<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" "http://jats.nlm.nih.gov/publishing/1.2/JATS-journalpublishing1.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="brief-report" dtd-version="1.2" xml:lang="en">
    <front>
        <journal-meta>
            <journal-id journal-id-type="pmc">F1000Research</journal-id>
            <journal-title-group>
                <journal-title>F1000Research</journal-title>
            </journal-title-group>
            <issn pub-type="epub">2046-1402</issn>
            <publisher>
                <publisher-name>F1000 Research Limited</publisher-name>
                <publisher-loc>London, UK</publisher-loc>
            </publisher>
        </journal-meta>
        <article-meta>
            <article-id pub-id-type="doi">10.12688/f1000research.52197.1</article-id>
            <article-categories>
                <subj-group subj-group-type="heading">
                    <subject>Brief Report</subject>
                </subj-group>
                <subj-group>
                    <subject>Articles</subject>
                </subj-group>
            </article-categories>
            <title-group>
                <article-title>Isolation of active Averrhoa bilimbi phytocompounds corresponding to brown adipocytes stimulation</article-title>
                <fn-group content-type="pub-status">
                    <fn>
                        <p>[version 1; peer review: 2 approved with reservations]</p>
                    </fn>
                </fn-group>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Hamzah</surname>
                        <given-names>Mohamad Faiz</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Resources</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <contrib contrib-type="author" corresp="no">
                    <name>
                        <surname>Amanah</surname>
                        <given-names>Azimah</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Resources</role>
                    <role content-type="http://credit.niso.org/">Software</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <xref ref-type="aff" rid="a2">2</xref>
                </contrib>
                <contrib contrib-type="author" corresp="yes">
                    <name>
                        <surname>Lau</surname>
                        <given-names>Wai Kwan</given-names>
                    </name>
                    <role content-type="http://credit.niso.org/">Conceptualization</role>
                    <role content-type="http://credit.niso.org/">Data Curation</role>
                    <role content-type="http://credit.niso.org/">Funding Acquisition</role>
                    <role content-type="http://credit.niso.org/">Investigation</role>
                    <role content-type="http://credit.niso.org/">Methodology</role>
                    <role content-type="http://credit.niso.org/">Project Administration</role>
                    <role content-type="http://credit.niso.org/">Supervision</role>
                    <role content-type="http://credit.niso.org/">Visualization</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Original Draft Preparation</role>
                    <role content-type="http://credit.niso.org/">Writing &#x2013; Review &amp; Editing</role>
                    <uri content-type="orcid">https://orcid.org/0000-0001-8877-7292</uri>
                    <xref ref-type="corresp" rid="c1">a</xref>
                    <xref ref-type="aff" rid="a1">1</xref>
                </contrib>
                <aff id="a1">
                    <label>1</label>Experimental Therapeutics Centre, National Institutes of Biotechnology Malaysia, National Institutes of Biotechnology Malaysia, Penang, 11700, Malaysia</aff>
                <aff id="a2">
                    <label>2</label>Natural Product Development Centre, Malaysian Institute of Pharmaceuticals &amp; Nutraceuticals, National Institutes of Biotechnology Malaysia, Penang, 11700, Malaysia</aff>
            </contrib-group>
            <author-notes>
                <corresp id="c1">
                    <label>a</label>
                    <email xlink:href="mailto:waikwan@nibm.my">waikwan@nibm.my</email>
                </corresp>
                <fn fn-type="conflict">
                    <p>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>18</day>
                <month>5</month>
                <year>2021</year>
            </pub-date>
            <pub-date pub-type="collection">
                <year>2021</year>
            </pub-date>
            <volume>10</volume>
            <elocation-id>398</elocation-id>
            <history>
                <date date-type="accepted">
                    <day>7</day>
                    <month>5</month>
                    <year>2021</year>
                </date>
            </history>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2021 Hamzah MF et al.</copyright-statement>
                <copyright-year>2021</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <self-uri content-type="pdf" xlink:href="https://f1000research.com/articles/10-398/pdf"/>
            <abstract>
                <p>

                    <italic toggle="yes">Averrhoa bilimbi </italic>is a fast-growing tree widely found in countries of tropical Asia. Due to easy accessibility and traditional knowledge, various parts of this plant are adopted as folk medicine and a natural health remedy. Recently, beneficial effects of bilimbi in combating obesity including its potential antihyperlipidemic and hypoglycemic activities have been discovered. This paper reports the successive isolation and purification of bioactive compounds from the leaf of bilimbi that corresponds to brown adipocyte activation. Bilimbi ethanolic extract underwent bioassay-guided partitioning and fractionation. The n-hexane partition exhibited highest brown adipogenesis potential via adipomyocytes differentiation. Further isolation of this active partition yielded 10 fractions. Active fractions with the highest brown adipogenesis potential were further evaluated via the adipomyocytes assay. Chemical structures of the constituents were elucidated by gas chromatography-mass spectrometry (GC-MS). Major phytocomponents in the n-hexane partition include hexadecanoic acid, phytol, 9-Octadecenoic acid (Z)- and squalene.</p>
            </abstract>
            <kwd-group kwd-group-type="author">
                <kwd>Averrhoa bilimbi</kwd>
                <kwd>obesity</kwd>
                <kwd>brown adipocytes</kwd>
                <kwd>white adipocytes</kwd>
                <kwd>adipomyocytes</kwd>
            </kwd-group>
            <funding-group>
                <award-group id="fund-1">
                    <funding-source>Ministry of Higher Education Malaysia</funding-source>
                    <award-id>FRGS/1/2019/STG05/MOSTI//4</award-id>
                </award-group>
                <funding-statement>This work was supported by the Fundamental Research Grant Scheme of the Ministry of Higher Learning of Malaysia (FRGS/1/2019/STG05/MOSTI//4).</funding-statement>
                <funding-statement>
                    <italic>The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</italic>
                </funding-statement>
            </funding-group>
        </article-meta>
    </front>
    <body>
        <sec id="sec1" sec-type="intro">
            <title>1. Introduction</title>
            <p>
                <italic toggle="yes">Averrhoa bilimbi</italic>, also known as bilimbi by locals, is widely found in many countries of tropical Asia including Malaysia, the Philippines, Indonesia, and India. Bilimbi is a fast growing and long-lived plant species that is commonly 16-33 feet tall.
                <sup>
                    <xref ref-type="bibr" rid="ref1">1</xref>
                </sup> It belongs to the same Oxadilaceae family as the carambola or star fruit. Scientific studies have been conducted on various parts of this plant, with medicinal benefits including antioxidant, hepatoprotective, anti-cancer, wound healing, anti-diabetic, anti-hyperlipidemic, anti-hypertensive, anti-hypercholesterolemic, anti-ulcerative colitis effects, as well as attenuated hyperglycermia-mediated oxidative stress and anti-thrombotic activities.
                <sup>
                    <xref ref-type="bibr" rid="ref2">2</xref>&#x2013;
                    <xref ref-type="bibr" rid="ref5">5</xref>
                </sup> Its beneficial effects in brown adipogenesis activation, a phenomenon that combats obesity, has also been recently reported.
                <sup>
                    <xref ref-type="bibr" rid="ref6">6</xref>
                </sup>
            </p>
            <p>There are two main types of adipose tissue. White adipose tissue (WAT) is the primary site of fat storage and its amount increases in obesity. Brown adipose tissue (BAT) on the other hand plays specific roles in promoting energy expenditure and maintaining body temperature via heat generation. Although BAT and WAT are originated from distinct lineages, the plasticity of WAT allows brown adipocyte-like cells to emerge upon appropriate and adequate stimulation. This process is termed &#x201c;browning&#x201d; or &#x201c;beiging&#x201d; of WAT, which induces thermogenesis in cellular and animal models.
                <sup>
                    <xref ref-type="bibr" rid="ref7">7</xref>&#x2013;
                    <xref ref-type="bibr" rid="ref9">9</xref>
                </sup> Fundamental to the development of obesity is an imbalance between caloric intake and energy expenditure. The induced BAT development from WAT may help to increase energy consumption as well as reduce adverse effects of WAT to improve metabolic health. The stimulation of BAT development and activity can also be strategised to combat obesity.</p>
            <p>Although the molecular effects of bilimbi in stimulating BAT activity to combat diet-induced obesity has been investigated recently, the corresponding phytocompounds to the reported pharmacological effects remain poorly studied. Here, we identify the phytocompounds of plant partition, and its active fractions that induce BAT associated adipomyocytes differentiation and enhance brown adipogenesis activities.</p>
        </sec>
        <sec id="sec2" sec-type="methods">
            <title>2. Methods</title>
            <sec id="sec3">
                <title>2.1 Plant extractions and fractions with Flash Column Chromatography</title>
                <p>Bilimbi leaves were sampled from an orchard with the owner&#x2019;s permission. The botanical authentication of the specimen was conducted by botanists of the School of Biological Sciences, Universiti Sains Malaysia (USM) and a voucher specimen 11738 was deposited in the herbarium of USM. Fresh leaves were dried in an oven at 45&#x00b0;C until the percentage yield of moisture content value was less than 10%. Dried leaves were then ground to a fine powder using a grinder then weighed, recorded and kept in a plastic bag. The powder was soaked in ethanol (EtOH) for 3 days at room temperature. After filtration with Whatman No. 1 filter paper, the solvent was removed under reduced pressure at 40&#x00b0;C using a rotary evaporator (Buchi, Switzerland) to obtain the crude ethanolic extract. The weight and percentage yield were recorded, stored in an amber bottle, labelled and kept refrigerated until further use. Next, the crude was sequentially extracted with organic solvents - namely n-hexane (n-Hex), ethyl acetate (EtOAc), and n-butanol (n-BuOH) with increasing polarity respectively. The filtrate was then concentrated and dried under reduced pressure at 40&#x00b0;C using a Buchi rotary evaporator. The remaining ethanolic aqueous layer was evaporated and lyophilised in a freeze-dryer. The weight and percentage of each yield was recorded and kept refrigerated. A flow diagram showing the step-by-step extraction and partitioning processes is depicted in 
                    <xref ref-type="fig" rid="f1">Figure 1</xref>.
                    <fig fig-type="figure" id="f1" orientation="portrait" position="float">
                        <label>Figure 1. </label>
                        <caption>
                            <title>Representation of continuous procedure of extraction, partitioning and fractionation of 
                                <italic toggle="yes">Averrhoa bilimbi</italic>.</title>
                        </caption>
                        <graphic id="gr1" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/55440/ba0c4bf4-16f0-4ede-a493-1f3b8462dc65_figure1.gif"/>
                    </fig>
                </p>
                <p>After the bioactivity of these extracts was confirmed in cell-based assays, 200g of dried leaves was soaked in 15L of n-Hex to yield 12 g of extract. Fractionation was conducted with normal phase silica gel flash column chromatography. Mobile phases in the separation consisted of gradient of n-Hex, EtOAc, and methanol.</p>
            </sec>
            <sec id="sec4">
                <title>2.2 High-Performance Thin Layer Chromatography (HPTLC)</title>
                <p>HPTLC has advanced separation efficiency and detection limits compared to conventional TLC Chromatography. The pre-coated silica gel Merck, TLC silica gel plates 60 F 254, 200 &#x00d7; 100 mm were used. 2 &#x03bc;L of the sample solution was applied on the plate using a CamagLinomat V automatic sampler applicator in the form of bands (length: 5 mm, width: 1 mm, distance between two bands: 10 mm) by using a 100 &#x03bc;L Camag Microlitre Syringe (Hamilton, Bonaduz, Switzerland). A constant application rate of 150 nL/s with mobile phase of n-hexane: ethyl acetate (80:20, v/v) was adopted. The plate was then placed in the mobile phase, and ascending development was performed to a distance of 8.5 cm, the plate was then air dried and performed densitometry scanning at 250 nm. Images of the TLC plates were captured using the Camag&#x2019;s TLC Visualizer documentation system. The system provided illumination with white light (remission, transmission or a combination of both), where the exposure of RT white was optimised at 1.482 s, R 254 at 0.185 s, and R 366 at 0.877 s.</p>
            </sec>
            <sec id="sec5">
                <title>2.3 Cell culture and adipocyte differentiation</title>
                <p>C2C12 myoblast was purchased from the American Type Culture Collection (Manassas, VA, USA) and was maintained in Dulbecco&#x2019;s modified Eagle&#x2019;s medium (DMEM) supplemented with 10% fetal bovine serum (FBS). Rosiglitazone, a PPARgamma agonist with known browning activities on both classic white adipocytes and inducible brown adipocytes, was used as the positive control.
                    <sup>
                        <xref ref-type="bibr" rid="ref10">10</xref>,
                        <xref ref-type="bibr" rid="ref11">11</xref>
                    </sup> Adipocyte differentiation in the myoblast was initiated after the cells reached confluent by adding 0.5mM isobutyl methylxanthine (IBMX), 1&#x03bc;M dexamethasone (DEX), 1&#x03bc;g/ml insulin, and 1&#x03bc;M rosiglitazone (ROSI). Fresh differentiation medium containing DMEM, 10% FBS, 1&#x03bc;g/ml insulin and 1&#x03bc;M ROSI was replaced two days later. These cells were then stabilised in DMEM containing 10% FBS for another two days until the formation of oil droplets was observed.</p>
            </sec>
            <sec id="sec6">
                <title>2.4 Adipocyte determination by fluorescent dye staining</title>
                <p>Cells were washed once with PBS and fixed with 4% paraformaldehyde for ten minutes. Lipid droplets produced by adipocytes were stained by Nile Red solution and were visualised using the IN Cell 2200 Analyzer High Content Screening System (GE Healthcare, PA, USA) at an excitation wavelength of 460 nm.</p>
            </sec>
            <sec id="sec7">
                <title>2.5 GC/MS analyses and identification of components</title>
                <p>The GC/MS analyses were carried out using the Agilent 5977A Series GC/MSD system with an HP-5ms ultra inert column (30 m &#x00d7; 0.25 mm id, 0.25 &#x03bc;m film thicknesses). Spectroscopic detection by CG/MS involved an electron ionisation system which utilised high energy electrons (70 eV). Pure helium gas (99.995%) was used as the carrier gas with a flow rate of 1 mL/min. The initial GC oven temperature was set as 110&#x00b0;C for two minutes and programmed with increasing rate of 10&#x00b0;C/min to 200&#x00b0;C in five minutes. Finally, the temperature was increased to 250&#x00b0;C at 10&#x00b0;C/min in five minutes and held at that temperature for 13 minutes. The split ratio was 1:25 with a scan range of 40 to 550 amu. Hexane fraction (1 &#x03bc;L, 1 mg/mL) and extract (3 &#x03bc;L, 10 mg/mL) diluted in n-Hex were injected into the GC/MS via an auto-injector. Phytochemical constituents were identified based on the comparison of their mass spectra of the MassHunter Library WION 14.L.</p>
            </sec>
        </sec>
        <sec id="sec8" sec-type="results">
            <title>3. Results</title>
            <sec id="sec9">
                <title>3.1 Biological Assay-Guided Fractionation of Bilimbi Extract</title>
                <p>Four partitioned extracts were in the yield: n-Hex, EtOAc, n-BuOH and water extracts. The yield of each component is indicated in 
                    <xref ref-type="fig" rid="f1">Figure 1</xref>.</p>
            </sec>
            <sec id="sec10">
                <title>3.2 Flash Column Chromatography and High-Performance Thin Layer Chromatography (HPTLC)</title>
                <p>Flash column chromatography was conducted on the plant extract with an equilibration solvent ratio of n-Hex and EtOAc (95:5). A Hi Flash Column with a size of 3L (46 &#x00d7; 130mm silica gel) was used. With a flow rate of 60 ml/min, a total of 80 fractions were collected and spotted on a plate, employing the HPTLC. Plate visualisation was done under short (254 nm) and long wavelength (366 nm) ultraviolet lights, as well as white light illumination. Fractions with similar separation profiles were pooled. A total of ten fractions were collected with respective TLC profiles as shown in 
                    <xref ref-type="fig" rid="f2">Figure 2</xref>.
                    <fig fig-type="figure" id="f2" orientation="portrait" position="float">
                        <label>Figure 2. </label>
                        <caption>
                            <title>Thin layer chromatography plates showing separation of compounds of f1 &#x2013; f10 using mobile phase n-Hex:EtOAc (80:20, v/v).</title>
                            <p>Two batches (1 &amp; 2) of fractionation samples were collected and run on the same plates (A) TLC plate image viewed under UV 254 nm. (B) TLC plate image viewed under UV 366 nm. (C) TLC plate image viewed under white light illumination.</p>
                        </caption>
                        <graphic id="gr2" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/55440/ba0c4bf4-16f0-4ede-a493-1f3b8462dc65_figure2.gif"/>
                    </fig>
                </p>
            </sec>
            <sec id="sec11">
                <title>3.3 Differentiation of adipocytes in Myf5 lineage precursor cells</title>
                <p>The partitioned extracts and fractions corresponded to the growth stimulation of brown adipocyte were investigated via cell-based study. The myf5-positive characteristics of C2C12 murine myoblast allow the co-development of mature myotubes and brown adipocytes upon appropriate stimulations. ROSI was served as the positive control. Adipogenesis was observed on day four after treatment with 1&#x03bc;M ROSI. A similar phenomenon was found in 200 &#x03bc;g/ml EtOH and 100 &#x03bc;g/ml n-Hex treated cells. Myotube development was observed in all treated cells. All positively treated cells showed intracellular lipid droplet production after 7 days of incubation. These lipid droplets were captured by Nile Red staining and imaged at 20&#x00d7; magnification (
                    <xref ref-type="fig" rid="f3">Figure 3</xref>). Among the four partitioned extracts tested, n-Hex exhibited the highest amount of lipid droplet production.
                    <fig fig-type="figure" id="f3" orientation="portrait" position="float">
                        <label>Figure 3. </label>
                        <caption>
                            <title>Differentiation of Myf5 lineage precursor cells upon stimulants.</title>
                            <p>Lipid droplets formation upon adipogenesis upon treatment by (A) 200 &#x03bc;g/ml EtOH, (B) 1 &#x03bc;M ROSI, (C) 100 &#x03bc;g/ml n-Hex, (D) 100 &#x03bc;g/ml EtOAc, (E) 100 &#x03bc;g/ml n-BuOH and (F) H
                                <sub>2</sub>O were stained by a Nile Red solution. Treatment of myoblasts with adipogenesis stimulants facilitated myocytes and adipocytes co-differentiation.</p>
                        </caption>
                        <graphic id="gr3" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/55440/ba0c4bf4-16f0-4ede-a493-1f3b8462dc65_figure3.gif"/>
                    </fig>
                </p>
                <p>A bioassay guided fractionation technique was used to identify the active components present, as well as to evaluate the corresponding phytocompounds in brown adipocyte activation.</p>
                <p>The cell-based study was then repeated on ten n-Hex fractions yielded from the HPTLC fractionation. Cell morphology and lipid droplet accumulation was observed for seven days with treatments by ten fractions. Myotube development was observed in all treated cells. Adipocyte differentiation was also observed in f5, f6, f8, f9 and f10 treated cells, with lipid droplet formation as depicted in 
                    <xref ref-type="fig" rid="f4">Figure 4</xref>. Massive death was observed in f7 treated cells on day 3, and cell death was also found in f6 treatment from day 5 onwards. DMSO was used to replace bilimbi and ROSI in non-treated cells. These negative control cells were supplemented with adipogenesis culturing media and were fully developed into myotubes.
                    <fig fig-type="figure" id="f4" orientation="portrait" position="float">
                        <label>Figure 4. </label>
                        <caption>
                            <title>Differentiation of Myf5 lineage precursor cells upon stimulants.</title>
                            <p>Lipid droplet formation upon treatment by n-Hex fractions (f1 &#x2013; f10) and 1 &#x03bc;M ROSI were stained by a Nile Red solution. n-Hex fractions with adipogenesis stimulating components facilitated myocyte and adipocyte co-differentiation. Cells treated with 0.5% DMSO served as negative control (-ve) and demonstrated a complete process of myogenesis.</p>
                        </caption>
                        <graphic id="gr4" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/55440/ba0c4bf4-16f0-4ede-a493-1f3b8462dc65_figure4.gif"/>
                    </fig>
                </p>
            </sec>
            <sec id="sec12">
                <title>3.4 Gas-Chromatography/Mass-Spectrometry (GC/MS)</title>
                <p>A GC/MS based qualitative analysis was performed on n-Hex and subsequently three active fractions, namely f8, f9, and f10, to determine the types of compounds present (
                    <xref ref-type="fig" rid="f5">Figure 5</xref>-
                    <xref ref-type="fig" rid="f8">8</xref>). Major constituents identified are presented in 
                    <xref ref-type="table" rid="T1">Tables 1</xref> &amp; 
                    <xref ref-type="table" rid="T2">2</xref>. Studies on f6 &amp; f7 were discontinued after the demonstration of cytotoxic effect in these treated cells, as shown in 
                    <xref ref-type="fig" rid="f4">Figure 4</xref>.
                    <fig fig-type="figure" id="f5" orientation="portrait" position="float">
                        <label>Figure 5. </label>
                        <caption>
                            <title>Mass spectrum of 
                                <italic toggle="yes">A. bilimbi</italic> n-Hex.</title>
                        </caption>
                        <graphic id="gr5" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/55440/ba0c4bf4-16f0-4ede-a493-1f3b8462dc65_figure5.gif"/>
                    </fig>
                    <fig fig-type="figure" id="f6" orientation="portrait" position="float">
                        <label>Figure 6. </label>
                        <caption>
                            <title>Mass spectrum of 
                                <italic toggle="yes">A. bilimbi</italic> n-Hex fraction f8.</title>
                        </caption>
                        <graphic id="gr6" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/55440/ba0c4bf4-16f0-4ede-a493-1f3b8462dc65_figure6.gif"/>
                    </fig>
                    <fig fig-type="figure" id="f7" orientation="portrait" position="float">
                        <label>Figure 7. </label>
                        <caption>
                            <title>Mass spectrum of 
                                <italic toggle="yes">A. bilimbi</italic> n-Hex fraction f9.</title>
                        </caption>
                        <graphic id="gr7" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/55440/ba0c4bf4-16f0-4ede-a493-1f3b8462dc65_figure7.gif"/>
                    </fig>
                    <fig fig-type="figure" id="f8" orientation="portrait" position="float">
                        <label>Figure 8. </label>
                        <caption>
                            <title>Mass spectrum of 
                                <italic toggle="yes">A. bilimbi</italic> n-Hex fraction f10.</title>
                        </caption>
                        <graphic id="gr8" orientation="portrait" position="float" xlink:href="https://f1000research-files.f1000.com/manuscripts/55440/ba0c4bf4-16f0-4ede-a493-1f3b8462dc65_figure8.gif"/>
                    </fig>
                    <table-wrap id="T1" orientation="portrait" position="float">
                        <label>Table 1. </label>
                        <caption>
                            <title>Major chemical components identified in 
                                <italic toggle="yes">A. bilimbi</italic> n-Hex partition using GC/MS.</title>
                        </caption>
                        <table content-type="article-table" frame="hsides">
                            <thead>
                                <tr>
                                    <th align="left" colspan="1" rowspan="1" valign="top">RT</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">Name</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">Molecular formula</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">Area %</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">m/z</th>
                                </tr>
                            </thead>
                            <tbody>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">3.140</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Benzene, 1,4-diethyl-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C10H14</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.34</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">119</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">3.675</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">2-Methyl-3-hydroxypyran-4(4H)-one</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C6H6O3</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.11</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">126.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">5.156</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">N-Methyl-N-nitro-4-t-butylaniline</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C11H16N2O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.06</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">147.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">5.342</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(E)-(S)-(+)-6-Hydroxy-8-phenyl-2-pivaloxyoct-7-enyl pivalate</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C24H36O5</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.11</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">131</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">5.569</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Neopentyl 2,2-dimethylpropanoate</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C10H20O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.11</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">71.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">5.723</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">5-Isopropyl-2-methylpyrrole-3-carbonitrile</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C9H12N2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.13</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">133</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">5.942</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Naphthalene, 2-methyl-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C11H10</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.67</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">142.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">6.169</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Naphthalene, 1-methyl-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C11H10</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.43</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">142.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">6.990</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(3-Phenyl-2-propynylidene) cyclopropane</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C12H10</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.06</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">154.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">7.331</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Naphthalene, 1,4-dimethyl-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C12H12</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.55</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">156.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">7.524</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Naphthalene, 2,3-dimethyl-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C12H12</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.50</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">156.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">7.655</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Benzeneethanimidic acid, .alpha.-phenyl-, ethyl ester</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C16H17NO</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.06</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">167</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">7.980</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">2-Naphthalenol, 1,2-dihydro-1,1-dimethyl-,acetate</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C14H16O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.16</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">141.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8.419</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Benzeneethanimidic acid, .alpha.-phenyl-, ethyl ester</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C16H17NO</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.10</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">168.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8.683</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(1'.alpha.,4'.alpha.,4a'.alpha.,5'.beta.,8'.beta.,8a'.alpha.)-1',4',4a',5',8',8a'-hexahydro-1',4'-dimethylspiro (cyclohexane-1,9'-[1,4:5,8]dimethanophthalazine)</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C17H24N2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.07</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">162.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8.936</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">2(4H)-Benzofuranone, 5,6,7,7a-tetrahydro-4,4,7a-trimethyl-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C11H16O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.41</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">111.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9.028</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Octanoic acid</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C8H16O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.08</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">73</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9.532</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Pentane, 3-bromo-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C5H11Br</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.02</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">71.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">10.073</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">2-Propanone, 1-(5-ethyl-2-furanyl)-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C9H12O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.09</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">109.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">10.644</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">2-Bromo-1-nonen-3-ol</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C9H17BrO</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.08</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">123.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">10.821</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Phenol, 2-(1-phenylethyl)-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C14H14O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.08</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">71.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">10.956</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">1-Heptyl-1H-(1,2,3)-triazole-N-{[2'-(hydroxyethoxy)ethylamino]ethyl}-4-carboxamide</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C14H27N5O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.05</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">74</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">11.825</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Acetonyl decyl ether C13H26O2 8</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C13H26O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.02</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">57.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">12.383</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(2S,5R)-2-Isopropyl--5methylhept-6-en-1-ol</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C11H22O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.52</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">68.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">12.456</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">2-Pentadecanone, 6,10,14-trimethyl-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C18H36O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.60</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">43</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">12.728</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">7,9-Dodecadien-1-ol, acetate, (E,Z)-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C14H24O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.17</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">81.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">13.231</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(E,E)-6,10,14-trimethyl-5,9,13-pentadecatrien-2-one</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C18H30O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.20</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">69.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">13.646</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(E,E)-6,10,14-trimethyl-5,9,13-pentadecatrien-2-one</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C18H30O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.08</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">69.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">13.691</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Hexadecanoic acid, methyl ester</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C17H34O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.28</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">74</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">14.336</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Hexadecanoic acid</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C16H32O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">7.87</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">73</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">17.794</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Phytol</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C20H40O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">14.82</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">71.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">18.399</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9-Octadecenoic acid (Z)-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C18H34O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">19.69</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">55.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">20.132</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(2S,5R)-2-Isopropyl--5methylhept-6-en-1-ol</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C11H22O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.59</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">68.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">22.192</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">geranyl linalyl ester</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C20H34O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.13</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">69.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">23.396</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">geranyl linalyl ester</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C20H34O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.24</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">69.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">26.281</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Di-(2-ethylhexyl) phthalate</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C24H38O4</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">2.75</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">149.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">30.355</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(E) and (Z)-7,8-Dimethoxycarbonyl-6,9-di(4-methoxyphenyl)-6,8-tetradecadiene</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C32H42O6</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">0.03</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">69.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">32.864</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Squalene</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C30H50</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">100.00</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">69.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">35.158</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Dodecane, 2,6,10-trimethyl-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C15H32</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">3.92</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">57.1</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">36.713</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">delta.-Tocopherol</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C27H46O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">1.52</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">402.3</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">37.210</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">3-Cyclohexen-1-one, 4-(1,5,9-trimethyl-4,8-decadienyl)-, (Z)-(.+-.)-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C19H30O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">1.10</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">69.1</td>
                                </tr>
                            </tbody>
                        </table>
                    </table-wrap>
                    <table-wrap id="T2" orientation="portrait" position="float">
                        <label>Table 2. </label>
                        <caption>
                            <title>Major chemical components identified in 
                                <italic toggle="yes">A. bilimbi</italic> fractions using GC/MS.</title>
                        </caption>
                        <table content-type="article-table" frame="hsides">
                            <thead>
                                <tr>
                                    <th align="left" colspan="1" rowspan="1" valign="top">RT</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">Name</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">Molecular formula</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">Area %</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">Mass (DB)</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">m/z</th>
                                    <th align="left" colspan="1" rowspan="1" valign="top">Fraction</th>
                                </tr>
                            </thead>
                            <tbody>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">3.527</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Nonanal</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C9H18O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">14.1, 19.8</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">142</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">57</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9,10</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">3.935</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Tri-o-trimethylsilyl, N-trifluoroacetyl derivative of Terbutaline</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C23H42F3NO4Si3</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">38.1</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">537</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">73</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">5.337</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">((6a.alpha.,6b.alpha.,10a.beta.,10b.alpha.)-6a,6b,7,8,9,10,10a,10b-octahydro-10b-hydroxy-5-methylbenzo[3,4]cyclobuta[1,2-c]quinolin-6(5H)-one</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C16H19NO2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">11.8</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">257</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">175.2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">10</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">5.581</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">3-nonen-2-one</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C9H16O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">95.7</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">140</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">43</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">5.815</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">1-[3-(Hydroxycyclohexenyl)]ethanone</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C8H12O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">36.9</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">140</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">43</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">6.694</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(1R*,2R*)-N,N-Diethyl-2-hydroxy-2-methyl-3-oxacyclopentanecarbamide</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C11H19NO3</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">17.5</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">213</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">157.1</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">6.727</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">1-(N-Phenylcarbamoyl)-1,3-diphenylurea</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C20H17N3O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">18.1</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">331</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">93</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8.326</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Phenol, 2-(1-phenylethyl)-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C14H14O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8.9</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">198</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">57</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8.526</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(1.alpha.,4.alpha.,4a.alpha.,10a.alpha.)-1,4,4a,5,6,7,8,9,10,10a-decahydro-1,4,11,11-tetramethyl-1,4-methanocycloocta [d]pyridazine</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C15H26N2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">25.4, 8.7, 24.0</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">234</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">191</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8,9,10</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">10.657</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">11-Acetoxy-8(12)-drimen-7.alpha.-ol</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C17H28O3</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">6.7</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">280</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">123</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">10.833</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Phenol, 2-(1-phenylethyl)-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C14H14O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">4.8</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">198</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">85</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">11.628</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-6-hydroxy-4,4,7a-trimethyl-, (6S-trans)-</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C11H16O3</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">64.6</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">196</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">111.1</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">14.319</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">Hexadecanoic acid</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C16H32O2</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">67.3, 100</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">256</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">73</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9,10</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">15.019</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">3-Acetyloxypropyl 2,3,4,6-tetra-O-methyl-.alpha.,L-(5-D)gulopyranoside</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C15H27DO8</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">14.3</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">336</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">88</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">18.367</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">(12R)-(9Z)-12-hydroxy-9-octadecenoic acid</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C18H34O3</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">100</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">298</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">55</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">9</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">19.061</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">spiro[2.4]heptane-5-carboxaldehyde</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C8H12O</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">100</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">124</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">79</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8</td>
                                </tr>
                                <tr>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">26.298</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">i-Propyl 3-(phenylamino)-2-(phenylseleno)-3-(phenyl)propanoate</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">C24H25NO2Se</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">89.6,37.5, 47.4</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">439</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">149</td>
                                    <td align="left" colspan="1" rowspan="1" valign="middle">8,9,10</td>
                                </tr>
                            </tbody>
                        </table>
                    </table-wrap>
                </p>
                <p>The database information search via the MassHunter Library WION 14.L revealed the presence of nine predominant compounds in f8, ten in f9, and five in f10. (1.alpha., 4.alpha., 4a.alpha., 10a.alpha.) -1, 4, 4a, 5, 6, 7, 8, 9, 10, 10a -decahydro- 1, 4, 11, 11-tetramethyl-1, 4-methanocycloocta [d] pyridazine and i-propyl 3- (phenylamino) -2- (phenylseleno) -3- (phenyl) propanoate were detected in all the three fractions whereas nonanal and hexadecanoic acid were commonly present in f9 and f10. The high abundance compounds include 3-nonen-2-one; 2(4H)-Benzofuranone, 5, 6, 7, 7a-tetrahydro -6- hydroxy-4, 4, 7a-trimethyl-, (6S-trans)-; hexadecanoic acid; (12R)-(9Z)-12-hydroxy-9-octadecenoic acid; spiro [2.4] heptanes-5-carboxaldehyde; and i-propyl 3 -(phenylamino) -2- (phenylseleno) -3- (phenyl) propanoate.</p>
            </sec>
        </sec>
        <sec id="sec13" sec-type="discussion">
            <title>4. Discussion</title>
            <p>Brown adipocytes and myocytes, precursor cells that give rise to skeletal muscle, are developed from a common adipomyocyte precursor. White adipocytes on the other hand are derived from pericytes that embedded within the vascular vessel walls. In this study, a straightforward cell-based assay was adopted based on the unique characteristics of precursor cells, where the adipomyocytes containing a mixture of brown adipocytes and myocytes, or solely the myocyte differentiation program, was activated via its initiation factors. Gene program commitments in myoblast-brown adipocyte transition were observed in bilimbi treated cells and the up-regulation of PGC-1&#x03b1;, UCP1 and PRDM16 proteins which influenced the cellular metabolism and mitochondrial function.
                <sup>
                    <xref ref-type="bibr" rid="ref6">6</xref>
                </sup>
            </p>
            <p>Our observations in this study showed that bilimbi n-Hex partition was able to induce a higher cellular response in signalling the co-development of brown adipocytes and myocytes than its crude extract. The induction of brown adipocyte differentiation indicates the presence of active phytocomponents that are responsible for the program. Major phytocomponents in the n-Hex partition include hexadecenoic acid, phytol, 9-Octadecenoic acid (Z)- and squalene, which agree with an earlier report.
                <sup>
                    <xref ref-type="bibr" rid="ref12">12</xref>
                </sup> Bioassay-guided fractionations yielded some promising bilimbi fractions from the n-Hex partition which further enhanced the brown adipogenesis program. Major chemical compounds of these fractions were then isolated and are summarised in Table 2. Some of these compounds are highly related to the obesity research in the past few years. For instance, Terbutaline is a &#x03b2;
                <sub>2</sub>-adrenergic receptor agonist that has been recognised to increase lipolysis and thermogenesis.
                <sup>
                    <xref ref-type="bibr" rid="ref13">13</xref>
                </sup> 2(4H)-Benzofuranone,5,6,7,7a-tetrahydro-6-hydroxy-4,4,7a-trimethyl-,(6S-trans)- is a compound that has been found to ameliorate hyperglycaemia, dyslipidaemia, and obesity in high-fat diet-fed mice.
                <sup>
                    <xref ref-type="bibr" rid="ref14">14</xref>
                </sup> (12R)-(9Z)-12-hydroxy-9-octadecenoic acid, also known as ricinoleic acid, is an unsaturated omega-9 fatty acid and hydroxyl acid. Ricinoleic acid is linked to the activation of EP3 prostanoid receptor for prostaglandin E2 regulations in adipogenesis and lipolysis of WAT.
                <sup>
                    <xref ref-type="bibr" rid="ref15">15</xref>,
                    <xref ref-type="bibr" rid="ref16">16</xref>
                </sup> i-Propyl3-(phenylamino)-2-(phenylseleno)-3-(phenyl) propanoate, detected in all the three bioactive fractions, is a short-chain fatty acid which regulates gut hormone release, suppresses food intake, and protects against diet-induced obesity.
                <sup>
                    <xref ref-type="bibr" rid="ref17">17</xref>
                </sup> Hexadecanoic acid, also known as palmitic acid, is a controversial component in obesity research, where over-accumulation may result in dyslipidaemia and other obesity associated diseases.
                <sup>
                    <xref ref-type="bibr" rid="ref18">18</xref>
                </sup>
            </p>
            <p>Evolutionary processes in natural products enhance their structural diversity and shape the pathways of secondary metabolite production. Many of these metabolites play important roles as the starting points for drug discovery in both traditional and modern medicines. Besides, plants are used in the form of spices and herbs based on their natural antioxidant values. Several plant-derived nutraceuticals are found to regulate BAT activity and induced WAT metabolis, including capsenoid, curcumin, quercetin, and resveratrol, without major adverse events.
                <sup>
                    <xref ref-type="bibr" rid="ref19">19</xref>
                </sup> Food ingredients that could increase BAT activity include chili peppers, turmeric, thyme, cinnamon, garlic, onion, green tea, mulberry, and cocoa. Their white-to-brown adipose tissue conversion and calorie burning properties are identified through 
                <italic toggle="yes">in vitro</italic> and 
                <italic toggle="yes">in vivo</italic> models.
                <sup>
                    <xref ref-type="bibr" rid="ref20">20</xref>,
                    <xref ref-type="bibr" rid="ref21">21</xref>
                </sup> Nevertheless, the involvement of bilimbi fractions and their bioactive compounds in BAT program enhancement are reported for the first time in this study.</p>
            <p>It is estimated that 40-50g of active BAT can increase daily energy expenditure in humans by 20%.
                <sup>
                    <xref ref-type="bibr" rid="ref22">22</xref>
                </sup> This could be implemented either by enhancing the activity of BAT or by increasing the number of WAT browning. Dietary components have been shown to increase thermogenic capacity by augmenting BAT activity via several mechanisms of action. Capsaicin that is commonly found in red peppers has been shown to activate ADRB3, a member of the G protein-coupled receptor family, that induces adenyl cyclase after activation, triggering a cascade of responses in the brown adipogenic program. Resveratrol extracted from grapes and berries on the other hand activates SIRT1 to recruit the BAT program via a series of PRDM16 pathway modulators including PPAR&#x03b3;, C/EBP&#x03b2;, PGC-1&#x03b1;, and UCP1.
                <sup>
                    <xref ref-type="bibr" rid="ref23">23</xref>
                </sup> Bilimbi, in our studies, acted similarly by up-regulating the expression of relevant proteins which influenced the cellular metabolism and mitochondrial function in adipocyte browning. Therefore, the molecular mechanism of action behind each active compound of bilimbi should be further studied to constitute a therapeutic option for obesity treatment in the future.</p>
        </sec>
        <sec id="sec14" sec-type="conclusions">
            <title>5. Conclusions</title>
            <p>In summary, n-Hex extract of bilimbi activated the adipocyte program of adipomyocytes. Several bilimbi fractions showed strong activity in brown adipocyte differentiation from adipomyocytes. Fatty acids that were found in the n-Hex extract and fractions are potential BAT thermogenic agents. Other major phytocompounds including phytol, squalene, and terbutaline, are believed to enhance lipolysis and themogenesis of adipocyte tissues.</p>
        </sec>
        <sec id="sec15">
            <title>Data availability</title>
            <p>All data underlying the results are available as part of the article and no additional source data are required.</p>
        </sec>
        <sec id="sec16">
            <title>Author contributions</title>
            <p>F.H., A. A. and W.K.L. conceived and designed the experiments, collected and analysed the data; the manuscript was written by W.K.L. and reviewed by F.H. &amp; A.A.</p>
        </sec>
    </body>
    <back>
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    <sub-article article-type="reviewer-report" id="report87939">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.55440.r87939</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Jani</surname>
                        <given-names>Nor Akmalazura</given-names>
                    </name>
                    <xref ref-type="aff" rid="r87939a1">1</xref>
                    <role>Referee</role>
                </contrib>
                <aff id="r87939a1">
                    <label>1</label>Faculty of Applied Sciences, Universiti Teknologi MARA (UiTM) Cawangan Negeri Sembilan, Kampus Kuala Pilah, Kuala Pilah, Malaysia</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>8</day>
                <month>7</month>
                <year>2021</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2021 Jani NA</copyright-statement>
                <copyright-year>2021</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport87939" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.52197.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>
                <bold>Title</bold>
            </p>
            <p> The word &#x201c;isolation&#x201d; is incorrect since the authors does not obtain any pure compounds from 
                <italic>Averrhoa bilimbi</italic>. Therefore, the authors should revise the title of manuscript.</p>
            <p> </p>
            <p> 
                <bold>Abstract</bold>
            </p>
            <p> 
                <italic>"This paper reports the successive isolation and purification of bioactive compounds from the leaf of bilimbi that corresponds to brown adipocyte activation. Bilimbi ethanolic extract underwent bioassay-guided partitioning and fractionation. The n-hexane partition exhibited highest brown adipogenesis potential via adipomyocytes differentiation. Further isolation of this active partition yielded 10 fractions. Active fractions with the highest brown adipogenesis potential were further evaluated via the adipomyocytes assay." </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>The authors performed the bioassay-guided fraction approach to afford the active fraction corresponds to brown adipocyte activation. Thus, the terms &#x201c;isolation and purification&#x201d; are incorrect. I think the word &#x201c;fractionation&#x201d; is more suitable.</p>
                    </list-item>
                </list> 
                <italic>"Chemical structures of the constituents were elucidated by gas chromatography-mass spectrometry (GC-MS)." </italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>GC-MS is used to identify the presence of compounds within a test sample. Basically, the chemical structures of organic compounds are elucidated by spectroscopic techniques including NMR, IR, MS and UV. I think the authors should revise the above-mentioned sentence.</p>
                    </list-item>
                </list> The authors can include the active fractions as well as compounds detected by GC/MS from the active fractions. I think the compounds identified from the 
                <italic>n</italic>-hexane partition are not that necessary to be included in the abstract.</p>
            <p> </p>
            <p> 
                <bold>Introduction</bold>
            </p>
            <p> Please include literature review on the phytochemicals of 
                <italic>Averrhoa bilimbi</italic>.</p>
            <p> </p>
            <p> 
                <bold>Methods</bold>
            </p>
            <p> 
                <italic>2.1 Plant extractions and fractions with Flash Column Chromatography</italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>Figure 1: Please rearrange yield of F1 until F10.</p>
                    </list-item>
                </list> 
                <bold>Results</bold>
            </p>
            <p> Please change title for section 3.1 to "Biological Assay-Guided Fractionation of 
                <italic>A. bilimbi</italic> Extract".</p>
            <p> </p>
            <p> 
                <italic>3.2 Flash Column Chromatography and High-Performance Thin Layer Chromatography (HPTLC)</italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>The TLC plates were visualized under UV (254 and 366 nm) and white light illumination. However, based on Tables 1 and 2, most of the detected compounds are weakly- or not chromophore in which their spot maybe could not detected by the UV light and/or white light illumination. I think the authors can use a TLC visualization reagent such as vanillin sulfuric acid reagent to detect weakly- and non-chromophore compounds to see the whole TLC profile of the extracts and fractions. Reference: 
                            <ext-link ext-link-type="uri" xlink:href="https://link.springer.com/content/pdf/bbm%3A978-3-662-02398-3%2F1.pdf">https://link.springer.com/content/pdf/bbm%3A978-3-662-02398-3%2F1.pdf</ext-link>.</p>
                    </list-item>
                </list> 
                <italic>3.4 Gas-Chromatography/Mass-Spectrometry (GC/MS)</italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>Please revise the title of Figures 5-8. The title should be GC chromatogram of (tested sample) not mass spectrum of (tested sample). A mass spectrum is an intensity versus m/z (mass-to-charge ratio) plot representing a chemical analysis, while GC chromatogram is a plot of an intensity/abundance versus retention time.</p>
                    </list-item>
                    <list-item>
                        <p>Please revise the percent area of each compound in the 
                            <italic>n</italic>-hexane partition. The total percent should not exceed 100%.</p>
                    </list-item>
                    <list-item>
                        <p>Please use subscript to write number of atoms in the molecular formula. For example, H
                            <sub>2</sub>O.</p>
                    </list-item>
                    <list-item>
                        <p>Why does the m/z of the detected compound&#x00a0;not match with the exact molecular weight of the compound?</p>
                    </list-item>
                    <list-item>
                        <p>Why were the major compounds in the 
                            <italic>n</italic>-hexane partition (i.e., hexadecanoic acid, phytol, 9-Octadecenoic acid (Z)- and squalene) not detected in any active fractions?</p>
                    </list-item>
                    <list-item>
                        <p>Explain why the majority of compounds in the 
                            <italic>n</italic>-hexane partition and active fractions were not similar.</p>
                    </list-item>
                </list> 
                <bold>Discussion</bold>
            </p>
            <p> 
                <italic>Second paragraph: Major chemical compounds of these fractions were then isolated and are summarised in Table 2.</italic> 
                <list list-type="bullet">
                    <list-item>
                        <p>Please revise the term &#x201c;isolated&#x201d;. I think the sentence should be &#x201c;The major chemical compounds in the active fractions were listed in Table 2&#x201d;.</p>
                    </list-item>
                </list> 
                <bold>Conclusions</bold>
            </p>
            <p> I think the authors should revise the statement related to the detected compounds in the 
                <italic>n</italic>-hexane partition and active fractions. I recommended to add further study such as isolation of compound from the active fractions.</p>
            <p> </p>
            <p> 
                <bold>Technical</bold>
            </p>
            <p> 
                <italic>n</italic>-hexane and 
                <italic>n</italic>-butanol: the alphabet &#x201c;n&#x201d; should be written in italic (
                <italic>n</italic>-hexane, 
                <italic>n</italic>-butanol, 
                <italic>n</italic>-Hex, 
                <italic>n</italic>-BuOH).</p>
            <p> </p>
            <p> Please give a space between number and unit. For example, 20 g instead of 20g.</p>
            <p> </p>
            <p> Please add a comma before the word "whereas". For example: "_______, whereas".</p>
            <p> </p>
            <p> The term &#x201c;GC-MS&#x201d; should be written consistently either as &#x201c;GC-MS&#x201d; or &#x201c;GC/MS&#x201d;.</p>
            <p> </p>
            <p> The abbreviation of litre should be written as &#x201c;L&#x201d; in capital letter.</p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Partly</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Partly</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>No source data required</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Yes</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>Natural product chemistry</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p>
        </body>
        <sub-article article-type="response" id="comment7396-87939">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Lau</surname>
                            <given-names>Wai Kwan</given-names>
                        </name>
                        <aff>National Institutes of Biotechnology Malaysia, Malaysia</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>5</day>
                    <month>11</month>
                    <year>2021</year>
                </pub-date>
            </front-stub>
            <body>
                <p>
                    <bold>Reviewer 2</bold>
                </p>
                <p> </p>
                <p> 
                    <bold>Title</bold>
                </p>
                <p> </p>
                <p> The word &#x201c;isolation&#x201d; is incorrect since the authors does not obtain any pure compounds from&#x00a0;
                    <italic>Averrhoa bilimbi</italic>. Therefore, the authors should revise the title of manuscript.</p>
                <p> </p>
                <p> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> The Title section has been rephrased to &#x00a0;&#x201c;fractionation&#x201d;.</p>
                <p> </p>
                <p> 
                    <bold>Abstract</bold>
                </p>
                <p> </p>
                <p> 
                    <italic>"This paper reports the successive isolation and purification of bioactive compounds from the leaf of bilimbi that corresponds to brown adipocyte activation. Bilimbi ethanolic extract underwent bioassay-guided partitioning and fractionation. The n-hexane partition exhibited highest brown adipogenesis potential via adipomyocytes differentiation. Further isolation of this active partition yielded 10 fractions. Active fractions with the highest brown adipogenesis potential were further evaluated via the adipomyocytes assay."&#x00a0;</italic> 
                    <list list-type="bullet">
                        <list-item>
                            <p>The authors performed the bioassay-guided fraction approach to afford the active fraction corresponds to brown adipocyte activation. Thus, the terms &#x201c;isolation and purification&#x201d; are incorrect. I think the word &#x201c;fractionation&#x201d; is more suitable.</p>
                        </list-item>
                    </list> 
                    <italic>"Chemical structures of the constituents were elucidated by gas chromatography-mass spectrometry (GC-MS)."&#x00a0;</italic> 
                    <list list-type="bullet">
                        <list-item>
                            <p>GC-MS is used to identify the presence of compounds within a test sample. Basically, the chemical structures of organic compounds are elucidated by spectroscopic techniques including NMR, IR, MS and UV. I think the authors should revise the above-mentioned sentence.</p>
                        </list-item>
                    </list> The authors can include the active fractions as well as compounds detected by GC/MS from the active fractions. I think the compounds identified from the&#x00a0;
                    <italic>n</italic>-hexane partition are not that necessary to be included in the abstract.</p>
                <p> </p>
                <p> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> The Abstract section has been reworded to extraction and fractionation accordingly. The last sentence which describes compounds identified from the&#x00a0;
                    <italic>n</italic>-hexane partition have been removed.</p>
                <p> </p>
                <p> 
                    <bold>Introduction</bold>
                </p>
                <p> </p>
                <p> Please include literature review on the phytochemicals of&#x00a0;
                    <italic>Averrhoa bilimbi</italic>.</p>
                <p> </p>
                <p> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> This was included in the second paragraph of Discussion section where two references were cited (Gunawan 
                    <italic>et al</italic>, 2013; Saini, 2016). &#x201c;Major phytocomponents in the n-Hex partition include hexadecenoic acid, phytol, 9-Octadecenoic acid (Z)- and squalene, which agree with earlier reports.&#x201d;</p>
                <p> </p>
                <p> 
                    <bold>Methods</bold>
                </p>
                <p> </p>
                <p> 
                    <italic>2.1 Plant extractions and fractions with Flash Column Chromatography</italic> 
                    <list list-type="bullet">
                        <list-item>
                            <p>Figure 1: Please rearrange yield of F1 until F10.</p>
                        </list-item>
                    </list> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> Figure 1 has been rearranged to align the label and yield of f1 - f10.</p>
                <p> </p>
                <p> 
                    <bold>Results</bold>
                </p>
                <p> </p>
                <p> Please change title for section 3.1 to "Biological Assay-Guided Fractionation of&#x00a0;
                    <italic>A. bilimbi</italic>&#x00a0;Extract".</p>
                <p> </p>
                <p> 
                    <italic>3.2 Flash Column Chromatography and High-Performance Thin Layer Chromatography (HPTLC)</italic> 
                    <list list-type="bullet">
                        <list-item>
                            <p>The TLC plates were visualized under UV (254 and 366 nm) and white light illumination. However, based on Tables 1 and 2, most of the detected compounds are weakly- or not chromophore in which their spot maybe could not detected by the UV light and/or white light illumination. I think the authors can use a TLC visualization reagent such as vanillin sulfuric acid reagent to detect weakly- and non-chromophore compounds to see the whole TLC profile of the extracts and fractions. Reference:&#x00a0;
                                <ext-link ext-link-type="uri" xlink:href="https://link.springer.com/content/pdf/bbm:978-3-662-02398-3/1.pdf">https://link.springer.com/content/pdf/bbm%3A978-3-662-02398-3%2F1.pdf</ext-link>.</p>
                        </list-item>
                    </list> 
                    <italic>3.4 Gas-Chromatography/Mass-Spectrometry (GC/MS)</italic> 
                    <list list-type="bullet">
                        <list-item>
                            <p>Please revise the title of Figures 5-8. The title should be GC chromatogram of (tested sample) not mass spectrum of (tested sample). A mass spectrum is an intensity versus m/z (mass-to-charge ratio) plot representing a chemical analysis, while GC chromatogram is a plot of an intensity/abundance versus retention time.</p>
                        </list-item>
                        <list-item>
                            <p>Please revise the percent area of each compound in the&#x00a0;
                                <italic>n</italic>-hexane partition. The total percent should not exceed 100%.</p>
                        </list-item>
                        <list-item>
                            <p>Please use subscript to write number of atoms in the molecular formula. For example, H
                                <sub>2</sub>O.</p>
                        </list-item>
                        <list-item>
                            <p>Why does the m/z of the detected compound&#x00a0;not match with the exact molecular weight of the compound?</p>
                        </list-item>
                        <list-item>
                            <p>Why were the major compounds in the&#x00a0;
                                <italic>n</italic>-hexane partition (i.e., hexadecanoic acid, phytol, 9-Octadecenoic acid (Z)- and squalene) not detected in any active fractions?</p>
                        </list-item>
                        <list-item>
                            <p>Explain why the majority of compounds in the&#x00a0;
                                <italic>n</italic>-hexane partition and active fractions were not similar.</p>
                        </list-item>
                    </list> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> Title for section 3.1 has been reworded accordingly.</p>
                <p> </p>
                <p> 3.2 
                    <italic>Flash Column Chromatography and High-Performance Thin Layer Chromatography (HPTLC)</italic>
                </p>
                <p> A TLC plate with vanillin sulfuric acid reagent development has been provided as follows:</p>
                <p> </p>
                <p> Hex&#x00a0;&#x00a0;&#x00a0; F1&#x00a0;&#x00a0;&#x00a0; F2&#x00a0;&#x00a0;&#x00a0; F3&#x00a0;&#x00a0;&#x00a0;&#x00a0; F4&#x00a0;&#x00a0;&#x00a0;&#x00a0; F5&#x00a0;&#x00a0;&#x00a0;&#x00a0; F6 &#x00a0;&#x00a0;&#x00a0;&#x00a0;F7&#x00a0;&#x00a0;&#x00a0;&#x00a0; F8&#x00a0;&#x00a0;&#x00a0; F9&#x00a0;&#x00a0;&#x00a0; F10&#x00a0;&#x00a0;&#x00a0; Phy&#x00a0;&#x00a0; Sq</p>
                <p> </p>
                <p> 
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"/>
                </p>
                <p> Hex: n-Hex extract</p>
                <p> F1 &#x2013; F10: Fractions</p>
                <p> Phy &#x2013; Standard Phytol Isomers</p>
                <p> Sq &#x2013; Standard Squalene</p>
                <p> </p>
                <p> 
                    <italic>3.4 Gas-Chromatography/Mass-Spectrometry (GC/MS)</italic> 
                    <list list-type="order">
                        <list-item>
                            <p>Figure 5 &#x2013; 8 have been reworded accordingly.</p>
                        </list-item>
                        <list-item>
                            <p>The % area of each compound has been revised and tabulated in Table 1 &amp; 2.</p>
                        </list-item>
                        <list-item>
                            <p>Revision has been made on the molecular formula according to the format as requested, as tabulated in Table 1 &amp; 2.</p>
                        </list-item>
                        <list-item>
                            <p>m/z refers to the base peak and mass DB refers to the molecular weight of compound.&#x00a0;</p>
                        </list-item>
                        <list-item>
                            <p>From the TLC plate developed under vanillin sulfuric acid reagent, these compounds did not present in F8-F10 and therefore were not being detected in GC/MS. Standard compounds of squalene and phytol isomers have been purchased to be spotted with the hexane partition extract and fractions as references.The squalene compound was only seen in F1 and F2.</p>
                        </list-item>
                        <list-item>
                            <p>Some of the compounds in hexane extract are weakly or not detected compared to fractions because during fractionation the extract was fractionated and concentrated. Besides, major compounds in hexane extract in this study could be present in other fractions.</p>
                        </list-item>
                    </list> 
                    <bold>Discussion</bold>
                </p>
                <p> </p>
                <p> 
                    <italic>Second paragraph: Major chemical compounds of these fractions were then isolated and are summarised in Table 2.</italic> 
                    <list list-type="bullet">
                        <list-item>
                            <p>Please revise the term &#x201c;isolated&#x201d;. I think the sentence should be &#x201c;The major chemical compounds in the active fractions were listed in Table 2&#x201d;.</p>
                        </list-item>
                    </list> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> The second paragraph of the Discussion section has been reworded accordingly.</p>
                <p> </p>
                <p> 
                    <bold>Conclusions</bold>
                </p>
                <p> </p>
                <p> I think the authors should revise the statement related to the detected compounds in the&#x00a0;
                    <italic>n</italic>-hexane partition and active fractions. I recommended to add further study such as isolation of compound from the active fractions.</p>
                <p> </p>
                <p> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> The Conclusion section has been rephrased.</p>
                <p> </p>
                <p> 
                    <bold>Technical</bold>
                </p>
                <p> </p>
                <p> 
                    <italic>n</italic>-hexane and&#x00a0;
                    <italic>n</italic>-butanol: the alphabet &#x201c;n&#x201d; should be written in italic (
                    <italic>n</italic>-hexane,&#x00a0;
                    <italic>n</italic>-butanol,&#x00a0;
                    <italic>n</italic>-Hex,&#x00a0;
                    <italic>n</italic>-BuOH).</p>
                <p> </p>
                <p> Please give a space between number and unit. For example, 20 g instead of 20g.</p>
                <p> </p>
                <p> Please add a comma before the word "whereas". For example: "_______, whereas".</p>
                <p> </p>
                <p> The term &#x201c;GC-MS&#x201d; should be written consistently either as &#x201c;GC-MS&#x201d; or &#x201c;GC/MS&#x201d;.</p>
                <p> </p>
                <p> The abbreviation of litre should be written as &#x201c;L&#x201d; in capital letter.</p>
                <p> </p>
                <p> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> Corrections have been made accordingly.</p>
            </body>
        </sub-article>
    </sub-article>
    <sub-article article-type="reviewer-report" id="report85583">
        <front-stub>
            <article-id pub-id-type="doi">10.5256/f1000research.55440.r85583</article-id>
            <title-group>
                <article-title>Reviewer response for version 1</article-title>
            </title-group>
            <contrib-group>
                <contrib contrib-type="author">
                    <name>
                        <surname>Andriana</surname>
                        <given-names>Yusuf</given-names>
                    </name>
                    <xref ref-type="aff" rid="r85583a1">1</xref>
                    <xref ref-type="aff" rid="r85583a2">2</xref>
                    <role>Referee</role>
                    <uri content-type="orcid">https://orcid.org/0000-0002-6052-301X</uri>
                </contrib>
                <aff id="r85583a1">
                    <label>1</label>Graduate School for International Development and Cooperation (IDEC), Hiroshima University, Higashi-Hiroshima, Japan</aff>
                <aff id="r85583a2">
                    <label>2</label>Indonesian Institute of Sciences, Jakarta, Indonesia</aff>
            </contrib-group>
            <author-notes>
                <fn fn-type="conflict">
                    <p>
                        <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                </fn>
            </author-notes>
            <pub-date pub-type="epub">
                <day>1</day>
                <month>6</month>
                <year>2021</year>
            </pub-date>
            <permissions>
                <copyright-statement>Copyright: &#x00a9; 2021 Andriana Y</copyright-statement>
                <copyright-year>2021</copyright-year>
                <license xlink:href="https://creativecommons.org/licenses/by/4.0/">
                    <license-p>This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
                </license>
            </permissions>
            <related-article ext-link-type="doi" id="relatedArticleReport85583" related-article-type="peer-reviewed-article" xlink:href="10.12688/f1000research.52197.1"/>
            <custom-meta-group>
                <custom-meta>
                    <meta-name>recommendation</meta-name>
                    <meta-value>approve-with-reservations</meta-value>
                </custom-meta>
            </custom-meta-group>
        </front-stub>
        <body>
            <p>Hamzah 
                <italic>et al.</italic> studied the isolation of active 
                <italic>Averrhoa bilimbi</italic> phytocompounds corresponding to brown adipocytes stimulation. Before indexing, several points need to be clarified:</p>
            <p> </p>
            <p> 
                <bold>Abstract:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>The authors stated &#x201c;This paper reports the successive isolation and purification of bioactive compounds from the leaf of bilimbi that corresponds to brown adipocyte activation&#x201d;.</p>
                    </list-item>
                    <list-item>
                        <p>I think the authors couldn&#x2019;t say &#x201c;isolation and purification&#x201d; because in their study, they only yielded fraction (mixture of compounds) not pure compound. I suggest to use the term &#x201c;fractionation&#x201d;.</p>
                    </list-item>
                    <list-item>
                        <p>&#x201c;&#x2026;&#x2026;.bioactive compounds from the leaf of bilimbi that corresponds to brown adipocyte activation&#x201d; -&#x00a0;in this case the authors did not know exactly which compounds were responsible/linked to brown adipocyte activation. The authors just checked the mixture compounds (in F8,F9,F10), the major compounds in F8-10 didn&#x2019;t&#x00a0;correspond&#x00a0;with brown adipocyte activation, except the authors check single compounds in F8-10.</p>
                    </list-item>
                </list> 
                <bold>Introduction:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>Please add information about bioactive compounds that have been reported from 
                            <italic>Averrhoa bilimbi.</italic>
                        </p>
                    </list-item>
                </list> 
                <bold>Methods:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>For Cell culture and adipocyte differentiation, Adipocyte determination by fluorescent dye staining, GC/MS analyses and identification of components were needed to add citations, from where did the authors obtain the method, did the authors develop the methods by themselves?</p>
                    </list-item>
                    <list-item>
                        <p>For GC-MS, the authors should explain why they use the HP5-MS column as the column was a&#x00a0;non-polar column, it is better to use more general columns such as DB5-MS, because in Figure 3, ethanol extract seems to show lipid droplet&#x00a0;formation upon similar with the standard (ROSI) and hexane extract. I mean maybe the polar compounds might be also responsible for&#x00a0;this activity.</p>
                    </list-item>
                </list> 
                <bold>Results:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>In GC-MS result, it is better to present Retention Index, so please change retention time to retention index following NAST.</p>
                    </list-item>
                    <list-item>
                        <p>In hexane extract, Phytol (14.82%), 9-Octadecanoic acid-Z- (19.69%), and Squalene (100.00%) (Table 1) -&#x00a0;How can % area be more than 100%?</p>
                    </list-item>
                    <list-item>
                        <p>It seems phytol, 9-Octadecanoic acid-Z-, Squalene were the major compounds, but why in the F8,9,10 they weren't detected?</p>
                    </list-item>
                    <list-item>
                        <p>I think you should conduct GC-MS analysis at least twice and present&#x00a0;as mean &#x00b1; standard deviation, to make your research more producible.</p>
                    </list-item>
                </list> 
                <bold>Conclusion:</bold> 
                <list list-type="bullet">
                    <list-item>
                        <p>Did you detect, squalene, and terbutaline in F8,9,10? There is no strong evidence to say that these compounds enhance lipolysis and thermogenesis of adipocyte tissues.</p>
                    </list-item>
                </list>
            </p>
            <p>Is the work clearly and accurately presented and does it cite the current literature?</p>
            <p>Partly</p>
            <p>If applicable, is the statistical analysis and its interpretation appropriate?</p>
            <p>Yes</p>
            <p>Are all the source data underlying the results available to ensure full reproducibility?</p>
            <p>Partly</p>
            <p>Is the study design appropriate and is the work technically sound?</p>
            <p>Partly</p>
            <p>Are the conclusions drawn adequately supported by the results?</p>
            <p>Partly</p>
            <p>Are sufficient details of methods and analysis provided to allow replication by others?</p>
            <p>Yes</p>
            <p>Reviewer Expertise:</p>
            <p>natural product chemistry</p>
            <p>I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.</p>
        </body>
        <back>
            <ref-list>
                <title>References</title>
                <ref id="rep-ref-85583-1">
                    <label>1</label>
                    <mixed-citation publication-type="journal">
                        <person-group person-group-type="author"/>:
                        <article-title>Antihyperuricemia, Antioxidant, and Antibacterial Activities of Tridax procumbens L.</article-title>
                        <source>
                            <italic>Foods</italic>
                        </source>.<year>2019</year>;<volume>8</volume>(<issue>1</issue>) :
                        <elocation-id>10.3390/foods8010021</elocation-id>
                        <pub-id pub-id-type="pmid">30634624</pub-id>
                        <pub-id pub-id-type="doi">10.3390/foods8010021</pub-id>
                    </mixed-citation>
                </ref>
                <ref id="rep-ref-85583-2">
                    <label>2</label>
                    <mixed-citation publication-type="journal">
                        <person-group person-group-type="author"/>:
                        <article-title>Antioxidant, Xanthine Oxidase, &#x03b1;-Amylase and &#x03b1;-Glucosidase Inhibitory Activities of Bioactive Compounds from Rumex crispus L. Root.</article-title>
                        <source>
                            <italic>Molecules</italic>
                        </source>.<year>2019</year>;<volume>24</volume>(<issue>21</issue>) :
                        <elocation-id>10.3390/molecules24213899</elocation-id>
                        <pub-id pub-id-type="pmid">31671906</pub-id>
                        <pub-id pub-id-type="doi">10.3390/molecules24213899</pub-id>
                    </mixed-citation>
                </ref>
            </ref-list>
        </back>
        <sub-article article-type="response" id="comment7395-85583">
            <front-stub>
                <contrib-group>
                    <contrib contrib-type="author">
                        <name>
                            <surname>Lau</surname>
                            <given-names>Wai Kwan</given-names>
                        </name>
                        <aff>National Institutes of Biotechnology Malaysia, Malaysia</aff>
                    </contrib>
                </contrib-group>
                <author-notes>
                    <fn fn-type="conflict">
                        <p>
                            <bold>Competing interests: </bold>No competing interests were disclosed.</p>
                    </fn>
                </author-notes>
                <pub-date pub-type="epub">
                    <day>5</day>
                    <month>11</month>
                    <year>2021</year>
                </pub-date>
            </front-stub>
            <body>
                <p>
                    <bold>Reviewer 1</bold>
                </p>
                <p> </p>
                <p> Hamzah&#x00a0;
                    <italic>et al.</italic>&#x00a0;studied the isolation of active&#x00a0;
                    <italic>Averrhoa bilimbi</italic>&#x00a0;phytocompounds corresponding to brown adipocytes stimulation. Before indexing, several points need to be clarified:</p>
                <p> </p>
                <p> 
                    <bold>Abstract:</bold> 
                    <list list-type="bullet">
                        <list-item>
                            <p>The authors stated &#x201c;This paper reports the successive isolation and purification of bioactive compounds from the leaf of bilimbi that corresponds to brown adipocyte activation&#x201d;.</p>
                        </list-item>
                        <list-item>
                            <p>I think the authors couldn&#x2019;t say &#x201c;isolation and purification&#x201d; because in their study, they only yielded fraction (mixture of compounds) not pure compound. I suggest to use the term &#x201c;fractionation&#x201d;.</p>
                        </list-item>
                    </list> &#x201c;&#x2026;&#x2026;.bioactive compounds from the leaf of bilimbi that corresponds to brown adipocyte activation&#x201d; -&#x00a0;in this case the authors did not know exactly which compounds were responsible/linked to brown adipocyte activation. The authors just checked the mixture compounds (in F8,F9,F10), the major compounds in F8-10 didn&#x2019;t&#x00a0;correspond&#x00a0;with brown adipocyte activation, except the authors check single compounds in F8-10.</p>
                <p> </p>
                <p> 
                    <bold>
                        <underline>Response to reviewer:</underline>
                    </bold>
                </p>
                <p> The Title and Abstract sections have been reworded to extraction and fractionation accordingly. The Abstract has also been rewritten to better reflect the current findings of this study.</p>
                <p> </p>
                <p> 
                    <bold>Introduction:</bold> 
                    <list list-type="bullet">
                        <list-item>
                            <p>Please add information about bioactive compounds that have been reported from&#x00a0;
                                <italic>Averrhoa bilimbi.</italic>
                            </p>
                        </list-item>
                    </list> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> This was included in the second paragraph of Discussion section where two references were cited (Gunawan 
                    <italic>et al</italic>, 2013; Saini, 2016). &#x201c;Major phytocomponents in the n-Hex partition include hexadecenoic acid, phytol, 9-Octadecenoic acid (Z)- and squalene, which agree with earlier reports.&#x201d;</p>
                <p> </p>
                <p> 
                    <bold>Methods:</bold> 
                    <list list-type="bullet">
                        <list-item>
                            <p>For Cell culture and adipocyte differentiation, Adipocyte determination by fluorescent dye staining, GC/MS analyses and identification of components were needed to add citations, from where did the authors obtain the method, did the authors develop the methods by themselves?</p>
                        </list-item>
                        <list-item>
                            <p>For GC-MS, the authors should explain why they use the HP5-MS column as the column was a&#x00a0;non-polar column, it is better to use more general columns such as DB5-MS, because in Figure 3, ethanol extract seems to show lipid droplet&#x00a0;formation upon similar with the standard (ROSI) and hexane extract. I mean maybe the polar compounds might be also responsible for&#x00a0;this activity.</p>
                        </list-item>
                    </list> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline> 
                    <list list-type="order">
                        <list-item>
                            <p>References for cell culture, adipocyte differentiation and adipocyte determination methods (Sharma 
                                <italic>et al</italic>, 2014; Melhem 
                                <italic>et al </italic>2013) have been included.</p>
                        </list-item>
                        <list-item>
                            <p>References for GC/MS method and analysis (Azeem, 2015; Suluvoy 
                                <italic>et al</italic>, 2017) have also been cited.</p>
                        </list-item>
                        <list-item>
                            <p>The GC/MS detection focused on hexane extract and fractions which in this study showed best biological activity for brown adipocyte activation. The non-polar column was used because hexane is a non-polar solvent which extract non-polar extracts and compounds. Please refer to Section 2.5 for further elaboration of the method.</p>
                        </list-item>
                    </list> 
                    <bold>Results:</bold> 
                    <list list-type="bullet">
                        <list-item>
                            <p>In GC-MS result, it is better to present Retention Index, so please change retention time to retention index following NAST.</p>
                        </list-item>
                        <list-item>
                            <p>In hexane extract, Phytol (14.82%), 9-Octadecanoic acid-Z- (19.69%), and Squalene (100.00%) (Table 1) -&#x00a0;How can % area be more than 100%?</p>
                        </list-item>
                        <list-item>
                            <p>It seems phytol, 9-Octadecanoic acid-Z-, Squalene were the major compounds, but why in the F8,9,10 they weren't detected?</p>
                        </list-item>
                        <list-item>
                            <p>I think you should conduct GC-MS analysis at least twice and present&#x00a0;as mean &#x00b1; standard deviation, to make your research more producible.</p>
                        </list-item>
                    </list> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> The GC/MS results which were reported based on the database NIST-WILEY have been re-tabulated in Table 1.</p>
                <p> </p>
                <p> Standard compounds of squalene and phytol isomers are purchased for this purpose. The standard compounds are spotted with the hexane partition extract and fractions on a TLC plate as shown in the figure below to confirm the GC/MS analysis. It is indicated that squalene and phytol compounds do not or minutely present in F8-F10 and therefore were not being detected in the GC/MS.</p>
                <p> </p>
                <p> Hex&#x00a0;&#x00a0;&#x00a0; F1&#x00a0;&#x00a0;&#x00a0; F2&#x00a0;&#x00a0;&#x00a0; F3&#x00a0;&#x00a0;&#x00a0;&#x00a0; F4&#x00a0;&#x00a0;&#x00a0;&#x00a0; F5&#x00a0;&#x00a0;&#x00a0;&#x00a0; F6 &#x00a0;&#x00a0;&#x00a0;&#x00a0;F7&#x00a0;&#x00a0;&#x00a0;&#x00a0; F8&#x00a0;&#x00a0;&#x00a0; F9&#x00a0;&#x00a0;&#x00a0; F10&#x00a0;&#x00a0;&#x00a0; Phy&#x00a0;&#x00a0; Sq</p>
                <p> </p>
                <p> 
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aWPYX8I5u2+8rVBT58SdYhBeHJV/edRdksX2ADZCYNVzvX8TNkS5VYj480NTblXL8BgTzYNRz2f+wle2UgjPJ3tm5cJzA2vfXQN6LxPBZIukjOA1UnBxlesWcY86BGHxrjEfEUZOfS3dr5shkLM1Yix+Fwqe537loGcHzSS/vIQJAeyoWTnMKFMRfyY13oqvRJryUC5vGadgiN1f0Bz75bCBj6Irbsk8dPgVk4fjqnESvNYtp3PKWPw0LWhzHiwWH8VZmata5Bu6w5nVPNHhORdg539ByvMb0Hr9AE1iQoxNHCTqNtxi6CqVWsaqONfr9Y7zt+/M8dgUIMoECCkjcIMegAMBMYNzII2m4pmgumzcmUCL24OysIAM7lsXSyIFoB0LSVtNgHXC83kMFd2sKFpcJAAdJ2XVcbUdFIVJKVOvKUpvnyX4LGNG5AgxVZy6PjV+y7wySU51qaAGDIFvlZDrhZVxrmz7YrXYWM5O6Se/pght4+s29brSGSS25rtnz2rd8R0EYeP7OnHlm2r4tBPM5ASUDENBikSP/aj6I2FwyyxJDx/ILAFYkhkj9INlqOT2dA5RB2bAs7suuY3QNNZ+8lwv5aOM4ZetoQGHEGbBc5jUpccWjNlCsA8Y3/epUngkeDBaH2Eup8QqG479XonEEjphT26dMiuN11MkZ8jqETulSVoO1ZrHwuB2yPOi/1YSx1PlDMbbrAucds26GQe1qTn2p0nFOsjAXcC7J31kJ2WcovCdidYnn8mMWmZZZ6yHc+WToVUd1Wsy5mSX0tq625tIFPhJCYJFFxtSxjj7fi5855fAAWd7YXLiX/BQbfeZFv7MG0EnoLJYvRtyikeN6pCh79k8IzFWoRdJ8rMK8BCpvpt2udVDuYyJOf7gFpgoMP7LHQbbegxbb5lrMAktIVnv+DS3IndeM1XXueR85BWy9GLSgrwfXBBpu85LT/UJejS3Wk4XsUEhyydl7xs+AbLxuJqgnTQm5CSaVRYx/BGIdPnXfF4I1Ki+Q5D227GSZ8YfUlne1NnACg76BaTXtoFAJl2zZM1GY3jtrEQKItziZlHhxNsDUAyoi8tXg6OfX4s/tAICGYmji5jQVEe9ne5bwES+Et/JAIMS7LrB8q3Lw5hcFYNOlQJ3/wG2HGx/8avMiUwJXYQYsPnvsmCBg76Nhesg2LWvsbX8IzrR2xHn7pD30LUjcYxsRJwZ+uU+k6/WVVtjqVBt+8xij9ia42/Ehxa4UToCxqjYSK+eSFBq2q97CGNi+KajkARPGTUSNMaEU3TvkKtvECbnysEH/idAQmsYkvaSa2UJdj0zKYk2XyR7vh2S9fQZc2/sg//vxO0urYzj8o/ZZwh2IXQO5FZ/Yz+FlmltQsmF5OpC1d5dFeVv+4Ba3bqloEqaqn3RHgBDjZs0UVrxxQKyOFTOUCOTWcX/b0l+eho68DsBTsrxWJUqg8x4FufQBVx5TRtFfdZryFWJKTDn4gUQcQ13ZvPKYkkxjE850V6UxXncG0JXjg+Z9N+WxI4gp2idi2aaLbDoqqMOwB4cp4R0ddw1a3Ei19j5z3hW2jVisjZIFqjXGa9pSN6Bw4448jM4oZ2LcM9IwNIy70yKOxSiu++5mQkU22gDa+zQHFKFTgcEEIOHUYDOX5LaBOkKEIkpJ3S1DLAUcU8T24jBupbEHjknMu7rOyZUSChOgFHzlRFbWhvFnomiq+dHhrY4Yej79VBwc+OslmWVGCjBNMLxzF75iziMdYFhzJ0pwGVjCNEzA7lbjJXZtEqXSpLQY5MruKlGOxM9W6xBdfI88EBDNx+wDletuMVPtNqydSfMHOPEjaaozLdpyYwDLhY59QfZY1d67Q8ReGXqbTEs3CQm88HKnilNU6P6XPYeNXYaGHO7xU5SSH4liM2XlxY4qR3NS7/OOaQHyWa+3eBCutZMDmRG5/nKFAx4FFIBnnZRcoKaHnYVt+Glu09N0i48hJxukiWirGJiHIZxSDFx9MLBlQMYiOLdROMTA2bVt1O1NNxGB5lwqqviimosKOvE/zDJqCKUuWcJgmsKGgkXUilIAR7+Rcy4k1p6WMmjfXm5/eUOQQzA46ueie4vM5TlmJxIuchE5y1q+8S5g/5xO70gPVVFX41E3W9YWjGGinUGF9iZKVg5cDoNgBNGrV6YMqfKKH7vHK4L4BqDBdw5b8s73Zbj/DMt225WXj4p/+26THkfTxM50LL3aQi2gy4Dq8HM3CxA/byJ4qIlnyGBuw+8ig6LFFBvF7h+xJwO4tQELVNmV1dY9h3cIJXpOTVAtEFXUwmy0nZYUPuWpAIs9kJiG83IGmioOndHR24lddfOpYM37JkzBk7RFB+JKQFrKiV0xu9MYscDAVOY8KcPDX/1qAsI099TgvSs4uGz37LARlj2yr1G7GN615ZdOS+JBK74ssDCYIOtErKe8mv0vYeSwAD7Si947cha695ByP7HKVloMPkjii7C7cu9JO8OJPeO18OGwX6qk7BPq6ZGQgAy+E2d+Fpp2Qn+Qf8oJVKPJTPEEs/W2eia2DDC+YUuDNBtadhOHxBx+RjEuaDUH9ylZookqo3PWcjSiaDBp8nOvwgs9+Nzo8DEL14m9CbeQXjDlXNC0pYrynJQFTmcTBULtptbccsnYa4CriU9gZxfDQrnwhAy39w80M6nOBl5I+Sg4yBg3q/E8yrmR8dF+C33XY9klxGlkQ2YObMIwqhezbmyUGdUdVnhavjRhTPmALEErtTROns+zYQ3QeWhxtP4eJtreIpZKat7LJsuQAsctrYcxeAArD/zTmkijVVjysb8gj4eyUQcLK2gGfQq5aTsxe0MwTgnilqjFMXS0IbToGLN30yrZOWlggxZIetzChb9uBFjdRMeebWng3MHPD3qjqW/umUScK/XFVJrwurqmdt6brGLVnhTfIEWWJJOI0dKFz4dLGePXV18YKByslcnVtdZy/cHG8/OoZ0dbkzI7eLMjXfKQ4YKrgVOLDUThehCPZSSXg1vXebdzEXtDiP1y1Yal40USkAazMRi91isW0IYn8xUoWElzDP58JA9igIvcBVdXYWOzduQvZYGZAIO9SNrvJ3gMGt0rX9u3T620gn8muTpKg44RYpe+Zp+muFzCeYEBbXi28rMVo+TJLWNTNqeeCBQPIZHLyPRQZDNv0tOwGzudlTXGmmK+waQTYrR1Ct/tAs4wGiea7jn/S30Uz3oJWcQd91l96M4vQl+EwfaDHvZaWN7OwIZdt6u2LYZAFY5HDKR6FTd1/JYesX9p1VtzDjQP+gmcH5KBPAnAUuTjMGYnZyaOswJu0OartBrKPpW5Rs+MFoigQm/MinOwtFJN2LPKxgzwuSVaAc55o80I3MnYGbBHtfpg+cZwSYYfBQxE5xed65rT1JOw4sKs/f8ATadvCK/1ZXfryIfFAKI8t6GbZSX2OSjUZ6ra59hsAeNkHMU1k6ANaTadFoe0r+TjvK2b8BqfHIXJTfQIwYExSpa99wm9VVFzsKVjCrGGtmkAmnJbUHlrTEdEFBgdXcuSDLEMGuv76uuf8+sVx9vx5PVsjl/TJGOfPr49zZ9flDocpyYvu+KVs29AMCFbKGtcyi9yHYcdXxulTZ8df/fXfkpNx730nxrvf8fbxwvMvjr/+2N+O1b17x4P33D3e8/a32lEUkUs+VZPlnWR/soajvowrhzJhSVXcY5vEES1oKT5gddP3NpIcgBGD5W91ICoaIk5d3pEWy3yf1UbCB6KyLxHV5pJJEcPzJGoJ6ZM4s5tWukmCGx7oxLtI8qpvGzB50nkINj85seq0mTEsabpN18hy9uJm3CGPavukRpWpT6S5tJN6Xc1aSEoOaQFWoSGbvtFtWGQkUuYnPzsLaEfecOa7R8CwP9U7i5w4bkAdWCCn8bDoFeOWeyHHGWy3XSp+u/+SFshp2xB4mPwSU/qUcbpwLcHuIoSPfD4XRjvR9955JB6VHsp9lum4PEaXXnT2ykXnAX4i83bKT/S8tRyIs/9GKNu+XSRbdXIqPzlByyqQ29vBMoxvr4sfUmyzYojpt0+Tq4Bry0GgBLWz9267JhV4qaPi/q1m8PEhfmcSw6w5ZQW4hU/bJX1lPYnP6WhgrorLGe+gtw4AaotvX6rZdWaBb9dHI5Y7BhlinTB0mULd8wFMpF/natGxH+HIUa+SFSCqlpowiEN0tfM4gQYSJcBOJGSo5A6R/IMmij+E6zEsnpz2gaCYxpjsoI+OgdIIIcabvDQLTd/E8dQPnxnf+sZ3x/mL6+OhBx4Yb37w3vH9J58a3/7OP46LlzbHg/ffO9740L25MmrMtlC4vX5obCr1RKv/THC5qTaD9lvfeWIcuerI+Nlf+LnxpS9+VRA749uP/+M4cs3R8f73/sL4zBe/Mjb0adq9ukrao4V1ZW2vw+mJmLNyWSxrDBDi6Y7Hl23fQlKyZLO48iOLFAex+LtIEwAq0O2vKnBNLp5jUT30mWgdNGWrLw9ze6M4HODQ0j6DQHLp6fIvcsh4EnKLzZaZuDOvdSRkffspm9EDH9lgFzyiLrNukGu5EI8clVDV42PRbF4ZdAhlw/KcDDjkCEKbcFQXSJ/FWKD52pM/inuGnNGgD6UTDoTXF/M6xzjNS4F0v6NhBGzYfjDyxhZpmyY76F1hCWw/58jZTNgiWhZx+HqlTT/PdqF1X8hEldiwKWnG5pKnumWR61f1So2TmT7b2pXjhmNSYcivBVzFGJcqt5iafMyV/dRcyPf4jXhhtz32UoIXlYwhnuYyNzMvVSeXzDX6dTaysC993VLx7W34frFkKw/owfM60vlrECyTK3vgUz/qnt8SwZbHe9iSo1S/N0SItZ0Fu7/CKGHv2OglcLyyj1A6MENEnkwr+onnd9O2+mSx8mKMEIEgemwg7ttW1G0TwcgVVJqmacO/fQmXJcfLjlXIX56oBR58K4pbsVutabHDySZyWYNYmVi/IpMDUOTIxi6YhDBZiJSwoKth65jlKKE7ZiuruvO1tjY2NjbH0888N669/rpx4o47x0svvzJefu3keFYXKMduOD7uvOuO8fyLL47TZ8+698mTi3bBDbb9MfzantHPj3QoydoqT/l75bXXxtVXHx5HrzkyNrY2xtbm1jhx5+3j+WeeHx//1D+Ml19+dWxubY+/+fjfjc986cvjT//8342LF/XMSE578oNCENq7tAcmhdaJg5T7l5H2wiiXhSRC7TuxCFcdvoc5E7zJ7lmlUDL+GgroxkBGL0PijCb3PALkazDEWBRoYDFclyUp5GF3Jhr7DITAMBB8RKgcwJsnJNK2ZzcUAwdxKfoPWsWHHNU8U6JFiU/sF2K04qPtxFYWmzxkdeeWvgcn0ny/WD/QFc8Dj20Bt5ynhnKIa3JGUK4ZzU5Uzs2rPCLt/gNrKQ6dWP0iPux5OSgx4kJnt56NybQnCSnogpwXUu3gL+jpJRFwuTgsuv6bMJJHYJCyJ/D4OpVlnLCrxHf81gdC+VqTxhYIUaePEJYHwmWc8KLOixIf2BITDKjtsRpuh8Z2LpKVnVzFi8qtDycFfwpvFk4tZyeouYvoz0qZ7XgeLnxATv/ipa9ATQ+F7hgQQoRN979vDaEGVa+S6aAdp7AyRNBf5h6g9D18+4Af9YL7umI5NnAY83PJ3Ec5GO5KgSGa8U0Ow2s/TfftfOZuRxxMNBO3AWJSprtMPGgKIFhwVW+hNE2DmO9dcxCThM9VkOAkFxznUQTJ16xOozSSS7AkYN+tobr6jHlF35efxNCxW906eMerxfhQMvFTLo+Tp86MT/z9p8dnPvel8Q//8IVx4fzFsanHNAf27R1XHTmouw5bsjHGtdceGy/qQPTFLz42Xn3l1Njewi6GC7sdNe48tdbsH77TOcjbWeorOgBdNU6ePD0unL0w9u/dP9Z1mXXrzTePD3/4g+OrX//2OHP6jOj7xiMPv2V85/EfjX/yT945DuoruvvMyrauMBwDcYotZxR09XR2bSfkNnsJkDTOZtgHj71ewsVVdF20Sy28piEVjHkglIYgROuGKrTzeR/oMDgwsHdSDNmb6JWydpYyXtGko6Z1g0W9edSrJEzljKspNaodbg0eLTK+3RjA1tQen9PEH/ugURrfymub7AEln5TXzoc1Wxi0qnfOAKSe/kEXQvKY2zNWigOaMLhic8oZouDRexTqybclSrLy74OmZJ0DC2uzKxGTPiun76Mb4UpMadmmmBRMAYMzTlTbprkbn35GzKIo6T8TgtzNi9sEpawgZBiIi0LOGJlTETvfT7fgxBAMi/nrVuRwZ6wRGX7MRTsmyabP2KF0fpfD1XFL0LFaAVkHZqCkpGnqP1VpBYsDG4SMF9RzEkVNdIQdIr7xDy2xqIOkpr6Z5rIFnEWnSnJJGXnV6Fqogo6P7Xva87Zlox+6rdYYCkWAKm0f7+Iasajgjiv0X8YQNl3YRT1rgVyc5sxCJMLBQRx8h8t+4RxrYadl8gH7+gPOV2oswstcGQ+upIQ1zRsBZFQRh61KpNcogxqR8YoLtuG8qI44Ot4r69rzZq0U7TFHX+hCxD5xUmH5lXHo8KHx8JseGmf0rOch3XY7cGC/7nztHZcubgoXab0EeN+9J8Ztb3jDePTRr+DBOHTwgHOIjPNjo9ihnRettZ1tEqB/Jy8LIa7xevjND47/5y8+Ov7mox8bP63fkfjEpz8z7jtx13j88SfGqydPjl96709Lamdcf+1V4+jRQ+OmG66LIvoYAURI7JxImlUcs+oWgWYvJYtSESFRfEXRdbdNbTEoekWga0l6OgOeO8XElgC369ItfHZQs+Usd3fCWjDuRjKDRJlQkpffHWcU2Wg+mD2omsc+uSF2NYQBNv0ly/bbvtgg0suy4JtMOz7NUuR/jsjvXlEzVK4is9hMerJtH0nIVEeePASV3dIM/rs0EXMicW8awQkbcslC8x86Vtc49IC5AivIWK9a+bfAmRjgLyWlMtlWncUEtSm+yosoosEjMcohgOUWJyW6ULSOXZ3dCI4A079EU6EEwRg58wfQzXDwhbYNMdlZjGnoFWIwi9o0NeM/lbbhuuiGkH75Z+iKJ+Ix6vitTaaQ195mW9ea4mTfJ2WOMYNS2hw40E+xbC12HICGrw7ZB6OdyhjCKvSZlxO9AtvFK3zhZNygM4/x9GUhedFsjPKtXOgxBzcuzbbbt9ac921TFOH0gUhh28M+IZuzEE2PLTk2jTuRm4bEbB8gzT0ln7+6kZN+n/ImuwSp0nt7DkmVcKirxr/p8wlrVJPtyGYLXp/IzRRT2dgfAPlDe6/ullx79Jpx7Bruil2lg9A+HWxuHl/72jdt+pZbbx4/fPaFsfXUj8apM6d1cNoYDz/80Dh0iIMQ47vyoSCdLxnNHLQ5fW3Psj+q3gLXHb1q/IsP/pYuvS6Oq646OO4+cbuf/9x40/Vjj27jHT54SEb0BezuGcJhIeEYyMIW4+CTDDADjxyzmk8RU8fF7OEjRegkHR0X2Ly6GUCzZkw3Q7Me+rLEWVnjiJvOjD1AYcUN0dQALwOMGIDzhspUnB8pTQOj8OMzYsIpWpSMqio6mEl8rY8MOUDFYdo9NhaGbTfanfgrbnVeo8eucNrnCYezJXG1iPS37NqWV9dg20bZsTAKLDoq9AjO0c0psciWsyCK401wbrPhxhb85KWwKvYIcdIDfl4xw4RkfPAfHQt1XbKdtyVu8K7Y4hoQdlEVJjzDclcRrcwguDwJsqoc9K3CEmLxTZmUEntTxV7OKUvPG0uhiR3H7hzPWB2bKeQKDQRVrIcfNPVKTyu/oiG564CHGDLIGkwVz1O1V/v3gqDpX/SMyRIFHj2Xxi1/kRWz2bNNjIBnY6plLQCiSMbMFb2B4XTFe2y2LxDyBp7YnQW73T4v+09Szo945UvnE/3lnJzjm5F31ypC8sO4QV+vOe7kfUqU5chb+xXJnuuxDQ0UCj6qrjTlbc6htjyt9ncZg6UaomKNZuQTOXbqClN2iNUY0qPnbLc6xbvi217JeImmLmXQnFdq+r+8sz3uvOPWcfPNx216n96kdnlbt/40lre39V5pPTfar1t1vgq0vcaQuCdH2uXCWPNzGFsxnjc0PbAFfGDfHt1iOyhAfTGibsnxOaGrjxxKUL4XqGXGE5NhB3jAfGR3suiU18186xO0RViqEEEVz6oj7UxvIlgiabBFxQ8UW26xJ/ndkZDTmaXrDDCg+4AZRWTslxLqDqAThON3W2FMFh1j+Rmt12/RQdZYNqkNRO/MnHyLXxJvzBLtg4DRxYt6dINtE84hJuiznM1bw7aoAUsXxW98og6DfzTVFnhqNNARlqqhaWsFJCepVFsWfYCrMNamuKBxMsBem1mO0/dlkYccf1pwwWr/7RO+XWGvRe03iVIBpvUCOgVh/u6NMoFvEokUFbIjn0QEsrFn/+3obhiNmxkjgD7ggIo4ibGAAvW0UNum6rZnhOwDi71tyrHlLbnEwsmee7R4QENLbmzMppiV+O5tfG0HbRrj8FLoEccnGbHtm7HsS7WNBRPs5KiDnnPUfLDjJ34b3x5lTSDnMU+s6OA/e+XDB+jkHnqwlyeHyQ/ShgTLC4+ltZnjmnFLWCz6tdeORQoMN/lVvsRxDICk4YwUFb2oc7u1c9Bjk33Pgd6j5iIDvu0lAL8Dzgtg+g9+5zHC1ca47Svv6msXHM2DabT0xy01fOKLBKih4iBc33VbX+y5/+rEhAk4xQJe9OkK6vQavcizdJ4JGRve3pWxpv3K2Bt9xeNvLsGq8dBDWkICuSz3OudrlxUA4JRO3oIgaToqQbDlVzIJzpfpcaEeBss1Hag84SSI3QwzA1vH+hyQZN3jS/pty/nCEfBr8BlEpKnIDwLhiNWdlD0SoPNKAa9lYiRRQqshpL3wrjhANn++gmLwN27tvUNXCK6D2TKxz3bGhqcOIPnQlRxTqhdiEw10AKWyxJO8ZdmDgFxs0yoStegjoNK4r5sA8SKpj2sTYPC0zX+AtI035B97NTjdVfHNlPKxdWe/cCYghAW9/RfVPPARMZ2NWsA5BueiJcSCK5lpvFa7vSzvImNhNrtL43K2jS1K9GI7BK7jRMWWCbQY1ZGHtPTDcVkwSPCJwVNPKul38SbBoAJnHnsslT24xGiLel7GCaNTwXMAwLW1J+jpRQt7fjjdOavFynQkjM0c1hySLFMtKGqIgOdWNT7LDQujijfsZz6mPDcMLjpYECdhVUUgXMvaEnKAFaV0o2NlACzT4xYb1INNNprvqjbYCKZ3sm9ZwaWNnBr5D00Mr2F0v31hn9J9at+FZX8dm+zIPNg8ysvxTpVF6cgWpOiL0PFYQ0lJzwYrPqDdsSnvhEry2HnddVV6bVz9xXNU+q38i7Aw6QPp9sVAn4S0D8EVDnl1f6lOx+tflbwYZ0k6YsaD5TlA23Xo+IG9wpIzPS6MbeeUaOkwooKPj+lLj7AyCaYLhjNQJF6LdPZLybjecg2MJWjYtXNCbVzAwXacUFVREw1t6N20naBKvh2aNmga2BTsQDFC2cSubUsuCzaB8kKPUhoYrjLHi45HpWOAnfiaTlw8J+IgqM63j407obky+9F05Ep2UgEvDefFLqkNiV1YapDTZbtjm/UJy5kujMbFetfZu27c6ofJRuTohslN4pv6y0h4YprTSBV942bfK9icXfRUyi/r2wCKs29tc+72xGwhMftdjPifXMG3cYm00+FZpxxAZPaF+INI9AyBYEATA9l5BY5gtVHD7uRn1cn5hCG2cdsGi0MpIA5vuqJRewKjFvPBEs8RsTeDxQQAlHjBF7KTRU3SqvefBWgxTvXXvASILRxknLMwBNM7/yCacoiOX2XHB6KcOIJoZ4sfPeSByt4N8TOviCQq9lwKzmOE4Ey11LudOGfabvqVGHAdv+emMsIJseKc56i4WrCRwWfmORj87S7lt4hwLF912qaVSj7OmRz1Al4sSab02Njlr4Tw197syllria84MJYrnijEb0dpvtcgulEY8BJY2qDnIClL5ieuWBAt5DTZcrIiEPzi5UBtNvZMclcmY5hDKC1hTzX0xfW/kVTFSZGwCTeBqB4UfYu2jFCvwW5pdx7iATGAo6yFF4WJFbkMNRmx8T5TM1qwq4oxTPkWBxWAbJ8NKBNwaywoDs94npT2OWJ2ryRBCiiiiqJslmR2JYFeBita8W0SEF7zPaFx1YbK71mw6GBpaMmmB3gckVTazrMHHT6BoW6QzGwj9cCS6zaA/pyXxIOv6HM+VT0l+fYsfrZ+9hNtwsUDvQqaXmtWeSeuaiVkMdnLHk73eolgxnFRSelm20nczTPSZLP9aN2gZgDjpCfmxEQ34wUb5IqXc0+iyzAxV3ZKM7lHbvcruWz49htQcCkTzXWM6p+xlYxIgH/8wr7+5bMzSh1lv0ASVWeOSCLoPX2pSveRY4HdtpGUfv85H+55oyHp4oMo8S8Uc1sLdoGpFn9EwabbyqXZeCO/ZWvRFA02fyzeakiccKYCDm3iIhYJTfySW+a79Xosc0Bc1ufxfoWRVqx9902TsZsXegsfaMGzIPTgdh4mX63DfJMgInp5F3HrT2+1NzsC9DUYjtEaqdvcFRvk4ne8uYK9aCJIXzDGEotzq3przn5L1lJEnb/kW5KwzFWVps80QWiU2gusKfSHx5Klyl7nL6YmWXPp/LKDLeqM1deXkpOt5q+t6ue97bKvAOaRxcKWcNgHDBc90Diy+UiNJrw+JyiTdAguIlO6xSm6kBVQkhRs+H0PcZ7Y8CRnWS3KXnSiizwYwHcnhS+CM82OlHZauw6m/LNvrmqTQgf/x0p8RkJxl//Bakz2ywIeOZhjZXLZbdyk08mV7SLbdTDiSwYZ9oJN22Eh7Th1ljxhxQ7aU9huZIO9H1dMlzl6rcFtTk1GR48FOt/9GlfFUWV2Bu3Y7QBpgotdoM2ugwqy1Z+zXyAgdWWB1nmLTEKpMyzbQAceW+3179tXEKJOTf40PrmWGHIMZ1W6/0VayEVm7m9Dz14KznkAS/npsRszOMY/iCnk1TlsGixjUOFmhfJd4u1pqWZnffnct9msJQjjpP/tz0KJNn/JM6gak7immN0/jWEdGMjIa8SoUsonV/uDhabhUPBbyL6IHGz6KHjo9njqudR57THQ/UNfpI6RuW/aH/qt+baDWJW5vSAWD4oy4VbmHjhFNZmckANb3aVlPd8Wqx6UmJ/DER71ku4xQBOs+AM3+UYS/6vTEHtdsY5PVKJnf7DhvkGcnKfgjevC5MoZEfKMjIvAnK/p2VFxit0o2WdsTWZEZPw6Ncu6dEmbIQSOB62DNO3yanIDDF7T7TfVKWvb27q91CYcuaig2wBgtDUxuPeov1C0hUZkmsEZDAgKydGiE4MNDXcukeszNJzve7q+TytBXHGxT7gOgX1KD9peOKCGu9xCnYDim6VKhuSB73iRU4TaNXYQI5vbdNS50gkuclbXpnWSi+hEP7iu8/xNLFOsA046BY3WMpF2DKmWQYQE9oLAPhocbE02a1FHsnybsRCai2NAW1A5c9e+HYFMjBJnjHiuliptxBgR/pdQD8q2Zd04XH5wQE6xX6oaRRDYSGnj3e59vLBFn0B0O/wy40bGhJDlu8dWQ3hP/6FLX8cW8cZX7acg4ZF3fEaAqZPnMoaBpPa04AjSdelnobBA4rPCvKGHQM8GK2n4ZKuJpi029ANBVqDJvvgG0p61vuut5liC7gfS0NVEbFp4J9nikQxULPR6SIuDC1+l59/kT8j2lZOXpDPyTnvh99hg3yWh0W+hhBf/bVACOXCwj5Pd143BXkuV+EtK6uCxOAMPP/sWjFHjqupzcjkcKgTksZt9tGVLAVpGsvCMJju2rw3cXQdz2U58lRvwgF/0Fd562NGpxgRF+ND1R6EeCi0CZq+d5TmrohFZMxBYONJrhm3HgcKwYlTYugloYsEMQ40TpaDLI9sUEePCsl8IQrLdktX6x5W0Lw6Kj4x+WRWHgdPLyAkTZd9aAkjOz58viSjYjlG6PRCtAxGHjUk1jqlWnsDHJDYXexZ3CJaHjlhwerHoaEsVaQkl6Jw9Ih8y20w8oWRETQza2G/X5A4uqfQCVfYdRzjtr71K4NZHa2mTeLmiC77qCFDoKQtC6bgKm7aFsgneLkr5Gj0BGYN9Fi41Ld46ZC6k2e/ClmC44kvc/qJc+stYQivWLh8jP/etcArC+moYGx8mQBtwO3nPSmFfSA336yVrzLhaW/qPajCXrGA3v2VoYyvjslStFg8ijy7jNovaIg8Enf/C0KTzeKHXyD+yEqm4pqsf8FCk2Kg3Uzu64feWbxlmXNCe4k5D+RRaDBnD/VRtkBlOU1F9zgV44MJV3blwNXZC9UKdCSyCbPWcsI518Sk8bRMzFfsHQf/S80mjHj747f9SsDn8sa+JDbX+RuVAk3fM0gqgTdVaarsoqdh/iVhestaoNrrBiWxvW/9K3pRjcB0zVlPsypQv/LZHxdROTSg+8YZqH3JIc1/Vum8HJZn+RmeBg54KFG8xih+s6kWllnEJhXiJWCVKvbOe537lAqnYQjjrdY9RlKzuxKjmMbc4UmPfCYiR2JT1mkdi6l/yQpkw68CX79JGQn8cJzjMxxiOWJ6x6tyr4rm5a/ByEOJKhkCwk56IwyB0b6KMA95KTsLY4Y5AO5zAUAqO5YVHcvIOjaTUBLCwRwMZGgL0gJ5shm+nCQJcjsEVSJBtTfQkCJ6B5Z89dLBlV6IZhLGNJiXJATv1zgE83Op9uxXKvLXrYqIH1pX6SxoxJJKym6B2xR2cGT+1EnQjKFTbpp1vZy2j+OXL0rYz4tyVvgPWxjmy0u56DOCocl85TLCSa/mqSIZkIce/z4Alssu+ReMTEpTyJAtiyZthAwxmeS3cTEp0qUci28bDPAzyv/tgxtyJX84A52ELjNQ7j5Yzvz2LbmzWGMM3zng8B2a5ySuxsERwwSuOSF3IZiYcFAs6p6Bl3Nbi3gqms1mA0JfNhwwk/UCZdpOE+WHC1qfi/VwAir2d9vEtephjmbWyGlUrLLWwSUbhMU7k03TQI/H2ERqiaHdZ9gG0OvkLpIWIzp6hrGIbrrkFxenAxylu0+ILnmVMSBQIyVGyq4ZbYXbqWEYtH3FvkfAQR16VyaYriNQaamnZkAK+W080ap2j6OJ77CDTtDbsNc96sQsscrsKfkAox8MvXAuKMuUOOb14Sx9V6eSnbYg2OMWBaxnA57yGjGS4es+k1376mNlOYSSIiIAJAFCNRmTgyaLmjoNGTWXVb61jINEiMCuSQhMMuLKin2oQg0TyNw1gCWEiZqxgx+WKBiQI1GLPBwoWEf7My56pTXNHn0libeKMmNt0ywHkOslGXy/CBj3+SE2+sS5sKyHwfLZi0MKBSds7ba4omUjzvXg7UjJLX0PqiBM1bk2+LnS6A+2jbEeGHCIfDM9TZa9lcdD15jvnsYNe5NLGVK5iVRGzMbzHKZMrVnTppVnV/AxAhkn+zHeezcZA6RSOcVMvE15LHZRVMJRbHpzcdLkyP0s6caXogwMu8qXszLzkL7dvUVAf+8qpNKYFVSNOZzydi3B7Cx2cZbv1F4lx7gm9cwLPo1TE7COy0CFNk3zV6TvZU4sBKo8nwzHqZzqVI7EyD5CJ3I+LAYum2xbdvhvT2nZLNWK1dRF2i8kdJprRnA/fPrSKxoLofG4F1w0lZcR7CoHFiJn2REOstcC5XrRGQNavqZI8TnkFz30en3xVZpXYiXJySMwWxYbwqJOGzsWcN4yKrhS3NROmDXMRe5Su+zR37HEOSsswkcvVYjTKmzTKzfYLafulfbygzfhDHFvsiyNafDYTxq4SWXRkMY1dfHDCg4wNWzc6FhjFNhzjxW/rkqCj6T/3oeqETZFaYkwTQNoU9l6j9WyQ1VgrJ+J6sa29q+ofbsGpLguRIQ5q5Q/ymrM5ANmAeRHoNj5d3NCXkupTsPSZP92rb1O9uLHhb1TN4JRz4uEe/xQ7JIMM6h4YV17pMBn6R8nIr5PJhOWmrkqSHsC5TlAY0+haBBJ9dHilo60jAnuvB+zjqPEl6X3HSgN9fasnNSq7Cjh5QW4bsxvNy2K3Wx+fg518IOuiXefHB5VQtYVPrPhEHX325IZX+9L5EAlq49IgTf2ivSgskJYFnzr2pGsaZrpAdj0yrsupeACvueAYIpp1ew28ji/ORD5CLMI9PqCnnduZOE67X5KsHDJmsExuyEUOTpxN4z9EM7yfbcNDvsvudnRbnzh8OiN49ineN/QME7stJSGhLBRUj6N2q8cfEm0zjiHXoD9+j45j3x2IhaOK/4u4XEcrBR6FnCDPnPABiD6A4dvIGmfMQUTRJxZ1QWmghaQjDJr6AWom2MToW4nBKbIxrf4f3bSfaNm+DcVu9ycyGQ/xAic4WJLqTrfNeTz8OHOlZ1aPJUBi1QGyuPGKaUnqIKWmXwxBC8tD50mtFtXecUdCUiKo5OTaim6zQS7xgmPKxOtKn3x17E2f9+odfERf8fpP/cEBIO+Ek03x0u8kKJpWsU0SpwOJB0ShGqsw2TmP3J1gMEheJ5mkZps3qfg36YLLhcCmvlEhvopWODYDtPQ44jjmIuqZEAYykJBJQpBdHWf143V/+Vf/n74LaH28U78b9KY3vnGcPnNKvzH08bG5uTHe+MC9453v+MkE5QxmoQSDZTwDNwZtw8Ywog5hwz+RQEK/Rg87Jps/SAi5zm6DgaxVnBB/g2u1jRE01o7KdQ1W0QVrZfySV05UsGjn0jQi+Owam12l44ityPXgyL51S01A7nwHRTAVq9jQwVliYhfR2Mdj9Jc6xbN/4ceXhZviCTW2sFMvqyAGS6A5eS1qoCJp40LAcJfAaWCI1irWiYzxNACrNQUBfS4da+xjhgm2S6SEd+upM+kzKSQv5CB60Q1vvtJRD8sssuxdn514XW2Zv8YjSI9N9BcaFXpoakhq5lqWsXQFvSSY4y0Ppm+ZEpON5sekm47zyzoQiZlY1cBWnIUl3Mw2vi89mhV3u2c8EGec5fMNOVGBxnsLtqyVZLdFaLPI8Vkc+eC42EOmocJBfFlMloBdn4CSq+gbsnyIphFZ8ScoLXhqur8wRqSySz4iy3hqH6BgTfl1wpC3kseDceCiW3lELqLooRkNt7CjPxZh3+WRK/GLxZuGsImvlZBWnEDHx/gqwanAi0yU1EyxQexTYeGPXZg9BiI4bx0Hy7sqiUe2pe7YOz6LQ4ze5Wku4ySy0OE7m6ZFUlt8QIiqvkvuuWdeGF/96rf0A6fnx1sefvO4//67xzPPPqfvk/v2OHfu7HjTQw+OB/Vlp/4ojTC522RtGaE7nb7AcVwiUdVdFUCSM8a3v/3dsbZv/3jvL/6z8cm/f1RyY7z40qv6iYfT401vfqt+b+hxHyj27ts31vRbQmv6Rm06xN82YG+JpyLGhvB9hila6qRZTkGXfNNy5AdA1HLUySwZOJTIIaAgEdXA9wNuH9gIFTvBBaeseB8tW3Xb31cG6KK0P20bljsCtSrzAIOAvWnG0PRrwnEs3gio8xGg2GjgHkjwkLN4BGmr5nncY5+OWZTECSF0fG5kqO4TEVC3DGDYME3aNCdMNHlVcW7VLhKyFB/ErRNC56xjjwJxqeZxEIBuG4NAXQq0WiGT2+JKaQ8fLTDWTIuqiIwvN9BpvZ5CkY9s6pm0qVu+gwNqKdjuiYYv5BFdvyxZfdP6yKFv4fhCkyJN0ye+2thy3ivQ5uFTF0eWwS4M6MHN4k8dn7XhHzYSqoCbuWiBmnPRZTX1dV/pWE2bZVf39aBnFQIegCjwX3vvdmUMju0jlasu10STP2bSpsu0japaKmpCjx0YNdidJ9ros+iqrv81jYf9Wqv27ddv3jAwdDDm4yf7tTYd2L9v7NO3+6eQHV5GYAHUurVm/R19JRnfhQkm3415YL/wDhzwHnzW7DW+nkZ4/Ko0dnfIqypse+rbb2i4oZL+UwNf/wPFuWA8KSakus+t4Vwp8xaaAdyvjiQ02j2WWVPpP8cahyxEX7V/OQuVBf3nRmStCJBs2Cxj7tPavrZ3j9b5PfpJn+3xgx88rS+tPjrecPsd4+nnXhhnz50fL7zwiu6O7ddv0B3TL2+/pt8d2gpADGCEqUlm5Etu++LUGoSQLOPgCWRVZyAvvvjKOH7t0XHdsSO+/baxedE/5fDayVPjj/+3fzP+xYd+Z2xvbo6Pf/Iz4/OPfWPc8ZG/GT//sz81DulbVjnLzuDUVnjYSNJ49pI0dKKcXGRE735iwEYM2eh0gjvZtDOY4XMASgxEant9zAUUMHYweLlI37LgxKfmsA/+3LFgEEnsx1bOvmdawCWFb8mAIY1vN8qO7S2wy3DwsJ2449fsX870uKANPvba9/a3oGzd/i488UKwyAEnmu4nlOxzIpizYacDKT45kIKsqiDkijfJC+RZ2XmI4Eyc/Uz8s//oQsseE+jaZM1wdH02qvwYcTFOjIMrvGZzHhs+EwMXSMpUJ5PpL8jGkEH+2k/7o7HljAMgXedV9R4/UFigsVBdp305IgDbJTaC0WrmE/wEWmN+ljHMj9ngD/j2zYBqQqoSn2mIKNvIc0fBcUDCC9F9QHGMeIxdveKg2vF/zqUVczcCaYJjdQMbO2qTC6C98Z66aMuCXUjMUd3OYbmbT3TQDEZCLEyDNg7AjkB3DZMrPdCWX6tj49L2eOLpp30mfvjwgXHv3fd4zXrm+VfHE997Sl+uuTmOHbtGv/Z5jw5Ker7tP+HKiXPnL4zH//Fp/TTByrj/gTvGvj17x5Z+WeDZZ58fT/zgR5JZ1Xek7Rs36cs6b73p+HjqR8+NV189Oe7Q4nvHG24cm/rCznhFvlNL7glebfdD1dXmP/2oquvQPJrE6gQhX4VbX+JbtVKROZKGcy9R561U8CK9KhnEZIIyVyGkz+kH6kFzQ00QcI726jh9+uz41KcfHY9+5es+IL/r7W8ZlzYujaPHjurnGg7qJ73PjU21r9OP3H320cfGqVOnxvvf/3P+tu0cmesqSGi2LFzbzaDTTzmIzAtmB+cBKA8OHjkw1nUrjk7Zu09nGPoNia9/82vjjjtuG3/w+787/uovPzLe87Y3jzfcetO4+cZr9c2qb+CyYsgAAEAASURBVNAZhW7ECZyxahtkRxXwCcoHCjW2Ny+PLQYz88CymSweXvqlVnzKgYhM6DitKyyjgG0gqEYFWaViMCAJBlRkzXZS4MGAmP0JyxNKHdwoiVs8/EV/UWjbE+g92ExBSLwaBZ7wErlS31CoSq558wBtQ+EVlGTxA53skWpeZrMI2ML+5EtjZc+VGXk0nwBUqLcOe5cYwnE3HY/qxTV6+50esen0M52tviz44C22gWbYCW9xMAl9zh2eRaatBiQxy+speHDgsZ+tOrd4zJhyQQfBlkkb3fQn9M753C+YMS5y4Cg2H2wKivz5n7EjMP6QNB15iki92Nt6+W4I4XlBThDOoTsWLL3mOANluJY1DnIdm7g2IGTAq9gntaecWF+ERW7A4KSGSenvhzSOdMDBF9rqNj8hRV8LNTzidG9Ds3CMokYTvd6DQ8mbIUSX/xyAVWOjogby1HqjCvXuW8gIzGr9xmDIq/qdm1PjE/qRTb6085AOQl/44jfHb/z6P9PB5Xvj0S9/bbzzrW/RIrmhA81l3dlZG1uXtrSWrYxTZ8+Nj338c+NZ/Ugnv5f2/aeeHr/167+o24wbOrjsjEv6/bRLeuzwtaeeHTfrAPSun3xkfPnL39TVwL7xRd2K+rVf+tnxwD13jQ191rKLfcVTBZA359jrZr++b81GXrFEOcmYNFJBLFeL9HvGXW4DikG+0CdJ/gcIjXnn/qQtXbi8qCPFlFQ6QtTetuCrQp2ryVu0xt+kX1O98cYbdSzYL3n9GKreTLa1pVwKh3H09DNP+xbcHh0DXtEPnp7Sj+EdO3a1efSnxGyXselhCEFFv9nKgMYaznVwGF8Zb3zwwfHn/+9fjpP66da3vfUnxkc/9nfjyOGrx5mTr6kzvqQzgTfoknfPuOvOO8etN96kHzy6X4pcBuaX9ngrqMegbOEEwbIIbW9d9sOrrQ2++ht3ZF98eyB/9kDSZR+JQae/Gdc+axN/cT+Ftp6FedJzH5/U9mTmRGJHg4/D2h7fu20tfJEBlblTSSbG52L/jRcaPtAxaNoP7+U5dBjmsp9L40NpHeRCx37LhhY56LE060SuY2Pf9ejYgQarvcDxDXv68y2ZCqBxjSHp1sYd89ox7CxQXbeQ5IA3HoTSK9n2bRk/q1rsVj9KFj7FB0yPx7STU267tXXoNiZZa1Q7dn2FJH6kg0HwOYiTK3RSyEWXtk/bvmoIOE+0WYUlypiK7V0gWWytKBmxFlyoIkp5aTjU1225jYzt9iU5Qh1aDuAode5SJzfSI2YZfr2pzgVe5WRsm7AlvK3KtmyyfmodydwxSOJAngmIfRbvvWv4kL5g0eFrhzDoW2LY15+hyZdYtoxZlR4HXUfP+Sy+5UlTpB2HqwEUXfIwpcebJ9IXzFMddlZ5u/nQunRkvOsdbxs3Hj82/o9/+xfjef3C58H9B8fb9GNsH/jtX9KB6tx4/Inv6QrnhfHzP/Mu+/TyiyfHiy+8OD74wV/TGndm/N0nPjdO60c8r9GdnxMnbhv33nNivPTSK+Ps6XPjrY88MO6985Zx14mbxrkLm+Njn/zs+OEPnx8P3XuvfOK2U+VGfpJG3O0+dCyLTeej97A6KmfRA6lyRFb9r02VxvUJnfJRIrIZwx5D9kNoJGdZaqDENn7KjkTcf+V3VGY9fsTuzttvG8/oYH3Hbbfo6ufQuOH49eNbelyD8RtuumE89dQzyuHpsb6+ofV9Y9x+561jv/SyHjNOJAo+sLyaoKq+McFhWwAuDvhNARrcNx2/bvz+7/3mWL9wadykM4EzZ86MI4eOjPvvO6Gf8T4v2i1a/Fn99e45HS0uc0CxERIYyN5jncC3OABJZ/3CZV3S6V0UHD3wDsMMetXXdCV0UMfHbeWXs6bKm+U6qf0ciedCxEB7a5OBSbStwxWUJpG+XdwFG5LjDKVvU+Bfjo6tSfxgqFsMxR6v3F2iV2ziAUdhIeALE2nHv+iEi8lglfnSSz6Qj53Wjbxak/3GYd8D0DT7EN9oJ3vtp4Wl0E7SnmXmuhUBzkviVK2F3xVk23V8+Fx/sRn5pQz1xCYwTDjOub6kxQSLsEW96QOwnSYyTczgd15mWWTQtd+qdEbmg9dS9vX12W8wGEMqpIO97ILoCMFmnIdTJAQxzr810HKZwtG4ztf1N+f1+2V+mtsxcYhw3m2h/ZEUZ1hTIS/kKInI+VbqFYnHKHWmKWewly4xF3d0pwOa7z0ITfgKg0MOUKuai3v1POTyvjF0Quy5SZ5dpopamNKffWIn0sxe9lnqYKenStZ203PhyQPNw9zuxBqIbPWsR/nco2fQ67qieemll8bF9Yt+JvHRT3xyXK3fQLvqqqvG3XfeMb7y2DfHl77+rbH34L5x3XV6rHD0mA4w1zq+La1ZF9bPj7161nH91cfGpfObCm5nnD5/ZhzVj3QyDC5dWvfzji09J7r3xJ30gvpxbbz26kvjpedfHj/x3geUt83yjNzzJEo+kgsAOGDGbVx3H/ZYSwqJl8OPTgbIXeVNTQnPEkSdlkQ8BhG0EY59GANepY3BTwt7GTuRYOt+YmFNQ1v0yDU10fU7C8jkdjJNMHBKL07SFdsD997lYwJyhw8f9MXEgw/cMy7ozhnHhKPXHPPzOceLf8tELE0LdY0fKCJeZHxyg0tqIMcB4tg114xrr+ZW2Ma45pqrLXvD9deIezSJYyvvfXtBVAcPHgCr84TF9y395vimHmqdPXd5XNCB7eK6HgRuMagkrMFOPvUDRzporOhe7bruKa6Oa3Uvl4dhXM5f1KDja4bwlUtEzhBpc9C4dJFbCsP4lzYu+sxoR/ZW9wjfE02X48IBa68G8N69etCoh4vnzl3Q5NojvDU/SAMrz5dIvPJNHCqqZgAoE35XibyVq+b3mxoiSxSK2w20KD0QAKsBJf7uhUd02yqD7gEre9ODadKRGFeI/e5CD7TA46HttHba+C+bjDS49LENSjb/DrI1Oaj7Aa/2bZt9Y03YxjCEcwQ9uKEt5RpnlokvLU++cSJ7cFqbg3znMDTHS4dLZxITq69gWtk2s6Q22LSf/BGA9QByjPHLgvDagsi5MpIvZtb4lsO+dVz5neQJgGDkJ2kHvv2yupumuj8c04KW+MP3ooZ9G26drAkFLGjoCBnEspFPX2tkew3Z1ry4pOco67rddPHS0Ljf9oEJHxm9+Ni+aKpornBHQ7fj9bdHPykGMLhCVVyqYNemMUy1aG71OHNjwkWcl0tV0nfBMLerJblXHxPhwPWqzrg//6Wvj89+8bFxlX56+n0//1O+I8OdlVdePDXeeP9d45qjh7Uy7Wi+7x8H9eaCg5rft992wzhx+81JGn7rPiNziOdBe3QA0lYPydfGXn0ERRkZp/Vz1o8/8eS4+8Sd4+DB/VpbNsfzemPWJ/7+8+OBB+8ZXC3xTIgx4fXOSVFOFrmoCBNm5dX9RF161PlLqGRURQ2gMmjoNah6iTjZwYja1rWyNhMNENgAuWJ1E82QLv/wtGcdKwem/iTPWS+ixXpA8YjHCeWOW20AoF6GdJWkdRkKV2iOTSwZyCMBvMWv3mivkttxyAPM0bsgM6DVMonNDOq5LwM2zgdZVXFdJjiutyX77SbYOoxtruiy+LJ+AvbCOHN6Q2cfWz4jW9HbI/aRYJ1xaZyNbz3xrfGZRz+rX+67UQel7fGH/+k/90T4H/6n/2Ucv+H4OKyDxgd+61f1oPBmTaYtXVVt640T2+M5vZHiz/7iL8clXQ7ef9fd45ff+14NqB3d+z0//vpvP+oBs6FBc0Hv5Phv//UfjX/37/92PCudDZ3x/P7v/poG2wkd1C7peZUOmGSLJCo4D430vhKo24VKqn5MUGHWLUPViDUTl1xRRx2Q7OFV07hNB4Xs5Uwfakr0u9XYC0zpucvQ7xwjHkdMc8cUhA8+knMp33r4xAdxcEUvFpaqRt5kKPY0cmBIriGJtReunlhoNN+a1XBeFgF2buJ6LPuM3hZlZzqjjA+QY68Epl31Q7Xdd9hxUJPQVIndwtQuU8TS3pCF3WeekNW/cpSRniWbvUgyk2wEpe/fm1n5dN+YsHvDyRSxg253iz37x9jBLuNEzBqLPgGBXPIw/eYeUXJFh2/L+HWyqKueS7pjsH5pR2+v3RkXNG82dUDSuZyBkDYgC4fUecZ78CAfl9BzYS3Ua7o9sYdfLxOux89s3DG0PmsEhbP8SNeYYpyYTkzzmLEQHJily4ktnyFZ06KwoQPnd/7xh+PTn/2yn/VcpzdM/eLPvGc88vCDvhLiltG73/nWcfjAwfGRj35inLjzdj9Ef/C+O8fP/fQ7/OvQ6xubY3NLvxJ96KDOefX86MBhrR8Xx/OvvqoT44uyfXlcrYX1tNaLwwf3jpdfOelbTL/0C/9U+eEAdHJ8/FOfH3fddUKYb9dtJ50QcyDRIklU/GXcqias7tOMw4RFeJSEmbUihOa4NW2QWI4JM5Z5K0ls9XNze+JxIa/Uj85z9wdgDFY8SMdUuuNzDS3zwMkDvMgiblU2Dk5GhJc4RassWMZXWjFgTPGsL2H4XGt1WTOA2g50ES23rDyRgJaQFxjbjnDTGsom4DNJF4W5w4DUSYTe5LCpA9C6HlpdGKdOnhsXzupchfupOvhwyc8ti70HV8ezT78ybrvx9vEHv/07408/8qfj33/04+NXfukXfV/6X//Rh52/TR0lvvn4E+Paa47rALQ5LpzZ0OeXPqbfQr95vO2Rh8e/+b//z3HXbXfp7Ef3JvceGB/4zd8ch686MD7z+S+OJ5/8vn4d9uB4zzsfGbfcfMv45D88Ov7srz42/pv/4j8fl9Yvj4t6GMmkJK3kihiVaudoVQdMPr/ELY0D8pXY1M/iIUWx1rTPYGRAhp/FAXnkCrlV0RKZQdvyIrnsbuONBr57W2zPeJxQvXHpMy3e9Eu61T1kLJ+1Q4dbA4AdLy98XLaqtFumF2WSURtzFDCi1xpQGdRm0sBSyeVsyldZIMh3/rrEHysWKfXJrkUjv2vrhvoG8algs/yYaJls4GHWe3tAI0JTPDQlAHk6qKif6WpTZcsTtWSYXdDtQpJu3TIS8NomzjRiL760bTizDPnNWTOwcVOWtEpPfuBD9XckxHNwOlz69jOApJvnQBrjOvhc5GpIt6G4Q8HtOPg56EpWCyvPW3a4k8DVge5ObG2pvU8HIeIsR7xDD1v4Rl1/zpHbIfokt3T63D4KRFp5LD1weNa8V69Teh7zpa99ZfzD57+ktePcePND940//Fcf1LvTbtK8Jid7FMclXe3sk497x6233qhnOXeOz37+y+Puu+/wO9uwx9UeHzn5wQ+eHb/zW+9TTJf9Zqq7dNvuj//XfzuOXXvdeMfbHx7P6RnRV77y7fEr7/sZveX4xXHrbcfH9Tcc1Unz2fG5z315fO7Rr45zZy+O5557brz7HT+hN2PdptzpGbjjl+MeEeReNdlg74zUOOSKAimTYal47kBXfrrPqSPo8RUpb3uzSw4pyWZtiZ4JYNsSzkQzawbyINOP4uFM7byOdV0+2wNDBqAoDHXzomckVYkNi/ypD6ONYOxDRM/4NFJ0EOIUSKVoHVxlL3BS7GRGWFuirkLNLZLu+sxDhMc+G7oNd+bs1nj55bN6aHh2vPbaOb+9e0tnE9yD5CC0pgV+r96D/5oG3va27sfqw0/ndNB64L67cnUiWQbiPt0eYAL92Z9/ZPz6r/2uzm70Thc9WPzBk8/qoeMb9e6XNZ3lXD2+8fgPdNZzXGdEK7pHuXfsrG2Orz722Pj1X32fPxtw712369Zffh/9CGdFOlKeu6ArJT145B2BXos9aJRahbSHW4bK7IH9mow6O6TD9h/gVkX4UxIJOhlx3pLTJLgHGjTqLAruOLH7AfQ8eA2kDbLoZyGSZ6HRO4F1BznravfZuz8tLRuI+EqImoS6jxeqksA+llLY98NylLADnxL9apSG4ymeJ5AUwGgpAFzXQYcDkM/ioTUostC5jK/67I0pZddsbYg9eU8u0RPNz+awTVbDx4uYQYdXxYCD6IivTereFz/EyUdY+YCma5YHjL/8lzJQi0Lc9EnnM/4S65XxozRlLNWSwWdisK3G1pgwNuOBo2/P5eYv95JlHmJTNwN0QOGbULZ9ANrY4MCUk5qMM1mRvbU1KezT9f623lEmebU8J4QgPvc8Mh7BpHjvavRRYGE0Sc4HWzTJ4nc+0Y9maEbUZ3Y4wfuB3gr9mS98cXzjm98dV+s5NFc5j7zljTrpPOz+YAbwLItnREevPqy7Hj+ld77t1a311fEz//Qdet5zUbfg9PPTOnhv6m7Jqm7DP/TA/eM+vdmAFXRbPh86eFi38t4zflLv8N0ru8euOaK15NK45cbjfuvxTzzyZt+d2dTnXfjM0M/+9LvH237yYdnkFt7qOHb0iHA25EmW20RFzPQSOej+Tew9XjPOkzM61Tmqsdy81iW1kiBZ2pOn0lOdkla2resjBMLml3yUhaM2/7JH3W8csA/kEiFby5YBp38Q8CMFGRowpE9TbfMtOwnGBpKlUlrWZtOSa3u4/0WxIEkztkkWMoLN2H8CDTPpcOChKCB1hyaDMWSChY+T9G1tLmhxf+01faDppTPjFb1OnjqtgfY9v9Hh6JHjWuD3jJdefdqXj+tbm+N7T35j/OiZZ8bdd9063vW2d+mW2+nxo6dfGH/8f/35uPbw4fFrv/L+8aHf+9B46ZWN8dpJHdReOTPOretK6+S6ngGd0i22nfGKaM89rzdTHF7VgD0ynnz6R3pDxeZ48N579KaITb8t87vfe3I8+qXHxof/+e+Nk6fXdSl+UVdouleugcfzMg+CSuJeHST36HVQl/6HN/WuHMWtY6fessg7dLIIViq0S4rTefBSOOh08UJMvvU3P1fKgEWmByKdE5zqNu2owa9RUHU6XPqi1xiLKdFK033inkOXjnJnhWu4WjTA7qsV2zEC8gVJu0D7kp/mNBEiZr8dR7fZY6iKefigMtvpehnL4LRaRDP2CkI78tOY4tkRzn+ZVmo0a1YwzcsjbCsgVLpLOdOymLQ3UxKk9x+Adh+QW+tUfJ2bK/cIlWT5gpZsatjYqwQ0ecVi7hMkC8SDjh9sypVtE8ViPsLjTUQ7+pgEz2g3eUOPDjI7K7m9hCx529aBZku3VXi2Sq52uEeNrgZXImcsk3v81J/k7Co0HSCInw+M7siWNVj0DK4FNSHqtpji9HPaPX4++7Uvf2N8Wlc9fCj+vvvuHv/yQ7877rnzNl3R8JwG/zUuwXCJH3xw+fChQz4O78jng3oL8UF9aFWdoH+SyNXcyti/f69OGnXSyTqFvq729KmTcfz6q9WWnyKt6aHX1YfIBe+407u7FCvziXXt2qMHxvXXHBIPba1tkuk8A8fBxH0Ll/gkw6vr9JtPBoUHjVygb1HX5/xFR0RnFh9U06ZtBLc1kaOACw0cRhR1vawMPwU//IcDYjNTeMt1XxW1v8hw0sXznx5XSOfFiLCyttpTykdOTjBJ77BlzLk+B2Xxfm+E3phQnQqAkyPx2lsS7EpUJ5g2BdP9cm0yEuM2rY7hE7Y5CK3rg15cBZ3xu1Ce/OHj48Zrb9VV0MHxwivPjm8+8dh420Nv15nL+rjlhrv19sq3ji9/7ZM6+Dwzjmlk3Hni9vFf/eGH/RZv3txwbv2Crqx0UHv5nK6QzurqaHO8oGc8O5f0PvVX9VbLq64X7/Q4f1ZnQ5oUX/jyV/S284f0nEjPcnSP+0l9LuBP/vQj40Mf+K1xg95i/sLzJ8fZM5f0LsALulfMpNPMVISExcGDT03v4/70IR2cdg7Ib10V6QC0to+cSVKCnRvy48laucrOSYa1KALXf/QYROQu02zu+FnccoAhptL22CO/611/4hvDstrYTlRVXYzN+A2G8TzJDO5JHz8qNtytOOcDp2QR1x8H44z5YPUALXeFH9lgxudQmk5MbQMvu9AP5Z9IwMijCa+lOh8+c2x18Eqn5SBw+5ffgwEkscMFeXeh/3cVERzXtBjv4tqWnI2PYAPQgZco9siB7+P7wStLIGe7MYY4C47PJnAJjIVvyQXLMnQWdYTAiKnk181FbOH5rdkcWHRw4PN6PNvQHSUf2PwuKGFxirWixXtbV//cvrus2+n4zMHPi4r7Qvhq8JzJMriDDPLan794UXP9Nb1t+rjfSBT/hLvCd1Hq7oeuPjhGPPfsi+Pz+iDkY/rKF94E8Y63PTL+1Yd+Qx+UP+YrEQ48myzw+ptvtyp/+qOI7fQ4eucSYuWLHCo/3MFAjo089AEkdwcYB7q9Lmq+bJOrO7IqORZgm9BGRL4dgQICsTC/U3qPWPrVFMRU6Xr7awRhc/IWSCQiXCZEh5b+RC+mkKniatrWbEWx08YXbPOXK1YOui7IkhfPc6QVW+UXHSN4POEDL7DMSN1bS4UbEYv6xxaB54UJcOk0+1KKauJVl7XLcsSu2YZWVdg4oJfPqDGul9nBqiAlZ8fSqVgxrAQ5RlLMETgD9KzehXb61Hm99KaEM6e1yJ/3La/N7Y3xo+e+Px5/8pvj/hMPjSO6jfbMyZM6+m6P/ePAuPPme8af/NlfjH/5B78noC29k2JN+Jt6aPia6H+jNyfcM9bPbek77c5L9zoN5C+Ma68/rrOqc4pj//jU5z6ltxPeN06dOzWe/MEPx6/+8s/5YPXyC6+O/+5//J/Hu9/1bl2BndKngjf1UPMaHbDWx3nhXdLbhnKLQqmg05QPdaVu33FmpzeQa1AfOLQ2DmoSr2kSr2pCuUiW9CUDTkLyock60SIZcc1q3pDBWUhPMCb6NGlK1ouOBi6DIV1SOXbnJdfTQKEjNAqCF7vuG2Ohl0FtTusbF2zxWWVUmIPIoOsXeYCnhUlJiSyC0FVYQnHOuBACQ82y3fTtOMmhZXxyNuEpscqF3cKMlVrTUJiwSXzNFagtmB4J9FqnxjOzQmBNZe96xY9e/GmEed95hd8dYyRDzlqWIw6J2b7r4su2bcm+x5Ia/l5EyTl++BW/vMVK5YO9hI0zO+rFj3isnTxaST1AkbhL/E5ukdUhx/3HXbvLuqrZ1hXQlm7Fbemqf0efG+q84qwvIPTllKv60N6O5LgaQsdX7x6HnOHC46MReuar2+M7Otnc0skbz5i40nrm+efGJx/9/Pjln3/vuOG6ayW/ree6a2MP70TV7fbvP/3D8ehjXxnPPv+Cn638zm+8b9x/9wm95Ze34OGq5oYv+TK2VnTiSM25RKD6M33dQUunT6IqH6QUGOfFY4b+qDnnPMLrPCIobOj+dw9hTSDSc4rd+yK0JyiQZ4RE9dhT20NZMoYMnjXozzRLFvulHIjqi9AyRqSJnuy4XeJclTFEwGWPHy409OoTYeAtI2aP5yginzECPX6obTuBYju51/DKH9WOA3QO6rmawlghUDV+cgbOJBeH/eYTQWXi+6gFkAHlMsnUv8/WOKypbhBlNR1NdjkL08ELb5C3t2AgrkEq2iXdGvNLt8K2dLUiogYzuhvjxZNPj4v6otT777pfX7Vxgx8yHtI9YD4cd0oPA0+8Qe8933pV7yUf49ff915PpIsXdZalAwWfJjp9al0fJrug22wXxnXX3KDj1IYOJGfHXW94QG8yuGSXLvDOF13VvPmht4y9OjBd1K1BzP/UT71zHNJ7UJ/6/pPj+PFbxv4brtKtCfy9pIPQZs7uiMlJTGj7NMEurert5Qd0n5y3mGtC54yq5DxKBa7ok77kxfkQOV0htmpeeFVbVT5WtLAzYJxfqUyTBlEVsPrgnjb4ImZWgIaU9ekqJgBecMmbPgNfOohJjv8uXD31wDSK5SLD5MQXDmjTREFXbbAYigsoMdKKb3Q1XuwusUUmGKjSII5FyfAtrgdqfE9wxIZP6Ai7cubhiY+iOq/CLPT2aJefMSkMOxCd9hV/OtbUw59clE+eSEZM/JNd7COopBu68MmLbz+I7j6wf7hfsdmVqlsdfU4K0SRO/oWhfiWyIhTfTNWlI9aUG7W5jWRZALSIMoa48uGzelsa63ymg/cGMR/JScZHNPCAz/V5virBzGW1/HCIKwc+XrGxrvlwemuc00nceT2/PXde73rVCRx4L7z24nj+xdPjie8+P05drYmid7pe3Do7nnr5Rzrp/J4+RD7GI29603j/L7x33HrLDfpIBs9a4j9eE0v3BVn280LRqGtjX3Kwx6kuOrgQiAr+9yjzwRyK9DyPFA/14PCxDxpVak5hgYMVLM/xEkkdH9JfrTv5Wliez/YyuM6eJ42AnMvyc2m7XIDU64HDYR7EpOwyCGj4XxqKK1CpM15stxw2ZsmUjn0xXdr4xH/5Qd4o3qbqFlVk2as37Ub6BVnGlnJqyXrvGy4nEI8reHMs7kVReAck6uLuaDVlQPJFpAh37nCMl2N03JyJYETdY0HJclBBp1xwRiDoswWXNcJ5J9sFLdhf+9ZXxrce//bY1AK/qYPCSydfHGfOnR43HD2uK5sX9KV3L4+bj79BbyY4Nvbrltm6Lue3L141Hnnjw+O6q68dd993m/C2/SHXLb3d+45b7xpP/+hFHVTW9QxoQynY0j3bG+UvjuoWnN5ocK2+ZG9bZ3pr+w+MG47pCun0xbFx/YbeGXOdLvk/4MmyflFveHh1fTz/3FlGKMlwTN5XZN4pYN6Nw9eVc6uEZ11+iupYEz63M5xomsoPA5MXkGDMPLAkYF9h6J8mJwSlyz7y4bujbRtOF/HIu3dgsoCiNy+kZpdhBpHPVmwQjJanhq8eEaLjMDhGr3owBY9waNZKvbc94Xf5ICZtl5pQLb/cW0cE2xA23tgvhKyffBIjh0BoSkm7A9F2MqEYuxUTNuHVBlTnc6HZkxCRjHuwwAhmVewbPrmI1zn1ZF7QXZUYtlCK+6UnZdc6J9gEcHkJbAL4zDn8VQHKY4a9kOnPBQYiU25o2EoBuWkAYXGlzGjTu918rNP8N1UuIK4Nz3N4s9Aqn6lTqn1bRSwOcryhaPPcxjj90sZ4+Zmz4yV9+8DJU2f1ifmLnnfcknvtwslxSrfDv/Ot743Hh76t4PTz4+Wzr44jew+PB+66b7z5/gf0FWA3jKv3HBrb+qT9Js8l9KafNU0W1llZdIzUVJ2Ke27KQxjJhTNquWV7OQ6Vrkq8dklF4aLrrDhjJlbfQ0DP/ewOiCQyOQGPXaDtDX3CuNMfOc0PyMlr04XFumFBW5n6r33mxMFrAwiBLl9pSLGJqktSJL3iYAC9jYFgih1PqPAf+1S6LUxkKfjNP+j8U3ywt3zWBdv1WsUc1OHHY6nvBmn4AOGBhK5AnGzqwaNifDX12VBVJcDVDmfjCMeJOEW+Lq6vj8NHNFB04NjWF9Wtb+qhvZT3824U7uuWo+g5LTaImxrYOght8YBfA+vwocN6J8u149KeS2ND7yo5febwuPG2m8c1R47+/4y92dMu13Xe1zjzgIOJxECAIMGZFClKsiRLMiNbipWkkricoTJUpXyRC1/mLlf5n1KVOFFKUiRFSiI5UmzZGihOEGcSJMAJIMYz4Jz8fs+zV/f7HVB29ve93Xuvvdazhj1199tvNwsgzumIDcDltge8PZR7829zZnObo63bnIF4eUx8f5j6OgPAM6jbPFT11p030eQieotaLXPxIw8WF8q4nsyZy50r2y1uOrjDLdh+EZtLC2DLrf8uHl6P9nue/ELca+GiGXMbJx9vROBoDYc9A/BuOXbBgIvk0aKjh0gA3MnRTEjUHbOl9VY4oajHeaBnQ427tSbVZpKBbwbTPuks3LQXfKNoGjfGDdm95mpOdMM12NaZ8CuydpDUlXy6lX5/Cq/EhTv1M6hSFaUq1wiPLuuPg61H60sKsPHzkKNTY5kDKSqyAUbF2slfzXK7KgOnjoUbjlWYSWDHOeUprgcZuyz2dpyAnoGFoLLZLZDJI+TR3zzWRhNNxlU7T9Opn7Fc5l1pOeNb6NrlYsQYSAytbzslCAt4MGdC6cFE42PMpbvo+GPti5d5OOedC9tN7hq9wB1whqjtz55ufIExeIkz/svccXaZvTfmnMM+x+ptrkT84IU3t288/+L2ja99b/sBDzV+9a0fbz/ksvfNt9/CSn6Swbj8zsvf3l545YXtDX6P4+Nenn3kfdt7H31mO/fW+e1bf/NiriY8/f7HtyefucHgupIbBXz6yLEWE7PMCfaBRCN+n/qs60fY6qO002Rc7Gd5Rh6QE5/IrTbyqsLZFpoKkKjw+8PEZwGnHZI/+GCJlYlk+jhl289/wf0s9tiw+GUxue8Ps5ufxagyi6ms2eqXaeRTWBu5c0mTjPUufuO341BR6akrzKn4mbwjz27nRye8nOyBiBj+jss59K3bN3lG39uZFy/wMIE+eRyBTIlEDl6nR204jeOFO1zQzeDQkubC4AT+6mtv5n1CnpH84i/83PZz3CL5lW++wH37f8UX+NxGzYL0T//pfwu/3thANVAFnrr7xIQeKTiIt+3Z93wA0lVkf8xlt9e59/4HOTt5/FEe/0Ov7eUQLe2R1nbRiYAP35r6J486vH36bb4fus0dbl4Ok37Pb06pvOfDsbjLJ8ECR8cN4NssnHdg9D1Iuf1aXKb+zkd0er4ovcSgvMyP1K7dvJygqiM6iY3uGBMfZ37lGo9452aEC1wydCFC2ggYPvxNLvwORGn6JU5iAiENoCMmgf3nMyQJKadNyiZPGG3ARU/EOeSwA/hXeie7HFQIKG/Bojd8Ayn9JGlXKPFBPdrhXpjqrUjlxg4hkl96Vi2yOE2ny8Qoj/jLyUSBeK5i5eGp/iUzlSpI0gY5WjET7qrcd7lULEvsl7t+dDBQtjMeMAsPQpwr9vi29yP8v+dBhylGihtkM9AmX5+yEIGXvQJLBgfIroUkYN2kusE9oXrQRUrl2LV8gaaNjUGZGv5lIySqkzI+UqansgDZz6/yfaZn8j4i6xJ3euZSG5hO1OIq6vefl/j43edlz1C8VkbdHb7/eePlW9sLX/3+9uXnv729+MPvs+C8sb16+7Xtxde+y0Eqj2EAwacJ3GJ+uc7PHx658QhfJZ7jR7Kvba+++Qo/2eCyOVcwfDmml/xcFC/za/u7l7xsp1+qUp8uHH3b0vhlvsl64kKhliMBwMRHnn59kF63MCsZMHWkoZNZFd3Fjl5HbTMsVrvh6JLTfM5+NIK8NteHEFZZuh+2ONFxoeQclFlTu/WxY88y30MDVv/kaRofLZU/ueTDgU8dL+0zPplGmViUOmyAUUqtUmftjX2hT00Y4eOPTuTPZ/78zz/LE7Tf2j796U9uH/3IB3gu3wvbl7/8jeyf5uTiM5/5pTzRQiXOtioR13SCylctY5RM6rd2efTZv/4iPx69uv36r/0aDzL9n3h50Sd5kN3T2/ve997td37/j/L9C79h41Eel7nj7CJfOF5hsqcDgmkj5RoueSdqf3nt0ZT37t/EiTusnvc4/X7gAR6hwyVAn2WV2wFpEI86LyHjj+WU8yun3q1TE2mRLD6ePzif+JiNW1z689yl54G4oA00XoK2XNYm74hSvp2u/qIGfd759kAeP377CmdiD1zNEZ+X/4CijBz7CwzGK97qyYDxcT/nEH4gz1ayweFZqbnV6C6UTsgrZXIGCwE+7FWQ2Vpai+0elPXATsifd57YmF0cqGGMRiAY5ptqhh2rKLEruOppSg5aeBKrxQv+mHa6AI1cTO4GWRLM6UNipAwhE0J9H57YOfXxITVSkuauPkHG7qmLEu1KnA6qfp2lqXtwNUys7iNlmY/JhSi3EbcoZRdtBKCAZT/SJsV6dhPWRYDm0XPssl2ogzG8bNMCkdcKqdbLVxtn34pux5+yLF/YNWcMyMeJE9+XzuEavJgVv/Qtgown7vLkQOryFQ+MGF9M/n4PevhQTS5G3vrMsVnOhvxt3CUGuzcceGXiB/zM4pvf4BlqP+JpA7de5UYDn6F2bnviMR6Lo6/8v3Hrje2lV85vzzz6JLK92aBV3PjDouULKe++7O3B57ZrD3JrNI/aucxNCQ5PhtQaEtiDI/KYtM6qFYLmocZ+iI2P/vKJQP0x23iYuy9lAZLWOEy7TJzLndYMj2dARdUSP26xr9mU5ZiaMlBeNg1+QeDyn099c0Eq+vSBfMc3DgM29fqsDu3ZD3ARrZ9FC9LC61mVerRNyQh3X2OSd05tghM250zfJ+RNJc7Vt/iawx/9PsYPfJ94/AI3lrzEj/6f5MfBH9je8/Qz25/8v/+aW+Qv5ekVfHdRG2tI84AbzTGBM+42rgT5ZhDY6N/jcdw+9O9RnhnH1yp8l3Mzp14e3Xzp+S9u/9V/8Z/lqOf/+P0/3P6fP/vX23v/2aPbb/zDX+O0+/L+fYl3tHhd+er189sNJu6Xb1yNE/e4LHaJBegcTxe1g2ai9ZANQ/LkBJ755Hs8LnoZgHv78xIzTE2wCarlXFJgEbjoSOFbzkiTtRUMdK5j0zku8eh1nw3nc+McgKEnBqurcITgbdZXrl3k9z8scPeubRe51OcZ03aXH7mu4BiTXK6Tz7MhBqZHizmyVuMaBEbSOBY93aT59o7EPqfyVPEVE3V63WSTuXTmHz97ai62nfzAXP0qQsLuHTMVRasN1lXWI/AMll2XasDcwVaHWfUHXf2jo9i1eeWtjG+lKq7veSYgM8r9OCsMlVm6Pb1P3wNSKw6ZZYyIy4bjUlnbeMcb1iz4Y4s2mifFXC2rnx7BeMOVpXoCjzoWqwOwdGWss9Kzk2ECyayTAJxZsDIZ2RPLDjV0/QmWxha0PItx6na/5bOjulc5+2T93gT/OkEuHbCoJ1BsNFPeptLjKzRvuXY8ejZ09y53qjHbX+KKgwdJEUG43xWBg/78gJxL7l7Ciw+4dpunLLzs7/14uv5r/JziNg/xfJvbrgHMpNWN2j2AE9Ux7bgiT1/kahssLGZcvXiAS3Y/4hLeSy8+uD313se2h9/N2dBVZYlX+D3U1CFpJGX1ds2Uelc2+akzzmRmf4itHPVn4xPUfbPHX0V7sg2jaelfFejx7Me67CMDn2ccXlIkO+0yUOkvFBoXMhojNKkWWqgPkbZanmGST0dNJ46kT8qTIFgpj8L2m1Ofmy+m1vnRfmWEXH3ZPB/pP3rlFV5q+kfM8X/FbysvbH/v7/18XnHx2PVHeKTTle3Hr7+eq1IuUi+99C1OTm5v73n/M8yNfH/uyUXsXIDs9rR0XvC2SbXFiHEOLjvMdRaMt97gNzOsfBeZpM9x+Um+L/BQv2t8v/MMvyz28tZzH/oQd7d8jpdCfSy/WnayM/kdxzkWBy9dXWZlvHbj0vbwG5xhcAru+PrUxz/JAnKFmxCusDhcyqTvLZlUIXNxu8L3UDf4EdoVFqLzTPiesbhOEQfquUzAj0avXVPOb3645MbRmMGfdvBXzU5uXp/0rYs+hPACTzjwQaYqSdsi4aTvj1A9y7l09YHtOmdVF/nRmr+nUF8aFFD98TuhqyyKl6+yoIEl1ADNkYYSOdJmAm6HYR+7VueRwbQaQWr41IFRkl0s4gflvdN1OVQyKW2LcDvUUFs2hrFLeX8fsHqZnU56Y7TsGiDqkjU496XqGPv162A47FtEdamGv6V2Mde/nAke4smJcegQRxsXXuHO0nZ5B9V99iDrRGo6OwCXUAO8Ct2Fe8VBm7XdyXJdiYHpPiFMM65nJqD4LB3eZXukmHhz56l0Qhu/VmDiob4va5bHqCuGZRfDSZHdCW1HEA/5xXvEs5K2qFeqz4tLP75Lf7/nI3jAso/7Uw1Ntke4+Mhve/gjch8ofNGxTNnvXm+/xd1wfL/6GpdieOMOcixgxgIs4xHzKHv70vmAShfQDfONfRuLWaJYiB7g+6I381icl/nR+Xtu3tiuxA640JdLzZHDSG1HzuLMMSlLPknaHf+htU+dVCKfGJ6SyA9NftUss8MlvHFJUjcGwNWyW7ItNSPnWiMVTFrNnfJg3W9b3Ct3hU62u0/Lt6kqhkrQHSXNF+vsuDijLzFdxmm8fYC/4RHKAxMPYK+w0LzvA+/fvv3dH27vffZZ1gJ/osICw+XWO6wN9iG/mrjF1yPfeeGlXL16irXBuyfH/wlSFvOl2/Fl6u0M2BInIUxwNeiTH//49j/+s/9t++Fv/ub2S7/wC9tv/e4fbr/4M5/evvL8l7e/+3d+Bt4a/f6n38OjLt7NIzGea2O2V+/6PbW/yqT94ENXtoffepCbAu5yJHaBpyW8n7vauCWaM457rJjenafhF+G/yCW+h/iB6g1+pXwDOR/V4zm6c/4lzmqucdr+IIvaG69e3e7xZdhbjJo7b3ttm2jGCxcMBhmrxFXOhB7icePXeUnfpWuczWCLz4CLz/Y2oZFxsbzH9W875HkWxfwC3GsDJlrVjvWAAxJMnxvnJbw8S056AjodeMlEDBoz2QP5ripIJxv5FFZegH6mw2YgQR87e6zq9NC4ewlLmamnQKm64chEEg175ywHQaqMxehM5iwOphgaLRIjOGUrPfZKPdLYW0rrxrapc2+6n16Z1LBx8PS22S7sy4ole/CaOzvQpIhdetuj1ltjOvjHt9K7ne9ydBzpBmBnWBK4cDoRJU4OrBNAveylvBLjdexnYIu32vugV4kTr7SdHvDWHVt4ggXXic4KVbYxOJGAL8ju+fRKAj2KvuGVB/HSm6gcPn9F70GW7xPylQ6ZO6i9xVh98w2er+gNRzkw4sCRg03njCwvsYkDOi7RP3ztoUxKznL23FjhmAJM3dwonjMpX575xms3+c0Sdfpmn2Wnf/EldlmW2P5LJnbLNP1Lmmn8H3pMSo3y70zDf2ANn5KkA6DlRUsfWRT9b7vKX/8iZsDjjNU/yVb9GPqSi59Lt9LEZGRVF0Rou5XDmv1gYF3sUL4yyio17CmnUloPRBKL8MNFW3kC8d6nntyefuoxHirNI404+H+SReazn/1cgJ98z3v44f+3crXJA3ffNXSNh8TmSRknIYmtUezmiNwFJ/9YqCHLtF5f3LYn3/3Y9k/+6/+E26tvbk/w6tZXeBXuDc4C/uGv/yqLwDXuWPM6MHIo1rFcOoqTumZSER0TnZ7OX73KwnOdSf4ul7s4Crt96woffmfA2ZR3z/lkBZ2W/wqPX3/k4au82+PadvUGl+w46/CMhcvYuQx3nUtiPjPqJr/5sY0veIs2q7J3gHQFX4sQi8lVvsN5iMdtPPgIixpnd75T3qM9k9/p3GPx01J/m3QbQ69c5ayKhe1tjxYTORuuRzd+v6UtXtbzCNEfmh4d2MYX1Y2BbrIDSXNchRqe1lneOffMqjvpsFZ5fGpXyfgkXrnMVta1LfDRvGCrO4OgmuRQzvYSkYBFfyWtO5U+W7ZGufo7EqoWe+iUqJra+i7PkSZesz9qJtdATOwNaiZnyAce1oTN/cRd+aGblz7l2S/ewWJPTQyOb4Vwe5Ki6Cgr43dBOfo/JvGEAa5w17hd5qC139mOto0xcOEbv2Y/gvfHurD1aymC1cPYsbGRdzsUc8FNQKHy76Vlh8AF363MAdLbyDu+ejtxz/jV5dUHryZoR8ryUPbp1nlJpD/2AWts2BdnZC9y8PcYY3c6gxpMh4XktYk+6O+W3mb8063BU7F7ud0YLXyWDnspRZFDjJ/Ul8onx0lSnzgrBXJBHbjG6wxb9KpY1vSXAVh76TmAce9HANplll4KST/JVudN28K+Wmnz6hfjsFXZoey6oCWuVOQwwnL8s48c0osEnmei1VWd6mn/KLq1J/4LolKT80bKd/hR8XP8zIWzHcgP+vULbWjVB2m4PAmD9lRMbNs0Z8n7pQXB1NLkbMxgSBPLmUk8DKwEKnz4xnWeOH2Do6W7dKjrqb/xkNeluUsGEzyVivHeexetOljwxDBgdmA7OLd1M4HfusW7SbgdM4+Qp+PdZmXxqmAeSIiwNyT4/g8Xjod4YOFlvrS8wITvl6i5k4b3DF198NL24KOX+X7pWo7W3uQHpv4Iz0Wo7xyCH5N8qOE1TicfvnGZF1pdYfH0+VGcIWFXgpAA2a3WYsnCc9cvavDBX4snIyPlXCN3QOK018r92NH0twvCchyqApbScWCYmuwRS2zsBdb4HxDlzIvXfDtUWEqkHQpWhgO5/JGHoR4pAt8J4EilxaOn7XxIy15UJ/4mGEmiZpxA3tsWehcBZMJ2GB+flKv4US8Y6fDNgdMOXpp6jf3or01aUEz7kwj3JWnajpFHve2TCqt2ujiNUY1z8kzKzuhQa54mct9Bn4xWxDTlzaas8Gmeqn1cBXg2YpuQWvZm58Y0wQJb3LYfdPHCoOTk4NHXGKk+GByG8W0xZweSNAbgOSaC+OLZOTB+JezvUb1sJm7aXCDqPFRRf2LJJosUVyMuX+ey+qPXtmtcrfjxLb4z5WcYubQiBqJvI58JtDCgkAKybIqhIaJEX8BmzPsdQg7sdvZI6nBtXsWFcuyEUPGpjmjVdisDUTvIlyLV+DU2liqvEj/9jnkgNXlqwuqGZMzcdvHt2Ey/QFD+d6bRt+Ia31c7oqR99WzfP4MhLsB1ue2Teohja9raQgxAj9OMiYOmjg31UG0HvM9I46V8FLjZceS3WFu9Tv3wjQfDlwPd4GtVIdEQ2d2WYK3KsFHPv4npVEUtVLdHPRorjMYaUsoqz2EKsnZk/jwSEscJRIodz1N7zyuSwLDxlDeNz94M4RfCPh7ea84XWcCOp/iyUHG2cZn3gjz00FW+R+K7Ib6r8fuYYLFxcbnGZbWHeDWDjwrxeuT1N7lrA/v80a0LmoOJd9ZxLdszGwYNvI9wFnSZMzH5ja2e6Yf5JPaZjNDRLz4d0RjKQCkLW0g5J1tC0r3cmIYjL9k4+mHrBnE6dAgtS/f8I48kQcBxmLEYCXRrl7FzH/vUksqUW1h6sLXQcMumHBk12TmyVyB1+y70oVl9mmwnU6zV/5WCP4V9r/4KtJ4+sXTVsMZE4ng/HX06qOXEeFe1fBgBdJ2Nb3VWX/3PxGoH2WVWPCK7Gxsc4xWdB5ncbnTy8SibxRRowRXmfy+v+r9lp0TaAWNnn7gwxhqo1VaWSWn1YLcUv4ndbop9M3w7N+X2E3WJvfNSNEb6a9p9tuzHOu1yHxLjPEtN611YPFjrWQ4dH915gC+L0GPveWx7/NvcmMDzG19dV1P8Hc3+fZ+dIGVsUwH/Y1t0pl4Wripwx9VVFrQHHZ9c+vHsS4v0S896gAcvGJ5bAFuDISw3FCCpRDVeJfBgUtnln4wmxyq7YCQn7hEDNZoSd/H9cGDLbhlgfrSGWJuU22NPHGK5FjWmXu4P1oKxpk4US/yOo9rcelj+LanzMxgA6Jl6Y6tyTl82vv1HdfHXzCrbBknu7Y/syIZXH/d6suI0aHLq6l5WQM0CaIWpdrFPMcyliy+PpJU4JYGAsZ6l7cGFQVmNMYT5j9ZKtc4tyepVd4JLhfVMtuw0yI7haSd37LGfASdHg+5ipMK+WZWFg7OVK3xv5B1ongF5TToTFQvWBZ7h4w+kLnOHz7XrVxDjkh58++NnsMd49RffnDUtvssPesMBlwdsHO3SRAOIPdoeN8xoJzSrEy/xktVav24Nd2TtwMt9ufd8ZJUh45Gmy84pXwrC4DbqWLzZ2Nk90xJo6TgWsBDZeMaw4ofMYEozH9nsqUO59iVBqy8t2hEkJVDyUXCwJa1dC8ofBEurLwY7OpF3335grCwfMu3ASC5abdW2ymSSQbd8p/FsvvrHDyHsp/IuuJipTerVuKNOHX7Kq76mPbP0tX0pxNXD8lN+FdS3UFWlPlLssE0OBQvnQJLT9pA5edpxaFmcvJJgnB0rgq6kjmVdqkseDhGGXNTWz/ZoB+OQh4yG3wma39phi1cfVOiQ1I9c0lEcXx2SnOQw1jxTIYYebHo33bXz27ufvLE998En84SEe9/nduw7r3OLAlcj4BNIrDiCWZ5DxQfuoEsEHXjge/bDLUdc9bjC+3we2h57/CEudffgMSboEjoDhv32G0mncQ4LuuxDqjRNX8klXOOHMSv0ZRjGllpHPnMCm+igbIt0IVMAuk45Xzr8sL8pFqR+X2RWjfK2ntt+akeRhz7utayd5XVProQW2MaXlIoZdvoOEYe6+tDYJulMwocISCRjHh4xm4yVOm0/K1zMnW9a7z75Jaqs9k39qa2jJqYoHtNWDBH0RaZFpX9F6QLL4pAvhNUvi0GTuXl3o1Rq7gCDq45RGWPHuTAHo8ZrLAMBXAdDvv6xMbmmnFhE7AhlLh3Y+dd3Lu0au2s5G/JMyrv23r57GT6+E+Ktjx0DBgtsOsxl3iXkwuNlQBcz1yCxdz+MkrwkXXah7MdRuej6yp/97wFuorjLgug1dHXo21y2WuwRmobRXwe8vGvtbbxG1hiA5/DqMGrMI0Bg5/u5WsI2jtVOaY1t2wVqxweY3pRhtDJw2kC7P8odCS7tw86xWV+DuwIwfUA8WHvmR90shgM8agb7iHHxpoxV+TcQsVEB4xHB1ecSXymlpio69bUlt7Epxba5djtwyrP6wWKvf+Id9ixRJiDkcoZ/IFaMsv/gnlqyIGuespksLZYv/DGCspDQs1vGNx9LqKd0Am5dDiAqsbYQl9DqAoJCWvrIn0DE/2lP+eYAwz5uPgeEHB1x6Z5yzi9CHxnnDH+hwLtectXCS9wmTeVK+nbt3Ze393/YR2SxiDx/ZfvO97/Pj1Bf3W7e5eaCXKinT8nPBHnR2JDucR37DquavjmWLtzjjlke4fP4I+/a3v+Bp3h53IPccUrbUemYirsKqnS8I19c4pnL2TKYHGPT7kNpfLJNv1gowA22c5Ip7UZ+6JPJWKBeuiPK4apILrW6p6xBytu/W+zeycjx0oP7VK2NsSyPW88+22/j2Slj6KeEhRxSrJWQs+qxwSpqwoh+fToROqNh8cShyJQxPGRXj017vANEJhP7scMYMAOPa613u9opIs69ZDpTlcVbwQLkPkHcDdaE1eCr4VUCqUrJt8OWRwWdZApsObwLL0cJBMS+bEM6GGZAKGfeL0W9Bdp6PHcbTM8GAqON2rCS9k1n9ahKeYUzOSpjT+doLt/d2LGBlBS/wDrTh7HNHy/aaVyMs2BgVCdajw+pjwBfNXIX3r18H1T7NGe5GctqIRR0+AgSk7iabgwhJ1kTv6VLtHcnyUWSNqRVlOzCJL8f5WVqW2hPdeSAAns9gdxTQctUDTL3w67tV9zJKxvshRt6fKAtDObUJ1fdE19593rlCcI+KKzSh+xsCxmcuJSuLWyrWx6J1tnG5mRn39KRCT3BPjshKXOaegn1hAKQ1og3R7yZaGIjjYde+2EWa/L79z3Y0QlpLK9t9LbIsAFxWRkddgRJcQg/evgRh/TXKnQaG3UmLQitWyEVffGqYUpld6t498oY0+qzw+RAkDMcx6JPvPYjXQ+mH8nuq7y3i32olHo9G/KKhN/ZXr6GgieubM9dfHK78ej17YlvPsabSH/A43t8GgqP0uL2XTF8jfYlbrFzOPvuIn/U6gLoweV1bvV912OPbR/4MD+A/8AT2/VHOC/ioDILeryrD+K0ZQxJ/ZlAaG/7KnXw1We25pUzjAtL/xI1x7rViW8D1Qg2X5jZhjNAuVRP0abz8T/lAEdhJ5okfBtr5Q0NzrSBpVKOva2Hd2N8Qf7W7f1jqjjGzJzYWkVO+8bAoJ3QdCB89kXy+T/DDMLyb5mbPrQAjV7IlM0ldhAyPowp/5FeatyVP1sqWar4jEbujWHgU7I63/mM5dCc4CxOx2wABqhNiuolLF8n+Zqg05VVQoye2srupM6fDGrufyZ6J395U6WVGGBnSZZik3JQUme9H0jBgdeCMtL9Y5/FyrJVaJogiNSWf4tdAABAAElEQVS0JnQM8PupPMsuA7O1hYdO8GKfdcvOtCls6tHu8LIxX57TWOj9YGpdIphF6h6DPM26Oom26lRikVwx46d2DtAJj/IeIaZN9/qEQ7GVzK3PavyxU4bR1/yKZ+gHwu5rbMxGK1DkjyAPGTFM4tcA/YG/Iq3MloaHVt31OW01NCqCD2/6eeTdHDYJo7y2SS+WbV6e2CAT7d+neaSwNh1Q0em0cKKvoI4H/dJMN2zHJYv4tFsSeiulWRd/KSRu0WhhPGIh8nKcmNR1QlKy/sinXhXUB3RZXsnRFMmKlJc622EKTjemnJVT8FKc7xB6m7OhPPqKBSkHXGJoB33xNn3MKcZ3b5335qH46FiCyveqF3n5G++L5CcPD28PP351e+bld2+vfJ/viHia9pvcreqCfZEbjK7wWz6/E3mTH4C/8fpN9PFAYd5o/CB3tz7+9MPbu555eHvoMZ41xmVzv8d1SnFiHttjEuTEpY1KVQOQbWjEJCIrVlQsViSb9himpcpvwMU3oMEMSCgRatsd/TlNaTU2YmB4uoGHP8lW1y73/EkAN/aPEcMj5xKIvba1tH9TglEOP32y+JIIgIIAgtOFyD7W+o4D8oxReVQryD4uFIt9QW7l8Mk7SR547S9jq/OreEqeTaUEUQZ19BfiO++FBHk55TVfuTRqGtnZ0ftEZuVX1V2OWuVxLYtqA6eW+y04MXKqjFODAsXWQfDt/IZGKtj5EqncOpazm4CPa9ZBZ6JNAKK7skjnAZOeEsYHBpI2pePgW75vWVjUkPCXy4/apFV+VH8HguPX1F3xvVxjiMrf+m4ThehUYXxEsEf+4htTJ4X6pUw6grapwE+q3IhFbE/KtkUO7q0h01tqEcvM0snbxdskr25nIeD6vX6kVcUgH9joIKdd/GmANppqq1yrHCobgIz50CUHq2wWazl80zGLRdmYR9eStt1tlMjU5ixMmUxDRmaAh08M69isbM5YSlnGWCi/nKYDJyUpZoq1/NdiU+yG5tSrvaUtSMuxyYlmtXE42MhuP0R3paZiaVv2yliIRigjSBtsG+vQfMa+aloGiGk92mOumvyAJX5wKt1YghdcWMKVJgyWunzKtXe32k/6EjqYxnixyGuP/crxyYNDgpL7J+ic5730Bc3jfxeO89xQcPXG+e2RJ67nZxe+q0g9+a0eZ0+eNfvASy/r5bI55Uv8Ji/PpeOnG5eugcnPNlDa70fta/rF5zRmy/n4q0Wm9C/3KYViQE7K8kDCF/d+dLaL6i4UWvhO+qY+GBaHrv3N30NNXO0ptkgn+yCuG6DbJkWOMiubEtjSlDedWVzLdbIdQe223zk3IC8O//bvsTHxgmboatPikY94syPZqr11LHgTE2paH6YVu/ruqLcy/QrdWt8aMnGl2NIsqst5J1VpwMWv3aTqObTl2XGtsNOqCGEyOht6aGykNWa0SMyqIadBUEHE3FQmT2SQHrBhwFwY4UgDzEShE+FDVxYMnNGWegYz2TRcemXLVsfwkQHXibq/baBBoJ/nqM1bUQ1O/1ZHig0GcNlLrfbLEzu0jz+nHfXG3tQQiATD2iWLTdqyd/SaxxYaDeGRp2hN7GVk52IU6vgEQ66rQk2M4qAcam+ae/L7XZN6E4TYHjXI+nsm47BfJohj5Rsc9/aR6qfj2HOS1D25xkfJ+Epm79DwSG8aWX0Si0logcxevuI046BOxwa5PiiLnh1q7NCX8rhPffjKGNmRWfQORLEOnmJrl5YsnSkcwuayuGloI1ObEU4TSQ/TcRQoQ9ipKDTYiqtwJXMz2bROTvhTAIt9ZNd2AcIjQwdeWJWBMmxmp7Crw6fgwajMxMx+ffD2YEW+HHumOy4NOOqv4L30NuOocjDJrAyfXEJlbHny4nj1d3X8NGjjsXQxWUsztpfBPdjkVTFgOCbP+cQGx6aLmd/XAm3fj+0xhc1qPxB/Ypp+08pGcHw8BNp/Wg4w2WVbAklkVJo0sVvl7I7I5ZKc0qiKNur1J4vBqLfeWhpXWMPhvvOcB8i2tzYtvbYXcxMcpTMOoUSmNPWVZuxVc/Sls7rHj+py5ip/hFSRjPsmmzNjZK8jE5qatZlt/NKifkKgf2Bh/iYWuhDbhFautZaag6REEr4HlsKFO5yTH+xDLp/TY6+BtxGPyYeVNNYLWmMq0cBplPSzaNA4bFJMUydYKS27pKVju9dgOzjXn62eQPmdkU+DCA5599LyhlgHjspJwQ8eMXXPSKG5akP01VaXbKpVEDkkk1XezhMnUkU0lpw8EbITmQ+fcsXobvB0mLxs++mKlBU3bE7lsIu1ICdG4sYWIcj754Khnnk0/Rx6KJM2MyY1GG7N3S1VQ9LSXL4YDSU+gW9ZGezrYNF84guuitP+2kle+2prCCkvFWtXW4dWsbO01jlAa1Xx5iy19o+8e8PmQBTrJyVhxiYxx8bEAaGoUVp1jVBgggfuqlg19quMhpA9xq/mAh1nQdJhUb4KKq9+bZUWbGEWjwLkNeMQi1HR0Zw8pnAFe/gjnrqJBbrEW/qtGh67g82nHseNbXiOs2XTXU+fF6d8orkIMQTzxAPzlSm/II5nkz3cUwUksqDQTeJ+eq1nTNhi//XnG84hXt14gFvv8v0SGF7a8zsijctkzOUaI5K78RIzFZxNbduDNu0rZfITz1kgLMcOefhIh0QhW3OkaI6vpcNDtfzOH1OfNoZyPNlD2SPtl2AhKZWoZTMjOYWFiM4GKHGTqBWT9qacjLGn2nnN1h7y8Hdva5jghXlHizOU3J+mPQbFPo3J3pcQGV3qN+XnPNoTJ2lH6OVXx8yZMvq/dC5dU+ZyXLCy2ZVZosKjknucazsheWSUZ7OBc57bo9fNMjASPTtXENjueCtT6w6f09cMEJN8hDxrqZi2GLgcWdE57eN2fAeOtsjuD+FM2ho6DA/wKnAvE3ip0cMpbzBQbY7kyHg050LVQRSlIvAhD15OStxL84vixaItfa+8vNYpks2SZWfS/8hTJ9uS3+Npy7Fw7slRij3qqztLQGFSG1of4GlBavRMnXvXNF0uxmIRwpgVasVp8KmbBIDU4BUUnNokZmoXXZFZiIKkKfvRXO0sz1Jq4SckfalPVsr7TrucGOUbv41ly/cBUqG1Jhdd02B3cCoXMhv1TlmdVlSPFcZvZ42IpfSGEaKJoSE6MZIjcpFv3CIq3Qz08igXSnUcRsEnD3XGJXl4k8c2yC76Ea34QhaPf/uc7FWlxjP+Sg+WAPnIXDxvvc4ZiLe/sZK8zcA555mrMvDmDzEPOPL8Rc5UXIwsCxWbWZT8UtIx5pMb/d4obafNAuWDH2KCOL+ti8cZ2GB5YEh7Oz7F5h9C4242ETRjHsXTJyyf5qfevUlek+Ojef2GZuwXjnMBhdCWimWr3kWztfnUh8WPkb3CUKkclFE140MVBiAxYILzqz6BXXhzUJiKmiJ6sMcWVexJfjiixrgvK9PXnZu1I9CZ1zIGhmdhOI4zn9mZzMabygx4dDhhpN7vc3sVKYS16VkahdgDln2PeTZ46PS5iJkPoHgwYT43RinAf5xY5mtFk75bad/iTMOsBEHd28D6c5O7WX7nd39/+97LP9x+/TO/sn3gA+/jzpq3tz/9s7/YvvC557ef/tTHt7/zc58ObgCRC4rC9S544krKGUk6HLd98qpfvn2BjcVIA+I8PA4QGvoigyM/UPUISbvZRQfbJPHstQtXNhc1a+3cBspqB4+yOeqDAKkptjarbQMrYGxN1dLIwKgPXQjDj8xguQBFTpBFVLKWljAhLyfsOh37y9VtlLI5Sna+TrKl5RJWetfgAzVi7B32hn4mbvNJrrTaKZ60mCWvASqWdLGm/SunjByNnf3ZuAvhpt6FE+iWjglCXeE8s49PFT8jH8idn0kN02rZaNFe7XDPR7I2LL2xAh9zppJZZkVGE8LHzjikCEF8J81QZiMj/4CrO3Wn+DubtfD2f6hn9iMLC3jaC2X5N30mANicmMOnTxOzXa1nYcglHifyZsf31SKak8/oHoMykbBo5IwMfT7I1J8s3OPdWw9wF5w/O7B7mGIv9VkYGEuOR/Oxh40vdXT02nX8Ybh0u3NvrLFejILlZg4HJuBpEpVQHLw85NguqMHukTMrqAhz9jgxsWpS+tGKR+ITA0EwVskHgbKIWrT2y2CHn7QFURtOyjnDTRkcMbDdH+oHZhlRWfWolyox4WWHCIvF8j2Tk/GCXjPl0Mfpz9JTC9204h3SEoS7Z2D0ccVJVg+ofucBtRDjsnXgjMleJVJfTn61075fM7C5utNGBVzSMMibUrK0I29Y+MEPtr/4y89vr/GA65/9FK/44anZb/F+uc/+xfPbt154YfvYxz60feSDH8AejziQY9ilf4xh6hWYxFLQWylzd84io4P689tfff4L2yuvvr595ld+cfvt3/vD3OHynRe/t/3Zv/zL7T/8D/697VOf+DCO3MkDRS9xvctH5PQQvM6qws844NGAB1AuNBeY2F0gMhhQ6AMVz+UUnT2Dw87JCrlPeGIYQMHEjIlkbEIHmC9ayhEVUfXoqq9+gA6mT3bQp7xaQN79r2c9sU9QZLtYYSD8xisDEX71+eii3CgR1vJ2qIiQ6Zt9LV2WhT4UClSvjhQ7JJwMgo7CdIzTAVauONsFh6wRtuPYd5Jnmz7MXsP7p/0uS5MUTBTDI1jtp94JarGN7nRMFSSxR9yU77copqYjqhXSdn5J4WCv7esjFZ5lhUwdMKleCqy1LfZKMsTL9nBgO6imLycAYgTTjNpksNe3zLb1p7aZD6+1Zi0rkELLzUZWjuCi3zvHVBGXzCt7XyrFPkEf4zMLEGIRdKIanujW5Doow/qE2U3iodOrJ1b2JPanFkRHpI5N26VXD4JHF3fSy0sZ+fG3Z0fn2PvJjQbcN+sVhozXHIQxJuNwjAmw/U0vuqA47rAvxIkVE94cTK445TDOvo+RTmZJjk3jKg3C/bEq09ltbFmk9BWF70tSsvhP31DHoinTUghVTDZxgilNQdlUP429PuKvtKjTZj0KpaM3p1/Iy8Of/9lEoHwlpiJ44Rw+2U9T6PYQD61WjE58tbptCwr0cNku6RBIZC8PPXGwskdw4VReIh9poVfQrX74HqFLdAwfEuDj0b70/NfoJzy/8+FHt7/52je213n79pe/+rXtBz98efv5n/309uHnnmO94AhG2ESDrMMhZWlkDAcpc2qPNnTRv5A5+rm3fevb39ueeupd27NPvScPMb3FgvOdb72wfeUb32JR+oPtj/70L8A/v/0Ji9Kff+lvtj/4v/85t2b6QjjMtuFzlLlrXXq9NBbNmSDzClg6ZS6ZweqP33I2RO/31M5+mrhgWRp1HQKImk4fRyxRa4cg2769GswBFJr1+saG/3haQjuZC1WrwmfVDJJeLqBemzKA2MtAsIzXfEQ371HhKTVYi4KU6pPCZtmOrb5V0VZo7QwK8YYeGtXl51DUxdFRwwBIp1uNu9SUcdnbllcRPUKlSd7lU3zlg4GV6knZzkMmflHvfBLJMi4MdhIHclFbXDjIHUnaCSFZNyc0Sj061EWMyCQM7ZQNv8RJLOzlzRxqJrfTa5F9NM6t+lDta8uBmlNblsSyTDmF2GBIfIA0coWzr5LoU52yrLec7Z73bMv28I8eFaZO4jKL75Z99FE8Ao/eajSEfuLOUrHUCHImqaftr4zIy37kOQahfbm5gPHl94zuHdue8XjVzkfyjCHmkmA59VtvNET9uN4686E5NsfHcJZXEeVatS9A0aFjeyKvDUtU8mn/mbz76KNefbaE5RnbyqXtI5+SFDOkQ58UWyUkdmkXY5aDG+ntc9GrXdqqeehMkQ3faDrKApGmVkVE2ZykiAZb/eqwMpvFdTY/vq7K+3Zg008OCXIWlsrQiYd2hWZjmc1OXjJlKp3tq6+9tv0r3qb9rz/3xe0v//JzPKrp9c23bT/Es0SfePwROsfb2+ucrHznhe9tX/ji89sf/9Gfbn/12c9zlzFrQZxZyoPYTSyMEZibO6KiVEPaYGGjQ/q6BR/rcZezHJ8ZdYmV7zagH/nwB7b//B//x9u/+Bd/wat+b+YtqK6QvtTIx+80SCruBLHr3v1zMrfXQVAnWV8znJfOcXrk21TzNlYnfISpXjHEUAmWG7UUvVzgkVwmc2WoyxmMeYSttxfOb4OKMNvqsDQmeXaWMynFFl4NcSAVO2Zo2BiU7DKuveikMzXA0bTbbUl+PvzLkQFkDnkn/HYlOnvE5bXjM5XEJhUiSqdxQUx3F9t89EeoGig7GEeR1cuM9jn1+3GAqVXdS6fH8SFHFxvqs4Uh9nfFVQqMLmXDU25rSAVcilW2asUhr+7Rf0Z+jd5GS0bl4knzu1vKW1meQx2S4+yKwVFeSNTHl4hWQWClK7vL6wsxOkOisGyMIzncK27NQYb6tC0Ej/J3M9OwlAGs7WJTaoPHF+2I2XANj/Wh66vSgxmcha/cSsppg63bx/VwIZxF/TZj+xY/VH2Lj69TeZPL7+bf4kj3TW6xfpNnwt3kafL+lvU2myxMakZtImZcGAPm7ZPGSVs69o6xkxhaD6+vSHfY9DsF9tJN7PTGSSsYCyt1q1515SrVYrBbzHbKbUUZJB8xt62Mf2MZkTOb6NC/AJjxHxm4tH0u0ypEVHdZ5eSrgDWNhSS7hxT7jjy2n/qVycGE7UmhtovxE9Iia/v9KQvfLiY2hZSLWdvulyqLbwveRbGQHrIzToy0y1fiOMef42pXvsfTXv48yPJ7Q98Hd57ffv30T39y+8jHP7q9+NIPth9xVuTNZeLL24SOZN3XGW7R5pqwveLQbWQ48nlg+9hHntt+8zd/n0tyr/ECuo9sv/N7f7A98dRTnI7d3X73D/7P7b1Pv5sfrF3dPvXTP7X9+V9/YfuVX/r5dKI84iOgo9fAkDfw7GKQmXRATv/bSqvzUos9LkheC0gnhdcmNJgm5YXTvZ6liK8TTW3gk8GQBQqpJS+a+ZiESOhrMs0AsVNAz8CKzXDC5CKVMyAqMxFkv5SyG72xNbYddR6d9PXl2G4nXDaEQ/9JMyAtTQdQb6o1Nn73jrF02mD0FDwIaVQZlaFBOavsTSXFn8scwSR6nqnmz8ZJA6nDFGXsRy7E8Br7o54c9kkJNTD4ucT1IQeOdq5CoYaM9ewGS1pivetd+nbHl7ASyYILhtmYLtwqR3Jhy7vHcSr0YtkXkija2cK+TXkxWptLubt+7SDuTNxYHtkiaJHJfftQY619cNSgVX/sduUqrTmRZ7PK6KnjRT1xoPSxHp27fKQLLZEmsIv7m6AsPLxC5U0WmCwu/GA1kPDoh5Yr4vD04NMDUd9MHBAGpw8LmbM4r/NLz6vnbQSAEhOUzWKaNgAwI5R9Lp+D45UF/XW8d0TXZkjAgKURJHf2kbZlaer5t6Xh6KQ/OEP9SdLRrDH8V1/isVSmXxOU7IXxg+3alXcySUifVZf+Kzh2V99xoEXU8psj5a2T3/1SZvYkOZydN35y/Wo/rYZHPD+Tn74zcI3jlNgv2DN80lLFy015sehP88LSl3/0yvaJj3xgu3H9+vboow9vzz//VZTw2zDyX//GC+i8u73+xpucEX2Hl5w+xfvb+tYF8T2THs/ud5PXghgkJwmjWcVGwzsennvfs9t/+o/+/QB/+MPPbd998bs8ZoP3ivOoje99/6XtuQ9+hnP1W0Yzoj7B2s6YloGS4LIZWAOoAW0gM+iVgr5caWDvpTrPRLq4GFTrI5QJ1YFcbQeqDPLJ2nz3JWhPqBR7JmM5tgXXISRDhGNr+KA6RqLbenXw5yKUtV19/DVpy5IH2EHmHXqm3SbyGbAOzhO5afjZKzMcHjmJor7m1KIPx0Lk4ERbpPZIn9g9uJJipQaR0f94YCaXOFPbuMirH+n0FIibl2iclJzIEhdBgoVl2rDi4VfWIu0dHRknrNzyLVQCIsf9qbSaVxvhhmn1o7Cf5q3SxuFtO8s2tteG2qaNYrU+22W+UTvqwvCOTfUaj/5ou3rtJ8Yjlxx2O8SatHwSn/po2flat3ofAuWpzVMnjrrqU/KhBG7F3Tr56Cf2u+TPygebei+r3cLgm7w47nVfBMkbUm+yGPmw3jyyauIDb75QJqQX+MrYM6bcYMA1fq/f+y6tCadR92h43q1U/ctfC04+YYaRlAXIPmxn4t9deFLbjTHIwU3k7XPETpeco5Iaf7Pj6fTz1nc7k7Al69svGi9juidAAg8teJZVGBn59TBm1s+0JGVo4WfruJRgeejTlnXDfrjql3Lp5WnbVZKtAEGpAosd42wDYn80BuoURH6TdPsZ+xLYOrv1Zy4LOLVypU3TADtzMzFMe+UisfEMWvz8/o/9Jz/2ke3dvG/Oy+QPP/QQBzd3tg9deC9v232TfvXW9gQvOfUVOhiEvDj+LVvtE9WU7QW/0HeaM01D6aCTnoPr2fc+Bd2j5jvbe595is56d7vKdcAnn3g4DjsIxQ4+drbxIFAhau6iST0bDQrz6nxweMaVDqY+81iXy2bu4R5rE/QUa75IsVE5cYONm+zFUU/7OXURacMdzVOc6oZ92bVYY0cgEZ64yLVCJRUJrTBnaj6CxMFlI5fV5ZBhKfKGi7Gx+NgrQgsCRZ97I5gFT5/4Uy5eu8tHG+wcJBtWWjjhduALK6MGiBFD5OmRtXChUW0KfPYBatk6ivnysNbIupKYZuX3o5UTl6gPORYGUmtqr5cB7tFAXVTFaFpspyV4ir6IicNOG6OtrAnLJgkxLvwpUSx7tKx683yMn/xk266l0aENVwa/seokKqHxzB1HE1dY87K7heeuNhTR9klfkl9DDMXkZSW1SH2FszWibTv7wPCU1j5RXOaDXc5cMdCND1Y5hj0LunmT/Zu3ea32OhNC0AUql+ajS1neFUZf9an2+X0YdC95X+BtqRc5YvSmoswL0HPcyeawkzrpxm75bCbxBTlrycRbVbrrZuK4k1KxqpEWrg0o7J5GXMKqTl37GnOXdkSi+8ayvF3XjKUsQaqsCgALL+TaftSHqQYt+xL8pUZuAIiJc4BsiUcGaKJUGUDO2EJ9FpHwOfsZvO6cC+JzNiV33gsIBCo0IbrciMXOG0MwIHMOhMSHanIwn6QwL52CJCuPmSZbcA4EPBDxbauGLOYms203OPuJw4GnkmQ28bBYUu1YJlxwNTQf47IRA84wsLGeS3ZeeHBhcaeEZy6eQTko+6Wl5k5jUidGQGJ6cm3QdqYctdFCaRJ4/fNf+tyQkEs0wpCUTRYb3BvUfChndWYUxY7YxUYZ+TR07x+lWxHzsKouz14JKxefIKTp+M2zRTiNKntSbXFQti8gb53ggbIMocoqQV6fPLPKn/WNUvapC0ipiu7q5IM/J7AQT39PknieDqZlxshiKThuScGo7UVvvr5hlQcfsbn8kQt2bR0+oUy91GAdHZezNSdFDxR2v+OjxYWnEYm1MnzIV52/VUBbjNT/ZqYMcyjGqDZQdlGzwoSNS1XLJe54lWa7AyLpP/pti/uTuFLt/ueyUVaiBPYUsviwn0X2xJhmYVWk2+amqM4c+S//ewQfjbC4r027uSCd8R1/xxf9PviUbBzV6AGhz4m7ScO8xSW4W7d7ac63mkYoNrIBxLHsEw7e5rK4ZznnecHjZfjf5gZYn9jRW2+5lA+mWtItVLIb0DY42kHDWp24tRg/RIiohh8CklNf31bfWLRU7pv6KMqu3rqsjmJUttC1uJgaoU54bUvyiVdwIIZVmvVWa2X3wWoxvltXviKEL7NmqCAu5iAstQO86mJKbDV2a9ICtZfhluDaqQUUth5gaDtdUaOWvAbX39qrWCyoOQvlZHdCl8+i2LaroWlqRjvLYwYeKxeTB79ZFxZCThQCtvjgFje2LQPP5VTcAI7FS1120FzJ/ahW5ZrWI6sFBC3VqbJO1tUgZvmbcMYhMHNJCyG/Y/HSjubZUTTBT757YeMlOYq7bekEBrkqmHg8gqPMx8XUa/QuSLModRa0XDsEEz8aAak/USAxcnJaHwkY1Dmdr/YFYOEoFWt2G52jQj2RG4NPYzz53YYIVVZ+/Zk0vCmf0MeIWGRwSYkOWdmk2OBJ2UGkogcIJctfTW6bG32nvp/a03gUd2LjfuRsSxu9aPVlsE5x2ha1Q+7cTQmsOGfdVNfqb8OutWGqXLxg4I6/p/InIsmKP7aeqYvZIC3c0zpp6Vc78HjXOGcBi5n2/cZG+XCxCSUOOyw7IlLUV/hyVBuF43uoZ5CMq2NjrXxKReKI9MguchQgQN9wTvOg5Q5jxBsNfEivY/8OC5Bxv8ONCLdZmO6w0PiMNz+2lU/Ynqdu34HP17DkjEkXBj/qltd7fJYNJzZmMbBvIOedpx7Apt2Ekj14E7P2p7aFVfW1fWPaCF70JZ4c9IjyDvU7rXWtP+aaU/7REWu0M9aBr+owavvYUntqOPVJpcmvdJh3hhLlyCf+mCun3O9M1hnoI42No6O2FSWxkiDs/480WLKar1j3aZkVA+uN8m7pMtt1YOgjbTn5gDXOUgyf8sUYA7vn+39uiTYgcOnEqWEjohGjpHnRsv7Cr4o6MXVjbI1kEDgCVLGboPEuNj3Sno6WRcdgwOvtqPmxqqrSAQLRAbBo/lDOui48J99FAO5V0Dxolb08frSzUGMhmBIZXFpkVj+rjzKE8lNBmnysT504lQpDNmuQyjz9Z9QtgInx7EEuzvBZimHqhMi/WqxeXiQ3dkYsPHDJmITProg+RiJYHtuOTzJTpt6/spMjW8ySZistZ6VDWPtTG82PrJNdUN0gW5OsV3DXaBfSjLRpz3rlnM/iRSaL2uGYFVHQ8QZ//tmgYM5malsUlv9kO3Zq5GFNGdSeeAWu8tpmH/O7ytUs5YFVrIyNjAele2Sa8St/8NwcCbil1/agEKaYD9NZmxsz1ZVpjyFsuy0LOpP8EreuvAwS23rHhcF+gdL0D44w89T49FXp1vPv+GOh8nFX9/gxa2rYiMM3t1G+vAtWYq8dh2JLsUHb/TtsgE1AXV++h3ltQjqpUH5vM8Xuq6sYgIKKu5JsaZ8AtiJzEh1nIGZfORltkwMnujHU8OSC42qQco4mpNS1Kwc/Z2HD5f5v8TVVrT/QJpcjjhQav6GPX9iFPblEnMGg3YcVaNz9HMmJnXFpbKp7RSdscTEN1JAa190S9cGlOj1ufOxSpatzkgcHD3Dzxfw+Ux05U1rYw8clbY2xyBlKD7POGKeSkRHehrA7HY2neXzEWIZMY8WIknNXTs8w6y7kJNFyyx+6VW9A+24gC85S/GvDyae61LssYlf1bLHBmmhJsYGK3QtDxbVf+UmC2Nkph+ymfspb/uLLcIYmZ4MYYX2qQKwIDpt3pMjAup+ZRIl6STue9XYszujqFVW109Nefzgqa9oxkwsdIqd+lYmOMNAh5AU6aqQljz4XI+sWrYDwySKm/+HJTLXkJv7i1WZ3gWAvpdrCTkGKH3mqu+Y2Lz1xU9dOKr91QdvpFKlqLVsEYn/43JzK/aTyYhQDo898DuDFVHnfodO+WZmTyrSPdQlYOrn99tTY5muVE7qx0yNNP2L/zsXW/neMtUFURpuNkx/TlKuj29bIQN/BLuU42Un/yBWQ1vQ3QlgzD8T14MDLdraPT34XwX7GmlSbBY4KauKn+9qwGwSn+pRwn260+ok8qUu9YE36cJoq3xgNHcmVNQYrzivWtbTVHtw06bexGuzG81T1YFaffOuz7JGuVi/3D++MxaWkVsmfjwinfaBxGF73h2/Ntzy+DedZuQnPaZwcM8v9COUxY+MqlIMXy4GLL8v5ow6+qB7ftX/ya3yA5TcbpWfGP/LST3RqiCrCm5hJmPKBMSK81I56ETLJUCDdb5zUTmA2gdDl2ydPMVYNlVGoqlLpgHZqNnZur+1pYDqlHcO28qhr/uw92KMNOfJExCSaabdtETQ91rDxjM58vptZvDm7WvNldYil8NEZtT0YkgX0Y5YjvviVYmmpsC588kgXS7+CQp69vgWnkVEu10qhDW+qrTBZmN4kD1hBG8MWdjuRsVSHYjPaHOhMb4mnurTLpbdJ8aigaLSzlWCb6IV5asSvGdWfsoRdj/ziQxMJseYXfiCZsFJdPLMqiohGk4prBt1M4C2Tt3LiYLYEqeTFI8NnWReGtDp1Ocpavsk3PtXJAAXnzAb9O9ZU1ERKY8+SjV3RFs7xOy7t8MZEf5Q2uMMfo2PdQSOHXHGUUfGunLLtoirripVSidSteKR++KIYfstuln6y8zbgXF6jge6w0niL9u21unh5O9AIe8MSPSoGnmfMcnUul/JyOY6axGz5bC+z7yEVnel7+rUoWmLKDQ60tRbJG9+hpwdT2OMJLZazOe12iYdA7bQwoSR6FJCZvR/OQhwfGYdOJ/ILSG76RPfSoAYnFpWtRGvMrS0A/OdYI/2ACuPkTlwZoyPkfoWRNkNe38rOdmEm0OQDUEFd0Lf2n+6lmBJv7Vxp7J8YhmNhNRQrvgUFM6cGsWNkB8t9oIc34xGbWeRri+hN8WQV9NobjPQhl5PRMXbGLukRC+fqkxBKLC9Fb/lHUTfu2hEB06DRDD3Rp6zOY2prp7WRF66caYCqtTP0mrO3WdqR+x0OPHTs4KvD8UVnWTHQo3S+fTERdKUORm2Wp5b4YymvEHh0IpydWv39HYKP8PFsC4oyi8d6DXCg1VbrlGMix1af4Gtt+aggeVkmOsGZGBgnebogq72YRYqY2eWc9eMnMsqCdRDVDs0egb3W50vJ9BC0LFb590FPg4QPueo8tVm8pUcjtMNkoMkfC5l4VharjAtvZBzUfoF3ktqZoacDld9sTga0p5Cx70TsTFZ/td8mjH7lEGxsQtzlNVu6uG2XEERQdO2RTTuLAZ/5xLLGSDubpPuxEx6Vxm0wE/eYUmzrTttCvNhlJnK1zk6pbfIWOZbKvDjBWzXSuhhbZT3l2F1fcwlM6sgCevgyvMhhtj63Thz9aNmxkzepMg5v0553/A6IReie7/ixH6kzIspIcwz4mz1GuN8N+QQFxvPbd32oMePBD39jE6wrKRUP1N6EDXs7LGL0DYcGpx+VPSwjvALo+Aub4xM2x5xlB3zs0AN8LIx5KhaGOywI+LG4p1iM1f89IBbSFgx2pCgFVARxwQkT+DBZVWTJ9mXbvTTptVGZiYG11sPta3EWttLVI2kQWy2GpGBFupuUhxW7Mp8U/oRLObDFSAyr57hD1XpBRrCAeQqLWT5IpNZXvyXBnxvBKJcUphbaiRZ91UNjFlYR4vgdoepbvxOysAKqqKcuKxnUNvYyHHpEA+JmGl0BeKlcehajnZSFCKKn9P3FNh2cgRAJjxaIrt/hEJ9iUyFyhnIyDYD8pbWhz52/kwXmLoNDAQdFgi2fmAweHEzg54dx+pNgCEbyUqATuikNb0v7rxz7GWDTKVLNxiBqupc5vA1SvgwAhPcGXbh61fqokYMMH3HCU/0huZomYSfkNhaEGNOzCScAjey9++VRp1DWXQIoPmkTnIudDDlwMD3Z1MgDWTs8W9XP2q+OSAbLhV6+RbIySblDRmRTDweMTx74CKVI3XbhHd/B1bck9/3E3YUtfybJGICcvORtyTkCC0lpeRbewEZMhnqe/eDXMhFrm1wmrUjsJduxkhqrNMyiKBasKS++AGihosu9tqc2i639HTvLrbTBAVOh9ruOx7g241hsWOruxFLp07xt1vEVbXQOW8YbePyphQuLeQ8MPSuapH35YTVd0ZOku96kcBEE5XjDKhB76kFSWuIga1c4Gi89ORFJXJd3GfPanD60o4aCzIn86t/3xzo49jP9Rt56A9wDOegQq5vAnSTbNhIrqM3DkAr11kL3TBFWRPr0R/EBl4rSnHlphXljvqcoiiHFLI/VIqYPLF0YtNJkRqcqxn5pxiuQZneZ4+BjaPftI3Oif8onE69wcAQ2a3iNDNCY6flDuCD0gI9+trypYOXl2nMxW93ODjW73jm8CJgVaQSlSFPpLBsbElQbhhIffodPI/f2THIBXVXhj43kOsGCDWEws6BB0PheY7WuvK59qhKrdpCRdwgSKQ++Dd477djnUT++f8gzHx/74zX8Pv4nZ0pKiQXE2BIdYhewdXvlYX8shK/SGAhOkq3iqb+SkZMjpVWWOB8llpxI4Km6HyYD2YSbPytCWv6Gt/LyehtG9mzyHVD0IIBNwRJs7JS88FRYZNHLEp1xiwU58T+1TaPG3sruWJGvTZ0sqyeqoqR11SOG9XTCZXPLxJXMMejlbjowxxVBbYe1VU7Wk33cDsehO9wpdhBYVnf88iBCFOqnZwlpikhiGC3hj8JVHKYzmqjTqravEzsBJR1Y1lMORmOSWvtSuWqbQklVJl6bs2V540Nkhga1wHvTZ4GXMcx4KKuLKurafSX41BLO7Zs9tMLjGX/PwqyE4AHXYaqUks3UIOH2FB3lOmhTDl8QIjrtrZ+9FAR0nMYK9vW/MCcqdlwtravtFzGHGTMLUoxKyzAv2OcygnZZM+Jn7MTBVskbh2omKO/UbP9Vd1gmBpXCHvhDm/aTyz4xOOy9sy9390nz0z5DhiR/lc+4mzilet/Y57ChrFAt75Up61uqqQhWYpren/rhlicHF5kLpNb2POJHHZah2td6Rcj94ovSw7/O9yC0I8S74DvRxBjOtJ0QegEfisRVJ4zOnueZQEr6i9g8EYFV8/yFy2G8u35fEJOU9ZMGXN5r2RoUDWCZ2qHsMAQDvBy9jkVCRDzC0T38qwKOqrLS08su5MWbWz+FywBiUYp84GwkKmIGG/RgQfDcxJ7Zp6GkuUgr4DFL77oz779UKru3s8Kg7fwn1Q/IxLj+e2RqlUgk8vVNG1uWuBDTEcyHGwYPCRSPjmAK0nI6mAcNuqWvy4jihy22VtESYpf2X9VijFzFsaVOLr9SWNzVO4WJUcvDp731vf4rI6ZxqLKelcLPvyT19YhOucGZOg5/9JtIJEYwx04xVVxjo3Mk5da20T9s0WWcmKTk6X/QZLEYpcWRcRKaW7nUlcNt7CKjNbWsdXFsiYfiho+7TBzuY7vAtZdM8BuD6YNSx83aulxuRRHv8zdaMkl4NdUfn+ZZXzyaxIcHX+TMxva4yyTI4Vv7nKDEXt4L/Co1zw1TjpMgL4n10jY+wjaxzVl5jJGIbem0jsBENzJafNjbNpEt0UVMIeDTh42DmXSTRNR6aPIB4q4jUlKjnfrIyaM2+XtpLFloHU1ILwyB8j6cWKFPyhVJW13AnJDTb6MJmgxUhrMGQlOXNalNtjLFUuRMLvOK4/5ob/nTL4Oz7Ais+cF1Xz4Rz6Z30qd9lI8/CIyOGRONpkjIe8bFAt1GsGU0gC1CGePuNYe4nONZcWLeY23wRhcvQ/pk7XxV4Ncw4avltV4dTepcoc7741StqiSwK8zep+r+8z/+k+2l732f1zn8Mk9MfXR75cevb7/z+7+1XeEXs5/+1E9tzz33PgRQpJbZoyCI8RY6de24TOJOQDS43wHYce2EMxDt4ALtp54WK76MonCS1Bm90MInKKm+rMow6LBum+r8+DuGpwNYjc2arU3TKYqoXfpFN3a2hsmyJiex1/5c1QhGK7oAkVehn2RX2faGJk8esroYjJ0NVH31LfGkoYXwcK02steWqGJIks0gJqinjwhK58HWsC0zhDHpj2DtOCEEO5XWYWPenugZn7jzMpIysDXm2hPLdjEzjV8sD91ybKHkXh/DE68mvioJOzzNFFo7/V+07MWAAtaJ0cXV7ugxyOJZmjR5KsjORJP9wpfjflylVVX+wQqjGyqYVNR1Ynd8iLognghpcgyDv+I1NMzha7WxNTYVPc1PLK2ZesEGV5rHRm+T8QfgPH+ShxCfY+zyOP5LtDmh9nL0Hc8KGM+58rGAXD68qcfvUy9demC7fBk5Ppd5zcpFsKwfnbEjrtg6KHUSMR7+j90WrDtJLbnFkEnBOQlJ5KiELfxs0gcIjmWsQEfzhbC9ZbKvSaneiUklpFOZ+uL1QFh6xUdtmICQtb44B1gY/a2wr9gvVJrDxSVTHUsRBW1KKXzxRKjSXQAKTVn75azsbv8wh9ONmPaRnbBoLe9yq3pnix3F3iXVt/yKrztopdxqogf6r/LU7C988Uu8XeGt7RM8wueJp57grQsvbp///Jdjzyc+9uHt6aef0LrxAMHOnyFEVzXPofluokcXDcCGkq9sX/jyV7dnn3lm+19+639H9tz249de3b70pa9sf/9Xf3V7zxPvzrslLtEp/QW1T1FVTb9j0UE+9q/leSZsyWQ8A3IlypFV8tKt8ojLwPJZcpkgqS7uIgbUPB95yTWZt1RKMMj2TjsXjZPOacARd3KNjHklI2rOj6lYtomTQAy1yhHut/CSXAFMEbZTeL1dfge7nY2uKct8wjwalmyABCM+7NaS071GWtPbzrL4iB04gKujndHvYWzHwXCAtXsHQqNiV0tiyG1CZtVJ0zov9dU18rGBSDVAkTDbsvyiVNLKoK5GnMHgounNL9GDLOrC6aGJg5fdmSRfdRTf6Bif8FGXPjE2y5vqckWnYgSi+nsJBjaJbppgb58ZWOruwxJYHmW9Jq6X4989b0k0KXaCpd3pL6qi4gG/u1wYYVaGJGYXKwRUtFLEAD3CfeTbD+WoDsvi1E/xWl5QKXubuYvI9cvnt+tXLmxXr17Yrl27SP4Kn0s86+vqyl/Zrl25zOfSdv0q9fBd54n6167wTplLLEIsZpmMFnjan3Y2+m0BDfE/BsGcXMrxdckZqzAi57hPO4UEipcHlYdc/MY4B11kbQv/TKuLdWE0zqFGFMxlVW4bLR7bZUutnQW1bVB+j2f3uAtIwT9trjfFd/HZ/ZSNkvbkKp56BIlBbqqZTEguWnNWKa31nX+D1AC1im1g9lK53RpTa2vv7E8YT7MyOf8iFLGTOu2ul8zBxi0E7KEtfJu2/eccPxxzzP01r+750Su80uHNO9vnvvjl7dbNW9tLL32PRen17eMf/eD21BPvCpbwahpX9CHTpsprsAc7Dv9wSu2HHibj17/xzSxAP+UTVF95Zbt7+83tKguN37f8r7/9O9tLP3xFgO35r31t+8a3v7P99ee/lF9d20PHRQM0i0iMiXMGaj7oIn8BB31WVRYhgpQ7sVZPOHhPm4E8elIH39wpp1/9EEQXtelMLnQE1S5mSmeBMb6XECw7ZIqCkEZ3C8qBmUIluwVHLPAjpmJTIfazHOvkz+JiA6ekzGJUZhLVdghZsjemsi2QWk4l8WysxSOxCz5SdjTVWJPJSV4+e0oFkO670gjXxD64iydWxM4DI3bBnQ49cqtcrQtK0OVjPdU26qCN52Kc+W6rontsqmO4V+XJ7p3tBDb48RexqodGEBuvE2FsGZoa0h7LXg2MlcOT8vSiYmRhTZQHc/UDijm7DxkNq80d3F1Ehr/76jZvrlt1T5vMQqO1Y178U3eYhl6/hydvRgXHy2mXWIBcfK6yuDx0/cL2IPsHXWRYjB60fI0P+xvXKUu7eml7kAXrGnzXWH2usIg5RteLyDAS3fqjzTG7vU+T4q72d9VOl8jBGXXxbwyk0Mvetd8bfeqg3jcWjrr2/1VViDPbBVvFVS5yPgdOST5NfOI6KvRCo6ffvLNdsQKWLFoLWRAtbJ8hc5qqGkjilQ981uN3qpgm1rCD6Jzhx5uc8FWGcIWdfOOaRc0qkfgPW8rtUw2p+RP/Un9srIu94TkQapyYjMVgW3eOd8S9tX3161/fvv6t72zf+Na3tjd4SOkbr76xvetdj23Pvu8Znkd4J29YffD6g9uPX/7x9i//1V9u337p+8jqpwcPgvE5nG1bLoUcH6yVF55JZnPdHo9CZoFwNn/g0tXt8cfftf0P//1/x2r30e1//s3fof6B7RsvfHf77vde2r7y1a9y9O8NCxjvUfPyTwwnGH9jYAD6C20nnX7UG51sM2BYMNLRbRMqWtf9dIC8YA77vF6ddw9xmcGH6uUNrciLkw93yDkRzLqgUVkUNdHGiPJsd0UurDbS7CffMmzpie1IaUwwayNbOzGY8lavNMoYkC9EkXVhrOLVGSi082iMCTo2J62deXVpuzd05KaOFd8yul12SKfRK6qd8zk4yw0jeuwfithHIooOU449qTtrW6qysbM6eSilr4olVqnVp9IszuUOZeTXIgXUraX7wIjs2c3UtQ01tvad5ZLcttWR/FGe9ilvtHIN2zMi/sRxwKNfQ6RNMpf6hTX07uFfqSeoOmGfkehkIXZjEQzpmRQTUQvhrW3NtxGQsxFO7KCwcPXFOvCj1JseGuPqNYraPNjmIaxk3/Ohoxc50LtyyTMhPpcvbTdcdPjccDHibMeF5wZnPNdYmFygrpP3TOgar3LwkhxrUJ6iEJe0Jz7T/lE+2tC91439kbBiMdW4KUlMrAp4AA2304nqVmcsbysHMogAph2Xuv3SvqwwKOfdgJkDckYOLbzETSfQY11IlvNv6Z0pPNoTm9QeC8IYMsLOVyW3zrsObb5pexW0ndpn6uP027ZvjBCH5LZI8KSt296ppEb/infQJ2blEaR9XXpRWzNXU0KMT8W4ees2r2f47vbid1/kTQovcgsBDtCf6Er0By7XMqF5d/MnP/Gh7Z/8N//l9sgjj26f++vnOSt6M/O47RE9waz9xnx0c26FAVLS+bWPPF7axT/ywee23/7d3+O3BDym+/3Pbv/XH//p9uzTT24/ZhX8Om9XfeqJR/Pen9/4+5/hveLf2/7xP/qPtru33gKShSiQDVcmThueW0LzLCpu+dQw/2OKkzJSTtwJYMWg2RjWkWtkI5ebEZBxwfEhi7n9OvYr7ySAEP8XXQi8fk2g/CwgMJ0wprFKrohy5ZtJVbXTiOmTqaZxFp6mZm7QGZjl9SgxU4Rw/onJJxNMfkRTHWqOX/HtoO30ZuqP8KuNlioI/o8fKdA5xEwV0gksqruXqpYcTS1blbcLKGRtt3LyJw4C6n1g/y7osLOhWnzwnuMI1ptXMkdGHuJKk5u9ZJFcnPzzoONIDkxjeVBiC4bKe3+aviG9sRYXjNz5doBYZ/+wTyQBlYMwIduIpVs0d2pAytIXHnXJRc/il+ckOai7cIwNtb0LRZSGW9+MfIJ9nx3xGB27KdTrW1Njn7xykgUbG8vUau2l3jFzge+ALvnbHybEBx7glc08nPSI4WA6ZuCnn1+E19cJ+XMHI8chqY2taVW5tlOKwmVDrTmxCbHdF9tYX/h30/ZpfdtfBdoz/bd8Kp4+3bPLagyvdQF0HxNbCZRt3c4Oh2XN4jD8XOKwmGOOraFtQzOTFnIXeimQk6bkSEp0Ft3+Nlnl+Syjhm4s9PVsn1hMSHQMW/azkvwU9VPUkS0mtBPWEZl92jntCBPwalBOmYl/kOO8B728quHhG9u/85lfZs47t/3y3/15npR9gzPnG9vffP2buYJ1lcu5X//2d7cXvvsSoue2H/7oB9u7eeXPRc6cxc5CvNukxkO3dvEqBydNs25ksHFoGPYf++D7tlv/7q9tr7z8yvYb/+BXtue/+k1ebnVxe/nlH27PvueZ7Wd/5qPbzTs3tztv3WLxubXdZgFyRUzg2DUY66jDwAHvhJYVt6oSAcs5a3GScQZDNt1OqNiUjIU9wA5un0mk/Z4JRYCWgRo+GzxnS6knDOy7+ByNNCa4aKRDGnigbGBtNWVSayZ1e7ms4ekGvdjTRoZyKg9YLzVhXwaUZ58BZUMm+aKcbtMp5ABLOPcalx+WJla1MzqpciLPgmGDJt7toNank0W8dWK7XMvXSY24kLdsQrJbSRhoOXpSX4NHbwXKD0q4gwGvPAuycQUnnVJ02lpffB3CLOoT304yDmr8rslIkhZYfTq1o/nh9bu6WCK2xIg2H4gEVc+os36VyYQ3/GwS8yElFmxOyulXmhlGJ3OyS6hqbXNpYQr20FM42XRCFRyBJGOHhbu+ZS+Aoberpz7j5sSwo21Gf0H0V7zccMDiYsvqQ2KCicY9ZwLwuOR4EUTeXmFoH9O0oAG0HxgJij0+K0z/80PGCZX9cU+TVzcpzunvYSfe7dwaBhpscO+8K0JTtqpoqTCO9rGidKs9eZ6bMSOmHqAkfJ6ZeIDF//STsVDIHXdZFDT0AtH1DPvSn62IPTJSH8YKySuWfii4s7U62+kzQ1JePtv1eB3D1FInjka7z7xj3U/GPvpC5VMGPDMAimznmh8jk9dm/vmog6dq3L7D/M6DbW/f5gzobW5K++j20MMPUnV3e/Lxd1N3e3udy3Q/Yq34+Ec/vL3/uWfzpm2Hgu3RfRzCH3Uf6UK+y1gax9idBQM//YkPw80Zx+2b20999EMx6Nlnn85hxO07tymTNVoebUdhQhGlOW1b6pwg872NvKFNV6uBPlIkX+TbTo5XeWSdaGTv4LOuSbxcm2agdDBYjx51wOgRbyY3g2AUziT5ipcaNk7CEV32DbuqtdZLavqYIGqb1AgXq6fx5BFwYuUpkLBwZlCmfW88xauuYsTldmtwAx5+zc5iAU1V+bARUtxT7DxHzlmDpC1KRhfZhN2KkzQhmYkk8uiJ7crIy4ZxijL9shzL0bsmBnlM8qEzesIvc5OmpE2y0suHVfKQkOg+PPUpcVlY418cd0Nl7WVPMYqDkKrEwyP46LOWNhPj1P/ipzI+dXIQZBllNvk4Qj6KQiIKqQ2NmMQ+AENdPozOxbh0Y9RJ0j4Pvk6x09dHF/v6N/sRRtPuDHptj5inn/JkA736hiZVXnuRNVo8/dg5uEPOWngSP3ige1DZHlUfF2z8ToxXiNyJqx61ZFyug6HBFr0ffYBPIWnuVYYmQxKyNZIoTVs6MWShViCVyGZfX4tTmeljsQeewK+4RTwLr5rg8EyIidysE3KTOuZADTpFIxAcGMW1v3u3rzUyJHZWhFc4/e2ffdacPmtyQqMibVt2pVhxsySYlQlPl9XmpYvvhq14AmOcB3UncHKQsCT1LTkXe6LR9quOIlJPZt4iHPXLBvWoz97DusOl2Uu82O5D0ZUzfviMhfPtHbDzRgN1KIS+alG/Oe1xl57h74RoWFEduVZYx0dWJ5+7rIAJr0Quy5nucF+4XDoxjjTgkJUx4AZOSfYa5wTu0ZTf4RgAG6WGxJqFhw1G0H8+SfvezDSYxtfY2GAx9htYpZZd5ih7lgXDgYnsaaNE4cJbRqXU0OlLIPc9JfJiyhGFZQiutcSAj5ObgyHP7LIOoMjFcaOqVaaJvaXijn8WlTGaJmVcALrXD3NN5r0cdt5HH4ejdGOwVJ5kbBsviy7OPSPLiS4hKGesmbVta/RIypGUvoCi3STkOtG2T6hJnqpyqw3sD8CFBKkmpFwenfBjZEVyI15L0V1SKhPrxGx4ZI+UArHDxjGSsSR2VOmUgx17wTipD0Cwyl9rzNsb2iba08EZbVQdug/+IB1w9WoRwUJmTNZ2MbVp8mdNUn95elZkf186FUpSuL67SEi1KnvHB5V+o5tywCtVzqDTBPXRmqDXoPinXA0EKW1a/Rl+rQy2m5gUmkL2f4ljr9hOd/YP61eKLvJL2N/3dMGHtPpk2O2vsPXhq1KMW0ajOcrQ1BV91ZFXbQfD/iVXjXPvxYsxjcr6vbgs5PI27Bk3cVbrq8ecqf1zYn5Cw9ZyV1+Y2Xg3b3t6KW3TqVXeTw9mjV1D0yU0MTsDBx27pv+3/xhzIGI3dgEgRu2snjP9K3Wly+g8cOce64B6Em+exu4Pbu9QR7COeDWSUQVgIiBWxNpoPGCAY53TBQhGjdFABXcl0kga5oJik04a5yxn4FVDquucR0Z+CK6Xzvwy2NmZTJex5wAAQABJREFU/yyCKPGvp/zlM2j5242BWVH+JBU3LlHoANq/61Bz5OUtjrL8k7S7A1QTTLv94Q0ltqVybRLUxiyUXqZe2DpCUpcWGaN28jZIKtnYSdMply2yBRK61imXReqkPlgBsNKMUrXZulkwSi9GzoKCibYFYOyVT8RS17Jw8vjHkLZByNm+TWNPCCFWh9/fBU3MpSO+LcHDLvklaou8mKEtKyVmEOVomx6Vgys9dzMpYwxdiGyrg3XpkKH2TVUxLLUvDKYKDUMXeLUfKbLZSE/Eoq8ylqnUDnnCsuxfRZFGj9X2HfduZ2HwAKD2a69A+BQsssKrhX10WSHL8JA7je/pYiVXU+OtDCMKkqAqUN/UOUxod7GzuMtJ+4etmhVDouLs632IVRMjqceH+DZqool+5BmGdqecHXzyHhiNHsgoFiN1GuV/5MVYNqtHe9ZHX2KpJtjJE0YK7O8SY0HEU8K8e9u8c94RB5ji9+5D+JSJhJlk01QpiLto7MyaYk/0ISdzKg7tWitRS4xzMTjXzARzyt4D6koGmk0nIBHaRvJEAQcPHOyjbw6+jKl9ZFXHrkFxHwwzifnwFWuRkVX7WRoCwSrujiLVqvjlWdnoLVYqioWI3gd2HVByVVg7qqw6bYy4oPyeyoPsjp4wEhY7LihCkFpvR4Lks6nAcmHyF7WdHA0lndNr5PB43/lgn/fuNs6ULnBPuo/hyTwHZqyL3na4mAtVCzz9c8pMwLVbvv5n9bdfepSUI9kCBS+2DvZ9hSxp6/RcMO3rJA6AYBS0QTgjhYoWyJnP4mYHhMmY2nWkpQthX+So81LH3iDiLCDr9ySAnSsKzeurHDIf+5DOYFgPekHD2c3CUoHiKxk/J/egpiNQEdClI7wxsG1Lndi1ZcWfagdBxcZOFSBsPNyzsx8YY01TPjGQjZTyjgs7+X6f1voOVrFMrdt9FJe2ETeK3JmiPsqCF1LmRRnVT1LOGMhsgvhvLitlHy5/eW1h8URZOGLRl6JjbUdnHxorg7wrCjAqW1ilqOM/fbIBC294TuI0GN3DolAwtWbKySQGpRZbZUInbsQgtoage/CQl+Y25QwGy2ix/ahvP6ieiFI5bdvWFmDJy7anaOul8xiKZ+zTP+DJ91V4kKs1egK7+NUHHgUPpDO35ACKRXQ1y3luB8yyKp2PN2F4m3oGIko8sDFN66yg62XoCdyJruhG2Alf3LS2sTMGxm2J6cBkgzFaYNhrHA8y5VKgDLtEuI+y9NoZLjDsG0M9ag4u+4Yx/NtSxtRiGF9bfKeQ9BwYLLC2aXVJd1673/LqNx7aQS2BUo/o3ZMLbkEvvM3tqre5zOaBR+9Oy+gsMhqiQGnSMdihGomV5LHOT6hsNN4F4S5P6r3Fl1Y337rTd9uz79FKV3ENzXc37rWBMyUXn+gSQ2z1JBruqyf64OtCxTJEzxsWvUZ18NxHXgzPmCiIKbV1cySIr9TlNJhMFhI44xNKcuofCKWxXVpQQII/jYNtWBEcbUlmaVOr0WkjKuvfJJhVLi+71FhuEIMQHcmNjPv6EF0hq3thBxy8vVJCiPB0nw6S7EHP4I5e2aEv0xxkTRBIg1Eb9F+qPqqfI1DYy7P605Kxg2YhP+OL2MVwEZkUnwE2HpFLhXyzWB68Vg1/JLTHA4boE70f/bMqN3FIpSAlTxQ+hQt9bF989NEsNhTL2vgDF5wFRmVaMLhFb33DLlalnS21RbQO2BQsJRNL7e+UmpfcusZW6VNZuAIoTd9JVQCdjCQn3tjnZXL6a3yCrnGpEp9P/zsWoZhshcTUNqkzso2KcIQOMW0UzOpTPgl9pumnziNenn/55df4Yvut7SrfNTz+7kcJDb89efPm9sorr+WW4Kv8cPaxRx7KmI5xYjAGWVu27//g5e0Sd2I9/NANfjx/Ozb5lIjXX3lje/Otm9u7Hns4d8q+dfPm9vJrr/Otwm2e/nBpe5S7vnrFBX9WuPa4gWvMa63+1GfLsR27MdsS//qQmug+ImJ0mjIzcPlkhU0QxSLu8a7tkDMISMLmYBq0CaEookUGfkXtRu3z5NEfc5bModk+URwxTOkLogGePLLGoXzD3301peVjS9sXWWTivzCFzci3Ry+3sMe5wBLBld8gpxKJFW9F8z6hXhLDBY2C2K2yKuNfzwFZCLCpaBpF51uOQ0aN/zwxG0i+UqJzvc1t3be3V3/8Jr+shQAUYYlTuSuOHx/witeYzM13cQ6IJo9gstKMLTEzdaqaSSsByZfyh539rQ6s+NEfwC3MtYvVsUUCPAZGTOPAPg0sKXnOW/C7+RXMSrFtigwAjUMQU5HQBZxmQV+vmQY0+oKp8GmK0Clh5VfsshC2FYKRoIrNR4KPXPJcS4M74UrFY9UMn+3LepEFXOJKYmiTjHgc/m7EbO/Rx3TIyLj0lDe9ARG7zAzMwsLhjEEchA4uW5M2aYo6J3YWUuuexrWu3VDjlOdvxayLkj2qtvlW0PwuC2xY/z/G3jxYz+q+8zyS7r0SkpCE2MQuEPvuDQMGY4PB+xYnsRPbnTiJu7NN90xVp6tSM9PVU1NT809mqro7W9uTzbGdeG3bcWxnsQ3eMJgdDJjV7AghAQKt90qaz+f7O+d9r2jPcu59n+csv/3s5znPeSb23p88WEgeyDc76KAhVs2gKcmESy8xTaiq3ZkagRzGgaUcwhurcItcrBESApnAJaJ7sdEq4LKjgVBJ5EG0hJ2kajXgMvwcclZq2Q4q4SmZYtDZRKeyrfqiJ4DqKbjlwzocnbhWh1iEpGfe2GEV8U63FxJzSchkhUnJpMIJPwu86SYVN4qAy2TOJ3xWvKw9y4uNf/2ZL7fdO/e0WfaCn3jC8e3tb76iffPa77cbbrq9nXLy8e3kjce1q99wWdoJX/OwTXjxpV3A3NBuue22toptw9dc9fr26gvOps2Zb08+ubl9/R+/zUv22+m8Dm+/8HNvbffdf3/77Be/1k5l59bxx21ob7ry9W0FnZvlZ9gLD/ZVK1ZutI+/Ej46Vm4ATZx667SA+mu3nhuVEK2NF6bKZvhYZrSLBLiJI8Hc9Yew5T6UR0TdgY+tBSKdYNy0Lhq0bnjv8gNqepW1uhfvwjZt8hhFtBKgfJEFb0Arr5MQhWnPVM5E2hs70sFX8DGLFj7yacgOP6lWpLEcxzdC+L78AQsGo4lUyKIUocPcsDqHWah3pSy6U+OaXsoWL0c4e/bubzt2zreXXtrdtm/fzayITqhPg1XAb5U4E5plCW6el+PYM972rkQmNz8cmNOM6tCVkzeZ1mvwGJGY+dYUzFsyW2vM6CisLKSETExDWDgzY8gZFtTt2khgW6lLgYotQlnt805BogzgBo3yl/6JJKEKNzD8OXZXzBDBbwOdXbvKmuhQj/9nXaSl2ml4EUy+PldzBhuyxI1xRxr/AJgGjLx65seAxFU+kdobNHMyL3FK179F9iPIQ0fXnMMpaRlBatfEgaMc4nKZ+CMn4dy5AB9ZgEvDV1GRIQfZQiNimpNllhB1AJHZMvAL/DHcte/Mi3Jj75ZlwsFU9JTuOEqH8uQJH/kmAViqq6VsYHwGKb+Ffezy5L50CaeBsKdD3ffR0GkHYQ92xcOUUTZNV9zKZQ/MNJUrt5TNnpr873qltBLhXbmXpVEiMcbirle6CohH/4jsUbF1pZifpYtQlSWFETqxM5xEhFLJUenmo7bL4hLpA1fQwAssWkmjJ67Sipe1I0AhP4YCFUX3RZJ6ahX/EIaxr4MWp1kejuAu2rWHrW0f/OUrqRNL2ifpkB557Om2fOXK9tZrrmw/9/Yr2wsvvphTWfYwm9nEqyO+cPo4p7R4Usvv/va/ag89+NN2/Q23tPPPPpW8b+34Y45qv/svf6Vtefb59unPfbk9xDstc7Mr2qsufEX7rd/4pbZ7z862h3PP0pZQDmIXEXXqTqOaXaLIOclnRK5yObVlwFW/1+OyhXapcqKdqjORqNDcuFf9EpFfYEqG8O6Awk2QAim80njX22kvuktv0E5dDKjyFkqhDUEOjqu6XPQHjeIP/MC3fkMzA2nQB07Kfip+pZGzkW9wiBheepmPPNhYN2MjlIaIZbDRaI4uWb6DSWwFThXSRfETk/SMgVEylruHbPui625mP7t27+P4B/w798KvZgTKaSWYoebPzdC8MG3y5F7f1N67Zn87ZFUZ1IppQ0QIxupWxrez8BcZNU5Ugqhxyq5u6knYRilLSrb8oUE8oKnzRAg7T4c5P485WUK0Q5pknMSAdZffPhqpkBiGICkOYEAiS+SQNmG3ndvw+kws9TlyKkCh2bCz9R47UC3x13JPvyuXYJ1g0S96CwwcbFHTVHIXyIbVSWO+F0NlzrMUzisL3chjBoIntHIEDzhlpNG1YkvTAijlif7AL6Eh389LjfnQWcpBhCLFOP2Rjnux8AOGxi/4DZoSz2yIExLqkTV5CWwvj4FNww1OqgOy7NmzixfhnqUczfM29tp2JG9kz1OWHt/2DIObnXRQrR3JESJrVq9Mvs24O1B7I/OLO+bbM89uSaO0kiNrbEV8FrOX0fJTj2xuKyhrxx69geeXC20bR1Ntee75KHAUS0Lr1qyNHbrYPc+wTVfV3Jnkl347rJhFAKuhtgTNjAdpVFxhTBsOrNjKvOtMQkf7pzHogNJKWaccFt2eEFrSg2qMbTzIqfB4bSA7rqNT8yHcjV6cyZ3ONKpopuBKz2CPwhd/lXrrvsGCcSaad3IyymIDhmfmxWiIRNwy5Jrh+C/rxJ6du0lbSrsw3+65+2HqFh3SmlXt8CPX5mXILVseb7fffS95vKw9QRl4cfv2dsZpGykTu9vzHKa8mvPujly3qm1fvxo1l7WXduzg5cqVbQ/148e33dVu+fF90FvDO48bOVbs/nbLHXe1j/3lZ9qrXnlOO/Xk4+wBo0dXAKXQQccteRuFe4TRgNvhJy9Ul3D6H+0SpML3OVXCAKSMJyRQT5cOf1kODR5JPU2fbrRzw+apDwoQV3SE0Y17AhVD3DSkb8BUe/CyxINA5UT6Ip0s2+qqjMlr/eRblaYpbdGCu4jeKJPFnzoh6dCqFmGmRslEQlkgw1MXirYYUq5Gizs+/kuoZMAUIXAREhhBQ05wfj4ITIPssSkUtigJ0OxSXoRiRrRv/1ybo0PasdJnSIxa6QzqnG9AU6AhKBrEbPc8BmgfHYcdBsHQC29oyttYZ1oH/AGQRkM4XQTj7tRbWjTCdkB7OJAvX5tMejQAqAy/lC2Icywd0m3SIENtBsTecAxDS1q3gI4LyCetvXuRk9G71PxNKyTsMwqjA3AQ4AsbAwrAdJpKjd++wy2ndup70dnG3Y7DRPXXVSYDjFwL0NPE2qmqgwQBz0U++qU5ZEUG5LTjyrsG0avgtKll34o1M4NVfeBrZFzdY3dg0h9JF921Z+SM8OhHfChiW8uNS7ApczRaGWKEh4x7MUY3LNfuvPvB9rFPfL6dfeap7cknnmxvufqqdsamTe0//slftqM4RHfdoavapRe/ph121hltL+cbancHM48/ubX95Sf+lncaZtOI/fZHP5RGy6Pnb7rpFpZ6fty273ipXfSaV7Q3v/F17W+++NX25OZtbeMJR7eLXnVeW3/YYck3XzBWEhufcggaRbyVZ9zLGsbpq9AUiqhe7mI//IHQGBrObOwooUp6VXL4LsITqzqswQc8nJ1+zbyom51WRCZgZxYXwvh6/lWuVHBxGa5kcUqzCVpR+W+uoR55O5/RWEIoy5dE+62vpXwaZjsvNd70w1vad757Yzty/WHtzVddwdLa9vaP134v1elDv/hOBhWHcTjmznYnnYiynHnqye2SSy9ESb/QPB+rW1az7AovBxxuYnAgZek5QNsxTx20jrxIh/USM6kLzjujrV2/jgOYH26f/69fbx96/7vbCccfTR47JKK+qJV5Ar8qgYu1rphUhMCUnmU/8gPQgtZexJKcuEQWrLjxjbwwhL+3Xj1L4IMA5sXID3H8VU5M+cTEZLB5DniglL3ciDNU2D2BUJUn6TkJqLKBbJZD6RSx4KlfPnCYOEPd4cmyMfIXy0QkbsQM0KR3wcSP3BNClgsaK5clDpCJi+Lxw5zWa3bZbGTa5+YFWyzjYTxLYfKcOD/3W4hgWJFURIa9QuRtaxqbWRrvubllzHbcEyasjSR3UGxL2XSeJR9H+AuMchfmKUCmk+TSjfeRMWnTWJXZu4eG0yOAaHRq5lLmGBmhgZ2b8wFWzrvCn1OMkT9gyuBACNx0FHQWmbHNZ3nSDm7a0JZKmQktRxLw3cE3g06jPA3YyKis6GfHtoflxz177HRtJMpWI5OUr1YCXJb0CCIa4+zogYA2Unwu0qzZFDpbudB5L28vZ9tybKQdS97QRs9ls9gms5Bpwxlrakgdd3EAobJCE1vaYY48GUDaKvZicpElNPJw4A47RlbpBtjBgbwpG8i6l/xM3qi/bCmBWI0Kin1kDp4H19K3pbdUphgYI9t50swE78Lzzmr/+l/9Wvv7f/h2++73bminnbixHXHYmvbRD3+QGQsj4Rdfat/73g/bqZuIpwHbw0zn+9ff1M4996z2vp97W/vjP/1E+xHPF6656nVpqF53yavb1Ve9od10+93ty1/+RrvmstdxptpM+7Vfeiej5As4qn47Nt6LSsN+0TIVLuVcu6Qy93h0V/ZUTOQmED00SWYz1rxEJaJghRAOoFF+RI0dCxgeVfbTDUo/6YHKpdDlLVoYBGfUh7JnkRSmUwhVJEmCdh4vlkujZK6Bgvz9879+eiIF98Wuyp/0zd+JI59n2fXqMOMJTlm+7vs/ajfceBu2Xt6uuPw17ZKLL2w7tr/Ujj76yPaBK17bNm/e0r5Dvh3LctraQ1fzbOiN7T1ve0PbuXNn6rqNkhLYZh2yfDkrLLvIa547M6Paz3srq1evoj3hkxM867ni0le2V5xzRvvcV/6p3cHhyu9525V0buuY+R7RNj/zDLy2tpN48d6vI5dyaEZ2ZxlOI0xi441WsbD5ZVRXU50T3+9pZSVEeFhr5GJRqqt5Eza9bEiV6owreYrmYgwoyjt4UPbOr4sBIP5JADjoVtlbTIPc6TDZeYh/LB1arq3jCm0H6pJPOiiV6G6UU3F9H3G68lJdqfXFwaVdhW3AgJ/gQ1xyi6pVn2dAUJuVoVSqjGcj8s1vX9ueeXpLu4KHgsdtOALKNFYUqC98+e/bGZyg4AnbISqXCKthNIeMqgCu4oDElSvn0sir5DwdkUtGToXduFCjPfCAr3qtBKWUBSIzgk5bA5pRNvA26vM08rXV0hGFr9r1xhJfvaSKYWnk5sxXz8jiFkFt3InzXQILgw3iPB2fyzwuy5khgYVgzIbcS3luZqNI/wM8FQuYnPZdiodO76cj04Idhp0qP4+9GE77mAk2YnwDqi1dgJbLVwBkhoi86mF+TOW1YSeM0I70pZtOiLA2EdJCZ0c5D/LsAt+JAc6erEbI0lvkCCi//aL54Oed52PTohfYjuBSodadYaAiLYWSZyWXrAQjQ2ThIpydWjq2GBoAENRRPAs5qgdOnWo0hmHjsDKzrn105Ls5BNG0p57a3L7/gx+1u++9r517Dg+fKeAvMKK+8dY72vq1a9phh63nhN/N7ZgNx7AbynK1r23buq297tJXYd897aTjN7SnNnO2lbWG/+efe6n93deuy+dKrrrydYzQ6TAZaH3iC3/fbrrjJ+2tdFaHrmJ5T7uqsJWTW5bUknkVLnnNF+0PpAbQqeTwCooORqTTMR2XGOn2QMC5JEY6Pa3gBCIfMJw8pFfJSS0SiTBM2Sev0ohEaGMKzjJXy+49DC11lFjq7ZCzpOCqVMKWq9AIT9P0mbuYthydxSyN0R5sejvfmPnmtde3n9z/YDtl4/HtVz/03nYeJ7GsoPDb+b30wosM/PbSqsy0s884td1zL/DX/YBNAyuoj3QyHAf2EuXg1tvuYbDxQnv7W67kE/bz7Zhj+JQMFe4P//Qv+UTFXDvzzFOCe9/9D7Szzjq93cYAw1msmxNe88rz2g/p/G678yc8FpinvC+001jWS0PZtaz8KW0n+ag2UQ6bl8lSBqZmqVo6ysiwTN0rjwatGiR0+3SyUIX+gCtmVYTKopreEhjWiaoaJM3RwBd9aQgwZkaWkSmv5G2EMhKPdCc0DJQMlSBP4AYBk8WijIhjG76N07LvuPNuBge72/nnn9OOO+boLLNu4csK9/JpB5fHTz/tZOCRt5AjTGS1nHYxjGR+wKYANiTMUzkdPQ0m9mh33HEPFfuJ9qpXvLp9hROzf+dfflhJ2p2stf7d17/VVrE+f/45Z7YZvjGyhF5xZnY5nQMPOOQAH4WenV2So+NXryKNht7DEBcWWM6yAWS5xpmIDZVT9llmAcs5yXeOo+OXsSyXnrwbdkhdoxbaTbiAZp/YG0YrE1kQ5ZxVlV8bxMEnFZeGDSuqBh0IGUm6owu3D9q+Cp4lqYFX2Z/Kr1KxqTj22imVdS/upbN55y/0uNt5EBM5tYnxdqxLx0iD6ie8rgoWwmnDxOkv54uF5k8y0uQg9QIKsPqV2DYrU1hpFt3OJOQ0AL90wupc7IQILveiXPG5piTjg++YIShCdi92WcqCIg+5x8hLBj1OtvFyUadF4PL0XbFnOYPq43/2txyie2Lzw4lPcDji1791Lcule9qHP/D+9vzWLW0Hz4Nu//GPeSawrl1+6Wvb+971VsoSs2hlgZ3r7Vnyg6bPCxyjLyXf3LOwlLK2mpH2oWvXtp888FPK+PntAz//7rZ5y7b23etv5mH237Xf+vUPMkumPCuvMkZZBe8RxtrqmuiN9Ji0hwX7bxwARiOCCKAWsLEZ4Q4elEfTJmYUXvsLl3siuAAVElLVb6DDCRJuBwtSeQfM4N8FCmpAO2/pSYKEDD4crBrW4ZFOwCk8li9nO87ul5B/z29/sd10853t29/5IZsKdrK8eUH7n//d29rxfOiMtTIGSKwS0PFYdtxC/bar38DW7LXZOv2et1/NAOL5dihbsmfRdS8DODcunUJZmOcBal70RYQNnFn2/l98F53bwxyYuZZnPCew1Xt7O/TQlTl087ntJyoRH13b2NYxUNm8ZXk7iS3aK1euIO4UPlmxgvrsMjmzq+hTdorW2oZg8oXEyQwEWHVP+SVde/ibdL74M2jmXilS6E5AaBlO/lsWJhkssw4rff5M0sb6hc8gXRr6O55pQo/6PSEXqLqo20FCKEOEygV0AOAjHduBQTtIHdY6uYTZp98VYvzHYPDBPHdbRt/hwHAd58gdsnwFHdM9rEDczCz3tXRCpxRpxYg9O79wgqdy4WjppxKWXF4tUAf4fsST7cQTT2pnbjqu/fO397I8sdBeoOLfesfd7U3XvLEe1DOC/jFCPMjuk5tuubWdezadkqMw9YKUa/PLmY77DZP9++ZylLyj1Mw0KIgqbSdk2CWplRwxv3LlcjYl8FEtCoudmIovzO+k4Hpm0XLosmmBKbczFoa5zMyqI1gGDfuGWR56LodvZkJLOXBP+yKLdFwSVC6f0RzgGchyDOdZSb47MDe7r+1htsRDKuSyQ9Z22qMyLYNoly/NEKLNLO2UgipUQLWn8DR2yGmlVK8FZjvTxqNwxfeBq/Kl8oJ5sJMyHGSGs5xALrx91oUvTEenUHJAE0H9hLP6O2qZFqqQ6ToFOzM5yctf2iGuRoNnx3c501kfbXkqQ3jCo4OFcBrp5Lv40ATex2aOGPDh0Vr154nM0nLGk06MPIx9uTz06JPtP/3xX7BUcmJ7yzVXtjvuvZ9dTee0j3zwA+1PP/5X7brv/aBd9MoL29E8D/qVX3o/yzarKAt7M+o2L91OvJRBzKHsrnqSJaBXgvs0mxNOZFvugRw1sr+tZ8nuPW+7ov30sWfb//mHf0qHxpIQzwuO5WNcILfPff5LlDdmrzVK6WbRQMNRTnBWHzXKneTUZ/hXZ1AwgUucDQpxHZgoLR0cL/6FkTYDJuWLeOGGc2SZ1w3Ak/PIp5E+vffyG1wJ4vTzy2ATMeTnbNql3hxxE74CFrzl2TLkoMkq5N2NNORU5NM2YzOMHzzbzzKwX9e8luXSH918e1tFJ3/l6y9tl7z6/LYG/wKdzjzPZyzIEYuL9nDp7KSTjqEN8NmMm0zWkrfrkK3sl2VqytIxR62PIC6/UwspLwfaUWxIOPp1F9SSPPl15OFr2hFHrkm2XclynKpkYEI7Zad1wjGWgarvDiipJYBEu8BGc4Xgl6Vw8yFSEYdTXkpXDwAtgvC45Hnyucp/4szreLiQlvwGSRSvqUfB7+XAWHkHAptj9Go3RpkyTDlKuRhx4dAv5GpnOY1VzhCdRBVMB0wBG35B8MsjEvqKza72E2Y3Dzz4SDvx2GNZATuFFYrddPyH0a4f0h5jh+Lu3fNZ3tzFYdannXpKPpAYFdTNhmsIFVE6ry7NjNPZ+b172gEaY1UvQ1ah8/lENcK0FhSCWTqAa7/2TU5KfZ713c3tha1rWXd9FbuPnmvPsavo6aefZoRxOjvdbKnMCAwG/5zYy9rLPJ8JtvDM7ZMuJYBZiRUgO6gIzlKoVq5ezp7/ufYMI92PffIbFNBlbS3LIu977zty/9hf/217dtvzUJlpF7/6lRyidzbLfOy4o8Aqu+Oab33/e4xuWbZhqfDqN70B/hQzWvp77n2AT5N/F7mWtHe/+y3t1BOPbV//p++262+5i5HWbDv/zLPoRM+rJSC2wWX01zPPfLJvnYGOHUBWO/pGgnQ+sZ72QxFgbdTV25G4lchw6FkWkjEWfZcsgKPTW+bdXiD/00yCWspExUMXelljt8Imc+FpoenODshnLHaUdkTilatiXeooi/jFM52FPaEFRPl1khSXu52PybTrvaM6uCIIrrOcqYKdgHbyuRQx0cu0dKZ4lDj2pHxFVspJVKdXuvn2e9qf/Nmn29VXXt4++L63sxSzE7o+YF5gMLOkXUN+fuJTn20b+ZgW6My83W05x7dLdrYvfeXv21VXXtZOOem4iP/qV53f/sbtvo88mV1V77jmqvZXn/p823QaGxz4Pso2loGee+4FvoNyVl52/MRnv9R2Uxceffzp9rqLXpkyMd2qPTFkdPVipzvqttYd+ZA6FKgqD5OOottTW2hYy35vjgOtUUwanbn2n+AWRA+HQGhkdj8Rrepc0RchRq9sJF9jeQTOAJAkB3ULnPVl+XRFwp8o4QnNvMxq1Q+m28jtiODBPflHfvk15d28EHoH34/55+uubw/SUG065YT20V/9AAcen0JZ5ZULBghuiU4JZJQYu0kjOsEQ50YeB0x2dBakfZmhmKIRFIs/4l22FgTIdAXG7ef5aGwPbbrFwDgaXYCvNEXQstp7T9b/sUT4g0W6qzBVKgMoeDiIqszhJ+/AClOdZeKTansmCrDgYODQg7CEYs+KTyBhmjrsbRhw7mkrZSi/HtafKIHiRDjYmVfCa4fye0e57uQTmiPiZ9wLrzSdcpIowFRMT87eto02nnZ323PbiNwISRNt61impyLupg2+h8Hiw48+QZ3dy/tey9rZZ5/eDuHFzzxiCOHSJyKBPdyyd15x6X84jzW9bEwgdShiLpmBP7j+hraVDsYp89NPPtE2UbBew9T6ySef4sHweo70PrttYD1wK/vxP8wHjXJ6LiWpRlfOODwxwR1ivNuB30auRt3Qp3HTb4O1nO2zfvVxNc+OVq9Z3p5+6sl28513tN/5jQ/TA/+0fecH17dLL3pF+8wXvtre+563t1edd3Y75uij4VP0lrGmb4N/6513tetvurH9/Hvf3b72jW9k99TJJxyDvgfaTtaXN516EjrOtC9++avtTW98PcuK38zU3B06Rx5+OF+OZCcVumtYd8LNstToBoRxX0GnqN8PfC2jUdRelVtmtT+rB3rSyGuDNCjE1wYNOxu6z3ROLoPKB73hFT9hbZL8AmfiekFz1JoGw7Y9DTedg/DYcNhS2S0Azj7nVjBwgL42jousU+oWXOktmF+Wp+Q/5Y4yPDnHj87R2az2cIarTXKsEnGl+yA96KI7Gji4qU7XDqxmTMrowMZdUt7tMJ3p2qkuYQPMN775nfbXn/58+8ivvL+96y1voMzshPgBBh9reGHxRD6udghLLYfSyG3MNt6zeSfkMJaEHRBI44ijjuRY+fX4/UbOEkbTh/Oc4AwGNavYUXd5O2zNyraOPN6w4ajk9ezcXLvwvDPbFa+7iAb3QFvFluzZFava5Ze8hvJ1Frap53iL9azMMY/Q13zhL27YuEKTa9WngKaYDCsJUPbuMdCa8JF0KEw8E3pTT/E1S6sjsm5NU4uCidI1nkRmKskb8pwBK4MtXpvwt5ulMTamWE991riXjTTZgemzTGA9DT5lDDrmoWXVcvUcGwq+fd2P2l984ovt5lvvYgB3OjPT97Zr2Gno5hCX3xfo6ewusmWbQSclI6IpU+mrcJRfO6eI7zwDOHVJhDjlquk3bMxU2ZFutP78uPjumY1/haVKzWSHq3xtpjMEAEC72W4bL6wDp+ISTAAlUimoEM7OkpJKR5wU0tNJEgp26IXY5CL98OoxoTApN4R6uhyKXfGcECiOCRbsgOuyB6k6RDhFjgAPU72cHImilEx4MHqVpYoMD2zmbOeII4+k09zHsvfF2TCylWc/D/30cdr9bdRnnxXvZzPJ0dS30/JO1xGHrWubNp4U+rEljOSVpgs9fGa5+Zmt7dOf/fL/wqoIKRGm7hKLY/q66eST2tvecg3ru8+3C849pz32yKOp2IdSqd/1tmvyYqlr5vOs02eqvZvRTozqiKZGAzbEGa3Q0HnXeZ9hJ5K70sxUjW6HZwPnUpzftl+Kfz27nzbxpvSJPhPYsoWysJ+PKR3CQ+bj2hGsQT7Py69/+KcfZ4vnlXzDYhOFfl/7yX33tNe/7pJ0Ug9efBEPyQhffH7bQ5qj5127/ErgVj7OVx2Tx46/yPZNvzi5ljXoHbyFbSPpMS6K5hXJFTINvnJaCe1MyLX8XB6sGWNFWUBtxJextGcHYCeTTpnkZLg25+ctDTKwWeqSXHjpsWYAQ+Wki8BrmH/okedtGQ+9/IuVrRCKyE9+qBIYqRQrC6uhqXt52Gw7IKJspFXeid8OQxj+40qPAjQ/pecPc8Ypmw2WcCiPwFb7wq86TTrEtKN587kvfKHdcsud7fd/73faWTww3r17B8Bym2Eph09Rs+Tms4FlPNA5ZeOxdHI1cnZ3ZOhCZyODjfpuD2rQONDF8gzicOKPYGmnBkEnHXd0nhkdyfKQW7EdLOxhQ8oSjLoJ/DN4dyS7JRmJjyoTBkNzVKnlEXiom81ZKu8oLwW92D7RWhtVUr9KSHOHCHFVzmLDInwQtIERrW0rYxJrCv+LqZsX1jfAjA4ryyCbW5j57KbT2fES33/h3Rw7Id/VzbIacG5QGfmfgQgrGHvmFtqqfbPUEZ+hLGs/ffTp9u3v3tBuvu1OnuMc3t75tjeyXHo2dfcQ2gFeIeCF0ugTgZXNIKVYU/FXJYHopFP2LTfKa2lWNZvPyC2ijjCpk/ToamNru9EtSD1JfmUHLF7qpLOApNLDFCW4I0QNysyPopkeSFhoKR+3wGfmI3uckKZNXWFPMoWE4MIr2J2G8OpXFPQbE2b9XreD06ENiBziCqn8/Vq2A6uDGA4OsMl74MzvWHux2AdRKXzJOzv2XrLKu36RAsLzLKXuo54sMJmQ0Xnnnd7W0T470Dxy/fqkr+LxyWre1Vt76OUZYFq/o+qEZ1nQ6CT0eAeMdAjL2Gui4r0FEUQ70fOdceqJ0DmRKe88219PzjTZnV5HH7E+DfUBCq1lomyh2fhRAMxYd6/xn9FVRtxuRDCOivCj23/UNh53PJ8L3wCyH0qayQj+jnvvZqS9L+v9t9x2d/vX/+P/zh7/l9q//3e/G3pP8+D4D/7zx9phq1e3d7/jmvZrH34/z3k8WQGeNM5O69cza9t/YDej5tWsUz6VBn6GyvPc1q3tzz71OR6kPdI+8svvA3xZe+2F57brb727/W9/8IftDZdd2t7CjKjxrtBSltps4CsrunaUL5coshSHzjFmLr3SAK/ddNpBiOo0qhNKDACLMzoNu/2ZTlwytXAJgGBIoplVBIjKFZ5eTFNtbY6HgI1wPTMA3VJlLEnijOWwihvwlSaoA4I8VCVg6lBGOr5/MRudTJB23a345QdnojzY6tl/LpZY/LI8CJodPwAZTT/PaPrPP/kZvlu/s/2v//5/oFwdluWdNO4RQlI0nn1wpD2yLR8K0rWcFV9t7w97uEwKjgMDK9d8cNXJmZ8VExnMXcpjjYK8E4cN97mdHoWyZKRikgI+FjE+oYohOimmyjhtY5Km6cojhcpT9R6OwhQbBiD4nVEBJLm4hXYa5imsdEf+DvtHQ4QYHdDgJKMaDPpaA2eycYTWCy/sbS9tn8fuvEDOeY5+qPLprZvbxmNOZmAwl7o0S0E/5BA+A77mEGB2t7vuvq/dcvvN7Wm2N5/DO1v/5jc/woujx1G2fObJ81Q6n9JTxfilPa68ryvRUQFJMYV1o2Qvu6TzEaSMRpoEynre6T5ALzvX0UlmX9kopoSnbY9dVuoLjZuDOHWPPNqMsuFfXKKB5p4Y7xAyl40smr18K2gHqmVwKRhRUHqDS3mLC4nKY/WJnsLwV6QmFomZJJOOOJ0pgCHbaQ+C4Vfkx9UyMLWX8J2/PtOI0kJTbvhTRgYF4JQveQVwt33PGMwgzc4jadKy7s6000/ZWDJbp8hLk93tuJ7ViejSbSGFKCSAyieiYk3JOT0MclNZjRgKOeLX7+GmNoy67BSKj0bJHW0IY+NmJk5Iysgwd9+VYck+0/tM9925hMAuht74o5vaimXL21FHHEOvubxt3vZs+8Znv84xHI+ynvzL8F6SL/T9+gff1/7443/FhogXeCP+aJZf1rWPfOC97MY4NA+elx+ynBHyfDq7GXi6FLWXdwdW0DHt2ruLT9GuTid1YN/OTCl//7//nfYI50r9H3RkF5x/FksHl7Esdxm7rB5of/Sxv2hXXXE5nSCFOI2VlUTNupLq6yzJsjUUVs/YpOKsXGb4sKOJyeR+Dy0RBj7erJnSlVhZXJYwzd16acTjt5eiI/LZCHmh7Wvh20T52+CWFD5g3scQ0CDUknXppEK0YLq48Cg5hXXVxQY5Jzt3WsIJsxSelkU7ZYtCUSGi+4Sb+I3OD1p4q6OwvOxrP7zlttjuMp7lMVbIJ4E//tefZrZydPu9/+6jLLeRdwx2JJAyCdOhV/Gsq3bS/tGRqJEXsXMZj6tCFEzlYeFW3PAPYbUhcfmXcP77pdSZMEmTUfhilw9PhCAkOnJXR1D3ToH41HSBAy7chEJv9MSzuc2AUH90FU9YneniD9sUrSTlUnk/DYdEZPLdsh0vLbTtz+9tz29jnf/ZnW37Cy9RX+ZZaXis3fPwPW12/xp2qB2SDsQlt/n9e9rT2x5l9nMfzyP3tdfyeeePUCdPYMv7EgaPvj9oh15iIYt5Fl26TZHTP0PKrBaGaiRVAwVNVykkkjTxG1Ddukzj1T12IskBRghQZmJPoGMziAa9ygqCga8ddSaUJAMmeVZVTwGmvPCNsiFmxjd6cNFLWHWmzll3o3tkLryMx2Ul0dyUwZphur+Sv9SEYvQifrHscgLduGm+h9pU1wq+7DosOe4vSyYo3WFCZXGWWOUyVhIiMANTW1R34OYO+oauVhoQ/NrCZTuRFnNVP0H9Df+gyUft6t2aEWEjO5bkonBQRmrd0ztCzXy0AZVwMS0GFoI8u+C+m1lTjsPxZcjdTuu0ruavXSkvstvuuh9+J89xLjrvwvYffu/3WSY5sv34gXvzrOjcMzcx43lz+y88rP6f/u3vUHYPsLPpUJ5RrWO77j62A97STuVB6Hp2ami801mWu46XFk8+eSNvxd/aPvC+d7Xrb7yd3vnQjNQ86PDRxx/H2DybQdeb77qLKeTa9qNbb+ddk3V0QFQ8RsPDZGZ8aWWMhWYS1Ddxo3BkREFjXh1LNerKlRmjuWeu41KouOdQTaLysiblU/pChFeAemWWN4Xc1Sff38oP4bR1HjSDVFWsGnDf+XFphSdcneegDHEd8OLq7NjsdJ0lGNW17OmkIUcWt+C7fw6AjjdRAqkntEhOZyVN4dM+Kcv+9vAjT7jxsLWLLmq3srX6k5/+bHvD617dfpltti7dzcPfwY8uOnVrDHbS2s8ypBaZzgyxcepx6ZLlGXjbKBSlkDvoMvJK8h1LinIFRztzr6FhdLWcOyCQYOALqWiaJo64yF64JhErPMijYamwcRIg0Wtu4hmaEpZWHPeKnaaJWjTkWf6St3gW4tRGyuCKhANBO6EdzIKee2ZH27rlJeLoRCwre6DFaoGfcD7AKsJz27e1R596iA1CT/L8bV171fmv4Fnw+W3Dsevacp6J7uJ4nDk3GiGAfzby6tTNFPXGzEV59EeTKqTElL2mOnd9Be5OWqOzkXz8spkwERDupsVKcKAwZIYVWUhQtCEjvC1Xw4180HbJI+kIr2y5AklExZV8kSlc5VgIdS+qwRuqmHednveAQ0CYkW9aRokqDLzpKFT5PwiB2uPlP8pGxb0cZhqWYZW/ks3rKNfSEL/oDfoVp0DWhklWARu9SlJoAE8b9zMd0emUwRgu+snIkWV3BkOOm/tm8YvgT6G6AQT6Gc70OG6IPIEw2lDwI369/Jj3AXjgeYAdcaWWozwaUYDvuu/HbP3+Jg+OV7bf/Be/3k459gSeMy3L28++LX3B2aex62IPGxIubA8+8GB79LHHeZ/j3Dxj8X0kl2h+8sBDbcPR66sTooC9/nUXt61bX2h/9cm/bVe8/pJ23tlntC9+6avsiDqlbXl6c7uB0fgazpL6LTY8+J7AXXfdn7ep168/ov36hz9E48420mqPDtIvhldDCkiedagAMyb/dMMuNojkUGYUnhYwnw5D/acZHgSyWcx97GGecxZEI+GauUt9k9EUvGJXSrCdeo7BoRP3dAM7ISdEIYIo4c/dB+y2yssYggm/nE4jszqfO5WoYQ9UcHyhNi+UQt9dRu6Q8k9XulkeyDMKnYdsLkBzH7velhoeDXXgR/mxo0Av6XF3sGRFcDlPeNuAb333h+2LX/lK+4V3v4v3CS7NmXBuTHF34JR3qSZ8flzSAaOzNtYuJaYeJIW9cSnnbFIRaDbT9NIk5bIbIH6i7Swt+4ZDT38RTZyYoQ0541OFhg2jrkyhMuKEhkfyIrgHXwaPakSUW77aV3+5gpmGS68wm8hY8OKJU3QcTDqAXNzolCwFUctl+zmjbaHtfGkfz0H35IXfBWfX/Dlj3buwqz3y9P3sdn2q7eD57pGHHdlee/5lbDLYkGdyO3ccaC++sDvvA67gXb4ZBkUpb+BGQvIoZUYblMjYTD8yqljk7Yr227BF2aE6g7KBAKMEgg7uFH1qH7WLBTr5YX/hxypN0sWmTFZBKUplv+IT8UCwBExpAtfzRi6lW7ViYxWhsEvSqrfiFAUxon9oSpaYwbQbyKB5VmXG9EHRe7nIhhyxC+IFNnDlHzTrjpRJK3i9VNvSikDZefCTvpFcg9P5qSsRY8UhnzkhLpZRDsC8ihLZ9C3CLyp1DYzEI/ziFNqHHmSjE68KxVTEAFzGiFwTv7BD+BA1IsIobIkkRgQiI9OQhS/bk3lz3wboezf8oD346EOpyC753PfwT9oTP3icB8FntqOPOabdeOPNbdlreLjMpgNHYyccexxvPW/M58XnoPnRX/0lGrR5duOdxRIfmyFo4Fw2+5UPvjcjeDu7pTQIy6mIH/zFdzOqQ0Ua4n3759sHfu6drjS1pXRgb3/rlcjAjIKlhHkeVr+fbcDvW3grfjq/XWwlZSlvgRFhLXeZgTY95lJ1PjZcPmPfj0xZJDMJ2mUXbaGtbIQZXdIAj1MYMjOCrvZKYRXQKbxv1WIfZwDuGKklOalNXdGyQ3PnEvfsNqTpsEGm+YBQmCqpSyOzbC9fygP8OY6JWOCXbdCkjTz0bsMTfCqmHdFengt4mnpmRcCqUxRB31iAzRXL2Fm0j1FwTmrQRNCxsCbfgffuT7rm4W6erbklVqvYCO7gHZEf3HAju9TWtg+//5fYwns6zyd2s9mEedYy9OCtd+2sjdIcYBeRxd+HnbSDS0qWJxmVlSIoEDi8bsW3gVABvzSpXfNnHK5ktlOtypQBA/LKwyWw8CUkuLqEJpcxQ5OGDhGB4hK6JbPwo0EQpngF2kt3EsYbEa0zdhxGDJ7KabicDZThxZ1LpXS7hxZSSzZownZk8Ugw7Fq9yycOivbsmW9bn9/a7r7/x+TJDk3Qdu7e3n769EPtJ4/c1dasWsvRNieyU3V9W738MAYnlA829OxhG+6+hRUMRBwEOWOSEaXDMo3fF9YtyxGZlLQNycshT9lHNO1UZaf0Lrstkj1qA0jFjYnNGY2OA2pCcLG9e2LowgGojit4cDXSBHWRRxkqTdnDWi7U8cCXAPHaoCcSwJQzQpKOTB1ZzlKx3ozMKJiX00M+QWM7geVcrmxUdEZcT+EWytyDPFgkWVGtw8MuU7nwIV9XpcMiqQ0jLl0C5JRHXMPOKE3JjkKBJuIJlCRjD3Yk8R8XcGiN+8GA0zpFqVFMMWHsQr0YCkHJ7DYN1aHUICiOZrbDofkXSSpcNSw+maORb9k6kl61chVnfPGymaxIX7lidXvHVe9iS+0GGiz2lbNF14eI7nBLIXcWQU+z3J1lIO3juY8COfuRj/zHMlIqgHSVhwzYx1ZsQ8qsZMxtaJT7sk0wieSu28N6uDR9FuEMYz7vTWCH3jCpQyob92U02H7vaIHZWnaooQcsorO0POhPqnaI1WGw1VWabBayorM+JViWt9LZUKLn3Bniozl20s2wzdhlOScY2h7GkdYBHCbiR4fh9lmWTnbjpyVIp6WwqaBE+ZA4o2KXt5gFzbNNe868jazJvchMbxWZ3Bkm3XlpM8uxg3m5c4nLJVpngLxG02bYnu6RO7ELFh68UTuzMzvfvdC67oc3tn/49rXZwml5enLz0+0Z3jOYPXGmfe7LfwfeUt6Uv7K98bKL0sEsY1ZYjW5JoA0QOxdnVc7sLAZjSU4oRBNgsvxGP0nIDp0Oldl3OiHzUED+qxyXXTWAAyLz31IlSKpMgZJPlb/iSlPZnKlqQGM6dO6RM3HGR+TOC8rSjem9mMHcoluFpa1vSqPgK2zKwNcHZ+QoeoZH2oj3bqx3KZdzmd4d53a+DlTcJHLI8tXQ8XgkYhisHbnuiHbSMacyaOFcSMScZYOCnReUMlM64EAAmqljZPY+Z0LFrDRwVkWeFk+x4I8sNtapGbEb8ox7vKQ50BNQF/juNwwxQ9IcsRkW9YA8MkBIBROhw5l38XOd0I5EA2BiwyTbUIRZz1s3OEQ3BShmRTGAAtcAVWR//qtX92PtoLnsb0cqM+UZTpLWq+D1lGIDFwDVqepCDQLzrNYHTHE1WCq/ZWFKeVrPjUdfWPgZFnUbYFVvVavii04gkN+78TgD4lHHjalY4vBkyQ2649GM4BM3oSGs+kin8DtlyU7cZIv2RPjgdUQIJH4CjmdoMombZorMSi+I2Btjf1/C9DieV154QTv3LF4sZYufzwqeePoxvv1xQTtr05kZofkuiu2xPbMP5cGO1MNOso0CVoqJJgiBP2EuE/6p6NUBKa9Fbygt3TSa3jGypPxZsayg9sMudU0zapHKqLqPGYbLHkGSKP89INd4vWeZy86IzjTLXa6ddWWs1lbunIvHSRJL4ed2SmnaORVR7/iUkRbBhtLZhXJKb58zDPzC+6Ju7TRCZzo2bWWH4tlxdcZeFaIsDUg/tiRdfTs9Ye38pSnA0F9arqRaFZbN28E5W7LxJhzlKz9iBjDVbR4ekrngnAt4cXRTJpU+n7ruB9+lo51tV19xJaPqPdmSv4YjVhyZL6GMUFQiu7SSZ10ORZauMtnRjWeWtQXVfEQ6GGr3JfTibs7wkxNxEBM/SldMrtJPowGeyQExJY2Z+g8MUywnRkhM6fhZAQeWUcWkYPGPMhbYrsciLt0r3KAJh7BQ7vLj6XGGLbfG1D1oypIGZsCPe+GFNzTVIOLpAcVdoatWHspJ5OemTLmj9Nnnn27tsZl26gksd/PdHcmGPCg5LUNW/iRBWsYqZrKj5ZEgLEhIEcCIZhy/zBpKwQiTUzIIA2lq9Cvihs1T76bo5FEwxUq/ZMp+IdsbykCXceBpY40Tlj9bBCiHXGK0aYqJMidFaFxnjs8ytTisXJPPL0T+wox+YVbQWcIWVuHCGQm6XB2s0y15RoeTtELpald5kGrJzhU62biiaLiwKG9PAxZCFS98yeR1Wo4GQmkYOlWDyj5DBu8QUq5s/CDPewkN0djVPMcwGYQn46JFGAR20IpdR5pCmcCgdmSkhcKZxVDO9A4ToapxAiMGgCHUTR8k02glwpgygs8QHDnO8EKiBcJTbm2RZG2TkR84NrIWl1meC/hi6HgBs4xXBgAFl6YoTE0LJ5UnJTQjsEa30bUhdOnM1HKhJz+BwaqiV36v4iTW2mcYwDQSxOsyIijkwJbOGAIZ/OslurdjUoeGFdV8DomKk1LexA+A8pbMVfMKxoZ1FOwhU1iHjrJ1vaAdC8DHd41CstPNCKwYV7w6wKtgsFEIcjffQ4c7iRWvTPqVVl5mODec5WzAJGxyT3MEF3WR7xAOoFyxZDmwzLboPQ49xJF3a+t50L3As7e8BOvynvjhUfY3VK7bn3Lkny79fzoZw0FMfDBTFiouqonTwariFo0gLMIVpmzcG63wwU6Cx86DSGFqALn4GxSrnBAGSR0Lz/RFUNIyaOxA5N5zHI+JlHHg4iU0RrNiiVd6hUjCB8WZ2JOGPBa/5G/w1dK1flG5s6SWEyd8h2o/J0+0PYQZ3HAUlpmSV26oPwfo0JXRzSU5sV5a0o1timHyp7Mf0qnE8HuXrQrUs6vK0SGnSRPhoRs7WrUwlP7S+yAoE0M/cghHcs0gwmZCTo9/E7siTGZfDtjkFWX6vYsxJB/yG528HB3bIjjNIO8phcIaeUgC5jROAwmlnAb1eec3CkTiCyZpSYTzhFjFFpJ+EcoJU0txw14OSkeqcWgV8EX+8B40pvfoT5rtkH2DKbFTb09tmxIGxvvPnBGZNkgexKdwjNLVTKgTDDH9Mo60wZyEI9ggKuOQiCgTI8V84Fp4XLlggJu9+Usc5tLB+B7LUkbyzA8Sv2w5GP2hOTu1c5qxb9F7urQiTOUIs35RabwTGfFq4GEgZPN9niwFImWNdgZ+1wkYCwGp0cNZm0tZyxhJm1lCURu1lsT5MYKEvp2qS3sW9sUFQ3+VKSuZogHLPdu9qfXKMzKNugwdloqMlxY07Xhd7gIsuNyAlya8YZoXX0mUt7h27K7uKbMi2qGTVOluNwlNacGHhPxJXFh7HLzKxz938kW6pPs4rCsdGDHU1eV+eZbu3b6hZ5cDVP7lwjZ54vcCb/J+lthcSktD0u1ofBqXbss8syLSoq480vJS9sSHJ5VJeGlppwFo1qQwiIPF1AngfIk1jIwv/uUrf/KuM0jDBW6VBq0ZASK/ONIsmSs+ccIAWssv6j0cdCShGzXQRMtnUEzsPIzvad7jpcxN9SGd2Nhq0EzY+OGKrjQnfDuODZLOq3ngUmueJzLz3EsH5OsXLn+7ceWAs2v7HsqGM20LkO/imN/7mf1nZs8qhsu3wiY/pYvtPSRUJuqX0Yz89SKQvEc9GfeSigRcyjeIwodIYYZWpcUCXNSPn0XX8gxCalQYdMyIQQT/sbX1V6geLhx5CuKFtGIcWgknvtJ7UnujDB0AAEAASURBVGgpXYpZCoOiRgDuymXdg5ayhYlxI1/EFLfaleStsLCfECYgWtVG7zjTS0ruwicicHp9lnhwORFeyLpal+JSBqhX8owND5YrGN0I8rA90cktvhKMUKfHLR1qiUNs0RUn/hI8/l4sOjFTyxsQdRYJx0ftLHA4iIZIlK1k+cxxBIrieH5Qtj8i8BwfCROi3uko8ZJBIqgs66EJE9Sph01llltoIPbTM135hje2DYcdhdKkkWBnZYOdBtm7DSNp8tFZgWxQ3Wwgh3j7dl0BrSzDCSfMYpcQF9NqiqxfPHlU5+KzjsbzFl0e2CmAqdwsaMrnDq90GPLzn8Q0EvJjZOU+AZM027xLTDyXCT7x1Zg4Q6ORZMOGtV6a7gzzqBllsYOxhGYWJG+CPuewQ/Wl4oW9fpfJWQybGHhpIZtAzFB1Bla7+aXacayQ5/ipHyJNnP7YXXh18lkUX7blyZnmBbZsQGrgrPTRHRkyQJCmuicf2FQwyhD0fI/C0yeE93R1dbFD8GGRZ88pDStymS0FxgEH9F22lU/qtuwjMHEUJHVzF5bvrbhemjgBLasI0kswXsuPA4nKpzRWISRECJYNol5hqYd2VwUbJmmYrN9r8lds4kMBWO+Zu3d/GkfihitO6gKOealdQ9EU6RaPKDuQEjsNKJcN2YCvsmM69CL/gNVG3Z972WzAVOOFjdDPpWGfge7mhJN9WRb3vTMaR9JWLV/TTj7udPLPZ0TofYCHkNjdeuBBvAv0/n4hWfz9+5bzU74uo3wPkmEqYOwJYKwHjFbPpiHvCD6F7DoYA9wkPh51Lr2K0bBBj4N+igPUq92xfkgPQv7zK1d0jLNs6wQ7SIoBK1Km6JavLv/AACnLT8HFPsZ3HUdZGHU95UZh+FEyi0IJN6HR0bnZtpUAggy5rav7JmUhJHraELbicu1RAR/UMIrPlPKX9LLbYh4aUFk1YEAIY60IUbS0GClGdUa2WTo3HWXFB/XmeLYvnJ/KcOk8OogAM+mADouiIK6O6uoyXIcGogptMXLt+OZb7+TTys/kqHy/r+L3Pb73wxsYEc23i3lx7dBDWD9WLIXvVsusBOJTpQ0ESp5pLM885fQ8y/DFJtsoz4+zxbNhsiNyd9egW0jS0yxRgyj4gRJeRhn21mUQuoLee1piRkZDTcMQJ0rNcgj77oPLDy5HkBid9Nh8KiKypTGVN7aTgBTDY+gfODprOg1Nu8/GdXEnBN4MtXg/z1hmmQn6bpLta2Ykyhg6yCFbnA8wayZEQ85scgb5fG7mcxx3BFoAlNMOzLuzSM+1y4vvNvwhVPaY5K/5Be3MQoAR1hGOszM1k07HIAh/Oqqlzk7pYNSf5NiuLkJqDedjyEQwHQs6HZAuNphjKvX6Sy8J3f30qnYUzq78ObuUQmTTE6cM3cc9NkffA86muZs5pUE18ZYNJTaPZugJ82xMQXQQknbyMmGiUsnkWzKnJAyG2lRUwt5iEflpM/GS1uMLrMqKsMmzksaSEdjEkRhKFYgsIS4scflXRmkMroVTDfDUHsZOHdgT+sIsCnd90sFy8VmsHZE75HJeIEzzxV/4LZ9byUaFVb1Tsj2AgzpD29mSAw2fGe5lJlTfnSqZS46qAbGmCMOORYJrtxk0MnNflD7yRBVk6TUWmUZomsTFE5gO13U152M/6qwDD41eVo1lK1zEIVTU0rUgx8A1ryjcUtJXF3VJPTcCP4mDjEnJptAzWUr+FYvMkPXnV4glk3GdiqLo57/MJkR38A2kEWEUqXrif3sbdjw4ZehfsYN25AjNsIa3KcpUEpYeYTxBDCVw3EtNdW07OAjg/h8/wq7KPXyywQ9MHsbhps/nsxp+reD0U0/mVZjVoS3lwbsIcjWyuwwZJkYxS8gYezL/7nvw4fbDG27JG9T/lROKZ2jdnKofffQxnGCwq33jn7/LQZnLaTzBo3X2swQ6R38OqVXJMpElHDOTQEadZJzr0VEswpREAw5EiShN6IWmmUJcSYaXoFFZdjOpR0ww8JR/kTGB01UdML5rjpe2NY21jfIsjTl2zGwmjXRvePUnjB6hQaWSinTScBCQov3pWF6rT1FAk84tI3TuOQzVMA10NcJ0LjbE6hehi2oVIW1Iuml0zDmWBFgGHOk47HBmoJVnacg8Q6cmLWchdkrKonM6LvH6U05+pJk/wpg34nlcUc309PcfdEU3LI5lpO5WHmWVWjnpBZZLOmx5iAfdwzmif/36NdiM2Yy0pTPgQ1daat3vZYZBuvhih2R8z4PMdsBVkfCVX2hW6TEOITuuMPUrmS2hpnsppz6juhs9Zs3C+yeT0KBCJuydn5NgnXYRrHSTecFlVqTsoeENLROkngi2SAjD044I/J4eeEFxFZcEQyXToElM8qUj2IFUPgXTS/GHp9KNqzDqVoMhZ8Gkok+eq+oXbXIhAflLAq5DfwAGb2mV6/ek9ahxk2f3SyKyGl4UaX4aUXlXdiudjB+uZE8HBHzBkkbeTIQTFBmiSeQtnaStqVzp6WoIie62hKZOf0Wv8jd4pC3GIVguy2UWhv5DjlGuBkFlLNzSbaDmbgJCDdpDUuWU49S2gf7/fQlHaJRU0J8IVXSjKelFX64aE3gb7qEKnh/f9UB75LEnmaBsbrfzaZ9ddEbCHMmxW49xQv1td91j5UndUOQ8W5KaZUKDRo8Sm36D1kBX2pUfQAvg/Q890k49dRPH2r+6PcsLoHuZBc1xwOg+Nhg89fiTbdNJx7a9TLue2vIsn1d4jm+JPEUaGVmtAJlop2Pjxs+RL0oYN0PJjk7wsBNJ/PDbcOIffxNhJ0JXgYlxiKuKA11xyOiMVoNfcVNtSSe+LDnUhEC3iXDytbG30U+HkM6V0VtG1mDGVBaAIlOdhoJVXFX0ylVnBXneg3LO7mx080N/OxMruiOGZKz0pOEtFz24NFrEI4f5IU7tDCOsLMqHbdMhRNauIzSUMfrG5t0K5nGnX7aQh3DFIzYMonDdBqRXYwSMsMipk1TIGbnImVoNsQXcSo09APRZYJ4/uI4TGkKSBno1+8AY5W/iKuB16GOz4K5CLEEkKUFSb6j5AxZKRUHEIpGw+VN5RFAQFfDWgZJO2Lzzv77q21mIK5IzxaDh924waT1amB6XBpFwtYNGQotkpAUHTwhw7fGm6C8Z8fy/OOEKX90N2NGUHRajycV8tR5moMFM0t2qngs3x/TSb2+lnApnOxO4GsBkCRgYy6kwLr/M8Y6CAzHrrGV32Dp36x+iyHPiSuESNWmkmlGKbENdRCbgGsikiRM/zrybxKpUkehpcq3ya0Tlc9lD/N7GKRk0whpls4REKpjgIkvnZVj6BWv59V868MTW+kuWGmjHvsILEieFkqEGMfh72qSMAj/OqjH7NKfzsDTWCihTOcEovCKbcpom/wBNZF4cHnoIWfHiAR8UZRui4pNxYgq2JhDGEdt5eizaM1ueow/Y3rZsfY7DBHa07ZyYcRzf5zrr9FOp17zEzMk3KzlC7SXOgPQTGodxrJrlLe1xUZNByVOiJ80kwCxJCoZAJMp3/DxqJs8oSLDQORPyecPaVav5tO4GTjB4GgGWtls4TddvSXzvB9/PScc5QDMjARiApzAWcPs7C3ONuLnTBieOeJdPfDDvqDwFXCMs+ims8ml724E0Nh2gjKXw/htZzgwwrX7GlRF6/g2oCaPJFHqCUwVz0BBvQqv7IxR0B0yIyrdLoi0VaSJL52q0nzMQLwDe/UG3nBqWM49if40FSD3H65rCK+9LubyXYU2XJYSsQNKRR5GPh8uQJ+HOWqgSQxr6EcbODVpkS4GaoL7+xT/ihS+ZpE32T8IFpq4DNthQUdmiXfQ0aslfkGWMgZdcZylPeYSHYpfLfCWm0y/cfv1ZcSQlGgRx9A/Z0+FSWG22pG/HL8RIt8aMFkXJ8+xOIpbxiQBSnLpBP5WAaMtRbBiwgh1xAytycSmai+mpa+FrZ+mUK116oOTt8tQKA4MhZskreCVg1epD2Lm4nGe7fGSSMxY9f3GOnx+Xm+V5r88eZ/CvAGbWH3HLV8zmO1+ecu8S8hhUjnddtNbLXYnJtYtpHqU0BTRdfYUrNzp66VEo6E15UM0KU9a7/+W8RtgyVe/UDHuMOxCS06YB7oMsWhKlSlpPcTBbMHLFiVe+ksMAclE1vCGfPNEsZXDKTw0dfPk16JST0bB1WiHT80gdq8OS2eAv4OCc6IPCpo56rF6pdxCqcmGqOOIbp1yVZouQoWAnrc10BRtvvygLS29+wfjOO9vd99zT7v7xPTlpIzmpnPyWsZyTT9TQPxzOstzc3PK2nYOJ53MOpLSxQ68f1qm4ftM/47LYftZ8ecECgtXAyUDUU046ge/43Mh3aVa0o/gGy+133ZujujXWOs5qe+zxx/Kuxzve9iY+ELazffCXP8C6sR8ZU8VOBZoqb901DzzhWINbaJ1SR0tDSeNuh5SZB0ACChtDcsc5E3BE7UkPMa58LJk6M0B+URRPIXf0wXdkhuAWEuHFV8Z6CCmpIZcNX2Vw0dWIoQ9sRCvg8B5YkUGdbcAVA5fGTX3E455dRUlEceP96SzVcSAmTnkL35bRBoUl/eqw82zI41qEoTA4Qs1dNvrrLn6cpBNQA2RL2JQAcjc/CCGSENKYytWJRCfhA9ovi2Q2PnjefxZMj+vMh9oFC0KXMR0gkYuXXUoP5LL2g2ilj4NWvEQLY6x2KF0JFGKB4q/KmiBJ6mlZHMjgRYZKt6w7OJJq2cNkeBODmbqvaBonbtDBGxsTIlESSYugZeeAh20xVITCFk6xK95YaVhHcghqj69GRTiJW74LfsR3tKQ74FtO57Jy1Wxbe9gKHhpTR7fT2bJBIfUH3Ox68gGeNlE5eDoIdSPMKr50vG79KjqipXRObHzhJegMJmFd7AtPu0cMRJnIoz/kygi1ozMxEVF7+l950f1auMALN5AVNcU0T9R73MMkcQXZZYGCeeyALLjqJ3FZqTcehnnhoGkzes/dDko487JoBz8dVxeIm0nKLq2USSKMi1zeg8t9wOHTRuKk4wlwhQEBV8iSqCQOpkk/05WdD06KLNCPJNzi007y5mqSVHUpPz3gzfJgmyKEcq4/fF178zVXkveHtjdcfgkHSa+iDPBFVT7+aDvsxGHz5i1tC184mMugZY6TZ9jAAh1b6TjbaHnKuP4n/DnAlEygQB6whPdEDWNmnHnGaflW/PO85f7Ot1zd7rnnPk6lXtnuvvsnGRn9wrvflk7HAz89y83luqJhpkdVg2k0wlsW0FYxC0QKbBp5KgMAWaJCeXegCR8h4sHLXcNqnFk6TBvMrtdEmYBKu/NWYXHCj2idcnHNv+FKM85A3XINjR4HeOWJFc0Gq4qIIg6kgZoe3zk3o59KY1kOn0/AymmXoqe8NdK2YHSRRCWQMP5gcVF/20K/xMp+BuQBJ1lmt+iSoYUWwAzPOjHgZRZ5USD5WhS5kmCHQrxyWDjl4T1IXHXR23hhglIweOOSL2aE9HTKyk069ZfYfik9Fah4Fc3IQlw1JgWqzP6kZDnxBPGeEjmCE1mloUy6WlItOK8OICq/RlyVB4UEh0oZiURWJpQ1vRMruhGEdO3NH9JUurhmVIC5A1cydXRopoEMhGWh0w6pJOIDA291TNISZxqOKBMeYcGlN4zASVN4IkOj/IanThqKmM5kBR3IIUvbmnV8+oRZ+NyKPdRZDjBlw4HvAOlSxyxYFFoHNX4scPkhzIJWzrSVfNdJ/OXLWZajBxq8IwcoZSGpEMB168Yv8MglbeUv+YZ+yYfoWaDK6/i0OphQ5yJOJyUlYTq4Pv/E81bx5kaXg9sUHSBoV8cgDC54UMidC7KFRvKYZNujnt+Z+YlSTApZ3Xr6lL88oCXNRU486dm5aOtKnsJFL2gZrxR4gS8ozTZpvxbRHF7hiuagp53kY1hqi1xoyUPawi0qn9jL3Z/Wu+hJ/XaJzQmGR6f5CODcc07jW20Pkk98zO64Y5L+4ks72hNPPtkOZynurDNOpZxw8kbXMXJ0PZSiZCm9ZvYwE8oOGaZUi4VMYSTm9ZxyTPOHAHvaxa+5EIX2812hjaT4hj1nSU0aBw1UxFVsWkGNdxTXFVVlbdIFKjHAoBMUxlHSxCDAODoLjAVDPDXg4hvxZWClJoJbmTmeKSCppZe09QHlrRMSJ7IQZ6UpuYqSiCUL8BMnTC/cxElKV3jI5TsXhpPQZR90w7PL2RFjCwcAwLg1NuMfdSa9gyTNQl7PyoDpdARyVmC8HVBG7PZUEhWbhiRyEUzhTMdo2vgVqPR8FrbgDDQdivDCQKPzkKb2SRxpVSE6iPQi7LBfqlhvi7R3/cIWVVNhk59FByoRRLGHHfHhFy+pxbaYEAf9xAtTchkW30FVzCktUEel7DkaYuLYyHWRhSpg7tKYuorPqwnD6HbcodzxqaziBE+bgTJd1g2H8PE9kZK0rFhsvNr495Do4nMZcpSeRafiMtQI3uJOW8YFO5Venzwdl4wOZcVKmhfK6FK2Oq7gw5K+L5TDZrFbuHAvm4LHwMYZlM+Q3PzCHqR0QPq1sY3U1A2rqD86ORDrGmdGOAVU1Ghs/sYFdOD3sgUDN7AIGHsAGmhhO5qeqc4Cmli0E48cGegSiHbBRTbu8abWSI9UYM1ZU7x6sVbFhXelFg8FKFjT5eXKhvLUr/K4BqJClCt1wYuNiZMEMpcZS+/C9xqJkyZ9bVV1QyTSe5xEUp7VgV9opRwKYNygL83F/pCpC6DFTT4RijuRcdhAUhHWNNpkOp7V7JS+hC9dJ5zKBIR12wvw2XimkPAv2xTu0FUYh8+6mVkK4nKW2/Y4+pHYIgcc63q8wcaLpSrox+fEyzeBpMIvRItWDNPp1o2MLaaCCg9jS2/3l9EI40JCQxP0NwpPbEFiKVc0Igs4nUywa4kGhZVJxXM3CWTRikHBJnOMIiHxZRDPErWA6CZSEReQRKuvxi4k+Wf0DHzJZCaTFtiypeVBGcxUG0iJRbbQQAIV5OdzNFFdJUlnKd7EUSF5FmIn5VS5CojFxl1zAGGwjFrw22CmhYCmMzCFCYh8hrITH2nmeSpg8U0xAC4PSEWXZvThYmtmODKbWH6gAySYUilXopDFkLGR2YYFYiOPAk1YiGISSQlYIUfjjl9y6Oij3OE0X/AW0wdiFGyjpaxsncNArTv4qiGNmAZPRm0WPl0iuRNfFVPZhO5O/NBGOO5WtjjjzUi5dnDLY2CCMUArcXT2QZGOkIN3QoYTG3pJ6/QqvtLKP+VprLaO7rFd7ez0s+8Ly+2IsCWfDZo5MEMnJDSuC6zFqoxbruCPSVx6c1kuz3FlFpnMGC2eQPgVIa6WlSHnyGMTwQ10oUzA5T21rvmPUMIkEs8E3ghp96SRAC/lHsFqDwaMbKBPx1h5M+I7rZAnLTSLRgZK0hsussABoOqcSo8KQ2+0nckIeIVM0QxvjRgGg6Y6Wi+8y6TiF/sH6591D5uubLU9BRW+pcjPQntZXJcz8Nqm6qePRKpcvtzuoHe95OmXjg9yoxyB5mDaHPWvLNUhwU/7QeyQe8Yv5OWQvbx7MSWpcezxykgaS/40IMAtdZtXd5DqpsDGZpQhlKoeWMMXDfWUVoGUAHZIIyMGkcCk5AspnU5TPuAPwWMNk+IGXClsQwr4xClXyWbUgJ0kh0eBl55WnjSUEbZI6Y1sU0IhWvIMZt4LyTKnS6PR6URf8M2e2DZ6ll7ViVgQil8956oGTlva8eegydiBBtO80Y8Ny7bFWXkk63tXB2V+5FL3khFfCZj7Ij/pUbHrORrEChacfnlHP7SRtKHww1MpiQhMyoO+UVEjS08CtzK58razja0XUQpw6MoboHS24sJNlYyrUG4HXUY5S3r0j8jJ48WAY+BTBlCsUmbQLiwwiB+Nz5BXpStOPaoDDe0ikTwKf+UPPp7AAbDIHsFZdCn60gSq1ysQI+LowAxXfbP8lD0GCUtBVoc7jtk1Zjg52LIParReJPFCATI8dHPcmI0IROSNeqp/2QRg/zuyWHAfrPFN7RBaATddqTq/QEvXGOK6TaqcGmNtSYLXiamMSznQMLocpSGNqnixRxL6RdlDyzo1yAjPDxLJO2CSNx3goI5ImAB2WGDCuXd+Uk9MZRT+Xi/FokEqWYkOj8F/tH/EV1LyUTpljmm+D4xRDkb4IBuERtkxBg5V+Vsu/A3/uBunSObbcPj7YHlEmr+T9E6+oJHTxPrv3ArW/CubYpcoI4WOvAheJN4pFEhE/3QlGJJHuHFPoQB2rIkGMjSD3MknApSiZYHKMyeepEtbA/qLTAl3HhLDaYyJA87lpeJroindmPgSb9QidxA+8RXuhla/SWaYZkMOf+CmRgaWCqjcVpMpb40d7kOMcCWqu+JRMlVsRpISh5G8iq78bBQostTsTCxMrHoT2qmeROUhcWdQS3zayqkry6bOMBDanYY0D4lHi8gdnYmWT/3HFzkAAFYcnffFfoLyM4N0KDxgvfuzEtsgDTwshN9fpZf+vbIfRAs+na4wESGyF3oaIEAy25mCBmUirnwIVBOiV+7KbwUfsUqik1jd1V6eJVsS449OxLuj0LSaBYlkWSXOUXNRmuDa4ceRcSGfQPdNBSVWfgUqoJJWELxU2sIplPIXfLdNpz7ihuwjLOXF/s7poNugnaUQgCN77IQ0sMxH/xi5ZvAzydOyk7jJHi7Kbj3UXrpYmnrjX1YnaBOiHwpGxwILrCj+HND5H5urGwLYQAUeSOvCDG9L2zlatvfv42R7pmfLeBnOA28zPuZ0Ec8bFKdEkaANGF/jYoamjRb81IS62k5RL/JeHjr4gq15mg9ZsnNLGg6mPQVGNwZaeW4L8XR7ACWruqyTDsl6IQ5/eeYjPyiWv3AiWGBCvYKRS935x95pT1QhbRwUKIcdUKTkr3KlnCuIiNwqH4TXD1/u/8+uyrDMlDJ/wcEnyYOcMAwazHNkGV8EiA2AK60PQiCgQBWnGEVTfOAJ+KfTPgG03KRgVewQgR3XNGoyFYF/MzOKTalCwgAuN1HxdAP0LCNukNQbQlnZcgnJUTv/FLD+A2TAT5S0RdZ5i4WVA6mkVdpFtsCkKphWsvbkSpJAxxtGqATh9RXdYtRRbFyhVYUQduAnQ6QfFK8iLwpDTFtVoZBm9ysulXs8kLN+qwf/Ewsphyq6Tq+rzQlWRsLdDks9lkf+KZtq4nIK73WwRr+f7wWlcIrMs7F89juCAmUBN55wYenFJ8OJK9lNKTQbcnIS/m5vjaxlrAmGTY3PBgsB2jIZjMpDBNTgI6tKhpax4W2MNuIWZ+PvjFoYIiMIt1EXTTHa+EItf+AlMCEEnI2QcVxGdL+robIPxpN8AjTaR6BiUkudyiB8katyju6EKcqVPaYd5CpiRKdMDPrCYVAbNskmLwZuLxRDdi0cRaY3EeBdlGNHveLFqYGMSj6BCcV5krjFyfLgoaOmDRvol6S7N3O0iqkxvGWRgGVIXLz5UQ51Qw5jk2cC4qKTgPiGv/LcKLUqYlVfBSw5pWLHsP3FF9t1372pPfbEY3zuZW177Wte1U464Zj2o1tubTfxEcpZHnCffOLx7fLLL+bkDcs7S80o5zLi/Y892q6//odt3WHr2xuvuJRPxK+AvN9M2t0evO/R9uyWre08vkG2bt3a9uBDP23f+s4NtEV8r+y4o/PRyxUr58hv5DETUFqZks+lDuKjETrkntRhY+Ar43q+8uoAMmmpqmvSlAiujIgnqaGSeBMCY/6BC8K4Wxer/XNlo5fvyFhstWURLkrjOuw+8io04WNZECM08RVtsYrvBJ+0yq90xRUNrip0Yfu9+Ef8pFVq4NAlJSM8HThaB3EIYXr6geliGtHEBrETmt4ismVoyrsnxrgmmNaRzRozbzht5PdHrNAFT7ol3ngiJj+QLFQy2s9pDPrLDIo9IR6yg3zomdqTB89xd8AakkYERn4jte7y8D93rkmvqKDIvRrejP+LmcZNKx+iKTBSGG7oZNhMloHXMdukxpU9wZnRQPITIKUeO0lLcuDG3ys5MaFj1oqWQmmHENvZmJcWNZMoqcVI1g8eoVCUAp94mfNTFFlPjGT81Blf342Cj/LhJqAV5OpAhnTpGjI/e4EYsN7HLzNk4PN9mgB0uSes9RAnvR4Xa2qbMZjxDm59lrzK30JowSgNbyFGr8RPhI2Q0jNaMfPlW+jxnL4GTH0gkcFTl7uULhwpjYqu/Q5yDHVDE+ITtnhiHwCRNNfgGM+oZbJpxkjoCduFTKOWhs2o8Cpbm76EjjzPP3qSGrO/hJRquHrfklTtoM7uPrVRMjOtK9rXRs+ibS9bNq+wL6yGD4lVvgAGwD/zfKKgBaOSiJ+6ob80y49NPJIruiA7fy9t35nTWU7ddFpbQ2fy2S9+rT3yxJO8kc8W4NmV7arLL29nnn5ann+queVC9CfZFvz1f7iurTp0fXv2ue3tK1/7Zo6LspN5asuW9s1rb2z/fN2NSdNwTz37XNv2/Ivtytdfxu6us/KiLh/bQZbSZ9T51C91REhTFjtA0bNsEoUto4HhDEX8/hnOxzCHnbxr+84nUyEJES+HnqnJA2kSja1iIMp6lSVlCF0LVow3yqFwU2eZzF+ihZUPNgdPknIzzug44ZQrcNJUJniaOEEI5JSushelkZA0EPkPwcivqNKwLBZ8MS1sOHQZlvq1xZRE0SmxQ3kBuh2KUb9WumkRE0+/y0bj+KPjcZRiZRNeA+SbOnzjxq93HmB5znOsfPaYjokGIzMmeURwR+R6dGXUustXPi5BVSaUMUut4S+Ywg7BGLlwpRMZlWuwCGgPTCKlWUWqrsEsuQJT9iqBlUn4qVscis0kr+zIYgE1g8ZpxFJ2eaBYD0zuXZYULAp+JORe38txo4INJrbE3vXTnmU7t9xaGVMQpmJNfMokbgqddEwJj7J3ZgMWtv5TFB9mZxwRxsBN7KpkJbf0PGzR/HTXVb5pFL/ylKxFi/TIgIzGU46meT4RMyZIERMGepYXO4bQ73SlU8/MKj27vRSJ+NhM6VJeKmR5zMcR5Q89y2o6Q3nEbj5/M76XW/39t3jVIFJCMlS9y0+a/BJJPssrujFomOfF7nl6uVnPa8IpHVtLiGPbPTIgZFVFzS4AdSnPX9nJxptxLFmxk6ASTJycYNDYXGC8nydwdslnp2If824sMIScWdTbg3Q6nZ9tqr/qkADSVsYZifNes3blAY5fjpFChjTUNnLA2dx4R2XkKL8bakzVDtX4Qo/yypUOw9Mb2C7OisChbOs9/5zT26svPDsvz+7me1Mr+RDmaZs28i2yM9uGDUe07//w9vbVr3+LdxOxGUo+/cyzOWD5ve94U7v41RdydtkL7cUdu9BlSTuZdxw/8AvvaBuZQSlQZvhwXcGs6qgNh7eTNh6Xg3YXt18Kr6yjGRvZiKhqCjZ39DK9tK27eHHeo5uhoqMt8x0eDG/yBEQbC9ZdlR0DxpZ9cg9ba2fHBq/SoZ808yAe4oGyDPJHK5uweNEDmAnqSOn5YIIw2kKpqB2wwJ/C0/l2/UUdlOMbAUQYNurkAzmkLfQBTNIimTlBh1NzJ9aeostJoiq12E0qM9RNK1oB7jJNDSIF6aQy8z7CXo6Qt8ERR7IW4Ew7iVgyi/Kc4pnGWBgybcg5jKx5dBbo8g3JzKSqMCWTcmv0DtVFCl95A27KwUYTpzeEE+LlKVnBCZ2OqcydijETO6XyEUMGi2cDl9ldmEqgCyM26S7TOwP0+KRQVG6gqiZMJbRYqWUabBvi/pzNCiadcmCic4K+FKwMpNuw1vFMxTt5qA38pVEvGs5c05B1aoNuGiOImiuISgPHWjwhR0ylBAhJtxHky7j9EwH5uJ30YeSUWwxpKZOaLaFBXmCkbee2n29I+K5apZQAw6bKgQoKm/KR8mreamtuOtvLkd2ma6eUhbSmciZdGUjLZg7Sq6NRBfxjo88gUlRztSwt047IbFm2HBV7qOrXzvkDvAvkzZ+8/AzCt753Q7vu+zfwafPVbf26de0Xf+6tbduzW9uff/LzLBWta4ceuqq99x1XchLJkTSweyOrzB9/ckv7my9+Nc81LrvownyFdoGvoC6wM+na79/c7r3/4faKC85sl73mgvbQo0+1z3zp64E99qgj2jVvvITPdB+FzPZwisqfjYsyI6/PTLIbc9JTJaFg9Vql0N06qp4zbNVOHMprMy2SWcPQWfsmn0IiuFnC7daBW/RaSifiSfIPPfZE++nDj7XDj1zPy47Ptj/62CfTSbz2ta9oG084od16x0/aP37rtvbAwz/lkMxT+Cjm6RzA6vOiGtzuoU2Z4ySHZei0ghdxfXN/567d7ZCVs5Fvfn4399Id0Tlk89C2i+dK//FP/qKdSOf0/ve+hS83+86hpWORU13LVqmd5CpBwBBHaQywZVVXcJNQwZNUVLV5Rwt0+e0kgm0iBLy93FWpelkscMmWztvUlGlt3/PBuZgyHezMM2M6r/DsQJZTvZ1GyaLGRqqrkpTOBACTFkhRnLvl3woBONQFmfiJxZZF4yAlRTMRN5NDR42wlod70MrfS1Q6AZgmBRjBpiYnVr4IZFz+ki550kRyBEtp99PUVoihniMWnaO9GU/3Jewx8Wk8jSe8lIIlTfG5pRIs4WVVjRCaqWDVYWmX0WF5MrXMhTE+uN6R0xFfOjLh+auDV2vskI96xToamtRuk0kG50Ei+B3XEhH1iUgDKw9iHKkf1MCbYRGkZJGeoJm221BifuMysux2MV/rLXY8AtuhMWT2GH5tmTwTj6TYNOZkhEmNm8GmY3YlW3/pCJJ52gWM8IQso8r5Xbyw6DEbHVZxqzArkzTZRWlhYxBv/pClpJfOYYsMmVUEX9oQ77Ndae6joRg29G7eutKTTRbYy+x1QB8BYlGsCN+yE50EOPIcuph/CYiCM2gjJ90lLgP3B76mIWWRVRDDQcUG8gRx7146ytCTpzoJZd6jhyg00vuhO+fnx5U5eWnJsVui8wQ+jRb0J/LhcfBh+36Azvb5bdvaK88/t73lqivaf/mLT/Fs4vp25mmbWEo6tP3ub34ETG3vF4e30liuydKTX5z96j9e184867R2zumntz//xGfamWef2jasW8OZjZgXG+8izzZv3kq9YAbEg/lVHL/zGx96V14q13barfJnXJGYqrEP+7AqljRASk+11ub8pdyDkg7IvNJPhombepty76CDBP6LhoQgQsBwlZCKsx77/Hk3Ot11x73tn77zfZ4BPd0ufsV57ZgNR7f1h61tJ550Ytv8zDPUG5ar4XMIL8m++hUXtDddcQnvJ81yOOZaaLLFfIFvzMPfOp58o2esl20P8Cb/CmSdxfaMKjIyUQ4butbO5gXKC887pz386OPtC1/6Bi9bPsws6yzo8QImetRzH4AV2XLQLRcC45K0qFyAHUarWV8ntkDOgW50TxpUqm6FkVGmlhtlL/aLDKRq4wEDaIIDod/Na+VVjv8vJ+1Aepe05TwsBi7Gmjgzt8LyWOwmsoaGZDp36Ll6JM2ySYhHBTcsUJwOcjNLqHwHsQQoDQhg8pxxhM7PnSQDN+fJ0ZLnE9PATIVDtY5fNG3wfaBO4aXjWLbMI0KgP3BsBOG/n+2VvtWS0T1h0x09b97yQnuBM4iWs0NmI9NrK71v5W5mGj5PY+zZVscec3QZseeMxoUp59o9njd2jyF9fx/mOiqdYWnjhe3Pc2bWbA5w3MOI8knWnnfu2pvljQ1HbYjhEAGnrOVLCNJpuJG7jGZEwNB7sRWtxrguU4CitFGkTEmGvuR0xap4auuKNz9MRHs8Lmfu3bXQdu/0eKQSRBn9ZacTiPs5pHIZndCKlcRrb21Ka5idKR02ukF2H0uk+cbMi7v5TAf5QAMgb6C54zOA86u3Szjy5RDsxudSiekJSVX2qVLiuTyYT4zTSDrjkKI45mFoUy72Uy4ogO3ACm0y0qVbfu9GG7JBd7yRzkgPkcPi4U25Gy8M2mDKI3ozI5Tr6NAgFVmlq1x+2sDOeoHylCVm6BykGQFxPaV8Pw39MvM0+VqyRRAwlHHk91iqNj9meMNzYR+NJuWDkk4Z30N9WNLW0vkkg4n3tBEL3ZatW9uXv/KN9uu/9sttFY3pjl0vtW10Xu97z9VtHec1bjj6iJxcfOzhh1O2G7OfC9uOF1+KvDZUdtIeNHnjzbextOUS1pEZtFQ5Ug+aBspBZoJ8vJHq2Bbga6dtPVWH0l17GVB50vlVJ1LtQR30WzN3M8hGJdu9yRbD2lNK5FjoLuNMseepxzfefGe77nvX5/j/yy95Zfvov/j5tuGoI9HpiXwi4LwzNrXzTj+lfeHL32hHHbWO+jnXVh+xsm3ceGzbwYGZTzy1mfxaaMcfvyHtkodkPvf8C+2Oe+5nxvg0ndZcVlseevbRduIJx1Ku9+Z0511sUtjFwO3F7TvorPZho21tF8t9fpomS4OIa3nJvyojfQWJM4wdpp1UUgtAuABgIxvqkSQ8KVU7A6QZ4yw6BWbEQKg0r5bLGgiZ9vL0Xs4U7mVpykglkUS1MdSOySAqsT1Pu18Soqiot1F/IzlCjrBleNLWFcKgwKDCGkj5dWIBPdu2GScNRDsIt2/pTHLXBvzjoAmtsCfEhLwqk6MbR9YmmCywp+M8/PhT7dlnt7Vzzz6zrWAkOE8B+8mDj7YXX9zJ9HhTDkQMOTlI20KIX3qOpL37VdU5jv04wDA6X21UGODSSKSG0AUxcPCUXhXzQ2t+/voP/tNf0pA69D5AgVnVfu/f/Eb78j98OztpPFLoWKbx73vPm3MisCe9loYH2qc+//V21913M1vY1d7zrre3N132qraHJaKtrBn/9ae/0G7lDLzf/7e/3c6l0N957wPtD/7o/2rnnHNOO2Ltmvbut76praDTc61+NCbJYG1CJzDNB5XFSBoqxsJ+Tv31TyIrgy2NNZoRJRYXCEe6NIqAnmmGUxDyMFo22CYHxgUX21jJLc08Y7Dy+GfY5t1ikYGLBYCOQJdYvBFNetJJb+qd/GLGYju4z4aYSmq6goSuPvgvzNOpAGcnkALqXUiJ8pPvaOwqRCT2ACz5THKoWlD9s6FytKTclpTaujmqbScqfRGFwAiTb0yBo/xCmWapDWTiu31TIVUaKGuFkBi7KlfJkG2k9ljoBWBAC15ow1Cl/CaVy+QOTeXqXIXkjxgj0StpNLzP8bD82u9+u516Cs8lQP7GN7/ffnDjze38007n20oXUUYfyMG///njn2pHU5Z/gc7mtz/6IeqAI3xmpyw7qfcKOjJH9p5cMM9MwkIjx3lOMfG7QMtT1/bToK9vb7js4vbgw4+2G265q/38O65u55zJKcdBcZbIj7x0GW6G5c95bFhfqlXfEj95ij+DltKKGSs5RWfujjS8yET+WU4YRBDVTYZEyoX+aYxytBbLiWweuPb7N7HT7ba2jiXHq696/f/N2HsGW3Zdd367u18ndO5GN9ABGWBGIBjEYQ4gKJKiJEoiRcmcoWYkjQJHlFxjWyWVqxw0NR+m7A/21NSUy1WusqwaDSmJHFGMYs4UCYAgMtCNDDQy0Ojcr5N/v//a+9z7QMrl8949Z++11/qvsOM599xz2mtf+fK2fsM5mXy9i201/X7PrvP50fxC28alyde++uq2b9+DbdeuXZmwTzHIHaN/773/ESaSg+0C7pxz0ryQ46uuubJ98tNfbBfv2dWue/sb2kPc0HAfT/9fxp2kf/eFr+Vy5pe+/r12mIdwrl69pn3+S9/i1/6r2xt5F9qll11E/GpxTVXTlqt9+PSEmnSoYWgsHRKcqmUZddqjUWOi9RmOiVXFgKRBqr7dO4VdscYR0Ci37aRRFIhA2YS21GZrufnIqc+tH4asOOpOu7YstnEAIBhz/GnPyQvesU0lCxKVa9qn/DuBOZBYVLqqXedMBqrzwEO8SeEEi8zdPLZnHWeqR44cz7vn7Ju7eML2ehZOLpTjg3AkVD/6YKDZ8W41Vte9Q5ezxeS9+w9xuvx3n/l828XpsqewH/rF9/DKhifbPfv2tZNE8o4772q/+ZEPYvXRDEJWV21WGgrRqBuOAV4iYhZDYa2Eh+MaJIV+wZkN13fpgN69c5ZOt3rNivax3/1I27F9W/uXH/vTNDAvIbyfieLDH/w5zoqOtTvu2cuZzcF2DZc6HEAffvwpnup9U/uz/+FP2v333t8+/tefbG/9JzxegoCew5eSP/9z726PshLy0oX++v3FNa+4sv1Pf/JH7eCB53KmpRfapf3zAZNfGv9YbCUbVKtJisGuQtowDD0fTsMhDxuHmpC6HHT9FXjiUSbs1cBCpz4WVvB2V659ryKOiZpzHnbGxgSUaR8on/fl+4SczFXroDGwbVtlhgnozP4LPDVjYWGRyYi4s/BwcqCEcj52SDqUPCt5ZphnBOqI7NQRtbfiZYkyni17tkkYGDBcjsRFcGvT3HqzKtrA539ugx+8uKQJlBiJDIas5Bfwz1vTpeu75lgTIeCwJQmp2srYitGkvViV8wv309rqoAl71TcFbEkLi3H6k9/LIGQ+CNromaY6TfNkkRX4fOz46fbNr3y/fepzn2fA3NVe9cqreP/Tw+1973wzz9S6ov3nv/m7rOAXaMxXv+yK9rGP/gtChiwmrOIU58TxY6mDVUxktqtjx07SN9bljahbN25kMlrg7Ir34NpXul12oE2b1rX3vutN3G12pH2Od33ddvvd7eUvvVxPuudlZ87a0W31eblXP20fHv3LmZJ527QVpn/Eyctxw/XiTGFCAVcGPuv8OGeXd+x7oH35699hrLi3XXrJRZz1fJA73C5tq6iPk/ia/geYdbCFJy+/991vJ3Uq9fim178azelEuVx9iol3PVc93vC6a2tS8BZGDFtLm3z7W17T3vLG12ZypDczVm1rV1/1IuaG0+2jv/Wr8BHDNELqmhnY78/0wYnYx45NxhOMRMn6roZEWRXbnqa2ICm8JWkbVS48NgtD4iH0CT1ki+1dOVYGPvtoCSlT25QIjuXalDoaCmRUR8cxO+qw+KSMrXTM8KEjp5YSt9wxtyi26enKjrr5WE9GKHdx0p9vv+vOtu++h7kV/gQPLn2i/cxrr2WR/1x78slnmfgfa4+yGPgnr38V3/Fxyj4mM/AzEXWbaVbZeDsDLZ8tDbGbhA4pKNrLF4JX8Py4n2n//v/8c1Zei1y73dx++ed+ru174IH2hS9+NbP6SV5odJQvCY9wSWsdj4CPAyj0aIe1qXnttiY7HO3KvWBu/P3WZ6UDlYPBSu7z1zoGx0Ua8w033Yr08lw3Pm/HeaxclrW7WOl88wc3cXa0Pu8x2s97jFwRuaB9jOvj23nq6xa+nFzcuSMz9cFDR/jid33ed3HxhTvzZaRWaYYPYvzxbXe0P/tf/vf2+le9kls3X8zKkVeZGwPwrH5bghXrFn/okMlpvAkrj6NZk8p4I4BPOIhY4ilffUbwc6rbB0mFTXZkGJduqRN4nMxXc9nBCSZnktHnDklt4COPj2NyUKvWU6junSzslBloXN3C69mmd5ctMOnnbE8h/QpeDRQraCfyOQlRm5TJq6fazfKDxYUO6mp+fIj/q1aiR2qPD0kz7ELN5OOEWXajZzggqmzZKiGu9ntHlQuns7QVdQ+W2QDCZK3fYqXtyYGN5KuTWVSx8mWMrkod670VVgwHGDXFZJNs0j1h6tkiJmcc/PeFjpCpwJtvvaf95cc/nTcP/8aHf7W99tpXtMXjxzkrP8nrEpbzPdAl7aUvubT9509+tr35jT9DG+G+N8BV+/QzB9oPb/hRe/vb35izg3Xr1rTdrPA/8befaZdccmnO5rezIPvrv/1ce/3rXsPlqafb/Q88xhtRV7YHH3qCOjjZ7n3wsfSbO3i45Nve+BpsMmqA975mG3MQ1j/7ow8vzd188MR/44ZNcYx9eClZTlCzWHAyou6dvK0TDc8ZEkF0Ufi9m29uX2Ly8SzwVde8vP3pv/4ov8nZiW287pkFn+8fQ0PimToy4w/rZlWJbcXjYqbI7E34PU/fbHmWLgNTiLNeFTCITq6uvunAvhfM1yik7+KwbTQ2U+l1BZQ2HOlquyYTqyQqFXVpE5TFBuJkLOUhP465+gOff7Lzn3RFFbPM90adqwHyph0KWttYeI32OS1Wo0d1/rmJPg7YAo5te7Rvj/G5M1ml/9im53HMMaHjFG+3CzttDsdov8dOnGnH+f74yNETeYvqxRfvyStB9j/6JAv4I3xlsqddyhtVfRHqwQMHI8cXA1lOqKO3mMDHlx4PLrDVCmG8Jlr/4irK7TwbNm5InkVXjHQ19NyhA+2zn//7dv3113F76Yn22a98s33vhze1rRvWtl/74PvzhaIYI5gJiAMVmAls9y/WGSDyzpC+r+jOvfvapz//qfav/+j3c5byXa4j77v7bvK/2zZvOoeJhtPyvXtz+nfFJRe2d7zjDSx6uLbPaaG3ep6hUa5aySCdJpCqT+PLr+oJdK5X47wdz7uyLmdQ+B//+KPt3ocfa//H//OX7Y//1e+0C/Yw2VGWurZC03LL/6qgKkqkui81JOqzDY4OwVmLfSuDk34rSI2ThKdXCDER2skiDyd9YWuRV9nI0GgdEUASU30WWOZlN/FN2q0EDQ94Tl5+7By1ebQDQGfnG0+5jNtWc7l0JZMQFRS2wqs6VFTeevU6mEwcdYkGvTCWCBbB529eaCIxJiso0uGRBm95rFCdXYRCma6lvcAcn2TXiL5Zt17qyqbd8VgeLx9W9KNUhmBxSLwKtwTl77EfPEpTH8xBiTUc8UPd+pNjaDrQY9br7WxOG10YLOOFjk+3v/yrv+Nutb08cf76dv11fJlOez61eCID7tUvZ3VO3LT3+re+nv5yc9twzpr2pje8FiUsevSDs7GVvGbbI9rzvccvvPsd7ctf/XY78Nwz7SMf+qV2jlcTONs4xYD8wEMPchuzL5pb1fZy5vGiSy9qj+9/goXXYnvXO97EJfTLc/luCqOJVFJR/B4sr+om4NZVtmngx3n9Jrb22ZWrGNwhZTHEJSjp3mjgZa9n+N7qm9++qX3rezdk8fUWflT6xp+5pm1l7MidkqeOgUR/oFG64Kx/9KHLTS1ZIaMgVqhaeu5UcXCUSvwtNx05mawTZbRNetWRaVuIf76Y0I5nSZ21prvM6lp6aSWVTM8p54ct7Q09ZtXT7ai+aXsZ2sq+LGQwb0wKGf8Ye2rTNjfByv5kh67KlC5o4Y1e7MYAq0+f5zeLZxs+AxtbQ8S7pQydVTp2Dyj8ihpi6CKgRKxl6pevMb769W9ziffW3Nb+zuveQizLNhcgNXbRvGnv93C35pPc6Xj5pRfz2p+1jLEuFbA4VwxIqQ+9bkM3Nz0x8LhZDtXARQHpXbt2tlt4bcNO3iW0ccNGXtv6ZBqer/q+5uor2xW8WZVhvf3Ce9/ZDh892j7yz341g6BfoMcJG4sBASt/REfs0sfg1wcJjVrB9zA33PLj9p/+6uPtX/7zX2tb+IXz+nPOaR/9zV9vTzz+ZPuLT3yyvfoqfkPAKuw917+j/dqvvKcd5kvZk1xWo5mWX0Cfy1nQk9z6epqV0IHnn8914I10hmx0glV5PAgd17to0LmGyeZlL7msXXH5Ze0b3LH0GLIXcfvmsqzsKyYl3CF65PQnDTJRtdLLLw8j7ZngCiajtD8LnDw4UB+RMt6OrQ6WiUWwq4JSIVEpLp/w1OWXCi7UdAiZ5Km99attDst1mYsp0bhbB9iQOjYNXx5jQh3ZaK0Km2G1j2pgMSfYAY3RmbiGvWrSBo7D56FT2bz7JvLgwZYQkYiI9kqAryY0h6fyozPGfllGpxJTfX6CBf902SAdepSXf/pabbrsHHmVLvO7G/wAMvYP3VCLwmH4RCrjm7cjG0s3LfU17c/z3ehn+JHkl7727fYafun/7/7sT9q5WzdxSY3va1gQ+V2XMbuSScgf0rpI2rRpfXvfu97G6v0UT6S/IN+NaMs2Xn3+s+96M22aFb9nA/i5ft3a9qFffg8mn+Uyl9/ZnWrvvf5trORPtPe/57osvLTHG1Z8ov2vf/B95Fy9nsqVi/LH9oDuxINidDkkuiDzN10VS2OhV+aRMgzVmIoXATmMt2evVsr9D+5vX/nG9zn7uy1jxIc+8N72yle8mO+wVjG58WoXbHTLYoAJRfzYgy8qGHnrJaqDz12fFufPyYUiL0tEhJ4em8aiI/DBFHJ8z4l7yOmhXquRs2d0SPM3O6lB9SvDTl3iy1sb+aQrb7losTnBkg5P/gevx7GVbjEcm/TTffyEEojhc8TEm+mu+M/wZHUbcYp2jQdTmwS0rNqrfWMmG8Fon3k30V7I1n3OS0lNB/p020p7fvfPvqNt5FKwT6Xw5wQ8+TpnQ4cOc8WJdnfo+NG2/5bb29577mMRv6tddumFtGttrPiLFVuX6KwMXy6wqqFWbFx1O7PKrfSz7aqXvTjX9m688Uftfe9+Z7vt1ltQzrVpOtijfCl1Ayuh1/AlIotpKpRLFkbXSlV3AlQmZORFnz9+Um3MgjdhIXg+nfebP/x++/znP93+8KO/297x5teh43A66fHjR9qrrnpJ+/svb25/9cnPM3EssNI7muvm3pb5je/yuI9HHmm/8eEPcKvtyXYhp/47zt3e/u3/+u/bUX64dt3b3tju2ndv+8bXvts+zOWRz33pi+12vkf69Oe+nInmGDcvfPu7N3Kdmm9v6fgvf8mL+mW0qlTr2NiMCq58Bc8eYj4bx7iNh3aU4rMEaoUzHcAzsnQCo2Cg7DFjMyacSSCNmJ1mxCsQcNGA5WflvYwbBWol1vUiOyGpPLObFHRMZRNHt8/vWTShuom8mmQbHg1+gCqZFU8ugxUOrIHXLrfqZBEG0zOj4qMkfOWXUvgZmyoSmlicyvYBJuByKiWDx4FXNqszaJ1XdYkpfOqvTYTO0CldSsg09QKhUHiJsNeX8Ny+zQ0y48fR+fEexSs5dfRM5Ks8auYTf/NZbh3e2v77P/6D9qLLLqRdnuTj71NUVhZbj6eYFGpwAZ5VyUl+rxJ13J14Vp/htz+4qFKOmiGsxJUCv7wXg2VbbPN3RPKc8DsN/jMpi0bFLZ7mrsnoduekyUQDjLQsdoTkTMafD+SONtpTfQeguBguDm3vEc8gmkUTtDoDO9PuvOWe9vdccnv40UdzF95/8we/1S67ZA/2MRni1yJnf71FQGFjEhG6NrnYOFTd1jGkTBz6I3PxZdKxUJoxgGys4m6M7GVFCl1eI+hfQfV0dMLIFYpgWZh+RhZQcQMdW5l4nPz6li/6k05hGKcJLDiSXJJXP5nktMGzhqmfVyy6CvwpZ9JSNAfB9D8dTK7yNTFTiq70V5Nw1B1o8IAz629Du0f5OQjqgcrUXLvH0C2PtK6uYpw2SdzUpyZX0gn82dwg48L95ptvy8LoIm6tP/DswfbgAw/xeu8j3O34MC9PXMsPjS9lEW4MMTZOeSQLZvwl6cYDTLtxo0KH4+R97Pv7f55VGNtpvgi97u1vDchbmSR8B5EZO1canODZZt7ESe3vJV5LXuAC4Be//nWeEbW5ve61r8mK6fNf/Ur7zne+3f7go7/fXsMvpk8sHma1tbz983/2IW5K2EonOtl++zf/aXv8scfbJs6QrNBxN9xLX3JFu4hrkd5C7rrHH5999Lc/wg/dbmVluYXfBlzGozoOtOve+RYq40zbw4/3/uj3/gX9+yTXM1e03TsvyhmVt5Fe+YqX5Tui3CVooLSdOFRl6QQE3etHV/02b/+zcUw45TEtkV14TKZNS6gG00MfHqu6w5YQbPMVJYp4o6H06qxIAABAAElEQVSL39tVp5eYA0ZunlU+CtRFGZtm5rr0oEuhLBMbtAxC8s75HBsGQMcRK1v3Y2RnRzW5RWOPX8VThWkvYvHJmQzHxIgARZKdDb8uP/QYB00uhYzC0IGsWNgSQPbZBpCZYT/JUZfGZpZGbwZABmBwjOHD/EjU23ivYGKxPTge+XuylZw933HPve0v/uq/tIPPH27/9EO/2N7ApSd/kHec70T1RH2apD2lQx/MxtDqyya7Xb00/LIFw4PlsafwMgYUsGQZ3NUh+coOeX30u1gXD57xpO4HC98F+oDQ6r080uaxx/L65je88Y25zh82ML1RyBtSDvMj0Btv/lH7hx/8gEnxVHsTXzr/3m98IHf1neRLA8/6NMqzll5Lk13xT/s0V8Oxp1seNXV2U2cI5bK1zB8Ox60h242qnjC8tLA0KltbCSQbAFSq1xgYROoyZNKxJEWktct0WmgNvt2CyR4xBk2QLA6gpWtDcMFinctVH/YYMtqB9g07xxWnUTb6aw9UbNG+fN+knLonLDHLW/00maO6hwIo+uvXILFPujYLUrNal8SjjhUUgbr1KWA9JMnYaID9Yfu2rbxt9W0phpLjtVypOmMfhs8TmpzgdMuUNyKBpjxHaG5efO5FDg2GF1YU6YgrtjOsarRZA/wSNeW2N90KfYRfJK0UvlSAkE7g+iDIfGmo795N4U0I/kL+r//2v7S777qr/dd/+LF20Z6dfLdzIr//MV5XXLI7ZyXec76JU8Btm64Ax9+deG+6K8vWzjt3M7ZuSyfIjA3HGs6s3vi6V2OGX4YuMuGt593nGznROdPexjX4M+h1OeTvM3zMzdu4u0YLT3BXk78dGXWYxpQM/KlcxNiqkRps/R2bMRtp6XwgWP0r8mVpxXaOKfZ7l07EUsOmRuznsdNvOh6IsPnjSXW4N6bi2lhTGxAjbWGMMlFb6Evslo6UMnzkLLiSkSZF5MiSS0fudlpabQWejutkpkQww+CuOkPJKlR6FCkdxWM0zEsXV/Ve0gi3dHyMv7K/YFMuYZGeBARF2YZtlQtBBXzKK3WJm4UCd1/96PY72uPcXuxNBF768k61p1nt/Q03Cfz41tu5JPz29r73vIPvaPhtCmcm1cG631Eyi4dGaIaaRuiLUjQ7cWyZBSIIKY0L4FJWq3FaeYLTAeWMnMZneo9sPcJHjcrmkJDkcUcQ8uQLyN5o5q3gB48e54eyfp/EXXpcanGhZ3966qmnWfH+uN1152086WFju/7tb2lveO2VbRM/mXAB6kRNZy7/1cNmLKdtLjnKUhdzPIlN9ym2TsI9YR3xqe8wSMtkPCbsinXqAJp9QFP8jK1kl9JSJpSc4yiR9ID2OFsMEUu7imGmYNRhGYIQlKmdmXUb7VCBOKetcGL8FKcUlQ9iWr8pU3n02PcKsHyzreotVIPgh7y6J8wolxarKA835SK4ydtNKkL20sIbG0mnf3jqWJvF2uhckFfEk9c69XoNRzO1ST45h72DFnoUhyE7HttT7OYqgLNAClWXAbqAjtgYHMQdrEPmPnmNSLoCVdx64XCNsXzvcorLfl4nTtDQ6a19//d/+ov26MMPtT/8V7/Pd0Cb87iSs3xXU6spJxoukWmD37J3p2vlQSuwEaLXlR5dSrY+OBkFOheTj4k8VgR27XAgzSUMWlHhgAvvcX4A56pkVBikbKNCU7mdpp6a7HpLlDAJVELsrFoEpDCxIfDJhVZgdVqNXislpSD3RqLu1AdFwRInDQK/Zecyi9e3y+bCMyK5hRK33IyMl5WyKTSnW5r8/pVu7expdRdD6Zacjzz67R1tEn7Kpu+Q065Ie4bRo6IjPR2GYKYe4lepEHGyCkFcnDbZjL0y+hKLSRYLdOtw9DFnFDcPHWOKqfQpHhZio7Hh35VcbhWmjXsr+ErOfg4fPsWTC77evvTVb3H56cXt3/3Pf9p27thCWzrBoM2lslgs6Au3bgMcIzVNwsNq1Udn50gdlY8Va+uiHAhH/EZmAJpMejhZNsw0UkhRQtbF6LpZbHmp0Ter+gggf/fh9zyeFUt7hEvcN954Y3uE/rlnz+72S+//pXbVKy5v2zau5XLkafqMlxN7XdO3ogTZqpoybrTfWSVUCysLZ/uylbLuQviFKJgwlo9wQhtsHpWVFnovKFFLCjM/JKVaqx/ZfjuwckkPxKiaBUvOwapj4vVuH6xOy6Uqrcq/WF0Ihwyr7Y7uGnnL5tth+r6OK9blphhCTwwpSbPu0PYpRUqoeOb7ozKOI7a1KTZi25kiVzYN7HlZUSMfeJnHR4JlnkgYhLI2caDel07yOlsd0dmhVHZbEosEIxjuFvLlaZwvZWlYgo5ODJMOp0K7rE0p32zobE6mlNJA27HKa+DRMBu8392ccCKicfu7lEVWXp/6zGfatVdf1X7/936Hy36ruL37WGvcAly3cSPkAIteK89KUYOTiKeW0UaZKwanQi1XdxLmyZi3gnXDX9XAGCDPIJZ5K5T8bgEvXRILrWSrccGigrEJWPFFR1W2ygpOo2DU9mANoToqFj4Ay74hZ3mVjoYhZb6xBjd2VAOw3Cg72BsnG0eKLYgWNYGPvVm9xVbt6psGKKBzymdirmiKJWY6gzwwGBnjP29xR5oOiR2Nn28hqHdu/2XhUGfBVZ9R1Huxl698RfuIcQVZQ7QQXR7TuD3y6RDqt4j/iJgfTbXqyZIq0/QRz9HRlsS0l0cVuvJ0ENovd/tyWdeHip5tP7j5jvbxT/4tZ9Pr2n/3h7/Dd4aXZFHlLavGzSDl0Uuxj/j1y1GUpP6KqeyIfd327sF0KLkIBbMKyKe9i1Z4+pOByyOfLGQE1gnrJ8GoGAwZj26y+J2Aq2jPiLTdP/0Q4gTfR93KWd6dd97eDnCL7Ute+uL26//Vh7lkvZPfe9R3xov8hmnlWi6gKNAdsm0EBNyYEW0WS+802aWrbpJNadpZCpUnUeUyd/kulwPy6nWArXo3ref43pUbn7FQUZ8iOSJTE1EZQxZyAKOqyswXNSXBtu3Dwi4LTO3SBnHdrCMyUQ/T1M8tk8k2b4dy8yAY21yyCAORgvREsDK4IwAC7Yy66zCRF5N/4fR9advusmAMSxUdfLYBh2ptHnGr+oIpsNXXjYnRlVhJJzf7J2M5bT1+l0iBTbLlX/eEjDLaIhzYQvaN3yBy6h10nRhGUWqag0Ya/HEJQJBluXNHhEBWTJMsRcrE4uAua1/+2jfaV1lFuuJzkf7g/kfrh02c6v9v/+E/Zox597uvb6/hOVFOQm71BNeyIUYYhBhTjU116YzhNqPSsmc4YvC1PQp0rjwqNmyrVSl+MxgyBqWiSzssQxdShV3HDPokp44SvjBBnG9+4Jrlcl/plafLVUKP+Os2Vyl7DXbrdLLVJaRVY6ynFhSuZVJzRiEvftZ9SPqHBthSr4kFDImDWKUp0kwYOVvlGK0FDQeJ+OfAxY8MnZzyF/G+6/ViTl2oyNTjF9/InuJuLiEKCm81gT/r0sc5nSL2ef5X6h3LVclfbFcMAlCpm/yuRVv5pHE74YGjuaIq52N15NNe89arGMGLIZVPW8IWj+Ld//Aj7VZ+3Okdg/7OxLtCv3/DTe0Tf/2pdu0rr2kv44ka4nqW4Fl57NQvW3SCXDbooD5apP6iVo+LzjI29HilgfGgeOMHydjcfbJ+S2x0+jB0WoeaDgNH2J6eSKzIMUWsJx5/nEuLn23PPX9IxnaEJwo89tij7YYbfgDPMr5nvTixO3frZsqNt7FcyfdiFcvUY19QpL66rxUYuMtEsqU8e9LGLa2ImKknYuVcGdZtTW32tEKFMxgxh4FRrGzy8RlXdTJRkC+yskJXmxt1UoJzAOEXhH9sU3bi0W4yS2WNSW2THXKJk0O1g9R8aObBRSz2IaSO4Bb4gKuJRhkZulZZ8hxJ89CjM7Gt+rBtpX3BZ9uprXhNF4V9EgHu+J2129Nz1JEX1+xDiAxXzeBASQ+sGl61s7Arzsa78hwIQnC6XS5MF3yXWDlI2jPpoBa0RhjEUWnJB99yPh4ClgT56mDyzXf0wjTiZ3kI4TU8x2oPt5HWD8u+9JWvckfRue3Vr341P37idxRAeROBT8g9c5Zf2PaZV0xtidKus+JejrrmqKq0ecRtmUt1dziPooiQ9lqoc4KW/a40krcSLR9FptnMFn/laiVkusrCnkaqfL8sAX4aXIzveuEvjREtu6O7kKwoPZg67Uysa5pJ62tWezSI2BcOI1FCs8a6BKT0W/HYp/yQqFNs7LOBGZtJTE2ws+Q8xeDjk6T10M0GX3dRFfOoewdwyxY5LnI51QHeVVdCQXPImEvGODI/8f0D5c4y0aQH/AmpjUmojQ3b8uw8eE+RdoD1KQMv3Ow6WXjS0O1E4/LmEqxJCEVgneQs/fCRo+0RbnxZvoJVPvkDh3g8DJehXsfdnz6vzLtBd5+3RbMSnrQbcWIru7hgbxud1EIjLE/2JipvSeTgV46gzEqUqlpNLORNdmBBcANTctXXDD9lP2U3BqYVdLaV3Gzgzy5+5Zd+kf5HHRHTR7nT7eYf38ot4u/kR91rcvXCBeo6fhidH0hzo9Aq2kDOFss8tFR7HfZGLabENXms49hpxnahDynAZbjwvc7kpFrv4olSaTMJXQccYRxtTU636B9yapc/dJKmI48OeSzmqHond6WzSSvGIimTrWrBvQVLfJ3KLUkHBrdQcsNXx5igMEAbbPtBCy+piSEqgiG0NuZoHEiUFPQ4AmEck+x+KMBme5/iJF+K8TfOV3aUB2bYImt4syuwgTcqYDK47JJJ7jK3Up6xDTuzyJQMcA4KuJUin6JTw4qF3dOyl2yEISb0CKiky3Uew2JFslFYZfBhbHghe0axhfvLly/jV/n91tJN6ze0zXz2nLedL3VrYlI2Zx9IzqSDnN00iKRm4FCBHzdGcAfvEVQLRoMWK3Z1XtNespC/YhrPC4b9sHt07lGZUROuuRTMXnbyNm8fT+GEV9swbAhU4zOCpbPoM83axIfLToM2KlCCLluiLU67+qCmcWo+8xsihQNDLWVRpyUQ5aOGaEs+MqoH+2vQkFBbBg7iO/OsfFuiE9ZZ/Zjxv/C97JOuXmJMIKqiDLpnqMbPb+tKwrLyT+3zOsTQVs6rIjPKPU66SXtL8vidWqIpdA/I4BuxFS+2MWt5l6V3RzqD+fsZv/95kgfl/tZv/BqPmDnO4MsTAxjAvZll3H4+8MRxG8eh0HwPefcF3zpT/YbKoKtyjrGgOl4B+zSHuACYFDFG+xx0BYy58Rt2ZcAJklzqYGImuWrVMp7jtpYflTvZsMDAXy+PPvDA/e1Cbg7y93m2g8hA9+aZNXxWc0nOpzsEKooFnxJmuu6yIXWZax8pmOqzJPxekej7ycAINQXoVXXfRl2Z1XYXOZHR0wLS4yqTNgZT+SNEm1AumMr2WFpWHEnVTomhXwE/CqsraFXsWDPXI5QNp7phK5ReFxayWd4diC3C0dKkwt/jNXSEJjtM3Qx9TJ1Honajnifb5soGbRwFGmPixFZGpc7s3/nujGNUEjDVa1v+OUhIXkLfMi6Y1fFIcoBPujhp7wFCsrfbmXQM4BcnAkceggkO+X5BukBB6kcoJVZ7Gbxs1nO94XbtHOxbfjGXp2OzmjQjngOQTyTwPTo+762MzvCKoQj+I5u2WpwB2yOf0GKkNg7h7tNUXp2vDLVsxgeLguWn/OYTEBOFnzgkNtEYnaV92OC3TrkxuoSyHzoKLn0n4HMs/5/JaqBqsCbSuHO61CsYrHQn7HIgTz3Cl8akrXxKnWn+7SAVgNBt0DVxy+rlSC+5SQM1cRy+gsh/IBmAnI4Kt4wf8R9Hqc7FPg/P97vUikgfahNaLI/jKRF184S6B1dnnpPRmsgGyQFMirQ6gsofae7UCj17d+JOmSWJ8f4iGZxc8jRr+F18nWRx5JPaj/ObH3+Xc2rFyTxKZxVPE6+t6sWBRM25FESVlTVYoinYaF712lm05KTwsY6HceNYJfXbEoqzFWrtKS/AqaQ6t9kZ3ohlxacktdMOtpJ6XLPauHC2yu+UvLdtJROs8wsnPKSFQkZ2LplyN3dbzTMLV3MG5c8+EvraxQZ3w7ZBiH7kHUs0OPEhAKn7bkciaFwQgnXaZnljVjKj0LYVuS6QVp2ASF9qRcWp2yA/hFoSdbS09SAUAfFCiIbQSj/2Dx34nThaGmb6g35mMu36+6FAZ/uqi0kwBaOeBpdtJAEOBsDoCy2XEMiibIZT+SH7048VqCxkUmfFVTimURSdYvWsapPp5Ul3HEhpb/1ITib87yDW55CN4TIav6LLPUJlJN2YhGhvjpBjuSCY/wjNDAlv8gM3wYE882tUDUGCKLwXJjQtWOIFht/y8FwzX88wbFUyMQ6PBndcBObtmIIPQ07jaRh1thbg7F5YSSUvTuchUbJF0IYM0GAOFjljK4XSotdO2RuC5cGDye8Q1vi4m2zw8F+VFMmiDmZQHZiEUsPwX2X0BynhD0jSYSy7Opz+wZrYpkAboE2raXTpz0AKILLz3gUVPo96GEx/w0WuVsBz0ipj817+2a/NyahnOvOTozY1O1H5jDefhOCNKHWREn1RaDuwjYCHiK+cUGJOY/HFLnmxUf/6n9xaXlFY6pcWlP4qNe1/9CKTcvK2mcIoCZ6xlJixC64TizfTeAsynsBU52pWWFlfemOXsZZezkWHpZC6RjXVNs9neTYL59uVxDI4xZEZCBO9hIMLLUdJJCo37KnYFaQ2wcS/l7+964+L8dC8e5XnETLJrOeRQL4cbs1K5fv3AVTSSuxbzQzFjwrT1paeiaAxMVBz2RXD2dWUaHy0LVaWrTGobPPcPoazN7q1EIqZciVRqDpHaqmK6KxYim+Ni9IFLY3u0mCZBKq5oDzKPWFabrvUf4mU4TN78oU1LebkG+ByyITHSo2FkEm3mE2B83FVNZikR3vwWB/1yIhVAarYVrtUgXZFUXCD3fOp204tWwUICOL1/egkRGLgcA7R/bOUEVsDO814OR7w0BdINX1Hu36TyEJQAll5NSW+FkkGQSEmypXu+xpWgsbZtQ8wFbPyYk/BkegqVQf9LYH5qhK1AuMXSh7ZVJNGmBouaxyM8yVuGKja3FFxliffviUr5TOcHTmoRBrrs9oYdqiCkvG9g06GLwMFsA4kmBTDU4kmoamzb/MVM2gei0e82rQ8mzqxw7wUXREjZw1zNEu1KzGe0zfQuqki8Bn2ICVep0QLutKQZel6y8+K6eCWZhOIP4DYMIIFWHzJoAp3J6rDz9gnObeLDYmZNcYfBI9qLWuLZi748lKQy1DwZYvzM9Ap7hTbiY0P9//X+2oCamWxjfaCreGhBXhG5JbLpNVLK7bSLOj2VX1qadkjvTYT3XIOISeepMGebJMLupudz7WXWxbrxFAfz/CbLif0l15xabuAVwn4TMJcOkwsXGwoPzCRmeKATTEjNSvsMKmS+CvvjF8UBCIDi8d+sL1ps23xhYuCtM/wlr8lxV5fNKDjJAHG5Lv0HhvxfT33aWK9YDxYCFx28YVtD+/eWsdrDnzkZJ3BspyQBxbbaaoGTPWoqpTphXrE6TZE1VL7FFMIzvDHd8/gAbXl5YwpuIBEh5jIBLTLRidp6OJYFpskRf+I/az/TP5Hq4wDJOKp866oYxZWrezhiX50absNJLbZxoXCl8RdPrc57CJAkVa+JGYFFaqL9CrvzGDFH3XpHjvzMXnEt+N1ieTUW1oigqmznhy+UdiFlvSJUYYvutKXHml7gqa9YscSO7Vb+zwt7nZGWqz0fo9syGXs1BlYE74EweaqYARc5lGaAZWGx0QTRoXhcSV7gJfIPc8z2C7YvQvQ+v7Ia8j+OnzdWn8djEzgbBYaK3g3PgqrVDU1Zy3jmvPa4IcTReNWv1qBEIp4GBj4Op6Y/Nk5ilT6opyywVdSM9kKuPaUTfIpP9lMujQIUsGJevVAcmAPhvIK2mkUzqa3ZiaEAA98WSwJj/KkAmFBNrF7KuUVvcn9TuvrcCqU2AQ8SBgLXgfQr9Fg6uaAqTmVEWkEXa5rj7FYlT8GBBuhBsGaTczRySVk0o+/VT72FeOKgVhu8QGSue5VOtOIVM7eLIOxNMZZRXtMlCyfhttOGr6vJu1URn0S3ZhUlKWSdFCzjGQvkzy7SQG6hciI5U8HXBBJE0nMC3fvyQB5lMtxaZd+MU/zDx58is/7Ldq0odNBseqjqC/kTdmweTQCWQHO77+ioPSo06y52D3yo+4tUqdd5wVYkGvr8RBDFj9ZSEF3pbucpyL4NO5MBvDk56edzzEtsYsRynf/iIz4qUNp1k94VMqnCuNl2il5YytLfMII/9SpnBNs5WEwHyx20GuIVE6iH7D8EfjITwOvujGkb5ng1BO+/u0j4pnM5UEuaW1TlWanTSqjljApQHdTrxu+1gG6dVKMqaUS6LZ3Jmhi68UQs2PlLETRlFnOph0DAx6/zpAuKd/ZUFhtSx8psTz6BTF6MmcfGYZwNu/mlFayGeXgkTTOfMRKjMTjo4fGQpC6wq13jAfyhbysPcudlb73zZcL1uN5+OGzX7HQp7zaVXcrDkCk6h9pksY9qVxlwRlyNgoHhnF5yxXQU8881z7+qc/xGI9VPM3g/Pau697SHnvqmfbnf/EJWu5C+28/9tv5UagDlWc0njWVPEcUxGwwjUkaIeXpxPBWQ4413ZSqogqW5vkxCmxicKgcUc1o2IOYSIVr2s13RNP8T5t5AzxtJruqUHtRQm0FmrdhCEKrGVDeJ6/XYwuunanLT4wA1Gp2UjPURdQKH2cCEtQ77E1XUjf0alelPR146AlK7dQjhxg/UQwhfsNQHVCO4sqZlbkYX50tDbtgCTd+0ZprgEJ+GDjK55weyToip+wgqmPIQsudixxTnWJpWy+fl4md8HkXl49nyi3aOmoV4ELFvnyxM5wSg05gl5FhVm7HMg9VvSj2ex/Pdk762JnerxFqyxZVUHxJneZ5cZwS+MZY30sz7JRnSotdhNi11AcLlm7GXQtjn0fy7LG4fBmHsoR9FUfHGNSXIo4cjCXUB5RBr6M6x6NgMuFJVjcyGRyY9dNGCNTQXd/pwJcmX1TjF99txBoXsjs/xgJf0nAps7gCHzdgSB14zCJ4mjhe0Hqxa4oHrCPWmjueg9YVUwpxbJgQc8xHfS8LvQqrH1CO/dXe53RHRgy0a7dspEs/ecvjNwVCyyM56SI5ybgpbTTSHsyE4pEUlWFZdYIUQjRfXlcSu6IrI0KKxah42mzrRCC4iMYL5GuMlS9w4TctR26PN6Uf5O2DKYv97tRlS2FDxlQWjqb5rvdOHl11y+138a6ro7mp51XXXtUefPCR9vdf+kY7jxvOfNL2Op4fl4CwKFStcYx6MbOirADxlQZk0jHGCJINI9G5hZfWXcITpT/4y7/Qbr7tHjqqj8/ZkLeVOtN5mUKRgzxB+zCv0n3+kI9sR2GUOan5pZNvxOxrXTDjiNOzHZljytQpzaO1YsLGKw6fDBg64WeqHMrZqlFoR9kirdJyj02Mkf5px6ooVGGfdhR2FBIYs2kS+htx+WcTkCR1and8D3+yFlU8OcohXwb9zm95LwlfNMOYYnbxf8ZCqseDVpPLRNpEhc4wS0cmjBibnQi1id29mCwrZbGtPFcGLlsmW8U4yfCUr5Uf5VM99MFE2Wpn4pQN8b03vmjwx6w5m8MXgdjJM/iHBss8Y/HVG/VqgFNMRnyXwRmMeW/t9+gZjW/uDY96+tbV/wSu71Dy91E+dueUNyJwc8LA8bZs6fnZADxOUq76xiJt2Dgfm9hfnmRf/pY/fu9avs181C7cD312lk0bmlqMDtgfqk+kEys02mf3z0ORe9+Rv1Z+VcUZwIxibdohKZrspFg7BrncsaZ+dFgS/4olNKXE7qWR7ag50DrD4tlo+JKtfh8ji7nzDslumwcd6TqSjCYNcBtHUto3ZSMosdsFKyS8isjgzQSL7eIqUcVmtLrHLt+flJxcdZbDkb/4ppxbWCYDChAoKcZWNDPK+Z/6TaEoENhs9dVKZdAu66GOyXe+MKs9YvPtGiQU6VchwklCnCiPIDzWF0aN9ld4tdcGx4/JE20WAw9E0Wu+GuVhAifaIa5+HeZdQsdIP8abDS7hEu41vMftwIFD7Xl+3HzxBXvatddezVPiN9NPWCGic+COthsbJgOqlHtyMXDmVwwonrO8wvtI3tu+wu9yuDPGTr6aleYGX6oGk47Zab/13e+3m358W/vcF77Ar655r0/XbLj9XYLDtZf2/LLah0A6EfnJI/yh5TXBADIv5VPGG4IKqLwGqgjuYSawY6tGVo3S9NhSGRXJkKoSnBxtrG7KmOoVXCpTMu2gSQ5/OqwlXZqCShVtXnc0pLx8VY28859e2wrrKLvR0JKMbTQRqKWl9jLPbQEcHGWrpbElAu7E7rSkamf9uJXdLh7M2RGKJr0aTfFVJwhL6GE3S8H4dEiBOt7AD4nYc3RyywRXjVTZsdVgaF7MTiVh7XsmxH8+Tro2CSdiB/h8oDkxOVFkssjZqgpHPRde2QoO+OKpxzhHa9dZNpWsr1O3szIHlekiVrACOOxXFJbEwmcbnsSW/Ag4R2908MNEydEyb4/2L3EUTzswpv4C5E6yBe6Tjr6eD5HdnDmDlGOkgZ63N6pKXfQtlY01XdZJUqPMxrPQuxWkLRDIdqJctxwZ9Q2dM8QuwiFb53HQDg75JKvQWit69pTEFu0w6aSeJLvSrXCPZhUkL7P8YpOANZNRH0Os2TCURJUPvS5I/VElx1occwzAYPYooEc0F/zIBvYn+DHasdPVx4jPaD+S80N08OprB+MqPHqxP7+tKnRZS74HYWCloO9iFvw1AXn0M+OodsRoKi3Gq4woFQH7Y307yG/mvvv9H7Qbfvjj9sMbb+InKT6qigfy4Mgq7inID7xp056YrOHjicWAS8jB79HXaDNdadmy4AM+y7BYAhUjsEXh7bwSwWdIPc1LjTacs45naB1qa9au5keIdCCY7Ei8kKj97HXvgOdI+9AHfgU5nsvLLJhQY2S+eLQePeOivnXLYIxVdig9Mv6CPr/HiMllg1YZrASQtPzapomFY+qnb5a7VShLxvw8nmNhdIRRuzrejGgJWyntkOjWJqnulOlxC29hprGlyJ2NKIewKxK7euctMTGR0l/KLYoP5EWoyVOQTh+qixA/lNOWju5hqvD4DaHQBkfxKiXviNVIy1udFhhZdNp/jlHFURUjpvExUCw4PNpSe3zIsiHV9XSEwqQk8dR3V3YBXxrTBFAFlJVu936kyWtlqpWhZYnOFBdf53foMYk2/jwrDyJxJ58g6lVtqaLud1GU0XpR5CetPv2CWTuc3GKTh3CbjdZAKKMGv2dIjJNL0U/s5JtWkhWkn+CR4MDgNrCH3iKaMzbyYSus2lxPnzDdSyrR+arp+Hgl7fSuuvIJrCk8lcg+tolffNpRSfkrTvNuVjHcClge7tolnklSaEEKw0im8LpgCfS6SCt10hCWXerDY7i6BsvKSpkKr9eDjMoEQJnuw8Subs1gq0NvBR26H4ph4iFBgfypARLh4zizQ1Ogdr5UCHaMGBZdKVEizZEUMqPvFdosjnLWJs6MV9qI7+ivTopzsEkrI98W3qb9zre/jTcrb2hvffMb2rpzVjPu86i1I7wx2O+ACIhXCo5yhsSUkJMV2/8y7zqlXoYdCaumB7csc+95cnICTR8jBflKX3UN6te+9R3ey/P69oMf/oj3hz/d/uGGm9uRQ4faP9x4cwYZf+i2zInHR7QomuCV6lQYSZ1xdvda9DBrCjBSIxiuNRxsNSCypsgPXlF1xsobgfQ4zwNL8qM8+jpY9IAXGTsc9G6poGp1p5Y6pLQ4JAxuRa03t7JNofEJmdiQDwmfiGk1116mj3yGr715Fr4ybNo4fBAnZP0wYb5j5H03fYDJoO/ZZSEUnvHpDXrYH/yOkd/rYOskpY7wq6Oo6prc6eh6VJ8yKSqRjXkLtsJOD406Voe2GQyZuLacVZfl6gRPPQ7WqR91h17R0U5F/fhqbg5lH/Q6m+aYe8ltL8K5G5u0KM0xJey8wWcZtyr7umq/0/S16dqXs3RoLp78gaa0XLpWKVtd/kSFsQ0uRMPmH0fxBxnuKnRvYbby1yTo/CnUP6SzyImuXmNdLnZHxl21YzHcsB4F0FCXM5OoktY/8Kjf71LcxtMsTvFoKc8qx8cB5DQ8Hn1+nnj8Z4uJpoBIXUHIn7HOtSGNthBqGRI5d8P3HCNnz+RP0NQ5Wrwsk0+PRYeLX0YpPrlLoh+1bpSRFsvvtbRjLvZk2LTN/36kjeWBzJYQz0GPPmM1yVMnKq//SbuIrl90QeNk0aucyVjYOS1209IaBynBBOkVR476ql3uNS++w4EvWlLb6KWVkx55FbOlzcUY03hjnIfoGLDQIUom29E2SlC0onMc9eVAd9qTCz88BcXXmVx2yQXt4MED7fEnn+Iljltzr4DfET3IScv9Dz3U7nvwIR5yW29gSFy0TTv4pFlolwaz5ceqlSySBmu8lzfW8/rhD33w53P5wEd9XMyvqTXsot3b27L3XE8jZeLJkq8ChyR/ypuqXAadODhUWl58M71w9+KcOQ2ATlOnMm4DxfwI0ihLeY/4JFNi8I5Gqa4XVJyo8llk2uxkQwco8MkOmWXp6izt27Bw5DnCZ/3XucGsPAMsDU0Nicicj7MqKnltCqOH+N5ttaVSFlsocxvRrbiIntadeFk2wzYO8gugggJKM08jGUiisgn1E5vEYYB4SHubM8dMWuJa7MwhJwFz4rUz2nul5vS9g1c80athbOZNOgHIq7tC+m4qcfVlqgMUlt4uH4iySb2jvVTbYOIBzMlmBZeIXYWmXGXwluc9VpB8A/F0+bgsm2y0r5S9MSil2plr8dhXWPqhQTEqPHhBLhFAP3xOIPxlC5S8yrMf9nVnw0XaWGYQV44BZRY35OKLdhTmKBMzgyETDBc1cgkoVvlkBupE9kggL/0UuwX4zni5nLSwKC0ejmZTKalr81BKZdnW0xGMbJePoMJssd1jcsFMcsqLScYFAYci98KI4K/1P182bEyMi1fbKl6a3NPdh8ERkHhhJfJv3WjRvL2a2vMeMqCTCEYCBM2C6EgiVo7dqItxlF7VZL2ZRqPODKXaoRUDz4kamu1dezOtcOxV3dUoJM5AEVhF9D+StrxREjsV7rYHG9S0R2j1Jztth7F0567z2/k7zw+CrVjFZ7j8fdVVLyVJ3we8+rmYsOVQ+GlDIUa8LfjYnBdeJqgiBT27sbPSSTnL0SQ/vnAOODolrbJfAsg1Vwzp+hIM07MAxP4QxHCzczhJ6Xsn9RhIQFqAvmXggNdgyD9v8+DxaKWOTmde3qrMmghow6WKAnm1RW0yamsy8ijnX9LhiD1yy+YmTwl4VM+gFdWKlpjvJ6ZCMecbS0eUV7yAl15y6Cjd2tIVFl8YKUv5zOfRwWql3n0COFYHwnRtwVSHtrnFxkpqyCBPFBt06JYpM/RrxkCVmy7RIZNLusoHZpHKvhmr3WLwDZ/U6NjjmQgThjk7nlsES1fs0QZi62YHKBsLR71lYo8JbJ792IT9xba/mamOz5G/GtBK1nzOwuArv8SI8ugaacIT9ean4tgKQo+PZTEzkvDHXozh3/VufYE9sCGaFDiLKNLKewAk5JrOJ7YUhq0wsndEkJ9P6fcynGc7fJhhbJ9iasFyGm2lQ6g0cotci1sJCYSyXwdhrBoj7emAgirsADkkEGVr2UC5sZaV2Ew2SYmxiHc7YcmW2IkTbLGIL867sAu8XNJk6GwRJLaGN1Oux8TaWKR02vXWNOUroafYiV+ZYCAqFhPc98laXu8wTIGZviUuk54pkdJRBx7HZrLG0LGgGSX9CGB+kE7svPIkYmrNWHWrSM62cjz+hxhVxZs7I1FYC+Pu0+SCIGVXmTcVTFhIpo5cOFRUSrE3daXfdfnoMTbhkmcu0mRHS1tYxS+kA+HOAjypvA1VAi6Co4OBYEJiURSCdDulEqbHrOx5vQHOnWZypyM6gUBXtYFTIOmAZTKKvFk2S39iS5Rn9ozKlG8+vVSudGWAoSC+RUAXyr/yWI2jNFb2RtuxO+iwq/RVWcmBMsThtQ0QAVLl85JCSuK+GhkIcqNdYiJe2VXHUjp8G5ex1OM1+qpS9LooimFlgxPtsKkQel6bOnB8D1lf2azr2GzTANCMq1PL2IWfjmdJhYnyvs1SxadMOuZUAEViz2tDfIqOiRwdsU9WnUIofKmnbnunOUm49aKqK+3NgIBcb++6W8pLflq8ICiEP6jN6ppLUtENja4UW7r3aeTiyO9n2DRsVYMbCHFRuin/il5l7vXWfdlV/cbGIk2itRo59eBL1RM4CmBzEL1u5sTiljqCnvIiuR944TdI0kppYubvrXwbhe8Uqps44AEycjKTVUoZ38rqWeBZZ6ERV3koq7NA0k5eGeSVm/kYzV2v/KO+FNc38bt5UiTLxlbp5LWLrAur+EBb7dVMgWVgyJ9/dtICIR8yo1zqPA+KfmqdwRPxjhmejlPApTM3CnS8anclV0piQRmCHsOmn9o5/B7c0UVZLUjGxKwRZa8B0f/EN5cqtai2ah/m/fNsJNpTHq7OGLSkq24iPUBKU2xTsJAKJ3wKz22xJRok1qg+K0Y6lUNJr1jVpJ6NoXLgpY67fh5gOqehV3Ju/QVIRppoFAlo3k6czikQaDXxwGJepfCoMMHQGLmk23A48J9dBc9M50+5eAJIlNlE0eSPDPSogW8eYz4dh2USRoiO4cAlX3T0Y/LxpPhVHnnFlOsBdfBxIpvfggNP2ZNmNles/WKKZ3quKDIV36y4LQr+jCeiksuBiOdlUh3H3yklmXJtcOKXv6u1kKpTtYzlU8UQzSFaVM/+K8awJi4UzPGYVmI0lapzaPAGQ50qhsEz21JMOUFbltNBKAKwjYaZ+ortYitT2/BXo2uAo6w7pl7Lw69PiPjRCt/pU7ZQHlzxpLDvwZRcZzu9TWU5BX+xpV3PqqqQB5b0uCaZbdAr91P28HVW8AtLO7XeY9khD/5Rjvnhsywbhynd4S1J/JwQpHW/RE3lJDbCjb4RkC49f1iWL5S9Df34YqyhsGyMXwFXf1F9ws9KLrGe8U4xaeJ7tK14RsCffumnrla5HHwKVrFsqYNwD0qn21aU7+NNp0a+6Ogo4UAKLb6aqy2VxLQftkxtCwH/qUhtnU9rs7EMPYj6UfwDz7Lw9fLQXY0rO21RYACW+N3ho2Mp/ySYhNVZV0kiMVdInrLIlrHJFr+wxoZyysoHcZTBQPktY8tIPvLyhl6FFUPbov9dWYpMTxDIwJOYhjEYsgkbe9BSl+Gsy2GNMsWvLbENykDl5ZH12J4Cqgodq5sype6KEdAF2NhUEa3QVF5N0VW/Ct0gahgpJzpvR/VBpsrVqs2BTyM98iIxQVidWfexhXwN/OJDr4ghXcGTNrsWH4XZTbydX7TYqkXieAynK46RlupmScXAXA0eUvkzytL8JCltzq4laeJgGIxXdOZCC5nahg8CiaGjw9xxlDy2YXdigO4sErTTDjUx1sSvWjEizrUmj6NGjET3ZjghN2aWHePsw5WvGH2oK7CIisZnvoN2g7UjOrMHrzjZu2lnDuBqt7ailfjkwgLYwpSKQjE36rxWiHDmOjj1jn2uQodGCrq+ArHvBS+VYFrk2kbStlW/9YKZGdK6jv7BEETjAh8fx99MHMaKv+IuzPl2UNHGMQVt2nEcPsCr3qRVsUR1ZqeOgpvtKYzuwdBLwgd+yZbvQ8iymqjUkVwBy9xtMeYuYbyTyTOisPG2VOOYL8F7LBRMjyaOPEu7cFSEgAvR6sexotKVjF6T4tYEomi3V3kapGUOVm5pE/aV2JcW3IWhwafsFGNEzGcvvmIZKGZ0i+MKZUm7w4dZG1XIf30IS+wPlrIlVN+bzOWFU05bgq9A37LI0p9OQ0wUr9ollfZFuW4OnrCmHBIZX8FiXCoOEJKWS33JhJZIwps+MMgaFE6xxKGGIYXKLnUxRgJl2EYbHovLokqn56fhgSPR+upGp22JO/Rqm2Xkg2dslOkMpseEri3aZEwsNu+2sIpHdeT7A8IjmBD+6txAeznOa/GiCmakNEKjSbIyLP6AsdP5AZyOr+dIeju3j0U57egAR113Tg8Nv13CdlT3yAcIrGgMpqqnzeB2LV6eUl91tuKoCtBANc/ZI98EQlrBYpqo+rQEC9eNTTg5ptNN3OVvyhRkUzYxsNNOX1aHw9LuiwNwYaa7IWtWWePp1ouTVm7aSKauYJDfkjQKjmlIA4sSr2bJm3gAK3JNWhNaJUpl6jqIAe14yKeOAfM9PYxXfavIzrJTqmwHMxSMSkwmMduONlW9zZErNtg/1V8slkNdXiBz0qq252ootsZ245euHTiHRxpY2REMmaoc1eCDyDF14FEsCE4QdW4Zy1PX1o/M1vsKT1UoCobH2OWg4jBddos1beHtWINo/fiXESsgdQY02Vn8YcfODOD0A7vCKPFYA0GcL7pGuXXf7LLJjIN002EgPOT9vd7CCn7g2/swlC6CL8oj4OV0r+v7KodVfh+nIPTUkSzRq/fF38dB8vL1NpJCoflDVjtcS8gy22wT5ohNx485ABovN3W+cAspoINvKDNmnYYy24/A2id1fos9GhPFM7MGV3xJsZYUbslTIl28gJBCwTAz8pRnSyZMWpLWqNkVxxQWBmVlXxfMoeQmoO5v5WPdZEO+gxFBXxLA3k+6Xd3FiI54xv6ubtxRGHmDxVYaTHU7TbIFKywzvsTHsrIIC7CFgIgnTsqB8e3WopWdTEIFBlD+MbpGM/XUoAi3QKrSpeLjIHigLKlPt5tcV0iJyk4v8qt07qSrZ35JKUMdWmSQ4mDnj2HVP4IwKlT+6NMZr4lwTH/nWO51fV1g8IorVk0ecs7hCKpm/RCXwnG2kyLKIEfGfMpjqShu41i5UCaspWXiDP4ReCmld/COY+myPPblOGwksuhw4PMpz7k8JJc+omN8QepUnzfUDgcCXZGK2WVQkN3liQs5Gs8aVHXYsXJcrg1E2kaJ6dKIc1HGPs4KyqCubXz4K7uLUI88qoiU7V2mQ8jrNmKVUnbWufZ4a3HVeJp58apDKouU097tpt58qm5lEnZgOp760lt5nDzEHWeCxkibRZQh9p7ltRTxv+pAPLHUOcUBBcBlgnMBJ3YGG5n7Znkhs3eQp9M4MKnKXQ4w2RblHVsVFy0+CM4mPe2758Va0o7jtHxqpRSDtHcN3++cYrfcCZua6lOpkGVDjraGeuL2Km5GyltVY1WsyTg3PfYndKXnNnV3epeIr7F8ODcKwicRSz04mPTgTROK0CHXSn0WoQEmQ9/kS7L2RS38wVK2zdrQMGGMY6VMrsLPd4V9EkicofdsjgM39d59nzSSDwqFscjykUaw2vzgRiNG1EIcvigpntS9pOGdDdm2Bs+gaUe1ySRIS1Fm4Kua9KyA0irLPiqjBCoUif/IJrTFI0Zhi76S10dNDMm9ccDWM17GLaIPMFWJTW2maTgq2fToJAlU2AavDXqsaovmPr4ly0BGgGzivr3Sl4URsTIojHSAzrecEWGRs6V09G5ddGu01hVjdybtH3p5EYjOU8EviqWzyiXDJo70EbTk0VGDkP7I030I/7wOcStP4qduQ//AKU36QBzjgwryHyT15uF/o0Ym1PJBkXEZJ6t1BkyfHJA4AaQ+INIQvcTlKkMbfRp0bfrrhTWBin/YGEehKW/diJsfbcaW4o3JANkBT9Nw8r1UNLCzEP2uzDWhYo02Vp/mT7HwyM0ttAAjEIvArjtoPEMGl9cIpG4VoKGJZGca9ZIyipwQbT4+IJGnx+UpHN4lVB1U//QaJnTzu7mqz2i09apD++ozzpzs254RLfpIHmc45L0LTix3OTNSUHuW9bZpmYbajgVMeYiFnyRedBAPehRAywpusqfylluGXGw0prJmV0eLO4+HIGqriroNyowyjxnILItPVU8p5/UN3rziXa65+sC1xsQdWrEXby3eiAnM/CbdKAClkhgZ5rH6Dq3bEbvlVdnYeqy6iUUljLMJRm5kaL/le7Gk7ccudXdEDnDJngGtqCEu1en3hICFd8gMe9QGnjXpNnjkF7c2PJnSkiuvTPkoRsnKVnaMhBT7CzrwyXqa1xXY+FNI9qGcgSIl6BTnASc6Qum7NNXUewwhwzH2R9ZdbVO8kpUjjMlVv+pWlzFl/4hT1GGTDuarQI5sFQ8NSY48iSqaHSnqxUrwb8spXWFP4WQxt//z3C16bPEqMhoL2vyr660DAulu8NSRIYkZXC1Efpwx2cGFXuQMxwdFNl6glUqGX5zgm0baSjjDyiwrSIRyxkPlIV6BpzwVoH49yQaCOtmkSB55aSM9LnNBkBwbRzrhCZ416pmY5TXgRQsiORqLNBoZ5K1NHXWbeceGXHbKX7QxcDv9ilUrVaKUWIdQYc2+tIk+mWscDCT80oxRXUaSCzJFUPioz6bmDw09G9BWy9zQDbT2aoV/iZc0sH3MTC6XUk3FF6HOBzdnGF6OcVCID+wSD8CDPyekJZ6J+T4evwvsBqg82A7u+qHIaX/gHDM1RDP1Ub46lhXsMVafT5zx1QrJlh3JdM8jDwTsNUmbkviCTTKbav2tW+4QY4YjakxwJW/5kJR9Ob+h8Ux92KcSLJzZqTPZGPjAVNoq85hmQHHFnmMGOvWhQTmD4RZM8/5HuOh9T0lvM+XvJNfLZ/aaCpjaUaENaOt6YzlpJ15/55e1YUmowVArXX2SxHImrWqzWkzrgtYtMDFLKz35Eu+g1Ba6oN00qWaXbABrYi7ZsuDJANjjWpMTmOBPuuUXZfg3B24vmMaaKEkkErLQ9QF5v5/QEmly1L+WpSCS6jAm0sITqrsZPTfgiAmtmxxdplPvJLqqOkZcSoRyWbbnQnJX402Xlz9b4VcdxajU74h7JrIyVmMKi2P6kwRpblFrARZ7KFIVuyjpMU1BBGqnP8bDEbDiNQB7OWVS5BHY+Jo31plopUNwzBobd8c5NCpUn9zZYIbBoyqoK1PSwaG0Rz53SqhBfnbjL/gOPlAccE4d52GQ3A/q90Kem1ky9TG9otO6Aj1+nEnIX+W21dHtdwDje5+6jN6VIZKmaINHbqoAYIMtfi8T3q1MrEwaRScayDR2tIZXFmSHm/pU2krPC/F+YrAUt28O2PUnpF/t1oo8tQ7PsMNQ1YRV/mm69aCsW1zBDvOnwHEwPM2kri12zkxIBL1u9YZII1yRlbuDTMcOHungyAK2wOTzLDN/teipiTp9PosN2exobgTk9HImKkeslI14GB9ITgbdxsp7ZsVQSp1jjSS2miqVKNX4Q8ZJWt9GbJ1Eq40Wto3FWJ100MTGTJoU2f7Oek2tDFVB/LLuTvrUxWBWTQZbu9O5Sqf8i4sMwn5faUtFRwYvXBxfmtelQzwgzl4Czc01vPRt/EV3wK0HA4Z94ORxJjZa9KEBOhLQE1L47YRY306v8TZxLRGxb4olF+7EqlLCgSEjBPWknag/2HIpXNKjBUVWv4NZ9ebTwD2rPM4k5ARjqVv69Fz0nRBWArDAbhU8dd1j6EEqstEQ+bHrHMlq56jchKpboiPWfGnXLlPkdWHgUsdaJkRhFo/ENOHB2/1LHUZJ4SZYKatJSawoALDqQz7aiDhq0FTKUE+rDTgk6aRhq0FVWbh7vgvH18KEN1tZnBaoA9nEzH9hRmE0l3559FkblEkwCi9ZSQL0LeleYDxyyQAjk9Zg+5bs8NSkVnaEBLnGPjFrM69Ko6J/sy2KkzUeGbPIxbxQI1S6yFuvinsMOvorhCJ3bWUKzxSlQ9kBvfE6XOrqjtsAR2eMNgD1Nx6a0GC+mHDACB5yBi86iEL+CMKBwzyB9cBh5pp6Z0t1PyX8dBlQVmkCZ2bKZatoTI5q/rDfwSADVQySG5n8u9NNjvraTyXJpbz8MMM2iFVjeVqsxJCLY2JJVnvUCsM4i+nh7NxzBwqsqEUHzQzc/vLcwciqUYMf7XSIxlpg83w9qcha6pajeWuQz8kTp9oJnmbrgzEdxKignHWMuhodavkZXj3gyt0N0drBn/SUw0YnC84wWCj4WA6VM3/NrFOvQlzeW8bZlXq1KdRg4V/ioo3WiUQu84Bxgu8CYyfOpUbijDZoOUcXQJSpvxqWsVF3GHsadmyw7nnMIY8CqTOhopRYr75IeofRGd5PbfsQqf7p1DFYZBqEl6Mo8nvKsyfLdyfiMt3JCOZhgnLY6A+zF3nlwxnO3Hh2AjY5qGkijIDbDO0rLghOgrl4ggkrOALJw0cm/wNffcvHBK0+jU263umpg2KF2PUg28WD4c64YzVJy4hw7KmBPQtIdEqaysBUiTKL2Hno+Ol2lB+rOyHJaB35r411lNNLcRC5MWHtai8d88nE4EBkLcARxd1g0nUJrcrCELDC1fc4wmGQVSiO35FlEIyF2iKmflpmFkzwY2fo0W7gCwxaysQKpepdavA9ikPZ2Ae754srisKjTPGRgpw4igHVkFVr1Ta16oPIlE5K1B/BwtEfeQVws37kRS71mBQ7MjkTEbVEOIqLxu4/ratjK+9WOOpP++WofembFFVNqpiM+R6rslxGFUUiuupMqPLq9FO4pN04lN/RGJ/K7dIRLiHzVyJVD5T4ZI76MWMKuNS7ki+GVnKlDLcYNaegxVl5FBUysLpY+SwJyrWUjl3Y6jsA39546Mix9uPbb2snDhwIQpyXZ6ookEl7iekIL0e65sUXpFFhZ86OstLHtrgDLQMdY+uiA5yDrGOKWAmiwcVag5Ygs4rj7r/Vq6MwvrArD2YiAkS/l6SmQHsX3oSj+mp8fq+zjB/4+ogy1Yrmf20jIbgrTS7vnGDCcEC05XmIDyUmPBSeRMtEzquVTYeUYv0YeJZUwzl48HB78qmnmdC9o7B8deAzQnlXO4ziLvKg2S2bWGYv3zCHqlnaUb4gIijPgDrcnn76WdI0bJ75lomsq04sxUTmBLHcvPGc4OtPsWiZn+oUNgvr+NjhY+053kflwsMy77SyiiIUx2XEd2K5bdN6ELSzb+rjE/wICeGLFI+057Azp/Iyy4NdepNWiZDtYyUD5uZ1rts3BDA+FFrX4cKLjo+CQ8ePtSefeQY79RuCA+HEpeXooMw7xM7xNe7bN1a5NhZ68n43UnG1LZ9pRw4fDX6iNPzFtgwmyddZxcKKte2ctVWPwsWX7nPw9VEjsM1XSXg0LomP5sqUo/5UHWSSgB55hqIcxYyN0sHiTPqEZ0L+TqgUVLnYfCYFiPn7IFUb65wdJCraLKNiMI3NJPSuKrZKqDMEMcwVU9p36s/BegCUfCESI+iVdtKWpxgLwTypMIxxanCjBdCyrbgncXRGiEPRiAEJ24O3PecogbzjT00A4GtkHDNNkl20sUu/6tYpagnaNTCpHDt+iOyqtPY13JPudZg+OBhFwo66hCVyxSK6Y09hyK5d0tUdN9UeRjRQVpyUyuiWMg4Wj3qkKDdhFEf2hQFf5JQV17MssFAUXcjrdf134K7HSdsC//yXP7xQuTuOAePEMVaYkBwhEXLwlIkuxyq7VmwLPGzSp2N7erWMVeEyfldwmhVfvaOeSyWsEo8dP5lbOU/xkDsr0rOr45wFuOp6hidwr1y2Cjrdn7J6mkIFVHMcRs+ePNqee+5AO3f3Vu6o4+tnOv6CX55iT7191Y7myp0BCV2uTP0eI8/0QpfX97NC0Q82fVvLYM29QNheq3x560tow9w1w+cE7NnACTA9u7Mj+8V6rqdz/AgrlwAAE61JREFUnStfwOKHEuv5nKLcu9OWcSdWxYG45NZ2/IDPi2+uCI8dO96OnzjNwOjlnGpaNigbm3B+6XuKgdhH0rCEb75uXfscUBbA1i7zTjh+f3L85HHe23SQ9+mcSqfMHVZYderkIvbUazJsYIcPr2ibN61r523fxhnRorWGLvEKO5MCrfrQ8RNtP+8GeeSR/egvf31pVSZjbMhDO5HVo3N4cOEGBvfztm9xhQAeE0vwrE984t8J1xg+zkR5/30PptyVtPUGhCx88CVtlLPf1Svapo1r2vbtm7jsw2AHX3gIhyKpK47HuXng3vsfaHvvvh+93k/jqAiWeu0E5qkQLfWVISt52Nnu3fiOfcUnr3hWoW248dTfxfbAAw+2227Zy2WW6kDVdMSsdq+1ns25clvON0YXXnAu9U3HIZdLdE5esBvbeo9Ra08++Vy7GzsJZxZSaBQmk1muHGgThAXuULv4ot28LoXLz6S906xuDvHsYwX90AmcNO3F9n6ct7zGXzqQEEBWu9BW/rTdOK9evbqt4ftVY2L7sZ26xQ/7LvSTOHGa79fyfSV5xNJ+jBW9H1wQ0WOfsg7O5syas0b7L3jVHo0j7QBDfNBpBiOAcmardf6zKqkbkqqtjzLbveNNPuA5Etg2zacroNcFZdKWEUvrwFso0iaQ8RK+Z7DGRBuy3DEu2ETLTBuzbrTVdjD49DWLVf3QXj7Wi3y55Is1WdBZpjyx4xA+edLnXQRKR84FIQjEz5Zt26ZvWubY4NglBmnHI6qjJnIB1Wu7ZaAFAiz4wPMRaWqscUJbq/9rf8Yx+Ly0nztgoVn3GJD6xgn+vXQNAR2Ody6wznL2X3HBPtsEf8s4q3UsFRe22BdztIE/YwRc6qDercVla9rkcfq3+vQ3/Rpr5XPMMD/iUPVpmbVbMXfMUJ/1sej9CGwLdz7wSLvt8U+1Zw4caRdduJvOu7bdvfeetmn9uvail7+03XjDjRixggfTvaLdd++9vOr7cLvo4gugLbS9997Hynh9u+TSi9vDjz7e/q8//8t2zZVXtn337W0Hnj+aV4KfxLpnntrPS5EOt62bz+VlSE9gxPK249xz24FnD7AaWwRjk/Fqjx460L78/e+1ZxYPth/densG4auvfmm75677eJnS8Xb55Rcw2J5t993/UNu4cUO74MIL280/uoW7fBbaK658ebvn7nvaUV66dNllF/Jeo1Pt4Ycfadu2bW6XX3ZJu+lHP24redfFtVe/ot16+93tKIPviy6/mEtbJ9oDDz3atm7d0nbt3h28BfCuBO8uXupn578MvqM8tvyR/Y+2c3ms+QV79rTb77yTzr7QXv7Sl7Xb7rozl38uvezidvD5g+2JJ55s2xj8z922vd3Me5ZWrVrVLr/00nbP3n0ZPC6+6IK8Mv2ZZ54N3zYw77vvvrZ+49r2khe9qN1xx13pdJdddlF75rnn2lO8zXbHju0M1hvajbfr42I7f8cOYv5IOv75O89rB5472A7zZPPNWzbyyPV17fFH92Pvw7xw8FDw7ByXX3EJZ1HPNPWef/6OtmHDOt6OuK8dPHKCWK9qj+7fb3NuOyg7ePAQeo7wEsONTBSrGFifaufwpkTuVG7/cPOPMghfccXlbf8Tj/NCq+d5oOF53Pa7Gj8eQP/atm7L1nbzHbelkZ9/3rntOV4Rf5yYbwZvBY+Bf+bZZ4nLStrB1vbQ/kfaTTff0l784svbQw8/ylnEEeriPAbpBfAepK7XtZ0797RvfOd77cmnWaRQV9rnIL1h/YZ03OewYQ1nahs2bsG/p9rjB56i95wF7yEeOX+CtrKTXrKMun6kbdq8vl24azdt4o52z72PsoA6Q/s6kg62npg4cB/GButtPfjPPPtMFhFnV7e2/6nH03kuwL4VTBT33/8gOje0Sy6+uN122210tpWcfa9tt/sSSM42Nm3exCTS2tOcba1ds4b2uC0T/gquQeyk3u64e2/bwBnrBRfyMEiWo9q3efOGtsc2dtudtNmV7cUvurzdc8+9+Excdu3MQLR/P21s26a8QOz+Bx6gbs5pu84/L9i+XuXCC3bRxo9Sb8/SDrewINnU9u69t62jX+/ZvbPtJa6Hjp2mvWxrR0+coP08R3oz/q5vDzMxr8PvHbwh85GH99M2WrsY+86eXmwHnn6unXsufNTjvn33oX9j28WDje+l3p2Edp63g0XS4fYcfXvb9q0sWs5JjNbzMsxd55/ftHUZi4g9PADzedrrQV6Gtn3H1rZu7VrGjQewcwNtaRfp+xhjVmLrDl6a9nx7jsv5O3ZsaevWnIOuB9ELn37su5eF7ypsOD86HZ/kW02sH3jgYfjWE+dd7Z59+3jZ7mrwzk99HmJ82n4efKvlewi+TTyQ87zEaM2qNW03Pj311FNpB+fT96yH+x+0bjby5lD59iK7Crzd7fEnnspi8zza+QpWFg89+HD64U78vfvufbRL9GLf4088TZ86Tt/bzqS5grb5cNtCW7Yv33XPXs60z2l7aKeP7X+CBdJx7D6X3rgi49gWxrEd284l5veywDiHN13vpt88zoJvkZjDxyj/0KOPMY5tpE9tx759nGGDR4wct7w8vHM3Cygmu/3wbdm6mYXf9nb3XffwBlTaxJ5deW3PSa4w7dy5I33rscdsY1vatnO3tVtvuT03mN186x3tYcbLNcR3KzY57hyir+ymP3j5fz/zwC4ebLqaRdAjjzxGvZ7TtmzbWOPTkaPw7aJfnGKR+gTvKTqCf8yVX/iP/+bsa970+naEKb9mRFdgWMrmI+z9gpWCXOLwhVzOaHWXFDMbOSePutWTWRBeLscj78qKuZROtchq6/Dhk6y4F/mSmhk6KzCvzDvcubpg5UovdVWzZu3KtpUOuYYKd3Xic73qC2H1OFu74s0cjaQrBe9YcpVQs60zr/aNh6IGg0LLDVD5x8pgCZ8rMFYuLkXZ9FF/XU27ilGfndrViKs47VxOr3RFELycXcAHZj2yxtUFqy71Upa7AjHKs4HENWnxWOnBJ48TumcrrhgWVq7KWY4ripXqxTbvMNN3L3UdY2DzrjNtzJ1q8AHSVzukExfLzrS1xHENA7krdCNoPbkCwZXo8vz+OGezR7lkeIyJzZWJZ1bG3IEY54XLqskzTi9zeenMy4fq92kb+koEybrCrvgRaRYXpzjDeobnk/HOKVeB6LWt5AyYROoRHSs5C9zKpLCRM6yFFX31DqI2qN+YGWciBR6PjmcSFcj2UK3C/ufKrdqBA6GD/vk7GFQYjM5Sn7rimYpbzm45ukI8woJl74NPMDEfgMf2CBK6OCQtFgR85GybyfeiPTvaJXscGMRzRedNIvabXr+0b+vtAAu6Z54/lJg42BiYU8QPififla1RBXvThjUMvGtjT1q2xsJX7aX6l2fJdlzbUla2GGjdS9de6yC2G7PEYQF5zxnAwh60omvWto2nd6weY0V7hn6vzdYPTsFLXNN+aQf2f32n4a1lUFlNW7JdyKWqnEGA5Qresx2t8JKOK211skt/8cyl+p7tqvqoftTWefuqXKpnPOPsKato7ePP1buK0+7BHnpVZCz1I/5a0M/AlLcvBo80IYvdGlvfS2C3/RAZr2TksqwyfLTVhYtV6EYVpq6te+NitK2DOlsffBU/9TpO1LijXNlnH9EeZY0l5IwBtrHEa64vx3bs8mzUp8brY+m1bryqUWeLtnebatopdOWiF37Hq0QcBkNu6/JqhXbrR87UjF+uOOmv9mgdESeeZWvFYjkTLBGiHSJviMGupkebAEtZx6qkcVSXyid8tQ65EqTd5deydtud+9ovf/h3li374n/4N2ff9K63NE70YXSABhwNYxCqy1HgGzxtE7VvBjaNd46u4W4eNdDLRhrm9eyTECx2EJyhFD+2MXs6EJWhYkd31zV/EFtHoquMIg0om9WbxphMDU4GRyxvEMgEVYwJmrw1aRWeejXSLiVWWReSUj0GA0CIssUGG5/0ryDC727EZBCMQSHDGKEZbspCq4KSFTT/afDqzONWAKw4dFu1m4Zned5ia2fsPPHJmJIHKntDZtTSmKmj6IqezkU68UDKs1cnLesJFVKCE0Rwp5jrAKiniDVVDrYX8uAKXdlqAzZuIpdOImYGpehTHlvA1J5EAZIDm3i54y52azkFBptttBefIeeEsZxByBJxLZvVgTLYJSadxK/rFrlc6l2HSxumPgssTg32q7iJZyV2i13fRVEMvtgzfGMhPjQT+t5/s9VDAC3U4KsjMdSoKESfR7LDf1G8zKw52QtkJviKGUu5us0ms8kHTVsmafqi8ny8b7EmMC3oGMoaM/40wu86vGRlH/EyT7WoMmaoCS+ksgGV1oni2Df7HVGsszCWqt+4is9/ycZW011eDPlUZEzcIoet4kCP3R0jOotSGCXR93PWBqsmTY3pTTsyFnlzS9oCfsQ+8taFvqtPS9L6SIS1Q1esLHWz0AJl5Ssm68ox1rJpDBNToLltZHNZ3HL+XrgVj+CFbXnFRH3ojZTxVjpRfUE76n0ZcecLpLKvA/zDiKIWZiYW8GzTxoePUlY5KroBhTNszvdGPRbF64J4Zbv51rva+z7wm3kFWFQ46TiQK+g1Tg0q12iQSVgeLeEfu15EtspHp0z/oLBWPDjEwLCCKTsY0HsTALsMXs6q38lUx9X3Av+jrvSX5qWD3vC9BkMxqjyKKkjETJtKW/GLFN5qFeZin3oKQ76llZEQxATpSKhCXP0wjQ/zdg4d87ThW+SCVRg9WXjCzZQBreV2BHwj1RfYqjRX+5odcqmsSNpYE7F81q3HGW7Z7GDgd3tprmAMn0wYB/Wm25EUwy32UJ7JfzTEDOQpTAdb4HtDr267Jl8S/I6T+DEpJXbWAYQ5l2NHdHXcjOXodGUeH2IK2Jqnrf4xoeklWQZOGADMhMCxfBHRzRU9exhX8lK75V4bD1UzAqw5scczHmkLgIvri3WyqAEjizBItRlBJ1ktgTEw+lelwe8ORkfocFKQuPayWf1opTj600FkFnhkA00meeNSuqY9hPry2KOiYsU6MjUQxr7sEIbHW8pdQVdEIJHuopNqQkKAhaieHHF9l1GY6EAuR/VIpiAG2kn027z6euxJ11Z+mFVPJgJZ+WhVyWkToKVYmGC5s01qR9q1El2PZcrEjigSlH91sGP5iy1utbdAd0SqtqATY+SKaHbRLZtYCsTH8kE5t5Bk4DPa1zgTqvIJxWznR3MAuk/QR13YxioeEvuHQ6qEo21e0XGEFK8qXN2o0HpMehzja+zEhvkBE95Zn0M3IbJO1adPNVmSRGlFeMQZJmypuMhZ5tZ3keUzV5rSFSk14A5YBRsP9Q7QCmCX5jA2V7l1FjEL7KhMkfw74woK98fkNBmrrgBXBSe4qHZ1kE3D+fv/s0Wiy4kjbmiT4brhGVYvGzqWgA9d2qP0zKclbD0jSwZg+dLiR3g5Uhj97KuhFeZscBm8gs2nZ/nCLjuq2rSHcieJzPD4Qt7BqewtpKo99+JWzsuitRIrXSNG9vDYyc7n+XlJzZgHD9aawJTxoxX+zVKGV0rVlCnqtBjSQG31C1xmSX3E+Aj3nYziIme8MllKGwNA6SxmkGlHrtcyqKWoKyqGuT32k1Nd4g1u+V7HUR9hcsLiclSd3UWq12nJF2i3z1jGTqn4JHs3cS6pN4m6XqQPuMymb40YxFchFKod5WSm+Mz8NmV0axM5QlBmGkcq4hGAYsWkcko6Z0KZNXpRt5+5HAX6MlDKlLooRxkYRC3qMwl2trDPRODQ24qT5gqbTRvYwjoRsSlnqdVuqu/AFHtLOJOmeEGy/YjJbuinrSb+c5NLFaoNW6CnncRnhEajVI04/M9vGX9dvfTNMSrjUApgl3+mPqokaU7oPZGbHSAMy43T6PPVxmXE9ujPToSfuslfsp2PvGei0kIBqtpDtQZBy+eYxHg75JfCK5sxWuM0B0Ka38TW9VkUFvMyslembyMmI58jrMUy6wNVjs0ZX7QRirgzNW3Bsx4vx1RFVYkKCoZEMuW8Ts+Co8KZo1OwY7CTjs1BefhqGaTAnHIKwquZGIdh3t0VUry3XJFy3NCrf+ip0hlCmPsudik7R5Rf2YE3iia8F5TF7QAMO8akqU1i9cESLemC2qwObaxE5WAeOiTXVrSyxXRhjlJRtL7o46hOPz0OaooiaOhQeyxwgloxbCsccZVl7y4xmMWh123iTDl+5DLB4IuE8jMsSdZHHU2X/lBiS5Wqw0HMtaPpn4iDOksoh2SZMONjnCsdFXDtdiJKtDmiswegfJtZY6rKq04qX/uZ38WTji1W77T1ZO0hh2fRydE2zFbVbMxorxqM5eWttpKHQXIN2nqP3xC0HIECmBkcEd0QQzQO8P+/XZgJkoMwDAT//+vt7pEr1FLE6ByNZZNATt4dBB4o4O4BjgD9ESFLbPDaAwIoi+Nfaq9OwBqDr2YTkJsM8zPEgyBFZOnqEs9yzkVNvbdDrh7qDl2Pm5EHrDR/6XKfYXPyQcjuCMDHOoqGMJoiy/Sz1w+96G8PvIxK9MNvRlkhzG4fsaaI69rMYIX1i77pr6a268/Lw/dDnRwcca1xqUQAsn0cmNb4lnEAbS05FK7xF2t8u6dYYnr71+pxnI5JIVugzU3MvvTBW7Mvy+rkXrvjrS4Brp7f45/6dTE9vKSZuVVCwLS32s3pi4elXgZiPc6H/wctUSVEXRMYYAAAAABJRU5ErkJggg=="/>
                </p>
                <p> Hex: n-Hex extract</p>
                <p> F1 &#x2013; F10: Fractions</p>
                <p> Phy &#x2013; Standard Phytol Isomers</p>
                <p> Sq &#x2013; Standard Squalene</p>
                <p> </p>
                <p> 
                    <bold>Conclusion:</bold> 
                    <list list-type="bullet">
                        <list-item>
                            <p>Did you detect, squalene, and terbutaline in F8,9,10? There is no strong evidence to say that these compounds enhance lipolysis and thermogenesis of adipocyte tissues.</p>
                        </list-item>
                    </list> 
                    <underline>
                        <bold>Response to reviewer:</bold>
                    </underline>
                </p>
                <p> These compounds have been removed from the Conclusion section and only their potential functions are being discussed in the Discussion section.</p>
            </body>
        </sub-article>
    </sub-article>
</article>
