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opencc-zeroJul 2022View details →

No evidence of predicted phenotypic changes after hurricane disturbance in a shade-specialist Caribbean anole

Extreme climatic events (ECE) such as hurricanes have been hypothesized to be a major driving force of natural selection. Recent studies argue that, following strong hurricane disturbance, Anolis lizards in the Caribbean undergo selection for traits such as longer forelimbs or smaller body sizes that improve their clinging ability to their substrates increasing their chances of surviving hurricane wind gusts. Some authors challenge the generalization of this hypothesis arguing that other mechanisms may explain these phenotypic changes or that they may not necessarily be generalizable due to system-specific idiosyncrasies. To address this issue, we compared body size and relative forelimb length of Anolis gundlachi, a trunk-ground anole living in closed canopy forests in Puerto Rico, before, four months after, and 15 months after Hurricanes Irma and Maria in 2017. Overall, our results show no clear evidence of a temporal decrease in body size or increase forelimb length (relative to body size) challenging the generalizability of the clinging ability hypothesis. Understanding how animals adapt to ECE is an emerging field. Still, we are quickly learning that this process is complex and nuanced.

opencc-zeroJul 2022View details →

Natural history predicts patterns of thermal vulnerability in amphibians from the Atlantic rainforest of Brazil_supporting information

In the Brazilian Atlantic Rainforest (AF), amphibians (625 species) face habitat degradation leading to stressful thermal conditions that constrain animal activity (e.g. foraging, reproduction). Data on thermal ecology for these species is still scarce. We tested the hypothesis that environmental occupation affects the thermal tolerance of amphibian species more than their phylogenetic relationships. We evaluated patterns of thermal tolerance of 47 amphibian species by assessing critical thermal maxima and warming tolerances, relating these variables with ecological covariates (e.g. adult macro- and microhabitat and site of larval development). We used mean and maximum environmental temperature, ecological covariates, and morphological measurements in the phylogenetic generalized least squares models selection to evaluate which traits better predict thermal tolerance. We did not recover phylogenetic signal under a Brownian model; our results point to a strong association between critical thermal maxima and habitat and development site. Forest species were less tolerant to warm temperatures than open area or generalist species. Species with larvae that develop in lentic environment were more tolerant than those from lotic ones. Thus, species inhabiting forest microclimates are more vulnerable to the synergistic effect of habitat loss and climatic change. We use radar charts as a quick evaluation tool for thermal risk diagnoses using aspects of natural history as axes.

opencc-by-4.0Jun 2022View details →

Data for publication: "Can size distributions of European lake fish communities be predicted by trophic positions of their fish species?"

Overview of the lakes used in the paper "Can size distributions of European lake fish communities be predicted by trophic positions of their fish species?" published in Ecology and Evolution (add date etc)   Lake data table: Overview of all lakes in our study. The first tab holds the 235 lakes with the good fit (of b) and the second tab holds the 129 lakes with an ill fit (of b). For each lake we note the country it is in, the year it was sampled, latitude, longitude, number of fish individuals between 8-2000 gr, number of individuals classified as predators and prey, the b exponent (proxy for size spectrum slope) of the fish community, mean trophic position of the fish community, the log10 of the predator-prey mass ratio (logPPMR), the catch per unit effort (CPUE, N net-1 night-1), number of fish species, maximum air temperature (°C), maximum depth (m), total phosphorus ((µg l-1) and area (km2). In addition the presence (1) or absence (blank) of fish species is noted.

opencc-zeroJul 2022View details →

Island size predicts mammal diversity in insular environments, except for land-bridge islands

Insular environments are among the most endangered ecosystems as they face a myriad of anthropogenic stressors. Forest mammals perform a wide range of ecological services, with their persistence being vital for ecosystem functionality in both natural and artificial islands. Studies revealed that shrinkage in island size usually leads to the decay of mammal species richness and abundance in patchy landscapes. However, mammal species-area (SARs) and abundance-area (AARs) relationships can differ among insular environments: oceanic, fluvial, artificial, and land-bridge islands (i.e., natural islands connected to the mainland). Large dams create vast insularized landscapes after river impoundment, leading to pervasive habitat loss and potentially causing even worse biodiversity losses than other insular systems. We conducted an extensive literature search and used meta-analysis techniques to quantify the magnitude of SAR and AAR for forest mammals across different archipelago landscapes worldwide. After a screening process, we ended up with 26 studies comprising 55 different effect sizes representing the magnitude of SARs and AARs. Our global analysis unveiled a positive relationship between effect sizes and island area, with mammal species richness and abundance increasing in fluvial, oceanic, and artificial islands accordingly with island area, but not in land-bridge islands. These results demonstrate that, except for land-bridge islands, SAR and AAR are still fair models to predict mammal diversity. These results could improve the prediction of SAR and AAR in insular environments under habitat loss scenarios and propose sound conservation strategies since the rate at which insular communities have been lost is presently unknown.

Foraging on anthropogenic food predicts problem-solving skills in a seabird

Species and populations with greater cognitive performance are more successful at adapting to changing habitats. Accordingly, urban species and populations often outperform their rural counterparts on problem-solving tests. Paradoxically, urban foraging also might be detrimental to the development and integrity of animals' brains because anthropogenic foods often lack essential nutrients such as the long-chain omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), which are important for cognitive performance in mammals and possibly birds. We tested whether urbanization or consumption of EPA and DHA are associated with problem-solving abilities in ring-billed gulls, a seabird that historically exploited marine environments rich in omega-3 fatty acids but now also thrives in urban centres. Using incubating adults nesting across a range of rural to urban colonies with equal access to the ocean, we tested whether urban gulls preferentially consumed anthropogenic food while rural nesters relied on marine organisms. As we expected individual variation in foraging habits within nesting location, we characterized each captured gulls' diet using stable isotope and fatty acid analyses of their red blood cells. To test their problem-solving abilities, we presented the sampled birds with a horizontal rendition of the string-pull test, a foraging puzzle often used in animal cognitive studies. The isotopic and fatty acid profiles of urban nesters indicated a diet comprising primarily anthropogenic food, whereas the profiles of rural nesters indicated a high reliance on marine organisms. Despite the gulls' degree of access to urban foraging habitat not predicting solving success, birds with biochemical profiles reflecting anthropogenic food (less DHA and a higher carbon-13 ratio in their red blood cells) had a greater probability of solving the string-pull test. These results suggest that experience foraging on anthropogenic food is the main explanatory factor leading to successful problem-solving, while regular consumption of omega-3s during incubation appears inconsequential.

Data from: Predicting range shifts of Davidia involucrata Ball. under future climate change

Understanding and predicting how species will respond to climate change is crucial for biodiversity conservation. Here, we assessed future climate change impacts on the distribution of a rare and endangered plant species, Davidia involucrate in China, using the most recent global circulation models developed in the sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC6). We assessed the potential range shifts in this species by using an ensemble of species distribution models (SDMs). The ensemble SDMs exhibited high predictive ability and suggested that the temperature annual range, annual mean temperature, and precipitation of the driest month are the most influential predictors in shaping distribution patterns of this species. The projections of the ensemble SDMs also suggested that D. involucrate is very vulnerable to future climate change, with at least one-third of its suitable range expected to be lost in all future climate change scenarios and will shift to the northward of high-latitude regions. Similarly, at least one-fifthof the overlap area of the current nature reserve networks and projected suitable habitat is also expected to be lost. These findings suggest that it is of great importance to ensure that adaptive conservation management strategies are in place to mitigate the impacts of climate change on D. involucrate.

Geographic range size, water temperature and extrinsic threats predict the extinction risk in global cetaceans

Despite that cetaceans provide significant ecological contributions to the health and stability of aquatic ecosystems, they are highly endangered with nearly one-third of species assessed as threatened with extinction. Nevertheless, to date, few studies have explicitly examined the patterns and processes of extinction risk and threats for this taxon, and even less between the two subclades (Mysticeti and Odontoceti). To fill this gap, we compiled a dataset of six intrinsic traits (active region, geographic range size, body weight, diving depth, school size and reproductive cycle), six environmental factors relating to sea surface temperature and chlorophyll concentration, and two human-related threat indices that are commonly recognized for cetaceans. We then employed phylogenetic generalized least square (PGLS) models and model selection to identify the key predictors of extinction risk in all cetaceans, as well as in the two subclades. We found that geographic range size, sea surface temperature and human threat index were the most important predictors of extinction risk in all cetaceans and in odontocetes. Interestingly, maximum body weight was positively associated with the extinction risk in mysticetes, but negatively related to that for odontocetes. By linking seven major threat types to extinction risk, we further revealed that fisheries bycatch was the most common threat, yet the impacts of certain threats could be overestimated when considering all species rather than just threatened ones. Overall, we suggest that conservation efforts should focus on small-ranged cetaceans and species living in warmer waters or under strong anthropogenic pressures. Moreover, further studies should consider the extinction risk of species when superimposing risk maps and quantifying risk severity. Finally, we emphasize that mysticetes and odontocetes should be conserved with different strategies, because their extinction risk patterns and major threat types are considerably different. For instance, large-bodied mysticetes and small-ranged odontocetes require special conservation priority.

Data from: Improvement of genomic predictions in small breeds by construction of genomic relationship matrix through variable selection

Genomic selection has been increasingly implemented in the animal breeding industry, and it is becoming a routine method in many livestock breeding contexts. However, its use is still limited in several small-population local breeds, which are, nonetheless, an important source of genetic variability of great economic value. A major roadblock for their genomic selection is accuracy when population size is limited: to improve breeding value accuracy, variable selection models that assume heterogenous variance have been proposed over the last few years. However, while these models might outperform traditional and genomic predictions in terms of accuracy, they also carry a proportional increase of breeding value bias and dispersion. These mutual increases are especially striking when genomic selection is performed with a low number of phenotypes and high shrinkage value—which is precisely the situation that happens with small local breeds. In our study, we tested several alternative methods to improve the accuracy of genomic selection in a small population. First, we investigated the impact of using only a subset of informative markers regarding prediction accuracy, bias, and dispersion. We used different algorithms to select them, such as recursive feature eliminations, penalized regression, and XGBoost. We compared our results with the predictions of pedigree-based BLUP, single-step genomic BLUP, and weighted single-step genomic BLUP in different simulated populations obtained by combining various parameters in terms of number of QTLs and effective population size. We also investigated these approaches on a real data set belonging to the small local Rendena breed. Our results show that the accuracy of GBLUP in small-sized populations increased when performed with SNPs selected via variable selection methods both in simulated and real data sets. In addition, the use of variable selection models—especially those using XGBoost—in our real data set did not impact bias and the dispersion of estimated breeding values. We have discussed possible explanations for our results and how our study can help estimate breeding values for future genomic selection in small breeds.

opencc-by-4.0Aug 2022View details →

Prediction of ternary complex structures using molecular dynamics and proteomics

Source code and structural PDBs that went into the Prediction of ternary complex structures using molecular dynamics and proteomics paper.

opencc-by-4.0Aug 2022View details →

Action-based predictions affect visual perception, neural processing, and pupil size, regardless of temporal predictability

Data supporting the findings in: Lubinus, C., Einhäuser, W., Schiller, F., Kircher, T., Straube, B., & van Kemenade, B. M. (2022). Action-based predictions affect visual perception, neural processing, and pupil size, regardless of temporal predictability. NeuroImage. DOI: XXX Details are specified in the readme file.

Annual egg productivity predicts female-biased mortality in avian species

Among avian species, the differential cost entailed by either sex in competition for mates have been regarded as the main evolutionary influence on sex differences in mortality rates. However, empirical evidence suggests that sex-biased adult mortality is mainly related to differential energy investment in gamete production, a greater annual mass devoted to egg production leading to higher female mortality. We explicitly tested the generality of this pattern in a comparative framework. Annual egg production can be relatively large in some species (up to 200% of female body mass) and annual mortality is generally biased towards females. We showed that greater annual egg productivity resulted in higher mortality rates of females relative to males. Mating system was secondarily important, species where males were more involved in mating competition having more equal mortality rates between the sexes. However, both traits explained only a limited fraction of the interspecific variation in female-biased mortality. Other traits, such as sexual size dimorphism and parental care, had much weaker influences on female-biased mortality. Our results suggest that both annual mass devoted to gamete production by females and mating system contribute to the evolution of the fundamental life-history trade-off between reproduction and survival in avian taxa.

opencc-zeroSep 2022View details →

Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

The ecological conditions experienced by wildlife reservoirs affect infection dynamics and thus the distribution of pathogen excreted into the environment, which have been hypothesized to shape risks of zoonotic spillover. However, few systems have data on both long-term ecological conditions and pathogen excretion to advance mechanistic understanding and test environmental drivers of spillover risk. We here analyze three years of Hendra virus data from nine Australian flying fox roosts with covariates derived from long-term studies of bat ecology. We show that the magnitude of winter pulses of viral excretion, previously considered idiosyncratic, are most pronounced after recent food shortages and in bat populations displaced to novel habitats. We further show that cumulative pathogen excretion over time is shaped by bat ecology and positively predicts spillover frequency. Our work emphasizes the role of reservoir host ecology in shaping pathogen excretion and provides a new approach to estimate spillover risk.

opencc-by-4.0Dec 2021View details →

Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma

Original file of Downregulation of ST6GAL1 promotes liver inflammation and predicts adverse prognosis in hepatocellular carcinoma.

Data for figures in Model predictions of wave overwash extent into the marginal ice zone

The data required to reproduce the figures in 'Model predictions of wave overwash extent into the marginal ice zone'. The data includes: <ul> <li>Coefficient values produced by coupled floe-wave motions, required to determine overwash of a single floe and thus the overwash extent model.</li> <li>The data for the transects used to predict overwash in Figure 11 (Agulhas II), includes wave conditions and the ice concentration along the transect (other floe field properties remains constant).</li> </ul> The code is uploaded at -  <pre>https://doi.org/10.5281/zenodo.7059554</pre>

opencc-bySep 2022View details →

Data files associated with the U-net prediction of urban canopy flow

Folder "testing_data" includes the input (urban canopy structure), output (LES velocity field), and prediction (velocity field from trained network) for general test cases. Folder "perturbed_data" includes the input (urban canopy structure), output (LES velocity field), and prediction (velocity field from trained network) for perturbed test cases. Input: dataX_xxxxxx Output: dataY_xxxxxx Prediction: pred_xxxxxx

opencc-by-4.0Sep 2021View details →

Immune signatures predict development of autoimmune toxicity in immune checkpoint inhibitor-treated patients with cancer

Immune signatures predict development of autoimmune toxicity in immune checkpoint inhibitor-treated patients with cancer   Nicolas Gonzalo Nuñez1*, Fiamma Berner2*, Ekaterina Friebel1*, Susanne Unger1, Nina Wyss2,3, Julia Martinez Gomez4,  Mette-Triin Purde2, Rebekka Niederer2,3, Maximilian Porsch5, Christa Lichtensteiger2, Rafaela Kramer6, Michael Erdmann6, Christina Schmitt7, Lucy Heinzerling6,7, Marie-Therese Abdou2, Julia Karbach8, Dirk Schadendorf9, Lisa Zimmer9, Selma Ugurel9, Niklas Klümper10,11,12, Michael Hölzel10,11, Laura Power1, Stefanie Kreutmair1, Mariaelena Capone13, Gabriele Madonna13, Lacin Cevhertas14,15, Anja Heider14, Teresa Amaral16,17, Omar Hasan Ali2,3,4,18, David Bomze2,19, Florentia Dimitriou4, Stefan Diem20, Paolo Antonio Ascierto13, Reinhard Dummer4, Elke Jäger8, Christoph Driessen20, Mitchell P. Levesque4, Willem van de Veen14, Markus Joerger20, Martin Früh20,21, Burkhard Becher1**, Lukas Flatz2,3,4,20,22**   */** these authors contributed equally  Affiliations 1. Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland 2. Institute of Immunobiology, Medical Research Center, Kantonsspital St. Gallen, St.Gallen, Switzerland 3. Department of Dermatology, Kantonsspital St. Gallen, St.Gallen, Switzerland 4. Department of Dermatology, University Hospital Zurich, Zurich, Switzerland 5. Department of Radiology, Kantonsspital St. Gallen, St.Gallen, Switzerland 6. Department of Dermatology, University of Erlangen-Nuremberg, Erlangen, Germany 7. Ludwig Maximilian University of Munich, Munich, Germany 8. Department of Oncology and Hematology, Krankenhaus Nordwest, Frankfurt, Germany 9. Department of Dermatology, Comprehensive Cancer Center (Westdeutsches Tumorzentrum) University Hospital Essen, Essen, Germany 10. Institute for Experimental Oncology, University Hospital Bonn, Bonn, Germany 11. Center for Integrated Oncology Cologne/Bonn, University Hospital Bonn, Bonn, Germany 12. Department of Urology, University Hospital Bonn, Bonn, Germany 13. Istituto Nazionale Tumori-IRCCS-Fondazione G. Pascale, Napoli, Italy 14. Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland 15. Department of Medical Immunology, Institute of Health Sciences, Bursa Uludag University, Bursa, Turkey 16. Skin Cancer Center, Department of Dermatology, University Hospital Tübingen, Tübingen, Germany 17. iFIT Cluster of Excellence (EXC 2180), University of Tübingen, Tübingen, Germany 18. Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, Canada 19. Sackler Faculty of Medicine, Tel-Aviv University, Israel 20. Department of Oncology, Kantonsspital St. Gallen, St.Gallen, Switzerland 21. Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland 22. Universitäts-Hautklinik, University of Tübingen, Tübingen, Germany

Climate data for Machine Learning based 100-year flood flow prediction model

This study evaluates the application of ML technique over northeast United States regions and compares its performance to the U.S. Geological Survey (USGS) Streamflow Statistics (StreamStats)

opencc-zeroSep 2022View details →

A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues

Although cellular senescence drives multiple age-related co-morbidities through the senescence-associated secretory phenotype (SASP), in vivo senescent cell identification remains challenging. Here, we generated a gene set (SenMayo) and validated its enrichment in bone biopsies from two aged human cohorts. We further demonstrated reductions in SenMayo in bone following genetic clearance of senescent cells in mice and in adipose tissue from humans following pharmacological senescent cell clearance. We next used SenMayo to identify senescent hematopoietic or mesenchymal cells at the single cell level from human and murine bone marrow/bone scRNA-seq data. Thus, SenMayo identifies senescent cells across tissues and species with high fidelity. Using this senescence panel, we were able to characterize senescent cells at the single-cell level and identify key intercellular signaling pathways. SenMayo also represents a potentially clinically applicable panel for monitoring senescent cell burden with aging and other conditions as well as in studies of senolytic drugs.

Uplink-based Live Session Model for Stalling Prediction in Video Streaming

Dataset to model streaming session only by uplink requests with the goal of quality impairment estimation like quality changes or stallings.

Dataset for Uplink-based Live Session Model for Stalling Prediction in Video Streaming

This dataset presents aggregated YouTube streaming data used for uplink based quality impairment estimation.