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opencc-by-4.0Nov 2024View details →

Quality-Assurance Package for the "Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework" Manuscript

<h2>Background</h2> Neuroimaging research requires consistent image quality and temporal signal stability, especially for functional magnetic resonance imaging (MRI) studies that rely on detecting subtle blood-oxygen-level-dependent (BOLD) signal changes. Regular MR system performance monitoring is essential, especially for longitudinal and multi-site studies. This study aims to establish a robust quality assurance (QA) protocol to promote data comparability across scanner models, vendors, and sites, as well as over a prolonged period. The manuscript titled "Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework" was submitted to the Special Issue <a href="https://link.springer.com/journal/10334/updates/26638300">Reproducibility and Quality Assurance</a> of the Magnetic Resonance Materials in Physics, Biology and Medicine (MAGMA) journal. This QA package proposed by the manuscript hosts materials for <ul> <li>all reconstructed images,</li> <li>instruction for data acquisition,</li> <li>instruction for image reconstruction,</li> <li>instruction for post-processing,</li> <li>example raw data and DICOM images, and</li> <li>images and scripts for T1/T2 fitting.</li> </ul> The detailed information is listed below. <h2>All reconstructed images</h2> This directory contains all reconstructed images from the fBIRN phantom on three Siemens 3T scanners (Trio, Prisma.Fit, and Cima.X) and one GE (UHP) 3T scanner. It contains four sub-folders for each scanner. And each sub-folder contains (some of) the following sub-folders: <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product spin-echo (SE) images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h2>Instruction for data acquisition</h2> This directory includes the following documents: <ul> <li><code>write_QA_Tran_EPIrs.m</code> to generate the <code>QA_epi.seq</code> file for EPI scans.</li> <li><code>write_QA_Tran_T1.m</code>: to generate the <code>QA_T1.seq</code> file for SE scans.</li> <li><code>20241122_QA_protocol_instruction_siemens.docx</code>: standard operating procedure for QA measurements.</li> <li><code>QA_record.xlsx</code>: Excel sheet for the record of QA measurements.</li> </ul> <h2>Instruction for image reconstruction</h2> <h3>Documents</h3> <ul> <li><code>pulseq2mrd_epi.m</code>: convert GE Pulseq EPI raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_epi.seq</code> file.</li> <li><code>pulseq2mrd_se.m</code>: convert GE Pulseq SE raw data (<code>.mat</code>) to MRD raw data (<code>.h5</code>) using the LABEL information in the <code>QA_T1.seq</code> file.</li> <li><code>siemens2mrd_epi.m</code>: convert Siemens Pulseq EPI raw data (<code>.dat</code>) to MRD raw data (<code>.h5</code>) using the information in the <code>.dat</code> raw data.</li> </ul> <ul> <li><code>default.xml</code>: Gadgetron configuration file for SE image reconstruction. This document is already in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/default.xml</code>.</li> <li><code>qc_epi.xml</code>: Gadgetron configuration file for EPI image reconstruction, which is modified from the <code>default epi.xml</code> located in the Gadgetron container: <code>/opt/conda/envs/gadgetron/share/gadgetron/config/</code>.</li> </ul> <ul> <li><code>specialCard_ICE.png</code>: Special card setting for ICE online reconstruction.</li> </ul> <h3>Procedures for Gadgetron offline reconstruction</h3> Step 1: Gadgetron installation (for more details, visit <a href="https://gadgetron.github.io/tutorial/">here</a>) <ul> <li>Download and install <a href="https://www.docker.com/">Docker</a> software. You may need to install/update the Windows Sub Linux (WSL) system for the Docker installation.</li> <li>Open your terminal (Power shell with administrative privilege in Windows) and navigate to the folder you would like to map to the Gadgetron Docker container.</li> <li>Run: <code>docker run -t --name gt_latest --detach --volume ${pwd}:/opt/data ghcr.io/gadgetron/gadgetron/gadgetron_ubuntu_rt_nocuda:latest</code>. If docker is not recognized, set <code>docker</code> to connect to <code>C:\Program Files\Docker\Docker\resources\bin</code> in the Environment Path in Windows. This will download and then launch the <a href="https://gadgetron.readthedocs.io/en/latest/building.html">latest Gadgetron version</a> in a Docker container. It will also mount your current folder as a data folder inside the container.</li> <li>Run this command: <code>docker exec -ti gt_latest /bin/bash</code>. This will execute your Gadgetron container.</li> </ul> Step 2: Data preparation <ul> <li>Place your SE/EPI <code>.dat</code>/<code>.h5</code> data in the mounted folder.</li> <li>Run the command in Terminal: <code>cd /opt/data</code> to enter the mounted folder.</li> </ul> Step 3: MRD conversion <ul> <li>For Siemens data, you can convert the <code>.dat</code> data to MRD data by using Gadgetron. If Gsdgetron doesn't work (e.g. for XA EPI data), you can then use the Matlab script <code>siemens2mrd_epi.m</code>.</li> <li>The command for Siemens SE data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -o se_data.h5</code>.</li> <li>The command for Siemens EPI data conversion: <code>siemens_to_ismrmrd -f meas_MID*.dat -z 2 -m IsmrmrdParameterMap_Siemens.xml -x IsmrmrdParameterMap_Siemens_EPI.xsl -o epi_data.h5</code>.</li> <li>For GE data, you can convert the <code>.mat</code> raw data to MRD data by using the Matlab scripts with the corresponding <code>.seq</code> files. For SE conversion: use <code>pulseq2mrd_se.m</code> with <code>QA_T1.seq</code>. For EPI conversion: use <code>pulseq2mrd_epi.m</code> with <code>QA_epi.seq</code>.</li> </ul> Step 4: Gadgetron reconstruction <ul> <li>SE reconstruction: <code>gadgetron_ismrmrd_client -f se_data.h5 -c default.xml -o se_out.h5</code>.</li> <li>EPI reconstruction: first, put <code>qc_epi.xml</code> to the mounted folder and then copy it to the Gadgetron container: <code>cp /opt/data/qc_epi.xml /opt/conda/envs/gadgetron/share/gadgetron/config/</code>. Then, run the reconstruction: <code>gadgetron_ismrmrd_client -f epi_data.h5 -c qc_epi.xml -o epi_out.h5</code>.</li> </ul> Step 5: Load Gadgetron-reconstructed images (<code>.h5</code>) <ul> <li>Load SE <code>.h5</code> images in Matlab:</li> </ul> <blockquote> filename = 'pulseq_se_out.h5' ; info = hdf5info(filename) ; address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ; pulseq_se_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ; pulseq_se_im = reshape(pulseq_se_im, [256, 256, 11, 2]) ; </blockquote> <ul> <li>Load EPI <code>.h5</code> images in Matlab:</li> </ul> <blockquote> filename = 'pulseq_epi_out.h5'; info = hdf5info(filename) ; address_data_1 = info.GroupHierarchy.Groups(1).Groups.Datasets(2).Name ; pulseq_epi_im = squeeze(double( hdf5read(filename, address_data_1) ) ) ; pulseq_epi_im = reshape(pulseq_epi_im, [64, 64, 27, 200]) ; </blockquote> <h3>Procedures for ICE online reconstruction</h3> Before executing the Pulseq-based sequences, you can enable ICE online Reconstruction following the procedures below: <ul> <li>Navigate to the Special Card (<code>specialCard_ICE.png</code>), set <code>Data handling</code> to <code>ICE STD</code> for NUMARIS/X (e.g. XA60A and XA61A), and <code>ICE 2D</code> for NUMARIS/4 (e.g. VB, VD, and VE).</li> <li>Select <code>Sum-of-Square</code> for coil combination.</li> <li>Be sure that the maximal pixel intensity does not violate the intensity threshold of 4096.</li> </ul> <h2>Instruction for post-processing</h2> The example post-processing is based on the reconstructed images from Cima.X over five days. <h3>Reconstructed images from Cima.X</h3> Note: All <code>se</code> folders contain a <code>structuralQuality_main.m</code> to call the <code>structuralQuality.m</code> function for structural quality analysis. All <code>epi</code> folders contain a <code>temporalQuality_main.m</code> to call the <code>temporalQuality.m</code> function for temporal quality analysis. <ul> <li><code>product_epi_ice</code>: ICE-reconstructed product EPI images.</li> <li><code>product_epi_gt</code>: Gadgetron-reconstructed product EPI images.</li> <li><code>pulseq_epi_ice</code>: ICE-reconstructed Pulseq EPI images.</li> <li><code>pulseq_epi_gt</code>: Gadgetron-reconstructed Pulseq EPI images.</li> <li><code>product_se_ice</code>: ICE-reconstructed product SE images.</li> <li><code>product_se_gt</code>: Gadgetron-reconstructed product SE images.</li> <li><code>pulseq_se_ice</code>: ICE-reconstructed Pulseq SE images.</li> <li><code>pulseq_se_gt</code>: Gadgetron-reconstructed Pulseq SE images.</li> </ul> <h3>QA analysis Matlab package: <code>QA_functions</code></h3> <ul> <li><code>circfit.m</code>: to find the center point and radius of the phantom.</li> <li><code>makeCircleMask.m</code>: to make a circular mask based on the center point and radius.</li> <li><code>structuralQuality.m</code>: to analyze the structural quality of the SE images.</li> <li><code>temporalQuality.m</code>: to analyze the temporal quality of the EPI images.</li> </ul> <h3>Post-processing procedures</h3> <ul> <li>Step 1: Add the <code>QA_functions</code> folder to your Matlab Path.</li> <li>Step 2: Run the <code>temporalQuality_main.m</code> or <code>structuralQuality_main.m</code> script in each folder to produce the QA results of all reconstructed images inside the folder.</li> <li>Step 3: Run the <code>make_figure_epi.m</code> and <code>make_figure_se.m</code> to produce some of the tables and figures used in the manuscript.</li> </ul> <h2>Example raw data and DICOM images</h2> The data and DICOM images were acquired from Cima.X on the fBIRN phantom on 06.08.2024. <ul> <li>DICOM folder: contains the DICOM images for four EPI scans (the first two scans for warm-up) and two SE scans.</li> <li><code>meas*.dat</code>: Siemens raw data of two EPI scans for temporal quality analysis and two SE scans for structural quality analysis.</li> <li><code>*data.h5</code> files: the ISMRMRD data of the four raw datasets.</li> <li><code>*out.h5</code> files: the images reconstructed by Gadgetron.</li> <li><code>*.nii</code>: the NIFTI-format reconstructed images.</li> <li><code>siemens2mrd_epi.m</code>: to convert the Siemens EPI raw data to ISMRMRD data.</li> <li><code>read_image.m</code>: to convert the Gadgetron-reconstructed h5-format images to NIFTI-format images.</li> </ul> <h2>Images and scripts for T1/T2 fitting</h2> This package includes DICOM images and T1/T2 fitting scripts for the fBIRN phantom. Images for T1 fitting were acquired using a product turbo spin echo sequence with an inversion recovery pulse (repetition time = 4000 ms, echo train length = 4). Images for T2 fitting were obtained using a product SE sequence (repetition time = 3500 ms). Both measurements were conducted on the Siemens Prisma.Fit 3T scanner on 05.06.2024. <ul> <li><code>T1 sub-folder</code>: contains all DICOM images for T1 fitting with inversion recovery times of {50, 150, 300, 450, 600, 750, 900, 1050, 1200, 1350, 1500, 2200, 3000} ms.</li> <li><code>T2 sub-folder</code>: contains all DICOM images for T2 fitting with echo times of {7.5, 15, 30, 45, 60, 75, 90, 130, 200, 250} ms.</li> <li><code>Do_T1fit.m</code>: Matlab script for T1 fitting.</li> <li><code>Do_T2fit.m</code>: Matlab script for T2 fitting.</li> </ul> For more information regarding Pulseq and the workflow for data acquisition and image reconstruction, please visit our GitHub repositories: <a href="https://github.com/pulseq/pulseq">Pulseq Matlab software</a>, <a href="https://github.com/pulseq/tutorials">Pulseq Tutorials</a>, and <a href="https://github.com/pulseq/Pulseq-Rocks-2023-24-ISMRM-Reproducibility-Challenge">Pulseq Rocks for the 2024 ISMRM Reproducibility Team Challenge</a>. If you need any further information or have any questions, please feel free to contact our Pulseq email address: pulseq.mr@uniklinik-freiburg.de.

opencc-by-4.0Apr 2021View details →

Density independent prey choice, taxonomy, life history and web characteristics determine the diet and biocontrol potential of spiders (Linyphiidae and Lycosidae) in cereal crops - Dataset

Materials and Methods Fieldwork Money spiders (Araneae: Linyphiidae) and wolf spiders (Araneae: Lycosidae) were the two most common families present in these field surveys, so were prioritised for collection. Spiders were visually located along transects in two adjacent barley fields at Burdons Farm, Wenvoe in South Wales (51°26'24.8"N, 3°16'17.9"W) and collected from occupied webs and the ground, between April and September 2018. Surveys and sampling were conducted five days per week across this period. Each transect was adjacent to a randomly selected tramline and they were distributed across the entire field. The areas searched were 4 m2 quadrats at least 10 m apart and all observed linyphiids and lycosids were collected in approximately 15-minute searches. The spiders included in this study were taken from 64 locations across 24 days (Supplementary Table 3) along the aforementioned transects. Spiders were individually placed into 1.5 ml microcentrifuge tubes containing 100 % ethanol using an aspirator, regularly changing meshing, at least every five spiders, to limit potential cross-contamination between spiders (spiders were also subsequently washed during transferral to fresh ethanol at the identification and, separately, dissection stages). Linyphiids occupying webs were prioritised for collection, but ground-active linyphiid spiders were also collected. For each spider taken from a web, the height of the web from the ground and its approximate dimensions were recorded, the latter calculated as approximate web area. Spiders were taken to Cardiff University, transferred to fresh ethanol, adults identified to species-level and juveniles to genus, and stored at -80 °C in 100 % ethanol until subsequent DNA extraction. To obtain data on local prey density, 4 m2 of ground and crop stems were suction sampled using a ‘G-vac’ for 30 seconds at each quadrat from which spiders were collected, with the collected material emptied into a bag, any organisms immediately killed with ethyl-acetate and material frozen for storage before sorting into 70 % ethanol in the lab. All invertebrates were identified to family level due to the restriction of many of the metabarcoding-derived dietary data to this level, and the difficulty associated with finer taxonomic resolution of many taxa. Exceptions included springtails of the superfamily Sminthuroidea (Sminthuridae and Bourletiellidae, which were often indistinguishable following suction sampling and preservation due to the fine features necessary to distinguish them) which were left at super-family, mites (many of which were immature or in poor condition) which were identified to order level and wasps of the superfamily Ichneumonoidea (which were identified no further due to obscurity of wing venation due to damage).   Extraction and high-throughput sequencing of spider gut DNA Given their prevalence in field collections, dietary analysis was carried out for the linyphiid genera Erigone, Tenuiphantes, Bathyphantes and Microlinyphia (Araneae: Linyphiidae), and the Lycosidae genus Pardosa. Spiders were transferred to and washed in fresh 100 % ethanol to reduce external contaminants prior to identification via morphological key 1. Abdomens were removed from spiders and again washed in and transferred to fresh 100 % ethanol. DNA was extracted from the abdomens via Qiagen TissueLyser II and DNeasy Blood & Tissue Kit (Qiagen) as per the manufacturer protocol, but with an extended lysis time of 12 hours to account for the complex and branched gut system in spider abdomens 2. At least one extraction negative (blank tubes treated identically to samples) was included per 12 spiders (each extraction typically contained 24 spiders, thus two extraction negatives), which was included in subsequent PCR and high-throughput sequencing to detect instances of lab/reagent contamination. For amplification of DNA, two primer pairs were used. BerenF-LuthienR 3 amplified a broad range of invertebrates including spiders, and TelperionF-LaureR, amplified a range of invertebrates but fewer spiders (modified from TelperionF-LaurelinR 3 via one base-pair change from Laurelin; 5’-ggrtawacwgttcawccagt-3’). Primers were labelled with unique 10 bp molecular identifier tags (MID-tags) so that each individual had a unique pairing of forward and reverse tags for identification of each spider post-sequencing. PCR reactions of 25 µl contained 12.5 µl Qiagen PCR Multiplex kit, 0.2 µmol (2.5 µl of 2 µM) of each primer and 5 µl template DNA. Reactions were carried out in the same thermocycler, optimised via temperature gradient, with an initial 15 minutes at 95 °C, 35 cycles of 95 °C for 30 seconds, the primer-specific annealing temperature for 90 seconds and 72 °C for 90 seconds, respectively, followed by a final extension at 72 °C for 10 minutes. BerenF-LuthienR and TelperionF-LaureR used annealing temperatures of 52 °C and 42 °C, respectively. Within each PCR 96-well plate, 12 negative controls (extraction and PCR), 2 blank controls and 2 positive controls were included (i.e. 80 samples per plate), based on Taberlet et al. (2018). Positive controls were mixtures of invertebrate DNA comprised of non-native Asiatic species in four different proportions (Supplementary Table 1) and blanks were empty wells within each plate to identify tag-jumping into unused MID-tag combinations. PCR negative controls were DNase-free water treated identically to DNA samples. A negative control was present for each MID-tag to identify any contamination of primers. All PCR products were visualised in a 2 % agarose gel with SYBRSafe (Thermo Fisher Scientific, Paisley, UK) and placed in categories based on their relative brightness. The concentration of these brightness categories was quantified via Qubit dsDNA High-sensitivity Assay Kits (Thermo Fisher Scientific, Waltham, MA, USA) with at least three representatives of each category per plate. The PCR products were then proportionally pooled according to these concentrations. Each pool was cleaned via SPRIselect beads (Beckman Coulter, Brea, USA), with a left-side size selection using a 1:1 ratio (retaining ~300-1000 bp fragments). The concentration of the pooled DNA was then determined via Qubit dsDNA High-sensitivity Assay Kits and pooled together into one library per primer pair. Library preparation for Illumina sequencing was carried out on the cleaned libraries via NEXTflex Rapid DNA-Seq Kit (Bioo Scientific, Austin, USA) and samples were sequenced on an Illumina MiSeq via a V3 chip with 300-bp paired-end reads (expected capacity ≤25,000,000 reads). Bioinformatic analysis followed (Drake et al., 2021; Supplementary Information 1).   Statistical analysis All analyses were conducted in R v4.0.0 6. Initial multivariate analyses used binary data (i.e., presence/absence) given the various problems inherent to quantifying metabarcoding data 7,8. Prey species that occurred only once across all of the dietary samples were removed before further analyses to prevent outliers skewing the results, which is particularly problematic for non-metric multidimensional scaling. Spider diets were compared between variables using multivariate generalized linear models (MGLMs) via ‘manyglm’ in the ‘mvabund’ package 9 with a binomial error family and Monte Carlo resampling. Model independent variables included spider genus, spider life stage (juvenile or adult, the latter defined by fully developed genitalia), spider sex and all two-way interactions between these variables. Pairwise two-way interactions were also included between the aforementioned variables and Julian day to account for how seasonality may affect these relationships. Coarse dietary differences were visualised by non-metric multidimensional scaling (NMDS) via metaMDS in the ‘vegan’ package 10 with Jaccard distance in two dimensions and 999 tries. For NMDS, outliers (usually samples containing rare taxa) were identified by plotting and subsequently removed to facilitate separation of samples and achieve minimum stress. For visualisation of the effect of categorical variables against the dietary NMDS, spider plots were created using ‘ordispider’ with ‘ggplot’ and the ‘RColorBrewer’ ‘Accent’ colour palette 11. Spider diet was compared against web characteristics for spiders for which both data were available using the MGLM process outlined above, but with starting models containing web height, web area, an interaction between the two, and pairwise interactions between genus, life stage and sex with the two web variables. This model used the same binomial error family as above, but with a ‘cloglog’ link function. For visualisation of the effect of continuous variables against the NMDS, surf plots were created with scaled coloured contours using the function “ordisurf” of the “ggplot” package in R. All prey taxa were classified as agricultural pests, natural enemies or excluded from subsequent analyses of intraguild predation and biocontrol (Supplementary Table 2). Intraguild predation and biocontrol variables were created by counting the number of natural enemy taxa, and, separately, of agriculturally relevant “pest” taxa (taxa containing species that commonly adversely affect agricultural productivity; Supplementary Table 2) in each spider’s diet. These resultant count data (effectively the diversity of pests and natural enemies predated by each individual spider) were separately analysed against spider genus, life stage and sex via GLM. “Site” (denoting the 4 m2 area from which spiders were collected within fields) was initially included as a random effect in generalized linear mixed-models, but no significant effect was observed when comparing this model against a standard GLM via a likelihood ratio test of nested models using the ‘lrtest’ command in the ‘lmtest’ package 12. Standard GLMs were thus used to avoid issues relating to singularity in the mixed models. The assumptions for the resultant Poisson error family GLMs were tested using the “testResiduals” function of the ‘DHARMa’ package 13. Intraguild predation and biocontrol differences between significant terms were visualised using violin plots with the quartiles, median and 95 % upper limit annotated using the ‘geom_violin’ function in ‘ggplot2’. In situ spider prey choice was analysed using network-based null models in the ‘econullnetr’ package 14 with the ‘generate_null_net’ command, visually represented with the ‘plot_preferences’ command. Binary dietary data were used alongside suction sample count data to represent prey availability. These suction sample data, as described above, were collected at the same sites as the spiders three days after spider collection. Prior to the taxonomic prey choice analysis, an hemipteran identified no further than order level through dietary analysis was removed due to the inability to pair it to any present prey taxa with certainty. Standardised effect sizes (SES) were extracted for all comparisons for each individual spider and compared between genera, life stages and sexes using permutational multivariate analysis of variance (PerMANOVA) using the ‘adonis’ function of the ’vegan’ package with 9999 permutations and a Euclidean distance matrix to determine overall differences in prey choice.   References 1.        Roberts, M. J. The Spiders of Great Britain and Ireland (Compact Edition). (Harley Books, 1993). 2.        Krehenwinkel, H., Kennedy, S., Pekár, S. & Gillespie, R. G. A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing. Methods Ecol. Evol. 8, 126–134 (2017). 3.        Cuff, J. P. et al. Money spider dietary choice in pre- and post-harvest cereal crops using metabarcoding. Ecol. Entomol. 46, 249–261 (2021). 4.        Taberlet, P., Bonin, A., Zinger, L. & Coissac, E. Environmental DNA. (Oxford University Press, 2018). 5.        Drake, L. E. et al. An assessment of minimum sequence copy thresholds for identifying and reducing the prevalence of artefacts in dietary metabarcoding data. Methods Ecol. Evol. in press, (2021). 6.        R Core Team. R: A language and environment for statistical computing. (2020). 7.        Deagle, B. E., Thomas, A. C., Shaffer, A. K. & Trites, A. W. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? Mol. Ecol. Resour. 13, 620–633 (2013). 8.        Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Mol. Ecol. 28, 391–406 (2019). 9.        Wang, Y., Naumann, U., Wright, S. T. & Warton, D. I. mvabund – an R package for model-based analysis of multivariate abundance data. Methods Ecol. Evol. 3, 471–474 (2012). 10.      Oksanen, J. et al. vegan: Community Ecology Package. (2016). 11.      Neuwirth, E. RColorBrewer: ColorBrewer palettes. (2014). 12.      Zeileis, A. & Hothorn, T. Diagnostic checking in regression relationships. R News 2, 7–10 (2002). 13.      Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. (2020). 14.      Vaughan, I. P. et al. econullnetr: an r package using null models to analyse the structure of ecological networks and identify resource selection. Methods Ecol. Evol. 9, 728–733 (2018).

opencc-by-4.0Jul 2018View details →

VSR Databases used in article "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition"

This dataset contains required volcano-seismic waveform DBs (dec.95M.16c and dec.09U.4c) used in the article: "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition", published in the Seismological Research Letters (<a href="https://doi.org/10.1785/0220180334">https://doi.org/10.1785/0220180334</a>). The authors want to thank everyone at the Instituto Andaluz of Geofísica (<a href="http://iagpds.ugr.es">http://iagpds.ugr.es</a>),  precisely to Prof. Jesús Ibáñez and Dr. Javier Almendros, IPs of several research projects which  have made possible the monitoring of Deception Island since early 1990s. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).

opencc-by-4.0Sep 2024View details →

Auditory stimuli suppress contextual fear responses in safety learning independent of a possible safety meaning

This repository stores the raw data that gave rise to the study by Mombelli et al. (2024) (Title: Auditory stimuli suppress contextual fear responses in safety learning independent of a possible safety meaning; DOI: 10.3389/fnbeh.2024.1415047, Journal: Frontiers in Behavioral Neuroscience).  Below we supply information on the provided metadata files which, in turn, refer to individual raw data files. General structure of the repository: ·         the raw data is organized in 5 subsets defined by the figures or supplementary figures they contribute to. Each subset is documented by its own metadata file. Raw data files were compressed into ZIP archives, one per subset; ·         the metadata files listing names of the individual data files are provided in “.csv” format, one per data subset. Field separator: comma; ·         the dataset is accessible at the following doi: 10.5281/zenodo.13524007   Description of the non-textual data formats: ·         video recordings of animal behavior were provided as unmodified ".wmv" files created by the VideoFreeze acquisition software (Med Associates Inc). Video stream parameters: wmv3 codec, color space yuv420p, 320x240 pixels, 30 fps, bitrate 300 kb/s. ·         movement traces were obtained from the videos, as described in the Methods section (Mombelli et al., 2024).

opencc-by-4.0May 2020View details →

Genome-wide association summary statistics of chronic musculoskeletal pain at four anatomic sites and their genetically independent components

The dataset contains results of a genome-wide association study of distinct chronic musculoskeletal pain conditions: back pain, knee pain, neck pain, and hip pain. Additionally, there are genome-wide association summary statistics for four genetically independent components of pain conditions, listed above. For more details, please, read the paper XXX. All files contain association summary statistics for genome-wide association meta-analysis of the 265,000 white British individuals from the UK Biobank and additional 191,580 individuals of European Ancestry from the UK biobank (total N = 456,580). Cases and controls were defined based on questionnaire responses. First, participants responded to “Pain type(s) experienced in the last months” followed by questions inquiring if the specific pain had been present for more than 3 months. Those who reported back, neck or shoulder, hip, or knee pain lasting more than 3 months were considered chronic back, neck/shoulder, hip, and knee pain cases, respectively. Participants reporting no such pain lasting longer than 3 months were considered controls (regardless of whether they had another regional chronic pain, such as abdominal pain, or not). Individuals who preferred not to answer were excluded from the study. Besides this, we excluded individuals who reported more than 3 months of pain all over the body. The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilization of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank. When using downloaded data, please cite the corresponding paper and this repository: <ol> <li>Tsepilov et al 2020</li> </ol> Funding: The work of YSA and SZS was supported by the Russian Ministry of Education and Science under the 5-100 Excellence Programme and by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project 0324-2019-0040). The work of YAT, ASSh, and EEE was supported by the Russian Foundation for Basic Research (project 19-015-00151). The contribution of LСK was funded by PolyOmica.  Dr. Suri was supported by VA Career Development Award # 1IK2RX001515 from the United States (U.S.) Department of Veterans Affairs Rehabilitation Research and Development (RR&D) Service. Dr. Suri is a Staff Physician at the VA Puget Sound Health Care System. The contents of this work do not represent the views of the U.S. Department of Veterans Affairs or the United States Government. List of files: <ol> <li>Back_output_done.csv: GWAS summary statistics for the chronic back pain</li> <li>gpc1_output_done.csv: GWAS summary statistics for the GIP1</li> <li>gpc2_output_done.csv: GWAS summary statistics for the GIP2</li> <li>gpc3_output_done.csv: GWAS summary statistics for the GIP3</li> <li>gpc4_output_done.csv: GWAS summary statistics for the GIP4</li> <li>Hip_output_done.csv: GWAS summary statistics for the chronic hip pain</li> <li>Knee_output_done.csv: GWAS summary statistics for the chronic knee pain</li> <li>Neck_output_done.csv: GWAS summary statistics for the chronic neck pain</li> </ol> Column headers: <ol> <li>gwas_id: uninformative field</li> <li>rs_id: dbSNP rsID (GRCh37 build) </li> <li>snp_num: uninformative field</li> <li>chr: chromosome (GRCh37 build) </li> <li>bp: position (GRCh37 build) </li> <li>ea: effect allele (coded as "1")</li> <li>ra: reference allele (coded as "0")</li> <li>eaf: effect allele frequency</li> <li>af_ref: uninformative field</li> <li>beta: effect size of effect allele</li> <li>se: standard error of effect size</li> <li>p: P-value of association (without GC correction)</li> <li>n:Total sample size</li> <li>z: Z-statistic of association</li> <li>info: uninformative field</li> <li>af_outlier: uninformative field</li> <li>pz_outlier: uninformative field</li> </ol>

opencc-by-4.0Dec 2020View details →

Volcano-Independent Seismic Recognition (VI.VSR): case studies with 'geoStudio' graphical interface

Video-documentation of the <a href="https://zenodo.org/record/3594080#.X9JP-XVudQJ">geoStudio</a> Volcano-Independent Seismic Recognition (VI.VSR) software, supported by the <a href="https://cordis.europa.eu/project/id/749249">VULCAN.ears</a> EU-funded project (H2020-MSCA-IF-2016 Grant) and referenced in the "Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears project" - (Cortés et al., Frontiers in Earth Sciences, 2021) article. VI.VSR aim is to automatically detect and classify volcano-seismic events in any volcano 'V' of the world by models built by other volcanoes data. This provides volcano-seismic catalogs of the given volcano 'V', without the fuss of designing a custom recognition system for it, being specially useful in real-time monitoring scenarios. The material includes 2 VDs: <ol> <li>"VI.VSR+geoStudio_intro.mp4" -> introducing the main idea and concepts behind the Volcano-Independent Seismic Recognition (VI.VSR) and presenting geoStudio and its role in the whole VULCAN.ears platform.</li> <li>"VI.VSR.by.geoStudio_case.studies.mp4" -> running the VI.VSR case studies presented in the (Cortés et al., 2021) manuscript.</li> </ol> This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).

opencc-by-4.0Jun 2021View details →

Maximum Independent Set Satellite Scheduling World Cities Data Set

<h1>Satellite Scheduling World Cities Data Set</h1> The Satellite Scheduling World Cities Data Set is the a set of cities treated as point locations used to simulate a set of image collection tasking requests for AIAA paper "A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations". It provides an open reference and benchmark for the satellite task scheduling problem. This could also be considered as a sparse Maximum Independent Set problem for a generic graph. The requests represent point collects, from which we can compute multiple distinct collection opportunities. The tasking problem is then to select a subset of these collects that it is possible for the spacecraft to feasibly collect in a given time period, subject to constraints on the spacecraft's agility and constraints on only collecting a single collect per request (no duplication of effort). The data set is hosted on both <a href="https://github.com/duncaneddy/aiaa-mis-satellite-scheduling-dataset">Github</a> and <a href="../">Zenodo</a>. The Github repository contains the original source data, the associated requests generated from the source data, and scripts to reproduce the scenario files. Zenodo (DOI 10.5281/zenodo) hosts copies of the output Metis graph files and collect data files. Due to the large size of produced files these are not included in the Github repository. <h2>Notes</h2> Notes Please note that while the source data and generation methods are identical to the satellite task planning paper it was created for. The specific generated problems do not exactly reproduce the scenario in the paper. Since the original reproduction, updates in upstream software dependencies have changed the output of the generation process (specifically, Earth orientaiton parameter handling libraries). This can be determined by considering the cardinality of the generated collect set. However, these differences are generally small and since the constriant rate is similar, the results should be comparable. <table> <tbody> <tr> <td>Spacecraft Count</td> <td>Orignial Publication Collect Count</td> <td>Reproduction Collect Count</td> </tr> <tr> <td>4</td> <td>59356</td> <td>59624</td> </tr> <tr> <td>6</td> <td>90777</td> <td>91204</td> </tr> <tr> <td>12</td> <td>180008</td> <td>180939</td> </tr> <tr> <td>24</td> <td>359170</td> <td>361519</td> </tr> </tbody> </table> This repository also adds additional scenarios for 1, 2, and 36 satellites. Note, the provided scenarios represent the largest 10,000 request data set. Should a smaller request set be desired, the requests should be filtered to the top `x` request based on city population and any collects not associated with those requests should be discarded. Note the Zenodo repository excludes the collect and graph files for the 1 and 2 satellite scenarios to avoid the file limits. These can still be reproduced from the Github source code. <h2>Acknolwedgement</h2> If this data set is used in your research, please cite the following paper <a href="https://arc.aiaa.org/doi/abs/10.2514/1.A34931">A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations</a> <blockquote> <pre><code>@article{eddy2021maximum, title={A Maximum Independent Set Method for Scheduling Earth-Observing Satellite Constellations}, author={Eddy, Duncan and Kochenderfer, Mykel J}, journal={Journal of Spacecraft and Rockets}, volume={58}, number={5}, pages={1416--1429}, year={2021}, publisher={American Institute of Aeronautics and Astronautics} }</code></pre> </blockquote> <h2>Licensing</h2> The source of the world cities data is from the <a href="https://simplemaps.com/data/world-cities">simplemaps.com</a> website, licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License </a>with the specific license found at `./data/worldcities_license.txt`.

opencc-by-4.0Sep 2022View details →

Infection increases activity via Toll dependent and independent mechanisms in Drosophila melanogaster - ethoscope dataset

Ethoscope dataset for Vicent et al 2022, PLoS Pathogens Original preprint available at: https://www.biorxiv.org/content/10.1101/2021.08.24.457493v1

opencc-by-4.0Sep 2017View details →

Linkage-independent SNPs in the Drosophila melanogaster Sussex LHM sample

Unix code for running Plink program for generating a list of SNPs (single-nucleotide polymorphisms) which are independent of linkage diseqiulibrium. Used for later statistical analyses incorporating the number of independent tests made across the genome.

opencc-by-4.0Nov 2017View details →

Diallel analysis reveals Mx1-dependent and Mx1-independent effects on response to influenza A virus in mice

Data and analysis files for diallel analysis of weight loss in 8-12 week old male and female mice (n=1,043), mock treated or infected with influenza A virus (H1N1, PR8) across 4 days post-infection, as well as founder haplotype effect analysis at Mx1 for pre-CC and CC-RIX.

opencc-by-4.0Aug 2020View details →

A molecular taxonomy of tumors independent of tissue-of-origin

This tarball contains the pre-processed data in .Rda files required to compile our manuscript entitled "A molecular taxonomy of tumors independent of tissue-of-origin"

opencc-by-4.0Aug 2021View details →

Source data for Synthetic dynamic hydrogels promote degradation-independent in vitro organogenesis

Source data and statistical analysis results for Synthetic dynamic hydrogels promote degradation-independent in vitro organogenesis

opencc-by-4.0Jun 2023View details →

Fisheries independent trawl survey data of fish biomass on North American and European oceanic shelves.

Publicly available scientific bottom trawl survey data, primarily sampling demersal commercial species, were obtained from the Northeast Pacific and North Atlantic shelf regions in 2021. The final dataset contains approx. 197,000 unique tows and includes data from 1970 to 2019 (166,000 tows between 1980-2015). For each tow in each survey, we selected all teleost and elasmobranch species and obtained species weight. We corrected these weights for differences in sampling area (in km2) and trawl gear catchability. The data processing scripts and individual survey data can be found on Github (DOI: 10.5281/zenodo.7992482). The data processing scripts are modified based on earlier work from Pinsky et al. (2013) and Maureaud et al. (2019). If the correction for gear catchability is not important, it is recommended to use the FishGlob database (DOI: 10.5281/zenodo.7484547). Please contact Daniel van Denderen (pdvd@aqua.dtu.dk) for questions. Column names Haul_id: unique haul identifyer Survey_Region: survey name or name of ecoregion (depending on survey) Gear: gear information (only included for northeast Atlantic region). Gear information is available for other regions. See original survey description (sources in manuscript). Year: sampling year Month: sampling month Longitude: longitude (EPSG:4326) Latitude: latitude (EPSG:4326) Swept_area: estimate of swept area of survey gear (only included for northeast Atlantic region) Bottom_depth: bottom depth in meters (as recorded in the survey data) Family: taxonomic family of the teleost/elasmobranch Name: species name (or higher taxonomic grouping) kg_km2: wet weight (kilogram) per unit of swept area (km2) kg_km2_corrected: wet weight (kilogram) per unit of swept area (km2) corrected for trawl gear catchability F_type: fish type (demersal or pelagic) Trophic_lev: Species-specific trophic level information Data uncertainties Data have predominantly been analysed at the community level and between 1980 and 2015. Any species-specific inferences may need further checking. To reduce the effect of potential outlying biomass estimates, it is recommended to remove all individual observations 1.5 times less/greater than the interquantile range per survey and year based on log10-transformed biomass values. Please contact Daniel van Denderen (pdvd@aqua.dtu.dk) for any comments/questions.

opencc-by-4.0Dec 2019View details →

Databases for exploratory mode of RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery

RREFinder is a bioinformatic tool for the detection of RiPP Recognition Elements (RREs). See "RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery". This database contains the required databases to run exploratory mode of the tool.

opencc-by-4.0May 2020View details →

Data set related to the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons"

Input/output files for Gaussian calculations, data sets for all plots shown in the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons", C code for the NICS calculations through the dipolar model and python code for the NICS calculations through the tight-binding model described in the manuscript.

dryad40/100

Data from: Independently evolved and gene flow‐accelerated pesticide resistance in two‐spotted spider mites

Pest species are often able to develop resistance to pesticides used to control them, depending on how rapidly resistance can emerge within a population or spread from another resistant population. We examined the evolution of bifenazate resistance in China in the two‐spotted spider mite (TSSM) Tetranychus uticae Koch (Acari: Tetranychidae), one of the most resistant arthropods, by using bioassays, detection of mutations in the target cytb gene, and population genetic structure analysis using microsatellite markers. Bioassays showed variable levels of resistance to bifenazate. The cytb mutation G126S, which confers medium resistance in TSSM to bifenazate, had previously been detected prior to the application of bifenazate and was now widespread, suggesting likely resistance evolution from standing genetic variation. G126S was detected in geographically distant populations across different genetic clusters, pointing to the independent origin of this mutation in different TSSM populations. A novel A269V mutation linked to a low‐level resistance was detected in two southern populations. Widespread resistance associated with a high frequency of the G126S allele was found in four populations from the Beijing area which were not genetically differentiated. In this case, a high level of gene flows likely accelerated the development of resistance within this local region, as well as into an outlying region distant from Beijing. These findings, therefore, suggest patterns consistent with both local evolution of pesticide resistance as well as an impact of migration, helping to inform resistance management strategies in TSSM.

opencc-zeroDec 2018View details →

opencc-by-4.0Mar 2017View details →

Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.

These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175. Use is free for academic purposes, provided the aforementioned article is appropriately cited. The directory contains the following files stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist. ratings.mat contains the variables arousal - mean arousal rating valence - mean valence rating valence2 - squared mean valence rating (after subtracting midpoint) motivationalValue - mean motivation rating motivaionalValue2 - squared mean motivation rating (after subtracting midpoint) All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive) Experiment 1 fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN. boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively. figure2.m uses these data to computes figure 2 of the article from these data dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. table1.R computes and prints the models for table 1 Experiment 2 fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial. boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally. figure3and4.m generates figures 3 and 4 of the article from these data files. dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively. table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.

opencc-by-4.0Jun 2017View details →

Genome-wide estimation of linkage disequilibrium-independent SNPs in Drosophila melanogaster (Sussex LHM).

Uses R to create SNP density across each chromosome arm. Uses Plink 1.9 to select independent SNPs with step sizes corresponding to chromosome density. Output data is combined_chromosomes_lhm_indep.txt a list of SNP IDs.

dryad40/100

Experimental evolution under varying sex ratio and behavioral plasticity in response to perceived competitive environment independently affect calling effort in male crickets

The operational sex ratio (OSR) is a key component influencing the magnitude of sexual selection driving the evolution of male sexual traits, but males often also retain the ability to plastically modulate trait expression depending on the current environment. Here we employed an experimental evolution approach to determine whether the OSR affects the evolution of male calling effort in decorated crickets, a costly sexual trait, and whether plasticity in calling effort is altered by the OSR under which males have evolved. Calling effort of males from two selection regimes maintained at different OSRs over 18–20 generations (male- versus female-biased) was recorded at two different levels of perceived competition, in the absence of rivals or in the presence of an experimentally muted competitor. The effect of the OSR on the evolution of male calling effort was modest and in the opposite direction predicted by theory. Instead, the immediate competitive environment strongly influenced male calling effort as males called more in the presence of a rival, revealing considerable plasticity in this trait. This increased calling effort came at a cost, however, as males confined with a muted rival experienced significantly higher mortality.

opencc-zeroDec 2023View details →