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  • Data set for publication in Cell Host Microbe, Bruggisser et al: Cell-specific targeting by Clostridium perfringens β-toxin unraveled: the role of CD31 as the toxin receptor.
    Data Types:
    • Image
    • Tabular Data
    • Dataset
    • Document
    • Text
  • The dataset is constructed for a project that investigates the coverage and the role of Semantic Scholar (S2) search engine in condunting secondary studies in software engineering.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Text
  • The following files are associated with the manuscript "Flavones’ and Flavonols’ Antiradical Structure–Activity Relationship—A Quantum Chemical Study", MDPI Antioxidants, 2020 (https://doi.org/10.3390/antiox9060461). They include geometries of molecules used therein and allows to reproduce the obtained results.
    Data Types:
    • Dataset
    • Text
  • The presented cross-sectional dataset can be employed to analyze the governmental, trade, and competitiveness relationships of official COVID-19 reports. It contains 18 COVID-19 variables generated based on the official reports of 138 countries, as well as an additional 2203 governance, trade, and competitiveness indicators from the World Bank Group GovData360 and TCdata360 platforms in a preprocessed form. The current version was compiled on May 25, 2020. Please cite as: • (Data in Brief article) Data generation: • Data generation (data_generation. Rmd): Datasets were generated with this R Notebook. It can be used to update datasets and customize the data generation process. Datasets: • Country data (country_data.txt): country data. • Metadata (metadata.txt): the metadata of selected GovData360 and TCdata360 indicators. • Joint dataset (joint_dataset.txt): the joint dataset of COVID-19 variables and preprocessed GovData360 and TCdata360 indicators. • Correlation matrix (correlation_matrix.txt): the Kendall rank correlation matrix of the joint dataset.
    Data Types:
    • Software/Code
    • Dataset
    • Text
  • The COVID-19 pandemic is a worldwide public health crisis. A vaccine with efficacy against SARS-CoV-2, the pathogen that causes COVID-19, is needed. While most vaccines under investigation are optimized to generate an antibody response, we hypothesize that peptide vaccines containing optimized epitope regions with concurrent B cell, CD4+ T cell, and CD8+ T cell stimulation would drive both humoral and cellular immunity with high specificity, potentially avoiding undesired effects such as antibody-dependent enhancement (ADE), all the while providing a platform with fast manufacturing potential and with high shelf-life stability. Here we combine computational prediction of T cell epitopes with recently published B cell epitope mapping studies to propose optimized peptide vaccines for SARS-CoV-2. We begin with an exploration of the predicted T cell epitope space in SARS-CoV-2, with interrogation of HLA-I and -II epitope overlap, protein source, concurrent human/murine coverage, and allelic space. The T cell vaccine candidates were selected by further considering their predicted affinities for MHC-I and MHC-II alleles across the human population (as well as H2-b/H2-d murine coverage to support preclinical studies), predicted immunogenicity, viral protein abundance, sequence conservation, and co-localization of MHC-I and -II epitopes. The predicted B cell epitope regions were selected by starting from responses identified in linear epitope mapping studies of patient serum and filtering to select those with high molecular dynamics-derived surface accessibility, high sequence conservation, spatial localization within functional domains of the spike glycoprotein (RBD, FP, and HR regions), and avoidance of glycosylation sites. From 58 initial candidates, three B cell epitope regions were identified using these criteria. By combining these B cell and T cell analyses, we propose a set of human and murine-compatible SARS-CoV-2 vaccine peptide candidates.
    Data Types:
    • Other
    • Sequencing Data
    • Tabular Data
    • Dataset
    • Text
  • The following files are associated with the manuscript "Antiradical Activity of Selected Phenolic Acids – FRAP Assay and QSAR Analysis of the Structural Features", MDPI Molecules, 2020. They include geometries of molecules used therein and allows to reproduce the obtained results.
    Data Types:
    • Dataset
    • Text
  • This dataset comprehends data and and associated R code used to run the analysis for the paper. We also include an R Markdown Dynamic document. We tested whether the amount of melanomacrophages and hepatic cellular catabolism substances are influenced by land use changes in the Brazilian Cerrado. Data contains the Environmental matrix (Q) composed of the land use classes for each samplimg site, species trait (R) matrix with content of each pigment in cells, averaged from all individuals, and species composition matrix (L) with the species incidence in all sampling sites.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Text
    • File Set
  • Raw data from single molecule experiments.
    Data Types:
    • Dataset
    • Document
    • Text
    • File Set
  • Sample of shale from Oligocene menilite formation was taken from outcrop near Gorlice (Silesian Unit of Flysh Carpathians). Kerogen was extracted from source rock with use of conventional method of HCl&HF treatment. The pyrolysis experiments of source rock samples and separated kerogen samples were performed using NETZSCH simultaneous thermal analysis STA 449 F3 Jupiter. The tests were carried under non-isothermal conditions over the temperature range of 40-600oC. The measurement heating rates were: 2, 5, 10, 15 and 20K/min.
    Data Types:
    • Dataset
    • Text
  • The purpose of this experiment was to compare between-strain differences regarding avoidance behavior. Active avoidance data were collected from 40 Wistar-Kyoto (WKY) and 40 Sprague Dawley (SD) rats during a foot-shock experiment. The experiment consisted of 12 sessions each composed of 25 trials. Each trial consisted of a warning period, a possible shock period (if avoidance did not occur), and an intertrial interval (ITI) representing a safety period. During warning periods, rats had an option to press a lever in order to avoid future shock. However, if a lever press did not occur, then a shock period began during which the rats could press the lever to escape the foot-shock. After an escape or an avoidance, a 180-second safe period began (ITI). The maximum time of a warning period and a shock were both 72-seconds each if the rat failed to lever press during both periods. Every session also began with a 60-second habituation period during which the rat could familiarize itself with the experimental cage. Lever presses during ITI periods were classified as inter-trial responses (ITRs) whereas lever presses during the habituation periods were classified as anticipatory responses (ARs). Lever presses during shock periods were labeled as escapes “E” whereas lever presses during warning periods were labeled as avoidances “A”. Lever press data were discretized to 12-second periods. Frequency counts for lever presses during each 12-second timestep can be found in the raw data files (located in the “ratRL_datafiles_Model_InputOutput” folder). An example name of this file is “S09.csv” representing an SD rat or “W09.csv” representing a WKY rat. A reinforcement learning, actor-critic model was applied to this raw data to determine different learning parameters for each rat. The code for this model fitting can be found in the folder titled “ratRL_ModelFitting_Code”. Based on learning parameters determined from the reinforcement learning model code, a simulation of theoretical rats running through the experimental protocol was created. This simulation can be found in the “ratRL_Simulation_Code” folder. Inputs to this simulation are found in the “ratRL_codedTrials_Sim_InputOutput” folder. The summaries for each experimental rat’s performance during the foot-shock avoidance experiment are labeled “Sum” files (ie S09sum.csv) and the learning parameters for each rat are stored in the file called “parm_listfile.csv”. Output files are labeled “out” (ie S09out.csv) and represent the performance of a theoretical simulated rat. The differences in learning parameters between the two rat strains can provide insight into anxiety-disorders as the WKY rats have been used as an animal model for anxiety. Learning parameters can also be mapped to certain brain regions in order to explore possible neurological differences in the two strains in future experiments.
    Data Types:
    • Other
    • Software/Code
    • Tabular Data
    • Dataset
    • Text