-4357930_CPPP_WT 2.297972 1.871720e-01 \n",
"chr1-4357766-4357930_CPPP_MUT-allCrxSites 0.239708 3.783122e-11 \n",
"chr1-73826292-73826456_CPPE_WT 2.290427 6.397380e-03 \n",
"chr1-73826292-73826456_CPPE_MUT-allCrxSites 0.293410 1.203730e-08 \n",
"chr11-87108697-87108861_CPPP_WT 2.718470 6.025624e-01 \n",
"\n",
" MAZ MEF2D \\\n",
"label \n",
"chr1-4357766-4357930_CPPP_WT 2.204502e-08 1.421229e-06 \n",
"chr1-4357766-4357930_CPPP_MUT-allCrxSites 2.204502e-08 1.421229e-06 \n",
"chr1-73826292-73826456_CPPE_WT 5.577725e-03 1.815852e-09 \n",
"chr1-73826292-73826456_CPPE_MUT-allCrxSites 5.577725e-03 6.339047e-11 \n",
"chr11-87108697-87108861_CPPP_WT 2.744230e-12 2.986062e-06 \n",
"\n",
" NDF1 NRL \\\n",
"label \n",
"chr1-4357766-4357930_CPPP_WT 3.064604e-07 1.001505 \n",
"chr1-4357766-4357930_CPPP_MUT-allCrxSites 3.064606e-07 1.411916 \n",
"chr1-73826292-73826456_CPPE_WT 6.713635e-07 0.993418 \n",
"chr1-73826292-73826456_CPPE_MUT-allCrxSites 6.713632e-07 0.993414 \n",
"chr11-87108697-87108861_CPPP_WT 6.477337e-07 0.040965 \n",
"\n",
" RORB RAX \n",
"label \n",
"chr1-4357766-4357930_CPPP_WT 2.370847e-02 0.005755 \n",
"chr1-4357766-4357930_CPPP_MUT-allCrxSites 2.340304e-02 0.004416 \n",
"chr1-73826292-73826456_CPPE_WT 2.922269e-04 0.000004 \n",
"chr1-73826292-73826456_CPPE_MUT-allCrxSites 1.239630e-07 0.000002 \n",
"chr11-87108697-87108861_CPPP_WT 4.672926e-05 0.190641 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Calculate predicted occupancy of all TFs\n",
"print(\"Computing predicted occupancy of 8 TFs on every WT and mutant sequence. This might take 2-3 minutes.\")\n",
"\n",
"# Load in PWMs\n",
"pwms = predicted_occupancy.read_pwm_files(os.path.join(\"Data\", \"Downloaded\", \"Pwm\", \"photoreceptorAndEnrichedMotifs.meme\"))\n",
"pwms = pwms.rename(lambda x: x.split(\"_\")[0])\n",
"# Reverse compliment RAX for display purposes\n",
"rax = pwms[\"RAX\"].copy()\n",
"rax = rax[::-1].reset_index(drop=True)\n",
"rax_rc = rax.copy()\n",
"rax_rc[\"A\"] = rax[\"T\"]\n",
"rax_rc[\"C\"] = rax[\"G\"]\n",
"rax_rc[\"G\"] = rax[\"C\"]\n",
"rax_rc[\"T\"] = rax[\"A\"]\n",
"pwms[\"RAX\"] = rax_rc\n",
"motif_len = pwms.apply(len)\n",
"ewms = pwms.apply(predicted_occupancy.ewm_from_letter_prob).apply(predicted_occupancy.ewm_to_dict)\n",
"mu = 9\n",
"\n",
"# Do predicted occupancy scans\n",
"occupancy_df = predicted_occupancy.all_seq_total_occupancy(all_seqs, ewms, mu, convert_ewm=False)\n",
"print(\"Done computing predicted occupancies. This corresponds to Supplementary table 4.\")\n",
"display(occupancy_df.head())\n",
"\n",
"# Separate out the WT sequences\n",
"wt_occupancy_df = occupancy_df[occupancy_df.index.str.contains(\"WT$\")].copy()\n",
"wt_occupancy_df = sequence_annotation_processing.remove_mutations_from_seq_id(wt_occupancy_df)\n",
"wt_occupancy_df = wt_occupancy_df.loc[activity_df.index]\n",
"n_tfs = len(wt_occupancy_df.columns)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Computing information content of sequences.\n",
"Done computing information content and related metrics. This corresponds to Supplementary table 5.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>total_occupancy</th>\n",
" <th>diversity</th>\n",
" <th>entropy</th>\n",
" </tr>\n",
" <tr>\n",
" <th>label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>chr1-4357766-4357930_CPPP_WT</th>\n",
" <td>3.516114</td>\n",
" <td>2.0</td>\n",
" <td>2.291861</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-4357766-4357930_CPPP_MUT-allCrxSites</th>\n",
" <td>1.679445</td>\n",
" <td>1.0</td>\n",
" <td>0.440493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-73826292-73826456_CPPE_WT</th>\n",
" <td>3.296117</td>\n",
" <td>2.0</td>\n",
" <td>1.743370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1-73826292-73826456_CPPE_MUT-allCrxSites</th>\n",
" <td>1.292404</td>\n",
" <td>1.0</td>\n",
" <td>0.378922</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr11-87108697-87108861_CPPP_WT</th>\n",
" <td>3.552689</td>\n",
" <td>2.0</td>\n",
" <td>1.867968</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_occupancy diversity \\\n",
"label \n",
"chr1-4357766-4357930_CPPP_WT 3.516114 2.0 \n",
"chr1-4357766-4357930_CPPP_MUT-allCrxSites 1.679445 1.0 \n",
"chr1-73826292-73826456_CPPE_WT 3.296117 2.0 \n",
"chr1-73826292-73826456_CPPE_MUT-allCrxSites 1.292404 1.0 \n",
"chr11-87108697-87108861_CPPP_WT 3.552689 2.0 \n",
"\n",
" entropy \n",
"label \n",
"chr1-4357766-4357930_CPPP_WT 2.291861 \n",
"chr1-4357766-4357930_CPPP_MUT-allCrxSites 0.440493 \n",
"chr1-73826292-73826456_CPPE_WT 1.743370 \n",
"chr1-73826292-73826456_CPPE_MUT-allCrxSites 0.378922 \n",
"chr11-87108697-87108861_CPPP_WT 1.867968 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"Computing information content of sequences.\")\n",
"entropy_df = occupancy_df.apply(predicted_occupancy.boltzmann_entropy, axis=1)\n",
"print(\"Done computing information content and related metrics. This corresponds to Supplementary table 5.\")\n",
"display(entropy_df.head())\n",
"\n",
"wt_entropy_df = entropy_df[entropy_df.index.str.contains(\"WT$\")].copy()\n",
"wt_entropy_df = sequence_annotation_processing.remove_mutations_from_seq_id(wt_entropy_df)\n",
"wt_entropy_df = wt_entropy_df.loc[activity_df.index]\n",
"\n",
"mut_entropy_df = entropy_df[entropy_df.index.str.contains(\"MUT\")].copy()\n",
"mut_entropy_df = sequence_annotation_processing.remove_mutations_from_seq_id(mut_entropy_df)\n",
"mut_entropy_df = mut_entropy_df.loc[activity_df.index]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"caption": "### Activity of putative _cis_-regulatory sequences with cone-rod homeobox (CRX) motifs.\n\n(**a**) Volcano plot of activity scores relative to the _Rho_ promoter alone. Sequences are grouped as strong enhancers (dark blue), weak enhancers (light blue), inactive (green), silencers (red), or ambiguous (gray). Horizontal line, false discovery rate (FDR) q = 0.05. Vertical lines, twofold above and below _Rho_. (**b**) Fraction of ChIP-seq and ATAC-seq peaks that belong to each activity group. (**c**) Predicted CRX occupancy of each activity group. Horizontal lines, medians; enh., enhancer. Numbers at top of (**b and c**) indicate n for groups.",
"id": "fig1",
"label": "Figure 1."
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Frequency of each activity bin in WT sequences:\n"
]
},
{
"data": {
"text/plain": [
"Silencer 0.173615\n",
"Inactive 0.192491\n",
"Weak enhancer 0.282099\n",
"Strong enhancer 0.218005\n",
"NaN 0.133790\n",
"Name: group_name_WT, dtype: float64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Frequency of activity bins vs. CRX binding status:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>group_name_WT</th>\n",
" <th>Silencer</th>\n",
" <th>Inactive</th>\n",
" <th>Weak enhancer</th>\n",
" <th>Strong enhancer</th>\n",
" </tr>\n",
" <tr>\n",
" <th>crx_bound</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ATAC-seq</th>\n",
" <td>281</td>\n",
" <td>363</td>\n",
" <td>430</td>\n",
" <td>211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChIP-seq</th>\n",
" <td>556</td>\n",
" <td>565</td>\n",
" <td>930</td>\n",
" <td>840</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"group_name_WT Silencer Inactive Weak enhancer Strong enhancer\n",
"crx_bound \n",
"ATAC-seq 281 363 430 211\n",
"ChIP-seq 556 565 930 840"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ChIP-seq status is independent of if a sequence is inactive, Fisher's exact test p=2e-07, odds ratio=1.49\n",
"ChIP-seq status is independent of if a sequence is inactive, Fisher's exact test p=1e-21, odds ratio=2.16\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>group_name_WT</th>\n",
" <th>Silencer</th>\n",
" <th>Inactive</th>\n",
" <th>Weak enhancer</th>\n",
" <th>Strong enhancer</th>\n",
" </tr>\n",
" <tr>\n",
" <th>crx_bound</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ATAC-seq</th>\n",
" <td>0.218677</td>\n",
" <td>0.282490</td>\n",
" <td>0.334630</td>\n",
" <td>0.164202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ChIP-seq</th>\n",
" <td>0.192321</td>\n",
" <td>0.195434</td>\n",
" <td>0.321688</td>\n",
" <td>0.290557</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"group_name_WT Silencer Inactive Weak enhancer Strong enhancer\n",
"crx_bound \n",
"ATAC-seq 0.218677 0.282490 0.334630 0.164202\n",
"ChIP-seq 0.192321 0.195434 0.321688 0.290557"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted CRX occupancies:\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>group_name_WT</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Silencer</th>\n",
" <td>837.0</td>\n",
" <td>2.822068</td>\n",
" <td>1.474613</td>\n",
" <td>0.013521</td>\n",
" <td>1.598510</td>\n",
" <td>2.724195</td>\n",
" <td>3.916786</td>\n",
" <td>8.028408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Inactive</th>\n",
" <td>928.0</td>\n",
" <td>2.232489</td>\n",
" <td>1.342345</td>\n",
" <td>0.001052</td>\n",
" <td>1.173444</td>\n",
" <td>2.048457</td>\n",
" <td>3.136282</td>\n",
" <td>6.759976</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Weak enhancer</th>\n",
" <td>1360.0</td>\n",
" <td>2.216861</td>\n",
" <td>1.220496</td>\n",
" <td>0.000385</td>\n",
" <td>1.235126</td>\n",
" <td>2.113810</td>\n",
" <td>2.988673</td>\n",
" <td>7.801177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Strong enhancer</th>\n",
" <td>1051.0</td>\n",
" <td>2.534010</td>\n",
" <td>1.169460</td>\n",
" <td>0.003694</td>\n",
" <td>1.616414</td>\n",
" <td>2.490314</td>\n",
" <td>3.285321</td>\n",
" <td>7.368500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% \\\n",
"group_name_WT \n",
"Silencer 837.0 2.822068 1.474613 0.013521 1.598510 2.724195 \n",
"Inactive 928.0 2.232489 1.342345 0.001052 1.173444 2.048457 \n",
"Weak enhancer 1360.0 2.216861 1.220496 0.000385 1.235126 2.113810 \n",
"Strong enhancer 1051.0 2.534010 1.169460 0.003694 1.616414 2.490314 \n",
"\n",
" 75% max \n",
"group_name_WT \n",
"Silencer 3.916786 8.028408 \n",
"Inactive 3.136282 6.759976 \n",
"Weak enhancer 2.988673 7.801177 \n",
"Strong enhancer 3.285321 7.368500 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Strong enhancers and inactive sequences have the same CRX occupancy, Mann-Whitney U test p = 6e-10 U = 566045.00\n",
"Silencers and inactive sequences have the same CRX occupancy, Mann-Whitney U test p = 6e-17, U = 477843.00\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x576 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Mapping activity class to a color\n",
"color_mapping = {\n",
" \"Silencer\": \"#e31a1c\",\n",
" \"Inactive\": \"#33a02c\",\n",
" \"Weak enhancer\": \"#a6cee3\",\n",
" \"Strong enhancer\": \"#1f78b4\",\n",
" np.nan: \"grey\"\n",
"}\n",
"color_mapping = pd.Series(color_mapping)\n",
"\n",
"# Sort order for the four activity bins\n",
"class_sort_order = [\"Silencer\", \"Inactive\", \"Weak enhancer\", \"Strong enhancer\"]\n",
"activity_df[\"group_name_WT\"] = sequence_annotation_processing.to_categorical(activity_df[\"group_name_WT\"])\n",
"activity_df[\"group_name_MUT\"] = sequence_annotation_processing.to_categorical(activity_df[\"group_name_MUT\"])\n",
"rho_ticks = np.arange(-10, 7, 2)\n",
"\n",
"# We can only plot points that were detected in DNA\n",
"activity_measured_wt_df = activity_df[activity_df[\"expression_log2_WT\"].notna()]\n",
"print(\"Frequency of each activity bin in WT sequences:\")\n",
"display(activity_measured_wt_df[\"group_name_WT\"].value_counts(normalize=True, dropna=False, sort=False))\n",
"\n",
"# Count frequency of activity bins for CRX bound/unbound\n",
"crx_bound_grouper = activity_df.groupby(\"crx_bound\")\n",
"chip_activity_bin_freqs = crx_bound_grouper[\"group_name_WT\"].value_counts().unstack()\n",
"chip_activity_bin_freqs = chip_activity_bin_freqs[class_sort_order].rename(index=lambda x: \"ChIP-seq\" if x else \"ATAC-seq\")\n",
"\n",
"# Different ways to format group names\n",
"chip_group_names_with_n = [f\"{i}\\nn={j.sum()}\" for i, j in chip_activity_bin_freqs.iterrows()]\n",
"chip_group_names_with_n_oneline = [\" \".join(i.split()) for i in chip_group_names_with_n]\n",
"chip_group_names = chip_activity_bin_freqs.index.values\n",
"chip_group_count = [j.sum() for i, j in chip_activity_bin_freqs.iterrows()]\n",
"\n",
"# Display the data behind Fig 1b\n",
"print(\"Frequency of activity bins vs. CRX binding status:\")\n",
"display(chip_activity_bin_freqs)\n",
"\n",
"# Test if CRX binding and inactive status is independent\n",
"chip_group_inactive_counts = crx_bound_grouper[\"group_name_WT\"].apply(lambda x: (x == \"Inactive\").value_counts()).unstack()\n",
"oddsratio, pval = stats.fisher_exact(chip_group_inactive_counts)\n",
"# Take inverse of odds ratio to match language of manuscript and be more intuitive to the reader\n",
"print(f\"ChIP-seq status is independent of if a sequence is inactive, Fisher's exact test p={pval:.0e}, odds ratio={1/oddsratio:.2f}\")\n",
"\n",
"# Same for strong enhancer\n",
"chip_group_inactive_counts = crx_bound_grouper[\"group_name_WT\"].apply(lambda x: (x == \"Strong enhancer\").value_counts()).unstack()\n",
"oddsratio, pval = stats.fisher_exact(chip_group_inactive_counts)\n",
"# Take inverse of odds ratio to match language of manuscript and be more intuitive to the reader\n",
"print(f\"ChIP-seq status is independent of if a sequence is inactive, Fisher's exact test p={pval:.0e}, odds ratio={oddsratio:.2f}\")\n",
"\n",
"# Row-normalize the counts\n",
"chip_activity_bin_freqs = chip_activity_bin_freqs.div(chip_activity_bin_freqs.sum(axis=1), axis=0)\n",
"display(chip_activity_bin_freqs)\n",
"\n",
"# Setup for some downstream stuff\n",
"wt_activity_grouper = activity_df.groupby(\"group_name_WT\")\n",
"wt_activity_names_oneline = [\"Silencer\", \"Inactive\", \"Weak enh.\", \"Strong enh.\"]\n",
"wt_activity_count = [len(j) for i, j in wt_activity_grouper]\n",
"\n",
"# Predicted CRX occupancy vs. WT group\n",
"wt_occupancy_grouper = wt_occupancy_df.groupby(activity_df[\"group_name_WT\"])\n",
"wt_occupancy_grouper_crx = wt_occupancy_grouper[\"CRX\"]\n",
"print(\"Predicted CRX occupancies:\")\n",
"display(wt_occupancy_grouper_crx.describe())\n",
"\n",
"# Statistics for differences in CRX occupancy between groups\n",
"ustat, pval = stats.mannwhitneyu(wt_occupancy_grouper_crx.get_group(\"Strong enhancer\"), wt_occupancy_grouper_crx.get_group(\"Inactive\"), alternative=\"two-sided\")\n",
"print(f\"Strong enhancers and inactive sequences have the same CRX occupancy, Mann-Whitney U test p = {pval:.0e} U = {ustat:.2f}\")\n",
"ustat, pval = stats.mannwhitneyu(wt_occupancy_grouper_crx.get_group(\"Silencer\"), wt_occupancy_grouper_crx.get_group(\"Inactive\"), alternative=\"two-sided\")\n",
"print(f\"Silencers and inactive sequences have the same CRX occupancy, Mann-Whitney U test p = {pval:.0e}, U = {ustat:.2f}\")\n",
"\n",
"# Generate the figure\n",
"gs_kw = dict(width_ratios=[1, 3])\n",
"fig, ax_list = plt.subplots(nrows=2, ncols=2, figsize=(6, 8), gridspec_kw=gs_kw)\n",
"gs = ax_list[0, 0].get_gridspec()\n",
"for ax in ax_list[0, :]:\n",
" ax.remove()\n",
" \n",
"axbig = fig.add_subplot(gs[0, :])\n",
"ax = axbig\n",
"\n",
"# 1a: Volcano plot\n",
"fig = plot_utils.volcano_plot(activity_measured_wt_df, \"expression_log2_WT\", \"expression_qvalue_WT\",\n",
" activity_measured_wt_df[\"plot_color_WT\"], xaxis_label=\"log2 Enhancer Activity/Rho\",\n",
" yaxis_label=\"-log10 FDR\", xline=-np.log10(0.05), yline=[-1, 1],\n",
" xticks=rho_ticks[1:], figax=(fig, ax))\n",
"ax.set_yticks(np.arange(5))\n",
"plot_utils.add_letter(ax, -0.125, 1, \"a\")\n",
"\n",
"# 1b: CRX binding status vs. activity classes\n",
"ax = ax_list[1, 0]\n",
"fig = plot_utils.stacked_bar_plots(chip_activity_bin_freqs, \"Fraction of group\", chip_group_names, color_mapping, figax=(fig, ax), vert=True)\n",
"ax.set_yticks(np.linspace(0, 1, 6))\n",
"plot_utils.rotate_ticks(ax.get_xticklabels()) \n",
"\n",
"# Add ticks above to show the n\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(chip_group_count, fontsize=10, rotation=45)\n",
"plot_utils.add_letter(ax, -0.7, 1.03, \"b\")\n",
"\n",
"# 1c: Predicted CRX occupancy of different groups\n",
"ax = ax_list[1, 1]\n",
"fig = plot_utils.violin_plot_groupby(wt_occupancy_grouper_crx, \"Predicted CRX occupancy\", class_names=wt_activity_names_oneline, class_colors=color_mapping, figax=(fig, ax))\n",
"ax.set_yticks(np.linspace(0, 8, 5))\n",
"plot_utils.rotate_ticks(ax.get_xticklabels())\n",
"\n",
"# Add ticks above to show the n\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(wt_activity_count, fontsize=10, rotation=45)\n",
"plot_utils.add_letter(ax, -0.2, 1.03, \"c\")\n",
"fig.tight_layout()\n",
"display(fig)\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Neither CRX ChIP-seq-binding status nor DNA accessibility as measured by ATAC-seq strongly differentiates between these four classes ([Figure 1b](#fig1)). Compared to CRX ChIP-seq peaks, ATAC-seq peaks that lack CRX binding in the adult retina are slightly enriched for inactive sequences (Fisher’s exact test p = 2 × 10^–7^, odds ratio = 1.5) and slightly depleted for strong enhancers (Fisher’s exact test p = 1 × 10^–21^, odds ratio = 2.2). However, sequences with ChIP-seq or ATAC-seq peaks span all four activity categories, consistent with prior reports that DNA accessibility and TF binding data are not sufficient to identify functional enhancers and silencers [@bib11; @bib29; @bib30; @bib62; @bib85].\n",
"\n",
"We examined whether the number and affinity of CRX motifs differentiate enhancers, silencers, and inactive sequences by computing the predicted CRX occupancy (i.e. expected number of bound molecules) for each sequence [@bib85]. Consistent with our previous work [@bib86], both strong enhancers and silencers have higher predicted CRX occupancy than inactive sequences (Mann-Whitney U test, p = 6 × 10^–10^ and 6 × 10^–17^, respectively, [Figure 1c](#fig1)), suggesting that total CRX motif content helps distinguish silencers and strong enhancers from inactive sequences. However, predicted CRX occupancy does not distinguish strong enhancers from silencers: a logistic regression classifier trained with fivefold cross-validation only achieves an area under the receiver operating characteristic (AUROC) curve of 0.548 ± 0.023 and an area under the precision recall (AUPR) curve of 0.571 ± 0.020 ([Figure 2a](#fig2) and [Figure 2—figure supplement 1](#fig2ab)). We thus sought to identify sequence features that distinguish strong enhancers from silencers."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting k-mer Supper Vector Machine. This will take a few minutes.\n",
"Fitting strong enhancer vs. silencer logistic regression model for CRX occupancy.\n",
"Fitting strong enhancer vs. silencer logistic regression model for 8 TFs.\n",
"Optimal regularization strength (C): 1.0e-02\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Prepare data for fitting models\n",
"# Mask to pull out the silencers and strong enhancers\n",
"silencer_modeling_mask = activity_df[\"group_name_WT\"].str.contains(\"Strong|Silencer\")\n",
"silencer_modeling_mask = silencer_modeling_mask & silencer_modeling_mask.notna()\n",
"# Mask to pull out the inactive seqs and the strong enhancers\n",
"inactive_modeling_mask = activity_df[\"group_name_WT\"].str.contains(\"Strong|Inactive\")\n",
"inactive_modeling_mask = inactive_modeling_mask & inactive_modeling_mask.notna()\n",
"\n",
"# Within the data to model, mask indicating which sequences are strong enhancers\n",
"labels_with_silencer = activity_df.loc[silencer_modeling_mask, \"group_name_WT\"].str.contains(\"Strong\")\n",
"labels_with_inactive = activity_df.loc[inactive_modeling_mask, \"group_name_WT\"].str.contains(\"Strong\")\n",
"\n",
"# Write strong enhancers and silencers to file for the SVM\n",
"seq_bins_dir = os.path.join(data_dir, \"ActivityBins\")\n",
"positives_fasta = os.path.join(seq_bins_dir, \"strongEnhancer.fasta\")\n",
"negatives_fasta = os.path.join(seq_bins_dir, \"silencer.fasta\")\n",
"all_strong_mask = activity_df[\"group_name_WT\"].str.contains(\"Strong\")\n",
"all_strong_mask = all_strong_mask & all_strong_mask.notna()\n",
"strong_ids = activity_df.loc[all_strong_mask, \"variant_WT\"]\n",
"fasta_seq_parse_manip.write_fasta(all_seqs[strong_ids.index + \"_\" + strong_ids], positives_fasta)\n",
"all_silencer_mask = activity_df[\"group_name_WT\"].str.contains(\"Silencer\")\n",
"all_silencer_mask = all_silencer_mask & all_silencer_mask.notna()\n",
"silencer_ids = activity_df.loc[all_silencer_mask, \"variant_WT\"]\n",
"fasta_seq_parse_manip.write_fasta(all_seqs[silencer_ids.index + \"_\" + silencer_ids], negatives_fasta)\n",
"\n",
"# Fit k-mer SVM\n",
"print(\"Fitting k-mer Supper Vector Machine. This will take a few minutes.\")\n",
"# Hyperparameter setup\n",
"seed = 1210\n",
"word_len = 6\n",
"max_mis = 1\n",
"nfolds = 5\n",
"\n",
"models_dir = \"Models\"\n",
"svm_dir = os.path.join(models_dir, \"StrongEnhancerVsSilencer\")\n",
"if not os.path.exists(svm_dir):\n",
" os.makedirs(svm_dir)\n",
"\n",
"# Fit the SVM\n",
"svm_prefix = os.path.join(svm_dir, f\"gkmsvm_{word_len}_{word_len}_{max_mis}\")\n",
"fig_list, xaxis, svm_tpr, svm_prec, svm_f1, svm_scores = gkmsvm.train_with_cv(positives_fasta, negatives_fasta, svm_prefix, num_folds=nfolds, word_len=word_len, info_pos=word_len, max_mis=max_mis, seed=seed)\n",
"plt.close()\n",
"\n",
"# Fit logistic regression models\n",
"print(\"Fitting strong enhancer vs. silencer logistic regression model for CRX occupancy.\")\n",
"cv = StratifiedKFold(n_splits=nfolds, shuffle=True, random_state=seed)\n",
"crx_clf = LogisticRegression()\n",
"crx_clf, crx_tpr_list, crx_prec_list, crx_f1_list = modeling.train_estimate_variance(crx_clf, cv, wt_occupancy_df.loc[silencer_modeling_mask, \"CRX\"], labels_with_silencer, xaxis, positive_cutoff=0)\n",
"\n",
"print(\"Fitting strong enhancer vs. silencer logistic regression model for 8 TFs.\")\n",
"occ_clf = LogisticRegression()\n",
"param_grid = {\"C\": np.logspace(-4, 4, 9)}\n",
"np.random.seed(seed)\n",
"occ_clf, occ_tpr_list, occ_prec_list = modeling.grid_search_hyperparams(occ_clf, nfolds, param_grid, \"f1\", wt_occupancy_df[silencer_modeling_mask], labels_with_silencer, xaxis, positive_cutoff=0)\n",
"c_opt = occ_clf.get_params()[\"C\"]\n",
"print(f\"Optimal regularization strength (C): {c_opt:1.1e}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"figure: Figure 2.\n",
":::\n",
"### Strong enhancers contain a diverse array of motifs.\n",
":::\n",
"{#fig2}"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"caption": "### Figure 2\n\n(**a**) Receiver operating characteristic for classifying strong enhancers from silencers. Solid black, 6-mer support vector machine (SVM); orange, eight transcription factors (TFs) predicted occupancy logistic regression; aqua, predicted cone-rod homeobox (CRX) occupancy logistic regression; dashed black, chance; shaded area, 1 standard deviation based on fivefold cross-validation. (**b**) Total predicted TF occupancy in each activity class.\n\n### Figure 2-figure supplement 1. Precision recall curve for strong enhancer vs. silencer classifiers.\n\nSolid black, 6-mer support vector machine (SVM); orange, eight transcription factors (TFs) predicted occupancy logistic regression; aqua, predicted cone-rod homeobox (CRX) occupancy logistic regression; dashed black, chance; shaded area, 1 standard deviation based on fivefold cross-validation.",
"id": "fig2ab",
"label": "Figure 2a and b, and Figure 2—figure supplement 1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model metrics:\n",
"SVM\tAUROC=0.781+/-0.013\tAUPR=0.812+/-0.020\n",
"8 TFs\tAUROC=0.698+/-0.036\tAUPR=0.745+/-0.032\n",
"CRX\tAUROC=0.548+/-0.023\tAUPR=0.571+/-0.020\n",
"Total predicted occupancy of all TFs in each group:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>group_name_WT</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Silencer</th>\n",
" <td>837.0</td>\n",
" <td>3.588419</td>\n",
" <td>1.848387</td>\n",
" <td>0.067069</td>\n",
" <td>2.167386</td>\n",
" <td>3.408131</td>\n",
" <td>4.845272</td>\n",
" <td>11.848887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Inactive</th>\n",
" <td>928.0</td>\n",
" <td>3.005903</td>\n",
" <td>1.690368</td>\n",
" <td>0.034470</td>\n",
" <td>1.777625</td>\n",
" <td>2.810142</td>\n",
" <td>3.968906</td>\n",
" <td>12.011682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Weak enhancer</th>\n",
" <td>1360.0</td>\n",
" <td>3.068334</td>\n",
" <td>1.582532</td>\n",
" <td>0.010029</td>\n",
" <td>1.935493</td>\n",
" <td>2.921969</td>\n",
" <td>4.031018</td>\n",
" <td>12.521734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Strong enhancer</th>\n",
" <td>1051.0</td>\n",
" <td>3.782727</td>\n",
" <td>1.622289</td>\n",
" <td>0.021160</td>\n",
" <td>2.577761</td>\n",
" <td>3.664645</td>\n",
" <td>4.762179</td>\n",
" <td>10.185356</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% \\\n",
"group_name_WT \n",
"Silencer 837.0 3.588419 1.848387 0.067069 2.167386 3.408131 \n",
"Inactive 928.0 3.005903 1.690368 0.034470 1.777625 2.810142 \n",
"Weak enhancer 1360.0 3.068334 1.582532 0.010029 1.935493 2.921969 \n",
"Strong enhancer 1051.0 3.782727 1.622289 0.021160 2.577761 3.664645 \n",
"\n",
" 75% max \n",
"group_name_WT \n",
"Silencer 4.845272 11.848887 \n",
"Inactive 3.968906 12.011682 \n",
"Weak enhancer 4.031018 12.521734 \n",
"Strong enhancer 4.762179 10.185356 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Figure 2, panels A and B:\n"
]
},
{
"data": {
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DpbwbzCEZL7fLB/wk0UPihN/y2rxP+P2st/EF7bz++uucfc450XwaooaqdrlKEpERwNvAdaq6aA+2fNng4fMicj5wGjC7i0009FLGjBljVha6iKgIBhFxRM5tB+wiEgcEVTXYZOoLwHMi8jJWENVtmNzwPklNTQ2bNm1iw4YNbNiwgblz57Jly5b67SNGjODiiy/miCOOICcnh/79+xPvdGAjhM0eSfbREGG/h5B7CxqsJlS7hG+XfsHzuyr53hfim1A6m0gHckjEx7PxHzFx2DH0zzkKr30Aj738R3xBO6+99hrn7KdioSEiEguk0yB9ujMKtYnIEOB94F5VbZqhsTeUDqZzGwyGfSNaKwy3AXc2ePwL4G4ReQZr+XGsqm5T1XdE5AGs5dF4YF6T/Qy9GL/fTzAY5KmnnuKxxx5j06ZNjbZnZ2dz2GGHMSX/IKZMHMLIodlk90/FbvcT8iwnXBUg3GB+qLaEmuIlhCu+JVy1iXc88czQ0ygho35OEl6muqo5fsgkhqdNJic1DUfyCOJduXzw4cesWLGCM844o5uegZ6JiIzCyp44hOZfzvY2HqPFmwKsOi4fAn9V1Sf2coxU4FCsoMgg8DOsmIc+l7ZiMPQG2lQaujdiSkP3TCoqKpg/fz6LFy9m+/btrF69ml27dgFW3EFGRgbp6ekcdNBBnHXWWaTG+RiQWMGAATnEuZIBAQ2DLQbfd/8iWLkeDXjw+6rQ8lUIYTwaywN6LE9zKAD58UFOykpngKsfg10JEA6SnZjOwbmTWLj4a3500sn17oz20NtKQ7cVEfkAqwrkvVgBkGOAmcAnqvpCG49xF83F/d1YKwR3AY3qNqtqYmS/W4CjVfVUEcnE6nkxBghFbLpdVd/b2/lNaei2YTIKDND20tBGMBi6hYKCAmbOnMnLL79MTU1No205OTlcdOEvOOHog3DYbRAOEfRVMiinP4OzHNjjMxDbD4thqiE8y+/B9/2C+rFtmso/mMiqmNEsD2VQGrJhF+HXw3I5Ls1Fut2GzeZgUIKLrLShxKXkcdtttzNr1ixuv/127rnnnnZfUx8WDGVAf1X1i0iFqqaKSCKwUlXzom1fWzCCoW1Mnz6dZ555hssuu6zP+P2NCGo/ndZLwmDoKB6Ph48//pg5c+awYMECwmHLgXDAAQeQn59Pbm4uGRkZjB01hITAFtLji4lPsF6zsc5U0jOTsce6ELFWwUO1xfi+fwdfwX8JeQoI2+JYOuAy/lbVj7crxQrh91vnHpsYz/VDB5DlSmZwag4TsoZhi+uHiA1V5dZbb2XWrFnY7XYmTpwYhWenR+PnB9dDqYjkAuVAVvRMij598YuoL2YUmLTKrsMIBkOnoqq8/fbbzJ49mxUrVrB7t5W6aLfbOemkk5g2bRqDBg1CRLDZbPRLSSDbuZPkpAm4Upp3itaQD9/uj/Fue4tA4RcQiVrY6hjEjc7L+GJ7CAUcIpyQnsKRaYkMjE9iVMYgqmzJjEjNZmRKGnabrd6+hmLBBDi2yKfAOcBLWN0s/wt42c8LLN12223MmzePyspK3njjjWib0yn0xYyCviiCegpGMBg6BVXlmWee4f7772fjxo3146mpqUydOpWTTjqJcePG0a9fP9JTnCTEhrHjJ1y9DbFlYItNaXbMYOUmKj+7DvVZWbQqdipSD+Nd14ncsdOB2x0iVoTjMvpx+dBBDE7JQhIG4g1DdTDAIRkDyE5IbGRjQ7Hw+uuvG7HQMj9r8PcM4DKsImp965ulndS5b/uqG7ev0BdFUE/BCAbDPrFhwwaWL1/O888/z4IFVkxBWloa06ZN45BDDiEnJwew3BCDcwcQqt5GqHINErCjYscWm9woPgFANUywfC3uJb9HfaVI0jAK0k7EnXkcC6tC3L9+EwENcXhqIndMPo4UVxbeUJCyUBB7IESq08n4tGzS4uIbHfcvf/mLEQttoEkPiQRV/Vurk/cjZs6cSWpqap+6c+2LbhZD12EEg6FDhMNhZs6cyV133VUfm5CQkMAvf/lLjj/+eAYMGIDT6SQuLo7c3Fzi7T78uxeBhrHFZyLSuFGqaohg+bf4d32Cb/vbhL3FADjSDmT9mLv51+5yHlu2AW/kXNMGZvG/w4ficSThDQXJineR7ownI96Fw9ZyE9Zzzz2Xp59+mnvuuceIhT0gIknAw1iVFRNEpAar4dyNqlq5x537MH3xztX4+w3twQgGQ7spKSnhggsu4L33rOy2SZMmkZmZyQUXXMDgwYOZMmUKiYk/uALC/ir8hcuwxboQe1yjY6mG8X73b2o3vki45oeaQLb4LBzZR7F14AU8UbCbl7fvAOCApEROy0zix9nplMdmc2BaFoMSU7C1khZZt3wsIuTk5LBixQpiYmI69fnogzyD1cnyGKAAq7X8nVguiWlRtMvQyRh/v6E9GMFgaDOVlZXMnDmTJ554ArfbTXJyMjfddBOTJ0/GZrORm5tLXl4ecXE/iIKwtwx/yTJsMfHNxEKoeieelfcTKLKq/9ri+xPb/0hsA09kvX0YJf4An5eU8/L2HdgFbhw5nDPT4nAkDqLUlsrIlAyGJKW2am9dzAJYy8kiYsRC2/gRkKOqdfmvZSLyC2B7FG0ydAF9cdXE0HUYwWDYK6rK3Llz+d3vfkdZWRkABx10ENdddx2HHHIII0aMICYmpr74UTjggXCAUE0hIfcWbM6UerGgIT++nR/g2/YWgeLlgCKxqSQeeAOxOSeg2FhRUUmRz8+XpWU8snkrAL8aMoD/yXAhScMpExc5riRGpabv0eaGAY4XXHAB48eP79onqu+wGcgBNjYYGxAZNxgM+ylGMBhaZfv27bz77rs8/fTTfPHFFwBMnDiRn//85xx55JGMHTuW1NTGd/hhXwX+yIqBiK0+XiEcqMb73b/wbn6tPj4BWyzOgceRMO4a7PFWiv9WTzUz123k4+IS3EEr9u749BR+njeOakcaIVsMQxJTGJ2Svkc3RNNsCCMW2sX/Ae+KyLNYqwqDgEuAF0XkgrpJqvpKdMwzGAzRYJ8Fg4hkqWpRZxhj6BksXLiQ66+/nq+//rp+LCkpiauuuoof/ehHjB49mkGDBmFrElyoQS/+0q+xxbgQh5WhoKp4t71F9apH0EAVAPbkPOKGTcOZcyK22B+6l9cEQ9z37Qbe3FUIwOD4OC7JzeD0McfhVTt24KjsXJz21l+2JnWyUzgJ2A2c2mCsEDg58gNWiWcjGAyG/YgOCQYRScFqL/tTrEo6LhE5E8hXVdMcqhdSVlbGggULePLJJ1m40KrPExcXx6hRozjiiCM48cQTOfjgg8nNzSU2NrbZ/hoO4C/5ClH9QSyEvLi/uhf/jg8AcKRNIGHUxcRkH9mod0NIlTK/n8c2beWV7TsQ4P4DhnJkvyQcqaMJSgy1QT9HZA8yYqEbUNXDo22DwdBRTKpo19HRFYbHAQ8wCvgmMvYl8BCmm2SvIhQKMXv2bG699db6Hg8JCQmcffbZTJs2DafTSf/+/Rk7dixOp7PFY6iGCZSuhGA1trg0wHJNVH1xA8HyNYgjAdeBN+IcdGojoaCq7PJ6WV/l4bEt3/GfyMrCZbkZHDMwj1Bcf9xqw+/3MSUrh6TYls9fh9vtZt68eaaCo8GwH3Prrbcyf/58KioqmDdvXrTN6VN0VDD8CMiNNKdRAFUtEpHszjPN0FUEg0E2bNjA0qVLefjhh/nmG0vzHXDAARx99NFMmzaN8ePHExcXR3x8fKOshxaPV7WFcG0x9oQswr4y/IVfUrvxBULurdji+5N8+MM4koc32iesyurKKr6v9fJxcQn/2VVIrAjXjxjCWSOnUBp2kCAOcl2JZMW76OeMb+XsP5CcnMxHH33E119/zemnn97xJ2g/R0QCWC6HZqhq8+Ulg6EHUXdT0pEOtIY901HBUAWkYfk5ARCRQVh+TkMPJRgMcuONN/LMM8/gdrvrx9PT07nuuuu4/PLLSUlJISEhoc3HDHvLCFVuwBafSbBinVXK2W/V9rEnDSP5iEexxzfvEbHFU82OajeFNVXM3mRlQtw6YTKnDZ9IsbeWMSnpDEvut9c3vary7rvvcvLJVovqgQMHMnDgwDbbb2iRphGiA7BKRP8zCrb0GMxSd+/gvvvuIyUlxdSW6AI6KhieAf4R6V1vE5FDgFnAk51mmWGf8fl8lJeXs3btWl599VWWLl3KypUrAavHw8iRIxk/fjxXX301Bx10ULMgxr2h4QD+spXYYlMIubdS+enVaLAaR7/xxA44mrihP27WI0JV2Vlby/ryXSQ44rl7025CwAV548kfOIpiXy3pzniGtlEs1MUs3HLLLcycObNd9htaRlXXNxlaLyIrgc+A/bZMtKmK2DswtSW6jo4KhlmAD6vyWxxWtPSTwJ87yS7DPlJTU8OyZcv4+uuvufPOO6mstO76U1NTue222zjggANISEhg1KhRDBgwoN3Ld+FANcGyVUg4CHYn7mV3oMFqYgccR9Ih9yK2HwokVQeDeIIhqn1udniqqAqGSEzsz93rtrCztobRKelcPGoiGXEuxvTLINZmbzVlso6mAY4HH3xw+58kQ3uIx1pp2G8xVREN+zsdFQzpqvoQVpBjPSKSAZTss1WGfaKmpoY5c+YwZ84ctm61lvvz8/M57rjjmDhxIpMnT2bQoEG4XK4O+fnC3lL8JcsRWwy2uDQ83zxEqGozNlcuSZPvaCQWfKEgX+wqwK9hHDEuXCkj6B/Xj+s+f58lxTtIiXEy85ATCKkyPLkfCY69V2I02RBdi4g81WQoATgeeD0K5vQYzJ2rYX+no4JhC5DcwvgGrNiGPSIiaVirEydjCYzft1QERkScwCPA2UAMsBi4SlV3dNDuPk9VVRW33HILc+bMQVVxuVxMnTqViy66iEmTJuFyuUhPb71CYmuoKsHK9YRri9CAB5szFbE78Rb8F++Wf4A4SMq/G3EkNNgnxPri7YQTshnYbxgi1svt8bXLWFK8g3RnPI8ddRoZcQnE2R2kOvccXFlnhxELXU5pk8fbsJpPvRUFW3oMJobBsL/TUcHQ7LZURBKxajK0hccAP5ANTALeEpGVqrqmybzrgMOBA4FK4Cms+g8/6aDdfZJgMMiuXbsoLy9n0aJFPP744wBcccUVXHrppbjdbnJychg8eHCHzxHybCNUtQVbXBriyEJE8O34EM+KPwKQOHEGMf3GNdpnW9kOdtgzGdhvRP1Kxqe7t/Hs+hUIMPOQExiRnEZRbTXj0rLaZMcjjzxixEIXo6q/39djiMg1WNUhJwCvquolDbadiPUZMBgrHfsSVS1o5ThDgWeBQ7GEyzWq+v6+2tcRTAyDIVr0FLHaLsEgIlux0q3iRWRLk80ZwF6TXkXEBZwDjFdVD/CpiPwHuBC4ucn0YcACVS2M7Ps6VttdQwS3281XX31FTU0NHo+HOXPmEA6H+dnPfsb06dPrYxeGDRvW7mOrKuorJ+jZTrjme2xxGYjNQai2iNpNr+Dd/HcgTPzIXxA39H9+2C/oZVdVMav9TvpnDkdECKvy6ubVPLLqS8Iol46aRH7mQPyhEHF2B2ltSJsE+NnPfsbcuXO56667jFjoIkTkQWCeqn7RYOxw4Meq+rs2HmYncB9wClb8Q91xMoD5wOXAm8C9WK6Ow1o5zqvA58BpkZ83RGSkqha366I6ARPDYIgWPUWsSl373zZNtu4MBPgPcGaDTQoUtrBC0NIxDgIWq2pCg7EbgWNV9cwmc/OxXBI/BSqAp4EiVf3N3s6Tn5+vy5Yt2/tF9WJqa2tZvHgx77zzDu+99x6rVq1CVcnMzGTu3LnExMQwbNgwcnNzcblc7Tq2BmvxlyxH/R7E4URikhAR/MXLcC+5pb7Mc8KYy4kfPR0RQTVEuLaMCo1lqS+B9KT+OB2xuP0+bvjyPb4q2QXAZaMncdUB+dhEKKqtZnykRXWrtjRoUQ3WiorDEf02KCKyXFXzo21HZyMiRcAgVfU1GIsDClS1XbVWROQ+rJotl0QeX4G1onBE5LELyy15kKqua7LvKGAVkKGq7sjYIuBlVX1iT+d1uVxaXV3dHlMNhh5LV68wiEiNqu71S6Jdn7qq+kHk4P1VtaqDtiVi1XFoSCXReuL1AAAgAElEQVSQ1MLcjVjNb3YAIawPj2taO3Dkw+gKYJ+W33s6oVCIgoICtm7dygsvvMCLL74IQExMDMceeyyXXnopcXFxHHnkke2sqVBCsHIT2OMitRTC2BMy0ZCX6m/+hG/722jQqgYZk3UYrrG/xpE6qsH+5dTED+abWgdpSXE4I1/qf1y5mK9KdpHmjOf3k47k+IHWakel30eqM44BCS396y3qYhaCwSD3338/ItIjxEIfR2judhQ6p1ndOGBl3QNVrRaRzZHxdS3M3VInFiKsjIw3o+H7v6Xy5ftKT1kWNux/9JSA2w59AKhqlYiMB47GckVIg2337GV3D80DJpMBdwtzHwOcQDpQjVU85m0sf2ZLdj2FFedAfn5+25dOehm7d+9m7dq1vPvuu7z44ovY7XZmzJjB1KlTcblclJSUkJeX1y6xEHQXECxfgy02CQ34EJsNcSSiwVoqFl1JqHKDNdEeT/zwn5Iw9kpE7PX7a8iLjxhW+GJxOhzERb7U39m+iQXfbybe7uDpY85kcGQlwRsMAspB6QNwtFL/oWmA4y9+8QsOPPDAjj1phvawGLgduLXB2C1YdRj2lUSgqTuhtRuGxMi2pnNzWjpww/e/y+Xq9Pd/T1kWNhiiRUebT03HCj78AKuz3XvAiVg+yb2xAXBE/JAbI2MTgZbcGZOAW1W1LHLe2cA9IpKhqvtl+qaqsnnzZr744gtmz54NwJ133slpp51GMBjE4/EAkJPT4mdqM8K+CsL+CoLla7HFpSM2R6Nby5oNz1mVHBMGkjzlD9hTRjdLxVQNE/JWUhI3iqAfUmKsu7uQhnl8reUWun7CYfViIaxKhd/L4dmD6oVFS9fZNBvCiIVu4zrgbRG5CNgKDMUS9Kd1wrHbc8PQnrldjolhMOzvtK+03w/cDJwWiTmojfw+F2sVYI+oajVW0NM9IuISkSOBs4AXW5i+FLhIRFJEJAb4NbBzfxULADt37uSuu+6qr2p43XXXMXXqVIqKivB4PKSkpDBhwoRWG0U1JOwrx1/4mSUI4tIQW+Mv76C7gNpNVrZrUv49OFLHtCgWwrXF2JKHsy1oJ7lBg6hPdhWwo8ZNjiuJs4aOrh8v8daQl5xGWlzLgY4mdTK6RDIWDsQKRP4bcBEwsbVMhnayBusGAaiPYcij5RuGNcBwEWm4+tDazYXB0GdZt24d06dPZ926pl677qWjPslsVf048ndYRGxYOdovAJe1Yf9fY5WXLsLK+f6Vqq4RkaOBt1U1MTLvRuBRrFiGWGA1Vk2G/ZLa2louuOACPvnkE5xOJ9deey3nnnsuxcXF5OXlMWLEiDaXd9ZwgEDpN9hik+vbUTfEX/gF7uV3QTiAc/DpxKQ1bi8Q9lehIT9g9YyoihuE172zXjAEw2Fe3LgKgPPzxmMXy64Sbw39ExIZldpyLQgjFqKPiIwDyhu8xxGRgSKSqqpr23gMB9bnix2wR4Img1j9KB4UkXOwPjPuAL5pGvAIoKobRGQFcKeI3AaciiVkovKCMC4JQ7S47bbbmDdvHpWVlbzxxhtRs6OjguF7ERkSuePYCJyOFekcaMvOERfDj1sYX4Tlt6x7XAr8vIM29hl8Ph+lpaVcffXVfPLJJyQlJfG3v/2NESNGUFVVRWpqKnl5eW0XC8Ea/CUrIOxHYhvX2VJVvFv+TvWqR4CwFdw4/oekFA0HCXvLscVn4ug3CFtsCmJ3sr10N3F26+W0s9rNzUveZ21FCYkxsZw52AqMLPPVkhobx4S07FZLP3s8Hv71r38ZsRBdXgGmNRlzRcYntfEYt9G41f0vgLtV9a6IWPgr8BJWHYbz6iaJyBMAqnpVZOg84DmgHKsOw7RopFSCcUkYokddplh7shq7gnalVdbvZMUw7FbVt0TkDOAfWJUYf6uqj3ayjR2iL6RVhkIhNm3axJYtW3j66aeZP38+sbGxzJ49m8mTJ1NTU4Pf7+fwww9vc4CjBr34ij5HAFtsY/dw0L2V6pV/IlCyHID40dNJGDMdUMLeUsCGOOKxxQ/AkZKHRFYNPAE/i3ZtIzMungq/l+kL/8O26iqy413cPfk48jMHUh3wYxMbU7JyiLXb2ROFhYUsX76c007rDJd519GH0yqrVLVZJdfWxnsiJq3S0JfolWmVdajq3AZ//1dE+gFOVW0a0WzoILW1taxZs4aSkhI+/vhj5s+fj8Ph4MEHH6wXC7W1tRx22GFtFwshP4HSFQjarItksHKj1W0yUIXEJJM48SacuScBVmCkPXEIjpRRzeIcAArcFcTYhKqAj+s+X8C26ipGpaTz5FGnkxRxUVSHAkzOGNiiWFBV3n77bU499VREhOzs7B4vFvo4O0TkQFX9pm5ARA6kQTt7g8HQffSUtMqOBj02QlW9WJkPszrjePs7W7duZeHChWzYsIHXXnuNhx6yenzdcccdjBw5kuLiYvx+P4cddhjJyW274QvVFuPb/Ska9DQTC4Gy1VQuvhYNVBGTfST9TnqjXiyAJTTsCQObiYXdNR4W7vqOre4KbAhXLnqLteXF5CQk8egRU+vFQlgVO9JiNce6mIXTTz+d3/2urUUEDV3MbODfInK5iJwgIr/Eij14JMp2GQz7Jb026FFELsbyY27Eynl2Yfkrr6Jz8rT3a8rKyvj222+pra3l6quvpqKiAoCLL76Y/Px8hg0bxpAhQ7Db7W0uThN0byNYvrq+YVQdgfJv8W17C2/BmxD2EZN1GMlT/tBojkbaV0sT90V1wM+K0t0kx8QyICGRx9cuY1NVGUMSU3j8qNPJiPth1aPK7yPHldys3kLTAMdDD22xvIahm1HVOSLiBqYDg7CKp92pqi9F1zKDYf/k1ltvZf78+VRUVDBv3l47MHQZ7e0l8QBWqtVnwPlY9d8PB5YDR6vqik63cD/C7/ezcuVKdu3axV133UVFRQUHH3wwv/71r8nLyyMxMZHRo5vXQdgTwepdlliI1FioI1C2ispPfw1hK07VOeQsEife1GwVQQNV2F1D6uMVAELhMKvLinDa7DjtDtx+H69tXg3AHQcfS1Z8Y1eYPxxioKtxXR6TDdGzUdUXaTnVeb/FVHo0RIvdu3c3+h0t2rvCcB5wjKpuFJEDsPKhz1fV1zvftP2P9evX88orrzB37lxUldGjR/PnP/8Zl8tFUVEREydObLNYUFXC3mKCZSua1VgIVm2m6subIRwgNuck4kechyN1bPMaC8FaVAVHYm6j8Y2VZZT5auuFwd+3rqU6GCA/YwAT0xu3GgiGw8TY7KTE/tC62oiFno+InI91c5CDVZr9RVV9NbpWRReTVmmIFjt27Gj0O1q0VzCk1lVnVNVvI5GVRix0Art37+bDDz/k2WefBeDCCy9k+vTpJCQkUFlZSUpKCqmpqXs8hoaDqL+KsL+CcE0hIX85dmcqYoupn1O7dR7V3/wZNEhMxmSSJt/ZYiBj2O9Gw35isw5FHJZ7ocrvY6u7nO89VfVioSYY4JVNVr2Fy0Yf1Ow4noCfHFdSozTKRx991IiFHoyI3AT8CvgLUAAMAe4VkUGq+kBUjYsiJq3SEC3mzJnDb37zG/7yl79E1Y72dqt0A2P5oXfEN1j97hv2ktjWmQZ2lN6UVlldXc3ChQu59tpr2bx5M+effz433HADwWCQ4uJisrKyGDNmzB4DHMO+cvylKyHkRcSGOBKaFWTy7VqE+8sZgBI39GwSxl2DLaZ5Jk3YVwH2eGLSD8QWY5XFqPB5+bLoe2JsNpJjnPWrES9t/Ia/rP6SCf2yeObY/2m2SlFUW81h2bn0axDwWFRUxMknn8ztt9/eq8VCH06r3AycrKqbG4zlAe+ravv7pEcBk1ZpMLSdrkqrdAHf0biTXcNysYpV2c3QRsLhMCtXruSTTz5h8+bN5OTkcPXVVwNQWlrKhAkTGDJkSKv7a8hPsGoTIfd3VtXG2Mzm5whUU7vxBWo3vw4oCQdcRcLoS1o5ng8FnJmTEbuTsCq7atysKisiyRHbqPdDpd/LS/WrC5MaiYXaYABPIECcw0FKbFyjFtVZWVksW7bMdJ3suSQBO5uM7aJBUbX9EVXlt9dcQ0xMDA9E+U6vszBxGYb20N60yhisEs0xrfx0fk/ZPk5xcTGlpaW89JIVgH7llVficDgoLCwkOzt7j226VcMEyr4h5NmOLT6rxRLPqmHcS26mdsPzEPLiHHwG8aMubuV4SthXQUzagfWZEhsrS1lZupvUWGcjseALBbnxi/co8dYwtl8mR/Uf3GhbdTDAQRn9OTxrEIIV5Xv99dfXCwcjFno07wPPicgQEbGJyFDg6cj4fktNTQ2PP/UUDz36KMFgMNrmdAp1cRkPPvhgtE3pNHpKCmJfpF2f2qoa6ipD9kfC4TDr16/n/fffZ8eOHQwZMoSTTz6ZkpISxo0bx+DBg1sMclQNowEPoZrdhGuLsSdktXqO2k2vEiheisSmknzoA8Skt97xUX3l2BMHY4+3Vin8oRAF7koy4hLqe0GA1Zr6pi/f4+vS3WTFuXhgyo/q7fQGg1QFfEzJyiE9LqFZgOMll1zCpEltrS5siBJXA08Cm7FWE8NYdRiujKZR0aaqqoqUWCe1Iasr7N5iinoDfTEuoy8Gp/aUlSBzmxdFdu7cyeeff85jjz0GwDXXXENlZSVDhgxh6NChzeZryE+o+nuC7u8g7AcEW3wrTZzCQWrWzaV2w3MAJB50a6tiQVXRQBUqdhwpI+vHd9W4UdVGYmF3jYffL/mAVeVFpMbG8egRU+mfYK1UVwf8BDRcH7PQUjaEEQs9H1UtB84VkVigP1YZeH+UzYo6brcbV4wDmwhut7tPCIaeUkGwMznnnHNYtGhRr46PakpPEUGdUunR0H5qa2v59NNPuf/++wmFQlx44YUcffTRiAgjR45sNj9YU4hv1ycEKzdii0nAHp+JPT4DkcYhI6HaIrwFb1Lx4c+p3fAsICSMuwbngKObHdNKvSwj7C3B5kwnNmsKYneiquyqdvNtRTEpDdpkLyveyc8/ms+q8iKy4108fcyZjEixmleFVfEE/RySmdOqWOhLb+D9AVX1q+o2IxYsPB4PLrudxBgHbrc72uZ0Ck8++SQpKSk8+eST0Tal05g3bx4bN26MaoGjzmbKlCkkJyczZcqUqNphVhi6kXA4jM/no6amhrVr1/LnP/+Z8vJy8vPzueaaa6ioqCAvLw9n5EtaNUyw/FvCvlI04MHm7IfYm4eJqIap3fQqtZteQX2l9eM2Vy6Jk35PbObkFvZRy53hysGRMqI+dRJgq7uCteXFpDvjibFZguSd7Zu4c/nHhFQ5PCuXe/KPa5T5UOatZWRKOsmxTiMWDH0St9tNos2GA+kzgmHGjBlUVVUxY8YMrryyb3ic+qKb5aGHHqKqqoqHHnooqv+nDguGSL/7Q4AcVX1DROIBVLW2s4zra6xdu5bt27cD8NFHH7FkyRKSk5O5++67sdvthEIh+vfvXz8/5NlOyFNgFV5yZLUYzxCqLcLz9R8IFH0BgDhcONIn4hxwDM7BpzeqwVBHOFCNBjxWQ6l+BzSq4ugJ+FlfUUJW/A9xCytKdnPX8oWEVPn5iAlcO35KIzdFMBzGZhOGJllLtNXV1bz55ptGLBj6FJWVlSSK4LBZ8Qx9gQceeIAZM2bwwAN9p7xGX3Sz3HDDDcyYMYMbbrghqnZ0yCUhIuOAdVilY5+LDJ8IPNM5ZvU9iouLKSgoIDMzk1AoxJw5cwBL4WdnZ+PxeMjIyMDlslJhw343wYpvIyWdY1oUC77v36PigwsIFH2BxCSTdOgDpJ3+PimHP0zc0B+3KBY0WIuGg8RmH05M2rhGYkFVWVtWRJzdgV1s+EJBnt+wkt9+8S5BDXN+3niun3BYI7EAVo2Gkclp9asRiYmJfPDBB7z55ptGLBj6DJWVlSQrJIvU93jp7Vx55ZVUVlb2mdUF6JtZEkuWLKGqqoolS5ZE1Y6OxjA8DtynqiOAQGTsY6C5o3w/xufzUVBQwGeffcbSpUspLi7m+uuv5+yzz6a6uprjjz+eU045hWAwSHV1NXl5eQBoOECg9Gur+FJLVRi9pbi/ug/3stvRoIeY7CNJPeElnAOOabV0tGqIsL+ScMBNbOZkbM5+zeYU1ngo8dbUuxVuW/YRs9csoSrg46jsQVw3vnlzqGA4jIgw0JXEf//73/q0yaysLE499dR9efoM3YyIvN7k8RnRsqUnUlZWRnIoREo4THl5ebTN6RT64pdrX0wVvfzyy5k8eTKjRo2Kqh0ddUlMAJ6P/K0AquoRkYTWd9l/UFW2bdvG+vXrUVUSExNZvnw5d955J6qKzWZjypQp3HzzzYRCIUpKSpg4cSLJcQF8uz5BQz4EsMWlNTquv+hLK02y5GsI+8AWg2v8dcQNO2evPSbCtSXYXbnYEwc1b28dDlHmrWVNRTGpTqvnw2ub1/DRzu9wOWKYecgJHJk9qMVzVPq95CWncdftdzBr1iyuv/56Hn744X17Ag3RoqnCewFIa2ni/kjR7t2kBYP4RCguLo62OZ1CT+mC2Jn0xSyJ7777jnXfl3Dn3fcwY8aMdjUg7Ew6usJQADRqHCAi+Vh523tFRNJE5J8iUi0iBSJywR7mHiwin4iIR0QKReS6DtrcbWzevJnVq1eTkpJCRkYGPp+Phx56CFXlpz/9KQsWLGDOnDkkJzqpKlzDgYNDZMgGAsXLEZsdmzOlkVjQYC3ur2dS9dl1VqxC2Eds/6NIPf4l4odP27tYCFRji+2HI21CiysLW6rKWVK0A4fYcNod7KpxM3uNtfR158HHclT/lutBhDRMWJUn7/9TfYDjkUceuY/PniGKNP0nd8mnUuS93PAnJCKzW5l7SWR7w/nHdYVde6Nw2zYybTYygd3bekQF/H2m7n0drS+grmDu3Lls3LixT8UxbN26ldjh+YjdQUlJSdTs6OgKwx3AWyIyB4iNNKu5GqthTVt4DPAD2cCkyLFWquqahpNEJAN4B7geeAOrkmQuPRRVZfv27axfv56srCxsNhuqyqOPPkplZSX5+fn16tAWKCWweynj8oaTkZkGthjE2fjOX8MB/LsWUbPuKULu78AWS8KY6cQNPgNbXMv1F5rZFPKjAQ8xWYe1+qFQ6q0lLS6eOLv1cvjrmqX4wyGm5uZxQk7LrQO8oSBl3lr++8zzPGWyIfoKTRvLtL3RTHtOolpfYlpEEoHdwD/2sMvnqnpUV9jSHr4vKOAoux2vKiu2bo22OZ3CfffdR0pKSp/KKPj2228b/e4LrF2/CUnOxpU+kC1btpCZ2bwFQHfQIcGgqv8RkV3AL4HFwGjgZ6r65d72FREXcA4wXlU9wKci8h+sVro3N5n+W2CBqr4ceewDeuyrYM2aNRQUFJCRkYHNZqOoqIhZs2axaNEi7HY7N954IwCVFWXYK5aSnjWIjIGjaOl7XIO1VH76a4IV1uXak4aSdMgfcCQPb5MtGqwl7HcjMQk4UscgLawsgFU/wR3wkR5Jkfy8cDsLvt9MrM3O1eMOaTbf7fdRGwqS6Ijl3bnP89TMPxqx0HdwiciGBo+TmzxGVTvbiXoOUAQs6uTjdjrbtn/PQLsdnyoF3xXsfYdeQF/MKNiwYUOj332BdRs34hh8CpqSzZYtWzj00ObxZN1BhwSDiPRT1aXA0g7sPgoIqmrD/+ZK4NgW5h4GrBKRz4ARwJfA1a11xBSRK4ArgD32YOgKPB4P27dvJzs7GxFh/vz5PPzww3i9XpKSkrj77rvJy8ujqKiInDQbyUm5ZOW2IhZUcX91r5UlEZ9NfN75xA09q8VeEQ0J+yvRkBWDKjGJxGRMxhafucflxppggDCKiPB1yS5u/PI9AC4eNZEBCUmN5npDQQKqHDNgCM888SR/vc+sLPQxTovCOS8GXtA9t809SERKgDKszKxZqtqtzRxUlW2FuxnULx2vKgW7mvbm6p30lJLDncmQIUPYsmXLHpv29Ta+27IF58QB1CZms2HjxqjZ0VGXxA4ReR94GfhPO2svJAJNk5grsTrkNSUXOBg4CVgFPAC8CrToKFfVp4CnwGpv3Q6b9pndu3djt9vrxcIf/vAHAI4//nh++9vfMmDAAEpLS8nN6c+o9ApsMaMQe/O0R4Da9c/i3/kh4nCRfMQjOJKG7vHcVsXGUmzOfsSk5SGO+EaFmPZETTAAagUv3rzkA3yhEGcOHsUvxxzcaF5YlQqfl8Oyc3HFxHLeeefx3HPPccsttxix0Hc4RVV/210nE5EhWDcK0/cw7RNgPFbc1DjgdSAIzGrhePU3DLGxndsHr7CwkDibjSSbjURV/H4/FRUVvb489LXXXst7773H9u3beffdd6NtTqfg8Xga/e7t1NTUUFleysDkTGypA1ixam3UbOmoYBgG/AwrtuCpiEvhFeDdNjSo8gDJTcaSgZZKp9UC/4ysZiAidwMlIpKiqpUdtL3TCYfDFBQUkJKSwqZNm5g1y/osu/HGGznvvPMACAaDCMqI7DAEg80qNqoqgaIv8Rd+jnfL64CQlH/PHsWChoNWXYVgDfak4ThSRiK29nUXr/R7cYjwp28+p9RXy6T0bG47+GhsTVYlSrw1jExJIy3iusjMzGTJkiXY7aabeR/iciw3YHdxIfCpqrYaEKCqWxo8XCUi9wA30YJgaHjD4HK5OvWGYcOGDQxLsES4iDAsMZGNGzdyyCHN3Xa9iTVr1jT63ReoW6za86JV72H9+vUkZeYgNjsx6YNY+9VHUbOlozEMhcCjwKMiMhy4APgTkIEVyLgnNgAOERmpqnVrKxOBll6x39A48KpHvgIqKirw+/04HA7mzp2LqjJt2rR6sQBQXl7OAUNc2ALF2OOt7pKqIYLl3xKq2oy/8DP8uxbWz08Y+yti+7eecRAOVKMhL7b4/jj6jccen9Eh278tL+b3Sz5gbUUJTpudOw4+tr4wU1iVKr8PfzhMujOeZx54GE9VFbNnz0ZEjFjoe3R3qPxFwB/buY/S/Xayfv16hjf49Mmz2Vi3bl2vFwxjx45l586djBgxAlXtE9kSOTk5FBcXk5OTE21TOoVVq1bhyLDcKzEZg/lu80aCwSAOR/d3duiMM6ZEfpKA6r1NVtVqEZkP3CMil2NlSZwFHNHC9GeBeSLyKJaguB3rjqTHrC4AbNq0iYSEBAoKCnj//fdxOBxceuml9dtrampIcEJGvBtbnPXFHihbhXvJLYS9P+RziyOBuOE/JSZjMjGZLX8Qhf1VaMgPdiex2Udgi0lscV5bCIXDPLL6S9ZWlJDujOf3k45icKKVqRFWpai2mmFJ/RjoSuT+u+/l/kg2xGWXXcbBBx+8l6MbeiExInLLniao6h8640QicgSQw56zIxCRU4GvVLVQRMZgfQbscZ+uYO3KlYwIBMEqU8Jwf4C1q1Z1txmdTn5+Ph9+9CGfff4ZCxYsYOrUqdE2aZ+pK6rVV4prfbFkGYF+lmCwOROIT8lg3bp1jB8/vttt6Whp6FEicqeIrAfexnobnaeqbQvhh18D8VjR0a8Cv1LVNSJytIjUO55U9UPgFuCtyNwRWKsZPYbKykpKS0spKCjgmmuuQVU588wzyc62FlqCwSDuqkomDI3DERuPiJ1gxTqqPvsNYW8xtoSBOAedSvzoy0g94RVcY39ldY1sovRVw4Rqi5GYJGKzDsXZ/6h9EgsApd4alhZbwVtPHn0Gxw0cWr+tOuAnx5XEAf0yeODue/ljg9RJIxb6LDasomyt/XTmJ9TFwHxVbeSKFJHBkVoLdVHLJwLfiEg18H/AfKBTREt7WL18OaNifri/Gm23s3rZsu42o9PZUbiDnAuyyT4xg1V9QAABzJkzh5EjR9aX3+/tLPrsC5wDfkhOihkwKmoloju6wrAU+CdwLfB+G+IWGqGqZcCPWxhfhBUU2XDscaxS1D2Oqqoq1qxZg6py7bXXUllZydixY/nVr6xyFB6Ph5rqag4amUy8vRpbbBYa8lO19HY0WE3swBNIyr+nxfLPDdGQn7CvHHtyHo7kEe2OU2iNBd9vxh0RBkMSG9eAqA0HGZuQZLpO7l94VfX87jiRqrbYvCCSAZXY4PGNwI3dYdOeWL1uHWNi4+ofj46JYVYf8PuvWbeGuKOceNXH6nWro21Op3DooYcydvwE3nn3XU47LRqJP51HbW0tG75dTfbxv6sfC2eO5P2PPuGyyy7rdns6WukxW1UvUdUF7RULfYWdO3eyePFi/H4/ixYtorKyknHjxjF37lzS0tLw+/1QvYXDRgbpF+fBFm8V2qjd/Brh6u3YE4eQNPmuvYqFsK+CcKCamIx8YlJHd5pYCIRDvLN9E0Czss+qiig8PHOWEQv7F73fgd0FFBcXU+v1MrBBzM5Qu4Oi8vJe37WyYMt3OLNjiesfy7oNPbbETbtYvnw5q9dt4MknnrA+h/+fvfMOj6ra+vC7p09m0hMSIIEQktCrQRBEQEHFglgBFdFruVfF++lV9NqwXAv2joiKgAqKDZUqKAgCinQIPRASAumFlJlJZmZ/f5wEA6ROzkwSzPs858nMmX32WYHMOeusvdZvtWA2btyIf9tOaKo4q8bonqxe82stR3mPekcYhBATpJTzK97eUHOTIzlXDcOaMzk5OWzdupWwsDC0Wi0LFiwA4MYbb0SvV0olC/Iy6RttxhIUiRDKhcZ5IpnSfZ8AYOn9nzMqJU7HZctFYwzCENoHoTWqYrvT7WZHbibZ9pKTyxGDI6JPfu6WklyHjQChZdnixa3Owt+Lpm2F10zZsmULPazWU5xqrRB09fdn27ZtXHDBBU1onefYbDYK80/QMaQtuCUph1Oa2iRV2LNnD3G9z8FRWkJycjLdunVrapM85pdVq5GR3U/Zpw/vQHZePunp6T5P7GxIhOHWKq/vrGG7QzXLmjHHjx/HYrGg0+n4448/OHToEGFhYb8htDsAACAASURBVFx00UWAshTRJkASHBR80llw2bI4sf5+cNkwRl2CoU3tSl1uex4aYxD6sH6qOQsl5WVsz80g217C0eITHDiRh1mrIzGsnWKjdJNpK6ajNZBz2nVg5cqVLFq0qNVZ+JsgpbyoqW1ojvy5cSO9ys/Uieol4c8/PdGuax4cPnyYgAh/hEZgCDGQl5Xf4p/IAbbv3EXbmDjaxya0+HLRZStXoW1/qsMghAZrTE/WrvW9OGq9HQYp5SVVXg+tYWuZrnYDKSgowGhUWkC//76SXjFu3DilzEU6KSvOpF2QE41JEXWRUlK89QXc9mx0oX2w9qs5EV1KiduRDzo/9KF9EZrqxZ0agltKduRmsCbjCHkOG6FGM9N3Kxe6CXE9MVWU5+TYSknfvIOuQWEYtTrCwsLOiqzpVlppDBt+/pm+mjMvlf2kZP3KlU1gkTokJydjClceRoROYA2zcORIy5e83rFjB9FxXWjXOaFFJ3K6XC52bN2Msf2ZEZLy8ARWrfnN5zZ5WiVRrVsthPi9ceY0f1wuFyUlJRgMBlavXk1SUhKhoaGMGzcOpAtdyV78yvYTEBh0Mj+hLH0l5Vm/I/QBBAx4ocaIgdtRgNuWgzCGYQjrX+eSRX1wS8nOvEzSS4poY7IQYjTzRXISW3IzCNQbuSW+DwBZthK+mjWHf103jvvvv7/R522llbMBt9vNhk2bGGA48zs7wGBk3e+/t1iBoIMHD0LoX7abI8zKvhaM2+1mz+7dRMd3I6pzVzZv3dbUJnnM3r17MfoHozWfKYJsbJvAug11tm5SHU+THmsSHVe7MU2zw2ZTVLD//PNPnn32WQBuv/12/MwGtPY0HCVZ+Id1wuCntKd2Fh6kePsrAFh63HtKl0npduKy5eCyZeOy5SCMIRjbDcMQ1rfOvhH1JaWogPSSE7QxWwB4Z9dGXtu5AYDJPc7FqjeQbSvh649nM/t/L6LVaFrsmmwrrajNjh07CNHpiKhGpCxaq0XndLbYJkdJ+5LQhP2Vl6EJo8U7DIcOHcI/MBBrQBAdu/Rg+/btTW2Sx2zbtg1DRPVKBYY2sRzctxu32+1TmxpUVimEmFXx0lDldSUxNONOkmphs9nYu3cvU6ZMwel0MmzYMK4ZMwpD0WbKy+wUO3TExEfiKk6l9MCnlB1fgyw/gSFyKMaOV54yl9ueizYwAa1fW4TWWGfFREMpLLOzNz+HMLMfLunmswM7mXNgOzqh4Yn+Q7miQwJ2ZzlzP5nN/OemodVoWhMc/6ZUCCnViZRyvbdtaU78tHw551ezHAGKRPT5BiPLly+nS5cuPras8STtTcLU/6/IiQiHnXtabggflJtsTBdFLiQyOoa83NwW2/Nj2/YdlAdGVfuZxmTBYAkgJSWF2Nj6yh81nobeodJreC2BzSiNYc5qMjMzeeWVV3A6nYwdO5ZHH30Uo/0ATrfghF1Pr149CAywkL/ydtylyj+Rvs0g/Ac8hxB/XXjcjgJF1jmgs9fkWNOKCzFqtSxNO8gbO3+nsMwBwNPnDOPSaEUK9q0PZ/LVa2+hFaLVWfh7802V1xJF5l2L0hguEHABOUA735vWdCxasIB/iJoDscOl5LsFC/j3v//tQ6vU4dDBQ4Rf9pf+iinCSNK2lq3FsHnLFtrHKWv+Gq2WmISubN++nWHDqmuG3LzZsiMJXWivGj83hXdg9+7dzddhkFI+CUqugpRysXdMat68+eabJ7XXp0yZgk6Uo3XmU1iqtFUNCgrEnrYUd2k6GksU/v2eQBfa+xRnQTptSDQYgrt7zVlwut0cKylmQ1Yaz2z+FQm0t/hzW0JfLo2OA2DWV1/y1Yez0JTaW52FvzlSyraVr4UQD6FEDB+TUp4QQgQCzwEpTWNd05Cbm8u2pCSGhNTcp2WY0cTDmzZx4sQJAgJO76nXfLHb7eRl5xEVFn5ynzHSQPLB5Ca0qvFs2bqNHiOvPvk+Kq4b27Zta5EOw969e9CPurzGz10B7di9ezdXXHGFz2yqdw6DEKJqJ6QiIcQF1W1esLHZIKVk+fLlADz44IMYjUY05Tk43W6E0BAREYF0O7HtnwOAX8Kt6MP6nuosuJ24y4owhPVD6EzVnkcNCsrsHCsp4vmta5HA5O4D+P7i8YyNUdJPcu02xo++nMR20SxodRZaOZUpwANSyhMAFb1bHgQeblKrfMy3337LMIsVcy1Ovb9Gw0CLlR9//NGHljWe5ORkpaRS+9fvZgwzkJuVh8PhaELLGkdSUhLRcX+l2LXv3IXtO1te1KS0tJSs4+nog2sJ6IV04PfNW31nFA2LMHzMX8mOn9cwRgIdavisxbNmzRqOHj1KQEAA/fr1A+lC6zhGXomkY0w0er2O4p1v4ipKQWOOxBh9akmilC7c9lx0oX3QGL23pialJLWokC8O7aLc7eaSqM7c2qXvyc8cLidajWBQxzh+X7+htetkK6djB/pxqpBTH6Dl3kk84LMPZnJzPQogxrglc2fM4KabbvK+USpx8OBBTBGnPrAopZVWDh8+TNeuNeW1N19KS0vJzsqkTfu/bkHtYuL4ee2yJrTKM3bs2EFA2xiEtuZbtCEils0+/t3q7TBIKbtWeR1d29izkZKSEj777DMAhgwZgk6nQ1OeR5nDhtHkT2RkJPbUpdiTvwChxf+cp85IYnTbc9EFdUNn8a46V2pxIRsy0/jpaDI6oeHu7omA4iy8O306maXFzJr6PwytjkIr1fMssFII8TWQBkQD16BEGf4WHDp0iKTdSVwYFFLn2EtNJqZu2sTRo0eJiqo+Sa25sX//fkTYmd6QX1sT+/fvb5EOw+HDh4loF4WmynUtIjqGw4cONaFVnrFu3TpEm/haxxjCY8g4dtSnSZ2ellWeQkWXyXplWbdEXC4XO3bsONkhrLLsUNjTKLK56Nw5FllymOLt0wCw9H4IfVi/U+ZwlxejMQSj9e/oNTuzbSX8mZXOttzjvL9nExK4plNXoiwBSCl5b/p05nz1JcvnfcnRPfu8ZkcrLRsp5cfAMOA4SsQwAxhesf9vwYx33uEaowljPXKMzBoNY8x+zGxB3RG37dqKts2Zl38RrtT/t0SOHDlCm3anPssGh0eQl5vT4hQsFy//GdGue61jhFZHQIdu/Pqr7/pKeCrctFoIcX7F64dQWs5+I4R4pPYjWyaHDx9m7dq17NixA71ez3nnnQflhRTlHaVDpwSsZHHi94fA5cAYfRmmmFMbcUpXGdJpQxfc/ZR8BrU4UeZge24GG7PSsTnLWZ6WTFJ+NhFmC/d0H4CUktdmTGf21wsQpXbmv/EOAwYMUN2OVs4epJRbpZSPAw9KKR+TUrZcBZwGUlxczMcffcQt1Yg11cStOj0fTJ9+UqelubN953ZM7c/8/XRtNWza1jLlrlNTUwmOOHXNX6vTERoeQVpaWhNZ1XDsdjsb1q3BFNO3zrGudr357odFPrBKwdO7Vy+gUtXxn8BwYCBwjwo2NSvsdju7du3i7bffBuC2227DavHDkbOTkDYdiPQromDNHbhLj6ML6oq1z5STlQ/SXY7bkYe7rBB9aH80BvWzqJML81iXkUqOrZRQo5mP9m3l/T2bAXis71AsOj2vzpjOF7NmozmYyoJX3mTc9derbkcrZw9CiAAhxCwhRAnKkgRCiCuEEE82sWk+Ycb06QwxGIjR1T/FK16vp49Gy6yPm38Qxul0cnBvMn7RZyZdmzuY2bx1cxNY1XgOHT5MSNszl4TC20WRkpLie4M8ZOXKlVjaxaE1132/MMcP4ocffvCZgJOnDoMGcAshYgGdlDKpoo993Qt+LYy0tDR+/PFH0tPTSUhI4LbbbkPaM5HlRXSIScB2YDa4yzC0H0ng0BknFRqldOO256H1j8UQMQStXxvVbcuz29hbkEOoyUyew8Y/1vzApwd2oBWCx/sOZXBEFG9Nn86Xn32O5lg2Cz6fx3XXXae6Ha2cdbxX8bMnUBnL3QhMbBpzfEdJSQmvvvgik2tJNquJ+7Q6Xnz6aex2uxcsU4+dO3diaWNB63dmDpM52sjRlHSKioqawLLGsWfvPiKjY87YHxEVw4EDB3xvkIfM/mw+7pjamxNWog9pjzQF8Ntvvukr4anDsB54E3gZ+A6gwnnIVcmuZkFZWRkHDx5kyZIlANx7773odDqKs/cTGR2P3pVN2fE1oNFj7fUAQvuXx+6256MNjEcX0BmN4Uwt8MZS6ixnS+5xrDoD36fsY+Lq79hTkENbPyvvDbmMqzt1xeFwsGHrZkReIQvmz28tnWylvlwC/EtKeRil8gkpZRYQ0aRW+YB33nyTc4WG7vqG93HpZzDQ3S2Z0cxzGX799VfMnav//TQ6DSFxwaxf3/IEPZOSkojqfGZ3gohOcWzf0TIULIuKiliy6Ecs3YbW+xhN/Pl88PEnXrSqyrk8PO5WlNKrfcBTFfu6A++oYFOzITc3l/Xr15ORkUF0dDSDBg0iKyONtmEW2kfHYkv+EpAYoy4+o0cEQoPO6p0ER4fLyebsYxwtKuSmVd/ywrbfcLhcXNEhni8uvJbEcGUdz2A0Mu3FF/nhs3mtzkIrDaEICK66QwgRBWQ2jTm+IT8/n1enTeNBvecdYqfodLz47LMUFxeraJm6LPppEYaEmiMo2jj4acVyH1rUePLz88nMyKBtx85nfNa5ex9+/8P3jZo84YsvvsAvpjdaS3Ddgysw97iQ77791idRIY8cBilltpTyYSnl41LKoop9i6SUr9V3DiFEiBDiOyFEiRDiiBDixjrGG4QQe4QQRz2x2RNSU1P56aefALj++uspLCykQ2QgHTp2BHsG9pSFAJg7jz/lOLejAF1AnCrdJk+n1FnOH1lHcbhcvJX0B+mlRXSwBPBc4giePmc4fjo9K375hSxbMTn2UrpFtueKS0erbkcrZzWzgQVCiPMAIYToB8wCPlTrBBWJ03YhRHHFVm3ZjlB4SQiRW7G9JLwkj/rKCy8wSm+gs85zh6Gb3sAQjZY3X6v3pdCnOJ1ONvy2Af9u1hrHWLr5sWi57xLp1GDNmjV06dMfbTV5J7E9+rB/3z5OnDjRBJbVHyklL7/+FtoeFzfoOJ01BL+YPnwye7Z3DKuCp1USOiHEk0KI/RU3/P0V7xvyTXsPZX00ArgJeF8I0aOW8VOAbE/s9QSbzUZmZubJbmcjR44EoFM7Mxq9HyW73wN3OcaoS9EF/lUvK6UbEGgtbaub1mOklCQX5rH2+BFcbsnWnONsy80kyGBi7oirT/aGeP396Tz69FPMe+1thkR2oGtQzbK2rbRSA88DS1AE2qwoVVC/AGrfBSdLKa0VW03dm+4CxqIIR/UGrkRJtFaVvLw83p8+nf/zYCnidO7X63nz1VebZR7A5s2b8Qszow+sOcJg6WzmyOFUcnNbzgrzosVL6Dqg+jC+wWiiS59zWLFihY+tahirVq0iu7AEU6d+dQ8+DV2fK5j2yuu4XC4vWPYXni5JvARcDtwPnFvxczQwrT4HCyEswLXAk1LKYinlb8AP1JBUJYToBNwMvOihvQ0mNzeXQ4cO4XA46NSpExqNhi7xMWhdJ3Ckr6Qs/WfQGPHrfvcpx0lHPlprNEJb/5Ks+pBjL2VvYQ7BRhNbczP439Y1ANzVrT9WveGkszD/s8/RpKQz9oIRBBiMaGvotNdKK7UQKKV8SUoZK6XUSyk7SSmnAU3RLGES8JqU8qiUMh3FablV7ZPMmD6dUUYTUQ2ojKiJWJ2ewXoDH82cqYJl6rLy55UYEmp/rtPoNIR0CfZpfX9jcDqdLFy4kAEjLqlxTN9hF/Plgq99aFXDefypZ9H1u8qj0ntjVHfsOitfffWVFyz7C0/vJjcAV0gpl1RUSCwBrgLG13FcJQmAU0pZtZH8dqCmCMM7wGNArUXOQoi7hBCbhBCbsrM9D0ZIKUlJSTmZWdu3b1/cbjdBFklZ1gZKtlUINPWcjNbvrzww6XIg0aDzj/H43NXhlpI9BdkE6U2sOpbCg7//RFF5GcPaduSamG643W5enTGd+Z9+iuZoBgs++7w1Z6GVxnCkhv1qS+a9KITIEUKsE0IMr2FMD5RrQyW1XSc8QkrJzHfeYZIHlRE1MUmj5YO33kLKemhL+5BlvyzDFF93IFgTK/l51UofWNR4VqxYQWhkOyI7dKpxzMCRl7Ns2dJmuyyxbt06kvYdwNJjhEfHCyHQJ17Ho1Of8WqJpacOgxY43Sp3A+azAqf/zxUCZ5QTCCGuBrRSyu/qmlRKOVNKmSilTAwPD69reI2cOHGCoqIidu1Smpb07NkTs8mEofxYRWMpiV/XOzHH/qVnIKUbt6MAfUjvk6WVaiCl5NCJfEqc5aQUF/D0ZsXrv7Nrf14dOIpyl4tXPnifL2d+hCY1gy8//azVWWilsZyRI1ARFVTzSvQIEAu0B2YCPwohzsxYU64VhVXeFwLW6vIYqj4wOJ3OehuyadMmdHYHvRuR7Hg65xoMlBYUnLyGNAfcbjdbNm7BGm+pc6wl3o9f1q7ygVWN573p73P+mNqfVQOCQ+k18PyT8v7NjYf++zj6xGtr7R1RF6bYcygs1/Dll1+qaNmpeOowfA38IIS4SAgRL4QYiVJeWd+YTzFnhjcDULKzT1JxkXoZ8Gmz+fT0dHQ63cn8hfi4GKLDdZSlLcddchSNX3vMCZNOjpdS4rZlowvqoqregsvtJikvm/2FuWSVlnDvb0uwu5xc0SGeu7r2RwjB/IXf8tWrb6HNyGXB/Plnhc7Cb7/9xuDBgwkMDCQkJIQhQ4awdu1aLBZLtdnn/fr149133yUlJQUhhNIYrAo5OTkYDAZiYmJ89Bu0TIQQB4QQ+wFzRV7SyQ1IR8lrUAUp5R9SyiIppUNKOQdYB1xWzdDTrxUBQLGs5tG96gODrgFLCz8tX84IjUbVVvNCCIbp9M1q3Xz37t0YAwy15i9UYulk5tC+Q5SWlvrAMs9JTk7mt3XrGHLZ1XWOveiGSbz2xps+EzmqL2vWrCFp3wGsPS9q1DxCCPQDx/PfJ57yWi6Dpw7DFGANSgfLJJTs6XUV++vDfkAnhKjaXaNPxVxViQdigLVCiAyU5Ku2QogMIUSMh7bXisvl4ujRo2RmZlJYWEh4eDgd/HMI1GZiT/kWAL+Eiac0lpKOPLT+MWj9Y1Wzw+YsZ0deJmklheiF4L71SyksdzA0sgOP9j0fIQR2p5MrRl3MwB69+PIsaVF94sQJrrjiCu677z7y8vJIT0/nqaeeIjAwkKioKL7++lSfdNeuXezevZsJEyac3FdaWnrKk928efPo1KnmcGUrJ5mM4pyXAfdV2SYDw6SUt3jx3JJqIhso14Q+Vd5Xd51oFBtXr6afFwov+kvJn6tXqz6vp6xduxa/uDPVHatDY9AQHBPIH828HPGFF6dx4bU3YzL71Tm2a/9B6P2sZ1xDmppHHp+KIfG6RkUXKjHF9KPIbWDBggUqWHYmnpZVOir05WOklIaKpKhHpZT1kjiTUpag3PyfFUJYhBBDUHIgPj1t6C6UTnl9K7Y7UGrB+1IhWas2JSUluN3uk2GdERcMQifKMMoCXEWHEMYQjNF/PQhJ6Ua63ej8Y1V7QjlQkMvqYynk2EtpY7bw2cGdFJWXkRjWllcGjsKg0VLmclFQbqdfuw6sX7furHAWQOmiBzBhwgS0Wi1ms5mLL76Y3r17M2nSJObOnXvK+Llz53LZZZcRGvqXDsbEiROZM2fOKWNuucWb97qzAynlcinlMiCq4nXl9pOUcnudE9QTIUSQEOISIYSpouLqJuACoLpevXOB/wgh2gsh2qF0zJytli2g/M3Fq5DseDpxOj179zSfRk5LVixBF1f/DrW6OC0rfm4+EZLTOXToEN988w2X3nhHvcYLIRhzx/1MffoZr1cT1Jc///yTXXv3Yel5oSrzCSHQJV7H1Gef90r+TIMchorlhzVCiDwhxEohRIe6j6qRewAzkAXMB+6WUiZVdL4sBpBSOqWUGZUbkAe4K9575X+8uLiY7Oxsli5dikajYeJVA9AYgihPXwqAKXr0KfoKsqwQrX9HhK5+nntduNxuDhcVEGoyE2JUJJ8XHNoNwH09B6IVgjfen87TL02jkzWQcLMFzVlUCZGQkIBWq2XSpEksXbqU/Pz8k59NnDiRNWvWnGwk43a7mTdvHpMmTTpljptvvpkvvvgCl8vF7t27KS4uZuDA+kmttgLAe5XN5Sqp+F5+rtL8euA5lDLpHJQoxlgp5f6q3/8KPgB+BHaiPEAsrtinGkezsmivYsJjJe21Wo5mZqg+ryc4nU5W/7KagJ416y+cjqWHiYWL6kwdazIeffxxRo2/Df+g+osc9R0yAq3Zyqefnv5s2jQ8/9KrGPpcrkp0oRJz50SyC0tY7YXoVkPvNO+irGXeivJFf9PTE0sp86SUY6WUFillBynlvIr9a6WU1f5VSylXSym92nA+Ly+PxYsX43K5GHnhMCLDzPj5mSlLVzKGjR2v+MseVxlINzoVW1YXlNlxSTdaoaGozMFDv6/A7nIyNLID3YPCeGf6dOZ9tYCVMz+hJDlVlbJJIYRPtvoQEKDoogshuPPOOwkPD2fMmDFkZmYSHR3N8OHDT37Zf/75ZxwOB5dffvkpc0RFRdGlSxdWrlzJ3LlzmTjxrG+BoDYX81dzuUp+R5GMbjQVwm8DpJT+UsogKeUgKeWKis9O+f5LhYellCEV28PV5S94SmlpKeUuF/5eWJII1WjILymhIQmY3mLVqlWY25gwBNc/sdMab+FISipHjtRUNNN0bNmyhZ9/XsVlN9/VoOOEENxw36M8/sSTTd7zIycnh+XLluLXa5Sq8woh0Pa8hDfeUV+ivKF3m3OAf0gpfwDuROlQeVaRnp7O8uWKLOrNN4zGWV5OcN6XSGcpuuCe6PyVtXDptOF25KML7YfQ1b1+Vl+OlxZh1Gqxu5xMXr+UHXmZRJgt/KfXIN6bPp25Xy9AZOfx5Zy5Z22L6m7dujF79myOHj3Krl27OHbsGPfffz8AkyZNOukwfPrpp4wfPx59Ndntt9xyC7Nnz2b+/PmtDkPDKUOJ/lXFDDSPOK6KZGdnE2oyqZrwWIlWCAKNRvLy8lSfu6F8MOsDTIkNqwIROkHwgADmzJ1T92Af88h/H2PM7fdh8qu74uN0EvokEt2lB++9917dg73Il18uwBqXiNZU/6hPffHrMYIVy5epXkbaUIfBIKW0AVRIQqtXP9gMcDgc/PrrrxQUFBAXF0fv+FCCCxeiz1tVIdL0T6R04yrNBDTowxLRmj0v3zwdl9vNsZJizDodz21ZQ1J+Nu38rHw09Eq+n/MZs+fPQ9gdzH93hqrVEFJKn2ye0LVrV2699daTSYzXXHMNR48eZdWqVXz77bdnLEdUcu2117J48WJiY2Pp0KExK2d/S34C3hVC+AFU/HwTaL4L2h6SnZ1NqEF9CfdKwoxGsrKyvDZ/fcjJyWHp4iWEDA5s8LEB51t4b8a7zSJKUsnGjRvZmZTEiKsn1D24Bq6+60FeevmVJo0yzJn3JaLzYK/MrTVZscb0Otk4US0a6jAYhRBTKzeU8qupp+1rsZSUlLBsmZJ3NXbsGPT2wwQ4toBGT+DQ6RjCB+C2ZaMNTEAfcZ7qLatLnOUcKsrn1tXfs+xoMmatjtcGXsw3s+cye84chF7HZ9NeY9z119c9WQtl7969vPbaaxw9qrQMSUtLY/78+QwaNAgAi8XCddddx2233UbHjh1JTEysdh6LxcIvv/zCRx995DPbzyL+A0QBeUKIVJTcoSjg/5rUKi9w/Phx2mjrnwjYUMJ1Oo4fP+61+evDO++9Q9A5Aej8G75O7hdjhiDZrCoLXnvjDS4e/w90jZDx7pDQjQ4J3Zk/f76KltWf4uJidmzdhCmm4TLQ9cUd3Y9vvle3J0hDHYYFKKWOldvXp72PU9U6H5OSksK2bdvQ6XRcNuoCzIWKcIm+/Wj0wT1wl5egMYWhD4zzSgjzhMPOqzvWs78wjwizhVcHXUy02coff/yBsPjx7pPPcON1Z6+zAODv788ff/zBwIEDsVgsDBo0iJ49e/JalWY+kyZN4siRI3VWPiQmJtK5c3VaQK3UhpQyV0p5EdANpc9LVynlxVLKltNcoJ6kpqbS1uW9uvx2UpKamuq1+euiuLiYt955i+CLPVf19h/lx1PPPdUsVCuLiopYsngJQ69sfIR16FUTmDW7aZZbfv/9d/zbxaExqJMsXx2mDr1Ys2aNqnM2yOWUUp7Vi8GrVq1CSknXLvGE69IwFm1CIrAkKL+2LC9FG3hmv3W1WHnsEMkn8gkymPhq5PX4VXTNe+fdd/hj5w7uUOFL0txp3759nTXEw4cPr/biFRMTU+NFbeTIkaSkpKhh4t8GKeVh4HBT2+FNDu7ZQ0cvhts7lJVzcF+1jTh9wjvvvoNfgglTO8972wT29efIj8f58ccfGTNmjIrWNZwVK1YQ36sf1sD6V0bURN8hw/nwmQfJz88nOLjx8zWEP//8E3eYdx9mdCHtyS3IJycnh7AwdZoQnj31eI2kpKSEnTt3AtArPhxT4RoELuzm3uj8o5UulBoNWlNoHTN5hpSSOfuVUvexMV3YsGYt7oqbn0Ov4/oRIzF4MXTayt8bIUReldflQoiy07ZyIURZU9roDfZs20asFzQYKonV6dizdavX5q+NkpISXn7tJYIvP0Nxv0EIIQi83MojTzzS5FGGn1aspOuA8+seWA8MJjNdevdX/Sm8PmzavhMREu3Vcwihwb9tDHv27FFtTu99U1oYBQUFJ5tN9eoag7FI+SMyx1wJVOgt+LVHaNTTm6/K4aIC1makohWCkjV/8sjHnzD26qv5138eINJsJdbftx5wK387qlY89WwyK3zM7n37SNB55zsNkKDT8+bu3V6bvzbe/+B9zHEmzFGND3sH9fMnXRwjewAAIABJREFUZdExli1bxujRo1WwzjNWrf6ViY+/pNp88f0G8ssvq7jqqqtUm7M+JB86jC6uFwBHpl1Rx+iG0/G/Su6C8I/g8OHDDB1afevvhtIaYajg6NGjHKhQGTynaxC6sqO4tVZC4y9RulBK0AV4L4Q0a+8WXFLSrtTFVx9/gkajoe+gc7EajPQJjWhtU92KV5FSHqjyel9NW1PaqDaFhYXkFZ6ggxcjd511OtKysrDZam20qzpOp5NXXnuZwIsbXnZYHUIjCBhl4ZkXnlFlPk84duwYGcePEdNFPX+2+4AhLF/p+66c2VlZaC3efwh0mQLJzMxUbb7WCANKIs2BAwfIzsnBajERZ9oG5WBsPxKh0eMszcQQlqiamuPpFDrszNq/DYC0H5aj0Wh4/sUX6TN4EL1D2rQ6C614HSHEzPqMk1I2TCmnGbNr1y4S/K1ovJDAXIleCDpZ/dmzZw/9+/f32nlOZ+nSpcgAiSVWPY2Y4HMDSPp6F/v27aNLly6qzVtfvvvuO0WpUcUlpM49+pCVlcWBAweIj4+v+wCVKCkuwmpU7/+mJlw6M4UqajF4/C8vhBgBjAcipJRjhRD9AX8p5a+qWecjUlNTOXToEAD3XBGCuWg9UuiwxI9DOm1oDUFoVNRbqEphmZ0P9mzmeGkxnChGHE7nmReep/fggfQIDsff4HmyUiutNICqFRDBwESU3g5HgA7ApZzZ66VFs2fPHuIrnIXoY0dVnz+tnSJKG6/Vsnv3bp86DLM+/RjzueoutWh0GgLP9efzeZ/z7DPPqjp3ffho1ieMulXdxsUarZbzLh7DJ7Nn88Lzz6s6d204y8tVlYOuCaHVYbOppzXhkcVCiHuAh4BZKE4DKOpwzwPqZKT4CIfDQVpaGhs3bsRqgruHK9dNbfy96Pw74SrNRhfW1ytllFmlxWzJOc77f6xW+vRt38eLL7xAr/MGkRjWljZ+6iuAtdJKdUgpH618LYRYDFwppfylyr4RKN/5s4b9e/bQsawcjN7Vn4spLz+53OkL3G43P6/4hZip7VSf26+3iYWLF/rcYdi4cSMZmVn0OW+Y6nOPuOYmXrp7PE9NnYrR6PsHtMp8g5aAp7HuB4GRUsrngMoi5j0oddstiqKiIoqLi1m5ciVjBgiMOhc2fSyBXcYh3U7Q6tCa1ClJqUp6yQk2ZR8ntfgEKcKJxuli6mXX0v/8wcQEBLY6C600JeejtK+vym+AOplTzYS0gwdp74PKo/ZCkHbwoNfPU8nhw4cRBjCEqJ/MaYk1sy9pn8+VH1974w0uumESGi/8f7WPjadDF9+KOOn0eqSr3Ovnka5yzGb1ltI9dRj8UUKVoPSxByVa0eLKrgoKCli7di0Oh4M7LlbWlMpDRqDVanA78tAFxCM06oaOTpQ52JGTgVWvZ8aeTQBM6n4OI4cPByDWP0TV87XSSgPZCTwthDAAVPycWrH/rCE3O5sQH+QHBWu05KqYeFYXBw8exNJWnWTH09GatJgCjCeVWH1BZffg4WPH1z3YQy68bhJvv6t+s6aasFisyDLvJ8JqnHYCAzwX7TodT++Ev6GEJ6vWt9wLtLj8hezsbH766SfiIqFrpAO3MGJodyHuskI0xlC0VnVrZZ1uN1tzjvPp7DmsMrvICDITZDAxpmMXJJJz27TH5MW68FZaqQeTgK+A/wghsoEwYB9wQ5NapTJ2ux1jxVJjZb6BNzAKfNqzIDs7G22A9xI5TYEmsrOziYmJ8do5qvL111/Td/AILP4N74VRX/oMHs6s5x8hOTnZJ+qwQSEh5NqK8F5Br4KurJjQUPW0gzx1r+8DxgshDgL+QogklCSpB1SzzAe4XC52797N3r17uewcRZe8yNgba2AY0uVAF9QNIdR9Akk+kcf7H33I/NS9ZASZMWu0vDtkNEadln6hbQn0olRoSyElJYXLLruM4OBgIiMjmTx5crUh0M8//xyr1YrVasVsNqPRaE6+t1qVJZ2YmBjMZvMp+48dO+brX6lFIaVMllL2B/oAtwD9pJT9pZS+i6v7AL1eT7kPdIjKJdV2VPUWxcXF4L1+WmiMGkpKSrx3gtP4aeXP9PBC7kJVtDodvQYOZdWqVV49TyVtI9viKvZBF9PSfCIjI1WbzqNHWSllekVVxGCUDOo0YIOUskW1vy0pKeHXX5WgyLB+wUAudn0MRq0TjTEcjaFxCmmnk2srZerbb7Bg03q4Yjha4O0ho+nkH4RTurE2opnK2cQ999xDmzZtOH78OAUFBYwaNYrp06fz73+fmiF90003cdNNNwGwevVqbr755mpDpT/++CMjR470ie1nC0KIQKAv0E5K+asQog2gkVJmNLFpqhEUHMwJ6b0+EpUUSjdBKj7l1YXD4UDqvOcJafTCpxGT/fv3M+Da2wG4sb/66ojztqQB0LZTPHv27lV9/uqIi41hy95sr5+n/ES2qt16PY59S0UjdF3F1iIpLi5m7dq1AHSrSCgu17fHoHWiC4hV9Vy787J45t23WLDxN7hiOAD/7TuUfmFtybGX0iUw1CuVGC2Rw4cPM3nyZEwmE5GRkVx66aUkJSWpeg673c4dd9zB0qVLcblcxMfHs2jRIiIiIlQ9T0tECDEYWAjsBs4B3kJRf/w3MLYJTVOVqNhYjm/43evnyXC5aN+pk9fPU4nNZkNqJZtv3aX63OfM7onQC58KURUVFeFnVffhrTrMVn8Kc47UPVAFuneJR/Ond7vFS7eL4pzjdFLxb8+jeLsQ4rAQ4lB1Wz2PDxFCfCeEKBFCHBFC3FjDuClCiF1CiKKKc07xxN6a2LRpEykpKYQG+ROoLwDAGBSL0FsRRvVUuPLtNp5+9y0W/L5WcRY0Gv7Z7Ryu7tQVt5S4JYSbvZOk1BK5//77+eKLLygtLSU9PZ2lS5dy6aWXqnqOOXPmUFhYSFpaGrm5ucyYMQOz2bvldS2It4FbpJTDgcq1oA3AoCazyAvEdetGig+qJI7o9cT5UOgoOzcbzF5cazEryeK+wmg0UebwfkSjzGHHz0fXgC5duqA54d22584T2QQGhWCxqHdv8TTCcMdp79ui5DXUty7lPZSKigiUsOdiIcR2KeXpj5ECZQ11B9AZ+EkIkSal/MJDu0+hcjniqou6IthKuSaEwAArWmtHVZ/2d+dksLowA666EITg9i79uKOL0gc9x15KXGAIliZcjjj+kW/WV9veUb8yogsuuICZM2cSEBCAy+Vi0qRJjB3r+YPt2LFj0VUkkg4fPpyFCxei1+vJzc3l4MGD9O7dm3POOcfj+c9COkspl1W8rrzzOFBpZVwIYQSmAyOBECAZeFRKubSasbcCHwNVH2mvkFKubqwdXbt25QsfVEkkA/d1813F+cGUg14pqaxEBro5kuqbJ3GAiMgICnKziY7r6tXznMjNpnuCdxtCVdKtWzdKM4/gTa3H8pw01R1VT3MYfj59nxDiZ2AJ8GZtxwohLMC1QE8pZTHwmxDiB5Skyf+edp6Xq7zdJ4T4HhgCNNphkFKyaZNS0nhBLyVBrszQnrahIWjNbRo7/UmybCU8sfVXsrso60j39TiXSQl9AMh32In0sxIX0FpGWYnb7ebSSy/lrrvuYv369RQXF/OPf/yDRx55hJdffrnuCaph4cKFZ+QwTJw4kbS0NMaPH09BQQE333wzzz//vE+T05ox+4QQI6SUVTPAhgNqrQvpUPKehgGpwGXAAiFELyllSjXjN0gpVReE69GjB/tKS8DovURjKSX7i4vo0aOH185xOkm7kzBd5z0BIkOknk3bN3lt/tPpEBVFXqbyNF6Zb+ANCrKO02HkEK/NX5Xo6GjcTgeu0kK0ft6p/ijPPsy5/fuqOqea9Xs2oD4L/wmAU0pZVfpsO8rFo0aE8sg/FPjAYwurYLfbT7az7hntAjdYQuPRW8IRusaHpaSUvP3F55R1jWH18SP46fS8MOBCzo9UHIdytwsJdA8Ob/Lchfo++fuCvLw8UlNTmTx5MkajEaPRyG233cYTTzzhscNQHXq9nqeeeoqnnnrqZFVGly5duP3221U7RwtmCvCdEOIbwCSEeAu4HsXRbzRSyhLg6Sq7FgkhDqPkS6SocY760LZtW9xCQ7bLRbiXlibSXS4sfn4EB/um22xJSQlph9LoERXHObO903TUEmNm05I/vTJ3dURHR5GS5f1c28LcbNq1U18dszqEEHTr0ZujGQcxx3onuqnPP8LAARerOqenOQxTT9umAWuBn+pxuBU4vRtGIYoYVG08jWLvJ7XYdZcQYpMQYlN2du0ZqHv27CE/P5/g4CBCjcp6nF9oHBq/xv/BSCl57PHHuf+Zqby+UpH9HBfb4xRnIcdeSs/gcIw+0BNvSYSFhdGpUyfef/99nE4nBQUFzJkzh969e6t6nlWrVrFz505cLhcBAQHo9Xo0rU2+AJBSrkW5eaejLDPmA0OklBu8cT4hRATKg0RNEYx+QogcIcR+IcSTQohqvzRVv//1USIUQtC1cywHnN5zmPc7y+nhw/yFjRs3EhQTiMbgvb9lY6SBEyeKfFaeHNGmDcWF+V4/T1F+HuHh3ukZVB0XXjAEZ/oer8wtpaQ0bTfnnXeeqvN6+lcVf9oWhJKXMLEexxYDp0tPBQBFNR0ghJiMkstwuZTSUdM4KeVMKWWilDKxrv/4yvyFxH7d0NkPA0r7ak0jowtSSh5//HGmvfYqdGpHZqAZvUbDuM49sDudZNlKKCovIzGsHW0t3s/8bYl8++23LFu2jPDwcOLi4tDr9bzxxhuqniMjI4PrrruOgIAAunXrxrBhw5g4sT5/vmc3QgitEGIZkCGlfFZKebuU8mkp5WEvnU8PfA7MkVJWV9O2BqVCow1KhGMCSgTkDKp+/3X1FD/r0acPB7woc3zA6aR7X3XDwrWxbv069LHedXyFRhCcEMiGDV7xH88gICAAe0mNtwfVKC4qJDDQe+JQp3PZ6Evh6DavzF2edRh/q4XYWHWr/Rr8eCuE0AIrgAVSSk9SV/cDOiFEvJTyQMW+PtTwdCGE+AdKbsMFUkrV9EjXr18PwDWDDGilDSyd0Frag9bz9cxKZ+HFF19EXDgQrh6FBK6O6YpZq6PU5aR/WFuCjWYMPsjObqn07duX1atXN+iY4cOHV6vBkJKSUu34CRMmMGHCBA+sO7uRUrqEEN1REo69ilBU0T5FSYCeXIM9VSuvdgohnkVxGF5Uw4Zu/fqxc/ESNaaqlmSdjgt86DD8vnkD+igf5OG0l2zdtoVrr1VllapWAgMDfeIwlBYX+9RhGDp0KK6iXMpz09CHqpts6dizmtvGqS/M2mBXtEKc6R0PnYXK9ctvgWeFEBYhxBDgKqppnSuEuAl4ARh12oWj0RysaAYzMEoJqxmilLI94aHDUNVZ0Gi1BIy/EglMjOvNv3sMxOZycl5EFBF+1lZnoZXmzrPA2xVLBV6hIifpY5RKqWullPVdF5Co6Mx0796dZK33nsgPCqUaw1ccOXIEY7j3HQZ9qI79hw7UPVAFwsPDKcr3ripimcNOWZmDABX7LtSFTqfjX/+8E8fWH1Wd1+0oxb57Fffc/U9V5wXPlyQWCyEua8R57wHMQBbKGundUsokIcRQIURxlXHPAaHAn0KI4optRiPOe5LU1FSC/KCN/ggSgaXjJaA1IjSe3cxnzpzJiy++iFar5bnPZlEonbQxWbiv57kUO8uJDQhuVXJspaXwPkrp9DEhRLkQoqzyp8rn6IbSRrtGFSAhxOhKx0UI0RV4EvheLSO6devGfi+JEEkpOVBSQjcfllTa7Hav5i9UojEIbPZSr58HoFOnThxP9cqK2Eky01KI7qBuOX19ePCB+7Ef2EB5vnr5IKWbvuOy0aNVX44Az6skNMC3QojfUMqjTqqESCn/UdfBUso8qlGMq0i2slZ57xV5tMLCQvLy8rh6kA4NblzWHgi9P0LneUvpCRMmMH/+fCZPnszPESY4cZgR7WIQgFu6ibL4znNtpZVG4p30+gqEEB2Bf6JoO2RUuUj/EyV5ejfQXUqZClwEzBZCWIFM4DOUqKMqREdHU+J0Uuh2E6hy0mu2241Gp6NNG/XKtOsiICCAklLvJwi6St0EB/qmHLxDhw44neXkZR0npE1br5wjOWk7/fr198rctREeHs6jj0zh9bkfo7vqiUY7LOUFGdi3L+W1z7arZOGpePoNOQC8gqL+dhQlm7pya/YcOKCE0ob3UhIc9eHngLscjaFhN3UpJW63okVvslh48YtPsZ7bhx+O7APgwvadyLHb6GANxKxrre9vpWUgpdxXuQHHTnuvxvxHpJRCSmmSUlqrbJ9LKVMrXqdWjH1IShkhpbRIKWOllFMbsHxRJxqNhi4xMV6plNjvLKd7XJxPn1q7xHfBnqFmIKh6nFkuuid09/p5QKlmGTHiQnas914z5N1/rOGSURd5bf7amPLggwS6Cinds6ZR80gpsf08nccefUTV/hFVaZDDIISYUGHYkzVtXrFSZSodhsRY5WZvjkhEussR+vpHGCpzFiZOnIjT6SQpP4v8Mjsf7dvKsdJigg0m2lustLVY6RoU5pXfo5VWvIEQwl8I8aEQoggoqJBm/7CiIdVZR/fevTlQrn6lxIFyJz18mPAIMOTcIbhSvd9Qy5UqSUxM9Pp5KrnumqvZsuoMIVBVKLPb2LZ+NWPGjPHK/HVhMBiY/+lsbGtm4Sot9Hiekp0riDS5efihh1S07lQaGmFQRTSpqTl48CBWE3QMdSDRYgrrDYh6JzxWTXD84ssv+ea31SxLS+a+dUv55vAeDBotU/oMxk9roGdwG7St9f2ttCxmoSQjXgCEV/n5cVMa5S269+/PQS8EAQ5qNXTv10/9iWth+PDhFO/1bmMol91FweEC1Wv8a+PKK69k77ZNFBWov9yybd0q+vXr36SN5wYOHMitt0zEtmaWR8e7ivOxrfuMLz6bQ31Lij2hoXeys6KdYnJyMud0FmgESGscIEFo66XweEo1hF7HK5/PZpdFw5ObfuFQUT7t/fyZcf7l9A2NJCEotNVZaKUlMhIYL6XcKqXMk1JuBW5GySc46+jevTsHvXCRPajV+DThERS5a41Lgz2zRrmaRlO0t4Re/Xqr2tSoLvz9/bnwoovYvHq56nNv+nkxN44fp/q8DWXa8/9DZOzBntrwLqO23+Zw1+23qS5wdzoNvZtphRAjhBAX1rR5xUqVSU5OZmC84vvognvidpZiaDMAoa1df/0UZ8Hix//mfkxR5yhe2r4Ol5RM6NyTr0ZeT3xgCCatjgiz50mUrbTShCQD7U/b17Zi/1lH9+7d2e+FbogHSkvp3t036/yVCCEYfemlFG73nm6BbZeDa8d4X3/hdK6/9hq2r12p6pzO8jK2rV/dqOZ2amG1Wnn79Vdx/PYJUta/26gj4yDuozt49umnvGidQkMdBiNKWLKm7SNVrfMShw8fpldFTog+sDNaayc0htqXZ08RZWrfhodnvkN+TCT/27qGcrebCZ178p9eg3BKNzaXk/7hbdG1RhdaaZksQekMO1UIcZsQYiqwHKWc+sbKrYltVI1OnTpRUFZGgVu9tf9MlwunED7rTVCVG64ZR/lO7+QxSLekaHspY6/y/Q129OjR7Ppzvaqtrvdt/ZO4uHgiIyNVm7MxjBs3jnCrAduB3+t9TPkfX/L01Cfw9/e+cnBD72glFZnKnWrY1C/8VBmHw8GxY8dIaKtEGPQBMWiMQfU67te1axERodw57X+ssUhe3bEBl5TcmtCH//QahMPlwuZ0MrBNFIEG73XA+zswb948EhMTsVqttG3bltGjR/Pbb7/x9NNPo9frsVqtBAUFMXjw4FMkan/88UciIyPJy/tL6OX777+nffv2FBZ6nlD0N2MUkAGMBu6q+JkJXIzSxv4+alBmbIlotVr69+zJljL1wvhbysoY0LdvkzSWu+SSSyg+UkJZnvqVH8X7SmgTGu5TMapKQkND6dmrF0kb16k255Zff2LsVU2T7FgdGo2GF56ZinPLd/WKMpRlpeDOTuauO+/0gXWel1W2WDIyMjAbJNFhAokWnaU9Qld7V/I9+dn8mZ/JlI+nc8Ubz/GxLYP1mUfx1xt4+pxhTO5xLiXOcoqdZZzbpj0BBu+1lv078Prrr3P//ffz2GOPkZmZSWpqKvfccw/ff6/o9YwbN47i4mJycnIYMWIE119//cljr7zySi688EIeeOABAAoKCrj77rt5//33fSr72pKRUp5Xj21wU9upJpeMHcsvUr2n8l+E5JKrr1ZtvoZgMpmYMGECeavVd5CLfrVx393/Vn3e+nLThPGsX/KNKnM5y8v4Y+Uixo9r+vyFqlx99dWY3TYc9WhMVbb1ex64/9+YTL55QP3bJT1mZGTQuSIZ1mmIQGh0NSY7SimZ88V8kgty0QkNf+Rn8mNRJi4pGdMxga9GXs8VHRLIc9jQCg1DIjsQZGyNLDSGwsJCpk6dynvvvcc111yDxWJBr9dz5ZVX8sorr5wyVqfTcdNNN5Genk7V7qRvv/02S5cuZfny5TzwwAMMGzasyUqmWmkZjL/xRhbZ7dgasHZcE8VuN8tKS7mhCW9Ejz78KAW/nqD8hHrloqVHbNgO2rnj9jtUm7Oh3HLLLezetJ5jKY1Pp1n9/Zf069OXLj7sJloftFotjzz0AM5ttUtGu4rzKT3wB/fefbePLGugwyClbPHtFTMyMkhop/g90tQOjTEYpQfOqVTmLNz6xH95aPYMpm3/jWnblVDYg73OY2r/YYQYzeTYSwnQGxkY0b5V+lkFNmzYgN1u5+p6PJ2VlZUxd+5cQkNDCQ4OPrk/LCyMt956i5tuuolFixbx9ttve9PkVs4CYmNjOW/wEObZGi93PNtu4+JRo4iKilLBMs+IjY3l1km3kf2VOmWI0i3JnpfPC/970afVEacTGBjIY48+ypxpj+F2uTyeJz87g+8+eJ1XXp6monXq8Y/bbsORtovygowax5RuW8K4ceMICfGN4iZ4Lg3dYsnMzDyZv6C1RKExnfmP7Xa7efjJJ3jtkw/h4dvZZzGz78h+9BoNU3oP5ppO3Shw2Clzu4jxDyIuMAS9hz0omgNi5jM+OY+8q+4s3tzcXMLCwmqtJV6wYAGLFi2iqKiIoKAgvvnmmzPGDxo0iMLCQm644Qaf9rhvpeXywhuvc+GgQVxlNBHmYYO44y4XH5Y52PDqqypb13CmPT+NhT2/I//PQoIHNG45LntJHp3D4/jnXeo3NGoo9//f/7Hw+x/45oPXuf6eajud10p5mYP3Hr2Xf983mX4+1smoL1arlTvvvIM5a35Af+FdZ3zuLrdj37GM/37kmxbjlfwtcxgSKhKXtZYohF6Rgy4ss5NefILtORnc9NyTvLZ6Gdx1PVjMJASGML5zD2YPG8vVMV3JspUQaDQxrF0M3YLDW7Sz0NwIDQ0lJycHp7PmUOoNN9xAQUEBmZmZ9OzZk82bN58x5q677uKWW25hyZIlpyRFttJKTfTq1Yvb//Uv/ltmb1BZWyVuKXnYYee+Bx4gISHBCxY2DKvVysKvvydrXh72DM8TOov2FFO0upSv53+DphlUful0Or79+is2r/iB1Qu/aNCxbrebj555iPiOUUx9snkLEz/0wP3Ydv+Ky3ZmiWzJzpUMHjzY58spf7sIQ0ZGBhe1r9BgCOiI0PmRZ7fxR9ZRBPDaZ3NYWZIJk64CIMRo5u3Bowkz+SGlJMteSif/ILoGhTVJBrQ3qM+Tv68477zzMBqNLFy4kOuuu67WsWFhYcycOZPExERuvPFG2rZVGtN8/PHHpKWlsWjRIhITE7njjjvYunUrBkPrklFNCCHqVf8npVSvrV4z5JkXXuC8JUv4JDOLf5hrT4Y+nRl2G/aOHXj8qebzfUpMTGTacy/x5CtP0PHRdmiNDbvhl+WVc+yjHL6d/22TLrGcTps2bVjx03LOH3oB1qBgEodfUucxUko+f/0ZnCdymP/NT83C+amNdu3aceWYK1m5fRn+g/5K7JZuF85ti3jqm/k+t6l5/4t5gZysdDqGg1uCwb8jDvRszjmGv97AG8u+Z2WIFjpHo0dwZYcEZg69gjCTcuE4G52F5kZgYCDPPvss9957LwsXLqS0tJTy8nKWLl3Kww8/fMb4Ll26cMkll/Dyyy8DcOzYMaZMmcKHH36I0WjkX//6F6GhoTz//PO+/lVaGkdROs8erWVLazLrfITRaOTrxYt51+nkT0f9n8rXOex87Hbx5Q8/oNc3r0Zz99x9DxeffwmZn+U2KHIinZLjM7N5+IGHGTVqlBct9IyEhASWLF7EJ889woEdW+ocv+SzD0jZvpEli37EbK5b1bc58OiUB3HsWIp0/5WvYTu0hbZtQhkyZIjP7fnbOQx6ewpajaDYFYjR2ob8Mjsrjh5i4qqF/EQJaDQMNQSy7PKbebjPYPz0erJtJWTbSmln8adLq7PgdR588EFef/11nnvuOcLDw4mOjubdd9+tUY1typQpzJw5k6ysLO655x7Gjx/P0KFDAUX57sMPP+TNN98kKSnJl79GS8MM+FX8rGlr2CN3CyU2NpZP5s/jXnspWfVIrDvucvJ/dhvzvvnGa10CG4MQglkfzMKU6Ufe+oJ6H5f1Yx49o3vz+KOPe9G6xpGYmMic2Z/w7n//RWFeTo3jdv6xlpXzZ7F86RKCgurW3Wku9O3bl04dO2BL3nRyn9yzgof+b3KT3IeEJ2t1LYHExES5adOmM/bfMyaCJ8fkkSvjSLjuO+7YuptPD+4EINRo5oFeg7g4qjO5dht+eh1dg8IJMpjQaTRoWh2FVqoghNgspfRdy75W6o3FYpElJSWNmmPqo4/yy4wZfG62oK3hu++UkhtKSxj7nwd4bOrURp3P22zdupWhFw2l8zPR6ANqX40uTbWR/lY2+3btazYqiLXx0JSTX04DAAAcbklEQVSH+X3nXu59cfoZn9lLS/jv9Rfx+dzZjBw5sgmsaxwzZszgyfe/xHLZQ7hsReR8dCdZx4+pquwohCiVUtZZ/vK3ijBIKYnwUzxsfWBHvjqWzacHdyLckim9zmPxpTcysn0sWbYS4gJDGBLRgTZmCwatttVZaOVvgxBCI4S4SwjxuRBiuRDip8qtqW3zJU899xyauDhm2mvu/viu3UZgzx7894knfGiZZ/Tr149bJ95Kzve1RxmklOQsKGTa/6a1CGcB4H/PPsOhnVtITtp2xmfLv5jFsAuGtkhnAeCaa66hOHkz0lWOLXkTQ4YO84kMdHX8rRyG4uJi4iKU7HtTSAL/t3qF8sGKdXQtdaPTaMhz2OgRHE5cYEhrp8lW/q68CjwE7ACGAD8Dsfx/e3ceb1VZ73H88wVkEFAEQRkEyRtapJlcza4ICM6KVpYDXk2LvJVmgZhzgldN7WV183q1TA21tLTUxCwzryIOmUMkyBVQwIGZA4d5/t0/nufAZrPPsMe11zm/9+u1XmfvtdY+z2+ds4dnP9MPXk0yqEpr3bo1E3/7W+7cuIE5OWbtzNy0iV9u2si9Dz1U9QPo6lx37XXUvraKDUs21nvOqhlraL+2A1+v0HLDpdChQwcuGTuGvz48cYf9W7ds4dlH7ufqK69IKLLi9ejRg3369WfDgllo/lt84eQTEoslsWe5pK6SHpW0RtK8+pLZKLhZ0rK43awCO28WLlzIAXGGxIQ3ali2S2tYuZrrv3wuBx54IEvWraF7h4707ZyePi7nyuB04DgzuxnYEn+eCpRsOegkXv+F6N+/P+Muu4ybNu/8AXv95o1cNX58Vc0eaEzXrl35j6//B8ufWVnvOSv/spbxV41vcC2UajRq1Chef/4vbN60PYfGrLfeoFvXbmVP+1xuw4YcwcaP/o/NC2cyePDgxOJIslp8O7AR2As4G7hD0sAc510AfB74NHAQMBIoaPWQxR/NoldXcfemQ7h3t/0AOK3HvnzuyMEsWbeWfp278Olue3n3g2vpOprZnHh7naQOZjYdKOV4jYq//gv13XHjeNOMtzdtrzS8uXEDs9u04cJvf7uSoZTEmO+MoeblWras23lA5/oFG1j//gbOPvvsBCIrTs+ePenXb19mT3tz275pr0xm5EknJhhVaRw26BBaLXuPNUsXVDxleqZEKgySOgKnAdeY2WozmwL8ATgnx+lfAW41sw/N7CPgVuC8Qspd+dHf+bEN5prWJ0KrVgxuuzvnDzmGzm3bMbhnX1+EybngHUmD4u03gCsljQMWlOKXJ/X6L1SHDh24aMwY7s3olrh36xa++73vpXJtjz59+jBkyBBqXtk5OdWKyasY/dXRtGuXzgR6x4wYwYzXXtp2f+YbrzBixPAEIyqNAQMGsGr263Tfu1ei03aTanMaAGw2s5kZ+6YCQ3OcOzAeyzwv1zeRRk1cuZjftB6GbCvHtu/Gf55wGgO6dPNU1M7taCzbE81dAtwFdAJKleUmkdd/Mc4fPZr9b7yRd1qFP8vstWu56/zzKx1GyYz99ljO+Nrp2OYdZ8ktf2klF95+YUJRFW/48KO4/Nr/5LCjT2bL5k28O2Naok34pdKvXz82rl1Fn76HJBpHUhWGTkB2J1otkGvoZ6d4LPO8TpJkWXNCJV1AaMLMOR96UJvNTNq6ntMW/oMfX/Z79mjX3tdUcG5nM8xsOYCZzQAGA0gq1eCesr/+S/3Nv2fPnvxj+nSWLg1z/Xv06EG3bt1KWkYlHX300Vw8+mIWLl60w/4DbzyQ/v37JxRV8YYOHUrrrd/njstDr9VpXzot0WRZpdKrVy9OPOULHHdMsjM9ElmHQdJngBfNbNeMfZcAw8xsZNa5tcAxZvZqvD8IeK6xzJn1rcNQs34dXdunY5UvV92a6zoMklaa2W459teYWdGp8Srx+i/FOgzOtRTVvg7DTKCNpI9n7Ps0kGspvunxWGPnNYlXFpxr1E7NbnHcwdYS/f7EXv/OucIl0iVhZmsk/R64TtJo4GDqn7Z1HzBW0h8BI/Sp3laxYJ1rISTNIrzGOkiamXW4B2FgYtH89e9cOiU50fZbwD3AYmAZ8E0zmy7pSOApM+sUz/sZYdGYt+L9X8R9zrnSuojQuvB7IHO+oAGLzGxqzkcVxl//zqVMi8sl4VypNOMxDF3NrCbpOIrhYxica7pqH8PgnKtetZKukPS2pNr48wpJ6Vr6zzlXUs22hUHSEmBePYf3BOrPhVpalSzLy6tsef3MrHsFY6kISbcAw4DrCa+hfsCVwAtmdmmCoTWZpK1A/VmjCtcG2DmxRLr5NaVDOa+pg5k12oDQbCsMDZH0WqWakitZlpeX/vKqgaT3gUFmtiRjXw/gDTNLT+KEMmiOzwe/pnSohmvyLgnnXLZc32Q2A75uunMtmFcYnHPZHgUekzRUUn9Jw4DfEWZPOOdaqJZaYfh5My3Ly0t/edXgEuBvwK+AWcD9wGvAuCSDqhLN8fng15QOiV9TixzD4JzbmaSzzOzBpONwzlUnrzA454D6c0g45xy03C4J59zOPHWrc65ezbLCIKmrpEclrZE0T9Koes6TpJslLYvbzSog33Ue5V0qaZqkVZLmSCpoTntTy8s4v62kGZI+LGdZkg6RNFnSakmLJH2nXOVJaifpzlhOjaQnJPUuoLyLJL0maYOkXzZy7hhJCyWtlHSPpHb5llflWks6StLw+rakA6y0+B7xMUk+Q8S1eM115bbbgY3AXoTENk9Kmmpm2VnuLgA+T8iAZ8BfgDnAnWUqT8C5wD+B/YCnJX1gZg+Vqbw6lwJLgAZTAhdTlqQ9gT8BY4BHgLZAIXP2m3pt3wE+BxwE1BIGBN0GfDHP8uYTFig6Dqg3lamk44DLgeHxMY8CE+K+5qIdcDf1tzQYIa9Di2FmJuktCnvtuIRJetLMTko6jlJK9JrMrFltQEfCB86AjH33AzflOPcl4IKM+18DXilXeTke+1PgtnKWB/QHZgAnAB+W8W95I3B/Bf93dwC3ZNw/CXiniLKvB37ZwPFfAzdm3B8BLCzlczfpDViZdAzVuAFTgAOSjqNM19YK6Jl0HGW8viuSjqE5XVNz7JIYAGw2s8z0vFOBgTnOHRiPNXZeqcrbJnZ9HAnU1ypQqvJuIyzrW8gyufmUdThQI+klSYtjF0HfMpZ3N3CEpF6SdgXOBp7Ks7x85Hqu7CWpWxnLdNXhOeBPksZL+pqkr9ZtSQdWKEldJP0aWA/MjvtOkXR9spGVlpn9IOkYSi3Ja2qOXRKdgJVZ+2rJ3aTYKR7LPK+TJFmsypW4vEzjCbX7e5tYTt7lSfoC0NrMHo2L7+Qrn2vrAxwCHENIRXwL8CBwRJnKmwV8AHwEbIllXpRHWfnK9VwhxrasjOVWkg96zO0IQlfl0Kz9RkjRnUZ3AssJeULejvteBm4Frk4qqGJI2h3Yn/Ba3cbMnk0mouJV2zU1xwrDaiB7athuwKomnLsbsDqPykK+5QFhoB1hLMORZrYhj7KaXJ6kjoQP7RPz/P15lxWtAx41s7/H8icASyXtbma1Oc4vtrzbCX3u3YA1wPcILQyfbWJZ+cr1XKGe2FLJzLyfPgczOyrpGMpgBNDLzDZJMgAzW6KQMyR1JJ1HeE9YDazNOJTacTfVeE3NsUtiJtBG0scz9n2a3E3/0+Oxxs4rVXnEZszLgRFmlveshTzK+ziwL/CCpIWEZX17xlH++5a4LAgDOTMrWoUs8JFPeQcTxhzUxErXbcBhcfBlOeR6riwys+bSuuAaIKmbpHPqZjbFrrA0J+KqJWRi3SZ2IS5IJpyi3QB8ycz2MrP+GVsqKwtR9V1T0gM4yjQo5CFCc3hHQnNiLTAwx3nfIAwI7A30InwofKOM5Z0NLAQ+Ue7rI7Qe7Z2xfZEwun9vQjdFqa9tOKGJ82BgF+DHhHTI5fpb3kvIb7B7LO9K4KMCymsDtAd+QBhg2R5ok+O84+P/7pNAF+BZmjCw1bf0b4SuiKWEWUCrMvY9kXRsRVzT5YRB30cBKwgzjv4X+G7SsRV4PYvyeV9Lw1aN15R4AGX6Q3cFHiM0Vb8PjIr7jyR0OdSdJ0KzfU3cbiGuflmm8uYAmwhNTHXbneUqL+sxw8hzlkS+ZQHfJIwpWA48AexTxr9lN0Kug8XxDW8KcFgB5Y0ntIZkbuOBvvH/0zfj3LHxRbySUGFpl/Rz3bfyb8CbhBZBgOXxZ3tCC1Pi8RV4TSJMTX47vtZmAN8t5P2vGrb42rwWaJV0LM35mnxpaOeca4Ck5Wa2R7xdY2ZdJbUClpiZz5JJiKQP2N71KULr6UayBiGbWb6ztRJT7dfUHAc9OudcKb0t6Tgz+3PGvqMJM3NSSdJU4AHgQStsLFU1+PekAyiDqr4mb2FwzrkGSDocmAQ8CZwO3AeMBE61OCsobeKU67MIs6heJyxM9rCZ1SQamKtqXmFwzrlGSOpF+PbXj7D+xwMp/ma+jaTOhAHRZxHGCf3VzE5JNqr8SWoLnEcYdJ29ZsG5ScRUrGq8Ju+ScM65BsQkY0vM7JaMfbtIamf5r6NSVcxsVVzxcQUh/0sx67YkaSJhqvMThIHJzUHVXZO3MDjnXAMkTQa+Z2avZOw7nDCtdlhigRUhLk0/HBgFfAGYR+iWeMjMPkgytkJIWg70N7MVScdSKtV4Td7C4JxzDTsQ+FvWvlfZcSGvtJlPmDb8EHCEmc1IOJ5ivU9Y+bU5qbpr8gpDmUl6AJhtZuOTjqUhkt4BRpvZC/UcfxqYaGa/qmxkziWulpBufWHGvr0I6xek1alm9mrSQZTQfcDjkv6LrOZ7S28uiaq7Ju+SaCJJcwlvElsydg8ws/mNPK7kFYb4O08nzM/dCLwGXGQ7Znks5vdfD/Qxs/NK8fsaKKcNYSGrtYS5xysIqzxeZmZbm/D4o4FfmNm+5YzTtWySbgU+A1wMvAfsB/wIeMvMxiYZWz4k7Wtmc+PtepcXNrP3KhZUiUiaU88hs5QuD12N1+QtDPkZaWbPJB1EdKOZjY9Jpu4mZM0bnHBMhRpoZnMlDQAmE1afyzeLp3PlchUhi+OrhCbi9YTn55VJBlWAt9ie+XU2oZKenaHUgNaVDKoUzKx/0jGUWjVeU3NMPlVRklpJeiQmdVoh6TlJn6jn3B6S/hjPq4mDqeqO9ZH0qKQlkuZIurAp5ZvZGsK38k/F39Ne0k8lLZD0kaQfxek5jZX/oaRhkk4mZH48W9JqSa/H41MknSepg6SVkg7IeOzektZJ6hbvnyJpaixniqRPNfFaZhLWtz8443ePljRD0ipJ70oaHffvThg93DfGuTpeXytJV8Zzl0p6SNIeTSnfuVzMbL2ZXUjIb7I30MnMLjKz9QmHlhfLyEZqZq3MrHX8mbmlrrJQJ85cOVLSGfF+x/iFKrWq7Zq8wlAakwjZIfcGphGSGOVyKaFJs3s892oIlY74O/5OSIR1DHCppBGNFRznUY8irHcP8H3gX4GDCM2oRwBXNFR+JjObRMip8Ssz62Rmg7KOryPkejgrY/cZhPnbyyQdCtwFjCbke7iH0A/XtgnX8okY7+yM3YuAkwjppL8O3CbpIAsps0cC78c4O5nZYmBMPH8I0IcwsOunjZXtXENiBfVQwgDIoyQNlzQ84bAKJinna0LSTyodSylIOpCQ7fYuQosrhARh9yQWVJGq8pqSTmaRlg2YS/jwWRG3x+o5b09Cs17HeP8BYHy8fSMhzfR+WY85Angva981wF31lPEAoVl0BSEd7WOE6TcQpkcdm3HuSYQxFPWWH499CAyLt68npI7OPD4FOC/ePh6YmXHsb2xPEnUXcG3WY98ljMTOLrNN/FutJAwgs3htbRv4P0wCLoy3jwbmZh2fBQzNuL9P/FtVTQIX39K1ERbPWUOovM7J2N5LOrYirmllPfuXJR1bgdczBTgn3q5LENaRAjLYVstWjdfkYxjy83nLGsMgqTUhNfKXCJWFusF6e7LzKOqbgAnAXyVtIWSq/CFh9bi+kjLn27YGnmsglpss90DKXoRKQ515hFaLhsrP1zNAF0mDCJWWTwKPx2P9CN0ZYzLOb5sRQy4HEaYQnUGorHQkDOYkdpFcQ2jBaQXsSmiJqU9f4AlJ2YMme7DjKHfnmuoG4Etm9lTSgRRL0lfjzTYZt+t8jJDGO40GEr5sQEzeZGZrJHVILqSiVd01eYWheOcSVkcbTvhw7gYsYefBRJjZSkKT+ZjY3PS/kl4lLDU7y8xyjn3I03zCh/Y78X5fQsrpess3s+ezQ22oADPbLOlhQrdELfAHC2MpiNcywcxuzidoC7MiHpT0ecIgs3HxhfEIcCbwpJltkjSJ7X/bXHF+SGjtyJ4371yh2gBPJx1EiZwTf7bNuA3htbQI+ErFIyqNucAgwowxACQdxo7dm2kzlyq7Jh/DULzOwAZC+tFdCd9GcpI0UtJ+kkT4oN1CaJF4Gdgo6ZI4aLG1pAPjN/h8PQh8X9KekroTvp0/0Ej52RYB+8bz6vNrQovAqHi7zl3AhZIOVdApltvUgTo3Ad+IsbcjvLEtAbbE1obMcR2LgD3jOI46dwI3Suobr7mHpNStje+qys3A1XGsUaqZ2VFmdhShhfKojG24mZ1lGatZpsw1wJOSJgBtJV0BPEyOcVopUnXXlPoXQBW4l/Ctfj4wnTDKvz77A88SxkK8CPyXmb1gZpsJrRSHEWqVS4GfEQb65WsCMJUw+PKfhPEFP2io/By/4zeED+qa2AKSy0vAZsIAym3fvuIbzjeBO4DlhEE7TU7ZamZvEipQ4ywsiToGeBSoIXT7TMo4dxrwO2BunJHRgzA//k+EbpdVMc5Dm1q+czmMIbxJr5L0fuaWdGBFmKwwjXkbSftLOiapgIphYbD28YT3o+cJraxfNLPUtgxV4zX5wk3OOdcASUPrO5ajOy8VJM0ChpjZgox9vYDnzGxA/Y90LZlXGJxzroWRVGtmu2ftE1BrZoW0bLoWwLsknHOuAZLaSbpB0nuSauO+YyVdlHRsRXgvxzoSwwjTRZ3LyVsYnHOuAZL+hzAt+CbgKTPrIqk38LSZDUw2usJIOhWYSFgQ6F1CfozzgfPN7PGGHutaLq8wOOdcAyQtAP4lzoGvMbOucf8KM+uScHgFi1P0vkpY3OwD4G4za2iNk6ol6ZNm9naO/ceZ2Z+TiKk58nUYnHOuYRvJeq+M036XJRNOaVhIb91cUlxPkjTCzLZ1qUgaCfwc6JlcWIWTdD+515rZQFhv5jEzm1rJmHwMg3PONexhYKKk/gCSegL/DTyUaFRFknSwpG9LmiDpurot6bgKdCnw5/i/QdIXCVPTT040quLUAqcSFqr7MP48hbB+zieAlyWdW8mAvIXBOecadiVh8aa3CIuzzSIsUDYhyaCKIekC4MeENVROAJ4CjmX7Eu+pYma/k7Qb8BdJtxMWPTrezP6ZcGjFGACcaGYv1u2Q9DngOjM7RtLxwE+A+yoVkI9hcM65JopdEUst5W+ckmYTBji+IGm5me0h6QTgTDNLxfLQ9ay8OQYYR6j8TIdty86nTpyR0y0u7Fe3bxfC82/3OA12lZl1qlhMKX/eO+dcyUn6WFPOM7P3yh1LOUhaWbfegqRlQHcz25o5qLPaxQRz2R9gmXlmBJiZta5oYCUi6XngFUL23/WS2gPjgX8zsyHxOfqcmfWtVEzeJeGcczubzfYPnfoYIatsGn0oaV8zm0tYvv1USUuJWWJTon/SAZTZVwh5elZKqgG6EhJRnR2PdwW+VcmAvIXBOedaGEnnAYvM7KnYFfEIIX/MxWZ2R6LBuR1I2gfoBSwws0Tzl3iFwTnnWjhJbYG2ZrY66VgKFbPSDgX2JKNlyMwqOpOg1GJSvR3GKSTVFeZdEs45l0XSn8zs+Hj7BXLPh8fMhlQ0sBLL/jCS1CON4zIkXQt8gzDV9cuEKZWjCJl3UynOgribndeRSKwrzFsYnHMui6RRZvbreLveWQNmNrFyUZVOxofR3uw4TiOVgwQlzQNOMrNpdStwxpUsrzazU5KOrxCS3gV+CEw0s3VJxwNeYXDOuZwkDQI2mNm0eL8HYd77QMLo9UvS2oRfjR9GxcjMvilpMdDbzDblysqZFnGgY7dqmsLrKz0651xuPyF8A6/zc+Dj8edA4JYkgiqRPYCfNYfKQvSupLpEYNOAb0o6B1ieYEzFupuQEKxqeAuDc87lEKcZ9jazDZK6AEuAgWY2M45cf8nM9kk2ysJI+iEww8zuSTqWUpB0IrDazCZL+izwK8LYjG+Z2e+Tja4wcezMYcA8YGHmsaTGzniFwTnncpC0AtjDzCz2+f88c5EcSavMrHNyERYufhh9FphLlXwYuR1V49gZnyXhnHO5TSeMuP8tcCbwTN0BSb0JyYHS6hdxazYkHUD4f+1lZhdJ2h9ol9Z8EtU4oNbHMDjnXG6XAT+Lg89OIiSgqnMG8GLOR1UxScMlDQc+aGBLHUlfBiYDvYG6dRc6Az9KLKgSkHS+pGclvRN/JjqmwbsknHOuHpI6E7IGzjSzVRn79yck/pmfWHAFkDSnkVPMzJqUR6OaSJpBSJw1NSOZ1i7AfDPrnnR8hZB0FaHycythHEM/QnKtB8zshkRi8gqDc865NIsJtPaM401qzKyrpDaECkOPpOMrRKzcDTOzeRn7+gGTzaxfEjF5l4Rzzrm0ex04J2vfmcCrCcRSKh0JM3MyLQM6JBAL4C0MzjnnUi4OeHwamAMcDjxH6Eo61sxmJRhawSTdRxiHcTnwPqFL4gZgrZllV44qE5NXGJxzzqWdpF2BkwkfrB8Ak9K6EieApN2A/yYMsG0DbCLM2LnYzFYkEpNXGJxzzqWRpNMJffoLGz05RSS1AoYRZuJsImTgXGpmWxONyysMzjnn0kjSTGA/4F3CtMrnCRWIeQ0+MAWqcWEwH/TonHMulcxsAGHthauAdcAlhLwS8yTdL2l0ogEWZ7Kkw5MOIpO3MDjnnGs2JO0BfB0YC3RPY7puAEn/A5wFPE4Yk7Htw9rMvp9ETL40tHPOudSSJOBgYEjc/g2YTxgg+EKCoRWrA/BYvN0nyUDqeAuDc865VJL0JPAZ4B1gStxeylyV05WOj2FwzjmXVgOADYT1F94FZjeXykLMYZJr/+JKx7KtbG9hcM45l1aS9gaOJHRHHEmYgvgioTtiipn9I8HwCpZrlkTMj7HQzLolEpNXGJxzzjUXaR/0KOkFwgDHzwEvZx3uA0w3s5EVDwwf9Oiccy7Fcgx6HAx0AV4D7kkwtEL9AhBwKHB3xn4DFgHPJhEUeAuDc865lJL0R8I38bbA34gLNwEvm9n6JGMrlqQDzOz/ko4jk7cwOOecS6vJwPXA381sU9LBlIKkQcAGM5sW73cHfgJ8itBFMS6pHBnewuCcc85ViTiGYYKZPRPvPw70An5JWMjpn2b2rURi8wqDc845Vx0kLQV6m9kGSV2AxcCnzGympH0I60zsk0Rsvg6Dc845Vz3aABvj7cMJ0yhnApjZB4QBnYnwCoNzzjlXPaYDX463zwSeqTsgqTdQm0RQ4F0SzjnnXNWQNBh4gjCNcgsw2MzeicfGAp81szMSic0rDM4551z1kNSZsOz1zMylriXtD6wys/mJxOUVBuecc841xscwOOecc65RXmFwzjnnXKO8wuCcc865RnmFwTnnnHON8gqDc8455xr1/28ElwSQ3MyZAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Figure 2--figure supplement 1:\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Generate the figure -- this has to be done in a few pieces\n",
"modeling_xaxis = np.linspace(0, 1, 100)\n",
"fig, ax_list = plot_utils.setup_multiplot(2, sharex=False, sharey=False)\n",
"# Separate figure handle for the PR curves\n",
"fig_pr, ax_pr = plt.subplots()\n",
"\n",
"# 2a and supplemental figure 3: ROC and PR curves with SVM, TF occupancies, CRX occupancy\n",
"model_data = [ # (TPR, precision, name, color)\n",
" (svm_tpr, svm_prec, \"SVM\", \"black\"),\n",
" (occ_tpr_list, occ_prec_list, f\"{n_tfs} TFs\", \"#E69B04\"),\n",
" (crx_tpr_list, crx_prec_list, \"CRX\", \"#009980\")\n",
"]\n",
"\n",
"model_tprs, model_precs, model_names, model_colors = zip(*model_data)\n",
"prc_chance = activity_df[\"group_name_WT\"].str.contains(\"Strong\").sum() / activity_df[\"group_name_WT\"].str.contains(\"Strong|Silencer\").sum()\n",
"\n",
"# Generate figures\n",
"_, model_aurocs, model_aurocs_std, model_auprs, model_auprs_std = plot_utils.roc_pr_curves(\n",
" modeling_xaxis, model_tprs, model_precs, model_names, model_colors=model_colors,\n",
" prc_chance=prc_chance, figax=([fig, fig_pr], [ax_list[0], ax_pr])\n",
")\n",
"ax_list[0].set_xticks(np.linspace(0, 1, 6))\n",
"plot_utils.add_letter(ax_list[0], -0.25, 1.03, \"a\")\n",
"\n",
"# Display model metrics\n",
"print(\"Model metrics:\")\n",
"for name, auroc, auroc_std, aupr, aupr_std in zip(model_names, model_aurocs, model_aurocs_std, model_auprs, model_auprs_std):\n",
" print(f\"{name}\\tAUROC={auroc:.3f}+/-{auroc_std:.3f}\\tAUPR={aupr:.3f}+/-{aupr_std:.3f}\")\n",
"\n",
"# Calculate total predicted occupancy of each class\n",
"wt_entropy_grouper = wt_entropy_df.groupby(activity_df[\"group_name_WT\"])\n",
"print(\"Total predicted occupancy of all TFs in each group:\")\n",
"display(wt_entropy_grouper[\"total_occupancy\"].describe())\n",
"\n",
"# 2b: Total predicted occupancy of each class\n",
"ax = ax_list[1]\n",
"fig = plot_utils.violin_plot_groupby(wt_entropy_grouper[\"total_occupancy\"], \"Total predicted TF occupancy\", class_names=wt_activity_names_oneline, class_colors=color_mapping, figax=(fig, ax))\n",
"plot_utils.rotate_ticks(ax.get_xticklabels())\n",
"plot_utils.add_letter(ax, -0.25, 1.03, \"b\")\n",
"\n",
"# Add ticks above to show the n\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(wt_activity_count, fontsize=10, rotation=45)\n",
"\n",
"print(\"Figure 2, panels A and B:\")\n",
"fig.tight_layout()\n",
"display(fig)\n",
"print(\"Figure 2--figure supplement 1:\")\n",
"display(fig_pr)\n",
"plt.close()\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"caption": "(**c**) Frequency of TF motifs in each activity class.",
"id": "fig2c",
"label": "Figure 2c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"in validate_matrix(): Row sums in df are not close to 1. Reormalizing rows...\n",
"Figure 2c\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 11 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Calculate motif frequency in each class\n",
"occupied_cutoff = 0.5\n",
"motif_freq_df = wt_occupancy_grouper.apply(lambda x: (x > occupied_cutoff).sum() / len(x))\n",
"# Sort by the feature importance in the logistic model\n",
"feature_importance = occ_clf.coef_[0]\n",
"feature_order = feature_importance.argsort()\n",
"motif_freq_df = motif_freq_df.iloc[:, feature_order]\n",
"\n",
"# Make the fig\n",
"fig, ax_list = plt.subplots(nrows=8, ncols=2, figsize=(6, 4), gridspec_kw=dict(width_ratios=[1, 2]))\n",
"gs = ax_list[0, 0].get_gridspec()\n",
"for ax in ax_list[:, 1]:\n",
" ax.remove()\n",
" \n",
"axbig = fig.add_subplot(gs[:, 1])\n",
"\n",
"ax = axbig\n",
"vmax = 0.25\n",
"thresh = vmax / 2\n",
"motif_freq_no_crx_df = motif_freq_df.drop(columns=\"CRX\")\n",
"heatmap = ax.imshow(motif_freq_no_crx_df.T, aspect=\"auto\", vmin=0, vmax=vmax, cmap=\"Reds\")\n",
"ax.set_xticks(np.arange(len(wt_activity_names_oneline)))\n",
"ax.set_xticklabels(wt_activity_names_oneline, rotation=90)\n",
"ax.set_yticks(np.arange(len(motif_freq_no_crx_df.columns)))\n",
"ax.set_yticklabels(motif_freq_no_crx_df.columns)\n",
"plot_utils.annotate_heatmap(ax, motif_freq_no_crx_df, thresh)\n",
"\n",
"# Add the logos\n",
"for cax, tf in zip(ax_list[1:, 0], motif_freq_no_crx_df.columns):\n",
" pwm = logomaker.transform_matrix(pwms[tf], from_type=\"probability\", to_type=\"information\")\n",
" logomaker.Logo(pwm, ax=cax, color_scheme=\"colorblind_safe\", show_spines=False)\n",
" # Right-align the logos\n",
" cax.set_xlim(left=motif_len[tf] - motif_len.max() - 0.5)\n",
" cax.set_ylim(top=2)\n",
" cax.set_xticks([])\n",
" cax.set_yticks([])\n",
"\n",
"# Add a colorbar\n",
"divider = make_axes_locatable(ax)\n",
"cax = divider.append_axes(\"right\", size=\"5%\", pad=\"2%\")\n",
"colorbar = fig.colorbar(heatmap, cax=cax, label=\"Frequency of motif\")\n",
"ticks = cax.get_yticks()\n",
"ticks = [f\"{i:.2f}\" for i in ticks]\n",
"ticks[-1] = r\"$\\geq$\" + ticks[-1]\n",
"cax.set_yticklabels(ticks)\n",
"\n",
"# Add CRX\n",
"cax = divider.append_axes(\"top\", size=\"14%\", pad=\"2%\")\n",
"heatmap = cax.imshow(motif_freq_df[\"CRX\"].to_frame().T, aspect=\"auto\", vmin=0, vmax=vmax, cmap=\"Reds\")\n",
"cax.xaxis.tick_top()\n",
"cax.set_xticks(ax.get_xticks())\n",
"cax.set_xlim(ax.get_xlim())\n",
"cax.set_xticklabels(wt_activity_count, fontsize=10, rotation=45)\n",
"cax.set_yticks([0])\n",
"cax.set_yticklabels([\"CRX\"])\n",
"plot_utils.annotate_heatmap(cax, motif_freq_df[\"CRX\"].to_frame(), thresh)\n",
"\n",
"# Add CRX logo\n",
"cax = ax_list[0, 0]\n",
"pwm = logomaker.transform_matrix(pwms[\"CRX\"], from_type=\"probability\", to_type=\"information\")\n",
"logomaker.Logo(pwm, ax=cax, color_scheme=\"colorblind_safe\", show_spines=False)\n",
"# Right-align the logos\n",
"cax.set_xlim(left=motif_len[tf] - motif_len.max() - 0.5)\n",
"cax.set_ylim(top=2)\n",
"cax.set_xticks([])\n",
"cax.set_yticks([])\n",
"\n",
"plot_utils.add_letter(cax, 0, 1.03, \"c\")\n",
"print(\"Figure 2c\")\n",
"fig.tight_layout(pad=0)\n",
"display(fig)\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"caption": "(**d**) Frequency of co-occurring TF motifs in strong enhancers. Lower triangle is expected co-occurrence if motifs are independent. (**e**) Frequency of activity classes, colored as in (**b**), for sequences in CRX, NRL, and/or MEF2D ChIP-seq peaks. (**f**) Frequency of TF ChIP-seq peaks in activity classes. TFs in (**c**) are sorted by feature importance of the logistic regression model in (**a**).",
"id": "fig2def",
"label": "Figure 2d, e, and f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>group_name_WT</th>\n",
" <th>Silencer</th>\n",
" <th>Inactive</th>\n",
" <th>Weak enhancer</th>\n",
" <th>Strong enhancer</th>\n",
" </tr>\n",
" <tr>\n",
" <th>binding_group</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>No binding</th>\n",
" <td>0.221493</td>\n",
" <td>0.286300</td>\n",
" <td>0.331419</td>\n",
" <td>0.160788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CRX only</th>\n",
" <td>0.203553</td>\n",
" <td>0.222276</td>\n",
" <td>0.346615</td>\n",
" <td>0.227556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CRX+NRL</th>\n",
" <td>0.192560</td>\n",
" <td>0.115974</td>\n",
" <td>0.238512</td>\n",
" <td>0.452954</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CRX+MEF2D</th>\n",
" <td>0.145000</td>\n",
" <td>0.165000</td>\n",
" <td>0.280000</td>\n",
" <td>0.410000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All three</th>\n",
" <td>0.099338</td>\n",
" <td>0.105960</td>\n",
" <td>0.284768</td>\n",
" <td>0.509934</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"group_name_WT Silencer Inactive Weak enhancer Strong enhancer\n",
"binding_group \n",
"No binding 0.221493 0.286300 0.331419 0.160788\n",
"CRX only 0.203553 0.222276 0.346615 0.227556\n",
"CRX+NRL 0.192560 0.115974 0.238512 0.452954\n",
"CRX+MEF2D 0.145000 0.165000 0.280000 0.410000\n",
"All three 0.099338 0.105960 0.284768 0.509934"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Figure 2, panels D-F\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 7 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Setup figure\n",
"fig, ax_list = plt.subplots(nrows=2, ncols=2, figsize=(8, 4), gridspec_kw=dict(height_ratios=[3, 2]))\n",
"ax2d = ax_list[0, 0]\n",
"ax2f = ax_list[1, 0]\n",
"for ax in ax_list[:, 1]:\n",
" ax.remove()\n",
"\n",
"ax2e = fig.add_subplot(ax2d.get_gridspec()[:, 1])\n",
"\n",
"# Calculate co-occurrance of motifs in strong enhancers\n",
"strong_enh_coocc_df = wt_occupancy_grouper.get_group(\"Strong enhancer\")[[\"RAX\", \"NRL\", \"MAZ\", \"NDF1\", \"RORB\"]]\n",
"strong_enh_coocc_df = (strong_enh_coocc_df > occupied_cutoff).astype(int)\n",
"strong_enh_coocc_df = strong_enh_coocc_df.T.dot(strong_enh_coocc_df) / len(strong_enh_coocc_df)\n",
"# Fill in lower triangle with the expected values\n",
"for row in range(len(strong_enh_coocc_df)):\n",
" for col in range(row + 1, len(strong_enh_coocc_df)):\n",
" strong_enh_coocc_df.iloc[row, col] = strong_enh_coocc_df.iloc[row, row] * strong_enh_coocc_df.iloc[col, col]\n",
" \n",
"# 2d: Make the heatmap\n",
"ax = ax2d\n",
"vmax = 0.25\n",
"thresh = vmax / 2\n",
"heatmap = ax.imshow(strong_enh_coocc_df, aspect=\"auto\", cmap=\"Reds\", vmax=vmax, vmin=0)\n",
"ax.set_title(\"Strong enhancers\")\n",
"ax.set_xticks(np.arange(len(strong_enh_coocc_df.columns)))\n",
"ax.set_xticklabels(strong_enh_coocc_df.columns)\n",
"ax.set_yticks(np.arange(len(strong_enh_coocc_df.columns)))\n",
"ax.set_yticklabels(strong_enh_coocc_df.columns)\n",
"plot_utils.annotate_heatmap(ax, strong_enh_coocc_df, thresh, adjust_lower_triangle=True)\n",
"\n",
"# Add colorbar\n",
"divider = make_axes_locatable(ax)\n",
"cax = divider.append_axes(\"right\", size=\"5%\", pad=\"2%\")\n",
"colorbar = fig.colorbar(heatmap, cax=cax, label=\"Freq. motifs\\nco-occur\", ticks=[0, round(thresh, 2), vmax])\n",
"plot_utils.add_letter(ax, -0.25, 1.03, \"d\")\n",
"\n",
"# Calculate activity classes for different binding combos\n",
"binding_combos_activity_freq = activity_measured_wt_df.groupby(\"binding_group\")[\"group_name_WT\"].value_counts().unstack()\n",
"binding_combos_activity_freq = binding_combos_activity_freq[class_sort_order]\n",
"# Ignore cases where there is NRL or MEF2D but not CRX\n",
"binding_combos_activity_freq = binding_combos_activity_freq.loc[[\"No binding\", \"CRX only\", \"CRX+NRL\", \"CRX+MEF2D\", \"All three\"]]\n",
"binding_combos_activity_freq = binding_combos_activity_freq.astype(int)\n",
"\n",
"# Generate names then normalize data\n",
"binding_combos_names = binding_combos_activity_freq.index.values\n",
"binding_combos_count = [j.sum() for i, j in binding_combos_activity_freq.iterrows()]\n",
"binding_combos_activity_freq = binding_combos_activity_freq.div(binding_combos_activity_freq.sum(axis=1), axis=0)\n",
"display(binding_combos_activity_freq)\n",
"\n",
"# 2e: make plot\n",
"ax = ax2e\n",
"fig = plot_utils.stacked_bar_plots(binding_combos_activity_freq, \"Fraction of group\", binding_combos_names, color_mapping, figax=(fig, ax), vert=True)\n",
"ax.set_yticks(np.linspace(0, 1, 6))\n",
"plot_utils.rotate_ticks(ax.get_xticklabels())\n",
"\n",
"# Add the n\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(binding_combos_count, fontsize=10, rotation=45)\n",
"plot_utils.add_letter(ax, -0.25, 1.03, \"e\")\n",
"\n",
"# Frequency each class is bound by each TF\n",
"group_bound_freqs = activity_measured_wt_df.groupby(\"group_name_WT\")[[\"crx_bound\", \"nrl_bound\", \"mef2d_bound\"]].apply(lambda x: x.sum() / len(x))\n",
"group_bound_freqs.columns = group_bound_freqs.columns.str.split(\"_\").str[0].str.upper()\n",
"\n",
"# 2f: Make heatmakt\n",
"vmax = 1\n",
"thresh = vmax / 2\n",
"ax = ax2f\n",
"heatmap = ax.imshow(group_bound_freqs.T, aspect=\"auto\", cmap=\"Reds\", vmax=vmax, vmin=0)\n",
"ax.set_xticks(np.arange(len(wt_activity_names_oneline)))\n",
"ax.set_xticklabels(wt_activity_names_oneline, rotation=90)\n",
"ax.set_yticks(np.arange(len(group_bound_freqs.columns)))\n",
"ax.set_yticklabels(group_bound_freqs.columns)\n",
"plot_utils.annotate_heatmap(ax, group_bound_freqs, thresh)\n",
"\n",
"# Add colorbar\n",
"divider = make_axes_locatable(ax)\n",
"cax = divider.append_axes(\"right\", size=\"5%\", pad=\"2%\")\n",
"colorbar = fig.colorbar(heatmap, cax=cax, label=\"Fraction\\nbound\")\n",
"plot_utils.add_letter(ax, -0.25, 1.03, \"f\")\n",
"\n",
"# Add ticks above to show the n\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_axes_locator(ax.get_axes_locator())\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(wt_activity_count, fontsize=10, rotation=45)\n",
"\n",
"print(\"Figure 2, panels D-F\")\n",
"fig.tight_layout(pad=0)\n",
"display(fig)\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lineage-defining TF motifs differentiate strong enhancers from silencers\n",
"\n",
"We performed a de novo motif enrichment analysis to identify motifs that distinguish strong enhancers from silencers and found several differentially enriched motifs matching known TFs. For motifs that matched multiple TFs, we selected one representative TF for downstream analysis, since TFs from the same family have PWMs that are too similar to meaningfully distinguish between motifs for these TFs ([Figure 2—figure supplement 2](#fig2s2), Materials and methods). Strong enhancers are enriched for several motif families that include TFs that interact with CRX or are important for photoreceptor development: NeuroD1/NDF1 (E-box-binding bHLH) [@bib59], RORB (nuclear receptor) [@bib36; @bib79], MAZ or Sp4 (C2H2 zinc finger) [@bib51], and NRL (bZIP) [@bib55; @bib56]. Sp4 physically interacts with CRX in the retina [@bib51], but we chose to represent the zinc finger motif with MAZ because it has a higher quality score in the HOCOMOCO database [@bib46]. Silencers were enriched for a motif that resembles a partial K50 homeodomain motif but instead matches the zinc finger TF GFI1, a member of the Snail repressor family [@bib8] expressed in developing retinal ganglion cells [@bib88]. Therefore, while strong enhancers and silencers are not distinguished by their CRX motif content, strong enhancers are uniquely enriched for several lineage-defining TFs.\n",
"\n",
"To quantify how well these TF motifs differentiate strong enhancers from silencers, we trained two different classification models with fivefold cross-validation. First, we trained a 6-mer support vector machine (SVM) [@bib19] and achieved an AUROC of 0.781 ± 0.013 and AUPR of 0.812 ± 0.020 ([Figure 2a](#fig2) and [Figure 2—figure supplement 1](#fig2ab)). The SVM considers all 2080 non-redundant 6-mers and provides an upper bound to the predictive power of models that do not consider the exact arrangement or spacing of sequence features. We next trained a logistic regression model on the predicted occupancy for eight lineage-defining TFs ([Supplementary file 4](#supp4)) and compared it to the upper bound established by the SVM. In this model, we considered CRX, the five TFs identified in our motif enrichment analysis, and two additional TFs enriched in photoreceptor ATAC-seq peaks [@bib31]: RAX, a Q50 homeodomain TF that contrasts with CRX, a K50 homeodomain TF [@bib34] and MEF2D, a MADS box TF which co-binds with CRX [@bib2]. The logistic regression model performs nearly as well as the SVM (AUROC 0.698 ± 0.036, AUPR 0.745 ± 0.032, [Figure 2a](#fig2) and [Figure 2—figure supplement 1](#fig2ab)) despite a 260-fold reduction from 2080 to 8 features. To determine whether the logistic regression model depends specifically on the eight lineage-defining TFs, we established a null distribution by fitting 100 logistic regression models with randomly selected TFs (Materials and methods). Our logistic regression model outperforms the null distribution (one-tailed Z-test for AUROC and AUPR, p < 0.0008, [Figure 2—figure supplement 3](#fig2s3)), indicating that the performance of the model specifically requires the eight lineage-defining TFs. To determine whether the SVM identified any additional motifs that could be added to the logistic regression model, we generated de novo motifs using the SVM _k_-mer scores and found no additional motifs predictive of strong enhancers. Finally, we found that our two models perform similarly on an independent test set of CRX-targeted sequences ([@bib85]; [Figure 2—figure supplement 3](#fig2s3)). Since the logistic regression model performs near the upper bound established by the SVM and depends specifically on the eight selected motifs, we conclude that these motifs comprise nearly all of the sequence features captured by the SVM that distinguish strong enhancers from silencers among CRX-targeted sequences."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"figure: Figure 2—figure supplement 2.\n",
":::\n",
"\n",
"\n",
"### Results from de novo motif analysis.\n",
"\n",
"Motifs enriched in strong enhancers (**a**) and silencers (**b**). Bottom, de novo motif identified with DREME; top, matched known motif identified with TOMTOM.\n",
":::\n",
"{#fig2s2}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"figure: Figure 2—figure supplement 3.\n",
":::\n",
"### Additional validation of the eight transcription factors (TFs) predicted occupancy logistic regression model.\n",
":::\n",
"{#fig2s3}"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"caption": "Predictions of the 6-mer support vector machine (SVM) (black) and eight TFs predicted occupancy logistic regression model (orange) on an independent test set. (**a**) Receiver operating characteristic, (**b**) precision recall curve. Dashed black line represents chance in both panels.",
"id": "fig2s3ab",
"label": "Figure 2—figure supplement 3 a and b."
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Only panels A and B are shown here. Generating the data for panels C and D will take approximately 50 minutes. If you are interested in generating these panels, the code is in the next cell, but commented out.\n",
"Computing predicted occupancy of all TFs on the test set.\n",
"Done computing predicted occupancy.\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
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" .dataframe tbody tr th {\n",
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"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CRX</th>\n",
" <th>GFI1</th>\n",
" <th>MAZ</th>\n",
" <th>MEF2D</th>\n",
" <th>NDF1</th>\n",
" <th>NRL</th>\n",
" <th>RORB</th>\n",
" <th>RAX</th>\n",
" </tr>\n",
" <tr>\n",
" <th>label</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>chr1_100559800_SCRUBR</th>\n",
" <td>0.274096</td>\n",
" <td>2.545296e-13</td>\n",
" <td>1.630613e-11</td>\n",
" <td>4.707551e-14</td>\n",
" <td>1.017487e-07</td>\n",
" <td>0.000854</td>\n",
" <td>4.694361e-05</td>\n",
" <td>0.008889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1_100559800_UBR</th>\n",
" <td>1.178397</td>\n",
" <td>5.862032e-11</td>\n",
" <td>1.102815e-06</td>\n",
" <td>1.221394e-10</td>\n",
" <td>1.066875e-03</td>\n",
" <td>0.000541</td>\n",
" <td>8.777171e-07</td>\n",
" <td>0.001608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1_100750470_UBR</th>\n",
" <td>2.430898</td>\n",
" <td>8.232504e-07</td>\n",
" <td>5.564299e-11</td>\n",
" <td>2.960941e-10</td>\n",
" <td>1.272582e-02</td>\n",
" <td>0.969272</td>\n",
" <td>1.295348e-06</td>\n",
" <td>0.001267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1_108920170_UBR</th>\n",
" <td>2.072197</td>\n",
" <td>7.323860e-03</td>\n",
" <td>6.147587e-16</td>\n",
" <td>4.758899e-09</td>\n",
" <td>2.658399e-10</td>\n",
" <td>0.808744</td>\n",
" <td>5.559077e-03</td>\n",
" <td>0.003341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chr1_11177090_SCRUBR</th>\n",
" <td>3.214338</td>\n",
" <td>4.034044e-04</td>\n",
" <td>4.444271e-14</td>\n",
" <td>2.389581e-07</td>\n",
" <td>3.627830e-10</td>\n",
" <td>0.000005</td>\n",
" <td>1.550753e-03</td>\n",
" <td>2.118118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CRX GFI1 MAZ MEF2D \\\n",
"label \n",
"chr1_100559800_SCRUBR 0.274096 2.545296e-13 1.630613e-11 4.707551e-14 \n",
"chr1_100559800_UBR 1.178397 5.862032e-11 1.102815e-06 1.221394e-10 \n",
"chr1_100750470_UBR 2.430898 8.232504e-07 5.564299e-11 2.960941e-10 \n",
"chr1_108920170_UBR 2.072197 7.323860e-03 6.147587e-16 4.758899e-09 \n",
"chr1_11177090_SCRUBR 3.214338 4.034044e-04 4.444271e-14 2.389581e-07 \n",
"\n",
" NDF1 NRL RORB RAX \n",
"label \n",
"chr1_100559800_SCRUBR 1.017487e-07 0.000854 4.694361e-05 0.008889 \n",
"chr1_100559800_UBR 1.066875e-03 0.000541 8.777171e-07 0.001608 \n",
"chr1_100750470_UBR 1.272582e-02 0.969272 1.295348e-06 0.001267 \n",
"chr1_108920170_UBR 2.658399e-10 0.808744 5.559077e-03 0.003341 \n",
"chr1_11177090_SCRUBR 3.627830e-10 0.000005 1.550753e-03 2.118118 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Making predictions on the test set with the SVM and 8 TF logistic regression model.\n",
"Model performance on White 2013 test set:\n",
"SVM\tAUROC = 0.800\tAUPR = 0.821\n",
"8 TFs\tAUROC = 0.662\tAUPR = 0.714\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(\"Only panels A and B are shown here. Generating the data for panels C and D will take approximately 50 minutes. If you are interested in generating these panels, the code is in the next cell, but commented out.\")\n",
"white_data_dir = os.path.join(\"Data\", \"Downloaded\", \"CrxMpraLibraries\")\n",
"white_seqs = pd.read_csv(os.path.join(white_data_dir, \"white2013Sequences.txt\"), sep=\"\\t\", header=None, usecols=[0, 8], index_col=0, squeeze=True, names=[\"label\", \"sequence\"])\n",
"# Only keep barcode1 sequences since barcode info isn't needed\n",
"bc_tag = \"_barcode1\"\n",
"white_seqs = white_seqs[white_seqs.index.str.contains(bc_tag)]\n",
"# Trim off the barcode ID\n",
"white_seqs = white_seqs.rename(lambda x: x[1:-len(bc_tag)])\n",
"# Only keep the 84 bp of the sequence that corresponds to the library\n",
"seq_len = 84\n",
"seq_start = len(\"TAGCGTCTGTCCGTGAATTC\") + 1\n",
"white_seqs = white_seqs.str[seq_start:seq_start+seq_len]\n",
"# Function to correct off by one error in labeling\n",
"def correct_label(name):\n",
" chrom, pos, group = name.split(\"_\")\n",
" pos = int(pos) + 1\n",
" return \"_\".join([chrom, str(pos), group])\n",
"\n",
"white_activity_df = pd.read_csv(os.path.join(white_data_dir, \"white2013Activity.txt\"), sep=\"\\t\", index_col=0, usecols=[0, 1, 2, 3], names=[\"label\", \"class\", \"expression\", \"expression_SEM\"], header=0)\n",
"# Correct the off by one error of the labels\n",
"white_activity_df = white_activity_df.rename(correct_label)\n",
"white_activity_df[\"expression_log2\"] = np.log2(white_activity_df[\"expression\"])\n",
"\n",
"white_measured_seqs = white_seqs[white_activity_df.index]\n",
"\n",
"print(\"Computing predicted occupancy of all TFs on the test set.\")\n",
"white_occupancy_df = predicted_occupancy.all_seq_total_occupancy(white_measured_seqs, ewms, mu, convert_ewm=False)\n",
"print(\"Done computing predicted occupancy.\")\n",
"display(white_occupancy_df.head())\n",
"\n",
"# Define cutoffs\n",
"scrambled_mask = white_activity_df[\"class\"].str.contains(\"SCR\")\n",
"strong_cutoff = white_activity_df.loc[scrambled_mask, \"expression_log2\"].quantile(0.95)\n",
"white_scrambled_mean = white_activity_df.loc[scrambled_mask, \"expression_log2\"].mean()\n",
"\n",
"# Pull out bound sequences\n",
"bound_mask = white_activity_df[\"class\"].str.match(\"CBR(M|NO)$\")\n",
"bound_activity_df = white_activity_df[bound_mask].copy()\n",
"bound_occupancy_df = white_occupancy_df[bound_mask]\n",
"\n",
"# Pull out relevant sequences\n",
"white_strong_mask = bound_activity_df[\"expression_log2\"] > strong_cutoff\n",
"white_silencer_mask = bound_activity_df[\"expression_log2\"] < (white_scrambled_mean - 1)\n",
"white_modeling_mask = white_strong_mask | white_silencer_mask\n",
"white_labels = white_strong_mask[white_modeling_mask]\n",
"\n",
"# Make predictions\n",
"print(\"Making predictions on the test set with the SVM and 8 TF logistic regression model.\")\n",
"# Write sequences to file for the SVM\n",
"white_modeling_seqs = white_seqs[bound_activity_df.index][white_modeling_mask]\n",
"white_modeling_fasta = os.path.join(svm_dir, \"white2013TestSet.fasta\")\n",
"fasta_seq_parse_manip.write_fasta(white_modeling_seqs, white_modeling_fasta)\n",
"\n",
"# SVM\n",
"svm_white_tpr, svm_white_prec, svm_white_scores, svm_white_f1 = gkmsvm.predict_and_eval(white_modeling_fasta, white_labels, svm_prefix, word_len, word_len, max_mis, xaxis)\n",
"\n",
"# Logistic model\n",
"occupancy_probs = occ_clf.predict_proba(bound_occupancy_df[white_modeling_mask])\n",
"occupancy_white_tpr, occupancy_white_prec, occupancy_white_f1 = modeling.calc_tpr_precision_fbeta(white_labels, occupancy_probs[:, 1], xaxis, positive_cutoff=0.5)\n",
"\n",
"# Setup figure\n",
"fig, ax_list = plot_utils.setup_multiplot(2, n_cols=2, sharex=False, sharey=False)\n",
"\n",
"# Plot White 2013 test set\n",
"_, white_aurocs, _, white_auprs, _ = plot_utils.roc_pr_curves(\n",
" modeling_xaxis, [svm_white_tpr, occupancy_white_tpr], [svm_white_prec, occupancy_white_prec],\n",
" model_names[:2], model_colors=model_colors[:2], prc_chance=svm_white_prec[-1],\n",
" figax=([fig, fig], ax_list)\n",
")\n",
"\n",
"plot_utils.add_letter(ax_list[0], -0.15, 1.03, \"a\")\n",
"plot_utils.add_letter(ax_list[1], -0.15, 1.03, \"b\")\n",
"\n",
"# Display model performance\n",
"print(\"Model performance on White 2013 test set:\")\n",
"print(f\"{model_names[0]}\\tAUROC = {white_aurocs[0]:.3f}\\tAUPR = {white_auprs[0]:.3f}\")\n",
"print(f\"{model_names[1]}\\tAUROC = {white_aurocs[1]:.3f}\\tAUPR = {white_auprs[1]:.3f}\")\n",
"fig.tight_layout()\n",
"display(fig)\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"figure: Figure 2—figure supplement 3c and d, static.\n",
":::\n",
"\n",
"\n",
"Static version of the figure to display panels (**c**) and (**d**). Null distribution of 100 logistic regression models trained using randomly selected motifs (gray) compared to the true features (orange). Shaded area, 1 standard deviation based on fivefold cross-validation. (**c**) Receiver operating characteristic, (**d**) precision recall curve. Dashed black line represents chance in both panels.\n",
":::\n",
"{#fig2s3cd_static}"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"caption": "Interactive version of panels (**c**) and (**d**). Note that this takes close to an hour to run.",
"id": "fig2s3cd_int",
"label": "Figure 2—figure supplement 3c and d, interactive."
},
"outputs": [],
"source": [
"# # Read in HOCOMOCO database\n",
"# hocomoco = predicted_occupancy.read_pwm_files(os.path.join(\"Data\", \"Downloaded\", \"Pwm\", \"photoreceptorMotifsAndHOCOMOCOv11_full_MOUSE.meme\"))\n",
"# hocomoco = hocomoco.apply(predicted_occupancy.ewm_from_letter_prob).apply(predicted_occupancy.ewm_to_dict)\n",
"\n",
"# wt_seqs = all_seqs[all_seqs.index.str.contains(\"WT\")].copy()\n",
"# wt_seqs = sequence_annotation_processing.remove_mutations_from_seq_id(wt_seqs)\n",
"# wt_seqs = wt_seqs[activity_df.index]\n",
"# modeling_seqs = wt_seqs[silencer_modeling_mask]\n",
"\n",
"# niter = 100\n",
"# nfeatures = len(ewms)\n",
"# # Track the cross-validated mean TPR and precision for each feature set\n",
"# random_tprs = []\n",
"# random_precs = []\n",
"# # Keep track of the features selected for each round\n",
"# random_ewms = []\n",
"\n",
"# np.random.seed(seed)\n",
"# for i in range(niter):\n",
"# if i % 10 == 9:\n",
"# print(f\"Iteration {i+1}\")\n",
" \n",
"# # Randomly sample PWMs\n",
"# sample = hocomoco.sample(nfeatures)\n",
"# random_ewms.append(sample.index.str.split(\"_\").str[0].values)\n",
"# # Do predicted occupancy scan\n",
"# features = predicted_occupancy.all_seq_total_occupancy(modeling_seqs, sample, mu, convert_ewm=False)\n",
"# # Fit the model\n",
"# clf = LogisticRegression(C=c_opt)\n",
"# clf, tpr, prec, f1 = modeling.train_estimate_variance(clf, cv, features, labels_with_silencer, xaxis, positive_cutoff=0)\n",
" \n",
"# # Store the result\n",
"# random_tprs.append(np.mean(tpr, axis=0))\n",
"# random_precs.append(np.mean(prec, axis=0))\n",
" \n",
"# fig, ax_list = plot_utils.setup_multiplot(2, n_cols=2, sharex=False, sharey=False)\n",
"# niter_rand = len(random_occ_tprs)\n",
"# rand_tpr_plotting = [[j] for i, j in random_occ_tprs.iterrows()] + [occ_tpr_cv]\n",
"# rand_prec_plotting = [[j] for i, j in random_occ_precs.iterrows()] + [occ_prec_cv]\n",
"# rand_names = [\"\"] * niter_rand + [\"True features\"]\n",
"# rand_colors = [\"#8080801A\"] * niter_rand + [\"#E69B04\"]\n",
"\n",
"# _, background_aurocs, _, background_auprs, _ = plot_utils.roc_pr_curves(\n",
"# modeling_xaxis, rand_tpr_plotting, rand_prec_plotting, rand_names, model_colors=rand_colors,\n",
"# prc_chance=prc_chance, figax=([fig, fig], ax_list)\n",
"# )\n",
"\n",
"# plot_utils.add_letter(ax_list[0], -0.15, 1.03, \"c\")\n",
"# plot_utils.add_letter(ax_list[1], -0.15, 1.03, \"d\")\n",
"\n",
"# # KS test, null hypothesis: random AUROCs and AUPRs are normally distributed\n",
"# # One-tailed Z-test that the real data is drawn from this distribution\n",
"# for data, name in zip([background_aurocs, background_auprs], [\"AUROC\", \"AUPR\"]):\n",
"# real, rand = data[niter_rand], data[:niter_rand]\n",
"# dstat, pval = stats.kstest(stats.zscore(rand), \"norm\")\n",
"# print(f\"{name}s of random features are normally distributed, KS test p = {pval:.2f}, D = {dstat:.2f}\")\n",
"# zscore = (real - np.mean(rand)) / np.std(rand)\n",
"# pval = stats.norm.cdf(-np.abs(zscore))\n",
"# print(f\"Probability that the {name} of the real features is drawn from the background distribution, one-tailed Z-test p = {pval:2f}\")\n",
"\n",
"# display(fig)\n",
"# plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Strong enhancers are characterized by diverse total motif content\n",
"\n",
"To understand how these eight TF motifs differentiate strong enhancers from silencers, we first calculated the total predicted occupancy of each sequence by all eight lineage-defining TFs and compared the different activity classes. Strong enhancers and silencers both have higher total predicted occupancies than inactive sequences, but total predicted occupancies do not distinguish strong enhancers and silencers from each other ([Figure 2b](#fig2), [Supplementary file 5](#supp5)). Since strong enhancers are enriched for several motifs relative to silencers, this suggests that strong enhancers are distinguished from silencers by the diversity of their motifs, rather than the total number.\n",
"\n",
"We considered two hypotheses for how the more diverse collection of motifs function in strong enhancers: either strong enhancers depend on specific combinations of TF motifs (‘TF identity hypothesis’) or they instead must be co-occupied by multiple lineage-defining TFs, regardless of TF identity (‘TF diversity hypothesis’). To distinguish between these hypotheses, we examined which specific motifs contribute to the total motif content of strong enhancers and silencers. We considered motifs for a TF present in a sequence if the TF predicted occupancy was above 0.5 molecules ([Supplementary file 4](#supp4)), which generally corresponds to at least one motif with a relative _K_~D~ above 3%. This threshold captures the effect of low affinity motifs that are often biologically relevant [@bib10; @bib15; @bib16; @bib63]. As expected, 97% of strong enhancers and silencers contain CRX motifs since the sequences were selected based on CRX binding or significant matches to the CRX PWM within open chromatin ([Figure 2c](#fig2)). Compared to silencers, strong enhancers contain a broader diversity of motifs for the eight lineage-defining TFs ([Figure 2c](#fig2)). However, while strong enhancers contain a broader range of motifs, no single motif occurs in a majority of strong enhancers: NRL motifs are present in 23% of strong enhancers, NeuroD1 and RORB in 18% each, and MAZ in 16%. Additionally, none of the motifs tend to co-occur as pairs in strong enhancers: no specific pair occurred in more than 5% of sequences ([Figure 2d](#fig2)). We also did not observe a bias in the linear arrangement of motifs in strong enhancers (Materials and methods). Similarly, no single motif occurs in more than 15% of silencers ([Figure 2c](#fig2)). These results suggest that strong enhancers are defined by the diversity of their motifs, and not by specific motif combinations or their linear arrangement.\n",
"\n",
"The results above predict that strong enhancers are more likely to be bound by a diverse but degenerate collection of TFs, compared with silencers or inactive sequences. We tested this prediction by examining in vivo TF binding using published ChIP-seq data for NRL [@bib23] and MEF2D [@bib2]. Consistent with the prediction, sequences bound by CRX and either NRL or MEF2D are approximately twice as likely to be strong enhancers compared to sequences only bound by CRX ([Figure 2e](#fig2)). Sequences bound by all three TFs are the most likely to be strong or weak enhancers rather than silencers or inactive sequences. However, most strong enhancers are not bound by either NRL or MEF2D ([Figure 2f](#fig2)), indicating that binding of these TFs is not required for strong enhancers. Our results support the TF diversity hypothesis: CRX-targeted enhancers are co-occupied by multiple TFs, without a requirement for specific combinations of lineage-defining TFs.\n",
"\n",
"## Strong enhancers have higher motif information content than silencers\n",
"\n",
"Our results indicate that both strong enhancers and silencers have a higher total motif content than inactive sequences, while strong enhancers contain a more diverse collection of motifs than silencers. To quantify these differences in the number and diversity of motifs, we computed the information content of CRX-targeted sequences using Boltzmann entropy. The Boltzmann entropy of a system is related to the number of ways the system’s molecules can be arranged, which increases with either the number or diversity of molecules ([@bib67], Chapter 5). In our case, each TF is a different type of molecule and the number of each TF is represented by its predicted occupancy for a _cis_-regulatory sequence. The number of molecular arrangements is thus _W_, the number of distinguishable permutations that the TFs can be ordered on the sequence, and the information content of a sequence is then log~2~_W_ (Materials and methods).\n",
"\n",
"We found that on average, strong enhancers have higher information content than both silencers and inactive sequences (Mann-Whitney U test, p = 1 × 10^–23^ and 7 × 10^–34^, respectively, [Figure 3a](#fig3), [Supplementary file 5](#supp5)), confirming that information content captures the effect of both the number and diversity of motifs. Quantitatively, the average silencer and inactive sequence contains 1.6 and 1.4 bits, respectively, which represents approximately three total motifs for two TFs. Strong enhancers contain on average 2.4 bits, representing approximately three total motifs for three TFs or four total motifs for two TFs. To compare the predictive value of our information content metric to the model based on all eight motifs, we trained a logistic regression model and found that information content classifies strong enhancers from silencers with an AUROC of 0.634 ± 0.008 and an AUPR of 0.663 ± 0.014 ([Figure 3b](#fig3) and [Figure 3—figure supplement 1](#fig3)). This is only slightly worse than the model trained on eight TF occupancies despite an eightfold reduction in the number of features, which is itself comparable to the SVM with 2080 features. The difference between the two logistic regression models suggests that the specific identities of TF motifs make some contribution to the eight TF model, but that most of the signal captured by the SVM can be described with a single metric that does not assign weights to specific motifs. Information content also distinguishes strong enhancers from inactive sequences (AUROC 0.658 ± 0.012, AUPR 0.675 ± 0.019, [Figure 3b](#fig3) and [Figure 3—figure supplement 1](#fig3)). These results indicate that strong enhancers are characterized by higher information content, which reflects both the total number and diversity of motifs."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"caption": "### Figure 3: Information content classifies strong enhancers.\n\n(**a**) Information content for different activity classes. (**b**) Receiver operating characteristic of information content to classify strong enhancers from silencers (orange) or inactive sequences (indigo).\n\n### Figure 3—figure supplement 1: Precision recall curve of logistic regression classifier using information content.\n\nOrange, strong enhancer vs. silencer; indigo, strong enhancer vs. inactive; shaded area, 1 standard deviation based on fivefold cross-validation.",
"id": "fig3",
"label": "Figure 3 and Figure 3—figure supplement 1."
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Information content for each class:\n"
]
},
{
"data": {
"text/html": [
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" <tr>\n",
" <th>group_name_WT</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Silencer</th>\n",
" <td>837.0</td>\n",
" <td>1.554721</td>\n",
" <td>1.872824</td>\n",
" <td>0.000173</td>\n",
" <td>0.195721</td>\n",
" <td>0.952877</td>\n",
" <td>2.240308</td>\n",
" <td>15.248629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Inactive</th>\n",
" <td>928.0</td>\n",
" <td>1.385812</td>\n",
" <td>1.646322</td>\n",
" <td>0.000105</td>\n",
" <td>0.150796</td>\n",
" <td>0.841681</td>\n",
" <td>2.050814</td>\n",
" <td>14.738741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Weak enhancer</th>\n",
" <td>1360.0</td>\n",
" <td>1.496780</td>\n",
" <td>1.683849</td>\n",
" <td>0.000008</td>\n",
" <td>0.201747</td>\n",
" <td>1.014613</td>\n",
" <td>2.216628</td>\n",
" <td>17.960698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Strong enhancer</th>\n",
" <td>1051.0</td>\n",
" <td>2.383258</td>\n",
" <td>2.178600</td>\n",
" <td>0.000173</td>\n",
" <td>0.635291</td>\n",
" <td>1.836731</td>\n",
" <td>3.453384</td>\n",
" <td>13.082139</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% \\\n",
"group_name_WT \n",
"Silencer 837.0 1.554721 1.872824 0.000173 0.195721 0.952877 \n",
"Inactive 928.0 1.385812 1.646322 0.000105 0.150796 0.841681 \n",
"Weak enhancer 1360.0 1.496780 1.683849 0.000008 0.201747 1.014613 \n",
"Strong enhancer 1051.0 2.383258 2.178600 0.000173 0.635291 1.836731 \n",
"\n",
" 75% max \n",
"group_name_WT \n",
"Silencer 2.240308 15.248629 \n",
"Inactive 2.050814 14.738741 \n",
"Weak enhancer 2.216628 17.960698 \n",
"Strong enhancer 3.453384 13.082139 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Strong enhancers and silencers have the same information content, Mann-Whitney U test p = 1e-23 U = 557959.00\n",
"Strong enhancers and inactive sequences have the same information content, Mann-Whitney U test p = 7e-34, U = 641607.00\n",
"Model metrics:\n",
"Strong vs.\n",
"silencer\tAUROC=0.634+/-0.008\tAUPR=0.663+/-0.014\n",
"Strong vs.\n",
"inactive\tAUROC=0.658+/-0.012\tAUPR=0.675+/-0.019\n",
"Figure 3:\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x288 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Figure 3--figure supplement 1:\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 288x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fit logistic regression models\n",
"entropy_clf = LogisticRegression()\n",
"entropy_clf, entropy_tpr_list, entropy_prec_list, entropy_f1_list = modeling.train_estimate_variance(entropy_clf, cv, wt_entropy_df.loc[silencer_modeling_mask, \"entropy\"], labels_with_silencer, xaxis, positive_cutoff=0)\n",
"\n",
"inactive_entropy_clf = LogisticRegression()\n",
"inactive_entropy_clf, inactive_entropy_tpr_list, inactive_entropy_prec_list, inactive_entropy_f1_list = modeling.train_estimate_variance(inactive_entropy_clf, cv, wt_entropy_df.loc[inactive_modeling_mask, \"entropy\"], labels_with_inactive, xaxis, positive_cutoff=0)\n",
"\n",
"# Setup figures\n",
"fig, ax_list = plot_utils.setup_multiplot(2, sharex=False, sharey=False)\n",
"fig_pr, ax_pr = plt.subplots()\n",
"\n",
"# 3a: violin plot of information content\n",
"print(\"Information content for each class:\")\n",
"display(wt_entropy_grouper[\"entropy\"].describe())\n",
"\n",
"ax = ax_list[0]\n",
"fig = plot_utils.violin_plot_groupby(wt_entropy_grouper[\"entropy\"], \"Information content\", class_names=wt_activity_names_oneline, class_colors=color_mapping, figax=(fig, ax))\n",
"plot_utils.rotate_ticks(ax.get_xticklabels())\n",
"ax.set_yticks(np.arange(0, wt_entropy_df[\"entropy\"].max() + 1, 2))\n",
"plot_utils.add_letter(ax, -0.2, 1.03, \"a\")\n",
"\n",
"# Add ticks above to show the n\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(wt_activity_count, fontsize=10, rotation=45)\n",
"\n",
"# Statistics for differences in information content\n",
"ustat, pval = stats.mannwhitneyu(wt_entropy_grouper[\"entropy\"].get_group(\"Strong enhancer\"), wt_entropy_grouper[\"entropy\"].get_group(\"Silencer\"), alternative=\"two-sided\")\n",
"print(f\"Strong enhancers and silencers have the same information content, Mann-Whitney U test p = {pval:.0e} U = {ustat:.2f}\")\n",
"ustat, pval = stats.mannwhitneyu(wt_entropy_grouper[\"entropy\"].get_group(\"Strong enhancer\"), wt_entropy_grouper[\"entropy\"].get_group(\"Inactive\"), alternative=\"two-sided\")\n",
"print(f\"Strong enhancers and inactive sequences have the same information content, Mann-Whitney U test p = {pval:.0e}, U = {ustat:.2f}\")\n",
"\n",
"# 3b: ROC and PR curves with information content vs. two classes\n",
"model_data = [\n",
" (entropy_tpr_list, entropy_prec_list, \"Strong vs.\\nsilencer\", \"#E69B04\"),\n",
" (inactive_entropy_tpr_list, inactive_entropy_prec_list, \"Strong vs.\\ninactive\", plot_utils.set_color(1))\n",
"]\n",
"\n",
"model_tprs, model_precs, model_names, model_colors = zip(*model_data)\n",
"ax = ax_list[1]\n",
"\n",
"# Plot the models\n",
"_, model_aurocs, model_aurocs_std, model_auprs, model_auprs_std = plot_utils.roc_pr_curves(\n",
" modeling_xaxis, model_tprs, model_precs, model_names, model_colors=model_colors,\n",
" figax=([fig, fig_pr], [ax, ax_pr])\n",
")\n",
"ax.set_xticks(np.linspace(0, 1, 6))\n",
"plot_utils.add_letter(ax, -0.2, 1.03, \"b\")\n",
"\n",
"# Display model metrics\n",
"print(\"Model metrics:\")\n",
"for name, auroc, auroc_std, aupr, aupr_std in zip(model_names, model_aurocs, model_aurocs_std, model_auprs, model_auprs_std):\n",
" print(f\"{name}\\tAUROC={auroc:.3f}+/-{auroc_std:.3f}\\tAUPR={aupr:.3f}+/-{aupr_std:.3f}\")\n",
" \n",
"print(\"Figure 3:\")\n",
"fig.tight_layout()\n",
"display(fig)\n",
"print(\"Figure 3--figure supplement 1:\")\n",
"display(fig_pr)\n",
"plt.close()\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Strong enhancers require high information content but not NRL motifs\n",
"\n",
"Our results show that except for CRX, none of the lineage-defining motifs occur in a majority of strong enhancers. However, all sequences were tested in reporter constructs with the _Rho_ promoter, which contains an NRL motif and three CRX motifs [@bib9; @bib47]. Since NRL is a key co-regulator with CRX in rod photoreceptors, we tested whether strong enhancers generally require NRL, which would be inconsistent with our TF diversity hypothesis. We removed the NRL motif by recloning our MPRA library without the basal _Rho_ promoter. If strong enhancers require an NRL motif for high activity, then only CRX-targeted sequences with NRL motifs will drive reporter expression. If information content (i.e. total motif content and diversity) is the primary determinant of strong enhancers, only CRX-targeted sequences with sufficient motif diversity, measured by information content, will drive reporter expression regardless of whether or not NRL motifs are present.\n",
"\n",
"We replaced the _Rho_ promoter with a minimal 23 bp polylinker sequence between our libraries and _DsRed_, and repeated the MPRA ([Figure 1—figure supplement 1](#fig1s1), [Supplementary file 3](#supp3)). CRX-targeted sequences were designated as ‘autonomous’ if they retained activity in the absence of the _Rho_ promoter (log~2~(RNA/DNA) > 0, Materials and methods). We found that 90% of autonomous sequences are from the enhancer class, while less than 3% of autonomous sequences are from the silencer class ([Figure 4a](#fig4)). This confirms that the distinction between silencers and enhancers does not depend on the _Rho_ promoter, which is consistent with our previous finding that CRX-targeted silencers repress other promoters [@bib32; @bib86]. However, while most autonomous sequences are enhancers, only 39% of strong enhancers and 9% of weak enhancers act autonomously. Consistent with a role for information content, autonomous strong enhancers have higher information content (Mann-Whitney U test p = 4 × 10^–8^, [Figure 4b](#fig4)) and higher predicted CRX occupancy (Mann-Whitney U test p = 9 × 10^–12^, [Figure 4c](#fig4)) than non-autonomous strong enhancers. We found no evidence that specific lineage-defining motifs are required for autonomous activity, including NRL, which is present in only 25% of autonomous strong enhancers ([Figure 4d](#fig4)). Similarly, NRL ChIP-seq binding [@bib23] occurs more often among autonomous strong enhancers (41% vs. 19%, Fisher’s exact test p = 2 × 10^–14^, odds ratio = 3.0), yet NRL binding still only accounts for a minority of these sequences. We thus conclude that strong enhancers require high information content, rather than any specific lineage-defining motifs."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"caption": "### Sequence features of autonomous and non-autonomous strong enhancers.\n\n(**a**) Activity of library in the presence (x-axis) or absence (y-axis) of the _Rho_ promoter. Dark blue, strong enhancers; light blue, weak enhancers; green, inactive; red, silencers; gray, ambiguous; horizontal line, cutoff for autonomous activity. Points on the far left and/or very bottom are sequences that were present in the plasmid pool but not detected in the RNA. (**b–d**) Comparison of autonomous and non-autonomous strong enhancers for information content (**b**), predicted cone-rod homeobox (CRX) occupancy (**c**), and frequency of transcription factor (TF) motifs (**d**).",
"id": "fig4",
"label": "Figure 4."
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correlation between WT activity with Rho vs. Polylinker:\n",
"PCC = 0.338\n",
"SCC = 0.359\n",
"n = 4751\n",
"Fraction of autonomous sequences belonging to each activity class:\n"
]
},
{
"data": {
"text/plain": [
"Strong enhancer 0.693103\n",
"Weak enhancer 0.208621\n",
"Inactive 0.070690\n",
"Silencer 0.027586\n",
"Name: group_name_WT, dtype: float64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fraction of each activity class that has autonomous activity:\n"
]
},
{
"data": {
"text/plain": [
"group_name_WT\n",
"Silencer 0.019394\n",
"Inactive 0.044565\n",
"Weak enhancer 0.090705\n",
"Strong enhancer 0.387657\n",
"Name: autonomous_activity, dtype: float64"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Information content of autonomous and non-autonomous strong enhancers:\n"
]
},
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
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" <td>2.073301</td>\n",
" <td>1.964160</td>\n",
" <td>0.000173</td>\n",
" <td>0.488725</td>\n",
" <td>1.624789</td>\n",
" <td>3.026204</td>\n",
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" count mean std min 25% 50% \\\n",
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"\n",
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"text": [
"Autonomous and non-autonomous strong enhancers have the same information content, Mann-Whitney U test p=4e-08, U=101739.00\n",
"Predicted CRX occupancy of autonomous and non-autonomous strong enhancers:\n"
]
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"Autonomous and non-autonomous strong enhancers have the same predicted CRX occupancy, Mann-Whitney U test p=9e-12, U=95541.00\n",
"Strong enhancers with autonomous and non-autonomous activity vs. NRL bound and unbound:\n"
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"Fisher's exact test that NRL binding and strong enhancer autonomous activity are independent, p=2e-14, odds ratio=3.0\n"
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gOz0WBcwMjtIAjM+IJa+igRMXfkayw87q35x8GL8jTdO6Sk/YDV89SV7OV0qt2H+jiMxqSWaUUr875Mg07TDYVOTkhv+uw24x4fEHUArGpMUwJi2m3XGLvthNo9vPJTMGkZ1uDCrmlNSxvayehGgrt77yHfeeO5HkYJ+jSKsZq9lEksOoR1Ld6OHutzfxg4kZnDOlY3KkaZqmdV1PkpfFGHNc9vchkHiA7ZrWbw1PjmbG0ATGpscgQFGtiyGJUR2OM5kEgKyEKKobPfz6zY3srW7EF1BU1HtYsrmUD7eUctr4ND7JKeeFa2az8fenYRbject3lLNkcymbi+t08qJpmnaIupy8iIgJEOOuSPB+i5GAr5dj07Q+FxNh5fUu1GK55tjhnDc1i4QoK+9uKOajrWWt+y6ZkcXX+TXkVzXx5c4qvH5Fca2Lo4MVdZVSnDYunVvmNXL86JQ++140TdOOFN1ZKu0DPEBU8L63zW0r8K9ej07T+pDb52fpllKcLm+nxyqliLCaEBEc+/Up+u388a31X+rdRg5/+gSjdcDzq/KZ/qePmHLvUuZPyuCoYXpwUtM07VB157LRcIzRluUYPUhaKKBCKeXqzcA0ra89sXwXD368gzMnZfDYj6Yf9Lj3NhTz+PKdbCup555zJtDk8XP25AzW761lclYcR//lE8wmIdpmoskTQAHHL/ycjLgICmuacLqMhKai3k12+mH65jRN0wawLicvSqmC4F1dhE7r1yoa3ETZzER3UmNl5rAEUhx2Thzz/ZdyfvnqenzBkZV7Fm/BH4B/XjKVRy6bTn5lI8u2lRFlt/DYZdO55KnVgDFBt7rRw/j0GOpc9cREWJg9/OCNGTVN07Su61KdFxF5Uil1XfD+8wc7Tim1oBdj6xFd52Vg+2xnBTUuLz/ITiPK2rGcfkWDm49zK4i0mjhvYvcnxnr9AV7+Zg9ThyQwKSsOgGe/2s2O0gbe31yC0+XFZjaRERfB/edP4ugRSeRVNPDgsu2YzSaOH5VMaoyNumY/pXUuNhU5eX9TKZfNHMyffzj5kL53XedlH13nRRvIdJ2XznV15GV3m/t5fRGIpnVFrcuL2xfA7fUfMHmJtpmJsppJibZ1+9xun5/z//UVW0vqyIi1s+o3pwBw1Zzhxtdjh/HTF9ZSUNVEQXUTC57+mrS4SO49ewIfBifwvr+pBBH46NYTOW9aFu+sL6K60ctN80YdwnetaZqmtdWl5EUp9ec29+/pu3A07fv9IDuNZp+fhMgDJydRNgvnTsw44D6ny8s3e2sYl+pgUHzH5dClzma2ldQBcPWxwzvsH5MWwy3zRnP7axuwmQVPcFVRfLSVK48ZyuvrCmn0+FEKTAIuj59fvrqegIK1BTUMSuj4mpqmhU5/rbCrda7bdV5EZD3wIvC/lpYAmna4RFrNRB5gxKUripwuKhs97KhobJe8tFw6HZoUzVMLZuKwW1qXOe/vgmlZpMVGsKO0jnvf3wbA45/nIUCjx49ZYESKg/yqJpJj7JhFEFHMHq5XGQ1U+j/A8HX33Xfzxhtv4HQ6ef3110MdjtYNPSlSdw9wGfB7EVkHvAS8ppSq7tXI2hCR0cAm4HWl1BV99TrawDY6xYHJJGTFRbZu8wcUJz+4nPpmL5/9ai6njEs76PPL642Rmdte3dCa8IxOdfBJTvm+8ynILW/g2ufW8PVvTubLu+YhCCkx9r77xrSQ0s39wlfLv+Pu9vjTQq/byYtS6i3gLRGJAS7ASGQeFJFPlFLn9HaAQY8Ba/ro3FqY8vv9FBcXk5mZidncfjTG6w/g9RvLlm1mE9bgLTM2gh3l9YxNdeCwW/EHFBX1zfgCimaPn9iIfU0Umzw+TCJc9tRqqps8OJs81Lra12LcWd7AY5dN48VvCvgqr5oJGTFYzCai7Wb+/tF2rjl2BGPT27caCDcicpFS6rUDbL9QKXXEf1zVXaXD13333Ud8fLz+3YWhbneVbvdkESswH/gFcIJSqicjOZ29xqUYSdJWYFRnIy96tdGRY+nSpaxevZo5c+Zw6qmnttv37pYSGj1+BIiJsHDmOKPAyvK8SorrmrGZhQsmZSIilNc34/UrsuKNEZllW0r527Lt5JY3kOSwUdngOeDrnzQ2hSibhRvnjuQ3b21iY6GTMyel47Bb+WhbKdWNXs6anMGjlx28hkx3hGq1kYjUHahLvIhUK6VCcj1MrzbSBjK92qhz3amwCwR7A4icLCKLgDLgD8AHGEXsepWIxAL3Arf19rm18JeamorZbCY1NZXC2iY+21lBQ7DCrd1iwgSIgM+veGtTMTVNHkYlGe8HXr+i2GnUVUyNiWhNXADueGMjO8obUNAhcUmIMkZmbGbh3nMnsmRTCWc/+gUXz8hiTJqDCRlxvLJ2L9WNXo4flcxNc41VRst3VDDr/o95d0NxH/9Ueo+IjBCREYBJRIa3PA7eTgGaQx2jpmlHpp6MlBQDDcDLwLFKqW29G1I7fwQWKaUKReSgB4nIdcB1AEOGDOnDcLT+ZNq0aUybNg2AZdvLqWryUOh0kZ0aw2ljUgnWleOj7WXUNPupdXkZnhRNlsPGjS99R0KklZV3zmNLsZO/fpjD5EFxRNssXDVnKA9/spMom5kfzRpMamwEBVWNRFotPPXFbuwWE386dyLJ0Xai7WYa3H4WfZHP7qomPtluLJlOjraxcmclJU4X4zJiWb2rivJ6NytzK8KpMeNOjAraQscSCaUYH1w0TdMOu54kL+cqpb7p9Uj2IyJTgVOAaZ0dq5R6EngSjMtGfRya1g/NGpJAYW0TI4IjKyKCOZjvWkzGAGNlo5uyBjeD4qOIsVs4ZqSxouiVNXtZkVvJitxKBBiaFIUCnvzxTI4dldz6GtWNHlbtqmLakAQumjkYgAd+OIXHPt9JlNXM7qom1hXUAuAPXo7NK29kcpYbj8/PLfNG8dPjRxyGn0bvUEqZAERkuVLqxFDHo2ma1qJLyYuIDFNK5QcfVgaHkjtQSu3qrcCAucAwYE9w1MUBmEVkvFKqdyYRaANGfKSV+Mg4tpTW4Wz2MmtIIhaTkb2kxdpxur0U1TXj8gZIc9hZddfJrc+9ce4o4qOsPPPFbuo9fpIcNoqdLlL3WyGUGG1j8c+Pb7dt/qQM5k/KwB9Q/O+bAu5+ZwsAAQV/OX8Sv3lrEy+v3UNeRSMZcRHcdurYPv5J9D6duGia1t90deRlE9CyZKLtUHJbCuhZAY4DexLj0lSLX2EkMzf04mtoA8zm0joCCsakeEiONpKPSRlxTMqIo7S+mSKni0HB+S0uj5/6Zi/pcRHcMm80K3ZUkFNaz5biOpSCmDYrj+qbvcz7+3JcXj+f3T63w9Jns0m44uhhIPCXD7bzo1lDyM6IxWYxMX1IAlMHxXNSduph+zn0JhEZDtwHTMX4ENFKKdWl67QicjNwFTAJo0bUVW32nYyxonAI8DVwVZteapqmaR10tcJuTJv73Z7k2xNKqSagqeWxiDQAzUqpisPx+lr/EFABTNL1P7njhydR7/aRFNWxAm96TATpMRGtj8957At2VTTy5g3H8JMX1uHz+Vl11zzOf/wrmjx+ouz7cvFfvrKeigY3AFuKa5kzMgWX18dJf19OaoydJT8/HpNJ+HSbMWk4ympm6uB4tv/xjEP47vuNlzDmvNxOm3+T3VQM/Ak4HWidHS0iycCbwE+A9zDmub0CHH0I8WqaNsD1pMLuw0qpWw6w/SGl1K29E1ZHSqk/9NW5tf7p79/8lVdyXuKRU/7N7MxjuvSczDYF6Jq9fty+AHGR1gMeG223AIpVu6pocvtQwOc7KimoamLmsARiI6y4fX4a3T5yyupbn/fTF9bhsFtYMHso1Y0eaps8NPv8RNks3H/+JD7dXs5ZkzL427LtDIqP5NJZYT+JfALG5PxAT0+glHoTQERmAoPa7LoA2NJSR0ZE/oBxaTpbKZXT85A1TRvIejKKctVBtv/4EOLQtA6KG4pQSlHdXNWj5y/dXs4HOWVUN+1b7lxc19w6gnLPORPwK/jLh9uxmk18dOsJDE+OxmISimpcnLDwM8b+34fMuv8TyuvcpMfaGZ4chVmEZm+ArAQjUQooeParfADS4yKYNSyBx5fn8dhnO/nNW5sIBMJ+DvkKujBxvocmABtaHiilGjFGeSb00ev1upycHK699lpycnSupWmHS5dHXkTkmpbntLnfYgRQ2WtRaRpw/4kL2VtXwMj40d97nC+gKK9vJjUmonWSLkCM3YLXH8BuMXL0Ro+P5XnGn+lFU7IYlx7LOZMz+HBLGX6lyK9q4q9Lc/j7xVP4xcvraUk5RIxGi6V1buaMTKKgqomAgnvf38olMwbxyrpCVu2q4sZgTZeb//cdOaX1nDgmmVPGpWEyHXyZf5jIBz4Ukbcwlki3Ukr97hDP7QD2vxTsZN8cu1ZtSyLYbN3vGt5XdHsATTv8unPZqGVkxUb7URaFUazuyt4KStMA7GY7oxLGdHrchmInOyoaGBQRYOqgBGJijP/35o1OaXdchMVMmsOO3WLCLGCxmHj4suk4XV5E4O/LtrOx0MlnORU8tWAm/12dT73bx5OXz0QJvP1dEdOGxnPRv1cBxoz1k7JTeWVdIanBuTS1TR6OG5VMTaMHi0m4OLikOsxFA4sBK9Db31ADsH/13ligfv8D25ZEiI6O7jfDWbo9gKYdfl1OXpRSJwGIyJ+UUnf3XUjaQOL2u7n83YuwmCz89+xXsJgOPP/kUKRE29hZ4mH5Gy+x0mTijjvuwGrt+Dpmk3RIaADiIq34/AHOm5JJYpSNy2YNITU2gttf24DT5eWrXVWcPSWTr/Kq+POHOa3F7xx2K1uK6/h/Z2RzxeyhAFzw+JfsqjTmtJbnVLCropFxGR0q64cVpdTVfXj6LbT54CMi0cDI4PawkJ2drUdcNO0w60mRuhUiMkYptaNlg4iMBYYopT7qvdC0gcDldVHYsBdBaPa5cdh6P3kZkhBF+pRBPLIiEovFgsn0/VO5/AHFG98WMi4jlklZcWwudnLeY1/iDyii7RZunmdcprrnnAksXJrDz1/+jsRoGxsKa/EHM5fBCZHsrXGx6MtdNHkCRNnMbC9roKjWqJh/0pgUTp+YTnaYN2VsISLZwEVAmlLq5uC/ebtSamMXn2/BeL8xY9RrigB8wFvAQhH5IfA+8Dtgo56sq2na9+nJhN3H6DikWx/crmntvLnjNc4ccTYvnf06Dpuj8yf0kM1m47bbbuOWW27p0GF6f8t3lHPP4i1c86zRqLzO5cWvFCaTMCQxipYpKudNzSI73Rg1+dXr6zlv6r6y/tnpMdxz9njcXkWExURWfCQvrC7A7QswOSuWf10+g0uPGsL3tbUIFyJyEbASyAIWBDfHAA924zR3Ay7gLuCK4P27g6UPfohRR6YGmA1c2juRa5o2UHW7q7SIOJVScfttE8B5oM6zh5vuKt1/NPtcHPfiLADePn8Jg2L7x/yPVflV5Ne4WJ1bwZrd1Zw2Lp0FxwwlLTYiuHwayuuaOeUfyxmV4iDSZubLvH0rntJi7Lx+/TG4fQF+8M+VDEmMosHto7zeTbTNzAM/nMyZk3u/f1EIu0pvAy5VSm0QkRqlVEKwo3yxUqrjdbjDQHeV1gYy3VW6cz0ZedklIvP22zYX2H3o4WgDSYQlkv939O+4ZcZtZMUM6vwJ3eD1B/gwp4zP8yrpSgKulKKq0YMvECDCaozMFDubKa5t5r9f5xNQtCYuAC99U0Bds4+NRU4evHgqE4LzVm6cO5IXrp3NBY9/xd+WbWfZrScwKCGShCgrcZFW3rxhTp8kLiGWCrRcHlJtvvabSbOaph1ZejLn5Q/AmyKyCKMew0jg6uBN09q5YOxFfXJety9AjcuLqdl7wF4V+9tV1cQ3e2vIiotg9pAEdpbVsz6/hrRYO2V1bv70/laevXpW6/HThyRiNQtXHj2UigY3MRFGwhNpNfPkijwqGjx8tLWMGUMTWJFbicUEvgA8tXI3n+SU8aNZQ7jj9Ow++d5DYB3GCsPn22y7FOjzBq2apmkH0u3kRSn1joicBlwDnAnsBU5XSq3p7eC08OUrLMS1+H2iL7kYU0ICACv3LmdwzBCGxQ8ETzX7AAAgAElEQVQ/5PM77BZOGZ2C1WzC1IV5JdE2MyYBu9nEW5tKaHD78AYUQxOjKKtzk1PafhrXCWNSeOCCydz7/lYWfZnPOVMyufKYWC6ZOZiaJi9bS+pp8vi4b4kxr3RSVjx2q4kRKdG8/q2XN74tAuBXp40dCPNebgGWici1QLSILAXGAKeFNixN045UPRl5QSn1DW0+dYmISUTmK6WW9FpkWlir/b/f07xsGQGnk7hf38kHu97n/1behVnMfL1g/SGff09NE1/vqWFaVhzxByn/31Z6bASXTB1EZaObXdVNDE6M4sVrZzM0KYpT/7Gc8vpmdpY3kFNaxy0vf8f8iRks21qKx29cGZkyOJ5rjjWSrtTYCN68YQ7XPreG/KomrGbhu721/PLk0dxw4kiGJkZz0/++5bHP87jm2OEkOezfF1q/p5TKCa42Oguj3steYLFSqiG0kWnaobnq2uvYvG0bANOnTOHJxx8NcURaV/UoeWkhIpMxajRcjrEEMiST97T+x3HljwlUVRF55pkApEenARBt7Z0VR7UuL76AoqbJ2277hmInRU4XJ45MJtpm/HkXOV0U1DQxIS2WBrefc8anAxBlM7OrshGX12jZs66gmme+Mua/LN5UQrTNjNfv57iRSZQ5XZz84OdMzopl5c4qmjw+rCbhoumDeO3bQgCsZhMiwpmTM6hsGI+IhH3iAu36mb263/Y+7WemaX0pEAjw4gvPkXjB3Si/n83P/Y0n/vXIQBgpPSL0pDFjKkaysgCYjDFp7xbg6d4NTQtnEXPnEjF3buvjaWkz+fKKtVh7qUjdxIxY0mLsJEe3Tw4Kappo9PipdXlbk5cNxU6czT6czT5qXV4yY+wU17vJTnUwNTOOMyam88HmUh5fnscNJ47kLx/mUN/sRSmjgF1pnYuVwdVG+ZWN+NtMUz1mZBKrdlcxLj2Wn54wonX7lXMO/dJYP3IVxr/x/f0Y0MmLFpaKi4uxR8cSOXw6APUmE1VVVSQnJ4c4Mq0rurzaSEQuEpH3gCKMybmvYPQ0qgBeV0o1902I2kBhN9sxSU8WuHVkEiEtJgLzfn2DEqOsRFhMfL2nhoIao9LtUYMTGJviYFRSNNE2c7su0yLC784az5RBcVw+eyiXHDWEv180BV8AXD4/9W4fFQ37RnfuPWdiu9f73bubee1nx/DUgplYzSacLi+fbCtj6ZbSLq2C6s9E5JpgHzNLy/02tz+h+5kBujFjuNq1axeRSRmtj6OTs9i1a1cII9K6ozsjL68AVcDFSqm3WjbqITatPyl2NreOjJTVuxmaEEWKw05K8PJNvdtHbmUjswfH8+/P87i3fAuxERZe/dkx2C3GiqK5Y1L55yVTsVtMPPNVPs1uY8QGYPLgeKwm8AaMScBNHj9VjR4y4owO0+c+9gX5VUbS9Mp1RzN7eNJh/gn0Kt3PrAt0Y8bwlJeXh8Smtz42xaWxc+dOZs2a9T3P0vqL7iQv12BcKnpNRNYCL2IkNOH98VILW76AwiztE+iTx6TS4PZhFiEtpuN8k/KGZnwBxZqCGl5dZ8xVEaC8zs3gxCgATCbh7MmZPPTJDr7eXd1acfcHE9K4+aV1BKfIEGkz88YNc8hOj6XE6cJhtzAoIYq91U2MSHEwKqXvKgofDrqfWdfoxozhKWfHDrzR+6Zp+hyp5O7cGcKItO7oTmPGZ4FnRWQoRhLzc/aVB58vIi8opfy9H6KmQUFBASUlJcyaNQuTyURFg5uPcyvIjI0gympmeFIUydF2kqJsJEXZDniOerePGpcPgPEZMdw4dyRRNjPpMRH8/H/fcuyoZBrdPtYW1HD2lEwe/nQno1MdRNnMbCh08vn2Cpp9RuYSF2nl5pNGkZ0eS25ZPWc8vJL0uAi+uHMegYDRamCgaElcgvPdHPvtO+LH2XVjxvC0JScXc/yw1scSl87mnB0Hf4LWr3R7AoJSqkAp9Uel1BiMyrrPAP8A9vRybNoA903JarZUbu7Ssa+++ipLly4lLy8PMEZdAGqaPOysamR9kbPTc/gDgdb7hc5m7jw9m2irmTve2Mj6Qif/+2YPz64qYHNxHQ8u24EAc0YkkeKwM3lQHDbLvoTE6fJy1LBEAIpqXZhEiLZZ+HBzSVe//bAhIqeLSBFQCuxsc8sNaWD9hJ7zEp525uVhTdh32cgan8GOXD3yEi4OafakUupLpdR1QDpwW++EZBARu4gsEpECEakXkfUickZvvoYWOoV1e7lp2XVcs+QKvH5vp8efcMIJjBkzhsGDjf5IGbERxNotuHwBUqJtTMnc126r2euntL4ZpRSNbh8rd1VSVOvCYbMQH2khLsLC8SOMuSgPLNuOAuwWE89ePYuM2AisZsHtD2AyCc+tLuDjnHJGJEfz8W1ziY+0YDUJZ0/OYHRqDG6fn588vxavP4Db5+f6F7/l5TUDLo//F/BHIFopZWpz+/4OmEeIu+++m6effpq779ZX1sJJYf5uLPH7Juxa4tPZk6+73ISLXln6oZRyK6Ve6Y1ztWHBKIZ1IhCH0ZX2VREZ1suvo4VAclQyk1OmcuLgk7CYvv/qZUAFmD17NpdddhkRERGt2zNjI7CZTUwfFN86IRdgxa5KPttZyRe7q3h3aymFzmY2ldZR2+wlKcrGyMQoom0WtpbUcdIY45r3QxdPZfKgeJbfMZdZwxKxW0ycOTGt9ZxOl5ej//wJtS4fAeDbPbWU1zVjM5uYMzKJCZmxnDIuDQHuWbyV+ubOE7IwkgA8oZRyhTqQ/qhlVVm4ry47ktTU1OD1eTFF7fvQY45JosFZS1NTUwgj07rqkIrU9SWlVCNGH6UWi0VkNzADyA9FTFrvibBEsmj+850el1O1jauWXM5JQ+bx5xP/1m5fTIQFjz9AbkUDs4cmtm5Pddhp8PjxBy8tZcTYmZQZx/K8StzBOSvbSxv4xcvfMW1IPLvvn9866ff5VfmtHaTPmpzFuxtLAfAHFAEFdrPg9iuKal28tb6In88bzfPXzAaMEZ93NxQjYhSsG0AWYZRH0LWcDuC+++4jPj5eT9gNI7t378aRnNVusr+YzDiSM8jPz2f8+PEhjE7rin6bvOxPRNIw+qlsCXUsWs98uOt9TGLmtOE/6PJznO5a/AEfxQ1FHfbZLUaC0NIlusXUrHimZsXzSW4FdouJ2UMSiLRZGJfqILeykeQoG9FWMzaLiZEpjtY3sLUF1byzoZhIq4kTR6dwcnYqN80dyYbCGvIqGjAB3oDiZyeMYG1+NZmxNraV1JGdHsP1/13H5uI63rv5OFIcdr7dU0OzL8BxowZEwaujgVtE5C6MeS+tlFInhCak/kNP2A0/+fn5WOLSOmy3xqexe/dunbyEgbBIXkTEirE0+zmlVIdZcSJyHXAdwJAhQw5zdFpXVLoquXvlXQDMyjia+Ih47vjsVr4rW8fzZ71MpiPrgM+bnXkMr533NqlR6R32DY6P4uIpkR0K1bWocXnw+hVuXwCzKcC4tFjGpcW27t/+xzO47dX1nPPoF5w1OYP7PzD+tDLiIthcXMfLa/fyr+V5tFwNsJjgnCmZZMZFsm5PLev21ALwy1NGsya/hpomDxX1bmIiLFz85CoU8OWd88iMj+zpj62/+E/wpmkDQn5+Pv6ojjWYlCOF/Pz8wx+Q1m09aQ9wGbBeKbVNRMYCTwF+4IYDJRaHSkRMwAuAB7j5QMcopZ4EngSYOXOmvvDcDyVGJHLB6AsxiZk4u3GdeXt1Dk63k8qmioMmLwDD4kYcdF/bxKW+2ct3xU6yU2OIj7DiDVar21RaR6GzmUnpMYxIiibCasYkglKK9zaW4PUH2FjkRACLSSirayagYOHSHEwi+JXCZhZ8AcWb3xWzfEcl8REWapuNZdcNbh9v3zSHygYPE7PiUEpx6vg0Gpp9JA+A3kZKqedCHYOm9abcXfkEHB1b8fmiktidXxCCiLTu6snIy5+AOcH7f8PoLt2AsSJhXi/FBYAY4/mLgDRgvlJqQM2CHEi8O3fien8JjisXYIqPb7dvXekaHv32n9x21B1MSpnSuv2Z+f+loqmc7KTuD9E2eXyICJFtLhntrm6iyNlMdZOHqZlxCEYFRWcwydhUWs/m0nqsZuGcCRl8u6eGsekOHDYLG4ucoBRNwQp0ZpNQG6wJ8+ilU5k+JBGn28u/l+dxwqhkLpg+iMv+s5pdFQ0s+mI3eeWNPH3VUYBRNO+JK2Z2+3vqz0Tkaowqu1kYLUJeUEo9E9qoNK1ndu7ajTl2Yoft5pgUduzKP/wBad3Wk1mFKUqpMhGJAI4DfgvcC0zt1cgMjwPjgLP1Sof+reY3v6XugYU0vPDfDvveyX2LTRUb+CDv/XbbkyKTe5S4uH0BFm8t5b2tpfj8+2q3pMcaK5Fc3gDfFtZy7kTjUlO928eUjBiswb92n1+RW9HAPz7aweaiOiKtZo4ekURCtFHcLj7SSorDjgiMy4jh8x2VzHngU77aWUlchJVjR6Wwfm8tp2SnkeywE1CQW17f7e8jXIjIb4G7gJcxGjS+DNwZ3N4b5x8mIktEpEZESkXkUREJi0vaWngqLCzEEtPxspElJok9e/eGICKtu3ryBlEhIqOAScAapZRbRKIwqqz3mmAl358BbqC0zazwnymlXuzN19IOXeyN11NvNhM5f36HfT+fcSujE8Zw5sizD/p8f8CP2dS1siHm4Goek0i71QJRbUZhhiZGEWm1MCQukj1OFzkVjVw4ZRCNbh/vbi1lfbGTkjqjl+hnOyqAfX/AkTYzn90+F7cvQFyklV++8h0CPP1lPkW1LuIirbzxXSHFtc0kBxOevTUuyuqaSYvdt5R7APkJMFcp1TqeLiJLgRXAfb1w/n8B5UAGEA98BNwIPNwL59a0DsrLSol0dExezDFJlJYMvEKTA1FPRl7+CKzDuJyzMLjtFGBDbwUFrZV8RSkVoZRytLnpxKUfijjpJFJefgnryI7zU1KiUvnxxKtIjDxwk8KP8pcy+4WpPLSm/VLoQCBAaWkpgTaVcQEsZhPnTczgnAnp7ea8uLx+xqU6iI+0MjYlBq8/AAKRVhOp0Xbe21LChznGYpmAgop6d2vCEhth4T8LZnL9iSN4+NJpfLmzknUF1QAsvHAKf/3hRIpqjcG/z7eXUefycuzIJB6/fDomMZ6fMgDmtxxENEb3+LaqgN6aiTwceFUp1ayUKgU+BCb00rn73JIlSxgzZgxLliwJdShaFwQCAZzVVZijEzrsM0cnUFu1/5+61h/1pD3AsxifkAYppT4Kbl4NXNqLcWkDRE1NDSWdfJL5rOATAJbsWtxu+/Lly3niiSf4+OOPOzxH9ht1qWw0eh3l1zRxRnYaDruF8gY3e2pd+AKKykY3DR4/ngDMHZnM5oIaTLKvq+joVAfXv7iOIYlRDE+O5trn13LNc2spr2/GYjbx8bby1tfaVFxPg9vP3LGpfLq9guNHpbDyzpMGVD+j/XwIvCgiY0UkUkSygeeApb10/oeAS0UkSkSygDOCrxkWbr31VnJzc7n11ltDHYrWBdXV1VgjIhGLtcM+sUUSCARoaGgIQWRad3Q7eQmu/mkGmkXEFHxciTHsq2mtlFI8/vjjPPXUU1RWVh70uB9PuIqkyCQuH7+g3fb44MTf+P0mAB9IlNVClNVMSvS+0Y8Yu4VBcXa8fkWzL0BSlJVZQ+KJjbCQU2okIDF248rpuj21+PyKb3ZX89LXezh5bAoWk3DB41/h8QVYttX48757fjY3nTSCQfERrNhRzhMr8lieW8GuisZu/3zCyM1APbARY3L+eqARozlrb1iBMdJSBxQCa4G39z9IRK4TkbUistbn8/XSSx+6hx56iNGjR/PQQw+FOhStC8rKyoiI6TjqAsaHosjYRMrKyg5zVFp39WTOi499H1j3p3ud9FMtpcvbjlb0lHf7dnwFe4g49ZTvPZ+IMGjQIGpqaoiOjj7oceOSx7P04s87bJ82bRpTp07tUsxRNjPnTsxot+2TXKMLtIBRxn9YIou3lWES4ZTsVMrrmnn6qqPIr2wk0mbBYbdw7+ItvL2+OFgAT1Hn8mISWHjhZEqdzby9vphtpXX4A1BYa8yZmT8xnQmZsR2DGiCUUnXAAhG5CkgGKpVSge9/VtcEP/x8iFHqYA5G1+qngb8Cd+4XR2tJhOjoaF0SQeuRiooKLNEH/0BkdSRQUVHByJEjD2NUWnf1ZM7LcGBEm9uxwHsEi8Rp/U+gtpaSyVMpmzsPFTj0/3MqfnghVVdfQ8Wll1F68ikEamsPeuyCBQv4xS9+QWSkMT3C5W1ic8XGLveBOZRkK9Vhx2oSFOD2B1izp4YIs4lYu4WbThrFw5dOY3NRHadPSOfEMSnMGJrADScYb1huXwBfADy+AG5fAJvZRFZ8JLnlDbRZ4ESUzcySzaW8t3HgTvITkQUiMlkpFVBKlSulAiIyRUR+3AunTwSGAI8Ge6RVYXSq7zjzu5/Sl43CS0VFRbueRvszR8VSUaHnvfR3PZnzUrDfbTVwJfDr3g+vdym/H//3XL4YqJTLRaChAX9pCfj9h3y+yLPPxjphAt513+LbvgNfcdf/475r+R1cteRyFue90257UX0h/1jzNwrrOi5TLKzfy/ry71of+wKKenfHywZKGUugy+qNEZFjhycxfdC+T1ilDR5GJkczOcMYJbnymW/41esbWFdQ03rMip3t/z48/gB//mAbv3hlPf/3zmbcvgDHj0oi1m4MMo5KiWZsWgzThxx4GHqA+CNGk9S29mLUfDokSqlKYDdwg4hYRCQe4/1k46Ge+3DRl43CS0VFBQH7wUdKVYROXsJBb9VSiAU6livsZ6pv+jmuxYtJeuZpIk89JdThHDbmjAzSV3yO2O2IteMkte5K+PP9AHh37MBfUYlt/LguP3dM4hjWlH5Nxn4VdZ/Z9B/ezn0Dp7uWPxzX/v/Ey9+5mEZ/A0/Oe4aJmdNZsq0UlzfA3JHJpMfYya1sJMZupqLBw5ayeqwm4cIpxvmHJ0bhsJmpavKSX93A5lKjHstJo5K5cs4wNhU6GZse0/paKQ5b6/3TJ6SxdEsZ76wvwiQQZTNGbC6ZOZjdlY08/GkuvzljHOMyBu4lo6BYjPkobTkxljX3hgswJu3+GqNa96fAL3vp3H1u/vz5zD9AiQCtfyorL8dncxx0v8/m0HNewkBP2gO8QPs5L1HACUDH6mT9jPJ4QKmDTtgZyCyDB/f6Oa1jxmAdM6Zbz7lp+i+4afovWh9vr87h4bUP8oMRZ+J013LpuMs7PCfFk4oKKGoKneyJbMIVrIJrNQt7a12sK2x/2SrKZia/uokhCZGU1DWzpbSO0ckO4iJt1Da7iLCYiLVbufP07A6vlR5nXN6KsJq4+phh1DV5WbXbWDJd2+ThmmOHE2E1k+Swt3aTPgJsBX4IvNpm2/nAtt44uVJqPTC3N84VCk888QR33nknDzzwAD/72c9CHY7WicLiUiTy4JeNVEQcRSU6eenvejLysnO/x43Av5VSHdez9jOB6ioA1BF46ai/em/n23xdsopYexwLTzrwsPvDP3ic7du3M2f6HJweRYzNzLg0B8nRdsoCxiWillYAYFTUXVVQDSTybWEtbn+Amr01XDQli4npPmLsloPOpRmXEUuKw85Pjx/O+kInq3ZXYwICwKhUB5/llHPGpH0Tgyvq3ZTWNTMp6+BvhgPAr4ElInIJkAeMAk4mjOal9KXbb7+dxsZGbr/9dp28hIG9RSWYHQev7G2Ojmdvka6y29/1ZM7LPfvd/hYOiQtAxGmnYc7MxDppUqhDGdCcTiePPPII77//fqfHXjXxWq6bciNXTbyW3OrtHfZ/XbyKB7c/QPZRY7Hb7WwocVLv8bOt3FianBYTwQkjkmhbYmVEYhRxERYSIq2MSjYuG03OiMMkQmyEtV3i8t9V+cx/eCUbCmvZW9PERU98RUWDm+dXFzAsKQqTgAT/lWwrrWfhsvYxXvD4l5zz6BetBe0GIqXUF8BEYA1GwbpvgIlKqS9DGlg/MXr06HZftf6tuKT4gAXqWpgdiRTpKrv9Xk9WGyEiV4vIpyKyPfj16t4OrC/E3ngDGWu+xjYxbIp3hqWqqiqqq6vZsWNHp8cmR6WgVICrP7iCy967kDUlX7fb/9SGf/P5nk/4KN+ohzYkPgowqmSuyq9i8dYSvi2sxa/AJDAqKZoZgxOYPy6dHRUNbClrYGhiFOPSjHkt28rqeWNjMeUNbpRS3PdBDltL6vjVaxsor3O3dqIurHHx1neFBBTtVhe5fQHK65tx+4yJzxOy4oiPspIaMyDbArRSSu1RSv1FKXVT8Kv+aBqUkpLS7qvWv5WVlGA+QF+jFpaYJEqKig5jRFpP9GTOy2+BBcDfgQJgKEaTtkylVG/0OdHCWGlpKYsXL+aoo47imGOOOehxDZ4Grl5yOSlRqeytK8Djd2M12UiM2Pem8l7uO7h9zfxwzMWcM+p8AMamxmAzm1i9p4bGGqNcf0sGfvLoFJLbFqmLsGAC4iP2TVIua2jG4w+wbm8N6/KqcXmNJCTGbmFHWT0zh8aTFGVjWU45n+Tsq7sYYzdT7/ZTVOvi+Ac+IzHaxle/nse/L59xqD8yLcy1rEzRK1T6P5/PR01lOVnfk7yYY1IoLS1GKdUrdbG0vtGTOS993aRNC2N79uyhpqaG8vJyEhI6Ds1uLF9v9DhSUFCXT1FDIS+c+TI7a3dy6rDTMYmJZzb+h5WFy0nZnMGw5jEMnT8cmqGZZsRsxdnsbT3flMxYhsRHElAQ2yZJqWx0s6HYSYTVTFZcBN8V1RIbYcFuNlKd2mYfW0r3LaAZnxnLn97fSqPHSGaibGZGpzo4blQyT67cxcnj0rj0qMHkVzbxh8Vb8AcCLN1SyqRB8WTF91aLHy0cbdu2rd1Xrf/au3cvUfHJiPngqy5NtgiskdGUlJSQmZl5GKPTuqMnyUtfN2nTwtiMGTOIjIxk2LBhHfZtr87h2g8WEGOL4dPLvuTpM/6Lw+ZgWNxwRsSPYtnuDxmVMIrXt79CWVMp55ONDx9lX1Xw8JKHUUox7rSL8EfEkp0azehkBw57+zehYqcLkwgrd1Ual3yUoqbJS055AwLMGBTPnloXaQ47eWX1rc87ZXwa28vqKapxMXlQHKdPSOfhT3dy7KhknliRx9vri3l7fTGpMTa+uHMeK3MruP7FbxmbFsPSW0/o45+q1p/Fxhp1QWJjB/yS+bCXm5tLRGJGp8dFJWeyc+dOnbz0Yz2Z89LXTdq0EFBKUfu731P967sOqQqv2Wxm0qRJxMTEdNiXEplCpiOLCSmT+evq+xARhsUNB+Dt3Df47co7uezdC/nJlJ9xdMYcTjv9NMaMHkOcIw4EdsXk0tRUgs0sDI6P6pC4FDpdLN9VxWd5lfiCS4+mZ8aSGG0jO9XBUUPiGZ3i4JKpg4izWShv8GASuP+8iQxOiGRNfg3Fzmb2VDfxytq97K5s5D8rd5Hq2DefpaLeQ2F1E89+lY/DbuaMiekH/Vn4A4oHlubwwur8Hv88tf7vpptuwmq1ctNNN4U6FK0T27ZtIxDXhYQkPkuPpPVzPUle+rpJmxYCqr6ehkVP0/TfF/GX9k2Ng8TIJN754QeMSRjDa9tf5o5P95VTHxY3HEGIi4hn0YYnWV3yFY+ueYgdO3YwadIk0s5MZkvSBtb4X+G8iZkkRdnandvrD1DsdHV4zcI6NwAmEZo8fgLBtgRTB8czb2wKAQVvrS/CbtnXlmt7aT2rd1VjNQufbq/g9tPGMGNIPJOz4lDAgme+YWOhE7cvwI+PHkogcODKQTvK6nn88zz+750t+A9yTDgQkeEi8pKIbBWRPW1voY6tP3jmmWfwer0888wzoQ5F68Sa7zag4gd1epw/bhDr1odNkecjUrcvG/VlkzbtwJpXrMC3O5/oH1+BmHq0QKxTpthYEv/1GMrjwZLZ+bDqoRgZbywp9QQ8rdumpc1gzZUb+bp4FTd/ZNTK2GLdyIkz5jFixAjKN5RzfOpczh1zLa9uKGJUcjRHDd43p+aT3ApqXN52r5Od6mBUUjTl9W62Bi8RRdssjEiKpqzOzfJco97Pd3tqiI+0MjwpCrcvwMhkByvzKomPtDE0KYp1e2pYt6eWk7NTGZQQyeSsOM6ZmkVBVSMz7vuYc6dkUt3o4f+zd9bxUV7Z/3/fZ1ySTNyJQJAgwaFQpIUudactdYO6UN3qbrvbbmXb7/5q23a3tnWBIqVQird4kOCEQEhC3GVc7u+PCYFAkISEAJ336/W8knmeK2cmk5nz3HvO5xTV2vnx3pFNsTfdo4O4b2w3Yi16VMppHfj3FX59l0cBWyfbcsrRvXt38vLy6N5KwcYAJ5/MdRvQ9D9cCPNQNFGprF476yRYFKCttKk8gBDCiF+oygx02x+RLaVc0X6mBdhPxW13gMOBJj0d3ZDBza7ZZs+meuqjhLzwV8w3XH9C8xgvu/SE+kspWfvDNMwbN9LzsUdRGgN2PR4PGzduJCUlhfDwcC5IvYg31r5Kvaue/Lo8ugQnNY2hVvxvyVhTHOGGcCaeN5Gl85exbt06xvYfT0pId4qrq3C4D9Ro8vhkM8clNkhHRlww6wtrWVrrYHDCAQE5s1aFze2lweVGNPWHIS8voFuUmYLieqaOTyOrqIabhifx4Lg0thXXkV3awORRqQxPPZCl8Os2/wqVw+1hdW4lHp+kssHV5LyoFMFjE3qc0Gt6itAbGBm4SWmZt956i9dff53HH3+8s00JcBScTie5OTuJ/lPKMdvqYrqyc+YWPB4PanV7VdEJ0J60JVX6ZuAdwAUcvE4v8VeHDdDOBD1wP+6tW9G0oE/j3r4DabfjWLjohJ0XX20t3tLSVkv+7yc/P5+5W7egQfLgnJ8x3+i/w8nKymLOnDnExsYyZcoUtlduo9ZZg0CgUdGnBGwAACAASURBVJrHrQyKGcLiSSswaowojepwhWH5LE76hTHdRpMUaiTMoMGo9b91dxeWsmbjZmKTelDs9rsjAxMs6NQK5Q2uZqUgBBBi0DBrawleKblnTCpvL96NIsDu9lFe78InJUnhZjY9P6Gp3+ysIjLzqnl2xmbyqmzcN7YbD4/vznnp0ax7Zjzj3lyKV8I7kwaSEmFq02t3irMMGACs62xDTkV69uzJRx991NlmBDgGGzZswBSZgKI9tiaTojdjCI1k8+bNDBgw4CRYF6C1tGUP4jXgKillhJQy8aCj3R0XIUSYEOJHIYRVCJEnhDixb+fTlJCHHyLiPx+iGA5P6DJccjEAjvnzkZ7DKy23hrIrr6J03Hk4V6067Jqvrg5PXl4LvQ4QHR1NclgY/T0edKPOBiAvLw8hBHFxcQwe7F81EgIEgt4RfXlm2ZM8tOA+pDzgZjg8dtw+N+tLMhn5xWC+2vs5DaKe7wq/BiBIr2nahllbYkfG9qDY1TymZFNxHRIIN2lYvLuSEJ2KiRlxaFUKQTp/yvTYHtH+5yYhNcLIrPtGsOjRsewoqWNN7gHF3HE9ozBqVeypsOL2Sj5entt0LdysIzHMiEmrYkhy2PG+1Kcbe4F5QogPhRAvHnx0tmEBAhwvv/32GyLm8HpmR0Id24vffvutAy0KcCK0ZT3MBSxpZzuOxLuN80UD/YE5QogsKeXWkzT/KY8mORndmNGo4uMRJ7i8qU5OwbN3L0rY4V/CpRdchLeggKif5xxRoViv13PTnXdSPHAwZTNmEbFqBZ999hlSSh566CFWVS3n3vmT2Vy+ifcnfES0KYYrpl+MIgROrxO9Ws+W8s3cPvdGeob14pqek3C73cTburDHvIshMUMPmzMlzEh+eQ0RISYqHdA7JohgvYYYs469lVaqrf7tpHqXF5Wi4PFJxqdF8tPmYj5dsadpnNxKGzaXj/wqG8/P2orFqGHjc38CYHByGAaNgs3lZXRaBHeOSm1mw+z7zz7TBa1MwE+ABji4wufpG4Uc4A/HT78sRIk//lUUGZvOnPkLefDBBzvQqgBtRkrZqgO4BXgbiGht31bOY8LvuHQ/6NznwCvH6CePdHzwwQdyPx988MER2/lflgMMHDjwiO0mT57c1C4zM/OoY2ZmZja1nTx58hHbDRw4sNn87fWcfD5fpz2n6LQoOejTPk3H0cbscmucnPDtOdLn88lLr7+0Xf5ON9xyu9ywr1r+kLVPvvLVz0cd80/PfCwn/WelLK21d8rf6SjPKfNY/zd/lMNoNMpThYKCArly5Uq5cuVKWVFR0dnmBGgBl8sl9SazTHjwS5n055+O64i/7zNpCrZIj8dz0u0FrPIU+D87lY+23KpnAy8C9x50pykaP3RVR+zVeroDHinlwQVysoAxhzYUQkwBprTj3Gck0mpFmM2dMne9qw6NEs8FKRdzVY9r6MPRi2MmB6cwc+ZM1I7jf4seHMR72DWPl+2NQnXHIlivYcXuSt5fuvu4UpyrrS7u+fLMDgcRQqQBk4B4oBD4Wkq5q3OtOjW48NIrKCivwet2csG5Y/j2q88726QAh7By5UqMEfGojMdf/V0dFI4mKIzMzEyGDRvWgdYFaAvC7+S1ooMQOcDXwLc0D9hFSrm73QwTYhTwvZQy5qBzk4EbpJRjj9Rv8ODBMjMzs8Vrjt+XY5s2jZBnnkYVEdFepp7y2H78EWEwYDj//BMey1dTQ/nV16Du2YPwd95udm3ZsmVkZWVx/fXXEx7uz8r5cOO/2Vi2nl5h6QyIGcTZCUdXo/1ux9e8tvpl+kT0o++OgTQ0NHD11Vej1+uZOXMm48ePp2vPdGZtLfEr5saH4PJJ0qOD+Gajv5hahFHDeT2isTk9zMsuxaRVM7xLKAtzynF6JGnhJiZ9sAK3D0Z0DeerO4cDkLm3imCDhnlbinlzwS60KoHHJxmWEsaqPVWkRZl545r+9I1v/gG4ZGcZt366lnCTlnXPnnfCr/GREEKsk1IOPnbLdp/3EuBL/FtHefgD8y8GbpJSdko+qclkklartTOmboaUElNQMOF3foi7shBL1pfs2LShs80KcAiPPfEkH6/II3jUTa3qV7/0E+4Zn87fXzy54V1CCJuU8oyM/m8v2rLyEg48L1vr9bSeBuBQve1g/AJ5baL2b3/HvWUL2n79MN9264nYdlphvOKKdhtLsViIXjC/xWtbt26lqqqK4uLiJudlSv97WjX+pd2uAAmhDeGk9Ehl7dq1/PDDD5x77rk88sgjgF+5NsKkRasSZBbWAqASEB+sp7DO0ZSJZNSpOadrJHuqrNQ6PLgaZXerbC48PrAYNOwpb+DZGZsZlRbJPV+uQ60oTBzoF7FyeSUCqLV7mHb3CDISLS3qtYxKi+TVK/vSM+aMlYd/GbhMSrl4/wkhxFj8WYd/aDGMkpISUGlQGYIhHPL35CDlGR3/dFoybcYsdMPvbHU/TfIgfpj+7Ul3XgIcm7ZkG30CtM59bRvZgLpxuXo/GUCbg3XVqSmgUqHqknjsxmcIvoYGyi67nKpHHm2/MW02ioePoOSccc0ynCZNmsTEiRNJT09v89h6tZ4MZSArfl7J7NmzKWwsTV9ZWcmu8gZ+3FxEpc3Fed2jGNM1ku4R/m2wDUV1mLUKPSPNDEs6IF63obCW7HIrq/KqmqJLK+1u3rt+IGqVoKTOyRer83nku410jw5icFIoX671C8e+fmU/3r9hINuK67jl0zUI4N3FOczYUNjMZpUiuDQjnhq7i/pDhPLOEBKAQ9Mufm88/4cmOzsbU6T/ZVAZgkFRU1raMQrVAdpGbm4uZWVlaGPTjt34EHQJ6eTl5VJUVNQBlgU4EdrivAwF/iuE2CmEWHbw0Z6GSSmtwHTgRSGESQgxErgMf9Bum/DV1IDPh6yrO3bj0xifzUblPfdS//4HePILcK1bj+3HGbTXYpm02fCWlODJy0O6D3xZWywW0tPTUVqpAvxL7lzu/uUO9tUVABAXF0dcXBwDBw7krLPOIjQ0FJvNxs/TvsFqt1PR4Gzq2z8+hK7hRrpHmNhZYWNHeQM7Sg/8fXvH+GsseRufukpAmEHNYz9kUdFwQOH32iGJzHtoNDcN92f8h+jVmPUqnp6xhSC9ij/1imJbSR3/nL+Tqd9tPKwkwFPTN3HLJ2sZ/PICrM4TS1k/BdmIX133YB5pPP+HJicnBxFyoL6VKTKBnJycTrQowKHMmDEDQ9ehCKX1IZlCpcbcbQgzZszoAMsCnAhtcV7+A0zGv5T80SFHe3Mv/mrVZfjjbO6RJ5AmHfzUU5jvmoJ+woRjNz6NcW/ahH3WbOreeBNtei/CP/mYqBnT220pWxURQfSihcQsXYxiMGD75Rcqbr8Db3Fxq8f6KWcmzyx7gsySNSzbtwSAoKAgJk+ezJgxYxg5ciQul4tdu3ZRU1pIgq+GHlEHij6qFMHQLmEMSLAQpvdvF20vs7KtpA6n20ukWdfUNj3KzBV947C5fdw2JrVRtE7N5LNTeP5if/q3T0KEWctNZyXx2i87qbS6qHd4eeGyvvh8koFdLDwxoQfKIdtHfRrjYBTOyPzhe4A7hRBFQojVQogi/AHyrdsTPAZCiOuEENsbdZ12N8a9ndLk7N6D2xjZ9FgER5Obm9uJFgU4lC++nYaSMqTtAyQP4cvvprWfQQHahbbUNvqsIww5wlxVwOXtNV7NU0/h3piFuksXzLfc3F7DnnJohwwh5K9/QZPWDQDDeeNPeExvVRVKaGiTA6RJPSCxXf/Gm7i3bsM+ZkyrX9caZzUAGVH9uSztSgByqrOZMu82zk+9iCeGPU2vXr1Yv349Pp+PMKOmWdyJy+Mjr8ZGeb2T2BADVY56PD5JVnEdWcX+FZikUD1eH6THBGNzeXB4fCSEGvno5sGM7RGF7aAspad+3Eydw0Pv2BDeWeyPPx+aHIpBo+LJaZvYXlLPlQMP3y25c1Qq1w3tgpQSs+7MkhOXUu4QQvQCzgJigSJgtZSy3fbIhBDnAa8C1wJrGuc55cnenYsqOLrpsdsYRt4xxBwDnDwqKirYtmUTUaPavm1uSB3EuvffpqamBovF0o7WBTgR2lrb6Db8cS/70yY/l1J+0p6GdQTm226j4bP/oR9z9IyX0x2hUhE0ufXBaUfCNnMmVffej+mWmwl9+aXDroe+/hr2+b9ivLL1gcE3pN/CqISxJAQlompc1t1XX4C6TkvmhrUUpu+jd+/eFBcXk5aWRp8+fZr1X763kpL6A9tIF/SMZktxLcX1DjyNlXg0isLwpFCqbW5+zS4j3KhhZEo4VQ0uxr25hL2VNu4Z25VJQ7vQ4PSgUQmGp4RxWUYcM7OKWLO3msn/y+TOs1OZsbGQc3pEtfhczjSn5WCklB4Oj3tpT14AXpRS7pd3Ljxa41OFgsJCVAm9mh4LYxi5+fs60aIABzNr1izMqQMQau2xGx8BRWsgKLkfc+fOZdKkSe1oXYATodXbRkKIZ4A/A98ADzb+fKLx/CmN6eqriJ49E3Vycmebcnrha/QCvC3X5dNmZBDy+GMoQUEtXj8aQgiSQpKbHBeAMYnnclbpaNJLM1i0aQFr1qyhsLAQp9N5WDxNtFmH6qAdHI0CpQ3+bKKhXSz0iDSTU2ljbUE1EokEFCEwadW89PN2cittSOC9JbvxeHwYNCq6hBmxmLT8v+sG8K9rMtAogkU7y4gLNfD5HcMIM2n5z7I9PPb9RsrqHa1+zqcDQojtB/1eIITIb+lop7lUwGAgUgiRI4TYJ4R4RwhhOKTdFCFEphAi03OCpTDai7LSUlTGA3fjKnMo+4pav30aoGP48rtpyOQT2DJqxJc0hC+++b4dLArQXrTlVvFOYKyUsmltVAjxC/7ibYfflgdoN6Td7v/ybaHGUUdivOIKdCNHorRBG8ftdqPRHCi+WGIt4bafb2BQ9GD+PvrVZm1XF61kQ+l6but3J70HpLO3cC/R9jgKrYWMGjWqRaGo9Jhg0mOC+WFTIW6vpNbhYWgXCxVWF8mhJgoVOzvLwaRREWHScXmfWLQqvwM0aUgiVVYn4SYd3aLMPPbDJlIjTHx314im7bHLByRgMWrJLq2nb1wITo+Xv8/Zzher/W//njHBh5ULOEOYfNDvN3bwXNH4Sw9cDYwC3MBM4Fmg6aZISvkh8CH4dV462KbjoqaqEvNBwmcqQzBlheWdaFGA/VitVlb8tpSIKSceImBMG8bijz/B6XSi0+mO3SFAh9MW58UEHPrfWYk/sDZAB+Gz2ykeMhR8kti1q1FMJ1e/SBXV8lbJftx5+dQ89zzBD96PrrEA4+bNm5k+fTqjRo3i3HPPBaDEWky5rYwNZesBqLJX8sLy5xmfPJ53179Fhb2CHmE9ueaSa/F4PLzyyit4vV5yw3J4dc7f+OTCL4gzxzfNu720nq0ldWTEhaAogphgPYoQJFqMACRYDPSJCSJI73egDBoVJXUOFmSX8clvudwyPImbz0qm1u7m7cX+LJGPl+dy3zndyK+yYdKqGNsjiv6JFoa/shCDRsX1Q/2p9mpFcFn/A7acSUgpfz/oYZSU8rDbTiHE1e003X6xy7ellMWNY7/JIc7LqYaUkoa6WkIMB/R9FEMw1VVVR+kV4GSxYMECzAk9UelPXFVcZQzBGJXM4sWLOb8dxD4DnDhtyTaaB3wphOghhDAIIXoCnwG/tK9pAQ7D6/Nv4Rwl5dlXW0vJOeOouPW2k2gYVD08FefChVROubvpnMPh31Kx2WxN52J8sXww/hPOSRzH8P8N4Mttn7O8cBn/WvsGFl0o47qMZ0isvwDj4vUL8Xq9aLVaVjtWUGmr4NUlLzVL+a6wOnH7JD4p6RpuQjkko6rK5mJLST0r91Y1Sf3n19hRFEHfxBD+Mnsr/16ymxCDBrPOv3W1saCaLYW1jHl9MYNeWsArc7djd3lxe314fD4mDk4kKdzIBX1iiAz6Q9yFHSmT8MP2GFxKWQ3so3mi1imxsnI06uvrUanVCPWBlUXFYKa2JuC8nApMmzELb8LxF2I8Fr7EAfw466d2Gy/AidEW5+V+/Cq3m/Cr4G4EbMAD7WhXgENQDAZi1672r7ocpT6Rt6wMT04Ojt9+bzddlyPhs9mwz5+Pt7gY96asxpM+XFv82eyDBw/mgQce4MILL+STTf/lrv/dyQcffMCWBVsps5XilV5iTDHc2XcKLp+LnJpdXJJ2BWZtEBX2Cp7f+jT5pr3oe2u4M+MuALL3ZbNs9YE6QsOTwjinWwRpkQdeE5+UlNQ5mLW1mF3lVrqGm4g0afkuq5Csoloy4kIYkhhKQpAeKWHhjlIcbi/3jOlGUpiRh8Z1518LdjZ9ey7JLmPMG0twenwMSw5j1GuLuWNkCm9PGtihr29nI4RIFUKkAooQImX/48ZjPNCeAT+fAA8IIaKEEKHAVPzlCE5ZKisr0Qc1zz5RGUKor63p8P+9AEdHSsnPc+di6Np+1TT0qYOZ9dOcdhsvwInRllTpOuBmIcStQARQIaVsOZIzQLtyNKdlP5q0NCKn/9AsrbmjqH3hRaxffInxppsQCKRaja+8HPv8+Wj79EYIQVhYGAAzdk3DZrcRr3QhPDyc+wc9TJ2zlvm588iu3oHdY+eirpcyPG4EAFpFg1qrZlt0Fo+PfJweYb2w7rGTuzGf3VZBX5uLMKMWjUohJkiP2+vDJyRqRWFNfhW5Vf6dCJ900Ds6mMJa/+O8KivlVicjk8N55Lwe/JZTQWZeNS/P3c7nK/OQwPOztrA+v6bped4wNInnZvkdMk1jvExBtR0pJV6fRK1qyz3AaUEO/hUQARxat6wE+Gs7zvU3/J8n2fidou84xWPoSktL0ZhDm50Tag1qrY6amhpCQ0OP0DNAR7Njxw5cXjCFtd+2riYqhcr6BnJzc0lJSTl2hwAdSltTpUOAHoC58TEAUspF7WZZgDajG3Ji0fXS7abun2+gTk/HdNmlTedd27ZT8+yzBD/8EPrRo9GNHIl97jz0544l5PFH8ZaV41q5EuPEq9m+fTvFxcWMGTMGlUpFSkgqvzcsY8B1GVycdjFfbfuczNK1zea16Cz8Y+WL3NzndpJCklk8aTmKUFCE3zm4cPT1rOxShVoRmLUH3rpOj49ZW4tRKYILekSTV32gXqjD7SNzXw1qBZJCDeRX27G6XWTtq+X5mZvJSLBQ53ATF6JH4o9jKa07sKAQalBz01nJdIsy4/T4GJUWyV1ju9IzOojL3lvOrtIGFjwyhniLAa9PsmhHGf0TLWfEdpKUUgEQQiyVUh5Wzb2d53LjF6W8tyPnaU8KCwtRmcIOO2+0RFJYWBhwXjqRhQsXokvKaNcbOCEEhuQMFi5cyJ13tp8URYC20WrnpXHF5V38W0a2gy5J4JROu/CWl+Navx79+PEIVeulov8ouNato/6dd0GnQ1ZWYpx0HYrBgH3WLFyr19DwyWfoR4/GeOklGC+9pKmfKjwcba+egF+S2+VykZycTGpqKvFBfmG3EL0/M+PC1Esos5aSENSF/8t8DafXybqSteyo2k6tq5Yru09kRPzZzezaUlyHze0lOdRAfo2NruGmwz6cJP6ieEL606L9qdHQOzqYeIuhybHZUVxLdmkDdXY3JXVOXp23EwCPT5IYZqSoxoEErC7/ouKzM7ewp9zKV5OHc1aqv+hkeb0Tt9eH3eVP2/1hXQFPTt/M4KRQfrh7RPv9QTqZjnZcTlf27NmDx3R4Bp46JIrc3NzDNIkCnDzm/roIGdPr2A1biS+mF/MWLA44L6cAbVl5eQm4Wko5t72N6Wgqp9yFa81aQv/5OqZJ13W2Oacs2gEDMN12K44FC6l57nlQBOZbb8U8ZQoiKAjjJRcfc4wJEyZQVFREYqI/M+fxYU/xwKCp6NV6pJRMmXcrZbYypl8xmz6RfdhQuh692sDMXdNZlr+EpfmLmXbFbLoEJzWNOSAhhNxKG9nlDeytthNq0BJu0lJrd6MI6BlpxqhV86fukazYW0Wtw0OPSDOJFgPhJi0CGN4lFJfHyz9+3oEioKTOL3B3cITC6G4RlNTYMes1dIsy0e+FX+gdF0xFg4uYYH1Tu3kPjabe4SYh1J/Z1Cc+hNgQPeN6RnMmIYRQ418RGYN/a6fJY5RSntmKj0dh09YdEHK4ELAvKIYdO3ZwySWXtNArwMlg1aqV6K+4sN3H1SWk8/u8V4/dMECH0xbnRQ3Mb29DTgb6887Du28fmr59O9uUUxqh0xH6979hHTSQhg//ixIcgpQSVVgowfcdeVU/JyeHOXPmcP755zNw4EAGDmwe0KpX+7/4JZIKewVOrxOH10GPsF70COvFOV+PpMFdT2pIV0J0IUQZm6dnhxq0hCZoUasEtXY3EkmD00213YXLK6mw+Qstrmx0XDQKRAfpWLCrnESLgTCjFqfHS029kyXZ5SgCjGqBzeN3Xa7sH8vSXRV8tbaAfdV2hqUYcHuhzuEhWK/hX9f0JyXiQIp6iEFDiOFApknvuBBW/nncib34pyb/B5yLP7voJfzpy/fgF6j8w7J+YxaaPhMPOy/Du7Bm/R++ZmWnUVRUhN3hxGyJOXbjVqIJT6C0qpLKykrCw8PbffwAx09bIg1fBZ4VQpx2UYrB995D7No1aPv07mxTTgtMV1yBdDqpeuBB/yrM839lX0pXnGvXttg+OzubmupqCpYubVZtetGiRbz66qsUFvoV3xWhMP2Kn/jxip+w6EI5//tzuXz6BdzY+xYUFPbU7mbqkCfQqw3Y3Db+m/UB2yq2kl3ewLcb92HRa+gdE8yv2eXM3lZKSpiRs1PCaHC4mb+zlPhgPQKICtIfvERAVlEtO8oa6BkXzMX9YvFJsHlkU5t528qotLrZ17i1NKJrBK9d1Y8XL+3N/G2lTP48kwXbS5j67UaqrH5HyeuTbCmqxeH2nskZJlcCF0gp/x/gafx5OXBO55rVebhcLnJ2bkMbdfhOuS66G2vWZnaCVQEA1q9fjzkurUMSFoRQCIrvxoYNG9p97ACtoy0rL1OBGPwlASoPviCl7NIuVgU4ZdANH4avqgp1SgoNH30MbjfeoqIW244bN46u6zdgevHvVO/MZs9j1xBuiCA/Px+Hw0FFRQXx8f7of4veAliocVRT76rDJlTc2PtmqhyV5FTvIikkGYD5uXN5f8M7LNw9n6mD/4tPQnZ5A66DShVsKKymzuGl1ukFvCSEGBgQH0JKmAmtWuHKvn5V3S0ldbi9PlblVRMZcmD7RwKRZi0en8Tl8WLQqOmbEMKVA+Ix6dRcMziRtXur6Bpp5sWftpFfZeesruFcMziR137ZwQfL9qBWBL3jgpl5X/M4nTMEI1DQ+LtdCGFsLNbYfiIapxkbNmzAFBmPojMedk0TlUxpUSFVVVVN2XYBTh6bNm3Cazm8eGp7IUMT2bRpE+PHn3jB2wBtpy3OS0dLhQc4hQh9+aWmYozhH/0Hz+7dh227SekPktXpdCQOGECVlBRrvdz7y2R0Gj3zr11CSUkJyQfVlNpVnc3UhfdxXvL5TL9iDmqhQq828MSwp5uNfVbcSP5UfRG6fAMxQ6ykpEWybE8FLu+BVY49Vc3lRgrrHFRYXTg8PjLiQtCpVTjcXraU1KMICDdq6R0fQoRJS0XjCkp5g/9nz5ggZt43Ep36QEC3XqPi7UkDsbk8vLskBwFoFEGNzUViY7yL1yfJr7JxhrIdGIK/2nMm8FchRB2nSfHEjmDR4sUosektXhOKiqCkdH777Tcuu+yyk2xZgA2bt0JoBzovlgT/HAE6lVZv/Ugplx7p6AgDA3Q+Prud6uf/QtW8eczZvZuCgoKmLZKKigpefvllvv76awCMF11I9icf8YPewjDr2YyW51JQUEB0dHTTMu7/y3yTSbOuosRaworC34kxxRBhjOS99W9xw+yJlNvKAHA6nXz89ifo6gwgod7uxOn1MTY1nLhgfyqyXq2QEKInyuSvGqtTCSJMGsxaFYmWAxUr1u2raWqvVgQ9osx8NXk4EWZ/IO/do1LQKIKyegcNDg9frcmnrK65U6RVKSSHmwg1apj6fRZ3/G8tNw5P4n+3D0EC1TY3O0vqO+zv0Ik8BOyvhPgIMBC4BJjSaRZ1MrPn/ooSf+RsIm9MOj//8utJtCjAfnZk70ITGtdh46tD49i+M7vDxg9wfLQlVVqDv+bITUAcUAR8DrwkpXS1r3kB2gv37j1U3nY7xmuvOWrQ7cFIKal59jnc27bhWrMWT3Awm667BsPiJSg/zsDy0t+xjR2D1+ul6qB6LqGRkQD0JJ2K3RV8vftrHCY7roF2eoT15Psdfkcn2hjN2+Pfb+r30+6ZlNnK2Fm1g9/3LWNZ3hLM3lDUqDnrrLPY6TDg3FNJiF5NrcODViXoExNMWqSZapuLeTvLcHolO8qsAJTWOwgz+p0ab6OzlRZhJqu4jvxqO9+u3EuP6CAuOS+WrH21qFQCh9vHh8v28MFve/g02kxaVBCvXdUPk06NWqXw69QxbNpXw1Xvr2B9Xg2r9lRi0vqDdsNNWpLCD99GON2RUq496PddwB96vdzhcLB+7Wqi7jqy76ZLHsDPc98+iVYF2M++/DxMw9s/WHc/aks0Bb/lHbthgA6lLdtGrwFDgbuBPCAJeA4Ixh8PE+AkYF+4iMrb7yDo4YcImfrwMdu7s7Lw7N6NfcbM43ZecDiwfvoZAIYrr8SX3IVLtmwlKiICpMSTm0uXm2/innvuISgoCMfSpTR89DF9//YieRkZZGVlgVGyW7WLOkMN+wryWVO8CofXwbik83h2xAsEaYMAWJS3gDJbGd0s3UkN6cbfV/yVCns5Pful8+ygv5IW250NhXXsq7VT6/AvAqgVwY6yepJCjejUCnq1wOGRmDQKVrePTcV1lDY46Rcbwtkp4TjcPgwahSqbi7/M3MLeCiteH6zLD1Bb8QAAIABJREFUq8bh8XHPmFRuH5lKYY2dxdllFFTZyC5t4IZhXRjR9YCeR78ECyO7RrA0u5y8Shv/mLudEanhfHHHMBSlY1WNOwshRBKQQaMw5X6klF91jkWdx++//445NgXlKAX/tNGpVFRVkZ+fT5cugVDAk4XL5aK+pppgc8cJBKqDIikrL8Hn86Eop13eyhlDW5yXiUCGlHJ/sO5OIcR6IIuA83LS8OzbB14vnl27jqu94bJLCVOp0A48doylz+XCW16BOjaG8E8/QTbUY7j0UgpTuhLl9WK8fhLmObPR9PZnbUVGRuKzWimecheywYpt2DAsvdNRFIVVCcsodZagVjTc0OsW+kT2ZV9dAZPSb2xKnf5i62fM2DUdgJyabK7/7mqu8lzHl4ZP2FG9jdvn3ciIlLN57Zx/0cViYHtpPUX1Tmxuf9BuYa2NLaUNODySWLOWGGHFGhJCdoWN4jonHm81qeFmLAYNRq2KIV3CePXKDEprHdz39XocHv84NpeXyCAdkUE65j88ht92lbO9uI5hKf6UyFq7mzqHm8RQIx/cNIjiGgdWl4c6h5uc8oYz2XF5Cv8NyjYOVIAGf6zzH855mTN3Hr74fkdtI4SCMaU/v/76K3fcccdJsixAcXExhpAwhNJxIqRCrUFrMFNWVkZMTMet8AQ4Om1xXo70Cd1un9xCCB3wHv7l6TD8dVWeOh2F8ToK8803ocvoh6Znz+NqL1QqjAdJ/R8J56pVlF99DUiJecpkLH95Hk9enl9xV6MBrxd1125oMzKa9fMWFyOtNlAUTNdeQ8OqVfh8Pq4NuoH1wZncNOBWBsW1XCTtm+1fUmItZlD0UHZUbiW0IQy308PzA//GwsWLMNnNOKIbmLsqi13ZOxk9ZmzTm00CawtqiQnS0uD0kLt9M0s3/M6AcZcgTFGoFUGEScvq/Gr0aoXze0Yzf2cpNreP71blEW/Rc37vGD5avhejtvm/w5Kd5XyXWcDw1HD6JVi44K1llNU5+fnBUXSPDiK5UfPl5wdHEW46/csBHIVHgcFSym2dbcipwE/z5qMdeNMx28m4vsz4aW7AeTmJFBcXowvueP0VfUgExcXFAeelE2nLmtf3wGwhxAQhRC8hxPnADPyF1NoLNf7UzDFACP4Ym++EEMntOMdpjRACbf/+CL3+2I1bga+mBhrjQ4TO/4Vc88LfqHvtdVQx0SAE2h5ph/crL0d/wQVEfP8dqogIJkyYwM0330x9bQPmdRbc+Z6mtna7HZfLHx6VXbUTpCTCEMm60jVYPVZiUmK4++672b5sJ0H2YHaFbudPaRewZeVCavfuoKYoD61aaVLFDdar0Gn8d1qayES0Wi3CHIoE3D7J3io7EUYNCSF6ZmwpblqxUQlBg9PL0xems/bp8TwxoUez57RpXw31Tg95jVlEUUF6NCoFo7b5XV3PmOAzopbRUagE9na2EacClZWVFOTuQRfb/Zht9cn9WbZ0yZms/3PKUVJSgsrU8TWlVKZQSkpKOnyeAEemLSsvT+B3Jt7FH7BbiF9p8+/tZZSU0krzirU/CSFygUGcwIeor6EB985stAMHdHjF5ZOFr76e0vHnoYSFEzVnNuIE92AN559P9NLFKGYzSrRf5t585x1IawMhzz2LKjISVfTh8vdVTzyJZ08uxSlJ9B0+DI1Ggy3IyqfuD4mIjuLOGH8tkPr6et566y10Oh2PPvoo323/ihJbCQaVAa2iJVQfxsTe12E0Ghk/fjzfL/+WXEMOr218CZVRzbX9nqc+KIFB8RbWFlTj9kl8PgjWquli0RNqCEZjuAlLsB5pc2N1e7F7fPgkDEkMJafS1vRlYnN7efrCnigCVuyuIDHMyMAuBz74/nvLEHLK6pvO/XjPiDO9ivSReBj4UAjxL6Ds4AtSyvzOMalzWLZsGcHJfRCqY390qkOiQK1n27Zt9O4dEMY8GZSUlOBrrJ/WoRhDKC0t7fh5AhyRtnwKh0kpn5dSdpNSGqWUaVLK54AOc3eFENFAd6DF5HohxBQhRKYQIrO8vPyI41TeOZnySy/D/uOMDrL05OOrq8dbUoonOxs8nmN3OA403bpR+8prFPXuS8PX31D/7rtY/vEPtH36tOi4ADimTCYnrRsrKiqpfuJJ3Nu3Y9KY6F2TwbCSsylqFLYTQiCEQEqJ0+1k/t55AHikB5fPxbC4szg3yZ/MktIzma3RWQR5gol2xOKyOAmJTsbm8aFWCfZXJKp1ethYXIdEUGN34ZNQXOdgVNdwos3+bCOn14dWJTi3awSKhH/M3sa2ojpW5FSyLq+ah77dyI0frW72nEIMGgYlhTU5ukIIauxu7v9qPfO3/aHuurTAn/DrvOw96MjtNIs6iV8XLsYd1ePYDRvRJvZm6dKAisTJoqi4GI+u450Xjy6k6TMtQOfQFuflSAnuHbIf3pia/SXwmZRyR0ttpJQfSikHSykHRzam6baEpk8fhNGIqrFY4JmAOj6O6AXziV6yCKHVHrO9fdFiXFlZx2zn2pSFrK2l4ZNPcS5Ziv2nn/CWlWGdNh3pcBzWPumGG9C9/BLnS4n1y6+oe/P/SA5J4bYBdyIQ6Bu3t8xmMwkJCdhsNtZtWIfL698+urv//Tw/4kVuSL+JkoZiAPLr8iloyKdeXUePkt78ePkiJGo0isDp8aI9SEhOAAU1dvJqHIQZ1AxKsBBq0HJuWhTndPNnCs3cVsqi3RW4peS6wYncNTqV5y7qxV9n+33icT2jWLKzjLcX7cLdqODr9Hj5ccM+/r00h2dnbGbelmJ+2lzM67/sPOw1qLW7Wbmn8kzcJngPeBp/RqHmoOPYb7gzjAWLl6JLOP5VFBndk18WLek4gwI0Y29+IUoHZho1YQwlb1/AeelM2iVgVwgRDPhaaNvyAEIswR/P0hLLpZRnN7ZT8GvIuID7W23pIViefQbLs8+c6DCnHJq0NDy5ufjq61GCgo7Yzr1zJ5U33wIqFboxY7A89wyatMPjVwAif/geb14eUlGoevBhpM9L1dRHcC5Ziq+8jKC7727WXlEURowYgScujurycuxLllL7zzcY89ijnHXWWWgPcqzWmFewNzaXG6NvZHrvn/BJHwlBidjdNs77dgw+fPx89UJ6R/Thul43sDpnFZfccAnVdjfOxsygLSX19IsNpqjOQV61HbNORb3TC8B5PaJRDtoWDDVoUSvg8YFKgFGv5vrhSaSEm5i2fh9biuoAmDgokQe+2UCt3U1GgoUGp5uHvtmI2ycRwh8K9Mmtg7l7TCrjW6gcPeXzTFbnVvHmxAyuHNhxCp+dgBr4RErp7WxDOpOGhgb27t5F7IUt/8+0hC4hnRWzp3WgVQEOZm9BASrLsA6fR2UOY29+i/fSAU4Sx+28CCEK8K/TG4QQh+5zhwNfH+9YUsqxxzGfAD4CooELpZTuY3T5w+Jct57yy69AnZZGzKIFR2ynSkhAO2ggnn2FOBcuxNqjO5Znnm65bVgYqrAwHL/9jjcnh4b3P8Tywl/x5OWhO+usI86hTk7GeNFFOBcvwbV+Ay6XiyVLlpCSkkJaWhoen5vV9SuQOoligThz/IG+ioYwQzgur4tb5kwiISiRqUMe4/6BD6NX6/0rGsmhlNQ52VNlY3VeNRP7x1NYW9TkuPSKMjdzXAB0aoWr+sVT73BT5/Twe24VawqqSbAY6J9oISXCyIW9YxndPZInJvQgc281g5JCeX/pbtw+SZBezUV9YogM0nN2t0gSQ41Nc/h8ksveW06V1cWFfWLYXlJHauSR9T9OU/4J/FkI8bI8A5eVjpc1a9YQnNANoT7+BSd1WDzVViuFhYVNdb0CdBz7CvahSryww+dRB0eQn/WHCvc65RDH+1kkhBiDf9XlZ+CCgy5JoFRKefg6+okYJsT7QH9gvJSy4Xj7DR48WGZmtlzR1bV5M7aZswh+ZCqK8cxRQnXv2kXpuPMQRiMxy5agiorCW16O9etvMF55BeqE5qsA7pzd2L7/HvPttx0xhmU/0uul4eNP0PTsiX7U8RUdlD4frjVryHK7WbZ6NVarX/H2sccew2Qy8cLy55md8yNdgpMYFD2EZ0b85UBfKdlbm8s1My9Hrahx+9yMiDubf417F4nA4/OhEoJ5O0qpd3lRALVKNNU60qoE0UF6KqxORqVGEG5s/kXj9Ul+2laCze0lMURPqF5D77jD98hnbSxk4c4yrhoQz9CUcPSN2UxVVhdDX16AELDhuT+hVSn0e/EXPF7JssfPIe6gkgTtjRBinZSy5XzzDqTxxiUG/wroKVGM1WQyyf3vq5PFCy/+jbfmbiRo7O2t6meb/RLv/mUqEydO7CDLAoD/s8MUHEL4HR+gMgR36Fye+krqv3qU2qojx1ieCEIIm5TS1CGDnyEc98rL/tpFQogIKWWHVqBrVPO8C3ACJQdlBt0lpfyyreOWT7wWWV+Pr7qGsDdebwdLTw00aWmIiAhkaSmuTZsxjB9H/bvv0fCf/+Levp3wf7/XvH23roQ89efjGluoVARNvrNV9ghFQTd8OFs+/hir1YqiKE0HQFqof9k9vy6P/Lo8JvW6kdTQrk39VxT+hkTSN6IfWWUbCV0Zxd9W/A1dkIWojFEMTQgmRBdMvcuLD4g0aal1ePD6fNg9koIav45aldVFrd3NuoJq4i0G4oL1bC2pJ9Gip6zBxbPTN5NXaeObKcObhOj28/B3G/FJqLW5MGrVaNUK/RIs6NUK4WYtQToNOrXC3M3FXD+0C9cO7tKhjksnc1KKsQoh0oDNwA9SylOuAOyCJb+hih3S6n7uiDSWLPs94Lx0MFVVVfh8EkV/5K3z9kJlDsVua6ChoQGz+YxbaT0taEvMy5+PlGYspXz+xMxpGiePdhS9248qNQVP1iY0A/u399CdTuQXn+Pevh39uecAYLz6KlxbtmC+5eZWj1X72uvUv/seEf/7FP2YlkOTHCtWUv30M+jHjqFsyxbExReTNKA/O9/7N+XnT+Dcyy8neXQX8n/di8Ubxo033IjBYKDaUcWV3SdyYeol3PPLHeyqyeYvy5/m2p7X8+8Nb1Npq0Sv9jsBG8s28M+z32Lx3iUAOOtrKMpcxOLSbkQY1JDcH41KUG33YHN7SQ41UG51oRLQOyYYh9vL1tIGPBLyqu3kVfudGpNTzfk9o5m1bh8VDU72VljpHhVEiEHTpJI7qlsEK3ZXsiS7gmW7KgD4deoYthTWUlrnxBKtRaNSeOT7LLw+ySX9Oq4QXGcihFABtwNTpJTODp7uXWDtMVt1Aj6fj/VrVxN2W+v/n3QJ6Sxc8k0HWBXgYLKzszFHJZ4UGQwhFMyR8WRnZzNw4MAOny/A4bTFeTk0VScGf/DtjyduTsfi3Vfo/5l9fJL6pxPa9F5o03sdeNynD1E/fH/UPt6yMpTg4MOE7tw5Of7SA42vV0vYf/4Z765dNOzahc9k4ru+fRi0fDkZP89lV0UF9ePG8dr6lxlQMYwqXxVTZt7O2X3O5stt/yMxqAtPDn+GXTXZKChc1eMaXlz+PL7GmG91o7S3Dx+PLX+QN859h+3bt1NcXIzXZSe85yB0zhq8iiBYp2FAfAhFdXa2lfp3FxND9GgUhZVF1QDEB+spqnNgUAt6x4ZQZXXx4+YiXr6yL6/O3cGT0zfz5PTNnN0tgi/u8Af7fXb7MNbnV/PU9E0IIXC4vUSYdfRPtJAWZeby/n5n5dmLerG73Nri1tOZgJTSK4T4E60IyG8LQojrgBpgBdCtI+dqC1u2bEFjtrRJAE0X2509P+wM3KV3MNu2bUMJO3mB8prwRLZv3x5wXjqJVjsvUsrbDj3XqLI7qV0s6kBUsbF4KitRp6Z0timdjmvTJsouugRNnz5Ez53T7Fr4//sXjmuvpeHTT1EsIRgvuuiw/iGPPYowGrH98ANFsbEAZHVJJOi88SSlp6P6fTnX9pzEctXvFJUVUUYJxTv24ZM+XF4nGVEDuLbnJEoaSiiqLyQxqAv59Xk8OvTPWF31/HvjO5g1QVyQeiFVpVUUl5SQMupiNDo9CIFTH8qV6dEU1DpweHz0iw1pcl4Kax0U1x9YJNCqBBMz4pm9rZh1BTUoAjwStpTUYdarUQT4JNhcB3Ry3l+6m1fm7eAfV/Rh0tCkpvM6tYJJp2ZVbhV3j5HcOuIP8V76P+AFIcRfOiJwvjFb8UXgXOCIe5RCiCnAFKBZ9trJYPHixWji2yY0J9RaQhK7s3z5ciZMmNDOlgXYz5p1G/CEnDwZDHdIIpnrN3DDDTectDkDHKC9pELnA5e301gdhunaa1ClpKAdMaKzTel81BpQlBbLCwiDAW9hIc5Fi6n711stdlcsFixPP0Xc+nU477sXxScRKhXLuiQS/dbbVE2Zwty5H+DSOSkzljA6cQz3DXyYITHDGJkwmoV7f+X2flNYum8xH2/+D3n1e5FILDoLE1IuZHDMUP468u88OfxZrFYruSF7WNzwDV69kVCDBp1KsDCngrUFNfyeW4lPQr9Yf5CeD/D4/AG8OpXA45NYXR7sbv/ajqcxRt2i15C5txqfhJhgHf+4oh8vzN7KL1tLeGWePw2yvN7V7HlXWl1kFdTw265yVu5uFrt6JvMA8DhQL4QoEELk7z/aafy/AR9JKfcdrdHBek5qdVsWjdvOrLnzIb5Pm/t7Y3oz95f57WhRgENZuXoNmuiux27YTmiiu/L7ytXHbhigQ2j1J4AQIvWQU0bgevy1iE5pbNN/xJubi2vVarRH0Dc5FlJKbD9MQ52chG5I64L3XJs24Vy5CvMtN7d7TaLWok3vRdy2LUe0w3jVlfiqqtCPO/eYY5VPm86t837BfvVVzEqIp3jgALxlZaQ6h5MYl8yb494hXB+OEIKe4T2ZPO9WZufMYPmNmTw29EmW5S9hTclqJmfcw4SUCxBC8P6EjwCYtWsGha59CI+PzZXzGGgYjkEzHJdX4vR6iAvRE6JTo1IEvWOCSQjRM3dHWVPdI4tBQ0GtA5NWzaiUcHIrrVQ73KSGmegTG8yUUanc89V6yuqcPPLdBrYW17O3ogEBpMcFc156NFkFNWQkWgCItxh49E/d+ef8bKZ+t5HVT4+nvN7Jk9M3cUm/OK4YcEamw3ZY8KwQoj/+AqzHLnfeSbhcLlb8voyIO9p+h61LGcCsOR/zrzffaEfLAuzH5XKxc9tWosc8ftLm1MZ1Z8ucf+L1elGpOq6KdYCWacvtSw7+9Oj9UVE2YCNwS3sZ1VGEv/cOzpUrMV7e9kUiV+Y6qh+eijAaid/Vuuzwynvvx5ubixIRgemqK9tsw/EgHQ4q77kX944dRM2cgSoq6rA2iunImXiKyUTwww+1eM1XW4tz9Wr0Y8citFrGZmTAvF8wLV7CvW+8zptOJ16vl9FDhnDhwOaaC30i+nF9+k10s6SxsnA5w+NGcl2vG3F5XWhV/q2ARYsWUVVVxcWXXsyry/7OefsuJpmuKFoVJm0aLq9EETAiOYxEiz/lvdrmYsXeKix6NQcKB0B8iAEhBMlhRpxeH4O7hGLQHPigmdA7pmnbaGtxPQD9EkN58fK+BOnUDH5pAT4pefu6AZzbMxqDVsUNQ5NYn1/DOT38as7LdpWzaEcZe8obzkjnZX+mYQcxFkgG8hsDLc2ASgiRLqU8JYIJlixZgjEqCZWx7XFN2tjulJSUkJeXR1JS0rE7BGgVmZmZmKMSUXQnTwJDZQjGEBpJVlZWIO6lE2hLzMtpW5VO3aUL6i4nJkuh6dUT3ehRaPv1a3XfoPvuxT5rFvqzR56QDcdD+bXX4cpcB4C3sLBF56WtVD36OI65c9GdN56gKVPo8sD91FRUYv3oI6wff0La5ZdRXV3NueccvmqjUWl4ZMgTfLb5Y15c8TxmjZkl169Eraj59/q3KbOV4V7hQ+PVUl5ezlWR11G/z0poaCiPTH6KRTkVeHw+XF4oqXM2OS+lDU7qnB7qnJ4mr1qvVugRFUSPqCD21dj5LbcSi17DBb2iefGnrUxbX8iXdwylT3wI+ZU2FAWqrG76xIWQGGrE55MMSgolt8LKfV9v4NKMON66bgChJi0f33Jg1e3CPrEU19oZ2TWC3AorHq+PtOiOT9c8WTSW6HgWuAl/MdYi/MrXL0kpXUfrexx8iL+w634ew+/M3HOC47YbX337PTKp9SnSByMUFcZuQ/lh2jQefeSRdrIswH7m/7oA4k5+8Uslvg/zf/014Lx0Aie8cdwo4X8BcIuU8poTN+nURjGbifz6qzb1NU+6DvOk65oe++x2fOXlrXKoPAUFVNx2O8bLLiP4AX/FBJ/NhuPXBejPGYsS7I/7ECYzKAqWV/+BdkD7rsjrx4zGuWIFzl8X4FqzlvhtWwh5dCrq2BgMF5zPtcnJR+xbYi0hWBvMvFx/kHC0KYYKewV3z7udvXX+On/nZJxHxO5IysrKiBbRKCFqBgwYwKr8amzuA0kveyqtZMSHoFYENpcXs1aFRlFQhKTS7sF3kACjQaOgUQQRZg0A6/NrqLW7ya+yM+s+v/ielJJdpfV0jfI7Hooi+HbKWSzZWca9X62nX0LLd94GrYr7z0nD5vKQ8eJ8pISVfx5HZJCu7S/yqcVrwFDgbiAPSAKew1/raOqJDNyoGdWkGyWEaAAcUsqOUf9qJW63m+nTpxN03WsnPJaq2wg+/t+XAeelA5g28yfU3S856fMqif35YcZP/PnJJ0/63H902uy8CCEy8G8VXY8/7uV/7WXUH4WKa67FtTGLyO++Oark/sG4NmzEs30HNpe7yXmpe+NNGt7/AOO11xDWuKce8cX/kA7HYUrCri1bca5ahfmmGxG6lr9cpd2Or77+iKs15ptuxHj5ZVQ99DC6YUMBUEJCMN01pUmI7lAyi9eQW7uH19e8Qpw5jn31/hCpqYMfZ31JZpPjohEaUivTKKstA/zCU08/7S9hMGOzP3VbqwhcPolKEQhgb5WNneX+TKP+cWZ6RJkprXcgpeSXnaUMiLewu8KK2ydRN9r36a1D+C6zgHiLnpJaB09MyyLMpGXmxiLS44Ix69T85+bBBOs1jO0RxbYXzj/GXwa0KoXu0UE43F7MupMbUNrBTAQypJT7I5R3CiHWA1mcoPNyKFLKv7bneCfKvHnz0ITFobHEnPBY+uT+7P3lLXbu3EmPHsdfmTrA0SkpKSEneydR41suddKR6JMz2DL3/6isrCQ8PPzYHQK0G636hBVCROEP3rsFSAeWASagr5Ryb7tbd4ajRET4M36OEntyKIaLLiTsnbfQZBwQ2tOPHoV91uxmwbVCURAtlECouudePHv2oFgsmK6+qsU5yi69HPfOnUT9PAdtn+ZLsd6yMnxOJ4rJTMTHHzWdX/rFFyzJyeGilBQG39I8/Omfq1/hmx1+YWS1UDc5LgCDY4cipeTRIU+SW5tLhD6cnM05hBuiGNB/AN27d2fLli38OONHuoy/DJ0ujHO7R2LUqFErApUiiDBpMWlV6FQKO0rr2FtlpUeUmS3FdVjd/5+98wyTqsga8Hs6TB5mGIYw5JyRIEEUFQOrIqZVd826rnnX/XbVjeac1jViWjGBYl5zwISKGAAFFCQLkhnC5NThfD/qDjTDhJ6Z7unpmXqf5z7dXXXvqXNDVZ9bdapOkM9X5zKqa1tyi8vJaWMclLcVlnP7+8vwuISbjh/C5yu30zE9AQVWbC3EF1BWbi1i/x7hr+vhcbt45/KDw94/jqhp1a/orwYWYx585HEYcFhEZInLTfLgiTz+xDTuubvxPTkWw8svv0xqv7GI29vkZbu8SaT33Z9XX32Viy66qMnLb83UJzDjO8AkYDHwDPCCqm4Skc2EdPs2Z4J5eVT8uITEA8cjNfQQNCXtnpwGFRU19oBUh7jdpJx00l5pSYceSs686qfsqc9nDCTHGz7t0ksoff2NWv1uJDkZRBCvB//atZR/8y0pvz4J//oNbD38CPD7kdRUcr5fsLtnZ9eaNQDsXPrTbjmBwkL8yQm7DZeRHUZz5di/cs47pxPUIIIwf/O3LN+5jIO6HMx/5t2FOq62V075O78a/CvAOEwGAgF8gQBeVcor/LRN3rPOR5skL8cPyeGVRRvwBaEs4GfRpgLKnAjUviC4BLpmJJOZZBq4LpnJjOyWSfesFE4c0ZX8Uj8T+mXTt30ay7YU8svOEkZ1z6z1Xjz86SrW7yrhphOG4nXH/nmKIi8Db4nIjcAvmGGja4CXYqpVlNm8eTNffPE57S+s/6q6NZE49EimPXkdt996S5OvVdNSeeSJp3APjN1KHdJ3Ao9Oe9oaL01MfXpeDgUKgPeAd1V1U3RUih7bfnMa/iVLyLj5JtLP32etvSZHRKAehkt9CeblsfnACbjS0ug0dw7i8ZB2xumknVHzeoIlb71F8vHH0e75Gfh/+IFdf/8H/tVrACVxwgRwuyEYRJKSdhuAxcXFJI0axXHbdzD8xBMA+OLvV9Jrxkt8flR/OBQ6pebw+NFPoqpMP+xFlpb8yMB2g7h01oUU+QrpndmHAVmDWLZzKQCHdNvztutyuRAEX0UemtCG6TOeY9QRx9I+I5WhndrsjvDcOSOZdbtKcQkc0KMtCzfmk1fmxy2wMreI7SU+krxuBndMJzXRw2uX7jHgLjl0z/oQI7plMqJb7YZLMKjcPcvMNjt7fE8G50Q3EFyM+RvGWJmKcdjdiHGyvSWWSkWbx594gtSBEyI6g8XbrhvurM68+eabnHLKKRGT21pZvHgx6zdsJOvo2IV8Se4zhuWfPMqyZcsYOHBgzPRobdTHeOkInIwZMvqXiCwCngO87JmZ2qwJbjN+FIENta6F1WJQnw8tLzeOq8G6V3dXn4+dl1wGInh692bHueeBKp5Bg0gcOxZPly50WfKDmSfvdiNe04uxYMECvlmwgJ49ezIq0/zpl+7aiQLePD9Z3nZM6XM8IsKzbz/D6u/7ePOIAAAgAElEQVTWcNSRRzFowBAuHnEZX278guEdRvKbgadx09zraJOQQee0PbGCKmOVDPN6WfTt+6R27M220iDbSgtJS/BQUObDBQxon8aoLpl43C48LqFTehKF5X6WbClgQ34ZndIT6dF2T/DEYFB56NOVdGmbwsmj6resuMslPHj6SDbnlzKoU8uZWVSJiNytqpWLZkxw4pZFJHZZPBAIBHj4kcfxHnVVxGW7Bv+Kex6Yao2XCPDve+8nYeivEFfs1lkRt4ekoUdy7wMP8tjDU2OmR2ujPlGlizFOuc86UZ/PxizVnQVMF5EHVPXd6KgZGbIenkrJq6+SflmzmYUZVdzt25Pz9VzE40HC6KIWr5f0K68gsHkz/o0bcOXk4B08iOynntxtQEhS0m5Hh/zb76Dss88Z/NADbB08mDFjxqAVFfhWrOCwqY/z9SmvsnTuMg5aKZx53LnM+PZZHtx5Dx3b53BymvG3ySvfxTebvuLJHx7n0hGXs6N0OxO7H7G7vBJfCfdtv4uUkSlcc+A1jBkzhg9W5lLsM8tDl/oC/LTNOOsu2VbECUM64XGCK27IL2VDXhk7S3z4g0q3zGTm/LyDAe3TyCv28Yfnv2NjXikCnDSiy+6gjOEypYUGY3S4CLOqLsDrmJlFrYYPP/wQX0IabTpFPsxSSv8DWfT4E6xevZo+fZpuRdiWxrZt23jllVdo97uHY60KycOPYcaz/8cdt95C27b1j39lqT8NmhLhRH2+BbhFRMYD52HWfWjW7tZJB44n6cDwZvVEG9/y5VR89z0pp5y8uwcjXLS0FPX7caXX/cbvbt++XrIzrvgLwZISNvUbACK0fflFAmvXIhmZuLP2rpTFL79CcOtWMtav59RTTwVg5xVXUvLSy2TceD0Tzj+f/Ip3CAQCpKWl8cO3P0AaDO0xlOHDhwOwJm81ipKd1J6Hv3+A55ZOJ8GdSJeUrsybN4/MrplsKFyPS9z41U9iYiL+oLMMnUDvdqn8vKOYwooAKV43CW4XQVXmrNnBlsIyAgrtUjx4XVBc7mdniY+V24tZsTGfjXmldG2bzPkH9arWcKnwB8kv9bWkKc/1YZGIvAIsBRJF5KbqdopUJPnmxtTHnkAG1L26dEMQj5eUQYcy7cmnuO3WFj3yFlXuvPseUgYejDu19iHepsCTnk1yvwP4z733cfNNN8ZanVZBo70MVfUrVb0YMxZuCZPtv7+QXVf9ldJ336vXcRoMsvmgg9k8cn8CW7dGR7mKCjxDhoDHQ/mPP7Ll0MPYOulX++zWfuZzZD3yMImHHmp0U8W3fAWo4s7JQUSYMmUKJ5xwAiLCsfsdx2k7z+UPw/68W8Z3W81Ceu+seYttxVsBJS0hjUWLFvHRRx8x5/05PHfcSzw06VGeWvwEW4q3MKFXNh1SE5jYJ5tkr5spQ3I4fWRXThiag8ftwhcIsrHAGC59s1MpKPPjC0KJL8Dorpkc0COL8w/qzb9PGc5rlx7IwE7pjL/jY95YuHcU7bOf/IZxt3/EvLU790qft3YnA659j1vf/YkWzCmYlbNzMLOKutWwtTgKCgr4aNYHpAyK3swx76CJPPnMdFTjYsS92bF9+3Yee/xxksZUP2MyFiSOOZn7HnyIvLy8WKvSKojYYhSqWl73XpZK0s4+i5K33iahlvhIwaIiimfMIOnISXj7Nk33csWPP7Jt8hQkMQH8frSwGElKwtN132XvvQMG4A1ZryK4Ywe+hQsBSBw3bne6qrJr1y62b99OcWExixcvpnv37tz+1c0UVRSSk9qZlbuWE9QAX5zxDcneFPLy8ujVqxejRo2ib9v+3PTltby56nW2l+Zy1ZhrOaxf+92OupUUlPlwiZCW6OGwvtkAdEpPwuty8dO2QrJTE+jXPm33/qfsb/xcnv1qLZvzy/h42TZOGLHnPBM9xrZ3u4Q731/GywvWM/38cWzYVUKFP8jyLQWNvNrNF1XdhuOQKyKe6qLJt1TefPNN0noOw50cPV+mhI59yA+YZe3H1DNGmgVuvPlWkgZMwJMRuZXDG4u3bWcSe4/h1tvv5O47b4+1Oi0eaWmW/+jRo3X+/PnV5hU9P5PiZ54ha+rUJjMGGkPho4+Rf/MtJIwdS4f/vbo7vT7DRvWlYvFitk05Hu+QwWTeeisJI0eAqpk6LXX7hJS89j8Q9prO/eGHHzJ37lwOPPBAgsEg48aNIzMzk0s/uID5W76ld2YfVuet4pIRf+CC4ZeYcy8spLCwkOzsbGbOnMmO/B383G8FJw+6kM27OtI9M5mDeu0ZpSz1BXj9x824gFOGd0HVWcTO0dkXCNY4nbnMF+DjZduY0DebjOQ9Q3gBJxp1myQvx0+dw+IN+Tx4+kimDMth0YZ8+nVII7UJF6MTkQWqOrrJCty3/A6Y2EO7UdU1sdAlNTVVi4uLoyL7mONOZF6gB2nD9+1tjCSFnz/DBQf34c477B9dfVi3bh2Dhw2n7bkP4knLirU6e+HP38auGVewctkSOndu+GCEiJSoavgLgLVCWtQyoHVR/Nzz+H5cQvncuXFhvCRNmkTprA9Ju+D3e6VLcnLUVgdL2G8/chZ+jystdY+TbxhGSyWJB08g/+FHmLNlC+0POICRI0eS5ESuLioq4rDDDiPTmZH0n8MfYH3hejqndWbOhs/xuhMIahCXuJg2bRr5+fm4XC6CzkypG8fdRpk7mc27zEKvq3cUI8DK7UUMz8nALRBQWJFbyMJNBfRsm8L4nqZxq20dliSvm2OH5ez+7Q8Eue7NJeRkJHH54Sb6+JPnjmHZlkIO6mOiY9c1lbolISJHAU9ihpBCUaBFhdP1+Xx8PvtTsn4X/Vkj3l6jee3N6dZ4qSd/uervJI04ttkZLgCejA4kD5vEVf/4F88/+3Ss1WnRNPuVtUSkn4iUiciMxspqN/VBMu+6k9TfnBoJ1aKOt09vOrz2CimTj2myMgPbt7PzT3+i+JVX6965GopmPEfx4/8l46mn+eCDD1ixczmp/ZI58sgjWbx4Mf/73/9275vsTaF/1gDSEtJ5bOHD/H32FXy+fjYAnTt3JiUlZbfhAmb4qXNGMr8elkNOeiLf/rKL7zbksbPEx8b8UtqnGcdaX9D0JlYE9p0evqWwjG1FtY9wrs4tZua3v3DPhyvwOzKy0xKZ0Dc7rN6nFsjDwM1Aqqq6QrYWZbgAzJs3j6SsTrhToz9jJLHLQNb/so7c3GYRxiku+Prrr/nwk09JHfPrWKtSIyljT+HNt97hu+++i7UqLZp6GS8i8hsRuV9ELnIizYbmRWu+2lRgXiQEeXr2JO3MMxCnJ6A1UvDwI2w5+BB8q1ZVm1/+5VzKP51N4QMPNkh+6q9PIumYowmedy5Tjp/COe+cxsUfnE9a51Q6dOjAiBHVLyY1qedR9GzTi/5ZA7j0/Qu4q/Rmkg7xcOmllzJgwADatGnDrFmzCAaDJHrcdEhPom2ylwHt0+iYlkhKgptDemdz3OBO7JeTwfFDOnFw770nv5X6Any6ajsfr8xldW4Ro275kP974ft9dOnfMY1rjh3Mg6eNxNOyV84Nl7bAY6paGmtFos2HH30MnYc2SVnictOmx1Bmz57dJOXFO8FgkAsv+yOJ48/EldB823BXUipJB5zGhZf90TpkR5GwW2YRuQoTXRZMdNlvRSS0G/msSCrmlHkakAd8HAl5xS++xNZjj8O35udIiItLSt94E/+an6lYuKja/OSjjyLj2mto90jDus09PXrQ/on/Mv6yyxgyaAgHdpnAoHaD6d+1P5deeikjR44kGAxSVFS013GXjfoTr5z0Jp3TurBw23egMGPpM3To0IETTzyRgoICVqxYwa6CInyBIGmJHo4e2JFubVPYWlTOwk0FBFQpLPcTCCqpCZ59HHoTPS46pSfSuU0Sn6/IZWdxBQs37DszQET4/YReHDfcTqBzmAa0Cofdd2d9jLvLkLp3jBD+joN4f9ZHTVZePDNjxnNs2FFM6tDIxJqKJqn7TWL1xlxefvnlWKvSYgnbYVdEVgPHqOoK5/eNGIPlcFVdJyKFqhoxD1IRaQPMBw4HLgD6qmq1BpKIXIRZVIvu3bvvv27dumplbj12Cr6Fi8i87VbSzo1cvJJ4wr9xIxULviN58jGIp/EuT1pWxq5//gvvoEGkX3RhnfuXB8p57KVH2bliF70n9uSJjY9y40G3cliPI3bv8+mKj7npi+s4JO0wbvytWQdj9erVlPqVJaVJJLhdnDjUTMUOBIN8umo7yV4X+aV+8sv9DOyQxsgumfgCQUTYHUkaoKTCzy87S5j8wBco8MGfDqF/nKyQGyuHXRH5AhgLrAO2hOap6iFNrQ9Ex2G3rKyMzKxsOlz8JK6kpvGVLN+yCu9nD/HLmpVNUl68UlJSQvfeffFM+gtJXQfHWp2wKF27CNcXj/HzquW7/f7CxTrs1k19/r3aA7vHGlT1ehHJBb4QkUlEPkTAzcA0Vd1Ql5+Bqj4OPA5mtlFN+2U9+CDlX3zRpD4vqkrR08/gapdF6vHHN1m5NeHp0gVPl32nPTeUih9+oOSllyExkfSLLkT9foK5ubhzqvp2Gq7/4mo+qviA/dJG4S4QSnzFLNu5dC/j5bD+R3BY/yP2Oq5Pnz6U+AIsXbpl9wq6AEUVAXKLK/baN9XrpswX4M2lWxCgZ9tkRnVti9slzF69nV0lPsb0zCLB7aJ3+7rbh+e/Wcdt7y3j3t8MZ9LgTny9Zgcd2yTRK7vVtC1POFuLZs6cOaR16tlkhgtAQode5G7fzvr16+nWrUUumxMR7rr739ChX9wYLgDJPYdTtLgr9z3wIP/421/rPsBSL+pjvKwD9sMsXAWAqj4kIiXAbCDsZUhFZDYm0GN1fAn8ETgSGFkP/erE27sX3t69IimyTvyrV5N/7XWgSspRR9UrgnQ8kDBqFG3+9U+8/cwy6jsuuZSy9z+g3ZPTSP7VpH32796mOy5xcfZJ5zC++0EclXsUozruH1ZZKV43vx7WGZfsiXe0Ob+UtAQ3vbJSaZvipSIQpFdWKmW+AAL4g8qqHSX0apdKdmoiOW2S8AWUJ84ZTXpSeCsbf78+j6JyP0s2FZCTkczp//2a9CQPi68/KryLFOeo6jOx1qEpeOV/bxDsFtEmp07E5Sa1z/689dZbXHbZZU1adryQm5vLv++9j4zT7qp752ZG4oFnc+vtV3PxhRfYsAERpj7GyzMYg2JhaKKqPiki5ZiekrBQ1Ym15YvIn4GewC/On1Qa4BaRwao6qh46xxxPz56knHkG7vbto2a4BIuL2fX3f5A4bhxpZ0fc9ahWxO2mzR/2NLqSZAIfSmL1sZQuG/UnLh15+W7jY2zOAWGXVVBmYhRlpSSwbFshgaDy07ZCfAGlXWoCOW32dM0med2cODSHX3aVUOILkpVi9BnZJZORXeo3zfmm44dywogujOuVRWGZn17ZqQzu3PJD/YhInevjq+onTaFLtAkEArz8yiskHd/00Q6k1zienD7TGi81cNOtt5PU/yA2PVb3sHRzo8c/3iax9xjuuOtu7rz9tlir06JolovUiUgKeweCuwpjzFyqqrXOK6xtkbqWStns2Ww/82wkI4MuS3+MalnBggIqvv+exAkTEPe+M2VVFS0qivgCekFVXlm0kaDCrwZ04IPl23bnZSZ5OGpgx30cdFsaTe3zIiJ1ebarqvZuEmWqEGmfl1mzZnHahZfT5oz/RExmuKjfx/bHz+fHhQvo1atpe4abO7m5ufTs04+259zPxqnnxVqdetPjH2/jz9tK3vNXsmHdz7vXuKoL6/NSN/WeByoivWvYuohIROaVqmqJqm6p3IAioKwuw6W1knjQQbT5219p9/BDUS9r55/+j+1nnEXxs9OrzRcR/D//jH/9+oiWK0C71ATSEj2kJ3oY3jmDHm1NL09FMMjsVdup8O+7roul4ahqrzq2iBguIpIoItNEZJ2IFIrIQhFpusWNgLvvfQAZtO8wZ1MgHi/JgyfywNTYR0dubtz/wIMkDzgQT3p2rFVpMJ7MjiT1GcODU6O/8GFroiHGxipgpbOFfv8FKBeRV0WkY+RUBFW9oaaZRvWWFQhEQkyzQrxe2vzfn0iaODHqZSUecACSmYl30MBq830rVrDt2OPYemRkl1YXEY7o14EpgzvhdbsY3DGdA3u2Y8rgTvgDsLWonB0lFbXKWLuzmNd/3MTmgrKI6mZpNB5gPcYPLgO4BnhJRHo2ReErVqxg7ty5MZ2CmzjiWJ54YhoFBS03XlZ9qaioYOojj5Iw4rhYq9JoEoZP4f4Hp+L3+2OtSouhIXNlLwQmAjdgGpzumMbmK+Az4E7MwnKnRETDCLLrn/+ieMZzZL/4AkkHjo+1OnFJ+iUXk37JxTXmu7Kzcefk4OnTNKMJ6YkeDu3TjrxSHx3Ta/cp2lxQTqkvyLai8r38Y0JZsbWQmd/+woUH96ZzZnI0VLZUQVWLMe1JJW87Q1b7A2ujXf71N99K0ojJuLyxW/jMm9mJxB4jeHDqVK7+5z9jpkdz4u2338bdtgsJ2d0BMwQTryR06kN5ShazZs1i8uTJsVanRdCQnpcbgQtVdbWqVqjqKuAy4FpVXQachzFumh2BjRtBleCOHbFWpcXizsoi59uvaT/z+SYrMzs1kb7ZaXX6vOzfLZODemYxpFPNzrb/nrWcp+au5am5rXchw1jj9Nz2B5ZEu6yff/6ZN954g5RRsX+7TxhzCnf/+z9EK+BkvPH4k8+g/WKyjFB06HcI/32qVUzcaxIaYry4MM6zoXRnT4C2YpppwMd2jz9Gx08+IuW4KbFWxRIDEtwuurdN2b1OzOb8Uv784vd88/MeY/YPE/ty7LAcTh/TPVZqtmqcsCPPAc84L0OheReJyHwRmR+p7vcbbr6V5OFH405Kq3vnKJPQvgeezoN4+JFHY61KzCkrK+OzTz8mpX/4sxGbOyn9D+SD99/D5/PFWpUWQUOMl/uAT0TkVhG5RERuwSzff5+TPxkzhNTskKQkvP37x1oNC+AL+Jj63f3MXhf+TNv8Uh/vL9vK2p2ReTN9feEmXl+4iX/PWr47bXi3TKaeMYre7WP/Z9bacBz+pwMVmLWe9kJVH1fV0ao62hOB1aG3bNnCyy+/TMqo2C8eWYl39Mnc+e97qKio3X+rpTNnzhxSO/XCndxyliRwp7UlOasz33zzTaxVaRHU23hR1buA84FOwAlAZ+D3qnqnk/+6qjbpTIHWSMXSn/CvXRtrNRrMgi3zeOqHJ7jhy2vCPmZzYRm7Sn2s3hEZ4+WUUV05d3wPrpkcP6t2tlTELPwzDegInKyqUX89nfrwI6QMnIA7JSPaRYVNYqe+aHonXn21YVHdWwqfff4FwU6DYq1G5MkZxOdffBFrLVoEzXKdl8bQGtZ58W/YwJYDJ4DXQ5dlPyHe8FaKbU6U+Uu5Z95d7Nd+OMf1PTGsY/zBID/vKCGnTRLFFX48LhftUqtfDK8lEqvYRk2BiDwKjACOVNWiuvZv6DovdYUaaY60tDY6HCZOOpql6fuTMuDAWKsSUYqXfsb+wRW8/9brte5n13mpm4as8+IVkRtFZI2IlDmfN4pI6/kXiTGujAzcXbuSMHQoRKD7PBYkeZK5evz1YRsuYAIs9nOGcz5ZtZ0PV2wjEKy+YfcHgqzZUUypr+VNjW9piEgP4GKM8bJFRIqc7cwYq2aJEcuW/YS3fY9YqxFxvNndWbp0aazVaBE05J/vLkyE2Usw8Y56ANdiVsT9S+RUs9SEKz2dnLlzYq1GzEjyuslOTSDR48JVw4v0T9sK+XFLIV0ykjikd/wucNUaUNV1mHUILRb8fj/bt26mS0aHWKsScTyZOWzZuB5VjctewOZEQ4yXU4Hhqlo5RWO5iHwHLMIaL5YmwOMSJvWvvWHLaZPEul2l9Gib0kRaWSyWSJCbm0tSahvEHX/D4XXhSkjC5fGya9cusrKyYq1OXNMQ46Umc9GakZZmQ3ZqIlMGd4q1GpZmRqX/SNv2HUk99Q48zfTtvvTn7+i6bhYLvmp9zp25ubkkpDYfJ+pIk5SeSW5urjVeGklDpkq/DLwlIkeJyCARORp4HXgpsqpZLBZL5NmxYwelJSW427SPtSo14m3fkxU/LWmVzroFBQW4k1puj6knMZXCwsJYqxH3NMR4+RvwESYEwALgIeBT4O8R1MtisViiwqJFi0jP6d2sfQ7cqW0JKmzatCnWqjQ5ZWVliKf2UB/xjHi8lJXZ+GqNpSHrvFSo6nWq2ldVU5zPa1W1PBoKWiwWSyT58su5BLP7xFqNWhERUrsO4KuvmuV6n1ElEAggLnfdO8Yp4nITaIEBgpuasHxeROTwcPZT1fCXS7VYLJYY8Mobb+PpfXSs1agTf6ehvPbm25xySrOLcRtVRATVYKzViBp2plFkCNdhd1oY+yjQNKGELRaLpQGsXLmSlStX0P7wf8RalTpJGTiBN2f8mcLCQtLT02OtTpORmJgIgZYb/0cDPhIS7LJojSWsYSNV7RXGZg0Xi8XSrLn+5ltJ2e+ouJiG62mTTVLPETzw0EOxVqVJSU9PJ1BeEms1okagvIQ2bVpOzKZY0RCHXYvFYok7vvnmG9586x1SRp8Ua1XCJnHcadxx591s2LAh1qo0GR06dKCiKC/WakSN8sJddOjQPKfoxxPN2ngRkdNE5CcRKRaR1SJycKx1slgs8UdRURGnnnYmyYf+HldS/ISM8bbrSuKIY/nNGWe3GifPTp06UVaYj/pbXmTtYHkJAV+FXeMlAjRb40VEJgF3Ar8D0oFDgDUxVcpiscQdqsqZ5/yOkqy+pA6Kv/ef1HGnsmzTLq6+9vpYq9IkeDwecrr1wLej5fU2+Xasp1vPPrhczfavN25ozlfwRuAmVf1aVYOqulFVN8ZaKYvFEl/cctvtfLbgR1IOuyjWqjQIcblJnXwVUx9/gldffTXW6jQJ48eNo3zTslirEXHKNy7joPHjYq1Gi6BZGi8i4gZGA+1FZJWIbBCRh0QkOda6WSyW+OG9997jznvuJW3KP3B543fhM3dqW9KO+yfn/f5ClixZEmt1os7xxx6Na/33sVYj4rg3LuT4Y4+JtRotgmZpvAAdAS9wCnAwMAIYCVxT3c4icpGIzBeR+bm5uU2npcViabZs2bKF0886l7RjrsLTJv4jiyd26kvihHM47qRTWvwKrVOmTKH4lyUEinfFWpWI4S/IpWTjCo4+uvmvMRQPxMR4EZHZIqI1bHOAUmfXB1V1s6puB/4DTK5Onqo+rqqjVXV0+/bNN16JxWJpOi7/85V4Bk4kqfvQWKsSMVKHHkm+tx133nV3rFWJKhkZGZx66qmUfP92rFWJGKXfvcXZZ51Famr8OIw3Z2JivKjqRFWVGrYJqroL2IBZ+G73YbHQ1WKxxB9btmzh7bffJmXcqbFWJaKICEkHncW99z+I3++PtTpR5abrr6Vs0fv4C+K/N92Xt4WyJZ9w3TX/irUqLYbmOmwE8BRwuYh0EJG2wF+AlmOGWyyWqLFgwQLSuw/EldjyohN723VD3V7Wrl0ba1WiSo8ePbjyir9Q8tHDcR1dWzVI6UdTufpff6dz586xVqfF0JyNl5uBecAK4Cfge+DWmGpksVjigtTUVAKlhbFWIypoMICvtLhVDD9ce/W/6JqqFH/zSqxVaTDFc2fSu10yf//rX2OtSoui2RovqupT1ctUNVNVO6nqn1S1ZXupWSytFBHJEpH/OQtSrhORMxojb/z48bjK8ilduyhSKjYbihd9wKAhg8nJyYm1KlHH6/Xy/ttvwNIPKF76WazVqTfFP3yMa9XnvPPGa7jdLTdSdixotsaLxWJpVUwFKjAzDc8EHhGRIQ0VlpiYyPPPPk3x+/+hfGPLWS+kZPlcKr55kRlPhRMrt2XQuXNnPv1oFr45T1GyfG6s1Qmb4p8+x//1DD7/5CM6duwYa3VaHOFGlW4RbOjSDYCuG9dHvSz/hg2oz4+3V08AguXl+H74kcTR++PftBnJaEP5F1+QdOihaH4+JCdTsWwFga2b0cREpLwcd04OwU2b8Q4bStDno2zWh3j69MaXux0Ki/D274t/+QoCO7bj6tELJYj/u4UQDED79lQsWAAuwd2hI55ePfH9tJzgzh2QlIimt0F++QWCCmmpVOzYSUJ5GWRlQXEJeL1QXk5ZURESCJDYpzf8/DPFZeWUZWXiLSjEj1CcmoLL5yO1uBhxuXFVlJOXmkZhRhu2Zfj5fMJ+lIsPbzCBnUnbKHGXU5C0i4rEMqgmsKqr1EViWQo9SnrSxpdJECWddApdBSS4Uxk09rckp+2Z9ur1uqgo9+NyuYy4BMxfYKILf3kQl8eNy733Y+4vL+fDn7bx+YqdYd/PZIHsdA9t0xIYlJ3GJ6u20y7FQ7+cTE4Y3pUtheWkJ3kIBoNszC9jbI8sEhM8DO+auVtGcbmfUl+AMl+AnIxk3C4Ju/yWjIikAicDQ1W1CJgjIm8CZwMNDv88adIkXpj+NGecfS6+/X9N6v7HIa74fPsN+soomTsT1sxl9sezGDRoUKxValKGDRvGJx9+wBG/OpriilJShx0Ra5VqpXjRBwTnv8Rnn3zEwIEDY61Oi0Ti2RGqOkaPHq3z58+vNq/SeIHoGjDBggI2DTIvjR3nfI63Vy82jz2AwMaNJE6aRPlHH0FiIpSVQXo6FBeDxwMVTRPLI/SOC/tO45Jq9qtk8X7DmD9uLEe//S6dN2+uVn7Q5cIVDALwxUFjWTFk2F75AQkwq+tbBFyBPYWFFNi2vB3jtxyCK6RjsMCdz+YhZRzXo/7e+iJ7Cgl93t9dtIk5K7bXW159uPvk/Th1dDdUlQPu+JjtheUEFE4f253bTxpWt4AQRGSBqo6OkqoxQ0RGAl+qakpI2lXAoap6XHXHpKamanFxcVjyV61axRnn/I6VGyUKiKYAACAASURBVHJJnHAuST32i4jeTYGqUrJsDuVzp3P4wQfx30en0pqXg1i2bBmHHjGJQN9DSR1/2l51uzmgGqR4znMk/PI1n338If369WuQHBEpUdWW79TUCFpVz0uT4XKZLRhEvF6TlmwWB3alp5nfHnPpxetFRZrUeKmuuoebluDzgdNg1NRsqMhuo6jC60YQggQRBEUJShAV3VeAc5BHPSFJ5hi3uvEFyk1aIxqsymNVlXJfsMFywiUlwR3y3YPLVUEgoKQlxmcPQJRIAwqqpOVjYprtRkQuAi4CSEioptuuBvr27cvXcz7jxRdf5Iq//ZN1M9c1Ut2mZcDQ/Xj4hekcfvjhsVYl5gwcOJBFC+Zx9JQTWHRntXZtzBl9wIG89918srPjf2HE5kzr6nn5wx/g9TebZNgoWFQEgQCujAzzOxgkmJuLp2NHgsXFkJyMf906vD16oGVlkJBAcMcOgkVFiMdD0O/HlZFBsKAAT4cOaDCIb8VK3O2zCZSUoGVluLKzCW7dihYVQceOiCqBNWsIBoO427ajbMkSXG4Xrqy2uLp2xb92HcG8fFwpSVQkJxNYvwGP3w9pafi2bYOSUtxdOhPIz8Odlo4WFRHctQtKSvGMHYP/xx+pyM2Ftm2hqJhCXwVlImhKCsmbt4DLRboG2LYzj7zsLLakVPDN0C6QFCBQpuTJLooDhRSnl1CUVlC9x5UPXPkuuhT1Io1UXLhoQyaKH5F0Bo/6NalJyXhShIBfcadlESzfiXjScHlcmIWZg4AL/H7wuNi3ID+zF//Cp8sKSTFF4hVI8kJCImjAjJplJieSkuxlZ2EFAzqk07FtIm3Tk+mbnc7sn3LpmOmle7t0RvTIoqjUT6LXRTAIBaUVdMtOwwVkpyftKTUQxB9U/EElNcFdbyOslfW8XAlMjETPSyjx2t41tx6GWNPc72Nj75fteambVmW8WCzxTAs2XlKBXcAQVV3ppD0LbFLVan1eGmq8WCzxgDVe6sbONrJYLDFFVYuB14CbRCRVRA4CTgCmx1Yzi8XSXLHGi8ViaQ5cBiQD24CZwKWq2vLDJ1sslgZhHXYtFkvMUdWdwImx1sNiscQHtufFYrFYLBZLXGGNF4vFYrFYLHGFNV4sFovFYrHEFS1uqrSI5AK1rUKVDURyWdVIy4uGzOYuLxoym7u8hsjsoaqtd3nVEEQkCJTGWo8QPIA/1kpYGkRzvHfJqmo7F2qhxRkvdSEi8yO5Vkak5UVDZnOXFw2ZzV1etGRaYoO9l/GLvXfxibXsLBaLxWKxxBXWeLFYLBaLxRJXtEbj5fFmLi8aMpu7vGjIbO7yoiXTEhvsvYxf7L2LQ1qdz4vFYrFYLJb4pjX2vFgsFovFYoljrPFisVgsFoslrrDGi8VisVgslrii1RgvIvJHEZkvIuUi8nQ1+UeIyDIRKRGRT0WkRz3l9xSRd0Vkl4hsEZGHRKRRgS9F5DQR+UlEikVktYgc3Bh5IXL7iUiZiMxohIxEEZkmIutEpFBEForIMQ2QkyUi/3POcZ2InBFrnWqQ3ehrVkVeVO5tvCAia0Vkm4ikhqRdICKzY6hWtYjI0yJyS6z1aImIYY2ILK3ncT1FRBvbxlril1ZjvACbgFuAJ6tmiEg28BpwLZAFzAderKf8h4FtQA4wAjgUuKyhyorIJOBO4HdAOnAIsKah8qowFZjXSBkeYD3mPDOAa4CXRKRnA3SpADoCZwKPiMiQGOtUHZG4ZkDU72084Qb+L9ZKWGLKIUAHoLeIjIm1MpY4QlVb1YYxYJ6uknYRMDfkdypm6fGB9ZD7EzA55PfdwGON0HMu8PsonP9pwEvADcCMCMteDJxcj/1TMYZL/5C06cAdsdKpKa5ZtO5tPG3AWuAfwE4g00m7AJgdss+BGIMx3/k8MCRvNnAz8CVQCMwCsmsp73dOHS3EGIoXh+SdB8ypsr8CfZ22wec8p0XAW07+IEeHPGAJcHzIsU9jjN13nPK+AfrU47xucZ6RIuAtoB3wHFDg7N8zTFlrgSNDfu9+foEkYAawwzmHeUDHGDwHTzrn9hrwUDXPSE36/+LcoyJnG495Gb8GEx5mG/AskOHs39PZ/1zn2O3A1SGyE4H7MC+5m5zviU7eRGAD8DdH7mbgRGAysALzDP8rTFk1PmvO98nAUue52QhcFeu62ly31tTzUhtDgEWVP1S1GFjtpIfLfcBpIpIiIl2AY4D3G6KMiLiB0UB7EVklIhucYajkhsgLkdsGuAm4ojFyapDdEeiPacjDpT/gV9UVIWmLqN91j7ROVWVE9JpF697GKfMxf9ZXVc0QkSzMn/8DmD/v/wDviEi7kN3OwBglHYCE6uSEsA2YArRxjrlXREbVpaCqPo75c71LVdNU9TgR8WKMillO2ZcDz4nIgJBDTwNuBNoCq4Bb63FepwFnA12APsBXwFOYXuGfgOvrIasmzsX0TnZzjr2EJo4VJSIpwCmY6/scpv1MCPPwQ5zPTOe+fIUxDM4DDgN6A2nAQ1WOmwAMAI4ArhORQU761cABmF7z4cBYjCFUSSeMwdcFuA74L3AWsD9wMHCtiPQKU1ZtTMMY1unAUOCTMI9rdVjjxZCGeXMJJR/TpR8un2P+dAswVvp84PUG6tMR8GIq9sGYSjCS8CtATdwMTFPVDY2UsxdOY/4c8IyqLqvHoWmY6xVKfa97pHWqSqSvWbTubbxyHXC5iFQNOHkssFJVp6uqX1VnAsuA40L2eUpVV6hqKaZnbERNhajqO6q6Wg2fYQyPhvoZHYB5du9Q1QpV/QR4Gzg9ZJ//qeq3qurHPIeVuoV7XqtVNR94D1itqh85sl7GPC/hyqoJH8Zo6auqAVVdoKpV62K0+TVQjrkX72DqxbGNkHcm8B9VXaOqRcA/MQZRqF/MjapaqqqLMC9Kw0OOvUlVt6lqLsbwPDvkOB9wq6r6gBcwQVXvV9VCVV2C6S0JV1Zt+IDBItJGVXep6nf1vAathhZhvIjIbMd5q7ptThgiijBvZKG0wXTd1SlfRFyYXpbXMEMh2Zg3rjsbqG/lG9CDqrpZVbdj3qomN/QaiMgI4Ejg3jCuR9jX1Dn36Zhu9T+GIzuEWq97Q2mkTqFy6nXNwqTe97Ylo6o/Yv74/1ElqzP7Rodfh3nzrWRLyPcSjEGBiDwqIkXO9i8n7RgR+VpEdopIHuZ6ZzdQ7c7AelUN1le3MM9ra8j30mp+10dWTUwHPgBeEJFNInKXY/A3JecCLzmGVxnwqpPWUKpej3UYP7iOIWnh3pd1TlolO1Q14HyvrMPh3peqsmrjZMyzuU5EPhOR8WEe1+poEZ7aqjqxkSKWEFJpnBkQfZz0OuU7Dr/dMWO25UC5iDyFGbv+W0P0FZENmLHQ3YfVtn8YOv4ZM+77i4iAqWhuERmsqvt0n4epo2C6OTti/H18dR1ThRWAR0T6qepKJ204jRvmaaxOoUykHtcsHFR1V33vbSvgeuA74J6QtE1A1Rl/3QljKFZVL8EMgwBmFhrmj/Ec4A1V9YnI64A4uxQDKSH7d6oqssrvTUA3EXGFGDDdMc9zXTT4vBoga6/zwgx9AODUixuBGx2H9neB5Zi6E3VEpCtwODBWRE52klOAJBHJdoz6GvWn+jpT9Xp0B/wYI6NrHSpVHlvZ9nR30hpCbbJqfdZUdR5wgmNI/hHTo9itgXq0aFpEz0s4iIhHRJIwMxzcIpIU0p34P2CoiJzs7HMdsDjc4Qanov0MXOqUk4kxhhY3QuWnMN3pHUSkLfAXzBtqQ3kcY5CNcLZHMV21RzVC5iMYx8XjnK77euH4Fr0G3CQiqSJyEHAC5q0wJjpVIRrXDCJ/b+MaVV2Fmd33p5Dkd4H+InKGU6d+CwymYdcpAeNEmQv4xUyf/1VI/iJgiIiMcOr/DVWO34rxoajkG8xb+99ExCsiEzFDNS+EoUskz6suWQsxwyZeERmNGaoEQEQOE5FhYnywCjDDFUGajrMxxt4A9tSv/pgh98rhtxr1x9zLIHvfl5nAX0Skl4ikAbcBLzrDbXUxE7hGRNo7L6PXYRyaG0Jtsmp81kQkQUTOFJEMx7gsoGnvSXwRa4/hptowD4lW2W4IyT8SM15cinEi7FlP+SOc43ZhPNlfohHe+5jx34cxMwG2YJzykiJ8PRo8cwbzZqFAGXs8/ouAM+spJwvjG1SMmQVwRqx1itY1a6p7Gw8b+84k6ebct9khaROABRg/qAXAhJC82cAFIb/Po8osjirl/QFjhORhjOMXgFtC8q926u16jCNm6AyQfpg/0jzgdSdtCPCZo9tS4KQQWU9XkT0R2NDA89prdqTTTq0KU1ZvjKFVxB7H3srZOqdjelqKnevyAOBpwvu/DLi8mvS/AfPr0t/JvwljxORh/JBcGENhvZM+A2jr7NvTuaeekON3X2uMM+4DmJlEmwmpk9XcP48jq2dI2hzgrLpk1fasYYzs9zH/IZUzyyY05Pq2hs0GZrRYLBaLxRJXtJphI4vFYrFYLC0Da7xYLBaLxWKJK6zxYrFYLBaLJa6wxovFYrFYLJa4whovFovFYrFY4gprvFgsFovFYokrWrXxIiJrReTIWOtRH5zl+fvGWo9Y4iz73juM/f4lIk80hU5Oea3+3lhaFyIy0Vkx2mJpUlq18RIpROSvIvKjiBSKyM8i8tda9u3p/MkVVdl+25Q6xwIROc8597DP1YmxdEFompoosmvqOlZVb1PVCxw5lde9XiExROSfInKb00gHnXtVKCLLReR39ZFlaT2IyAQRmSsi+U48pS9FZIyTd56EF3PNYrHUQIuIbdQMEEzclMWY5eRnich6Va1tufBMDW/Z6rhCRDy1nNe5wE7MtXqx6bRqFMdiggZ6gE2q2tWJn3QM8KaIzFXV5THV0NKsEJE2mCX6L8WstJ2AiWBdXg8Zbt0TCNDSCOy1bJnYnhcHEUkUkfvERFjd5HxPDMn/m4hsdvIuCB0iUNW7VPU7NdFRlwNvAAc1UI+nRWSqiLzjvOF/IyJ9qux2pIisFJE8Z19xju0jIp+IyA4R2S4izzlxliplrxWRq0RksfNG+KITX6My/wQRWSgiBSKyWkSOdtIzRGSac/4bReQWJyZK5VvklyJyr4jsYN+4MJWyewCHAhcBR0mVgGTVlS0it2Ia/YecHo+HnH1VRPqKyDgR2VKpi5N3kogsdr7fICKVMUU+dz7zHFmHOm/Ew0KO7SAiJSLS3vndFhNv5atQXdXwLsYQ2y/Me+MSkWtEZJ2IbBORZ0Uko7prZYl7+gOo6kxVDahqqarOUtXFIjIIEyNrvPMc5sHuev+IiLwrIsXAYU69e1ZEcp3n5hoxEdN3996IyL9FZJeYHt9jKhUQE9/nc6cN+ch5FmuM1SMiU5z6lyemx2i/kLxa2w1nnyud53qzhPRIisixIvK9U6/Xi8gNIXmVvaHnisgvTpt1dUi+W8zQ72rnPBaISDcnb6CIfOjU4eUi8puQ46q7lpNFZKkjZ6OIXFXvu2ppXsQ6PkEsN0Liq2DiZHwNdADaA3OBm528ozExaIZgIoLOICT2SRWZAnwPXFJDmT2pEmOjSv7TwA5gLOZt/znghZB8xbzVZWKileYCRzt5fYFJmCB07TF/2PdVOd9vMeHZs4CfKvV0yst3jncBXYCBTt7/gMeAVOf6fAtc7OSdh4ncermjb3IN53Ut8K3z/QfgypC82sqeTUisl5BrUBl3ZjUwKSTvZeAfzvcb2BPLZZ/rjokvdGfI7/8D3gr5fRow0/k+ESe+iaPj8ZigaSPDvDfnA6sw8VrSMAEpp8e6Dtgt8hvQxqnDz2B66NpWyT+PKnGYnHqfj3npcWHi4zyLeRFKd57fFcDvQ2T4gAsxwWYvxUQurgz58hXwb0yvzwRMrJxq43IBI4FtwDhH1rmYtiLRyV9Lze3GREz9vwkTs2syJmhl25D8Yc457YeJo3Sik1dZJ/8LJGMiypcDg5z8v2LaigGYdnU40A7TDq0Hfodpc0ZiYgUNruVabgYOdvLbAqNi/ZzYrZH1LNYKxPTk9zZeVgOTQ/KOAtY6358Ebg/J60vNxsuNmMihiTWUWVlh86pslRX2aeCJkP0nA8tCfit7B197CefPupqyTgS+r3K+Z4X8vgt41Pn+GHBvNTI6Og1Kckja6cCnzvfzgF/CuNYrgT873/8JLArJq7ZsJ282tRsvtwBPOt/TMYHmeji/b6B242UcJhhkZYM/H/hNSP504Gzn+0SMsZLnXI9A5fmEc2+Aj4HLQvIGYP58miwYnt2absNENn8aEyXZD7yJE6iVmo2XZ0N+u4EKnD9kJ+1inMCVjozQAI0pzvPXCWM4+4GUkPwZ1Gy8PILzohaSthw41PleW7sxERPMNrRebQMOqKGs+yrrekid7BqS/y1wWogOJ1Qj47fAF1XSHgOur+5aOmm/ONevTayfDbtFZrPDRnvoDKwL+b3OSavMWx+SF/p9NyLyR4w/x7GqWtf4draqZoZsP4XkbQn5XoJ5U6eufBHpKCIvON2iBZgGKzucYzFRfVdXo2cPzBvVZqdLOQ/TUHQI2afa61GJiBwE9MJE8gV4HhgmIiPqKDscngd+LWaI79fAd6q6ro5jAFDVbzDXYKKIDMQYpW86OrswPUHvhxyySVUzMW/WDwCHVyO2putb3fPlwRiHlhaGqv6kquepaldgKOb+31fHYaH1KBtT76o+M11Cfu9+1lS1xPma5pS1MyStquyq9ACurKzfTh3vxp72b6+y2LdN2qF7+7mFtknjRORTZ+grH7iEyLRJ46roeybGcKvpfE/GvAiuE5HPRGR8NXItcYQ1XvawCVMpKunupIHpcuwaktet6sEicj7GsfMIVY3V1MHbMG8yw1S1DSbcuoR57HqMs3F16eXsbWy1UdUhIfvUFZr8XEePhSKyBRPmvjK9trLrlK2qSzGN+jHAGRhjpj5ynsFcp7OBV1S1zEkfA6xT1dxqyiwH/o4xwE6sTb8Qqnu+/JhudEsLRlWXYXoDhlYm1bRryPftmJ65qs/MxjCK3AxkiUhKSNo+bVYI64Fbq7xMpajqzDDKqovnMS8E3VQ1A+PvE4k26bMq+qap6qUh++x1jVV1nqqegHnpeh3TK2qJY6zxsoeZwDUi0l5EsoHrMD0XYB7034nIIKdBuDb0QBE5E2M4TNIwpvBGkXSgCMgXkS6YMeNwmYY5xyMc59IuIjJQVTcDs4B7RKSNk9dHRA4NR6jj2PcbjKPuiJDtcuAMMVOXqy3bEbEV4ydSG89j/FUOwfi8VEcuZtinqqwZwEkYA+bZkPTJwDs1FaiqFcA9mOckHGYCf3EcKdMwz8uL2gJnnLV2HGfSK0Wkq/O7G2ao9Wtnl61AVxFJqEmGmtkxLwG3iki6GIf3K9jTJtWI0/M4H7hBRBKcXobjajnkv8AlTi+JiEiq42ibHsbp1kU6pheoTETGYl4wwuUJ4GYR6efotZ+ItMP4lfUXkbNFxOtsY8Q4Q++Dcw3OFJEMVfVh/H+CjT0xS2yxxssebsFU+MUYJ7HvnDRU9T3MMMGnGKfLykaoPOTYdsA82bNuy6N1lJcne6/zckUEzuFGYBTGWe0djFNoWKjqtxgHuHud4z9jz1vfORjHv6XALuAVICdM0SdixsSfVdUtlRvGj8iDcWitrez7gVPEzKh4oIYyZmJmMn2iqttrOL8S4FbgS6er+QAnfT3mXivwRcghxwLv1nFuTwLdRaS2P4bQfadjnKh/BsowBpyl5VGI8af6xpnt8jXwI3Clk/8JsATYIiLVPq8Ol2N8uNYAczBG+pNh6nAmMB7jOHwLZmmCaoeyVXU+xvH3IUz9XoXxqYkElwE3iUghxtCvT4/Hf5z9Z2EMjmkY37tC4FcYh/pNmGGnOzETFWribGCtM5x+Ceb6WOKYSkdFSz1wLPwfMU659s05zhGRJzH+LNc4vztiZox1UVtBLC0AEXkR4/h/fax1sVgige15CRMx64ckiln7407MlFpruMQ5ItIT4+g7LSQ5AzOV2xoulrjEGUbp4wzDHg2cgPH1sFhaBNZ4CZ+LMVMAV2OmyV5a++6W5o6I3IzpQbtbVX+uTFfVFRFyVrRYYkUnzDIDRZgh70tV9fuYamSxRBA7bGSxWCwWiyWusD0vFovFYrFY4gprvFgsFovFYokrrPFisVgsFoslrrDGi8VisVgslrjCGi8Wi8VisVjiCmu8WCwWi8ViiSus8WKxWCwWiyWusMaLxWKxWCyWuMIaLxaLxWKxWOIKa7xYLBaLxWKJK6zxYrFYLBaLJa6wxovFYrFYLJa4whovFovFYrFY4gprvDQBItIu1jpYGo+ISKx1sMQntg2IPrZ+ti6s8RJlROQo4DYRyYy1Lpb6ISJjReQwETkCQFU11jpZ4g/bBkQHWz9bN9Z4iSIiMhm4BZipqnlV8uxbQjNGRI4FpgEnAdeJyBUxVskSh9g2IDrY+mnxxFqBloqIdAOeBe5R1dki0gnoDmSr6ruqqiIi9m2h+SEiQ4HbgPNVdZ6IHAqcIyKJQIW9Z5ZwsG1AdLD10wK25yWa5AM3AoNE5CzgeeAc4H4ReRRsN2dzI+RNOAm4V1XnOb83APsBHew9s9QD2wZEEFs/LaFY4yXCiEhPEckAgqr6IPAV8CDwpqr+ERgOTBSRM2Opp6VaOgCo6nzgOQARcanqamA9UOSkDYiZhpZmj20Dooatn5bd2GGjCOI45t0OfA20EZHLgJnAQlX9SkTcqloiIq8BhbHU1bI3IjIFuFpEfgS2AU8AP5ssESADSBSRs4GLReR4Vd0ZO40tzRHbBkQHWz8tVbE9LxHCGXe9F7gSeAAoBQTIdxotl6oGROQMYDKwNHbaWkIRkT6Ye/ZPjI9CCfCCiPRT1QDGyN+Ocbz8A3CxbRgtVbFtQHSw9dNSHbbnJQI4XcSjgEtU9XMR6Qkci+nGTBSRe1V1pYicAFwBnKWqq2KmsKUq24FPHadKAeYAfmC6iJymqmtFJABMAE5U1WWxVNbS/LBtQFSx9dOyD2L9mxqHiJwIjAceVNUNIpKOGd9eArwLTAGOxrxp9QF2quqmWOlr2YOITAB6YsbPPwfeUdU7nDwXcDWAqt7sdEd/q6rLY6SupZli24DoYOunpTZsz0sjcLqJ7wD+pKobAFS1UESuV9V1zj47gYFAQFV/jJ22lkqchi8FeAxTB4qA3wCzRKRUVe9X1aCIfItZRwJVnR4zhS3NFtsGRB5bPy3hYI2XxrE/8ISqzhKRzsAwYCewImSfw4HeQCpQ0fQqWqqiqkGgSESeAQKYhrEt5l7NFRG/qk4FcoB+ItIGKLTTMC3VYNuACGPrpyUcrPHSOPxAgvP9FWCdkyYi8jfMmPdlmPHtXbFR0VILfsyiYdOAC4GuwA/Ab0VkLDAW+I2qFsRORUszx7YB0cPWT0uN2NlGjeNT4EIReQH4r6qeDlyPmQJ5IDAE02gtiaGOlpp5A9iiqh8D84DrgEWqegimsTxYVX+IpYKWZo9tA6KHrZ+WGrHGSyNwKs5VwDigl5O2BvMmVq6qf7aNVrOmFBggIhcCl2CmWo4VkUtUtUJVt8dWPUtzx7YBUcXWT0uN2GGjxvMe5k3rBhFZ56QNxyxUZWnGqOomEVkPXAv8QVXfEpHDADuF1VIfbBsQBWz9tNSGnSodIURkFHAKkAg8bbsz4wMneF4HVV3g/HY5DoMWS72wbUDksfXTUhPWeLFYMN6VdraCxdI8sfXTUhVrvFgsFovFYokrrMOuxWKxWCyWuMIaLxaLxWKxWOIKa7xYLBaLxWKJK6zxYrFYLBaLJa4I23gRka4i8oyIrBORMhH5SUTGRFO5loCIXBRrHSwNw947SySwz1F0sNe1dROW8SIiKcAnwDlAGTAd2AV0jp5qLQZbweIXe+8skcA+R9HBXtdWTLgr7E4G+gGbgZGqWgIgIt5oKWaxWJoHIvI0cC5wv6r+OcbqWCwWS9jGSy/n84dKwwVAVX2RV6lxuFwuTU5OjrUau0lISCA1NdUuphOHNLd7V1JSoqpq/dSAJBFNjxOXvTSE9uJuNs/R/7N33nFSFEsA/upyJh05HTknSQoqPkVREBERFVBBURDEgIpiRsSI2YeKCk8lCSIgGcQEguSghCOD5AzHJbhQ74/Zg72cdm737vrz17/d7amprhm83Zru6qqiQmG6rydJPqmqZd1tR1Eip87LXsdrExEJVNU4ABHxUdVEe0zLG4GBgcTExLjbDIPB5YhInLtt8BRC8aIHQe42w2DIEWOJ3p+9lCE35PTRZT6wE6gIbBCRsSLyB9DFNssMBoNbEJGrReQfEYkRke+AAHfbZDAYDM7kyHlxLBXdgBWoG4S1/l0OOGyfaQaDoaARkZLAHKAxsBIIB3q61SiDwWBIQ06XjVDVA1i7jQwGQ9HlVqAksAvoqKoqIuuAK9xrlsFgMFymcES8GQyGgqKy43WnUxXfHe4yxmAwGDLCOC8Gg8GZQ47XOiIijvd13WWMwWAwZIRxXgwGgzPzgHNAbWCJiMwHWrjXJM8jqFQpHpkxmY+jj/LGvi207pVxWFBgiRL0/WYs7x7bw7vH9nDrq8+nk7n+8UGM2vMPH0cf5dWtaylXp7bd5nskrrqnVZo14emli/jw7EHeOhBJ55eeLQjzDQWMcV5sJDIykv79+xMZGeluUwyGHKGqZ4DbgC3AVUAU8KNbjfJAeo15n8SLF3m2fC3G93mI3p9/SMWG9dPJ9fzwbfyCAnkxohHvtLmOtvfdw1X97r10vH3/vrTrfz9jutzJEyEVGHNrT6JPnirIS/EYXHVP+08ez66ly3mqdDXe73ALHQY/TNOunQvyUgwFgHFebGT06NGMHz+e0aNHu9sUgyHHqOpSVW2sqkGqeo+q9lRVySy7roj0FpG1IhItIkdEZIFju/UIEUlw9J8VkRUicpXTwWODYgAAIABJREFUeV1F5KiIlHbq6yYih0SkREFca17wCwqiRY9uzH55FBdiYti9/C82zZ5P2/t6pZNt2vUWFr/7EQlxcZza/y/Lx02g3YP3ASAidHl1OD8MHc6RbdsBOLlnL7FnzhTo9XgCrrqnAGUiqrF60lQ0OZmTe/ay68+/qNioQUFejqEAMM6LjQwbNowHH3yQYcOGudsUg8EWROQp4CPgTaA8UA34DOjmEJmqqiFYW65/A35IOVdV52DVTPvQoask8DkwSFXPFdQ15JbydWuTnJjI8Z27LvUd2rSZSpn8QF4OHQIRqNTYkitZpTKlq1ahcuOGvPnvNkbt+YdbR7yQSr644Kp7CvDLR5/R9v7eePn4UL5uHWpe1YbIJb/ZZ7zBLRjnxUbq16/PuHHjqF8//dSnwVDYccyOjAQeVdUZqhqjqgmqOkdVU3nsjkzck4DKIuKcJv1x4BYR6YTlxPyhqrML6hrygn9ICHFR51P1xZ2LIiA0JJ3sloU/02n4U/iHhFC2Vk3aPXgffkFWZuBSVayNXQ1uuoHXm1zJh//pQutePWnfv6/9F+FhuOqeAvwzdyFX3NmNT+NO8Nr29Swf9x371663/RoMBYtxXgwGQ165Civ77szsBEXEDytP1CmsivQAqOpJ4Aksx+ZWLGcmMx0DHMtTa+NxX0mbC9HRBIaFpuoLCAsl/nx0Otmpjz9LQlwcI3duZNBP37NmynTOHrQ2dCXEWdUeFr/7IXHnznFq/78sGzuexp1vsv8iPAxX3dOgUqV4bOEM5o18h8cCwhlepR4NO3Wkw6CHCuQ6DAWHcV4MBkNeKQOczKa+2V0ichaIAx4G7sxAfiVQAlisqicyU6SqX6pqK1VtFYD7llaO7diFl48P5WrXutRXpVljDm/Zlk429swZxt/7EM9VrM3Ixm0QLy/2rV4HwNHtO0m4cAF18sNUC0WdQZfjqnsaXjOC5KQkVk2YQnJSEmcPHWbt99Np1LlTgV2LoWAwzovBYMgrp4BwEckqU/c0VS2JFQ+zGWiZgcyXwHdAZ+eAXk/lYmwsG2bMpuvIF/ELCqJWuytp1q0LqyZMSScbXrMGwaVLI15eNLr5Rq4Z8ADzR70LWDMv66b+yE3PPol/SAglK1fimgEP8PfchQV9SW7HVff0+I5diAite/VERAgrX45Wd/fg0N+bC/qSDDbjsc6LY4eCc0sSkU/dbZfBYLjEX8AF4PbsBB3LQwOAESJSMaVfRPoDVYHBwAvA144lJo9myuCn8A0MZPTxPfSfMp7Jg4ZyZGskta9ux0fnj1ySq96yOS//s5KPzx/h9rdGML5Pf45svZw64fshz3AhOpp3Du/g2b9+YfXkaawY/507LsntuOKexp8/z9g7+nDD0Ef54MwBXty4gsObt15ybgxFBykM05QiEgIcBTqr6tKsZIODgzUmJqZgDDMYChARiVXVYHfb4YyIPA08CwwEFgMJQEfgP0AsUFtV73WSnw4cUNWhIlIJazamm6ouc2T0/QP4TVVfzWrcsuKtPQjKSsRg8BjGEr1OVVu5246ihMfOvKShB3AcWOZuQwwGw2VU9X3gKeAl4ARwABgCzMrklNHAABEph7Wl+ntVXebQpVhxMU+KSCO7bTcYDIWXHFeVdjN9ge+0MEwTGQzFDFWdhLVbKC0rMpBdBaTMHqVbblLV7VjBuwaDwZApHj/zIiLVgQ7At1nIXNpCmZiY1cYHg8FgMBgMhR2Pd16A+4A/VXVvZgLOWyh9fArLZJLBYDAYDIa8UBicl/vJYtbFYDAYDAZD8cKjnRcRaQdUxqkeisFgMBgMhuKNRzsvWIG6M1T1fLaSBoPBYDAYigWFIs9LbjB5XgxFFU/M8+IuROQEsN/ddhgMOaS6qpbNXsx+RKQ0MA64CTgJPK+qkzOR9cdKadARKA3sdsgvcBz/HbgSSNkpc0hV69l6AQ5MdKvBYCh0eMoPgcFQCBkDXMQq2dEcmCcim1R1SwayPli5mzoA/wKdgWki0kRV9zlkhqjq1/abnRrblo1EpIxdug0Gg8FgMICIfC8i/xORu0SkZDaywVhJX19W1WhV/ROYjbWrNx2qGqOqI1R1n6omq+pcYC8Z1ygrUOyMeflXRH4SkTsLQ60Sg8FgMBgKIc8Cq7EckP0iskxEnheR5hnI1gUSVXWHU98mIEcZrUWkvEOH8yzNWyJyUkSWi8h1ebqCPGDnslEE0At4DvjSUdPkO4enZzAYDHkmPDRYI8KzfMg05AFNTHC3CUWS9QdPnExZ6qwqPhpP1rGmJ0neAsQ7dX2pql9mJKuq/wKfA587YlSuB14F3hSRBara2Uk8BIhKo+IcEJrdNYiIL1Ym7W9VNaW66HPAVqxlqHuAOSLSXFV3Z6cvv9jmvKjqCeAT4BMRqYflFU4QEQUmAuNU1QTcGQyGXBMRXpJVIwe724wihx47kr2QIdf4Dhtz6bfuAkpPso67/5zz8bkp5CgiZYFOwC1YgbgHgTeBmWlEo4GwNH1hQJY7ekXEC5iA5aQMSel3lPtI4VsR6YUVF/NpTm3PKwW1VbqCo4VhRStXBjaIyPACGt9gMBgMBrcjgI9Ili1X+kTGA9uBbsASoKmqtlDVF1V1bRrxHdbwUseprxmpl4HS6hes3UnlgR6qmtX0nDou0XZsm3lxVIW9F+gNxGBlyW2mqgcdx18H/gbetssGg8FgMBg8Da/sft5zl8HkHWCAqmZb2E9VY0RkBjBSRB7C2m3UDWiXxWmfAw2Ajqoal9LpCA5uC/yBtVX6buBa4IlcWZ9H7Jx5WYq1jtZTVRuq6jspjguAY5vVRzaObzAYDAaDx+GVTcslY4EEEdEMWkYxpoOBQOA4MAUY5LxNWkQWiMgLjvfVgYFYTs5REYl2tD6ALzAKOIGVL+Yx4PY0wcC2YWfAboVsppdQ1VdsHN9gMBgMBo9CyP3SUFao6nW5lD8N3J7F8Vuc3u8n62Wg1rkZ25XYOfPyvqM20SVEpJ2ImNkWg8FgMBRLBGvZKKtmyB47nZdeQNpgoXVYMTDFgsjISPr3709kZGT2wgaDodBwOjqWHh9PIuyh16g5dDRTVmzKUE5VGT51EeUGvUG5QW8wfOoinEuy/Lp1N61fHkOpAa9T5+n3+eq3NQV1CR7H6dh47vxmPiVeGEutN75lyoaMVx9+33WQjl/MoszLX1H7ze/SHd93OoqOX8wi7IWxNH53Er/sOGC36blDwFsky2bIHjudF81Av7fNY3oUo0ePZvz48YwePdrdphgMBhfy2Hdz8PP25vB/h/PdIz159NvZbDl4LJ3cV7+tYfa6bax/Ywgb3niMeRsi+dLhoCQkJnHnx5N5+D+tOT32JSY/ejfPTF7Apn+L53blx2cuxc/Hm0OvPsC3vW9kyIw/2HL0VDq5ID9f+rVuwNtdMo4xvXfSYppXCufYa/0ZefOV3D1hISei4zKUdQeCy2NeiiV23qdlwCjH/vCUfeIjHP3FgmHDhvHggw8ybNgwd5tiMLgcEblHRFaJSIyIHHe8HywW34jIRacAv2gRudtx3j4R6eh4X1FEZovIYUeAYYQ7ryknxFy4yIw1W3mtR0dCAvy5ul4EXVvUZ+Lyjelkv/tzA0NvaU+V0iWoXDqMobe059tl6wE4HRNHVNwF7m3fHBGhdc0qNKhUlm2Hjhf0JbmdmIsJzPhnNyM6tSXE34+ra1Ti1oYRTFqffvalTbXy3NuyHjVLp01XAjtOnGXDoRO8elMbAn19uKNpLRpXLMOMf2zPmZYrfCTrZsgeO52XJ7AqUR4RkdXAYeBGrIjkYkH9+vUZN24c9evXd7cpBoNLEZGngY+B0Vg5nMoDjwDtgZRyIO+qaohTm5qBqmRgIVa9lULBjiMn8fH2om7F8Et9TatVZGsGTsfWQ8dpWq3CZbmql+XKlwjhniub8s2y9SQlJ/PXzn/Zf/Is7etWt/8iPIwdJ87i4+VF3bKXsyY3qxjO1qOnc6Vn69HT1CxTgtCAyxVpmlYMZ+ux3OmxEyvmRbJshuyxM8PuQRG5AmsfeBWsypSrVTXZrjENBoP9iEgJYCRwv6r+6HRoA9DHIZMjXap6DPhMRApNhfvoCxcJC/RP1Vci0J/z8RfSy8ZfpERgwGW5IH+i4y+iqogId1/VlIHjZjJ04nwAxvTtStUyxa/sQcyFBML8fVP1hQX6cf7CxVzpib6YQFhA6lJ6JQL8OHQuJt82ugorSZ27rSj82PqF4XBU/kpZOgJr+Sg3DoyI3INVp6EacBTop6rFZunJYPBArgL8gZ8KclARGQAMAKhWpkRBDp2KEH8/ouJSOypR8RcIDfBPLxuQWjYq7gIhAX6ICJGHT9BnzFR+eLw3Nzauxc5jp+j2wUQqlgqjS/N6tl+HJxHs70vUhdSZNc7HXyTUP3c1fUP8fDkfn9rhibpwkdAA30zOcA8mriX/2HYPReQKEflLRGKABEdLdLzmVMeNWNkDH8BKeHctsMcGcw0GQ84JB046Z/QUkRUiclZE4kTkWkf3M46+syJyMr+DquqXqtpKVVuVDc26Noyd1K0YTmJSMjuPXr6kv/89SsPK5dLJNqxcjr+dAnCd5bYcPEbdCuF0aloHLy8v6lUsS+dmdVn0d4Hk+PIo6pYtSWJyMjtPnL3Ut+nwKRpWKJ0rPQ0rlGbP6ahUDszfh0/SsHzu9NiJq8sDFFfsdAC/BX4DWgE1Ha2G4zWnvAaMVNWVqpqsqodU9ZDrTTUYDLngFBDuvNSjqu1UtaTjWMr3ynuqWtLRwjNSVBgJ9veje6uGjJjxCzEXLrJ8x35mr9/Gve2bp5O97+oWfLRwBYdOR3H4TBQfLlxO32uuAKB59YrsPHaKX7fuRlXZfewU8zZup0nVCun0FHWC/Xzp3rgmry1eTczFBJbvPcKcrXvpc0XddLLJyUp8QiIJycmoWu8vJiYBlhPUrFI4r/+8hviERGb9s4d/jpzijia1CvqSssTsNso/di4bVQdeVOekBrlARLyxHJ/ZIrILCABmAcOc6ysYDIYC5y/gAlZNlB+zkS2S/LdvVx76eiYVH32LMiFBjOl7G42qlGfZ9n3c+t53nPvKSh4+4D+t2XP8NM1ftIrsPtihJQP+YyUlrVW+DF8/1J2hE+ax/9RZSgQG0KtdM/p3aOm263Inn97RgYen/UqlEeMpExzAf+/oQKMKZfhzz2FuHTeHs28MBGDZ3sN0/GLWpfNCXxjLtTUr8cug7gBM6nMT/af+QtlXvqZaqVCm3nczZUMC3XJNGSEmEZ1LkDz6FtkrFvkWmKyqi/J4fiXgEFZiu65Yy00/Ab+r6otpZC+thfv5+bW8cCF94JzBUNgRkVhVdd96iRMi8izwNPAosAir+GpTrNnW7kA/4KCqvpTBufuAh1R1ieNzAFYOqGigPrBfVeOzGr9Vjcq6auRgV12OwYEeK545ZuzGd9iYdaraCqCat48+F5h1UPaQmFOX5A0ZY+fMSwAw01EY6qjzAVW9Pwfnp8yufKqqRwBE5APgJSCV86KqXwJfAgQHB9vjjRkMhkuo6rsicgh4FvgOy3nZAzwHrMByXnKK80xqSjpq82xqKJKkJKkz5A87nZetjpYnVPWMiBwkdXHwQuWYREZGMnr0aIYNG2ZyvRiKHKo6CZiUyeF+WZwXkeazcVQMxQZXF2YsrtiZ5+U1F6j5H/CYiCzEWjYaCsx1gd4CIaU8AMC4cePcbI0hNxjH02Aw2IWJeck/tuZ5ERE/oB7W1spL/1yq+msOVbzuOHcHEA9MA95wsZm2kVIWwJQHKHwYx9NgMNiBAN7Geck3tjkvInI18ANWMqswIAorV8sBcrhdWlUTgMGOVuhIKQ9gKHwYx9NgMNiFlwnpyjd2xg19iFXbpDRw3vH6OvCZjWN6FJGRkfTv35/IyMjshQ0ehalLZTAY7ECyKcpoSgfkDDuXjepiFW5z5m1gL/CejeN6DGbpwWAwGAxpMf5J/rHTeTmHtVx0FquydEOs7JshNo7pUZilB4PBYDA4Y8W8GPclv9jpvMwAOgOTgfFYyasSgOk2julRmJgXg8EeND4O3brZ3WYUOU4t2eRuE4oFxnXJP3ZulX7S6f17IrISK2A3Txl3DQaDwWAoChjnJf/YnuhPRCqLSGtgr6ouUNVku8c0GPLK8uXL8fLyQkRStddGvu5u0wwGQxFAAC+RLJshe+zcKl0NK/vmVcBpoLSI/AXcq6r77RrXYMgPc+bOo2S7uwi75r5LfXH7NjFtxgxefeVlN1pmMBiKCqY8QP6xM+blW6yiijeraoyIhGBtlf4WuM7GcT2C+Ph4pk2bRlJSEiLCbbfdRunSpd1tliEb5ixYjG+DO1L1BVRpwK6ftnHu3DlKlCjhJssMBkNRwcyu5B87nZeWwE2ORHOoarSIPIe146jI8+effzJ46DCCarQg7mAk8fHxPPLII+42y5AFZ86cYfeOSMrf2CBVv/j4EVa9Eb/++ivdu3d3k3UGg6EoIJiYF1dg5+zVSqBNmr5WwF82jukx7Nq1i8CaLQm68TF86l/Htu073G2SIRt++eUXQiMaIz5+6Y4lVWrCnPkL3WCVwWAoanhL1s2QPXY6L7uB+SIyWUTeEZHJwHxgl4iMTGk2ju9Wtm3fQWJwOQB8SlVkS+RON1tkyI5Zc+eTVKlphscCIq5g/oKFqBaqwuYGg8HjkGz/y1aDSGkRmSkiMSKyX0R6ZyI3TEQ2i8h5EdkrIsPSHI8Qkd9EJFZEIkWko4suMq0dK53ev+oKnXY6LwFYuV4uAOUcrzOBQKCqo1WxcXy3siVyJz6lKgHgW7Iiu3fvdrNFhqxQVRYtXERgzSsyPO5btjrRsXHs2rWrgC0zeCKn4y5w54/LKPH+D9T6bDZTtuzLUO73/cfoOPlXynz4I7U/m53u+KtL/6b5uAUEvDOVkcv+sdlqz0bCSlDi3U8ot3Qt4bOXENCpS4Zyvi3bUOrz/1H2t1WE//RzhjKB99xL+KzFlFu6ljLT5uBdrbqdpucKEauqdFYtB4wBLgLlgT7A5yLSKKPhgPuBUsDNwBARucfp+BRgA1AGeBGYLiJl83xxmVNXRAIc7592hUI787w8YJfuwsDuXbvwveYmwJp5OfTvXlQVMYFaHklkZCTxickElc7YnxYRAmu0YOHChdSpU6eArfNcRGQf1hdoEhANLASGqGq0k8wI4FXgSlVd5dT/GDAQuEJVLzr6ngT6Aq1VNbGALiPXPL54HX7eXhx67HY2HjtLt+lLaVquFI3Kpg7oDvL1oV/TGtydWI13VmxNp6dWqVDevq4ZX24wDzdhz74EiQmc6HQtPnXrU/Kjz0nYuZ2kPakfGDQujrjZM5DF8wnuNyCdnsBuPQi8rQdnhg4iae9uvCtXJfl8VEFdRo7Iz6+AiAQDPYDGjr+zP0VkNnAfMNxZVlXfdfq4XUR+AtoD34tIXeAKrNjUOOBHx99fD+CLfJiYET8BOxzfF4EisjQjIVW9NqcKbZt5EZGamTW7xvQUkpOTOXRgHz6lKgLg5R+EX2Awhw8fdrNlhsxYtGgRAREtsnYuqzRj5pwFBWdU4aGrqoYAzYEWwPMpB8S6ofdjpUu4P815Y7DKh7zokK0JvAb092THJeZiIjO2H2TEtU0I8fPl6qplubV2JSZlMPvSplIZ7m1cg5olM66Kcn+TGtxcqxIh/nbunSgEBATif/1NRH/xCRoXS8Km9VxY+huBnbumE03c+g/xC+aQdOhgej0iBD88mOgP3yFpr+UQJh06gEads/sKcowA3kiWDQgXkbVOzdlLqwskqqpzIOUmIKOZl8vjWn+L1wBbHF2NgD2qej43evKCYzKjN/AlkAiMy6TlGDv/YnYBSmonMyVgwNvGcd3O4cOH8Q8Kxcsv8FJfUHgVdu7cSeXKld1omSEzZs1bCFWaZykTENGcleM+JyEhAV9f3wKyrPCgqkdFZBGWE5PCNUBF4CHgExEZmjLLoqrJItIfWCMi04GPgM9UdX1B254bdpw+j4+XULd02KW+ZuVKsfTf4260qnDjUy0CkhJJ+vdyCrDEndvxu6JVrvR4lauAd/mKeNeqTfirb0BiEnHzZxPz1RjwoHi1HEzAn1TVzC4+BEg7lXQOK4N9VozAmrD4n5OetF7dOcCWHylV/RNrlshPVb/Nrz7bZl5U1UtVvR2vXkAlLK/rvmxOvYSI/C4i8SIS7Wjb7bLXlezcuZOAMpVSd5aoYOIlPJSEhARW/bWcgOoZB+um4B1UgsAylVi9enUBWVa4EJEqwC1YDy4p9AXmANMcn1M9SqvqduAtrNpnVbBmXjyamIQEwvxTO69h/r6cv5jgJosKPxIURHJMTKo+jT6PBAXnSo93+fIA+Ldtz6l7buf0oH4E3NSZwG49XGarK/BCsmzZEI1V9NiZMOB8BrIAiMgQrJnPLqp6Ia968oqIOC8H7ROR6zNqudFZYIn+VPUo8CTWF1VuGKKqIY5WzwbTXM7OnTshrEKqvoTgcmyLLBS+V7Fj7dq1BJauiHdQDhLQVWrMz0t+sd+owsUsETkPHACOY8W3ICJBQE9gsiPf03TSLx0BLMMKGJyuqvGZDSIiA1Km0U/GXshMzHaCfX2JupDaUTl/IYFQPzMbl1c0Nhav4NSOigSHoLExmZyRiZ546/+LmAnj0OjzJB85TNzMafi1y3Eohe1Y5QHyFbC7A/AREefgu2ZcXg5KPZ7Ig1ixMDeoqvNa2xagpog4z9hkqieffOb0PrMlo69zo7CgsxTXA4IKeMwCZ2vkdhJDy6fq8ylVib+3RrrJIkNW/Prrb0ilxjmS9a7ciAU//2qzRYWO21U1FCtzdn0g3NHfHWt9e77j8yTgFufdDCLiB4wFPsXaCZFpTJyqfqmqrVS1VXiQv+uvIofULR1KYrKy8/TlB9RNx8/SsKzJvpxXEv/dB94+eFe9vCvIp049EvfkbrY6cf9e9OLF1EtEHrRclIJk07JCVWOwdvKOFJFgEWkPdAMmpBtHpA/wJnCjqu5Jo2cHsBF4VUQCRKQ70BT4MT/XlonNjZ3e18ik5Soe1s6A3WUistSprQVWAR/kUtVbInJSRJaLyHWut9T1/L1lG75pdq34lq5szcgYPI4FS37Fu3LDHMkGVG3EpvVrSUgwSwRpUdU/gG+A9xxdfbHW1f8VkaPAD4AvVuBeCi9jzdY8gbXDYWxB2ZtXgv186F6vCq8t+4eYi4ksP3iCObsO0adRRDrZZFXiE5NISEpGgfjEJC4mJV06npCUTHxiEqpKokM2KbkY1q6Nj+PCbz8TMnAIBATi27QF/h2uJ27+nPSyIuDnBz4+Tu8ds14X4on/eQHB9/VHgoLwKleewO49ufDn7wV6OdmRz2UjgMFYaUeOY213HqSqW0TkGhGJdpIbhTWrucYp/MJ5J9E9WMljzwBvA3eq6glXXGNmOHY8ZdQ/I1d67Eq6JSJ903TFAJtUNce/4CLSFtiKtZ/9HuC/QHNV3Z1GbgAwAMDPz6/lhQvum1IGqBxRC73xGfzCq13qS06I5+infYiNicbHp5jvLPAgkpKSCC1ZijIPfpGzZSMgatKTLPhhIm3btrXZutSISKyq5i4IwGYcWx8fUtUljs9lgX1AZ+BXrBiYv51OeRLrKbCliDQDlgItVXWXiAQ6ZN9U1f+RBS0rltZV/Tq5+nJyzOm4Czw8fzVL9h2lTKA/b3RoSq9GEfx54Di3TlvK2afvBOCP/cfoOOW3VOdeW7Usv/S5AYAH565kwuZ9qY5/3bkNfZu6Z1PmqSWb3DIuWHlewl4ehX/bq0g+d47o/35A/KJ5+DZvScmPx3KigxW/6ntFa0qPTR3veXHdas480s/SExxM2Auv4de+AxodRdys6cR8/XlBX04qKqzdti4lALe+r5+OL1M+S/n2xw5eki9qiEiUqqaNtUFETqtqjgsA2ua82IGILATmqeqnmckEBwdrTEzu1kldSXx8PKElSlLpie8R79Rr4GfGP8LqpUuoV69QhO4UCzZt2kSHm2+jZL/Pshd2EP3LFzx/93U8/bRLci3lmMLgvDj6Psd6mNioqi3TyFcC9mM97Y0DpjnnonDMrk4HGqnqsczGdbfzUlRxp/NSlHF2Xhr4+uk32TgvVxZB58Upo/6zwLtpDtfE+ptvkVN9di4bzRCRa9L0XePYEplX0m699ji2bdtGaLkq6RwXgIDyEfz9998ZnGVwF8uXL8e3Uv3cnVS+Hkv++NMegwoZqhrh7Lg4+gY5dhq2zED+sKr6quomR/zKu2mO/66q4Vk5LgZDYUdEsmxFlJTM+l5O71My7R/ACu7PMXauX3QgvTF/AbNycrKIlATaAn9gBf3dDVyLtTbusaxfvx6fsjUyPJZQohpr1q6jZ89c/RsZbGTJH8tILlc3V+f4V2nAqukTTMZkg8GQa4SC3ynjCaRk3ReRFar6VX712XkP44G0U9whQE4jHX2xgo1OACeBx7B2NXh0eeZlK1aSWDpj58WnQh2WrliZ4TGDe1ixfAX+lRvk6hyfEuW5mJjM/v37sxc2GAwGZ7KZdSnqD0Sq+pWI1BGRV0RkrOM11zVX7HReFgFjRSQMwPH6X6zaJ9miqidUtbWqhqpqSVW9UlUzrsLlQSz7cwX+lTNehvCvVJe/N6wjyWm3gcF9HDp0iKhz5/ANr5qr80SEoGqNWLZsmU2WGQyGooy3t2TZijIi0hVYh5VW4TRWCpW1InJbbvTY6bw8jZWt77SIHMcysgTWboMiyenTpzl4YB9+5WtleNw7qAT+JcqycePGArbMkBF//PEHwRGNEcn9n0FiufosWGyS1RkMhtwhWDu8s2pFnDeBbqraW1WfV9U+WHlq3syNEjvLA5xR1S5YwThdgCqq2lVVz9o1prv57bffKBHRBPHOPJTIq3Jjfl6yJNPjhoJj7oLFJFXIWXK6tARENGNB0puyAAAgAElEQVTJL79QmHbrGQwGD0DAy0uybEWcKlhZtZ3509GfY+zcbXSTiNRV1aOqusZRtK2eiNxo15juZtaceSRWyqY+TrXmTJ81t4AsMmSGqrJw4QICa6XbEJMjfMtUJT4hkW3btrnYMoPBUNQp5jMvG7FWZpx5ytGfY+zcbTQGa3eQM+cd/bnb3lEISE5OZt68eQT2GJWlXED1Zmxe8AFnz56lZMmSBWSdIS2rV68m2TcI31KVshfOABHBt0Zrfpwxk4YNc5ad1+A6pFxlvJ94w91mFDnKPlRkJ8bdS+3UD0lFPSg3GwYBc0TkCawt0lWBWNIUbc0OO2NeyqnqkTR9R4AKGQkXdlavXo36BWf7Y+jlF0BojWbMmzevgCwzZMR3EyfjXeuqfOnwrdOObyZMMktHBoMhx4iAt5dk2YoyqhoJNMBKf/I+cBfQQFVzNY1tp/OyJ4MS19cBe20c021MmfYDXjVzli5eq7fh28lTbbbIkBkXL15k4qTJBDS8Ll96/Ks24sSZc6xfv941hhkMhmJBMV82QlUTVXWZqk5T1T8dVedzhZ3Oywhghoi8LyKDReR9rGqVr9g4pltQVaZMmYp/3fY5kg+seyXLfv+NqKgomy0zZMQPP/yAT3hVfEtXzpceES98G3Xkg0/+6yLLDAZDUUcQvLyzbkUZEWkmIr+KyGkRuehoCSJyMTd67Nxt9BNwE1aiui6O106O/iLFypUrSfDyw7dsRI7kvQNCCIlowk8/Fblb4fGoKqPeHo13ky4u0RfUtBMzZ87k6NGjLtFnMBiKOFJsywOkMAVYjhUT28DR6jtec4ytWYpVdbWqPqKqXRyva+wcz118O3EyXrXa5ep/Oq3VjnHfTbLRKkNGLFq0iCOnowis08Yl+ryDShDUoANvvzvaJfoMBkPRp5hvla4AvKKqm1V1t3PLjRI7t0r7ishrIrJHROIdr6+JiJ9dY7qDpKQkpk6dSkD9a7IXdiKodltWrljOqVOnbLLMkBZV5annnse3zd15SkyXGQGt7+Crr8dz5Eja+HSDwWBITzGPefkW6J1fJXbOvLwLdAQeAZo5Xq8H3rFxzAJn6dKlSHApfMvkKr8OXv5BhNZuyY8//miTZYa0TJkyhSNn4wiq186len1Cwwls3JFnn3/RpXoNBkPRo7jvNgLeBl4XkS2O2JdLLTdK7HReegK3qepiVd2uqouB7ljboooM306cDLXy+GNYsx3/mzjFtQYZMiQ2NpYnn3kW/6v7uXTWJYWgtncyY9ZPpvRDMeH0mbPc8cAjhNRsTESra5g8Y3aGcqrKc6PeIbxhS8IbtuS5Ue9c2lq/Y/debu83kHKNWlOmwRXcfE8/tu/aU5CX4VGcPnuOOwY9TWiT9tS4tguTZy/IUE5VGf7uJ5RtdT1lW13P8Hc/uXRPT54+wzV3PUjZVtdTukUH2t/Zj+XrPO9vspjHvEzH2nX8OTApTcsxdjovmf0LFJl/mcTERGbOnElgvavzdH5grZZsWLeWEydOuNgyQ1pGvfkWyeG1CajWxBb9XgEhBF7Vi/6PDC40eV9EZJ8j0j88Tf8GEVERiRCRbxwy0U5tk0MuwiGX0bEuIvKniJwVkaMi8rWIhDqN8btjOfm8iESJyDoRGS4i/gV7F/LGkBdexc/Pl6P/rGLimA8ZPPxltmxPX/D+ywlT+Gnhz2xcMpdNv8xj7uJfGfud9cByNiqKrjfdQOSfP3P071W0btGU2/sNLOhL8RiGjHgHP19fjqz8mQkfjOLRV95iy470YRBffj+Dn37+nQ1zprBx7vfM/WUpY6dYM9ghwUF8/fYrHFu9hFPrf2fYwL50GzCUxMTEgr6cTBHAyyvrVsRpDtyiqv9V1XHOLTdK7LxNP2Bl0eskIg1E5GZgFjDNxjELlKVLl+ITVhbfknnLu+flG0Bo7VbMnDnTxZYZnNm9ezcff/IpAVf3tXWc4KY3sefwab77boKt47iYvUCvlA8i0gQISiPzrqqGOLVmaY6XzOBYCWAUUAlrF0FlIG1U8xBVDQUqYqULvweYLx7+6BkTG8uP8xYx8tmnCAkO5uq2rbjtpo5MmD4rnex3P8zgqYH9qVKpIpUrVuCpR/rz7TTrh7ZNi2b0730XpUuVxNfXl6EDHmT77j2cOn2moC/J7cTExjFj0S+MHDqIkOAgrm7Vgq43dGDirPTJPCfMmMvQ/vdSpWJ5Klcox9D+9/Ldj3MACPD3p17NCLy8vFBVvL28OXMuitPnPCgthQjilXUr4iwD8p2W3E7n5VlgCVY5gHXAp8BvwHM2jlmgTP5+GtTIWWK6TKl5Jd9NLjL+nMehqjz0yGACW3XHp0Q5W8cSL28C/jOAoc8M4+zZQpNmfQJwv9PnvsB3+VWqqpNVdaGqxqrqGeArIMNESKoao6q/A7cBV2GlVvBYduzei4+3N3Vr1bjU17RRfbZu35lOdsv2nTRrdHkHaLOG9dmSgRzA0pWrqVCuLGVKl3K90R7Ojr37rXtao/qlvmYN6rBlZ/pltC07d9Osfh0nubpsSbPc1rzL3QQ1uorbBw6l/123U65MafuMzwPFPGB3L7BYRMaKyEjnlhslduZ5uaiqr6hqbVUNUtU6qvqyql6wa8yCJDk5mRkzZxJQN3/Bn4E1W7JuzSrOnCl+T1sFwU8//cT6zdsJbtWtQMbzr1QPr+otefb5FwpkPBewEghzzI56Y81+TLRhnGuBLVkJqOq/wFogd1v3CpjomFjCQkNS9ZUIDeV8dEyGsiVCQy/LhYUSHROTbmnx4OEjDHlhBO+PKDT/37iU6Ng4wkJS39OwkBCiY2IzlC3hdP9LhFpyzvd047ypnN24lIkfvkH7Vs3tMzwPCMU+YDcImAf4YdU1SmmeUVXalYhIHcf6uB1fqnlixYoVqH9ovrO0evkFElKjmUlYZwOxsbEMfPQxAq57GPH2LbBxA9vfy+QpUwtT8G7K7MuNwDbgUJrjzzhiV1Lat2mOn3Q69kxa5Y5K8n3JWXbtw0CGj8kiMkBE1orI2hOnTudAlT2EBAcRdT46VV9UdDShIcEZy0Zflo06H01IcHCqoMwTJ0/R6Z5+DOrbh17db7PPcA8mJCgw1X0COB8dQ0hw2hXMFNnLjmKUQy7tamOAvz+9ut7Mu2O/YdO29PFI7qQ4B+yq6gOZtAdzo6dQOC9YS08eleBu8tQfkBzWMsoOrXGlqXVkA6PefIukMrUIjCjYJy/voBIEXNWb/gMLTfDuBKy8C/3IeMnoPVUt6dTSBg+FOx17z/mAiFwJTAbuVNWc/IJUBjL0TFT1S1VtpaqtyrpxGaBurRokJiWxc8/lMm1/b9lGw3p10sk2qleHTVsu15vbtDWSRk5yZ86eo1OvfnTtdAMvPvmovYZ7MHVrVLfu6b5/L/VtitxJozo108k2qlMrlTOyadsOGtVOL5dCQkIiew4cdK3B+UGKfZI6l+DxzouI3AOcBX5xty0pJCcnM3XaNALyuMsoLUG12/DX8mWFKU7C49m3b58VpHtNP7eMH9z0RvYcOc2kSZ6fRVlV92OtQ3cGZrhKr4i0AGYDD6pqtn+/IlIVaIkV0OexBAcFcUfnm3h19EfExMayfPVaflq0hPvuvD2d7H133sGHY8dz6MhRDh89xgdfjKPvXT0AiDp/npt79aNd65a8/eKzBX0ZHkVwUCDdb7qeVz/6gpjYOJav28jsJb9z7+3pw5/u7d6Fj/43iUNHj3P42Ak+HDeR+3t0BWDlhn/4c+0GLl5MIC4+nnfHfsOxU6dp28yeXYZ5pZjHvLgEj3ZeRCQMGAk8lY3cpenkgtgSt3z5cpJ9g/ELr+YSfV4BwYTUaG52HbmQR594ioAruuITVtYt44uXN/7XPsiTzzxLTEz6WAgPpD9wvaq6xFgRaQwsBB5T1TnZyAaJSAfgJ2A1MN8VNtjJmLdGEhcXT/nGbeg96Ek+e/t1GtWry7KVawitdfmHcuD9vbj1putpen1nmvznFjp3vI6B91ubu2YuWMyajX/zzffTCa3V5FL79+Bhd12WWxnz2nDi4uOp0LYjfZ58gTEjn6dR3VosW7OBsKaXHxQH9urBrddfS7Mud9O08110/s/VDOxlOYQXLl7ksRHvULb19VRtfwsLfl/OnK8+plJ593wPZIQIxbowo6sQV09ri8gnwHOqGpfBsbrAV6raIYe6PgYOq+o7IjICqK2q92Z1TnBwsNr9Y9Gv/8PM3pNA2JU9XaYzZtsyap1aycplv7tMZ3Fl2bJldL7jLkr1HYOXr3vThkTPG83g7tcx8rUR+dYlIrGqmj6wIu/69gEPqeqSNP0+QAJQA6s6fG/AueJrvKqGi0gE1oyNr6omptHxP6w4F+eIy/2q2shx/HfgSsc4ALuwkle9r6rx2dneqlkTXbPIxIm5Go0xs7924F275TpVbQXQIjRIf29RO0v5ksv+uSSfESJSGhiHVfz4JPC8qk7OQO4/WLFmVwBnVDUizfF9QHkgydG1QlVvyuFl5RgRGa2qwxzvr1fVXGXTzQg7Zl6qAlsdQXoAiIi3iLyI9VS1JNMznRCR5ljlBT60wcY8ExcXx/Tp0wlqeJ1L9QbVacvfmzby77//Zi9syJTk5GQeeexJ/K/q43bHBSCg/f28/9HHHln3SFUj0joujv5EVRVV3aeq/VTVL02el3CH3D6HXLrpTkcAnlea8xo5Hb9OVQNUNdTRWqjqGzlxXAyGQo+3V9Yte8ZgPVCUB/oAn4tIowzkYoDxwLAsdHV1+ht1uePiYIDT+/QJkfKAjyuUOKOq3UXkbmCCiMzHSvn7ARANXKWq27JUcJnrgAjgX0f0dQjgLSINVfUKV9udU6ZPn05ApbouX44QHz+C6l/DV1+P4/WRr7lUd3Hi+++/5/CZWMJuztHknu34lCxPYCOr7tGEb8a72xyDweBuhHztKBKRYKAH0FhVo4E/RWQ2cB8w3FlWVVcDq0WkYz4sdgWbRGQ6sBXwzyyni6rmZEciYFPMi6pOBVoDXYHFwFJVbZ8LxwXgS6AWVirh5sAXWHvDO7nY3Fzx/idj8Gpgz/8Hfo1v4vMvv/KoVNaFifj4eJ4aNhz/q/vaUr8orwS1vZMZM2exefNmd5tiMBjcjuRk5iU8JY7T0ZxnLuoCiWl2720CMpp5yQmTROSEiCwWkbTZs13FncBGrGzaQur8LnnK8+LymRcAR/DdV1iZdZdh5Yk4DryV0RRzRqhqLE7r5SISjbXW7rZCQOvWrWP3nn2Uvt41W6TT4le+JhdCyvLjjz9y99132zJGUebDjz8hsWRVQmyqX5RXvAJCCGzTk0efGMofv/zsbnMMBoMbESEnJQBOZhHzEgKkrXdwDgjNQDY7+gDrsRyKJ4BFIlJfVV0a/KSqx7HKhSAiPqr6QH51uvzxVES+xAq8e0NVb1bVN4A2wA3ABhFpnRe9qjoiu2Bdu3n9zXfwa94F8fK2bQzvprcy8s13Ckt+EI/h5MmTvPnWO/i3vz97YTcQ3OIWNm7ZzuLFi91tisFgcDf52ysdDYSl6QsDzufWDFVdrqpxjjIeb2GlJbE1w7WqPiAipUTkfhF53vGa68RNdsytl8Zai7uUhVNVd6rqdVglsBfZMKbt7Ny5k8U//0xws5ttHSewTlsOnTjDL794TFqbQsHwF17Cv97V+JbJ1cxjgSHevvi3v59HhjxhlgUNhmKOeHtl2bJhB+AjIs5ZEZuRTfmNHKJYszC2ISJXAbuBR4CmwEBgl6M/x7jceVHVO1X1WCbHPsO6yYWOF14eQWCLLnj5p09X7UrEyxufVj14ZviLZvYlh2zZsoUp034g8Kp73G1KlgTWuZKzGsjnX4x1tykGg8FdiIBXNi0LHLmYZgAjRSRYRNoD3bAyZacZSrxEJADwtT5KgIj4OY5VE5H2IuLn6B8GhAPLXXzFafkIGKyq7VS1l6q2BwYBn+RGiS0xL5khIk2w9py7LkFKAbB582bmL1xImQe/KJDxghtcy751M5k7dy5du3YtkDELK6rKgMGPEdimJ96BaWdSPQsRIeCaB3jplVfp07sXpUt7VqXbwsS5LZHMa5RhkWpDPph7Ojp7IUO+ycHsSnYMxtoCfRw4BQxS1S0icg2wQFVTKldeC/zmdF4c8AfWbt5QrNWQWkA8VkDtLap6Kr/GZUNdYFqavulYm3JyjB0xL0Ei8rqIzBGRD0QkTERqishM4C+sm11oUFUGP/4kgW162j7rkoJ4eePX7j6GPPk0CQkJ2Z9QjJk1axZbdu4juPkt7jYlR/iVr4lv7asY/uJL7jbFYDC4AyHf9QFU9bSq3q6qwapaLSVBnaouc3JcUNXfHbmYnNt1jmNbVLWpQ0cZVb1BVdfadNXO7MSqXu9MT6ylpBxjR8zLGKwt0luxksz9iOXpbQEiVLVQVR+bPXs2m7btIri5vbEuaQms1YrzPiX46ONPC3TcwkR8fDyPPj4U/w79Ee8CnUTMF4HtejNpylSzddpgKKaIt2TZijhPAv8VkZUiMlVEVgGfAY/nRokdzksn4CZVfQ6r0NsNQG9VfUlVT9ownm3ExMQw8NHHCOjwEOLtW6BjiwiBHR5i5KhRHDp0qEDHLiy8/+GHXAirXOBVo/OLd2AYAW16MvDRx01ck8FQ3BBBvLJuRRlVXYG1VPVfrHQqn2KV/lmRGz12OC8hjj3dqOpBIFpVPbpKbGY8/9IrJIbXIbBGC7eM71umCv5Nb+ahgYPNj1wajh8/zlvvjHZb1ej8EtL8Fjbv3Mv8+R5fg9BgMLia/JcHKNSo6hlVnaiq7zpeT+dWhx13yUdE/iMi14vI9WAVh0r5nNLn6axatYrx33xL4LUPutWO4LY9WbH+b6ZNSxvfVLx56dURBNTvgG+pSu42JU+Itw/+7e7jsaHPkJSUlP0JBoOhSCCO8gBZNUP22OG8HMeKgh7naKfSfP7ahjFdSlxcHHf1vo/Aax/EO7ikW20RHz+COg5h4OAhHDuW4Q70YseBAweYNGkygW0L1aa1dATWbsPZRB+mTp3qblMMBkOBkaPyAIZssCPPS4Sq1sii1XT1mK7mmeeeJzqoIkENrnW3KQD4V66PT4MbuLfvg2b5CBj11tsENL4R76AS7jYlX4gIvq178uKIkebf1WAoRpiZl/xjx1bp0iKS4dYcEblZREq5ekxX8uuvv/LtpCkE3fCIR/1PFNzuHtZs2cFXX3n8xJWtnDt3jokTJxF4RdHIfxNQ4wrOxiXx88+m5lFhwrdkSVp9N56b/93N9RvXUKlH9wzlag4ZxLV//sbN+3dy/fpV1BwyKNXxus8/y7XLfqXzsQPUffbpgjDdYwkqVYpHZkzm4+ijvLFvC617ZTyzeuMzT/DyP6v4KOowo/b8w43PPJHq+Bt7N/NJ7HE+On+Ej84f4fFFswrC/Jwj5CtJXWFHRJ4QkfD86rFjfuoloGUmx1oAL9owpks4e/Ysd/e5j+COj3pcwjPx9iWo01CefvY5du/O1Xb4IsXkyVMIimiGT2gZd5viEkQEr4Y38clnhTfrrojsE5HjIhLs1PeQiPzueK8iEiMi0SJyyJH/ydtJ9ncRecgNpueZxu++SfLFi/zcoAkbBj5Kk/feJqRe3XRyIsLGwY+zqGZ9Vt3Vm4iHHqRS926Xjsfu3cu2EaM4/vOSgjTfI+k15n0SL17k2fK1GN/nIXp//iEVG9ZPJycifHP/AJ4qVZVPb76D64YMoNXdPVLJfNb1Lp4MrciToRX5pNPtBXUJOSaf5QEKO9cD+0RkrojcLSL+eVFix13qCmT2TfwlVhpjj6T/wEFotZYE1szM93IvfmWr49+6B3fe06fYBnmOnzAZqdvB3Wa4lKCG17Lk50XExsZmL+y5eGNVpc2MZo7kWR2AuwH3RsLnA++gQCp27cL2t94lKSaWM6tWc2zhYqrcfWc62d2ffkbU3/+gSUnE7NrNsQULKdX2cm3ag9//wIlffiUxOqYgL8Hj8AsKokWPbsx+eRQXYmLYvfwvNs2eT9v7eqWTXTz6Iw5s2ERyUhLHduxk00/zqNX+SjdYnUeyS1DnQTP+dqCq3YDqwAKsnC9HReRrEclVnIYdzkv5LPK5nAbK2zBmvvnxxx/5+Y/lBF3T192mZElwy9vYf/YC74x+z92mFDhRUVH8vXEdARGFsjxWpngHhBBauS6//vqru03JD6OBZ0Qkywh3Vd2FVTulcCXncSK4Vi00MYmY3Xsu9UVt3kJovXrZnlv6yracj9xhp3mFkvJ1a5OcmMjxnbsu9R3atJlKjRpke27ta9pxeMu2VH0PThrH6ON7eXzRLCo3bexye/NLMZ95QVVPqeoYVb0K64GmNfCbYxb3RREJyUaFLc7LGRHJ7K+4LlbJbY/i9OnTPPzIYIJufAwv3wB3m5MlIl4EdhzCG2+9zY4dxetLcM2aNYRVruPx/0Z5Ial8fZYu+9PdZuSHtcDvwDNZCYlIfeAaYFdWcp6MT3AwCefPp+pLjDqPT0jW37d1n3sG8fLi4OTv7TSvUOIfEkJcVOp7GncuioDQrO/prSNewMvLi7/+N/FS3/g+D/FiRCNeqN6Q7b8t4/FFswgs4UHB/cU85iUFEblBRP6H9b1xDLgfuA8rvGRBdufb4bzMBD4RkUDnTsfnD7EKMOUIEZkoIkdEJEpEdti1Lv7k08OQGm0JqNLQDvUux7dkBQLa3Enf/g8Xq10qmzdvRktVc7cZtuBTtgZrNmxytxn55RXgMREpm8Gx9SISA2zD+rL6LLfKRWSAiKwVkbXnNDl/luaDxJgYfENDU/X5hIaQGJ15UcOIhx6gyt09Wd3rPpIvXrTbxELHhehoAsNS39OAsFDiz2d+T697dABX3t+L/3a5k0Sne7p7xUoS4uNJiItj0dvvE3f2HLWvaWeb7blHivXMi4i8JyIHsapIRwJNVPUmVZ3kSGjbC8uByRI77tLLQGlgj4j8T0TedHhXu4EywKu50PUWVj2kMOA2YJSIuDQgZe3atcz4aQ7B7e91pVrbCbmiK9v2HeGHH35wtykFxs49e0kKyeh3sfDjU6I8+/f/624z8oWqbgbmAsMzOHwFEIIV79IWCM5AJjv9X6pqK1VtVULc9wUfs3s34uNNcM0al/rCGjfi/PbtGcpX7X0PtZ4YwsruPYk/fKSgzCxUHNuxCy8fH8rVrnWpr0qzxumWg1Jo98B9dBr+FB/d0JWzhw5nqVtVPWrnKFCsY16AAKC7qjZS1XdUNVX9G1VNAFplp8SOPC/ngXZYTkyAw4gAx+drHMdzqmuLql5I+ehotbI4Jbe2MvjxoQRceTdeAbn+LnUr4uWN/9X9eOLpZ7lYTJ7kTp46jVdAtkuhhRKvgBDOR51ztxmu4FXgYaBy2gNqMQ2ruvwrBW2Yq0iKjePI3PnUHT4M76BASrVpTflbOnFwavpJ5cp33kG9l55nVY97iM3AORUfH7z8/a38Ho73eBXtJ++MuBgby4YZs+k68kX8goKo1e5KmnXrwqoJU9LJtul9F93efJWPb+zGyb37Uh0rVbUKtdpdibevLz7+/tz4zBOEhJdh9/KVBXQlOUCw/o2zakWbt0izbCwipUTkUrp0VY3MToktd0lVE1T1a1Xt5ZgO6qWq41Q117+yIvKZiMRiTS8dAdIVg3GeTk5MTMyx7mXLlhG5ex/BTW7MrVkeQWBEMy4GhjNhwsTshYsACQmJiJd39oKFES9vkpML/w4yR0DuVLKuEPs28LCIVHDq8xGRAKdWsJVQc8nmYc/jHRjIjZGbueKrz/nnmeFEb99B6SvbcvP+y9/L9V54Dr/Spbj65wXcvH8XN+/fRZP33rl0vOlH79H58D4q33kHdZ5+ks6H92W4a6k4MGXwU/gGBjL6+B76TxnP5EFDObI1ktpXt+Oj85dnrG4b9TIhZUozfM3vl3K59P78IwACQkPo9fmHfHDmAG8f2kGjmzvy6S13EHM616VzbESKu/MyC6iSpq8KVshJjpHCEDPhyAlxFXAd8I5jWilDgoODNSYmZ9sOb+jUmY1etQhtnmFOvUJB3L6NBK6ZwN6d2zxvatTF9L6/H4tOhBbqf6/MSDhzhMS5r3Ps4P5MZUQkVlU9bopQRPYBD6nqEsfnqsBOYKWqXiciCtRxODYp5ywAtqrq0458MGn3v09S1UzXcuv4+OqHJfKd58qQhrmnM48xMeSdsUSvU9VWAC0rlNJVfbIu8ef7wYxL8kUNEYlyhIKk7T+nqjmOrPZxrVn2oKpJwJ8ici8wCCvQJ18cOnSIv1YsJ3xA/3zb504Cqjfj7B9fs2LFCtq3b+9uc2ylXHgZkg+kf4La//atbrAm71QfPjddX3J8NKGhnpUYMaeoakSazwewlopTPqfzqlX1Fqf319lonsHgeRTxB81sOC4itdM8zNTGqoOYYwrb/JQPLop5mTptGkF1r8TLr3BvuxURvOtcw7cTJ7nbFNupWrkSXnEet9PeJSTFnKFCxQrZCxoMhkJOsV82Gg/8KCK3ikhDEemKtQs5V7VvPPYuiUg5EblHREJExFtEOmFtofrFFfp/mDkHr4jW2QsWAvxrt2X23HShQEWOqlWr4h3nSWvXriMp6gQ1I6q72wyDwWA3JmD3bWAi8B6wBivB5URHf45x6bKRiEzA2hGUJap6fw7UKdYS0RdYTtZ+4ElVnZ0vI4HExEQ2rFlF2UcG5leVR+Bbtjono6I4cOAAVatWdbc5thEREUHS2WPuNsMW9PxxGtTxzLIUBoPBxRTjZSNVTcZyWEbnR4+rY16ctz+FA32BOViORzWsukff5kSRqp4gfRCfS4iMjCSgZDjeRWTbrYgQXLku69evL9LOS+3atf/f3nmHR1F1Dfx30isQikgLhF4UkCYgCnYsCIodFfVDfLH397WgWF+76GvH3rAiiA0rKKgoiqBIFxBCCT1tk+xuzvfHTKSIs48AACAASURBVGCzySa72d3sJrm/57lPdu69c+fMZGf2zLnnnkPBjmzSvOI2VOZDUteIz8+hc+fOkRbDYDCEHYHYerpq0k/sKPx9sGI/7UNVX/J3jJAqL6p6Z9lnEZkDnGRHzCurG4YV7yWiLF++nMQW9ctE72rchuXLlzN6dNTmvQyapk2bEhsbQ6kjl9iUKAr3HQKce7YY5cVgaAgIDdryIiK3YMV5WgJ4ZqNVLH8YvwjnaqPBgHdkoIVYS54jytq1a3GmNie5+q51Bml0IH+trLPpYvymbWYHcndvqVfKi6pSsD2bjh07RloUg8EQZgRBGrbl5RpgkKouDWaQcHoGLQbuK8txZP+9F/g9jMf0i3X/bIKUZpEWI6TEpjdj/T91O7y8P3Tq1AnX3vrl91LqyCU+PoHG0ZQ8zmAwhI+GnR7AgRV0NijCaXm5EHgL2Csiu4EMrMyz48J4TL/YlL2F2LSQZRmICmJTm7BtRf36Ua+Mrp2y+PG3nEiLEVJce7fRul39msYMN4mxMXRsUp9sp9HBkxcMjrQI9ZLnps7cv1G22qjhMhn4n4hMwcomvQ/bmdcvwnYFVXW9qg7FistyCtBZVYeq6rpwHdNftm3PIaYeTTsAxKQ0ZtfOgGL81Ek6d8witrB+nadr73YyM+tntmyDweBN8HFeRKSpiHwoIgUiskFEzvXR70gR+VZE9tqRsL3bO9jthSKyQkSOCf78quUVrPxnmwCnXVz2X78Ja4RdEWmGFdK/lao+aCdeilHVTeE8bnXs3L6D2O51M5qpL2JTGrN3d/36Ua+Mdu3aEVPPlBd37nY6dTOWF4OhQRAay8tTQAnQEugLfCIiS1R1mVe/Aiwn2OnALZWMMx0rUeqJdnlfRLrYq33DRVb1XaonbJYXERkOrMSaJipbYdQFeCZcx/SX3bt21DvLi8RbkYLz8+t3bpLMzExcueG8r2ofKdhB5yyjvBgMDYYgfF5EJBUYC0xW1XxVnQ98BJzv3VdVf1bV14G/KxmnK9APuENVHar6AfCHPXbYUNUNqroB2AiUlG3bdX4Tzom3qcBZqjoSyyQE1mqjQWE8ZrW43W7y9u6pV6tVwIr1ktyoKTk59csfxJsOHTpQuGMLdSGhqL/EFewwK40MhgaDX9NGzUVkkUeZ6DFAV8Clqqs86pYAvQIUpBfwt6rmBTlOQIhIExF5CyjCjg0nIqeIyD2BjBNO5aWDqpaF8i/7pSkhwskgc3JySEpNR2LrRE7KgEho1JTNmzdHWoywkpGRQVx8PKUF9SfHkXP3Zjp1ql8O5AaDwQf+pQfYoaoDPMrzHiOkAbleo+4F0gOUJM3eL9hxAuVZ+zjtsXQCsKauzgpkkHAqL3/Z+Yg8OQbLLBUxsrOzSc44IJIihA1JbUZ2dnakxQg7nbt1p2RHQBbGqEXdTvK2Z9OtW7dIi2IwGGqFoB128wFvp81GQF4lfWtjnEA5GrhKVbdgGzZsH5uAfpjDaX64HvhYRD4BkkXkOaz0ABENAbtp0yZi0yvGeNlw/8kRkKbmVBYS352cwaZNEfWFrhUOO3QQ0/9cQ3KHvpEWJWhKctbTrkNHkpPNst+6REzjxrS+/37Shh2Oa/duch56iNzZFdOupQweTIsrrySp10G49+5lzfAjyrUn9uhBqzumkNi9O6X5+ex+ezo7nnyytk4jqthVVMLELxfz5YYcmicncM9hPTmne8V0J3M3bueehStZnLOHjMR41vzf/nfknMJirpu7lO+yd1LgdNGrWSMeOuIgDm3VtDZPpXqCc9hdBcTZjrWr7bo+gLezbnUsAzqKSLrH1FEfrBAn4WQvVvqgLWUVIpLpue0P4Vwq/RPQG+sCvQSsw4qq90u4jukP2dnZlKZE2Rc5RGhKU9Zt2BhpMcLOUSOOIG7b8kiLERKKN/7B8MOHRVqMgBGR9SJSIiLNveoXi4iKSAePuil23aFefZeJSL5XKRYRv2M9RIpWd96FOp2sPHQQ2ddeS6u77yaxS5cK/UoLC9nz3ntsu/+/lY7T9rGpFPzyMyv7HcL6c88hY9x5pB19dLjFj0qu+mYJCTExZE88gVdHDuCKb5awbKf37AikxMdxYa9M7j/8oApt+U4X/Q/MYOG5I8j510mc3zOT0bN+Ir/EVaFvxChLD1BDh11VLQBmAHeJSKqIHIZlFHi9wqFEYkQkCYi3NiVJRBLscVZhBY29w64/Fes3+4NQnm4lvAB8ICJHAjEiMgQr5+GzgQwSztVGN6jqZlV9UFUvV9X7VXWTiFwXrmP6w6bszTgT6pezbhmxaU1Z/0/9t7wcddRR5G5YRmlJUaRFCZrYTb8zZtRJkRajpqwDzinbEJGDgRTPDmJl0LwA2GX/3Yeq9lLVtLICHIi1KuLucAseDJKcTKPjj2f7o4+hhYU4fl1E3ldf0XjMqRX6Fi1dyt6ZM3H+U/lLRXzbtuydNQtKS3H+8w+ORYtI7NI13KcQdRQ4XcxYs5kpQ3uQlhDHsDbNOLnjgby5vOJ1G3RgBuf1yKRj45QKbR0bp3Jtv860Sk0iNka45OAOlJSWsnJ3NK3CtBMzVlWq5zIgGcjBWu48SVWXicjhIuJ5skdgRbT9FCs5sgP4wqP9bGAAsBu4Hzg9zMukAR4A3sFa7h2PZdyYBTweyCDh9Hm53Uf9bWE8ZrVs2ryV2NT6qbzEpDZhWz1fbQSW0+7AQwfjWLMw0qIEhStvJ4Wb13DcccdFWpSa8jrlFZLxwGtefQ4HWgFXAWeXvfX54AWs5ZN3VtEn4iRmZaFuNyXr98fbLF6xvFLLS3Xsevllmpx6GsTFkZCVRfIhh1CwYEEoxa0TrNqdT1xMDF0z9icZ7tOiMX9VYnkJhN9z9lDiLqVzk9RgRQwtQaYHUNVdqjpGVVNVNVNV37Lrv7dfBMr6zVVV8SojPNrXq+oIVU1W1W6q+lU4TtdLdlXVx1W1py1/D1WdqgEuIQ25z4uIHGV/jLXNQp7/iY6E3xmoSnbt3kNMYsWVHZX5kNQ1YhJTyM31dh6vn1z5r0u47Nb/Qs/hkRalxjj++JLTzzijLvu7/AScLyI9sObhzwYOAzyXPI4HZgPvAk9g+b1VMEuLyFXAUKBfICHCI0FMSiqlXvGU3Hl5xKQG/gOZ9+03tHn4YZpNmIDExbH9iSco+iOofHV1kgKni0YJ5X+OGiXEkxfEdE9usZML5/zG5EO70zgxPlgRQ4eIv9aVeomHjlABVf3G33HC4bD7ov03ifLprRUrj8GV/gwiIonA01grlJoCa4GbVfWzYIQrLHIgaVW9/NVdJC6BoqK6P5XiD6eeeiqXX30txVtWkdiq7pnZS51FFC/9nBuf+jbSogRLmfVlHrAc2LfcTURSgDOAC1TVKSLv233LKS8iMhi4DzhGVXf4OpAd62IiQKu4yD38SwsLiElLK1cXk5ZGaUFBQOPENG5M5ksvs/XOKez96CPiWrSg7ZNP4dqxg91vvhFKkaOe1Pg4cr0UlbwSJ+kJNfuJcrjcjPnoJw5tlcG/B0Xh80EadG6jF722WwAJWOkC/A54FfIrqKpZqpoFvFn22S4dVXWIqlZ0ya+cOCwT8nCgMdZ007uejoCGhkt8fDyTb7mZkoXvRFqUGlHw++cMO2wIBx1U0emwjvE6cC5WIlbvKaNTsQJUfmpvvwmcICItyjrYDr/vYb2Y/FTVgVT1+bK4F01jIqe8FK9bh8TGktChw766pB49KF692vdOlZCQmQmlpez98ENwu3Ft3Uruxx+TNmJEaAWuA3TNSMNVWspqD9+UJTty6dks8DQuxS43Yz9aSJu0ZJ45OgpXJEpIfF7qLF56QRbW7/u9QEDL7MKp/j0qIuXWuYlIOxHp48/OqlqgqlPsOblSVf0Yy0GwfzBCpSanoM7iYIaIWtRZXJenIALm0omXkJC3Bcf6JZEWJSDcjjyKFs3g0Qfvj7QoQWOH9F6HlRdlhlfzeKxAWP+IyFYsJSUeS9lBRGKwlmUuUNX/1ZrQQaIOB7lfzKHFNdciyckk9+9P+jHHsnfmhxU7iyAJCUh83L7PxFtTGCXr1oEIjUadAiLENm9Oo5NOonjFilo+o8iTGh/HqZ1bc+ePyylwuliweSez125hXI+KS6VLVSlyuXG6FQWKXG5K3NZMo9Ndylmf/ExyXAwvH9+PGD/8RyJCkD4v9QlVdWMpLzcFsl84lZc3sB5UniRQyXIufxCRllhhkSusZReRiWVhlF2uqudIWx7QHHdRRN1uwkapI4+mzSrGsKmvJCYm8sRjD1P83YuoO4qWQlaD48e3OOuM0+nZs2ekRQkV/wccZS/hLKMNVjCqk7ESx/XFiiHxAPudfKcA7YAJtSZpiNhy++1IUiLdfv6FtlMfZ8vkyRSvXk3KgIF0X7o/DmfKoEH0WL6CzJdeJqFNG3osX0H7V14FoDQ/n42XTaLZxRfTbfHvdPr4E4pXr2L7Uw0zzsv/juqDw+Wm9XOfcf5ni3jyqD70ataI+dk7aPLU7H39vt+0g/QnZzNq1o/8k+cg/cnZnDDDcnL+ccsuPlm3jS//2U7zZz6hyVOzafLUbOZn+5yNjAwSU3VpeBwLBOTrJuHKESMiuapawebnq76aseKBz4C1qnppVX1TU1O1oIq557vvvptHP/+DxkeMD0SEOkHeki84usku3n2rRvphnURVGTbiaFbGdyJt4JhIi1MtxVvX4PjoHv5evZKmTQOLNyQihaoaFcsmRGQ9MMF7dYKIxGGltr8ZOENV+3u1twY2AIcAS+2+zkoO0VNV//F1/IMSE/Xdtm2COgdDRbqe4pdh3BAg8VNn/qqqAwAGZLXRhVP+VWX/uAtv39e/viEiG9mfMgis8ApJwGWq6j317JNwRtjdJCL9VPW3sgoR6QcElHzHNi2/jpUD4YpghcrKyiK+oM47SVZK6d5t9BjYPdJi1CoiwsvTnuWQgYeS1O0w4hq1qH6nCKGlboq+fY5HH3owYMUl2lDVDj7qXexfYVhhXkxVN7PfItsgXzENhoY2NeTFeV7bBcAqVQ1oXXw4lZfHgFki8iDWSqFOwA1Yc1t+YQe4ehFoCZyoqpW9oQVEly5dKN1TP5MXJhRso1u3UyItRq3TtWtXrr36Kp6Z8SLpo/4TaXF8UvD7Z3Ru1ZSLLrow0qIYDIZI0cCXSqvqvFCMEzblRVWnicgerPnwdlgrh65X1fcDGOYZoAfWEkpHKOTq1q0beTkbSVVF6pn269qdTdeuUbgssBaYfOstvPLaGxSu+YWUzgMjLU4F3Pm7cfz0Dq/9tKDefe8MBkOANEy/FgBE5HXKTxtViqpeUFV7WK+gqr6nqiPtMOAjA1FcRKQ9cCmWo99Wj9wn44KRqUmTJiQlpeDO3xnMMFGHail52zY12OzEiYmJvPT8MxTNewF1lVS/Qy3jmP8qEydcTI8ePSItisFgiCgCMbFVl/rNHmAMEIsV2yUGKzfTHqxZmrJSJSG1vIjI+ar6uv35Yl/9VPUlX20efTZQPjpvyMjMymL77q3EpTevvnMdwZ2/i5TUVNLT0yMtSsQ47rjjOLR/Xxb/Npu0QWMjLc4+iresonTTUu6a4r2S2GAwNDgEiGnQ1teuwEmq+n1ZhYgMAyar6vG+dytPqKeNzmH/UujzffRRykferXWyOnRgS264c0/VLu7c7bRqWzEmQkPjyamP0G/QEFJ6H09MUlr1O9QCJT+8yX1339WgFUuDweBB/beuVMVgrNQiniwEhgQySEinjVT1RI/PR/ooPvMa1BZZmW3r3bSRO383rVu1jrQYEadbt26ccsooCn71N5BzeCna+CeJRTv5v//zaYg0GAwNCRHL8lJVqd8sBu4TkWQA+++9wO+BDBJS5UVEYvwpoTxmTWjZojlSHE0p0oPH7cil5QH1ZxosGO66/TaKlnxGaUlIfLyDwvXrh0y57Rbi46MoMZzBYIgsDdvn5UKsBK57RWQbsBcYhhWR229CPW3kwg8vYixHnYjRqFEjYlz1K4GhljjIaNw40mJEBV26dGHY4cNY9Oe3pPc7sfodwoRz50ZcOX9zwQW+ZlANNSUuPoYDW6ZEWox6x/svL4i0CA0AadCrjVR1PTDUTh/UGthSVUBKX4T6CmZhZYXsiJU9eh4wEmu580jgW0IQaC5YkpOTidG6E07eH9TlJC3VPMzL+Pd111C6bA7hiiDtD8VLP+fSiRNISkqKmAwGgyHKKHPYbbjTRohIM2AEMFxV/xGR1iLSNpAxQmp5sVcIlQl3HTBAVffYVatEZBGwCCt+S8SIj4+HOpQLxx/U7SQxwUxNlHHkkUeSEltKyeaVJLap/ajD6iqh8K95THp7aq0f22AwRDn1f2rIJyIyHPgASxc4DHgQ6IIVxHaUv+OE03bVGCtngScpdn1ESUhIQErrl/Ii6iYxMTHSYkQNIsKkiRNwLv86IscvXP0Tffr2pUOHDhE5vsFgiFYEYmKqLvWbqcBZqjoSy9UErNVGgwIZJJxX6VXgKzvj8wkiMhGYY9dHlJSUFIjCQGbBEOsusc7LsI8Lx19AwYoFlDqLa/3YunIul11iVhgZDAYvhIbusNtBVcveKsvm9UsIcCYonLmNbgLWAGdhO+UATwLTwnhMv2jatCmljoByQEU9MSX5ZGRkRFqMqKJt27b0HzCQlat+JLXXiFo7ritvB47slYwdGz2B8gwGQ7QgDT0x418icryqzvGoOwb4I5BBwmZ5UdVSVX1WVY9W1R6qepS97Q7XMf2lXbt2FO3eFmkxQorm76BdOxOkzpurL7sUXfFNrR7T8efXnHHGGcYSZjAYKiJYiRmrKvWb64E3ReRVIFlEngNeAW4MZJCwKS9icYmIfC0iS+26I0TkzHAd018yMzNxO4twF+ypvnMdQEvd5G1eS+/evSMtStQxevRoXDs34ty5qVaOp6VunMu+5KrLJ9XK8QyRQdIbkfbfx8n4+mcaz/iChGMrX5If128g6f97iSZf/EjjD+ZU2ifxzPNo/P7n1lhvfURMu/bhFD1qSWjShGFvvMLp2esY9cevtD/9tEr7db/yckb+MI+xG//m5CW/0P3Ky/e1JTZvzpAXnmX08qWctmENR3/+MU3796utU/ATe6l0VaUeo6o/Ab2BZVjR9tcBg1T1l0DGCedVugsro/Q0INOu2wT8O4zH9AsRYeiwI3CsWxxpUUJCyZZVtGrdhhYtWkRalKgjISGBSydOoPj3T2rleI7VC+nQri39+kXbA9MQSlJuuA11Otl98nAKpvyblBsnE5vVqUI/dTgo/uRDHE89Uuk4iaPGknjyaeTdcBm7jx5E3o2Xo3t2h1v8qKT/w/dTWlLCzK4H8eMll9H/kQdp1L2SRLMiLJx0BTM6dGHe2LPpMvFiMk8bA0Bcaiq7Fv/OnBHH8GFWV9ZPf4fh775JXGpqLZ9NNTRQnxcRiRWRucBOVX1QVS9X1ftVNeC3y3AqLxcCJ6vq2+x3ylmHFQMm4lw47mx01dxIixESnMu/5fxzzoq0GFHLNVddSeHy73AX7g3rcVQV1++zmHzzTWE9Tm0iIutFJEdEUj3qJtgPIERERaTAzvi+07a0nuU1xlwRKfLIDJ8vIkPstrtF5A8RcYnIlNo8txqTlEzCiGNxTPsfOBy4li7GOX8uCSMrrvJ0L/+Tks9n486u5NksQtLFkyh8/AFK1/8NQGn2RjSvfvnj+UNsSgptTzmZP+69H1dBATt+Wsjmz+fQ4awzKvRd8cST7F7yB+p2k7dmLdmffk7zwdZClYING1j51LMUbctBS0tZ++rrxMQnkN65omIZMRpwegDbbSSLEOge4VReYoGyGPxlykuaR11EOfPMM2FPNsWbV0ZalKBw5e6gcOUCrrj8skiLErW0atWKs846E8evs8J6nKL1v5MuJZx2WuXm7jpMLHB1Fe19VDUN6IY1d/2kiNzh1ecKVU3zKD/a9WuwnPtrxzQWAmIz24PbRenGfWGtcK9eSWxW54DGiTmgJbEtDyS2Uxcaf/gVjd//nOT/u7xBOnOmd+6Iulzkrf17X93uP5bRuEcllhcvWgwZzN7llT/Hmxx8EDEJ8eSvWxcyWUNCA7W82NwJPCMi7W1LTI1SB4VTefkMeFREEsHygQHuBmaH8Zh+k5CQwMP3/5eiudPQ0oj7ENcIVcXx3UtcfeUVHHDAAZEWJ6q58/bbcCydg7sgPCZ5VaXkp7e47647iK1/DncPATeISJOqOqnqDlV9HZgE3GxH0awSVX1VVT8D8kIjaviR5BS0oKBcnRbkISmBTU3EtDgQgPhBQ8k9/1TyrriYhGNPIHFUvVN+qyU+NRVnXvn3WmduLvFpVWeGP+jmmyAmhnVvTq/QFpeexuBnn+TPBx7GmRtNX6/gfV5EpKmIfGhbPTeIyLk++omIPGBbRXfan8Wj3dNymi8iL4TwRH3xAnAB8DfWEmknVrwXZyCDhFN5uRZohZV0qTGWxaU9Afi8iMgVIrJIRIpF5JVQCzh+/AX0ympNwQ8Vv/h1gYI/v6JR0TYm33pLpEWJejIzMxk/fjyOn94Ny/iFK3+gRUos55xzTljGjzCLgLlYETD9YRZWGIaAgk5Vhx0zapGILNrpjNwLhzoKES8fCklNQwsLfOzhY5wSK79a0Rsvofl5lG7dTPGs94gfckTIZK0rOAsKiE8vr6jEN0rHme/bUN/lkovpcPYZfHfmuZSWlI/bFZuUxBFvv8HORb+y/LEnwiJzMIhIlcUPnsL64W8JjMOyZPSqpN9EYAzQB8tJdhRwqVefPh4W0Qk1Pin/yaJ8KqGOHtt+ExblxdbsmgNnYDnrDgY6qeqpqhqICrwZuAfLIznkxMTEMOPd6cia7yhY/l04DhE2ijb9RfGC1/lk1gyzJNdP7p5yO8WrF1CyfUP1nQNAXU6Kf3iNp594jJj6Gx3zduBKEanWK1xVncAOoKlH9RMisscuv9VEAFV9XlUHqOqAZvGRs265/9kAsXHEtM3cVxfbuRvudWsCG2fDerSkhHK5bCOYiyuS5K35G4mLI61j1r66Jgf18jkdlHXeOfS45iq+PeV0HJu3lGuLSUhg2JuvUpi9hV+u8VffrkVEICau6lLl7pIKjAUmq2q+qs4HPgIqywA7HnhEVTepajbwCJY/aq0jIgeClUbIVwlkvLA8adXKhvcHUKqqOar6i6purcE4M1R1JrAz5ELaHHDAAXw95zOK571QZ1YfleSsI//jB3hv+pv07Nkz0uLUGZo1a8adt0+m+LsXQ5qwsWDRTA7t15djjjkmZGNGG6r6J/Ax8J/q+opIPNAC2OVRfZWqNrFL3V6KVeSgZN5XJF9yBSQlE3fwIcQffiQln1cyIy4CCQlIXNy+z8TZP07FRZR8/TlJ4y6GlBSkRUsSR59OyYJ5tXs+UYC7sJBNsz/h4Fv+TWxKCs0PHUSbE0ay/p33KvRtf8ZYek++lbmnnkHBhvK/dxIXx2GvvYi7qIiFk66IXmUwOIfdroBLVVd51C0BKrO89LLbqur3nYhsFZEZItIhsBMJCE95EZEZwQwWztfExVgXOex4mpNdrsBzFvXu3ZtPZ8+icM5jOP7+NQwSho6SbX+TO2MK055+kpEjR0ZanDrHlVdcTmMKKVz5Q0jGc+Vux/HbLJ598vGQjBfl3AFcArSppt9orDnsn8MuUYQofOhuJDGRjE/mkXrngxQ+dDfudWuJ69OPjK/2n3Zc3wE0nfsb6Y8+S+yBra3PU/cHGS945F7UUUjGrG9p9PybFH/xKSUfB/VMr7P8ev2/iU1O5tTVyxjywrP8ev1N5K5YSYshhzJ2036H24Nv+w+JTTM49psvGLtpHWM3rWPAow8B0PzQgbQZeTwHHjmc0zas2dfeYsihkTqtiviXHqB52W+aXSZ6jJAGeC9J2wukV3K0NLvNs1+ah9/LcKAD0B1rpuNjEQlX5H1vrWxEMIOFMz3AXOBz21dlIx62UVUN6TSQqj4PPA+QmppaI1V72LBhfP7xR5xw8mhKR1xCavdhoRQxJBRt/JO8jx/kpeee4ayzIh7rr04SHx/Py9Oe5eSxZ5PcsT8xCUlBjVf0/ctcfeUVdOoURUsxw4SqrhGRd4CrqCSUt4g0BU4AHgUeUNVqLaa2lSYW60UqTkSSAGc0ROKuCs3LJf8/FRdguZb8xu5j9rv6uBb/wq6hB/keqLCAgttvJDBvmfpJyZ49zB83vkL99h8X8kHb/dNJH/cZ6HOM7Qt+5O0m0b54Qfxxyt2hqgN8tOUDjbzqGlG507t330ZAvj07gqqW+UuUiMjVWEpRDwIM1e8nITWDhVN5OQwrrstwr3olTD4swXLYYYfx/dyvOerY4yko3ENqv5MjLdI+Clf+gOObZ5jxznSOP/74SItTpxk+fDjHjDiC7xe+R9rhlU0T+4dj/e/E7VrH5Fs/D6F0Uc9dVJxbXyIiiuVAuAS4VlXf8nO8aVjz8mXcClyEteTaYKifBLccehWWot9FVVfbdX2wItZ6s8xu+7mafmUoFS0koSJORI70GN97G1X1O5dL2JQXVT0yXGOHkz59+rBo4Y+MOOY48vO2k3rEeAJcfh5yCn77GPfiD5n79Zf0798/orLUF5564jG69jiIxIOOIT6jVcD7q9tF8bwXefmpx0lOTg6DhNGBqnbw2t4IJHlsV/ugU9URVbRdSIQcCA2GiCB++bX4RFULbH+Ru0RkAtAXa6p2aCXdXwOuE5FPsRST64H/WWJILyAey8qSjLU4JhtYXmPhqiaH8oaLnV7bSgArjkL+qywiKSJyn4h8JCJTyuK81HCsMjNyLBArIklhnI/bR1ZWFot/WUh792byP30EdZVUv1MYUC0lf97LpKz+il8X/mgUlxDSunVr/n3j9RTNf6VG+xf8/hm9unRgzJgxoRXMYDDUf4IPUncZlsKRA0wHJqnqMhE5XEQ815c/hxVb7Q/gyyYNHwAAGwdJREFUT6xgkM/ZbS2Bd7Cmiv7G8n052V4tGHJUtYOqZlVRIr5U+imsteQrgNOBh4MY6zbAgbXC4Tz7823BCugPTZs2ZcG8bxjSsSm5H95FaVHtzkqr20n+51PJdGWzeNFCsrKyqt/JEBA33nA9sbvWU7Txz4D2Ky0qwPHzezzzv6n+xmQwGAwGm+CD1KnqLlUdo6qpqppZNk2rqt/b0a7L+qmq3qSqTe1yk4e/yzeq2s0e4wB7vNW+jhlthEN5GQkcp6o3YTnv1dhxRFWnqKp4lSmhErQ6kpKSmPXBe5x+zGBy378t7Llxyih1FpP30X/p3yqZBfO+oWnTptXvZAiYpKQkHvrvvZT8+FZAS6cLfp3FSSeeYLJ4GwyGwPFvtZGhGsKhvKSq6hbYNz/eOAzHqDViY2N5/pmnmTjudEuBCVN4+TJKnUXkz7qHI3tn8ensmfXanyIaOO+880jTQoo2LKm+M7bV5fdP+e/dd4ZZMoPBUD8RiImpuhiqJRxXKU5EjhSRo0TkKO9tu65OISI8eP99XH7ROPI+vBN3UXhyS6rbSf7sBzhmQA/enf4mcXFhd+9p8MTGxjLltptx/zbTr/4FSz7jhBNOaBBLow0GQ3iQmNgqi6F6wvHrGFKP4mji3rvvYveevbwz+7+knzYFiY0P2diqSv6XTzGwc0veev3V+pjcL2o577zzuO7G/+DcuZH4Zu189tNSNyVLP+e2Lz+tRekMBkO9QmiQmcNDTciVF++llfUJEeHJxx9j7cmj+W3uC6QdPSlkYxcumklL9w4+fG+BsbjUMgkJCVwy4WJe+u5L4kdc7LNf0brfyGzbmkMOOaQWpTNURkx8LEmtq0xybagBc38Mbd4vQ2X4FaTOUA3mCgZIbGwsH7w7naRtf1GwYn5IxizevJKS32by+cezTJLFCPF/F11I8Yrv0FLfgV1LV33PZZf4Vm4MBoPBL2Jjqy6GajHKSw1IT0/nw/ffofDb54NegaQuJ4VfPMG0Z58mMzOz+h0MYaF79+4ccEALijdXnsVWXU7y1/zC6aefXsuSGQyGeoVI9cVQLUZ5qSEDBw7kgvPG4fjhjaDGKfh1FgN69+SMM84IkWSGmnLGqaMpWVd5Ys6iTcvo2LkrLVu2rGWpDAZDvcMslQ4ao7wEwX1330nxmoU4d26s0f7uonwcv84ywc6ihBNGHk/M5srzkTn/WcopJ5qcUgaDIRRINcVQHUZ5CYKMjAyuv+ZqihfVLIV94W8fc8qok+natWuIJTPUhEGDBpG3eR2lzqIKbbHbV3HkCO8cowaDwRAoJs5LKDBXKUiuvupKClcvxJW/K6D91OWkeOln3H7rzWGSzBAoKSkptO/UBWfO+nL1Wuomd9MqBg0aFBnBDAZDPcNYXoLFKC9BkpGRwZlnnolj6RcB7Ve4cgEHH3wwPXr0CJNkhpowsH8/Srb9Xa7OtWcrTTKa0aSJWZprsElLJ+HG+0h+40uSnnmf2GHHVtotptchJE55guRXPyfp6fd8DhfTsy8p788n/uxLwiVx1JOSkcG/ZrzF4/lbuXf9MgaeU7kf4LE3XM3kPxYyNXcz9/z9B8fecHW59nvX/ckThTlMzdvC1LwtXDXHvwCUtYZgZZWuqhiqxQQUCQFXXT6JGSNHoUPORPxcv68rv+G6e/4TZskMgdK/78HMeWduuTrn9g307NkzMgIZopKECdeDy4ljwinEdOhC4s0PUrR+DbppXfmOxUW4vvkEEr4i/rTzKx8sNpaEi67GvWpZ+AWPYs556hFcJSXc1LITbfv25opP3mPTkj/Y8teKcv1EhFcumEj20j9p0akjV30xk90bN7HonQ/29Xl61Jms+HpuLZ9BIBgFJViM5SUEHHLIIbTIaEzxP/5lJ3btzaF42zpOOeWUMEtmCJQePXoQk7ulXJ1zVzZ9D+4VIYkig4isFxGHiOSLyFYReUVE0jzah4rINyKSJyJ7RWS2iPT0aB8hIqX2/nkislJELvI6hopIgd1nh4hMF5HoN28lJhF76HCcb78ARQ5KVyzFvWg+ccMrOnSXrlmO+7s56LbNPoeLG3UO7iU/o9kNN0BcQkoKh4wdzUeT76G4oIC1C35kyUefcuj551To+8VDU9m4eAmlbjfbVq1myaxP6HTY4AhIXVOMz0soMFcpBIgI/5pwMa6V3/nV3/HXXE4//XQSExPDLJkhULp27UrRjk3l6uLzt9Kze7cISRRRRqlqGtAXOAS4GUBEhgBfALOA1kAWsARYICKeqT822/s3Aq4FpomI94XsY/fpCGQAU8J3OqFBWreDUje6Zf8qw9INa4lplxX4WM1bEnfUSTjffyWEEtY9WnbtTKnLRc7qNfvqspf8Sete1U+rdz58KJuXLS9Xd/GbL/JQzjqumjOTNr0PCrm8wWN8XoLFKC8hYty4cylY+QOlzuIq+6kqpau/55KLL6wdwQwB0b59ewr37kRdzn11mruNzp07R1CqyKKqW4E5WEoMwIPAa6r6uKrmqeouVb0N+IlKlA+1+BTYBfT2cYxc4CMg6ufnJCkZCgvK1WlhPiQHHh07/v+uwfn2NChyhEq8OkliWhqO3LxydY69uSSlp/nYw+LkKbcQExPDjy/vj7f10rgJ3NqhF7e078nKb7/nqjkzSW7cOCxy1xgTpC5oolp5EZGmIvKhbVreICLnRlomX7Ru3Zq+/frhWPtLlf1Ktq0lUdwMGTKkliQzBEJcXBzNDmiFKzdnX13Rri107Fgnc4mGBBFpC5wArBGRFGAoUJn36btABc9VEYkRkVOA5sCaCntZfTKAMVgKkC85JorIIhFZtKPYFfiJhAgtckBKark6SU4FR2FA48T2PwxJSsH9wzehFK9OUpyfT3Kj9HJ1SY3SKcrL97nPiMsnMviCc3jypNNxlZTsq1/7w084i4pwOhzMuf8RHHv20vnwoWGTPWDEzm1UVTFUS7RfpaeAEqAlMA54RkSi1vng0ovHw+qqp46cK+Zx0QXnmaB0UUxm+/a49m4DrCXtjtxdtG3bNsJSRYSZIpIHbMTKFn8H0BTrubGlkv5bsBSUMlqLyB7AAXwIXKeqi732+c3uswPIBJ7zJYyqPq+qA1R1QPPEyK010M0bISYWOXD/dyKmQ2dKN66rYq+KxBzcn5hO3UmeNovkabOIHXo0cSedQcK//xtqkaOebavWEBMXxwGdO+2ra9vnoArTQWUMveh8jv/PdUw9ehR7sn37E4Fl7Y66562xvARN1CovIpIKjAUmq2q+qs7HMiv7cNmPPGPHjiV//VKf+Y601E3Riu+5cPwFtSyZIRC6duqEa4+lvLjyttOsxYENNdP3GFVNB0YA3bEUk91AKdCqkv6tsJSQMjarahMsn5cngKMq2aef3ScJeAb4XkSSQnYG4aC4CPfP84g/ewIkJhHT7WBiBwzDNW9Oxb4iEJ8AcXHlPwPOt6fhuOocHDdehOPGi3Avmo/r69mUPHVfLZ9Q5CkpLGTxjI8YddetJKSk0GnoYPqMPomFr0+v0HfQuWcy+r47ePzY0exYt75cW0a7tnQaOpjY+HjiEhM59oarSWvejLULfBr0IoJITJXFUD3R/ETuCrhUdZVH3RIgasOcpqenc9zxI/nqq+dJaN6+Qrs7fxeZme3o1q1BOn/WGXp178LMT54gZs13uIoK6d6Ap4wAVHWeiLwCPKyqY0TkR+AM4FuvrmcCX1eyf7GI/BtYKSJjVLVC4A1VdYrIC8BU4CBgUajPI5SUTHuEhMtuJvnF2WheLiXTHkE3rSOmR28Sb3kYx/nHAVb8lqQ7/7dvv5Tp3+BetpjiO660/Fw8fV1KiqGoCPLzvA/XIJh+2XVc8NLTPJTzNwU7d/HWpGvZ8tcKOg8byhWffcA16Za+fMo9k0lr1pT//DJ3374/v/EOb026hqT0NM555jFadMrCWVTMpt+X8r8TTqNgV2BBRMOOsa4EjahqpGWoFBE5HHhPVQ/0qLsEGKeqI7z6TgQmAiQkJPQvLq7aaTacrF69mhdefMln+6ljRjN4cF1a1tfwyM/PZ+nSpfu227dvT5s2bSIokYWIFKpqavU9Q3Ks9cAEVf3K3m4BrMfyd0nHcuC9GXgZ6yXoeuAKYKCqrhaREcAbqtrWY8wrgItVtZ+9rUAXVV0jIrHAJCxn4LaqWuWvTb+MNJ1/dN+quhhqwHUfLIm0CPWS58j/VVUHAAzo20cXffNZlf2lWZt9/Q2VE82Wl3wsc7MnjYAKryWq+jzwPEBqampEtbEuXbrwwP0Nb866PpGWlsbQoVHk4BcFqOp2EXkNuF1Vx4rI8cA9wH1Y00jfA8NUdXUVw7wETBGRUao6265bYisxpcBK4NTqFBeDoU4jGMtLCIhm5WUVECciXTweiH2Ahh2G0mCoBVS1QyV1kzw+z8fyhfG1/1ygrVddIR4OvapqnuCGhonxawmaqL2CqloAzADuEpFUETkMGA28HlnJDAaDwWAIAhOjLmii2fICcBmWqTkH2AlMUlVjeTEYDAZDHUWM5SUERPUVtCN3jlHVVFXNVNW3Ii2TwWAwGAw1psznJYg4L/4GcBWLB0Rkp10eEI+gNyLSV0R+FZFC+2+d8YKPauXFYDAYDIb6R9DzRv4GcJ2IFbm6D1ZqjlHApQAikoCVn+wNrLxirwKz7PqoxygvBoPBYDDUGsFllQ4wgOt44BFV3aSq2cAjwIV22wgs15Gpqlqsqk9YwlUaTDLqMMqLwWAwGAy1SlCWF18BXCuzvPSy2yrr1wtYquWDvS31MU7UEe0OuwFTWFioIhJNKVrjgMhlkTMEQ7T975IjLUC0sHhPwY7UDxZsiLQcftKc8mkTDKGhLl3XfSHXf138+xxJy2heVWcgSUQ8o0w/b8czA0gDcr3678UKHulNmt3m2S/N9nvxbqtqnKij3ikvqhpV1iQRWWQiJdZNzP8uelHVFpGWwV/M9yg81NXrqqojgxzC7wCulfRtBOSrqopIIONEHVH1Q28wGAwGg6FK9gVw9ajzFcB1md1WWb9lQG/P1UdYTr11IhyJUV4MBoPBYKgjBBjA9TXgOhFpIyKtsXKQvWK3zQXcwFUikmjnHgP4JpzyhwqjvISf56vvYohSzP/OEArM9yg8NOTrehmWD1wOMB07gKuIHG5PB5XxHDAb+AP4E/jErkNVS7CWUV8A7AEuBsbY9VFP1GaVNhgMBoPBYKgMY3kxGAwGg8FQpzDKi8FgMBgMhjpF1CkvIrJeRHLsKIJldRNEZG4ExaoUEXlFRO6JtBz1DTsfx98i8leA+3UQERWRehcCoCFhngHRjbk/DdFA1CkvNrHA1ZEWwhAxjgAOADqKyMBIC2OICOYZEL2Y+9MQcaJVeXkIuEFEmlTWKCJDReQXEdlr/x3q0TZXRO4WkQUikiciX4iIz2iGInKRiCy3+/4tIpd6tF0oIvO9+quIdBaRiVgJsW4SkXwRmW2397Bl2CMiy0TkFI99XxGRp0TkE/t4C0WkUwDndY+I/FB2PBFpJiJvikiu3b+Dn2OtF5FjPLaniMgb9uckEXlDrAyke+x9W/q6fmFiPFbCsE/tz/uoSnbgO/vvHvsaDRGRGBG5TazMqzki8pqINLb3LXsTHC8i/4jIDhG51WPsRBGZKiKb7TJVRBLtthEisklEbrLH3SIiY0TkRBFZJSK7ROQWP8fy+T2zP58oIn/Z35lsEbkhRNc5mjHPgOh9Bpj709yfkUdVo6oA64FjsNax32PXTQDm2p+bAruxklDFAefY283s9rnAWqz8D8n29v1VHO8koBNWQonhQCHQz267EJjv1V+BzvbnV8pktLfjgTXALUACVoKrPKCbR/+dwCBb9jeBtwM4rzW2rI2Bv7CCFR1j938NeNnPsdYDx3jIPQV4w/58KdbSuhSst9/+QKNa/P+nYIW+PhEr+dgOIMH7++FD9g72/yfOo/1i+7p1xAqHPQN43av/NPu70gcoBnrY7XcBP2G9ZbYAfgDutttGYKUOuN3+v18CbAfewgqv3QtwAFl+jHUhVX/PtgCH258zsL+f9bVgngFR+wzA3J8N/v6MlhKtlhewvnRXioh3GPCTgNWq+rqqulR1OrACK9V3GS+r6ipVdQDvAn19HURVP1HVtWoxD/gCOLyGMg/GugHvV9USVf0G+BjrwVHGh6r6s6q6sB5cZbL5e15rVXUv8BmwVlW/ssd6DzgkgLF84QSaYd2YblX9VVW982iEk9OwHlBfYMUkiMc6n5oyDnhUVf9W1XzgZuBsKT/vfqeqOlR1CVbisj4e+96lqjmquh24k/KZW53AvarqBN7GyrXyuKrmqeoyrB8Xf8eqCifQU0QaqepuVf0twGtQVzHPgOh7Bpj7syIN9f6MKFGrvKjqn1g3/X+8mloD3gnZNgBtPLa3enwuxHqYICLP2ubK/DKToYicICI/2WbEPVhvFNUlzfJFa2CjqpYGKpuf57XN47Ojku1AxvLF68Ac4G3bfPqgiMT7sV+oGA+8az9wi4AP8DJNB4j3tdiA9SbqaQb393+ywa4rY6equu3PZclA/f2feI9VFWOxvpcbRGSeiAzxc786jXkGVLpvpJ8B5v6sSIO8PyNN1CovNndgmfs8b7jNeGTotMkEsqsbTFX/pappdrnPntP8AHgYaKmqTbDmcctyPRRgmUkBEJEDvYf02t4MtBMRz+vql2wEcV41GKvceQH7zktVnap6p6r2BIYCJ2NFYAw7ItIWy8x+nohsFZGtwOnAibLfZ8Gn7FT8f0DFa5GJZU7eVklff/bd7Md+gY5V5fdMVX9R1dFYJu2ZWJaEhoJ5BkTJM8Dcnxbm/owOolp5UdU1wDvAVR7VnwJdReRcEYkTkbOAnlhvaIGSACRizYW6ROQE4DiP9iVALxHpKyJJWPO3nmzDmqstYyHWm8FNIhIvIiOwzLRv+yFLKM+rurF+xzLNxovIAKwHEAAicqSIHCwisVhz206glNrhfKw5/G5YpvS+WH4Lm9hvdvcpO9b/sZTy/5PpwLUikiUiacB9wDu2mb06pgO3iUgL++F8O/BGNfvUZCyf3zMRSRCRcSLS2DZ/51J7/4+IY54BUfUMMPenuT+jhqhWXmzuAvbFe1DVnVhvAtdjOb7dBJysqjsCHVhV87Aeiu9iObOdC3zk0b7KPv5XwGpgvtcQL2LNde4RkZlq5YQYBZyA5cj2NHCBqq7wQ5ZQnld1Y03GcvrbjTW3+5bH7gcC72PdhMuBeVSe8CscjAeeVtWtngV4lv2maZ+yq2ohcC+wwP6fDAZesuX/DlgHFAFX+inPPcAiYClWbpDf7Lqa4HMsP75n5wPrRSQX+BfW/HxDwjwDAj+vcDwDzP1p7s+oweQ2MhgMBoPBUKeoC5YXg8FgMBgMhn0Y5cVgMBgMBkOdwigvBoPBYDAY6hRGeTEYDAaDwVCnMMqLwWAwGAyGOoVRXgwGg8FgMNQpjPJiCDliZ3SNtBwGgyE0mHvaEG0Y5aUWEZFhYqWz32vnUVkgIgPttgpp1w0GQ3Rj7mmDITLEVd/FEApEpBFWaO5JWNE8E7Ay1xYHMEasR6IxQxCYa2kIFnNPRxfmWjYsjOWl9ugKoKrT7TTzDlX9QlWXikgPrBDbQ8TKdrsHQEReEZFnRORTESkAjhSRxiLymohsF5ENInJbWRK4sjc9EXlYRHaLyDo7Vwt2e5aIfCcieSLylYg8JSI+c4GIyMki8rsdyvsHEent0bZeRG4QkaX2W+c7dt4Pz/2vF5EcEdkiIhd51J8kIotFJFdENorIFI+2DiKiIjJeRP4RkR0icqtHe6yI3CIia+3z+FVE2tlt3UXkS/sNeKWInOmxX2XX8kQR+cseJ1tEbgj4v2poyJh7en+9uacNtYuqmlILBWiElWPkVay8Jxle7RcC873qXgH2AodhKZpJwGvALCAd6ICVKO3/PMZwYmXhjcV6I9zM/jQQP2Jlz00AhmHlLnnDh7yHADnAofZY44H1QKLdvh74GSttfFOsHCj/sttGYGWGvQuIx0oXX1h2znb7wfY59cZKbjfGbuuAlX12GpAM9MF6k+1ht9+IlXukG1bm3z5AM6zcNxuBi7Asiodg5ZbpWcW13AIcbrdnAP0i/T0xpe4Uc0+be9qUCN5/kRagIRWgh33DbbIfBB8BLe02Xw+61zy2Y4GSspvXrrsUmOsxxhqPthT7oXEg+1PNp3i0v1HFg+4Z4G6vupXAcPvzeuA8j7YHgWftzyMABxDn0Z4DDPZxrKnAY/bnsgddW4/2n4GzPWQYXckYZwHfe9U9B9xR2bW06/6xr1+jSH83TKmbxdzT5p42JTLFTBvVIqq6XFUvVNW2wEFYbzhTq9lto8fn5lhvPRs86jYAbTy2t3ocr9D+mGYfa5dHnffY3rQHrrfNy3tss3c7e5wKx8J6C0vz2N6p5dPa72sXkUNF5FvbTL4XKxNrc6/j+xq7HbDWh7yHesk7Dush7+t8x2K9QW4QkXkiMqSScQ0Gn5h72tzThshglJcIoaorsN4cDiqr8tXV4/MOLBNye4+6TCDbj0NuAZqKSIpHXbsq+m8E7lXVJh4lRVWn+3Gs6ngL6w21nao2xvINED/33Qh08lE/z0veNFWd5NGn3DVW1V9UdTRwADATy+nSYKgR5p4297Sh9jDKSy1hO55dLyJt7e12wDnAT3aXbUBbEUnwNYZanvTvAveKSLqItAeuwzIVV4mqbgAWAVNEJMF+IxlVxS7TgH/Zb1QiIqm2U166H6dbHelYb4xFIjIIODeAfV8A7haRLrZcvUWkGdaqj64icr6IxNtloFiOkxWwr8E4EWmsqk4sX4HSYE/M0HAw93Q5zD1tqFWM8lJ75GE5yi20PeN/Av4ErrfbvwGWAVtFZEcV41wJFAB/A/Ox3nhe8lOGccAQLCfDe4B38LGsU1UXYTkJPgnsBtZgzb+HgsuAu0QkD7idwN6OHrX7f4H1cHoRSFbVPOA44Gwsh8atwANAYhVjnQ+sF5FcLDP3uADPw9CwMff0fsw9bahVyjzWDQ0QEXkHWKGqd0RaFoPBEDzmnjY0FIzlpQFhm1w7iUiMiIwERmPNCxsMhjqIuacNDRUTYbdhcSAwAyuGwiZgkqoujqxIBoMhCMw9bWiQmGkjg8FgMBgMdQozbWQwGAwGg6FOYZQXg8FgMBgMdQqjvBgMBoPBYKhTGOXFYDAYDAZDncIoLwaDwWAwGOoURnkxGAwGg8FQp/h/RpiZwnSciSUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x576 with 8 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Keep sequences where the WT and Polylinker were both measured\n",
"poly_measured_mask = activity_df[[\"expression_log2_WT\", \"expression_log2_POLY\"]].notna().all(axis=1)\n",
"activity_poly_df = activity_df[poly_measured_mask]\n",
"wt_occupancy_poly_df = wt_occupancy_df[poly_measured_mask]\n",
"wt_entropy_poly_df = wt_entropy_df[poly_measured_mask]\n",
"\n",
"# Setup the figure\n",
"fig, ax_list = plot_utils.setup_multiplot(4, sharex=False, sharey=False)\n",
"ax_list = ax_list.flatten()\n",
"\n",
"# 4a: scatterplot of Rho vs. Polylinker\n",
"ax = ax_list[0]\n",
"print(\"Correlation between WT activity with Rho vs. Polylinker:\")\n",
"fig, ax = plot_utils.scatter_with_corr(activity_poly_df[\"expression_log2_WT\"], activity_poly_df[\"expression_log2_POLY\"], \"log2 Enhancer Activity/Rho\", \"log2 Autonomous Activity\", colors=activity_poly_df[\"plot_color_WT\"], xticks=rho_ticks, figax=(fig, ax))\n",
"ax.axhline(0, color=\"k\", linestyle=\"--\")\n",
"plot_utils.add_letter(ax, -0.2, 1.03, \"a\")\n",
"\n",
"# Display some numbers for the manuscript\n",
"print(\"Fraction of autonomous sequences belonging to each activity class:\")\n",
"display(activity_poly_df.loc[activity_poly_df[\"autonomous_activity\"], \"group_name_WT\"].value_counts(normalize=True))\n",
"\n",
"print(\"Fraction of each activity class that has autonomous activity:\")\n",
"display(activity_poly_df.groupby(\"group_name_WT\")[\"autonomous_activity\"].apply(lambda x: x.sum() / len(x)))\n",
"\n",
"# Information content of strong autonomous vs. non-autonomous\n",
"# Set up grouping\n",
"strong_enh_poly_mask = activity_poly_df[\"group_name_WT\"].str.contains(\"Strong\")\n",
"strong_enh_poly_mask = strong_enh_poly_mask & strong_enh_poly_mask.notna()\n",
"autonomous_occ_grouper = wt_occupancy_poly_df[strong_enh_poly_mask].groupby(activity_poly_df.loc[strong_enh_poly_mask, \"autonomous_activity\"])\n",
"autonomous_entropy_grouper = wt_entropy_poly_df[strong_enh_poly_mask].groupby(activity_poly_df.loc[strong_enh_poly_mask, \"autonomous_activity\"])\n",
"\n",
"# Set up for plotting\n",
"strong_color = color_mapping[\"Strong enhancer\"]\n",
"autonomous_names = [\"Non-autonomous \", \" Autonomous\"]\n",
"autonomous_counts = [len(i) for i in autonomous_occ_grouper.groups.values()]\n",
"\n",
"# Do stats for difference in IC\n",
"print(\"Information content of autonomous and non-autonomous strong enhancers:\")\n",
"display(autonomous_entropy_grouper[\"entropy\"].describe())\n",
"ustat, pval = stats.mannwhitneyu(*[j for i, j in autonomous_entropy_grouper[\"entropy\"]], alternative=\"two-sided\")\n",
"print(f\"Autonomous and non-autonomous strong enhancers have the same information content, Mann-Whitney U test p={pval:.0e}, U={ustat:.2f}\")\n",
"\n",
"# 4b: Make the plot\n",
"ax = ax_list[1]\n",
"fig = plot_utils.violin_plot_groupby(autonomous_entropy_grouper[\"entropy\"], \"Information content\", class_names=autonomous_names, class_colors=[strong_color]*2, figax=(fig, ax))\n",
"ax.set_xlabel(\"Strong enhancers\")\n",
"# Add ticks for the n\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(autonomous_counts, fontsize=10, rotation=45)\n",
"plot_utils.add_letter(ax, -0.2, 1.03, \"b\")\n",
"\n",
"# Differences in CRX occupancy\n",
"print(\"Predicted CRX occupancy of autonomous and non-autonomous strong enhancers:\")\n",
"display(autonomous_occ_grouper[\"CRX\"].describe())\n",
"ustat, pval = stats.mannwhitneyu(*[j for i, j in autonomous_occ_grouper[\"CRX\"]], alternative=\"two-sided\")\n",
"print(f\"Autonomous and non-autonomous strong enhancers have the same predicted CRX occupancy, Mann-Whitney U test p={pval:.0e}, U={ustat:.2f}\")\n",
"\n",
"# 4c\n",
"ax = ax_list[2]\n",
"fig = plot_utils.violin_plot_groupby(autonomous_occ_grouper[\"CRX\"], \"Predicted CRX occupancy\", class_names=autonomous_names, class_colors=[strong_color]*2, figax=(fig, ax))\n",
"ax.set_xlabel(\"Strong enhancers\")\n",
"ax.set_yticks(np.arange(8))\n",
"# Add ticks for the n\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(autonomous_counts, fontsize=10, rotation=45)\n",
"plot_utils.add_letter(ax, -0.2, 1.03, \"c\")\n",
"\n",
"# Differences in motif frequencies\n",
"autonomous_motif_freq_df = autonomous_occ_grouper.apply(lambda x: (x > occupied_cutoff).sum() / len(x))\n",
"# Sort by the feature importance in the logistic model\n",
"autonomous_motif_freq_df = autonomous_motif_freq_df.iloc[:, feature_order]\n",
"\n",
"# 4d: Make heatmakt, but put CRX separate\n",
"ax = ax_list[3]\n",
"autonomous_motif_freq_no_crx_df = autonomous_motif_freq_df.drop(columns=\"CRX\") \n",
"vmax = 0.25\n",
"thresh = vmax / 2\n",
"heatmap = ax.imshow(autonomous_motif_freq_no_crx_df.T, aspect=\"auto\", cmap=\"Reds\", vmax=vmax, vmin=0)\n",
"ax.set_xlabel(\"Strong enhancers\")\n",
"ax.set_xticks(np.arange(len(autonomous_motif_freq_no_crx_df)))\n",
"ax.set_xticklabels(autonomous_names)\n",
"ax.set_yticks(np.arange(len(autonomous_motif_freq_no_crx_df.columns)))\n",
"ax.set_yticklabels(autonomous_motif_freq_no_crx_df.columns)\n",
"plot_utils.annotate_heatmap(ax, autonomous_motif_freq_no_crx_df, thresh)\n",
"\n",
"# Add colorbar\n",
"divider = make_axes_locatable(ax)\n",
"cax = divider.append_axes(\"right\", size=\"5%\", pad=\"2%\")\n",
"colorbar = fig.colorbar(heatmap, cax=cax, label=\"Frequency of motif\")\n",
"ticks = cax.get_yticks()\n",
"ticks = [f\"{i:.2f}\" for i in ticks]\n",
"ticks[-1] = r\"$\\geq$\" + ticks[-1]\n",
"cax.set_yticklabels(ticks)\n",
"\n",
"# Add CRX\n",
"cax = divider.append_axes(\"top\", size=\"14%\", pad=\"2%\")\n",
"heatmap = cax.imshow(autonomous_motif_freq_df[\"CRX\"].to_frame().T, aspect=\"auto\", cmap=\"Reds\", vmax=vmax, vmin=0)\n",
"cax.set_xticks([])\n",
"cax.set_yticks([0])\n",
"cax.set_yticklabels([\"CRX\"])\n",
"plot_utils.annotate_heatmap(cax, autonomous_motif_freq_df[\"CRX\"].to_frame(), thresh)\n",
"plot_utils.add_letter(cax, -0.2, 1.03, \"d\")\n",
"\n",
"# Add ticks for the n\n",
"cax.xaxis.tick_top()\n",
"cax.set_xticks(ax.get_xticks())\n",
"cax.set_xlim(ax.get_xlim())\n",
"cax.set_xticklabels(autonomous_counts, fontsize=10, rotation=45)\n",
"\n",
"# Test relationship between NRL binding and strong enhancer autonomous activity\n",
"print(\"Strong enhancers with autonomous and non-autonomous activity vs. NRL bound and unbound:\")\n",
"nrl_chip_vs_autonomous = activity_poly_df[strong_enh_poly_mask].groupby(\"autonomous_activity\")[\"nrl_bound\"].value_counts().unstack()\n",
"display(nrl_chip_vs_autonomous)\n",
"oddsratio, pval = stats.fisher_exact(nrl_chip_vs_autonomous)\n",
"print(f\"Fisher's exact test that NRL binding and strong enhancer autonomous activity are independent, p={pval:.0e}, odds ratio={oddsratio:.1f}\")\n",
"fig.tight_layout()\n",
"display(fig)\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TF motifs contribute independently to strong enhancers\n",
"\n",
"Our results indicate that information content distinguishes strong enhancers from silencers and inactive sequences. Information content only takes into account the total number and diversity of motifs in a sequence and not any potential interactions between them. The classification success of information content thus suggests that each TF motif will contribute independently to enhancer activity. We tested this prediction with CRX-targeted sequences where all CRX motifs were abolished by point mutation ([Supplementary file 3](#supp3)). Consistent with our previous work [@bib85], mutating CRX motifs causes the activities of both enhancers and silencers to regress toward basal levels (Pearson’s _r_ = 0.608, [Figure 5a](#fig5)), indicating that most enhancers and silencers show some dependence on CRX. However, 40% of wild-type strong enhancers show low CRX dependence and remain strong enhancers with their CRX motifs abolished. Although strong enhancers with high and low CRX dependence have similar wild-type information content ([Figure 5b](#fig5)), strong enhancers with low CRX dependence have lower predicted CRX occupancy than those with high CRX dependence (Mann-Whitney U test p = 2 × 10^–9^, [Figure 5c](#fig5)), and also have higher ‘residual’ information content (i.e. information content without CRX motifs, Mann-Whitney U test p = 1 × 10^–7^, [Figure 5d](#fig5)). Low CRX dependence sequences have an average of 1.5 residual bits, which corresponds to three motifs for two TFs, while high CRX dependence sequences have an average of 1.0 residual bits, which corresponds to two motifs for two TFs ([Figure 5e](#fig5))."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"caption": "### Independence of transcription factor (TF) motifs in strong enhancers.\n\n(**a**) Activity of sequences with and without cone-rod homeobox (CRX) motifs. Points are colored by the activity group with CRX motifs intact: dark blue, strong enhancers; light blue, weak enhancers; green, inactive; red, silencers; gray, ambiguous; horizontal dotted lines and color bar represent the cutoffs for the same groups when CRX motifs are mutated. Solid black line is the y = x line. (**b–d**) Comparison of strong enhancers with high and low CRX dependence for information content (**b**), predicted CRX occupancy (**c**), and residual information content (**d**). (**e**) Representative strong enhancers with high (top) or low (bottom) CRX dependence.",
"id": "fig5",
"label": "Figure 5."
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Correlation between WT and MUT activities:\n",
"PCC = 0.608\n",
"SCC = 0.706\n",
"n = 4123\n",
"Information content of strong enhancers with different mutant activities:\n"
]
},
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" count mean std min 25% 50% \\\n",
"group_name_MUT \n",
"False 586.0 2.321663 2.067846 0.000346 0.641760 1.849581 \n",
"True 344.0 2.857066 2.411316 0.001591 1.145032 2.413095 \n",
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"Predicted CRX occupancy of strong enhancers with different mutant activities:\n"
]
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" count mean std min 25% 50% \\\n",
"group_name_MUT \n",
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"text": [
"Strong enhancers that remain strong vs. do not have the same CRX occupancy, Mann-Whitney U test p=2e-09, U=124411.00\n",
"Residual information content of strong enhancers with different mutant activities:\n"
]
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" count mean std min 25% 50% \\\n",
"group_name_MUT \n",
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"text": [
"Strong enhancers that stay strong vs. do not have the same residual information content, Mann-Whitney U test p=1e-07, U=79938.00\n"
]
},
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\n",
"text/plain": [
"<Figure size 504x720 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Helper functions to visualize sequence\n",
"def hex_to_rgb(hexcode):\n",
" return tuple(int(hexcode[i:i+2], 16) / 255 for i in (1, 3, 5))\n",
"\n",
"strong_color_rgb = hex_to_rgb(strong_color)\n",
"weak_color_rgb = hex_to_rgb(color_mapping[\"Weak enhancer\"])\n",
"crx_color = mpl.colors.to_rgb(\"orange\")\n",
"other_tf_color = mpl.colors.to_rgb(\"red\")\n",
"\n",
"def visualize_sequence(seq_id, ax, title, below_text, basecolor):\n",
" seq_occupancy_df = predicted_occupancy.total_landscape(all_seqs[seq_id], ewms, mu)\n",
" visual = np.full(((len(seq_occupancy_df), 3)), basecolor) # (number of positions, RGB values)\n",
" text_mapping = [] # (name of TF, center position of motif)\n",
" # Loop over each TF, identify motifs, and fill in the representation with the predicted occupancy for the full motif\n",
" for col in seq_occupancy_df:\n",
" tf, orient = col.split(\"_\")\n",
" for motif_start, occ in seq_occupancy_df[col].iteritems():\n",
" if occ > occupied_cutoff:\n",
" motif_end = motif_start + motif_len[tf]\n",
" # Check and make sure all positions of the motif are zeros\n",
" if (visual[motif_start:motif_end] != basecolor).all(axis=1).any():\n",
" print(f\"Error, motif already in the range {motif_start}-{motif_end}! Skipping.\")\n",
" else:\n",
" color = crx_color if tf == \"CRX\" else other_tf_color\n",
" visual[motif_start:motif_end] = color\n",
" text_mapping.append((tf, (motif_start + motif_end) / 2))\n",
" \n",
" heatmap = ax.imshow(visual[np.newaxis, :], aspect=\"auto\", cmap=\"Reds\")\n",
" ax.set_yticks([])\n",
" # Add text showing which motif is where\n",
" for tf, x in text_mapping:\n",
" ax.text(x, 0, tf, ha=\"center\", va=\"center\", color=\"white\", rotation=90)\n",
"\n",
" ax.set_title(title)\n",
" ax.set_xlabel(below_text)\n",
" \n",
" return ax, heatmap\n",
"\n",
"# Setup for sequences where both WT and MUT was measured\n",
"wt_mut_mask = activity_df[\"wt_vs_mut_log2\"].notna()\n",
"activity_wt_mut_measured_df = activity_df[wt_mut_mask]\n",
"wt_occ_mut_measured_df = wt_occupancy_df[wt_mut_mask]\n",
"wt_entropy_mut_measured_df = wt_entropy_df[wt_mut_mask]\n",
"mut_entropy_measured_df = mut_entropy_df[wt_mut_mask]\n",
"\n",
"# Figure setup\n",
"gs_kw = dict(height_ratios=[5, 5, 1, 1])\n",
"fig, ax_list = plt.subplots(nrows=4, ncols=2, figsize=(7, 10), gridspec_kw=gs_kw)\n",
"gs = ax_list[0, 0].get_gridspec()\n",
"for row in [2, 3]:\n",
" for ax in ax_list[row]:\n",
" ax.remove()\n",
" \n",
"axstrong = fig.add_subplot(gs[2, :])\n",
"axweak = fig.add_subplot(gs[3, :])\n",
"\n",
"# 5a: Scatter plot of WT and MUT activities\n",
"ax = ax_list[0, 0]\n",
"print(\"Correlation between WT and MUT activities:\")\n",
"fig, ax = plot_utils.scatter_with_corr(activity_wt_mut_measured_df[\"expression_log2_WT\"], activity_wt_mut_measured_df[\"expression_log2_MUT\"],\n",
" \"log2 WT Activity/Rho\", \"log2 MUT Activity/Rho\", colors=activity_wt_mut_measured_df[\"plot_color_WT\"],\n",
" xticks=rho_ticks, yticks=rho_ticks, figax=(fig, ax))\n",
"# Plot y = x line\n",
"ax.plot(rho_ticks, rho_ticks, color=\"black\", linewidth=1)\n",
"# Show cutoffs for different classes\n",
"strong_cutoff = activity_df.groupby(\"group_name_WT\")[\"expression_log2_WT\"].get_group(\"Strong enhancer\").min()\n",
"for line in [-1, 1, strong_cutoff]:\n",
" ax.axhline(line, color=\"black\", linestyle=\"--\", linewidth=1)\n",
" \n",
"# Add colorbar to show the cutoffs\n",
"divider = make_axes_locatable(ax)\n",
"color_ax = divider.append_axes(\"right\", size=\"5%\")\n",
"color_ax.set_ylim(ax.get_ylim())\n",
"color_ax.barh([(-1 - ax.get_ylim()[0]) / 2 + ax.get_ylim()[0], 0, (strong_cutoff - 1) / 2 + 1, (ax.get_ylim()[1] - strong_cutoff) / 2 + strong_cutoff], # Midpoint of the bars\n",
" [1, 1, 1, 1], # Bar height\n",
" [-1 - ax.get_ylim()[0], 2, strong_cutoff - 1, ax.get_ylim()[1] - strong_cutoff], # Bar width\n",
" color=color_mapping)\n",
"color_ax.set_xticks([])\n",
"color_ax.set_yticks([])\n",
"color_ax.set_xlim(right=1)\n",
"\n",
"plot_utils.add_letter(ax, -0.35, 1.03, \"a\")\n",
"\n",
"# Setup strong enhancer->mutant activity groupings\n",
"strong_mask = activity_wt_mut_measured_df[\"group_name_WT\"].str.contains(\"Strong\")\n",
"strong_mask = strong_mask & strong_mask.notna()\n",
"activity_strong_df = activity_wt_mut_measured_df[strong_mask]\n",
"\n",
"# Group the data based on CRX-dependence (whether or not it stay strong) and name the groups accordingly\n",
"stay_strong_mask = activity_strong_df[\"group_name_MUT\"].str.contains(\"Strong\") & activity_strong_df[\"group_name_MUT\"].notna()\n",
"wt_occ_strong_grouper = wt_occ_mut_measured_df[strong_mask].groupby(stay_strong_mask)\n",
"wt_entropy_strong_grouper = wt_entropy_mut_measured_df[strong_mask].groupby(stay_strong_mask)\n",
"strong_mutant_names = wt_entropy_strong_grouper[\"entropy\"].count().rename({False: \"High\", True: \"Low\"}).index.values.tolist()\n",
"strong_mutant_counts = wt_entropy_strong_grouper[\"entropy\"].count().astype(int).values\n",
"\n",
"# Differences in information content\n",
"print(\"Information content of strong enhancers with different mutant activities:\")\n",
"display(wt_entropy_strong_grouper[\"entropy\"].describe())\n",
"\n",
"# 5b: Information content\n",
"ax = ax_list[0, 1]\n",
"fig = plot_utils.violin_plot_groupby(wt_entropy_strong_grouper[\"entropy\"], \"Information content\", class_names=strong_mutant_names, class_colors=color_mapping[[\"Weak enhancer\", \"Strong enhancer\"]], figax=(fig, ax))\n",
"ax.set_xlabel(\"CRX-dependence\\nStrong enhancers\")\n",
"ax.set_yticks(np.arange(0, 13, 2))\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(strong_mutant_counts, fontsize=10, rotation=45)\n",
"plot_utils.add_letter(ax, -0.25, 1.03, \"b\")\n",
"\n",
"# Differences in predicted CRX occupancy of strong enhancers with different CRX-dependences\n",
"print(\"Predicted CRX occupancy of strong enhancers with different mutant activities:\")\n",
"display(wt_occ_strong_grouper[\"CRX\"].describe())\n",
"ustat, pval = stats.mannwhitneyu(*[j for i, j in wt_occ_strong_grouper[\"CRX\"]], alternative=\"two-sided\")\n",
"print(f\"Strong enhancers that remain strong vs. do not have the same CRX occupancy, Mann-Whitney U test p={pval:.0e}, U={ustat:.2f}\")\n",
"\n",
"# 5c: predicted CRX occupancy\n",
"ax = ax_list[1, 0]\n",
"fig = plot_utils.violin_plot_groupby(wt_occ_strong_grouper[\"CRX\"], \"Predicted CRX occupancy\", class_names=strong_mutant_names, class_colors=color_mapping[[\"Weak enhancer\", \"Strong enhancer\"]], figax=(fig, ax))\n",
"ax.set_xlabel(\"CRX-dependence\\nStrong enhancers\")\n",
"ax.set_yticks(np.arange(0, 8, 2))\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(strong_mutant_counts, fontsize=10, rotation=45)\n",
"plot_utils.add_letter(ax, -0.25, 1.03, \"c\")\n",
"\n",
"# Differences in redisual IC\n",
"print(\"Residual information content of strong enhancers with different mutant activities:\")\n",
"mut_entropy_strong_grouper = mut_entropy_measured_df[strong_mask].groupby(stay_strong_mask)\n",
"display(mut_entropy_strong_grouper[\"entropy\"].describe())\n",
"ustat, pval = stats.mannwhitneyu(*[j for i, j in mut_entropy_strong_grouper[\"entropy\"]], alternative=\"two-sided\")\n",
"print(f\"Strong enhancers that stay strong vs. do not have the same residual information content, Mann-Whitney U test p={pval:.0e}, U={ustat:.2f}\")\n",
"\n",
"# 5d: Residual information content\n",
"ax = ax_list[1, 1]\n",
"fig = plot_utils.violin_plot_groupby(mut_entropy_strong_grouper[\"entropy\"], \"Residual information content\", class_names=strong_mutant_names, class_colors=color_mapping[[\"Weak enhancer\", \"Strong enhancer\"]], figax=(fig, ax))\n",
"ax.set_xlabel(\"CRX-dependence\\nStrong enhancers\")\n",
"ax.set_yticks(np.arange(0, 11, 2))\n",
"ax_twin = ax.twiny()\n",
"ax_twin.set_xticks(ax.get_xticks())\n",
"ax_twin.set_xlim(ax.get_xlim())\n",
"ax_twin.set_xticklabels(strong_mutant_counts, fontsize=10, rotation=45)\n",
"plot_utils.add_letter(ax, -0.25, 1.03, \"d\")\n",
"\n",
"# 5e Visualize the two depresentative sequences\n",
"ax = axstrong\n",
"become_weak_example_id = \"chr16-43945747-43945911_UPPE\"\n",
"become_weak_text = become_weak_example_id.split(\"_\")[0] + \"\\n\" + f\"{wt_entropy_df.loc[become_weak_example_id, 'entropy']:.1f}\" + \" bits, \" + f\"{mut_entropy_df.loc[become_weak_example_id, 'entropy']:.1f}\" + \" residual bits\"\n",
"ax, become_weak_visual = visualize_sequence(become_weak_example_id + \"_WT\", ax, \"High CRX-dependence\", become_weak_text, weak_color_rgb)\n",
"plot_utils.add_letter(ax, -0.05, 1.03, \"e\")\n",
"\n",
"ax = axweak\n",
"stay_strong_example_id = \"chr11-114685176-114685340_CPPE\"\n",
"stay_strong_text = stay_strong_example_id.split(\"_\")[0] + \"\\n\" + f\"{wt_entropy_df.loc[stay_strong_example_id, 'entropy']:.1f}\" + \" bits, \" + f\"{mut_entropy_df.loc[stay_strong_example_id, 'entropy']:.1f}\" + \" residual bits\"\n",
"ax, stay_strong_visual = visualize_sequence(stay_strong_example_id + \"_WT\", ax, \"Low CRX-dependence\", stay_strong_text, strong_color_rgb)\n",
"\n",
"fig.tight_layout()\n",
"display(fig)\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Strong enhancers with low and high CRX dependence have similar wild-type information content and similar total predicted occupancy ([Figure 5b and e](#fig5)). As a result, sequences with more CRX motifs have fewer motifs for other TFs, suggesting that there is no evolutionary pressure for enhancers to contain additional motifs beyond the minimum amount of information content required to be active. To test this idea, we calculated the minimum number and diversity of motifs necessary to specify a relatively unique location in the genome [@bib87] and found that a 164 bp sequence only requires five motifs for three TFs (Materials and methods). These motif requirements can be achieved in two ways with similar information content that differ only in the quantitative number of motifs for each TF. In other words, the number of motifs for any particular TF is not important so long as there is sufficient information content. Taken together, we conclude that each TF motif provides an independent contribution toward specifying strong enhancers.\n",
"\n",
"# Discussion\n",
"\n",
"Many regions in the genome are bound by TFs and bear the epigenetic hallmarks of active _cis_-regulatory sequences, yet fail to exhibit _cis_-regulatory activity when tested directly. The discrepancy between measured epigenomic state and _cis_-regulatory activity indicates that enhancers and silencers consist of more than the minimal sequence features necessary to recruit TFs and chromatin-modifying factors. Our results show that enhancers, silencers, and inactive sequences in developing photoreceptors can be distinguished by their motif content, even though they are indistinguishable by CRX binding or chromatin accessibility. We show that both enhancers and silencers contain more TF motifs than inactive sequences, and that enhancers also contain more diverse sets of motifs for lineage-defining TFs. These differences are captured by our measure of information content. Information content, as a single metric, identifies strong enhancers nearly as well as an unbiased set of 2080 non-redundant 6-mers used for an SVM, indicating that a simple measure of motif number and diversity can capture the key sequence features that distinguish enhancers from other sequences that lie in open chromatin.\n",
"\n",
"The results of our information content classifier are consistent with the TF collective model of enhancers [@bib39; @bib78]: globally, active enhancers are specified by the combinatorial action of lineage-defining TFs with little constraint on which motifs must co-occur. We show that CRX-targeted enhancers are distinguished from inactive CRX-targeted sequences by a larger, more diverse collection of TF motifs, and not any specific combination of motifs. This indicates that enhancers are active because they have acquired the necessary number of TF binding motifs, and not because they are defined by a strict regulatory grammar. Sequences with fewer motifs may be bound by CRX and reside within open chromatin, but they lack sufficient TF binding for activity. Such loose constraints would facilitate the de novo emergence of tissue-specific enhancers and silencers over evolution and explain why critical cell type-specific TF interactions, such as CRX and NRL in rod photoreceptors, occur at only a minority of the active enhancers in that cell type [@bib28; @bib32; @bib85].\n",
"\n",
"Like enhancers, CRX-targeted silencers require higher motif content and are dependent on CRX motifs, but they lack the TF diversity of enhancers. The lack of TF diversity in silencers parallels the architecture of signal-responsive _cis_-regulatory sequences, which are silencers in the absence of a signal and require multiple activators for induction [@bib4]. Consistent with this, we previously showed using synthetic sequences that high occupancy of CRX alone is sufficient to encode silencers while the addition of a single NRL motif converts synthetic silencers to enhancers, and that genomic sequences with very high CRX motif content repress a basal promoter that lacks NRL motifs [@bib86]. We found that photoreceptor genes which are de-repressed upon loss of CRX are located near _cis_-regulatory sequences with high CRX motif content, and that genes near regions that are bound only by CRX are expressed at lower levels than genes near regions co-bound by CRX and NRL [@bib86]. In the current study, we find that silencers in our MPRA library are more likely to occur near de-repressed photoreceptor genes, while strong enhancers are enriched near genes that lose expression in _Crx^-/-^_ retina. These findings suggest that the low TF diversity and high CRX motif content that characterize silencers in our MPRA library are also important for silencing in the genome.\n",
"\n",
"The contrast in motif diversity between enhancers and silencers that we observe could explain how CRX achieves selective activation and repression of its target genes in multiple cell types and across developmental time points [@bib60; @bib72]. CRX itself is required for silencing, and we previously showed that some silencers become active enhancers in _Crx^-/-^_ retina [@bib86]. The mechanism of CRX-based silencing is unknown, however CRX cooperates with other TFs that can sometimes act as repressors of cell type-specific genes [@bib7; @bib65; @bib84], while other repressors can directly inhibit activation by CRX or its co-activators [@bib12; @bib26; @bib57; @bib75]. In _Drosophila_ photoreceptors, selective silencing of opsin genes is controlled by cell type-specific expression of a repressor, Dve, which acts on the same K50 homeodomain-binding sites as a universally expressed activator, Otd, a homolog of CRX [@bib70]. Other transcriptional activators selectively act as repressors in the same cell type. GATA-1 represses the _GATA-_2 promoter by displacing CREB-binding protein (CBP), while at other genes GATA-1 binds CBP to activate transcription [@bib21]. Selective repression by GATA-1 is also mediated by chromatin occupancy levels and interaction with co-regulators [@bib38], which is consistent with our finding that sequence context enables a TF to both activate and repress genes in the same cell type.\n",
"\n",
"Given the central role of CRX in selectively regulating genes in multiple closely related cell types [@bib60], we speculate that CRX-targeted silencers may contain sufficient information to act as enhancers in other cell types in which a different set of co-activating TFs are expressed. This hypothesis would be consistent with the finding that many silencers are enhancers in other cell types [@bib11; @bib20; @bib61]. Our work suggests that characterizing sequences by their motif information content offers a way to identify these different classes of _cis_-regulatory sequences in the genome.\n",
"\n",
"# Materials and methods\n",
"\n",
"table: Key resources table\n",
":::\n",
"| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |\n",
"| ----------------------------------------------------------- | ------------------------------------- | -------------------------------------------------- | ---------------------------------------------------------------------------------- | ---------------------------------------- |\n",
"| Strain, strain background (_Mus musculus_, male and female) | CD-1 | Charles River | Strain code 022 | |\n",
"| Recombinant DNA reagent | Library1 | This paper | | Listed in [Supplementary file 1](#supp1) |\n",
"| Recombinant DNA reagent | Library2 | This paper | | Listed in [Supplementary file 2](#supp2) |\n",
"| Recombinant DNA reagent | pJK01_Rhominprox-DsRed | [@bib47] | AddGene plasmid # 173,489 | |\n",
"| Recombinant DNA reagent | pJK03\\__Rho_basal\\__DsRed | [@bib47] | AddGene plasmid # 173,490 | |\n",
"| Sequence-based reagent | Primers | IDT | | Listed in [Supplementary file 6](#supp6) |\n",
"| Commercial assay or kit | Monarch PCR Cleanup Kit | New England Biolabs | T1030S | |\n",
"| Commercial assay or kit | Monarch DNA Gel Extraction Kit | New England Biolabs | T1020L | |\n",
"| Commercial assay or kit | TURBO DNA-free | Invitrogen | AM1907 | |\n",
"| Commercial assay or kit | SuperScript III Reverse Transcriptase | Invitrogen | 18080044 | |\n",
"| Software, algorithm | Bedtools | <https://bedtools.readthedocs.io/en/latest/> | RRID:[SCR_006646](https://identifiers.org/RRID/RRID:SCR_006646) | |\n",
"| Software, algorithm | MEME Suite | <https://meme-suite.org/> | RRID:[SCR_001783](https://identifiers.org/RRID/RRID:SCR_001783) | |\n",
"| Software, algorithm | ShapeMF | <https://github.com/h-samee/shape-motif>, [@bib74] | DOI:[10.1016/j.cels.2018.12.001](https://doi.org/10.1016/j.cels.2018.12.001) | |\n",
"| Software, algorithm | Numpy | <https://numpy.org/> | DOI:[10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2) | |\n",
"| Software, algorithm | Scipy | <https://www.scipy.org/> | DOI:[10.1038/s41592-019-0686-2](https://doi.org/10.1038/s41592-019-0686-2) | |\n",
"| Software, algorithm | Pandas | <https://pandas.pydata.org/> | DOI:[10.5281/zenodo.3509134](https://doi.org/10.5281/zenodo.3509134) | |\n",
"| Software, algorithm | Matplotlib | <https://matplotlib.org/> | DOI:[10.5281/zenodo.1482099](https://doi.org/10.5281/zenodo.1482099) | |\n",
"| Software, algorithm | Logomaker | <https://github.com/jbkinney/logomaker>, [@bib40] | DOI:[10.1093/bioinformatics/btz921](https://doi.org/10.1093/bioinformatics/btz921) | |\n",
":::\n",
"{#keyresource}\n",
"\n",
"## Library design\n",
"\n",
"CRX ChIP-seq peaks re-processed by [@bib72] were intersected with previously published CRX MPRA libraries [@bib32; @bib85] and one unpublished library to select sequences that had not been previously tested by MPRA. These sequences were scanned for instances of CRX motifs using FIMO version 4.11.2 [@bib3], a p-value cutoff of 2.3 × 10^–3^ (see below), and a CRX PWM derived from an electrophoretic mobility shift assay [@bib49]. We centered 2622 sequences on the highest scoring CRX motif. For 677 sequences without a CRX motif, we instead centered them using the Gibbs sampler from ShapeMF (Github commit abe8421) [@bib73] and a motif size of 10.\n",
"\n",
"For sequences unbound in CRX ChIP-seq but in open chromatin, we took ATAC-seq peaks collected in 8-week FACS-purified rods, green cones, and _Nrl^-/-^_ blue cones [@bib31] and removed sequences that overlapped with CRX ChIP-seq peaks. The remaining sequences were scanned for instances of CRX motifs using FIMO with a p-value cutoff of 2.5 × 10^–3^ and the CRX PWM. Sequences with a CRX motif were kept and the three ATAC-seq data sets were merged together, intersected with H3K27ac and H3K4me3 ChIP-seq peaks collected in P14 retinas [@bib72], and centered on the highest scoring CRX motifs. We randomly selected 1004 H3K27ac^+^H3K4me3^-^ sequences and 541 H3K27ac^+^H3K4me3^+^ to reflect the fact that ~35% of CRX ChIP-seq peaks are H3K4me3^+^. After synthesis of our library, we discovered 11% of these sequences do not actually overlap H3K27ac ChIP-seq peaks (110/1004 of the H3K4me3^-^ group and 60/541 of the H3K4me3^+^ group), but we still included them in the analysis because they contain CRX motifs in ATAC-seq peaks.\n",
"\n",
"All data was converted to mm10 coordinates using the UCSC liftOver tool [@bib22] and processed using Bedtools version 2.27.1 [@bib68]. All sequences in our library design were adjusted to 164 bp and screened for instances of EcoRI, SpeI, SphI, and NotI sites. In total, our library contains 4844 genomic sequences (2622 CRX ChIP-seq peaks with motifs, 677 CRX ChIP-seq peaks without motifs, 1004 CRX^-^ATAC^+^H3K27ac^+^H3K4me3^-^ CRX motifs, and 541 CRX^-^ATAC^+^H3K27ac^+^H3K4me3^+^ CRX motifs), a variant of each sequence with all CRX motifs mutated, 150 scrambled sequences, and a construct for cloning the basal promoter alone.\n",
"\n",
"For sequences centered on CRX motifs, all CRX motifs with a p-value of 2.5 × 10^–3^ or less were mutated by changing the core TAAT to TACT [@bib49] on the appropriate strand, as described previously [@bib32; @bib85]. We then re-scanned sequences and mutated any additional motifs inadvertently created.\n",
"\n",
"To generate scrambled sequences, we randomly selected 150 CRX ChIP-seq peaks spanning the entire range of GC content in the library. We then scrambled each sequence while preserving dinucleotide content as previously described [@bib85]. We used FIMO to confirm that none of the scrambled sequences contain CRX motifs.\n",
"\n",
"We unintentionally used a FIMO p-value cutoff of 2.3 × 10^–3^ to identify CRX motifs in CRX ChIP-seq peaks, rather than the slightly less stringent 2.5 × 10^–3^ cutoff used with ATAC-seq peaks or mutating CRX motifs. Due to this anomaly, there may be sequences centered using ShapeMF that should have been centered on a CRX motif, and these motifs would not have been mutated because CRX motifs were not mutated in sequences centered using ShapeMF. However, any intact CRX motifs would still be captured in the residual information content of the mutant sequence.\n",
"\n",
"## Plasmid library construction\n",
"\n",
"We generated two 15,000 libraries of 230 bp oligonucleotides (oligos) from Agilent Technologies (Santa Clara, CA) through a limited licensing agreement. Our library was split across the two oligo pools, ensuring that both the genomic and mutant forms of each sequence were placed in the same oligo pool ([Supplementary files 1 and 2](#supp1)). Both oligo pools contain all 150 scrambled sequences as an internal control. All sequences were assigned three unique barcodes as previously described [@bib85]. In each oligo pool, the basal promoter alone was assigned 18 unique barcodes. Oligos were synthesized as follows: 5’ priming sequence (GTAGCGTCTGTCCGT)/EcoRI site/Library sequence/SpeI site/C/SphI site/Barcode sequence/NotI site/3’ priming sequence (CAACTACTACTACAG). To clone the basal promoter into barcoded oligos without any upstream _cis_-regulatory sequence, we placed the SpeI site next to the EcoRI site, which allowed us to place the promoter between the EcoRI site and the 3’ barcode.\n",
"\n",
"We cloned the synthesized oligos as previously described by our group [@bib47; @bib86; @bib85]. Specifically, for each oligo pool, we used 50 femtomoles of template and four cycles of PCR in each of multiple 50 µl reactions (New England Biolabs \\[NEB], Ipswich, MA) (NEB Phusion) using primers MO563 and MO564 ([Supplementary file 6](#supp6)), 2% DMSO, and an annealing temperature of 57°C. PCR amplicons were purified from a 2% agarose gel (NEB), digested with EcoRI-HF and NotI-HF (NEB), and then cloned into the EagI and EcoRI sites of the plasmid pJK03 with multiple 20 µl ligation reactions (NEB T4 ligase). The libraries were transformed into 5-alpha electrocompetent cells (NEB) and grown in liquid culture. Next, 2 µg of each library was digested with SphI-HF and SpeI-HF (NEB) and then treated with Antarctic phosphatase (NEB).\n",
"\n",
"The _Rho_ basal promoter and _DsRed_ reporter gene was amplified from the plasmid pJK01 using primers MO566 and MO567 ([Supplementary file 6](#supp6)). The Polylinker and _DsRed_ reporter gene was amplified from the plasmid pJK03 using primers MO610 and MO567 ([Supplementary file 6](#supp6)). The Polylinker is a short 23 bp multiple cloning site with no known core promoter motifs. Inserts were purified from a 1% agarose gel (NEB), digested with NheI-HF and SphI-HF (NEB), and cloned into the libraries using multiple 20 µl ligations (NEB T4 ligase). The libraries were transformed into 5-alpha electrocompetent cells (NEB) and grown in liquid culture.\n",
"\n",
"## Retinal explant electroporation\n",
"\n",
"Animal procedures were performed in accordance with a Washington University in St Louis Institutional Animal Care and Use Committee-approved vertebrate animals protocol. Electroporation into retinal explants and RNA extraction was performed as described previously [@bib28; @bib32; @bib47; @bib86; @bib85]. Briefly, retinas were isolated from P0 newborn CD-1 mice and electroporated in a solution with 30 µg library and 30 µg _Rho_-GFP. Electroporated retinas were cultured for 8 days, at which point they were harvested, washed three times with HBSS (ThermoFisher Scientific/Gibco, Waltham, MA), and stored in TRIzol (ThermoFisher Scientific/Invitrogen, Waltham, MA) at –80°C. Five retinas were pooled for each biological replicate and three replicates were performed for each library. RNA was extracted from TRIzol according to manufacturer’s instructions and treated with TURBO DNase (Invitrogen). cDNA was prepared using SuperScript RT III (Invitrogen) with oligo dT primers. Barcodes from both the cDNA and the plasmid DNA pool were amplified for sequencing (described below). The resulting products were mixed at equal concentration and sequenced on the Illumina NextSeq platform. We obtained greater than 1300× coverage across all samples.\n",
"\n",
"_Rho_ libraries were amplified using primers MO574 and MO575 ([Supplementary file 6](#supp6)) for six cycles at an annealing temperature of 66°C followed by 18 cycles with no annealing step (NEB Phusion) and then purified with the Monarch PCR kit (NEB). PCR amplicons were digested using MfeI-HF and SphI-HF (NEB) and ligated to custom-annealed adaptors with PE2 indexing barcodes and phased P1 barcodes ([Supplementary file 6](#supp6)). The final enrichment PCR used primers MO588 and MO589 ([Supplementary file 6](#supp6)) for 20 cycles at an annealing temperature of 66°C (NEB Phusion), followed by purification with the Monarch PCR kit. Polylinker libraries were amplified using primers BC_CRX_Nested_F and BC_CRX_R ([Supplementary file 6](#supp6)) for 30 cycles (NEB Q5) at an annealing temperature of 67°C and then purified with the Monarch PCR kit. Illumina adaptors were then added via two further rounds of PCR. First, P1 indexing barcodes were added using forward primers P1_inner_A through P1_inner_D and reverse primer P1_inner_nested_rev ([Supplementary file 6](#supp6)) for five cycles at an annealing temperature of 55°C followed by five cycles with no annealing step (NEB Q5). PE2 indexing barcodes were then added by amplifying 2 µl of the previous reaction with forward primer P1_outer and reverse primers PE2_outer_SIC69 and PE2_outer_SIC70 ([Supplementary file 6](#supp6)) for five cycles at an annealing temperature of 66°C followed by five cycles with no annealing step (NEB Q5) and then purified with the Monarch PCR kit.\n",
"\n",
"## Data processing\n",
"\n",
"All data processing, statistical analysis, and downstream analyses were performed in Python version 3.6.5 using Numpy version 1.15.4 [@bib24], Scipy version 1.1.0 [@bib82], and Pandas version 0.23.4 [@bib54], and visualized using Matplotlib version 3.0.2 [@bib33] and Logomaker version 0.8 [@bib81]. All statistical analysis used two-sided tests unless noted otherwise.\n",
"\n",
"Sequencing reads were filtered to ensure that the barcode sequence perfectly matched the expected sequence (>93% reads in a sample for the _Rho_ libraries, >86% reads for the Polylinker libraries). For the _Rho_ libraries, barcodes that had less than 10 raw counts in the DNA sample were considered missing and removed from downstream analysis. Barcodes that had less than five raw counts in any cDNA sample were considered present in the input plasmid pool but below the detection limit and thus set to zero in all samples. Barcode counts were normalized by reads per million (RPM) for each sample. Barcode expression was calculated by dividing the cDNA RPM by the DNA RPM. Replicate-specific expression was calculated by averaging the barcodes corresponding to each library sequence. After performing statistical analysis (see below), expression levels were normalized by replicate-specific basal mean expression and then averaged across biological replicates.\n",
"\n",
"For the Polylinker assay, the expected lack of expression of many constructs required different processing. Barcodes that had less than 50 raw counts in the DNA sample were removed from downstream analysis. Barcodes were normalized by RPM for each replicate. Barcodes that had less than 8 RPM in any cDNA sample were set to zero in all samples. cDNA RPM were then divided by DNA RPM as above. Within each biological replicate, barcodes were averaged as above but were not normalized to basal expression because there is no basal construct. Expression values were then averaged across biological replicates. Due to the low expression of scrambled sequences and the lack of a basal construct, we were unable to assess data calibration with the same rigor as above.\n",
"\n",
"## Assignment of activity classes\n",
"\n",
"Activity classes were assigned by comparing expression levels to basal promoter expression levels across replicates. The null hypothesis is that the expression of a sequence is the same as basal levels. Expression levels were approximately log-normally distributed, so we computed the log-normal parameters for each sequence and then performed Welch’s t-test. We corrected for multiple hypotheses using the Benjamini-Hochberg FDR procedure. We corrected for multiple hypotheses in each library separately to account for any potential batch effects between libraries. The log~2~ expression was calculated after adding a pseudocount of 1 × 10^–3^ to every sequence.\n",
"\n",
"Sequences were classified as enhancers if they were twofold above basal and the q-value was below 0.05. Silencers were similarly defined as twofold below basal and q-value less than 0.05. Inactive sequences were defined as within a twofold change and q-value greater than or equal to 0.05. All other sequences were classified as ambiguous and removed from further analysis. We used scrambled sequences to further stratify enhancers into strong and weak enhancers, using the rationale that scrambled sequences give an empirical distribution for the activity of random sequences. We defined strong enhancers as enhancers that are above the 95th percentile of scrambled sequences and all other enhancers as weak enhancers.\n",
"\n",
"For the Polylinker assay, we did not have a basal construct as a reference point. Instead, we defined a sequence to have autonomous activity if the average cDNA barcode counts were higher than average DNA barcode counts, and non-autonomous otherwise. The log~2~ expression was calculated after adding a pseudocount of 1 × 10^–2^ to every sequence.\n",
"\n",
"## RNA-seq analysis\n",
"\n",
"We obtained RNA-seq data from WT and Crx^-/-^ P21 retinas [@bib71] processed into a counts matrix for each gene by [@bib72]. Each sample was normalized by read counts per million and replicates were averaged together. Genes with at least a twofold change between genotypes were considered differentially expressed. We determined which differentially expressed genes are near a member of our library using previously published associations between retinal ATAC-seq peaks and genes [@bib60]. For de-repressed genes, we determined how often the nearest library member is a silencer; for down-regulated genes, we determined how often the nearest library member is a strong or weak enhancer.\n",
"\n",
"## Motif analysis\n",
"\n",
"We performed motif enrichment analysis using the MEME Suite version 5.0.4 [@bib3]. We searched for motifs that were enriched in one group of sequences relative to another group using DREME-py3 with the parameters -mink 6 -maxk 12 -e 0.05 and compared the de novo motifs to known motifs using TOMTOM on default parameters. We ran DREME using strong enhancers as positives and silencers as negatives, and vice versa. For TOMTOM, we used version 11 of the full mouse HOCOMOCO database [@bib46] with the following additions from the JASPAR human database [@bib42]: NRL (accession MA0842.1), RORB (accession MA1150.1), and RAX (accession MA0718.1). We added these PWMs because they have known roles in the retina, but the mouse PWMs were not in the HOCOMOCO database. We also used the CRX PWM that we used to design the library. Motifs were selected for downstream analysis based on their matches to the de novo motifs, whether the TF had a known role in retinal development, and the quality of the PWM. Because PWMs from TFs of the same family were so similar, we used one TF for each DREME motif, recognizing that these motifs may be bound by other TFs that recognize similar motifs. We did not use any PWMs with a quality of ‘D’. We excluded DREME motifs without a match to the database from further analysis; most of these resemble dinucleotides.\n",
"\n",
"## Predicted occupancy\n",
"\n",
"We computed predicted occupancy as previously described [@bib85; @bib89]. Briefly, we normalized each letter probability matrix by the most probable letter at each position. We took the negative log of this matrix and multiplied by 2.5, which corresponds to the ideal gas constant times 300 K, to obtain an energy weight matrix. We used a chemical potential _μ_ of 9 for all TFs. At this value, the probability of a site being bound is at least 0.5 if the relative _K_~D~ is at least 0.03 of the optimal binding site. We computed the predicted occupancy for every site on the forward and reverse strands and summed them together to get a single value for each TF.\n",
"\n",
"To determine if there is a bias in the linear arrangement of motifs, we selected strong enhancers with exactly one site occupied by CRX and exactly one site occupied by a second TF. We counted the number of times the position of the second TF was 5’ and 3’ of the CRX site and then performed a binomial test. We did not observe a statistically significant bias for any TF at an FDR q-value cutoff of 0.05. We also performed this analysis for silencers with exactly one site occupied by CRX and exactly one site occupied by NRL and did not observe a significant difference in the 5’ vs. 3’ bias of strong enhancers vs. silencers (Fisher’s exact test p = 0.17).\n",
"\n",
"## Information content\n",
"\n",
"To capture the effects of TF predicted occupancy and diversity in a single metric, we calculated the motif information content using Boltzmann entropy. Boltzmann’s equation states that the entropy of a system ${\\displaystyle S}$ is related to the number of ways the molecules can be arranged (microstates) ${\\displaystyle W}$ via the equation ${\\displaystyle S={k}_{B}\\mathrm{log}W}$, where ${\\displaystyle {k}_{B}}$ is Boltzmann’s constant ([@bib67], Chapter 5). The number of microstates is defined as ${\\displaystyle W=\\frac{N!}{\\prod _{i}{N}_{i}!}}$ where ${\\displaystyle N}$ is the total number of particles and ${\\displaystyle {N}_{i}}$ are the number of the -th type of particles. In our case, the system is the collection of predicted binding motifs for different TFs in a _cis_-regulatory sequence. We assume each TF is a different type of molecule because the DNA-binding domain of each TF belongs to a different subfamily. The number of molecular arrangements ${\\displaystyle W}$ represents the number of distinguishable ways that the TFs can be ordered on the sequence. Thus, ${\\displaystyle {N}_{i}}$ is the predicted occupancy of the ${\\displaystyle i}$-th TF and ${\\displaystyle N}$ is the total predicted occupancy of all TFs on the _cis_-regulatory sequence. Because the predicted occupancies are continuous values, we exploit the definition of the Gamma function, ${\\displaystyle \\mathrm{\\Gamma }(N+1)=N!}$ to rewrite ${\\displaystyle W=\\frac{\\mathrm{\\Gamma }(N+1)}{\\prod _{i}\\mathrm{\\Gamma }({N}_{i}+1)}}$ .\n",
"\n",
"If we assume that each arrangement of motifs is equally likely, then we can write the probability of arrangement ${\\displaystyle w=1,\\dots ,W}$ as ${\\displaystyle {p}_{w}=\\frac{1}{w}}$ and rewrite the entropy as ${\\displaystyle S=-\\mathrm{log}(\\frac{1}{w})=-\\mathrm{log}({p}_{w})}$, where we have dropped Boltzmann’s constant since the connection between molecular arrangements and temperature is not important. Because each arrangement is equally likely, then ${\\displaystyle \\frac{1}{w}}$ is also the expected value of ${\\displaystyle {p}_{w}}$ and we can write the entropy as ${\\displaystyle S=-E[\\mathrm{log}({p}_{w})]=-\\sum _{w}{p}_{w}\\mathrm{log}({p}_{w})}$ , which is Shannon entropy. By definition, Shannon entropy is also the expected value of the information content: ${\\displaystyle E[I]=-\\sum _{w}{p}_{w}\\mathrm{log}({p}_{w})=\\sum _{w}{p}_{w}I(w)}$ where the information content ${\\displaystyle I}$ of a particular state is ${\\displaystyle I(w)=\\mathrm{log}({p}_{w})}$. Since we assumed each arrangement is equally likely, then the expected value of the information content is also the information content of each arrangement. Therefore, the information content of a _cis_-regulatory sequence can be written as ${\\displaystyle I=-\\mathrm{l}\\mathrm{o}\\mathrm{g}({p}_{w})=\\mathrm{l}\\mathrm{o}\\mathrm{g}W}$. We use log base 2 to express the information content in bits.\n",
"\n",
"With this metric, _cis_-regulatory sequences with higher predicted TF occupancies generally have higher information content. Sequences with higher TF diversity have higher information content than lower diversity sequences with the same predicted occupancy. Thus, our metric captures the effects of both TF diversity and total TF occupancy. For example, consider hypothetical TFs A, B, and C. If motifs for only one TF are in a sequence, then ${\\displaystyle W}$ is always one and the information content is always zero (regardless of total occupancy). The simplest case for non-zero information content is one motif for A, one motif for B, and zero motifs for C (1-1-0). Then ${\\displaystyle W=\\frac{2!}{1!1!}=2}$ and ${\\displaystyle I=1}$ bit. If we increase predicted occupancy by adding a motif for A (2-1-0), then ${\\displaystyle W=\\frac{3!}{2!1!}=3}$ and ${\\displaystyle I=1.6}$ bits, which is approximately the information content of silencers and inactive sequences. If we increase predicted occupancy again and add a second motif for B (2-2-0), then ${\\displaystyle W=\\frac{4!}{2!2!}=6}$ and ${\\displaystyle I=2.6}$ bits, which is approximately the information content of strong enhancers. If instead of increasing predicted occupancy, we instead increase diversity by replacing a motif for A with a motif for C (1-1-1), then ${\\displaystyle W=\\frac{3!}{1!1!1!}=6}$ and once again ${\\displaystyle I=2.6}$ bits, which is higher than the lower diversity case (2-1-0).\n",
"\n",
"According to [@bib87], the probability of observing ${\\displaystyle k}$ total motifs for ${\\displaystyle m}$ different TFs in a ${\\displaystyle w}$ bp window is ${\\displaystyle p(k)\\sim (Poisson(k;\\lambda ))}$[,](https://www.codecogs.com/eqnedit.php?latex=P(k)%20sim%20Poisson(k%3B%20lambda)#0) where ${\\displaystyle \\lambda =pmw}$ and ${\\displaystyle p}$ is the probability of finding a spurious motif in the genome. The expected number of windows with ${\\displaystyle k}$ total motifs in a genome of length [N](https://www.codecogs.com/eqnedit.php?latex=N#0) is thus ${\\displaystyle E(k)=p(k)\\cdot N}$. In mammals, ${\\displaystyle N\\approx {10}^{9}}$ and Wunderlich and Mirny find that ${\\displaystyle p=0.0025}$ for multicellular eukaryotes. For ${\\displaystyle m=3}$ TFs and a [w=164](https://www.codecogs.com/eqnedit.php?latex=w%20%3D%20164#0) bp window (which is the size of our sequences), ${\\displaystyle \\lambda =0.123}$ and ${\\displaystyle E(5)=1.6}$ meaning that five total motifs for three different TFs specify an approximately unique 164 bp location in a mammalian genome. Five total motifs for three different TFs can be achieved in two ways: three motifs for A, one for B, and one for C (3-1-1), or two motifs for A, two for B, and one for C (2-2-1). In the case of 3-1-1, ${\\displaystyle W=\\frac{5!}{3!1!1!}=20}$ and ${\\displaystyle I=4.3}$ bits. In the case of 2-2-1, ${\\displaystyle W=\\frac{5!}{2!2!1!}=30}$ and ${\\displaystyle I=4.9}$ bits.\n",
"\n",
"## Machine learning\n",
"\n",
"The _k_-mer SVM was fit using gkmSVM [@bib19]. All other machine learning, including cross-validation, logistic regression, and computing ROC and PR curves, was performed using scikit-learn version 0.19.1 [@bib64]. We wrote custom Python wrappers for gkmSVM to allow for interfacing between the C++ binaries and the rest of our workflow. We ran gkmSVM with the parameters -l 6 -k 6 -m 1. To estimate model performance, all models were fit with stratified fivefold cross-validation after shuffling the order of sequences. For the TF occupancy logistic regression model, we used L2 regularization. We selected the regularization parameter C by performing grid search with fivefold cross-validation on the values 10^–4^, 10^–3^, 10^–2^, 10^–1^, 1, 10^1^, 10^2^, 10^3^, 10^4^ and selecting the value that maximized the F1 score. The optimal value of C was 0.01, which we used as the regularization strength when assessing the performance of the model with other feature sets.\n",
"\n",
"To assess the performance of the logistic regression model, we randomly sampled eight PWMs from the HOCOMOCO database and computed the predicted occupancy of each TF on each sequence. We then fit a new logistic regression model with these features and repeated this procedure 100 times to generate a background distribution of model performances.\n",
"\n",
"To generate de novo motifs from the SVM, we generated all 6-mers and scored them against the SVM. We then ran the svmw_emalign.py script from gkmSVM on the _k_-mer scores with the parameters -n 10 -f 2 -m 4 and a PWM length of 6, and then used TOMTOM to compare them to the database from our motif analysis.\n",
"\n",
"## Other data sources\n",
"\n",
"We used our previously published library [@bib85] as an independent test set for our machine learning models. We defined strong enhancers as ChIP-seq peaks that were above the 95th percentile of all scrambled sequences. There was no basal promoter construct in this library, so instead we defined silencers as ChIP-seq peaks that were at least twofold below the log~2~ mean of all scrambled sequences.\n",
"\n",
"Previously published ChIP-seq data for NRL [@bib23] that was re-processed by [@bib31] and MEF2D [@bib2] was used to annotate sequences for in vivo TF binding. We converted peaks to mm10 coordinates using the UCSC liftOver tool and then used Bedtools to intersect peaks with our library."
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