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18 results for “3D Deformation”
3D cell tracking dataset of bacterial biofilm deformation and recovery under shear flow
<p>This MAT file includes dataset in the scientific article "<i>In vivo</i> microrheology reveals elastic and plastic responses inside three-dimensional bacterial biofilms" by the following authors: Takuya Ohmura, Dominic Skinner, Konstantin Neuhaus, Gary Choi, Jörn Dunkel, Knut Drescher. This MAT file can be conveniently opened with Matlab. </p><p>When you open this file with Matlab, you will find 4 variables stored in the file "Data_v3_loop2_newRxy_bidx1_274.mat"</p><p><strong>Variable 1: name_parameter</strong></p><p>Names of 31 parameters for columns in 3 variables: 'deformation_all', 'recovery_all' , 'plasticity_all'. The parameters have cell displacements, orientations, coordinates, biofilm indexes and experimental conditions. When the parameters have units, they are shown in the names. </p><ul><li>'x_Frame1[um]'</li><li>'y_Frame1[um]'</li><li>'z_Frame1[um]'</li><li>'Normalized_x_Frame1'</li><li>'Normalized_y_Frame1'</li><li>'Normalized_z_Frame1'</li><li>'LocalDensity_Frame1(VolumeFractionAround30px)'</li><li>'LocalCellNumberDensity_Frame1(VolumeFractionAround30px)'</li><li>'NematicOrderParameter_Frame1'</li><li>'AlignmentFlow_Frame1[rad]'</li><li>'AlignmentRadial_Frame1[rad]'</li><li>'AlignmentZaxis_Frame1[rad]'</li><li>'d_x[um]'</li><li>'d_y[um]'</li><li>'d_z[um]'</li><li>'Normalized_d_x'</li><li>'Normalized_d_y'</li><li>'Normalized_d_z'</li><li>'d_LocalDensity'</li><li>'d_LocalNumberDensity[um^-3]'</li><li>'d_NematicOrderParameter'</li><li>'d_AlignmentFlow[rad]'</li><li>'d_AlignmentRadial[rad]'</li><li>'d_AlignmentZaxis[rad]'</li><li>'CrossProduct[um^2]'</li><li>'BiofilmIndexNumber'</li><li>'Biofilm_width[um]'</li><li>'Biofilm_height[um]'</li><li>'Biofilm_volume[um^3]'</li><li>'FlowRate[ul/min]'</li><li>'Duration[min]'</li></ul><p><strong>Variable 2: deformation_all</strong></p><p>The rows indicate 704198 single-cell trackings in deformations of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.</p><p><strong>Variable 3: recovery_all</strong></p><p>The rows indicate 685991 single-cell trackings in recoveries of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.</p><p><strong>Variable 4: plasticity_all</strong></p><p>The rows indicate 665749 single-cell trackings in plasticities of 274 bacterial biofilms. Each of the 274 bacterial biofilms has a different 'BiofilmIndexNumber'. The columns indicate 31 parameters which names are shown in 'name_parameter'.</p><p> </p><p>To plot the cell tracked data in the figures of the article, use our MATLAB code uploaded in our GitHub (https://github.com/knutdrescher/biofilm-rheology).</p>
Data from: facial growth and development trajectories based on 3D images: geometric morphometrics with a deformation perspective
<p>Developmental changes of facial shape are commonly investigated through geometric morphometrics. A limitation with this approach is the inability to investigate patterns of morphological changes at local scale. This could be addressed through quantifying the deformation required to deform one shape to another. This study aimed to investigate changes in mean, rate, and variance of facial shape at local scale using geometric morphometrics through deformation perspective. 2112 Europeans 3 to 40 years-old from the 3D Facial Norms project were included. Shape and rate trajectories from partial least-squares regressions revealed that the developmentally protrusive nasal bridge was due to local expansion in surrounding tissues as opposed to shape changes in nasal bridge per-ser. Local expansion of the supraorbital region, in particular the medial part in males, resulted in the sloping forehead and deep-situated eyes with development. Facial shape variation increased non-linearly with age (p < 0.05), with features having larger rate of change becoming more developmentally diversified. In summary, our deformation perspective facilitates unravelling morphogenetic processes underlying shape changes. Our extended analytical scope inspires novel measures worthy of consideration while establishing facial growth charts. The analytical framework in this study is broadly applicable for analysis of shape changes in general.</p>
Studying gastrulation by invagination: the bending of a cell sheet by mechanical cell properties using 3D deformable cell based simulations
<p>This dataset contains the scripts and end results of invagination simulation experiments.<br>The data are the results of a 3D cell based model that was used to investigate invagination: the bending of a cell sheet, and uses cell-cell adhesion, apical constriction, cell volume conservation, collision detection and handeling.</p>
Data from: facial growth and development trajectories based on 3D images: geometric morphometrics with a deformation perspective
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Multi-modal dataset of a polycrystalline metallic material: 3D microstructure and deformation fields
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3D Coseismic Deformation of the 2023 Türkiye Mw7.8-7.5 Earthquake Doublet Deduced from High-rate GNSS Observations
<p>3D displacements of the 2023 Türkiye Mw7.8-7.5 earthquakes derived from high-rate GNSS data.</p> <p>This Zenodo repository contains 3D displacements for the paper "Coseismic Deformation and Kinematic Rupture Process of the 2023 Türkiye Mw7.8-7.5 Earthquake Doublet Deduced from High-rate GNSS Observations " by Dong et al. in Geophysical Journal International. If you use any of the data or models please cite it accordingly.</p> <p>For any questions or issues, email Zhihua Dong at <a href="mailto:dmelgarm@uoregon.edu">dongzhihua93@163.com</a>. </p>
Additional data - Using a micro-device with a deformable ceiling to probe stiffness heterogeneities within 3D cell aggregates
<p>Additional data for the article <em>"Using a micro-device with a deformable ceiling to probe stiffness heterogeneities within 3D cell aggregates" </em>by Jain, Belkadi et al.</p> <p><strong>additionaldata_jain-belkadi_2023/data_fig_3/data_fig_3_bc:</strong> One .tif file per spheroid shown in Fig 3b,c. the air cavity pressure levels are the following (respectively for each frame): 0 (mbar), -50, -100, -150, -200, -250, -300, -450, -500, 0</p> <p><strong>additionaldata_jain-belkadi_2023/data_fig_3/data_fig_3_def: </strong>One .tif file for the cyclic compression of the spheroids.</p> <p><strong>additionaldata_jain-belkadi_2023/data_fig_4_d: </strong>One folder per spheroid. See readme.txt</p>
Evaluation of Using 3D Printed PEEK Facial Implants in Repairing Maxillofacial Deformities
ClinicalTrials.gov study NCT05348434. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
3D Photogrammetry Versus Commercial Scanners for Accurate Repositioning of the Maxilla for Correction of the Dentofacial Deformity.
ClinicalTrials.gov study NCT05755022. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Experimental Evaluation of Back Braces for the Treatment of Spinal Deformity Produced With 3D Printing Technology
ClinicalTrials.gov study NCT04282408. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Dataset for article titled 'Quantifying 3D time-resolved kinematics and kinetics during rapid granular compaction, Part I: quasistatic and dynamic deformation regimes'
<p>Dataset containing the raw X ray CT data, experimental XPCI images and input files for Abaqus simulations for the article titled ' Quantifying 3D time-resolved kinematics and kinetics during rapid granular compaction, Part I: quasistatic and dynamic deformation regimes' . The README file in Part1.zip provides information on the data.</p>
Studying the zippering process during gastrulation in Cnidaria: Aligning an invaginating germ layer with the help of filopodia using 3D deformable cell based simulations
<p>This dataset contains the scripts and end results of zippering simulation experiments.<br>The data are the results of a 3D cell based model that was used to investigate zippering aided invagination: Pulling the cell sheet inwards, and uses cell-cell adhesion, filopodia, apical constriction, cell volume conservation, collision detection and handeling. </p>
3D Characterisation of the Skull Base Deformation in Congenital Muscular Torticollis
ClinicalTrials.gov study NCT04421898. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Full-3D Computer-Assisted Workflow for the Diagnosis and Correction of Deformities? Dentofacial
ClinicalTrials.gov study NCT06806605. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
3D Facial Scanning for Evaluating Autologous Fat Grafting in Craniofacial Deformities
ClinicalTrials.gov study NCT06306326. IPD Sharing: NO. Countries: 0. Publications: 0.
MRI-based 3D Hip Labrum and Cartilage Morphology in Patients With Hip Deformities Compared to Asymptomatic Volunteers
ClinicalTrials.gov study NCT06095219. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Myocardial Deformation in Real-time 3D Ultrasound. Normal Values in Adults and Validation Against the Tagged MRI
ClinicalTrials.gov study NCT02508727. IPD Sharing: Not stated. Countries: 1. Publications: 0.
3D grain boundary and 2D slip transfer data of pure Ti after plastic deformation
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OpenNeuro
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