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356 results for “In silico”
Dataset from "In silico assessment of collateral eddy current heating in biocompatible implants subjected to magnetic hyperthermia treatments"
<p>This dataset from the publication entitled "Dataset from "In silico assessment of collateral eddy current heating in biocompatible implants subjected to magnetic hyperthermia treatments" contains simulated data of magnetic hyperthermia treatments for three different indications: colorectal cancer, prostate cancer and head & neck cancer. Since the aim of the study is to evaluate the risk of thermal damage caused by the collateral heating of two common types of passive prostheses (hip and dental implants), eddy currents induced in these implants upon interacting with the externally applied ac field during treatment have been computed for all the evaluated regions. Two different alloys for the implants have been considered for each case as well: Ti6Al4V and CoCrMo. At the same time, besides temperature, the specific abosorption rate (SAR) have been also computed to work out the energy deposition in tissues.</p> <p>Calculations have been carried out using a het exchange model with and without thermoregulation.</p> <p>log-log SAR vs T plots have been obtained and proposed as a quick means to pre-check treatment feasibility in each patient. These graphs are thought to be included in treatment planning prior to the clinical procedure.</p> <p>Other parameters taken into account have been the treatment time (5 and 30 minutes), and the maximum tolerable temperature threshold (1 or 5 ºC, as indicated by the ICNIRP commission), all for three main types of tissues, namely fat, bone and muscle. Each tissue have been simulated using three different field intensities (5, 10 and 15 mT).</p> <p>The field frequency has been 300 kHz in all cases.</p> <p>The files "Dataset_description.doc" and "file_scheme.txt" contain the structure and description of the files that make up the dataset.</p> <p>UPDATES FROM PREVIOUS VERSIONS: simulations of the dental implant without thermoregulation have been added.</p>
Experimental validation data for in silico OA study
<p>The folder contains, ALP assay and PCR data, experimental protocols as well as scripts for plotting and analysis.</p> <p>This is related to the following git repository: https://github.com/Rapha-L/Experimental_validation_for_insilicoOA </p>
Supplementary dataset to publication: Oxford nanopore technologies - a valuable tool to generate whole-genome sequencing data for in silico serotyping and the detection of genetic markers in Salmonella, Thomas et al 2023
<p>Bacteria of the genus <em>Salmonella</em> pose a major risk to livestock, the food economy, and public health. <em>Salmonella</em> infections are one of the leading causes of food poisoning. The identification of serovars of <em>Salmonella</em> achieved by their diverse surface antigens is essential to gain information on their epidemiological context. Traditionally, slide agglutination has been used for serotyping. In recent years, whole-genome sequencing (WGS) followed by <em>in silico</em> serotyping has been established as an alternative method for serotyping and the detection of genetic markers for <em>Salmonella</em>. Until now, WGS data generated with Illumina sequencing are used to validate <em>in silico</em> serotyping methods. Oxford Nanopore Technologies (ONT) opens the possibility to sequence ultra-long reads and has frequently been used for bacterial sequencing. In this study, ONT sequencing data of 28 <em>Salmonella</em> strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the <em>in silico</em> serotyping tools SISTR and SeqSero2 compared to traditional slide agglutination tests. Moreover, the detection of genetic markers for resistance against antimicrobial agents, virulence, and plasmids was studied by comparing WGS data based on ONT with WGS data based on Illumina. Based on the ONT data from flow cell version R9.4.1, <em>in silico</em> serotyping achieved an accuracy of 96.4 and 92% for the tools SISTR and SeqSero2, respectively. Highly similar sets of genetic markers comparing both sequencing technologies were identified. Taking the ongoing improvement of basecalling and flow cells into account, ONT data can be used for <em>Salmonella in silico</em> serotyping and genetic marker detection.</p>
In silico Database for Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1)
<p>Modern methods of mass spectrometry have emerged recently allowing reliable, fast and cost-effective identification of pathogenic microorganisms. For example, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has revolutionized the way pathogenic microorganisms are identified in today’s routine clinical microbiology. Furthermore, recent years have witnessed also substantial progress in the development of liquid chromatography-mass spectrometry (LC-MS) based proteomics for microbiological applications.</p> <p>In this context, we introduce a new concept for microbial identification by mass spectrometry. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS1 data are then extracted and systematically tested against <em>in silico</em> libraries of peptide mass data. The first version of such a database has been computed from UniProt Knowledgebase [Swiss-Prot and TrEMBL] and contains more than 12,000 strain-specific synthetic mass profiles. The database is stored in the pkf data format which is interpretable by the MicrobeMS software package (requires MicrobeMS version 0.82, or later).</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089.</em></p>
In Silico Local Electrical Impedance Measurements in the Atria
<div>This document describes a dataset provided in the context of the manuscript “In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human” [1].</div> <div> </div> <div>Authors: Unger LA, Anton CM, Stritt M, Wakili R, Haas A, Kircher M, Dössel O, Luik A</div> <div> </div> <div>The dataset contains in silico simulation setups and results from forward electrical impedance simulations with EIDORS. Geometrical models include the commercially available ablation catheters IntellaNav MiFi and IntellaNav StPt catheter measuring local impedance (LI). </div> <div>Catheter geometries were embedded in different surrounding conditions of clinical importance. Catheter tissue interaction with and without scar, the insertion of the catheter into a pulmonary vein (PV), the withdrawal into a transeptal sheath, and catheter irrigation were modeled to quantify the respective effect on LI measurements. In vitro and clinical data used for validation purposes are included in the dataset as well.</div> <div> </div> <div>Abbreviations: </div> <div>LI: local impedance, all numbers are given in Ohms</div> <div>MiFi: IntellaNav MiFi catheter</div> <div>PV: pulmonary vein</div> <div>StPt: IntellaNav StPt catheter</div> <div> </div> <div> </div> <div>Simulation results, in vitro measurements and clinically measured traces are provided in the following MATLAB files in the subdirectory „results_LI“:</div> <div> </div> <div>• impConductivities.mat</div> <div>In vitro measurements and simulation results for MiFi and StPt in NaCl solutions of different concentrations as described in section III A of the related publication [1]. The struct "impedance" includes the following fields:</div> <div>⁃ conc: concentration of NaCl solutions from in vitro measurements in weight percentages</div> <div>⁃ cond: conductivities of the NaCl solutions from in vitro measurements in S/m</div> <div>⁃ temp: interpolated temperature curves from in vitro measurements in °C</div> <div>⁃ condSim: different conductivities of the NaCl solutions from in silicon experiments in S/m</div> <div>⁃ LI_MiFi_iV: 41x9 matrix with interpolated in vitro LI measurements with the MiFi catheter in 9 different NaCl solutions and at 41 interpolated temperature values</div> <div>⁃ LI_StPt_iV: 41x9 matrix with interpolated in vitro LI measurements with the StPt catheter in 9 different NaCl solutions and at 41 interpolated temperatures values</div> <div>⁃ LI_MiFi_iV_RT: LI values for different NaCl solutions at room temperature interpolated from in vitro MiFi measurements</div> <div>⁃ LI_MiFi_iV_BT: LI values for different NaCl solutions at body temperature interpolated from in vitro MiFi measurements</div> <div>⁃ LI_StPt_iV_RT: LI values for different NaCl solutions at room temperature interpolated from in vitro StPt measurements</div> <div>⁃ LI_StPt_iV_BT: LI values for different NaCl solutions at body temperature interpolated from in vitro StPt measurements</div> <div>⁃ LI_MiFi_sim: LI extracted from simulations with the MiFi catheter for different NaCl solutions</div> <div>⁃ LI_StPt_sim: LI extracted from simulations with the StPt catheter for different NaCl solutions</div> <div> </div> <div>• impSheath.mat</div> <div>Simulation results and clinical measurements of LI with MiFi and StPt for different overlaps with a transeptal sheath as described in section III B of the related publication [1]. The struct „impSheath“ contains the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and distal edge of the sheath in mm. Negative distances describe an insertion of the catheter into the sheath</div> <div>⁃ LI_MiFi_sim: LI extracted from simulations with the MiFi catheter for different vertical distances between catheter tip and distal edge of the sheath corresponding to the field distance</div> <div>⁃ LI_StPt_sim: LI extracted from simulations with the StPt catheter for different vertical distances between catheter tip and distal edge of the sheath corresponding to the field distance</div> <div>⁃ LI_MiFi_cd: 281x2 matrix containing clinical LI measurements with the MiFi catheter in the second column and corresponding time steps in the first column</div> <div>⁃ LI_StPt_cd: 301x2 matrix containing clinical LI measurements with the StPt catheter in the second column and corresponding time steps in the first column</div> <div> </div> <div>• impTissue.mat</div> <div>Simulation results for MiFi and StPt with variable distance and angle between catheter and tissue as described in section III C of the related publication [1]. The struct „impTissue“ contains the following fields:</div> <div>⁃ distance: 25 different distances between catheter tip and endocardial surface in mm</div> <div>⁃ distanceSel: 5 selected distances between catheter tip and endocardial surface in mm</div> <div>⁃ angle: 13 different angles between catheter and endocardial tissue surface in degrees</div> <div>⁃ LI_MiFi_d_alpha: 5x13 matrix with simulated LI values for the MiFi catheter at 5 selected distances (distanceSel) and 13 angles between catheter and tissue.</div> <div>⁃ LI_MiFi_d_90: 25 simulated LI values for the MiFi catheter for different distances between catheter tip and endocardial surface corresponding to the field “distance” for orthogonal catheter placement</div> <div>⁃ LI_StPt_d_alpha: 5x13 matrix with simulated LI values for the StPt catheter at 5 selected distances (distanceSel) and 13 angles between catheter and tissue.</div> <div>⁃ LI_StPt_d_90: 25 simulated LI values for the StPt catheter different distances between catheter tip and endocardial surface corresponding to the field “distance” for orthogonal catheter placement</div> <div> </div> <div>• impTissueScar.mat</div> <div>Simulation results for MiFi and StPt interacting with tissue in the presence of scar as described in section III C of the related publication [1]. The struct „impTissueScar“ contains the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and endocardial surface for all simulation setups in mm</div> <div>⁃ centerX: horizontal distance between the catheter tip and the center of the line of scar for all simulation setups in mm</div> <div>⁃ LI_MiFi3mm: simulated LI for the MiFi catheter for all combinations of horizontal and vertical distances with a central line of scar of 3mm width</div> <div>⁃ LI_StPt3mm: simulated LI for the StPt catheter for all combinations of horizontal and vertical distances with a central line of scar of 3mm width</div> <div>⁃ LI_MiFi6mm: simulated LI for the MiFi catheter for all combinations of horizontal and vertical distances with a central line of scar of 6mm width</div> <div>⁃ LI_StPt6mm: simulated LI for the StPt catheter for all combinations of horizontal and vertical distances with a central line of scar of 6mm width</div> <div> </div> <div>• impPV.mat</div> <div>Simulation results for MiFi and StPt insertion into a pulmonary vein (PV) as described in section III D of the related publication [1]. The struct „impPV“ includes the following fields:</div> <div>⁃ distance: vertical distance between catheter tip and tissue surface in mm. Negative distances describe an insertion of the catheter into the vein.</div> <div>⁃ radius: inner radius of the PV in mm</div> <div>⁃ thickness: thickness of the PV tissue in mm</div> <div>⁃ LI_MiFi_d_r_th: 31x4x4 matrix containing the LI simulation results for the MiFi catheter for all combinations of 31 distances, 4 radii, and 4 thicknesses.</div> <div>⁃ LI_StPt_d_r_th: 31x4x4 matrix containing the LI simulation results for the StPt catheter for all combinations of 31 distances, 4 radii, and 4 thicknesses.</div> <div> </div> <div>• impFlush.mat</div> <div>Simulation results for MiFi and StPt flush with NaCl at different flow rates as described in section III E of the related publication [1]. The struct „impFlush“ includes the following fields:</div> <div>⁃ radius: radius of the NaCl spheres at the irrigation holes in mm</div> <div>⁃ LI_MiFi_NaCl: LI extracted from simulations with MiFi catheter for NaCl irrigation spheres of different sizes corresponding to the respective radius</div> <div>⁃ LI_StPt_NaCl: LI extracted from simulations with StPt catheter for NaCl irrigation spheres of different sizes corresponding to the respective radius</div> <div> </div> <div>Additionally, exemplary geometrical setups and results are provided as VTK files in the subdirectory „selectedGeometriesAndSimResults“:</div> <div> </div> <div>Each VTK file contains the following data fields:</div> <div>⁃ Ids (point data): integer specifying the Id of the respective vertex</div> <div>⁃ Voltage (point data): electric potential of the respective vertex with respect to a reference potential in mV</div> <div>⁃ Conductivity (cell data): conductivity of the material of the respective cell in S/mm</div> <div>⁃ Current (cell data): current density of the respective cell in nA/mm^2</div> <div>⁃ Ids (cell data): integer specifying the Id of the respective cell</div> <div>⁃ Material (cell data): integer specifying the material of the respective cell (for MiFi setups: 1: distal ring electrode, 2: middle ring electrode, 3: proximal ring electrode, 4: tip electrode, 5: outer insulator, 6: inner insulator, 7: mini electrode 1, 8: insulator mini electrode 1, 9: mini electrode 2, 10: insulator mini electrode 2, 11: mini electrode 3, 12: insulator mini electrode 3, 13: tissue, 14: blood, 15: sheath, 16:NaCl, 17: scar tissue; for StPt setups: 1: distal ring electrode, 2: middle ring electrode, 3: proximal ring electrode, 4: tip electrode, 5: outer insulator, 6: inner insulator, 7: tissue, 8: blood, 9: NaCl, 10: scar tissue)</div> <div> </div> <div>• mifi.vtk: MiFi catheter in blood </div> <div>• stpt.vtk: StPt catheter in blood</div> <div>• mifiTissue_dist000_angle0000.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 0°</div> <div>• mifiTissue_dist000_angle0450.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 45°</div> <div>• mifiTissue_dist000_angle0900.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 90°</div> <div>• mifiTissue_dist000_angle1350.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 135°</div> <div>• mifiTissue_dist000_angle1800.vtk: MiFi catheter positioned in 0mm distance to the endocardial tissue at an angle of 180°</div> <div>• mifiTissueScar_dist0000_angle0900_centerX0000_line3mm.vtk: MiFi catheter positioned centrally and orthogonally at a line of scar tissue of 3mm width</div> <div>• mifiTissueScar_dist0000_angle0900_centerX0000_line6mm.vtk: MiFi catheter positioned centrally and orthogonally at a line of scar tissue of 6mm width</div> <div>• mifi_PV_d0060_r030_th20.vtk: MiFi catheter 6mm above the endocardial surface with a PV of 3 mm radius and 2mm PV tissue thickness</div> <div>• mifi_PV_d-070_r030_th20.vtk: MiFi catheter inserted into a PV of 3 mm radius and 2mm PV tissue thickness; insertion depth = 7mm</div> <div>• mifi_flush_050-0.50.vtk: MiFi catheter within blood with NaCl spheres of 0.5mm radius at irrigation holes</div> <div>• mifi_sheath_0100.vtk: MiFi catheter within transeptal sheath extracted by 10mm</div> <div> </div> <div>[1] Unger LA, Anton CM, Stritt M, Wakili R, Haas A, Kircher M, Dossel O, Luik A. In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human. IEEE Trans Biomed Eng. 2023 Feb;70(2):533-543. doi: 10.1109/TBME.2022.3196545. Epub 2023 Jan 19. PMID: 35925848.</div> <p> </p>
Data supporting: "Calcium-driven In Silico Inactivation of a Human Olfactory Receptor"
<p>In this repository we deposited trajectories and input files for the paper "Calcium-driven In Silico Inactivation of a Human Olfactory Receptor".</p> <p>The data is organised as follow:</p> <p> </p> <p>DATA:</p> <p>CA / NA / NO_IONS / NEUTRAL</p> <ul> <li>centroid.pdb # centroid calculated with GMX</li> <li>step5_input.gro # input file from CHARMM GUI</li> <li>topol.top # topol file from CHARMM GUI</li> <li>MDPs # folder containing mdp files from CHARMM GUI</li> <li>toppar # folder containing topology files from CHARMM GUI </li> </ul> <p> </p> <p>TRJs:</p> <p>CA / NA / NO_IONS / NEUTRAL</p> <ul> <li>ref.pdb # reference pdb file</li> <li>trj1.xtc # trajectory from replica 1</li> <li>trj2.xtc # trajectory from replica 2</li> <li>trj3.xtc # trajectory from replica 3</li> <li>trj4.xtc # trajectory from replica 4</li> <li>trj5.xtc # trajectory from replica 5</li> <li>trj6.xtc # trajectory from replica 6 (NEUTRAL only)</li> </ul>
Supplementary Figures. "In silico research of new therapeutics rotenoids derivatives against Leishmania amazonensis infection"
<p>Supplementary figures corresponding to the submitted manuscript entitled "In silico research of new therapeutics rotenoids derivatives against Leishmania amazonensis infection"</p>
Extended data for Manuscript: Identification of potential biological targets of oxindole scaffolds via in silico repositioning strategies
<p>This is the Extended Data for the manuscript "<strong>Identification of potential biological targets of oxindole scaffolds via <em>in silico</em> repositioning strategies" </strong>submitted to F1000 Research.</p> <p>Extended Data include a list of all the accession codes as mentioned in the text, the results of 2D fingerprint-based similarity analyses and ligand-protein complexes predicted by rigid docking and Induced Fit Docking calculations.</p>
Official logo of the H2020 Project In Silico World
<p>Official logo of the H2020 Project In Silico World</p> <p> </p>
Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"
<p>Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G.</p> <p>The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales.</p> <p>The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications.</p> <p>This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.</p>
In silico 2D photoacoustic imaging data
<p>Here you find the data that was used for the experiments in the paper <strong>Confidence estimation for machine learning-based quantitative photoacoustics</strong> by <em>Janek Gröhl</em>, <em>Thomas Kirchner</em>, <em>Tim Adler</em>, and <em>Lena Maier-Hei</em>n.</p>
Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)
<p>This repository concerns the supplementary material data of the research article entitled "<em>Personalised in silico model for radiation-induced pulmonary fibrosis</em>" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.</p>
Dataset related to article "New in silico models to predict in vitro micronucleus induction as marker of genotoxicity"
<p>The .txt file contains the dataset of the in silico model for genotoxicity as induction of micronuclei.</p> <p>The .doc file contains the descriptors of the models and the structural alerts.</p>
APARENT2 Genome-wide In-silico Saturation Mutagenesis
<p>In-silico saturation mutagenesis predictions for all polyadenylation signals found in PolyADB V3 using the APARENT2 model (transcript-wide). The file 'aparent2_ism_scores_polyadb_v3.csv.gz' contains all data. The file 'aparent2_ism_scores_polyadb_v3_cutoff.csv.gz' contains only variants with more than 1.25-fold increase or decrease in isoform odds. The data columns 'delta_logodds' and 'delta_usage' contain variant isoform log odds ratios and isoform proportion differences (wrt. PolyADB measurements) for polyadenylation occurring anywhere +/- 100bp of the canonical cleavage site. The columns 'delta_logodds_narrow' and 'delta_usage_narrow' contains log odds ratios and proportion differences for cleaveage that occurs +0bp to +50bp immediately downstream of the canonical core hexamer motif. The data columns 'pas_position_hg19' and 'pas_position_hg38' indicate the start coordinate of the core hexamer.</p>
Cortical cell assemblies and their underlying connectivity: an in silico study
<p>Dataset linked to the article with the same title</p> <ul> <li>simulation_config.zip: contains SONATA config files needed to re-run an exemplary simulation (after downloading the circuit from <a href="https://zenodo.org/record/7930275">10.5281/zenodo.7930275</a>). In order to run it, paths in circuit_config.json, and simulation_config.json have to be updated!</li> <li>assemblies.h5 is a dataset produced (and can be easily opened) by: <a href="https://zenodo.org/record/8112725">assemblyfire</a> (see GitHub README for more documentation) and serves as a basis for the manuscript. As the assemblies are the results of an unsupervised clustering (of high activity time bins) the resulting labels are not necessary meaningful. In the manuscript we have ordered the assemblies (from early to late responding ones, and from pattern A to J responsive ones) but the HDF5 file still stores the original labels. The mapping from the "random" labels to the ones presented in our article is stored in the config files on GitHub.</li> </ul> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods"
<p>Supplements for "Understanding the interaction between a human transferrin receptor aptamer-short double stranded RNA conjugate and its cell membrane target by in silico methods". </p> <p>This supplement includes the following files:</p> <p> </p> <p>1. Structures of the most stable Protein-Aptamer complexes predicted from HADDOCK</p> <p>Haddock_Cluster1.pdb <br> Haddock_Cluster2.pdb <br> Haddock_Cluster3.pdb </p> <p>2. Structure of the most stable conformation aligned with Protein-transferring complex PDB</p> <p>cluster1_aligned.pdb <br> transferrin_aligned.pdb </p> <p>3. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer</p> <p>aptamer_new_rep01_Decomp.dat <br> aptamer_new_rep02_Decomp.dat <br> aptamer_new_rep03_Decomp.dat </p> <p>4. MM-GBSA decomposition analysis of the three replicas for Protein-Aptamer-Conjugate<br> conjugate_new_rep01_Decomp.dat <br> conjugate_new_rep02_Decomp.dat <br> conjugate_new_rep03_Decomp.dat <br> </p> <p><br> <br> </p>
Phloem anatomy constraints root system architecture development: theoretical clues from in silico experiments [software and dataset]
<p>Simulation software and results for "<strong>Phloem anatomy constraints root system architecture development: theoretical clues from in silico experiments</strong>"</p>
A battery of in silico models application for pesticides exerting reproductive health effects: assessment of performance and prioritization of mechanistic studies
<p>Dataset of Table 1-7</p> <p>Data of Table 1, “Pesticides and their classification”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Tab1.PNG). Corresponding raw data is regarding classification in the hazard class reproductive toxicity available on line. All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK__Tab1_PPP_27_1_M.txt) in txt format.</p> <p> </p> <p>Data of Table 2, “PDB structures of nuclear receptors used in VTL and ED” </p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Tab2 15 meta data files as pdf-format with information sources of PDB structures used in employed in silico models (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_2_M15.pdf). All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab2_27_2_M.txt) in txt format.</p> <p> </p> <p>Data of Table 3, “Results of in vivo studies (Shepelska et al., 2021; Shepelskaya and Kolyanchuk, 2021; Shepelskaya and Kolianchuk, 2018)”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1_Table3.PNG). Three meta data file as pdf-format with data of in vivo studies (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_27_3_M3.pdf). All further related information are provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab3_27_3_M.txt) in txt format.</p> <p> </p> <p>Data of Table 4, “Results of in silico modelling of pesticides interaction with nuclear receptors”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab4.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf). All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab4_24_1-2_M.txt) in txt format.</p> <p> </p> <p>Data of Tabe 5, “Combination of in silico results with in vitro results by considering as positive result only where both in silico models predict a hit (Combined 1)”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab5.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf).</p> <p>Corresponding raw data with ToxCast results provided as seventeen files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_2.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_3.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_4.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_5.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_6.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_7.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_8.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_9.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_10.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_11.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_12.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_13.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_14.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_15.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_16.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_17.csv)All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab5_24_25_1_M.txt) in txt format.</p> <p> </p> <p>Data of Table 6, “Combination of in silico results with in vitro results by considering as a positive any in silico hit independently of the employed model (Combined 2)”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab6.PNG). Corresponding raw data with in silico modelling results provided as two files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1-17.csv) and seventeen pdf files (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_1.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_2.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_3.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_4.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_5.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_6.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_7.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_8.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_9.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_10.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_11.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_12.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_13.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_14.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_15.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_16.pdf ; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_17.pdf). Four meta data file as pdf-format with detailed in silico modelling descriptions (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M1.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M2.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_1_M3.pdf; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_24_2_M1.pdf).</p> <p>Corresponding raw data with ToxCast results provided as seventeen files in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_1.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_2.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_3.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_4.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_5.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_6.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_7.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_8.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_9.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_10.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_11.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_12.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_13.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_14.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_15.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_16.csv; IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_25_1_17.csv)All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab6_24_25_1_M.txt) in txt format.</p> <p> </p> <p>Data of Table 7, “Metrics of performance of in silico models separately and combined.”</p> <p>The Dataset (TIV-D-23-00280R1) contains the original table as PNG-format (TIV-D-23-00280R1 _Tab7.PNG). Corresponding raw data with calculation of relevant performance metrics provided as one file in CSV format (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_26_1.csv). One meta data file as pdf-format with detailed description of the method used for calculation (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_26_1_M1.pdf).</p> <p>All further related information is provided as one meta-data-file (IZSEZO-2L2269_TIV-D-23-00280R1_SK_PPP_Tab7_26_1_M.txt) in txt format.</p>
In silico prediction of ARB resistance: A first step in creating personalized ARB therapy
<p><strong>AT1R Model preparation</strong><br> The crystal structure of human AT1R bound to olmesartan (PDB: 4ZUD) was downloaded from the RCSB Protein Data Bank. 4ZUD contains apocytochrome b562RIL fused to the amino terminus, and many of the flexible regions, as well as helix 8, are not resolved. In order to generate an appropriate starting structure, olmesartan and the apocytochrome b562RIL fusion were removed from 4ZUD, and the missing regions were added to the protein with MOE software (Chemical Computing Group ULC, Montreal, Canada). Specifically, the N-Terminus (residues 1 to 25), intracellular loop 2 (residues 134 to 140), extracellular loop 2 (residues 186 to 188), intracellular loop 3 (residues 223 to 234), and helix 8 (residues 305 to 316) were added to the AT1R in accordance to the human AT1R sequence and PDB:4YAY. The remaining carboxyl-tail of the AT1R (residues 317 to 359) was not modeled. The AT1R model then underwent an energy minimization within MOE using the Amber10:Extended Huckel Theory (EHT) force field.</p> <p><strong>Molecular dynamic (MD) simulations and analysis</strong><br> The MOE minimized AT1R was loaded into CHARMM-GUI. An 80 Å by 80 Å lipid bi-layer composed of 13% cholesterol and 87% Phosphatidylcholine (POPC) was generated around the receptor. Water was packed 17.5 Å above and below the lipid bi-layer, and 150 mM Na+ and Cl- ions were added to the system via Monte-Carlo ion placing. The all-atom CHARMM C36 force field for proteins and ions, and the CHARMM TIP3P force field for water were selected. A hard non-bonded cutoff of 8.0 angstroms was utilized. All molecular dynamics simulations were performed using the PMEMD module of the AMBER16 package with support for MPI multi-process control and GPU acceleration code. Orthorhombic periodic boundary conditions with a constant pressure of 1 atm was set via the NPT ensemble and temperature was set to 310.15°K (37°C) using Langevin dynamics. The SHAKE algorithm was used to constrain bonds containing hydrogens. The dynamics were propagated using Langevin dynamics with Langevin damping coefficient of 1 ps-1 and a time step of 2 fs. Before the production run, the AT1R model was minimized for 5000 steps using the steepest descent method and then equilibrated for 600 ps. The protein coordinates were saved in 10 ps intervals. The production run lasted 150 ns, at which point all three replicas were stable for at least the last 20 ns.</p>
In silico identified signal peptides of Chlamydomonas reinhardtii
<p><strong>Overview</strong></p> <p><em>Chlamydomonas reinhardtii </em>theoretical signal peptides identified by<em> </em>SignalP 4.0 in a protein data set described below:</p> <ul> <li>Protein data set came from "The Genome Portal of the Department of Energy Joint Genome Institute" (http://genome.jgi.doe.gov/)</li> <li>Protein sequences were evaluated in SignalP 4.0 Server (http://www.cbs.dtu.dk/services/SignalP/)</li> </ul> <p> </p> <p><strong>File used</strong></p> <p>Chlre4_best_proteins.fasta.gz -> Protein dataset version used for analysis</p> <p> </p> <p><strong>Workflow</strong> </p> <p> ______Chlre4_best_proteins.fasta.gz_______</p> <p> | |</p> <p> Chlre4_best_proteins_fasta_protein_woSP.fasta Chlre4_best_proteins_signalPeptide.fasta</p> <p> |</p> <p> ___Chlre4_best_proteins_signalPeptide_unique.fasta___</p> <p> | |</p> <p> Chlre4_best_proteins_signalPeptide_unique.aln Signal Peptide Anotation from aligned.xlsx</p> <p> </p> <p><strong>Info</strong></p> <p>Chlre4_best_proteins_fasta_protein_woSP.fasta -> Mature protein sequences from proteins identified without signal peptide</p> <p>Chlre4_best_proteins_signalPeptide.fasta -> Identified signal peptide</p> <p>Chlre4_best_proteins_signalPeptide_unique.fasta -> Unique identified signal peptide</p> <p>Chlre4_best_proteins_signalPeptide_unique.aln -> Align signal peptides (UGENE)</p> <p>Signal Peptide Annotation from aligned.xlsx -> Signal peptide list, highlighted in orange theoretical tested.</p> <p> </p> <p><strong>Citations</strong></p> <p>For use of signal peptide dataset, please cite:</p> <p>Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433</p> <p>and </p> <p><strong>SignalP 4.0: discriminating signal peptides from transmembrane regions</strong><br> Thomas Nordahl Petersen, Søren Brunak, Gunnar von Heijne & Henrik Nielsen<br> <em>Nature Methods</em>, <strong>8</strong>:785-786, <strong>2011</strong><br> <br> doi: 10.1038/nmeth.1701<br> PMID: 21959131<br> Supplementary materials: nmeth.1701-S1.pd</p> <p>and </p> <p><strong>The genome portal of the Department of Energy Joint Genome Institute: 2014 updates</strong></p> <p>H. Nordberg, M. Cantor, S. Dusheyko, S. Hua, A. Poliakov, I. Shabalov, T. Smirnova, I. V. Grigoriev, I. Dubchak, ,</p> <p>Nucleic Acids Res. 42, 26–31. <strong>2014</strong> </p> <p>doi:10.1093/nar/gkt1069.</p> <p> </p>
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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.