Mixotrophy emerges as the optimal strategy in mature waters of the Amazon River plume

Mixotrophy emerges as the optimal strategy in mature waters of the Amazon River plume | 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 Article Mixotrophy emerges as the optimal strategy in mature waters of the Amazon River plume Ana Fernández-Carrera, Noémie Choisnard, Dirk Wodarg, Iris Liskow, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4742841/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Communications Biology → Version 1 posted You are reading this latest preprint version Abstract Current evidence shows that phytoplankton are mixotrophs, combining photoautotrophy with osmotrophy (i.e., uptake of dissolved organic matter). Additionally, some unicellular eukaryotes are also capable of phagotrophy, representing an intermediate step between autotrophs and herbivores named mixoplankton. Mixoplankton seem to provide higher-quality food for metazoans, and to improve energy transfer to higher trophic levels. However, field studies on all aspects of mixotrophy are difficult due to the challenge of distinguishing their activity from that of autotrophs. Our April/May 2018 and 2021 cruises focused on the base of the surface planktonic food web in the distinct Amazon River plume habitats, where we used nitrogen stable isotopes of amino acids (CSIA AA) in seston within a multidisciplinary framework for resolving trophic function. Mixotrophy dominates in the Outer Plume Margin, a region with mature waters aged ca. 27 days. Mixotrophy seems the optimal strategy for growth in these heterogeneous outer margins as part of the succession of phytoplankton functional diversity along the plume. Our study supports the growing evidence for the cosmopolitan distribution of mixotrophy among unicellular aquatic organisms, underscores the urgent need to study it in situ, and paves the way for a novel application of the CSIA AA in field research. Biological sciences/Ecology/Stable isotope analysis Biological sciences/Ecology/Biogeochemistry Biological sciences/Ecology/Biooceanography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The traditional dichotomy between plant-like (i.e., phytoplankton) and animal-like (i.e., zooplankton) organisms structuring the base of pelagic food webs has been challenged by increasing evidence that most “phytoplankton” are in fact mixotrophs, that is, they combine photoautotrophy and heterotrophy to make a living 1 . On the one hand, eukaryotic and prokaryotic autotrophs are able to use dissolved organic matter to some extent to support their nutritional needs 2 – 5 , i.e., they are osmotrophs. However, if osmotrophy is used for acquiring organic compounds or recovering leaked metabolites is still unclear 6 . On the other hand, most unicellular eukaryotes, with the exception of diatoms, also rely on phagotrophy on varying degrees, thus constituting an additional functional type recently recognized and named mixoplankton 7 . From the perspective of classical trophic ecology, this nutritional flexibility coupling photo- and heterotrophy produces intermediate steps between pure autotrophs and pure herbivores, which are likely to reduce the overall transfer of energy from primary producers to secondary consumers 8 . Nevertheless, the impact on biogeochemical cycling of mixotrophs that rely solely on osmotrophy differs significantly from that of mixoplankton, also using phagotrophy. The former does not remove preys, competitors or other grazers from the food web affecting its structure, whereas the latter does 5 . Mixoplankton do not work in binary fashion, switching between pure photoautotrophy and pure heterotrophy. In most cases, they couple both nutritional modes to maximize their growth efficiency 9 . Heterotrophy, via osmotrophy and/or phagotrophy, can support growth directly or supply limiting nutrients to photosynthesis, which in turn compensates respiratory losses due to the former, allowing the increase in biomass of the mixotrophic stock relative to the autotrophic 10 , 11 . In addition, mixoplankton provide better quality food to metazoans 12 , and, in seasonally matured systems (nutrient poor) where they dominate the community, they drive the accumulation of refractory dissolved organic matter, which directly contributes to the ability of the carbon pump to mitigate atmospheric carbon dioxide 10 . Overall, their better performance seems to lead to a higher transfer of mixotrophic carbon and energy than that of pure autotrophs 9 , 11 . Yet, laboratory experiments also suggest that photoheterotrophy is the dominant mode in some mixoplankton, resulting in no enhancement of primary production or compensation of respiratory losses, as the photosynthetic system is used as mere energy provider rather than carbon fixer 13 . Furthermore, mixoplankton compete for resources with both autotrophs and heterotrophs, implying additional energetic costs 14 , and some groups seem to provide poor quality food affecting the overall performance of their grazers 15 . Thus, it remains uncertain whether mixoplankton universally implies an increase or a decrease in energy transfer to upper trophic levels in comparison to pure autotrophic plankton. Therefore, it is imperative to investigate their occurrence and activity in natural systems. The development of gene-based predictive models for identifying the potential activity of mixotrophs in the field is currently being explored 16 , 17 , as there is no specific molecular marker for directly quantifying heterotrophy. In this regard, the amino acid nitrogen stable isotope analysis (or CSIA AA) offers a complementary tool for identifying the imprint of osmotrophy and phagotrophy from phytoplankton-derived field samples by combining the differential fractionation in nitrogen stable isotopes of the trophic amino acids glutamic acid or alanine with the source amino acid phenylalanine 18 – 21 . The Western Tropical Atlantic Ocean is the site of energetic surface circulation that plays a key role in the transport of heat, salt, and water from the Southern to the Northern Hemisphere 22 . In this complex region, the Amazon River discharges about 50% of all annual freshwater input into the tropical Atlantic, contributing up to 20% of annual fluvial freshwater into the global ocean 23 , 24 . The magnitude of the Amazon outflow changes markedly through the year, with a maximum in May/June and a minimum in October/November 25 . The interaction of the river discharge with the strong tidal currents (> 1 m s − 1 ), wind stress and the energetic North Brazil current results in complex circulation patterns on the shelf over the year 25 . The Amazon River plume displays a strong seasonal variability, and spreads as a buoyant lens of fresher water at the surface, reaching hundreds of km to the north and the east during the peak outflow season. This freshwater lens acts as an effective barrier to vertical mixing 26 , so the plume works as a semi-enclosed system slowly exchanging material with the adjacent environment. As a result of this extension and complexity, at any given instant, the plume is composed of a mixture of waters of different age and salinity 26 , potentially reflecting different stages of phytoplankton succession. The strong dynamics along the plume and its large extension define distinct planktonic habitats capturing the aging process of the plume 27 , where the mature systems optimal for mixoplankton can emerge 10 . Here we apply a multidisciplinary approach to explore the environmental drivers of the dominant trophic mode at the base of the food web (mixotrophy or autotrophy) in the varying habitats along the Amazon River plume from the estuary to the northern end of the plume at 15°N in two cruises in 2018 and 2021. This system provides a unique natural laboratory, where it is possible to study different stages of phytoplankton functional and community structure succession as the plume matures from nutrient-replete to nutrient-deplete conditions. Our approach by the use of nitrogen stable isotopes of individual amino acids reveals widespread osmo-phago-mixotrophy in mature waters of the plume, where the potential accumulation of refractory DOM will likely contribute to the sink of carbon dioxide found in the margins 28 , and also confirms that this flexibility in phytoplankton nutrition is more ubiquitous in the global ocean than traditionally considered. Results Environmental variables The Amazon River plume reached as far as 15°N during both cruises as shown by the extension of sea surface salinities below 35 derived from the Copernicus Global Ocean Physics Analysis and Forecast product (Fig. 1 , Supporting videos 1, 2). Our samples covered six different habitats along the plume 27 , from the freshest plume in the Riverine (RI) to the oldest stages in the margins (OPM and WPM) and the Modified Oceanic water (MOW) habitats. Samples were also taken in the habitat without influence of the plume named as Oceanic waters (OSW). In the Amazon shelf close to the river mouth, the geostrophic velocities revealed an intricate and strong circulation as the riverine waters spread at surface in this energetic region (Fig. 1 , Supporting videos 1, 2). Additionally, some differences in the strength of the North Brazil current and the variety of eddy-like structures appeared north of 7.5°N affecting the overall distribution of the plume in the region in each cruise. The physicochemical variables measured in these six habitats reflected the changing and dynamic characteristics of the Amazon River plume (Table S1 ). Sea surface temperature was warmer in the estuary, where the lower salinity surface waters (< 6) were found. Moving to the north, surface temperature decreased to reach a minimum towards the older, saltier regions of the plume (OPM and MOW). The base of the mixed layer in the area under the influence of the plume ranged from 8 to 40 m, shallower than that in the oligotrophic oceanic waters, where it exceeded 70 m. These shallower mixed layers were related to higher stability of the water column, shown by values of the maximum of buoyancy frequency (N 2 ) above 0.02 s − 2 , which in most stations was found at base of the plume. This suggests a slow vertical exchange between the plume and oceanic waters beneath, which intensified towards the plume margins, where vertical stability decreased (lower N 2 ) and surface salinity increased. As expected, surface dissolved oxygen concentration decreased with increasing salinity, but in most stations, it was consistently below the saturation concentration estimated using temperature and salinity (Benson and Krause Jr. 1984) (Table S1 ). The concentration of chlorophyll a at the surface exceeded 0.5 µg L − 1 in the habitats representing fresher parts of the plume (RI, YPC and WPM), and decreased towards older sections of the plume and the oceanic realm. The significant nutrient load carried by the Amazon and Pará Rivers is evident in the elevated concentrations of nitrate and silicate found in the brackish estuary (RI) compared to other habitats, with a sharp decrease at stations on the shelf (YPC), although values remained relatively high at around 2 µM. Phytoplankton community structure The hierarchical cluster analysis (HCA) performed on the relative contribution of the different groups of phytoplankton to total chlorophyll a revealed the existence of four different phytoplankton communities along the Amazon River plume during our cruises (Fig. 2 ). The clustering tendency of the HCA was validated by a principal component analysis (PCA), which evidenced the separation of clusters along the first two dimensions (Fig. 2 ). While diatoms dominated in most parts of the mouth, they were also accompanied by a relatively high abundance of presumably filamentous freshwater cyanobacteria in the oligohaline section of the plume (salinity < 5.0, community 2, RI stations). Additionally, two stations within the mouth exhibited a distinct structure dominated by cryptophytes, with a complete absence of picocyanobacteria (community 1). Along the margin habitats, representing older plume waters, we observed a transition to a community dominated by haptophytes, where filamentous cyanobacteria (presumably Trichodesmium ) were also relatively abundant (community 3). The community in sections of the mature plume and the ocean domain resembled one of oligotrophic waters, dominated by pico-cyanobacteria (i.e., Synechococcus and Prochlorococcus ) and haptophytes (community 4). This community also showed a low percent contribution of Trichodesmium or diatoms, presumably associated with symbiotic diazotrophs (DDAs), in the stations affected by the plume. However, the PlanktoScope image set suggests that these two phytoplankton groups do not fully coexist in similar abundances. Instead, they trade dominance, that is, either DDAs (in station 32.06 in MOW) or Trichodesmium (in station 30.12 in OPM) were in greater abundances relative to the other group. Dinoflagellates represented a small percent in most of the samples with the exception of stations 8.02 in 2018 and 24.01 in 2021. It is notable that each phytoplankton community consisted of not only a single dominant habitat, but also samples from the habitat that preceded and succeeded it, highlighting the gradual transition that occurs along the plume as it ages. Amino acids nitrogen isotopes and trophic mode of seston The trophic mode of our samples can be graphically illustrated in the space defined between the nitrogen signatures of phenylalanine (Phe) and glutamic acid + glutamate (Glu), where the combination of their values using the equation proposed by Chikaraishi et al (2009) 29 defines different trophoclines, that is, lines that represent the same trophic position (Fig. 3 ). Most of the seston samples fell around the autotroph trophocline (TP 1.0), pointing to a clear dominance of autotrophy along the Amazon River plume and oceanic realm in both 2018 and 2021. However, we unexpectedly found mixotrophs (TP 1.5) at three stations in 2021 (in the large size fraction of 17.17 and both size fractions of 21.07 and 24.01), and at five stations in mature plume waters during 2018 (in the total seston of stations 6.04, 22.04, 24.03, 25.03 and 31.11). The apparent isotope fractionations of the trophic amino acids Glu or alanine (Ala) relative to the source amino acid Phe in the literature dataset of different trophic modes of plankton from TP 1.0 to TP 3.0 showed a significant linear correlation (ε Phe/Ala = 0.59 + 1.1 ε Phe/Glu , R 2 -adj = 0.81, n = 118, p < 0.001) with intercept and slope not significantly different from 0 and 1, respectively (Fig. 4 ). Our seston samples were consistent with the literature, the autotrophic samples fell within the range of phytoplankton, while the mixotrophic ones occupied the same space that strictly phagotrophic unicellular eukaryotes, where we would expect osmo-phago-mixotrophs (Fig. 4 ). The trophic mode of seston did not show any linear correlation with the environmental drivers found along the plume, hence, we applied a supervised machine learning algorithm named C5.0 for exploring the variables predicting trophic position capable of handling non-linear relations. The C5.0 decision tree model 30 selected the depth of the mixed layer, surface oxygen concentration, the absolute value of the maximum buoyancy frequency at each station, and surface chlorophyll a concentration as the most relevant variables for predicting the trophic mode of seston among all the environmental variables chosen (Fig. 5 ). The relative attribute usage, or the fraction of cases included in each branch of the classification tree for each of them was 100% for mixed layer depth, 73.9% for surface oxygen concentration, 52.2% for the maximum of buoyancy frequency, and 21.7% for surface chlorophyll a . The model only misclassified the large size fraction of station 17.17 in 2021 as autotrophic, rather than mixotrophic, for an overall error rate of 2.2%. Discussion The dynamic nature of the Amazon River plume is evident in the daily evolution of sea surface salinity and geostrophic velocities provided by the Copernicus Marine Service (Supporting Video 1 and 2), which illustrate why distinct habitats emerge as the plume ages. During our cruises, the plume extended as far as 15°N, delineating three habitats in 2018 and six in 2021 (Fig. 1 ). These planktonic habitats, identified by Pham et al (2024) 27 following the approach proposed by Weber et al (2019) 31 , were based on physicochemical drivers and capture the age of the plume better than single proxies like salinity or distance to the Amazon shelf. This age accounts for the history of the waters as they travel along the path of the plume, and determines the direction of the phytoplankton succession as the habitats suitable for each phytoplankton group emerge 32 . The apparent age of plume waters (Fig S1 ), estimated by concurrent radium isotopes (Supporting Information) measured on CTD samples during our 2018 cruise 33 and a 2019 cruise 34 , both included in Pham et al (2024) 27 , showed that the four habitats sampled in these surveys captured the aging gradient as follows: Young Plume Core (YPC, apparent age 12.9 ± 6.8 days, n = 8), Old Plume Core (OPC, 14.1 ± 6.8 days, n = 9), Outer Plume Margin (OPM, 26.6 ± 4.3 days, n = 23) and Western Plume Margin (WPM, 31.7 ± 5.8 days, n = 9). The evolution of age between these habitats seems gradual rather than abrupt, therefore, habitats with different physicochemical forcing may contain waters of similar age as a result of the varied gradients in hydrological and biogeochemical properties of the plume 27 . Two additional plume habitats were sampled in 2021, but not in 2018 or 2019. They seem to represent the youngest (Riverine Input, RI) and the oldest (modified Oceanic Water, MOW) plume waters. Seston in RI exhibited bulk δ 13 C values below − 27‰ (Fig S2), which are consistent with previous studies reporting suspended terrestrial material between − 34 and − 27‰ in the Amazon shelf 35 – 37 . Hence, these waters containing fluvial material are younger than those in YPC, where seston δ 13 C up to − 19‰ points to a clear shift towards marine particles 38 . Lastly, the physicochemical similarities between MOW and OSW (Oceanic Water without influence of the plume), discussed elsewhere 27 , suggest that MOW represents the oldest waters of the plume, above the average apparent age of 32 days in WPM. Machine learning unveils the drivers leading to dominant mixotrophy In May 2018 and April-May 2021, we sampled surface seston along the Amazon River plume as a total sample (> 0.2 µm) in 2018 and size fractionated into picoseston (0.2 − 3 µm) and the combination of nano- and microseston (3 − 200 µm) in 2021. The bulk δ 13 C, C:N ratio, and the relationship between particulate carbon and chlorophyll a suggest that the seston was clearly dominated by phytoplankton (Fig. S2, Supporting information), thus representing the dominant trophic position (TP) at the base of the food web, where a canonical TP of 1.0 indicates pure autotrophy 39 . The TP is derived from the comparison of nitrogen stable isotope values between the source amino acid phenylalanine (Phe), which barely changes with trophic transfers, and the trophic amino acid glutamic acid (Glu), which integrates the effect of trophic steps 21 , 29 , 40 , 41 . This seamless integration of trophic transfers in the isotopes of Glu at lower trophic levels is due to the metabolic pathways in heterotrophs, where Glu is central to the metabolism of amino acids, acting as a precursor to most of them in reactions involving deamination or transamination, e.g., to alanine (Ala), proline or asparagine 42 . Consequently, organisms combining both autotrophic and heterotrophic nutrition should occupy an intermediate position between TP 1.0 and TP 2.0 (pure herbivory), as it was previously observed in studies of mixotrophic foraminifera and corals 43 , 44 . Therefore, when mixotrophy dominates the community, the TP of phytoplankton-derived seston will distribute around TP 1.5. Indeed, we identify this imprint of mixotrophy in our Amazon River plume samples. Interestingly, it was prevalent in mature sections of the Amazon River plume in the Outer Plume Margin (Figs. 1 and 3 ), a habitat representing waters with an average apparent age of 27 days, and comprising a large extension of the plume, where a significant sink of atmospheric CO 2 was previously described 28 . In this habitat, mixotrophs seem to find the optimal environmental settings required for taking over pure autotrophs and heterotrophs 10 , 14 . Elsewhere along the Amazon River plume, our samples predominantly reflected the dominance of autotrophy, clustering around the TP 1.0 trophocline, and falling below the range defined for mixotrophy (1.4 − 1.6, Fig. 3 ). In the intricate dynamic environment of the plume, where waters of various ages coexist throughout the year 26 , and the plume ages delimiting distinct biogeochemical habitats 27 , 31 , it is likely that non-linear interactions among multiple physicochemical drivers influence the TP (autotrophy vs mixotrophy) at the base of food webs at a given time. To identify these drivers, we employed a machine learning C5.0 classification model 30 , which can handle non-linear relationships between variables in large and complex datasets with missing values. The most relevant predictors identified by the model include, in order of importance, the depth of the mixed layer (m), the concentration of oxygen at surface (mM), the absolute maximum of buoyancy frequency (s − 2 ), and the concentration of chlorophyll a (µg L − 1 ) at surface (Fig. 5 ). Mixotrophy was predominantly found at stations with relatively shallow mixed layers (≤ 37 m), in areas where the plume had a thickness between 5 and 35 m (Table S1 ). The concentration of dissolved oxygen serves as a proxy for dominant heterotrophic processes, as heterotrophy consumes more oxygen than is produced by oxygenic photosynthesis 45 . Mixotrophy-rich regions presented oxic but undersaturated surface waters (Table S1 ), likely due to photoheterotrophy, which has been suggested as the preferential growth strategy for these organisms 13 . In this situation, mixoplankton may use the photosynthetic apparatus for energy production rather than carbon fixation, contributing then to oxygen consumption. Moreover, mixotrophy dominance was observed at stations with higher chlorophyll a relative to similar locations (> 0.171 µg L − 1 ), likely reflecting the higher growth success related to this feeding mode suggested by Ward and Follows (2016) 11 , which also results in a higher transference of carbon up the food web 11 , and potential accumulation of refractory dissolved organic matter over time contributing to mitigation of atmospheric carbon dioxide by the biological carbon pump 10 . Interestingly, we also found a dominance of mixotrophy at stations 6.04 and 24.03 during 2018, with waters close to oxygen saturation and a relatively less stable water column (Brunt Väisälä N 2 ≤ 0.006 s − 2 , Table S1 ). The pigmented community at station 6.04 consisted mainly of diatoms with minor contributions from cryptophytes, prasinophytes, haptophytes, dinoflagellates, and Trichodesmium (Fig. 2 ). Although all of these groups, except Trichodesmium , are known members of the mixoplankton, combining osmo-phago-mixotrophy 1 , 46 , it is very unlikely that they were able to override the potential dominance of diatoms in the signatures of Glu and Ala, as the sum of all of them accounted for only 10% of the chlorophyll a . Rather, it seems that diatom osmotrophy of ambient organics, previously described as relevant in other systems 3 , 17 , was responsible for the signature of this sample. This suggests that under certain environmental settings osmotrophy actively contributes to meeting nutrient needs of phytoplankton, rather than simply recovering leaked metabolites 6 . Phytoplankton community succession along the Amazon River plume The functional diversity of plankton identified and quantified using nitrogen stable isotopes of amino acids (CSIA AA) can be complemented by phytoplankton community structure measures using the matrix factorization procedure named ChemTax 47 , 48 . Despite a number of caveats discussed extensively in the literature 49 – 51 , ChemTax is a simple method for evaluating the community structure of a mixture of pigmented pico-, nano- and microplankton through a single analysis, and detecting the presence of protist combining auto and phagotrophy, such as dinoflagellates, prasinophytes, haptophytes or cryptophytes 1 , 14 . This taxonomical classification method assumes that all phytoplankton require pigment assemblages for harvesting/dissipating light for photosynthesis, with some of them exclusive for each group 52 . During boreal springs of 2018 and 2021, we identified four different phytoplankton communities along the Amazon River plume by chemotaxonomy based on pigment markers (Fig. 2 ). Each community included phytoplankton not only from one dominant planktonic habitat type but from habitats representing the precursor or the subsequent stage of this dominant habitat, illustrating the gradual transition between habitats and underscoring the role of river plume age structuring the phytoplankton communities along the Amazon River plume 53 – 55 . In RI, we observed two distinct communities: one dominated by cryptophytes (community 1) and the other by diatoms (community 2). We hypothesize that these communities represent different phytoplankton assemblages originating from the Amazon or neighboring Pará river, as indicated by the consistently fluvial bulk δ 13 C values of these samples (Fig S2). Given the intricacy of swirling patterns in the estuary, suggested by the geostrophic velocities (Fig. 1 , Supporting Video 1 and 2), it is possible that the communities were eventually trapped and transported unaltered within coherent eddy-like structures. Nevertheless, both communities in the RI harbor varying amounts of filamentous cyanobacteria, likely Nostoc , Anabaena , or Oscillatoria , known to be abundant in the Amazon River basin 56 , 57 , which could still be viable in these brackish waters (salinity below 6). The diatom-dominated community (community 2) was primarily distributed in the pathway of the North Brazil current, where younger waters swiftly travel along the coast, reaching as far as 8°N 26 , and still containing ample nitrate and silicate to support a non-diazotrophic diverse diatom community 58 . Adjacent to this “fast highway” transporting the young plume, older saltier plume waters are typically found in the northern sections 26 , in the regions of our study included in the plume margin habitats OPM and WPM. Overall, the plume waters undergo slow exchange with adjacent oceanic waters, acting as a barrier to vertical mixing 26 , thus resembling a semi-enclosed system where the different phytoplankton communities reflect different stages of phytoplankton succession 32 , 58 . While diatoms dominate in younger waters, once the riverine nitrate is exhausted, residual phosphorus seems enough to sustain Trichodesmium along with a larger contribution of haptophytes and Synechococcus (community 3). However, as the succession progresses, a community more akin to the nitrogen-depleted oceanic end emerges, dominated by picoplankton ( Synechococcus and Prochlorococcus ), with diatoms and Trichodesmium also present (community 4). Notably, previous studies in the region 53 , 54 and our own image set at stations 31.11 and 32.06 in 2021, indicate that diatom diazotroph associations and Trichodesmium do not fully coexist, but tend to occupy different sections of the plume with little overlap. As shown, prasinophytes, haptophytes, dinoflagellates and cryptophytes were present in all phytoplankton communities. These groups of unicellular eukaryotes are known to be mixoplankton, coupling osmo-phago-mixotrophy 1 , 14 . Therefore, in locations were mixotrophy was the main mode of nutrition in the community, these organisms will drive the dominant TP, where the consumption of other microorganisms by phagotrophy coupled with osmotrophy will unambiguously impact the signatures of Ala and Glu. Phagotrophy and osmotrophy potential impact on trophic amino acids The nutrition strategies of autotrophs, heterotrophs or mixotrophs reflect on the difference in nitrogen isotopes between source and trophic amino acids. This effect is captured in the apparent fractionation of Glu or Ala relative to Phe (ε Phe/Tr−AA ), whose comparison allows us to define the ranges of different trophic modes in planktonic food webs from herbivores to carnivores (Fig. 4 ). Recent evidence suggests that the apparent fractionation of Ala is greater than that of Glu in strict protists phagotrophs compared to what is set by primary producers 19 , 20 . To our knowledge, this thesis was only studied in cultures of strict phagotrophs, but when mixotrophs are actively grazing on other microorganisms, a difference in ε Phe/Ala similar to that of phagotrophs should occur and occupy the space between TP 1.0 and TP 2.0 in Fig. 4 . Additionally, Yamaguchi et al (2017) 18 showed that different groups of Archaea , fungii and bacteria growing only on organic media (i.e, as osmotrophs) presented an apparent fractionation of Glu similar to that of herbivores (TP 2.0). Since all phytoplankton are capable of non-auxotrophic osmotrophy of amino acids, among other primary metabolites 1 – 3 , we would expect mixotrophs that combine osmotrophy and autotrophy to also be distributed in the space between TP 1.0 and TP 2.0 (Fig. 4 ). Evidence of phagotrophy in the values of ε Phe/Ala was found in most mixotrophic samples along the Amazon River plume (Fig. 4 ), suggesting that prasinophytes, haptophytes, dinoflagellates, and cryptophytes actively grazed on other microorganisms to some extent. However, the ε Phe/Glu values in these samples are also consistent with the uptake of ambient amino acids (Fig. 4 ). Hence, it seems that all mixotrophs along the Amazon plume combine phagotrophy and osmotrophy as feeding strategies. However, future studies addressing the impact of mixotrophy on trophic amino acids are still needed to extend the application of this amino acid approach in situ. Nevertheless, our study demonstrates the usefulness of nitrogen stable isotopes of amino acids in seston to address mixotrophy in the field when these samples primarily reflect the signature of phytoplankton. In summary, the Amazon River plume encompasses a complex and dynamic array of habitats as it moves and ages in the Western Tropical Atlantic Ocean. The interplay of varying physicochemical drivers intricately regulates plankton structure and trophic mode, ultimately influencing the energy available to upper trophic levels. In the present study, we observed dominance of mixotrophy in the mature waters of the Outer Plume Margin, a habitat shaped by specific environmental conditions 27 comprising an average apparent age of 27 days. In this dynamic system, it is likely that the phytoplankton succession, occurring after the depletion of river-derived nitrogen by diatoms, favored mixotrophy as the optimal growth strategy in mature plume waters. From our results, it is not possible to define the implications for the energy available to upper trophic levels, but underscore the urgent need to study the pathways of mixotrophic mass and energy in the field applying complementary approaches. However, it seems that trophic transfer as well as mitigation of atmospheric CO 2 may be more efficient when mixotrophy dominates through the production of a larger, better quality biomass 11 , 12 as well as driving the accumulation of refractory dissolved organic matter 10 , which may contribute to the sink of CO 2 associated to the plume 28 . Methods Sampling and hydrography To investigate how the changing environments along the Amazon River Plume (ARP) shape the structure of the plankton food web at surface, we conducted two cruises along the plume at the beginning of the peak discharge season in May 2018 and April-May 2021. Sampling took place during expeditions EN614 on board RV Endeavor ( doi.org/10.7284/908137 ) and M174 on board RV Meteor ( doi.org/10.1594/PANGAEA.935041 ). In 2018, the Endeavor departed from Bridgetown (Barbados) on May 8th and arrived in San Juan (Puerto Rico) on June 1st. In this survey, a total of 67 CTD casts were carried out at 19 stations covering different regions and age stages of the ARP. In 2021, the Meteor departed from Las Palmas on April 12th and arrived in Emden (Germany) on May 30th. The survey along the ARP began on April 21st and ended on May 13th, and a total of 114 CTD casts were carried out at 23 stations from the mouth of the Amazon and Pará rivers to the waters east Barbados to cover the largest possible geographical extension of the plume. For the scope of this study, we collected suspended particles at surface at 12 stations during 2018 and 17 during 2021 (Fig. 1 , Supporting Video 1 and 2). During both cruises, pressure, temperature, conductivity, and chlorophyll a fluorescence were measured continuously by a SeaBird SBE-911 + attached to a rosette equipped with 10L Niskin (2018) or flow (2021) bottles. In 2021, additional photosynthetically active radiation (PAR), turbidity, and SeaBird SUNA Nitrate sensors were attached to the rosette. The Brunt-Väisälä or buoyancy frequency (N 2 ) was derived using the potential density and its primary maximum was used for defining the depth of the mixed layer at each cast. Dissolved inorganic nutrients Water samples for nutrient analysis were taken from Niskin/flow bottles fired at 5 to 12 depths through the water column. Inorganic nutrients (phosphate, silicate, nitrite, and nitrate + nitrite in both cruises, and ammonium in 2021) were analyzed onboard colorimetrically according to Hansen and Koroleff (1999) 59 using a 4-channel Lachat Instruments QuikChem 8500 Series 2 autoanalyzer in 2018 and a QuAAtro, Seal Analytical continuous segmented flow analyzer in 2021. In 2018, the limits of detection of the instrument were 0.01 µM for phosphate, 0.02 µM for silicate, 0.02 µM for nitrate and 0.01 µM for nitrite. In 2021, the limits of detection were 0.01 µM for phosphate, 0.3 µM for silicate, 0.02 µM for nitrate, 0.01 for nitrite, and 0.03 for ammonium. The depths of the nitracline (1 µM), phosphocline (0.1µM), and silicocline (2 µM) were estimated by linear interpolation, or assumed to be zero when the concentrations at the surface exceeded the threshold. Habitats delineation Habitats shaped by similar physicochemical forcing were defined following the approach developed by Weber et al (2019) 31 and based on simple, commonly measured, and relevant environmental variables in our study system. The procedure is described in detail in Pham et al (2024) 27 who made the classification of habitats in six cruises along the Amazon River plume from 2010 to 2021, including our 2018 and 2021 cruises. Briefly, a hierarchical cluster analysis, complemented by a principal component analysis for validating the clustering tendency, was performed on the depth of the Deep Chlorophyll Maximum (DCM), the depth of the Mixed Layer (MLD), Sea Surface Temperature (SST), Sea Surface Salinity (SSS) and a custom-defined nitrate availability index (NAI). This NAI was calculated following Weber et al (2019) 31 as: $$\:NAI\:=\:\left\{\begin{array}{c}{\left[N{O}_{x}\right]}_{surface\:\:\:},\:\:if\:{\left[N{O}_{x}\right]}_{surface}\:\ge\:0.5\:\mu\:M\\\:-{Z}_{\left[N{O}_{x}\right]}\:=2\mu\:M,\:\:if\:{\left[N{O}_{x}\right]}_{surface}\:<\:0.5\:\mu\:M\:\:\\\:-{Z}_{\:bottom},\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:else\end{array}\right.$$ where [NOx]surface is the concentration of nitrate + nitrite at surface, Z [NOx] represents the depth at which nitrate + nitrite concentration reaches 2 µM, and Z bottom is the bottom depth of the CTD profile. Eight habitats were defined in the meta-analysis of six cruises made by Pham et al (2024) 27 . During our cruises, we took seston samples in three (2018) and six (2021) of these habitats (Fig. 1 ), varying in plume influence and age, and named as Riverine Input (RI), Young Plume Core (YPC), Outer Plume Margin (OPM), Western Plume Margin (WPM), modified Oceanic Water (MOW) and Oceanic Water (OSW). The habitat with the freshest plume water was RI, WPM and MOW were the ones containing the oldest plume waters in apparent age (see Apparent age of the Amazon River plume section), while OSW was the habitat with absence of plume influence. Phytoplankton community structure Samples for the analysis of diagnostic phytoplankton pigments at the surface were transferred directly from Niskin/flow bottles into opaque polycarbonate bottles. Soon after collection, a volume between 50 mL and 4.3 L, according to the density of particles, was filtered under low vacuum through 25mm GF/F (0.7 µm nominal pore size) in 2018 or 25 mm Advantec GF-75 (0.3 µm nominal pore size) filters in 2021. Filters were then flash-frozen and stored in liquid nitrogen (− 196°C) until analysis at NASA GSFC using an Agilent RR1200 with a programmable autoinjector (900 µl syringe head), refrigerated autosampler, degasser, and photo-diode array detector with deuterium and tungsten lamps. Diagnostic pigments were measured by High-Performance Liquid Chromatography (HPLC), as described in Van Heukelem and Thomas (2001) 60 and Hooker et al (2005) 61 . Diagnostic pigments were assessed at all stations in this study except station 24.03 in 2018, where pigments where not sampled. The composition of the surface phytoplankton community was determined by the concentrations of the HPLC diagnostic pigments and the ChemTax matrix factorization procedure proposed by Mackey et al (1996) 47 in version 1.9.5 of the software designed by Wright (2017) 62 . The groups selected for this analysis were based on existing literature and represented groups previously identified along the plume 54 , 55 , 63 , 64 , also confirmed by the flow microscope images collected during 2021 as explained below. These previous studies in the region reported the presence of representatives of cryptophytes, cyanobacteria (in particular, Trichodesmium , Synechococcus , Prochlorococcus ), dinoflagellates, diatoms, haptophytes and prasinophytes. The initial pigment ratios (Table 1 ) were averaged using relevant literature studies contained in Higgins et al (2012) 52 . ChemTax was run three times using 60 random ratio matrices until the contribution of each group to total chlorophyll a converged. For the second and third runs, the initial ratio matrix for randomization consisted of the average of the six best matrices in the previous run. The final ratio matrix is also shown in Table 1 . The contribution of each group to total chlorophyll a provided by ChemTax in µg chla L − 1 was converted to a relative percent contribution for better assessing the changes in the dominant groups along the Amazon River plume. Table 1 . Initial and final pigment ratios introduced in and yielded by the ChemTax analysis, respectively. Ratios were taken from Higgins et al (2012). Note: Cyanobacteria1 contains fresh and seawater filamentous colonial cyanobacteria such as Trichodesmium , Nodularia , or Nostoc ; Cyanobacteria2 contains mainly Synechococcus ; Cyanobacteria4 contains mainly Prochlorococcus ; Prasinophytes3 contains groups like Ostreococcus, Micromonas or Prasinococcus ; Diatoms1 contains microplankton diatoms such as Thalassiosira , Skeletonema or Chaetoceros ; Haptophytes6 contains mainly Emiliania ; Dinoflagellates1 represents peridinin containing dinoflagellates such as Tripos (former Ceratium ), Prorocentrum or Peridinium . The pigments included in the analysis were Chlorophyll b (Chl b), 19´-Butanoyloxyfucoxanthin (But Fuco), 19´-Hexanoyloxyfucoxanthin (Hex Fuco), Alloxanthin (Allo), Fucoxanthin (Fuco), Peridinin (Perid), Zeaxanthin (Zea), Divinyl Chlorophyll b (DV Chl b), the sum of Chlorophyll c1 and c2 (Chl c1+ c2), Chlorophyll c3 (Chl c3), Lutein (Lut), Neoxanthin (Neo), Violaxanthin (Viola), Prasinoxanthin (Pras), and Chlorophyll a (Chl). In selected stations during 2021 cruise, microplankton samples were collected at surface from the ship’s clean water supply into a 10µm plankton hand net, filtered by a 200µm sieve to remove mesozooplankton, and imaged in vivo by a PlanktoScope v2.1 65 for qualitatively describing the composition of the microphytoplankton community. Individual cells were segmented from the full-field pictures using the built-in segmentation function of the instrument. Afterward, image data were assigned to taxonomic categories using random forest algorithms and validated by experts on the web-based platform Ecotaxa 66 . The classified image dataset is available at https://ecotaxa.obs-vlfr.fr/prj/6346 . A partial and qualitative validation of the output of ChemTax was conducted by combining different concurrent measurements during each cruise. In 2018, flow cytometry samples were used to determine the presence or absence of Synechococcus and Prochlorococcus , while 18S-RNA analysis was used to identify the presence of diatoms, prasinophytes, and dinoflagellates at stations where data were available 67 . Additionally, in 2021, the presence of diatoms, dinoflagellates, and Trichodesmium in the microplankton fraction was confirmed using a fluid imaging microscope (i.e., PlanktoScope v 2.1 65 ). Although the application of all these complementary approaches does not enable validation of the absolute contribution of each group to total chlorophyll a , it confirms the presence of the different groups, thereby supporting the structure of the pigmented plankton community found along the Amazon River plume. One possible caveat in our analysis is that the literature ratios for Trichodesmium and Synechococcus depend on contrasting ratios of the same pigment marker (zeaxanthin). However, validation with the alternative variables mentioned above suggests that ChemTax effectively resolved meaningful biomass of each group. For instance, ChemTax did not identify the presence of Trichodesmium at stations 19.06 and 24.01 in 2021 (Fig. 2 ), where this organism was not observed in the PlanktoScope image set. Additionally, ChemTax did not assign any contribution of Synechococcus to chlorophyll a of station 6.04 in 2018 (Fig. 2 ), where it was also absent in the flow cytometry data. Elemental analysis of bulk and amino acids stable isotopes of seston Seston samples were collected at/near the surface, using Niskin bottles (2018) or a water pump deployed in the upper 0.5 m of the water column (2021), for the analysis of bulk and compound specific isotope analysis (CSIA AA) of amino acids carbon ( 13 C/ 12 C) and nitrogen ( 15 N/ 14 N) stable isotopes at 29 stations: 12 during 2018, and 17 during 2021 (Fig. 1 ). In 2018, samples for the bulk analysis were collected after passing 2.5 − 19 L through precombusted (4 h, 450°C) 47 mm GF/F (0.7 µm nominal pore) filters by a pressurized air system designed for large volume filtrations. Samples for CSIA AA were collected by passing between 6.4 − 18.7 L through 47 mm 0.2 µm Nucleopore polycarbonate filters using the same pressurized system. In 2021, we decided to size-fractionate seston to separate picoplankton (< 3 µm) from the combination of nano- and microplankton (3-200 µm) as well as the free living (i.e, captured in the < 3 µm fraction) from the particle-attached components of the microbial community. Bulk samples were collected by vacuum filtration by sequentially passing 0.1 − 4 L of water through precombusted (4 h, 450°C) 25 mm GF/D Whatman (2.7 µm) and 25mm GF-75 Advantec (0.3 µm) filters under low vacuum pressure (< 100 mm Hg). Size-fractionated CSIA AA samples were collected again using the pressurized air system by sequentially passing 0.5 − 20 L of water through 47 mm 3 µm and 47 mm 0.2 µm Nucleopore polycarbonate filters. All samples were immediately dried (24–72 h, 60°C), frozen and stored at − 20°C until analysis in the laboratory ashore. Samples were not acidified prior to analysis to prevent changes in the signature of nitrogen. The stable C isotopes of the 2018 bulk samples were analyzed using a Micromass Isoprime 100 continuous-flow isotope ratio mass spectrometer (IRMS) coupled to a Carlo Erba elemental analyzer (NA 2500) at the Georgia Institute of Technology. In 2021, bulk samples were analyzed by EA Isolink CN (Thermo scientific) elemental analyzer coupled via a ConFlo IV (Thermo scientific) interface to a Delta V advantage (Thermo scientific) isotope ratio mass spectrometer at the Leibniz-Institute for Baltic Sea Research Warnemuende. The stability of the instruments and the contribution of blanks to our measurements were checked using a series of elemental (methionine) and isotopic (peptone) standards in each analytical run 68 . We have carried out multiple intercalibrations of these two systems. For both cruises, stable nitrogen isotope compositions of individual amino acids were measured using an isotope ratio mass spectrometer (IRMS, Thermo Finnigan GmbH, MAT 253 MS, Germany) connected via a ConFlo IV interface unit to a gas chromatograph combustion periphery (GC-C, Thermo Scientific Trace 1310 GC, Italy; Thermo Scientific, GC Isolink, Germany) at the Leibniz-Institute for Baltic Sea Research Warnemuende. After 24h of acid hydrolysis (6N HCl, 110°C), amino acids were derivatized to trifluoro-acetylated isopropyl amino acid esters following Hofmann et al (2003) 69 and purified according to Veuger et al (2005) 70 . An external standard of 16 individual amino acids was derivatized in the lab and run with each set of six samples. This external standard consisted of a mixture of alanine (Ala), arginine (Arg), aspartic acid + asparagine (Asp), cysteine (Cys), glutamic acid + glutamine (Glu), glycine (Gly), isoleucine (Ile), leucine (Leu), lysine (Lys), methionine (Met), phenylalanine (Phe), proline (Pro), serine (Ser), threonine (Thr), tyrosine (Tyr), and valine (Val). Additionally, an internal standard (trans-4-cyclohexane carboxylic acid) was added to each sample to assess the overall performance of the derivatization/purification procedure in the laboratory. The separation column in the GC consisted of a non-polar column coated with 5% phenyl-polysilphenylenesiloxane (BPX5, 60 m, 0.32 mm inner diameter, film thickness of 1 µm; SEG Analytical Science, Ringwood, Victoria, Australia). For each run, 2 µL of sample was injected via a PTV injector in splitless mode. The temperature program was as follows: Initial temperature at 50°C, heat up at 12°C/min to 120°C, hold for 17 min, heat up at 3°C/min to 180°C, linger for 10 min, heat up at 5°C/min to 200°C, hold for 6 min, heat up to 250°C at 10°C/ min and hold for 7 min. This procedure allows us to separate and analyze 13 amino acids (i.e., Ala, Asp, Glu, Ile, Gly, Leu, Lys, Phe, Pro, Tyr, Ser, Thr and Val). However, here we report only the results of 12 because Gly in our seston samples from the Amazon River plume co-eluted with a substance unknown to the NIST (National Institute of Standards and Technology) mass spectral library as identified by parallel analysis of subsamples with a single quadrupole GC-MS (Thermo Scientific ISQ 7000). Samples were analyzed in triplicate. Reproducibility for individual amino acid values was typically better than 1‰. Bulk and amino acid specific carbon and nitrogen stable isotope abundances were expressed in δ notation (‰,) relative to Vienna Pee Dee Belemnite (VPDB) and relative to Air, respectively. Trophic position and imprint of mixotrophs in trophic amino acids The trophic position of seston was calculated according to Eq. (7) in Chikaraishi et al (2009) 29 , based on the nitrogen isotopes of the source amino acid phenylalanine (Phe) and the trophic amino acid glutamic acid + glutamate (Glu) as follows: $$\:{TP}_{Phe/Glu}\:=\:\frac{{\delta\:}^{15}{N}_{Glu}\:-\:{\delta\:}^{15}{N}_{Phe}\:-3.4}{7.6}\:+1$$ According to recent laboratory studies, deviations from the average difference in the isotopic signature of photoautotrophs between Phe and Glu or alanine (Ala) can be used to track the effect of uptake of ambient amino acids (osmotrophy) or the grazing activity of phagotrophic protists 18 , 19 . We calculated the apparent nitrogen isotope fractionation of Phe relative to Glu or Ala, following Yamaguchi et al. (2017) 18 , to account for these deviations as follows: $$\:{\epsilon\:}_{Phe/TrAA\:}=1000\times\:(\frac{{\delta\:}^{15}{N}_{Phe}\:+1000}{{\delta\:}^{15}{N}_{TrAA}\:+1000}\:-1)$$ where TrAA represents the trophic amino acid (i.e, Glu or Ala). To investigate the relationship between the apparent isotopic fractionation of Glu and Ala relative to Phe and its ability to track osmotrophy and phagotrophy in mixotrophs, a comprehensive review of the existing literature on plankton was conducted 20 , 71 – 77 . The literature data included autotrophic phytoplankton, eukaryotic strict unicellular phagotrophs, and zooplankton with different trophic roles, ranging from autotrophs (TP 1.0) to carnivores (TP 3.0). These components form the basis of the planktonic food web. The approach facilitates the analysis of the degree of isotopic enrichment of the two trophic amino acids with trophic transfers due to the metabolism of heterotrophs, providing insight into the potentially different fractionation patterns associated with mixotrophs that combine autotrophy and heterotrophy (Fig. 4 ). Remote sensing products, statistical analysis and graphics The median sea surface salinity provided by the Copernicus Global Ocean Physics Analysis and Forecast (doi: 10.48670/moi-00016 ) was calculated at each grid point for the duration of each cruise (09 to 29/05/2018 and 21/04 to 13/05/2021) in order to illustrate the average position of the plume (Fig. 1 ). The median of the geostrophic velocities retrieved for the Copernicus Global Ocean Gridded L4 Sea Surface Heights (doi: 10.48670/moi-00148 ) were also calculated at each grid point over the duration of each cruise to show the dominant direction and speed of the currents in the region (Fig. 1 ). Daily data was used for producing Supporting Video 1 and 2 in order to illustrate the dynamism along the Amazon River plume. Statistical analysis and plots were made by scripting R v4.0.5 78 in R Studio v2022.02.0 79 . Package ggplot2 v3.5.1was used for plotting 80 and ggpubr v0.4.0 for exporting the plots 81 . The different pigmented plankton communities along the Amazon River plume were separated by a hierarchical cluster analysis (HCA) on the chemotaxonomy by HPLC samples during 2018 and 2021. The HCA was done using all the samples available at surface during both cruises. The clusters were separated by Manhattan distances due to the existence of outliers, and the clustering tendency was confirmed by a principal component analysis, both done using factoextra v1.0.7 and FactoMineR v2.4 packages 82 , 83 . The physical, chemical and biological drivers potentially predicting the trophic position of our seston samples (n = 46) were assessed using the decision tree classification machine learning model C5.0 30 in package C50 v0.1.6 84 . This model was used because of its ability for handling non-linear relations among variables in datasets containing missing values. All our seston samples were included in the analysis with the main goal of defining the variables explaining or predicting the trophic mode of seston. The predicting variables included in the analysis were the thickness of the plume, the maximum of the buoyancy frequency, the depth of the mixed layer, the depth of the subsurface fluorescence maximum, the depth of the nitracline (depth where concentration reached 1 µM), phosphocline (depth where concentration reached 0.1 µM) and silicocline (depth where concentration reached 2 µM) and, at the surface: dissolved oxygen, temperature, salinity, nitrate to phosphate ratio, nitrate to silicate ratio, nitrate to nitrite ratio, the average size of the pigmented community estimated using pigment markers according to Bricaud et al (2004) 85 , the concentration of chlorophyll a and the relative contribution to chlorophyll a of all the groups identified by chemotaxonomy as mentioned above. Declarations Competing interests: The authors declare that there are no competing interests. Data and materials availability: Fernández-Carrera, Ana; Steinkopf, Markus; Liskow, Iris; Wodarg, Dirk; Voss, Maren; Loick-Wilde, Natalie: Stable isotopes and mol% of amino acids in 0.2-3 µm seston at surface during METEOR cruise M174 [dataset]. PANGAEA, https://doi.pangaea.de/ 10.1594/PANGAEA.971293 (dataset in review). Fernández-Carrera, Ana; Steinkopf, Markus; Liskow, Iris; Wodarg, Dirk; Voss, Maren; Loick-Wilde, Natalie: Stable isotopes and mol% of amino acids in 3-200 µm seston at surface during METEOR cruise M174 [dataset]. PANGAEA, https://doi.pangaea.de/ 10.1594/PANGAEA.971279 (dataset in review). Fernández-Carrera, Ana; Wodarg, Dirk; Montoya, Joseph P; Loick-Wilde, Natalie: Stable isotopes and mol% of amino acids in seston at surface during ENDEAVOR cruise EN614 [dataset]. PANGAEA, https://doi.pangaea.de/ 10.1594/PANGAEA.971946 (dataset in review). Subramaniam, Ajit (2020) Phytoplankton diagnostic pigments from HPLC from samples collected on R/V Endeavor cruise EN614 in the tropical North Atlantic during May 2018. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2019-06-05. doi: 10.26008/1912/bco-dmo.769601.1 [21/02/2023] Umbricht, Jaqueline; Mohrholz, Volker; Montoya, Joseph P.; Subramaniam, Ajit (2022) Nitrate uptake, primary production rates and phytoplankton pigment concentrations from the Amazon River plume from three cruises (EN614, EN640, M174) – Dataset used in the publication “Nitrate uptake and primary production along the Amazon River plume continuum”. http://doi.io-warnemuende.de/ 10.12754/data-2022-0008 PlanktoScope images: https://ecotaxa.obs-vlfr.fr/prj/6346 . Funding: Deutsche Forschungsgemeinschaft LO 1820/8 − 1 (NLW) Author contributions: Conceptualization: NLW, AFC Acknowledgments: We would like to thank the captain, crew and science parties of cruises EN614 on board RV Endeavor and M174 on board RV Meteor for their invaluable support at sea. A special thanks dedicated to all people involved in M174 as well as to chief scientist Prof. Dr. M. Voss for making a memorable first international long term cruise in a German vessel after the hard lockdown due to the Covid pandemic. We are grateful to Prof. R. Peterson for the radium isotopes during EN614 and EN640, Dr. A. Pham and Dr. J. Umbricht for vivid discussions about the habitats and community structure along the Amazon River plume, and C. Burmeister, L. Kreuzer and E. Strope for providing state-of-the-art nutrient measurements. References Flynn, K.J., et al.: Misuse of the phytoplankton–zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types. 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(2012). 10.1017/cbo9780511732263.010 Subramaniam, A., et al.: Amazon River enhances diazotrophy and carbon sequestration in the tropical North Atlantic Ocean. Proc. Natl. Acad. Sci. U S A. 105 , 10460–10465 (2008) Goes, J.I., et al.: Influence of the Amazon River discharge on the biogeography of phytoplankton communities in the western tropical north Atlantic. Prog Oceanogr. 120 , 29–40 (2014) Gomes, H., do, R., et al.: The Influence of Riverine Nutrients in Niche Partitioning of Phytoplankton Communities–A Contrast Between the Amazon River Plume and the Changjiang (Yangtze) River Diluted Water of the East China Sea. Front. Mar. Sci. 5 , (2018) de Fiore, M.: Characterization of nitrogen-fixing cyanobacteria in the Brazilian Amazon floodplain. Water Res. 39 , 5017–5026 (2005) Kraus, C.N., et al.: Interannual hydrological variations and ecological phytoplankton patterns in Amazonian floodplain lakes. Hydrobiologia. 830 , 135–149 (2019) Margalef, R.: Life-forms of phytoplankton as survival alternatives in an unstable environment. Oceanol. Acta. 1 , 493–509 (1978) Hansen, H.P., Koroleff, F.: Determination of nutrients. Methods Seawater Anal. 159–228 (1999). 10.1002/9783527613984.ch10 Van Heukelem, L., Thomas, C.S.: Computer-assisted high-performance liquid chromatography method development with applications to the isolation and analysis of phytoplankton pigments. J. Chromatogr. A. 910 , 31–49 (2001) Hooker, S.B., et al.: The second SeaWiFS HPLC analysis round robin experiment. NASA Tech. Memo , 112 (2005). (2005) Wright, S.: Chemtax version 1.95 for calculating the taxonomic composition of phytoplankton populations, Ver. 3 , Australian Antarct. Data Centre (2017) Ferguson Wood, E.J.: A phytoplankton study of the Amazon region. Bull. Mar. 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Environ. 2nd Ed. 1277–1302 (2008). 10.1016/b978-0-12-372522-6.00029-3 Hofmann, D., Gehre, M., Jung, K.: Sample preparation techniques for the determination of natural 15N/14N variations in amino acids by gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). Isot. Environ. Health Stud. 39 , 233–244 (2003) Veuger, B., Middelburg, J.J., Boschker, H.T.S., Houtekamer, M.: Analysis of 15N incorporation into D-alanine: A new method for tracing nitrogen uptake by bacteria. Limnol. Oceanogr. Methods. 3 , 230–240 (2005) McMahon, K.W., Ramirez, M.D., Besser, A., Newsome, S.D.: Primary producer amino acid nitrogen isotope values from published literature to examine beta variability in trophic position estimates. (Version 1) Version Date 2022-03-08. doi: (2022). 10.26008/1912/bco-dmo.870320.1 Décima, M., Landry, M.R., Popp, B.N.: Environmental perturbation effects on baselineδ15N values and zooplankton trophic flexibility in the southern California Current Ecosystem. Limnol. Oceanogr. 58 , 624–634 (2013) Doherty, S.C., Maas, A.E., Steinberg, D.K., Popp, B.N., Close, H.G.: Distinguishing zooplankton fecal pellets as a component of the biological pump using compound-specific isotope analysis of amino acids. Limnol. Oceanogr. 66 , 2827–2841 (2021) McCarthy, M.D., Benner, R., Lee, C., Fogel, M.L.: Amino acid nitrogen isotopic fractionation patterns as indicators of heterotrophy in plankton, particulate, and dissolved organic matter. Geochim. Cosmochim. Acta. 71 , 4727–4744 (2007) McClelland, J.W., Holl, C.M., Montoya, J.P.: Relating low δ15N values of zooplankton to N2-fixation in the tropical North Atlantic: insights provided by stable isotope ratios of amino acids. Deep Sea Res. Part. I Oceanogr. Res. Pap. 50 , 849–861 (2003) García-Seoane, R., Viana, I.G., Bode, A.: Stable carbon and nitrogen isotopes in bulk samples and nitrogen isotopes in amino acids of mesozooplankton (200–2000 µm) of NW Spain (NE Atlantic) during 2001 and 2017. (2022). 10.1594/PANGAEA.947073 Hannides, C.C.S., Popp, B.N., Choy, C.A., Drazen, J.C.: Midwater zooplankton and suspended particle dynamics in the North Pacific Subtropical Gyre: A stable isotope perspective. Limnol. Oceanogr. 58 , 1931–1946 (2013) Core Team, R.: T. R: A language and envirinment for statistical computing. (2014) Rstudio, T., RStudio: Integrated Development for R. Rstudio Team, PBC , Boston, MA URL http://www.rstudio.com/ (2020). 10.1145/3132847.3132886 Gómez-Rubio, V.: ggplot2 - Elegant Graphics for Data Analysis (2nd Edition). J. Stat. Softw. 77, (2017) Kassambara, A., ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.2. (2018). https://CRAN.R-project.org/package=ggpubr . https://CRAN.R-project.org/package=ggpubr Lê, S., Josse, J., Husson, F.: & others. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008) Kassambara, A., Mundt, F., Factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R Package Version 1.0.7. (2020) Kuhn, M., Quinlan, R.: C50: C5.0 Decision Trees and Rule-Based Models. R package version 0.1.8. (2023) Bricaud, A., Claustre, H., Ras, J., Oubelkheir, K.: Natural variability of phytoplanktonic absorption in oceanic waters: Influence of the size structure of algal populations. J. Geophys. Res. Ocean. 109 , (2004) Additional Declarations There is NO Competing Interest. Supplementary Files FernandezCarreraLoickWildeetalmixotrophyARPTableS1.xlsx Supporting Table S1 Supportingvideo12018.mp4 Supplementary Video 1 FernandezCarreraLoickWildeetalmixotrophyARPSupportingInformation.docx Supporting Information Supportingvideo22021.mp4 Supplementary Video 2 floatimage1.png Cite Share Download PDF Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Communications Biology → 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4742841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":404651298,"identity":"41353c09-e99b-4f7a-b183-c9d876ed7311","order_by":0,"name":"Ana Fernández-Carrera","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYJADxgcMBSRqYTZgMCBRC5sEUVr4Zzc/YPhRcy+fX/rws2oegzt2/Q3Mhz/g0yJx55gBY8+xYsuZfWlmt3kMniXPOMCWJoHXmhsJBswMbAkGBmcYQFoOJxsw8Jjh1SF/I/0DM8O/BAP7M+zfiiFa+D/jdZjBjRwDZsY2oC08PGbMQC12QFsY8DrM8EZOwcHevgQDiTM8xZJzDJ4lSBxmM8OrRe5G+sYHP74lGPD3sG/88Kbijj1/e/NjvA4DgQPI7MQGZkLq0bXbk6hhFIyCUTAKRgAAAPbtRT3rr7fyAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8347-0996","institution":"Institute of Oceanography and Global Change / Universidad de Las Palmas de Gran Canaria","correspondingAuthor":true,"prefix":"","firstName":"Ana","middleName":"","lastName":"Fernández-Carrera","suffix":""},{"id":404651299,"identity":"4c88bbc2-14be-4b58-ab11-ae5635848473","order_by":1,"name":"Noémie Choisnard","email":"","orcid":"","institution":"Leibniz-Institute for Baltic Sea Research Warnemuende","correspondingAuthor":false,"prefix":"","firstName":"Noémie","middleName":"","lastName":"Choisnard","suffix":""},{"id":404651300,"identity":"1ea4c966-8d3a-4641-bbad-d3cd37173796","order_by":2,"name":"Dirk Wodarg","email":"","orcid":"","institution":"Leibniz-Institute for Baltic Sea Research Warnemuende","correspondingAuthor":false,"prefix":"","firstName":"Dirk","middleName":"","lastName":"Wodarg","suffix":""},{"id":404651301,"identity":"cc44fcd0-2d2d-4971-8d4f-a3b7bba303c6","order_by":3,"name":"Iris Liskow","email":"","orcid":"","institution":"Leibniz-Institute for Baltic Sea Research Warnemuende","correspondingAuthor":false,"prefix":"","firstName":"Iris","middleName":"","lastName":"Liskow","suffix":""},{"id":404651302,"identity":"9410c7b1-ef10-4c45-951a-aadb3f631ca7","order_by":4,"name":"Ajit Subramaniam","email":"","orcid":"https://orcid.org/0000-0003-1316-5827","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Ajit","middleName":"","lastName":"Subramaniam","suffix":""},{"id":404651303,"identity":"cafefa79-54eb-44dc-b1d0-1bb561b7bc7a","order_by":5,"name":"Joseph Montoya","email":"","orcid":"https://orcid.org/0000-0001-7197-4660","institution":"Georgia Tech","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Montoya","suffix":""},{"id":404651304,"identity":"455be24e-7ef7-4405-8d53-a4acbd8774a6","order_by":6,"name":"Maren Voss","email":"","orcid":"https://orcid.org/0000-0002-5827-9062","institution":"Leibniz Institute for Baltic Sea Research","correspondingAuthor":false,"prefix":"","firstName":"Maren","middleName":"","lastName":"Voss","suffix":""},{"id":404651305,"identity":"61352da6-d098-4d69-b57b-0690a223039b","order_by":7,"name":"Natalie Loick-Wilde","email":"","orcid":"","institution":"Leibniz-Institute for Baltic Sea Research Warnemuende","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"","lastName":"Loick-Wilde","suffix":""}],"badges":[],"createdAt":"2024-07-15 12:27:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4742841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4742841/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s42003-026-09893-4","type":"published","date":"2026-03-25T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76482314,"identity":"bfeef1b8-7272-4291-b0e4-4ddfca8b5916","added_by":"auto","created_at":"2025-02-17 15:02:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":957795,"visible":true,"origin":"","legend":"\u003cp\u003eStations sampled for compound-specific isotope analysis of amino acids of seston in surface waters along the Amazon River plume over sea surface salinity (SSS) contours and geostrophic velocities for the 2018 (left) and 2021 (right) cruises. Shape of the symbols represent the dominant trophic position (TP) in seston, with inverted triangle for autotrophs and asterisk for mixotrophs. Both size fractions presented the same TP in all stations, except station 17.17 in 2021, where the large size fraction (\u0026gt;3 µm) was mixotrophic and the small (0.2−3 µm) autotrophic. Colors of the symbols represent the habitat definition according to Pham et al (2024) ordered by apparent age: Riverine Input (RI, dark gray), Young Plume Core (YPC, red), Outer Plume Margin (OPM, purple), Western Plume Margin (WPM, yellow), modified Oceanic Water (MOW, dark cyan) and Oceanic Water (OSW, blue). The extent of the Amazon River plume is illustrated by the median of the sea surface salinity at each grid point during the timing of each cruise (09 to 29/05/2018 and 21/04 to 13/05/2021) of the daily surface salinity provided by the Copernicus Global Ocean Physics Analysis and Forecast (doi: 10.48670/moi-00016). Arrows represent the speed and direction of the median of the geostrophic velocity throughout each cruise as provided by the Copernicus Global Ocean Gridded L4 Sea Surface Heights (doi: 10.48670/moi-00148).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/c218d13c11f60d1ffc383574.png"},{"id":76484056,"identity":"d95f526e-0f55-4119-8121-79565f7b626c","added_by":"auto","created_at":"2025-02-17 15:18:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":322495,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of the pigmented plankton community derived by chemotaxonomy based on specific pigment markers during 2018 and 2021 cruises along the Amazon River plume. Four distinct communities were identified by hierarchical cluster analysis and Manhattan distances as shown in the dendrogram on the top left panel. The principal component analysis on the top right panel supports the separation of the clusters along the first two principal components with the data points colored as the clusters in the dendrogram. Colors of the sample name in the bar plot at the bottom left represent the different habitats defined by Pham et al (2024) ordered by apparent age: Riverine Input (RI, gray), Young Plume Core (YPC, red), Outer Plume Margin (OPM, purple), Western Plume Margin (WPM, dark yellow), modified Oceanic Water (MOW, dark cyan) and Oceanic Water (OSW, blue). The stacked bars representing stations where mixotrophy was dominant are marked with an asterisk. The gradient at the bottom left illustrates the potential decreasing impact on the communities of the physicochemical forcing driven by the plume as the water ages.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/e265b9f04fd9a02fe3651da4.png"},{"id":76482311,"identity":"ec54cc6b-374b-4653-9719-143c058d19dd","added_by":"auto","created_at":"2025-02-17 15:02:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":135944,"visible":true,"origin":"","legend":"\u003cp\u003eδ\u003csup\u003e15\u003c/sup\u003eN\u003csub\u003ePhe \u003c/sub\u003e(phenylalanine) vs δ\u003csup\u003e15\u003c/sup\u003eN\u003csub\u003eGlu\u003c/sub\u003e (glutamic acid + glutamate) of the seston samples collected along the Amazon River plume expressed in delta notation (δ\u003csup\u003e15\u003c/sup\u003eN ‰, relative to atmospheric N\u003csub\u003e2\u003c/sub\u003e). In 2018, total particles (combined square and triangle) were collected, while in 2021, seston was separated into two size fractions (triangles for \u0026gt;3 µm, squares for 0.2−3 µm). The dashed lines represent the trophoclines of dominant autotrophy (TP1.0) and herbivory (TP2.0). The dotted lines delimit the trophic space around TP1.5 where mixotrophs are distributed. Colors represent the different habitats defined by Pham et al (2024) ordered by apparent age: Riverine Input (RI, gray), Young Plume Core (YPC, red), Outer Plume Margin (OPM, purple), Western Plume Margin (WPM, yellow), modified Oceanic Water (MOW, cyan) and Oceanic Water (OSW, blue).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/3c46e991b3d4c843668a916f.png"},{"id":76482322,"identity":"bbc49ca0-e4ca-4afc-a65d-61281b7fb88a","added_by":"auto","created_at":"2025-02-17 15:02:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":219390,"visible":true,"origin":"","legend":"\u003cp\u003eApparent isotope fractionation of the two trophic amino acids (glutamic acid + glutamate, Glu; alanine, Ala) relative to the source amino acid (phenylalanine, Phe). Literature data for TP1(autotrophic phytoplankton, dark green circles), TP2(herbivorous metazooplankton, yellow squares), TP2.5(omnivorous metazooplankton, orange squares) and TP3 (carnivorous metazooplankton, red squares) as well as strict unicellular eukaryotic phagotrophs (herbivorous protozooplankton, light green squares) are represented along with the autotrophic (dark purple inverted triangles) and mixotrophic (light purple asterisks) seston collected along the Amazon River plume in 2018 and 2021.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/86c79bc18b7aee8d00b51397.png"},{"id":76483723,"identity":"26d4c1f7-94ec-409c-9df6-22c23777c935","added_by":"auto","created_at":"2025-02-17 15:10:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45143,"visible":true,"origin":"","legend":"\u003cp\u003eClassification tree based on C5.0 showing the most likely predictors of the trophic mode of seston and the thresholds defined by the model for each of them. Note the model misclassified one sample (17.17 mixotroph was classified as autotroph, accounting for a 2.2 % error). The stations classified in each branch are shown below the tree colored by the different habitats defined by Pham et al (2024) ordered by apparent age: Riverine Input (RI, gray), Young Plume Core (YPC, red), Outer Plume Margin (OPM, purple), Western Plume Margin (WPM, yellow), modified Oceanic Water (MOW, cyan) and Oceanic Water (OSW, blue).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/cd2c1a090c2a4c22ce25fba5.png"},{"id":105535380,"identity":"9955c8e2-0a34-45d0-9599-1568a831193a","added_by":"auto","created_at":"2026-03-27 07:06:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2633383,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/932c25ff-4297-41c7-a30d-be6f97fac2f5.pdf"},{"id":76482310,"identity":"d829b722-44d7-4fb7-87cc-6708ec1621c9","added_by":"auto","created_at":"2025-02-17 15:02:53","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23770,"visible":true,"origin":"","legend":"Supporting Table S1","description":"","filename":"FernandezCarreraLoickWildeetalmixotrophyARPTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/8c2842a3479a2b0c2fdcff65.xlsx"},{"id":76483717,"identity":"d0d0268b-cfec-4153-b363-0d86ae5f740e","added_by":"auto","created_at":"2025-02-17 15:10:53","extension":"mp4","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1898705,"visible":true,"origin":"","legend":"Supplementary Video 1","description":"","filename":"Supportingvideo12018.mp4","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/3c89fe72cf9286e4a5315755.mp4"},{"id":76484057,"identity":"a9eca024-6718-4e23-81c0-bebfe8961bb7","added_by":"auto","created_at":"2025-02-17 15:18:53","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1124373,"visible":true,"origin":"","legend":"Supporting Information","description":"","filename":"FernandezCarreraLoickWildeetalmixotrophyARPSupportingInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/965c4f216b1b42de5450c6ef.docx"},{"id":76485094,"identity":"a023ac16-c37a-43e1-b7be-9c61579d893f","added_by":"auto","created_at":"2025-02-17 15:26:53","extension":"mp4","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2210819,"visible":true,"origin":"","legend":"Supplementary Video 2","description":"","filename":"Supportingvideo22021.mp4","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/437da7a9ca8b8ad0769723da.mp4"},{"id":76482331,"identity":"e0725b64-27ab-47ad-a982-5254081e9f93","added_by":"auto","created_at":"2025-02-17 15:02:54","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1387844,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4742841/v1/1a7b4efbaa01c51f67ff90cc.png"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Mixotrophy emerges as the optimal strategy in mature waters of the Amazon River plume","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe traditional dichotomy between plant-like (i.e., phytoplankton) and animal-like (i.e., zooplankton) organisms structuring the base of pelagic food webs has been challenged by increasing evidence that most \u0026ldquo;phytoplankton\u0026rdquo; are in fact mixotrophs, that is, they combine photoautotrophy and heterotrophy to make a living\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. On the one hand, eukaryotic and prokaryotic autotrophs are able to use dissolved organic matter to some extent to support their nutritional needs\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, i.e., they are osmotrophs. However, if osmotrophy is used for acquiring organic compounds or recovering leaked metabolites is still unclear\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. On the other hand, most unicellular eukaryotes, with the exception of diatoms, also rely on phagotrophy on varying degrees, thus constituting an additional functional type recently recognized and named mixoplankton\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. From the perspective of classical trophic ecology, this nutritional flexibility coupling photo- and heterotrophy produces intermediate steps between pure autotrophs and pure herbivores, which are likely to reduce the overall transfer of energy from primary producers to secondary consumers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the impact on biogeochemical cycling of mixotrophs that rely solely on osmotrophy differs significantly from that of mixoplankton, also using phagotrophy. The former does not remove preys, competitors or other grazers from the food web affecting its structure, whereas the latter does\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Mixoplankton do not work in binary fashion, switching between pure photoautotrophy and pure heterotrophy. In most cases, they couple both nutritional modes to maximize their growth efficiency\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Heterotrophy, via osmotrophy and/or phagotrophy, can support growth directly or supply limiting nutrients to photosynthesis, which in turn compensates respiratory losses due to the former, allowing the increase in biomass of the mixotrophic stock relative to the autotrophic\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In addition, mixoplankton provide better quality food to metazoans\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and, in seasonally matured systems (nutrient poor) where they dominate the community, they drive the accumulation of refractory dissolved organic matter, which directly contributes to the ability of the carbon pump to mitigate atmospheric carbon dioxide\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Overall, their better performance seems to lead to a higher transfer of mixotrophic carbon and energy than that of pure autotrophs\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Yet, laboratory experiments also suggest that photoheterotrophy is the dominant mode in some mixoplankton, resulting in no enhancement of primary production or compensation of respiratory losses, as the photosynthetic system is used as mere energy provider rather than carbon fixer\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, mixoplankton compete for resources with both autotrophs and heterotrophs, implying additional energetic costs\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and some groups seem to provide poor quality food affecting the overall performance of their grazers\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Thus, it remains uncertain whether mixoplankton universally implies an increase or a decrease in energy transfer to upper trophic levels in comparison to pure autotrophic plankton. Therefore, it is imperative to investigate their occurrence and activity in natural systems. The development of gene-based predictive models for identifying the potential activity of mixotrophs in the field is currently being explored\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, as there is no specific molecular marker for directly quantifying heterotrophy. In this regard, the amino acid nitrogen stable isotope analysis (or CSIA AA) offers a complementary tool for identifying the imprint of osmotrophy and phagotrophy from phytoplankton-derived field samples by combining the differential fractionation in nitrogen stable isotopes of the trophic amino acids glutamic acid or alanine with the source amino acid phenylalanine\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Western Tropical Atlantic Ocean is the site of energetic surface circulation that plays a key role in the transport of heat, salt, and water from the Southern to the Northern Hemisphere\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In this complex region, the Amazon River discharges about 50% of all annual freshwater input into the tropical Atlantic, contributing up to 20% of annual fluvial freshwater into the global ocean\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The magnitude of the Amazon outflow changes markedly through the year, with a maximum in May/June and a minimum in October/November\u003csup\u003e25\u003c/sup\u003e. The interaction of the river discharge with the strong tidal currents (\u0026gt;\u0026thinsp;1 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), wind stress and the energetic North Brazil current results in complex circulation patterns on the shelf over the year\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The Amazon River plume displays a strong seasonal variability, and spreads as a buoyant lens of fresher water at the surface, reaching hundreds of km to the north and the east during the peak outflow season. This freshwater lens acts as an effective barrier to vertical mixing\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, so the plume works as a semi-enclosed system slowly exchanging material with the adjacent environment. As a result of this extension and complexity, at any given instant, the plume is composed of a mixture of waters of different age and salinity\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, potentially reflecting different stages of phytoplankton succession. The strong dynamics along the plume and its large extension define distinct planktonic habitats capturing the aging process of the plume\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, where the mature systems optimal for mixoplankton can emerge\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere we apply a multidisciplinary approach to explore the environmental drivers of the dominant trophic mode at the base of the food web (mixotrophy or autotrophy) in the varying habitats along the Amazon River plume from the estuary to the northern end of the plume at 15\u0026deg;N in two cruises in 2018 and 2021. This system provides a unique natural laboratory, where it is possible to study different stages of phytoplankton functional and community structure succession as the plume matures from nutrient-replete to nutrient-deplete conditions. Our approach by the use of nitrogen stable isotopes of individual amino acids reveals widespread osmo-phago-mixotrophy in mature waters of the plume, where the potential accumulation of refractory DOM will likely contribute to the sink of carbon dioxide found in the margins \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and also confirms that this flexibility in phytoplankton nutrition is more ubiquitous in the global ocean than traditionally considered.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental variables\u003c/h2\u003e \u003cp\u003eThe Amazon River plume reached as far as 15\u0026deg;N during both cruises as shown by the extension of sea surface salinities below 35 derived from the Copernicus Global Ocean Physics Analysis and Forecast product (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supporting videos 1, 2). Our samples covered six different habitats along the plume\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, from the freshest plume in the Riverine (RI) to the oldest stages in the margins (OPM and WPM) and the Modified Oceanic water (MOW) habitats. Samples were also taken in the habitat without influence of the plume named as Oceanic waters (OSW). In the Amazon shelf close to the river mouth, the geostrophic velocities revealed an intricate and strong circulation as the riverine waters spread at surface in this energetic region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supporting videos 1, 2). Additionally, some differences in the strength of the North Brazil current and the variety of eddy-like structures appeared north of 7.5\u0026deg;N affecting the overall distribution of the plume in the region in each cruise.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe physicochemical variables measured in these six habitats reflected the changing and dynamic characteristics of the Amazon River plume (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Sea surface temperature was warmer in the estuary, where the lower salinity surface waters (\u0026lt;\u0026thinsp;6) were found. Moving to the north, surface temperature decreased to reach a minimum towards the older, saltier regions of the plume (OPM and MOW). The base of the mixed layer in the area under the influence of the plume ranged from 8 to 40 m, shallower than that in the oligotrophic oceanic waters, where it exceeded 70 m. These shallower mixed layers were related to higher stability of the water column, shown by values of the maximum of buoyancy frequency (N\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) above 0.02 s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, which in most stations was found at base of the plume. This suggests a slow vertical exchange between the plume and oceanic waters beneath, which intensified towards the plume margins, where vertical stability decreased (lower N\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) and surface salinity increased. As expected, surface dissolved oxygen concentration decreased with increasing salinity, but in most stations, it was consistently below the saturation concentration estimated using temperature and salinity (Benson and Krause Jr. 1984) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The concentration of chlorophyll \u003cem\u003ea\u003c/em\u003e at the surface exceeded 0.5 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the habitats representing fresher parts of the plume (RI, YPC and WPM), and decreased towards older sections of the plume and the oceanic realm. The significant nutrient load carried by the Amazon and Par\u0026aacute; Rivers is evident in the elevated concentrations of nitrate and silicate found in the brackish estuary (RI) compared to other habitats, with a sharp decrease at stations on the shelf (YPC), although values remained relatively high at around 2 \u0026micro;M.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhytoplankton community structure\u003c/h3\u003e\n\u003cp\u003eThe hierarchical cluster analysis (HCA) performed on the relative contribution of the different groups of phytoplankton to total chlorophyll \u003cem\u003ea\u003c/em\u003e revealed the existence of four different phytoplankton communities along the Amazon River plume during our cruises (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The clustering tendency of the HCA was validated by a principal component analysis (PCA), which evidenced the separation of clusters along the first two dimensions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While diatoms dominated in most parts of the mouth, they were also accompanied by a relatively high abundance of presumably filamentous freshwater cyanobacteria in the oligohaline section of the plume (salinity\u0026thinsp;\u0026lt;\u0026thinsp;5.0, community 2, RI stations). Additionally, two stations within the mouth exhibited a distinct structure dominated by cryptophytes, with a complete absence of picocyanobacteria (community 1). Along the margin habitats, representing older plume waters, we observed a transition to a community dominated by haptophytes, where filamentous cyanobacteria (presumably \u003cem\u003eTrichodesmium\u003c/em\u003e) were also relatively abundant (community 3). The community in sections of the mature plume and the ocean domain resembled one of oligotrophic waters, dominated by pico-cyanobacteria (i.e., \u003cem\u003eSynechococcus\u003c/em\u003e and \u003cem\u003eProchlorococcus\u003c/em\u003e) and haptophytes (community 4). This community also showed a low percent contribution of \u003cem\u003eTrichodesmium\u003c/em\u003e or diatoms, presumably associated with symbiotic diazotrophs (DDAs), in the stations affected by the plume. However, the PlanktoScope image set suggests that these two phytoplankton groups do not fully coexist in similar abundances. Instead, they trade dominance, that is, either DDAs (in station 32.06 in MOW) or \u003cem\u003eTrichodesmium\u003c/em\u003e (in station 30.12 in OPM) were in greater abundances relative to the other group. Dinoflagellates represented a small percent in most of the samples with the exception of stations 8.02 in 2018 and 24.01 in 2021. It is notable that each phytoplankton community consisted of not only a single dominant habitat, but also samples from the habitat that preceded and succeeded it, highlighting the gradual transition that occurs along the plume as it ages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAmino acids nitrogen isotopes and trophic mode of seston\u003c/h3\u003e\n\u003cp\u003eThe trophic mode of our samples can be graphically illustrated in the space defined between the nitrogen signatures of phenylalanine (Phe) and glutamic acid\u0026thinsp;+\u0026thinsp;glutamate (Glu), where the combination of their values using the equation proposed by Chikaraishi et al (2009)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e defines different trophoclines, that is, lines that represent the same trophic position (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Most of the seston samples fell around the autotroph trophocline (TP 1.0), pointing to a clear dominance of autotrophy along the Amazon River plume and oceanic realm in both 2018 and 2021. However, we unexpectedly found mixotrophs (TP 1.5) at three stations in 2021 (in the large size fraction of 17.17 and both size fractions of 21.07 and 24.01), and at five stations in mature plume waters during 2018 (in the total seston of stations 6.04, 22.04, 24.03, 25.03 and 31.11).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe apparent isotope fractionations of the trophic amino acids Glu or alanine (Ala) relative to the source amino acid Phe in the literature dataset of different trophic modes of plankton from TP 1.0 to TP 3.0 showed a significant linear correlation (ε\u003csub\u003ePhe/Ala\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.59\u0026thinsp;+\u0026thinsp;1.1 ε\u003csub\u003ePhe/Glu\u003c/sub\u003e, R\u003csup\u003e2\u003c/sup\u003e-adj\u0026thinsp;=\u0026thinsp;0.81, n\u0026thinsp;=\u0026thinsp;118, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with intercept and slope not significantly different from 0 and 1, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Our seston samples were consistent with the literature, the autotrophic samples fell within the range of phytoplankton, while the mixotrophic ones occupied the same space that strictly phagotrophic unicellular eukaryotes, where we would expect osmo-phago-mixotrophs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe trophic mode of seston did not show any linear correlation with the environmental drivers found along the plume, hence, we applied a supervised machine learning algorithm named C5.0 for exploring the variables predicting trophic position capable of handling non-linear relations. The C5.0 decision tree model\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e selected the depth of the mixed layer, surface oxygen concentration, the absolute value of the maximum buoyancy frequency at each station, and surface chlorophyll \u003cem\u003ea\u003c/em\u003e concentration as the most relevant variables for predicting the trophic mode of seston among all the environmental variables chosen (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The relative attribute usage, or the fraction of cases included in each branch of the classification tree for each of them was 100% for mixed layer depth, 73.9% for surface oxygen concentration, 52.2% for the maximum of buoyancy frequency, and 21.7% for surface chlorophyll \u003cem\u003ea\u003c/em\u003e. The model only misclassified the large size fraction of station 17.17 in 2021 as autotrophic, rather than mixotrophic, for an overall error rate of 2.2%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe dynamic nature of the Amazon River plume is evident in the daily evolution of sea surface salinity and geostrophic velocities provided by the Copernicus Marine Service (Supporting Video 1 and 2), which illustrate why distinct habitats emerge as the plume ages. During our cruises, the plume extended as far as 15\u0026deg;N, delineating three habitats in 2018 and six in 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These planktonic habitats, identified by Pham et al (2024)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e following the approach proposed by Weber et al (2019)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, were based on physicochemical drivers and capture the age of the plume better than single proxies like salinity or distance to the Amazon shelf. This age accounts for the history of the waters as they travel along the path of the plume, and determines the direction of the phytoplankton succession as the habitats suitable for each phytoplankton group emerge\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The apparent age of plume waters (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), estimated by concurrent radium isotopes (Supporting Information) measured on CTD samples during our 2018 cruise\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and a 2019 cruise\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, both included in Pham et al (2024)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, showed that the four habitats sampled in these surveys captured the aging gradient as follows: Young Plume Core (YPC, apparent age 12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 days, n\u0026thinsp;=\u0026thinsp;8), Old Plume Core (OPC, 14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8 days, n\u0026thinsp;=\u0026thinsp;9), Outer Plume Margin (OPM, 26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 days, n\u0026thinsp;=\u0026thinsp;23) and Western Plume Margin (WPM, 31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 days, n\u0026thinsp;=\u0026thinsp;9). The evolution of age between these habitats seems gradual rather than abrupt, therefore, habitats with different physicochemical forcing may contain waters of similar age as a result of the varied gradients in hydrological and biogeochemical properties of the plume\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Two additional plume habitats were sampled in 2021, but not in 2018 or 2019. They seem to represent the youngest (Riverine Input, RI) and the oldest (modified Oceanic Water, MOW) plume waters. Seston in RI exhibited bulk δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC values below \u0026minus;\u0026thinsp;27\u0026permil; (Fig S2), which are consistent with previous studies reporting suspended terrestrial material between \u0026minus;\u0026thinsp;34 and \u0026minus;\u0026thinsp;27\u0026permil; in the Amazon shelf\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Hence, these waters containing fluvial material are younger than those in YPC, where seston δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC up to \u0026minus;\u0026thinsp;19\u0026permil; points to a clear shift towards marine particles\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Lastly, the physicochemical similarities between MOW and OSW (Oceanic Water without influence of the plume), discussed elsewhere\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, suggest that MOW represents the oldest waters of the plume, above the average apparent age of 32 days in WPM.\u003c/p\u003e\n\u003ch3\u003eMachine learning unveils the drivers leading to dominant mixotrophy\u003c/h3\u003e\n\u003cp\u003eIn May 2018 and April-May 2021, we sampled surface seston along the Amazon River plume as a total sample (\u0026gt;\u0026thinsp;0.2 \u0026micro;m) in 2018 and size fractionated into picoseston (0.2\u0026thinsp;\u0026minus;\u0026thinsp;3 \u0026micro;m) and the combination of nano- and microseston (3\u0026thinsp;\u0026minus;\u0026thinsp;200 \u0026micro;m) in 2021. The bulk δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC, C:N ratio, and the relationship between particulate carbon and chlorophyll \u003cem\u003ea\u003c/em\u003e suggest that the seston was clearly dominated by phytoplankton (Fig. S2, Supporting information), thus representing the dominant trophic position (TP) at the base of the food web, where a canonical TP of 1.0 indicates pure autotrophy\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The TP is derived from the comparison of nitrogen stable isotope values between the source amino acid phenylalanine (Phe), which barely changes with trophic transfers, and the trophic amino acid glutamic acid (Glu), which integrates the effect of trophic steps\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. This seamless integration of trophic transfers in the isotopes of Glu at lower trophic levels is due to the metabolic pathways in heterotrophs, where Glu is central to the metabolism of amino acids, acting as a precursor to most of them in reactions involving deamination or transamination, e.g., to alanine (Ala), proline or asparagine\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Consequently, organisms combining both autotrophic and heterotrophic nutrition should occupy an intermediate position between TP 1.0 and TP 2.0 (pure herbivory), as it was previously observed in studies of mixotrophic foraminifera and corals\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Therefore, when mixotrophy dominates the community, the TP of phytoplankton-derived seston will distribute around TP 1.5. Indeed, we identify this imprint of mixotrophy in our Amazon River plume samples. Interestingly, it was prevalent in mature sections of the Amazon River plume in the Outer Plume Margin (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), a habitat representing waters with an average apparent age of 27 days, and comprising a large extension of the plume, where a significant sink of atmospheric CO\u003csub\u003e2\u003c/sub\u003e was previously described\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In this habitat, mixotrophs seem to find the optimal environmental settings required for taking over pure autotrophs and heterotrophs\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Elsewhere along the Amazon River plume, our samples predominantly reflected the dominance of autotrophy, clustering around the TP 1.0 trophocline, and falling below the range defined for mixotrophy (1.4\u0026thinsp;\u0026minus;\u0026thinsp;1.6, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the intricate dynamic environment of the plume, where waters of various ages coexist throughout the year\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and the plume ages delimiting distinct biogeochemical habitats\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, it is likely that non-linear interactions among multiple physicochemical drivers influence the TP (autotrophy vs mixotrophy) at the base of food webs at a given time. To identify these drivers, we employed a machine learning C5.0 classification model\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, which can handle non-linear relationships between variables in large and complex datasets with missing values. The most relevant predictors identified by the model include, in order of importance, the depth of the mixed layer (m), the concentration of oxygen at surface (mM), the absolute maximum of buoyancy frequency (s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), and the concentration of chlorophyll \u003cem\u003ea\u003c/em\u003e (\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Mixotrophy was predominantly found at stations with relatively shallow mixed layers (\u0026le;\u0026thinsp;37 m), in areas where the plume had a thickness between 5 and 35 m (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The concentration of dissolved oxygen serves as a proxy for dominant heterotrophic processes, as heterotrophy consumes more oxygen than is produced by oxygenic photosynthesis\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Mixotrophy-rich regions presented oxic but undersaturated surface waters (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), likely due to photoheterotrophy, which has been suggested as the preferential growth strategy for these organisms\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In this situation, mixoplankton may use the photosynthetic apparatus for energy production rather than carbon fixation, contributing then to oxygen consumption. Moreover, mixotrophy dominance was observed at stations with higher chlorophyll \u003cem\u003ea\u003c/em\u003e relative to similar locations (\u0026gt;\u0026thinsp;0.171 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), likely reflecting the higher growth success related to this feeding mode suggested by Ward and Follows (2016)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, which also results in a higher transference of carbon up the food web\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and potential accumulation of refractory dissolved organic matter over time contributing to mitigation of atmospheric carbon dioxide by the biological carbon pump\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, we also found a dominance of mixotrophy at stations 6.04 and 24.03 during 2018, with waters close to oxygen saturation and a relatively less stable water column (Brunt V\u0026auml;is\u0026auml;l\u0026auml; N\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.006 s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The pigmented community at station 6.04 consisted mainly of diatoms with minor contributions from cryptophytes, prasinophytes, haptophytes, dinoflagellates, and \u003cem\u003eTrichodesmium\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although all of these groups, except \u003cem\u003eTrichodesmium\u003c/em\u003e, are known members of the mixoplankton, combining osmo-phago-mixotrophy\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, it is very unlikely that they were able to override the potential dominance of diatoms in the signatures of Glu and Ala, as the sum of all of them accounted for only 10% of the chlorophyll \u003cem\u003ea\u003c/em\u003e. Rather, it seems that diatom osmotrophy of ambient organics, previously described as relevant in other systems\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, was responsible for the signature of this sample. This suggests that under certain environmental settings osmotrophy actively contributes to meeting nutrient needs of phytoplankton, rather than simply recovering leaked metabolites\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhytoplankton community succession along the Amazon River plume\u003c/h2\u003e \u003cp\u003eThe functional diversity of plankton identified and quantified using nitrogen stable isotopes of amino acids (CSIA AA) can be complemented by phytoplankton community structure measures using the matrix factorization procedure named ChemTax\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Despite a number of caveats discussed extensively in the literature\u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, ChemTax is a simple method for evaluating the community structure of a mixture of pigmented pico-, nano- and microplankton through a single analysis, and detecting the presence of protist combining auto and phagotrophy, such as dinoflagellates, prasinophytes, haptophytes or cryptophytes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This taxonomical classification method assumes that all phytoplankton require pigment assemblages for harvesting/dissipating light for photosynthesis, with some of them exclusive for each group\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. During boreal springs of 2018 and 2021, we identified four different phytoplankton communities along the Amazon River plume by chemotaxonomy based on pigment markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Each community included phytoplankton not only from one dominant planktonic habitat type but from habitats representing the precursor or the subsequent stage of this dominant habitat, illustrating the gradual transition between habitats and underscoring the role of river plume age structuring the phytoplankton communities along the Amazon River plume\u003csup\u003e\u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. In RI, we observed two distinct communities: one dominated by cryptophytes (community 1) and the other by diatoms (community 2). We hypothesize that these communities represent different phytoplankton assemblages originating from the Amazon or neighboring Par\u0026aacute; river, as indicated by the consistently fluvial bulk δ\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC values of these samples (Fig S2). Given the intricacy of swirling patterns in the estuary, suggested by the geostrophic velocities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supporting Video 1 and 2), it is possible that the communities were eventually trapped and transported unaltered within coherent eddy-like structures. Nevertheless, both communities in the RI harbor varying amounts of filamentous cyanobacteria, likely \u003cem\u003eNostoc\u003c/em\u003e, \u003cem\u003eAnabaena\u003c/em\u003e, or \u003cem\u003eOscillatoria\u003c/em\u003e, known to be abundant in the Amazon River basin\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, which could still be viable in these brackish waters (salinity below 6).\u003c/p\u003e \u003cp\u003eThe diatom-dominated community (community 2) was primarily distributed in the pathway of the North Brazil current, where younger waters swiftly travel along the coast, reaching as far as 8\u0026deg;N\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and still containing ample nitrate and silicate to support a non-diazotrophic diverse diatom community\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Adjacent to this \u0026ldquo;fast highway\u0026rdquo; transporting the young plume, older saltier plume waters are typically found in the northern sections\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, in the regions of our study included in the plume margin habitats OPM and WPM. Overall, the plume waters undergo slow exchange with adjacent oceanic waters, acting as a barrier to vertical mixing\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, thus resembling a semi-enclosed system where the different phytoplankton communities reflect different stages of phytoplankton succession\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. While diatoms dominate in younger waters, once the riverine nitrate is exhausted, residual phosphorus seems enough to sustain \u003cem\u003eTrichodesmium\u003c/em\u003e along with a larger contribution of haptophytes and \u003cem\u003eSynechococcus\u003c/em\u003e (community 3). However, as the succession progresses, a community more akin to the nitrogen-depleted oceanic end emerges, dominated by picoplankton (\u003cem\u003eSynechococcus\u003c/em\u003e and \u003cem\u003eProchlorococcus\u003c/em\u003e), with diatoms and \u003cem\u003eTrichodesmium\u003c/em\u003e also present (community 4). Notably, previous studies in the region\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and our own image set at stations 31.11 and 32.06 in 2021, indicate that diatom diazotroph associations and \u003cem\u003eTrichodesmium\u003c/em\u003e do not fully coexist, but tend to occupy different sections of the plume with little overlap.\u003c/p\u003e \u003cp\u003eAs shown, prasinophytes, haptophytes, dinoflagellates and cryptophytes were present in all phytoplankton communities. These groups of unicellular eukaryotes are known to be mixoplankton, coupling osmo-phago-mixotrophy\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Therefore, in locations were mixotrophy was the main mode of nutrition in the community, these organisms will drive the dominant TP, where the consumption of other microorganisms by phagotrophy coupled with osmotrophy will unambiguously impact the signatures of Ala and Glu.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhagotrophy and osmotrophy potential impact on trophic amino acids\u003c/h3\u003e\n\u003cp\u003eThe nutrition strategies of autotrophs, heterotrophs or mixotrophs reflect on the difference in nitrogen isotopes between source and trophic amino acids. This effect is captured in the apparent fractionation of Glu or Ala relative to Phe (ε\u003csub\u003ePhe/Tr\u0026minus;AA\u003c/sub\u003e), whose comparison allows us to define the ranges of different trophic modes in planktonic food webs from herbivores to carnivores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Recent evidence suggests that the apparent fractionation of Ala is greater than that of Glu in strict protists phagotrophs compared to what is set by primary producers\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. To our knowledge, this thesis was only studied in cultures of strict phagotrophs, but when mixotrophs are actively grazing on other microorganisms, a difference in ε\u003csub\u003ePhe/Ala\u003c/sub\u003e similar to that of phagotrophs should occur and occupy the space between TP 1.0 and TP 2.0 in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Additionally, Yamaguchi et al (2017)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e showed that different groups of \u003cem\u003eArchaea\u003c/em\u003e, fungii and bacteria growing only on organic media (i.e, as osmotrophs) presented an apparent fractionation of Glu similar to that of herbivores (TP 2.0). Since all phytoplankton are capable of non-auxotrophic osmotrophy of amino acids, among other primary metabolites\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, we would expect mixotrophs that combine osmotrophy and autotrophy to also be distributed in the space between TP 1.0 and TP 2.0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Evidence of phagotrophy in the values of ε\u003csub\u003ePhe/Ala\u003c/sub\u003e was found in most mixotrophic samples along the Amazon River plume (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that prasinophytes, haptophytes, dinoflagellates, and cryptophytes actively grazed on other microorganisms to some extent. However, the ε\u003csub\u003ePhe/Glu\u003c/sub\u003e values in these samples are also consistent with the uptake of ambient amino acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Hence, it seems that all mixotrophs along the Amazon plume combine phagotrophy and osmotrophy as feeding strategies. However, future studies addressing the impact of mixotrophy on trophic amino acids are still needed to extend the application of this amino acid approach in situ. Nevertheless, our study demonstrates the usefulness of nitrogen stable isotopes of amino acids in seston to address mixotrophy in the field when these samples primarily reflect the signature of phytoplankton.\u003c/p\u003e \u003cp\u003eIn summary, the Amazon River plume encompasses a complex and dynamic array of habitats as it moves and ages in the Western Tropical Atlantic Ocean. The interplay of varying physicochemical drivers intricately regulates plankton structure and trophic mode, ultimately influencing the energy available to upper trophic levels. In the present study, we observed dominance of mixotrophy in the mature waters of the Outer Plume Margin, a habitat shaped by specific environmental conditions\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e comprising an average apparent age of 27 days. In this dynamic system, it is likely that the phytoplankton succession, occurring after the depletion of river-derived nitrogen by diatoms, favored mixotrophy as the optimal growth strategy in mature plume waters. From our results, it is not possible to define the implications for the energy available to upper trophic levels, but underscore the urgent need to study the pathways of mixotrophic mass and energy in the field applying complementary approaches. However, it seems that trophic transfer as well as mitigation of atmospheric CO\u003csub\u003e2\u003c/sub\u003e may be more efficient when mixotrophy dominates through the production of a larger, better quality biomass\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e as well as driving the accumulation of refractory dissolved organic matter\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, which may contribute to the sink of CO\u003csub\u003e2\u003c/sub\u003e associated to the plume\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eSampling and hydrography\u003c/h2\u003e\n \u003cp\u003eTo investigate how the changing environments along the Amazon River Plume (ARP) shape the structure of the plankton food web at surface, we conducted two cruises along the plume at the beginning of the peak discharge season in May 2018 and April-May 2021. Sampling took place during expeditions EN614 on board RV Endeavor (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.7284/908137\u003c/span\u003e\u003c/span\u003e) and M174 on board RV Meteor (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1594/PANGAEA.935041\u003c/span\u003e\u003c/span\u003e). In 2018, the Endeavor departed from Bridgetown (Barbados) on May 8th and arrived in San Juan (Puerto Rico) on June 1st. In this survey, a total of 67 CTD casts were carried out at 19 stations covering different regions and age stages of the ARP. In 2021, the Meteor departed from Las Palmas on April 12th and arrived in Emden (Germany) on May 30th. The survey along the ARP began on April 21st and ended on May 13th, and a total of 114 CTD casts were carried out at 23 stations from the mouth of the Amazon and Par\u0026aacute; rivers to the waters east Barbados to cover the largest possible geographical extension of the plume. For the scope of this study, we collected suspended particles at surface at 12 stations during 2018 and 17 during 2021 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, Supporting Video 1 and 2).\u003c/p\u003e\n \u003cp\u003eDuring both cruises, pressure, temperature, conductivity, and chlorophyll \u003cem\u003ea\u003c/em\u003e fluorescence were measured continuously by a SeaBird SBE-911\u0026thinsp;+\u0026thinsp;attached to a rosette equipped with 10L Niskin (2018) or flow (2021) bottles. In 2021, additional photosynthetically active radiation (PAR), turbidity, and SeaBird SUNA Nitrate sensors were attached to the rosette. The Brunt-V\u0026auml;is\u0026auml;l\u0026auml; or buoyancy frequency (N\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) was derived using the potential density and its primary maximum was used for defining the depth of the mixed layer at each cast.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDissolved inorganic nutrients\u003c/h2\u003e\n \u003cp\u003eWater samples for nutrient analysis were taken from Niskin/flow bottles fired at 5 to 12 depths through the water column. Inorganic nutrients (phosphate, silicate, nitrite, and nitrate\u0026thinsp;+\u0026thinsp;nitrite in both cruises, and ammonium in 2021) were analyzed onboard colorimetrically according to Hansen and Koroleff (1999)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e using a 4-channel Lachat Instruments QuikChem 8500 Series 2 autoanalyzer in 2018 and a QuAAtro, Seal Analytical continuous segmented flow analyzer in 2021. In 2018, the limits of detection of the instrument were 0.01 \u0026micro;M for phosphate, 0.02 \u0026micro;M for silicate, 0.02 \u0026micro;M for nitrate and 0.01 \u0026micro;M for nitrite. In 2021, the limits of detection were 0.01 \u0026micro;M for phosphate, 0.3 \u0026micro;M for silicate, 0.02 \u0026micro;M for nitrate, 0.01 for nitrite, and 0.03 for ammonium. The depths of the nitracline (1 \u0026micro;M), phosphocline (0.1\u0026micro;M), and silicocline (2 \u0026micro;M) were estimated by linear interpolation, or assumed to be zero when the concentrations at the surface exceeded the threshold.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eHabitats delineation\u003c/h2\u003e\n \u003cp\u003eHabitats shaped by similar physicochemical forcing were defined following the approach developed by Weber et al (2019)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and based on simple, commonly measured, and relevant environmental variables in our study system. The procedure is described in detail in Pham et al (2024)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e who made the classification of habitats in six cruises along the Amazon River plume from 2010 to 2021, including our 2018 and 2021 cruises. Briefly, a hierarchical cluster analysis, complemented by a principal component analysis for validating the clustering tendency, was performed on the depth of the Deep Chlorophyll Maximum (DCM), the depth of the Mixed Layer (MLD), Sea Surface Temperature (SST), Sea Surface Salinity (SSS) and a custom-defined nitrate availability index (NAI). This NAI was calculated following Weber et al (2019)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e as:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:NAI\\:=\\:\\left\\{\\begin{array}{c}{\\left[N{O}_{x}\\right]}_{surface\\:\\:\\:},\\:\\:if\\:{\\left[N{O}_{x}\\right]}_{surface}\\:\\ge\\:0.5\\:\\mu\\:M\\\\\\:-{Z}_{\\left[N{O}_{x}\\right]}\\:=2\\mu\\:M,\\:\\:if\\:{\\left[N{O}_{x}\\right]}_{surface}\\:\u0026lt;\\:0.5\\:\\mu\\:M\\:\\:\\\\\\:-{Z}_{\\:bottom},\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:else\\end{array}\\right.$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere [NOx]surface is the concentration of nitrate\u0026thinsp;+\u0026thinsp;nitrite at surface, Z\u003csub\u003e[NOx]\u003c/sub\u003e represents the depth at which nitrate\u0026thinsp;+\u0026thinsp;nitrite concentration reaches 2 \u0026micro;M, and Z\u003csub\u003ebottom\u003c/sub\u003e is the bottom depth of the CTD profile.\u003c/p\u003e\n \u003cp\u003eEight habitats were defined in the meta-analysis of six cruises made by Pham et al (2024)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. During our cruises, we took seston samples in three (2018) and six (2021) of these habitats (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), varying in plume influence and age, and named as Riverine Input (RI), Young Plume Core (YPC), Outer Plume Margin (OPM), Western Plume Margin (WPM), modified Oceanic Water (MOW) and Oceanic Water (OSW). The habitat with the freshest plume water was RI, WPM and MOW were the ones containing the oldest plume waters in apparent age (see Apparent age of the Amazon River plume section), while OSW was the habitat with absence of plume influence.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePhytoplankton community structure\u003c/h2\u003e\n \u003cp\u003eSamples for the analysis of diagnostic phytoplankton pigments at the surface were transferred directly from Niskin/flow bottles into opaque polycarbonate bottles. Soon after collection, a volume between 50 mL and 4.3 L, according to the density of particles, was filtered under low vacuum through 25mm GF/F (0.7 \u0026micro;m nominal pore size) in 2018 or 25 mm Advantec GF-75 (0.3 \u0026micro;m nominal pore size) filters in 2021. Filters were then flash-frozen and stored in liquid nitrogen (\u0026minus;\u0026thinsp;196\u0026deg;C) until analysis at NASA GSFC using an Agilent RR1200 with a programmable autoinjector (900 \u0026micro;l syringe head), refrigerated autosampler, degasser, and photo-diode array detector with deuterium and tungsten lamps. Diagnostic pigments were measured by High-Performance Liquid Chromatography (HPLC), as described in Van Heukelem and Thomas (2001)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e and Hooker et al (2005)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Diagnostic pigments were assessed at all stations in this study except station 24.03 in 2018, where pigments where not sampled.\u003c/p\u003e\n \u003cp\u003eThe composition of the surface phytoplankton community was determined by the concentrations of the HPLC diagnostic pigments and the ChemTax matrix factorization procedure proposed by Mackey et al (1996)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e in version 1.9.5 of the software designed by Wright (2017)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. The groups selected for this analysis were based on existing literature and represented groups previously identified along the plume\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, also confirmed by the flow microscope images collected during 2021 as explained below. These previous studies in the region reported the presence of representatives of cryptophytes, cyanobacteria (in particular, \u003cem\u003eTrichodesmium\u003c/em\u003e, \u003cem\u003eSynechococcus\u003c/em\u003e, \u003cem\u003eProchlorococcus\u003c/em\u003e), dinoflagellates, diatoms, haptophytes and prasinophytes. The initial pigment ratios (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) were averaged using relevant literature studies contained in Higgins et al (2012)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. ChemTax was run three times using 60 random ratio matrices until the contribution of each group to total chlorophyll a converged. For the second and third runs, the initial ratio matrix for randomization consisted of the average of the six best matrices in the previous run. The final ratio matrix is also shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The contribution of each group to total chlorophyll \u003cem\u003ea\u003c/em\u003e provided by ChemTax in \u0026micro;g chla L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was converted to a relative percent contribution for better assessing the changes in the dominant groups along the Amazon River plume.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Initial and final pigment ratios introduced in and yielded by the ChemTax analysis, respectively. Ratios were taken from Higgins et al (2012). Note: Cyanobacteria1 contains fresh and seawater filamentous colonial cyanobacteria such as \u003cem\u003eTrichodesmium\u003c/em\u003e, \u003cem\u003eNodularia\u003c/em\u003e, or \u003cem\u003eNostoc\u003c/em\u003e; Cyanobacteria2 contains mainly \u003cem\u003eSynechococcus\u003c/em\u003e; Cyanobacteria4 contains mainly \u003cem\u003eProchlorococcus\u003c/em\u003e; Prasinophytes3 contains groups like Ostreococcus, \u003cem\u003eMicromonas\u003c/em\u003e or \u003cem\u003ePrasinococcus\u003c/em\u003e; Diatoms1 contains microplankton diatoms such as \u003cem\u003eThalassiosira\u003c/em\u003e, \u003cem\u003eSkeletonema\u003c/em\u003e or \u003cem\u003eChaetoceros\u003c/em\u003e; Haptophytes6 contains mainly \u003cem\u003eEmiliania\u003c/em\u003e; Dinoflagellates1 represents peridinin containing dinoflagellates such as \u003cem\u003eTripos\u003c/em\u003e (former \u003cem\u003eCeratium\u003c/em\u003e), \u003cem\u003eProrocentrum\u003c/em\u003e or \u003cem\u003ePeridinium\u003c/em\u003e. The pigments included in the analysis were Chlorophyll \u003cem\u003eb\u003c/em\u003e (Chl b), 19\u0026acute;-Butanoyloxyfucoxanthin (But Fuco), 19\u0026acute;-Hexanoyloxyfucoxanthin (Hex Fuco), Alloxanthin (Allo), Fucoxanthin (Fuco), Peridinin (Perid), Zeaxanthin (Zea), Divinyl Chlorophyll \u003cem\u003eb\u003c/em\u003e (DV Chl b), the sum of Chlorophyll \u003cem\u003ec1\u003c/em\u003e and \u003cem\u003ec2\u003c/em\u003e (Chl c1+ c2), Chlorophyll \u003cem\u003ec3\u003c/em\u003e (Chl c3), Lutein (Lut), Neoxanthin (Neo), Violaxanthin (Viola), Prasinoxanthin (Pras), and Chlorophyll\u003cem\u003e\u0026nbsp;a\u003c/em\u003e (Chl).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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PDuZTP6+urr6IM1QJTKZzGl6TXYLIcDiXVVeXp5samraxzCMMXgDqlLq/fXr1y/PZ1CFRKW7u/tVpdQSzcZfbBjGOMbYGr3Wvi+EWGEYxodCiJOwttavX/9iWVnZboSQcUKIAtd13xo9evSytra2g13XBZMsHMd5eeHChZta8fm3Sykaje6EtazfWFrDGPuDdtKvYc6Kioou6OnpOUAnl8WwKSAtvMoqAsNlsCtSypdM08Sz7CCEiBNCujjnhzHGgigQ6ilpjsfjJ2/JWv7H73qOHIHoe7pacLVlWYc4jjMxkUi8OWXKlN1hN+GwOefvjhgx4pMVK1aA/SsnhIxQSn2eSqVezOd44HillGdqxjeczWYPs/52IUHAPJ8lhLiZMTbetu2pnPOQDjhmmaZ5Xu4NnbFY7DTDMGKu655bUFDwdldX1+69vb0fFRYWzsKeNQwDe26cTir/VFlZ+WFzczMSzY808XApmHE40EwmczYh5DBCyFxK6SjXdV8cNWrUu/kkWMLh8ISCgoJfZ7PZc5VSDwkhdiwoKFiZzWYng7CSUiZN08RLRRFQIhi4Vkr5w8GDBx86e/bsPiYYWIFcQ0AjBJaz81vO+XcZY0dalgX7dHouIcXnY7EYbOfxtm3flqtGRCKR8YwxBEPFWK8eNnjN7buMsT8TQt4qLy8/Pl/PjmBnyJAhpxFCzlFKney67ueMsUpN8jVxzhdp/7adUmo253yIbdtPmKZpB4PBW1zX7dh+++0vyI0DCUFbWxvsfhkCRtu23zdN8yd679zFOZ+olDovGAzuO3fu3A35XJ+eXUTSeouU8vj6+vo/eXOxr2VZIIsaKaWlhJDvua6b4pzvSylNMsYeJIQ8XFdX91o+xxOLxfbSvmIuY2xsJpOp4pxDQfETx3Hin3/+effOO+/c50t0YtyF5KG8vFw1NzejQogYAnbl3WQymc84BqThiXr+7hBCnC6E+AvnfDpjDDFXL2MsopR6AD4rGo3Cp+0rhMCeX7lgwYJXw+EwxnsTpfTsQCDwQSaT2UcnfEPw1vTi4uJHc2s/Hxh6VaYT9Vu7L2OM/dJ13QBjDBXZK1GV1GRgiUculAkhnmeM7YzYTylV4rru70C6Ih7x2PwSkOHJZPIv+Rgb7oFKod6b9zHG3shmszcVFBQMcl3XopRi/hqUUkhQkWR/NHz48Ffb2tr21UqaBY7jTKmvr+9bZ0jCHccZZxgG4tagbdtPe3HIJg9zixODSCSCQGWpx9KcDqfY1dV1izY0h+PNq8uXLz98t912O0MnD5iMnzHGDtNBFQL92xGku657PmNsJ0rpLNd1EbzXGYaxtxDiII9J2h/BCBxFLjEAUwTDIoSA0fwVWGdK6bpsNnuJaZpnoCqhg5wW0zSvyWQyqBSUggEGkHpxPialPArBmuM4PzUM43hsLkIIWJp5jLFfE0Ie1wnKHVLK203TXCelBIP7+3wZTMwSmJvm5mYw0Cv0IjspJ5FAeU5KeYkOJo7gnF+jpTtXgJ0PBALXuK77qZTy7xKDadOmXQ4G37btuMZgpl5jqzo6Ok5ubGzszzRt8sLo/4VoNLp/IBC4y3GcB3B/IQQc6RzDMD4QQrxFCLmOMbaioKCgccOGDZOw0XSZK+k4zvWc82fXrl27Ilf6D4fDf6GUroSDVkq9g01XXFx8u5RyWTabvV4p9ZIOtJ/RQcp9juM8hIROKTVOJ1BvSCnbwMhqw3EjjI6UsoNSWpVPNjMWi31bJyP3KqXuJ4T82TAMsMB7YWNms1lIH2q9dXSiZh/ahRBzTdPEfwNzPEiPq3ndunX3eowCjcVisxB362Q0AUazsrLytNbW1qlKqemBQOD7cOgIDrDBLcsah/2glLrLW/sIOLGelwsh1imlLsO6TSaTcOJ5u8BoBoNB7L+7gDsh5JHOzs45Q4YMOQjPhsTAsqy067p/8Awpkrvbwc4yxnYA693b21u9aNGi5nwNCnJBBGkIkCmlbzLG/tTT0/N8KBTCHm7lnNcIIcB0omJ4l5ZT1MOBw5BKKdd6AeA8pRTwO4FS2ptIJBBw5+2aNm3ajkKIJ7XzuE2vj0+0w76bUnqxUmoDEkuwuDoxuJQQAmZzhZYd1TuOs4AxVoGES0oJ8uGX2Ww2qhOdkxCgalnmRR4L90VdXd0peRvs325EJ0yYwMeOHXtIMBhcbNv23I6OjhtKSkogl8Cau0sphcBtN01UVOlgFUE6nCSCTNjIH22qXOXfjR/BrpYynaGUqnJd9xBdGe4KBoOXwb5IKWH/QBigelclhNibMZZJJBKoGvRVdaurq+s55+VCiDAqXLnfisVil6PKwDnH3gOuy4PBIJK1Rvx7/8Qgm83iM5AYXcMYOx2/oZQ6s//9tmQOEJC0tLTM4pxfi2dIJBJIDPuuaDSKBKYKFQJKKQiE17xEB+P/u8SguroaFQcE2XOVUqdalhXlnHdkMpkJhmHUSSmnITFAMD506NCQbdsHM8Zwz2mWZa3s6empwPojhMCufU/f//nKysrFS5YsIWPHjj0cDD5j7OR4PI49npdr1qxZGAf81r7t7e2oDHzJ6MdisYfA2BJCkpZlRWzbXgjiPRAI3CqEaO+fGESjUagKrjEMYxrssFJqbnl5+VurVq06jnOOuXugoqJiUT79cw6ASCSC+OBmyND6JwaFhYUv9fT0nI7EPxAI3JZOpw8BYaTXY4IQMpsx9lRdXd3neQHy/6+XPSmlqCJOQJLEGJuuCan9wIALIRDnQIlxt2VZ5+kKzWwhxDuaUIKvxJh2MgzjKMdxoiC48jWuWCyGxBuV5EcZYws0UXM0xsM5R4DdqCuTN6FqYBjG/SA9EdhSSquFEFALFMNmK6ViXvJyOCHkyVAodEs2m708kUhgv+bl8pQrJzDGLgURIaVEgvwrJDJCiOMsyzrBcZzbhRC7U0pfUEr9H+f8Zv1mYPiZ+03TvC+TySDhe0pK+aFhGKXxePzuvAzub7K+caZpPgfFSHt7+339JJCoCKa0/LqGUoq5RoJ1EiqrSin4k77EwKtuIYndCWNEciaEQJVr8eYQiXlNDKSUx4VCIZQ3EDhi8Ah4d45GozGlFMqdR+syIjIaOJxzEOCEw+HrKaX7ZDKZaCAQgA57eynlREgHstnsTwoLC/fRvQTXfEViACOHhAJBE1jVakg9EFAopaAnPQjsOgJI13XhtMEIXVZeXv5cS0sLArVTtSE+khCCRGBleXn5zKampkVaGgA2GAYVyUjQcRws7OcaGhq68rUIcB9PWvAeyquu656C+3ultgAhBOW53XTfxSklJSV3EkJCnPPzwMyCYUkkElfnxjJ9+vQrOOd7ZLPZaUKIc4PBYF8CFo/HwQzk5UJiYJrmXbZtQza2Sin1c70wF3nMMTY7GJV3CwsLl2Sz2RPAbHuBB1j2N/sNgkYikdfRo6Ad4ipK6YuaZXmMcz4H2fp22213VUtLC6RGCHxXg4nQDExFOp2G9lhlMpkkpXRnwzAOQwCNNgvDML6DSkJeHvRvTBsqNfdCHkIIAQMCaRGY4UlCiCMCgcCs3t7eq3TF61tKqTEVFRUntrW1TdEtE1dLKW9IJBK39X/eaDQKRnK6Drbu55zvZVnWcZlM5ngkwiUlJT+EXIwxdhqCEinltahmZTKZowcNGoSixHjHce6ilJ7HOX8biaFmdK9MJpPYD3m7pk6deiCcLgw8WFlK6aec85lSyt21/Kmuf2KAJBysPGPszBEjRoxrbW0Fk1eHADyVSqGikJcL+1gI8SKSL83Wvc8Y+102m33SsiwE+12maV7qOA7WSZ+8TbPMM1zXRSLTGQgEQr29vVg7uzuOc7BhGNWU0ovguPOpoYak0TAMVMsgYwET/SfTNB+HzcBv6317o17jkC1+rJT6ELYGdrCoqOgPTU1NorS0NGeHUKmB03nWcZwLkXwyLWzOd8UAE+Np2GF/93Rd99CRI0d2tra2nmwYxjzdH4FgukwHWnsIIc7hnAeUUsO0Mz8FtpxSenie7co/JQaWZSH5PQRkRyaTKTIM4zGd7D2gneBIEEq5PjM8SywWu5sxNiSbzYahs4X9RA9ZLBarQRXWsqzdXdeFDGQPzvkk13WBPxj5/hUD2HpUlrAP8Q9Y/dPyxQZ6WuoLIFnQ1YJvoZqL/wa5AkgDKeVP4btAAunAuNx13YmoGOtK2YRcxSAajQ721vn75eXlU1euXFmYkz3FYrEfcc4ROFQnk8lnIpEIKqxIRsEoVmDtUUqROIhQKHRRT0/PUcXFxX1J1Jw5czKRSASB3cUgA8rLyx/LySjysYm9Ho7rdYA43rKso3IyX/xGOBzGvHIp5amGYTwqpfwAdu4fE4OZM2cWZ7NZBJxFyWTyp+gpCYVCNsigSCQCEgoEDfpKYv8gu8rHI6CS2pcYIJ5JJBIvX3nllWZra+to9JzZtn2CEKILJFg2mz3EMIxCLQFZ6LpuNRjxvAyg300ikQiIhB9qme4nlmXdb9v2IjDXgUCgIZvNgvAEw3wbGHFN1ryACiuqKVrBcYJlWQVKKQTwZ6ZSqUfzNTbID8eOHQtypkJXQmtBiqxfv/7R7bffvjKbzT7AGJsD0lBKCb8+mXP+jvbZv81ms0iGIQ2/FUl8b2/ve/o5RqFnAUQLzF9dXR0UCXm5vMRgIpQKUsq++EIH2o8vX778uV122eV62AsQgOvWrUOcB/lkpSbodmCMQQr8kq70X6kJSigJts9ms3MDgcBj+VxvXoUP8upzR4wYcT/2SE1NDW1tbf25rrJhf0N+CHUN7NmR6KvqnxgAJOwVIUQF4l/HcbA25lZUVNzcT4a90VjmIzFAuX8pYwzsx/GhUKgmk8n8SDe0wZmcUF5evmtLSwsywtNc1z1KM/K7KqXu1MzMhWCjUNImhOChLwXDAfmRUmoE2LNsNns4EgNdUrkObHj/ioFhGHcrpVaD6TEMY5FS6gkhxGyl1EyUW70A9lqwTSiDK6XqKKWQ7iD4Qd/CqY7jHGFZ1l2MsQ7btmdCLwxJEQw1Y2yUaZo/BZvkOM55qVTqXjSEbTSyG/FBXdpt9IJOLFgEQAc6jvOiYRgXKKV2R2JQWlqKSkbAYwHA0P9dxQCJgWma3/ISgwtN0zzCdd2j88kKhMPhAwoKCu7KZDIXFBQULIEkTEq5AyotYAFQXYGBCoVCjyPoVUo1B4PBRb29vTNCodCfco3mYCwjkcibuqHm6Uwmc6eu6BiWZW0wTXOO67ooi16LCgKkU2DedcP6PMdx9stkMh/BGRBC5lNK906n00chQNf680lIAOvr61dsBNwb9ZFIJALNLSoGZ6VSqZyUaHImk4EDOwy/C2mDZu8+1drOIi1la3Ic58dgJ9Eoq5nhKf3YRjpt2jTsCyS6pyilCoYPH76sqanpZM1gXxAMBidks1kwmhOllEgOrkAJE8lVKBTqyGazkM8gmEGi+DZjbCnn/MK6ujokUnm5PDkFfhtNT9BzH+A5nxMNwxgCXbdOSE6jlKJisETr9S/Te87A+G3b3ksHbvtq5udW7YRm1NXV9ZXa83F5FQNIcMCi9PXUZDKZDZ7+Hqwuqil/VkpBHvIp2B9tO47WWuNPhw4dyl3XBRN1kE7qD+ach0Ew27a9+8KFCzP5GB/u4SUGT+vK6HWdnZ2LPd02NOI3U0p3gfwAAT4kEloqgYQTJMSkZDL5BL4fjUahdwUbGkUZmRCCCtqvwNSDiUwkElgbebs8HSsqYAjWruru7v5tYWEhpHDjOOd32rb9C8Mw3mOMBbLZbI+W79wopUQDHErYCFgPmz9/PoiAvFz9KgZIkA7BTU3TRGLdqQMu9AkgcbidMYZ5nO8l3V/2gFVXV1+CRmPXdSPYVy0tLYdqaSOICOB6im3b40OhEBINyMxOcxwHbDkYy4tzUiLIe7TEFInBcYZhnIp8Q+vDIcvp6yfKwwW5BfziAq96/XsdoH0HFVLOOciyI8GYEkKuQXDlMcLnSykPKSkpmQA5BRIDXW0EC4hDJk4rKChwt99++zQY8kgkAglWCsGoiyaNAAAgAElEQVQoKgZg6devX48AC9UWaKov1wHsB4FAAKTGZZlM5iTDMJa6rgsNMtbqWZq4gD799yDYbNt+65577smLJMeTP0FGhErHEb29vesKCwvHp9PpT0Kh0EI0v+setNMDgcDDmgxcqivN5weDwVtx8EOuYoBn14EOCCEcCAGpqmNZFg5zGN/Z2flaSUnJMaZporp/RF1dHarXeb1isRiILshej0ezKuwJpXSZYRhv9/b2TtbJTI9hGHcKIb6viUiwOfdAiolq58KFC4HxVzZQb84gkRggOYdNxv5FMIsqi2EYVzuOA9mpwl7Q2n4cEpI1DANVOKwlSGiKtU1CFX5mfX19Xw9Uvq5wODwlEAjAV4PcuBZVMRzi4bouKv63GIbxiSZtngWZRCkF2fYo1AS6+r1UKTUHyQqlFFVL9JEh/rkSZFMikUC8lpcrJyXinF+iyWRUxr7c39FoFGoS2OG9Kisr1yAYR0+rlPJuHZfBlvwRlXJPMny4TnSmCCGuTiaTc/MyuL/5AsS86FWDvPi2rq4uNOuDENjBMIx6JC26Yv8D7QYvzCUGunpQD8kkJGsgmrfbbjvEGHsKIeCnE0gSly9fPntzDtjZosTA27Qv6E3zPlh2gBsIBM60bRsBG07WwYkPBzHGkI0hUUCzCXNdFxsdDqYBjhLMqJQSmeXNaDjTzCkcAkpSExljcKiXQ4cWj8f7TirAaSCmaaInobewsPAUx3FQDjoWJWAYWXwfQQNj7GIp5VWGYUCjD2fbrnsLUO7fR2t9T3Rd9xjLspCB47cegIPmnN+r9XJwhvt7mmEY7NvLy8sfzHepcurUqd8OBAIoMb+slOpGqaiwsHBGT0/PZYSQ3RljYKmRWUP3C5kNylh3d3R0XJhrfpk2bdpFXo9BCrEKpfSvI0aMuLqmpuafTnjazEXc13wcDAbvsG37QcbYhwh0pZR/6uzsnF1SUoLG0G5UDEKh0PnQ7OoAAnIgBBrIyhcGAoE/gy3ydHQv6iDgj5BL1NbWdkDGMmjQIJQ+DwTb4iWYL7que08oFJpn2zYYLzTsQctdiYRSKYVg+VBdRkYT8015lhL9GHI2OOp4PL5gxowZWEO/0Lp2VAXSnHM0x6EUiwDjHV3dcXQi8wPvFAZoAdHYdD2kYTBGX3zxRQ2cv66iVOWMETTHhmEgubqfUlqp+zD2VUpFveZLNKOjjwN9Oo96iTOkMZBR/YJzfkaegzNUQyB/WZhKpV6KRCLHQNrAGLuAc47nvda27TNM00xD14y+HATrKO1r1vlB74QblNCvQQPcZq6xf/qaJ9NBEPWSPnUMjd9dCDT0PkEgBznifTiUgFL6vD68YJFhGNhHKN3/hTEGmzRCSoky+m2QcDHGmuLx+I35Gh/ugyQSUiYEr+Xl5fOx57wmt2O9dQq8sAcgidqVUoqm4rMqKiruu+KKK9BAdgnn/Gd6n0wzDOP/KKVwSNgDh+OUo9WrV5/4+OOPp/M1ZpyANmjQINhBMI83oToFVplzDhkHkvJepRTYtOXoq7JtG8zi77UuFw28J3POT6itre07ACIfF5qPQYLgEAIknAiMwaprjWztsGHDoJtlra2t6PEBCXBSPB5/vf/vhsPh3fRJX1iH78JmQ2+P+2BqEGBLKdHYjcB3J9d1URFbDAZdr+vzYXu8pHgGpfRnruvWoIFS+7ENtm3fmNPl5+M50ZQaCASw35GwPmuaJthl7CsEbKgonQ2pgK5ql5mmOdlxHMibfoiqOSoknkwggsqjblyGBGdZMBh8eP369UX6NJhJlmWBlID9b8iREpB4oLLrum4d9g6a+XXvXq0QYqXrum8XFBQ8mMlkvgOpCaW0WSffOBUKPTiz8igXQ4MpNNIg/9DwDhkXerQSSqlb4deQ0Gsibx6Ilmw2e1FxcTFkOyAhpsGue/sJ1SpISCHdwFyvNk3zek1OgchDr+DIgoKCabfddlvfyU75urxqD4J/BOE5MgaxCvpzHkKvg/bRUCOgx+Nk0zSbkcBqaVuTbrJeWF5e/ud8xgxoPobqAgm7lzRDvvNCV1fXZaFQCPHWDC0zWiKlXGNZ1hO2baMv5UrImkB46qBzV0iT+1fd8oEVqlSmaT7vuu7Dtm2fC/IlGo3u6VVI56HvEOw15N4gEHHwC2w3kjutyrgB+1FX6A9gjKHn6h6vavdkeXn5xZvDdn/VM3lqjDD8B2LEXHXbk3RDFovY9EcjRoxY1traCnUGCOUrOeeTkcQHg8GbM5kMCKZ39UET0yHZwuEk+cAP94AtArmrK+OoCjzmuu4+WFu64oPE80bEt0IIkHBQhvwc1VBgJ6Wcibh44sSJwbKyMlQKxwJDTULCTzcWFRVdN2fOnE1uyt+ixABHwOnmuwMZYz29vb1voUEQ5VAAqU+sKXNdF2XoV7ROGc12oznnf16zZk3LkCFDwKaVdHZ2Li0pKcGCwOkUf9GaVzRh4p9W0zR3hnQG/QEo6SC7xKTlTqPAaQ6Q+vT29v6+qKjIdF0XTZttlmV9LoTAd6FNhl6s3TTN1zOZzGhkX/rkkDfAkHla2tdDoVBLNpuFTroSxqmwsLA5m81yfUIDGpiHg0V1HOeTfDKNucUEw7Ny5crhqKKgLwIGP5vNfhEIBL7HOUcT5VLDMHb2jghdjaYjlC4zmcwLuePJPOeIxrzBnPMN3d3dy/J5dBnG2NrauisSFSGE1Bk3mojBLPcdsRiNRsFOjXYcBw1laABvcRznTdM0R0E2ZlnWn2tra2GwFY6mdRznu2iOCoVCr3iGHA26OCEAp3B84DgOGrCDgUDgle7u7gDnHM5rVe7kgLKyspFCCJxY01JQUPDZ5iz6f7eZq6qq9jBNE86suba29sVYLPZjr8Hstbq6uhVoovLWVZOuInyMfhg0BruuC90h+mLQGPinBQsWrPGwAxOKUzTejMfjkJv0ycjKysrA/uB0mg4cvQtnPXLkyObm5uYdIWfLZrPvNzQ0NIXD4VI0hcIRgOkbOnRoRz6dzfTp00diTkzTfH/evHnLpk2btrfX8I1TYXDqF7SiqAiC+YI8YWVxcfFb69atw6k2exuGscFxnGXxeBy6+ryxY2CcdPkZhwKsz2Qyr3gnloFZQcMinPPnWt6GqhX6idD0i9PO0Iy5wrKsZTgdbcWKFSOCwSAaatt0RebTfMqIMI9YK4ZhjEe1q6Cg4NXcSRBolly9ejWaisFUL1u1atVno0eP3h3affx/OJfDDjtMPvfccz9Ac7Su8OGQhrWojuHITu8EKNjRP25u89hXrfGamprgF198AX1yIaXUgfQA+zUYDL6RTqdxaMN3gbdlWUvb2tp6Bg8eDFnKdpAkoAlYCLEsn82LXmB+gJZO4aCIXthvzdp/3P8IQfgYx3F2EkK89o822Eu8IT1FFXqQ1i1/DLsgpdwPtlsTP2/gOFnvSEsEpbBjSLReReKOYGHQoEGQhQzCoReU0ozjOO81NDSgPytva9lz/MU9PT07U0pxdDekPMs8BhJ9SR/DP2KPa70zKjZjYHMMw8CBCziUou9o0UAgADuA423f0/0ybTNmzBiFqp3XU6cgV0skEpCn5k6ACcTj8V7sSzSVRyIRVCTGK6Vwshx+E4nVHjgqlsOwO86qkSNHIpjN2+k1uaNFLcuCdA04f2IYBk4fBJniaikRiB/EDQ5j7G2v7wYltqdz8411smHDhrE47ACN8bZtf6H17Jg3JHzrOed47rwfVwo7PWTIEEhGQXzArqNSukYrDV4NhULljuNgnnDMJgLcps7OzjdLS0txeAN8+mt5tje5JAsx18s4qELLsfbFUc7Dhg3769KlS1lpaSlO9AJT/8Enn3zSPHToUIVjf03ThN1JI57C9/K9tr3KH3osPsv5OBzMoHXu+7muu7KysvLNzz//HEcD74ojdhzHWQ5mfvXq1XtoeR/25HJ97PrnoVBor2w22waZDPYC5FD5wtBrhId9G+XFLn0SZ+/EMCg1hmq1xau1tbWfTp8+HRUpHNLQhqNYDcMYrA9HgJwIp6YB/+atEROi2tfd3b0jDu5BjJTNZpfq3k7EpiDvPsTx7q7rIq7CCZywmTvYtv0uDtqA9Gj69OmIs3fFSZCQzTLGaG9v72ubEw9uUWKQr2zJv4+PgI+Aj4CPgI+Aj4CPgI+Aj4CPwDeLgJ8YfLP4+7/uI+Aj4CPgI+Aj4CPgI+Aj4CMwIBDwE4MBMQ3+IHwEfAR8BHwEfAR8BHwEfAR8BL5ZBPzE4JvF3/91HwEfAR8BHwEfAR8BHwEfAR+BAYGAnxgMiGnwB+Ej4CPgI+Aj4CPgI+Aj4CPgI/DNIuAnBt8s/v6v+wj4CPgI+Aj4CPgI+Aj4CPgIDAgE/i4xmDhxYqi0tBQv5cELVQbEALfFQQghXH2uu7Etjv2/ccw4Fg/Phfdb4OUv/43P2P+ZsHf1S5zwHgsc+UYZw6moA+PyjrwdOAP6Z1gk3hKL95INDMT+/ShwFCXGSykdEPYGa48xhnd74GjH/1riSQiBd8vgRXt42+x/7XP+4+rzbEufDR1otmVj9iv2ijd2YyD4gr5zZCl1vXU0YHyTN65tYV33Bar6hWM4cXfA4OetMcDoDmRfgtiAUro+GAz+pv/R73838TNmzCjDS4xwBqqfGGyMmfnnz+g3TWKh4u2qQaXUQA6ANu8Bt81v5V7wgfc9/K9cOK+6QL+obUAEjP1AR5LG8RbsgTgR3vn+WcZYwTayf4V+QZ1NKQ0OEEwR6PQQQkJ4D8ZAnON8jMnLBTrx/gRvPefjttvEPTyipe+9m9vEgPsNEu+q0G9Nx3/BfhkIF+wh9i/szUAYT98YtqHEAPgBuIFI5LiUUsQehQPVlyAx0G9PXkEIObL/u0D+zjkjea2qqirCGxoHzArdxgZiWdaXu9u27QEZ/GxjkG7xcDEnmIvc/27xDQf4DXJr8H/pmfM1Jdva/h2I4/1f2Gf9ccfa+1+x9du6bRlovmAg7t982dKtfZ+Bvha3lbnFm+1HjRrV0//FqX7gurVXr39/HwEfAR8BHwEfAR8BHwEfAR+BbQABPzHYBibJH6KPgI+Aj4CPgI+Aj4CPgI+Aj8DWRsBPDLY2wv79fQR8BHwEfAR8BHwEfAR8BHwEtgEE/MRgG5gkf4g+Aj4CPgI+Aj4CPgI+Aj4CPgJbGwE/MdjaCPv39xHwEfAR8BHwEfAR8BHwEfAR2AYQ8BODbWCS/CH6CPgI+Aj4CPgI+Aj4CPgI+AhsbQQGZGIwderUQZZlla1bt66lsbERL2ryLx8BHwEfAR+BAYLA9OnTi+bNm9eDF6wNkCH5w/AR8BHwEfARyAMCm5wYTJ48OWgYxgGMsfFKqZGEkLeTyeSv8zCWL28RDocrGWNnKqXuSiaTSzfl3hMnTuSmaRa3tLR0LVmypO+Nt//uQhLCGDvWNM1Xa2trP/yKz9JoNFpMKf0RpbS9rq5uydfdc2P+XlNTw1pbW/cVQnyfMdZrGMaT8+fPX5X77syZMwO2bR8updyFENLkOM7TCxcu7Jw2bdouQogf4+VB6XT6wXvuuacpHA4Po5QeRinFfLxZXl7+XE1Nzdc++8aMc2M+E41GByul8Ps7KaU+6ezsfLKxsbHvpWLec+6plPoBXrglhLi/vr5+BZ4vm80eoZQaSyldEQgEnpk7d+6GWCy2u1LqUEIIk1Len0qlVm/MGP6Tn/HwPpwQUkkI+Qul9I+JRMLBGCZOnGiVlpZ+lxDyHbzR0jCMe+fNm7du0qRJhcFg8BeU0uFKqXeCweCSO+64w45EIvtTSr9HKe3u6Oi4t7Gxsfs/8SzRaBQvP/u5UqpcD3up4zjPL1y4EC/mwzPw4uLi/Rhj38cLWhhjT9XV1WHOrEwm82NCyK6EkM8opY91dHTIwYMH741nwEtmOOcP1dXVfb4lz+CtpyMIIaMZY+9ns9lnc2PDS7yqq6tHYw8wxoYIIf64fv36vwwfPtxwHOdI13XHMsY+IoT8LpFI9E6fPr1SSvkjpRT2yPPDhw9/vf95zVsyToxl6tSpOxuGcahSqtC27YfvuuuuT3L3rKmpMVatWrU/5xzrIW0Yxj0Ipqurq8diTIyxIqXUc+Xl5W8vXbo0UFJScgwhZBSe2bKsP8ydOze7heMjwJIQcoiUchz2WTAYfLTffen06dMLhRCHM8bW1dbW/hG/BxtvmibW986MsVVKqafLy8u7WlpariaE7OK9/OjZRCKR9F4utKXD/MrvY43CFiildmOMfd7R0fFof6IoFouNI4T81HsD74N1dXXN1dXV22NtAEel1OuVlZXP19TUiFgstpsQ4jCc2S2EeKa+vh7zNKCSGtjE3t7egzjneyulNvT29t63ePFivCyu74JfVEodxRgbJIR4vKGh4cNoNIq3m2Pv7Sel7BFCPLpw4cI2z4diPQW0j/l9fX09/Og39rxYU5ZlYTxjKKXvtre3P9vY2Nj3FmL4iLa2tr2UUocopXqklL9NpVJNsJmhUGiClHJ3znmTZVkPtbW1uSUlJftSSg+SUsLmPplKpWBvtvjZsF9bWlr2I4QciL3NGLunrq7uixz+5557bmFXV9cPlVJ7UEpbstnsAz09PQ7sH2PsYEKI47rukw0NDZ9Fo1FDSvljSunu2HeEkEdyPmJTNgvmlzH2AynlPpTSTsuyHps7d+4a7x40HA6P4ZwfKoQYSghZAls4fvx41dzcvB+lFGNaL6V8HH4Ua4JzjthhmJTy2WQy+eaWJvY5X0gpHaGUwv2+9IWY15aWlp2VUhMopcVSyqeTyeS75513XsH69et/TggZIaV8r6Cg4HnM6+DBg79DCEFs2dvT03PfPffcg5d0bvHlzesPKKXYI6sdx3kcsVTuxtFoFLbiaLx9Wq/BP44cOfKv69evD3R3d2P+duGcfySE+G0ikXCBN2MM9qWEMbZk3bp1b+TW8RYP9GtuMGHCBGPcuHE7EkIOdV33wQULFuTWQd5/epMTg2g0ejiCvEAg0JDNZgdTSr+NzcAYGxwKhT7t7u4utixrqFKqGYGC67rFSqmuhoaGtVOmTBltWZbMZDLCsixr/fr1KwFqTU2N1dTUtL2U0ho8eHDrhg0bQpTSX0kpnzAMo8O27c6FCxeurqmp4U1NTWUIMEOh0DoEktjACNwJIdthY3LOQ47jVCHQtm37jVGjRvWuWLFiBF6XXVlZ2aKdG5dSIqBLU0q3k1J2m6aJQOc9OBY4UtM0S3p7e51Ro0a1YYH09PQcQAiZgUAoHo8vyscsRKPRnbQxu0IIcZVnVAZRSufnjMe0adOOcF33MCnlHYZhTJJSvhEMBp/LZDLXKKUeo5TC0e9r2/a5lmWdgLeNWpb1SjabvUpv/NpkMvmXfIzz6+7hbbpjCSFDCCEP6rdiXkoIeSqZTD6D71ZXV1dIKacqpR5njO2m/3e/wsLCy9Lp9GHakE0QQtzJOZ+MAC8UCv0pm81erZR6hFK6h77fnsuXL5+1MQne140zX3+fOHFiqLS0dBKldK3jOK8wxq6QUiYbGhpex29UVVXtwTn/meu6D1iWdZyUEhWv2wghUwkhRZTSp6SUUyili0Kh0BeZTOZcKeVCQsgvEYTH4/HZW2qsv+5ZPYM9VUq5PSHkYUrpVG0A70skEm/iu9OnT9/Ntu2oZVm32raNxLW8uLi4Lp1Of9d13YOFEPdwzi/gnN+dzWZXYX1SShs957V7cXHxdbNnz/4yoPm68fT/O5KSIUOGnOS6bjAUCj2ZzWZ/hbWTSqUQtKqzzz67JJ1OR5VSrxJC1mK42uncqtcMnPN3XNdNGYYR1k7gcdM0X8dzCCFWMsba9BvJLySEnJVIJFZuypj+1We9oPtMrF3DMODkfmqa5sz58+f3JXdVVVVjsbYppfcKIU4GsYB5J4TEdDD3O+++pyilMOeHEEJ2ME2z0XXdiBBicX19/VtbMk4PyyMJIRXAQwhxBWPskXg8/iTu6wUdSFrOIIQ8V1dXh0CfxmKxowkh3xZCJLE2pJRvcc6f1m8zvlE7xT8RQtY5jvMZEvwtGd/Xfbe6unqC4zjftSzrASnlVYSQu+PxOMZBZs2aFerp6bmBEIJ1N44x9q3CwsKLu7u7T+CcpzOZzGuWZV1NKb1dCLGKUnodIWQukh3809nZeUuOvPi6cfyn/j5lypR9DcM4UvujXwshQIy9k0gkUvh97NmmpqbLKaV9doYQcgqltAqBp/YNxziOUwt7QymV3d3ddxcVFV3vuu7jlFLGOT+kvb398m/yeWOx2KlSypGcc6xDPNucVCr1AR4kHA7vwBi7Rkp5DaX0+4SQQRUVFbc1NTUdzBjbR0r5CGPseqUU9tGHhmFgTc4FGaGU2lFKOa+hoaFrS+dp8uTJ4wzDOFEI8aBhGCAeQHhd388n/1Bjuh/ID9d1sbYwrneEEDHYeBAAeow7u647zzCMvZC0CiEWGoYxC4lBPB5/dlPHGIlEQBodmclk5gSDwZPgI/QzL8KYJk+eXGJZVhViA0LIakrpWYZh3OS9oPYKpRRiBTyHaG9vjw8ePHgmEmwpZZt+w/slOrmJJhKJ1k0dU+7zHkk8hXO+Gkm4jqnO1wl5Q86PIGlgjIWFEM/q+TUIIScqpa5ljP1MKVWqbcwTOmmaopRaEAgEEOdhfPdIKeELM/F4/MbNHVv/71VXV2NewkqpG5VSJymlPkqlUvf3baJTTikOhUKXc86fUkpJzJnjOLMty9pbx5/fZow9QAi5VErZYBhGbq5fklJuYIxFOOdzamtrP83HOP/dPUB+zJgxo9JxHCTXv1RKnbw1SdNNSgw843QnpfRPyWTy3pyj7u7uBqtzlG3bFwWDwbFYAIZh/F5vHgThLud8B7C/hJCjKaUHekHiQVhEHR0dn5aUlCCzRZIRAPOGwBcVAx1AwfFkKKVgGRYyxsa6rjuEUooASwUCAVQqint7ew/jnDtKqfGaFXqJUnqONhq/CwQCjY7jIBgd5AVBy13XfduyrLOllC1guCmlH4E11QboUcbYStd1f4ZNpjfy3pp9vHv06NGtq1atQsUAicGnyWTynnwsgkgkgsn9aSqVikQiEQTJxxUVFc2aM2dOLxx6aWnpBYyxNcOHD1/Q0tKCwBrYYCNdqVmV8wKBQIVt2/NN0/y567rTgSmSgUgkchsMRX19/eJ8jPPr7uFtrJmU0jeTyeTTkUgEgZxdWVl5J5jZqqqq71JKj9Us1y2maeINxPVI+gghp+qgY6WuGNyt8Z5GCMlyzp91XfcS13UvNE1zR6XUfErpoYlEYv3XjeM/9fdoNApm5GxK6d2JROK9SCRyvd6o7+b2QyQS+T8kQJzzea7rfkcpNTmTyZwXDAav4ZynwJDAgFJK30EyB1apo6PjgpKSkqN04HhmYWHhMVgDW/N5wHxt2LDhFkrpg5zzl13XvQiVv0Qi0YjfxdpExSOZTF4Yi8W+TQiZ4jjOtYZhzNAJzspEIhGPxWLXSSkzYIEMwzjKdd3bg8Hgdtls9opAIHDxvHnzNitonDZtWqlmPs+D00VASghBEAHJSj2c4dSpU3cxDKPacZxbdRC+TrPusxE4KKWO1A70tfLy8l/raiHWV9Z13QWc82ullAuEEB+Ypgm7cvPmOOivmo9p06btKIQ4RwhxMwIwxtgiIcSU+vr6j/H5WCz2C5147VVQUHBDT08PGCkkB5frasvpjLGbtHN2hBA3McaS+B6qHEVFRU/39PRcrpR6P5lMbtEe9oLnmYSQ5eXl5Y+1tLScL4SQ9fX1N+WCzVWrVpUgwGSMdSAIBUnT3Nz8K875Ss3U/gZJrEfE1Gaz2Ys0C/mcdvZNa9euXbG1mbJwOHyxhrE1lUotiEaj2GdSr4FLPGz30ozxJel0OlxUVAT/MrewsPC0np6eKiEEWNuXY7HYbZTSt4UQn2E6KKXAHYFmNXKLgWRX8EzRaPR013XLWltb540ZM+ZkIcT34vF4FL5uxowZZY7jwKZcbBjGBqXUbznneNZdGGOjlVIIlA9WSv1Ek0b3CCFu0P5sKue8WAhR4zjOmVuTYfwaewXy7i4p5WOhUOjxbDZ7CyqtiUTirtw+EUL8MpVKnRoOhw/hnE9qb28Pl5aWIgZYV1lZeXdbW9tlIA4Re6BCUl5efjmqDFLKiGEY1/evtm+u7fRIz+8KIW4nhJSbpgmbjUQfpAL281lCiI5UKrU4EomcyRgDwfgCIeSgeDx+YTQa3dPzZTdIKUFOrEkmk3MjkQj2fAHs6aaMzYsDsP+CiUTiTiTKUsqjhRDX1dfXt4N4oJRiLc/DngQhyBi7jxCCqtlPdCVlcjQaPUopBUXE2Y7j3CylTGzYsOGdkpKS3+v73pAj8DZlXLnPTp06tdwwjLNd172nq6vrg9LSUiRLr8bj8Ye89Qw8JunYbo7rut2c8zsNw7gDxAch5AHTNF9xHOdSzvnz2u87HhMPYhBrONrR0XF0PmxMNBqFPyjVjP+l2GNKqX2SyeTZnq8brwnlS6SUZ3DOkaxcyBiDzwgzxj6uq6tbGIvFbpFSdiilHqSUxmCzCSFdpmnepJT6TSKReH5z8NvE79Bzzz23oKurC9Xmix3HOQuVwU28x0Z/fJMSA0/+8QgmOpFI5BgvGLRdlVIzXdetMQwD7NReKHUqpRzDMEqFEDM554sopWCPTwsGg9WZTAZBxnLO+YtKqQsQaBUVFS3v6upqAIuMRENK+XwgEHhLs/rn6nL4H3t7e/9immavaZr74O+u615jGMYxCPo1q5kwDKPMtm1Ds7lnmKZZ293d3aED6CTnvAGsEiFkp97e3gssy8JvfyalXMI5L2KMnY4AJxQKveU4zmB4eCEEFtPj8Xj8BWzQwYMHY4wr8pUYhMPhKGNs30QiUR0Oh1FqOx3JRyKRSHvl4Rs552+sW7fu10OGDAGjDtnEHwzDuMgwjKjrupCA3K1ZJchTzlJKLU6lUqh4GowAACAASURBVG/o8jsW60fJZBLs31a/TjvttLJAIHChLu1CWvBsLBY7Q2fmofLy8lshZ4pEIpCE4Pmu04ye3d3dDYYPmw8sy4sVFRUPtbS0wGkbSqkXpZS/cl13hmEYkFTc5zjOhG/Qof0TfmC3KKXnK6XuSKVSy6LRKBK1VZ2dnQ2NjY0yHA6DPa8oKiq6raen51tSylmMsUvAPOF/R4wYsaKlpeU8yMNQusSahMOorq5GOfgG7fh+vLUDFjBNep+CDZ5TVFT0Vm9v70Wu636cSqUWgZmIRqNwaiMTicTlVVVV+3DOpxNCrvZY2xdTqVR9NBqtQQVEVwkeBcuCQBeVNtd1YTQvjcfjkPNs8gVno5P8SzjntcOHD1/W3NyMpNEMBoPzIIGprq5GYBexLOsa0zQ70un0jUKIF3Si9QtNHIBRfjIajcLoD9JVrDu8gOlmTVYs81jz2lQqhTFv8YXqEEgIzKvrupRz/iDGVl9fj+QPFQOs65Hr16+/qqys7HAwpZTSy+AsHcfBunECgQD26yJvncQDgcAfs9ksmOFV8Xi8bksGiT4A27YRSL+STCYfQ2CjlCpOJpNg3/suzHcsFgNeXUgMPPnOlfjORx999Oguu+yCauVwbRfBWJ6I4IwQ8i1K6ZJ4PA42batd4XD4dsbYmwggw+Ewqh1DEonEWV7QgYBncmdn5yklJSVjCCHAcBpjDIHUYpAksVjsBtd1sc+aKaUnjRw58sSWlhbIdM5xHOeM/nKCrfYQm3DjcDiMKjkZNWrU7a2trUjOf9be3j4JwdGkSZN2C4VCN8L2E0K6Hcd5TgepZ3HOd5FSlo4cOXLu6tWrD3Qc5xdCiEcMwwAD/zPLskqklDdqGcrZqIhvwnDy9lGPUARrfCuSXynlbM75x/F4HBUcVAzgj/cGSRaNRr+PWOKjjz46ZezYsdgjy+rr6++OxWLABoH4K5rQ2LO8vPyypqamPbQEbqZek9elUikkf1t0hcPhUxljowoLC+c4jlNq23YdCMqcNDIcDiM5fSeVSt0XDodR9d2fEPIaY2yHeDx+GWSwmkkGKXm9EOJSEBWpVKo2Go1eqJQak0wmEcRv9OXFW7OQYMTj8fpp06Yd4LruJM75lZA4wf5o8nCGVkncqO0O5na+lPJhQgiIqR3j8fgMkI5g5fWaOVeTrjeDsUelJhKJPEspTeTIoI0eVL8Per7wAlTiKKXLtS2B3XgnJy0HKcgYg627VsdTXaFQaB6lFPHdTNM0bxFCvAPbKYR4FTED1CfBYPBS27ZRPb143333/VEsFuuT6G7BRSORyGzG2BeoQEQikRMppYfrqjEq+GTq1KkHaukSYsJTs9lsmVaWXME5vwYEJaRZIGc8UoKhKq6Ummrb9lUFBQXdQgjg/kQymXxqC8a3SV+NRCKQZl0FcjuXsG7SDTbyw5uUGCBALikpWayUeiiVSvU5BU9TrRDQUUrfg4YaOjPNbNjQKGtn2M45x+JcqJSq1AzOz8vLy2e0traGpZRrdGb7MgIBKeUcMG2RSCSlqwrPQFfoui4c+LJAIIDS+wqwaFg8uuSIstQeumR2Nef8DGwcr+RKp0yZUmma5jlelmpoNmUBslQ9vi44iEwm87lpmhFK6bKRI0f+bvXq1YOllGcrpSA9+quW4/RpVqHjgqNHT0EuMUAZLpFIoFKyxVc0Gj2ZEHIYFigSA1QMBg0adN7gwYOhK4Y273Loe5ctW7Zg3Lhxp2HjmKaJKsxVYDGUUpAuoHpzPCEE438AzjAajaJchs2Zl8rG1z2oF2QiOHo1kUggKIOhFh9//HF8woQJsq2t7UA4KM2Y3KSZ6axhGAtd1z2Pc47E6ENUDPS8gEEAe/oC5/wy13VnmaY5UikFQ/LTrR0of90z9v+7J41CooqS6XvhcPhqaMLBVC9dupQWFxcjQN1FB8m32baNBBZJ3SWWZSHovzObzb5nWRaqKu9zzhGYfb+9vf38IUOG/BjJqOu6x/TT02/K0Db6s+irAXuDQDqdTv85FAqdp5R6N5FIPHTllVfS5ubm4yml+2iN/gXRaHRf7xkwflQ6PvScXQ16KAghfyCEHOO6Lpw92P7LtAb9is2VmUSj0e2UUhehwjhy5Mg3V61aBceHCsoCHQyI1tZWkBDTHce5ERUDraG+HsbZSwxeiMfjv66qqjqbMeaijM85R2WkLhAILM1kMg94Cd0ml/S/ClwYaThcndSCoFCu695l23ZszJgxn9TU1KhIJII9Pr6oqOjqnp6enyilTkUFA9UBOJ9MJmMbhnGdDhoWghRgjD1s2zYqMBcD5y3dw16Qfy5j7L0RI0Y80tzcjASAJRKJWzDPqOjlEgOd4GxA8IFgRI8LTP3yioqK+1tbWydp2cfgwsLCeE9Pj4uqjcdChjs7O4/LB6P3rxZuJBIBe/h5LhGF3U+lUlhfqGrtzxg7z3XdyZr4gUb4Tvh5xtg0TUw9HI/HX45EIkgIl+qSf7NSKrZ8+fITd9xxR0jOYoyxcweSXQEG0WgUZElxR0fH7WVlZRM1EfSDZDI5raamhra0tEAXjmc813XdDkhlKaURSKjgVyFl8XoNsM7AbM42TfOkbDaL6jokq+d8gwQLtPCIF+6rrKx8rLm5GdWMt+PxOGIJJAYncc6PSiQSqBj8UCcNp7a3t8c8Mq6loqJiUVNTE2xPIaX0Dd3zckBFRcXFra2te2qZ4FQhxOwFCxZ82Z+30YbwHz4Yi8WOEULsa1nWLbZtD6WU3gBmdsyYMV9gP0ejUZCDLRh3NBpFkA8lw6tSygOTyeR5HlFQDZIEPhlEYjwevz0SiaDSVpxIJFCZ3egLMt3m5uYo55zV1dXdCVkRksVsNnvTjjvu2LFy5cpxiH+EEHMMw0AvEIiQRk9hcXAikZgci8UQzxyrGftzOeeQtM5NJpN/1ff6ncbz5v4E70YPzPug18eC6m5De3v7ByUlJSA9oBx4BGvWq+hMdl33xmAwuMG27TlSyjjiF1RX0+n0K6FQ6ALdb/WitovolTkUMuPu7m70hMUqKyt/lo9+sHA4DKIpiOQNyR/6cVKp1DkYY3Nz87d0JfJyjV+V4zionp7vJVpnMcberaura0BigPjRMIzHXNetRiIKIgXyNsbYg/+hikEf6uirAkEH1cKAkRJ5A6vSTcHQwiZt2wbbvls8Hn8wEokcrPVv2Dh3FRQUPNbd3Q3pDaQSaNbFJloESREWaWtra2TEiBEzlFJrdKD/BAIBaA+hW+WcQ6NeCzmSbrr5g5TyAyklJvNFnZWiFP+KUgollKOllFdQSlF22tMwjLtc11Wcc/wmnMd9QoiPdEUAUoN6rUtcqfXKmMwezTSiJPh+ZWXlI2vWrAEzcDGkRGjo1XKh0/QCgV6wytPKPz1z5sxBvb29YPFWZDKZhv4NYZu6mXKfj8Vi0CBeiQ2LyoEnpYJcCgnJZ9AyokQJyQSMJoJP3Vz5mmEY2Nx3CyGGcc5xDzC5KDe36QbglxhjZ0MDvqX65I19LiSGJSUlYFrQxJjTq6MKhIy6yLKsz4QQCHoe8hqQDrJt+zrDMBB8fkezXbfrygiqPq9LKd82DAN6cciLkETusXz58ssGUo+Bx8KCxV5pWdafHcdB4Hq/lj9B8oZelSYEqbpp/D7oZb3GyIQ2JEickUA/QQg5Vj8bdNNZSikqQHdKKQ8RQtBUKoX53aqX1xeC5BnyNMwL5GhPBINBKoRAT1C33pvYn9CogpUamclksK+PgO5SJ6VxBGCEkMeg84cUBfvNC1DGZzKZOzZ3j6BahvXMGJNoepZSnoJqoa7qfYYkynGcFzX7gwrGc5CZEEJOQ48HYwwsE4IksFfHQ8qYyWTesSwLjBaSsA8dx0FgfDkaG/MBMOQdkHbohrmnhRBoEvxJJpMBm7OnaZqQyRWgggD7A5YKkig4F12WPgvBq4fzcUKIuGEYJ2inDbnIvYZhHIc+m1Qq9c6WjNMLLNB/NBR705uz36PXgTEWqqur+zOajx3Hgd3e4LpuXVdXVxo9HpodG2/bNqqtx1BKIcP6MJPJHBQIBF7UbDUkK9+trKyclQ/H/a+esbq6+lit98bBBYth19DjBclENpsdEggElukq6h16bdYLIdArgwZ4rFcEy+v0YQwvIRgGIQUZACRnWu6FSgmSiLG2bddu7QR8U+cuHA7/gHOOAxnQhwJm82Mtq3gZY95vv/1eeuONN2br4AnVMfSqnJLNZs8IBALoEUFAC9kl7A0qr48ahgEfepfu+XIRSDuOc9M3+bzRaBQ6e5Bw91NKJxNC6lF94pwXwidDngFfGAgE9tGB7uCKioq6lpaW/yOEgIWHPAb287fQx3POMcc36YMsdoMcGGRTrq9nUzHv//kpU6bsbprmZFSqhRBoGq5wXbdWV8cm4HASbSO/zTkHUYLxoMrxglZAfOoF59frXqKd0awKCaNhGD/SNupgSNw0wYdY4g+bE0CGw2H0CBwppbyFc46ePIJKhLa9+7qu+wf0NHlrAvYxAtsM+ZiU8lp9oAX61w5ijNHy8vKFLS0t0Mp/CKIAMRcI1C1hnT1fCGL2Uynlq14/wQM6hsOaG2tZ1puu60JBAJk24rLjOOdz0XOo4xYO/6F9/s8hWQwEAiAGz9E+BMTwIRBt1NXV3bEl89kv1vqRJ+261GsyRrP6K5xzNHS/C4IZ8juQk+ifNU2zzrbtvsZxyFAhZ7UsC0qHj0FaCiFA3sDvnayrWKl8VKs25jk9ew6yEXML3D/bnIb2jfmtTaoY4IY49YEQgs52TD6y6nXxePy+6dOnD0dphTF23bBhwz5avXo1+g720pr9t+BkENijuQ7Np9qw31FQUIDgCdph9Ansj055aEgZY9BPvo4JxIIWQqCxJaj7CF4qKSk53LZtNBJ/IoTY2bKse2zbVnpyodeHkXk1nU7jxBc4w4JMJnNfKBRC6fgAZPpogtElmE4wduhf6Ozs/G1xcTGkQ2CQlvT29q4MBoNobIL+ewwWwieffPLM2LFjd9fs9knIGjnnjSNGjAAjiEaVzb4QUA8ZMgS640EIEBAY19fXr4zFYpAbvJ5Op5cVFhYea9s2TjNBQvNSbW1tRywWg7EZCx2/DkReTiQSn0YiETQio3ejFycXQI6QjxNNNvbhvNMOIIVBoNsrhHiec45TTYYEg8FFaGDVjYyQmEHmhVMLPgEzjCYalBg55+sRcKCZPBqN4mSKHbBJpZQvbC7zvLFj39TPeQzrLsAbDezQwDqO81IwGESZ94eaoZtdWFiIZygBO6uUgsSqdcqUKSNN00ST9lo4t/Xr1788fvx4Z9WqVUcwxsoYY1nvs2io3eqXx/agktNBKV1dUFDwZ68hfLt0Og1DjRNcSrCmDMN4ff78+Z/FYjH02iCJwwkgXU1NTc/suuuuLJ1O/1DL/8D0w568nNPYb+5D4JQIQghO5OpCwGqa5otCiFGQIabT6etCoRDW+144vUIfovO51/iG03d+geBbG/L2TCbzwk477dTryQ0gPxJwYBUVFa9s6d7NPZenAca+w36UlmX9JRAIfN7d3Y1Gy79u2LDh7cGDB0NKh/4p/P4z5eXlnd5JRfgOTnz6tL29/a2hQ4cOdRwHJ3WgsvlFKBR6IR972JNmwdGiguJYlvU7x3EO1bKO4hH/j73vDm+rOv8/415J3paTgGM5gZYEKHTQDf12pKyWVUpL6ADSEGvYCQHCKhsDZQcChDjWlZSYQKAlhbJXaUsHlFGgLWXv4JGBY8m2bEn3nnN+5+PfFY+aJiR2nJA/rp6HB7DuOOdz3vOOz/u+R5Mn37527drpQoifoukYbGM6nX6tsrKyThv1Hwghhjjng9A9OCxiaGgIjdIIxkwwt6M9MW608uDW1YMBx9iA1SO6th59Ow2ZTOauiRMnfh019mhUtG37qVQqhdNgirrQQSa5UCj8BSfHBINBnCqHLCQCoH+nUqk3tneT/2jn6x7TjQCyDHsMWQGlFEpgUUsP0ghZchBGcKLQR/af+fPnV+P0OgSmkDF96syTKBk66aSTvu73+/ezbRtM58vt7e2wadt8cs9o51S8Hn6Dbdvov8I6rps8efIfOjs7D+Kc16E/DiWUOEAE68MYew7lOyeddNIkONhYf8zNNM2H1q9fL+rq6lCBAOe3IIR4cbxOmEKGDUGAduZxwAkY7MfKysr6c7lci5TyrkAggAZZjBNOrQgEAg8NDAwUTNPEeCag54pz/gL8g9WrV1eYpgn9StwymkfGsp+LJ7QBA/RR6uoLBLw4neq4N95447xp06ZBD0IuQEx9EAqFnu3t7eWFQuEI3SsRhD8lpfy7LtPqCofDn8OBJS5B9XpjY+Oz26gLUXaKU8rQBN4P3wO2UFc2oIEcPhdKbfbXgdvuDlhbXRaGE396e3sb8vn8iO2Bf6dPEXty9913d2ALXXlAX+rvS0+EGqvc4T701LmnIJk4pckwjCd0gzh81yP6+vqWBYPBvZEhd8fz1ltvvfWvPfbYo1rL4RFFn3T16tXo/xo5EQuHokAfoRydMfb89nLON54zZAGnY+ryNQSKCKr+hl6TbcFmc/eOOjDAg5Bu9vv9xoYNG9jAwIBTX19vDg8Pf4FzvufkyZOXQ9gQ3fT29qJZohAMBinS0IVCgZeXlxvr168fDgaD/oqKClVstm1sbPRls1na19eXR602jkbUJ9RwXNPZ2SlwTB3YxGw2i5Kags/nY8VrFyxYEMhms6h9HcKRUrNnz/brBhJz6tSpWZR31NfXg82WbW1tWaQuZ82aVa5LVgQYFNQ/rlu3rnz9+vUFvANRsOM4Eu/t6+tTHR0d+ZkzZ5r19fV4B5R1brwEARitW7cuMDQ05CxfvhxONdKVOEbSxjuAMxR+cWzAHs5IAF2eEyfKYpMq5oDTk4DXK6+8kvsEGHYKzLG2KBcqjt0wDIYxYp5vv/22v6ysTKGHoiiMONXAlYcR7Evnl8vl5Cd5isbHbTY3OCjD/HbZZRfIkINALxAImGDKIae6Ic6PdS1l6bC21dXVtLKyMl88TnZz126Pzb7xM9GcijR1cTyQtzVr1jDgXhwXDPGdd94JozPiVBRlsrjO+FtRjv1+v1i0aBGOPN1WB4RiT0Oei+8pvgN7GCngt99+uwzvzuVyuWI5S1GeNhobyvJALFAECttoCP9nWYr7sUS2R8ZeUnYzIgsw6kXHoPSehoYGyM8IyYDxT5w4kZfKxzjIwSb35sDAAIVsYp1xAlQ+n6eFQgFYjuzDjfdmqcyP4zpvaXoUdgA6sLim2GcVFRUMerkoB64uHJG7zelCYD5p0qQyjB3lmuMtB1uayNZ+j/lNmjQJJNeIvGB9KioqDFePMsi9K2twTEb2WfGejfQNPfHEE3F6nyqVsa0dx/a4bmP5Ll3LGTNm8H322WfEFmJt3bn9z/qX6puNddN4jBl461PVIHMj9hVyD3/B1TMjfsnGNrlU/11//fUfqyvHMsaN17eom5ElcU+YG9Fvpbqw1Lco4ll67XjpwqJeKLVrpbZwc7oOtufDDz9ke+yxxw6xhRvtqwLkbc899/ThSGvokcHBwbL+/n74fPA/R47R3YytG9mDG9uesazraO8prh+CVviI23NfjykwKJ2Qe+b1TDTgcM6tbUlNjRYo73oPAQ8BDwEPAQ8BDwEPAQ8BDwEPgfFBYJsDA0SMJ598cp1pmuKGG27AsZLbyhaOz8y8p3gIeAh4CHgIeAh4CHgIeAh4CHgIbDUC2xwYbPWbvAs9BDwEPAQ8BDwEPAQ8BDwEPAQ8BHZaBLzAYKddGm9gHgIeAh4CHgIeAh4CHgIeAh4COw4BLzDYcVh7b/IQ8BDwEPAQ8BDwEPAQ8BDwENhpEfACg512abyBeQh4CHgIeAh4CHgIeAh4CHgI7DgEvMBgx2HtvclDwEPAQ8BDwEPAQ8BDwEPAQ2CnRcALDHbapfEG5iHgIeAh4CHgIeAh4CHgIeAhsOMQ8AKDHYe19yYPAQ8BDwEPAQ8BDwEPAQ8BD4GdFgEvMNhpl8YbmIeAh4CHgIeAh4CHgIeAh4CHwI5DwAsMdhzW3ps8BDwEPAQ8BDwEPAQ8BDwEPAR2WgS8wGCnXRpvYB4CHgIeAh4CHgIeAh4CHgIeAjsOAS8w2HFYe2/yEPAQ8BDwEPAQ8BDwEPAQ8BDYaRHwAoOddmm8gXkIeAh4CHgIeAh4CHgIeAh4COw4BMYUGMycOZNXVlbWmaZpNDQ0rG9tbXXGc8hNTU149mdM03xp8eLF/eP57I2f1drayrq7u6v6+vqGV61aVdjUu6LRqGkYxoRcLpddtmzZwHiOZ968eROGh4cLm3quUopGIpFdMplM/6pVq4bxXow3nU5X9/f3i9J7zjzzzIr+/v5qy7J6xnN8W/us2bNnBxhjdQMDA2tXrVolSu/DPGKx2ATHcfKlYy7Oz3GcTEdHR644v7Vr19ZQSu22trbBrX3/jr7u4/DG/pgwYQLkpb84L4wPciSlrGOMbbAsy8bfZsyYYey55561hUJhsPTaHTGf1tZWY82aNXVSyr7ieErf29zcvItpmrnSPThz5kxfZWVlzW677dZXsu/p/PnzJ27YsCG/cuXKcdmvkCefzxfs6+tbt7E8uVjW+P1+3+LFi9cXx4y98fbbb0+sra3NLF68OF/8+5w5c6rKysp8S5Ys6d0OuNKWlpZa7MdNzR0Yv//++8HKysr+0jG1tLQEhRDcsqwPS8Y/sh69vb3pzemisYy/iGVDQ8Pa1tZWuRkdV9PX1zcIrOfPn++3bXtiPp9njDE/5zxtWVZva2srXbNmzUQpZd6yLKyzGst4RnMPxpJOp2v22GOPDzceO/THggULajalC9PpdGUymVxb+i4Xc2lZVmY0Y9iR10JHMMaCm9mT9Pjjj68yTZN1dHRgDkX86dy5c3ctLy8fWLhwYbZU7v1+v9HW1pamlG73tdoSTpgbIaSGEJLZlL6ZNWvWhMrKynyp3ofsGoZRs/FaQje5cjiua1nUIRMnThxYtGjRiM0t/UAeh4eHaxOJxLpSTDc1HszX7/fX5vN57J8RfT/GD41Go/UubkMbPyMajdZgn7a3t68r+Y7Omzevrr+/P3frrbf+l0xQSs1UKrVhjGP5n9tKbOGaTekE+HOBQICW6t9N2Z7taQuxru+///6EwcFB6Lj/Wdeijbj55ps3FNcVaz04OFj97rvv9j3xxBMf+bizZ8+uraqqMkttz3hhuTXPgVw1NDSo8fa7S9896sBgwYIFZf39/ccYhmEopT4lpRxKJpPXbs2EtvaaefPm7Wbb9rlCiHgqlXpxa+/DdXBcJk2atCvnfF2pId7cMyAQhmH8iBDyjGVZr218XXNzc8hxnJ9TStcrpb5GKb0tkUg8NZoxbe7aWCz2I6XUZEopsHy09P3uZptNCHGUUtw0zbvb2trWRKPRbyqlvugq2AcSicQ/o9HoHkqpAwkhXErp9Pf3rxhPx2JLc50zZ06DYRiHSykF57yaMbZi6dKlfbgPCqCzs/PbnPNPEUIwzt9alvXC3LlzK4UQTYSQPAy8bdu/Xb58+fpIJPIdzvnnpJRBpdTdiUTi5S29f0d/P3fu3GlCiIOllIRSOuz3+39dlLVoNFpOCDmUEDKRUvpppVTKsqy3YTgcx5nDGOtjjA0xxn7X1taWjcViR0opd6eUVldVVcWvu+66jxzF7Tmv+fPnT8rlcr8ghMDQDAkh7ioJ2hDI/UAptauUsoox9phlWS9hLXt6ek5USlUQQlhDQ8PNL7/8slFdXX0g57yRENJIKX0gHo//Y1vGjj1HCDlGKZVVSsG4At+PAo6mpqYvcs73J4T4pZTvZjKZBxobG33ZbDailHI457YmFn6zePHigXA4vDdj7FtCiFrO+buWZf12vBxaGJuenp6vSym/QCndlTH2eHt7+5PFuUMX1dTUHMYYayCE7DI0NGTddtttPdFo9OuEkP0opQEp5RuJROKR2bNnI9D5hVJKSCmz+Xz+zlKDPlY8586dWy+l/D4hBEFp0O/3t29MtkQikX0ZY+cqpebBaY5EIl/Vcv1LKeUwpdShlLZzzt+wbXsupbSLMfYpSuljpXMd6/g+7j5XR0BHVkkpyzOZzI2leq25ufn/hBBfIYSUSynvT6VS/5kzZ86nOecHUUrLINcNDQ0dMKAtLS0HCyH2kVIyQshfk8nkC+MlB+M1d8zXtu3DQbAIIRqEEIuhE4vPb25u/qyUcgZ0v1Lqb4lE4s/QN5TSo4ABZIwxds/SpUtfj0ajX1JK4VroqBcbGhr+vLmgcLzG/3HPKdoz2DpCSP/kyZNvKRnPiL5x9SBTSj2YSCTeQCDnOM6RjLEKSumkbDZ7fS6Xy9XV1R0O3QSdqef4BOzJeMwBQYhpmgfj2YyxPR3HWbZs2bLXi8/GeKSUR0AelVKThBCLli1bNhiJRPC3Btg+Sukf2tvb4bOA1PsFpbSSUso3bNhw86YIji2NG2Py+/3HCiEQpMOvuWvp0qXvFO+LRCLQI19TSpVxzle3t7ffC7mOxWLHSCmnMMa44zh3pVKp92Ox2HSl1Hdhmwghr1iWdc+W3r+l7yORyJ6U0oMIIRhgoays7DfFgAp2PRqNHkAI2VcpVcsY+5dlWb9vbm4GdrOUUnDQB3w+392vvPJKbq+99jqMEDIF4zNNM3nzzTd3b+n9W/M9xjF37txZjuNUw275fL7bSoOUWCz2FSEE/I6CUur1hoaGP7733nuG3+//mZSykjFGJk+evATy6u6r/SmlPiHE2/39/Q+NZV23ZtwbX+OSBl+TUh6Wy+UWrVixYnsQXSOvHXVgEIlEfqyU+orf718ohAg4jvNdwzAecxynwnGcntra2kAul6sdGBjYMHnyZNHf3z8hm82mC4VCtqamZjIhxAZzjKh1cHBwA0CFAa2oqKjz+Xy0oqIi3dfX5zcM4zQp5QMVtlGSKAAAIABJREFUFRXvDwwM2Lvvvnt/b2+vmc/nwUxX9vb2ZlatWjUSCc+ZM6fSMAwwcIOccziUp1JK7ySEvNDX1+fU19fX5PN5MCaZhoYGsF6TpJSDeA5jTORyOThA74OFASNTUVEBx8duaGjY0N3d/SnGGLIXf8nn8/MRDIVCoeZtjdbC4fBntMK4LJ/PxwKBwPdgbHt7e68tGr5wOPwjODQ6K/Mr27ablVKvhUKhBzs7O6/nnC9TSu1JCDmioqIiPDg4+AvGWJdS6g9aQVmGYcSXLl3617EI32jvgbPY1dX1Exjivr6+lTU1NVcZhvFEe3v77/Cspqam3bQzETFN81ZCyF62bR9RVVV12tDQ0A+EEPtrJveKQqFwMqX0uVwu97hhGAsNw0jZtv15xtj33nzzzVml0fpoxzfe18MQK6UilNK3CCF/opTerJ3iZFtb20iw6DqtR9u2nTJN8zgoRErppUqp+ZTSghDifsbYAh0o3eI4znrG2FmEkOsIISfCUYzH4xdtb4cFGY3a2tqTpZR+Qshv9L5YoJT6zbJly/6OOcRisS9IKaN6vOe5Sn8f27ZvhDwSQmCEbjIM4zpK6V1CiDeUUrO0Qk8ppb7AGDswEAhcMNZMH1ij6dOnn0QIydm2fb/P57uYEPKwZVmPYWzIsOVyuXmMsYcppWuVUhdTSq+FcZRS7m3bNsZ2pmEYD+Ryuad9Pt8pQoh/K6Xe5Zy3Mcaa4/H4m+MhF6eddlrt8PDw2TpQhzGeqJQKU0pnFxnpWbNm7enz+cKMsZuhpmCUpJRLCCHzsR8cx1GMsTOFEFcyxr4PZ4NS2qEDIsjEHdtKQGCda2pqjmGMTfD7/XcMDw9f5wZ5q4rzB9tICAHeTbZtHwhHNBaLwdDvlc/nb6+trSW1tbWD7733XqPf7/+mbdv3UkoR4O6dSCRaxgPHzT1jzpw5h2kZ+5x2kqHT2hljd1mWNTL2o446qnzXXXe9USnVZpomHH4QI9DPkMW10EGO48QJITfk8/n3fT7f9ZphPtc0zX11UPMNKeVllmX9D/u6PeezpWcjIFNKHY5x60zdOUqpD5LJJHQDcZ2sawghjyqlJOSGEHKsUgqO4RHZbPbq8vJy6OEgISQupbxZCJH0+/0gi47Tzvjpn+R8I5FIMyEkSCldqffBpZzzxUuXLn0ec2tpadnLcZwrOefzhRCHgGAIhUJX9PT0HKr10mfKy8uXZ7PZOOf8Pk0Y/MtxnEg+nz+vvLwca/61fD5/XUdHR3pL+G7p+2g0urcO1H8OModzjv04DTqwyPZHIpHvSSn3IYTcYhjGTUKIx3RQhjnAhl+Qz+e/5Trp1+PfhJDv6qDmWsdxrmCM/T4ej9+9pTFs/H04HD6QMXZQoVC4zO/3z0ZgVV9fb7W2thbAxDPG5mo7+ghj7ANK6TVKqas451QpdSl0Xz6f/6HWjxUNDQ2Lurq6zlJK/YtS+j72jc/nO6Gtre2D0Y6pRHfAFjZr4vSNmpqavw0NDV2t/aNbinoLhCHnvAVrDnITMujq6+MQoMN+EEJOo5QuF0Ks45yfAbmQUh5PKa2Kx+PnjHVspfeBbKSUwkc6RwgR5Zx3tre3d+Ca+fPnVw8NDV3DOV8upaSMseMNw7hcCIG9uG8ul0sEAoGb3Dk8K6U8mTF2n+M4aR1InKcD8msQxI7HOD/uGW51BQhE7OUjOOfHbpQhGtchjCowcMtuEkqp3ycSiV9jJDA+wWAQjBSivVawwpTSY6WUf2SMlSmlkN4CQ3Y7pXSGEGIGmDX9jP/TTJrl8/netm37ECEEUqgQnpBS6k4sAKUUwi6llFPheDDGAAwUH1iFWr/ff+vw8PAkbBw4CUKI/bTNeJ5zfgqMNVgkGAEIJaJtpdT72tH5K4SZUjqolAohiiWE7E4IeQQGxDRNbKT3pZTfEEKsWL58+cjGgUMopYQg9CQSibZtTc3GYrGfgOXXpT8xbH7O+U+VUqdBebuO2/mEkM5QKLSiq6sLhrvctu2HDMO4SAhxumEYGHvctm1kb+Zq1vJ3yWTy+UgkAqWEyPyWcZWUzTwMjoUQ4lRCyLOpVOrRaDSKgE5lMpnFCPrC4fD+jLGjc7kcAkm7oqLiVo39mY7jzKKUvskYuwOKFYwDpfRxIcS5mlE/W0qJgCJpGMaBxezDjpjPlt4BZccYOx3KLJFIvBKJRC5njL3W3t6OealIJHKMViLTtVwuKSsrQ2YnpoPQMxljVyilFtu2/ZppmmcTQl6jlIIRn6HX/OxoNPp9sLQax8O3twEHM1koFBZJKW/XDOWzfr//XM75S/F4HEECiUQix1FKv2hZ1rnhcPjLWg7DOkP4K13ecKpS6s1EIpGIRCKXuXvgQTgzhmHcUCgUJug9fJlS6rxkMvnulrDc1PeusfslpRRjeUkIAUUMIiEBA42AmhACJ+NalGTp7AWylQ8wxo4QQoAJ/k0sFoMBoo7jWJzzq7ROSXR2dr7a2Nh4H2NsUTwef2QsY9v4ntmzZ083TfM0x3GuMk0TxhiyfFLRUITDYThqCKquNE0TTu5JjuNcyDmfxTm/OhAIFLLZLIz5MmBMKb3Ptu3HTNO8yDCM15cuXbpNe9gNYrFmrzQ2Nt7f3d19BrKKlmUBk5Fs3tq1aw9wHAcZmp8EAoEo0uOuIUVW5q9Syn6l1HvA3i0F2VspdSTn/IX29vZHxwPHzT0jHA4j6Hs/kUh0hMPhX4FMSiQS2CPElctz8vn87Orq6t0LhUIcxh8BGPR+Mpl8OhKJIHh9VQjxrrYlTW+99das6dOnfxHOlKtrx7UMZVuxCIfDyKBWZTKZttraWsjOdy3Lwt8UMnz5fH6JEOIsn8/Xb9v2o5TSqJRyOuc8hOzdmjVrvimlPBK2Vkp5hWEYYGVrpJS/UkqdsnE5zraOdxT3gz2/nRByZzqdfjAYDCJAfbG9vR1yP6JvlFJHJZPJE5ubm2dIKef09fWdpEsCFyDIC4VCK7u7u5HRQvbySSnl10Kh0MVdXV2fh36llF5pWdbqUYxnk5dGIhH4MF82TfOGXC6H7Mu1Usr5y5YtG2Guo9HoGVLKtclk8rZoNNqi9eeejLE/w7+BrkRGB/oKOkcIAR/jA8uylkQikQs457Xt7e0I5rb64/oBEfg78DlQLUAI+ZFpmpeD8UYGQI+nBXaFMdapMx1LQfDowHAyYwx2ZU4sFvu+lPInQohTOOfAfUk8Hv9XJBL5g5Ty+lQq9eBWD2ijC91qigWO49ySzWZfDwaDl0gpn08mk8jKjhBMmlU/kTF2nWEYA47j3KzJjyVKqSghZGVtbe1z/f39F2g/6C+EEAn/sKKi4tLBwUEEhyen0+nDxoONj0Qiv2SMlcfj8YtjsdgJUsoDEonEPFePQIbOq6ysbB4cHETger7OUl+OeFUp9XIqlbpF61EE5EM6UFiFPUcIucpxHJDQ+PtdiUTi8bFiOIr76MyZM826urrddFB1qWmaC1BBMor7R3XpqAID1FwVCoW7dZ30TYlE4iOj4KaTTsvlcpf7/X44dLtrBucv2viVOY5TyTmHcCyDswdmVBt0sHhRxtgrlNK/CSGQtr7Ntu3VgUAA7JqllDpEC8oThmG8atv2PDj0mvnA9ahT3JsQgvKAS7VjP8KIaUc5USgUpnDOUXpzsmmaS/L5fIYx1qZTaggq8O7Jw8PDV5eVlYFZes80zX84joPUJkqFHt6wYcM/J02ahOCD2bYNpfvbZDL5VxhEzvmhhUJhT9u2V4xHCiccDmP+X0ZgAGWojRfSjkjlD7kG+EpK6T83bNhwR21tLQxeHSEEAni+YRhR7fSgPOE2n8/3w0KhAMb3VjcwgDJ7PZVKJUclCWO8uMSR+wPShLFY7GTNWgTAUCCr0tzcDJblwEKhcGU2m83X1tauklKegyyCxvSpyZMn39Xd3Y3AgMOxc4MGMMK7cc4RTH6ntA57jMMct9vC4TDKKM5USt2cTCZfi0ajl4DZC4VCqdbWVgQGJ2jGaUpVVdWibDYLpYPswHlgxHTwc2E6nX6vtrb2bBgNbbANBBGJROKc5ubmA3UG7qpAIHDwWNn2rZ0kaiRN07TAplZWVr44ODh4Hhz+ZDK5AlnEcDgMRng3y7IuQgaEUjoXDr8unULg/2QymUxFo9FWlBQppeBsgxi4plAoBH0+3zWGYZzX1taGjMqoPy7LdD7nfGl9ff1rXV1dYKXNQCCwBOVabkYmwhi7HHXYOoV+tW7VeIJSinLA2y3LejAcDp+mjVGV3jM36fIZZDrAur6mlEK2Zmk8HgfDv80f1xFYIIS4EA/jnIMRbCqWv2GPE0KmNjY2XrpmzZpDYZx1qd8FjLETHcdBFsk2DAMBzi0IuhAIBwKBJ2zbvkg7R6stywLjPeaPW5qCtX0mkUjcF4vFECRUJxKJS12jiCDrGM75cwheTdO8bmho6N/l5eWmbdvI/kCXgiB5IJlMPnfccccF6urqUPIxQ2csb4duHPPgtuLGSCRyA8pgQHIgSCCETEgmk6fgVteJa0qn0z+vra0FsbPCNM1m27ZRfrkykUg8F4lErpJSdhqGgb32c1wbDAa/oJQ63bbtk8eDZd6KaWz1JZFI5HS3bOGmnp6eH0spjw6FQieghGHevHmwPQiCm+FkFQqFP7kEGDLHQQQGCPJ0duxHlNLfCSEQDPwQBBocGM75qe3t7V1bPZhxvBCEYldXF4JxMK+P6vKMhZqZfdOyLNh6ONzY41+yLCsC5xek4FtvvXXC9OnTseYotYMjfqaUEjbvaULI5xsbGy/s7Oz8LGNsPoKgsRIRpdOMRCLHK6V2z2Qy1+tsVDCfzyNLMb+9vf09d5zQOf9JJpN3hMPhk7TPAMLzaV1ePM2yrAtisRgyVyCNrgQ5Qin9h2VZSyORyLnQA6PNsMHfyuVyINp6Yc9BslFKT9QZgUvAFqMEEOV/hmFcI6XE2sLX+R2lFMH7tGQyOTcSiaA0CsQiZGuhi9WrkUjk9yAW4/H4iBM/lo9rC0Fy3UwpBWsOm/BSkTRuamr6GnSdbdtg4AcCgUCbUipJKQXZcwOyF7r0E/rw71LKMl2GikqUCwuFwndBwiJbvY29GSPTikQi2DcfJhKJqyORyE8ppYciaHJ1IEpSz0H2JJ/PT+CcX8w5/5XjOCCB/5xIJG6NRqNYT5DRd+rMTFOhULi0vLx8sFAoXEMpvT+RSDw8FvzGcg98bTcbdNpOExi4Eewyx3EeWb58OdixkY9bewyF3aWj0GrNtj+GBiqwZSjfUUp9D4yf/mekdvj111+fN23aNJRjrBNC/F0bIESNiF7fikQicGgB9L5aWYBBe80wDDCEYCA3MMamwPEkhCA6xyIiQv+wvb09gbHMnTt3im5mBdNwAxwKrRhhXOahnKmiooIODw87fr8/Aud56tSpj6LRFaVHUsqHhRBvcs6P0mUfvajblFLekUwm/xwOh7/EOd8vn8/fNV7GBGwisizJZHJ2OBxGLegxVVVV56A+zy0BuFAp1fP222+npk2bhmAKNW1/0KnEXxmGgYzIZCnlIl0T9zNKKeaLyPW5pqamqxhj/yxuzrEI32jucUsRTiOEQAk+FIlETkYZVigUQroTNXlQnj+GUaOU5hAgKqXO5JxHlVJvU0pv1Y51k1IKvQZwNC6GctXlUlOxdrlc7ojxamgdzbw2d63ruJ6Bkg/Lsv4TjUbBnINtR+mackvAPqNL2250HGc/lDYgHQ3HWRs2y3Gcf2rDjvuRqUL970G66fPMiRMnHmrb9imZTObo7d0f4jqMi4UQd+hs3ZOBQADj+Xex5jQajc50WbAzwcwyxk4wDONq27bBOL8L9gqOGph87STCKYezuBBlfGBfDMNoHWuK2m0QPVdKeXdjY+M/uru7we4OptPpW8AeId0PY2ia5jWO4/QqpZCJQXCCWnQ4wLe6gcGw4zi3c84hd9YHH3zw8tSpU38jhLgpmUz+cTxkoampaRrn/CwhxBXaoOgsuUoZhhEr1gA3NzcfhyxmOp2+rK6u7lDHcX5qGAbYRBilK1BSaZrmZQgMdFkZDPz9+Xz+IZ/PhzKS17Z1D7v9Lgs0Y4hsyT3d3d3Q0dSyrEWYf0tLy5dt24ZzgT4C6NE7dB/VQ/F4/J1LLrmEo84WpVDoM3B7ZZwFCxYEBgcHEYTBuKK0Ybs1tUaj0YuFEF1wihCIIhOZTCYvwdibm5u/iIBsaGhodnl5+RSdSb5RSonxQP/ch3KGWCwGkuTfjLFuMKuhUOinPT09+0kpmwKBwC+3dwA+WhlramoC1kH0UtTW1s7UfWcHJJNJzIecccYZEwcGBhAonl1eXt47ODj4AEq63H6zqXC0tNN9gJQSbOtdYIPz+fzxfr+/gjF2vs/nO3M7Nd9vzTSRMUCFwV3pdPqe6upqlA09b1kWsggIDGZqh/vYZDL5E9hCSulP0+n0vGAweAYqDhKJxC2RSARkkk8phfr9b4RCofNWr179OcMwfoG5JpPJzq0ZyMddA3ZdCIGeoevLy8vRfH95IBA4tdhkGolEEJysT6VSK5qbm5uFEPBFnkGJpWVZZzY1NX3WMIwwmHkEnpTSHsuybohGoyhhqUilUiMEwtZ+3GqMMPydRCJxYyQS+Ya2s0dWVFRcc8MNN6TdjAFKVG/UmbLVlNIbhBBgtbEfsD9PdAPoI9Pp9C+DwSD2/VL0ZITDYfhoyLT8aWvHs/F1yBjYtn0WpXRZJpN5ra6u7nxkDEBC4NrZs2fvxzmfrZS6pq6uLtPf338d+kYppRjz7TU1NX/PZDIoxfwzCF9K6SEVFRUXDg8PHwQdmUgkjkUmYazjK94XjUZRgRBMJpPnImOgexc/jyw9vg+HwyDvLjZNM2zbNvogThdCIHBF8PJaPB4HCXa5JhP6NZF7v5bHuSgNQ28EyCn05i1fvhxZox3ywZoLIS6pqKhYcOONN/7X4QrjOYBRZQzwYkTViO51U81CXWtcBya9vr7+xs7OTjTmQjld19fXdy+EBGlcKSVqrxegIVWnzOHsHd3X19dcU1MDFhJlRo+50TUyEe8xxsB0wJifhLo8OFJ6g6EcAw7IPM75bW6wcUyhULjE5/Mdgp4HCBwyFDhBQ9ceXeTWhL2plMKmQY3qO0IIRzvVG5CBQPZhypQpD77zzjvBQCCAUo8HdLPXBDCPhmFcaNs2WPgHdLXUy8hwQAFQSjvBpPb29j6yqc720SwM2AVCCJR3M+f8q2iwCoVCt3V1dR2CsTqO8wUEDrr8BhmC4+Ds19fXP93T07NUCHGL2+y0t+7HuLKmpgaM+4fZbPaRqqqqC8EGL1myZIc07SK74aYLUUufApuO2kshRAFGSUr5FtL2juMguzEFjlImk/lVMBhEKdUhjuNgrX7KOX8yFAq90NPT0yaEWK7rOMHM7xEKhS75JJvmNl5TNN9ns1k0Ya51HOchZAOEEB26xEui70M7HSh/+ynK5IQQP2CMDTQ0NCQ7Ozth5Ko55x0akxN8Pt9KNHeC2fP5fAsLhcJhUD7JZBJM/vb+wFCfgSZHNNhxzn/GOb9TK3A0MKL0AE1NZ+u+oLNM00St7K62bd9qmiaaqr+jyxMWUUpRrrPSNM1Bx3HA+CEwRyZvmj6x7KZNneixNZNyjSGYdgT/6EuBY/QwGDHG2Ldt2wbTdapOlT/BOX9LCNGCFDXn/LuU0m+jn0MHL7/gnN87PDz8Bsq24Bgrpd7QuuE0HGrQ0dExLilYNyiGw3q/EAKHGHwHZAVj7KvYw1JKzAFZAqT60Uy9RuP5EIwpGHedKR1GCaFhGKgHR+kIsq1xnb1Bje2d8Xj8la3BbHPXgLDp7u5GmQZqfVcIIVCze6/ua8mizLPYh+SWxy0zTfNnH374Yba6uhqsM/AGbmjofsXn871fKBQOchwn5fP5jsZaW5aFgHe7BQbNzc1HwlHL5/Ptfr//AsgDpRSnhqCPDIF1QkoJwqkeLGmhUEBJFzKRKFtAD0qrziYvxv/btr1UH+SAZzSin8vn81lbczjFtuA/2nvh+EFOMFYQJ6hfNwzjRa0Pd3v99df/PG3aNJRG/YUxltZrcpw+Zapl9erV3+CcHweWUwiBRl3H5/Pdk8vlUqiFh45hjB2QTqev2d6Ew8fNNxqNYh+jTBi6HWuEkhI0TldC34A0wV5njKH8EmUfqXA4jPmgpC3lOA7WEiVSIO1gj+FfoA58t3w+3zEejfogHdzST8s0TTS2I+sIRxY2+W+65+nLKIHWJciQufP12O4tFAqrOedno3lfZ2pQOrOH9hGWQxe5jcpXI1hFFseyrGdGKxMoM0bFdiaTOae2tvbHlFKcivgCgsDh4eGHfT4ffKs/6EqMt5FpQVbGNM0K27aXmKY5z3EcjLfQ2Nj4687OTmD2utY1rxqGcY4+oGXutpTpFksV0Z9pmibKgJGpvQ2l28iEU0qfAUmLslAcigLyBmQmynE0YQPfAPodDD7siAPdjLIo4OaehDYutnDOnDnfRE+O3+8/1bZtkBovm6b5fKFQQL/cC1oXIut8Owgot9y9Hf9GWRkCKZT4onQ4l8thnmdxzlEtg9K1MHrGxiMo3Rq5QOZt7dq1n4We45w39/X1dY1HqdWm3j3qwACn+KAplP9/igzNct1oqpk9e3a9z+e7Skd+l8fjcTD/B+iaWQQQ/wa7r08X+IdhGAgMUOOJlBdYxpzjOHeYpvk5bHBKKTrb1yCdHQgEvq+DBJT5IOMgs9ns04FA4FtwzBljMLr7UEqRBkOdKNgVikCkoqLi+Ww2CyMcyOfz9/p8vn200/8VsO+MsefBQGhBRmptNWqOcZSgaZpQwihjQb8D6qtfRzkUyo2klG8bhjEfLLh78gNOQFg2DikuOGff1yUTuzqOgxN8nkYNaDQaRZnV8+Xl5S8VCgVgBMP7IaX0aTQ1trS07K+Z3r3Q0e84zt/RA9Hc3IxU+v+huVU3Kq/JZDJPbS+B2ZQQ4eQTpP+wfjr7glN3ntTjRfNWXV9f34qampqv49QarJFuLnsSJyTAqdJlAkfjJCMEiLZtP41TcXDSiC4rm4b55fP5J1esWPGJpL8/bqO2tLR8ulAo/B/nPKcdp65QKPRsV1cXFPC38/k82KZvaWdwAphY7Sz+CWnfk08+uSGfzx9BKUWTXE+hUPhHR0dHPhKJwNnehRAyKIR4fLyPw93cPNz9eiT6HJDpy+fzz6KMSQgxMRAIoFH120qpeq1QUUv5LMoQ3FM7foBMnJuafdQ9vu8A9Lygh8S9diT1PtZPOBxuRPmZZuPRg9FbVVX1jDb+u9u2faJpmlfYtr2bLgv6MjJQulHtzSlTpvwLRw5DntwxrNuwYQP2QG7OnDl7mqb5Vc0u48SsN7f1xKTSOSGIqa6uRkYF+9GBjgsGg53ZbPZ4NDxzzl90m7dxxCdO+Hkcx3xGo1E4Pzh5K+s4zusdHR0v4ahGn8+HQBKZsy6t/54eD8cVp2HhVFzbtnEaT1r3eD3h9/sPQWY3kUigEZScdNJJ+yDzyhhLNjQ0/HP16tVfNwzjczoBi4yuCAQCT+XzeQOGHY45dJJS6rnxKN/4OBmZOXNmZV1dHfSIT0qZbmxsfKSnpwelJg3pdPruuro6OGoIxrPDw8NPr1y5stM90Q66ELqoq6+v7+/QhbFYDI7aNEJIn7Y9L6RSKRj27RbUjEX2TzzxxIpAIIDGb5x6M+zz+R6zbRuHVIAkQr8JmmNR2ldAwIDTh3CPz+fDXoETO2QYxtMoMUB9tz7d56uufXzJsiycrvOJzdctXzyquC6JROIvkUgEc61DFqG2than5UyG/XAc5xkE77hHO+QHu5n/wf7+/keDwaASQoAACMFJRvA0Hv0FWC+QPgMDA9+klEJ3o0fpT1VVVUMg75DtKBQKvX6/H446Dm0Y3LBhw+PBYNCRUqLpGEEPAm5kzle75cAo5fLpcqfesfY1Ibubz+cP1uVJfpyAB5sfCASmSilnxuPx8yKRyGdQkuye8PO2e0ITTnkC0TQJeOrswVM4bjgSiUwnhODEMRx68Kp7etJYRPWje2ALpZQ4KREkVzf0P4IipdT+DQ0NV65du/aLjuOABMXx0S+jzDIajeIkxsPd0/A6Hcd57r333nOmT58OeYC+GrJt+w/jVZ2Bw21qa2tHTu6C/hoeHn6ivLwcp34dMTw8vKympgZz+Cr8MPiQ6XT6tcD//xxRauvcMlvoxf0YY9Dnb0yePBmn9W1zVmNrFgEHb9i2/T33BMrHTdN8YnuVE406MMAE3GP4cAqQHBwczEycOLFRCPF5OOPpNI7gXiXcRuU6nM+uUy6oEYYxQbrI7zhOv2maZbZtU5z8g9psnAWOZyOCxb04BrG+vj6gewLKiue+F5/Z19eXnTgR/ksgB2YSDgtOEpo0aRLule653X4YYZy9jXKhfD6Ps8cGZ8yYwT/1qU8FcRpRKpXqi0ajBs6WLysrG8bxhjjhKJ/P05qaGlg/c3h4GEdq1eLEJMdxaD6fHxzP0hYsdulZw3CYC4VCHmfaF8/Dr6+vT+MUAuBT/B2DoaGhQmmTKhQIMiaf1Nm6UKr5fL5ml112WYc1gMHSJ39wYIUxr1+/Pmjb9nDpmDG/+vr6ugkTJmRK54f10k3IOCt9pzo1pHTzAm8coVg8GWDmzJllkMmlS5emIVM4v3rNmjU4Oeuj38aAE61P0MBvZvQVAzewuu+8807N8PDwwI5m9FpbW33d3d3VDQ0NkC8HDJDjOBz7BE4EnMpsNpstZeOw93fZZZeql19+OVM8Laq4L6HQx2vNXCNd3djYiN9JkXhvdXV15ZQpUzBW7HHUTrPS87hdhhx65L/OScfJEwMDAzj7fZtPLtlYgbssTo3u0ygAJzgUp5xyStXAwEABexgOgmma1bZt95eSCejNgf4sHRPmGAwGP1qPrTEWW3MNsNQnElW1t7fj6EtVss4jv8nX79ODAAAgAElEQVTilhzhuNRssbwG+JaXl+PUud7iuCEvnZ2d0J0D47XOWxo/9gyODCzqdlfPcBz1C92O33Tp7e21S2UUe1M3hAc27k0C5ujr2FHB95bmtqnvizJQ1BGY/4YNG/wrV64cWav58+dXZbNZVToH6FHdYzAxk8l89Hsw7rXVsGU76jcntjRfzCWTyVSuXr16RHeUriXkEsGxUuq/zt13bXnlxmsZDod39fl82fH+rRucigb/oPi7I9jf7733XjXehX2A8eDEnNJjZDFv6EocQ106nuLenzRpEuzbmH/rCes7adKkSUNDQ2nolOKeKJaGbUoXwomdNWsWbOtQSfZ25HcwUFI9nr/lsbHvgXXN5XKwhSNHlmPfcc5F6TuLtvCNN95Il9gRY/369TXr1q0bd1tYtFF+v38AhIsbuJU3NDQMuCXP8LtoqT4u4rxu3To4tR/9NtNm8N6S+G/z9y5mdfilgFwux/TJnfh9jO3iI40pMNiINfPV1dXhLPYpSA2N5w9nbDOS3gM8BDwEPAQ8BDwEPAQ8BDwEPAQ8BLYKgW0ODLbqLd5FHgIeAh4CHgIeAh4CHgIeAh4CHgI7NQJeYLBTL483OA8BDwEPAQ8BDwEPAQ8BDwEPgR2DgBcY7Bicvbd4CHgIeAh4CHgIeAh4CHgIeAjs1Ah4gcFOvTze4DwEPAQ8BDwEPAQ8BDwEPAQ8BHYMAl5gsGNw9t7iIeAh4CHgIeAh4CHgIeAh4CGwUyPgBQY79fJ4g/MQ8BDwEPAQ8BDwEPAQ8BDwENgxCHiBwY7B2XuLh4CHgIeAh4CHgIeAh4CHgIfATo2AFxjs1MvjDc5DwEPAQ8BDwEPAQ8BDwEPAQ2DHIOAFBjsGZ+8tHgIeAh4CHgIeAh4CHgIeAh4COzUCXmCwUy+PNzgPAQ8BDwEPAQ8BDwEPAQ8BD4Edg4AXGOwYnL23eAh4CHgIeAh4CHgIeAh4CHgI7NQIeIHBTr083uA8BDwEPAQ8BDwEPAQ8BDwEPAR2DAJeYLBjcPbe4iHgIeAh4CHgIeAh4CHgIeAhsFMjMKbAoLW1la1bt67ccRze19c3uGrVKjGes5w5c2ZlbW1tQ2Vl5QeLFi0aHs9nb/wspRSNxWJlDQ0NhdbWVudj3kUXLFgQqKmpybe2tsrxGtPcuXMrh4aGnI6Ojtymngmse3t7zZtuuqlAKVVF7P1+vyhiM3/+fL/jOOWFAi6hJmNswLKsofEa49Y8JxqNmoSQ8k3JAzCeN29excbzxN9POOGEqoqKimHLsmy8p7gemN/ixYvzW/PuT+IaYJ7P5wOWZfVj2KVjwBqtXr26YmBgIL9q1apC8bsZM2YYn/70p8sGBgaGinumZC/lixjsqPnMnDmTV1VVlZeOp/Tdc+bMqZJS2qWyuak5EELo3LlzK9avXy9WrVo1Lvv14+QJY1ywYEFZJpMxli1bNlAccxH3qVOnDpfu5dmzZwfKy8uNtra2we2ALY1Go2UVFRVqU7qqiLFhGLnS9Y1Go+WGYbDSMRWvfeedd4afeOKJj9NFo5oGsPT7/WUTJkwY3Fh3lYxPlOqMmTNn+ioqKsqz2Sxk9SMZxlrPnz/fN2HCBHs89eDmJtTa2mqsXr26LJVKDUL/beI6Onv2bH9HRwd0xcj3M2fOLKuvrzcXL14M2cDfRuRzcHDQHwgERmxeQ0PDhh0x/lEt1P8f+8ftSTpz5sxAMBikpWsFue/u7q7y+/25Up0JHNxrsSc3hd1oh7dN12Mt161bF1i/fv3wpnyGE088sYJzLkr1DWQ3m82WrVy5sriWI2M4++yzq2zbdsbbPyjRIbDzpXI/8l7M4c033yyfPn36f+0l6Mqampr/Gs+W9OvWgunaxOoPPvgg9/DDD/+PTdyULsSz4Vtwzu1SmYAuFELwW2+9Nbu179/SdUVbGI/H+ze1R7Guu+66K1m4cOFH7/wEbOGIDnAcZ5N2trW1NTA4OMgXLlwIv2lkrxR1z9SpU7OlumJzeG8Jp/H6HuMihMjtqb9GHRhACHK53LcYY2VKqQalVCaRSPx6vCaN5zQ1Ne3GGJvrOM4ty5cvf2U0zwZovb29wXw+n94aRwsGmlI6wzCMl5csWfL+5t4VjUb3VkodTSlttywrM5oxfcwzv+liSBljf7csa/XG186ZM+crnPO9hBD3QUA7OzsP4pxXwQlnjD334YcfvlVbW/tNSumhSimplEKAcXsymXx3PMa4Nc+YN2/ehEKhcABjzCelpEKIx4oOG5TjhAkTPi+E2J1zvosQ4k+JROINbK6hoaHDpJQGpRQK+E/ANRaLfUFKuRfmxzl/or29/b2tGcOOvKa5uTkkhPga59wUQmQopX8syhoUr2EYX6WU7kIIqfP7/Q/efPPN3aeddlrtwMDA4VDUSqn+ysrKvyxatCgXiUSA2xRKqa+3t/e+VatWjYtsbQmP2bNn15qm+X2llOKcZ5RSfyk6GzBE0Wj0AELIZEqpX0r5d8gTlPn06dMPIYTUMMYKkydPvqe7u5sLIb5omiYwCRJC/ob13dL7P+77aDQ6UWP6bfcae2ho6I+lhmzu3LnTbNv+AsamlHovFAo9jeB5eHj4CEqpwRgbVkr9wbKs4Wg0OoUQ8iUpZTXn/L14PP7X8XKS4ER0dXV9hnO+t+M4tYZhPNPe3v6f4tygi7q7u/fHHoc85/P5+1esWNEbDodxz2cIIX5K6dvt7e3PwWgJIb6P/QD5cBznj5sjC0aDbVNTUx2ldH+8nxDC8/n8A0UszzzzTASvRyql8kop6Nwn4/H485FIBHvwC6Zp9gohdqGUPmxZVk+Jbj6UUvo7y7I+HM1YRnutqyO+I4SogcPhvnOEQCh+YCs450cahnHLLrvsMoT10PpvT0IIU0oNZzKZxzHv6urqKGMM6yCUUi8xxn67NfZhtGPelutdR35/IcREwzAq8/n87zo6OtLFZ4bD4U9xzr9MCIH+fyEej//Ldcq+oZSqVUpV+Hy+P7e1tX0Qi8WmCyG+whijSqlXEonEv8ZL7scyR6zl4OAg9E2AUpoOhUKPljo2kUgEc9gN66yU+lsymeyEs805/waltBp7vby8/C4Qcz09PV/XdrheSllGKX0uHo+/OZYxbXwPguG6urovSynrCSG7Oo7z4PLlyz8oXlccj6v/Akqp38bj8eFYLPY1pVQjISRg2/azHR0db0KHNjc3f09KiXVxGhsb7x6LI4cx1dbWfpsxVoH1de3kyF7EJxqN7qGU+jxjDOP5IB6PP0kpxd//T0oJuwI8/5RMJteGw+FGxhhkota27TdTqdST24obngl7Z9s29G5/IBB4oiQQAen6GdeeVzLGXoV+aWlpqXUc53uMMSWlTMMWdnZ2Fmpra79OCAGO8MkeGC/94gZ7B3POoQtBYj9W6sNFIpE99fu+Av+JMfbWhg0bXoTOqKurOxh2o2jrsH7YV1LKz0MeHcd5d+rUqc+MZV3HgrtrT6Dfvl4oFH5bqhvG8ryPu2fUgUE4HD6Ec36QUqrNtm0s9rcKhcJttbW1MDrDFRUVBpi8ImsHp3HSpEl9F198sYrFYtV9fX1gL0YyDKWsAYzU0NAQBYPmGrPTCSF3ZjKZ1/bdd1+nCD4czfr6+opS9gt/mzhxYvWuu+6aWb16db1hGBEhxF3d3d1vIsKGUnIcR0JgS6NAOBNlZWW+/v7+3XK5XCeA3hTbi8hbCDFfCHFYIBD4KRy9bV0IGDTtDF7hOM75nPPvYLNqZ7Kt1FjNnz+/Op/PXw4h1RHiRVJK3BPmnF8NZ4xS+r1CoXCa3+8/HA45IeQlznmht7e3eyOWb1uHu9n7gX1tbe0PXEf4Lr22FyqlHkokEo/ipjlz5jRwzucQQh4wDOOzQoj9+vr6zqupqTlYB0NQnDdoh202IeQvcE4JIZczxu6VUn5WSvm5xsbGU3bUxtsakOD4+3y+EyilG3w+318LhcLljuMsTaVSUCbkpJNO2scwjB8JIVaZpjlTCJHPZDLXB4PBk2C8GWMPKaXmUEqXKaXWgfxSSt1CKZ2pifCMZVmLtmYc23gNjUQiJ8FRKhQKd5umOUcpdVsikfinu2Z7GYYRcxxnoWma31FKwVDGpZRfZowdRAjB2C8ihNyq54Jgdhal9Lec8/30vPfN5XKXj5WRwv7r7Oz8CQyh4zgPGYZxpnZSf5dMJuHQw+DVEELCQojnKKXrCSHzCSHXcs4/7zjONwghCc45nMDf9vb2/rOmpiZCKf2AMdatjcKFUsr5mwrAx4Kn6yicCkeGc76rUuqw4eHhecW5R6PRqYSQmJTyNsMwTpBSfiiEuIVz3qKUehDvpJRiHa4RQsyAQ6uU+rUea1QIsaIoU2MZG+4Blj09PXDGdiOE3KNjwMu0E3F3Mpl8CN/Pnz9/Ui6XO4pz/qjjOD9njO3b19fXVFNTg32JjMV/DMM4ixDyVENDw10vv/yyUVNTcyKl9Iec8/nbO2gPh8PfUkodoB2LO6DzKKW3FPWKO36QVLOUUj8vFArHmqapGGOnEkL+6DjOK6ZpXmYYxgql1KtSyvMcx1llGEafz+dLL168GEHNJ86il65tNBr9nJTyh4yx25EUk1K+mEwmUyVOIHQr9AyjlP68r69vVjAY/Cwh5IdDQ0NLy8vLf+zOaTml9FpCyP2I+wkh3/X7/ed/khnYSCTyU0rpHgjuNJm4oFAoXNfR0fGau6exT67Udu4y+BNw+BsaGm7+4IMPvglHVil1J2Psen3/rY7jvMoYi3HOr5VSfkdKuYeUcklp5nCs+8UNvI7nnN9l2/ZhcFDT6fSVRT8F8ojxEEJg564mhPwGNlcp1ayD10V6H4FMmWaa5hLtuH2WMXaUqzfPUUrdlUql/jDasYXDYQT1R/r9/kVCiOMdxxlobGy8FRlR+AfabwlTSp/VeqZHB8K/VEpdCV+Ac36pzgxA/r9HCMn39fUl6+rq5gohVnPO10kpL8zlcrNvvfVW2KAxfdygdDb0sG3bT2vS63xKacqyrBdcWziJcw79C5uHgPB4pdSvXFxAEt5nGMZJhJCUYRhrbds+E3aIEHKctotDiUQCGG/zJxqNfolSijW6UhMxJ+rE3MuJRAJrCHsCwuRSKeVDCFQIIYfZtn2t3+/fVwgB3XM75/wyKWWisrLypaGhIfiWT5mmmXEcp1kH8Au3tx50AUCguZsQ4mdKqUMNw/hJe3v7mNduS6CONjBABLiYEPJsPB5fgYeDRdxjjz2OZIzN0GwdFn0vpRQcosdcZzctpdzd5/P9xnGcwxDdKqVe047EZw3DWLR+/fruurq6r2lnA8qhkhAyaBjG47Ztw+B2K6UQ5YHVTCml9nAcZ5phGBg3WK67C4VChWEYiAahAME0vyilXKBZskdyudxKn883xTCM3eGUwZlwHOdtOEKUUgQoYMb+TCmdTAh51DTN94UQMxHFUkqxwZetW7cuPWHChP2llIh8EeWeUmTPtgTux30fi8V+JKX8fiKRiMZisYOklMdWVlaeXkyNIoVKKT1BCIENNQnOJwIIQsj3ysrKTh4aGprOGFumlcLhlFIIcL/P5/uPz+cbGu/06sfN4/jjj6+uqKg4mVIKBuvRaDR6GhiSUCi0BA59OBwGw3UsY+y68vJyZ3BwsEOXOp0hpYRz/T6ldCUUK7IGUsrHKKXn63TfuT6fbzel1FLNQB88XhmabVmv4r1z586tt237NDjFiUTilWg0eoWUEopmJeS0qakJxuAzfr8fgTPmPsfn853uOA6uS1ZWVv5Hl2ecTSl9CYEAnMmGhoazu7q6DoczYNv2EePBFH/cXBGEp9NpOA6/q62tfSqTyZxDKf23ZVmrcF8kEjmGELJ/IpH4pZuxOkkIcblhGHOllKuTyaQVjUZ/pZRCpudhzdodwTm/kVI6wbbtVl1+dH4qldps9u3jxuY6/mfrwPdezvmLSqn5UsrslClTUjCGLrsDhbzIcZxeOEA62LyHMXa4UuofiUTi9mg0ejql1IHOQOCmlOoQQrxqmuY9Qojrly1b9th4yEJLS8unHcc5jVK6UAiBrN9tUsqTUqnUW3g+9jj0XaFQuMo0zSMg85zzi5RSs+DYBAKBwtDQEIwfAq3ZhmE8+uGHHz5aW1uLAObVVCp167aME4RINps9mRDyZkNDw33d3d1n4nmWZV1T+lw3gJgHPZ1IJM4ofudmlc5AJqG9vR17ex9KKXQVdPD129sgRiKRc+HwJBKJDq0PQZBg7OeXOMqfg34QQuw1PDx8biAQKINzopSKNzY2vqKzWQiE3rFtexX2oBAihQyfz+fL7mzZAswpHA7PQpaxsbGxraur6+dKqf9LJpNROPvuvkBwfq7f7+8vFAoI7ua49ha2cwkh5JtwbBCIUkohc3Ns267WjuIlCOTGi4Edg0yCiOjQmbv7AoHAA7lcbiGl9B+WZd3i7pOjtU/wU8uyfhYOhw+E85ZOp8PBYHC+3v+9oVBo5Zo1a2AXYAv/BnvX0NDQ2tXVBV8CJMHVpcz+GMY3cov77m+YpnmDEGKylPIK27bnd3R0rHH1IkiIdCKRuDUajZ7sZjie0IHot3V28pfNzc0I0ubato0gtklXPayzLOvmcDh8odYRldCnoxmbuy9BKJXhObFYDFnUoxzHuTKVSm1wswXNIGkZY52EkDYhBIiFCbqa4Ih4PP6LWCwG0vBHmsg6RUq50DCMRHt7+4vRaPSPuurjauzr0Yyp9FqQfjrQRiB+W19f3+u1tbWXKqWeTSaTd7tO9+eUUrDzNxiGMWDb9mLDMG62bRvkJoikZ2zbPk8Hrn9WStl6bQ+urKy8bGBg4FBCSHNjY+MR40EKwidhjE1qb28/PxaLYY99KR6Pw4bD1u2r7dh5sDMucXeuYRiXSynDeuxvQffEYjEEoagM+C3UuuM4sJ0DhmFAd6+yLOtPY8VwFPeNlLLn8/mQbduXmqZ5eltb24hcbo/PqAIDt4wIi35TCXODTY+0/jzDMC60bRvpq6/o0panELlSSlHaAgZsGWPs06gUopQiGxDWSuwZlB4opRApwrgjEIDSuE4pBdb1WZT4aGYVke4DYHocx8lxzlHC9F3DMC5zHAfpnumFQmGZ3+//ovuMuYyx9kwms6aqqmqhUuoO7YBOR0q/pqbm8v7+fjgOnVLK19300ZGU0geRcoXQuN/No5TeoTMFvYyxg3Ww8zKl9Gww4qZpvrWt9cqRSCSiGZ0vJxKJ5ubm5hmO48xG+RTKOZDV6OrqOgQMi5TyH5xzBCvL9CYqmKYJw41NjfrvUwzDmFEoFBAkIChCEMX8fv9tixcvBpu63T/I7ujSjV9SSlG68ftoNArl6W9oaFgERy4ajaLs4EBdj39VMBjMZ7NZMOm/zOfzUc75U5MnT0ZmJ4asiGaj/yqEOMO27ZP9fv9uiNYppd/5BA3a/+AHVolSCnm9OZlMvhaJRC7VBnp1KBSC46rC4fDxmjWZUllZeUM2m/08nH2fz3duoVC4Eix7KBR6r6urC4HBB2DzIJeWZZ2L4FAHgVeVlZUdtHjxYvQtbLeP6/BZhJAbKisrXxwcHIRifDOZTCLYp+FwGA7HbpZlXdTc3PxFIQT2AliVVhjmRCKxLBqN4r+R3kZ25zCw3oyxoJTyGgR3Y03vR6NRBOnnY//W19e/1tPT06KzSKbP51sCxjMSiewHfc45R6DVB+ZOKQXjfAz2q2VZD8IQuOUWN0kpb0RQSgh5TSl1P2NsaTwev3c8wIUjoB3NBUKIC/E8nbWAbmxKJBIvu45GVCk1pb+//7IJEyYcKoSAcb6AMXai4zgworbP54ORuUXP5VRKaRKp+Hw+j2zMasuy4tsyTmQ6XcP7tGVZ98disVOllDXJZPKS4nOR8aupqdkXLB0CqGJQA8Jnzz33BNs4VQjxG855JaUUmaG/CSEQ8NxeVlb2yvaU1aamJmQTX4QDGY1GL4Z+sywLjgjqp+sLhQIIHrClP9alHEkpZQ9j7HiwjYyxfxBCTkFJWSAQWDk8PPxzLVc2Y6xRZzz++frrrz8+nn0c27JOxXsjkQjsIgmFQjf19PRgTj9AVgCMNQJi7DGlVAucLMdx/gRnz3EclEEEGxsbb+7p6UE5zjFCiN9RShEUHePz+VC2cQ3n/NT29vau8RjnaJ/hZgEfoZTepJ36R+GgatILThdIRgTQYHO/nEwmI2DlCSEnv/XWW8dPnz79YqXU68lkcmU0Gj1LCNGgs+R/14w9MskXdXZ2gpUHcXDFeJTOxmKxn+mM3qcymcyiCRMm1Akh2kszYwhOKaX/icfjd0QiEWS598d44H8kk8kL4GS6vg10E4La55PJ5FIEuJrEmxqPx6HLtvrT2trq6+7uPk1nOXuROUL2AEET5/wSsMWuUwu/6xopZZdLpN1FKd2bEAK7MrelpeVgx3GQEV6gs7BwcK9MJpOv6lKj32tCNB6Px+HsjukDW8gYO0sptURnsN7Q5VPQK/8ulpZHIpGvEkJONE3zioGBgYHy8nIEMEnYd875DVo+/6VJxAsIIU+DADUM4yu6t/DCoaEh+HawSQeNRwDf1NR0rWEYH8bj8auxxlLKQxOJBDIVCAaB6S9N0zwxn89P4JxfzDn/lW3b52q/5S9uEAjbjdLEOymlCLYvKy8vH8S+gk1JJBIPjwnAMdwEPaDHcqn2hU/baQIDOKw9PT0rhBBIR38kUC4zBZYeKaSJQojnUZflOM4ehmGgoRSOd0L/E0L0mk6nW4LBYNRNQT0FlkcbrEUwSrFYbDlSoPodYAPuBdPn9/vngvnRQvY26p71BthVp8ZRttCq02JzKaV9lmXB0UEpxxSd5lkAp4cxxqSUEEaUayDDEBgeHn7H7/dHkLWYMmXKY2vXrq1RSsFgPuz3+1/N5XIH6ABHOI7zYzAvnHMHKXYp5QCuo5SC8X54W5VsOBxGJH0QBDQcDqOM4NiqqqqzwPajWaesrOxY/b4+KH3gRwj5dTqdvr+urm43XeJRaZrmV6SUB33wwQe/qKysRAoMdbSouYTSXbWjhBVOJuf8DCjIZDL5cCQSOVlKqfr7+5fCoDU3N6PW8WjTNOHA5QuFwi2maZ7lOA6CgVdDodBt3d3dCJJQO4wg8QKfz3eqDiSnOo7ThizT9qylG+3eRE0lAgOw/6lU6mU3MHjNsiwEMSiXOxYMZj6fv8Hn8+3HGJtjmub5YJF09usmx3Fe9vl8Z7hBKNP1jt8IhUJnrVmz5hAhxJm2bR+1vTMGbgnMEiklylpg2M6CwSumV6E8daC+H1gwpGHBZiNo4ZyfzRh7LR6Pt4fDYThqknP+uJTyKMMwrs3n80HDMC4SQly8DRmDiVpOYEjvmDx58j+7u7vnEkJyDQ0Ny9yMAYxvC/BEDTwMHVLVjDFk4NC/8utwOAwWXwwNDd1SVlYGkqGtu7v7lVAohIzI4mQy+fvRrvumrnezFyi1QaoZLMgK27ZjqDHG9ZFIBE7qvmB4/H4/ynNQTnQFY+wkGJ9cLodAH+wUSskQUP/WcRxkLs91AzWk1cf8QZrcXdt/h0Khe7u7u5GF5YlEYmHxoTDuuozvQMhBPB7/qJ9rzpw530Vvk1IKZVxrW1pavoWadaUUdDDS8X/T8n3Px/VljXng7o1wxKSU77hOEeTNhAPmOpMHKKXgCL+Hshop5R+hw7WjkmWMhTTZhGblSxFgtre3PwGdOnHiRDk4OHgQiB6t1y/YVnJnW+e38f2RSKQZWfN0On1TMBg8FqUyyWQSf0PGAP0+cMBOdwPih5VSsGOodw6FQqEbEBggCw37jIwU5/wnyBggS+X3+8/YUWTRJnABeXiXEGLlwMDA/dXV1Sgb+ncqlRrJGGCfaKb58GQyeXxLS8t3HMeZlU6no8FgECU4XYlE4pZwOAzyCTX28DG+HgqFznMDgyZd0rxwPDIGsVjsKCkl+qVQtoS+uWtQuVCSMcC+7Eomk8gYwGZNQ2CqS2e/pgm9s93AAOsFBxz+0HuJROLGaDSKedQmEolzRiMzCM6nTZsWQ79CKpW62e3D+KH2Y65CxqClpQW9h/Mdx7neMAz0QtyoS3DBxH9KCPGtZDI5OxaLQR6OgQ+keyMWwQahZDQSiSAwuNayrDFnT+FrodQQQTlKvoPB4AUodUskEr/DPEEqSSlnO45ztW3bGb/fj0AfWS8E7B35fP4Zv9+PLMpf3f7E72YymQuDweCBUsrmTCbzw/E42MbN2PgSicSFkDUQ17oKAT4iZA9E00VuSSeIrbOx7qZpIsvwr3g8vtzVQyAb7nOrG37lOM4gyClK6d07KGMwIjpuj8Ml5eXlC2688ca1o5Gn0Vw7qowBHhyNRucppaaifjOXy6Ex47DGxsbla9asmQE2TDNkqcrKyruz2SxY1Vccx3kWjjrnvEM7U41gMfr6+pqDwSA20DrG2J/BRCLtPzw8/LbP52uXUl6LulHDMO71+Xz/zOfzuP9JKeUvdCS80nEcLNLRCAxM04QDPU0zWjB2wVwuZ0MRGobRpoVrjVIqoVnQSyilaMZFgyIaZFD3+0pJYIBNjNTsBMdxjg4EApfm83mknu/p7+9/OhgMokEWzZU3+Xy+KMqftlVgY7EYHPtrDMP4sW3b39Y151PR2IyMhRBiTXV19bq+vj5lmubeUsrjkd5sb2/vnjlzJgsGg7sTQsDiPqaU+jsCDGRXMpnMurq6OrCMt8Xj8VHXM45GcIrXunWGUJI46aOts7PzUj2ev/r9/ve1QihDiROMGWPsJtu2Q5rJQ73kufl8HozLl9waexi4F9atW/dcfX29pVOnVxuGMV03YH77zTffXLAzMXsuCwtmDxmk+7QhR0bqNp/Pt254eHiKlj2cWPVjN0ADe1ehS+rAjlyklfMQMmeUUjAmv5FSokkWsnkepRTlO+XxePyq7V37DJY4GAyCjXNs27ZQIgQFRykd0A5FhS59AWaW6nAAACAASURBVHtzOnohNOMKBm/fmpqaeCaT+REhBI3kSGVeRim9B2wW9JUeOzIF+wghvunz+a4aq9OFEjoE4IyxwXw+3wHmiDH2p6qqqpfS6fQX/H7/y7Ztn20Yxp1CCJTsXCiEWIxGavQ3VFRUnDs0NHQq6uYDgcC/stlsq2aekIl6BalzGLLxKoFB/5Rt2+iJQSlQlVvrfoZpmmi0Rx8SiJDmXC4HvI5njDmmaa4SQpyD/SCEGEZAoJu2UYaFjCEalG/S7BaC63uWLVsG1nvMH2DJOf+FEKKyoqIins1mYch+rwOUt7RR82Wz2Xf9fj8yuP+PvfcAj6M624ZPmdmiXrAjS7IhAQPBKZQUCAklBBJSCCEIQjHY1hbJxnQIHUEopgQDLtLurGxjgwk49NDLawg9EAglgE21VVywd1W3zJxz/nP7m+XT6xiwmmP+b/a6uC4szcyeuc85T7mf+zn6EPgYhrGj3+9/vr+/fw/G2Mm6/I5A1K8TPVVUVPThp59+ylCtNU0TlcrZn3766SfDtYNf9HKhUOg4sJ+maV7nOM6fwYRj/rPZbCnn/MN0Ok0LCgpQPT7bMIwrxo4d24nk0ZU3HqMrSLsVFxc39fT07ALZaCwWWxIOhw9DYtDf39801D6YIU/Il9wYDodRmf6FbiSFDUDV62P02VBKv8Y5R1N7Cxpe0d+kpQxnJZPJE9GYSin9bTKZPLeyshLJMXTndwaDwaU62EIPGwiXI1Kp1GUjdWLYUN4/EolATmPoXpxmSEV19T4eDAZ7MplMgRACP49p0qTOMIz9UdWpqqq6qb29/RjO+c4lJSU3J5PJ2YZhIDBb4zjO9OLi4sZMJrO/1qnv3dfX13LbbbcNu8oaCoXQxAtfhioe+ht2hY9GP59WMLwBP6t7JL4bDAZnowKu5dPLsZcgje3r64vogyZw6MQP0e9RVFQESRfGdyGUFKhcDeyP2VoMI5HIrwghkAXNcINan06IH3MPJHkLZI3jOHcIIVaYpgnfiwppiVLqJsiiIT2GjAnELPr3pJRoOMa7zDVNs344rDP2WTAYBFkEO4VYBL7sdiSupmki1lstpQTZNT8YDOZs2wahc53P5wMJgtN/YDdB+i4FsWIYBux6UzabhfQ4YFkWEshhf5AcSSlnFBYWTtY272TOeVtJScljGzZswCEQqNxD4nQ91Cjw3Y7jwAbjIItv9PX1XV9YWDhLKXWHbdvvGYaBSsKt6K2jlOL/58TjcZAl2+TjVgxQnQ+NppJi0IkBpCOcczBffZzzCWioQ0mwoaFhLBo0KKWXxGKxN8LhMLTWKCVBLgTtIxgOyF1+4vP50AgGh9RbUFAwr7e3F8YNmnJoljfi5Bq3IVVQSiEvKgQbpFlNLBhUDN6nlO7j8/nm4BnZbBYLDRKjf2ez2SdN00SWjS75BY7jYIPjZJDVlNLnent7P9LHY6JJZ2Vtbe0tHR0dY/SiROkejVorGGPQ5L6oHTPY3ndramoWQ+eGbB3MIKX0nHxzzXBWgnvyC5hDVATQ0/AkApZwOHydPiVpOSQReD4061qPiVM3rsbvZ8yYsWM6nf6dnoP3U6nUw4H/86ljjJWiqqGbigTnHLq3bXZcqSuvASuRRNMiEiq32XIs2ALTNA/HKRIIfG3bfhgnTbknz8AorMcpFXqOHmlubk6Gw2E0MqPzH2X/B7flptva+XRPTkIT/kbo3Gtrax/u6OiAxh3J6qk6mT1Sl8thjHFyF04/WQU9KApaSFCFEJ8yxh6vrq7OtLW1oXKENZDz+/13zps3D4H2qH+mTJmC4LVeN+q3SynXZjKZxwoKCo7BiRw1NTWzOzs7IS0pwb6ilD4NaRB6E7q7u+E4HZ3sdaPPCNXCdDoNpg2n11Awt3kpzVBfwmXioYvtkFJmhBAP4+QfsF4+nw8MIZo00ZSH9f5+V1fX05WVlUjq0ZS4XjNSG03TfGjevHl9DQ0N6PM4APhqmcnK4WhqN38fMHq77747NPd7IJkDyYHvyOVyqKA9Z9s2WPVjId/RJWqcDnI7jsns7Ow8GBUZnOLhOM67aKx2GeEw7CljbE1BQcHDI9Er5DZAg2VEEiqz2exfAoHAsZhbxtjdaNxE05174snbK1euPHPixIloaEQD4Keu7bW6u7sfWLZsmWxsbPy2ZsvOFkI0L1iwALKOUfu4krfJGDfnPLNx48ZFFRUVOEXp636/vxnSMgRzkHhKKW9ubW19ubGxsdxxHFSoUbm+Ja/FBqcFWYrLTL5aU1Pz6kjol0fy5d0+AgRzOK2KZbPZJagM62Ruf7/fP8u2bZBJ8GVYy6jwvIJ7NAGHJAjVR4I+LchqIpHIwbgPc4hKfnNzM5r1/2vN1m6jO9ZVj24e7qyqqrqns7MTEtmxgUAg7kq9KqDhh1QW7zB16lQ0ryI5wElSNJfLLSwuLla2baOaAtlHv5aFPD+w0jWc+XBJH5w8WAT7rUmQu3HqImMMEiIoD9YKIY4GzLB1hmEswSlzmUwGcUkFbJV7otI7rgwIhAvG3p3vpxjs+LAHfD7fCS6JgHjtUcbYRBAKsVjs6HA4vC/iG+AK39Lc3PyES/ygNwH2OwWSBKRiNBrdRymFWAyn77zV0tLy1GDHs/n18IU4BAU+DQmr3+9/OJPJ/BYJiVv1wWEV6E1CfPbG/Pnzn3erlIh9ENtBJ4+qBXoMYCsxr8I0zTtGqsKFo0g7OjpARIO0w6l/OIgBkmCQJn/inOMdfkIp7dTJFGzgy7vuuiuqU5AbOe7Jm2j4xil0uA4N6JDzrezp6XlmNMmRgXgjZlJKgQg/XleFoLx4eCQOwtnSGhh0YoCHuMfI1ZqmiWbSzuLi4uJsNouyM/SOS6ALc08KQhf1Br/fj2MsJXRkYFC1FAAbDMcl0qqqqk1HdLa1teEoOYJjytyFjWMScVwZTuZZDXmFq4edEAwGN9i2DY19LxpT0Z1v23bFhg0bVmOSIJWAxnjChAlr3n77bVpUVFStWepsS0vLet00aTiOMx5Sp9ra2g6cTKT7E8DWd+H3DQ0NaPSlfr8fC6Jg3rx5bTCoLjuIUzzW48jB4W6o/P1ugNYNB4afTZkypQrl8PwpC2jwoZTi6M4OBPvAPplM4txuLPD8ud2+0tLSGsMw1EixoYN9P9dBfS2VSn2AOUACWVBQwLG53QCqNpPJpAbKgtyz6tE415lPZCBXa2trq0KpbnuSEG2OBzYpjq3LJy5Yg11dXcWLFy9ux8lFwWCwWlemOgcGd3A8uVxuTFdXV3v+1Cg4kNWrV4/L5XKfbmsGE/IK3SQ9NhgMdiDIOv7448v9fr+xcOHCTSe2IJkRQiTzaxMYQJ4ihBjT3d3dmX+H/L7s7+9PLV26FLr/YX+wnnDS1bhx4z4CC4x1n81md8hX69zGN2PgCUOoXqXT6Wr0Fg1gR3Gaw5hcLoe/eTDs08Q2fzH33cfpkngaSR2cxwcffLBDcXFxP6omWAua6UVSvGagRMw95g/79TPdN7BljI3VxEbnSJ4gAywxZ+PHj/8QwTCCZ/zNEwTbuVwOdgOMLc41hCRzTSgUwhhwpCFOv9EtFKIzb4/ck1CqEASMJmOVxxl7Rkq5Q962g6XEMcGLFy+GvVT4d0FBQRWcNyRPbkBWsvnfKWhqaipYu3Yt7P6n2yr5HsomcOVfX0MzKfyoexZ9EKcoNTU10Y8//nhscXEx/sbLZ6cquVW2rwcCgTUDez5CoRCO3KQLFy5c+99MCvI4uBLZSq0YwP7MYV2apmnMnTt3o3tww466SbwbBFH+HvhyxtgOWhaDY6s/S2xwZDEkxCM9lzgeFL40m82ugz12/xbB10A2YP/mx2NZFiRsn42nvr4eqgUckf7ZEb44frawsPBrxcXFw9rPmF/HcdCf2YF9mMcxb/u2ZAvRK1ZfXz/BHVP+COxNtlBKyUfiAJX8HCGB0/KrkrwvxLwyxorytm369OnjHcdxBn4n/lZVZWUl9vVnJyjCfvf391eB3BlpYtM9Nn3T/oddxtyMGTOmdP369esRr6BnSVd2WWtra2d+XkGEpdPpHWzbRuz12THJwBuSzJGQrw3GRsAWOI5T6zgOlCRgmtpH62CWISUGA1/GPWcXJTawvA+MRBPQYMDyrvUQ8BDwEPAQ8BDwEPAQ8BDwEPAQGD4Cw04MkJniL3/29fUZm/91wuEPz3uCh4CHgIeAh4CHgIeAh4CHgIeAh8C2QGAkEoNtMU7vOzwEPAQ8BDwEPAQ8BDwEPAQ8BDwERhEBLzEYRXC9R3sIeAh4CHgIeAh4CHgIeAh4CHxVEPASg6/KTHnj9BDwEPAQ8BDwEPAQ8BDwEPAQGEUEvMRgFMH1Hu0h4CHgIeAh4CHgIeAh4CHgIfBVQcBLDL4qM+WN00PAQ8BDwEPAQ8BDwEPAQ8BDYBQR8BKDUQTXe7SHgIeAh4CHgIeAh4CHgIeAh8BXBQEvMfiqzJQ3Tg8BDwEPAQ8BDwEPAQ8BDwEPgVFEwEsMRhFc79EeAh4CHgIeAh4CHgIeAh4CHgJfFQS8xOCrMlPeOD0EPAQ8BDwEPAQ8BDwEPAQ8BEYRAS8xGEVwvUd7CHgIeAh4CHgIeAh4CHgIeAh8VRDwEoOvykx54/QQ8BDwEPAQ8BDwEPAQ8BDwEBhFBLzEYBTB9R7tIeAh4CHgIeAh4CHgIeAh4CHwVUFg0IlBJBIpyOVyvuLiYpnNZp14PJ4mhKghvDCdPn16oeM42Xg8bg/h/s9uqaurCxYXF5cbhrF+uM/awjhoXV2dWVxc7LdtWy5ZsqR/iO/7H49uampi69atK/D7/eKGG27IUEo/w1EpRadOnYrv9JmmmVu0aFE2/724r6OjI1BdXe00NTXl8v/u6+szKioqsnPmzMG12/Qzc+ZMPyHEv2bNmsyyZctyA7+8rq6OjxkzJtjT06MG4heJREzHcQITJkzI4j1wD97lgw8+CAaDQRWPx4H19vihU6ZM2TQ3EydO7G9qanIGDrKpqclYtWoV3tdZtmwZ9semDzDq6+vzGYaRya/TPDb9/f3OokWLMtvyZbc0nvz3Y1zFxcUFhmGIWCyWzq/N/Jxt6R0cx5HDsAf/69Xz6ymbzaa3sKdpJBIJOo7De3p6+pctWyZwc35sPT092QFrkJ5xxhmBbDbLx44di7mSI4lxfr3imUuWLOnb/NkHHXSQscceewQGzm/+nuLiYjpwTHV1dbCr/oHYjsRY8VyfzxcoLCz8DywxzzvuuGOgu7v7s/2G8b399tt+nw+m5//aHuBrGIZ/8308EmP8vGd8ESawkSeddFKBayvyfmjT3mSMmR9++GF6+fLl+b1JJ0+evOna6urqzEivg5HCIG87CgsLc5vbcbxvNBoNptNpOtCOfp69ga/GtTvvvHN6e3jfpqYm36pVq/wTJkzAeDa3mZ/5wtmzZ8MObvKFW5r/z7NNIzEHeXsMnzx79uzPbPcAu7hpj25mY+C3jLfffpsNtDv5dbiluRzMWPHslStXFmweB+AZ+TUx0Ba68UCxEIIHAgFq2zb2/SZfiverqqoyRjJGmDJlSuALfOEmfz7Q1uXjFXfMeVtNZ86cWdzf32/Ytg27mLn++uv/w54OBrfNYxD4M3cuEGv8r5gVYyKEGJdeeqk9MA7D+issLGSub97kd4QQATxbCCH7+vp68v5nqGMb5H3AyVdZWWmP5p4edGIQDoePIYT8WCn1CqW0VCn1fCKReHWQL7dpI3V0dNQTQp6Ox+PvDvb+gdc3NDTUCCFOl1LGWltb3/+iZ02fPr1ISlna3NzcMXABfN49kUhkByHEjxljAUrpOCHE462trW8NZ7z5DVpSUrI/Y2w8IYRRSp+Px+Mf5hfs1KlT9+Ccf5sxximlfkrpYy0tLe0wru3t7d8lhOzOGHt13LhxKzo7OycJISYSQkxKqUEpfSIej3cOd4xbe//MmTPHZDKZ/QghpZTSTC6Xe3zRokWpfLDGGNtLCPENSmm5lHJ5IpF4JxKJlEopDyGEFFFK+wzDeKq5uTkZCoX2oZTuTinFBlz+ZfO5tWMcweuQ0NbmcrkfMsYKpZTrGWPL84b3jDPOCPb29v6AEFJLKS00DOPh+fPnrw6FQl/jnP9UCGESQj7FPQhQ2tra9qeU7qSUMnw+H65dM4Jj/dxHNTQ0jLVt+6dI5jCeXC63PB/YwhiWlpb+kFI6QSmF9fdiPB5fiaBEKfVTSmklIaS/sLDwb0gGcrnc3kKInQzDwN76+3D3M7DSaxh7DsF/b0FBwVNz5szpxsvAgK9atWoiY2xPznmBlPLDVCr1Qnl5Ocb2M8yJUqrbMIwn5s+f31dfX78z53wvrE1CyAfV1dVPj5RBxVja29u/o5T6plKqhHOOPfxmHnQE0kqpHxFCJmBvmqb50NixY9d9/PHHkzjnkwzDCBJC3n3vvff+MWHChNJAIID5KFBKbSgqKnpyS4HJYNfGjBkzKnO53H5SykrGWJZS+nA8Hu/Cc5C07LbbbnsSQiZi/K4tfi8cDu9BKd0DaxW2hzH2iGEYPblc7heEEKwHzP/zlmW9PtjxDOZ62GrHcQ4UQuyAsWcymQcGJl/hcHhPxti3pJRItp9rbm5eEQqFajjnP6CUlgghPnVxzITD4e8ppXaDPYXvqqmpeWek1sFg3umLrkUg766XcVgvjuM8sHDhwvX5exobG3ezbXtv1+68Br87efLkwoKCgn0dx4F9MV2b+UkkEtldSvk9PIdz/mZLS8urW+PvRupdNn9OY2NjueM4WN/FUsoN3d3dD+UDKjcY38+27a/jPtcXfjBz5sySdDr9Y875DlJKFQgE7qusrMx0dHTAvk7A+vT5fC9i3keCrEOA6/P5vkcpHa+UKpNSPpJIJD7KvwvGk8lk4LPHICAvKCi4r6ysrHv9+vXl2Wz2CErpesuy/ub6vd2VUthbsAG+/v7+u5cuXZocLL5nn312YVdXF2xhBQJ9vW3hP9vytnDt2rUTHcdBPFAE+wbsbNsu04TOxS6WhlLqbsuynpg2bVoxpRTjr7Qs67bBjmVL10ciEfiI70sp4cf/ly/EvJaWlk4ihHzT9fNvpFKpfxYXF/+AMTYBPp4xlrZt+yGQroFA4ELsW3yPUuoBy7IeHYkxgmTK5XIHE0LGCiHsbDb75JIlS9a5z6Znn312QVdXF/bKuEAg8NAAX4NY60jGmC8Wi92K/SmlPI5SinkFGdXW19fXOpR5Hcp7YX1yzr/JOf9RT0/PHbfffvunQ3nO1twz6MQgFAr9klL6GynlLMbYryil+zHGzsJi45y3O46DAOHjnp4eZhjGBMYYWPxNQepJJ51U6ff7x1NKN3LOe4QQR/h8vscdRBdSFuvn9CilxmWz2ffy7KkbUOHZq+bPn98Lg2cYRl82mwWzRmOx2EoE73qiLlZKLaSUYsI6UYkwTXO3bDbbVVBQ0CmE2Fkp1auU2pVSejCl9J5x48b9A0F5R0fHNxhjG1taWtbX1dWxMWPGVNu2XVlYWPhxd3d3BWNsd5/P908hxAwhhD+RSJxHCBkW8xgOh3dVSl3gOE6TDooPAH6EkLkuO0pDodCBUkrHMIxVOlCYiYC6tbX1oWg0+lMp5V6O4zxlmiYSKizW0ymly30+32u2bZ9LCPlXPB5fujULYLjXuMHP7xhjxdls9kHTNM8ihDxlWdbDeHYoFKpljE3FRmeMfUNKiUAYRuswpdS+lFJLSnm8lPKlwsLCpzOZzBWMsbuR+BBCvjdu3LjTNmeXhjvm4dyPwL+vr+8kpVQn5/xVIcQFhJBFlmVhLeF9v0MI+TXeQQcgv3YDqRvwKyRtOsl8glJ6MqUUhhkb+wydGCySUv4W91uWNWsknNwXvaPL2ISVUthzD+ng+iRCyF35dwiHwzDm9ZzzP0spf6T31Y6mabY4joP//4Ft27cahnEupfRu/S5wnMcLIe5hjCGg/K6uAs1asGBBz1BwBmHQ1tZ2PKWUcc4fF0LM1Dbm0Xg8vhy4IMAQQoQZY885jrORUjpdO82bhRAImL5NCFkopZxGCHkkGAy+nMlkGgghHyml2hljF8BWtbS0fDyUsW1+D5JbpdRpSPIQeBNCjggEAqflHcuMGTN2zeVykxljt0spj1ZKpQ3DuEVKGbZt+2HDMGBDTqKU3kgpPUQIAeLhXsMwphJC7szPx1DHCsdcXl7+G6XUGMdxHjFN84/AMhaLPYBn1tfXV3DOTxVCPMQ530Uphb15jpQSjhsOu8M0zTOklE9IKV/nnMNuPkUIOZFSOjYWi50z1LFtzX2NjY0/cxznO7Zt322a5qVSyrtbW1s3jR1OWie1V2sS4S9Syl0opd8rLCw8r7+//w9Sym5K6T8opZdRSptzuVwb5/xyKSVwhl35ZnFx8fUjkXhtzXts7TXRaPR7QojDdNK4TEoZoZS+H4/HY/kAJhKJXKaUeoFzLh3HmVpWVlafTCYnMcZ+oZRaAH+MQBTFK2CDgNANTA/p6uq6eGD1cmvHNFLXhUKhqW5A/aBS6hTG2Px8Eh2JRHbGXEkpL2eM7a+Uqqiurp7d2dl5sFIK73c//CTn/H7O+TuO45wkpZyHBBD+3O/3z8/vueGMNxqNglw7VghxF2PsAE00juvq6roqXwWIRCIILr+rSYl7hRAX6bE+DjInl8tNUko1UkpXxGKxK1w/cCj8dyAQeN+27etzudy8W2655ZnBji8SiYCEPZRzPk8IcRSl1Ekmk4sxJjfZqgdxQyldK4Q4g3M+2w2ssW8XM8YUY+yj5ubm9eFw+NuU0jpCSIVlWTMGO5Yt2D8kslNgW5VSIAnOMAzj1lgs9gqunT59epXjONNAamazWWqa5gmc8+uklN9HEuOSTjNyudyfCSFrDMM4jXP+LOI0jLmlpSUfvA9rqNOmTduPc34S5/waIcSxjLFVsVjsdjwU/mbdunXfchwHa22XbDZ7yeLFizcgPw2Hw1gP8HNt8Xi8CfZSj3syIeQ9QghiWJBVH4yCSuU/3tetDCF2hp84Vvu0kxOJxNphAfMFNw86McDm0EHc4el0+orCwsKfK6WOV0pdyhg7h1L6V5cFflc7nI1SSpQEf6aZsWtyuRwyyp8ppVaBIcWmIoTcwBi7UAiBLPFkBI94cSnl87W1tUs7OjqQmWHCwKLukMlkEsFgEAvxW4yxl5VShzPGWiilCIibGGNvU0qLlFKFrsNoUUo9KaVcxDn/E2Psf8AuMcZ+Twi53efz3Z/JZH4LJ4jgxzCM25RSO9m2DQawjHO+NplM3oHSm+M4qEpM18HsI8i+hzsh9fX1dYyxQyzLaqivrz+Ec45KzBkDSn6+8vJylLsQ7PxCVw/uKiwsbOvq6roJQSel9N9VVVVtq1evLmGMnYFAqbi4+O9aWnGu4zgvtLa2PjLcMW7N/S7zf4pS6tXW1tZHw+EwxiLGjRs3B4xcfX39DwzDONIwjD+bpin6+vpuYYyd7TjOiToo+pBzvlRK2ajXge33+x9Lp9PnM8bOQ4JpmmY8m83+NF992JrxjPY1kUgEbB7ecXFLS8vbkUjkKqXUvy3LuhWBa319/ZGEEASE8woKCvaRUoY453jfKxljzclk8p3S0tJzGWP/hgFEkqorB+e1tbX9AkFZUVHRL0c7YHGZ2OuFEMs0C/yi3+8/nxDyZiKRuAP4hcPh3zPG9onFYhcgWJFSIkm4AokxpfTDeDyeCIVCV8JJaSP/IOf8l5zzm9Lp9A46Wb/MMIwLhhp8u4H/uZzzv2pH+AaSYkIIZHYWDLDLhEa1zbjBNM0Neq9fRwi5nxDyK+1An0skEndGIpEzwY7atm1pFvVKzS4uCAQC72QyGVx3w0gxUQholFKnZzKZ63TgyUzTRLI31bIsMJgIXuGEJyWTyWsqKioOl1LCxl1MKZ0MB0kpqvz2tYSQVtwHLHt6eh4vLS29hDH2bjwev2U469lloGcqpd6tra19oKOj42wdfLFEIoHkc1MSSymdoiVDV6I87jjOUu00pyWTydX4/Q477PBtx3EOxzrp6el5v7CwEJLKHZDsSCn/OVI4ft47RiIR+Ia21tbWW9z1xi3LAilDGhoaUIU83zTNadq+7ySlbOacn4yAGcluLBZ7IRKJ3CClfBuJIaUUAQoICviU6ZTS0/OVk+FgPJL3RqNRjK80mUzOLykpOY5zfkA8Hg/Brhx33HE7FBUVIRj+o23bSHweRvLgJnTja2tr537yyScHYC/CjwkhrrBtu97v90NS8ifDME4dqUBrsO/sBjWLkaiAkc1kMtcTQl6zLGsBnhWNRpE0HxGPx0+ORCIHKaWmplIpJD5IuteNGzduaVtb24WMMSSDzxJCflhdXd20bt267ziOEyGEzIrH46sGO67Nr29oaPg5qiyGYdyUzWarNKN9ja5uzFywYEGHu59B4qyPxWK3hcPhRinlrgUFBX/s6elB0BtF7BGPx2FvNslG8XO/37+XSxDNjsfjg2J4kdiXlZWB5DAty5ofjUYP0AH4kbr0eOW8efM2TJ8+fZdcLgfiYx5jDFUExEJ/UUph/07lnMf6+vq6Fi9evBFrCGNKp9PwMwdYlgUCb1gfKDUcx8EcLUkmk++VlZVdru3wPyzLuitvXxhj8POzUflFckMImRMIBN7A7/v7+w+GKsI0zQWQDyHJgCKjpKTkkxtvvHGT4mAkPqFQCLFpkWVZl4bD4cl67/xQz8Up7rM3SXPS6fS3EMtqSdRVSAzAzpum+Xvtd2oopTwWi13tEtt1lNKXQSRXVVWt2pZVR1fKuYvjOJcahnH6aKoLhpQY6IUHCdCdKPdracEzSimwpRdzzucHg8GO/v5+BPr9uVyu1+fzxXXZCBOPMtZUwzDAfvdWV1en29raFhiGkQDzB1vvOM7lhmHsRAj5TVdX19VlZWXzXKYZSUQTWH7N+uWEEMfA8CHAZ4z9yHEcVCwuQ/CsSz3vaWbuj6ZpxoQQlZdVigAAIABJREFUB6LkmMvlZpumeazf71+czWbhDA/y+XzX5XI5bLRoLpe7xjCM47UhfgoVC0IIKgbzNEsF3e2n7gZEZl6uJR9X5Et5w1m0kUgkqgOavXU1JRoKhQ5yWeQZA3X1oVBoX5dt/1CXN1s1MzI2m83OJ4T8VZPLWLCFGzdu/GNZWdlBhJDDtbPDZoJkAoZym0iJ3M1yni5RPh6Pxx+PRqNIEoLV1dU3gOl3K0wH2rZ9dXl5eba3t3cZIeSPmG9CyN9ramru7uzsjEgpUXJ9ljF2Zi6Xm+FWm5DVHzhYgzqcefmye0Oh0NeRBGuGeE4ikXg3Go026eC/raamprWpqUmFQiEYnpqioqIb+/r6IDNBNecipdTVOgq8cKeddvqko6MDTGsbSsya3dsZwU4oFIJEZ1YgEPjZSLBfX/QeU6ZMKTNN03IcZ3ZZWdlrvb29FyilViYSCThv6JixvyfE4/FLEIA5jgNW/k/6d2Asn00kEq2RSKQJ5WFdvboPjKWuXF1rmmZZLpe7ljF2ESp5X4blln4/bdq0atyvlJo/fvz4dzs7OxvhGMeNGzcXfSj19fV76cokKgZXBoPBjb29vddAAkMIOVozx7e2trY+GAqFTkcFSzNjNyul5uh9guThXdeWNMdisfuGMrbN76mvr/+WZo3OJIRciN8h8MHYWlpaNkkNw+Fwg1Kqtqur6/LKysrDEGToSuMlUsrJQghgaRuGcR1jDFUEJJsxv9//NFgrSunqWCzWMpxxugnghVLKFy3Luj8ajZ4GyZBlWXDgGB+Imt+k0+mLy8rKzFwuB1yilmW9HQqFfoJqBmNspWmarQhEpk6dOsYwDMzHGFQ54vH4B8MZ35fdGwqFbgLpY1nWonA4DEwq4/H4abivvr7+V7CNqVTq+LKyMiQGiwkhIBhQbVmCaks4HL4GRBRjDIzm8TU1NX8A2aSlZmfatn3K9kQ4uIHnWZpkUzU1NTd3dnb+HsFyMpk8CZIbJMTYY4ZhIBDutW0bvupUVECklOXjx4+fs3btWshxwCrfq9lY7NcjfT5fmeM413DOT4cU9cswH43fo0KpJZOPa+nxbFSstN24HuuqpaVlrrsOQbjtlUgkwlh3WmUwY8WKFSfuuuuu2CPvgnQJhUJnwy9TSl9SSn27trb24ra2Nuy/U3QV/+qBkp+hvkMoFDqRMbZjYWHhDbZtl+dyuRYdACKh2lRhjEQiqFC9AbY5HA5PIYT8yLbtU6FuCIfDpw1MDNw1uj/nfBPDnMvlmgfbQ4ZAXise8slIa2Nj475CCIzxciR5sD+MMfjba5AYKKWaESNpwvaF7u5uVKgzUDsgWYjFYpuqFe6++elIJAauLzxPCDGHcw4WvUkp9aZlWX9xvwuVx8m2bV/p8/mQzMY4583z589/PhwOH6GTQ1TJ/65li3eiFyYQCJysY5eAlHJPEEGJROLvQ53LAffR+vp6rLd1lmVdEw6H/0AI+bllWbATn30gYYaNCAQCV82bN29jNBo9Qkrph4/W1X1ILee7KhRUTSElw8/WSSlbhlodH8q7uUqTy30+3/aVGLhBLLLY223bfmfRokUfg0WVUp4rhLhq4cKFn0YiEWj0vgNpkS7rnqmdY4JS+jpj7NfIyBljLxUVFT2QSqXA6FuGYXQJIf6gdfyz9OKGvhilkpvxHQjEa2pqXmlra7uAUvovxtgaKeWhehNeD20bIWSKy6IgK1wAXSnnHIEM2M8uPblXo/RHKe0ZN27c02vWrPmxEOLgoqKiWel0+lgpJeQeKDH7OecrMpmMMAzjGNhnsNlVVVX/2rBhg4nmRb0QpkM7XlNTc26+WXYok4t7QqEQtGq/sCwLLAkW2+/APriNV2AcNjWYtLW1wQkju30JwSQh5KJAIADnTDKZzF0auzMcxzlCKfVEcXHxK729vWAk39qGUiL0CpyFLNqyrAfD4TCcVS6ZTMaXLVsmp0+fvp/jOEfZtg0njaqABWbccZyoNhoILMCKhpRSaJhGGfFSGMOCgoIJ2uHdFAgEDh/tQHkwcwiWRCe2YF4XJhKJNyORCBzwWzU1NXfiOe3t7b9TSu3h8/lmQ96ilAK7ehEhBAEs5Div+/3+sxljb+jyOPS0B69YseKcXXfd9eeU0pnZbPbIwTqQwYwf1yJgzOVycymlt2cymWeDweA5Sql/WZZ1L34fjUax/r8fj8fPhsGEc4PzEUKcrUuyqBjMQ2KAOYOm2TCMI4QQ1/n9/gokP5qtv3SoDJ4rb7lASvlX9NDoHo4ZusIEImFRU1OTCIVC6K1B4niN4zgo+cJZ368d0u8RACcSiSWRSOR0bA/HcW6FHEon+zHHcd72+Xx3MsZubGlpgRxm2J8pU6ZMhKTK7/dvkg9ks9lWxljU7RVC4A07tmdRUdFlPT09hxNCjkHyB/8MuwZH4/P5roCUDPIKLYf5W0FBwYN9fX3nSynfSSQSm8rdQ/24cpuz8Kza2tq7Ozo6zoAEMh6P3+gGOmAzp4KQ8fv9Pt0nskgnJPWrV69eO3XqVOepp56qlFKCEfznhg0bHqiqqqKwg6h8oAfBsiwkOqP2CYfDl8HmWZZlRSIRJDMYO36GNYpqHNYaAjT0cMyGRIsx1qirAvdalvV8JBJBQPAvIQRIkuk1NTXHdHZ2IjEI+f3+c7cnu4J3CofDYcg8ampqZq9Zswa+ab9YLIYqHfzFWMiiYDuFEEnDMO4PBAL1/f396J/ZUWuzUR2Bfhx9Nnch6WeMwcdAe45E+6z/IsECWcYyLQtaFgwG79UyYPjkV1taWjbJXWFvCCFHxWKxY+ELlVLHpVKpxvLycrzruvHjxy/q6OhApcinJZevQW6EKmtnZ+e3pZSoslw/QmQdpFjfdxznz4ZhjEVy5SZU6PNQkUgEld51zc3Nt0SjUcQCE6qrqy90CbBTGWOoGFyFd4Lk9LXXXrP32GOPHRAYE0LuQ3I+mM2CHqCJEydGEJwmEokbIStCZVT3Vl3b3NycgtQFPgPxCyEESgwQIfBDqKqQ6upqtWbNmhOllN/Uh0KAjMM6ghT8YMuyhi0DdCsGkCcuSKVS/0alExWDRCKx6T3D4TB6H6ZBwqNVDV269wFx1vxcLvfvvr4+u7y8HME1pKKt1dXVb3R0dNB4PO5EIpHpkBslEgns7WF/wuHw+ehdiMfj50ejUVQwvgsZJEiwfN8NCCfGGCTNV3d3d/eWlZXBVqJn4ptSyp21tPkvSGqTyWR60qRJzqpVq6pQuQHxFI/H/znsQW7lAyB3A6mEisFoVgAHXTEIh8O/Vkr9Fqx8a2srmH5k0jDMkPJcDnlLe3s7SoNIDsDwQ1OGTPFjyHjAaGtjDU36zY7j3KyzL5SX8Jx6wzDOF0J8HZpzgO44DhiD99D85gaPkGvsoJQ6SjOC0KUhIK6A49cd5XDOLT6fr9227fOguUTgFg6HwebVIrABqxAOh9EMiGTiZq1THAO7pLXWf0YlwufzfZTJZHZDwEMpRUaLnof7KaXfh46PEHIkdIeMsUuGqytraGhA0xxkKJBxoIkuMG7cuFvAFGkN2TuQNrlNuR+CJdBVFARJLxmGMUsIgRIsTnKCYwRbD+Ox3DTNV3K5HAzFG3lZyFautyFf5vYYnAA2TxugOzjnUWiStXQLAUSJaZqQec10Nehwbt/1+/1XZ7NZlOkOZIzdgF4TyECQPBJCbtLNnHegKVsptXMymbx0G3f9fyEWaPYLBoMN+aZj3SeAgA6lewPzqNfI61rmBUd3O2Psp9CVr1ixwpo4cSIqB4WEkLullFi/dwgh0pzzi8A6wFijymZZVvNo9xi42nMEz2gGhyNBMnMX57wMEjrG2Gpo53WyfLXWdX/H1QaDkT0MBw9oWR2SeczznaZpJlHOh45eKYW9uzP29lBPlILms7OzE4kimCMw2NDgP+73+ztzudyvtOb8bsMwwMS/AO0n+hvAtEsp0ZODhnA4x6OFEI9oXf9bSMKklDiQAEnodL/ff+HcuXM3SQOG+3E1vpu0xm6vzwGQUqFSCb0xJHWaqICeeoGU8pdgmBD8I26QUqLnBqduQGY3XwhRB5sJtlsn9kcbhnHnvHnzIIMZ8sfFsk4IUYsqI9hVpdTf0IclpcSJcK/A5lJKHwAho+019NN/QlM6GL5sNrvKNE3gj14a2OGfUUof0tgjid0lH2wMeYBfcqPLKn4fsj0hBORhYCK70N+Qy+VeNk0TsspbpZQI4tBYPAuJjZSyT++np1GF4Zxbei0n9YETs5FMSimrIT1yHKdltBPwweIC36Rt4JH6oIbFqGBrOwHJxZtgLP1+/8PZbHYWkl+lVBfn/Hd+v/9U3deFxk8kxXNM00RQ7fj9/nuy2WwMQSKattGrlc1mr/1vvm8kEjnF9Z2Yw5MZY3NhVyAj5px35HK5Gyil54NQxKED1dXVVmdn56/QT6clO0t1XyECWZAFa1HBdFlyBJ61JSUlC0fiBJtoNLqHrjBBmgbbje9FrJLAHkUvZDab/bZSaj9KKRKamZzzR1taWh6sq6sLuPI/nFZzIU5mC4VCIFe6IGHM5XJIKB6IxWKDlvc2NDTgwArIh66zbftQ+AjdJwDJNCRXtyMxwJpwe71A2KIKgxjpB5zzB9CX4EqRbnB9NRjzn/X398+87bbb0Ac2lBMlNy1tNEZ3d3fPQL+dW+1HNQtyRBwcgmrOo1LKUwkh6AuypZS/0XMOFQgk6DikAScAnQySDc/DQQKmaT5n2zZ6mIKoWA92D23p+nA4DHsMMulSbTeQGEFVAqL6gHQ6fV8mk7HLyspAzp6AJDqZTLaXl5eXu/06wAvB+EUgTyBtFkL8w+fzFYNUgTQqLx0dibF+0TPgtysrK/dBBRAESDKZXDlasdFgEwMc1/RLaPwIIc15uUp9ff2OnHOAdH88Hn8tFArhNB1MwOto6NDOewWMM8qFPp9vlW3bDhYGJBlwqugh0Izl70zTnANNruM42HxoIgxAn6eDTSw8yJKeSafTByAQhgOFYaGUPgKWXycEYc2YoBnoY8dxwghEcXoQNhYa2BhjcQQr0GoWFxdDt7nRMIyluVzuSEopmIe3oKX1+XyHuMmGwzl/JZ1O9/v9/qMh00FgRym9byRKspjkkpIS9A58XbOFqHL8Hc+NRCKnuic+4bQYVEQQ/NhCiCdbW1uT0Wj0J2i4wik22iG82d3d/WJxcfFemkHaWzP3SGQo5/zBbamfnTJlSpVhGJAy4bPW7/c/kclkUIkB+7Wwra0NDepoTMXnAbwnWOtsNotmJAUpDuYWzeX19fUov+6pK1D9Qoi/DTyVY7Q33lY+nzY2NiIAPgyVEd0I92Ftbe2z7e3t+0P2VFRUdF1fXx9O36iBk9Zl0XuRQKNBXimFIKsbsh2cpIOTDUpKStBYhsbPZFFR0d2j3V+Qf0e3Yf94OBoEzX6/H6dZHIpGVZ/PtzSTyUDaBA09Av9nFi5c2Iaj2tykphDa7/Hjxz+KI/oqKiqwJnEtzhF4fOHChZs06kP9YD2ZpomAGRWJ9mQyubyiogINppP1UclXQW4opUTDfhYMYm1t7WtaeoijYCF9wTFyH6dSqWeWLVuGEj8ayOAYYOteicVir43U6SyQSKxZswYNo3tjLtEsLaVcp+f3BCnlv7q7u18vLy8Hpkhye7PZ7D04OrK9vR1la5x+gt6mf9bW1r6VSqVK+vv7j9MyKmCI83qfG4kjBd0Tng7DyRpIZlOp1CMVFRXQUpekUqm/VFZW7gtpBo7ecxzn/p122mndmjVrDtJN0NiDazjnNuf8iXQ6jeNL0d+F96Ow20OtCm3tukD/EhhSSNbA+nd3dz9aUVEBOUVNV1fXXSUlJTiZCgcY9PT39z+4ZMmS9aFQaCf0smG+NcG04v3333/2oIMOkqtXr8bpNqgWbOCcP9fS0vLJcIKirX2HwVwHm4g96EoV+lKp1N2VlZWYm++iqgTZoZZe4CQ323GcJxYsWPAhmGlUo0DMoLJGKX0STGIkEkEQ+1POOarmOFUMUo8hB4GDeY8tXetWKZHEIwBcUVVV9czatWtxkEZlNpu9LxAIILEHO7vWPeWtE9VDSimSUFQ9NqZSqQcmTZok29vbgcHXYUuFEM+1traiv2DY7wbpTiaTgax3R7wDKvKBQACxSgMa4MEgw88ZhlGiA96N1dXV9y1fvlzusssuOCVtqttzdduhhx767lNPPXWgPlUKJ+ytB8Hn9/vvH8p+drXuR+imYhx00K1PaXoiGAwiZqhLJpMXuKf+YA9gnP+CLVy3bt03crkcyM9NvU62bWOtdLjKDkjNcGLSnbW1tS8NU/mAePAbSDRgi4UQH9XU1DzX3t5+EOf8h1VVVVfDPkIaBLsmhHgJhInrQ6AyQQPvumw2u1zHZ9WmaR4lpUQfBk5Du2ekKlxIiLQE7Cj4Nt2X0xEMBp9wHAcHPfxSH4bTmk6n0af5W11B3w0xoJbGYr/gOFCjvb39KM75TlVVVTesXbu2GFI9cJ5KKUifYbdfGq3gfPN95NpDkHM49Od+9MziJMfh7s0t3T/YxGDTcYHQUrsP+4+zYL+sGQPlMZwtPaCMg6YTSCoUnn3ppZeivpMfK56PvyOA84E3nVUejUZ/gRIrMmgEkgNfyj2LFmOUYHbxUUoh0H9vMOWegc/5bCADyk6jMRGf90wX781PQMoD9L/wH1ga25Zj9L5riwhgjrbkrD7v5x6M/4mAh9W2WRVbxPlzbM+2GZH3LR4C/z0EBmN3aFNTE3dPzsvHKvDXm2KXLcVJ2+q1No8HQEZOmjQJcZZEsByLxZyRIkmG8k7bi31BTAryAHHtQQcdxJcvX45Y83/N38Cx/rfGje/dY4896DHHHCMw5jFjxqjRSkoGnRgMZQGM5D0NDQ0nCCHQUHPTF2WU06ZN241zDsnGW9lsFufqb9M/HjWS7+w9y0PAQ8BDwEPAQ8BDwEPAQ8BDYLQR+ComBjXuHwvq/KLSHEpwhmGM002unV5SMNrLyHu+h4CHgIeAh4CHgIeAh4CHwFcdga9cYvBVB9wbv4eAh4CHgIeAh4CHgIeAh4CHwPaIgJcYbI+z4o3JQ8BDwEPAQ8BDwEPAQ8BDwENgGyPgJQbbGHDv6zwEPAQ8BDwEPAQ8BDwEPAQ8BLZHBLzEYHucFW9MHgIeAh4CHgIeAh4CHgIeAh4C2xgBLzHYxoB7X+ch4CHgIeAh4CHgIeAh4CHgIbA9IuAlBtvjrHhj8hDwEPAQ8BDwEPAQ8BDwEPAQ2MYIeInBNgbc+zoPAQ8BDwEPAQ8BDwEPAQ8BD4HtEQEvMdgeZ8Ubk4eAh4CHgIeAh4CHgIeAh4CHwDZGwEsMtjHg3td5CHgIeAh4CHgIeAh4CHgIeAhsjwh4icH2OCvemDwEPAQ8BDwEPAQ8BDwEPAQ8BLYxAl5isI0B977OQ8BDwEPAQ8BDwEPAQ8BDwENge0TASwy2x1nxxuQh4CHgIeAh4CHgIeAh4CHgIbCNEfj/TWIwc+ZMP7CbM2dOjhCitjGO3td5CPw/h0BTUxNramqS/8+9uPfCHgJfIQSUUpRS6vnEr9CceUP1EPhvIjASiQFtaGgYI4TYhxDydSllLhAI3DNv3rwNQ3wxGolEgtXV1bmmpiZna5/R2Nj4DcdxLpRSXt3a2vr+F91XV1fnKywsZIsWLcps7fNH4TrgtqMQ4ieU0p5MJvP3xYsXf4ZZJBIxKaXfVUrtKaV8v6ur66Vly5alI5HIOErpAVJKZdv2Y4sWLUpNmzat2DCM/ZRS32CMvbBx48a3li1bJkZhzFt85JQpUwKGYXyfEPItSum/KKX/iMfjNi5G8NjW1rYjY2w//Lu3t/fhpUuXJvF+Qoi9DMP4tuM4K4UQL2M+GhoaahzHOYAx5jiO88iCBQt6ttV7bO33AG/TNPcnhOwkhHi+q6vr7TzedXV1vKKiYjdCyA8dx0lyzh+Lx+P9wMjv9+9PKd2REPJaVVXVm01NTSIaje4ihNiPMbYhl8s9ua3WJBJp27b3cxxnZ0LI67W1tf/K77e6urqi0tLSvRhj36CUCtM0n5o7d25nfX39BMbYKUopn1JKGYYRV0qtVEpNIoTsSQjJUEqfiMfjn24tllu6DlhRSn9kmuYujLHXpZSv5dcTIQT2oZIx9hMpZaUQYvn48eM/7Ojo4FLKfSilezDG3tm4ceOry5Yty82YMaPScZwfOY4zBu/R0tLyyQgSB9jD1YSQ/XO5nIn1OnAPY+13dHTsirUghPhU73XMbzYUCo1ljGEPFzmO8z+LFi36pK6uziwpKdmXUjpRSvmvCRMmvD4Y+/d5eAPLQCCwj5RykhDibc75ywOxnDFjxgTgA3tCKX04Ho93YW8SQvZWSn0H8+s4zotYl7A9SqkDlVKOaZpPNjc3J4czz192L+x0eXn5XpTSb+VyuXc++uijl5cvX/6ZT5gyZUqVaZoHYY1yzp/AeE444YSSwsLCfV1f9EJ+b5544onjCgoKDsbYbdt+ZtGiRWtHcB182ats1e+bmpqMVatWTeKc7+M4TntRUdHyOXPmZPM3Y16UUrsyxko2btz48kAbHwqFvsY5/7VhGPfC94ZCoZMIIVhPDqV0RXV1dctIrKetepEtXIS5rKio2FdKuQvn/LXm5ubXByQrdPr06bW2bWMue4QQz7S2tm5samrydXR07EUI+baU8j3G2IvV1dVi7dq1O8FHKKV6GWPLh2tv8sOF7S4uLt7FNM0fEkL6N27c+MiyZct6B/zeV1JSsifn/Du2ba80TfN5jAf+zTCMA6WUffodli9cuPDTuro6VlFRsZeU8jtCiBU9PT0vDMUnH3TQQcauu+76TaXUvoyxds75M/Pnz8+PaZMtJIT8GPbNMIynqqurP2pqalKhUGgn2BghxEbcg31dX19fwRj7EWNsjFLqyXg8vnq4eyDvC5VSO8JO9PT0DIw96LRp08bpvfljpVShtpOPJxKJ9ilTpvh9Pt9+UkrEK69VV1e/AV8YDodh+zb5wlQq9STinaGut4H3wQ63t7d/kzH2AyFEG2PsOfjk/DWNjY3lSqmf2rZdQCl91rKsj6PRqAF/QgiZpJR6s7u7G/5EAm/EX4QQ3LPcxXubkGNYn6WlpTswxr4nhHgBe2Qk8NnSM4adGABUIUQYDkRK+U/O+dFYdEqpTsZYRgjYbI4gxCgsLMSmXlteXj7WcRzq8/kc9n8iQCyAgurq6rUrVqwoLSgoOJlS+lY2m33tlltu2XjGGWeU9vf3B5PJ5LpJkyap9evXl+Nl+vr6hGEYRbW1tWs6OjoKlVJ/5ZxfyBhb1d/fnyouLlbpdLra7/d39/X19QWDwep0Ot1vmuYPlFJlhmE8U1VV1fbxxx8bRUVFFV1dXV1LlizpwwTg36ZpBgsLC9fPnj17RBbowAmYNm1aNef8EiFEwjRNTLRdW1t7S954h0KhnzDGfqeD/cWEkCOwuaWUz+nE6wLG2CtwfISQsclk8ory8vLf6oQIFZM3GGMzlFI3W5b19mgtmoHPBVZlZWWHUUp3Vko9xhibLoS4K5FI/B3XwWExxk4mhDwvpfw2pXRMKpWaVVlZiWDkt4SQJUopjP+x8ePHv9TR0XGhUuplSunuhJDSZDL5p6EY1NF6d9dZHc0Yo0IIOLdGpVRzHu9oNDpRG5LfYw9wzo+UUn6STCZby8rK6gghcIqP6cAP87rQMIz1juOcyTl/REp5uFLqQ8uyFg7XWG/Fu8OhHA2jRyl9ND8P+XcIh8MI8n8khLjXMIwTlVLZQCDQks1m91RK4d8vIQlAcCiEQOD7M8dx/mqaJn7/dcMw5g5wXlsxnP97iWvEf6uUqtbBP+xIvQ5ql7W2tr6Mq6ZPn45gGuvpYyFEijF2lFJqtmmau+skDevwTiHE7ymld6RSqX+XlZWdCCfPGEtpG3UCY+yslpaWdYMa1OdcjASqvLw8Sgh5F+NFUkIIuTDvdKZPnz4+l8tNo5Q+Tgj5HWPs3zrYvpsQMg3JmBAC5u8QzvmNjuPsRyn9nk4iHnavXRiLxf49nHECy87OTgTD3ySEPC2lPJVzviQWiz2D50YikVLGGMb/ip4/JPVjampqLuvo6PgJHCWwJIT8Xkr5THFx8Qu9vb2XEkL+hxCCvRmwLOva0Vyr4XD4+5TSg6WUTzLGTlNKtViW9TzGjkCzrKzsYtgVlxCpSCaT18AWUkqDUso3lFKnCiGuDwQCnY7jXMo5v1cIsRNsfzAYbB4YdA8H55G6NxQKfZNSeqQO9J7AGsH6j8Vif80/PxQKfYcxNgXBs87Lr8gneAjOGGMNlFIEpzNef/31jn322edqKeVaBJNKqY9rampe/G9W98Lh8K8ppQiUlxNCjtWB4nWtra1I0omb4F2uiZZFlNK9HcfJtra2JqLR6A9ghxD8CyHO5ZzHOOcfOY4Twv5WSn3fDTgXDAz0hjofkUhkAiHkeMMwnrRt++eEkJ5UKjU373/c9YiEBHbpbO2Db2GMvUcIiZqmuTSXy+1NCCmjlLY6jjMRsRBj7D6l1MmMsSUtLS2bbNhgPrDFlNKjOOeLHMc5ijG2+r333rsLCTJsoRDiRCHEKkLIRk2IHCul/LNhGEoIcalhGAts295fKbWuu7t7aWlpKdZOn1Kqm1J6opTylOEEl/CF7e3tx+o4RjqO8yZjbCql1MrbLSQier5OBtFBKUV88hPDMK6TUh4mpdyNcw7/8RvGWKthGBtt256Jta/9/y8xzy0tLdZgsPq8a6PR6B5KqVM45622bf+aEPKPRCLxkGsDC9y5fJMQIrBGOec327a9G2Psp0op2O4GKeVc7fve55xPIYS8j6SUUoq47MaWlpb2kRjnlzwjn2QdAZ8nhJicSCRAbozKZ9iJQTQa/Z5SapqU8qpEItEGZ4NkAMER57yYEPIJgkZCyA46TmwjhCQopfVIBjjnnUqpnzNw4al2AAAgAElEQVTGHlFKgdX6u37hDwzDuEkHi68xxu6VUiJ5gNNCAkFLSkru6+vr+ykYSqXUK8ikpZQvBAKB5/Vk3q7ZJRjScr1IP8HzdMJxLaX0IaXUc5TSKwkhDwkhkD0isL6fMfa84zjfJYSYjDEEI/crpWAgxupFXoNgLRaLPTnS6NfX12NDHGlZVn00Gj0UwXFhYeE5SEJclv0sbGJsNARHqAXbtv03nWX9CcmBYRjjCSHXM8Z+L4SAU3g0Ho+/FA6Hb4aztCzrLyM95i09D8Ypm81OR9BjWdaD4XD4XBjUmpoaMFRy+vTpezqOcyxjbHYulzMMw5gjhDiTUoogbUMymVxYWlo6HfcIIR7jnF+qE8aLbNsGW32D4ziHbk9Vg4aGhrFSSgQpyyzL+lc0Gr1WCPFKIpFAEKXC4TAC/O9KKee5VZxjDcM4W++JP+mA6i+GYbyey+Xg5P4hpexG0kcpPU8phQ0/zefzHTXaAUtdXV2wrKzsGs0UP6YDr6cNw8D3v2JZ1j2Y48mTJxcWFRX5MpkM5ut4N7i4Dywy/svlcvcUFxcnKysr7c7OzqOklBMppTcRQlDNatIM7sUtLS0fD2X9zZw5sySbzZ6t9/UjqDxpJ3amNuqf5pPm+vp6VBEapZQ353K5TwOBwPUIYF078t7GjRsXl5WVnc0Y6+Kc35rNZi9TSt1eUVHxTiqVuocxdnU8HkdwO+xPfX39jpTSM3UwcCPn3FBKJaSUUxKJxEd4OIIhKeUPc7ncrGAwiGQH/zXBgcKBm6aZ0yzVtaZptti2fZJhGM/19fU9GAgEEPC+Ptw9jGoB5xyJ66qenp57y8rKzkeSZFnWDW5AtrtpmiB1rnUcp8Tn8zVj/IyxOlS7DMO4zbU9kKE8L4S4gHM+EwmQlPK8VCp1JKoywwbycx4QCoXOIoSkEolEazgcvo5S2huPxy8bMPbLDMOA04Z/uUYIgQAshOAiHo8/q9m9OTrhfolS+iEh5BTbtqf5/f69dCA1LRgMnjVnzhzsv+3mEwqFjiOEVGv2d57P5wPjv2c8Hp+RT75gezRTiIC1llJ6PRIDBGdr1649xLbtMW5icHl3d3dneXn5BUgyhRArP/jgg+6BlZb/xgvralNMV5v+p6io6L7e3t7rlVLPJhKJ293g7FcgHGpqak5oa2s7hFJal0qlGktLS+HX+lKp1JLy8nLMO6pFSAQPqqmpuaizs3NvKeXJnPNZIxGcNTQ0HKST0J/4/f6b0un0TpTS8x3HOXXhwoXr3XFiL6VramoWt7W1ncUYQxLwdyTROjE51w3iQ7ZtX+MSKulEInFjOBy+jDFGYrEYEuut/iAOWL169WR8j2VZN4XD4Z+BhLFtexaUAqFQ6OuU0gYhRMwwDLD/LXovLNGBbYVS6sjq6uop7e3tCIQP18nkWUop7JFbCgsL38xkMo/qmOKy4dhCkH56f54uhFjW29v7Vnl5+RUgLxOJBHwFQUCO+clmszcHAgFUORDX3YikF/Y9GAw+m8lksE6f0D4Q6o1fGYZxpeM4h+vg+8Tq6urfjUQyGw6HEV/saFnWH6PR6FQQJZgvjLGxsXE327YvzmazZxYUFJQiSRBCXMUYA+6dsVgMtudGSmk7pRTxaAOIqHQ63V1QUHAtY2xpnmjZ6okd2oV05syZxb29vd80DOMs27ZPXbRo0ZqhPerL7xqJxOA3UspfIrhBucr9ShqNRqdIKfe2bftKn89Xhs3DGKsF06GDyGPcsrYjpZwFIlAH7z/QFYcjsBFN00TgiAD9FSklAvv/MU3zZdu2sfCv0kaxCKyDYRiXU0ohIToGi55zDsc8F0UIBAp+v//SbDY7U7PY6xzHQRJwlt/vvzmbzR4ipUSp6E6dlOyrlDrMMIw74OwIIfdQSqullG1FRUUP27ZdOm/evE3Mxkh+wtpaUEr3icfjDaFQCCVUVElmgPlwS/lXo8y2YcOGv1RUVABLVEnAJCEpiDqOA4ZyiWmav7Nt+3Rdor01kUi86jrQ9+LxeGIkx/t5zwIrgLlnjMEZPx6NRiE1CVRXV89G9SMajf5CSnmwbdtXl5eXZ/v6+pZp4/pHXVGKomxXU1Nzd0dHRxTKFNfInmnb9ima7ZhAKV1KKT1opErFI4EHjDFj7BwkOPPnz383Eokg8FxdU1PTihJufX09GOrakpKSG/v6+iDFON0wjAuFELNyudyFxcXFqzKZzDk6OV6NNQjpiGVZ59XX1x+CoDUQCPxstAOWKVOmlJmmGUdAW1RU9Fp/f/8FCCASiQSqU5s+kUgEwVYIjJwQ4sYFCxZ8OG3aNLAokymlOTBAJSUlV/X39++BgAvMGKW0UsvewERNzwfHg8XcraRBEtg8fvz4d9va2mAbjJqamrlNTU25+vp6MDphxtiVOkFL6rUG+/G0Ugps2tJ4PP5gKBQ6nRACFvVmvW9uYoz9Gaw+iABKaUssFtvkuIb7CYfDkFDBVl0E0oJzfhcwy1deIpFIRAgxobu7+/LKykqwZDOFEBczxk50HOdPSinb7/dfi6qgUuo0kAB+v//pbDZ7CdZHLBZrGc4YkbTbtn2BdrQvLViw4P5oNIqEtsSyLLCzqObBJv9WV14v1nYV8sp7KaXYl8frRPWfK1euvG/ixIkn6QS9REqJCmRjNpudXFBQgArEDf39/YejwjqcMX7RvfX19Ui4ICO7JRQKXarnsyIej5+Ge5CAI8BAMNne3r6TliUsBmsOApoQcptlWf8Ih8OzsDfdxPa4mpqa4zo6OkAobbIxCK5Ga+xDeW44HEaSSWpqam5ub28/mlL6m2QyedLAimk4HAaRgMrUn+PxuNPQ0ACpA/4DuXYDY2zWxo0bPyktLYV/KYdcjVL6Sm1t7d0jEWQN5b3cKuAjhJCb3QolCK2V8Xh8rmtrkOjvrWUm4Ugk8mMkcStWrDhxl112aVJKvdfa2nprJBI5B8SDlPJFSItqa2svaWtrA2EIf3P1UO3NwPeJRCLHSyl36urqmh0MBisCgUAzY+zUPMkRDoevgqwECU1DQ8MUIcQP3er2NyzLuhj2QCl1Buw4/LSUEoRRczgcRkKOwBTrc6s/kHtmMpnTtXzzUwSojY2N+9q2fSLiHlQ93e+bYRjGtVJKVIaaUZFEtV1XKHaOxWIzkEzoqvVUkC0IZF0C951wOPy4UiqWSCQ+q0ht9cDcC93EBAH2XMjVdC8Lkrc38oRGfX39D2DrbNu+yufzdcOmm6aZcBwH/n22EOIN2E53TgNKqe8Fg8GLc7ncwcBv3LhxP4PNH+y4NrsesSje+1PLsq6ZOnXqcZzzQxOJBKq2sIGQHZ5XVlZ2QjKZhEznEjc5gTJjeSwWw9rDfDI3XqzP5XKXFxQU9OZyOcSmD1iWhSrvNvlAkSClvNw0zTPmz5+//SYG4XD45yh1CSHOd3WBaEgEcwrJwfhEInEVEENm5rKml4B1NE3zUThNx3HOKy4untLX17cjyoucc2RkYAruzuVy7zPGFmkp0tWxWOwVXepbgCBAM0MfUUoPy2azV5umWWoYBlhFBDqztG7xUlQXtCbxOCHElTpYgNThd5xzSFlKUqnU3RUVFSdAe1lVVXV7R0cHNit0ydgohblc7p+U0gIE39hsjLG5oxGYRiKRk5VSB6Bi4DIVf0CQEY/HUTHg7e3tcN7vp1KpW8rKynBtUTAYfCKTyYCRRQYMvW88m80eHQgEztBZ8B2uM0Sp7q3W1tZbtsVKPf3008v6+/vPAQNkWdYjoVBoJprduru7UYKVoVDoQMbYL93AGJKUWxlj50opIzCytbW1S9vb2yPQCeMZhJDzs9nsaaZpQs+eKCwsPOTGG2/cbhw45CG2bZ8jpYy3tra+HQ6HL4eMrra29las+2g0iqR352w2e5PutUElKso5h5OY5TjO9YWFhe/CSOuS9Qp3nX7Psqxzp02bdihjDDK4w0eiLP5Fc++y8s2O4yQMw3gJiRoqPrFY7A5o+JuamjYRBh0dHQHYTshGYPyTyWQWc+pqROcjkQNDP2bMGPSJIDhjhBAEm01D3TMuK3qBTvgXQ3vqJgbQSVsDAiGwZLO0096IxMCtLkDy8khra+syJAacc+bz+RKZTAbs+Nyampp/t7W13cM5nxOLxRCkDPtz2mmn7ZZOp8+2bftyXemCRn+pYRjh5ubmFWB5o9HoSbpasVsul/uT1iMf5jJlfwIJoCsMV2i2LGeaJsa/SFfPwMT/JZvNPu7KIT+wLGvRcAaJyk8gEIDjfr2mpua+jo6O0yA5TCQSSKZoY2Mjeh9gJy8zTdNwHAfyjLBhGJOFEO/W1tYuW7169Uk62EQP0Zu5XA6s2sn9/f27o/rqOM5vRrMnJhQKXa0riysty1oQiUQQJEK+BPKG1NfXow/pVF0NOElKicoNWFNUP8KQtSUSiRdDoRCSrhVIDIB9KpX6Q2lp6Z6QWxJCzhhAZA0H5hG7NxQKzYAMCtU3TZzVcc4PicVikM3gOzY1DyMxIIRAcnSDXlfc7/efoJQao98HpNxkXXl/IBAILKmsrOxAIhCNRtF7Nt0wjMahyvuG+4LwB9FoFJKahalU6sHy8vJr4KNQCXITA0gDD4nH4yeFw+ED3bmaVlZWBoLgk9bW1sWhUOiPlNIy7UtehoSourr6QjcxQCJ7TTweh5xmWJ9oNApZ4qR0On1DMBisVEr9OZvNnr548eIOd5wX6b37EYJFEHuYB8gqIT/BukSgDnKPMYZAFAz9Csuy5oRCIRBnO8Tjcdj9rf64PSUgRnLxeBwJxo8QayHOicfjGyKRCHrZTtHVvhvcisEczvlf4X+AkWVZoUgkArvzh97e3rM0Iz4bCX0ikXhDPwvV4ptApGz1gDa70JVenZvJZOLpdPrd8vLyS9EP5iYbNBQK7c0YQ+VrFhh2n88HoibuVh2tTCbzit/vP59z/ixjzOc4zk9SqdQlpaWlB1NKT02lUr8aCRkxbIeUkiQSiab6+vrjtZz3R9o/nQIbiDFqMcaFlNKpjLFyLWM73zAMyPAwf6/CBkejUSSEWcdx7uWcI1a5wnGcXlyne0DvtSwLhO02+eQTg4KCgtNvuumm7VdKFA6Hd0WZSgjRyjl/TwiBhllosX5BKd0pHo8jEYAmFFp0yCnKUeKvqqp6ob29HYwqAuCwXtDfIoQcp5Q6T0oJZ7bcLTVhMd8XCAReSKfTsw3DaBZCGAj2Hce5UusOxxFCUDGwGGMoV15omiaB9k5/bxPkEH6//zwkAlLKG91gAsYUTTu3cs4PQmlJCLEQwamUssvn86HBBNIlSCyw0JtHetaRqXLOz7NtezrnHM1OE1KpVHNlZSWajdfoIBllw2+Zpnktgn9o7goKCiA1AOsyT0pZheRICHEe5xzGA9g/o6slF+n7/prX4o70uDd/HliNbDaL7FuipAjDSAh5lRDyEWMsqGVD3ZB+aO35LdrQgsU9KpPJXBIMBjE/E2HUKKXHoqfEcZx/6OoSynZzbdtGQ9chhJDTBjRLjvbrfOnz3aAayc+b2Wz2Ob/ffy60+JTSDsMw0IAF4fivoDNFkxKcdjKZvLGiogK9E+sYYw8KIRBs3aelGWDez4Bjo5RCTva1eDyO/TCqJ4i4DuciyG30fkWFDGVTaC670IClpU/QT47t7e19u6ioCBr1bzmOg3X3Hc455BeQAF4CtiyRSIBRKXATVWhY36iurr5rqOyku55gtD9ljD2sk/OwEOJFaMZ9Ph8a+d4DS+hWKDAOOGKwULA3aAS+UWuqp+iK4QtuEAFG6gVdYXwHpXPOeVMsFlv5pRO9FRfMnDlzTCaTuZhzjmojGrJBOCAIQG8BjHY1gmwwpSiTo49KCHE75xzVASTu/Y7j1BNCmjn//9j7EiipirPtW1X33u6etYcBZLoHJCqYaKJJNH4xq0bjErOZBKNBDDDdtwcUFTXGuA7GfUMFZqZv94yIkkXc16iJojFqNGpciAY3hFnYZt+6+96q+uuZc9szElSYBfX8t8/5zhfpvt1VT1W9y/M+bw2DAxWU0pWQ80AGAmngDgzjQz+CdRZCgPEPBIPBVQhyvP6QNzzNLzA+x3XdFWCX0U/Q2dl5RklJyUxVfa3UdR0SpxnqO96BHWSMQS4GOSaqSV+LRqPnDnedd2ResVgMNmKKrutpyJigT2eMvQg74rruZsMwlkBS47ouGhyP9Jw6iJ51lFIQSJBkQV7aTghBAgZnvxdkq8XFxYvHondsR+b1YZ/xJKWHqLOUhJRC2b4O13XvUeswqaOj42U8h14lVAwGBgau3HPPPQc2btw4XkkbIN+FzwJpdIlKEN7mnH+1oKDguYGBASRxvzIM4zefVGKAcaPigwBXCIEq8AL0usA3MMZCnp/4vVcJR8P07hUVFdc1NTX9EtVXTdNWMMbAuj+FCpDyLUgkUFVDrAFpY2o0qj+eFAjVsjRiAsYYFA+QKsLurAkEApAwo4cMFT5UJdHThx4DVAZRCdwHyodsNpsMBAI/RPM+JIOwV7quP19XV3fvzu6P6upqyBHRRAwi5HBU71Cd55zvpSRXa1BpVM3q9+VyuXcZY2fiQgjHcSClht0DwYogG2cb1Y+LIa1TvQoY99WonOX7PHZ2XF6iVKriJZCSLxBC0D9xqq7rqBxvdRwnCuk45o4+QtM0+7PZbAwyccdxqlVygItW7sM+z+VydxYUFOBSANh12G8kM6gOIl4c8SuRSPwIChPHcSDhRf9fR39//wNFRUXTHceB377cdd0lSmEiVZXnhyCD1Zx+hL4xJDKwkSCfVO/aq2DqXdddhfGDwEaPaGNjI/bAmL+8BPuLqJChl6ihoQHJ8JjECiOWEsGRu657pKdxhDPE7TQrcRMHZD6RSOTSoQ21KFWjCQ7VhXnz5u3nui4WAUa7HMFvIBB4fmBgAMZvqmmat2Wz2d0gt1AszxbHccp1XYeeGE1pKD8uUxsetzF0IGM1TfMKxlgtpTTHOUdzJSQEXSpoRsJR3NHRkVq1apUzd+7cA6DNV+zsPw3DeMlxHDRME9XYtW5gYODxYDCIxt9ONMigcQbs02ivuhdMwcChMRLSp5eSyeTrlmVBX/0cY+wtL4CEU2Zo+rNtuzUWi0EygVsFhLqF4l+TJk16GVpLr0djCyEE2vD7xrLEvw0WaGQFTtCGgllBz8GDKqlDYgPWZbkKRo5WhqjQK2s/a9v2i2DeXdcFo/o2sEfDF8qjsVgMJXR0/BPDMJ6tq6t7ZbSxH8n3eWVx3H4D6VsrSozhcPihnp4eSNK+xxhDoIJGO9wKBZnVwwhEPb3lL4QQYDCznPO/FhYW5jKZDPYm9h56XB4YDa3sjsxv/vz5SIbRpItGKmjdH0Ui5rGPzxNCjsKeVAnOZCnly52dnS+Gw2HIGKC9Rf8OEtPbwFzF43E0d0EOt4Fz/vhIe0I85g3Sm/cYY0Y2m30YgSpjDGXcRYFA4AAEeOjRIIR0BoPBxxzHGQ89K+d8na7rHM9MnTq1e+PGjbh5aV9d1wfQdCelfHC0Ek3v5jDgBK0t1XUdyS0a0qHTfxGlcsgm0dSGva/r+v1btmzZhGZ9nGHVUNmvgqQ29HkoYgI3d8HuoScL6/HwSHEEI4Zbr3A2IUdAMz+l9E5I+/C/TdNEw+QxijAJoyqEBKqhoeEl9E7kx4LeK9geXAzR2to6g3NeyhhDdQ+3G6Fhb8xeuKEM8lI4ck8+usq7eSra2dn553Hjxv2Qc74bJFmMsX9GIpE1TU1NuMXmIMZYuxCCwBZmMhln3LhxxwkhQoyxfiHE65FIBLc+7ZLbRHYUIMuyxhNC0IeCyy7G5XK52wOBAHrhvtLe3p4uLy8Po8KDM6gSpsaOjo7/5BlV9N4wxiBRRCDbBTkfqoCYMwiJzs7OR0aDfd3RuWz7uZNPPnl3z89uxP6ORCJ3tba2ItAtV0279+q6jv4KAWYWEuJ0Og1fiF4/6ONRMcZNZEjyHHwPpRQY4ea+NfCBo7GWIH0cx/kR54MX+sF2o/EU5MNp6F9U1TfsHQTqIEfGBQKBP3V3d2dCodAvOOdI6jVUQiZPnvwyLkhxXRdJBgJg3Fh253DOM/YEglo0kkPRQAgB+TcRJERnZ+dZ48aN+zZux4NtQyzR0dHxqNL6o98J5EhWVR8d13WfbmhoeDuRSMBH7eclaFs6OzsfGsme8G5dQwM47DHWlZSWlv6lp6cHY/qmaZqX9Pf3Q6Zd5pGuLWvXrv37tGnT9kaTvdf7gzE+0t/f75qmiUQQLxAoD44kaRm6/9D36l34gAqzwM156KnC5SCO4yCJ+wb8CcgopWRowa1F3i1PP4N9xi18AwMDt4dCIRB5IJL3wB5Aw7S6Fe0DN4cN93zsyHOeNBSXQkBBArLkqeHsqR35rREnBl7maAQCgVB/f78Op4ENqrKwgJTS2Lp1azduEsJ1gh5r5ti2DXYSpfb9Hcc5H6VONJ/09PT0Y6OiaQ6Oqq+vD1dK8UmTJhW2t7dr48aNy6IxM5FIgIk9GhWArq6uvn333TezZs0aWlJSUqRY+Ewul0PQjM3Vr/RtZlFR0eGMsdehCcfv4gqwqVOnFiFLnThxYv/bb78dQrNlb29v7pZbboHGHw4kACO0ZcuWgZEcno9aBO9vLwQ6Ozs52B8YN9wwgYARf49h1qxZBeXl5UZzczMkHGjOkV7/QYHrumLKlCl9eAbVmAkTJmDMkHIMYrQjiz9an4GBAIYYa2dnZ8a72hDMMwVLhQa5devWFVBKeX7MCFrOOusszCP/DMYsvSsKQ5hfQ0MDmOsxyYhHMnfvWrvB+UUiEaxbDnvWNM2AbdvdkNqEw+FgW1ubg/3kZfW4VaBICIEr8Qbya+Qx5MGBgQF3FyZzmP6248ktXLgwuHXrVrrnnntm29raCnCeA4FALn8GMFYwfP39/RJ7NM+45vfxq6++2j8aTY7YT5s3b8ZtEYau6/3ACvs+l8sVIthHEL5+/foQJqEapzP5QB+GE2PGjUm4FjR/1qPRaAHYoClTpmCtdvgK5B3ZI/lxDdnbcv78+YUTJ07Edcvv74uh64tncElaaWmpmDhxYsYbE65sLFS9B5CHZEbrDOfPZiQS0Tdt2pTF2Vy4cGEI6+zdwIbegv85m7A9sIlbt27N5G1P/mwi8YLdHI1g7GMwHry6uq+vT0ejNsaOvdbZ2al7dhprPWgL87Yibwu7urrotGnTgO2gRhljnzRpEnyKzPuZHVnfXfwZgjPY0dGB6xwhHwHG5ubNm83a2to+YJHJZAb3PeY8ffr0rvwagGiCz8J5waUA69evBxEDWyOy2ezAaCXDI8ED53Ooj/JsJoV8dsGCBaamaYFAIMCLiooGfaHHkGK+xrb2pqenB9VoJ5PJYH+O2tXc+dgDsQEwh6yyra2tqLy8vP+iiy7C9dLbHU82mx204UPHgzUZuneHix3OayaTCSq5djaZTA4sWrQIPrMAElvvittQMBiUuM0pv87biy1wNkzTxLWh2rRp07C3RmwL8+cNdms7vnDw6mPEhkic8rYO61pVVfU/vjDvQ0tKStxrrrlmVHuXgOHmzZuNiRMnOosXL8bVy5BOBhGPwJ/A3wEXrHPeHiM+yWazrKenZ9D24P083tv6nuGu7c48BywV8YibqJhhGJJS2jtafmLbcYxKYvBxk4vFYrhFAXKF/oqKimRNTc0gyLFYDFfLLcQVUDt6i4kXlP1UNaWBWfnNR13Z5LEo0ONDSvDAWGu3Pw4H/30fAR8BHwEfAR8BHwEfAR8BH4FPKwK7JDFA5gw5EO5THtrwNXfuXFxJWYnSbv5KsI8DKv8HswzD2K27u/s/K1eu/NAr57zSyxcMw0C14P0/VPJxv+G/7yPgI+Aj4CPgI+Aj4CPgI+Aj8P8bArskMfj/DVR/vj4CPgI+Aj4CPgI+Aj4CPgI+Ap81BPzE4LO2Yv54fQR8BHwEfAR8BHwEfAR8BHwExgABPzEYA1D9r/QR8BHwEfAR8BHwEfAR8BHwEfisIeAnBp+1FfPH6yPgI+Aj4CPgI+Aj4CPgI+AjMAYI+InBGIDqf6WPgI+Aj4CPgI+Aj4CPgI+Aj8BnDQE/MfisrZg/Xh8BHwEfAR8BHwEfAR8BHwEfgTFAwE8MxgBU/yt9BHwEfAR8BHwEfAR8BHwEfAQ+awj4icFnbcX88foI+Aj4CPgI+Aj4CPgI+Aj4CIwBAn5iMAag+l/pI+Aj4CPgI+Aj4CPgI+Aj4CPwWUPATww+ayvmj9dHwEfAR8BHwEfAR8BHwEfAR2AMEBjzxEBKSU4++eRC13WZ67qisLAwt2TJkpymaXLGjBlswoQJodra2j789xjMb7tfWVNTQ9esWaOvWrXK2ZW/u+1gMI7e3t4Q51wsXrw4s+1Yampq9M2bNwe3bNmSW7VqFTDT8ExLS0uwsLBQLl68eMD7TjJ79uwApdR45513BlavXu3uKizzvzNjxgyzuLg4oOt6xrZt4Pr+C+scDAaDoVBI2rbdn3/DsixD1/XAtvN7++23Q+PHjwcm+fnt6ul83O99JN6HHHKIvs8++wT7+/vd5cuXY10HXzU1NebmzZvNiRMnZmpqagbXCNjsvvvuwa1bt/Khn/24AYzG+1izCRMmmK7rZrddM+yzzZs3FwQCAT50b+bXbOgz+Tl0d3djfbFmIz7LCxYsCPT19Znb7qf8uKSUBjDA2dE0rT8/fswJ/7xq1So+BHfa1tZmLFmyJDsauA39jvx5xL8N3dtDzsWgjWOMOfnfh01MJBIhXdfpxIkT+2tqajAH7IUPXY+RjPujzibGv73zhnV2XTfY09OTzdue/Lg92/M/9mokY/ywZ7e334Z8lliWFdpmPO+fzZ6env4h+4DMmjWrADYoEong/A1i/ml75W3+0P0ydL4LFy4M9vX1kfw5Az65XK4wFAoN+nJCiDNkTw1iMXXqVDdvbz7J+eb391D7N/SMDhgGYjAAACAASURBVNmH7++t7a3/h9mm0ZhbPibZsmULbMj/+J+PGg/im6H2b3v+bThjxJ5Yv359SAjhbMdHEOyJbDbLhtqSfGzh+RXYPdhkMmPGjODuu+9Or7766n5CyIjtNOYze/bsIGKPKVOmDGy7z/L2saSkhBQVFeH9wXO3q31hfl29czUYfw5di4ULF4a2bt1KM5lMJm8zPsT2DNoc2O4tW7YMDPUzw1nbYTxDFixYYJaXlztjacPGPDGwLKuAEDJbCDGeUvqaEMKUUv7nrbfe+s/06dMnaJr2046OjuXbO4Tz58/fq6enp/WWW25B4jAqrxkzZoSKi4v3NQzj81LKO7fnzEflhz7mS7BRx48f/w0hRCUcLmPs2bq6unfzG3bmzJklRUVFh0opkVRlQ6HQ6mXLlrXF4/EvU0q/wDkPEkL+kUql1sZisUpK6YFSymIhRHtJScljuzKonjNnzgRd1/+PEFIipcwSQv5q23YXIMDhklJ+mRAyVdO0Mk3TnrRt+w3LskqllN/TNK0AgZ2u66vr6uo6LMv6KiFkb9d1Q/hsQ0PDW7tiPXbmN+bMmTNZ13XgXUgIaSsqKlqdxxsGpru7+2uEkCghpEhK+VA6nW6qrq6e6DjO9xhjlBDSrub+JAKUlpaWb2iaNoUQonPOH06n05t2ZizD/eyCBQsmZDIZ7C8Tc8hkMk/mzxnWTNM0rGdUCKFTSp+rr69/C8GspmmHEkLKOOcZ13XvLy4ulgMDA19hjO3OOS8yDOOpurq6tSNJDoAV5/ybmqYFsDeCweDqJUuWdGOuJ598crnrujFN0yo0TYOBNznnjV1dXW+MHz++knP+Pdd1H2tsbHwHn587d24xpfQAzCUajf5xNI0pnN769eu/BFvium4xIeS5dDr9an7uwJEQcrCUEmfcdBznL1OnTt3c3Nz8BZxh7H3Hcd585513nt9zzz1LCCHfI4SEsD80TXt8NGwT8FK/+3UpZVhK6VJK/5I/m7BBpaWlOJt74uwyxv5eV1f339mzZ4cNw/iOt39xnp+wbXsrzqamaZ/XNA3B6NPLly9/c7j7b0eemz9/fhHn/Duapo3jnDvZbPb+ob4gkUjsr2naPsDWdd1nGxsb11ZXV0eEEF+DLQSOuVzub8uXL88mEokDNE2bxjlnjLEX6+vrXx+twGhH5rIjn4GvVOvzdSHEJCEEiJ77gHv+2Xg8Pp0x9mXYHSnly7Ztv1RdXY25/lLTNCGEgE94W9O0m7DGVVVV4yilx0spnxq6L3dkLKP9mdNPPz3c29sLe19EKW2LRCIP54NIL3gctIP4Xc8XvgMfoWziwbquj+ecawMDA/fmcrlMeXn51zjnkymluuM4z2PdR2Jv8nMFGdHf338gpRS2u4xS+nB9ff26Ie+XZDIZjAc2iDmOc59pmn1SSqzBZMYYfN2/IpHIWhARuVzuMHyP4zi57u7u+7cX43wczrNmzSosKCj4Bs4vzh2l9In6+vpmL7imzc3Ne2ma9iWcVULIu1LKZzo6OkRJSck3GWOV+Jyu63+vra1tmjdv3udc1/0KpbRE07S36+vr/z7SMzBr1qwpgUDgQMYY/PhW+LW83fLsy76U0r0ppYWc8zXRaPSFjRs3Yj0PoZSCxBl8pqOjI1tWVnYw9oDnkx62bbv14/DZkfeRkIbDYfitciEEVzblsZtuumlL/tlYLPYFnCv4OkLI2kgk8gIIWdd1v4v1gw/KZrMPTp06Nbd+/fpphJD9dF0PSSnhY/65Lam2I2MazmeQgAUCgb2llN/gnP+5oaEBfmJMXrsiMTCEEDFKKdjHFY7jfFUI8QPO+RLGmK6C8/06Ozvv3rJli9h3332jmUymuKCg4M1sNhvUNO06zvnfTNN8drfddlvX0tKymxBighDi3cbGxh4ExLquh4UQGzVNK+3o6FgXCoUmFRQUGG1tbRuQzSFQGxgY2FMIMRCJRN5taWn5nDo8R2qadkB/f//ClStXDgYcu/qVSCSmqcDmXM75xZqmfRvOj1K6zNtkyEp/oGnad5STqKeU/lJK+ZrjOH81DOP3hJB7hBB7EEL+r7Oz86zy8vJfcs67DMP4l9r1FxBC0vX19c/tijl5zOKxCCyUgbifc36mms/fksnkX/D71dXVUSnlHCHE/VLKveD4lBO7UCU13xdCfIMQYnPOZ+q6/oxpmk9ms9mLpZT3IABBQPff//73tE+iAvJh2GE/9fT0zNI0bZOu68D7XE3TlqdSqefxjGVZXxJC/IhSeqemaT8CSxOJRK5rbm5GMAvH8VckylLKlZTSLVLK0ymlN0spf0oI4clk8qrRcHIftfZwxM3NzVVwNoSQB5WTmCWlXJVOp1/Ac4lEYh8hRBVjDOfvm4QQJELJXC53MGMMCcOtnPPfUkpvl1LCcf5K07S7pZRfkFLurwKUK3A+h7P/wI41NTX9ihBCgZUKIhZQSh+qr69/Argg2NF1/Zuc807GGOzXUa7rNgohskjWCCEnYNx1dXXPYp7vvvvuFwzD+BmcvW3b1cMZ04c9g8BFJeKnqoD+SUopnM6PCCGn5wNvnHEp5SxCyJ+klDMIIX25XO5mwzDimqb9RUoppJS/ppQuRpKM5EUl0XcTQuYoLP+c31PDHbNHPhzDOZ+kEveHHMc5RyV8D6VSqfvxncBSJYULkLwahjFNCHFwJBI5o7m5+Si1nl82TfNmlbjMlFK+Qgj5u9q/l2qadgshBPtjb9u2fzfSwOKj5pZIJA7TNG1/5Yjv5JxfRAi53bbtB7xzVqACiMtUgvVnBPxCiK9Go9FzmpubEQj3IpklhCwSQtRyzpt1XV+kbOyNGDdj7PMFBQXX7kryZEfWMJFIHKhp2vc1TcO5Sqhztda2bdt7lsRiMWDwT/w3IeQk13UtwzD2QJLsum47yCFN05zOzs5bEYRWVVUdQymdzRhblj8/OzKOsfiMZVm/1jRtNxVEPyClPCUYDNYuXboUSbQ2b968PVzXrVEx7CWUUhACZRUVFde3traCuEBgeS/nfJEK3u4WQryuadqvHcep1XX9IErpXqZp1uWJg5GMHedVfT987R2U0kMIIROklJfnAz/LsjCe/Rhj93h+Fmf4ZUII4psbFFEC/L8QCoVqEedwzg9VhMlNyladrmnaw6lU6qGdHV91dfU3hRDfp5RiH/9MCOF2d3evQBVv3rx5SDrmUkr/SQjZJKVcyDm/jlJK1T44TyVOl+BZdQ7I2rVrk9OnT18ghHiHENKifM0FlNJ5+SRjZ8eFz3u+cLZyey26rr8MX6Zp2q3JZPJfeH/+/PmTXNedK6V8VAhBdF3/FWPsCtd1fyKEKNJ1/WE1npMopSv6+/s7gsHgfJVE/FEIgfe1dDoNezPiVywWQ8zxaxXMXymEgH1Yl0ql/uTZkVJCyPmITzyfc2QwGLw2l8sdgLUWQqxSVbjzGGN/cBzn35RS2O6/M8a6hRD43zfato1kfExfnvKm0nEc2IfjGGMn1dfXbx6rHx3zxMBz9LMopQj004yxItd1L4KDopRCTlHluu4piq06DIGSEOIgLJzrus/our4KB8p13QfBDlBK96eUbhBCTDUMY5Xrursr53qht5n+T9O0VzRNy6hgB99Rjwxa0zQ42Y1gxfr6+uoLCwsHcGh1XT9OsRA1ww1gRrogsVjsF5TSwxGsVFVVHcYYOw5nDdk2HHpZWdk5hJCNFRUVNzc1NeHwA78HFC10YSgUOstxHLCjmONPYVsJIffiQKpgZbE6WC+l0+kVIx3jjjy/YMGCkoGBgQWMsReSyeTDiURiIbLyaDS6BOxsPB7/mqZpx5qmea1hGLyvr2+5YRhnua57onJu70gp/+iNPwcDoioOCDYw96lSSgSjhy1fvrxzR8ayKz5jWVaFEGKhrusr6uvr1yQSiUsJIa/X1dXdiiApFov9BAxJSUnJsoGBgQNyuVwsGAyemclk8Ln6jo6O10tLS8+mlP6HENKDgFBVDs5pbW09Skp5dmFh4dFjHbDMmDGjKBwOX0MpXdXf3/9sIBA4lxDyciqVug0YxuPxnxNCDkTgh2DFSxIu4ZyfrAzsu6lUKmVZ1iVSSq7Y2QcYYz9gjN0ARiaXy12s6/q5Q5m2nVkXODvO+dmMsduVI3xFSrmAEIKSfhoOGgZy0aJFrKamhsfj8cN0XY9yzv+A38hkMkhOz0XCkkwmn8G/HX300YHKykoY08NTqRQc16i9LMvaE86QEHK1Clzg+FbC3iSTyUEmvaqqagZjbN/CwsIrs9ns0Y7jnAR7xRhDsnA1ZB9CiCsV29jAOZ+jEv2HiouLH+nr67tQ07Q3bNu+eSSDBQMN/BRr/EZFRcV9LS0tZwkhaDqdvgLfG4vF9kOSaprmpdlsNqSCrFsZY3M5578ghGyQUt6O+XgJ7XMIPFzXnR8IBJAAXhwIBH44FvKs/JwtyzpPStmcSqWWx+Pxy7D26XT6d3i/urr6K1LKcxhj8B+f0zStljGGgHEuY+wBrL9lWdcJIdZQShEMIUDB/30ZMcvQBG4kGI/ms7FYbI5KEksjkUhtS0vLCSCGbNsGoSBPOOGE8SUlJUthGx3H6WaMIaGfv3bt2lcOOeSQQXlGc3Mz7PCT9fX1L3nJ/WFSygmMsb8mk0kkdqMiHdnZOXsStBUgSwzDeDCTyVyjadpLqVSqcYi9+alt2ydZlnWI8v+zOzs754bD4dNUkXBLNBpduXHjxvOEEGCln0I1MxKJ1GzcuBEkBIKzK2zbXr+z49r289XV1UeqAPFAXddvyGazk5TPvYJzfmpjY2OLF0QuJIRsSSaTK+Px+DxKKQjHJ1AVhK0EKSSlPNlxnCtM08S6tdq2vcyyLJB1Jclk8jc7M0bEAeFweC6qybZt1yUSCRCFPzFN8zKoB6qqqvZijFUrsnQZpbQJMY+mafCnu6H6iL0Tj8ePIoTM6OjoONWz+XUVFRWvtrS0gKC6JplMYh8N6zV37tyIruunM8Zu3bp16xvhcBgk5/OpVOqOvH2hlJ6oSNvFruv2qkrLEkLIUvgTEFGmaT6Xy+UQdD/hOI6r9i7IkUs80nB+Z2fnD0ZDqhOLxX5DKS22bfvCeDwO2/t/qhJ3CsY4e/bsLym56jmwk6ZpoqoK+3Ip59xSJNN/YYPj8Thsda8Q4jZKqeW67tWapvWoKslVnq/527AA3MmHQMIKIVAhusg0zdNra2tBiI/Ja5ckBhs2bDiJMQZZQBqaciWNQfD3nK7r/87lcot1Xa/K5XKQobTrug6G6PsFBQVX9PX13aJKqpe7rvu2ruvnU0rvDgQCrwwMDCBweCoUCj07MDCAABgL+y3Fwn09l8udEQgEYkKIV3Vdf45zDmd9meu670yZMmUzypdz5sxBYjDzE04MLDDiqlyWiMViYCd+rYzOyUgMPPnNlQi229ra/hQOh8G2jINEglJ6rmmacdd1I8jOGWPHuq67UNO0FWB8vU2MDZ0ekx2zzZeedNJJ5YZhwEE/atv2o4lE4hQpZTASiSwG1vF4/GhCyCG5XO7ysrKybG9vL5K938K3I/OORqN3tra2WkIIsOlPCSHOFELM13Ud8po/4Nmh5fRdMaeP+o1YLPY5SulvYORqa2shiVokpdzQ2dnZuGrVKhGLxWB4okVFRdf39fXt5wWO54N5opSeV1FR8V5zc/PZXuCFUuqeqVTqnFgsBqN4RTAYPHw02K+PmgOkIiqgThFCFhcVFb3U29t7rpTyTSSTcOKWZcVQJYAhRQDmuu7JioW6WAWVMPx/T6fTDWAvYWyFEPdQSsEwX2UYRjiXy8FYnp8Pjnd2veBs8LyUsnby5MlvNDc3z0NgGo1Gl9bU1Az22eAFtqq3t7cG1ZZkMvkf/BvK7sFg8OKhiYEXeBytadqRo50YVFVVfZEQcgbWFb8DaSJjLF5fX/+aF0gkhBCTu7q6Li4vLz9CCLEA1TIhxCwwoFJKR9f1qzEHJJuU0mQgEHgim82iorYhmUzC0Q/7BSkOHK+qqjybSqXura6uPlUIUZpKpbCOkFkdTin9USgUumDr1q1mQUHBXZRSnMuZqtr1wptvvnnPtGnTTkJQ47ouEtl5uVzuxKKiIkinFmcymaNHU+a57URjsdgNlNIX4Zw9trw8lUqd6mF7DALIaDQ6s7m5eaqqoMIPIFhDInMLqi3xePwKnE0hRAtj7IRoNHp8S0sLpDhnOI5zyqeJcPDmBNunVVZW3rBx40awwz/p6Og4CcGRZVmfV4HxVaZpWlu2bOkNh8OPYe/Zto1AGaz7Aa7rHtHZ2XlVKBQKB4NBBNVImFGJXAOJySdlR1G5a2pqeoQQcr0Koh9WciKQEm/W19cv9QJIEA5fTqfTccuy4MdPWbt27Yl77bUXzsgbDQ0Nt8ZisbNUG08EFRNCyJcikcgFTU1NOH/wN5en02mQgCN6xWKxEymluxcWFl7X1tY2LhgM1lFKT82THJZlXS6EeCWdTv8xHo+DKQcZ+TwhZA/bts+Px+P7InmmlF6uZHuwYZAW1lmWhfhkaiqV2qmKJaRNAwMD+L6t8Oce832i0rdfDLYY9ke9h/lficQA1TFVYbsLVQuMKZVKnRyPxw8XQszJZrNnhUKhq1Vv1qXpdPr1eDz+iJTSTqfTSP6H9YIvVKQNfDkCfsi5aqSUr+bZ+Orq6oM457Mcx7nUNE0w7HWGYaQdxznVMIxrQfzAdnLO/6nmAGnOgQUFBef39/cfCnKpoqLi8KE2f1iD1DQSj8eRiG5OpVJXVlVVHU8IOSKdTs/1bCCq4OcYhjEzm82WU0ov1HX9Utd1YTdXp1KpW0BKEELQI7tK1/W5IL8KCgp6FZkCUuf+xsbGna4EDXMuIO2mg5T5zCcGaMScNm0askboHxu8isH5ipG8XwixjlJ6pWEY8xzHAQsVZIyNF0J8HixbX1/fH1AuVqU6ZMMoBV/T2dm5IRwOz1MM23uBQOAxSG2Q1eu6Do3iPm1tbVeXlpYiOGuJRqMPb9iw4WjG2Hc1TXsrl8utQPNOVVXVVwghM1V5a9EnWDEAI3SUMmi/9kqUx6rmnN+WlpaiUQgNxggOWioqKtLNzc0nQUutGKTHUG71WFRorK83DON4JEO6rt8O+ZBiCa8ihLyUTCbBHIz5y5NTnIlEL5VKPWBZFoIfZ+3atSkwWc3NzV+nlB6LgNEwDAR2DZRSVAwSlFKU4BAUIRDNcs7/wRi7IBgMLhwYGJhCKb0xEAgcPdaB8s6ABGmUEOIslIjr6upetSzr95B5RaPR29asWUPC4TAqOJCvXJ/L5aDnRKkUbBckQnW5XO5l0zTPhAFFEI6SdUdHx1njxo2DvO3UbDb707FuQvYCRrBMKwcGBv4RCoV+g5J4KpW6u6amhjQ1Nc2glH7Ntu2zYrEY9Pk4TzCCv4GG2WPB4ASwZqtV8PZjpXsGiwI53HmqcnLRcBk8JJrBYBCsDRLIF4UQSEp6sU9UMMDzPQKWZR0jhNjfY78HGVMkBoFAYJFhGLdDSpRf16qqKrBmR6bTaSTQo/YCYwfHKKW8BF8K+0YpTUCyWFNTI+PxOGQJ+xcXF1/c29uL5Ol41TdxOYoJeAa9Q6ZpgiFbzjlHZeTezs7OB5UNwPxfRwAyksGiYqCYLey1/0Sj0btaWlowfzRIXo91RpCMioDjOBcTQgzG2M0qIalSUk2QFOvQkwHbA1E+AiIl1/pdUVHR7N7e3s+jvN7Z2fnzfGPySMb5Yc/GYjGMawMqVPF4HFISWV9fvwhj37BhA7Tg5wSDwdn9/f1TGGPXgz2GPAJyrFQq9XQsFruGMYZxt0gp57/00ku/PPDAA8Eyo4p39qfJrgCDuJokNM0dHR3Xh8Nh7J2DKysrB5nNdevWTTQMo1bJ0X7DGGt3XRf69sSyZcvWYJ1VQlGT9wGo8nHOf84Y+zeSC7UnWyFdtG37xbFYpx34TlJVVYWgalV7e/td4XD4CpBedXV1g5U+y7J+icq36j07Hr4QcsD29vZ5ZWVlsDeb6uvrUTECmRiQUr4IuRGqrOjvYYxBEXAN+rh2YBwf+ZF4PP5DVEpVv9C1gUBgguu6lzLGTps0adJWnGfLsmAnEWCCRcY+Q1/B89B8w1Z6gTrkXVcj+MW+a2hoWJxIJH6HakcqlbpgZ8boxU4WiJFUKnUDZEVSymNc172mu7u7KxwOozcIifJileejYoJE+jYhxO6KwDkMFRiPmPuR67q/xRlR8dXS+vr6fycSCSRoVyaTyWGz3ZByK/392Yr8aOjp6Xk9HA5fgB4LkBCLFi0izc3N+4F/UP2QVziO0xUMBhejag75pZL/rTRN8xnXdUGSPaGwRv/EEZD1KNniYegjmzx58rGj0ROGvaNpWnEqlTo3kUiciKoh1stLWJFc1ngxCGRFqKqC3IJE9HUkZEgIVYWxE3IjVOk455A4oWJwGWR/qVQKMtdd8oLcDaQSKjWfaSmRV/4AOw4nBbkFdHjY4NDMlah/R8CEEttszjlKjVNQzjMMY5FqMEKJ/W9SSjDlCB5fEEI8g7Kwpml/5Zyv03W9wSsngnXf33GcCwzDgD5zveu6T6GUTymFFGGOYRjnLV26tN2yLDS0VcGpJ5NJyIx2eYnV06JfZprm2WgIQiNiJBJZ3tTUhMByrdIlo8kP2T42KQIv6NsQ7CA7hz4O/Rb7oPSmyu0op8JZrEbQieazZDL58q7Yqd76QhZUxhj7s5JtVWPNgCmanAghrwohwGrcSQiZqPoHvhIMBi/LZDK/UA2WSNiu8eRQT3uB4I1gugghcPbT2traLhyNcuJoYYHgMxQKVcNBBIPBxzOZzGmeBINks1lUu14jhBwHR0wIOZwxNvDGG2+kpk+fDokVpB13MMZ+jrIk1C+oACFh8Bqx+1OpVN1Yl/y9EvVC9BhAo62Cvp9Bv431EkKgya0VDodzDg035jQRgTl6cwgh6DlISikhX1ulHGA7YywBOY9hGGBu0Zx1w3AbZ73G5xga0EzTvNtxHDj+RxhjKOej1I8qIhpLf4v+FDSye2uLW2cmBINBJCh/iEajj8KpeHOFvh/9HvHRujUJvwnZk+u6FzDGHuac45IFVMYuMgzjW4SQfNP8yZAKEUKOQaOdpmn3grFGn5CqPPVBN6zwBdM3Q1VU4Whv8f73Ktu2BzXYw31BxtnS0oK9GPGkWegneEDX9awQopBS+m9PunePlHI3TdO+0t7efmFpaemglEzX9WWu6/4MwZhnf5YxxpKc86kK+4ht20hyxsx2erI8NA1j752FvgvYOaWZn5DNZl8wDAO2YoVnV8CSgtHF3umFPAE4U0pt13U70ceBpAzVPNUTNDWXy9WPdQK+s+s2d+7cb+m6/mMkil5vyitecL/ne++992hlZeWVmqY9zRjrQuBfVFR0Gm4Mi8fjR+BSgGAweBGkXWhShG8tLi4uQWVESVafQuP2Jzlfy7IQDGKPwXf9mjEG/w/7U5LNZjczxq4Fc+v1lIQmTZqUampqQs8O7A9sKRLw2z0tPbToWGs0ziM4v6m2thbkwYheYPwRyLquu9IwjK96PVgpSukPhRCo0OwvpUQjPwhLBI5/AbmJ5IFzDtYe/niqqnY3Oo5ziFqrI1Tv0RIpZYxz/kBDQ8OTOztA9NkIIX6qei6vCAQCR0opBwgh6PlBDLMKQbaU8mnTNN/F3nddF1UYXLhwFaR2UFGAqKusrLylubkZvuZtSul/VU8iSIIzRnLZhUcwIXFt0nUd1X7EXfDdUIfsgyZfBNicc9i6HPy84zjXo6oHglgIAZ8D+wI8HUIIkoSUlBJ9EX22bQ9WlEb6isfjiDMQg0B2/gMpJeb/InrVXNcFGQlyD30liBO/lclkak3TPJRSCgJ5uYfxylAo9HYul4PfXO267ibG2C+FEMmGhob3RjrGHXneS2QwpqsNw7Bqa2tBrI6J/R1zKRGMlGEYaCbErURvCyHKCSGPV1RUvL1p0yaUmk5wXRf6UHSkI3l4W+nO985ms38KBoNgXA8nhNydzWY7AoEAEgp05AtIiRzH2V2x6haMjBAC+j7c9AFngQaTzY7jPBMIBMBG4DuJaZqP4KaTXC73YyEEvus+IcTfP4mqgRewoJoxGRIqFUQ+hUageDx+GhKgjRs3vjxp0qRfqtIWGhTRpIpu/y6V6R+s5Bu4BQbXfj5aX1/fYlkWGpGhzXSVfm9DZWXlk7vyejro7hE0YqyapnVks9lHDcM4xuvoh1PH7UvTVOM0o5Q+WFtbuwG3xTDGZiqG2XFddyPm5zWUf1tJQVAiHXBdFzf67JJbenbkYOYD0Kqqqj29nhhUd9ZNmjTpqebmZgTMcAYo136PUgoniLUbvFnEu2nnRI/tQ5P8Mxs3bnRRKZBSolemu6io6M6x7i/Iz9MbzwmU0m5U35QM4elAIAAt/vj29vaVZWVl3wcjpuQK3flbLXBNGwISr9+lNRqN/mXNmjW0tLT0uyjBo3kacreRGkrIiQzDwBnFbWRIwFbncjnoeU8CW0MI2R0Jy9BbhjzWFDdxILH+F24wWbFiRdvs2bMnmab5c03TwF4h6IY05f0rc3di3f/no56hPgjyR/TVZLPZxwOBANjZmUri93JbW9u/PQkRJBD9juPcM2XKlD6w3bqu4/YtJAavRqPRVzZs2BCG3EVpyHFV4zrTNJ8aDf0+5m8YBpoXcetVe1dX10PhcPgoFYgVQ6ZYWlr6DdyQBAlLMBi8f+nSpS2QmjHGfmGaJnpI8jeNdMdiMcg8gGOPEOKvee31SDD8qGe925FgR3BV4xZv7AerYD/S3t5+R3Fx8QGqMfWrqCiFQqG/3HDDDZvnzp0LeQNkeVLX9XffeOONJ1G5bGpqwi1L8BFIzp5Np9Nomh8TpzpcPBBoKZ+F5LvMCwAh7foigmXVV7dcBchoZkeQ46BShxvbkPw1NzcjgH5v24qAJ/MEAfZSZWXl30aDfR3u2nwRVQAAIABJREFU3NCLls1mj0fxSfXavFNZWbm6tbUVzeXj2tvb7w2Hw7Cd0E9v8W7BavUuGgAeIdd1u7u6uu7dd999UYVG4Ajb1KMqgc/U19cjMBvxWnqxChqMIWdkgUAAspxsJpOZp+KLO0zTbHMc5yjcsAO7OWnSpLtXr14tpk2bhnngUhPo6OHH38OVkgMDAyAksHc3Tp48+aHh+GRIJvv7+38M4gE+X0oJ0g39d8epm3x+V1xc/EXTNA8SQqBHD5XBF7zKNSqqU3Rd32Ka5mNLlizZ4jVXg0DFxQ7/TKVSIA9HghsqQfCFOG8Zzvn6ysrKp9avX38IY+zr0Wj0sqampoNw7nBNqLqc4QXbtl+LxWITCSGonmL93g0EAk+Xl5fz1tbWI3CDG2ylaZr3jlZFzyMvQUTCrm0yTfOvrutWcM5xwUsj53wPqEqUEqMNlfPKysrX1q9fXwhfgiTHU24gdsTegzwY8iPM95XKysqXdtW5gjoDySYuNUFMDCJ4rG4mGvPEABV2HJKenh4ydepU3F/7/r3uWLCOjg5SVlaG+89xLysFyDB2a9askdtjihEA5B17/nkYK3zHAQccoLW2tsq2tjZWXl4ua2pq8J0omQeTySQybZQDjZKSErOoqCjb398feu655z6Re//zBhZzxYYbsrmwJu8fVtz3u63Obnv4IPGxbVtPJBIf+BsCwzXkw3kOhnUIK/Wx88BvfNj8tsFkOMMZ82e2HTvWIF99gj50e4Hd9uaLkjGCl11lYIYCg6vchshBiJQSkpjB/YezorSzuCDg/f2IOR566KFs9erV+DsBH7lPR7oA29lP+MrB+7i3dWjAcI899sD90plIJGJUVFQ4OAtYB9w5XVpa6ozVeQdOyWTSHVJ5/MD4tvO+hvHOnz9fHnfcce//vQXMSzV1o9Fw1M/wNvvuf8a37ToD6G3wH1xOEBr4/7uwikcOOeQQ7Lft/m0W4DhhwoQP+IohDeofeAb+BTLN4QRoI93LO/O8d+62uwe2Y/vzPvx/Arz83+vx1uv9/pydGctofvbj8Pfeh0/4wN+Y2B4e2M8XXXQRWOaRBLbbnd62tnuoXR9iFz+wPtuzlTjP3t79gK0cDqbb2sKhdhpncsaMGdo2tmTwbx55PvZ9PMfK1+CSh4ceemjo34l538ZsJ8bBsPA3FYxtpYj47EUXXcTHYl0x92381lA7+D+29yN8Hd1nn33ItngPZ1135hlg09vbG8Dfg8DfGBr695925nt25LO7IjHYkXH4n/ER8BHwEfAR8BHwEfAR8BHwEfAR+AQR8BODTxB8/6d9BHwEfAR8BHwEfAR8BHwEfAQ+LQj4icGnZSX8cfgI+Aj4CPgI+Aj4CPgI+Aj4CHyCCPiJwScIvv/TPgI+Aj4CPgI+Aj4CPgI+Aj4CnxYE/MTg07IS/jh8BHwEfAR8BHwEfAR8BHwEfAQ+QQT8xOATBN//aR8BHwEfAR8BHwEfAR8BHwEfgU8LAn5i8GlZCX8cPgI+Aj4CPgI+Aj4CPgI+Aj4CnyACfmLwCYLv/7SPgI+Aj4CPgI+Aj4CPgI+Aj8CnBQE/Mfi0rIQ/Dh8BHwEfAR8BHwEfAR8BHwEfgU8QAT8x+ATB93/aR8BHwEfAR8BHwEfAR8BHwEfg04KAnxh8WlbCH4ePgI+Aj4CPgI+Aj4CPgI+Aj8AniICfGHyC4Ps/7SPgI+Aj4CPgI+Aj4CPgI+Aj8GlBYESJwZw5cyYQQiKEkA2TJ0/ubm5u3oNzTqZMmfJuTU1NbiwnaVlWBed8MmPsJdu2nbH4LcuyCiilZaFQqH3x4sUDo/0blmUZlNKoECLb0dGxedWqVXzob8yfP7/Idd3dGGPtdXV1nZqmyZqaGv29996rIITwxsbGVu/f6HvvvVceCoVKXddtsW27f7TH+nHfN2/evDLXdScEg8GNS5Ys6R76+QULFgT6+/snGYbh1tfXt2DMeB/zk1JODAQC7ddff31X/t89XCYKITaP1dp+3Hw+6n0pJZk3b94EKWWplLJ5KN41NTV006ZNpblcrpwQkkmlUs2EEIk5aZo2nnNeyBjbYtt29+zZswOBQGBv13Upfk/X9d6KigqcHXck49vRZ7FmmqaVEUI219bW9g59rqamxly/fn2lrusDQ/fmggULSlzXHa/r+tb8OmN9Hcep0DTNqaura8F8d3QMH/a5qqqqcbqulzuOs7GxsbFn6OdmzJjBiouLdwsEAkFd15uXLFmSzb8/a9aswlAoVKJp2tb83sGa4HwYhqHbto0zM2ovrKsQYjd8YTqdbs7v4fwPLFy4MNTT0zOJUtpj2/ZW/DvGX1paCryMrq6u5lWrVg3aytmzZ4dDodC4bDa7Zds5j2TAp59+erinp2d8JpPZvHLlyg+cTaxzS0vLJNd13cbGRpzNwdfcuXOLCSG7mabZVldX15Ef94QJEyL4bCQS2VRTUyNGMq4deJZYllXCGCvfHibAEePJZDJ8ypQpGzGe/FpTSktLSkqa83Yb/97S0lJJKeWTJk3C2HfJGduBOX7gI2eddVZhe3v7boSQzoaGhvZtn58xY0aorKzMhP3AXsN5zGazu0spmWdDujnnGyKRiNy8eXNFLpfTKaWbPgmfsO3YsQ97e3vLstns5ltuuaVv2/dxdg3DoPm9j/8uLCz8HM4J5qfr+rvLli1rw3PeeuLc9du2Dd8xKq/8eXVdd2D58uUbt/1Sy7JKYcc9+zL4uzhDTU1NUV3Xc21tbRvzfhxnKBAITOCct41kjIhDdF2PMMa6lixZAhvyvn3N20Jd10MdHR0b8rYkH1tgHpWVlVtwNrzzMqG/vz+4bt26ptWrV4/GGSDV1dUTKKXwC/8Te+TjlUAgwCZNmtSUP3fb8z2zZ88OBoNBxAl9S5Ys2TIqC+p9Cb5b1/WKQCDQvXTp0vZtfBQ55ZRTKgYGBoyCggLEL/An27U9wLC8vHxSLpcLFBYWfsD3jOZ4P+y78PslJSWl3d3dvfm1HovfHVFikEgk9lEH9vdSyisIIf8WQvyGEFJWVFR04Y4G0ggq+vr6Pp/NZt/d1ml91ITj8fjXNE07V9f1uXnHtb3Pw4Bs2LBhD8Mwuuvr6zfvKIiWZe2padpRlNIt2Wz28ZtuumlUNyoOTEtLy3FSSkEIKaGUPl9fX//v/KGvrq6eyDn/BSEEv1um6/pDtbW1GxKJxPeFEJOklOWU0n/Ztv2PRCLxBSHEVwghCDDGhcPhP1x11VUfCKZ2dN7D+Vx1dfVUzvm3CSE9QogppmneVltbO2hUcSADgcAhQohCQsgehJB/JpPJJ0877bTdBgYGZkgpNzPGSnVdf2Dp0qWDgUksFjuAUnqiCuQuyTuC4YxrjJ4hVVVV+xJCvkoI6aOUhk3TXJUPki3L+rymad/hnLcSQr4rpbyroaHh6UQi8XMhRABrJKVEcPUHxth4leBdQAjZKIQYh7V2HOeC5cuXZ8Zo7O9/7bx58/ZwXfcYSulWjEvX9Xvy5whrZprmj5B8Sinh2F6aPHnyv9ra2oocx/mZlDKLf9d1vbGnp0cWFBR8T9mAoIqNK4UQ/0qlUk+NJDmoqqraC9gZhrGFc15BKb0rf3Zxnpuamg6ilGIvUc6547ruPcCsqqpqd8MwjhBCbCkpKXn0mmuu6ZsxY4Y5bty4r0spv8Q5f7WhoeHJ0cLWO8PfZoyBpJioadp/bNt+NH+GgaNhGMcgsCGETCWE3NHe3v5OOBz+lpRyEiEkhABQ07QHdF2Hs/kx/ptSakop7xxJMJGf4/z58yc7jvMdrBnGIKW8JZ1Ob8L7wKasrOwQ2GwhxGQhxHPAB6SLpmlY/61YZ03THrJte30ikfiBEAJ2JyylfM627X+OFpbb+x4vOfwBIQS5yGTDMNJDbX0sFvs+sBdCIFh7IZVKPWNZ1t5SygM8u1rKOV85ZcqUvqamph9pmoZkB5i/HolEnt4Fic1OwYOgk1J6JOeD/NB0xtit9fX16/JfEovFdqOU/oRSmnvjjTduffzxx7llWd9R8/klAmRN04oIIe/qup7O5XJYvz4vYQgHg8GbhibQOzWwUfjw3LlzI7qu/1BKOUAIASFyS348QxLlExhjbySTyftxhuLx+Jc1TasmhAwm/opES9XX17/mEWY/llIWCiEeb2hoeHvbhHw4Q8b3ZrPZw3Vdp1JK+NS/pNPpF/LfNWvWrIkFBQWHSSldIcTnKaXAuUPXdexRSQgBWfRaNBp99u233w4VFhbOcF03SymdEA6HbxqOT/YItx8SQpAkThVCPJZOp1/3EpJBW4hzDRuDfWGa5t2vvvoqnzZt2i89G1xKCHkimUy+XlVV9WVK6T6EkEKsQyqVunWkuFmW9SUpJb53QAhRAhuXt1seGQZbB19RIoTY1N7e/mBZWRnsEDBDkgfC7G7sX0IIzmhASjlZ07R7bdt+YzjruO0zSKyEECd4vwEi7H7Ys/zn4vH4dzVNQ7wHomNrMBh8tL+/v9Dbry7Wtbi4OF1UVDTQ2tr6f1LKz+FcgfjL5XL37Qp/jbGCNOjq6gKeh1BKr80TTaOB0bbfMaLEAGxFJpNZRQg5DYtoWdbpUsqpqVRqYU1NDWlpafkcpXRSW1vbyyUlJSal9EuMsdfBsGUymfYVK1a0zJs3bzrn/HxN0/4qhPjbW2+9tXH69OkIDAL//e9/10yYMIGWlJQgEOs0DMPM5XIF6XT639XV1XupYPNqIcRZuq4XFhcXv7V58+ZwIBD4opRyLaU0QwjZnxDSJIRAAJ4jhDxq2/Zzc+bMGW8Yxh6apr0KJsVjCXBgAkVFRS/19PSA7Z0vpXzINM2XJk6c2D/aTgRGjxDy246Ojng4HD6SUrpvRUXFFflKSyKROIFzvq9hGNdyzudKKTdgQ0spryWE1GIjE0JO6O/vnxsKheZomrZW07SnpJQ3Sin/kE6nHxuLDbPtdyK4KC8v/xWMJYIZTdMu0DTtRdu2b4fR8ZKGKsdxVhiGEZFSniiEOINSeiwhZJppmtdns9kExp9Op+8AQ+i67hxd179rmubJ+WRhV8xlR37Dc0pxIcTLlNJnsR6U0j8i2cHzCGpRZSKErEGirGla4VtvvXXuXnvtdWgoFPp3LpcLCyHO0TTtOiSrqLJkMpn3TNM8hhCip1Kpm0ZqrD9uHh7rcaamaT2u695HKT3VM8RP4Vkk3YSQEzVN+x0h5Duaph0kpbyBEPJ9zvn0rq6uG8Ph8JVSysdVfLmGUnoCpbSRc76Xpmk/0XX9wo9K1j9qfJ4ziSG4p5QiyAZWT9m2/aC3nya6rjtf0zTstU2U0osppUsymUyTaZpXuq57h+M4/1ixYgUcDeZyMKX0cM75LaFQqGU0gyM4bc752ZqmrfKc4ummaVblk9lYLLafCnSOdxxnKWPsV2AaFfu9xHXdeSqBbtR1HWfmd47jXBcMBhE0Ecdx/sQYw9rcm0qlnvi4tfyo94eQDybn/A7G2O+94P8PeA4VX7UHzxBCrGCM7S6EOKmoqKiqr6/vWDhnVQVKqopMnBCC/fm3bDZ7nRDiMl3X98NeqKioqB5tuzh0PpZl/ZQQsjtjrMF1Xey/x5PJJIKZwYqGOmc3MMauUfZuupQS9gS4ziGEvJPJZJ40TbPOMIxGZU/eI4Rcyjk/yzCM/aSUhw4MDFy8PdZ6JHiP9NlYLPZtTdO+HQqFUo7jLBBCgA2/Ykhi8HVFBmGfwJddHolE+ObNm+HHtGw2i8DxO5RSptbxHULINbqun+C6LgiZ3xuG8TsQSyMd43Cfj8fjZxBCEHj9mRCyiDG2vLa29ml8n2dTvyGlPEpK+UY6nU55icHXQAD09/c/bpqmq87FINmlyKT5mKfaow2RSKRvtKo/HtF5nOM4DYFA4GCVGHw9EAj8Lm8z5syZ80PDMKbmcrlbDcO4HH6OUvoc53w2pfQ8lUAjaPyeSlQXM8ZgN/fv6Oi4tqys7GJN00DiDZ67nXnF4/EjCSEHd3R0XBkOh38BQtB13XoEo975rVak4D2GYWzgnF+jadr1XjJ4rhACSRUSyYpIJHJVc3PzuZzzZ03TXMs5bySExG3bRlI1rJe3bvMopS8Eg8EX+/r6LhZC3J4nX0BKuK4bZ4ytzGazjmEY57iueylj7ATEc4yxB1RidRpj7E8gIVTigO+60XXdGcqfTFFVsTOGNbBtHpo7d+4Ruq7/PBwOn4V4CwmzbdtJfOzkk09GVfoyZZuXIv7jnM+ilF4hpfy2SvwqHcdJKeJyMaX0Qc75PxAXapp2G+ccCeGFhJAbk8nkf0ZjnB9jy+nGjRuncM4PUQTUcYyx2TtDdO/s+EaUGHgb4y7FOOKQrMVG0zStIBgMng/GQkqJjHC6ECJEKb1VCLGMEPIGWEovy7xQSgm27ULG2D2O4zxkGAYOlk4IgTMLc85vDAQCM4UQX1Zs4NNqY8N43M8Ye0EIAQNysxAC7O1LXmD8R8UwnQ8mWjFhNwghLlLVjJ8iQ8ZzlNJOKeU3KaV4/9vZbPZq0zRx+NqFENN0XX/ecZxxlFKwjDdhs2ia9vZol2Ity0Kg8C3btudblnUoIWSmlPJU/A4Ct3A4fCGl9O329vaV48aNmy2lLMnlcg/pun4epXQhYyyCww0GiXN+smJE7wC7YVnWNUKINel0GgHmmL8gf9B1/TTFdD0DttSyrNOklLSzs/MGlFQTiQQM7I+EENeUlZVlu7u7/6gydwRTsxBURiKR21paWqrBOHd2di4uLy//LthLKeXXDcNYVFtbC3ZzxNKU0QKiuroa0q+FhJBGsDCJRALG8O1oNLpiaJDkscUwKKuTyeRtmINlWeNVMA12AuzZrfngGdUhIUR8YGDg+l0RrJx99tnFHR0d11NKb5ZS/ksxcr9zXff1hoaGQccVj8ePJ4R80bbt8y3L+qqmaZY6k5eoROJMQsirqVSqMZFILJJSmnBKKkE6SiU81xcVFZXlcrnLGGPn1dXVvTMczGGoc7kckoGV0Wj0tebm5pMppUIx7PWQBsF5CyEsKeU1KqFpV6zh1ZTS+zjnUVQZOOeX6rpOOjo63issLJQq+AbpANv0ihCio7u7u2lbyd5wxolnTjrppOmBQACO7TK1V0k2m8U6z80zXd4Zn57L5a5AJUPTNCTA5yHpchwHiZUTCASuEkIsl1LGNE273XXdxwKBABzOW/X19Y3DHRueA1OmadpCKeWr0Wj0/tbWVhA3Idu2L/Xex9qeiIRBSmlwzm83TTOWzWZ/QSl9MxKJ3NXa2vpryN845/+ilC7IZDKJ4uLiz6vvubKiouLompqaMatuWZaFs/VWOp1egf2mbEhRKpVC0oS5IQg7s7Oz89cTJkyY6rpumhACOzkXDCSqGYlE4mpFRLyladq7Klk4qaKiYnZraytY6AWerR01CcpI1in/bCwWs7yKRi1jDGzzkbZtzx5afbMs6xgp5RdVlfm6oTJLT655GiHkvkAg0J/L5a6VUiLYAZONvXfVaFSghjNPJLyWZd2mgvwV7e3tD48bNw7J3Gu2bdve9yEOQYVgFmxKOp3Gvsd/f0357AM1TXsOJEBXV1drSUnJfvB/SnJ0MfYlpJzJZLJtJBXK/JyArRBif1URuz4YDI4fGBi4QcUwC9LpdJO3587G76VSqZXV1dVxlWx/SdO0J1A9VmtxXjwe31fTNPi/y1WgewrOUDKZrLcs61xUCFOpFAiYHX55cUCCMcbr6+uT8+fP/wbnHCTnJWCL586duzdjDO/fqPwHZIy1am/cruKiKGPsYNu2415iMbOvr++UwsLCa5Wi4Ebbtl+1LOuvIBFTqdS9OzygbT44Z86cybquIxZpbGtrW1tWVgaf8FIqlYIdhB9BJWEmY+zagoKCnp6enlpKaZ3rujHFut+czWZfLCgoOE9K+Q9U+JAUO45zcSgUOsx13TM6OzuPGA1bXV1dfR7nnKVSqYs9m/xdJSmFLR4co6Zpv1VV+2oQeiCplS281Fu/l0BEWJZ1pRDC0XX9T0h0kDi4rttLKb2SUgpb88hwMdyZ5zyZ8lTHcX5vmuaZeVXGznzHjn52RIkBNIAqs36YEHIrDoFi6hHsbu7o6Dgf2s9sNouSHIKgYxljl7uuiwPzWiAQWJHJZM7zgvu/a5p2uioJJ6WUvZzzeiW3QLYLzXOjYRjVruuCzT8oEAhc4zjOwZzz7yGoEUKAMQJ7iwP51cLCwmt6e3t/Qyl9R0lR3uKc76/r+spcLofD80oymfyrZVlnKfazjFL6OOf8JAQclNJvgT1F1cJxnG7TNE9CgsM5f4EQggThXcWG3jqaJSM4ARXQHIANWl1dDakNfvMUJAZgTTnnV+i6/lJ7e/sfw+Hwr1Hu55w/BmYCLB5kvoSQWwzDODaXy8H5QyKAxOAqzvnahoaG9I5ugpF8DuV+VVI7RyVZf0ulUo8kEolTIE158803r4eGEYZJ07TvOY5zORKD3t5eGA0EfglK6T8qKiruaG1tRaAXEkLcSSnFXP8upZzNGLuZc75mtHXhI5lvLBb7HGPsLCUDWlpbW/tGIpGocV0XPTaN+cQA5yIYDEJCAq1wQz6prK6uPkgxtj9QwVoX5/xmaIhx2Jubm1ExCieTSSRNY/5CMqeqNzZj7IZQKASm53dSyjdTqdQt0FYmEglUqHa3bfvCqqqqryiN8rxMJnNJKBSqEUL8I51ON1iWdRGqIUIIVByOQuABw+q6LtiW8xsaGhCQ7fTLk7GAfUtGIpHXm5ub56GSEolElqGa5lXacHYuRaCPAJVS+oiU8kjYD8XYPckYO1TTtGccx3kUdoUxhrPeQwiB479+qDxgpwc45IGqqqovMsbOUDoXJB/oEbldBQ7xVCq1xgskEkhyKysrL960adP3XdcFC4yEf5bqK7k4nxgQQm52HGchpBLBYHB1NptF1W1DntUa7hhB3DiOg6DknwgAqqurTwXBYNv27/Gd1dXVIGF+okroF3o67rsopQm1F0DEvPTWW2/dvffeeyOBhwwAczrZMAwEb3ujykEpPXq0CZOhc62qqrre6yG72dtv42zbPs1z6EejQBcMBmdmMpndQRA5jjMvEAjAbqysra19Ph6PYy9uYIyhajwzGo0e39LSgkBgYS6XW7B8+XLIuD41L7DqGEw0Gr2xtbX155zzH3d1dZ00NDiKx+OQm+2jZFGQErzfW2dZ1hGEkL3b29try8rKIMc4C1JAIQTkpWZXV9d1q1atGvU+uR0Ej8ZiMcQJqPo8jGReEWFv2ra9bOjziUSiCjKqfGIA+REIOi9ZOgrPAx6V6P4cVST4dyllWPXh1Y2G1NcLGj9XWFh43cDAwDglD6w1TfPUZcuWveedZwSMr8FOx+Px2agQEEKeVSTGNJAoIC045zjHqPKc61UJ6izLOgcS23Q6DbZ5h19e/8/pmqa127adjsViwAKB9u/BFnuJyClSStjeJiV5WaYSm7sIIUjcp6dSqXnxePxwKHqRRBNCrjUM49La2trX4/E4gtlkKpW6Y4cHtM0H4QsJISD5kIBCsVADAiaZTP4ZH62qqoLkcxZ+UwX66ImB0iGNZEvJo7EXIJ0GUYLKe1BKeZBHKh+KSqoieg8fhR5DEo/HrwKGqVTqcpBeUsoj0uk0CATIlr+OeMQ0zROz2Ww5YwwkNcaL9XsCPjEej6OigFh2FaW0SknNUAHqBbnDOb+/sbHxoeFiuLPPxePx6cpHLzIMY+GnNjGYMWNGUTgcRqD3m2g0+npLSwsy4ko4GpTUFOBgk9AU9T1d169ApkMIeaqzs3NVaWnpGYyxTYyxR5TTPhOZJEBSjY1LpZRw/GADV2GDEEKiqgT5BcdxwKR8QbGWP6eU/lkx0xejx8BxHGT5Xy8sLLxhYGDgS0KIX3gs4RPFxcUt3d3dJyv92gvJZPKxeDyO5KSJc34fYyykMsBmSim03qgaoESOBOVIaB1ra2vvSyQS31Qa65+oz145mpquWCyGMR6bTqdnomKgadoxuVzu/L6+PmffffclKPtRSjsqKirqmpqa4JzRoIrE4EqwEupQV6C3gzEGac7CPEuGxIAQ8lwymYSUZ8xfM2fOLAmFQmDQwRTcF4/HT0Wpbu3atcsnTJggw+Ewqjm/hOyruLjY6evrA2twJue8WiUH0OGnVPN6lefAoFn+P1TGNU2bCTmFSggfHKoHHPMJfcwPzJ8/H42aSC5vTaVSL1uWdQmCqK6urru2bNlCIH0rLS39lkpa0dj7yJQpU+CMRVdXV6CpqSlXWlo6RVWtwNzi+eehWw0GgyitPjxamsqPw8hjkpeg/8EwjNUqGUZAAvnXA8cddxwdN27cT1BKVQzjwnnz5h3AOT8B64cEXgWOrbZt3xiPxxd5+vinIeMwTfNq13XhTM/Wdb2mvr4eDNZOv7ykBWf+Pk3TsB9QDWuLRqN/XLNmDfYTJHRwLBjPVlVZvIwQcreq2swSQjyUTqdvh4MHq6qa7K4oKCi4Do7LW6vrhRCvILHZ6YFt54G5c+fuQSmFA4O8RoLUME0zMWHChA01NTUykUgcq8aE4GERgmgEepB4gPDO5XJg7TNgPoUQN6leJszzb8XFxXf39fX9Fiz/SJw2hguJZH9/P/baW5FI5I7m5mbIOTKRSAQOWtuwYcM+jLFq1aB6iWmaJgIFVDzwf1JKnM1bFGN/IuYG3BSrd6EQYi6l9POEEOyZE0bBcX/oUsTj8fPRd4MEKR6PI5npj0ajsH9aS0vLvkIIVDownimapsEWwo6cgp4N27afisfj10kpnzcMo8V1Xdgo9DRBwnkSHP9oNniPxn5CsEkImRQIBBZns9njlU3/ciQSOXPNmjUknxxUV1f/0HXdL4DVzmOPMxMIBC5mjNUh6EskEgcKIS7GPCmluMAC63ZJY2PjsKp4ozA3BGcrIYUNBAJ39Pf3X0YpfTqVSt0JyXGeUPGCbYqKJH4TVVf4w9tVWYKBAAAgAElEQVRuu01YloVAEpKwd4UQB0aj0fMhrZBSYn+jmjhiPTr692D3kHQpuc4kVD5N01y4efPmzlWrVmEMpwkheiorK29qbm6GDRqHnipK6WGpVOr06urqL3rn41p1cQaSgE7btq+Ox+MgXmg6nR6s1O3oy6sY4DyWplKpaxKJBHrXDndd97p169b17rnnnlMZY6dxzkGQvqeSpOtVcvhHKWWllPKn6XT6eFRBNE1DxQh2ChUQO5VK/QuJAewSzsmOjmfbzyFxY4ydLYS4ubu7e004HEa/3LOQfR5yyCFsr732QgKLQPqqtra2rnA4jEoR/D8qW5A2Pun50scppbqqXv9wYGDg/EAggGTmhFQqBb84YrWAl3BHUW2MxWIgYKen0+nzPXzRE3gJ1C6MMciYT/ds9Dy1BdfB9liWdbmUcqOX1C5AhTiXy3UjgRBC/LmhoeEfw8VwZ5/z+u8WoVLzqZUSQRqhado90Pw2Nja+7BlyNI4AMJTdkDSgoeqYTCZzoepM/z39f+19CXhcVd3+Pefce2dLmkxb2iSTlq0FtcInCPop+lEUUFAR/AzygYW2mZmkxSIgqAhIEEGUCkhpm1lSCrUIjYCy/UEBWcoiZecrWwsU2ixdkkyaSSZzl3P+581zp0+MLWQtge/M8/AA7V3Oec85v/X9/S6l7+u6fguiZzLysYYQ8j6oSISQvxmG8YRt27j3DkT/GGNntbe3L5w4ceKxcDRQjOoVVU2AEtU0rV6mJFGgC2EP7hW4lAE8A3oP6VQA7/P5FsoIvJ8xBi7wURC44ImBixcIBN7t7e2Fh/0q+MpCiPsQadE07RBKKRTjt5HChuMymtExFDeDWgVlSyn9LIzhTCZzW0lJCZyY5yB0QLfxeMHglT6+YcOGZ2fMmLFcZjEeQDQa2Pr9/mvz+TwUOQEnm3MO4+36RCKxYaibbjjXF3jM4JfD2JXOHiK8felMyReE4HyAEIIoyv0o2sG8KioqFjc3N6N4DHUUMKqA8cOdnZ3rysrKdMdxUMwMPvT5lZWVzWPJYx7qnL3uBnFZS+FKZ/QBIcTZ2Idw1KQj8GXGGGgtECqPUUrBIUdR7n0yYnEO5/xRSmk3IQRR10QymXxz4cKFsx3H+Q9ZXNmwFw0VpPbBTUXh2EqcT2QwYPChUE46xk8LIS41DOMa27ZxVnBObu3u7kah7SkyagdnLopaF2lgge+8SNbIoNYIhZ8TCtH9oWKL6z1hfRY47oyxW5E5kucDkfg8jFS/3w+jCfSi11zXfRe1KkilW5aFGo1Zpmn+wbbtHwghdti2fauu62iI8KZhGC8gTe1FyUbE3S/My+O5X8Y5f1qmy4sRSTdN80rLslAM+hLGAMeBMdYnR1zXfZkx9hCodJBfMvOEDOnx+XwedMnvgtOMSCgKf13XXTncrEthfF7HkpMhXxBI4Zyfg0yrXDfUaRRrmvaA3KOXyIwpaAXobDMpEolc5Z3Nb8tOI9e6rgsa5mOWZb3k9/tB3bwNa4PC71QqtWQ4azzYexDtBO9c07Rb4Ey7rrvEMAxkckEbQ4Eqgkg4W9izxbKj0w35fB6RXHCFEURB9nIxmiLIWrd6zjkcIgSBSjKZzIqx7Oox2Dn2v85runCm55Ahcwr5so1SirqIW9BpaMuWLWfJ+ppDOzs7L25sbERWnUSj0VPl+n66oqLiV5CV0Wj00zCWdV1HpFraf2QeMs1jaUh82Hzj8TjONIrCb5VnF+O9Gpl+uSZTEFSZNWuW09zcfDHn3K2srFzsZQexlr3IeMmCWQQCVsog7xbQ7mRNDzJWKLpFM44l/TtqfdhY9vT3NTU1M2VtE+Q57BY4CJ2o+TFNE478GsMw9oWDr2nanzw9exM68TDGLsF4ZK0RmAvFnZ2dq0pKSo6QzAUwEkCfPhN0wXQ6/cpQxyYN+C9LmwnO71WO46A4913QmeDk2rYNVgGclf+VLId3UGBrWda16FAkHUwwL6BDQRdsBsbhcBhsijYpUzdxzk/3+/0/HthBcCjj8+iycIBQz/kInCLGGGQEHLYvymxpgxdA+LvXDOBoUOBg10BWCiFWS5rYiSjiRncBxlidRzmHg/ZmOp3uqyca6Q90WGkfXS6Dq7Dv0LBgLWoyJJXpJMbYn+SrEbBGjSCC0dDF9YwxZGcQLIa8rNV1/Q89PT0tPp8PmRc02gG9DBTpJYVmDiMd5yDuh4MNat31pmnOW7p0KbI0Y/IbEZVo0aJFn7Es64dykUGBeMM7AKgxuNmyrK+4rruvruuvOI7zKV3XH5A8LfDJUOV9LyLEoJJ0dHSQ0tJSpEFRZNQo6UBF6LDjGVUvozgG6VN4wEgRu66LDhQoAgFfG9GshCwQAafusAkTJty0Y8cO1+/3I130TiKRAE1JnH322QfZtn0yCu8cx3nJMAwUtYHX/ZxpmigegkeNtKwB+kE+nyfBYBCV3yYUoOM4j43F4oN/L7MisyilrbZtPwVqCdKpjuO8ihS64zgnmaZZ7DjOJhQ5wTHxONbYtKA+/Q280blz56LFF8YLrxudUV4Yk92yh4eiDV13d/fXZTYj4Lrue52dnU9NnDgR1I7SioqK2zZv3oyMDmgXHHUkmGcikTD++c9/nuIJsXfz+fyzBaoWBLScx6nSOUOHgxFHgkYbC2QN4KhKQxPpxfUNDQ0vemsJxYcoNxQf2lgGZa3EAxs3brz74IMPRnr0ACFEB9oJSmcIxWhOPB4/ynGcrhUrVkBpjDg6Mti5osPBzp07UczGKaWvy6j/y55zPbG8vLwR3RekgkTEB/VATyJb5rVKgyBHcf5GRJu8FniHg+6HzhSMsYdH2knKK+qFAtZQSFlZWflsS0sLukZ8v6en5wbDMKbquv5VyBKZaVuXSqXeAsc6l8vBCEa3iG6MAy1Y0amIUoq5oPsF2jY+PBpRKA9nGGWfkkV0R6I7kuu6D2Wz2Wxpaen3ZMOD195+++1XDzroIBSMznBdtz0SidyPQskFCxZAKeLs59CNDPUYHkf8v0GbQtecSCTy4mgUVaLTjeu6x8gmBsWyicH7yWTycS8yWhyJRO7asmUL+OqgZDidnZ33NzY2Qp70dVOSkTPI4veKi4v/iS5zoC6g9kdGKTtM03xoJEbFYPYpMh7ZbPbrMOaxD9Lp9BOxWAxrie4895umeWA+n0fR6k7O+YNwrNG5hzE2Gw45pfSV+vp61J6hJgF8cGC+nTH25EdpJO9p7l7b36NAP3Ndd8e0adP+X1NT06G6roOi0ojmDXCA0eUFjTQkNW4dnIWWlpZjgQ/OQeHZ1dXVR+m6DtpBD2i1U6dOxX4a6/aye1xWBJBaW1vR0cwPemg6nX4hFouhQBfBrQdt255p2/Z3ZA0d9PD9KOj0DLrPYw5o1VlWVoYGBMgWYc1xrhF4gY3w5mjIToyxqanpC2jsgQ4/xcXF97a3t1Nd15F5ua+7u3u7z+c7GsETWefUnE6n0RwBlDxQROG8tfXbWwi+oPMOuguiTga2yJB/s2fP1mfOnIlOSNjXCHY8IZ17UHhOQq1QdXU1qK1HQbdalvXcihUrgIXm1fbhbCNb9jjOhkfTRD0jZMzj/TteDXlg3g3QhZZlHQuZCpkHXYgietRdpFKpP8BJlWcVzSsQ2Hm6oaHhPa829b+he1zXfX369OmgFHHoHASXICsJIeiENmpt6FGATClFQOM927bXMsYqJQagUv4JjBGZwToeuhkZxnQ6vamqqsooLS1FUAL1pm8mEomnPTmCvfFV2K/yLK5buXLlXgnA4t0FOwtOK6X0cQSLR5PF0n8PjNQxQGEuOiJwdEjYtGmTuXXrVlFUVOQg9QmuPBYX/7YsixmGkUCRajAYvPnaa6+Fl7nLEELEu/D/XmcSDfeCg93S0oIUMLzxGyoqKsDLRlrPkOlTindjDBUVFWzLli1YYHj9aKn3aH/Q8JyCYMR/P/roo/inr48vjBs00F21ahVavu0aE4wntDwc7qEZzH2YR0dHBy+kir1xYgx944CyGNhJBQIMtIr+3FPMAdH20ey6Mpjx978Girx/33D8XQHz3c1jT/ODMDzkkENYa2sr5jim38MY6hwL12Od2trajH54IyVOkPaXXSj6vksAp7exsRF7rG+PFs5B/1oVrBvS1KOh2IYzl/7rgjn1X7MTTjjB98UvfhFncJdBgWvWr1+vNzY2QmjvOiswJkezBsc7l4ECLxryATSnfucE7X7JAOVBqqqq/LNmzcr3H3NBVozV2UBnrjVr1tgF+TXwDOP9iUQCAZFdeOHPYNQNMP4xfmMM9jxZtGiRWZj/wHXG+AeuJ/Df3Zpiv+LvRqMocDD7deB+Gzj23cnCfvf8i+zAtd7+Ho3+7YMZ/rCu6b8e2PeXX355H93GayNMLcviwWBQ97rlYQ/touP0fyHm29bWxsZq3w91ct63B1j/74sU5A2Mxe3btyNb0PfYgh7Z09mtq6vzr1+/Hnpz1PXDQF2FPQ86k3d+gbUx8DtNu5OVWLuamhp8OwX7bURBn/66FVmiqqqqD5OF0L3/Zid4cscdZSeR4Js8/eR/ny7sZ2+Z69evdwfIjN3iOEbj69tTA549cIwFvbZrP+1J13ksiYG6Z6jHYcjX473ZbNa3c+dOS9d13/bt262x2P8Y2Igcg6HMDDxI0zTPRZeJjRs33jbYj2tgMdHfFlGU3t7e9Ad1bVmwYMGhKEyWRSz3RyKRjaO8+YcyXXWtQkAhoBBQCCgEFAIKAYWAQuBjhcBecwzgfeGjH7LWAF4+ipIH60H3eaOmadJkMolCzj3eBycil8uZBx54YE45BR+rfagGqxBQCCgEFAIKAYWAQkAh8BEjsNccg494nur1CgGFgEJAIaAQUAgoBBQCCgGFwAcgoBwDtT0UAgoBhYBCQCGgEFAIKAQUAgqBvVdjoLBWCCgEFAIKAYWAQkAhoBBQCCgExi8CKmMwftdGjUwhoBBQCCgEFAIKAYWAQkAhsNcQUI7BXoNavUghoBBQCCgEFAIKAYWAQkAhMH4RUI7B+F0bNTKFgEJAIaAQUAgoBBQCCgGFwF5DQDkGew1q9SKFgEJAIaAQUAgoBBQCCgGFwPhFQDkG43dt1MgUAgoBhYBCQCGgEFAIKAQUAnsNAeUY7DWo1YsUAgoBhYBCQCGgEFAIKAQUAuMXAeUYjN+1USNTCCgEFAIKAYWAQkAhoBBQCOw1BJRjsNegVi9SCCgEFAIKAYWAQkAhoBBQCIxfBJRjMH7XRo1MIaAQUAgoBBQCCgGFgEJAIbDXEBi0Y1BXV0e3bt1a4rousyzLKS4u5pMmTcrW1dXxeDxuOI7jb2hoyBJCxGBHP3fuXL+u6/tTSidpmlZCKV1fX1+/abD3978O42tubj7acZxex3HeCgQCRxJCXq2vr2/azfMK8x70WPGMuro6va2tLViY93DG2f+eRYsW+bq6ugLAsrW1tbuxsdHt//fxeDzIGPMRQuwpU6b0FLC2LCuE67q7u7twT1VVFdtnn30CQgjD7/f3XnfddbmRjm0o9wP7bDYb6Ojo8IXD4fzixYt7NE3bhW1VVZUZiUSCeOZLL72UffTRRx3895w5c0JFRUVmKBSyioqKcnua31DGsjeuBd7FxcVBn8+n7wZvMnfuXF8gEAi4rssrKiq6MC9N08j8+fOL+t3TC4ywBxzHCeq67i5ZsmTn3hi/945d43FdN59MJrFndq0ZxoW9XlRUhDl019XV9a0Z9qRlWabruvaqVau68We4ljEWyGQy2n777QeZ0HftcH/YT9u2bQvm83mTMdY7cGyFc9jV1UWLi4tzS5YsyRf2k+u6uMdatWpV3x7EWpmmGTIM41+uHe7YBt4H2adpGjAhA+eOeWzfvj2cz+d1SqmPc87S6TTkWx/Oc+fOLdU0rXflypXYC1rhHE+ZMqV3pBgWxokxvP322wGcM8iRZcuWYc3+5WyGQqG+s1mQJ9irc+bMwdr33TNQ9pimKTo6OrID5dVoYdrvOSQejwcKMnDZsmXZAfLRGKwsLKwT7u+/n8dgzCN5JKmqqvJPnjzZv3PnTnf16tVd/dfK2zN+0zRpMpnE/oZO2rW+kCHQI/jzcDgcxn7Df+u6Ltra2tr2wnp94NwXLlxYZJqm3tPTYxXG32+fmps2bQoO0IW7Xf/+enPSpEnQiyOSN/0HPUCW7fRkd+GSXePJ5/POihUrsD598g96HOfiw2TlcDbHeeedF+jt7e3bE5Zl/YudAFm4YcOG4ED5VhiTbdt85syZfXZaQRZOmDCBTZ06FTLdGs54+t/zIboQ+mKXfOzu7u5pbGzEO3erewrYQ28uWbLk3/b+SMZaVVUVCIVCPkqpO336dMwdOrnvN3v2bP2ggw4KBYNB0tTUtGuMkD0Ddd2edM9IxjaUe4Fnd3c3xvVv9uJQnvNh1w7aMYARbxjG5UKICYSQvxFC9nNd9/Vp06b9bevWrftxzg8PBoP3DcUojcVi8xlj21zX3UoIqSKEvJJIJP74YYPew9+T2trakx3H+Y4Q4gJCyC8IIY+mUql7B14fj8cnc84rKisrXxusUDnjjDMmhEKhrwohTpTOz2XJZHLHMMfZd5uH52mUUs45LyGEPJtIJJ4tOFbRaHQqY+w0KXfaOOdhIcS9MCqqq6tPNAyjAn/GOX8ulUr9IxaLHcIY+zznHAKy1LbtVStXrsyMZHxDuJcsXLjwQNd1j3Zdt4cQUs4Yu73gkEGo5XK5r3POJxFCpmma9nQikXhk4cKFU23bPp0xtt113VJCyF+TyeT71dXV32GMTeWcTyaEPJ1KpR4fqByHMLZRv9QzWg91HOcIuccsznmRbdu3FvCGsafr+jd0XYfxPEMIcXc6nX4hFovN1DTtJEppB+cci34HY6xLCPHfMCyFEGX4sxUrVrw56oPezQOrq6tnEEK+SwjpIITohmHcsXTp0jZcWlVVVRQOh08ihGCck1zXfb6rq+vpKVOmlFqW9T0hBPbZZCFEg2VZeb/ffzylNMQ5jzDG1pWVlT06QKEOaUrz588/2DCMYzRNywoh8M4/r1y5shUPgWBuaWn5Mucc2OpCiBxj7A7HcUoppacSQjKEkCJK6ZqysrIdra2tRwghZrmuG8R6+f3+WwqOxJAGtZuLMZbm5uavYV9jv0IeTp8+/d7C3OfNm7ePNBYu0zQNZ1xjjN1ZX1//KB4Vj8fL5br/TsrRlYlE4mE4XJzzz1NKj9M07dZkMvnGSMeH+6PR6P6EkKO9vbqf3+9feeONNzZ762xOnDjxWM55GexlzvnTDQ0ND9fW1kY45ycTQjqFEBPlXr93+vTpm5qbm08SQuyDNdE07Zl0Ov3EaIxxT8+ADCSEfAv4aZpW6ff7E0uWLNleuL6mpuZEuQchUyA/nykvL1+7ZcuWzxJCjmCMAfQiTdNuhrHW0tJyMuccuiugadprHR0dj3/UhvLAeVdXV0/Udf1ETdMoZAdjbFUikdhQuK62tnaKpmmQF05ZWdlNdXV1ridXjmaMtTuOExFCPMMY28w5vxQOsqZphqdfzh2pzhrJWnt76hRCSM5xnEA4HL5p8eLFfU6MpwurKKWQmcVCiKcaGhrWxePxMiHENzRNQ8AM8qo+l8vt9Pv938F9lNJ9OOevZDKZtaOxlvPnzy9mjB0P+aFp2oGapt2XTCb/WZi3d2aPx/8LIWbk8/l6v9/fIYT4NmMMOE8SQryM8YRCoWLTNE8VQlg4Mz6fb0VBvg4FxzPPPHNSIBA4SdM0OIIR13UfSqfTr2IIMGhnzJjxJUrpAZDhCNZQSv8cCoVENpv9AaWUwV5zHOeRFStWvBKLxSALD2WMFXHOuwghq5LJpD2U8Qy4llRXV39O0zTILQSWinVdv3358uUdBfkSDodnCyFwjidwzrd2dnbePXHixH0559+hlHYi0GwYRqOu6z2WZUHmBBzHmUYp/UsymcQ8R/yLx+Mlmqad5slA2E13NzQ0bPT0CW1qavoapRT6RHDOtzuOc7+u65Ap0NeOEGIyYyw9derUrqampqOwN6B7OOfduq7fNdDJHfGA9/AA7E9K6VcJIcf7/f4r+8vC0X7noB0DeHnxeByOgZ7JZK4Ih8OflwbCJUKIBYQQg1K6r2maj+dyucMYYwCzWNM0O5lMrsWgIdRc191X13VGKd1g2zYRQtxKKX3ccZxVjLEfaJq29a233lo9Y8aMaaZpTnYcx1dRUfEM7n///fcPNAxjouu65ZTSJp/P91I2m8Wf4T07Kioq3m1ubv6yECLW29u70O/3/wRCY9q0afdu3bp1OhZXCMHa29ufnTRp0gmu61YJIf6q6/pa27YdQsj+EKKO47yE9wWDwf055xNh+HR1db0YDofhCH2GUno2IWReMplsGcli1NTU4JD+WAr0c2zb/ro80J/1+Xy/8YwWUlNTcwYhZIbf778+m81WE0Ka/H7//fl8/mpK6TLXdSEMfug4TjVjbK7rum9KA+0p27av1XV9zfLlyx8ayfgGey+8fMuyfsg57yWE3CXX/GLgnk6n10B4xeNxHKK5nPObGWMwQOZls9kLQqHQd4UQ+/p8vhsty4rLg/uG3+9/OJfLXcUYw/w+RSk93efznTFahtxg5/RB18FBDAaDcULI87qur7Nt+7ec8zthUHmG2H9SSr9umuZNtm2fwDk/yLbtywzDuEDO/T3G2COu6y5yXfevnPMdhmEsMAzjd47jfF/TtJkdHR3nj4aS+6A5eAYtxtNOKb2Pc34OIeS+RCIBJwzG5H8SQk5zXfdSQshRlNIv6bp+Pef8m5zz6bquJ6VAv4JSujafz79iGMapmqatcF13JmPsZNd1L2toaGgfDt51dXVmc3Mz8G0WQgDTC6Uz/mwymbwHTvPcuXPLgBml9E5CyA6MkVK6nHMOgV1immbStu0fS4NyXTAYfCibzf4Ehoau6+9wzpdpmvazUVQ4kzVN+wmcEMg7zvn5hJBowQCbP39+hWmaPyCE3G/bdndxcXEbAidQVJTSb7uuezqltD6RSNwTj8enO45zEPa8rutL6uvrXxwOfv3vQXSJEAK5Sm3bvksay3WU0hcLwZeFCxeW5fP5c6XsuxmGN6V0vuM4cXkNHMayUCiU7u7ujsotsYUQ8hgh5CrO+W9c10Ug4sRIJBIbiQP4YfOLx+NVmqaVW5Z1C84IpRRBhZtw33nnnTexu7t7MZwrqUcOEkLAEFqoadpZnPON0sF6Ujow10PHmKb5ruu6dblc7mc+n++zmqYd29vbW1fIeH3YOPbW30ejUTiZX3QcJy0DZgthlCSTySsL76+trcXYv805RxT26o6ODlJaWnoW9CwhZA3nfLamaYdZlrXSNE0YVzhDn5aO5kF+vz/5UcrRWCz2M0IIHIE7pJNcxxhbXZA3tbW1h3HOL9I0bSEh5OtCiEMqKip+6TlzZQi8mKb5W03T1kE/IwAgsyGQP5C1sw3DuHY4RvfAdY1Go4dqmnaKaZorLMs6klJ6tKZpFxUMv3g8Ducy4jjOasMw6gghCOLAcYAevkzX9SOwt6Qcuk4IgbU8mDF2o+M4l+i6/sry5ctxzob0i8ViJ8DRDYVCi7PZ7PdwLjVNW44xeY5zjQwK3qNpWpPMll4l2RdLCCHIQp9PKf0RnBYpm6dHIpGrmpubYa+t5Zxv0HW9AXoonU6/PqQB9bt40aJFE/L5fC0CR/jHNE3Yh3emUqnHcFl1dfW+0kmFfbLasiyXMXahaZpX9/b2/o88q9t0XX/Qtu1zDMOA/NzpOM48xtgS13W/B12YSqXOGe7YBshBBBe+xRi72HXduQhspVKpJZ6um0op/RVwg71JKT0TZ0vapV/jnO/DGLtZOrK/lQb5w3JNHxdCLOSc384Y2ymEuEg6PcvT6fQrozHOD9HZ9J133in3+/2wcTGH+el0eutYvXcojgEMhkvhGDQ0NFwGpSO9ZkT37+WcN1NKEUn/FQw/efgPEUI8yDkHyD8nhGwnhMzhnD8rPa9KCHvbttfour6SEALl/0dCCA77Vs75fYyx78O4pJTWCiGSjLEc5/w4CHsZFb/EEy7vCCFgqL8ghDhOHsI/McYQQajO5XKLAoHA+TJC+wKi6og6CSFekxGw8zRN+w2yHZqm/YBzfpthGK9Kj/q/KKWbXdc9Utf1Nqmw3yKEfIox9pzjOMdkMpmls2bNsrds2YKo940+n+/HhajbcBcmFovN0TTtqFQqVRuPxxEd/SGykjjwMNyampouE0Js6OzsXF1aWjof3rgUhA/IqMDFPp/vnHw+H5HOGIzPkw3DwGb9czqdfj4Wi/1eBn3Xp1KpFcMd21Du8yLkEEJrk8nk36PR6I8Rqejo6LgeBm5NTc1/CSFOYIwhOmrbtn0bIeQCb3O/XFlZ2djc3FyLdxJCHpFe+IUymvETwzD2dV33Zii74RqZQ5nHYK9duHDhNAhdQkgqkUi8Ho/Hfy2EeDsSiaysq6uDIwQDH07mjZTSw1zXPVsI8VMZUbqSc/47v9+/MZ/P/1QI8Q6lNM85PyKVSl0Uj8ePE0JcbhjGcQMpE4Md22Cv8wT6DYwxKIfnOefIrr2eSqVW4xnxePx0TdM+k0wmL4nH44cLIWodx7mCMYZM3MvYW/F4HMIUVI67GWPftG37ukAggOgyztcvksnk24MdT//rEGU3DANGxKry8vL1zc3NUG4ik8ksRxoaxpHrunFJXYJDhsgU9hVkzXd0Xb9/+fLlf4nFYuczxkzLspbqun4NHILKyso3mpqaMNYb6+vr/y2LOJyxIrPBGDuXMfZrx0FsgTR6QYO+aD8Mb8dxYoSQdymlG5cvX/4MMk4tLS3Hc84RYTtB07SnUqnU3TjzmzdvRkT7F0KI1Q0NDSN2DEDVQ4AEDL5IJHJvS0vLuUKIQMHYjMfjX4QcDAaDv8pms1CKf3YcJ6rrOhy9NyoqKu5qbm6GEgrBERZCwPWNYq8AABkFSURBVKGNmqb5aezlUCh0wlAyxEPFGGeLc/5WOp2+BfsN40ilUpgP9uhXhBDnRCKRM7ds2YKsyAooS0rpXErpHfX19c9Go9HFMngCJ2GTPKNzZPDorJaWls/hPsuyztmLWdVBTT0ejy+Q+smnadpSJO5k9P+bkUhkbsH5gs5ljCGo9RmpD34PxyAcDtfCUZAZ8KTMkM+WOguOwy/T6fQ2UBp1Xf8uAmnJZPLJjyrzKuUakVntO5Ady2QyD4TD4d8LIaCj6gFMbW3tHBjSiURiHvQF57w2k8nMKSkp+Zmu6+8tX7781ng8DjsCEfknZXb5sIqKirqWlpZDcMxkdvaqpUuXvjcokD/gopqamu+6rjuLUnq967owCm/Qdf1Hy5Yt2+ztuYul4/luMpm8tba2Nua6LhyJJwghh0JWVldXf5Yx9mPIQMMwzkGwLp1OL4/H4xcTQqYmEokhGbpednSBNEBB9UzW1tYeBQfYNM0rEC1esGDBwY7jLGCMIWjTBCMVdgCldBqcCRm8rInFYt8QQsBxWSSDqL+X2aPrGhoa1sdisYcknkuSyeRfhosbghnSoIesTbe1tb0VDofhGLyYSqUQFMS6HoZ3M8auCQaDXZ2dncsQCJH2YtwwjAYEXH0+3yWEkCcppQgUf1lSMi8PBAJfdxzngkgkctxgGR0fNId4PN6XtU0mk1fEYrEzhBCz0+l0zFtT6DfoGwREkE241HGcK7F+Uqc8n0qlVklKERwD23GcNQj8GIZxVW9vb7dn0yBj8LfhYjiU+0Dbmjx58gwZVL/cNM1zly1b1pdFH4vfsB0D2HE1NTUQxs9Aabiue1IgEFicz+e/hghFIpG4pqam5loYezLA08k5/25PT8/lJSUlQXjQhJBlWARN065NpVLrotHohYyxrfX19avOPPNMcA2LpbK9HPe7rgsO7kGdnZ03lpaW/lrTNHikRyJTAGdBprW/6TjOfbqu+6Ez2tvbF5WWlp6PtB6Ubk1NTUDX9Qm2bd+oaRqcEdBsjs/lcr/1+XxTKKXYODBCpzHG9nMc51lKKSgTK+S/QW/K9YtWLhkNxyAud6ukB8EojMdiMaT6Efn5ERwDKADXdX+r6/oL7e3tf0JUiBASppQ+AuwMw4g6jlMB48kwjFPy+TycoFvgGEhKAgylNxOJRMNYbJiBz0T6Wxq4iPY8lEql/haLxX5ECPFXVFRch0NdU1ODKPMxtm3/pru7Ox8Oh28nhPxcOoGI+q59880375g5cyYyBgwCAjQw27Yh7KdTSmGoHvNRpsAHzhfUDErphXAQ4RjEYrE66YxunjZt2go4BvPnz59DKa0sLi6+LpPJHKrr+rm6rl8MZSFT4pdmMplNJSUlF8qAymakzpGSTqVSP/eihb/p6ek5bvXq1WNaawBnzjCMNJTEhAkTXujq6rqIELIBQhBKPBqNIsozPZlM/rK6uvowKF/Lsq7w+/0Q/GvT6XRDNBq9zKMP3QPHT1J1fscYK4HB7rruJYVU7VD3IKLsiOxIOtnysrIyGPPISOodHR1L4RhgPIyxmHRK4GiBlnW13F+P4bwiOADqYCwWQ8YAdIAbhRBLhBCLKaWIjN0N5QmZMNRx7e56GAKEkPO9YAWoDX9mjEUTicRruB7Zj9bW1q+4rgtjD0ESOCTrhBCgQtwOuSOEQMblL1OmTGnatGnTBF3XIRv/mEql+jKXI/mB0+04DjJ4z2DO8XgcmaEJUJCe4kZU7Ls+n+/SHTt2mEVFRcj41SITKSONz23YsOGvBx98MAI6ExBY0TTtbNM0f2hZ1sGU0t/ncrlvjWXUPRaLXQ+nJpVKrYzFYr+klE5MJBLnYuzRaBSBqPmRSOT0pqam/YQQNxNCsFfmIWAFnRKLxbA33meMIXJ+eiQSOa25uRnUh/Msy1o03hyDaDTa5/TIed4gA2PYz9/JZDJn9c8gxmIxUPz6HAPQQGpqaj6DucEIZYwdLIToM24QTIHhpmna97yg0ZaR7KWR3AtneMuWLaAfXw8nXjp410DeJJNJOECILCNTcHg6nY56Dt/ZGzZsmDNjxow6TdPgGK6KxWLABnQ3BBeRUbgUtDFk8GX9zm+GW5vYf14wGhE0DIVC13Z2doYhg0zTPKfgdESj0d9I6uerDQ0NcAzmSsrJF0CdFEIcCMcAa+G67nm2bV9tmibOMTKdcAx+jqh9MplERmvQP2Tje3t7kdFrSyaTaS+TewZj7Ir6+vptsVhsFmwGIQSyaVskZQl43oWAKWPsgEQicXYsFjsWtoWu6xfatr3YMIwrly1bBr0FYzaRSqXuGPSABlzo0RR/JmmUNzLGkD1BFgX20u3eugKfObquX9mFIoxAYKm0sWCXnMMYuxb2GRgGki72NEgasOn8fv8llmUdI+veLpL2wbEjpDphGNBn18jsyY5kMnl1LBYDPfu4VCpV7ckRZMcvMgzjjHw+P0lSu3/puu6V0EGy/ABUdOy9q6AXNU2D01Wt6zoC4Nl8Po8s1j0NDQ0PDBfDod4Xi8WQHf3VuHIMYrEYUlF6Op2uA2fQdd0kNgMmB2UHx6C3txfRexyC6+Px+BWehw+ayYm2bSPKGKSUwjC8iVL6C9d1lzQ0NDwZi8UQjWzO5XIPBQIBRJjfdF13nqQy/F3SEBHxgecL7xac2PsopTWIjMMxcBzHQp2CYRiHw6NDxsDn88FYBt0Dm+84SR1a70VusTF7kGLL5XLwTsFRRQQ3bdv29uLi4kwmk+kJBAJHYcxCCEQLbsAGRTRT1/Wl8CZH6q0hLQnem+QSn1ZTU4P06fGWZcFB0YqLi0Uul7tA4gSH6AZCCIwKDq6gNMaugZcuMykwoC63bftMXdfPAybAMR6PLwbmqVQKSn7Mf+C9wfjlnK9Pp9N3xWIxZGUySP/i5UjdQ3FpmnaNnJ9A2pcQApoXUqAd5eXly5ubm2MICtu2/SSEiN/vvzCfzyOrgwj6KWMdQR8KSEjfeo7BbYlEAo7YVV46+b6Ojg69uLgYma3DA4HAtZZlfVEIcRKoNZTSqyXXvTGfzz+FvSmj7M9wzgUh5GTLsi7w+XzYaxBaZ4yCMPzAKYHPDpoFMlC2bf+dUorxQMH93efzUcuy4Mwdm0qlFnmUN/B/EW36kaZpHclk8rpYLHaFNMS2ykzhs5zz79u2fY2MdCLaj4jZ5StWrOjjsQ/1B5qN5CH/DGORZx1pbyiRre3t7WvC4bAuMzXgUSOac20+n9/m9/uRoVjjuu4plNK3M5lMorS09DyZ8t1mmuadsrD7d5zzW/L5/Kt+vx978repVOqpoY5rd9fPnTt3Pzh9oDnIoAfx+XygriywLGtbd3e3HQ6Hi1DQdv3112ei0SjoUdgPv6aU/geCGjjHngy7pby8/G04BoZhQCGtHg3HAAV3paWliFK+l8lkGuGQgtfb0dHRMGvWLK2lpWWG5NSeDUWo6zqaF/xByjsozHmohfHoYWdJJQmqykuyNuZXyCgwxj4laVwIAPxwLPcqDCpQDFKp1DIoZ1DfZIDkDx6FBjU7VyIjAydWpmsuD4VCse7u7kWapj0IOgMcCwQfoBsopRdYloVoK7A/HQGWQvHoaOyF0XgGssgyGAVK2e/hgAkhDo5EIhdt2rRJLxSo19TUoIbuU7KW7PoC9sgASmPUzzk/DZTOyspKZC+tmpoaZN5RnPrnQsOH0RjncJ4Ri8XguD0SCoXWZLPZKwkhoC3eV1FRQTZv3vwNadhGM5nM90pKSsD3/jYoleFwGOe8u7OzsyEcDiOI2C2E+F9wrGVTh5+0trb+B+f8f2TQ79qR6mPMydPDX5Mc/MWWZSEj/xNd189Hg4mSkpJ8U1MTsr95jAcyRlJOEIh8XtO0EyEr4/H4IZxzUNlA50WwC40TYIxeIrOyVkNDw++Ggh0cqqamprkyiDoFzwGzgHP+X5JSfUM2m7UCgQBqMJChR/ADNhIc6dWUUjhQP8hkMqdNmDDhRNCtioqKLslmszjfCHYi+wce/aXIYg5lTP2vBa1T13VE22/NZDIvl5SUXEYIWUsIeSgUCund3d3g7UO3w3FBPd3VXrDmXFBYfT7fI729vaB6gvaMQvlTEGSRlEHUlZycTqfBqhhSg5jdzSUWi0Em9FGT4vE41me/ysrKKzZt2mSi+Q0okqCn5vP5IumEISu6GMEnQkgrZA8yBjLg8B7nHFmWc6Ss/G1nZ2dnMBiE3Fw9EgyHij1qA0F9wjjGMmA66IwBOst4KUAY9ohofIkQMhECSkYBvgTjz7btnxuG8QNpZFf29PRcFQqFbpSFb/+QNCCk/6qlwXQ35xwddQ4xDOPPlmUhQnl9Z2fnvaWlpeBSgrf/IKJWSKdD+SDapev6Mx5XDl5xUygUejqbzZ7CGEO05H4Zge71IlrHgPaAqBHnHOm7f3oUojMopTAKLuOcw2B+AbQdyWW8wzTNt6BcYNiCS8sYa5Ub2C83BwoYJ8hi2W9IR+GnxcXFOmMMqXeM86eZTObZxsbGYXf/8aI5iEDcBK69TNO37dy5857S0tIfCyHWod5B1mKcoWkaOLVHOo7zRFdX1yvSOAJ15WUUlFFKtUwmky4tLQWPGQU2MO7A3U+l0+l3h7rphnM90lsTJ05E+hr0sf8HDixj7C/g/UqnBsV0f7VtGylyCKN9NE0zN27cmDrggAO+pus6CoJWCiGOZIz9fceOHW94+wDCFtfakUgkOZY85qHOGVGcXC73Q0ppAEaxzC6hGPwW1M9AaFNKb5cUMOw38BG/II39NyKRyF1btmyBoodRcj+lFIXLf7JtOwfh7EXRDpXC/PWGhoZhp3YHOxconNbW1v8B7o7j3IPxyBR6o4yIfBa1PbZtPw5n03EcRLX3lQ68ZZrmX3p7ez+vaRqoUn8FzQHFx1gjT4iuxbmXXG5t586dt3jdJwY7pF3XeVQb0ChmIAvFOYfTfLeMrkNBzA0Gg4uBP5wSSSlqhrENShSKwaFYoBg1TUMU6E4vOwMB70r5AwcfnOErk8lk55AHtpsbvGJFGAigTSErsI9t2wnUFXh7Q8pwigDDU3AQpdHwbmVl5c1tbW2GrBWCUXGLxP7etra2FWVlZTrwFUKAMvHH3t7e+0YajfcMCwRqgM1DMjsB7u6foBilo1ds2/Y9pmnCOXlVZlbQGc6IRCJLm5ubEWXEmb5NUkIh25+wLGu97LQF+haoE6BUdieTySFzpoeCe3V19VGgzqDZBSLosvgyBRkCwwf7FpE7ZDaQHUJELxKJrGhtbQVFNOw4zguYg2yGkJCO+E5EcWGIerVvoC3eNpZOzVDmWbjWo6KcIetl4Kx/Q+rYB2UQArz8z3V0dNwUDoehj0ArBYXkd11dXRvXrFnDa2pqUPwOfTyTc76qoaHhPTTY0DTtF7quL1u2bFlfoeVH+YtGo9+jlH7Zo/19RdJ+l1qW9TlJ15mI8wFKIBx4FJMjQp5Op++Ix+OgzoBiCYMMTk/aNE0UrMJ4uwsBSNSLBoPBO0ajfsLLBkdlncMTnHPYFtuKioru7enpQaT/dnDO4QSA/lw4S6iL9CjKdyBYBwPXcRxkDlHkC9rKnQhmyrVZPZyGAl5gZo5Xh/kFSc9+RwYm213XPa2np6cuFApVY5weFfqrlNKE67ohZG6hWx3HmQE6Vnt7+/2lpaWYBwq530cmHnU3I3GOvXowBC0hy9Zxzo/XdX2VZVmolUJR9FLYY5Ku+DqaRICeLRunoD4NbAxQ+u5H4bKsrbrV5/PlXddFQOhhSunnXddFZmZUdOGCBQsOxbNlHVVaZo0Phz0qqVVtpmmeZFnWatM0YXNtkMY2cK3s7e29paio6HMyO4VGAH02Dec8gUYp0lmAbboZmGPsjLGbkL3ZG2cLAT3XdY8hhNQZhgE684tjFTQdtGMAvmooFOrr7AADnnPelsvlXkYrrK1bt37Wtu0jpeGDArcvIS0UCARQiIwq83fKy8v/sXXr1gNRiAlakOM4iN6hUwcO+//m8/l/BoNB0BHs0tLShzo7O4+XlJKsxzszYXxAqUvvuAPpZGxsFKw4jvMVpOgopY/t2LFj/eTJk7/sui4KtFBQeZRpmm29vb1Pg7eN8aIoB/QNwzD+btv2bK8bBzZfOXhn3iF/ENECxtgsRMo456+hGxC66KDzDpQq/owx9vhIDAwosoULF+6PdCSKxBhjL2CRkUnwNulb6DTkOA4i52/Irk3rocg8qgUw7sTcYDwgyiqEgDE0gVKKrjDv701juq6uzt/a2vqfjuOUI5UoIxVvhMNhGBTFkUjkwebm5v1d1z1cRqZ3QugW6FKUUhjOlbJ49bXJkye/DupRNBqt9Iy9jkAg8PRYcpiHe5iBN/aXruugaqyrrKx8z+uGckgmk7lt0qRJn0YnHDixPp9vHZQWOm8wxsABRtcNdK54u7Gxkc+fPx9CFELo/a6urueHa1APdS4YTyAQAM0ljGgwItbNzc1o8QvayKOSmrE/Ywx0vSYZwXvO25vgOGNPIkL7SjKZfLOurg4t3mDEg6LRXlRU9ORI1wyR7pKSEpxtnPVXOjs7wV9FpgAK9jaZnQjpuo6/13K53DOrVq1qPfXUU42SkhI47lDOffeAguHVLHwBRq9hGE+MRpFiP6xxhitt2z4CXTl6enqeKC0tdZAKZ4xtLCoqQhcOUCsRaGjGXilEfr1OGafDGdy4cePaAw44AI4msk2oT4Hi/sdoRISwzsFg8AhPpr3a3t7+ZklJCdY16HU0g3xBR7OdaMSAs4kg0KRJk+A0Yp1fL8ieM888MxIIBGCAomj92bFSSgV8vcYGUOTTQOHAmpaUlMzSdX1iWVnZ2m3btk22LOsroIYahvGMt0f7ZCEK0W3bfna//faDLBS1tbXYFzA02/L5/PPjjUaEOXt1Jofoug6ZsFEaUK84jnOAYRj7l5eXP4QmGpxz6EHM8cVMJvP0rFmznJaWFmBU1tPT83ihxSkCT+hWU1ZW9vje1AV7kkPenkLh5GTUBS5fvvydefPmHa7rOlL0T0+aNKkCTqhsRAFn/wUYrFj/7u7uw1FIjUwB9u6sWbNES0sLbIkvgGGABhCjtQ8R5CoqKkLdECL/29ClC11z/H7/t/L5/JO5XG57SUkJWAn74Uy89dZbr8+ePZtv3rz5AF3XsedastnsOlBB8axJkybBwMX5Wh+JRKDfdrXIHKy8LuwJWb8Ee+Tt8vLy50GdQ3eaRCKxora2Fl3CoGuJzPQ+s3Llyq2QyRgTY+wLjuO8b9v2i56dAGcRMtLIZrOP3XrrrX3dg0byQ7aqt7cXwQ90OnoOuhCZHGS1EonEbdFoFLYUHBpL1/Un0bEIMsnn830VZ1QGc16G7rnssstETU0NqHCo29iSyWSeGy1diLWA3IBNZ9v2BgRYi4qKpvp8viPgfLuu65c2JAzuXl3Xn1+2bNnWqqoqo7S0FAE8jB+F428BJw9v1Ddx4H3TTTfBMRxxVmMwa4AOVaZpgnYOKicYNU+OVQHyoB2DwQx8LK5Bu8tsNov0zwYAgYJOHEKfz9ewl/u+j8X01DMVAgoBhYBCQCGgEFAIKAQUAuMCgXHvGHgfCZphWRYiaeCp4aNo74xGsdG4WAE1CIWAQkAhoBBQCCgEFAIKAYXAOEBg3DsGBYw8KkZw2rRp+IrfiL/YNw6wV0NQCCgEFAIKAYWAQkAhoBBQCIwbBD42jsG4QUwNRCGgEFAIKAQUAgoBhYBCQCHwCURAOQafwEVVU1IIKAQUAgoBhYBCQCGgEFAIDBUB5RgMFTF1vUJAIaAQUAgoBBQCCgGFgELgE4jAvzgG6JPqfUwLX0/8BE537KeE1lWEEAcfgvO+ljf2L1Vv+EAEsB5YF8658X8Iqrz8OrAhP0pFx9mcIVjG25h2QSS/fMmFEA5a+n0czi/GK790i97k42ZvCyFQA2bIj459YgNP+IaM/OZEL+fc90me5x5kB84Ipv2xW1/oAtg2+Jr6OJGLkIeuJ6vHyZA+VsPg0O1CCHwgbVz9IJtd13WwtuNVl3hyLKNp2s1oVV0A8F8O9oIFC/BhmDsppejVv1d6s46rlRylwUBhyM+0+8brZhilaX4sHgPdhfXwBhsYjS8pfhwmju+AyK/8BjwFOJ7OMozYcSfEC2sqhMD48KV2fIhx3P+gETVNyxNC8JGhcfETQnQTQnDWxq0DOApAQXfiQ3lFn/B57g4qnBHMH+d4PMmWwSxr3rsI52U8jB1Y2vLrwAjKjptoLMTKx8Tvw8crIQYRGBkP69l/D8KBxofdxq0uwbfCXNd9v7i4+JTrrruufbeOAQ77nDlzgoZh0K6urvEG8mAO/bi4Zp998IFETdu+ffu4GI8axP89BLAHx+P+G6/jKuyQwtn9uJxfJWv+753tj3rG4/0MfxA+4+28jLfxfNR7a6jvH8/4fRx0SXFxMbFtm69atQrZgl02/8cuFTjUjaOuVwgoBBQCCgGFgEJAIaAQUAgoBD4cAeUYfDhG6gqFgEJAIaAQUAgoBBQCCgGFwCceAeUYfOKXWE1QIaAQUAgoBBQCCgGFgEJAIfDhCPx/D53GXw7/3SAAAAAASUVORK5CYII=\"\u003e\u003cbr\u003e\u003c/p\u003e\n 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\"\u003e\u003c/p\u003e\n \u003cp\u003eIn selected stations during 2021 cruise, microplankton samples were collected at surface from the ship\u0026rsquo;s clean water supply into a 10\u0026micro;m plankton hand net, filtered by a 200\u0026micro;m sieve to remove mesozooplankton, and imaged in vivo by a PlanktoScope v2.1\u003csup\u003e65\u003c/sup\u003e for qualitatively describing the composition of the microphytoplankton community. Individual cells were segmented from the full-field pictures using the built-in segmentation function of the instrument. Afterward, image data were assigned to taxonomic categories using random forest algorithms and validated by experts on the web-based platform Ecotaxa\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. The classified image dataset is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ecotaxa.obs-vlfr.fr/prj/6346\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eA partial and qualitative validation of the output of ChemTax was conducted by combining different concurrent measurements during each cruise. In 2018, flow cytometry samples were used to determine the presence or absence of \u003cem\u003eSynechococcus\u003c/em\u003e and \u003cem\u003eProchlorococcus\u003c/em\u003e, while 18S-RNA analysis was used to identify the presence of diatoms, prasinophytes, and dinoflagellates at stations where data were available\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Additionally, in 2021, the presence of diatoms, dinoflagellates, and Trichodesmium in the microplankton fraction was confirmed using a fluid imaging microscope (i.e., PlanktoScope v 2.1\u003csup\u003e65\u003c/sup\u003e). Although the application of all these complementary approaches does not enable validation of the absolute contribution of each group to total chlorophyll \u003cem\u003ea\u003c/em\u003e, it confirms the presence of the different groups, thereby supporting the structure of the pigmented plankton community found along the Amazon River plume. One possible caveat in our analysis is that the literature ratios for \u003cem\u003eTrichodesmium\u003c/em\u003e and \u003cem\u003eSynechococcus\u003c/em\u003e depend on contrasting ratios of the same pigment marker (zeaxanthin). However, validation with the alternative variables mentioned above suggests that ChemTax effectively resolved meaningful biomass of each group. For instance, ChemTax did not identify the presence of \u003cem\u003eTrichodesmium\u003c/em\u003e at stations 19.06 and 24.01 in 2021 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), where this organism was not observed in the PlanktoScope image set. Additionally, ChemTax did not assign any contribution of \u003cem\u003eSynechococcus\u003c/em\u003e to chlorophyll \u003cem\u003ea\u003c/em\u003e of station 6.04 in 2018 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), where it was also absent in the flow cytometry data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eElemental analysis of bulk and amino acids stable isotopes of seston\u003c/h2\u003e\n \u003cp\u003eSeston samples were collected at/near the surface, using Niskin bottles (2018) or a water pump deployed in the upper 0.5 m of the water column (2021), for the analysis of bulk and compound specific isotope analysis (CSIA AA) of amino acids carbon (\u003csup\u003e13\u003c/sup\u003eC/\u003csup\u003e12\u003c/sup\u003eC) and nitrogen (\u003csup\u003e15\u003c/sup\u003eN/\u003csup\u003e14\u003c/sup\u003eN) stable isotopes at 29 stations: 12 during 2018, and 17 during 2021 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In 2018, samples for the bulk analysis were collected after passing 2.5\u0026thinsp;\u0026minus;\u0026thinsp;19 L through precombusted (4 h, 450\u0026deg;C) 47 mm GF/F (0.7 \u0026micro;m nominal pore) filters by a pressurized air system designed for large volume filtrations. Samples for CSIA AA were collected by passing between 6.4\u0026thinsp;\u0026minus;\u0026thinsp;18.7 L through 47 mm 0.2 \u0026micro;m Nucleopore polycarbonate filters using the same pressurized system. In 2021, we decided to size-fractionate seston to separate picoplankton (\u0026lt;\u0026thinsp;3 \u0026micro;m) from the combination of nano- and microplankton (3-200 \u0026micro;m) as well as the free living (i.e, captured in the \u0026lt;\u0026thinsp;3 \u0026micro;m fraction) from the particle-attached components of the microbial community. Bulk samples were collected by vacuum filtration by sequentially passing 0.1\u0026thinsp;\u0026minus;\u0026thinsp;4 L of water through precombusted (4 h, 450\u0026deg;C) 25 mm GF/D Whatman (2.7 \u0026micro;m) and 25mm GF-75 Advantec (0.3 \u0026micro;m) filters under low vacuum pressure (\u0026lt;\u0026thinsp;100 mm Hg). Size-fractionated CSIA AA samples were collected again using the pressurized air system by sequentially passing 0.5\u0026thinsp;\u0026minus;\u0026thinsp;20 L of water through 47 mm 3 \u0026micro;m and 47 mm 0.2 \u0026micro;m Nucleopore polycarbonate filters. All samples were immediately dried (24\u0026ndash;72 h, 60\u0026deg;C), frozen and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until analysis in the laboratory ashore. Samples were not acidified prior to analysis to prevent changes in the signature of nitrogen.\u003c/p\u003e\n \u003cp\u003eThe stable C isotopes of the 2018 bulk samples were analyzed using a Micromass Isoprime 100 continuous-flow isotope ratio mass spectrometer (IRMS) coupled to a Carlo Erba elemental analyzer (NA 2500) at the Georgia Institute of Technology. In 2021, bulk samples were analyzed by EA Isolink CN (Thermo scientific) elemental analyzer coupled via a ConFlo IV (Thermo scientific) interface to a Delta V advantage (Thermo scientific) isotope ratio mass spectrometer at the Leibniz-Institute for Baltic Sea Research Warnemuende. The stability of the instruments and the contribution of blanks to our measurements were checked using a series of elemental (methionine) and isotopic (peptone) standards in each analytical run\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. We have carried out multiple intercalibrations of these two systems.\u003c/p\u003e\n \u003cp\u003eFor both cruises, stable nitrogen isotope compositions of individual amino acids were measured using an isotope ratio mass spectrometer (IRMS, Thermo Finnigan GmbH, MAT 253 MS, Germany) connected via a ConFlo IV interface unit to a gas chromatograph combustion periphery (GC-C, Thermo Scientific Trace 1310 GC, Italy; Thermo Scientific, GC Isolink, Germany) at the Leibniz-Institute for Baltic Sea Research Warnemuende. After 24h of acid hydrolysis (6N HCl, 110\u0026deg;C), amino acids were derivatized to trifluoro-acetylated isopropyl amino acid esters following Hofmann et al (2003)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e and purified according to Veuger et al (2005)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. An external standard of 16 individual amino acids was derivatized in the lab and run with each set of six samples. This external standard consisted of a mixture of alanine (Ala), arginine (Arg), aspartic acid\u0026thinsp;+\u0026thinsp;asparagine (Asp), cysteine (Cys), glutamic acid\u0026thinsp;+\u0026thinsp;glutamine (Glu), glycine (Gly), isoleucine (Ile), leucine (Leu), lysine (Lys), methionine (Met), phenylalanine (Phe), proline (Pro), serine (Ser), threonine (Thr), tyrosine (Tyr), and valine (Val). Additionally, an internal standard (trans-4-cyclohexane carboxylic acid) was added to each sample to assess the overall performance of the derivatization/purification procedure in the laboratory. The separation column in the GC consisted of a non-polar column coated with 5% phenyl-polysilphenylenesiloxane (BPX5, 60 m, 0.32 mm inner diameter, film thickness of 1 \u0026micro;m; SEG Analytical Science, Ringwood, Victoria, Australia). For each run, 2 \u0026micro;L of sample was injected via a PTV injector in splitless mode. The temperature program was as follows: Initial temperature at 50\u0026deg;C, heat up at 12\u0026deg;C/min to 120\u0026deg;C, hold for 17 min, heat up at 3\u0026deg;C/min to 180\u0026deg;C, linger for 10 min, heat up at 5\u0026deg;C/min to 200\u0026deg;C, hold for 6 min, heat up to 250\u0026deg;C at 10\u0026deg;C/ min and hold for 7 min. This procedure allows us to separate and analyze 13 amino acids (i.e., Ala, Asp, Glu, Ile, Gly, Leu, Lys, Phe, Pro, Tyr, Ser, Thr and Val). However, here we report only the results of 12 because Gly in our seston samples from the Amazon River plume co-eluted with a substance unknown to the NIST (National Institute of Standards and Technology) mass spectral library as identified by parallel analysis of subsamples with a single quadrupole GC-MS (Thermo Scientific ISQ 7000). Samples were analyzed in triplicate. Reproducibility for individual amino acid values was typically better than 1\u0026permil;.\u003c/p\u003e\n \u003cp\u003eBulk and amino acid specific carbon and nitrogen stable isotope abundances were expressed in \u0026delta; notation (\u0026permil;,) relative to Vienna Pee Dee Belemnite (VPDB) and relative to Air, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eTrophic position and imprint of mixotrophs in trophic amino acids\u003c/h2\u003e\n \u003cp\u003eThe trophic position of seston was calculated according to Eq.\u0026nbsp;(7) in Chikaraishi et al (2009)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, based on the nitrogen isotopes of the source amino acid phenylalanine (Phe) and the trophic amino acid glutamic acid\u0026thinsp;+\u0026thinsp;glutamate (Glu) as follows:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:{TP}_{Phe/Glu}\\:=\\:\\frac{{\\delta\\:}^{15}{N}_{Glu}\\:-\\:{\\delta\\:}^{15}{N}_{Phe}\\:-3.4}{7.6}\\:+1$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eAccording to recent laboratory studies, deviations from the average difference in the isotopic signature of photoautotrophs between Phe and Glu or alanine (Ala) can be used to track the effect of uptake of ambient amino acids (osmotrophy) or the grazing activity of phagotrophic protists\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. We calculated the apparent nitrogen isotope fractionation of Phe relative to Glu or Ala, following Yamaguchi et al. (2017)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, to account for these deviations as follows:\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:{\\epsilon\\:}_{Phe/TrAA\\:}=1000\\times\\:(\\frac{{\\delta\\:}^{15}{N}_{Phe}\\:+1000}{{\\delta\\:}^{15}{N}_{TrAA}\\:+1000}\\:-1)$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere TrAA represents the trophic amino acid (i.e, Glu or Ala).\u003c/p\u003e\u003cp\u003eTo investigate the relationship between the apparent isotopic fractionation of Glu and Ala relative to Phe and its ability to track osmotrophy and phagotrophy in mixotrophs, a comprehensive review of the existing literature on plankton was conducted\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. The literature data included autotrophic phytoplankton, eukaryotic strict unicellular phagotrophs, and zooplankton with different trophic roles, ranging from autotrophs (TP 1.0) to carnivores (TP 3.0). These components form the basis of the planktonic food web. The approach facilitates the analysis of the degree of isotopic enrichment of the two trophic amino acids with trophic transfers due to the metabolism of heterotrophs, providing insight into the potentially different fractionation patterns associated with mixotrophs that combine autotrophy and heterotrophy (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eRemote sensing products, statistical analysis and graphics\u003c/h2\u003e\u003cp\u003eThe median sea surface salinity provided by the Copernicus Global Ocean Physics Analysis and Forecast (doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48670/moi-00016\u003c/span\u003e\u003c/span\u003e) was calculated at each grid point for the duration of each cruise (09 to 29/05/2018 and 21/04 to 13/05/2021) in order to illustrate the average position of the plume (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The median of the geostrophic velocities retrieved for the Copernicus Global Ocean Gridded L4 Sea Surface Heights (doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.48670/moi-00148\u003c/span\u003e\u003c/span\u003e) were also calculated at each grid point over the duration of each cruise to show the dominant direction and speed of the currents in the region (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Daily data was used for producing Supporting Video 1 and 2 in order to illustrate the dynamism along the Amazon River plume.\u003c/p\u003e\u003cp\u003eStatistical analysis and plots were made by scripting R v4.0.5\u003csup\u003e78\u003c/sup\u003e in R Studio v2022.02.0\u003csup\u003e79\u003c/sup\u003e. Package \u003cem\u003eggplot2\u003c/em\u003e v3.5.1was used for plotting\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eggpubr\u003c/em\u003e v0.4.0 for exporting the plots\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The different pigmented plankton communities along the Amazon River plume were separated by a hierarchical cluster analysis (HCA) on the chemotaxonomy by HPLC samples during 2018 and 2021. The HCA was done using all the samples available at surface during both cruises. The clusters were separated by Manhattan distances due to the existence of outliers, and the clustering tendency was confirmed by a principal component analysis, both done using \u003cem\u003efactoextra\u003c/em\u003e v1.0.7 and \u003cem\u003eFactoMineR\u003c/em\u003e v2.4 packages\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. The physical, chemical and biological drivers potentially predicting the trophic position of our seston samples (n\u0026thinsp;=\u0026thinsp;46) were assessed using the decision tree classification machine learning model C5.0\u003csup\u003e30\u003c/sup\u003e in package \u003cem\u003eC50\u003c/em\u003e v0.1.6\u003csup\u003e84\u003c/sup\u003e. This model was used because of its ability for handling non-linear relations among variables in datasets containing missing values. All our seston samples were included in the analysis with the main goal of defining the variables explaining or predicting the trophic mode of seston. The predicting variables included in the analysis were the thickness of the plume, the maximum of the buoyancy frequency, the depth of the mixed layer, the depth of the subsurface fluorescence maximum, the depth of the nitracline (depth where concentration reached 1 \u0026micro;M), phosphocline (depth where concentration reached 0.1 \u0026micro;M) and silicocline (depth where concentration reached 2 \u0026micro;M) and, at the surface: dissolved oxygen, temperature, salinity, nitrate to phosphate ratio, nitrate to silicate ratio, nitrate to nitrite ratio, the average size of the pigmented community estimated using pigment markers according to Bricaud et al (2004)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, the concentration of chlorophyll \u003cem\u003ea\u003c/em\u003e and the relative contribution to chlorophyll \u003cem\u003ea\u003c/em\u003e of all the groups identified by chemotaxonomy as mentioned above.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests:\u003c/h2\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u003c/p\u003e\n\u003ch2\u003eData and materials availability:\u003c/h2\u003e\n\u003cp\u003eFern\u0026aacute;ndez-Carrera, Ana; Steinkopf, Markus; Liskow, Iris; Wodarg, Dirk; Voss, Maren; Loick-Wilde, Natalie: Stable isotopes and mol% of amino acids in 0.2-3 \u0026micro;m seston at surface during METEOR cruise M174 [dataset]. PANGAEA, https://doi.pangaea.de/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1594/PANGAEA.971293\u003c/span\u003e\u003c/span\u003e (dataset in review).\u003c/p\u003e\n\u003cp\u003eFern\u0026aacute;ndez-Carrera, Ana; Steinkopf, Markus; Liskow, Iris; Wodarg, Dirk; Voss, Maren; Loick-Wilde, Natalie: Stable isotopes and mol% of amino acids in 3-200 \u0026micro;m seston at surface during METEOR cruise M174 [dataset]. PANGAEA, https://doi.pangaea.de/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1594/PANGAEA.971279\u003c/span\u003e\u003c/span\u003e (dataset in review).\u003c/p\u003e\n\u003cp\u003eFern\u0026aacute;ndez-Carrera, Ana; Wodarg, Dirk; Montoya, Joseph P; Loick-Wilde, Natalie: Stable isotopes and mol% of amino acids in seston at surface during ENDEAVOR cruise EN614 [dataset]. PANGAEA, https://doi.pangaea.de/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1594/PANGAEA.971946\u003c/span\u003e\u003c/span\u003e (dataset in review).\u003c/p\u003e\n\u003cp\u003eSubramaniam, Ajit (2020) Phytoplankton diagnostic pigments from HPLC from samples collected on R/V Endeavor cruise EN614 in the tropical North Atlantic during May 2018. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2019-06-05. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.26008/1912/bco-dmo.769601.1\u003c/span\u003e\u003c/span\u003e [21/02/2023]\u003c/p\u003e\n\u003cp\u003eUmbricht, Jaqueline; Mohrholz, Volker; Montoya, Joseph P.; Subramaniam, Ajit (2022) Nitrate uptake, primary production rates and phytoplankton pigment concentrations from the Amazon River plume from three cruises (EN614, EN640, M174) \u0026ndash; Dataset used in the publication \u0026ldquo;Nitrate uptake and primary production along the Amazon River plume continuum\u0026rdquo;. http://doi.io-warnemuende.de/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12754/data-2022-0008\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ePlanktoScope images: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ecotaxa.obs-vlfr.fr/prj/6346\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eDeutsche Forschungsgemeinschaft LO 1820/8\u0026thinsp;\u0026minus;\u0026thinsp;1 (NLW)\u003c/p\u003e\n\u003ch2\u003eAuthor contributions:\u003c/h2\u003e\n\u003cp\u003eConceptualization: NLW, AFC\u003c/p\u003e\n\u003ch2\u003eAcknowledgments:\u003c/h2\u003e\n\u003cp\u003eWe would like to thank the captain, crew and science parties of cruises EN614 on board RV Endeavor and M174 on board RV Meteor for their invaluable support at sea. A special thanks dedicated to all people involved in M174 as well as to chief scientist Prof. Dr. M. Voss for making a memorable first international long term cruise in a German vessel after the hard lockdown due to the Covid pandemic. We are grateful to Prof. R. Peterson for the radium isotopes during EN614 and EN640, Dr. A. Pham and Dr. J. Umbricht for vivid discussions about the habitats and community structure along the Amazon River plume, and C. Burmeister, L. Kreuzer and E. Strope for providing state-of-the-art nutrient measurements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFlynn, K.J., et al.: Misuse of the phytoplankton\u0026ndash;zooplankton dichotomy: the need to assign organisms as mixotrophs within plankton functional types. J. 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Ocean. \u003cb\u003e109\u003c/b\u003e, (2004)\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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4742841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4742841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent evidence shows that phytoplankton are mixotrophs, combining photoautotrophy with osmotrophy (i.e., uptake of dissolved organic matter). Additionally, some unicellular eukaryotes are also capable of phagotrophy, representing an intermediate step between autotrophs and herbivores named mixoplankton. Mixoplankton seem to provide higher-quality food for metazoans, and to improve energy transfer to higher trophic levels. However, field studies on all aspects of mixotrophy are difficult due to the challenge of distinguishing their activity from that of autotrophs. Our April/May 2018 and 2021 cruises focused on the base of the surface planktonic food web in the distinct Amazon River plume habitats, where we used nitrogen stable isotopes of amino acids (CSIA AA) in seston within a multidisciplinary framework for resolving trophic function. Mixotrophy dominates in the Outer Plume Margin, a region with mature waters aged ca. 27 days. Mixotrophy seems the optimal strategy for growth in these heterogeneous outer margins as part of the succession of phytoplankton functional diversity along the plume. Our study supports the growing evidence for the cosmopolitan distribution of mixotrophy among unicellular aquatic organisms, underscores the urgent need to study it in situ, and paves the way for a novel application of the CSIA AA in field research.\u003c/p\u003e","manuscriptTitle":"Mixotrophy emerges as the optimal strategy in mature waters of the Amazon River plume","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-17 15:02:48","doi":"10.21203/rs.3.rs-4742841/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c03ee415-2e20-4712-b6f7-62829ecf5576","owner":[],"postedDate":"February 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43138660,"name":"Biological sciences/Ecology/Stable isotope analysis"},{"id":43138661,"name":"Biological sciences/Ecology/Biogeochemistry"},{"id":43138662,"name":"Biological sciences/Ecology/Biooceanography"}],"tags":[],"updatedAt":"2026-03-27T07:06:19+00:00","versionOfRecord":{"articleIdentity":"rs-4742841","link":"https://doi.org/10.1038/s42003-026-09893-4","journal":{"identity":"communications-biology","isVorOnly":false,"title":"Communications Biology"},"publishedOn":"2026-03-25 04:00:00","publishedOnDateReadable":"March 25th, 2026"},"versionCreatedAt":"2025-02-17 15:02:48","video":"","vorDoi":"10.1038/s42003-026-09893-4","vorDoiUrl":"https://doi.org/10.1038/s42003-026-09893-4","workflowStages":[]},"version":"v1","identity":"rs-4742841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4742841","identity":"rs-4742841","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}