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371 results for “Likelihood”
Fig. 3. Maximum likelihood tree estimated from the 215 in Morphological and Molecular Identification of Isospora sepetibensis (Chromista: Miozoa: Eimeriidae) from a New Host, Trichothraupis melanops (Passeriformes: Thraupidae: Tachyphoninae) in South America
Fig. 3. Maximum likelihood tree estimated from the 215 bp long cox1 sequences. Numbers at nodes represent bootstrap support (1,000 replicates; only values> 50% shown) for Neighbor-Joining and Maximum Likelihood, respectively. The scale-bar represents the number of nucleotide substitutions per site.
Fig. 2. Maximum likelihood tree estimated from the cox1 in Morphological and Molecular Identification of Isospora sepetibensis (Chromista: Miozoa: Eimeriidae) from a New Host, Trichothraupis melanops (Passeriformes: Thraupidae: Tachyphoninae) in South America
Fig. 2. Maximum likelihood tree estimated from the cox1 sequences. Numbers at nodes represent bootstrap support (1,000 replicates; only values> 50% shown) for Neighbor-Joining and Maximum Likelihood, respectively. The scale-bar represents the number of nucleotide substitutions per site.
Data from: Accelerating maximum likelihood phylogenetic inference via early stopping to evade (over-)optimization
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Supplemental data from: Inference of phylogenetic networks from sequence data using composite likelihood
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Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
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Data from: LoRaD: Marginal likelihood estimation with haste (but no waste)
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Variable species establishment in response to microhabitat indicates different likelihoods of climate-driven range shifts
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Insights from Fisher's geometric model on the likelihood of speciation under different histories of environmental change
<p>All code and simulation data necessary to repeat the analysis described in "Insights from Fisher's geometric model on the likelihood of speciation under different histories of environmental change."</p>
Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
<p>The data part of this release support the results presented in the paper <br /> "Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data", by W. Wang and G. Casale, accepted for presentation at MAMA workshop 2015. </p> <p>When referring to the dataset or scripts please cite the paper above. </p>
Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
<p>The data part of this release support the results presented in the paper <br /> "Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data", by W. Wang, G. Casale, A. Kattepur and M. Nambiar, accepted for presentation at ICPE 2016. </p> <p>When referring to the dataset or scripts please cite the paper above. </p>
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 10. - Maximum-likelihood phylogeny of Epicephala species based on sequences of the COI, ArgK and EF1α genes. Numbers above nodes are maximum-likelihood bootstrap support values based on 1,000 replications. The Japanese Epicephala species are marked in blue. Symbols right to species names donate ovipositor morphology: inverted U-shape, rounded apically; inverted V-shape, acute apically.
Figure 10. - Maximum-likelihood phylogeny of Epicephala species based on sequences of the COI, ArgK and EF1α genes. Numbers above nodes are maximum-likelihood bootstrap support values based on 1,000 replications. The Japanese Epicephala species are marked in blue. Symbols right to species names donate ovipositor morphology: inverted U-shape, rounded apically; inverted V-shape, acute apically.
Comparative analysis of patient-specific aortic dissections through computational fluid dynamics suggests increased likelihood of degeneration in partially thrombosed aorta
<p>Aortic dissection is a life-threatening cardiovascular disease associated with high rates of morbidity and mortality, especially in medically under-served communities. It compromises the hemodynamics of the arteries that originate from the aorta, and its outcomes include visceral ischemia and aortic rupture in the acute phase and aneurysmatic degeneration in the chronic phase. Understanding patients’ blood flow patterns is pivotal for non-invasive evidence-based treatment as they greatly influence both the disease onset and its outcome. In this paper, we combine diagnostic imaging techniques and computational fluid dynamics to analyze the flow patterns of three aorta dissections (fully perfused, partially thrombosed, and fully thrombosed), and compare them to a healthy aorta. Besides flow kinematics, we focus on time averaged wall shear stress and oscillatory shear index that are recognized risk factors for aneurysm and rupture. Our analysis shows that partially thrombosed dissection is the most prone to false lumen degeneration. In all dissections, the arteries connected to the false lumen are generally poorly supplied with blood. Further, both true and false lumens present higher turbulence levels than the healthy aorta, and critical stagnation points. Mesh sensitivity and a thorough comparison against literature data together support the methodology robustness.</p>
Likelihoods for the CTA sensitivity to a dark matter line signal from the Galactic centre (S. Abe et al., 2024])
<p>We present likelihoods to estimate upper limits and detection prospects for monochromatic gamma-ray signals from DM pair-annihilation in the Galactic centre, based on the Cherenkov Telescope Array (CTA) consortium publication "Dark Matter Line Searches with the Cherenkov Telescope Array" by S. Abe et al. [arxiv:<a href="https://arxiv.org/abs/2403.04857" target="_blank" rel="noopener noreferrer">2403.04857</a>]. These likelihoods are based on the analysis benchmark assumptions summarized in Table 3 of that publication. In particular, they assume an Einasto profile for the DM density distribution and 500 hrs of Galactic centre observations. We further supply likelihoods for the same analysis benchmark assumptions, but for an Einasto profile with an inner core of size 1 kpc.</p> <p>As indicated by the filenames, the four files contain likelihoods for setting limits and claiming discovery, respectively. For more technical details, see the headers of these files. </p>
Data from: The influence of the number of tree searches on maximum likelihood inference in phylogenomics
<p>Maximum likelihood (ML) phylogenetic inference is widely used in phylogenomics. As heuristic searches most likely find suboptimal trees, it is recommended to conduct multiple (e.g., ten) tree searches in phylogenetic analyses. However, beyond its positive role, how and to what extent multiple tree searches aid ML phylogenetic inference remains poorly explored. Here, we found that a random starting tree was not as effective as the BioNJ and parsimony starting trees in inferring ML gene tree and that RAxML-NG and PhyML were less sensitive to different starting trees than IQ-TREE. We then examined the effect of the number of tree searches on ML tree inference with IQ-TREE and RAxML-NG, by running 100 tree searches on 19,414 gene alignments from 15 animal, plant, and fungal phylogenomic datasets. We found that the number of tree searches substantially impacted the recovery of the best-of-100 ML gene tree topology among 100 searches for a given ML program. In addition, all of the concatenation-based trees were topologically identical if the number of tree searches was ≥ 10. Quartet-based ASTRAL trees inferred from 1 to 80 tree searches differed topologically from those inferred from 100 tree searches for 6 /15 phylogenomic datasets. Lastly, our simulations showed that gene alignments with lower difficulty scores had a higher chance of finding the best-of-100 gene tree topology and were more likely to yield the correct trees.</p>
Full-likelihood genomic analysis clarifies a complex history of species divergence and introgression: the example of the erato-sara group of Heliconius butterflies
<p>Introgressive hybridization plays a key role in adaptive evolution and species diversification in many groups of species. However, frequent hybridization and gene flow between species make estimation of the species phylogeny and key population parameters challenging. Here, we show that by accounting for phasing and using full-likelihood methods, introgression histories and population parameters can be estimated reliably from whole-genome sequence data. We employ the multispecies coalescent (MSC) model with and without gene flow to infer the species phylogeny and cross-species introgression events using genomic data from six members of the <i>erato</i>-<i>sara</i> clade of <i>Heliconius</i> butterflies. The methods naturally accommodate random fluctuations in genealogical history across the genome due to deep coalescence. To avoid heterozygote phasing errors in haploid sequences commonly produced by genome assembly methods, we process and compile unphased diploid sequence alignments and use analytical methods to average over uncertainties in heterozygote phase resolution. There is robust evidence for introgression across the genome, both among distantly related species deep in the phylogeny and between sister species in shallow parts of the tree. We obtain chromosome-specific estimates of key population parameters such as introgression directions, times and probabilities, as well as species divergence times and population sizes for modern and ancestral species. We confirm ancestral gene flow between the <i>sara</i> clade and an ancestral population of <i><span>H. telesiphe</span></i>, a likely hybrid speciation origin for <i>H. hecalesia</i>, and gene flow between the sister species <i><span>H. erato</span></i><span> and <i>H. himera</i></span>. Inferred introgression among ancestral species also explains the history of two chromosomal inversions deep in the phylogeny of the group. This study illustrates how a full-likelihood approach based on the multispecies coalescent makes it possible to extract rich historical information of species divergence and gene flow from genomic data.</p>
Profiled Likelihoods from A Search for Dark Matter Lines at the Galactic Center with 14 Years of Fermi Data
<p>This repository contains the profiled likelihood ratios calculated in "A Search for Dark Matter Lines at the Galactic Center with 14 Years of Fermi Data" [arXiv:2212.07435] for the unmasked analysis. Profiled likelihood ratios are provided for each of the 30 annuli considered in the work joined over the top three quartiles of data in energy dispersion.</p> <p>The data are provided in an hdf5 archive containing 532 datasets. Line rest energies in units of GeV are provided in the `Line_Energy` dataset, which is a length 531 1-D array. Each of the remaining 531 datasets contains a 3-D array of shape (30, 2, 1001) corresponding to the profiled likelihood ratio as a function of line intensity, in units of counts/cm<sup>2</sup>/s/sr. The log of the profiled likelihood ratio for the i<sup>th</sup> line rest energy in the j<sup>th</sup> annulus may be accessed by:</p> <pre><code class="language-python">archive = h5py.File('./Archive.h5', 'r') line_energy = archive['Line_Energy'][i] data = archive['Line_' + str(i)][j] line_intensity = data[0] log_profiled_likelihood_ratio = data[1] archive.close()</code></pre> <p>For details, see <a href="https://arxiv.org/abs/2212.07435">https://arxiv.org/abs/2212.07435</a>. </p>
Genotype likelihood (beagle file) and genotype (vcf) files of North Atlantic and Black Sea Harbour porpoises (Phocoena phocoena)
<p><span>The Harbour porpoise (<em>Phocoena phocoena</em>) is a highly mobile cetacean species primarily occurring in coastal and shelf waters across the Northern hemisphere. It inhabits heterogeneous seascapes broadly varying in salinity and temperature. Here we produced 74 whole genomes at intermediate coverage to study Harbour porpoise's evolutionary history and investigate the role of local adaptation in the diversification into subspecies and populations. We identified ~6 million high-quality SNPs sampled at 8 localities across the North Atlantic </span><span>and adjacent waters</span><span>, which we used for population structure, demographic, and genotype-environment association analyses. Our results suggest a genetic differentiation between three subspecies (<em>P.p. relicta</em>, <em>P.p. phocoena,</em> and the recently proposed <em>P.p meridionalis</em>), and three distinct populations within the subspecies <em>P.p. phocoena</em>: Atlantic, Belt Sea, and Proper Baltic Sea. Effective population size and Tajima's D levels suggest a population contraction in Black Sea and Iberian porpoises, but a population expansion in the <em>P.p. phocoena</em> populations</span><span>. </span><span>Phylogenetic trees</span> <span>indicate a post-glacial colonization from a southern refugium. </span><span>Genotype-environment association analysis identified salinity as a major driver in genomic variation and we identified candidate genes putatively underlying adaptation to different salinity levels. </span><span>Our study highlights the value of whole genome resequencing to unravel subtle population structure in highly mobile species, shows how strong environmental gradients and local adaptation may lead to population differentiation and how neutral and adaptive markers give different perspectives on population subdivision. </span><span>The results have great conservation implications as we found inbreeding and low genetic diversity in the endangered Black Sea subspecies and identified the critically endangered Proper Baltic Sea porpoises as a separate population.</span></p>
Determining the Efficacy and Value of Immunotherapy on the Likelihood of Peanut Tolerance: The DEVIL Study
ClinicalTrials.gov study NCT00932828. IPD Sharing: NO. Countries: 1. Publications: 1.
Kidney Response to Sepsis Affects Angiogenic Balance and Likelihood of CCI and PICS
ClinicalTrials.gov study NCT02276066. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.