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123 results for “structuring mechanisms”
Diffraction images used to solve the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-alpha-1,2-Mannanases."
<p>Raw diffraction images used for generating the structures published in the article "Exploration of Strategies for Mechanism-Based Inhibitor Design for Family GH99 endo-a-1,2-Mannanases" (available <a href="https://doi.org/10.1002/chem.201800435">here</a>). Full single-crystal datasets are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Supplementary codes and datasets for "Modular-topology optimization of structures and mechanisms with free material design and clustering"
<p>This repository supports Tyburec, M., Doškář, M., Zeman, J., & Kružík, M. (2022). Modular-topology optimization of structures and mechanisms with free material design and clustering. <em>Computer Methods in Applied Mechanics and Engineering</em>, <em>395</em>, 114977. <a href="https://doi.org/10.1016/j.cma.2022.114977">https://doi.org/10.1016/j.cma.2022.114977</a> (first published as preprint <a href="http://arxiv.org/abs/2111.10439">2111.10439</a> at arXiv.org).</p> <p>This repository contains:</p> <ol> <li>MATLAB source codes for <em>(modular) free material optimisation</em> and <em>hierarchical stiffness clustering</em> (folder <code>./mFMO/</code>)</li> <li>C++ source codes for <em>modular topology optimization</em> (folder <code>./MTO/</code>)</li> <li>Input/output data of the test suite (folder <code>./data/</code>)</li> </ol> <p><strong>1. Data flow</strong></p> <p>The test suite considered in the manuscript covers 4 problems:</p> <ol> <li>Messerschmitt-Bölkow-Blohm beam (labelled as <code>mbb</code>)</li> <li>Inverter compliant mechanism (labelled as <code>inv</code>)</li> <li>Gripper compliant mechanism (labelled as <code>grip</code>)</li> <li>Reusable design of both compliant mechanisms (labelled as <code>invgrip</code>)</li> </ol> <p>Each problem in the dataset is stored within a separate subfolder named according to the labels mentioned above. The final level of subdirectories <code>{X}color</code> comprises of the results for problems with <code>X</code> denoting the number of edge codes considered for each edge direction during the clustering (<code>0color</code> stands for a non-modular design and <code>1color</code> represents the design based on Periodic Unit Cell).</p> <p>Each of the folders contains outputs of the modular free material optimisation in the following form:</p> <ul> <li><code>{label}{X}.mat</code></li> <li><code>{label}{X}.til</code></li> <li><code>{label}{X}.tset</code></li> <li><code>{label}{X}guess.mat</code></li> </ul> <p>Files <code>*.til</code>, <code>*.tset</code>, and <code>*guess.mat</code> are then converted into a JSON input file for the modular topology optimization code with generator scripts which can be found in <code>./MTO/scripts</code> folder. Note that each of the problems in the test suite has its own generator script <code>generate_modular_problem_{MBB,inverter,gripper,inverterAndGripper}.mat</code>. The generator scripts make a directory named according to the key <code>MTO_{n}_kernelSensitivity</code>, where <code>n</code> denotes the resolution of each module (i.e. the number of nodes along one direction). The directory also contains the outputs of the modular topology optimisation in the form of the initial and the final state of the optimization in <code>VTK</code> files and visualisation of the final state in <code>SVG</code> files. The log file <code>log.txt</code> stores the optimized objective and progress of the value along with stopping criteria quantities during iterations.</p> <p><strong>2. Running codes</strong></p> <p><strong>2.1 Modular free material optimisation</strong></p> <p>MATLAB scripts and functions for (modular) Free Material Optimization (FMO) are contained in the <code>mFMO</code> data folder. The codes have been tested with MATLAB R2019b. To run the codes the user is required to install the <a href="http://www.penopt.com">PENNON optimizer</a>. A free academic license is provided by its authors on request.</p> <p>Input files for individual problems are defined in the <code>mFMO/problems</code> folder and are launched with the <code>runproblem(problemName, numClusters)</code>, where <code>problemName</code> refers to the file in the <code>mFMO/problems</code> folder without the file extension and <code>numClusters</code> denotes the maximum number of color codes in Wang tiling formalism.</p> <p>If successful, the optimization produces output files in <code>mFMO/fmo_fig/{label}/{X}colors/{T}/</code>:</p> <ul> <li><code>{label}{X}.mat</code> (contains clustering and tiling information)</li> <li><code>{label}{X}_tmp.mat</code> (contains results of non-modular FMO)</li> <li><code>{label}{X}.til</code> (the assembly plan)</li> <li><code>{label}{X}.tset</code> (Wang tile set)</li> <li><code>{label}{X}guess.mat</code> (guess for TO)</li> </ul> <p>where <code>T</code> is the optimization time stamp.</p> <p><strong>2.2 Modular topology optimisation</strong></p> <p>All results were obtained with version <code>v1.1.2</code>, which is also provided in the folder <code>MTO</code>, and linked Intel® oneAPI Math Kernel Library and the incorporated PARDISO sparse solver. For the recent development of the code see the open git repository at <a href="https://gitlab.com/MartinDoskar/modular-topology-optimization">https://gitlab.com/MartinDoskar/modular-topology-optimization</a>. The repository also contains a detailed description of input parameters and code design.</p> <p>Modular topology optimisation code uses CMake for the cross-platform build automation. For instance, under Linux, the whole code can be compiled in the standard five steps:</p> <pre><code>cd ./MTO mkdir build cd ./build cmake -DCMAKE_BUILD_TYPE=Release .. make </code></pre> <p>All executables are automatically stored in <code>./MTO/bin/</code> folder. Individual problems can be optimized by parsing the JSON files obtained from the generator scripts as an argument to the MTO.Application binary, e.g.,</p> <pre><code>./MTO/bin/MTO.Application.exe path_to_data/mbb/2color/MTO_100_kernelSensitivity/input_modular_mbb_2colours_100.json </code></pre> <p><strong>Acknowledgement</strong></p> <p>The related research and code development was supported by the <a href="https://gacr.cz/en/">Czech Science Foundation</a>, project No. 19-26143X.</p>
RMSD and Trp assays for "Structural analysis of a motor with increased mechanical output reveals new transitions in kinesin microtubule motility"
<p>This entry is for our manuscript, "Structural analysis of a motor with increased mechanical output reveals new transitions in kinesin microtubule motility" by Satoki Shibata*, Matthew Y. Wang*, Tsuyoshi Imasaki*, Hideki Shigematsu, Diego Ugarte La Torre, Yuanyuan Wei, Chacko Jobichen, Hajime Hagio, J. Sivaraman, Yuji Sugita, Sharyn A. Endow & Ryo Nitta.</p> <p>*Equal contribution</p> <p>Corresponding Authors: Tsuyoshi Imasaki*, Sharyn A. Endow, Ryo Nitta</p> <p>The deposited datasets are 1) beta-strand residue all-atom RMSD between kinesin-14 NcdY485K ADP and ADP + free Pi nucleotide states and 2) fluorimeter assays of NcdY485K intrinsic Trp fluorescence changes upon addition of free Pi . The data are presented in Fig. 6e, Fig. 7b,c and Supplementary Fig 10. Files are named for the figures in which the data are shown. A computer script for the RMSD analysis and methods for the fluorimeter assays and data analysis are included in the deposited files.</p>
Tensile2d: 2D quasistatic non-linear structural mechanics solutions, under geometrical variations
<p>This dataset contains 2D quasistatic non-linear structural mechanics solutions, under geometrical variations. </p> <p>A Description is provided in <a href="https://arxiv.org/pdf/2305.12871.pdf">the MMGP paper</a> Sections 4.1 and A.2.</p> <p>The file format is PLAID, see <a href="https://plaid-lib.readthedocs.io/ ">the plaid documentation</a>.</p> <p>The variablity in the samples are 6 input scalars and the geometry (mesh). Outputs of interest are 4 scalars and 6 fields.</p> <p>Seven nested training sets of sizes 8 to 500 are provided, with complete input-output data. A testing set of size 200, as well as two out-of-distribution sample, are provided, for which outputs are not provided. </p> <p> </p> <p>Tips to access the data:</p> <p>After decompressing the downloaded file:</p> <p>from plaid.containers.dataset import Dataset<br>from plaid.problem_definition import ProblemDefinition</p> <p>dataset = Dataset()<br>problem = ProblemDefinition()</p> <p>problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))<br>dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)</p> <p>print("problem =", problem)<br>print("dataset =", dataset)</p> <p>sample = dataset[0]<br>print("sample =", sample)</p> <p>for fn in sample.get_field_names():<br> print(f"{fn} =", sample.get_field(fn))<br>for sn in sample.get_scalar_names():<br> print(f"{sn} =", sample.get_scalar(sn))</p> <p>print("nodes =", sample.get_nodes())<br>print("elements =", sample.get_elements())<br>print("nodal_tags =", sample.get_nodal_tags())</p> <p> </p>
Structure and Mechanism of a Cold-Adapted Bacterial Lipase
<p>Input files and structures used to obtain computational results published in:</p> <p><a href="https://doi.org/10.1021/acs.biochem.2c00087?urlappend=%3Fref%3DPDF&jav=AM&rel=cite-as">Structure and Mechanism of a Cold-Adapted Bacterial Lipase</a><br> DOI: 10.1021/acs.biochem.2c00087</p> <p>See included README file and publication for more details.</p> <p>Funding<br> This work was supported by the Swedish Research Council<br> (VR), the Knut and Alice Wallenberg Foundation, and the<br> Research Council of Norway through a Centre of Excellence<br> and project grant (Grant Nos. 262695 and 274858).<br> Computational resources were provided by the Swedish<br> National Infrastructure for Computing (SNIC)</p>
Research data in support of: An Interplay of Mechanical and Structural Properties of DNA Determines Its Electrostatic Interactions with Lipids
<p>The data collected and reported for the publication entitled: An Interplay of Mechanical and Structural Properties of DNA Determines Its Electrostatic Interactions with Lipids. by Diana Morzy et al.</p> <p>Data is divided by the technique used, with folders named accordingly. Each dataset has a readme file, explaining the basic technical details, as well as how to open each file type.</p> <p>Please do not hesitate to contact the corresponding author (MMCB) for further details.</p>
Figure 5. A chromosome structure in case we have 2 visible states and 3 invisible states-Neuroevolution Mechanism for Hidden Markov Model
<p>Generating a population of size n of HMMs at random can be performed with some<br> restrictions:<br> - The weights representing the input layer in the chromosome should be always negligible as<br> initial values.<br> - The weights which are involved in summation of 1.0 in the hidden layer part of the<br> chromosome should be exactly 1.0.<br> Let us assume the following case<br> Visible states are 2 and invisible states (observations) are 3, , then we shall have a<br> chromosome as shown in Figure 5.</p>
Figure 4. A chromosome structure for HMM shown in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>The chromosome which represents the HMM can be extracted from its corresponding neural<br> network. The general structure of the chromosome is divided into two sections, input layer and<br> hidden layer. Each section contains many slots, and each slot represents a weight from one node in<br> that layer to a node in the next layer (from input to hidden and from hidden to output). The number<br> of slots in the input layer is the same number of input nodes in the neural network. In the hidden<br> layer, number of slots is equal to nodes in the output layer multiplied by the nodes in the hidden<br> layer.</p>
Genomic Data for "Structure of Anellovirus-like Particles Reveal a Mechanism for Immune EvasionAnellovirus-like Particles Reveal a Mechanism for Immune Evasion"
<p>All genomic data supporting the paper 'Structure of Anellovirus-like Particles Reveal a Mechanism for Immune EvasionAnellovirus-like Particles Reveal a Mechanism for Immune Evasion'. Include amino acid sequence alignment used to produce supplemental figure 6 in the manuscript.</p>
Revisiting lactococcal abortive infection-like antiphage systems: Diversity, structures and mechanisms
<p><em><span>Lactococcus</span></em><span> spp. are cornerstone species in the production of fermented dairy products such as cheese. However, the non-sterile dairy environment exposes these bacteria to phage infection, leading to potential environmental and economic losses. To counteract such phage threats, bacteria have evolved multiple defence mechanisms, including abortive infection (Abi) systems, of which many of the first (AbiA to AbiZ) were discovered in <em>Lactococcus</em>. Recent discoveries have expanded the list of Abi-like systems experimentally confirmed in <em>Lactococcus</em> species. In this review, we examine and revisit the antiphage activity spectrum, the interference with the phage lytic cycle, and the emergence of phage escape mutants associated with these systems. Since many of these systems were identified before the development of advanced functional and structural prediction tools, their domains and mechanisms have largely remained unexplored. Here, we group certain Abi-like systems based on structural superimposition and use predicted domain information to hypothesize their potential mechanisms. Additionally, we discuss established mechanisms for some Abi-like systems. Our findings confirm that the co-evolution of <em>Lactococcus</em> species with their phages, driven by their extensive application in dairy fermentation, has led to the selection of a diverse array of phage defence mechanisms.</span></p>
Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures
<p>This dataset comes from the following paper:</p> <p>Chiara Pasini, Oscar Ramponi, Stefano Pandini, Luciana Sartore, Giulia Scalet, Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures, J. of Materi Eng and Perform, 2024. <a href="https://doi.org/10.1007/s11665-024-10199-x">https://doi.org/10.1007/s11665-024-10199-x</a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. m of the neural network</li> <li>. inp of the Abaqus finite element simulations</li> </ul>
Mechanical Simulation Analysis of skeleton gyroid structure (standard, double and graded)_Spoke11_WP4_Task4.1_MOST
<p>These data provide a foundation for understanding how each skeletal configuration handles stress, with practical implications for applications requiring tailored energy absorption and strength properties in lattice structures.</p>
Dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7
<p>Open dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7. https://doi.org/10.1186/s40069-022-00568-y</p> <p>File 1 - X-ray diffractogram and particle size distribution (laser granulometry) data - *.opju (Origin)<br> File 2 - Rheological test results - *.opju (Origin)<br> File 5 - Mechanical peformance (early strength - ultrasounds and compressive strength) and density test results - *.opju (Origin)<br> File 4 - Mercury intrusion porosimetry test data - *.opju (Origin)</p>
Data from: Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing
<p>This dataset contains the data for the publication " Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing "</p>
Strain, Young's modulus, and structural transition of EuTiO3 thin films probed by micro-mechanical methods (Dataset)
<p>Datafiles of the Article "Strain, Young’s modulus, and structural transition of EuTiO3 thin films probed by micro-mechanical methods (Dataset)"</p>
Genesis of column sprites: Formation mechanisms and optical structures
<p>The dataset contains compilable files and input files for the paper "Genesis of column sprites: Formation mechanisms and optical structures".</p>
MOOSE Structural Mechanics FEM Demonstration
<p>Demonstration of <a href="https://www.mooseframework.org/">MOOSE</a> finite element workflow for a simple structural mechanics problem in Windows 10 using Windows Subsystem Linux (WSL). Targeted for Windows-based MOOSE users who wish to simply use existing functionality of MOOSE -- not focused on developing / contributing to MOOSE. Datafiles to replicate are provided.</p>
Extended Data: Structure-dependence of the atomic-scale mechanisms of Pt electrooxidation and dissolution
<p><strong>Abstract:</strong></p> <p>Platinum dissolution and restructuring due to surface oxidation are primary degradation mechanisms that limit the lifetime of Pt-based electrocatalysts for electrochemical energy conversion. Here, we studied well-defined Pt(100) and Pt(111) electrode surfaces by in situ high-energy surface X-ray diffraction, on-line inductively coupled plasma mass spectrometry, and density functional theory calculations, to elucidate the atomic-scale mechanisms of these processes. The locations of the extracted Pt atoms after Pt(100) oxidation reveal distinct differences from the Pt(111) case, which explains the different surface stability. The evolution of a specific stripe oxide structure on Pt(100) produces unstable surface atoms which are prone to dissolution and restructuring, leading to one order of magnitude higher dissolution rates.</p> <p><strong>Contents of this repository:</strong></p> <p><strong>1. SXRD data:</strong></p> <p>The experiments for the acquisition of the raw SXRD data were performed at the the European Synchrotron Radiation Facility, Grenoble, France at the beamlines ID31 and ID03. We thank H. Isern and T. Dufrane for the help during the SXRD experiments.</p> <ul> <li>tomo_tomo.spec is the file with the X-ray diffraction metadata for the CTR scans. It is a plain text file.</li> <li>Each HESXRD dataset is saved in a folder, which denotes the potential (e.g. 1V0.zip). The png-image files are previews of the corresponding dataset. The individual raw cbf files can be opened by pyMCA or silx. From python, the images can be accessed using the fabio library.</li> <li>calibration.zip contains the pyFAI calibration files and a list with indexed Bragg reflections for the UB matrix calculation</li> <li>CTRs_parameters.zip contains the averaged CTR structure factors used for the structual analysis as well as files with the atomic coordinates of the refined structural model.</li> <li>steps.zip contains the full datasets from the potential step experiments in Fig. 1e.</li> </ul> <p><strong>2. DFT:</strong></p> <ul> <li>CONTCARs.zip contains the atomic coordinates of the optimized computational models.</li> </ul> <p> </p> <p> </p>
Underlying data for "Global alignment and assessment of TRP channel transmembrane domain structures to explore functional mechanisms"
<p><strong>Output Files from Structural Alignment of TRP Channels</strong><br> The archive contains the key output files resulting from the structural alignment of members of the TRP channel family described in "Global alignment and assessment of TRP channel transmembrane domain structures to explore functional mechanisms", Huffer et al., (2020), eLife.</p> <p><strong>Contents of trp_struct_align_outputs.tar.gz</strong><br> <em>Aligned_structures</em><br> For each alignment where the transmembrane (TM) domain of the mobile structure was aligned to the TM domain of the stationary/reference structure (6co7):<br> 1) mobile_stationary_full_align.pdb: aligned based on Fr-TM-Align output matrix, contains all residues<br> 2) mobile_stationary_tmem_align.pdb: aligned based on Fr-TM-Align output matrix, contains only TM residues used for alignment<br> 3) mobile_stationary_hole.pdb: aligned based on Fr-TM-Align output matrix, contains HOLE profile of mobile structure</p> <p><em>Alignments</em><br> Multiple sequence alignments constructed using the TM domain of each structure as the stationary/reference structure, with all other structures aligned pairwise.<br> 1) stationary_full.ali: Full multiple sequence alignment containing all TM residues from all stationary and mobile structures.<br> 2) stationary_nogap.ali: Multiple sequence alignment containing all TM residues from the stationary structure. Any gaps in the stationary structure removed, which removes any residue in any mobile structure that does not align to the stationary/reference structure.</p> <p><em>Fr-TM-Align_outputs</em><br> - frtmalign_output.csv: contains all information from Fr-TM-Align output files for each pairwise alignment of TM domains, including PDB IDs of mobile and stationary structures, lengths of mobile and stationary structures, length of pairwise alignment sequence, RMSD of alignment, TM-score of alignment, and sequence identity of alignment.<br> - aligned_length.csv, RMSD.csv, sequence_identity.csv, and TM_score.csv: csv files containing the specified values for all pairwise alignments of TM domains, with the mobile structure in rows and stationary structure in columns</p> <p><strong>Reproducibility</strong><br> Code to reproduce this alignment and analysis is available on GitHub at: <a href="https://github.com/kehuffer/TRP_Structural_Alignment">https://github.com/kehuffer/TRP_Structural_Alignment</a></p>
Impact of intercept trap type on plume structure: a potential mechanism for differential performance of intercept trap designs for Monochamus species
<p>Studies have demonstrated that semiochemical-baited intercept traps differ in their performance for sampling insects, but we have an incomplete understanding of how and why intercept trap design effects vary among insects. This can significantly delay both the development of new and optimization of existing survey and detection tools. The development of a mechanistic understanding of why trap performance varies within and among species would mitigate this delay. The primary objective of this study was to develop methods to characterize and compare the odor plumes associated with intercept traps that differ in their performance for forest Coleoptera. We released CO<sub>2</sub> and measured fluctuations of this tracer gas from 175-point locations arranged in a 2-by-3-by-2-m grid cuboid downwind of a standard multiple-funnel, a modified multiple-funnel, a panel, a canopy malaise trap, and a blank control (i.e., no trap) in a greenhouse. Significant differences in trapping efficacy between these different trap designs were observed for <i>Monochamus scutellatus</i> (Say) and <i>Monochamus notatus</i> (Drury) in a field trial. Significant differences were also observed in how CO<sub>2</sub> accumulated in time at different positions downwind among these different trap designs. Turbulent dispersion is the dominant force structuring odor plumes and creates intermittency in the odor plume that is important for sustained upwind flight in insects. Methodological and instrumental limitations resulted in the inability to determine instantaneous plume structures and vortex shedding frequencies for different intercept trap designs. Although we observed differences in the odor plumes emanating downwind of the different intercept trap designs, we were unable to reconcile these differences with capture rates of the different trap designs for <i>M. scutellatus</i> and <i>M. notatus</i>.</p>
ScienceDex guides
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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.