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5,805 results for “Data model”
Data from: The socially mediated recovery of a fearful fish paired with periodically replaced calm models
Social learning is an important mechanism for acquiring knowledge about environmental risk. However, little work has explored the learning of safety and how such learning outcomes are shaped by the social environment. Here, we exposed minnows, Pimephales promelas, to a high-risk environment to induce behavioral responses associated with fear (e.g., neophobia). We then used the presence of calm conspecific models (low-risk individuals) to weaken these responses. When observers (individuals from the high-risk environment) and models were paired consistently in a one-on-one setting, the observers showed no recovery (i.e., no weakening of the fear responses), and instead the models indirectly acquired those responses (i.e., a socially-transmitted state of fear). However, observers paired with models that were periodically replaced with new calm models showed a significant recovery, and each new model showed diminished socially-transmitted fear. We argue that our understanding of predation-related fear and social information transfer can prove fruitful in understanding problems with fear and stress across animal taxa, including among humans who experience post-traumatic stress and secondary trauma. Our findings indicate that the periodic replacement of models can promote fear recovery in observers and reduce socially-transmitted fear in models.
Data from: Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records
Objective: Acute kidney injury (AKI) in hospitalized patients puts them at much higher risk for developing future health problems such as chronic kidney disease, stroke, and heart disease. Accurate AKI prediction would allow timely prevention and intervention. However, current AKI prediction researches pay less attention to model building strategies that meet complex clinical application scenario. This study aims to build and evaluate AKI prediction models from multiple perspectives that reflect different clinical applications. Material and Methods: A retrospective cohort of 76,957 encounters and relevant clinical variables were extracted from a tertiary care, academic hospital electronic medical record (EMR) system between November 2007 and December 2016. Five machine learning methods were used to build prediction models. Prediction tasks from four clinical perspectives with different modeling and evaluation strategies were designed to build and evaluate the models. Results: Experimental analysis of the AKI prediction models built from four different clinical perspectives suggest a realistic prediction performance in cross-validated AUC ranging from 0.720 to 0.764. Discussion: Results show that models built at admission is effective for predicting AKI events in the next day; models built using data with a fixed lead time to AKI onset is still effective in the dynamic clinical application scenario in which each patient's lead time to AKI onset is different. Conclusion: To our best knowledge, this is the first systematic study to explore multiple clinical perspectives in building predictive models for AKI in the general inpatient population to reflect real performance in clinical application.
Data from: Residue proximity information and protein model discrimination using saturation-suppressor mutagenesis
Identification of residue-residue contacts from primary sequence can be used to guide protein structure prediction. Using Escherichia coli CcdB as the test case, we describe an experimental method termed saturation-suppressor mutagenesis to acquire residue contact information. In this methodology, for each of five inactive CcdB mutants, exhaustive screens for suppressors were performed. Proximal suppressors were accurately discriminated from distal suppressors based on their phenotypes when present as single mutants. Experimentally identified putative proximal pairs formed spatial constraints to recover >98% of native-like models of CcdB from a decoy dataset. Suppressor methodology was also applied to the integral membrane protein, diacylglycerol kinase A where the structures determined by X-ray crystallography and NMR were significantly different. Suppressor as well as sequence co-variation data clearly point to the X-ray structure being the functional one adopted in vivo. The methodology is applicable to any macromolecular system for which a convenient phenotypic assay exists.
Data from: Spatiotemporally explicit demographic modelling supports a joint effect of historical barriers to dispersal and contemporary landscape composition on structuring genomic variation in a red-listed grasshopper
Inferring the processes underlying spatial patterns of genomic variation is fundamental to understand how organisms interact with landscape heterogeneity and to identify the factors determining species distributional shifts. Here, we employ genomic data (ddRADSeq) to test biologically-informed models representing historical and contemporary demographic scenarios of population connectivity for the Iberian cross-backed grasshopper Dociostaurus hispanicus, a species with a narrow distribution that currently forms highly fragmented populations. All models incorporated biological aspects of the focal taxon that could hypothetically impact its geographical patterns of genomic variation, including (a) spatial configuration of impassable barriers to dispersal defined by topographic landscapes not occupied by the species, (b) distributional shifts resulted from the interaction between the species bioclimatic envelope and Pleistocene glacial cycles, and (c) contemporary distribution of suitable habitats after extensive land clearing for agriculture. Spatiotemporally-explicit simulations under different scenarios considering these aspects and statistical evaluation of competing models within an Approximate Bayesian Computation (ABC) framework supported spatial configuration of topographic barriers to dispersal and human-driven habitat fragmentation as the main factors explaining the geographical distribution of genomic variation in the species, with no apparent impact of hypothetical distributional shifts linked to Pleistocene climatic oscillations. Collectively, this study supports that both historical (i.e., topographic barriers) and contemporary (i.e., anthropogenic habitat fragmentation) aspects of landscape composition have shaped major axes of genomic variation in the studied species and emphasizes the potential of model-based approaches to gain insights into the temporal scale at which different processes impact the demography of natural populations.
Data from: Testing models of speciation from genome sequences: divergence and asymmetric admixture in Island Southeast Asian Sus species during the Plio-Pleistocene climatic fluctuations
In many temperate regions, ice ages promoted range contractions into refugia resulting in divergence (and potentially speciation), while warmer periods led to range expansions and hybridization. However, the impact these climatic oscillations had in many parts of the tropics remains elusive. Here, we investigate this issue using genome sequences of three pig (Sus) species, two of which are found on islands of the Sunda-shelf shallow seas in Island Southeast Asia (ISEA). A previous study revealed signatures of inter-specific admixture between these Sus species (Frantz et al. (2013) Genome sequencing reveals fine scale diversification and reticulation history during speciation in Sus. Genome biology, 14, R107). However, the timing, directionality and extent of this admixture remain unknown. Here we use a likelihood based model comparison to more finely resolve this admixture history and test whether it was mediated by humans or occurred naturally. Our analyses suggest that inter-specific admixture between Sunda-shelf species was most likely asymmetric and occurred long before the arrival of humans in the region. More precisely, we show that these species diverged during the late Pliocene but around 23% of their genomes have been affected by admixture during the later Pleistocene climatic transition. In addition, we show that our method provides a significant improvement over D-statistics which are uninformative about the direction of admixture.
Data from: A test of genetic models for the evolutionary maintenance of same-sex sexual behaviour
The evolutionary maintenance of same-sex sexual behaviour (SSB) has received increasing attention because it is perceived to be an evolutionary paradox. The genetic basis of SSB is almost wholly unknown in non-human animals, though this is key to understanding its persistence. Recent theoretical work has yielded broadly applicable predictions centred on two genetic models for SSB: overdominance and sexual antagonism. Using Drosophila melanogaster, we assayed natural genetic variation for male SSB and empirically tested predictions about the mode of inheritance and fitness consequences of alleles influencing its expression. We screened 50 inbred lines derived from a wild population for male–male courtship and copulation behaviour, and examined crosses between the lines for evidence of overdominance and antagonistic fecundity selection. Consistent variation among lines revealed heritable genetic variation for SSB, but the nature of the genetic variation was complex. Phenotypic and fitness variation was consistent with expectations under overdominance, although predictions of the sexual antagonism model were also supported. We found an unexpected and strong paternal effect on the expression of SSB, suggesting possible Y-linkage of the trait. Our results inform evolutionary genetic mechanisms that might maintain low but persistently observed levels of male SSB in D. melanogaster, but highlight a need for broader taxonomic representation in studies of its evolutionary causes.
Data from: Selection, genome-wide fitness effects and evolutionary rates in the model legume Medicago truncatula
Sequence data for >20 000 annotated genes from 56 accessions of Medicago truncatula were used to identify potential targets of positive selection, the determinants of evolutionary rate variation and the relative importance of positive and purifying selection in shaping nucleotide diversity. Based upon patterns of intraspecific diversity and interspecific divergence, c. 50–75% of nonsynonymous polymorphisms are subject to strong purifying selection and 1% of the sampled genes harbour a signature of positive selection. Combining polymorphism with expression data, we estimated the distribution of fitness effects and found that the proportion of deleterious mutations is significantly greater for expressed genes than for genes with undetected transcripts (nonexpressed) in a previous RNA-seq experiment and greater for broadly expressed genes than those expressed in only a single tissue. Expression level is the strongest correlate of evolutionary rates at nonsynonymous sites, and despite multiple genomic features being significantly correlated with evolutionary rates, they explain less than 20% of the variation in nonsynonymous rates (dN) and <15% of the variation in either synonymous rates (dS) or dN:dS. Among putative targets of selection were genes involved in defence against pathogens and herbivores, genes with roles in mediating the relationship with rhizobial symbionts and one-third of annotated histone-lysine methyltransferases. Adaptive evolution of the methyltransferases suggests that positive selection in gene expression may have occurred through evolution of enzymes involved in epigenetic modification.
Data from: QuLinePlus: extending plant breeding strategy and genetic model simulation to cross-pollinated populations – case studies in forage breeding
Plant breeders are supported by a range of tools that assist them to make decisions about the conduct or design of plant breeding programs. Simulations are a strategic tool that enable the breeder to integrate the multiple components of a breeding program into a number of proposed scenarios that are compared by a range of statistics measuring the efficiency of the proposed systems. A simulation study for the trait growth score compared two major strategies for breeding forage species, among half-sib family selection and among and within half-sib family selection. These scenarios highlighted new features of the QuLine program, now called QuLinePlus, incorporated to enable the software platform to be used to simulate breeding programs for cross pollinated species. Each strategy was compared across three levels of HS family mean heritability (0.1, 0.5 and 0.9), across three sizes of the initial parental population (10, 50, and 100), and across three genetic effects models (fully additive model, a mixture of additive, partial and over dominance model, and a mixture of partial dominance and over dominance model). Among and within half-sib selection performed better than among half-sib selection for all scenarios. The new tools introduced into QuLinePlus should serve to accurately compare among methods and provide direction on how to achieve specific goals in the improvement of plant breeding programs for cross breeding species.
Data from: Competition both drives and impedes diversification in a model adaptive radiation
Competitors are known to be important in governing the outcome of evolutionary diversification during an adaptive radiation, but the precise mechanisms by which they exert their effects remain elusive. Using the model adaptive radiation of Pseudomonas fluorescens, we show experimentally that the effect of competition on diversification of a focal lineage depends on both the strength of competition and the ability of the competitors to diversify. We provide evidence that the extent of diversification in the absence of interspecific competitors depends on the strength of resource competition. We also show that the presence of competitors can actually increase diversity by increasing interspecific resource competition. Competitors that themselves are able to diversify prevent diversification of the focal lineage by removing otherwise available ecological opportunities. These results suggest that the progress of an adaptive radiation depends ultimately on the strength of resource competition, an effect that can be exaggerated or impeded by the presence of competitors.
Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling
Remote sensing techniques offer an opportunity to improve biodiversity modeling and prediction worldwide. Yet, to date, the weather-station based WorldClim dataset has been the primary source of temperature and precipitation information used in correlative species distribution models. WorldClim consists of grids interpolated from in situ station data recorded primarily from 1960 to 1990. Those datasets suffer from uneven geographic coverage, with many areas of Earth poorly represented. Here, we compare two remote sensing data sources for the purposes of biodiversity prediction: MERRA climate reanalysis data and AMSR-E, a pure remote sensing data source. We use these data to generate novel temperature-based bioclimatic information and to model the distributions of 20 species of vertebrates endemic to four regions of South America: Amazonia, the Atlantic Forest, the Cerrado, and Patagonia. We compare the bioclimatic datasets derived from MERRA and AMSR-E information with in situ station data, and contrast species distribution models based on these two products to models built with WorldClim. Surface temperature estimates provided by MERRA and AMSR-E showed warm temperature biases relative to the in situ data fields, but the reliability of these datasets varied in geographic space. Species distribution models derived from the MERRA data performed equally well (in Cerrado, Amazonia, and Patagonia) or better (Atlantic Forest) than models built with the WorldClim data. In contrast, the performance of models constructed with the AMSR-E data was similar to (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of models built with WorldClim data. Whereas this initial comparison assessed only temperature fields, efforts to estimate precipitation from remote sensing information hold great promise; furthermore, other environmental datasets with higher spatial and temporal fidelity may improve upon these results.
Data from: Exposure to predator models during the fertile period leads to higher levels of extra-pair paternity in blue tits
1) The perceived risk of predation can affect breeding behaviour and reduce reproductive success in prey species. Individuals exposed to predators may also adopt different mating tactics with potential consequences for the distribution of paternity in socially monogamous species that engage in extra-pair copulations. 2) We experimentally increased perceived predation risk during the fertile period in blue tits Cyanistes caeruleus. Every morning between nest completion and the onset of egg laying, we presented a model of either a predator or a non-predator (control) near active nestboxes. 3) Broods from pairs exposed to predators had higher levels of extra-pair paternity than control broods. This mainly resulted from a higher proportion of extra-pair offspring in broods with at least one extra-pair young. 4) Females exposed to predators first emerged from the nestbox later in the morning, stayed away from the nestbox for longer and were less likely to be visited at the nest by their social mate, but we detected no behavioural differences once the model was removed. 5) Our results suggest that the higher rates of extra-pair paternity resulted from the disruption of morning routines, which may have inhibited within-pair copulations or increased opportunities for females to engage in extra-pair copulations. We conclude that the perceived risk of predation can have substantial effects on levels of extra-pair paternity. 16-Sep-2019
Data from: Species' ecological functionality alters the outcome of fish stocking success predicted by a food-web model
Fish stocking is used worldwide in conservation and management but its effects on food-web dynamics and ecosystem stability are poorly known. To better understand these effects and predict the outcomes of stocking, we used an empirically validated network model of a well-studied lake ecosystem. We simulate two stocking scenarios with two native fish species valuable for fishing. In the first scenario, we stock planktivorous fish (whitefish) larvae in the ecosystem. This leads to 1% increase in adult whitefish biomasses and decreases the biomasses of the top predator (perch). In the second scenario, we also stock perch larvae in the ecosystem. This decreases the planktivorous whitefish and the oldest top predator age class biomasses, and destabilizes the ecosystem. Our results demonstrate that the effects of stocking depend on the species' position in the food web and thus cannot be assessed without considering interacting species. We further show that stocking can lead to undesired outcomes from both management and conservation perspectives. The gains of stocking can remain minor and have adverse effects on the entire ecosystem.
Data from: A novel iterative mixed model to remap three complex orthopedic traits in dogs
Hip dysplasia (HD), elbow dysplasia (ED), and rupture of the cranial (anterior) cruciate ligament (RCCL) are the most common complex orthopedic traits of dogs and all result in debilitating osteoarthritis. Herein we reanalyzed previously reported data: the Norberg angle (a quantitative measure of HD) in 921 dogs, ED in 113 cases and 633 controls, and RCCL in 271 cases and 399 controls and their genotypes at ~185,000 single nucleotide polymorphisms. A novel fixed and random model with a circulating probability unification (FarmCPU) function, with marker-based principal components and a kinship matrix to correct for population stratification, was used. A Bonferroni correction at p<0.01 resulted in a pgenome of 6.96 ×10-8. Six loci were identified; three for HD and three for RCCL. An associated locus at CFA28:34,369,342 for HD was described in the same dogs using a conventional mixed model. No loci were identified for RCCL in the previous report but the two loci for ED in the previous report did not reach genome-wide significance using the FarmCPU model. These results were supported by simulation which demonstrated that the FarmCPU held no power advantage over the linear mixed model for the ED sample but provided additional power for the HD and RCCL samples. Candidate genes for HD and RCCL are discussed. When using FarmCPU software, we recommend a resampling test, that a positive control be used to determine the optimum pseudo quantitative trait nucleotide-based covariate structure of the model, and a negative control be used consisting of permutation testing and the identical resampling test as for the non-permuted phenotypes.
Data from: Correlative changes in life history variables in response to environmental change in a model organism
Global change alters the environment, including increases in the frequency of (un)favorable events and shifts in environmental noise color. However, how these changes impact the dynamics of populations, and whether these can be predicted accurately has been largely unexamined. Here we combine recently developed population modeling approaches and theory in stochastic demography to explore how life history, morphology, and average fitness respond to changes in the frequency of favorable environmental conditions and in the color of environmental noise in a model organism (an acarid mite). We predict that different life-history variables respond correlatively to changes in the environment, and we identify different life-history variables, including lifetime reproductive success, as indicators of average fitness and life-history speed across stochastic environments. Depending on the shape of adult survival rate, generation time can be used as an indicator of the response of populations to stochastic change, as in the deterministic case. This work is a useful step toward understanding population dynamics in stochastic environments, including how stochastic change may shape the evolution of life histories.
Data from: Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation
Proteins have distinct structural and functional constraints at different sites that lead to site-specific preferences for particular amino acid residues as the sequences evolve. Heterogeneity in the amino acid substitution process between sites is not modeled by commonly used empirical amino acid exchange matrices. Such model misspecification can lead to artefacts in phylogenetic estimation such as long-branch attraction. Although sophisticated site-heterogeneous mixture models have been developed to address this problem in both Bayesian and maximum likelihood (ML) frameworks, their formidable computational time and memory usage severely limits their use in large phylogenomic analyses. Here we propose a posterior mean site frequency (PMSF) method as a rapid and efficient approximation to full empirical profile mixture models for ML analysis. The PMSF approach assigns a conditional mean amino acid frequency profile to each site calculated based on a mixture model fitted to the data using a preliminary guide tree. These PMSF profiles can then be used for in-depth tree-searching in place of the full mixture model. Compared with widely used empirical mixture models with k classes, our implementation of PMSF in IQ-TREE (http://www.iqtree.org) speeds up the computation by approximately k /1.5-fold and requires a small fraction of the RAM. Furthermore, this speedup allows, for the first time, full nonparametric bootstrap analyses to be conducted under complex site-heterogeneous models on large concatenated data matrices. Our simulations and empirical data analyses demonstrate that PMSF can effectively ameliorate long-branch attraction artefacts. In some empirical and simulation settings PMSF provided more accurate estimates of phylogenies than the mixture models from which they derive.
Data from: Competing metabolic strategies in a multilevel selection model
The evolutionary mechanisms of energy efficiency have been addressed. One important question is to understand how the optimized usage of energy can be selected in an evolutionary process, especially when the immediate advantage of gathering efficient individuals in an energetic context is not clear. We propose a model of two competing metabolic strategies differing in their resource usage, an efficient strain which converts resource into energy at high efficiency but displays a low rate of resource consumption, and an inefficient strain which consumes resource at a high rate but at low yield. We explore the dynamics in both well-mixed and structured populations. The selection for optimized energy usage is measured by the likelihood that an efficient strain can invade a population of inefficient strains. It is found that the parameter space at which the efficient strain can thrive in structured populations is always broader than observed in well-mixed populations.
Data from: Modeling and quantifying frequency-dependent fitness in microbial populations with cross-feeding interactions
Coexistence of two or more populations by frequency-dependent selection is common in nature, and it often arises even in well-mixed experiments with microbes. If ecology is to be incorporated into models of population genetics, then it is important to represent accurately the functional form of frequency-dependent interactions. However, measuring this functional form is problematic for traditional fitness assays, which assume a constant fitness difference between competitors over the course of an assay. Here, we present a theoretical framework for measuring the functional form of frequency-dependent fitness by accounting for changes in abundance and relative fitness during a competition assay. Using two examples of ecological coexistence that arose in a long-term evolution experiment with Escherichia coli, we illustrate accurate quantification of the functional form of frequency-dependent relative fitness. Using a Monod-type model of growth dynamics, we show that two ecotypes in a typical cross-feeding interaction—such as when one bacterial population uses a byproduct generated by another—yields relative fitness that is linear with relative frequency.
Data from: Posterior predictive Bayesian phylogenetic model selection
We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection, and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand-Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (Pm) and goodness-of-fit (Gm), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML), which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models, and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data.
Data from: A strengths-based data capture model: mining data-driven and person-centered health assets
With health care policy directives advancing value-based care, risk assessments and management have permeated health care discourse. The conventional problem-based infrastructure defines what data are employed to build this discourse and how it unfolds. Such a health care model tends to bias data for risk assessment and risk management toward problems and does not capture data about health assets or strengths. The purpose of this article is to explore and illustrate the incorporation of a strengths-based data capture model into risk assessment and management by harnessing data-driven and person-centered health assets using the Omaha System. This strengths-based data capture model encourages and enables use of whole-person data including strengths at the individual level and, in aggregate, at the population level. When aggregated, such data may be used for the development of strengths-based population health metrics that will promote evaluation of data-driven and person-centered care, outcomes, and value.
Data from: A model to identify urban traffic congestion hotspots in complex networks
The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable for improvement and control. Here, we propose an idealized model, based on the critical phenomena arising in complex networks, that allows to analytically predict congestion hotspots in urban environments. Results on real cities' road networks, considering, in some experiments, real- traffic data, show that the proposed model is capable of identifying susceptible junctions that might becomes hotspots if mobility demand increases.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.