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1,066 results for “bayesian”
Power of Bayesian and heuristic tests to detect cross-species introgression with reference to gene flow in the Tamias quadrivittatus group of North American chipmunks
<p>In the past two decades genomic data have been widely used to detect historical gene flow between species in a variety of plants and animals. The Tamias quadrivittatus group of North America chipmunks, which originated through a series of rapid speciation events, are known to undergo massive amounts of mitochondrial introgression. Yet in a recent analysis of targeted nuclear loci from the group, no evidence for cross-species introgression was detected, indicating widespread cytonuclear discordance. The study used the heuristic method HyDe to detect gene flow, which may suffer from low power. Here we use the Bayesian method implemented in the program bpp to reanalyze these data. We develop a Bayesian test of introgression, calculating the Bayes factor via the Savage-Dickey density ratio using the Markov chain Monte Carlo (MCMC) sample under the model of introgression. We take a stepwise approach to constructing an introgression model by adding introgression events onto a well-supported binary species tree. The analysis detected robust evidence for multiple ancient introgression events affecting the nuclear genome, with introgression probabilities reaching 63%. We estimate population parameters and highlight the fact that species divergence times may be seriously underestimated if ancient cross-species gene flow is ignored in the analysis. We examine the assumptions and performance of HyDe, and demonstrate that it lacks power if gene flow occurs between sister lineages or if the mode of gene flow does not match the assumed hybrid speciation model with symmetrical population sizes. Our analyses highlight the power of likelihood-based inference of cross-species gene flow using genomic sequence data.</p>
Inferring human immunodeficiency virus 1 proviral integration dates with Bayesian Inference
<p>Human immunodeficiency virus 1 (HIV) proviruses archived in the persistent reservoir currently pose the greatest obstacle to HIV cure due to their evasion of combined antiretroviral therapy and ability to reseed HIV infection. Understanding the dynamics of the HIV persistent reservoir is imperative for discovering a durable HIV cure. Here, we explore Bayesian methods using the software BEAST2 to estimate HIV proviral integration dates. We started with within-host longitudinal HIV sequences collected prior to therapy, along with sequences collected from the persistent reservoir during suppressive therapy. We built a BEAST2 model to estimate integration dates of proviral sequences collected during suppressive therapy, implementing a tip date random walker to adjust the sequence tip dates and a latency-specific prior to inform the dates. To validate our method, we implemented it on both simulated and empirical data sets. Consistent with previous studies, we found that proviral integration dates were spread throughout active infection. Path sampling to select an alternative prior for date estimation in place of the latency-specific prior produced unrealistic results in one empirical data set, while on another data set the latency-specific prior was selected as best-fitting. Our Bayesian method outperforms current date estimation techniques with a root mean squared error of 0.89 years on simulated data relative to 1.23–1.89 years with previously developed methods. Bayesian methods offer an adaptable framework for inferring proviral integration dates.</p>
Data for AJ paper: Mass ratio of single-line spectroscopic binaries with visual orbits using Bayesian inference and suitable priors
<p>Data and plots for paper "Mass ratio of single-line spectroscopic binaries with visual orbits using Bayesian inference and suitable priors" accepted for publication in The Astronomical Journal.</p>
Raw, processed and merged Data for Swiss Cat+ East A1 project related to the automated and high-throughput Bayesian Optimization of CO2 hydrogenation heterogeneous catalysts
<p> All files generated during the fully digitalized automated and high-throughput experimentally-guided Bayesian Optimization project, which led to the synthesis of 144 heterogeneous catalysts with a Chemspeed unit (6 generations of 24) and their testing under CO2 hydrogenation conditions with Avantium fixed bed units. Below are some indication to understand the naming of the files.</p> <ul> <li>A1 stands for the internal project number.</li> <li>G1 to G5 stands for the catalyst generation number and G2NC for the alternative second generation suggested by the Bayesian Optimizer without considering the cost of catalyst as an objective (No_Cost).</li> <li>Three fixed bed units have been used, named XDB4x (a 4 parallel reactors unit), XDC4x (another 4 parallel reactors unit) and XR16x (a 16 parallel reactors unit).</li> <li>Individual fixed bed testing raw files (FB_RawData) generated by each unit are then processed to extract the mean and standard deviation (std) values (e.g conversion, selectivity) and to compute reactions rates.</li> <li>Then the processed files for each individual reactor (XDB, XDC, XR) are combined into one file (All_FBData), and finally aggregated with the synthesis details, viathe catalyst barcodes (AllData_Processed).</li> <li>Finally, the processed file for each generation are merged together (AllGen_Merged) and a condensed file is generated for a given reaction temperature (AllGen_275CDataProcessed_Merged)</li> </ul>
figure 6 Bayesian 50 in Zebrus pallaoroi sp. nov.: a new species of goby (Actinopterygii: Gobiidae) from the Mediterranean Sea with a DNA-based phylogenetic analysis of the Gobius-lineage
figure 6 Bayesian 50% majority-rule consensus tree estimation of phylogenetic relationships of analysed species from the Gobius-lineage sensu Agorreta et al. (2013) based on the nuclear gene rhodopsin. Numbers on branches are Bayesian posterior probabilities and maximum likelihood bootstrap values, respectively. Only values higher than 0.9 for posterior probability and 70% for bootstrap are shown.
figure 5 Bayesian 50 in Zebrus pallaoroi sp. nov.: a new species of goby (Actinopterygii: Gobiidae) from the Mediterranean Sea with a DNA-based phylogenetic analysis of the Gobius-lineage
figure 5 Bayesian 50% majority-rule consensus tree estimation of phylogenetic relationships of analysed species from the Gobius-lineage sensu Agorreta et al. (2013) based on the mitochondrial gene cytochrome b. Numbers on branches are Bayesian posterior probabilities and maximum likelihood bootstrap values, respectively. Only values higher than 0.9 for posterior probability and 70% for bootstrap are shown.
Data supporting: "Bayesian diagnosis of climate feedback evolution and forced temperature response"
<p><strong>This data set supports the paper:<br> Calafat, F. M., & Cael, B. B. (2023). Bayesian diagnosis of climate feedback evolution and forced temperature response, Geophysical Research Letters, submitted.</strong></p> <p>Please cite this paper when using this data set.</p> <p><em>Data description:</em></p> <ul> <li><strong>EBM_estimates.nc:</strong> this file contains Bayesian estimates from the energy balance model, including estimates of the climate feedback parameter and forced global average temperature change.</li> <li><strong>EBM_input_data.nc:</strong> this file contains all of the data used as input to the Bayesian energy balance model.</li> </ul>
FIGURE 3 Bayesian consensus tree representing the known Bathynellidae taxa constructed using COI, 16S, 28S, ITS2 and 18S in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment
FIGURE 3 Bayesian consensus tree representing the known Bathynellidae taxa constructed using COI, 16S, 28S, ITS2 and 18S alignments and model partitioning implemented in MrBayes. Numbers on branches represent Bayesian posterior probabilities followed by maximum likelihood bootstrap percentage. Bathynellinae and Gallobathynellinae clades are collapsed for easier interpretation.
FIGURE 2 Bayesian consensus single gene trees for COI, 16S, 28S and ITS2 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment
FIGURE 2 Bayesian consensus single gene trees for COI, 16S, 28S and ITS2. Numbers on branches represent Bayesian posterior probabilities followed by maximum likelihood bootstrap percentage. ABGD and PTP results are reported next to the trees. ABGD method: major partitions are showed; PTP: partitions with the highest support for each group are represented.
Predicting time of failure of Internet of Things devices using Bayesian workflow
<p>Repository includes the environment details in ```energies_iot_env.yml``` file.</p> <p>All code for model analysis is included in the ```iot_tests_refactor.ipynb``` notebook.</p> <p>Code for computing simulation based calibration is in the ```compute_sbc.py``` file, and can be run by ```just_csv.ipynb``` notebook.</p> <p> </p> <p>The data set was created in the project NCN OPUS "Process Fault Prediction and Detection" (UMO-2021/41/B/ST7/03851)</p>
Fig. 1. Bayesian 50 in Revision of the land snail genus Landouria Godwin-Austen, 1918 (Gastropoda, Camaenidae) from Java
Fig. 1. Bayesian 50% majority rule consensus tree based on mitochondrial 16S rDNA sequences of species of Landouria Godwin-Austen, 1918 with Mandarina suenoae Minato, 1978 and Euhadra peliomphala (Pfeiffer, 1850) as outgroups. All species of Landouria in this tree are from Java except L. omphalostoma Páll-Gergely & Hunyadi, 2013 from China and L. timorensis Köhler et al. in press, L. montana Köhler et al. in press and Landouria sp. nov. (= L. winteriana (non Pfeiffer, 1842) in Köhler et al. (in press)) from Timor. Values at the nodes represent Bayesian posterior probabilities (left) and maximum likelihood bootstrap values (right). Numbers after taxon names correspond to MZB and ZMH catalogue numbers, AM catalogue numbers or GenBank accession numbers (see Table 1).
Performance of akaike information criterion and bayesian information criterion in selecting partition models and mixture models
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Data from: Full Bayesian comparative phylogeography from genomic data
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Data from: Diversification dynamics of Cheilostome Bryozoa based on a Bayesian analysis of the fossil record
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Data: Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis
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Cophylogeny reconstruction allowing for multiple associations through approximate Bayesian computation
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A Bayesian extension of phylogenetic generalized least squares (PGLS): incorporating uncertainty in the comparative study of trait relationships and evolutionary rates
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Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
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Data from: Bayesian estimation of muscle mechanisms and therapeutic targets using variational autoencoders
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Power of Bayesian and heuristic tests to detect cross-species introgression with reference to gene flow in the Tamias quadrivittatus group of North American chipmunks
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Allen Brain Atlas
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International Brain Laboratory public data
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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.