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212 results for “Invasion modelling”
Supplementary files for "Tristetraprolin Affects Invasion-Associated Genes Expression and Cell Motility in Triple-Negative Breast Cancer Model"
<p>Track1 and Track3 - raw numerical data on cell tracking; Morphology-DXR treated - raw images of the cells, treated with DXR; Morphology ecTTP+WT - morphology of wild-type and TTP-overexpressing cells; RAW data qPCR - rew data of gene expression experiments</p>
Dataset of High-Resolution Micro-CT Imaging of Tumor Invasion and Metastasis in a Murine Esophageal Cancer PDX Model
<p>This dataset features high-resolution micro-CT imaging data capturing the progression of tumor invasion and metastasis in an orthotopic patient-derived xenograft (PDX) model of esophageal cancer. Using contrast-enhanced micro-CT, we visualized detailed patterns of tumor invasion, including budding, multicellular streaming, and expansive growth, across multiple abdominal organs such as the stomach, pancreas, liver, and spleen. The dataset includes two specimens, highlighting both the primary tumor site and extensive metastases throughout the abdominal cavity. Our imaging preserved the native tissue architecture, providing a unique three-dimensional view of tumor-host interactions. This collection offers valuable insights for researchers studying the dynamics of esophageal cancer invasion and metastasis. Detailed descriptions of the micro-CT scanning parameters, image analysis, and sample preparation are provided within the dataset archive.</p>
Potential distributional shifts in North America of allelopathic invasive plant species under climate change models
<p>Occurrence data for invaive species used in ecological niche modeling for predictive studies. These data are cleaned to removed data with duplicates, incomplete coordinates, unlikely coordinates (e.g., 0,0), or those lacking environmental data were removed using the scrubr v.0.1.1 package in R (Chamberlain, 2016). Points falling outside of the respective training region for each species were also removed. These data represent downloads from iDigBio and GBIF.</p>
A high-resolution finite element method (FEM) human head model for non-invasive brain stimulation
<p>High-resolution finite element method (FEM) model of a human head for non-invasive brain stimulation modeling using SimNIBS or other compatible software. The original head model (Ernie) was downloaded from the tutorial dataset of <a href="http://simnibs.org">www.simnibs.org</a> and further refined in grey matter and white matter regions.</p> <p>This supplementary dataset is released as part of the NeMo-TMS toolbox (<a href="https://github.com/OpitzLab/NeMo-TMS">https://github.com/OpitzLab/NeMo-TMS</a>). Please refer to the corresponding article for more information:</p> <p>Shirinpour, S., Hananeia, N., Rosado, J., Galanis, C., Vlachos, A., Jedlicka, P., Queisser, G., & Opitz, A. (2020). Multi-scale Modeling Toolbox for Single Neuron and Subcellular Activity under (repetitive) Transcranial Magnetic Stimulation. <em>BioRxiv</em>, 2020.09.23.310219. <a href="https://doi.org/10.1101/2020.09.23.310219">https://doi.org/10.1101/2020.09.23.310219</a></p>
Scripts to perform dynamic optimization of a model of invasive aspergillosis
<p>Matlab scripts to perform optimization and analyses of a dynamic optimization model of invasive aspergillosis:<br> "Dynamic optimization reveals alveolar epithelial cells as key mediators of host defense in invasive aspergillosis"<br> Jan Ewald, Flora Rivieccio, Lukáš Radosa, Stefan Schuster, Axel A. Brakhage, Christoph Kaleta<br> https://doi.org/10.1101/2021.05.12.443764</p> <p>File structure:</p> <p>--- Core optimization files ---<br> -- simulation.cpp --<br> Description of ODE model in 'C', used by solver to calculate the time course given the parameters, initial states and control variables</p> <p>-- simulation.mexa64 --<br> Compiled mex-File from simulation.cpp used as interface by MATLAB to 'C' files.</p> <p>-- optFun.m --<br> Definition of the objective function. Uses a control vector 'u' to calculate the, objective function value F, the time course of state variables 'x' and gradients used by the solver to determine the optimal control of 'u'.</p> <p>-- single_opt.m --<br> Function which performs an optimization run for a given parameter set and a randomized initial solution u0. Optimization can be conducted by SNOPT or in conjunction with IPOPT. Both solver are not provided here (proprietary).</p> <p><br> --- Additional files ---<br> -- solve_with_ipopt.m --<br> Configuration file and interface to call the solver IPOPT.</p> <p>-- lognorm_mu.m --<br> Function to provide log-normal distributed samples given the standard deviation, reference value (mode of distribution) and number of samples which should be calculated.</p> <p>-- mice_param_alv.m --<br> Reference parameter set of the model for mice as host organism and cell numbers normalized per alveolus.</p> <p>-- parameter_model.m --<br> Definition of parameter container to handle interface to optFun-call by solvers which do not allow parameter in function call.</p> <p>--- Optimization Runs ---<br> -- rand_params.m --<br> Performs optimization runs for multiple randomized parameter sets and several scenarios to determine parameter sensitivity.</p> <p>-- doseresponse.m --<br> Performs optimization runs for a sequnce of initial conidial dosages to determine the dose response of the system.</p> <p>--- Scripts for post-analysis ---<br> -- rand_analysis.m --<br> Loads the data created by the optimization runs (rand_params.m) and performs parameter sensitivity analysis as well as produces plots for analysis related to parameter influence.</p> <p>-- doseanalysis.m --<br> Loads the data created by the optimization runs (doseresponse.m ) and produces dose response curves as well as additional plots and analyses.</p> <p>--- Optimization output files ---<br> -- rand_param_out/ --<br> Folder contains optimization results as MATLAB session-file for randomized parameters used for parameter sensitivity analysis.</p> <p>-- dose_out/ --<br> Folder contains optimization results as MATLAB session-file for dose response curve calculation.</p>
Predictive modelling of brain metastasis risk and non-invasive biomarker detection using DNA methylation signatures
<p>Methylated cell-free DNA was sequenced for 123 BM plasma and compared to plasma methylomes from 107 gliomas, central nervous system (CNS) lymphomas (CNSL), and non-CNS tumor controls. Plasma methylome-based classifiers of BM from other entities were built in fifty 80% discovery set iterations of 92/123 BM samples. External publicly-available tissue methylation data on 442 LUAD, 85 BM, and 146 glioma/CNSL/control samples were acquired for validation and the remaining 31/123 BM plasma samples were used for additional validation.</p>
Supplementary movies for the manuscript "Modelling of Tissue Invasion in Epithelial Monolayers"
<p>Supplementary movies for the manuscript "Modelling of Tissue Invasion in Epithelial Monolayers"</p>
Evaluating niche changes during invasion with seasonal models in Capsella bursa‐pastoris
<p><span>Premise</span></p> <p>Researchers often use ecological niche models to predict where species might establish and persist under future or novel climate conditions. However, these predictive methods assume species have stable niches across time and space. Furthermore, ignoring the time of occurrence data can obscure important information about species reproduction and ultimately fitness. Here, we assess and compare ecological niche models generated from full-year averages to seasonal models </p> <p><span>Methods</span></p> <p>In this study, we generate full-year and monthly ecological niche models for <em>Capsella bursa-pastoris</em> in Europe and North America to see if we can detect changes in the seasonal niche of the species after long-distance dispersal. </p> <p><span>Key Results</span></p> <p>We find full-year ecological niche models have low transferability across continents and there are continental differences in the climate conditions that influence the distribution of <em>C. bursa-pastoris</em>. Monthly models have greater predictive accuracy than full-year models in cooler seasons but no monthly models are able to predict North American summer occurrences very well.</p> <p><span>Conclusions</span></p> <p><span></span></p> <p>The relative predictive ability of European monthly models compared to North American monthly models suggests a change in the seasonal timing between the native range to the non-native range. These results highlight the utility of ecological niche models at finer temporal scales in predicting species distributions and unmasking subtle patterns of evolution.</p>
Evaluation of an Artificial Intelligence Model for the Prediction of Human Blastocyst Ploidy Without Invasive Procedures
ClinicalTrials.gov study NCT06762704. IPD Sharing: YES. Countries: 1. Publications: 23.
Deep Learning Radiomics Model for Predicting Post-cystectomy Outcome in Muscle Invasive Bladder Cancer
ClinicalTrials.gov study NCT06092450. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluating niche changes during invasion with seasonal models in Capsella bursa‐pastoris
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Forecasting suppression of invasive Sea Lamprey in Lake Superior: data and code for Bayesian forecast model
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Data from: Powerful yet challenging: Mechanistic Niche Models for predicting invasive species potential distribution under climate change
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All simulation results, figures and code regarding the manuscript: Calibrating models of cancer invasion: parameter estimation using Approximate Bayesian Computation and gradient matching
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Data from: Combining geostatistical and biotic interactions modelling to predict amphibian refuges under crayfish invasion across dendritic stream networks
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Predictor complexity and feature selection affect Maxent model transferability: evidence from global freshwater invasive species
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Data from: Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma
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A multi-state occupancy model to non-invasively monitor visible signs of wildlife health with camera traps that accounts for image quality
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Integrating univariate niche dynamics in species distribution models: a step forward for marine research on biological invasions
<p>Aim The development of approaches to predict the distribution and potential expansion of invasive species is still an open challenge. Here our goal is to improve the modelling procedure for marine invaders by coupling Species Distribution Models (SDMs) with an analysis of their univariate niche dynamics. In particular, we tested for the first time whether choosing model predictors among the stable niche dimensions was effective in improving predictions of invasive species expansion.<br> Location Mediterranean Sea<br> Taxon Dusky spinefoot, Siganus luridus.<br> Methods We analysed the univariate niche dynamics for S. luridus across its native and invaded ranges, by applying a standardized framework that allowed the identification of cases of niche stability or shift. We compared inter-range transferability of SDMs fitted with different combinations of labile or stable predictors. Finally, we evaluated interactions in SDM settings (calibration area, model technique and predictors set) on models' predictive ability, using independent data from the most recent phase of invasion.<br> Results We detected a pattern of niche stability for several variables, especially salinity and bathymetry, which positively influenced model inter-ranges transferability: when the models calibrated in the native range include only stable niche axes, predictive ability is improved. We also identified a shift toward lower surface temperatures in the introduced range, which were almost never experienced by the species before invasion. The model calibrated within the combined ranges was the most ecologically congruent. Also, models calibrated in the invaded range allowed a correct prediction of range expansion, with the predicted suitable areas only slightly underestimated.<br> Main conclusions We provide the first evidence that using conserved predictors in SDMs improves inter-range projections of expanding invasive species. Variable selection, calibration area and modelling technique all matter when modelling invasive species, with important interaction effects. We provide guidelines on how to improve SDMs applications in biological invasion research.</p>
Dataset related to article "Optimization of a Luciferase-Expressing Non-Invasive Intrapleural Model of Malignant Mesothelioma in Immunocompetent Mice"
<p>This record contains data related to article "Optimization of a Luciferase-Expressing Non-Invasive Intrapleural Model of Malignant Mesothelioma in Immunocompetent Mice"</p> <p>Malignant Pleural Mesothelioma (MPM) is an aggressive tumor of the pleural lining that is usually identified at advanced stages and resistant to current therapies. Appropriate pre-clinical mouse tumor models are of pivotal importance to study its biology. Usually, tumor cells have been injected intraperitoneally or subcutaneously. Using three available murine mesothelioma cell lines with different histotypes (sarcomatoid, biphasic, epithelioid), we have set up a simplified model of in vivo growth orthotopically by inoculating tumor cells directly in the thorax with a minimally invasive procedure. Mesothelioma tumors grew along the pleura and spread on the superficial areas of the lungs, but no masses were found outside the thoracic cavity. As observed in human MPM, tumors were highly infiltrated by macrophages and T cells. The luciferase-expressing cells can be visualized in vivo by bioluminescent optical imaging to precisely quantify tumor growth over time. Notably, the bioluminescence signal detected in vivo correctly matched the tumor burden quantified with classical histology. In contrast, the subcutaneous or intraperitoneal growth of these mesothelioma cells was considered either non-representative of the human disease or unreliable to precisely quantify tumor load. Our non-invasive in vivo model of mesothelioma is simple and reproducible, and it reliably recapitulates the human disease.</p>
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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.