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908 results for “Mesh”
Data from: Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation
<p class="MsoNormal">The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDE). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, <em><span>Eptesicus serotinus</span></em><span>,<em> </em>in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness</span>. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in light of these findings.</p>
Maximally Error-Tolerant MZI Mesh-based Optical Neural Networks
<p>Codes.zip contains code that trains and tests maximally error-tolerant Mach-Zehnder mesh optical neural networks on MNIST, FashionMNIST, and KMNIST.</p> <p>models.zip contains all the trained model parameters.</p>
The dataset of "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction"
<p>The dataset used in the study "Deep Learning-enabled 3D Multimodal Fusion of Cone-Beam CT and Intraoral Mesh Scans for Clinically Applicable Tooth-bone Reconstruction" is available upon request. Please contact the authors of the study or the responsible institution for access to the dataset.</p>
Repository of Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes
<p><em><strong>Repository of Thoracolumbar Osteo-Ligamentous Spine Virtual FE Meshes:</strong></em></p> <ul> <li>16807 FE input files representing thoracolumbar spine hexahedral models, including point coordinates. To reduce the size of shared virtual finite element (FE) models, only point coordinates are shared here. The mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" is also shared, which includes point coordinates, mesh connectivity IDs, and element sets. To generate virtual FE input files for any of the 16807 models, the corresponding shared point coordinates can be replaced into the mean FE input file (<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>). Mesh connectivity IDs, and element sets are the same in all of FE input files. Mean FE input file includes vertebras and IVDs hexahedral meshes; pelvis, sacrum, and the femoral head triangulated meshes; and ligaments. Each point coordinate file is almost 38MB (totally 655GB).</li> <li>An excel file: "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" reporting measured spinopelvic parameters for 16807 virtual FE hexahedral models. The Excel file includes measured spinopelvic parameters (PI, PT, SS, LL, LL-PI, GT, RPV, RLL, LDI, RSA, TPA, and scoliosis cobb angle), GAP and IVD centric thickness for FE virtual cohort. Model ID in the excel file is correspondent to the model's name.</li> <li>One video file: "<a href="../records/8107354/files/how_to_replace_point_coordinates.mp4?download=1">how_to_replace_point_coordinates.mp4</a>". It shows how you can replace point coordinates here to the mean FE input file "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Mean_Model%20%28Template%29.inp">Mean_Model (Template).inp</a>" in order to generate specific FE input file.</li> </ul> <p><em><strong>How to download the data:</strong></em></p> <p>You can find and download the point coordinates (16807 models) in the following 7 subsections:</p> <p>1- Subsection Data 1 (Models 1 to 2700), DOI: <a href="10371461">10.5281/zenodo.10371461</a></p> <p>2- Subsection Data 2 (Models 2701 to 5400), DOI: <a href="https://doi.org/10.5281/zenodo.10379757">10.5281/zenodo.10379757</a></p> <p>3- Subsection Data 3 (Models 5401 to 8100), DOI: <a href="https://doi.org/10.5281/zenodo.10379535">10.5281/zenodo.10379535</a></p> <p>4- Subsection Data 4 (Models 8101 to 10800), DOI: <a href="https://doi.org/10.5281/zenodo.10380397">10.5281/zenodo.10380397</a></p> <p>5- Subsection Data 5 (Models 10801 to 13500), DOI: <a href="https://doi.org/10.5281/zenodo.10380512">10.5281/zenodo.10380512</a></p> <p>6- Subsection Data 6 (Models 13501 to 16200), DOI: <a href="https://doi.org/10.5281/zenodo.10380633">10.5281/zenodo.10380633</a></p> <p>7- Subsection Data 7 (Models 16201 to 16807), DOI: <a href="https://doi.org/10.5281/zenodo.10396267">10.5281/zenodo.10396267</a></p> <p><em><strong>Notes:</strong></em></p> <p>1- Model number in "<a href="../api/files/bdc067d7-cb7d-40b7-9974-5ada43e1a0c5/Descriptive_List%20%2816807_FE_virtual_models%29.xlsx">Descriptive_List (16807_FE_virtual_models).xlsx</a>" is correspondent to the same model number in the 16807 stereolithography (stl) files (.stl extension) representing the virtual thoracolumbar spine triangulated meshes (DOI: <a href="https://doi.org/10.5281/zenodo.8108354">10.5281/zenodo.8108354</a>; <a href="../records/8108354/files/stl.part01.rar?download=1">stl.part01.rar</a> to <a href="../records/8108354/files/stl.part09.rar?download=1">stl.part09.rar</a>).</p> <p>2-These point coordinates are sampled by combining the first 5 shape modes of the morphed-mesh statistical shape model in which each shape mode is discretized into 7 standard deviations: -3, -2, -1, 0, 1, 2, 3.</p> <p>3- Generated FE inp files can be opened by Abaqus 2019 and later. Any other FE software which supports .inp extension also can open the files.</p> <p><em><strong>Developed by: </strong></em>Morteza Rasouligandomani (Ph.D. in biomedical engineering, Pompeu Fabra university, BCN Med-Tech group, DTIC department, Barcelona, Spain).</p> <p>Email contact: morteza.rasouli@upf.edu</p>
Porous Subvolume - Planar Specimen - Fine Mesh
<p>Geometry of specimen: Planar ROI, with cylindrical ends</p> <p>Pores: Contains a subvolume of pores (1/9th the volume of ROI)</p> <p>Meshing: Fine (~395k elements)</p> <p>Additional info: These pores were segmented from a tomography and remeshed via GMSH</p> <p> </p>
Porous Subvolume - Planar Specimen - Coarse Mesh
<p>Geometry of specimen: Planar ROI, with cylindrical ends</p> <p>Pores: Contains a subvolume of pores (1/9th the volume of ROI)</p> <p>Meshing: Coarse (~12k elements)</p> <p>Additional info: These pores were segmented from a tomography and remeshed via GMSH</p>
Mesh data for multi-impeller mixing performance prediction in stirred tanks using mean age theory approach
<p>The upload files include mesh data for all configurations studied in the research: multi-impeller mixing performance prediction in stirred tanks using mean age theory approach.</p>
Publish Meshes
Japanese House made in Blender 2.83. Source: Objaverse 1.0 / Sketchfab
Lion Paestum mesh
created with micmac Source: Objaverse 1.0 / Sketchfab
Three-dimensional Bone Regeneration Using Custom-made Meshes With and Without Collagen Membrane
ClinicalTrials.gov study NCT04286334. IPD Sharing: YES. Countries: 1. Publications: 0.
Open Abdomen: Vacuum Pack Versus Sylo Bag and Mesh Protocol
ClinicalTrials.gov study NCT01864590. IPD Sharing: NO. Countries: 1. Publications: 1.
Safety Study of Pelvic Organ Prolapse Repair Using Transvaginal Mesh
ClinicalTrials.gov study NCT00402844. IPD Sharing: Not stated. Countries: 4. Publications: 5.
Male Sexual Experience and Its Impact on Quality of Life Before and After Their Sexual Partners Undergo Polypropylene Mesh Augmented Pelvic Floor Reconstruction
ClinicalTrials.gov study NCT01320631. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison of 2 Lightweight Y-meshes After Laparoscopic Sacrocolpopexy
ClinicalTrials.gov study NCT02248935. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Use of Human Fibrin Glue Versus Staples for Mesh Fixation in Laparoscopic Transabdominal Preperitoneal Hernioplasty
ClinicalTrials.gov study NCT01641718. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Does the Use of Prophylactic Mesh Reduce Incisional Hernia?
ClinicalTrials.gov study NCT04700956. IPD Sharing: NO. Countries: 1. Publications: 9.
Mesh Fixation inTEP Inguinal Hernia Repair
ClinicalTrials.gov study NCT02419950. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Mesh Ablator Versus Cryoballoon Pulmonary Vein Ablation of Symptomatic Paroxysmal Atrial Fibrillation
ClinicalTrials.gov study NCT01061931. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Prevention of Incisional Hernia by Mesh Augmentation After Midline Laparotomy for Aortic Aneurysm Treatment
ClinicalTrials.gov study NCT00757133. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study to Compare Ventral Incisional Hernia by Laparoscopic vs Open Repair With Mesh
ClinicalTrials.gov study NCT00240188. IPD Sharing: Not stated. Countries: 1. Publications: 5.
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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.