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249 results for “3D Structure”
Datasets to quantify the impact of lianas on 3D tree structure and biomass
<p>This dataset consists of terrestrial laser scanning (TLS) point clouds and their corresponding quantitative structure models (QSMs) of 182 trees. There are two point clouds for each tree, one containing only the wood points and the other containing the leaf points of the tree. The QSMs of the trees are in .mat format and the detailed readME file to read the QSMs can be found here: https://github.com/InverseTampere/TreeQSM.</p> <p> </p>
Anisotropic coverage control for surveillance of 3D structures
<p>This demonstration illustrates the execution of a distributed algorithm for the deployment of a team of Aerial Robotic Workers (ARWs) for coverage of a 3D structure. One physical ARW and two simulated ARWs are used in the demonstration.</p> <p>The position and orientation of each ARW is represented as a colored circle and arrow.</p> <p>The structure to cover is abstracted into a finite set of landmarks, and each landmark is represented as a colored arrow. The color of a landmark corresponds to the ARW which is currently responsible for covering that landmark. The direction of the arrow corresponds to the outward normal to the surface at the position of the landmark.</p> <p>The control algorithm prescribes the motion of the ARWs and the distribution of the landmarks among the ARWs. In particular, an ARW is allowed to intermittently yield responsibility of some of its landmarks to a different ARW. The motion and the landmark transfers are programmed in such a way to gradually increment the coverage of the structure. The algorithm is based on a generalization of the concept of Voronoi tessellations.</p> <p>The algorithm is implemented on a ROS architecture, where the controller for each ARW corresponds to a different ROS node. The pose of the physical ARW is measured in real time with a motion capture system.</p> <p>In the demonstration, we can see that three clusters of landmarks emerge, and that the ARWs position themselves in front of the landmarks that they are responsible to cover, at a suitable distance.</p>
Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'
<p>This is the supplemental data for the manuscript titled ‘<em>Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’</em> submitted to <em>Ultramicroscopy</em>.</p> <p><strong>Motivation:</strong></p> <p>Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network (CNN) is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts.</p> <p><strong>Authors and funding:</strong></p> <p>The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1 and under contract number INST 144/462-1 FUGG.</p> <pre> </pre> <p><strong>Dataset description:</strong></p> <p>We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data.</p> <p><em>training_data.zip</em> contains the training data. In the 3D case, the subfolder <em>mask</em> contains the ground truth segmentation masks; the subfolder <em>reconstruction</em> contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder <em>HAADF</em> contains the 2D-projection images. In both cases, the subfolder <em>json </em>contains the annotation. Each file within the <em>json</em> folder provides for each image or reconstruction the following information:</p> <p>· aggregat_no: image id, the number of the corresponding image file</p> <p>· particle_position_x: list of particle position x-coordinates in nm</p> <p>· particle_position_y: list of particle position y-coordinates in nm</p> <p>· particle_position_z: list of particle position z-coordinates in nm</p> <p>· particle_radius: list of volume equivalent particle radii in nm</p> <p>· particle_type: list of particle types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></p> <p>· particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</p> <p>· rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</p> <p>· deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</p> <p>· cluster_index: list of cluster indices for each particle</p> <p>· initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</p> <p>· fractal_dimension: the intended fractal dimension of the aggregate</p> <p>· fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</p> <p>· fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</p> <p>· fractal_prefactor: fractal prefactor</p> <p>· mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</p> <p>· mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</p> <p>· particle_1_rho: density of TiO<sub>2</sub> used for the calculations</p> <p>· particle_1_size_mean: mean TiO<sub>2</sub> radius</p> <p>· particle_1_size_min: smallest TiO<sub>2</sub> radius</p> <p>· particle_1_size_max: largest TiO<sub>2</sub> radius</p> <p>· particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</p> <p>· particle_1_clustersize: average TiO<sub>2</sub> cluster size</p> <p>· particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</p> <p>· particle_2_rho: density of WO<sub>3 </sub>used for the calculations</p> <p>· particle_2_size_mean: mean WO<sub>3</sub> radius</p> <p>· particle_2_size_min: smallest WO<sub>3</sub> radius</p> <p>· particle_2_size_max: largest WO<sub>3</sub> radius</p> <p>· particle_2_size_std: standard deviation of WO<sub>3</sub> radii</p> <p>· particle_2_clustersize: average WO<sub>3</sub> cluster size</p> <p>· particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</p> <p>· number_of_primary_particles: number of particles within the aggregate</p> <p>· gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</p> <p>· gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</p> <p>· mean_coordination: mean total coordination number (particle contacts)</p> <p>· mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</p> <p>· mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</p> <p>· material_1: the name of the first material (TiO2)</p> <p>· material_2: the name of the second material (WO3)</p> <p>· radius_equiv: list of area equivalent particle radii (in projection) in nm</p> <p>· k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</p> <p>· polygons: list of polygons that surround the particle (COCO annotation)</p> <p>· bboxes: list of particle bounding boxes</p> <p>· aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</p> <p>· n_pix: number of pixel per image in horizontal and vertical direction (squared images)</p> <p>· pixel_size: pixel size in nm</p> <p>· image_size: image size in nm</p> <p>· add_poisson_noise: 1 if poisson noise was added, 0 otherwise</p> <p>· frame_time: simulated frame time (required for poisson noise)</p> <p>· dwell_time: dwell time per pixel (required for poisson noise)</p> <p>· beam_current: beam current (required for poisson noise)</p> <p>· electrons_per_pixel: number of electrons per pixel</p> <p>· dose: electron dose in electrons per Å<sup>2</sup></p> <p>· add_scan_noise: 1 if scan noise was added, 0 otherwise</p> <p>· beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</p> <p>· scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise)</p> <p>· add_focus_dependence: 1 if a focus effect is included, 0 otherwise</p> <p>· add_partial_coating: 1 if some TiO<sub>2</sub> particles were coated by a thin WO<sub>3</sub> film, 0 otherwise.</p> <p>· data_format: data format of the images, e.g. uint8</p> <p>For 3D reconstructions, the following information is added:</p> <p>· add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise.</p> <p>· add_projection_noise: 1 if noise was added to projection angles, 0 otherwise.</p> <p>· N_SIRT: number of SIRT iterations for the 3D-reconstruction.</p> <p>· proj_angles: list of angles used for the projection directions of the tilt series in rad.</p> <p>For the 2D case, there are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder <em>COCO</em>. For training, validation and test data there is a file <em>annotation_COCO.json</em> that includes all information required for the CNN training.</p> <p>For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script.</p> <p>The zip archive <em>models.zip</em> includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file <em>2D_projection/logs/fit/20240209-095952/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook <em>2D_projection_prediction.ipynb</em>. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>2D_projection/logs/fit</em> <em>20240209-095952/config_file.py</em>. In the 3D case, network weights are stored in <em>3D_reconstruction/model/weights_best.h5</em>. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook <em>3D_reconstruction_prediction.ipynb</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN.</p> <p>The zip archive<em> experiment_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs.</p> <p>The zip archive <em>simulation_measurement.zip</em> includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks.</p> <p>The zip archive<em> simulation_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process.</p> <p>In the zip archive <em>code.zip</em>, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed.</p> <p>· <em>2D_projection_training.py:</em> This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox.</p> <p>· <em>3D_reconstruction_training.py</em>: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package.</p> <p>· <em>2D_projection_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data.</p> <p>· <em>3D_reconstruction_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data.</p> <p>· <em>Evaluation_experiment.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations.</p> <p>· <em>Evaluation_simulation.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations.</p> <p>· <em>particle_detection_functions.py:</em> This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments.</p> <p>· <em>ASTRA_CM_plot_results.m</em>: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions.</p> <p>· <em>ASTRA_CM_particle_detection.m</em>: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.</p> <p> </p> <p>There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.</p> <p>The dataset was generated to discriminate between TiO<sub>2 </sub>and WO<sub>3</sub> nanoparticles in STEM-images and STEM tomography reconstructions. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO<sub>2 </sub>and WO<sub>3</sub> but there is no guarantee.</p>
Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations
<p>The supporting data and utilities from the publication "Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations" are recorded in this dataset. </p> <p>Non-isothermal phase-field simulations of SLS process on SS316L material and subsequent elasto-plastic calculations were performed to analyze the development of plastic deformation and residual stress in SLS produced components during the processing. The dependence of the fusion zone, residual stress and plastic strain on the processing parameters namely, Beam power (Unit: Watts) and Scan speed (Unit: mm/s) were investigated. </p> <p>To promote FAIR research data principles, the processed simulation data from the thermo-elasto-plastic calculations for all the process parameter sets (hereby refered as P-v sets) are curated in this dataset. The raw temporal data obtained from the processing simulations and the elasto-plastic could not be included in this dataset due to its high volume. However, the corresponding raw data can be requested by contacting the creators of this dataset (Yangyiwei Yang: <a href="mailto:yangyiwei.yang@mfm.tu-darmstadt.de">yangyiwei.yang@mfm.tu-darmstadt.de</a> and Somnath Bharech: <a href="mailto:somnath.bharech@tu-darmstadt.de">somnath.bharech@tu-darmstadt.de</a>).</p> <p>This dataset includes: </p> <ul> <li><code>average_value.csv</code>: Contains average values of mechanical properties (such as residual stress, plastic strain) for the powder bed and the fused strut of all the process parameter sets.</li> <li><code>mesostructures_tep_sls.zip</code> : Contains resampled mesostructures obtained at the last time step of the SLS processing simulations with thermo-elasto-plastic calculations for the P-v sets reported in the aforementioned investigation. Nomenclature of the sub-directories indicating the P-v sets follows: <code>tep_<beam power>-<scan speed></code>. Each of these sub-directories contain the mesostructures from last time step of the thermo-elasto-plastic analysis of each of the four layer scans and is named as: <code>TP_layer{1..4}_output_final.e</code>. These files can be opened using Paraview v.5.8.1 or higher. The nodal values are explained as follows:</li> </ul> <table> <tbody> <tr> <td><strong>Nodal value name</strong></td> <td><strong>Symbol</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>T</td> <td>\(T\)</td> <td>Temperature field normalized by \(T_M\)</td> <td>-</td> </tr> <tr> <td>c</td> <td>\(\rho\)</td> <td>Substance order parameter</td> <td>-</td> </tr> <tr> <td>eps_ij </td> <td>\(\varepsilon\)</td> <td>Strain</td> <td>-</td> </tr> <tr> <td>epsp_ij</td> <td>\(\varepsilon^\text{pl}\)</td> <td>Plastic strain</td> <td>-</td> </tr> <tr> <td>peeq</td> <td>\(p_\text{e}\)</td> <td>Accumulated plastic strain</td> <td>-</td> </tr> <tr> <td>sigma_ij </td> <td>\(\sigma\)</td> <td>Stress</td> <td>MPa</td> </tr> <tr> <td>vonmises</td> <td>\(\sigma_\text{e}\)</td> <td>von Mises stress </td> <td>MPa</td> </tr> <tr> <td>u</td> <td>\(\mathbf{u}\)</td> <td>Displacement</td> <td>µm</td> </tr> </tbody> </table> <p> </p>
Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application
<p>Dataset for publication "Composite MAX phase/MXene/Ni electrodes with a porous 3D structure for hydrogen evolution and energy storage application". The dataset contains relevant data and figures from the publication. Description on how to work with the dataset is given in the readme file. DOI for the original paper: DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3RA07335A">https://doi.org/10.1039/D3RA07335A</a>.</p>
Workshop Material - 3D-e-Chem Structural Cheminformatics Workflows for Computer-Aided Drug Discovery
<p>The workshop at the KNIME user meeting (Berlin 9th of March 2018) is set up to stimulate participants with varying degrees of experience in cheminformatics to learn and apply the different structural cheminformatics tools and workflows developed within the context of the 3D-e-Chem project. You will learn how to construct and apply integrated cheminformatics workflows using the 3D-e-Chem KNIME nodes for the exploitation of G protein-coupled receptor and kinase data (two important pharmaceutical target classes) to obtain useful information for drug discovery.</p> <p>Information on the 3D-e-Chem KNIME nodes and workflows can be found online:</p> <p>3D-e-Chem GitHub website: <a href="http://3d-e-chem.github.io/">http://3d-e-chem.github.io/</a></p>
Computational model results for "Uncertainties of Glacial Isostatic Adjustment model predictions in North America associated with 3D structure"
<p>The mean GIA signals of RSL, u-dot and g-dot with 1σ, 2σ and 3σ uncertainties in North America. </p>
Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders: Synthetic dataset for validation of trace gas retrieval algorithms
<p>This data set is described in detail in a paper submitted to AMTD:</p> <p><strong>Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders - Part I: Synthetic dataset for validation of trace gas retrieval algorithms</strong></p> <p>by Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and<br> Bernhard Mayer</p> <p> </p> <p>The subdirectory <em>boxcloud</em> includes synthetic reflectances for clearsky, 1D cloud and box cloud.</p> <p>The subdirectory <em>les_cloud</em> includes synthetic reflectances for the LES cloud scenario for low earth orbit (<em>leo</em>) and geostationary orbit (<em>geo</em>).</p> <p>All data are provided in <em>netcdf</em> format.</p> <p> </p>
3D structure model of the TgREMIND F-BAR dimer
<p>Coordinates of the AlphaFold2 3D structure model of TgREMIND F-BAR domain dimer (amino acids 80 to 345, UniProt S7W754_TOXGG). The model of the dimer was made using ColabFold v1.5.2 (Mirdita, M., Schütze, K., Moriwaki, Y. <em>et al.</em> ColabFold: making protein folding accessible to all. <em>Nat Methods</em> <strong>19</strong>, 679–682 (2022). https://doi.org/10.1038/s41592-022-01488-1; https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb)</p> <pre> </pre>
Parameters & Complexity Indexes of 3D CAD models from "Unleashing the Potential of Artificial Reefs Design: A Purpose-Driven Evaluation of Structural Complexity"
<p>You'll find here data frames related to the paper <strong>"Unleashing the Potential of Artificial Reefs Design: A Purpose-Driven Evaluation of Structural Complexity"</strong>:</p> <ul> <li>df_parameters: displays parameters from 3D CAD models of the study</li> <li>df_complexity_Indexes: displays the complexity indexes computed with the parameters of the 3D CAD models</li> </ul>
RNA 3D structure modeling by fragment assembly with Small Angle X-ray Scattering restraints
<p>Structure determination is a key step in the functional characterization of many non-coding RNA molecules. High-resolution RNA 3D structure determination efforts, however, are not keeping up with the pace of discovery of new non-coding RNA sequences. This increases the importance of computational approaches and low-resolution experimental data, such as from the Small Angle X-ray Scattering experiments. We present RNA Masonry, a computer program and a web service for a fully automated modeling of RNA 3D structures. It assemblies RNA fragments into geometrically plausible models that meet user-provided secondary structure constraints, restraints on tertiary contacts and Small Angle X-ray Scattering data. We illustrate the method description with detailed benchmarks and its application to structural studies of viral RNAs with SAXS restraints.</p>
Supplementary data for "DNATCO v5.0: Integrated Web Platform for 3D Nucleic Acid Structure Analysis"
<p>Supplementary data for "DNATCO: efficient and accurate analysis of nucleic acid structures"</p> <p>The data in "dnatco.datmos.org_1ehz_4qvi_5hix.zip" contains the DNATCO-annotated extended mmCIF files, full validation reports and NtC-specific restraint files for the three example PDB structures (1ehz, 4qvi, and 5hix) is deposited.</p> <p>A snapshot of the core structure processing library source code from the https://github.com/cernylab/libLLKA repository is included in the "libLLKA-main.zip" file.</p> <p>The fully offline multi-platform CLI version of the dnatco.datmos.org using Node.js is provided in the "dnatco.zip" file</p>
Data for: Melt electrowriting enabled 3D liquid crystal elastomer structures for cross-scale actuators and temperature field sensors
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Models from: Non-destructive in situ monitoring of structural changes of 3D tumor spheroids during the formation, migration, and fusion process
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High-resolution 3D forest structure explains ecomorphological trait variation in assemblages of saproxylic beetles
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Data from: Anatomy of an agricultural antagonist: Feeding complex structure and function of three xylem sap-feeding insects illuminated with synchrotron-based 3D imaging
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3D structure of the HII region Sh2-235 from tunable-filter optical observations
<p>We present observations of the H-alpha, H-beta, [SII] 6716, 6731, [NII] 6583 emission lines in the galactic HII region Sh2-235 with the Mapper of Narrow Galaxy Lines (MaNGaL), a tunable filter at the 1-m telescope of the Special Astrophysical Observatory of the Russian Academy of Sciences.</p>
Data from: The effects of aging on neuropil structure in mouse somatosensory cortex—A 3D electron microscopy analysis of layer 1
This study has used dense reconstructions from serial EM images to compare the neuropil ultrastructure and connectivity of aged and adult mice. The analysis used models of axons, dendrites, and their synaptic connections, reconstructed from volumes of neuropil imaged in layer 1 of the somatosensory cortex. This shows the changes to neuropil structure that accompany a general loss of synapses in a well-defined brain region. The loss of excitatory synapses was balanced by an increase in their size such that the total amount of synaptic surface, per unit length of axon, and per unit volume of neuropil, stayed the same. There was also a greater reduction of inhibitory synapses than excitatory, particularly those found on dendritic spines, resulting in an increase in the excitatory/inhibitory balance. The close correlations, that exist in young and adult neurons, between spine volume, bouton volume, synaptic size, and docked vesicle numbers are all preserved during aging. These comparisons display features that indicate a reduced plasticity of cortical circuits, with fewer, more transient, connections, but nevertheless an enhancement of the remaining connectivity that compensates for a generalized synapse loss.
Molding 3D curved structures by selective heating
<p>It is of interest to fabricate curved surfaces in three dimensions from homogeneous material in the form of flat sheets. The aim is not just to obtain a surface which has a desired intrinsic Riemannian metric, but to get the desired embedding in R^3 up to translations and rotations. In this paper, we demonstrate three generic methods of molding a flat sheet of thermo-responsive plastic by selective contraction induced by targeted heating. These methods do not involve any cutting and gluing, which is a property they share with origami. The first method is inspired by tailoring, which is the usual method for making garments out of plain pieces of cloth. Unlike usual tailoring, this method produces the desired embedding in R^3. The second method just aims to bring about the desired new Riemannian metric via an appropriate pattern of local contractions, without directly controlling the embedding. The third method is based on triangulation, and seeks to induce the desired local distances. This results in getting the desired embedding in R^3. The second and the third methods, and also the first method for the special case of surfaces of revolution, are algorithmic in nature. We explain these methods, and show examples.</p>
Classification of Binding Modes for Kinase-Inhibitor Complex Structures, 3D Activity Cliffs Formed by Kinase Inhibitors, and Structural Analogues of 3D-Cliff Compounds
<p>The classification of crystallographic binding modes is provided for 884 kinase-inhibitor complex structures that were assembled from PDB. In addition, a total of 105 three-dimensional activity cliffs formed by 3D kinase inhibitors are listed. Their corresponding potency information is also given. Furthermore, the 2D structural analogues of 3D cliff-forming inhibitors were identified from ChEMBL database, on the basis of matched molecular pairs. These analogs and their activity information are also provided.</p>
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.