{
"cells": [
{
"cell_type": "markdown",
"id": "fa68bc89-7268-4f8b-9fb5-4d8bdf670699",
"metadata": {},
"source": [
"## Inspecting what Cis-Regulatory Features a Model Has Learned\n",
"\n",
"`tangermeme` was created for the purpose of helping researchers use sequence-based machine learning models after training. An important question that most people have after training is \"what did my model learn?\" Specifically, one is likely interested in the motifs that the model has picked up on. There are currently two main ways that researchers do this right now: (1) use a de novo motif discovery method like TF-MoDISco, or (2) manually inspect many loci and cross-reference the spans of high attribution with an external database like JASPAR using a tool like TOMTOM. However, TF-MoDISco can be slow when all you want to know is, at a high level, \"what motifs are present here?\" (and conversely, what motifs *are not* being used), and doing anything manually is a pain.\n",
"\n",
"In this vignette, we will explore the functionality in `tangermeme` for uncovering what these sequence-based model have learned. There are pros and cons to each method, so keep those in mind when deciding what to do."
]
},
{
"cell_type": "markdown",
"id": "b3f5173c-be1b-41c1-989a-42e9e711a2bb",
"metadata": {},
"source": [
"#### The Model\n",
"\n",
"In this vignette, we will be using a BPNet model that has been trained to predict GATA2 profiles. Although this model can make predictions for the total counts in a region as well as the basepair mapped profiles, we will only be using the total counts here to make analyses easier. Because the BPNet model expects a control track in addition to the sequence, we will use a wrapper that always passes in an all-zeroes control to factor out that influence. Basically, just view this as a model that takes in genome sequence and outputs GATA2 counts."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1e5bb254-0934-46ad-bd3d-edba1aae78f3",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
"\n",
"import numpy\n",
"import torch\n",
"\n",
"from bpnetlite.bpnet import CountWrapper, ControlWrapper\n",
"\n",
"bpnet = CountWrapper(ControlWrapper(torch.load(\"../tutorials/gata2.bpnet.torch\")))"
]
},
{
"cell_type": "markdown",
"id": "1d87beea-566f-442f-9574-4dc2e018b7ed",
"metadata": {},
"source": [
"#### The Methods\n",
"\n",
"tl;dr? Skip to the flow-chart below.\n",
"\n",
"Now that we have a model we want to inspect, we have to answer a few questions. Most importantly: do we want to do truly de novo motif discovery or not? If we do, we should use TF-MoDISco. But, frequently, we do not actually want to do complete de novo motif discovery. Rather, we may have a set of motifs that we are specifically interested in and we want to quickly see whether the model has learned any of them. This set of motifs can be quite large -- it can even be all of JASPAR! De novo motif discovery can be an unbiased way of discovering what the model has learned but it can be quite time intensive for large data sets and, in my experience, researchers want a quick answer to \"did my model predicting GATA binding actually learn a GATA motif?\" and plan to later on do de novo motif discovery (say, running it overnight after telling your supervisor that the model seems to work). \n",
"\n",
"Next, we need to decide if we have a set of sequences. Frequently this answer is just \"yes, I have a set of peaks\". However, keep in mind that performing your analyses with respect to a set of sequences is not always the right thing to do either. Biological sequences are extremely structured in terms of cis-regulation and so your results may be convoluted by what sets of motifs appear together. \n",
"\n",
"If we do not have a set of sequences, or we want to calculate model response in a manner agnostic to the distribution of cis-regulatory elements in the genome, then we need to do a \"marginalization.\" Here, we insert each motif from our external motif database into background sequences and consider model predictions before and after. This idea can also be extended to attributions, where we look at model attributions before and after substituting in each motif. For more details, see the tutorial on marginalization.\n",
"\n",
"Now, if we do have sequences that we want to consider (e.g., all peaks genome-wide, all promoter elements, distal enhancer regions, etc.) there are two options to consider: identify short spans of high-attribution (\"seqlets\") de novo and then map them to your database (seqlet calling + TOMTOM), or identify motif hits based on sequence similarity alone and then threshold on attribution score (FIMO + attribution threshold). You can read more on these methods in the tutorial on annotations. Identifying seqlets de novo allows for better identification of the actual regions the model cares about but the motif library being used matters a great deal because variance in the seqlet sequence can cause the best motif match to change when many motifs have similar motif composition. In contrast, FIMO will find good sequence hits for your motif database but if the seqlet does not actually align with that hit, you may be assigning some attribution incorrectly to a motif that is actually not relevant.\n",
"\n",
"Here is a helpful flow-chart to help you navigate which method to use.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"id": "c7982321-091c-4f92-a690-1417c308961c",
"metadata": {},
"source": [
"#### TF-MoDISco\n",
"\n",
"TF-MoDISco is a stand-alone tool that is not a part of `tangermeme`. You can find instructions for how to use it at https://github.com/jmschrei/tfmodisco-lite. As a short description, you will need to calculate attributions for your region set using `tangermeme` and then either use the built-in Python function (https://github.com/jmschrei/tfmodisco-lite/blob/main/modiscolite/tfmodisco.py#L260) or save the attributions and sequences and use the command-line tool."
]
},
{
"cell_type": "markdown",
"id": "80c5b011-2587-4672-9e28-e10b1675ab0b",
"metadata": {},
"source": [
"#### Marginalization (Predictions)\n",
"\n",
"Marginalization is the process of substituting in a motif and observing the change in model output before and after the substitution. Usually, this substitution is done into a background region so that one can estimate the \"marginal\" effect that each motif has on model predictions in isolation, and this value is averaged over many background sequencces.\n",
"\n",
"An important complication in using marginalizations is the choice of background. Uniformly generated random background sequences are likely much higher in GC content than the genome these models were trained on and so they may not be the best choice, although they are very convenient to use. The best choice is usually to use regions of the genome that are known to not exhibit the activity you care about but have similar GC content and dinucleotide content, e.g., regions that are not peaks, regions that are not accessible, non-gene body non-promoter regions, etc. The second best choice is to use randomly generated sequences with proportions of each character derived from the genome. As a reference: for `chr1` after excluding N's these proportions are `[0.2910, 0.2085, 0.2087, 0.2918]`. When prototyping, it is okay to use uniformly randomly generated sequences, but make sure to switch before presenting your results. Models can be very sensitive to local GC content and can also be sensitive to dinucleotide content and so when you substitute a motif with a very different content you may get a \"mirage\" that is driven entirely by this local change in GC content.\n",
"\n",
"In general, more background sequences is better as long as you have the compute for it but this betterness drops off quickly after around 100 sequences. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "61bde225-7e27-4ae6-ab41-cbd13bd15687",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[0.2919, 0.2167, 0.2039, 0.2876],\n",
" [0.2805, 0.2029, 0.2152, 0.3013],\n",
" [0.2862, 0.2124, 0.2010, 0.3004],\n",
" [0.2838, 0.2219, 0.2058, 0.2886],\n",
" [0.2819, 0.2242, 0.2020, 0.2919]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tangermeme.utils import random_one_hot\n",
"\n",
"X = random_one_hot((100, 4, 2114), probs=numpy.array([[0.2910, 0.2085, 0.2087, 0.2918]])).float()\n",
"X.mean(dim=-1)[:5]"
]
},
{
"cell_type": "markdown",
"id": "edf1ab58-b54b-4c98-85ab-c6ee004c5b6d",
"metadata": {},
"source": [
"Now, we can load up a few motifs to consider (and make sure to put the GATA motif in as well). In practice, you want to be comprehensive about this, but for this tutorial we will only do a few. We will use the JASPAR database for this."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "54bc9de1-efd5-46b1-b3b0-ae72a78e0bee",
"metadata": {},
"outputs": [],
"source": [
"from tangermeme.io import read_meme\n",
"\n",
"motifs = read_meme(\"../tutorials/JASPAR2020_CORE_vertebrates_non-redundant_pfms_meme.txt\", n_motifs=5)\n",
"motifs['MA0036.3 GATA2'] = read_meme(\"../tutorials/JASPAR2020_CORE_vertebrates_non-redundant_pfms_meme.txt\")[\"MA0036.3 GATA2\"]"
]
},
{
"cell_type": "markdown",
"id": "c52a7ec8-06a8-4405-860d-e28aba3d1149",
"metadata": {},
"source": [
"Then, we can do the marginalization."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ea14c4e9-ac94-406f-ba8d-ebe42ac9332c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MA0004.1 Arnt 0.007374629843980074\n",
"MA0006.1 Ahr::Arnt 0.005128492135554552\n",
"MA0019.1 Ddit3::Cebpa -0.0021654844749718904\n",
"MA0029.1 Mecom 0.17749778926372528\n",
"MA0030.1 FOXF2 -0.007398463319987059\n",
"MA0036.3 GATA2 0.11949684470891953\n"
]
}
],
"source": [
"from tangermeme.marginalize import marginalize\n",
"from tangermeme.utils import pwm_consensus\n",
"\n",
"for name, pwm in motifs.items():\n",
" consensus = pwm_consensus(pwm).unsqueeze(0)\n",
" y_before, y_after = marginalize(bpnet, X, consensus)\n",
" delta = (y_after - y_before).mean().item()\n",
" print(name, delta)"
]
},
{
"cell_type": "markdown",
"id": "f83fbf25-e497-4fe3-a935-1bd264c6e7cc",
"metadata": {},
"source": [
"Looks like the BPNet model makes larger count predictions when the GATA2 motif has been inserted. But.. it seems to make even larger count predictions when using this \"Mecom\" motif. Is this a mistake? Let's take a look at what those PWMs are using the `plot_pwm` function, which takes in a PWM where each column is a probability vector and plots the information content of the PWM and its reverse complement."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "540a0560-5951-4138-a1bc-cc61eea28f32",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from tangermeme.plot import plot_pwm\n",
"\n",
"plot_pwm(motifs['MA0036.3 GATA2'], \"GATA2\")\n",
"plot_pwm(motifs[\"MA0029.1 Mecom\"], \"Mecom\")"
]
},
{
"cell_type": "markdown",
"id": "189ffa97-14da-4221-8fd0-cd3ad79bdd1d",
"metadata": {},
"source": [
"Looks like the Mecom motif is just two GATAA motifs next to each other. Because the core GATAA is flanked by a G at the end and an A at the beginning, the beginning of one motif can actually snap in to the end of the second motif. Regardless, the reason the model is responding to the Mecom motif is likely because it's actually just responding to the GATA2 motif and Mecom is two of those."
]
},
{
"cell_type": "markdown",
"id": "855cfb24-9903-407e-88ca-5ff3547e30d7",
"metadata": {},
"source": [
"#### Marginalization (Attribution)\n",
"\n",
"Rather than looking at the difference in predictions before and after substituting in a motif, we can look at the difference in attributions at each position in the input. You can read more about this in the marginalization tutorial, but all you need to do is pass in the `deep_lift_shap` function (or whatever other function you would like to use) to the `marginalize` function. A small difference in processing these results is that, because you get attribution values for each input position rather than just a single output value like with the prediction function, you may want to limit the window you consider to just surrounding the motif of interest. Further, doing this in attribution space will likely be much slower than in prediction space if tyou use `deep_lift_shap` because you need to do a number of shuffles for each of the examples you are marginalizing over."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "b73f9810-a4e1-4fb8-9a74-2987012b210c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MA0004.1 Arnt 3.4735185181489214e-05\n",
"MA0006.1 Ahr::Arnt 2.23364113480784e-05\n",
"MA0019.1 Ddit3::Cebpa 1.0883775757974945e-05\n",
"MA0029.1 Mecom 0.0013928902335464954\n",
"MA0030.1 FOXF2 3.253892373322742e-06\n",
"MA0036.3 GATA2 0.0010769471991807222\n"
]
}
],
"source": [
"from tangermeme.deep_lift_shap import deep_lift_shap\n",
"\n",
"for name, pwm in motifs.items():\n",
" consensus = pwm_consensus(pwm).unsqueeze(0)\n",
"\n",
" n = consensus.shape[1] // 2 + 1\n",
" s, e = 2114 // 2 - n, 2114 // 2 + n\n",
" \n",
" y_before, y_after = marginalize(bpnet, X, consensus, func=deep_lift_shap)\n",
" y_before = y_before[:, :, s:e] * X[:, :, s:e]\n",
" y_after = y_after[:, :, s:e] * X[:, :, s:e]\n",
" \n",
" delta = (y_after - y_before).mean().item()\n",
" print(name, delta)"
]
},
{
"cell_type": "markdown",
"id": "2c3a9de1-3b2b-4184-be5a-43b292bcaf35",
"metadata": {},
"source": [
"Likewise, we are seeing orders of magnitude higher attribution signal for the GATA motifs than for the other ones. Further, it looks like taking the average across the motif length has caused the two to have similar scores because one is just two GATA motifs in a row. "
]
},
{
"cell_type": "markdown",
"id": "1f10ea57-f4a9-4b34-901b-4e26be4b36b6",
"metadata": {},
"source": [
"#### Seqlet Calling + TOMTOM\n",
"\n",
"Now, let us take a look at method that relies on actual genomic sequence. As mentioned previously, sometimes using actual genomic sequence can convolute your results because many motifs can be present at each sequence. However, sometimes this is actually want you want because either (1) you want to quantify cis-regulation in actual genomic sequences or (2) you want to evaluate the model in the same setting it was trained, among other reasons.\n",
"\n",
"This first method involves first identifying short spans of high attribution called \"seqlets\" and then mapping the sequences of these seqets to a database. Importantly, this first step is entirely attribution-based and so focuses on what the model has learned and the second step is entirely sequence-based and so focuses entirely on prior knowledge. Please see the seqlets tutorial for more information about the seqlet calling algorithm, and the annotations tutorial for more about using TOMTOM for seqlet annotation.\n",
"\n",
"First, we should load up some sequences. These will be GATA2 peaks from the ENCODE portal. You can download the file from `https://www.encodeproject.org/files/ENCFF497ISV/@@download/ENCFF497ISV.bed.gz`. You will also need the human genome hg38. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8f1fb4fb-4251-40ec-8a3c-0fd4a86aac88",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading Loci: 100%|████████████████████████████████████████████████████████████| 11447/11447 [00:00<00:00, 12315.26it/s]\n"
]
},
{
"data": {
"text/plain": [
"torch.Size([11443, 4, 2114])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas\n",
"from tangermeme.io import extract_loci\n",
"\n",
"peaks = pandas.read_csv(\"ENCFF497ISV.bed.gz\", sep=\"\\t\", usecols=(0, 1, 2), names=['chrom', 'start', 'end'])\n",
"X = extract_loci(peaks, \"../tutorials/hg38.fa\", verbose=True, in_window=2114).float()\n",
"X = X[X.sum(dim=(1, 2)) == X.shape[-1]]\n",
"X.shape"
]
},
{
"cell_type": "markdown",
"id": "fe6d35b6-9631-4519-9de7-efae7a80cbc1",
"metadata": {},
"source": [
"Then, because seqlets are called on attributions, we will calculate attributions for each sequence."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "37d22730-0374-4aa3-bd34-6ead627414f8",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|█████████████████████████████████████████████████████████████████████████| 228860/228860 [03:30<00:00, 1085.63it/s]\n"
]
}
],
"source": [
"X_attr = deep_lift_shap(bpnet, X, device='cuda', batch_size=128, n_shuffles=20, random_state=0, warning_threshold=0.1, verbose=True)"
]
},
{
"cell_type": "markdown",
"id": "6198290e-e745-44f4-a157-6c0092020534",
"metadata": {},
"source": [
"Now we need to call seqlets using the `recursive_seqlets` function. This is explained in more detail in the seqlets tutorial, but the only important parameters to the function are a p-value threshold (default is 0.01) and an `additional_flanks` value for how far out to extend the seqlets after they are identified (this can help include flanking regions that may be important)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7f688d6d-5cd7-442b-8cac-22477f6c5395",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
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"\n",
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" \n",
"
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"
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"
example_idx
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start
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end
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"
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" \n",
" \n",
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0
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616
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1
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3470
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1044
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0.390759
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],
"text/plain": [
" example_idx start end attribution p-value\n",
"0 616 1001 1007 -0.029226 4.289254e-07\n",
"1 3470 1065 1075 -0.042681 1.475572e-06\n",
"2 15 1035 1044 0.390759 1.520215e-06\n",
"3 4568 1089 1096 -0.034386 1.572707e-06\n",
"4 2141 1084 1094 0.403397 1.634249e-06"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tangermeme.seqlet import recursive_seqlets\n",
"\n",
"seqlets = recursive_seqlets(X_attr.sum(dim=1), additional_flanks=1)\n",
"seqlets.head()"
]
},
{
"cell_type": "markdown",
"id": "2a6a62ac-8723-4b82-8d8f-a69cb9c69420",
"metadata": {},
"source": [
"After calling the seqlets we can now annotate them by using TOMTOM to match the sequence of each seqlet with the PWMs in a motif database using the `annotate_seqlets` function. There are a few optional parameters but this function takes in the sequences, seqlets, and a motif database, and returns the index of the best motif match(es). You can read more about it in the annotations tutorial."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a4f79d1e-5e58-49af-b6b9-ee15383760a5",
"metadata": {},
"outputs": [],
"source": [
"from tangermeme.annotate import annotate_seqlets\n",
"\n",
"motif_idxs = annotate_seqlets(X, seqlets, \"../tutorials/motifs.meme.txt\")[0][:, 0]"
]
},
{
"cell_type": "markdown",
"id": "f43cad27-850b-432f-bb08-15112f0568c0",
"metadata": {},
"source": [
"Finally, we can count the annotations. This will return a tensor of size `(n_sequences, n_motifs)` where the value is the count of the number of times each annotation (motifs, in our case) appear in each sequencce."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "11f201ee-2b47-49b9-8684-1afea000c46d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([11443, 2193])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tangermeme.annotate import count_annotations\n",
"\n",
"y = count_annotations((seqlets['example_idx'], motif_idxs))\n",
"y.shape"
]
},
{
"cell_type": "markdown",
"id": "5314c79d-1889-43b8-8c14-db208d7502df",
"metadata": {},
"source": [
"We can sum this across all sequences and show the motifs with the top five number of counts."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4e6b0328-ec6f-489c-afbd-2df8b437f422",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array(['Gata1_MA0035.3', 'Gata4_MA0482.1', 'Mecom_MA0029.1',\n",
" 'GATA3_MA0037.3', 'CPEB1_RRM_1'], dtype='"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_1499319/495163197.py:10: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" seqlets_['example_idx'] = motif_names[motif_idxs[idxs]]\n"
]
},
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_1499319/495163197.py:10: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" seqlets_['example_idx'] = motif_names[motif_idxs[idxs]]\n"
]
},
{
"data": {
"image/png": 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E2T0LsmRYMuw9CjT0QMDJREFsbGxRxyEiIpI3a6YxfVM2yzkyrZlYLzjIERERkQLYixg6foearTl6FGjogVCIGgUiIiLFKmW/0ZMgBxUyFBERKYys078Lku1mixkvDy+jRoGGHghO9iiYN28et912G76+vsybN++i6w4ePNgtgYmIiACQcjDXorTMNHw9fUsgGBERkctLqjmVo0lHuaLCFXjmM/TAXsxQPQoki1OJgjlz5nDjjTfi6+vLnDlz8l3PZDIpUSAiIu6Vnnvq3VRzKuV8ywGQac3E09Mz1zoiIiIC6w6vo/tH3fn3qX+p6JndodyxqKF9ekTVKJAsTiUKfvzxxzz/FhERKXLZiYK2H8Lx7wBj6EGAd4DxtzkVX2/1LhAREclLckay/XfFACPJntfQA29Pb7w8vDT0QAAXaxSYzWa6devGP//8U1TxiIiIOEpPAA9vqNYPoh4DsoYeeBnJgfTM9JKMTkREpFRLyUgxfptTcsx6YHFYp7DFDG02Gy+seoFtJ7a5LV4pHVxKFHh7e5ORkYHJZCqqeERERBylx0NwNPhUgPCrwDuEVHMqfp5+AKRaVNhQREQkPzl7FGDKPv3L7lFgnNcVdnrEdEs6MWtj+PP4n26MWEoDl2c9GDRoEO+//z6ZmZlFEY+IiIij9AQIrGn8bfKA8LakZqbi52UkCtLMaSUYnIiISOmWYs7qUZCRknt6xMArgKyhB9k9ClwYeuCQhJAyxakaBTlt3bqVX3/9lbVr1xIdHY2/v7/D/dOnT3dbcCIiIqSfhIBq5297l3MYepCWqUSBiIhIfhx7FGQlCqx5DD3I6lHgytCDpPQkh+eQssPlREG5cuXo2bNnUcQiIiKSmzUdfEIdFqWac/QoUKJAREQkX441CrI6lFszyNm5PMOSwa+Hf+VM2hmXhh5kJwiSMpLcFq+UDi4nCmJiYooiDhERkbxZzbkSBWmZafh6qkeBiIhIQfLsUWBJBQLt65gtZu7/+n7jbxeGHmQnCNSjoOxxuUbB4MGDSUxMzLU8OTmZwYMHuyUoERERO6sZfMo7LHKoUaBEgYiIZItfa59KVwwp5hQCvQMvSBQ4fnfmTA64MvTA3qMg/b/Vo+C/UK7P5UTBxo0bMZtzv3nS09P5448/3BKUiIiIndUMHr4Oi1LNqfYaBamZmvVARESAjLOwpi/83Avi1hTtc5lMuX8AMlNg28uQtKdon98FyRnJRARGZA1ByIrzghmDciYHXOpRkF2jwPzf6VFw4ACEh8MDD5R0JEXL6aEHsbGx9r//+ecf4uPj7betViu//PILFStWdG90IiIi1gwwOX5dpWWm2XsUpGeml0RUIiJS2uyZDhkns/5+ByKvLv4YYifD9lfg4CfQewd4eBd/DBdIMacQERCR1aPAZNQpyNEbz2azOSQHXK1REOgd+J/qUfDkk3D2LLz/PgwdCm3alHRERcPpREHfvn0xmUyYTCbuvvvuXPf7+fkxatQotwYnIiKCzWwcaGWcgZRD4BdJamaOHgVm9SgQEREchxxYSmBYmiUNdr1u/J20B05ugIiOxR/HBZIzkokMjLRPk4jJC6znvzszbRfMgODKrAcZSVQMqvifqVEQFwdLlpy/vXevEgWsWrUKm83Gtddey2effUZo6PnCUt7e3oSFheHp6XmRLYiIiBSC1Wwc1Bz7Fn69Exq9SFpmJn6eWT0KLOpRICJ5yDgLx5ZBtZvAK7Dg9eXyZrPBma0lG8PJDcbQg2w2a8nFkkNKRgr1QuudP5n3DHBIpJitjgPuXRl6kJyRTMXAiv+ZWQ/WFPGIltLE6RoFVatWpVq1asybN48GDRpQtWpV+09kZCQAv//+e5EFKiIi/1FWc66umzmnR1SNAhHJ05/D4deBsO5W4yRSyjbzGTAnQsTV0GpaycRwdofx2+RyGbgilZyRTERAxPkeBV6BYDlnvz/D4pgocGXoQVJ63j0K9u+Hf/8tfMyl1e7dxu927eCKK0o2lqLm8rv47rvv5uzZs7mWJyYmatYDEREpArkP8HMOPUgzp+VfVEpE/pvO7oL9s42/jy2H1OMlG48UveQDxu9GIyHqEajco2ifz2Y7n4DK/jt5n5EkuG47RD1atM/vguxihvaTea8AI6mSJVePAhdnPagUWMmhRsGOHdCoETRsaCQMypJ//gEvL1i0CD7/vKSjKVouJwpsNhumPA7Azpw5g7+/v1uCEhERsfPwBpvjQUzOYoaaHlFEcjm+4oIF6lFQ5qUcMH6Htzd+1xxY/DEk74fIa6B8A2g8GrKGyJW0FHOKUaMgI6tHgWcAZJy232+2XlCjwJVZD/KoUfDgg5CaCqdPwwcfXFrspc3evdC6NVSvDi1bwpVXlnRERcfpGgWPPPIIACaTieeeew4fHx/7fRaLhb///psWLVq4P0IREflv8/ABa+b5uZ9x7FGQasl/6MGP+3/k67+/5s1ebxZ5mCJSiiTuNH7XvAOOflWysUjxSDsBPqHgHWTc9ilf/DGkHDg/04JvKHi3AuCXX+DNN+Gpp6BDhyKOwWaDDffByV+h/UJsFZraixme71EQCOkJkHWNN+OCxICrsx5UDKxIijkFq83K8WMerFt3/n6LJf/HXo4OHIBrrjl/u3btEgulyDndoyA4OJjg4GBsNhuBgYH228HBwURERHDbbbcxceLEooxVRET+i0zexswHOaSaU/lsx2dA1tCDfPxy8BembpyKtZQUlBKRYpK4GyI7Q7v50HJqSUfzn7ZpE4weDSdOFPETWdIgoFoRP0kBzGchoMb52x6eHD4MffsalfJvvNG40l6kDnxkDLtJjIU/HiPdko7VZiU8INyxmGHy3vNhX9ijwMVZD8IDwjFhIiUjhT//NJa3aAG9e1/yqyl1kpOhRo2C1ysLnO5REBMTAxhFDe+9914CAgJyrXPq1Cn3RSYiIgLG0ANrJpSrBUFG6j4tM431h9dDI0izpJ0fJ2oyORQti0uJw2qzcir1FOEB4SUQvIiUiKTdED3c+Ey4YhBYnb9CKu6zezd07w5nzsCyZVCkdc+taeATUoRPAJzaDHE/Qe0heT+XJRX8qzgsev99yD5FOn0a0tKgSEdr7597/u/MZJIzkvHx9KGcbznHYoYn/4QIwGa55FkPgnyCCPIJIikjie3bgwH4+GOoW9f47Yr4eOP/ExTk2uOKS2oqVKpU0lEUD5drFDzyyCMOSQKbzcbq1at59NFHufrqq90anIiIiJEoyIDwdkY1axxnOrhYj4K4c3HG75S4oo1RREoPmw3S/oXgusZtD29Nj1hCXn/dSBIAxMYW8ZNZ0sDDN9fic+dg+HAYOxbMzp//5pa8H366FjY/BT9fl38MF7zXsqfTu/NOKF/UoyFsNjj1h5FUb/IKePiQkpFCoHcggd6BjsUMrVlTC6ceuaRihknpSQT5BBHoY2x/+3aoWdMoZOjjY7xugKOJRzmedPGiosuXQ9WqxmwC27c7HYLTVq+GuXML/z6w2YxEwX+lLJ/TPQoudPjwYT7//HOWLl3KuXPn6Ny5M2++qTGgIiLiZh7eRnfOHHIWMEyzXCRRkBKHCRNxKXE0jGhYZCGKSCmSfQIUUED/4LxmR9E0im61ciV4e8P99xtd74uUJQ08sxIFiytC0BXQ4zduvx2+/tpYHBRkJA0KZfdUyMjqGpC46yIx+MHprZCwHmtYJ/78szH9+8P8+fDll4V8bmedO2J8X141B6r1hZAWJGckE+gTSKBPICkZKUZhes8cPcNtFjIsmfh5+dGpRicOnj1Ihgs9cBx6FKQnceIE1K9//n6/rHqOAxcPpLxfeb68Pe9/QnIyDBxonMQnJMDSpdC4scv/gXwtXgy33AJWK2zYAO+84/o20rION/xKR43KIudSoiA9PZ0VK1bw+eefs2XLFjp06EB8fDxLly4lKiqqqGIUEZH/EKvNyld/f8WVVa+kSnAVo0ZBjurMYNQoyHaxWQ/iUuK4IuQK9Shwks2mmSWlDMjMmh/eO7hk47icZJwGDz/wct+l0hMn4OBBGDXKuJrfo4hnK8SaYXxfAKTHgXc5tm8/nyQAo+t/ocWtAd8IaPAs/PNe7vttNmPogYcvnPgRNj/J/siPSEpqzI03Gqv06WOcqBaZpL+N3xEdjd9Vrifl2O/YbDY2HNmADRupmakEXNDrwWzNpHq56nw/6Humb5zO2kNrnX/KjCQjEZHVYyElBZo2zb3e0aSjDjMjXGjVKqP3Se3a4Ju7Y8glsdngySfP/+///rtw28lOFOSo6V+mOT30YPTo0XTq1IkFCxbQq1cv1qxZw3vvvYfJZMLDw+URDCIiInn6N/lf+i3qx7Ldy4wFnr65EwU5hh7kTBpcKC4ljgbhDTiRXNRVtC5vcXHQpQsEBMD48SUdjcglsmQlCjz9YO+HsO52OP1XycZUmh36DJZUhKWV4d8f3bbZrVuN39dl9dK/6Sa3bTpvHj65Ct/+9JPx+/334ccfL2HcuyUNzm6DpuOgwVPQfn7e65lM5JyK8+Rp49Jzo0bn7/b0zONx7mI+a9RO8A23P2FyRjJHk47Sa34vAGOKRE/HWnNmqwVvTyPJ4u3h7XKNgvWH15NuSbcnCgIvGOljs9k4mniUo0lH893OunXGCfjPPxsFMBs0cDqEAu3ebSStevSAzz6DyMjCbccr6xJ7WZvJIT9O9yj49NNPGTp0KEOHDiWotFaXEBGRy96BMwcAOHj2oLHAJ+x8d88sOXsRpGem57kds8XM6dTT1A+vX7p6FBxbARuGGAdyV82D0JKfWvihh4yxmwDvvgvPPVey8YhcEkvWZ4KHD5z+Aw4tgtr3Ahdc5synCOp/ijnZmErPagbrWTixEip1dcums6/e5zxJLlKefufbPsu6dcYV6vvuM56/ZctCbjvloPE/isia2zDsytzrmExGr4wcMSSlGCfflSsX8nldlZlyPkmQJSUjxeF2ckYyERf0KMiwmvH2yEoUeHo7PT2ixWrhnPkcQ74cAhi9C1JSco/hP51mvBniU+IxW8z2pEROf/wBV14J1asbt/v3dyoEp/zyi/F7+nSoVw/atSvcdrJfV3rehx1ljtNdASZMmMBff/1Fp06deOKJJ/jpp5/IzMws+IEiIiIuOHjmIB4mD3vCAN9w4yAth5y9CHL2Lsgp4VwCwb7BVA2uWnoSBRlnYN0tRqG1s9vhaFEPWC1YUpLRNbdtW2P8bKtWJR2RiOsOnjnIm7++aZwUeWT1C3bhquh/VvwvkJkEraZBxy/Au5zbNp2YaFyBLee+TV6cp9/52S3qDQPg8GHj5DM7SVHoYoLmRON3UL0CYvA3amT4VwHv8iSlGO/FC6+wF5nM5PO9BdbfCT/3ztXdP8WcAj4VHJbl6lHgZDFD+ywKWZIzkrFa4cLO5kcTj1KtXDXCAsI4npx3QcOTJx2HLLgzsbR3L0REGLMwgFEwsTC8vIyaG2n5j3gsU5xOFNx4443Mnj2br7/+mtq1a/PKK6/QsWNHrFYr//zzT1HGKCIi/yEHzx6kSWST8z0KfMMhcef5ccdc0KPAkndqPy4ljvCAcCICI+yzH5S4f783rvh0+AyuWQl+hez/6EarVxvFo9580xg/O3duwY8RKW1W7lvJk98/yY74HUZFdzC6i+N4tjF782xiE4q6/P5lJP4XKN8Yoh6B6v0h6nG3bTopyThBLra6Jx5+54epVe0DwNmzUK2aG7ZtTjJqD3j6wLFvYd9s47P8Qp5+xsl6zdsgvB1p6cY4A6fGtJ/6A7aOhMNLCt/DxWoGj6wO4/Fr4cSqPE/m8Y1wWGa2Zjr0KHB26EFSelKu24GBxswAOR1NOkqV4CpUCa7C0cS8hx+cPQthYU49rcvOnDFmUnDHe9Hf/xJrXVxGXC4uUK1aNR577DF+/PFHXn/9dXr06MEzzzzD1Vdfzbhx44oiRhGRopF8AHaOhwPzwcludlL0Dp45SPvq7Tl4JjtREGYc8P/7PZw7gs1mFGOKHWYc7OdXo+BEygkiAiIIDwh3f48Ckyn3jzPi1kBEJ6hxM1TqBnX/5964CuHQIaNwVJs2xu1g1X+Ty1D2yX9sQqxxVReME7nW0yHIuIx4IvkE9351Lx9t/aikwix9Uo9DaJvzt91YzLDYC6N6+kGq40nomTOXUJcgJ0uqsX2AXa/Dhnsh/VTu9Tz9IPWY/WZggHHCfeGJcy5xv8D3V8LO12Btfzi9uXBxegbABb3sLuxRYCQKHIcnZFgzHXoUODv0IK9tBwYaU1LmdCTxCJWDK1M5qDJHEo/kua3sxFJROHvWfd9tAQFGTxV3sFjg44+N4X4//5zPSlYzxL4Jv/SDXZPBWnw9+gtdhdBkMnH11VczZcoUfvnlF+699142btzozthEpIxITYWnnzayuVdfXfhqs26VGAvLG8Ffo+DXu+DQpyUdkWQ5cPYA7aq141jSMaP7Y/YBzbpb4cRK0q1GFaEgH+PoL79ZD+JS4vD29OZ06mn3JwpstvNXfHL+XZC0OAjJUZPAVPLFgE+ehPDw80WaLtXatcZUaI8/Dupw6DybzSgkWbUq1KhhFNwS58WejKVJZJOsREHWCd05x6P5bXHb8DR5si1uWwlEWEplnDaSsWlx8NeLsP9jt206ONiY8q7Yyj/4VYT0BIfeZ56ebpplwNM/q4dKAXxCHd53wYFGoiA5/2L/ht1ToXxTuD4Wrvro/HvYVV6B54t5NnwByF2jICUjdx0Ds83k2KPAyaEHSRlJuW4HBsKxY47rHU08SpWgrB4F+RQ09PcvurH/7pzRp2JF2L///O0LkyKuGDwYBg2CGTOwz4yRy45XYfNTkLQX/hoJGadJTzcSDGPHwvr1hX/+grjlCKVChQrcc889fPXVV+7YnIiUMaNHw7RpcPvt0LDhRRIFVgsk7THmAS5qu6dBUG3onwB9DkKFJkX/nOKUg2cOEhUWRQW/CsaVh+wDmqyukGkWI5vu52UcSF0sUbD20FruXHxnsdQosFrhvffg5pvh3nvzOUnOOA0+5eHsTvjqCvj9YcC4qrB3rzF3dHFLS3PfVE8rV0KnTrB9O+zaBYsWuWe7JSqPs5yiOPH54gt4/nno1w+GDYN9+9z/HEXpyBGjqvyRYvj4zktsQizX17ueXQm7jAScbwQk73VYZ9uJbXSp1UWJgpxsZjB5GSfYO8bBYfclzcuVMz7biq2bdmBN4/fJDfZFFSoYV6ovmXewUXvAkgFNX71IDLXg9Bb7zexEwYEDBWw/7meo/xSUi4Yr7oLyDQsXp1eQ0avCmgkVuwDGVf67m93N9oe20756e6MXwAXD3sw+YXhlDVnw8vByeuhBckYy5X3Lc2eTO2lWsRnJGcmEhjoe51mtxtCD7B4F+Q09KFfOqGtRFNz2PsCoc7BxI2RmGj0VVq0q3Hb+/hsWLICJE+HUKVi+PI+VbFZjKs4mY6D3X3B9LDbPQHr2hKFD4ZtvYODAS3o5F1XylzJELkFCAqxZY1RKLdJ5aeWSfPABPPUUxMQYJ1I33JDHSueOwLdN4YcOsKIF7MljjmIXpadf5H1x8ne44m6joE9gDQhpdsnPVxjHjhnZ4JI6uC5x547CH0/A6hth8whsFjMHzx7kaNJRyvuVN+oUBNRweEiq1YyHycN+UJOWmYYtjzO3nMmBxPTEfBMKzrDarFwx5Qoe+PqBfNd56SWj50yzZsZV4R078ljJ08840LRmQMoBSI/jl1+MKs/t2xvVmL//vtBhFkpoqNE91x0mToSOHY339Pffw2OPuWe7JcKcBOvugM/LwRehcHAhO3dC585G19MqVYwpvJxydgf83Bu+rgM/X5fn2OY5c6BrV6Mq97PPwogRbn01RWrMGKhTx4i5efNLeD/ZbMYV7XV3wK+DIOE3px6WnpnOgTMHuK7edefrDwTXgxOOR/B/xf3FjVE3cizpGInp+Z+RnDx3koRzRZe1c6UTUpHzrgCZicaUeuHt3brp7PHm27LyMkV+nBZYy/i941U4YcyLWL68kYS9ZNlFHpP/yXvGg2xBV0DcaohfB6n/UrWi0ZXgr6zZOdPS8rhqbjUbiZrgusb0lFtfgFN/Fi5O/yrG9nIkyVLMKdQoX4NGkY2IDIw0ahb4VQKPrGEmHv6YPQMLVcwwKT2J+uH1md9/Pnc3u5ukjCQaNjSmI4yLM97ny5cbQw9eWf0Kb214iyNJeR/wVKhQdD1OK1QwegG4Y7+rUwfi4426PiNGQEoepSqcsXGj0cvh4YeN35065bFS+klIOwE1bjV6yviE8MMqX1avhhUrYMOGy6BHgUhJmDXLyOrNmAGvvQbq0HKJLOnw70pjPmU3zjd99qyRKc1ZSf3CargA7HjN6DbY9yj0/ReqFX7C5cWLjfdGu3bGSdu2vC4eZZw0MupHvoTPysG2l/nzT6PXQ/v2cP31ubvOuZPNZlw1bNHCmI7u1luNL9X/FGsm/NTdaIv6T4F/ZRJSTnDOfI4Bnw5g3+l9Rp2CoDpgOj/xdKrVgp+XH6asfoQ2bHmOp7ywF8Gl9CrYdGwTFquFL//+Eqst76Pd994zEgUvvmh0B8xzznDfMONqT4WmxnzcwJAhxgni8ePw77/Ge6I4hYcbV/yO512I2iW7dsG1157fx0tLvYPMTKOHx/btTowVzrbrdeME/4Z/jJ8KTbn1VuOqV1yccZDn1FzcNiusucl4H3f9Ceo9kufR6oEDxkl2tmIf311Iu3cbvcbmzTMSJwcOXMI44/1zYPNwqNYXqt98vjBdAfac2kMFvwp4mjz559Q/xklOcBQcWwabhsG5Q4DRo6BJxSZEhUWxPW57ntuy2Wx0nN2RNu+3yTMBeTF79xpXF7duzfukePdu6NYNoqON/Xz2bJc2XzR8wyDxb/CvbJyMuFH2Z9mXWZO7fFTUpSF8Qowr6idWwa7xgJF8/e034zMAnLiyn5/AWuDhDQkFnJUFXgHYYGVHOLOFShGpVK5sFIk1m2HSpLy6qpuwF908+SvsjDFmxSmM8g2Mbe2fa6+9lJyRTKC3sVMGegcaPQpMJiMxARBct9DTIyZnJBPoY2w7yCeI5IxkGjc23v/Dhhnfh2vXGj0KkjKSSM5IzrdHQYMG8OuvkJH11HkeuxVS7drG53Z20uho3iE4JXvmhBEjYObMwm8nIcH4jgwIuMhK5jPGb+8K8Ofj8EUFtq4/QFiYkbSGop1687JLFMyfP5+uXbvSpEkT+vfvz6YC0vkbN26kf//+NGnShG7duvHJJ5/kWue7776jd+/eNG7cmN69e/PDDz8UVfjM2TKHulPrsu7QuiJ7jv8CiwWeeAJGjoT5840um3kelBchmw0+/9zIBD72mHFicNlKPwXLG8ORJcYYvGPLiIuDe+4x/q/33Qevv164Tfv7G99HBY7PO7sdKnWHpH9g/e1weHGhni8xEe64w3h//PmnkcWPispjRd9wI0tboRmEtODMGRPXXAP16xvz7c6eXbTTGW3aBO+8Y0xL99FHRkY4IiL3evHxMHUqPPmkcZUxz6vUl6vkfyBxF7R8E2yZ4BXAwZM7HVY5ePagUWW63PlumGmBUfZhB/ZlefQWiEuJY+w1Y9n6v61UCa5ySYmC5XuWc1uj2wgPCOePY3/kut9sNsb6165dwIaCo42DWGsGePiQmOLP3r3Qt69xcu3rm/f7oChddZXxO3u2g0sZG+/tff4gz90yM42T0cceM/bvuXMxao388bhxMrjpEUjJXWFqwwbjCtD48cZ3xfTpTj5hwm9GFXhrGmwfw8l//mLHDqP+QnCwUUm9Ro2CN8O5I8bVvQYj4MxWOLXJuHp4geDgout2W1hJSUbya8AAeOABIyl/oZ1Zu2yPHsbvoCDjfVAoR740enpVutYYM+/h3IZiE2JJOJdAx9kdybRmsvf0XijXwLhzzztgzcBitbAjfgdPrHiCQ2cPse1E3mch2+K2YbFaKOdbjo1Hnau9lZlpDBm57TYjGfX553DwYO717rvPaOfYWNiyxUgQl7iQlkZ1/JSDbu/mEBpqnFS99ZbxHnrySbduPjeTyfhOz6FjR+ME8YknjB5Pc+YUctuevlC+iTErwY6LDD3IYxhjq1bG8cgVVxgnzrl4eBlDZRJjodFIiOhYyCAxahQE1TYKNf/YBTB6FAR4G2ejgd6B52sWZCcKQltitpgdexQ4O+tBRpK9XlCQTxBJ6Un2KQ4//xxezfpXHUk8QsOIhkSHRedbzLBDB+N79J57jBo32QmmAlnNxoWufbONi10ZZ0jPTKfDhx148OsHgfNX6x991LiY9MwzTm47D61bF/6xOUVGGp/5F/3c983KRqceM6b89A3HZDISMcXRK8lNpYuKx/Lly4mJieHll1+mZcuWLFy4kKFDh7Js2TKqVKmSa/3Dhw/zwAMPcMsttzBx4kT+/PNPxowZQ2hoKD179gRg8+bNDB8+nMcff5xrr72WlStX8sQTT7BgwQKaNXNvV+RMaybj1ozjrqZ3MX7deL6u8XWudSwWo7vm/v3GVQtPT+Okp0z7e6oxniukmXHlpcatEHDxCU4tFqPrVs75cIv76su8eUaSYNcu42Axv7FPVquRuUxMNE4CatVyX9Ewt0lYZ1zVbTUdtr0MyXt5fpKZHTu87V2jCvuB5ONjXFWYMwfuvNPIpJ86ZXxhOvCrZBxM+1cy5sA+9Xuhnu/UKeNEpUHWMaLJZPzfcwm90si4174Pgupw6FAFEhONYRGenuevFKalGcMCMjKMbpSJibB0qdGNzdvbmPO3ZUvX48xOnOScCujC97DNZvRuuPZamDLFuF3YLm6lUvZczxlnjYrSfw7nYM2XaVOlDfP7z+eDPz84P/NB9f5wdht4BpBavlmuREFqZirlcZwgOy4ljuaVmtO0YlMqB1W+pETBt/98S+WgyniYPFi+ZzltqrZxuN/b2zgZXbvWKE4ExpVr/wsLiFfuCX+9AKu6QGYKQUH1CQoyrjRmy6/o0r//Glf9LZbzJ6gbNhjvCS8v6NLF6B3gqqgoI/bnnzd6amVkwC235LOyNdNIJnr6g4dnrrtbtTIOwp5/3njtBw4Y23aHl16CTz81XnNYGJw+ZYNV10Cz1yCys3H135q7EtYbbxgJwA8+cPEJfcOMk3yvIEg9TjnrMnx8bne4Inn2LLz9tvF9VKWK0W4PXDg6xTurW0V6HHiXh91TjHiDajms1ratMV71lVeMdty50+gRVZJeftnoNrxtm/Eez+sqefXqxu8dO4yTsuxu9Xn2HCuIp78xX701E44sNk56Kl1b4MNiE2KJCouiacWm/HLwF2ITYqlfuQdsfda+zt7UFDKtmZwzn8PbwzvfOgULty+kReUW+Hn5sWjHItpWa1vg8+/aZXwvbNx4fvaQvBw5YlwFzP7fFGUy2mkVrzH2m2+bAiaIvNqtm+/Z09hHFi920+wDBanc0zimASjfiG7NjP/3228bi/I8UXdWZGf4+03YPjr/dULbGFd/s68EB9XluuuMseQXvYpdqRvsmgjl6jvdkyZfFa8xjqcyToOnv8NV/0CfwPMzFQTVhUwgtDXmuJOFKmaYnJFsTxRkbzs62ujJsWePsY6ZVE6lnmL/4/tJTE+k7tS62Gw2e6/AbN26GW2VfU3X6QtwP/UwEoN17oOUQ5Aez9zdP1GzfE1W7l/JzvidNIhuSI0aRnf9FSuMXnyF1awZ1Kx5PhmY5zGmEzp0MF7v+PHGZ+033xgJNQfe5YzX9veb0Ppt8PCmZdMkTp+GH34wErQHDhjnFkWhUKcrZ8+e5fPPP2fv3r2YTCZq167NgAEDqFChgpvDczR79mwGDBjALVlHMCNHjmTt2rV88sknPPXUU7nWX7hwIZUrV2bkyJEA1KlTh23btvHhhx/aEwVz586lffv2PPjgg/Z1Nm7cyNy5c3njjTecji01I5WlO5YSRRRxiXFElsvdH3HxzsVkWjI5cuYIvx36jb+O/0WjyEYO63zyiZHt2rnTOFHJzDQOCsssmw12vQVNXja64v71EvhVh+r9HNdLPmhcfQyoAnjg6R3C8OGRjB9vHCSnpxsHWVcX4vstOdnI7Jcrd777T926YMn6x1sslqwztENgPWeM6bLZqFKpBn5+nmzdatx9/HjuA4SEBONq3bBhxhXDtDQjoVCunOtx5h38QeME3zvrBMm/knFg5apyLcAUBLumQGg72PUGV7VNZeWPHnz/vXHl7MyZ81ceL+rQF5D0N4S1NbLj5Rrw2mtdufVW44MsPR3efz+PK3H1HoM1fY03fNIBo5BPId781apB//7GVDMPPWRkp6+/3ujq6dCmdR6BA4tgaU3ARlS9KK65xsLzzxsFYuLjjROmKVOM/TIiwrgqMXSokfiYNs04KTCbC7ePZr9f77vPSAbu3WtkuENDz69jsxknH4mJxlQ8VqtxsF6+fP7bLQ5pacZJq8lkfMkFBjrG7TTfKlD1Flh3F1S5AfDjSFICjcIbUbtCbVpWbMncv+Ya7VXtZtg5CWo/QJrVgxDfEHt7hviEcC7jnHE7MNDeIElpSUT4RWCxWKgWXI2E5AT7Y1wRnxLP3oS99K3Xl6pBVflx34+M6jTKuDPH88XEGFebDx823jtDhmQlesuXN84oAco1hco3wYmVxu2qN/PqqxZeftn4sk9JMYa/9O7tGMPw4cbn1AcfGCfgJ08a758XXzQSlhbLpX1fTJhgFGA8ftx4f1u2TzJO1so3Mvbj8g1h+xiIftxIFCT8Cs1fB1/Hhh81ykhs1a9vvFcffDDvKzdWm5Vz5nP4efnZa0047J95qFrVeH1//238feoUlAusA2f+hoC6sPVlYy74AMcsZO/e8MILxtWtatWM/albNyf+KXUfgTX9AC9ITcAjsAZjxlgYN84oNJiUZMQxdapxFblKFSM5lCt8z3JwxQPw671QpQ/YvMFKrhWHD4clS4yCr/7+cNdd0LixE3Hm4XTqab7Z/Q02m40BDQfYTxJc1aKFUZDyxx+Nz+/s77ScmjUzrqTfdZfxv96xwzgprFCh4DbNpc7/YO0t4FkebF6AT67/05kzRkKucuXzJ9r7Tu5jWKthDLtyGMOWDWNPwh4s9W6EarfBsW+gUi+2p5voXKMz3931HUt2LWHmnzPPx5W1H9tsNr6K/YrGEY2xZFr4OvZrJnSbQIYlg/2n9xPmH0ZkUO7ju1q1jP/V9OnGZ8DJk3DllUZ19JwefdQY11yxIvj5QaVKuff1YudXDa64Hw5mnZ2FtMv3g8Tl9sTY/1esML4zOnYshmPaOg/D3++CJQUavkjVEAvPPWd8l4NxEaHQMdR5BA58BubTEN4JfCKNjeX4HgBPaDAKtr8MYe0hoiuDBlmYP98YktK4sXGsmSuGek/AT71gVVZizCus8IHWexoOfWnMfhD9DOat6wjyCsJisVDeuzxn0s9gsViwRHSF42CpeB2W43MJ8ArAYrHghReeeObaP/KSmp5KiI/xfRzkHYTZYsZqtTB1qlHY18MDrrr2KBF/RBDgGYCPnw/eJm/ik+MJCwhz2FbVqji0VaNGTv4LLGbwDofMDNj+Kpk17mLab7OoF1aPSgGVeHP9m7x3w3u88YbxvWy1Gp+zl/JenDTJ6PlQvTp07164bVWvbvScmDrV+AkONs4VcmkwEn5/EA4ZPW2vvsZMnz4Wbr3VOF9JTITdu3Mn7t3BZHNxANbGjRt5+OGHCQoKonHWN9iOHTtITEzk3Xff5corL1Lg4xJkZGTQvHlzpkyZQvfu3e3Lx40bR2xsLB9/nHs6l4EDB9KgQQNGjRplX/bDDz/wxBNPsGXLFry9venSpQv33HMP99xzj32dOXPmMHfuXH766Sen4/t53c8E+5WSwZgiIiIiIiJS5rXKWQjMjVzuUfDKK69w3XXXMXr0aDw9jeyFxWJhzJgxvPLKK3zzzTduDxLg9OnTWCwWwsIcs0/h4eHEx8fn+ZiEhATCL+iHGRYWRmZmJqdPnyYyMpKEhIRc2wwLC8t3m/m5ut3VZGZmsm3bNpo0aWL/37jDb4d/o9+iftzd/G6+2PUFfzzwB+V887kknfPKVX7Lsi9HXrjsYo+74BKm7cwZHvv2Mc6Zz3Em/Qzz+88/3xU4r+crKCZnX4szCvt8F7BYLIVvzwKeb/rG6ew+uZuUjBSm956Ov/eF/ZPzZ7VZ8bjI3OvxKfF8+bcxsOuupnfl3y5OxJmvHI/759Q/fLvnW0wmEw+0egAfTyfmWft3lZEd7fKDcbVn+xgY4EKF6ULGbalcmW3ffuv2fdQeE8DZs3z4odHdedYsCAkxri5v3Gh0x77vPuNKa+PGubt3T1g7gX+T/+XNXm8y+qfRmG1mXu2az1jIAtpzwgSj18PQoUYPgGuuOT+GONs7v7/D7C2zaRzRmA1HNzCx+0QmrZ9EaEAo5XzKsWr/Kv557B9SzalsOLqBUL9QWle9yOC8HP+DTGsm3ed1p221tizcvpDFty7mq7+/YuX+ldQNrcsvB39h0c2LLr69AhRqHz13zCgylXESIrtCp8Wlt2qcG/bPE8kn6PlxTzrX7Myx5GMsunkRX+z8grd/fxtfL18GNh7I4OaDiyzOTGsm9abWo131dqSYU0hMS+Sne/JOwufZnoX9HxTgSOIRnvr+KYK8g7ir2V1c4+8BG++Hq5fCmb+MWVFaOt+rME/uir2A72yLBa67zhiSAcbQhccfL1xM69cbV7TS040hWH//XbiaGdvjttN5Tmfua3Yfd1a8E1NFE83+/Qji1hjdg3e/zak6j9H4m5e4tva1nEg5QURABB/3z33RpzQa8cMINh/fTFRYFCv2ruCzmz+jZZV8xqC5+D746y+jS/FVVxk9S/z9zw+lc+r4Kqf1d0H5xkZvw7Pb4YrBRu9NZ2O6YL08v0NzrDPyx5H8uO9HIoMi8TB5sOS2Jbk2GZsQy7XzruW+FvexJHYJ6+9bb3Rdz/E+X7R9EQt3LGTJbUtYvGsxH27+kG/u/Ma511yAQ2cPceX7V/Lv0/9yJvUMV0y9ghNPn+C7f74jNiGWAO8AHm7zcK5u8e605+Qerpp1Ffe1uI9Zm2ex/t71RIdHO6yTkpFCo3ca0TiyMSdSTtCuWjsevmIq3boZvVWis1YfP568PyOcUOjj3EI+X6EV0/NtPLKRB755gC3/28KWf7dwxxd38HT7p3lv03u0rtKaDUc28HS7p7mz6Z35x1mYczBnt1UCXE4UHDp0iKlTpzq8oTw9PbnnnntYunSpO2PL04U7bl5jXApa/8Llrm4zLx4eHvb/iaenp1tPQjrU6kCvqF58tuszxl4zlpCAkPxXTkkxvt3B8eA3e1B8zkHOOWPM+bi8luVRie7tG962F0hxOHHNuW52DDmfP5/t5eLMOnkp6LU4w2QyulqtXo1naCieKSmuDdIvIPbH2+V3FFcwTy7+OiqVq8SDbR48vyC/9wG4/n/JluNx0RHRREdEF/CAC/gEgTUF0g5BZFtoP9v5OPJ7Tznj+HHYssXt+yjg0OZDh8LddxtPl55uDLPo39/o8jZhQv6baFixIS+ufpH3t7yP2WLmw5s+zD/Oi7zPbTajG/zo0ca0lPnp26Avz656llGdR7Fk9xJ61OtB7KlYfjrwE+X8y9G1Tlf8ffzx9/HnuqjrCv4f5Phs8fT05P2b3mf4d8N5qsNTtKneBpOniZlbZvJgmwdZc3gNbWsUPPbXGS615/6ZxvsO4N+vIeNfCKjmljjcrjCfgRfsH1VsNhbfvphNxzZxfdT1eHt5c3vT2wn0DSTTmkm/Bv0usjEnXeRzxNPTkx71ehDgHcDBswe5sf6NBbaVQ3sW9jOqADVDavL5bZ87Lrx+CyTthXJ1IbLDpT+vu2Iv4Dv7u++MoQEtWhi1G86cucjTFhDTa68ZqwwdCj//bAytKsxLaFa5Gb3q9eJA4gGoCM0qNcFz/QzolFWdzHKWCH9/mlZuSv2I+uw5vYf7W93v/s/lInJ99PUs2rmI3tG9yfwnk9bVWud/3Oji+6BFi4vMfHLhtgradqfcBbwB5z9bLtx+Vrn+/PbR5zo9x6Kdi0g0J/LZLZ/l2Z6NKjZiRMcRfLrzU6ZdP43y/uXPb8fYOFERUaw+tJpbv7iVvaf20r56e7d9JtQMqcnZjLNM+nUSiemJVPCvgL+PP30b9i30Nl1VP7I+A5sO5JfDv3B7k9tpWLFhrnXK+ZejT/0+1KpQi9lbZnNvy3uZMtqTihXzmOUrr88IF7h8THSJz+eyYnq+qIgoYk/FMuSrIRxNOkqNCjW4MfpGHvn2EZ5q/xRzts7h+vrXF+q4zOE2FPxaCnsO5GYuJwoaNmzIvn37qH1Baed9+/bRwJ7ydL+QkBA8PT1JSHC86njy5MlcvQay5dXb4NSpU3h5ednrKYSHh+fa5qlTp/LdZkn5oM8HnEk7Q2RgPnMx5fyCyv7bmROowj4OI8GSXcAkXyUxUbA7ntNmMy7TbNliZPQuk4OXPLmzDS58vxR22xHtoecmiP/FmBe2fO4vyXyVmsmnL87Hxyh2k+3s2dzjVS/UrGIzbDYbtzW6jYXbF9KsYuEqmZnNxvFcQc9XvXx1mlRswje7v+H6etfj5+XHgAYDmL7RKAv/YKsHL76BAkSHR7N84HL77dZVWhPgHcDqg6vpX7//JW270OJWG5XUK14Lx7+9bN5PTsvj9dQLq0e9sHoOy26MvrFInzOnWxvdyqu/vMo/p/5haq+p7nted/MuB6H5naEVQlG+t3Js+8svjav+q1cbY1yzZyFw4MR3fVKSkXAYMcIoIpaYWMiChFliusXwyV8XnKiaPCCwJrSZASHNuaVhGt/v/Z49p/ZwQ9QNhX+yYnZ1zatJy0wjPiWePtF9ivTq8+Wkgl8FvrnjG5IykmhSMXf1/2zD2w1neLvh+d4fHRZNYnoii3cZY7KHNB/ithg9TB7UqlCL51Y9B0CXWl3ctm1XjOs6jnWH19Gheod813mw9YNcM/carqhwBa2qtOLo0XxmcRK3CAsIo7xveT76y5jH86HWD1E5uDKtqrRi64mtdKzRkVD/whRkuny5/BUwePBgXn31VWbNmsWmTZvYtGkTs2bN4rXXXuPuu+8mNjbW/uNOPj4+NGrUiHXrHKcVXL9+PS3ySb02b96c9esd5ztdu3YtjRs3xjtr7p7mzZvn2ubatWvz3WZJ8fH0yT9JAOfLDOf8cUZhH3e5MJnOHxTl/FsKx53vlcAaUGsg1B0KEfl/UZYV111nFKLbssUofPbnn7nXqVWhFsG+wYzsNBIbNhpEFC756uNjDDWYPv38NFy7duW97s0NbmbOljnc3PBmAOqE1iHUP5R1h9bRs05P5580rxORPAxoMIC5W+ban6/YndlmTFN39YWXZKSodL2iKzvjd1KtXDVqVqhZ8APEJdu3G8W0grPKJDXMK+/qxHf9gQPGMKnrrzdulyt3aVXqrwi5gmc7Zs06YPKAWoPgzyfgxE9w8jfITKZf/X58+feXdKnVpeALD6WIl4cX19W7jikbptC3ft+8Vyrq44/iPrbJ+RwXqajbKLIRV1VzpvJx/oJ9g6kcVJkF/RcYU+qFu9hrsQB1QuvQJLIJ9ULrUSfETdOyuCjEP4Qbom4gxD//XsJNKzblgxs/4MObPjRuNzWGB505Y9x/6lQxBPofExUWxahOo+hSqwtRYUZWpm9034vv685y8jipNHG5R8GTWZOgTpw4Mc/7TCaTvev+rvyOTAtpyJAhjBgxgsaNG9OiRQsWLVrE8ePHuf322wGYPHkyJ06c4PWsCd9vv/125s+fT0xMDLfeeiubN2/miy++YPLkyfZtDh48mLvuuouZM2fSrVs3Vq1axa+//sqCBQvcGruUkLKW+JDL1vTpRkX4gQONE/lJk3KvYzKZaBLZhE93fEp0WLRz9R6MB+b6e/6/Np59Fvr0MU4gZs7M+6EPtHqAK6te6XBgN7//fJIyklyqneHsvvZcx+foV78f7au3d37b7nLuqDFlVeWSLjP+3+Ll4cWWB7fY5+gW90pIMKbcu1TZHTCrXnx24sJr8y4cXGjUgPCvAv5VifAOYsuDW3JVP78cDL9qONFh0VxT65q8Vyjq44+i3H5evQcv7GlZxM8XHR5NsG8w8SnxRIe5N1FQu0JtOtfszNHEo1QOruzWbbtbzuFhI0bAypXGlLNVqhgzjnw83009PUsrd/VkdVJUWBQVgypisVrs77u7mt6Fp4cntzW67dI2fhm2jcuJglWrVhVFHE7p3bs3p0+f5p133iEuLo6oqChmzpxJ1axvtfj4eI4fP25fv3r16sycOZOYmBjmz59PZGQkI0eOtE+NCNCyZUveeOMN3nrrLaZOnUr16tV58803aVbSkxeLFKVi/uAV4wLMtGkFr9esYjMW7VhE04pN818p55UkyLP9KgJz5jgRl195OtdyPMu4cNpWdwoPCCe8RgkN7Uo/afwuV79knv8/rGq5Qpx9OvE+L/Oc+Kw+d84oeHepsqf3KrKRdiYPqHUn4FgIrCg/b4pSy8otaVk5nwKG7lbc39nFva/l8Xz1w+rzx7E/SMpIcntPpDqhddh9cjdHk47SsUZHt267KIWEGEVLd+0yhgq1bAl8XMxtVcbfi9Fh0fxz6h/2nt5r71FQtVxVnm7/dLHGUVq4nCioWmSpZucMHDiQgQMH5nnf+PHjcy278sorWbIkd9XVnHr16kWvXr3cEp/IZeG/eMB9mWhWqRnvbHqHQU0H5b+S2q9wMpONkxXfUPij8AVFRYqNE/t6SIh7LvBml2aKizOuVkopUho/8y+hxpUzosOjWbh9IfVC6110pqfCqBNSh+/3fs/RpKPUCS2ZoQeF5eEBjUoyt1Ya34tuFBUWxQ/7fuDkuZPUqlCrpMMpcS4nCrL9888/HDt2DLPZ7LC8W7dulxyUiMh/VXZPgov2KJDCyUwGz6xLr2f+KtlYpGBl/IDUXSpWNHqDX6oaNYyTkB9/hObNITPT+PHzu/RtSxlUxPtndFg0vx/7nf4N3F/4tk5oHfaf2c+xpGMlVqNASqeosCjWHV5HnZA6eHpcxoXM3cTlRMHhw4cZNmwYu3fvttcjgPNTDLq7LoGUAHVLFykxbau2Jen5JAK8A0o6lLLHlgmmQufHRUqlq6+GV16B334zTvbXrIGs0k0uCQuDq64ypkgMCIDly+Htt6F6dffHLFKQ+uH1sdqs1A9z/1Cx2iG12XtqL8G+wRctJni5WLt2LQsWLCA1NdXlx2ZkZODj42Q9JDfw9/dn4MCBdOhQOotY1wurR6Y10/UCmmV0qJzLR0yvvvoq1apVY86cOXTr1o3PP/+c06dPM2HCBJ599tmiiFFE5D/DqWlHpXC8AsFizAPOVfOMv/1LdyErkYIMGAAvvwzt2xs9Al54ofDbevxxuO02eOgh4/a777onRhFX1axQk4FNBtKttvt7Kgd4BxARGEHV4JIdTu0uS5Ys4ciRIyUdhtOWLFlSahMFfl5+XFn1SlpXbl3SoZQKLicKNm/ezNy5cwkNDcXDwwOTyUTr1q158sknGTduHEuXLi2CMMVtnOktUEayYCIiDryCwGoGSzoE6jKplA0NG8Ibb8DTTxszqrRrV/ht3Xor+PrCggVwww1FOAOCSAE8TB583P/jItt+vdB6VAkuG8U4+vfvz/z58y+bHgX9+vUreMUS9Ot9v7r+IJuNd955h1q1atG7d+mbWenEiROMGTOGKVOm4O3t/AxELicKrFYrgYGBAISEhBAXF0ft2rWpWrUq+/fvd3VzIiIixcMra6L5pD1QoXHJxiLiRk88AXfcYdQTuMgU90656SbjR6RUcXPxxDVD1lxiQKVHhw4dmDJliv12RkYGHh4eeHkZp3kNGzZk9OjR9OnTBx8fHzw8PPD09KRWrVp07NiRXr164enpyfz589m0aROTJk3CM2v6k99++41p06Yxffp0goKCmDFjBlu2bCEpKYnQ0FD69+9P9+7d7c/9zTff8OOPP3LgwAFatWrFyJEjC4x/9OjR7Ny5E4DMzEysVqtD8uLTTz/lhRdeIDY2Fi8vL0wmE+Hh4dxxxx107GjMWrF161ZiYmKYOnUqkZGRgDEb3mOPPcaIESNo0aKFU//LFStW8M4773DfffdxkwsfhMeOHWPTpk0MHTrUvuzw4cNM69CBfTffTHh4OEOGDKFt27Z5Pt5sNjN58mT++ecf4uLieOGFF7jqqqucvh9g9erVLFq0iISEBKpVq8ZDDz1EvXr1AKhYsSLR0dF8++239OnTx+nX5XKioF69evz9999Ur16dZs2a8cEHH+Dt7c2nn35KdQ1kK/3UW0BE/qsCaxg1Ck79YSQKzmxXwkDKjIoVSzoCESkpn376qf3vF154gbZt2+Z5ovv6669Tu3ZtMjMzmT9/Pp999pl95rfbbruNjRs38umnn3LHHXeQmJjI22+/zf/+9z9CQkJIS0sjJCSEsWPHUqlSJf7++2/GjBlDeHi4/UQ8NDSUW2+9lS1btnDy5EmnYh89erT97wULFrB///48Ewx33303N910EzabjU2bNhETE0NUVBSRkZE0a9aMLl26MGXKFMaNGwfA1KlT6dSpk9NJglOnTrF48WJq1nR9Os4VK1bQsWNH+9X6zMxMxo4dS+eEBMb9+CNbt25l4sSJvPXWW1TJZ1qZBg0acOONNzJ58mSX79+5cyfvvvsuY8aMoW7duvzwww+MGTOGGTNm2C/wd+3alenTp7uUKHB5vpGHHnoIq9UKwBNPPMGxY8cYOHAgq1evdiprJCIiUiI8/aBcNOwYB8dWwIZ7SjoiERFxhs2W+0cKzcvLi86dO3Pq1Cn7DHZeXl4MHz6cJUuWsHfvXt555x2aNGlCp06dAPDz82PgwIFUrlwZk8lE/fr1adKkib03AED79u256qqrKFeuXJHFbjKZaNOmDYGBgRw9etS+/J577iE+Pp5ly5axbNkyTpw4wZAhQ5ze7nvvvcdtt91GcHCwyzFt3LiRpk3Pz1a1o3Jlkg4d4rbdu/Hx9aXNlVfSuHFjfv755zwf7+3tzU033USjRo3w8Mh9el7Q/Rs2bKBt27ZER0fj6elJr1698PPz47fffrOv06BBA06ePMnhw4edfl0u9yjIfrMAVK9eneXLl3PmzBnKly9vn/lARESkVAppBQfmwerrwLvoDmRERERKq4yMDH766SeqVq3qMGa9Vq1a3Hzzzbz88st4eHgwffr0i25jz549dO7cuThCtrNarWzcuJGMjAxq165tX+7n58cTTzzBmDFjAHjppZfw9/d3apvr168nJSWFbt26sWrVqlz333HHHbz44os0bNgw133p6ekcO3aMatWq2ZcdKFeOGsnJeOVIaF1xxRUcOHDA2ZfpEpvNZp+JMKecz+fl5UXlypXZt2+f06MA3DJPVIUKFdyxGRERkaJ1xSAjUQDg6dwBhIiISFnw/PPP4+HhQVpaGr6+vgwYMCDXOk2aNOHjjz+mR48e+fYMsNlsTJs2jSpVqtDuUiqouuCjjz5i4cKFmM1mMjMzGTRoEOUvKMpSs2ZNfHx8CAoKokGDBk5tNzk5mQ8//NBhCMSFPvnkk4s+HiAg4Py01qmengRm9dTIFhgYWKiCk85o06YN48aNY+fOnURFRfHdd98RHx/PuXPnHNbz9/cnJSXF6e26nCg4d+4cM2fO5LfffuPkyZP2YQjZ8srCiIiIlAqVroVGo+DAx9Aq/yslIiIiZU1MTAy1a9fGYrHw119/8dprr9GqVSv7lfmMjAymTp3KDTfcwMqVK+nevTvR0dEO27DZbLz77rscPXqUsWPH5tkVvigMGjTIXnfh2LFjjBs3jsDAQHuNBYCZM2cSHR3NsWPH+PLLL52aYWHOnDl069bNoUeAK4KCjCmtz507Z0+s+E+cyLlff4UNG+zrnfv4Y6d7OLiqSZMm3H///bz99tucOXOGNm3a0KxZs1zDKFJTU+01C5zhcqJg1KhRbNy4kZtuuomIiAgNNxARkctL07HGj4iIyH+Qp6cnTZs2JTQ0lC1bttgTBfPmzSMkJIShQ4cSHh7OlClTeOutt+yzENhsNt577z12795tP1EvCVWqVKF169b8/vvv9kTBhg0b+OOPP5g2bRonTpzgpZdeok2bNgUmALZs2UJaWhrffvstYPQQ2Lt3L7GxsTz77LMFxuLr60uVKlU4cuQIlSpVAowhHIsWLSIzM9M++8S+ffuoU6fOpbzsi+revbt9BorMzEyGDh3qULgwMzOT48ePOwzXKIjLiYI1a9YwY8YMWrVq5epDRUREREREpATZbDZ27NhBfHy8vcr/9u3b+eGHH5gyZQomk4m+ffvy22+/MX/+fHtRwBkzZrBr1y7GjRtnv5Kek8ViwWKxYLVasdlsZGRkYDKZHOoguENcXBx//PGHfbrBC2doCAkJoXfv3kyZMoXx48fbp3vMy+TJkx16yMfExNCyZUtuuOEGp+Np06YN27Zto3Xr1gA0atSIoKAgPv30U2655Ra2bt3K9u3buf/++/PdhtlsttcayMzMJCMjA09PT3vsF7s/MzOTQ4cOUatWLZKTk/noo4+oWLEiLVu2tG8/NjaW0NBQl2YpdDlRUK5cOdUkEBERERERuYyMGDECDw8PTCYToaGhdO/enebNm5OWlsbUqVO5++677VfFPTw8eOyxx3jqqado164doaGhLF++HG9vb4cT3i5duvDwww8DsGjRIhYuXGi/7+abb6Zx48a89tprlxz73LlzmT9/PmCM92/Xrh233347YMxY0KhRIzp27Ghff+DAgTzxxBMsXbo0z1oM2S6sc+Dl5YW/v79DIuTWW2/l5ZdfplGjRnluo1evXrz44osMGjQILy8vvLy8GDVqFNOnT2fx4sWEhYXx5JNPOkyNeOE2H3roIeLi4gBjGkuAxx9/nG7duhV4v8ViYcqUKRw/fhxvb2/atm3LqFGjHIaF/Pjjj1x//fX5/h/yYrLlVSLxIr788ktWrVrFhAkTimycxeXKYrGwZcsWmjdvftHMlVwe1J5lj9q0bFF7li1qz7JHbVq2qD3LFrWne7399ttcccUV9O7du6RDySUuLo6XX36ZqVOnutS7w6keBX379nWoRXDw4EHat29PtWrV7OMusi1ZssTpJxcRERERERG5nA0bNqykQ8hXZGQk7777rsuPcypRcO2117q8YREREREREflvi4+Pz/dE+uGHH6ZLly5F+vzDhg0jPj4+1/KcwyYkN6cSBY888khRxyEiIiIiIiJlTEREBJ9++mmJPf/bb79dYs99OXO5mGFO6enpLF++nHPnztGhQwdq1arlprBEREREREREpCQ4nSiYMGECZrOZUaNGAZCRkcGtt97K3r178fPzY9KkScyaNcthGgYRERERERERubx4FLyK4ZdffqFdu3b2219//TXHjx/nu+++4/fff6dXr1689957RRKkiIiIiIiIiBQPpxMFx44do27duvbb69ato2fPnlStWhWTycTgwYPZuXNnkQQpIiIiIiIiIsXD6USBh4cHNpvNfnvLli00a9bMfjs4OJjExET3RiciIiIiIiIixcrpREHt2rX56aefANizZw/Hjx+nbdu29vuPHTtGeHi4+yMUERERERERkWLjdDHD++67j6eeeoqff/6Zf/75h86dO1O9enX7/atXr6ZJkyZFEqSIiIiIiIiIFA+nEwU9e/YkODiYn3/+mQ4dOjBo0CCH+/39/bnzzjvdHqCIiIiIiIiIFB+nEwUA7du3p1atWlSqVAkPD8dRC8OGDeP48eNuDU5EREREREREipfTNQqydevWjdOnT+dafubMGbp16+aWoERERERERESkZLicKMg580FO586dw9fX95IDEhEREREREZGS4/TQg5iYGABMJhNTpkzB39/ffp/FYuGvv/6ifv367o9QRERERERERIqN04mCnTt3AkaPgt27d+Pt7W2/z8fHh/r163Pvvfe6P0IRERERERERKTZOJQrmzZvH+++/j5+fH88//zwjR44kKCioqGMTERERERERkWLmVI2C8ePHk5KSAsDSpUtJT08v0qBEREREREREpGQ41aMgMjKS7777js6dO2Oz2fj333/zTRZUqVLFrQGKiIiIiIiISPFxKlHw0EMPMXbsWMaOHYvJZOLmm2/OtY7NZsNkMrFr1y63BykiIiIiIiIixcOpRMFtt93G9ddfz7Fjx+jTpw+zZ88mJCSkqGMTERERERERkWLm9KwHQUFBREVFERMTQ6tWrfDx8SnKuERERERERESkBDidKMjWr1+/oohDREREREREREoBpxIFV155JStWrCA0NJQ2bdpgMpnyXXfjxo1uC05EREREREREipdTiYLnn3+eoKAgAF544YUiDUhERERERERESo5TiYKcww3yG3pw7tw5duzY4Z6oRERERERERKREeLhrQ4cOHWLw4MHu2pyIiIiIiIiIlAC3JQpERERERERE5PKnRIGIiIiIiIiI2ClRICIiIiIiIiJ2ThUzBFi1atVF7z9y5MglByMiIiIiIiIiJcvpRMGwYcMKXMdkMl1SMCIiIiIiIiJSspxOFMTGxhZlHCIiIiIiIiJSClw2NQrOnj3LM888Q6tWrWjVqhXPPPMMiYmJF32MzWZj2rRpdOzYkaZNmzJo0CD27Nljv//MmTOMHTuWnj170qxZM7p06cK4ceNISkoq6pcjIiIiIiIiUio53aMgp3379vHxxx+zd+9eTCYTtWvXZuDAgdSpU8fd8dk99dRTnDhxgg8++ACAl156iREjRvDee+/l+5j333+f2bNnM378eGrVqsW7777LkCFDWLFiBUFBQcTFxREXF8ezzz5L3bp1OXr0KKNHjyYuLo6pU6cW2WsRERERERERKa1cThSsWLGCp556isaNG9O8eXMAtm7dSp8+fZg0aRLXXXedu2Nk7969/PLLL3z66ac0a9YMgLFjx3Lbbbexb98+ateunesxNpuNefPm8b///Y8ePXoAMGHCBNq3b88333zD7bffTlRUFNOmTbM/pkaNGjzxxBM888wzZGZm4uXl/L/HarVisVgA7L/l8qb2LHvUpmWL2rNsUXuWPWrTskXtWbaoPcsOT0/PItmuy4mCiRMn8sADD/D44487LJ86dWqRJQo2b95McHCwPUkA0Lx5c4KDg9m8eXOeiYIjR44QHx9Px44d7ct8fHxo06YNmzdv5vbbb8/zuZKTkwkKCnIpSQCwfft2zGYzANu2bXPpsVK6qT3LHrVp2aL2LFvUnmWP2rRsUXuWLWrPy1+rVq2KZLsuJwoSEhLo27dvruV9+vRh1qxZ7ogpz+cMCwvLtTwsLIyEhIQ8HxMfH29fJ6fw8HCOHTuW52NOnz7NO++8w2233eZyjI0bNyYzM5Nt27bRpEmTIsvsSPGxWCxqzzJGbVq2qD3LFrVn2aM2LVvUnmWL2lMK4nKi4Morr2TTpk3UrFnTYfkff/xB69atXdrWtGnTmD59+kXX+fzzz/O9z2azFTgl44X322y2PNdLTk7mwQcfpE6dOjzyyCMX3WZePDw87DuZp6endrgyRO1Z9qhNyxa1Z9mi9ix71KZli9qzbFF7Sn5cThR07dqVSZMmsWPHDvtQgK1bt7JixQoeffRRVq1aZV+3W7duF93WwIED6d2790XXqVatGn///TcnT57Mdd+pU6fy7GkAEBERARi9ESIjI+3LT548SXh4uMO6ycnJ3H///QQEBPD222/j7e190ZhEREREREREyiqXEwVjxowBYMGCBSxYsCDP+8C4kr9r166Lbis0NJTQ0NACn7NFixYkJSXx119/0bRpU8BITiQlJdGiRYs8H1OtWjUiIiJYt24dDRs2BCAjI4Pff/+dp59+2r5ecnIy9913Hz4+Prz77rv4+voWGI+IiIiIiIhIWeVyoiA2NrYo4rioOnXq0KlTJ0aNGsUrr7wCwIsvvsg111zjUMiwV69ePPXUU3Tv3h2TycTgwYOZMWMGtWrVombNmsyYMQM/Pz9uuOEGwEgS3HvvvaSmpjJx4kSSk5NJTk4GjCSGuuGIiIiIiIjIf43LiYKc0tPTi+0K/KRJkxg3bhz33nsvYAyBeOmllxzW2b9/P0lJSfbbQ4cOJT09nTFjxnD27FmaNWvGhx9+SFBQEAA7duxg69atAHTv3t1hW6tWraJatWpF+ZJERERERERESh2XEwUWi4X33nuPhQsXcvLkSb777juqV6/OW2+9RdWqVbnllluKIk4qVKjApEmTLrrO33//7XDbZDLx6KOP8uijj+a5ftu2bXM9RkREREREROS/zMPVB7z77rssWbKEZ555xqHoX1RU1EVnKBARERERERGR0s/lRMGXX37J2LFj6dOnDx4e5x8eHR3Nvn373BqciIiIiIiIiBQvlxMFJ06coEaNGrmW22w2MjMz3RKUiIiIiIiIiJQMlxMFdevWZdOmTbmWr1ixggYNGrglKBEREREREREpGS4XM3zkkUcYMWIEJ06cwGaz8f3337N//36WLl3KjBkziiJGERERERERESkmLvco6Nq1K2+++SZr1qzBZDIxdepU9u7dy3vvvUeHDh2KIkYRERERERERKSYu9ygA6NSpE506dXJ3LCIiIiIiIiJSwlzuUQCQmJjIZ599xhtvvMGZM2cA2LFjBydOnHBnbCIiIiIiIiJSzFzuURAbG8uQIUMIDg7m6NGj3HLLLVSoUIEffviBY8eO8frrrxdFnCIiIiIiIiJSDFzuUTB+/Hj69evH999/j4+Pj3351VdfnedsCCIiIiIiIiJy+XA5UbBt2zZuv/32XMsrVqxIfHy8W4ISERERERERkZLhcqLA19eX5OTkXMv3799PaGioW4ISERERERERkZLhcqKgW7duvP3225jNZvuyY8eOMXnyZHr06OHW4ERERERERESkeLmcKHj22Wc5deoU7du3Jz09nUGDBtGjRw8CAwMZPnx4UcQoIiIiIiIiIsXE5VkPgoKC+OSTT/j111/ZuXMnVquVRo0a0b59+6KIT0RERERERESKkcuJgmzt2rWjXbt27oxFREREREREREqYS4kCq9XK4sWL+eGHHzh69Cgmk4mqVavSq1cvbrrpJkwmU1HFKSIiIiIiIiLFwOlEgc1m46GHHmL16tXUr1+fqKgobDYbe/fu5bnnnuP777/nnXfeKcpYRURERERERKSIOZ0oWLx4Mb///jtz5szhqquucrjv119/ZdiwYSxdupS+ffu6O0YRERERERERKSZOz3qwbNky/ve//+VKEoBRr+CBBx7g66+/dmtwIiIiIiIiIlK8nE4U/P3333Tq1Cnf+6+++mpiY2PdEpSIiIiIiIiIlAynEwVnz54lLCws3/vDwsI4e/asW4ISERERERERkZLhdKLAYrHg5ZV/SQNPT08sFotbghIRERERERGRkuHSrAfPPfccPj4+ed6fkZHhtqBEREREREREpGQ4nSjo169fgetoxgMRERERERGRy5vTiYKYmJiijENERERERERESgGnaxSIiIiIiIiISNmnRIGIiIiIiIiI2ClRICIiIiIiIiJ2ShSIiIiIiIiIiJ0SBSIiIiIiIiJiZ7LZbLaSDkJERERERERESgf1KBAREREREREROyUKRERERERERMROiQIRERERERERsVOiQERERERERETslCgQERERERERETslCkRERERERETETokCEREREREREbFTokBERERERERE7JQoEBERERERERE7JQpERERERERExE6JAjeaP38+Xbt2pUmTJvTv359NmzaVdEjihBkzZjBgwABatGhBu3btePjhh9m3b5/DOs899xzR0dEOP7feemsJRSwXM23atFxt1aFDB/v9NpuNadOm0bFjR5o2bcqgQYPYs2dPCUYsF9O1a9dc7RkdHc2YMWMA7Zul3e+//87//vc/OnbsSHR0NCtXrnS435n9MSMjg7Fjx9K2bVuaN2/O//73P/7999/ifBmSw8Xa1Gw2M3HiRG688UaaN29Ox44dGTFiBCdOnHDYxqBBg3Ltt8OHDy/ulyIUvI868xmrfbT0KKg98/o+jY6O5oMPPrCvo/1TsnmVdABlxfLly4mJieHll1+mZcuWLFy4kKFDh7Js2TKqVKlS0uHJRWzcuJGBAwfSpEkTLBYLb775Jvfddx/Lli0jICDAvl6nTp2IiYmx3/b29i6JcMUJ9erVY/bs2fbbnp6e9r/ff/99Zs+ezfjx46lVqxbvvvsuQ4YMYcWKFQQFBZVEuHIRn3/+ORaLxX57z549DBkyhF69etmXad8svc6dO0d0dDT9+/fn0UcfzXW/M/vjq6++yk8//cSbb75JhQoVGD9+PA8++CCLFy922LeleFysTdPS0ti5cycPPfQQ9evXJzExkddee42HHnqIxYsXO6x766238thjj9lv+/n5FUv84qigfRQK/ozVPlp6FNSea9eudbi9Zs0aRo4cSc+ePR2Wa/8UUKLAbWbPns2AAQO45ZZbABg5ciRr167lk08+4amnnirh6ORiZs2a5XA7JiaGdu3asWPHDtq0aWNf7uPjQ0RERHGHJ4Xg6emZZ1vZbDbmzZvH//73P3r06AHAhAkTaN++Pd988w233357cYcqBQgNDXW4PXPmTGrUqMGVV15pX6Z9s/Tq3LkznTt3zvM+Z/bHpKQkvvjiC15//XXat28PwMSJE+nSpQvr16+nU6dOxfZaxHCxNg0ODnZI0gKMGjWKW265hWPHjjlcOPHz89N+WwpcrD2zXewzVvto6VJQe17YjqtWraJt27ZUr17dYbn2TwENPXCLjIwMduzYQceOHR2Wd+jQgc2bN5dQVFJYSUlJAJQvX95h+caNG2nXrh09e/Zk1KhRnDx5siTCEyccPHiQjh070rVrV4YPH87hw4cBOHLkCPHx8Q77qo+PD23atNG+ehnIyMjgq6++YsCAAZhMJvty7ZuXJ2f2x+3bt2M2mx2GD1WsWJF69eppn71MJCcnYzKZKFeunMPyr7/+mrZt23L99dczYcIEkpOTSyhCKcjFPmO1j16+EhISWL16NTfffHOu+7R/CqhHgVucPn0ai8VCWFiYw/Lw8HDi4+NLKCopDJvNRkxMDK1atSIqKsq+/Oqrr6ZXr15UqVKFI0eOMGXKFO6++24WL16Mj49PCUYsF2ratCkTJkygVq1anDx5knfffZfbb7+db775xr4/5rWvHjt2rCTCFResXLmSpKQk+vXrZ1+mffPy5cz+mJCQgLe3d67EbXh4OAkJCcUTqBRaeno6kyZN4oYbbnAY2nXjjTdSrVo1wsPD2bNnD5MnTyY2NjZXbwQpeQV9xmofvXwtWbKEwMBAe4+ubNo/JZsSBW6U8woXGCedFy6T0u2VV15h9+7dLFiwwGF579697X9HRUXRuHFjunbtys8//5zrA1ZK1oVd7po3b0737t1ZunQpzZo1A/LeV6X0++KLL7j66qupWLGifZn2zctfYfZH7bOln9lsZvjw4dhsNkaPHu1wX85ieFFRUdSsWZMBAwawY8cOGjVqVMyRysUU9jNW+2jp98UXX3DjjTfi6+vrsFz7p2TT0AM3CAkJwdPTM1fm9OTJk4SHh5dQVOKqsWPH8uOPPzJ37lwqVap00XUjIyOpUqUKBw4cKJ7gpNACAgKIioriwIED9vF22lcvP0ePHmX9+vV5dpHMSfvm5cOZ/TE8PByz2czZs2fzXUdKH7PZzBNPPMGRI0f48MMPCywU26hRI7y9vTl48GAxRSiFdeFnrPbRy9OmTZvYv3+/vbbaxWj//O9SosANfHx8aNSoEevWrXNYvn79elq0aFFCUYmzbDYbr7zyCt9//z1z587NVdAlL6dPn+b48eNERkYWQ4RyKTIyMti7dy8RERFUq1aNiIgIh301IyOD33//XftqKbd48WLCwsLo0qXLRdfTvnn5cGZ/bNy4Md7e3g7rxMXFsWfPHu2zpVR2kuDgwYPMmTOHkJCQAh+zZ88ezGaziqddBi78jNU+enn6/PPPadSoEfXr1y9wXe2f/10aeuAmQ4YMYcSIETRu3JgWLVqwaNEijh8/rirql4ExY8bwzTff8M477xAYGGgfNxscHIyfnx8pKSlMnz6dHj16EBERwdGjR3nzzTcJCQnh2muvLeHo5UITJkzgmmuuoXLlypw6dYp3332X5ORk+vXrh8lkYvDgwcyYMYNatWpRs2ZNZsyYgZ+fHzfccENJhy75sFqtLF68mL59++Lldf5rS/tm6ZeSksKhQ4fst48cOcKuXbsoX748VapUKXB/DA4OZsCAAUyYMIGQkBDKly/PhAkTiIqKsldYl+J1sTaNjIzkscceY+fOncyYMQOLxWL/Ti1fvjw+Pj4cOnSIr776is6dOxMSEsLevXsZP348DRs2pGXLliX1sv6zLtae5cuXL/AzVvto6VLQZy4YBUZXrFjBs88+m+vx2j8lJ5NNg4jcZv78+cyaNYu4uDiioqJ4/vnnHabXk9IpOjo6z+UxMTH079+ftLQ0hg0bxs6dO0lKSiIiIoK2bdvy+OOPU7ly5WKOVgoyfPhwfv/9d86cOUNISAjNmzfn8ccfp27duoDRg2T69OksWrSIs2fP0qxZM1566SWH4pVSuqxdu5b77ruPFStWcMUVV9iXa98s/TZs2MDgwYNzLe/Xrx/jx493an9MT0/n9ddf55tvviEtLY127drx8ssvq41LyMXa9JFHHqFbt255Pm7evHm0bduW48eP88wzz7Bnzx5SUlKoXLkynTt35pFHHqFChQpFHL1c6GLtOXr0aKc+Y7WPlh4FfeYCLFq0iNdee421a9cSHBzssJ72T8lJiQIRERERERERsVONAhERERERERGxU6JAREREREREROyUKBAREREREREROyUKRERERERERMROiQIRERERERERsVOiQERERERERETslCgQERERERERETslCkRERERERETETokCERERccmgQYN49dVXSzoMt3ruued4+OGH7bfL4msUERFxlldJByAiIvJfEh8fz4wZM1i9ejX//vsvwcHB1KxZkz59+tC3b1/8/f1LOsQCTZs2DS+vSzuEOHjwIO+99x7r1q3j1KlTREZG0rx5c4YMGUKTJk3cFGnhueM15rXNlStX8uWXX7p1uyIiIu6mRIGIiEgxOXz4MHfccQfBwcEMHz6c6OhoMjMzOXDgAF988QWRkZF069atpMMsUIUKFS7p8du2beOee+6hXr16vPLKK9SuXZuUlBRWrVrFhAkT+Pjjj90TaB7MZjPe3t4Frnepr1FERORypqEHIiIixWT06NF4enryxRdf0Lt3b+rUqUN0dDQ9e/Zk5syZdO3a1b5uUlISL774Iu3ataNly5YMHjyY2NhY+/3Tpk3jpptuYunSpXTt2pVWrVoxfPhwkpOT7etkZGQwbtw42rVrR5MmTbjjjjv466+/7Pdv2LCB6OhofvnlF/r27UvTpk0ZPHgwJ0+eZPXq1Vx33XW0bNmSJ598ktTUVPvjLuyWn5GRweuvv07nzp1p3LgxPXr04LPPPsvzf2Cz2Xj++eepWbMmCxYsoEuXLtSoUYMGDRrwyCOP8M4779jX/fvvvxk8eDBNmzalbdu2vPjii6SkpNjvt1qtTJ8+nauvvprGjRtz0003sWbNGvv9R44cITo6muXLlzNo0CCaNGnCV199hcViISYmhtatW9O2bVtef/11bDabQ5wXvsauXbvy3nvv8fzzz9OiRQu6dOnCokWLHB4zceJEevbsSbNmzejWrRtvvfUWZrMZgMWLFzN9+nRiY2OJjo4mOjqaxYsXO9XWIiIixU2JAhERkWJw+vRp1q1bx8CBAwkICMhzHZPJBBgn0w888ADx8fHMnDmTxYsX06hRI+6++27OnDljX//QoUOsWrWK9957jxkzZvD777/z/vvv2+9//fXX+e677xg/fjxLliyhZs2a3H///Q7bAJg+fTovvvgiCxcu5N9//+WJJ55g3rx5TJ48mZkzZ7Ju3To++uijfF/biBEjWL58OaNGjeLbb79lzJgxBAYG5rnurl272LNnD/feey8eHrkPQ8qVKwdAamoq999/P+XLl+fzzz/nrbfeYv369YwdO9a+7rx585g9ezbPPvssX331FR07duThhx/mwIEDDtucNGkSgwYNYvny5XTs2JEPP/yQL774gldffZUFCxZw9uxZfvjhh3xfX7bZs2fTuHFjli5dyp133sno0aPZu3ev/f7AwEBiYmJYtmwZI0eO5LPPPmPOnDkA9O7dm3vvvZd69eqxdu1a1q5dS+/evZ1uaxERkeKkRIGIiEgxOHToEDabjSuuuMJhedu2bWnRogUtWrRg4sSJAPz222/s3r2bqVOn0qRJE2rVqsWzzz5LuXLl+O677+yPtdlsxMTEEBUVRevWrenTpw+//vorAOfOnWPhwoWMGDGCzp07U7duXcaOHYuvry+ff/65QwxPPPEErVq1omHDhtx8881s3LiR0aNH07BhQ1q3bk3Pnj3ZsGFDnq9r//79fPvtt7z22mt0796d6tWr065dO3r37p3n+tkn8bVr177o/+vrr78mPT2dCRMmEBUVRbt27XjppZf48ssvSUhIAGDWrFkMHTqU66+/ntq1a/PMM89Qv3595s6d67Ctu+++mx49elC9enUqVqzI3LlzeeCBB+jZsyd16tRhzJgxBAcHXzQegKuvvpqBAwdSs2ZNhg4dSkhICBs3brTf//DDD9OyZUuqVatG165dGTJkCN9++y0Afn5+BAQE4OnpSUREBBEREfj5+Tnd1iIiIsVJNQpERESKUXavgWyff/45VquVp59+moyMDAB27NjBuXPnaNu2rcO6aWlpHDp0yH67atWqBAUF2W9HRkZy8uRJwEhMmM1mWrZsab/f29ubpk2bOlwFB4iOjrb/HRYWhr+/P9WrV7cvCw8PZ9u2bXm+nl27duHp6UmbNm2cev3ZLvw/XGjv3r1ER0c79L5o2bIlVquV/fv34+fnR1xcnMPry17nwm77jRs3tv+dlJREfHw8LVq0sC/z8vKicePGuYYfXCjn/8lkMhEeHm7/fwOsWLGCuXPncujQIc6dO0dmZqZD++TF2bYWEREpTkoUiIiIFIMaNWpgMpnYt2+fw/LsE3I/Pz/7MqvVSkRERJ7d/XNe+c6rKv+FJ7sXnpDbbLZcy3Jux2Qy5dquyWTCarXm+bpyxu2MWrVqAUYioEGDBvmul1ecOePJ6+/8HpffUA9X5fV/yf5/b9myhSeffJJHH32Ujh07EhwczLJly5g9e/ZFt+lsW4uIiBQnDT0QEREpBiEhIXTo0IGPP/6Yc+fOXXTdRo0akZCQgKenJzVr1nT4CQ0Nder5atSogbe3N3/88Yd9mdlsZvv27dSpU+eSXktOUVFRWK1Wfv/9d6fWb9CgAXXr1uXDDz/MM/mQmJgIQN26dYmNjXX4X/355594eHhQq1YtgoKCiIyMdHh9AJs3b77o6wsODiYiIoItW7bYl2VmZrJjxw6n4s/Pn3/+SZUqVXjooYfsQwiOHTvmsI63t3eu1+yOthYREXE3JQpERESKycsvv4zFYmHAgAEsX76cvXv3sm/fPr788kv27duHp6cnAO3bt6d58+YMGzaMX375hSNHjvDnn3/y5ptv5jsE4EIBAQHccccdvP7666xZs4Z//vmHF198kbS0NG6++Wa3vaZq1arRr18/XnjhBVauXMnhw4fZsGEDy5cvz3N9k8lETEwMBw4cYODAgaxevZrDhw8TGxvLu+++y8MPPwzAjTfeiI+PD8899xy7d+/mt99+Y+zYsdx0002Eh4cDcN999/H++++zfPly9u3bx6RJk4iNjWXw4MEXjXnw4MG8//77/PDDD+zdu5cxY8bYExSFVaNGDY4fP86yZcs4dOgQ8+bNY+XKlQ7rVK1alSNHjrBr1y5OnTpFRkaGW9paRETE3TT0QEREpJjUqFGDJUuWMGPGDCZPnsyJEyfw9vambt263Hvvvdx5552AcTI9c+ZM3nrrLV544QVOnz5NeHg4rVu3tp8kO+Ppp5/GZrMxYsQIUlJSaNy4MR988AHly5d36+saPXo0b7zxBqNHj+bMmTNUqVKFBx98MN/1mzZtyhdffMF7773HqFGjOH36NJGRkbRo0YIXXngBAH9/f2bNmsWrr77KzTffjL+/Pz169OC5556zb2fw4MEkJyczfvx4Tp06RZ06dXjnnXfswxvyc++99xIfH89zzz2Hh4cHAwYMoHv37iQlJRX6f3Dttddy991388orr5CRkUGXLl146KGHmD59un2dnj178sMPPzB48GASExOJiYmhf//+bmlrERERdzLZCqrcIyIiIiIiIiL/GRp6ICIiIiIiIiJ2ShSIiIiIiIiIiJ0SBSIiIiIiIiJip0SBiIiIiIiIiNgpUSAiIiIiIiIidkoUiIiIiIiIiIidEgUiIiIiIiIiYqdEgYiIiIiIiIjYKVEgIiIiIiIiInZKFIiIiIiIiIiInRIFIiIiIiIiImL3f1+rBZijly5uAAAAAElFTkSuQmCC",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_1499319/495163197.py:10: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" seqlets_['example_idx'] = motif_names[motif_idxs[idxs]]\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from tangermeme.plot import plot_logo\n",
"from matplotlib import pyplot as plt\n",
"import seaborn; seaborn.set_style('whitegrid')\n",
"\n",
"\n",
"for idx in range(10, 14):\n",
" idxs = seqlets['example_idx'] == idx\n",
" \n",
" seqlets_ = seqlets[idxs]\n",
" seqlets_['example_idx'] = motif_names[motif_idxs[idxs]]\n",
" seqlets_ = seqlets_[seqlets_['attribution'] > 0]\n",
" \n",
" plt.figure(figsize=(12, 2.5))\n",
" ax = plt.subplot(111)\n",
" plot_logo(X_attr[idx], ax=ax, start=950, end=1150, annotations=seqlets_, score_key='attribution')\n",
" plt.ylabel(\"DeepLiftShap Attributions\")\n",
" plt.xlabel(\"Genomic Coordinate\")\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"id": "2fb15ed6-70b3-4c40-a098-303a367df8ce",
"metadata": {},
"source": [
"These examples provide some insight into the underlying process. The first two examples show that GATA has been found. But the first, third, and fourth, show that sometimes seqlets that are close but not identical to the GATA motif can be assigned to a slightly different one. Genome-wide, the noise gets filtered out because only a few seqlets will map to these other motifs, but individual examples might be challenging to interpret. An alternate strategy, which I go too deeply into here, is to get the top N annotations for each seqlet (a feature that is currently supported) and consider a set of annotations per seqlet rather than just the highest scoring one."
]
},
{
"cell_type": "markdown",
"id": "93ecd603-f27e-443b-9375-a2748dd1f3ed",
"metadata": {},
"source": [
"#### FIMO + Attribution Thresholding\n",
"\n",
"The conceptual opposite of using attributions and then sequence identity is to first use sequence identity and then attributions. In this approach, we use FIMO to find motif hits from the same sort of database that we would map motifs to using TOMTOM, and then only keep those that reach some sort of attribution threshold. You can read more about this approach in the annotations tutorial.\n",
"\n",
"Running FIMO on the set of sequences is simple enough. This will give us one dataframe per motif containing all the hits for that motif."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c1557e14-fcaf-44f6-841d-cf5c922dfc38",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2193"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tangermeme.tools.fimo import fimo\n",
"\n",
"hits = fimo(\"../tutorials/motifs.meme.txt\", X)\n",
"len(hits)"
]
},
{
"cell_type": "markdown",
"id": "fe0f86e9-3879-4558-be93-47ec802cc465",
"metadata": {},
"source": [
"As you can see, we end up with one dataframe per motif in the database.\n",
"\n",
"We can then count the motif hits and see which motifs are more present."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "0fa6363c-fbb5-4cef-b1c7-af384f7920cb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['ZNF384_MA1125.1' 'STAT1_MOUSE.H11MO.0.A' 'IRF3_MOUSE.H11MO.0.A'\n",
" 'IRF3_HUMAN.H11MO.0.B' 'MAZ_MOUSE.H11MO.0.A'] [128131 122595 122256 122256 112572]\n"
]
}
],
"source": [
"n_hits = numpy.array([len(h) for h in hits])\n",
"idxs = numpy.argsort(n_hits)[::-1]\n",
"\n",
"print(motif_names[idxs[:5]], n_hits[idxs[:5]])"
]
},
{
"cell_type": "markdown",
"id": "f170d10f-19fa-4864-a5df-4728f8f333f0",
"metadata": {},
"source": [
"Of course, this list does not include GATA because it just includes any motif present near GATA motifs even if the model does not care about it. If we want to get a list that is more specific to what the model has learned about GATA binding, we need to now cross-reference the coordinates of these motif hits with the attribution scores. We can do this simply with the `extract_signal` function, which can take in the motif hits for each motif and return the attributions, and then we can add those attributions to the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "24d83ccd-7ea5-4042-b51b-ec36bcfb29ff",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_1499319/4011365143.py:3: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
" hits_ = pandas.concat(hits)\n",
"100%|████████████████████████████████████████████████████████████████████| 18649732/18649732 [04:20<00:00, 71480.31it/s]\n"
]
},
{
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"
+
\n",
"
11.193491
\n",
"
0.000081
\n",
"
0.000004
\n",
"
\n",
"
\n",
"
3
\n",
"
DNASE_6
\n",
"
0
\n",
"
6
\n",
"
392
\n",
"
422
\n",
"
+
\n",
"
12.022779
\n",
"
0.000045
\n",
"
0.001523
\n",
"
\n",
"
\n",
"
4
\n",
"
DNASE_6
\n",
"
0
\n",
"
18
\n",
"
1192
\n",
"
1222
\n",
"
+
\n",
"
11.185351
\n",
"
0.000081
\n",
"
-0.010708
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" motif_name motif_idx sequence_name start end strand score p-value \\\n",
"0 DNASE_6 0 0 1905 1935 + 12.641882 0.000030 \n",
"1 DNASE_6 0 4 1313 1343 + 11.679096 0.000059 \n",
"2 DNASE_6 0 4 2066 2096 + 11.193491 0.000081 \n",
"3 DNASE_6 0 6 392 422 + 12.022779 0.000045 \n",
"4 DNASE_6 0 18 1192 1222 + 11.185351 0.000081 \n",
"\n",
" attribution \n",
"0 0.000054 \n",
"1 -0.018434 \n",
"2 0.000004 \n",
"3 0.001523 \n",
"4 -0.010708 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tangermeme.utils import extract_signal\n",
"\n",
"hits_ = pandas.concat(hits)\n",
"attrs = extract_signal(hits_[['sequence_name', 'start', 'end']], X_attr, verbose=True)\n",
"hits_['attribution'] = attrs.sum(dim=1)\n",
"hits_.head()"
]
},
{
"cell_type": "markdown",
"id": "6252140f-e2e8-404e-99b9-458832a69320",
"metadata": {},
"source": [
"Now that we have these sequence-based matches, as well as the attribution sum across them, we can set our threshold. Using the groupby function we can see how many hits there are for each motif in our database."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "801a2ee5-7962-4520-8d77-1b31e2f93d31",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"motif_name\n",
"Gata1_MA0035.3 10414\n",
"GATA6_MA1104.1 9952\n",
"GATA2_MOUSE.H11MO.0.A 9742\n",
"Gata4_MA0482.1 9666\n",
"GATA2_HUMAN.H11MO.0.A 9628\n",
"GATA1_HUMAN.H11MO.0.A 9569\n",
"GATA3_HUMAN.H11MO.0.A 9257\n",
"GATA4_MOUSE.H11MO.0.A 9233\n",
"GATA4_HUMAN.H11MO.0.A 9233\n",
"GATA2_MA0036.3 9107\n",
"Name: motif_idx, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"threshold = 0.01\n",
"\n",
"attribution_thresholded_hits = hits_[hits_['attribution'] > threshold]\n",
"attribution_thresholded_hits = attribution_thresholded_hits.sort_values(\"attribution\", ascending=True)\n",
"attribution_thresholded_hits.groupby(\"motif_name\").count()['motif_idx'].sort_values(ascending=False).head(n=10)"
]
},
{
"cell_type": "markdown",
"id": "9090ceaf-2b93-4e20-9937-eb29e1fc872a",
"metadata": {},
"source": [
"Looks like there are a lot more hits to GATA motifs now that we are getting the attribution score involved. There are likely a lot of pure sequence-based motif hits in these sequences, but the model is highlighting mostly the GATA ones because those are the motifs that are really affecting model predictions.\n",
"\n",
"Remember, when using this approach, there is no guarantee that the regions counted for one annotation are not also being counted for another annotation. In contrast to the seqlet calling + annotation approach, where each seqlet is explicitly given one annotation, each genomic region can be labeled with any number of annotations. This is because we are considering sequence matches for any motif regardless of where it appears. If those happens to overlap because, perhaps, you're scanning very similar motifs, the same coordinates will be considered hits for multiple motifs and the same attribution scores will be summed over them all."
]
},
{
"cell_type": "markdown",
"id": "6b749606-7401-407c-9fc8-cddb48d09656",
"metadata": {},
"source": [
"#### Conclusions\n",
"\n",
"`tangermeme` provides several ways for identifying what machine learning methods have learned. These differ in terms of their speed, robustness, and usefulness. Seqlet calling + TOMTOM is extremely fast but fairly sensitive to the redundancy of motifs in the databasse. FIMO + attribution thresholding is slower but can be a bit more robust. Marginalizations do not even require specific sequences to investigate, but are then somewhat artificial in nature. De novo motif discovery methods like TF-MoDISco may be a great solution for robustly distilling what a model has learned down to motifs but can take quite a while to run, and so may be more suited for after the initial inspection steps."
]
}
],
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