diff --git a/04-contam/README.md b/04-contam/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2e4ff0bdae592d96d79f54c7f01bb325b189dcbc
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+# Repeats, annotation, and possible contamination
+
+After the trials and tribulations of genome assembly, scaffolding, repeat masking, and basic
+(protein-coding) gene annotation, we finally reached the point where the genome could be useful. At
+the same time, this was another checkpoint where we could compare the genome to other known
+metazoan/arthropod/chelicerate genome assemblies to get a sense of whether we had something
+reasonable or whether we might need to go back to the drawing board.
+
+We got three red flags, which on their own would not have prompted much further investigation, but
+together were very concerning:
+
+1. RepeatMasker/RepeatModeler suggested that 60% of the (rather small) genome was repetitive, and
+   that 45% of that was "unclassified" repeats. While lineage-specific repeats are common, _that
+   many_ of them certainly aren't.
+2. In the process of investigating the repeats (read: BLASTing them against NCBI `nr` for funsies),
+   we were surprised to find that some of them returned hits in sea anemones. In particular, two
+   persistent results belonged to _Metridium senile_, a sea anemone that we actually feed the _P.
+   litorale_ larvae in our culture. While the matches were partial and the e-values were barely
+   significant, this was too much of a coincidence.
+3. We used a set of bulk RNA-seq data from various developmental timepoints to annotate the genome
+   with `BRAKER`. Despite the extremely good coverage of development, BRAKER only identified 11.4k
+   protein-coding genes, a far cry from the number we'd usually expect from a metazoan genome.
+
+We have three objectives:
+
+1. Make a decision re: repeats. Are they real? Can we come up with an idea for how we have so many
+   unclassified repeats?
+2. Is the _Metridium_ contamination real?
+3. Can we find more protein-coding genes?
+
+### 0. data and setup
+
+- `FLYE=/lisc/scratch/zoology/pycnogonum/genome/flye_full/assembly.fasta` - the original, unscaffolded _P. litorale_ genome assembly
+- `DRAFT=/lisc/scratch/zoology/pycnogonum/genome/draft/draft_softmasked.fasta` - The scaffolded, manually edited _P. litorale_ genome assembly
+- `METRIDIUM=/lisc/scratch/zoology/pycnogonum/raw/GCA_949775045.1_jaMetSeni4.1_genomic.fna` - the _M. senile_ genome assembly
+- `NANOPORE=/lisc/scratch/zoology/pycnogonum/raw/20230601_0904_3C_PAK64436_4d39d6a6/basecalls/nanopore.fasta` - the ONT data that we used to assemble the _P. litorale_ genome
+- `HIFI=/lisc/scratch/zoology/pycnogonum/raw/m64120_240312_152428.ccs.fasta` - PacBio HiFi data from a collaborator that weren't deep enough for an assembly
+- `REPEATS=/lisc/scratch/zoology/pycnogonum/genome/draft/repeats/repeat_modeller/pycno-families.fa` - the predicted repeat families from RepeatModeler
+
+### 1. Repeats
+
+We need to figure out if the repeats are real. We will go back to the raw reads that we used for
+assembly and ask if they already contain repeats. If they do, and if the percentage of reads that
+contain repeats is high, we can be more confident that the results of RepeatMasker are not an 
+artifact.
+
+First, map the repeats on the reads:
+
+```bash
+$ OUTPUT=/lisc/scratch/zoology/pycnogonum/genome/draft/repeats
+$ bwa mem -x ont2d $REPEATS $NANOPORE > $OUTPUT/ont.sam
+$ bwa mem -x pacbio $REPEATS $HIFI > $OUTPUT/pb.sam
+```
+
+See the [ONT](check_repeats-ont.sh) and [PacBio](check_repeats-pb.sh) scripts for
+details.
+
+Let's first have a look at the ONT data:
+
+```bash
+$ samtools flagstat $OUTPUT/ont.sam
+$
+$ 97,952,355 + 0 in total (QC-passed reads + QC-failed reads)
+$ 30,502,516 + 0 primary
+$ 0 + 0 secondary
+$ 67,449,839 + 0 supplementary
+$ 0 + 0 duplicates
+$ 0 + 0 primary duplicates
+$ 85,877,260 + 0 mapped (87.67% : N/A)
+$ 18,427,421 + 0 primary mapped (60.41% : N/A)
+$ 0 + 0 paired in sequencing
+$ 0 + 0 read1
+$ 0 + 0 read2
+$ 0 + 0 properly paired (A : N/A)
+$ 0 + 0 with itself and mate mapped
+$ 0 + 0 singletons (A : N/A)
+$ 0 + 0 with mate mapped to a different chr
+$ 0 + 0 with mate mapped to a different chr (mapQ>=5)
+```
+
+Let's break this down:
+
+- ~98 million total alignments are produced, from...
+- 30.5 million reads (primary).
+- The total map rate is 60.4% (primary mapped: 18.4 million reads). Since our mapping reference are
+  the _P. litorale_ repeat families, this means that 60.4% of the reads have at least one repeat
+  element on them. However, there are also...
+- 67.4m supplementary alignments, i.e. alternative alignments. If our reference was a real genome,
+  this category would denote chimeric reads or split alignments. However, since our reference is the
+  (rather short) repeats, this essentially means that every read has an average of 2-3 repeats
+  mapping to it.
+
+The 60.4% map rate is surprisingly similar to the proportion of the genome that was marked as
+repeats by RepeatMasker/RepeatModeler (60%). This suggests that the repeats might be real.
+
+Second, let's have a look at our validation:
+
+```bash
+$ samtools flagstat $OUTPUT/pb.sam
+$ 
+$ 11,081,282 + 0 in total (QC-passed reads + QC-failed reads)
+$ 1,715,700 + 0 primary
+$ 0 + 0 secondary
+$ 9,365,582 + 0 supplementary
+$ 0 + 0 duplicates
+$ 0 + 0 primary duplicates
+$ 10,739,929 + 0 mapped (96.92% : N/A)
+$ 1,374,347 + 0 primary mapped (80.10% : N/A)
+$ 0 + 0 paired in sequencing
+$ 0 + 0 read1
+$ 0 + 0 read2
+$ 0 + 0 properly paired (A : N/A)
+$ 0 + 0 with itself and mate mapped
+$ 0 + 0 singletons (A : N/A)
+$ 0 + 0 with mate mapped to a different chr
+$ 0 + 0 with mate mapped to a different chr (mapQ>=5)
+```
+
+Let's break this down:
+
+- 11 million total alignments are produced, from...
+- 1.7 million reads (primary).
+- The total map rate is 80.1% (primary mapped: 1.37 million reads), so 4/5 of the PacBio reads
+  have at least one repeat element on them. However, there are also...
+- 9.37m supplementary alignments, i.e. alternative alignments, suggesting an average of 8-9 repeats
+  per read. The average length of the PacBio reads is three times higher than the ONT reads
+  (5,892nt/1,848nt), so this makes sense.
+
+#### Takeaways: 
+
+The repeats seem to be real. There seem to be multiple repeats present on each read.
+
+Ways to improve this analysis:
+
+- repeat analysis with short reads
+- filter mapq30 alignments, count #alignments per read
+- analyse the CIGAR strings to see what portion of each read is matched by high quality maps of
+  repeat content
+
+
+### 2a. _Metridium_ contamination
+
+Map the ONT reads onto the _Metridium_ genome using the [backmap
+script](../nanopore/eval-backmap-ont.sh) We wrote for assembly qc:
+
+```bash
+$ OUTPUT=/lisc/scratch/zoology/pycnogonum/genome/metridium_cont
+$ sbatch eval-backmap-ont.sh $METRIDIUM $OUTPUT
+```
+
+The first thing to ask here is how many reads mapped well to the _Metridium_ genome. This
+information is included in the `.stats` file produced by `backmap`. Consider the SN section of that
+file here for closer inspection, but the important takeaway is that ~290k/30.5m reads mapped to the
+_Metridium_ genome (0.95%) with mapping quality >0. This is worrying and should be investigated
+more.
+
+<details>
+
+<summary>Summary Numbers from `samtools stats`</summary>
+
+```bash
+SN      raw total sequences:    30502516        # excluding supplementary and secondary reads
+SN      filtered sequences:     0
+SN      sequences:      30502516
+SN      is sorted:      1
+SN      1st fragments:  30502516
+SN      last fragments: 0
+SN      reads mapped:   419599
+SN      reads mapped and paired:        0       # paired-end technology bit set + both mates mapped
+SN      reads unmapped: 30082917
+SN      reads properly paired:  0       # proper-pair bit set
+SN      reads paired:   0       # paired-end technology bit set
+SN      reads duplicated:       0       # PCR or optical duplicate bit set
+SN      reads MQ0:      129520  # mapped and MQ=0
+SN      reads QC failed:        0
+SN      non-primary alignments: 1413288
+SN      supplementary alignments:       1742261
+SN      total length:   56368688445     # ignores clipping
+SN      total first fragment length:    56368688445     # ignores clipping
+SN      total last fragment length:     0       # ignores clipping
+SN      bases mapped:   3057365257      # ignores clipping
+SN      bases mapped (cigar):   356727603       # more accurate
+SN      bases trimmed:  0
+SN      bases duplicated:       0
+SN      mismatches:     79904074        # from NM fields
+SN      error rate:     2.239918e-01    # mismatches / bases mapped (cigar)
+SN      average length: 1848
+SN      average first fragment length:  1848
+SN      average last fragment length:   0
+SN      maximum length: 1533915
+SN      maximum first fragment length:  1533915
+SN      maximum last fragment length:   0
+SN      average quality:        34.7
+SN      insert size average:    0.0
+SN      insert size standard deviation: 0.0
+SN      inward oriented pairs:  0
+SN      outward oriented pairs: 0
+SN      pairs with other orientation:   0
+SN      pairs on different chromosomes: 0
+SN      percentage of properly paired reads (%):        0.0
+```
+
+</details>
+
+We can then extract only the high-quality (map quality >30) primary alignments (exclude non-primary
+alignments with `-F 0x100`):
+
+```bash
+$ cd $OUTPUT
+$ samtools view -q 30 -F 0x100 GCA_949775045.1_jaMetSeni4.1_genomic.fna.ont.sort.bam | cut -f1 > primary_mq30.txt
+```
+
+This leaves us with only 32,490 reads.
+
+At the same time, since `flye` doesn't keep track of which reads were used for contig building, we
+need to map the reads back to the assembly, which I'd already done for QC reasons anyway. However,
+we should be focusing on high-quality alignments that are not secondary or supplementary (0x100 and
+0x800 flags):
+
+```bash
+$ OUTPUT=/lisc/scratch/zoology/pycnogonum/genome/draft/backmap/ont
+$ sbatch eval-backmap-ont.sh $DRAFT $OUTPUT
+$ samtools view -F 0x900 -q 30 -S $OUTPUT/draft.fasta.ont.sort.bam > $OUTPUT/draft.fasta.ont.sort.sam
+$ BACKMAPPED_ONT=/lisc/scratch/zoology/pycnogonum/genome/draft/backmap/ont/draft.fasta.ont.sort.sam
+```
+
+(feel free to use more threads to speed `samtools view` up.)
+
+We can combine the two outputs to see where the high-confidence _Metridium_ reads are concentrated
+on the _P. litorale_ assembly:
+
+```bash
+$ LC_ALL=C fgrep -f primary_mq30.txt $BACKMAPPED_ONT > metridium_scaffolds.txt
+```
+
+This leaves us with 10,170 putatively _Metridium_ reads which have mapped well to the _P. litorale_
+draft. We can then sum up how many times each scaffold was hit by _Metridium_ reads:
+
+```bash
+$ cat metridium_scaffolds.txt | cut -f3 | sort | uniq -c | sort -k1,1nr > metridium_scaffolds.txt_summary
+```
+
+The summary file is available in the [repository](./metridium_scaffolds.sam_summary). However, the
+most important message becomes clear if we just inspect the top hits:
+
+```bash
+$  2478 pseudochrom_16
+$   623 pseudochrom_22
+$   373 scaffold_1844
+$   355 pseudochrom_38
+$   272 pseudochrom_13
+$   238 pseudochrom_7
+$   233 pseudochrom_24
+$   214 pseudochrom_8
+$   213 pseudochrom_17
+$   174 pseudochrom_23
+```
+
+This looks a bit concerning. We need to go back to the contigs that were used to build the scaffolds
+and do the same. However, if the offending contigs are highly repetitive and the "metridial" reads
+are also highly repetitive, this is less concerning, and can be explained by the similarity of
+low-complexity regions or tandem repeats.
+
+Mapping scaffolds to contigs is not straightforward because of the manual editing of the hiC map.
+It might be faster to just look at the locations of the putative _Metridium_ reads on the current
+assembly and try to make sense out of that.
+
+We cross-referenced the predicted repeats of the _P. litorale_ genome with the mapping locations of
+the putative _Metridium_ reads. Out of the 10,170 putative _Metridium_ reads, 6,039 (60%) are at
+least 25% repetitive, and 4,593 are at least 50% repetitive (see relevant
+[notebook](metridium.ipynb)). This leaves us with maybe 5,000 problematic reads out of a total of
+30.5 million.
+
+For context, if we do the same thing but with contigs, and then refer back to the original flye
+assembly (before scaffolding):
+
+```bash
+$ OUTPUT=/lisc/scratch/zoology/pycnogonum/genome/flye_full/backmap_ont
+$ sbatch eval-backmap-ont.sh $FLYE $OUTPUT
+$ samtools view -N primary_mq30.txt -F 0x900 -q 30 metridium_locs.sam > metridium_contigs.sam
+$ cat metridium_contigs.sam | cut -f3 | sort | uniq -c | sort -k1,1nr > metridium_contigs.sam_summary
+```
+
+This file is also [uploaded](./metridium_contigs.sam_summary). Let's have a look at the top hits:
+
+```bash
+$ head metridium_contigs.txt_summary
+$
+$ 2197 contig_8782
+$  324 contig_21131
+$  151 contig_28132
+$  146 contig_6939
+$  123 contig_19945
+$   94 contig_11056
+$   79 contig_5985
+$   78 contig_19987
+$   77 contig_4985
+$   76 contig_4041
+```
+
+If we go back to the alignment graph from `flye` and have a look, the alignment path is very
+illuminating:
+
+```bash
+contig_8782     209547  86      N       N       1       *       10576,-3609,-3607,8782,20233,-24417,-24416,-28783,13730,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605,3605
+```
+
+This contig's path starts out normal, but very quickly devolves into a self-repeat.
+
+Overall, it looks like _Metridium_ contamination is not a real danger; rather, we should start
+contemplating other possibilities to explain these weak sequence similarities, such as horizontal
+gene transfer.
+
+
+### 2b. Other contaminants
+
+Widespread _Metridium_ contamination seems to be out of the question, but this doesn't mean that
+other contaminants can't be present. To check for that, we implemented an approach very similar to
+`Kraken2.0`. Briefly, we aligned UniRef90 onto the draft using `mmseqs2` and then then used the
+`taxonomy` module to assign kingdom-level taxonomy information to each hit; we then excluded
+contigs/scaffolds that did not overwhelmingly contain metazoan genes.
+
+First we need to download UniRef90 and also build the `mmseqs2` database from the genome sequences.
+
+- [mmseqs-download_uniref90.sh](mmseqs-download_uniref90.sh)
+- [mmseqs-prepdb.sh](mmseqs-prepdb.sh)
+
+These can be run in parallel.
+
+Then we do the actual alignment/taxonomy assignment:
+
+- [mmseqs-align.sh](mmseqs-align.sh)
+
+We then used a custom notebook to parse the result and save taxonomy information per
+pseudochromosome/scaffold:
+
+- [contamination.ipynb](contamination.ipynb)
+
+Finally, we used the outputs of this notebook to filter out the contamination from the assembly:
+
+- [filter_draft.ipynb](filter_draft.ipynb)
+
diff --git a/04-contam/check_repeats-ont.sh b/04-contam/check_repeats-ont.sh
new file mode 100644
index 0000000000000000000000000000000000000000..28eec49df4122309bee8f8744fc035deae97f128
--- /dev/null
+++ b/04-contam/check_repeats-ont.sh
@@ -0,0 +1,33 @@
+#!/usr/bin/env bash
+#
+#SBATCH --job-name=read_repeats
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --mem=5000M
+#SBATCH --time=20:00:00
+#SBATCH --mail-type=ALL
+#SBATCH --mail-user=nikolaos.papadopoulos@univie.ac.at
+#SBATCH --output=/lisc/user/papadopoulos/log/pycno-scan-ont-%j.out
+#SBATCH --error=/lisc/user/papadopoulos/log/pycno-scan-ont-%j.err
+
+# tests:
+# 610050 FASTA rows (~1% of file)
+# /usr/bin/time bwa mem -x ont2d -t 4 ../repeat_modeller/pycno-families.fa onepercent.fasta > test.sam
+# needed 3.2min with 388% CPU usage and 359MB RAM
+
+# 6100500 FASTA rows (~10% of file)
+# /usr/bin/time bwa mem -x ont2d -t 4 ../repeat_modeller/pycno-families.fa tenpercent.fasta > test.sam
+# needed 54min with 384% CPU usage and 2GB RAM
+
+# first run timed out at 11h (--time=10:00:00) and processed 17,467,334 / 30,502,516 reads, so we 
+# need at least 17.5h. Going with 20h to be safe. At least now I know the real memory consumption.
+
+# second run went OOM with 3.5G of RAM, so I'm going with 5G now.
+
+module load bwa/0.7.18
+
+NANOPORE="/lisc/scratch/zoology/pycnogonum/raw/20230601_0904_3C_PAK64436_4d39d6a6/basecalls/202306050/nanopore.fasta"
+REPEATS="/lisc/scratch/zoology/pycnogonum/genome/draft/repeats/repeat_modeller/pycno-families.fa"
+OUTPUT="/lisc/scratch/zoology/pycnogonum/genome/draft/repeats/"
+
+bwa mem -x ont2d -t 16 $REPEATS $NANOPORE > $OUTPUT/ont.sam
\ No newline at end of file
diff --git a/04-contam/check_repeats-pb.sh b/04-contam/check_repeats-pb.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d32253d666c0fb4b250a90b02ce72606ce52ffda
--- /dev/null
+++ b/04-contam/check_repeats-pb.sh
@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+#
+#SBATCH --job-name=read_repeats
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=4
+#SBATCH --mem=300M
+#SBATCH --time=80:00
+#SBATCH --mail-type=ALL
+#SBATCH --mail-user=nikolaos.papadopoulos@univie.ac.at
+#SBATCH --output=/lisc/user/papadopoulos/log/pycno-scan-pb-%j.out
+#SBATCH --error=/lisc/user/papadopoulos/log/pycno-scan-pb-%j.err
+
+# tests:
+# 34314 FASTA rows (1% of file)
+# /usr/bin/time bwa mem -x pacbio -t 4 ../repeat_modeller/pycno-families.fa onepercent.fasta > test.sam
+# needed 49s with 392% CPU usage and 271MB RAM
+
+# 343140 FASTA rows (10% of file)
+# /usr/bin/time bwa mem -x pacbio -t 4 ../repeat_modeller/pycno-families.fa tenpercent.fasta > test.sam
+# needed 480s wuth 392% CPU usage and 271MB RAM
+
+module load bwa/0.7.18
+
+HIFI="/lisc/scratch/zoology/pycnogonum/raw/m64120_240312_152428.ccs.fasta"
+REPEATS="/lisc/scratch/zoology/pycnogonum/genome/draft/repeats/repeat_modeller/pycno-families.fa"
+OUTPUT="/lisc/scratch/zoology/pycnogonum/genome/draft/repeats/"
+
+bwa mem -x pacbio -t 4 $REPEATS $HIFI > $OUTPUT/pb.sam
\ No newline at end of file
diff --git a/04-contam/contamination.ipynb b/04-contam/contamination.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2892efa5cdcda52045d3f8c8c8a5865f8504c939
--- /dev/null
+++ b/04-contam/contamination.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Contamination analysis\n",
+    "\n",
+    "I don't want to run [ContScout](https://github.com/h836472/ContScout/) because it requires insane\n",
+    "amounts of RAM/storage. Instead, I will align my draft genome against UniRef90 with mmseqs2, append\n",
+    "taxonomic information to the better hits, and calculate the taxonomic makeup of each scaffold.\n",
+    "\n",
+    "We start by [downloading the UniRef90 database](mmseqs-download_uniref90.sh) and creating a mmseqs2\n",
+    "index. While it is running, we will [prepare the draft genome](mmseqs-prepdb.sh) for alignment. When\n",
+    "the download finishes, we can [align the draft genome](mmseqs-align.sh) against UniRef90, add \n",
+    "taxonomic information, and save the result in a .tsv file, which we can afterwards read and parse\n",
+    "at our leisure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "print(datetime.datetime.today().date().isoformat())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import os\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "columns = [\"query\", \"target\", \"alnScore\", \"seqIdentity\", \"eVal\", \"qStart\", \"qEnd\", \"qLen\", \"tStart\",\n",
+    "\"tEnd\", \"tLen\", \"queryOrfStart\", \"queryOrfEnd\", \"dbOrfStart\", \"dbOrfEnd\", \"taxid\",\n",
+    "           \"level\", \"level_value\", \"taxonomy\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The end result is close to 20Gb. To parse it, we first need to know how many lines it has."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m8 = \"/Volumes/scratch/pycnogonum/genome/draft/contamination/contam_tax.m8\"\n",
+    "output = \"/Volumes/scratch/pycnogonum/genome/draft/contamination/chromosomes/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash -s \"$m8\" --out lines\n",
+    "m8=$1\n",
+    "\n",
+    "wc -l $m8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_lines = int(lines.split()[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will parse the file. Since we don't want to keep 20Gb in memory, we will extract only the\n",
+    "relevant information: what we need is the taxonomic information of the hits _per\n",
+    "scaffold/chromosome_. We will keep the corresponding columns of the `.m8` file and save them in\n",
+    "separate files per chromosome/scaffold, for later processing. This is somewhat time-consuming, but\n",
+    "can just run in the background, and we will only need to do it once*.\n",
+    "\n",
+    "\\* unless, of course, we change the genome, which has already happened once :D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chromosome = \"\"\n",
+    "\n",
+    "with open(m8) as f:\n",
+    "    for i in tqdm(range(total_lines)):\n",
+    "        line = f.readline().strip().split(\"\\t\")\n",
+    "        query = line[0]\n",
+    "        xx = int(line[12])\n",
+    "        taxonomy = line[18]\n",
+    "        if chromosome == \"\":\n",
+    "            chromosome = query\n",
+    "            out = open(f\"{output}/{chromosome}.tsv\", \"w\")\n",
+    "        if query != chromosome:\n",
+    "            out.close()\n",
+    "            chromosome = query\n",
+    "            out = open(f\"{output}/{chromosome}.tsv\", \"w\")\n",
+    "        \n",
+    "        out.write(\"\\t\".join(line) + \"\\n\")\n",
+    "\n",
+    "out.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def approximate_taxonomic_distribution(sequence_path, columns, resolution=1000):\n",
+    "    \"\"\"\n",
+    "    Approximate the taxonomic distribution of a scaffold/pseudochromosome by aggregating hits within\n",
+    "    ORFs. Essentially breaks the sequence into bins of size `resolution` but only uses detected ORFs\n",
+    "    instead of blindly scanning with a sliding window. Assigns the taxon of the hit with the highest\n",
+    "    score to each bin.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    sequence_path : str\n",
+    "        Path to the sequence file.\n",
+    "    columns : list\n",
+    "        Column names of the sequence file.\n",
+    "    resolution : int\n",
+    "        Resolution of the approximation (default: 1000).\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    pd.Series\n",
+    "        Approximated taxonomic distribution; contains the absolute number of hits for each taxon\n",
+    "        (Metazoa, unknown, Viridiplantae, uc_Bacteria, Fungi, various viruses, uc_Archaea,\n",
+    "        uc_Eukaryota).\n",
+    "    \"\"\"\n",
+    "    raw = pd.read_csv(sequence_path, sep=\"\\t\", header=None)\n",
+    "    raw.columns = columns\n",
+    "    raw[\"queryOrfStart_approx\"] = raw[\"queryOrfStart\"] // resolution\n",
+    "    raw[\"queryOrfEnd_approx\"] = raw[\"queryOrfEnd\"] // resolution\n",
+    "    first_pass = raw.groupby(\"queryOrfStart_approx\").first().sort_values(\"queryOrfEnd\", ascending=False)\n",
+    "    second_pass = first_pass.groupby(\"queryOrfEnd_approx\").first().sort_values(\"queryOrfEnd\", ascending=False)\n",
+    "    return second_pass[\"taxonomy\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir_contents = os.listdir(f\"{output}\")\n",
+    "sequences = [s for s in dir_contents if s.endswith('.tsv')]\n",
+    "\n",
+    "result = {}\n",
+    "\n",
+    "for sequence in tqdm(sequences):\n",
+    "    result[sequence] = approximate_taxonomic_distribution(f\"{output}/{sequence}\", columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat(result.values(), axis=1).fillna(0)\n",
+    "df.columns = [k.split(\".\")[0] for k in result.keys()]\n",
+    "# normalize each column by the sum of the column\n",
+    "perc = df.div(df.sum(axis=0), axis=1)\n",
+    "\n",
+    "df = df.T\n",
+    "perc = perc.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.to_csv(\"/Volumes/scratch/pycnogonum/genome/draft/contamination/scaffolds_taxonomic_distribution.tsv\", sep=\"\\t\")\n",
+    "df = pd.read_csv(\"/Volumes/scratch/pycnogonum/genome/draft/contamination/scaffolds_taxonomic_distribution.tsv\", sep=\"\\t\", header=0, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viruses = df.columns[df.columns.str.contains(\"vir\")]\n",
+    "df['viruses'] = df[viruses].sum(axis=1)\n",
+    "df.drop(columns=viruses, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv(\"/Volumes/scratch/pycnogonum/genome/draft/contamination/scaffolds_taxonomic_distribution_collapsed_vir.tsv\", sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "suspect = df.loc[perc[\"Metazoa\"] < 0.9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "suspect.to_csv(\"/Volumes/scratch/pycnogonum/genome/draft/contamination/scaffolds_taxonomic_distribution_suspect.tsv\", sep=\"\\t\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ascc24",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/04-contam/filter_draft.ipynb b/04-contam/filter_draft.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dada3a3a2674935739eb476038bedd1ab16148c5
--- /dev/null
+++ b/04-contam/filter_draft.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "draft = \"/Volumes/scratch/pycnogonum/genome/draft/GAP_sort_scaffolds_by_hic_insert/output/assembly/plit_q_0_50000_0.5FracBest_output.fasta\"\n",
+    "suspect = \"/Volumes/scratch/pycnogonum/genome/draft/contamination/scaffolds_taxonomic_distribution_suspect.tsv\"\n",
+    "filtered_draft = \"/Volumes/scratch/pycnogonum/genome/draft/contamination/plit_q_0_50000_0.5FracBest_output_filtered.fasta\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "exclude = pd.read_csv(suspect, sep=\"\\t\", index_col=0).index.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['scaffold_1542', 'scaffold_373', 'scaffold_405', ...,\n",
+       "       'scaffold_6737', 'scaffold_4062', 'scaffold_1888'], dtype=object)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exclude"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "copying = False\n",
+    "with open(draft, 'r') as f:\n",
+    "    with open(filtered_draft, 'w') as out:\n",
+    "        for line in f:\n",
+    "            if line.startswith('>'):\n",
+    "                seq = line[1:].split()[0]\n",
+    "                if seq in exclude:\n",
+    "                    copying = False\n",
+    "                else: \n",
+    "                    copying = True\n",
+    "                    out.write(line)\n",
+    "            else:\n",
+    "                if copying:\n",
+    "                    out.write(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   11648\n",
+      "   10258\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash -s \"$filtered_draft\" \"$draft\"\n",
+    "draft=$2\n",
+    "filtered_draft=$1\n",
+    "grep \">\" $draft | wc -l\n",
+    "grep \">\" $filtered_draft | wc -l"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ascc24",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/04-contam/metridium.ipynb b/04-contam/metridium.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5b049d8490d44fb4dc65979a99cf362797c34ae4
--- /dev/null
+++ b/04-contam/metridium.ipynb
@@ -0,0 +1,192 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Convert the SAM file to a dummy GFF so we can use `bedtools intersect` to get repeat coverage:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"/Volumes/scratch/pycnogonum/genome/metridium_cont/metridium_scaffolds.sam\", \"r\") as f:\n",
+    "    with open(\"/Volumes/scratch/pycnogonum/genome/metridium_cont/metridium_scaffolds.gff\", \"w\") as w:\n",
+    "        for line in f.readlines():\n",
+    "            tmp = line.split(\"\\t\")\n",
+    "            seqname = tmp[2]\n",
+    "            source = \"Metridium\"\n",
+    "            feature = \"read\"\n",
+    "            start = int(tmp[3])\n",
+    "            end = start + len(tmp[9])\n",
+    "            score = 0\n",
+    "            strand = \".\"\n",
+    "            frame = \".\"\n",
+    "            attribute = f\"ID={tmp[0]}\"\n",
+    "            w.write(f\"{seqname}\\t{source}\\t{feature}\\t{start}\\t{end}\\t{score}\\t{strand}\\t{frame}\\t{attribute}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "in the `metridium_cont` directory, run:\n",
+    "\n",
+    "```bash\n",
+    "bedtools intersect -F 0.9 -wao -bed -a metridium_scaffolds.gff -b ../draft/repeats/repeat_masker/draft.fasta.out.gff > metridium_scaffolds.repeats\n",
+    "```\n",
+    "\n",
+    "Now read the bed file, which is like two concatenated GFFs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overlaps = pd.read_csv(\"/Volumes/scratch/pycnogonum/genome/metridium_cont/metridium_scaffolds.repeats\", sep=\"\\t\", header=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def repeated(x, offset, length):\n",
+    "    seq = np.zeros(length, dtype=bool)\n",
+    "    start = x[12] - offset\n",
+    "    end = x[13] - offset\n",
+    "    seq[start:end] = True\n",
+    "    return seq\n",
+    "\n",
+    "def percentage_repeated(read1):\n",
+    "    start = read1.iloc[0][3]\n",
+    "    end = read1.iloc[0][4]\n",
+    "    read_len = end - start\n",
+    "    # print(start, end)\n",
+    "    masked = read1.apply(lambda x: repeated(x, start, read_len), axis=1).transform(np.array)\n",
+    "    absolute_repeated = np.sum(np.sum(masked, axis=0) > 0)\n",
+    "    return absolute_repeated / read_len"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metridium_repeated = overlaps.groupby(8).apply(percentage_repeated, include_groups=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metridium_repeated = pd.DataFrame(metridium_repeated)\n",
+    "metridium_repeated.columns = [\"perc_repeats\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_length(x):\n",
+    "    start = x.iloc[0][3]\n",
+    "    end = x.iloc[0][4]\n",
+    "    read_len = end - start\n",
+    "    return read_len\n",
+    "metridium_repeated[\"length\"] = overlaps.groupby(8).apply(read_length, include_groups=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4593"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(metridium_repeated[\"perc_repeats\"] > 0.5).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='length', ylabel='perc_repeats'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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qrTxW5Im67ua87D1ePEYXZUtvPZES7BcvjyV405HLm3RlrorMtreBlN2Ec2SdZ5RoxBtMib5osX6W39E5F/hbxoMI29dbhwKQcLKuOd+03NvP+cmNyVsv9lfHJdo5PFvvuNiG62uWx98aRdAwMhsw4N1iKG4GXONlJMMb04RNbb6ybPRVN/ODUczROGFdG5reMs1ibs5SRsnzsQPTLBj1ATuZ+/OjoasL+b39EaNUXxOOBbA/iqk6G5RZHo7GKRebjjyOOBgHB+FNZ5BKcGOa4LygNe6arHyFFwu6q8KhSCKUDCo1JRVaBWJyYyzv7RVhxPeK94Cn3Y7ltuWzRU1nHYIwkluUPT84KNgfp9fv8TZjpBe7MhBGdd5Da4IZYqIV0yziF6dbytaQJxopBI2xdMpSdua1XZRnz2+sBW3veLxqgLBYP1+EwmbW88l5yb96uGTbWlZlzf2Lms6DEqEQ+X98MOf/9WvHHE/y111eLz0uvfNcbFsQnlXdo5XAWP+NK4Le1RhwwIABTzEUNwOu8SqSoZbtV5KNftHNfNtYbs+zLyQ2OuffiP8C4LxnksWMkwghBcLDum75p7+44HzTYZwjVoGrM8sj7sxypkVEZxx7RUysAzlZi9f7gDxbOMS7bd42wafHAQdFTBqr50Y1xvnr93i229Eax0enW4xz3JhlxCqQujd1z4NlzTSPv7BguTo3LxZOz3voSNZ1z6Z5qvDayyP2RylFrImVoNdhHKiV4OYsI9bytQvu1fmN9U7JVvX0NhgpGuPCe0fquSJ0P1I0xnKySsl0zx8+WnBadmFcqWBR9dRdRxppfvtPv5kNwYvHZbIrTldVz7Y1zLP4W6EIepvzN2DAgC+H4Ts04HN4kWT4VWWjb3IzT7R65cX4Ji38V41FnPNkccRPboz5+Kxk2/SAYJSEcVEcSZSUZJEkizRlFxbBWaG5LPvr7XxZQfciZ2iWx4wSTdUFcvEoUXx2+XwnJlZh1KaVZFG21/ulhGDb9UgEvXE7tZfCJp5V1dMa+9LPfV037dlx46rqWDUGFbjijHPNZdWjlSRPNEUaMR8FdZqUgVRunX/tgnt1ftve8mBRh0IKgfHBj+hgEnNr9rzPj7GOundkqeaj8y2LqkcLwSiPEB688Kwqy7/47JL/548O2B+9PlDzxeMipWCWx0RSMPMRd+fZK40Uvw68KTl4MNEbMODdYyhuBrwWX1U2+qY385ctDm/awn9ZAfR41dAbx6yI+bN397gzz7l/UfF43WCc47BIcN6zbXoORgmtDVL3WaHZL1K0lF9Y0L2Mp2R2o6+DUYzzHnajMXFd3jzlmjy7X9YH4zolJKumJ9Ly2h7f8zzp5W1N2KZZjHGOnz1u8XgSpRkVmkka0VnHujZkkULLEHWRRE9f/yYL7tW/3b+sqPtgoKiloOph21lOVg1H4+y57bqy+B8nmnXVUXWOcaKCYWFnmGYRpTdcbA2rsmeUxp+7Lp6VuF/998uOS+/CKOpdFTZvSw4eTPQGDHj3GIqbAW+MLysbfdnNvO4NVWu5MU2QQrAo25cuDm/S9eEVBVBnHOeblnkRFpmjScYsi7lVtnx6WaGlQItABHbeY3vPB/s5+0X6xgXdq7paiZI8WNbsjRLSSOEcSAnGeraNJY3sc/uVKMUk0Txc1HTO03UOpSTee+7OMxL9/HjrbbppUgomWczBJCHWkvgZLlO0i3UQ8uWFwesW3KttihScbRuKKCigjPfgPQejhGVt2DQ9WaQQUuCd3xU3nnGm2RslKCloO4cTBkVQ6VWNJYssZ1WH2xXI40SDgE1j6Kyjbg0gyJIwGhynmoMiZtt+noD+rvBlyMEvnj8BTDNN8Q47SwMG/EnC8E0a8CvB1c18WfU83lY0rSNLFJvasG22NMaRx/pzi8M4jZ7r+lwtpsaEUY+xjsY6qs4ySp+/nJNI4giZUsmu6IkjxbxIKBJNHika44JEWcA8C/yTV5GVX8TVtozT6DmybNkZ7q/qaynyLI/IE4VzQU3V7cIvn90vrSSjVFP3FiHBAdYZvPUUqWJRtSzK4MCcaHld/L1pNy3IyiXihd9rOoPzHu/8WxVMz3YrOuu43LSsqp5SGTadYZRGHBVxkHZvGn6qBHbX1ZIyKOMkYBz86GjMv7x/ycmqxrSh4Ktaj1SCUaJRBI+c3jo+Oi8Bz94ooW4tZ2WLFpJIpyRacL7t2MsDURwC+fxddkK+LDn4qnguYs2i6ti0hm1rafp6kIQPGPA1YChuvoP4po2/vgyubuZhMRDsjyLyJMJYxycXJftFQlq8fHGYZhEnq4ZV3VF3IYpgVfVMM81l1VK2lrLumeQxR5P02tPEe9jL4usORKTCyKPqLcfjhMNJ9qWO5ReNIV6UIvfO8cn5lkgpikRjrGOcaKL5890sAfTGc2uek2lFEksSpUgiwYOLmp+flmSRIo81WSTZtobOOPZHySu9cJ7Fi90z7zznZcOi6tgrEh4sK0aJZi9/s/Hjs90KYz1lZ8lihRQyFEPes6w7VrUhi8K4rukdl2XLXpEgRBhDgWM+jvk3frjH//6HZ7RGEEkocs041fyZe3OElHj8LhTWUrWGzvpg0iclRQJlaxgnmtZYfvak4WCcPlcEvqtC4auSg8vOsNwVR1fdskESPmDAV8dQ3HyH8F03/up6y6NliHFojGPbWRItd7Jjd+1ODM8vDtMsZll1PL5owgLXW4xzXJYdm9YyTjUeWFZd6A44T77LZLq9F6z9l2XPybam7gxprNi0Bl2210aAb4NXjSGMc8+FefbO88n5lmXVE0dh9NAai5KCsjPPdUrKPuzrh4dByu4JBc9n51v+4NGKw0mCIMH5nssydK0utx235v21F47z/rUjNOc8D5Y1Dy5KFo1hP49oe8tJZ6l7y9Eo4d5+8YXX1LPdCi1DhtmsiPlAjHi4rPHeUXeOi03L8TRlnofRXGtcKPCcR0mwHvZGCXj4nT99l5vjnCebDuscRRyRxJI78xxjPc4F7lHVGlaVIY81kZRoJdg0BuMcaSRZVz2O0LERgndeKHwVcvAgCR8w4N1hKG6+Q/iuG38tqo6zbcskiwKHxDmWVUdvHNYbms6RxkGlc7U4eOeprcF6uLeXUzaGy22H84KTVY3w8N5B/jSmQQhONw13dP6c2Z9xjm0nmc4ysujN3YJfxBctSM+GeRrn0EIgCfEGDxcNedwySqOQvH4u+Y1b+prX05rQWQrJ7uH9LjYNH19WRFqwn6cIAeeblqa3jNOILFJ4DyerhifLGkNQQeWJfmnRK6UIf4SgSCP2xilNb7l/WXM0SRklmottcBK+t5dzOHm5QunZboVxYayXRYppHorQTW1ZlC0XVcfBKKK3Dr8rzLseOtftVGyKeR7jCMXor9+e8cPeopRE4Lmswsgr0QrnHb1xrOueaKfuqvtQ5CkJdWvZ1D1SCSIhcDgSGc7R2xQKb9vJ+yrk4EESPmDAu8Pw3fmO4E2f8r6tIytjHZs2qHKCW+/T/KXT9ZbahE7F1dhFKUEeKU7WDXVvebJuyLSk6h1CAtaxbQwOS90njLOYbdNTxIo81dyYpiQ6jIW88ZStZZJFX/kJ+YsWpKa3ODznm4bOeuo+pGAvy5Ykktye58Rasm16PrmouDFLuTHNn4kpeFpwCQFPNi14OCwShAzHZ1X1bNqedWvwHuZZzINVyS9PS/ZGCaNYsT9OqNrQFboaBdodr+bqGsrioGjaNCEs9bJsMdaxqHomqeZs3fLn3vMcjrPrAqnpDL3zCO+vuxVahs7FlSdP2RnSWHMrzsljhQVOty1VZ9i2BoEg0woPrJue1W4sdmUkeGqCaiwozHoW245prjlZhVDUZdkzK3YS/0hyvu1wLkjxq87SW08SSfw6cJryWKOVeG2h8FW6ol/WKmGQhA8Y8O4wFDffEbzuKe9Ngx6/KVxJf/eKmMUu7DJIrXvWjeH9g4Ii1jTGUneGWaZpEMF/JQ6OuR+fVxyOE7SUPN7WtLuOwMmmQRDyiqo+FDHVLjjR7JQ526bn+IVOxJd5Qv6iBUlLQdtZHi5qpkVEJASruuOs7Pjh4YhJFkzljHN0nWNVGQ5G7hkX5qeL5LY1eODGJEUqQd1aVlXLo1VF0xlGecx+HvMvPrvkybphlsfsFQnOOx4uagCySF+PysxuXLdtDXtFjJKCbid9t87xaFmjpWCUBpLyZdXxy7OSWCvGScT9RXmt5Ipl8PCJIsUoCV24h4uK0/UVB0aD90yygqpzrOuWR6uecRqFseCuwCxixaLuubOX7TogT8dmy6rDebDe8XjZMB8F8u3tvSxc660hjSSzLKKzlkgq6r4PMvwsuTZBPF03zIv4tYXCV+mKflmrhEESPmDAu8NQ3HxH8LqnvDcNevwq+CpdoavtV7FiXwq2raFse5ZNx61ZxofHYyThCbtpDWfblnH+NKxzL495uKgoO4OxnrNtg8ITxYqmszzZNNyZ5TTG4rx/7lg0nQnjDCk5noYCx7iQmyUFr+VFvBgE+eKC1BrL+aZBCcmy6fE+OCpHSlIkinEf0TtP1VqQHusgTxUO/1xh9ewi2RpLrAVVZ1mVHWvr+Ohsy6NlTawkPzxIGMeKj8+3dMZxN4sROysdKeBs3aAEjLqYSRbS3Os+kFe1EsQSLrY9VdNzWfd0vSMfxczzUKxIEcJHL8uO803DR+cl4yxilkQ0xvB41XI8jbHGc151tMbSGoNVEglMi5g81myans4YytaRR5JxGorTNJKM04hYS0ZJdL3/UoZwyxuTjLozXG5bGuNw1jPNIuZ5IJcrKTgaJdd8rWmqebJuWDfBQPFZvC4b6+vivnwZq4SvapA5YMCAl2Mobr4j+KKnvL08eo7ICl8vMfHrIDI/u/1hLKLY1CGx+95eQazC9mrCotpajxJPt3maRRyOUi7KFucde1lMpIMKx1jPNI3pvWOWhdiFRD89FkUasT9KON92ZInCmLBo173lcJSwafrP7UvX22uJLoQRR5GETkURa47GTxekqgleK3mkOFnV1MbS1Z5prjkYpbRd6CC0vSVPNHt5RB4nxOrlo4dnx1S9afDARdnRGM/RKGWSa3Qkqa0DD3XrON9UPFrWlJ3Be49wDnNnxp+7l10fh7Dtkt/75IJIS3rrOd+2nG9ajsYp+3lMpCSdDcRf5zzLnavxOIuYpGHBjXX4/7NVx/E05e48Q+3lfJqVPFxUxCr46kDg/xxPMvZHCbfnOaMkwnmHEAJjHEo+NSt8mtUVyOCrpkdryX4ceDsX246DcczxJOV009A7Tx4pbk5DEVz2lizWVH1Q1GklOJ6kqNeMpb5J7suLXR/vPEIG36UwnBswYMCXwVDcfIfwqqe8NFKsGvPObs5fF5H5uXToXWTB4Tjemds9Hc9450mUwHoHPPWnOZomGBsIp8fjjMuqw3vPXpGQxoptYziepHTWP10wncO5kDNkrGdV9izqjiLW3JplZJF6bl+63nJZtTxa1FxWwXhulkdsreOj855ZFrFXxEyziJvTlN45HlMzFhFP1jX3lxVaSSIp2TQG7xznZYNxIeeq7R2PVi1ZpNgrPq/UerZTNE4iTkXNx+cbLssg/T4YJ9yeZ3gv2NQGpcB5y6NFSxxLRolm23Qsq57TVX3tqWOc49Gi4l9+uuSPTjfs5TE3Jyk3JxnWehIlEbviTktY7/Kuqj5ca7enxfNqNim5qDvu7OfkicY5OJ5kbBvDedmRpxolJVVrKBLNwSimsx7nPZFS187B80J/LjMrVuLa9K81IeTTeUm062TN8og78/yaV3XFNYuVRGjBJI+eM03Ef3F37tvAfZFCfKvHygMGfNcwFDffIbxqtm92fI93cXP+OuWqz25/bx3btmfb9Hx2WZHHmnkek+0k1LdmGZ311/40V/vyw+MRp6uO1lomaYSSkCdBNXRjmnJQJJxuW9odd2dVGTwe7yGLw5homhc7omnYbtlblmW/KwBqTtYN28Ywy2NiLXmwqADBONM7WbLndNNyNA6dCRBs256Hq4ZZFtFaj4SdEsyGEM2RJtIaCIaBsVbPudG+rDvmvefxqsV5wQcHGXkkWVY9q8pwPElII4n3jvNtRxRBHqldrpPkR0djlJKcrhumWcSjZcUfnWy4rHruzjNmeULTWW4WMQfjlM8utjt1F6wbg3GeUaJJlOIXy5JNY7k1yxklilEaUXUWJUIO1uNlg3VPE9jZdpStJVaeeRFzZ5YxzeJAhN4V5m7XdUmeuXauiozW2J0CSzNynierhliHaIqyNcRKcmeeUSRPU9uf6wyi3oq/8m3gvnzXlZADBnzbMBQ330G8ONt/lzfnd9Gy10qyaQJHaJKHJO5F1fFoWXM4Tri3lzNOoucWw0hKbkxTxkkEfsNHFyWJVtdxAqlW3Jpn5GnE2Fj+5WcLTjcdWj3tRhyMUzxwMw92/8/uy8m2Zt0Itq1llEY0xlF1hlhLegvWG6ZiJ/HWEuW5zmQCz+m6Ae+Z5Qm9dSwrg3GWs1XDZJRwY5YTSUWRKJJIYa2nd46Y4Lp8sW25KLvgnBwrqtbwBw+XLOsW66HpPXujGE9QYZ1tQyHx3n6BsY5tF2TRkVYcHMQcFAmP1zXrpmdZdTxcNigRHJL3RoHTUWrLouz54LDgcJIyShUPFoFYfDjJGEWSzsOtacrjVcOm7qja0JFyzjNOFMu6Z7xToRnnqDrP7XnGe/s5UkiySF1nOs11QqYVn1xuOVm3OA+fXghuzzPuzgv0znTv8arGOs+mCb5FkZLksabuDVoIbkxfzkn5KvyVl8UhzLIIBYE/JcU7y6Ya/G4GDPj6MRQ33xN8UcbRV7k5v4uW/Ys38zzWTPLomuB7dTOf6893qRZli/Fwc5LRmFDANY1hbz++PgbGOjaNoXchedo4T6oVs9xSNZ46N9ckVoDtrmOyXyTXyd3BC8ey3kmv297R9g4pINGC/SLF+BBpaa3n4bKm7i1laxknmlGiiFXKk3XHOAl8Fe+h7sOYRSuBcz74wZQtDxc1WkqUFMRaUvfBt8X0sDeK2bSGznnmecy27ZFCcG+v4N5ehtKK03VNFivyKJB0t62hSBU3p2nIs2oNo50XzdU5S7Rg01q2jSFTikkas1e4nWeNpurCqOeDwxFSAoIgd/eO37g1pmod68Y8d25741hVHedJhNrJxJ8drzxe13x6UYWCSGuqtuenj9cY6/jRjWkI+bSOT8+3/Oy0RMnAt0o0TPKUe/Ocw/HL/Xe+SsDrs3EIl1XLsur5w8drzjcNUSSZ5zF35/l1EfZ1YvC7GTDg68fwnfme4HPEROt5vK6fk+/enmfcnGQIJb5RuerLbuZBQiw+dzN/tkt1VRTlsSYt1DWfprOWpnecbRoSLVjVBq0lYzSRViQ6KJrOtj37RUzV2lBICMFl1fJwWXO5bbksW8rGkqeSXCt6B9vGsNg0GA+rSiAUnG1aDkcJPzwes67VLnU65bLsA1els9ycpfQIJokOviuAUgIsnG9bfnw0puktZ9v2mhMSacnFtsPa0DUaZRGt7UgjTRopllVP1RumecIHBzm/dWtKnkY0vWNRtjSdI5KOures645b04wPj8ZEUnBRdcQyBEx+dlEhpaC3nt5aLstAKJ5mEY2xrOuen580tMZzMA7jrzvzgr0i3knHPbfnOY9XNWmsqTtL2fW0nWPb9awbC0JyY5Ki4qecpixSPFzUjLOIURyxbXuq3lJ1ln/1cMko0xyPc7atwXnBDw8LtrXhsu4423T82rHn/b0C5/wX8lBep1r6ItVf2RlWteF82/B4WZPGCu8Edef42ZMNAB8cjt/yiv9ifBs4PwMGfN8wFDffM1zd2D9ebPjZk821fLfqDL/32ZKjUcXNef6VTcr2it0o4hki8Jviy97MXyyKtJR0zvLRkw0/P91SJBGJgkgLPIIsjhgl4RKXMpBRQXAwiugMnG4blnVPriVrIag7y7JpOSuD/X8RC8rW0XvPtjNM04j9IqU1jofLhlGqkYgQPXA4IosbDsYxZWvYtj29gz99Z4olqK9A4Jwj1oK9ccT9y4p1a1BCsKw6RmnY3lUT/FqmaUTZGmoTOk2TTGO95/Y05cfHE/I0dJ/uzguc9/ziZMtl3aGl4L39nF+7Mbk+v3dnKT873VJEiqNxwsNlzarpuDcvuDFJubtfUKQRugxdCyUFFkvZ9qxrz+iwYJRGlE2PECC8J1aSRIfr6LJsqdua3ngmqSJPFIuqZ1+Ka5dggM55ZkkobBZlRxwppmnMZdXyeBmKoIeLmvkoBg/Oe4o0SPyr3vJo1VwX8m+L16n+ropnCSx2I9NRoq9J2WmkeLxsuDnNvtYR1beB8zNgwPcNQ3HzPUTTmesn5Cv5btOHJ9azsuX2fo4Q4kuZlPXWsW16ytaGDslXlIXD62/mzzrsvlgU/fxkzb96uGKSRhyNE8rO8IvTLUUiubev6UwgW9e9RQrBOFXsjVK8C2nho1Szrg15YzhZNYySCGs9BsfjleFonHJrnrGpg9xbiiD5zrRECMll2TEv4muDvm1ryCNN2fVMMs3xKKP3QVLdmbBIjhPNpjb84cmGPL7iWXhOlg1Hk4RYy5AwLuDHx6NghNf09MZykMd8cDB6joysteSHRxNuz/LdfkKRhNHe1ehLSEEWSe4vKtreoYTkzizj5jzD+sArkRK8D/LpsjVs2o7eOvaKBGM8j5YVy6pjkkWcK4n3nroLhO8n65aLsuds23JjnGKtJ9qNx7JY0dkw0ouloNp1bOJIESvJ1oTQy1GqudyY4FsTRVyULcaF89/2fudy3LOs9Ct5KMY6WmOBzyeCX5F2tRJoKbH+edLuVfHsgM45prG+zioz1jFJI8qdS3P6Rlf6m2Pwuxkw4OvFUNx8D9E7f/2EDOGGv20DMbExFu8EafrlTMo2Tc9l9dXNAt/kZv4qBVHdhcWrM5Y/Pt2SRYpbs4w80eRJ4HF8erHl7l6BdZKqN1jnOR7HHIxSlBBYGYo2LSWdaVFSsD9O6I0LqeF5ihlbUqWJlEDmkjTSCMFOkQRxJK8X0yRSzPKYUap33KGUSaqvj9XNaUZrbOhOKcEnFzVpLHe5SSFEcpSG4MfpKGaWRVjnOBinKCnYNoaLbUMRRzTW8WhVf66oTGP9uY7Csyqcm9Oc3nmW24401syziMuqZVH1PFk1HIxiBII0khjvKeKISRa4M082DXGjOB4n7I8SBKFgTCPJRdnxeNUwjiU3xglJLFmUIbU90oq2D9ydIom4Pc/46eM1VWeZpjFbY9g2hh8cBg+cpe12XTbDpu5pjCfVEiE8SaSouyDVvzXP4JnxkhSCRdnyYFGzrDs8MM8i7uzlzPME5z2LqqPtLZsmjNaUFMRKsKyCe/JVR9E7jxaC822LFIEnpUS4XsaxJnoH0uyvwhcaMGDA5zEUN99DRFIQS0FjDLEOadHGhT+JkujdWX9bwuK7koW/7Gb+MgVR3YeFMNWSrndc7qTWd2f5TpIdcDTJWZQ9CEgiyTjT5JEijYNnjVYSdvJ56x3WOerOMs1jeucYuYh5EYWnexfSrTe745TooHQqktB1mCXRddcpUpK2s7TG7oqAFK1k8PVxjlgpZlnERdkRKUGuFSeb5noMF+26YD86GnE8Tlk1HcvS0OEwxpFGmnkRk0RvVlQ+l96tBGVnmecJbe9CDpOxXJQdxoUM8mXTs60NQsAsj7k1zYgiydmqpWzDmMY4ON90jNPAA3K7MeGdeUaehGOzKDsQIfhylnt6G3xutJLcnQd11796uOSyahknmh8c5tya5/TWBf+hScwfnWw5XTe7Ys3TdJZ7+zlprKhby+WmpbVut+0eaz0XZeiOXV0Li6qns1s+PAoxHouqo+rcdbfMOMdqRzy/Nc+e5lttWmIt+ei8ZJRotFQoBY9XFTfvzr/USOpVPJ+XOWAPN+UBA746hu/R9xBprLk9z/jZkw3OeqSAcqf4+cmNMemuunlbwuK7koU/+5qrbs2zCiIhQmjkujGcbRrq1nJ7npFEglGicYLnLPfr3nB7nvGn704xvcAR1FKTTF93hrSSjBPNR+cl68bweN1wum0pYs3NSQo+PNU7KXF4PjktKTvLwTjivb2CJBYUSnHvIEcKwbLsOdnWITspVmxag1Yd0yx+roCzPiz61nqqLjgqNyYosc7KhuNpwmERxmtla68jGoxzHIyS64X7dUWlsYFY3BoXPt897VY0nWVVdSSxYpzGdM4iEbSdw3lL28OducZ5z9m64/6iYl21HI4z4lzivOdi2zHPIzwghODGJGVRhciJwMHZRWtMU25O06fHXUt+dGPKKNM8XrbB6yZWoSi0jlRLZJiP8XjVYlzN/ijm/b2cSR5hTbgGL6pgxNgZx/m24ZOLEu899/ZGpJFCihDO2vaWy7IjkiGfS6ngXn11DFtjabuQUQZcq7VOYsWtSRq6NlIwzTTzPGe8+/c3Dal9Fc/nyurgVfyfbd3R2mBmORpGUwMGvDWG4uZ7itvTnFXV8cvzLXXv8dYxySJmeYx1/ksRFr8sEfhtMqmuxihShKJJK8lnFxXgibWiN+B3kuQiTdgvEu4vSqQQFHFE2fUsqpY/f3fOB/uTL3xi7mzo2oxTzTiWfHJeoZRECsH7saTuHWVr8F5wc5pwtu24LHug4s8Xc35wOGKeJ4GM6hzbTjKdZaHYay2fXVTcmjsOx9nTL5oNi2VvHVKG4mycwLYzxEowy4PZ3dU4a7wjFT+qetJYP9eheraovBrRWONYtz3b1mBdyJhqe8t89DQsc9uaEHsgJFJCrvR1Rtb+qEAgcJ7QwVCSWRphvWNZd4xSfR2tsKg6DkYJUoKS8jozLNKKee65OU/48eGIJNGfixM4LDKaznH/sqI2jkwHg8W2k3g88yLhN25NdqnlCqRgXfWhmLXBALCzjmXVESlJphXLsmdZ9SRaMsniMHIkdKnaXVp424cxopYyqO2sJ0/0tTuzlKHLs1ek3J4XIYHeCbQGQRgP1l0wOXwTN+FXmfMtq47O+s/9vLOWx4uaX5xuaawnVYIPj0b8xs0Z8QsPFQMGDHg1huLme4qyN2RJxG/dmYMXeDzruqPqLEKIL0VYfFsi8NtmUj03RpGCWAfVUG/djpdiibSgUDFFoumN59eOx8RKsO0sp9uGPJL8+btzfuPm7Hqb2fFiWmOJpKTszHVnSIqQ5B1HivcORrS9oe0Nm9aQRZqu82wag9aa44liYnq8hTSSz6lsyjaQk5ve8ul5dU0Afrys+XP3PIeTLHB8dh0jKQWTVNNbT2uDd8wPDguyWHP5wugvTxRZpFhWHdPsaZemtw6FYF13bOoQivlkVdMYx+E4Ya9IGGeax6uQEl7EistFe837qY3BdcEkrzeeJIINMEoV4yRhmofR3MNlRRRJIIyc0khhnQ+8mSzC4Xi8bBmlmsNxEhReKpC3L+seU3afO/erpuN82+JF8BVaVh1/fNpyNEqRAkZpxHt7OaebDo+jt5afP9kSKZBSksYSKQVJpFBCkMWK1W6ktm0tReLprGXddHTWgvcsyh5FkOT3IhTkkzxiHAe+zbNdw8uyRdeSg3HCKA3RHuebFus8zY7DdTBOyWP9yhHhq8a41oYU9huz7HPj3d/7+JJHq4a9UcJRFor137u/AODPvrf/xt/VAQP+pGMobr6HeNlNFbiWbj+byfO2eBtVx9tayj879lJSME4jTlY1vXV4B531JFJS5BFJFBxr4yTmRzen7GURXojn2vhXSqFnSaaKkFM1zyIiJdk2hp+dlhyOYt7bT6l7GxRBiaLsgrrMOk8ea5wUZEKzsj2PVg2/aSxayevt7o3js8uKqrNkceDkXNYtvzjfhs/c7fM8jzkep5SdIQVmIhB3s0iFeAf/NBsLguR9r4h5sKyoOkPxDM8nVoLLsqfaJWhvOoMkhGIuq469POZonHBRdhwU8U6+LemsZ1WFwjOLFZM0okjDv18pw25O893YxrK34/pclh1lawIBu4hYVR0n6yCpb3tLEkmmaYwHFmW7y+GKryMrIJg0PrisguFhFlG3hrb3wQSxMaSJYtv0zIuYw3HMTx8tOdsGCfqtSUJtPL9/f8nxNOXDowlKCkZJRCQ7qrbHWkuRKC63bQgrjRVP1g0PliXLquf2LOfHR2PGqcYCsyK6Noi8ul4Pxymn6+B1k0WhiwewX8SsW0NvHJPMkcf6lSPCV41xhRSfC4aF4Nf00UV1XZgCJFH4/4/OSz48Gg8jqgED3hBDcfM9xBdxY/odX+DLKjHeVNXxZcjHL469JjuuyPm2pbOOLFbBAC6J6F148vbO757A42seRGtC7tGm6fnovKRsDXmisc7x6XlFkSjGiUZJETpc8S6wU0CkJUWiaQ3UnWFZdSRahhyszmJtkKRnStEZS5FE1+O4s03oiowSTaxDuvY0jXEOLsvuep/jSHFvP5jgRSoopjxh1LZXhIT3F0d/aaw4HqVIIZ56DeURq6ananseLGo656g7xygW4XhFikfrhliG9142LZFS/NqNCY/XDZESsCsIIyWxHm7NUrQWfHRa8mBRIgmOyavaMPJwMIrZL+Igl+4tH1+UjLOIO/OCJ6uKB8uaUazJ4oh148K/px3zXU7Xsuxx3nG6bRntXIyr3jHOI2pr2fQ9RaaQMhjnZZGg7jxFLJmPMm7NUrZNz8fnJZ9dltwYJcRx6JhNc73L3uqRIhRxx9OUbRv4SwejlEgpzrdBDv7h0Zh7e/k1z+bZ6zVWwS36dN3wyWXFrWnG0SQl1ZKyt0SJDq7PqUbvks1f5J29aoz7smBYgLKxWOdDxMgzKOKI021Daz2jL/OlHTDgTyCG4uZbgLfhpLzJ676q4+mbbM/rVB1fhnysVeBdnKxajHNkkaZINLdnKdYHj5RNF9x3ASZ5dJ0sfSUFvhqBAayrjtaE7kCiFb1xZLHG49m29lp1pXYLad0atq1BSsHJukF4z7rqqY0hihSpUjjv6I2lbA1Va5kXT8nJZWfod2nnnXV0vb2WUne7ouvquL6YkP5sB0zL7rrLIURIEu+t4+5+TnT91O9RSvHLsy3rusdYzziJabqG5c4EMBKCzxYVWawQwONVj/eCo1HCtu5D8niqyWLF3VlOliq0VByOElbbnnUXiq1IS842DU9WNcl+QRZHxErw883m2kvJWIdSkoNRwqLu2LQWf6Uuc+F6WJYdl9uOeRNzvmlDwvfOOymLFUWkqBpDItUu26uj7hStNewVCftFFDhAecydmePTi5LTsiPvw2cdTFLGeUQiQ3CmIzhYr6rAkel3afHjNGKcaiIpiXUgSL94vTrvyeJgeFj3lpvTjHEWPRNS6+mMCz5Ksby2BzDWPad8uhrjWusQO5n5q4JhgWuX6Ge5VWXXk0eSRA1OxQMGvCmG4uYbxNtyUt70dV/W8fTLbs/L8LYF1tVnb+pQIJwsetJEspcn/PBwzLrpebSs2TQGYy37RUoRafZGYezx4gisbA1PdsTk8c7IUEhItKTuDa217I8yDicpjxYVm75nXQVllOvdTjataXrDqu5C6KaCURyh0oh5EbNt7bVy5mrU9IuzTXAbTsLvXBF1687suC8CCInbe/nLO2DTLMj3H17WnFcd3oeQyo930QarpqPrHFp5Hi0bsl3CuRcwSTWbVR9GVI2h6XddMim42LY0naXqgvmi8DJkZSnJOIuIdZDbV10gBd8tIlaN5XzH2dkfJRSJ4nAUYwHjYbRT3jkfSOo4z+NVA3iKJA4p4XhiqVj2HRfblr0iYj8PRdCTzu5iNEK34uY8YzqKsJsOt/P+yWNNHodCF4IyLo01t+YZv3ZjxNmmC50k4xFeopSkNe6aYxaMB2MSLWl6S2dCoSWlBAF5HAJNJdD2ls46Nk34DnQmkI4BjAvSc2MdH5+WSOnDuUn1NZ+r2/GZ9otQqI6TiOUuFPZK/XRrlnF7mlP25rnx7nv7Oc5Zfv/BEuBzBPlhJDVgwJtjKG6+QbwtJ+VtXvdlHE+/7Pa8iKvOTxErLqseeH2B9exn35hmNJ2h6i2jJHCDjINb85y7e5LOWtreMck18yL5nJ+LdZ44EhSx4nzT0hSGIg7k2DSSrGoHPnRFpqlmm0XcHKes2p51HdREe0VEEitGWcSi6Um04HiUEmmJ1pJ5rq9l2prQvZkXEdFlkKxLCZESNMKCD265SoRAzEXV8XFXcjRKuLdffK54lFKAh6o3OwKs5LPLisfLlsNRKEJa6/jkskF4hxQKcDxZ1UQqyJ+ruqcylnt7Iw5GoRADQWc91llGk5i6dZysG1ZtTxor8kgT6SCBL3Y+NlXnOJ5kJFpeK4Q2rWGaRc95KUkhaHrLk3VDEoWU9s55nqxb5nnEomn5+LxCy+A/Y51j23TUvaUxnmTTcjBJ+fHRmDzTjOKIWa4YJTHOw8mqoersLicsFC7v7+Xc2x8hZU3TWdaNIU8UWkpqYTg9bXi0DKRx40JBqZRACMfZNrhPj1KNMY4n65ZECZa1uR5jyh3JWCv47KIkiRSbpuf+ZUVrggnhyarmswtHnkgOxyHMNfCNen5wUCBlOOY3ZhlKBFfkznrK3rx0vFtE4XM/Oi9fSpAfMGDAm2Eobr4hfFlDvDd93ds6nn4dBn0vdn6kIHjFWE9lX11gveyzizRCKcm6MXh47t8yFI22VK3D5KGQ6qzDWk/ZWTpjEQiUBKRgVfaInQzZ4dkvIsDz2WUFHuZ5FBZjH6z3Z3n47EhKbs1zqs7RO9CaXYdDkGgdeBm7DtSq7mh6xw8Ox5yvOy6qlifrhluzjFkeMc3j56TLeayDv86qAT6vsnmwqGl6txuFeE5WLauyZdP03JqmOLFLC697xpkGBL3puNj0HE0TDouUTdMxziKc92zqnk3ds2pCdlKsBJsuRCJME82q6vloW3J7lu54Qo7LLizyV74w3gRy7rY17I+Say8lgEgG+XvdW25NC6IokIIvtz0PFhWfXJScrhqyWCKcp/ee+4uaLJaMk4i9UUIWK1rnGHnBKFH0Fi7KwNfZVB2bpqcEtBbcmqb85NZkt22ey6ojj9X1tl7FQsyyYMy4aXoa49jPgzdOrBV7RULdWTZNT9VZrHMIL9BKXCe335xllG3Pw8uQaXW6qemtZ5bH3N6ZHP7R4zWtsdyej0h0MAfc1D2fXZaMs/gFYr+i6e1z36lnb8JxrPiz7+3z4dF48LkZMOArYChuviF8WUO8t33dmzqefh0GfS/r/ASSbPBGeVWB9brP9h7yF1xhn90uJQR1a7ioOiSCsrU0fRi/7OUR++OIsrF4PAd5QpEqms4RR0/JvOuqY5rqXYfJ76TogVw8SsIT+yfnJVqFTsDBOGEv1+CLp2nliWYvSjgYpXTGUnYGYzwI8D4Ufq1xbFtLbxy9C1yTK/v/q2PTGsuy7q4Li0UZlEV5pllXBgus6/5a8RUriVSQ6ZRJHmTgR6OE87LjbNPyeFmzrHq8ACWg7x0PVw2Rgr0iuCgnWvCTG2NmWcS0iHiyqjnbtNyYhu6F8x7rgtLL787Z3XkBwONlw6LpiLTkN2+OKbKIqrForcgTy3rZoySEUyi4v6hxOPZHKcY49kYJt+cF00wzSiLSWLBuAncploKtECitSK0niRUHo5gfHI6YpjGbJhRt9xcV41gzzQ2JVlyWLceTjCwO/Ju6tayans6ELtrdeY5UYVSXRCpEP1QdjbHcGefM8hi5u1SXVc+0iDgoYs62TeD+RBLjISbEgCghr40DlVTYxLMoDVop8tGrr91XfadGWfydJA9/Wf7ggAFfN4bi5hvClyX9flWy8Ne9PVd42WhIK4F2gmVpGCcR+hUmZF/02bGS+N1/f/F2CTZ1z7rpd2GLnrozaAl/djpjdiO5/qyTdcMoe14mTx5jnGPZGKquZ5YldNbR98H8sG4Nl1XH4SimyGLmWcTjVcs0L7k1z58rzqQQtL3jZNkGYjKeSR7RGYuWIUpCK4HxwQjPe8+tefbcl9Hv/j/4qvhd3pNDqrCAJkpxUXbsjxNmeUzZhzHeONUcjRImRYxUgqo13F+0JFpxumnwHu4dFGxbg/c+jJcixXv7I/Ik2snLFQdFwqPLmgeLCth1MSYZAG3n6HtLksV8cDjm5jSjai2/PNvSuUCsHaWCJJJ8crph1YQAzM4E6ToixCUcjSVWhkiQPFH0ztM7x7r1192OZdVR9Y6DcYLYddnc7hhv2p7TTcskj5kkmtN1y6NVzSSLKGKFx2OMxzoXsrSKiM54PIJ5EfZV70wbe+soUoXugspur9iZAJpABE+1ItKSSIVzJwRcli3rRrCoezSSdd0Ta/nULVsCgt2YK4zL3uY79XXiXRcdXydfb8CArwNDcfMN4cuSfr/s697V9lzhxdFQb0Nek/ehc+GE52iUvPRm90WqkqNxKEpetV0AdW9RAqz1XG47jHPX8QnLyvDosuH4vRy9I/e+qktUpBHva8UfPFhxvg3uvJ11TJIIM3bc2M+4NxshJGghySLJ42XD4ShBS0HTGbSWbJue+5f1jsiqSbXi4aLkvOy4Nc3QKmZd9cRR6Nqsq54784zjSR6M6XTw4VlUPc57BDDPNR/vZOzOg9s5Bt+eZWSJZj6KuNh2xCpIlTeLmkgG7sw0izkYJVjnqPrQLTLeIzzXBV6yy1rSSiBlIAtLDdaCx1M2lv/v6QVSCu4e5CyqltvzjLvzgjTW1L0FEbx+rpQ+9y9LHi5r8jgUBtZ4ehtSv0FQm2ACKGQg8hrnKSINMpwP4xybxpBEMvBoekOeRngfpPVX48qmD75C0zy69kC+2LacbVp+dDwm2pkEfnJeEyvBnb2M023Lpu7J4wiHx1jPvb0wQnywrNhrYvJEU7ZmF2GhUEowihWbxtJZw7a17GUR+S5ja1HtMsMSzbbpiaWg7y1PVg1ZpNgrYtJY0Rn3lb6rV/gq8Q9fd9HxdfH1Bgz4ujAUN98gvgzp96u87l1tD3A9GlpU/bVkdlX3NKZnniaIZ0zcXnaze5WqZJxE1zfhZ7froAiKogeLitY4Hm/CU7txDmOgd5Zl7fG+ZXyi+LWbE8ZXo7FXdIkSpbg3y9m2Hf/isxV1a3dqo5D0/aN5TpForA8S3rHWgavhw9jp08sSZz0XVUvVBb7MwShhlEZ473m4qvnF+ZbpNsjDD0YpqRbEkeJ825NGHfMiLHp39nI6u6XZxQVkseaDgwylNMaG4uC9ecHRJCWPA7ekbgwfX1QUqSLTEVqF/bo5Szkep+RJMAksWxMM73rL3ihm2xo2TU+kVFA37TpKiVKU1lK2jtNNzZNVw14e8WtqAsBPH68x1vHB4ZhV3XMwTplkIYG+akOmU9uFhTyLNY+tIwS6O1IlMcayqoMD9OmqJY0lNyYJkQwFrBShA3gVcikAax1qp0rynp0HT7+L6hBsmjCOtM5S7FRWSaS42LZs2/7a4bmqOx4ua/YKx81pRpQILrZdGL9Zzy9OA5coTyL63vDpecesMDjvKbuORdWzl8fEkeLuXg4+NGrOty0HAN4TR4HwnsaaZdXxYFlxPEq5u59/pe/q2xQsv4qi4+sM1B0w4OvCUNx8g3hb0u/rXmesozfuS7eev+z2PEW4sVoXFqzWWDa1xdmO2Sgmj0P+z8tudpu2f6mqZNP2zIvkc9u1afrrm/Y4jci04OPzCiU8kyyMmGpj6DrL//1wxZ++M+NP3Zm/tkN1tR1Hoxibw2XVUreW1oWC5mr/IilDUreSVH0gpEZKcFK2PFo2bFrDT6LRteomjRSTOEJrQaRUcKk1llESczROSSLJ+aYji1QYoeQJHx6JEPqoBOu65739AgEsm566s9yaptyeZ8zyhGXZ0VjH/ihBiDCW66zncBRMD63zCCHIE0WeaPJUk2kZukA2FEHzPMRaVJ1h2/TBF0Yq9keadd1ze6+gM4ZPFltuzTI2reX3PrtEKIFzT0nfnbEsrGFdW6xwPF53HIw8mQ7RF5vWUIwUgmBIOM1itJbkkWJRdewXCY1xJFG4RjZNz7oOvB2zCwBNlGCax7S9Y131VL1lksakkaVqFXVnGCUa7+F0HQqzaR66Po9WDesqBH+GHCqNtZ6z3TVxZ57R9BbnIZFArLFtMHCMI0UWa5SQ3JymZIlmmo5AwKLsWDeG+S6/bZJFxFqiVFDudbuCbZxGX6lr8qYFy6+q6HgXgboDBnxVDNfctwBvSvp91euuYga+rtbzl9ke6z1Zooh0yrruOS9bjHWhFa8V3sO6CmZzt/zz/JKXx0W8RFWy265nfz9WMuQ4OY/zlvNN+FxP8HARQlB3lj88WXM8STme5S/tUB0UMZ21/NGjDaeb4B0zTjV5LHlw2VB1hp892dBbzyTVpLHmsjS8v59RtsGcTwrJ7WmOFiKom2rLsuwoEs2y7pmPI7TUREpQ7DKJOuux1rIooezMc+O7eZHsug+KR6uak2XNsjYcjGJ+eDAiTzXrxiJF6Aosqp5RHCGFYJrt8peEJIlV4Kqsg4tyHitujFPSWFF3lg8OCrQSVG0o8sLrQydtlie43eLlvGNZ9pyuWsrGkMea2lr+9YMlkZSMs5jWhHPjraOxhoMsBGt2vacxBuE9e1nEKNNEkeTmOGWvSJmkGrfzyKk7xzyPMEaCcHx2UVL3hmkecbZpKVvDXhFzY1csLKqWJNJXFkLcmKacrVuQnv1RTG0sWotrUrlSIeCzszIUftZzVoaEcqWCdHuUhhHX6brhcJoy3XGyjsYpt2YZj5c1x+OEcRZfFwixkuyPPcfjhCeblrq3XJTt9XcyjzVa8ZUW+7cpWH5VRce74gEOGPBVMBQ33wO8y9bzm8z1jXUYG2TFRaJJtOThqkI6RRwr5C7YsOo8TWfxzj/3+re9CT/7++um53TdIITg1jT4jKxbS6qD26tHkUWSqnM8WNbsj9OdJ81LOkGLDuM9SRRUUyerQMA9mCQoRQhP3DZUbVhY8kjxaC1pL2syra5N+2Z5spN51/zRiWOax5ytG44mCff2Ui63hs654N7b9dTGkUSCUayJhHju3JWdYd0YZnlM23tGmUUiQqcj1khhub8IyqZMh8VZyaAY01LgsMyzmINRyl6ecGPSBTIxoSjeL+IQjRCpa1m9EoI0kvzytKTug1Hhk3XNo1VN1zsgGNmNUs2deYGSks8WDUe9Q8tQVJ5UHYmUjMfBnK/rHbEGl2qOxwl7o5jOwK1Zzt4oprpSu4kgxR5nwbm4EIpsR8BelB2nq44sCYXbouwZpQrvPHXfowRMsog0UmRxMOy7rFrO1jX3L0vKzvH+fkGuNUsdCvB5kXAwDgVc0zvKtqfsAgE+1orGOpQInB9jPVoGhd0o7TE+dJKECAaGjdllcGlF3QY/o/FVZphznK4b5kX8hYt90xl654lkMCp88Tv4Nt+VX1XR8a54gAMGfBUMxc13HO+q9fw0Ibnb+YJI9nchiFfdoBdn/2Vj6GzPOFYIB082IfX5YBSzLNXOWVbSO4/eOfvC29+Er36/anseLkJQpSCMe3KtqbwhjRXFzrRtmihGaSDvtruwS3g+Mfyi7MgTxaiL2OwkwyGjSjHLNdLD8TSl6SynmxYhJQeTjDRRnJ1uedLWGBdIy711dMZiTbjBT/HM84gs0uwVKbM8dFA8nqoT4AIZen8SUaTRdccqi9T1ub1S4BRx8K3ZNmHsIgiy8CwO/x1CLUORc7ZpGWcRsyI4GKPgeJoxbnouqo6qDSOiurfXnb6rY3M4Srm3l/Hz0w1VH5RnZdNjTOh8rNueVd0zShQ3bRpMDyNFa9zO30hyY5ahBWx2/JsiURRa8es3p8RK8XjdUHcG6zTb1iKVIPaSLApcmcZYPl21u3gMz/mmD6ojAefbnlVluLefkSaaw3GMFEFZJ4Xg1jxjVfUsK0NnPHjJOBWsqo51Fbb9smqJhOTHRyPKNnRZbkwzehvMGS/LNqitvMOakEzvcDgr2MsTRkkwOdy2YUyI96ybEOGxrjsczxfxxoexsXnm2r/6Dre95dGq4smqpXOeWApuzlImaUTZ2evuz5WT8pt8V36VRce74gEOGPBlMRQ33zK8rWTzXbWeF1XLR2dbOuODU6sPo68fHI7YH6XA5ztGSgrONw1PNi3b3lKkYaFSSnJeNhSxQoiYR8uaRMvnFtQ3vQk/637889OKk1XDLE9QEvZGCWXb060sGkhjidsZxrW946JsWVYdWaSvt39V99S95fGqYZ5FWBdM3ZaVoe0McRyyjULnQYTUbueZZJpmpzy6NU353U8vKU8MPzwao6TEecHRNOP2LOPD43Hopq1bLsuOW/OMqdH88mzLsuwRwDjVNJ2li+z1uetdOLdpJFnXHafrBusdidYkWrA/jgPHSgr2iphVbULoZ++ou1C0/MatCftFOF9XxehnFyWn25Y81szzGBWpz3X64kjxg+OCk3WNwKDELveod7gd/0gJ+PisxCK4O8s4HCVMMo0kSMd769krYs43Hc47DkYph+OEHxyOqXrLsu5Z1h1FpSkbg8Mz2hWi1nu8g1XdUnaWqrUUqaY3jpNNQ6Ilt+cF3gl6Arn4cJxcK+2anRLr9l7EfhEHcrOxXGxamt4xyTTvzTOM9/zybEOiVbge84ims5xvu8Dt0fB4UdH0YV8eOE+sxfX3wFjHxbbFWE+xKza3Tc+2M+zlYWGvOkPZ9vTGc+k6Pl1UHI0SxknEZlckfnJe8tmi4niccjBKaK3l9+8vmWcxPzweX3dkL8ueWIlr/tfrCpZfVdHx1fl6AwZ8vRiKm28Jvqxk8120no11PLgMxnHjLMQWXLmuPrysr2+ML3aM8lizXyTUXcVPjkfBMC0Kvh+nm5qLbceNScZ4F5b47IL6upvws8en3RngNZ0hVoLeWSKt+cFhwfEk4f/38TmdDRELaaQ4GCcoKXdxDB2xluBDZymodhyLbcsvTzeBkJxGyFzzadNxsey4NU/Zz4Pq6cGiYtsZnIM4lWxag8LjrOey6ciWNdM0IosVaSRJd12I0HkRITCyDaOlG5OUeRphPDTG8Ycna/byiMNJwixNiGQ4t+ebhifrlnbX0fGiI9WSvSIiizWzLGKUhjHKtjXE2hKpkPB9d55fXz8XZcPDRcOq6ZlmMUoKllWHGiWkkbru9EkhQs5XFbpc1kPvBO8djMm05JenG7Zd8P/pTOjmnG469saGHx2NWFZBedX0HVIKZoUm0iOKWHFnryCOFFoHQq4QOxsBZ5mkEbM8YtuEUVzdWTa1Cd2LxlDVPSebhqq3eAeTJCKJJLemGVmkrkdtoViM2LbB0C+LJWmkeLisaHtLEiluzlIiHUaLB0UMUnJ7ltHuuiqzPMI5zbpp6Z1nlCqSSBK+ToJnmzJ1b8l2kvTQ9XO7c9zz4c2UJ8uaRWUATyIlVdNzYj3LqKOzHgks645xojHW0+6CM4UXLJsO8NfmgFffhb1cU3XutQWLlIHAHOtQaCRavdOi48vyBwcM+LoxXIffEnxZ3sy7aD23xrLYOe5e2dlfu67W3fVo52UdIyGDOd2dcUZvgyw4pFp75kXMXpE8d6N+dnT2RU9+q7rj4aKm3eVK1b3lZN1xkEdEkaZIFanWxFrxazenSAGNIUQlqGDGFu/UOPcXFeDDZ+3SokeJ4pPznsZYms4zzSNuTVNWsWEvSxntyLebNgR7PlrWaCmuk6GLVHN7nlOkmrb3RBL284Q0lqG7Zh3WOqaZ5ngSs6h6pkXM6brmF6dbhBA0xvLRecvjVcOfuTPlvYOCIlH8ywdVGG/lMUoJLqqOzjrONx1//v0CKQTn2440UiRaclG2VB1oLTndtoyNxRjHz0+3tLsx5iz1zEcxSaTYNmEE1rnQFds0PSerhsZYhBBoGRb1vjccjYrAq8HS9oYs0dyaJtTWsakMsVKM0sCtOhil4AIPa5bGOMK4KhxzR5Zo/sL7c/JYs6haHlzU1F3wybkavUgJUoVrYVm3XJQtWRSk71vj6FYNHxzkjLMQXHqlxqq60KW52DQgJe8dFKzrntNtTW8c5+uWaREzzSOsBy08SaSYj2KcC+OnVRXcjw92IzeAWRaRJWGMNt1xlK4CNi+2LVqFQmqWxzxYVCi5M/mT4TiOdkVXZx2nG8edvRzvQ4E3TYP787oxu++coHfhu5MG6lLo6lnDJI+ZF+ILuySDsd6AP8kYiptvAb4qb+ZdtJ5fdeu7ymh6VcfI72S6npC/M0o1TRfIlqMdQTJwWcRLR2cve/Krmp5fnm6CAqUzpJEmixRKwlnV8cPDCC2CyZtzng+PxuwVMX/8ZIt1DmPC+GiaRVSNYVG2OAR5pNm0PU3vMDYULsY6tOwpu55RosETeBp1eAq+NU3ZVoZl3fPp5Za2DzlF1jmEEIySiIORpOlC8rhvPL88WfPxZcmqNszSiIuyY5QqjsYZnrD4X5Y9IEh1xP4o5rLqudg2wfFZCDIdgjqnWczxJMEYT9VbpIB5niCFYF0bLsuOVR1CIfdGCdZ5Pjrb0luP8R48LKsw4jpsUo5HCUoFWXqsAjl3Vff0LpgwTrMY6+DGOObhsqKzUO3Iu62xzIqEOFJMsojeO9ZNx36RcjxOsC7IuN3ueolUuHaWVfCT2S8i9osUKQWRlFzs0r2NdYgdV0sIaDobXIwjza1phhDBdDCSgv1RhLXhqqxNOC9ppIg1KCX41w9X3JoVHE0Tbs5SzjfBC6m2juMojC1XjeHmNNlxpQRNFzhCj5ZViMbIIiItaXvH403NkQsS8KvCwjnPZxcl7TPFYKYlB6OI03XgnU2KhFGqgodQ3VN1PZ2FWRYzyTSxDKGq3nsWVU/da56smxD2+cxX/9mO7Ou6JIOx3oA/yRiKm28Bvipv5uuedydaMctiFlWH2j1xGueoWsN8pwZ5Vceod55bs4zOBl5GpCQIj/eezlrOtk+lsbGWjOPolaOzqyfPX5xu+OmjNZ11zLKItg/v450LRNA05ocHMa0Lr7s7zzHe0XZhsdvUPZM0IpKSddszSiIQPqh/bGjtP17UJJHicJwSKcHDRY0QgTcykoFzNIk1R5OMVPc4AYttw7az/PrNMdb664DINJIkkSSPJIttz1kZFpi7s4wsUfzyrCSLxM723xMpxY2JonMWvOBomtB0jkXdc0MrRqkmUZI4CpEC55uWzy4r6s4SR5IPD0fcnRdkkQpp1XnwtgEQwtGZkA3VG8u2dUzSiMttx7LqQkJ4HBQ9s1m0899xbJtw3VkXODajNMb5mlXdECtFtJOXH09TlJQsakOyiyfIE4lEsKxDSOgVD6buLFXfs27Cey7LEDZ6d17gBWSJRsrAL9o0lqo3dJ2h7AxFEnFvv+Bi2/Jk06KlYJZpYiHZtD2pLihbS6zDd+bxsuFi07CoDHW3QYhA0kWEbp6Sgk1r2bYNNycJsyzmcBzzeNHwZNuQ7jg4dWv56eMVsQrqpc46Hi0afv3mhHvzHK0l1jo+PiuJI0m060x5HD88GKHVTvqfaOzOxVgriRIKLR3n25Y0VtyYpvzBwyVN7yiSCDz4XWTFsuxItP7CjuyLXL03eWACBn7MgO8thuLmW4Cvizfzdc27tQpOsWVnqFqD2rWwi0RzZ5Zd3whf1TG6Iko++/P9PGQxSSlItaYxhotty/hYv/LGuqo7Hq9q6t4ySoNXzEUZ5MKjRNMDo0zw8ydrHq0qJmnMNItw3tGa4CGzqjpOtw0PlhV/9GSNVvDrN6YI4HzTkibhidzBzp3WsqgM284iAKk8RRJ8eoQPmVFCCD48GtHM80BQjiWL0nAwSrisOp6sWySeG6OERdWzLHsSHfgj0zwm0wrnHFoqDsYxVd+Dg3VrGceK02VLFimcDcqkq0JTSsHjVZA1A+wXKbGU/NHjDZ1xHIxj6t4yVk+vAue4JoQb5ynbHufBezhfN6wryf4ubHPTGjrjeLQo+fmTLY1x1K0j0QIhPIfjhN57IkArxe29HPCcrBriSAR3YSU5WYUYDakEG+Oui9nzdcPptuWDwxFFFlN2Pf/60QrrPO8fjK7l086HGI3GWKq2RwhBZ3usdeRpxK+Pg4tx6xzr1nB3P2eSRTzZtPTG8dllUNAVaczhxLIoW87WDZum332/QkGCD9uV7jg5sZJoHcjRwgv+Pw8W/OtHa57skts/OMy4tz9iXfYcjWLKziCloOwNcRQKJikFUkr8rmuVJxFFEkwFl3WPEgLTW6QUvD/PaHrPxbblYJQwSSPqrgUCAf7mLEVI+Oh8i3FwUCSf68i+avQUXJ1f/sC0bXsutsGHZxhXDfi+YihuvgX4NvlEXN0sy96SRJJtY4iEYH/nB/LsjfWLOkZz/fTn3gU5rZRBBt7b4PEymmpAfE4aC09HdZGSxFqxXyQsqo6y60n0zv/DeWIFXggmacT7+yHA8l/eX+I8lG3Px+c1Z5savGeeJczHcUgM7x2TVJPFESfLFVoQ/Fbq4E8zS4Ob8F4eirVtZyh7wwe5Yj8vyBLFw0VNrCQfn5fkid4tYpZZqrE4TsuebrevjXFggn1/Ns2orWVVtRyMIs7WDXUfDOLSOJx730O7e+2tacpF2fDReeDnGGODyZxwnJctq6rlZ0/WzPL4Wgn0wUHB3b0CKdmZBVqsCcRU4QLRO7cSJRWzPIyHNnXH//FwzUfna06WPVJ6pplmVQf+xzSPGSWatrMcTmKsl5ysaxBwPEpACM63wUTxdFVzNE4ZFzFZpKiajl+cbXe8IEXTG+rduPJf3l+SaonzDuscZ9uO2hjSSOHQjHZS+AfLmqNcsz9Osc5TtT37eTA7FEBnHE9WDb11jBJNrCUTE7GqOs62HU1vmI8ihA8Fza1pvuMc9ayaDreEk3WLFvB7n17we58uEELgnOWy6jndNHxyWvJb92Y0veV03WKtZ91Y3t8PcvLgQC2pGsPjVcv7+4pFZVluO07WLZEUCCn40dGI2/OCbWM43TTUveVonPHh4RjjQ+dsnMXXmVUAo0x9bpz0qtHTXhG98oGp7uy1umsYVw34vmIobr4l+Lb4RDx7s7w5zanzkBM0K6JX3vhe1TG6+nlrgpX9wTjF40M3QXJtv/+ysdvVqC7Raufvobk5STnbdDhvaY0jlpJN03NzmjLZGdHFgHXwR4/Xu8DJGIngsm5prUUjWNU9zsFsmjFONUUcYZzlfNOwqHoSLeg9+M5wOE6QUoaneiU5KDLmRUzdG4zzTAvNuFRIIak7S9lZhISmcax3pGzrHQ7POI5orOXJpiGJFU3v2dQGt1NrVY1lnkXc3s/II4nYKXKkCoqXzngiIdFxcLs1DharhgcXWy6bng/2RljnuNzA6bplVXUcTzOWdUvbO87LjmkaMck1ZWtJo4wkUigpKbueh8uGk3XDqjFYb3hwGRb6W/Oc43ESjlkWs9WGPE44msTEWuAdRFqR7owFq87weN2Qp5oDnYU8pt6yrHoS7fnodIvzniLVxFJTdj2PVi1CemZ5xEfnZUjfloLDIkEpSRZLlnV/PZb0eA5GKQfjEMHwaNWwqXs+uyhRSnI8Cd0S5xzjWFP1wY9mqhWxDoVAZ0IXbl33tL1jXgjWVcdPH6353U8uQ9zDTsJvdtfsyaZjdF4SS4UD3tsfcbZu2C9i9kfRTuVlWNYdxlqOphlNZzjf9NSdwUeagyJh1QSPpnmRcHOacTxJOEvacK1WHUUa9vOy7LHekXaWx4uGaRquc/hirl7VOopYcVn1wNMHprLtwYfCZsiBGvB9xlDcfEvwbfCJeNnNcpQEKXjVOkz++Q7Lm+DZsVsaKQwO56CzBrH73Ge5AlfdHi0FniDrvdi27I9S7s7DuOvKJwcpGCeBTxOCFh3GOVZNz+GOxGmdR2lYl4bSWG7qIOvdND1Swk9ujllsWx6tGkrpMNZwsa5DGGVr6W1L2fb85q0paSSvi88P9vOd2Z5GSUlrXCBSZzFlG9RZqZKcdwbrBY3sgzuwjQKfo+v5F5+FrtIkjXFCcFY3mHPHn7ozI4pCivmmMRxNMqZFzKcXJVopikTx4LLG4yg7hzWhU+UQJFrgrOP/+mzBj25YPjwaYS1s2gX3L0tGScQsj6l2BOx0FHO57bjctpxuGh5eVozTmHGqWdeGi7JlNoq4McqQSlCgaXpD1Smsg97YkIdVxMRa0lnBLA9ho2Xb07SGJ+uGIGkOZnmLsiPWQb5dRJL39nPazjNL9fXCO97xhjyElPdIcGOesJcH355YKy7KlnVrmI9ijicp59uOj862NH3o/EgpsMAoUSQ6I4s1eRI8hdZNT2os81xzc56xLDvOty1l1+F94Ck9WtRUvQ35ZYnEOVhsWn5qlhxNUn79hmSaRzxa1cx3vjYX24bPLmoOxzHrpqM3cDxJ6J2l7T2zLGR1/Z8fXXJjmvCTG1OcD4X441XDpjE461hUBqUEBztV25Ntw1GVcGOaA6/n6o3S0PV99oFpr4hZNyZw4V7ymiEHasD3BcN1/C3DN+kT8S4MAZ813TsvO1ZVR9076t6wqnv28lDMaSEQO/sQ5wmFjQ8k1Ctp7bIKOU2jRHFnL2ecxPzBwyVlZ9gbBTNA50NnSAtweOre4XYqoVmumeUJP7wxIY8Fv3hScTRJGSWaqjHMswjnPA8WXehoxIpl3TNNA+l5mkXcnmUIKVBCPPWDqTt+drbBGB9k0Z3Hi7DP4yxi3ESUfceiCvyV+ShI160DiaBzIJSgiCS2CllWjb3gN7tpkKD3lizSRFJyPE452bSULSybliLS1H2QDhdphJKSprOkkaSxniKOgiQbuLFJaLoerQR5EsixjbEgAj8kOPq2CCFI41AYhDRuQdUYRplmnER479jWhkXVYZ0LHZU8ZIi1xtIbx81ZEQI0tzvpvRBM0yiY43kTwj13MQM60zy8rDHOcyo8T1Ytzjlmo+D1IwWsKoOznj98vOX9fc+tScam7niyrjkeZ9cE6g+PRyyqhifrjnke4jAeX1Z0znFnL3QO+x3RfZZHHI0TilSjZMgD2x8lXGw6pNzuOjth9BkpT99ZULCqPavG8ovTECA6LSIuNoLPLitGqWLbObJIMcpimtZxWfVI4bkxyVnXHeebhtNNT28Mszyians+OttyNEpIlOBs3XC6bShiza1ZxjgLlgVFHDpuV2PcKy5e2Zrg3CxDwXLF1YuU3GWkPX1gAmj6asiBGvC9x1DcDLjG12kI+CLRUQpoO8Nl2QeFyc75uDceYz2Nszxe1uyNYqZpQucddufOK0VQVh1PUt7fL1ASmt7TO8fBKOZk3WCMo+0sp9uasjPM8wghYVMbIMiotBaME804liSR5s48I480dWfJEk2RarQMSdwHRYITsGo6kJ4f7o0wjp0a6el4bl4k/MX395kVMX/waI2WkMQwzhSLqmfbGI5nKc7GO9JzMMwrG48gFCGjRNFbuNy2O2KqZ12H4u+PHq8xzu8KF0EcSQ7HMefblr73iMiTaY2QwVXYWYeQYEO7Awk0vaE1jstth/EC4Tx5LBnFGU82DW1rqY2jsYFgOt0t9snOI8ghWFc9ygtuTBOa3l5nMgH8/GTDpjVcVqGgOJqk1K3hs0WNJYya0khT9YqqNZxsWxIliSLJ+wcFiZY82bTkWrE3SjgYwYNFxaZu6U24/m7OMt7by2md518/XPHwsmJ/nNL2DimDHFtKQR4pjsYp1jla61hUHXEkmSUxozihSDTn2xbpPR8ej7g7z0MERRuMIVMdCtI7s4SPL+prTybjoOos00QTxwolPUrB6aalNQ4lPalWaCG5M41xU0+sJQZPb4O0/KaAtresGsMsVSRJIID/X/cXIc9Mws1pzp29jN558ljTGs+i7Ei0ZJYHHxzrPdIFP6KyMZxtAwF9r4hJd+O2Z7l6Lz4wfVv4fQMGvEsMxc2Aa3ydxOYXiY51b7isA6FzmsecrlpmOrTxN02PdY5t0/Nw0TDONKlWYVwwy7k5Ta67JVfb0PVBWXNjFrxizsqGB8sa6+EnxyM205STVc226XFe0hiHMZ69ImJvFFKoR2nwsREi8CkWZc9slLA/7sgSRds5EhlGEZdVz6oxHE1SbvTuOmwSIE00v3ZzSqQVxnpGWSDALrYh6mBVG4wN3RXvoDGeXDs6B9uu52iiUMazaXtoHL33jGLNcttS9ya4Pq8tnfFsO4MQnmkS8es3R1gPxjkuy9AR6HqPklC3PQg4Lxt4ErxTys4igbKzPFjU3NvLQoL1tmNdd3TGogWMMo21nqqzxLFmEisiJUKadm0R0nN7mlFkV92SMZ9ebMHBJFXEkaK3lnmmQQh650FAFil0rHAeeheUYLGWlL3F4RESjmcp91TOjWnKZdlxsqoZZ5oPj8ZMs5ht2zNOQ+F6Z55xse0437S7TkyMFxDpYJbXdI5NZ4iAREuMd+SJ4pbKSGLJhwcjJkWCLlvWdUXXW6wNQZi3ZjmxkvyLpmNTW6RyjBPJpAjJ53kaczhOqVrD2aZh0/bMshgpY27lweX5ZN3waLHmfNvxyXmJsY4kUiQaRmnKLJNEWocMLilZNx1uVfHjZMTNaUa9i+CoGsPx0YhsR6xWu47h6aZlsvPgWVYhGf54lHJ3P/9Crt63hd83YMC7xFDcDHgOX8eN72XcnXhHEO1NkFRfmZ0B1J3h8bLmbNsFFVWicR4+WzT0znNnL792Sr7Cqu4427bkkeInN6Ysy5Y/eLhgHEccjFPyJBA4t20wDPy1GwXv7RfsFQmTVHM4ydBle12AjeKIT88rtBTcnqVcVh3W+13OT3BETpXkwWXF2ablcJRwb7eISBmKrqNxEgpDL1BSMs1jfqAkRSIpO8fd/ZxPzmuq3pBqxem65tPLCms8HkHZGlIdOESHecKqs5iyY1F2OC8QHqQGbzxt6siS0AGb5zF4eLQK/i/zIoywhAjH98mm5bJq0SJInveLGCEE2zZEG9yZpvhZymr3WWfrjnGuGaeaRAryTPGjoym/dWeGsZ4Hy5rGOgq49s8x1rOoWlBwOEqY5xEgSCJJkWgeLisutz1d78iisB0IwYNFjZSeURQhlcQ7T5QojiYZAJ31jBPFKIlweLatZZbH1MYEgvc44fGy5mLXvTDW8XhZ70I9NU1rOW8NvzwvuT1OmWcRSkg2jeHBqubYh+gMAUjgo7MtEs/eKOFoGly2Pz5fU/Ue6QVd7xB4ikhyvm45L4M6LNWaPFJcliEm42Cc8MvTNT97siHWiq4PJpHtNmSsJZXh573j126N+dGNCZvakMWaaZawKA339nOW9ZWMPzxodDZ0ZML1//T7lSc6GFR2BikC+fyLJN3fBn7fgAHvGkNxM+A5fNUbn9kVAlUbrPmNDSOh3ln0LmLAiyBJNi78m3ewLHsiJRinQSqspAicjbKj7+11ceOc56Js+OOTDQ5oYsXY7eINnMATugFKxkyLsEDlUcR7B/m1kmfdGOaFe66QU0owTjVaSIpUsSgNk0TQ2DB+GCVBZQPB76fsgmQcuD5WiZLMsmCvfzXKuzlNiaRkVW9pe0+2i4D4xZMNHpjtnG7XVcem6YmKwBPx0rOuerZNT9pF/MbNCZGS3L8sWdU97xcRqY64M4lYNgZESJHWKuyjlpKjSeCs3F/UpEpTmx7XOZSSeOd4uKz4jZtTfnA0BoKU+sOjMXkcXHzLLoRbjtKIG9OUSCmiKOzDsgpZSCfrmvuLGus8uRaMEknZ9PS95cm6oTaOItYsyo6yC9wQ6yKmmWaUR1RVz5NtR6UsUaxoOsOdec5BEUZIqe6Cx5AM14m9ChOV6toczznP6aZhVXcIBN6HZPLzTcuq7miMC3Lwdctp1XJzmrFfJAgBDy8rplnM4STlR8djHJ5PzkvONy3Hk5R/4wcHzPKE++uavncYG/Z/L9f8/HzDpuqJlWJv5Nl2IbTUuaAOO113jNOYqjNkiURIzaO1ZVUZZoWg7hwfn26ZJBE3pjl5Eswymz5wao4mGRfbwD+SAg5GCUWsqfugFrwy4oPQdU20ouzMdTzK1ffxVd/jIQdqwPcZw7U94KU3wC+68b3s9684Noudtf/H5xV5JMMNWEAWK9ZtT2c8tyYpRax2ChooEoXSgq5zO0dj6KxDAk4IWutJdyTKVd1xsmpxBCWXx3O6bmh7S5FoPB6JQCpBrBXCw2iXJv24rumMu/ZBORynTLMYYx2NldyYpizr4Ab7/n5Gazwnm5qjcU6iJVmsgOBjYpxDePjl2ZbRrtip2xB4OEo0k0wzinRY5OoQyvlgUTNOFBGCWIWFKI8kWRwxShRqE8I1e+s4WbXcX1Qo4fnBUUS+S/suO4v3UDUWXIeSMM8itMo4HKV05srnRDMtEsrGMM0DUVrUntkoxnpYlMEFON1FJxjriHSIhHA+cIsOPGyaDikVf3B/xcmy2Y1QYNNaHq0q7l9UqN3CWsSKk2WLcY4sjvAIrPOsmyC9d94zTjXv7RUY69i2jmVj6IxlnkfESvFk1fDgsuK9g5y/+P5+8Dequ+suYt0aqt7wkxtj0l26exorJkmEQPBk3fBo3dN3hmXZcFZ1VK3DOyi7MP70OyI3IgRWHo1TYi2ojeNHN6Ycj1I+XdT0NvBjjmYZHxwGd+RPzxvWXc9H5yGaIYlBa00SKYQXeA+t8Wyqjt55xqmid562twgpyTRUHYxjhRSKsu/549MNiZasGsXJekmhJeM0WB+kkeS9vZzDXW7Wo1VNaxxnu2v+cJJijGO5++55H7o9k7RHAvUzJopfl1HfFxVMAwZ8WzAUN3+C8bbBel/0+6u642QdLO9P1h2tMdxftBSRZH+cMt+lUI9iQdlbslixt+NIKGCcaGQa/FuaPoySjHN4D0927rJFrNjslCFlK2lNkGFHWnJZduSxZN1aOmeJdoToVROygXpryZOnHh4XZXedlHxZhRZ/Ns1oe8snF/WuWFLcmWUcjFOqLuQdyd121a1lVQZjtkmm0EoxLyKq1iBlGJuVraXqLGms8A6wno/OSpQMi1eeBlfccaRprSOLNI/XDb2FOJJMYsW6NdRdz8WmpTGOtnMoHZRgozQQdYM3jySOBM6F4ujBouG87MjikE11UraM0pi9PIzZrHUkOrhNO+cBQRFrEgFOeLJIUvWWB5ees01H3fWB07EOMvGjIuHSeRrjmMUh/+h82/LJRYmzgUz746MRN6Ypq8pwWbX0xlPhOBoJkihCScPZRnB3r2CURNS9JVKKsjf84smW9+YFv3V3xrY1PFm1bHpDEQfTwb0iuQ7gPN80oXjpgyFe03ZcVB0X64ams6AkjpB7VvWGVdXzk1uKprOUteWPyw1N57g9S2mM5bxqKTvDNItoe8co07SdoUg1RSI43fas2pBO74UklqGjw27UOisUx5MUgaTHMss0i23LqunxThAriZCSUSIQrcVauKw6Jmm84+RIfnEanJF/cDRilodcr6oNcRtZpJjkEQ8uKi62wZX5vGoBwWERc7pu+MNHLVJI3j/M2csTrPdf2ahvCOIc8F3CN17c/L2/9/f4L/6L/4LHjx/zm7/5m/zdv/t3+bf/7X/7lb//j/7RP+I//8//c37+858znU75S3/pL/F3/s7fYX9//1e41d8PvG2w3qt+39jQUWg6y5NNWFDzJGVZhigC46BsLO8d5OztWus3Z+n1qMl6Txorfnm2pYgVWsvrxeDHx2NmeUxvXQjO7A1FHPgFF2VHFinySNH2oQtxbx46D5umo911Ux6tarJIUnSOREsOxwmREpyuW5QS19yFZRW6FO8d5JR1sNavesvltsXYYKa3V8Ssqg7jPJdlKOLOthY8xEqwN0q43HQsqpaznTdP1YWoCSXhYhvcZtNEMY01SRwUQiebBimhSDWHozR0k3pD5x1V6/jjJxsSLQL3R4an/nGqGSeazgbjuNNVS6TVLhHdBp7PXCOEpzGWmfK7xHCLFJ69PKiujscJ6W6EWBlHHoeFv6otT9YdZ5uWIlFkWtL1nkXf0nWWw2lKrCTrqqPpLYZA5l11LeeVxXq4M085ngSui3ddIEBbz7rt+PnJmrNtx+Eooe89kZbcmqfc9BnLqqXeKep+eDTh9iyQaxWhiFmWhmXVIQAlJF47nlwEgu29/RH3L894tGyuk9+1d0glUQiQ7HyBQhaV6z2Xmy4o43zoTBRJxDjVbJo2hHL2jqrxeCFAhAJlNlVYJ5BKhpiQzmARzLOIG+OMeRHx8UXPVVBU1RocMM8D32iSavbyiHVraA2ksebDoxFCwPk2jANTreiN54+frMNnFjFSgPeesjX8/HSLFLA/SikSyXnZcSRDnpe1YRyX6GClYK27Vlal8dNb/5t2YoYgzgHfJXyjxc3/+D/+j/wn/8l/wt/7e3+Pf+vf+rf4b//b/5bf/u3f5qc//Sn37t373O//03/6T/mrf/Wv8l/+l/8lv/M7v8PDhw/5m3/zb/If/Uf/Ef/T//Q/fQN78N3F2yaRf9HvL+oeY31Q40hw1gWn1t7SGQDPfBTCHDetxXrPXZU/HYEB7++PUFLweNmwbQxVZ/nx8ZgPDsbXclxrHQ8uKtbaMMkjIiVZ1z0n64ZYCcaJCuqbNix8267naJqwrizndcdj1zDLNL0LnYltZ8ljyd29AuMcm8aQRBItNVIA3rOue1Z1SMFWGvaK4CXT9T2LquVolGEdOByPVy1ahoVO/P/Z+5NQW9M1vw/8vd3XrXZ3p4v+3mykrJTKVWmLUhrXwAMbY2zjkUYGgyWskTASAhmDwcaQ2ANjsEFg8MTgmccCo5pU4VIJF0KWVcpM3S6aE6fb3Wq/9u1q8KxzbkTcGzcjlTfz3gydBwIidpy9ztprr/V9z/s8///vDyglIuDOJ57ed4QQsU6Ro4iqb9rxzUncGTDa4aw4jdoxsa4LaiNp51pnjLJkIsMYKLWsYzofSQmKwvBgUXB9mGgqS10ahjHikzzOh+cZazXDlLmYl5w3TqzuJy2MNppFackkPr458k9fHn/cQFqF05bjEIGJZV1wfRhPzUjN03vPrpu4mBWMk2hKyJln247DMPJ82zOvCt69aOj7yHEU+3JlNWeVQevEy0OPQnHpxRlXa01lFZveczYrT4DHyIv9wH70tL1nylAZxeATU4iA6HGM1lROcxwDh2GidpZF40ApOh8kMmH0b1ZJIUb244SLCqcNPiXqUnK5zmeOMUSOg+flbqQdPMd+YuYcy9qx7ydA0U8Sb/HkrOLRUlAGi9KycIZX7cBqJrEf2ynglCScn82Enr2eFVyuKj66nDMvHS82A0qdohJ8pJ2CxDGUlsJodr3n5bYnw2lCBCjF4COz0jGGhEIazZzhxbanmyamIE45Mlx9IQPum0xivun14u3K6m39stQvtLn5r//r/5r/4D/4D/jLf/kvA/Df/Df/Df/L//K/8Lf/9t/md37nd37iz//9v//3+fDDD/lrf+2vAfDRRx/xH/6H/yH/1X/1X/2JPu9vQ30R2PfFC9LXAft+FuBv8JF0WhHEkNiNCWsUU8w4A92U8EGmM2kKjFMip/ylx7FW89HVgsermnYSMeq6Kd6EdoLogGJKmCwAvWUtY/zCas6bglmp+fR+wBpFzPBgWRNjJpK5WlVMPrLrPfftSGE0PiSu94l2iLx71jCcpj9jiEw+Y43infOaB6HkrCnYjx6rJE3ap8yDec2isbRDJKMZfeb2ONANkcuFox1hM4xoNCFlXu4nYo4s6oIP1yWHUSzqN+0k9F8NuyFw204sKgm7zDGRlOQEKZVZFo6m0vQh8eldS+WMuJ1Q1IWjLhMXhUxh9ibQTh5nNeezis5HHq9lIpER5soHlzNmheXRqsKsFP/bx7f88KblxbaX9GwUY4QyJGaVoSoc5Yk9lDMMXsCEKLhvxVKuFThnmEIkJcWul5vzu+uK33h3xf1pXVYVllf7jh+8OrwJtZz2PQbF2aygHaNMB/YDL7c9//vTzQkICLPC8MHlnItGAl437cQYJKyzKSyLwrIoDH1QOCuTkuADIUrSeTdGFlVBU4i4+jh4bo4ByKxqh9EQY+Y3niy4O04cp8CiMlQW7oeCEOEwynRvHCXsM+vM2azCOQ0psR08ldOcNbIO/NWHC273Pd2UWM9KCqfIIWGMQSXFrvNs2okfvjywGzyVs9zse56cNZSF8IZizviYKZzm5jBwtSpJQRLYd33g0RKMlgv70Qdu9krWlk7CcJ+cNRijuD6MbLuJKeZvNIn5gwCfPiYBQb5dWb2tX5L6hTU30zTxD/7BP+Bv/a2/9aWv/2v/2r/G3/t7f++nfs9v//Zv85/8J/8Jf+fv/B3+jX/j3+D6+pr/+X/+n/k3/81/82v/nnEcGcfxzX/v9/ufzw/wp7yMUmjg9jgwfUF0WFjNonA/Aewzp2iDTTsyr+wbMaePAj5b1oaX+0FC+XKiHxLtGAgxMcbIsS/pvVBmm9KivuaCVxWSEn4Y/E/ABMcQaSrH1cmePfiINYon65rj6Hm+D1ijGKbIsQ8sG8v9cYIswZ1jSDinmULi9jDx6w/noBTXx4HWywqrKiTRuXCGJ+safSIRz0+xAD5mqiJiT89/08nNfD9E+XuiQhkICerSsO89Q4giZEV0O/shcHeYeHRWk3PmrLH8S98553Y/8vd/cMem9ZQO+jFy2468d9bwZx+u6LxkFimlKU7xE4VWjEGmJS92Pe0QMFZRWXMSNwv8bxyjiJedYTo5uS5OK8J2CqQMt8eef/rqSONEHHyYPF1IQgRMsGoK+ft85qxxXC1KEplpSkwhkXJiXllWShOBbgwnsa/h2MvErikk3V1rxegDZ1XBk1XDzX5LHyfOG8eT8xl1YbjvR253BYfO86PbI5/vOprCsB8jm9aTU4t5pFDA3XHkvvUnMrBhPyXO5gWFTyxPK6ajD+y7iUVluZqXXC0Kcs48D5IgvhsCg8+0vaebIk0ZOJsJ0PByXjKFxLbNzJ1lVAGfIj5AUIZlpXkwL5kVsu68mlUYA8YYHswNF4uSWel4tKqJIdN5z6GPFLXEmxgNN8eRz25bbg/S2C8qQ8yKT+5a3l3XNKUcJLbt+EbjlmKiKq1oulSmmyTSxIfAthMxuULciZ9vRs6agnnp5POyGXi8rr7R5PYPAnweR8996/9QK6u3U5639cdZv7Dm5vb2lhgjDx8+/NLXHz58yMuXL3/q9/z2b/82/9P/9D/xl/7SX2IYBkII/Nv/9r/Nf/vf/rdf+/f8zu/8Dv/Zf/af/Vyf+7ehrNEoJSPrRe2orGUIog1ZPLRfXkmFxNNNy4+uj9y0E00hQturRUVI+Q0HZ38WeLnrCVMSoq2CqBSlMTzddcwry0cPZFLw1cnNV5/bT4MJ+phY1pILVZcGssJa2LWeXRdEIKwUQ0j0IVJOCq00Uwzok4tFAVln1o1lNS+waPaD59AHphgxGpZ1waEPbI8TCXBG8QpOllzFg1nJxyedSQZZHYyB6kSRdcaREfFxbyK7bmLfe6zRPFmUbIbA/TBRHhVXy4rCGg595DglzhYFr/YD3RQZpiSTk5BIKmOM5oOLOe0kYuerRUU7SlhkTCL4DCQ+e9lROM2DRc2TdY3RcLkqGKbEWeMoCyPNrRbU/6EPxJj4vZc7PrtvmReGh2c166ng9jByd+w5Tp4QErawMh0pxYq97SMPz0qMkQZv005oG1gWjvOzhvOZI+dMaSTAsR8D19uB3391wMfIui7IKXFWW0I7EROMMVJkxTBl7vuBZSo4jnLDJcGhH+hD4NAH7g8j71xWzArN59tIzontMDH4keqkZzLaUjmNtZpV5fi1qwUPlxUxJg5jwGqLUol1U9KOAdTptQyJf/z8wHfPGq7mJc+2EnTZTYHPt5EQxA1W2YwzBm0ML3YjMSYmn1k2MhFrCoEiXp1cWdt2ovMF76zhclkyTolP7lqev9rzYj8QfWSqZbq5qAybNnF3nPjwckY7Bp5ue0JI1Eaz6T1NypxXBbVV9D4wLyS7qrCG/RAIKbFsCla1EW1cCBilGVP6iUPG101ufxbg87xxpyDWb7bifitMflt/EvULFxSrr0wIcs4/8bXX9bu/+7v8tb/21/hP/9P/lH/9X//XefHiBX/zb/5N/upf/av8D//D//BTv+c//o//Y/76X//rb/57v9/z3nvv/fx+gD+lFWIiA49Xgnr3MQlAb2UB9Sa/BuDppuV7rw4smoJZYblrJ/7pywM+Jn7znfWbi9JHl3N2w8TH1x21M9SlJWch5u77wA+uj2x7z689WuCs4qwpvvaC9ppBc98K/0VlKJ2mn+AfPd0whsS8dCcbtueds5oQ4eWup3aGi8axG4MkVhtN1mIJX1SCp2+cMG0GHxl8oio0OgjrprSGgwq8PIxczBzrRk7tN4eRbgq8KvvTv0dKayFLnpI52cQXtayFNq1MrgqruJwVdD4RUVSlQWdFPD23/RjofcBphVGaeW2hz4xKIgU+ve/op8B3LhfEJJOaD88bHq8q/uEnHS92AxdNgTaa7d5zd5woC01VaC58QUiWylhUIcJvlMD17vYjL3Y9D5YVOcP1ruf2MDI1jmVdYI3h8VlNRNxhMWecUVwtSxSKf/pK8pdWMwcx0xSK60Pk+m7gyaICJO1823sKoyiMIqXEx3ct7Si27F0/cr2fMEqxri3zytCOCZ88tTVs2yDPXSlCzNztpxPROjOSebrt2HQTH141PFiUnM8lJdyHxFkDdWHFoeQMfdDMnOXDBwu+ezXn5WFgNx65nBe82vfMSktpFXetaMi+czWndPokKA6U1uGs4snZjMoZnm86Xh0HVNQUVnG967lpJyqjWdYGZ2ZMvsAomV5eH3pmhWU/eD44n4kw2WoapzhOnh+8gsZpZrVjVjl6L++5wmlxGpYFx3FgWRpGrbic19x1nn3vuUueP/tkwapyvNwP/Oiu5TvLhlXt2A9e1pZObOkhAEro2189ZPysqJWvA3xWzrAbwjfOpPtZwuS3YMG39fOqX1hzc3l5iTHmJ6Y019fXPzHNeV2/8zu/w7/8L//L/M2/+TcB+PN//s8zm834V/6Vf4X/4r/4L3j8+PFPfE9ZlpTlWyX/VyvmTMpwuZAwwZQkgkChvnRBGqbAs41Md5aVXNyWs4Jd58lZgGavm5PCGd5ZN7zYjuLgWFRsuonnm57CacYpn0B4iuMQ8BKA9LUXtJSz8DtaTzd6EqDIXO8Gtn0g5MR5U7CsCz4404wp8HzbU5cWqxTBR0CBhRQz764rei+hj8tK9As3h4FNO1JZg9aaGGFSkYfzgqe7gVllGabAy5OzqnGap9uBVWW5nJXs+sCz3fCmqVnPijc3iMt5wc0p62pWObTyPN8PlE4ozJnMpg38xpMGEIv5cQo4K1ocpSS2gJT5fDMwLyyzxnA2K8gant61/N7LA9t+QmtFPwXu9gOz2lAbS2EM3RQ4DJpPbg+cNSXWKva9J+TMMAbqQtMNgc/agZtWtBljyOSYRWTde3yEX3204M88WnHRFBynwIvdwK73XC1KGuc4xolhCKdTfODZduB7ty1nTcHDecGichynxPPne5a14911zfV+pJ8S5MymlxVWA8ysWKqXtTQC89LIiu4oDrQhBGI46UBiYjcEXhxHPjybUxrF1aJGkfl8050aSMVx9ISUeH/tuFiUrObSVL/aDzxcVFijeLbpCRmWpcUtNE/OK3JSvNr3vNxOlE69semfzUpqZ+RzFCGrTOtl+jeGQMLxYIq0PmKNYVkVPFzI+rQ8uZ6ebXrujgKy/PhawIFkyEYI1ySxrzeF4511hT+5nR4sKsmQipnWJ4yG2lm+e7lgVgnk7/Gq4nxecj4rqAvDzWHkuh04b2Q9rJXiyVnFFDPDKUj0D4pa+TrAZ4jpG2fSfZ0wOeXM07uOppLrw9tpztv6o9YvrLkpioLf+q3f4u/+3b/Lv/vv/rtvvv53/+7f5d/5d/6dn/o9Xddh7ZefsjHyAcn569cc/7zWz9ppf3GHXjkj7HmQC90XLkg+ZaaUWZdfoKFqgfNtewGVVfx41NyOkZwyN+3I+pTG/dHlTBw5C8XFrKQu5ATZKElSnpX29LhyQZsVlk038fHtkcEnZqWhGwK3x4EXu55lVfDh1YzRJ7opMk6B798cOJ+VnJ04LvfdxKEPfHDeUDmNQkk4ZqGonH5DUZ5CJOXMlBIPmoKiEMJuP8Hcado+8Gzbo7SkWrc+MfpIp4Sq3PvI6AMpGfbDa2eVwhnFvLC8u6oYQ2b0AessRovT5qx2uMIQg9wYppjePK/tSQBqoiERuJorolJEFH6CB+cl3RD5h5/e8enmiNMKXzrRi/jM1A9YY1jPHU1huT2MfL8deWdd852HS1anTK1/eHMABcuqYNOOFAqcMqDzyS6dmWLk1x82/EsfXXHeFHRT5Lod8Snz/rkEj6YsDdPNcWRRWh4tSz4JkamLbPPAeeOYV5b7buJ3X2y5amq0NTgFBlnXKDWggX0vYZBofVr5CD9n8J5+DIQUUSgO44Q5NZBaa8gwTJ7rY2b0GaWgNIr9ECkLCWG9rAuGwMmiL41eaTX33UjOsO9Hdr045i6Lipu9TOomnzn6wH0vxObHq5rzUpq4fkocx4kxCEW5cYYxZJzWHKZAcRjQCh4sKwqnmdrIYQj8/vO9xFUA87Lg5UHgfM7A5DMHhKMTj4mQEn/m4VxWyTljraE0milGGmfY+CTXv5PmJgG//mDBfec5joHSGJxVPLuVcNL748SqdlzNCrRRHIf4M6NWvnod+Srg8w+TSfd1wuRhirw6DnxYzk7atrc287f1R6tf6Frqr//1v86/9+/9e/yL/+K/yF/8i3+R//6//+/57LPP+Kt/9a8CslJ69uwZ/+P/+D8C8G/9W/8Wf+Wv/BX+9t/+22/WUv/Rf/Qf8Rf+wl/gyZMnv8gf5ZeqvslO+w/aocecISacVhRaMYRAYX980RtCoDIad3q8L46av/twIQ6KIMTh5kQnXhaOwhoKI6Po9mQJbgoR644h8oPrA0Yptr2XdO55KanPJx3E4COrSlFaQ3maGuWU+Py+52JW8c5ZzQ+v9/Qn2N/tYWAMAj/TGj44n/ErV3Myirt25NP7jsoaxpjY9p67buQ4yrTi8aogZcXZzLGoSnyK/P6LPdth4qxydD5QGCPToEmE1TllslZ8fNsC8N3LGVfzwO+/HIhRAjGrQiZms0qzOU784ObI1bzkwbKitopNO5ISzCtHbTWbLrKoDIvCcjaTpvLTu47r40RjNFprCc1MUBg49pl5qdFZfi/73kuAZc7cHkee3rd4n/jh3VGSvtdZRMUo0AmdNedlQaqhtIp3z+eAZoiS4J5iIqdEipqnbUdtRXw8TIHRR95ZNzxe1pTacJzExXTfeq4PAy+3IzHBe+czyIrN6LFK1hPLynKcMiFnaq1YVg6lFK0PPFrVjCfEQFUojC55MC+5nJV8su1JGYrCyqRLiaPoalHzwaXhej/yfDeitOLBXCZr3395wFpF7z33R+H+VIXhvpsYvQZkkuAjfHjZUFnN831HCFnowE1i18o0pw+acQyUSqG0pjAJT+JmP54aNEuKifuD/F6VTjzdtviQOQ6eftrzyV2HNYrKaLTJ4r4iEYKEbS5njvXMUVrN//F0Q+UMl4vyDbxRYIbCVXowL2lKy+I48nI70GZPyokPLxr+z++fsawkXfy+9zxYlFzMHGPMlEYx/0Jj8zqc9jBK6OsXDx9Z8aVD0zfNpPtpwuTXwa+zwtIU9g364fXjfVWz87be1jepX2hz85f+0l/i7u6O//w//8958eIFv/mbv8nf+Tt/hw8++ACAFy9e8Nlnn7358//+v//vczgc+O/+u/+Ov/E3/gbr9Zp/9V/9V/kv/8v/8hf1I/xS1jeFbX31gmQQXcRhDOyG8OZi9nhd8YPrI8Ab4fGhF8BedbId73qP0wqlYF07fvXRgu+/Op7iDDTz0oJWzEtDJgOZ3eBpCkNTvs6SSrRjRKnTPUrDi12PM4rdELjv5DTrUyTnTM5QWY2PYiXPSvRaMULpLFbBq25kVlq0VrSjXKxjhl95MOf9suF7L/bgNMvG8Wo3iKAzJZaVJWfHcfInZ1imHUTEWlnNmDKllbXEYfDUJ+7Jfe8Zx8CLw0ih4eG8YFY4nqxqXuwGqkICK7vJE5PhOHjaKcmKY9vLKXr0+KSoS0NjLYtKmoxFY6icpvOJy0XJ4CM+CDdm8HIjzIAxiserisdnNbcHmXAYC1qf7O+7gVf7kSlK0rgyilkhwYyjz8wrSFoow+taGozKKGpnaUfRIpGgLjWl0Xx6d+TpfU8fEsvSMitHnNKEDFYbQoLrfU87RpzV8v7qJqrCcuwjx3Fg0Uhzq5RmWZVonWknz6p21NrywcWcj67mzIo7+jHS+VN+klZUhtN6BLTKFEYRjWY3eB6sKqaQ6YPAHH3OWKXQWiIY1o2jKTWvdgMXs4JHq5qX2552FEv+vDTknNFGcTGvAPmztdWMTtNOEEKmtprSyVozGY1WIkD3KVMWku/16X2LsxKSpbVgDdoxcHecyGSmKTNMiUfLkstFAUqs9v+nd9YYZd7ktRVaEaPo5LoxUDvF2aKh85ELJdqy26M0katZwegjLfDdhwuuFvWbz38MiX/ybEtMEIFCK945q3ln1dD6wGd33Rvo3/msQDnND64PFMYwq+xPHJq+SSbdTztUdWOk95En6/pL3/N1mp239ba+San8z9k+Z7/fs1qt2O12LJfLX/TT+blXiInPN53ckNyPR7+DF3jXO2f1T1x0Xo+d9/30xs751UnOcQy82A4MMVEZzeN1xXtnM6zV9FPg917s8SeRstUSaDhNkY/vjzglTpWUMovGkbOidsLfeHct1OKQEi+2A+mko0gp8/mml7iFwlJZxfdeHnh5GDir3SnsMRNT5jB6Zs7ynasF88ry6V2LM4ZP7w483/Q0pSGjsRr+L++tmVXuFEKo+X9+75bdII3Z/XGkKi0pZApnuJiXzEtDO0ZiivQhsWs9Q4pEn1md4hY+uW05nzs+vFiw7z0JsTyTobCah2tx5nzv5YF+9Lw6TNKEKCiMgPCenNcch8ToA5/eHPh8M+BjpiwMlVPorHn3ouKd9Zy6lAiCQ+e5aSfGKXF3HPAkVMpYrXnnbMa8NHxy354oxomUFJqMR5LYY8ynkFI4Dl5eIwMfnM/51Qdznu96tt1I4STo8mpeEBLcdyPtFOnGyOgj9+3Ii21PzvKem1cGZzU3B0kpX5Ty/kk5EVNiChBSZgqBcYoYq1iVBe0UiSTeWdWUzjKrLErBsjS8fz4HFJ9vjrzYiRXaasVFU+AR2rLWhnUpsRZ3h4m73lNYTT9GZoXwiVKC3ehP4mbFu2cV371asO0kr2xZulN0w8j1cWRVOd69qMX9NHO0g+f7r/ZczCruuolunOinRDt6DmPCaUl4t1pRWc27Fw2PVg0GxW6cmJWWmbVcH0ZyThx8oh084+RlBRwzF7OSeel4sip5uG74F94/IyZp+LPKArIcvIinp0Q8rX5TzpzPCvbdRB/iG1xDOwSu9wO/8mDOu5czrJbP/8c3B/7pywPvXTTMCscUZVX7cFFi9On3ZxRlYYgRnBGqs9HSPGulhPe0KP9Qq6PXk+V9H/BJgIPHIbCsJUPtm1yz3tY/n/WHuX+/bYi/ZfUHwbZ+2inIGg0xfa2ds5sS753P3jirnFZfwrcfR8+2F2DZrHCEJCPypjD8mYcrFrWlHxP70TNMkbLQLCuH05rqBBEUmnF8k25cWMWPbg+SHxSEVXI+d/QhkHKmO4li+5h4MC/48GJOSJm748jgk7ia9sMbuF04QcY+uTsyr8TiXDmxla9wvNgPHMZE4TLL2rGsLYUTQa4zmrq0tLsJZ6ByFjezlE5z3pRoxH207z2ZzLoWhkrMcH8Yud4MWAtGK45TojDCNRlTxqck1u8x8HDdMPlI6zPLynLbedrRM3jNWeMYPYzBY4zCacV3HsyJLw88HXraEKiN4WxeMi8L1o2FnOVUrTJTUMBJuOs03svq5mwm0watNevGMcXMGAKbdhQHXWG5mpX0IfH965bGGT64mnFz6LnZD9wdJkKCy0VJZTWv9hP3x4llY7Eqn6zsImAtrUFjKReW4+h5sQt4Mo11zKuCoojcHwZujxO/9rDi4aJhCoHPtzJB+M7lkoeLWhrafiSRUdqycJnCSMTAonbcH6UJL61i23nujxNTkKY95gw5E4yh0JofXR94tetwVvKjYoaLRYnVSgJCJ8/kCza9NLXb1jP5TBcDhVUUpuRipni+aTmMIzlLY2O0vI9XlRNHWV3ifGTyia7v+eFtz+gDClkbaw2FNRgFdSmE6WXt+OhyTszimkunaeHr1c26LtilCbSYAMYTmuD6MDAvHe+fFbzYD7w8dHx82/HxfctvPlnxaw8X1IXh6X2H1qJRGv2EOa2L/uHTDcvKcXv0knFWO5ancNjGWTova6q6EIDmtvV/qNXRT5vyHAbP9WFEf0Nx89t6W39QvW1uvmX1B8G2fprFE75ZU1QVluor3xdOTdHlrOA4Rnrv6YbIXTvhY+LXH895uKq4msue3vtIVnJzbsfAj27b01Qh8+owUBrDrz6YUxeWR6uGu/3A0Ud8Nnz3wYL3ljVPd53oOLqJxslKqw8Bqw0pIQ6hfqJ0MvUAWWHNSsuuj2Q8dWGYV5Y+RKxSnFUFKmasEbjdvHaEEPn0buTds5onq4YY4eaQuZwVXCwrNseRkOHxmbBM5HUoCTmhlOaysXSj5/uvjtSVph/kxlo5TRsSxz5yPnOA4jAllkPmtvVsO0/tFJez8k1EhDOKs3nBO+s5s9IwhAjA+bzg6X2HVYrKWVaN43xmuWhK7jrPO2clP7zuMCZTYPi0a8kKVqUjIhZ2rTVDFMjhWS0i5OvjyOWy5P7oZVpnNd4pNt2EuhNUwNW8xGlZY+YsTrZEovOJJmYulhU6n/AOWnPbeiYfWCoIITJOiVXhTk61xLGb2A6BwgamKP9oJULhoDXXh5FPfWSYAtooBg/BB2a1cIXqU8PdT5Fu8lTOUTuN0onDIFOJWWVx2tBPieXSsu0C2y5S2Ew/eqJSJJV4/2zO+awU2N2uP0U8JCYfWc8KhjHhVKYqC5xKxJhZlpacwTkR/tZW83I/sqodldUoEs92AyFmnIV2SEwhM6bIqnRcLjSmkM/UWe1Y1WKzvj9MXC0LyAJGvG9H7tuJV4eBfSeBsutZwbvnM2l8lGLfT/zwJvFs1xOT4tGy4uYw8qPbIz4k3juvhYRd2lMjpwgpc3uY+OF1y689WlIXklN26ALdEOjGwLpxzCvHrBTK9b4T2/yTXP+hbyZfFCZ/U83O23pb37TeNjffonq9XpqVhvtWoGnulE3UjZFHq68/Bf1Rm6L1rGAMPT+46bg/jpTOcFaLQPD6MHI5lxDEL4qcYxKqreLkwCotxzEwxkijLbPCYlYVf3YuF7neR673A080hAhWKTwJoxRTgLJSzErNk3XJrtDcHj27YXqjk9EKfIwoLPNCWC77fuLZbiQrqCvDFCSuobKa29Fz1ljOZyUoxeVcwj6tguATxzFyGDzrmdxE+yly9AGFojCRmAwZ4fOsqwKdJjaduLAuCotKMnqvSyuOpyT4/RQifTbMG7ExGy0ipNpqmkITkozxx9Dz9L7DaMX//VevWFQFt8eJ+3Zk9HDWOJrScV97pgRTkKDIMUTRUGjY9RHyhDnZ+q21xAyfb3pxUpWSf9T5wK6PDD6y7yaulpWstlJmPwRSkhXheVPweKUxyvBgXnDXjvzg5si6dpCkIXu57RhO+qDCScjjYQiy8lAZi+G+83T+SKEUCYmb6EbP/JTnNAWYQqKuNI/XNdebgR++2qOsou0F6Bh9YNNNkEVLdpwiVYLKgkrgQ8ZYEV4Pp9DLlLKszWLinbOKfnQ83fbUJzF3O2qa0nC9nRhyYmETx95z9AlNBq1wCpLSTDnTd6JZuWk9KSW6IRDE2ERI0uSMA+QcmUJiURfEkz371aGnLBU6K/JJqXaz7xljOsWJJK7DwLYfuVwUrBpHiqJVSyHzyW2PtaKBS2SsM5Azt0dZj5VGvZm+ABikeQopMSs0lbNsO4+zEkR7cxTmzpN1fUI6CDV8mOLPhHJ+k/qmmp239ba+ab1tbr4F9VV3lEYSqkNM3B4HhjFRl4ZDH7B6/KnsiG9i5/xp1vLXTdG2nRhOcLzFhSVliCmTE1SV4dl9j7WKWelOqdOBz+47LmYlF4uSlABVszlOHIdA5aIEBVrNqpaMqft2ZNt52lFO9S92PcZqjDY4a5i8pqkMi7LgclZiTc+ydvRT4DDIqqUq5OZkjLiSIopF49DA9UGYIxaFT4nJJ37t4YLHqwalwK5EDPvxzRGfM++fNywqsf7ed573L5z8HBqOQ+TFtscZw3tnDc5qjqOEO05TgkJRF5p9Lyu52hlChH7ylNairEJlWevsh0hp1I+FtFqfcrYygw/MCn1KMJfVRGklH+s4KT7fStRBbaAdk6wPJo3PmQfzCsPEzTHyYFkxrwrOGqFHd5PQf797tUBr+OzW45MQnC9njnVdSAZXhHlpUWRu9hPRQmMNs7JAaU3nIwoReC9rxxAzk0/gI+WJ51NnRRuivGdiBic/q4+JwmrmhWbbZaaQGEOkn4RWbYyiHzM6a5KCKWUelyUWJbwmHxhDprSWsznEY+bYv4710FwfJkor7+GmtJRWn973sO0CSgmAsPeBJ6uKh4uSz3xHDIonZ7VYsa1mcwhAoikdVWkZxogBDoNkVSUyOYvTrHSaofPUTizz+zEw+sTgYZ4USisua4n56MbIvo9cLcuTXs3w7D4zKwtmzjJzsChXfHbfcXsMVDctg0/s+xGjxWW2dO6kY4OH8xJnNHfHkYtFiTHC+XFmoikcx3FiiJHzuSOeGDu10+wHye7yIeBjZNN7lFKUzhATNIX+CdLxP2u0wldt5m/rbf2z1tv30begfpo7avARjezoz+qCqviD2RFfNxpelI5NO/5Ua7k1mllp+NFt+6bRMUYRY2LZOA5TwBjNTTvyzqpCKVlTWKsxWm5uwJvT4+WionSeJ+ua0mjaKbDvA+0kDhvIkvrde8aUGdtAyj2VEbeV0SWL2vJoWZ0gZwGVKm6Og1CR64J3z5o3YYurWhqhutTUhcVoRHA8K7BakVG83A8oYNU4Cq2ZlZamFNLxq/3Ii90gN3cy87qgsNK8+ZD58+8tqZzh+jBxHAPbtmA3ePBCdNVKbiIfXcwoneW+7dn0EwpN4TQQ0UmiF4xR7IfAvBT3SlNafMj03vOD6yNnTcHZvOTuMHJ7HN8Ejx464fFYrXjnvCHEyMvtwOE44YyRddTM8WhVyUpvilwsCmKUKAQdNbth4nY/Yq2mOE7spoBF8f5FxeAzL7c9Q4gYo7jrPCFBUpln257ipBPJJHTIlM7QTZqrRcnNYaJNcoP3Ufg2VktzWSrHMEVmTqFI9CGR+0iOsnYqjOGuH/nRzZEpRLRSjEkcc5t+Yt955qVlVhqmINqiV6Hn7uhZz6xEEEyR0grp+mJRse88YwqElPn4tiXmxLww3O0nbo+eV9uewhkerUqWlePlYSAgUQlTTCyVhgK8lwYs5Mw4ChZhmCKjz5RWURSaVWVPEMDEEMS+PysMzliyypzNHaU1eJ+56Sde7WTq4rqR3leU1tI4jc6S5H6775kyWAWQSVGai9o61o2lKS3dFHFOk3PCnnRBt4cRaz1zZzirHcdJcd9NvDg50kJMlFbzztmaR0uBCb7c95zPKy5nAmh8Pdl9fdC6ayemIM3pxezrSeRv6239cdXb5uZPeX0d8TOkxMvNyON1xaxyb74OX8+O+LrR8KYd3zRPhVWMPvFiNwDSJM1Lx7p2jD4ypUiBZtU4csy82A7cHyZe7ntCTCxqR2E0jTMURp0SxQEtvItulJtw7QzWaApnWFTiSrreDRwGz36UtcPVvGbTDrSjMGr608l4N0zsh4hREHzmth2YlZbvPpiJoHOUyIW7wyip4U2i88LjmZ00COuZpfeB6/1EypBy4tP7jrPaYY3i7hDY9YG748CzTc9+GIlJ8/is5Fcv51wtaz7bdMSUuZhX+JTQGc7nlsopfMyEFFnPJGzxwwczNAqf1myOI5susB+98HwKTaUt75zVOGtJOXI+L6mdZVEP3HcTWinaKaCOslKS5zgyhMhumEQ7FSIrHFlpmtJxuSo5q0v2o+fz+55tJ5lDISdyEgH07W6UEMzJo7XEA+w6z/Nt4DgG3jureLisuZyXlE6zHSY2B8/kJaE7Z0WlFVWp6QY4mxnaweOM5uG6Zl5arvcTYxwgGz5aV8zrktuTm6iwhsJZFkoz+EQ7ZXzOzMuSs7klJWHF3LdCaW6cwhhDPwpF+XxRsKxqLuaSYXUYNFbBWV1KkvkhUDkgy+Ps+5H+lArf+ciH5zUhZm7aiXllMSozhciPbnvOqkniLHLGWRFMtzHSdhODF1aNyhltDJClqVCRuijJObHvZYW5agourebBsmZdFaBP0zANz7e9TJN8YDt6uiEwhcRvFCI4fr5p+f0XB266gXUjGq6icEwho1VGkSFLMO6u/XG6udGaq4WjcIYUImPMOAP55EDsgzyHIcg0cNUUvLuuqAp5//uTTs4asdO/vpZsutfNpqyhU85s2pHvXM1PVvq39bb+ZOptc/OnvL5OCPzFYLwvjoi/CTvii6Ph181TYTXtKAGV+STQDSExKyzOaM5nBSll5pVl2034mLk7TmgjGoZMZtt7mkJWLtvey7g8ZVmhnG5QvY9czUsOg39z2rNGUxYylXq+7zHA6BMxii116TQxZsYpcHsYKYwBNdFYw9WqEHjbosBpw4v9IJMjrd5MNVLKnM0qjkM8rcTMm5tVYeXkCkYQ+6Nn9PFE0AUfEnfdxL71zCqLD4l/9GzHR2OgtIbvXR8YglB19YnB82BW4JxlO06kmJgVjugzZ6uC9+KMx8sGo5BATMQC/Ghd8eeenPFi358mReqN9ffzTY9Sil3nuT1IpMG81Lw8jNSnVcvrKcmunziMiUeLktJaxpBoCsOnh4Gb48j7Z3PWi4K7wwg5Eck8XlZ0o+c4RPaDJJtbrSiM6JqMMqfoiZLr/cgYhOJsraY5BVYehsCxC7QexpxZzQsuZwWXTcFhjDzQNYfRS3SGVtRO1qLnM4POhkVhIAsDaeYM2ghDxzp5f7Wjpy4NQ4Bh9Kxqy8W8YFlY1o3EhQw+05QFl3PFo1X9BnqoOGXc5YzWhqaAbvQYrQg5C47gZG9HaWaFxWoYg3y+UlIsmxKjNAkYfKCbEovCUFYOZwz7KaBzotQGDfgkDqmrRkCEhROBe11Z7nYTo4/cHD3dJPqYforsh4nGWYE27kf2vefFfuCT+5aUM5WLHHpFTppZZZg5S2Etg4/cdRMaeHdd82hRcbmoTm5BoRin3nPfDrx70bCsC+6PI2NEJmgOPrhsmNWORenofSRGJfEnp6nM62vF5/cd7RhZnNLOX0d4PLvv30x639bb+pOot83Nn/L6OiFwzAmnFNtOYF6v10mF1SwK97UC4a9WzDJu3xxHnu1GjBJkfWEU7eR5sCp5tGre6HXmp5iG77+UVcGDWgjDi3rOrgvse8+sdKBgmhLvXdR0Q+DVcWBWWJ6sa2pnuD6MhChpxkYpGa8XhrafeLHt2fWepBSlQfKOYpRgz9JwMa+wWnMcA/suosh8ctdhTkRljTQp7WkaVDrNouklysEnHi1rNl3F+xczLhclVmumEJmC5f4oK8BFLQGNd92E3PM0SkvcxLbzfH7f8Re/c8VdN/D7zw9cLBwhZWJKvGojBgHM1YXhcl7w7nnD5aLg3VXD59sOHzOXIRFjktygqxmz2mKOmc1xRAHz0r1B8Ftt2LYTu16az/ZwElE3DkXmOEasAZ9EPPvqKJOJ86agHQPHIZ40IkcwDYvKEqLm/jiyHzzHPsh0LiYSQJaVlISHKpLKdJ1njIlCK7SRoEerJdfp+8/39EFWMuva8sH5nJgUWmfeWdWknPl003PoA6mUxrOfMptuYlWDLRzkzDAFjDGsTrZuYwwznXFGOEqbbhStz5g4bxylLVjWEpVQV4bCGha1CKenyXM+r5l8oHSGx6sadh3doFidWHchwn4IrJTCGgVKUxeGZWM5DMIKEi2bOiXXA2iMSmAFftj5wDTJ76WeOR7MSpyV4FSloC4LSiNW8BgSIUfujhPHXj4v+3bCOk2IikMMPFxVXB8GjpOkqgvdW4T1lRMoZs6JTR+ZVwV/7t0V4bSiTQluj55FE6ms2OfnlaV2RpLtjSOWAixMWaZUL3Y9i7KArFhUjlXj6MaIVnA5r96sm8YgehzRL/14ihzLzKafGE+oh7f1tv4k6m1z86e8vk4IHGJmURle7UcWtXtDFr47jiwe2m98kTFKcew9n9x2zGtHXRhiOtGFndifQ0xf0utYo1nPC84bx6Ky3BwnKmsojBbw2SRrpdVM82hZ8+ogmTJNIc8rncB833s1iAbHys/Ye8/z/Yi1huUMfIi0Q+S2HbDG8v55Scyy6pqXmtJqro+SxNxNkrETFDzd9IQUZQ1VWZ7ed9y0nneWFY9WNd5LDIRC8RtP1hyHiZf7gftWTtTHfmLdOHyM3LcTlbWczRQxZY5Toq4sIWU+3xy57wM6K+4OA2NKlM5iTnC+wmq0UjgrOqlPb3rOFwW1MzidWFWWV4eRgsRnm5bffbHj+jCw7yOHUW5yq7pgVTt2/UROYg33Jy2QVRKOKhFfidIYSqOYqsSx89wdB+5bx74LHIeRPiRuj+LUebyuWTUFChH3pgwoxbJ2WKV5jf4sNOyGQLeXm92TZcW8sLxspQHTCoZjBKNZu4TSQovedCNkJdTq2lI5aZg3g2fdlOSU+N7LI92UqGzC6cgQxfYNIgDWWovFWqmTi8pzGCOXM4OykjC+qCWZvrSG0kdspTBGE2Lk/jCRSKSkWWvYDRMvdz1ZWS5rR0iR+z4wBmEkVdZQO8W8tBx7YeLonFnUmkMf8AGMgdrB4KEbAjllHhQOrWWaWjjDsrZobUhKkuTfOatEwJsjn216ulEmiEPwJBLXxyBrpLmItF8n2ddWtFf7PqARHtPgM0YHfExs2oEYEzknlNZczErWM8dh8LzYistuWQsM8uObA5/etWw7j1HwYFWdMr9k9Tz4SK1kAhuihNrOK0PvIy6mN9yrrzsyqa/9P2/rbf3x1Nvm5ltQP00IfN44dvqE3E+Cai+tYb6ygKyqvmmDk7JMcOxXBIHGCBsjZuHDvNbrjEFAXM7oE0PDM53EplfzgsdrOalbpU+nPiEam9Pj7wfPvpMU8NIalILPNx0vtiMP5iU3x5E0JpwryGliPwSWFVRO0YXI7c3EqhZdwb731Cu5tN53E7tOVl+lMRigjwmjJT9qOS84n8lKLIbI7XHi803HcZDRvTGas8YwhMTtYSKmxH7w9NrjA6ASWonteBsT205Oqiln7ttAbQ21NWz8hAowBkOImRfbnm3rZeq0EIHmq1MMxHlTcNcN/KPPjuwHmZ48XFZkBJ9fGEWzKNic9DWlkUT0VWnRRtEOgZwzD2YFU1Y4q6kneOUj23ZiexyZoqI7vSa1NWy6icpYZqVj7jQ+K2KIPL3vmVcyKSBnrg+DrAA19L3nYl7hnAYFq8pJltSmI6EoNThjWVYOnzMvdyOtTzxa1KxWJSHBxaLg/csZhzHwyW1HXWqWZYW1MtWoU0ZnJVEKU8AYRQpJJkRKcT4Tt1pdWs6akkziclbyYFnzqw81x8Hzvz/diKNo8Lw6TjRW8XB1EscrjVYWVMTZgraT5PraKsaQQGdWVYFWsOu9uPlOJGpvM4WDGODOZ0YfKZzBR+jGiclnFk3BeWVZ1DJhvD6KA+m+8xiV6X3ChyyJ8UkxlpZN27PvJdrhYllw6CK3+wljM7UxjDHiDJSuwKfEYfSE6EhI03k+i3y+7TmflRTWnGzehikmXu56utHz+XZg13neXdeEBLvB86ObIyHK2vniBHcsjWYKr4nCE8/uJaSzspp3zmoeL2vWdcGmEyDg67VUNwbOZsWbac7belt/EvW2ufkW1E8TAsec2Q2By0VFJpMSaC0nqJ+lufmqhTPmzKyyPFhWonPJkhp+OS9FMHkK0Htdr1ODL2bizCpObotP7lpSggcLaR6c1pyv5IL3xbXaa5KwNoraivbFas1xEEbO5VLWXGOUFc/2qFnWIlS+Pk6URgOJ4whKBZ5vZdXx/mXDujK82nd0U6KcCbsnhUw8TTdSFACcRnG1qokxc73vmGJmXjq0kjXCe6uKT+879uPEeW25PgiZuDCajGg0Kmc4ayxdSIQkWP9EIitYVJbJp9Ofl4nPFBP77SQTrcbxbNMxxMy765rPN0ms9jEzTBFn1IlSC+0UcQqOPrzhnHgFVSWPrbwA5saUWJYlkGit4bwp2LSeV4eByopVu7ESPDlFOIxCz0UpFqU0JST5PX1+K1Z4rTSmVNIoaknqDmSWpWPVFNwde5TSzKwmayWPh8JZmbw0VqISfu3hktFH/uHnWy7mjotlSc6Zh8uSu95DAq0Ui7rget/jlCIrYdJEoA2BKUYqY5hS4vmmJ+XEeS1Axdt2YFFYgdIhae7tNFKbTFMaZoUhhkyXAv0kfKidm0BlnFHownLmNL/2YMUYIjeHEUWidJqq0NztR7bdRF0a2kGmXPNSVnijT+yHKNoxDeu5Y3Oiax/6kXZM+JjxKXO7G2Sdd9KrJJ/IWVNacSEde5k2khObIdCryCIXnM8KshId2jhFtIE4Rc4baaT2fWDTH3kyBWpnWdWOd9Y1T4+d0HMyvHPWcD4rZap6NNy1ouk5n5Usq4L1TAIzY878k2cbfvflkdoZGmeZQuL+2Y6YMu+e1aeMsohI0QWe+e76bYTC2/qTrbfNzbeovsSIiOlN01A5QyCREkwx/FQo39clic8K2cc/WJTseo81iuJ0Yow+n9xDP3nRej1N+uy+Yz8Ezk5iQmMUt8eJjy6aN4LhL67VUs50U5RVQ2nf5ODUhcFqzThlCmsprTQRY0wYBfPScBwSRalxVrHr5IbXjhI3oBVYkog+c2AKmYvGcrUoGUKinWTtUSXF5bw8BWFaFPD5tuf2MIqFW2VKY9gcB56saz68nPODl0fSKezTT5FFIdbnu86fEsYVRmc5xU6JyiqmmDiLIjaNKdNPgVVTkLNimCI/uDnitGHynt97uZM4AaO57z2vdiNogcP9udKRjGZWOfqdCEwzipxlWlcZzbo2/JNnW0prCUkaosrCooTDoDifF4BCa5ky2QwhRZaFoywNF03J0QfmpViP/98/uKPvJyorYZqzUm6Gu/1EXQbu3cQHSaYyKWWmKM32EBJ9COio4UTyPasqQoJH64Z3jyMhJZZFxaouMdZzs58oCkU3RXbjxK7zPJqXLKqCwXuMM8xL+HwTOcZAYxVdhpf7kRAz75w1XM0Lbo4jTzcd286jtAAYU86ELtJOLbPS8d66ZFZIzMDdwfPuumLdSCxDU1l+64MzzucF/6/ffyU26yy5VQmZXr7cDoScccZgdKKPmcJpVqVj3ljmzuKMZVYISLGwhhgVrjBMvacLERMl4LU00IXMtp3wpyDXECOTgcfrGVc5EaMwZrbjxK71LGrHbz5Zsqgcm24iKUhJyWoywf1xpHCekBPvnDe8f95wGEXXU5wOJctaDhyL2r7JcHvtXAR4enfk914emDnLalZw6IWorZXiHz3d8n/7zgXfuZqx7TxTlAb+i6Ljt/W2/qTqbXPzLa3XWpyX++G0ihEAWkiZjy4a9Feam69LEn+w4KR3iaxqd2oEwpvH+Tp7Z8qSKl05w0eXc5pSRtLSYEW0khupRn1prTaFBDm/SQt/XUZr3j2veXbf8+I4EJMEbB66iaZ0tFOkcJqHy0qSmJ3YcevSkJKIN33OVM4wC4ZZoXm0qphVhk3v2XUT88Ly+DSx6afAo6VliIpXu57n254H85LzZcX9ceTFceLRquHPPlozLxwhydTnOHrIiU/ueqbDJCd/BVMSgfeDVYnTmetD5DhEPrqs2A2SzXM5l0brZj/w8jBQaMXkCw594DhIFIExmkSmGwTo1w7SFF3NHJ/dJLadZ9UUkrA+JR5cVlzMCz7dThyGSOMUOWVujl7It0nJjT4ha7FZgc4JYyxTSuz2kiPkg4DvrIazuePBsqTzgYymnSLeJ6wxrErLmBPPtiPrueHd8xkvtyN9CJgsImafIxdNwVldsJ7JiivExHld8GovVuJnu57BR0qnaaxm3wcOnQh3D1OgixmVI5XL9FhKZ9h1YjG/mFu00liliDmhlEy6PrlthZE0BDqfyDlD9MSs6KvIw7kwkhRQlYZlLXlZvjTUznDbjgw+8nQz0MfA0WcKZxl8QgNTzqiU6FJi8mCtorGWQGJRWs5qcRTOasOmnbiYV9wxcug8nc+smoKQErtu4tXBs6wsdWGZkYkJ7o8Txom9fl2LFdxHRU7S/D9sCh6eVWhthbUUM4dxwgxw8BEfoQyGy5msa99ZVxTWMHpxRL6eGHEKdJ1X7k1jk1Lmrh34x8/33B4mLuaKEAdxjCmJ3phi4tVh5KPLGR9czBhPESGlNX8kxs0/KxDwF11/Wp/3t6XeNjff4lrVBdtu4tVuQGtFZQ1VIbyQXT+9Afl9HSsHpOF4vKp4tKzY94EyxDc3pYuZOCW++CGWiAWZAPU+8mo/8HBRkbKGLPbXWtsvrcZer9VmhZXE6dFy33oGL1bTqhBg3p95tOTBsmTzuyOf3neA4slZRVMUGC3RClorzuuCpjCMPrMbDf4UVjkEOVU/XNSsF05WRLXjtz444zh6Kmc5dNK4Gav4wd2AzonPNy0+wn3vOYyiM7CIDug3h8B6VkKGIQSqTsIaS6eYFQWzQvNiJ1OpZeO4bBx9SFzM4WxW8MFlw107yQ1m8GwOA893I2QJNNzkiWPnOUyS33Q2K9h3ElS6qC27znMxLzhMkmR+1hQncaeiKjLLxvF8P3IxKym14ugjfZzAiyPpSWO57wOHKVGetC2lNWiTOQyRwgovp7AGZ05NpxMgnOk1m37k7uipCoONmTEK30TbzO3B853LBeWl4flmoJ2E2XJZOc7m8nstrYGTjV0bxdWyYArCZ7lpPVZlWW9qxarS9AE6H2GU6Z6PAe9OKIRGGEqrqmBKidFnaYZ3A01hTnEfGasUy9LSjYEOJQGWWvF83/FoVrLJkTJr9uNESpKaPij4/ZcH3IkDRM6EBN1x5Pm+J5/I4EYLOTqmgMsGRWTbKQo78P7FjIt5wcWs5L73xCTwv30fsEqQCK/2Pe0Q2A2BcZLXuXKakDOz2qGy4jBMvNwG1o1MPnNWvLsu+eDBnBAShVXoxpDaSNdLHsPlrGCIkXGM7EfPrvcchoA1CmcU+8GzHgJaK3bdxKwU4fMX+TXfe3ng/jjJ67ppGabIvHbMCsdx8jxZ1ZRGcb0fGU506K9CP7/a5PysBuDrpsm/7EDAP63P+9tWb5ubb3GJJkbx/nkjRODTBWTw8Usgvz8oNDOfHBNfhftNPnJ/GDmOARBLes6ZwSea0p54IIofXB+ZlfaNaLgw6ktU09fVTgLGO2vKU67NxOfbjofzivdOa6xlU/Dbv5p5eH0gpETlxNWSoog8d33gwbLEGEsIHqsUdSMaEE7aGlTmzzxecdaU+JjIGa4WBdvW87svtsQorhClFC82A92UOJsVJ2aH2Kc/uKy53k98fHfg4bKm85F+DNROwClPVjVGKUKGsgvkBEplIorVrOCMgm0/8eldhwa6IOngt4eB0hoezwWu93I74lMipozWEs9wFwSg9p0HM2a14Th6PrlpiTlzPi8l5buSDKtPrlte7AbOZoZ1U0v+Uc6sS0M4sVashmURGKbIi33PWeWoS4ENPlgU3LUShVEVRiZ+GYmuyBI2eXPsKY3wX5aNeQNw/N0XB/73pxseL2pmjeFdU4PKnFUlZWkwKKaUWSkxTpVKHG7zStNUlkzP9aFniqJnWpQFhYN2FNcaiHi4HcQddDkvWDcFg490QySrzF0/MnOWYYpvXF9TSpTOkFH0J1aS0pn74yiNYRIyduk0AWiKgkJDzJKHRpa4hJClIT32XnQ2hSYkjbOZQkkG2JQUi1J0VqUxLKuCq3nJ9WHgs/uOLgR6L8Lu/RCZYiaGQJwyh+RRQO9hVRUsa0tOWiZgU+KunXi0qCgKSx8knsKdDihhhKrMoMGd3FIuaXSpKDR0vef5piWfJlxGwatDz7ab0FlzsSj45LbFx8S6KoRf42VyVlr4+HZg0008WlY8WitpZELi2abnMHnqwvLuWcN5UxJz5sWuZwyRy3n1ZhL0BzUAXzdNhp9OWP9lqT+tz/vbVm+bm29x/bhp+bETCfgJkN9PY+W8pgXrLwiGX2t6UhLq6Gd3LdfHkaawnDUFEfh003E1Lzify4e4tJpdP+GziCH3vec4eH7jyepLJ7WvTo+aUoSP3RRETFo5tFaYLPTiy0XFvHQoJU1cPwn8ryw0q6qkP/SUznDhtLg8Tvbyyhl+/eGM81lFzFmmEE7ydv7eD275fNMTTpEQj9YVx0kcT6XRrJuSSOKyKbk7DixLx74P3B53LAvDopGgyNvjyNWyYlU77g8DRmfamDjuIjm3vH81wyBC3U0/0o+JeWlJMZFS4vlW2ESlhU0/MgbJWaqUYYqREGS9VjtD7SzdlNgchYVyOSsxRvNi0/HJvhPxttVkNJ336FPu0n4MtJOnsopVVaLLguGU5XS1rJgXjohM8H79UcO2mxhjxKJ5clbyg1ct3RTE4RMzwUcapzFK0w2R++OE0hL42PpAmTWL2rAqHVkZHi5kwjQvDIuZ4+V2wCiJukgZaqclfHVWEDNURnPfTqRJqL4hKQYfaJzhwcIRkdXVcUh0IVJq0RxFFOezgum0ejE6042BQ3dCCfiIIdP1gU5HVnWBU3AYM1pPXGlLRIS9KUWOY+Dz+47tIE6qGIXmbJ1kLXU+MfnMrLEopXiwKDlvBCRpjKJ0mi4kCmNOHCrP9X5g00pkRe00Q4KgoFCKwhp8yowh0Q2Rd85KujFy9vo92Ejch9KZ+y7wwYUcAH7lQcHT+wEfE7eHgW0X8ElYOJ/eHkEblFZvrPKP1hVOGZpVw7K09CHxw5uWzzcdj9c1t4eR6uRcnNWW87mjD4GX+4G6tHznUvLXfnB7RAGXM8UL1Z9gmZpN57k5jBzPIhcn4OdtO31tA/AHTZN/GmH99XXkF7kK+md93m/r519vm5tvcX3TpO8vsnJSFkfO19GCQU4mL3YDx1FuBgp4et8BmU3nGUaBoi1KR87waFnxYj/y++0Wn8BpzfdeHViUlqtljdbqS9OjkNIbd9esdF9qxMRy7vh829P5ACmz7TxDiCJurS1PViXrmeXuMDHFSDdFsk+sKsdHVw3fvVqScn5zEfzk9sg/+PiO+95jtWIC9r1Y0WeloXGKm3biIstqadtOPNv2/NqDBbPC8HQrK6Kq0PSTprBimf2nL3Y83QxoJSneYqOVnKhF7ViWQnfucuDlruP3r4/c7wcGL2LTdSPTrSEkcpYpkAqwqizOmTep3F0IJ12JIZ/cTXdOsek8503JWWO5OQqoT5G4PU6EDDYr6qKkmzJTmLDGEELi914cWNSWVWkpjJyqD2Og7Sf2U6IyYKxiaSwRsAZebHp2QyTSodBCVV4UzAqH0YbLRUlIItz+1QcNl8uKdpKMsNZLVILTmpvDgFIaRWbVlFzMaqaQeHrX8uowcL0bmZLoZQpr8Fns2SFnrncjxkjTMZLYdwKMdPOKqBPdFKmtrI3248Qwia05GcU4SoTC3XFkVhVYlTl2Hh9amt5QacUhRDaHns+3EypnvAxGSIDJmZAVTidCEujkxazg4bKhn7xkrZ0yz55ve5SCeeO48AWHo2N/WgmV1ohjT2WMltd+9JF9SCiVuWpKxhjxwTN4yVtbzwpI8Go/8mRdYpTio8sFl/OKIQRebQeMFgxAP05ctx6L5/dfwTtnM0ojn+vaGX7l8ZJ8SqtfVI6YEocucHMQJ+LFojwly4sGa9tOvHteUTnLbgikpFg3lmUjn9sfvjqwnhWUzqJR5AwvdgPDdKIYn1Lvv9oAvL4eFEYxhvimWfk6wvovyyroD5qC/ywy/Nv6+dbb1/lbXN8k6ft1vRb1Pr3rfiotGL58onInd0VpDe3o6aYg6cqFBDFe70fiPJMQtH1KidI5HtRCMN71E9+7PpBPlFPR68DtYZDRfMpfu8K6mFV8dBH4/32+5zBNzJysv/b99MYN9mRdURWa7U7E1Ap496zivfUMreX2q1Pm1a7nf/v4nrvWk6IIXpe1wxnNq03PzioqY6h0ZD+MqBPE7NGi5mpR8Wzbs25KSmO42U8YI+C6H7468Go/UjtNUZrTDSEypcxxClzOhe3y+X0rNzOjqa1FaY0xshKLKXMYZVVx0RjWs5LP73v2Q2KRFTHLmkqlzNWyorCaYYp8//rI09sj/RjwtaX1UNqMzaL/CQmuGklcL5zjrpu42U5UpeGsKsgEfDQ83fS82ve8f7nk4bKkLCznOvHx7ZGmsFSl46x2rErDMEW2fSAkUDnxaFmxamSy1VSGbgzctSPGGN4NSaIiupFj7zmOER+FDi3UI8VxinRDQJ83bLuRm3aSsM/BU5UOYzX1aXX5owzvXTQUBropMoRIzjAmxUJJM/lo1lAXhk0b6EI4nfChMIIaoMgQE90UqAuLKwsKIkYLsPDVYWQ/Srr86DMGyBpChlNvgMqZRVmyHeSzMKstSollXAO3h4lNN51CRDkFtSq2R09QisnL1KGwwmjiFPcxxIgPmUIlrtsRnzKoxMW8ZDsGksrMSsvVzHIcInmZsEZxtRSt3KezkgsFzzY9x6SYV44YI/fHQF1MvLOuGUKmDxKRkRUUzqCAXS/ruUWleb4bWc2cUIrrAgu8e1ZTFY7b40DtHBerH68vC6t5vu05n5enBkZRFZrdbuLj6yOP1jV1YVlU9s1n7nUDoDK0Q+DVFE7MHLkOFEb/VLfnL8sq6JseKN/WH3+9bW6+5fV1Sd9ftWbq08WjqcyXaMEA+gsandcnk9JpjJbVwHGUG0I+JRLHJIC/4xBOIsmB0mou5nLqG04Jzi92Az7B++cyqs5JdvNfJip7FpV9k0782oHxcFFxfz5y6C1jiHRT4vG6pioM7RT43qsDSknK+DBFdr3n+W7ER3j/pN/ZtCPfe7Xn1aEnBogR9q0nJYgpsvMTxmsGG1jNSi6bCm01wxh4vK65aUcOQ+BqbgDFtp24XJTMiwKrRspSU1vHvJR10fVx4NAnyAMz69AkfnR35OGioNQWZ+SGVxtN5yMJpCk0mt5nwmGkCwmdEk5nbnYDBgk4PF80vHde00+R4cWOIWbWdSEiT2vofSLnyBASV8uCd9cN//jZnt0Q8F6aEnu67vZjAia2nacbAyFGYljweN3gnBHG0BRYzR2lc+IIyoplJ5qVfT+xGyZuTmGWRaHop4TKiofLkl038f2bIwYoneHZpuPxsqZy4oQiR3GM7RI3/Ug/RpyWnCNj5OZVGI1VGpUTd+3Io7OKppS1ybxyjD4wy4reR7btyKw01IXh9jiitdwgsTJJm0IkRImmGNvAzEnyelLSLGoktVumiZrCRsgCtwwJ5nOHzbBsZD27agzKOGrrGHxiWTp8StzsJ/6/P9pwtZJp1qouWFWOurLMY2LU8TSNkwNIRphFpdNom5myQBlTyjSVOPs6H+inzORPTZQRK/nn2455aSWzqrbs2gmlxa2oFSSlqZxoX/qTlqZyIqJ2xpBT5vl+YNuOrOqCi8YRY2bfeeoiYZWmPLkLC6OJMVIYw5O15FZsOs84CWMpJmHwXMwLulHwDElBzoJHuN5LE1I586YBOIyeKUZ8SLjSkjK82PbMSsuvPJj/zJU2/OJWQX+YA+Xb+uOtt83Nt7y+Lun7p1UUte2XaMHw5ZHq65NJziKSfLmTTCYB0kFRGt6dOTJKmh6rUUomEemUU7VtJ1KCqhD+zOtR9XTC/vuQmaIQc+drS0rwat/xcjuw6UVkOTs5gt49b7jZjyzqTGUNKWfuOuF+aAPdJGLc83lBlaXxebHrCSnxfDMwhszMWfYh0lSKWW049BPHKVIZx+N1gbWGQmserSUh+tPec3McMcpwXjsKpzmM4qR6clZjdOZqWbJaCDG3sIbBB6zSTNGztCVZJ+6PnrYPDKXlOIy0U2AK8dRAJqagWFQFpMzLY0/KmkUlN/duirw8jEwx8eHljAc6o4AYM+G1lqjUEryp5NR8e/CoDA/mFZezisvZwHUY8EahlEw9xjBistiaOYmYnXM82w5sB8/jdUNVWPopsGs9zibm504s9utafoYEU4AILJxmf5wwzlJaxb6b2GTFvveSyYXm1X7EGEVlLLfHgSlIvMBxlOymXR+YFzLTuZxbqsJRWRHCK+SG+WhZoVGsG01h4OVu5Dh6Cq3xb/LRJqzWrJxhqxVjgHaU9WdhQSdpVq6PA+MpYd6cgjWHSQjEzgjJuJ/ESTZGSZ8fY2DwmjFmrHEURpFSkBTzmJg5iyszh3HEHhJZKRpnuFpU1M6wNwqbTooorQinJkRSL2QcWBrN2dxhsqKsLM5pFqZgXsHl3DKrHO+vG5rSkGLmk9uW28MICUEUICvE1gs/Z91YUO60BhOMQj8ljjHwbNuzbQOzSrQ5uymic2Q/eBISZ3HeVFgjup2LWBFiwlktPKrTdK6yBlTmYl7QlIZXO/ld19bwaj9QnJqRbvK8c9bwzqk52vWey0XFspb3QYiClngtXP/qdeuXaRX0TQ+Ub+uPt942N/+c1JcAf19T32Sk+sWTSWE1503BbTuy7QKP1jWPljXLkxB4VTkqp7g/Tny66dgPkbPGoY2iKQ0aEQc3paEb4fYw8u5ZjTpNkQsrgLsX257nu0hM0JTyU+x6TzsFIAt3Jya2ePbdxBDiyamlT2GQsAiJWelIWUIWbw8Td8fxNCq3fL7tYQCnReNSOsV7Zw2ruqRymmUluVqVkwbpvvXMS0hIMGQICavhMHocEkUxhMDNcaJEhKfHMdAUhqsTh0USlyUQMWZhEBmlmHykC5GUEtW8oioM1eiYgjSLVhuMzlQGUlY4a9Eq83I/UhjQKB6uCnKC7BN308RxmBhD4qJ2xJj4bNPRlIZIovNJQjb1aRqRgRCpCotLhsppxgjHMZNiZtmYU/5XpD+OOKOZlRqrLFPMvH9WM8V8WlVqNu2EzZ4UFZ9vOs6bgilKhpVWhmHyfHITcNoSUsRYTe8DQ4gssFSlIqFBiUC5ckK1rozGx8C7dc26Knmxm2gs7MfAbpDfibGK0ihmpebVNqKMoSyhLA3HMUrncqJTJ8BZeQ36aeJiMWdVWfZDRCH5TkZrfIiEwlBqqApFDDAi07uzugCVxTU0elZ1yYN5ybyyb9xWCc3VouDj+5azpmBZGrYtRJ14fFZROsWmnVAq004RDczLikSmnZI0cUvDbzxZU1qBar7+Oea1rG7CybXoTjqZ59uOm8NEjKItWtSnKcdJH7Q66YHGEPneqyPXh4HLecWTswqtNK92A6VRPFnXLCt3Yg/De+uKd89n7LuJzzY9PiQ8idJqvnM1Y1VZ8inQM0bopkA7eAqrWdaO0csq8G6Y+PBixqqWGInXRoimsMwrOeBkxBGWv7LZ+WVbBf1hDpRv64+v3jY33+L6wzoHvslINZyIx+czRzcm6tLy0cWcwQceLGuaQnDsctJSDCHz3uWMjGTWTCERc0JnmFeWdVNgtcYZiV14ei+hkvpE/O3GyPP9gAYeLCsqJ/t8oxUhJT696xh9Yl47jFbsh0BpNTHJKbU4UZElFVufNB2SYLwfPTFmrhYlWsGz+56Xx56cM09WM1a1k3G/1YQIxzGQcjoBzjS107RDFJhdSmSgGxMPlo7CQOwMV4vX+PtA7yMP5rKC0ipTl46QEjfHgUUpuqFjTPiQIGWS1pIKXhnOGkcfpPGaUuRyXnI5lwgFyAw+c7kwzEpDsenxyRCC3BDOTkDE2hreW9f4mPjRXcdhGCQ9uy55sip5vhmZciTFxBQyZYazxhG8vF5KwYtdjzWKmTNUVYH3hsZpnDHUhSZrKyLomDlPltvDBCS6Ud43PkT2vbxmWsNFY+mGRNtnsBGVMitdoE9usrJwuGjYD55Ky2k4ph6nNcuZJQTF5UymhiFEPt1PxAyzynBWldwePds+cN5Fqsox+YDWmrlzbPV0yk3jCwGjkBSMWjg/WoHTELRBIY1B1hmXZEpRG8foEmdNwaKxRAWbg6xUQubkvArsxoBGtCyl0/x6s5DoBR+4XJY4p3m5GTmbSRhmDIltH/hs25Nioq4dKiUMEtXQj5FSAyhmlaUdwxvxcW21fA6cZLedzR2f3xe83PZsfaLSivNVjT8RlD+6mnF2smy/f1nTTZFZYagKSZ0/Tp4pBFI2KKV4vK6F55NPri+juVrWFM5w305fIhMvSsdh9Ox7WVGTQGnFxaykLiwhJnovk5nXOWxfbVas1qBF5PzTmpVf1lXQNzlQvq0/vnr72n8L64/iHPi6keqidGza8UuPOSsM86rk/bOGdgpfDu6cOQ59eCPw4wKu9yO37cjuGLiYlXxwPmN5EhjvB8nb8TlRZLH9Xu9HuSkbsf4eTo3LspaGqHaSStxoQztK0vi6dpTOcOgDdYEEC2ZFyJkpBJrCMYaBEBPt4EkKZpXj0bJmXlrOdo6sMh9cNFwfJnyZqJ1l9JGb7cCiEjFo4xzn84KzJnPfatoxcblwPJjXrBvHkCLns8j1YcSgmRcF370Qu+zRJyyGRZlxukApRdcHDpOwZhKwKAwXi5r3LytSVIyhx2jNoCLLU1M4hszlouS7Vwuu9wPtKCfidWPpfUQpsXZPKVNZywcXNe+dz2jHic5nlqWhcSXOKTSZQxe46yL9JHlYZyfReOsnfMg4a5hiBq04+EhRGB6uK75zsSDETFnoN8LyXSdNBXpiDOIsIkackbww42WtedY4bo7gM8y0Etuz9+zHRGMVh95TnOCClc6kUaGzYl2fiM4+sigdq8bxZx7M+X987xqnNR9czpmXjvUsoBNgFVkl7iYRrs8qaYZzlnVeU0KO0EdpSC6bgkUpIEjtFBczh9ICvIsh4XNCKy2TBZ0pFezGRNcODD4QIwQfOY4T2ovmRnLYjCSJVwVOaZQR8N9vfXDBp7OOzbGnsYqdj1QxM3OKQSnIwuaxBholE8+o4MOzGh/heuxlNZgt28Hz+X2HQojWhdGUWvFbH57x7L5nM3pKo0VjV1gqZ8VK3gp24PmuJ0VAS6bXui4JEYbTSrdyWtalgDWeB0tpVL5uWnFmf/z1eWH4vZcHCX49he7mDBfzkpAyvY/Uzvyhm5W3q6C39dV629x8C+uP4hz4upHqph3FrmskoThnuO/8mzTwwpk3ieCva9N5xjHQTpGYMvPKsqwN7aJgVZfUpYiQ29Fzdxx5b10Tc+b5rufZpmfyidELZr6dIrNs2Q+a2WniEZPoSVaNpZwSnRcbsLOZuhS3xv3R87QdqLTmyVlDYUVECZnjGJh8Yt+GU0iioSzkop+y4uGy4jB4bg+TxDtoCetc1iVayY1uNS8pjbBL/tyTJU3lOA4BJkWpjazW1sIpuT0M3LWeudNknenajNaadV0wTJFZaVlURqzUJ8fKoirZdYHzeSmQOeuoSuEFQeL/+t4Vf/79Nf/46fZ081N8+EBu7L/3Ys9hlLVcoRWlVRz6ie+/OjClRB8jysDcaokKqCxTToDoRFTOHAbPGBK1ldXTe2c1zlp8ihzHhEJErJczizYGTeYHtx0pJbQWHQ/A42UJSnKtrIJcWd5Z1SQkAqI08udjhu0Q2A+RTsOV0oSYqYqC9aLkYql5vK6prWHbTTQzi7aKj29aqkKdgiUhRpl+Xc4rtIanNx3bThK4YxKekNFQaBhPWpsofD4CsC4dH13NMUaTk0Acl1WB0ZoX+45DG3h0XmGVrNp++OqAOQWSxmTYdLJWOw6B5czgtCIrMCqx7Ua+92LHO2eNPN52YHec6GPidj9xtaqorWIj0acYpalOeU8XTcG8FEDeg3nF2bykMJqmULzYy5pVmpCO+4NnPbP0J8bP1bzk4brhIkZQCqc0V+uSq1nBzXFCKc2qKajdyPfvj/S3nnfWNRfzimGSdPnCaEafOWtEb9T3ieMg0D74+mnF669fLSpujxPtFGRFrDVns4JxirTj67gGzaK0XM4LjkP8Rs3K21XQH71+0Yygn3e9bW6+ZfXzgl998SLVDZ4fXh8kTdtJAvSiEivxfStJ1k7rE2FYJjsAr7Y9PklmTmE1227iejcwKw2rJrNrxX4MsKwdhZWb9uW84tgFDtkzntxC3mZuDiNTjCwqiw+JECMqy6l+UUmDcKsHjr3HGsNZXbCsCyqnuGtPSeOFIZ0CLC/nFZtWRLkJWFWO9cwRcqYymnldUJ9EGJMPKGtpx8ijleHhsmTfS+jgFDMXp9TkWWWZl5YpJp5tWnYHT+EMVknO1uR7piBul9JofMxs+pEp5DfTpKayp5tk4uVuJKeEM4pHZxX3rTiYEnBeF2iruTmMnM0di7IgIxqENFd85ypTF4rr7ch1O7FpA98/Hvn4vpMGKytCjjzfJGalwP0WhaN2jsF7DmPGpIi10sA4a+hDQhtJ/m4KTuRox307cTkznM9r3ouZH14fCTHhfWJeScO4rB337YjVileHie0QqJ3mYlHRjYF+jGidsWgMkd7D3XGgLMxJZG5ZVwUKxeebgf0YeLAq0TFzDANmlGiImBKTT3zaHdEb0ecMk6d2mhyjZJKNkUMvlmOAYZJ1pVEiLr4fA9Wm57sPZvis+c7FnPeuZmxaL47AFaInGiac1gwx0hiNM4rOZ2KM6Jw5+oQbJpS2+JSxKpIx9EHIy7WzpJzYdRJxoYDJex4sawqtaQrD/XHioiloSkskU5eWZVNyPi95vKhAwaadMFrxct/z4r6X5ldlhjEyxsw0Jb7/6sA7Zx6jjdjWm4JfKWdEFD5kHq4qrNYsSsfVsuTFLnEYApGBUmtmpUwMQQT/ZDifF3RTIsT0jW6IhTO8f9HwYidRHOWpSX11GHi8rllUTsCD7cSDRck7Z/UferX+x3FT+7bd+L9YvyyMoJ93vW1ufonrn+UD9YdxDoSY8DFxHD3tGH/ijQ0yBfrB9YHfe3lgVTvOlWQC3RxGrIYxJMaQ8EF4LJfzkqaw9D6w6ScUmlUDx0HSg1FQFVamFeG0YqkLPr1vTxlAFq2Ec9J6SfzejoHLE9X00MtN8HxeoJEV0vV+5D5MKCXW37vW892rAqU1lVH8+qMVrw4DlTWsa0lMnlcCPnux74VsbC1aQ1M45qURCmzt6IymC44nSuB87SRpxyHB2bzg09sj993EmCI+ZZZ1wZOluEi2ncfHTFNqGmconTCAPr/vuZwV3KWJ633LzWHEn1xSu1EaOqPBak03dJSFBFo+nNf0Y6L3iUVhKEvDj66PfHrT8WefLJgvjSD9vWDuf/XhnJvDyBh7MpnvX+95et/S+8gNivNZxaNlidOeZ7uR2gqj5HJREhPsO9FKhBQxRnHelBgDhZEIg4tGgHGvdiO7fqIuDMeDrKUqZ9jHiDXQecXLw0A6RVrsBo8PpwmO0RiV0SSs1YSg6X3EGcHjxQQZiYkolCRc//D2SPl6gpgyo49s28CyFsHxtg8MIdMNAWfFeVRYRVlarJVsteLkhAoZGg0G0EamN1VhKE4aj6ebjotZycOzmqt5ST9GqtJyPi/RAPeJp/cncKBPvDr0jFOmcYZUBI4e2tOqxWkYoyLEyOgDMUacEUKwxJUYLtcVo8+sGstvPFpSOM3vvdhyfwgcQ2BVFqyqgvtuIIRASon9EPgnz3aczRzWGG6PE4cxsKos2ynhfQSdySR2faQuZLpTOydiaRU4mxXMK8l0O5sXlIU+gRI1j5cV81pE8CGI2FcrxXpWMCssw+m9+01vJl9cIfU+cugFrXA1l6y6rx7GSmt+1sP9sda39cb/xfplYQT9vOttc/NLWH+UD9Q3cQ588fHv24lt77mcSWRCTPnNGxtEQNr7yLpyGC1CRXMKy3y2GSTHSStuBk9OMoFpSkthDMu6IAa5+dy2I4UxXM4LNCLcrKyhHxNGe/a957P7jlXtIMvKyIfEaiGZUJ2PnM8cv/Jgzp99sqKwmhe7gXGKgJLcppTQScbtj5Y1D1Y1+tQT7odA74XoG1LGKjhOAkc7joHOix7kw/MZ54uSF7uOl/ueV/uRlDLbwVMa+OhqQYyZ55ue4+C560ZmhRELb1ZsjiPtGFjX8tF6tKzofZI10BiYfMbHyPNNx3U7kVRiNXPsjoq2HymMvDqDT9SFpigMldVMMXPbTZwvSrTW3BwHSYyuDMYY4a1UBR9dKf6Pp3uckt/F800rk6kYebkbBXLnpRGzGhaNpZ0iU0isSuhC5tCf9E9JnCmPVhJ8Wjh9ihvIdD4BI3fdCBl+5aHQmq/3I93o6afAy+3IXeuxWtYy+94TE5ATv/5wzrIqGWJgvEv0U6BU4hwrrbyHyQptFWenaUHSmkzmvg08WpVcziybLpwcbhE7iSi4cvIZyfrHQmirLMFHNq3H58xZbelLQ5fjG0Gx0cI6ykoo2jFJ7ti6cewHz/5F4GY/iDBdIcL5lJlby6oqKIxCKc28jEwTjFGjMtTWUltFzBqNvO6gGSIoFVFK0/qAIqMS1I1l1wdWdeBBXfFkPWOajuw7zzHLhMYo+PSm5fvXezKKm/3EXetY14YuSNbWlDMpRKExj4EpwLzMrBqDVpmk5H1ZGlnhhhM8s3SauXa8d14DmnfOa8gKnxLJiGX8bFa8yan7OkfSFw9nwJcOaq9XSP0pI+x1vMrr+mUh+v6y3fh/3hOkXyZG0M+73jY3v4T1R/lAfRPnwKYduT6MuFMTVDnNcYyULpzGznDfTmTkhKaUEt3LGECJTXU8gf3WTcHNYWTTehalZd975qVMQWpnGZUA+mLO1NYIxn+QaAONCH4vZhVnp4nHtvO8OoyURvFwKcyLlKG0Bmc1DxYyupaSpuN8XtCf6LFZQVKZp/ct88ZChmMfuDmIFV2lzH4Qq/C2k7iFRekETT9FxhOlVmdxnuy61+P9wD4lVvX4JgPJp8jMWR6sKlLMDCHirCb4hKkVlbM0laVwiR/dHnlxP+BTOMHKFOeVo5u0NFomoFEkMiEqjoNYsTWZQktTOEVOULQkU6op8jKPvLNuKI2iHQNX84p10/HJ/RGzNbzaT4SYJMIhvcbYyzTj1W7iOO1YFPYUI5DRGXZjJOd8CqcUou3FvKCdIreHgU1WPFhVnM8K2t6zG8RhNfqMM/B823PfesYYaQqBLFal5aIpxc1TWf7Cdy+52Y/cdpL99HzTE6PHWIUOmnByBSmfuT8OOGN4sHTUhcYnibfovOQ67SfPpvcMo2FeGZw17DrP6IV8XDvNfe8Zo2SmxZjZj5HGWooabvaRKb9uamCcIi98z6JyPFqXDF5Ac49WYove9yNtN5JPq9hFU/CdBw27XujFOcM+TqznBctSM/jM3ieszqicT59LyaIyCvypuxqC5uW+x1lN5RTP73s2x5HeB/4/H9/ybNsTIyxqw1klh4ghwWUjq9Bn9y3fG6MIu0cJYl3VVlg/WlOYzK6T5uhyXqGV4u44ofmxlmyYAv0kh6FZUfBg4QRuGCKrWthSs1JyswYff6rI94uHpylKUw+KuhT44uuDmjUaF9MpHDQwL92bx/hlIPr+Mt34/7gmSL9sjKCfZ/1pfd7f2vp5fKB+lnPgi49/CnhmVohA9zgG5pXkHR0GTzt6YoS744hWitJqUoLDFNh3E4WzrBpHYQxWT2w6AXw9XFWnCRIcQxIUuzHctRP37cS8lJDDbT9ye5j4F97XPFguee9ihtad3NTIWCun9/NZSeM0xymyqH9MTp6Xlo+nlmFSHEYZ9R+GwBA8/+sPDvzj51uZ+kyZRa14dz3jrpt4uRvoQ+TddYMzipc7CRi0RnF76NGIuPZs5phiYlFZ3nnSsG09P7rpGENgXRfMy4IhJO6P4ogqCs25KygLw6yyFPEkWo6Ru4NniIFuDBijIcLlzOJz5nLhsDpzGCbGSTErFHVZsKiE82Od5uFSHGk3h567U+AgWrHvA6SW81nBkyxuk1lhuUFcMnWheLYJfL7pGMaEjzKpICWBHPrEooRl5ZiC4sGyoFCaF8eBlBKz0/uhKiy73jOEjFFii3961zKGTMqJ//WHt8wKS06ZH94eiUlWM+5EwfVT4jqMVIVmZTX3x5HtIA3dg2VF7fa86gNxVCgloa3DhHTAOWEbyxQihwGIGU/i1bZj3ZS8t5K0890w8WJ3gtVZg84QfOYQA8ZYjBatEifMgLWauig4jD3dKOnopZO1FXD6jIgu6tAHKus5jiP37cSrg0RHaODBqubRqmZZiZvoeh9RWnM1K4GCbecxY2CaIl2Sz0N/clylkFEaCm2xNtNNkVe7gRASfpF4vK74x59v+fi2wxqh+l4fAq/2I5dzx1lT8qPrkaQy/RAZc+bBzBJC5rYNGAOVNqAUF0uHypqYBVw4rxzOKGpnCSkz+iBRHHtZ4T5aFbwgkmLmybpm1TghO2d+psj3rh14uROnY4ySNwegdYlCyOQpy8Fp13uOY/iJ6fEv2sYNv1w3/j+uCdIvGyPo51lvm5tfsvp5fKB+lnPAh/Tm8XOWE0BICatlxJwSpJyYgtBIZ6XjclZycxjpUmJWaFal5Th4rhbFm9PW+azg6aZj33ruDgPdlGgnEYz2UwQSm5OYdD9EpuAZfCSkyKe3Ry5mBRfziriqOQyBzgeWZSE3IKdJGa4W5WlFIXXelFw0BR/ftUxBphOFVXgvTcX1fsRoWDcl66YipMy293Ian7JclBWk1HNzGDmfFacUcnhxGHg8VVwuqjc6n7O64PrQEzF8dDXjMESebY/sh4nSaS7nFakRns+jXPLeec3Htx0/ujnyat8DmVnluJgVfH7fc9d7KqtRWjGGBEpRO1g1Jeum4MGyZNtNFE4TsgDZdl1gCpnKSZaRMEAk+iFFya1qKstZXZBJJ51EZJgSPkXC6Xc+pUyIMmUIUb5WFxJg2iXRvFwuKqw6IfdLTWGFxeNUxiENJQmmFLhvA4WdMEosviEmNj6+WVHux4gzkSFIOrbW8GBeU9WGp7dHtBWheopCyU5ZhL0+CFwvRM/z7UTOiqo06JRQWlK4Q4bKKHqt6VLAWSH9DlEauJA0xERlHKYRzU+hNY+WBbeHkZQF3odW9F7E5fZ00Z9CxmjNFBMf3x5o+4A9IQeMUtyfGvbKaoaYqK1lVmauCk3lJDagnRJz4D4kFAq0koYD2PoRBazWBZURvlMXArNgmFLk/jjybDuwqB0+BMYoK7MpeK73oqfxUaz0TWmIU2Y3RB7OC5JS0sxESfisjCEhDXAmM/hAO0Gzqul9lAT4h3POKstxSjIpSv9/9v4sVLcsTc9Dn9HN7m9Xu7toM7OyWktHVp1j6chgbIFANxIqhAwCX0j2hVwgkAUGG4FADcgYLMkXlrHBxpbxhbFFGV/IlurCCBuhG0k+llyqyqqMPna32r+b3ejOxTf3yojIyMzIyIiMyCQGxEXsvf+1/m6O+Y3ve9/nhdNFwYN1jdOGMSaOZ5ZlXdztK2FiM6ksN+HfeLwVUX5vGHxiVomG7e3LA8fzQpyWu571vGBRFZwvRKN2vR/xKXE6q74UNu4vy43/8+wgfVynv/NBqN+rn+y4iK+Kmy/Z+iwvqI9zDrx4/GEINKXYt6/2A4OKWK0ZpwBDozT3ljX7XhK+Txclz7cDb191nCwKKmfYd56LXc+6KXBWs5zGUk+3A6va8dJRM7FoAvdnNTnDt5+37PrAvHKsKsdNp7luA49vO45mJSfzksEHLvYDrx032GlclHLmpaPmDtcO4rx4dNTwdDtADsxLy3vXLbed56gpKK1s6sva4mPi6uDxURKlK2eZ14bRpympXN2NlV5ez3i86XnvpuPesuZ0XnIYE2MYKbRmOROY2Rgju8HT+sh+DHQ+QYb764oMHDUl4yrxT96+mgq8FynPiXVjef+253iuUTmy7TyDTzSlI2XYTgLd83nN6bIgpEyIkZAjViee70ZKozhbltROsnhe6EDGkFlMeTxn8+mEbhUxGwrk9WuUEJE1DCHDJALOZOZlwYNVxeAD7932ZCK3e+nadGPClprH24FZqafojIGYMnVh2XSJeenoxpFtlzgMkVEFuiDAN2s0PvbsOk8/ROZ1wXUXmFnNuinwGRRxCkIVjU7OMPpMN4IzUtzHKXk850Q/eoakQRucjsSYKXSktIrDmBnHQF1ZmsIQlTinGqdovSTGV9aQgKYwjCHiYyYr6KMwcAojLqTb3cjR3BGDQPTUpD27HgPaZLQytL1n3TjqwtKNkdIaHiwqrvYdT3ygKe30PsMYIpUVQnBpFGOQ986qzOW2p/UBA+LQMrAfEzEk+b0B+phwJlJaISfnlCU3KsGQNCczEVA/XNWonEgZbnrJTtseAls18mAlQbL9kIlpZFYYjhcVuh0ZQ4GzAux8sh1YNwWzUuCdizLjydy0w50ZYdeJ62nbhiljTLR0J/OClNUdHTykxLevWn6uNJwt5Hq+t6yZF5aQM/eWJVXxxd+avixwwM+7g/SiiLxtPU/20uGtS2GFWT38xIqnv/hv0Ffru0Rin9cFlZJwSw69FA+1E/dQUxpuD56yljDC+cyy6T2Ns5TWsO/lNLysDClbfuZszmEIbPrAbesJMXE8K+9Sfu8vSxZ3XREoMYSYmZcOozMPj2rIWWi7IdENkfduOl47m1NaQ1VYXj02bAbPbhOxSubLKWVSyh+60I6agvNFyZuXIqLcDYFFVRBzIiNi0pQyN0PgvjUUxjCGRGEyz247YsoMIaCtjHGSNA44WxRcHnqu9gOvny3IyjObUPAKgQ7mBLWTFnE/RHLMXOx7TucFt53nyaZl3wcOk8VZKU1I8HTbo3VmVhgMiifbkaY0fPN8gbaKdkgYlafRQMXXzubcW5X8zvMdv/XswKrRPDhyVMaQtditK6dg6rIoFK0PXLcj7RCpC8PXz+a8fdXiA0gjLbNsHIvCUDnHspLE7D4EHq41hVXcdiLqnhWOPkqUQu0UV4eRLiSUcndBjEYrYlIoNLPCMARNUcgGc9OOaG04nTvmlUYlxeVhYEyZV44U88Kw6xQJRWUUKSlSThilqMuMs5rRy825KjRl4aZiX/RSGS1aq5hl5GIMZWGZVcABiFJ4zWrHGBJVIQV8zNK9q8vAvs8kMhlFSMKBMTrjU+Td2wPdAPMSrDYUTojIXe8xSsB/4wgvmorHs5LTpuDpbgAFJ7OC1QRWnNUFlRML9BgT71zu2Q+RhGJWiFbtMAS6UYjXvU+ElNBa3vtbn1EhojRUShLUtdI4q3HaUBUwK0QwPCsNOSuOG8e8KggpsX28wRiB561ngkl4/7ol5syicrx52bJuJEC3clYIzcYwRs/N3k/j5YFtL9+t3RA4mRXMKsvj257nO2FhzTQU1vJsM/D25YGXT+Z3r8+P0nH0Id91jUFclO0YUD+GG+knFeZ+GeCAn3cH6UWnP8TEYRSERlXYL1w8/aOur4qbL3B9L5HYYhr1fNYX1Iu57bIWAe1tO/J423FvXvGLD5fTDF4u9M6LvXM9QcPGII6aR5XjeCHQvoRspChYVpZ2FMtrVVj2k8A3Tswba2BdO0KGbT/io7hTZoXBGdFwXO56Hiwbqum5Xe9HKms4mjnmpeNySpn+4LitcIZXTxpuuoEUYT2bcO+tnBKNkuynnMTJYlQmJsVJbdmOGaUE4GaynB4Po6Scz0rDw0WNT5mbdqQpDOeLijGKbiWkxG0n+P5Ca5YrybKqCkNWmWebnt96vOWmHXnzYk8XE/cWFYXRdD5xu/e8elzz9XtLLna9nHz7wBgzj9YKg8IYw+tnc37ppdW0uSh+8UHLVTtyOq+IMXN1GLgZA4sSNq3EWzhjWFaOx5ued29arIKvnS4orOWtqz0uZLTOnDaVZH1pLRk/IeLHSAwJp2DdSIcu5Uw7ZOYzEZVvWk8fIv0YWVaOs5nFIxEVZwtFZTWltaQ4oLShcI57i4LzVc0YJh6KZrqBZ07mFTedp9Ay8gkxEaNok5zVFEYxhlFCLJWSkZGVvKmb1lNYwf33YyRrxbywlM7iJ9z/rC65tywop6yyk6ZgN0aWpcUYgfD5OOBTJoZMO4DRmZ2NHFURXSrWtaYppEO5rhzPQ+C6T9SFJanI2bJgUVva3k5OPM+zXUdKEiy5ri33lhXKwkvHS9aVFFpt7wlxoCkcVaFpfaINCXRGGyAo5oVmOyY0cs0MoxTKD5cFaIPWQlx2WuNTxmklnagJlFiXIuquC839VYWzDe/ddHRD4u2+xWmoy4JlZbk4jPzOxcD5rKSLkZQyrZew2nYMvHO1Zz8EFrVjCAGy4smmZVk63r5qUWS00fQhcTwrWFaG33zW0pSWs3nBk5vIYZRA1Ot25GFosNO29uMY9/ywwtwvAxzwx9FBksImsqjcFy6e/qzWV8XNF7h+kEjss7ygPjq3bUrLagq41ErdVeYvft+qdjzZdIxBWushZYYQOVs3WC1BkiA31O3g2XQerRQHH/nNp1vREdRiHx98pBsz92Yl68rwf72zwVnNvBRB8rooeLg2HM1KqlJxc4i0Y+JoJjbzXR8orDgt3rlumU3hmS82pqOm5BtnC759caAwBp3EIdMNoi3wMUn3pu05npXcWziM04z9yCtHNfNS89ZVC1NS85Ntj4+W18/lteasOFsUHM9K5qUh5MRvPN7xdNuzqizUTui6CULKvHPZUliN1hoUgvE/BG7bkdIZnJ1m2xMfyGpFVUhGz2GI+JQYUua0lsJtVRcMQezbr5/OKDeGi0PPYZDE83mpOV+V7PrIunGC+Teae/OC9y4PaKNRWvON8xnOKC62PZtO82BdsW4K0bSkTGGhj47VrKRwFptBaXjjWUtExjXXB7HGOytumdJkWl8QYmaIicY6doO4smZ1QUhQhSQ3/D5IZ85oYmHoRnGmvb9pJd7BICGWScZCAk/05CwZSY1TZCCrTGUdPid2baCyhsIqMpaKzKzU9CHiQ2I5txyXJRh53OmE+bed53zuyCnz9tUBH2X0lRUUBdROYzTCbqos//JLR3Q+cXUYuG5HbncDz29blJFNtDKG3kcqq3n3asBnGQ8W1nCzH9l1wqQxUTOMgcFK52LVFIwpk1Pi9uBJ0+uvnZHxjhOH4GqIPNn0AMxLx71lwdfPF7x321MYxYNVjc+Z9y47+ijZY68fzzmeF1RWc76quN7LgUMjBOAx5KkbYFkUhjHJyNKqqcM7BtGizQtWjSMeAsELLbt0hmebHm2Entz7jk0/UljLkZUgztt2pB09JEkhb8fIybzk4bKiHQUPcd0O3LP1j23c82mFuV90TtTn3UH6MomnP6v1k/Z8f2rWJxWJfVYf0Md9ea2RKIP94Lnc91MLPE8cGrECX0wX/rKynM4E4Adyolk3BU4rlM6URrOcSMT/7L1b+pDYdR7nDDElzhcVl+3IvBL6qdUirDx4GZu8ejxHK8WuEzGrneilZjpN7ftAYRQXu4GmsMzKD7dNXz2ZM68sq8ry1nVLNwTpwrSBnY6sKs037i95+aihKS3vXx2ISUS856uGnOHpdsDHxKI0nC/EtVFZxcmipC6McGEqR+EM3ifevWk5qgqMVrx905KT5BLdhMSqkSRon4QE21SOTRcYfcQaxb1lydmsFM3KrmdRWO6tGgoTeLzpGXygWNYCZOsFVf98P/Bs15MyWAyzAnJWrGYVD49mxCSt9nVVoBW8/NoxWiveu+1Z1w6tNA/WImJZ1wWP1jVlJQ6nfowcusChl5iNm9ZQO4UPgf0gN4NdG4hJYie0NWglxVU3iqNsXgmYMSbFSWOY1SX9GHEaoTIbzem8kK5gFLrxvUXFpg9YJaRnnwMhJOZlgcrC2bFKOhLtEGhDpB0k2mAcJYzzdO5QxlCnyH7ItGNiXlpO1zVZSSs/JAEIaqM4dKJPSlH4SU1paYdAjAkfoTKi5zJG0xSa06agKCwRz6K2bA6eq8PIGCMqKlxlaH1gjIFhDBzGzKySfKSEgC7npaFwUmh0YyKrgaOq5F96tOKN5zu+ddGxHwKFNsxLw6IuuG1HnLEs6pKfe9Bw0/a0faAsDGfzmqow3FvXvH974OClUH60rjiaCWm7cBqFpioc88LydtfSjZKvtmoKnm4G+jFhlKSjxwgPVg2H3vPtiz2ZzP1VgZ6y28qpWC+cdNPUlCJfOc3zzci6KenGQJ8kOysE0eT87IOldNyMPC4B88YxrwzdENn1nsqaT3Sz/lE4L18ma/cPuz7vDtKXRTz9Wa6vipsvaH3WlfIPuui/35e3HwRJPysdTWG43Pc8ue14sKp5/WzO4IVkXDvhi2gVv9MaDYlCG5bTCANEF7HtPZucuF9allWJD4n3Dy2vnc9JWbEbRjSaRGJZFhgtrh2lYF6Zu9TvF6nA227kejoZF/aFw2si4U4b09miZlUVvHzSiKsoirj06aYjKcXprKB0luvDQBsSR7XDKhE6rucVi9rRjYnjuaO0lt4HTufisrppR55ses73A6+czDialbx8VHN1EG7/unZYo4Tdk6TbZY2cYIeJBzKvLKXTLEtHiImtjzzf91zvem6UYduN+CyfZWUt58uS68NI5wMK2PUjTzcjQwjMCsuqLni2FRv6rLBcbHqe7wbpWKlMXWh+5nyB0cJU2ewHrncD7286Ysw8PwzUVrJ9APoQSGQaq9AG+pC42XkKpVnWjrYLVJVhKKRrU1iLQWG14eXjmtIZujGTlbBLauu4tzScLjyPbwYeHlXMSse4GVg1jvtHNcMQUUrJ686ZxjmUy6QkqdqzwpBQdwXNXAvfJcaE14rjpqBwhps2osgURnzei8Ywry3PNz0Xu46koBsjy9oxqyylVtz0AadAI2JtH0WPFBIYl2icBkR79M/fv0VnoRePMZFR3FtVDEMkK6TbFjJtn2gqQz9GCqOopmuqD4lEpnAlx01BylJEaw1NXXDUeEYvMEaltFjPtUFrEYv3wfPK8RwfBNmwmhUUWiIzVnWJ08I9ev18zqvHc8iJm9ZPFHEJQy2dpnaO/RAkV8sqNsPI421Ek/nm/RUzp9FY1rVjiGm6mSZyUoDmyc2B8AEwnw+RfhQ+07qxhKn70w6eNgSWVcE37y/YtEG6vzFy6AOvn81YNgWHIfBwXVM7831v1p8F5+WnoTvxeXWQvizi6c9yfdk/y5/a9VlVyh+96CEzLy3HTfkhZ9H3+vIehiCsm1JmrSGKyHdRO3zKGC0Av95Lm38+bdQvnufJrGDT+zutjjiCNOfLGqtfMEwsF9uO216Sux8d1TzfapyVhOfLw8CblwfmlWXwiZNFybwwXLeelEUke7kf6Ebhy/iUqacxTlNYrFF3G9OLE868sGSlSCnRVJYQM90YJWqiDzxYyYa6rB1DSIQYqKzl5x82LIqCp7uekyl1Wk7Q8ru2feDJpudsXvDSUcOskO7MrHAcRo9B0RSjMIKSZd2IffrNiwPRZ5SFYfTc9pllZehiZlE73r/puNhHtDYYDS+vG7oQuNgLGwelGHzgwbpk12veeH5gUduJKN0TUuL5bccQM1dNz7y29D4yKw3OyO//5+/dcH3wzGvDcmbZ+8Sz254xZr522kx0ZIVPkSrLyfwQIqTM9jAypIxNhm4qNE5nJWiFQkJUjxrR6XQ+0vuMj5kU4aQR+N2yLtBK8dJxw/1lwX4M/LMr0WMcNaXohZQmZvluKWsgwaYfUEpozycLgc+NUzTHelbQ+8D50rBpI4bEvHLU1nKxGwgpcbosuToInmDbdXzzrObsbM4bzw8860Y23ci2zwxRsDohw6aNjF5ghqhMUziOZpaDF3q0MUo6f0ajAY245+aV5mhe8M51RztmUAGtLZt2pDAi/v2F+wvJpWoHtn1gXhhWjWXbWW5bQSRUxjCbWQzgtKHtE3sXWNaW9axm8Jlnu57b1vPaScOrJ3Ou2oHeR96/OeBT5mI7cOgC944k1uCFU9Ao6H0mxcSyKKhtoiwc/Rh54+qA01CWmqGNvHm5p3SaVVWQc8RoxWGI0qEZJSZknKjGi8qxrgsCiUVRMMbM2bzkdFmilL5j2wwhMq/d1Bk1P7Cwgc+G8/LRPffFgTCEn9zuxGe5vgzi6c9yfVXcfEHrs6qUX1z0hZGuyk078uZ44Hxe8srJ7EMnm4/78n60OHlxuqmsnbQqQvxth8CzXc+9ZYUz6q6A0lrynF5sGOpFrlAf0Frsupe7nudbKU7eu+54sKo4XRS0Y+S96z1PbgdJC/eRfRf4nec7fu7hkrN5xXvXLU+3PWeLktJK7lDuQCsFKJ7cdpwuSlSGm8PAxb7n5jDS+cSycsxLSzcGlk3BqnbTSCQJwt6ou67FC5LqvCx4scep6f0dQmI/SHHnU6KymiFEoeNO+UWn84KiVzy56dEo3t8cyBlWdUnKQoB9+aSmdpr3tz0vH9dCf+5GLred2I9RNIVA41qfeHzdsu2F7vpwWdOlRM4iqD6eS7erLh2X24FvPd0ToxSqYeLfHHTgcjuwnjliTGhjmNewqCyLynJv5Zg5Qx8ykczRvJwKsT3vbAac0ZJ+bsV23A6BkMMk1M0MZSQrzfFMo5VhP0YKIx0XO410broBbQpmZcFRU3K6dBKQaRRMFv99N7LtAhf7nm6MZGQUdTK3lNayrixKi93ZKHG13V9YlrXjtvc0peH+suHZvqMdIsczISF3PnJvXU+uHsv9lWHXe276QHHb8WTTctOOQn2WHEjEsC/p4OMAMY2orPnGeYEzlvWsYN8Hnt60Yv0HQog08wpTFlP3bhqlDiOHUeOsZwgRnQ39IEDH5czRx3SnaXv5aI4PCmN6bvcDVWN4tGo4XRTUVoTPhYW6cHReRO29l+Kij4mQ5fv+/nUr3B+tuD4IIfg+wHQISjmRkM80K8tLlSVNad97H0gJgtGSLYVwhowGHyOtl67HvLS0IZO1aJQeLEtO5yU+MdGPp7EzmbNFSeMcY5W52o+gpJAIIeFT/kR73Wc1Tnqx5z7d9Gy6UYjkIRFT4rWT2bSnfPnX5xXi+WUQT3+W66vi5gtcP2qlHKIIHLWCbhTKZ+UmYer4HQHii5PNx315gQ8VJy9ON30Q8afWsO08z7Y9hZXxRwY2nfz90az8UJGmkNZ9P2lpblvPtvesZ46juYRfPt30nM1Lcko8uelpfURrxYmFs2XJ8y288Xw/neosv/TSiuOm4M3LAxFhubx5tacpZDxRGsXjm5a3b1o2nacbZWzWDRGzrujGyGFsOZ9XdCHxfD8whsj9ZS1U5sKy6z27zmOMltNpH0hOs2k9nU/T6VKB0jzbDQw+8s17S+4tK3a95+2rPYNP1M6idcnVfuBqPzLGnsZZVnXJy8cNALsxsawLDoOn0Ion257BJwRtL2OJHBNvX7doJdbgmLOAFbuem05s6TGLayaTGVPCKMO9VcG8KGjKF84ZTdtHooZEQmUZsW37wKL05CRjJI0hpyy+bi2f4XpWMIyB929aEgoi9N5PXBy4bkfOZiV+zPg0kpGHr6Y4iyFGLnYjhyGxaBy1leLt+WEkxUg3RK72I9e7gTElyIl2SDgnr2nTBXwKnM4cR7MSp0FpJXqWWYEzmmIMdCFz2470g4yC5qVl3we8Fx7NthvxMVNoxaouuNz1vBV6SFIgK6CwGRWhzxKi+WJJMkLGTk6ry33HrhPKs5oQ3yHDxUEyxhbGcrsf0SmxHxOJRO4zTkGHQpvMTT8ym0ZXuzGyqDMP1w2lVZininaILBvHw1WFc4bKaY6bgquDZxwj798IK2rfB6pSc3Xb0xSaZV0yKx3bPnDdy/V34goq50Rbtmy4OeyYFeJCu957YT5N2q1NP2Cx+CixFYXVnK2K6buXmDkZP58tKtoxcDpvyMCDdc2qdPgsKeuF05RG8AN9SFMH0U4ZcyPr2mC05mhmP9Fe91mOk1aTlunJVX831q6sow+JTTd+qS3PP64Qzy9aPP1ZrZ+G1/ATu36USjmlzNV+4P2bDqPUlHQtlmmrBXzmjP7Yk81Hv7wf7SAVVnO1H5ivZOxxOf3dyeSSeLFe/OwPFmljkhNkZWUMcbEbKJ20fI+bAqUVV/uBt64OZMA5zbqwVE4yeConm+VtOwpbp7asG2F0VIWhGgLf3vRse8/ZvKQuNf+/9275J+/cECfa7r1lwbKpaYfAO9cHlnXBvvdcbkeGGIR54iyFM1xsey6mAuLBSnKrfEyMUUZYm17YHigIIVM7zdtXB+mwLWvqwrAoLU+A57uO2jmuDyOX+5HbQ0/SmnkhepqYImeLitophugJGdooOH4Je9QopSiMYj96btrAunECmnMjOSduO9FHLSuLVUaKsJQ5nTkOY6TUMgarS8uT6wPOGGKEPnhudoN0vrLCOc2h11PKt2ZeKMaYeLrp2Y/SaRqGyBgDYwKtMiHn6ZQPRgnHRFlFVbkpHyixOUQaYzDW8PSmZddHxpCJSGfsth95sKp51kae3nbknIlKuh9jlsgNqxXWQMoaqwObLqCU5mReMnOaRObJbQtZ0ZSG05mj9aL/YoTnumffjzzZdrQXAWuUjMjIpEmHZa0lKylat0E+i4xsiAop0kAiGYaY2A+e0mme7wRY2AexSRdGobKgDJzK1ItSBNBZkxF3nNFSsGQloMB5IZ0YYxR+TPgoN+/VrOR8GdgPI+tKxsLZSz5X6xOHwUv3LGWWpSNm6Tg+70e4gldO5GeuK8euG8hKobQU7uUezuYVVsFhDJRJksysVRTW4kNCZ81uHJmVBpLmdFYyWClOks28fNywqkuO5g6NEsfcGCUnSivOZk7GV0pGQBKvIF253su/e7CumJfuzjTwSdZnKXZNWYTlr5404qzU3NHZv+yi4i9biOeXfX1V3HwJ1qeplDfdyNVhFACWEkT9vveUVlNOXYZyEgD/oJPNRztIi8KxuCfb/H5i1Zwvqzv7N3zk1GT0h4o0lWUDvdyNjDExL8TyvZySf1+IH08mvcRhSFTWCPrfRxoFpdGTWFU2NTtpO57uevajpywkIPF61/N4I+CwV05m7MfI092A1gqrDE82Pb9wT4L+9qOceFaN4aiR0+yuF77Oa8f1nYbFGsXpouJ6CvDspsRmqxXvbQ68eb1nXZW0o5Bjt13kncuWzRCZleIYev/6wOUhUDjQi4rRZLb9npwzL500DD6zqgybw4AzCnCczB2lNXRj4PF+RAPzwjAvHT5GNr2MGVKCZ7uB+wvRUmz7wINFJQGNw8ghSAGwGyLzSiBsYVQ4o3l2GNEps9QFnkjMikorNl3gZFHRlAa1g3FMXIcBkti4U4qMEQpnaZzlMHiUzhzVJVplSmNROnIYPE93mWXI+CBW7z5EFI5b5XnzYs+8MjSlwWgBPg5jpOs945gwBg5DFFF5TmT1AmKX0GQO/cCQEOu4hqOZ43Knue0jhVacLh2bTrhEBrjYd5TWSIQHkmVkMtRaUtwrawlFYvRxCg+dxlJKChtXGEqnpGPTj7QhsyqEMj3GjK0c89pQZcmJEE5RjdE9hVOsqgKfEkqPlMYwJMmVGnzCKsW8NixKN3UXA6va8P96ZX1Hu65KyX3qe/k+PNsPLAoLRtGUljR4tDJsh0AImcMYGENiOwRKYwhFmg5CUnw+Oq05bgrev+54b2gxGFSWcVJdGg5jxBkzmQIC5VSEVdZMFGzNorI4m7k5RB6s6jut3MVO9qMX+wB8NkiLz1Ls+p0ukL1zYn7XfvZDP8PPf/0kO72+qPVl/By/Wj9gvfiiz0q5QJ/veozS5Jy4OXiWtRQjOfOJTjbfq4MUYmIIkcIqnDEfan1+3Knpg0Va4UQomFRGpUxVCAxOM4l/jWI5c6wOJRe7HSiorGGIkZwyy6ZgUTuWteX6IBf1METevjpQO8PMSfjit57vKQ1skthv+0FO1TdT2Kezhrcn/cCyKlgXlt6nO7LqsrS8ex252I88243UhWVZORaVJHq/7hYMIXJzkGKy95mZc6SceOuq5WxeYJXmvU3PvUUp7qxtz6YLaJOIUbKfrNEyOjyM/PzDNY1L4srRkh5uFFRWAktvWs8QAmezkqIwnM8rbvuRm85TasXxqgIUzmoudwO7zhN9oguRwxBQU0fjfO7ITtOnwE07iEtoCwMZn8Su/mBRcLYoeL733DOKZVEwK0fG4Nl2gZgl7TuTYeqsaJ2JOdENcLU/gHKczSWx3PsMOnIVW27bwBAipbPcdAFyYjt4rvbw2tmMpshsQxTOC+KUewGq8z5yiPJdakrN3Fqu+xGnNavKMa8tF7uRRORkpkgxsfeZpncc1Zb1vOBJ1d4RqYeQKS3MnCWjIGfGlCkQ8GIxRvog9nAQS3/pFLXRZKW46QZK5TheVhK8qSJGR1KOzAqHoqCwCp1hP0p3RSzsQkMmZY6WExQxRm7agUXp+H+/dswQI09vBrZDxBrRt/Rj4tXjudCBgySZn69KHm869oNnpiVzrdQFuzHQDQJWfMEiOqocY8rsOk9hJbw0k/mlRytePZ1zPCvRRtGP0qUkKc6XNevKctXKiPx2GMiF5WxZUjnJh2pHz+gdShnuLSsKq3m+lW7WGCLXu57ztQjIPzg2+VFvvJ9khP9JtCg/qZbnnwan1497ffV+/ASuD37RCzsFYsaOi51Hq8ijI9l0vt/J5uM2go92kOzUOj6ZpU91aiqsodCKt25ajNaUVlNPkQPr2omNtZSRz+Mb0ZfMSstiXXNcO45nxdTy1lwfRvqYZDPLkjQ8xkxKiX2M9GPmPd2hUGxbL9j7RcHaaG4PnuN5SSbTjoHDGPHR8GwjnZmchc45Kx3dELBa0Y6B40ba6Bf7kcMQuL+sROxrA0qBUnDTBrTKjKPntlW8fXngredbnh0GdAaroDQKaw1OK24GLbEBVSmvtXb87kcrfudiz7efH9gPYht3WlM4oe6+fXUgk+jGwPGs5NFRwzCJIZXSqJzZ9+OU8C7fiW5MjEn0D/PCse08mzZSFZal0TxYN6waw7IuWE4RBpXVZBLLwrLrBJjXjwKjAzCWKT0+CrNEwU2XmBcBlR1WG6oSYogMPhNCxiA3kn0fmBUaq6U70A2SiXR7CKDEbQaS7J5BQh/JlFZTWcveZ7SWMMoQNbmQ4m7wnvdvReSsteYw7LmuDauD5el2pHbSJRLdkqIwmTxlOS2MnUaQMi6aFXC5E0FzYYW03JSaYcwCoTx2/MuvHPF029OHxL7P+JTxQa5FyfOS+ZazWuIbQmJzkCBYpTu+cbagKa3ACjOcL0revekYUxYuUkhctiM+BnyMKCzOKmrnGCIsajd1RyxGi+Dbh4g3mnaI+BTpfCapyNxZnLM4JQ64h6uKk0VJiJmjWcHrp3P2QyCTeXY7gMpobZgVmdqKk7ApDA/XDcvaorKIlwcvhfngI/sXVHJnOPSed24OVKXh4Xr2mY5Nvt8I/+O0KLPC3BHXPzqS/0m0PP+kFmVf5PqquPmC16dRvn/wi145w7opaErDqrJTlpBFoz5WnPxpRGmfVvi86Ub6kDiZlXfCwsMYef2kYVlXvHGxZz9GvnE251nZcrHzOK04W5R8/Xx+95yOZiWF1bSj6Bz+xdMthTHMC8WF1dx2CTfpF1bzkl0vdlqrNatZQW0Nx/OCm3bkdu+pS4OrLE/3PdYomlJyeQqriVpxGIQMmxXMCktXRp5uweRMN3pQAq4TqzGczyyFzlwdRm4PAwlwSjH4TNKwHSJrpZg3BTorHt92PDyqOV+WFKbkYtdTFZp7RyXnlDy/7Qghsa4LbvY9b910U6ERyRneeL4TlL8SSrMrDCelREM0hWVdORICs1tUJS8dSQDpm89bhiCJ3T5Hvn0xQtoxayy1kcgMpaCqNHkjXb/1rCTEQIgyauxDZlaK5XvfBbYHT/AenyRGobAarGFZWDSwGQIxMtn1FZUFCk3WkqFFlgJGZ7mx5hwYvHSBjJauTek0my5gTCL7QDaazQDtICOrunAURqMM3OxHrvaSoC4ia1iXBTFphnGkHTOLQrOqK4yFfRspCpiXFfPC8tazPV3wNJUU1X2IoCT/yFnDfhgJPkj8Rj9CzKxKQ86Jd6475qUCZUXbo6A0Bp8CSmUudiO13VOXFjs54/7Fs53kuhnFEAUF0BSGbRd5+/pAXWjO1w2lNmwHz1FdkFKPBgojhdV1O5KUFKOVc5zPNWNOPDxq+IWXVsSQeevqQF0Yrvce8JLAnpNc8wmebDo6L1lihdO0o2VdG37m3orXzmaMIXLbBoERaolieeuy5d6qoqxFE9eOkXnp7hLoK2fv9o0fdmzyvfbFjxvhf1CLUlnNdTvw5uWeZe04mQwPP8g1+mW3PP+kFmVf5PqquPmC1o+ifP+4L3pKkv78yknDsi6+Z7H0aURpn0b4/GJ01hSWamYISbQiY4xopamnEMvKSRDmql7zCw+UzP4Lw6r58PtQWsOishwvCtRTsbJqreW158TxoiJMzqmmkEyd108bmsJxCJFxzNzuR7G3D5l/tm059JFXT+a0Q+KoFrFhjDKyeTCrGGPk3U0Hk4nIGhmbvXcjTouQhBXyeCd6lNJkjNEczST36RZxQ5VGkZTA7k5mkmuklTh3ut7zm4eREJXcuDLsnOeQAu9eH4SanBOr0jFrNGhxcPmQOVtWrBtLG8TR1faeEBJNIb8np5GQIn2IlE4T1QS5y4rtwbPtR8gyrsgF7MeIzoqQAt2YWTSW80XFth252I/0PmBQnM0kWO9615MzaKXJKPbtgNWKsig4XZeolHm6G8mIZiqRUU5SprVRNE4zc0rGUs6itLzHt/ue1id8gMOQGMJAN2RSglsilVMoNaWZTx2Q2aLEx4zSGZUl+TsDKEvnk+hKrEblxHom+Wj7zlOYzNHcMgREuF1baiROwxlNGcR6fhgCOSfevx3ohoBOmRwSIWuu20hpBNKnKodSQr62ZiogFJRWkzxct4JUeLSqyEp0ZaMXoON2DCwnjU1O8Pi2pzIGa+0EuMy8dFSRcuK9m55MJkbBP9xbVZwuSnxMkqQeJE2cpDBGOojdGIlZAJF9COy6yPmy4Ol2uHOqrWpLXRo2h5Fv7yUJfFE7DmNgUVm0UtSFZVFJAOyu83ccrKvDQF0Ynm47YhbGzbIp6Kecuk9ys/lh98WPalFu21FiPpwhJulyf3R/+16uUZ8SJn957c8/iUXZF7m+Km6+oPW9ioyQ0vctTl6s7/dF/17F0Y8qSvthhM8fnRFbLWGJZhr5RGBWWY5mhXQZ+iChgX3g+b6nKTUPV7O71/KioLs5jBzPC+FxpMi8NMQo/9+OkZMZfO2kpikLtDUkpQg+8cblnpt+4LQpeLbpJteLuEXmlWXWWR5VorfRamLctJ5FYWlKx3FT8M/ev+HdawmiDElGNNZATondkDhfFFQuobOiPKoxU9ZWyglD5njuOF/WU5yD5fYw8nTbc7Ef2XUehVisnTGsZoqLfU9MsK4KzlcVdWHkxh0ltPJ05qhKCSy9OgyMMdO3nmXjJCGbRKE1u27kuvXEmFg2BftuYD9GKmuJZHyALnsZ/SgRxYYYSSkRQiIrLenQVjLFvnF/wdsXLWjDvBEwmwbakCFHGhTjaDFWM68MgxfAXTcGQlLYxlDETHDwtZMZlbM8ue24HTxGZbAanRK9h7FLFE4KmC7Id6s0GesUg5dA1hAj7SiAuXlh8TFziIGicDSFwAxDTKScmVclr54taJxh8JkhJK4PI2MSHU5dGnSWTKiqNHRDQimFUpFNH9mNPaOPbPsRayyLUpxdMjK1FLagdIrtEMQqHjKVK4CMMwlrDbt25O0x8XOPLIui4g2/56aVaIeQMp2PPN70dCHyeNehLWi0kI69hIEe1Y5N73ly6NkPkbL1XOwkO60sFCHBGETbU1rDo3XDcuaIUQ4YpTE8OrZYpbHa8/r5gpdTYt9HrvdCytY6s+k9+37k8jCSk8SQnMxLCqM4aoSRdRhEW6UQoXVMmZiElv1gVfJoNfvEY5MfdPj6aEfng/tMiEmcYc7cOaAKY7A6f0/XqP4x2as/i/XTxqH5vNdXxc0XsD6uyFBKs2lHvvVk4HQpwLrPOq328xKlfVwL+QfNiFXO8nyiwLyuW4+PiW3v8SHym092+JB59WT+oXbyS8eJxzcdz3c9KYlGZ0hygn+laVg0JYpMFyKlM8xrzaKs+e1nG8Yh8u39gS4EFqUlJM1vPj6wqDT9KHbfl9YN99cV7950FEZz2weB02lFjJlDL+ySWelYVYas9GTNzpSF4VTVFCajtUD6Lg4j88LyYFXzjfMF45SS7mPkvduOfe+pnaYfJfaiHyOrymGMaHRmlaYuRQSbsqIyii5A5QxKadZ1wbiKEoioM04l0UL0npykSKtLsWrHkEArhiA2a2cUS6fpfaBPkIlSbObE+zeeq33kMBxonKUwisY5rNK0fWA/eBRKRnlRCs0QFD4ljhrFkOV79s3zBe9tWi62kco5ZqVGGyid5mTuOJo5zpYN+zGyj5GysNRjp8qgYQABAABJREFUJBpLVQRCYrohC2APYO+hFlkwKUDU0sU5nzsqo7ntR3Rd4GPGe+HnFE7hk+J4JlbsqrS8cqL41mNPFxXz0qBylg7YGHEmYDu5Yc4rgzMOpUdijB8QghvuL0qGLIWERpxVp7OSdkhcbHtCTjTOURrNmBSltVN8RKYbpuwsM4Ebh3jXXUFlVqU4FlOEo0XB4D1PbztCypRGcd0GNm2AlAlJ8WwjRcBiov/eW1a8djxnPROydu0sSokV2iiFUoqrfU/KGacNpTOMITMrNFUphYRRggjQwLb13FuV5JS56j0JQQYYLUGjdan59rMdi9phjKbrvEAkfwhu1/c6fN0ePCElDkP8EIl9Wbq7fUYpCYCtnZkOHgqtwfC997efRHv1TwuH5vNeX71HX8D6uCJj23k2vYj7Ciu8k886rfazFqV9d/QDLErL0ZT383Ez4nbSs1weRva95+YwyokJxW6MkDP3lzXaKN6+apmXlrNl/Z3XqyUB+9mu49m2Z9lYHhaSvNxUBfNKkrUba6gLw6wqCFFurKUNaK2mBG/Z4MtC3yHhd12kn0c2B09MifUUHRFS4t2bjt4njpeOMCas0+z7xKKCRVmwqpyQeZV0UYzRzGuH0qCypirl1H3Teq52Pd1YURWOfRe53A9se08mAYk+RoKXtOvRZVAvQHOKdhQ32fmqpDKaYYy0XjZ2rSNtL79jNrGDLnc9LxWGQ5/YjZHCGnIWrQYkrM1oY9CTfmsIgTEI3K4dI4OPxAq5+atMUok3r1puOo/KMCsK+jHTxyiEZDWNSUNGOynCF5UjTAVZaSWR+nRRcroouT14xL8FJ03BaV1yUzu+/XyHj4pSZbKRxHUmFo1DiiOnM/2UJl45hY+R/TjiQ8ZYg8mRTTdMBZ6lNNLVuWkHTB/Z9KOQgydxeIgw+ERphYDdeQks1aNmWWZq67j2wx1nxkydwUJplM5UheaoLphXBetm5LZV0nFxCp/EDbeuDHVl8SniHHQh8vpxQ8qR33iyZ/TiLqsKTWMMVWGpCxHrfvuyxQdPiIJfaCrLsrRcjiPX+56hNCI895GzWcX/9xvH/My9uWAjdgMJCZ1dVEJ3HoNotE7mBb/1ZMdhFGdkVhpFpnGWRS0Or/NFxeV+4NCHqfjTzCvHqrLsB0EADD6xnklgbM6yz217z28/21EVZtK/fO+OyPc7fD3dd+xHzby030ViX9ZipS+tsKEPo4esOJkXdx2cj9vfvrJX/3Svr4qbL2B9V8ZJSux6CbMrjaX4gML/s7zIPqko7eM6MR/3Z3fRD1bjp/b+Wz5yNi955aRhUbq71/BidFY5TT9tqveWNWT454+3GAVHTcnRTECEGQhR7M9HM3luL37fUVPwYFXThYhGczIv+Ln7K/ogFtqcRh6tG4leCJGrdiSrTB8k/LP3ikimHSPryrJoHGfLivuLktdPZly3npNZiZtoxSFC6wPbfiT5zOXe46zg5Y3OFMbwjbM52ijev25576ZDq8Ss0szmkoU0jIk3Lg40ThMT/OO3bskk5lVJXRoap9kPiZDhpNKcLxreuz5wuNqz6wZiCBxGSSNf1pZV7Xi867i/qHBquulrhS4dZ4WhKSy7wZOUdFxmFZLinGA3jKQkVOhuSBQ2s+tHYk5oRPi8mOCBYxIdizYwMxanFa1PWKsZB8/mIHC70mraFJk5w8nMctMGLnaJ5czx0qrGGSEgx5RoSofRmv2U8n1uRG8UUuJyP9AGIS6PQXKQlJbiY6qfcYWmMJaoIjHLaHJeGIYgoyynwarMkDVGGZyV+OqBkZwbeh9RCrbtyM4HLIpsNT6laVRpcdaQsp+s2CIgt4XCaU0XB0xZopD4jUILL6lJifshAomvnS2ZFYa3r3t8ECF4oWW8l1PmuCrQ0xinLiyFMRRG8WA1kxt0L6LclWU69Mg4SQo8xeAjTWU5+IjPicYYSSCPmdEnfuHhnJ+/f8RNO/BsN1Baeb/7MdB5KVpLZzidOnX7fuBbz1uKKacthcS8sowxsjmMfP18htOKt65bchb+0r1VxcN1QzsEfudyTztEXj6eYY1i13nGYDhbTqOj+N3al4/uK9/r8NWPgW4MrNY1Y0zctuOHSOzGaGaFQSvR4B06z+msYFbKSPJ7iW6/slf/dK+vPrsvYH20yMgZ2jEItG1m7y7Cz+Mi+35anY8T8y0qsa3uJjLqi5n0rLB3p54XHYmqkBvJYQw82XSwgkXl7uzqRimebnsRGU8npfvrmptu5GIzcrYoaKaCaAiRyhpCkJOo+8ApS5LDHY/WM2KSEdH9VUVKmbcu9vhCOCdk2O8Hdp0A1mJOHIK8/103cnMYibWMtayCRWW47SSk86V1zWEIbDvPrve8e9XyxsUBp2BZWW66kd0Q2Peer500nE2aGBmFFdxbFuyGwJvPW4yBPkQutgM+SYL45b7n6uB5oOQmsa4Lnm16+tsDPsLJvECTuN613BwymxhwIbF0hgfLkuNFSeUs79/29CFgFKRJPJqV4uAl4dtheH8z8HBZcn9d41AYlXmsFRaB/YWUUSQ2ndB8rbOEGNFGU5nMonSYSQw9eBH4kqSj1KdE20pquXNih+5TJmbwMTAEzeUho0lStISMz5E2KMYusigtJ8sKdiPv3rRc7Qcudz27IRIEv4LLonFKyIaVktjitco0zrCoC47mBSjFvhWy9BgSOSfmpaZ2hn2IjMFw23tuO8/ZvBAd0RRfsRsCB58ojFyQpQEfYVlaBpfY9hGdNKczh85iATdkmkKzKp0wlbSmsIYhwqyAr58vebCa82zb8puPtzzb9fhccqqlm3OyKHHa0PnAxX6U8FprJlhiplaOTRc4nVuWlcQ6oA0hSpjsZj8wRAFgLuuSxhpWMyduxKrkdy62PL7tOQyB1kdiSGilKRzEWPGLj5YSo7Efub+eMXq4PPS8f9OxaT2PjhtJeteZm7dv+fppzfmyoSksSknHdz/I4WtRGN7shLVjtOZqN1JYSU53xkxRLurusPaCXvxRrcuislzux7v9z8dE66PsLVpzc+gpnfy8mDI5i9lAK829Zcn5smQ/eNoh3XVsvpfo9it79U/3+qq4+YLWB4uMIUQMikVjP0QB/jwusu+n1bk5DN81f37jYg9IuvcHZ9LrOhFSprByapXTu2w4KUua+LtXLU1lYCINV04zTpvxEOLd776/qHl629NNLfkw3bwAblo5mSkEcX86K9n3nttWkrd7Lynmx7OCIQjq/7WTBjJSvJB5fNNy3Q6QNdt+oO1HtFE4pUhJQxL0OglKJ5bwPoiN+LqVWf/tYcRoOFkIkfV4KHmyaaXTUYtGatcHNIrjmaMPmU0rBeR+9MxLS11OdN8xSJt+2lxHn/EuY63oa87mFQnF49ueIWvO5gU3bY/KijZlnm1HtG15tGrQSjKTrNO820fprJiRlDLr2rFsDP2YuDiMnKmCunaczcQ2vh0iJoMzCmMMRstY8NCPJBQqgjaa/RiZmYSvZLsojZq6DVpaRiozRMWskFP6xWagDzLW0FlxGBN9PzKmjDGGXR/YtRLVsW4kQLUwMg5aVZbLHegMtZ3CLKeOjQGckfFcztJN8jlxc+gJKVIXJTmDjzK/MgY0hi6KIPeo0vgkY4vdEJgVhlVt6Xyk9RFFJmVoBwn0lFiEkXVdcNQoSqsJMbOa6NZmgkSerxq+XipihldOG8YxcXHwkDXHC8e+N0L5nUaFxsDbNz1Ka37ppSOM1mw7z82u5/lh4HRWcjKvCSnxrad7UuqJsWJZGa5bz7b15JzYjYmYYT03wIvIDM+bl3t+89mO0mohORvJINv2EecknuHVk8TXz2f0ozBrXjmZURoFzxX7LmCNwVrDvo+cLY24+MbAQysusHK6hvd9wGlF7SRQ82LfExLsupFy6kD/7L0FhdMo1N1hbdf7j9W6rCsrcSW9jJAra7i3KNkNgXbwkqdVSAbeCxjoCxK7mv59XVhC84MRG1+UvfrzCr78an14fVXcfEHro0XGceO4bgWv7/j8L7KPanU+bv6cUYxBLKhWi037xQlHTlzipokp3z0mJCnIRp94tu95rZwxKwWWdrUbuT4M6GncYyc9Rl0YXl7XtGMQW28hz2zXBR4dSd5T5yUYtB0DWinmlSPlTMqeTed5tukorOH+uuL18znX+4E3Lg68f3Og9cL+ebgseesy8ew2UhZiSa4tknVjzd37/8pxw23reW/T00yWUp8y89JJcrbSnC4MZaHYdYHL/SDZRSmxaiz7PrIfA4c+0oXA5W6gn8I3+xzY9YEhSBJzSJKxNKbAorCcLSr+pUdrnFH8PznT9Z79GGhHUDpR5szzHWgrbpVlVfB4cwAU56sClaEbIs+7gVIrZqUhT9+nJ7cH9p2jdFpyoDqxgo9eWC6kxM5nnE/UpaMpNf0Q6DxEp1mEJDlhRlMaOCT5bszKglmWk/Bh8KATpbMoJfwhhaIFGqsoSgtJ4IMGOPQjl5sebTSvnS74xj1FSqIpGbwQlw9jxKqMj+Ac1E4Rk3x/mJxahzFzGAeWlRVYo484Le6zCBgUXc4UhWbZWGaFRHKcLgpGD9f9SHiRtn0neg9s+sAwBonryIbew7yynC0rHqxqtkPgpWWJc5ZDH7jeB0KO3Bx6Kqd5ch35jad7jucV5wvpzl3uJdz19uCpreX8uMSHxJCyiH615DLt+0ihExmNM1nmc0BScniYO8voI20vAahGaS73EqlxMo1yn962ZKVYViJATyjGUZxjD5Y181rs3WQpZI9njreMZm4Nq8ZyPpdIjhASo5cR7DCloSsk0sRo2U9eP5tJPMiUSh8n99KL7evFYS2n/N2GCjS37cA/fb7DOYMClpVjfWw5akq2nec3n+54fNsJ3XwyW7x+MrsjseeUP3Ro+iQ3tx+nvfrHFXz51ZL1qYqbrpPAu6aRlOO3336bX/u1X+MXfuEX+EN/6A99pk/wp329uAhP5hXW6C+MYfBx8+eURFiZyXejsZQz7SCsjtpJtyKkzFFdYK3ckNZNwc1hZFbYuwwXow2bbuS2G2kKx6J2pAzvXB1IOXMyL2iSZTdGUorknHl0VHO2FM3KvBSs/m893fHouGFdF+KWyYpVLRvh1+41DGPm9jBy6EUjsx0CZ7OCWeVIKdNMtuqTxnA6rwlIYbeoHIchTpj7it6LJbY+qpmVlvNlyU0bOHRiNTUqE5O4M2KSnKM+wfOtQPwaZ0X/k5Eg0l2PcZaZE32A95nDIALf9dIws44xRCqruOk6vv3swG892/P+bQtZU1lxzWx9YNWIu+bxTUfMifdvegG4WRh8ph8j/Rh4b/AMIfGN8xlnq5KL7UCegpOs1rx6PGfXjTzddIChcZaQhRGScmLXRqwzNEpu+u9ftywrGS8MIdD2GaUTRmkKnSkKRySjk6ayLzQgiRgFtncybziaOXEGeRGTv3XdcdTsee14xjiWzGYlIcOyqTj0I2OE2UTYVYMAIQujOXgRtS5KxbbPGGREsk2SvB1CoAdWlUZPlOTeR8qoKZ1iTwAFs17z0vGMutBctz3doKiLjDOWwWuWKuIMZDRjyNSF4cGqZF4V1KVGK8d1GzieaVaN47Yb6Q+JECX6YD8I46UwisMYaUOmcJoYA9fdwD9844qvbRt8hm8/O6BU5sFRxWz6DGZFQVkYUoLLbY+zmtN5RTuOEzlaREmltRidebIRpIJRAjfc9YG997y/kXFo7Qyjt+Sceb5v0WqGMYCSrtXRvGBeWlBwPhOi8TAGBh9JCpaNhazYD+KYs0pxNi/ZDgHnI18/X9CHyPVu5LLtCTHxdNujlPCrvnY2R02MqA8ZKnrP45uOm9bz6qkkvu+HwDvXHe0oaeJGK9ZNyU07crUfMUAfK/IAldM83fYfa2z4fuvHaa/+PJxZX3WBvvf6VMXNH/2jf5Rf+ZVf4c/8mT/D7e0t/8q/8q/gnOPy8pK//tf/Ov/uv/vvftbP86d+fdEMg4+bP2v9IkWXu9HYtvM82/YUVnNvWVM7z5tXB55ses6XJetGtC7b3gsZ9wOC5G6MLOpiIhaLK+nJpmUYM5U1HM8KZqXhMCaMyZwupLB5sZZNQV0KfXmMiUXtOF+UlIXoEM4XDY9vWp5sOipnmJVi5T0ExcposoGTmeQ/WaN45WyBM4rL/UBOGWfhtht576pj2wcerioerOsp82lk03uu25GTpsCHyG70WKN56ajheF7yztWeJ7c9J4uSNkfsRJ0traI3mhAi14OnLTTBy0hBGSUMl1F0Ip3OvPutjmfbgavdIJtXgsIY1o3lupUE85gCqJLBy+fTOE0CNl3LoU8T/l8TcuIwBl61lq+dSXp2YQEtDqRN79FKoHd1LSnfRsmNRRmFydPPUorCGSHVHgLOKuaVQSvDGBOHMVF5AdYZK/qTPkSe3vZYpRhTIqXAvtdc7+WGFVOmtjLiuO1H+pQ5neIvKqM4AFM1Rs6KWeV4dFLRGMfzQ8+qckRg23d0Y5q6UQOlBbJETxQuAwajIaskUzStICa00bQh8GTb4cfIYZTP66h2oh0qFF+bzTkMnqvDSGUNLx3XnC8rsY0Pkd4H9r0U4yFGnm47btogwaBG8tC0gl3v5T2yUohcjYmuT1Q28OZVx/mywFlxxe36yBvDjlnhaGqH0YgwNsEcOF2UDKPh+hAYrWdVFwwhc3MY6fooJOIuwj6y7Qf6MWMt9NbQDWJz731i9u4txaua43lFe/AMk9C6KQy7zqO1YtOObFuBRC5rQ9tHzpYV1igKo7m/KjlqSvaXe8aYOZ5JzElh1JScLh1gKTmkQ/TBvSZnxRgiV/uBbkysajEUmKlT3A6BNy8PWKN5eNSglWLbjWy6wBBkvDlbG/ogbr9uDFzuR8aQuL+seOWk+YHsrxf7bWm/fyH0o6zP2pn1VRfoB69PVdz8k3/yT/gbf+NvAPA//U//E/fu3eOf/tN/yt/5O3+Hv/gX/+JXxc2PsL4ohsHHzZ9DFE0NyEkr5sjl9PcncwHR1cULoqkEebY+yMbjZcMqrGZZS8E2TC6p43nBph3ZdYF55ZgXMMTIb1/sWFSSjN32gaOmZFG7uw0ohsTprOB4XgicyyK0VR8hJ/ox4GPmwbqWQqp0dx2eIQj34nhe4GPgcu9JE6xtDJHbzvO10zmltSLw3PW8tKrRUzK6RrEoHONUlFzue44qx0snDWfzCmNkFPCtJzv2Q6ByluPasXCWN7cdY4gc1YY2GrbtiI9wb1lwtqg4mzsZVfjAxaZnDJGQISIdAIgcgsIOnsJomkJhlWVROU4XpViZs2Lbj8zrimWd2OwHlNHkbHhvM6DVhp99sBBHURBd1LbzHDovkQwhoSopMLsxYbzoX3yAstTMrGVWGtohsO0DRukpDRo0EZMU1pkpsDFxdRhox4jSmdpYrFUMQTGmgauDZ4iJWWGkO7eoaQfPwUeu20yplURohEDIwkEKKUmsRFT0OWKUYV6X9IPEYYQkAMLKvsh1UpKyjmbImcNkxV5UDqU0MSdeO65xRrMdAgk4mRlaD92YyHnkeFaxrAqGmHjtdMbprOLRccWmFXDd5WHk9jASk7jmZqWjGzPBZ4KLxChjyX6M3IZIjODmjsvdwKYPWA03u4FLNbAq16yaghQjdgpQrUslha0W0ezDdSV5W07RDoqi0Ox3iVRCaTQqK7LJ0+/N9D4wBuiDiLJnZUApK2NhJUXU29cdxkr8yKaTsexrpzP6MbDp/OQcLPjm0YzzdcWmE77N6azipaPi7mZ6XDse37TsBk8MiXeuOnzIzGvNcVPw6lGDMpr9EFk1UFvNbz3f4oOIgh/f9vgY+ca9xV1at9US+NmNkXkl3Ub5XCvmleh9lrUjZ3WHPXj3pheGT8483QhJHL67M/LjLg4+a2fWTyKf58e9PtV9tG1bFosFAH//7/99fuVXfgWtNb/v9/0+3n777c/0CX61fnzr4+bPXzubE2Ji2wnGPyZJHP+g8FlSkdUEFhuwGprCCp69D3ztbEbtDDElKiuPa8dEUxp2g1hwD4PEMowhMS8tB+955/rAUeMYoxRGPkackmwmZ0TAXJpxcoxohpC42PWcLSoergrR7mR4vGvRWZFi5um+I2u4tyjvnD8KzS/cX7KoCxKJ0komjRB2NW9d9iS4433UzuBj5P6q5nxZE5GASNDi5NCKmVNEEj5n9AROK6xF2Yz3IjA9nlcijEwZqxQ+JG66gFGZurCUxtDbDCniY8JHxVmjKErHshZ42eYwyvvXB8HOG8V2zGwjzDUoJQXSwUuKeeXEqRYj+CQjR0WmcIYUFcpqAchVAp7bhMC6dMI5OYy0IQmQLiT6MWO0plAKY5XYtqUpQlkoEpIMP6bM6cyKDb2LOCfeboltgGEMgGbdKMiastTEw0htLa5SHHxiCAlnFbshCpgtJy63HVpramvZ4QHFsrZUhSVGqK2hKgznpeP2MHA7RIw2GAMzJcnWY0xYpThe1fgIYxzwKcAIt2oADSFm1vOCeWX59rMdjzc9lTOYrKagT0VpLPOy4LgZOfjArs00NtOUVrRkMXDjE082EVDMC3luTze9dDB0pLGOi34gj1MkRU6sKhGny81RWEwxKRa1pfSKi1tFN0ZO5gXzWnN10GwHuWFnhOCsp7TzfZ+YNwK5qwszZVFpUoZv3lvwtdMZT3fS9VQZ3rrqePkE7q0qzua18GSGQMiZe8uSqvjO7eNkXvHaSeCfv3/LTRsIMXE8cxgjXZshSabXfhCS8m88ub1j61itCFGiTFa1nRyWgsjQUxyLUoqQPuxqckZROPkOHXoZYVXTaxtDYtuPdMF9bGfk44qDJ5uOIUROJ4nAZ7k+Dv/xIo7mhzWNfMXn+WTrU70D3/jGN/if/+f/mXfffZe/9/f+3p3O5vnz5yyXyx/qZ/2tv/W3eP3116mqit/7e38v/8f/8X98338/DAN/4S/8BV599VXKsuTrX/86//V//V9/mpfxha8QE0MQNPyXYb0YjT06qnn5uOHBqppgZpKbYzRCzC3Mh043nQ+EkHly2+ODnGDPFxX3lhXtGHjrck8GXjuZ4eyUXhxEjzKMkW03chgihz5ysRt4etsRMzy9bfmNJzsu9wOF0XKz7aO4ZrSA99656Tj0nmUjNx+rNc+3Pa2PnMxLfvGlFS8tG7Fi73v6lDifVTw6bsgpsqotP/9gwTcfrFg3jrNZMfFtNO9ct2zbICMZIzyd+8sSnxOH0fPbFweu9wM3bc/7Nwfevt5jdWY3Bm67gPeZTTtKMKRKPN97YoSyEFfZ1b7nMAbaITHmxPVBNAxKyw1HIcGYWoFB8pGyMZwtSla15tm2YzuIQyrlzLN9x9vXe652PSUJZ2AMmcFLOvXVYcROnJWr/SA35iQ61eN5yaoyzCrDqjZURnPwolPZjZ7N4IXOmyeooFKTG0aAek0p48bKWrRWFMZwuig5npeczUT7YMyU36MUjVXUTuOjYjcEnBPWzaq22KwotKL1gS5G0Ws44djknFmXlrKwgv7vB5zVHM9Fk7WeFZwuSk7mliFMRFsyD48b7s1KZpUhhsBN13OxaXl6c+DZbmA3BIyBl48bZk6zGUae7nsuNx2HzvNbl3veu9qLOHwIXO5Gnu8HupC5aUe+9XTH7zzfCRhwjCSyXDNK4bTiZNnw8KgQ+F4hgMsYJR+tscIbArDaUZWK44n7UzjNg2WFQrKiaiefv7NiO//GvYaUJTJjXjjmTmO0/NzaiYV8UcGsAmNk5FhZRVVolrXl/qqZRkYiKC6spfOZunC8djrj97x8wuunC9ZNgVbi+NKTU+mDe5fWikfrhkfrGS8d1RzNC2al48Gq5nhWsO+FsdP2gX/y9g3/9/tbBi83+JDlevY58X+/v+Wfv3fLs03H5iA8m9dP5XC0mwwF7RjYdR5nFKezAqNltGyNonHipDJa0TiLD1kOZTnf7VcfLQ4U3OEsfuvpjreuDtwcBlL6zmN+1PWiM96OgWebjveuO9683PP2VTvlj33y4uZFF8h9pIBxZmI15c/uef8kr0/VufmLf/Ev8if/5J/k3/v3/j3+4B/8g/z+3//7Aeni/J7f83s+8c/5H/6H/4E/9+f+HH/rb/0t/sAf+AP8F//Ff8Ef/sN/mN/4jd/glVde+djH/Ik/8Sd49uwZ/9V/9V/xjW98g+fPnxNC+DQv4wtbX8Z56UeFaZYPW8MXlcNHuVlc7nrOlzVGK673A5eHkcpq3rtuOZmXOK3RWnG2qKitiD/P5iXzyrHpRq4PI0OMFEazqqXDc2QNWkPbBa4PI68d19jSsW4cZhIjXh2kKNrkzJnVLEsRRjqjKazGas3pouTJbcfVfqB2hlnp+Ob9JbNKXB5VYclZ0YXA4CfBr5MIhaaQ2b33kTFEcoLferZj24/cX9RkEk83A9ZoZpXjcjvwL55sJNcmKbRJd8DBnKEdvIQEKPAhE1NgdJqQMovSUBpD5xOli2xbj9Gae0vHVSfgsTEmYogkpVk1htdPZqznFbWWk6nRirYXe6yPErbY9QljxX3SDRlMZFkaQk5En3HGcLos2HYBP46UheZ43rCuHTedZ9NHuiFz8IFVbclYbrvAtj9gSBglhVlhNVklDn1CG8WqtjSFYTME7i9LQpIbzBAERvfedUSbTEoZ5wSMGEIk5Z5rn9h2nu3M8WhVsh8T/ShBjocQmGmFcxqlDIXR1KVjZQzfODPcHDx9TKRUkLK8B9ZpBqWpnCGljEmKfS/v76zQ3OwG2jERgljeZ6Xi/duOdVPwM/eWPDqZ4ZykzIcsHYXgE2+NkS6IW8tqJVlbruDpbcvFfiAqSYOvnPCerFEEJFvqZKHRuWR7uGUIkScbSVLPU4fm21cH7o+RwoCPmkUt1vPRZ+4vLUfeAZnzec35qmQ/SCf1/mJJ9XTLoY8opzg7qpjVDh8Cnc9In0g6JzFEjJLOgTMGa0RDt5rJaHc3BJwRAXbhNFftyH70zKrvEMJfONiebHqAD+1dWcGykVHpphu5bUV8r1AS16Ey71+3vHF5oB8jKUs31IdIn0Sv40Ni04/kW8XXTxu+djpjVRcsK8d7t8LfyeSpMKuEz5Vl7AwiYDdZMfrIsnb49GHNIHz3iGjbe672w91rz5nPZcSzqgtu25Fnm/7O5l4Vmn5yLn7S35WTXEedD8zLzxcd8pO8PlVx88f/+B/nX/1X/1WePHnC7/7dv/vuz//gH/yD/Mqv/Mon/jl//a//df7tf/vf5t/5d/4dAP7m3/yb/L2/9/f4z//z/5y/9tf+2nf9+//tf/vf+Af/4B/wxhtvcHx8DMBrr732aV7CF7q+DPPSD+pYtoNnPwRe8Gg+Cum7s4ZnxaoSTkuIicu96GZO5wW1tbx/03LbjtSFYTmNuLTR2JgwRn1IND0rDRfbgTFGTmcFYxILs9WKR+satIxrZoVDKXi26blpRxaVI+eM0opN7+/C8VICNBNWPvJsO3C9HwRRX1suDpbjuQQp+pC4aQe8T3RBQHAhZkkvjpn9KNj982XJ7WHkjd1A1wd2gzhs1k3BonC4tWY36RK+djxj0RSMPoptvDBctwPrxnG5HwkJupiYxcSstKxnjrmz3LQjVmusVZyVBcvS0scd21bYK1ln1pXjbFlzf1kzawpIiS4oFgbev42g5KS+rCzeB+mI+YRSkUprFpUEbg4xcdt5lpVjXjmOF5LYHELCGsPP3m9w2vDPHl/zm49HEpZ7y4Kn2479MJKSZl2L0HteGnqfOIwjFjhflGQFqQskFIvSUVgRf757LTyianKKOQ27w4DPiluADHVhcEbhk4QcagvzykCXiEpBVMwrRVUYUsy8er+mtpZ23OGzJFwXRrFqBHwXc+KldcPZsiAlxW8+3WF0wqfEfhptoQ2FgsrAzRC5bj2bfmT0kdELxymQGSZg4DZMuVPO8NK6YVkX1E5zvTe0o+i+tn2kHSLFlNmllaF2in1XcNTArDIUUaz5Q4hkNJBQSGfSx8yiMbx20rCq5bWMIfFg3fDzDxdCYDYCALRaIiLuryrKY83JrOTtq5JvPWu5aTu0yRRac6UyfRdICXovVu1NJ0XxzS7wy19bc7GX9O8XHYTKGZal5dmmp7aG2XS4udwPQGbdFHdcmBd716KSUWkGThcVhTUfclVZBc/3Lw4Him3rGYMU8i+6fY/WDYvacTovmZeWVSPdrpNFxaop6EeJzZAMsMTj0FEXmpOZ4+kmsWlH5qVjVQuLKMbEUf3hMc0HR0Q+ZK4OwwdQF0qs7/HjwzZ/lCXmDMUrxw12EulbI/EQn+R3ffBQvB8EjSEaxJKY8ufO5/lJW5+quPnTf/pP85/+p//pd3VpfvEXf5E/+2f/7CcaE43jyD/+x/+Y/+A/+A8+9Od/6A/9If7hP/yHH/uY/+V/+V/45V/+Zf7j//g/5r/77/47ZrMZf+SP/BH+yl/5K9R1/bGPGYaBYRju/n+73f7A5/Z5rs9zXvqiYMlJbv4f57h6cYHctPLf803PEDKnc7lIjDM83w2s6nh3uklJMmJ2vTh6fBRo3qywHNVisxarb8XFvudiN0x5PaIHOG6KDzkRrNGcL+Tzum5HThYV237kbC42YWdktGS14um2wyrNxb6TzBqgsIbaWYyKXO2Gu/k8U/jgbetp+8AFmVWSjkw7BJrCknLmyebAezc9eZr9dKOkG98cPDFnjmrHsnGECNbISTzlzPPtQD3FJywqR2UspwvNuzcd58uK01XNthv5ned7Oh8YgoxgDAqlFSpkbg89zjScLyqawmKt5nhmuWoNOWYu25FhFLdJsgqlCr5+Nuflkznb3lMZw2pZ8fblntsuMassduKUVAaSUgxjwIdEMelgrltP7RImw7u3ezaDOL6UMtL1CYmHxyVMGp1tH4gpcdsNOFMyd4Zx1BxSQmvLK0c1TeW43PU0pSFFWDUOHzLFUvF8P1I7ea8vdwMqZ84bSwC0khsiykIWPc/JskYrICu2vZB0K2coteF4XnK19+w6T4riBIoqsTl43h87Yso8WjSo3PH+7SBjswwP1gU/+2DBelbw7HYg5sRV5ym0JJU7rUhKAjnndUnWnnbM7LqAyjBOoZEmSbdizBB9xKfIGKUDURcaoyvSFNKYgbKwzEsZmd20nmWRqEyBT4nLLuKMoQ8RHyR8Uykw2nK+KDmZV/SjhLo2lcNa6RDOSsf/5/Vj1rXjW89Ep7KunRwcOs03zizzynIyK3nppMGaK/7htwcaB1UpAuLzWWQ/SDewKUSYa6ZMrJv9iNUd9yeytDFSeNx2IgpHwfFMuidGwawqsObD3KsXe9cHTQmyl8lI93hmefeqox3k/YtJdHTOaLoQcZMzc1451k3B0axgDOlDQltrND6Jvf5F/MIQIu9ctfRjxBhoSoPVgmhIZF4/aTiZVx/aA62RfLM3Lg+0Q+D5NPaunObV4xlWS7bWZ02H/07HyN6JpuGTi4o/eCg+X4hj7XovmrvTWfVjRYf8JKxP9bn9t//tf8t/9B/9R3ei4her6zr+9t/+25+ouLm8vCTGyL179z705/fu3ePp06cf+5g33niD//P//D+pqopf+7Vf4/Lykl/91V/l+vr6e/7Ov/bX/hp/6S/9pU/4yj7/9XnkmdwVLIdRQHdDoC4NR03BUVN8aNz14gIZfGTXR9oQIYlb6WLXczYvmU3MF+COD3K1HyiduSuWblt5rstVcSeOO5sXXO163r9paYdAUWiOqoIH6+8W6GmtOJ1X7I+kWxJzw673PNl03BxGnDXcW5b0Y+IQvGT3GMXGJx6uG5SS9nHrI2EnycYh5AkiliiskcTwkFFZCo2n1y1o+O1ne+nVT9qP53vJq0oZfEhYa+jGRFNaNp3ifF5z2w5icXbSlfI+0WcPPk+xCzI+W9YF54uCx7cDOWacNZwuCkJMtFZQ+z7Kc3WN4ve9fsLXH8z5B7/5nG8/b6k0rOcF61nBwQcqozle1lirccbgjDB9aqf57WcdxhiChpgyN62n0VCUhuws/dSVYwQ7K/BkUlY8H3v2feDRqqY0sGkjpMihyzzuRq73I1UxIe5jZrjTCcGsLJmVboLzaSqjKWvR4GSVSEwJ0VbjU8YDs9JxMsUjHEbPTZtBZay2zOuCR0fibnty25EmMOBJVZEdmAyxFgeQMpZxolA/V+LEc1rRJYEO3lvLeKJUitI69l0QzkyE83lJCAGfpeBXyrAsRLdlrebU1vSFZ1FZYpDuW1CZfohTt0NGY/iE0rAfA483LSkbkoKzueN81bCoHE5rfuPJhk07UDgnqfKFouvEpdYUipQ1vc9orbCAykLgNsBtF7hpNxzNCx6uK+4vi0lkH6lKw2EIXB1GmtJy8gKfMESe7wZ8Srx83PBzhzlXe6FCL0rH6azisvP4MTNvLKvSEXIiTyTsFBWHPvDSSYPRmovdMDGsEv0QaK0I5beDACh3fbgL4Pzg3vVxpoT7K0s7RDnIzEuuDz19EPjith/Zdl40eouSaiIdvwDzfXDE8nEHw3HSVVmjeGndsBsEkjmvLK+eNJzMqo8f9SuAjNJSZEak9Zunf/p5jHh+lLiHj3vt95Y188J+rMD7q/VDFjfb7ZacMzlndrsdVfWdijjGyN/9u3+X8/PzH+oJqI98oHlq3X3cSimhlOK//+//e1arFSCjrT/+x/84/9l/9p99bPfmP/wP/0P+/J//8x96DS+//PIP9Rw/y/V55JncFSwh0g4BbRTtmHBabqQg464XF4g1il2fqZymspZuDDy5HZiVItp79XRG6QyrynF1GLhp/Z14LcTMybzEGsXj645ntMQsI6T9INTik4VYnF8EZX50c+nHgE9ZhJazgue7gdl04oXMv2h3PFiWnC0qrvcD1zcDo49cHODhur4DCd520iJvCsPNYeTiBeRsIaLS0hquDwPPDh6Ab1/sOXgPaOaFQ6lMSGIx1VoSh3cExhg5dBJSOITIotKMyXDaFNz0kRQTTaUZfeByGkmhZaNNeSL2Fp7ei4NrURc4rXjnpsVHTSDydNNjdGb96hGPljN+8eGa2lmu9iPrw8hhlLBEo+QErJDnF7KAFRe1kJ2HkIlRvld5ig/QSoNVmChp4U4pNv1IjJrSZZzThAi1lW7ZqrHcdJ5dJ0ye07njcjcSE/QhsPOB4IVFVFrNEKTjVRYSUnhcVPJd2Sf2vacpLetG9A4nc4fOCucU/RAZQmIYIiEnlpWlsJrtIWBMpguRmKDScq1LgrloFYzKZKUnrUbmveuWWeU4WTie7Ue8l0Tr2zYwxMht77nYjzw8KmlciQGssxxVjq7TXO9GDt5QW83mMGKNgCK9z1hjKJQSe/ognYvCSqyD0pqmFAhjoS0nM0tlofMRBTxcVxwGgRbOSyefWcqkxBTzEPiZ8znbqfByRmze1mpKq9gcAmMK1M4xcxqtDE82A0154KXjGY/WM45nJfsucLooKIzmuvWsGhHWXrUjoFjVMs4ptWHTB7TNHG76iTYuafWHNt5BHQunebbrubeq6IbAfgwUTnNcFmitudqPXB06jmc1xkjH6WrKgKqcudu7Pjh69lG+D5vO885VSztKyGmmpO0DpIKYE/Wy4tXjhmVTsJq+Ex83YvnowfBF4HBTSpdwNSs4XpS0E1voqCk/trAJUR53vqyxRnHUiD7IGKGil0YK8896xPOjxD18r0NxVYgbT33Ftvmu9UMVN+v1GjU5Jb75zW9+198rpT5xl+T09BRjzHd1aZ4/f/5d3ZwX68GDBzx69OiusAH4+Z//eXLOvPfee/zMz/zMdz2mLEvK8svj+/8s80xeuK2uDgNOK3YhUZf2LvfFp8zMfCes7sUFYrUWUagx9F64JZLRMlkib2WTe+14RibLmAk1MSaKiS0BIScebzxHswKnNZvWEzO8fjzj0dEMPdlo931kVSfI8O7NgfdvOvqQJodFyXFd0vokWUN1yWsnMpboQwStOFuUnC9KbjvP8aKEBEZnBh9Z1SUP1jV+EhSOXlxXRksw39Wu5+ATtZVT5/V+RCtFmAlV2ecJEW8U9xcVVsNvPz9wue8Zg5xGD30mKcX5osIZKfQGHxmCFDR1oSk09NMoalFafu7BkjcvWnZjoJzAY0ZrZqU4uqrCkFD81pM995Y1q0aSzX/7YotSsL8ONO6FKy3Tj55F7SbI28DVXrQFPnmBzlWOrBPvXXQczeSk/zREUlL0JMKYmVWKWelIScYTV/sRHxI//2jJ1c5TWMUY5SadgGGQIEStMkZb7i1LTuaFEKtR/NKDFW9cHjiuC+rCcAnUhcNZzW3n0Shm1rEfPLebkX3nCTnRehGatmPGh5ZDHdH5RXaUFDFjyIzBT+nVmqwUiiwJ0FrTjT03+17ghzGx65OMYaxY3fd94OYwcugHXj5ekI1kKqkMWYO20q3ss7y/y6bkwbrilZMZl7uB968PlGVBg2fbDihlyEmSuOfO0Ga59p7vRFhLyhyG6flqw7wSku+8FCpwFyPaKLqbgPeJ43nBvJIOiFJSOLZDYD9OWVaLks0Que52zEtNVRqOZuX02TqsFiFq58Uttx88t51o0mKUA+LVXvRvTeG42Pd0PlOYLI8bB4lXiYnaSu5TyonLXc+YhPB7PCuonBVeTgqEAK8ciejeaIM1iqv9wLopeLD6cHfWGmH1XLde+ENOOsnPd4PEKtQFi9pz7B0/czandMLAuW5HQkrcW9Z3MSwv1kcPhilJx1Kp73R5rNbMSvV9u+AfLBSMVpwuSgqr77Qs6+w+txHPp417+Crk84dfP1Rx87//7/87OWf+jX/j3+Dv/J2/cyfqBSiKgldffZWHDx9+op9VFAW/9/f+Xn7913+dP/bH/tjdn//6r/86f/SP/tGPfcwf+AN/gP/xf/wf2e/3zOdzAL71rW+hteall176YV7KF7p+1DyTDwrL2lHGSataTkpNIep52fwiRn3HHvjiAolZnDY+RRRq4s9Y8lT4aCNt8g+OjkLMlE5cSXrqBOSsOJ+XaK3pfEBreLiqMUajNd81u3736sBvPNlitQACt/3Imxc7fvdLK37XS8fkyaZcOj2NJ+DZbmBVS45UWVhqJ86ObS8ixPNliRBshdlhtOLJpuOdyx2bPrLpPd5n6kKsy+tZybYfMcImJE4Wdq0UL60rmrLga2ewqCy7XnKjrFJYY8SG3DisAo/mWGnO5iVdgPduOh6sa146qqkLy/2VFKq/8XgLU9bOqjIM0bAoHS+tauYzx1UrROSzVcm8tqyqghiFEyQFsOQNPToy3FvWbFvPfowopTlfVZIdpTTrymFPlqgoN+sIzEuhRd8eRrrJmRNywsdMbeVm3IXAz8Uly9pwvY/SXRgnkfVUrJUGFpVmUTvKwtANntgnrg8DY4i8ed1iTcJozbw0E29oIMPkQMnctgMX+xFnFFYpEkAK7HwiZAVZsawVJ03F8UI6wp2PPOk7nBVR9PGsJKXM5X4kpci2z3Q+QU60Y5iiE+IEfpQA0DfHSFMVvHI847gpuNyPLBTMS3vnskkJ7i0rFnVBCHA8K3l41HDbjoQYGHxmPwScAa0ipXGTAFbzteMF2Mw7Vx3PNgOF3fPKyRyHZjcKZG41c4R9QpM5nhVoY1jX4u6aF8IBUjmzS4njpuB8WTH4xMU+UBUwRhGuP9v2UwSBiHl3vefQy7X1bNfjtBQXg488OKo5DF50SEkKx8ZlQoJNP9KPcUoTdxzPSlxhWKUSH2W0dDQrmJVyrb0YgylkHFc7EXWHJAylk1nxXXvXB8coVisKK8Tj05zpfGRRW2alRZE5mkun5zyLuPfqMJIQ3dMHXaQfPRgqJb/Hx8SDdX1XXP2gG/5HCwWt5D21RnAELx/Vn9uI59OS6L+okM+f5PVDfYL/2r/2rwHw5ptv8vLLL6P1j/aG/vk//+f5t/6tf4tf/uVf5vf//t/Pf/lf/pe88847/Jk/82cAGSm9//77/O2//bcB+JN/8k/yV/7KX+FP/ak/xV/6S3+Jy8tL/v1//9/nT//pP/09BcVfxvWjRi18UFg2ryw3B83VfphAceYOEmWNFDJ3p5oPXCCFUWKtTolmKgo6HzmelzxYCnE35jw9Ft7ZdGJ/NZoYI7tRbNmz0jArNEeNbC5aKYFT+UQ0Qr41KJ5vOv6vd27YDZF9L8Jd2SwjV7/5nKa0/Nz9Ndqqu+eolRRyUWUGnziblywbS06ChX+6TTy9bdkN4oLpBykW20E4GD7KCHVVO27aQFVo7KQjqZzFKs02eHKUEV2IoI0Unw+WNd++2IlQOUtIoE8RYxQ+Rk7mFbPScW9R0ofEm5d7nt12okvSkpb9Sw9WhJh457oTm/uYBar2Qswakljuu5FXTho2fZCTKEIHViqjlSQ+LytJFNdzTR1En/JsO05MC8+8UMysoiktl/uBpDJ9SFilmVVy2h58phxFF1M4Qx88q8JxNq/oUxQQ4qV0SmZlwTB6FBJYOQ5pKk4MkcToJapiVVl2o0Aed0NH7Rxni4oQEtshyO8dPGBYVo6cRLNy6CTeQSuN02C1bNizypKQoMtl47g5SASFTxoQns9NOxCSYu4yhxjxHkafUToTOslbqp3GGUtW0I0RssJoTR8ChdWUxoltX8FhFJ3G5W7grf7A6ycND48q9oMnJgHh+SRxJD5mbruepnAczRw4zaI0nMwjGkU/yni2qQ1ZiVtp242T1qbg66cFOWvO1iW1s6wqx/VhwMfIW5ct9TR2eL5vcU7fjdoudh2vHM/YD6InCVHIvdeHEaWV0I+VFH69FwP4elZSOkfjFG0vndGTeUEO8Ob1Aa01R3UhY9x5SVcYLvYD95zsBRnYdhJlkJLiaGapnITgrhuLM+KuOpl/9/jng90RpURu8O7V4e7vzhYlTW0gT+J8Z7htRwYvWreY5N991EX6wYPhGKVLOsREYaQb/Ulu+N+rUJAIlx+PduXTkOh/nCGfPw3rU32Kr776KiCk4nfeeYdxHD/097/rd/2uT/Rz/s1/89/k6uqKv/yX/zJPnjzhl37pl/i7f/fv3v38J0+e8M4779z9+/l8zq//+q/zZ//sn+WXf/mXOTk54U/8iT/BX/2rf/XTvIwvfP0wX/APOqE+KiwTtkuiD4l95xmcaBdW0yZ4vLB3F/qLC+FyP1D0Cp1hPSs5mZV3FuWUuMuA2XQjvU+czgv6MXGx77k5jLx8XFNUBTFnblvPyVxz1BS8f91KpyiJlXYIkXVlCRFaLwXItvcMPnG8cNKq3nb83+9uOFtUHDUiKjyeCVl0CJFd73GTrXQIicIoZqVlDIHfudhLoeEsh2HkvZuOeWV4aTHj+W5gN8hjT6Zu0HU7sqw1ZSEahyPteP24oSwcrY+sbcFq5rAa0Jr7i4r9mJiXYtPtBs/7B8/LR457y4qmtDDICX3XBYwSkfP/82THS+uKX3iwYlFbbnYj76aOo6bifFUwhsjTy55lZdi2Yj1fVZY+RA6j53LMzErN0dywcI5DiFx3nqtdRzdIjlFKQcZ+Y2Tbj5RaEWJijIGQFYfOC81XWaz2ZCU3jEVtxHFWZB6uao4WJc93A85kxpCZl8LRCVnTFFJUJZ2JKfH+TUdpNS8d1dxfNaKjUElCVC/CREsW6zcachB3zHrm2A+KbedJKVOVjhAjzijQ8l87ijXfObEsZ58onGHbem4OgTG0qJxpx8QYpMO3MJY9nm4EH6E0coMjJ0nDLi124u2gEhpFbUQE7KxiXTs2l/J9dEa6Hs8PI8MgwanzymJ0w3U7UGiJ/MhJYabWX4yJflQ0hWVx7MRxc9oQYuafP75FIQ6gh4VjzAnvMz5Ftm0g1xI14YyeivHEYTfydDuw6TzH85KcNIWFnOBiJy40Gbn1XO4HLrYDj297hhCYVQXr2uITLEr5vhutqMqCgx94tGpY1AWryjBvCvbjCFm6rM82HYcxsCg1x4uCq52nGzwhZma1YV4aminzCTS3rWfdCKTvg0XEB/epF92Rbghc7UZiFq5OmDrFP3t/hjFminqR8fCLw8ELxIPVH7Zkf/RgqLIUpz/sDf8nsVD4ovMHf9LWpypuLi4u+FN/6k/xv/6v/+vH/n2M8RP/rF/91V/lV3/1Vz/27/6b/+a/+a4/+7mf+zl+/dd//RP//J/09VHgX0rSIj9ffEfMvawl8frpVrDwMWWaQnJ01o372AtWKaHSFhPV8nRRCgPmAycfgE0nAlGrFa2TsLq5k5vjrNDshwQqs2k9x42DqWNQxkSMklD9T5/v0UZxvRu4bkdKZ+SEnhRZZ46akpDgjecHThaC0bdasaws95cVb122VE7iFvoQuNh6XjqqyGiOppToPkRiVNxf1RRa8bP3F7xyWvNbT/eSbJ5lkxwDYGVGf9yUrOeO107m4rZKiWpieuy6kX03chiCBEsqsaEfxkj0wj3ZdCPPp2IvxkRM6s6l9Xzb8U/fvuVrZzOOmpJfeLRA6SRREiN0k1XbWcOikUJOQhiDOHMsgOLZdmDnIveWFfs+8M61WO17H0hJ9BOAUGatplQGqyM6KzrlGZOMoI4aS8yGTMQHzS57aiMZS7dtx771bHtJbV81kuc1KwzLSpOyYtMFikoKPGPh4VFF7ayEg/rE9aFn9AGjnYRGWkXvFTErdipSW8XFPqEQd4rQXQyr2qKsEXaPj1xuZRRXTc68MkmnMCIalxgTIUbGFLHJ8HBdYvfQDpE4JLKBQkth5kNkdEZqJwLDqBinKIrSKN662GOVZohCakYp1KSdutkP9EFGNyEnZoVlVjkaa6ZCKdPHRIyRmz5IXELKOCvd0pdOGipnOQyBm0MgRXHmDGPmwbria6cL9n7kaue5v6qYV4Ynty1vbQZu2wGFuJds7bi/rDhfVWy6gcPomPWGmGE3BNpROm3bPrO56bjcak4XFpUkJLW0Fh8jPsi/8yGyqiuyUrx1EXi267nYduwHCdM8XkhH67XjOYHMtg+iAWocGSTwdGJCzcuacrJjvygyPggmzTmz7wPv3bZSOFWWyoog2yjFvo/MKxlpOyNwy9p9p+usNRg+3kX6wYNhMUFGf5gb/k9yofBF5Q/+pK1P9R79uT/357i5ueEf/aN/xL/+r//r/Nqv/RrPnj3jr/7Vv8p/8p/8/9n7s1jd0iw9C32+ZvZ/s/rdRmRENpVVlU4DZZdkfFAhJCOXBNh3vrFAXCCQLNtcULKMkDAqsC0kzgV3tpCQECCBEKKR4IILMMJW+hx8yqc4VZXVZGb0u1vd3832687FmHtlRFZENpGRRURmDOVFxl4R/55rrf+fc3xjvO/7/D8/6Wv8ma7vDfzrnYQ3WaO4t5JVnFayjnj1pOb+WnhFH5Vz88HX05TWcHUYxb0AHzjBuBiZ5nFtN3na0fPuTSc6CgMP1tU8DlYc5pvXaVPwyqllmCKbzhGijKcro6kLy5vXLXb0ZGthxXivuX8kjdrlYeBkIbt+FyKXh5F29FysinkK5Bknccy9c9uz6Rz3FgVKKYYQCCHj4briuh0JMXG+qJlOE998tmdy0nC/clSynxwqiWvoYlkSUrpr5p5tBw67nl03ibB001FnlvN1iVKafe+5GRz/51s3s7uLuzDBr1w0NLnhMBOgkxLdQIpgjeHrj45466bj+W6k1IpFVVBZwytHFVMI/MFlS24UhzHN2TqRXe/ZdJ7VvK4hRvbdQDuJmygzCmsVwySNQ9KiH+lCwIecwntWTYZW4ji6PIi9d1UIJNMn+Hu/eykrLAOj80RviDP2wYfEbpR1mUtQF4omM/i5yY4k9t3I27eDADW1QFYVoknJCsN+nHAoMpUYo4A7tYJca5JKmCiWeWMUjbWz00+hleFyPwpssSmoc81t59h1MiFyWiYI1mrOFxk3SqCOyWYsjCbXerZOB964abm3bKitobCKy13Ps/00i2xnnpCVh2/nAsqaGZrpmFySCY7SdFPEaFiVFh9lUtY5caUVVhFj4uluZFXl8vAsxHHYDoGyNBSFQRsDOpEbyytnGZnSXLcjdSFiaKNlVde7SG4CZSF/7zLPeHRUzeGWQuhWStHkOeWJpF07HzgMgcJGFqWVvKI2cJg87SThe0VmuJ9b3ro6SOMwQ0eN1bS94BHurSpO54PDyaK4S8JdlIHn24HBw/PdwNvXHXVh0Qha47jOMUamRmOIKBL73mGN4CKO65w6t1wfRt68bnl0XDP4yHElaePt5CCJceHlBOeHEcx+3Af+543CT299rN/r//q//q/8D//D/8Av//Ivo7XmC1/4Av/8P//Ps1qt+Nt/+2/zL/wL/8InfZ0/k/Vh2QaLIuOsyWVkngsk8P3TluZ9cdw/zOvVheViXme8vzGK82mmH4MkA1cZJDkhu+i4WBVkRtxVVW44bQouVjmXh0mcB50nt/KgXlQZJMXjk4JtN/HeduDZduB8WfH4tCTPDM5F1lV+F3Cl0FxNgW+9OHBvVVJkGlLCKDhZ5HSjp7Ce3eS4t6g4WRZUmWNwnlWZMfpI7z2rKuekyUlJ1i0+zjiEkLg9DDw6Lnl0XN1NtzbdxNPrAVKitKLzGGOgGyIxedrpu+DIA0LHJkKM0A2Rt65bbjuHi4HSWg6j5/66osoNuyFwXOdcHiRszsdIVRQklTj0fhZtCy8nkrjcO55vexa55a2blpNaXB1KK5xHhOGZYZlndNPIzW6iLiUnpLaGGw19SGRT5KjKCDEJcBNFVVr6KcyTwECdW3IDqcy56R0G5PuYc39OGotFgt+Oqoxt57lqW/pxYnCJNFvyBx+53E/84oMFo4/cttKIb9sJPQeljc6hZ8dYSrKSanLLxUrYT09uRnoXOUw9V/sRreZGKyZCiBS5oUKC73ajCEqXleQzjS5ijWZRZESgMIrD5EhRtBlfPGk4jBOXB1lHoBQJy26MFN6zPq54UBdcHhzPxpbByRrUKC2TiMmxKi1ni5J1abhqJe1YAbnR7J2nyAwvdiOnTc7psiSpxNPbgbGTJujQe6JzDClxf1Wz6ycuDwPV3HC2o6ObZMU2hsDlduSmnfjK+ZKn24Hn+3H+/YjmSpoiS0yeZaZxpaXJJQRwVVm6Xizol/uelCQSYXLSpECiKXOZTiFC7+f7id9+d8uf+YV7RCvYEqsDmdHcHiauDxIbMfiEMYp97+ingEvpzpotImKNVTJdrXJz5xTc9RObbgIF54uS3ouLEcSld/Y+MfPngtnP6+PWx2pu2ra9y7M5OTnh8vKSn/u5n+PrX/86v/Ebv/GJXuDPcn1UtsHJosBF4d78KPvi7xcg+FLbMswEZgUsS5kegGRK9C6yrDNuDtNdtopXsO0cj48qmiLjthP9gpwGJbW1zsyc6aH4wumCPDNc7UcerkuOm4IQI7lWc37OHBLYTzMjapLk10zTjp5HRzWNFs3NSZ3x20/3bDrH43XD4D1PNj0XK2HUvHfdExE68etnC1KSROQyawghsR8cCiUi6BkEGBJ84bQmuMgb1y2rMmNwgWe7QZhCRoTBq9pyvpDJWT95muC5bkd+8+0bzDwxsLXmMHj60dNPjv/vW7coFFf77wbQNYXhunW82PW0/cSugzLX7OYm8MmmZ1kaxhBYl5IdkxnD+ZEm+ARKcxgczjlCkkToMQApMU3i8HEhctNNpCSJy9VMXd4N7g55YRSsy4LXFjnv3rTc9CNaJZJJFLnCGE0iMrhE6xyNSuzakSe7WfBZZzw8LjEz96p1kcerCucS5cmCy+0wT7giVhvqTJMbOIyR00aBktRYYzSLXPPs4Nn1o1DiKwNKblbrOmPymuAVTZVxtNCMU6TMhU02BmGDpQQaDSoC8nsrjKLIFG9vPEZDkUnOzegT3eTo5tXUosyocpke+BiYNLSjoyosy0LYXFVm+cVHa25n1tG+jzO5PVGVZiZYy+Tu3euO9zZCrDYWYki82PWsKotB006yjmxnFlmmNbmN5FpjrawCOxd556bjwapimRsGF7jcOVzwnC4Lno4dRhtOF/ndqvmkyvA+8cZ1y+AjlTUcJklZHkPCp4QyeoaSKqpC4+ZQvLc3LZvBc7zIhUzuRBOz7z0XqxI3NzaFNexxvHvbkxlZNx7VBYnErnMUmfDj2jEQMgljvGknfEqS0l0aVjpjkVtcSKxqw+S4m9h82nUwn9entz5Wc/PVr36V3/u93+O1117jn/wn/0n+7t/9u7z22mv8nb/zd3jw4MEnfY0/s/VR2QYhJs6aknur4iPXTz/K67kQGUaBRUrYmOgb3rwOWC1JmLvB081TkVVpGYKcBgtrKCrNYhb8rauMp9sBHyJac2fpvjfDGjsfOJ4nNMe16DNO6hKFkJ9BJkxPbjteHEZQsO8d7SjiZv1y3aGFW/WF45Kr1rEZJm4PEyollNL0IVDmmiaXG2hdWG5budmOU+B2mFBJsR8cv/3uhot1JZba3cC9ZXmX96INWDRLHWl9ZPCw6UWkvCgNTW4ZXMblvuONFy0uwKK2qEmxQdxE72163rhqcT7Q+UTrA5ftROvk4aFQPNsMLAvDpvN853LPs/1IMZ+8iYpvX3UQYUoybTltcm57z653dzkp2pi7NWA7BCaPZPBYSaNdFZohJvopsihl1TK4IKF1RrMfJ1onqdVGaY6aEqMiU5DJRZ4ZVoXgOVwJx4uMg4ukGAlJsesSXzjNWBxZ6izj64+P0NbgvafMNPdcYjM4bg4ykRk9M3HdUJmE1pr94Bmi8Lvq3JDZgWEM7NrAOA8l951MqNrRcFYXnJ9U5JmlyTT70XPbTXfaHqU01TCzvVzgyW5gPzg2oyNGmYSWKmKiopvF7vvesSw0WabIrcXYSGlzylxx1pScrSRbKbcZMXnJazopaEfPFDzbIbAsMozR9MPEe5sBlRTHdcFhHGldQqdIP0nT7LzEIlwdJlalrKWOasumG8hmUXdIItB/sh/nCWSiLBKbXaAZPT4IOiPEyMm6YN8HhjHggpDU16XhxidIQqGPEZyL7EdHCIlFbtm04hoLMXLoNb/97i1ff/WYsyZnXRW4mZJdWMPz3SCuQMBo6CY/u6Y0SkFu5qwtn/jCWSWi5xkG2zsRCN9fl3evUeaWOHlOGpkef9Z0MJ/V+l5g8o/69U9zfWzNzdOnTwH4G3/jb/Bn/+yf5b/8L/9L8jz/UBHw5/Xx6gdlG/yolsWPer12lDTcGOEwivCwzHI0E+/edhzVOQ+PRRfzEi4Xgtg5Iwmr9F2C8ctTlveR54cBa/ScKSMam4frimVhWdWWGIVZ82Bd0TuJj9dObshv33a0Y+DhUUluNLe9Y9wmnu96Hh1V3LQOlCLLLK+eZqzKjG9zoMktj09qQkoMk6cpRby47WTFs+smrnuP1fCFkxrvE29uWzKr5eas4MmmIybJJNn1jpv9xBQhBLFyn9aSu3N9mOizwBQTVwdHYRQni5zjKmPTO67bERcDOsn1N9ZQ2ki5kkDCQ+/5zbducA+OhHdkNatc863BoZAV4LLMKawwfd7bDSzmoMVN7zitMx4fFdx0smJ5eFTSu8jNfmQYZaKwGyKFDYhPSGNSYu81yzJitRJ+TxjpxsTBycotJshQRBJHjeW4yeYAwpwyg7duevIscJTlgmnQGUlDmnNPQgiYIsdYxb1VhguWurRMLvLmdUtKMgGqMnEZ9ZNnNwXJ3XGBXMMX7y/ZD4HF1vDWjYT1HXr5ekTxoC5ZZIYnu54uRP7kqyf8E6+u+c6LPf/Xu070QWUm3+8g9v1Ca968btl1Eii4LmRaUBUGG6HJJPdlUVjULGBa1RmHfgSr7x7O+86zyDyD6+hHx5vXLedNybKRRrXtJ9HMjJIea7SiyTVKJQaXmFxk8oHd5KlyQzsE3ttIgm87GjIFqzqnzDPOlwXni5yrdqLMxI0lzavjfFEx+chtP5LbjAJNjFBmlspanmzEAfV8L1bzbvIUueXpdqApLXkGmZNJ4pBP9EEyiValQaP4xneuebrr+cq9NQ/WJV++WJCZ7+ZkyXTqu5T4wXl5304ekkx7F6XlpCmp84ybdqKbwjzdyTmq53tFkJwirb57UPtcB/OTre81qryf7q61+oFf/yzUx3oP/cW/+Bfv/v8/9U/9U7z55pv87u/+Lq+++ipnZ2ef2MV9Xp+8ZfHDXu+0ybnpJna9p5hdSQDLKmc1eC73E+s656jKeLLpiSlxf12htWJy8QNW85cuhCa3XHQF+14E0G9ftxzVORfLkmVpIcF1N/H2TUdIcFxlnC1yDkOgHYSPdbYoOGkKtFI0c7rs29cdN4eJqKDJLeOcoLsbHE1hyY2Ecrkg9OebVq69yhTv3nRctiNVZsiLjJuDTD2s0jzdDYQkAtrbdmLwidM6J9eaKte4PmDnZOJXj0t8lNXZ823k/rrAKMWD45qjKsNaaRoOvefJbYcxmhAiJzOSwszZIIXRhFmw+6guebSuubYjxhiqpNj4iee7iYfHhvUiZ9oOLKsMlxI+KA5OuEjrwrLINacLSYDdT4FIwiePC0qyfWJkPyRIkRADb98GlE5ElxiSNLhVZklG3G5DFOv34BP314YiS3TTgNWQm4RRMxspJQafKCx4/5JFZjiqDYWVBvb6MLEqLAcfmGJk24s75qjK5mRaz81ulCkUUFjNSZMTSOx6x+gd+87TOnloLmuLVpZ1k6ONJjffFaDWZcYrJw2Xh5EUIxOKR8clKonw+9l+pNDmbgXWu0CKso48WYgIuC4MN/uJzMChD2w6j9ISbbzpHEopnrcjr2Q1rxxXfPvFgSe7nodaURrDKyfNnPztZi0OKK04jILnyDQkFE2eoZVmDI6khMGkZtRB6xyvHi85XhQUmSakJA3KKG4xayDLNF8+XzH4NENSlTSmWkOKXB0GllXOvWXO27ct3RSIIWEV0AOIyL93gdsusCjls1tahU8IlNVIEvAbVy0uJF4/b8QxpxK3vdC+Jccn8mLrGWPPm9ctTWk5qTKWxQKjPuhM2vUFN61jmML8d090k+diUbAf3GfqAfpZre81qryf7n7cFD/w65+F+rEa5GmaeOONN/jSl77EL/3SL31S1/R5va8+ScviyxHjssw+8Hoglu/RB8rsu02TnxNTjQHvRUzpZlKvPDwSj06qD2208sxwf11zthCLb5MbqsKSG3mYXbfjnF6r8D7ydDdwb1nw6LhiVVkeXh0YXWTwgcKaOWTLExHnx+TFFv/ouEIpcTmpOXW4m2RdY424LbxPPGlHikyxrjOOq4LMKG4Okob66omAOK3WoAK7ceJq74gx4pMIVI8WBbkRWGeeGTRQucBUiAYhzyxHpWHTOr71vJWJRqYxk2FdCrtp007UuZUpl1Ki76gsEQE0Hi0KYoKjyrIfHJWztN6z7Sb28890UVpciuRKcno6L6Fxk5cJkgsRq8RBN3lIKmKwKEQzFVKAeQKlUZJM3DmmAFWeyJVGWUXSiuQ9YxQR+jLJClGE5hKYVuZwsS55vh3Z9YHcaGKKJGR19u3LPdeHgTcu2zsKd3CRTENRWnofOQwerQyZFWfeSyL3//vNG86agv0ouiBlI2XU6Dlf5p3rjn7wPDqtqAoJigsRVlWBOdZkVhqUozrjaJHTjZG6HJl8YnSeprKS8D169j5yviz4wnHN2aqmHZxY9XVk00uwnAuJ3ERaA6dNhlZi+84yy+miJIZIoRWrKqMurXCphkDvPY+Pa1yU0EZUxmHOjmoKjZtZesdVRl1ocmNxPgGJyXsyVQCKRSHTlDF4SIp961A4fu7+inuzUDekKNyx3PCdm44isyzLnMwonuxGTmq4bR2p95wv9IzikAnlW9cdx03BaZ3Ru0iW4HSZ0ftIOzqCgnduO+4tS4pcPsP7wbPtBrQyLHLJ36mN/OybTMTlm87RTp58tvZb4LSRddQbVy3PdwOLwvL4qKbMzWfuAfpZrA8zlryf7l5l5vt+/WXm0Ke9PlZz03Udf+Wv/BX+s//sPwMEgfDFL36Rv/pX/yoPHz7kr//1v/6JXuTn9eNZFn+YEeNxk/HupqedHE2e4WNkdIFFKUiA3IKLlp+7L6Lf0cv6SM+wPPjw/aw1mgpxZb0Eor4M64pRYIl6kBj8bTfx5QuBMuZG82I7sh09q8LKuNslHh/XfPX+WjQK+0lCxgpLmL8vjYQcGq24bUduW4dZS17MsrDca2TyEecmJaRIZmQ9MvpAPwaO65LcZKxK0Q00hSFGaUZOFzlxzuPIjKbINPfXFW9dHXhv03M7OJ7uBqpM8kXWZc6itGxGx+YwMTlPUVimIEwgW1qOCks7Rd67bjFWc1RaaZAyRZkJzLR1kV+4vyAC7eB5Ma8PY1SUVrJm3th3c0rrRD/JCV8Bt35AzT8b5yGzmuPGkKJmiJHaKlKd0xQQk2JZWorCsOs0FYpci6D40ZHQu1ur6SfRaS1WBffXJdt+4rQpOF0UNHnG4GDT9bSjIzeK57uBo6Yg0xmV9Rw3lpvOs/GR0flZNG4lDLATwn2RKazVWGPIULRBGs3MCqhVW+HcGWXY9IEvnClWJkMDm9HTu47pEGido3MRN02EGFFKU+eyRhkcpCCC5iwz3Fvk3Gh4tut583rEGs3ZuhBm2Qz+3PSei3VNiJF3rw9isx4db206zpuCx8fVbLlONGVJ7wL95Lk8TLgpsBsiy8qglGLTjpAUF8ucMco66nSV0Q9xXhFl+JnX1jrP851w0FJKFEZTl4bSGDbDhEpQzw3Cs+1Ak2tKq1gXJbtjTzc69mOgyTRVbph8EmyKT3RToCkCLllCSkSEMm+VYj+Kq+xp31Hnip+7f8TD45oHR/D2TcY4BZKKnFMxuDA32pFXjxtyq7lppz/0QPQhshschZV1rNbyXtdKfaYeoJ/F+n7Gkm6GGn+/r38Us+vTVh/rGv/tf/vf5jd/8zf5e3/v7/Grv/qrd3/+Z/7Mn+Fv/I2/8Xlz8ymrH2bEeNqUvH7qeeu6w4eJ0sqUINPyIGincBeTDuI0GVxg13ua3P6hAK+PYsLol9gBo7lt5RpslZEZw2F0fPuyFRt2Lg+x3kWeDwOZVdxflDSVRWu4v67IjDioIojQubJoBW9et2z2jqvecbEsSAqcj2zjyOK4QSdhZGVGcX0QJ5JOiXa+iYPiuLZcLEs6JzoSTZqdSpp1bfEp3eV6LEpLXRiGKTB5cclUmSamQJ6L1TkEEao+38p6694y59664KSy/NyDFbet48luRJGwmeZiKTlDgGTZaCEsP90MDE5Shq3WTCmyHQOKRJXDfkoMo2SMGKVIIREVEETYbaxgCLS2JBXIgX3SqOjoR4PWEWcUxmuUUrx+VhN8YjMGbtuJKQQWhaXKDUGy7Ghyy2md8/ikYnCRbpJMoc4FOhfmTB4Rco/BcdP5mVIuLqDeBapMUxjLuha+EUrjPDSZ5XShcD4R0sTgI94pVJQguc3B0eTiyBGBsGbwmkUmkQb7wWOUkod+DyRpmCqrGH1GSAqlHatctFnvbjoG52kHAVEuCkuBYlXlVFmUEMEktvRNPxG9ghi42g1MIdIOnv0wcbosOF8UfPFoyW50nC4yximRGgnzO25yCqO53DvqHKrCUs6NpEZzusx57aSizDXv3Q68aMUq/YVTmZROU2DTO9657KhmjYxWYgYwWnFvmZNls6NOw1GdEWLk0VHJsjC0oxx4FALAXZRi+fZOYLWXm5HeR07rHK0NVSZ6u3dvR14/E+L55CXcsLCa9246Cb10IiJOwFmdk9uCcZ72vnzYbPuJ57PFf1VnJNIdYXxZZp+pB+hnsX4QhDPTPx2Qzo/1/vnv//v/nv/6v/6v+VN/6k/dncYBfvEXf5Fvf/vbn9jFfV4/fv2gEeT7Y82/cLpgUcgKJSVmsJ4Ei20H/5Gd/G03sZn/jo9qntZVjg9R2Dc+kIVIZmYY40wxz7Tipp246SasVhTWUphE5wLPdj3nixKtNe/ctJzMqzp57YwH9xcMLvL2Tcdt63neDjxc17x+1uCinFAPg8dFyLVAMENKNNbyaF1hM83tfhAhZJjJ1LOdVhvNg+OScYoMXpwppdI8WpRsese3Xkxc7keKTASZD1bVzPjSZFpzs5/YHAJnixwXEq0LDCHivEIbxbZ1c1q04o2rjnGMfOn+iiYzFLnmctfz2+9u+dZlN0+ZNDFIanBpRbxtVGSMsr4YJoPyiZQ8Sal5jZaIKGoD1lhyoxi8QeEpM8VkDOOU8E50EMZ61qVBRbHMxxDZITkzWmu+fFpjreWothSZQBDrzPLG1R4fEotSsoA0in7wdC7QlJoHx9KUPtv2HCYPKTIFiCmyKKEbRVdzvsrJjeVkkXN16HnWj4wuCpHcO1yAceuZfGBZad697kAlvvZozSI3XFvDqycN3TTxeF1LGOUUeHNoOa0sMUnzU2hFURes6hKl4MlG7OcxKdalYBraMTA5WBRGwLFaQQocBuinyLcvWza9JGsXNjHFiNaJRWa4aUd6nziuM04aS0qBIitZlwJqPBsmnm8n3rhsef20YdlI0/j4uOLRSc2297xyVlNsjGQjedFHhRDYz9bx22HCKCFhN6XlrCk5WeTCd1OS3H2xzNl1E9YIcf3dTU9MkbNFQa0zFoVm1zu6ELEetu04A0s1U/Bc7R2vnJTUuaVzEgQZicQo/LbrduRyL9M5iS5IvHnd8SBGHh5Vdw/El/ejOjMMuSGR7jR+h9FjjfpMPUA/i/XDGFV+GiCdHxu/8DLn5v3Vtu0Hmp3P6ydbP4xN7weNIN9/QtJacb6qJHvmfa/rQ/zITl4hMfDfr3nSM6OqnYTkvSrsfMIEo2TFNbpISjKq7gbPus6IJG7akW6UDJ7L/cDZouC4ztl0jsPgWJQZD9YFuZGQvAerkl0rrhqQB5MPgZdvy8PoOVtIeFyuFdW8ErImQYAhRJo5KLHMLFf7ia6dxH5uBK5pDXzpohHw5hAgJnmg55bzVUk222B9iGxbISrn1nCxrimsZnSew5jQRuL42zzwODecNEvGGPn9Z57aKupS1nG7TpKA11XGsjIi3E1IFooVJtZmiBTWEJOsCY2S07M2hirTpChuqFWZoTXsBkdMinH0oKDJDTpLpEwzOMWy1KwLw5vXHdvec7woOF8YJqcYJ8/btz2PjmsKW3G2yPnCaY0L8GTbk2Xy0PMp0nk/k8gjuYEYkLToJBb7IXgUkclDO0z4QtZKxMCyKUgx8HTTcbkfCR6MYSbAG6rCkBuYAvzusy3ffrFj2znOFyVXh4FCK85OanxUPG+nmTWVSR7TYSL4SFSKRWbw0XNzkKnTotT0XtZhvfdopYkx0E6JhOJrD0ruLSvevOm4aSdEE2PJtPCtQkikpLnpRnZT4JXTmn6UpnEKcFxbqsKQErx2usDHPc+3A9+6jJyPBa+fLbi/LAQMWVqa3LLtPH6YeL4buNx3bPrAstTcX9YSEDk6rg9OVngWSInOeW4Pjv3UsbCaCNRWz5+xCWUkpDO3iV4r8swyzcTu83VB8HBwiTx46sIwukTvHG9ddagEWmsOo+P5fuTmMAi+xSdikinvOEV2naPO9d396bv3I8syJq4PM+EbxWF05Ebz+Dj/zDxAP6v1g4wqn0X21vfWx2pufvmXf5n/6X/6n/grf+WvANw1NP/Jf/Kf8E//0//0J3d1n9eH1o9i0/tBI8gPOyF9r77noy3kbh4jB+r8gzej9zdP+8HdrcWWZUZuNc+3PZvWo5S4nI4qy2aIaK0Yg0BASZJY2rlAZQ370fF8P/BoXVFmiuvDxPmyoJ0iL4aJVZWh1RxUZw2bduLFbkCTqEor65wi4/6qYvSeEBWvHFV3os6rmelzf1WyKMXujfxPyMYkOVkaSSXee8/ZsmBvJq46RQqwKDNSTBw3Ne0YcEFO+OUs7AwxzFODyK4LbPuRhycVmZXkVqMUy8Lw4jByDjifuOkdeZZxvjKsK0nhvT5M9E7I25NLNJlmWVlhTSVJna1yizaKMrOEkMj7iRQFVdFksg7YJVgWhocnFaUxXLYTOiWUknXdGMRBF0NERTitS7bDxFU7saxyInC+KrgjXCWwKvFiN4gNedMTmadhKfFiN3LdDpAUTWmIU0aVhIzeTpGjRoLhJufx3vP04DgMEsSHSuRaEZAHqMHiouK2c5RWIgreuTrw1lXHth1JSnG+yvnSecNpnVFqhUuJQs9kdsSZdVxntFMgkTBao5ThvNFc7weG8aUYVs2YE8OyLHjldMG294wuMnl5v6YELjp8gKtdT1dk/PFXGl5ZNYwhsB8s99aGZZVxtiiYXOTtW3ERaqXQyOQz+Mi7247cGr728IhFZQnvRb7zoqV1kf0Q2A8Tt53ixW7iyxcLinmKlhtNSprr1gmPLdecLxraaSLzGoqMMjdcHibGl5lGBrajZ5kbmsLgoqwam1XGxbLGxSAhjKNn7XN+//mWN6/2vHa2ZFlYvA+gNKsyB5WIUQ4tx7XBZpJ582H3o9U8eT0MnsMk+p7768/WA/SzWj/IqPJZZm+9rI/V3Pztv/23+dVf/VV+53d+B+89//F//B/z27/923zjG9/gf//f//dP+ho/r++pH8Wm94NGkD/sG/b9nfyum2gnOfErpbjej4w+zGsjaZZeNk8fRjGvc8uDo5rCjGgNi0qmO1ftRGkkFTmmhJsZRKMLrEqZSux6x37wLErLcZ1zsRLWzuVBxuiQ6ELkqMmYouewF0Dlyzj4syZnWRn0IJONIjcoH8jnzJXJR3ajZOiUmcYYyeK5vy4pMtFl+JjYjp6UpNE8TJHTumDfO+pcggCrPHC+yNFKBKikxE074Xzk/lGFsRo3RS4PE2e3PQZ4vhs5rnNO6oJnu4G3rzpyKyRlTSJf5AxeuE3HTU7sJszc3Da5PNiMCvRTYHATy7rAKkWeaSYPdam53k1YJevGurCcNhkJRTtGHp43BBTtIDkxTWaYghWRdud5sZvoq8gwebJMsyozKis/1+vDxIt9z+W+4/oQqApNlWnWC8vV3pNbUErjgqd1iUVuZPqkNUdNxrq2bAbHWZPzYN1wO0y0fWA3DTw8KkixYjcMRKUYhshh8igtdurCCv9gCpFn+5FVkZHlQpH/vWcH3r3t5b1uDbWBzos25+kuMfnA031imgJKw8IaMpNxtCw5bjJciMQUWZc5D49rzpeFrDd9oLCyegsxcehHpqiwCgyJ7QQRT0qJzjtiEvbU083IVTsSUmJ7cLy37fE+SNPqIzd7xzs3PWfbgdfPG75w1lAWmoRCa02VwV5rtDHkyGr1MEW6yc0hfmIHt0bExQrFxbrgzeuAx3Nc5aAUr50tuOkmximQW9GgTT5R1Tmt8zw6FoK488KG2vWOEAMvNOymSKk1RXZgef+Ii2VJmi36r5+uSEgMwlUn07Yn255IEpfU99yPlmVGphVFprm3LDhfVj/m3fHz+lHqBxlVPsuZQx/ruv/0n/7T/IN/8A/4j/6j/4gvfelL/C//y//CL/3SL/GNb3yDr3/965/0NX5e76vv1dD4ILyaTH+0y+CTGDFqrUQ3EyOXe1m1LApLnUFZaN6+7ggxcm9Vf6B5UjNI8cPWYmUhHKTJiw3YKHh4VBIIXO4nDmPA+wgKjJU02qM6471tx/ky5/5RJcwpoMoM14dRmhgNV93E1WEkJghBs+88949LMmt472agnzwKJdkfSQTHh8mJpTYknm4PKMTpUVqL0fD4pOGokglMO3oRKfeOprQsCouZ+UdWKa4PjqPGchQLzhaON68llLDJLcMUCDGyriy70fGNt27J3r4lM5rHJyUxwXUrTrA6V+JmQijKea4ZxjAHFFq+/uqKVZHzZNvzxlVHIFFmcBjg0E0cNTmZNYTgUcpw3BS8elKilMFHAUZu2omuG2hHx6Yfud57ThvhlsWU2HQT3SSCanGXaXIlduDWBd657rhpHZe7cW4YZc3VDonXT5e8ehR5tpu4Ogyi50mJwzhhlPClJg+lzViVipOm4N66oHWemCekbzEYk4gYgg8kBKx5viopjWhRDkPgMIlou75YsqpzxikwTZEErMrEV44LDlNAxUBTikbIa2lq9i5wMzuOVlWOjwljtAh/M8t5k/OLj48BeC+2uJh4eFJz047cdhMuiFBeZwqIhJCYnObJthPsxBSxFp5vB9oxcBjE0bcfxBY+RSF4V5XorMycIfWdFwe8T0AkyxRBtmDUmcEUgttwMWIi7CfPV+/nGCXTxtvO8eiopLCGFGR1amZa9yvHleBIwniXDr6qDH/slRWH3nMYA7k1HJWKt24OtM7LBBBNmYHVmv0YKa3hqMkZfWDXBzrnUBFeHAa2nePequS2c1weJl4/FXxKmRlOmoxujHf3o0dH+ecTm8/rE62P3ZR9/etfv7OCf15/dPVyZ11azaab2A9upjBLw3Cx+sPTmI8zYvwwPc+2F+HsGCLHTY5S8PZNO4/y4TsvWrxPnC3Ku+YppkSIEoy3qAyllQfm1X5g30sqKYi1+vFxxW3nOalLqszy5lXHjfM8OpLTnAuB21aC3d657rhYljNUUXHS5Lx51TK4wGH09D4weSFYWwPL0rIqM17sJ1ISp01KCZciBsgyQ4rgU2LTOd64aokpsCgz6pVlNwTevm7hVBqhfS/Bbu/cdFwsC/JsRge4yKLOWBYGgyK3hodHNWeLLf/47S0+RG7aiYdHuYTcpcB15whzs/H/eesW5tXYas67Oa0sYxDejrVCpK5zwz/xyoqff7gmBni+H1jWGWWUB2NuLGWmMEbyRR4sBV647yPHi5x+8rioeHBcUeQSOng7OHaduFbGAJtNz6Hz7EcRkJZZwTB52iTXsK4yrnbSFB1GWct0U2TXT1wfJnxMNJPh0bLkfJGRUuKml4bzMDiWtUElzRAiRaE4q0p6n7jcj0xefheZNrSTCE2VSow+MTpJXO4Gx2QCxlpiFHrpAOwGT0iKy4MDLTe5dpIV6qrKeb4bWVUZv/BQs+s9l7sBYwI6wWF0kOCNBMEHqtJy1lj2o+N631PlhvM5j2hZWM6PSg6jpxs9pfVYKyDbKhNx9+V+4jDF2aIfsUrRZJpDF1hUGdYYtJdmdl3BUueElFAhkRtNncvad5wiOsH9dUFKCAuu9zgV6QdPIhKCiMUjiaPMclCw7T1PNwMHFziMjm7K0FqRWcO9dcVxZTFWc9YUnK9KXj9tuOpG3rvp2A+SBk2EZZVxUgqMdJigGye6UfPWTcvkEy4kThaZJJPvR2JMnC1yHhxVlJllP0781ntbbtuJVZ3P00bDoizIjP5Urzw+ywiCn+X62M1NCIH/7r/77/jmN7+JUopf+IVf4M//+T+PtZ/VIdZno17urG+6kcMgBOIqM7STo+0dh9FRfQSW4YcZMX6UnqfJJfRMz/+OzRSDk8TU3CYuVhWH0WGtZlHJhMD7yDu3Ld95ceCqnagyzSvHoi+53I/cX5eEKE6Lq/1AbiRYrBs9Y4g8WBdolahyw7PNwOUgSAGVYMwS79x25FZz/0jSkk+ajG8+6dlNntOmpMoycqvkwWAkp6bJDS5GtBJdQJkpnh9GLsocU4vAVkSOicJY7s9rLx8io1e8cXlAAY9OalZFxq4PvL3psArOFiX31iUv9j1X24H9FDhe5OhC89rpkv3o5aQdFGWe0U8elGVVidjzMIheg5joRodPiZuDxyI31To3rMqMi6U0j3/s4ZqbbuKdm56n2wFrFAbFUZ1zel6yyBVvXHfibNGak6bgYgXXe4fRAq683A7c9p5HJ+U8EQqMQaZZOimyXGGcJjOiZbJGg4+EELjcdmwGT1NlDFNkUUj+zXZwEta2yNkNnttuT240pdEs8pymjJQ7aANoHM5Dp0EjzUWVaS5WGW/feHIDm87LNEIpmkJT2JzCKkaXQCcI0vRlUTGMkZuZSSaZNom9i4Tk+PbVgYtlOTvKJByyn3ou24n9OJHQKERfkxuw1pKMYj/FOZBvy6NVxWvnDYNzDD4I9XpRUG56yrIkKzKc9ywKi4uJp7c9hZWQwZTgZFkQYyQlxfki550bT0gRFwQ/MTrPOCE8K6M5WRQcVTUPjkuebHtuWicHhhn0aq0is5qUFA+PxH4ekc/UoXMMXojzYwgzbLPnfCmW/SpTPFovaQrD6BP31iV1mVH7QJ1nws8yhvduJMPJz8JvHzw3hwG04TB4/CKglOLesuTBUcnJouDQe+rS3jmhUoT9II3y2bIkATedBG1+1P3q/+76aUAQ/CzXx3pX/dZv/RZ//s//eZ49e8ZXv/pVQIL8zs/P+R//x//x89XUT7Cs0TS54Y2rA0UmWSc+RkiKs0aSWH0dP/YJ46P0PMtCUAaDk3Thl2RfWTcJb2ZVZqzL7O4a3rlt+f3ne5ZVRp0bbtqJ33q6ZZ1n/NzDFbnW3HSyfolJMfnEooB7RwWjk5yWcT4JlpmRhFxEZKuQsf1vvnPLi91IkRmWpSE3ivNFwXFd4Hzgup1QGnIrq6tlbjlucnofSDNC4Z1bQUocVzlXIXJU52RWY43i0UktwXWDJ8QEKfFzD5Z3+qJXTypuOoEInq9KYpRU1tvWsR0dxmgMmsPkCF5WO6U1KBIvtj2dC3z1/gKtDN8ePIUxdNHxbDtRV5HCKHwEVKLUipM653wpltzrwyi2fZLoloB+ClwcFfPk68Dltqf1AXfV8uRabPeL3KCMpswty8LSj4HdIDH4ETipC7SByx3oABbFMFOg69wQrOQLveV7NOISszqx7b1YziPsnCPPwAXD5APYyDAplAKtofeJwXmOCovNDKVR7DtPZjUvDhMxBHbtSO8l7TgqhVXyAH5w3LCsjCQfG4O1ChWhnxzvhYFN54hAruW94hOoLGdyiRfbga/cX6JQfOfywG3nyTSUWUauxf2XW0VIaiaQR7adk8kRief7nsPo0Epx1OR85d6S4zrjxX6gNIazZcE7Nz2d86QQ8FHWSVYZVoU0mE82HZuDJGaHlNh1nsF5bvYTKEWdGdZ1zvPDwPTWDf3g+JWv3gPg//f2hqddTz95HqxrzhY5IQQ2Y+AXH6746sM1ziX+0Xcuee4i6yKTqaXOOIyOdZ7x+ESo3j5GEUobzToTntC+d8SQWBQS8rcfJFXbqkQ3SFPVT5GEQSe4PYw8Pq754nnDcZXTTRGrNUlxB8UMs+5uUWRzOrfETMCnO/H2pwFB8LNcH6u5+df+tX+Nr33ta/yjf/SPOD6WPfTt7S3/6r/6r/Kv/+v/Ot/4xjc+0Yv8vD5YizJjVWWEOK8qZrZOUwjl+eMGYH2/TJznu57r1tEUhtOm4Nm25/owcdrk1IXF+cjxKqfMBRbYjo73bnuWVSYuCmBd5yz3lst2Itea235CKYWbU4Zfog1ImvurAj9TiJtCRvPDbO9+vus4dIHdIPk645T4f/zcGYXReGCdGbQWAeaqtnSjaGQ2reO4yQkxEUJiWclN9eF6orAapQVboKzCd4KpKKyR02eSYD5rDRfL6u7kVmSaVZExBAnwk2tMrJqckGCaIldh4DBPCspcEoffvhm4bh1j8LzYDzw8rhidZ9NP+BRIUaEnSFbTTz250fQu43xZAol2mvj9Zz1aQZZZshmJUOeWfojcHgae3PSYTKN8YHLz9EEHdp2Iur/+sOBkWdJOnpPcUBhhbLmQOClz4gJe7CKFTSQlFvkm04xBCNsP1hWJhE+JRZHR9gMvOnEYjQGxZS9znAetLNX8MB8mWXOlGElasy4tRW6YDhPLSqIBnuw8mTVcZJZMG4JPHC0yvnpvycOjBh8jbR9pp4l7S0lF7n2gyAxPNwNTEJEwSnHvuORiUbEoLS92A4fO8cpZLTb16EElFCL8NUYxBWiniWGaWFQ5BsVZU1Bklhf7nnYKvHZSc1Tn+JC47kaOyoz9GLg8jHROtFtllhEQJhXAspJ8pcvteLdyW1eJq71iQJonQY0kbruRwzgxuUCTGd67bTluSn7+8RE2k5Xc8aKizDQhRh4cyyHn2e2A85Fn+4naGr72eM1RXaCUTAYHF/ilV4+JEW66iTKzFJkmJSTEkkSeGV5dFDO81XGzH3jrukOlIOnVVgkjy4iz7YsXDcs847qTuIcmM2y6Ea0VyyKjd9K8HdcFhTV3Ds1Pc+LtR90LfYxc7SeqzPzI4OLP64+2PtZv5zd/8zc/0NgAHB8f8zf/5t/kl3/5lz+xi/us109qV5sZzemcRZMbg9ZyShpc+LECsD4qE0chOoaTOmP0clM7rnOuDiNPdwO/+GAlfCWruG0nMiOOjSkmjors7nXsHNr3xnXLG1cHOh/ItGHynqaQRqOwWhhDWhFCpCkz6sLyrcsDmy4wTmK/XTYWkkS2j1GCzc7PFlysSrad42SpWRaWpDJebAfqXPPKcc0QICmZcDzf9milWZaW7ejpJjdTzMWmXGUG5yPj3LSc1NmcqCoj693guO1GOieYgzoTttK9dUUIiWxOjB18JMaItpo6k3yTcm4U3rnteLYdGMbIYXT4FAghYRBERDtEssxgdcTHwHs3HaOXwLgXu1G4PMc1ZS606MPguN6OouXQikxpjuqCZR7YziGGKYoj7PB2YJHt5Xuayc6L+YS66SbOlxnt6OhGaIymyAw+IViI3PBgXRFi5LbzDN6zGUduDhMqJWThJJR5rRV5iJApitwKndwEusmgtIhcd8NE7zyxlTTchObxUUlCCTnbORpr2fWepDpihLpUKGVZlQVKJXQ0HDUFVW55vhvY9olMa3TS9KNnY2BdZ5Sl4d6q4pV1zbPbnm07sR88U4TjyhJCZIywHSO5CTIpqjNu9oJKyC3EBN0USAy82E6sm4JFERlixGr5em4M6zrj+X6Un4OJxKDwyVNaw37wACxKSwJIYpd3MTGFRDCKGyaGpzvQ8EtfOGVVZJw0oqu7ty4pjGLTiRtv004YNaFVwsVAXebkRpxQISZQMpGyRtP6wKrK7x7cwF1z9OCo/EAS+StnFb/5rhL3VABvE6tK8CSL0gKJm24CBYvCclLntKPndj8Ro/wOFmWGmr/Xl/fCT3Pi7ffeC2MSrd9LVlZUiYtF8fmK6lNcH6u5+epXv8rz58/52te+9oE/f/HiBV/+8pc/kQv7LNdPelf7fjul1QmDNDY/boLkR2XijF4mJseLnOv9yPPdyBQii8ISYuRsmbFpR9657eld5KzJiUHYOIP35Pa7LohNNyGLLHU3bbhpHYOLfPF8QYI7C3luNMlIM/fl8yVt79i0MlFRStG7wElTkBvNdTvx+ETSUK/2A+/d9litiCScj/zCwxUP1jW73nEYPTFJ8vGDdc6ytCzHSO/mFGal+MJpw+gjV4eRTTtx3OQsKrlBv9gN5FaJINoo6swyOM+7mw6FCJxRiq8+XEOCm27kO5cdKomqY1XLz0Mh0Murw8ST3UBpFaeV5apzKCVi2zwXEOL9dYVGMl7e3Uy8clyyrvKZFp0oM02VSRR+OwWMEtHv3nlUVHQusBu9aGeAGGe+T5hYVJbD6CmM8Keu9iO3g2c7SCjbTTuiU+JsVXLSlDw4qkS0bA1NnhPVyBsvRg5DwBhNpmQVYYxi9AmjRD8yBMN5bciVwkfNIrfs+ondMJEbQ51bjJb1iFKy/phiYFVmjN7z4tDTe88QA36KoGXV+O6tIBMenyw4qnKuw8iytNx2E1PgLv1613qatSVGxeg9RW55/byZ9Tkdfgxc9xMWwTOUShIDRydk8N0Y2PUjubGMruXFYWScJ6UnVc66kvdlU2T4kDhfFlSZTCY748mM5bLrCTMDqsglAXo126+1UWTGknnPECVbafIJkue2Hdl1I9tuYrOfWNQCvZyAROS6ndAG2tFhlKbQGq2EhJ6SpGLftI46U1zuRdR98j2rFaM0Y5SDxfvr3rLm4XFDP3lChIu8RAPbUbJyXmxGjpcFJDUHTWa8frHgth2pMouexcNjiORG34mrX96vXt5jPk2C3e+9F+56J4gIJWvYTKnPV1Sf8vpYzc3f+lt/i7/6V/8q/96/9+/xp/7UnwLgH/7Df8iv//qv8x/+h/8hu93u7t9drVafzJV+huqPYlf7k0iQ/KhMHBciR1XGofe4IKFtBk3nPNtWcjleHEaWRcajdYnVim9ft1RWse8l3q20lnZyvNiL5uFiVfHepudyN5AZOcX5mIijZ1VnuJjubnwv9iOL0vLgqOIPLg8cxkSei0Nm23sergumEHAhEUNiVRWsSiuk6CBpvAY1YwEsZa7pR0lR/cJpLboYpRidvMaqNgxT4uow0E4OrQrurUrJksk0zzYdz7cTZZGhA4QY6L3oMfrJ8/i4Yl0X1IWVyZqoYUkE3rjqGacgD39t+LkHR1x0A996vifTkoqLEsfL6AKZUmhjuW49pdXcX+YsjQSmlTbS+YA2QjRXCprC8qXTBSfLjGfbjt9+2jKGwG6Yc3mSwpMwVv4bN7OzlpVmP3PCThYZymgmJzqjJjesK0uW5dKUWhEHX3UTx3VDaST5WSt4vMrpA/RjIMVIiLIaikmJ/T7lHM3asERgNwBRBNVnjTQIT7cD3SQP+4frmq/eW/J/vX1DP0asNlilSZliVVqOyxwXA29ee6pMobXhMDjKzLIscnaTw4XAGBQuRZ7vB4rMopQEA14sK7rJc70faZISXtjs8isN7J3kIY0+EKOsHttx5GqfWFQ5KUUWmeGFH2jnMMumsGTW3pHZHx3XlFbeB99+oQhXUGQZq9IKnHIOnRxtoB0mXIJCiwiZKNqYZ7uJ33m2l8DUlChyw673PDgqUUrx9nUnn1edUCZyusi5PjjevDpwupg/r8OEURn/5xs3QOJLFwteOWnQSqM1hBQp5oPF+8toxWtnDU83PUbLdFUpKHOD1Yrb3nOyLDhZ5KwqmdQW1rCqch6sJdtGJWgn/4H71VkjP+d3b7tPnWD3/fdCP+voUBJSuW4ymjK7Y+t9WjVDP+v1sZqbf/Ff/BcB+At/4S/cpROnJB+If+lf+pfu/lkpRQjhk7jOz0z9sCynH7d+UgmSH9Y0PVhXTCHw20/2lJmmtEINt1px76jgt97b0MyurdvBwTzSbUd47aRm8omNk4nNK8cNXzhZkFmNa76bY7PrHftu4t5RxTLPJNjtfY3arvecr0rh9XSORiuUMjA7Q86o8D5y0zteO6vv1nbRR3772Y5vPtvz6MhTzaA/nyJ1bmcrulSZW+LkOWlKdmpi24sAtMxkonDdTRSzJmPTe46U5IxoJY3lUZXx9nXH6CL1rGPY9mKJ/uOvrMkzxW37jN97PlDnhuNFzrKwHPqRkBQ6JtLsDlMKwrzSWmtpoEKC21GEmVMIGGM4yZRMYVLkrCp5dFKRvQwa7Dwn5cQ3Ny3bXnJljDIcpjBDRgUiedNOdJMGlSgyS10WVIU0V6MPWC3086Mym91ZLbk1FFbx+y923M7i2CMy6jIj9AFTgE+Cl8hkVCegzMlz1hQcH+X03rM5OKJNGKDKDUVuuVjnPN9FisyQJcU/fuuGbz7ZE5Xk7JTW8NppQ1lY+slzb1mz6RxPtiNNYUTnk2RyknXyuz24QGONWLhLw9vXPV+8t6C2BqMsVW4xJqCS5fXzBdbI1OTyMHG2yIhJsTmMxCCicqUSKQR2Y6QbPUeVBQWjVzzfDzxYVzw+Kuc8qIx762rOmIHbTtKYE4kq07x7PRBDYl1mOJcIXlxeWmsUCR8Uh8EJpLbUNFmGixHvRLydzXDapjCs64JuDGSZrJKv9xMndUGyUOWWi1VFYTW37cRvvHnLs+3Ao6OGmBK5Vdw/KnAzlPTucBMTX75oCFE+qySF1nC+LDlf5Nx0E8d1wVH93c/ry5VTYc3dfSmfE8pf3q/en1z+aRTsvrz/XO0ltHSRW9ZNdpes/GnWDH1eH7O5+d/+t//tk76On5r6UVhOn0R90gmSMSXqwlJlBqXVXdPUT56jSsbtvZOm53RRsO0mbjvP6XkJwL6TdN9lYXAxYa3hlbOCRZGjUuKqnUhKpim70XO6LFhWltFFjuqMB+uK08UHV2svmzi7hQfHFcYajuscozXXh55d7zlbSFjbUZVxUst/r2Pi3bbnaj+x7UcypVhVGS5F+tGjliVPtnFOjxVtwjIX/Uk7BUqr2fMy3ViEwC92IqI2Gjado3ORs0VObjXt5KlyTUiJ57uRV08N95YlJ4uM00bcVX/i1YmrdiTTQtLezlbuwmqOq4wpRA5jlByaFEXErGVKcFTmYqftPU1luGgyDqOkEZtJc1SJjf58UbAwmrrQlIWimB8eKYHVkQgYNKAoLaAllTgmhVIj503Js53kEPUukBuoZwr0bTdRGNhFxbLKWORWph/O8/Z1y66b6F3AR4FKZjrx6smCi1VFPwX6ENlPnuOmRCu4d1RyMk/qQBoiqwzroqCy8K2rHVeHiSkkqlyz7T27fs+yNByrkn3vqXMrWqteXFYv9p5n24EQE5OP+BTJtMVaLTEGRuGiOKea2U30xYsFN3tPHzxWC7tqPzpOm4xH64rd6LFWUnRbnxhdJDOQec+hl6maS57DbIMHxTgJvV3AkYhTLjM8Pqq53I84j6wUC9EZTSFRFyJgbwqDCwISzUsJFjyepyK51WRWsSoKTpcSrNnkMpEcXGDwgabMeXhUURrLvVXJ1WFkWeWMTlxqLkSK3HC7d5wvwpzuLewta/UfmghX1uB8Yjd4lJJU6nUlKBVjNDF9sCH6qBX5y/vVH9Uh8MeplwfIKjNElciUoim/qyH8NGuGPq+P2dz8s//sP/tJX8dPTX0cltOnob6fTgikOTtpcnk42Pn7UPDepmORWWKItCGRWYPRkmlxsshZlJbJQbWQE5yLiafbntvOzSBKYfNcLIu7Ufu6+vAb28EFHh3XrKucF7uR264nBNEOrOqc3Mo0Z/Jys77tRt6+7VkUlipXjD7wrcuB0lp6N/FsN3C+KmmyDGuksfv5+yvJ4Hif7X3bOdZ1xmH0DFPgJk1MLrHpHUklutEQ48htO3EyJwJXmcZozaIwHNcFMSbeuj5w0zlKa7jcD+wGUUyczM1SmWfsh4nWTxS5ocwF1rnpI6cqQoXYnj2oWUvSjhPD5Mmt5XJ2r62qnMv9SG4NX7xYcbGq6QfHW7cdV/uJmCZcDPRTIs4ZLFOAKot4L0Lp3okmqRs8zmraaeC2HTHaklWWMhMhtjUK52fW1RQZfSAh7/cpRo7rgnVTUOSa185qvnXZcnUYOF+WvHpSc74qePe2x6hEnglKYYqR+0cFz7Yj/eS5WFb4EDhMMtFoB8dvvbfjixeJs2WB1mJRH72lHR2DE1v/TTsyhMgwRY6aSGFzSqOEPdVPvNiNHFeGZWVZVRkxQuUULimUj7x6VNKNnqvZJbgsM4FiamF99WPAKoOxiUBiPwNb61xDSuwmhzJir17XGTHB6KO4BuucXCmeb0cWpbSaRila7zi0gSklNt0ISqOTBP/FlBhCIg6Oh8clR7WlGz0+JI6aDKMUz3YjTS4NQ6Y1Icqq7fIwcm8lYY3t5Lk5TLx6UlJXGSeLgpNGUpk7F3m0+PCJ8KunDU+3A5nRdw6rwQUezzlTu96zHxwayZz6fivyP+pD4I9TZW65WBS82I8/VAP3eX066mO/f/6P/+P/4O/+3b/Ld77zHf6b/+a/4dGjR/zn//l/zuuvv84/88/8M5/kNX6m6pNiOf1R1w/SCb3/+zIJlBZ7dYzw+pkQkjedEzJwTDgf+PJFw6LI7m5WhEiZGdZlxvPdeMfoyazmybYHJAflw5wIISViTAxTYNtN7PuJbvIscwnqm3zkO1ctdv73j+qMQ+eJUey1C2sYXKIqLN3g2AxQZYoYEpMK9A5OG0uIsOlGbtqRzGiOqpzbbuLZtue2G0lRTq2vntRku563LjtehIGTJudsWcx8qoBCNCbfvmzpfeDQO57uBpZlzpfOlyQUu36isJblSY5PnphETHtRK6YkybOLMufeGgyGk0VONwb6UcSN0wwfPCqt6D9QvH3TsywzkhKbro9wtijockvSsv67WBeS25MgpghojElYnWE1PL0dwCT6yZEUOBcIWjF6x3mj6CZFaSXsbgqJ3ejYD5NwM2OiLCxNkZFi4MFJzUmdE5XiYlXjPFSZ5XRRcLqQpu9qP/Fi56ijiGAfLEt8Cvz+0wPtFKkKcfpYrTiuMoKXh/XFquDhSgjgu34iBFnPLXJNSoYnW8/oAu0UsAYq6xmUNG+vn9Z0LtL5xM1VT5kpzlcVX7pYiPOoc6I9UiUxSiP53k3HuztxIzV5pEWamlzrOTcG6sKyyOV91PeORWV5uu2py5zzRS6TMGsoLHz7RSvkbysTMGsUa53RFuKyXJWW6/3AdhTRcV1kVCkwTJGujxTHgiRBSaDfpps4W+ScLgoG59n1DqUSL/YD+9GRdZrjKscAWiW+fdVxfxXZDY7cakGDBBFIF9b8oYfD+1fWk49/SOfnQ2QIgZgS7RiwevpI/cxn7RD400DJ/lmrj9Xc/Lf/7X/Lv/wv/8v8xb/4F/mN3/gNxlEegvv9nr/1t/4W//P//D9/ohf5WavP2gfhhx0Rf+/3pRWcLwp8Cmz6id0gawythEO1qnNcELr31W7gMHlJvlXiFILExbpidEFWDz5wuig+1ImgEjzbdvze8z237URSggx/t+/wt/CVi4bmYklRWEqrebGT5ie3kuwrp0QNyvBs05OIlLagcwF/l0AcGHyadTWR285RWE1pNVsXeboZeO2s4eFRxaLIKHJDPwW2g6PINFYbdoMjxTivDjLJlxkd33y652SRo2cn0cOjiiozvNgPRJ1YV/nMp5IVWDc6TuucIjPkVlO9DHbzPZPWrCpxFy0Ly2bwrIxmXeZs+om3bnqOaslCKqxm9BFIrIqCB2vJe7k+TEQkC+YwedopMUZPcprBOZLXFEZjrdioIVEZy3rOTAlErvZOoJwukVtDTAljJazuYl3RjvLzE4u/IZHQRvOli4ajOue9TY9RigfrEgUYI6usbT9hMsOi0qhbYViVmWVdWfn5pUjjDad1zm0/4oKsasZJGGVlk5NbS5zXQMs8kwRjq7g+eGqrKMuCdaN457bjECLPBodWGqsVRW44bXIeHtXUVvO8HXmyEc3X9UECI5siY/QjKRi8iezHwKLUVNYw+ETnI+dNTmk0uTXUmWRRaZV4uh1493agc4H7y5KTRc5xXXCYPOPkWR1lEBXeR/bWcW5fuosk5NAnRecm7q9yvnS25LafGGZ91G3veG/TQhINzsWq5t3bnneve272k4jWLfPKLrAq5Gf6dNOzri0ndfmRjcX30/ndtqNMJTNzd6D7fvqZz9oh8EfROH6Oa/h01Mdqbv6D/+A/4O/8nb/Dv/Kv/Cv8V//Vf3X353/6T/9pfv3Xf/0Tu7jPan3WcPE/7Ij4w76v23bkt55seXy64GxR8WzXY408HHa9E5SCi9x0EyElloWlyAxPbgeawkLDDOkTordRohExMd01VloJvO8Pnh3Y9RPGKF5sRzmRz5k0LiQOY8AozSsPGhZlxpObnkVpaCcJe2tyy2F0tFMgs4pE5HI3sB88Loqg8qzO0MbwcxdL7q9Kdr3kWixLwxfPGy5WQgdPQGY199cVZWbYdhNX+5FlZbDaME6BSy+8pZASvQ/0o2c7n3q1VlS55ajOKBRshkA3Ba7aSfAORxUPjisyrfnd5zu6MXCYuhkOqudk2sR+ineQRZdFloXFahFrLkrL45OG65mYfr6MaC16kZASV7uR57sBn5iBoLDrA+umJDeJZb1kHAOdd5Ra44Jk4UTESv28n2gyw6oSvZLVsKozfJCfTZUyNIl+ClS5/I7qQvH4pEYlxXubjjozbMfAbT/KhCREvnV54GyRM3qwNs2WYk9rRJkcifzCvSUnixIfI+uqIITEMzWw6RMRTZ1ZFkWOD7IGUkqDjqTdiIuG2iiUEbJ5P3k6Fxhj4jBK9oyzht97uiWzorc4ruV9f30Y2U8S9lcWhmVhQMOTTY9GE5USZIjVLEqZYt5blnzl/ooQEq+cNJwscsa3IxqN0po6txw1OUVuePNyJM+E2n6xLtFaJomdS+x7T1UYHq4rylzzfDfh/J6bTkIoXzttKHcDv/d0z8W64pXjWj7fc2zDEAJXh5EQErmFe6sCazVX+4nBB55tBv7Eax8U2X9Yfa/O7+PqZz5rh0D4/hrHz3ENn676WM3N7/3e7/Erv/Irf+jPV6sVm83mx72mn5r6rODif9QR8fu/r/enJVe54v5RjUpySt52jlVl6SeJ0K9ySz+J9dgncYtMIdA7sSCvFwXGSIrt+xur2zknZlFZ7ifBHRxcoMkNJ3WGB9op0A6O0moikUWRsaxF12O0ZtNPgheYadxKwx883/H2zUBhJQywsIpFZjExsOkd949qVlUuCatToC4sR9UMD5yToR8dVdS55vlO0U+RJjfcHCau2oFlnvHqaU1hNKOLvD12FEYmMJnVInYNcLTI8ckxhsR5UxCAByeSa5Nlkgdz209MXqzcxez6cTHefX+33YRSiZO6ILMGgojBq8xwuii4Poy0k+P104bOB3bvSkOnreY4E/v3861oChqt2Q2JMvOEFJg8aBPIjeAvFoURVtHomZzD6pw6zzk7KlAobvuJtvesasvZooQkDjBrFCdZwe1hoh1lLeZDZPCem86JyDXT7HvHfgjUGdiUcE6suJmGoxk/MYXI773YcbYoyDPD6ESMm5JkE00+gEIo4ExChDcZ61m8PASxPZeFIUYJqCuMsLEKm5h84kU7oNCgFNdqosoNrxzXXHcTIUSCD5gsw2qxcu9Hj4maVZVxthAUASkSVeSNqwP73vHHHq85W5SCCKlyBpfwSdxtkwv4qHj9rMFozemy5HefwvPdKJOO2nJUFXzxfMG+d7x52RLPEkd1gdGK0UVeOalJSaz5V+3IMAn6YAyBdZNxWuU83Q5UmRgCRh/xMVJnFvJE72Vy9qO4lT6ufuazdgj8QfU5ruHTVR/r2fvgwQO+9a1v8dprr33gz//+3//7fPGLX/wkruvz+iOsH2dE/GFpySTYD44QRSx73R4orWbykguRZuhfSvDV+ysMGm0kZdmHSAgRP+/0U0zcto7Matal2FSrkIkuI0bGEBkmscQaJGOEJI3aSV2wKAzdJDfw29axri3vXLd860XLtvdYlWZtSqTEUpUWq+D6MLEfJpZljlYiynzluEIpYU4ZpQlJJj+7MfD4tIGYeL4feOdGYJVdFYhKcVJnFBaeXI3cX1Xy/c84iVUpNuQyE9hokSnaITIETzd43rrs6KfA6BJaK44KS55rCaGbf17bwVHajHGKXKWRewpeP1/w6KSim8MJY0qc1DnHVY6PgTdetML6UZIkfb13bHtHYYTDlRK8t+kJIdL7RFOIO+cLpxWBxLTxLHKF0hKKJ9oducYmMyQUj48qXjmtyYwEI64rCZ77x2/e0E4RbaALCasUwQe2nUOh2Y2CL/A+YYzm3jrncj/RT2LBrjIjwXxK002R24PDxcBpU7AsMm5aCa6rMsNeO0ISW3yZyfsV4mxfVncpwfcWxQcSpPsQ6Cf5nqbZ/i1uOk0IkJTmC2cLCqu52Y/sjWGdK0ymqQrNFMD5wIOjkgdHNSEkDkPgO88PnC1LXJAGpMpefi4m+ilwf1Vwb1lx1Y44L+ynurBzLkxBPwWebiS0MCTN1V6C8tZVDpk0vFVuRdDf5OyU52RR4ENichFqxaK0KKUZXeLL9xacr0o52Cg1Byv+aG6l73c4UkgD+/0al8/KIfD71WfB/fWzVh/rPfVv/Bv/Bv/mv/lv8p/+p/8pSimePHnCN77xDX7t136Nf/ff/Xc/6Wv8vD6B+rA98Pv/7OOOiD8sLdnFKCuIGaop9mVZBxWZ6BJqq3lxGNkNE0d1ztNtTzsGqkw0IiFGXjttZmUONLlFA7edZpjEFvzkdqT3kaYwxKTxKdD7wPVhoMwsp03OcVNw3MDFquAwOG47x7detFijOF+LhbvKLZlRko6bEsxwUpAJTYiRe4uS104W9HOD5qLcuM08el4WGUYreufRJuG9PAwLIzkyzkdO54yMm3aitIbjyrKfPO/dttQzW2sKidyo2XVjWFZGNCgGdodRsl4OiiCXySJTTBFef7TgfFkSSRitOW5y7q1qLnc97WQ4Ps7JM8PNYRRHTWk4biwbldAh4cPEusqoMoOPgUUlkfnj/IDtewfz76LrE00pExSlEocpkUi8d9vxynHDuslZZBatDCHC6+eNIA2CrJiiUjw6KTlpCn7/2Z7vXLa8c9OjjWJZapaFEf1MBJOYnUoFeS6/96vDQJFbSiNpzM93HU1ueW/To5LiuLE8Pq3JlQiDE5H9MDHFINT0KhMBcoqMUyLPRMCtpkShpEHZdQ6rDUd1xqYf2XRisSZmlHODc1xntFNg2RSSzGz1PEmDbooU64JllbPppLnUOnHZOeoy5/G65N2trF6bQnFcFdxfyQSodZ7n+4Grw0imNevK0o0RFyX88el2IKbISVPSjoE3rw+8frZgWeaMLuK8rHkN4KIIfOvSkvk027gt7SD09WWVEZNoztaL4o4N96O4lT7scDT6wNV+IDeGpww/9Suaz5L762elPtbP+6/9tb/Gdrvln/vn/jmGYeBXfuVXKIqCX/u1X+Mv/+W//Elf4+f1Y9SH7YGXhQSO7Qf/h3bDH2dE/L2NkaxDNLdt4Mmm580rsUAfVRnjpNhrx4OjktdOGkYnFufMKEIKoAyZURxVBYMX3lJhNVWmcVFzb1Vw6Ce+ddmzHT2vndS8frZg8IF9L4ydy93I+Qq2gzQJL7+3amWpC8uLnYzuR+cpzCCZJcpQZGLh7cfIaycFD48qJi9hdg+OS8rCUs4ZQC4mYpxTlZMT3cbkefO6Q3IrEyGCUooqz8g1nC4Kvvpgxc1+5Hef7Xnz6sCTTQ8qcd7krJuSe+uSwSkG5/BBUnRPlyVNYXnT7PiD5weqzKCN5jB42l7xyknJsrT4JJym3Gi2vaMbpKFclhllZth0E4cxUGeG4yqnzCybbkNmBPRZjoG9C2w7L/ywRYGNiVUpjdvNQVYhnfMcVTmqsJAgN4EyM4RocBFOc8OysXS95x+/3XG9n1gvcpmKAA+Pq7uVmVWJF4eew+hZzgLoFEVc7HxgChNGiQMopUSIiet+JAweXRgOM9F8Zz3KSLLvw6OC4CIuJZa5ZjcldkOgjopH64KHxyUkjSJyHQN1piXrZ/TsnacI0py8clwRUayLXDRdvee6H3m9XnBxIiudwxg4a3IOmcZqy7o0XKwK3tv0bHuH0YrCaPIZdulCYAqBuix5pBVGd9SF4fFJxfmy4KYd+daLA5nWRA8xg9yKqDzXhq3rOYyiYSqspsoNm97x3k3Hq2cyPVFacVpn3HaOy/3E1b6nsIbzdcn9Vc0weV7sFblRDC6QG1lRvZ8N96O6lb73HtANHpBcqSL76V/RfNbcXz8L9SM3NyEE/v7f//v8W//Wv8W/8+/8O/zO7/wOMUZ+8Rd/kcVi8ZO4xs/rx6gP2wN/56oFEher6sNt33DnHvphmpzv3Z3vuombLrIsLW9cHci1JvnI5IU9pAM4n/jyqwvK3HLW5HSTJyUlNG+l71xC3Sg6lt4FVlhyo7l/VHHdTpwuCh4d1aAl/fSV45rC6jmFNqMpsrvvzYfIqs4xSnGxKjmMgW2vaKeZDD3jDrSyfPG04t665LabGMZIVRj2vSeGjkCimwJzVCvd6Bid562blk3v6EbRFynk4dFkmiK3tINkf+w6Jw++zqF1QhsJA3y6HalsyyunNfeWhYiqURw1OUtj2faOGOV3EaLQyM+XBZHEfgy8dd3z9VeOOF8VxAhXh4nTZro7TfoY2Q+eItMoNPlgqHLLVy4a3r488LRzGAWLTHPQMrHa98LSaXeByhrWpRFQ6JhYlIkiE4dSCInbabprTu6vazKt2Bw8z+cwvVdSzROX0Erx6LhkP4qwuigs9xYF+1b0UKOPJCUhiilGMisA0MlLiJ6LkRQSTgFKUZcZSkvI5CLL7iL9n+0cg4Mxgo+J09qyXuRz0rDji/eWLErDxZTYzPwlnRL70bMbIwEBrAYf8SmxLDIqKzlNVa5nmKxYqK0xGBVJKdKUJTEpQhBI5KN1zdmyJM72cu0CGrGWl9bw8/dX5Nbw6LiisIbbbuJ8WXAYPTelIga4t5JmMCWZUt1fVTw8roR8HhKL3HDdTjSlBA5qJZ+v+8c5dWHp59/l6ETgjlI8PKpYlpbjJic3imebnvduewYfOFvmxBh55aShyMwPdch5/z1g9IGn9KxN/jOzovmsub9+FupHbm6MMfzZP/tn+eY3v8nJyQl/8k/+yZ/EdX1en0B92B7Y+UQ7eQyIdkSrD9x4mtzSTv5jKf6t0RCipPtmBoWsK06XJWEOMLu3KCkLQ2EUmZG8lN57blrJxHipf7k+TBzXGZnVLEq5Oex6T+EDlV2yKCyHwZOU2IBXhaXKM9642nOxzO8gjArNtp/4/ecDZ8uSwmoyI42G0bDINe9uet7bdGileX1Z8MdfPaa2lut+4rjKya3hphv5rfe2hBS5WFUc17ngJlrH5UEyegqreb7ruWknzpc5Sime70ceHMGyMiwKy5NdxxuXLdvBk1KQ5sCDUWKR3/XSaJ01BSdNwbK0NIXlMExcHyYGL82eVohmxioutyO9c9wcRspMs6py6rkh1FpOj1pJ2N7kAzfdxOgji1wYWklrvnLRYIximsR2vG0dk0vYlwyqmNCAC8xsrsTopfHrnaebAosqE5t5O9INgSdbmch0LqCUrG12/cQUPI9PagbvePe2u0sfjslAYrZZR4IDNALgDJFhCviU0CqCEi2VAspMY03GaZWhSPzOkz1NabBaghz7KVBmGa0XmOtJk3HWl+KsO6v5wowCuO0cJBhCYN9NvNgN1IXlwXFFYw2dCzifmIJkwxRzY7rtHE2puVjkhBh593bEBc9pLQGDgwtMs5XbKFlnGaNIKdE5wSxkWh6Gt92ED2C14d6yZvKB40accYfRcbqoeHRccb6UgMGbdqIdHcuq4GsPVtxblfzBi70wq7TEN5wvClSCyQUG7ziuCuHDKYUi8f/69hW/f9mi52TxbnJ8+8WBr1ws+MWHRxw3+Q+9TrJGErpBzfqm79ZP+4rms+j++mmuj/Ue+/rXv853vvMdXn/99U/6ej6vT7DevweOUcLLbg4jl/sRo+HoUHC2kBvwyxvPbTexmRuij6P4H32gnTwxJDb9xOgSeaZ5fFRymAKPj2uUSgwuMoXEea7ZDu6uGXl/s3XbTdxblWRGdDEvJ0MpJpaVFVLy4KkyQ5FZNv3I4COL8rs31d3g2HWOiMD8lILeBU4byZB5+6olRHi4qjlfF5w2JZf7kcKKEPPlOmfbOXyQdObMaDbdBLPrqcotY+cYpoBBc7TIyDJpSnrnBcZZCByyzjJKq9kpmNzLyHqFT2C05mSZc1JnlMbMSb9CQV9XBVpDaQ1HdU5txfI9usBudEQS1rY82w68elrz8w9WaK1YFIZt79Eanu963r3t6L0n14a2yLjuBhKJB6uK3eC5GnqU0iStWGSa0YNSItiuq4zORUqbSEmRoohiE2ANOBc5DJ7D4LlpRxHMFhKeOHqPViLW3g6O48HRT5F+jGRWs6ozOi88J2M854uSVWFoJ4dSMo3LrKYfHS+2iaq0GNTdetAqQzt5Rp9AQQyK7eh5vhsgRGgUqIhzgRQTw0mgLq1MFef3xpfvlTA7jca15zsvWjbdiFWKMhN8Q51rfEh8+/JAN4pWzWrFojT4qDCKGbJqiCSGyfNuO9FNgW6aSDHxzqbn0bpmMU9VJiOfGR8iu97d5ehkVnFzmGgHT2YMeWZ57awmRphmvZxPiXaK3D/SLCqL0oqqsJKV5AI+Ro7rXD5HVvHoqKYprLDHWnEQbifP+bIgJeQe0DpWVcY7m4F7R8MdGuOHXSf9rK5oftrcX5/1+ljNzd/8m3+TX/u1X+Pf//f/ff7En/gTNE3zga//LJLAP431/pvM4AIvdgNKIVRlFdl0E7nVLAoREcYI+9F/LMX/S23PdTvxncsD7Rg4X+ScNDmb3uGS0KVR6c7J8eioYlFmbAfPcZ1Lw4C4pkKU9c+isHd/5/tdFesqo59kzTVMcU4VdpRGcxgDT7c9udbsR0nZbXKLUiKoLGb79OsnBS92HU1hWZUZVS6vvu0dPsjay4fIfnASRDf/TEQ0qtjNIttMabRSVJnlK/dKBu95cttLuJ/RWKVY1RmrMue2HeXn4YM0kt1EXUg0/+AC45iYssRxbTlbyMNkP3ON/tiDFb/9dC/sq+CJY+RqN+Ji5GxZsCwso4fb1nHbjqzKnJO6oLCGN64OPNsN3B5G+pjQyHpsch6rFW/eHKiynMzIugsFpVW82E8ED6vaclJljE5gp+LiEbq6MZoHxw0PVzk3nQhaL/cjEZh8oMwsWmmUhmWRsaozntz2jDFxuihksgRM+56gFZmxfOFE8oOsqcmsZhgjexe4joqiCJwtMnRSvHPb0waF8z2dk8nMxarkeGEYNgESZLmItYdJOGLOS57NeZ2h58/HbnQ0pQiNAS5WFZXN+J2nG5xPJBVZ5PbOedS7SIpJuF2F5dl2YBoc+97ztccN54uS71wdSGgerIvZPZhxGDwaxW03orRECeQzvDQhuqzD4PExkhtBQ7Rj4Ev3anFY9TnP9wO37cRucKgkCdSvHNXctG7WQGmUlfdcjGKff7EbyK2mzMwMtfX4JIiMXBuSFnH84CKBiNZaXI99ZJlHbtrph14n/TArmp/moLufBvfXT0N9rN/Br/7qrwLw5/7cn7ujgsPPLgn801ovbzJPbnveue2YXCQpxX5yYtdV8O51R5YpRi/gSq0U91bVB17no8bJ779BvST8WqOoM0s3BnaDp84Mo09s2pHcSMT72aLgS+cN56sKH4QubjKDmW/svRNswvmy4Ph9pOGPcncVNmAGgV+WmfCprg8TN91INwWWecbZIiNEebgpvuvoeG87sK4K1rWAITe9E71I79n3E1Upp+vSyiRFIboRrRRDCjgf6F3gbFnw3m1HStJULudJ1OOTijrPCFHWPQloSpkGbXqHCwkfAtYYrAGtE5HIusr4pVePsUbxdDtwfRhYLwpeOXZ889me3gcypRhj5KzKub8QKrPWkqj83nbkK/eXaC24iMxojuuMy/2A9tCUGbmNbGKiHR22Dzw6yjmqLW9fd4QQWTbSfN4eJvLcsB/Fvj/FyL2q4GJZMOaBKcEff7RmUWX8w29fMXlYNxnbg2M/hlmbE7BasXMSLhiYUQ1GQ0x0Y0Bpw1mV8cpJw+v3Gp7eDnQ+cFHmpBRBQ3Zc8Oi4ICXFmzct3Sh5QykFyZ6Z128hKjIDqzKfdTJmnpR4Xt6xisKwLDK2nehR9r0EKC7rjCqz6Ebx8w/WjN7TDZH7S/l8TD5w0hQsSivN3Utqu4/4CJe7Eefl9y2kcOGlNYXltnPkRnPUZGRaDhZKKbb9RErcNaMvPwdWaU4bw8WyYnABFyLny4J29CwLcfl94bTh4UnD5CPdJO7Bm9ZRYuapp4A4c2uY5unJaZNzdRB0RGYVm8HdfY5DjIyzaPzFfsBKn8OisJwvyx9qPfVRK5plkXHbjp8H3X1eP/H6nAr+U17rKuf5rueqnWhyQ2UNj48qRh+42Y/sRs+rxzWPj2oyI3H0mdbcW0uD42OkGwNacTdO/l4HFkA7eFYz8LIuLI+sOHYGH1gUGsjwUa7pwbrktBGK+PtPeZlWrCuLD0KufrCuyDPzfZM/7wSMW1mt5VbjQstVO2AQXQMknu9HlNacNAXbbuTt65YUo9i6Q+Iwysn50Hv2oyfXit/SgUWRiwVYKYiJusgYvMf7REwJazTqDtwYePOqZTeI3uHVk5qTMkPPQtHb1nHU5Lx6WrMfPKvOEn0iAPcay+milKDAMuPrj444WRQcRscUIu0YKDPNyaLgq/cV28ExuEA3ejkNWwlxawqZTFmTmFzi3duOdvT8wfM9b9+0HIbAqspoSkOVCQj10ItDBh05dAK+fHhSQlTkWrMzim07kWeW++uSFJM0YiisNWQ6cXUYeXfTc9tOjD7QZJZYRcykGH3ishtJRDJjsFpx0hTsuom3bzsyo8gLDVqYZataslaebwesgeNasnkgSZKvMfgQuWxHXjmpuGkd68pyupBVbHi5tokSJhmmxLLKKHNBQIA4yp7cDjw4kgC9RaElRXqKuADjFLFW8+i4YnKR33m2YwiRLDN3gl+AqjB3D39tNBfLjLq03PaObTfxcF3SzEGXep6MjCHcacterllzI+uxkBJHtaA4Qkp4HzFamtPCyuTwaj+xrBz3MsNxk3NcSxbTy0PIoiiw+oNk7y9fLGlyS3rf53g/OqyVRkeymRIxictsdIncBjkIWIFkXrcy6f1h1lMftaK5bcefaNDdT/NE6PP60eonSgX/S3/pL/Hrv/7rnJ2dfZy/5vP6BOrlA/jVk3pmFMlEoZ0cb1+1PD6qeP28oczkrXA6c56qwuB94qad6F3gfCGj9XWV/yEHVjt6LueI/3Wm75oMGdf3lJnmbFGiZhbVFBL70XFs5Wa2LDI23cSTTc8YhO308KhiWWTAD07+FAGjTGNilOnh4+OGlODZpmPbO7JM1kgxJp5sO/a9px0cPiXevjzQ+ci6tBgjwYEXqxLnYU8g0woXEqXVGJV496ajmyInVcarpxKF/85Vx2GIlMaQLzQ6JW67iSe7gV967ZT9oPnHb91ytijRGJrC8vrZguqR5jAEUlLUtaaxhtcv5Hf1zac7rtuJyXnOVwW37cTTrZDM760rLvcj/eDIrKF1ieNGplet81il2LSSqrsfnMAOXcKFQIiawwA+JDINpRUNRjsEJh+5v6pYVobbdqIfA2OQBuBeJSf3IrMoAmVuOalKXhwkp8YaRZNbWTW2I0ZpHqxzXuxHxsHz9ui5WOZctZbzZU5SgAar4bTOOYzSNIaYMCFSZqLBskpTVJq3Lg+8edVjjawZnY/cW5WMIfHoqJImaj8w+cAiM+xwaK0pssiy0KzrjNsE7Sg2+95JE70bPYNLXCxzwNF7SfR95bjiqBYn3/1FyUmVs15klFY+Q083PUYrXILMGLSK1IWEMoaYeOEjkUiVy9TPak2ZaTonU5nCGLyPuJi4WMpn4f2rnBQTLiaOm++uZo+bgioTJESmFE2Z3X3WX2pavlej9lEP+tOm4LZ1EgJYZDzZ9QwuYgycVjmnq5yEuM0uluXdVPRHcTu9f0Xzkwy6+xx98Hl9b/1EV4P/xX/xX/Brv/Zrnzc3/zfWS+fC2aJg0034KBRo7xODT5w2xV1jAzIW9yGxbR23vfCYvgt5FEv1SzfUyxtUXRiqWXi7rjKWM5gxJAnzW5Vi5z1dFKzrnMGFD9zM9qNjCnIDlauTMLvbfqTOLdft9H1viO/XFik1n9pD4vow8t6mE63RPMU5XeacNCVHVcazzcBhnLjtRQfxdOo5XZQsc8myOV6UnDQZKcCiNjy9HXlxGHm4LrlYSJDe871Qoq9ax4OjgmHw/MFlK5DDzPDuTcfPP1xh0STUfGqXG2+mFZnVPFgvuL/OMVrTjv5ORD25iX5yvHXdcdLkKBIuSrrug1XFo6OKIqv53Sd7DsPE2BT0k2PfTyxKAYaOMfFiOzBMgd45Jh+4ahMqwegC6zqnc44xBh4eVZzUOXmmeb4dJDW51mz6kUxlKC34hZTEVj54QWm8dyO8oinIWq+0mk3v6UZ3N1HodWCRWXJjKayimwI3rdjP92MisxFrDF8+r++agy9dLGkKyxQiz7YDz3cj1+3Aq6cLfEzc9o52chzNgYISAOnpvaYqMo6XEatEa9NNick7Ygo8PK44WpT0oycFcZ8dN7KKOlmU3LQjaX4PbvuJfe+wFl4cRvaj6MXyTMCUSikRoGvN0XFFntk5O8bwYF2ilWAn+tEz+oDWiotFzhQSTa7mwEX7AUfND3LblLnlYlHwYj/OgvTvalpOamloCPEHaj/WVc5rpzX7wWGsaK00ikVlUAh3q7BCEl9V2Z3g+OO6nX6SQXefow8+r++tn2hzk+YT9ef1f1+9fPD/IU1LkuC4pvjgWyCkxLrKZuBgRp1/99SonVCH05wj87Ks1pw0Oe9uOrrJ0xSW0QXe3UgzZeaU1dKKkPD9NzNCvFtlTF5C8bQSIeqL7cBRk3HdTtxbluRW30H93v8ahTUfWG11g+d6FusOU2RV5TgXGELER8XoxdJrrSIPhkWRKHLN801AJXjlpEIrQwyyRvEqsiwyDpUnsyUndU7nInUmwufr/cjtfuSmjbRjZHRBHghNTjt6vvV8j9WaplASzGcMZ8uCw8yIuljlVLnFhcjFsuDeukYDT3cDl/uRdgyMbuDBuiQ3il3rWOUZlJZVlaG0gDgP04RW8nB6th14sZ/42sM1zdwsDC5Ilk1u2HWO7ehZlBn3liXWaq7biW6MnK0KjAKjE5sxojGsK9HyWK05TJ43rjpOFhmTT0TgMDlu2kks/0bhU6Rz/m4F86VlwxgS3iUSijC7de6tS06bjKM6R2mFSonrGR5aFyWF0fgITWFZlIbb3s7cpQJS4r3NQJVHmVZZS2llKhYTPFpWnDQFf/Biy1U3ctpUPFgvKQqDBY6ajHvriptuIjdmRnZY2tHx3m3PYQrcHDKa3PLqSS2YisPIm1ct99bCeDqMojXzMc0QTAFhhphYlJYm1yyLHIW46ZrCcFIXlJmizqV5KPPvfgbfv8pJMaHm1GzNB6cPfyg4c85V2o+e7eB/qMmF1jLROV8UfO3hilwrjDUMTjKIqsJSZZbjhay9Bh8+4Hb6UVdAPykX1efog8/rw+pzUfdPeb1f01Jm5o7L40Lk8RwE9vL01ztPNwZOmoxDTDMT6LuvlRnN4GQP/703qDI33FuUaKXoJg8JzhYZtwe46QZ6ZylzQz6vrJa5TFxCSnP4nZ9vpoabduTZpmdd5zw4kinG890gbKVZYPy9N8SXN/ubVjJc3BxD/zLU7smmJ7eGVZHxYjfg50nR3noSw0zNVqyq/z97fxJrWZqeZcPX26x+t6eNJiM7V5a7svkw1gf+/cMnsITkCc0AYYwxDBBiQGuBMBIMDDYgkABPbFpZQoCEzIARQlgCGWRA+sxvYxt3VVmVGZERJ+I0++x2dW/3D94VJyOyjYyKrCrj80iWK3bG2bHP3nut9aznue/rTjmclDxaN9gQcCH+OwDWQ5FqdsaihEQC297y5vmaB5uW1kaRZ54qtsbRrloQgf/9YMW6MZSJZFSkVFlCqiVuuLsORPHpOItWXkG8E320aoYQUcHFro9rNGK+kgseJaL+Itea11+aDEnjMfLi3qKhd1EArJQEBDdnFV3vORyn/FKzYpInjBJFOoDaeuvYGsPUKPI0umoSFadLgYDF8XBl2XYWpWBeZsyqlEerFRc7Q5klpEpgrGPXedJEkSUK5wJISd9a1r1h0qRUqUSp6EyblAlVntAPDrLXDyI8btlYvnRRM84UxgZ8ENyY5hSpou4s83GGFNCZgPOetnNMinhBDgJemhUxBmSpeSVLeWV/RJFpFruOIosZYY8zsVprh+9ZE98768mkiCFbIq5mlBQkWpAl0d1UP0559zH8tV5bmj5hr4pBllWmefVg9FSzEiAm03eOXd99YBPifQyLjboe8YF/533gzKaPIuJEkQ+TnI+bXDxuCiZljOa42HZX0L7LXceutcyLFEFk9Tx2O0khnksU/GmB7q6jD67rg+r6M/9NUE/e5fWDxfRgFJ0Lm85EQu62pu08WSo5uRw0NImiSjTTUjOvomU314oyk9F2yrsnqN567uyXjPOE822LdZ7jqsT7mi+cbtlqx2GVYZTnbG35hpsTtJK0TXQNpTKmXUdXUYjiTx/TwQ9GGSfLhottF/ULg236vSP4eZWR6tiAzauEn7trMC7qUF6a55xtezweqQI6xIuU9YqpS5EKZkWC84K6jxZuATSd42gSR/LGOZxzrFqHAmrjONs0rFvLrNC8ed6yruNaZpRpNsN7fdkYjLWIKkd2Hi08+6OUi3UM57y9VwKwGC5Qi12HlhI5rGwyJZjlmvOtobWWV+YVX3c4osw0vfEY53E+kKUKJSXGeqSIeqtF3bE3yqg7g5BxlTJKFWWheX2asWwM76waBDHXqRq0GqfbmESdKEi1YNM51ltD21tuzjJuTkd0LlqqN33PybpmnEYqtFICIWGSpIyLBDdcRFMtOEhStIb7lzVCxBVNqiRNbzhf9+SZ5M5+DKTc3F9yue1Z1uB9nAS/NC/ojOOyNjRdhPpVmeb/fmWOVIptY3m4btkbpcyrlLcXO/IsYb9MGZcJ+6OURMH9ywbvG8ok6o1a65kXmnuXDa2LFOEy0VzUPXmqebBuSaQgTxXg+JX7Kx6sOkaZZFZlaBGT6bs+NpRvHI94ea+6uuA/PtFe7rqrJuS965PHera7FztOt3ElOy9T1LAShvc3KlfgzM594snFk01BOvz3bRt5O5mOa7VUq/etyL6cFdCnAbr7zcrVua6Pruvm5jdBfRRcaq6zQUcT84Za63m4bFg2hm7TUmUJ95eCG5OO/XHOjUk+gMvkB56gfIiAvipL0DLGKdyaFpxuO94837I3SqlSzfk23rVuO8emNRgb0ft5oth1JsYXpNGJNMkT3CRETsuup0wUqRJcNoazbXRw7A8U1URGyFrbO3rj2bYte1VKnioORikhwJ35iEQITrcdxnlGeVx5NCbexXcWbkxzDqoMrYcUcCnZL1PuXTZsWoM1ntNtx+mmpRogdMdVxtm2p3MBekemRITbORAhNjypVmx6x/2Lmtp4xoUiU5JN60DAJNMsd9Eh1TlPkQi2bXyfexu5LUHA8SRjf5TTG8cXz3f88oMVSokB8Ga5v6hJEsnd80j9zbRi1XbAwORJFQ8vGx7tDIWOQuxFbTE2guHKVFOmim1nEUJwOMmo0sFinKSMcw2di02L0hRS0liPkpYcyX45iJSbeLG82Bl04qnSFHyMPJhXCeMiQ6uYv+Vo6U3gctdHeq6Q7I+iU6rMNI7IZ6k7GxO70+gMm1UJeZZQpoo7eyVlrgghAg6FEByNM/ZGKb2NP7/rLYttS90rTIBprhilmvtLw6LuOahyZmUUDb+zbHi0bilSxe15Sds7vni2ZdlatI7flbp3TDLNzVmBC3BjlvPZGxOqLHnqOPy49Yl1nrNtzP+aFilKCpZ1jxpgkh/WqDzv5OLJpiBP1JVLq+4tUkSL+ePnf3zOaHvL2YB8yJPoWhMCEimeaQX0aYDurqMPruuD6rq5+T+knmX//UECw8cC4XEem5FF3SOG8EUhBmaNsdxfttyYFld3oh92gjKDbuZxntFjsmyuJbJMOBzltL3l5+5eslelHE5iJEJvLF8425BpjQ/RuTTJ83f3+94PEQWCVWNYNz2Jjoh9H2La9OuHo8ghcY5d78lTyXZruNh1FL0eWB+Sl2cFWRZFzhFqKHAeRrnnoEwZlZoqTcgSyTRPGOUJAnDeM+8dy6Zn01mcD1SpZFFb0lTxmZsT9PmOy20XLbdK0dvIWHE+6ibyRNGZjovac1BmjPOU2jhGRQyhtM6jpIgRBp2nUBIpYZQnIOKFyFjHl8535KnmbNPRGhPjCWrHtrdc1h3jTF1Rji/qHuccmzaKhuejjM5afuFsF+Ma0pxlE2m1cprxYNlyPM5IB6fSpExwjivdTaLiymhWpOzamF5+OMnpXaAzHi0lO+Mx1jMrBWmi2BvDrjbsaoPwcFhlZKli08YJ0XLb8daijqvTbEWZKqZVxvEko/eBW/OCSab5n3dXtNZzMExmxrnm1qwc1qGOWRFDNqNrLsEFaFJNbxzGezaN49dO1vQ2cHNeMs7iZ+uDoDae27OcSZ5eAR33S8fdRc3+KGFTW95abHm4bPACQhCkSeS/hEgKIFGR3Bx8eF822wc1IdZ5fAg0vaN1UXytB9u3GlY824HC3Q/W8fcew887ufigpiDaweFglL4Lz2RYle2ioP7+sqHQioWKwusn2VFHk2drJl406O46+uC63lufanPzfd/3fde04k+5vlwL5JMnXOvjeqk1jlGeXJ3kIHI9Qnha3PhBJ6gnT7RaxnDJZd0Pzhp4cNlwWUcB8cEoZkudbzo2rWO/ykhlvJifbluOnY9ulG3L/UUTpweZ4v6y5nzT8dK8ZFLoGArZGL50uqPMFeMiubqQl6nmfNPjQ0y3vjXL+dbbM5Zdj/OeTRvpuJNCMUrj7zwqkqu7v0Vtrtg9xge0iLoPJaC1DtPGdYkPUKUJe2XKpjUQAtZH7Q8iJgCcblvyVDIqEtrOkqSSIlEoES3Pwcc1UplqJrnm5LLFyIAaGk3ZhYjo7yy/9mjDvYst2wHalkiBkfEz2a8yIJAqQW0suz6uEH/LSzNmo4yTZUsADqqETR9J1cjAzVnOvExorGHTKTatxeEptCZLYmZQlSZc7izbxtD20bkVA0cluQ5Y7whCIAJUqWRZW7QKzMuEcqxonLtKo+6cZ1nHlV2S6kHYLFjXhvNtzx0fgXp35iV5opmPc16aR3dakWkgfn/qAW43KxNa6wcHVtTQZEqya2P8Qa4kp5uWVWu4OSpiYrWOWUrGBurW8tKswPno/IpRIJIyiSvCk3WND4GDUUbnPO8sGiA6z1yI3JhJlmB94P66vToWHh+PT7n6kDH6oOlpjUcQ12uH4xwlxSDEj817Y6LTKlXqAxuVL2dy8axNweNVVCIFVapY1oZl3XM0ydmrMna9YdcYtp25agy/kiWlYJxHLRvESeX1xOY3dz3Xt/AnfuInGI1G/KE/9Ieeevwnf/InqeuaP/7H/zgAP/7jP/7lv8Lr+sj6ci2Q72tGBiv14zvHdHBPJEpghjtHPmJK9F4Bc64VD9ctrQkcj1NaF0l+MYQxIGRkhFRZoEg1rXWRtlsl4GGxaXkw6DOMi1C6s03LeCDPBqIux/nArz1aM69SJsN67Pa84uV9QdtbGuu5McoilXnZ8HAT12JVqq4Ivr2LkLgPWhkkw3v0cN3iQ2BvVJBqzZt+Q23ifw/eo5RkWmQcVglbYzhfx9yn/UnCqrbcW7TsVwmjPGWvSJmXUc/TW8+q7lnU/RAVIalyxcmqpUwUe6N0EMoKCq1Ytj1v1z1aCI6nGXmq4++w7Umy2JjsjXMSqck0XNSG23sVB+Mo+v78Q0tVpBjfEhxopTiaFGyNwbSB33Kz4GzXcbYxdDpwNMopteThuqG3ARcCq7ZDK8XXH4053/WcblsOxjFt2uO5v9ix6zw3J5oqVRgXp0iL3lFkmipXYAOrxjITksNpTpVoOmd566zmi+db3jieAIIiURQ6R9yEdWcij0VE/s75NoZjftPRmIORYFlb1p2JUMNUIgiM8riea2xkNo0rzemqRytDbRx1FzVBZ9uY5O1dXGF11vH1NyfkqWS5c+xMFAOL1qJVdAoa52ldnH5OCh2nGEKQDw64J4/HaZHwcN1yb9vycNmCjBOgo1HKurVkiblCKQA4H/AD3fpg9OGNyvNOLp5lTRTXih1SQJYoxpnm/rIm1ZLOeDrrIAgOqpS689jSf0Ubi2vGzXV9UD1Xc/N3/s7f4R/9o3/0vsePjo74U3/qT101N9f16daLsEC+txkZp5q7/Y7WOG6M8yiArHsyLQkhTgOkEHj40JPIkydaPWQ55UkgTRSNDdyYZiy2htpYnIsn1FGWMC01e6TcmuUkOsLntIxx2XtVxNJv2sC2dWQqWoyDh60xLBuDUpJqoNCumvizB+McqyRYz3nds6o71o3lYJwxGeVY72l6j0hh18UE5idLCcHptqUxlpPLml+4dzkQZzXOQZYqprlm3Rpu75V8440x66bnbNszTTKmecZi1w0U3AABhISjSXLlbPLe8+CyZdta5lUk00oh+JZbM6psy93zmrpzFJliPkrZdQ6789yZlew6y+XOonBoLbDOYT3slwkSSWs9Uiog8PbZDuvjBXPXW7adQQpFmksyHR0yjXV87uaUm3sFjXMkSbyAoQKbzsU4i1xzlI/oXaDuDV0IlIUmbxOqVDEtNOebltOtQRCpvnmiqV1HbSLt+niSczjKeLRtuawN3RBoeTDJ2DSScdFjXKDK4uf5zuWOSa65s1fyyycr6s4ShuTpOCXSPNp1lIXm5rRkkqcs6p6Ty5p163n9cESeRGDivcsd5+uOLJFopVju4qplf5Sx6xyLreGN4xHTPKGYRJfVprNM8sCjVcum2SIEvDQv4uQpeOZlxit7BRaYj9IruN57j8dpkXK+aXln0cTva6KvvuMZsNj2vDQvmJcJl3WcOB2OM24OK+EPqy9Xy/JhayI/2N/vXzYDnNOQqOgEcw5qY5mFKNSussj4+STupBdBFL5m3FzXB9VzNTdvv/32ByaCv/LKK9y9e/fLflHX9Wz1oiyQT+U0pTGe4aI2w91vvCPNU40gpiHfnBUcjPIPPYk8eaJtRimN9dRD+GMiJQjItItrHRt/B4cjeM3eJCVPNdvOEHxc/Yzz5CoxfJRr5lXK2bYbQgBjqJ91nqNJzuE4OoBGmeJi16OUYLXtMd5TDcwerSUX255tZ4fcJ8+2jVOrujNXAZkAi7qLzYnTeAIH45wHq4Z10zMqUo5HKUeTjNY4XjuoOJoUbBqDOt0MVFpBqgUnyxhcGEJsuMa54mTZ8GjTI4isnkQLNi0kWnNnljMuUvTg/ooiY8c7lzUPVz3GWvS+BOHZDg3urg80veWyMfhxBMYVSRTX7lcp91YNiICWCmM9CsHROEEpHV10GuZJ4DNHY3yI9v6jLGe5MzxcNyyansMqYzIABaXxNEZwuemZlgn7I30FvquNZ1YkCB+oe09tLbl8HH8Aozy6sjadZV6lJErggqDpHNvOkmvNJBOA4OGqwbiAd4GbsyKGsdZxeqOlYFql7OUJZ7uOzvoYV9HbqA/Scaq32PXcmObsjxMebWRMzDYB13Y8XHekWvLSLK6qTjcdJ5c1v/XVAz5zNKJKNZ31ZFrwmaMRe6OEs00XSd46YT6K4EolFU1nhiDXPkYuPBGL0NmYuWcCHE1yILJjXIBNE+GOk0LjiTq140nOKIuOqTR5+hj/sHrRWpbHYbhaRi2QEJH0nQxMn7mMuIZcx8bmWd1JL2racs24ua4Pq+c6Do6OjviFX/gFXn311ace/1//63+xv7//Il7XdT1DvSgL5JPNSGcdNyc5y7rnzbMtoyzm4hRJTNuWSsaU5BCuTiYfdhLRSlIA+1VKlsSf0ypCz4IIZErxaN3QWsckT5iWCUWieLR6NwurNY5MS5o+ZvloKTkoUxa7/oqp0xrLJE84HmXRoTIEW9ZtzaN1w+XORH1FiDyUMIioA5pJLth1jpNlw16VohX0PrBXZrR9XI8oBfdXkXK8P8ries7DNE+QSnM0jm6Ww1HGS/MCMSsQEn75wZplbUiU4miSUXcOEwLOBX71wY4qjz+7bHoSKdAiussSKRCD00wrwZ29ktVuyVsXNQC5Ai0V91c1Ughmg5PIhsA0T8kSxa5zHAvBjXERXVQiYGzgdNNRZZob85zjacZl49grFFIqci3ZdDbSf+ueZuCsdC6GU07ShCRReO/oLZRZwo1EM8o0+1VK3RkWW4NOBImMnBwRAdksdwYtRAxhErFhO296ZmWCIMIPfXCs2o6zTUeRKI6nWVR3CUGVKUwIMburiXqaSZEwyRLmVcz/0q0ghGi37l1g2xlO1g3r2rDrHbvOMi00RSpojMT0jot1gzWOvWlJYx0TmVDlmp2N/CU7YAaenG4ejmM0yMHIcDiK3JeLuieVgstG4UK4WivNypTOxH/7ZNXG1eaqZV33OGAyuKKM9ZxtOvbKlJfnJUKKTyUf6YMmJR82PXncOFRZdI5dbHuUFGSJZtf3dMZye16QSPUUB+dZXvOLmrZcM26u68PquT737/me7+HP/bk/x3g85nf9rt8FwE//9E/z5//8n+d7vud7XugLvK4Pr+cREn7Yicz7wKY1V3dSLgTKTHMwyskSifewaRtyrendu6PnjzuJRP5M1MeMUoEQgzPEevbG6bCagaNxSp4knG1bNo3lYJQyyRPuXzbUnWM8uIkaY8kzzbfennAwyrEhBlgmSrDrHds+QgY7a1FKELygTBSFknTW886iZjeIHhMtWbcmuoSkYG+Usl9lnK5b6s4OWZmeeZGxri1aCbatQYQY8Hk0yjEhrlnGmQbBldPl5XkZwzlDwAUYqSQ6uBLJprFkqUQpiUIwKVMSBGfrdkjejsLtXRdDPEWIsQuZVggCvQ+UIv6+jbGQM4hxFTenGUpKvni65TNHY17br9j0jl+8f0lrXbTNjzJSIUlSRZl25IkkVZLLxiAFvL3YIQVUuebegzXr1jGvIl/ofO3JM8V+lUTnlov8osNJxtkahJBseovSkYC97ixaRrbOpnOM8xRCJD/vF9HmnafRDt9Zj2oc8zLl9jQjSEmZJpRJFA1f7GLMQ6olktjI7Do73Om/q73qrKe38TvWmQhezLRk11tSLZlmKblSnG56skxzMM7JMs2qtjT9joNRRi4jZPHRphuOs/drWl7Zr8iU5N6qoUwUVZ4QhOBi2+GC53zbEXzgou5QIjY6qZJoGTVQqY7QSHgXEigGavCLbmo+aFIyzuMx9bihfe/05CkOziDU3XZxlZxpya33cHD2quTKHv5Rr/9FTluuGTfX9WH1XM3ND//wD/P222/zXd/1XWgdn8J7z/d///fzt/7W33qhL/C6PrqeVUj4QSe3KlOMsugM2rTmqTuptrfsOkuuDVVWYInukdbGwL/HJ41nOYk8SQ9et5ajSc6sTK7WRNZHLcp+lbAzlirRjMskTmnGEeBnXODGJOoJjPPcnBZX+oJMCb5wuqXMoEw1i13PO4sd0zLh9rzi7vmOu5cN0zLBEzjdGlJpaDrLutBkSvL6QcU0T0mUItWKVWuRAfJEkai43vA+MCk0i7ojhEDvHMYFXAbLpkd3gkmumJVxkvPSXsnFpuftix29c1jnkQhWXc9UxGypXghkACMD+RAx4ELgzdMdhyPDS/sl69aClExLTWcDonPU1pJITwtc1o48c/zWozFfdzyhcw4pBAJJbT3WWcZZQq4lkyIjTyRvn+/YdDEqo0okh5OMO7OSRMKbFzuMD7RdFMrmSdREeSEwvSN4T2cDmYZERUdTnmpePqjItOLRpmW1M5zvWmrjqXtDM2gx/q87U3IteXvRcm+xI4hAlWhe3q+YFUkkE9eG89pG9kqRRVK096RKIqUYwikVF7sO42Fz6ZiWit56ur6Lydh5Qm9j+KaWiiyJ39HjSc7DEHAIytQhEJxtDaru6SxIBqeUEgghyJW8uuA+qWkRAXa95Z1lw/1lEwXTPlAmirUSPFq0nG87ylQhJdyal7QmTijnZUqeSAQSYzw77wkBXtkvGeX66kbhefQoH/YzHzQp+eLZFhAcTfIPnJ58IAcn19RdbOgfc3CM82zbKLReNR8f/fAipy3XjJvr+rD6xM1NCIGTkxN+4id+gh/+4R/m53/+5ymKgm/5lm/hlVde+TRe43V9RD2rkPDJk1ueSBbbji+e75gVURy7ay2T8l2nUJUn7I8yzrf9EJugosW2MYxmms46lnW0WL80Lz92SvSYHty7qH15UtciRFwhLOrA6bolU4plY6gyxaRIOJ7knG5adr0lG9Y/j0+c3rjoXhGCy7rnfNdj+pjRZIhNXKIl1nvunu9IZKThLjrD+UZxMMn47PEYKaOWoDGOPJHR/QQYG1gOmp3TTYv30BnPLI98nVwLikQS+7PALz1YUSYqrhsQvHYwwvq4ihCF4KLpWdaGxbZnr0zJ0pi5NS4SMq25NS2RInDX7cgzxbjQBAK3J0VcfRjHfFpyXnecrGvyJALUlBD0PrBtDEmqmOUJD1YNJ0vLura44OJaEcFqFy8uqZSkSnC67em859W5ZAN01iODYNUbRlnKtNAkSjHK43fnCw831K1lNk/YGxUcjTKKXJHKSK8+37TU1jJ6TNhVoKTgeJLxrXf22HWGZWtZ1RZjHEUVvw/bPjCtEl4qUpqHG7ad5XTdcjTNmeYJhNjEOxc4nESx9qru6JxllOe8WmXsjOF/3V2yXXe0ffwsA4HzbR9jHFxcc2qlaLo4Rdl1Bi2jq8oDq9rw0tzjvKP3AWcdjXEUvKtpudx1nKwa5NAAxVVUx1oJjCNa/nPN4SiPmjAb/zvArEg5Hke2UJUrxlJFunOhSaVCDKu1T6JH+SgNiw/hfZOSgKC3McpDD3q2D5qevI+D48KVHf7xMb9pDYv6g6nLH7RietHTlmvGzXV9UD1Xc/PGG2/wv//3/+aNN97gjTfe+DRe13V9wvooIeF7x8DLOlJQ8ySerKyNtN1UK8onGBWzImWx7bl/2RCIF9H9UcJi0/G/76+xPjAvI8l2nCXoYXT9YSfaakjbfm+cqnGepnd0xqEHHU1jHPcuHftVyn4VyalKyriK6B266ZkW6VVDszfKIvG4tZzYhiLRdF0UDSdK4qznfNMhVQzllFKRDRbwi23P6aalUHqYQkiKVDJK453q5S7qC/JE0RrPXpXGC+amZ1olLOueXEdn0mVtaHrLqEjAB+ajlPNtGwXFicSaQKpg0TqyxHI4Snm0idOGG5Oc1jgCgVGZMCtS9socJRTWBk63LY9WLZ1zLLY969rx+n7OrVnJzkSekBSSo1HCou7Jk5gptWwMW2PYNoa3FlsSqZjmCXvD+9r0jm3nebTpCURitPMxCylPNA9WDc565lXOpNC01jMtNTenOVmqo33dKT5zNKZKNXtVhnExTLMqYpaV8yDV0BSs4+pRiBiS2VqPc57DacHlruf2vGRaJsxCQucCo0yRacXdRc1i13Hz9oxxnpJqx8N1zaxIuTMvqPKUiUk4nXZ88XTLtjdYn3Aw0kzyBCVi4yYELHeOh6sYc/HyXs62c7Qm6pZSLci1prGeX36wYn8UL9CZjhf7QivuXtRseztMM90w3YhQxVmuaYzjxrQY9EBxkjAuEratZZRpjqY5HigShfWw7iyLxvDakNJ9vus/tFn4oOnMR2lYyky/b1LifQRiBsJTk5L3Tk8+rnF4nhXTi562fBrU4+v6jV+fuLmRUvLGG29wcXFx3dj8Bqn3gvo2rY022AEQliWxqbmseybDOgiIcDfg9jQnSRTGOH79dMX9y5ZJkTLJNL0J/Py9JQBfdxSBjR92oj0a84EntV0XoXeTMrqO7i+bIVE5Ydn2nG97DqqEg3F+9TMnq5a6jyGORfIuk2dcwIMlvHmxpUo1D/uGy7bn7mWN8R4ZZFwL6Ehe3nUGJSVaBja+ZxQ068bFNUyA9a6jt54b0zLmVvXRVbRuLKum585ehXeez5/taHobp1xJdAJtW8MXzzasdpZJpRBB4bzndl7x0syz6wJqSLEWBPIs/u9Jpni07bise7Z9z6a1LNuepresmo6LTU9jDftVSpompKnm5cOcxbbj3sWWk2VsAqdl1EBMK43delwiEETdiUey3BlGmSZTCmcsy13L3csa5wJhaASORtnVKvBglPFwXZMqxWGVYj0kIWpgUq2o0kjqHRUJe6MM5yO7Z9lYVq1htet5e1FzvmnpbSTz9i6mzb+92JFlQ8gmAWMcHuhtYL0zJImjt46jcc6kSAghNnOXtYEQp0/zgQwM4AXcmuZYL+hdoNBQZQm/+nBFoRVax3VoPQD0Eh21YZ013JyUTMuMuo0i4IMqZZwn7zYMIXC27a4ggEoKFtvYoG46yzRP4hqrjDEls0LzcB3FxBbebRJGKcsmaoZyrchTya5zrBv7gbylZW2uiOLvvWn4qAajSNT7JiVSxuwxIXhqUvLe6cnHNQ7Pu2L6NKYtL9opdl2/seu5vgt/9+/+Xf7yX/7L/PiP/zif+9znXvRruq4XXE+OgaUQOB+ubNSJjBemeZnyYNmwbgxFEkXD57ueg1HKeAjLe+tix//44oLGeN44GFPMFFWhWe56vvBwy+1ZXE9dDE6mmMn09Lj75jTnaPz0SW2vigAzN+QpHYxSPILeeLwL8XlUzHgSIoZmXu56TpYNQhKzmRqDzSIRuLfRCvzSJKexnoerhsWu43hSkCUqToiU5LjIWTWWO/NySBN3aKXYG8U1yTRPON86OgvWBbIEjiaR1jwtJbWxbFtLay0PVg29cdycFcxKTZVqlBA8WrXkqeLVvTGpVpxuWnoTYxKqXPKZ4xGpgHuXDXf2SmZlyqazdCbi+3/l/obOWE7WDbs+ppyvWodoArf2CvaqjM7E4EzrIiE4S+La8O5iN7inBD7whDDUU6QKCSQ6IfQtde+QBJouRHeUjO/zA+fJlWBvVNBYz6q13JxoZqOo3TkeRwFz73yMnBguip31lJlif5KjlWHXx/yw3ZDLtTOWde3YqzRFlrBqDCfLhsNRoLWOb7g5wQXPxcbSOsek1HzL7SllngzOo5p1YzgY9E3Oeu4vG6x1hMcU7SAJ3uF9YB0M29Zw76JmnCeMshgIG0Rg3TiWu555mXB7PkYJFdePwXNrVpBoSWcsqY4k6HuXDamSV3iCQmoOxoJNY7g5yTmepOzMu1EJWarYG2UY43DEqcnBKGXTySgoliCCIE0k9eDcey9vKVGSk23NrleRxKsEnXWcrKLL76MaDDE0Qe9dL6U6Wu2tj+upj5qefFjj8Lwrputpy3V92vVczc33fd/3Udc1v+W3/BbSNKUoiqf++2KxeCEv7rpeTD05BtYqnmx2vYki3mF3nitJrgVnqw5LiwwgBczylHVrePsigs+cF1RZQmMcZ5uOW0oyzhMWTc+usxjveeeyJlOKpJGMc81kiDOoe0sQvO+k5n3g0apjWfec1zEYs0wl3j3WvXgutj37VQ8CLoawTK1iztSj1lKkimUL28aRZZIbMqe2kYGzP8poOxvBewLaXtBbS5ZklKlmVib44CmTjCKLcD1rY7PQGc8oieN7QmwM3eBs0UJyvm0Hnopg00S+yayIXJ7HBOZKq8hJSaLmY1n3dNbz2kHJQRVXa9UwtVk2hlVruD2La4tfebDiwXLHuraYIcSn0BKVa/CCTEvWjeOX76/ZdoZxljArNcsmsG1h0RhKIym0oneBTIKxAi0lLnhaa2h6F0Mx6x6tBZnWJIlEDCDE3jqECFSJZLZfobVm2xqkiE1nCJDICNPbdIZda3m0afFDqrtSAq0UN2Ypi9rE966TKGEZ5XGqkEtJ3QU2ieFgnLFpes63hiwRNL2i6Tw3Z3m00vsQU7BTjQiBZW1Qu2hZv3exo0wTDkYpszIFITjfNCxrS6pgVGqcC+x6R6IE0zz+mSDZdp5xHzgcSY6mKdvOMUkVp9uYoZQnKgaz+sBBlbLrI7dGy+jSMt5zZ14ghCQNsK4NnXVYG1eRrQmMch2t+SGSwIOPa9bH36lMC2yI1N9scB65EOh6R9t5pllCa+Lq0A7fxW6IS/moBuODJiWvH44gwLZzzz09+XJXTNfTluv6tOq5vlf/8B/+wxf2An7sx36Mv/f3/h4nJyd88zd/M//wH/5Dfufv/J0f+3M/8zM/w//z//w/fO5zn+Pnf/7nX9jr+WrWi6B1flg9RQ1W0DT+KqG7NY6Lur+aViAEznlONh0Xu5bORUbKqEhItCCTgirXOB9YN3EVExO+zXDRkIPbhCveR54oxPA7Bh+e4nhctl10HgWPFhLrPPcW8bGX5xV172iN5ddP1+w6G8W6Q1OkBh7MY8rxercjWMH+OKPKYiQBAqwDLQPOwVpYlm1kuEwKza63JFpyMMkpkhhdYENg3fZoLXlpXtB0Lk4Sdo62jzqeMtOMi4S2d2QqJm5VaYL1cLZuOR/ggkmeQPDUxrDc9ZxtuhgkKmWcmAjBJE+ZFfFwHBdxJbduewKBdWsJIjDKU6yzaK2QwrHpLWfbllQoeu8IAfJMYq1jWXc8Wrf0LgZZjgrFNEupUoVI4nu8bqP1+3CUoVRg13uOJwrjQQwW+2rIuJoWKVWRIkUMmTQusN717BqDVPGiuOstp5u4rmmM5e2LGhc8x6OC/TLh0cZxY5Jy2UgypSlSSzNEYxzNM/aLhNZD3Vm2reNokkXNivF86XKL8RYXYnOdDaGgZ9uGREq0FFgfacrjLFKCvY/rl8Z4FnXUwuCjnTnCJAUEj/WeLNFIoohYyAhfzHVMnp8UCWWqCcD5tsNYT5Vr8iE1vTUO5z3Ho5xX90ZsOsNiB1kSIZU+BHob4kqtTK54Q8tdhwswLpKrSerFticVcWr5yDRRi2Y9rbGkKjbJq9bEhlFKQnBc7AzlcBzDhzcYHzYpmZZf3nnnWtB7XV+L9VzNzYuKV/g3/+bf8Bf+wl/gx37sx/jO7/xO/vE//sd893d/N7/8y7/Myy+//KE/t1qt+P7v/36+67u+i0ePHr2Q1/LVrK9ENsqTY+CjSca2NdS9p7WRC6MlCClZP8G8SBWcrjuEiEyRRElGWULT2YFx4nF1Q240X3c4xvhArhWTPOohyixBK8HZpkVLQaYVy6an6RxlppkVkey66SwH45xJkXJ/2XC2js6ocZpQpopF3XG6aemGxHE93I12zvHSrGRaJJzvOqSEItG0zjEtomah7qPY8/Y8Z7HtUSpwa1qwbQxpGi3evYkam7NNS6aj6Pl4lLLtHS+Ncw7HOevWsG0t1lkEkOp49z3OU6rM87ofMck0jfGcLHdkg8h6ryzJtUYoQdMYPHA8zjiY5ORJ/N1uTSNor7EB4wOFkmxbw+kq8nYkgjxLUBLOa0fSO+SgE6q0ZlKlsAXn4ms8XXckItKJV03HrvekO8FeZZhVGTfGGalOmJfRKTcrM3yAL1204GGaaba9IVVRl9P1jtN1zao1lKlir0hZ1jDKFLNNxmv7JYVWPNy05IlCD5OaVw+qGG/ROTad43IXm8ZUCTZD06WlQgrPS7OCG5OCR5sWazxHw+fqQ2DbWXrj+bVHO17dLzFDI6wEVEmE7t2YFoOGqiJPBdbBONfRmTaIlsdFSh8C284RBBAcu95xMMp4/bDCe6hN1KGcrnvWdUdjA0IINk1PkqjYZGaK3jqqYcrXmXgsHI8zGhufExEv+kUir/5sbYiuOSJS4WzVMq0SIHnqWB0VMZH+7mUTm6whz+ls23F3UZOnCtOFqyliIkEgmFcJdec/ssH4oEnJlzs9uV4xXdfXYokQwnvNK89Ub775Jj/xEz/Bm2++yY/+6I9ydHTEf/gP/4E7d+7wzd/8zc/0HL/9t/92vu3bvu2pgM1v/MZv5A/8gT/A3/7bf/tDf+57vud7eOONN1BK8e/+3b/7RJOb9XrNdDpltVp9zSSWX+66KwHue++6Ps1slMeTIus8v/ZwQ91ZikyjpaS1lk1t8CFmE13uOtIkriAeLhsutlHoOioUr+xXfOutKbWNvA4hoOkdCEGmBMvaMK9SykSx6R1KgneBSRnx9J113JyWhBBorGO57XjrosaFeOJeNYZ1Y+kdbJuOo0nOKwcVxoW4VqqiTfhonLHY9vzyww2vH1aMsoTFruN03TLOFQ+W7RVG/nCccjzO2PaWt843pCqhMZ7WWqpU8ZnjEYlSHE+KOP0Z3q9++Gwmmeatix0uQJVpCiVZdT2/cn/N3UXNXpXy6mHFy3slckiAfmfZALG52R/nQFwdWOc5HGeUieIX31lGcJ8QrJv4Hn/pvMZZT1Uotq3lsjZIERjnCf/fzxwQBgDgo03HqukJxKwlEHTWsmoMghjOOStSjmY5d2YFrxyOmFcJ5+seH+CLp2vOtjFH7GzT0Bmi/kZEMa7zAR88t+cVsyLlxizn649HSKWYFTFKoUzjRO/BsiFPFKtdx5cudhSp4uG6YVUbZnkSE+idR0hBoTUv7xeM8pRcCb5wXnM4ysjT6Oy7GKjKi7rnc7encVoHdDaw2HVI4GiaYx201qIQ7IzlYJQRfODXHsVw02mekWrB6arlzbMti7pnlCpePxpxa1peQfROV80VMPBonCGlonOeo3HOzWlOmUtuTUr64fiRCKZlDMxcNvap43jV9BHq52A5iH5nhQYh+OLZhlf3K6oswQz6t8daKCkFSglSFTk5WkruLXb83NuX8canTAnE4yxRcDDK+JaXZmRaPVOD8WlOia/ruj6t+iTX7+dq2H/6p3+a7/7u7+Y7v/M7+S//5b/wIz/yI1eRDP/sn/0z/u2//bcf+xx93/M//+f/5Ad/8Aefevz3/t7fy3/7b//tQ3/ucUP1L//lv+SHf/iHP/bf6bqOruuu/rxerz/2Z76S9dXMRnl8xxZ8iBMCFXNwtq1h2zlWjUECn71RYKxlsXvsTJIE4bk1z/m6wxHTMuEL51sutob9ccp+mZEoQWsDQomB95Gxai1lGgGADZamd8yLhMvW8kg0URTrAwIoUoXpPZ1zzMqcVFuMi8C+IouCTyUF69aQacnNWUGZJZGBUqV4H2iMpUwU+6OEuxc17yxrDsucG7OcW9OCWZly/nCNFJpbeyWJkLTOUncOJSKV1vnAFZJHRGFxqiTH04I0kTxcdZRZvHvvTODmvGR/kjPLE3Z9fM/KLAZX5ongpVlJOxB08yQefrve8noeAXiTMqU2ka676S3LpkcJh5fgQ8xkUlJQaMm0TFjszDCV0BwEz/mupemjnXyWp6R5ZNRoJWg6y7jIyIRESUmRaA6qgipNGKWaVEN7d0lrPCEIEJ69MlrG0ySh6SPosW4czrdIBcfTnCqDTSNARlEqROjervM8WLW0zlMJjURGXpKSzKsshl5KeHmvYr/KmFWaQinWbUT5+xCoO8fBOEME6JIoiM60orMOKTwPLxsMnq3pmRURPHdeG2ZFwtE4w/vA+a6n7DTrzpDqhMNxzqbtaV2kIXsPvXMcjwqaPrJ1vBRUqab3UCaBFIkQnt47zs5a2s4hpMQ6h5aSdZvQ9NHlNMmT2JxIxWLreXDZkCaScR6jFlaNJUsEozRBAAfjFCHEVcp5Z+OKsUjiZ/24ZlWc3iHid0EKKNNIvL63qJlXKUfj/Ll5ONcJ2tf1f1I9V3Pzgz/4g/zwD/8wP/ADP8B4PL56/Hf/7t/Nj/7ojz7Tc5yfn+Oc4/j4+KnHj4+Pefjw4Qf+zOc//3l+8Ad/kP/6X//rFRn54+pv/+2/zQ/90A8909/9atSXQ+t8UXdfQsaGoe49i23HphkaHalINCxrS6IUB+MYV2B9tEZ/49GYaZly2RgEMZF7XVveuWiu8pHwnlePKgIFxnqc99TGYazHeE+uY2bQ/cuaaZmSKkXvHLvW0FlPnkaOzq4bdA2TDAQYH0MTvQtMB3dSO7hRvv7mmMZE7U9rHKebuFp4bb8acPeCe5c1rXWRIptpvPesTBR29s7zaN1yY5pT9/Eiu20N59ue3noORxkXWcc0T9BzyWLXc7aJwZjRTeWoO0tnPSFYZmVCj2C5c6S6I1WSi7qj0poq0xyPciZZwoN1gxIB5zx154YmA5SMQLhJlmKCZ5oHXjscUaSKX7m/ZlQkTIs4WSmVRKQggyRPhrBDHI3xlLmmzCVlqri/rDmcZNyc5aSDKPT2vGJaRNfcL92Hy9owyTVBCJSMAZjBQy8cR2UJCDZthOrZLHBjknN/2dAPxNpHq4aTVced/YLWxDiPw3F25WZ743jMvEz4zNGYYqBVA3zDzTG/erIe1qAKKSXLXcckV6x2BiUND9ftVTyFc9FWvthE4KSzXIlsb88KXt0vsSHwhdNdzPeyjipN+KabKdMiYdc7auO5f7Hj0brnZN2yX2XsXOALp2vu7JXMRzlvPtqxbWOi/EVtGKWay8awX0Xbf0w8j+vXWZkO0ykYDQ4vQWzIEYFV67g5zaiNp7cx/fzxxHavjK/pvQLh4AXH44IilZR5Qtc7FnWPEHE9KoX82Hym6wTt6/rNUs/V3PziL/4i//pf/+v3PX54eMjFxcUnei7xHqtgCOF9jwE45/je7/1efuiHfojPfvazz/z8f/Wv/lV+4Ad+4OrP6/WaO3fufKLX+GnW81gpX/TdlxKCeZki6Hlw2eOHUMsyFeRJHK+Xmeb2vKAxjofLdsDZS7Z9DFXMtKR3Etv3tNZipCSXik1vefNshw8C5z3GBiZlihouZI/WLb21jPOUVW3oXbz4H4wyVq0hBKiNxRNFxVLoqG+YZNyeF0zzhFRrWuuudAbjLBlEnT1n247eO149GJEowfmmZ9sZzjaO823Pxa7jcBTvqHMdwyON86ybmCF1MEr5lfsr3rqsSaQkU5JHPuYGHU9yXt4vORpndNYzyjWZVkDLO5dNFHwCnXU01pEncLaJbiof4GHdcDjO+LrDik1n43TpckdrAntVdD0tdj2LbYx+SBJJJhUvzXM+czDm4bblaJohhGSxMbQu8MrRiPUuEoDtsEZqrUcrwSjTdJ2j7yM08eSyJlOSw3F+1dAVOk4Y3rgx4nxlaK3j0aYheLgxy1AC5qOMg3GO9XGCEAS0fbRgR/YtUZtVOE6WLZ3xKBXtz+M8oekdhZHcnhUoJUkT9VRzfmcesf73FjXbtePResu26QlS0rSW4ANpJpjkGVWuOFsb3n6wjZPOTPPq4YjbWUEzrE0Pxhmnm45vOJY82jQsdoa9UUqRKLadY14mvHVR86XLOmY/aYEnsGwtu95xvutIpKR3UcAePCCh955RphFCkqeaXRcFvY9BfT7Ez/6gyqgyRWcCLgxTwVyxV6ZUPiAF79PJ6KEJgXij05gYh3I8y0i1oukdi6YnVZKqjE1ymSmsCx868b1O0L6u30z1XM3NbDbj5OSE11577anHf+7nfo7bt28/03McHByglHrflOb09PR90xyAzWbDz/7sz/JzP/dz/Jk/82eAmGcVQkBrzX/8j/+R3/N7fs/7fi7LMrLsa/eO5HmslC/67kurCLZbN5Y0iaJhISLJt0w0u2ARCKSQjHPJNo/TjM7Gk3mZaGyI6P8s0dzZS9g0FkTgZlmiZGC566l7S6YVearxLjAuIk9n21nGRcrhJEMhcXjaPrJojsYZp+uOItHkKmow6j6uTfarjFf2R/gQ3jfBmusY9/DYGRPvng3rtuPhKrqYjiYph6OMR+uWANycllc5RnujjM55lIPWee7My5g4XRta45kU6soifzyO2TyP1WtVljDJIiSwd57eRL7Q4STnZNkCMRm8dZ53LlvydEOZKJwPtCaGEq7raF2fVxmJkHTOcTwtuD3LOZrGKZh1gTt7FavGcLpuo6U/0eSpo3KRmLxpDFIFbo5Lggs4AoTAwSRBKsmiNhxNcqpUI4XgS+c1x9Ocl2YVwdcs657DkLJtLc5FcnMiI9OlSBWX2x4l4wRkaxz7VXbFbXlpLzJiLrYthY5uuc54OuvZLyOQUX1AA6+15LXDMTenBU33kF992FMoidKKS9Nzvm1JFHzjzYS9quD+ZcNlE+MeqjwhTzTvrBoEgm3j2d/XHI1hqSS9GyB5Rcq0SrgB2BC42BoyJalyRQgpvYthlrM8QaJoXeB4knMwyrh3WVMkOsL3EkVv43QuSyS99/g+fu5+mMrORgkHOhs+I4EI4ImgwZf3yg92MA1C4GVtONnWtJ2nyOJKtzOeSabpypRES0IQTIrkKkLiwya+1wna1/WbqZ7ru/y93/u9/JW/8lf4yZ/8SYQQeO/5mZ/5Gf7SX/pLfP/3f/8zPUeapvy23/bb+Kmf+in+4B/8g1eP/9RP/RS///f//vf9/clkwi/+4i8+9diP/diP8Z/+03/i3/7bf/u+Rus3Un0SK+WncfflfYj/FzyX244HlztynbA3Tqk7y7qLJNvHwsYyVdy/rElUTJOOotPIgumtIwRFOziQ9qsEgcAHDzL+W87HSVCVa2Yi4WzdYWxgWqqBjhwv9KmQpFKhlMA0np2NHJ07ezl35jkQ07M/zO2RaUWVRbvuso7iXCkUR5MMYz1loilzwb0Lz/3Lht54qjxhlifcmRVYH7jYGFIdA0bPt128IydwuesRCnqbxjvqSR45NERBdZZItl2gShMQcNkYus4wLSJ/ZdlY7hQpm67n4WXDwTgj04pV02NdoPeesAjsjVJuz3LGecnX3xxhHdSdQ0vBzUnOqomcmiJTaBGFtJs2ZmuNioS3znY8WrecrFrGWcLRNGN/lHI0yTEucFBl2EG/tFem3L+s2bSG8azkcJxFTovTLHaG3oBWil3Xc7K2dH1Aa8lnjyu+4caE5a4DIpsopk4nvDQtWOw6Np3hZG3JlWJeJszKAuMD82oIT/2AFat1nkVjOSgzHIFtaylyzc0k596iYdEYZl2kH+daMy1TCFGvFULg0aZl2/YEUT7lFJyVGVIIyix+3+restozA3m4Z1YI1q0jeI0RURgfHMwqTd07lo0ZEr4VrfXRFj88piU0fQRJ5lrx2n5JazypjtPIy7qPMMZxxs1pcTVtfe/397EDKRKJFfMiJU8jnPF809JaRwBCgP1RSpmqganz4RPf6wTt6/rNVM/V3PzIj/wIf+JP/Alu375NCIFv+qZvwlrLH/2jf5S/9tf+2jM/zw/8wA/wx/7YH+Pbv/3b+Y7v+A7+yT/5J9y9e5c//af/NBBXSvfv3+df/It/gZTyfTTko6Mj8jz/DU9J/iRWyqeiFJ64IHw5d1+rJuYz7Y9ymrnl82dbgvDkShIk7DpHKgW99ax7w8W2Y9sZ8iSGOjoTqFIZ7asXNZ3zjFLFnb2CNFFY6zkc5ewNGoSX98u4vhGB01VL7wJn25bWuHgHnCq8C+xPMg7GGQ9WDULAQZWxVySUeUJtPdQdt+bFh/6+Wkn2q4zzTc/pqr1C7lvvURIaa7lYeIpMQhBUmY4rvlKTqAjsk4OFvXeO3kSn17aznG06bs6KgekSwzsnhYYBziZCwA0XWms9Z+uWk1XDawcVfni8GQjHjfGkiULJeIFd1nEqEkQEw52tDQfjnFTGxsL5EHPAsgiY2x9lcTVkHBOZkApBPoST7g+W87eXO87WDVoLRqm+auTKLKF3MbF7WiQcjnIudh2XdVwPvjwv2RaG23slVaK5e7Hh5+5uI3Au1aRKcH/RoATsjXIOxvnA+vFcbLuob8kU4yylt57axNRoIcTVCvFy13Gxi3qmVEv2q5RpkdK5yIeZlimNdRSJYtd7eqN4qNoYalr3+GFd1HVx8ni5i0GnnXEsGxs/t+GYitPAOOm0LsSwzBDfz1cOShrnKRPJ0RTevqjpTJxqljqSge8v29g4ZpreBVrjqDLJsu6GJkPz0l5yxWKSQrBqetZNZCkdT3JGWQwUTRP1Id/cWI+jFsZ5cnUjU6aao0kRJ4/jnMsBFLise5o+RlS8dlghP6BRuU7Qvq7fTPVczU2SJPyrf/Wv+Jt/82/ysz/7swgh+K2/9bfymc985hM9zx/+w3+Yi4sL/sbf+BucnJzwuc99jn//7//9Vbr4yckJd+/efZ6X+BuynoU3EXy0bj9a1bjAu0waLRmnySe++3pyEqSVINGaV/dH7DrLoum5mRZ85rAiwNVFqEo1X39jSioFtXX0xrJqHJlWBAJNFwXBMkiO93IOR1lc89iocxEiTgrONxE0d2MSdQSPc6XmZcrhJGWWZ8zLyDEZ5TpqEYYTsPE9Xe+vsoQ+rKZFyu1pzqN1Q2M8xsULXaYjNG2cS1KZcF73TPKURAtOli3rQTiqhSSV8GDZ8dYi5kddbnu0gpuzmJ9UZZpRoZFCcjyJWUw+eCZlysWuZ1Eb9sqEzjpaazndQJEIegtVpkilRAL3LztGmSLRUTuhlGCSJwhiqvMvnazIdAz8DEQ7fSIl05FGSVjVliqTnCxbnIeLbWTVTPbiuu2dRU1jHI92HUkio9Yj0xQ6rjukFGSJYL3rWdYdvY8OsEmueeNoRJlolk2E3PkQpwCJknjveXDZMa/ywfEW13DLuufN0x2vHJSMi+jIqgqFtdHpM84TVk3PF8+29DYgRZzEXe46Xj8ckakoir7Ydnii5R7hWLUdZRLfh9Z6hIICgfEBMYAdfQAl4Gzd8Ounmv0qu9KlPTkp3bQGieDWNArBj6qYV6V15PmoMcyKjMNxzvEkx3viZyIFTW/JtEAGgRegkexVCftV/pT27XkZMB+1RjIurk575/ni+Y62d9gQzwVvL6Le65X90fs0eNfAvev6zVLPzbn55//8n/MP/sE/4POf/zwAb7zxBn/hL/wF/uSf/JMv9AW+6Ppa5Nw8Sz0pIv7S+ZZ3LmuOxgUHVUZtLava8PU3Rrx2MP5EJ9HOOu4t6vfxSZyPMLDbs4Is0UNStI9k3iecLbvWcLJqyBLFg2XNxcbQGMvJsmbdWV4/qPi/X9vnxrSkNTGJXAoZnUrrjnGpSaXkfNfT9ZZlazAusD9K+czhiBvTgl85WbNpetJEDQ0UNJ2lzDTffHs6iHjfX71xXNY9y8Zwf9kQvEcKOFv3JImMpFcR1ye9sdElNtB8P3s0YVImnG9aVk3P2brn4aph1RqWO0OeCg4HwN833pxwc1rSWsedvRKIYthUS+4tarwPFEnMlXrzfId3nt4Hbk8Lxrkm1ZLzbcfdi4aAH0S3gf1KczyJjq1db3n9cMw4j6uJzniKVNIaz61pQZJI6tayqHv+f3cvEUAA9qqMpnes6ph8Ps40RaZ5ZS+mT4/zhK+/OeFglHO+bfn1kzXLxmB8wHvP/csaQiQ5T7KEu8saEUBrxfE0o0g067bn3mLHd7x+yGePJzTWsapNXMF0hm+8FT+jznj2B1Fx3cecsS882nBZG8aDZsT6qBPaK1O+6faUX3rnkv/y+XPWTXytznvuLWq0gKLQZFKhBFzWPZ3zzIs4EVk1Pa/ulXzuzl6cBo1S7MCtmVfRIn6xbbnY9WwaQ2OjU27XGi52ES8wzmPDanzg5ixjr8zYtpYqi1MxH3ykJK8aNr3nxjijzPQLs1ZbF6NMhBBXkxuIEzoCHE8yHq5bLnc96yGCJNOKXW/ojeebb004nBQf+tzXnJvr+o1Wnzrn5q//9b/OP/gH/4A/+2f/LN/xHd8BwH//7/+dv/gX/yJvvfXWM/FnruuT1WMRsVaCPNEcjXM2XU/dGxIZJya//mjLurVDsJ58ppPsk3v4x0GXrbH4EHOBUh3XJ9ZGcmuVJU+xN4QUNCaSg0EwqxKSTsZJQmtiDlUfxZU3ppHB4UOgMQ7n/AANFGw6w+nGDFba6N5qrWfd9kgiQv+yMQBMipT9MmGSpx/pJrt7UXO27SgSRZVIdibQWx+jIQg0nWWUJ6zrloebjvuXDVWqee1gRJ5JUi2ZVSmP1i1aC+4cVIwHaFyexvdBSzFoeRpm5buvRwo4Wda8s6jRWkRBtFZ8w/GIunV8/mwboW1JhK6ta8Om6el9YJqnvHqYo0TMrKKL5GdC4HTTsqwNmzo2e9MiRkzcnBZMisjISaXC2Lju2sqedrBmH41yZlXCurUoGWMMDkYZhVZsWsPlLmZ2zasoVF1uu/gcg2tolRrO1jHR+9WDkiKJeiKBJBGKxa5j2xtcCOx6S5UpyiRqgR43oMumH2jXAuviZ1pm+uq/K6lwWeCyiUGm33Rzhifw/755yf11TQiCl+Y5rx+NEUKghcDiebRouLdssCEgveelvYJvujWjSBTWB1Kl0PJdJ9GmNSxqQ++iwFkriRBwc56wN3LRhTQcR5NcM8pjPtqmjayfeHwozjcti9rEwNQqe6HW6o9bIwkp6Kynd+EJp94QAeJ6LhvDvPrgddNXK9Ppuqm6rq9UPdf3+8d//Mf5p//0n/JH/sgfuXrs9/2+38e3fuu38mf/7J+9bm5ecD25OpLDHv/GtERt4l3b8STFWM+DVRMnK4lGaPFMJ9knT6Cpj1Cyty5qfPAcVBnGubiaKBI6E0ffh6N3x+5h0KV0JrJZVk2P8xF/n0iN9zHV+8Y0p8oGzLyH1lgWux4/XFDrznFrWoIAKQQ3ZwXWBR4tO1obqa/TMsH7eOfau8Cselo8/fjEuW5iftO2t0yGEMumd4wzTSs969qwP8o5muT86oM1b1/u6Ixj1xhWteFk1bJpO/4/bxzHlY2UHFQ6iqDTGFewaUy0QRPf54erlq+/OWKvTRlnCcu642ffWrLadSSpotSKPJV8y+0pNyY5m97QO8fJOuYopYnkG26NOdt08QKNIFWC821PoiQJcLJu6IxjURuCh2bTkR6NgMBl3UcLe2v5pptjlq3lzdM1J+sGGQQMuAAE3J7mHI8zpJTsjTJuzeN7fb5paYxn09oIv9u2cVU0XFA7F2M3zrYtQgjSVOO9YNsaMgm9DVgHeaJxvqPrHceTDDsAFbe14eG6YVyk3JmX5APx+vFn50O40oq4EInKWa74tlcO+MzhmHcWNW8vGrJUMS1isneZKU5XLbnS7E/ySJnuLGe7ji+cbTkYRWE1hCtdWmcjoDKRgo2NDfZjMCDA8bSAADemOZl+2qb+3pXWprHcnBVXx8SLtlZ/1BrJh2i776wjT95dK9mBHxUCXzMOqGt44HV9peu5vvfOOb7927/9fY//tt/227DWftkv6rqerid374FBPGkdxgUSLUmUYLGzTPKEaZlQG8ekTMhRz3SSfXwCfet8y/m2Z5wrcp1RG8vD847XDyoORxnnu5b7ixqAg1Ee86V84M4s54vnO07WNc5BleuYhzS4YLbD+udxrZqexS4GEq5aS2c859sITitSzXyUDEnLkeVxc5rjXGDVGoSIYtxMySFi4OkTZzeId/M03olnWj01abq9F+nJeaJIhbjKLVo3jkQrRokGAl88a7gz3zIfR12NUgofAgGYVXFC82AZxdNHk5QkUSgheeeyQYmGt852kcZMJDNvnUVIxd2LGiXjai14CDKGPC63PVmi+aZbU1aNjY6cImGcRVfcg2XD+a5nVRumZTrweCTdAERs+p62dxS55nCaczSFXAu+cLaNouXecF63lFpTpppt77gxTch1XGXUXUdtogOHIaxz01nWTc+2t7R1bAYSLVAEzncdxYWKWpY8Nn7H05w8kfQ2uudGVUqVRc3UF0+3vLWI06q9UU4IgfNth3eeB4sarWPwqA+BdWsY54qTVcuqMVcXwa+7oVHD6020uFpjWR+PkTxRNCamZSohOVnWIAJZEgNK8ySml1vnI+RRRiJwMax8tIzQRyUkNvgrAfKT9aT4vxmCKscDkfhxvUhr9UeZDeSQJ/XOsmHXmyshdwQYxknh14oD6hoeeF1f6XquY+/7vu/7+PEf/3H+/t//+089/k/+yT/hj/7RP/pCXth1vVtPro7yRDHONQ9XLbu+p9QJvXE0xnJzUpDrSOr1/pOdZK3zrJu4tx9lQ2DlrqPSis44Hm7aeKcoRcTJS0mR6ivHi3Gen7u7xHlP7hVlKvEOci2vLj6P/53HU6hJkZAlhmXTI4BF3fHZUTrEFUQmCgF64+mcv0LOj7LIFAnDefvxiTORIiaP+8CujQ6g2NyoqwuXc3A8KRhlivvLls5BIhWv7KV0w4pCScHlNlJwp2XGrEgGC3QMkWx6h3WBItNMc03bexZ9zL+y3tP2FhMCt6clk3nBtrNsB6jgvcuG1w5Lvu5wzK6zvHm6IyklmZYkQnAwjknYdRf1Kc5HOrIQkkQqkIZt0+PShDt70a7++dMdiYoTvZH1GOt4Zb/iG29POZ4UfP50zaruaDqYjxKqRNPbQNd7Jnsa6zwXu55UCXrnWdSGxablYtPyxfMdQkiyVJAphRSSSZHhid+vWZlxPEvo+ui2KoZsozJVrDs7JLfDtjPsVxk3JjlFqjlZtQgJl9uWZW2j4DjTPFw3nG46Pns0ZtNGJ9CqNtyae+ZlRqZj0xqRAeB9jK9wNrA/Tml7T9s5CIFVYxnlBmaBs3XHvEwoM8XZtufRukUOcRpKChIkbR/F0i58vDVaK0kBZAP08dO2Vn/QGqntY1L4rUnGw3WHdT25VoxyTSIlk0J/Tax+ngdfcb2+uq4vt577xuKf//N/zn/8j/+R3/E7fgcA/+N//A/u3bvH93//9z9FBH5vA3Rdn7zeu3uvMs0k1zxYAkkUd+5XGVkaHUBaCaR89pPsqul5sGqxITDJUyDw9sWWZW3REha14Y2jEYfjHC2j1bbKNDdnxdWJ585exTfcGLOuDSYEOhOQMpApTd1blrueItFPTaGkEMzK2Mz0veNLix2nm45N6yizqPdREi52/XDxe9diPB+mJ9Z5LndRn7GxkS+yaSOb5jFszqQxxiCEmH10MMqHbCPB/jhmJs1HGcudJdOexa5Ha/AI9qoogB1lii+eOy7qjtUA2EsHXVHbx6Ro46KWaLnryBPNejih71UDw2Ybf495mcWJRhbzp0KAw1HG3csdb5/tIqFbQqYii+XmvGBVG1ZtzyRJEEoyThVVpnmwaFh1cbqhtcS2gZ2xiACfuTmOKd6jnM8cjwne09pwtQpKdYTanW06fu3Rms64qOGh4GLb8nA92PqzOCXzPsTmT8DhOOPGtGBaKsZZhnMxgiJRkmmZUk40bFoutpFYLaVkmmuMh8tlDKasUo1SkjsHJW3v6KzDe8FrBxWjPGHbGJZtfA+3reWzN2LS9/m2B2Jz1bs4udJakiUaRJxmHj1myCjB2cZgSzgcRx1XmWqOxzmP1i2b1nKxbVEianMmhUYCrx+OPvai+tWyVlvruXe5i2wmH9ASRrmkSuOKJ9eKSaG/ag6o9zYmnwQeeL2+uq4XVc/V3PzSL/0S3/Zt3wbEdHCI0QuHh4f80i/90tXf+6AYhet6vnpy994ax16V8e2vKpo+IuDHecLpugXgeJJjBwbHx51ke+O4e7Fj1Rg2rRlShkXMNrIW76HIJI2J7qlsmBz1zj/1PEWqee2g4nzbDdlCjixRGBthfqvWUDZ9vEuTgsbYq8TjunN03lMNwtaLXQ8ePnOjpEwVl43BWP/Ut1UMF2gXAsvGXCWaV1lCZz0PljVaCiZFyvm2pTWeV/dKjifv2l5nVc7nbo5583TLxbZDiMjyEULwyl7Bqwcl0yIh1YNNXklemVe0lWPdG94+35FqzUt7Bc4FikyS6OhakkLQuQj7M85xtu05q3teK+NkbddFsfV+lXGybDm3LZd1fP9Bclgl9N5hO8+2i/qkxa6n7WJKd18ktL3jwaYhVbEhKFPN2bbjfNtzvukIIkYBWOfJlCBLolalSjVCwvmm43TdxylYEDFgU0j2R5K9XJNnGdNgMM5xvu5REtJERtG3jMyWWZkwKzSrtuOLp7srsGM2rB+qec6mc6ADrfXkicC6QJkmLJuecaa4dTyls453LnYcTlLmZcZi12GdIk80IkBrHe8sG25Nc47G2VMalNcPS+5e1OxaQz2k0acqup2yRFEkQ4MvIk07HwI4pYxhoqe1Y14JpmXkyRgXaAdtzsdNDh5/lxYDgTtVn761+t7ljl9/tGFcJMyyhNZaLraG6WHKy/vVV23a8WGNSZXqZ4YHXq+vrutF1XM1N//5P//nF/06rutj6oN2708CwrQKzKsUEUApAYFnOsku6o7Tbce0SDkQgofrhgfLqPdIFCxay52qJNMyBikWkWobeFqsqJXkpb0yTi6WkbOhBGS55pX9kiLVrBtLlWpCCLx9EZsPLQXrJq4fRkXKvIwMka53XGwMQoJE8mjTEgJMioTjcUai4x3hk4nmj90i+6OMxbbl4arjYJQzm5UkWlDoGDHw5B3g//XSPmfrjv/37SW9dRAEx+OUvSplv0rxxOTlB8vYIE2rlEkJeSN5Z1Gz2LaMi5j1UySKVEn2JynOgZJwtuk4G4I7j0YZVZpwuomvbX+UopWgsz0P1z1H45z9xyJu63m47Pji2ZYQAjfmBdMsYddY1p2jdY5aad4+33FjmrPtoui6SKLA1oY4QVAy8nRaE0NIl3Uf1zADp2U6K0iVYpRqtp0bMq423Fs2aAFVruitYFk7JplmlEnWreVs13Owa7Au0PSWVEoOxgVaCR4sG4pUXjnrlo1BCE8io27J+ZhBpUUU4NoBMRAk5FpR947eBia5QmvBo1VD4RRSCurW8dkbI25Oc4Lg6jjQUvL5023UgZlAr2BVB0Z5Qq0Ve6XG+igqhihaH+WaaZWRpoo784LOerZNtNM/WNa8sl+xP8qYl+nHTg4e04Kfi6vxCartLfcvG8ZFMkxZIdXx/5+te+7MAzr9aDjgp1Uf1pgcjXmmCdd19tV1vcj6WhDSX9cnqPfu3t/b8ADPvKu2Lk4FylSjhimH9XESsqot83HCJEsHPH7ABj9g4BUS8b5117zMeO0gUBuPAhItGecJVaZRA+Z+UXe0xnMwivqITWs43XZMUs1elVIMIuFUKS7rjtXW8spBxd6oGlYQ8eI4UlEs6YZpwrZzaBmzq1prCUhuzwtuzQrKLE4AWuPed5JMU8V3feNNbs0K7l42dNahpWSvyrg5KdkbpWgpWNQdnfWcrlt2vWfTdlzWPX0fgYWrxnLuA3tVxsFIczSNjqr7naEqNIejnFEqqG2cWljruNh2nG1btJDsVSm35wWJkmw7QecMy11H00eyrXWBIAQ3ZyWXdUvTOuazjDvBo4UcSLX+6nMsUsGsyBgVcT22rA1FFh00p+uWca7JU0UxUKYnZUJjLKtdz9nGUKYaORKcbVpmZc4499S9w4ZoIZ+XGS4IOhODUw/HKeM85WCcxbiLxnA01QgEo0xxujHsV5Hg3NiIFrg1y0l0XDkhAlpIZAhc1B1KSLQSXGxaVrXlxjhiBLad4dGmi3loT9zJv7I/ItWCpje8dV5zse5QUuI9jErHpgvUvWGUJVcXTO+js2icJVgX2LSO3nms9QQE286RaYtx4epYe289fUHXn/qkwfhA7wOzx87DoXKtWTY9xgfyF/6vfnx9XGNyc5pzNH7a9bVXxUmZdf4Tr6+u67o+rq6/K/8H1Hsbnmf9UKPINzJllnUPSZyM7FcZ69byxuGYPNWcrls8gVSlFJmit/4D111SCvZHGS/Nc9aNiTlIvWPXWbSKQuBtFygzfXVS623U+CzqHiXffb7WWDzx9XgfCASKROOD4XzXc3OWI4XgctfRGkfTWerORvdPGp1WSqohy0cxziNJeNdZGuMo4GrytWoM4zLjG/KEIlHM8oQs01cN4qaJAL+TVYtzARtg2xoa41jtWvIs4WCccLnrebhq6YzG+piv9NkbY+7sVSDgwWXDeCAQnw5i0M8cjq+yh1aNRQBlpq+mANXA9GldzORKpeSgyrmUhlf2Ckap4sGqRSBY1waH53hccGuYVmkpmRQJy13Hw1WDH8Tdn6vGjHRC21u0luSDmPixLilRIrqBhMDYKDTvLUzLlJvjlDTR7JUJ0zJycjyBG0NeVdNHJ1IY/q3XDqt4gTIRgLdXJqwbQ20tpjVkUtH6KITuekeWRPJ1dxE1VHf2Cg4nBcZ7qlRTJorFgBB4bNX2IaCkZFYl5CvF4ThnlCX01oMDKRVt79l10c35OGm76R1lKmNDLmN0hpSCUsc4EeMDlRIfODl47wW9NbERknx6k4ZEClIpaK29mtgAEZegYqjpV6M+rjEJ4t0bMTM4KHedY9XY51pfXdd1fVxdNze/ieuxC0slCjWK9NXee4pUoyUxYVlHvsxi25NmIk4Zxh8uVtRKkkjJxbanyhKcc1zsetZtz8vzklmVcTTWV383Zv3kPFx3bLqemcyi48g4JIJZkTArU3oXWSlaSGZFdHStmhhtMCtTJIIwuF+MdXQuMCujBsd6P6yHYlI0RJdLCIHWeDItUVIgidqicR4t5Nb5mNvTdGwaR9NFHVFvPE0fhduPabjOxwDFTEuKTPJw3TLPo6W9s55JkZDoKJKuMkXda26OM9JE89bFliyRrBvDrndXqzqAWR6JwmkI7ALRlm0d1sVJSgggZGDd9nRD9pWvAuN8CKT0Pj6XkByOUpz3xHDLyKe5bMzQVMacpvO6I4RANmiJ3j6vuWxiHMHNWcUbByXTKhmAinHlRBbYn+QcTwq2XWTZaCkREvaLNLKGhOTtxY4sEczLnCpLebSq6VwgzRUHpebRxrPpDK1VOOexTc/LexW3puVVVtTeAHc827R01pMnMgqdjefRtsNZkHAlPm+NIwSBc57L2vDqQYV14SppW0vBybIhIDgoU843PY2xTPLHza1jXqSY4N83OXh8QU+V4O7Floerlt57lBDMipSDUfrCm5s81dyeF/z6o038s9a01rJpDJ89jjcjX4161lBOPYAQF3VsClMVsRYnq2YIEr3OvrquF1PXzc1v4nrS7fFYr9FZxziNcLTHMQnTPOXmJL+itH7USca66Ey6OSs43/YsBkLwy/MKpQQXux6tBPtVdrU+KxLFzWlO03sEPamKF9t1Y5AyNhC5jrZjASgZmShXlvI8IRvWGxtnWOwMr+yV0U3lPVpKdl3H2ablm29NB0aJ5a2zHVki4xTpiYyuxbbHes+ui9qXB5c1ZaYouqgHURK0gk1rQUrKRCFFnFzIIc4h14Gbs4LGOE6WDc7Htcu6NXF1lCfkWcKy7ultoDcGFzznq4Z109Maz/E4Y1omPFx1SAHGe3rrcC6QKLh32TDOE/bKjAeLlsY6jiYpo0xTG8dYCJaN43TdUqQqJogby36Vsax72j7azY2HTd2zqQ25krw0L7isLdZHXYodpmavHpTsrGVx0XNrWvHqfokL8HBVE0LMwqrSGAo5L1NuzIory3aeKo5HOevOoJRgnqVRkzWA/H7l/praOQ7GUd+Vacmjdc26MWy6GL0wKxJ6FxvVIo3Boe9c7PjSxY5xnuAD5BJ670mCIFWKahynYAjBtrVIBKNMsukkMgOpBHVvefui5mzTYBzMy5S9UU5rHHXbs2p79srsfZODxxf0u4st9y9bRnnCNI1RJXcva27Pc97Ipy/8uL0zrwA4WbYsm55cST57PL56/KtRz+ocezztSlVEM2zaKD52PmBc4OuPxu8TjF9nX13X89R1c/NVqK8lhsOTLqzee1KlOBi9S0B9nrA/D8yKlLp3FPOSIlUIBK1xlGng7bM6CltFjC7obeDmNEMpcbU26R3cmGZse887yxpnoy5kv0p5/XCEGABsZRqFprMyHVxjUTh6Z15h/BAd0Bm63g4XrQwl44WvtY5F3fPyfkmR6DjlqA0L37NvMiZFQqoliDjvyROJsYE0lTjnOd+G6MSRUHcBFzrKVJF3imqYnIyVIJWSrnc4At4FpqXCWThdNyxry41BIP2lix3WeyY65dW9EgcY6ykTwbpxBBnzqNJUcrLs6K3DuMjcmVUJN5KMg3HOrMpwFmrhGWeaCxmpzmMluTkp0Fpy92wbbdh7JXmm2KtSHiwb3jzf8vJ+RZFYvnRR01nHNFVMS00zOMQ8glGWcLHT7FcpszIDD03vyLXi6w5HtNZfpW4bF9dLt+YFZROztLyLsL5NazlbNfz8/UuOxhmZkoQgOZpUZMmY03WDkoHOWla153TbQYCvO6rYtobTraFMopYnhEBtIZWKR5ueW/MSKQXWx/+2N4rhmMpGflJjIuDv5f0RAvi1R2tuT0vKQZ/lfKBKNava8NIT2IPHpZUk04J3FjFbrUhVTPSBqSUAAPkdSURBVGxXiuOxHgS+9oVPU7SWvHY45ua0wPhAIsVXZGLzceetZwnlfDzt6q1nWfeDmy3eVJ1tOm5Mco6nxXMFjV7XdT1Z183NV7C+FhgO7z1BfRwB9ZN8Qazz8fmdZ2sc3ocrx0xno/A0kRIbHN5r2gGDX6aKYrCzL7cd60FP0fXi6i4vUYKmdbg8YG38dxgunEqqq9/LOYeUAuf9VcPTGkdvPWWm0Sq+zz54zGBnV0TNBkTNxcWm4/asIE8UvXWEEMXUi23HsnWURmKMJ1eSXCmagbMyLjR971nWHfujhN7GjKJZlbLt7BA+qUm15nTTstgZ9qqU+SijtzFU9Le8vBdBinnCtrVc1h1TlXE0iZEbB1WKI/CrJ2su6563zusYxzHJ2B9laKXYG6UEH/OGbk8LausQRKuzRHCxa7m3bOitJxAiqDBXCAFlmsSgzCrljofWpFjnubNf0naWbuqv4Ijn256dsXz2cMT+KOfWrKAYIkIeu/gep27vVQnTPGXTGazztH2M6uiN59G2H/5s8CEwylIORhlaSoIPnFw2nGy6SDD28OpeybZ3XGx6OhNxBRElIOmsQ4iA9Y6mN7QIJrmmTDRHo4zeOXAxDy1L5JXD7nCa82jbobUgTSLFuEwUo0KTKMEoTz7oK0+ZJhSZIpExGFSrSA3OtGLVmk9V4Jun+isiHn7W89ZHnUseV5x+xbiQfAjCBVBSUKaKbWfZHwTG1xen6/py6vr78xWsrybD4eNOUF/OyeTxc1/uHidw1+xaCwIORzmTMsHYqIG53BmOJwXH05zTdcesSiGEmFCdaUzwPFh3V9bcaZEgiBqKVAv0EClwMM4ji8T0ZKmk6QdtT20Y5Zq7C8+hdewNqyzjHVKoK6+uEDEkdN0Y3lps6HtPbfyQ1xODH0dFQt07jPM4F1BKMi8EZ9ue3jlGRcJekZBn0W3W9oGWjlEeRc2ZluSppjGOTEle3ivZr3J8CMzKmHcUgqAdGqhZmbJXxiymg3FKlcbMo01nSLUkTSRZqvEhcDwtBpebIlGCKtOxGSA6zbbWkCUKpQR5ImlNHP1vO8Pd8x1175hkCmMDn3+4pjOedRdDSuteMtMpo1zS9JHEnEgJWcreKE5BtJJRY2EC91dtDL98YmU5LWJT1DqHD4FNY9m2Eeb4cN2yaSxKwsmqoekte+MUa+M0JdfxuFAi2tWLTPHy3gghAg+XLVvjqazndNfT9hHYmGuFkLBsLU3r0CJqvw7GGXkiUVKy6Qx00W7eGHd1zHXWUrdRZzOvMl7dH6F1RBD0bniuD5keZFqyX2YEAaM0QchIT163/VdV4Psi65Oetz7qXKKVZJRpvtRHJpLzYYiM8MzL9H2Iieu6ruet6+/QV6g+bYbDx42MP83G6vFzt8byaNXQ9Z5mIM4uth2vHYx49WCElIJtZzie5EjEEK0Q2Sq1ieuVXWMwxnG6atn2nmJoEnadwYcoNBUy3nGnWvKl0y2PNgYpJI11TKsELQR6CJ3sjSfRccJysevpbGBepqRSYJyjNy7mKDWGVCnyBMpUc7HtSHW8k5SIwU0j2RslUbNjHaWSLDvHK5NIPD7ftGgZ2Srew+1ZSZUqWhf1M4fjAogTsSrTHI7zGCyq44TibNtRG4uWkrqzbFqHUjAvUgieL53VOO85npSkOmoW5lVCIiXrxoKIFvu6M9S9oe8195cx0LS3Hmsdl4Nd+GiS0fRuIEJ3XNbRRXQ0ySkSxaaz4EEqyWGVRppx72NKfB8zp44mBYIId1w1lou6vWKvRNt//L4nSnK6aXjz0YY8UexayxfPthA8m85xNM2ZFAkPVg1KxPyq03XDKEs4HueURcIki/lNPsDFpmOxg13bk6exQdPS07uo30qQvH5UYhyMMoUQgiSROA/TPBKzH2ufNq3hZN3S9A4FWBu4s1dSyOjqsS6wN/7wGIMnBb5KCnKtWff9JxL4fi2tqd9bn8Z5a6/MOBpl7AZBvFaC/VH8jn0QYuK6rut56rq5+QrVp8VweJaR8cedoIpEIaT4xCdX6zx1ZzlZt2gYUPsRdDcqos3Y+gH3L2A3ZA1dBVxuG4wN5KmiSiNg796qwQ4rJKWi26LuLIGoo0myGIT5eC2ltWQkE5SU5FbhAjQm6nhuTDMCMWn6pf2Ktndc1j0Plg17VTo4egQpcHteIgQsa4PW4DycbdqYdN72aAmv71cEYEFP5yJV2XnHso7rlCxV/I6v20cNOVZ5Gt/XgyJ9nyDySQGmChEAt20Mb17smBcJgTim98HjfHR1Xew6ztctn3spRkh8w80Jy8Zwtuko00gOXuxaLncGQWxeD6p8aOQ8Logru3IInstNx9Y6rI3AwVmhSZWkSBRfNy/pjGPdWapE0liP9Y4qk3Q9CClj2nfvmJSK813Dm7+wYb9MGZUpxjhuDoyhzjjO1x02wKZzJFpwY1ayqbtotU4T9scJe1XGou4JgBKKl/cLxkVKbwZXkowC57qzNL2HENeck0qy7R3W+7iim6W8djzicttz97zmYBLTvUeZpkwVWRI/n7sXW05WLZM84aCMwEbjPafLBjkXzyxmfV6B79fCmvrj6tM4b6WJ4uX9ipNVjOt4LCq/dkVd14us6+bmK1TPapX8pPUsE5kPO0EpKXi0bmidu3p9z3Jy9T5wtmm4t2g423ScbzumuWbVWg5GGamSeBEIieaw0NGyq+MJ7Pa8ZNXaGNC464cwTMnhOK4VWuuZZopbs4LzTcdF3RH8Y1dNYLntMSFwsmwGO7RlXkQxsnOQZ4oqTdj1EVzXGcdnb04p05iGPSkTto3lYtexa2Oq86o2uCyuGLQS7BpPX3p2fdSMZFoxL1O8BGMCzgFBsj/OEDK+1wTB8Shlr8pxIfDKXglCsOks287RmubqvX0s1K5SzUEVeGfZcPdix8llgxOBZR0t38ngJupsoLUOYwJ13zDKBS8flNycVvTGcTnt2TSWL11sebjsafueVe+Y5AkayfEkQ0nJYttysmypsiiU7gOx2VWQpZJRmjAr4xrN+0CRal49qOit53TbcnK54+2Lnm1rOR7nqLZHK4UIgnuLhmVtaIznhg801rFtLYudQUr4/NmWcaqHFPWc/UpivIVW0JvYfI+zhJfSgk0XU+RbE1g3DUoBCDItaU1kpEBgkmvGhSb2uZbDKqMYMssWG0NnA52LuV8Ho5TWeB6tW3rraWwEWBaJRkqJDTF7qsgU3sPhKCaaR4Clx1j/oc3/8wp8P+rY/VoR1H5a562njAzWX7uiruuF13Vz8xWqTyNk71lHxh92glpsOzaNHajDz0ZX9T6Gav6vd5acrTs8gaZznG7aIXtKUmbRsaRkROMDNCZC9FItyRIzQNgUhBATxAF84NYkw7j4/ry0V1JlimVtSLSgd45tF0W6ozzBqcCjdU/Tt1SZZl6lpErSO0+ZaJQI7IwbLo7D5yAjCfV828WVkwIhBY/WLdMiYX+c4T2UmaLpoEolzkHvAg/WNYWOEMMsEcyrlNvzHO+jq+nhqsMGwX6Zsu0tvQuUqb76rB+uWpZ1jxDi6k49hBBzsgTMRhlVpmn7uDJZ1YbzdYNOdEy0TiJ48N6i4RfvrZjkKZlWHE8LPDvWraHMFEmS4YQBAYumw3qPkIG3LxuEj+u4XefRgz5kZXpGImc+itOsIpccTDKqVHNnXnJ/WfOrj+JUThI1NyfrmlwpjicZq1qway3TMmGUSM42HT54lkGQaEmhJZvG8GjT4o3He0+RJuBjBlTvDetVoM4sICgSyeEsJ1GSX3+4ojOezgZ666gyzThLmFcpZZaQSkFZ6Cjm1QI/QAirXKMklIm6YgYZF8iSKJze1IbLneXVg5L9UX51A+AdeEBKeQWJfNbJyicR+H7Yset94O6ipsoGFtRXeZrzaYWDPov4+Lqu68up6+bmBdaLsEp+knrWkfEHnaAaYznf9RyMUqrBCfIsu/SLbcsXzjZsmsgrqXS04e56Q2c89y525FqBEIxzTWcckzyukhIlr5LA8zSeJJd1T5HFu23nh3RnDUKCD7A/zjkcx7XCqu7Z9TvyVJFoifSBaaFpOktvDa0ZsPrOMy4SxLDace7pz+iy7gemTcxdmhYJq21MtZYSjqdFvCvN4t31vEyx1nG2k9S9w3vP4aRkf0gMP9u0qFRS5ZqX5wVSCN5e7NivMvLq3QvXquk5uWh5Zb+kTDWNiXyVKpVDInWcqAigt9C7jgfbls8cjlFKsK4tSsGqsfz3Ny/IE8VLe2W0e297fIj5YsvaUKUBT2DXWM63Ow6rDIXgYJyD9KyXDd57eg8jrakyRfBw2XbM90doGRPRARZ1z7Y15GnC/ihHACfLHSerjmVjKLOUIAO1NTxYW3adp+1jA6iEYH8cm7Zl3dFZWNSWAylRKl64R1kEHEJACUnvHaNEI6VgVKQQDMY7skQyK1MORyl3DkYQYDNkak0KzWJrSFQEMD5at1jnGeeKznreuthxa1ayaQwPVy3rzuB9nB4ejnPSoWldNB2HZU4ixaeqU/uwY7cxblgzaqrs049zeJZ60eetJ+vaFXVdn1Zdf69eQL1Iq+QnqU8yMn4yvXg3iGRnRQTAPVkftUu3zrNoDITHjJkYo6BkFPCGEIFou96yV6UkQ5jlnVlBY/1TrzPXmuADi13MlQrSczHEF8xKxe29immeEkSIHJciIRCYdykBcWW7fWVesu4sy12HEoAPQzq4JtMyOrWGO83Hv9vjVdOdvYqzTce2j5Texhha49gvU/ZHGavaMCvjqiOLYxO2veVy23NjEBHXvaUznlmZUKSaItXxs5Uysl4GiKB1UWQtAILA+BjSCdAZj/Nx3eIDGBuoTU/wMefIB2h6iye+F70LFDqw6+Lqp+5dBB4KgRCCVAmMFmw6z2XT431AjQWpVsxHCaM8Nn5CRAFtaxwCycN1gxzCMo+HLKfTTcPJZYuxsD/olDa9IVEpaWLZ9Y5JHh1Z29ZirGeUJWgFCHi4ahiXmsMqY9cbmjba+Jd1z+Eou0or/8xRRWNigOaDVcPZrqM2DhlkxASUCeNUsz+JgDc5HDtlCByNMw5HGZ9/uObRpiNVikxIpIJVa3HWc1b3OOsxfuAzh7jyu3uxY5Jrbswqdp1hsTV8/dEYrSSrdfupGQA+6NhtjeV005JpSTloy74WgiO/0lOWr2WB9XX9xqnr5uYF1Iu0Sn6Sep6RcSAgRDzBq4F6m/Du3/uoXbobAo9SLXAeHv+VREmklNyY5mRK8sbxhDSRpCoGQk6L9Oo9gqj1OVu33F82FInmZBOBdHtVymePRxjnOd/2hAD7VcbeWFOlmk1nGOUJWr277vIhMJZcsUUmeUKiFS5Ep8tBFRvMx3edUggOqpRt7xjnCZ11nG86ahsbvirR7FUx7PN827FXpkxKTdPHWIrWRkeSUtHu2/UOMbx9owHch4tY/01r6G2GTiXGe863Hb3z9KcRapcqyc7EbB08nG5aJkV0O13Whm1royuqNzgXJwuXrSE4z95eSZUrjA9MEoVxYeDyRFFtCAHvPG1vKTKNUoJX9wtSrWJ+VaoxzpFm0WKeJ4qDkHFjlnI4ygdnUmBVGxIlCCKAgN56No1hUXfkWmI9zErNydpyWRtsiNlMxgb2spRxrhgXSWTMeE9fxWavtZZplWKco0qSGCbqTVzZWc/ZuqPIosVbBdh1hiLRjNKUVR2bKDQkMkZadMaRZ5o7qbrKLVs3MaS0V5a9kHGx7chTxY1ZiWoMUsV142LXY0MYmtTYwF0Mn9XoPQGVn0RI+1EX6SePXR8Cbe94uG65d1lze1qwbS2TIkEK8YnFu59Wc/BpT1l+Iwisr+s3Tl03N19mfdoW74+rZx0ZP92AxSZo3VrONy1Hk+KZGiMloqhzUqRkScO6MVQZrOt4QppVmv35aIgASPADD8WH8NTrfLRuWGx7pmXCQZXxcNNGt06ZMCkyWuOYFBotJPtVQjKA4farjMW259GqJXiwIXCx7UgUvH44Ih8iGJLEUyTq6n2QUjx11zkpNL94f835JlqgtRLcmhU4HxBKcLLqosjUB2alYn+UY11sak63DZvGR52QJzZbMk6IJnn8nbddtBqfDeC5wwEet9h1lImixyOVoHOe4D3LxpFKQZUpHq1qHq7byFwZpYxyxcW2Y9t1jHOFtQ4lJb313L9s2Csd8+MJZRoJy5e7hsa4yJXpLfNSc2Na8OrBiINxRt3FJkQpwShLURKKNKFIFdM8GYCCnmVtqDvLw3WLDzEAdbHpQMSp2boxjHPNJFc4Hy88wceGLVOavUpFNg4CM0yfykxzNFLkWSRWH00yvnC6pTaWyzpqetresjOxyfEEWhPJ0gDGOTpjGWeabW/QRnI0ybE+UBvHKNcUieZi28ZARhVXfLs+MMokXzjvyXtFlmgCHmvh1YOKxlimeUI11dzZq8hTzcWux1hPptUnFtI+60X68TFx76Lm0bYl14pbk5wsVVxsewBmZfrM4t3f6M3BV5MDdl3/59V1c/Nl1qdhlfwkd17PMjL+oAYsBME01+x6G90g7uMdC4/vNpve8epeyRfPd5wsd2w6y81xzs1pyV6p+dL5DucCDhDEKIaX5gXzKqNIFK1zTLKEVWsxLq6LMq1obSC18aJUpPGi7tfhaoQ/zjXzIuHRuqV1jotty9mwhoDYaAgBt6YlefL+O+XHn8N+lXNn3vM/vrBgUXeMsoRbsxTv4lSrGTJv7sxzrIsWVSUEq9aw/f+z92exvmV5Xh/4WWvtef/HM587xI0pMyKngiqqXJSrGFxQYJtGQKvbWC0Z1NBqIXhBPOEXC/vBPCBL9kOD8AuW1XIZYSFhq3HT5bbL2F0MRVFzTpEZGTfucOb/vOc19MPa59w58mZkZEZExvlJqYi898T//P/7v/da3/X7fYfa+E09ixjFikkWs+mDANtetn5/VlDUmjSUnG8a7s9L0lBxe5pyXviOQRopqtbQCrg5TTnfNERIGmO5MUnZHaakoWTTaUIJD5deIo2DcaKIlOTbpxtOEkVnLcM4xBlHoy1KSG5M097YzhFJRSAFrfbOr3EomKQpr+7kbBpDHCjCQPSRBBprYF56n59ASrZyxavbGb/yzgXa+UiHrTwijxXTPGIUS6ou4GCSsjeMOZxkjNKI2abheFFRNj4PaxgpzooGCsHeMOZsXVM3hij05n+5ECzLlqZ17I8iOsOVs/WtScam7Sg7zTSPiKXCCe+RgoP9Ycy60Qg8p+uiaH1IqoA08FyvQajQvau1w7EzCLEClivN7WnOK9v5VbcEYKlbisaTkb8XIu3LbtKXoDtLFK/GOVkUsGk0F5sGhLdMCJRAG/dS5N1PMzj4uA+J1/WjV9fg5vusj1Iq+f2cvD6oZfw4ALP2MtOno+15MHujhK08/q6hmPDotJmEntQbnileDWHYy2YXjeHerEAAb90YE0rJvGxpjeVNKbx/jfD/tMDpynvfIDz3JFKSg3HEquyeUXKdrBo6bfnizTGLjZc/748keRywqTWbuiGLJePUsI94YmF/HDBKIUgjhROWRd1RdYbORNyYpuyMYrrWMs0jbk1z6s6wqjSnm5pF1bGTx2wNYox1zEvfYdjK/MZzsW5592zDbNPSWXvVmWq14XhZ88o0YJL6zaruDIEQTNKY21sZFkGnDZ8LlU8Ql969teo0YRjx1n5M1ac/P5xXnG38705Lwemq5g+/vcc4S3gr8tL7SAnCUPnwx6bz91bZoqRgL0+4uZUyjEM6U+GcY1VpjhYVy9rL58vW8MWDIZM0YllrtgcJhxPPMRomEXvDlK1BSNUZNrXh5lbK7ijpU89Dz/ORkptbKa9t51Ta0DSW7f5aIeDerObW1BPGi8bQCeetBKTg9d0BYejNCZv+Pt3OYl7byRkknmyshHjCnykoGk7XDXkUsDdM6IzDOMO6FhjrCANJ0xjS0IduVj1XKZL0XaZHFSpJEitGSUDTuZcm0n6vm7TpTaDy3uV61JP7l2XHpvH3/8uQdz/t4OAH5QN2XZ/dur5fvs/6KKWSP6iT1+MArO4MF5uGOFRX721ZeW7HKI08X+QD3vPlaTMKZD9Giui0ZVV3xIG8er+tcXSdZTyMfLZUZ5gVLWmort7LKPULeWcdD+cloZKMkgQpuVJyxX0YYaA89+B83TBMAsrOE2zHmX/9edUxTgOElKyqDiE8AFsU3VXC9+Py6+NFhZKKg2FCZ/2IrrsoKBvDNItIAu+sm0bBVbDfOPPKHmsdRefHO2frhptTw3YeEYcC6yz0Eut1rTleVkRKsmkM75ytORin3iwvVBStxvVBodM05GztZe44emBjCKQikJ6fkuqA81VDbR1KOcaRQglB0znOlg2DOGKYRnRFw2nRcmuaMohDikZTa8skC3q1Ucw0i5E9eP7W6YbjZUVrvHy/rDVn65avOe+nM0oCWm0RQvoIiixklPkssGEckkaaw5GPlZiXHavadzvSQOCEYlFrjhY1oRLc2Y45nKRo4yMw4sC/Thp53pAUvqs1K1vAA9HWeiD42l5GEnin6afBPzw5ok1CRa07BIKq0eRxwHaeEKuWdd2xaRrO1i156EngZ0VNYyyvbGds5TGdscRKsTPw4u6X7aR+r5v004ejyxDYUAomLuT2NH0pz5xPOzj4QfnpXNdntz7J9/unpj4KqeQP8uR1CcCOlhXzsrvKydHGsZVHtNryzaMNO6OYOJDPmM1dLupPd5bAUXUdZesdhQX9JiAVkeIJtVCDoTUW0W9IlyAojwImacB84/1HLoqW1jgGkQIL71+UOAdRID2B1bk+sdkTkwH/XhzEoaLqrAcY+IX9eFOxaSWjNCSLFFWnefeswFjLzUnKu2eaRWWJA5/0fbZurtRfl9dbSIGUgjT0j8u89LLxKFBESuEcHC1r6tZrzjeN54m01jsil40nL1+sPUl6nIY9cG2JAsGDRcgbeyl745jzdcu69h5AcaAoWkcUeBXafNNxXnqzOxwkgWTTWAyWb5xsGCYhYuzHaIEURFKxqFpa7RinirZ1mMTyoA/N3B+n5FGAEj4wVCJoOg90d8cxzjk2tfYqNAdJIAhUyM4wQ0k4WdYEgeTONONwkjArOoZpiHN+U7p/UXrH3iBgGAcIKfpr77i1nZFHHqTOioZG++yry+DVIOh5PDhC6bOdytqwcuaF4P/xEe3eKGZRNrxzsvF+S1Ly6naGExknq5rfev+CRkuSSUhjDO9ftORJi8MRBfKZUdDLLpTf6yYdKEkeK++RZC1p6LuUnfW/P4mClxpTf9rBwQ/KT+e6Prt1DW4+gvoopJI/6JPXOI1otPfQkPjQyO2BBzDLWl8t6kIIjlfPms2NU9+xOC/aJzaXurOcr2tuTnO/gDqulE/O4TsT3qKPqDcUfBwMnm5q5mXH/ijGOKg7DzDWZYO2kKchaRiQhJLzzqCEzy2S0gcgruuuBx6SqrN02jCZpF4h1fuz3NzKrgBjpBRSwKY1TNKQYRZd8WoaC1t5yKvbGeAjHh43QSxbTVF3fOe86MchviOQRBJaOKlqpPBjkk5bZmXjScpSeJ7KIMbieO98Q6Md2wM/3gkDyfmqY3cYsTdMiALBvNAsq5qH84pAOLRzzMqWi03NIFII4UeIgRKE+FDIs02NVD7xOo8CKu0J48Za7latz75SkkjCZJDwhf0hB5OEIJCkYUCrDUVrfOCnlKyqlmXVUneeDC2lZKAEddcRSIWTPjdifxyznfsOx/nad1WkwN9DfSL8sul8gKmxfO1oDQicsyzLFmMswzRCCK+KWjUdb+wM2BulV89T0xmOlhUHfVo7vBj8B/14VUrBfm0YpxGrpiOJfLjobNOyaiyHo5CDUUJZdywqzbwoWZUd0zTizf3Bh/Jx+V426cvDwrrSHgzOGh/EOYjZG8YM4/ClTQR/FMDBD9JP57o+e3UNbj7C+n6kkj/ok5eUgp1Bwmbqw+qy2P+Oo4VXKsXK5woFSrIsWx7MS25NUrI4xOE7E522TPLoic1lf5Cw7mMOosDLfrUxtNbzJGrtfWDynoR6uchekoubPhH7fNN6oqeDe7M13zzesDeOuS0HdNry3nnDpvYdkdpYhHVI4f1KMiXR0kt6dwYheSy5P6tY1R3GWnY7S6sMDu+um4YBZ6uaojWkQcBkN6LqvLz4cJKyP0lptb0ClJfS6q8+XDAvO5rW+9ooKQmVVyZFgaQxhrgnDr93XnCy8kBwkni58o1JQlEbvnWy4dXtnN1h7OXjQnCy1nz7vGQvjyn614+VYLGp+fZ5wSSJGacxoai4KGq2BgmB8lL4k5XfFI8XDc46wigg6xVmjbasG42zjs44Oq1ZO5Ci7btkhrq1LIq2J/YqH0HRaDrrCdJRr3jbySMqbVk3HbFynlM09Mq4edHwcF5zvPJdIWMNp5uaW5OcKFRI4N3zkkmmiKXvoNWdIZTCewNZS9XZPq/K8t55QRyqq/FZayyN8SZ/j1eoJOueN5XCE5v4pbovCiRJ5ENQi0Z7lV+k2BunWOtoDewMIjaVJAoFUvjv/HtRGD3eXcmjgHFqPI/oA4j6l2PoSHkp/qa3AAgDwc4gZlm1zxwmPmhM/WkHB9euxdf1UdY1uPmE1A/j5BUoyXYecbpu0MYHWq7rDpxjMvbAo9U+KXpWtMSBIu39YJQQnFcd0/zJhTJLQvbG6RVJeH8Us8hD7l2USOHotHfNvTVJn1lkhRQ4YF50lK1hEAcUdceiNDghCIXnn7x3vvHk4UnCNAsRwPG6YRQH7I9jbJ87dHsrQ/R5ToM45NYkZV62fOtsQx4p4lABjnnRsq41jS77EZgHH4MkYHsQ45wnmDrraLRh1Y+WIhUwTuDc+NP0rS0voX/3bEMWB3SdZd60xIE3NzTGUDrLOE3ZGoYIIbgoapyzTNKQKFQ8mFc4B8NIUhhLayxpKAml4MJYlnXHujZo07IbJBxOEu6ee7+esu7oEIzi0Ct/pOOibPnxnZz9cYaSgqrzxoGVtmjtCEJB1xhO1i13dnzw5enSA71F6WisIQs9z8kY+Px+Th6HaAdOelPGsjXIQDArGoaRYlN3fOei8N9TY2g7y/sXBUXb0WnL7sh/D0o4juY1wyTgVZOzP/Zg72CUsGl856TrFV+b2o9P39z3G56zjlgJjLNA70RtHeebmnUfr/D4SFVK8cQzlYSKNFKs+y7PrWmONo6y8YokIQTWOiZZwjCNXnoU/PiotjWWqukjJHrX7WEc+BT68NGBRRtLow0XPXCpO8OibMnjkCT0URL355V/P48dJpzzwGtWtM99bz8q4ODatfi6Poq6voc+QfXDOHldvtai7Djf1FysvVnaxaam7gzWOU5XNcPe5dcBF5uGYRJi8Zt9HD7ZWZqmEYNUUTaWzlqmacz+qzFR4L1Osjh4oW+OtY552VyNJc423pk2EIJVbTj0al5GWUgWBn7zq7yT77zu2B4mRErw6k7O7jDh/qLn6Cif+F202rf2S8EoiZhXFSerhjtbGduZ//mjVcOdrZSDUYIxllmjGSSK45XfaM9WtfeByQL2gphpFnG2aViVHUHg6Iym6jqMhbsXJbOyY28Qs5NHGOc3nVBKjuZVr7CSHK1qBm3HyaKm0pZQCcLAk61DJajWvmtSNF655ZwjV5LhVo7pzfUq4zgYROwME4xwtI0jCyRt59gZhiw2BusAC0XVMi87otBzhCLpvWqWlaXUhlvTjKLVHC0rTtc1u4OYUR6QR55DM4wDr7AzFikkaaxwjR9TRrOC+7OSWdExzmK0bulwVMZwd15irOPhvEIGEq0th+OEtifsFsLQasN5/73nYcgwVqycY912PJh5QnZnHTcmKW2vNAuV5HxTc7SoOBynDJPwuZ2NJwIajSUJJbe3UwLl77V1o3FOXqW6H44SstibITbafFeQ8LgIwBhPqAYIg8TnolUdUSCJQvUEEKo6w8mqZjuLqPpnKg48qHTOd47mVcs0j56rcBzEAbvD5LndpWtwcF3XdQ1uPlH1wzh5Xf4ObSxFG3Bnx49Q7s1Kms7SakMSSvZGYx9s2de67hglwRNRBo93lqZ5jM78wrupO4rWULQdgRSY3sTveTyBURrQGEeiDYWxzMoWJf3Io249+fV80xAFntDcpIHvKAWSTWsQPRl2Vnae0+I8UXpZNbTacrapUQJmVYdE4JzkxjgjUIq9ccxrez6C4WLdcPeipNEGa31K9qvbOaM0wgKbWmMcPsKg3zi/cbLG0dE0BicgVj4q4EagevNCSdyPFE5XNYtakwWSnUHIvdmGVWXQxiCkIA0VO1nECT7U08cjCOrOfx8GgXGQSMEk98nrQajYy2M2raFsDHEgsEJxd17ylVcm3NlOOVoPOF6U3J0ZEJJY+XR1pSRFoylbf922hhGHQcrt7YxZ0bIsO5xzOOe7BzhHp73rsxC+s3VzklJ1huNVw9mmJYsC1lXLd2Yblhsf89G1Dt1ZNm1HrBQHIz/qPFrWtNpwZydn3RjuzgqEk5SNJ6FvZSFBIDnd1NyYphyME4ZxyLrpWFU+QmNddRyOvQRdikdRBT6U1XsnBUo+80yt68iP6Iy9+kxKSnaHMYeThKr14yvwMu0XcV0eFwEESlC0hmGvAKxawzgNSUJ11QVa190VEMr7QNWjVdUbGqb+Na2PFUljid34w0T1HIXjRf8ZP+n+Ndd1XR9XXYObT2D9oE9eHtj4cVNRd6wb623uBWzajsh4M7VpHvsUbesoWs2rOyPSMHhhZyno+Q+zsntpnsDuIOHOVsq9WdVzOSRxFlO3mihVtMayqQ2DBLa2Ah6uahIV0jlL13k5+SBS1F3JnWlG1XiF0jD1YYzaOE7WDYHwCdV140+9TkDRGrYHCaPU8WBRkgQBN8Yp50VLrQ0nK29kl0UBrTHY1lI2HVkceofhTlNrh3AWhySQls44xknIwTilM4Y0CtDG8M2TBmM12nolz6buWBQd50XH/jBif5Rgcazrlk1ryUIPbI21LCrH7kBRW42pPLH0K7fGbCrL/WXp3X9Dv5lvqpbWWr55tOIPvbXPnWnKvYsNW73zcNn5sUkiBQ+XJeA4HKUsipatQUwSBGznAongxjhBKa9SW1YdTnjH3EkWMskiDxaUpGg6iqpjXTUsKkPbGlQgCYyi1QZBwCT1Ls17k4RxFrKuNXdnNW8dDBEI1rUmjSRDFTFIvM9PHEgGUciNScK4z0CbBh6oVJ1Xpg2T8IkojrLRnKzrfrynrkDJ48/UOI14c3dAEiqkkMzLju08ZGsQMa86TpYecLy+N2C3Twx/3j38uAjAWK/2uhwhVZ0XAVwKAhptnlFD7gz8fb6oOwatt1doOsv2IEIJyVYaUbeGVaOvFI5NZ5hkIUkgPxX+Ndd1XR9XXYObz2BdLsoSv2jvj2LSMKDpNEopn1JtHI3WaKEw1rI/SNjJE6JQvbCz9GHk7FGo+OLhGBzcX8AgUmwaH0dQdhanLWkkScIAbRzzoqPqGsBxMIxZ1xqJwyJZNi3+tO1LIqhbv6kcjlJGaejHTJuWaS9Z9iORGoRgd5T4sVCjCZWi0ppl759TFQYp/WZatoajeUnZGAZpQNU5iqaj6iTWWkIpiHpX3Mt3cjBKwAlKrVnXGusUt7c8L2Yri8mT0EvRK8MwksRRQCQDyB1ae7l7LhVpJIjDkFd3h8zWDQ9WJdMkoDNwUfgxyzAO+NfvLzgcp9zZzrmzlaMNFI1GSYk2BikckVBMhyFbeUTnHJtKk8YOax0H44TP7Q2ptDcxjJUHuYPIGyA22tAZh7OWTa25O9vwYF6TxwoDpIFC4LD4LsO4V6YN4pDOOCIp2MpCrBXEkeKN3QFFoxmlEVEgqVrNrGx5a39A/lS+U6AkKZ5j8zgBf1V1nKxqokCSR36k+iJn4O1hwjiLeGNnwLJuOVk1nG8atLG0nUMoeOd4TbNtuLMzeO49/LgIIFBegahtb0PQiwAuCcXAM2rIURJyOMmozzYsyv4axQHg76ebWymdscyOW3CwMRacY934sNRQSfZGnw4l1HVd1w+7rsHNZ7CupM2NprWOcRT23i6KSRaAhdY6pmlCoPwCfTj2wOaDPDc+rJx9mITc3skoOws49keCZd1yf14xSmLu7GSESvIb9+Ysy46i0dyYJj5HaF1TdwGf2x9QNpYoVOyPFKu6o+wMnbVksUJKhxCez3K26lACbpLSdF7VMoj8uMta248lDE1rqRvDWwcjhrGibCCNArLIe/rUnQdz1noVj7GWsjEkoR8lOOfQrWWcRuwMY6rOkKO4Pys5XlUMIsUwDYhjQddZrHBICbe3chBe3r6pFEmkmFetT0YXsJdHGAtIL70/XlYYJ1CBjylIIsmibPndowXbg5jX94aM0ohF5ceEs6JmUbQ0xjHNIhZNRx6FRMonxVvnOBynJHFAEnuTvZ1BhBTwzlnBsu+yREqyqbyNwDgJeM84FrWm7TR1p3FIxolkZQWd8dL3NPQcrKrVTLIYIX0Y541xwt1ZybryQZ1SSJJAsT9Kn7t5P03AFwLO+3/fHsRP8MJeBKwDJRmkXklVdT6t/KJosTiEhVIbfuveEpzj1vbA+ys9dg8/QVhGkUeKk1UNcJV5dTm2jQP1jBpSSu9M/Pn9IUkoOdu0vXkhTPrx1lYWc3NqWFYttnFksc8yW/fcnUXZkL6Eyd91Xddnra6fis9gXS7K66oD66jaDhcGtMayN0goWgNN571vlGJnEL2U58b3Kme/JBPfn5XMq45aa7RxvDJOCIMYY0BKyY2Jt/W/d1HQNp7LsDdMcEJQdxpqwd7Ikyudc7TWYp1jVbXUrSGRirNVy6aeYZzPWFrOWrYy37XYG0WUred6NJ3x6dubhtY4jAv56tGSw1HCl26NmWQxbWeYbWqqVrNpW9alZV23KOVHKaEUbOqOOBBkYcitrQxrLN88W9NpRxoqtjIPKNM+LkEoUK7nXMUh00FEqx2IhiRQjPOULAhpjfeiqU1FKBQ7w5ivr1t2hyE74wTnYFF4j6L784p3zla8tT8iTwKiQLJuNPNCU3WWW1spB+OMovZdkkYb9kYp+6PoCRL75bgxS0Le2h9S9vEMp6uGQICSklvbQ0ptWVWGexdrNo1hKw8Y5TFNYymajrN1y6xoCZVkmERsZyF1p5kVHeermlnZ0hmII8FunvDm3oCdwYs5JY+ThTeNxljH3ii5ijCAl/OJ6oxlVjQ8XNZs6o7jZcMo9Sqnpet4f14RhwFbefzMPfz4e1BKsJVFT2RePR7e+kFqSIB1b/YYhxLn4HzTIoXPgLs389J4iWBeNMzKljiQvHtW9CrI55OLr+u6Pqt1DW4+ozVOI9jyo4xvnxWMU8POwFvoa2v54sGI1/YGVx2aeZ/b80FcmpeVs192f1Zly3sXBUVjyGIvnz5b1dxb1Sgp+wwnSxj4FOzGOoZ5yDiJrvKJIil910n6k36rNfdnJdY5mg6EEggryCPBrNJs5SH7wxSLQAiJlPDKNOed0w0X64a6NVjr6JwjDGA6CIikYtNasBAHilXpPWIWZcuq1qjAE4mN8ZwbpSSv7WQcTDLa1n+2SEkO247fur/CYdkfpeje5TCNAtal95XZyiOaXqY/iBWHkxFx4Lkj9+cl75z4lO4wkDR0nkiroDSGqjEUraZpLdYaTlctRaURON4+nFA2FiEEWeg7RkkQMi88iI2VJA0Vh+OYwVPqvMtxYxb5DV5bS91acHCxrik7wySP+MLBmHdO17w3o4/ZgKqxbGchYSs4Wzecryumech2GHFWNNSd4XzdcHdWspVFjJKAQAlWdYsUPCOhfrxr+DgBv9GGKBCESj2xyb+MT9Sm7lj04E4IwTgNWNcagY/w6LTleFVxa+vZLtLzRADw/LiGF6kh8yjg4bLyhpDhk13PVX/PjtIQY/3zuqpbplncj0g1x8uGQF6Ti6/ruh6va3DzGazLFHBtvWldayzzouXhoiJSgu1hTBBKykYzTqOX5tJoY0n6rkTZ2mdIx49LYcvW8HBe0hjL3jC5aq2HU8mm6uis4/Y0A+GVSq3RxMp71dyYpGwaTdX6vCiHNyPMIsmm0TgHx8sGB2ShIkBy0VpuTiLiSDJMAnbyGGsd7xyveX1vSBgIWu3luRrLrWnG4ShhkAQIIdg0mmWjGdYdDxc1y6rleN0QSkkSSSSCTkjubHtlzzT3fjmtsWxWNTuDmJ08YS9vKDovD7YGtHM0rcUljjf3x+RxyKrSzKuOw1HCdp70hn6S37y3YFV17I28q7APpxRMM/8dtcaAc8w2Nauqo3WwqFoufrXhT/wewx/83AFbg4izVcXx2od7WmOZlT6Qc3cUsmm1J0w/BhCeHjcGUpJEkMeKo6WlaQ1poBjlEWkkcdZL8bWBYRIglexjBTqSSLI9SLgoW8qm42CScVF07A9jtPW8lDQK2BslbBrjieWB+sBA2UtH4u3cfs8+UZfk+t1hwqLoqLXPFnMWTtf1lT9SEkpvlWDdS8mvn7ewvkgN2WjzgeNcKQXbeUynLcZaRqlPEG+0IY8Dslhdk4uv67qeqmtw8xmqx8HFrGj7lOuIL96YcLyouDcvuL2Vc2OSPdGZyeLgAxffzljWdecdb7UlCiTjNGAQP5k0Pi8ajlfe6XhVaY5XHiRI4OZWjhS+AxMGkkR4V9pRGpKGijxW3N5KqDtLKBXGdr0xnODGJOVwEnPWS5L3RgnWOoSQaGcxoT95hyoEJ4iUpOgM86LlZFWzNYy5NcmZJBEGi7GOO1vDK95Gow1J4P1hLoqW41VNGChuTVLWjcFon4oe5hBIgbYQKq+yUlJwuqx4MC9oLbTOe9okYcDeVowUkvfON6Sx4pXtAeMswlqvthFOcDhOSKKAovHS7EhJtH304MahYm8UEwnBorHcu1hxsqlpO8sk912BRa35X79+xivbA17dHvRAIPQeQBtNHCl2hhFh4Lsr8WNyd3j+uDGQkizyY5JNrVnUmlgqNrUhCyUGGGYx00HExbrmdN0icTy4qHlz1weoauPDSXGC3VHaZ0s5hmnA9iChaDVVZ1hUfpyVx+EHKvA+jE/UJXDbHSQ0255js3AdQkEUCLIoIAoFrYGvPlyjjePO9uD7GgE9DYS+2zj3snP3YFHRGUcceEB0qaxKw+BTEY55Xdf1w6xrmP8jUpeup9rYF/7MpeGYtd7jIwl9WvWqakEI9scpAs9buezSrCpv3X+5+D5el4vvqm751umak2XNsuw4Wda8d15QtvqJUdSy8o61m8YQhoJpFhMqxf1FzbLwm9Wl2mQrjzgYx+Cg1t71dm+UkkWKi7JmWbYM49B71exk/UgNXJ9rFEcBceRVM0J6kmkWKQaxwviczT7wU1G3PtU8iQJuTnKqxrKqWlpt+k3EkETSk7A7n70kBOyOUm5OUnbHEVLBMI2QQrGdR2SxQvWqKSFhXmr2hjFv74/ASarWq42MswgJB6OEquszuALJIA4R0rs4X1aeeLly3WmK2ncRslAxzmJ+6vVtpqnibNP6uIs0ZJwGZKEgCRSLuuPbpwWttgyS0Ev/kWwPI/9zkWKSxiS91P/x++hy3Fh3pg8t9UTZRht2RxF3djICKZhXNUVnuDXOiAPFIBQ8nJV8+7RgtmnAWh6sKv7Rb9znqw8XGIcfsSlB1XsWaWO9s3LdUdUd9+cVXz9eMy876t5t+fF78/H3edkZuTlNub2VcXOaXgVqvqgugYVxjjs7A37s1og8UjTa9qZ60HQOCRS15nfurzhbVS/1TL5svej61p1hlAb930fsD2Nv19B4o8DtQcSoD2H9NIRjXtd1/TDrGuh/yut5Sd2DOGAri5/hK1yOlvp8S/IoRFvLsvKjnCT0fi6XJ8DLzszTSd6Pt/y3spCHi4qi8QZmgZRoa1lX3l320mPEOO/2W3WWuPdkMbljWQVcFB50Bb3PRx4H7DxmDHi+qdHG8spWThxINu0GhDddO5h4/syi7AiVYpCENJ3vjqxrjbWWsrEcjCIuNh1KOaZKUdaeFPvKlnfL/frDFdPcS8WbVvPebEO+CdnKY3YHEaH06c2rWhMpwcWmo2kNtuerqFAxSUIqbb1zrBDMNw335yXvnm5onT9x745iD1Baw6xo2R3EbOex744ZR9tZjHJo/WjDstZRtpq2MzTGEivfkbJO4Jzgta2MG5OMw62aNJQ4400BF6V3cg6EAyF4cLGhe2ObSRpirLf4b4whkD5/aQSEgQexxjl4jOPydFfEB6Ra8igkj0MGccDxqmFnqImk5GZRcbpseb8oaTrtjQiF5HhRoa3j3kXBv/2lA/JIIfBmjc5dnrYc77eWYarINw1Va1Cp4LRXIk2y6AOJwt+LT9TTPLFb2wOE9OaJdeMIpGRnFPWZaY5ZUXNvUbI9TD7SEdB36zpJ6btbCD9yzWLVh51+usIxr+u6flh1DW4+YfVBUuvn1ePhe622zMuW77QFe4OYV7bzK17CJbiIA5+vFPSeHIGUNM4TKWvteS2XJ8DHT4QvWnz9ib0ji4P+tT0fx8SOeeVJmpfJ2oJ+xBP61xokIfvjBOEctXaefDp8Noeq7ix5HBL0/IrP7w8YxSFSuSuCq++EGE96HXiTuHnZcLZqCJRkb5hC7nh/4Z1zpYJQOC7KmvW5oe4s0yyiNYYkVLy6leGsv6YIwcE4IY8CTpY1tbbsD2OOlzW11iyqjnESkEcBe0NFngTMNw3vnhf+lK0EAxmwbjR64Ugixc7Ig5nDSUJrHA9mJZ31nAptHcZaXt32o7pl1TIrOnaGMedFw8nKc2ryOODGNGFnFFO0hle2Mm6ME75WdRSNQVhH3XUUrUNJ+PUHS15774IfuzXldOUQ0jGJQuLAK+U661iVHcMkZFW2FD2n6XGOyyVf5HxV02jHqHeMVlKwaS07g4hGOz53MORbJxsermqaTmNRlI2PqOh6QP2v787YHqVY540Pl1WHlH70tzMIeXV3QNNa7s5KRknAqAcYWaR8x+cj6lY8fm/XnSfWf/7A8d5ZwTSPyWO/TDpnGUYhq9pc3dcfVb2sO/l2nhBI+akNx7yu6/ph1TW4+YTU0x2YF1m+P16X3ZhQCjaNV3xkcUioJEVrOFr6k+44jViVLWerGgtkUYBzvvV9ScgMpeB0XTOYBFe2/0Wj2e6DMq1zZHFAGiqEFFeLb9F0vGh7EY/9jbfBD7m/qCja7qprFCnJ6/sD8ijkla3smRyqx8msut/4syhkd+g4XtVUnSZSilprAiUIlSSPQ4xxZGHA7anyAErAiXXcmfqNeFF23N+UzC+8jf0wDrgoG5JQElrJ+bLl5lZOqATW+O5MZ70aqmg8yXOUhKhWECkvq/49tycESvDts4J7s4JZ2XojwU4zHYWMkpBW+3FS0WqyMEBJSYQD4ai1Jeo5S8M4otaWi03NourY1B0Xm5aTZX0Vz9AZA1ZwtmrIk5BJGvLm7oDZpuWsaGnbjnXrcFjyIMBYx//nd0+wzrE3StnOYx+cimNnECEknBctWaSYlfbFyjhjqbR3AHbOAzEpBHEoaTvL9iBmEAds56G34pGSAEvZCQIFykkCaThatbx3vuHtG1PeOsxIlGTddnTaUXQarf01mWTeODE1ltN1zTBVDJPoQ3crnj5APO/eTkLJe2elB5rOuw+3nc+AstZ94Pj3+6nv1nX6UQnHvK7r+kHXNbj5hNTjAXwvE1sAl/4cLXVnOFnX/iQrvTLFOT8+uuQlzMqOURayKjtaY9DaIaWj7RxxKq9s9XEeKNWN8YndVcvxsuIy6ThSj5KXwUujJ2nkyb29JFtbH/o4zaOrbg74U+dr25q7FyWdbtDa0VlH1TmSQGGce8zV19cTLrBSXHWc4kixNYiRwr/fQAje2M0JpGRWtLx/UVJbwygOaY2laAxpGFDrhnVlaJ1lnEfcn5esau9qLIRAOkckJKtG81ogcQjem23QznNVHi5LmtaSRAGjLCSJ/Aa/m8dsDX2sgnWWxjjqzuKMozGW43WD6AM0o9CRBYo8Dqh6Bc4w9XLfuvUbZyslojN862zNg3lF3Tlmm4aiNoyTgFqDdYJNH3a5Y/BxD0nInZ2c1lrulQ1CwiRR3BxnvLKVcVG0/Mq3Lvj5L+zz1sGIde1VZz5AQhDHntSbPiZLfloZZ5zDOt8Nuj8vaTrf4ak6zaY27AzjPgtK8a/fu+BoVaF7bg0OjHGEUlBpy3tnJT/12i43JilKCrqFI48Eq1nHvGoZRH406FyD7QMly8ZwZzv8nrsVTx8gJFyNaK3jiQPF7iDhla2Uh8vah2r2IOhk5VPNj1dekv9Bh48fZD0Ogr7Xbu91Xddnoa7BzSegPkxsAXhi4aLqUEIQSYVSgnnRUoXeW0UpT8at+1HLKAl9/lCt0UYjkbx1kDHJHqmatHnEccnjkLLRTyQdx4F4AnQFSnJr6uXkTWdo8Jk/eRxwMIqvuBuXviR3tgcM4oC7s4KLrmWUhkyziCRSHC0rGu3HApef9wkX2NArkC65F7uDiFApYiW5MU3YHaZo48nBTsCNcea9c9Y13zpdM0oiikYzzUK07TsfeLXVKA7YtB0P5g3bI8coCSl64marPVCZpNBphxUwSvyjU0jf4Xq4qri1KukM7I9T1rVhVbYM8xglBUXdcbqqiIKAN9MBr+5kWAebfsRx76Kkag1JrAil8oq2dU3RWi/5Drysu2g1loA49OPDJFCcb1oUsG5Csijg8wcjWuO42HSMUsWN6YBIwbLu2FSa83XN+G7MIAm4McmxzncntLZo566A8eP1eEYSwKZ6FAGwKDUA1sBWFlJ1mtN1zf4w5Y//2AHfOF1zf16CFTghEMJL2BGOu7OCX797zts3RkTCk7Zb44HkbNNgjPNO09ogRcjNccLOMGH0EqDi6U3/6QPE46niO8PkmQPFF2+OCQNB3Tk2dcfZusYBcRhzvm5ZVR2v7w7YHiQv96B/xPVhur3XdV2flboGN5+AelFsgRCwafRz5/u670bs5BHLusMBrs+deTCvuLOTYa0jCSR5HBDGHlxMsohB7DkWrbZsDZInuivwGMflJZKOL1OX35SCWdFS9YqWOJAUjWHdlM8sutM8ZllrxmnsoxEQrOqOedlxtm7YTA3befSYs+sjTkSgBJMsZFN1nG0aAuHBz6Wh3Lxq+c55yab2YNFYS915o7yy7RDCZwClgaJWipsTL7VujJeAW0zPu4g5W7e0RnNjnBEGEikl4yxiWbYcrWpCKVDKh0yGUnK09BvjwTj1LrxpiLE+Rbu9zGJyBmMs755580IEaGP4nftz5pXh9lbKwTil6TTvnhcMk5Abk5R11XlpdGuQQhAFAhUopFQ4NJWFtycxq8Yw2zTc2cp4/3yDVAJwrCrDpulotMEZwb3Zhl/5Fvzcmz5nyWdFWXYHnr/ztCy50Yay1hxRYSycrCoabT3RdRCjnWFRGNIwJFE+H+z2RPBzbxzw7uma/+c/u0/Z+hGYECCcIJY+6fxff2fJVv6AP/T2AUFP1t7JY9ZVy8NFiZKS/WHC9jCm7TthH8S1ed6mn0eKdaOvDhC6v/+HaUhnHY5HoZeX9/Y0i3nrYMzZuuF3HzYkYcCNng92CeCAK9L8D7s+TLf3uq7rs1LX4OYTUE/7XPjogI7zdYOxjigQbOf2iRPZJSDa6nN0OlNx76Kk7gxZJK8WOyUFjbZPepQoibauTwJ/cpP4XpKOL5UqlwBEG3/yXxUtJ2sPvLzL8JOpysb5MVAeBz3/peV0VSOF6CMUngw8fJpnsKpazgJJqLzqygHvXRRYB0rCqmrRFr76cEUeSQ6nGcM44H7ZsTPwwYyN7mg6y8EkYVVr1puWOFRspTEiEIyzwAczWu/cG0rfVRglAcYYvnG8JgwlaRRitU//XpUdi8qHHMah7MdAPl5iXRtujGPiQLJsOzZL7Q0CEczKhrKzffq2Roma42VF2WiCwIO/nWHMbNNy0fsESeHHV512DCJJFkq2hwn7E8EsC5nlEUfLkt85WqPKDmM1dWsRCG5sJRyOU5ZVx2/cm3Nn1ztRT1Jv3jeMA84Ln3F0qYw7X9eAYKwilPQclVZ3HC1rokBQdQajHXHouTq1tpysa5JI8e//1OvcP6/4p++cU3UapARr2TSONDBURvIr3zrDOPiZN3Z4c2/AouhYVR3WCbb6QEkJVNrSaZ8D9SJA8bxN/2Td0GjD4Th74j5PgoDOWKwF5FNxDT1wlwLuzSMOxl6hdLKqPAivNXfPS/JI8fn9MUHwwwM4H7bbe13X9Vmpa3DzCain5ahlo58I4AuVeuZEduXPYR2TLCIKPThalx1hIIkCxSSLUMKDh1XZQi+h/SDn1u8l6VgJ8USUwqz05GYLVx46caiZZI86L8MkfOJ3GAMP+oRt40AJ2M5j0uhZ19VASeg7VnkcXi3q2lpa7dg0HXHgAU8WBUjhuHtekoUBgzTg9Z2cJJAsqg6E5MY4wSmHsaDzmJ1BiBDe5+fOVk6oFPdmBatacxB70DmIAx7MSxywP8zojOaidkShN9jLopDjZUMUCLYGMftKMts0DNMQ5xzfOivJQkVj/Hjn9d0BIIgCPzZclB4whMJHOiT99VdO8drugLOyou0sFoFSkiySTAaR5yxpS6uhs44oVHzl5hSc4+GyYVlaskgxzWJuTGLGScTJquL9ec2P3Z6wO0qv8ox2Bp6se6nIEUCkFKO+a+dHlgFla7DGkmcJkQxoA828D34cxCFhIHh/VvKFgxH/1z/0BqvW8M3jNRbHqu4Q2hGFvrs1qzS/82DOWwdD7uzkLMuOw0nKoYOHy5pfff8CJbyp3nnhfYQOx+kzI5gXbfrGWJZlS9VpBvGje7DWmjhQ9MHdz/WMCZQHtkpKf80uSvIkYJCEbJqWb52WpFHAa7vD73cpeOn6sCG113Vdn5W6vv8/IXU5epkVLSfrmiiQbA9iRskjK/zHN/tnUpGd7zAkQ//fjXuFyUXRsm46xmmALVvSSBEr9UL56MsmHe/kEeu6u4pSOF7VTDPvr/O4h86m8R2KxxfdS8fV03XDum4529SkYYgQkCcBi9IrsMJAPrNIP29Rtxacg1XVsT9OGaeSVdUxzmImgw6LYJiEHE68CeDpuqasvTHfoujYSmMuNj1hVcJuHpMEAWWnMcZwvCqYlw1RIImVZN10Pj1dODoDh+OELPFS7+08Jo4Ep4uGWdF4UzqhiKTgaFFhrAUpEQYWpQexqVL99emQwrGdxyjlQWkaKUZ9vIK1jkkaEw0UB5OEaeZN3ITzbrrnPbk8i/31HucRP/3GLnfPSn7z/pJhKpkOIpZFy1ePVpyvWwIpGSeKn39rn91JCnge0M1petUp08ZyRN13AY0HGaGiaLwFgOkdqrX1ALVsDDcnGUkYcLZuOBi1HI5z/tBbu1StZVY2dNpglfCdHAQ03nG6at8FAXEY0GjHbO1l74PeJTsOFfNSc7b2+WPw5Aim0T6RPX8qKTuJAj+abLyvT6i8t8/FpmEw9h20S+O8p0H/JWn+ZFXxYFGRRP77KjrD7ihlOgg5WtQ+Sf2HlND9vYbUflbrmmz92a1rcPMJqcvRSxRIWmPJo+DK/h+efyLLo4BJ6gGEtg6FYDgI2R7ErHrpsLWWNJCM4xDtYJz4v/+gB/1lko6tc4+iFGrN6armYiO4PbU4x5VE+7Llb53fnFell17nUcBWZjleVj78UcA0jxgk3nF1XrbsDOIeHDxamJTw3ap50TJIFUngQca8bDjb+ATtMFAESvQmcwEWyzD1PjTGOQZxyCvTjEEScn9ecG9eM0hC6s4y6KXoUjrq1tBY552EjcFY2FiNc47XdwY0xpsmDpOwVwsZBknA1iAmDQKkgvfOSuJAUnaGLA1J+1HcxnUECtZNx0ld+RgLbekcgGB7EDJMQm5vZYTS5xoNkoCffWOHcR5QtV5ZlkUBaaTAOspOEwU+UTpQgpvTzMvKk4BF07BpLA/nFe+dl71CyICDf/nuBY3W/Knf+wqjzIPWqjOkoSIOFMJ5d96TVveKOM8xCgOJ6rxKal766I29UcwoCxkmAQhBFqkrgPv24ZSiMfzj335Ia0H3DtCtBme8/80/X62Yrb7K//2PvM0gDPj6psU5n1UVKEkaBUwCyaYyuKHjfO2jHC7zpy76SI1QSXYeOxx0xjJNIwapomx87tkwChnuB4D4QM+YS9L8vGooWs049ontaSQ5GMZEgbrim/2wwM3LhtR+VuuabH1d1+DmE1ZxoLx/yFN//viJ7HmuxOM0YJL4HKGy08w2Leu6o2h7U7bGb3xlY9gefPB7+G5Jx+AlwJdRCj5LKqJsO945XZP3JmtCCiZJRNEEPJiVXrZuLAq4MUl5c3/I3ihhkkas6o449PlNnbGcrCqiXmF1uTDlYcCDpXf8PS9a0lBye5oSBpKqM55/IwQIKFtDHAjePhwSSskojai1eTLI0zlCpbiznYGDVd3Sam92eLz0idV5GLI/CrHOMd80CCEoW8vRquHmNKHqfFhnKAXjLCINFWWjaYwlwr//NJYsKg/qwlAxL1uKThOHkm8cLZkVHXuDmINxStF2lE3H/jjmKzdHDLOITa0ZxIpXdzPmm46iM15WLSANfceu7gxxGDDOHgVqppH3LIrCgNe3B/zOwyVH86r3C/IjQRk65nXH//7Nc6ZZxE/c2abRtr8X/QZqraM1hk5bwjjAOjjf1BS1ZhAHHOwPuDn1370ntkPVGbShV6b5uzkOJT95Z5tF0fA//MYDTitD00HrMRYG/89vnLX8t//s2/xffvYNskjSag9e90YJ0ywgCQIeLiqCQCClxApHJAV1Z8nigP1hwsnKK6HMyJHHwdWmf+l6bZzDWYeQ4uqfH3S6n+Yxb+0POV02WGCchYziAAfcuyhprOX+vEQbz41zgpfuFnzY7sKHydL6rNQ12fq6rsHNJ6xe5kQ2L5pnHtxlpa+4EudrP+pptGVnEDPNop4X06GN44ZLX+qLf1HScaN9dMGy8unWWRSgU8u8aFiUmjRUjJOQWdmxqjuaM9/ZMQ6UlJSt4dfen3O2qXhzb8Qki0kiL1GvOs2y6Aik997JouBqYaqaDSdrz1+JA8l50fDbD5ZMkpC3Doc0reFBD0oCIZBSkYchNx4bsVxuZNY5Gu1l3LZ3b9bWIYBBrCga7c0ArSMKJFWj6azfsNJIsa5a5pFikgSU2lC33uDtwbJiUbYMIsWt7RwBtJ03/rvY+KBDa6BpLaui42heI51jHXiOzc4gIgsD8ljxym6OwMu9D8YxgZScrho67TdxgONlDcJ3j06WNbPSj7bSOGCaRoyzgKox/NgrWyRRwN1ZhXUtVWMoWk1jJK7vOv0Pv/4AbS0/97kDhn0H7Wjpu3Neft13CY3nF0VSeCWe8qPQi03Ng4XnBHXaE37XTcskidjKPCn4XFt+8o0dfuPenNNNQ2s8qBH9/y7vsa8eFfz/vn7M9mjQp2Zb8kiRRiHvnq2ZbVriQJLFAeuyZVlrdocxSaj67pUnsR+vau5sZU9GGQhxNVJ9+lT/opJScDjJ+fE7mm8cbxjEAcY4Hq4qlmXH/jBhVrR856xkmgXsT9InPKGe1y34frsLH5Wh34/a6OaDeFdnm8aP739IHbbr+vjq+hv+BNYHncg+SCVxyZUIpeDeImSqfDgl+C+66TdhZ5/uC724nnFztV6tdLKsOFk3TNKQVltC5ccheawAwSANuTnN0M7yuw/X4ATCq5JBgsPxjaMNoVKESjHs07/LpmNdt9yZ5FcSdCUVjTZ867Rgb+jVK9o5P2YyllXjHY/3RimDJGRZexBnneuDIf31vNzQWmOpGoN2fkxTtIb9UXIFAs/XHkBM8pBVZWi1pWgNUeB9hBqtUSjuXhSkkWJnEIGQhEoQSp86jhQsyxbdk3tvTjOO5iWb2o9vnPNqokDB9ihD4vkniUzYGyaEMqCoDJMs4tY0Io8CHi6rZ0BGZy21toyziKozzIuGOAyIlONs3XC6qjkY++vy+YMRr2ynfMsYVrXASkkooDJ+oz1aNfzm+wv+2JduoqRASUWrLefrhmkeeRuBJMBafy+11rGTx+jewXeQhIwTg5SOKPTXYVN5X6FZr1TbG8YoBF++OeGdk4J17QnIUkCguMqXag38zsMVf2Q8oHLed+i8aDlenXG6bjgcJeAcwySgaC11a6hbS9sZyv5/Sgq0NiShfAIwfD+n+te2hwRScm9Wcn9ZYQ3sDGIOpymttlSdplpqpnlMHDwrBHi8PqruwveSpfV4/aiObp7m5VnrCeyLPlIEB7v9evpp/pzX9cF1DW4+gfVBJ7JO2++qkghDxTgNKFufFH7pGmwsZJF8Imn6RXW58F0UfjOOAsl27kc5s6JjkkWcbhrONjUPFpZAeWByMErYG6Xc6MmVy9J3CTrtN7yqNYShZJzGdF3N2brl8/v4IEZrkUqylcdsDZ5a3J2gNpbWONZNSxQq0n5EcrRac7QseT0asj1MGOcRZWOQAnYGCVKKJ7pdTWd6krTDGIsQsCi80isKFa19lLhd9R2qstOUteFoXjLKQu7spkSB5Hzd0naON/dTtoeJfz3pFVcny4bJICQJJCsJ7TiiqQ3fuShZN96zZ2+YMEwihIB1rVm1hmFnuLWd8+pO1vsNyb574ciigCwKGCQBZa2pO00gPGckDhW3t3Oq1lBrxyT1Ro44qFtNHge8tTvgt9+bMytbQgmaACUcw0FMZyzfuSj559864xe+chPwoySL73LFoSKQEiR02pAEkkHqnYxbbbHO9n4/FZFSDGJv0BgHAYESV+A7CiT/1hcPWZQN/92/fkjZQuDxIIj+n/jU9iSQ3JgOWVWak03NqtRs5yHbeYITHkBFoWJRtZSt5qJoWJQdaay80aEULCtNFrV+JPV9SqiDQPLa7pCt3AO9otFkvfpqWfnnotaGTauZDiISnlX9wSdDyv2jOrp5mmzt+YcNCBjEAUqJH4nPeV0fXNfg5hNczzuRvaxKYppFhFLT9eqmQAnGSXAlxf5uNS8b3j3b0GpPXLXOcb6uiAPF1iBhlIbMi4avH28QEqTzWUPrWnNrSzxq+zpHHAo2jaaz8kppUrWaNAnIYs8L2R/FV/yH41WNsY5QeZm3tWCcJZKCedV66Xtvh1+0GoHjW2cFDg9m0tDLtnd64vTlRhIpSVF3vHdeYAFrHEVruDFJKRrNdy42fXepj6DQht1RxCgJOF1VnBcNozzmjZ2crUGMto5p7kdaRW2YZD5jSeAND8+LFtd/j9bCjVHOwa0EzSn35iVxFID1Ro1SeEPAZdUxShpuTRPG2aOF9/HvXSDZ1JrZpvGycQlRp5D4xOxR4qhaw94owVqHE5ajlU/dzuKQSRbwrQtL62CQwDBRNNqyqDpc0fHf/Or7XGxq/g+/9zZBoNhKo6vR6OWYtLOOG5OU1ji0dLTasqr857g5TdkaxIRSXuWUKeGzuS7VcsMk5P/8k6/xrZM1//w7a4yBS/58642hWTeaf/7ejD/4uS1ubuXcnCS8d1EheyDUGsvRouK1vSESuOgdh4WAoE9xf2134D2IHouO+Cgk1Hkckichm159dRl4KoUgVsqT6O2LX/fjlnJ/EsDVD6oeH+0bY1mUrZ95Ot+ZGsQhdWc+9Z/zuj64rsHNp6xeViUxzSI648iVQAmJcRZtHJPsuz/M2ljuz0qKxrsTX3Z+LjY1rW7ZHibe8yWNeOtgQNVaam0YpgFt52g63zFyDpwQvL0/5J+XLbNNw7ZIaDvDujZMMsXByHdWhBRXTsnjNOR4Vfe+JF7Z0hrLMJE8mNcE0ku7zzcNJ8uG17Zz4lDRdpZ7s4KDccorW9nVOOpyI2m1n7lbvFS96TTHK680GyYhJ6u6B1s+9PN8VTMvOt7Yy/k9tye8f1ESK+9SrPsgxXEa8mBRcW9RYvDjmU3T8XBecbZpURK2BzEW15NmLQejhJNljbUGIzx/ZV23WOszl97eH/HGzpOeKY9/78uqZVX6iIlAStJYsq60B7yBJBCSOFR9bpIjlorOGpaVprGOn3pjj+N1y7JqmeaKovF/JwCloGo1/+NXT1lVHf/Hn3qVm1spUohnxqTDOGReNXzj4Yr35xWzoum7NZ6EHISyd9cWGPcIfF9+lkZb/m9/6HMsqt/lvVnjc6esn1xGIaShouoM/99vnPLm9oDXD0ZUnWaYBKy7DtPCSeejI5SEptUcrzqMdSAE40wxyfvNzJhHXdCPQEIdKMlWGvJwXlK0XQ9ovHvyJAuJQ4mUL37dj1vK/XGDqx90XT77Z5uGojUM4oBxn98GPzqf87peXNff66ewXkYl8fjPdNYvmFvD4KWUFI02zCvvYXIJOJT0p+17M2+4F4eSVlsmecwgtVgDh6OYsudobGpNHgXsDWNe28qxzvK/fvOcWVF74uwo4sYoYZKHSARd503tlPBux4uy5WhRUXWe89IZS9VqqlZzsqx8IKi1vLadkqUBdWMYJOoqwTmPgqt5uurJPvPSy4Yb7aFGFCi28oh54QEPvYHfyapBCDAGqrqBU8e/+eY2bx+MeG9WsGn8ZxulIWX/nvI4oOkM68bQtp6kvDeMWJQdkZTcnKYgBMtS89bBkPsXBb9zXLOpW6T0XZdRFvClwwk/9/YeSfzso3nJufrmSU1rHWmouDGJWFaaVltOVjWLsmXURzaUjSYJvYR6MogQomWcR3x+f8Smavmfvn5KYxyLskXiyd7jLGRrkKCt5dfur/n9b2z4wuHYOxc/Z0y6qTXrRnNzmnIwSjjbeBJv1RoOt1Kc8x3DsjEcjB8lvl/eh5GS/Pmfe4N/+K/uMysaLooGpQLyUBEHsOn5U7P1jE1n2c5CHDBKQmptqU3LOydrJmlIGEgPspOAaR7RaMu3TtckofdfehxYfRQS6u1Bwqvbmu9clGjjnbyd81YBWajQxr3wdT9uKffHDa5+0HU52k9DBQ6UEgzi8Orvf1Q+53W9uD52cPO3//bf5m/9rb/F0dERX/rSl/jP//P/nD/wB/7Ac3/2H/7Df8jf+Tt/h9/4jd+gaRq+9KUv8Tf+xt/gj//xP/5Dftcfb72MSsI6b5Gfhuq7ylyfV8975H3iuOJsXeGc4HzjT9t5oriznZMmIaIzxIHkYOwzqy5/5+97dZdhGvLOSUGgJKM0wFnHrGhZV5qTVUUaK6ZZxLCX2E6zEFULtLJXIY3gDeSiUKKEJI0U52svEx6lEY02zIqWedmyP/amdIGSDOKAb9cb8iQkVoJZ0YEQ7A0SVm3LxarxKpqyu1JIpYlEKUFjLN8+K/jKzTE3J4kf3zjL+brl3qIkDQN2BzGVtp6TEkpyF/LGTk7ZWiqtkVISKEEaSaZZzO2djLOy9WowbXAI9kcR/8YbW+wO0hd+74PEb+IOd5Uvtq478lSxYyNw0PTE1jvDAbujmHWjUcJ/D2nofXb+4BcPqLXmN+9vOF/XJKFiksVMs5AsCnFYjpYNv/1wzds3N9ycDJ4Zk9atzzEb5xGjxPOxor5b82BR0VlHqASrXgUWB9KPi6IAJ2CYeC+f7ewmSih+9b1z/uV35kyykEEccLysaTuLwLFqDfcvChwZOwJsqJACDsaJD9Y0Bmm9/87ZpqUzMM0C1rXhZFlza5I+A6y+Xwn1VRBsErAoNNr5vCrRb6aXnlAvet2PU8r9wwRXH6caK4kCdocxp+vmibHqtR/Qj359rODm7//9v89f/at/lb/9t/82P/uzP8vf/bt/l3/n3/l3+OpXv8orr7zyzM//03/6T/mFX/gF/tP/9D9lMpnw9/7e3+NP/sk/yb/4F/+CH//xH/8YPsHHW8/j5HyQAuJl69KRdV62vWmbH0uVjWachITKc1ryWLEoO1otMf0p9XLRyB87JYHfCD6/P2F7kLAoNBdFzaLW3q/FOmQoKVtLKH3adNlqnBMkoWKpLUlvfocTZLEiDiRHy5qq0+yNErYHseetyEfGcdt9/pDtwyutdRwtfJBnoAR5FCAUTNOI/UHCvGp577wgiXoegoMo8COs1lha63htZ8A7x2vuzksPMtOIvVGKEz7odBAplrVlXdc8XNbs5H6z2h8lIBw4z7HZHWX84bdiNq2m7nwYZh4pxqk3SJTPhZe+k7GuO7QBbRxHqwprHbvDmM8fjNgZxtSNQSrBm7uDnoxcYpy9itJQ0hOD/9hXbvHazpL/5le9E/PeOCVS3iTxfNPijOXBvOR//toJP/O65c390RPqks46WuuY9N+1FAIpBIM4QI4ld7YTztcdm1oTBxLrau7PC0ZJSBaHVyT1JFR88daENFK8N6tIAonpE8TBx2MIAaum5fy9ir1xxu1tzVYeo9KQ3WHE3fOSquvYzhPiyPs5nW4MWRiQxd7k7zKP6qOSUF/e17vDlGHs+W2h9K/1Mq/7Ub6PD1M/aHD1SVFjXfsBfTZLOOdeXhf8EddP//RP8xM/8RP8nb/zd67+7Atf+AJ/+k//af7m3/ybL/UaX/rSl/izf/bP8h/9R//RS/38arViPB6zXC4ZjUYf6n1/kutxVdDTp5TvRRlwsa5597ygM/bqz6Twxm5bg8S3tK2lrDWLyo8Obk5TRn3mTtg7yj5d1jqOlgW/dnfOpjEUtSf13Z6mRKFCSsEoDniwKH1sQhx45+HQB3la6+W/e8OEo2XJsvLgJg4U2lqazjLNfHfj9lZGHKira9J0hlnZIYQDC6M0IAwU+/0J7nfuL/hXd+dY51PRW+1IQ8lW7hfjN/eGvL6bc7Ss0NoipeTurKBpLZ1xvD/boHAEkWJTWVptCKTgxjTjSzdGJJFiK4uYlS3zor0CgNY5nIOi6RilIW/uDZ4Bh9BzoeYl9+clD+YVcSRZVwZtvF/PW/tDpoOEqtM0neULB0MOJ9mVKqbpDMtao6QnU4+ykDhQ/NJv3+d/+vo5o8QrSc76yIO9LGRvOiAJBVtpxL/7Y4d8+dYWUU9ArVvNv3pvhlSCURKhreV01VA0HVJCEijK1hD1Aa1Kwrtna7I45JVp3idxS17byak6S2ss/92/usvXHq5oDZwvKyqj0RqCwBO1V7VmfxTzC188wFrBom75wsGQZa0pG8Mki4kCQdFqwBEIwduHY25OsytTwo9yc/2kbODfT/2gOisf1Vr0UdWPmp/PZ7G+l/37Y+vctG3Lr/3ar/HX//pff+LP/9gf+2P8yq/8yku9hrWW9XrN1tbWC3+maRqaprn6/6vV6sO94U9BfZQKiGke86YUzIqW1lgi5dUnq1oTKuk7OkqShgGDNGRVdSShpGj9BvqiRX5Ztdy9KNnUnuTXak8Yvr+ouDlJkVJg44AkUCxrTSNNrz7S4AR5L/G1OLZyn4iujb2KHNjuU78l4irY8/KajNKQOPRdnaLRVK3l1jRje5AA8PnDIQ8XJV872WCA7SxmkoU+5Tvw4YnGOEAwymLWdec9X4xBa8vRsmLdaAaR7yaM0uAqtPSiaHgjGzDNItZNh8MrweJAISzMyoaLsqHrQykfT4G/XJS1sTTaEgjB3iih6SwL1xFIRRT4bosUgqIxNMby1eMlZau5MckYJYqVgzgwaO1HlsMoZJKH/LmfeQMc/O/vzpmvfY7YVqJAKU4XFVWnKTvLO6cb/vRP3ODn3txne5iQRAE3pynfPFn7e01IllWL1pabk5RZ1ZJGAbGSFK1mWWkcsNh0DOIGKQUnS3/tPnc4pCktP/3aNkWt+erDlb9O1iGUJAsErfXE4fNNwzsnK754Y0rYCs7XDYfTjCoyPrizBYkklIIOSxL6+7Vuvfnk5f39UdTLyKk/6Zvqh/XJ+aD6JKqxPuhzftK/o+v63utjAzfn5+cYY9jf33/iz/f39zk+Pn6p1/jP/rP/jKIo+Pf+vX/vhT/zN//m3+Q//o//4+/rvX5a6mkFxKWMWvSy2e9FGfCiCIa6K58hITpHb/bWMkiDJ1yF4clF/qJo0QbyOLhSSFkHnXHcmxekceA3cAk7g5BlqXHOxymMEh+uWTXejG+Shr1KRTPJQtLIxzc8Pk9/5A+jkEJcGdFVjaHWPmTyEnztjzL+8Fv7DJOIi6IhjQKUkFRao7SPFDjdNBS1ButY1x1beQQ43lmsrzgWVWvQxpEPQnaHMUIIDsYJUviRyHYeMy861lWHiR3rquNoWTFOQw7HKaGSnK4bP57q/VMuYzZWtY+w2MpjnPOdj5NVTWP8d3xRtoRKsj/0HKBfvTtnf1YSB4o4lIySkHESMsmjJ7prf+nn3+b33Dnl//3VIxablrLzjtPaWKwAieP+vOT/9ZsPyeOIn3ptm2kec3uaA3C0qFl3HRK4vZ2yP0q4qNr+PnQ452X31goqowmk50EJBHfnJXd2cv+dScHPf0EQCEFnDGWjicKAtjVsKk1/Gfja0RptLJMswhjYGnquV6QU87LB+ygKBknAt0/XfPtsQxwq8lDR9sGalx2oD1vfbQPPo6AHdZ/ers6HrU+LGutHofN2Xc+vj/3+Ek+x1Z1zz/zZ8+oXf/EX+Rt/42/wj/7RP2Jvb++FP/cf/of/IX/tr/21q/+/Wq24ffv2h3/Dn8C6PHW4/uH0YYSWda0x1p/4h3GAeMkB5OOv9zQZ+WkSYtl0PFxUrOqOURpRacMgMozzkCR80sCs0Z78WjSaVhtWtcEYi3aOrtNoJ3jrICJQkjySKCm5MVUIFKu6pdOOpg+w3BnGbGXeeK41lqb1jrSX8/Q8Crwcvb8ml0Ge4Chbw/nGb9xJqJ7okuyOUn76dcn7s4JZ2fnQxyjklZ2U7TzBOJ+zVGy8hDwNFZ2BrTzCORimnlw6zQKUkiSBQuNHNPVjidWv7+TcnZWcbRpmm4bdYcwr2zmTLEL29/+Dmc9PyuPwqitgtHeI9uGjIXkSkDc+LqLpDNM8YmfggU9bGTqjOVk59gaJ9zuSAiEESaRIey8ibSwGx0+9toMT8D9/9ZhvnZUI8CnfraXTDmstXz/Z8E9+5yEHo8R/r72p3eE4pbOOTd2yqn1y+DAKuChalJQ+I6ozLCsfiJr3oDLqAcmmMRxOMoZJyK2tjFemGVJKfvP+HPDu0haIA3/fGW15/6IiCRSHk4i2s5yWNYeTjFuThFnR8HDhx4cXhUYbQxopZlIwLzv2Rgk3ptn39dx9tw18VjYsK/0jZ5L3MvVpUWP9qBoZXtfHCG52dnZQSj3TpTk9PX2mm/N0/f2///f5i3/xL/IP/sE/4I/+0T/6gT8bxzFx/KN5kz7v1OEzdWqq1l55tnTG0hhL0eoPPK1evt68aLnYNKxbwzBWbPf5VOP0UZTBoug43lScrqreoM+rRAyC+7piK484HCco5Re4dd1xUTS8Pyu4d1GxPYjYyUPWjeZkWbOuWm7v5EwHIZEUaAubpkMheH0v4pXtCcZYHqxqQiHIk96vIpDs9aObg3FCKP0I5OGy6k3VYLapOS864sDzdi6l2493ScAvZrIPwPx85J2Mj1c1SaAe/T5gZ5gwLxrWtWZda6q2Y5pFCASrugPhqLWlLDsWFWylMQ8WJRIPEEMl+gyrS6NDuDFJngA2QsCsark5SZ/oCuwMYspOs+55TkmouDnJEAi0cdyaZkghOF37LK+y9mqtog/zHM4D3r45IupVS493FqTw3jKhUixK7/jbdN7DKAkUSShpreV37i34X75+zOcORlegN4kCEnxSfRK2rCrNKAs5W1c+dNN648Gi6XhtL4e+m1i1mmnPabo0+AuAN/ZH/NEv7nFvXnCyqj0wjSxF66g6bz2walvKbkkWK5Ig95uRdazbDm0lnYMHy4bdgWWSxUgpe7O/lruzNXuj5MrksdF+XPW4wu+71Qdt4AJPHH9RV+fDqhg/LfVxS91fpj6Jo7Pr+ujqYwM3URTx+37f7+OXfumX+DN/5s9c/fkv/dIv8af+1J964X/3i7/4i/yFv/AX+MVf/EX+xJ/4Ez+Mt/qJreedOja1vopLsM4RSslhH+L33R7YZdVyvKy5KBqOlxUOwWzjfW+azhOLp3mfrGwty1qQxgHjLOLuRcndWcnhJPVjn1bzcFGxO4jZ1B2z0scb5GFIHrfMi45wJJnmMcY6kkjx47e3SCKvwIpD70uxaTouio4k9G7GOBBKXClf4NHCGShJ0eira5IEkvcu1rx7tiEK1NU1uJSqT/JHYOLpMUKjffhj1WlGyZOqijhQjNKI7Tzidx8uOVs1nKuWrr9OceBHRXGg2M593MC3zzZ8fn9EGilOVzVnq4abWyk7g5iqM1xsWqJAMcn872o663lDUtBqy2XHaVG2OATDWOHw6pxRGjJIFO9flFfJ5+tasywazjYNDkenA6rW8n5XsmpavnRzShJJVvWjzsL5uuZ83fH2wYhvnW/41klL0XTEoSRWCte7UKtA8VsPVnz9wYLfc2f7ifb94+PMUPkE+854IngeBHz7HNrWUjUagCwK2B1EJD3p+LJrKBx8/mDMT9ye8k++doIQfuy0KC3G9UGbBuYbw6/dnbOuLT/1+g6zssYA4zRGScHFpkEJL4HvjEEIr7763YcbdgYJkyTi4bJmUXk36WnqO0fTLP6uY4kP2sAnqQfu4VPPmpKCk1VFbcwVOPpRHYN80lVKn5bR2XV9uPpYv7u/9tf+Gv/Bf/Af8JM/+ZP8zM/8DP/lf/lf8v777/OX/tJfAvxI6cGDB/zX//V/DXhg8+f+3J/jv/gv/gt+/+///VddnzRNGY/HH9vn+DjqRacOH8ooOBglBMEjR1hj3Qc+sJevt2laHi4q0tB3K6rWcLpuyOPwChwBLCu/cIfSpzCHgezHVH7s0jgfXKmdY1n79ymE98R5Y3fAw2XNvPQE2BvjhFobL+FtvUFgHPiwzLwnpJ6uGqKw4eG8BCEY9lESoz69OpQ+3frxa3KxaThZNQyTiDQKyEJFoyu28wgp5RWn5XIxm5cts7LtQY0nOh8vfbr6G7vDq83n8vc1WlO1hmGmKBpHFHmJPFifGZUqdgYJjTYcJqF3UL7woyhrHBfrlmnqx0hHi4qLTXPFG2q0QeA4WtQetLWaThufp2Ud87LjouwYJS23tzJe2fLhm9+ZlZRNw+mqYl40XKxrwkAS5Io0kgRByKLQvHO8ZhAr9kcZSeiVZq1xjPOQrTziZ16bcjKvWZY+aDSQHQJJHir2RgkC+K0HSyaDmNd2h8+5o3xn5nCSeSJ2D1i2hh4IR4Hn3ORJQCglg1g9kdRd1JrWGP7A5/coa80vv3vGxVqTRAIlHa2GxlsVcbzQbKoLAmnZHvrO1c7AJ8HncYA2cH9ekkYBSeDv0XWtqRufN7U/Shn1oHJedrRmw5t74qXGEi/awPMooOrMM12d2aZhXWmm/X9XNr4Lx9bzxyCfZqLrxy11/271aRmdXdeHq48V3PzZP/tnubi44D/5T/4Tjo6O+PKXv8w//sf/mDt37gBwdHTE+++/f/Xzf/fv/l201vyVv/JX+Ct/5a9c/fmf//N/nv/qv/qvfthv/2OtF506LoMOjXPkwcs/sJ2xnK1r7l6ULMoOm3hCpt/4/IaQRX6xXlYt9+clSgoWRUcSSCIp2R3GrGrNqu5IA8XBOO3HYo4skjjnCJUkChSfi0PWdcfhOCFUknWj6bQHYIM4pNGGqvVdlvOi5d2zFc4JIuU5I60OKRvtAx0Dxd7QZ1NdXhPdv0/P3wnojCONFcPeSbjpDNbiQyD7MYJ/D9bnBSnvyFt1mvfOC/JIsT/Ork7mozjg3syrdNIwYFW1VK1lXjacrhp+7PaYvXGCcJKTVXUV/bA/6jOXFJwXNTtVxM4gwYzcE87OeaQoA+XTrQWsK591tal98JIUimka4JxgUfjohtd3B9x2lq8+WHvidee7JlWrSYKOQRoziAOazmEdzIuOg/5MYC0Y66Ma3p8XZHHET7wyYVnVFJ3FOhglAfvDmE5raif47Ycrjpc1P/u5Hf6NV3eIHrsXH78/lRRXC82r2wNiJUnjgLDnJI3SAOvcVcctUoLjumNedeSR4iuvbnFaNvxqOfOeRXhuXmvB9K/bNPBPvjbntemGf+P1HcrGEEnBTp5Qdh2b2qeDn2389dzKFYvS8GCxIosUBxPPv1FSeMuAon2pscQHbeBPd3U2TcfRsmYrD/ruqAfO2jouiobf/9rOlTP1jxLR9Qehxvoo6tMwOruuD18f+z33l//yX+Yv/+W//Ny/exqw/PIv//IP/g19SupFpw7neG7Q4Xd7YDdNx6zo0Nab1hkHi7IjiyxpFND26dmbumNWeEVOqCTDxPFwWSHxMu1xGjJMFAejlDwJvUwbR9eTd4dJeJXQm8UBQSDpjOPWJMXiKI40m6bzHZtA0mivEFqXhq2BX9gDKShb2y/6ksP9hHHqHXKvwiW9NyCR8nELUd9ZmmQh788LhjrE8ch4cJwGzIqORdX1Cdd+s3dOECrRx1F0JD2QkkJQa8sgCBB4K37/ex2nq5pVZYgC7VPYheCibMjCgDwKqTt/PbIwoGxNb4gYXI3LlBAcr2p2RwmtscyKBu18BlXRGPLYp4Ir6UMps9iDt4tNS1EbRmnA7mBMHil+Rxsuypay86Z3oZKkgWCU+w5cWWtIPFhQUnC6rjhdNxyOE/6tL93AYPnX7y1JQ8H20I8YL4qOUaKYrSvmm4bvXGx4/2LD/+n3vXoFcJz1ALtu9RVfCTzo2RulV0Gpl2D7/ry86rg12lBrS9sZJLA/TvnZz+3yYLbh2+caowHxCNhclgW+Pe/o3jnhT+YxYShJApgVXnV1ZAxpqNifZuyNElaV5rys+fZ5ycE4v8pRa/GS++9lLPG8DfyKn1Z2HG1K1qUH4/dmHbNNS574DK6i0zxclOSR4ve9uoOU4pro+kOqT/ro7Lo+fH3s4Oa6Plx90KnjRUGHz3tg61ZTdYZZ0bE7jChaQ4uhMz6jaV62JKFEIBnGiqI15HGIkpKLTUOeBBzYhIeLirO1zxg6HGckkboCVNpajpcN2lo/skoMs01LnPqQx8vMKym9Zf3JuiFWklnZ4bAsi44gEBgHReMDHvdHMYPYS5pHmf9vJeLqmgjnwDoclk1t/GaKACkYRt6DptW+m7UziNDacryq+c7ZBgdsZ5HPi9I+hTsLFTcmKWnoJeUn65JF0TAv2z5p2I+LFlXHVu4lzUWjKRrPN9lUmsnUK8FCJZgXHTuD+GpcaHsZed53rXzXw/OMkkCC847EVVdijUXgnYQl4urfN41mVnnVWhYFvCrgougwzsdCDNMAKSVxIJmmPg7h/qIkChSB9EDn/rwiDiRZ5D/PT72+RxYG/O6DNfPSYKxglCiElHQW8kSChV/62inTNOQXvnyLedUwLzpWVeudogde2Wbco6ylq9R4eEKuDx4YVa32fkXSe9S8sTfm579wg+I3H/jP1MvM4VFUiMADnHsry9eO5hxMBqyqjmGk2B2EnBUdkzxmO/dqrXsXBcuiY1HMsdbxxu6AW9uZDxsNHnU5XzQa+m4jo8uujjaWolWMpiFyCV87WlFrg3OOySBGqRDh4BunBbe2M3YH6TXR9YdUn/TR2XV9+LoGN5/i+qBTx4uCDi9La8u9ecGDeUXZGeZFyytbKYfjiAeLGmMdxvlUaSEFd7YzJlnMg0VFFnlyLvjgxCRS3Jhm7A4i0jAACRLBziDCWneVWH0870hiyVYWszuIyKKQOJBPbHTbA69gOV+3FK0mVpIoFKwbdxWiWLZ+rDTNQvaGyROjtmEcsihbHi5qLsqWdaPZSgPisDeYs44v3xpxc5LhhO+AreuORa0ZJj4MVEk4Xteoje8sRKFkWXYoPKicFw2rynAwTnh/XlM0mk3VUXR+w7ox9knni7Kl1oZBqLi9lREpRdVpBnHAYDen6XxSu+eIPFKiPd2VSyIfBPn1oxWLovWv3bQIB6M09CaCxnIokidCG4ZxxK1pyv1Zycm6Jg0lN6YZgygikJBGilgpzouWk7KhaAydNkRKsqxbhnHEwSjh1o/dIg2POVo3nK5qyk4QCD9mtMaRxgGrTccvvzMDBGEcooRACpDA2bpBG8d2Hj8XZD/9eYX0UvVlqZHCXnGQXtsb8kfe3uOXv3nKN4tH4MY955+/+u6SV7Yb0jjiRDi+cmPAdu45ONYJ7p4V3L8o+nBWy6Yy/Oa9BZ/bSfkjXzn0rtRCMC+aZ0ZDwzhk3XQvNTLywMYwTLw1wrrpaI1DG0cjfXfQ9PEZtbbM1ppRYq6Jrj/k+qSOzq7rw9f19/kpru926vigB/bevOCbJ2uGacg0jNjUmm+fFXx+f8AXD8esap8JFErB5w8G7I+yJ8Y+PmgxYhAHlK1GCsGdbW/odvle1nXHaR+dcDBOqVvNpvGeI41QFG3zzMbweJqvFQ5nXD8G08z7Dk6eKBzw/qzizb32CeXU5eZxMPEdpGXdemDS8y8mecB2nlw5/zbacFE0hFIwSWP2RhFH85p1q0nDgBuTBOGg6iyrpkNKwdm6IVCC1/aGxKHidNUwb1o2dcdr2z7yIUtCJllI1X/mm5MU03OO4sC//6LRbOcR24P4me/t6a5c0xmM8x2rZe3HGg5BGigEou9yCCIpKBuNkoKy7ihbw84wYpAGDGMPNkd5wDSNmOQxrbbEjebmNGNZttydGcpaY51lEPkOwabpGKUxW8PIS+lnJVEosNbnbbme33KyKPhHv1Hz9uGQV3dHIATWOQ8qwoD90ZMdmxd9XoGXpNeB7UdulkAJdgYx/+7vvc0Xboz5f/wv3+S3HpSY/ueftnBaG/j2Wc0wrokixbxsuTWM2JsOWVQNq7JjVXeUnSMOFUI4ms7w9eOCtw4LfvrV3ReOhhZlS9vL8L/byOhpbtw0ixhnAUcLjew9m8apTzrPpKDPN/2hEV0/zYTl67quD6prcPMjUN/rqeMyzXmYhlcy54Nxwr1Zyfuzkq/citnKI/Io4GAcszv0KdWPj33Ab7raenLqziC6WhwDnq/mypOQTau5t6i5s51dORkfLSsabdjpuzbgfVP2BjH351UvZXbEoaSz1od41ppWO37j7oKTdctrWxm3ptkV8Bj02Ux5EjBOOrRz3J6mJP245fJEvmk0d882xGFA0vNj4lCSRwlWOAaxdz4OhOBoUTErO46XNXkUMMlCbm3l7I0Tytry/qwgDiVZ4jObwG+6QgiUlGylAWXrTQdDKTkcJy8kiD7elVvX/n1+bm/AOAs5W9b87sMlRavZdIbXxgm3ph5ULaqWpjOUdcd7FyXrWnNzmnNzkhKHinXVkYc+uPISgGZXJGu85Ftaqtby7dM1cejVcIGEV3eG/UjOd2KkkqzKDqUktuuYV5paa755uuH13SX/5pu7jLOYk2VDFoWIDyDCPv55W2sZJr6rN8kjQqkwzne5tvKI258/YFk1/K3/8Wscl88Cm8tqHaxrGAiDdvCe9oaRW4OUsjOsG8vuIObWVkpjLauyo9OWf/HegptbZ4ySiHEWEUhBrTXCCSR4i4NRghSiz8d68cjo6a5UFod8bnfA+aomUZJpFtJZb+Fwc5L0XDP1Aye6/igRlq/rup5X1+DmM1aXbfJaW7YeMzfMeoXOexcFSbhmmkbcmCZM0ydPoi9LwHuemksbS9V6l9xIKQQ+KmFedpytGzZTw3YeXS2w49SHMZ6tapSCLIoIFawbQ+E0YQinm4ZV2/Ebd2e8sp2xN059iKNxjNLQK74i31263FwfP5H7lG7DpjXcCFMGccj9ecV2HjBKQqaZP1XLnr9yK/TXyTjLxcaPRiZZBLF/7xdFQxYF3hm6T1IfJgHGOUZZxDQX33NidNV56mweBz4TS1vCULEXBSSx4rWdnEHiuyq5DbgxTjlbN3znouDGxEchDJLwSva+aTRD5664LoEUPFiUnC4bT+TVmnVtuDVNGSZ+o/3Oecnd84JxHvP2bsqv31vTGkuswGE4KRqsBSmh6jS/fX/B6arm5z63xyCNyCKFORxBoD7w86ahorMOBTTGsqq8QWQoH3GzllXLm7sT/u0vHfKLv3pE89xX9PybGjAVpMoQ5rBqHUPjM72yKOD2VkqgfMBnZ5znN1nH/YuSIKw5HCV0FpZ1hwBCIZlV3iAxDrwSbJgE5HFA3ZlnRkbP68LtD1Pe2B1yumk4WlRe2RhJKm2xziLFo3t/UfjU+Esi+0dFdL0mLF/Xj3pdg5vPSD1+Uitbw6JoMNZxOPZhlWWrKVrD7iDhznZOrAStcaybjmnwaLF7WQLe89Rcxjka7UdaUsKq7rjYNITKB106xzNuwbvDlC/c8CfuWHk/na8+XOKAprVMBzFpELCqCr5+vCGUkjwOngAej/vgFE3HRdGShIpAecXT3jhhUXasKs3OIGKaRSx7wm8UKNJQMitbskgxzHwHwiu+vK9OoLw78O3tlFAJitZgjKPSPl6i1oZYdWzlIdt58j2dvAMlSfHBncerkpNFQ6O9mmfTWVJtqRrDIPFji1h5MCKlVxmNkvDKm+jqNYVgHAcs6w5jHYui5t5FRdMZ7zejLZ01VK2lbg1beczOIKbVPmX7czcnpHHAN08Kys6waTRpILFOULYdVWfpNBRtRd0e89NvbLM1iFg1HVnybNr50/fn452Ew3FyxY26dBNeVh0P1xVZmvCzb0z4V+8vKDrfwQnhGbBjgcKAqg2v7SjyOCAJBetaUXUWaRzr1hPVo0ASRwonJN85LfnGwxWHWym3xgOEhHsXBWebmu1BcgVALja+WzZOo+eOjJ45ECjJz31ul+NVxe8+WNIZyGIP9k9XNYMkIJBeGWfxjoVZJD+yrspH6cz7WR5rfZY/+6ehrsHNZ6QeP6lNsoiDccI3T32a81Ye8XBRsSg7DsYxRatp+tyfRdE9d7F7fBT2vIf8eSdWrS3GWpLAJ22v6474anEVZLFCG/fMArudJ9zZ8kqeuu6oOo22hiz2UQ2zokVKSdN2HC8rpkOvhrkEHq22JIHkeFVTdYaTVc3+MCGLfYdl0o8eLsqG1lpujFMWkeLm1HeBmj4T6tYkI5A+fBJg2WdPTdPo6lQdKcXRsqZoO8pNR2P9OGWchHz7dE23bdkbpk9cz0vFmhSQx89ea9k79/76+wvqxjDOYiTQWkNoJcfrhjQJaDufgH288v4p1jjO1zWB8p2yy07SNI/Y7T9/3Vm+ebT21yiUtH0w5eEkxRjH/UXNwTjlYJRwvKrYHsZsE3NznPHGbs1vP1jw9eM11ga0naa23o4gkNBZ31n73YcLbk9z1pVmZ2CfuxG8qJOwN3yyk2CcY1m0vHO8odOW3WnGa1XH/VlBjzefKYWXjZcdFHXH9HBMqBSvbQm+dlxSaw3OkkbetHKYSFqjOVpXLMqWZaOx1nFznGP75+Vxj6XSaY5WHbuj6Lmf7UUhtPfnJduDmCwOfLxE72f0jYcrtnrl3LA3qZyVHYGSH0lX5aNw5v0sj7U+y5/901TX4OYzUM87qb22511lT9e+g7OpNVtZwMEkJQkCdM9B0MZxw6XPvVG+20P+vBHWq9u5z11qfUyEP407tgcRgZQIHjkpS+u42NTMqg7nYJqF1H3w5LL0HjKt9U7IbWcwBo5XNflFwW6ekMSKaRqRBJK68+TUPPLmcQ+WJZMspDMO0XvuxIFidxijnWW3jklD7+8jhWBvEJP0m4GUPl08lIKJC6+4POBP6XVreOd0zaqXhY8yRecc711UnG9a3j60bOcReRhwb1HwzeM186ojEILDccLbhyO2B8nVQrmsWta1v35RqkB4hc3hKEEgOF56sDZIFLW2ZJF3bz4cJ3z7vGC2bhmm/v3lccCtSUoUKqJQEUpJ2WgcvYmekkzzCCUkhdNEgaDVjlEq2Br47K7O+ITu29s5SgrePdtQdQZrIJaSzqvDUcL7BR0tKv6nrz1EScnhKGWQPaXc+x46CapPST9d1yRR4Plgh2CM43xTsW4cge5fl0dkY4n3PbqoNGVj+MLNIW4aYyzcW9Q0nSGNFdtZxDgNuT+rsNpHaTSN5tfeW1AdWPYniTcaDHqjyLKlbA1CwLLUJEHzwk3u8QNB0XiTwmEv27+sNjS8PyvZGSU/MBn4R+HM+1kea32WP/unqa7BzWegnndSC6Tk9b0RW4OWcRrw9WNJGinyyHcllOzN1FqfrP28+m4P+fNOrFL0BmWrhrIzxMZ6nkzqf+/lAisc3L3Y8N5Fgeq9WdJIMQ4Un98dcrHxMu9RHFI0LbOqIwTq1vuI3A1Lbm2lfG4/x1nh/WJChbWOQML7FwXvHHnAY4xlexjzxp6PWLAd3N7Onnjf67rjdN0gxSNjxM66JzxbLsHeoupYV5osVIySACmgKvUVgVobDySqZsO3zwu0cUzSCG0dd2clnXH8+B155ZGyrDrSKOgBlSQOJa021J3naFjnqDtNa/0I6XJT3J+kV5L0YRKQRQFbefQEbyOJFAeTlKozKCkJlKBqtc/aCgLi2Kul4khyc5wyziKOliVCQhIGfPn2lNNlxT/49Xus2o5YKayEVlvqFgIBBY53zwr+2199DxVY/v2ffB2hHoVGvkwngceS6qNQovr3qQQoKRllIUI6dnAsNh0oWBWGqgMlfExDEvpgz+9cFLy2k/Pm3pBXtoacrxuOFiVSeHfvuxclTadZ1p03cwwljbZ882yNUIKbk5TtYUQW+Hs+7cFO9FQI63er50EIa8FZ/3y+6Fp8v4v29+vM+1kOnPwsf/ZPW12Dm09RfdgZ7wed1PJ+05xkIWXrpdGBlGhrMdbP+p+ncvleHvLHT6y2B0pKCQaxYlP79r4xAa17tMAu65bvXHjX2jwK0b2iZJAEHE4zfm+n+d++dYHRDmMFwliWjWYyjBkkIcI5jhcNv3l3wSs7Aw7GXvG1qjvmm45AKoyENJBsrKNoDFXn05rHqe/wPP6+8yhgnBrvBdMDsKcJnpdgz7svK0IpmJcNnXGMUi/Dri9VX8byzvEGJGwN4ivfICUFi7rlbN1cgSttHVmsGCUhy6ojCiSdthyvKgLpnaEXVcfJuuZzuwNe2RkgeyC5M0yIHzMgfN54cTuPOF97s8AolAzjgFA1SCExxnJetGQ9R6doOr5xsqYz3jQwChRfvDnh54uaf/K7p5SdQQiBtX7z7m2PMA7OVg1/7397j7NlzR/78i3GfZckj4IX3p9KCFZlS9F68rNxDqUEXz4c8svfPON0XRMrCQiUVLy2nWOmHd84KyEVDFODNgJjBdu5B3irsuVX7804Wjd8+XDI2zfGvLGbc75p+bX3ZlxsGkZJwChSrBvNxab18RxFy3zT8PmDAQeTmNIJwt5gcZyF5InvLL7MJhcHikkaMS9bH0/RP3OXoZviqf/0RV2VD7smfD/OvJ/lwMnP8mf/tNX19/ApqO93xvvdTmpxoJhmEaHUdNa7yAZKMO7DKZ/Xpn7RQy4EbHo+wjPGgcZysWm4KFo/HpnmzDYN55v2CZO3PAp476LwG3cUoqS42vTq1hIHjp9+fRepJA8vSo4Ln8gdBIpRGNI0mjCUVG3Hr9+fs2k1g1CRpyHz0kcZbA88b2VnGPvOR2twFhptKdtHjs3TNH7CsM1YRxYqtvPoCXLs42AvUH5stSw6hIRV/+eez+E7OThBaSyDXnFzWXEgqVpH1StvLoGpc3A48REPZddxumzoOksYKw7GGdMsYl1r3rsoGabeOwf8ppgE6rnA5rLyKODOdk7TWu4vSyKluDnNCYRXCb2WhqSx37i/eb7i/YuSg0lG0znfNSsafu7z+wziiH91d8bxqqGsK9Ko70QIyAJB1TlO1x3//W+dcLRs+Le/csjN6YAbk/SF92ekBLOyu+oO1q1mU2tubGe8upXyznmFcT6QdXcQM8pCdocDJoOIf/bugk4LhmnAOA6JFSwbz9USDr5xvOIbx2t+7HTDH3xrn/1Rwms7A5a1vx+DOKBzjotS02iNMZZNZ2jueVPKzx+O+NzemO1BfMXDetlNLlCSW9OU1lgvw+/DJEZpyFYWoo37wPiU73dN+H6ceT/LgZOf5c/+aatrcPMpqI9ixvvd3IynWURnHLkSKCGvfEUm2fNPoE8/5NY5VlXHec/hiQLBdm6vfu+yarkoGh7MK4LeUn+UhuyPU/IkwBquTN4abXD40622jxYRryBpmYiQLAn4iTtbTLOI4GjJplf+rBoN+NO9EIKiMXzjaE0aSr5yc0rbWTptUUoyHfi0cGMd86LleFWSRWEfPeGN426ME/LERzF02jIrWqrOsDuIeWU7u7p+TwdF3hgndNpQNoayNZyt6t41OuCs/+4SKbE9YFLKL4qNtkgpSEN1teFcbvxJqHhlK+PhouJctUyjhNvbqQdqQrA3jPnOecHDRUkSSZSQHzhqeHqDPNxK2B1GOCEQwLxseW1nwO4owTpH1Ri+ebJCIEmCXhJvJYuq4+6s4t/60gG/95Up//1vPuRsXQOOUEIUCoyWWAzOQddpfvXunEWp+fkv7hLKPW5MUrbykLKxV/fnVh6yrvQT3cFQ+ZHl1x8WRGHIF/dCDI6y0+yPUsZpgJCCX/jyIbenJ/zKdy7Iw4DtUcy7pwWBdKShwhjv0t1oxz97d0YkBK8fjkDBl2+M+Zd3ZxStoXMwiBRV36UaxiFJLLk/b9BmyeEw5Y29R6noH7TJPd1lmeYxb/aE+NZYIiXZyqMrB+QP6qp8VLyPD+PM+1kOnPwsf/ZPW12Dm094fVQz3g86qenecfhyc3naV+R59fRDXjaak1UNwN4oQSJ4sKiu3IM9X8UvBoGST0i10zCgdI98aJQQnmMTSjaNP9EGUlK0Ha025JemgJH3F1FKIgQYa64CGzeNZZwEhJFimkRcFJqLPoDSCsckCRj0p+2605ytm75j5UcXRdNx/6Lim0cbvnxrxN4ooTGONFKEgaRoNfdnJc3Imw8+DfbGWcSrQnCyqihbTaMt+4OYYRpS1Jp107E3iphVHbONH0Fp61hVLTfGKbuPLZSPA9MwkNzeysgiRdV5jo3snYBb7cnPJ8sG8CTom1vpC7/DxzfIKBA0HSBhbxiRhP66Dnt/HCkE2vlQ0UkWYp3FOIlUXlV3vmnJo4Avvz3lzm7GvKr51tGazoEUklZoWg1K4tPbjePbJ0uOlmu+en/Nn/zxGxxOcvJIMUh8wKdxjmWl///s/XmsZVl21ov+5lxz9bs7ffSRXWVWVl/OctlVYPDl3lsuFwJsECAewrSW69E9Y0puhAQCBCWEkTCNMfCHEZ0xek90Ugmoe8FlX7uwy2W77Oqzi4zuRJxu96ufc74/5j47T0SeaDIzMiIyc31SZGTsvfbaa69mzjHH+Mb3EXkuCJwUNVeGOTuTAq01TQOdxENJyZkgIvSdd5Y2hqaBZx5bYyX1+fK1KXlhAEs/VihPMC0aAt8jDixXJxW/9NKQea05vZbw1FaXUmu+dGnCOK+prSHwpSOkh77TWio0F4cFn/nKNSyCp0/2sYJjJ7nbZVlu9UyuqFtnVR4G3serKWu91VqmW7PNNwfa4OYhx72u8d7Mf7l50D06udxpIDp8mA/mFdenBYGSrCYBCDhYdJEczEo6kWIlCQmUJFD1QrFXMCsdh6bR9obV7mHg5DgdgqrRTOuaaVEziH2mVcPelRHawH5WsdIJePpEh2/uZOxnc6QQeNbSWMNqEHF6JXFdRKHPubWASwf50g0cYDgrmZU1/SigF/lUjaZsLJ3YraCnhWZeZWx2Q7xF+/ikqJ1jd1YtxQfjQLIzqWiMIfYdgXklCQg8QdkYxlnNczsTmsbSiRRxoDi3ErE3rRnlFWrh4fXOE70bBsrjAtOVxOfLV8fMq5o08DmYl+zOKta7Ib1I0Y3deTXGHlumOJwgA+WyO9OiQRtLWTfMq5qnNruESt6QfldCLq0RepFPXi0IzdowiH1WEnfM59Z6/L8+dJ5/8fkXeG4vp6icJYa1EPhgjEFry6gAPdFcHl3l2b0pv/t9J3nmzDpn1iVxoECbZcBY1JqdRaCYhD5JoFCyQEjB2sLtPPAEO7MSgWVvVtCNfD5wfo0zqynP7cywWCZZg0GjhCRSHqU29KOAQaQoDQgkO9OGxzd7YARfeOmAotaLYNvDCsu0cvozwgh2pwX/11evszvN+dbH1tnsRq+Y5O6UZblV9uRWrz8MvI+7KWu9VVumW7PNNwfa4OYhxxtZ4z1u0D3U04iP8QC6GcZaktBxSKoFOTmvNfuzktD36ISO43JtXBL5zuG6G/nsz0rXJaMtWakx1r6CT2CMXb5fG4PAlcg2ezF1Y9ifV4yzilml6YQKKT2eWEvIa81wWiF8wWY34tGN1Ll6B06dtxf5nF8VbE8KZoVrgV5JA+Z1A0IgBMwrx/Uw1p3fRmtnMDlzHV7DWUVWNjyynpBEPtpYntuZ4kuJgRsNQruhE92r7UIQMCCMJdYYytoQ9yI+sJoS+eqWOjeHODrZrXUiHllreHE/o6xLJkXNIPGJfdfxVNSOOzS/PgXB0k/rEIcTZN0YhlmN7wnqxqlFXzhwngbrnZC8cgGg70mEZFlWk1IipTO01I3lXaf7y0wYwEef2AIp+He/8iJfvjKjMZrYt+57tCUrX27RxsDz16f8m1+quP50zvd8yyPL7EM/9tke5wyz2mWPzMJotBuSRoqroxyNoao0cSdiPfWJQsXpfuJKnLVGCsmZlZRxXvFLwyG10UTqMGgy9BKPfuRTVI4MvJoGzPOGd57uUzeGz1/YY5w79eKycVkhISD0pVMnDjy+dm3GVi9isxvdcM3eiCzLw8T7uF1Z663eMt2abT7caK/NQ467qfG+lrTv6xl0b16RgdOZOewOEgtFWW3tYj8No6yiH/tL4uX+rKTWBincJHozn2BvXjFIA1bSgFlR8Y3rU2JfOULyrMJiaSw02hJ4kkB5VJ7Hhx9Z4WtXp3Qij0fWUhoDeVVxZjWmaQzXpwUg6MWKrW5IL/bxPYnveTx7fcpMOP2dajHRCwGjvMYvXbfOyUFM3mh6ic+sNHjKCRPOS40nDSf7EZ3AozKWTuiRBordiSM1Z5XFExD7EusJ19YsBVUDG91bn+/jrq+UgvNrHTqRYmdcUTWGTqgA18WUhj6d0GdW1lwblyh5owDc4QR4MK+IAo+y1kvRw0EckNeaSdEQKXd/HU6cj60lTIuarGoIpETgjExP9W9UXo4ixe961ynec7rPf/riZf7713fYHuU0uqZpnOaMBDzlCMdlDVfGJf+/L15hb17yZ37HE5xb65Iu2td3pyVSCJRwfmGdyCcOFNawKElaVpIAbXxW0oAkdENbpQ1ZrQl8yXe+cwurLT/37AGzQpNGgjSQhFJQaedntZL6rKYhs7Lm9CDmvSf7nF2P+cxvbjPODZXWYMHzHG+s47uAe1yUXB7meN6Q0/2Y8+spUaBuaUNyWEZ8LVmWo0FftRAStLiS2GriUzZ6KTD4oDIKD0PprMXbG21w8ybArWq83dBfGkC+2rTv60ltH7ciK+uKa5OZ02RRHkJW+J7g/GpKN1RcHmVklZt0nUqyz2oa3GCWCccPiuMc8srgSbtw87YcZCX92KcTetTGEPkKrCslPbmVcnVccGGYEcpFiatoUFLSTwMELuMwXphGdmPF+bWEUVZxbVKwO8soKtcGf6Ibk0Y+z+5MMcZibeTahMOAvNEu0MsWNga54xHFgTObnBXa6b00rq1eG42UkuvTglh5bPRCfN8d/3Hn+05pfSkFa2lEWWkujSzXx3OGCwLuWscSKkknUiShd2x7fjdUXKg1nnBBTmMMEskgUVSNI1lLIdjqhXRCxbgouTIuCZSi0YZO6HF6NWGQBijp3eDOfojNfsqf/B3v4B2nuvzM51/im9enlE1NiGsPt9b9rSRgoNCGX/j6LvPS8Hs/eIYnT/ToRs4vCyFY64SMsopaG7SxzuwURz631jLM3X0XBU7TaFq4e2lWNggBH3xsjaIxfPX6lEHsY6275+LA4+xKzEY3QilBVzhieRQq/s93naaf+PzCN/f52tUJM93QjxWdQHFyJaIRjmh+YW/OF14cIj1452bK73rXSR5b7y6zLALJpKgXwaE774eWHK+mTGMWwpV1Y5bkdNdS73F5mDEunJXEIA44sxIvNafuJx6G0tmDxFuNZ/RmxFv5/nrL4FY13uG8fM1p39ea2r7ViiwMJPnEScM21hIsJMqsgMj32OpEGOMCo8CTnOzHrwjCGm3Ia03ZmKUfUrPIiqShwmLxpCDwJQgoarPoLvGYFM3iXEnedXrAZr8kq5xtQlY2XNzPAIFgjsY5nFtwHWKBYiUNef+5Ad3dGfOqxhOazV7MqV6E8t2klPguqNzPKrQ1rHcCLDDMavLKBRXuOGGS1ZS1IQ09VhKfvWnJKKuxOG8nEVnSIMUae8vzfae0vjF2KXQ4yiqujXOMFQgrGGcV4LSEYl8dO6GsJAFracCVUcblUUGkPLqRxygzVMZyepBgrcVay5cujdidFoSB4uxKSt40TPOaQAnWO9FtJyylJL/jyZNsdGJ+9gsX+IVv7DAqGoR2Fg2edFmcxrqMTq4tv3V1TKU1v/Mdm7zn7AqDJKDSLtOVBopJUVM0mtiT+MrDV64zbm9WsjstebzRbHYjGmOZlw2ToqIXpyTK46NPbRIHHhoQVnCQl5zsxWz0IkIl0dqdG4ubpEPl8e2PbfHoepdffn6f/+e5PXrxgvfjeby0N+XyQUalLWnoUdaGnUnJN3am/P5vOcuTJ3rMSs04r5hkNXLRydeNFAfz+hVZtTthnFfszSoGachKJ6SsjVPynlVIKZZZq2HmurCeWIwf9xMPU+nsfuKtyjN6M6INbt5EuNnP6fWkfV9rS+Ot0uwu0IjoRz6jvCZevD+cV/Rilx3RFnRjuVnv+OiAUDbOBTwra1Y64aLsIOiEilFRY6ylE3g0tWFeNpwaRE5vRgiSwPF8PE9wZjXl+jhjnNUoJUEKnrs2wUpY78SspgGBJ5kXDSd6EVv9mLWOk7wPF9L6jQGDRSI4NUiQAjb6EWmoGOXOJVrgpPSNhZU0IFi4XpeNZlbUNFozyiq2JwXKc+qzvoJx7rSAamNZSdWxmkB3ur7DrOTF/Qzfk3SjAGPg6rhgVjUEgeJkL1h2YPneKyeUYJFBuzIUrMY+ceBhgEsH+cItWzDJGp6flfz6pRFgWUsjfE+wmoYoKdmf1qylNYHybjthSSl4+tSA//d3Pkk/9PnMV6+yM6mxGrBQHR6TgEAJZnnNVy5PuHhQ8O5LB/zv7zrBE2s9rucVpXblvVP9CCUk5ULgMfQ9TvdjroxyXtid43tOyfnapGBjoefkSUGoPN5/fhUlBcoTXDrIsEA38kkj5wYfKGfmevibpBScHKR8/D0h3Ujx65dG5FVDrTXzwjqbDgRKKKTvCNpfvTplb/oCH3l8jfec7uJ5CiEksfLohIpe7FM15lWVaY6/LwTbI5e1OrOSLO0cPCkoa83BvLrvZaC3a8v0W51n9GZCG9w8JHi1acx7kfbtxwGNNgzzmqLWRMq7Y0vjndy+V7sBvnKZjqaxaGsQuAm5E6ulS/fRB/7ogJAGil0BX746ZjUNWUkCJkWNRHCyG4KFwJesdwJGhSO7am3pJz6+lPQTxax0gcXVcUUa+YRKsjcueekg59xagifcynmS1yShK1msLUoqofLoxT5i8Tu1tQig2mu4tJ8zL92KrGpcWWSjExIuPJp8JdHGLtWdtbUMsxpt4EQv4mBWcW1eIHAeT/O8YXDy5Xb7onIiir4Url36Nte3bDTDeY2SglgpZkJzYhATKMnBvCSSYK3l8jBjmFWc7sdMi/qGFWSjDdbCufWEvVnFwazEk4JBrDBYdicFeeX4G76SBJ5HrTWXD3KkcAKLB0XJrGp4pOsG7rLRt7yHpRScXEn5ge98kkc2Uv7NL1/guZ2MxoAvIFKAdV5W1oKQlnnV8IUXD/jG9oTveMcmn3jvaU6vRGhryErNpKwxQLgISv3F916b5OxOS4R1TvBRINEjiHyJ70nXYaUkJ/sRZ1fS5f0X+hJrj2/pBohCxe986gQnehFf2Z6yPc5BGpRwbvRWQlk6J/h5UfNiUTMuG371xRHvPdPlE+85zaAXLu0VXm2Z5rjn3hj3t7WObH0IJSUlmkq/Nm7P68XbrWW65Rk9XGiDmweMu01j3hz8vN607+H3zisnrCaAJJR3TJ8eXZFpbRBSUNcabQyBp5aidcZYSq0R1nJ96tqca2NpIrv0kZrkzu7g6IAwytwafrMbL92492el41X4CQhDoCRPbvXwFTTWabCEStIJFd3QBTd7swpPiGUGyQqLJ6ExrpxlLSAESsilrL9a/L408Lg6drL+SaQYziumpWYl9ZkuuoeU57HV8+mEitQYYuVhEEt15zSQYDx6ocdLlbMkCAPFqUGM0ZYza4lTHl5ci5f2Z1wZ5lQLkvFWP0QgyOuGwPOQ0k1Wh9cXWAodagyeFGhjWU1DKq0ptOXapCAJPc6tpKSResUKUluLAdY7EStxwLgTMi1qro5zysoQRE7TJ/JdpxgC1tOYWVUxK135MPAEm50AYy0X9mfU2pWxuqH/ChXnQ3SSgD/wrY/y7jN9fvrnX+QLFw9omgaLx7SoqQ3Eyl2fvKiZl7A90VwaXeKLFw74PR88xbc/tkkSeuzOSiSwkrqhTApBHCpOryQYYwmU5JyFujGO/1RrzqxERIGHxGVx4q4iUJJJ7sjkt5uEjbFMy5o0DnjX6T79RLEzLTiYV84s1FpKbZgUDaW2dEKfjq+YVw2fe3YHrODj7z/NesfxYF5tmea45/7Qhkos9I4O0SyinuCYrN39wNutZfpeLDhbrs69QxvcPGDcDa/iVsHP60n7Hv3ebuS7NvC7rP93Q59RVnF1lFNqS+gJVmKfvG4YZY6kqzxJNnO8l27kc6IfY7FL8b5u5JMtMhXNQtE4K11XVRwoulHAvKydmqw1WOMG6dpY8qyhH1tUEEBjmNcNRSWoG8OoqGkap2EjDjtStKFuLHGg2JuXGAudSLLRjVjEOHhCYIxlmJVcHmZcGeXklSb0PZRwwYa2sAIE0mNe1czLBktDVjZkvsdaJ2SjE2KFa2FPI8FqGjApNJeGOZ6UdCIfJZw9g8A5SR/MJlzYnzNIAwahT9E0PLczI/U9GtxkFiqP2HfZoRO9iFB5NwgdBp5gmjc01mUR4tgj9D3OrLhyG4AUN/oeHZ0oI99jw/dYSQN8JdidlHieoDFukO1EPtO8xlhDqJRrhVaS95zuEfmKF3bnZHXD3qRgZ1YhLJxbTXj6VI+zK6krDS5weE8Hyud7P3iWbuLzKy/sM5zXeB5IAUGgnNWCu72RQFnBb12dc2H/WX7p3AEff/8Jtroxs0ov9X6aRYt95Evy0rCWhvTigJ1JsTDpdByuyG84sxK/LJp3l5Pw4XMTeBIpBJ0o4GQ34rnrU2ZFTeBLpnnDLK8JfY9+4hMoQW0M81rwyy8eMG8a3nOiz7vODfCEfFVlmuPKPY22xIFkJQ7IF4E0OGHNNHTdZg9yony7tEy/ngVny9W593g73HMPLe4mjXnoRn1c8PNa076vN306LZ0o24lBvLRqyEtNUzYESmKs46mkkXs4J7njniShW8XPygblCffAA/Oi5mpe01gYzks2u25l7XmCSlsGsfN/2uyFeEgu7M94bjdjq+cCl2nZYIyhGwZEofMfssZleKZFRd1YZ7eQBGSlZqsb4ElvIc7msbLIJO1OC17cm1HUhs1ejDaG4bwmrxs86cwqI89jWtZcHmbszgpO9xOSUNGPfIZZjbWwloac6CtG85paWza6Efvzkkh5WCGQuDJeIODC3oz9WYnvS9JAEfmWXhQwzWuujAqe2EqxVlI0mrxqOH/E8qEf++S1pgNkEuZlQ1Ea1hMfpSQnejGD9OV74eYV5LETpbGkgU9/02d7XDDOKjzp8471DrO64fqoZFY3rKchT211OdmNeX5vviDt1uzOamJf0WjNflbxpUtj5mXDE5vdpXv6OK/YHhd4QpAmAd/x5BYrScDXt8c8vzNnXjVUjSZzySI86YjHFkc4HhXwyy/t8+LBlI88tsonPniGeWlodEXke3RC1xodh4IoUMS4ks32qOAgK6kbSydUSz2lw8njdpNwo52p7P7cPYtF7bhUke/xwcfWmDc1X7o4ZrjQQvKkpZ8ookA564raIoVllOd89arga9sT3rM94bvfd5JHV9PbPq8347jn/rGNDsa43zjMKwSClTTgzODWCtWHv6vNFNwbvB6eUcvVufdog5sHiDulMctG3zEIeS1p39eTPj0uMALPGSRawYlehFJOoM9O3T5L7cpLnud0UWZlTeBJzqw4PZWdacH1SUHgubLEOK85OYg5O0iYH4rISUnoectBQHmuC+ulccG1SYaxgkHS8Nh6h2QhFLgaS3pJzOVhRuwLZsZydt1lMuZlw0GmeeZcihSCC/szLu5nHMwr1jshke8hhUJKwUt7NbvTgjhQXJrkXJ+UDGcVyneiblbUnFlJ6MQ+wgo2OgGldiJ6u7MSD7d6G84rKmtJlWRWNBgLCBhlJUkQMJpP6cc+m72IurEIKVhNHcm5ajRlrTGWZfB4dJILfc1aEtKJJd0g4Nq0wF9kFw5x3AryuIlyvRPQiXw6kcKTgrzRhKEiDBXCChpj2OqHBL7HxVHGpeGcXhwwyRt6kRNrzApnv7GrSl7YnXFhb847trqcHSRc3J8zK909Mc4rVtOQb3lkjXOrKb+a7vGFFw6WJG0DC2L3y6J/BhiXMC8rro6u8fXrMz7+npN86NENAuURKY8kdBmUw6yUEALlOU2lwHO+Ynvzalk6uRWOrqizSrM7LVhNnAzAIc9HSclvf+IE7zm9wm++NOQb12e8NMxIfEXswbQ0NFpTlJoo9DmzElNrzc6s4NcuHjBIQ57Y6t3xuV0e00I8M/Y9hBQ32jakIWXjnpmbdW6OBjJSiDZTcJd4NQHga1lwtlydNwZtcPMAcac0JnBXQcirTfve/L2HD2/T3Dl9enNg1BiDMeB5rrNIW0uqPBrpeCBF0zCIA9LAI68Ns6pGCcGJfkisPL5wYZ+9aYUQkkIbtIUsq+hFiih0fJxaG04OYgDGWQ1AJwjIq4Zp0QACTzhi8f68YKsb0w0VtXbZjOuTEmmdpohzOYcT3YgokHRD3wn72cMsgSvvBMqVYurarbwuDzWBB5dHBZ1QIaST45+WDV08Xtqb009ckIkwWOuMQX0luXiQLXyhBKfikGph1NkNnbrti/OKojZEgeLKuGBvVlI1hlODCGucKN+0cL5OtS7phIqNbnQsp+HwGvVj12Z8eL/cagV5dB+1NszKmnmpGRcNUsCJfsQ0bxgXNQJBJ/Lwlc96N3ZdZY1mlLuMVa0t3cij0ZZLBzOe35/Tj3yyypWCvnxlzGPrMd0oZNAJFp5miknRYI1irRvxsfeeIg4U/+v5Pb5RZE4LB5YddgvurGvjB+oGvnxpxvb4Jb56ecj3fvg8j6132OjGKFkuuWGjrHLCe1aw2gnoLFzO7zR5HF1RdyLFcC65NilAOF7Y4TMQBR7vXVvh/FqH78xL/vOvX+bZnTl5ZaiNpl5YfZzqxwySgFnZUFaG7WHJL7+wj7aa9U7ssne3UAe/XeniEOoY25TjPmetdZYkgWozBbfAaykVvRae0dtdE+iNQnvOHiDulMZ0q8J7rxVx+L3XxgXjvFp0xDil3UfW0htW+zfjMDAqGydQd+hJ1GiDwnXLFLVeev3sz1wWZpCGRFVD6Eu2uiEb3ZgrB3MuDjO6UUA3VJTaOLn/ToA2zoX68PXAk8tgxhgn4182FqUE1dyQNRorICg9yiajG3oL4T+BsS7z43uK1FcEvqQTubbx7XFB3mjHf8mdNo0KJfPK/b7daeEmtsDjIGuWpYxQCSLloTzBvLKEvlNbBsFL+5nzeEpcS/Eg8Xn6VJ+q0e67tF2s+iVh4LGaBlzYy9nouYlyt2jYnhSUTQ+lJEoKNnovix3uzysC9TI3SnkSaVxgN5w7LR1fCmchYSyZvvMKUnmSaVFzMK9vSI0Xteb0asw7gi5aW3ZmJb4nlyvMbhywnobsTktqo9mbNYyziq9em4KxDLW7VuO8oDaWb+5MeXI95b3nVmmsa5+eFQ07o5xe4uw5nnl0nXec7PH//V8v8ptX5iya4l4BiQtIawPbo4r/NNrjF765x1OnBvwf79nke993ns1uyO6sZL6w6ejH/pLQfqfJ47gV9Xo35PJBwzir6YSKQDldm7VOgLWOj/bkZpczg5T/8bXr/MblIRf3MkQAK3HAVj9mXmonEOh71MLwte0xl8cZgyhkvePzyHqHpza7dBZeXYeLj0lWcZDVr7p0cXPJo6gaXjqYs5aGRGmbKbgVXk+p6NUsON+umkBvNNrg5gHjdmnMQ17FG6EV0Y8Dp7+yX6CkcAO48ikawzivbvnwHgZGz+1MmZeaJHTk0lobAuV4NIdy/d3Ipxs5ff3D33Z6ECxb0MeF87EKlERKQSydVkpeu7bxMytuJTstai6PcvbnJaO8whMwr2q0BWENO1NHFu2sxMyLmu1JTiw9OonPehpwsh+RVU4LxZeSE0HEMG8IlcdBVtGLfWcaGip2p3PKpoG5WOiYWPppwOlBTBwqjNbk2pAGjhCd12515gkXmCgpmSxKGHXjRN2MtfTjgKxqiHxF6EtCVVHUrjvHkx5x6NrVR3lBNwk43Q+xxvLliyOS2GU/VhKf82spcaBumIQOu61e3M9eQT5eTwN6SXDHFeTtUuNZaVhJPLRnl/fh0fvh7GpCXjdUs4YLBxllrVHCUhjLNKtJA4mQLkjDGC6OMgoLkRT4ymUa5pVGSsvOpORkP2K9H/G93/oIiJf4+rUZw/L44/aAhpczOtdyGD4/4stXx3z54pD/z//+bjY7wcIyQdAJX+7eutPkcdyKuhf7nBokFPWM4bxmkDjBv0MD0s2uK9edXe/wRz+a8F3TE/zKC/v8wnP7jkCvNXphPeErGM0qfE+xkgouHcz5+vWGX3x+nye3Uj7y2Lpzo2/sUv+plzgLEynFXQUkx11XpSSelBSNWd6zh9e1zRQ43M9S0dtVE+iNxtv9Hn7guFMa843Siqgat3o8sxIT+2rZanw3qfo0UE7zRDkZeE+IpTLrtUnBiX5EP/ZZTdxAf7RmDVAbQ6MNnpSsJQHTssGTYpFlscyKhtP9eHkMsmrwF6rGgyhgf14yLWt2JwW7kxK5ULi9NsrISovFsKcbBh2feWXYDBVpR3GQVezPS2ddsRCsC5UkKxsqbZ1acai4Ps5orGsvP9GLWO+ENAangiwE1kI/UihP8Oz1MbGveHSzQxIoskoTKIWxBotllLmyGrAkW/ueJA09irpmmjfsz0sGSUjpa9JEESmPwIeyMiBBN668lgaKsjEkITdol+zPCp7fneEJQeB5OH+pBdG4Mqyk9yY1fqsVZhIozq91SBfX69IwZ1bWVGVDN3TaM5Hy2J3mFJVGeoJZXnFxWjKvGyLlcXIl4VQ3xGIYZQ1J2JCXhvMbPSI/4OpwzsVhSW5ePrabAxsAH4gCmOaWz3xll1n1Zf6Pd51wwWngo6S868nj2LZr4RSAn9rqEYfSyQrgFK9vfi6VJzkxSPnE+2K6sc9nv3qNce5sQHqRZFLW1BZO9wKyWjPJatLQY1zWfPGlMS/uznlkPeXDj6yz0gkxOOXrUHkMFlmdOwUkx11XbyGdUNTaaeQsfv6tgr23I+n4fpeK3m6aQPcDbXDzkOBWacx7rRVxWEfemZVcGeWkgcIm3JCqnxY1ea2JF8d1M+zCvHAlDUDALK8Z5TWB76G1RQrJeJEZCXxvWTa50WwTikqz1gmotGFW1E6rZcHbOLMWL01Bh1lFow2VthS1I+I2taWsDUmoeHyzw6xo+Pq1KXEoOdVLuD4tEVZgscxrzYkkYFUI4oXQYKA8rIXAE1wYFXQi5VqrA9d+/fTJDv0kIFRqEbQ0bI+chs3Ad1m1UDldoM1uzKNrCaO8oRNJ19GTOZ0dz3eDYTHXnFlJ8D3J9UmBtZaT/Yhnr80pG0MnsvRjn7oxTKuG63sFUeDjeZbG1iS5ZBqUPL9rGMx9+qHPqV5EZeHFvRmXh/lC+A56oRNLlAJCX99xIG60CzaB26bGb7XCrLTh9CAmDRWPraVcHuZ86ZLHV65OQDhz0/1Zwc6kRHpg5xJTF+RaUzQwLUqKxjDMSs6tJFgt2JtaLhxkjLKabuRzZiOlXnTTzUrHt7kZCgh9kBY84VrHv3plghCSTujx1FaHp04OWO9FhN6dBSvvtKJeScO7mviVknzHO7bY7EX86gv7PH+QoXAcMF9AFCi2Rzl+4FFbJ2AopWWc1zx7fY4UkndsdvB9DyldGa+zkFu4U/bpuABNee4+n1eaSms8KY4N9t7O7cn3u1T0dtMEuh9og5s3Ce6VVsRhHdmXgjRwGjKH2jO92GdvWjDNnU+Tm7xfOZgdPviHPklZ7TpHwBFyk9CRSo9mgI6rXxeNI8pKIdFGk9cNie/z5IkOvShYThzDrGJeOmHAojbkVcPOvKCfKNYWnlXz2rDVi1hJfLb6EUp6GGEwFiZZQ+zXGGPoRD5SOF2eceEyNpu9kHqRuUHARhqwkrjJa5RVlI0TOjRYOoGikwRESiIEbHYCJqVmVmqKyrlP+57gzEqMFHLpznyyHxEoZ5K5mgRYAUoKzq5bNntOC+cgK5mUDdOiJKstW32fSjd8c3vKRS/j7DQm9B3X5uRKhJBO++fFvblrCQ8VviedgFyl6aUBa2l4y4H45slrXjRUWrPedTo6x014N68wBdCPFbGSjITLbKx3QrZ6CWVj+PLlCQdZgcAS+ZJYKUJfsJNVWOt4QdZYDqYFl4fONmGjE5EqyyANqRfXbLUXUteGfqiotObSQUGpXWv48hlxlmNY4bI5yoPSaC4dzJlVmq9vz3jkypRnzvX5zqe3iBfBwu1w3O8dxD7pgvR7t8+lUpJ3n17h8Y0ul4ZzdsaOB/Ti7oy9WUHZmEVpU2AMiEXwXDUNz+/MuD4pONmLGSQ+672Q1Y6z1rhT9ulWAZovJY+uJUghb5kpeDu3Jz+oUtHbRRPofqA9j28j3FxHro1lf1aCcKvErGzYmRacHMTL7pnjBrOjD74UUDVmkWWxrHUClHS2lIfpW25ltqk8dqrSTRZhRFk3lI1hmjdcGeWOP+I5/6e8NjTaEviSJFSOM1DDSuJTNlBqvRyADHBuPWaY1YzmFZHvgidPSk50w6Vpp7EFFxqz4NxIKq2ZF4LY8xbihJJu5EpNWaUJPcnZ9YT1NMIKN5HuT0smpVNR3pmWdCOPR9ZSF3RZdw6kEJxfc1omZaNdmzwWgSD0PYSA3XFOXtVM85pR1hCHHtZqRnP3eV/C7qzA4r64F3k8e23GJK8W/l2+61rzHX9qmtc0tiY4dev74ebJyxOCvZlhmtXoyB474R2uMNNAMcwqJkXNlVHOKKvJyppGw2YvYCX28Ta61LXmK9saKcXCdsJHeYJZqZHS2RzMKw1CIj0oq5pLBzXT0rCWKgZxyLyoeWKjw7m1lFFcEfqSd5/pszMpubQ/49Kwcd5UxgkAZhoQ4HuubbqoDEpCVle8sGv42pUxv/L8Ph9+YoN3n+rx5GZvSd69GUd/70FWus61siGv9WvKYkSB4vGNHuudiosHGdtjl52aF85ZvtIG6UlO9kOms5rSWFZTjyRUWM+yN6uQuADLWBgkahlo3QrHlTxc6dgpSx+XKWjbk9tS0ZsdbXDzNsLNdeTeQhp/nNVMcteifXIQs9GJloTFxhj2phWx793QopoGin6sGWfNklOy1gmX5a2j6dvD7w2UoGrMUi6+bCz9JODUICarNNfG2hlK1hmh77qZ9hclqUlRuwlYCvSCvKyEh5CSja4j987LEqst3dC5fAvhSkSPrCVsdCPSwPlGxYHTr1lJAjY6IfOqQRtDURmEcErHV8Y5edXQX0wioZK851QPpMTguq/2pgXXpwWn+u58ZVWzJDfLRau4sbDeCZBCMJyXXB7mjPIKC6zETktmZ1KwN6uJQ8UgUWS1RgjLtUlFqCRr3Qi98Ks6tRKi8HjpIGMQ+8xLy9VJwfVZxUbqs9GLsAvfrNh3QcTlYUYaeHQiH3/RKnzc5JWEik0paLThRD96hU7KUcyrhlHuJuXnrk+5MsrZmeVo7QjNJ7oB692Qj75jg7U0JKsNFw8ypOfI10moqLVBGxfQ+splBGeVcRYCC5uM0teMs5q60ZweRCRByPvOdFnvxWAlB/OC37iww//9lT1mDRTaZW1CAUpApBRJpCjrhnlj0aZmXml+5VLF87sz1nsx33J2wLe/Y4MnN7r0bqHmO68axrlzfj8Mol9rFuNowKQklJXm69tTtmcFElAIdGOprKUfBUSBR+x7BNLDKMNLB3OuTwoqY5BScn415kOPrXKyl97gG3Y0aLlVyUMi7pqrA28v0nFbKnpz461+f7Y4gpvryFI6GwB/0SkkBI6rsSD2TvLarSqrBiMsm52QbugzLetlKcPznHDfYWurtVAs2sEP07em1gsV4gYphSPUBt6iDKUoG70o/xgGSUhtNPuzikB5JL4HwrVde8KRIH0p2exEFLWmrA2n+j6nBhHGQj/2iH2fWdkQKsmHH13hVD8hqxvmpeb6tETJarnqPreWsD3OyaqGSlh836OoBaESjLLKieUlvnPC7oaLbihXTpvmDScHMYEnOZhXnBi47q5RUXHpIONEP+LcqlMU3p8VfHNnSlFpuouV3zBzFg6jrHKZIeVxqp/QjZ25aFE2KOmxPS+QSPqRx2oSsZ+VNNqwO6sIPEkvVES+JFsELJ1Q0UsUZ9c6pIFiUtS8uDcjVpJOHLCWBAzS4JaTV70wEb3VQH4YGClPcGmUc2mYUzbaGTVWDeN5xTgvmJcJjdGEocd6P+axzZRRXjPLGnanFbOi5jDpIQxUxpUefSmxnnNlB0FtNS8elOzMSrY6ERbNO09Yntzq04kSIu8k5ze6/Mrz+1wcFYzmFUkgWe2GNNpS1Q1lbWlqA55EGMBaKmN5fm/Cxb05/+ulIU9tpHz7E2u88+SArW609MW6mywG8KonQCkFaejzu951gvefX+HLl0d84/qMYVZRG8t6GpA6N1F3vLVhOC/45s6Mk4OItSSkqBt+5cKQ66OCDz22RhJ4NFbjS4XnyRt4MrcqeRzHG2rbk19GWyp6c6K9Zm8j3KqOXBvLZjdgXunlYDbJa8fFEY5n4gvBzrRklFVUi0DmsA5fa0MaemA5Nn07Ld2+dqfOr8iNixYpJf1VV/bxF+2pUoCvFHHoMSsaNroh3VDRGEPiO78kY+3CENMgcbyWlcQ5iGMFlTEMhM9K7LPWiRjnlfPN8gRKOruIoxYWjTZ883qFkBJh3QDei5zWTtFozkQxnchnkjec6Eds9ULyWlM1mjRQ7MxKrHGlpn7iVtr9OCBaWCrszwu+sj3h+qQgDRRxYOiEPp4UjOclk6Lh7EpCHCrmnYCro4L9eUlRG8JQkpQSJZ0HWKUNZWVcYBJKVpOASdEQeJIQg+9L8kpzfj3lRD+m1IbxvOJg5spXcaiQwJNbjjD9Wiavw1W91pZrI+cQ7lb0kGtIQo/hvOJ5nfHiMGczVWz2Ip7Y6LLRiVCi4swgAGvYn2uUEkgpkFpica3SnpR4AmZlTVY0rqvHCvazivIa7E5rDmYlgyigshalAr77fedQnuX6ZM4LOwWzuuL56zlgUQpkAwjHNpbWcVussVQYqkrzW1fHvDScc25ln3ee7PHeMyu8Y7OL8G7t0D4ra/ZnJXmtXzXpdpkdCX26ccCpQcL7z+V8/cqES6OcaV4jkPRiJyZpgb15RRooeqHrMuxFPqFq+IXnrvM/vnEdjSXwPB5Zifnok+s8tt6ldFHiKzJMtyMMt+3JLd7saIObtxluV0dWCw5GYwyjrHYTgYV+6pNGvsu+jHJODGIi35WspBBOp0ZItnrhK+TgG224fJBhsCSh57gZlaYxhs2OK/lklSbwPIqmQWs4NYiIlCKvnQXFeidiJQ24OMwxtSbyPXoLx+qbdVxuXoUedluVtWZaWLSxeAuBu8NOnF4SLEm0o6yk0oZuFKCkYF417M0qsrIhX7hKN9rSWOMyL/mcnUmOVAJPSiLlDDlPDTwaYznISnanFcZAKJ3n0d5iwkhDHyucWzpSECyk/MXCxHNWNngWenHIWqyojWFnVmGxpIFH6CvCQHFuoXg7zWushsiXnF9LSXyP69OCUV6zNyuXE1ZWNXzl6oSnT3bxlUekJIHy0NY6Neh+fNvJ63BVn5UNtXEt77WxzApNrCRF1TDMa87HAb1Y0VjLszszXtrLiENH6H7HyS7nVhOe3Znx7PUZ+1mD71nH4dIGz4LveWgrsHj4nl0IE0qM1uxOcj47zAkDyTtP9DgziOmEHk0DHzizztOnLDvjHF9c56WDAk/KhXieK+9FynGXPM/DWIEUhqwyi+xIw4VhzhcvjvjWRwY889gak8ypDK92o+V5qBeik81CQuA40u3tuqluzo4EyuPsaodBHHBib8bFg4xh5lrlKwSzsmFSNpzoBkgpiXyY15qL+3MujQunE+XBqKm4PCz4xvUpH3lig3ef7JNX9bK0fLeigC3npMWbGW1w8zbD7erIh4PW3rRiXjV0AkU/9ZfcHCEFpXZE2FFWLdWJwXWmbPZCInXj6rZsNMPcSfdL6SZ+KRyHYVY2eAKysuZ6kVMtWpKnhVoSbmttWO+7AbWf+IzmDQZLpDx6sXrFCvnmFPJht1VWGZLAWwZl48JNzKdW4qXuhzGGotHEvvNUshZi3xksXtjP2OpFlLVmmLm2dasNV0c516cFG6lPEvnsTSp8KZh0HadiVjZEgUQbw0FeOT4OoI3lxAACKVhNnKN65LvgJg4Um72QR9dTtvohF/dyLo8z0oVScVUbroxyPAm9SNGNfaJAcbIf4UuB73v0Eh8rIC8bhnNX4hPAtKipjWVnWjDKSk71Y6aVBgsb3ZAT/YhZUaMW5cObg9XDc9yPnalrGirGWUFWNhSLjq29ebUsjflSMi8bfCUZZzVCSipj+MoV50P1+FaX1U7Exb05+1kFi3tLCQgk1FqDsPjCEgVOjymvDL4SSGnASF7cnXEwr3h8oTE0nOY889gav+Od6zzz6Cr/91e2+bVLYw6yEtNA1xcYAY3WaCtIfEHZuGvSSEtsJZEvMNryf31lhy9fnrDRixwxfDXmsa0egRQ01pWL0lC9olw1ymoabZhXt87oHJcdyeuGedmwmoacW++wOym4NnFt9GmgWIl9As8FzoHnsqk7kwo0hKFH2WgC6eFh2M9KfunZXb55fcbJQci3P5LzyGbHde1pe1eigC3npMWbFW1w8zbFcXXkw8An9j2MsPjCOXsfwhpL6An2ZwVZZYgCSeQr5lVNnjutmviYzg1jLLPK8WsCtdDQwOmflI1TMkZIhHXdIF/bHhMoybtP9tnqRssJYaMbs5K8OkExu7Bx8DxHdAU3AZWNpqwM1lhU4NGPfa6McqrGEZIP5hUIwUriM8wrpkVNN1KM8goBC8fxmk7kMa08ro4r1gzEvsesrrk+LnjXyR55Y2gaR5INPLFQJBZsjwsshrOrKadWYg7mLrtU4TIhvdjnsY0Oa52I04OU3WnBpHCcJQm8Iyu5NCzI64Z65soOc2vpRj5biU/VGIR13mTTskHiRBLTSJFVrrX+QBu2ejFJ4FrOJ3lF0Wi+fm26nIw3ehGrScBKGtwwMR8GwgfzioNpxawwaCzTskY3LiBVnnNSt8LpF43zhrVuxIk0Iqu16/wRsJZGPPbODmVt2JlmfOniAQd5wyhvkJ6gr5x+jyfdvYS1KKnwhAJhyGtNt7HUxrA/nnFxWLA9KXniRJdzg4j/830n+fBja3z++T1+5cUDJrkL+LR1gXnkC0rjOrqU9EgiRax8GmvYn9f0E580cD5nn/3aNU5cHXJ+tcdWL2CtE92g2AwuSNmeZcwrV0q8XRv14XkcZTXbs4yiNHgeZLUhDhSPbXY5sRJzeT/H86Cf+LywN2eelUhPsDfNGWUVQoK2hlpDN/KcqnHlLEq0NexPK/7Xi/t89dqEDz+6xuaiZDmcVUtRwMYYrF109B0hDLeck9eOt6MA4sOC9p5t8QpEgWKzE7IzLZc+UbU2rqtFwjeuTRerzoDI145s2wnIKpd5OVqSAtdZdW1cEC1KH9pY8qphECvmteF0P8aTJS/tu/JVGjhyc1ZpJkX9ijb0V3PTCukUZbOyWZJeG2Mw2jkriyOTdaMN46yiBvppgFiI4k0y56fUCRTXpgVFbZgXDbO6ph8q1pKA1Jd049Bp7uQN9FyAkk9LDuYVq52QTqi4Ni0ZzUuEsITK49xawloSsZrW7E4ddyP2PTaOpP8D3+P0asrWYqC0xnJmNeHUoOQbi06lyJdsxhFrvYBASnwp0MZNtI0xWANbg2ipP6SkIAl9slpzoh9QVIKXDjICX9MJPPZm1TIrFyhJ0ehlidB1g7lA+NseWWOzG/KN61O+emXEpWFOGik6kU/iewxrQ6wEV+bOjHQQh0ShRHgSKWE4qykaw/5cIYVAeYLf9e6TxErxjWtjticlw7xmlpc0BurGlRUjJbHCUlaW9TQkDj22xznDWU1T17ywO+W56zNmVcO5lZgPP7bGB86v8PRWny9dGfH17Qnbs4KycEG30GbhRdYQqgAhLOOsJg0lSeDUrYvaEvqKF3dzdqZOn2k1Ubzv7IDzqx18X9IJldOfKQ0rcXBXBOTD8tW88lhZ8F0uDTOuT4ol6f9E37I9yjm7krCW+PzGxREv7M+oGsPKYWACSAll0zAvDVI4wr82EPoeia+oGss3FuelqF3g0xhL3WiKxkkXeLhs4tqia/KtgEabW7qlvxF4KwogvtkCtTa4aXEsbq63ewjKumFv0dWktWFoIFSCc2sJq0lIsVjxHVUjdoacFmNdZmfZdhwo+onPtHQZg2vjgkobikrTjxWB7zg1z+/OiHzJaie6wfX6bh8wTwgGsdNWqRtLXjvuQC/x6Qb+cp9SCjZ6MY0xXJuUdAJF6HvO12o/Y73rRPNmC72bMJAcZJprVcNaGrHRS1hJA+rGMK8koXItw91QcaF2hOlOFHBGSVbigH7sAoBBEi4Hu8NOMikFjXGD8WHrNjiBvGlR36DyPIgD53AeeYSeh8GyMy7Ym1asL9qxnz7R4TcvT5jlmkDJpdDdZidEC7AL3pNuNDWWg1pTaUPUaPazGiULwkCyOy0Z9+ultYaUAuEJ3rHV49H1Dr/tiXWu7Gf86oV9vrk7x2DZ6ASMs4pZWbPVjYgDSWMsAgiFZIzFExCHHkmgUAK0hrQT8L3PnGN/WvKVq2N+8/KQy/s50oPVOOTkSsi80BzQ0E18PCnZm+Zo3RAoxaxqEELgS4+dWcMLuzP25hVPn+zxO995gmceWWOclVzYn/Pibs72JCMvCoR1Bq1aG/bnJSd7EUKKpaGo1obhvGazFyGwXDmY883rUwaxz9YgoRcptnohm90IpV6Z0TmOgJyGHtPCBT1Lg85OyPYoZ39WEgcegXKWHYHyOL2acGqQ8NLBjLx2uj2/fmlMrZ1p6rzU5LUhDTySwKcXKQaJjx94zIuGaVlT1AErccDurOKF3RmzsmGjFyKBbqI4yOplWerNDGMsw6zk8kG2KI+7Z+bMSsxKGr5hgcZbSQDxzRqotcFNi2NxMzfnYF7w4kFDNwoQA49Ku7bdXqyQQlI1etllM14EK7VxhMvaOPfnom7ohD6xL0kj50V1MC25MszJa42wEC2yBp6U0BMcZAV51fD4epfaOCJvqBx3Zu2mUslRHF1lrKQBtbF0AoGQThG3NpZB+rIQ2eEDnNeuhLQzLYgD5yJ+eiUB48oqPd9jZ+EyLYB5oanqjPU0RBtL2WinYhz5iJu0dIx1bc6nVwICTyKFWJ6vw4Ew8j0OZiUXFzo2q2mwHEhuHjDnZcNBVnFq4PzBJrkT1NudOr2UXqLohwEneynlliVvNMYKOpFyGYnQaeq4QMqQa4tvGhojXCBXai4fZBzMQj54ps+s0lzaz3jBWDqBRy8OCJWktpaO77HVi/jAI2s8ttHhy1dHXDrIGRUNdWM43Q/xPMk4qzA4e4TaGHqRx7tOD1hLQkpjqGrLtKjQ2pL4ivVTMafXUj54bpVpUfP89QnP7uYoBOfXIpSYM8xqQsmCK+TEJMva0k99Ik9QVIas1DS25Dcvjh23xjiF6A89ss5HHxdc2J3zuWd3uDzM8KTA9wVSSOalQViDAHwlGGY1nVixloYczAr2s4ZAeWRVwygree76GCU9Tg5CLh7MeXKzy2Y/JvJdRuc4AvK1cUmtDSf68fL+7UU+umfZm5bMCucr9sRm19mlCHd/p7GPEoInNjr0Yo9ffWnMtVGGxbKa+AxSnzRy98paGlJoxyk7GSYoKenEPrWxHCwyhp4QrPVCeouuvLeCWN84d8HbockvwDCrqLThicUYd6/xVhNAvFOg9rBmdNrgpsVtoTwJ2jDNNUoKelGAJ2qGmbM2LivLVNSEvnP8BhjnNbVxLtyh7zHwXfnn4t6UomnwPJ9sUi6tGqywlA0c5BWdxmNWGVYS1+Jd1y6o2JsVjAunVbOaBqShYjgvl7yUQ2RFzd58MVgvnKi7kWI9DZiVLtDypWQlVbeUmj/Zdy7XWanppR6+SjjISkZDZ3QIkPoSFcXEfsMkL8mahrCWDOKA1Y5PL3QdXAjY6ofsTlzgFirXMXXYUnt4vg4Hwv1pwTCrHGFVW4yxroNtQU71pWult9Z1n8W+xyirwFr2s4qs1CS+hxSSadEQB4peGnBOdLDWZbKGWcmzu1MuDuf0Qp/9acU0KymrGj8KKHXDIHCBi15k3C7sz8gaRzouK82lfeej1TSWeaUpF/5S7zvd58xqyocf2+B9Z1yHWV7U/OaVIT//jT0uj3I6viKNJXVtOTGIONFzq+jGGLe/2vl8GeGkBULP412n+85Z/YkNvrk74cWdjP2FHsyqNmx0Q379wgHXZjUehlBBpCR5bRDCMq0bBioiaxrmlWaQhM4ktqg5NYg5vRbzv6kttke5K8c2mli54M/3JLV1/JR5qTm/HiOl4CCrsVZwchAxyWvq2tKNQrKy4sJexrPX5/w/4S5Pn+rzjq2uE5BUHkknQnliYRbrCO7XhjVF1Sw5bnKhPRUqyXrHlaqOllMOO62EEJxZ6/AHv/VRvv3RGV+9PmZnUlHUmuG8AilYTUOiUDGZ5CgJSSCdJIKxzipkLaYTKja7IcnCNd3nZbE+HtLJ605otGF/XjkeXewf4dwJylpzMK+ODTRe72T9ZhNAvN3vvV2gdjek+QeJh+kct3hIoa3F4mrVzcLrB9wqZFRUxKHHWuL4BWWjXRagcn5ThwOKtCCFI1ieGsTsTErHgVGSU3GCFAXXxznX8oYzgxilBJOiZpAGxL5zmg59j+5CMdhXrgvnykHughQLl4Zzvr49YXdekQQeZwYxG92IvVnFZjfk9Ep811LznXBhyVBDN1IUjWYQ+2hTcWbVWSn0Y0WjLdcnBRudkLNrCYHn+CmhLxnOy4U5oVlYMWh3Ho+YNtbGcR4iJdmflTy/O0MvFJH9xrDVC7ECro1L5lXjAhtYBG0+g9jn0iijbAzBIhiRwErqE/oes7IhDjziwKMf+VybFBSN4VQ/Zj0NyGtnd9GNFIWxNNqwkvpYBNOiWbqMv7CX846tDp3Q5/q44No4p2401cJZPlCSb26PuXQw592n+jy+1XOZtcinF/u8XwgsgqvDnFIbBE65+kQ/JFpMAkpKtHQZwI1OxOlB/IpurU4S8C3n13lys+L53TnCrpI1mqujnOeuT7k+c/woIcQyaxgriUUghcVf3IOd0HXE5U2DtTCvDBudiPNrHSptlpnJF3dmTApNMcspas1K4pMuOFzTsiEJJBioassg9ahrw6R0AXQ39GiAZ3emjPOarW646GqrHS8pdH+kFAS+82jzvJddy7OqIVKSYXZ3OjSn1zsMuiH9yEcpuLib8fXrU3ZnFUXV8MRahxqDtQJPugDJdS+6hUagbtQ78hBMchfQ3e3k9TCt4rW1ThFdOH2rQygpKXHP5dFA416VX94sAoh383tvF6jdLWn+QaENblrcEYet0rEvmZWOlJeGrlwlhM9mxwmKTcoGTwqmeU1Rawa+09SojOuM6USOxyJwRN1ESYZ5jZ9IHt/oILF85coEYy1i0YYdKY84kFwdF/STgMT3KBqD77myyjB3xpY7k4Kvb0/JK8N6GmKBl/ZdiWGzFy/TweFNrepw55VWJ3KdP1nVcHGYM4glKwuxvrI2PL6RUiy4RdOmAWu5Ms6YFQ1rnZDVJCRUrozUj3zWOi+LoHnWDYQHWclw7jIRweK7amPpjCVCeFwdzZmVLtuz2Ysx1hFMpbTESlJri5UWD7GYOP1lhqisDaHnOsKmZcOj6x2S0P3WqjGUtUEbw96s4PmdOUIIykqDL8kFZI2mG3usdELGWcXerHCmp7lzaP/G9RmzoiKvNN0oYDR3rvLdwGe1G5CEPtvj3An5rXdpcJNfVjVsjwsmWY1dVGSysiENPVbT4Aa7j5vh+x5p5BzbPenMOi3Qi/Z4fi93LdVFRcdXREqCtmhj2OjHy+BcAMK6LjohBNq4IL4T+oBP2WieOtmnF/kIYbmwO+eb1ye8uJ8TBg29yKNqLJOyoRt7eBL2ixoMhKFgcxAzzxs8JQGnZ5TPKvZnJVpD1jTEvs96J2C96/PUiS5VrckqJ8oYKUlRG5JbaOgcp0Nzsh8vVcQ3+jFx5DPMSpRw92ypDXlVL7oi1YKjUwNOqFAIuxTrC7yXuUZ3mrwOJ8r9eUmtXUZobXGMD2oV7y00uIy1NOblQKMxrtEh8G4MNO4VT+bNIoB4N7/3VoFaXjd3JM0/6N/ZBjct7ojDhzWvNR2gaAzz0tWtYyW5NnWr5VB5xL4zoBzNa4raYK0To9udlpzshZS1Zm9esTcrkTibh3mhUYlka5AwKRsEgo1OiAG3whYulSyEG4A9KZCLQUngUsxXhrmrqQtB6L88aO2MSwZxQGMdH+a4B+5w23nZkITecpV3uNLyPac98y5PgoW8NgTKQwrBRjd0A+gi+zIuGkJfsjetXOakaJattuD8km4+t2ng8fzulLzWXB8VTKsGjCAKoKw15wYxHoL1NGSUNXiyoKo137w+Y5RVnFyJGYQ+j2ymPLLmzmFtXLCljVOQXu8Ey86wdJG1AFCBJFSuS+bxzQ6Vdi3XaeBjrOHaOGd7VKCk4MpBxsVhxt64Iisq9ouKTiCwwmOY1UgLgaoZZh4v7MyIA5+zTcxa1/D8zoysqok8j04UsNr16YbOTVxKy+64JAwkm52Q06vxHYXibh5040BxdiUh8bc4tTJjb16yN3HlyTTwWOuEbPVDNrvxsh0/Kxv6aYBSgvUkIKudEKIOXe3xMNA6veKO59Qg4ZGNDl+9MmR3UjGJaq6NS3wPPN9jOnemp4GEXuwjrMtOrYSK2lq0cS7ls8JlvJSwzErNOK+YFyF5pTnVT1jvRZTWkJUNg9i/7eRxnA7NcF4uJ63VxfvzsmElDVhJAsZFxWjuzD8j5fHYRges41gdBkmrC7L/3fJGhvOSF/bmVNrxkywwnNc8tp6ydkT48H5CeZK1NGA4L4+5rorVIz5i95on87ALIN7t771VoJaVmjj0XrEAeZhKbw/6+1u8SXD0YQ0bzUocoG3DlWFBHCrSwKcxhlmpSQJF4FVcH+ckkUeoFGnkgiBtLY9sdFlPQ66Oc4y1bCkXoGRVw+MbHfzFoF01zmOq0oaNNGBW1ATKY73jWl+zxYAthKAyliSQzAoXxCS+IvQkV+c5Fw7mBIt0/1pqlqRMT4hlB9K8aNidlcT+YdbAo2rMDSutJPJ5fLPL9rhwwZwvnZdWrVlNfealJg3VMvBKA5+ybjiYl/gSJ2DXmFc8+J3IqfYeTCvypkEKSGLJNG/I64J+5JNGio1ORJxVPLc7Y29aYI1grRuwmoYczEq+sT3h/edWGCQBo8wJMW51Ik72I9JALQ1Ob5Uu3+xE1BuWl/YzysZNfOfWEgSC7XHGxb0CYy392OPysOTapCQNPNLQp2kMg0iRlRbPq9iZKWLVMMpL+pHPKG9AWOY0GMBiGc8rro1LIl8Q+Iq+UZxdiehHd17tHzfoRoHzS/voE+vEgSKvGmZlzWYvJvYk12fOhmKUlwTSDeqrsU+jLadXY7BweZQzzuoFKTdYBlpHCfZPbHXJy5pSW64dzHl2d85Xro450JpICVZSDyUF47J2GRhf0lQNlbYEnk83kAyzymV0tKUXKnwl2Z/VGJvje4LawKVhzslexFlYZCydZ5vWlpXEJ1w4uR+VR7jdpJVVGikq5pXGYEE4/s3KovOtf0RDSlvLuGiIjtHwuXnyarTh8jBnXjZ0Y38ptzDNay6PcvrJ8Wak9wP9OOCxjQ5XDnKGeYXANRicGdwYQN9Lnsxh23mgjldtfxjwan7v8a7yIdO8eahLb21w0+KucHRwLxs3wF4eG3zlkQb+khwJLrPhB4qnToZYnIdP7EteGs5xxQBHrh3EAVjXubLVC3lsI2UQBeSNZpK77qLuQoumt+CLBEoihMtopKHiRDcELFlZMZqBWQzK3dAFJ9Oi5kQ3ZHMQ4UnBcztTAs+VNJQUWGspGkMv9vGVZJRVXB5lrCchp1acEeZRHH3Qq8Y9yKupU409aJydg1NXdqvZrGzYzyq2R44z1IsUq6nPWvqyhogAhBBEoccTaXch6++4MApBIJ1+il6QiMtG048DOpEr262lIZHvMcoKDuY1pwaKrV5EGnrL83l1nNMYy7xoqLRe2k0cTZcHvsf5tQ6dUDHMa6yFSHlOwC6reXTdY29asj3NCX1F7NfU2pLVDVprpgUgJWkjyQpN7WmaucD0HAl6e5yTBIrVbshwXrI3K/EQRIOIQRxQNpovX50QKI9HN7p3vCdvHnS7gU93S8GCz7OaRjyy3llu100q9uKS/bmiagxppOgnAYPkZZ5BPwluq4dyGEgclje3ejFPnRrw29+xwaXhjC9dHPON61OK2pD6kjB0pOZQOl2fNJTknqWDAitIQ+hEwcLyw6fRhivjgjOrCb1YMc4rims1o7x2/mC5Ez58dK3Do+spq52QbqwYRI67dLcciW7kU2tzQ8v3DRpS2tw1b6RsNKO8IgnVDUKZOnRaQbfKmB7ijeTpSClY6zgh0Ntd13vBk7lT2/nDhFfze2+laq9k+VCX3trgpsVdwxi71FnJa821cYHRhirQxNLdSkpKRlWFtIKT/QTPc8FNqd1qIKs0eaWJA49H1lNCX5KVmjOrMemiUyPwPRptKLSgE/t4EhJfcWolYjh3k3PiK0JfMq81l8c5k6xhUjR0QkVR11w6cO2f7znT5dxaSj8JmOS18xdSlpXUifa9dDBnLQ1ZTV15KVSC/allUtYkuSKv9Q0ku6MPeq1dJ9G81Bw0NbuTgrLWbPQihHBEY/fAN8S+xzhv6EfKmXjKlzVEhBSEnmRW1JTCIKWkMhpfCdbSgF4a0Amd3UFWaudT5QsEkAQenhTEvkfp+/QXhO3Y915RokgWK/29mWGa1ejIviJdbqyllwTLgQygqD1WOnOEFczLhkgpHt1ISQPJ9qSg1hbwqK1lPXYdXPvzisCXJIHk2iTj7EpCJ/TRxrI/rcjqmnmhOdGLKGpDVmniwJX3Lh1knOzHt+XcwK0H3VtNlofbnltPscbe0lri1Q7MUaA4EShODBKe3hrw7O6U56/PnPhg0SAkbHVCslpTNpamcuTNqrakiY+wYKwLcAF8KYmVYjWxPL875dLQWSykgQ8GxmXDbxVjrowzeqGP8jxO9EPOrXY40QuRwnEiDjOIgZIUtX7VHInD6wh3nrzsLc6NveU791c/5U7X9V7wZB5E2/lrxWv5vTcLqD7spbc2uGlx1zhKQEsD16Z6UDboWcl613UkzKsaY2EtVYuuHgkSpFio1gaK04vuGiUl88K5dR9dKezPCq5PS4KFT5JuDJeHGWWtqa2zVJjIhtiXrHUjGg1n1lMu7EzYnZckvmKrG1PHmpP9xBlRWsu0aEhChbEuJa8WTuRZpdmflWSVk+Uf5zVJoBZt1+JYUqHynP3CofHgYUZre5SjFx1QvUg5xd7QpxcH+J4gDBTKEzdMKJ4Q+J7gMOMgMHhIYuWMIK21dCPntzUramJfYgyOaLoYRMvGICV0IrUMbI4rUSShYlMKGm040Y+Wq1hjLMN5yTivqbQhLxtAEIeeCwQE+BK2ViIC5c7ZOG/Y6DjNmqIy5NoQ+QojnEhgoASJ71EbsfARc5NX6AuuHpSUxjItPTxPgnA2ET6CvHZO9XfL1Lh50L2divUbbSXQSQI+eH6Np0/2yWuNbgyF0eSl8wN7YWdKFUg8A0VTkxWaNBR4ANYgpEfgS4R0HYFSQF4a9EIDqmwsHeEyT9fHFU1XsBoLZqVmb1ZS1hql4NLQ8bI8T5L4HmnoeElKudLoYfPQzXYLRwOOShvqxjBuKqLQu6HL7yhC5bES+0vPtcOyVFY2rCbBsSR+eGOE7l5PFuj1TNavte38QeL1Bie38yl8GLrm2uCmxV3huIlyveM0T2rj3JTnpsEYyxMbKb3IZ+/Iqq9ZdAHhWaQQWAPXZ06BtRe7klM3VDTW8I3tKbNSo41xPAoEu9Mci+SR9RRwvJDI9/CVhzaWTuCz2onpRIYT/RBjLNcmJUpKZqWT/tfLSfrltGuopJsUGk0cKBrDokVYUzSatSNt77HvLVf74PRpjurObCz0dvZnFY1xg9w5IVjthMvBrqg1npCO8Hukru17krUkZJy7lZ6w0FhQGGLfnb9+FHDyXMSZ1ZivXJ5QL1ruG2OZ5BWn+hEbR1ZdN5coDgccrQ3Vgn9zuO3RiUZryzCrF++HaGuZLsoinnSk6m7sc24QU9YRs7phmleEjXZGnpFPEikCT1LWmlmlMRaktBRaM59ZMu2EHSe5QYiKTugE52ZFw4mBMwB9vffrgxxco0DdkHlqtOHMasI7NjtcHeVcHWVcHuXMCk0aeQQSisYyykoCT5AEFWDphQHbsiTwQGCY5LVz9raWRlti5dGLfWrtCO0HeU1WVkjpuG7auudWCKeZdKk2SCnIa2eWKiX4whGI+3HAQebsQtLQpxP6iy6/ml6kltYbN0N5kjOrCZWe3eCPloYep1ePd5i/1wTee5EFut1kfSe82rbzhwGv5/cexdEFw8OkZvwwnesWDzGOq+X3Ih89iNmblos2b8lK7C9F9aQUN6wKHltPQcCs0OzMCkZ5zXoastpx6r4v7M2ptWZaNjSNQSnJvHCia6N5TSf2nYYJuNblaUNZOzPJbuhjLfieoGpch1ZZG3ZnJXmlSRcEU73wZTp8iAPP6bn0Yn+hRVPjCcFqEpAvvLI8Kbg+ySm0XtaqA88FRYftw4e6M6tJiBICs9CqifwaIZxgm+MeCLR9ua7daGf8GPgeG72AaVlja4uUkrVUEvkeW/2IzV68HHxWkpDQ83h2Z8Yoq5ESHllNeOpU74ZV1+Gxlgs7hXFWMSkaxllF5DuBw/WOI1iPc5dBM9YFNlHgusF2JiXKg7OrKUnoBAMvHhSkyuPdp/soJXn22gy1nrLRDfCEI1lrHN+i8jVbPUGtYT8rUdLDF4aVOMTzwBiXHbs6yuhFijhUnBq4CbFs9KsacBvtOsNmZf2qtFnuBw7LIqdXFVv9mHed7lPXhnFRcW1SMJzVCAl5qZnVDTuTgjMrMUJaZlVNqBS+8gD3TFV1Q+B7hL4EIbAYhIBp4bIHj6yHbjGBC7yHWcUwqzlsXBpmNdoaeqFiZRBzYT9Dyoxp0eB7LqPpyNDueS/rW5eXAFaSkCc2hTOAbQyhkgt17eOzAPda6O5eZoFeS3bv1badP0y4l9nMh8l2og1uWtwVjiOgHVVRPVriOMStVgVp4Ews+4m/0BQBrRvmlQtqau2Iyp1QMbUVB/OCaWYoGsO1cYEQhnnZkFcaa50B5fZohq/cSrdsnDrx2ZWYea3ZHmU0Cwn9ShsGuSINFdpYPCnZ7AZUtWFn4kiuaaDoxz6Vdun60bximjeOrxAoiqrhuYMJB7OKzX687BTbn5WUkaYfBSSh5GBeEyjJZEGq1Ab6kRP+G3S8JX+pbAzXJwVFZXhis/uyJYR1onpVc+P5U0ry+FaP0ysJea2RwukOHUd+7cc+z+3MmJcusBtnFY21CCnJK83OtKQbNezPXRB4MK+4OsrpRooo8JgVNY+tpXhS0Al8Hlvv8o7Nit1pSRL4KCX54PkBnifYSiNKbXj2+pSi1vRiRV47cTrlCR5ZTegkPvPCcE5YAs9jb1awN6vIqoZe5PPOE116seLyMLvr4OToavFgXi2C5mAZND9MwmJwhP8RsrBAgNU0Igk9jLGMsorRvKLU7v48v5JwkDnFbClhntXUjSMgB57TOOpFPtZaqsbZaYzmNVK6oDrwnCDmShyw1gm4fJDjK0EsXMcVAobzCs8ThJ7zRNufuXM2SIK7CjhebRbgXgrdPQx2B6+m7fytiofhOhxFG9y0uCvciYB2SAY+7nM332RiQcyNfYUxlklRczAr2Z2WC2YlCCupPENRG8aZE8aLF2n+nWlF1Th7h26k2OyHSAkv7c0pCsMjGx02OiFh4JEvXJOVkHQjxbRseHZnxrTUnF9NOdmP0MZwdVQ4aXqlmBWar25PONmN6ASKnWnJZi8kDhSTomaYVWyPS6rGEMwrfCkJlEcpNAezipO9iLU0AgsHcwiVptauVb0b+QwS39kqzMolX2da1LywMwJiNnsxFkvTWFaSAMvLvIijk8bNpY/jkAaKwBMUAkalG3hW0gBfSWpjST3B1VHOpYOcWdEwKxv2pyW704I48BjNa3ZGORqBtXBmELPVdzL9652Q2PfoJZ4znKw1sZI8upE6R2tPEHuKYV5yfVgudFScN9hWL8QgeGyjy8leQ9FYepHH6UHCpNCvauV3uFr0F5NltBCbDP1mqS/0sAiL3YxD0vZSe0jCZi+mnwQM5xVracD5tZTndmdc2s/IKkkv9PBTJwZZak2oJL1YUTQGa6HRgHD2E42x7M5KsrLmzCChH/tkHWfkKixOFXunRAiBBdLQEgbBUqPp0OX8bgOOu80C3Euhuwdhd3Bc2bMfB5xbS7i8nzMuDpsGXtl2/lbFw2Y70QY3LYC74yjcK3b80VVbUTsyr+cJAs+jFg2NASEt2lhGmbNgkIASzi27qgzjvGG1EzBIQoyF9U5E7Eu0xbkbC7kUa+tEPuO8ZrUTciYJmcTOgykKXTlre5TzlasTqsa1WocKsqLh2aLGSsfR0U3A/rxgOK9dxgdB5Ls09MG8IlAS3xN0Y6eaO86dnggC+klAN/YYxCH+4txeHmbLFY6x1pWJPMGLezPmVcNqGnKiHxF4jjO0PS4AbpvJOO4aWgFp5DvPIilIA3/JPypqjbVwbVy4f1cuC6SU4GDekJUN87Lm2gTOrkacGiQcZDUvHcx4+mSfJ090mWQ137hWMIidzUI3VJzohIBgZ1qwV7qVbN40BL4EKziYl8yqmhO9iNBXGARrHcVG12mCHBqjAkvvpb1pRey/UjTs6Grx0JriZc2lhk6kHiphsZtxqwyGtU7A0uImhw8/usZ7Tw8oKs20rMlLTeALJlmzJO73Yhe8jbKKqtFLocZGGxojiALnKu7kFBYO80W98K1SWGvQ2nIwK5eGiFnlVKjfiPbeN2I8eaM1V27FKTlUhc4rQyf2iUNnd7LRjQj84wnVbzU8bLYTD9uz3uI+49UQwO4lAa0fO0n+YVYvavyCyBdEuIFtVtQkvrNeODuIsQLnZWQM2hoCT/LkZo/VjstsNNrQj13rtr9oP6+1YZw7DkA3VMSLtuleFGBtxSw3DKdTticFSeARB4LaGHantRuYegFKCIpG81tXRxSVxlt0Ne3NKmIl6Kc+ZWXpxgolBCf7EZOyYpLrZVam1sb9WznSctnoG1Y4k7xmkmvODBKuTZ1onxRQL7JTYJflgeMyGbe7hocDjjGW2PeWrbmNMShPUNSavNRsLko4u7OSJFQIYGfiCMShskTK+THllUEISVnDNKuZldqJ1GmLBEa5IyIbHIHcWricz9ib19RG0zSGqtIc5A392OfdJwc8thmz3gnZ6kbkC2sNcGW5SV4zymrnqi4sm50bJf2Prhatdb+9MQYlXfuzMWDswyMsdjOUJ0lDj2vjksYYYl/dkMEAHIcBz3XweRJfSVY3fHpJgDV2mf0BuDLKWe+E7ExKxoUTrXP+USAXFgtJoNgeZezOKtYSn1I74cutXoivJLOioagbsE7kcr1za+7M68G9Hk/uh+bKrTglo6yi0vaGZ35WaZKquWfBzWshyd9PYv3DZjvRBjdvc7wWAtjtUs93+zAdimrtTp0NgycF59dSLK4LqWgaWLQOp7HPRjck8RcOyqOMF/YyQl8iF22njbF0IoU2hv1ZxSANSAIfrQu2xxkrJ/rLLobGGKKF4uv1SUE39PGVR6w8NBYWuiMekqIxrCYBF/M5F/bnpIHHejci9SXb44Kr45wT/YgTvZC8MWyPCyxwbr1zy7rz0RXOoUFl6Et85SOEWAj1GUZZRSdU9OJg2ZF1uM+jZZY7XcPDAedW/J8kVAShR6f2lxpEReS64LSWJIFP4HtL64iVJMAIy0FW0YmcIm1eN6613rrrNy2cIu+0aBjONRKY5e56n+xHbPUVdW25NslpjCHwFd3YJy8diTgJFZO8Zn9WgbB0AoV/TFv+0XN5OLHsz0pKoZ3xqdY02j40wmJHcRiUHiq9XhvWRKFkNQmXGYxGGwaxy0ItlaS7x3s2NdoF/Yd2H9VCtM4pHwg6sUdWuqC2nzg7lST08bShblypyhNysQDwOdmPbvBBe6NwLwitjiennY7VTefpXuFWnJLGGK4OC04OojeEa/JaOpDu9jP3Ovh5mLRv2uDmbYx7SQC708N080MkpWC9EzFbcaWRo55OgXJt0Sf7IbOq4WBeEflOD8UAg07EuwPFKG8Y5xWR8uhEyplGhopeElA3ltoYVlPXgZRVeqlQWtaGTuhazMGZLl4YZlTaZQ2kB/OsodSajTRm0AnYm5YgQHoe2gq2BhGTsmGW12gNnic524uoa832pOD0WgK8vGI7WhoJlbcMOGQjqBuD57lzdGolphMqKm2YZBVF4/QzjnZkpYGiWJCduYtreDiwjOY1jXblp0P+TyfysNaV+SJfoHEK003juqiyWpOGPqtpQC/x8WRFoZ3WzmHba2NezowI6YT+lICXxk6S/9rE/T2clWgLndDjRC/Cl04LSAiQi46ySmv2ZoZVXEkS4YLNfupKa0Wtb+t9kwaKzK/ZnZVLrtVqV72uwfXw3r2V8N9rxdGg9EQ/pqgaslrTCT36cXDD8wSWTqhYTcJbZgIOfcquT0uSRQnvcOW8mroguRu6TM9WN6QbBUjhyqtZpRecqxolBKdXohtUtB9W3DzuAHRDxUoS3PNy0K04JZ6QlMYsvdsOca/Koa9lAXqnz7xRLdv3Kht3L/DAg5uf/Mmf5O/+3b/L9vY27373u/n7f//v8x3f8R233P5zn/scP/RDP8RXvvIVTp06xQ//8A/zyU9+8j4e8Z0xy12nQ+gJokAtB8bD9HGoPGZFxe68pCydJkTge/geNAZmCwEtqwXSM/ieIg49p6kiBU3jOoewll7iuiR2J24CTAIPFsJctTFUjcZqwe6sICsa1voB652YSwdz9sYl47xkXjZsT+foBmIlCCPFOC/RjUB4hn4U0gkCOoHCCsHuLAckWZ0zySqElfi+oNSGIi/RDWS1WzH2UwiVjxGGxmh0BSXgg+vYCEKmZcFwH8YN5EACdD0IUpAWQg+KGmYlSA1+CFHkDB49JNPCOLPHCMoKsjk0AmwDSgEWhILQh17XRwlBnlcUNdSA9KDKIaugwb0WLI4hil0WZ5pBbaEfQhKD8OBgClUFBvdHAVEIkQfra5JOkOIJ8AXMKkOpHW8EBFXTMJy5wEhKiCPnRxT7PknsMkNZpbm4M6JooBvDIO2CNKR+gFSQKB/TuNbhwPdIPB8hNMoPUFZjhKQbKvppyDRr2M8zdAUrHZ9Y+RwUJTuTAikhsw2i8qhFhdUKbTWRsnheyDeaGoFENzVlpVGh4qmNPr7v7rVp7Zyw08BZWjTaYK2gNJYoUIzm7h6bFDUWw8UDzWhesZK6wbTIG17am9OLfPpJQFFqp5tUNXQCRT/16S30ho5OGCyCjjRQbHZd8Lafu66vlYXKchLKW2Y5biXHfxjMCAvzhbDjKK/JyoY49OhGit6i7f92E+jtVsVHFxbKc47kYeAC+KwyMCuWApFSWPLKkFclofKO/U5jLPuzgr2sIq9cu38cKPqR7+QO8oZh5rg6YIlDRVFp8rpZis71FnIOW92QjW58d4PdA8Y4r17h9TbKawyGSKllFvC48/9qJt9GO1kIeNmbrTEGY6DSmlBKrHm5PFhUDfOywXud5dDXsgC9m89Mi/oNbdl+o8Uy7+oYHuSX/+zP/iw/+IM/yE/+5E/y237bb+Of/tN/ynd/93fz1a9+lXPnzr1i+xdffJFPfOITfP/3fz//+l//a37xF3+RP/tn/ywbGxv8gT/wBx7AL7gRVaX56vaI53ZmFI1FG+2k/bs+43mDMU6H5cX9CV+7Mual/YLdeUHdaDwJtrEY6Sbows0XAIQKEh88b6Fb0hgMEHgeQliK2lDVrkuiti/nC5rFn9eP7LV/dAYuXDgOFihe8eoEmOjF/xyH4ujHjrhsz47Z9vAEaFxUNbvVsdyIEpjqV+5zUi7evN0HgQuZAabHbHCr79eLvxtciHcTpsv/APPbHMC9xnHXvuLzF+7unoiAXuL+v2hAa4gDw2hWMsyUI/wqhRxDYw1nV1JWOwFn+zHSE/hCODL0ArU2eEIwyRxh++iqM4kks0rSH8RL7srNVheHSsyXhzmj3C0IVmKfM6sJ/chl+Q5Xs/OiXphdOgdxhGBn7Lr6PCnY7ITO2uOm4OluVsXaWifspi3zSi9kCQTpojW70O5Z35nkXBsXVMZ1Qo2yim89v0YUqhu+76X9GRf25wuOlCQJFRKBwdBo4cqwZcMwc8KaSkqMMbywO6OqNWHgsxL7vONEh5Xk4WiZvxOqWnNxf86sdN5VnhQkgWRnkvO/nstQnofvCU4OIt651Vu6k7+ajMXN13JeNBRNQ6icWvQhf241VpSNgbxmZ1ZwZZgzrzUbaUCgBGdXUpR69RmM19KBdKfPlI1+qFq23ygIa4+w0e4zvu3bvo1v+ZZv4Z/8k3+yfO3pp5/me77ne/j0pz/9iu1/5Ed+hP/8n/8zX/va15avffKTn+RLX/oSn//85+/qOyeTCf1+n/F4TK/Xe/0/4gh+46V9fu3S0A0OxrI9KTiYl6wmPmvdGCUFV/Zn/NIL++xnpfPUyWtK7QZ+CwvH5FfC2QGyKE+4rIU1kBv3msfLn2/R4mGCBwSLmzcIIPYFtXZK1WdWE955ss9jG108T+BLwclBzPvPDFCeXK4uj5ITA08syZuHr89Lp/0yWJiIHqJYKPGeXomXXluHuj+H1hVOi8RjvRMu9ysEXNrPKGqNEI6jUtaa62Nn3rraCbHWEb1P9qMbVrtH/bxuJlUebtdow1eujJyw3k1O2p3Q8Y+G85KX9jM6kU+4sLwYzku+/bE1njrZX37f7jTnt65MiHy57BQra8edygrNyUFE6Htsj/Olf9U4r13nVVETKsFKJ6JpNL3Y54nN7kOjCXQ7XBtn/NaVMf3YWTw0xnBhb8blgwwpJWdXEzzhfuupQcQHz60C3PHaHMXN17KsNS/uTZmXmn4SECmPKHDZmSTwuDLMeH53TjdRrMYhSgnmRcOTW927MoO9Gc5xPXPGure5r1/NZ9a7AdvjgiRQy446AG0sWdVwdjW5pWXGg8armb8fWHhWVRVf/OIX+djHPnbD6x/72Mf4pV/6pWM/8/nPf/4V23/Xd30Xv/qrv0pdH78iLsuSyWRyw583ArO84rmdGStJSD8JqC2sdpxT7/ZkwRmRgm/uzpyqrXGp6DQKORrQHwY2N99aevHHLDZSAvRiY8nLpZEWLR42aNy9GoUwSBRpHOD7ypGUG8Nm1wnu+Z5kXmp6kU9WmUW5KQQLWeWi/9XUKVEfrjpdl52beEZ5zc0VAN972erC+f+UzoE7dj5nSeACiaLSXDrIUJ7bn8CVKyLfiS1q7Ui9SaiQUi5VfH1PMsmbZcni5pLA4fFFvnfDdg7Hlys86RS4L49yOpHrUvM9SRx49CJ/IfjYLL9vOK9RizZ/b9FKH/oeWanJG42QYrmaVwv7jGFWUDaaQRIQ+q7sN0hDau2kDW48zocPzeH1WEzQbpK2jOY1RWWcNEHkPN1W0oDxvGF7lLO/4O/d+docfy0DJQl9xSD2ObMac3olZqsX04l8qsY6deiNlMfXe6x3IwZxSDf22R69fM1eDQ45ZUWtKWq9lHA4FMm8lR3G7T4TKm9Jwj+KB9Wy/UbhgQU3e3t7aK3Z2tq64fWtrS2uXbt27GeuXbt27PZN07C3t3fsZz796U/T7/eXf86ePXtvfsBNKLWl0JY08LHGRcFyUc+ttF7Kcue1AeEcnR1B0X3ecmPG5ubszdH3rQWOBDeH7781bskWb0VIIA4Va92Y072E9W7E2ZWEE/2YNAxcSt86xd5BHFAbgxWu/n96JebsasLplZheHGBg2S5+iMOVZlnfesDW1lJri4BX+P9oIG8M3uKBlNJ5jEnp2trL2pUfLO51Yy3KE4T+y8ETvFwSuPn4jgZZh9vFocdmz4k95rUL3jZ7EelCeTirtLMaWZSwqtqwlgY0Fmrz8n7s4vcfSv0f/qZKOx8pa+yyq6wxhko7Er+3EO7zpFiSwwWu7K0fXEL/rqAXg+BKElDWrlmgqi1506CNIQ29ZVYiVIoGS1YbqiNSA4e4+doc/Y6br6UztsWVvOTLXC3fkxSNpraWbhjckBGJlKLQZnnNXi36cfCKIP9OHUi3+8xrCZjejHjQnJ9lmvQQ1tpXvHan7Y97/RA/9mM/xg/90A8t/z2ZTN6QACf0BJEnmFc1/SRYDoCuPdP59AgpiX3JJHMDkpDOQBJeGZgc9+/D18Qixe8dCXAOS1YtWjyM8H0wxmUkw8ijby1SWFbTkLWOs7XIq4bVToSnBEq8vIK8gZyozfGidzirgMMyw7EaG9rge25Cv9n/xwNiJdHWAK5zrxsptkc1iVLUiy4zC6ymAY229OOFn9mR1e7dCpl5Qizath3n45Dc2hjXHbYSKzbSYFkWU55gJfUxQGRZGot6QhAqN67MSsfbUlIyr2oEcGYQURuLt9C32ZkUaON0n+alQVdO3+bQ+8xiCdXDv3o/PM+e7+F1QmZFQ2MM0jqOVuy/zNMqmwaFIPElgZJ3LTJ33LU8DAjBciQ+dlIEyiPyJEXTEKiXA4+iaYg8+ZrNYF9LB9KdPvMwtWy/UXhgwc36+jqe570iS7Ozs/OK7MwhTpw4cez2SinW1taO/UwYhoThG18/7sQBT2x2+LVLQ8B1yGxPS4pKc7LnBiklBU9udNiblgjpCITzvKQ5stg8DFL0Tfv3jryHcI7R3uIFQ8u5afHwYiuG1V6MBHpJQCglYazwfI/H12Iaa6lqQxr4rMY+jbasdm+fcj9OKOzMIH6FWevRAdv5/4QM5/Ux/j8vc24Og6NAOYG9QeJjrPMlKxYikSuJU3q+WaDsboXMjm53HP+jl4a882SPr29PSYJFR4sxzPOaJ7e6S6Xmw/3ktaYDFI1hXjpX+kfXEs6upEzLmkneLAKkAGGhMoaqccR1KQVZ1SzPw5vBB+nm8xf7HmWjyKsO18YZk7LCEqCNXnJuTg5cB9jdiswddy0bYxeGpM6VXWBf3kcvJFCCb153xP9IKYqmYXrTNXs9v/nV7uFWn3mYWrbfKDyw4CYIAp555hk++9nP8r3f+73L1z/72c/y+37f7zv2Mx/5yEf4L//lv9zw2n//7/+dD33oQ/j+8d5G9xPvOjkA4IW9OVlt6IU+59YSVjs+07mmsZZ3nhoQhZJvXp3wwn7OLoKgaegJC9qihWsvLuqXA5zDbqnAkwgJ1WG3lPIQ2IUuCVTatZIfrklq7lW3VIsWrw0nU/jI4xucW0uxxjKqnLjeqX7I4xtdNrsxk7KmagxppOgnAYPEv2PKHY5fdcqFFtCtBux+HPDYesrlUc44q7FYVpOA06vxslvq6H6f2OySBgor4J1bXaal89+yOMXf41a7d7sqvtN2Z1dSALZHBdPSrf6f3OouXz9uP2GjWYkDBqla6tSsqBsnMXCZhrMrMdcm5SvPw5tk9X70d1fGZcg/eHaF0UbKCztzDvIKX8AjawlPbfVu+F13m7E47ho9tp6CgFmhX7GP7sJjb3tUMMqrW16zhwUPQ8v2G4UH2i31sz/7s/yxP/bH+Kmf+ik+8pGP8M/+2T/jn//zf85XvvIVzp8/z4/92I9x5coV/uW//JeAawV/z3veww/8wA/w/d///Xz+85/nk5/8JD/zMz9z163gb2S31CHeLDo341kFCIbznMsHM7QQhNKjE3vkhWYvqzCiuSudm16iqBHMspyqbMi104qJIkngORsAKy1VpZk3LnLzlaCbpDQ649quYZhBBSSeC+ZkAJ6FOAKjXRu2qEEEEAUu6Iukz0FeI4XTgmk0DCfOUwnt9G1SBWEq8aShE6X40mM8mzOvNUridEZqS6FdYCksxMmCxyE0dQ15Bcp3JYlOENFYTVHXZGXFOHPaOhjo9mGt59ONQwKlUOLlgLSs6iUvom4007zCkxAEgk6QoDH4wqOTRKxEHtrChf0ZWVkShZJTgwEK4ZSAjXGdctqAEvgCAs9HYlC+QlpQvodnDEkSMM0ahnmObQSrqSIKA/bmBdfGOZ3QIwkkjVZ0IxjEMXnd0FjLiZWISPlcG+XsjDI8X7CZhPS7McKCUJbprGFcVHgIkkBRW9e19PhmlzMrCWnkkQZucp0XNVYIUt+js1Dgfa0Cea9HXfVudG5utd+7/d57tV1RNdTG4ktx29X/a5Xnv9V5eLPguN9dVA15/bLa9b3Qubl5+9vt426vWYtXh1czfz/Qs/6H//AfZn9/n7/xN/4G29vbvOc97+Ezn/kM58+fB2B7e5uLFy8ut3/00Uf5zGc+w1/6S3+Jf/yP/zGnTp3iH/yDf/BQaNwcRScO6Bz593EneZBGDNLonn3nYxuvbvuP3LNvbtHi7nGze/zrWTm+7s/eYlK7035fjfP1vdguChR3M1K85rLFmzCgOYrjfncUqNsGFa/2XB23/e32cbfXrMUbhweauXkQuB+ZmxYtWrRo0aLFvcWbQuemRYsWLVq0aNHijUAb3LRo0aJFixYt3lJog5sWLVq0aNGixVsKbXDTokWLFi1atHhLoQ1uWrRo0aJFixZvKbTBTYsWLVq0aNHiLYU2uGnRokWLFi1avKXQBjctWrRo0aJFi7cU2uCmRYsWLVq0aPGWwtvO9OJQkHkymTzgI2nRokWLFi1a3C0O5+27MVZ42wU306mzoz979uwDPpIWLVq0aNGixavFdDql3+/fdpu3nbeUMYarV6/S7XYRQtxx+2/91m/lC1/4wht2PPdi/69lH6/mM3ez7Z22ud37x703mUw4e/Ysly5deug9wNp7pL1Hboc3w/3xWvfT3iP3Bm+Ge+S17uNuP3c321lreeaZZ/jmN7+JlLdn1bztMjdSSs6cOXPX23ue94Y+FPdi/69lH6/mM3ez7Z22ud37t3uv1+s91IMStPfI3W77dr1H3gz3x2vdT3uP3Bu8Ge6R17qPu/3c3W4XBMEdAxtoCcV3xJ/7c3/uod//a9nHq/nM3Wx7p21u9/4bfY7faLT3SHuP3A5vhvvjte6nvUfuDd4M98hr3cfdfu5eb/e2K0u1eHPg1Vjbt3h7or1HWtwJ7T3y9kWbuWnxUCIMQ/7aX/trhGH4oA+lxUOK9h5pcSe098jbF23mpkWLFi1atGjxlkKbuWnRokWLFi1avKXQBjctWrRo0aJFi7cU2uCmRYsWLVq0aPGWQhvctGjRokWLFi3eUmiDmxYtWrRo0aLFWwptcNPiTYfpdMq3fuu38oEPfID3vve9/PN//s8f9CG1eEiRZRnnz5/nU5/61IM+lBYPGZRSfOADH+ADH/gAf+bP/JkHfTgt7jHedvYLLd78SJKEz33ucyRJQpZlvOc97+H3//7fz9ra2oM+tBYPGf7W3/pbfNu3fduDPowWDyEGgwG/8Ru/8aAPo8UbhDZz0+JNB8/zSJIEgKIo0FrTyjW1uBnPPvssX//61/nEJz7xoA+lRYsW9xltcNPivuPnf/7n+T2/5/dw6tQphBD8x//4H1+xzU/+5E/y6KOPEkURzzzzDL/wC79ww/uj0Yj3v//9nDlzhh/+4R9mfX39Ph19i/uBe3GPfOpTn+LTn/70fTriFvcT9+L+mEwmPPPMM/z23/7b+dznPnefjrzF/UIb3LS475jP57z//e/nH/2jf3Ts+z/7sz/LD/7gD/JX/spf4dd//df5ju/4Dr77u7+bixcvLrcZDAZ86Utf4sUXX+Tf/tt/y/Xr1+/X4be4D3i998h/+k//iSeffJInn3zyfh52i/uEezGGXLhwgS9+8Yv81E/9FN/3fd/HZDK5X4ff4n7AtmjxAAHY//Af/sMNr334wx+2n/zkJ2947Z3vfKf90R/90WP38clPftL++3//79+oQ2zxgPFa7pEf/dEftWfOnLHnz5+3a2trttfr2b/+1//6/TrkFvcR92IM+fjHP26/8IUvvFGH2OIBoM3ctHioUFUVX/ziF/nYxz52w+sf+9jH+KVf+iUArl+/vlxlTSYTfv7nf56nnnrqvh9riweDu7lHPv3pT3Pp0iUuXLjAj//4j/P93//9/NW/+lcfxOG2uM+4m/tjOBxSliUAly9f5qtf/SqPPfbYfT/WFm8c2m6pFg8V9vb20FqztbV1w+tbW1tcu3YNcIPRn/7TfxprLdZa/vyf//O8733vexCH2+IB4G7ukRZvX9zN/fG1r32NH/iBH0BKiRCCn/iJn2B1dfVBHG6LNwhtcNPioYQQ4oZ/W2uXrz3zzDNtC2eL294jR/En/sSfuE9H1OJhwu3uj49+9KP81m/91oM4rBb3CW1ZqsVDhfX1dTzPe8UKfGdn5xUrsRZvT7T3SIvbob0/WkAb3LR4yBAEAc888wyf/exnb3j9s5/9LB/96Ecf0FG1eJjQ3iMtbof2/mgBbVmqxQPAbDbjueeeW/77xRdf5Dd+4zdYXV3l3Llz/NAP/RB/7I/9MT70oQ/xkY98hH/2z/4ZFy9e5JOf/OQDPOoW9xPtPdLidmjvjxZ3xAPt1WrxtsT//J//0wKv+PPH//gfX27zj//xP7bnz5+3QRDYb/mWb7Gf+9znHtwBt7jvaO+RFrdDe3+0uBOEta1ufYsWLVq0aNHirYOWc9OiRYsWLVq0eEuhDW5atGjRokWLFm8ptMFNixYtWrRo0eIthTa4adGiRYsWLVq8pdAGNy1atGjRokWLtxTa4KZFixYtWrRo8ZZCG9y0aNGiRYsWLd5SaIObFi1atGjRosVbCm1w06JFiweK7/zO7+QHf/AHH/Rh8HM/93MIIRiNRg/6UFq0aPE60QY3LVq0eNvhYQmoWrRo8cagDW5atGjRokWLFm8ptMFNixYtHhpUVcUP//APc/r0adI05du+7dv4uZ/7ueX7/+Jf/AsGgwH/7b/9N55++mk6nQ4f//jH2d7eXm7TNA1/8S/+RQaDAWtra/zIj/wIf/yP/3G+53u+B4A/8Sf+BJ/73Of4iZ/4CYQQCCG4cOHC8vNf/OIX+dCHPkSSJHz0ox/lG9/4xn369S1atLhXaIObFi1aPDT4k3/yT/KLv/iL/Lt/9+/4zd/8Tf7gH/yDfPzjH+fZZ59dbpNlGT/+4z/Ov/pX/4qf//mf5+LFi3zqU59avv93/s7f4d/8m3/DT//0T/OLv/iLTCYT/uN//I/L93/iJ36Cj3zkI3z/938/29vbbG9vc/bs2eX7f+Wv/BX+3t/7e/zqr/4qSin+1J/6U/flt7do0eLeQT3oA2jRokULgOeff56f+Zmf4fLly5w6dQqAT33qU/zX//pf+emf/mn+9t/+2wDUdc1P/dRP8fjjjwPw5//8n+dv/I2/sdzPP/yH/5Af+7Ef43u/93sB+Ef/6B/xmc98Zvl+v98nCAKSJOHEiROvOI6/9bf+Fr/zd/5OAH70R3+U3/27fzdFURBF0Rvzw1u0aHHP0QY3LVq0eCjwa7/2a1hrefLJJ294vSxL1tbWlv9OkmQZ2ACcPHmSnZ0dAMbjMdevX+fDH/7w8n3P83jmmWcwxtzVcbzvfe+7Yd8AOzs7nDt37tX/qBYtWjwQtMFNixYtHgoYY/A8jy9+8Yt4nnfDe51OZ/n/vu/f8J4QAmvtK147ipvfvx2O7v9wP3cbGLVo0eLhQMu5adGixUOBD37wg2it2dnZ4Yknnrjhz3Hlo+PQ7/fZ2triV37lV5avaa359V//9Ru2C4IArfU9Pf4WLVo8PGgzNy1atHgo8OSTT/JH/+gf5fu+7/v4e3/v7/HBD36Qvb09/sf/+B+8973v5ROf+MRd7ecv/IW/wKc//WmeeOIJ3vnOd/IP/+E/ZDgc3pDNeeSRR/jlX/5lLly4QKfTYXV19Y36WS1atHgAaDM3LVq0eGjw0z/903zf930ff/kv/2Weeuopfu/v/b388i//8g3dTHfCj/zIj/BH/sgf4fu+7/v4yEc+QqfT4bu+67tuIAR/6lOfwvM83vWud7GxscHFixffiJ/TokWLBwRhX00xukWLFi3eZDDG8PTTT/OH/tAf4m/+zb/5oA+nRYsW9wFtWapFixZvKbz00kv89/9/e3ZMAzAMQ1HQKpsA6JQhPIIrAIKjCosCCI5uZdFK1h2CPz7Z1xWttXieJ8YYsfeO3vvf04CPeEsBqRzHEXPOOM8zaq1x33estaKU8vc04CPeUgBAKi43AEAq4gYASEXcAACpiBsAIBVxAwCkIm4AgFTEDQCQirgBAFIRNwBAKi8GeohXOLdD8wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metridium_repeated.plot(x=\"length\", y=\"perc_repeats\", kind=\"scatter\", alpha=0.1, logx=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ascc24",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/04-contam/metridium_contigs.sam_summary b/04-contam/metridium_contigs.sam_summary
new file mode 100644
index 0000000000000000000000000000000000000000..f608aecd99a794f47fef30cc2b348ba12cfacadd
--- /dev/null
+++ b/04-contam/metridium_contigs.sam_summary
@@ -0,0 +1,971 @@
+   2197 contig_8782
+    324 contig_21131
+    151 contig_28132
+    146 contig_6939
+    123 contig_19945
+     94 contig_11056
+     79 contig_5985
+     78 contig_19987
+     77 contig_4985
+     76 contig_4041
+     74 contig_20675
+     69 contig_12257
+     69 contig_16877
+     56 contig_1671
+     56 contig_5096
+     54 contig_6508
+     53 contig_11454
+     51 contig_4780
+     50 contig_19125
+     49 contig_10149
+     49 contig_14339
+     49 contig_4794
+     47 contig_499
+     47 contig_9983
+     46 contig_3762
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+      1 contig_7490
+      1 contig_7680
+      1 contig_7707
+      1 contig_7736
+      1 contig_7855
+      1 contig_7991
+      1 contig_8070
+      1 contig_8127
+      1 contig_8304
+      1 contig_8440
+      1 contig_8469
+      1 contig_8475
+      1 contig_8487
+      1 contig_8644
+      1 contig_8882
+      1 contig_8888
+      1 contig_9045
+      1 contig_9059
+      1 contig_9118
+      1 contig_9122
+      1 contig_9167
+      1 contig_9224
+      1 contig_9290
+      1 contig_9408
+      1 contig_9456
+      1 contig_9510
+      1 contig_9541
+      1 contig_9573
+      1 contig_9669
+      1 contig_9690
+      1 contig_9771
+      1 contig_9897
+      1 contig_9970
+      1 contig_9988
+      1 contig_9992
diff --git a/04-contam/metridium_scaffolds.sam_summary b/04-contam/metridium_scaffolds.sam_summary
new file mode 100644
index 0000000000000000000000000000000000000000..3919800056b8b8e9d69b06718449563f5d0bcef8
--- /dev/null
+++ b/04-contam/metridium_scaffolds.sam_summary
@@ -0,0 +1,199 @@
+   2478 pseudochrom_16
+    623 pseudochrom_22
+    373 scaffold_1844
+    355 pseudochrom_38
+    272 pseudochrom_13
+    238 pseudochrom_7
+    233 pseudochrom_24
+    214 pseudochrom_8
+    213 pseudochrom_17
+    174 pseudochrom_23
+    164 pseudochrom_19
+    160 scaffold_875
+    146 scaffold_126
+    140 pseudochrom_21
+    134 pseudochrom_9
+    132 pseudochrom_59
+    127 pseudochrom_1
+    116 pseudochrom_10
+    116 pseudochrom_15
+    115 pseudochrom_35
+    107 pseudochrom_37
+    107 pseudochrom_54
+    104 pseudochrom_30
+    101 pseudochrom_3
+     98 pseudochrom_2
+     97 pseudochrom_58
+     94 pseudochrom_28
+     92 pseudochrom_11
+     92 pseudochrom_26
+     88 pseudochrom_12
+     87 pseudochrom_27
+     86 pseudochrom_31
+     85 pseudochrom_6
+     84 pseudochrom_39
+     83 pseudochrom_33
+     83 pseudochrom_57
+     83 scaffold_5103
+     80 scaffold_152
+     78 pseudochrom_41
+     75 pseudochrom_44
+     75 pseudochrom_47
+     74 pseudochrom_48
+     71 pseudochrom_20
+     71 pseudochrom_45
+     70 scaffold_601
+     68 pseudochrom_14
+     68 pseudochrom_50
+     66 pseudochrom_36
+     63 pseudochrom_32
+     63 scaffold_6579
+     61 pseudochrom_34
+     60 pseudochrom_4
+     59 pseudochrom_25
+     55 pseudochrom_18
+     51 scaffold_1883
+     50 pseudochrom_29
+     50 pseudochrom_43
+     48 pseudochrom_40
+     47 pseudochrom_51
+     47 pseudochrom_56
+     38 pseudochrom_49
+     37 scaffold_139
+     35 pseudochrom_42
+     33 pseudochrom_46
+     33 pseudochrom_5
+     24 pseudochrom_55
+     23 scaffold_10842
+     23 scaffold_2738
+     22 scaffold_365
+     19 scaffold_1105
+     17 scaffold_342
+     17 scaffold_4829
+     16 scaffold_1377
+     13 scaffold_7325
+     11 scaffold_2532
+     11 scaffold_3335
+     10 scaffold_246
+     10 scaffold_296
+     10 scaffold_5270
+      8 scaffold_2824
+      8 scaffold_3119
+      8 scaffold_3681
+      7 scaffold_462
+      7 scaffold_8788
+      6 scaffold_1578
+      6 scaffold_3633
+      6 scaffold_8483
+      5 scaffold_10507
+      5 scaffold_254
+      5 scaffold_5366
+      5 scaffold_6651
+      5 scaffold_6748
+      4 scaffold_2128
+      4 scaffold_817
+      4 scaffold_9451
+      3 scaffold_1281
+      3 scaffold_1615
+      3 scaffold_1732
+      3 scaffold_5394
+      3 scaffold_5725
+      3 scaffold_7012
+      2 scaffold_10435
+      2 scaffold_11049
+      2 scaffold_11619
+      2 scaffold_11825
+      2 scaffold_12344
+      2 scaffold_1553
+      2 scaffold_1642
+      2 scaffold_195
+      2 scaffold_204
+      2 scaffold_2520
+      2 scaffold_356
+      2 scaffold_3880
+      2 scaffold_5554
+      2 scaffold_6479
+      2 scaffold_804
+      1 scaffold_10482
+      1 scaffold_10792
+      1 scaffold_11196
+      1 scaffold_11454
+      1 scaffold_115
+      1 scaffold_120
+      1 scaffold_12273
+      1 scaffold_12277
+      1 scaffold_12312
+      1 scaffold_12338
+      1 scaffold_1238
+      1 scaffold_1291
+      1 scaffold_135
+      1 scaffold_136
+      1 scaffold_141
+      1 scaffold_1484
+      1 scaffold_158
+      1 scaffold_1639
+      1 scaffold_166
+      1 scaffold_1686
+      1 scaffold_175
+      1 scaffold_1756
+      1 scaffold_1779
+      1 scaffold_185
+      1 scaffold_1946
+      1 scaffold_2174
+      1 scaffold_2249
+      1 scaffold_2265
+      1 scaffold_232
+      1 scaffold_242
+      1 scaffold_2471
+      1 scaffold_2474
+      1 scaffold_248
+      1 scaffold_249
+      1 scaffold_252
+      1 scaffold_2536
+      1 scaffold_268
+      1 scaffold_2829
+      1 scaffold_2836
+      1 scaffold_2865
+      1 scaffold_294
+      1 scaffold_2969
+      1 scaffold_3003
+      1 scaffold_306
+      1 scaffold_325
+      1 scaffold_3299
+      1 scaffold_3487
+      1 scaffold_3502
+      1 scaffold_3736
+      1 scaffold_437
+      1 scaffold_449
+      1 scaffold_4666
+      1 scaffold_4697
+      1 scaffold_484
+      1 scaffold_4875
+      1 scaffold_502
+      1 scaffold_5241
+      1 scaffold_525
+      1 scaffold_5360
+      1 scaffold_549
+      1 scaffold_554
+      1 scaffold_5543
+      1 scaffold_573
+      1 scaffold_6082
+      1 scaffold_6518
+      1 scaffold_6880
+      1 scaffold_7139
+      1 scaffold_7200
+      1 scaffold_725
+      1 scaffold_7415
+      1 scaffold_7589
+      1 scaffold_7631
+      1 scaffold_7836
+      1 scaffold_8100
+      1 scaffold_8207
+      1 scaffold_8239
+      1 scaffold_8555
+      1 scaffold_8989
+      1 scaffold_8991
+      1 scaffold_920
+      1 scaffold_9571
+      1 scaffold_9603
+      1 scaffold_9690
diff --git a/04-contam/mmseqs-align.sh b/04-contam/mmseqs-align.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5eda140a4cc79d3bba051b751ed2dfce196acf88
--- /dev/null
+++ b/04-contam/mmseqs-align.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+#
+#SBATCH --job-name=mmseqs2-align
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=32
+#SBATCH --mem=120G
+#SBATCH --time=36:00:00
+#SBATCH --mail-type=ALL
+#SBATCH --mail-user=nikolaos.papadopoulos@univie.ac.at
+#SBATCH --output=/lisc/user/papadopoulos/log/mmseqs-align-%j.out
+#SBATCH --error=/lisc/user/papadopoulos/log/mmseqs-align-%j.err
+
+module load mmseqs2
+
+OUTPUT="/lisc/scratch/zoology/pycnogonum/genome/draft/contamination/"
+UNIREF90="/lisc/scratch/zoology/db/uniref90/uniref90"
+
+cd "$OUTPUT" || exit 1
+
+mmseqs search asm $UNIREF90 contam "$TMPDIR" --threads 32 --search-type 2 --split-memory-limit 90G
+mmseqs addtaxonomy $UNIREF90 contam contam_tax --threads 32 --lca-ranks kingdom
+mmseqs createtsv asm $UNIREF90 contam_tax contam_tax.m8 --threads 32
\ No newline at end of file
diff --git a/04-contam/mmseqs-download_uniref90.sh b/04-contam/mmseqs-download_uniref90.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5046bd840f17263e267d5cf5abf540b55f021725
--- /dev/null
+++ b/04-contam/mmseqs-download_uniref90.sh
@@ -0,0 +1,20 @@
+#!/usr/bin/env bash
+#
+#SBATCH --job-name=uniref90
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=16
+#SBATCH --mem=50G
+#SBATCH --time=1:00:00
+#SBATCH --mail-type=ALL
+#SBATCH --mail-user=nikolaos.papadopoulos@univie.ac.at
+#SBATCH --output=/lisc/user/papadopoulos/log/mmseqs-uniref90-%j.out
+#SBATCH --error=/lisc/user/papadopoulos/log/mmseqs-uniref90-%j.err
+
+module load mmseqs2
+
+OUTPUT="/lisc/scratch/zoology/db/uniref90/"
+
+mkdir -p "$OUTPUT" || exit 1
+cd "$OUTPUT" || exit 1
+
+mmseqs databases UniRef90 "$OUTPUT"/uniref90 "$TMPDIR" --threads 16
\ No newline at end of file
diff --git a/04-contam/mmseqs-prepdb.sh b/04-contam/mmseqs-prepdb.sh
new file mode 100644
index 0000000000000000000000000000000000000000..41f31e266b60c8d27c3844209be30552571848c2
--- /dev/null
+++ b/04-contam/mmseqs-prepdb.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+#
+#SBATCH --job-name=mmseqs2-index
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=8
+#SBATCH --mem=10G
+#SBATCH --time=10:00
+#SBATCH --mail-type=ALL
+#SBATCH --mail-user=nikolaos.papadopoulos@univie.ac.at
+#SBATCH --output=/lisc/user/papadopoulos/log/mmseqs-prep-%j.out
+#SBATCH --error=/lisc/user/papadopoulos/log/mmseqs-prep-%j.err
+
+module load mmseqs2
+
+OUTPUT="/lisc/scratch/zoology/pycnogonum/genome/draft/contamination"
+ASSEMBLY="/lisc/scratch/zoology/pycnogonum/genome/draft/GAP_sort_scaffolds_by_hic_insert/output/assembly/plit_q_0_50000_0.5FracBest_output.fasta"
+
+mkdir -p "$OUTPUT" || exit 1
+cd "$OUTPUT" || exit 1
+
+mmseqs createdb $ASSEMBLY $OUTPUT/asm
+mmseqs createindex $OUTPUT/asm "$TMPDIR" --threads 8 --search-type 2
\ No newline at end of file