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29 results for “multiscale analysis”
Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis - Image data
<p>The dataset contains raw imaging data from the work:</p> <p>"Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis"</p> <p>The dataset is organized as the following: the "FigureX_" or SupplementaryFigure_X" suffix in the filename refers to the figure in the paper in which the raw data is analyzed and/or visualized. The data is "raw", i.e. not processed. However, in many cases, maximum projections of the original 3D image stacks have been uploaded due to size limitations. The total size of the image stacks approaches 0.5TB. To access the full 3D image stacks please contact the corresponding author (Francesco Pampaloni, fpampalo@bio.uni-frankfurt.de).</p> <p><strong>Authors</strong></p> <p>Lotta Hof<sup>1</sup>*, Till Moreth<sup>1</sup>*, Michael Koch<sup>1</sup>, Tim Liebisch<sup>2</sup>, Marina Kurtz<sup>3</sup>, Julia Tarnick<sup>4</sup>, Susanna M. Lissek<sup>5</sup>, Monique M.A. Verstegen<sup>6</sup>, Luc J.W. van der Laan<sup>6</sup>, Meritxell Huch<sup>7</sup>, Franziska Matthäus<sup>2</sup>, Ernst H.K. Stelzer<sup>1</sup>, Francesco Pampaloni<sup>1§</sup></p> <p><sup>1</sup>Physical Biology Group, Buchmann Institute for Molecular Life Sciences (BMLS), Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>2</sup>Faculty of Biological Sciences, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>3</sup>Department of Physics, Goethe-Universität Frankfurt am Main, Frankfurt am Main, Germany</p> <p><sup>4</sup>Deanery of Biomedical Science, University of Edinburgh, Edinburgh, United Kingdom</p> <p><sup>5</sup>Experimental Medicine and Therapy Research, University of Regensburg, Regensburg, Germany</p> <p><sup>6</sup>Department of Surgery, Erasmus MC – University Medical Center, Rotterdam, The Netherlands</p> <p><sup>7</sup>The Wellcome Trust/CRUK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom. Present address: Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany</p> <p>*contributed equally</p> <p><sup>§</sup>corresponding author: fpampalo@bio.uni-frankfurt.de</p> <p><strong>Abstract</strong></p> <p><em>Background</em></p> <p>Organoids are morphologically heterogeneous three-dimensional cell culture systems and serve as an ideal model for understanding the principles of collective cell behaviour in mammalian organs during development, homeostasis, regeneration and pathogenesis. To investigate the underlying cell organisation principles of organoids, we imaged hundreds of pancreas and cholangio carcinoma organoids in parallel using light sheet and bright field microscopy for up to seven days.</p> <p><em>Results</em></p> <p>We quantified organoid behaviour at single-cell (microscale), individual-organoid (mesoscale), and entire-culture (macroscale) levels. At single-cell resolution, we monitored formation, monolayer polarisation and degeneration, and identified diverse behaviours, including lumen expansion and decline (size oscillation), migration, rotation and multi-organoid fusion. Detailed individual organoid quantifications lead to a mechanical 3D agent-based model. A derived scaling law and simulations support the hypotheses that size oscillations depend on organoid properties and cell division dynamics, which is confirmed by bright field microscopy analysis of entire cultures.</p> <p><em>Conclusion</em></p> <p>Our multiscale analysis provides a systematic picture of the diversity of cell organisation in organoids by identifying and quantifying the core regulatory principles of organoid morphogenesis.</p>
Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization - Supporting Dataset
<p>This dataset contains the files used to substantiate the outcomes of the publication "<em>Multiscale analysis of triglycerides with X-ray scattering: Implementing a shape-dependent model for CNP characterization</em> <em>" </em></p> <p>The dataset includes:</p> <ul> <li>X-ray scattering profiles - in absolute units</li> <li>Images used to measure CNP distributions</li> </ul> <p>Relevant abbreviations: </p> <ul> <li>SSS - Tristearin</li> <li>OOO - Triolein</li> <li>FHRO - Fully Hydrogenated Rapeseed Oil</li> <li>HOSO - High Oleic Sunflower Oil</li> </ul>
Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales
1. Multiscale codependence analysis (MCA) quantifies the joint spatial distribution of a pair of variables in order to provide a spatially-explicit assessment of their relationships to one another. For the sake of simplicity, the original definition of MCA only considered a single response variable (e.g. a single species). However, that definition would limit the application of MCA when many response variables are studied jointly, for example when one wants to study the effect of the environment on the spatial organisation of a multi-species community in an explicit manner. 2. In the present paper, we generalize MCA to multiple response variables. We conducted a simulation study to assess the statistical properties (i.e. type I error rate and statistical power) of multivariate MCA (mMCA) and found that it had honest type I error rate and sufficient statistical power for practical purposes, even with modest sample sizes. We also exemplified mMCA by applying it to two ecological data sets. 3. The simulation study confirmed the adequacy of mMCA from a statistical standpoint: it has honest type I error rates and sufficient power to be useful in practice. Using mMCA, we were able to detect variation in fish community structure along the Doubs River (in France), which was associated with large spatial structures in the variation of physical and chemical variables related to water quality. Also, mMCA usefully described the spatial variation of an Oribatid mite community structure associated with a gradient of water content superimposed on various smaller-scale spatial features associated with vegetation cover in the peat blanket surrounding Lac Geai (in Québec, Canada). 4. In addition to demonstrating the soundness of mMCA in theory and practice, we further discuss the strengths and assumptions of mMCA and describe other potential scenarios where it would be helpful to biologists interested in assessing influence of environmental conditions on community structure in a spatially-explicit way.
Mesolithic Plant Processing Unveiled: Multiscale Use-Wear Analysis of the Ground Stone Tools from Vlasac (Serbia)
<p>This repository contains the raw data and code to reproduce the analyses presented in the paper "Mesolithic Plant Processing Unveiled: Multiscale Use-Wear Analysis of the Ground Stone Tools from Vlasac (Serbia)" by Zupancich et al.</p> <p>The repositiory includes:</p> <ul> <li>CSV files containing the raw data of:</li> <li>3D surface measurement of experimental and archaeological ground stone tools</li> <li>Use wear spatial distribution</li> <li>Ground stone tools techno-morphological data</li> <li>HTML files of the code utilised to perform the analyses</li> </ul>
Pole Reversals and Delta Q Dynamics: A MEFI-Based Multiscale Analysis
<p>This study examines the phenomena of geomagnetic pole reversals through the Modified Einstein</p> <p>Field Interaction (MEFI) framework. By exploring Delta Q stabilization, resonance deviations, and</p> <p>their global impacts, the study demonstrates MEFI's potential as a universal model. This journal</p> <p>entry provides visual evidence and simulations to highlight the quantum, geophysical, and societal</p> <p>effects of pole reversals, underscoring MEFI?s predictive power.</p>
Retrieving 2D laterally varying structures from multi-station surface wave dispersion curves using multiscale window analysis
<p>Here are the waveform data used in the Geophysical Journal International paper entitled "Retrieving 2D laterally varying structures from multi-station surface wave dispersion curves using multiscale window analysis". The dataset is used for the reader who wants to reproduce the result in the paper.</p>
Gramtools enables multiscale variation analysis with genome graphs
<p>Singularity container and data files supporting the article.</p> <p>Instructions to use these data are at: https://github.com/iqbal-lab-org/paper_gramtools_nesting </p>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Data from: Bringing multivariate support to multiscale codependence analysis: assessing the drivers of community structure across spatial scales
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A multiscale analysis of factors influencing Blackpoll Warbler occupancy and abundance during the non-breeding season in eastern Colombia
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Multiscale analysis of canopy arthropod diversity in a volcanically fragmented landscape
<p><strong>Dataset and code associated with the article "Multiscale analysis of canopy arthropod diversity in a volcanically fragmented landscape" (Tielens et al 2019, Ecosphere).</strong></p> <p><strong>Article abstract:</strong></p> <p>Habitat fragmentation resulting in habitat loss and increased isolation is a dominant driver of global species declines. Habitat isolation and connectivity vary across scales, and understanding how con- nectivity affects biodiversity can be challenging because the relevant scale depends on the taxa involved. A multiscale analysis can provide insight in biodiversity patterns across spatial scale when information on dispersal ability is not available, in particular for community-level studies focusing on multiple taxa. In this study, we examine the relationship between arthropod diversity, patch area, and connectivity using a mul- tiscale approach. We make use of a natural experiment on Hawai'i Island, where historic volcanic activity has transformed contiguous native forests to lava matrix and discrete forest patches. This landscape of patches has persisted for 150 yr, and we selected 10,000 ha consisting of 863 patches to analyze landscape connectivity using a graph theory approach. We collected arthropod samples from Metrosideros polymor- pha tree canopies in 34 forest patches during multiple years. We analyzed the relationship of arthropod diversity with area, as well as with connectivity across increasing scales, or dispersal threshold distances. In contrast to well-established ecological theory as well as prior work on birds and fungi in this system, we did not find support for a canonical species–area relationship. Next, we calculated connectivity across spa- tial scales and found lower Shannon diversity with higher connectivity at small scales, but no effect at increased dispersal threshold distances. We examined the landscape structure and found all habitat patches connected into three subnetworks at a 350 m threshold distance. All patches were connected at 700 m threshold distance, indicating structural dispersal limitation only at small scales. Our findings sug- gest that canopy arthropods are not dispersal limited at scales shown to impact both soil fungi and birds in this system. Instead, Hawaiian canopy arthropods may perceive the landscape as a connected area where discrete forest patches and the early-successional matrix contribute resources that vary spatially with regard to habitat quality. We argue for the utility of multiscale approaches, and the importance of examin- ing maintenance of biodiversity in fragmented landscapes that persist for hundreds of years.</p>
Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer's Disease
<p>MEA recordings from wild-type and 5xFAD mice retinas used for the analyses in the manuscript "Multiscale Entropy Analysis of Retinal Signals Reveals Reduced Complexity in a Mouse Model of Alzheimer's Disease".</p>
Data from: Patterns and predictors of β-diversity in the fragmented Brazilian Atlantic forest: a multiscale analysis of forest specialist and generalist birds
1. Biodiversity maintenance in human-altered landscapes (HALs) depends on the species turnover among localities, but the patterns and determinants of β-diversity in HALs are poorly known. In fact, declines, increases, and neutral shifts in β-diversity have all been documented, depending on the landscape, ecological group and spatial scale of analysis. 2. We shed some light on this controversy by assessing the patterns and predictors of bird β-diversity across multiple spatial scales considering forest specialist and habitat generalist bird assemblages. 3. We surveyed birds from 144 point counts in 36 different forest sites across two landscapes with different amount of forest cover in the Brazilian Atlantic forest. We analysed β-diversity among points, among sites, and between landscapes with multiplicative diversity partitioning of Hill numbers. We tested whether β-diversity among points was related to within-site variations in vegetation structure, and if β-diversity among sites was related to site location and/or to differences among sites in vegetation structure and landscape composition (i.e. percent forest and pasture cover surrounding each site). 4. β-diversity between landscapes was lower than among sites and among points in both bird assemblages. In forest specialist birds, the landscape with less forest cover showed the highest β-diversity among sites (bird differentiation among sites), but generalist birds showed the opposite pattern. At the local scale, however, the less forested landscape showed the lowest β-diversity among points (bird homogenisation within sites), independently of the bird assemblage. β-diversity among points was weakly related to vegetation structure, but higher β-diversity values were recorded among sites that were more isolated from each other, and among sites with higher differences in landscape composition, particularly in the less forested landscape. 5. Our findings indicate that patterns of bird β-diversity vary across scales and are strongly related to landscape composition. Bird assemblages are shaped by both environmental filtering and dispersal limitation, particularly in less forested landscapes. Conservation and management strategies should therefore prevent deforestation in this biodiversity hotspot.
Multiscale analysis of canopy arthropod diversity in a volcanically fragmented landscape
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Data from: Patterns and predictors of β-diversity in the fragmented Brazilian Atlantic forest: a multiscale analysis of forest specialist and generalist birds
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SynopSet: Multiscale Visual Abstraction Set for Explanatory Analysis of DNA-Nanotechnology Simulations
<p>Dataset for the paper below:</p> <p> </p> <p>SynopSet: Multiscale Visual Abstraction Set for Explanatory Analysis of DNA Nanotechnology Simulations</p>
Multiscale analysis of a regenerative therapy for treatment of volumetric muscle loss injury
GEO Series GSE114799. Rattus norvegicus. 138 samples. Type: Expression profiling by high throughput sequencing.
Multiscale Analysis of Extracellular Matrix Remodeling in the Failing Heart
GEO Series GSE155495. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Sex disparities in influenza: a multiscale network analysis (Female ferrets)
GEO Series GSE168511. Mustela putorius furo. 116 samples. Type: Expression profiling by high throughput sequencing.
Sex disparities in influenza: a multiscale network analysis (Male ferrets)
GEO Series GSE168117. Mustela putorius furo. 169 samples. Type: Expression profiling by high throughput sequencing.
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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.