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75 results for “spatiotemporal model”
DAPI images, molecules and segmentation boundaries for: A Spatiotemporal Atlas of Mouse Gastrulation and Early Organogenesis to Explore Axial Patterning and Project In Vitro Models onto In Vivo Space
<div> </div> <p><strong>Data Description</strong></p> <ol> <li><strong>Stitched & rotated DAPI images</strong> - tiff file format filename indicates sample and optical z-slice position, i.e. embryo3_z5.tif is the DAPI image for embryo 3 in optical z-slice 5. Also provided in PNG format.</li> <li><strong>Detected molecules and cell segmentation in MoleculeExperiment objects</strong> - RDS files to read data using the MoleculeExperiment format in R/Bioconductor. Filename embryo3_z5.Rds indicates MoleculeExperiment RDS file for embryo 3 in optical z-slice 5. Coordinates are provided in microns. Note that z-slices 2 and 5 are only provided for embryos 1,2,3 as they were originally provided in Lohoff et al, Nature Biotechnology, 2023.</li> <li><strong>Pixels-to-microns conversion</strong> - pixelSize.R Simple R script/text to indicate the size of each pixel in the DAPI images, this is to align the coordinate systems between the DAPI images and molecules.<br><br> <div> <h4>Project Abstract</h4> </div> <p>At the onset of murine gastrulation, pluripotent epiblast cells migrate through the primitive streak, generating mesodermal and endodermal precursors, while the ectoderm arises from the remaining epiblast. Together, these germ layers establish the body plan, defining major body axes and initiating organogenesis. Although comprehensive single cell transcriptional atlases of dissociated mouse embryos across embryonic stages have provided valuable insights during gastrulation, the spatial context for cell differentiation and tissue patterning remain underexplored. In this study, we employed spatial transcriptomics to measure gene expression in mouse embryos at E6.5 and E7.5 and integrated these datasets with previously published E8.5 spatial transcriptomics and a scRNA-seq atlas spanning E6.5 to E9.5. This approach resulted in a comprehensive spatiotemporal atlas, comprising over 150,000 cells with 88 refined cell type annotations as well as genome-wide transcriptional imputation during mouse gastrulation and early organogenesis. The atlas facilitates exploration of gene expression dynamics along anterior-posterior and dorsal-ventral axes at cell type, tissue, and organismal scales, revealing insights into mesodermal fate decisions within the primitive streak. Moreover, we developed a bioinformatics pipeline to project additional scRNA-seq datasets into a spatiotemporal framework and demonstrate its utility by analysing cardiovascular models of gastrulation3. To maximise impact, the atlas is publicly accessible via a user-friendly web portal empowering the wider developmental and stem cell biology communities to explore mechanisms of early mouse development in a spatiotemporal context.</p> </li> </ol>
Construction of an evapotranspiration model and analysis of spatiotemporal variation in Xilin River Basin, China
<p>The publication for this dataset will be published in plos one journal, and can be accessed here:https://doi.org/10.1371/journal.pone.0256981. Please cite this when using the dataset.</p>
Making virtual species less virtual by reverse engineering of spatiotemporal ecological models v3
<p>The most up-to-date version of the archive that contains all the necessary data to perform analyses supporting the paper <em>Making virtual species less virtual by reverse engineering of spatiotemporal ecological models </em>(<a href="https://doi.org/10.1111/2041-210X.14176">https://doi.org/10.1111/2041-210X.14176</a>) .</p> <p>The research was supported by the National Science Centre, Poland (grant no. 2018/29/B/NZ8/00066) and Poznań Supercomputing and Networking Centre (grant no. 403). </p> <p> </p>
Open-population SCR model to estimate spatiotemporal variation in individual birth locations, detection rates, and survival
<p>This is an open population SCR model developed by R. Chandler and K. Engebretsen. The full model incorporates 4 spatial covariates in birth location density submodel, 3 location-specific, temporal covariates in the detection submodel, and 4 spatial covariates in the survival submodel. </p> <p>Formatted data is provided for the 2015 and 2016 fawning season in south Florida and the model can be fit using the script fitFawnModel.R.</p>
Synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution
<p>This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NO<sub>x</sub> emissions at a fine spatiotemporal resolution in the summer of 2017 over the contiguous United States (CONUS). We leveraged the partial convolutional neural network (PCNN) and the deep neural network (DNN) to impute gaps in the OMI tropospheric NO<sub>2</sub> column and estimate the daily complete surface NO<sub>2</sub> map at a spatial resolution of 10 km × 10 km, showing high capability with a strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). We then used the Community Multi-scale Air Quality (CMAQ) model at 12 km grid spacing to conduct an inversion of NO<sub>x</sub> emissions that allowed us to promote a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion suggested 3.21 ± 3.34 times higher NO<sub>x</sub> emissions over CONUS, significantly mitigating the underestimation of surface NO<sub>2</sub> concentrations with the prior emissions. The results displayed the primary benefits of incorporating DL-estimated daily complete surface NO<sub>2</sub> map, which in turn greatly reduced bias (-1.53 ppb to 0.26 ppb) and enhanced daily variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) over the CONUS. </p>
Open-population SCR model to estimate spatiotemporal variation in individual birth locations, detection rates, and survival
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The impact of varying spatiotemporal scales on different joint species distribution models: A case study of pelagic fish species in the northwest Pacific Ocean
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Data from: Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture-recapture movement model
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Data from: Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma
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Data from: Delineating important killer whale foraging areas using a spatiotemporal logistic model
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Modeling Glioma Oncostreams In Vitro: Spatiotemporal Dynamics of their Formation, Stability, and Disassembly
<p>Movie 1 of 16 is a supplementary material for the manuscript 'Modeling Glioma Oncostreams In Vitro: Spatiotemporal Dynamics of their Formation, Stability, and Disassembly.' It visually demonstrates the effect of various treatments on the formation and dynamics of oncostreams in high-grade glioma cells. The time-lapse videos provide insightful observations into how various pharmacological drugs influence the initial formation and structural dynamics of oncostreams."</p> <p>It aims to enhance the understanding of the foundational stages of oncostream formation in glioma cells and their response to various pharmacological treatments, supporting the findings discussed in the manuscript.</p> <p>Individual Movie Descriptions:</p> <p><strong>Movie 1: Low Density Oncostream Dynamics: </strong>Demonstrates the formation and dynamics of oncostreams in GFP+ NPA glioma cells at low seeding density (1 × 10^5 cells), over 24 hours using time-lapse confocal imaging.</p> <p><strong>Movie 2: High Density Oncostream Dynamics:</strong> Explores the effect of high cell seeding density (2 × 10^5 cells) on the formation and behavior of oncostreams in GFP+ NPA glioma cells, analyzed over 24 hours.</p> <p><strong>Movie 3: Spatiotemporal Progression of Oncostreams:</strong> Showcases the sequential self-formation and developmental stages of oncostreams over a 24-hour period.</p> <p><strong>Movie 4: Collagenase Impact on Oncostreams:</strong> Description: Illustrates the disassembly of aggressive and malignant oncostreams with 15 U/ml collagenase treatment over 15 hours.</p> <p><strong>Movie 5: TC-I-15 Inhibition of Oncostream Formation:</strong> Details the impact of TC-I-15, an integrin antagonist, on adhesion and oncostream formation in glioma cells, recorded over 20 hours.</p> <p><strong>Movie 6: Cytochalasin D Disruption of Oncostreams:</strong> Captures the effect of Cytochalasin D on oncostream formation by inhibiting actin polymerization, observed over a 1-hour period with rapid imaging intervals.</p> <p><strong>Movie 7: Myosin II Inhibition in Oncostreams with p-nitro Blebbistatin: </strong>Presents the influence of p-nitro Blebbistatin on oncostream formation by inhibiting myosin II, documented over 1 hour with dynamic cellular responses.</p> <p><strong>Movie 8: Control - Oncostream Formation without BAPTA-AM:</strong> Demonstrates oncostream formation and dynamics without BAPTA-AM treatment, monitored over 16 hours.</p> <p><strong>Movie 9: BAPTA-AM (Calcium Modulation) Impact on Oncostream Formation:</strong> Reveals the effects of BAPTA-AM treatment on oncostream dynamics, observed over a 16-hour period.</p> <p><strong>Movie 10: Glutamate Influence on Oncostreams:</strong> Shows the effects of glutamate treatment on oncostream formation and dynamics, monitored over 16 hours.</p> <p><strong>Movie 11: Histamine Impact on Oncostream Dynamics:</strong> Demonstrates the influence of histamine on the formation and behavior of oncostreams, captured over a 16-hour period.</p> <p><strong>Movie 12: Oncostream Formation on Non-Laminin Coated Surfaces:</strong> Illustrates the compromised organization of oncostreams on poly-D-lysine coated dishes without laminin, observed over 45 hours.</p> <p><strong>Movie 13: 4-HAP Treatment Effect on Oncostreams:</strong> Shows the impact of 4-HAP treatment on the structure of oncostreams, monitored over 20 hours.</p> <p><strong>Movie 14: Rho-Activator I Untreated Control:</strong> Presents the formation and dynamics of oncostreams without Rho-Activator I treatment, recorded over 16 hours.</p> <p><strong>Movie 15: Rho-Activator I Influence on Oncostreams Dynamics:</strong> Details the effects of Rho-Activator I on oncostream formation and dynamics, observed over a 16-hour period.</p> <p><strong>Movie 16: Rho-Inhibitor Effect on Oncostreams Dynamics:</strong> Demonstrates the impact of Rho-Inhibitor on the formation and behavior of oncostreams, monitored over 16 hours.</p>
Data and results from: "De novo spatiotemporal modelling of cell-type signatures in the developmental human heart"
<p>This repository contains data used and results produced in the manuscript:</p> <p>Sergio Marco Salas, Xiao Yuan, Christer Sylven, Mats Nilsson, Carolina Wählby, Gabriele Partel. "De novo spatiotemporal modelling of cell-type signatures in the developmental human heart"</p> <p>In situ sequencing [1] and scRNA-seq data [2] generated by Asp et al. [3] were downloaded and redistributed under CC BY 4.0 license.</p> <p>[1] Wu, Chenglin; Qian, Xiaoyan; Nilsson, Mats (2019): ISS data in "A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart". figshare. Dataset. https://doi.org/10.6084/m9.figshare.10058048.v1 </p> <p>[2] Asp, Michaela (2021), “Developmental heart - filtered and unfiltered count matrices and meta tables”, Mendeley Data, V2, doi: 10.17632/mbvhhf8m62.2</p> <p>[3] Asp, M., Giacomello, S., Larsson, L., Wu, C., Fürth, D., Qian, X., ... & Lundeberg, J. (2019). A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. <em>Cell</em>, <em>179</em>(7), 1647-1660.</p>
Spatiotemporal monitoring of the rare Northern dragonhead, Dracocephalum ruyschiana (Lamiaceae): SNP genotyping and environmental niche modelling herbarium specimens
<p><strong>Aim: </strong>We have studied spatiotemporal genetic change in the Northern dragonhead, a plant species that has experienced a drastic population decline and habitat loss in Europe. We add a temporal perspective to the monitoring of dragonhead in Norway by genotyping herbarium specimens up to 200 years old. We also assess whether dragonhead has achieved its potential distribution in Norway. Location: Europe (mainly Norway)</p> <p><strong>Methods:</strong> We have applied a microfluidic array consisting of 96 SNP markers on 130 herbarium specimens collected from 1820 to 2008, mainly from Norway (83) but also beyond (47). We have compared our new genotype data with existing data from modern samples. We have modelled the species' environmental niche and potential distribution in Norway using sample metadata and observational records.</p> <p><strong>Results: </strong>The SNP array successfully genotyped all included herbarium specimens. The captured genetic diversity was negatively correlated with distance from Norway. The historical-modern comparison revealed similar genetic structure and diversity across space and limited genetic change through time in Norway. The ENM suggests that dragonhead is anchored in warmer and drier habitats.</p> <p><strong>Main conclusions: </strong>With appropriate design procedures, the SNP array technology is promising for genotyping old herbarium specimens. We found no signs of any regional bottleneck. The regional areas in Norway have remained genetically divergent, however, both from each other and more so from populations outside of Norway, rendering continued protection of the species in Norway relevant. The ENM suggests that dragonhead has not fully achieved its potential distribution in Norway.</p>
Pretrained Model of Channel Mixer Layer for Spatiotemporal Predictive Learning
<p>Model supplementary material for <em><strong>Space Weather</strong></em> journal paper with the title: Channel Mixer Layer: Multimodal Fusion Towards Machine Reasoning for Spatiotemporal Predictive Learning of Ionospheric Total Electron Content</p>
Dataset for downscaling, used in downscaled Spatiotemporal Precipitation Model Based on a Transformer Attention Mechanism
<p>In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct the neural network in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. This tailored module is instrumental in enhancing the model's ability to generate outcomes that are not only more true-to-life but also more consistent with physical laws. It adeptly mirrors the temporal and spatial fluctuations in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of four months. The datasets vary in their spatial resolutions, with one featuring a 50-kilometer resolution and the other a 12-kilometer resolution, both sourced from the Weather Research and Forecasting (WRF) Model.</p>
Data from: Spatiotemporal diversification of the true frogs (Genus Rana): a historical framework for a widely studied group of model organisms
True frogs of the genus Rana are widely used as model organisms in studies of development, genetics, physiology, ecology, behavior, and evolution. Comparative studies among the more than 100 species of Rana rely on an understanding of the evolutionary history and patterns of diversification of the group. We estimate a well-resolved, time-calibrated phylogeny from sequences of six nuclear and three mitochondrial loci sampled from most species of Rana, and use that phylogeny to clarify the group's diversification and global biogeography. Our analyses consistently support an "Out of Asia" pattern with two independent dispersals of Rana from East Asia to North America via Beringian land bridges. The more species-rich lineage of New World Rana appears to have experienced a rapid radiation following its colonization of the New World, especially with its expansion into montane and tropical areas of Mexico, Central America, and South America. In contrast, Old World Rana exhibit different trajectories of diversification; diversification in the Old World began very slowly and later underwent a distinct increase in speciation rate around 29–18 Ma. Net diversification is associated with environmental changes and especially intensive tectonic movements along the Asian margin from the Oligocene to early Miocene. Our phylogeny further suggests that previous classifications were misled by morphological homoplasy and plesiomorphic color patterns, as well as a reliance primarily on mitochondrial genes. We provide a phylogenetic taxonomy based on analyses of multiple nuclear and mitochondrial gene loci.
Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change (Data)
<p>This repository contains the data necessary to reproduce the study developed in Legrand et al. (2023) "Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change"</p>
Data and codes for Landslide hazard spatiotemporal modelling: a unified and data-driven framework
<p>Data and codes for Landslide hazard spatiotemporal modelling: a unified and data-driven framework</p>
A Markov-switching spatiotemporal ARCH model (Datasets)
<p>The dataset used in the original article titled: A Markov-switching spatiotemporal ARCH model. It contains the prices of 26 Asian stock indices and 2 US stock indices spanning from 4 January 2011 to 30 December 2020.</p>
Spatiotemporal monitoring of the rare Northern dragonhead, Dracocephalum ruyschiana (Lamiaceae): SNP genotyping and environmental niche modelling herbarium specimens
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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.