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86 results for “Mixture models”
Heteroplasmy Benchmark Dataset - mitochondrial DNA mixture model - MiSeq - U5-H1-M1-M2-M3-M4-M5 - BAM
<p>mtDNA mixture model of 2 mtDNA sequences belonging to haplogroups U5 and H1. Run on Illumina MiSeq with 3 different polymerases (Clontech, Herculase, NEB Taq), and different DNA extraction protocols - <strong>BAM FILES </strong></p> <p>M1 = Mixture 1:2 i.e. 50%</p> <p>M2 = Mixture 1:10 i.e. 10%</p> <p>M3 = Mixture 1:50 i.e. 2%</p> <p>M4 = Mixture 1:100 i.e. 1%</p> <p>M5 = Mixture 1:200 i.e. 0.5%</p>
Status quo in data availability and predictive models of nano-mixture toxicity
<p>Supplementary materials for manuscript: Status quo in data availability and predictive models of nano-mixture toxicity.</p> <p>This table contains the list of 183 curated literature used in this study.</p>
Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space, and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.
Methanol-water mixtures obtained with the OPLS force field and SPCE water model
<p>These are Gromacs trajectories, 20 ps each, with MD information corresponding to pure water (one trajectory), methanol (one trajectory), and 2:1 molar mixtures of the two (10 trajectories). The pure water system contains 800 molecules, pure methanol contains 400 molecules, and each of the mixtures contains 400 water molecules and 200 methanol molecules. The force field employed is OPLS and water was simulated with the rigid SPCE model.</p> <p>These trajectories are made available with the main purpose of being used to run one of the DoSPT tutorials:</p> <p>http://dospt.org/index.php/Tutorial_2:_entropy_of_mixing_of_methanol%2Bwater</p>
Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
<p>Estimating the abundance or density of wildlife populations is a critical part of species conservation and management, but estimates can vary greatly in precision and accuracy according to the data collection and statistical methods, sampling and ecological variation, and sample size. N-mixture models are a common method which has been applied to a wide range of taxa for estimating population abundance from non-invasive data representing the distribution of the species. We used population estimates from an aerial survey of moose and videos from camera traps to assess the sensitivity of N-mixture models to ecological conditions, the spatial scale at which they were measured, the criteria used to define independent detections, and model choice based on the common statistical criterion of parsimony. The most parsimonious N-mixture models were considerably biased, producing implausibly large and considerably imprecise estimates of the abundance of moose. Most of the other models produced estimates of abundance that were ecologically realistic and relatively accurate. The accuracy of population estimates produced by N-mixture models were not overly sensitive to the formulation of models, the scale at which ecological conditions were measured, or the criteria used to define independent detection and by extension sample size. Our results suggest that parsimony was a poor measure of the predictive accuracy of the population estimates produced with the N-mixture model. Collecting and processing data from the aerial survey was less expensive and took less time, but data from camera traps can provide valuable information on behavior of the target species as well as insights into multiple species in the community.</p>
Fig. 1 in Assessing The Abundance Of Caucasian Salamander, Mertensiella Caucasica (Caudata, Salamandridae), With N-Mixture Model In Northeastern Anatolia
Fig. 1. General view of study area.
Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
<p>Imperfect detection in field studies on animal abundance, including birds, is common and can be corrected for in various ways. The binomial N-mixture (hereafter binmix) model developed for this task is widely used in ecological studies owing to its simplicity: it requires replicated count results as the input. However, it may overestimate abundance and be sensitive to even small violations of its assumptions. We used a 33-year dataset on the Marsh Tit, Poecile palustris, a sedentary forest passerine, from Białowieża Forest, Poland to validate inference from binmix models by comparing model-estimated abundances to the true number of breeding pairs within the plots, determined by exhaustive population study. The abundance estimates, derived from six springtime (April-May) counts of males on each plot in each year, were highly reliable: 116 out of 132 year-plot estimates (88%) included the true number of pairs within the 95% confidence intervals. Over- and underestimations were thus rare and similarly frequent (9 and 12 cases, respectively), with a tendency to overestimate at low densities and underestimate at high densities. Marsh Tits sing rarely but the frequency of countersinging increases with abundance, leading to non-independence in detections. When accounted for in a submodel for detection, the per-survey number of countersinging events positively affected detection probability but only weakly affected abundance estimates. Simulations further demonstrate that this property, overestimation at low densities and underestimation at high densities, may be a systematic bias of binmix model even if density-dependent detection is absent. While the behaviour of binmix models in specific situations requires more study, we conclude that these models are a valid tool to estimate abundance reliably when intensive population monitoring is not feasible.</p>
Discovering molecular regulators of ageing using mixture models with RNA-sequencing data
<p>Identifying the molecular regulators that control ageing is challenging because the ageing process is influenced by a combination of genetic and environmental factors which makes it difficult to source the contribution of a single gene. Multiple studies have demonstrated that as humans age, increased gene expression heterogeneity results in the dysregulation of key regulators and pathways. Given the dynamic nature of gene expression, it is vital that this data be modelled by statistical approaches that can appropriately account for changes in variability to understand the contribution of heterogeneity during the aging process and properly identify its regulators. This study demonstrates the utility of using mixture models to model biological variability of gene expression occurring during ageing and how novel potential regulators of ageing can be identified.</p> <p>Our mixture modelling approach was applied to gene expression data from the Genotype-Tissue Expression (GTEx) cohort. For every gene, the expression profile was modelled using a mixture model across the cohort where the subset of donors corresponding to each mode was tested for a significant change in age group. The multi-tissue aspect of GTEx was leveraged to find ageing regulators based on this mixture model approach genes that were common across multiple tissues, suggesting that the regulation of ageing may also be controlled through a set of genes that have non-tissue-specific activity.</p> <p>Our approach identified well-documented ageing regulators <em>mTOR </em>and <em>RICTOR</em> and other potential ageing regulators such as <em>IL4</em> and <em>GPR4</em> which were detected only by our approach. Genes identified by edgeR, DESeq2 and the mixture model-based approach were enriched for similar biological pathways. This suggests that while the specific ageing regulators identified from our approach may be distinct, they generally belong in the same pathways as the genes identified by standard approaches. Overall, these results indicate that modelling gene expression variability using mixture models in conjunction with standard differential gene expression can help uncover new regulators that have a potential role for understanding human ageing.</p> <p>I</p>
Thermophysical properties of hydrogen mixtures relevant for the development of the hydrogen economy: Review of available experimental data and thermodynamic models
<p>File: 1-s2.0-S096014812201271X-mmc1.docx</p> <p>This file (DOCX) contains additional figures associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc2.xlsx</p> <p>This file (XLSX) contains tables with the coordinates of the VLE associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc3.xlsx</p> <p>This file (XLSX) contains tables with the density data associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc4.xlsx</p> <p>This file (XLSX) contains tables with the calorific data associated with the hydrogen-containing systems.</p> <p> </p> <p>File: 2022_Renewable Energy_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in Renewable Energy (2022, 198, 1398-1429). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.renene.2022.08.096</p>
Northern shoveler data for "Sensitivity of binomial N-mixture models to overdispersion: the importance of assessing model fit"
<p>Repeated count data for Northern shoveler analyzed in Knape et al. Sensitivity of binomial N-mixture models to overdispersion: the importance of assessing model fit", Methods in Ecology and Evolution.</p> <p>count.csv contains Northern shoveler counts repeated 10 times at 50 sites in a 50 x 10 matrix. Each row corresponds to a specific site and columns correspond to visits.</p> <p>date.csv is a 50 x 10 matrix containing the julian date of each count, using the same ordering of visits (columns) and sites (rows) as in count.csv.</p> <p>site.csv contains covariates for each of the 50 sites. Sites (rows) are ordered in the same way as in count.csv and date.csv. The first column represents the area of water (ha) covered by the wetlands where the counts were conducted, the second columns is the percentage of the wetland area covered by reeds, and the third column is the latitude of the wetland in RT90 coordinates.</p> <p> </p> <p> </p>
Heteroplasmy Benchmark Dataset - mitochondrial DNA mixture model - HiSeq - M1-M4 - BAM
<p>Illumina HiSeq data of mixtures M1 (50%), M2 (10%), M3 (2%) and M4 (1%) of haplotypes H1c6 and U5a2e (decreasing).</p> <p>See <a href="https://doi.org/10.1371/journal.pone.0135643">https://doi.org/10.1371/journal.pone.0135643</a> for technical/lab-related informations</p>
Modeling the thermodynamic properties of saturated lactones in non-ideal mixtures with the SAFT-γ Mie approach. JCED 2023
<p>All computational data in the publication.</p>
A hierarchical N-mixture model to estimate behavioral variation and a case study of Neotropical birds
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zigzag: A Hierarchical Bayesian Mixture Model for Inferring the Expression State of Genes in Transcriptomes
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Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
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Data from: Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements
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Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters
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Data from: An updated 18S rRNA phylogeny of tunicates based on mixture and secondary structure models
BACKGROUND: Tunicates have been recently revealed to be the closest living relatives of vertebrates. Yet, with more than 2500 described species, details of their evolutionary history are still obscure. From a molecular point of view, tunicate phylogenetic relationships have been mostly studied based on analyses of 18S rRNA sequences, which indicate several major clades at odds with the traditional class-level arrangements. Nonetheless, substantial uncertainty remains about the phylogenetic relationships and taxonomic status of key groups such as the Aplousobranchia, Appendicularia, and Thaliacea. RESULTS: Thirty new complete 18S rRNA sequences were acquired from previously unsampled tunicate species, with special focus on groups presenting high evolutionary rate. The updated 18S rRNA dataset has been aligned with respect to the constraint on homology imposed by the rRNA secondary structure. A probabilistic framework of phylogenetic reconstruction was adopted to accommodate the particular evolutionary dynamics of this ribosomal marker. Detailed Bayesian analyses were conducted under the non-parametric CAT mixture model accounting for site-specific heterogeneity of the evolutionary process, and under RNA-specific doublet models accommodating the occurrence of compensatory substitutions in stem regions. Our results support the division of tunicates into three major clades: 1) Phlebobranchia + Thaliacea + Aplousobranchia, 2) Appendicularia, and 3) Stolidobranchia, but the position of Appendicularia could not be firmly resolved. Our study additionally reveals that most Aplousobranchia evolve at extremely high rates involving changes in secondary structure of their 18S rRNA, with the exception of the family Clavelinidae, which appears to be slowly evolving. This extreme rate heterogeneity precluded resolving with certainty the exact phylogenetic placement of Aplousobranchia. Finally, the best fitting secondary-structure and CAT-mixture models suggest a sister-group relationship between Salpida and Pyrosomatida within Thaliacea. CONCLUSION: An updated phylogenetic framework for tunicates is provided based on phylogenetic analyses using the most realistic evolutionary models currently available for ribosomal molecules and an unprecedented taxonomic sampling. Detailed analyses of the 18S rRNA gene allowed a clear definition of the major tunicate groups and revealed contrasting evolutionary dynamics among major lineages. The resolving power of this gene nevertheless appears limited within the clades composed of Phlebobranchia + Thaliacea + Aplousobranchia and Pyuridae + Styelidae, which were delineated as spots of low resolution. These limitations underline the need to develop new nuclear markers in order to further resolve the phylogeny of this keystone group in chordate evolution.
Field evaluation of abundance estimates under binomial and multinomial N‐mixture models
<p>Assessing and modelling abundance from animal count data is a very common task in ecology and management. Detection is arguably never perfect, but modern hierarchical models can incorporate detection probability and yield abundance estimates that are corrected for imperfect detection. Two variants of these models rely on counts of unmarked individuals, or territories, (binomial N‐mixture models, or <i>binmix</i>) and on detection histories based on territory mapping data (multinomial N‐mixture models or <i>multimix</i>). However, calibration studies which evaluate these two N‐mixture model approaches are needed. We analysed conventional territory mapping data (three surveys in 2014 and four in 2015) using both binmix and multimix models to estimate abundance for two common avian cavity‐nesting forest species (Great Tit <i>Parus major</i> and Eurasian Blue Tit <i>Cyanistes caeruleus</i>). In the same study area, we used two benchmarks: (i) occupancy data from a dense nest box scheme; (ii) total number of detected territories. To investigate variance in estimates due to the territory assignment, three independent ornithologists conducted territory assignments. Nest box occupancy yields a minimum number of territories, since some natural cavities may have been used, and binmix model estimates were generally higher than this benchmark. Estimates under the multimix model were slightly more precise than binmix model estimates. Depending on the person assigning the territories, the multimix model estimates became quite different, either overestimating or underestimating the "truth". We conclude that N‐mixture‐models estimated abundance reliably, even for our very small sample sizes. Territory‐mapping counts depended on territory assignment and this carried over to estimates under the multimix model. This limitation has to be taken into account when abundance estimates are compared between sites or years. Whenever possible, accounting for such hidden heterogeneity in the raw data of bird surveys, via including a "territory editor" factor, is recommended. Distributing the surveys randomly (in time and space) to editors may also alleviate this problem.</p>
Data from: Estimating transient populations of unmarked individuals at a migratory stopover site using generalized N-mixture models
1. Migration counts are popular indices used to monitor population trends over time. Advanced analytical methods for estimating abundance of unmarked, open populations now incorporate population growth models and simultaneously test for covariate effects on abundance and detection probability. However, estimating population abundance at a staging site is complicated by daily immigration and emigration of unmarked individuals. 2. We applied a set of generalized N-mixture models to simulated count data to test their applicability for transient populations. Using simulated datasets, parameters were unbiased when the apparent survival rate varied within a season or was mis-specified in a model, but not when the immigration or detection probability was mis-specified. 3. With knowledge from the simulated data, we applied these models to daily counts of staging migratory shorebirds and estimated daily abundances accounting for variation in the detection and immigration rates. Daily counts of ruddy turnstones (Arenaria interpres) staging at Westhampton Island, New York, were collected during northward migration (1997–1999). We tested the effects of weather and tides on detection probability, and we modeled within-season variation in immigration rates as a function of time. 4. Covariates affecting the detection probability differed among years, but tide height consistently was correlated with detection probability. Accounting for detection and immigration rates, the predicted maximum single-day populations of ruddy turnstones were 172%, 165%, and 129% of the observed counts for each year. 5. Synthesis and applications. Management and conservation plans for migratory species require abundance estimates that are near the true population size though they are difficult to obtain. Our study is the first empirical application of the generalized N-mixture model that incorporates temporal trends in immigration and estimates daily abundance of a staging unmarked migratory population. Correct estimation of population sizes and the environmental factors affecting them can aid the conservation prioritization of species and staging sites. Moreover, the use of generalized N-mixture models can improve our understanding of the environmental factors that shape migratory movements.
ScienceDex guides
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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.