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1,007 results for “three dimensional”
Data from: In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrythmia risk assessment
<p>Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. <em>In vitro</em> human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing <em>in vitro</em> and <em>in vivo</em> models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established three-dimensional human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted <em>in vitro</em> to <em>in vivo</em> therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for two physiologically relevant metrics of proarrhythmic risk (<em>i.e.</em>, action potential duration and triangulation assessed by optical mapping) across concentrations spanning three orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of four compounds with a range of known proarrhythmic risk profiles (<em>i.e., </em>quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human induced pluripotent stem cell-based three-dimensional cardiac tissue models as new approach methodologies that enable <em>in vitro</em> proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.</p>
Instantaneous, three-dimensional velocity fields past a bio-prosthetic aortic valve measured in-vitro with tomographic particle image velocimetry.
<p>Each folder contains instantaneous, three-dimensional velocity vector data obtained in a simplified model of the aortic root with a distinct size and geometry (small, medium, large, and sinus-less). The specific geometry of each aortic root model is contained in the corresponding folder.</p> <p>The velocity data is structured in the following way: Two separate folders for velocity data in the "ascending aorta" domain (AAo) and in the "sinus of Valsalva" domain (SOV). Each domain contains velocity datasets for instances t=0.00, 0.03, 0.06, ..., 0.39 s (t000, t003, t006, ..., t039). Each velocity dataset contains N=16 phase-locked instantaneous 3D velocity fields.</p> <p>The data was acquired using tomographic particle image velocimetry and a custom built hydraulic setup capable of replicating normal physiological flow conditions in the human aorta (heart rate = 72 bpm, cardiac output = 4.8 l)</p> <p>Data format:</p> <p>- aortic root geometry: STL (the geometry is provided with respect to the reference frame of the velocity data)</p> <p>- velocity data: NPY (NumPy), shape= (N_nodes, 6), columns contain X, Y, Z, U, V, W data, where U, V, W are the X, Y, Z components of the instantaneous vector field</p>
Three dimensional characterization of nickel coarsening in solid oxide cells via ex-situ ptychographic nano-tomography
<p>Three-dimensional dataset of a solid oxide cell (SOC) fuel electrode microstructure. Data were acquired using ptychographic X-ray computed tomography (PXCT). </p>
Micro-CT scans, whole-test meshes, and internal chamber segments of planktonic foraminifera for three-dimensional analysis of inter- and intra-specific variation in ontogenetic growth trajectories
<p> </p> <p>Here, we release tomographic reconstructions of 42 planktonic foraminifera from plankton tows and sediment traps, along with meshes and shrinkwrap meshes the whole tests and internal meshes of segmented chambers. Shrinkwrap meshes are test meshes that have been modified to close all pores and apertures in the test. Additionally, we have provided sample metadata for each specimen and volumetric measurements for the tests and chambers. This dataset was used in a study of ontogenetic growth in planktonic foraminifera and its variation within and among species.</p> <p> The CT-scans and reconstructions were obtained at Naturalis Biodiversity Center in Leiden, the Netherlands with a Zeiss Xradia 520 Versa micro-CT scanner. The meshes and segments were created at Yale University.</p> <ol> <li>Sample_Metadata.csv: Spreadsheet containing information on the sampling localities and dates for all specimens.</li> <li>Scan_data.csv: Spreadsheet containing metadata for all micro-CT scans including current strength, pixel size, voltage, image height, image width, and the number of images taken.</li> <li>Whole_Test_Measurements.csv: Spreadsheet containing measurements of linear dimensions (axis1, axis2, axis 3), total number of chambers, calcite test volume, calcite test surface area, shrinkwrap volumes, and and shrinkwrap surface areas for all specimens.</li> <li>Chamber_Measurements.csv: Spreadsheet containing measurements of individual internal chamber segments, including position from the final chamber (F-chamber), position from the first chamber (Chamber), volume, and surface area.</li> <li>CT_Scan_Stacks.zip: reconstructed micro-CT image stacks (.tif files) for each specimen.</li> <li>Meshes.zip: Meshes of the test calcite, the shrinkwrap, and the internal chamber segments for each specimen (.stl 3D mesh files). Regular test meshes are named with the format “SampleID.stl”, and shrinkwrap meshes are named “SampleID-WRAP.stl”. Chamber meshes are named “SampleID-CH#.stl” and “SampleID-CH#-Wrap.stl”. Chambers are numbered in relation to their position from the final chamber, with “CH1” being the final chamber and “CH2” being the penultimate chamber.</li> </ol> <p>This data is described and analyzed in the manuscript “Three-Dimensional Analysis of Inter- and Intraspecific Variation in Ontogenetic Growth Trajectories of Planktonic Foraminifera” submitted to the journal <em>Marine Micropaleontology.</em></p>
Video supplement: A three-dimensional palaeo-reconstruction of the groundwater salinity distribution in the Nile Delta Aquifer
<p>Videos of validated models. The filenames indicate simulation code, for explanation see accompanying paper, submitted to HESS.</p> <p><strong>Abstract</strong></p> <p>The Nile Delta is an important agricultural area with a fast-growing population. Though traditionally irrigated with surface water, the delta increasingly relies on groundwater. However, saline groundwater extends far land inward, rendering groundwater close to the coastal zone useless for consumption or agriculture. To aid groundwater management decisions, hydrogeologists reconstructed this saline and brackish groundwater zone using variable-density groundwater models with very large dispersivities. However, this approach cannot explain the observed freshening of this zone as observed by hydrogeochemists, who hypothesize that the coastal saline zone is the effect of the Holocene transgression. Here, we investigated physical plausibility of this hypothesis by conducting a palaeo-reconstruction of groundwater salinity for the last 32 ka with a complex 3D variable-density groundwater flow model, using state-of-the-art model code that allows for parallel computation. Several scenarios with different lithologies and hypersaline groundwater provenances were simulated, of which five were selected that showed the best match with the observations. Amongst these selections, total fresh water volumes varied strongly, ranging from 1526 to 2659 km3, mainly due to uncertainties in the lithology offshore and at larger depths. This range is smaller (1511-1989 km3) when we consider the volumes of onshore fresh groundwater within 300 m depth. Regardless of the variance, in all cases the total volume of hypersaline groundwater exceeded that of sea water. We also show that during the last 32 ka, the total fresh groundwater volumes significantly declined, with a factor ranging from 1.9 to 5.4, due to the rising sea-level. Compared to a steady-state solution with present-day boundary conditions, the palaeo-reconstruction improved our validation for the saline zone (5 g/L – 35 g/L TDS). Also, under highly permeable conditions the marine transgression simulated with the palaeo-reconstruction led to a steeper fresh-salt interface compared to its steady-state equivalent, while low permeable clay layers allowed for the preservation of volumes of fresh groundwater. This shows that long-term transient simulations are needed when estimating present-day fresh-salt groundwater distribution in large deltas. The insights of this study are also applicable to other major deltaic areas, given the wide-range of lithological model scenarios used in this study and since many deltas also experienced a Holocene marine transgression.</p>
Data for three-dimensional active nematic turbulence
<p>Selected data from numerical simulation of bulk three-dimensional active nematic turbulence. Data includes nematic Q-tensor and the velocity field. The outer layer of data points is used for allocation of periodic boundary conditions. Symmetric Q-tensor is written in the order (Q<sub>xx</sub>, Q<sub>yy</sub>, Q<sub>zz</sub>, Q<sub>xy</sub>, Q<sub>xz</sub>, Q<sub>yz</sub>). Data points are written with x as the fast coordinate. The data is obtained by numerically solving the Beris-Edwards model for nematic hydrodynamics, generalized by the active stress term [1,2]. The details of the data and the numerical model are available in publication "Spectral energy analysis of bulk three-dimensional active nematic turbulence" [3].</p> <p>Files:</p> <ul> <li>"active_turbulence-135x135x135-0.300.tar" — A timeline of active turbulence including Q-tensor and velocity profile in a box of size (135)<sup>3</sup> at activity 0.3 L/Δx<sup>2</sup>. The timeline includes 26000 steps, relevant fields are written every 50 steps.</li> <li>"active_turbulence-407x407x407-0.025_Q.raw.gz" — Q-tensor profile in a box of size (407)<sup>3</sup> at activity 0.025 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.025_u.raw.gz" — velocity profile in a box of size (407)<sup>3</sup> at activity 0.025 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.100_Q.raw.gz" — Q-tensor profile in a box of size (407)<sup>3</sup> at activity 0.1 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.100_u.raw.gz" — velocity profile in a box of size (407)<sup>3</sup> at activity 0.1 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.300_Q.raw.gz" — Q-tensor profile in a box of size (407)<sup>3</sup> at activity 0.3 L/Δx<sup>2</sup>.</li> <li>"active_turbulence-407x407x407-0.300_u.raw.gz" — velocity profile in a box of size (407)<sup>3</sup> at activity 0.3 L/Δx<sup>2</sup>.</li> </ul> <p> </p> <p> </p>
Fig. 11 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 11. Vertical displacement after circular and pressure-shaped curvilinear quadrilateral. A. Vertical displacement after a circular pressure. B. Vertical displacement after a pressure-shaped curvilinear quadrilateral with concave and convex alternate sides.
Fig. 10 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 10. Deformed mesh and vertical displacement in layer 1, scaled 20 times. A. Deformed small footprint, mesh after footfall (surface of layer 1); scaled up 20 times. B. Vertical displacements after footfall (surface of layer 1); scale in mm. C. Vertical displacements after footfall (surface of layer 2); scale in mm. D. Deformed mesh after the footfall (surface of layer 1); at true scale.
Fig. 9 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 9. Deformed mesh and vertical displacement in layer 2. A. Deformed big footprint, mesh after footfall (surface of layer 2); at true scale. B. Vertical displacements after the footfall (surface of layer 2); scale in mm.
Fig. 8 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 8. Deformed mesh and vertical displacement in layer 1. A. Deformed mesh after a footfall in a plastic layer 1; at true scale. B. Vertical displacement after the footfall (surface of the plastic layer 1); scale in mm.
Fig. 7 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 7. Results of the calculation model. A. Calculation model for big footprint after the footfall. B. Deformed mesh (at true scale) after the footfall (surface of layer 1). C. Detail of simulation model with phreatic level. D. Vertical displacement after the footfall (surface of layer 1); scale in mm. E. Position of the vertical section. F. Vertical displacements of the section shown in E.
Fig. 5. Track 95 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 5. Track 95 in layer VIII-B of the Miraflores I tracksite, Berriasian (Early Cretaceous), Soria, Spain, showing footprint section. Scale 120 mm.
Fig. 4. Tracks 86 and 95 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 4. Tracks 86 and 95 in the Miraflores I tracksite. A. Plan view of the autopod, without toe marks, that formed tracks 86 and 95; horizontal line indicates the zone where the section was made. B. Section of the track/ footprint to be reproduced, with mean values of the vertical displacements and measurements of thickness, both in mm.
Fig. 3. Scheme showing traces from layer VIII-B in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 3. Scheme showing traces from layer VIII-B of Miraflores I tracksite and view of the tracks 86 and 95.
Fig. 2 in Three-dimensional elasto-plastic soil modelling and analysis of sauropod tracks
Fig. 2. Stratigraphic column of the Miraflores I tracksite, showing the layers containing dinosaur tracks (Roman numerals).
FIGURE 5 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 5. Principal components analysis of 3D fixed and semi landmarks. 1, PC 1 vs PC 2; point size represents relative centroid size of specimen. 2-4, Landmark configuration of shape represented by low PC 1 score, typical of largest specimens, in dorsal, anterior, and right lateral views, respectively. 5-7, Landmark configuration of shape represented by high PC 1 score, typical of smallest specimens, in dorsal, anterior, and right lateral views, respectively.
FIGURE 4 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 4. Slice through volume rendering of Cryptolithus tesselatus (AMNH FI-101479), shown in dorsal view in inset. Red line in inset shows the orientation of the slice across the specimen. Bright white area is sediment trapped within the bilaminar structure of the cephalon. Blue arrows point to suture between upper and lower lamellae; red arrows point to fringe-pits.
FIGURE 1 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 1. Cephalon of Cryptolithus tesselatus (AMNH FI-101479) showing morphological terms used in this paper, following Whittington (1968) and Hughes et al. (1975). Concentric arcs are labeled according to their placement relative to the girder (expressed on the ventral side): E = external; I = internal. "Fringe-pits" are circled in yellow; the "F-pits" represent a subset of these interior to the labeled concentric arcs. Specimen is 6.6 mm long.
FIGURE 8 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 8. Length vs width of Cryptolithus tesselatus cephala, coded for the number of concentric arcs of fringe-pits expressed in each specimen. The first three concentric arcs (E, I1, and I2) are complete when first expressed. Based on clustering, I3 is likely completed over three molts, first by only 1-3 fringe-pits, then 8-10 fringe-pits, then 13-15 fringe-pits with the anteriormost in line with the 10th radial rows of fringe-pits in arcs E-I. The dataset includes the 23 2 specimens, which were CT-scanned as well as 31 additional silicified specimens from the collection; specimens that were CT-scanned are outlined in red. Arrows indicate the specimens shown in Figures 1 and 7. Inset in upper right corner is a magnified view of the specimens in the dashed box. The scaling component describing the relationship between length and width is 1.153 (1 = isometric growth).
FIGURE 7 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 7. Additions of fringe-pits associated with early meraspid stages of ontogeny in Cryptolithus tesselatus. 1, meraspid stage 2 showing two concentric arcs of fringe-pits, AMNH FI-101498, x35. 2, meraspid stage 2 showing two concentric arcs of fringe-pits, AMNH FI-101499, x35. 3, merapid stage 3 showing three concentric arcs of fringe-pits and first few fringe-pits of I3, FI-101496, x20. 4, later meraspid stage showing complete set of fringe-pits, AMNH FI- 101494, x15. Scale bars are 1 mm.
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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.