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901 results for “Modality”
Data from: Illness beliefs in end stage renal disease and associations with self-care modality choice
Background: Interest in self-care haemodialysis (HD) has increased because it improves patients'clinical and quality-of-life outcomes. Patients who undertake self-management for haemodialysis may hold illness beliefs differently to those choosing institutional care at the time of making the modality choice or moulded by their illness and dialysis treatment experience. Illness perceptions amongst predialysis patients and in those undertaking fully-assisted and self-care haemodialysis are being investigated in a combined cross-sectional and longitudinal study. Study Design: The study data are derived from the BASIC-HHD study, a multicentre observational study on factors influencing home haemodialysis uptake. 535 patients were enrolled into three groups: Predialysis CKD-5 group, prevalent 'in-centre' HD and self-care HD groups (93% at home). We explore illness perceptions in the cross-sectional analyses of the three study groups, using the revised Illness Perception Questionnaire (IPQ-R). Predialysis patients' illness beliefs were reassessed prospectively, typically between 4 and 12 months after dialysis commencement. Results: Illness belief subscales are significantly different between in-centre and self-care HD groups. In a step-wise hierarchical regression analysis, after adjustment for age, education, marital status, diabetes, dialysis vintage, depression, anxiety scores, and IPQ-R subscales, personal control (p = 0.01) and illness coherence (p = 0.04) are significantly higher in the self-care HD group. In the predialysis group, no significant associations were found between illness representations and modality choices. In prospectively observed predialysis group, scores for personal control, treatment control, timeline cyclical and emotional representations reduced significantly after commencing dialysis and increased significantly for illness coherence. Conclusions: Illness beliefs differ between hospital and self-care haemodialysis patients. Patient's affect and neurocognitive ability may have an important role in determining illness beliefs. The impact of modality upon illness representations may also be significant and remains to be explored.
Integrative single-cell multi-modal analyses reveal detailed spatial cellular organization directing human heart morphogenesis
<div> <p><span>The heart, which is the first organ to develop, is highly dependent on its form to function. However, how diverse cardiac cell types spatially coordinate to create complex morphological structures critical for heart function remains to be elucidated. Here, we show that integration of single cell RNA-sequencing with high-resolution multiplexed error-robust fluorescent <em>in situ </em>hybridization (MERFISH) not only resolves the identity of cardiac cell types developing the human heart but also provides a spatial mapping of individual cells that enables illumination of their organization into cellular communities forming distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, supporting their specialization according to cellular ecosystem and anatomic region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell-cell interactions within these communities revealed signaling pathways orchestrating the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. <em>In vivo </em>conditional genetic mouse models and in vitro human pluripotent stem cell studies confirmed an intricate multicellular PLXN-SEMA crosstalk among specific ventricular cardiomyocyte, fibroblast and endothelial cell subpopulations that directs the compaction of the ventricular wall layers. Thus, these detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodeling the human heart offer new insights into structural heart diseases as well as engineering complex multi-cellular tissues for human heart repair.</span></p> </div>
Raw data for: Too much information? Males convey parasite levels using more signal modalities than females utilise.
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THE PRAGMATIC ASPECT OF MODALITY CATEGORY IN ENGLISH
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Unified Cross-modality Integration and Inference of Single-Cell Multiomics Data with Deep Contrastive Learning
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Machine Learning Mid-Infrared Spectral Models for Predicting Modal Mineralogy of CI/CM Chondritic Asteroids and Bennu
<p>This is supporting data for the paper titled "Machine Learning Mid-Infrared Spectral Models for Predicting Modal Mineralogy of CI/CM Chondritic Asteroids and Bennu". Figure S1 compares model performance of nonnegative LSMA and PLS from Pan et al. (2015). Tables S1 through S5 provide XRD data, metadata, MIR spectra using the laboratory conversion, MIR spectra using the OTES conversion and quantitative XRD results for Murchison meteorite. </p>
Evidence of cognitive spezialization in an insect: proficiency is maintained across elemental and higher-order visual learning but not between sensory modalities in honey bees
<p>Individuals differing in their cognitive abilities and foraging strategies may confer a valuable benefit to their social groups as variability may help responding flexibly in scenarios with different resource availability. Individual l<span>earning proficiency may either be absolute or vary with the complexity or the nature of the problem considered. D</span>etermining if learning abilities correlate between tasks of different complexity or between sensory modalities has a high interest for research on brain modularity and task-dependent specialisation of neural circuits. <span>The honeybee <i>Apis mellifera</i> constitutes an attractive model to address this question due to its capacity to successfully learn a large range of tasks in various sensory domains.</span> <span>Here </span><span>we studied </span>whether the performance of individual bees in a simple visual discrimination task (a discrimination between two visual shapes) is stable over time and correlates with their capacity to solve either a higher-order visual task (a conceptual discrimination based on spatial relations between objects) or an elemental olfactory task (a discrimination between two odorants)<span>. </span>We found that individual learning proficiency within a given task was maintained over time and that some individuals performed consistently better than others within the visual modality, thus showing consistent aptitude across visual tasks of different complexity. By contrast, performance in the elemental visual-learning task did not predict performance in the equivalent elemental olfactory task. Overall, our results suggest the existence of cognitive specialisation within the hive, which may contribute to ecological social success.</p>
Data set for displacement-based multi-modal formulation of Koiter's method applied to cylindrical shells
<p>These results complement the results reported in the AIAA SciTech 2022 publication "Displacement-based multi-modal formulation of Koiter's method applied to cylindrical shells", presented in the Special Session in Honor of Prof. Dr. Johann Arbocz.</p> <p>A preprint of is already available as:</p> <blockquote> <p>Castro SGP, Jansen E. Displacement-based multi-modal formulation of Koiter's method applied to cylindrical shells. EngrXiv Preprints, 2021. <a href="https://doi.org/10.31224/osf.io/92xvf">doi:10.31224/osf.io/92xvf</a>.</p> </blockquote> <p>The results are generated using the bfsccylinder module version 0.3.12, see file <a href="https://zenodo.org/record/5667978/files/bfsccylinder-0.3.12.zip">bfsccylinder-0.3.12.zip</a>, also available in PyPi (<a href="https://pypi.org/project/bfsccylinder/">https://pypi.org/project/bfsccylinder/</a>). The composites module is also needed, see file <a href="https://zenodo.org/record/5667978/files/composites-0.4.18.zip">composites-0.4.18.zip</a>, also available in PyPi (<a href="https://pypi.org/project/composites/">https://pypi.org/project/composites/</a>).</p> <p> </p> <p><strong>Main analysis scripts:</strong></p> <p><a href="https://zenodo.org/record/5667978/files/koiter_cylinder_von_Karman.py?download=1">koiter_cylinder_von_Karman.py</a> has the analysis function using Donnell-type or Von Kárman-type shell kinematics.</p> <p><a href="https://zenodo.org/record/5667978/files/koiter_cylinder_Sanders.py">koiter_cylinder_Sanders.py</a> has the analysis function using Sanders-type shell kinematics.</p> <p> </p> <p><strong>Verification case: Water's composite cylindrical shell</strong></p> <p><a href="https://zenodo.org/record/5667978/files/v00_DIANA_Waters_shell.zip">v00_DIANA_Waters_shell.zip</a> reference results obtained with a converged model using DIANA Q40L elements</p> <p><a href="https://zenodo.org/record/5667978/files/v00_Waters_composite_cylindrical_shell_sanders.py">v00_Waters_composite_cylindrical_shell_sanders.py</a> script to run the verification case</p> <p><a href="https://zenodo.org/record/5667978/files/v00_Waters_composite_cylindrical_shell_sanders.results">v00_Waters_composite_cylindrical_shell_sanders.result</a> results saved in an object file saved using Pickle, can be loaded with `pickle.load()`</p> <p><a href="https://zenodo.org/record/5667978/files/v00_Waters_composite_cylindrical_shell_sanders_b_ijkl.txt">v00_Waters_composite_cylindrical_shell_sanders_b_ijkl.txt</a> b_ijkl coefficients for this case, with 10 modes in the Koiter expansion</p> <p><a href="https://zenodo.org/record/5667978/files/v00_Waters_composite_cylindrical_shell_sanders_output.txt">v00_Waters_composite_cylindrical_shell_sanders_output.txt</a> printed output</p> <p> </p> <p> </p> <p> </p>
cyber criminal triangle modal
SciDraw upload
Multi Modal Workload Data Capture Experiment 1
<p>EEG, Video, Game Video and IMU dataset 1st experiment</p>
CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal - Appendix
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M3: A Multi-Image Multi-Modal Entity Alignment Dataset
<p><span>M3, an MMEA benchmark equipped with multiple images retrieved from respective search engines, which better mirrors real-life challenges. We utilize the widely used DBP15K dataset as the foundational dataset, which includes three cross-ingual datasets: Chinese-English (ZH-EN), Japanese-English (JA-EN), and French-English (FR-EN).</span></p>
Dual-Transformer Architecture for Cross-Modal Learning on Tree-Structured Data in Regression Tasks
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Demo Datasets for Non-light-sheet Imaging Modalities for PetaKit5D
<p>Demo dataset for PetaKit5D (<a href="https://github.com/abcucberkeley/LLSM5DTools">https://github.com/abcucberkeley/PetaKit5D</a>). </p> <p>There are four datasets for non-light-sheet modalities: 2-photon, confocal, oblique illumination "phase", and widefield. Some demos in PetaKit5D use this dataset to demonstrate the usage. Please refer to the readme.txt for the file structures, parameters, and other information.</p> <p>Please cite our paper (<a href="https://doi.org/10.1101/2023.12.31.573734">https://doi.org/10.1101/2023.12.31.573734</a>) if you use this dataset in your research:</p> <p><code>Xiongtao Ruan, Matthew Mueller, Gaoxiang Liu, Frederik Görlitz, Tian-Ming Fu, Daniel E. Milkie, Joshua L. Lillvis, Alexander Kuhn, Chu Yi Aaron Herr, Wilmene Hercule, Marc Nienhaus, Alison N. Killilea, Eric Betzig, Srigokul Upadhyayula. Image processing tools for petabyte-scale light sheet microscopy data. bioRxiv 2023.12.31.573734; doi: <a href="https://doi.org/10.1101/2023.12.31.573734">https://doi.org/10.1101/2023.12.31.573734</a></code></p>
Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes
<p>The SIM2 testing set.</p>
Aligned Cross-modal Integration and Regulatory Heterogeneity Characterization of Single-Cell Multiomic Data with Deep Contrastive Learning
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TAMU - ARPA-E SMARTFARM Grain Sorghum 2022 Texas site comprehensive sensor modalities data set.
<p>Comprehensive Year 2 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Texas site of the project. </p>
UF/OSU - ARPA-E SMARTFARM Grain Sorghum 2022 Oklahoma site comprehensive sensor modalities data set.
<p>Comprehensive Year 2 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Oklahoma site of the project. </p>
TAMU - ARPA-E SMARTFARM Grain Sorghum 2021 Texas site comprehensive sensor modalities data set.
<p>Comprehensive Year 1 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Texas site of the project. </p>
TAMU - ARPA-E SMARTFARM Grain Sorghum 2023 Texas site comprehensive sensor modalities data set.
<p>Comprehensive Year 3 data of ARPA-E SMARTFARM Grain Sorghum project titled "Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains". Data sets includes Eddy Caovariance measurements of GHGs (CO2, CH4 and N2O) along with sub acre level soil moisture, soil temperarature, soil N and carbon, plant biomass and yield. This data is from the Texas site of the project. </p>
ScienceDex guides
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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.