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139 results for “bayesian analysis”
FIGURE 6. Bayesian inference analysis 50 in Rotylenchus castilloi n. sp. (Nematoda: Hoplolaimidae), a new species with long stylet from northern Iran
FIGURE 6. Bayesian inference analysis 50% majority rule consensus tree as inferred from ITS1 rDNA sequence alignment under the GTR+G+I model. Bayesian posterior probabilities and maximum likelihood bootstrap values more than 50% are given for appropriate clades in the form: BPP/ML BS. New sequences are in bold font.
FIGURE 5. Bayesian inference analysis 50 in Rotylenchus castilloi n. sp. (Nematoda: Hoplolaimidae), a new species with long stylet from northern Iran
FIGURE 5. Bayesian inference analysis 50% majority rule consensus tree as inferred from D2–D3 expansion segments of 28S rDNA sequence alignment under the GTR+G+I model. Bayesian posterior probabilities and maximum likelihood bootstrap values more than 50% are given for appropriate clades in the form: BPP/ML BS. New sequence is in bold font.
FIGURE 1. Maximum clade credibility tree after a partitioned Bayesian analysis using 8945 in Phylogenetic analysis of the Neotropical Pristimantis leptolophus species group (Anura: Craugastoridae): molecular approach and description of a new polymorphic species
FIGURE 1. Maximum clade credibility tree after a partitioned Bayesian analysis using 8945 sites depicting the phylogenetic relationships among Pristimantis including the Pristimantis leptolophus species group. Numbers on nodes represent posterior probabilities and ultrafast bootstrap (as obtained in the ML analysis) support respectively. Asterisks represent nodal support larger than 95% in both ML and Bayesian analyses. Two dashes in ultrafast bootstrap indicate the node was not recovered in the ML analysis (see Appendix 2 for the ML tree).
Fig. 6. Single-gene Bayesian trees for 16S and FoxQ2 in Cryptic species complex or an incomplete speciation? Phylogeographic analysis reveals an intricate Pleistocene history of Priapulus caudatus Lamarck, 1816
Fig. 6. Single-gene Bayesian trees for 16S and FoxQ2 markers. Numbers above nodes indicate bootstrap values from Bayesian Interference (BI), black numbers below—posterior probabilities from Maximum Likelihood (ML), only bootstrap values> 60 are shown.
Fig. 7. Bayesian Skyline Plot analysis showing population size over time. The x in Echinoderes galadrielae Grzelak & Sørensen 2022, sp. nov.
Fig. 7. Bayesian Skyline Plot analysis showing population size over time. The x-axis is the time to the present in years, while the y-axis is the product between the effective population size (Ne) and the generation length (t) in a log scale. The mean estimate (black solid line) and 95% highest probability density limits (grey area) are shown.
DMSO-TMP-ACN-H2O Co-solvent Bayesian Optimization with Reproducibility and Gas Analysis via OEMS Data
<p>The zipped files contain the data collected and used for the Bayesian optimization (BO) of Coulombic efficiency (and discharge capacity) from the exploration of 4 co-solvents (dimethyl sulfoxide, trimethyl phosphate, acetonitrile, and water) and 2 salts (lithium perchlorate and LiTFSI).</p> <p>The cycling data and the BO clients are contained in BayesianOptimization.zip.</p> <p>The gas analysis data via online electrochemical mass spectrometry (OEMS) are contained in OEMS_data.zip.</p> <p>The cycling data of select repeats from the BO are contained in Reproducibility_data.zip.</p> <p>These are the raw datafiles. Preprocessing and analysis is not included.</p>
FIGURE. (A) Summary phylogeny showing relations between genera in tribe Phyllantheae from Bayesian and Maximum Likelihood analysis of five markers (ITS, PHYC, accD–psaI, trnS–trnG, matK), modified from Appendix 1. Classification is shown of genera (right column), subgenera (middle column) and sections (except for the genus Phyllanthus. Sections not included in phylogenetic analyses and those for the genus Flueggea were omitted. (B) summary phylogeny of the genus Phyllanthus as envisioned here with subgenera and sections of groups included in phylogenetic studies shown. in A revised phylogenetic classification of tribe Phyllantheae (Phyllanthaceae)
FIGURE. (A) Summary phylogeny showing relations between genera in tribe Phyllantheae from Bayesian and Maximum Likelihood analysis of five markers (ITS, PHYC, accD–psaI, trnS–trnG, matK), modified from Appendix 1. Classification is shown of genera (right column), subgenera (middle column) and sections (except for the genus Phyllanthus. Sections not included in phylogenetic analyses and those for the genus Flueggea were omitted. (B) summary phylogeny of the genus Phyllanthus as envisioned here with subgenera and sections of groups included in phylogenetic studies shown.
Figure 6. Chronogram resulting from Bayesian analysis employing a in Phylogeny indicates polyphyly in Cnodocentron (Trichoptera: Xiphocentronidae): biogeography and revision of New World species (Caenocentron)
Figure 6. Chronogram resulting from Bayesian analysis employing a relaxed clock. Most likely ancestral distribution recovered in DEC analysis and estimated mean age are displayed at the nodes. Dispersal events are indicated as a black line below the distribution boxes, vicariant events are indicated in a green line, as recovered in the biogeographic analysis. Highest posterior density (HPD) 95% intervals for the ages of the nodes are indicated by light blue bars. Timescale and global surface temperature estimated from δ18O benthic (Zachos et al., 2001) are displayed on the bottom. Eocene and Miocene thermal optimum are highlighted in grey. Cnodocentron and Caenocentron species distributions are shown in the maps.
Bayesian Network analysis for Single Cell Multiomics
<p>The data from stratified random samples of scRNA expression, surface marker and SNF cluster membership were integrated with high-resolution CT (HRCT) Scores of COVID-19 patients. The healthy and recovered individuals were assigned an HRCT score of zero, indicating absence of active pneumonia. The integrative modeling analysis was carried out using the wiseR package for end-to-end Bayesian network learning, inference and dashboard deployment. All continuous variables in the integrated data were discretized using the k-means algorithm with k=3 for biological interpretability as low, medium and high. A discrete Bayesian Network was learned from the data using hill climbing optimization for finding the directed acyclic graph encoding the structural dependencies between variables. Eleven Bayesian network structures were ensembled averaged to derive the consensus structure. The consensus structure was then parametrized with marginal and conditional probability distributions using Monte Carlo Markov Chain (MCMC) approximate inference method.</p>
FIGURE. Phylogenetic tree derived from Bayesian analysis, based on nrLSU data. Posterior probability (PP> 0.95) values from the Bayesian analysis are added at the nodes. The scale bar represents the number of nucleotide changes per site. (T) indicates the type specimen for this species. The new species are in bold. in Four new species of Entoloma (Entolomataceae, Agaricomycetes) subgenera Cyanula and Claudopus from Vietnam and their phylogenetic position
FIGURE. Phylogenetic tree derived from Bayesian analysis, based on nrLSU data. Posterior probability (PP> 0.95) values from the Bayesian analysis are added at the nodes. The scale bar represents the number of nucleotide changes per site. (T) indicates the type specimen for this species. The new species are in bold.
FIGURE. Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, ex-neotype, or reference strain). in Six new species of Cladosporium associated with decayed leaves of native bamboo (Bambusoideae) in a fragment of Brazilian Atlantic Forest
FIGURE. Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, ex-neotype, or reference strain).
FIGURE. (Continued) Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, exneotype, or reference strain). in Six new species of Cladosporium associated with decayed leaves of native bamboo (Bambusoideae) in a fragment of Brazilian Atlantic Forest
FIGURE. (Continued) Multilocus phylogenetic tree inferred from Bayesian analysis based on the combined TEF1-α and ACT sequences. Bayesian posterior probabilities are indicated next to the nodes. The tree was rooted with Cladosporium herbarum CBS 121621. The species in this study are indicated in bold. Types of species are indicated after the culture collection number (T = ex-type, ex-epitype, exneotype, or reference strain).
Input data for Bayesian and information theoretic model selection and similarity analysis
<p>This data serves as input to the codes found in the following repository https://github.com/MariaFMoralesOreamuno/Bayesian_Information_theoretic_model_selection.git</p> <p> </p>
Supplementary Data:Risk Analysis for Real-time Flood Control Operation of a Multi-reservoir System Using a Dynamic Bayesian Network
<p>The files in this record contain data for risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic bayesian network considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Reservoir data and river flood routing parameters</li> <li>Flood data</li> <li>Code and results of the Monte Carlo simulations</li> <li>Code and results of the Bayesian network</li> </ul>
A Systematic Review and Bayesian Meta-Analysis of Acoustic Measures of Prosody in Parkinson's Disease
<p>The folder contains the dataset used for this study and a file defining the titles of the dataset.</p> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p> </p> <p> </p> </div> </div> </div> </div> </div>
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>
Efficacy and safety of different monoclonal antibodies for osteoarthritis: a Bayesian network meta-analysis
Open the record for dataset details and reuse information.
Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019)
<p>Supplementary Data for Bayesian material flow analysis of the construction aggregate cycle in England (2019) by </p> <p><span>Adam R. Mason <sup>1,a</sup>, Tom Bide <sup>2,b</sup>, Junyang Wang <sup>3,c</sup>, John Morley <sup>4,d</sup>, Mohit Arora <sup>5,e</sup>, Alperen Yayla<sup>1,f</sup>, Julia A. Stegemann <sup>5,g</sup>, Rupert J. Myers <sup>1,h,*</sup></span></p> <p><span> </span></p> <p><sup><span>1</span></sup><span> Department of Civil and Environmental Engineering, Imperial College London, UK</span></p> <p><sup><span>2</span></sup><span> British Geological Survey, UK</span></p> <p><sup><span>3 </span></sup><span>Department of Mathematics, Imperial College London, UK</span></p> <p><sup><span>4</span></sup><sub><span> </span></sub><span>Department of Earth Science and Engineering, Imperial College London, UK</span></p> <p><sup><span>5</span></sup><span> School of Engineering, King’s College London, UK</span></p> <p><sup><span>6</span></sup><span> Department of Civil, Environmental and Geomatic Engineering, University College London, UK</span></p> <p><span> </span></p> <p><span>Author e-mails: <sup>a </sup></span><a href="mailto:a.mason19@imperial.ac.uk"><span>a.mason19@imperial.ac.uk</span></a><span>,<sup> b </sup></span><a href="mailto:tode@bgs.ac.uk"><span>tode@bgs.ac.uk</span></a><span>,<sup> c </sup></span><a href="mailto:junyang.wang21@imperial.ac.uk"><span>junyang.wang21@imperial.ac.uk</span></a><span>,<sup> d </sup></span><a href="mailto:john.morley18@imperial.ac.uk"><span>john.morley18@imperial.ac.uk</span></a><span>,<sup> e </sup></span><a href="mailto:mohit.arora@kcl.ac.uk"><span>mohit.arora@kcl.ac.uk</span></a><span>,<sup> f </sup></span><a href="mailto:a.yayla22@imperial.ac.uk"><span>a.yayla22@imperial.ac.uk</span></a><span>,<sup> g </sup></span><a href="mailto:j.stegemann@ucl.ac.uk"><span>j.stegemann@ucl.ac.uk</span></a><span>; *corresponding author:<sup> h</sup> </span><a href="mailto:r.myers@imperial.ac.uk"><span>r.myers@imperial.ac.uk</span></a></p> <p> </p>
Data from: Bayesian analysis of hybridization and introgression between the endangered european mink (Mustela lutreola) and the polecat (Mustela putorius)
Human-mediated global change will probably increase the rates of natural hybridization and genetic introgression between closely related species, and this will have major implications for conservation of the taxa involved. In this study, we analyse both mitochondrial and nuclear data to characterize ongoing hybridization and genetic introgression between two sympatric sister species of mustelids, the endangered European mink (Mustela lutreola) and the more abundant polecat (M. putorius). A total of 317 European mink, 114 polecats and 15 putative hybrid individuals were collected from different localities in Europe and genotyped with 13 microsatellite nuclear markers. Recently developed Bayesian methods for assigning individuals to populations and identifying admixture proportions were applied to the genetic data. To identify the direction of hybridization, we additionally sequenced mtDNA and Y chromosomes from 78 individuals and 29 males respectively. We found that both hybridization and genetic introgression occurred at low levels (3% and 0.9% respectively) and indicated that hybridization is asymmetric, as only pure polecat males mate with pure European mink females. Furthermore, backcrossing and genetic introgression was detected only from female first-generation (F1) hybrids of European mink to polecats. This latter result implies that Haldane's rule may apply. Our results suggest that hybridization and genetic introgression between the two species should be considered a rather uncommon event. However, the current low densities of European mink might be changing this trend.
Bayesian Analysis of Quasar Lightcurves with a Running Optimal Average: PyROA Fits to COSMOGRAIL Data
<p>Available as .zip files are the data/results of using PyROA to fit to the COSMOGRAIL gravitationally lensed quasar data. In each .zip are the individual lightcurves for each image as a .dat file, a plot of the results, a corner plot of the sampled parameters and three pickle objects. These are samples.obj, samples_flat.obj and X_t.obj.</p> <ul> <li>The first of these, samples.obj, contains all the MCMC samples in an array with shape (Nsamples, Nwalkers, Ndim).</li> <li>The second, samples_flat.obj, contains the flattened samples, where the burn-in has been removed and is an array with shape (Nsamples_final, Ndim), where Nsamples_final is the number of samples with burn-in removed. This was used to generate the corner plot and obtain the best fit parameters.</li> <li>The last, X_t.obj, contains the driving lightcurve, X(t), as described in Donnan et al. 2021. This is an array of the form [t, X, X_errs], where X is the value of the driving lightcurve at time, t, with errors, X_errs.</li> </ul> <p>For objects with more than two images, folders where other images were the reference are included. The sigma parameter in the corner plot is the extra error added to the flux data which is labelled as <em>s<sub>i</sub></em> in the paper. The original data file downloaded from the COSMOGRAIL website is also included as a csv file.</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.