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107 results for “mechanistic model”
Supplementary Data from, "A Mechanistic Model of Annual Sulfate Concentrations in the U.S."
<p>These data are used to perform the analysis contained in, "A Mechanistic Model of Annual Sulfate Concentrations in the United States," by Wikle, Hanks, Henneman, and Zigler. This is purely for archival purposes, to facilitate access and replication of the aforementioned analysis. All data were obtained from the following publicly available sources:</p> <p>1) AMPD Unit Data (U.S. EPA, "Air markets program data," https://ampd.epa.gov/ampd)</p> <p>2) 2010 U.S. Population Density (U.S.G.S., http://dx.doi.org/10.5066/F74J0C6M)</p> <p>3) SO4 Concentrations (Randall Martin Atmospheric Composition Analysis Group's North American Regional Estimates, version V4.NA.02, https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03)</p> <p>4) North American Regional Reanalysis Meteorological Data (NOAA, https://psl.noaa.gov/data/gridded/data.narr.monolevel.html)</p> <p>Code and supplementary material from this analysis are available at: https://github.com/nbwikle/mechanisticSO4-supp_material</p>
Climatological data from mechanistic model experiments of Boljka and Birner (2022/3; npj Climate and Atmospheric Science)
<p>Some climatological output data from mechanistic dry dynamical core model experiments used for the paper of Boljka and Birner (2022/3): "Potential impact of tropopause sharpness on the structure and strength of the general circulation", npj Climate and Atmospheric Science. For more details see the manuscript. </p>
Dataset supporting the paper: Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach
<p>The necessary image files for the paper titled "Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach"</p>
Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population - Supporting Material
<p>Supporting material for the study:</p> <p><strong>"Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population."</strong></p> <p><strong>Authors:</strong></p> <p>Cathleen Petit-Cailleux1, Hendrik Davi1, François Lefèvre1, Joseph Garrigue<strong>2</strong>, Jean-André Magdalou<strong>2</strong>, Christophe Hurson<strong>2,3</strong><strong>, </strong>Elodie Magnanou<strong>2,4</strong>, and Sylvie Oddou-Muratorio1.</p> <p> </p> <p>Adresses</p> <p>1INRA, UR 629 Ecologie des Forêts Méditerranéennes, URFM, Avignon, France</p> <p><strong>2</strong>Réserve Naturelle Nationale de la Forêt de la Massane, France</p> <p><strong>3</strong>Fédération des Réserves Naturelles Catalanes, Prades, France</p> <p><strong>4</strong>Sorbonne Université, CNRS, Biologie Intégrative des Organismes Marins, BIOM, F-66650 Banyuls-sur-Mer, France</p> <p><strong>ORCID:</strong></p> <p>Cathleen Petit-Cailleux: <a href="https://orcid.org/0000-0001-7714-6583">https://orcid.org/0000-0001-7714-6583</a></p> <p>François Lefèvre : <a href="https://orcid.org/0000-0003-2242-7251">https://orcid.org/0000-0003-2242-7251</a></p> <p>Sylvie Oddou-Muratorio <a href="https://orcid.org/0000-0003-2374-8313">https://orcid.org/0000-0003-2374-8313</a></p> <p> </p> <p>-------------</p> <p>Raw data of the Table_Massane_moratlity_trees.csv and climate can be obtained from Joseph Garrigue, Jean-André Magdalou and Christophe Hurson.</p> <p>The inventories files and daily climate are the input dataset to run CASTANEA models.</p> <p>All details are provided in the article.</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>
Dataset and R-script for simple mechanistic model of Heracleum sosnowskyi seed dispersal by wind
<p>The dataset contains:</p> <p>- primary data about Heracleum sosnowskyi seeds traits (terminal velocity, mass, area, wing loading) and release heights for <em>H. sosnowskyi</em> populations from two geographically distant Russia regions;</p> <p>- results of experiments of model seeds launches under different wind speeds;</p> <p>- R script for exploratory statistical analysis, linear regressions and mechanistc models testing.</p> <p>The anemochorous seed dispersal was generalized with a number of empirical and mechanistic models of varying complexity. The aim of this work was to develop the simplest possible mechanistic model of <em>Heracleum sosnowskyi</em> that allows to determine the distance of seed dispersal by wind with an accuracy comparable to that of empirical measurements. We measured and compared the characteristics of the seeds (terminal velocity, mass, area, wing loading) as well as the release height for <em>H. sosnowskyi</em> populations from two geographically distant Russia regions. We tested two simplest mechanistic models: a ballistic model and a wind gradient model using identical artificial seeds with characteristics similar to those of real <em>H. sosnowskyi</em> seeds. The wind gradient model gave the best results, despite the fact that uniform in shape, weight and size artificial <em>H. sosnowskyi</em> seeds, when dropped simultaneously from the same height, fly off at different distances. This model provides an estimate of dispersal distances with an accuracy comparable to that of empirical measurements. We plan to use the presented model to develop an individual-based model that will allow us to calculate the flight distances of <em>H. sosnowskyi</em> propagules, taking into account real weather conditions in different years and in different parts of its invasion range. All primary data and R-scripts used are freely available at the Zenodo repository (https://doi.org/10.5281/zenodo.3766035).</p> <p> </p>
Simulations from "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors"
<h1>IL6R/IL8R Antibody Binding Model Code</h1> <p>Christina M.P. Ray, Huilin Yang, Jamie B. Spangler, Feilim Mac Gabhann</p> <p>This dataset contains all simulation output files generated for the article "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors". The model is comprised of a coupled set of ordinary differential equations (ODEs) where each individual ODE describes one molecule (antibody or receptor) or molecular complex (antibody + receptor). The terms in the ODEs represent each binding interaction (binding and unbinding processes) in the system.</p> <p>The code for the binding model and for the analysis and visualization results is available on GitHub at <a href="https://github.com/christyray/bispecific-binding-model">christyray/bispecific-binding-model</a>.</p> <h2>Specific Simulations</h2> <p>The <code>.csv</code> and <code>.rds</code> files in the correspond to the results from the simulations performed for the article "Mechanistic computational modeling of monospecific and bispecific antibodies targeting interleukin-6/8 receptors". These files can be read into R using the <code>import_data()</code> function included in the <a href="https://github.com/christyray/bispecific-binding-model">GitHub repository</a>.</p> <p>The <code>id</code> files contain simulation IDs to link the molecule concentrations (<code>yin</code>) and parameter values (<code>params</code>) with the simulation results (<code>out</code>). When applicable, the <code>norm</code> files contain normalized simulation output, and the <code>occupied</code> files contain receptor fractional occupancy values calculated from the simulation output.</p> <ul> <li><code>optimization</code>: Optimization of binding rate constants (association and dissociation) to experimental <em>in vitro</em> flow cytometry data; results displayed in Figure 2</li> <li><code>binding-curve</code>: Model simulations using the best-fit parameter set for comparison to the experimental data used to fit the model parameters; results displayed in Figure 3</li> <li><code>compare-opt</code>: Model simulations using each of the optimized parameter sets; results displayed in the Supplemental Information</li> <li><code>time</code>: Simulations of antibody binding dynamics over time; results displayed in Figure 4</li> <li><code>concentration</code>: Simulations with varying antibody concentrations and receptor expression levels; results displayed in Figure 5</li> <li><code>monovalent</code>: Simulations restricted to monovalent antibody binding only; results displayed in Figure 6</li> <li><code>compare-ab</code> and <code>compare-recep</code>: Simulations of both the bispecific antibody BS1 and the combination of monospecific antibodies tocilizumab and 10H2 for comparsion; results displayed in Figure 7</li> <li><code>local</code> and <code>global</code>: Local and global univariate sensitivity analyses; results displayed in Figure 8</li> </ul> <h2>References</h2> <blockquote> <p>H. Yang, M. N. Karl, W. Wang, B. Starich, H. Tan, A. Kiemen, A. B. Pucsek, Y.-H. Kuo, G. C. Russo, T. Pan, E. M. Jaffee, E. J. Fertig, D. Wirtz, and J. B. Spangler. Engineered bispecific antibodies targeting the interleukin-6 and -8 receptors potently inhibit cancer cell migration and tumor metastasis. Molecular Therapy, 30(11):3430–3449, Nov. 2022. doi:<a href="https://doi.org/10.1016/j.ymthe.2022.07.008">10.1016/j.ymthe.2022.07.008</a></p> </blockquote>
Data from: A new mechanistic model for individual growth suggests upregulated maintenance costs when food is scarce in an insect
<p>In order to calibrate and evaluate a recently developed growth model, the Maintenance-Growth Model (MGM), for the case of growth under food restriction, empirical data for house crickets (<em>Acheta</em> <em>domesticus</em>) were collected and analysed. This data set contains data for individually reared crickets growing under two different regimes of controlled food limitation as well as data for food-limited cohorts of growing house crickets. The sets include temporal data for body mass and ingestion as well as age and size at maturation (imago emergence). The data for food-limited cohorts were collected prior to this study and parts of it have previously been analysed and presented in a publication on animal self-thinning. </p>
A low-dimensional, mechanistic water balance model for piñon pine-juniper woodlands in southern Nevada, USA
<p>This is a low-dimensional water balance model developed for pinon pine-juniper woodlands in southern Nevada. It may require extensive revision for use in other similar or dissimilar woodland ecosystems. The model will require parameterization before use in any capacity.</p> <p>This model is mechanistic, but is driven from randomized precipitation. This framework allows the user to estimate the mean and standard deviation of water balance variables by simulating the same average meteorological year 1000s of times, each with randomized precipitation. This technique approximates a normal distribution of precipitation, and will need to be modified for locations/sites that have a non-normal precipitation distribution. It is not recommended to use the model in any other way without first testing and validating its output.</p> <p>I have provided documentation throughout the wrapper, model and parameter files to assist with your use of the model. You are encouraged to learn from, build on, and improve this model. You will need to set your own pathways and build your own meteorological inputs and site parameterizations. You may need to update, revise and add/remove different submodules depending on your intended use.</p>
Estimating drivers and pathways for hydroelectric reservoir methane emissions using a new mechanistic model (estimated methane emissions for hydropower reservoir surfaces and potential dam emissions)
<p>Methane emissions data from hydropower reservoir surfaces and dams, as estimated with the ResME model. Emissions estimates available for hydropower reservoirs in the GRanD database (Lehner et al., 2011). </p> <p> </p> <p>References:</p> <p>Lehner, B., Liermann, C. Reidy, Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., and Wisser, D. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment, 9 (9): 494-502. https://doi.org/10.1890/100125.</p>
Data & code repository for the article "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". </p> <p>In detail the following data sources have been included:</p> <ul> <li>the relevant code and supporting data (code_to_upload.zip and supporting_data.zip);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results.zip);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results.zip);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures.zip)</li> </ul> </li> </ul>
Using Monitoring and Mechanistic Modeling to Improve Understanding of Eutrophication in a Shallow New England Estuary
<p>This data repository contains the names and description of data files used in the “Using Monitoring and Mechanistic Modeling to Improve Understanding of Eutrophication in a Shallow New England Estuary” manuscript by Cashel et al (2023). These data files, formatted as .txt files, include simulated and observed data of various water quality components with time. Time is always provided as the Julian Day in the first column (left). The type of data in each file is indicated by a parameter code. Parameter codes are defined below.</p> <ul> <li>SAL = salinity (ppt)</li> <li>WT = water temperature (℃)</li> <li>PAR = photosynthetically active radiation (W/m<sup>2</sup>)</li> <li>TN = total nitrogen (mg/L as N)</li> <li>NH3 = ammonium (mg/L as N)</li> <li>NO3 = nitrate + nitrite (mg/L as N)</li> <li>TP = total phosphorus (mg/L as P)</li> <li>DIP = orthophosphate (mg/L as P)</li> <li>CBOD = carbonaceous biological oxygen demand (mg/L)</li> <li>CHL = phytoplankton as chlorophyll <em>a </em>(µg/L)</li> <li>MACRO = macroalgae biomass (gDW/m<sup>2</sup>)</li> <li>DO = dissolved oxygen (mg/L)</li> </ul> <p><strong><em>1 - Simulated Data</em></strong></p> <p>1.1 – PRE Model Data</p> <p> The primary simulated data for this study includes model output on an interval of 0.05 days. Columns two through eight contain the average concentration of each WASP Segment per each time step. Data presented in these columns from left to right are from WASP Segments 8, 9, 10, 12, 14, 16, and 17.</p> <ul> <li>WASP_SAL.txt </li> <li>WASP_WT.txt </li> <li>WASP_PAR.txt</li> <li>WASP_TN.txt</li> <li>WASP_NH3.txt</li> <li>WASP_NO3.txt</li> <li>WASP_TP.txt</li> <li>WASP_DIP.txt</li> <li>WASP_CBOD.txt</li> <li>WASP_CHL.txt</li> <li>WASP_MACRO.txt</li> <li>WASP_DO.txt</li> </ul> <p>1.2 – Macroalgae Scenario Data</p> <p> Simulated data files also include a model scenario evaluating the impact of macroalgae as a state variable. This set of model output comes from simulations with macroalgae removed as a state variable. These files have a model output of 0.05 days with time in the first column, and the second column contains the average concentration within WASP segment 17.</p> <ul> <li>MACRO_PAR.txt</li> <li>MACRO_NH3.txt</li> <li>MACRO_NO3.txt</li> <li>MACRO_DIP.txt</li> <li>MACRO_CBOD.txt</li> <li>MACRO_CHL.txt</li> </ul> <p>1.3 – Dissolved Oxygen Parameter Analysis Data</p> <p> Simulated data from model scenarios evaluating the impact of parameterization on dissolved oxygen concentrations are listed below. These model simulations have an output of 0.05 days. Rows two through ten contain average DO concentration (mg/L) for WASP Segments 9, 10, 11, 12, 13, 14, 15, 16, and 17. Files containing “1” indicate the high condition of each parameter analysis, and those with a “2” indicate the low condition.</p> <ul> <li>CBOD1_DO.txt</li> <li>CBOD2_DO.txt</li> <li>Phyto1_DO.txt</li> <li>Phyto2_DO.txt</li> <li>SOD1_DO.txt</li> <li>SOD2_DO.txt</li> </ul> <p>1.4 – Heatmap Simulations</p> <p> Simulated data for heatmaps presents average concentrations from noon of each day. The first row is time, and the following rows of two through nine have simulated data for WASP Segments 10, 11, 12, 13, 14, 15, 16, and 17.</p> <ul> <li>WASP_TN2.txt</li> <li>WASP_TP2.txt</li> <li>WASP_DO2.txt</li> <li>WASP_CHL2.txt</li> </ul> <p><strong><em>2 – Observed Data </em></strong></p> <p>2.1 – Sonde Data</p> <p> Observed sonde data is presented in .txt files from various years and location. Each data file has the first column of time, and the second column as concentration per each time step. File names begin with the site name, followed by an “S” or “B” (surface and bottom respectively, if applicable), the year, and the parameter code. Site names are: LNB (Little Narragansett Bay), Pawcatuck (Pawcatuck Point), Avondale (Avondale Marina), Greenhaven (Greenhaven Marina), WYC (Westerly Yacht Club), PR (Pawcatuck Rock), Viking (Viking Marina), and R1 (Route 1). File names per location are provided in the two tables below. Row 1 of each table has the site name, and the corresponding files are listed below.</p> <table align="center"> <tbody> <tr> <td> <p><strong>LNB</strong></p> </td> <td> <p><strong>Pawcatuck</strong></p> </td> <td> <p><strong>Avondale </strong></p> </td> <td> <p><strong>Greenhaven </strong></p> </td> </tr> <tr> <td> <p>LNB_SAL.txt</p> </td> <td> <p>Pawcatuck2018_ SAL.txt</p> </td> <td> <p>AvondaleS2019_SAL.txt</p> </td> <td> <p>GreenhavenS2018_SAL.txt</p> </td> </tr> <tr> <td> <p>LNB_WT.txt</p> </td> <td> <p>Pawcatuck2018_WT.txt</p> </td> <td> <p>AvondaleB2019_SAL.txt</p> </td> <td> <p>GreenhavenB2018_SAL.txt</p> </td> </tr> <tr> <td> <p>LNB_DO.txt</p> </td> <td> <p>Pawcatuck2018_DO.txt</p> </td> <td> <p>AvondaleS2019_WT.txt</p> </td> <td> <p>GreenhavenS2018_WT.txt</p> </td> </tr> <tr> <td> <p>LNB_CHL.txt</p> </td> <td> <p>Pawcatcuk2018_CHL.txt</p> </td> <td> <p>AvondaleB2019_WT.txt</p> </td> <td> <p>GreenhavenB2018_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_SAL.txt</p> </td> <td> <p>AvondaleS2019_CHL.txt</p> </td> <td> <p>GreenhavenS2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_WT.txt</p> </td> <td> <p>AvondaleB2019_CHL.txt</p> </td> <td> <p>GreenhavenB2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_DO.txt</p> </td> <td> <p>AvondaleS2019_DO.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> <p>Pawcatuck2019_CHL.txt</p> </td> <td> <p>AvondaleB2019_DO.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_SAL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_SAL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_WT.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_WT.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_CHL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_CHL.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleS2020_DO.txt</p> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> <p>AvondaleB2020_DO.txt</p> </td> <td> <p> </p> <p> </p> </td> </tr> </tbody> </table> <p> </p> <table align="center"> <tbody> <tr> <td> <p><strong>WYC</strong></p> </td> <td> <p><strong>PR</strong></p> </td> <td> <p><strong>Viking</strong></p> </td> <td> <p><strong>R1</strong></p> </td> </tr> <tr> <td> <p>WYC2018_SAL.txt</p> </td> <td> <p>PRS2018_SAL.txt</p> </td> <td> <p>Viking2018_SAL.txt</p> </td> <td> <p>R1S2018_SAL.txt</p> </td> </tr> <tr> <td> <p>WYC2018_WT.txt</p> </td> <td> <p>PRB2018_SAL.txt</p> </td> <td> <p>Viking2018_WT.txt</p> </td> <td> <p>R1B2018_SAL.txt</p> </td> </tr> <tr> <td> <p>WYC2018_CHL.txt</p> </td> <td> <p>PRS2018_WT.txt</p> </td> <td> <p>Viking2018_CHL.txt</p> </td> <td> <p>R1S2018_WT.txt</p> </td> </tr> <tr> <td> <p>WYC2018_DO.txt</p> </td> <td> <p>PRB2018_WT.txt</p> </td> <td> <p>Viking2018_DO.txt</p> </td> <td> <p>R1B2018_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2018_DO.txt</p> </td> <td> <p>Viking2019_SAL.txt</p> </td> <td> <p>R1S2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2018_DO.txt</p> </td> <td> <p>Viking2019_WT.txt</p> </td> <td> <p>R1B2018_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2020_SAL.txt</p> </td> <td> <p>Viking2019_CHL.txt</p> </td> <td> <p>R1S2020_SAL.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2020_SALtxt</p> </td> <td> <p>Viking2019_DO.txt</p> </td> <td> <p>R1B2020_SAL.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2020_WT.txt</p> </td> <td> <p>Viking2020_SAL.txt</p> </td> <td> <p>R1S2020_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2020_WT.txt</p> </td> <td> <p>Viking2020_WT.txt</p> </td> <td> <p>R1B2020_WT.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRS2020_DO.txt</p> </td> <td> <p>Viking2020_CHL.txt</p> </td> <td> <p>R1S2020_DO.txt</p> </td> </tr> <tr> <td> </td> <td> <p>PRB2020_DO.txt</p> </td> <td> <p>Viking2020_DO.txt</p> </td> <td> <p>R1B2020_DO.txt</p> </td> </tr> </tbody> </table>
Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction
<p>(DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning </p>
Data from: A new mechanistic model for individual growth suggests upregulated maintenance costs when food is scarce in an insect
Open the record for dataset details and reuse information.
Prediction of mechanistic subtypes of Parkinson's using patient-derived stem cell models
<p>Data and pipelines used to predict mechanistic subtypes of Parkinson's disease using patient-derived stem cell model.</p> <p><strong>Lists of files included;</strong></p> <ul> <li>chemPredPD2022_Imaging process pipelines: pipelines to extract tabular data in Columbus Image Data Storage and Analysis System</li> <li>demo_data_images: a set of data for the Demo</li> <li>ImageData</li> <li>TabularData</li> <li>New test data_PINK1_isoCTRL</li> <li>New test data_SNCA_isoCTRL</li> <li>Tabular_demo_data</li> </ul>
Supplement to Mechanistic phylodynamic models do not provide conclusive evidence that non-avian dinosaurs were in decline before their final extinction
<p>This repository contains the supplementary files for:</p> <p>Allen BJ, Volkova Oliveria MV, Stadler T, Vaughan TG, Warnock RCM. 2024. Mechanistic phylodynamic models do not provide conclusive evidence that non-avian dinosaurs were in decline before their final extinction. Cambridge Prisms: Extinction.</p> <p><strong>Description of files</strong></p> <p>This repository contains the cleaned tree files, tables of age constraints, XML files for running the analyses in BEAST2, and R code to process the datasets.</p> <p>Benson1clean.tree, Benson2clean.tree, Lloyd1clean.tree, Lloyd2clean.tree - the cleaned tree files containing the phylogenies inputted into BEAST2, with tip names matched to age data from the Paleobiology Database</p> <p>Tree modifications log.xlsx - a description of the modifications made to each input phylogeny compared to their state in the data supplement of their original papers</p> <p>dinosaur_ages.csv - the raw dinosaur age data downloaded from the Paleobiology Database</p> <p>tip_constraints.csv - the dinosaur age data converted into a format to input into BEAST2, to provide age constraints for tips</p> <p>change_times.txt - a text file describing the change times for the piecewise constant trajectories in the two phylodynamic analyses</p> <p>BDSKY.xml, PiecewiseCoalescent.xml - XML files describing the BEAST2 configuration for each of the two phylodynamic models</p> <p>BDSKY_logs.zip, BDSKY_trees.zip, Coalescent_logs.zip, Coalescent_trees.zip - compressed folders containing the output log files and inferred phylogenies for each of the 16 analysed phylogenies</p> <p>Supplementary_tables.zip - Tables containing summary statistics for each of the parameters inferred in each of the models</p> <p><strong>Description of R code</strong></p> <p>The R code is subdivided into the following files:</p> <p>Wrangle_trees.R - code for checking, cleaning, and splitting the phylogenies used in the analyses</p> <p>PBDB_tip_constraints.R - code for converting the raw age data from the Paleobiology Database into a format ready for input into BEAST2 as tip constraints</p> <p>BDSKY_post_processing.R, Coalescent_post_processing.R - code for cleaning and plotting data from the BEAST2 log files, for each of the two phylodynamic models</p>
Understanding complex spatial dynamics from mechanistic models through spatio-temporal point processes
<p>Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially-explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements, and spatio-temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio-temporal relationships or patterns. An alternative solution is given by meta-models and meta-analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a meta-modelling approach based on spatio-temporal point processes (STPPs), we characterise spatio-temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially-explicit population model simulate hierarchically the pest-predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio-temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi-type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio-temporal dynamics of pest-predator systems. We are able to differentiate the contributions of different driver categories ( i.e., spatio-temporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where, for example, we show that the organisation of cultivated patches and semi-natural elements play different roles for pest regulation depending on the scale considered. It aids to formulate guidelines for biological control strategies at global and local scale.</p>
Dataset of paper "Mechanistic modelling of solar disinfection (SODIS) kinetics of Escherichia coli, enhanced with H2O2 – Part 1: The dark side of peroxide"
<p>Data of the experimental and predicted <em>E. coli </em>inactivation and H<sub>2</sub>O<sub>2</sub> profiles under dark conditions to study the effect of rising water temperature.</p>
Dataser of paper "Mechanistic modelling of solar disinfection (SODIS) kinetics of Escherichia coli, enhanced with H2O2 – Part 2: Shine on you, crazy peroxide"
<p>Data of the experimental and predicted <em>E. coli </em>inactivation and H<sub>2</sub>O<sub>2</sub> profiles under different conditions of UV radiation, water temperature and initial H<sub>2</sub>O<sub>2</sub> concentration.</p>
DFT Calculated xyz Files in Support of "Bidentate Rh(I)-Phosphine Complexes for the C-H Activation of Alkanes: Computational Modelling and Mechanistic Insight"
<p>Theoretically calculated xyz files for propane, carbon monoxide, butyraldehyde and multiple Rh-phosphine complexes as well as transition states relevant for the C-H activation and subsequent carbonylation of alkanes.</p> <p>All quantum chemical simulations were performed using the Gaussian 16 software package. Closed-shell equilibrium structures, i.e., minima and transition states (TSs) as well as electronic properties of educts, intermediates, and products involved in the C-H activation of propane (methyl group activation) and subsequent steps mediated by the Rh complexes were obtained at the DFT level of theory. The range-separated B97XD functional was employed. The def2-SVP basis set and the respective effective core potential (ECP) were utilized for all atoms. TSs were fully optimized at the same level of theory using the rational function optimization (RFO) approach as well as nudged elastic band (NEB) method as implemented in the pysisyphus<sup> </sup>software suite. Subsequently, a vibrational analysis was carried out for each stationary point to verify that a minimum or first-order saddle point was obtained on the 3<em>N</em>-6-dimensional potential energy (hyper)surface (PES).</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.