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7 results for “quantitative structure model”
Mangrove terrestrial laser scanning (TLS) point clouds and quantitative structural models (QSMs)
<p>Datasets for a publication entitled, "Terrestrial laser scanning for the estimation of above ground biomass of mangrove roots by modelling them as inverted trees."</p> <p>See the file "Data dictionary for Mangrove terrestrial laser scanning.pdf" for a description of the datasets included in the zipped folder. </p>
Structure of Complement C3(H2O) Revealed By Quantitative Cross-Linking/Mass Spectrometry And Modeling
<p>The slow but spontaneous and ubiquitous formation of C3(H2O), the hydrolytic and conformationally rearranged product of C3, initiates antibody-independent activation of the complement system that is a key first line of antimicrobial defense. The structure of C3(H2O) has not been determined. Here we subjected C3(H2O) to quantitative cross-linking/mass spectrometry (QCLMS). This revealed details of the structural differences and similarities between C3(H2O) and C3, as well as between C3(H2O) and its pivotal proteolytic cleavage product, C3b, which shares functionally similarity with C3(H2O). Considered in combination with the crystal structures of C3 and C3b, the QCMLS data suggest that C3(H2O) generation is accompanied by the migration of the thioester-containing domain of C3 from one end of the molecule to the other. This creates a stable C3b-like platform able to bind the zymogen, factor B, or the regulator, factor H. Integration of available crystallographic and QCLMS data allowed the determination of a 3D model of the C3(H2O) domain architecture. The unique arrangement of domains thus observed in C3(H2O), which retains the anaphylatoxin domain (that is excised when C3 is enzymatically activated to C3b), can be used to rationalize observed differences between C3(H2O) and C3b in terms of complement activation and regulation.</p> <p><strong>For more information</strong> about how to reproduce this modeling, see the <a href="https://salilab.org/Complement/">Sali lab website</a> or the README file.</p>
Terrestrial laser scanning data Wytham Woods: individual trees and quantitative structure models (QSMs)
<p>This dataset was used for the analysis of the following publication:<br> <em>Laser scanning reveals potential underestimation of biomass carbon in temperate forest. Calders, K, Verbeeck, V, Burt, A, Origo, N, Nightingale, J, Malhi, Y, Wilkes, P, Raumonen, P, Bunce, R G H and Disney, M. Ecological Solutions and Evidence (accepted)</em></p> <p><strong>Any use of this dataset should cite the paper above </strong>(Creative Commons Attribution 4.0 International Public License).</p> <p>Contact: kim.calders@ugent.be</p> <p> </p> <p>================================================<br> Dataset<br> ================================================</p> <p><strong>General</strong>: <br> TLS data were collected in leaf-off conditions during late November 2015 - January 2016. Windy days were avoided to ensure data quality. We used a RIEGL VZ-400 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH). The instrument has a beam divergence of 0.35 mrad and operates in the infrared (wavelength 1550 nm) with a range up to 350 m. The pulse repetition rate for each scan was 300 kHz, the minimum range was 0.5 m and the angular sampling resolution was 0.04°. This resulted in 22,500,000 outgoing pulses for a single scan, resulting in a beam diameter of 2.45 cm and beam spacing of 3.5 cm at 50 m (for example). The azimuth angle range was 0-360° and the zenith angle range was 30-130°. Therefore an additional scan was acquired at each scan location with the scanner tilted at 90° from the vertical to complete sampling of the full hemisphere at each location. Scans were done in a larger 6 ha area using an approximate 20 m × 20 m grid, to ensure the best possible data quality within our 1.4 ha study area. Trees which had at least more than half of their stem at tree diameter 1.3 m inside the boundaries of the study area were included</p> <p>[ Note that this dataset contains 876 individual trees, but after applying the boundary conditions, 835 trees within the study area were used in the analysis of the paper >> see TLS_Inventory.ipynb]</p> <p>Full details of the methods to segment individual trees and generate the QSMs can be found in the paper <em>Calders et al. Ecological Solutions and Evidence.</em></p> <p><strong>Tree ID:</strong><br> Tree IDs can have numbers only or numbers + letters. A number only means this was a base with one stem. A number + letter means individual trees (split below 1.3m), that share a common tree base.</p> <p><strong>Datasets:</strong><br> 1) DATA_clouds_txt & DATA_clouds_ply: Individually segmented trees in *txt and *ply format. File naming is [tree_id].*txt or [tree_ply].*tx</p> <p>2) DATA_QSM_opt: optimised QSMs using TreeQSM v2.0 (https://github.com/InverseTampere/TreeQSM). File naming is [tree_id]-[dmin0]-[rcov0]-[nmin0]-[dmin]-[rcov]-[nmin]-[lcyl]-[NoGround]-[iteration].mat </p> <p>3) Raw scan data can be found here: http://dx.doi.org/10.5285/ed9156e1697343e4ad82e83ed550e345</p> <p> </p> <p>================================================<br> Paper analysis<br> ================================================</p> <p>We have provided all scripts (analysis_and_figures) that were used to:</p> <p>1 ) analyse the data (TLS_Inventory.ipynb):<br> ----- Analysis of point clouds and QSMs using TLS_Inventory.py.ipynb > tls_summary.csv (#876 trees)<br> ----- Link with census &1.4ha > trees_summary.csv (#835 trees)</p> <p>2) generate the paper figures:<br> ----- various *.R and *.ipynb scripts in the main folder and /allometriesTLS/</p> <p> </p> <p>================================================<br> Funding<br> ================================================</p> <p>The TLS fieldwork was funded through the Metrology for Earth Observation and Climate project (MetEOC-2), grant number ENV55 within the European Metrology Research Programme (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union. Funds for purchase of the UCL RIEGL VZ-400 instrument was provided by the UK NERC National Centre for Earth Observation (NCEO) and UCL Geography. The census of the forest plot was supported by an ERC Advanced Investigator Grant to Yadvinder Malhi (GEM-TRAIT, grant number 321131).</p>
T2* and quantitative susceptibility mapping in an equine model of post-traumatic osteoarthritis: prediction of mechanical and structural properties
<p>Dataset for the manuscript titled "T2* and quantitative susceptibility mapping in an equine model of post-traumatic osteoarthritis: assessment of mechanical and structural properties"</p>
Modelling Cell Shape in 3D Structured Environments: A Quantitative Comparison with Experiments
<p>This repository contains experimental data and computer scripts for the following publication: Link R, Jaggy M, Bastmeyer M, Schwarz US (2024) Modelling cell shape in 3D structured environments: A quantitative comparison with experiments. PLoS Comput Biol 20(4): e1011412. https://doi.org/10.1371/journal.pcbi.1011412</p> <p>There are two directories, “data” and “scripts”.</p> <p> <strong>1) </strong><strong>Directory data</strong></p> <p> WRL-files for experimental data generated with Imaris from Zeiss image files.</p> <p>The WRL-files can be converted to STL-files with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>).</p> <p>The STL-files can be converted to FE-files for the SurfaceEvolver with our script CreateFeFile.py.</p> <p> The WRL-files are named according to the scaffolds:</p> <p>L*.wrl cells in L-shaped scaffolds (n=6).</p> <p>V*.wrl cells in V-shaped scaffolds (n=7).</p> <p>TRight*.wrl cells in right-triangle scaffolds (n=3).</p> <p>TEqui*.wrl cells in equilateral-triangle scaffolds (n=4).</p> <p><strong>2) </strong><strong>Directory scripts</strong></p> <p>ClusterSurfaceLinearPlugin: New CompuCell3D plugin needed to calculate linear surface energy functional for cells with nucleus (using the cluster concept).</p> <p>CompuCell3DScript: Hamiltonian_Comparison.cc3d is the main script for our simulations, uses the directory “Simulation”.</p> <p>CreateFeFile.py: generates surface evolver FE-file from STL-file. A STL-file can be generated from a WRL-file e.g. with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>). </p> <p>SphericalHarmonicsAnalysis.ipynb: Python notebook that calculates the Fourier spectrum and Delta_30, needs WRL-file as input.</p> <p> </p>
Tree species recognition with quantitative structure models
<p>A quantitative structure model (QSM) contains the geometric and topological structure of a reconstructed tree. As such, QSMs enable computation of detailed tree properties that have been laborious or impossible to measure before. The computed tree properties can be used as classification features for tree species recognition.</p> <p>The first half of this video illustrates how we define the 15 classification features our research group has used for a species recognition study. An example QSM is used to visualize the relevant tree parts and key steps in the feature computations.</p> <p>The second half shows how the feature values of over a thousand Finnish trees of three different species, Silver birch, Scots pine and Norway Spruce, are distributed, and how well the species separate in the defined feature dimensions. One example QSM of each tree species shown on the right-hand-side with the only the tree parts visible that are related to the current feature.</p> <p>Table of contents:<br> 0:01 Feature illustration<br> 3:46 Viewer guide on screen elements<br> 5:20 Feature value distributions<br> 9:07 Credits</p> <p>The contents of this video link directly to the paper titled "Automatic tree species recognition with quantitative structure models" published in Remote Sensing of Environment (http://dx.doi.org/10.1016/j.rse.2016.12.002).</p> <p>For more information about QSMs, please visit the groups homepage, or watch the other videos on the topic: "3D Forest Information" (https://www.youtube.com/watch?v=wANRdliE1zQ) and "Cylinder reconstruction" (https://www.youtube.com/watch?v=j0Emjwp-fmU).</p> <p>This animation was produced by the Inverse Problems research group in the Department of Mathematics at Tampere University of Technology (http://math.tut.fi/inversegroup).</p> <p>Animation created using Blender 2.77a (http://www.blender.org).</p> <p>Music:<br> "Life of Riley"<br> "Thinking of you"<br> "Jarvic 8"<br> by Kevin MacLeod (http://incompetech.com)<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p>
Quantitative Structure Models
<p>Summary of how Quantitative Structure Models are reconstructed, and how they are being utilized. The process starts with the laser scanning of a forest. The produced point cloud contains millions of points, and numerous trees. The point cloud is segmented automatically into trees and then into branches. Each branch is reconstructed by fitting several cylinders to the data. The branching topology is stored together with the geometric structure to a single tree model, which is called a Quantitative Structure Model (QSM).</p> <p>QSMs can be used in many applications, as they allow easy access to tree properties, such as, volume, area, branch count, crown shape, taper curve, and size distribution. These properties can be used by forest scientists, forest industry, and forest owners to, e.g., evaluate the current or future state of a forest, or its value. The model properties can also be used for automatic species recognition. Furthermore, QSMs can be visualized in various ways to produce realistic virtual representations of locations, such as, national or city parks, for the travel industry. The models can be textured and augmented with leaves to achieve either a realistic or a fantasy look, to suite the needs of game developers.</p> <p>For more information about QSMs, please visit the groups homepage, or watch the other videos on the topic: "3D Forest Information" (https://www.youtube.com/watch?v=wANRdliE1zQ) and "Cylinder reconstruction" (https://www.youtube.com/watch?v=j0Emjwp-fmU).</p> <p>This animation was produced by the Inverse Problems research group in the Department of Mathematics at Tampere University of Technology (http://math.tut.fi/inversegroup).</p> <p>Animation created using Blender (http://www.blender.org).</p> <p>Music:<br> "Delay Rock" by Kevin MacLeod (http://incompetech.com)<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p> <p>Textures:<br> "Bark 0007"<br> xoio (xoio.de)</p> <p>"Cherry Leaf"<br> BrianHanson2nd (deviantart.com)<br> Licensed under Creative Commons: By Attribution 3.0<br> http://creativecommons.org/licenses/by/3.0/</p> <p>The animation builds upon but does not directly feature the "Prunus avium - Cherry Tree" point cloud data by Jan Hackenberg (http://www.simpletree.uni-freiburg.de/openData.html) shared under the Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International license<br> http://creativecommons.org/licenses/by-nc-sa/4.0/</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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