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44 results for “contact model”
Research data related to switchable contact model (SCM) development
<p>Research data of the linked journal article, is composed of input and output files of the LIGGGHTS simulations (Project folders) and an Excel data sheet (Results_Excel) which includes calculated data.</p> <p>The contents are the simulation data and results of cake formation in centrifugal filtration using conventional (mesh) method and novel Switchable contact model (SCM, primitive) method. (For more information see the linked article and the source code of SCM: <a href="https://github.com/DamlaSerper/SCM">https://github.com/DamlaSerper/SCM</a>)</p>
On the formulation and implementation of extrinsic cohesive zone models with contact - data set
<p>This data set contains data relating to the paper "On the formulation and implementation of extrinsic cohesive zone models with contact", <a href="https://doi.org/10.1016/j.cma.2022.115545">https://doi.org/10.1016/j.cma.2022.115545</a> , specifically:<br> 1. the meshes used to conduct finite element analyses,<br> 2. the results of those finite element analyses (in the form of vtk files and numpy pickles), and<br> 3. some images of the meshes and the total displacement at the end of the analyses.<br> <br> The corresponding code to generate and read the data is available at https://github.com/nickcollins-craft/On-the-formulation-and-implementation-of-extrinsic-cohesive-zone-models-with-contact (which is the preferred method), or alternatively via https://doi.org/10.5281/zenodo.6939391.</p>
Switchable Contact Model (SCM) Research Data
<p>This is the research data of the article describing the implementation of the Switchable Contact Model (SCM), link: https://github.com/DamlaSerper/Switchable_Contact_Model-SCM.</p>
The adapted Activity-By-Contact model for enhancer-gene assignment and its application to single-cell data
<p>In our work, we implemented the ABC-model and could show that one assay for measuring the openness of enhancers is sufficient. Further, we propose a generalised calculation of the ABC-score, which describes enhancer activity in a gene-specific manner, and which includes all TSS, without requiring any additional data. We combined our implementation of the ABC-score with an approach to quantify TF binding affinity into STARE: a framework to derive TF affinities to genes. STARE was also designed for potential application on single-cell data. You can find the code in our <a href="https://github.com/schulzlab/stare">GitHub repository</a> and more details in our <a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtad062">publication</a>.</p> <p>We provide the data for the validation of our ABC-implementation on two CRISPR-screens. We also provide the results of our analysis of single-cell data of the human heart with STARE. All data is in hg19.</p> <p>Content:</p> <ul> <li>CRISPRi_screens: One file for each CRISPRi-screen with interactions that were used to plot precision-recall curves, containing columns for different ABC scoring versions.</li> <li>Enformer: Similar to the CRISPRi_screens, but containing columns for different calculations for Enformer's predicted expression change upon in silico mutagenesis of the enhancer region.</li> <li>K562_CandidateEnhancer: K562 enhancer with the 4th column for enhancer activity, one file for each activity representation that was measured.</li> <li>K562_ABC_Predictions: Regular ABC-scores and generalised ABC-scores for each activity measurement. The files contain all scored interactions for a 10MB window, without any cut-off. We also included the results of the implementation of the ABC-score of Fulco et al. (2019).</li> <li>STARE_Hocker_*: Whole STARE output for human heart single-cell data, one for regular ABC, generalised ABC, generalised ABC with average Hi-C matrix and one based on co-accessibility analysis. All approaches were run with a 5 MB window (except for GeneralisedABC500kb), the ABC-based runs with a score cut-off of 0.02. Each folder contains two subdirectories, one for the ABC-scoring and one for the Gene-TF affinity matrices. The 'ABC_output' also contains a GeneInfo file for each cell type, summarising different attributes per gene.</li> <li>INVOKE_Hocker_*: Folder with the input and output of INVOKE (see https://github.com/schulzlab/tepic), based on the STARE runs. CS genes stands for cell type-specific genes, defined as genes with a z-score across cell types of ≥ 2 and TPM ≥ 0.5. The INVOKE commands were as follows: <ul> <li>Rscript INVOKE.R --dataDir=<TF-Gene matrix> --outDir=<out_path> --response=Expression --regularization=E --performance=TRUE --outerCV=10 --seed=1234</li> </ul> </li> </ul> <p>Importantly, the results are based on data from the following publications:</p> <ul> <li>CRISPRi-screens: <ul> <li>Gasperini, Molly, Andrew J. Hill, José L. McFaline-Figueroa, Beth Martin, Seungsoo Kim, Melissa D. Zhang, Dana Jackson, et al. “A Genome-Wide Framework for Mapping Gene Regulation via Cellular Genetic Screens.” <em>Cell</em> 176, no. 1–2 (January 2019): 377-390.e19. https://doi.org/10.1016/j.cell.2018.11.029.</li> <li> <p>Schraivogel, Daniel, Andreas R. Gschwind, Jennifer H. Milbank, Daniel R. Leonce, Petra Jakob, Lukas Mathur, Jan O. Korbel, Christoph A. Merten, Lars Velten, and Lars M. Steinmetz. “Targeted Perturb-Seq Enables Genome-Scale Genetic Screens in Single Cells.” <em>Nature Methods</em> 17, no. 6 (June 2020): 629–35. https://doi.org/10.1038/s41592-020-0837-5.</p> </li> <li> <p>Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</p> </li> </ul> </li> <li>Enformer model: Avsec, Žiga, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R. Kelley. “Effective Gene Expression Prediction from Sequence by Integrating Long-Range Interactions.” <em>Nature Methods</em> 18, no. 10 (October 2021): 1196–1203. https://doi.org/10.1038/s41592-021-01252-x.</li> <li>K562 predictions and average Hi-C matrix: Fulco, Charles P., Joseph Nasser, Thouis R. Jones, Glen Munson, Drew T. Bergman, Vidya Subramanian, Sharon R. Grossman, et al. “Activity-by-Contact Model of Enhancer–Promoter Regulation from Thousands of CRISPR Perturbations.” <em>Nature Genetics</em> 51, no. 12 (December 2019): 1664–69. https://doi.org/10.1038/s41588-019-0538-0.</li> <li>Hi-C matrix for K562 predictions: Rao, S. et al. (2014). A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping. Cell, 159(7), 1665–1680</li> <li>STARE and INVOKE runs: Hocker, J. D. et al. (2021). Cardiac cell type–specific gene regulatory programs and disease risk association. Science Advances, 7(20), eabf1444</li> <li>H3K27ac HiChIP for STARE runs: Anene-Nzelu, C. G. et al. (2020). Assigning Distal Genomic Enhancers to Cardiac Disease–Causing Genes. Circulation, 142(9), 910–912</li> <li>INVOKE software: Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction Schmidt et al., Nucleic Acids Research 2016; doi: 10.1093/nar/gkw1061</li> </ul> <p> </p>
Relative role of border restrictions, case finding and contact tracing in controlling SARS-CoV-2 in the presence of undetected transmission: a mathematical modelling study
<p>Data and code for publication on <em>Relative role of border restrictions, case finding and contact tracing in controlling SARS-CoV-2 in the presence of undetected transmission: a mathematical modelling study</em></p>
A new biomechanical approach on cranial sutures function: The role of contact elements in linear and non-linear models
<p>Understanding cranial sutures and how they relieve and dissipate stress is essential to assess their role in cranial biomechanics and to develop highly accurate predictive models. This involves examining how ontogeny affects cranial sutures, as well as their morphology and function, and how these changes through time may impact essential biomechanical loadings such as chewing or direct biting. In this work we study the cranial sutures of <em>Crocodylus niloticus</em> in detail using contact elements under finite element analysis. Contact elements permit the creation of a physical relationship between two bones that are in contact and even to configure these relationships, e.g. in terms of movement or flexibility. The definition of bone contacts may require linear and/or non-linear computational solutions to attain higher accuracy. Herein, skull geometry is tested to determine how they may be altered by different types of contacts under various conditions. As predicted, the absence of sutures or cranial kinesis leads to a reduction in stress distribution across the skull, whereas sutures and cranial kinesis help the skull relieve stress and prevent certain bones from sustaining high stress levels. The type of contact used in individual sutures has a significant effect on the models' outcome. Additionally, feeding behaviors significantly impact cranial biomechanics, reflecting the influence of other variables that may be applied to the models. As highlighted by the results, in order to obtain accurate results when analyzing fossil taxa, the nature of the cranial sutures should be taken into account. Therefore, developing predictive models based on living taxa is invaluable because it facilitates the study of extinct taxa for which there is a lack of information on the fibrous joints due to poor (or non-) preservation in the fossil record.</p>
Three-point contact data for "Multi-contact statistics distinguish models of chromosome organization"
<p>Three-point contact data for the publication "Multi-contact statistics distinguish models of chromosome organization".</p>
A new biomechanical approach on cranial sutures function: The role of contact elements in linear and non-linear models
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Data from: Lessons from movement ecology for the return to work: modeling contacts and the spread of COVID-19
<p>Human behavior (movement, social contacts) plays a central role in the spread of pathogens like SARS-CoV-2. The rapid spread of SARS-CoV-2 was driven by global human movement, and initial lockdown measures aimed to localize movement and contact in order to slow spread. Thus, movement and contact patterns need to be explicitly considered when making reopening decisions, especially regarding return to work. Here, as a case study, we consider the initial stages of resuming research at a large research university, using approaches from movement ecology and contact network epidemiology. First, we develop a dynamical pathogen model describing movement between home and work; we show that limiting social contact, via reduced people or reduced time in the workplace are fairly equivalent strategies to slow pathogen spread. Second, we develop a model based on spatial contact patterns within a specific office and lab building on campus; we show that restricting on-campus activities to labs (rather than labs and offices) could dramatically alter (modularize) contact network structure and thus, potentially reduce pathogen spread by providing a workplace mechanism to reduce contact. Here we argue that explicitly accounting for human movement and contact behavior in the workplace can provide additional strategies to slow pathogen spread that can be used in conjunction with ongoing public health efforts.</p>
Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation
<p>This excel file (DOI: <a href="https://doi.org/10.5281/zenodo.3755675">https://doi.org/10.5281/zenodo.3755675</a>) provides the collection of raw data used for developing the first integrative Quantitative Structure-Activity Relationship (QSAR) model using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD<sub>50</sub>) and ii) to profile the Mode of Action (MoA) of pesticides active substances in honey bees (<em>Apis mellifera</em>)<em>. </em>Chemical identifiers (e.g. SMILES, CAS n., InChI) and acute contact toxicity data (LD<sub>50</sub>) on honey bees were used to develop and validate i) a two-category QSAR model (toxic/non-toxic; n=411) (sensitivity =0.93), specificity =0.85), balanced accuracy =0.90), Matthews correlation coefficient MCC=0.78), and ii) a regression-based model (n=113) (R2=0.74; MAE=0.52). Similarly, current study proposes the first MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD<sub>50s</sub> data points from three different databases such as EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase. Such classification allows to further define MoAs and the target site of Plant Protection Products (PPPs) active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals.</p> <p>The full data collection and analysis of QSAR models, toxicity data (LD<sub>50</sub>) and Mode of Action (Moa) data are described in Carnesecchi et al., 2020 (DOI: doi.org/10.1016/j.scitotenv.2020.139243).</p> <p>This work was supported by the European Food Safety Authority (EFSA) [contract number: OC/EFSA/SCER/2018/01 and NP/EFSA/AFSCO/2016/02 (Edoardo Carnesecchi)].</p>
Material model calibration of Hertzian contact situations of a sphere and a plate with implemented pressure measurement film
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Osh4 Membrane Contact Site Model 2024 (Klauda Lab)
<p>This is a set of NAMD trajectory files and associated PSF files (final PDB files) for two membrane contact site models associated with our journal manuscript. </p>
The dataset used in "A max-margin model for predicting residue-base contacts in protein-RNA interactions"
<p>The dataset used in "A max-margin model for predicting residue-base contacts in protein-RNA interactions"</p>
Data for: Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model
<p>Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. </p>
High-quality Image (NIR and RGB) Dataset Synchronized With Contact Vital Sings Recordings and Clinical Data of Stratified Healthy Population. Algorithms and AI Models to Obtain a Set of Vital Signs Im
ClinicalTrials.gov study NCT05947721. IPD Sharing: NO. Countries: 1. Publications: 2.
System Dynamics Model for Acute Non-contact Lower Extremity Injuries Prediction
ClinicalTrials.gov study NCT05425303. IPD Sharing: Not stated. Countries: 1. Publications: 14.
Data from: Modeling multi-species and multi-mode contact networks: implications for persistence of bovine tuberculosis at the wildlife-livestock interface
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Lessons from movement ecology for the return to work: Modeling contacts and the spread of COVID-19
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Data for: Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model
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Machine learning based root stress calculation model for gears with progressive curved path of contact
<p><em>A machine learning based model for calculation of root stress in gears with a progressive curved path of contact is proposed. The prediction model is used as a surrogate model to the finite element analysis simulations, from which the data was collected. The results of the validation of all the methods are presented. Further validation was done with new simulations.</em></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.