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Provided are the raw data for "Humanisation of the zebrafish C5a receptor confers targeting by human-specific staphylococcal virulence factors", available at https://www.biorxiv.org/content/10.1101/2020.02.18.955021v1 This mostly includes raw imaging data that we used to make the figures, in addition to some Prism files we used for data processing. Below is a copy of the abstract for this work. Staphylococcus aureus (S. aureus) possesses high host-specificity, including many human-specific virulence factors. Current efforts towards vaccine development have largely failed, citing inappropriate infection models and insufficient understanding of staphylococcal virulence. We sought to create a humanised zebrafish infection model susceptible to human-specific virulence factors, focusing on the human C5a receptor (C5AR1) which is targeted by three human-specific staphylococcal virulence factors, CHIPS, PVL, and HlgCB. We demonstrated that the zebrafish C5a receptor (C5aR) responds to serum-derived zebrafish C5a, mediates phagocyte recruitment, and is not targeted by any adapted staphylococcal virulence factors. In vivo expression of C5AR1 in zebrafish neutrophils conferred susceptibility to PVL and HlgCB and enhanced S. aureus infection. Lastly, we designed a humanised zebrafish C5aR with only three amino acid changes that maintains endogenous signalling capability yet gained sensitivity to CHIPS-mediated inhibition. We show that a partially humanised zebrafish is a valuable model for investigating host-specific virulence factors.
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We simulated the surface evolution for a pre-Nectarian surface unit and found that relative to their size, large complex craters are less destructive to the surrounding terrain than small simple craters. The data is structured as follows: 01_CTEM_Outputs - .dat files in which the craters from the simulations are stored - Python script to convert the .dat files to shapefiles 02_Shapefiles_From_CTEM_Outputs - Shapefiles in which the craters from the simulations are stored - Shapefile of the surrounding area (created manually) 03_CSFD_Measurements - A modified version of CSFD Tools to conduct Cartesian crater size-frequency distribution measurements - .scc files which contain the results from the Traditional Crater Counting and Non-sparseness Correction techniques (for further analysis in Craterstats) 04_Crater_Statistics - .stat files which contain crater size-frequency distribution statistics (obtained from the Craterstats software) 05_Crater_Equilibrium - simplified .stat files which contain cumulative number density information - Python script to fit a power law function to the simplified .stat files 06_Geometric_Saturation_Levels - .stat files which contain crater size-frequency distribution statistics (obtained from the Craterstats software) - Python script to calculate geometric saturation levels from .stat files - .txt files containing geometric satration levels Data by Orgel et al. (2018) are availiable here: http://www.planet.geo.fu-berlin.de/Orgel_etal_2017_Lunar_basins.zip
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Developing and validating a University Needs Instrument to Measure the Psychosocial Needs of University Students
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The dataset contains:
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Data set extracted from https://globalclinicaltrials.com/ for key works combination related to TBI tDCS TBI TMS Stroke tDCS Stoke TMS refiltered using matlab custom code to match the cross matching conditions and treatment as defined in downloaded description.
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The database, version 26 (first version was available in 2002), contains now 13239 site forms, (most of them with their geographical coordinates), comprising 16695 radiometric data: Conv. 14C and AMS 14C (12922 items), TL (10143 items), OSL (6510 items), ESR, Th/U and AAR (2093 items) from the European (Russian Siberia included) Lower, Middle and Upper Palaeolithic. All 14C dates are conventional dates BP. This improved version 26 replaces the older version 25. 170 new sites are incorporated and 267 sites have a corrected or an updated content.
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Using the PiKh–model, a test data set for training the neural network is formed. Training data is presented in a separate file.. The architecture of the neural network can be arbitrary and is set by the settings file. To build the architecture of a neural network, it is necessary to determine the names of the input nodes, the names of the output nodes and set the parameters for hidden layers and the output layer. Each output layer is characterized by a name and parameters that determine the number of nodes, the type of activation function, the optimization algorithm, and the method for distributing errors between nodes. The settings file allows you to set the number of epochs during the training of the neural network, the interval between epochs when the learning results are saved (the interval of data recording on the hard disk), the error value (MSE), and the value of the task stop time for cooling the processor.
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This dataset contains raw data generated during simulations, as well as analysed data and graphs. The folder called "Scripts" contains all the script used to generate the data. Readme.txt file inside the folder contains a description regarding how to run the modelling. There are 6 compressed folders corresponding to the 5 different groups of experiments we set up: 1) Simulations where there was no reproductive decline, and where we varied the number of progeny produced by live adult at each timepoint. 2) Simulations where there was reproductive decline, and we tested different reproductive schedules. 3) Simulations where reproduction happened only at day 1 of adulthood, and we varied the number of progeny produced by day 1 adults. 4) Simulations where 4 progeny were produced only at day 1 of adulthood and we varied the number of food that adults consume. 5) Simulations where 4 progeny were produced only at day 1 of adulthood and we varied the number of food that larvae consume. 6) Simulations where 4 progeny were produced only at day 1 of adulthood were a control. We varied amount of food source, shape of food source, grid size, the number of founders. It includes simulations when adult food consumption was declines according to Huang et al 2004 or after day 2 Each subfolder in these folders (i.e. "2_progeny_per_timepoint" subfolder in the unzipped "1_No_reproductive_decline" folder ) corresponds to a particular simulation experiment where reproductive schedule and food consumption rates for adults and larvae are fixed. So, the subfolder contains the result for 4500 simulations (5 different lifespans * 9 different dispersal speeds * 100 repeats). Each subfolder (that corresponds to reproductive schedule and food consumption) also contains a file called run3_fast.py where all the parameters for the simulation are written, and 4 subfolders: 1_Raw_data_by_timepoints, 2_All_conditions__by_timepoints, 3_each_condition__over_all_timepoints, 4_all_conditions_over_all_timepoints 1_Raw_data_by_timepoints – contains raw data from the in silico experiment for all 4500 simulations for each timepoint. Each timepoint is a separate table. 2_All_conditions__by_timepoints – contains different folders where results over all 100 repeats are averaged for all 45 lifespan*speed combinations for each timepoint 3_each_condition__over_all_timepoints – in this folder it is shown how particular metrics (i.e. number of dauers) changes over timepoints for each of 45 pairs of lifespan*dispersal speed 4_all_conditions_over_all_timepoints – the folder contains graphs where all 45 lifespan*speed combinations or 9 speeds for each of 5 lifespans are shown over all timepoints for: number of adults – number of adults at a timepoint. share of food consumption by adults – cumulative share of food consumed by adults by a timepoint. L2S__max – maximum number of dauers by a timepoint. L2S – number of dauers at a timepoint L2S_sum – sum number of dauers by a timepoint.
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Dataset and R-script for: Salivary cortisol measurement in horses: Immunoassay or LC-MS/MS?
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In this repository are the native and processed (raster, shapefiles, csv) files used to calculate the landslide susceptibility. You can find the scripts in python 3x for each of the proposals analyzed in our research, as well as the ROC curves and the calculation of AUC. Spatial information is projected in the MAGNA-SIRGAS coordinate system EPSG 3116.
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