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Dataset results
317 results for “Multi modal”
Cell Volume (3D) Correlative Microscopy Facilitated by Intra-Cellular Fluorescent Nanodiamonds as Multi-Modal Probes
<p>RAW files</p>
Sex-driven modifiers of Alzheimer's risk: a multi-modality brain imaging study
<p><span><b>Objective:</b> To investigate sex differences in late-onset Alzheimer's disease (AD) risks by means of multi-modality brain biomarkers <span>[β-amyloid load via </span><sup>11</sup>C-PiB<span> PET, and neurodegeneration via </span><sup>18</sup>F-FDG <span>PET and structural MRI]</span>.</span></p> <p><span><b>Methods:</b> We examined 121 cognitively normal participants [85 women and 36 men] ages 40-65, with clinical, laboratory, neuropsychological, lifestyle exams, and MRI, FDG- and PiB-PET exams. Several clinical (e.g. age, education, APOE status, family history), medical (e.g., depression, diabetes, hyperlipidemia), hormonal (e.g. thyroid disease, menopause), and lifestyle AD risk factors (e.g., smoking, diet, exercise, intellectual activity) were assessed. Statistical parametric mapping and LASSO regressions were used to compare AD-biomarkers between men and women, and to identify the risk factors associated with sex-related differences. </span></p> <p><b><span>Results:</span></b> Groups were comparable on clinical and cognitive measures. Adjusting for each modality-specific confounders, the female group showed higher PiB <span>β-amyloid </span>deposition, lower FDG glucose metabolism, and lower MRI gray and white matter volumes compared to the male group (p<0.05, FWE-corrected for multiple comparisons). The male group did not show biomarker abnormalities compared to the female group. Results were independent of age and remained significant using age-matched groups. Second to female sex, menopausal status was the predictor most consistently and strongly associated with the observed brain biomarker differences, followed by hormone therapy, hysterectomy status, and thyroid disease.</p> <p><span><b><span>Conclusion:</span></b><span> Hormonal risk factors, in particular menopause, predict AD-endophenotype in middle-aged women. These findings suggest that the window of opportunity for AD-preventative interventions in women is early in the endocrine aging process.</span></span></p>
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>
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>
Multi Modal Workload Data Capture Experiment 1
<p>EEG, Video, Game Video and IMU dataset 1st experiment</p>
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>
Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes
<p>The SIM2 testing set.</p>
Figure 2 from: Klein A, Ghosh S (2016) Graph-based clinical diagnosis and prediction using multi-modal neuroimaging data. Research Ideas and Outcomes 2: e8835. https://doi.org/10.3897/rio.2.e8835
Figure 2 - Examples of automatically extracted features (MRI) (a) Example structural features (left lateral views of volumes, surfaces, curves, and points) (b) Schematic feature hierarchy: 3-D gyrii surround a 2-D sulcal ribbon with 1-D fundus containing 0-D pits
Figure 1 from: Klein A, Ghosh S (2016) Graph-based clinical diagnosis and prediction using multi-modal neuroimaging data. Research Ideas and Outcomes 2: e8835. https://doi.org/10.3897/rio.2.e8835
Figure 1 - Examples of graph-based representations of scientific data among hundreds on the www.visualcomplexity.com website (categories on the site include biology, food webs and semantic, social, and knowledge networks). Lower left images of DTI, connectome, and network hubs are from Olaf Sporns (2010, Scholarpedia, 5(2):5584).
Figure 3 from: Klein A, Ghosh S (2016) Graph-based clinical diagnosis and prediction using multi-modal neuroimaging data. Research Ideas and Outcomes 2: e8835. https://doi.org/10.3897/rio.2.e8835
Figure 3 - Example of a graph-based representation of MRI and DTI features (a) A gray/white matter surface (left lateral view) with (visible) sulcal pits highlighted. These features go by different names (sulcal roots, buried gyrii, annectant gyrii, plis de passage) and may be well conserved structures formed early in development. (b) DTI connectivity graph computed on the same patient with depression as on the left panel. Vertices represent automatically extracted sulcal pits and each edge indicates a connection probability greater than 0.01 between two vertices.
Data collection of toy horses and zebras for multi-modal image synthesis
<p>Motivated by building a live demonstration of horse-to-zebra synthesis, we created our own dataset consisting of images of different toy horses and zebras. We used a wide variety of backgrounds, camera angles and lighting settings. In addition, images were also created in which the toy animals were held in the hand to better demonstrate synthesis in front of the camera. </p> <p>Besides the raw image and video footage, we prepared different compilations of data in form of datasets.</p> <p>Following the horse-to-zebra synthesis by Zhu et al. in the paper "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks", we partially adopted the horse2zebra dataset from real horses and zebras in our datasets.</p>
Deepurify: a multi-modal deep language model to remove contamination from metagenome-assembled genomes
<p>The SIM1 testing set.</p>
Data underlying the paper titled "Enhancing hyperspectral imaging through macro and multi-modal capabilities"
<p>The data is in .mat format and contains two variables:<br>data(x, y, lambda) is the variable that contains the hyperspectral dataset, after being converted from temporal to spectral hypercube.<br>WL(lambda) is the variable that contains the wavelength calibration.</p>
Effect of Cold Therapy Implementation on Multi-modal Postoperative Pain Management
ClinicalTrials.gov study NCT03129464. IPD Sharing: NO. Countries: 1. Publications: 0.
Exploring the Impact of a Multi-modal Nutritional Intervention in Patients Undergoing Chemotherapy for Pancreatic Cancer (FEED Trial)
ClinicalTrials.gov study NCT06149546. IPD Sharing: YES. Countries: 1. Publications: 0.
Pilot Study of Multi-Modality Imaging in Patients With Hematologic Malignancies
ClinicalTrials.gov study NCT02042924. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Sentinel - Triaging Acute Breathlessness Using Multi-Modal Biomarkers
ClinicalTrials.gov study NCT05304494. IPD Sharing: UNDECIDED. Countries: 0. Publications: 5.
Phase 2 Evaluation of Multi-modality Algorithm for Non-metastatic Adenocarcinoma of Pancreas or Ampulla
ClinicalTrials.gov study NCT02626520. IPD Sharing: YES. Countries: 1. Publications: 0.
Emergence Agitation and Pain Scores in Pediatrics When Comparing Single-modal vs Multi-modal Analgesia for ENT Surgery
ClinicalTrials.gov study NCT03062488. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluating the Impact of a 12-month Multi-Modal Lifestyle Management Intervention on Disease Relevant Biomarkers
ClinicalTrials.gov study NCT06987708. IPD Sharing: Not stated. Countries: 0. Publications: 152.
ScienceDex guides
Understand access before you commit
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.