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882 results for “3D models”
Fig. 8 in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 8. Hydrodynamic restoration of the Nautilus pompilius 3D printed model following underdamped harmonic oscillation. Apertural angle (θa) measured in degrees as a function of time after rotating approximately 38° from the equilibrium orientation. An angle of zero represents a condition where the aperture is horizontally oriented. Open dots represent the peaks used to calculate decay in amplitude with time (grey dashed curves).
Fig. 2 in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 2. Shell and septum thickness measured from three specimens of Baculites compressus (WSU-1400, WSU-1401, and WSU-1405). Exponential curves were fit to these points to define thickness for the full 3D model as a function of whorl height.
Fig. 1 in Mode of life and hydrostatic stability of orthoconic ectocochleate cephalopods: Hydrodynamic analyses of restoring moments from 3D printed, neutrally buoyant models
Fig. 1. Three-dimensional reconstruction of a fragmentary baculite Baculites compressus Say, 1820 (WSU-1400) from the late Campanian Pierre Shale of Meade County, South Dakota. A. Model of a fragmentary specimen generated by photogrammetry with the software (3DF Zephyr). B. Broken septum isolated from the photogrammetry model. C. Suture pattern. D. Complete septum created by reconstructing the higher-order frilling with the suture pattern as a template.
Klecany airfield 2023 - Revetments 3D model, DEM and coordinates
<p>Data from Klecan airfield archeaological survey in 2023, 3d model, DEM and coordinates of type 2 revetments - April. </p>
SeisSol model setup input files and supplement videos for the 3D dynamic rupture models of Wirp et al. 2024
<p>Data required to run the dynamic rupture models presented in Wirp, S. A., Gabriel, A.-A., Ulrich, T., Lorito, S. (2024). The README.txt file contains detailed information about the data and data format.</p>
Repository for: "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model"
<p>Set of numerical experiments supporting the paper entitled "Using automatic calibration to improve the physics behind complex numerical models: An example from a 3D lake model" by Marina Amadori, Abolfazl Irani Rahaghi, Damien Bouffard and Marco Toffolon. Submitted to GMD. </p> <p>The folder contains: </p> <p>simulations: DYNO-PODS + Delft3D experiments on Lake Morat. See https://github.com/louisXW/DYNO-pods for more insights on DYNO-PODS and instructions for installation.</p> <p>scripts: extraction and plotting scripts</p> <p>source_code: modified Delft3D src as available at: https://github.com/eawag-surface-waters-research/Delft3D/tree/d3d4/research/surface_heat_transfer</p>
Real-time monitoring of a 3D blood-brain barrier model maturation and integrity with a sensorized microfluidic device
<p><span>A significant challenge in the treatment of central nervous system (CNS) disorders is represented by the presence of the blood-brain barrier (BBB), a highly selective membrane that regulates molecular transport and restricts the passage of pathogens and therapeutic compounds. Traditional <em>in vivo</em> models are constrained by high costs, lengthy experimental timelines, ethical concerns, and interspecies variations. <em>In vitro</em> models, particularly microfluidic BBB-on-a-chip devices, have been developed to address these limitations. These advanced models aim to more accurately replicate human BBB conditions by incorporating human cells and physiological flow dynamics. In this framework, here we developed an innovative microfluidic system that integrates thin-film electrodes for non-invasive, real-time monitoring of BBB integrity using electrochemical impedance spectroscopy (EIS). EIS measurements showed frequency-dependent impedance changes, indicating BBB integrity and distinguishing well-formed from non-mature barriers. The data from EIS monitoring was confirmed by permeability assays performed with a fluorescence tracer. The model incorporates human endothelial cells in a vessel-like arrangement to mimic the vascular component and three-dimensional cell distribution of human astrocytes and microglia to simulate the parenchymal compartment. By modeling the BBB-on-a-chip with an equivalent circuit, a more accurate trans-endothelial electrical resistance (TEER) value was extracted. The device demonstrated successful BBB formation and maturation, confirmed through live/dead assays, immunofluorescence and permeability assays. Computational fluid dynamics (CFD) simulations confirmed that the device mimics <em>in vivo</em> shear stress conditions. Drug crossing assessment was performed with two chemotherapy drugs: doxorubicin, with a known poor BBB penetration, and temozolomide, conversely specific drug for CNS disorders and able to cross the BBB, to validate the model predictive capability for drug crossing behavior. The proposed sensorized microfluidic device represents a significant advancement in BBB modeling, offering a versatile platform for CNS drug development, disease modeling, and personalized medicine.</span></p>
Fig. 1 in LiDAR sensors in smartphones can enrich herbarium specimens with 3D models of habitat at high precision and little cost
Fig. 1. Example of a 3D point-cloud model of specimen habitat obtained with the LiDAR scanner of an iPad Pro. A, Plan view of the model with potential use cases, including annotation and extraction of general habitat characteristics; B, Side view with measurements that can be extracted from the model at centimetre precision (DBH, diameter at breast height); C, Average times needed for physical herbarium specimen collection (orange) and LiDAR scanning (purple) in the field over 20 replicates; time for scanning depends on the area scanned and the habitat.
Data products for "3D modeling of long-term slow slip events along the flat-slab segment in the Guerrero Seismic Gap, Mexico"
<p>Data products for '3D modeling of long-term slow slip events along the flat-slab segment in the Guerrero Seismic Gap, Mexico' by A. Perez-Silva, D. Li, A.-A. Gabriel and Y. Kaneko</p>
Parametric 3D CAD model of human foot
<p>The parametric 3D CAD model of human foot was developed from CT data. A CT (Toshiba® Aquilion 4 equipment) scan was performed on a 29 years old male (65 Kg). 345 slices were captured with a slice distance of 1.0 mm (see Figure 2.a). Scans were made for both feet in their neutral posture in which there is the least tension or pressure on tendons, muscles and bones. Medical images were, then, exported into standard .DICOM format (image resolution 512x512 pixels) and processed by using ScanIP® and SolidWorks.</p> <p>Bone structure was composed of 19 bones: tibia, fibula, talus, calcaneus, cuboid, navicular, 3 cuneiforms (bones of the metatarsus), 5 metatarsals (bones of the metatarsus) and 5 components of the phalanges (bones of the toes). Phalange bones (a proximal and a distal phalanx for the great toe; proximal, middle and distal phalanges for the second to fifth toes) were fused together since their relative motion do not affect plantar pressures.</p> <p>More details can be found in the publications below:</p> <ol> <li><strong>Franciosa, P.</strong>, Gerbino S., From CT Scan to Plantar Pressure Map Distribution of a 3D Anatomic Human Foot, in Proc. of COMSOL Conference’10, Paris (France), November 17-19, 2010.</li> <li><strong>Franciosa, P.</strong>, Gerbino, S., Lanzotti A., Silvestri L., Improving Comfort of Shoe Sole through Experiments based on CAD-FEM Modeling, Medical Engineering and Physics, doi:10.1016/j.medengphy.2012.03.007, 2013.</li> </ol>
U-Net model for Arabidopsis segmentation in 3D
<p>U-Net segmentation model to be used in Vollseg notebooks and scripts for 3D segmentation of Arabidopsis membrane datasets in 3D.</p>
3D crustal velocity model for the wider Zagreb area
<p>The 3D structural model covers 60 km by 80 km area around the city of Zagreb, Croatia, and extends to the depth of 60 km. It describes seismologically relevant parameters, density, P- and S-wave velocity, on a working grid of 125 m in UTM (zone 33N) coordinate system. The model is represented by four main layers: sediments, upper crust, lower crust and mantle. The format of the model is suitable for simulations obtained using software package SPECFEM3D Cartesian (<a href="http://geodynamics.org/cig/software/specfem3d/">geodynamics.org/cig/software/specfem3d/</a>; accessed Aug 2021).<br> Description of the formatting of the file can be found on the SPECFEM3D 'engCartesian package documentation site:<br> <a href="https://specfem3d.readthedocs.io/en/latest/13_changing_the_model/#using-external-tomographic-earth-models">https://specfem3d.readthedocs.io/en/latest/13_changing_the_model/#using-external-tomographic-earth-models</a><br> (accessed Aug 2021).</p> <p>3D seismic model for the wider Zagreb area was assembled using publicly available geological and geophysical data. It describes in detail main structures observed in the uppermost part of the crust (e.g. sedimentary basins and high-velocity structures) and is embedded within the regional EPcrust crustal model (<a href="https://doi.org/10.1111/j.1365-246X.2011.04940.x">https://doi.org/10.1111/j.1365-246X.2011.04940.x</a>). The performance of<br> the model was tested by simulating ground motion for several moderate earthquakes. Results show that the 3D model is able to reproduce main characteristics of the ground motion, primarily shaking duration and amplification effects. Therefore, it is suited for simulation of shaking scenarios in the wider Zagreb area, mostly for T > 1 s.</p>
Text-fig. 1. Spermophilinus bredai (VON MEYER, 1848): 3D models of a right upper jaw with (abnormal) P3, P4 and all molars (mirrored; NMA-2019-1/2352). a – the specimen in occlusal view showing the relative position of the three roots in place of P3 (yellow) compared to P4 (green); b – same view, with focus on P3–P4; c – lingual view; d – mesial view. Scale bars 2 mm. in Dental Anomaly In A Middle Miocene Fossil Of The Genus Spermophilinus (Rodentia, Sciuridae) From Southern Germany
Text-fig. 1. Spermophilinus bredai (VON MEYER, 1848): 3D models of a right upper jaw with (abnormal) P3, P4 and all molars (mirrored; NMA-2019-1/2352). a – the specimen in occlusal view showing the relative position of the three roots in place of P3 (yellow) compared to P4 (green); b – same view, with focus on P3–P4; c – lingual view; d – mesial view. Scale bars 2 mm.
Early 3D Evolution of the SARS-CoV-2 proteome -- Supplementary Tables and Models
<p><strong>Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic</strong></p> <p><a href="https://iqb.rutgers.edu/covid-19_proteome_evolution">https://iqb.rutgers.edu/covid-19_proteome_evolution</a></p> <p> </p> <p><strong>Legends for Supplementary Figures for 29 </strong><strong>SARS-CoV-2 Study Proteins</strong></p> <p><strong>Separate analysis of protein changes was performed for each study protein and complex. Description below applies to all figures.</strong></p> <p><strong>A</strong>: Observed frequencies for all USV substitutions of Native Residue (i.e., amino acid type in the reference protein sequence) changing to Substituted Residue for a given protein/complex. Red boxes enclose conservative substitutions for hydrophobic, uncharged polar, positively charged, and negatively charged amino acids, respectively in order from upper left to lower right. Cysteine, Glycine and Proline are excluded from these groupings.</p> <p><strong>B-D</strong>: Normalized Frequency histograms for ΔΔG<sup>App</sup> calculated for all USVs for a given protein/complex. These were calculated using three methods, which we refer to as hard-hard (B), soft-hard (C), and soft-soft (D), based on the scoring functions used for sidechain rotamer optimization and gradient-based energy minimization respectively (see methods). All energy values described in the text were obtained using the soft-hard method. Overlay of energy histogram with fitted bi-Gaussian curve (solid red line) and fitted single Gaussian curves for subsets of USVs with surface (green), boundary layer (yellow), or core (blue) substitutions. USVs with multiple substitutions were included in single Gaussian fitting when all substitutions mapped to the same region of the study protein. The data used for fitting includes the energies of all unique protein models produced by a given method, excluding extreme outliers with energy values greater than 3 standard deviations away from the central mean.</p> <p><strong>E-G</strong>: USV Count histograms indicate the number of USVs among the full set for a given protein in which each site included a substitution. Sites are separated by burial layer. Substitutions at sites that are absent from the available crystal structures are excluded from the histograms. In most cases, only a single protein is analyzed, and only panel E is included. In the case of complexes, a separate histogram is provided for each protein in the complex: for methyltransferase nsp10-nsp16, E is nsp10 and F is nsp16; for RDRP nsp12-nsp7-nsp8, E is nsp7, F is nsp8, and G is nsp12.</p> <p> </p> <p><strong>Legends for Supplementary Tables for 29 </strong><strong>SARS-CoV-2 Study Proteins</strong></p> <p><strong>Table: USVs</strong>: All identified USVs for a protein/complex. Columns are:</p> <ul> <li>date: Date of first collection of a strain with the USV reported to GISAID</li> <li>gisaid_count: The number of sequences in the GISAID database that include the USV</li> <li>id: The GISAID strain identification for the first collected instance of the USV</li> <li>location: The country in which the first strain including the USV was collected</li> <li>substitutions: All substitutions in the USV, in the form [chain]_[sequence][site][substitution], with multiple substitutions separated by semicolons</li> <li>is_in_PDB: whether a substitution is present in the PDB model used to generate the USV structure, with multiple substitutions separated by semicolons</li> <li>multiple: whether more than one amino acid substitution is present in the USV</li> <li>conservative: whether a substitution is conservative, with multiple substitutions separated by semicolons</li> <li>layer: Identification of the burial layer (surface, boundary, or core) of a substitution in the reference structure, with multiple substitutions separated by semicolons and substitutions absent from the PDB excluded</li> <li>sh_rmsd: The RMSD of the USV to the reference structure when modeled using the soft-hard method</li> <li>sh_ddG: The ΔΔG<sup>App</sup> of the USV when modeled using the soft-hard method</li> <li>hh_rmsd: The RMSD of the USV to the reference structure when modeled using the hard-hard method</li> <li>hh_ddG: The ΔΔG<sup>App</sup> of the USV when modeled using the hard-hard method</li> <li>ss_rmsd: The RMSD of the USV to the reference structure when modeled using the soft-soft method</li> <li>ss_ddG: The ΔΔG<sup>App</sup> of the USV when modeled using the soft-soft method</li> </ul> <p> </p> <p><strong>Table: Substitutions</strong>: All substitutions identified for a protein/complex</p> <ul> <li>chain: The chain identifier of the protein in the PDB file in which the substitution is present</li> <li>site: The residue number at which the substitution is present</li> <li>reference: The one-letter amino acid name of the residue in the reference sequence</li> <li>mutant: The one-letter amino acid name of the residue in a USV</li> <li>conservative: Indication of whether a substitution is conservative</li> <li>in_pdb: whether the substitution site is present in the PDB model used to generate the USV structure</li> <li>layer: Identification of the burial layer (surface, boundary, or core) of a substitution in the reference structure</li> <li>date: date: Date of first collection of a strain with the substitution reported to GISAID</li> <li>location: The country in which the first strain including the substitution was collected</li> <li>gisaid_count: The number of sequences in the GISAID database including the substitution</li> <li>usv_count: The number of identified USVs including the substitution</li> <li>ddG: The soft-hard ΔΔG<sup>App</sup> of the USV that includes only the substitution, left empty if no single-substitution USV was identified with the substitution</li> <li>single: Indication of whether the substitution was present in a single-substitution USV</li> <li>multiple: Indication of whether the substitution was present in a USV with multiple substitutions</li> <li>associates: List of all other substitutions that were identified in a USV that included the substitution</li> <li>strains: List of all USV-representative GISAID strains that included the substitution, with the single-substitution USV strain listed first if one was available</li> </ul> <p> </p> <p><strong>Table: Gaussian Fit Statistics</strong>: Fitted models for the energies of all USVs either together (ALL) or by study protein.</p> <ul> <li>fit: The number of Gaussian curves in the fitted energy model </li> <li>protein: The protein/complex name</li> <li>method: The modeling method used to calculate energy values</li> <li>layer: The subset burial layer (surface, boundary, or core) of USVs for which the energy model was fitted, excluding all USVs with substitutions not in that layer</li> <li>μ<sub>1</sub>: Mean of the first Gaussian in the fitted model</li> <li>σ<sub>1</sub>: Variance of the first Gaussian in the fitted model</li> <li>wt<sub>1</sub>: Weight of the first Gaussian in the fitted model</li> <li>μ<sub>2</sub>: Mean of the second Gaussian in the fitted model</li> <li>σ<sub>2</sub>: Variance of the second Gaussian in the fitted model</li> <li>wt<sub>2</sub>: Weight of the second Gaussian in the fitted model</li> <li>R<sup>2</sup>: R-squared value indicating the goodness of fit</li> </ul> <p> </p> <p><strong>Description of Computed Structural Models </strong><strong>for Unique Sequence Variants for 29 </strong><strong>SARS-CoV-2 Study Proteins.</strong></p> <p><strong>USV Computed Structural Models</strong>. Computed structural models for all amino acid substituted USVs. We are providing the structural models of all study proteins modeled using the soft-hard modeling method (see Methods). Structural models are named according to the GISAID strain identification of the first strain in which the USV was identified, followed by an underscore-separated list of substitutions in the form [chain]_[sequence][site][substitution]. Atomic coordinates for each computed structural model are provided in the legacy Protein Data Bank format used by most molecular graphics software tools (see <a href="https://www.wwpdb.org/documentation/file-format-content/format33/v3.3.html">https://www.wwpdb.org/documentation/file-format-content/format33/v3.3.html</a> for detailed description).</p>
Ecological signal in the size and shape of marine amniote teeth – 3D models and landmarks
<p class="MsoNormal"><span>Amniotes have been a major component of marine trophic chains from the beginning of the Triassic to present day, with hundreds of species. However, inferences of their (palaeo)ecology have mostly been qualitative, making it difficult to track how dietary niches have changed through time and across clades. Here, we tackle this issue by applying a novel geometric morphometric protocol to 3D models of tooth crowns across a wide range of raptorial marine amniotes. Our </span><span>results highlight the phenomenon of dental simplification and widespread convergence in marine amniotes, implying strong functional constraints which limit the range of tooth crown morphologies. </span><span>Importantly, we quantitatively demonstrate that tooth </span><span>crown form (shape plus size) is strongly associated with diet, whereas crown surface complexity is not. The maximal range of tooth shapes in both mammals and reptiles is seen in medium-sized taxa; large crowns are simple and restricted to a fraction of the morphospace. </span><span>We recognise four principle raptorial guilds within toothed marine amniotes (durophages, generalists, meat cutters, and flesh piercers). Moreover, even though all these feeding guilds have been convergently colonised over the last 200 million years, a series of dental morphologies are unique to the Mesozoic period, probably reflecting a distinct ecosystem structure.</span></p>
3D Models of Devil's Throat and Twin Pits Pit Craters
<p>3D models of Devil's Throat and Twin Pits pit craters in Hawai'i Volcanoes National Park, from 2017 and 2022. Estimated absolute positional accuracy is ~40 m, and estimated vertical orientation certainty is ±~7°.</p>
A Mixed-Flux-Based Nodal Discontinuous Galerkin Method for 3D Dynamic Rupture Modeling
<p>This repository contains data produced by a mixed-flux-based discontinuous Galerkin method for 3D dynamic rupture modeling, using the software DRDG3D (<a href="https://github.com/wqseis/drdg3d">https://github.com/wqseis/drdg3d</a>). Input scripts for the SCEC/USGS dynamic rupture benchmark validation problems (<a href="https://strike.scec.org/cvws">https://strike.scec.org/cvws</a>) and other cases are hosted on DRDG3D's GitHub page. The preprint is published at ESS Open Archive (DOI: <a href="http://doi.org/10.1002/essoar.10512657.1">10.1002/essoar.10512657.1</a>).</p>
3D models of bead, pendant and softstone vessel fragments found during the 2021 excavations at Kalba (K4)
<p>The 3D models of the three objects were created within the joint excavations project at Kalba (K4), United Arab Emirates, of the Sharjah Archaeology Authority (SAA), the Austrian Archaeological Institute (OeAI) of the Austrian Academy of Sciences (OeAW) and the Leibniz-Zentrum für Archäologie (LEIZA).</p> <p>For each object the following data are available: 3D model with colour values reflecting the curvature (MSII filter) in ply format, texture image for the 3D model in png format, metadata of the 3D acquisition and processing in ttl and json format.</p> <p>The 3D models were created with the Structure from Motion (SfM) technique using Agisoft Metashape software. The images were taken with a Nikon Z50 mirrorless camera and a 90 mm lens. The models obtained were post-processed using GigaMesh (https://gigamesh.eu) to calculate curvature with the MSII built-in filter and to create scaled 2D views. The export of the 3D metadata with information about the 3D model and the 3D acquisition and processing was done with pyhton scripts in agisoft metashape (https://doi.org/10.5281/zenodo.7468298).</p>
Qesem Cave, 3D model and orthophoto, DFG-Project UT41/4-1
<p>A 3D documentation of the cave as a whole and its surroundings (by drone) was carried out in cooperation with D. Hoffmeister and produced a 3D-model of the cave and the current excavation areas available Open Access under Zenodo. Surveys included terrestrial laser scanning of the cave site, drone flights on the slope including the cave site, and tachymetric measurements to establish a combined result of the previously mentioned measurements and an incorporation of the excavation grid and plans. Terrestrial laser scanning was conducted with a FARO Focus 3D LS120 laser scanner set up on 22 single locations. Single laser scans were combined by using the tachymetric measurements of reflecting spheres and a following fine-tuning of this initial registration with an iterative closest point algorithm. The final point cloud consists of 46 million single points. Likewise, 8 ground control points for the drone flights and 10 connection points with the excavation grid were measured by the same method and within the same local surveying system. The drone flight was conducted with a DJI Phantom 4, and the processing of the single images was conducted with Agisoft Metashape. By a mean flight altitude of about 35 m above the terrain, the final digital elevation model resulted in a resolution of 2.6 cm per pixel and the orthophoto in a resolution of 1.3 cm per pixel with an accuracy of about 2 cm. The final 3D point cloud from both surveys was manually cleaned from shrubs, trees etc. and consists of 24 million points used to establish a 3D model by meshing the single 3D points (software: Geomagic Wrap), presented in Figure X. The 3D model was used by the team for a better understanding of the setting and are the basis for ongoing research using Geographic Information Systems (GIS).</p>
3D structural and probabilistic modeling of geothermal reservoir horizons in the Northern Eifel and its foreland
<p>This repository contains the supplementary data to the submitted publication titled "3D structural and probabilistic modeling of geothermal reservoir horizons in the Northern Eifel and its foreland" which was submitted to the Journal "Geothermal Energy" (https://geothermal-energy-journal.springeropen.com/). </p>
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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.