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124 results for “quantitative modeling”
Figure 2 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 2 Histogram of STEC (main pathogenic serotypes MPS-STEC) concentration (log10 (CFU/ml)) in milk put into production.
Figure 1 from: Basak S, Christy J, Guillier L, Audiat-Perrin F, Sanaa M, Tenenhaus-Aziza F, Bect J, Vazquez E (2024) Quantitative risk assessment of Haemolytic and Uremic Syndrome (HUS) from consumption of raw milk soft cheese. Food and Ecological Systems Modelling Journal 5: e109502. https://doi.org/10.3897/fmj.5.109502
Figure 1 Schematic diagram of the batch level simulator of the risk assessment model. Modules are denoted by pink coloured boxes with the blue boxes denoting the set of corresponding input parameters \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} \theta = \{\theta^{\rm farm}, \theta^{\rm cheese}, \theta^{\rm con}, \theta^{\rm post}\} \end{equation*} \end{varwidth} \end{document} and the orange boxes denoting the outputs, namely, milk loss per batch \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} M^{\rm batch} \end{equation*} \end{varwidth} \end{document} , probability of rejecting a particular batch \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} P^{\rm batch} \end{equation*} \end{varwidth} \end{document} and batch risk \documentclass[12pt]{standalone} \usepackage{varwidth} \usepackage[utf8x]{inputenc} \usepackage[T1]{fontenc} \usepackage{lmodern} \usepackage{amsmath, amssymb, graphics, setspace} \newcommand{\mathsym}[1]{{}} \newcommand{\unicode}[1]{{}} \newcounter{mathematicapage} \begin{document} \begin{varwidth}{50in} \begin{equation*} R^{\rm batch} \end{equation*} \end{varwidth} \end{document} .
Model outputs: Quantitative assessment of fire and vegetation properties in historical simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project
<p>This dataset contains the fire model outputs of various variables used in the initial FireMIP benchmarking paper.</p>
Data from: Comparison of non-Gaussian quantitative genetic models for migration and stabilizing selection
The balance between stabilizing selection and migration of maladapted individuals has formerly been modeled using a variety of quantitative genetic models of increasing complexity, including models based on a constant expressed genetic variance and models based on normality. The infinitesimal model can accommodate non-normality and a non-constant genetic variance as a result of linkage disequilibrium. It can be seen as a parsimonious one-parameter model which approximates the underlying genetic details well when a large number of loci are involved. Here, the performance of this model is compared to several more realistic explicit multilocus models, with either two, several or a large number of alleles per locus with unequal effect sizes. Predictions for the deviation of the population mean from the optimum are highly similar across the different models, so that the non-Gaussian infinitesimal model forms a good approximation. It does however generally estimate a higher genetic variance than the multilocus models, with the difference decreasing with an increasing number of loci. The difference between multilocus models depends more strongly on the effective number of loci, accounting for relative contributions of loci to the variance, than on the number of alleles per locus.
Data from: A multispecies coalescent model for quantitative traits
We present a multispecies coalescent model for quantitative traits that allows for evolutionary inferences at micro- and macroevolutionary scales. A major advantage of this model is its ability to incorporate genealogical discordance underlying a quantitative trait. We show that discordance causes a decrease in the expected trait covariance between more closely related species relative to more distantly related species. If unaccounted for, this outcome can lead to an overestimation of a trait's evolutionary rate, to a decrease in its phylogenetic signal, and to errors when examining shifts in mean trait values. The number of loci controlling a quantitative trait appears to be irrelevant to all trends reported, and discordance also affected discrete, threshold traits. Our model and analyses point to the conditions under which different methods should fare better or worse, in addition to indicating current and future approaches that can mitigate the effects of discordance.
Utilizing Qualitative and Quantitative Methods to Understand a New Model of Type 1 and 2 Systemic Lupus Erythematosus (SLE)
ClinicalTrials.gov study NCT05426902. IPD Sharing: NO. Countries: 1. Publications: 0.
A Novel Imaging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis (CHESS2104)
ClinicalTrials.gov study NCT05068492. IPD Sharing: NO. Countries: 0. Publications: 2.
A Predictive Model Based on Quantitative Fecal Immunochemical Test Can Stratify the Risk of CRC in an Organized Screening Program
ClinicalTrials.gov study NCT06607614. IPD Sharing: UNDECIDED. Countries: 0. Publications: 4.
Data from: Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models
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Data from: Evolution of female multiple mating: a quantitative model of the "sexually-selected sperm" hypothesis
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Data from: Quantitative analysis of the complete larval settlement process confirms Crisp’s model of surface selectivity by barnacles
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Data from: Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits
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Data from: An ultrasound image-based dynamic fusion modeling method for predicting the quantitative impact of in vivo liver motion on intraoperative HIFU therapies: investigations in a porcine model
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Data from: Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model
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Data from: Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models
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Data from: A multispecies coalescent model for quantitative traits
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Data from: Generalized linear mixed models for mapping multiple quantitative trait loci
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Data from: Phylogenetic ANOVA: the Expression Variance and Evolution model for quantitative trait evolution
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Data from: Comparison of non-Gaussian quantitative genetic models for migration and stabilizing selection
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Real-time quantitative PCR analysis of ccRCC model rats III
GEO Series GSE255822. Rattus norvegicus. 6 samples. Type: Expression profiling by RT-PCR.
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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.