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137 results for “mixing models”
Protea repens whole transcriptome count data for control and drought treatment for 8 populations, climatic data for the 8 populations and phenotypic data collected, and data used for linear mixed models for climate gene expression/trait correlation testing
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Bayesian stable isotope mixing models effectively characterize the diet of an Arctic raptor
<p>1. Bayesian stable isotope mixing models (BSIMMs) for δ13C and δ15N can be a useful tool to reconstruct diets, characterize trophic relationships, and assess spatiotemporal variation in food webs. However, use of this approach typically requires a priori knowledge on the level of enrichment occurring between the diet and tissue of the consumer being sampled (i.e., a trophic discrimination factor or TDF).</p> <p>2. TDFs derived from captive feeding studies are highly variable, and it is challenging to select the appropriate TDF for diet estimation in wild populations. We introduce a novel method for estimating TDFs in a wild population: a proportionally balanced equation that uses high-precision diet estimates from nest cameras installed on a subset of nests in lieu of a controlled feeding study (TDFCAM).</p> <p>3. We tested the ability of BSIMMs to characterize diet in a free-living population of gyrfalcon (Falco rusticolus) nestlings by comparing model output to high-precision nest camera diet estimates. We analyzed the performance of models formulated with a TDFCAM against other relevant TDFs and assessed model sensitivity to an informative prior. We applied the most parsimonious model inputs to a larger sample to analyze broad-scale temporal dietary trends.</p> <p>4. BSIMMs fitted with a TDFCAM and uninformative prior had the best agreement with nest camera data, outperforming TDFs derived from captive feeding studies. BSIMMs produced with a TDFCAM produced reliable diet estimates at the nest level and accurately identified significant temporal shifts in gyrfalcon diet within and between years.</p> <p>5. Our method of TDF estimation produced more accurate estimates of TDFs in a wild population than traditional approaches, consequently improving BSIMM diet estimates. We demonstrate how BSIMMs can complement a high-precision diet study by expanding its spatiotemporal scope of inference and recommend this integrative methodology as a powerful tool for future trophic studies. </p>
Evaluating Bayesian stable isotope mixing models of wild animal diet and the effects of trophic discrimination factors and informative priors
<blockquote> <p>1. Ecologists quantify animal diets using direct and indirect methods, including analysis of faeces, pellets, prey items and gut contents. For stable isotope analyses of diet, Bayesian stable isotope mixing models (BSIMMs) are increasingly used to infer the relative importance of food sources to consumers. Although a powerful approach, it has been hard to test BSIMM performance for wild animals because precise, direct dietary data are difficult to collect.<br> 2. We evaluated the performance of BSIMMs in quantifying animal diets when using δ13C and δ15N stable isotope ratios from the feathers and red blood cells of common buzzard Buteo buteo chicks. We analysed mixing model outcomes with various trophic discrimination factors (TDFs), with and without informative priors, and compared these to direct observations of prey provisioned to chicks by adults at nests, using remote cameras. <br> 3. Although BSIMMs with different TDFs varied markedly in their performance, the statistical package SIDER generated TDFs for both feathers and blood that resulted in model outputs that accorded well with direct observations of prey provisioning. Using feather TDFs derived from captive peregrines Falco peregrinus resulted in estimates of diet composition that were also similar to provisioned prey, though blood TDFs from the same study performed poorly. The inclusion of informative priors, based on conventional analysis of pellet and prey remains, markedly reduced model performance.<br> 4. BSIMMs can provide accurate assessments of diet in wild animals. TDF estimates from the SIDER package performed well. The inclusion of informative priors from conventional methods in Bayesian mixing models can transfer biases into model outcomes, leading to erroneous results.</p> </blockquote>
Numerical model code, input files and output data for publication ``Mixing and Transformation in a Deep Western Boundary Current: a case study''
<p>Contains numerical model data (code, input files, selected output) to supplement publication ``Mixing and Transformation in a Deep Western Boundary current'', by Spingys and co-authors. All umerical model data, including any errors, is the responsibility of Sonya Legg. This data set will allow reproduction of simulations used in the above-referenced paper.</p>
Dataset linked to manuscript entitled "Changes in Arctic Stratification and Mixed Layer Depth Cycle, A Modeling Analysis" by Hordoir et al.
<p>This dataset allows the re-create the fingures showing changes in Arctic stratification and mixed layer depth, as in the manuscript. Additional information can be obtained by email.</p>
Data from: A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies
An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor–response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their ability to incorporate latent variables and model direct and indirect links between state variables and capture probabilities.
Data from: Integrating genetic analysis of mixed populations with a spatially-explicit population dynamics model
Inferring the dynamics of populations in time and space is a central challenge in ecology. Intra-specific structure (for example genetically distinct sub-populations or meta-populations) may require methods that can jointly infer the dynamics of multiple populations. This is of particular importance for harvested species, for which management must balance utilization of productive populations with protection of weak ones. Here we present a novel method for simultaneous learning about the spatio-temporal dynamics of multiple populations that combines genetic data with prior information about abundance and movement in an integrated population modelling approach. We apply the Bayesian genetic mixed stock analysis to 17 wild and 10 hatchery-reared Baltic salmon (S. salar) stocks, quantifying uncertainty in stock composition in time and space, and in population dynamics parameters such as migration timing and speed. Our results indicate that the commonly used "equal prior probabilities" assumption may not be appropriate for all mixed stock analyses. Incorporation of prior information about stock abundance and movement resulted in more precise and plausible estimates of mixture compositions in time and space. Inclusion of a population dynamics model also allowed robust interpolation of expected catch composition at areas and times with no genetic observations. The genetic data were informative about stock-specific movement patterns, updating priors for migration path, timing and speed. The model we present here forms the basis for optimizing the spatial and temporal allocation of harvest to support the management of mixed populations of migratory species.
Data from: A new analytical approach to landscape genetic modeling: least-cost transect analysis and linear mixed models
Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics mostly uses landscape resistance surfaces and least-cost paths or straight-line transects. However, both approaches have drawbacks. Parameterization of resistance surfaces is a subjective process, and least-cost paths represent a single migration route. A transect-based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least-cost transect analysis (LCTA). Habitat-matrix resistance surfaces were used to create least-cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between landscape predictor variables and genetic distances was then assessed following a mixed modeling approach to account for the non-independence of values in distance matrices. Subsequently, predictor variables were selected making use of the R_β^2 statistic. We applied LCTA and the mixed model approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least-cost, effective and resistance distance analysis and showed that LCTA not only outperforms existing methods in a statistical way, but also provides more information about the migration ecology of the focal species. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.
Data from: Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components
Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g. genetic variation), and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods, and apply them to empirical data from a wild rodent population. While MM show high false positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false positive rates, but have always low power. Indeed, MM analyses of the rodent population data show significant fixed heterogeneity in reproductive success, whereas NS analyses do not. We suggest that fixed heterogeneity may be more common than is suggested by NS, and that NS are useful only if more powerful methods are not applicable and if they are complemented by a power analysis.
Data from: Linkage into care among newly diagnosed HIV-positive individuals tested through outreach and facility-based HIV testing models in Mbeya, Tanzania: a prospective mixed-method cohort study
Objective: Linkage to care is the bridge between HIV testing and HIV treatment, care and support. In Tanzania, mobile testing aims to address historically low testing rates. Linkage to care was reported at 14% in 2009 and 28% in 2014. The study compares linkage to care of HIV-positive individuals tested at mobile/outreach versus public health facility-based services within the first 6 months of HIV diagnosis. Setting: Rural communities in four districts of Mbeya Region, Tanzania. Participants: A total of 1012 newly diagnosed HIV-positive adults from 16 testing facilities were enrolled into a two-armed cohort and followed for 6 months between August 2014 and July 2015. 840 (83%) participants completed the study. Main outcome: measures We compared the ratios and time variance in linkage to care using the Kaplan-Meier estimator and Log rank tests. Cox proportional hazards regression models to evaluate factors associated with time variance in linkage. Results: At the end of 6 months, 78% of all respondents had linked into care, with differences across testing models. 84% (CI 81% to 87%, n=512) of individuals tested at facility-based site were linked to care compared to 69% (CI 65% to 74%, n=281) of individuals tested at mobile/outreach. The median time to linkage was 1 day (IQR: 1–7.5) for facility-based site and 6 days (IQR: 3–11) for mobile/outreach sites. Participants tested at facility-based site were 78% more likely to link than those tested at mobile/outreach when other variables were controlled (AHR=1.78; 95% CI 1.52 to 2.07). HIV status disclosure to family/relatives was significantly associated with linkage to care (AHR=2.64; 95% CI 2.05 to 3.39). Conclusions: Linkage to care after testing HIV positive in rural Tanzania has increased markedly since 2014, across testing models. Individuals tested at facility-based sites linked in significantly higher proportion and modestly sooner than mobile/outreach tested individuals. Mobile/outreach testing models bring HIV testing services closer to people. Strategies to improve linkage from mobile/outreach models are needed.
Model data and namelists for Sterzinger et al. (2022) - "Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations"
<p>Model data and namelists for "<a href="https://acp.copernicus.org/preprints/acp-2022-36/">Do arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations</a>"</p> <p>Horizontally averaged data is provided in NetCDF4 files (oliktok.nc, ascos.nc, summit.nc) for all output variables. Horizontally averaged vertical momentum flux is provided in a separate file for each simulation (*_vert_momentum_flux.nc files).</p> <p>Info on variables is provided by the RAMS model variable guide PDF <a href="https://vandenheever.atmos.colostate.edu/vdhpage/rams/docs/RAMS-VariableList.pdf">available here</a>.</p> <p>Model namelists are provided for each simulation (*_RAMSIN files). ASCOS initialization sounding info is provided within the ASCOS_RAMSIN file - initialization soundings are provided in SOUND_IN files.</p>
Benchmark for work "Temporal Verification of Mixed Sync-Async Execution Models"
<p>This benchmark is for validation and evaluation purpose for <br> the work "Temporal Verification of Mixed Sync-Async Execution <br> Models". It is constructed by manually annotating ASyncEffs<br> specifications, including both succeeded and failed cases. </p> <p>This benchmark consists two folders:<br> 1. validation_tests: <br> The validation tests are synthetic examples to test <br> the main contributions, including the preemption interleaving computation and <br> the inclusion checking for the parallel composition and the waiting operator. </p> <p>2. evaluation_tests:<br> We select 16 programs, varying from 15 lines to 300 lines, and annotate <br> ASyncEffs specifications<br> with a 1:1 ratio for succeeded/failed cases. </p>
Scalable mixed model approaches for set-based association studies on large-scale categorical data analysis and its application to 450k exome sequencing data in UK Biobank
<p>The ongoing release of large-scale sequencing data in the UK Biobank allows for identifying associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a novel method for rare-variant association tests, POLMM-GENE, in which a proportional odds logistic mixed model was used to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole exome-sequencing data for 5 ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.</p>
COAD-MS Model, Figures 2,3, and supplementary mixing model.
<p>COAD-MS Model created by Philip Staudigel. </p> <p>Includes the scripts to generate all components of Figure 2 and 3. </p> <p>Science Advances 2024, "Resolving and correcting for kinetic biases on methane seep paleotemperature using carbonate ∆47/∆48 analysis" adn0155</p> <p>Patch Note April 25, 2024: Corrected issue with Seep_Function.m, which caused fatal errors in model (uncommented line 10).</p> <p> </p>
MOM6 Model Output from "Improved Upper Ocean Vertical Mixing in the Equatorial Oceans in NOAA/GFDL's OM4 Model"
<p>Datasets produced during the MOM6 experiments described by "Improved Upper Ocean Vertical Mixing in the Equatorial Oceans in NOAA/GFDL's OM4 Model".</p> <p>These datasets are needed to reproduce the figures in the version of this manuscript as submitted to ESS.</p> <p>The location of the notebooks for creating figures from these datasets is indicated in the manuscript.</p>
Integrating Bayesian groundwater mixing modeling with on-site helium analysis to identify unknown water sources
<p>Analyzing groundwater mixing ratios is crucial for many groundwater management tasks such as assessing sources of groundwater recharge and flow paths. However, estimating groundwater mixing ratios is affected by various uncertainties, which are related to analytical and measurement errors of tracers, the selection of end-members and finding the most suitable set of tracers. Although these uncertainties are well recognized, it is still not common practice to account for them. We address this issue by using a new set of tracers in combination with a Bayesian modeling approach, which explicitly considers the possibility of unknown end-members while fully accounting for tracer uncertainties. We apply the Bayesian model we developed to a tracer set which includes helium-4 analyzed on-site to determine mixing ratios in groundwater. Thereby, we identify an unknown end-member, that contributes up to 84% to the water mixture observed at our study site. For the helium-4 analysis, we use a newly developed Gas Equilibrium Membrane Inlet Mass Spectrometer (GE-MIMS), operated in the field. To test the reliability of on-site helium-4 analysis, we compare results obtained with the GE-MIMS to the conventional lab-based method, which is comparatively expensive and labor intensive. Our work demonstrates that (i) tracer-aided Bayesian mixing modeling can detect unknown water sources, thereby revealing valuable insights into the conceptual understanding of the groundwater system studied and ii) on-site helium-4 analysis with the GE-MIMS system is an accurate and reliable alternative to the lab-based analysis.</p>
Dataset and codes used in the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data"
<p>This contains the codes and dataset for the manuscript entitled "A rainfall-tracking travel time distribution model to quantify mixing and storage release preference in a large shallow lake by two-year stable isotopic data". Detailed information about the dataset is described in the Readme.txt file.</p>
Data and materials for the "Steady-State Mixing State of Black Carbon Aerosols from a Particle-Resolved Model"
<p>Data and sripts for the "Steady-State Mixing State of Black Carbon Aerosols from a Particle-Resolved Model"</p>
A Dirichlet-multinomial mixed model for determining differential abundance of mutational signatures
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Study on a quantitative method for determining mixing proportion of transparent cemented soil for visual geotechnical model tests
<p>An effective mixing of transparent cemented soil is necessary for visual geotechnical model tests, so a quantitative method for determining mixing proportion of transparent cemented soil was generated in this paper. Firstly, quartz sand, Nanoscale silica powder and N-dodecane mixed 15# white oil were selected as the raw materials, and a series of orthogonal experiments were designed. Concurrently, the main physical and mechanical parameters (volumetric weight γ, internal friction angle φ, cohesion c) of transparent cemented soil were measured, caused by the change of "particle size of quartz sand" and " mass ratios between fumed silica and fused quartz". Subsequently, multiple linear regression equations of various physical and mechanical parameters (γ, φ, c) were obtained by fitting the original test data. Finally, the rationality of multiple linear regression equations was proved. The research results indicated: (1) the volumetric weight changes from 16.13kN/m<sup>3</sup> to 12.53kN/m<sup>3</sup>, the Internal friction angle is between 27.07° and 14.82°, and the cohesion varies from 31kPa to 2.3kPa, the parameters meet the similar requirements of the surrounding rock (grade ⅳ and ⅴ) and clay; (2) The values of Multiple R values (all greater than 0.88) and the Significance F value (all close to 0) proves the three regression equations were valid; (3) Combining the three regression equations and particle size of quartz sand, the mass ratio between fumed silica and fused quartz and geometry similarity constant were solved. All the conclusions mentioned could provide theoretical support and data reference for transparent soil model test implementation.</p>
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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.