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244 results for “Model Organisms”
Supporting data: Reporting phenotypes in model organisms when considering body size as a potential confounder.
<p>This directory contains the data and associated scripts used to generate the figures in the manuscript "Reporting phenotypes in model organisms when considering body size as a potential confounder." submitted to the Journal of Biomedical Semantics</p>
Systematic review reveals sexually antagonistic knockouts in model organisms data and code
<p>R Code and data for manuscript titled "Systematic review reveals sexually antagonistic knockouts in model organisms".</p> <p>Drosophila data is from Ruzicka, F., Hill, M.S., Pennell, T.M., Flis, I., Ingleby, F.C., Mott, R., Fowler, K., Morrow, E.H., Reuter, M., 2019. Genome-wide sexually antagonistic variants reveal long-standing constraints on sexual dimorphism in fruit flies. PLoS Biol. 17, e3000244.</p> <p>https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3000244</p> <p>Human data is from Harper, J.A., Janicke, T., Morrow, E.H., 2021. Systematic review reveals multiple sexually antagonistic polymorphisms affecting human disease and complex traits. Evolution 75, 3087–3097. https://doi.org/10.1111/evo.14394</p> <p>https://onlinelibrary.wiley.com/doi/full/10.1111/evo.14394</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
(c) simulation on Repast: after queen adaptive development-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>On figures (b) and (c), simulations on RePast [11, 16, 18] are<br> provided at successive times. The last figure shows the adaptive mechanism<br> of the queen which grows with time according to the material density around<br> it, like in natural observations.</p>
Figure 7: Cultural equipment dynamics modeling-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>The multi-template modelling can be used to model cultural equipment<br> dynamics as described in figure 7. On this figure, we associate a queen to each<br> cultural center (cinema, theatre, ...). Each queen will emit many pheromon<br> templates, each template is associated to a specific criterium (according to age,<br> sex, ...). Initially, we put the material in the residential place. Each material<br> has some characteristics, corresponding to the people living in this residential<br> area. The simulation shows the self-organization processus as the result of the<br> set of the attractive effect of all the centers and all the templates.</p>
Figure 4: Complexity of geographical space with respect of emergent organizations-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>The applications we focus on in the models that we will propose in the<br> following, concerns specifically the multi-center (or multi-organizational) phenomona<br> inside urban development. As an artificial ecosystem, the city development<br> has to deal with many challenges, specifically for sustainable development,<br> mixing economical, social and environmental aspects. The decentralized<br> methodology proposed in the following allows to deal with multi-criteria problems,<br> leading to propose a decision making assistance, based on simulation<br> analysis.</p>
Figure 1: Complex spatial organizational model-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>On Figure 1, we describe a two-level model of spatial self-organizations with<br> interactions in both directions between these two levels: the emergence of organizations<br> from entities interactions but also the feed-back process describing<br> how organizations are regulating their own entities.</p>
Figure 2. The model 6R30 in High seeding rates, interrow mowing, and electrocution for weed management in organic no-till planted soybean
Figure 2. The model 6R30 Weed Zapper™ used in this experiment. The generator is attached to the back of a John DeereṜ 5100R tractor with a three-point hitch. The 4.6-m electric copper boom is attached to the front of the tractor with a three-point hitch. The Weed Zapper™ was purchased from Old School Manufacturing (Sedalia, MO, USA).
A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms
<p>The unified and integrated metadata accompanied by referencing identifiers from the A3D database is available for download in CSV format.</p> <p>Aleksandra E Badaczewska-Dawid, Aleksander Kuriata, Carlos Pintado-Grima, Javier Garcia-Pardo, Michał Burdukiewicz, Valentín Iglesias, Sebastian Kmiecik, Salvador Ventura, A3D Model Organism Database (A3D-MODB): a database for proteome aggregation predictions in model organisms, <em>Nucleic Acids Research</em>, Volume 52, Issue D1, 5 January 2024, Pages D360–D367, <a href="https://doi.org/10.1093/nar/gkad942">https://doi.org/10.1093/nar/gkad942</a></p>
Code and data associated with Christiansen et al. 2021 "Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing"
<p>All code and data input and output files (except reference genome and raw sequencing data) needed to reproduce the results of Christiansen et al. 2021 as released on <a href="https://github.com/notothen/radpilot">https://github.com/notothen/radpilot</a> alongside journal publication. See published paper:</p> <p>Christiansen, H., Heindler, F.M., Hellemans, B. <em>et al.</em> Facilitating population genomics of non-model organisms through optimized experimental design for reduced representation sequencing. <em>BMC Genomics</em> <strong>22, </strong>625 (2021). <a href="https://doi.org/10.1186/s12864-021-07917-3">https://doi.org/10.1186/s12864-021-07917-3</a></p>
CATH Structural domains in AlphaFold2 models for 21 model organisms
<p>CATH structural domain assignments for AlphaFold2 models in 21 model organisms.</p> <p>The table cath-v4_3_0.alphafold-v2.2022-11-22.tsv contains the domain assignments with information on model quality, CATH superfamily and Class, organism, average pLDDT, percentage of residues not in secondary structure, globularity and domain origin (CATH-PDB, CATH-HMM,Pfam,newfams).</p> <p>Organisms included:</p> <p>Arabidopsis thaliana</p> <p>Caenorhabditis elegans</p> <p>Candida albicans</p> <p>Danio rerio</p> <p>Dictyostelium discoideum</p> <p>Drosophila melanogaster</p> <p>Escherichia coli</p> <p>Glycine max</p> <p>Homo sapiens</p> <p>Leishmania infantum</p> <p>Methanocaldococcus jannaschii</p> <p>Mus musculus</p> <p>Mycobacterium tuberculosis</p> <p>Oryza sativa</p> <p>Plasmodium falciparum</p> <p>Rattus norvegicus</p> <p>Saccharomyces cerevisiae</p> <p>Schizosaccharomyces pombe</p> <p>Staphylococcus aureus</p> <p>Trypanosoma cruzi</p> <p>Zea mays</p> <p> </p> <p>The archive</p> <pre>cath-v4_3_0.alphafold-v2.2022-11-22.by_superfamily.tgz</pre> <p>contains all domains assigned by CATH in the dataset as PDB files, divided by superfamily.</p> <p>Alternatively, if you're interested in a particular organism, an individual tarball containing all CATH domains as PDB files is available</p> <p>e.g. cath-v4_3_0.alphafold-v2.2022-11-22.arabidopsis_thaliana.tgz</p> <p>All domains included in this release are named as af_[UniProt_ID]_[start]_[stop].</p> <p> </p>
Trained Models for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the trained model weights for the baseline model and the winning solutions in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body", and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directory contains:</p> <p><strong>trained_model_1_weights.zip: </strong>Trained model weights for first place solution (Team 1).</p> <p><strong>trained_model_2_weights.zip:</strong> Trained model weights for second place solution (Team 2).</p> <p><strong>trained_model_3_weights.zip: </strong>Trained model weights for third place solution (Team 3).</p> <p><strong>trained_model_weights_baseline.zip:</strong> Trained model weights for the baseline model.</p>
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets
<p>This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology.</p> <p>Every folder contains two csv files:</p> <p>- parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis</p> <p>- outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample</p> <p>In ventricular_cell_model, there are four folders:</p> <p>- ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features</p> <p>- contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0).</p> <p>- contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1).</p> <p>- contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic.</p> <p>The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient.</p> <p>The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model.</p> <p>The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation.</p> <p>The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model.</p> <p>Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function.</p> <p>The details about the model can be found in the original publication.</p>
Soil organic carbon models need independent time-series validation for reliable prediction
<p>Supplementary Data 1 to the paper: Soil organic carbon models need independent time-series validation for reliable prediction</p> <p>By: Le Noë, J., Manzoni, S., Abramoff, R.Z., Bölscher, T., Bruni, E., Cardinael, R., Ciais, P., Chenu, C., Clivot, H., Derrien, D., Ferchaud, F., Garnier, P., Goll, D., Lashermes, G., Martin, M.P., Rasse, D., Rees, F., Sainte-Marie, J., Salmon, E., Schiedung, M., Schimel, J., Wieder, W.R., Abiven, S., Barré, P., Cécillon, L., Guenet, B.</p>
Dataset for the paper submitted for peer-review with the title "Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model"
<p>The proposed dataset is related to the following article submitted for peer review:</p> <p>Hasanyar, M., Flipo, N., Romary, T., Wang, S. (2023), Quantifying heterotrophic bacteria parameters and dissolved organic carbon biodegradability through oxygen data assimilation in a river water quality model, UNDER PEER-REVIEW</p> <p>It consists of command files for the prose-pa0.74 software available here: https://gitlab.com/prose-pa/prose-pa </p> <p>To run the model :</p> <p>1. Compile prose-pa0.74</p> <p>2. Copy the executable in the current directory</p> <p>3. In a terminal launch</p> <p>> ./prose-pa0.74 simulation.COMM test.log</p> <p>The “simulation.COMM” holds the settings for the ProSe-PA simulation related to the paper mentioned in the front head of the current file. </p> <p>The information on different parameters of “simulation.COMM” are included in “bathymetrie”, “Cmds”, “Inflows”, “layers”, “meteo”, “o2_obs”, “param_bio”, “Reaches” and “Singularities” folders.</p> <p>The “bathymetrie” folder holds the geometric information of several cross-sections along the river. </p> <p>The Cmds folder holds the “simulation.COMM” file. </p> <p>The “Inflows” folder the information about the boundary condition inflows to the river such as discharge, concentration of organic carbon, etc.</p> <p>The layer folder holds data of the initial conditions of the model (Table 2 in the article).</p> <p>The “meteo” folder holds the meteorological information.</p> <p>The “o2_obs” folder holds the observed oxygen data needed to do data assimilation. </p> <p>The “param_bio” folder holds information on the physiology of bacteria, phytoplankton, and other model species.</p> <p>The “Reaches” folder holds information about river reaches and their manning coefficient. </p> <p>The “param_range” file holds the variation range of model parameters considered in data assimilation together with their perturbation percentage.</p> <p>The output files are written in $HOME/Outputs folder. It is possible to change it directly in simulation.COMM, last entry “Output_folder”.</p>
Genetic architecture of dispersal behaviour in the post-harvest pest and model organism Tribolium castaneum
<p>Dispersal behaviour is an important aspect of the life-history of animals. However, the genetic architecture of dispersal related traits is often obscure or unknown, even in well studied species.<em> Tribolium castaneum</em> is a globally significant post-harvest pest and established model organism, yet studies of its dispersal have shown ambiguous results and the genetic basis of this behaviour remains unresolved. We combine experimental evolution and agent-based modelling to investigate the number of loci underlying dispersal in <em>T.castaneum</em>, and whether the trait is sex-linked. Our findings demonstrate rapid evolution of dispersal behaviour under selection. We find no evidence of sex-biases in the dispersal behaviour of the offspring of crosses, supporting an autosomal genetic basis of the trait. Moreover, simulated data approximates experimental data under simulated scenarios where the dispersal trait is controlled by one or few loci, but not many loci. Levels of dispersal in experimentally inbred lines, compared with simulations, indicate that a single locus model is not well supported. Taken together, these lines of evidence support an oligogenic architecture underlying dispersal in <em>Tribolium castaneum</em>. These results have implications for applied pest management and for our understanding of the evolution of dispersal in the coleoptera, the world's most species-rich order.</p>
Practical Cell Design for PTMA-Based Organic Batteries: an Experimental and Modeling Study - Supporting Dataset
<p>The cycling and impedance data used in the work "Practical Cell Design for PTMA-Based Organic Batteries: an Experimental and Modeling Study" are provided in this repository.<br> The data are reported both as original .txt files with the raw data from the instruments and as processed Matlab files, where the data are structured in cycles.<br> The Excel sheet "Metadata" explains the type of battery associated to each code and file(s).</p> <p><br> </p>
Export of organic carbon by ocean biological pump from GYRE ocean model
<p>Model data of the biological pump of organic carbon computed from the idealized ocean model GYRE used in: Resplandy, Lévy and McGillicuddy (2019). Effects of eddy-driven subduction on ocean biological carbon pump. Global Biogeochemical Cycles. Readme file describes the data.</p>
Genetic architecture of dispersal behaviour in the post-harvest pest and model organism Tribolium castaneum
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Model executable, output, drivers and parameters for modeling organism acclimation to changing availability of and requirements for substitutable and interdependent resources
Files used to generate the data for figures in: Rastetter, EB, Kwiatkowski, BL. An approach to modeling resource optimization for substitutable and interdependent resources. Ecological Modelling (2020). https://doi.org/10.1016/j.ecolmodel.2020.109033. This paper presents a hierarchical approach to modeling organism acclimation to changing availability of and requirements for substitutable and interdependent resources. Substitutable resources are resources that fill the same metabolic or stoichiometric need of the organism. Interdependent resources are resources whose acquisition or expenditure are tightly linked (e.g., light, carbon dioxide, and water in photosynthesis and associated transpiration). We illustrate the approach by simulating the development of vegetation with four substitutable sources of nitrogen that differ only in the cost of their uptake and assimilation.
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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.