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Animal-associated microbial communities can play major roles in the physiology, development, ecology, and evolution of their hosts, but the study of their diversity has yet focused on a limited number of host species. In this study, we used high-throughput sequencing of partial sequences of the bacterial 16S rRNA gene to assess the diversity of the gut-inhabiting bacterial communities of 212 specimens of tropical anuran amphibians from Brazil and Madagascar. The core gut-associated bacterial communities among tadpoles from two different continents strongly overlapped, with eight highly represented operational taxonomic units (OTUs) in common. In contrast, the core communities of adults and tadpoles from Brazil were less similar with only one shared OTU. This suggests a community turnover at metamorphosis. Bacterial diversity was higher in tadpoles compared to adults. Distinct differences in composition and diversity occurred among gut bacterial communities of conspecific tadpoles from different water bodies and after experimental fasting for 8 days, demonstrating the influence of both environmental factors and food on the community structure. Communities from syntopic tadpoles clustered by host species both in Madagascar and Brazil, and the Malagasy tadpoles also had species-specific isotope signatures. We recommend future studies to analyze the turnover of anuran gut bacterial communities at metamorphosis, compare the tadpole core communities with those of other aquatic organisms, and assess the possible function of the gut microbiota as a reservoir for protective bacteria on the amphibian skin.
Species diversity promotes the delivery of multiple ecosystem functions (multifunctionality). However, the relative functional importance of rare and common species in driving the biodiversity multifunctionality relationship remains unknown. We studied the relationship between the diversity of rare and common species (according to their local abundances and across nine different trophic groups), and multifunctionality indices derived from 14 ecosystem functions on 150 grasslands across a land use intensity (LUI) gradient. The diversity of above- and below-ground rare species had opposite effects, with rare above-ground species being associated with high levels of multifunctionality, probably because their effects on different functions did not trade off against each other. Conversely, common species were only related to average, not high, levels of multifunctionality, and their functional effects declined with LUI. Apart from the community level effects of diversity, we found significant positive associations between the abundance of individual species and multifunctionality in 6% of the species tested. Species specific functional effects were best predicted by their response to LUI: species that declined in abundance with land use intensification were those associated with higher levels of multifunctionality. Our results highlight the importance of rare species for ecosystem multifunctionality and help guiding future conservation priorities.
Behavioral choice can be characterized along two axes. One axis distinguishes reflexive, model-free systems that slowly accumulate values through experience and a model-based system that uses knowledge to reason prospectively. The second axis distinguishes Pavlovian valuation of stimuli from instrumental valuation of actions or stimulus–action pairs. This results in four values and many possible interactions between them, with important consequences for accounts of individual variation. We here explored whether individual variation along one axis was related to individual variation along the other. Specifically, we asked whether individuals' balance between model-based and model-free learning was related to their tendency to show Pavlovian interferences with instrumental decisions. In two independent samples with a total of 243 participants, Pavlovian–instrumental transfer effects were negatively correlated with the strength of model-based reasoning in a two-step task. This suggests a potential common underlying substrate predisposing individuals to both have strong Pavlovian interference and be less model-based and provides a framework within which to interpret the observation of both effects in addiction.
PaRDeS. Zeitschrift der Vereinigung für Jüdische Studien e.V., möchte die fruchtbare und facettenreiche Kultur des Judentums sowie seine Berührungspunkte zur Umwelt in den unterschiedlichen Bereichen dokumentieren. Daneben dient die Zeitschrift als Forum zur Positionierung der Fächer Jüdische Studien und Judaistik innerhalb des wissenschaftlichen Diskurses sowie zur Diskussion ihrer historischen und gesellschaftlichen Verantwortung.
We analyzed the population genetic pattern of 12 fragmented Geropogon hybridus ecological range edge populations in Israel along a steep precipitation gradient. In the investigation area (45 x 20 km(2)), the annual mean precipitation changes rapidly from 450 mm in the north (Mediterranean-influenced climate zone) to 300 mm in the south (semiarid climate zone) without significant temperature changes. Our analysis (91 individuals, 12 populations, 123 polymorphic loci) revealed strongly structured populations (AMOVA I broken vertical bar(ST) = 0.35; P < 0.001); however, differentiation did not change gradually toward range edge. IBD was significant (Mantel test r = 0.81; P = 0.001) and derived from sharply divided groups between the northernmost populations and the others further south, due to dispersal or environmental limitations. This was corroborated by the PCA and STRUCTURE analyses. IBD and IBE were significant despite the micro-geographic scale of the study area, which indicates that reduced precipitation toward range edge leads to population genetic divergence. However, this pattern diminished when the hypothesized gene flow barrier was taken into account. Applying the spatial analysis method revealed 11 outlier loci that were correlated to annual precipitation and, moreover, were indicative for putative precipitation-related adaptation (BAYESCAN, MCHEZA). The results suggest that even on micro-geographic scales, environmental factors play prominent roles in population divergence, genetic drift, and directional selection. The pattern is typical for strong environmental gradients, e.g., at species range edges and ecological limits, and if gene flow barriers and mosaic-like structures of fragmented habitats hamper dispersal.
The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.
Antibodies against spike proteins of influenza are used as a tool for characterization of viruses and therapeutic approaches. However, development, production and quality control of antibodies is expensive and time consuming. To circumvent these difficulties, three peptides were derived from complementarity determining regions of an antibody heavy chain against influenza A spike glycoprotein. Their binding properties were studied experimentally, and by molecular dynamics simulations. Two peptide candidates showed binding to influenza A/Aichi/2/68 H3N2. One of them, termed PeB, with the highest affinity prevented binding to and infection of target cells in the micromolar region without any cytotoxic effect. PeB matches best the conserved receptor binding site of hemagglutinin. PeB bound also to other medical relevant influenza strains, such as human-pathogenic A/California/7/2009 H1N1, and avian-pathogenic A/MuteSwan/Rostock/R901/2006 H7N1. Strategies to improve the affinity and to adapt specificity are discussed and exemplified by a double amino acid substituted peptide, obtained by substitutional analysis. The peptides and their derivatives are of great potential for drug development as well as biosensing.
Antibodies against spike proteins of influenza are used as a tool for characterization of viruses and therapeutic approaches. However, development, production and quality control of antibodies is expensive and time consuming. To circumvent these difficulties, three peptides were derived from complementarity determining regions of an antibody heavy chain against influenza A spike glycoprotein. Their binding properties were studied experimentally, and by molecular dynamics simulations. Two peptide candidates showed binding to influenza A/Aichi/2/68 H3N2. One of them, termed PeB, with the highest affinity prevented binding to and infection of target cells in the micromolar region without any cytotoxic effect. PeB matches best the conserved receptor binding site of hemagglutinin. PeB bound also to other medical relevant influenza strains, such as human-pathogenic A/California/7/2009 H1N1, and avian-pathogenic A/MuteSwan/Rostock/R901/2006 H7N1. Strategies to improve the affinity and to adapt specificity are discussed and exemplified by a double amino acid substituted peptide, obtained by substitutional analysis. The peptides and their derivatives are of great potential for drug development as well as biosensing.
Air pollution is the number one environmental cause of premature deaths in Europe. Despite extensive regulations, air pollution remains a challenge, especially in urban areas. For studying summertime air quality in the Berlin–Brandenburg region of Germany, the Weather Research and Forecasting Model with Chemistry (WRF-Chem) is set up and evaluated against meteorological and air quality observations from monitoring stations as well as from a field campaign conducted in 2014. The objective is to assess which resolution and level of detail in the input data is needed for simulating urban background air pollutant concentrations and their spatial distribution in the Berlin–Brandenburg area. The model setup includes three nested domains with horizontal resolutions of 15, 3 and 1 km and anthropogenic emissions from the TNO-MACC III inventory. We use RADM2 chemistry and the MADE/SORGAM aerosol scheme. Three sensitivity simulations are conducted updating input parameters to the single-layer urban canopy model based on structural data for Berlin, specifying land use classes on a sub-grid scale (mosaic option) and downscaling the original emissions to a resolution of ca. 1 km × 1 km for Berlin based on proxy data including traffic density and population density. The results show that the model simulates meteorology well, though urban 2 m temperature and urban wind speeds are biased high and nighttime mixing layer height is biased low in the base run with the settings described above. We show that the simulation of urban meteorology can be improved when specifying the input parameters to the urban model, and to a lesser extent when using the mosaic option. On average, ozone is simulated reasonably well, but maximum daily 8 h mean concentrations are underestimated, which is consistent with the results from previous modelling studies using the RADM2 chemical mechanism. Particulate matter is underestimated, which is partly due to an underestimation of secondary organic aerosols. NOx (NO + NO2) concentrations are simulated reasonably well on average, but nighttime concentrations are overestimated due to the model's underestimation of the mixing layer height, and urban daytime concentrations are underestimated. The daytime underestimation is improved when using downscaled, and thus locally higher emissions, suggesting that part of this bias is due to deficiencies in the emission input data and their resolution. The results further demonstrate that a horizontal resolution of 3 km improves the results and spatial representativeness of the model compared to a horizontal resolution of 15 km. With the input data (land use classes, emissions) at the level of detail of the base run of this study, we find that a horizontal resolution of 1 km does not improve the results compared to a resolution of 3 km. However, our results suggest that a 1 km horizontal model resolution could enable a detailed simulation of local pollution patterns in the Berlin–Brandenburg region if the urban land use classes, together with the respective input parameters to the urban canopy model, are specified with a higher level of detail and if urban emissions of higher spatial resolution are used.