An optimized probe-based qPCR assay for monitoring invasive lionfish (Pterois volitans) using environmental DNA

An optimized probe-based qPCR assay for monitoring invasive lionfish (Pterois volitans) using environmental DNA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An optimized probe-based qPCR assay for monitoring invasive lionfish (Pterois volitans) using environmental DNA Katherine Viehl, Zain Khalid, Kathryn Greiner-Ferris, Eli Taub, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3953940/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Mar, 2025 Read the published version in Environmental DNA → Version 1 posted You are reading this latest preprint version Abstract The Indo-Pacific lionfish Pterois volitans is an invasive species in the western Atlantic. Since their introduction in Florida in the early 1980’s, populations have exploded with lionfish now found from North Carolina to Venezuela. As their range expands, these generalist predators threaten native fauna, and while they are primarily a marine species, their tolerance for low salinity conditions may allow them to expand into sensitive estuarine habitats undetected. Traditional approaches for tracking invasive species such as direct observation or trapping are impractical across large spatial scales making environmental DNA (eDNA) an attractive alternative. Currently, there is only one published PCR assay for the detection of lionfish eDNA. However, the specificity of this assay is unverified, and the critical performance parameters limits of detection (LOD) and limits of quantification (LOQ) have not been established. Here we evaluate the specificity of the currently available lionfish assay, determined that it is not species-specific, and is likely to provide false negatives in the western Atlantic. As an alternative, we developed a new qPCR TaqMan probe-based assay that is species-specific for P. volitans and highly sensitive with a LOD of 12 copies per reaction and a LOQ of 598 copies per reaction. While our assay does not amplify the closely related P. miles , which is also invasive in the western Atlantic, the low prevalence of this species in the invasive population means our assay is effective for most monitoring purposes. eDNA estuaries invasive species TaqMan quantitative PCR western Atlantic Figures Figure 1 INTRODUCTION The rate of non-native species introductions has increased dramatically in modern times, correlating with human population growth, advances in transportation, and increased international trade (Mack et al., 2000 ). In aquatic ecosystems, new species are often introduced accidentally via ballast waters from ships or in some cases introductions are intentional efforts organized by local governments to improve fisheries (see Gaither et al. 2010 , 2012 for examples in Hawaiʻi). Aquarium releases are a common invasion pathway, with numerous examples of popular aquarium fish ending up in freshwater and marine systems (Padilla & Williams 2004 ). While most introduced species never become established, those that persist and thrive can have serious economic impacts (Pimentel et al., 2005 ) and can pose a significant threat to biodiversity and ecosystem function (Simberloff, 2003 ; Burgiel et al., 2006 ; Albins & Hixon, 2013 ; Gaither et al., 2013a ; Reaser et al., 2020 ). Documenting the spread and range of introduced species is essential to understanding their overall consequences on the ecosystems they invade (Gaither et al., 2013 b; Coleman et al., 2014 ) and is necessary to plan effective management strategies. The Indo-Pacific lionfish Pterois volitans (family Scorpaenidae) was introduced in the western Atlantic Ocean in the early 1980s (Whitfield et al., 2002 ). Later, genetic analyses also identified the presence of P. miles mitochondrial haplotypes in the invasive population, but only at low frequency, indicating the introduction of both species (Hamner et al., 2007 ). Since the first report of lionfish in southern Florida in 1985 (Schofield, 2009 ), they have spread throughout much of the Caribbean. Individuals have been caught as far north as Massachusetts and are well-established along most coastal regions between North Carolina and Venezuela (Schofield et al, 2022 ), with an individual captured as far south as southeastern Brazil (Ferreira et al., 2015 , Luiz et al., 2021 ). They are typically found at depths between 2 and 55 m but have been recorded at 304 m off the coast of Bermuda (Nuttall et al., 2014; Gress et al., 2017 ). While an accidental or intentional release from aquaria is the most often cited source for the Atlantic introduction of lionfish (Whitfield et al., 2002 , Semmens et al., 2004 ), ballast transport cannot be ruled out as the invasion pathway, nor as a factor contributing to their spread (Wonham et al., 2000 ; Whitfield et al., 2002 ; Semmens et al., 2004 ). Lionfish are generalist predators that consume a wide variety of fishes (Côté et al., 2013 ), including rare and endemic species such as the critically endangered social wrasse, Halichoeres socialis (Rocha et al., 2015 ). A decline in the recruitment of native fish species has been attributed to lionfish predation (Benkwitt, 2015 ), and while they are a marine species, their broad salinity tolerance may allow them to invade sensitive estuarine environments (Barbour et al., 2010 ; Jud et al., 2014; Schofield et al., 2015 ). For instance, in central Florida, lionfish are suspected to have entered the Indian River Lagoon (IRL), which spans nearly 400 km of Florida's eastern coastline. The IRL, like other estuaries throughout the region, supports a diversity of wildlife (Gilmore, 1995 ). While observations recorded on iNaturalist ( https://www.inaturalist.org/ ) and the United States Geological Survey (Schofield et al., 2022 ) Nonindigenous Aquatic Species (NAS; https://nas.er.usgs.gov/ ) database show that lionfish are found at IRL inlets, the extent of encroachment into this and other estuaries is unknown as the rocky substrates suitable for lionfish in these ecosystems largely go unmonitored. Traditional approaches for monitoring marine invasive species typically involve either direct observation or the capture of individuals. However, these techniques are time consuming, expensive, and often impractical (Eble et al., 2020 ). An attractive alternative is the use of environmental DNA (eDNA) for the detection and tracking of invasive species (Fediajevaite et al., 2021 ; Forsman et al., 2022 ). Environmental DNA is the genetic material shed by organisms into their surrounding environment. This DNA can be extracellular or contained within whole cells sloughed from an organism (Ficetola et al., 2008 ; Goldberg et al., 2011 ; Taberlet et al., 2012 ; Kumar et al., 2020 ). By isolating DNA from environmental samples and utilizing PCR methods, it is possible to detect the presence of an organism in the nearby environment. These approaches are now widely used to track invasive, at-risk, and rare organisms across a diversity of habitats (Ficetola et al., 2008 ; Goldberg et al., 2011 ; Taberlet et al., 2012 ; Goldberg et al., 2013 ; Takahara et al., 2013 ; Roux et al., 2020 ; Rodgers et al., 2020; Kumar et al., 2022a ; Gaither et al., 2022 ). Moreover, in many cases, eDNA approaches have been shown to be more sensitive and time-efficient compared to traditional monitoring methods (Fediajevaite et al., 2021 ). Due to the increasing interest in eDNA approaches for biodiversity monitoring, there are a growing number of species-specific assays designed to detect and monitor marine species, including one for lionfish. Whitaker et al. ( 2021 ) is the first and only published PCR assay for the detection of lionfish P. volitans and P. miles based on the mitochondrial cytochrome oxidase I (COI) gene. However, the specificity of this assay is unverified, and the critical performance parameters limit of detection (LOD) and limit of quantification (LOQ) have not been established. Here we evaluate the method of Whitaker et al. ( 2021 ) and design and test a new qPCR TaqMan® probe-based assay targeting the mitochondrial cytochrome b (Cytb) gene. We determine the specificity of these assays using tissues collected from 23 non-target species (primarily from the family Scorpaenidae) known to inhabit the western Atlantic and report LOD and LOQ for the new TaqMan® assay. METHODS qPCR Primer Design Primer design and in-silico primer testing : To design lionfish-specific primers for a new PCR assay, we downloaded Cytb sequences from GenBank, including sequences from five species in the genus Pterois and two sequences from representatives of the closely related genus Dendrochirus (Table S1). Sequences were aligned in Geneious 2019.0.4 using the default settings of the ClustalW algorithm. For primer design, we focused on regions showing high sequence divergence between our target species P. volitans and other species in the genus Pterois , and manually selected the best sites. We tested the specificity of candidate Cytb primer sets using NCBI’s Primer-BLAST tool with default settings (Ye et al., 2012 ). The resulting Cytb primers (Cytb-ptv-F and Cytb-ptv-R) are listed in Table 1 . For comparison, we also tested the COI primers LFeDNA-F and LFeDNA-R of Whitaker et al. ( 2021 ) for specificity using NCBI’s Primer-BLAST tool as described above. Table 1 . Species-specific cytochrome b (Cytb) primers, probe, and gBlock designed and tested here. The gBlock contained a mid-sequence modification (11 bp reversal; underlined below) to permit differentiation of synthetic DNA and genomic DNA amplicons as a cross-contamination control. In-situ primer testing, protocol optimization, and TaqMan Probe Design : To test if our candidate primers amplified target DNA at the exclusion of other taxa, we obtained tissue samples and DNA extractions from our target species P. volitans as well as from 20 closely related species across the family Scorpaenidae that naturally occur in the western Atlantic/Gulf of Mexico regions (Table S2). In addition, we obtained tissues from three species that were flagged as potential sources of false positives from our in-situ testing (Table S2). DNA was isolated from tissue samples using the E.Z.N.A. Tissue DNA kit (Omega Biotek) following the manufacturer's protocols. DNA was eluted twice for each extraction using 50 µL of buffer, for a final elute volume of 100 µL. After extraction, DNA was stored at -20 ºC. To determine the appropriate annealing temperature for our Cytb-ptv primers, gradient PCRs were run on a Bio-Rad T100 Thermal Cycler using DNA isolated from P. volitans . All gradient PCRs were conducted in a total volume of 20 µL and included 10 µL IBI Taq 2x Master Mix (IBI Scientific), 0.4 µL of each 10 µM forward and reverse primers, 1 µL of template DNA, and 8.2 µL Invitrogen UltraPure water. PCR conditions were as follows: 5 min denaturation at 95 ºC followed by 40 cycles of: 94 ºC for 20 s, a gradient annealing temperature of 56–66 ºC for 30 s, and extension at 72 ºC for 30 s. This was followed by a final extension step at 72 ºC for 5 min. PCR products were visualized using electrophoresis on a 1.5% agarose gel. The highest annealing temperature that produced a clear and strong band on the gel was selected and used in subsequent PCRs to mitigate nonspecific binding. We repeated the gradient PCRs for the Whitaker et al. ( 2021 ) COI primers. Based on our results, we determined that the optimal annealing temperature was 65 ºC for our Cytb assay, and 60 ºC for the COI assay, a few degrees higher than described in Whitaker et al. ( 2021 ). Once optimal annealing temperatures were determined, we conducted qPCRs for both assays in triplicate on the 20 P. volitans tissues and all non-target samples (Table S2). For both the Cytb-ptv assay and Whitaker et al. ( 2021 ) COI assay, qPCRs were run using 1 µL of template, 10 µL SsoAdvanced Universal SYBR Green Supermix (BioRad), 0.4 µL of each primer at 10 µM, and 8.2 µL UltraPure water, for total reaction volumes of 20 µL. All qPCRs were run on a BioRad CFX96 Real-Time System with the following protocol: 5 min denaturation at 95 ºC, followed by 40 cycles of denaturation at 95 ºC for 20 s, annealing for 30 s at the assay-specific temperature (65 ºC for Cytb-ptv and 60 ºC for COI), and extension at 72 ºC for 30 s, with plate reads after the extension step in each cycle, followed by a final extension step at 72 ºC for 5 min. We also tested assay performance under more natural conditions. To do this we collected ten 500 mL water samples from the Indian River Lagoon which is a highly turbid body of water with high levels of background DNA (Kumar et al., 2022a ). Prior to filtration all equipment was soaked in 20% bleach and rinsed with deionized water. Water samples were filtered using 0.45 uM pore size 47 mm mixed cellulose ester (MCE) filter paper fitted onto 500 ml filter funnels attached to a custom-made 6-station PVC manifold vacuum filtration system and a standard laboratory vacuum pump (Kumar et al., 2022a ). After filtration, filter papers were carefully removed from each funnel using sterile tweezers, folded, placed into a 3 mL screw cap tube, and stored in Longmire's buffer (Longmire et al., 1997 ) at -20°C until DNA extraction was performed. Prior to DNA extraction, filters were cut in half with sterile scissors and DNA was extracted from one half of the filter using the E.Z.N.A. Tissue DNA kit (Omega Biotek) following the manufacturer's protocols. DNA was eluted in two steps, each using 50 µL of elution buffer (100 µL combined volume). To determine if our assay was effective in samples from natural systems with high levels of non-target DNA, we ran 20 µL qPCR reactions as described above with either 1) 5 µL of the DNA extraction from the IRL samples (our template DNA) and 4.2 µL of UltraPure water or 2) with 5 µL of template DNA spiked with 2 uL of gBlock (10^3 copies/µL; see below) and only 2.2 µL of UltraPure water. These qPCRs were run in triplicate. Following the qPCR results above we decided to design a TaqMan® probe to eliminate late-cycle (> 38 cycle) dimer fluorescence (see below). The probe was designed using the same sequence alignment used to design the primers (Table 1 ). Determining Limit of Detection and Limit of Quantification To determine the LOD and LOQ for our qPCR assay we designed an assay-specific gBlock™ (IDT; Table 1 ). The gBlock was 197 bp long and included the priming sites for our newly designed Cytb-ptv primers and a mid-sequence modification (11 bp reversal; Table 1 ) to permit differentiation of synthetic DNA and genomic DNA amplicons as a cross-contamination control. The gBlock was resuspended in 25 µL Tris-EDTA, 1x solution (pH 8.0) following manufacturer instructions ( https://www.idtdna.com/ ) resulting in a stock solution of 5.15E + 10 copies/µL. All subsequent dilutions were made using a tRNA solution at 100 ng/µL by adding 100 µL of 10 mg/mL Yeast tRNA (Invitrogen) to 9900 µL of UltraPure water (Invitrogen), which helps prevent the gBlock from binding to the plastic tube and causing inconsistencies across dilutions ( https://www.idtdna.com/ ; Bustin et al., 2009 ; Klymus et al., 2020 ). The LOD and LOQ were determined by conducting serial dilutions of the gBlock stock solution until we had template concentrations of 5000, 512, 64, 16, 8, 4, 2, and 1 copies/µL. Dilutions were run in 10 qPCR replicates of 20 µL reactions, each with 10 µL TaqPath™ ProAmp™ Master Mix, 6.7 µL UltraPure distilled water, 0.4 µL each of 10 µM Cytb-ptv-01F and Cytb-ptv-01R, 0.5 µL 10 µM probe, and 2 µL of template, effectively doubling the copy numbers listed above per reaction for more accurate pipetting. All qPCR replicates were run simultaneously on a BioRad CFX96 Real-Time System using the following protocol: 3 min denaturation at 95 ºC, followed by 40 cycles of 95 ºC for 20 s, 65 ºC for 30 s, and 72 ºC for 30 s, with a plate read after the extension step in each cycle. We used the curve-fitting LOD and LOQ model as described in Klymus et al. ( 2020 ), where the LOD is the lowest standard concentration for which at least 95% of technical replicates amplify, and the LOQ is the lowest standard concentration for which the coefficient of variation (CV) value is less than 35%. The Generic qPCR Limit of Detection (LOD) / Limit of Quantification (LOQ) R script calculator (Merkes et al., 2019 ) was used to calculate LOD and LOQ, as well as the slope and intercept of the linear regression and the resulting R-squared value using the default settings. To visualize the results and determine the correlation between Cq-values and standard concentrations, a standard curve plot was generated using the data output of Merkes et al. ( 2019 ) in RStudio (2020). Based on the results from our testing of non-target species, we did not calculate LOD/LOQ for the Whitaker et al. ( 2021 ) COI assay. Assay Validation in Aquaria To verify the performance of our assay on water samples with known lionfish present, we collected water samples from a 2650-liter saltwater tank containing eight lionfish and a few non-scorpaenid fishes. The lionfish made up the bulk of the biomass in this tank (see Fig. S1). One-liter water samples were collected in duplicate from the air-surface interface and were placed directly in a cooler with ice and transported back to the laboratory. To test for the presence of lionfish eDNA, the water samples were filtered, DNA was extracted from those filters, and qPCRs were performed as describe above using 5 µL of template DNA and eight replicates per sample. RESULTS qPCR Primer Design Primer design and in-silico testing : Based on our sequence alignments, we selected the Cytb-ptv-F and Cytb-ptv-R primers for in-silico testing. These primers are 27 and 22 bp long respectively with no ambiguous sites (Table 1 ). Our NCBI Primer BLAST results for the Cytb assay showed that only marine species in the Indo-Pacific genus Pterois were matches for our Cytb-ptv primers. One freshwater species was also a possible match for our primers, the silverjaw minnow Ericymba buccata , but this species is not found in the introduced range. On the other hand, BLAST results for the Whitaker et al. ( 2021 ) COI primer set identified several matches for non-target species, including the king mackerel Scomberomorus cavalla , the round scad Decapterus punctatus , Sloane’s viperfish Chauliodus sloani , and the silversides Menidia beryllina and M. menidia; all of which are found in the western Atlantic. To test the specificity of these primers we obtained tissues from three of these five species (Table S2). In-situ primer testing, protocol optimization, and TaqMan probe design : Both qPCR assays amplified all 20 P. volitans samples collected in Florida successfully and early. Cq values ranged between 15 and 25 cycles (data not shown). Since we did not standardize template DNA concentration, we do not attempt to interpret these Cq values other than an indication of successful amplification. While the COI assay also successfully amplified P . miles , our Cytb-ptv assay did not. We tested both primer sets for specificity by running qPCR assays (40 qPCR cycles) on all twenty-three non-target species (Table S2). We found no indication of non-target amplification with the Cytb-ptv assay. In contrast, the COI assay of Whitaker et al. ( 2021 ) amplified two non-target species: the king mackerel ( Scomberomorus cavalla ) and round scad ( Decapterus punctatus ) after 27 and 34 cycles respectively. As above, we did not standardize template DNA concentration, so we do not attempt to interpret these Cq values other than an indication of successful amplification. Our qPCR assays on the environmental samples collected in the IRL resulted in unexpected late-stage amplification at around 38–40 cycles. Although not sufficient to represent target amplification (they did not cross the threshold before 40 cycles), and while melt curves indicated dimer polymerization, we decided to design a TaqMan Probe to eliminate the issue and to minimize the risk of false positives (Table 1 ). When we ran qPCR on eDNA samples with the probe, the fluorescence caused by the polymerization of dimer was ameliorated. Determining Limit of Detection and Limit of Quantification We ran our experiments to determine the limit of detection (LOD) and the limit of quantification (LOQ) using serial dilutions of the gBlock at concentrations of 5000, 512, 64, 16, 8, 4, 2, and 1 copies/µL and 10 qPCR replicates each. Using the Generic qPCR Limit of Detection (LOD) / Limit of Quantification (LOQ) R script calculator of Merkes et al. ( 2019 ), we found the LOD for our Cytb-ptv assay to be 12 copies per reaction and the LOQ to be 596 copies per reaction (Fig. 1 ). Our standard curve plot based on the output data of Merkes et al. ( 2019 ) demonstrated a strong linear correlation between Cq-value and standard concentration with the R-squared (correlation coefficient) value > 0.99 (Fig. 1 ; slope = -3.298, intercept = 40.012). Assay Validation in Aquaria We evaluated the performance of our Cytb-ptv assay with water samples collected from a 2650-liter saltwater tank containing eight lionfish and a few other non-scorpaenid fishes. Lionfish DNA was successfully amplified in all eight PCR replicates. Cq values ranged between 19 and 21 cycles (see Fig. S2). DISCUSSION Early detection and tracking of invasive species are critical to implementing effective mitigation strategies (Simberloff, 2003 ; Reaser et al., 2020 ; Sepulveda et al., 2022 ). Where lionfish are abundant in the Caribbean, massive citizen scientist efforts and lionfish culling events, or "derbies", have been organized to keep lionfish numbers under control ( https://www.reef.org/lionfish-derbies ). Regular culling events can keep population numbers low and targeted efforts have the potential to slow or even halt the spread of invasive species. In some locations these efforts have succeeded in lowering the overall abundance of lionfish and resulted in fewer large individuals (Frazer et al., 2012 té et al., 2014 a). However, to control or manage the spread of an invasive species, we must first know its geographic range and be able to identify potential new habitats. Tracking invasive species, particularly ones that spread quickly, can be time-consuming and expensive. Here we present a fully validated qPCR assay for the detection of P. volitans DNA in environmental samples. Our optimized assay was able to detect P. volitans at the exclusion of other closely related species found in the introduced range, with a low LOD of only 12 copies/µL, providing an important tool for monitoring this invasive species. Our testing of the Whitaker et al. ( 2021 ) COI assay showed that it amplifies lionfish, as well as at least two non-target taxa that are common in the introduced range. Both king mackerel ( Scomberomorus cavalla ) and round scad ( Decapterus punctatus ) readily amplified with the COI primers, indicating that this assay is unsuitable as a standalone qPCR assay and would require a subsequent sequencing step for species confirmation. In fact, the original protocol of Whitaker et al. ( 2021 ) recommends bi-directionally Sanger sequencing the PCR product of any positive detections found with their assay. Subsequent BLAST results for these DNA sequences that returned matches for lionfish were considered positive detections. However, this presents an opportunity for a type II error. If non-target DNA was abundant in a sample, it’s possible that PCR followed by sequencing would only return non-target hits, even if both species’ DNA were present in the sample. Alternatively, if both target and non-target DNA were amplified and sequenced, the data would be difficult to interpret unless a massively parallel sequencing platform were used in lieu of Sanger sequencing. While the data generated here indicates that our Cytb-ptv assay is species specific, it is not without limitations. These primers are highly specific to P. volitans and do not appear to detect P. miles , a cryptic sister-species. However, this species comprises just a small percentage (~ 7%) of the invasive lionfish population (Hamner et al., 2007 ), making it the most useful and only fully validated qPCR assay yet published. Currently, culling lionfish during derbies by free divers or scuba divers is the most common means of removing these predators from reef ecosystems and has proven to be quite effective at lowering population numbers (Frazer et al., 2012 té et al., 2014 a). Site selection for culling events or derbies is typically based on citizen reports of high-density populations or from anglers who are catching lionfish on hook and line. However, there are many areas across the region where recreational diving and fishing are less frequent. These places represent important missing data where the extent of the lionfish invasion is not well documented. Furthermore, at the front lines of the invasion, where an invasive species is moving into new habitats and expanding its range, the species is presumably rare or at low densities and so can go undetected using traditional survey techniques. Moreover, commonly employed survey methods typically do not detect larval fish and/or fertilized eggs. With an organism of high fecundity and durable egg masses such as the lionfish (Morris et al., 2011; Eddy et al., 2019; Bustos-Montes et al., 2020), even a few individuals or the presence of a fertilized egg mass can lead to the establishment of a larger population if not addressed immediately. Detection of lionfish eDNA using a species-specific qPCR assay could be an important tool to promote early detection and aid with the selection of sites for targeted culling efforts. Early detection could allow for removal strategies to be implemented before a new population becomes established (Simberloff, 2003 ; Reaser et al., 2020 ; Sepulveda et al., 2022 ). As eDNA techniques only require the collection of water, they can be implemented in habitats and places where traditional surveys are not advised (deep-water, poor visibility, etc.). For instance, in an estuarine system such as the Indian River Lagoon, traditional survey methods are hindered by low visibility and habitats that are unsuitable for many types of fishing gear (e.g., seines). With reliable species-specific detection assays, eDNA analysis will increase confidence in traditional survey results and allow for expanded monitoring into the front lines of the lionfish invasion. Moreover, metabarcoding techniques may also prove valuable for use in detecting lionfish. While not yet tested, there is evidence that some of the primers commonly used to characterize fish communities might also be able to detect lionfish (Kumar et al., 2022b ; unpubl. data). Declarations CONTRIBUTIONS Katherine Viehl, Zain Khalid, Eli Taub, Pavithiran Amirthalingam, Girish Kumar, and Michelle R. Gaither designed the experiment. Katherine Viehl, Zain Khalid, and Michelle R. Gaither conducted the environmental collections. Zain Khalid, Kathryn Greiner-Ferris, Victoria Marciante and Eli Taub processed samples and conducted the aquaria collections. Katherine Viehl and Zain Khalid analyzed the data. Katherine Viehl and Michelle R. Gaither led the writing of the manuscript in close consultation with the other authors. All authors read and approved the final manuscript. Financial interests: The authors have no relevant financial or non-financial interests to disclose. CONFLICT OF INTEREST STATEMENT The authors have no conflict of interest to declare. Funding: This work was supported by a Student Research Grant from the Office of Undergraduate Research at the University of Central Florida. ACKNOWLEDGEMENTS We would like to thank Eric Reyier from the Kennedy Space Center Ecological Program in Cape Canaveral, Florida for his insightful discussions and expertise on the region, as well as lending us his assistance in sampling along the Indian River Lagoon. We would like to thank the Reef Environmental Education Foundation, Florida Fish and Wildlife Conservation Commission's Florida Biodiversity Collection, Smithsonian Institution's National Museum of Natural History, Kansas University's Biodiversity Institute and Natural History Museum, and the University of Florida's Florida Museum of Natural History for tissue and DNA samples. We would also like to thank the Sunrise Fish Dive Surf in Port Canaveral, Florida for allowing us to sample their aquarium, and the Office of Undergraduate Research at the University of Central Florida for funding. Finally, we would like to thank the reviewers for taking the time and effort to review this manuscript. We sincerely appreciate all your valuable feedback. References Albins MA, Hixon MA (2013) Worst case scenario: potential long-term effects of invasive predatory lionfish ( Pterois volitans ) on Atlantic and Caribbean coral-reef communities. 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Mol Ecol Resour 20:29–39. https://doi.org/10.1111/1755-0998.13107 Kumar G, Farrell E, Reaume AM, Eble JA, Gaither MR (2022a) One size does not fit all: Tuning eDNA protocols for high- and low‐turbidity water sampling. Environ DNA 4:167–180. https://doi.org/10.1002/edn3.235 Kumar G, Reaume AM, Farrell E, Gaither MR (2022b) Comparing eDNA metabarcoding primers for assessing fish communities in a biodiverse estuary. PLoS ONE 17:e0266720. https://doi.org/10.1371/journal.pone.0266720 Lionfish Derbies : Reef Environmental Education Foundation, Key Largo, FL. https://www.reef.org/lionfish-derbies . Accessed 4 December 2023 Longmire JL, Maltbie M, Baker RJ (1997) Use of Lysis Buffer in DNA isolation and its implication for museum collections. https://doi.org/10.5962/bhl.title.143318 . Museum of Texas Tech University, Lubbock Luiz OJ, dos Santos WCR, Marceniuk AP, Rocha LA, Floeter SR, Buck CE, de Macedo Klautau AGC, Ferreira CEL (2021) Multiple lionfish ( Pterois spp .) new occurrences along the Brazilian coast confirm the invasion pathway into the Southwestern Atlantic. Biol Invasions 23:3013–3019. https://doi.org/10.1007/s10530-021-02575-8 Mack RN, Simberloff D, Mark Lonsdale W, Evans H, Clout M, Bazzaz FA (2000) Biotic invasions: causes, epidemiology, global consequences, and control. Ecol Appl 10:689–710. https://doi.org/10.1890/1051-0761(2000 )010[0689:BICEGC]2.0.CO;2 Malpica-Cruz L, Chaves LCT, Côté IM (2016) Managing marine invasive species through public participation: Lionfish derbies as a case study. Mar Policy 74:158–164. https://doi.org/10.1016/j.marpol.2016.09.027 Merkes CM, Klymus KE, Allison MJ, Goldberg C, Helbing CC, Hunter ME, Jackson CA, Lance RF, Mangan AM, Monroe EM, Piaggio AJ, Stokdyk JP, Wilson CC, Richter C (2019) Generic qPCR Limit of Detection (LOD) / Limit of Quantification (LOQ) calculator. R Script. Available at: https://github.com/cmerkes/qPCR_LOD_Calc . https://doi.org/10.5066/P9GT00GB . Accessed 23 April 2023 Nuttall M (2014) Lionfish ( Pterois volitans [Linnaeus, 1758] and P. miles [Bennett, 1828]) records within mesophotic depth ranges on natural banks in the Northwestern Gulf of Mexico. Bioinvasions Rec 3:111–115. https://doi.org/10.3391/bir.2014.3.2.09 Padilla DK, Williams SL (2004) Beyond ballast water: aquarium and ornamental trades as sources of invasive species in aquatic ecosystems. Front Ecol Environ 2:131–138. https://doi.org/10.1890/1540-9295(2004)002 [0131:BBWAAO]2.0.CO;2 Pimentel D, Zuniga R, Morrison D (2005) Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol Econ 52:273–288. https://doi.org/10.1016/j.ecolecon.2004.10.002 Reaser JK, Burgiel SW, Kirkey J, Brantley KA, Veatch SD, Burgos-Rodríguez J (2020) The early detection of and rapid response (EDRR) to invasive species: a conceptual framework and federal capacities assessment. Biol Invasions 22:1–19. https://doi.org/10.1007/s10530-019-02156-w Rocha LA, Rocha CR, Baldwin CC, Weigt LA, McField M (2015) Invasive lionfish preying on critically endangered reef fish. Coral Reefs 34:803–806. https://doi.org/10.1007/s00338-015-1293-z Roux LD, Giblot-Ducray D, Bott NJ, Wiltshire KH, Deveney MR, Westfall KM, Abbott CL (2020) Analytical validation and field testing of a specific qPCR assay for environmental DNA detection of invasive European green crab ( Carcinus maenas ). Environ DNA 2:309–320. https://doi.org/10.1002/edn3.65 RStudio Team (2020) RStudio: Integrated Development for R. RStudio. PBC, Boston, MA. http://www.rstudio.com/ Schofield P (2009) Geographic extent and chronology of the invasion of non-native lionfish ( Pterois volitans [Linnaeus 1758] and P. miles [Bennett 1828]) in the Western North Atlantic and Caribbean Sea. Aquat Invasions 4:473–479. https://doi.org/10.3391/ai.2009.4.3.5 Schofield P, Huge D, Rezek T, Slone D, Morris J (2015) Survival and growth of invasive Indo-Pacific lionfish at low salinities. Aquat Invasions 10:333–337. https://doi.org/10.3391/ai.2015.10.3.08 Schofield PJ, Morris JA Jr, Langston JN, Fuller PL (2022) Pterois volitans/miles: U.S. Geological Survey, Nonindigenous Aquatic Species Database. https://nas.er.usgs.gov/queries/factsheet.aspx?speciesid=963 . Accessed 27 March 2023 Semmens B, Buhle E, Salomon A, Pattengill-Semmens C (2004) A hotspot of non-native marine fishes: evidence for the aquarium trade as an invasion pathway. Mar Ecol Prog Ser 266:239–244. https://doi.org/10.3354/meps266239 Sepulveda A, Smith D, O’Donnell K, Owens N, White B, Richter C, Merkes C, Wolf S, Rau M, Neilson M, Daniel W, Dumoulin C, Hunter M (2022) Using structured decision making to evaluate potential management responses to detection of dreissenid mussel ( Dreissena spp .) environmental DNA. Manag Biol Invasions 13:344–368. https://doi.org/10.3391/mbi.2022.13.2.06 Simberloff D (2003) How Much Information on Population Biology Is Needed to Manage Introduced Species? Conserv Biol 17:83–92. https://doi.org/10.1046/j.1523-1739.2003.02028.x Taberlet P, Coissac E, Hajibabaei M, Rieseberg LH (2012) Environmental DNA. Mol Ecol 21:1789–1793. https://doi.org/10.1111/j.1365-294X.2012.05542.x Takahara T, Minamoto T, Doi H (2013) Using Environmental DNA to Estimate the Distribution of an Invasive Fish Species in Ponds. PLoS ONE 8:e56584. https://doi.org/10.1371/journal.pone.0056584 Whitaker JM, Brower AL, Janosik AM (2021) Invasive lionfish detected in estuaries in the northern Gulf of Mexico using environmental DNA. Environ Biol Fish 104:1475–1485. https://doi.org/10.1007/s10641-021-01177-6 Whitfield PE, Gardner T, Vives SP, Gilligan MR, Courtenay WR Jr, Ray GC, Hare J (2002) Biological invasion of the Indo-Pacific lionfish Pterois volitans along the Atlantic coast of North America. Mar Ecol Prog Ser 235:289–297. https://doi.org/10.3354/meps235289 Wonham MJ, Carlton JT, Ruiz GM, Smith LD (2000) Fish and ships: relating dispersal frequency to success in biological invasions. Mar Biol 136:1111–1121. https://doi.org/10.1007/s002270000303 Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL (2012) Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics 13:134. https://doi.org/10.1186/1471-2105-13-134 Supplementary Files SupplementaryTableandfigures.docx Cite Share Download PDF Status: Published Journal Publication published 14 Mar, 2025 Read the published version in Environmental DNA → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3953940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290233284,"identity":"830cb44c-67d5-4f02-9f92-de83cd65d10a","order_by":0,"name":"Katherine Viehl","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACCQbGBmaGApsEEOcAkGRsIE6LQRpJWhgYgFoOJ8AECGuRnJHc+LjA4Hwe/4zkhwceMNjIbjhAQIu0RGKz8QyD28USN9IMgA5LMyaoRY7nYJs0j8HtxIbbCSAthxOJ0dL+m8fgXOL82+kfgFr+E9Yizd7YxsxjcCBxw+0ckC0HCGuRbG9sBjosOXHj/TcFBxIMko1nEtIicZj94WeeCrvEeWeOb/74o8JOto+QFjRgQJryUTAKRsEoGAU4AADXhkmiQxf5yAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0000-0797-2580","institution":"University of Central Florida","correspondingAuthor":true,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Viehl","suffix":""},{"id":290233285,"identity":"b444e81b-08d6-40b9-81cc-489f12de5ca6","order_by":1,"name":"Zain Khalid","email":"","orcid":"","institution":"University of Florida Whitney Laboratory for Marine Bioscience","correspondingAuthor":false,"prefix":"","firstName":"Zain","middleName":"","lastName":"Khalid","suffix":""},{"id":290233286,"identity":"71d4b977-8d1c-499b-822f-be2355c2af58","order_by":2,"name":"Kathryn Greiner-Ferris","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Kathryn","middleName":"","lastName":"Greiner-Ferris","suffix":""},{"id":290233287,"identity":"f40cc0a8-0a99-4346-ba1b-bad30c6d65d2","order_by":3,"name":"Eli Taub","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Eli","middleName":"","lastName":"Taub","suffix":""},{"id":290233288,"identity":"a0a6bcae-0dd5-4a73-aad8-aa3c31f085c6","order_by":4,"name":"Pavithiran Amirthalingam","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Pavithiran","middleName":"","lastName":"Amirthalingam","suffix":""},{"id":290233289,"identity":"a5356e02-a4f1-4aa5-81fc-6601bc2352ca","order_by":5,"name":"Girish Kumar","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Girish","middleName":"","lastName":"Kumar","suffix":""},{"id":290233290,"identity":"992445b3-3cee-457f-ae72-0a5b393b3545","order_by":6,"name":"Victoria Marciante","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"","lastName":"Marciante","suffix":""},{"id":290233291,"identity":"6d91dc81-fcd1-40b4-b057-e18b83f7f660","order_by":7,"name":"Michelle R Gaither","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"R","lastName":"Gaither","suffix":""}],"badges":[],"createdAt":"2024-02-13 16:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3953940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3953940/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1002/edn3.70078","type":"published","date":"2025-03-15T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54741894,"identity":"0d3504ad-2682-47e0-912b-f9ff0bc813fc","added_by":"auto","created_at":"2024-04-16 06:10:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40248,"visible":true,"origin":"","legend":"\u003cp\u003eThe standard curve, where Cq-value of each qPCR replicate and copies/reaction have a linear correlation with a correlation coefficient (R-squared) of \u0026gt;0.99 as calculated by the Generic LOD/LOQ R calculator (Merkes et al., 2019). Made using RStudio (2020).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3953940/v1/9e3e326fc4fb1d65a4785462.png"},{"id":78906656,"identity":"4fdee226-79c1-4a4d-ac43-e77b8f291ac0","added_by":"auto","created_at":"2025-03-20 15:18:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":711298,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3953940/v1/8a38e89c-7510-49ce-8cbe-b8a3a45dce72.pdf"},{"id":54741594,"identity":"ca170976-aec4-42bc-b28b-5b98b1cd5a89","added_by":"auto","created_at":"2024-04-16 06:02:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":396411,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableandfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3953940/v1/119ef9476700d7b39f0b828c.docx"}],"financialInterests":"","formattedTitle":"An optimized probe-based qPCR assay for monitoring invasive lionfish (Pterois volitans) using environmental DNA","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe rate of non-native species introductions has increased dramatically in modern times, correlating with human population growth, advances in transportation, and increased international trade (Mack et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In aquatic ecosystems, new species are often introduced accidentally via ballast waters from ships or in some cases introductions are intentional efforts organized by local governments to improve fisheries (see Gaither et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e for examples in Hawaiʻi). Aquarium releases are a common invasion pathway, with numerous examples of popular aquarium fish ending up in freshwater and marine systems (Padilla \u0026amp; Williams \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). While most introduced species never become established, those that persist and thrive can have serious economic impacts (Pimentel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and can pose a significant threat to biodiversity and ecosystem function (Simberloff, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Burgiel et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Albins \u0026amp; Hixon, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gaither et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e; Reaser et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Documenting the spread and range of introduced species is essential to understanding their overall consequences on the ecosystems they invade (Gaither et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003eb; Coleman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and is necessary to plan effective management strategies.\u003c/p\u003e \u003cp\u003eThe Indo-Pacific lionfish \u003cem\u003ePterois volitans\u003c/em\u003e (family Scorpaenidae) was introduced in the western Atlantic Ocean in the early 1980s (Whitfield et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Later, genetic analyses also identified the presence of \u003cem\u003eP. miles\u003c/em\u003e mitochondrial haplotypes in the invasive population, but only at low frequency, indicating the introduction of both species (Hamner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Since the first report of lionfish in southern Florida in 1985 (Schofield, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), they have spread throughout much of the Caribbean. Individuals have been caught as far north as Massachusetts and are well-established along most coastal regions between North Carolina and Venezuela (Schofield et al, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), with an individual captured as far south as southeastern Brazil (Ferreira et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Luiz et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They are typically found at depths between 2 and 55 m but have been recorded at 304 m off the coast of Bermuda (Nuttall et al., 2014; Gress et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While an accidental or intentional release from aquaria is the most often cited source for the Atlantic introduction of lionfish (Whitfield et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Semmens et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), ballast transport cannot be ruled out as the invasion pathway, nor as a factor contributing to their spread (Wonham et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Whitfield et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Semmens et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLionfish are generalist predators that consume a wide variety of fishes (C\u0026ocirc;t\u0026eacute; et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), including rare and endemic species such as the critically endangered social wrasse, \u003cem\u003eHalichoeres socialis\u003c/em\u003e (Rocha et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A decline in the recruitment of native fish species has been attributed to lionfish predation (Benkwitt, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and while they are a marine species, their broad salinity tolerance may allow them to invade sensitive estuarine environments (Barbour et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jud et al., 2014; Schofield et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For instance, in central Florida, lionfish are suspected to have entered the Indian River Lagoon (IRL), which spans nearly 400 km of Florida's eastern coastline. The IRL, like other estuaries throughout the region, supports a diversity of wildlife (Gilmore, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). While observations recorded on iNaturalist (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.inaturalist.org/\u003c/span\u003e\u003cspan address=\"https://www.inaturalist.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the United States Geological Survey (Schofield et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) Nonindigenous Aquatic Species (NAS; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nas.er.usgs.gov/\u003c/span\u003e\u003cspan address=\"https://nas.er.usgs.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database show that lionfish are found at IRL inlets, the extent of encroachment into this and other estuaries is unknown as the rocky substrates suitable for lionfish in these ecosystems largely go unmonitored.\u003c/p\u003e \u003cp\u003eTraditional approaches for monitoring marine invasive species typically involve either direct observation or the capture of individuals. However, these techniques are time consuming, expensive, and often impractical (Eble et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). An attractive alternative is the use of environmental DNA (eDNA) for the detection and tracking of invasive species (Fediajevaite et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Forsman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Environmental DNA is the genetic material shed by organisms into their surrounding environment. This DNA can be extracellular or contained within whole cells sloughed from an organism (Ficetola et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Goldberg et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Taberlet et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). By isolating DNA from environmental samples and utilizing PCR methods, it is possible to detect the presence of an organism in the nearby environment. These approaches are now widely used to track invasive, at-risk, and rare organisms across a diversity of habitats (Ficetola et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Goldberg et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Taberlet et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Goldberg et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Takahara et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Roux et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rodgers et al., 2020; Kumar et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Gaither et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, in many cases, eDNA approaches have been shown to be more sensitive and time-efficient compared to traditional monitoring methods (Fediajevaite et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to the increasing interest in eDNA approaches for biodiversity monitoring, there are a growing number of species-specific assays designed to detect and monitor marine species, including one for lionfish. Whitaker et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is the first and only published PCR assay for the detection of lionfish \u003cem\u003eP. volitans\u003c/em\u003e and \u003cem\u003eP. miles\u003c/em\u003e based on the mitochondrial cytochrome oxidase I (COI) gene. However, the specificity of this assay is unverified, and the critical performance parameters limit of detection (LOD) and limit of quantification (LOQ) have not been established. Here we evaluate the method of Whitaker et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and design and test a new qPCR TaqMan\u0026reg; probe-based assay targeting the mitochondrial cytochrome b (Cytb) gene. We determine the specificity of these assays using tissues collected from 23 non-target species (primarily from the family Scorpaenidae) known to inhabit the western Atlantic and report LOD and LOQ for the new TaqMan\u0026reg; assay.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eqPCR Primer Design\u003c/h2\u003e\n \u003cp\u003e\u003cem\u003ePrimer design and\u003c/em\u003e in-silico \u003cem\u003eprimer testing\u003c/em\u003e: To design lionfish-specific primers for a new PCR assay, we downloaded Cytb sequences from GenBank, including sequences from five species in the genus \u003cem\u003ePterois\u003c/em\u003e and two sequences from representatives of the closely related genus \u003cem\u003eDendrochirus\u003c/em\u003e (Table S1). Sequences were aligned in Geneious 2019.0.4 using the default settings of the ClustalW algorithm. For primer design, we focused on regions showing high sequence divergence between our target species \u003cem\u003eP. volitans\u003c/em\u003e and other species in the genus \u003cem\u003ePterois\u003c/em\u003e, and manually selected the best sites. We tested the specificity of candidate Cytb primer sets using NCBI\u0026rsquo;s Primer-BLAST tool with default settings (Ye et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The resulting Cytb primers (Cytb-ptv-F and Cytb-ptv-R) are listed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. For comparison, we also tested the COI primers LFeDNA-F and LFeDNA-R of Whitaker et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) for specificity using NCBI\u0026rsquo;s Primer-BLAST tool as described above.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Species-specific cytochrome b (Cytb) primers, probe, and gBlock designed and tested here. The gBlock contained a mid-sequence modification (11 bp reversal; underlined below) to permit differentiation of synthetic DNA and genomic DNA amplicons as a cross-contamination control.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cimg 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OzgoniGnki51F3DbxZMAD5EuF7YzrudAOrTtRudOrahXuSgFKzRl+LqLmLp4iPyx5u6hcQxqW5X8WdJQpMsSLl89z66Z4+hQJTtpVemoNmM9x64+4eb2Lczu2YV6RVOjKlyDUUfvYeWsfMZJK+5a5ugoRKbLE/ZOGkn/9u1pXbsk2dX5pqJc29nsfWmHn3uM+HbxCuL1zt70Gj+NEy+9RN5pi3KlY8UJi1e3WTVyME1qliSTKjW5StSkRecO9J69k7uGznh6eOFjcZ9tsybRq30f2op7tm1RheIFi1G/7zwOP7PE21sIdnVHoX5TPgGmjLz7elnz/PgGpo6YxKqrb3AJ9IqZvaOMTjs5cWl2NbosPcM7aze1cNd1LWnSpEmTJk2atH+q/f3iudw4jhg74O1lx7WVHSgoGpGZi7Zh+UUT3IJEI9rVK5549hJCxNXZmts7e5FblYXqk65j4xPBpyALdsxqrhYw5fqs4aFzOEbnljK+TXlSp05JyioD2Hj4LC/tAwh0ucfOAcVRpc1D+SFLOHHlFbZ+Xwi2OMKQKllQpWzBuruG6k143NycsVZGFmtWo824c1h4hvMpzB/LfXOpm1NF+gpTOPjcAS9POUXx32rKaLGNwSVW965NHrUQSkuhul2Zv+UKpm7KlGBXzXRsR9zcnTG/uZP+VWrQdeYN7P0+8SnUB8NNE6iQWUWWOos4+1b4ubciPl5ysE8N8hdrxspjb3ALC+dDoDMnV3Qnf0pxnxqD2ffIhZCQMDyMdtO0pDJ7ow2bn5vi4OmJt587NlcW0yq9uG7VTux67i/KjzPeHpacn96VGtUGs+26E8EfPhJs/ZwtzUqK8MVpu/oVlmaPOTOvCx0r5hHHilK35yx23ruDlWMQH70eMKNuHlKoarLkngmO7kLki+eyvbeNfjXLUqbvVl66+PLl+0cMdw+jtJImFcZw4rmtetTX6f1VZjetQcN+e3njGMrH8GCczm+kU+EUpCg0gHW3xHle8UdZFVNGo51cvAn76ovxldlU1kzbtoz8zMfQMMxubmPToKaUzZ5WxLkavZYc5LXDW86umcH0zZd57eGC9fXVdKhUnvoj9mHr4U94uC/2xqeY1rEIqnRl6LbpNnaOdtzZNJB2VfOJ62SnTOeZnLzxEDPvD/i928LIqllR5apBt1nbufHCDq/gEKyvTaNSqlRkKDOFm+ppzkmMuto74+HrT6CTI7Yvr3Ng6RBalFXinJpivZby0tZPPX1e6ZixfnyC01dv88IiZop+/OsoI8EeBH9z5+Wh0RRXZaPl1HPYR0UQ4u2hnhXgbv+aw2MbkbdCP/bdNCEkLIRAPxMubRlA5Wwpydd0Bmfe2+MvhLje2TNOzrjZW/D8/DqG9m9O6fSZKFCsLfNOvcLK1Q1PFyVvHNX1n9H1XRy7Z4Slnf4OBGnSpEmTJk2atH+q/W/E83tLXHy8cbG9z9o+VckkGtt5Kw9n/2tb/IICMdqvJZ493HCzM+XKxApkSJ+d0cff4i0a774fPmF7dQkthAAp0HkR100DCQgIJvD1JuoXyEKaatO5Y+uKh7u7eCBbHuwZKYR6BupO3M37D/64O7iIRvBL1repREZVSSacfI6Nty/utu85NLU+FRov5E3oF8JDxHXFvwS/YW2/cqLBW4DBm29i5eWFi2ws/ivN3t4RVy8P3C2fc2DhaGrmUnYcFoIxc3la91/AGQN7IZrdcBFiyNVKiNSRtajWfh2mEd/5GByAv+JP/o+Y004IOOF7Ew89wcHXB+MLE6mQKRMNlz3GU1lPK/zL1TsUOyFSexcT1682RIhnZ/VaW0/jQwwom1sI2jZsEuLZUZQTd083TB7uoXsuIZ6rxIhnPx9/bG4vpEWlukw9akTwz6+EBgUT/iEY97uzKaBKSfaq47hiH8jnEBvOLWgprpmTTnMvYPMxCE9leq67Edt7lyJb6mwM3f8We3d/PKyfsqlfYbIXacf2V94EeAjx6OyFm+lVZrTMgSpNQ1beMMbL25lrK1pTrvo4brmH8VHcNyDkM9FhFhyfVlc8fxbazz2JiZcPrjrLkyI8bXl6YjIVVBmp0WsrrwK9cLVXprwHEep6lXG1CojrVGLeNSMcPH3w8QsgKCAQT7O7LO2Yl+xl+nDQ0A9/EUd13nkE4WW0ix5F06HK05Fdj2wIDvLB5OBgsopnrz7iMNYB3rg5u+HvZ8CuoXVJpyrKqFMPcQj2xcPVGXubkwzOnJlsObqyy9AhZtZKorgnMGXjMHdv/H19MTq7iHblUqPKWIohO9/g6qksG1HWVIu8VXxH79IQFzwDrLi9fQhFVVlpMvYQ75UNw0TaObt68fbIEOFDWei4+h527qKOUotyHwK8zTkzt76oa7PSaOoxLDySqL+UjRvtzXh6aj3DR42gTdMy4vmF/+Wpysit5zFx8FWvp1fOdRLP4ymEuOxIlCZNmjRp0qT9G+1/Jp4dXVxx9/HE+f0ppjYuLhq/GSnacSHXLX1xPDGarrHTtpVNlRztsLx/kkNbd/LQWtmB256HJ3Yzql0D8qVXUbrHEm6aB6k3IHJ6vYNOhXOQsepkrpnZCQHjhqe/LY+OTKC8uEftPjt5ozS+HVzx9HvMypblyaDKw5A9j7H29MPJ5BbTG6QhY9GatO3ckY4dOtChY2e6dmxChTzKaJGKyiN38MQuAA+X+Ikn7V9k9jHr85Wpy4/O7WJmt1rqTiDFP4rVH8vRF7a4eHtj8/ocY2oIMVuyLu27dKJDrD91aEjpbCnV59efeZp3QiwdGlSSTFlLMPOilbp8OIn7uHoFYnZ9PV2LiGtX/yWe3Q0P0KdMrgTi2RXjezvomjNWPAcSGOTJ7TXdKJMrBxUatqGzJg6dOnagdf1SpFXinLkii+76EOL5hsPTm4hr5qbDzFOYCFHm5uiMi+s7NvcoQZZUKnpsfoW9pxDkL47Qv5CKog3nc0fZc0ARYiJNXLxcMbp1hM3bDvPAWAhcRwNWdcxOpgIVaS7u21Hct0OHTnTt1IIaRTOrn79o5/mi3AtBq7M86RHP4jdHZ3chkI8zrJoyYtyU1Q8McfByi0k3T0/M7u+gi0iLMu3W8MTbR/19YuWaytIMd7+3bGxfmsyqtPTe+QonD2feHhtDYSGeqw7bh6myltnRFe/g9xwa01iI6uIM33kXa38v3IV4tjU7RN/MGciUsznrHv+meI41Jze8vV14sm8IZVUZqCKeyUDnztq6TJ94VtaV23N4VHlypKrKwhtGOCgbJKrDOOER4Myrswuom0JFvmbjOWMSqCe9Y0wZWXYV9WFgUABWprdYObyhepZQmqqD2fPYmYAE67GlSZMmTZo0adL+jfa/E8+iAao0jL2EgDa7uIzmxUUjPmUO2i6+xNMjs+g3e13cmmdl/Z2rXyBBwVZc2jid/g2aMnLENOaM6UDFrCpKdNcSzy+30b5QdjJoi2c/Gx4eGicaqkI899clnvMxbN9TbH2E2Hmzi+45s5CrWk/mrFzJiuXLWbZsKUuXrWTths1s37WLU7dfYG7nmmC3Xmn/GlNGY4W4VXaKdlDW/gYI8WLylPNbxlI3bzoh5FJRZtIxLN1csHi8gTYZM1Gw/gAWrlkl/GlZnD+t27RF7U8XHhpibX+diWVykiVDZ3a+sxblJWZZwH8mnkMICTPiyJim5FKVosOYOaxau4rly5Q4LGP5qnVs2bGDPUeO88jEE3+318mK515b3+Dk44HR3WU0SpWBci3W8CxI2fQsJm2UDcJcPH0J8PfF090Ve8szjCyRi6wl2jFl6TJWrlDK0zLx/CtYvX4T23fu4tjVBxjbKBtvJUhntf2ueG7MynvvNeLZCTcfB15dnEPNFBmp3m0br4NjwqivKYSmi5szp6ZUIXc6FR3WPsdB/P3myCghEBOI56B3HBjViCyKeN6dWDxnztmSDU//UDwLU74C4Gx8mSk1ilK25WqeKLum6zgvsekWz24iru7ej1nerBSZVM1Z+9gYJ/XyASWMMlPCB7Nb2+leQEXW+kM4+iYYb9eE19Zljnj4B+H36hBDlenr6Rsx98RLPH3d5WizNGnSpEmTJu1fb/9T8azeidhJiFt3Z25u7EuVbCpS56pLn+6taDtnBzffxow8u7g4Y33/OBPq1qBaub4sW3OQB29sMbm2hHZ5VRTq/N8Qz0+w8fbB7v0R+hfIStH6y3kT/oHQYGU6eECMhYQRZv6Y21df8tLYRTSY4yeetH++OQp/sn1xj8vH93HN3D3mU0/2jurpq0G+DlyZ347cGYXQrTGD+zaOWL3aRedsWSjXfiumXz8RFqTlT8GhhBrf58ZNQ96bnWNUgRxkTNOM9U+tcHJRPov2n4rnYIJDrTgzqyW5VRWZftaYwB+fCIm9f1AwIV7O2N+/xqVH7r8pnl/j6O2F2cMtdMyagpxVe3LI0D/B5lYinZyUcK442lxlcuU85Ck5iVs+gYSGBhMYe39RnsJtXvH42iMeGegrT39FPDvjroQ5PZWKqhSUbqNsHqZ8G1pzTeWzYW7eXJlXg/wZ8zD8oAEuHk68Pvy/E89OruI6RjdZ2Lse1QaL+/l6/EcjzzHi+TZzhRhPqyrHjIsG2Ln/+hyXi2cAVg/20KeEisKt53LN2l+9djnx9RObvaM7AX7v2D+6DukyN2Hhhff4JvyMljRp0qRJkyZN2r/Q/lbxbPHuCOPLjeeooZVGPCu/Kev8vPFyN+Lo9JYUUgkBnSYNpQdv4r6xaLwKQWBvcYfFzYqQM2Nt5pw0ws1HaXh/xOziIlrmFo3BLkI8W4TgqzR4X22ngyKeq00R4uaXeH4UJ553xRPPqzTiefj+J1h7+uNi/oxlrVSkzF6ZiaeM1OsPXYWQUYSAh68PzzavYtH6Uzy09lR/ikc78aT9881JiC67B0fYuGAU6x/74ev+a6MoJ+HHHq8207pQZlIJP79q5YqT6T1m11ORKn9D5l+xQPnWuIuL8t1mRex48XDVIhbvusEr21csb5KTdGmzM/qYuSgvinjTiOcbG3SK596lFfHcli0vzWLEsxCOpg92xYjnql3Y+zqYoKAAnu8eTOUMqSjcdwMPLT3i4uDu6Yn92+fsnTKTzU88CHR/zRE94nlLzxjx3Hvba+w9AnEwuML0OiJOaYrSZektHHxFeVCeS5QVVw9PnO9s4tjZJxia2bCnv3iuDPnpu+uZ+rncXEV5Eud6+PlhcGA7y5bs4qq5N+5JiOdnJ6cIIZyRmr238ToovngerhbPTVh5P1Y8K+uavbB4dIhBpUXal27PujtO+Hu5xFxTmeLs6c2lWVXJl7E+y++ZiXs4CPE8WtQ/QjwP3x9PPB/UJZ7ND9NPLZ5bsfGZEONJiGd1R4LyXWSnmE22lGOuHr7YPj7K5N5VGbL7LV4emrgla7HieTBF1OL5cIx4dnbGzdOJ05NrkDN1KmrPPI+Zo4c4rtnJ2zsQ6wfb6FYwDdX77eGNMlsi0bUVE3W1Ut+5aG8A5oZvmDknxjWhZNne7Hoq0lLL76VJkyZNmjRp0v6t9veIZ0cXPLzcsX5/hFHKyLORrWjoK5970vxuL4SHhw9e5heY2ULZAVhF/l5reWjqjq+3K5bv9tIlpYoMhbqwx8QHH3EtDy8/3hydRRMhFAp3Xcptc3/RABXXFOK5XaFsZKgyhZuWouHt6YWflyU3NvYkmyoT9Qbu5n2oH54unvgGPWd1K0U852X4wWfYefvj42jB7ZXtSCnul6ZgVaasPMl7Azts7BwwfLSHUd2Hs/zAA3GuO86xzyftX2OKeHZ5dYrJPRvSYuYZLNxdhRgU5uau/pazw6W5lM+bhfIjDvDe3hM/RyMuzGtKihQqMhWvx7yNFzE2tMPazp63d7YwuPNoNp57hbOPK7eXNCdT+hQUHnEQS3shhj2EOPQR4vnqGjoX/iWe/TzdxT2vMaVSPtKqmrPuuQXuvl54ejjx5vpm9TrfzBXasPGOELKuXng930OvWjlRpc5Cra4TOXf1HdZW9liZC7G8Zhy9+q7nqYsPId7vODJTEc+56Dj7LJZhyuikmyhDxmztrhl53m6AkxD0/nZvODmzASlFWU2ZrQY9Fl3llakoJ1bWWNteZ8XIoaw4fR8b8Qzvdw8gR3rlvJIMnbeXVy+tsbF1wOzlSaYNGMKMdVex8vHQWZ4cnYRwC3Li+cnJlFOPPG/hbag/3orAc/fG1+oUw6rHjDyvfmiKq58Q7kpYJ3e8nN5zdkFL0qXMQqMR+3nn74Onkk9uyjTzG8yqV5xaPdbz2MqdAF9HIZ5HUkCI52rDDmAp0tNN5HWg+xPW9K0snrMko/fFbBjm5emBk9VRzchzCza9cFbPkNEtnh2xtTLHzMxS1CHKiLfSeSDqL/t3XNgxj/4j1vFciHTdm6XpMo143jaMYiLtqwzYwDOPINzsbLAVItfh+SY6Fc1Eigxd2f7CHBcvT/G8oq7ztebGpoHULlSP2adN8fJy0R1fexsszcyxsLTB3lnES6SXm7sXPkYnGNu0BS3HCb/2EWnz2/GVJk2aNGnSpEn759rfIJ4dcbAxFwL0BcfX9ydHtlr033iGVwbvMLf5NRKjnOcixK/bva20q5iR7J2WcVc98iwEieUr1nfNRpo02Sk/5SgPzQ14de0sS5rUI5eyiU29MRy+dJzTZ29waG4vCqQVQiNFO9Yef4aJtRM2by8zd2gltSgv3mQiR9/aCuFgjtXz3QyslFt9vOG0ddw1tBFiww0X0ydsHlGVNMrngVKlIXPmbGTPnov8+YvTcdo+ntmKBqn8pum/0tTi+c1FZrfLT9Z8NRi8fCuPXj7F4O077pzaQc/yJclWZSiHXljgooywCqHk9P42K/qVJbXan9KRJUs2cuTIRYH8pem16BRvHDzxcHPF3uwGs1sIQZwxN23H7eG6gSFWFmY82zqGJpmE+KwZK56FqPEwZPfAKmRTpaDq+H08ffmOZ9eOMmVkFxoVEPdJnZnipdqzVgg+l0Bf3p+YTeNSqdS+njZ9ZrJmzUbu3HkpVaUHG+9a4ezihNmTE4zuqmzep6JqryWcN3LAzsoM43tHGVU4u/p4lXH7efTKHEd38VwG11nUqxRpUoj7qdKRKfa5Chek6oA13DJ0xNNTlCer9xyd3oTMqcV5KVORKVNWUZ5ykj9fIZoNWcstK/H8CaZ9K+YoBJq9jRWGz6+zZKyyM7dKpG0zNl5/hrGJqRCkFjw7N58KBZXnys/gLZd4amyNnSa8i7sQ0m8uMKNNfjJnKkrHWTu5+8IUw8cX2TWqDuU6TOLMM2v12myrV7fZOqyM+h4FK0zlzBNRP9g4YHJ/O+3rKM+elpZTd3PLzBE7Uf88Oz2Laspzp87O8N1XMLSwwynRmm1HnIXwvL+xKzWKlKLtiNWcefqCV29fcGrnSuYt3i7qMgdR5/zJKK4T7j4uvDm3mCbpVWStM4gDz0w5u30P5649wdnHnYfr+1Ikm6hDG/Zl6/nnGLx5wf2dk+jUtBY919xS7wSfuKNCmR3khLXxcYYXKUiewq0Ye+gat98b8fbZBVZObkWXySu4Yqb4qqz7pEmTJk2aNGnSFPsvi2dHnJxdcb6zkY5NqlG6ZDHRsC5CsZJlqN60M5vvueCmPd3RUTQM3T14e2gygxfu4NY7J/VnYZQprhavTzClaWEKFitNhQadWLTjLUbn9zGmdVEKlWvJmM0H2DGlKw1LF6NQoYIULFiM0jWaMXrDKSGEy1OiRBEKiuNFipWgTIfpzF80mVEtS1O8qLimOF60ZCmqT9yPoZW4p4cnTubP2L+6C1Wr5hUiIxdFC1diwoLDvLRyx1UIDd2jTNL+6eYkxLDt0+sc376aYw9ecnv3bDo3r0mF8hUoW7oh9Tqv4fgzcyGaXDTTXh2F/3rgaHKfbUtaU758HrU/lSxeg+krz2Bg646L2p+EeHFzwdroGnMHN6Ng0VKUr9WIKTvucuvkRrprTdv29XDG0dkZ++cXmNWrIoWVMlG1D4v2X+LGjW0MaVKWWt1msPvINd4qszqcRTlzEYLrxjqG9CpJnny5ySXi0LTlMA7eMMPF1xPTB/sZ16gMxYsXUZefosVLUbb7QvZsWcDgVhUoUaiwOF6IIiXKUqfpAq7aibLq4Y2j0W02LWxBuXIxz1W2dD2Gz97Ng3dW6qm/SjlxcnHD1eY9p7cNpF6dmPJUMH8phk7awkMTZQq7jlFbURe4udjy7vIKOpYvSwkRrwIiXoWKFKZUmXI0GDGHpbPG0rVqSQoXKSTiVpjipcpRs+86Hlra4eysdMwpae+GtcF9ds7oRP1KJSkr8qlCo/p0W7GDu0Z2Qty742D1iHV9alC6eCFRF4hnLFyCyh3HsWLLVmZ0KE1RUUcoz16sZGnKDd3IllkdaF6tJEWU+IjjxSs1ovemO0JUiudIMCLr6OyGw8s9zO5VluJFRN1TvjyV+vRn4dmHWFnb63725MzJDS/Hlxye3YNSRYtTrU1/dlx9jLWoS5W8dnG04d6xVQxtVUqkVXkqVKxAjcHDWXvlOfZOrrg66Ra/TsKnHKyM2DqwOXnzCB8oXZay1avScPh4Np65zTtlrbg4R9Z90qRJkyZNmjRpMfY3jDwLs7PBwtwccwtLrK2tsLQQ/ze3UE+F1r652kSjWVnraW9vp965N+a4IsJFw87WGmvLmLDWtuKYaATai2tbq6eK2mFnbYmlpXIPG2zE/y0sLLC0tsVGhLGwtMLGxgYrK0sRDyvRcI2Jh6UIqz4uzjW3som5p2gkKvdTPpFlayuuJX63sRHP4+gsGuVSOP/rzV75xm3M2lAne1u1H5mZmQm/FL5npxzXXi+q2C9/stHyJ2UtrLOTtj+J8xTBKa5ppfZzc+zd/DG9voFuRbXFszJSqfi/+Fdzrpn63vbimqIQi+vbirKlvqcilJT4qn1XlBf72PvbYKc+J0bkK+VImY1hqS4n1uKaojyIcqL8X11OrEUZE2Fi4mWNrRJfrXISe01bTTlRrwHWSgNHJ2XNr71WebIRx0VZT6Y82Ysyb6GeRhxTfq2trERdItJa/G0tyrKlqAusRNxi42lu+WvkWW3qOCqbr9mJukM8k8gnMyWMfYwQVOeTqGuUeiXmHuI5rZRnF+kp7qc8b0wdIdJEc30b8btSt1iJesZa3NtSqWds7OLFW9scRRyU6dDqNFXuL+ooG8V//oO6RLmmo/JMIg0sRFzt1P4QI4rVa6yVvLaNSSu1b4p60EER1nqE8y9T4iquq9SPSp2thBXPby/rPmnSpEmTJk2atET294hnRRCLhqqyaU6sKX/HFxhaJs5XGoLxG2oxjWB1+Niwmoax+m/lfPUnhH7dI/a4k2i4xx1TH1eurxml0TqubOijHQ9HdYNR85vmWtq/S/uXmiJc1OJF+b+2b8cIVH0CQ+2f2v6kiKpE5yn+qrmeOMfdOxiLGxrxXGMoBx+74KcWz4nPVa6nxEsRtGqhk/D6ym+x56vjoCVw1eFijseZ8rsoJ7FxjrX4IkpT7n6jnDiq00pzntLB8DvlSQmjde9YUzodlDKcdNx+mXYclbSKX9Zj0izhdXTVETF1h9a11McSXi+hxU/3mLzSdd6fWWx9p6R5whHvmPzUymvxLInO0WPKdX/F9c/CSpMmTZo0adKk/Zvs7xHP0qRJ+0Ozx97BGe+QTzjc20jXfEI8VxnCzlfehPi74age/dYVTpo0adKkSZMmTZo0af8Lk+JZmrT/F8zJBXc7M55f28W4nnXJrRLiWVWYagNWcOapEXbKmmpd4aRJkyZNmjRp0qRJk/Y/sf+KeI6IiMDf35+goCBp0qT9FQv9QIS/I89PrmLkmPFMnj6d6VMnMX70Io4+tMQ3IpxQXeGkSZMmTZo0adKkSZP2PzFF84aHh2tUcNLoFc9Xrlyhb9++9OvXT5o0aX/J+tK33wAGDx/NhAkTtGwMI4YMpL8sX9KkSZMmTZo0adKk/V81RfNu3rxZo4KTRq94fvHiBStWrGD58uXSpEmTJk2aNGnSpEmTJk3aP84UzXv+/HmNCk4aveJZIpFIJBKJRCKRSCQSSQxSPEskEolEIpFIJBKJRJIMUjxLJBKJRCKRSCQSiUSSDFI8SyQSiUQikUgkEolEkgxSPEskEolEIpFIJBKJRJIM/9+J5+jvnwnwDiLow1eiNMckEolEIpFIJBKJRCL5O/nPxHP0V3zsrbEwfM/798JMjLH2D+WH+rcvRHz7FvP//wJR3z/gZ3qcgzMHUTZzY3osukuo5rd/HD9DsbcwEmlqiJ3nJ81BfUTy6XMgzk6ueAZ9En9pERWBr50xhoYmWNjYYWdjjbmxYUxeaZmJuTU2niF8+6kJ948niq8ffHGwMsc4Lh0MMbW0xtYufhoZGptj6+TLp3gJm5AoPnq6EvjhO9GaI7/LB1937MX9DDX3M3JyIVBzkagv4UQk0UP0OcAVB+Nf8TcydcYrPCaikd8j+BgbWPhBgLsj5qaKT2mey8gUKztbbKzNMNIce//eSKSBHd7BEfH9KAGR38MIcPPi05/2XkVG4GVriXlsfWFqgm1geMy9xG+fv39X/z/6WyjudlaYGsXGS6SLqQU2Ir5W5iZxafVe8WtrV4K//Ewy3b+G+ODnG8J3zd+/y7ePgbiYmfzyEWtr3GPT9Fs4n37qv+vnEF+cTIx+pa2tSNfYCHwVYdWX+cnnYC9sLePngZmVjfBD5fl/+aHy/I7uwXwTvvYlRFcYxXetMNMOY2KBg1sgEXqi+T3cB1dzI630tMPFPyLmR1F/h338nQrhCyGubgjX/0MiCfNywCbWF94bY2LpTsDXmF+/Rijvj6Ry9QuBzla/3j3vTTG38+GDOl1Fefz4lR9JRf9HGD62ZpjGPvt7S2xcg4i5/XfCwkVZTuL2ESF+OInypJ2/Xt80P379kEx9ofCDMDdXQvRlThJ8Usp9XNmIKffe4TF++fObVrlPgsivofi7efM5idtHi3eup4MNZlr1oZGJOdZKObTQKoci7yysnQn89ENPOYxIkFdmWNj7avIqMl5eRUYE4WJjiUncuYYYm4m8sbPB0sxYc0yYoSmWth6EftP9rD+/hOJmYforfy0scBZ5qubHR1F2lXDR/PgUgJO1Rbz7mZhbibJkg4Uov3H3MzLD2sGb8B9/nl8SiUQi+WfxF8XzNzxsX7Fr01x61mxH1xp1qVtXWMsW9F66ju0Pn3N9yyy23nyHd7KNCD2Id1TUp1A+RkWpX8jfAy25PLkGzQtnRaXKR5f5dwmJOfN/R4R46f4QL3rNn38HX/3tOLJsPHXr1RFpWovmnWZw5K2PjlH2r3xwNufF8X3MntWZXNmL0W3lfQI0vypEfXXm5KSW1KlWhlypVKjSZKJQ+doxeRVntSmVIzWq1ssw9Eu+0fXP4APv9gyjaJqclK0Tmw41KJU7EylVGSlUoXpc+lQvk5tsuVtz1CGJRtPXtyyqV5gO02/h+ZuNq8/B9ty/sY/hXfrRq0J9Gir3q1eXpiPHsvj8Ay6cO8y+zeu4G6wJoMXXD17cvLSNWcPa062a4idKXBtRv9lwRi46yNVrJ9i8/yCX32ta8x8fM6V6MXIWrUAtzXPVqlaC7KqUpMtchCqaY3XrVqZgpkw0n3waN72PEYnDzSm0KtKQDe80QitZInC1fM6WNTPpWb0NnWtq7te6Ff1WbmHXg2dcWz+N7Q+sRc4IWWSyjaYZslCoUuyz1aFyybykVaUie6Fycc9Qt0YJMqmKMvWCo5BSeogM4PLUejRpMIMHAb/ZOxTpi8mra8waN4oe5ZvQKvZ+PXswce8Zjty6x8kVkzliG6uWfvEjwp3Xj88ycdBQepVvSDNN2Pr9BzDzyBWOX7vCkVVzOOulnO3LjfkdyZsxPxVj71G3KkWzpBd+mI3iVWtpjtWmUrGs5K84mhfh4Txd0Y18GfJRQStMsaxKmKwUr1JTc0ykWfHs5C3Vn6v+6qjFEf0jmJePTrJ4Sk/61K5LA8116jXoT9+pGzl29hLHjm5k+53kuyfDHXbRvVApJl3wT7IDQ5sPvo6cObqMyb1b0qFa7DM0p3G7sUxZc4Z7N/exdO8V3jrpzi9vZyP2b5/GmPYib2J9qW5bWnSfysKtV3hwfSPzD7zAM0RXffYNe9OHbF4xkqFNG9Asrvx3pf3g+Ww4cI1b51ex8ISdzs7En188ePP4HJOGDKVnhUa/8rffAGaI/D127SpHV83mjJsmgD7CrzOubAH6bDLlo+ZQcnwJ8+TGhS3MHCreuVVj492YBs1HMGrxIa5fP87G/Ye5apTYL+PzA8tzo2hRvCXbTfSWHL7bHaJDjmwUKP/Lp6qKOKcX9UaWfGWoqXn2urXKkC1lPkYeNEn0LN5OhuzbpsmrWrFxbkfLHtNYtP2qyKsNzDvwCq8w5ewofB4to2bKTBSL84vaVCiak9SqNOQuXonasfesWoQMKcqz9JF/TOdbHEFYvb/Lohnj6VGxOe1i87dzJ0ZtO8z+u085v3wCe02VmP7E8cI0yqXMRsk4P6pJ2QLZSKVKR77SVaijuV/tigXInLEu24x/t86TSCQSyT+VvyCev2J9YwFNiqjIXasPm8+68jX2/StaTx8enWVmx3KkUalotOQh/n9VPEd94M3KvTwM/aglHMN4taOHuHZOuiy89z8Wzz9x2HuS628t/r77frblUN9yQqz15ZiN8nL/wfMVrShUthdb3yVoAYtGgu297WwcO4ZeNUqSQpWeHusfxRPPsUQGv2NZnbSo8tdm2dOEjaUoTPf0pWzPZbx2/zu7Bf5fIoAX+2bSt/9+7DRHwJ+zQ+uKhmENVr320RwTWWJ1jEmtG7H0mT7h9RP3A8MpnkmFKt8Qrrp8TlZEhLs8ZF7fQmTMU5ae8y5jq93QdrHj0oJ+lM6hInXVvlxVi6xfRHi+ZLWIZ84SZRm26iRGvpofBJERNlxZM5mmuTKhKt6dQxaaOIffYXrHkWy55SZiG8Nnz+N0FD5TvN5GbDTHlHS5u7gD3cZu5Y2iYnUQGWbBrjZKB1YOmk6/JkpkcnzG+Ox0audTkb/BUHZf8eZn7CilqBuCbh9lYqsSwn9VtN1oiDLP4rPpDvq2mMN975jTlBMtT4+jiBCUnZY9+dVAjzBiW+8aDN1lQpCefp8Iw8MMKC3yRuTr/Et2cc+vj6jPzpxc2YZ8mbNTo99q7sV2QCiEhWG2bxEtS4r0TZuPxU/iX+1bkBnbp9ciW5aCNB2zhzdWWp7g78uLTZOpXigNqjzV2WmpHHTj4tLxDJtymV/Z7Mi2ZqWFH7blkGvsk0bj+2QtQ9v34qiNP/fWT2TohPO4a34VTsPu1uVIp2rJXvtfGRfwYhPDW7dns4nmgCAyTDzfol4ULJyX1hPXcs9eM9SrxpuXx1fTt2Q+VOnKMfVmoOa4PgK5O7q8SNvUFOiwHaff6HsLtbnExHbFyFmxHrP3P8AxXPOD4HOgAYdnDKBy2lSkbTyPp94JLxiN8+OtdKmZiwINurDhijn+cSPekQTY32FD/5YUFL5UaNgx3D9rfool6iNvj8+jVqmslOs0hkPPvYR3xvIZx1cnmNu0EplF+AarXifqJP0eLHx/Zh2yZ8lP41G7eGWpFb8AX15tnkLNwqKezVWFbVppnpgvmC5rTYaUwi8rzxFlLfmEi3B/zopBtUS5L8eINacx1nod/PxszaXVE2mcIyOq0n04ZpW0l/8MfM+GZhlEvuWhzfw76jKniy+2BxnYfBpXHH6VAdd78ymvykizydd/vQd/WnNgSG36rX2CZ1yiReH8aAudauSiYIOubLxqQUDcb5H4295ifd8W5BdpXXj4CTzUmvQ77o/X0quNeBfFufEX3m7pS25VfobuNyYuJiHPWNW5BmNPuvI5Nvl++nJjW291R1K5zvO4+ExLykd8wenkenpWySGeOy0Trio3+IbVxcX07roJk7gLf+DevDbCB0oy7Yp9XF0e6XGNuW1rM+NmqI6ObIlEIpH8m/hD8RyJ59OlNBIvvBTlunHTQ3M4IZ9MWNQpH/VnnsBFf8d2kkS6HmNAqclc9dZ6ARLMi22KeM71vxfP3wxYWH8oK06Y6h/l+o/4gfO5iZTLlIsBe01+NWg+PmF8pRykb7gUqw+6ZFkUhtsHCWGRgk7rHusUz3zz5eqUkqgK1Gbp44QtSsH39yzecB5Lx+Sl0D+CCBcMHxznsKH2XFNfzgyrRyZVLVa99PolgKPDCXq9mY36xPN3YxYMbE2RTGnJqCrE6AuOfE9CPX/xfsHKpqVRpSrCyONWenzpCya7+lKuQUsO2/26WFTAOzZ2LSaETUnm37LSK9J9Ty+mRvVOzHig8Qani+y4boSLVvZ+dD9GZ1UmStbfiJXmmBr/65y5cZcnOst2JL6mW6mVoxBFUgsx3GiyzpHxX3zH6dYMaon6Il3tITzy0xxOSMhbprXOSbOltwkUjdivJofZ8EB7JPM75qfGU0zpNFv2SD06HYftTjbdcsFHh1sTHcCtPeMokTUneVXpqDfrPK5J9A9FfvHi1oKO5FBloOrEw7jo6fj78X43LaoXYOK1X+LyR6gtx4Y1EAI2B61W3dFdDgWBtxZSq0ZZlr8R/hRhyYNb57lko90ct2db87LCD9tx0FnrSb97YP/8IEfv2/H6wTnOW2pHzondbSoI/2vFXlut0eIf3jg/2c6ON5q/P3txdXZzsiv156Ybejs+vr4/y6Ba9Wi+L55nJCI64CztypajaDoVmYu2ZZ9d0qLtm6sQzjXToMpXnYNm+oT5D6xWDyZ/w7EcsYyfqUEG6+iQV/GlPjz203evEB6NbkW2busxCdBKo+jv2JwZT7WUKSjSdzFW+irxMFv2dqhNvkmX+KR1i59h9pwc2Vjkb3aaL7+JPlcOvreU2tVLs+RZEm+JL48Y1qwuhTKKZ8lUicXPkx7hj/J7w7pOhVGlL8Wiu7aao4nxPj6fyjW6MudRUp0e33F9s44q2QtSJJWKQq3m8DhegYolii+mR9hw14NvcckYifPd+VRUZaH5lGsEaY6qcdrPthvWOGs6QwLfrqW9yKv0dfvxWG8PehAPRjYnW/cNmKrrkc94vz7ElqfanvmZ11v6ifJbkGH7DOPXl+ZbWX83APXqgp8hvNzal8KqlBTrvxZLfcnvcIFutXMx8ISrcIlQnJ4fYddr7dHkUO7Oa0s2VWmmX7bV6mz7yRfDTax79IkfUj1LJBLJv5o/E89BTxhRMRUqVQG6nnDRHNRN6NOZjFi2H2OtkbHfJsiQLYOqkiX3eB7FUwiBPN/aPUY8L7j3G6Ne/yWifXixpQvFszRk8dPkRmP+Gj+DX7O2YyFUpYZyyla7YfiRCyMKkS1tYWbcC9ApmGzOTKCyECgdVusRzxFeXJxQXC2elzzS7oyI5RsWZvZ8CNOlQP6BfArG39YsQQPam9NDY8TzyhceopmoIforn/yMMXHW3WIKuLmaIRv3s3FyW0pnFoJyyHl89bXrI4O5vrQNeUReFem3FwudjdYYovyes2flIFbe1XSjfPfh0oKG5BZhS0+8jK/WgGgifjiwd+ceNp1xivnb1QwzryBCtZzng+tROqnF8wbUg6CxfHPE1tENLx09Uz8/u3F/0yT6bDvC8npCBOWqL0S8fvUc7XWLPkVVqNKWZvDlpCuCwHvjGbLyLA7+UUQ5GmIUGvkrD/iK2clxavHceenD+HsdhJtibP+BzzrS47PNDdasXcq0NYvoV0hFymrTuaNXPX/D9e5SqgtBkanCIA7YJZHAUZ95u78HI+LE5SeMj42htMibXI1ncyvRiKkWEe7c2NKDyWf9Rdx98XCwxiHeemFbtmrE8wFHrSeNCiPY1wobMz98nKyxjxfGgV0a8bzHRis/osIJ8TbFXJnZIP5vemIslTKoyNN6Jc/1qXs1Iby6vo+JGww1f+siAuu9k+iy7QzbOudElSk/3Xb+mseRiI+ObOqXX70sos5GI6281cFnA+YsPcDVF1rDq95vGF8nxpeGXNEnXTUE3mHYrDNYu8bWZz8IMz5Bp2IqUhfuyBrjpOu5T7Zn6Df7Fp++xhaYz5ieHE8Zkb85G8zgmqe+Ai744sXtbT2YeEJfz3IU3qfn0HPLSbYNrUK2DKmoNve5Zq21Dr55c25OHXKKe5cRgvXXSLsOftixY9setp7X/27+EWbPtQ2T6b/1AItqpESVtzkrH+qoiKKj+enwHqMg7WVKP3C8My9GPE++Gv9d89ECU7tglGXF0T6vGVtb5FW6sgy7mkxeBdxi6Owz2HiKFIj+xmc7Q4y1ZiMo779Xm/uqxfPQve+1ZgqI6jTUGCOHL8KXfhLyficNMqpIW7gdq9/rG0uPwfpkHwZvN+Br+Gc+2BtjFs8dgrkzN0Y8T7tkw6/k/sm3QCOMHX8QpeslLJFIJJJ/DX8knn1O9yJrahVpSk/gaXLL4X668tbcgwB3Y06sXc7SNWtZu3YNyxavZOsVC00PchgWt4+wYtlSli9dxfk3Vhjevc3s5kJEisaCKlUluk+cxoxFq7loobzhlJHn7qRW5aXv+mf4+73hwPK5zJyzkGUHr+EQ76WbDD99MLt7ja0b72AdEYqL0XV2zprJ3IUr2PnIWsj0GIIs37BraCvypBXxUeWjYY8RTJs5k603b3Jm+SqWLlopnmstq1cuYt3OGzhp2tyfnR5xeP0iVqxYxuL15zDySXqtVODzLbTLpCJLh8U8TzBD+/36umTOlJIqy17pnDJmJYRFRZFefy6eo/Dx8CYsKOnG5L8DPeI5SXw5OGk6W8+aE+p8gGZ5FUHZhCOWETrz6YfLRcbWy4QqZXnmXLD5NQVRJx/wdLPBWDQOFb6a76BDmbRCPNRn03PvZOIXja+dNz6W+jt69IpnvUQR5nCTKT2WYPUhkAcrGpJClZ26Ew/gqedBnA+2FWJJRdbqczDQNxIUy3dnXpt6EfpJV8s0CfGslwjentjMzOlnCfpgxtJ2eUT5Lc3EU6bovMVnN86OLyLOyUazyefjyr9ufvI1xIYXZiHqzqzoEEM298ytrh/6rnucaN1nfL4Q6mfDa0t9PSd6xHOS6BHPWkT5PWVR22wijkUYudcgngjRxYeAIFzexM2bT8zn98zrNo0HToHYnRtKFlU6CraZjZGexwq9N4GSWVOgytmbS656paKGSFwN3Qlx+1WhOx1oJeKuInvNubxPLrgoWY4vXIkI0zjm9yDuzi0hwmek+sCD6JO1sfyM/IT9U1ei1JtKKflrwrY+iv/kodeqhyT9mvnKB39rkb96zop2ZO2ASZx44U7Am6WUSp2CtOX6cVtPUn8x2UzrEqJeSdeIba98kyn3oj638cLXKt6YsBaRBJhfZnLvVTiG+3Ftbi3xTDlpMuskPkmJ8jiSEM9aOOxtps6rHLXmY/ibefUlTN+J+sVzHD9CebSwjLhnBqr026e1nEEXUUR9tOOFaQDfdfZx6RPPEolEIpHE8Afi2Ycj3Qqr1zLnGHqG4CQGV+Lx0ZyTU3tRSIRTXqglao9g17XY6aphWD1cR5v8xclZuB+73lpjfP8uG5aOoVmurKRPWZHuU2YyZ9l6Lqun8CniuS+ZxUuyQtcJHLx6mR2rFjJ1SD1yps9G/R4reeWXxLxMDcFG+1k4oD4V8mQXcarBOCHsr9y4zJ65s+lRtxyqPLUYseORkEbiXCsD9i1fSL+6pUgnGhp1e41m1ty57Lh9m/MrJ9GmasxzqdLXY/reWzhr3rafnZ9wbHI9MmTIQ6WBezHxTUo8/8D63GyqKOkzYC1GCVrfwVcnkj1LJnIMOqEz3X9LPI8vhqpQfda+1xxTiLJk9vbDXH+XzOjAv4I/F8/hFhuYOW85120U3/Rmf58ipBf5UHX1Ox0Nsx/Yn5mpHh1RFR/GUdMkhp0TEcH7zQOokE6ErTyDu87/+aY1fyyev3/k3ZGejN5iKdLmJ4Gv99I2n0o0/Luz21yXMnZnV8vsQmCnoujE6+IJ/hP+XDz/8H/GwVUDWX5VkcGfebO1P8VE+uXquQOzkMTq+aPTNUYXEumbsQqDjuqfGpuYKPzf7KVHLhE2R0vm30qwSP2P+TvEcySeD9bTLpuIY+7OrH38n5d39+tDGb7sNt7ff/LD+xFjy6pIkaMcE3XO4w/h2tha5FTW+Lbbhv3H3315xOLK9ubZ1L5UePy1+FN3f4NvwhdmFBf3Tl2MNpuTGk3XTcC7A/TKLcJna8bs68lJ76QJejmDsfP3895fCPtvtixvnFrEKz0dDuvaYSyCt+t6U1bpuK02m4duySrRpPkaxPP9PRm301788QPvR5tpJp4rXaUBHLFJuisvht8Qz9HObGuaRbwTU1NElPv/MMaC5MXzt8AXzCop0ihNUdpsMtK7nOX3kOJZIpFIJEnz++I5/A7jSuZSC8VcU67y5feG5jR85M3K3hRMmZMWc19ojmn48IQt8+ax+uoHrdE6O1aVL0y2NKN5rDkSgzJtu7doJGakas+lPIpbUO3D+3U9yKBKQ9lxF/DU8/mKWD7YP+DkljE0KJBRPE8DFh25gbemdf/N+SnTaqVAlaEe827FTn/7yeM5bciqqsKK1/Ebh74WO2iVNyWFay3BXHMslh8vdzF/5UaeJzu/PALDgxMoIdK2+IDVvE+gq348WUTuDOnJXH4h73R8Hud3xPPlaRWEwM9B+cZd6dmzp9p6tK5GptbTOKUe1f+386fi+TNP5oxn8cpTeGiyJPzaCPJlToWq4jLME/mgHzcXd1FPv1TVHMs5yz+Rk04cGdGIdCJsjj5bMAnQ0Tz84sez/ZPp1WcAg4eNYtSokQwfMoC+cw/wRsfQ8J+K5+9hxmxo0o1TsTsCfbTiwKBCosFalmE7jBM3koMuMThvJlG+0lF8/gPNwb/Kn4rnn7jePsCCbpN5pYnYT7sj9KmSQQigbhw1STgy9wP3R0uoruRNrpoMP++qOf47fML0xHhKKmGLtWdpkutNf4e/QzyH8mrnEPKJOKauN5JTVjo6GKO/43h9Of369GPAEMV/RjFi2CD6j1vOKZOE8fDmQLsubH/hGiMuosJ4vrQyqhQ5qTfuRuL8+faWRQ1iNoUrNv0qgboUVYg159YOp3ffQQwdodx/BMMG9af3uhu4Op5jRP7M4vqZabHdVBMgPtFuj9k8uz99+gtRP1IJP5wh/Qcx9vAr3A0200Sdt5WZcE13x8En05PMGt2bvgOHM1I8+6iRQxk0ZCLLb5pie36qekq+qkhrFj74TzoeQrg8qB+rjz+PSyOXA23Fc6UlX5t9eGqOxRHtKMpYfdKKe+ccsB3zIB3lPsKHJ/smJSr3feYdwsArvvT74v+GNU17cj72JRGqbLiXT5ThSkw4aPkbQjF58Rzlf56+OdKJZ8pCyx1mmqPxiXJ9wMaZ/egz4FdeDe4/gJEH3upoVyQnnqMIMd0Sk78ZStI+mXX6ySPFs0QikUiS5vfFs89FBpRSpv2lpMGa18QtB/tdPj1nbMkUZK7UhYta86rcLqxhwaJ1vI7XoDJgQZmCZE0zmKsh2g1/RTx3I7UqNz2Xv4g/AvHzOWPK5BQNgTYcsUt6zVMMRsxrUFg8T0eOKeut4ojE4epYIWRTUaHvHs0OsgFcndycLKqKzLvjKJoQWkQFcn5wQVIWasDaeHNT3Ti2eBnbL7yPf75OvmB8eBKlRANAl3j+9mQhudOmJUOuiTzS8Smk3xHPl6aUEcKhGJ1m7OTAgQNq279qOCX6z+e4yZ/Md/+n8ofi2e0ay2cs5dDDsF8jHZ/vMrhgVlKJhtfaX9u3anDj4pw26pkbmdrM477bn/Q+WbJrcG11x1XRkQew0dUZ8z0cx5cX2LmoM7WyiIakODdtuS7MPv0Sl9DEazT/SDxH/8T59myGTr+Pf1yfwGcMD4+koCgnlYZsSbwBk/NxuuQXjWhVZlrrETy/zx+K53AHrm6byvQt9r8av1FO7OpfkwwiPn0PGiWYevsF56sz1Z1XqqKNWfr4T3ZTCOXd3qHqTpFUVfqwz/h3RvCS4u8Qz3483tBLXFNF+uaTuKFzSexPAszvcGjLBDqXiPEfVbY6DNlymfee8Tt6PhquYcT445j6x9ZsUfi9XkNlVQry1RnJ/YRR+PSIqfWVulZF/WWPCNFVIX72xej+CTZOqkUR5d7CcjcZxpp7FgTZHKN7gfRCkGVjwDHNOv4ERAfb8+jKPpb0ya+e/aFM4S3fZx4n3zjj82SFek8IVcGaLH2i+93wzfMdV06vY1IzzbMLsdZk2i7uWDphdmSUep+ClJV6sNvwr+fvT5sDTJ24hVsWv9Lzh/txWqRIQaZ8zTmacPA52pyt/Wqo41NizGHsdVXT38Owf3menQs7UC1zTNzTV+jKnDOvcA3TKvdRX7G7NpNhs55qlZ9wXu4cSD5VaqqP3Yd9so+WvHiOdDhC+9ypUaXMzsDjzpqj8YkOsuXh5b0s7qV8fk6Jc0Yq9JnNoVeuOjbjSk48RxL4fCVVlevkqsiE5NZYJ4sUzxKJRCJJmt8Xz99fMa9CXvGiS0HpBff5kvTgbmKiwzFcq6yFykaLZa81wteTM6sXM3/dwwTTOpMSz5oNwxLttu3D0W4lyCpexNMeBBHm/YhlzZvStKm29WPladOYF/DHx0ytU1DEpz0H7eNf6YPdWXpnVTaHGcFNdQshCfEcHYXny8VUTZGFuoMviFjEEPRqFXPmzeVa3Gc+HDgxug+t4sWnLUOmXRNXj8T88ET16JUu8exxegjZ06cmW+MN2OjYq+aPpm2/0xxT48eBc9d4ZfpXdnX7p/Fn4tnhxDLmL9uOYTxn+Mq1kUXUArn8GCVftQnlyboBFBa/pWk4VcsvfgdvLk5uoxZo6dqv4q1PErGLNGXfCGV9Z356rHiid7r0n4jn6B+enB3dg7UG8UdVw02O0b+4EI25O7Dhya/Pe6n58pBJRZTpm+mpsfqV5uBf5c/Ec5jlLTaMGcXpeHsHRGN7YBAVMqrIVG8pz3y11X4kPgabaCrSV5W/HlNv/El5+IrNlbnqxnuKMl3Z+Oo/7Yj6O8TzJ4yPTqSsiGPKygPZb5TUTJMQ3q6rL/ItA1W67iexzo7g0ewBLLxkGG/Dxp/+BiyuJdIvYzlGHrKOP3U20pJNLWM+X5h11CmSXMESfJ1JLXKK+1di2kWHmGNfHzChYGZ1nKqtSsaXrDfTrJTyGaa27LKOEcqf7A7SUcnbrOUZcCrpjS6/3h1H3nSpSZtnHHc0LmJ/fb56VkKKUp1Y++KvblMZhfHGiczbdYl4ew/+8GJ/e2XqdjZaLn2doLx6cXZsC7KLe6frtBZDvyTK/U8jdgwWdbyqAL3XJN6ALOqLCydH9WSjaXz/DH2zhx6FRRnO350dr5MTnr8x8vzxLuPzZ0SVIpMo9681R/VguYHGJdKLOLdnt62+nQKSE8/RfHA4TGclf7OUpf9J3YL995HiWSKRSCRJ8wdrnr051qsEqcVLKnOn3bgmseGoPr67X6d/4ZTkqT2dV0p416vs3byc4+YJhcRfEc+BQvyUUm9oNvF2AAGOJ+iivFDjWV4hKB7HfOomKfFse5HBuUVDr+4Arqjn0iUhngVRoRYsqyMa5WW7cF49qh7CvfmzWbLgfJyYBkOWlIuZ9q5tRettQZkk6nFrKfXE30X6JBbP9ge6kzlTOvJOuMoXHSP+f33DMNFY/BzBj6+yifBH4vmzNTtH1adAwRJUrVeb2rVjrT6VimVQT0/NULQ/l320Mysau/OzaZBG5Htu0VB9lWBXuCSJVH+OrLyyZjRje3a+9Y0vTrT56sCxqdWEbxViyIaXer/h+ifi+ce7TdQsnJdSVWpqPWsdalctSwH1aFcOOi25SfwncmZ3u5jOtvx9j+n9tM/v8QfiOfoDJvuHkj9HfioocYyLb11qlMtHFlE/qFRVWXLfPV7D+KPzVcYIEaFKVZqeW97pT18d+BnspUd2ETZ9XaacUdaT/if8HeI5Gs9H62mXVcQxbWXGH9AxzT4WkX5me7qLNMpM3d7HEk8ldr1Ar1rFKFa2MjXrxKZtberUqEYJZV2wKi1V+2zDNl7najjXJ9Ulh1Ln5RzJzXgdFwkIecnMLgXEdaqx9KajZimPE7vaKRuypSBXrVVYqI/pwfkUrSopnTadOaH51Nd3/yfMKqfELRu1B50SJV0/US+XUyBjWtLnm8oLzTsu0PAAvXOI8OlqM+Hkr6+i/xFBL5jVsSKFi5elel2tdKtVm7IFlS9YqMhfdwYv4lXPPzHY1JcyKcS9M3Vij6H259sS8NWOAxMqiesUYfiWN4lEZsTL1VQukIfS1WrF3VtdhquUJp/yjXpVHnqufSjeoknxG2ueoxzY0Vrp/EhB7tqrk65bHI/TvLzSKdKVk64JXnpx/Maa56DnzFHyN0V+mix8lMxGjMkhxbNEIpFIkuYPxHOUeNf1J49owKfJN4EHSQ1eaPj54ydR2t91+OHPzSllSJmhEiNvGGBxZBsLxp/AJVGLIFY8D+Fa6O+K5wBODChNtrSVWPfuA5E/PuHn7ISTk7a54x/6JaZBFieeO3DIIf6VwmxP0zebELatl2Cgvn2seK7E/LuJxTNREVgcG0jOlAXott2cL563WDVvDvteRWg1dr4R6umOc7z4uODh/UF9vY/Gh+ijfE6n8UzuJdgu9NniCmTJkJpW4tq6Bvz/E/EsicWHM8Pq/5Z4drq+kHEDB7P/hTUWpqaYapml+QMWNcqIKkN+hp2Lv3nUT7sLjKmbWvhcZaafs0nsR4mIFmUoJiZfDffQvYISNg2dN75M4PtaRNhweFJlcV5BBq59pndn4A9usd95Tk48+3JuTG1GbLnLe3OzeM9qZmHFwwNTqa183qn1Ip7EG7CNxGJPZyHoVGQtNYdXuhw3AYnqizi+qr/zXPw3xPNH1zus6l2feVdssTSLnzfmVuYcm1if3KJhX3XOHQK0WsbR4Y4cG11UpFs2Go86l6AjQDc/vsWoqyg/A9Z1y6FO857Lk9ttO4Yf3/X1PtrFfef598WzY9x3nnWLZ5EbHo9Z1F7pvEtB9eE79X/nOCoEox2dxHmZqN3zMPFnEn/j8dI2DJ63mwcmFphppa2pmQVvb22jWy4VqSr05GC871CD342ZVFE2VRPXHXvRU2c9pibwCdM75hfnVWXhNTtNGfmJ+Z5uZBW+lCJ7ZdYZ6pWQYH+UlhUUQdaRI7Gdol99uD6nunrn95zVems6OHXz/eki8mcQ4jnvZJ5o9i2ICnjPxp6KIMxPt8UP439nXA8J89dk31CGjZ3D+XeWmMdLNzNMXp9jnBB/KfLWZNHj+CU2wmA7ncqkFPdOQ1chivWOe0dYsXdsBXFeYYaI+iGeD0Z5cHJETUbufIyJReIyfG/nWKqJtMnWcTWvk3R8IZ7vzqdSUuJZyE3TXZ3Fu1LZQK4KG4w0h3Vhe4hmZZV9ETpzVK+vf1J/5zlfEuKZb6JdMUfpOEhP5c77dMyWSIw6f3S6UQh357WT4lkikUgkevkD8Sz4+ox5ZZUdqrPQY59Rko3/HxFuXLv1Ah9f7aZGNAEm22mRVrzkarShx+yFTDypaw3bW+aXEuI59SCuBGuL5wCebelGKlVuui99HH9ULcqA+ZXzkKPmIgy013rpI048t+WwU/zmkN/rFVRXZaDeiPOaUQp/rkxqRmZVeebedtD53B+drjKkYGpyt+jHkmVbWbVsO6a/EY1Yoj+YsLV/MVT5OrEn3ncqfdjXNT+ZsjZkV/wPUsZhfXqCaNCo6Lj2qe6Rgy8+XJ5UAlXBOix78hu9Hv9KvDg9pK4QILVZ88pLf+MeVy5smMvszS/1nmO+swPpUqajxIAj6h3b44gK5sGKnuo1nbl6iYZqUtMwoz9hZPSKZ68dY/6OiuDZxh4x63JLj+WcnR6J9s2B41OrCr8uwMA1T/WK53CXI3QUQqZUg01Ya47pxP00owev4bmnnmak1wNmN8uIKn0zVj2I31nwI+wuk5S1qqp8DDlhLSSQfr5/duLKzZcEBukaK/+C2YmxIt1y0VWIU/3i5RuO99YxdNwpveI34vlyGhRMSYoKk3gcL/1/4v5sDc2UmQFlO7MryW/FRuPracrVO8bK53DFn1+wODudailVpG4whgtJLh79jpP9O2491NdlYcOWZmWEH7bnsPPvThG2Z2fr8mRQtWa/nR4REv0ThxuLaK6MMmZuxbK7rno6iD4IodpV5FlGanc/FF88f3zE/DELOPFSz7T2Lw4cGlqYlCnLMmiPSBvNYTU/fTk3oZqoQ1Wka7yS1yF6Sk/oC2apR56rsPCqbZx4ifpoxoa2yrsnBWWHnsJNnzM5n6R1JUU8t/8lnkVMwh2vMb6CMsJbkA4r9ZeL6JfLKKCI59yT4sSz8q13q4szqZFKRap6Izhrm1T+/sDF8T03H2iNj/80ZfucOaw7o28zqzAeLKxBmhS5aTTndnyBGPmJx+s6U0wp92UncMFRj19+s+OgeuRZmXHyIp54jnQ6xohBGzEI0JPmbjeY1CCt8Is2bHqe1ByRHzjdnkt5VVZaTrmOvg9iRYWbsK51zP4o5Yedxl1fXjkeo0U5RTx3TEI8h/NqUx9yi+cavt9I7zKUQMv9dFG+RpC/AcufJD1+HhpoyaWb7/j+XVcJCOKOenPQMsy4Ett5I5FIJBLJL/5MPAs+P1xF78KZSJkuL9PPvyLoU8JGdQQhnvZc2bWVi6YWfEw4kvTFnbMTK6pfrIXaTeKuzne1IQvL5iR9ilrstFPC/yQsUNmNO5gXW7qSQpWexmPO8etzoV+w2teH4gXyMuqks/4pidoI8TytrvI96aKMOWv/q8Hy04UzE2uSomhrtr3+pGkABnNNPfKcgYFHLNTX/xQYyhfNqKCan35CGLUVwl40sEq0Z8XDJKbW6iSa4Deb6Jw3D20XP4xrmHw330bLwnkoPukCoXre5A7Hx1BB3Lf9Sj2jXtF+nB9VGFXemiz4nSkD/0osWd2trPCtmqx4rn9ip9+TdcydPpDrSbUxQ28zJG0K0uapIPwgfqMw2v85BwdWI0OqFNSYsJr3XqEJRGU0X0P8MbpzjgPHT+OgPYL12YD1veuRSZl63HoRJ6wC+ZSwYfrBkM0DlO8VK9Mwn+sVmiEGKygt/LlY/fX6R56jP3JnSW2mnTLW7FGgi++8WTOAgqI8Vxy+HZPg+II09PoCOuTNQKrsxVl4w5jgiAQRjv5MsIc1F3Zs5qqNA190jjx/5MnmLqQW4rnzkkd6R3ajvA3YObk26+Ot60+IKzualSWdKjU9t70nTDu63714t7YHxbOkJlfjnpwx9+VzgvZ19JdQ3C2ecXjbDl6Ha5WlzzbcmdWSXOlTUaz7eO44BCSqhyI/BWH39g4Hd+/HWDNqnYgfz5lYp4Dww7bs0yeUEhL5imkNC4kwLdllo08WCqLdubKoFwUzCBFZYQDLH7sSkjBjo/x4tKyB8J9UVOxxRGsU7ztGOzszcePRJEf2PG4pu5anIF/LsdxyTpAC3teZXK+kepOo7EN289jrQ6Iptj89rjGigSKoKjHrmtaGb4IPRnvoWjA7KVPloMrC61gHRiTqAPhitIU6BUX5UHVgv7122YvA7vIC6uTMiCpbFbrufYt3WOIKNejqeLXAV+WezEPtHfMj7Lg/pzW5RdoV6TKGW/a68jcY+3d3ObhrH0bfYq8djdP58UxZOJ83SWRnhM1B2or7Zq7YmgOGCTz80xvW9qhDJiHeVW2XcNomKLFfhr1jbW/lfZaPPhtf/eociPzA9QU1mHneJonOqy88XdSDfKIMVxt3AKswfd2C3zE+OohMqiw0m3RVvBX1E2a4my4FlLzKSbVFN0ReaWZ8aRHxfiM18yl51ZEDjvo6igK4tKCp8O1CDNljqF/MRodgt3solbOnJXPl5ux960Z4ws01f3zA19GA41u28DggGJ1VjagfDowW738hnqdetNMck0gkEonkF38snhXCLY4zoEpx8hcsTMvxKzly4zrPnj3j9esXWLy9wu55qzhx0UxPI/eremfbkjkL03XjOz2jd4FcnVxdPU2v0+qbPH/7gisnlc2PfuJyfwv9GxYhb/6qtJp2gnvivk/PLWF0o+r0XfOAgN/tKv74hOn1FZGRlaJtZrHlxAvxDE+4uG445QvXof+2l1qNo2iszkymqmi8FOuzhptmFlw7dA2HQO2GahTeLzZSP62KHG1n8SqploVewnmzcShVyvVkwvEnPLt3htWda1Kz/3RuhiZsqsG3ME8sX59mQ/vS6rXo+eoO58TVdzgFRMSka9RnPM1fcf/Cerqq1yOmp0L/TTx8YYyD7282zP/xfMXfyZxnF5dTvWTM9MiqI9Zy9akx7h++x3WARH4JwuHhAUZUE+lYtDqrr7zD1iMsUeM9wteGZ4dX0EJpgAtLk6M5604+xS5AS6X8sGT/hI6ULJKTsq0GseDgCZ4IP37+/AXmpve5eWA7G5aewuGDjtLxwYmTi7tQqVwJ8jXoxvQdxzn//Jm6/D17+YRNm8bRoFwRKtQcyIJ7LommOUZH+GL55hFb5zRRx0+VpQlTDt/E0MqDj3GNyWjC3C15sLo7eYTPVxu/ivuvHQhKuBXutzDcrB6ypUujGMGhSkvTPou5YeLBp+9xKUfgu330qFiM/EVK0m7KWk7dvqmO75s3zzF7fYnts1dz7qadjlGlSCICnHj34ATDOyqzRIToqjOKHbdeYuMZ+qshHf0VH8unbB9YHFXatAzecQUjC+9E14v64In5sxOMKxK790BxRi89xjsX7UnwQbw+OI2apfNTqGoThq/Zx60nj9TxNTR8yusbR1k7YwsvbHWVH09urh5GhRJ5KFavI5O2Hua+qFOUsCYmj3lybj9r5+zB2EuHjIn+hKe1EXePTaewsr5WlZOmM3fx4LUFPhG6a0mh2PG2Neb+yVkUV5fvbDSauoN7r8zxTqiuYokM4en+iTSpUpaCVZozeNkujj/R+M/z5xw8sYJ2tQpQulILBu0xUO/ZEBHgjPGhaVTLoiJPh9GcfWyJ98cEnaaRXwlwMuDElH4UVaetirINxnD6uR0BWnGJ9nrHxrENKFW6CCXbDGHp0ctcVe4t7NGr+yyd34OqJYpTuekMdpkHJRJLH8yvMr1HZYqXLEP1PlPZdOk2dzTh7zy7yrSRjShTojTVem3iekBCaR6J68OdDGhWkaKlqtJi7BL23H7EQ034y3ePMqRzWYqUKUejyWd5nygJPbm9bgQVS+SlaJ32TNh6SLx/YvLXWOTv0wsHWDt7N4YeMbH+/sEb68tr6VBE1AM12rPnrgkugZ/jv/OiI/ngYc6NtVNjPpUmLFepHuy9YYSbdm/pB0eOLexERVHu8zfszsxdJ3+V+1eP2bhxDPXKFqVC7SEsfugmPCOKUFdzbi/tQPYUKupO28jjt46ERCZQjN9CcbG8x/o2tcmgvn8GWg1ayS1zLyLibh/F1xBXDB9dYGq/Uuo4ZqzUn/VXn2HpGqR3jXGY2RWmdY/Jq5p9p7H50p24vLr97ApThgs/UOfVZq4HJupGIdzHjjd3RN3RVFkSoaJA62kcuvcWe9+PejoCPmJ5cQmNyhakQLna9F2yg6uP7qnv9+7dM94/OMWG6eu5bxiYqN5WOgZC3K14dX0zLaorG86pKNVzIWcevcc5KLHwl0gkEsm/l78kntX8cOXBiXUMGDCQRo0aqa1j+/5sPPgU8/BIHS+nX0TY3mb16BasN9QzFVTB/wELuremSePmzD94O/4Usc/vuHJoKG3bxdy3T6/J3HwfGCd0fgtl2nbdwqRUdWTv6XXM7tZCfa2WHedw7KVjItGhfHPz9ILhNG3SmP5zt2ERlljMEmbF8XWzmXcmyYmwyRCK/ZFtDGzWTMSnN0MW3MNOz1y1AOOzzO7QiMZNmtKseXN13JQw2x65xTRovjtzempbGovnaty0Gc3FNZs0FmnWfDjrb+n+5Mu/Dx8ebJtCy0aNaaKkUXNNGjUZyXGzkLhG02evF2zsL443biLOayrStC8rTml2btfC4+YSOrVqTFOR1s1i07vDCFY/TDia/RW3d2dZNFMImWbN1b7XpFEzpszazV2bYP1rGzWEBllzfc90xo2KKQNqa92SllMXs+X6W0J+6m7S/vC4yaxOLdXP0Uw8a7NmTcSzNGbI3BPYxRXan5gen00fEffGTZRnaEzL9ut5+SFBeQ005fDcTnH+16ypci0Rj1mnsE8wLTf6qz23Dq+iT59+cfHt0mkI246/xvpztJ6yK9Lo0TZ6i3Nj7tFM4+MdWHza6Nfa5ygfrszrSnNNHjZp3JohM67gofk5lu+WZ0Tjv6n6nLjy0qQtffcm7sT76PmMvWtm07Z9x7j4Dhkyn1OPHfFMsqKJIsjuFhsXT6dFq7ZxYUeNWcVFA0/9a6mjHDizcBhNxTMovtO8uYinSP8W7adz3U1PPRntzMVlI2PCqH03JkyzNlO4knDUNwFfvvrw7Owq5kwSadEiJo6Nmjam8dgpzD9yH5ePvzoG3R/vZFyrmDxo2qSJOHcGl+yC4+fZF18ebhHvArVfKb4gyoji+8PX81BHXHzdX3Nq/UgGD9bcW7FO7ekwZz3Hniuze5KSK9G4mN9gz5Le9OilFb5XD3ov3cstC+ck3wXRfMHi2VHWz+lIh05a4QcNZsSGM7z1SGpaichf+ztsXjKdlq218nf0Si6+9Yi3MV6I5WUWdP6Vbo0bjWTPU9f4HQJREZifmERTkW5NlXRrFpOHjXrN4ohR4logNMiKa7umMnakVrzbtKTVtCVsu/GO0KhYX/mO4cGp9NAqw207b+Fdwg8p+79n74z2icvw/Au4x3Xe/cD37X4GieNx5VD4SqNGbZi1/4Wetc+xROOsM6960mfZPm5buujJq3Csrqyii+aeSr0cU/a7se6Wnd6NEBW+Br7j+OYFdOrSLe5+fftM5dBNqyQ2Og3G4Nh82mm9B5qKNGvUqD+7Xnjp7SCQSCQSyb+Pvy6e/zLRuL04yZyhy0n0Kdz/JVobhh12lqOwEolEIpFIJBKJRCLRz/8F8ezI5aWDmbDDSv/6pf8Fn58yS73muROHnfRtViKRSCQSiUQikUgkEsn/SDwHub9i5+6tbN68joUT+tBm0Aye/qU1wf9Fvj1mYK08Qjw3Yb+j/q1UJBKJRCKRSCQSiUQi+Z+IZ59ro9TffVRvNNJiFBtM9O2X+78gHJPTW5nco476u59KnMq2GcLmB67E7XEkkUgkEolEIpFIJBKJFv+badteBtw/uJ+9B+/yzOf/9qeSPmF7+yRbVm9g56497N27i83r13HqjTcJv2whkUgkEolEIpFIJBKJwv+FNc8SiUQikUgkEolEIpH8/4UUzxKJRCKRSCQSiUQikSSDFM8SiUQikUgkEolEIpEkgxTPEolEIpFIJBKJRCKRJIMUzxKJRCKRSCQSiUQikSSDFM8SiUQikUgkEolEIpEkgxTPEolEIpFIJBKJRCKRJMP/d+L5e6g35m+tsXEL4afmmEQikUgkEolEIpFIJH8n/5F4jvoegtmD21w/dZKTJ4VdOMcdW3c+Kj9+CyLwQzhfo9Wn/sdERvhjd3MJS/u0IKeqIm1m3SRY89s/jajPrjy4elak6SkeGPkm3UkQ9RVff1uePX6BkWMg3zWHFaJ/hmH98BynTp/j8s073Ll9k2sXzsTklWKnb3H3oQVuAd80If4t/CTcy4KH1y9zNjYtTp7mwtUb3L5zg0tnT8el0amzl7jzxBx/7YTV4nOALe+vn9O6zl2emHmjTtGoENx8v6rP00eIqxEvLp7mlDrsKXG/Jxg4f1D/FhkRiFfAD/X/kyLyqy92L97i/UVzQBffA7B8cZ9L53892+kzF7l+5zY3r13kTFz8z3Dp2gPMPcKJjArF5tUDLp8/o4mfSI/YMNfjh7l47T6mbiHx/C8h30JdsHptQuBf7PUKdzPHwtiBcM3fv8v3Dy6Y3b3Ehbj43uSegQsxqfwBN5/PRCVRT330teStKI9xz3vqPs+t/GPK5c9g3P1i8zgCH5u33LxygdNx9zoryt4t4VdXOR9bT4p8PnvxGs+M3fnCDwLs36nD/ErPs1y6oYS5xvnTp+KFeWrozAc9cQ1xMeT5BS1fOvcnvvQduzdPuHtaEwdx30uGFviqf/vMh5AAwpX7RgZh8+YRl0U9EucTpy9wTe0Tl+L7xNV7GDsH6fGJz1g9e8Dt2PuJMnfNzI4g5aefHwgJDeFTlPrEROh679z+zffOt0++GN6+xpXYsJcv8dDFP6a8fvYj4FOEyBHd/PzsgcWDK1yMe8br3H7lQLD6XuG4+3zip544KwS6WvP84tlfdcW9+xiHxQSIDvHUW8cofPAy5/XlM7/86sxDXtkGob719yA8kgqsIToqCMcXr/D4pDkgkUgkEonkj/mL4jkM40fHmTV5EO1q9GBIq0506iSsdy+GL1zKggOH2TptAEvOvcLzrw4PizbFjwAvAiN/qhsI34PtuL2oLV0r5EGlyke3hfcIiTnzf0Z0cABBEeExDa2/iTC75ywf2Zc6zduING1Dq5aDWHHFisQS7BN+Zg+5tGYpwwbXJ5UqB22X3MFf86tC9Dc3Li/sQ/tm1citUol0U5GtQpOYvOrUXhyvS5msFWnWcwZ7n7rpbTT+8wjj9ea+5FXlokZ7je92akGlnGlEGqWiWJ3mdFQf60jL6nlJm6YWu63it8ajvvlw49QGxgxpR5+mbenSMfY6w+gzcjZLl2xi3drpLL7gowkRn4ggO47tms+wPq3p3aS9JmwXOnUZxcDxi1m/fi3z1yxnz5PPmhD6cX8whVoZijD+UoDmiA4CLtM3b3byVGxMO82ztWpYmnSKX2QqS1P1MWEd65E/RQqazrxJYOgLRhXJQe7yDTVhOtG6SVkyKGEylKZJbJhO9SmYSkWDCadx1yceor5geLA7lbPWYuXLMM3BP+CHD+cmVqR8sQGccUqql0CbUJ5d28fEUZ3o16INXTrExncIPYdOZ96CTWxbN4FpRx2I1BHvyAgPLh1dzahBbUUet6Oz5nk7dhpB39HzWL5iA6vWzmbVtUBNCAf29a1JlqylaByXNvUooi572anSOjaf21CzSFpyVZqIYUQw50bUJUvmkjSKC9OAoimUMNmo3LJdXJjaxdKRo/Qg7iSo+BRfOqr2pTb00valroovLWHD+jVJ+NIPnE1usG7pKNrU7cmQJpo4dO1Cvxmzmb/vOFsWTWLh5kMYKhXf57uMLJGHnKXr0UYT3zbNKpBZeca02s/QiKJpVdQadgjnyJg7xfAFm7cXWTp3OG1q9WRoc8353bszaM585h84ytY5I1m0/yo2iSrapN87C5N87/jy6uYhxo/sT8fq3RncRhO2b1/GLF3Ngn2H2DKhJyvv2hOWSHh/5M2dI0wZ14W+LdvQLa7OGET3wVOZNX8zOzeMY+J+SyJ0VKJfwky5cHgtPTr2oXfNLvSKrStGjGDaxp0s3rKNFROHcdAh0Y35Hu7M2QPLGDGgragntHyw80j6j1nA6tXrWbpmPhtvJ1+mAo2W0SBjbgYddidelkgkEolEIvlt/oJ4DuX5jqFUzKqiaNsZnH4aqjkeQ5TRPTYMqUUa0Ziqs+AB/n/1LR3lz72pm7kbHK7oaA0/MNrfR1w7J13+5+L5C6Zr9nLuienfdt+oYAPWt8xD1qJjuOkXc8xqTw8KF27OzNsuMQfiCMHpxTEOLVnAqEblSa1KR8+Nj9Epn37Ys7NtVlSZKzHgfMxYUgwW3FvRmVxpRMO3SBs2P/bgmz7x84/Cj5dHFzNmyjViZY8yEnZtdEMyqeqyyTRmtE6Nzx3WDmrPkqe/WuM/g23YP6EdeQrnpcvc3bzw0vyg5hsuL04xr2EFkSc56HXUUXP8F5+cHrG4VzWyl6rI2M0XsdS6HQRjfHEbQ8vmR5WuJkueJT3OGvnFhr3NswihlZ6yPXfjlrj9HYMQz1P6LuSq5a8Rqu+uB2mlykiVbse10uETrzf0YuiSC1i6PGfhkAVcNP3VdRPpe5z2IkzFdodEKsYSgcGWvgyZd4T38Z7lF998nrCoSgoRzxw0n3b5j8vQh7f76F1EEZSlGbPfUJTGZPjixeWl/ShWKBuNx67mjl38EH5m11nfuQ5ZVClosPZtzAieFt/9TNk+qjm5ihSmz5KDGPx6WEEE9g+PMr12aRGfggy74BlzOMqCg/NnsHiXmVZHlC2rKxUmS4ph3NMSdMHPNzJl+GQuOXhyaeUsFmwz1hqhdWRjjWJCkA7gplavWdjrrUwdPIwDtpoDAsWXFglfyla6IuO3XsIqgS8ZXdjC4DL6fOkH9jdX06akiqxVu7D+vD1ftetrVwtOz+1K4QwqcnVdg5mShBG3mD1wHqfeqsd5Ywi5QHdVJso02YWH5pBSDkx2DWbIzD28isvsCMzPzqZ+XhW56w1mz01PrbpdYPuWfZNakiOVihIjjuIQTzwn/d6JNnog3ju19bx3fLi6VDxHRhUVei/jxvv4qjzi+UUWdS0l8lJF553WoibQ4mcQd9cPpUzhzNQetohrFvHTMNjuHtv6NiGnCFtlwSMiEjjSZ4+XLB1YmvRZitFt4TmstOuK71+xPbmazhUyoMpUho3vNMc1fPV8y/rB9clRrCSDVh3H6FchFXzE6vZ+JlQtpi4Tk25qd5smJjragzNdCopzU5Ov5Vps/mqntkQikUgk/3L+UDx/xfrsaMqLhkKquuN5r6+zO9qNnUNK0HjmIRz/4hSxrxbb6Fh0Ale8tS8QyPOt3UUDKdf/XjyHP2Jytb4sOmH+N408R2B5YDCFMhZh3FmHXw3pH4bMqZWDVJUm8yZQt7K12D+cEkIEdFynRzx/9eXm9DKigVae3kfsNAc1/BQN9YGV1CNHmTtswTz0X9Cq+uiCycNTnLTWbul6c3poPSGea7HyhYfWyMxnPhjvYtszTbp8dOPS9AZkVeWm48Y7Qmrqwd+QNd2aUWXtG82BGKJ83rKhi2jwpi7PrKtWmqOJ+Wl0mn4t29PrjLPmiC6iCbfZQY0yVamaSUWW8t054qSn98PyNDsfORIYJ8aiCTDfTjMhhCt13IuT5qiawKucvm8o0ug6R57Z4aelO4Pt9tJahCnfagf2mmNqgq9z9s4L3sbrSIjlCw7PVlC2UAUqpVORp+kUbscTAskQ6ceNQ5Mpmb84pVKloszwPVgkNSD/XQit9R0opEpLtamH483GiMc3d04NaUGRmTf4pu0KYQ6cHFODDEIY993zTH95d3/O/E4tqCuErxrfV1y7e4en3jF/xvCOxRUKkSXlAC4FaSnhb/aYvz7PpTumPHl8i4ca/R2DMSuqFhVlsjfn/CI0xwTfnbB6foSjRjF/avvS7Os2MQd18NPwlPCldol8yefVJjplS4mqQAuOW2sOJuIjD5c3p2av8TxQKhfXi+y5b4mnVvp/cD1KJyGeSzXcSDyPDrkjnu8RojgJonF7sJBGQhirSnTl5i+VnYBAzk+rRu3hyzBQz+FW+N33jnvi9050GK+3daGwCJu720ps9WXmFyshoAvQbu19AmN7PiIjMNrTg5IibKkR27Q6BhIQHcDNMW3IO+EcIVqzp7/5G7Oze2VSqPLTectzvXVFpPEBOjUuwex7WsI8yIL9gyqSWlWckSfe6V+6Y3+HiW1b0WK//rpEIdL9EA2r1qFKFhWZijRnm3ny07wlEolEIpEk5s/Es8cVuiijPylLMTKZnu6vxquYtPY4JrpnrSaN83WmtS5GuhzjeBhvBEFLPC+4z1+Y/PnXiLDm3Ox65ElfnyVPk5ga+x/w3fsBC5rkRFVhDBcctQXQV66NL0K2NDkZfcUr0QiZgs2ZCVQWDbwOq/WI5wgvrkwqqVs8C96sbhcztbT0VO4nuXD2H0JEOGEeTrjFm2KpTzx/50uoI04+IuVFQ9zs5ATKCAGYv8UiHia56P47LoaXmLn8leZvwXdPrixtrp5CX3H0aZwSz8XXIoQHl0+xbo+l5u/ERH73EcJmDKNO3mZPV+E7GUsx6EDikW413uIZQj9rjdgmIZ5FPN18QglzdMUt7BNa8k2/eBZh3L0CCdGhEL4FWnB2ySSmnLrEmkZpUWVvyMKbv1+Owiyus2HZElYe2cnYssJPiw7hlLk+9fwFr/urqZ5NReYKQzjmmvRUinDnm0ye/4DvPzQlKzKYt/uGUDy1iiKd1vFaz0h6DF+wenGeuWsMYv4M9cXHxxv/eLfUI56jwggO8hTZEkiAnxe+8cLoEc9R4YQEuOKudDx80/KlMadxTtKXghP70gcTFrTLj0qI3qab3+tYFvKLKPezrFy7nHMmosAEuuAcHB5PCOoVzz+88fTyJ1g5OfA5o2tlFvfLRacj8TwnET/t9og03co9W00p/CvvHU0HxlfzA9TNJMLmFD73NqkeF/Ecz2czeuMtPNR5/oOgFzupl1tF2mLd2WmrYz62Fj987jFxzm3CP2niHP2B9weGUFDkT4EWC7gf1xGgA+Fzb06MYspxV83f/jze0oNCKYVo77MLM32qW81H3t8/w+JNmh4VnYTxZs0Yhh+/w6EBhVCly0eHjWY63yUSiUQikUiS5o/Es9P+NmQSL/R0VefyLskXuiA6EAe3ID64v2DTuKH0HzKUYcOGMrDfCGYdMNBMjQvC4ORqhg0YwMD+I9lx7z1PT59gYKVs6il0qpRFqd+xC90Gj2PvW6VXPoQX27qTWpWXPusf4eF6h+Uj+tC9Zx/6z9vBG694SjsJIoUOsuLBgR1MGH2Ql8FeWDw+zLwe3ejVZzBTjz2LExO+BvdY2LoGGZX4qLJToUFrunbvzsxjx9g2YhQD+w5i6LBhDBncj/HzjmKpaYWGW15k9cS+DBo4gH7jd/DMLekE83u8gZZpVeTssozXCRpaplubkjlTKsoseKJzrZrVyXFUFPH7a+I5hCujG6unHaqar8Ys6N86n0+feP5FVKAh6zsrAqAw/TY+T1JwKHz/+pUA61+Z+dX2OIOrpEaVqhZLbzolu1v85+CPhDjp7yL66nGTcd2WYR72AfOj/cmiSkfxzvN4H39Gqx6SEM9JoFc86+UHPoYnGdN3Kz7fgrm3opkov5mpPX43TtqqXC8feXt6E5OnXODzdyd2DFCmSueh55ZHBOrKpM8enB6VU5yTg0aTLiY7OyUy+gf+VkKNapTET++nLGqZQYQvyci975KdZfL1cwSBtkn1ougRz0miRzxr8cXm6H/kS8GPZlM7b0pUuXpw1jbhdO6EfMDDyw8fPXWDXvGshe/loRTPkgJVoeHc8U5u1DMEFw9/AjWbaf2l946SbNGRvFtdReRlSgq13YSOJcXxEaLVxiWUL4pOFr56Y1peETYT1YYc1VqioJtokQP+lgHiljE3iQo0YlPXrCJ8Yfqse5bMMoOffP3ojY1bTF7/cLvFtAbpRNjyTD1pobUEQDdfwj8TZJeEp4c8ZnK3BbzyCsXl2nhyq9KQp/FYnv09/cASiUQikfyj+QPx7MzOlgVJJURWztEX+JD0gM4vvrjyeMsMaqjFp4qq7Rdzy8BdMy35M+6mhxlYvialaszigp0HTiZGXD6zmp4FcpIxVX0m7TzA0dOXeOOmNDyDeb61nxAJaSnechA7rjzgyvkT7FnRi/JZM1KqzmguOSTXugL/FxsY07osBTMpDZTKDJ+3mOtPXnD76GFm9mxM2kzF6DD7NA7iGT+623HnrDjerjIZVAXoMH0dB48e5YahIS/ObmREuxihnypHZzbdeY+vpqXz1deCh2u7kq9AWVrNvYxTSFLN8O9YnJpBBXGdEgPWYpSgLfvh1jRyZMlE9t6H8NOR7n8invscc9Ac1OB8lsFVFUGootZSIUiSa6n9Y0lOPEfh+2Y3nbMoo1hNmXk93jzb3+Ab5ocnUSOtCF96BCfMkhMsyRAVidHe9ozfbSoa5lF8d77DmAoqUuSpx6wHSQ1zxfK/Ec/REX7c29iWaSeUocBIgt/up2shUV7K9GaPadIjgQrfvF9waEl3Vj9QhgO/Y396KtWzqsjQbAnPvBOLuXCXq4zIKdI4Q3l6JDOVNTFRuD9cT+uMIny+9ix/mJxk+h3+DvH8FbNDE2N8qcxf8aVQbk9vSQEhSFUt1mAc8J91mCUvnn05M7AaWZVN0LrtweXTn4x5uvy1944g6psJSyspmwBmpuKCxzo7xPTxxe8Bk/KJ+KYuTIvNSY3q6sbPYC9dNHXFjGt/Ulf8wPHqYhpnEGEL92LLy//8mxLWR7sydvMzAiOjiPR/w+ya4tpZyzLyivb+FxKJRCKRSH6H3xfPITcYVUIZ0VGRd9o1vvxJS0TZmOboBEqnzkG9ybfjbxLjfZP1ixaw/Ym2uLRmRfnCZEszkofxTlambfcivSoLDYbt0Gr0heMmBGSetKnJ22MfDhFJR+6LjwUvr6+iY0lF+DZlzY23hGpEY7S/CWvbZUGVsiJjTsROc/zGw1mtyaqqwrIX8eXpB9ez9CuWigLVZ6OZvBlH2L3trNyyF+NkZ0JHYHRwAiVE2hYfsDrRpks/niwid4Z0ZCoxh9ex00u1+Csjz99+hmHycA8jmhcjbcqs1Bq9nnd+X/+ogfnPIjnx/AmLMxPV09tTVu7OLiPdo2ceNxfTsUVDGjZqTNOmTWnSpDENO40T/m3I05XdxPVVZG6/gEceOgTEjw+8OziKhg0b0qhxExG+CU0aN6Jh34WcTbBRUdR34acNB3HaQeMsP324Pbea8Oe8tJ5xJ9kR1/+VeP7k9ZAlDUZyK9anP1mwZ0hxUb5KM3JXcht//cDlwWHmtp/H+9ii7n2d8Y1zoErXmA1PEmw4Jc73fracqiKNVYXqMu++7lH70JfbGNDxVx41FXnUqFVf5lx+h/nRMeQX4VPXHswxS12l4SeOl2bTuql2Hos86jyOrQnqhhj+DvHszdV5HZL1JYP9OnxpwApumb7kYL+apFTSqesmLPXspfC7JCuev7xiXn2R5+J+qQYdwktrr7FkUb93RH6LsH/63vku4tUlcyoRNhtVVr3UHP0dogg23EQ9JX1ylmfCVd1TxT+938/IbjF+0ETxo6bCj5p3Y+Lpt9henEYpJXzhFsy5+yedMME83dxPPRMoXePxXFJ6cBMS9RWr01Np2kjkbSMlb4UPKnnbfRoH3iUU23ZsbTaIQ++9Yuq06A+8WVtfpEl26gw7r38/AIlEIpFIJDr5ffHsd5lBpbKLl25Kmqw3iL/Bzu8Q8Z45VVORrkRjtAeEbE6sZOHCzRjHG/wwYEGZgmRNM5ir8UZsFfHcjdSq3PRY9jT+rqiikTqzYh5SpGjILgvROouOJvLnT34msMi4D7oaMLt+YfE8HTmqmS4Xg7KxzVz1GuIy3UXDUh2vAK5Obk4WVUXm3XGMP40uKpQbk8qQKm8NFj7QaqxHWrJr3hJ2XLbQNPCjiYpMHJ+f6m/kfMH40ET1xjS6xPOXR/PInS4tGfNO4clfFc8zKgnBkp4cxSpRq1YtqlUtT+EKhWnRdyJnH5jgFv4HrdJ/JMmJ5w+YHhtDPpHOKav34bCepchf/Gx5++QA81qIhrM4V5W9GdPOvcM50JHrC9qrdwPO3nUZL3QN+kT/INTNlKeXlzCoYkz4DJUGsOG+BV4f4o8Oet6cwbBlD/HTOuzxeAXVUqSgYIvJPEp2Q67/gXiOjMD81HhGbjTVzDRR+I7x4XEUF/VI+SFb0bt0WeGDEze2jmPmce0duIK4PK01OVRpaLn6Pr7xkuU7nvcXUU5J96INWPFcl/AUujLYGaO3F1jfS5meLc5NUZ3Be+5j7ePFu71DxLVVpGkwgnOJtwcQRPPJx4o393cyraEmj/O1Y97F1zgH65pd8neIZ3cuzG7zW7705NJiBlTQ8qVH9vh9eM/G7pXVx4qOPY7zfzgJIlnx/PkJM+oXUd+vwuybBCY3F14b9XtH6eT88/fOd7sDtMmaAlWaPPQ6nGDGTZII8fx6bcxsqTyVhPjV3QnzM9QN03fX2D44plNZmWbda/M1zD19RZ08ngLiWMpKXdmRYHfvpPHn0bpeIu9FfrWYwk3NMuj4RBHuacmLWxsZVysmb1MV68rS64a4hcbv1At6NJ8RC6/iqNVhEWy8gwYpVeSqOZCr8Ta3k0gkEolEkhy/L54j37GkSj7xok5BkZk3+fzHWisCu/09hfDNQt3pd4hprzlwfM1iFm5/E1+QJime9e227ceJPiXJmjINU+4GEux2jbGFClCggLbVY+KuN6jbER8fM7WO8umO9hy0j3+lD3bn6ZdDNEzrDeGaesOzJMSzIMB0C41SpqNy1wPEflDK+85C5sxfwIO4ddiWbGtbh2Lx4lOKpr2O4SMaQ5ZHJ1NaNIJKDFyDYYLGrOvxAWRLn4bsbbbhpCPdf0s8Ty6NKkMJ2qy4haWlJTc3DlWLjMqjfsX5301y4vknzg+WU1+kmapwSxbeTWrMJhLXYz2Fb6WjuBAUMRsZf+b5psHq2QUpa4zjvLVuYReDD3cX1xbhs9NiwhUhFxPiy/GhnZh/4R0eAQEEaMzj3VlGlRfxS1uNCceshMxLir9fPEd9tGZH7w6sfOJMoFY8He+sp1NBEc9sbVkXf5vpeIRZ32RJr97ssQokKDZ8oD9G+4ZQJbMQDCXHccVBu7BEE2S1Xwg55dqVGXg86af6fGci2VOlIXPRWTxXH4nC5vJctWhKUbIz654lNX7/Bes97UUeZaBiu93o3xP97xDP4TzfoPGlmsn5kje3F9ZK4EteHB/ckGwifLruW7AM+uPKPB7Jiudoe7a3raD+pnjGIUfxTH5lzS8i3//l905k+D3G50mLKkU2aifY9T45wl1P0UeZ1p6+GB13656MHkv0szkUzpyWVNnHcEfTGeT6aCUNFD8s3IqF93XWynr4gsmxSVRSfLB8P/YYJOhJjUcY7zY1F2mTidp9jolcTUgIF8d2Z9bRhzj6B8aVP2/LO8yoIeKWuhT9thsmu15eIpFIJBLJL/5gzXMAZ4eUEeJXRebW23WKuOT44f+EiWVSkqOKaGQo8zWtz7F7/UouOiecmvZXxHOgED+lyJY6NZPviMa26yWG58xGtmzaVo1RW18lL55tLzAotxDP9YdwXd0zn7R4jv5gz+Y26UhfohWH1d9g9eHK7HksW3ZXxCoWczY2rUyeePEpQJ3OB9SfQPG+t1IIcBUFe63iXYKBDtt9ncksGmcFp97kuw5F9PvTtivS75R7zLFof27OakvODJWYfMYi2c2v/vkkv2FYiPlJBhcWjc4UheiyIKmp0d9xPztM+FZ6SjfeQuxneV2uLqCxEH2qVNWZed5K/2ZUkb48WNlUhM9Bu0na36KO4YfBOhpULE6pCpWoUqXKL6tUkRL5M4sympbaw/dhl+Rs3L9bPEcTcmsGZYoWpWwlrTiq41mOIrkykFKVjQ7LbqFsZJ6IqFBMdvUhV94SVKhSVSu8+H+FkuTLoqxlLc6EszbxdgP/4v2YWZVFGotr1xlwRP/nhQQRD+cJ8ZyWrEXn8lpzLNBgLz2Vta4pitN/3VNNJ58uPmN7qK+4T0aqdtiXRAfU37NhmPPleb/nSz99uLe8SQJf+sGbdd0opqxBLjWZex5Jie/kSX7N82fuz2pELuV+1RZh8Eefw/sP3js/HNml3pMiLSUHnk5UjpLie4gBS2srfpSBCh13omcPezXRr1ZSJEs6IZ7H81Dzoecwy9MMLaTkTxVG7jdXH/tdvJ9spIOybj9lWUbtjt1cUwfRoRju6CzimJn6/U4kEs8/LfbQvmZJSpSrSGXt8le5EqUKZRXv0VRU6LYe04QvNIlEIpFIJHr5A/EMnlfHUEQ0gFLmGMrV39jR91P4Z35812oo/Qjm0eKapElVkn5H7/Ji7xZmjb8gpGZCYsXzUK6H/a549udYn9JkTV+HbSbhREf/5Nvnz3yOZ1/49iMyZkQuTjx35LBj/CuF2Z6kt2iYFm+zCiN19GPFc2UW3NW1s+1XHC5NpIAqJ61WvyDY/gor5s/miLn2mVH8+PKFiHjxieDL1x/q+Hy2OM7AEqLBVG8qt+JN1Yvm4ZzSZEmXji77bcVVEvNX1jyr8XvE1Ja5SJm1Azvf/c4mU/9kfDg7rL4QLPrFM6E2HBpTnrQirYu0nsFjvQNKX3E9NVj4VnpKNdqkGXkWOel6j9ltRHqL8DXHHcdRn+L56S0ET2MRPgdtJl5JsC4xmFNjGzF59xNcg0MJCQn5ZaFhuDzZTpfswo+qDOW0TVLqOZpAi520+FPx7LCftr8jnn86srNfHeaetyUgVCuO6nh+wPLsbOqmVpG+xRKeeidWz5887rO4W2M2vgonLEH40LBQnq7uQmFRFxUecYZ4qy6++HJ7cUMyiDTOUaUnp/V991rw6f5sjXiezUvNadGBJmwfXFLUMSpKdV3GW7313EesD/QSeZSRKu33JJF+hiz9Y/FsyqqqxZIUz9Gud+P5kpO+Dax/eHFnaaNEvvTFcBttiqUUx5uwwTD5sv/1y1e+fNZ9kw/ux+mSpHgWRefJYmrmUMRoBw78xjbrEZ8i+Kbe9vqvvneUEvwdi0PdSC/SKGcFkce/IRI/hn0i8qdwhh+hPFvfWr2uPFPpVuy30u9HkS+WacTzOO5/0tT5YTYcGFVMne4tp13RXS/HI5Lw0BiZHO3zmtU9C6k7DCr036L/U1VRwbzb1kHcIzP1+h5L0FH0iSszWjF+3SVsAxPWE6F4vj/OgPwiP0p1ZpeRVM8SiUQikfwufySeiTZla/2CosGWgqbLb5FUO+ajvxlnrj3G2y/+2E2Y3Ql65sxA6VI1aT5xNtNjhnYT8IZ5JQuQJXV/LgZoNzgDeLa5q7h/bnquSPD5j6+PmFgqC3mab8Ii9lubSREnnltxSHtBmMDn6TzKqrLQbMptTaPHn8sTm4nGbGlm3bDXWr/5iwjR2J9SPiNZ67Rm9KTVrFxzBDtdI2r6iLDjyLhKpM3Zgi2vtYeeXdnSJh/pc3fglL1utWV9ZoJ6ml+ndc90j64IQXFjWmlUmSvSN8FUVt+Hy2hTIBW5Oi3nYfwPH//L8ODE4DpCdNVh7WtvnZ0UCsHmJxhePK3wm3L03/FaTxn4ie/FkeIcIZ7rb4gTzwpOtxbSVNnNOWMLFt5y0jNiGMDTtcp0zOy0GX8pnniO9jjOpHFbeOgQ32fjCDNn99CiQvxVYORhE92dAGqiCDDeTEMheqp0PiC87HeIJsRqJ81EmIptdicpuL9ZbGPkuMNYhOqJgddD5rTOTsq0DVh6L+H4cBQud5cybMZNvaOFPy32061celIU6MVZx/ii9LPHA+bWyiTSLy/N517HO3Fvl5qfzxaSQxHPhWbGiWcF/3d76SvKhEpVjVGHjfSMPn/F+Xh/cU4GqrTZpT8tIl8zv3xBMqcaxLUwfQo3AeolMkWEcOuXpODW9qVFt110+1K0P49XN9PhS95cGdtM/Z3ookO3YRmuv7L6Hu7CtVv3MLXRvTtzuONB2gqfKN1oCzqXiatx4nj3qqIOVVFx4lHc9HUcCb6E2HHh+n3sXTSi/i+9d2LKx48PT1lQIqN4/lz03v0m3vepExLi/Y5T114QFh4TuW8Br1nZVPncVFZqTzyLq744v1tDsaxCPGcZ80s8izrA7fF62imzA8p0YNurpOTzFyyNH3L50a+NFLyebaRzrhSoUtVjyhkrPaPP4Zjv6Sril4l6vRLMsgi4xJxJa7hsrGe37ghnTk4qR3pVcXpufpFoNpVEIpFIJBLd/Jl4FkSbHmZanULiha2i54Yz2PomWJMVHYzL+5cc37CV2/aO8aZUqvnmy835yhq8lBTrOFvPyI4JiytkI22KSmy1EX9Gf8HD3ks0R0J5ta27uhFVtc9+TOMGjAN5urgJ+YuXZ+Edv99rCAjxPL2esmFYHrrvMPq1rjTCmM09SpOiQl9OWcVeKVSIzxZkVaWmz0FLtXj2d3Aj7ItWYzg6hJfbeqrX9amKtRECLKm1arqJsDnO0GK5qDPuPG6aY+GP51Ezf35qrHqod6dZx0Mj1OuX2y69r6eR5cPZIQVQpS9J9wMJx4aiMT88WL0zbJ6uy3goGshJtGv/wZizuEsp4Q/VWPQ48erBX3zA5OwiGhYRDfIs1YXvPMbGL6HHhfB0VUN1GcndZCsWmqMxeHJj1TDKKNMyi3Ri2kVj3BJM04/+ZMn+YcruxBmoP/kGcZIlMoBLM6sx7uD9mKUHenA5OUf92bO8zcdy1Vq/H7rfnUYeVTrKtd2Z/OZfGnwezyafCFOqxRaUoqmTCDcOjCnFzGtJb9L0dkl/9aZKpXqt5pXnL5EY6faYFUPKsd5Ec0AnIZzpUks9Mthg6kVc46mir7g+3ErHMrlQpSlM/SVXMfH4nKBD5Ac2B3qo80hVbC5P42nHEN4cnkWtgmlR5azPwAMvsU/4UekoH27Pr6kOX7Tjfv3pF3KZLsWzkTpVb0776x+9jEf4dXqWzkEqVXeOeSVVm2n5UtEuTL9kgnvC6vijBXuH6vAlBa977OpbTf0N+zqTVvPWyTfBrJpP+FoZc3HnTs49e4m+jyYFvV5CEeETReqtwUxzTCdOl1jTvrR6RLXF3F2YuSUc8f6Ap6kBZ7Zs47KRifjrF8m+d0TsXAx1vXci+fRsEwMr5Ba+kIPRB+/iHJTgrfTDD4e3jzi0YTtPfLTfHz/xfbuPXhXyo0qZi6qzzvLeJTxBh1QUbqeGqNNQlWMCt7QXZf/0xnTPCCrnTEmG6q3Z9cSSwAT9J98CnDG4cZ4De48nKE/+PN45iar5UqHK04IRx9/iFJygg+O7C+enVhRpkpIK/U/FvTMgjHuLGzBqy8kkv0/tf3sVtUW8s9fpw3HD//yTWBKJRCKR/Bv4Y/GsEOV9nwXdmlC2aD6qdBvH/I0b2bdvH8ePHufh7bPsWr6Nqw9ddI7QKlPpvO4vp1aJEvTbq6/5HcbTFc3JJV7sdcds4eytu1y/9Iovotni/fYk8/rXolTR4lTssZjt4r5H1o1jZLv2zDpupn99WEI+PmF6/SKkUOWlUnvxDCsOimfYw9rx3alYqRNzzlmrp1PH4nx3MQ0zioZG07HsuvWISyfu4Bys3WKPJvD9HlrlSEf+HitRNvz+c75jc3IerSp3pufivezbvpaJzRvSbuYm3n1LrJw/e5tz99hSJtQQDUORVhlLtWXJ+jO8dgqNaQRHhmB26yg7Vo+jbhrRuBPnZKvTixXbT3H/vccvkRxpx4nRyudLVKQp2ZARi5Zz/IwBvn8ycv7/LeE4vr7J/tUjKZI7Jo0KtxnD+r2XMfH9Es8HfhGNy5M9jOvVkspVa9J6xEQW7tnHXuGL+/bvY/GWOTRvVIZG7Xox5YihkDgJ+cy7s4vp264Bpas3oMfUOSxTwip2cD8zlo2mQa3KtOoynlV3ndUiIsjmKaentiCLiF/2Zv3YsO8hDuEJStjXYCyfHGFu6zoxjXlhBSt1Z80FA7y0dlP/HmTJjUPbGNWtpPocVcbq9F+ynStPrPWO6v0MtubW0Z3imcvEhElflT6Lt6pHy341u3/i9f4mOwZXUp9TqtcE9p14heePBKLxoycGt8W1qmnuL6xik+FsvfcGg1snWdRG2dVfRdNJKzl33TzB8gxR/wRac+/USnrlzaIJn4bmfedy/I0bkVoZFmRyntlD2lGzahUa9hvBnJ172K1J51W7l9G2ZUVqt2zDiC2P49al/yIS+7tbGNGtORWr1qb9mKks2qvJo/37WbBhOk3rl6Vph/7MOm2eWKT89OXdjbOsndle1DFKHLNSc9gCDp9+iJO+4c9IPwxvnWPDnE5qgalMx602eB4HTz2It1tyfGJ8qY/Gl3pOS+hLo4QvVYnnS/H4bs7eKT2oWTIHpVv0Z8LyVeqwhw4e5t7tcxzdtIMjJ94mDieIDLPlzrE9TO6vCDgR31QV6D5/Mxfvm6L361fhb9kwvB1ViuaiXLthTF+zRn2/I4eO8uDOOfav3cbpyxaJO10Fyb53Vuh/73y0uciE9nUpUawYtftNYfm2LeqwJ0+c4MHN02xbsoMH7wP+D3tnARZV0/7/tTsfu7seu7u7u8UEu7uxUbE7UcruQMVW7EQpAQEVBEWlGz7/OcsCu7C74PO8z/u+v/97Pte1l3L2zJ45M/fcc39n5szRuuIk8P15Fo/pSoNaNWjYdwRztu1gu1S+4rNuzyq6dqpJ3VbtGLrmCq+jk3uMYF4dW0mXJhUpUa0hXSYvEH3WLmXa06ctuWB1CNNVR3HQusQikrfn1zK8ewuq1GpI94kzWJpgg/v2Mm/tFJo3rErrbiNZeNpBOQD884Mdp+f3UL4VIEv97sLOr/AuxWBBIC73LVnSswV5lXamIH/ZTiw/ehe3HzqWacjIyMjIyMgo+UviOZ5fvLG1ZOG8OQwaMID+/fszeuQ09p94jnsqEywhjlfYMKEL29+lFISJBL9i96yJjBgyhA0n7iVbbueK3ZVljDccyABx3clT1mDnlqb55iSUy7aLi8C2J0cuH2DjhCHKexhuuIVrztqeAfThxp4VjB4+hOkmh3HT9s6UEA+uWGxn5w192xSlRiTfrlozb+hg+g+YyvydLzWW7arj/+4Ca0cPZPCIUYw1NGTMyOEMHDCJQw8/xweQUV6cX2HAwIGDGTHGEEPDcYweMVScM54NVq80y/T7A9YunMxog+EMEeePmWyO3kdm/7/Bl/uHjBk6cAgjR48VZTSWUcMGM0CIsdMOAVqD6SRicH10jN3rDBgxor/SfvoP6M/AhcvYePYJvjGpBaIBvLHZzXrjIQwepEo/ZBDDjE05KISsesjrfn0ns4cPY5Sox9HDRR0O3MRd32RDRUHC/rZNYNBwA8YIezAcN5aRUn0vNeetb1L7CHO/wlJxj0OGjRT3a8i4MQbi/gczf7uNFqEfT4SnDSsMRD6HaqaZu/kynolNIZI31sZMGDqCMWMNGSmuYTD2IK/Dk7Xzb68xXzWKIQYjlXZrOG4MBqJdDdxwBPOVEzEyEPY8diwjBg9n6pILKTb9inGzwWTWUIaNHsM4KS/jRjNcXNPo8DO0vMmNz/aXOLJJ2P4YVRmLz8DZ8zC2usPHkNTWWUTifN+c7WuGM2yYKr3wd4MWr2TrxRf4x+mwkAgHLJdNZsCgYaIsxokyGy182SBGTtzMQ12TfFHOHF85jYFqaQxEGoPxG7n3TcuNaRDAa5tdmCS3pRWmHLqtualaSqLxenEBk5VLGTxwoDLt0MGjMN11jTfaVLOKqC+2rB49VNiEQZJNDBrErPVncdfj2qVlyh/sTrFi6aLEvmPEMCO2H76LY6rvxNfe7+w7/iyVa0r4YHfhEDOnzxA+MD6t0bh5HL30XstO1SnxcbqB5dbxwt5U5Ss+A2bOZslRW5wD9JdwzI9XnDywmTHCZqU+S0o7d952rr75rncVSTyhONw+xJZVwxg6VHXtQQMYvGwtu66+1hjY+PzgMIsMhjJyzDjGjBjGwP6rsXH/pTkIGObLvf3TGDRsBKOV7W8cowzEufP2Yuf1v7nuSEZGRkZGJq38DfH8V4nhw81DTB9pilOqwc4/SOIzz5054J7WjXxkZGRkZGRkZGRkZGRk/hf594vn8FeYzezNzKOeqczq/cOE3GFWQ+kZum4cdNW3BY2MjIyMjIyMjIyMjIzM/zr/FvHs7XCWqVONGDd2FAO7t6WFkQkv0/xw8j9EwCV61c4nxHNDttrrX3InIyMjIyMjIyMjIyMj87/Nv0U8f78wnuLKjUlyUWL4cqw+at9K7N/DT+5vm0+fxhVUG/koKFarLXNPOqHtMWYZGRkZGRkZGRkZGRkZmX/Psm3/D7y9ZcuNW29wDPxP7+YZzqfndzh/6hxXba5z44YNF86e5o6Tv8ZOvTIyMjIyMjIyMjIyMjIyCfwHNgyTkZGRkZGRkZGRkZGRkfm/hSyeZWRkZGRkZGRkZGRkZGRSQRbPMjIyMjIyMjIyMjIyMjKpIItnGRkZGRkZGRkZGRkZGZlUkMWzjIyMjIyMjIyMjIyMjEwqyOJZRkZGRkZGRkZGRkZGRiYVZPEsIyMjIyMjIyMjIyMjI5MK/8fEcyxBnvZctLzOzZefiVAdlfk7BOD5/CHHze/i8D1UdUxGRkZGRkZGRkZGRkZGnb8lnsODvLDZt4MtSxezeLH4rFnFrgdv8ZW+/OXOR99vBMcpT/3bRAd68njPcMY1+5NMitK0mnYef9V3Mn+NX87nOD67B63LF0ehaIPJ48/8i6rrv5xI/Bxs2bdxLcZLVLa7eCmr1puyZaspa42XqY4tZqnxWnYcuoarjnGF7653ObdpJStU5y9evIcj1x0JkL6M8OT1h2DledqJ4c2Ncxw2XsoyKa3Ii7HVSR4rrxXJTw8HPkUqT9RCGE/OW7N/2WKWSGmXLmHNmSu8jRVfxQbi5+mCT3T8mboI//GO2+ancQpSHUgjYaItXt27nc1q7X73Q3u+SV/+dMPd1x/pFuJ+vuPcns2sXr5EVTZLWL5yHZu2bmHjuhUsVR4TnyUrWGd6lIceAaJEUhIZ+IH75iJNYl1tYNPhWzgESt9+552LH8Hh0v/jCPn0GPNNJqxMyJu4pvGaDaJeN2Oyanl8WUmfZavYuP0Mb79HSQlT4OP6nFMb1rI24Zoizydc/FGe/eUdroGRWvOaRBhOF6y4/cIHZdYSiSbA/T6HTdexQs32VqzbKPK4iXUrk/K4ZPlqNu+5jEtQJEFejzhiasKKxPsSadamvC8pzaZd53n/U1vlx/Hl8SWu27z4bd/pZf+QY+tWsUp1ncW7dnHBM962Yz7Z4xasvzQgEPtTR3ng8FOUwO8TF/eFJxYWvPBWHfhNEm1dX3PUQuq2/l1nH/frqyMXtpmyIaGeN5hw+NVHlM3Nzwk3/2BlWUT4PMNqy3pNm129ns2ibtevNk6y2aUrWb/1FG++hRP+7TXHt21glWj/mmm2sH5NsjSbjvHcJ0yrb//u9Zazm9ZjkpDHTRuxePdFWK/gqwOuvyTrjSPY044jydqVVvtbthrTHWex99fWruLweXqZ61ee8z1ZZmKUvmKLhq9YlugrVibzFUd48FG7rwj0c+Hijs1JvsJkLQeff4j3x9+ccfsekKw9JieGT/fPcd3Wnp+qI2nFX7TrC5tXafQFZjbv438n0otXLvocbSxvb57HLKEvEB9ji+PYKe01ml8e7/HS2ReE8/TCcfaLslPWg+gLVp+6yBtlAQUp+wLvv9LoZGRkZGS08hfF8ycuHVxIv17t6dDUiPmjpzBlivjMmsWCJQsxWjCPSX1aM/XgHT79VactRECIqwOeUZHSf4n69ZH7Ow0Z17yMEHqF6LX4Gj/iz/y3Ee3pjuev70ph8P8DP9+f58SiATQsmk2UaVu2vfyqNcD6/4+f3FvXj8LpStNlgsp2pxjQsngWUQ6ZqNFjBJOUxyYzqnMFsiqqYvpSssIkwn+9Z8cKI7p2bcXQvmOYPDnhd+Yy1nAyRsOMGDdhKHOOa4v2Y3B9doR5EwfQuPFgJvWewnRl2inMWLiQOUuWMmbMcMYYTePMV1WSRMJ5c2sXk0d3p0mj4cwYoLrutKnMXryQGSKtkUF/xs1Zxa1fqiTaiIvg7dG+lFYUZZSFRxrr3YsL+xfSt2cHOkrtfozq2sp2vwjD+aLd927FtCN2ygG0GPvNNFbkplZvQ1XZTGRQuwqijBXkrtSRMap7njKuA4UVGeht+hjNLAdw3cKE/n1aiWsOZoKh6vwpczCaOJXRwyYzfVJPBpjcwkcZZEbhdno65RX5aTEy4VxDutcqqLxmyaZ9Ga+65qRB9ckuzpt+/qtGEB4R9Ig9K6fQuk03BrSdzPyJqt+ZO5eFSxcxYsosDLu1ZOX9n/rFs+9FRldIT+n2m3kVoO4EQ7A/NJaSisK0HZeQxzF0rJRbmcfybQcxUZVHo741yaYow1o7X5yOTaWs8HttEsp8yli6VMunTFO21QAmqNJM6F9bpCnM/Os/UtZpiANb+/xBkWJDsXZJm4oM8rvJ5kXjad6yJ8M7TGZugp3Pm8cCUR7DJk5jTK+Oon1oH4RIIPbTUXoXFvc34ChuEZptKS38eLKYWhky0nLZY1GCv0mirRdjjKWn6mBqeKbJ1qebPeBLCkN4j9XmOXTu1IkeLSawwEiVdvYcpQ2Nnj2bCd2aMP+8m3L1lN+1BVRR5KHJMNV5U4zoVb+Ism6LNuiFkfKY+AxrQk5FXqaeduPT3TXUFGkaDZmsSjOePo2kQVAFher0wDAhzYhm5FZkZ8zRDxqiMSbyJYfWzaBD+y70aTWR+eNV58+Jz+PIGXMY36UpS2/4ibPjcDs2mTKKArQcpTpvyji6Vs+vvF7pFv0T7W/igDrC/gow67Kfst/WIMSJnf0LUqTwICySjWKEvtlCE0UuavYep/r9iQxuX1H5+7krdkjyFYYdKKJIT8/1dsl8hTPHt82lW5dOdG0uyjzBV4iylux0jLiviT2aMeeUPT/0ObvAV6ztmIui5Yw455G2nj4y0JE9qyco+4IhfbT0BUONMJowiJmWn1Up1InD/aU5CyYNFH2BaPu91PuCBaIvWMLosQaMGTeRkym6kkjs7+xm6tgeyr5gev8pTJXSTpvC7EULmSn6AsORAxk705gbvzsSICMjIyOjk78gnr04MbUdxbMoqDNmB7fsky2e/vAMq4VdyJtBQd35NvjqjTD1EP2RY8NWcckvQK0TjuXl/kFkEkFvj0U2/2bxHMDdORs5cvlFsk77/zrvWd2xnAhSWrDlf0U8x3nzwHoNs1Y8iJ9hURLORaPmIjhtxKa3auF5wBOOzB7IsttJAijU4y7Lu9UiX4XKTNp5nvfKaY0kvr2zZceAViJozUu3fY6qowlE43JhA52qKshVsy8mZ94RqK6tQgN4bb2K7lUyoKjcl7OfVMeVhPHSbD5NSigo2Hws+657iPBJjZ8+PNg/i2bi+4zNpnFPz/RihP99llZKL+o9M1X6bMU1VT3jgfWk1hQV7b7e2F3cfp9sGsTlKRbzOpErvYKGS24rZzajPxxk0oTDOKtF7b62C6kqlcuy+6ojEkE82ziISXuf4JVwbqwfF5Z0p3yx7LSctpGbzpqzNnEB77m8bjJ1RHCdf/ABPikrMhTX25uZNOsC35VnxfN62xAKKUoz6YS76oiEJ1eW9GHmaW9CVPce5nOPJT0rki13MQasOYtDsoGLQLvzrOkrBfRZmXhWnxcI5rlxLwpnUqDI3JodT/zUhPZP3l7ewNT51+Jnw1TcX9aNvIoqLLiudtFoJ84sFLZn44n99a1Mm3dVw/c8WduH/IoKzL6sFpTHfeD8wj7MueBPpEZjjuHz+eW0zCPypCjPpIMvUhWhAR8uM61jcbIUqMKYrddwT+Zw/W5ZM69jCRSZirD4lj5H/4s7U1qQPZ24du6eWDgH/6af8eBIJ3EdUdfZqk/j/m+ulEiy9SxU67sd11Qv7oHVxFZKW68/Tr+tN1p8k29qbScu7B3bh9Ynb7ZMtJt9hKfqJieIfnOLvUYNlPfSY6ezcubZ9/E2Jk47jbq5ORwcRVEh9sceVvcfX7lp3J85J5xwenSAqdNP8EX1jcQHcyNKKIowfJe96ojEN+6s6stUyw/8UI1vRP18xtr+1cmZQ7TDJcd4reFjRDt4bsP2ETWUeRxuLg2DReF2cxOTZl/SWLHwfGM//lCUY8Y59QEJdy4u6s2sc76Ea/iUWLwvraRNXsn+ymK075ma/cUR7HSIyRMO4ZjkkPG7vZhqijx0XXxPdURCtK1NQmTueYRngq+IcmLviEbkz6qg5fRDPPqgOq4i7t09DkxuRkZxPx1NX6FcsKKVaDyt59Agp5THqsw69l6tf9BO2KeHrOpVl3xlK2K07Sz2yUTqd4db7B7cVrTtXHTcqV4vEjG4XtlC1z/Tk7N6L9acessv9TGo8CDsT6yl958ZUZTvzkkP1XEl4byxWEzzUsL/NRnF7mvxAzGJBPhid2guLUsqSN94ErfkZXoyMjIy/zJ+UzwHcH9jN4qITih75xW46VxG9IPj02vQZtZuHJMJi7Ty4+5SmhUz4sxX9dHfb9zd3FuI5z/+/eLZ+xTDK/dgvsW7+KWb/78Q/pBFbUqLYKHl/454DnDnje0xjn9U/a3kC5YGjcihqMfKe15qYieUIPsD7LkXr3Djvr1hz5AKwgbLMfzgE92zj0FO7Bvfk1pr7FQH4vn6aC9Di+VAkb81G+7png7wvbKUrl1bsPVF0hU+2qykYy4hqkv3wspBt1hxPjKGNn36YemiOpCcuEg+262hSu1WtMon2nKVXux30i9+bq/rSGHR7nN2X81HnQ3AH8sp1Wgz/zCuIkKNe3GUPc9/qZVRJI5nZlBJBMQd5lzSELj4WHLQxo3Pyog6mmc7OlBKiJNiI7bgozxBG3F83Dqa4iN38V5yBjGBfL5zhEPv1aP2QO6s7UsBRUnGCMGoEQy7HWSX7S8ihNFH/7Jn78DKol7z0dz4qu4A+9dLFvYuztCD75KJUzV+3mLCpOHUK1uYoumy0c70EQEJhRD6VQh8c45o1M13Ls3pQB5FRaafclAbEIkl+oMZBy574HLLkqMaaX5wfUk38gkhMsnaXiNwjvtwgJ03g4lQr9JwF/ZvmUSVSn9SPWN6yo/dz3s9k89h3o8w6SCt8ilJr+2PNGYtNfh8kyndS2N4Qtusmgq/CwwcPpjGpfOQP0Nu+h0U9/g7jsZ9H03ad6dlESGe81dj/s3f6FTUbL2lEG7Zq/URtq5vpEgIfZWt5+q5Fo9UbL3tvIMkrMaNi/rM2Zl1yS3SljU8GP/4kjZiPrFlRGm6rr/Br5AoftkdYb+9+oWCsds6ROShCMNE2WsMcngdZf9VD9xsLThor17roTzdbUAxRSEGbLgfvzQ8AU8z9lz3VdpgXKgrFmOrk02RjVqzT+pevh/xgdWDStFn53MiQgLxvm/OIY1xwF/YGvcgv6I0481fa9qHq2R/AYSpDwqGf8Bsu7C/ytWpkSkdZUftwT4hk3ExhD4+ksJXOJ2bRWVFbtrPuqjpK75acfCaa7yviPvKpfmNRDtQUMJgFzpX9cf5sHtMeTqvOIe3LmMOfcfWdeOpVrEylTNkoOoUS52P60jE+b/nwIhKZBFlMHCPne64QJS52aRe/LlSfRBANIunBxlZMheKvC1Ye1u3uvW/sYruXRtj+jTpCp7X19ItrxDVJbpj9lbnlXG1NKJt714cST6GKyMjIyPzl/k98ex4iGYFFSgy1WLePd3jtxIxLrtYsPUkb/TEVLoIfLyTvjXzkyH3GC4FqUdZSeK55+JrGrMw/yhf7rBhWDlyZqrPIhuN4d//+ySK51ZsfSUt0fsfICKMkO++fNeIoXWJ52giQ77i+1PYYYwfD7YPpJAQdeV6mvBI76q+GD69u8FadfEc6szOcTWE/SpoOve8/kcaQu05a7aMTVdV7ezXMxZ1L0k6RXY6m9ppBsfJ+XmXg3tNMHugPUqMCnDi2ExDlly6x8GBBVFkrsDQ3e9U36Yk7t0+GgqRrchal4UP9S/3jXHazrytZ3n3RRTuDx++RsSqrRzRI56j/PD1DycyNo6QpzuoLV2vQHtM7fUVkiDyNauXXeKLt5DFcaKuvnnjq5FEl3iOIzb8K1/9pZoOxe3UZAqJevmjjiEnffQrO587i5mx97WmOEgkDoeDi5i+9Thn906hzh8KMjc34c0vlUVFRRD+3Qe/JAMT6BHP4b74+YUR/u1rsjS6xHMsMWHivn7EIIoyEe87h1m+dD2Hz+5jVGVRtiUMOGavw4fH/eL5vqFKQVKyzQJu6NWq4XywWcjsw7pGaqJ5tXk6U/dd4sKmwZTNJURp9718TLN6/sWtxWOZceIeZ2bVQZExJ3VnXkvz0m11W98/oACKLBWFrb9XfZuSRFvPVo/Fj/RfJd7Wz/BeOboTR8D9VZTNoCBTsS5sc9Rvt2Ev1jBjxy18fsYQ4++Dr4b+0S2e4yJ98ZWeefaLT5NUirrFc1yEr7DzKGEZkXy1mS/OUZCz0mAOe+gbRBBuxM6Y6bsfExgUTZSUR41b0iWe44iR2lUy+/t6/ygrlqzj4LmDjK0qyrfYMCzeJNifaIvfvVP4Cp3iWeUrosQFQp9soFImBekKtmOjHhEpEfl6PbO2XcNLx7rtT9d2s3jFVsxPbmVgOZHHcuM5/0GHDcT683j3UAqL+i7TeSUP9JpKLD6Otqxe+UD1tyDsA/sm1hbCW0HjGSfx1GcuYY5cOrqUDZdVEU/AS5b1Lk16RRY6rLvHL31NKeABZnvXcuBuanPoMjIyMjJp5bfE82vTpsqld9kar+CtzqmIBEJEBxdMmNd1FvduT6NGjWnStClNm3Zh1MY7IjyQ8OXO7lm0b9KExo27YHzmHlf27qJrufjn/xTpClK5fiMad+yHyW2p4/jBvS19hPgoSJ9Vl3B+d5xp3VuKtI1oNHAWZ96kcUYiLpAA95uYLZlNh9bGnPvowKMLWzBs2ljkswXdVlrzVNUZfr57lgkNqpJVWnKoyEHJKrVpJPI7ascOVnTrRstGIn/K+2pKX6NtvFD1Ub9emDGnn3RfjWncy5ir0lScTqKI/vWaW/tX0bPpVNaef4ST60VW9O5IE+neRhtj5ZYQHosAI8yZ56KDH95mIjN23sT9wznmdG9Ew3ZzOfM+aTYzxusJJhMM4/PQuCkdey3h+AsvzeVdSvEszTC1ZtvL1zwyWyjqQ5zfqCk9Fx/mg5YiDfN+z/FVg+jcQvpdcf+jlmPpmqpB/JejSzwnEe39gOVtMomyqsDwbY+VSy5TI9w3qVyCXu5hcOUMQoS2ZM11L7VAURtRhIYE8E218ZP/rSW0LpYORb5eHH6V2hq8CAIDA/kRqO0uYvlhf4RRA7fiGRXE2yMGIgDOSMkuM7mvEaEm8cKkAZmlgLvZat7pXG2SgGj330MI0yqO9IjnRCK5vaCsKOP0lOixk7Q8oRruKxqdzsLUM/OcQLAnR4bnFNfMT7Pp5/Qs65QQ9xUTiLcQMOriIJHY92yaNp/Nx52JDrVhRJU8QvAVYs5lbz0rVnSJZ33onnlOyVeu7BRCaOlNomM/sGlAFXGvuelpejuZII8n1v8tW3pK+yCUoOfKm6n8tvBHUYH4fNdxVuQzlo+Zz2FbL6L9jtG9WFZh/+VYlda11/5XmdR/GXc+/sL/8SohphRkr9xZ+BvV93rRtPXXZiNEeaVu69LgVq7ma3DQr8UE8bYeLlVYTCg3ZpcUZZaJsv32kuqYcVwQPt9CidLWRPXNPOtEz8xzAhHfOTNBek45J7VGW+lof2rESnkMI0Zr29Iz85wCP67tWcm0RTeE/bmxbUg1kYdcdF9/I5kgV0ePeE4kijuLpMco0lO8+w5SH9YO5uu3ECK11usXTm9cyty1D4iJec+qbuXF7+Zl4K7H/NBy/zFfH7O6Q2ZxTlkGmT5MY1+Q5H1C3hxieDXRF2RpyorL7qn2BWGiL/BT9QU/7hjTroToC/J0Y+9TP7UBFG1EKvsCf419F2RkZGRk/g5pF89x71jfuAjpRGCRf/wpgvR7+ySivouAeRs9MkjiU0Hz4Xux9/BXdTYR/PA8x/TmHWjUdTt3v/7Az8uTV8+PMLZUIXJm7MRam7vYPX2Jm3L3zu/c3TyI3CIAKt60H5vPP+bp88fctJ5D+xK5+KNkF7Y9+aZV+KjjY7uCoU1Lkj+z6LwU1TCYsZCrz5ywf/SQ3bMGUSBLHuoNNuVZqMjhj6+8e36TjYMaCGFVhuGmJ7hnZ8dbDw8+PD3FCsP4DZBylhyN9TvPxOWZUQFfeG9mRLUazRi54wHfQnV3XhEfb7JucHlK5pMC1kI0bDsS61v3sX/xlMv7llEvd14KVBqCpXscMUEfuTCtEi0L5RLnFqVk4yVc/HiLYwvaUKBAa+Y9jJ89jniwhU6ijJZsPoedyO9D220Y9ShNgRLNmXL4VVIZCfG8uK0UKOSi1aQ1nL7rwFO7exxc1I6Sohzq9lzFAzU1EetojVGb3hgtOM6jR3bYXdjDkKLFKFh2EKvvpG2Dlf9OUhPPMXy6u5F2mYUdF27DvMvaFxPHRoURHBxMaFgkUdHRQmBFChEcSkR0MM93jaOa1A6qjcPybdo2bIpHCKVFPSkpDeA0WcgtT/1yRi9C6Fxf0YLZx6VNwuKIdL/OtNridws0Ze5VLQtN496ypl4B0e7TUXDSWUL1R2qpkLp4jhX2OK+sFJTm5M+ld7QHhjFSMBlMcGg4ERFR4pxowsNCCYnQ1sZSF8+B7scZll2UQbaK9NijewY+LfhcW8jcRUu48VmyoHBuL2lAvowK8ow5nfjMaUr+WfEc7HCKTbP7sfulUuXhaj2ROrkVZGq1ArsUW7LH4vdqHz2zivLIU48x1ske2v1NPE+NZ8aKrTxRLvMI5cKU8mQSdlx6lm2a7vPNjm5M2X4dn2hhCaFubO0iREOWInTZ7aQ6Qw/JbD3CzYYpabT1wpPPEfYbth4ddJeZJaXnqvNQc/k97XabZv4Z8RzudwWj/OL+Mxaj5cbnqqN/lbSL5xDnC2ye3YedzyUrjcHtxFTq5VWQsfky7n/R1ShSF8+xEY+YXyGrKPMcVF5082+VecCbo6ybPZTD9pJVRuNwyIBqORVk7riR1+oPtSuJwfvhVjpmkWypBTPOqz95nkRsVLiyLwgJi4jvC5QDoqGEi77g5f5J1JD6giojOfwyjQNJSkS5L+tD6fQibcM5XHP/vz5oLSMjI/N/j7SL529nGVkufnfX4jMvEJaaQk3G12vLqJUlN9VHWgj3r4azNSuWLOLAC/UO6g3LKxcnT6ZRXNWICqVl233IoshL2ykWuIUkdJdRBNoupULuTGRvsSbZ7rYpiQr8isfrwwz7s4C4n7ZseeiStLlJ4AcOGEi7lpZhwJZHKpEfzNWZ7YRor86SW5odZbjvdSZVyUiB6hOwTRaZe57cyPo9R3HSnx2hdIP46nGLzaPriOsWZ+jaq/glFnAMr7b0IKciAyUHHMI9KpJfn55xdnl3sisyUqP/Fl7FRBMXGcBXb19+RMQS8+kU4+uWoN6kk/io9a1hHtdZ0iE3GYt1ZvUNn/jR7sSZ5/osu+5IYpFGf+aWcWtxvChdVt0WoZl07D37RvWkj+FhPqjdU/iFmfyRISt/9NiLe2r3+l9LauI5iNfm4ykq7D99rb7sfaW9AXicmUvrhtWoXK4EpUqVolSZ8lRqYcD6G49EefYQv68gXYOJnHTQOgeqA0f2iLxJM2IZ2ouA81OqU2I6ifC/wcLG07j8VWUYUV+4tKihqOcCdJp9MeUzkF9PMbSUtBIkA6XmXCYyeRz5W6QunqNcD9E5lxAhmQow6Iib6qgmMQ7HmdavLtUqlaaMVMalylCuch3ar79Nyo2cUxPP0fjcXaHceEyRuwr9zXQtP04DcT6cnTmX5caX4l9lJIh9tprqBbKgyDmcy1pf3yPxD4rnuGBeWZoyq99WnFWH+HKWUY2FL8/QDNMH3slmvcJwPT9b1JEoj0KNmHjmLzx3k0CsB0dHTcdk1/1Enx9+ey7FsmQkc4kp3E9tDCjmNevbT+TQvY+qMonA5VBv0iuyUbHTVlKbfE5p65+5sEDarCt1Wy899wpRv2Hr4U576ZhNCPt0f9DQ5LHq6F/lnxDPcQS82EQzqV6zlKTDzreq43+VNIrnuFDentzCrD5bcEzoW7wvMrbZH8L+GrP+7mctvlYidfEc7X6Y7nmklUC5qbVKfQPC3yQugCf71zFn+F4SPY6HNf1rS6tG2rDrZUJrTiCEd9aTKSH58j97sOO59nb96fx82jRK1hc0G8rqq4+4u7aP8tn4dHXHYvn2dwadndk/pqlyJVD6Ngu55ZE2byEjIyMj86/jN8TzOQzK5xUdVXpabniSxgBPjfC3rG6ahQxFarLuUULqWF7sM2bRwh281xg2fszCisXInWk4Z3+oR1gJzzwXoNdS22TLK1+xtHYR8V0tNrwIIDY6jB9fvvBF4/OVH0Gq34t6wCzlqz26cNBdM9T4cn81DUXnVLbrSp4ps+rH2SmtyaWoxvwrHzSXX8YEYru4LpnyVWXqSZUgFcSFPWT9nMVsP58QjEcR5PcVb438eOP7Pf5dn9IS9vMLWon8lGTs/peawXDYdcYXzk7WvB04rNzkKpo3h0cIQZ2LtlPOiNxp8tS0OcWyVWDJneQhRwyOJyZTXtxbbcODuEiZjUp45rk1O95qnv/j7XZaplNQvMV8nopzw+zW0q5GRUZsvomr+wdcXFxw+eCKy82V1JFG0TM3Y6/zb46q/NeQFvE8Qfm8YPpa/dj3Rvs8R3TID3w+3mDXYOm1VwoylujPRrvPBIR5cX5hF7KJYzm7LOLmJ+3ptePI3pGNhWhQUGjoNt4kf0lqmhH1b2aI0d7XGu3X544JjbIoKNxmKjY+yRTD19MMLS2tcshEm03PVLb6V0mLeD5Ml9zx4nmwucaObklEhuDv58bdDU0pnlHYnaIqQ3dfxzMgXMvsU1rE80pqibJVFK7N7CupLYnXTfRbC4yXr+CEhv52waRxMbIosjLmpLeO8vvnxHPc1+ccXTOBVbfVfzWQC1Pa8Ifw5a1X3uSrRqYk8TyHClJ5lG/Lmnt/fTVJ+KMdzDXewnUN/f2KBRVzkilTEWZd0z/j9v3SfMabnMNVrdJivE8zIKeCHOXasOedvkEk7bbufWttmmy97eYXOkSddsKd9tFBWr2QpYjOQZ+080+J5800leo1T0WMTv/FF2YnkjbxHOf3Gqu1RqywVbfQIC7N6CDaZDpaLL/Olyht/iwt4tmMbnkzCBGenz4HEoeGfpu4T/c5sGoyGx+o17g/x0c1EW0yPZ1MH+GvYSqSeJ5CSVGW6f7syc6X2i0lOlT0BZ632Ds8h7IvSF+kDyYPvPgV9pnLy+MHUnN0mIuNx+/4c2cOjG1KBpG2wEBTXvj91b5ARkZGRuav8hvLtl+yqk78uycLTT6N1scp9RKJ1/Fx5EqXgxpjj8WP+se85dDaJSzd/zrZ7Edq4lnbbtt+mA8pR+4M6Zh85RvfXC3pK/Iq5TfpU5JBq2/Hi+6gm0xrUEwc68x+Z81fCnA6wZA/FORpOJQzytd46BHPgl/OB+mcOT0V2q3HQXUjH4/NYd6CpWrP1r3AuGYxpQBKyk9mKrbYhIPyey9OzmmhzOPo3U/jZ3oTiHFlc7ucZM9dkIV3pAPBPNs3RHS+eWg/5VziLFc8fliMqEr+DN054pkyhPJ7tJuuIsgr2Hku96TnmWMTxHPK3baDP15iXDEh9mr04uxPkcPjU6mbPTtFK9agbp061En41K5F9apVqdm0I3te6Ato/5tJfdm2x601ysEEReEWzD77Sc8ywWg8LYaIMs1KuWamQvpKhPJwyygqinpPV2McVr+5bPvSnK7xs94t/say7QhHTLq1VG7eZPf0KU9Vn/sn1tO7tLiv9LWYePiN5r3HPWN5dWmFRjqKTjuX+Fqnv0Yalm2H3WKG8n3bOfhzzo0UbU0Dh900KZddnNsHy0+6RF7qy7YDXM0ZIA3+ZK9I7326N5PSTyzPds9i5JBJmD15nli2T589ZeewIspgN0/rjbzTqjL+OfHsc+8A4zt0Z/2t5zxTy9OpJW0pJT2CUNqQ827qfiKWr8+301laFpqvPoaaW9L/BpHcWjWOkeMWcOzps6TyePqQ9crnqRWU7LMP3brBH4sx3Ri7fA/X1NI/sT3JtPoib4qCtF1gIzyhDnTY+r3j69Jk68Wnnyf0N2w9KvA6kwtJjwHloe7K+8n6s9/ln1m2HeZ7llE5xL1nKk6bzS/0+K+0kDbx7PvoCBPad8XEVtP+zizrQBnJ/kqM4ZRzgJa8pGHZdvgdZpaUbEn4iqW3fmuwQx2Pa9sx7NybzXc183hsbmMKS4NzFaZy7bO654jh070NtJHaSMEmTD3pqacsY/lyzEDkMQulG5gQ/1BIOE92GVJF6gv+HInZby3b/snVBT0pLqVtIi/blpGRkflP8Bsbhv3k3Phqyncl5mxuit63fegg+scTFtfOQM5KQ7H+FUXs06NsW7mWlI+P/hXx/E2In/LkyZCVaTbf+PnZhvn161K3rvqnB4vMVO83TUU8DyugIH/TMVxSvnxTv3gmyIN9A/ORpURTNj8XXXicGxazFrBy3QO1d7k6cWBoNxpr5KcZ/YysiA9P9YjnKFe2ds5NjnylMX4oHUgSz+2mnE028/ya1XXLkE3RhcMfU3bKoe9PMrK8giLdlmInRZ56xHPIx8sYllSQu+YgLotCe7t9kAjoajLn7AdiYqKJjk74xIp0wiBCfuAf8PdCsv8cqW8YFuBwnDEVRMAkAveOM09pvJdVkwg8LKWAKSvlhXiOHxyBT1eMaSu9ZzdHJ0xva39OThfOR4yoJT1jV3Aolu90v+JKN3EEnJ9C7Vq1qNeoEY0aNqSh6iNt5le7cjFx75moM3YvjhoG/p1TYysp9zrI02oLf2NRsyANG4bFeHKwfyFRdgr+qDKNe/rU8/t9KvHcFysvXQFo6uI58tsjjBtJwqcQrWZf03pOakR9vYWJQT3K1BJl2yipbBs2bETjxjUpkVNBlrzCP7zW9uv/jHiODf+IzdI2FK0i8iDqXD1PjRo3oEKh7KRXlGLiSRcN8RPiYcOMPyU7L0u/NQ//khCM9DjD3L61qFAnZXk0avAnRbJK+0R045COF4yHPt1A75Y1qF5fM71kq/VrlSdfOgUlWs7kptbd3f6+redtnfqycHXiIt3Y00NamZWFCgPNk/nk3+WfEc8xwfaYtpX2ExAC3/Bs/CDyXyZ18Rwb4SXOaUeRytrtr2LhHML+SjDOykHLXgpp2DAs5hOH+xcW95ORUr0P6X5NlR5igp05M685xaqlzGPjxvUp/0dWYQ/lmXnJU6PfD3I+w3hp13pFftpOsdZz7Sg+Hx+rtAtJPCe86dn7+ho6Sru6Z2/HuuteqqNpw8ViEnWlPQnyD8Ds9V9fKSMjIyMj89f4DfEs5OntuVSWRltz9OJoGnoqX+/vhIaohYLRgdiZtiG7ohjd15tzdttGpk7R8uxZongeyflfaRXPPhzqVYFcOVqx910aZvUSxXM3Dn7QFCM/HczonV1BuU4mvFHGdgniuToLbVy1LL2MwstmEWUVOWk8/zwfXxzHeMFcrH5r9Z6nSjyXZuze55oBfNx7TOr/Qe7c3TFXDjQE6RHPH9ndqQxZ0xVn2jXvFCPiEc4nMCitoOaww/GiPVKPeBYB8JA8GSktyuGD+Nv7wmzqZ8hNu5V3NcW9klh8Tu7jiF1Eimv+38AH65GNyalHPBP0kRPzGguRo6BQ4/Fc+KRLVoTz0WK4KFNJPG8kcS7T5y6LO/8hjpdg8K4nWspQk7CwYPx8VE+Lup9hTD1plqUKU866plrGQYEB+Puph9AebB7UkuVnPmgVWgHP9tBb2kyo6iis32tauO+Nmcql/uly98NC58trk/D98p2wUG0SMEoZEFfRJ56FHbmfnUZ5aaAgb1lmXdIzK/NuT9LMs6cuKRDEPZP+FNQjnon6yf3N7cimSE/Zdqt5qWsaN5EYvnh+IzZxu+1oXlsvYpzBHB5rzcYPjg0rjiJTVtqYvNJSdz+4MrcjeX9LPP/EdqkkXnSJ5zi+vbFkSvdenNQRmz9b14UiGRQUH3WCL+o/EObDxaXSDutZqDFoNy6prtWPUpZHEpE82DkRw8nreKutwGM92dstB4pshei3V5tEDeLYjO5M23Ce5CurJaJ9bjO7mrCPwk1Zdl1bgf99W0+fpz9WaVDAUh8XFirJqmicjw1T+oY8lSZxLdUuKA7vT9+IjNTmaUJ4vH0YRX9LPIfxfO8oSugRz8SG8XJ/L+HjFBStv4CHqY4SxeLt9Y2YaG1+LoBbK3vxh07xHMcPhxNM696dYzq2wX65sQfFMiooMtwSzxR5icL5whyq6hPPkq84M0a0bQU5So/loh5XkYDPZ1HmiRsLivt7coBJvQZwTms8E8PDpS3JJ2KeMhNFHtSrKsSLs4uaizaroECD0ZzR+dqvSD4dGy18lCSeRXtQHcX3AcY9Corjxei/9UGqdRweHixsTdUXfDyHUUPJ71Vkwgkn7X2VGsFBgXz3+3tDJTIyMjIySfyWeAY3rHpWJqvoMGqM34OHnlkhX5c7HDpzgy/fNHu0EK8LjC2TkxKFylF71FQW3tLWLdoxv1wRcmboj7W3erfsx23THqRTFKTfqnuagdGPswwvkZkSvQ7gmrj7lx4SxXMr9rlo5vHT1SmUSJePLovuihBV4iunJ7YSQUcZZpx11DrKHv71Pksb5yNr1bp0GzQfY9OT/N54siSeW4r8FMBg9zPNe/t5nO65s1Gs8SaVEAsUQdAwkZ+8tJ96PkVg8fHUECpkFh1+X8sUs6PBz7bR7Y+CDNz8Jr7DDr/P/BbSK1basT/Ze6nczxtRRgRwA1Q7EIe9PUT/8ulQVOyFydmXBKuPi/g8Zf2YZVz4krbQ/7+PjxweUo8sioaY2H3RKU6lpewza0mbChWj/ZKLeGiNXGLxPR0/21C+kUmSeBa16mgxl6bSs5G1BnDope7oPCbEk4vnT2L7IiHyDOLpRgOqiUAuawsjLn7UHZ2H+Tty4sxpHr1Lmt2OfLOeCTPNeJ6weVJyfr1j97hKQkBWwODA82QC7gNHu5RXvpO01uR9eOpp9z7Otzh4xhaf79ryF8Z764kiwBdta75N4iZSKYjx5vSEysqlzn+0XoK2jZGVuJrRoqL0PGFPLD10vabOH9sV3ckl2u74I290CtPgz1eYVzEniqylGL7ngXbxoSSad4/OceTaC6ISREX4e46tn84yc91zld62s6icJSN5m8zgaQpF58PZ6a3JrqjCnHMuWgbntOHL5fkdyCHqa8ZJRyE1khPCS4upTFirY8BAEOmwn95S+RXugqWbpiH/cLBmfKnMKPL9yVTrN1p9XjyhPL97GqtbCXNqgmA79hrPZeM5XR4wGhfRPkooslKi6wqckrWhuM8WLJq9jlPPdcyqRX7jhkkb8glf2Xz2cZWPTuJfYevShkx1phzQ+/5dydYPnZVsPX4YLDr0GZvrScu+s9POOPmgpiaeb69w6OIDAtSdaCK/uLehnxDixRkl+gLdZa9OII+2DhJlUpShmx/pHJiL8L/Limp5UKQrQB/TG8kGoNWJ48Pzi5hdeUK41l3sv2GzqJPog8oxxfqdlnYVzttjUxm/+pnOvEQ5HaZ/lVykK9iRw87JfyEMh+OTKSn6uM5zr+r0FbGRr9nZuJCICTLRctEJPauB4PN7Gw6dv8ePQFWJxv3E7tAUJpu+1dJ+4gl7u52OpbKhKNGL08kWC4V62jCvnrTaoAitF5zDTceP+J+foOwLStdZnSSeRYk5H19IS2kZfY0+7H+uO+exYZ+4fPEE158lPEIRzPMto6mZQUHGpmM5q+c1mOE/nTkl+oKHb//Gxn8yMjIyMhr8pngWeFzGtE8tZWDbaOIabjsk61GivXh+7hS7V+/k7qfPKUf+o79zc3Vz0ZlkpELfFbzXOuT6lhU1c5M5fTnWSzFZdAD2jxxFdxOA3bZ+yiC+bLu1XE/QFbFuHB9TlQLVWrH9cYCQLmlAiOcZjUqIfOSi2WLbpCDJ/wYzmxQjU8OJ2CTGfkFcmd2OvIp09NodH4C7PnrD1yD1sCaIZwdGiCBddIZlOrDpVdpCniQk8dweaUfl0t1Wc0X5rLXEF85PEeVVsBHL7iaEYzHc39NPBHjZaDNFy3tpwx3Y2r2cuLfyGFg9El2tii8P2TS6CU16m2CX8FOhLzDuJD3LXoweJvfwVpVDiNMxJtTOTtlBK3maEMOGe3BpbW8KSc/95q3M2Knr2b17N9u3b2Ty4C6MXHUT/5j/m/PO8JLZXaVdx6sz54qOqRIl4bje2k3/uiJITlecBrPNuOUUpGlz0Z85PUt6l6mC/G2380Z1WEmkO492jqZqNgXZ6ndm66Un+GqIhxB8Xt7FynQ7lpdvaQbgIe+wWdlXuVFNoXZDMLv9Ru2xAImffLx/HbN12zh171FSwBn0nk2DS2JoYadzUEDCdfdMpPfo5mswHPPHyYI59wus71lDuaS16eR13HFMNlUT5cmzcyfYtWYn9794awmmJSKx3dpD+I4cNJt0Sq+4iPQWQX63+sr2lKXDfA7dc08RQDufMIrfMCz9QA5+0jWY4M2+afVEXRRhkN53c0fw8/42htctiCJbQUZuPsnbr5q/Ge1nz03zw+zeZo5DdFKk/P7wOPoP6841fTN5MS+YmzcTGTLlpK/xfVSvbFXhiomBZC9lGHMkrbsge7DFsKZIUxKD/S9T1Gvgo92M6VGZXXrfNOXP4VbVlIOhtUYextlf/VeC8LZZTU8hbhR/lGPy3ks4/dD0aWGfX3L14H727T2htsQ5ksebe9PXcDSP9Rnbj1uMS68gQ67CjN6i9phK7FdOTq/NwJU7hOfTTajdbtpItlG2MSvPqG0WFfivsfV1PVS2PsXkt2w9yt6C2a0l35uebkv28fhjsgGAYBfsTlqya/0+ngcE6Jg1/MbReU3EbxSg17q7aRTPPzi2RFq5lJ+uy22TfH4Kogl5to/xTYqLdpOT/mvMefE52cDTL0fuHzvKrk2HeRsWqqMcP7FjQm1xveIMEwI/eZ8b/Owg47qXZ5ve5zx+crRdDbKLcq4xbD8O39RLI5Jb23sqfUXTCSfRt+Al1uE4C9tL73tW0HHBLuzcku2OHebGo1PW7DTZwxP/hNdkwvfbGzHoWZODenWlDzvrlVUOpjQ0OsZHjaIK5+Pd/QyqLz1mUpi60w9h65CsTmN8OD+vljJvuZtvFr2MGlEePN0zjupCQGet25HNFx/hoyHAw/j66j7Wm3dgccFWswxC32O7diBlxO/+0Xogh269TjaIFICHnS1HTLZx8o6dnkESGRkZGZnf5ffFs0TQK/bNGUGzP0tQpnlvBhqOY8aMGSxeuBxLC3P2bjfn9nNfHZ1uNH73NtOhdiVGHfVUHUtOCC/39Fe+CqJ854lstjjNtRsvRVcVx/f319izqAcN/yxK0SZDmCSuu2TiAMYPGc+O6zqW22oj6JYQzyVFgFSWln1HYTR2triHaYzt0ZH6bY3YfjtRvSrxfryJLgXTk7lqF+buteb06Tt8+qU5ph7w9gi9S+ej7PDtuKe6JC45knhuSxYRMNVp1pcxRnOYKe5txsi+NK3fnYl7Hitnw6J+OnBm9UgaVovfdCdTkQYMmredy8l2yo5wOsusIY0pVroEnYaL35k5g6nTBzJi7hrOuqr10JEBvLIxZZ5BbfKVrUSj3lOV5TB3ZA8mTNucNECRQKAjJ9b0pkpJIVrE9eM/2WkzdBsv/k+uDPPnxZmdzBrVgTxC0Er3k6tmJ8ZON+WGWzJRrIa//UVM5o+lfbN6tOjTnSEzpjNNqi/xGTJ7FI1aN2bIpNlsvuKkRSQG8sR6A8M6/kmpGs1oPmC4Mu3MGbPZtXs3Rw9bcNTyPj+0NiBfbPctoV/zcpSs05Z2QwyU15w9ax77D+zn8L6jHD/zQhU8x+Fxx4xlXaRgXkH22m2ZNNeC1wHJpG2wF7fM5tO3SqnEOs1dvAljtl7l4y+1FhX4kt2zhtG0mmj3LfowKKHdL1qOhaU5e0S7v/vym5Z2H833dxdYO8WA+pXjdyFX5K1Bt3HzOHDNIdkAgBq/HLDeOovBXZtQr21r+k+dwARVGY9fPJkW3VrSceBI5u+x5U1I8pYfgvs9cxaP70vZIvH3lKF0UwZPMubUc++Ug3oqfr2/yKIxnalWpRJVOw1i3NTJyuutWb0eaytLdu84hcPX+DuM/OnEmWWDqJVL+v28dBq9DDMb5xQzvf7PLVli0IVyqrKV3mHdxsCY4y8+4HHbjIXjulNUev5RfJelYiuGT1nDefvvOmbDgnC2PcxCw16ULBifJlO5FgybvIpzwgcoBxj3z6WH8tl8BXX6jmfjvodCjmkS5X6TnYuG0SBTQp4U1GxrwNrLmrsW+zw5xsxhralUpRo1uw1h/LQpyvJYv94Ua0tL9uw6h5sqag/1eYHVnJ5UlH4zS1F6Gq7l+ANPDXEppA7eDw4ws19b5c718dcuSJfpG9hrbYmZYeP4AcjiNUU57OCm60/NNhgVgP15Ewxa1EbarViZPmtlBq02w/roHjb0TsXWgzzTZusBL9g9U7L1kr9p68Lav9xj/eS+1K1ahgpt+2MwabwyrfGy1VhJNrTNmmcftI0ah+FlZ82yCQOoVEJ1b8UbMWDiMqztvHSsIAjn85PjLJ80iKql0sWnKVKfvhOWYHn/o85Z31DX66ww6kHNKuWo1H4goydPVOZx5Yp18Xa+7TivvbRZYDAfbh9hkVFvShWKz2PGss0ZMnklZ19/E3UdxONDC+itfB5Y2FRvQ0x230/hA6M/3mLXkuE0zKzKs/hUb22AyRV7vr2/jInwFQ2qSO9wFt/lqU5XyVfYvNfpK+J8H7JpygDqVy1N+Tb9GD4hvsyXLV2pKnMrHjmqOqgoH27tmEZHaeM48fsNB05ls9mTFAIzwuUqW+YMorYqf9KnbmdDNlzTfB7rp8jvhgXj6Ni8Hs17d2Ow6D8T+oKhc0bTWPQFgybOxPSSg5aZ8SCenzBleOfqlK7RlGb9hzNVpJs5YxY7d+3CXPQFRyzuJtvtO4Fv3D6wnP4tK1CqdhvaDh6hvObsmfPYt28fZvuPYn36uZ6BFBkZGRmZv8JfE89Kovj09jb7d+8Uonkh8+fPZ6WxKeduOusdJZYIeneFTRP7cshNu7xWEu3BpT1bWLV0CRY3NV83Im3s4vLCjI3rF7FAXHeD6VGckkeHqaFctl1ciOdeWNw7h/X6Jcp7WG5yile+2sKUQF5ftsBk5VI2H72Ej7asR/jx/PYFLrz6K5t4SOJZmpGvxMxtx7h53JTFIj/zF+7myssviQFk1E9nLmxehPHKdZiammKyxpjFy/Zy7b2Wa/504671OpYukn5nASvOXMM1WSyZSOQ7bC9uYPmSBaIcFnLk2MOUM9oJxH3l6b19bNlqqszDseM3+abrd//r+cHrC/tZsnAZa9dtFPezgbUrlrJw4XZufwzWKZ4T+O5ylwuWK0VgLMpYqq8F81m0+yDH7dzil8XrIdrfgSunLUXaZcq0CxcsYr/Zdey/p3ZVEfx+ecVpKzOWLom320Ui/0esH+CiESnF4XXfkk3LV2KywZR1K5axeOlx3gYmq6zgT9y1XMuSlatZL+rT1HQja1YtZ+HBG3gGJBelkXi9ucW+XTuS2v2KTZy/5aJnJjkaf4ermIr7M15porSZDWtXsHShMUdtnXTbmYqIXy7YXdgq2vsCZXuXrrlgw0Z2XnrCxxBdc8kheDw8xsoFS1i1Zn18W1m9nMUL1nHu5Ved4jmeb7y+ewHTDetFnUjtYT4bNx7m9ttvGnUaKfJ1cctijNdsZKPJOpYtMsHS9kMKkfPj5QnWCL+xemN8e9m4YQ3L1uzizGs3vO6J+l8ovlu7QVnu61YtY9GCjVwW7VmXeP5wx0KkWZKYxkTU1aIFG7ikTBPIk6OrleW8ccMGViwxZpvZ4xSPdkR53GGf6RJWmMSXjampCcbGqzC9Ju1ukIzYLzy5cQaTtWtF+ceXx5YtVjxw/qUxKxrm+5qTG5awcp24x3VrRP1u4swjrxTi+eujIxgvX84a5XWFLaxfzdIt+zh88gSW68VxIczXr14p6mofd9yTi+dA3l/eyqLlK1inSm8ibGnxHmtOWh9k75rUbN3rH7b1BIJwfmLD9i1bRN3El9k6Uec2jz6lWGaeRBifnpxizYLFrFydYLPCvy9Yw6knn3SK5y/Pz7BWLc16qU9YsJoTdp46xXM8P3n/8DJbRJoEO19vso8bz330tMlg3O5ZsUKyvzXq9reei8oBn2Cema9R9k+S/a0U9rfloF2KwZtoz3scEPZnrGZ/K4xXs+X6e747XmOLFl9xJFVfEYLrs+vs3CbsQ3U/a1Zt5/IDDw1hHBfly72DK1i2ar3I43pWLF7JTsvnKeolwvUGu0RcsGK9dJ8ijxvXiTaylq03E5ZPa+Lvep+LVitZsVytL9h1gGMPPqQqYGN+OHD1tChX0S4kH7dg/iL2HbLhrV+01gEadcJ8XnPW+gjL1PoCM6u7OKXmWGVkZGRk/hJ/Qzz/VSJ4f2U3k8buxTO1XuGfRG237QMf9YfS/x4SNgwrwah9Ggt9ZWRkZGRkZGRkZGRkZP7D/NvFc+y322w17MziC39ldvZfSOgdZjYsLsRqVw666ty66N/IZ87OkzYMK8XYvS9SmRmTkZGRkZGRkZGRkZGR+XfybxHPXs/30aNLG1q2bKF852bzuWZ80L4m8d9GuKc17armFGK1Bqsf+Ka6PPcfJ9yeFaOlTYOy0nv19RTL3GRkZGRkZGRkZGRkZGT+c/xbxHPA1ZnUUW64UYmGCw9wV2NX1383/txcO5G2f5Yme/aMpE+flaKV6zPB/B1pecPVv54oPt7eiUGdqhT6I5vIT3ryFqtA+8VWOP03TIjLyMjIyMjIyMjIyMjI/JuWbYf64+vmhqv7V77/h2ecJbH665Mr7968w9nFFVdXFxzs3+LqF/Ifmn2OI/yXN86v3+Lg6CLy44qTgz0OHn6E6n6vjoyMjIyMjIyMjIyMjMy/kf/AhmEyMjIyMjIyMjIyMjIyMv+3kMWzjIyMjIyMjIyMjIyMjEwqyOJZRkZGRkZGRkZGRkZGRiYVZPEsIyMjIyMjIyMjIyMjI5MKsniWkZGRkZGRkZGRkZGRkUkFWTzLyMjIyMjIyMjIyMjIyKSCLJ5lZGRkZGRkZGRkZGRkZFJBFs8yMjIyMjIyMjIyMjIyMqkgi2eZ/0HC8Hpkwbxxoxg2aBCDlJ8hjBhjyISJhowaNkR1bBCDh41k2qJDvAxQJdXKD+5tNcba1pNQ1ZG0EciNI7swFtcbKl1v8CAMNmzl8nfxVfRPvF4/wCk8/szkxMb68eSUKSuGJ+R/GMPHmXL43hfxbSjuLm946RoRf3K4M6c3LGDUiKEMTrivIQaMmzgBI8OR8ddWfoZgMGYauy+/Q9/t/nK/ztEV23j6U3Ugjfh8fMb+OdOZKu5TeT2jsSy58hhP6Uv3R7z09FWWX9SX22yaZoTB0IR8DWboiDGMnziesaOGJ97DoMHDGW24kouOP4mRfkMr0Xy4tJuDe6/gpfskrby5exbTsaMZk3C9efPY89aXEPFdkP0tXvpHxZ+YgmgeXbBg3cgRGKjSDl5mjIVbMFHE8f31bd4ES+cF8v7KbqaONVCrg6GMHGck7HCcuP/BqmPS/Y9m/obz4h7Ccb2xj+lj1etNW5pBDBFp5q45gaNWGwrH4a4ZW4yGJ7aBwcOWs/H0a37Gipz5vOLumyDVubqJDnvLmeWruO6uOpBGwkNcubl/GfOHJNzDKMZM38MZe+ma33j1xhE3n+j4k7Xwy/cZZ01mJtnSoAlMWmbJHa848e1H7j37yI8g6f/a8XERNrzAiHGqex80aCbztl7m3S/xZbgDtk+/EqXTXmJ4ctlK1K9BUv0uXc5R12Aixbf+on5fB8afqRsvbNYs49yLQD22m5KYGF8endzIimEJ+R7OcKNNmD3wFt8G4+r0hlduqnavB3+nS5it3sMLPQ09xs+OnXMmadjU0OGjMBJ+Y9zoEQxRHRs0eBijxi3h1Ctf5f0n59dXUVfrZjJFra4mL7fm3mfpW1VdKdtDHIEfrrBm/BhGJNrFYIaNHKts+2MMhiW1/SEjGDtxA7YeoSJVSpyfX2PbBCOMEs6fPg3Tx27CU0OEwx2e+0llFMP3N6dZYjia4Yl5G8zw0eNEWzJitPCX8cdE/Q41YMKsPTzzD+OH40WMk6cZlZQmMY8izfjpO3jsF8ovlyusTJEm4b7U0gi/bDhlM3e+aHf87x9dZrPwm4l2O2smW595Kv112PvbPFf6pCi+Pj/OQql/S7ye6N9U9zVquPp9jWTy3L088ReNXgser86wb/poRiVcb9B8lh+8g0eY6IMC3mL73F91pj5Utv7yX2/rb9x123pYoDPXdy9mbqItjWbsrP1ccJA8uC/PXzvj4Rd/39ptffS/1tbjJFv34Gewbr/k+OQqW8aPS6rfmTPZIupXcktS/b7wC9NZhi9unGTD6JFJdbVwIfsd/EW0AwFvRX/1S3sdJ+HDzfXLOWXnp/XedBGHP5f2bmaZKDulHQ8ZzOite7GVXHnYV9zfPuaDZJZxXlzfu4JxI9XasejDx0yYwPjxozViseGjJrLR6jF+yitoEh3tzUNrE5ap2cWI8Vswf+Qrvg3ExdEeew/VHYS+x3rNXM3YR7RLZewzTrMPNRg7k/03nNHW6wV8d8J82UJmJ9Sr6NtnHrvKe+lLn9e8cXHlm97ijePzQ0sOmlrpjOmkc0I/3sBk0lhN/2egin3Uy034vzHjTbD9GETIl7uYThmXehplvLSaKy4BetphFE7nd3Bw/3U+/U5jFby6dZqNY0YxOuF68xew7913pf0FSvb3M7Uf9OXWhuWcfKC9baVGTJQj54xXYOPymxn/i8jiWeZ/kB/cXTuA0rkaMHHLDnbskD4rGVw1LwpFLtpMNmab8th2Vk9oRoGMFVj7RHeDjHHaT9ciCv5osZ4Xv3QJKnVCeHppOf07N6FWk3GsmLmDXdL1du5g9xZTFk4dS7t2Lek80JDTUpygQRz2tvvp37UBrbsPYP7Crar872bLJhOmDB9B306taTJ8NpYvVOIj8AqjSpWmTp9ZbFSeu4P1y/pTUqEgfd6OzFMd27FjEV3LZubP3qbY6/JeUQHcNWlKHkVxRhx20hq4Jic2+ilbZvehfqOm9O69mn3rVdfbtYedqxYwYHBfOtevjOGh58ogMOTxGuqnL0XP+VtU+drMnIG1RN0oKNXKCJOE/K41oFL6zAzY9kp0mTr48YJ1XbKTM1cP9r6UQufU+epqyfTBnfizQU8mjdrC/m2q64nP+gVTaNe5G20bN2DNo+SBWyyuz3Yytlcb/mw4mLnjt7FXlW7n9m2smjOBFh070r5dJ3Y5Sed/xtqoNaVKdGCO6rwdOxbQpVAmca/F6L3YRHVsM/MHVOWPAu058/knV2Z1oFTRtsxKTLOI7kWziDRF6LlgnerYFhYNqcEfuRuxz0WZuUR8HC4xcXArGrXtyJSZG9iu+p3t20U5CwHev20HWvXux7JLUsimjzg8zo6ikCIjjRbcE+FsWgjhttUaOrapQ+f+Y1m2MuEe9rBx/QoM+w2md5dmNJ20nfseWqKROG+sNk6haYu69DOYydoEWxL2v2bFAgy69KFn5/q0X3IRr8CU1hkRYI/p7IE0aN6I4YZLMU1s/7tYvnA6Q9p2oUv7Bgzc/pbIFJePxf3Fbsb1bivqdyCzNep3K6tF/bbs1Il2bTqw/Z0qiQ6Cni6nThYFJQdb4RWdplbEm+t76SvurXX3gSxYtC3xvjdvXsdkZbtvRROD+Vi/TiV4iPzO1WV1yK0oh6GVq+pgSsLtt9Myc3E6TV+vutZWFo9tShbRDgvVM2CN6t53mI6nZtaMdDa+zXf1WxF1ZbFhMk2bx9fVug2q80Vdr1kxX1lXPaS6WnqZT8qINRrPi3P5U+Rr0IqEczcypXMFZduv2nMGpgnXXNZP+K9cjLPy0Ai0Ar5eYNGoblRv0IlRQzdyIKF+d+5k85IZdO7Zi/Z1qrHghmTbUbw/NI4Kmf9kVKIdrWNsk2LiehloMHwBW1TXM53diSLpS2B82wfH4zOokrEqBusS0phg2KKkSJOOOkPmJabZPK8bRUWbXHj9M85nxH1lqMSw1Qlp1jO+TRnlfdUcMJvNqjRbF/WkuCIfk8/4aAS4/p9OM3dEF/6s3xXDEZs4sFX1O+K+TBdPo2O3HrSvV5Ol96RQNYznO0ZQLlttDE0TrrcGg3qFxPWy0HTsYraqrrd+elsKpSvJ0rtSuiQCfexYYtSV+i2bM3byKrZsT/idHSyaNYlBLTrSoUsbDI8ohz71EvhkObUlWx9inUZbj+H1tT307ZK6rR9/o83WA7lutpx2rerQVfShxqsS8r6HDeuWM67PQOFjmtBs6l6efo7PjzZbXzS6SdptPfZLmmy9w7IrfNaizn5+Oct8g65Ur9+FscPV63cXmxZPp2OPnqJ+q7P4ZsqB4k/vDzGpfweqNejL9LFb2JdYV9tZN38yrTt1oW3z5mx8ob/sw9+a0jy3uN/u+3EJT01oS4hY4MQ8egp/Wa/5JNbN3clO6bq7drJn8wbmTB5Dh9ZN6TZqDjbKgXY75tWtRJVW41itzJ+w942jqS7KWKFoxGTVMSkWG1InD6Ubz+C+RqcSzYsrO+jVqT5tegxh4eKE83ezyXQtk4YOp0/nljQZvZQz71Sl9P0MgwqXoOHgBWxS/f66BT0pIq6ZpWh3FiVecy7ti2Wk9rC9uGiEcC4cWTOYZk3q0r7zEnYltN/du9ktbGnUyEF0blydIevO8EGf4gtx4/CY4uRM3xDjmz6qg8mJ4avtUmqlK0O/pQn52sSMnlWVfqJC5ymJ8duOlYMpq8jJOHNHvB6Y0jBDKfosSogFNzGrb3VlmnLtJ7IhIc3qYZRXZGXwnnfKCQCtfH/Eig5ZyZm3L4fepm12xNvpKFMHdhS+qTdTxoh4Sc1XmAj7a9O5K22bNcXkmf5+KcJ+K63yKijYZTdOYfrP1caXqxNEn5CO2jNu6J38+Vchi2eZ/z1iP/Pw9GaW7XwvQtIEQjlv2Iycwombqk8bhTtybcM4jO/omgX7xpVJrcidTnQAGZqz5fE3PaN6EsE82TODOkJsF+s0i2OPP2uOskWH43nfnMmthJCv3JvTXqrjSuJ4Yz6TZsWzU2XwDC6+8kzmBMP5/Oo669vXIn2eNqx/rOql/S9ivOww99Tj5J8n6S2cb4XmO1C/xM9bq1m6+Si3pUFcLQR7ncOwgNTZZaJ6vy04p9LHxoY8YU3rCuTKnp0BG2xx/qr6IoEvztzeZkCxHAparLJTjvqGvNvPzGU2omST+Hh2uugs/qDvuieqIxLhuB42ZLble3y0TkBE4XpiAQ2zSfktxoit95Uj+LqJ4+szMwbVyEv28i1ZfuY13zRjSoLfP+TQ+PooshVk3nX1i0bjesWUDhWykqfOALZedyYwmcn4v7Jh3UAhBP74ky2vpSMunDu4CVMr9RrwYW+HqsIOu2OpXgC+d7Eymce+Z57ctNzEBnMP1RcSfhzuWp0cis4c8VarEP+HnFg7hQ1PVX8LvO/tYVDVwhRu0ZM9N9/il8xYf7o9w8qoB38oSjHY6qPqqHZio16wpmYeZSedp+YUbqfaYwVxa20fqhRMR/PpG7H74C9KTZ1ffHhwnBlVhHipYsg592TRSORHzMbVpWiRvAxYf4y33smG8KN8eHN2G/3yZidHt004JxtRifjygBUdq5CvXCVmHLmFazJjiPnlysPtc2ioyEyFWZcJ14g1Y3C/tplOFbORu1ZfNl9zIvk42Y9X19gwpBKK/FXY+Ex1UCsemHWrrCw3RcE+WH/UF3VJxPLabCpNimWl2tDZXH7jlazdh/Hp5TXWthYBU/4OmD7VP4zxy9ma4fmlNpGFusP24KY6rkkcYc5mzF5yHi+17H27b0wNRW46zLEVrSuBGDytJjP70BPcE5beRLhjNlbUVdF8DNxwnLfJG2ikN69Pb6F3rmzk7LEFF6WrCsfLbiezV93VuL+3u4eLYLcEhkedVUckAsXxMcw98ymxnQW4nsWoXmGyFf2TGUef8DlZ/Ud9eMHZeW3JkCkzY09IA2nBON7YygJRWUm5i+X+8m7kVVRmvo36yOUXHm4zYoWNJ+9u7WLBhscit0k8XtOb/IryzLqo3pZ9ebJ9HMuvfOT9nb0sMHmonIVJ4JnpAAooyjDltPqyDX+e7xzNvPN+hKqasv/744ysXZBsJesxz/o5PsmqN9z5KcdmNEeROSeTL0gV8IvXV7awSDiZpPYVhe2cDuRW1MD4vppjifnI7U1jWXIjLLEvDHC8yLSGZclfoyErzjzGK5kPDPvqgM2yMVQSdtB4vZpz0crv2/qrw1NoLNn6sNRtffPz5Lb+C5vlXalYMCOt527jqdtPtT5e4gfOdyyZVL4QCuGzrn+OLyFttu53d1nabD3cjUNj6lCkSBpsvedWXJOJ55+OJxlTtxDZitdmjtVTvJN9H+H8jJOzW5IuczbGn1YfAI7l8/299Kqai5xVOrH24jv8k7nEQPs77BxdE0WukhjfUx3Uijcnh9Qlk1RPuTpzSDk7r4+f3Fk/jqrCj5Tru1zEIn6a5RwRjKvtXkY2yEb6eqO5Ia2mi7zFptW7Of9U3aDuMjFnPoqWWcwb1RGJ0Oe7MTHZxOnEbi6a5/uMaFAkMzVHLuCa/WeNtiTFb17Pr7CiWRUUhbqx+42qcnzPsGTJUZ4qV7nEE+t9hM6KHFTvelhjZvubzVIWbT2BnZRXJU4cGNqIAhkUtJl/kteeyRy+/yeemM+hViEhbA0t0G3asXjf30LnXJLPzUuX+eeEZ9BGOF+f72HWipsa4s/5qCElFEUYvvut6ohECI77jVhw0gGnJ0eYt/KGclVNAq7HJlJaUZDBW1+qjkiE4XxgHLOtnfmmNa8ROIn4sm5WKZ8lGL37se5JCSVxeD86QL8/85C9UltWnnvL92T2F/TuHnvG1kaRoziLb2u2RE18ODOigXKwSpGjA/vsg8Sv/w7v2NRIGhxUkL2yIdfSNk/yt5DFs8z/Hr88eH/7DOc0ZnW/YGnQSAiQeqy856UmgEMJcrTgyEMdktjrFEOmTqBTzRIUy5CZluuEANSjnt2vrKJTrowoygzg0CvdHVTAgy2MGNiKDY+THLb3DWO6lkiHotpYrn7UufZHaLKzjB47kRnnVT2G/Tmsn7jireYwAz4eppvoQMo1Xo+96piS4HvY3H3I00+qv9WJDcLhyhKqtu1Hz4IKMlftzb6EEV5tRHthMa4K2YRDKzvJWs9oYAgXF9Wly1IrPH/FEfXUmiMaa43DeW0+XnQG+em+7IbottXwOcHRm1/4pq0og+zZvXU6dZu2oLHoEAoP2MwLPYOpQe42zKuXH0XGKow4oj6wkoyfb1gzoiKjzJNGI769NGdM2Rwocjdj4VVp6bwO3K8ye5AI7m1E0PfzPY/uXOWmxspHRza3riTssBP7PqhlNtaXz+/Oce6yM68fXOGGurAWInyHENzZFe3Y5aD2Y3Hf8X5jjYjHlIQ4nGZ6w1wo8rZl7W09efz1lh1zRtJum1Lh6yTk2Xpq9zKgXxlhCwX/ZM51/SPVDuZD+FMKINovw0mPvou020pLg0UceKambmPDubmyHvlFIFNmgrnekeWAMwuoOmYHz78kGXzcLzcOjypJtnRF6bjpnp5lYeG83TmeSjPPEqh20vfXVowrn1MElk2Ye0ljREsTjxvMHVyNaec1KkgT5/20NTCkhwh6c2fLx6Ajumd/Jb7YLKZTMVFu1Y247qk75zidZNiYycy5qKduo3/w/NwCqrTpQ/c/FGSpNYSjLlrClLhYIp5YceRdgJpwiOLDlflUU+Si9ZSzmksqv53F+qYbX5T1GobtirrkTy8C60kWeuvq16l5VBm3kxe+wo/EheB33woLV/WWF8QD04EUUhTDYPdTTSHlZcXh2z+VgxxR356wtl1h0ovzOm22UxPDyYj8zC7DigzY84rooB98un+c4xqTp/5cmdeRPIoKTD/loGYnsUR9PIaljRcfb5/guPrYlfBI15d0I5+iLJOs7TWuHed5DItrXrjfOsExjbGoAGHPvZSDVIZHXmkIcTwsOXwnCGmSNsL7HstbFBBBYRl6736uVhfJCHVl85gKDDnkJIrMD/d7Jzml4cP9ODulNbkU1Zh/5YNGnYa7WHD4fvydRns/YWOPwmTMVI0xFm91B7AxX7FZM4oaS2+rDuhAaevj6FklF7nSaOsdJVuvMV7Yus5aVNr6cGHr8y5rLs16e6gfFbMLf9R9Lfq6yPC7pjQxWI7123hHpM3WXS7NTYOth3JjeW3yCVsvO8kyDba+i1c+SVcJ93nIylYFSacoSfftT3TXb9hHto6tyOC9r4lQNY9fTueYVj0Piqy1mXDig+668n3EsqGVMDqupiCT42ZOd8PxdKtegD+ESO+++53uvAgcT8yhuSSyqo3m3AfdMYCvEKQDBndjj7S++ZMtZx++xjlh0EEQFXSJMTnyUrjUbO6r30D4S+weXOfmh/g/PS/OobUQqYo6k7n7JdmotBpx7yzpP3oay66rRunfnMbimSff1JL4O+yinSI7VTvsED2nGkG2XLr9jDfKiWFpEKYNBUXskq2XKbrdbhSPTDvRafJKlKvGtRHmyYUjs6jevBPtcirI1XYu11OsKBREhfLjgSVHndVLPpSnuw0opijEQNMHmkvKvU9gcd0TD9sTWLqot5UwXhwcQ0lFAfquvaMpgL2Pc+SmD/7JBsSUBLxiy6Zp1GvclIZZFBQdsoM3emYbAlwuMqtWXhSZazDGykl3vPTtGSuGVxLn6Fml8tGSXuPH07V6IQpkykLnHW90+3AtRL3eTMP+o+hbLh1Z8lVgysXEEZB/DFk8y/zvER1FZEgwIRq9jS7xHEtMVDDB2pyNcJx2JjOYv+8q96xmUD2fggyNlvPil47OJPAl64aUFx1lFrqsvIGvzt5OEOnCjbM72Gurcn1+t5jVpagIovIxZN8r4VL1Ecjts7ZcPaMKVEKDCI6M0pjl0yme48IIDQsnQkvPGeb7nF1GE9nz6AWHhhVBkaEiQ3e80Nlph943pnQmBekKdmaXs76bFZf9eIRVB2/zUXoGLTRQOQCRlEKPeI4RdRMaTYwWz+1x/QDL5pty8f5JJtQWHW++3ux5okPUxPhjt7UnWUVnWb7zCuy01ncCUXx5tIUVVqqAJcqT0wubKkft643ej6Puvl3c1A/e2W7B5Izo3KMjiAgNSTaCrkM8i9qLigwRRRNJZJj4V3U0Hh3iWVhxVKSwcymIDHPBfGb8yG6zacf4qLOnkxAi6cUDju56pfpbC7GfOT5pNGtuvOHKwnrCLvNQb9oF3SPVzhZ0rZpZnFeF2Te0Pcmmji8n9l7jhV3SMoWQe2uoIAIPRcEu7EnFlsCd/etv8MVLpdDjgnEyH01eIbyLNJqJjf7lB0T+esuutXeJSFi6GPmJ80taKOu3rsFu3uurXxF0OdzazLpTugRsENfmG7L45CPubO9H/izpKNR7r+7n8b/eYEoHaUT9D4YffJvMVpITgO2pG1w7p/sB9CDPB2wdP5WDj5+yq29+0Yb/ZOxBe61tOC5Eaodxat/pEc+xIaIdRinbYfDdlZTLIdp9oW7s/ZBaXbmxf4Mt3p8lWSx8rbhmsIZt6hbPcdHBBCmnZ0NxsTAQbUZB4YZTuZLKrMOv1ztYdsSesPAYcT3hGzWyqEs8xxErrhccHE10cPI0usSzvjS6xLMo74T7igvi7cFBol0rKNFiHgldgXbi+P58G8vMHYmKENcT96XZv+kSz7HERom8hYqTY/y4Z9qDnOJ6lXqa8EzPOI2Ev9cz9qx/pPpLG2q2vq0P+TKn1dYLMOKQqB/VYe3E2/r1C2q27mBGu/IZRPqaLL6rfyBPmumy3nWdt8/j+wNttq5TPKvZepCw9bLZFKQv3F3Yuup7XcQJW19vi4/S1qW/g3l/eKiyfos1ncX1VPzSj5fbWXb0XfyKmBhfbq7roPTn1fpu5IXeugrl4/0trNIc8VEjjNvLJrLw6B3uHRhFSdF2c3bYhIYeU8f/AfO6FhflnJv+21OZnQx7w7kTezF/IO45QvRbEREaA5c6xXNcBOFhYYRLJ3+5gmFL4asURRhl4ahn4FPiJ1etb3DriupepdgnSsQH8X8p0Sme40IJCRP5k/z7+700K5ZeXLMWSx+kYku+59h06CzP3LUbtt/Ls6yduoQTj26yvLXoA7O2xPiyFiEZJ9piCv+nRzxLsU+IaOtB4h410ugRz8p4KYZYDd8Qj9ulXSxdtI3Ld60ZW130tQX6c+iFjj0NYr5xZ0NnZbxUuedanuoZqJLy4/lQxEvWuvqlcO4YT2KB2W3uHR5LGdHPZ22zAQfJJ6UJP85MHc3KKy+5btwMRbqc/Gl0SmMm/p9AFs8yMkp0iWfdxIXexXjSIvbbfBL+wZYxVfOgSJ+TCWe9tab/IRxInzLCKeXqhOkdbUOP6sQSHR1JqDKAj8P74myaSsulCw3B0j61wCB1dIpnXcRF8umBKcNHWYjwMoS3R8ZRWOr0O03nptYR1yhuzC4vOh8FhXru1lgarp1oIdrjA5KU6BHPuoj15MKORUxfZSf++MTBsfXJoMhEiyUX+KSl9432e4lJeynwKkPPdfc1Bhq0EhclOtr4Wg5zvczMeqKjzVCLSWbv1AIwbYgALTYpbUp0iWd96BLPSQS/Ex1iNWE/GRsz/3Syh6B1oacRRLodZtTQzbzxD8H/sQm1hDDNUbk9B5203X0s73Z0oVhmcf3qc7HzT7V0pWJSI5Lrs8om2pK2RREpUMt7XKAbe3pJdVuQ5nOvpDLwpEIti+HuNsxpIOo3fQ2MDr75W/Ub53+eqQYruGT/g5jPVnT+Q4jMPBUwfqAtJIzj09mpNJSWWBcZzjEHffNaaSAujA+26xkx7gRCnvFk13Ah3tJRuucC7uuIkTTRI54TCcdmeillXRXptQc9c11J6LEzfeI5kWAP9veT9gkoQNNZl7Wfo4Gon9BoHfWoSzzrQ/fMs270zDyriP3lzI4eGcV9FaHt4htaz9FE333pEs9JRHnbYdxSEgzl6b/pkWi1aUBPU1a39ehPFnQSdpwmWy86guOOv2vrsbza3IYCGUX6ukt5HpCG3OttyHrEcyLhXFXaejqK9dknIog0oO6XAlzZ1Uuq30K0WmCTymCBhORX4us38stDlrUQPi1dJYZse5pKXYkUav1VcuICrjF37FKOPfJV1lm/kkLgZSnKfK2jNXH4Xl9Ku6KinAv04+Dz1Gb4YomKiiIsYbo8GTrFcyJxeJwwonYecb0SYzgT/3zH30KneE5E2JJpawpKA//1l/MyMDVbiiEsPIoobc/zx33nkbUx42bbiDvx5+LiTkJwZqCm0R7ep+6oBHrEs070iGddxLpzavNCZikfw/Bgj0EdZbzUesVVvLU4iyifx6xoLdmf8BWmdvpdeGr2F3iDBeOWYHn/K3E/LzKwdFYRpxRkVvyD8qkSK3zL2GEbePwlkIDXW6kvfEDWMs3YZa/VoP5lyOJZRkbJ74tnN/OJzFm+nic/pUYay5NVDcgtHG6u4cf5keIHRBC4yYAKIqhU1DDEyj5NnlOFHxdmdVGK1QxtlnH/kxZvFhtJgI8bjk4fcPPw4vOXz3h5uuH80YdALR3X74rn2FBvTk5tzHLb+PG8qI/XmC0JigKtWHhZywYYUQ+YWz6XCAwyU3quTSqde2r8vngOeH2C9ZN6Y6Z8KDsO7ysLaK5c9jWHmx7Jw9tYfF/soHN68X3euhhYa38KVDuRfLi0lEbSM+/FO7H0uq61W2nlnxDPYby1nEFNkcd0lQay/bG2wDSOMH9PnByd+eDuxafPn/n86SMurl74BSePkGO5s7glcyxexHfMIe7s6SONqJei+453Kes6TuSvc1XlKHWWYYdImHjRIDqUb59ccHR246PnJ754f8LT/QNOn/wJD73L3HKSLWWhypJbUleckvBffHZ3wunDRzyU6b346OrCh68B/PhgQX8h7hU5KtL3kPpzs0nEBPvi/sERZ1cPvD5J6T1x++CG549A3G2MaSLVb9H2LLTRtdlL2ni9uTczt5zETamEQrk4uRiKDFmoOve2Fp/jKwRPB+XSwQztV2D3RYtSEe3+l49rsnbvirPHV4IiNEsqJsAdy8lNWHUvfvgg3Oksk2ulQ1G4I6ts9SwzTyR18RwXfoeZpbKLuspGNV1LesN/pqgrd1FXLj6BWmZEUhfPQR8tGSiJpqxl6bRT/bnAv8J/i3iO46fTIXor7bYKfQ46qo7/VVITzzF8eWhKG8kHFmrC1HPapWBUgDcfnJ1wcfPk06fPfPniwYcP7nz+lfKuNW09mIuTigpbz5qqrWfssJLH3mm0dQ/J1n0JDnvP1nYVlatDco6xxE/bSENUML5ezji6JPkYD+FjHD//ICJFhlIXz5Ktz1DZevXld1RHk6HV1j8IvxTID5ej8X4pe0V67FXu25xGYvj8YGN8XRVsyqSzqQ9N68NxzzBmrdvPO+VCnWhs51YinchXyclXtdjyD64t6UMJUc6KRjO5mvBGj79I6uLZm+PjWpFfsosuJrz4qiUyi4ngp7ewC2dX3JV28UlpF06SXUSm/NFUxXPse0xblSejuGZmgyPabSmNhHs+ZPeEtmx7Fd8r/nq0nT5lRdmVN8A8/pUbqfDvEc8/n1mwdvIALFyl8orj84VZNJYmaxos5G6K2YYYvB9tpp1kf/kbYnhS14qGtOG0z4BZa3fzVnlzMdxaUI2Mwv6KjL+YhoHuOB4at2H24fvxG/hFfOHwoCwoshSh7cZXfzPu1I8snmVklPymeI504fD4uazb9kiEQSrebKBGHtFwcwzk/I/kTtuTUyIok57/zdB4Mqd1v69AC+/ZNaKB6KQVlBi1h3faNFXIJy6t6U3tqsUplDMjmTNnJccfJag8eA228dGLBr8rnn+5WTO9xUoeBqmCmqhPnF3UlAyKP+iy4AIpxp89zOldOKsysKhhfFe74EkzvymeY3/x1NyE6YPMSHzM8Jstk1tLy95rsOyGR7L6Dcft/BwqSgFBoYYiGEnTfJkKca29o5QDG+nKd2dtqssFU+OfEM++3N4wQATOQry2msg5bY8dxkXw7uhEGtUqT4l86YX9ZCZr7sKUaTOO7Q80azcu1JZFbeZx2t5PVY4hvDcbRBZFVqp13UyKVdUht5jRWNqNWEH9Jdfx16ZI/F6we3pbalQoQL5smcT1s5O7SFmqzzDH+YUZg4qLIDVdbgYc1DFX4HKOeUMaUrVULnJmySzS5yR/qRp02mCD+81V1JHqtmgdFt3QHkoEPtzK4E7VKV8kC1nFvWfOmpci1Tsw0fIB9keMKCrVb9kurLz9NxaDhT9nY5/Z7L3gnCiWft6YJQLDjOSvPAO75Hohzp6tg+sqy63U2P1onYwL9uT8ql7UrlKcggntvkBJKg814dZH9cA2ju8OR5naai1PEwLKCDesZtYnvaIgfVZeS8OgVOriOcbNjK5/ZESRPg8DD2lfxxrrfJo5gxtQRa2u/ihVjTZrbQlNoZlSE88xfHuwmrpS/easSP/D2u0j7fy3iOcovtgui9+JOF91RqSyeV/qpCaeQ3E8PjVeFFXpzLZn2uamhZS5sozOzatSpqBka6Lusv9B8Xo9mXMuWf5S2Hocv27MTMXW6yhtvfS4gzj9jq0bbMHu7UUWNon3MU1X3U2xWaMSHzu2TGpF9fJ/kFflY/IULUf1mVZ4pJipTl08K209v2TreRlspv1Z7pS2nos/Steiy8bruNuuiLfb3FUZYv47A7YhOBybIvpDkbZES+Ze/RsDthH27Bw6h61H3yQKlbAHSyiRLiM5ihtyM4Uxu3DIqJlSWGZqu5CbHtrtJK2kKp5jX7G+T01lvZabaM4Hbeox0I1Ty7pTq3JRCuSIt4ucBUtRZcQmHqjvAKciVfEcYMPE2kWU1yw48USKTbDSjLTS5+YOpnTZjoPqEMEvWDtIentAWSYefZWG1Qb/BvEc8wO7Q2uZOcIyaYWgnw1GzQuLfNZm1Z1PyURoKE4npQ1chf0VbcqMi39jQDniHbtHzGHz4ZeJvj3SbjllMmUiS6HRXE9NPYfeYVm7OVg//aJaBBOBm/UIsiqyUL71Wt7/g+pZFs8yMkp+TzyH3t/FkjWbuKURcXqzs2URpYgYdsQr2Yq2L5xf3E35PFnOzguxVb6PNq04c2B0/DO1BYfvwF7HEsu4uFhCPxxidgvp2dKc1Bm1lzdBsVqF62+J57hQnm0ewcwLmrMR/vc20TKXggItp3E5eSflc4JBRbIp8/H3Z4N+TzzHfHnK0RXj2aRx2VgeLutJ8YwK6sw4y0eN2fgIPGwWiUBJdAZlWmJ8J21dVDyBvDhsRHGRNkPtQex/rS1q+x3+CfH8nftbh1FA5DFz0/Gc1LZBlISwn+ifDzkwQprlVVC48TwufgkVdqX6XoXHkUlMOfQwadBI4uslRhRUkL18W7a/SNbjhT1gQdMypBO/WWXBFX5oVSRxSvv1t1tCr4rpxPWL0WXVJXxixMV9TzCwYFYhnnPRdW9iGJIMabl0OB+PD6R6XlGPotMff/wNUSLzP5+sp4FUt4VqMfOqDvErzpPenf50fV1lG1Xk6IbJU19iRQD0xnyiCEQUpK/Zjz0v/3qw+OPCUmZuOYajxk+8YVG5DGTOU04EwckUQ5wju4c1UgaqhUfuxkGHOSjbvfMBpjaVli7not64A9gHa7b7uJgAHpqOYM5VTfv4em0VjXOI3+8wH1tt6/M0SIN4/mxFvz+E/0mfm277dM2WxteVu3U/quaW6qoOE0+8IjS5oSlJTTwLm3m6gcZSnRX4kykX0jKDro//FvEcjc991aBA0XrMTeMSRt2kJp7DcTk3O34AsWJH1j/WoRikdhLpytW58Uvzs5QcwX5H/xQ+4q/ZekOlrRcR/ZZjGm29vtFB3ocJe4q4y6x6JZR5qml8k4QxXk0ku4vl2925dC4n+ZhS9NpwHd8obXaXBvEs2Xp+ydbz0OOA8t2DWkhu63WZdMqeGHEfPx6vo75U3oVrM+uyjk5dK2E4n50Vv4qtcmc2Pkpdguki8LoJ89Yf4LmGy3ZhdfVsZMpaiIlnRN2qjsbjjsWk1spnrfP1M+GJ3o1bUid18WzPlv51ySCuV8zwMB+0DaoIJLsIeb+T8Q2kZfB5aTzlKI6houxV36uTqngOvsWMutIz3QpqLb1BwF90+bEB7lxaPZLlyTabddg3nspZFFQYtovXAalGmv+4eI7+9JBDxpPYodG1xnB3QReKZFBQf84lPmnM4Ifz4cJcKkn2V749a+9rWw+UNoJsNzJ//V6eaEzCu2JSOycZM+XD8GSyXdyT8dlyGlP23dJsnz9tGVdYQbZSTdjw+K/nLTVk8Swjo+R3xHMY11aOpHvnwayxMMfcPOFjxZyOkmBUkKfZKt5qRFHhPNk5Ll6gVR7JkVc6egGtBGKzsIcygFdUn8qlD3ocQqQLZtOkkdriDF9/P3E0OTm/JZ5/PGBqo1oMX7GLI4n3as7BNePjn8NWVGfSoVfJAs1nLKsmvcYoI6WmX9ASHP4OvyOe4/h8czt96zZh+j4LtbqxYNP4BsrOQJF/AObvfmp0rN/tD9A7k/guZ02GH9EVCGkjBvcbq2klLest1I6Fl9P05Jse/gnxHIXjqfk0lJZZFe3MKhtvrUFFPIG83tlT1FsOGvQ3S/nMarQ7Jj2a0G+yMXvVbP/ozlX0qyjZQiHaz72UrMP+xIHe0qu0xPctN/I+UE/r+nmPWd2lFQK1WHpRNZsT94QlVXOLY5koOeqU/gDCzZx21XKKc7tzxC3+zFCvk4zILq6dpSQdVz/RK4hi7y+naLbMZC00jXuqbHreWhe/RLJAa+ZeTNMT11rw5+j4rvQcPJnN6j7j0B4MG0rlloVKA/bzSaNiArg8p4tyYEZRcwZXE9+No4VIJ/ZN+FP8TklGbrJLOaPx9RYTGtTCYO3+pGuLz37jkTTMJ34/XR1mWevfYTct4jku+jGLKknln5nSo0/rf/f3BzNaV84hzu3J0Y+6ajX1ZdshX04zUqrfzKXpsEX3BoZp47/nmecAdysGSzsaZ61Ir93qL/L5K6QmnqXX9O2gq+QDc9dk5H5HPX1gHN/PTVBufFm8lujnVEeT+Lu2PpNrH9Nm66O3PFKV3Ud2damsFHUZOmzHNeE9X9r4bsukjtKsWn1W23rosJc0LNtWs/WyY8+m0dZ7YeERb8UhnscZJtVvlvJ02/HqN+w2Dp+n2+kiPaqQtyHjT/zOrLU6gZye05/uvcewwVKtno4cYnILqZ7SU6LrVjQ2vxct8PaqgfGzjrWncMFJTz2lgdSXbf8QdttOubJLUWc+t5K/N02dcHu2jakk8l2W8XvUXz+nSerPPLuzs1MlMotrZhq8n89p3rhKk0B7a4bUrI3RVsukshVxyM7Znako1Xu2dmy68zmVWPOfFs9xeNqY0qdeM2Yd0IyXNo6rQ0FlvzeEY06BavYZh++LXXST7C93XUZbprZTni6COTd/IN17jmK9uv0dNWNaS2lwS0GRjqY46yqgmE9s6deCPkaL2aXuZ3avZ1BVkTcRLzabeuYf2zhMFs8yMkrSLp5Dna2YP7A+9br0o1eP7nTvnvDpQZ++XaiRX0HWfA1Z/0IzJPD3ahIAAP/0SURBVPK7uY5O0nO3Wdux3vb3gnCP41OoK+02rKjInAsfdAd1oQ4cnCy9IL8YQ/U4zrSL50jc9/ajRvMu9OjVkx5q99qjV186Na2qXIpXw3AfDhq9lej0jCoqHWChVtt1vEs2raRdPMcEO3J2dmMqtBpAn549NOqmV9++NKv0hwiwCjLk0BuN3WilV8IsaSyNWpegx5LbqYgITYIcTmBYWdRNuj9Fp63/9U6p889sGPbrxQEGS89aiXvvteJyymX2CcT+4Pm2buK8HNTve5DkTzN9uzSN9u1a0aFnb3qq2X4P8Xf3jo0oITrbYm1mcUMj61E8XdeRgtIAg6Ih2+MfrtPOt1vM6CotmavJonNOqtUb/pwxit98LkexwVzQFw04m9G2qhTQduWQc3wmYn6+ZmNH6RGCzFRsuwx9k8eRt5dQRCmep3BL9cxwiNNpJkibrSmqMnbny98IcpMIeLyBEd0a0bx7H81y69FT+JC2ytfr5KnYR4hIzaDfzXI8NZTvKa8sRI+bbrsMfcduo6rivBKM2HA/WZAVjtOOHlRt3o1evUS7VV1b2YZ796VDo0rkFWVTd6o5H/QqvzRsGBb7jVPjyoh8KMhZYgiX9EV7jgdpVUkSFN05rNPWUxfPMQH2bO4k1W8+mow/n8YAUxf/PeI56vtz1raRVhEVos30qzoHQtNG6huGhXleZ3Y9aWO9HDQcsQtXnZ1gFF9PGSnFc7GaK1B/k6zEv8LWF9h8TJOtjxSCIt6bRGC3srWoN5E+XQvhO/UM1361YUJ7aVfveqy45pZshVgCadgwTNj6ybHxtp6r5DAu6VPPibbegyOu8QPnMT9esr5dFnGsAC2mXNIvvpMR6nGN2XUkgVGOwetS27BJO0GvdzGxdwMad+urGcf06EHvXh2okku04ZId2eOkWUKfz8+nqbSBV84e7H2mtWTSTOriWbj0w6OoIolNYbtLb+qJzYJfsXmUFHOUwXDnY62+QiJ18RzOg+WtlG9nSFdxEU9Cfr9046J8ebCiFWWb9qVvsjikZ5/+tK5ZQvTXueiw7iZ+2g1QxT8rnqMD33FiRiMqttYeLzWtkI/MisKMMHdAfQwhzNOWefWlzQVL02/lPR1tSD/Bb/cztXd9Gmmzv94dqZZbQfaibdjxXrsn8L82j67tW9AuRSzSix6dm1JK1F/hJhO59HcXI+lAFs8yMkp8ODayMTlTFc+h3NszE8OJm1IIi3hCOTeqBIpMOWi5+oXm7/x4jkm/kiLoKECXDbdJ5c0UBAR85+MH1fNMPo9Y3qOEcmS90vCjfNQVWaRRPAd6HqVnWsRz1GsW9+zCLjvtM+XRr/fRWxoQqDia4+81w8ZvDxdTReQ3Q/7uHEp1rCCGD46ehARrCz0jeGs5kbKpiucY/F6ZY9htBDd1TOx/3DeIUlkU5OtzADf13iDqB3e2dCK3IgNlu60h+crj5MSJenay94pfrhjqxYm59UXdZKO2oRnuOqO+eCKjAnF5r+u5ame2taks7PB3xLMruztWE4Jbt3gm6CPH5jYVwbqCQk1ncc1bh4XrFc/e7BjTk6VHX2jvLP3vMa+GsIUCzVmebJlppMtJhteSBI6CxgsfppwZTSCZeE4QL9/tNtJcmiHN9gfDDumZ3Xc+rCaeVWPOMaHYW0+gYiYFmYo1YsUt3as+Im8vVhPPquA+7BOnFzYkqyIrNcccxDUVRRUVHYjze3WDD+TUYgOmrjypYxTcg12dsqPIUZbhh5I9O+p9n0VdiihnQSobWOl+zY8+8Rz6jHndurL/pXZBEflkK52lXY6rTeK8i76bi8bVZiE19AkKwbcHJjSVls5nL8CIw3reKuB4IA3iORi7zYMpokc8EystrR+itO3CDWdxKzXHKvyJk70nMdHaZid/YrOgM3l/SzwHYLusB/l/SzwHcXt1HwrqEc9EB/N0f18hyhUUb7mIB/qnkARhOIr7itX2Hhq+c35aW+HfdItnIr5zd2s/5QqnHJV6s/+NrlYqiWdDHeL5X2PrVUYew0tXRJ5MPCfYeriTFQOrSoMNClqteK47oE+TeI5WDhSlZut+99fRRGnrhRhhlhZbl8SzykBjQnhxqL9yM6yizeZzN9X6DY+3W8kHRPhxdU0r4fMzUXnAVt7pGSuQiJFe12ev7pOCuLzWkIlzD5L0QkB1/DDrkxtF1iL03pFsJZbfXRa0KyjupRgD9r/Qbrtq+H/3w9Nd+3BtVOhVDFMRz3y6zex2BZRL+quNO42PrvPSKJ5/OO2lo17xLFqSw07a5pPEYT1Wv0x9WOOj6yd++CdcMU50GVeY0rlPsnetJ+F/bhZ1cirI0mINT/10R5tSm36+dxQlfks8h/Pq8DhKpyqeo/F+eohxPUah60k1l519lG/J+GPAUTyV70hTEfGN6yZtRJySkQq9N6LTVaiIFbUhxUtJBHNt40QmzN4nIm9t+GM1IB+KLAXoulnbZnp+7JvQh4V7H2j3t0HPWCa9ASVfA+ad/2fUsyyeZWSUfGD/oNqi426IySPdy1rjftxn/8rJbLqqe8T168Nl1EifkXwNxnFPw+/G8OnKWnpIS51Lt2DtTV2uW7g/fweOWR/ltkNSh/zNbgMd82dAkf5PBhx+rV18R7tjOauWcPqFGbrqlg7HGccv+220EAKpQtNN6Fuk/NN2BuOXXcRV+0Nkwkk5sn98TdGJl2TI3ifJZke8uDgkfrlu5WGreafHwTo/PMn+S3fxD9TWFQfxbO9wEUQWpM+K27o7g7ggHh8YydSdrjoDp9jPZxhT6w/S5a7P1leaGQr5dB3junmFwxZBweYrepb7/OLO+UNYP3JKfNbv51srJlUQ4jBfNSZZPtItDqO/cPnkIc690rFDZfRzVjYrK+ywG2YeqUZT8cS8Yl3r8mQSgvvAB93KIczzIjNq/yFsowjNF5zFWWshheCwr484Jxv1e+7TEM/hbzcwe8EObulaPhz5jZubu1BIkY/GMy1Ivo3NxxPjqSzNPudrwawb3tqfZQqyY16vYuL6f7LojPo7PUN4tLadCP6FAK46gh3vdZSwpzUdq0vPbHfikFPSQEJcuGgXo6SAOwPFOy/nkq449/EqimXPTJb8E5PEs+Dn+2NMrZQNRd7KjD/6MJmdqxHjw9VThzj7MkkYRHtZs2bxEiye6Kib2HAcThtSTpR52T4rcUimbPwerKWdFMhlqMHAo2+FVNNCtCuHp0qDZkUZse6uRpDld3UKRsbX8dL1erSAN2w3qEJ2EXSOPvpaTzAsxNnJySKQy0OH6Rf1tI9gHq5po6yrzNUM2KWrrjwsaKcc6OjCYRddv/Yd21Xdlf5l7P6XOvMW+esp25tLgy656bDUCt3DK+E8uXqEI3feEKVVPH/h9LSWZFVUYe45F90CTAMfLsxpSzZFRWacdExjGj8uL+wgyrwcky3tdc6yhn+7z4bGQqhk+INuq0+naFNJhPDwshnm99/pEM+eWBk2JouiJktvfNQ5OBz94zmbu5UW5ZiTqiN28lCHyQZdnhovnqst0xDPf9vW762hTZ508bZ+JHVbH7n+npqtx+FqMYoywu4UBdux4J6OoPnXHaYJkS7ti7Diqq6+IgyH4xPTYOtBPFjVWrlPQpZqI9Ng6105ojZQFOH/kE1NCqNIl48uK07oEBISodiJ+j16z55oVYaD3C4wv4bwdTnKMGKPrr5e4js3zhzixLOkTc1iv15k89K57L2rY7A1Lhq3q9OpLNVxh3lojrtF4n5sAW1yiXKu1IldT3VvsBnk/RJzK0vsnLVbboyPNf2z5aZQmfk8UR3Ths+t5TSTVt5lrs1wa0fts/RRjuw2qiLKuCSG2+10iOdYvj81ob6Ifap33pO0oWgKfvBoTgflYwQF203h7nfdrdrr5WX2nb6Ch1+SJbocG8P49S90i12/2yxsX5KMmauxyFbfY1Q/ub+pP7mErQ/f+lh3v6NBAI+2DyaPiAEHbXigoxwEMT95uHck0/fo8QdeJxhRLR/p8jZkl73mUGLQx0ssrpUHRbZSDNlxXc9kkD83zx3i2JOk5d1xfjZsWzaTXbd02J/IkaftbKoqMlO49UyeJWtWEY7bmL/AlKtOOgY2on7yYHdvior2W3fifj39wV9HFs8yMhJxj5nYUdpwpDITT+na6CaImyvb0ap/P+7qm10MeMjUbOlJlzErHaac4ou694rzw+XCUloVFB1BmdrM2nseZw3B6I/rzTPsWrqZs/dfJgsefvHymAmdq4i02arRdpkl9z9GaDhe77fmjKwtjb6XpOf2p9qDD4HH9ZkiuM1IwVrzuKnLI3tdY0qb3Iw78UJ1QDv2a42Uz0DlrNKLXTbJdh31teOgUUvlbMKfA6Zz/JGLZqAY+JarO3eybb059gEBOpz4T44ZtxL3lINmU06iS/f4XjWmW4vSbNU9JiHw50jrGsrXJpVqZ8wDN3XnG0nQa3MmtyouOum89Fy4i3sf1UfMowhwvo3l2k0cNL+eLB/B+D7cwaAauVHkLyUEzFGef1UPZ0LxfWnDoRWbsDj3AJ1joYGX6Fkrn7jXJix/oH1OIAUhNvSrJ4niBiy+ra+biMDTzoqxraTn/QpRfexmzrz01xD6wb6PWNtbmlXITKXRJ3BXHZfqaUO/4vRcfUiUoG5+XDalqSjbjEVrM/fAU83ANM6bW5um0UD5vvImjNhxhbfJJiScb6ykRRHxvaIRM6591Jz5C3bGcnpfSkvPWlXrz0Krx2i+dSyaJ2YGlJC+zzaI3e6a4WToZztMhjdRDubkaj2FbVecNOsh7juXljUV1xbpKy3imsYmKSH42e1kaK28QkCXYPgaM576qLeuUPxeX+PwClPMz9xP+t2Yz1hN+pO2E+eg92VDjifpL66bLlcRhq28jubmvz94brWGjpVEvrL/SYcV1jz00AxkvrwxY2gNaROlsvTZrXqFmIT7RYxa5mLSef2vOnq+eIRyxjH3nwM5dMtTdTQ54Vzb1p30IqCuPfwg2vcXVhHkhPm0PpRS1tUAFlk9wVMjy1E8OjiMYtIzddkGs0fnM8+f2TFN2nE8P11W3tDpz6SgONLlLEt6VBbnZqLN1PXYqA08SoR/suPUxi3sET7XI1ZXuOjI8qHSbxRn8G79z8cn8YE1o6qJNEUZsPWhnsEHddzZYCgJwEL03nhXT1AcTZjDCeZ2KicEVjY6zdqMrYu6EIkl1OM+x9dvZt/By3zSGYa/YV5f6V3pZRhp9kr7wJWSGPydr7Gwt3Q/WSnWezGH73hplHt0qDtHJsY/kpOj5RakN8Mq+ZfY+k+eWaymo7R/Qhpsvd+el5qiMeYT19ZOoI60SqVYK8bsvc77ZIsaHK4uprHU/ypasODuZx3iOZyrW7qpbP2QflsPdOTolN6UlPbT0GPrRSVbzz6UfR7qfU4M4Y6nWNBV2oE5K+1nmHLDWd3vS/X7gJMbNrP3wEW8NGounJ/PDzK2cSEhoAvRf/l+7LzUZX4EP97f5OjqTZgdu5U0wxz3jXOz69N61Hieqw5pxesKI6XXG2bLR9+Fl/im3nHHfMH+2GwaZFeQuUoTlh69hkeY+gm+OFw+zvYlW7n8zF672BUEvVyn3Fk5a5HRnND5LJHEd+HbjWlXTpRhzlp0XXOSR580a+7Ti330ryI9dlCJoWZvdV7T8dx4sovYp3hTY+z0NfAAey4t7alc0VKizVAO3nitaWtRLtw5uJ8tK/bx2Ns70VeEPtvHwGYFWKb1lZAJRHBlWDvlqpJCDady8bWum/fl4LzG4p5y017Ugd4iSuQ7Rxc3F2ly0Wr2OZ2rJr5cWESXluXZldjJa8OPg02rKlc8lum0mscaexGEE/DSjPHNiggfnp8+S/bwwFM9Oojkp+MtzIX9Hba+qTYw9INL8xvTevgonuoeNQCfG4yVls5nzU3PuReSXhkW4si2waXpunS7nsEmYVu3dtFG5DtDoapM22WXxhVBaUcWzzL/43zl7r7F9G1bi6zSZimisWUpVZv2PWdy4v2vxK4q1Ocxu0Y2ppjUQQqnVL+DIZtOv0sR9Hy5vp4hrWsrdzZWBuFChNTsNIGtd9U7xEjcHpxgydh2VKndiCrNWsc/59GzL4uXbRAd3SUu33qvY8QwDq+Xl9i+dipd2jegdZ/mtO7Zgx4ifY+BfWgwsCcdBo9n06FzPPgUlGJGI/rLdYyH96F+VSEClPnLQ/mmnZiy6qTaM24xvDuxkhE1pA2/FGSrUJveQ3fwJChZT+P/DutVg2hYWBJ78feb7Y8qdFh2Crefap18uBsXdi2gd4s/KVm3DS06dVTm12D4eHbss+LoiRu899QWPobz6f4+xnVtSanC8RtIKHKWoUHHIaw/8yYpoIv9ypXVQ2lUOD4PZRp3YeryyylGGyMdRXA9ug0VlXUY/ylerR1GRzQ3awn++JDdy0bQuF5NyjRsS6fu3ZT5nTJlAQcsznLs/EN8dES73+yvs2F2H+rUqUv5xm3o0kN61q8Hs+YYc8jqPKevPOOHtmgt1p2z66bRtYUUuMfnLXfFhvQashSbFO9ZVBHnycWNM+nWsirpVGlyVmhA90GLueyhO4QPcHvA0V3LGdK9Ga27N6ZVb5X99O5F0+G9adpzMMu3H+LKOz9lAPL5/iHmtorfLVtRsDxtuy/nkqvmhmuE+3J37wRaVSyp3BVeeQ8Zi9Ny4g7uearnJYz3946zdtEI2rWpS5t+bWkvXVt8ug3rR70+3ek1Zja7j9/mlX9EyiA/2peHVw6weFIfWrWtS+v+neisSt95ZH/qivIePnMph668xFXbTOsvF64e28a04R1p2aEerft1o6sqfdtR/ajbtScTVq7D+t4Hvmrp2L+/u8HGOeK8OnUo17htUv3OXq6s31OXn+KvanQ/HS+ztncN5QysIlthmnWZidlDL802GRuGw8mFdKldGelVYvF1n5eGg5Zy7I168BWL5/OLbF0zmc7t69OmT4sU7b7jkIlsNrvAw8/B4hqRvLZcwqBq0lJRYReV69HfYC+vwpOVybdXHFnej/oFpA3Z4q+fs9CfdFp1gc9BCQUQhd9zcyZ3a0vFEpJoEedlKU7t9v0xNn+sO6CT6uryARZN6k1rUVdt+nVOrKtOUl11F3U1axmHrr7ELUVdBeF0ZQsGnZtSWBJC0jXzV6Rpl1Fsv+GqU2xGfX2B2drxtGlQnVINhG1166K8npHhLPYeOYXV6bt4al0h/pMXx9cwpEMD8uaIv176ItVo1W0Chx/76BDRv3h9yoShHRuSX5qFE2nSiUCtZVcjDjz8rCNYC8D+3AaGdWpEAeXuy+JToAotuo5j711PnStWwj8/Yf+qsbSoH39fHbp1Vd7XxAlz2G9+Gqsz9/isOVak4huPjhozsH1dcimfJ1aQsVgN2nSbjPnLbzqEo1SOrzljtgHD/q1p2bkBrfr2UPZR0jVbjuxLw269mbNhK2eeeilnZf9Ttm6ntPXkBGN/x5LV84aqfEw7Oqjy3nW4aLu9u9PHcC57T97j7c/IZMMNSbZeoYS0B4bIY1psPSqttv4at/AUXo2IL885uGYcrVR2m1C/E8aL+j0aX7+fdGixAJc7bFs0hAZ1alOmUVtx3fj+atr0xRy0PMeJi48ShUeg+01MB9RVCkJFpj9o3Gkye667JrPVKFzPG9Or4Z/kTaynnKLcZnPwqbo4CsXh5lHmDWtGxdpN+bNFG+V1+/QegPHKLco45to9F+2DST8fsXnCMJqpXgklDRwUr9eGMXMO6FkCHMPHp+fZtGoCndpJdtGSNqoy7jGoLw36iz5g2GS2HbkixGxICtsO97jM4kHdqV0p/m0S0oBc5RZdmbnxIh5afH08Ptw2X82IdtUpWac1jdq3U15v8MCRbNp+mKPHr/H0fUKZBHJ/+2Q6lo6PVQrXacXoqdYplobHed7CdHpnqueIf8RA+hQo04AB2++SFGKF4ma7k9FdmlNMuSmr+OQpT+POBmy54qRjRjsMj9t7GNOlBSWUg0Pik7scjToNZ+P5d0nCP8abi8aDqK86p1yT7sxYaZNCiEa8P8lCg1aUlwZ9VPksWaMjEy00Ny8MdL3HjiXDaFS3Vny8pLK/qVMXccDyLMcv2OGrqtMgz7tsGVxf+T53RcZ8Im+T2HnVJZn9iXq+tJK+TWsoBxfir52dOkNmY7x1MybdKyiX8CvylxF+bDFnHb5rxgmR/jw6NIU2VcooRb8yfboiNB+3iRuuOo3rt5HFs8z/OEG4PbbB/Iglp06f48KFc5w6ZoGZ6JjffA1L7FijAj2xO3uUY6fOc/7MKSxEIHbP/muKjlvqyKytLDl5/oL4rQucO3sSi+MXue+uxd2FefPy0V2sLSw4fPgwZoePYHPjJZ461/qoE8M3l4fctDnKEZFWSn/Y3BzrW09w8NW5UIfYABduWJtjbnVSmb/zIn/WR49wzvYN3xM9UCxf39zgtOVxzp67wClrS8wtH+IVkSzADf3KK9tjWJw4yTnxW1LZnTxuidmNt/iHJe+NYvnuYY/NpYscMTNT5veY9XmevPPVvbRJdH+/3B9z8rAo9xNnlPk9e8oaczMrbtt7J3XKcSLQvnkcq2NnOH/uHMfMrTh33THFLFWMn7j+GVFGZ84qf+vChTOiro5x+vmXZAGURCBu9k84ffKkqJf48j137jbvP4fqDDgTif2O48uHnDh2LL5exOfipYe4fEseqKkR54/9rfMcOWrFmbPnRd7OctJK1JPlNZx/aRGBSn7icPdCijRHLWxw1KrQNQn8/IZHtyyxOKqyH1Ev5ja3efpRc5FiwMenXLa24tRZUf4nrIW9XcfhezL5EhWE++OzHLU+zhll2Z4XZSfOvfgQ95/a8hLCZ3tbrlw8kli+h6VrPHiDR6COwQJ1Ir/xQQTY586YJaY3O3mKi09d8EtD8qhAD+ztTos6Ul1bfI6cv4jt20+6l7klEOuP00s7Tor6Tbj2RXGfzuLC6p14mJ8jt05YcOKM8AOnTgi7vcAzj1+i5aoRF4Xvm6uizqw5pSw3UcZnjmN++gavvbWFndH4uTzQbPcW5hy7/RRHP/U6icH7pQ2nLE/Et2ErCyyPPeZLVLLAPeQLz69bizZ8KrENnzgm2vCt9wSoNkyTfiv48wvOCv9kdfy0Mo/nTot2b2bBjReeqZdXpJ+yrs6eVqurU6e59OyD5myWBhH4Od3nmJk5x09K7fU8Z05YcdTsOA9dvusQswmE4uX4XPjpM+J68b7m9KnrvHYP0DP7EMrn1zexFPd08pTUDwj7Fb7syOHTPPMM1KyzRMLwfnsbK5HHE6o0Z45biTSneOIRoMNPhPP13Z34NOr3dfgkj91/ahGC6gTj8f6ZKMdTieV45rQtbz2C9JRHCJ4vrivr6qSyf0u4r7O8+BKiGXRqIey7My/uHcfaUmVr4nPkkg33HH01rvmftXVdBOH15jqXL6j5mGPWnH74Fi9djyGp2br1v83WExB15fBMXO90YtrTomz0128Cv/jw5jGnTpxITHv+/F0cRbmq10G4/wfunhRt6rTo+0+L2MTsLI+cvyez1Rj83wubsRT+OLGeRJ2evMpTr5TlHhvkxdMHt7EU8Yd03SPCtm/csuezPp0S5sm908LuLY5zXrrG+VOi3z7CiUtPSPWNeaI0fIVvuCE9fqG618Mihjp25xnO33VfNPqHIzYWR7GwOqW8p/NnT2AlYp8Ldx30LMuPJ8jHiZvXrwq7PqK8noX5SR489xQ9sDrhuD88K+Ia4UvPneeEpQXHz71Kucrspyt3L1pgdUrEK8ryFQJT8s8P3UkKsSLx//CQ42ZS7BMfr5wV/am52THuO/np8GNR/HB7xAn1eEmZxpq7778mxUuxgTiIuM3qeFK8dN7WWXNWXRD99Q1XT4v2lhAvnY+Pl8681Lb2LwDXt5r2d+7cHRy+hGnYX8RPN+6fUre/M9g5JR/Ei+WHww0sraw4qSwf0QaF/Vmct+HG/XvcPpUQixwTftMGexHvasRVMaF4PhWxlJRXZXrhY0+Jc8/fwyVhZPtfgCyeZWRkZGRkZGRkZGRkZGRSQRbPMjIyMjIyMjIyMjIyMjKpIItnGRkZGRkZGRkZGRkZGZlUkMWzjIyMjIyMjIyMjIyMjEwqyOJZRkZGRkZGRkZGRkZGRiYVZPEsIyMjIyMjIyMjIyMjI5MKsniWkZGRkZGRkZGRkZGRkUkFWTzLyMjIyMjIyMjIyMjIyKSCLJ5lZGRkZGRkZGRkZGRkZFJBFs8yMjIyMjIyMjIyMjIyMqkgi2cZGRkZGRkZGRkZGRkZmVSQxbOMjIyMjIyMjIyMjIyMTCrI4llGRkZGRkZGRkZGRkZGJhVk8Swjo4Enp6YbYHrCmSDVkbQQHOyO2cqFTGjRktYtWtBCfDrOW4qFRwSBfq68un2JlyGqk5Ph//0du+ZOZ6wqXYvWLem+0pTz3yDE8yWP7t/GMUqcGP2OQ7OH0bql6jzlpxVt2rWjbdvWtEw81oKWrTozZ68dQXGfOLZgRBrTdGLmdlt84rOlwVfvF2ydNpnRCeeLtH1MdnL1p8ij6yMe2D3ELUacGPaC7RMG0Er9ei1V12vTKtn1urPk6BP84y+hla8vzVg1ej7Xv6oOaCHW5wZL+3fW+O0WLVvTVrpm61ZJx8SnZevBbLV1J0yVVp13z6+wYuAgBiac370rhuZXeBUNgU/PYevgya841claCePZvrksX2qJc4TqUBqIEZb2/PouVvbvSMfEvBpgtPokL76E8svrFsdv6y6AiEhvjm1axeRW4p5V6dtOn80+x1/8+OnN+5tneByoOjkZgYGuHFw+n/EiTSsprai3TguNsfKKIvCrMy/vXOZ1qOrkZPh/e8vZzVMxapWQ53a067kQ04vOhIR4Ymf3gMfOkfEnBz1h89h+abSLnhgfe4kwLZ18ebyflWMWc0u0kbQTx6uHZ1natx/9Eq7XqweTjt/EXtjur0ensXXxJlhrHUfy8KoFC7p1p0dC2kEDmHf5OW7CPvzuHuOGZyBSM9XNN64uHcsa0S79YlWHUvCLV8dX0FP4gITyaCF8Sqs2bWnXrk2K8us7djNP9DqqD1hNHM7Wi15abV4XAb+c2b9kLkbiOsq6EXXcZclqTnwR1ur9nmd3bHir4we/+77i1MbJSf5M2EX73ovZctWVsJCP3HtgxzNXVUkF2LFxVO8U96Vsu8IuEo+JT8tWvVl18g0B8Sm1EIvnvZ2sMDTmnj6nkoIYXF6dZvv4vvRKvF4/Bk7dzWXHIKJ/POLEjQ8E6GgHsQTz8uZeVg/qTKfE9AaMXXmcp59DCfx8m+O3fIjV4TvCI75y98Qa5ndtQ5uE9K0nMnv3Tdz9A/B0vMm5R7rvWqqrS7vnMKlNwrXF73Sdw5pTb/kV7MOLp3e5Y5967fu+MWfNqDlc/aI6kEbePbss/OZABiTkvYfkN68q/WaAXr8ZjePz42wZ25ueCWlbDGDIzH1ccw4m6rsdx2+4ERyuOj0FgVy13Mms9h3pnJB+1GhWP3DjS0Q4njePcdtXZEIL0TF+nN61nmmt29JelbbVxKlsf/P/2HsL6DaSZv1bYaYNMzMzMzMzMzMzMzMzM9uJEzscM3PMkDiJ4ySOY4bfv0eWHUmWZHv33ffe77t6ztHZ9WR6pqe7urqe6urqL3wNC8bt2Q3eyps8mHenl9Ojrep4bNdBMR6Tr4mfkNuhs0/xIfY3NpdW0VOtjLYxPHjaQay0yFYinDk3eRSHngQJLZR2fA9x5v6B+Uxrl/S+DnTouYRtd5z49TsAc7NXvHZSTFQR1hyeMVS1blrthJ6sOPte57ydJEtPPioupBEO5g9ZN2Swkiz1ZMpFQ2zlsnSHZy5+qczB4ZgdWcjatVdxT09jJeMzD5dPYMtpC75pfc93LC6toVcadfTAKfuxDFMUVUJk1CdeXN3E4u7tk23G1u2ns/ioMT4hP/Bxfs59HeM+CZEhJuwbPZHL9jobRiv+DRvroIkvESHv2DSsp+YyKXT7EPYaeRPx05ytI3vRRqUNtZUZxI6HwtZQ1FEZfu5GnFo0gqHJ9/eiz7idXLb4RvwvW+49t8M/WOsknCboybMeeigh7O0mmuWXkaPxBiy+6zaFE/GJe4dH0Lh2Fep3Wc6ZI88wfv6c59Lv6nnWT+pP7eoVadBzEvdSsFIPzm3sRZ0aFWnZdzPXzijKGT/n2fljLBzVg9qVy9BijDAGJeUb+5xZderRZeo+7ire8ejaYhrKZGQvPJSDSe99fpH57UtQq+dWXMQEuqppfTpO2M1txb8/vrWcpqJM1gID2Zdc5jKLO5eiZsdlvFUxxh05sqwDNapVoMMg8YyLivuNjXl25hCzhnahVsVSwvDYi5Vk5Py+z9iKtem94DgPFc++e3YWNcT78lYYz4nk951mSuOCNBiyFzttdl2kaNsFVcksK8HQQzbCSNWMaNdjdCtUlwl7bvNM/uwnol0HUEi8s1yHZVxLeuejXfQtm4cuyw35pPSwsK8PWDioLuVrNmP09Is8u6+43+gZD/asoV+nJtQoXZgRR8z5qkvfBjxjUcsMZMrRnYOmXxQXdeOj4x3G9ahHteYdWbjtCo+eKt79/AGHN81nQK061KhZizFnvBQllPEd48vTaFWnGjXazuXIPqNk2TO+eZUdM4dQq2YVarfpz/kUxQO5tX8oDWtVoWG3VZw//kzxXlH2ylnWTOxLLdHnDftM59FnRZFkfMfg+CIa1atEh+FzOHjOSFHWmId3T7Ns1EBa16pMxb4Lue2iaLCvtxhRtjb9l53mkeI9t05MpYroowJVJ3Nace358xOMr1eAJqOPkmTXpcBvf27MrCDkoixjTjgISpw6vgfcYGavOpSr1YZJc65h/EDxvidG3N25nO7tGlG9VCHGnXMSJrkyEgh0Oc6o9rUpW7cHC5be4YWhoqyBIVc3zKWNGF9VS5Vk3sMvOslzrN0J+pQV467CbAz8tVnLgdxZ2Ic6tcZwJLlNzjCtxl/IZBWZdOym4poh51d3p3LRRux3UBTVgB/Gy6mdU0bOdrtx+J0WY8GPa7sGUr9mZZr0XMelk0l1EL/Lp1kxvg+1q5an8cB5PAlWFElGMA8OzaNB3Up0GrmAwxeSyhrz4M4JlgzvTyshF5UGLufhB0Vdgq4xpFQdBq8+j4H83mdcPzye8kIuCtWZwbmkdxsdYVTNfDSbeFq7URzmxcXJpYVcVGLyeWfFRd0IDzZn7cSOVKrXgPHLDnPzUVKdn3DhyHrGtGxB3WpCv617TogGEvfZ9SGTejegatN2zN9yiQfJ4/chR7csYGDtOtSsXo3hx90VJZQRj8uL4/RqXY26nQaw7sA9nii+1+jpTXYunkx3MfYrN+3Eikc/FGWU8ZtXl9fSskElWg2Ywp5TTxTvNsbg4XlWTxhGu1pVqdB1MmesJc+mDkQJ0rC4JllkxRiwz0rQ2tQR9uUe8wdIerM5Y2dc+qM3RRvc37OaPgq9OfKoBcFqg/R30DtWjG1HpfqNmLzyOLcfK8oKuT53aA2jmjWjTrWSdNz0ip8pBlU0Tq820qtJTSo0HsHGjY94aaQo/+A+p5ZNon7d2lSuVofNpuoy/4v3dxfQoW51qracyt5dTzCRv1f87txk/9yR1KxdnRqNO3FM3mU+nJ/UhXpNpnIy6b7nxxhdOpcYj3WYe/Ge4tojji9oS8Vy3bntF8K9eT2o22Aix5PLCL1WIa8oU4NZZ+8qrj3m1BIheyVbcsxNXjmNCDacS+WsMvJ1O4x7ZFq0XSjG51bQTMhF28HT2X/2qeJ9xjy+f5aVY4fQRpKLnjO5aKeQi5+PmFC5Dr3mHeOBos53z82mphiHecqNVfqOM0xvWpj6A3fqmLeDkmVp4H4rUpE8ObTL0jPu715N706NhSwVErJkmUKWVOD3hPnNZGTK1YvDZunyrMoRbXOY7iVlZKs6n2eB2j7Qn+tzelK33niOJbfLKSZWzif6tyrTT91WXDPgzLLOVCrejMMuiqJyxOFkfISerapRr/Mg1h+8x1PFc4ye3GDHwkl0q1mTSs26sNowNfIcg8OxXuSQZaburMdo0hI68a/YWHnpvtqIgA83GVC8LmN33lR831Mubh9CUVGmdOvFXEkqY7CXAWJsdF5mQIDXPYaUrMPIrdcUutCIy7tHUEKUKdFsPpeSyjw5wODKeWg39ya+SsoqPsKNQ0sHUbVOTYbM2cbFO4r7xbxy4/xOpnbpSKMaZak38wJuIX/P2ZAEPXnWQ49k+HJuRDPyZJAhy9yE7e++alUmcsR/4sGSgZTMJaP+1GOY+v9SNeQT4vj+wZitg8qStc4A7vgprkuIE8R5XDsKZpPRfsVtnL6oWWaxkXyxv8Pc9vnJ234urySt+OsR2/dd552n0lvC7jFMlody9bbhqrgkIcbhJPtPXeeF3TuOH73KK3elL4l8xJiMeSlVcwPKdnes01kOHTvFfd/kCxwe0JA8WWT03PwEj2A1KyY6nE9WlxnfNCeFe6/DSnIBfrvNxj33sFXyOMd+ukgvWW6qtj8kWvgPIi32sf3kLV5o8XiGuJ6lf+6MYkLKRI1+27HXYtFFuZ9jxZ6XBCmRraDX66gr2qXtzIcoO31Dn61mzUVT3BUOgohPD5hZoQDZ/yrNwuvufFV3Y4Z+xeP+WuqXkNFq/Qu0OyvDsTo1kxqirWSyIgzd/VKnZ17C5/cnGFL2L4q06ss5Cz9+qlkZcWFfcL6wiXY5ClNv42vFVQUSQni5YxKV82Wk8rAtPPP4nsJI+eVjxvFJtchcsQ2nlYUjXhC0BX0pLkhVo1mnsAhQ++iEWELcjNjQvxTZGwzjgfJKVMIPDNb2oFye7HRefQLnoF9q743lh78954e2JkeJHuy1UrR+4E3W7X6IoxIRj/I9TRdZLmp2Pyko4x9EmO5iy6kHvNXof0jgq/0JeuXKINo5M3WG7sNZ5yAVXeh9g/Fl8pO9eA1W3fckRN0m+vkZ95uLqVokA113WymR53iCLI7Rr2RuclVtz54XvoSqEbeEbwHYHZ9A0QI5GHMpQAfp+MqDhT0omlGSj2osue+FxgW1aDeeXD/CgXvKH/+RM71rk0vWkeNeSpUPc+TFyaVsf6/4OwU8ONKnLtmE4SHL2YYDNqnE0sT7cX2mqGMOGc3mX8DmoxrBj48h2NmAVT2LkaPpOAyVx22CIM7LO1EqTza6bziL6+cwNd0Zw3c/G073b0a20v05aquQOf9rrN3zGHelwfL7w2HaCbmo2/88yn6b32+3sen0Y8xTkHYJCQRZHqKbkGmZLCsNxxzBLRXbKOqjMStaFSVfxVqsfWjLF3V/RuxPAt5cYXKJQhQYdoIANbn5anmOkRUKUqhZD06Z+vJDrfPjf3/B7ep2OucqSI1VxoqrSUjA7dZq2hTOS8VBszBwDRIaRBVRIf682TadKtkr0u/cB8VVBRLCeS2Mymr5ctJi/m6sAr+rOW7i+fXRhZuTupH3rzasfKErjgN+fLjAQLmuzUjVnpuxSWXVLuLjfaaXK0C2gmVZdNOdYE16894a6haX0WbjK5VVvAhBcBY1Fe1epR6bDR34qj4eY37gb3KBcUX/otCYc3xRcaJF4Xp3PS0KZ6dQq3FctA4iQm38Rwd5YLSxBzkKlWSFsVKrJPzC7MhsahXITJney3jo+i2Fs+t3gA2X5jYhS6n6HJAmx3hH7lw8xsknIYk3yOHF3paVyC3rz1XlDwsxx+DUJk6/9+TZjWMce6w8A/hyqH1VUaYXFz8pCcoPa56eWMFuC8XfKeDCnk5VBREVcp2/EyectRE6BRLCMN46kEp5c9Bm6UExD/9U00lxhAY6cXVMR3IV7sTGtwq69fkOm/bcwSYg8U8JsZ8v0UfM21XaHsBHcU1ClNVBdp68jrGmEDWB7+7nFbKUgWq9t2CbwvmhiiRZyl6oPItvftAoSx/urqJ2sZSypIrfWBybRjX5HFyUYXvFvYp/SRs+c2d2FwrJdXRNVhj4al7pj3Lh0ZWjHH6oTM79Odq5utDR3TkXoFQq1I7nJ5azy1zxtxiXrtdX0KpQXioPmccTt88pxn3kNz9eb5pKxRyVGXTRQ3FVMxLCXzK/Un5RXxn5a07EIH0f/C/ZWGtYd9kcJxthCx4wIVDpA7+Zb6WRkKmWk++qRBD9Ml7P+kvvcbS5y+YDRvgqPfCH7S6aiTJNx15XiUYLe7GB9edf4JDkMQh3YP+gchQoUpSZF8R7f6p/zG+CHJ+yum4ZsrZZhennVAyHVKAnz3rooUCs4ykGLFrKyDaVKJk1I03Xv9ESwikhDpuzM2iYSUaGRnMx8NaidQTCrE4wa0IXdrxVzCIJEZhs709FofAyddqItUZjMBFfjFYzetJIzjiLing/56H1B/yVJqPwoKsMEoqlTJ31WCmuyRFhyVtLG+yfvsHYwRUfJX0eGXyT4RnzULL6KswU1+SIsOG92VvMJYabEMrjVZ0oLuqYud9ePihrRzUE3JvL0KlTuSWRendD7tr7oRwt98PjJN2EMVy5zW5U1oN+v8X4rTUOGkK7EqK/Yn5tMfWHTWN0KRkZq/TmkBQHqAExlne44fZTmFVJiMXzyQpqi3ZpNeUGKtw8xJD7r735KH1PlDO7ehUhoywndVY/07FqGMf7Xe3os+ocH7Q4guO/vGPHrkV07N2LdrllFOizAwut/ZpAuMdTFrfMTqZ8zVn4UNmloI6fmJ9eSLNVzxR/S4jHTRgT7QXJkVUdzTl7dWvjD2Lc77J8YkuWP0nqwDgsTk6irjAQMjZdwDM/7RNIqPkhpk/owR5TRbsnxGB/ZhS1xfdl6rIFB132uN9DJs9bwUYDRefaPeSOQyDBSkz7m/NhOoq2r975ICrrcmGvePrGXhBzxd9KSIj8xOtLi2gwbAojS4hvqDGIkw7avyFeGKcbO+YRffwXLbe/1UFuYzHZ3IK+G26R5EsI9zNifr3sZMhSmcGnHUWvaYEYK7eW1WPQ3nf80vKCOM+bTF69hEGdGlItq4wqs+/yURN7/uqG3RsjVLnOB450r0lOWTv2OygZbPEhfHa+x11rxd9qiJVWUeYtY2yLUhTKlpn2O610hH3G8P7wGPlKU8ZWy3ilI9zyx9vdTJrYl0MWinYXcmF9fDDVBXHN3HMnblq2CMjhfYcxc1ax00hBi23vc9sxiO/JXZjAZ9s9tBZyUbv3cUFTlBD6gidvHHHTsKCUEBHAs/OLaDhkIsOEkZ2pzkjOSTpTG0J9OTYqH1kzF6bd7vdiVGjHr6cbaTDzPF4hSZ2bQJTPC1a0zUGm3I2YeUdTVEgSwrAT9Wq8/AlxydWJJ/j9UUZUyUTGskM4bqVjvSjCg0vb59Fzr43igoRY3G/MomVB8Z3Nl/H6o3apJsiExYsXM++6Mv1RQ8w3rK4vof7w6YwpI/q/Yjf2mutgz+FO7OpZWIypXNRb81yn3ny3sy19Vp/HI0lvfvfg4JC8ZMlSgm4HLdQcLKoIebSO+nMuE5DsVUwg2OYcI0tlRlagNUsMdcSX/3TjyKy6jL+Q1Dfx+Bhtp3u+DMjKDGC/mfY2T/A3YePkxsw3EDozxBmLdy94pyLTDmxrVkEQpd6c9VX6h/gg/FyNePbUHUdzE96ozBXO7G5TWZTpxkkPpXfHfyHQ8T4PbBV/qyHWYi8dZi9jfNMi5M+RU7SZo442i8Xl8mSa5Bdy0WY1ZrrIQaARsxcsZekd/8S/HQ24Z+erOm97naKH6ONKrXbipLgmx+93vHhnhb2m5o8OxlIuSzMYU1rIUqUe7LfUwZ7DHZNlqcFaY52y9HZHGyFLF/7Ikhrig96wdeciOvXqTutcYg7uuwtLHbaVOuI+XGPcyqUM7lhXvtJfY8EjvmgaBl9csXn7PHFBIxmu7OtQVejozhxzU3K0xAfz0eke9+XDN56vbw8xtFJGMpYfzmlbHYrytzvnts6jz34tgiGH0CPP1tJo4kIm18xIpoLVmH0/7avt/56N9YSHb3zwff6Iex++E56s9+LwfSHGtOjrZuOuqDjM+W7Eo7de+Dw34L77NyW7O56Ad5tpLMo0GnEehbQm4sczHr12w1fqh7jf3F1cnezCrqm44K5SPVMizuIAraftw+SDdrspLdCTZz30kOM3j5fPZMPZV9g+WkqNfIKY1F/E+29aJqCvJizpUQKZrCCjDpnq2IcnEOvFm2fXuPYmceUnwecGwxtJIT5lmXPLXedAJ9KOBw/uYmAjLO24WOLiE1SMeK3kWSiduPh44mNEmYR4lTJaybNUJi4OUYx49zP0qJ4TWYbqrDAKFP+iA+EW3Lj7kBfO4kukOiao1lEreRbKNC5O1FHDw3/5vmTrmLnccXbm/PhSoq0qMHj3G6KVH5yE2Fg1UqRDscvfmVi/H4ZzKCiUbZaSg7msopU14PNDDl03w0+jPCTgfGs/K5ccxUyQxGkNMyHL3ZX9bz5pbreYTzxd31oYDDLqDtuPk87GlRZGXbl12FLxl8BPK3aOrCjaJCe91z1WWZ1LAWHQ2by+zJlniWws4fMz5nUpKsoWZtwJS937+mM8ePH0OjffJxLveK8rDG6QR5StxML73joMHQm/eXv/PRbPFQ0r+khdLrSS5yS50NDXPz2esXnsAh44O3JyVDFRlypi/JlpqUsCQbcnkUO0c54q47ibWiR94F32X7fmi/S5CeE4HOsr76NSrZbwKpV5NsLzOntvuPFbI+eI4eXOpaze+xDbV3toX1IY8OUn8NA7XKU95JDGrBiDqmROC3kWpRPipbZS/KmCUO7Mn8pmIbM2N2ZQVhDbHC3XYJvkQ1FDwkcDprUrJNqzGJPP2Yne04FoV4wMb3LXLHFJId79PH3qSGGs1VhuGKBWd3WE8er2O2xeKkynFHKhgzzrkItvzgZsHL+Exy52HB4ifUd1xh+31CIXcfhfGydfkc9fcwqPUlutiQ/g1sF3hCbFbcd+5eXWDmQS5Wv034mdDu4qIeybOzcPWYi+Ulz4Zc/hidVEHbPRbeV9dG8LFQankx2Pzjoq/hYtFPiQqW0LivLFmXLePsXKlSoisTIy480D7QT/d8Abto2ezS0nFy5OKiueW5Z+214QpaGdJXw3mM1fGWRkKz2Eq0orlRoR9ICDQgYD5OonFq+LI+WrqIXqz8JIh89AQkKsHzcPvCPsp2JQxXzEYE0LMsgyUH/EQXT5RqTvDrK/zIF7Cv0T7szJqTXFt2Wh44LrKAeBpcQ3XN9f4oS0L0HjeNRCnqXxmCDujdFURgt51jmGv3N95iS23bPB+uJ4imbNQL4Om3HWGLIinuR3m7HNpVXI0sy86qI5siUZEZgZmPLOQOFU0aCftZLnpHGoYd4K83slZGmOQpbKiLqUY8COl9pl6fGsZFm6liZZMicgRPMc7HRtLyuXHcfc9h6T6ok5OE83Dr7VMgenQDQmWxex+qAhti+207K4sP8qT+GJf6RKm8ihUSa0kGfl/g21Yf+4KqJNstNzzSM1u0QdcXjb2/L4nGrLqyDWk6NjJnDsvRsmm1qJ5+ajzpRLureWKeFft7Ek21P89edxOsiz1jI6yLO8jLBtxc2RL1ZSLo/oswI9OPVB42BSwlfun3hDoKdOqz1V6MmzHnoIxAXfYcnMNVx8IyzsqDfMrCvtT8rKyKuaiGM8nwzX06WYGKyFB3HUNG3evgRplIsB/+HCJOrnFWXLT+Xeh1RCKRWQF9UA7eRZO7ST5yTE4nhkEFWkEMiaCzEJ1D0NJ0FbHbWTZy2Ij8DNcAWjZj8URnw4TpfnUDazjCLtZ/AwUMtLVKBLsSfhN3enlJIbYsWHnk1lIkuE/Ps0vD4hypmrOxewUB7n95EL01vKCVuDxbfx1eAZiQp4xYrGom0zVGXIfq3xelrx7f0RhpQT5XN3YoOBbjMwCYmyF0/go5V0KCLKFh3Gacu0xXgl9mscrmfGyFedZZVm8Nj7n3ltJWgnz1oQ/wvHB8sZPe8JUaL/bM9MpVQmGcW6LOBJkIaOifvGlZEF5OO40vhrKJs02pAkw/Gh7uzvKr5VVpiWS5+mYogmQpv8x/96zpbFS9l7X+ore9Z1LCMnXt0P2/NbSxlVaCPP2hH7+Qpzp27kjo3o49BnjKuUFVmWfEx9oKnP4/G9s5BWBcX3lhjNRftUWI0Cid8rdMWxIVSTdEW1eTwLSJuu0A5d5FkL4n5gc2spYxc+F7UJw/zoBIoJuSjRcxkmX1M2cELMZ84Nlsh+TqpOuanb8akB0Z9MWdtcfG+GCvTd8V7D/KAb3y1PM6qiKJ+9LSvveyuuphUJeN+YS/MConzx0Vyy1xX+kQbER+HxdBUjZ93nl9C1LtfmU17o2kKtJ3PPX9OXhXFnckm53iwx7Jxux50CSeMiITKAk/2EHMryUmfWPZVQz7Qgyu8FyxqJ785UnRGHtYRbqCHp3aF2l5lQRZTN1pJFN3SHwiYhQVuGN63kWRe0kWftiAk4x4xJ2zB0EfcHP2JY6QzIchRlzhNNNkMcnpen0lBy+peewE2XtNZLO7STZy2Ij8TdcCUjZz/4I0tZhCy1mcL9AE1t+Yvbk4r/LVlSR0KUIxd3LGDxYcnJFMC5yU3JLnRswyV38dO5OpGI+NCnbFy4jEOPJUpnw8o2JeSO0z4nXEjTNnOt5DkJCXw3P8Hw8qJ/crRnzSNd0WZpQ5j9TkZPPIlraDg/rA7SQujg7OVbsy81b56E/4qNpQ5d5FkbdJHnJERiOK+q3Bmau8chfNLw+f8J6MmzHnqIAWq/dzyLNx7CSeHGt9vVgpxZZeQacEGDJ+8bT9cPpqQYrLKG07nprNv3rwp/Lk9qRwGpbIc1vAvQvX6XGv4V8pzgyYnBjcREIOrYaxfO/zArYXrJc9wvT06Pas5Oy0RDPM5f2psojK78rVh8208Tf1VDGhR7xDNmlZZWUbNSZsmTNDxTO76+OceGCUO5rVhC+vZ8FW1LiLarMZen3up71OIIeL2VllLbFm/BvIeap52IABuePn7IY6OXvHr9mtevnvPkyUus/T5idWIKlaXyVYdy0DQ9pn8wBiv7yZNvyJrM4b5beoiOL+fGtCSPKJu9327sNLm3Y0LxsTbi/sMnPDN5xes3r3jx3JCHbxz5oiFZVXrJc0yICydGtGSvlHpV+tv7MQubZkH2V3tWP/yYog9jv99nwl/i34WxXmPtS8XVtOGnxxn6Se2UrSwdD9oprv4dJOBxfgnLFq/knaKrvM4OpFQu8ey2+/gQlpaxlV7yHI/V5iEs2XUeD4XhaLahriAdGSk25pYG0vKZewt7UET63haLeOKZBmszCQlenBzSWJACGTkGH8RVUxKW6J94WT1RkQuTZ0Iu3joTHK7+/eknz9Gf7Tg6ojUHFBZ+tMddZjUU/V6oC5s1pJCN/nqL0bkFEclYmLYq4dB/EB/iwbvnD3hoaMKLV6958+YFRoZPMHb5xEezPbSX2qpwI6bf1mwCRgbaYmTw4M/4fW0sxq8Jlr6fsL8wi6qifMaawzmqKQ2v6L/vHu948OARhs9Fe4nyr14YYfDMlA+ffXi0sLs8UU/WTqt56adh/ogNJ8DemHsPDDEyTmzvl8ZPePTSloBQ1VWZ+N8+nB3VjB0Wic6wuMCXrG2bDVm+Ziy44ZNSL4Y/ZWbJ3GJMZaPc0qeKi2lDROBVhmUT7Za1BF0Oac5yFxfszhsj0e6irV7K291EtPtTXrp9wv/VVlpJ7V6oKVNvps1pmIgI7K/Mo5ZUtlxvNpqkI5ZXI/4b5DmO92v6suTgHRK7OJZXK6qIMSzafcoDDZnzA7k2rYPcrsjWfSOmHzWsvsX+xs/2GfceCrlI1s9PePjKjo+/Ut6fXvIcH+bNGfm8rYhIEbK0Wi5LzVl0yzelLP1+wvQSkhMr/bKkji8vT7F+4gjuKvZhBxsto420elxjHs98UtknLmrmfmY+S5etx1zRnR4ne1NMcgh2OoxPmthzauQ5DIuTU+Xzdsbaozih6eiVhDhCPkjj/rHKuH/87D0uqhv/BaJ4OK0Vq+86JUaeRPhxYWReMV+Vofv21BP+/VdsrBT4l8hz9GsWVy0s5CgDFZY+IVLTlPorEJvXD3lg8ByTl5JeecnzJ495ZO1HVGoL1VqgJ8966BFmwd5Jy9lz1vnPpOS8n/o5s5M5Ry+up7BXvbg8t708/Cxr24U88tSxP0wdCbbsHiwMWVE2V9/tWH3+myNXgX+FPEebsrZbVXkd8404irvusyFSRbrIc0ICX2wPM6XbYeyjFVowNpB7a9qTTZCgjovSoqjToNi9ztG7WHbxjTlptOltGiYLLYj5jMnxtcyd8uiP5/zHK+a2L00GWWWWPlEPbw7H7focSom2lVXrygFzzdPcF+OdDO/diCrSCpN0r/iWUq3Hs/2pKa93DCS3uJapwTjOak15qgkenJ/RhsyibPYOyzDyTofjJt6Krf1qy+tSZeZlvDQtfvzy4cH2kbStX5x88jqLX4HyNJxxFIvAlO9KF3kWhkWgxQGm9Dj2J0FYjB/Xl7Yiqyw/3VfdR32YRjsfpWO+jIIk/UXb3WkdHRLiCX61iXpS/fNUYui5tK1UaUS4M+cWL2f9NvM/icj8LtFV7rhpxlH3tDje0kmeQ9+wbeQKjt70SN4SEm29jSqyzOQqMoyHKfiaC8cnNpevhufouoaX/ulw3ceasb67FIIso8b8m2hMIv7Tg9tbhtGmXlG580W6N8NfFWk0+xS2QervSid5jo/B9+1eJvc6+UeGor24MK+p0M8F6LPJMEXEQbjtAVplF/XIVYppNzVnPYpxusGSse2pX1ohx6Lt8ldrx6Qzr/G8v1ier0JWsT273mvW/cEv9zCqXxOqSqv58vK5KNFiFBsfm2K6b5jc2ZC93Uzua/rAhGhcbyyhe9ualJEMeKl8pkJU7ruCq+ZvuDK9pfxayTEHsddkp0cEYXxgPO0al5VvS5GXz12auuN38txL1WEW7HCEKV0PYhep6Ie4Txhs6kR2WR7az7uaIqQ8wfMsvYoIQiS+p8nmd4qraUOoxS6aZhJ1yVeBOdI5jBoQaX+JeaPaULekot6yLPxVqzPTL7zF7cZcyknXSrVmmWFanEhJ+MbrfcPlxDJjzcHsN03vmrc6/gvk+YcJGwav5Izhn6RVYe/XUUaWiXxlx/E0hdq3Z9/wBvI2KzvxJM6afKrhgTzZM5a2jUpRQEqKKrVl3jLUm7SPVxpCpNJHnqVEjkKWuh3CPomNCFl6tKGTmLfz0GHB9RRHYMZ7nKFnYSkSIf2ypILoTzw7soZ50w3+zAHfTZjTToosq8IKIx/dZDLcgVMLlrFJzBHJkuFzjvbFcovyrTnlnRb7LjXyHMTTLf3lCxI5O8zhkaYUBAlROF9bTNc2NSgt5TOR+idzIar0XsAZS9Vnxn29ztyumzF0/66wXcJxvTKevLKsVBHXHXR98H/LxkqBf4k8B15jaHkpUjQzvQ/ba96q42fCztndaVo5u3yek9o2Z5ladN72hJB0+IqVoSfPevyfR4jBDlbtOoZqINgPznQpTJaMWeh70E0t0U4gd5f2FIpKChNZhYnG8DYtSHDj2KhmwuCXUXjkIRy1p45ME/4V8hxnz67edeQkq+TUC/ikLbJcK9JDnhPif/JywwhWv1P1zIaZHqSrMETzNZrCjQ+pEY40KPavtxlVLKd84u54QFdSDt2I8n7FyVUzOKNmGdiIibJMNhlVJ1zB/beygyQab4OViasg5Tuw4bWu8OdQrLfXJ6MsA7krTOeWfMEklBe7hstXCbO2msYNt/Q4XwK4uaCrnHjn7bOe12kKz1IgwYVDQxrJ5bbI+JN80GX/BV1jSQdpdaoIXVY+TEFqk5Ae8hwfE8zzdSNZp5bI6OfLXbQvIKNA8zncU1vlj/G/xIA8meQGSJ8TyunGU0MC323300bqo/xVmXxLS1rZNCDs3Rk2b9zMM5WqxfBgdA3yCGLTar1pKueWSkgfef56Zz3L918UlFgZnzjYIgeZs+Vn1Bk/NWPSh8szOsgNu/wDt2H+OR1yEefAnr515bqi2NTz+OrSFYEXmNtaWmkqTs8NT1Qyp/5B+shzfOQnnqwdyUZrVR387ekmWueXUbD1Yh77qRLGSO+z9M6eQb4COiA5oZRmfLs7nMp5pWy0ndmqMGA/mqxPdKyUbsVKY13k6TcOe5oIgzQD2UtN4qq8XX/z9tDYxESMTSdx2UXX+HXn7pyS4t2ZKNdhM+/lRp4f56e0lfdVvkE7sdS1j/+HIVv6SHtg89NixmWVkw4kJCT84vXGEax6o/oNkdbH6VlI6Ij6E7nqpqprE77cYmSRHMgy5KbTgfRFZIS5H6ebRJ5zlmX4Fd1hq5+vD6BcTtHumXuw2z6xft4Gy+UJ7WQVO7JJp95URyjmJyfLHZaZGo7ilH06nIYa8e+T509Xl7PkyG2VLNfSON3VIDOZchVj4uWPglYo4wOnxrWQh67+NfwA9rp25Hy7z9ruEtn4i7bzb2olMekhzwkJobwS8/Yq1XMuibA8SnchS/kaTua6mqMw4fNNRhTO/rdkSRlRHsYcWzWLc2px3zab+lE6q4xqk6/hGaF9nIW+PMbGzdt5oUKiorg9VPRXBhnttyiRaq1IjTyH8GrfKIqJ/snafCrXdc7brtyaKeXTyUKFzkIfawgQc9k/kUU37VXt0hATZpSV9o+3Zssr7ds5/ms2Vgr8S+T552OmVJCOcsxEkx2m/EnMmBKR75bQoVxmcW995txNj02QEnryrMf/cYRwbWEfmjfuyJRVy1m+XPFbsZLBDcRELRkpjZajiG5RIBrzQxMTJ/JKYzlnk5YwrCSEYSiId2mpbMOFGKUI600f/p09z9+5M70NhaU6tl6HedA/MzbStfLs/4Ah1crTZeKCP32xfAWLJ3SnurTfVlaZSUfME0OVtCItit2OTfUlwzITJafdTMPkqAlxeN5bR6vy1RmyeIVSfVcyvVcFuXNFlqsnp2y/qRg6wfYnGSCF7easxtA9VjoTxn1/MFcQk+wUr70Be/mVOLl3WjqnW1a8DztM0rJTLAlRvN8zhupS2aqTuKIwStOGXzxc0CUx5LvybAx07XmOcOLAJGk1sjzTj1lq3S+cLvLsfYv+VcrRbfIipXZewaJxnRP35suqMeO0tUo4Y0KUKSuqSKtkOam84FGa9i0nITzoIVP/Es/NVILWW9+KVv87+M3z7eNoVqsZ41Yqy8dqxrSUSKR4foVpGH1N7enpIM/xnzkztQvNW3RnmrI+W7aU/rWldspI8fabcFIZ0hG82jqUSlJ9ak7ntkt6RsMP7s5omxjyXW0hz7WejSrw247tY6REd5WYe85OzSGZhPSR52i3a/SuWI7u05Yqte8KFo5uR0X56k0t5l9yUBljceGvWVwhk/i3XFQbf0f32aiOR6hfWiKLI7n3LbHRfricZ6iUnCZ7RfpuNdMpV2GGi0S/ZaVQlVUK/RyP+72VtMwsyhdsx6o7PmokSBm/sDs6QNQzN00GnSMxn1IELzYOSlz5LjuWy7r2PMd4cWlBfVG+OCM2vhBPU8PHxwyrXoHOE+axTKntlkzqTU0pJ4esIuMPvlfTtTZsqCslu8xMqem3hISnHdE/jZknreRnyEvdqQ/+RGJogs0+ahQV7Z5tPI9DE1so2PYE/aWIgZz1GXsuPYZvPF4GG2gnEfeCnVnxSPPOybTjXybPcYEcGtOGFm37MlNpDK9Ysoje1aV+yUzZ7rtQzYsUjtGqPpSV5KL8JG656qhXlDunZklRRKUYt/Ot1j5M18pz4EOGVhfz9oQFqrI0sSc1pbEixvzEw2aqspRgzfo6iSuG6ZWlP4iVH/nWskINhi5Rm4N7lpM7imW5RT85JK3QqiMMA0Ekm9VpyXg1HT2quRSZJiNztdlqpx9oQmrkOQ6XW8toLslgoY6sve+rY9z/xPpQH/HuPLQYdjFlQsFwWxa1qUmb/uNZsCKpvuI3ZwKtpO1iklNkzk3tx3T912wsdfxbe55d2d06cY963i5H8dW2MV6C/10GNZHsvg4ctUstg6hu6MmzHv+nEWK+j9mDWtFn5hLmz5nDHKXfwkVTaS+UUbYC9Vn/TlWVhLzYQY9SQtFkac/2pIzCaUTgrXk0lMJxcw/gnKMmRZt2/DvkGTzPjaeqpEjzj+GO3z8Lc0s7eQ7DenN7mgyYztwFc1X6Yu6CxUwe0EKQt4xUHHsQO51VSoti/8WThXXkk2OhJrvUVunShqhv5pyd2oBmYwSJm6sqO/MXL2ZwkxLkkOWl7xErfinNlNGf3rCuvTAMZdmoM2gbjlqjwuL4cnuaIM/ZKFZrbXL//rI+w0gp+U3Ghiy54qLFKNCMb8ab6SztBcvWiT2v0jZ9JcHv2vTEhGGyMix8pCPbdpgNe8ZVFveVY/IhU61GUdrJ80/MNrSh8cBZzJ+vLhdLmNi3CcVkmag6+QQq6Qfigrk7XSJrmSnT4bDaKo5uxId94PhgKdlYXhqNvJbO80IT8cv1CmuHN6HL1KUsUKqz9FuwbA7dK+UgW5YyzHmcmgMk7eT568uNTOrfnoGzF6vps7ksWDCRloVl5CjRmt22qi6br4ZraCslksvencNmaTOFkuB1YUJiwjDR38uMdGTbDrVgy4jy4r6KzDxlpWHfpoR0kOf4b7xZ3YpGA4Wunq/8rZJcLGVi74aC1Gem1szzuCu/LCaI6/Ks0jIKVBrBQ12Gsc1e6pWUVseGcTMoUbhiv5izuYvk/MhK9d7rUWtKJcTz/cFsOXkuWHk5poqrYQ4XGVdDWs3OS5flN/mobQAnfMfyQC9xnzAcB5xKlt8vj5bTopDU3sWZfNFOu5Eb6ca5OZKOK8aQdc/UnAS/sd3Wiab9pzFXfUzNX8yUwa0pKXRthVH7sFHRtaEYLpCyVsso3GyXMFvTjoRIfy6OlVbUZBSpNY6nutI1mG+nehHR7tnGcO9bYgPHBL5mbVspzLcU/Ve/0Ol0VEe4yw2m1ZHarCbTj9r8TWdYEv5d8vzZaCWj+ndm6JxFzFPqF/kYnjuOZsJ2yFWuC4ecVeNHPt5dQCP5Np8SzLzhomV8CYQ7cWxaDXFfSUZte6nViZF28vwbm60dhCxNZ576vC3J0sBWclmqOPqAarb/hJ8YzJfqkShLborL6UHUV1NOTW5A87HLWahhDh7UWJqD89H/uC0p0isIhDqeY/nQZnSbllJHL1w2i67ls5I1e0UWGaU2A6RGnsV8YHuOUVUziO/NR/dVdwjSNu6FXjPb213cl5umQ86myAzvdW4EnfsOZdK8BcxVrrP4e/bEvlSTEv61mslDjdzwv2ljqePfIs8RvFqdGM2ZOVcbTurKweh9jQHyk27accDyU7psJ3XoybMe/4fxnUe75jF9yXktqw9RGE4rhyxzHpqveqNKFn45sn94VbIIRdh6vQGpLR59+uSLk71i6Ie8ZlkbKctkYfoet0plRSwBL09PPNw1G9gRX64zNJ3kOSrkNqNSIc98fcLMelIoTAlGXXXX4SWVEI+76wd8vDUnYvnpdZqeaSHPv94wt9dwrqgui/2B02mGlBXGQamRXNB25o4ccXgZraJuKor9p6N0+L6MDHlas9NRKxVUIBIrc2d+/kgySWLwe3ucsf3mY6Ol7z+eGyU/IihX14O4KieGig3D9tIMameRkbVEJ7a/1uZA0UyeifDiwuzGwijIQd3ZF/FOxYr89i0QW0vFjPLLll2DKoln5qfdlmdoPPVDCYEBPjg7Kqa5YFu2DiwrzyReevAZvLSR/jSS5xDXo3RJC3n+bsKsniO4+UHxtzpsjtBX2iNZbjzXnVTf9tVig5ADGVmKduOIe2pTZTiWpi78DhOykBCD+8NZVBBl89Yay83UjlGRQkPfuRATkyQMYbw/v4aJk/ZoNRLMVzchi5CBStMe6l6Fw5NjPWoJwzs18vyV62unMWedtlWcn9wYXRRZ1iJ03WajOqZ/WrK5Vzmk87C77H7Fz1Says/PG1dnxZrIVws29C4pDxktO/wy/tp0YZrIsyCH9vtomwbyHP/ViOk9RnFPWwSw5T66y4+cmcZdFfacwFfzA3SUnAU5BYE46am4rgEayDNx4TjemEu9bJJctWHDc20JqDSTZyI/Y7ill3yVMG+tKUKutbSEFvLMDzcOj68pzytQrOtubEO1dJYu8vz7HfN6D+OivZbZx+0SI6SM/iWHccZaVdf+sN8pj3zJmLcNu5x07iYVEHrTzIXQn9J74vn0ZhdtpYiOPOWZcE6HO0uFPCvqGPuTt0cGUljKztxtDW9SWQ2MjvmGxXuFZon6yN1Vbcgj+qLq2CM4p7LM+TP0E9YW2uTCiZ3NK5I7XeRZkKu2VUSZ1MjzZy4sm8TCHQZanANfuTC4oGiXUvTf9+f4Mjm+O7FnZBV5XoESvQ9p/8Y0kuefPmfonRbyHPZWzNvDuKTNC+x6nmHyeXs451LI0o5kWdqt5gxICWVZkhCNz+sjjB2wEHst81jgmRHyvAG5exzFI0J9nITy6uQKJk0/pNVGeLe0nrD/MlFjzpNUVmLdOdixmtDR2smzlIvg4cZu8qjDfHWmc1ct/0AydJHnBHe2jB7O9rtaZsxQM1Y3zYwsb3OW39NgM/6XbSxVxOP3agMN00WeEwg03SpkRBd5lkyiG4ysmglZxky0XWshqL0W6MmzHnr8c0T73GXv6ukcN9U+k36z3UFzWSby1h3CIxVPXgLfXh9kjLR/olANFt620BKGKJ7h/Y4z507z3i/JexnLxzsr6S4lkynViK0vtbECCLB7wolLV7D7pNlNH+5yWBiauShTb5MirDd1RHw4TqcMuSlZY53aPm9lRON/cS6tc4mJrVwLDlloZw/e5vc5du0Wrl80bXhMIMRyu1CYOanabp9OkuR7fRQzdrzgo8aDBgUivbk0p7kwGgvRe7eJlj2TEiJwuT6dErK8tJt2G+25Vb9huqCtfB9S8Y6Tef5Zm8qNweLBKU49e8f3cEUvR33GaHc/llzRkbk15Amz6hYhY46KrH+j2n8JEe6cn9CADLIsFO+6iht+mr859OFsMgmDr0i1pLDPRPy0u8i8WrnkyXcmnn6WMiRTgbDPtlw8c4znXknCG8/XF3sYLh17UqQWyx5Ya51ogj1ec/rcWcz8/xgDobYH6VU8myATZWi3922KJDByRDlxcJJ0nmVpJh14r4XIxfPl3UbqCNmt2fWITpLkeXk4M3a/5WusFrmI+MCZqY2E0ViEgYfeqR099AmTaU3kyYLK95nHW62BHpG8v3uMMy8t+RWZyP7iIly4MaQ6mUX715u4GQetSzk/eH7lEJcsBHlWbLiK+/KeM+tHseOZduM60vcUPfNnIWvJdlzQ5S2PtWJTh4pCDjpy1EXrBxDpfp4tqxZwwUabmScMkTdraSBkrnDLyWqhiKI/nm5jkEQ2i9VltaGDuKIZn11fcPL8eSwTD++V46fFbroUErowUzk6HTJH46JHhB07x1QQclGOGScstZDnWAJfrKaakIt6/U7pPI/X7cIQZuyzQFOCbznCXTk2rr4gr0UZLt6nagqGYb67JwWFXGSrMoiNploIjdMh6peUQv8Hc+OTUrtGeXF1WmPRJ5kp0mEJl700ewwiniwgh2jvAuWX/CHPArHfXrO+Yznx3LxUnXAcNfWgQBgOR/uKe3LQsO8JlciJ36KvR1WSQl6L02TDE7w0tUGcN1cWCONfVpjBq5+qkGf/m6OFrn2Gv7ZMwlG+XFvQivyygvQU96lKXTDv57ehqGi7Ep2mYvxFm7ckGov7pzj5zJQf4UnG+g/ebukqnisjR80R7LDSorls91BDSkyWSQqX/0Myoj6/Zl/Hksiy/EWX1efw11L9hBh/7p07yB2XP/NWmPtdVjTML08UN+zAgxSJ5JIQ+d2Fq2cO88RTS0RI5GsWNyhFFll/LgWmwsKTEP2OFU3LCF3Si3M6kohEOB1j/crl3HTSpmzi8Hm+TOjNrGLOmsM7NfUS5nyCwWWlfBMlab7VWPP4ifnA6VlS9EAxRm4x0UKexbxttYPGYt6u0nqvzlVhvxuSLBkTqO0w5ygfrsxrIebtgvTa9Vyt3b/+kaXOUzH5ok3rRGN+/yQnnyvJUsRHDHf1Z+k17TqREENm1ClMhpyV2PxOVQPEBr3gxLqx7HmhvT/CPI7ROVcmcpTvylVdAYbRgrS2KCv0QXdO6zg/OCb4BavbSedf56PapFMp+i8RodgekpxmOWky8LRKH4a8XML8tScx07aVLu4n744OpZQgtfWminlVbXL/79tYyojC495cSsvy0GrC9TQdTSb1u/fjRZQX39Ns1GXN9oYcCYIXT5Y7u2V/tWK6YYDm+SvwDoObSOS5NfstUp7QkR78f4g8i8+M9cH+/nGm9F7EmjPmWg1GPf4ziPJ5wdkl01m7+Cz2WkNL/xP4iNfLyywetpDZm56kcVD9Q4T7cHFadWr1Gshz7fYtCV6PmSjtU5GSEQzZj2OQsj84lE+mBxlQMTsZilZg5PqTvPNXrr0P1jdPsWPJXp45eqsaiwnBeD/dJE92lK1sDabvuYb9VyVVFePO69OH2L78KO+8A7WGqLk9nouUuTVnuYlc9tPtG02C59OF8v242UuN5Zyuvatxn3G/s5wm0pFdFesy/9BdXL4rTQxRzjw/sp8dq09iERikJYw3Dusbk+SZyQvUmsM9LUo/wvIk/etkYeT5t1qdEJIifbd0tNxzm7lYKzZcMNfyzhBub+8q+iwDlXvvwEyXffPLDYNNg+VHv5RsNYBdd98LkpZUg1iiP7/jyvotHNh/H6+IcIVCjsPp9Hga1C3NLm0rNxLifTjcuHLit9eZyj2hrJUR/tmGwzPbky9jFvK3GMfGG9YqZxSGfLFl1+jE8NKC3Q+jeiJ0ON8czjO+YSFkBUrQb8k+jD2Vnx+I8+ML7Fq0k8fmH9QI7E8C3+6jT9ksZCxekTEbz2D2UWlFM8Eby2snhdzux8TJRy0y4jceT04wvFEWZDnK0XDmPu7bB6sYYLbPttO1nLSntD5jrzhrIc/RmF4cLYxJGYUbLOLRV80UPszsCL1qZWHcFUutZE6ayF/OGyLfj521RHu2XlVzCPx05PaqPnKZL99pJAceW/I9PklyookIeMXFNZs5fPQxftFRKhNqgiDBJ2a0EX2Ygdr9Z3D6paNo+aQ7fvPD/Smnlm/i9KVXQm8pahgbjMGqVtRs04qbulzyIaYsKSQlrZNRtfMmzDSmMBd1CLpFT/n+/JpMv+mo2dkR6srR0WWoM3g877SSfDFk7W8yVLxPlik77cYd58M35af9wP/lLnqUyEjmklWYsPUClkFKZlG8J2aXjrFj6UFeufmqyUUY7g8PM7ieeHbOCjSbe5BHTt+V5ugErAw20qG0FK7cmCm33bWQ50henR4mz4harPkKjL5pJmY/3uylW62sTL6ly2UYzrNpfeX7sbOV7MyuW3Zi5Coh3J+HmxTbUyp2YeqhJzj8SEju3ajoIG6s65CYOb7cPK4pwoeTEPHVnuNzO/NXpkzkaTKatVctUE7M+/2bI/snSM4CGXk67EM1n3Ac39yNWTNUyg2Qj9K9FnL46QeVFZwAD0MWdpYMvXzUnnUXVf9KJL5vrjCxrfj3LCWpOWEbNyyDVIxd57dHGFRDCnOuQr/DQuYV1yNsTjOwbhaGn3mpI/Q5BrNV4+Sr45mLtmDtWVPVe3+58njDILnzoVTrgey+Z0qwkt6MCkrUmwcP3Mc7IkJl7CaE+XBn/SgqSo7Zqj2ZffQ5zkqiHxkRwJUVLZEflVh5Mbd/Kmv5OCJ9DFg3oAayrLmEEb6aWzaeSmPiOx8t73Jw8RauPLJRXW0XbfbT9Rqz2gjynacw3eZux9BVmRF9xsP4CnsWbufeK1et9mW0z3laVpLC9huz5GnaMvHH+l+mfTUpw34DFjxyV9Exyfhhz94hxak/ahbWOuytcItL9JfaJktuukw5g/cP5daNwNvkPONaCr2StTR1puzkts0XFYeiw0uh+6sI/S30yZCTtmrOxiTEY3NzsjwUNn/N2dz5pLlCEVanGCDN22df65Ql0+VjKCPJUrEWrDtnpjrHK8lS6TaD2X1fkqWkPpdk6S2XJVk6+ADvyCRZisX+xBjq1yvLXu37nsRneHOgYSW5Pvmr7gweWCroV8xn7i1pSs32Hbivi/l9ecO8fFJbyajZYzuWfppns7jAa3SqIfVvXebed9UxV8UR7PqMVYOrIMuQnzJ9FnPsmaeKzevv/ph5HSTHWAHqzX34J9GfjyFzW+eh/epjKfdBK8H34noai/rK8lZgwsZHhCiaJ/x/ysZKxk8e7ZOcAhko33ULarnltCCMp4f7Ip0FXrbjBl5p90uIpv3KuyOLaV5UfHvRJgzeckPYNdHJ9U8QI9rwyMjEXBj5h7DHOUTzOEwj/gF5TsDR8hFrBg9hULNmNJN+nTsy6th13oYnEGJ2hyc2rmh1JKUT0V8duDWjOq2KSkJVhI7zH6R7H1qo7SWWDEqsa/MWLWjRojnNk+rerAXNmo5nwbKjuH3/MwWFOVxjxZBmNG3alGbNpTItaN68GS3HH8H2u6pY/XB7yOo+4lni35s1a0rTznM5Z6ZKBT9a3GfL1InJ723bugtL11/mVUC4mjHyDxEfyK0lPWktvUdR7xbyein9eveg59JdXDf7qjZAxMRufYoRNUvJPcTlGs3DJG28LN2Ij/jF8zXD6FVaChHOTbXuR9WMhP88wnyN2dy7OoUFKZRlK0D1hoPZeNVezZiLx+v2EjrUrSwnVpLylDyBlVqPZutzZf9XPF/dX3N8zQiatmhNudoNaCratkXz1syev40Lj95h5hyoRQFFE+BgyN4FfanXtA0V69ST90vrVh1ZvPIAV56aY/vhq0ZFnPDJkGV92lO9YsHEumXKS8kaDRgy5wQO2jTjZyNW9utEjUqCcEllMualRLX6DJx+GFutwheJj809ts7oTp0mbahcN7GO7YTcLl93lKvPLHDw1pyMI8bnLnO7tKJKOWnvqHhflgKUqdWE0csu4Z78UTFYnZ5Hz4rSuBZGZtkqtOm6jbdS6KwyPltyZH4HqhUuICej0r25/xLkbcElPJONhxi+mJ9gZNP6lCmemJApY66iVKrflrkHjbWHFsUHY21wlNmD21KxfitqN2oo/8Ye3Yaw7dAN7r6yx1+RLIhYP24s6EIN+RE0mSlVswWjF9xWJPP5g0jbc0zvU5cy2SXjNbG+xSo2ZcgRc8UdCoQFYPbqNpvmDaJnj5o0aCf0hjQ2xa/OwJ60HDGdvdfvYOn1TW3lLBE/fCy4sH2S0COtKFu7IU1EueZCl02bsZYz91/zxl6d5CQhTr6CeHTVUJoIWS1fu778nS1biLZauIMLj99j7vJJM1ET+OZtw+1LW5k4qCXte9ShnqLOTTu2oeaAQQxftI37r6xF30SLN6kiwuMmMzs2p3JZiRiItsn6l6h7U8atvo5nsiBFYXZ0piDh0iqKjHzlqtG+5y7MItRGw6f37J/djqoF8yfLRZ5CFWi05Bp+yuGswlgye7CfKf3Ft9ZvSd1GjeT17d1zJDuP3eb+Gwc+/tCyghbmz4vrWxjZU7Rx3ebUb9JYXnbgwMkcOPeYR+9dCFbox+hgKw6OqENJKSFcptxUrt+TRQffqhnxwiYz2kz/ltUomknaAyfVOytlG/Zh4V2lNfgEL26sGU/zOuXJLiVKEvKWt1Q1mrWfrpK5Vj7vdKlMASkJVc5C1Gw0gp0PXFWN2YQYXK7MpWWNColnzMt/eajacQoH3ihbj7F8cnnOoWWDadS8DRUUctGqRTvmL9nNJQNTLF2DUvRpIhL46mXFzfObGD+oOW271/0jF53aCrkYzKilO3j4xhavn9Ep9FqY61Wmtm1KpTKJukCWrSDl6jRn4oY7SnNCBG8PTqVjmUSnQ4EKNejcbx9WMWoaKPAVu2e2ocpf+eQOGunevIUr0mzFbT6GKd0b9wMX66cc2jiZAb3qino2prGizg37dqXBoHGsPHKO10KHf9OUxvX3Ryxe32XrgiH06lmLBu3/jN+6Yvy2GD6NXVduYuEZrJGMRQV7YGJ4jqUTu9NVlK/fKrFss5YtqDWoD53HL+LMQyPsA39pJCc//e15cH0v04a1pUPP2tRtoSjfvjU1Bw5k4NyN3DIxw+1bpOjZWGzOLqR3xcRxl7tMZVp32cLrUDVd+9WGk4s6U73IX3LyJN2bq0BZGs47zwflfR7CWLUyOMIsoTcrKOnNnt2Gsv3QTUFAJb2pRYPEhuBk+YQD6ybQr2cdmnRqktzuDfp1o+HgCaw5cYG3Lh8J0SBs0V/deHByKb07taRMnSY0atpEXnbUqLkcvfYMI8sPhGp5dViALdf2zaBT21aUqd1I6Exh24mykyav5NSdl7y09dYcohvnwrlFI2hcq4x8u4WUDTl/2Rq07r6Qh35aXhbvzqVlo2lSuyxZJXtDlMlXpjothW14V8nT8s3xFss6VCSfdLRY7iLUbjyWg8+8VO2G+Ajsz8+gaZXycrtMPkYy5KNat9mcNFddI/zuZ8e9a7uYPLS10M9CLpJsP0kuhH4eMn8zd15YiP6MTDGWY3zvMa9ra6omz9v5KV2rMSOXnMc1+eZYrE8voFfSvF2mipClrYIQqcuSNScWdqJ6YSVZkubt+Rf58D2lLM0cJHSOmiztOHyTu6/tCQhRtHGMH9fmdaKaYg4uXbMVYxffTREGHGFzlmm961JaeQ6u2Yb+Gw6yb3gdikskKnMeqjTow9JjZilW4D8ZbqB302oUSTruTZaN8k0GsuyBUgxI/AeurBhD09rlyCbv38zy/m3RaTa3vbS7E6K+fsDY4CxLJnSjS8+a1FMa9zUH9aPLxMWcffQMh4+J49776V4m1C8un+MyFy5Dg6bzuen2TdXuCg/EYMdg6pYpnuh4Er8sOYpRZ8pGtq2YxvCa/1M2VhzBVqcZ06wB5eRnxMvIkLMIFeu1Zubep1rCt+P5YX+e8S0bUb6UokyOwlSo24ppOx+nsLf+IBJvpxec2TuPob0b0LJrQxpK7Sp+jXt1EjbVSOZsP4axjSefotRnoPThb5HniG+PWTSoKmWrNWTkzKu8NHzHu3fi9+Y9z45sZkiXxlQu8Rd9tz/jozbrKzXExeJ7/yEW4YmrPfHRvwhyNuTUnFZklBWkz4qU5zemhtjQQFytX3Jr3XDKSufc5ezI+itGiXV/d41Tq/pRO2seStfpyI6rbvL3xv76hJvFQzaPrZM8+BtNXctjWz9CY1QbP+b3F+wNVtNJyohYqhHzbpgSIIxHCQlf7Nk/sz01K7Vn/N47vJa/8x2mr49zcGYnKlVqxtjDz9E216QbCZEEuVryWpCCkfKzKnNSfcZpxbeK3/PX3Ds0nQb1ClKgZCW6rbvDH6daAjE/AzC9tY3uBWSUarOcNzpWM/4R4mMJ/mDNoy0DhVGXk0aDTqc4UuM/jbiIb3jamGFl64ijnS3m723wEEpKfRIJD3LF3MICG0dHnJyccLC3wdzKAc9gDUoxNpQAbw8szM3l7fv+nSnOLgGo+Vc0I+oHPh5umJuaJsrEe3NcPwShSDKqGVFfcbM0x8zCBkdRN0cHWyzNTLFx9lNJTqWCqGDcraQy1sllrMzeY+3oq/tdAvERIXh9EO2RVEdTC9w9v2gkdEmIj/iMs7mZaEMbefs5ivazFOXt3AL5naz1E4QB6IyNqJO9gxO20jeZe/Jd3VCN+omfiwXm1rY4iGc5OTlgY2XBe9dAkiMCxbOif/hhK9re0sousc/srEX/muHs802HZzwRET+CcBHPNX3/Xv6NFhZ2eAu5UPFFJEQIcmGJhXi+o4ODaD8L7FyCUhDU+NAAHCUZs7eXt7WTox2WlpbY+GkOD42PCCbA0wpLC8X4lNrYwRmPL2GqE6QmJPzmk58nVkJWE8u+F+/0JTgt3jght/7eH7AwM5OXff9eyK1o0zTJrajZ7y+euDqZYqqo8zvxHKsPfnxVJ7lKiA8PwknIqrmlbbJcWAi5sHf/pGS0isnTzwlrITv2Do5yuTC39OZHvLpcfMfHWciFjbJcmPPe7RMRGnTp75CPODnYJ/expaU9vkG/tTi3lBFP6NcAIU+2YmwnlrW2diZANLKy3oiP/omvvZA/GwfxXXbiu6xw9f2e4vnRwZ5Yi2+yFt8mbwMxFi1Ee7h8Vjbbw/n0QdRVjDU7e+k+B3k7mJo58knp+DNp3nG3Fs+ycxT1EzrqvSCnQm5UeyCesI/OYmxZYOsotZMj9vbWmFk74fNdg7ctRow3L2W5MMPF7SM/0jQ/JRD2xQNXRyW5EHrRysNfyKR2uYgL+4ijkIM/cmEtlwsHjyAVufju4yhk3QYH0XY2FuZiLPqk3KMdGSKMKPFvSnJhLeTC1D2IpGNoVRD/i6++9thYvxf9m1jn96Ksc0BIqnpDQkLkNwI9rdXGrxMfPqv3g2bEhH7Ex13IsamivJBPM2d3/AS5SQsivvng7mIm5FpRXowvS3cfgsKUOyyB0ACXFLo2JFathtFCJwjbwdxatHHSmLIWutZF6G11J4WAXG86/tGblkJv+qjrTW2IC+Wzj50Ys0rtbmuHS+D3NJSP5tsnX2ytrZLL2tm58+lnWoQ0gs8B3mIMWiaXdXDw5uufiSklEsIIcLUV489SrpOcnIS8WJiJ+dSFLyn20yogdHOgm50Yw6KMfAwrypg781kpg1XMryDchHxa24k5y9YGs/eiDYOTIp0USIjjV6ATpuaW2Mr7RYxhMb+Z2bjg/0OzBgsP9sLNWVU/W7r78kXl+ERVaJu3bcW88MfvJOZtDbL0XX1rjdCHfmK+VJm3dcrSJ5xTk6WkOdhazK1Jc7Dr51TnYEdpDrayFnrIBx970Q62f3S0m5iX1aUm6qsHVuK7ku0/SUeLed/ti7Ix/JuPiv5N1NGify3FOBT9G6QpQ5kaogUn8XYXc5vKuP+A33dVrRMe7I2Dheh30db21paiP13EHKDW53GRfPWywdRS9IlCPuxsxL1OHni6iblBfPv/jI0lyvz0x058m9R+yWVMzXDyDtbi3Bcc5Fcg9vIyifOBVEaaDxy9vmgpo4xIQgKcsbdV0itWVtj7fNFuH6cT6SbPUcEGzK2Sn2x5irHghhch6i66iJ8EvNpHh4oZabDwLoGpWyUakfDrJeuaLeH+R2V/UDimhweRRZDn3n+DPCfD7wYD6+VGVmISjwOVlEhcAC+2SGEFMrJX68W+939iEaT6LO8oJTv5i6k33ETXasFvU06tm8ays47JAz7uqxFLO1Yjd46ObLnkRIham0T/tOPc3HbkyJ2Pnsuu4altb9/fwi+ezq+OLEcZOh9V27kilNBXiyN0q54DWZZ8jDpuy08lwYoLMmRSCRmlWy/798izAv6P51NclouG/wXyrIceeuihhx566KGHHnrokV6kjzzHfODAgNJkkI4FWWqg01vvcLwP/ZbsxzaVYyk1Iv4X7/cNolyxKTxUiXX6yss9/RLJ83JDHZvZU4H/LQZLGdeKjeX2B9VgjWh/A2bIz9IrSY8Nr5Q8XnGYbu9LyYwZqTDtBn5ayGSk0x1O79nM/aRd/rHuHB/aUL4vtdtOMy37SwT8nzC/gyCxGasw6Zgpv/5T/Dn6O8+X1xPkuTQd9tkoLiojmicr2ssTNsj6HMTjx58Xh3ndZVzx/wZ5juDD7VkU05NnPfTQQw899NBDDz3+f4KwsDBcXV31v/9lP3d3d2Jj0xROlQLpIs8RxospLZ3n+Fdvznmm8sLvxpy+8x4Pvy98sLPB1skZZ2cn7KQwKu8QRYhENCF+7tjZ2WFva4fX5+989ffj6aa+FJPek70nO56+xtTSBi/5fodgOXnOLCvMgPXGhER9wdVaCkGwxs7zo9qeVR3wvcGghnk1kuf4bw7s7C6R5zw0GXJWNTW6z22GNMhHhmJDue6uaYd8MK9vH2Pj5lfJYWahJutpIx1cLr7llM4zfcN5t2c05QWJzdFxOUa+2sNq/iCaIA87zExNsbDz4KOmeIToEIyW1tFNnpe2kydVyTz4GF5K+wRTI8+hH33kYX2mpuZY2XgToiNMJSbkI7bm5uJeM6zcPAlSicPRRJ5j+RHghZOQFyl0RPpGe98Qov9DIRd66KGHHnrooYceeujxb+L169fUr19f//tf9pNyQX37lt7sWYlIB3kO5/HsquTIICNv7yP4ppWsfzZiRad68kynUpa1ImVaMn7nC0XyjM+8ODadWvkLkTtfY5befsXjY4fo36wmRbNmJlOGQlRp2ISmnQew1UTaKxjCq70DyS7LT9tZ+3n6/DTTe7WmUa0S5CjZkHkHX/I1LWHiSuT5rhp5jvW/x7jyoq4ZCtJ5zmO1Izeieb22PYVEG7Rc+5pgtf13sYGvubBrBVtMk5hmFC/W9pBnGczWew8OwbqZn9fdZbSTMn/m6sb2537aQ8MlxP7A4cIaBtVrQMOmTanRqAGdZ+3k8fMXPH9iiOE798R9i7HfBXmujSxnWbocV88MGccv7ytMrFMAWeYiTL/kprSnRRd5/o3Hm9usHjeJ1s2a07RpHSqWqkuXmXt5kuIYhhiCnJ6zefZsWrdoJ+5tSPn6lem05igWn5Ly1moiz+HYHJpMncL5KFCuijwhyKj9L9G03VgPPfTQQw899NBDDz3+tyEhIYG4uDj973/h7+8i7eQ56hWLqxWR7wcuMPM24el5Z5A9+9tXJqssL71XvlZcVCDqPXsmTmfSJjv+RHhbsbpKSfJmGcMDlfhlKWx7KHlkWSjdciQnXn+Ub1CPC3rK7j5VySArzpDjNqlvCE8izyUmcitI6fmhztxf2Vd+NEWWeqO46pLSQxBhvYcOJbIhKzkOg4/Ky6cxOD89xfKZO3FIIvAJXpwa3kye+a7CzEt46cquJBBmfohhtaSjPKoy86y1liNeBGJ/43B9BrWyFqLfIjP5Kr6/xTLalpMytxamRtPWdJp6EgeJx8b/5Nny+siyFqTGqE1cunQp8Xf5FGcOLGV8+5IULt+QnpuNCFZbXdZMnqMINFpP8xrtmH3wjSKCIASLe/OpWyI7OVou4XFg0s1xhNidYmiTpjRfdAc/uczEYrK4EdlEm5SdepvEk4ui/pDngUlne4bg8uIoG7fu47xpYvI2PfTQQw899NBDDz300EOP/ymknTx/vMFw+REHGai58hk6kqlqRLz/Zbrnz0LRjkuwVkoy9vP5PpavXsN9ldOvzVhRuYQgz6O5q5JFMHHPc2aZII1rjFUz/X66QtdSuZAVH8/jj6nkYpPIc+P8yPJ2YcK+Sxw9epj9+3cxe2AlQeqyUK7xIFbdclXcrI4gzvUpTw4pdPyS0nl9UV48ubKO5deUTmCLs2BTH+kwehlVZqVOnvG4w/S22cX9uRi8w1ht1fsP4r9asaljJmTV+nEuOQeYK/v7VCdDzg4ctlVaTZfCtlc2QpYpBwVrtKZnz56Jvy7daVu3itxRkLPlRA48tsJTbdVeE3mO+nSP6fVyU33kCdxVQq/B+/ooisiyUWH85cS6h1qxq3dJSrRcxCulJMO/rQ/TuZQMWaV5ikPioxXkOSeNRlwTtDkCt4eHmL/xDKZu+qVmPfTQQw899NBDDz300ON/Hmknz6GGTK8knSmbkcZb3hKtvCCcBiTEBHB9TElk2Yoz7mKAYiXxFwY7V7Ns5SVUuDOmLJeT51HcCVEmT0oJw1Jk2/bkQLsy5JYVZ8lztfPP1CHI8+AmBZBlb8bIaSsZO3Y0Q4cOZcTYCcw6dALjD7pj4AMfTqRWnozI6i/FMjjxTWEehhxfPY9bysc6xztzYGAD+RFXhccexy2VDGdRtqcZ3TCzaONSTDj6XmtysWifF8yuJSNf87EYJL8vlEfzO1BYENAx5xz/OBbke54VYdsnPBUXExEX4MDt7TNpmV8Q8aylaTn7Lh5KZ59pIs/eN6ZSO1NOhh+xSnFMUdQ3AybklZG3XE+ui2+NsDpA91wZaTn3rtr5b9HYXNjAsl138Zb7ORJXnovL8tOsz04uP97DlKUHefVBbSlcDz300EMPPfTQQw899NDjfwjp2PPszr52JckoiGDekWf5rLZKmToS+GWxh7o5s1Cm23ZBdQVCTTi2azU7H6sfrf13yPNXLo2pIMrIGHflc4oze1WQHLY9CUPNp3rrRrQNyxoVkCcum/5EsMSE31g82kyvedfV6vSLh/M6UkK0mazJcl76615F/fFuJwMrSnvD27Dm4Qet3xAXYsvunvmQle3FIYek5d8oni/vLMhzcWbecZeHs8uRasKweFyPTKWiIL0yWVmm33YhiT+nJM+RvNkxQJDcysy94ZIi23rcD2e2tJWRvWxDTniD38MF1JD9Rb8Nr3SeB5y057mUrDS1q3aiUetclJ55HK+/vx1BDz300EMPPfTQQw899NDjP4p0kOdI3q5vRfYMMnJUW4vV39iEmhDhyYleechUpD0bXD7z/f4Rti7Yj0WKKOu/R54vjq5E/kxFWPQ09ZVnbdm204ZoXPf3IZcg6mV6X8bnZwCPd01i4cuU2bT9r82gbn5BTPMM5coHrQdVCcTy4eoSmkkktvokrjpp3fEsEC5PLlavQG7azruXmGX890vmNsyJrOIk7inHh6dKngV+PWFcg8T97HWWPklMNCaQkjwHc39BF3KL9p941jolIQ7z53C/jOSv0o87oll9Hy6njiw7DWeewUtngrlE8lxCVpSGveaycFZ9smUtz8ijpvy9PHh66KGHHnrooYceeuihhx7/WaSDPEOU70l5GK4sY21WJm5W1YFILM0dCQ5WolgJ0bjfm045WW5aDF/Amk2bmbzR9M8qaTKSyPMY7v3QQp5XCpKnuJoID/a1LU/uEiO5F5CGPc//iDwLfLtOz7zZyZSpMqO27GPpjF14aHIofH/J4mZFBTEtzIBT1olEVyN8uTCzMZllpemz+1kaskr7c21NW8rkq8XAXgMY0KoBNTuPYtdrHyKV65EW8vzlHkPrSiH5Mnrus01OuJaSPEfjeHISNbLJqDzzCl7qzfzbj4O9c1Ci1hIspT8tTzKkmIyspQdzwlY9C7eA5wPeOUoPicbzrrTnWcjFxCv4fH7LshaCzBeswaqHHkKS9NBDDz300EMPPfTQQw89/meRLvIs6BTOW/pRSZCs/A0HcMdbWzBuDJYPTnPyyUu+hqmywLgfNmzrUpxcmXJQquckDjtrYonvWFqhCLkyduakpzJ1+sKLXb3JIPuLvmuMVbJR/7bcQstCGWm+7CXfUlsVD7zNkMb5kBWfwH1vXSu8uvCdp7ObkV20RaYi1Ri2x1lLRuhYgl7tp1uJLOJ9vTnnopQtTQkB1zfTvIAgql02YfojtXjlnzjeO8uaJad4YPKMx9eucvWGIZYBGmLp40IxWVlPTp47HrBTXFSFy4nRVMghhW03ZKf51+Rw8XC/h0woIaNM25W8Vzw69pshi1oXRVagO8esVd0X0UEPmVaxGN3XvZf3TcLXN6zvW1I8Nzdl2y3lvKnyt7twYdFMLjrLU4Ljc28OxaVs24MvySMKftocp3uNLOQsO5DTVtpSp+mhhx566KGHHnrooYceevx3kE7yLBAVgNmxKZQXpLFQ3Q6sufAMn19Jq7dRRH1+x9V1m9m3+y6ev3+nJJQJYdicGExWWT6azbnMd42M04sDXYqSPUNeFhl9J+SzBwZXXhBOBLbnplFREL18FYax7k6AfNU60vceK1sVoUr/NbwLSo05x/L5iSDaxTLIV9DXPvFFLWl0mhFmsYnaEnnO14YjbroIbyR+JjsY1TAnJWoOZ4uJ158EWrEOmByeSIPCpWk4chNvfH9qIeF/EPvFgi3d/iJ3rqq069WTnr1707t3L7p36UCbVkNYut+aX4qPig21Y2e77MiylqDpFlsilPwdUbHfuHd8Bk2LZhUEtxgtZ73ANzrpO6IJfr6Rbhll5K3Sk1u2Sa0UQ+CdpdQvkIHCLSZwzVoRqu7zgsMTGlB39GG8QpOC5iMJenWAPrWkI7Qyk7dMA5q3akXLli1p0bw67RfcIiBe+tpfPDkyQL6fvkqXTVjKiXoUgY9W0rSQjL/qzsfA429GCOihhx566KGHHnrooYceevwHkH7yLMcv3N9dYdWEblRv1JrqdevSqFEjOrbvy8a913hg6kLAN22UNIEwu6vM7N+ARQba9gDH8e39NjoUyUbRSo2YvfcKjv6JK6JRPwP58GYvC8bWoGiVGtQX7+3VsRdbDz7A6bNuGhxqc4GF/RpQq1wxcmfJRKaMWSlcvib1m7Vii9HXFAmwUkWYF3tHlKPGzCv8TI3xCjL6I8CIW3vH0L7vAGqKektt1rJpQ8aMXsihG6/x+ZnainMiEqJ/8P7YeKr/Ja0Wp/xlyVOW+ZdNOLugP63qVqFoDkHws+QgT6ma1Kub+F7pV79BbRrWKEHHEfM5cdWOgOTIatH+VscZUqkIeTNmIkv23FTpMZ9broqPjP2Jo+ExpnYpR8XKteXPajy0H3NO3MLpm/rm5ig+Wpxixohior0T61e4cD3GLj2F/dco+O3MsfGNKV86PxkzZSJnvuLUHr4Ko+BfuB2bTp28EvHOTalqkowt5oHff4FEywn930BCHDGREUREKH6R0cQqbb5P0LkRXyA+lmil8pGRMSqOlNTKJ8RGE5X07ohIIqPjVPb+p1Y+PiaKSKXyUTFKb0+t7uKGuGjxzuTyUUTH/SmU6rert1202rcn/Tde3BetWs/o2Fhi42KUvl20XVQMcYoHJEjPVvs27WV0VFStfyJi0llHlf6JIEp8o1ITpUScmjzExKr2p+K/OvE3ZTntsiT6PVa0o3I9o4Tci7rHCHlIuiaXh9j41OssPThNH6YODfKnNPhSlb8k/N2x/z/YV8lIfZClua9iUumrf6orYqOU3hcVhcaUGAnxxMZEq71HGrdSW/8pHynpWZ0DSYG/275pHPeakCB0jIpcCD2gfL/OstIYVCmrWv/EsgnES+2h3J6Rov/i4kTbqemgmDidjvl40dbJY168NypJgaYHaR5oqohVHrtCHmLS/Jg0ypImCPmKiVKUk37R0SpJWnVX4T8/16u8O7n83+nftJeRxpPuKU9tztIoE4r3pRiTYqxqqGPi61LWUSrzn5Hb1Br/DxJio1TmuajkxaO0IFH2VORW8S+pQ5K91OU2QYsOjFPXK3I9rvhurXpTKqPU3splNOIfjI802nM68U9sF+W20ag3/zn+JnlORMzv7/h6eeJgb4+dnR1Ojm4Efg5LdSX3i8UtNk2cgUHK/FpKiOarrzeuTg74flY6JFiOeCJ/+fPBzQF78V5XNz9+p0Hm435/wcfVDgcnVz54euHl5SF/vr29AwE/Yv5Wo0b+DCDwV3po908CfUSbiXpLbeZg70LA5/SGjv/C5s0JVm87h4OX9B3KPwtu7u9Bjx2PsHFwxdHeEdcP4rqnBx9cpG9NfK/0s7ezx8PTm2+/1HcVC6UQ9pkPop3cxTM9PT7g4uHDtwjVFor45oObU2Lf23n5Eqy1DxL4HeqPt09iHQMDgwlP0hRx4QR5OODk4i7/Nw93F5zc/fghBvvvr4F4e0h1d8PZQXqPDyFR6VFufwfOHB7YlnmHrAhJs0DE4uP1lrObJzK6YT3q161L3boNqN9sPDP3PsLxgyPGz25x672WbQ5xUTjZPeTAwsEMqC2VlX5NadJhFqvPm+Ln/Z7r9w1446p5ZMVG/8L89Tk2j+tO1zpJ5dvQpt9y9j5wJsjzCSduvcfzs2ZlFP77M8b3d7Oib1vaystKv250H7+Viy89CXS+xcHbLoRq2bAf8s2De+dWMrt9U5ollx/A4IXHMLTyxM3iKkcfBiruVkNCFIH+luxcOJuRtRrSWFG+8YSpbH/ribOrNcZXT2As97P5cnvVUGpWqkbt5PfUpnqlSlSsUJWayd9eh5pVK9Fu1EE84n5gtGkktUSZWsplKmsu02bIVizUvzM+Eh/vd2yeMZXhNevTULq/Xl2aTZvNPgt/3BzfYXTjHG/k3fuB83P6UK1KdaU61qJapYpUrKhchzrUqFKZ7lOO4azWLQmx4bi7PmfNuHEMqVU3UZ6EXLWet4zjDl/xsDLi0Z0bWGreAZKML3ZHmNWmP6cc0z4JxUSHYv5KXZba0qb/cvYly5IpXl8T7w9zPM3Q6lWpKuqZeG9dateoSqUKFalSvVbytbp1qlOhXEe2GwfoMDBCMd4ynAnj9mKlbUeQBnwP8eTBhdXM6dCU5knvq9ufQfOP8NjSC3erqxzRJn/KiDdnR/fWLLvkqSM3hSriRV+5OT9j9dix8r6qJ727YX3RV8s57ij6yvIpj+7exCqVvvpsI/qq7UBOp6Ov/iAahyvzmdhrPoaftSmtBH7ZnWJQtcpUS1NfdWbny48p+krSFSYP9rCiXxslXdGVbuO2ckGuK25z8JYLPzV+b6zoKw9O7VzDuHrNaJVUfsAAFt0zw9LPH4vLe7kXIN0r5rjzC2haoTI1k98jxkzVymLcVqZ6rTqKa9I3VKZuq5k80nlyRvr1ekJcBN6eb9g4bQrDainGff26NJ8+l/1Wgbg7vOXpzQu81eDPjY8Jx8XhCStHjWawkly0Xbia0y7f8DR/zIP7d7HVIGixUWFYm95mUb/B9E+aD5o0ouvanVz1DsHr5VVuP30hdJu4+cdzlnZqrNJ/dWrXoLLo08pValBHcU3SQVUq1GP6kTcEJ74mGTGRP3hrfIm53frQO2nMt2hK761HuOsv3vf0LDdfWhCgk5VGYnlyOhMGrODFt7ROnHH8+uXH1ePbmNi4Ne2S6tqzBzMuP+OtsBPsr+7hpvtPDTojRsjSB07uWM24+sqyNJDF982w8vNLlCV/TZWO5nOQA/tXLmJU7UY0VZRtMGos602csfd05vWVwzxR6DhVxOHvY8b5bVMZ26g+DeRl61O/6Vim7RT96ebMK+MbXHujIceLhPhoXBwNOLR4KIOS9WsTmrSbwfIzb4QdYcatB48wcVLM9d+NWdalMZXV+reK6N9Kon+V5xh5/x4141eELZt6N0tbmYq1mbjHGPWNcbFRoZi+usb8nv3omySDzZrQY+N+bvmG4P38IjeN3+MndfXXR8xpVZ8qNWr/eV+t6okyWFVZBmtSuXwjFly0J+zHW5a1b6BapraijIrcijIVGjD7pJlajiMhARE/eP3sAnO69KJXUlu2bE6/Hcd5ECjk1vA0N17b8FGDyRgVGcKbp0dZO7QDHZP7oSMdh63j+FN3Pn24z+FbtgT90CTLQm5/+nLp8BYmNGr1R2579WLWNRNMA79gJ+T2lmeYCtlMRAzfvrlxfOtKxtZrSsuksoOHsOyRJda+Pphd2ceDj9K9PzE7MZtGFaqo6kBhN0g6sEZtVR1Yv+MS3v78jf2FBTTRpjfVytRrPZvHysfryhHNl8+OHFyzhDF1GifbdPVHjGLtM0fshD3+9vJBpWNylSBsJX8/c7bPm8mI2g1opCjbdNJ0dpp64+JiKey5k5ioUzo1RH65x7K2kr2QdkNA0ptW72+xqO+gP3qzaWOhN3dxTdKbLyS9+RKP9NA1HfhH5PnvIZjXp1cxafFjrecY66ELcQS+PcyCUYO57KO4pIY470tsfubL1zS7YPVIRALB9xdSM6+MDNUX8+pLam4ggegvPDw4kxqVS9JqxELOG7nh7eODj48vHq4mHFsygdali5G3di/2vE+Z+izut1CkS/tSplJ5+s7dxYP3UtnEn6PVXbaM60e9Yn/xV6cFPPRMaViHB71m1ahmFK9Wh4kbL/LSPqm8JxYm51jSsy2VCuahwrgTuHxPOREEOdxkbOeqlKrfhiWHn2DtnlTeBeNbe5jcpA4l8+WkxdoX/EwhTzE4GR2gc4OSVG43gG1XzHDxTipvw92jqxhcqRyFC5Zh+GkPRZk/iI/05vLWblQsWYYGfTdy74EXvopv9337guOz+1OhZAGKNezNJV+phDOHRvWix6gDvFHc5+Nzmykl8pJJ1o2DNm6Ka47c29CLxo1G8jjwM5en9qHb0D28Ti5zjxllCpBR1lmQXxfFNWceb+tPk7q9Oeclr54cceGunFjVhrKlytJiyG6ePPFOrqPPy6fsn9qLcsXzUar1KO7KjXcrtvbpSv8ZZzBPus/zEiPz5CBrFkGOXLwU77Pl6uIONGk5XlEuETG/rNk9uz4lS5ej07jjvDRRPENMqp5G99g0pgNlCuemcq+FGOtKRR/9mYezqyKT5aP1EkNBRVJH+KdXrBipTZbOsrhHG7ksVRx/ClfF5Pft/Xa6VOrH3pceinvdeLBtAAVk2Wgz+wIO8mviZ3uOsTWLM2y/DRrEUI449ztMbyBDlrkt2577p8GZGYPL88N0bViaSq37suXSe5y9kupsy73jqxlSuTyF/yrFkJPuijLaEI//pfGUzCojS/Mt2IWlTmJjQi3ZObMuJURfdR5/ktcvFO8WfeX19C4bRyv6qs8iXuhyFEd94v6sKvK+arPsaSrH+qVEwhcLdvTIJcpXZepZBxUP/x/E8fXNNjpX7MeBV56KNnLlwdb+8r5qO/fSn76yOcuYmiUYccien0qd8NnxNuO7SLqiNYsPGWDlprhfPMfkttAVTRN1RbPVxvxQN1Div/Pq2jQalC1BhTbTOX7RBb8kXWFrw/11U6lTvhi5i1Viw3upwDeebR1H25aLeOCZ9J63bOtSWf6d82+a4iW/5oXllXm0q1SPzabyN2lA+vV6nBQRtaIVZcS4bzl8H0+NhE6Sv88H7xeG7J3cg3LF8lGm7Tjuf1IUUiDmpylbJtcQclGe7pPO8PaVov7SGDa8yZoR7ShdKBfVBq3irZoRGfnNkMUDSlG0bFWGzruNjbmirJhP3O6cZU6fRhTPn53GM07iLH1GyE1G1+rI/JOvcZff543loxXUlcko03IdLxR19vE2Ym23CrSbLMopEfZQvxtM7VSQIpUaMmmFIU42ivu9fHESBHJCl9oUyZuN9ivv4qvD6IwPfMW6DtL2r7osuO6ahrEbjq3RCtpULkqpJsPZecwOXw/Fux2dMN65gFY1SpE3z1/MfRCiKtNCll5enUr9MiWo2HYGJy4pyZKNtZClKdQWspSneBU2vlOtSUL0R+4dHkS1EqWo2X0F125/+DPnmL3jwqLhVCxdkILVWnPMWVEoCTHfeHp8HrWrlKT5kLmceeKqkEHRr24vOblqMu1LlyBvtc5seZlyQSQ+3IMzqwZTrlJZes3cyp233ol1Fj8Xm4fsmDyIBsULUqDdTG67K774yy3G1OrA3GOvcFPca/1kFfVF/5ZquhpjxTUfn2es71GR9lPP8+HjW+Y07sCsQ8a4Kv7d9vl6GosyJRqs4FlyGRO29K1M69EHsFVydv3+eJfZPYpQpEIdxi55gIOV4n4hEy43jjOtR32K5ctOq0VXE48T/XiRIVU7s/yyBZ7y53phenM+VcX7KnfZxtuk93k+Zmm7snRddFfYSE+YULszi8+b8kH+796Y3VtMTVGmQrvNf+ZqL0NWdixLx1kXcVdKj/Td+woT2/9FkcpNmbraCGe7pHf44HBpP2M61qRInmx0Wvs4hdPnu5cBs/vVoUStJszefQdTZ0VZH3fePj7K7PZNKFsgF7XE+AtM4QT8jZXBElpWLEbppqPZc9IevyS5dXDEaNtcmgn5yJuvKIsM1bxqcd94fnEidYWMVGo/m9OX3f7IrbUld1ZPpGa5ouQtVYOtFlKBzzxeO4p27ZbzONmuesmG1mXFOKvD0gfmClvTE9PzM2gtyaxDMC+3j6dd68U8TJ4L37C1cyVRpjoLbv/RmxZX5tK2cgO2mMlrJ0dCTBAPjw+nRslSVO+6hMs3P+Anv1/8LEy5vHQUlcoU4q/KzTjkoCikQHyEJxc2dxE2WxkaD9jKg8dK9tzr5xyd2Y8KJYQ917gfV7Vwl0REYrmtPRlkGSk39DxpcHsTGWzAov4lKVK2GsPnK+lNb6E3b59hdu9Evdlk1ikV/fdP8F8hz1Hh3/D09sTDww2zB7uYPqQPJx1TV696aED8F56v6U6+ioO45PErhaEU9f07b+7cx/Hzn8RfeqQRCa7sGdycApmEkZWxFquNP+luw5gfPFnXWxjceei87hzeYSmjF+KjfmK3dzZVijVh0n01NRD+gZNjapIrR34mn3/Bj+iUZm9M+CeezOhB/kqDOGCtSoHivpqwvHUuspeowlYTNyJTVDaeqBAXTnevR47Wy3n1UXUW+W53hpHlspG3QVcuOgYTnWJIxhLu/oJVtctTcMxZglS4fywe99fSJlcOyvSbxYvACHFFDXFRBL84z7BS5ai57qXiYiLiI/24ObsHf2XJTvOFl3FJycyJC/Pl8er2FKjRGjn3iTTlypXL3LFWNn5tWFNNTFaZRnM/TOkDQkwxunGEK2+ceHz7EjdUzsOzZ2PtMuSRDee2cnK+H1aYXNvJUUVS+vhwd86Pa0fuLDnotO4h3hrCW2J+unNtfhMK1OvBVT9x4fcLTl24xVMn5e95y/wSRSiYfzrPlR/x2Zh7wkC94JL4Z9wvW/b3qU+2LDkZuO+NaG/1DkkgOtiGQ2OqUKj1eAzUjHZlhPlepHf+nOQQxkjJFnN5loqnMu7Lc5a1FLJUsirbTNw1y9I3Z051q0uOtit4EyTVLY4Q6xOsu+imtDKUgNPlaZSVFaL/ltcqmfJjXm9g/V0PPmmcvEJ5dXQalbNkJqssPz02GvBJMwtUIA7vR+tplzsHpXpP51lAeEr5i48i5PVFRpYuS3VB6HQiXshRh9rkyijGfu5m7LLS7W6I+2XNnh51ySr6asgBUz5Hae6rA6MrU6jNBJ7oWBX95XOBXoJ0Sn1VqtV8jNO1MyUa96fraZQtK9llWWk44RguKcVUVCeSYKsTrL/0Qamd4nG8OJUyoq8GbHur0ldRrzaw7q4XXxQG6w/7s4wuL3RFvS5cdPiqWVd4vGRNnfIUGH2aT8oPiwvF/MQMqmbNInTFCkw+RqUkV5GhuF+YRqkyJVn8XNQwzo93j05y+Kkyu/zM5ZGNySVrxk4rpfXTmI+43l3L1lcpnpqIdOr1+HA3zoxqTS4x7rsIOfQJ1zDuf7hxeXZDCjToww0lQzA21ILtnaqTOUsuRh6z4muKhkog6osFu4aWpUjHGZgorW5Ghzxlcd1iZMian7nXvfmtLtAJcUT4PWN+xwKUG7YdO4mffbzKZmnFUqmZfrocop0sJ7V7n0S+iK9AgtNR9l0xwlSxzBj56RaTS+ckQ95yrH0aTIqgrnjRpx/uMrZJTmpNP4tK7lYVROJwdzl1hQxmk+WgxeyLeOpq4IQo3O6vpEm2zBQUeuyuR0TK/ogO5+vj1VQvk5dxl/z//HvcT8yOTadKlqyUHbCKFxpl6Sdu56dQskwplhj9UTYJ0UEYrh5M8SzZqDPlMDYhKb0B8WK+fbOzD4Uq1mWPlZICiv2FydZBlMuam7bLj/EhNDrFe+Ojf+F8dDG1i9ZnxA25t/cPIrw5N6keebPlY+xJI75piKCLjfiMyfz+FKzQhx3mCiXgc43Np1/jpbTs+uvDUTqK/q3Z/RjKb0lwPs7+6y+wsDDgwIUXuCsNkd/ep+gqylTrdAgl3zC4nubQ5Ye8UswlUcEPmFUpHxmyF2fZw09EqsugJBNej5naKg9VJhzGTdIPvpfYcMacICV/QbDVTprJctF45GUxav8g3m4/u26+x9b8HrsvvCdQSc2G2O+llahj/YHnUZ7aEhwOsvvqC5KGfLj/NcYVz0bGApXZ9Px7SrmNi+G36w1GNMxJvTmX8VEi3RHed5hROys5qzTliHkAUSnmmDgiAy3Z3aIqufrsxkV55TkhAqcbS2gg9Fjh9lN54KVBbqPC+XRvKZVLF2DKjc9/ZCT2B28PTKaikNsKQzbwOiil3CZEfsfp1HiKly3LylfiybGevHhwimPPlSeEQM4I4p9T1pb9jkpCEeWH471tHDL2wtTgFEeMlPVmEJeGNxR6swW7bJQ87jGBuEh683ViTRJivmC0YQSlxPioMWEfFsEpnYzxQkZN9w6kcMWabDf/8/XxkT5cn9GVAsKea7X0Ou6hKWZjMYS8ebCiDQVqteOMq/rXK+H3EyZWKiY/FjlP+YFckYc3aEd0yBMW1S6KLGsB5t300aw3fY2Y1yE/5UfsxC4tKwlpwH+FPHsbLKCIaAiZLBPZKrZg0HmHNK2E6KEJEXgabKZN+bI0rTeYHVcMeGRigsnbt1hZOvDu9j1MbD6hFmGtRxoQ/n4/A9dsYU7fOpTOLqPWkucqKy8qEIrU8dI0GuWVkbvdOt7qsvR/O3F451pmnnJVXBCIDeXpxrYUFwZdgfEXVY1NdXx5xqxlWznwRGlKifzCyXHFxZjKToNNbxQXteDDJXrPOcILl6RRl0DsR0uWtxfGZM6qjLiSclVYGb9N99F23nU+/kjSSnH8sLnMmOoyspTsydZ3upbVfuN4dxOdVj5X/C0hCquz0+SrIzmaLhCTkPaVoFhhLG6b14nlT8U9XxyxsrHCUYV8mbGiaglBnkdw65tSI8b64+VuisULd5ztLbFTKWPJ2lqlBXkewrXPSq7luI/4OL/glZv4f0E23hwYKfee52q/hlcftfdvlPst1szpyaZXon2CbHhv56jiJSfWhDnFC/NX/ikYKhviMZ64OLzF3Fv8f/wvHq3tSinxvrz99uKgo0nDrI4wb/YwDpmmmLrlSBATtd2FBbScv4H5tTIiK9aU5U90sOfIzxwfKyafDDlpuOWt4qIWfLhILyFLr9yldosj3MoAw0BlQzISm3OTBSErSN8NYvworsoR/IwnFsH81CDrMf7GbNu5nEETx9CjgJCLDut4/VHb4Isj1P4642rKyFy8Gxvf6FqCj8Dl/mY6rnim+FszfhtvpdvK7SzsWo4CObLQarNlSjKehPhQHqzuQgnRV/kGHhAGluK6BoRZHmLO7BEcVTI0lJEQ8x1bqa/mKfqqeDNWG6WdPcd9d+L6oQV0mrGAMSXFHFtrHJecNLwrNkrUxYAnH5UdfBFYn5lEadFX/TaZqPbV12cYWnwjLC6B+M/WrOggdEWOKgwT5FsXIkz303bONQK+/9EVH9/up3t+0Vele7D5rQ7BjvjIjdUtGHtODIjQT3hbvuCNSlP4cW6YZAQ2Yev7wD+O44Qwvns95am9ZnlJr15/uXcYlUXf5u60Qeh1bTcKSXe9xso5vdlqohjscT+4vaQdRUXZ/MOO4aHDyAk13c3M2eM4nUTQYgI4Maa6MIozUGzmLUK0Ch98NlzI6HlLEiOR3I15+uELP/40Bl/s9tFGkJBaPY/iqbgqR5gpry3d8ZG6IMqNHb1LyG2y6qufa5d1Ab9b4xmyeBtvAzS3RdxXa87sX0z3aTMZVlRGxgYzBCHWpi/jCXW5xrBS4r78TZj5UJneqyOS59taMXTfexLVppCl1/volk+SpV5CBnTIUngA11e1YNyZJHmNxuXWUpplkZGt1gTOO2vcVyBHwhdLDi9sw5xbCs+GmAvcbs6nxV9iLmixjOcaQ8EViHDnzL61TDjiqLggECeI944ulBHvzj/iNL7aXy105EsWrNzMzkcKR7vTM9G/n1US6wY7HaS96N8aXQ+hMhrDzHhj44XPy7e88vxEsFKZELdjdBZlqnbYh5IVIhSfJe+snPGQiGm0F/sHlyeLLCMVlhjotCE/3p/OiIXrMZGIjavQFYLd//FdxxP4fitNpNNThp0To1YJv97ywsqXgFcvMfH5RnJuWSG3nyx30kLUsV7/U6gsTP56z0sLD/wlPRvlzOZuRYTcZqXO+pd/dIAG+FwfzeAluzGXh0AL/PBgR9/syDIXpcsRe8VFLXA4Tqc553AKTJqs4gW5v8DA4kJu/2rBfMOkh2qA6G/DzS0ZftBS4WSMxf/FTjrlEfZSuf7sNtcxF4d5c2llCyZeEuPiZwAelq94r6JHvDjep7bQE63ZbR30R5cn/OSbj7BdXgXgY/1SbSuJL2eHNJA7HbeZfVTSm78IEXrTSN4UMXy4v4qW2WRkrTaaU/Y6tpJ+s+PYklbMvJ7kEY7C/NRkefLknC0WY+irfXzE+BiyZV5nVhloU45xBN1ZRsvF21nSIgcZ85Zm5HkVCVJFjD/HRleT683is++g66Ciz4bzGTV/GY8+6JKatOO/Qp5jLY+zqHN72nScKwwDJ3249n8AMQ7vuDZjAH1btaKV9OvXh2n7L2MTrIuF6aEdP7g6bxbbL5nj9XYjdfJnQFZlHE8DVRMlJSE+8CGz2v0llHgppp+1UTk2LSVi8bJ1xdpQYkmJiLHbQ7sK2ZBlacyOt190TgJyQiI03AeLPz7c0MfTKSIUXYaiQ7ipxaj5g1De3rDhi49CoyZE4Xiki6i7jBItl2OaShhLXPxXTC7aEpG0tz/Sj9sLa4vy2Wg+6YyK91sTfoX48/LSH2MiIfg16/pKhlshRu55lWIvkyqCcbM14p6pNq2hhTzrhBbyrIT4T4bM61RI1LEEU05Z6g6lTfiEncVzHltquUsbeVZCnOdlhjfMIwhsZZbcdVdZBUyBOG/evzXBxEHzXVFf3rNu0CwMvPwwWNlQ3s4t5l4hWMuc9vPRNApLslR8KLcCU5elN9dt+OqnbfLTQZ61IhbbK3tZtvAUbsIAmCcmTVmW5qw38BQmrwZEBXB/SR3xXVlpOv4kf0aVZvz6HsCLS2oxZir4wulJUzjw0JEPj+ZROmsGcjSeh5mWysd5XmRoA9FXGauy/IGWOiZB9NXbty94qaWvIoPeir6ajaG3H49X1BffVJjWC6+RIu+iRsTj/+4qy8ZtwibAiX2DJaOyKuOPC7KRWjfKoYM8J0HoCuejXcVzZRQXxOF9KroiPj4YkwtCV4QqdEWUP3cXS30ldMVENaM4BaIECTbi8nNtIRVayLNOaNHrmjZCCsj1evuCor6lU9frCYHYinFvaJV4V6zbafrWyoUsU03WPPFVisbQgFgPXr1+yRvFUZ1R5ptpVCIzsuytOWD1XeOck4woBx4/N8XOW9MbdJBnJYQ/X0i5PKIt8vfmoqdOCRY3W3LnqRUfPmlqszi8np1l6eSdOPjbsLVXAdF2tZgl5guNEh8fyvsdLcU9majSYyv2OuU8gaiPz7ko2lK+Qhj9Z95pkeq8EyUnBpefKwz8UBv2jy4nyuah9+qHKfb4quInvk5Pufk6cWZK+PyMJd0Ki7JFmSDtKZZf1YY4fB3dsHj4Z303xukoXatKpK0+m0w+ijt0IQp7Ywdc32sPK9JKnnVAK3lWQuSblVQvmBFZzq6c0nKkajIibLhvZIGLxo3wOsizVuggz0oIfzqXUrlkyAoO4KqP7pbktwW3ntrg9SVxNAVcGUpmocfyVJ/GU91Gh8B3Xl62IfSrQuHFhvByUzMhA5mp0W8XmvyTfxBPuP8zLj71T9QBkV5cm1NDlM1OmxkX8Jffow0RfPV4yhVjTRuKJWghzzqhhTwr45cDR8dXFHXMTfdld/6cBqQRofi7POXGq0SndcLXF6zqVUyULcyYA290z/kJX3CxMeKBqRYHcawbu4ZO5pKVL5bH+pBFyEP5gbvxUl6MUEKU2UYaFJf0ZlsO2ejwYkuIsueR0Jv2Pjo1c5rxP7DnWQ89/vchyvc0C+au57owXIizY2VLaU+sjB6nPDVko4zD585y2hYQSrzYcE5a6lr50oRoTLf2pIIgLLJ6y3nzSYtm0IpQbk2oQLYMMnL1P0FgmgxtJUR8YHc7KRKkAPUWGeomahoQLu0ZqibKZ6nNyOOpeHA14NPznfQuKk2AvdlpoltNp45/gzzH4Xd/FR0KijoWH8YJ8/T2rxpSJc8xuJyZQsPc4n0Vp3LbVbd5phPxwpg1nMPIRQaCNkQR9HI3zfLIyFm9F0eTktCoQJKl8nJZyjPglMbkKulD+slzfJgV57dPZ+lpydQPFsS4CwVEfSpOPoebBn9EhLcR82qItspcg6FHbBVX/z4i3PcybdYujD6Idg97y4wamYRs52bUTU0Zg0RfnZpIfamvKs/gnnt6dygrIT4Wz8dSXz0RvRDFJ5OdNJX6qmZfTrikPsEnRAVifGoKU7ZJbRCGxdFJ8tWtIt1XYaIwGHUjdfIs7dPc21468SA/dRcYpF9XeD9hbnXRVllrM/qELgdGWpB+8qxNr/cUej1lCynp9ZIjOW2lK6JGHdHYHxlFLcmwrz4PQ+9UyIcKIni9tj0lRN/JWm7EOm2eEy1IC3kO49Hs2uTJKCNbz0N4pdgeknYkRHjx6MgUZu5xEn+F8mbXSIpnllFy0A7MNWRmiw2xYUNj8Z2ZStJx01udK97qCPcyZLYkS9nqMOak0spuqkggxPwUw0uLsrnbs+xeau5eZcQT8HgDXQqJsoUGcOCtbtqdEjFY7R1EteyifK1FPPdP7whKiX+HPP/m2eImFJS2NnTeg+vvtLmmNOPfIs+/uD+tGjmF3Obqewy/dHGgz5zpn1/oscyUnnAjTfOSMmK+mLFano+jHN23m6ZJ9yQhzP0+M6pIclufSWcVe7T+Nv4N8pzAd+sLjC4r6pirNQtvqgT2p4J4Phlto0cRaXz0Y+8rbaQ/bfhuupIxsy/i/itSmKnX6VtYRuaitVj5WpM+DefVqrYUl/Rmq83Y6ArX+RegJ8966CEmOLN141m26SQeCn7hdbwrubJnIGvHI/inYM9feLSqH4WEEZat/WIMvDRo8bjfeL48y7Ydezlw9BSnTp3ixJED7D79CEd/G04Oakh2UT7f6BN4aMroGB6E+b1DbNt1gMPHRfnTJzl2aC87rr4hIMiIpdWlVaYMVFv1jBTbLSWEuGJ4cRc79hzm2IlTnD59nCP7drP3sT2f3c/SXxg4shxl6H1S81Qa4/eOS0e3s2v/MU6Iup8+dZSD+w5x9u0HfJ6vp5Gou6x0G5Y91Uwsfzo94vDenew5eFL+7adOHGbfobM8dvTE/NA4ykrla4zgqEXaQ1Q1498gz8EYrhtIMamODaZwzTE9hrAGpEqe/bkyrQP5pPe1Xspz71RWgnQhLoBTfVuxx1LhWv/pzNFBBZBlr8bIg3aJnnBlRDxnTkVphT0jNVYba9jLKvDNBYMLClk6+UeW9jy053eKB6aXPAujyfg0a4ePwVBR5d9m2+lWXhDYMuO5nbzVIAmx+JpsSJS/ki1ZZKCeOzgRoS4GHNm7Q0X+9h84yS1b9fujMJk3gJVH7iU7Dux2NEaWMSvFB18hpS/bj0tT2iX2VdsVvPD9B30V68/Jvq3Za61opR+OHB4o9VUNxh5xTJVY/PIwYXufblxSRBDGeN5iRuNsyAp0YeezT2kwrFInz+G+5+grDGpZtlL0Oq7Z8Ivxf8/lY8q64phcV5x5I3SF6KumUlsVasLkm+khLZqQXvKsRa9ny0D2TkcJSHGeUKJeLyLVt9F0buoI7U0Jb86OaSl0iijbaR2m6fFoClK/vUM1+aqYbPAhvDVm+E0r0kCeo96yvG4pMeZl5Jh0mR/p9d0q4YfDIzb378MNhc0c5XKOsXWyIivcj2Pv1ceaILGOhwSJE9+ZoyLdjqWHAMfg82xd4rgv3JSpt9JGyRLxG+tzM6gila3Qm40mmnWGZoTwbMsQ+VyQpdVs7rhpGO9xkfi8Ps/W7XvYf0Qx1x8Vc/3J+9j52XNxdHN5PoO8ww7irCkTeeRXrB8eYdvO/Rw6ljTX72PnJRMhCynni3+FPMdYsK5ZebljKdOYM3z5R1Pev0SeI1+yqIa0VU1GnmnXCUsHeY4PucOYAtmRZchLi12WiquqSAiy4fbpnezce5Tj8nnuGIf27eOosSuBlnvpKMlPnmr0PZUeAhyNh8FKeYI3WdGWzEo8SuAf4N8gz+HYX5lDdamOZbux2ig9DqLvvNg5Uswhomyt0ZxKJVeIboRxc0R7Nj5yEjUSiP3G47klkGUuQsvFJmK2UkOsLVvbK/TmkCP4aN2L8+9AT5710CPYmC3TVnHohu+fEEzfM7TJnZ0sWVpzOoX29+TSnHZkEIO20OAtyQlYVBAbiv3N9Yzp35BykmdMGuC5KtF2wWneuRsLo7e6/FrthXcI0DRR/fLhydEFDO9UlgJSWfHLUKIBvbfd44PTNcZXk8LjMtHnkL3msNEgC85tmUq/JrnJpCifu0Z7Jp1+TeAbMUllFdcKVGbufc3kN8rtIVuXDKej5DGVl89CidYjWH/fCrfrc+R7c2U1enDYWrMJ+830NAun9aRJsaTyBag+YCmn31nzYks/ofyFMdJsClflKWP/Cf4N8uzN1QUdySLqmKP9Ih57psvFnRKpkmdHDo4RhE28L2/vzbzXGB6ZNoTY7Wb8gFM4hCraNeEnZsdHCBnKSp0Re3FVt+kDrjGkfB55//Y94pCSXEv4ZM6ZzUKWGueSG1dSPfPUbMfYk2/4maL70kmeI/wxPLyUOYve/rn3tyWre1QScluc6Tdd1FY7w3G/oZC/al3Zb6G5rULMzrBoei+aFU+Sv3xU6zOL/S/VkvYF3WfF6PVcNA5KJquRjnupLctInmJ9uZ0ivM+eg6MayZ+Zr992zIP+QV/ZSn11Gsek7RAJ0vn9w8kvy0b90Qf5oIt/xf7C/v5qJox78CchT4w3Z2Y1J7ssL312PCX1eInUyXOo+S6aSI62/BWYc08z6Yh2f8i2pcPpVDWprbNQvOVw1t6zwv3mvERHWZk2rDJKz0quJqSTPOvS69nacDZF/GSiXpeMsRwdl/JEY1i0FiTYsGtwXfn3Fxi8F1vlDaepIdyEha2lkGIZxadewv8fBDNIJCRV8hx8nwm1JIeZjGpLDPj5d9Vb9DfMr61g8hRDkmM0ot04NL4BWWV/MfToOzWZiuOz8TpqSfJQoAYTrusOYFXFb1yuzkoc92XbseZZioGpA8G82jtc7vDKVGcYRyxT2XugAl9uLe0ql4n8vVfzUtNW19jfON3ZyNhBTakkRZNJdcxenlZzjvHS9SUHhiTKRfU5V/HWtA/gdwDGJxcxomsluUNeXr5oPXqsu47jl5RK4F8hzz8NmN4wkZhWmHeH4H/gUPnXyPPnW4yuKm2Vk1FrhVGajqdNQpzrCTr/lQlZpoKMTTy6IwXifV9wcM04etQR9yn6oWDDPiy+Zo6P4Wp5JnBZ4XpMu61jv3MKhOF4YRrFRdkMFTqz8UWqM2Iq+DfIcwjvD42S25kZaw5kn1l6PCf+3FvVQ74QlLXFdG5qOU41LYj2OcX0gXsw9ghVfFc0/k8WU1KKFmg+L+WWoTBj5rcsI++nEtOuEKB77+R/HHryrMf/eXy8tYnVu08KCqOMKG4MLEqWDBlot95Cbe9bEA9X9qWgGLTZOy/nma8uLe7A+UnSAM9D80mXFWn3XdnTuy7ZRPm/Jp4laSuyRoTcYXEniSiXYeje94kGfrQJsytLxk8Gam98RawuDeq8nR7SfithcC5/lvj2WJ8z9MooJoKcZRlwQXeITrjBBCrkyYws20BO+CRO5L5GaxI9qWXbs/qFrsrH8uFIW0G4MlOg2iKeySfk37zcM0qebElSttdd/yEx/VfI8xcM1g6QGzI5Oi3B0PvvEyQ5UiXPPlye1l6+apW3/zZByNJheKtAtO1KQVreqpEc7zuMKyfau0wP9qj/2+9nzKok7fHMSJ1Nr4nTJUuOW+leRZKlpqx4rs2Dnj7y/Nv9OUcWTeeK2hKv+8HhVM4hJsW+h7FVOfcoBt/n62koyV/pNix/rmufUzxex9sLwzcT+SrP56kGm9n7zBKWH7ujtg/tKyfaZyVjtgIMOOCq5lDw4eKUduQW7883cCdWWs5OTx2/eSHvKzWK63lbHj6XrWwf9plqp78x3924vnAUB9Q2fIc8XkPrAsLQb7qYRz6pGUGpk+ffXmfpI5Hn7GXof07bDtpERBhOpGJeoSuyDOC4V6Ku8H++LlFXlGvHap19lRakjzzr0uuZM2ak/Ubtej1Hl+UY6dTr6vDk9JgWcqdggWEHcEjzWccCsTZs7Zi4glJixlU+/iMjMA3kOfINS+uVFONYkOcVTwnT5aTRgaggWy4uGMMxdX/UjYU0zCuIR9v1vPiorJMTCLGXiJ+QhwI1mXRLraBOROP9VDHvlG/PWpP0kJAwrM5Op4Iom6neCI5Zp4cZBmO0eYg8CVy2tvN5oDONuDs3Z5UX7ZqLBiPPKAigJ0eGNJGvPBcYfQx3XZw/1IA1PSVdXIL+216mXGlT4F8hz9FmrFGsPJdfdJ/v/xvJc4QJC2pIe2sFeV5jTEQ6VG988G1GFcyGLEM+2h60U1zVjPj3i2lSQjp2rRM7bBLtm+/We2gt3isrUo8Z99Kz1Swaj0crqCPKZqjYhc2v/snKrIR/gzz/xu7SHCqJOmasPZgDKieTpIYQTHaMkEdmZGs9i9vuf1OZiFrZbpvAirt2qotBkTasqiXGbcE6zL+jZnPEWLO5fdVEvTnrOp/Sw/n/A9CTZz3+byM2gFPTO1CtUl26Dx/M4MGK35DhtK4klKUYmLlrzualCkeMxfrEVOpKBLRgT/aY6DDk4j7xeF0b8ZyC9F74WJEcK5QHc9rJvZGyUtN55KfDWgqzZstIKZFDZeZdsFcY8l4c7lpavqKcs+kOnHWx58D7DGkmke/2HLZNXB+I//mKxdK3ZchDzZHXBFXUAeu9VCssCFP2CTwOTTQcQhzOMVTaF5itIgM2vVUzQJUhjKX7s0U9s1Oq7iYSdzzG43pzGS2lUNDCvdlurD0xStrwb5DnWOxOz6SB1D+lh3LcLD1hfhqQKnmO5s3WofIMv7KqU7nt/Dcn2IB79KtaknrtezFoSJIsD2Fwj1ZUyiOeLStMr1WP1FYkvTjYuZRclnI124mLLvYsnj+oqbRvrANH7bRJTXrIcxQuVxdQvXBJWg8a+mfsDR5Gr6bF5ERElqUNe999Tl4VlhDifJHhf4l/y1qevutf6ZA/KRnaPLLIslKs5npSmExRH9gxtAnVazah17Ckdw9myKCBNJP2f8kyUrzValRt7WhebRksNzRk1Wdwz+1v9pX/bfpWKUG9Dr0ZqNxX3VtSUdpPLStK37UGwjTRjB9v99CgcBGa9lZut6H0b1eVwhLZldVgwVUnrQZ4IlInzzGhr1lSWXpebqoPv6pbV9jsp0bRHKJfxvHoZ6Kcf3c6z7B8ony22ow4kf78CKpIB3mO9U9Vr+epPRdVW1ah16W+LTOCU5bpWSmPxHhNH8pJZcVzDbzSY8mFcGNik8QIozYbsA7+Jw7FtOx5DuR0/8py522W3gfxTlt2OTXEE2S0iVp/FaV5v2EqMtivdQUKSHOjrB6rH3qIUf4H4UEGzJDyXWQsRbtNb1TGdWr4Js07gpRLe55HnkzP/vkEfI220lmKtsrXniX3NFIzLYjD8eJcGkljqkBnNhv4iitaEB/Mi22dxHcXoPP0O4J2S/jNk6WJpyjISkzijocOZ3O4I/smSmfzl2fGCWuVdlPGv7Pn+QsXh9eWOwVlXXfh+isdzDQF/q09z74c7yllA5eRo/9R/NJD8OPt2Nwor2jbzJTodlx30q4P52hbJbe4dwBX/BPHcbj/XSYXFm2TuRxdtr/XLgMa8NXmBIMknZ69PhPOqR8enl78O3ue/V/uorsUNZGnNfNv6naSqiIOl2uLaSaN92L92P3yb+55/vGO6S0qU6tpR/oN/aOvh/TtTh2p3YVcNBh9Uk2WvnFjfKNEvdl2Ezb/KFdE+qEnz3r8n8ZH441MH9Kd2ftOc/zwYQ4n/45w4sQ2hlbOQOZ8VVmscm6e4LRmB+lfSUqkk4d+O54RrE2Lxfhxf2Vzcd9f9Jh3Pzm8LfDGDGrnl5RCYebc15GZ9acpG4ZKIX2VmHXGWhHCGoPVzs7kziAjY7a67HHQocp9bzCosUR42rLPQkFUo4O4NVM6NF9GgaoDuaPLkWq2jaqFsgnFP5Z73xJnq7jP5myTZ1XNTLW+a7HUyl5i+XxriiBm2ShRZz3WiqsRjpeZUFf69rosvOSULgMqJf4N8ixMHoujDJbCUDM2Y91DTcmF0oFUybMgGc830qGUeF+WzuzXkWlVO8IxXd+OblPWse/YMY4oyfKRYyfZvXwEtYS8/NV1OcYqDCgayx2dxMQqZCl7PfY66jCafK4zoFE+0W/tOGCpbU9t2slzxKdXHBpTn35rznPyqPLYO8yx06dY1LMqeWXZabf9DcmnpAnEfrVmVx8pfC8zVXquxFxrmGscwXeny8lz0RprRK+rwufuPMaKiXrJwVMcU3r34SNHOXZoHb1KyMhavDlbzFUp6Pdn62kn/k2WtRuHlDLgpx2/eb+2NV2nrGf/8ZR9tWvpMHmIYMEeq3ihwW8TH+nFrRn16bLgGKeOHflTb/E7evIs68a1prgsE9VmXcJTJ4dLnTwnRH/m9qzKcl2Rv3J/bunSFeY7qF4ku2iX0dxVnPoQ99WSnb2lbQHF6LHIgH8UkZwO8vzx+Xqtev2kQq9nyV+dJc9VvzrM7BADJHKdsQUbDb3TNe6/GayghUQMs/XmhF16HG4JeF8Yn+g0yT2Wux/TQ7zVkRbyLAjhwT4UyiIjQ7F5vPgbyaFiQh25OLkeXZec4ZTa2JVkcNWwRhQW47Pe0nskn/YjEB/mxfmx0uphHhoPVz0DODXEfrZge69EWeq15Gm6ZCnK8yHzm2cWZaswcb+56upWKgi3OcWw6lIoby66b3iI1sCg2E8YbWwr7stPhyk3kjMWf76/gAZS8kkxzqbd+KCVFPNL6LWx0rxcjilHzBP3fWrAv0Oe43E7MYQSOUQ9C8zgqUq0T3rxb5HnWGz3did/ZumoskW8SQ95TgjDeGXidptsRdtzVlf6BefjtK6US9zbl/NeifohPtSNEyOkjOv5aT72hsIxkjbEBr1nUzfpeSXpv+p5Kg7N1PBvkGdhBfg8YUkrabW9ImN2vk/X+IiwO8/YWkJuMtRn6TUXnXpZMxJwPDyQvmNmsfnICY4q6ZIjR49zcOscWuaSkaveSC6pKLQEvM6PpYJcb47n/qd/1rLphZ486/F/GEHc3r6QOWvvaVFosbxcUgtZplzUn2eousIVGczDtV0FSRCDuu48HvtoGbix/jxY1UIoJVXyzC83Do2qKvf0/tXzMC7axn2oGRuH/SHPSbfFfH3BwmaSQpZRd7ahdpLid5PBTf6Q50RlK4XPnWWglH00awkG7rPTroTNtyeS52wSeVZYQfGRuD9cSSuhtDIWaMWqR9pCeOP4entqInmu/Yc8E/2RO8s6UECWhYoTjuCoi2EJVezv78b7d9rMBHNWKcjz7ZC0kmcr1ivI83VtmVGivLkwvZnon2zUnHsJ3clz4/DydMHcXEsIfNwL5inI85MILY4OadVhYA2yCYOj2ToDvui0XWJwdnLEzlbJfx72inmDpnI5KfmUOoKeMr9RRmR5OrLFSNVkjflizPwmibJUb84T7bLke4OBjVMjz1HYnp9CWYk8bzRG+zpLND6vDjJ66CbcFVfU8f3OTKrnl8L7N2OtnDgnPgqPx2tpk0fIX77mLL+vzTyLJ+TejETyXF2dPPtyevkslu17oWWy/8FtabtF5iJ02vBe1bkl+mpPv2ryvmq58YnWI8ASEYOTkwP2dkp99eslcwZM5YoiJDAFAg2Y3SADsrxd2GGcMuN3uMc1xvVdyDstHZVgvpuu0tiuMI37HroENxKbs4mOjn6bX2jpd6ErHM4xqIx4XpbiDNjz/9p7C+gokjZse3C34O5ugeAui7s7C4strouzuLu7u7sHCU6QBEkgrsTdbeb6qyeTZDKZmcC+3/uf73u3r3P67NLpZ7q66qmn6i7ptjLcObLcmCies6SIZ5SRWJ0ZT0XhW3lbTOFmOttcw8PdeGRuaFbRnRNDGqjF87rX+juBiXhxae0so3H9yV9SXM+N2az7qUVKtCNHxzUU9T47tWeexVBYTyQBRwcb3llq1s5HfGRdl0rC3/LQZv1jo98clfz/86dPfP2i2T8ZIupgHalzbkK/I0krjAwRyds3ViLm6OvCq/D7tJ3WavG8T3S19ZMQeJVRpXKK+5Vg7NX05E04L55/5MePpAFk6Zu3xxjZZz7vDQiYmCfLaV5MiPMaszH30LpIlYDLo4WJ3/iv1p8j34x3/+PivTF/aEWclCHClz6eGqtefp2v5TQMNjsawsLcePxIs2g/3p8H63tRVJGJEv3W8Dod9ePj48gzC43MjAnk/tqe6vvmqD6RS/YGRosTvHm4qo3I09TimXAheP6oSV5hX6DDVqzDDDxz+Ec2j0pfPAfY7KKdWjzvxkFzLj2C7PbTUS2et/Ndc04XZdAdJlSS4nthRpxP75cjRJv8ETc3fevQlfx4vY7GavF83PgsbzIqvN9vorlaPB8W0Vk/8QGXGFZU2jpUhok301v2HybK8ANeXolxNtL5Kn9Ib2vPmJt2S18aHsSwPUCrZPGs8XlVHPZ3/lLv189ZaygnDTVaGmJifgi/tSZBigEJEVgeHaVelVKg3Wzup7NlOiTElSdJfpsGZw72qqMWz1s+/qx4duP4IGnQsQnr33rpj5vxQTze2o/iiowU7bmc52mbnVT4+Tli8VTz4rRYDy7OaUN+0Z+rPO4AtkZHtZTCZ0R/7pWWf8V/YuXo8Wy9bqCPF23Dlq55UGSvzZ/HU1+jCjFnuvr9DSYMOPbl5+Km068MfRhGFs8y/1rCPx9hzYKxnLQx3A2LcDhAJxFQclXuwAmdup0Q9IhFbaXGLiclR+zhsZ+e30lw5/qCRuKavHSadi1FPAui7Y4yqKIQthnyU3PxLfS9yJOw1yzvX1rYl2fi4fepOoP+j5bQSPoERvYqdDv4Uf8ST7fz9K0vLUFqwRYROFOCbSTWB0aqX+aTuVR7pt520z8D/GYNlfJnEiJiBFe0Z3bj3bk6t4MQoJnJ03Ac2z7q63qr8L04Vtw7E0VqLhWSNYVoxxusbFUMRY6CdF97Cg99UU+lxOu7BYcO7OOVrwGhEXyT38sVJLvosF8L+ck57LC7jK1cmKwZBnPJ3/AYa+SXs8xraIIiZxGG7BRlp68zLNLobv2AA4cP8d7fwBJen3P0LZSfPAUm8tDg7VSiqPcwoZro0OYpyYSTj/V/T1QVj/3La+w/eZovgSmduO/H+zNly23cDP5+CI839KOEIgcNph3GXqfn4PtwMQ2lZVvZq9LtkJV+X3I9R+96UqeipdZAjC7BWGztSw7RAeu76qnhJdWhzlxe/htLbhkRdzFv+btJaTJnLsy0a56p/TPekxsLOokOaWZym/3BJkv9+2kDrkwgg6i/BasuxlJzTiLo9QYWL5rDDRfD3Q/ft+tFZy4TJg2HcD1VR11F6MtdjKmaQ11Wk05b6J8FE2Vl9+KquqxsglJy4tvRvkzZel+/z0uogjFf05tiIq40mnkMJ+0yVUVjsak9M0866a+vEgmOHBgqdZby0W/3Gz1vDE8iiKeb+5BdlFX/NcaWv0fy6dDv6liRqWQ7Jt8yECverqOKSWbROR3G5STxLEgIsebs8NqiHHNQb9wK9IYKkaeRgbac2bOF2+6Gem7WrO9RlYyKpmx4a3jOMuzTIVYvGGc0roerxURGclfpxCkdnRDx+SSz6xdAkasow/bcRO+7v4QIdLW6x4EjR/jon1T6KkIstjCyQlYUeUsz/cJL/QJI+IXt04vsP3OO78FJVyQIX51P14IKMppUYeVDG/2dXGU0728eY//127iH6RssVOEpxGlF4Tt1ehwwMIMnEYvX0T9pJup8tjIN2flWv8RRKSN4cXE/h+4/wTdS47BxwUJMtuOv80aWIcTZsq1HTbJnKMCIo19S+ZYq1oWHU5qRL2NmKvaewhMfvYGV+GhXruzfwuXvbiRoMiMhxIozw2qRKXNO6o9fhZUBX4oIsOHM3q3ccU/pKMd6PmJbl3IosuWj3cJ9OOgda1UR4PqWY3t38tQn5ccTQp+zsov0ks8cFBu8lbveemqAyocHy1uIa3LTauz5FPEsiHE6xYhq0n7mfFSZd5Uv+u4d8ZGNw6U906UZs+uNQfHs/XQxVUT51uyy1+DgiC5+L5ZTQ9hUa7/DyGx1PH5nptMmt+gTFKvL5hcGfl0ZxZsrhzh4+yFeEfoanDicbs6klBDPDYec0LznJT0ScLv3lxCYuajX74gRwR2Lx8Gx6pcYZi/flL3v9f+6ShnG8/N7OWz+DP+opECbgMulqYlvlTZpwPDzdvrz2HYfzcplE+XQnaNJ4lmgjHbg9oRG5M4oROKAWTzT198T/hMX4czFfVu46uBJ0gv944Pec2JQDTJmEXkyeR2f9DfuhPt94fSerdzzNLBlJOE9y3+rIGJga7b/9OoWK9Z2qyJsmrHpneGNN7FeT9nVowKKrHlpNXcn3/UHLwLd34n6sYPHWvUjyv4KS1sUFf25QvTZeI4f+hoI0Vf6YfuUQwf38cYvJQM8bkxkxqo9fDD4PoBYvl6YQjVFVir0X4JlqjofT8DleXRRx82qrHpkYOZbxM13N46KuHnHQNz8dWTxLPOvQ6WMI8rtJet6F6RYiy5ccowgMiZehIXUJMRG4vvqJIOlYCuOCs1mct/ai6i4pOqpJMzlNetG1SFn7sKU6DCBDZct+RIUTHCwOEICsbi3jd61cpM1ex1673yrs9wnjoD3V5nWsTCKfCWoOngRR57Y4iDZiiMw1Jtrx2bQuEQOspl0YuZdF52ZlCicbu2kX53MZCxUlZZTtnLVyg0Pjb13gDNH1/ShQt7sZK84jl12IamfMS6IN4fn0ayM+HuZJvRfeoJHjr74aOxd3D+wcWJ98mbPRu7mq7gZmjoixke4cXXNYCqa5CFf7a5M3nmLN15BBGrsbWzuM6dXMbJkzUelEcdFCNcmniiveyzsUo1sBUxoNmIeJ55/wF/YhQSHEB5qw5tze1g9cyvP7PzSjiiKDmh0eBgOT1ZQTr38vQHTLr8nOCyS5OJJg7CJCMP52VoqFcoobEyZdN5S5HMksXptYolwvcb0lmXJblKYtuOWcf6tNUFSGkNCiAj5xLNjO1k9exdvXALSCgqRxqiwUL7emUdhSZhmbMn8O18IDYsiTq9miybk2ylG1S1MzsLF6DptAzesvqrzMiQkmPDAD9zfs4k1Cw5h7R2svl9CTCQ+DzfQrpyCNsuO4Bwsflv302qiox8T5cujhYmj34rMpRg2/xROgdHEJz93JI43t9OntgFf8nfiyKpelM+bjeyVxrPHXseXxL8S4qII9X3PivG11PWlSv8NPPUI1qlbSmLCfTBf1ZGSZQqx2NyZCNEB081+VXwMEeK31tZNfAtxxkJd2HH5A2HRKbkcH+HO9XXDqGySm7w1uvDnthu8Ep2OAE2abW0fMLdPCeF/eagw9Kh65YMqIZZI+/vMa5udUl2Gc98jkqhY3c676LjHROByexedNXW/TqclPLcLIDr5/QLRBNucYEStguqy6jZjIzetbVKV1b1doqwWHsbaJ1h02xLLyvv+GtqUUdBuxUkjZSXyZ+4I9aoWReYyjFh4BuegKKKE7349OpZqxTMx+tQrQsKj1b+bCim2hTtwrGezxP2LORuwaJ85fiKPU77MJMoqVpSVjyXLx9YUz5eBagM3YuERojcOqhGx4u3R+TRXx4rG9Pv7OOYOPimxwkN0/Cc1IF+ObORquoIbqWKFCmXIRw5PbU+eXLmo3nkUW249xzPIX20bFuaM3dPTbJi6iuvPXTRbU7RREhsVjr/tCTrVl5btFqfnhlu4BoUTo/W+BymuR7q+ZE1PExHXuxmN6z4vTjBQU7YVm8/WieuxhLtcZkrz0mQvWJj241dw8d2nlHof/AmLoztY/ddu3rjp1vsogj4dZnCVfOQqItI5eyu3P9um+IX/O25v38jaxcf44heiU37h+D7bTMfCmclTvDzDlh3Ewt5eYxtAqPcLLqxaxca1F3EMj0hbZxJiCA925/Lm7urnyltrAse/eBEeGZvWTySUIaL9WEaTnAryl63NuA2neOvirL5faIgoG/cnnFi8nG3bbuEWHa2+X2xkKO92D6ViiexMvmJFeHhM2t+W6liILXvb1VO/hVeRuzkrjz0nUIiY5LKIsOfain4UFm1LuZb9WHn+ES7+Php/cMft3SW2TlvO2dtfdQYRhS8Ff+DQ1HbkyZ2LGl3+YOvtF3gGJvmSE9+fnGL9lNVcf+Gq40sJxPo9Zd2g+uTIlw+zgdM4+NgS3+AgtW142Hc+Xj/EmmkbMf/kI7xAGyFsPN+xZXwDcd9CFGs3hjUXXvMlMNH/g0OCePFoDwNM85A1W3W6bHyusyQ9nkDrW8zqXgJF3uJUHDCPAw+/Yi/ZiiMoxJdbp+fSokxOsuVvx+TrDmmEnbp8Qzy5tq2nunzz1BjL0c+eonz1lIEGKd5FhHhxa1dftU2uyqM4aO1JmCEbVSjuD1bTMm8G8pWsxug1x3np5KROo+SDIZ7POLNsJVs2XcMlMlJv3A4PcuLUqg7q+xVsOI1z33wJj0ob45NI9FtXzq/vrLYpYDqZ07behm0SgrG/tpgG2YXfVjBlwqazvHN1UacxNMSPINfHHFu4jB077+IRE526/ivD+XJuFR0qZyVL8bp0mbufu7Ze/NCUg6ePLbvntaVYTtHO1ZnDKd2lwOG2XFrSi4I5slOhzQBWX3yCq7+v2jYszBWXNxfYPHU5F+5/1xmMFDE36B17J7Qid+7c1Ow+lh13XuEVFKCxdcTW/ATrp67h1it3PTFQ+G6kiA+fD9O6prRipDT9tt0T7bOIgQbfU6KJmzbH6SD8Ulpl0nvT7cS4qddGXO//gk3DG5Ijb17q9ZvCfvO3IsYHqtMYHm7Hp5uHWSv6JQ+sfHRm7kXs/XGHeR2rkL1AQVqMWsjpVx/VbbG6PxfylVdnpf7cdp7bJ/bnlHHRhFkeY0jNDNQSfauPfpEiXTolLgR3XHQwVnvnJ74sUJGXDqN38vlHBLFJz6AKx8diM78VkuJmBYYvO5Qmbp5fuYpNay/rjZv/FFk8y/zrCHe+z9LOVShdJC/5i5aiWq3uLDlppdNYKXE4P4NmtStTulAhCpoUoECBolRqOpjlOutu4iMC+PzxNpvn9qd3r+rUrFeHOnXE0dCMGgOHMXrFAV5+dyAgMl5vxY0O8ebN05MsndiJTt1qUFOylY4WjakzdBxzdp3ns9sPgoXCSxvyVIQGuGJ+bRuzfm9Bqw61qKWxr92+FQ1GTmfN6ds4+AQSrjdgxuLn9Z1rR/9mwhAzGreuTW2Nfc1uHWk5biEH7j3FPTCcaH3m8eG4Ob/nxKYpjBhQm3pNNWkXR/XePYUAXMPl1+/wDolOK4AFsaGuPD27nEF9ulGplqnarm6deoweu5xTj7/wzUe3g6kh+j3b/uhGzarlKFS4IAULFqVUxWq06DKXe4YmpWKs2DWuB7WqladwEVGmGptmHWdx28g24+hgRx4cXUifHl2pWDPx2erVbcC4SWs59+wbdn5h+gNy6AtWDfyN6lXKUrCQSGOhYpSuVJ22fZby1PB0oHrW5PrumXTt3IWKNWqp72dWvwlTZu3g8hsHXAKSmmUVX07Po2e1kpgUyE+xcpWo22A+d7115hADv3J8bjuqlS1DsYIFMcmXn6LFy1FTNELvvbS7/kpC/F0M+lLD32ew9sxtHPX6UiQOt9fQuXZ1ypYpKvK2IEVKlqNqrVbMPf5O86I8gdKds1PbUKOUCXlNilC+WkP6TzyXZl9czIfDjO1aiwrFilJI/Fa+PCaUqWRGj+2vUteBhAjcXT5waus0Rqbxvx50mbaKi68s1f4ndYYDPl1gTqtKlCiUlwLFy1C99kA23rZL3RFQRmF9ZAymVSpRSty7oMhbE5PiVGk3jp0vUw9/RQR85dquGXTu3FmrrJoyZXZSWSVFFSXWJ+bQvaqmrMpXxrThAu7pbhsI/MKROW2Ty6qAuqzKU3PSGjZM6UuHKoXJJzonpSvXpH2vnWlfguZ0m7+HN6ByyRIUEfb58xSgZJnKNF58nZSFIxHY3VxFpzRl1Zp5J98ZmalOjBXXRaz4c6iIFW1Sx4pWYxey/94T3APCidITK1TR/lib72HS8F5Uqdsw2bf6SZ20q2957xmg/zvjuHJ1xe+Y1ahEseJSvS1M8XKVqVV/KEesg5PrXpiTiOudqlCq8M/GdVG2RuJ6dJAD9w7Pp1d3qd7XVqe1vmlDJkxZz7nn33AQ9V5vcsXZcL9PXN42hY6dUvyiQf1mTJ+7m6uWTrgFGloPnkDIjzccXz2WVu06UKVGDbVtk0Ztmbf8OLc/ueGV9vtwaiJsTzG6sSmVKpTARJRpIZEHFarXY+iicwb2PkvE4e/0mD2LRtK4VXuq1qypvl+LZp1Ysv4892088Ut+HXcAtxb3xKxMIeGDhShbpS6dBx1A96u3SrsrzBlQj0olilNY44OlytWg5cr7qb/NGx/K95cnmDumD9XNmlKzdmIe9+g+hh1nnvHa1Q9Du1wkX7J6uJuJw3qm8qX+/aeqfemDQV+SmiwvXl9dJ+JFdyrVrp/sw8NHLuTYPWu+aAYm9ZEQGYjNp/tsWzCYvr1FW18/0bZOg/pUHziEkUv3YmFjh39knN52KybUB8vnZ1g+uQudu1enhubedZo3pvaQP5ix7QxWLh4ExySk8a1IuzOMaVqfyhVEDEkuX1OGzD+Dg4FnjXE4z/jmZlSuqLEpUkrE3LoMnHMCO4MqIp4gt2ccXDqaZm1+o4rGJ5o3/o2Fa05z74s7Pkmf10uFkmCrQwytX5uK5TU+WKy08H8zRq28ZmDvs4qwL8cZ2bAulZJsigqb6vVFXl5C52MCWohY5GjOrgXDadRStG2aNLZs3oVlmy7xwNYTf4Pfsoon0NeRu2fXMmV4I5q1S2nnanVuR9PRf7H96kNc/EOI1B3clIgL4duLo8we3YdqItbXrJPotz17iPbh3AveuPkTbchvo/z4cG8HE4ZKftsg+b4DB87k4I13fPwRqLefJO11vrB4GPWlGFgsKQZWoU6j3znxNdxAHHLhynJN3CxWODlu1jYbJuJmSHLc1CUhQvRHb2xk1MAeVK6TUj+GDp/HkbtWfPYOMlg/YkNceHzqbwb0Ev25mnXVdqbiN8aMX8XpJzZ88w1Nvq/rg638Xq8cRUzyUai0aOPqTODkZ7/U6Yry4v66PtSoUJ4SwjdM8ucXPlya6n3/5maqqXEpbr7m2KoxtGybEjebNmrHvBUn1HHTO+nznf+HkMWzzL8OVXy0EAm++EujxkGB+Pr4ESLNzGj+nkR8ZDC+vn6Jo2chIeLaAHz9AgkxEBlVcRGEBfvi4+2Nt3T4+OAbHGawA6CLMiZU3MMnxd5XpFGaFTXQMKZGRZxIb2CAlr2fH4H6Zgb0kkBMeCD+fhpbcfj4BxCctFQvPVSxRIb54+erZR8YRFiMwRZaC6V6RthfPG+SbaAQ60ZNxf3CAv3w8fXXzAgFEeAn8tsv2GDDJdmEG7BJv4wSiIoIFc+XlEYfAoMiiDFWNsoYQgOEP4j7qUdBg0X++or7iUY5/WyJJyJMup9PYl6K+wUFR6ZpWKUy9/eTnieEQH/xbD7S8+skShlHRLCfuG8AQcKPpRmwwABxrcjjNJOuav6JL0nLLEV6RTr9A4LU9wgK9MfX25fgVDPLCUQG+eHnnzjbI5W5f1Bkmt9VxUaI55H+Jq6T0hwcJJ5T2IUZ2Kn2k/6njIsk2M+XgCBRnwNFffbxJyxKd3ZSWnoXKMpNPLM6v0IIkuq+SHO43oKTyioEP1HfjZZVhG5Zhfx8WQWFEhbkj79/4ixAgHgGX//wtB0tKbYJn/MLFNep7YWPi3z0CYkipR8oyirqZ8rKEP9ZrIiLjhBp8k+29Zc6qUaDXDxRoaI8RJwODJLqrcgT6Zl8AojQWmai/OW4nli2xuO6qPfhaev9z8XkRL/w1fYLUQ4/lUuqOMLEcybb+vgSEqZnpYEWKuHbgeJ6P+EjSc8l+WRASKTBzm4yCSJWiTqWdD913oXr+oKSqBBRx5J8UOSJX4A+H4wiWO2DmrorfNBf8tfQaK3VDynEx4h0B/gnxxo/vyAifqrdMOBL+pf16KAiJjJcHVOSbAMCwtBa2GIckdfqtt4n0TaxrQ8l8iftlTFhhGi39cLeP9S4X6niE8tX7ava5StijaHbGraJSN8nlLGEpvJB4RMi/hrzQaWI2wHiel0fDAyNMminFP0mfTa/4rc+mjRK8Tw0jd8aQkmsiPMB/poykA4pPuuJGfrQ9Vt/0T78rN/GRkl+m+J7/gEhJK8u10s8kSEB6jJIjIFBmhgYKGKgodSKuBmSftzUj6gf0qx1qvoRimgqf4LE/lxKzBT9OWm2W+eW8dFhBEr9CtEWB6nzUXoWnYtUCUSHir9J5aKOJeIZRFvlo84vPc8t2s/UcTN9n/2nyOJZRkZGRkZGRkZGRkZGRiYdZPEs868jKCiIGzdu/CuP27dvEx39f+aFCTIyMjIyMjIyMjL/JmTxLPOvw8bGhnbt2v0rjy5duhAQEKDJCRkZGRkZGRkZGRmZn0UWzzIyMjIyMjIyMjIyMjIy6SCLZxkZGRkZGRkZGRkZGRmZdJDFs4yMjIyMjIyMjIyMjIxMOsjiWUZGRkZGRkZGRkZGRkYmHWTxLCMjIyMjIyMjIyMjIyOTDrJ4lpGRkZGRkZGRkZGRkZFJB1k8y8jIyMjIyMjIyMjIyMikgyyeZWRkZGRkZGRkZGRkZGTSQRbPMjIyMjIyMjIyMjIyMjLpIItnmX8hKuKigvHycMXJwR57e+lwxNnNgx8/PHB1ctScs8fByZUfPiHEqlTER4XgncrGwaCNp7CJkWyijdg4O+KQbOOCp3cw0UpNEnWIjwrFx8UJJ8319u4e+Mcl/TGaGLWdkrjIIH64i/slXSeey0VcK93PxclBc84eR5FGL99QkUb1L6QhNiIYb5E+R831Dp4/CEzQ/DE+SmOnJCY8EE93l+Tr1Pfz8BT3c8fFMel+Djg6u+EdEE68yPvYiECRRh0bdwM2/mHEqm+alpiwQLzEMyWn8YcXwUnPE5eURv1EBvnxQ9xLnf+ibBx9fAnX/E0VFy3KW/MPPYT7e+PhqEm7gyhPv0CiNH9LiIshzoithCohijD/QGI0/9aLuCbIxzNNmXn8+IGHm3NK3jk44erhQ2h0AipVDMG+P4Qvap7LmI27NyFRUmnoI54gURauST4rfNs9JILE4o8jNj5e8//6iCXU2w2XZH93wdVL+LX6byqio+NI0Ph4QkwYvh5uOGvVDSdXdzx/eOKmXTccXXD/EUhkgv7UKmMj8HN1Tqljbm74JFWkBKluqAw8ZwoxQf6EGcwPiYRf83Xht0nVU5fo0AC13yY/n5coi6Qbp+O3ceH+eCfbiv86eRMQqXlWZSxRiYHAIKHCP9yTfFek2SVAxCn1X5TEx8WKkjdEAhH+nrgnl5UTzu4BhGscITYmjjjDxoIYQrxS+4WbV9K9VURFx6M0lvT4cALdRRmrbaVD+Il/hEi1RAKRouzSR0mUvx8RxjLYALFhfuoyS7y3VMYi36M0v5OQXr6rCBF1OTnvRL67BoZq/CMhnXxPRBkTTqgoK0M+lR5x4UGEhEQaqbf6Sb/dMZyXseKeum1IUFI2JbchCUSHBeDppr9eOevUK5+AiMQ25P9QXUxE4xeGGpp0iSXcz58ooy4QQ6hoN1K1+eq49gNPD53ncPMiSCRG/88l+pJbsi85/oIv6cZ1p1+I64nER4YQEiy14zIy/25k8SzzLySUdwfHUyVPESrVNcXUVDpqUbGYCblyFqB01Vqac6bUqlSMouUHcNUjjK8np1Itd2Etm9pULF4w0aZKzWSb2pWLU6R0Hy64hvLt3CxqCpuKdVJsKpWQbPJTqnKKTZ0qxSlcvBMHbXWaeWUg3z4/YunMSQyo3piWmutN+/Rh2vGbXHn+kmtbFnLaRbo4mBfbR1Ihb1EqJ10nnqt8kQLifiaUrVY7+X41KxShZOVBXPBQ3yUZZawvn9/fYd74sfSv2pCmmuvNBg3hr3Pm3HjyiItbl3LJW7ram5tL+lCqQEmqaa4zNa1B2YL5xP0KU75mHc25ulQrW5DyjabzOjyUp2sGUCZ/Capq2ZQrlF/YFKJcjRSb6uUKUbbuGG75q5OWTHyUNx/eXGPW76PoV9WMxprfaTh8JIuuWHDH/Bbndqzhuo/GQIvoUA9ePDnNpH5DhG19zCTb+qY0GzuRVXctMb99jhP7dvIoRGOgRXigCw9u7WdM5770qa5JewMz2kydw+Ynn3l6+QCHj53CMlJjoBcVTg8W0LtaO7Z/MCKf3c4zpHwJilfS8sWqpSmQMxcmxSpSS3POtG5lCucuw5j97wnxu8+YqqUoXjHFr2pVK4OJsClQtEKKjWkViuQuyYjtzwjU3C6RePzcP3Py4CqGNupI19qa61s2p8+KLRx6+wmLg8vYd+89P/T0svw8bDh9aAFTujSndbK//0ab/nNZd/wJlo/3sPyYJT5h0tVxuN9dSgPhl+VqJV1bhyqli5A7Z16KV6hO3aT0VhfPna8pG18Hqe+TQihO356zduFMBtZoRpuketmtK+P3X+DCq/fc3jKXo1/DjHcK4z+xoWM1+s25iXu8ITHw49d83exP7qXOXOIivXj36jIzho1U+20jze80GjGaJdeec+/hDc7sXMctP42BFgnRQbx8eoqVU/rQV5RLPbVtA8yajmTM8uM8fHybo6cOc/5V0jBOakJ8Hbh+cRu/t+lBz5qa9DduSIe//mbn889YnNnKgfM3sNEjIML8Xbl6bi2z+7elQ3JZtaJZlyks3HuPd8+OsebIA2w89Xf3fSSf2j+XSR2b0SrZLzrQbuB8Np54wlvzXSw//pGApNGrVMTiKMp4z7oJjG7WkCZqW+noSfdxqzh0+SlPbm5jzQUXUbPSIeYxcxtUYsSWD8Jzfo74qACePzrG8ok96V2rrubeDTFr/jvjVp7m8dObHDx5jMtv9eW7iiBve66c28KIVt1S8r1JIzrPX8HuV595dnIj+y/fx86ouovG8uAIutYbwlnnfyBblL5cmdGKLl2X8yLYmMLTQrQ733+i3TnlmLZmJcT4Yv3uFn+NGaNuQ5LKzGzIMOZdeMzNJw+4sG0FV9Tx2ZOr83pQ0kS7XlWnjElivapQK6le1aFqGRMqNZsj4msI5sv/s7qYish7zDKtxOhdX4nQnPoVgmx3M7SSKYvu6nXgRMLMmdm4MkXKVqWOJs11apansIjN+QqWpUbyc1SnZP7CdF9wGY9UDi35kh1Xzm5mRMtu9EjypaaN6TJ/JXuSfemBHl9KjOsnDoi43rDDL8f1ZFTB3P27M53bzOaBX3oyW0bmfxtZPMv8C/Hj6c6p9OyxgXeRccTFSYcLR4fUJ6uiDkvMHYlWn4vB9/V2RjRrwYaXPrw9NJteXdfyNjzJxo1Tvzcim6IG8+/aaWziCHi3h9+bNWWNhRfvj8+lZ6dVvApLsvHg3LimwqYKs67ZEKmxCbI6yB9N6jHvXkRyJ1AZ5cq5jd0plb8gtXr9zbUnIcRJMzySjacHzzdNw6x0bhT5KrDylWThw/0Nf9K77zasopPuZ8++XrXIomjA6pduxKrPReP5ZB2DmzRnldoukfiQ7xxc0pIi+YrRaOgmzF+Ha35DHM4O3F0xhurFs5OhZANEP0Pgwtn5oxk05hjfk66Ls2Jt47IiH39jj62f5lw4dlfn0btVb0588+XO8jEMGHkI22SbL2xqVVGksTXbP3trzkXgeGsx/Zr9xlZrdfLUxAZ8YscsM0wKlKLl6N28+BCluV4cDjZcWTiUykWykKXybxyz0xhpiPC0YOnI8uQrUpEeM85gLZRCkm30Z0uOTO0uRH0G8jb5nZteGiMNIY7XmNo1P3lLVmf4kjs4OGnuKY6It4/YPLo1RXMqKNljCa/0CO8klGHfONS7EAqFCa2FWDPUiU9wOc6wxiPZ98JH41fRODxYRC1FVsyGHcdFc++4yI9sH1iXvktu4+Bwi/EthrPr6Q+i1H+PwfnpMkyFTZ1+B3FMsomyZvdQU3rOvYBd4pSwIA7Hp2voWEVBwVpdWbr/K0H+musjovG+cZgJv1UmewYFbZaZ452qD67C5fk++jQuTvGGnVlxxhL3UI1tXCgOb86wsHMjCisUVJlyCR+1QIvG7sbfdG85l4e+Sdf6c3dJV/IqijPq4FtCNemNcrrItJY1mHTuR8qsbLwvd/cPo7JJHip2msGpO/7ERml+x9ePD3sW0a5yfhSZ8zPrtlqtG0CJz/np1MqnQFF0JFcdDXWAnTg7L62vr1H7egf2ftfx9ead2PFZYyqI8fvApql1MTEpQ9tx+3ltpeW3dl+4MH8QFQtlJlv1Lpx21BhpiAv4yr453ShWpjQ9Zm7l4fcITT2OIzzImvMrJ9KqcD4ylO/Gzne66ldFgO1xRjbLTN5y9Zm87jmeHpr7ilgSYnGdpQMaUiCrgmqjdvNVRwMG2l7iz86VKFizMbN238XGT2MbF8mP77fZMLwzFbJkomCfTVgF6MpXJY5PdtGjQVFKNunG6gsf8EiOg8F8f3GCee3NKCj8ouasGwToJj0hjFenFmFW2YTqXcdw4JELgclxzZu3V7cyplZZciry0X23OiAZIQ67Hf0pmEWUc625vAxKv/Mf62/FjqkdKFq2LH3n7uKJfUqZhQd84PTf42leKA8Zq/ThgJWuqFXi++kggxtkIF+FJkzf9AovL03axTMEPbrE/D71yCvSU2fiUeyMjKPFez5lacssImaUY/j215pZxp8n2vIgQ0S9VmRoxso7zunOLqqi3LiwqQel85mk2+6seKEx0hAXbMPeBc0onL8ETUds5cnbiOQ8i3O259bSUVQtlo2MZZqy75tk4cjJWSMZ8udJ7JOui3vPinqlRL3qzAGHAM25MGwvzKJn6/6csfPhxhLdumjNuqblhE37tO2OTl1MTSw2m3qSN5OCDPUWYRmW7hBMalT+3PmzmiibrFQYcEhH8GoRfJPJbQax8oJtclzz+76XDsKubOM1WCU/hyNnpjSn85htvE0OWyJGSb5kJnypYlNmbH6Nt64v9TZN9KVJx7ViuoR2XO/GsgMirgdobNON66mJ+XqeCbWFHynqMff8t3T9SEbmfxlZPMv8+4hy5u29I+x7rj1F6M2Z35uQS4jMVS88NecEymC8Lbaw/a4TVg+Psve59ti0LxfHtSC3wpSlj9005wSqMPyebWbbHSeszY+w20K7Q+7P1cmthU0tFojfTGmrIgl8tpY15jHqc8poL+4s6UUhRXbRudrHN/0TSgTeE+KoeinmPBS9r1gnXtw9xqHX2q2nO0cH1BcdzGZs+uCrOSdI8MXVfD0bLDT/DHPk7MT2Il15aLHgIm56W8ZYXM5NoVqt6qx6Ky6I/sSdG2c4b6XdnfvGlpaVRD525bCzlmiJdubLw70cfmjLs7unOfNBu6fswM7fqos0dmCffbDmnCDGFdt7m9imEfjxwV85NLwZ2RQF6LT6Pt56OyoRfN4/nAqmDdn2MeUhYnxfsba96ORkLsrIQ9bolVOiI2S+sgNlm3ZLJWAi3W8xs1ZRFDnK8NeNH/qXrMW5cnJyXcp3Hs999ay8PpT4fd5J0zyFKJJRCO2W07mvO5mqIf7rMdZe/kawVqfaw2IF9RW5aDr6nPBWLewPsOPaBz49v8i2618J0Cp+rzdraSRsGgw5gZZXiyw/xI4rr7EOkP4hOmdv19NBpClDhd848119RVr8LJjatgCNZp/FRStdAW8207OYgpwN+3Hb3YAKiHTmdM8G5O2/ne/SiIEqBOdnB9j0QDsDwnm8qrcQU2UYf+KT8LYUYsRzrL4bmLg0Mj6IFztGUFaRmbIDV/LewAhE3JudtKhZkDFnPbTqmQ4JNqwb1YHi2bOQXVGa8Rfs9S+bDv3I7WtpfX2z2te7ccRVx9fvbmHHm8R/xgV9Yq8QqJkzmNBz01P0TCwLwviwaxDl6zVj92et1IbaceQPU3JkKsqow08MzIwpcTu+CNOqbZlw84fmXCJhjqcZVSonijyVWWdhYBAh0oatQ8tRZdDfvNWufs7nGV9HdJbLNOTIZ/2phiDMJ3YkX6vZ3HFJrX59n6+layEFeZoP5f4PA5IvzIFj3czIP3gPLuFaGa+M4vOJSdTJkJXKI5bz2cCUoMrxCfMb1KTCgvvJg456iX3J9A4NKZJNQebstVjyVO34hhEicN/wGmTNUpw/T7/SbDvQRYnzwb+oWb0jM+6lXuYSYn+EIYWF4M1Xgy0GVgMQbs2afiWpPnIdVganwqP5eu9vauUqQKEMGak2eBPGFqykQeXH9S1/UC5HbvIpctP27xt4GJm8VkZ5cXdpHwqLdqfWhD3ptjuztWZbE0LtOTm2tYjjeWnz9zU89LYhMTicHE+VWrVY90H4edRHblw7y6VP2on6yvrG5UW96slxD62Cj7LH6sE+jj604endszp18TtbW1cWNl3StjtadTENsRZMbF2PwsIvsuSux8qXRkY+9RDvc4ZuZUpTJLOCvJX6csLVgBc6nWfzhTc4aYW7MNcjdBOxuVLLzSKSaOFxnqOXb/NEsyosxP4wg9W+VIttb/R7ImFWrO5TghojN2r50n8W11MTxOMjU6mUMy8FhG80mXkap18dxZGR+R9CFs8y/z7C/Pjx+S0fUnXIPDk1srFofM1YYeGWMqqqiiLM25J37/3w+aJr48X5sc2F4KzLEnPtEf1own3eYvnBD9/Pb3iXysaHy5NaCZuazLttrzWLEEuk10tefpM6zjE43V6CmWiQc9YYzgFbAz0YiVg/7m/uwh9HncVz+eLx9R1WqZYNu3C4fz3RoWnKBkuvlA6mKpxgj1e8Vs/ORmJ9ajI1FAryNZ7OFRcjrWKYPefXdGXKeX/RwXTH0cYam1QN7lc2tZAERRcOOmj1xJVB+Hp8xNrSE/dvVnxJ1Qf4znYhbHNKs9XftDq1ymD8XN9gKQlZVRgv9gynokijSdvFPDSyvkzl/57Dy3oy55LmtxKCuLakI8WEbfGBu/iilSxd4t1usX7hYNY+1OR5rCcHxtYV5aWg3JTL+BjpuEZ/OcC8+RM59FZ/71QZ5cHj7VPovno3i80yoyjSlEV39Ktnpa3whR/hyXupxUPg+mQZ9URnq8moM6mFcMRH3n31xu+dJZbeYaI0k1Di8XI1DYWN2eBjaA3viI67lbDxxE/yzeDnTDTNikJRmE4HU3Xj0uBzZyLDFu/lg2ZmXun5ismNhMDKUY2Rl1MLtzS4nqPfzDM4+Aj/Sogk5PMLXqfqq4ZgvqKXWjyPO/ZR69mVxPm94oVNtPDfePxeb6dVbgVZS3dh1VuDqkMQy5s93Ri2w1LzToC0BD3awsi121g6viPVcikoMfI8enWevwuOX9P6+ka1r3flkJOOr7u+5Z3Gb59sG0Q54T+FOq3kmY9BGY/S9zX7lvZmwTXNb8UHYr6xG2UziXQNOsA3o49qz4GdW1hyVKuHHGXHul5lyKzISvW/H5O0NVof4W9WM2neAq7aaC4KdWJNn3zCJ/LQasenxHOG8L/HH38d4JF1ki+LUnJ9yrj6wi9y12HcTYOjSYk4nqHPzHO4+SVlbix+r/bTrbSCbBX6s93a2D6IODxe7KPv/AfCSwzjd3UJfdbuY83QOhTKqaDO3Cda9USHeH/urv6NktIA1+/HcU4Vv3WItmXntq2sOuWgOSGI+MqyTsXIIA18rn6RahBIl9DnSxg/fxl37fSnPs7vI6c3zmTginVMriTys9wA9r8zEoR0iLS5yrK1S5m4YCq9CguB2GQRTzwNxc4YHG8toaHU7lQfxj6b9Nud0QeSlvdE8v7oeKoJP8/ffA433Y0o9BAbTq/sxrRLwl+CXbG3+YRtqkf6xNpGknjuwVFXLadXBuDtbsVnSw/cvuvWRRs2t5LEc+e07U5SXUyDCt+LC+m5bj9rB1UjX47MNFj80mh5pSYCm4PT6Lz2EKvb5kaRpxzDDqv3T6XF4S1vXH1T9nwLgp0O0VUSzy02ikiiRZQNn2wccZOWmkd8YWnHompfMl390uiqg5Dnixk7f3mKLwX9k7iuX/xHOT5g87oF/LF4HkOKCzFedxo3HY2UsYzM/ziyeJaRUWNAPBvFkHg2hiHxrEWEAyfGlxONXl7az7xgYKYqiRgh7j9w39LQTIoB8ayFKug9WwYWE/crRL9VD4SEMUYE/u4fMP9gSIEaEM9GMSCetVB6P2Zxl4IijcX5fccL/TPHyYTyw/kDT6wTr4p3OssfjUXnJmM15l2y1bykyBD+2Atx/1ozYBFttYUOlbKhyN6ULc+9jJexyhOrT5/45Kyv+6Ui1PEus/rM572/N3eWNBLPYkKzaUf58VO9NSPi2SBGxLMWPucGk1d0mLNUGMf9dCbkiP3GozcO+EhFq1LxbXdr8RwKijRZiKWBSZEUIvh8146oEEMlYEg8ayE67XfnS8skc1Bv2H5cNaf1oyQ24CN3X/sQr7fgAjk5eybrDr/F1+EQbUtkQVG4Pce/Jr2IKj0MiGctlF73mdvBRKS3NOP2vzUwc5xECO5OH7D4nDibF2d/nMGmOYTf1mTRdft0OvWirG3ccHqTst8g/NkC6hbNJDr1nTn2NUxv3U8mwYXXH2yw0wxKBV4dSaGsopNcYiS3jK3lVBOD7WM7gjw0tVIVj/W2ZuKZM1Ky1TJSTRDqQSVy5dMdO6LDNE8oLRueUl7Y56b5nydFlDVOVGQgn+84SO5oAHe2/z6JfXe+4/3yb6pmz0CmKkO4Y2CsJ9b2AH1qZkeR2ZSV912MChYp392/uOLyLmWAINR8JlVNMqLI35szDkYEqES8Ey/ffcPJS5+DxuP6/CQzhq/HOciZg6OlPClFv433Uokww0Tz/OA65s49h7f/K2a3yi98qRbzrn7TX7e02p1208+jtU5JD5p2521iwFAFvmZd36LCtgiDNzxOpw0JF4L2A48+GrrKgHg2igHxbBQXNg+byOFHjng+mU/ZzBnIXmsUD40/eAphb1nQayr3HH34cKA32RQ5qdBnJTbGK3kyBsWzFqEPZ1C5QKIvnU3PlxKcePHOFifvRF/yOf8P4rpex4rhw7ntzJp8iB+B1qzoKm07qsqkkx+TX7IpI/NvQxbPMjJq/u8Rz6G2F/mjtAJFTjPGntDZuPvLpCeeVfi93k+/IuJ++dox/4a75vw/5b8hnhPwuL+OLiYijYV6sO6RnjeBGSSObyem0ziXsK0whlPWP9sZk4jkzfp+VBEiQlF/Po/d01WHhomP5MOpoYxe806kKA6fp9tpU1BB9up92PPlZ2aS/lvi2Zdj/UqTNYOCgsNP4/9zqlGNMs6ezS2ziY5UDipNu2lgaeuvkL54jvZ6wixpBi5rRXps/6A5+8+ItN/DvLmLOKNe9+3G7v7lyK5QYLrmrcG33qcmPfGcgNutlXTIL9JbrB9bnxsfBktNLF8OTcAsp+S34zn7xfhwUVrCuTenJcVF51nRdj2ffmKPbwr+nB1eiRzCJ7L1O/iTgztaiI74usZCtAsRVnPOvV+YyUsk3OkmE8uJdGeryaD9Bjer/jQhH5Yycc5WHrtEinDwlb9bSAMSmelyyFlzhTYxWO0eRZ0c4v5Vp3Dd7ieVUDKh3JjckILSHtrO2/ge9gsVSgdlhCv3dg1m0i5p2jSab2fmUEvEsbwtZ3HLPf3yjPd7zJ4Vf7D8iiTsQ3i4vDvFRbqKjzikdxVDSrtTn9HHDa3x1YcSn2c76V1Y2BboyNK7PxedDPP/j3gOtlzI+Dm7eeEpIk2UNfMbCp/NkpNex3XeomkA11tjGLnoNt7xcUTZXWaQyLtMRU2Z9/Tn0py+eA7m5qQGmEjLrn/Zl/55XNclPtCSE+tGMv+klC/hvNwylDKiTSzcdwvvA40OycnI/M8ii2cZGTX/t4jnGBxuzae26MRLy3onXjY0X/izpCeeI/h4fKJ6ObSiXBeWPvyVDr4+/hviOZhn20dQUkpj1f5se/mzvyvxgytzu1JAsm00mcu2vyDxVHbsG9JYLagUHZfz1uirSI0TH/6ZzW07c9hJI5RDP7NnUFEhAqsxeqfVTwiM/5J4DrvDn+WlGX0FhaZdJfoXHjHW8yR9c4oOZ6aCdNrzRXM2NQnOD9m6cCxjJ85g1py/+OuvmUz9cxJzzliR9sXW6YnnBHzerKeZVB65q9PnsPGliMaJ5uWyKSxctBcHzerDoCujKZ5HPE/N5XyK+pmMSE88B/Jo4xCKS+mtOZR9lr8igD04N62DiBMKcnVfjoWHnt5vbBDvzy7hj7F/MmXGHJG3c5g1bRLjlx7D0uElO7rWIpN07/47MTAxrp9Ic6ZVk2YRFdRY8pBIfVnh+5GT6ycyZsJUZsyWynUW0yeN588DT/H8doT+OTIKIVKMngaWi8bZ32Xj/LGMS/aLGUyZNJ3Flz/g/Gg5ZlK6SzVn3n39r0kOsjzG3CljmTBlNnP++os5s6cxaepCtj7WFT7h3B4/mKV7buGr8Ten/R3JkDELJh32iuigiwsnxkv7doVI7bOOt/pm3WP8eX1yocj3icn5PlPk+7gVp7FytGBD2ypklNI/7ACevzrmoUWI3QNWtu+LtLpZQvXjHvPa5kWRqznLrruk094k4HRtFwsGTuWFpiLFWG2nW5WsKAoP5byN7qyvVrtTuAkTLv1KuxOG5cFxlJdsK/Rg9ROdzyP8Mv9/iOdQro8ewPLDD0lK7fedrVFkzEbxbodSv1NCL94c7NxJtEXuie/AiPPhzqyKIhYWo83sh+msjEokXfEc85rFTSqQ4Z/40n8Q11OjxPPxcRb3GsNDzTRz3LejDK2THUWBXhx8+5+WtYzM/5vI4llGRs3/LeI5ApszkxKFYoXfWPX0P+h9qUlPPAfyetcItbjMVGcge9//zCyoMf4b4tmb+6v7iN9UkKXRGE59TWcdaCocODmtjVpE5O68mEc/MWOTjOojG/vXUXdAigzbxZd/OsquUuJmvogR469pfQoplNf7R1BEkZk6v2/DNl31/F8Szx7nGFRBdMgVmWiy+rnRbwzrEvNlN23yCD/NVlT9Ui59KL3eceHACv5sl0OdjwpFdir1mMQ2c3vSfrY5PfEci+e9Rep9lYqi9Zh+/WfXV+rhx33WzlrIjis+KfU25CaDS+UVvlKJdR9/ZpAlPfH8g1vLepBDpDdb80lc/P4rewS/c2hCc3WeFRu2lff6qkVcGN/ND7FiRifqSisrpHwp0YLfd97lm7s5yzpUVp8rM+ks2u9dShffq4ysJi01V9BpyzuSPmecimB77p/ezJx+Zcgr3VccRVoMZfkNK/zf7aBNbnEuVykmJn7TLg3xHm84u38p41pLezIl+1xU7TubQxa2OF2do94jrqjUji2v9df1sG932btlOv1qJt5boShJ8z/XceFj6sE/pdMZ/pqwmnMvg5NjX4LrUVpnykhOk9YcSbPQ5it7RklbKhSUHrWbz/peSRAbjM39A6yY2p6a0gy1dP/SrRmz9wH2bkLgtiqrPldp5hV8/+lyjNgQPpyfzeiZT1OWQKu8uLSgo8jv7LT7+6rxzwqFfufiphnM2mCbsiIk9jNretQgq6IAf5yx0dnzrdXulG/Hyic/K1ol/LHYOoR8wjZTvWEcsvrVtQa6/PfFc4LDcWaOW8cVy9Bkv4hx2E9TRQZyF+vIqXQCbOjHdYwafZSPvknPGovrvflUVGSkdKvpPPuJZjtd8Rz6gKmNSv4zX/oP4noqIly5u2cGk5dbpfhLvD27hpuRXZGXgfvfiJZMRubfhyyeZWTU/N8inuNwMV9BI6kTU6YNCx+kt1kpPdITz1F8OT9L/bKwjNX7sPXVry5T1OW/IZ7DeLNvHBXUnbORHLb6lV6EH3eW9BEiVQiYdnO55/wrwtuF46NakkfY5um/BWu/f7b2TaX04eqU/qyxTJ0fMV9OMaycgszFu7PJIr25jv+SeI5+yqwqhUUnKyM1ljwy+GItfcT5XWZk/swoMhagxdZ0llC/XUz94pJQ6sE+B0OvakpPPCvxt95Je6lumNRh3MWfW16pD7eL61i8bDOWmn8nouTO+IrqlQbVJtxInqk0THriOZQXO0erhWDmRuM4/VPL85Pw4drcbhSS/Pa3JTxyNSa8v3JMpFuhKEiH2TdJ1Hvf2NKlNtmEvckYUfa/sjkx9iUL6pZQd9rLzr1NpLFbux5iYP3c4tqGLDbXeKXfBYbkzIgiUxHa705n2fXLudQuLPwiQ1+OuCXeyPvVepqLeyuKNWH6LeNbNHxO9xXPmJl8lebwRE8AttkziwU7zpL6fVGBHOuRA0XmXLRd+lpHRHpycVpHTMT9s3ddycsfRiqEyor9oyWhXJhui+5r9n9+Zm3rKmQR9oUnncPHkKunQ2zAF47+ORj1im0tAu6vpF1hBTmqTuDMV8PxNdjqOhunTOKSjrL5tnMAlYXgL9B+I5apvg2m1e6UbsX8dDfJahOJ1clp6kGtjDUHssvyP21D/vvi+fP2aSzYc1UnJvpwoFMWMmTJT+fVlka2oUTxaN4Qlty2Tb1iyP8l8+qIPMhbj2nn9L6hLBXpiud4K9aKZ8r8T3zpP4jr2oTbP2LHxD84qTPB7HJ8HHXyKMjbZCmPf6SN1DIy/+vI4llGRs3/PXueA7+eZFgh0YnJUothe600Z/8p6e95/vFiOz2lmaKczZhx3l5z/p/y3xDPKpzvrKBddpHGvO1ZeutXlhQq+XRkEvUzCtsSgzmgecHNzxGLxareatGuqDaFW47/sFP4aQ/NyhShSv2mtGjZkpaao0WDGpSQ9rQqCtFz2e10Xgz339rz7MaBHmXUM/N5e+/D2EtydVHGWrOmSS6R/kyUartdR6DoYHOARuVzimsHcOHHPxXPEOF5m8nSvsyMZei4+tlP1jkdYpw5MrUNpYqXp0HrlPJo2bINpuUSZ0Kzlh7KFQNvnk0hPfGswuHaElpLe+YLdGHNg58rtUQS+LB3DHWkPct5OrHpibsoTQPEuXJpfmOR7qL0X5L0wr8wbk5rof62tqLREl77/sqgkRfHBlZWC+/MVebzSu+6bQ3edxjZUloe2oItrxMXQaui3rOigbQXPgvlO+9F387iZD7top70Ka0MQ7nmnyhFwuwv8kcJke5MJegw+45mMEA/oXdmkV2RlcLV/ua95lwyYVas6G9KmXLVaKRdzi1aUrOk+H3xfEXMZvAs1cBCPG+3Dae6FC/ydmfXa289MVNDrBOnZtUTv1OcoaufasRzMJfHNyS/lO8tVvEx+FdWGyShJPDhUqqaFKR6k1Za/tmK5qblMJG+Va0oz7hD7/QvD1aF8m7vKEoVKEZdYZNi35rG1Qup97IrMjZh/XPvVJ/dS253MtdkyJ5faXdUeDzZRFdp9UOulsy9nL5wNM5/WTyHvGNRz1qUKV+Dxtp+0bwFNYpLeZuB4k3n8dpQmHK7Qt+6palQy4xmrbTsm5hRXnq/gSInjUYdMO73gvT3PAdxaayZekb/133pn8f1FCL5dnISpfIVpnaL1H7YpFYxckt1ROr73HH5iW1HMjL/W8jiWUZGzQ9O/wPxfOEfiOcr6YhnZYA1OwZKb7QsTKeZN0V3LH1USkNdPFeOGBXPopvuacGyztLsURmGrLHQmYnRj+H7iU7ML4tnO3YYFc9CHzjeZEYLSdhUZtweS35mDi8pjTFWhxlSS2roG7D4hoNhEaJFkm3ok1W0kcRapo7sfP9z+8FVqV7768+NqQ0ZtOw0dx8/wtzcPOV4bMHFTeMwFZ3ZvJ2X8MToJJsQz0+Xq7/z/Evi+dUa9XeeDYvnBL7u7UV+6eVQpWbw5KcyVpX4ZmNlOM9XtVR30LIWb8ZeY1uQhUhqWFZaut2P026G1jSG8Cgd8awKd+H0BGm2Lw+NhpwQNTB9dH3V/eFqpgzqxeqrz3iiXR7ieGpxmdlNcpEpe1FGnU9vZttGM1BkSDwLv7W/wuSmWUR6qzP5iJXe+q5LUnqj3+2lX43MwjYbXdc8wC/1Y6QQ48i5OWbiuiL0WXiXpF3CPtdmULuA8N2sAzjt8nMDP4m+q8TuyFBKZBO2Gcuw2MKIrcc1hjeXlng3Y8NzjcBXhvJ4aVNxTkGO0m05ZGw87sMWTEsKv8gwmIveiZFHGWzLgd8Tlz6XaTMFc4PjXUJkXp+iFs+Fqi5C91O+tqcnMnrkWHbdtuCxTjk/uneU0VUUZCxUl4WPUgu0yNfb6V5ZeuFZdnptfm5YvEd/59g0aVtHMQateKQZtBBC8sKfVFZvZxjGRa+fWCUjslw7Zigjv3N0lCnDN9/j6WOddD99zrH5PdX7i0v9vp+vel5YHeZ0k9UDGzPl2EueatlKx2OLR2wZWluI+8w0WfGUYK1GK6XdKUTH6deNDlokkeSr8W7mLPpNGhwrx4hNL/TWXV1062UKn1n3y+LZli0/KZ6/HB3DyNGT2HtH1y9EfL51gBEVFGQq1pBlFvqWa8TwdOlvDJy1mUvmj7VsxfHoCTeP/U373CKW1hvBqXTGoYOdDyd+59mgeFbhfn48laSB7WzDufRLvvQfxHUNUV5P2Ty4PuMPCD96pPWc4nhkYcGucU0orMhInbk38f5Pd3vJyPw/hiyeZWTUJM08N2BVUicwXRJnnnMpTFn62OUnbRJnnnMparHgrkOqkf9kVDHYXv+LeqLhy1JfNMK2xoaNVfj8+MzNu4ZmCpJmnpux6b2BpcFCBL09PI6qokOWq+10brkYe5J4XJ2tuPtQ/wuiUmaeu3LYSU/PTi9JM88d2Gen/wVB0vc6n2warF4CW6D7Yp78MNTxkojF7vt7Hj62TfxnvBeXZ/1GIUUGyvyx2+h3nqUleVZWb3n+SjN7EmfHvoH1xPNkwnTeJTyM9V9Uobx4+QqrT1qiy/sSEwYv4n7Si8J08bjDjKZZUeRsz9qHxqRgAm5PlmEqOltNR5/V86IjfSjxfLmaBsKmwZDjGJKCyrC7TCotzSAXZvhJW6n/ZRBl3A9u3XmBt3eiAI7xfsxfjSRxmJNGM29hUOd83UujcpJ47puueDZRlGXCcSsDyyaVuL9YR6uMooNbsQvbLY0P9YQEfuParXckJCT5tCfXt81n6vK7BmdLPm3rQLbMWSg39Fg6Lw5KmnnuxhEXA76uDMR8bT9KC78t1HclL40OkIh6b/NOCCTNKER8BI839lW/zC9jpfGcNfSh5zgXLs5rqC6/PgvuJItnYj6wrnUFsiiy0HqtEHdGqrUqIYBHj1/wzS4xgaooG7b2TtxvWbL/oeSXqqXB66Zm5rkpG56lxM0ozwdMr59BnM9Hy3n3DQ8AWm+nXinJLwYli2eRGLze7KGfNAuYqRZjDn8yODAZemt6oniusjC1eFZ948DCWfy9763mhC4RPJhXh4wZTWg65474lxbxodxf1Y2y4tkzVZ/CFUcDa95jHTg5s65Ie1EGLTfXiGdB9BuWNS5FJpGuDlueG315lCrBj4fmL3B0SlkXG/H1MCMHr+Wjgdvy6QD9qmVEUXQwJz7r+l0UtnfWMXLcIYMDSyG3Z1OnkHg2swW8CdKq7Zp2p77U7tQbxgmb9NudG3c07Y4yjFf7RlNJ5FmeDn9xz814G+Lk+JH75volY8rMc0+OuxvLPW2SZp67GG93lF/YNXcWK44Y2mYSxK1ZNciQsTBtFj1MG4PCHzN31ByOvzBQkcM+iXoj6kPm2kw4pml/DBCimXmu3HKTkP4GiH7N0oYl1b7UcetzzeoG/Ui+9EDtS4lR+J/F9aQYE4/7060M/32X6EHoJ0q0R81KSNu9pvHQ61dWtsjI/L+PLJ5lZNR8Zm3vqmRQmLHy2c9JE2m0e9PA6sLGlL/Nf3YpsR3bh9USNrWYf9tJcy4tqih77s9ui0n2LJTpOYE7okOQpnmKDefH95cc3r4NixBDs0MfWNpJemNnMza8Mzxzqgz+yIXxjcmZLQvVR8zjuWdYGmGvjA7D2cqcg7t38ybcgBiMfszk+sXE/bpy8GeXOcc9Z2bTUsKmA3u+G55pUPq94OjwumTLkoV6f67E0ic8Tac6ITIUu9e3ObD/AB8jUtIY73KLDV0qkClzNtot2cf3oEidzoSKuPAgrB5cYP/xk9hGpkir2M/HmNO4MBkzZWfA9kt4pnl2JTEh/ry6cpSDl6/iEp1UUjE8XducaYdfpXSs0xCFxaoBFFdkpu6EvXwOMdTFScD2/HgKKnLTdNTZn3gbrIQS+6uTKKyeeTYsnqXrgq7MpXOBLGQuUJG/71gTFqObs7GE+7tyY99Wzlh+JDQ+qXOsxMt8BS3zCAGUswLtRQfPIzxtpzv2zTpqSZ+yUfThuEHx/IPTM5uRUVGO8ccMCybiPXmzogelc2amSMsBnLcJSPtCnPhIAtw+cHzbZh54pwzIBL3Zy9JZAzhtbE1l0A2G58xA1sI1Wf3UiDgXvj5J7evdOORs2NcTfCw4MKgmWbJkpeHU9Xz0Tfsd6fjIEL69vMn+A4ew1vJbIl6ztq8ZWTNmJGvnpZy1C077Ga1oWw7/KX37ugBdF9xNNYAR83IbY6vlFmIgN2OPPiAgUjeKxBMV5MPjU3s5cvcB3rEpZRf95QB9y0ovHcpDtflX+RISkybdCU5n6FtXEr+NWZ1q0FF0vu8soUnu7ChyV6Xzztd46fGLmBcrqCrNjisGci5JPKsJ5OXOP6iQLSsZy3RkyjV7QtIoGSWeZ34XtgpyVV7IK81ZCc/b85kzfzoPjCwWifh2kE6KDOSp3oFDOu9RUIU9Y3m3umTJkIFsPddwySk0rY9Ffmb3H4l7zXsvf5RqgCD6yQaGV8gh8j0/k04+JSgqbb5HBnrx8Phujpo/wS9W4+1xgVxb1IC5V4x9MtCP83+0JJ8QVG3/vomHlrvE/3jL3lnNWP9ac0IvX1nTqCyZM+Tg9yOpv/kstTsP/mpHweyZKdN9nNF254hOu6MMeseZPxqQI2sWao1exCuvsDR1WBkViuP7+6IN2Yultp9rE/mQcbWLiDjQk6NuP7mfNuYJUxqUEHWxi9F2x+36TGYtnMtTA2O0EqGfdtNO+EXeOt05nuo9BQlY7+vF5M2ncTYYnFR8PzeT6opMlO45lyd6v9+dSMCr5VRVZKdCi01C+htCJXxpfaIvZSrA5NNPCTbgSw+O7ebYI4sUXxL1QzuuLxVxPTzduK5x8kBbjs5pxPLHxhZkO7KjQ3WyiWcYsPM9BpsuGZn/QWTxLPMvJ5YgT0c+PthM02rSsuCcNJ66i8cf7fCJiDcwWhtL8A8nrM2307KW6BwqctDgz+2YCxvvsDiDNiFeTnx6tIu2plJnMyumYzZz//13YROr3ybejZtrh1OxhAllG3Vl5t6zvPz4Hmtra+zs3vD25mFWT9/Ik6/BaTq1knAL9HDg/Z211K8oXbEuAAAKYklEQVQgLUHMR8vZ+3n2yQFfQ3sY4+w4Pa83pYsXoGLrASw8cglL64/q+9nbv+T5hX2snrmd187Rae+nisLXxY4XlxdQXto3pyhOl+UnsfziREC0oVY1Cj9XO15f+5sq6r1mReiw5Dhvvjjib+BDu6rYzxyc3IkSxQpQtcNwlp28wjtrK3UaHexf8OjkLlbP2cMHz2idPFWhDH3BmgGNKVa0AKa9JrHp4g0+CrtP1p9xsnvKrb1bWLvoGLaBMTq2SuK9bjOrXXWKFilAk2Fz2XPzjvqenz99wumbORc2rGXjmsu4io6NZBvh68ybXaMol0NBw5lbePnZnRDdbzPFReDjYsnBIb8lfkpLkZdOozZg8d2HyLika1XEh/tg99GclROl/ZVCKNQcwbaH73D0DBJepQ9hE+GLvdUT1k2VlvMqyF5lMJvuWeLgEWhgyXscvq920K1MYQqVrEDPOVu4bGGhfsavX6xw+HidPX8t49ApS4KVaSQUnk8PMaJVGYqUrkbzP5ZyyPw1r4WtZP/83T3mjm4k/KoE5btt4HqgbqrjCfdz5euLY/RvnfiW58oDVnD1rS2eQXp8TULpw/O9k6hR0oSSdVszYcsJLN5bqu/37ZslVuYnxbOv4c4bH/Hr4vLYUDwtLzGntRBjFUzZ9OArrj5pO/exgW58urqDnuryUJCjaFd2XH6Da5BWrqXx9RJ0XXHKqK+roq3YPa49xYXfVu80ipWnr/Je7befhN8+5+GxHayeuw8rb12/Fbkb9J0j8zpSvnQpijbrz9x9l7mjyVvrzx85engubaoVoVSFzvx5zlZnlldJnMM5xpiJsilckDbjlnHs/kO17efP1jh9vceJ5avYvv0eXnG64lZF2KcbzOpZniIly1Kz/wy2XX+Khebelp+es3peN6qWKEa5ZrM5YB+iU1YJuJnvZUhzce8yNWk5fgVHHr3hjcbewvI2s0fWo5Twiwq9tnInVEcUKMN4d3oB7WoK+0pN6DN/J2dffeSDxv7u45OM6lRapK2ciNkXkOZA4yMDcH18gOE1Rdk16MKR5/bCh6J08lRJpJ8Tzw8sTXwxmTiKVR/G8Ydf8NJKQ7z/V/bNbEMZKd9bDmLBoavc09zb+vMHDu+fScsqRShdqQfTrtrpzDAnEGV7khG1S1K4cFHa/7mSk+bmWvl+lyNLVrJztzk+CZIXKgnxtOPeii6YZFfQbd1pPn/zSj0jLhETjLuDqNet65BVnfbiDJt7kHeuXng5iFgypiqZ8+Vj8kkL7J0D0sQHpYglju8uMa1M4meMFBnqMm/7Db57a81pJrhxe90IKv2TdifOluOzeogyLUDltoNYcix1G2Jxbi+rZu3grYu+NiQSH+fvop2ZS2n1gEpJuq86w7uvzgQael20pi6+vLKQCurBOf3tTnyEPy4PdzOwqoLcTXtx4qUDP4J16ppKKeK2I093LaCxxi9K1v6d0xa2uLi5Yn9+Po1MFJQf8hf3PriI39epLwlSm/uZy3OGJ74tXpGRBt3mc9fKgxCt9kwVHYDTF0sOLO2QWAaFO7P4ynPsXHz1v9le40vDNb7025+rOKXxpS+frXD8ckftS7v2PMJX7UvaaMX1UiKu/7WVK8+S4vpHPXE9jgBXa05PrUe2XFkZffA+3x390szAqyL9cf50i3nVSyU+g6Ia09Zf4OuPlLeXy8j8LyOLZ5l/OZ7c2TiB5mb1Ma1niqmpOOrVx6zRcA5/DEzbwKv5wYMtk2iha2M2hH1vfdWd9bR483jnVFqmsRnI7lfeaUf3k4nD++s11sydQNOWbcT1ZjRo0IDBogE/9uAbzjH67ybtdb62agxNde7XsOloTnwx1sBF4/b+LIunj6VR05aYNUi83++jl3LmmRMecQaEt/Ibx2cPpmH91Pdr1m4KV5wNjF4r7Tg1bxiNdGyatpnIBQf9i3YTicDx5XHmTv6DBk2aa9LYkDET1nDpjbsQAQbSKKEK4vP93UwaO5L6DRsL2wY0bticyTN3cPOTD37JM6p6UHrz+tJGRg0fQj2zBup8adakDbMXH+WhQyDByd9eSuDjkZn0MqtL7Tp1MRXP16z1cp4E6+SD30cOzPoNs3r1qCeevW6dOtSta4rZlMPY+ielI4GA9wcZ1tCMeuI6dR6ZiuvNmjNh4x0De5+VBFsfYWTjBqlt6jdj7NobBvY+JxIX9oUrovPYo2dv6mt8rX3bPqzefQ9LvyijnzyJT4jAyuIYa2Z0pGNXM3XeSvZmPbvTY67otL23JVp0UNP+RBDvTy6gfSpfrSfu3511dxyM7MFXEuh0n22LRV1sI/JRk95+fSez/7ro6EemDH5F/njO+v7i9+vWpk7deuLa7sw/8DbNklqPG4vo1MKM+up8q0ud2nWo11oIp5taufZPfF1NOHbPjjB74ijMGjfV+G0jxv25nsvvPPA25rfiWX28PnJu80RGDxd52ygxbxu0aErjMdOESLmHW6iueNUizpXHJ1cyaOBA4TuJ+dSyeScWrj7PU7dQwo24vUoVy3era2yf34eevbXKtdNvtJ++kkOP3hMWb2jQUPKLMD48OszK6R34rbOWfe+e9Jy/nWsfvxGr1y8SiYz2weLieuZOaEGLtin29QcPZsSqo7xwclN/9kyyD7S5wvxO9URdEuUsfN6swWA23vyeWkSqYvhyYhJNG9RX1zvTuppy7vInO17ofrdWiZenJac2jGfk0PoiZiTlezOajJvBspMP8QgPM5jvqlgnHhxbzoABA5LrU6sWnVmy/jLPPcOJSDYM4/H64bQ3raOOGfXqN6Rjn51Y62aKywNW/tFC7Z/qmFG7NnVNG9Bc5OOOcb/Rol5taosYUt+sFcNmXUmz2iTqw2HG9myo8W9T8dy1MW3YhgF7Ur93Xt3u2Fz/B+2ORBQulqdYOO0P0d4kxecGjBqznHMvXAy3IfE2HJ4+QNQrkTatetW8w3Sua97EnoafrIsB1ueZ00HyizrCL0S722AY2x44pvYLZaSI2+PV7VGKX9QVfjaVuX/PY1p7kV+ibOqK3zdrMINLDjq7wiPcuLG6D2ZJ9tK96oiyGLGex84pg2+xzleY0q4Z9cVz1pWuE4dU3oNnH+ebsXoY48T9o8vo3z/Fl1q36MLfG66ofSnSiK0U1y+LuN49VVzvqyeue3NlXjeamib5UXP6TTiN7hq5+K9nmd6/UWo/Eu1Lty3PSB73lZH5H0YWzzIyMjIyMjIyMjIyMjIy6SCLZxkZGRkZGRkZGRkZGRmZdJDFs4yMjIyMjIyMjIyMjIxMOsjiWUZGRkZGRkZGRkZGRkYmHWTxLCMjIyMjIyMjIyMjIyOTDgbF8+XLlxkyZIh8yId8yId8yId8yId8yId8yId8yMf/7LF582aNCjaOQfH89u1btm/fLh/yIR/yIR/yIR/yIR/yIR/yIR/y8T973Lp1S6OCjSMv25aRkZGRkZGRkZGRkZGRSQdZPMvIyMjIyMjIyMjIyMjIpIMsnmVkZGRkZGRkZGRkZGRk0kEWzzIyMjIyMjIyMjIyMjIyRoH/D7q4TLJ4DwHGAAAAAElFTkSuQmCC\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn-situ \u003cem\u003eprimer testing, protocol optimization, and TaqMan Probe Design\u003c/em\u003e: To test if our candidate primers amplified target DNA at the exclusion of other taxa, we obtained tissue samples and DNA extractions from our target species \u003cem\u003eP. volitans\u003c/em\u003e as well as from 20 closely related species across the family \u003cem\u003eScorpaenidae\u003c/em\u003e that naturally occur in the western Atlantic/Gulf of Mexico regions (Table S2). In addition, we obtained tissues from three species that were flagged as potential sources of false positives from our \u003cem\u003ein-situ\u003c/em\u003e testing (Table S2). DNA was isolated from tissue samples using the E.Z.N.A. Tissue DNA kit (Omega Biotek) following the manufacturer\u0026apos;s protocols. DNA was eluted twice for each extraction using 50 \u0026micro;L of buffer, for a final elute volume of 100 \u0026micro;L. After extraction, DNA was stored at -20 \u0026ordm;C.\u003c/p\u003e\n \u003cp\u003eTo determine the appropriate annealing temperature for our Cytb-ptv primers, gradient PCRs were run on a Bio-Rad T100 Thermal Cycler using DNA isolated from \u003cem\u003eP. volitans\u003c/em\u003e. All gradient PCRs were conducted in a total volume of 20 \u0026micro;L and included 10 \u0026micro;L IBI Taq 2x Master Mix (IBI Scientific), 0.4 \u0026micro;L of each 10 \u0026micro;M forward and reverse primers, 1 \u0026micro;L of template DNA, and 8.2 \u0026micro;L Invitrogen UltraPure water. PCR conditions were as follows: 5 min denaturation at 95 \u0026ordm;C followed by 40 cycles of: 94 \u0026ordm;C for 20 s, a gradient annealing temperature of 56\u0026ndash;66 \u0026ordm;C for 30 s, and extension at 72 \u0026ordm;C for 30 s. This was followed by a final extension step at 72 \u0026ordm;C for 5 min. PCR products were visualized using electrophoresis on a 1.5% agarose gel. The highest annealing temperature that produced a clear and strong band on the gel was selected and used in subsequent PCRs to mitigate nonspecific binding. We repeated the gradient PCRs for the Whitaker et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) COI primers. Based on our results, we determined that the optimal annealing temperature was 65 \u0026ordm;C for our Cytb assay, and 60 \u0026ordm;C for the COI assay, a few degrees higher than described in Whitaker et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOnce optimal annealing temperatures were determined, we conducted qPCRs for both assays in triplicate on the 20 \u003cem\u003eP. volitans\u003c/em\u003e tissues and all non-target samples (Table S2). For both the Cytb-ptv assay and Whitaker et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) COI assay, qPCRs were run using 1 \u0026micro;L of template, 10 \u0026micro;L SsoAdvanced Universal SYBR Green Supermix (BioRad), 0.4 \u0026micro;L of each primer at 10 \u0026micro;M, and 8.2 \u0026micro;L UltraPure water, for total reaction volumes of 20 \u0026micro;L. All qPCRs were run on a BioRad CFX96 Real-Time System with the following protocol: 5 min denaturation at 95 \u0026ordm;C, followed by 40 cycles of denaturation at 95 \u0026ordm;C for 20 s, annealing for 30 s at the assay-specific temperature (65 \u0026ordm;C for Cytb-ptv and 60 \u0026ordm;C for COI), and extension at 72 \u0026ordm;C for 30 s, with plate reads after the extension step in each cycle, followed by a final extension step at 72 \u0026ordm;C for 5 min.\u003c/p\u003e\n \u003cp\u003eWe also tested assay performance under more natural conditions. To do this we collected ten 500 mL water samples from the Indian River Lagoon which is a highly turbid body of water with high levels of background DNA (Kumar et al., \u003cspan class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Prior to filtration all equipment was soaked in 20% bleach and rinsed with deionized water. Water samples were filtered using 0.45 uM pore size 47 mm mixed cellulose ester (MCE) filter paper fitted onto 500 ml filter funnels attached to a custom-made 6-station PVC manifold vacuum filtration system and a standard laboratory vacuum pump (Kumar et al., \u003cspan class=\"CitationRef\"\u003e2022a\u003c/span\u003e). After filtration, filter papers were carefully removed from each funnel using sterile tweezers, folded, placed into a 3 mL screw cap tube, and stored in Longmire\u0026apos;s buffer (Longmire et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e) at -20\u0026deg;C until DNA extraction was performed. Prior to DNA extraction, filters were cut in half with sterile scissors and DNA was extracted from one half of the filter using the E.Z.N.A. Tissue DNA kit (Omega Biotek) following the manufacturer\u0026apos;s protocols. DNA was eluted in two steps, each using 50 \u0026micro;L of elution buffer (100 \u0026micro;L combined volume). To determine if our assay was effective in samples from natural systems with high levels of non-target DNA, we ran 20 \u0026micro;L qPCR reactions as described above with either 1) 5 \u0026micro;L of the DNA extraction from the IRL samples (our template DNA) and 4.2 \u0026micro;L of UltraPure water or 2) with 5 \u0026micro;L of template DNA spiked with 2 uL of gBlock (10^3 copies/\u0026micro;L; see below) and only 2.2 \u0026micro;L of UltraPure water. These qPCRs were run in triplicate.\u003c/p\u003e\n \u003cp\u003eFollowing the qPCR results above we decided to design a TaqMan\u0026reg; probe to eliminate late-cycle (\u0026gt;\u0026thinsp;38 cycle) dimer fluorescence (see below). The probe was designed using the same sequence alignment used to design the primers (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eDetermining Limit of Detection and Limit of Quantification\u003c/h2\u003e\n \u003cp\u003eTo determine the LOD and LOQ for our qPCR assay we designed an assay-specific gBlock\u0026trade; (IDT; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The gBlock was 197 bp long and included the priming sites for our newly designed Cytb-ptv primers and a mid-sequence modification (11 bp reversal; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) to permit differentiation of synthetic DNA and genomic DNA amplicons as a cross-contamination control. The gBlock was resuspended in 25 \u0026micro;L Tris-EDTA, 1x solution (pH 8.0) following manufacturer instructions (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.idtdna.com/\u003c/span\u003e\u003c/span\u003e) resulting in a stock solution of 5.15E\u0026thinsp;+\u0026thinsp;10 copies/\u0026micro;L. All subsequent dilutions were made using a tRNA solution at 100 ng/\u0026micro;L by adding 100 \u0026micro;L of 10 mg/mL Yeast tRNA (Invitrogen) to 9900 \u0026micro;L of UltraPure water (Invitrogen), which helps prevent the gBlock from binding to the plastic tube and causing inconsistencies across dilutions (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.idtdna.com/\u003c/span\u003e\u003c/span\u003e; Bustin et al., \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Klymus et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The LOD and LOQ were determined by conducting serial dilutions of the gBlock stock solution until we had template concentrations of 5000, 512, 64, 16, 8, 4, 2, and 1 copies/\u0026micro;L. Dilutions were run in 10 qPCR replicates of 20 \u0026micro;L reactions, each with 10 \u0026micro;L TaqPath\u0026trade; ProAmp\u0026trade; Master Mix, 6.7 \u0026micro;L UltraPure distilled water, 0.4 \u0026micro;L each of 10 \u0026micro;M Cytb-ptv-01F and Cytb-ptv-01R, 0.5 \u0026micro;L 10 \u0026micro;M probe, and 2 \u0026micro;L of template, effectively doubling the copy numbers listed above per reaction for more accurate pipetting. All qPCR replicates were run simultaneously on a BioRad CFX96 Real-Time System using the following protocol: 3 min denaturation at 95 \u0026ordm;C, followed by 40 cycles of 95 \u0026ordm;C for 20 s, 65 \u0026ordm;C for 30 s, and 72 \u0026ordm;C for 30 s, with a plate read after the extension step in each cycle. We used the curve-fitting LOD and LOQ model as described in Klymus et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), where the LOD is the lowest standard concentration for which at least 95% of technical replicates amplify, and the LOQ is the lowest standard concentration for which the coefficient of variation (CV) value is less than 35%. The Generic qPCR Limit of Detection (LOD) / Limit of Quantification (LOQ) R script calculator (Merkes et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used to calculate LOD and LOQ, as well as the slope and intercept of the linear regression and the resulting R-squared value using the default settings. To visualize the results and determine the correlation between Cq-values and standard concentrations, a standard curve plot was generated using the data output of Merkes et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) in RStudio (2020).\u003c/p\u003e\n \u003cp\u003eBased on the results from our testing of non-target species, we did not calculate LOD/LOQ for the Whitaker et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) COI assay.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eAssay Validation in Aquaria\u003c/h2\u003e\n \u003cp\u003eTo verify the performance of our assay on water samples with known lionfish present, we collected water samples from a 2650-liter saltwater tank containing eight lionfish and a few non-scorpaenid fishes. The lionfish made up the bulk of the biomass in this tank (see Fig. S1). One-liter water samples were collected in duplicate from the air-surface interface and were placed directly in a cooler with ice and transported back to the laboratory. To test for the presence of lionfish eDNA, the water samples were filtered, DNA was extracted from those filters, and qPCRs were performed as describe above using 5 \u0026micro;L of template DNA and eight replicates per sample.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eqPCR Primer Design\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePrimer design and\u003c/em\u003e in-silico \u003cem\u003etesting\u003c/em\u003e: Based on our sequence alignments, we selected the Cytb-ptv-F and Cytb-ptv-R primers for \u003cem\u003ein-silico\u003c/em\u003e testing. These primers are 27 and 22 bp long respectively with no ambiguous sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our NCBI Primer BLAST results for the Cytb assay showed that only marine species in the Indo-Pacific genus \u003cem\u003ePterois\u003c/em\u003e were matches for our Cytb-ptv primers. One freshwater species was also a possible match for our primers, the silverjaw minnow \u003cem\u003eEricymba buccata\u003c/em\u003e, but this species is not found in the introduced range. On the other hand, BLAST results for the Whitaker et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) COI primer set identified several matches for non-target species, including the king mackerel \u003cem\u003eScomberomorus cavalla\u003c/em\u003e, the round scad \u003cem\u003eDecapterus punctatus\u003c/em\u003e, Sloane\u0026rsquo;s viperfish \u003cem\u003eChauliodus sloani\u003c/em\u003e, and the silversides \u003cem\u003eMenidia beryllina\u003c/em\u003e and \u003cem\u003eM. menidia;\u003c/em\u003e all of which are found in the western Atlantic. To test the specificity of these primers we obtained tissues from three of these five species (Table S2).\u003c/p\u003e \u003cp\u003eIn-situ \u003cem\u003eprimer testing, protocol optimization, and TaqMan probe design\u003c/em\u003e: Both qPCR assays amplified all 20 \u003cem\u003eP. volitans\u003c/em\u003e samples collected in Florida successfully and early. Cq values ranged between 15 and 25 cycles (data not shown). Since we did not standardize template DNA concentration, we do not attempt to interpret these Cq values other than an indication of successful amplification. While the COI assay also successfully amplified \u003cem\u003eP\u003c/em\u003e. \u003cem\u003emiles\u003c/em\u003e, our Cytb-ptv assay did not. We tested both primer sets for specificity by running qPCR assays (40 qPCR cycles) on all twenty-three non-target species (Table S2). We found no indication of non-target amplification with the Cytb-ptv assay. In contrast, the COI assay of Whitaker et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) amplified two non-target species: the king mackerel (\u003cem\u003eScomberomorus cavalla\u003c/em\u003e) and round scad (\u003cem\u003eDecapterus punctatus\u003c/em\u003e) after 27 and 34 cycles respectively. As above, we did not standardize template DNA concentration, so we do not attempt to interpret these Cq values other than an indication of successful amplification.\u003c/p\u003e \u003cp\u003eOur qPCR assays on the environmental samples collected in the IRL resulted in unexpected late-stage amplification at around 38\u0026ndash;40 cycles. Although not sufficient to represent target amplification (they did not cross the threshold before 40 cycles), and while melt curves indicated dimer polymerization, we decided to design a TaqMan Probe to eliminate the issue and to minimize the risk of false positives (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When we ran qPCR on eDNA samples with the probe, the fluorescence caused by the polymerization of dimer was ameliorated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDetermining Limit of Detection and Limit of Quantification\u003c/h2\u003e \u003cp\u003eWe ran our experiments to determine the limit of detection (LOD) and the limit of quantification (LOQ) using serial dilutions of the gBlock at concentrations of 5000, 512, 64, 16, 8, 4, 2, and 1 copies/\u0026micro;L and 10 qPCR replicates each. Using the Generic qPCR Limit of Detection (LOD) / Limit of Quantification (LOQ) R script calculator of Merkes et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we found the LOD for our Cytb-ptv assay to be 12 copies per reaction and the LOQ to be 596 copies per reaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our standard curve plot based on the output data of Merkes et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated a strong linear correlation between Cq-value and standard concentration with the R-squared (correlation coefficient) value\u0026thinsp;\u0026gt;\u0026thinsp;0.99 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e; slope = -3.298, intercept\u0026thinsp;=\u0026thinsp;40.012).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssay Validation in Aquaria\u003c/h2\u003e \u003cp\u003eWe evaluated the performance of our Cytb-ptv assay with water samples collected from a 2650-liter saltwater tank containing eight lionfish and a few other non-scorpaenid fishes. Lionfish DNA was successfully amplified in all eight PCR replicates. Cq values ranged between 19 and 21 cycles (see Fig. S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eEarly detection and tracking of invasive species are critical to implementing effective mitigation strategies (Simberloff, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Reaser et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sepulveda et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Where lionfish are abundant in the Caribbean, massive citizen scientist efforts and lionfish culling events, or \"derbies\", have been organized to keep lionfish numbers under control (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.reef.org/lionfish-derbies\u003c/span\u003e\u003cspan address=\"https://www.reef.org/lionfish-derbies\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Regular culling events can keep population numbers low and targeted efforts have the potential to slow or even halt the spread of invasive species. In some locations these efforts have succeeded in lowering the overall abundance of lionfish and resulted in fewer large individuals (Frazer et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003et\u0026eacute; et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003ea). However, to control or manage the spread of an invasive species, we must first know its geographic range and be able to identify potential new habitats. Tracking invasive species, particularly ones that spread quickly, can be time-consuming and expensive. Here we present a fully validated qPCR assay for the detection of \u003cem\u003eP. volitans\u003c/em\u003e DNA in environmental samples. Our optimized assay was able to detect \u003cem\u003eP. volitans\u003c/em\u003e at the exclusion of other closely related species found in the introduced range, with a low LOD of only 12 copies/\u0026micro;L, providing an important tool for monitoring this invasive species.\u003c/p\u003e \u003cp\u003eOur testing of the Whitaker et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) COI assay showed that it amplifies lionfish, as well as at least two non-target taxa that are common in the introduced range. Both king mackerel (\u003cem\u003eScomberomorus cavalla\u003c/em\u003e) and round scad (\u003cem\u003eDecapterus punctatus\u003c/em\u003e) readily amplified with the COI primers, indicating that this assay is unsuitable as a standalone qPCR assay and would require a subsequent sequencing step for species confirmation. In fact, the original protocol of Whitaker et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) recommends bi-directionally Sanger sequencing the PCR product of any positive detections found with their assay. Subsequent BLAST results for these DNA sequences that returned matches for lionfish were considered positive detections. However, this presents an opportunity for a type II error. If non-target DNA was abundant in a sample, it\u0026rsquo;s possible that PCR followed by sequencing would only return non-target hits, even if both species\u0026rsquo; DNA were present in the sample. Alternatively, if both target and non-target DNA were amplified and sequenced, the data would be difficult to interpret unless a massively parallel sequencing platform were used in lieu of Sanger sequencing. While the data generated here indicates that our Cytb-ptv assay is species specific, it is not without limitations. These primers are highly specific to \u003cem\u003eP. volitans\u003c/em\u003e and do not appear to detect \u003cem\u003eP. miles\u003c/em\u003e, a cryptic sister-species. However, this species comprises just a small percentage (~\u0026thinsp;7%) of the invasive lionfish population (Hamner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), making it the most useful and only fully validated qPCR assay yet published.\u003c/p\u003e \u003cp\u003eCurrently, culling lionfish during derbies by free divers or scuba divers is the most common means of removing these predators from reef ecosystems and has proven to be quite effective at lowering population numbers (Frazer et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003et\u0026eacute; et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003ea). Site selection for culling events or derbies is typically based on citizen reports of high-density populations or from anglers who are catching lionfish on hook and line. However, there are many areas across the region where recreational diving and fishing are less frequent. These places represent important missing data where the extent of the lionfish invasion is not well documented. Furthermore, at the front lines of the invasion, where an invasive species is moving into new habitats and expanding its range, the species is presumably rare or at low densities and so can go undetected using traditional survey techniques. Moreover, commonly employed survey methods typically do not detect larval fish and/or fertilized eggs. With an organism of high fecundity and durable egg masses such as the lionfish (Morris et al., 2011; Eddy et al., 2019; Bustos-Montes et al., 2020), even a few individuals or the presence of a fertilized egg mass can lead to the establishment of a larger population if not addressed immediately.\u003c/p\u003e \u003cp\u003eDetection of lionfish eDNA using a species-specific qPCR assay could be an important tool to promote early detection and aid with the selection of sites for targeted culling efforts. Early detection could allow for removal strategies to be implemented before a new population becomes established (Simberloff, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Reaser et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sepulveda et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As eDNA techniques only require the collection of water, they can be implemented in habitats and places where traditional surveys are not advised (deep-water, poor visibility, etc.). For instance, in an estuarine system such as the Indian River Lagoon, traditional survey methods are hindered by low visibility and habitats that are unsuitable for many types of fishing gear (e.g., seines). With reliable species-specific detection assays, eDNA analysis will increase confidence in traditional survey results and allow for expanded monitoring into the front lines of the lionfish invasion. Moreover, metabarcoding techniques may also prove valuable for use in detecting lionfish. While not yet tested, there is evidence that some of the primers commonly used to characterize fish communities might also be able to detect lionfish (Kumar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; unpubl. data).\u003c/p\u003e "},{"header":"Declarations","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCONTRIBUTIONS\u003c/h2\u003e \u003cp\u003eKatherine Viehl, Zain Khalid, Eli Taub, Pavithiran Amirthalingam, Girish Kumar, and Michelle R. Gaither designed the experiment. Katherine Viehl, Zain Khalid, and Michelle R. Gaither conducted the environmental collections. Zain Khalid, Kathryn Greiner-Ferris, Victoria Marciante and Eli Taub processed samples and conducted the aquaria collections. Katherine Viehl and Zain Khalid analyzed the data. Katherine Viehl and Michelle R. Gaither led the writing of the manuscript in close consultation with the other authors. All authors read and approved the final manuscript.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cem\u003eFinancial interests:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eCONFLICT OF INTEREST STATEMENT\u003c/h2\u003e \u003cp\u003eThe authors have no conflict of interest to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by a Student Research Grant from the Office of Undergraduate Research at the University of Central Florida.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eWe would like to thank Eric Reyier from the Kennedy Space Center Ecological Program in Cape Canaveral, Florida for his insightful discussions and expertise on the region, as well as lending us his assistance in sampling along the Indian River Lagoon. We would like to thank the Reef Environmental Education Foundation, Florida Fish and Wildlife Conservation Commission's Florida Biodiversity Collection, Smithsonian Institution's National Museum of Natural History, Kansas University's Biodiversity Institute and Natural History Museum, and the University of Florida's Florida Museum of Natural History for tissue and DNA samples. We would also like to thank the Sunrise Fish Dive Surf in Port Canaveral, Florida for allowing us to sample their aquarium, and the Office of Undergraduate Research at the University of Central Florida for funding. Finally, we would like to thank the reviewers for taking the time and effort to review this manuscript. We sincerely appreciate all your valuable feedback.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlbins MA, Hixon MA (2013) Worst case scenario: potential long-term effects of invasive predatory lionfish (\u003cem\u003ePterois volitans\u003c/em\u003e) on Atlantic and Caribbean coral-reef communities. Environ Biol Fish 96:1151\u0026ndash;1157. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10641-011-9795-1\u003c/span\u003e\u003cspan address=\"10.1007/s10641-011-9795-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbour A, Montgomery M, Adamson A, D\u0026iacute;az-Ferguson E, Silliman B (2010) Mangrove use by the invasive lionfish \u003cem\u003ePterois volitans\u003c/em\u003e. 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BMC Bioinformatics 13:134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2105-13-134\u003c/span\u003e\u003cspan address=\"10.1186/1471-2105-13-134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"eDNA, estuaries, invasive species, TaqMan, quantitative PCR, western Atlantic","lastPublishedDoi":"10.21203/rs.3.rs-3953940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3953940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Indo-Pacific lionfish \u003cem\u003ePterois volitans\u003c/em\u003e is an invasive species in the western Atlantic. Since their introduction in Florida in the early 1980\u0026rsquo;s, populations have exploded with lionfish now found from North Carolina to Venezuela. As their range expands, these generalist predators threaten native fauna, and while they are primarily a marine species, their tolerance for low salinity conditions may allow them to expand into sensitive estuarine habitats undetected. Traditional approaches for tracking invasive species such as direct observation or trapping are impractical across large spatial scales making environmental DNA (eDNA) an attractive alternative. Currently, there is only one published PCR assay for the detection of lionfish eDNA. However, the specificity of this assay is unverified, and the critical performance parameters limits of detection (LOD) and limits of quantification (LOQ) have not been established. Here we evaluate the specificity of the currently available lionfish assay, determined that it is not species-specific, and is likely to provide false negatives in the western Atlantic. As an alternative, we developed a new qPCR TaqMan probe-based assay that is species-specific for \u003cem\u003eP. volitans\u003c/em\u003e and highly sensitive with a LOD of 12 copies per reaction and a LOQ of 598 copies per reaction. While our assay does not amplify the closely related \u003cem\u003eP. miles\u003c/em\u003e, which is also invasive in the western Atlantic, the low prevalence of this species in the invasive population means our assay is effective for most monitoring purposes.\u003c/p\u003e","manuscriptTitle":"An optimized probe-based qPCR assay for monitoring invasive lionfish (Pterois volitans) using environmental DNA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-16 06:02:40","doi":"10.21203/rs.3.rs-3953940/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a38b652-718d-4362-addf-8ff5874060e4","owner":[],"postedDate":"April 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-20T15:18:02+00:00","versionOfRecord":{"articleIdentity":"rs-3953940","link":"https://doi.org/10.1002/edn3.70078","journal":{"identity":"environmental-dna","isVorOnly":true,"title":"Environmental DNA"},"publishedOn":"2025-03-15 00:00:00","publishedOnDateReadable":"March 15th, 2025"},"versionCreatedAt":"2024-04-16 06:02:40","video":"","vorDoi":"10.1002/edn3.70078","vorDoiUrl":"https://doi.org/10.1002/edn3.70078","workflowStages":[]},"version":"v1","identity":"rs-3953940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3953940","identity":"rs-3953940","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}