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244 results for “Coalescent”
Figure 3 in Considering gene flow when using coalescent methods to delimit lineages of North American pitvipers of the genus Agkistrodon
Figure 3. Posterior probability of speciation inferred by BPP, version 2.0 (Yang & Rannala, 2010) with an increasing proportion of hybrids included in the dataset for Agkistrodon contortrix (A) and Agkistrodon piscivorus (B). Results are shown for both species with hybrids assigned to the population that contributed the majority of alleles with a fully resolved starting tree (1) and a fully collapsed starting tree (0), as well as hybrids assigned to the population that contributed the minority of alleles with both starting trees.
Figure 1 in Considering gene flow when using coalescent methods to delimit lineages of North American pitvipers of the genus Agkistrodon
Figure 1. Map of the USA showing the sampling localities and distribution of inferred lineages of Agkistrodon contortrix (A) and Agkistrodon piscivorus (B). The distribution of both species is indicated by a bold outline. The key to the right of each map indicates the population membership of individuals and also identifies admixture in individuals.
Figure 4 in Considering gene flow when using coalescent methods to delimit lineages of North American pitvipers of the genus Agkistrodon
Figure 4. Niche predictions for each species of the Agkistrodon contortrix complex with hybrids included in training the model (A), Agkistrodon contortrix complex with hybrids excluded (B), Agkistrodon piscivorus complex with hybrids included in training the model (C), Agkistrodon piscivorus complex with hybrids excluded (D), and East and Central lineages of Agkistrodon contortrix inferred by mtDNA (E). The distribution of colubriod samples obtained from the Herpnet database used to correct for sampling bias is shown in (F). Overlap between predicted suitable habitat is indicated by the darkest intermediate shade.
Figure 5 in Considering gene flow when using coalescent methods to delimit lineages of North American pitvipers of the genus Agkistrodon
Figure 5. The distribution of species within the Agkistrodon contortrix complex (A) and Agkistrodon piscivorus complex (B) inferred by Bayesian species delimitation. Putative hybrids zones are identified with cross-hatches. Distributions are adapted from Gloyd & Conant (1990) and the results from ecological niche modelling.
Figure 6 in Bayesian Poisson tree processes and multispecies coalescent models shed new light on the diversification of Nawab butterflies in the Solomon Islands (Nymphalidae, Charaxinae, Polyura)
Figure 6. Habitus of Polyura bicolor and Polyura epigenes across the Solomon Islands. Pictures of the upperside (left half) and underside (right half) of the two sexes. All pictures taken by Bernard Turlin.
Figure 4 in Bayesian Poisson tree processes and multispecies coalescent models shed new light on the diversification of Nawab butterflies in the Solomon Islands (Nymphalidae, Charaxinae, Polyura)
Figure 4. Nuclear haplotype network and maximum-likelihood phylogenetic reconstruction. The haplotype network reconstructed using the concatenated RPS5 and wingless alignments is presented on the left. The phylogenetic hypothesis inferred using the same data set in RA×ML is presented at the top right. A table highlighting the nucleotide substitutions found between Polyura epigenes bicolor and Polyura epigenes* is presented at the bottom right.
Figure 3 in Bayesian Poisson tree processes and multispecies coalescent models shed new light on the diversification of Nawab butterflies in the Solomon Islands (Nymphalidae, Charaxinae, Polyura)
Figure 3. Comparison of species delimitation results. Graph showing the result of each analysis of species delimitation. The posterior probability of either Polyura bicolor or Polyura epigenes (including Polyura bicolor) as a valid species are shown.
Figure 2 in Bayesian Poisson tree processes and multispecies coalescent models shed new light on the diversification of Nawab butterflies in the Solomon Islands (Nymphalidae, Charaxinae, Polyura)
Figure 2. Molecular phylogeny and molecular species delimitation results. Bayesian phylogeny as recovered from MrBayes analyses. The nodal supports of both Bayesian-inference (BI) and maximum-likelihood (ML) analyses are shown, with colour coding as indicated in the figure. The cloudogram of the *BEAST analysis showing all posterior species trees is presented at the bottom left. Denser regions indicate a robust support for the inferred relationship.
Figure 1 in Bayesian Poisson tree processes and multispecies coalescent models shed new light on the diversification of Nawab butterflies in the Solomon Islands (Nymphalidae, Charaxinae, Polyura)
Figure 1. Map of the geographic range of Polyura epigenes in the Solomon Islands. Male habitus of the different subspecies are shown above the name of the taxon. The distribution of each taxa is indicated by a coloured dashed line. The colour of the line refers to the pastille on the side of the taxon name. All pictures were taken by Bernard Turlin. The map is from National Geographic's MapMaker Interactive.
Figure 5 in Bayesian Poisson tree processes and multispecies coalescent models shed new light on the diversification of Nawab butterflies in the Solomon Islands (Nymphalidae, Charaxinae, Polyura)
Figure 5. Divergence time estimates derived from the *BEAST analysis. Chronogram derived from the posterior trees of dating analysis conducted in BEAST. The 95% credibility intervals are shown at each node of the phylogeny. A map of the Solomon Islands with bathymetry is shown at the top of the figure. The island of Malaita is highlighted in violet. A picture of a female Polyura epigenes bicolor f. cinereus (orange morph) is presented. Picture taken by Bernard Turlin. T in time bar stands for Tarantian.
Test sets and results for paper 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning'
<p>This folder contains the input data (signal-to-noise ratio time series for gravitational wave detections) and results (sky localization areas) obtained from the deep learning based sky localization model 'GW-SkyLocator', and the rapid online gravitational wave sky localization tool 'BAYESTAR' on a set of injections of gravitational wave signals from compact binary mergers.</p> <p>For details of the work, please refer to the paper, 'Rapid localization of gravitational wave sources from compact binary coalescences using deep learning' (https://arxiv.org/abs/2207.14522).</p>
Data from: Assessing species boundaries and the phylogenetic position of the rare Szechwan Ratsnake, Euprepiophis perlacea (Serpentes: Colubridae), using coalescent-based methods
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Data from: In the shadows: phylogenomics and coalescent species delimitation unveil cryptic diversity in a Cerrado endemic lizard (Squamata: Tropidurus)
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Data from: Divergence and biogeography of the recently evolved Macaronesian red Festuca (Gramineae) species inferred from coalescence-based analyses
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Data from: Coalescent models characterize sources and demographic history of recent round goby colonization of Great Lakes and inland waters
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Data from: Species delimitation with ABC and other coalescent-based methods: a test of accuracy with simulations and an empirical example with lizards of the Liolaemus darwinii complex (Squamata: Liolaemidae)
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Data from: Pathways of cryptic invasion in a fish parasite traced using coalescent analysis and epidemiological survey
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Data from: Tests of species-specific models reveal the importance of drought in postglacial range shifts of a Mediterranean-climate tree: insights from integrative distributional, demographic and coalescent modelling and ABC model selection
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Data from: Assessing the impacts of positive selection on coalescent-based species tree estimation and species delimitation.
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Data from: What have been and what can be delimited as species using molecular data under the multi-species coalescent model? A case study using Hercules beetles (Dynastes; Dynastidae)
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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)
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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.