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207 results for “spatial response”
Data from: Grizzly bear response to fine spatial and temporal scale spring snow cover in Western Alberta
Snow dynamics influence seasonal behaviors of wildlife, such as denning patterns and habitat selection related to the availability of food resources. Under a changing climate, characteristics of the temporal and spatial patterns of snow are predicted to change, and as a result, there is a need to better understand how species interact with snow dynamics. This study examines grizzly bear (Ursus arctos) spring habitat selection and use across western Alberta, Canada. Made possible by newly available fine-scale snow cover data, this research tests a hypothesis that grizzly bears select for locations with less snow cover and areas where snow melts sooner during spring (den emergence to May 31st). Using Integrated Step Selection Analysis, a series of models were built to examine whether snow cover information such as fractional snow covered area and date of snow melt improved models constructed based on previous knowledge of grizzly bear selection during the spring. Comparing four different models fit to 62 individual bear-years, we found that the inclusion of fractional snow covered area improved model fit 60% of the time based on Akaike Information Criterion tallies. Probability of use was then used to evaluate grizzly bear habitat use in response to snow and environmental attributes, including fractional snow covered area, date since snow melt, elevation, and distance to road. Results indicate grizzly bears select for lower elevation, snow-free locations during spring, which has important implications for management of threatened grizzly bear populations in consideration of changing climatic conditions. This study is an example of how fine spatial and temporal scale remote sensing data can be used to improve our understanding of wildlife habitat selection and use in relation to key environmental attributes.
Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks
<p>Abstract</p> <p>Catchment water storage capacity (CWSC) links the atmosphere and terrestrial ecosystems, which is required as spatial parameters for geoscientific models. However, there are currently no available common datasets of the CWSC on a global scale, especially for hydrological models since conventional evapotranspiration-derived estimates cannot represent the extra storage capacity for the lateral flow and runoff generation. Here, we produce a dataset of the CWSC parameter for global hydrological models. Joint parameter calibration of three commonly used monthly water balance models provides the labels for a deep residual network. The global CWSC is constructed based on the deep residual network at 0.5° resolution by integrating 15 types of meteorological forcings, underlying surface properties, and runoff data. CWSC products are validated with the spatial distribution against root zone depth datasets and validated in the simulation efficiency on global grids and typical catchments from different climatic regions. We provide the global CWSC parameter dataset as a benchmark for geoscientific modelling by users.</p> <p>A global terrestrial CWSC dataset with 0.5 spatial resolution is now available. All input factors and the global CWSC data are publicly available as NetCDF files or download from smsc_data.zip at Zenodo. Python codes are available to calculate the basin average CWSC value from grid values in any interested basin on a global scale.</p> <p>The Fortran codes for parameter calibration of semi distributed global monthly water balance models are available at https://github.com/xiekangwhu/CWSC_monthly_water_balance_models. The Python codes of deep residual network we developed for the global reconstruction map of CWSC are available at https://github.com/xiekangwhu/CWSC_deep_residual_network.</p> <p> </p> <p>Major code contributor: Kang Xie (PhD Student, Wuhan University), Liting Zhou (PhD Student, Wuhan University), Shujie Cheng (PhD Student, Wuhan University), and Shuanghong Shen (PhD Student, University of Science and Technology of China)</p> <p> </p> <p>Citations</p> <p>If you find our code to be useful, please cite the following papers:</p> <p>Xie, K. et al. Identification of spatially distributed parameters of hydrological models using the dimension-adaptive key grid calibration strategy - ScienceDirect. Journal of Hydrology 598, doi:10.1016/j.jhydrol.2020.125772 (2020).</p> <p>Xie, K. et al. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships. Journal of Hydrology 603, doi:10.1016/j.jhydrol.2021.127043 (2021).</p> <p>Xie, K. et al. Verification of a New Spatial Distribution Function of Soil Water Storage Capacity Using Conceptual and SWAT Models. Journal of Hydrologic Engineering 25, doi:10.1061/(asce)he.1943-5584.0001887 (2020).</p>
Prediction of Therapeutic Response to Neoadjuvant Chemotherapy in Muscle Invasive Bladder Cancer Patients Using Spatial Transcriptomics
ClinicalTrials.gov study NCT06373055. IPD Sharing: NO. Countries: 1. Publications: 0.
Immune Spatial Features of Guselkumab Cutaneous Response
ClinicalTrials.gov study NCT05858632. IPD Sharing: NO. Countries: 1. Publications: 0.
Spatial microniches of IL-2 synergize with IL-10 to drive lung migratory Th2 cells in response to inhaled allergen [scRNA-Seq]
GEO Series GSE245109. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Single-cell and spatial transcriptomics analysis of tobacco-associated lung adenocarcinoma development, and in response to targeting inflammation alone or in combination with immune checkpoint blockad
GEO Series GSE300288. Mus musculus. 31 samples. Type: Expression profiling by high throughput sequencing.
Data from: Grizzly bear response to fine spatial and temporal scale spring snow cover in Western Alberta
Open the record for dataset details and reuse information.
Single-Cell and Spatial Transcriptome Analyses Reveal Tertiary Lymphoid Structures Linked to Tumour Progression and Immunotherapy Response in Nasopharyngeal Carcinoma
GEO Series GSE206245. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing; Other.
Transcriptional programs of cell identity and p53-induced stress responses are associated with distinctive features of spatial genome organization [ChIP-seq]
GEO Series GSE278977. Homo sapiens. 80 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Comprehensive evaluation on the effect of spatial fractionation on anti-tumor immune response: the case of proton minibeam radiotherapy for glioblastoma.
GEO Series GSE279905. Rattus norvegicus. 11 samples. Type: Expression profiling by high throughput sequencing.
Paired single-cell and spatial transcriptional profiling reveals a central osteopontin macrophage response mediating tuberculous granuloma formation in humans and zebrafish [Spatial Transcriptomics]
GEO Series GSE296400. Homo sapiens. 4 samples. Type: Other.
Paired single-cell and spatial transcriptional profiling reveals a central osteopontin macrophage response mediating tuberculous granuloma formation
GEO Series GSE296119. Danio rerio. 15 samples. Type: Expression profiling by high throughput sequencing.
APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge [Spatial transcriptomics]
GEO Series GSE212323. Mus musculus. 6 samples. Type: Other.
Transcriptional programs of cell identity and p53-induced stress responses are associated with distinctive features of spatial genome organization [RNA-seq]
GEO Series GSE278979. Homo sapiens. 40 samples. Type: Expression profiling by high throughput sequencing.
Macrophage heterogeneity and spatial localization in granulomas characterize early responses in Mycobacterium tuberculosis infected nonhuman primates.
GEO Series GSE296788. Macaca fascicularis. 22 samples. Type: Expression profiling by high throughput sequencing; Other.
A pairwise cytokine code explains the organism-wide response to sepsis [Spatial transcriptomics by Array-seq]
GEO Series GSE248904. Mus musculus. 4 samples. Type: Other.
Spatially clustered type I interferon responses at injury borderzones
GEO Series GSE269054. Homo sapiens; Mus musculus. 37 samples. Type: Expression profiling by high throughput sequencing.
Dual and spatially resolved drought responses in the Arabidopsis leaf mesophyll revealed by single-cell transcriptomics
GEO Series GSE273926. Arabidopsis thaliana. 14 samples. Type: Expression profiling by high throughput sequencing.
Spatial dissection of the Arabidopsis response to downy mildew using Fluorescence Activated Cell Sorting
GEO Series GSE67100. Arabidopsis thaliana. 18 samples. Type: Expression profiling by array.
Dual and spatially resolved drought responses in the Arabidopsis leaf mesophyll revealed by single-cell transcriptomics
GEO Series GSE273033. Arabidopsis thaliana. 8 samples. Type: Expression profiling by high throughput sequencing.
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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.