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11,837 results for “Stress;”
Outdoor mesocosm study evaluating how mass, NaCl tolerance, and pesticide tolerance affect oxidative stress biomarkers (CAT, SOD, GR, GPx, TBARS) in larval wood frogs (Rana sylvatica) exposed to baseline and NaCl-contaminated conditions, 2019
Biomarkers of oxidative stress can aid in wildlife monitoring by allowing conservationists to detect sublethal environmental shifts. However, interpretation of stress responses can be complicated by multiple interacting factors (e.g., individual development, evolved physiological tolerance to stressors) which alter biomarker expression. Here, we investigated how individual ontogenetic traits and population-level tolerance traits influence oxidative stress responses under baseline and contaminated environmental conditions. For our model contaminant, we used NaCl (common freshwater contaminant due to factors such as coastal flooding, irrigation, airborne salt circulation, drought, runoff from road deicing salts). For our model wildlife populations, we used larval wood frogs (Rana sylvatica) from six noninteracting populations known to vary in two population-level tolerance traits: NaCl tolerance (calculated as average time to death from lethal NaCl exposure) and pesticide tolerance (determined by proxy of distance to agriculture - a consistent and highly repeatable relationship). At an outdoor research facility, R. sylvatica tadpoles were exposed to either baseline conditions (0 g/L NaCl added) or NaCl-contaminated conditions (1 g/L NaCl added for 21 days, then reduced to 0.5 g/L NaCl). Exposures were conducted in individual units with 40 replicates per population for each treatment. The experiment was terminated per individual to capture the full term of larval development (Developmental stage: Gosner stage 36), lasting between 33-48 days. For each individual, we measured mass, Snout-Vent-Length, and developmental stage before processing for biomarker expression. Individual homogenates were assayed for oxidative stress biomarkers superoxide dismutase (SOD; responsible for Reactive Oxygen Species capture and peroxide production), glutathione peroxidase (GPx; responsible for high-affinity peroxide reduction), catalase (CAT; responsible for low-affinity peroxide reducti
Leaf spectroscopy and active fluorescence datasets for early drought and nitrogen stress diagnosis in tomato
<p>The dataset contains different plant physiological parameters collected during a 14-day stress and recovery experiment on tomato (<em>Solanum lycopersicum</em> L. cv Moneymaker) plants, undergoing a nitrogen deficiency, drought or control treatment. </p> <p>A full description of the experiment, together with the scientific results, is published by Pescador-Dionisio et al. (2024), and can be found through: <a href="https://doi.org/10.1111/nph.20253">https://doi.org/10.1111/nph.20253.</a></p> <p>The goal of the dataset collection was to obtain a non-invasive proximal sensing dataset at leaf level (reflectance, transmittance, upward and downward fluorescence), in parallel to gas exchange and active fluorescence measurements. The leaf spectroscopy dataset was further processed by a pigment spectral unmixing algorithm according to Van Wittenberghe et al. (2024), to calculate fluorescence quantum efficiency (<em><strong>FQE</strong></em>) and effective absorbance (<strong><em>A_eff</em></strong>) changes associated to the activation of regulated heat dissipation (<strong><em>A_eff_535_Xan</em></strong>). The latter absorption feature is linked to the xanthophyll ('<strong>Xan</strong>') absorption in the 500-600 nm range, which is modelled by the sum of three Gaussians. For a full description of this feature, see Van Wittenberghe et al. (2021).</p> <p>Gas exchange and active fluorescence measurements were carried out with a LI-6400 portable photosysthesis system (LI-COR Biosciences, Lincoln, USA) equipped with a 6400-40 leaf chamber fluorometer. Steady-state measurements were done at 300 and 1000 μmol m−2 s−1 ('<strong><em>PAR300</em></strong>' and '<em><strong>PAR1000</strong></em>'), i.e. growing light conditions and light saturating conditions. Light response curves were taken on different days. Common fluorescence parameters (e.g., <em><strong>Fv/Fm, Fo, Fm, NPQ, YNO, YNPQ</strong></em>) are provided together with 'sustained' and reversible' NPQ parameters calculated according Porcar-Castell (2011).</p> <p>Leaf spectroscopy and active steady-state fluorescence measurements were performed on the same measuring days ('<em><strong>d0</strong></em>', '<em><strong>d2</strong></em>', '<em><strong>d4</strong></em>', '<em><strong>d7</strong></em>', '<em><strong>d14</strong></em>') and on the same leaf, both at 300 and 1000 μmol m−2 s−1 ('<em><strong>PAR300</strong></em>' and '<em><strong>PAR1000</strong></em>'), taking into account an adaptation time. We used a LED light source and several filters, placed in front of a FluoWat leaf clip, which was connected to two high-performance VIS-NIR spectroradiometers (QEPRO, Ocean Insight Inc., Orlando, Florida, USA). The spectroscopy measurements are presented in the Matlab structures for each measuring day, e.g. "<strong><em>2023_d0_Leaf_Spec_Tomato_Stress.mat</em></strong>".</p> <p>The outputs of the pigment spectral fitting code are presented by Matlab structures, e.g. "<strong><em>2023_d0_Leaf_Fitting_Tomato_Stress.mat</em></strong>", which contains the effective absorbance fitting (<strong><em>A_eff</em></strong>) of each pigment (<strong>Chl a, Chl b, Carotene-b, Anthocyanins, and Xanthophylls</strong>) for the wavelength range [500-780] nm, the absorbed photosynthetically active radiation by Chlorophyll a ('<em><strong>APAR_Chla</strong></em>') for the wavelength range [400-800] nm, and the fluorescence quantum efficiency, calculated as the ratio of the emitted fluorescence photons and the flux of photons absorbed by Chlorophyll a. </p> <p>Additional metadata from HPLC photosynthetic pigment analyses, xanthophyll-related enzyme expression, biomass and total content of elemental nitrogen are provided.</p> <p>Please follow the README files for more detailed information.</p> <p> </p>
Increased inflammation and oxidative stress caused by accumulated metal particle exposure among metro station staff, Tianjin, China, 2023
Metro is a significant part of world transport, delivering over 58 billion passengers annually. The dilution effect of particulate matter (PM) from natural ventilation was limited in underground metro stations. What's worse, train operation processes generated PM rich in heavy metals. Though PM pollution in metro stations was reported widely, there is limited evidence of the adverse health effect of metro station PM. This dataset collected urinary samples from 74 metro station staff from three different metro stations in Tianjin, China, for inflammation and oxidative stress biomarker tests to better understand the potential health effects induced by metal particulates in metro stations. Also, an indoor air quality survey was conducted simultaneously in the metro stations.
Effects of Long-Term Soil Warming on Microbial Yield, Acquisition, and Stress Traits at Harvard Forest 2014
Soil microbial traits drive ecosystem functions. This relationship can explain why microbial functional diversity is typically positively correlated with ecosystem function. However, microbial adaptation to climate change related warming stress can shift microbial traits with direct implications for carbon cycling in the soil. Here, we investigated how long-term warming affects the relationship between microbial trait diversity and ecosystem function. Soils were sampled after 24 years of +5\degree C warming alongside unheated control soils from the Harvard Forest Long-Term Ecological Research site. Ecosystem function was estimated from six different enzyme activities and microbial biomass. This data was coupled with metatranscriptomics sequencing, where reads were assigned to yield, acquisition, or stress trait categories. We found that in organic horizon soils, warming decreased the richness of acquisition-related traits. In the mineral soils, we observed that heated soils exhibited a negative relationship with the richness of acquisition related traits. These results suggest that the microbial communities exposed to long-term warming is shifting away from a resource acquisition life history strategy.
Stress-associated brain activation across the hormonal contraceptive cycle
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Data and software: Stress and heat flux via automatic differentiation
<h4><strong>glp-archive</strong></h4><h2><strong>Code and Data for "Stress and heat flux with automatic differentiation"</strong></h2><p>This repository contains data, code, and related artefacts supporting the following publication (<a href="https://arxiv.org/abs/2305.01401">preprint</a>):</p><p>Stress and heat flux via automatic differentiation</p><p>by Marcel F. Langer, J. Thorben Frank, and Florian Knoop</p><p><i>J. Chem. Phys.</i> 159, 174105 (2023) <a href="https://doi.org/10.1063/5.0155760">doi:10.1063/5.0155760</a></p><p>This repository is available at <a href="https://github.com/sirmarcel/glp-archive">https://github.com/sirmarcel/glp-archive</a>. Selected versions are archived on Zenodo, under <a href="https://doi.org/10.5281/zenodo.7852529">doi:10.5281/zenodo.7852529</a>.</p><h2><strong>Overview</strong></h2><p>Each subfolder in this repository contains a README.md with additional information. The subfolders are:</p><ul><li>results/: Data and code that produced the figures in the manuscript</li><li>work/: Computational workflows, models, etc.</li><li>infra/: Project-specific infrastructure code</li><li>meta/: Scripts for assembling this archive; can be ignored but is retained for transparency.</li></ul><h2><strong>Related external code</strong></h2><p>The work in this repository relies on a few tools that the authors maintain separately:</p><ul><li><a href="https://github.com/sirmarcel/glp">glp</a> implements the quantities discussed in the manuscript</li><li><a href="http://github.com/thorben-frank/mlff">mlff</a> implements the so3krates model</li><li><a href="https://github.com/flokno/tools.mlff">tools.mlff</a> provides tools for the equation of state experiments</li></ul><p>These tools were developed during the work in the manuscript. The following versions/tags reflect what was used to obtain results:</p><ul><li>glp @ v0.1.0 (tag)</li><li>mlff @ v1.0 (branch)</li><li>mlff.tools @ v0.0.1</li></ul><p>We additionally note that the GK-MD functionality has been factored out into <a href="https://github.com/sirmarcel/gkx">gkx</a>.</p><h2><strong>Versions</strong></h2><ul><li>v1.1: published version, archived at <a href="https://doi.org/10.5281/zenodo.8406532">doi:10.5281/zenodo.8406532</a></li><li>v1.0: arXiv submission v1, archived at <a href="https://doi.org/10.5281/zenodo.7852530">doi:10.5281/zenodo.7852530</a></li></ul>
functional MRI study on the language stress perception in a foreign language
<p>fMRI dataset of 91 participants during a linguistic task about language stress perception in a foreign language.</p> <p>Participants listened to pairs of words in a foreign language (Spanish) and had to indicate if the words were the same or different. The different pairs differed either by the stress pattern, or by the final vowel.</p> <p>This dataset was divided into two groups: 51 participants with French as native language and 40 with Swiss-German as native language. None of the participants had knowledge of Spanish.</p> <p>This repository respects the BIDS standard (<a href="https://bids.neuroimaging.io/">https://bids.neuroimaging.io/</a>), including all the raw data (func, fmap, anat) and metadata in order to reproduce the processing.</p> <p>These data have been used in two papers:</p> <p>S. Schwab, M. Mouthon, L.B. Jost, J. Salvadori, I. Yakoub, E. Ferreira da Silva, N. Giroud, B. Perriard and J.M. Annoni, Neural correlates of lexical stress processing in a foreign free-stress language; Brain and Behavior (2023)</p> <p>L. Rogenmoser, M. Mouthon, F. Etter, J. Kamber, J.M. Annoni and S. Schwab; The processing of stress in a foreign language modulates functional antagonism between default mode and attention network regions, (submitted)</p>
Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019
<p>This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details. </p> <p>After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. </p> <p>The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). </p> <p>Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details. </p> <p>The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution. </p> <p>Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. </p> <p>Filenames: </p> <p><strong>monthly_windstress_wsc_upwelling.nc</strong>: 480 time steps from 80W to 45W and 30N to 45N.</p> <p><strong>seasonal_windstress_wsc_upwelling.nc</strong>: 160 time steps from 80W to 45W and 30N to 45N.</p> <p><strong>annual_windstress_wsc_upwelling.nc</strong>: 40 time steps from 80W to 45W and 30N to 45N.</p>
Monthly and Annual contour lines of the zero and the positive maximum of the Wind Stress Curl over Western North Atlantic during 1980-2019 and the Gulf Stream path during 1993-2019.
<p>This dataset includes multiple fields: (i) files for monthly and annual fields for the max curl line and the zero curl line at 0.1 degree longitudinal resolutions; (ii) files for monthly and annual GS path obtained from Altimetry and originally processed by Andres (2016) at 0.1 degree longitudinal resolution. The maximum curl line (MCL) and the zero curl line (ZCL) calculations are briefly described here and are based on the original wind data (at 1.25 x 1.25 degree) provided by the Japanese reanalysis (JRA-55; Kobayashi et al., 2015) and available at https://zenodo.org/record/8200832 (Gifford et al. 2023). For details see Gifford, 2023. </p> <p>The wind stress curl (WSC) fields used for the MCL and ZCL calculations extend from 80W to 45W and 30N to 45N at the 1.25 by 1.25-degree resolution. The MCL is defined as the maximum WSC values greater than zero within the domain per 1.25 degree longitude. As such, it is a function of longitude and is not a constant WSC value unlike the zero contour. High wind stress curl values that occurred near the coast were not included within this calculation. After MCL at the 1.25 resolution was obtained the line was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. The smoothed MCL lines at 0.1 degree resolution are provided in separate files for monthly and annual averages (2 files). Similarly, 2 other files (monthly and annual) are provided for the ZCL. </p> <p>Like the MCL, the ZCL is a line derived from 1.25 degree longitude throughout the domain under the condition that it's the line of zero WSC. The ZCL is constant at 0 and does not vary spatially like the MCL. If there are more than one location of zero curl for a given longitude the first location south of the MCL is selected. Similar to the MCL, the ZCL was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. </p> <p>The above files span the years from 1980 through 2019. So, the monthly files have 480 months starting January 1980, and the annual files have 40 years of data. The files are organized with each row being a new time step and each column being a different longitude. Therefore, the monthly MCL and ZCL files are each 480 x 351 for the 0.1 resolution data. Similarly, the annual files are 40 x 351 for the 0.1 degree resolution data. </p> <p><strong>Note that the monthly MCLs and ZCLs are obtained from the monthly wind-stress curl fields. The annual MCLs and ZCLs are obtained from the annual wind-stress curl fields.</strong></p> <p>Since the monthly curl fields preserves more atmospheric mesoscales than the annual curl fields, the 12-month average of the monthly MCLs and ZCLs will not match with the annual MCLs and ZCLs derived from the annual curl field. The annual MCLs and ZCLs provided here are obtained from the annual curl fields and representative metrics of the wind forcing on an annual time-scale. </p> <p>Furthermore, the monthly Gulf Stream axis path (25 cm isoheight from Altimeter, reprocessed by Andres (2016) technique) from 1993 through 2019 have been made available here. A total of 324 monthly paths of the Gulf Stream are tabulated. In addition, the annual GS paths for these 27 years (1993-2019) of altimetry era have been put together for ease of use. The monthly Gulf Stream paths have been resampled and reprocessed for uniqueness at every 0.1 degree longitude from 75W to 50W and smoothed with a 100 km (10 point) running average via matlab. The uniqueness has been achieved by using Consolidator algorithm (D’Errico, 2023). </p> <p>Each monthly or annual GS path has 251 points between 75W to 50W at 0.1 degree resolution. </p>
Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler
The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics
Chamaecrista fasciculata Survival and Biomass in Response to Microbe Stress History and Contemporary Stress, 2018-2019
This dataset includes Chamaecrista fasciculata biomass and survival data collected as part of a greenhouse experiment that took place at Indiana University in 2018. Rhizosphere soil was collected from Chamaecrista fasciculata plants at the end of a field experiment in which plants were treated with four stress treatments: salt, herbicide, herbivory, and no stress. These field soils were used to inoculate a greenhouse experiment in which Chamaecrista fasciculata individuals from 50 manternal families were treated with these same four stress treatments in a full factorial design (4 microbe histories x 4 contemporary stress environments), plus a sterile microbial control treatment. We measured the days to first flower, noted when plants never flowered (i.e., did not survive to flower), and measured aboveground biomass.
Dataset Publication for "A Comprehensive Stress Drop Map from Trench to Depth in the Northern Chilean Subduction Zone"
<p><strong>Abstract</strong>: Stress drop catalog data publication supplement for "A Comprehensive Stress Drop Map from Trench to Depth in the Northern Chilean Subduction Zone" (Folesky, J., Pennington, CN., Kummerow J., Hofman LR. (JGR: Solid Earth, 2023) <a href="https://doi.org/10.1029/2023JB027549">https://doi.org/10.1029/2023JB027549</a>), obtained from wave form analysis using the spectral decomposition technique. Time duration is 2007 to 2021. Seismic events were taken from the IPOC catalog (Sippl, C., Schurr, B., Münchmeyer, J., Barrientos, S., Oncken, O. (2023): Catalogue of Earthquake Hypocenters for Northern Chile from 2007-2021 using IPOC (plus auxiliary) seismic stations.<br><a title="Follow link" href="https://doi.org/10.5880/GFZ.4.1.2023.004" target="_blank" rel="nofollow noopener">https://doi.org/10.5880/GFZ.4.1.2023.004</a>). Wave forms were obtained from the EIDA/GEOPHONE web page (eida.gfz-potsdam.de/webdc3/ or geofon.gfz-potsdam.de/waveform/)</p> <p><strong>File descriptions</strong>: table columns <br>ID, cls, Lon, Lat, Depth, Magntiude, vssource, fc1, fcbound1, fcbound2, sd<br>------------------<br>explanation<br>ID : origin time<br>cls : event class<br>Lon : longitude <br>Lat : latitude<br>Depth : depth in km<br>Magnitude : magnitude (MA)<br>vssource. : s- wave velocity at the event location<br>fc1. : corner frequency in Hz<br>fcbound1 : lower bound for fc1 from 5% variance reduction test in Hz<br>fcbound2. : upper bound for fc2 from 5% variance reduction test in Hz<br>sd : stress drop estimate in MPa</p>
Inter-Chemical Correlation results for the study: HHEARx2016-1534 (A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function)
Title: A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function <br>Species: Homo sapiens <br>Number of samples: 5789 <br>Number of named analytes: 17 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=14 <br>
Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR
<p>Dataset for demographic, psychological and physiological data obtained for RESTORE (Experiment 1 WP1). Study results are published in the article titled "Investigating effect chains from cognitive and noise-induced short-term stress build-up to restoration in an urban or nature setting using 360° VR" in the Journal of Environmental Psychology. Explanations on all variables (column names) in the datasets are given either in the second spreadsheet in each Excel file or in the csv files appended with _legend.csv (see latest version of the dataset). File 'Psychophysiological_participant_data_aggregated' is aggregated per participant (single or mean values), and the file 'Restoration_EDA_baseline-corrected_aggregated' contains EDA data aggregated per time point per restoration setting (Nature vs Urban) and prior cognitive demand condition. Methodological details on how the data was obtained and processed are given in the Open Access article.</p>
DNA methylation dynamics during stress-response in woodland strawberry (Fragaria vesca)
<p><strong>Genome sequence and annotation of Fragaria vesca cv. Reine des Vallées</strong></p> <p>In order to generate a reference genome for Fragaria vesca cv. Reine des Vallées, we used MinIon long-read sequencing data to substitute the <em>F. vesca</em> genome v.4.0.a2 genome. The detailed method used to obtain these results were the following:</p> <p><em>Genome sequencing and assembly NIL Fb2</em></p> <p>Genomic DNA from strawberry plants was extracted by a Hexadecyltrimethylammonium bromide (Cetrimonium bromide, CTAB) modified protocol (Healey, Furtado, Cooper, & Henry, 2014) and purified with Agencourt AMPure XP beads (cat# A63880). Long-read sequencing was performed for the genome assembly; Genomic DNA by Ligation (Oxford Nanopore, cat# SQK-LSK109) library was prepared as described by the manufacturer and sequenced on a MinION for 72 h (Oxford Nanopore).</p> <p><em>Reference genome polishing</em></p> <p>Reads obtained from nanopore were filtered with Filtlong v0.2.1 (<a href="https://github.com/rrwick/Filtlong">https://github.com/rrwick/Filtlong</a>) using --min_mean_q 80 and --min_length 200. Cleaned reads were then aligned to the most recent version of the <em>F. vesca</em> genome v4.0.a2, downloaded from the Genome Database for Rosaceae (GDR) (<a href="https://www.rosaceae.org/species/fragaria_vesca/genome_v4.0.a2">https://www.rosaceae.org/species/fragaria_vesca/genome_v4.0.a2</a>), using minimap2 v2.21 (H. Li, 2018) with parameters -aLx map-ont --MD -Y. The generated BAM file was then sorted and indexed with samtools v1.11 (H. Li et al., 2009). We used mosdepth v0.3.1 (Pedersen & Quinlan, 2018) to verify that coverage on chromosomic scaffolds was over 50 X. Sniffles v1.0.12a (Sedlazeck et al., 2018) with parameters -s 10 -r 1000 -q 20 --genotype -l 30 -d 1000 was used to detect structural variations larger than 30 bp. The VCF files obtained from Sniffles was sorted and filtered with BCFtools v1.14 (Danecek et al., 2021) to keep only structural variants (SV) with smaller than 200,00 bp (we observed that larger SV were most of the time false positive caused by misalignments in regions with gaps or Ns), supported by 10 or more reads and with allelic frequencies above 0.8 (we were interested in homozygous changes). The complete filtering command used is “bcftools view -q 0.8 -Oz -i '(SVTYPE = "DUP" || SVTYPE = "INS" || SVTYPE = "DEL" || SVTYPE = "TRA" || SVTYPE = "INV" || SVTYPE = "INVDUP") && %FILTER = "PASS" && FMT/DV>9 && SVLEN>29 && SVLEN<200000' “</p> <p>From the VCF listing all the structural variants that we detected in our <em>F. vesca </em>accession, we generated a substituted genome version based on the reference <em>F. vesca</em> genome v.4.0.a2. The reference genome was first indexed with samtools faidx v1.11(Danecek et al., 2021) and a sequence dictionary was generated with Picard CreateSequenceDictionary v2.25.6 (<a href="https://broadinstitute.github.io/picard">https://broadinstitute.github.io/picard</a>). The VCF containing the SV produced from our Nanopore sequencing was also indexed with gatk (Van der Auwera GA & O'Connor BD, 2020) IndexFeatureFile v4.2.0.0 (<a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037262651-IndexFeatureFile">https://gatk.broadinstitute.org/hc/en-us/articles/360037262651-IndexFeatureFile</a>). FastaAlternateReferenceMaker v4.2.0.0 (<a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037594571-FastaAlternateReferenceMaker">https://gatk.broadinstitute.org/hc/en-us/articles/360037594571-FastaAlternateReferenceMaker</a>) was then run with the reference genome and the VCF file to generate a substituted genome representative of our <em>Fragaria</em> accession.</p> <p>As substituting our genome with the detected structural variants changes genomic coordinates, we also corrected the public GFF genome annotation of <em>F. vesca</em> (Y, Pi, Gao, Liu, & Kang, 2019) using liftoff v1.6.1 (Shumate & Salzberg, 2021). Liftoff also detects and annotates duplications within the substituted genome.</p> <p>Transposable elements annotation was carried out using the EDTA transposable element annotation pipeline v. 1.9.6 (S. Ou et al., 2019) on the substituted genome using default parameters<em>.</em></p> <p><strong>Differentially methylated regions</strong></p> <p>The file Stress_vs_control_DMRs.zip file contains the DMRs that were called using the reads submitted to ENA (ERP135585) and obtained as follows:</p> <p>First, bedGraph files from wgbs pipeline were pre-filtered for a minimum coverage of 5 reads using awk command. These output files were then used as input for the EpiDiverse/dmr bioinformatics analysis pipeline for non-model plant species to define DMRs (Nunn <em>et al</em>., 2021) with default parameters (minimum coverage threshold 5; maximum q-value 0.05; minimum differential methylation level 10%; 10 as minimum number of Cs; Minimum distance (bp) between Cs that are not to be considered as part of the same DMR is 146 bp). The pipeline uses metilene v.0.2.6.1 (<a href="https://www.bioinf.uni-leipzig.de/Software/metilene/">https://www.bioinf.uni-leipzig.de/Software/metilene/</a>) for pairwise comparison between groups and R-packages ggplot2 v.3.3.5 and gplots v.3.1.1, for visualization results (Fig. S1). Based on our <em>F. vesca</em> genome transcript annotation and methylation data (overlapped regions with DNA methylation cytosines and DMRs), we detected the methylated genes, promoters, 3’ UTRs, 5’UTR and transposable elements in strawberry. Global DNA methylation and DMR plots were performed with R-package ggplot2. Gene analyses by methylation patterns and analysis of per-family TE DNA methylation profiles were performed with deepTools v.3.5.0 (Ramírez <em>et al</em>., 2014). DMRs comparison between treatments were done by the Venn diagram v.1.7.0 R-package.</p> <p>We produced several genome browsers tracks with DMRs that we integrated in our local instance of JBrowse available at the following url: <a href="https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub">https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub</a></p>
Historical Sea Surface Temperature (SST) data and thermal stress indices of the Tara Pacific Expedition's coral reef sampling sites, from May 1st 2002 to August 31st 2018.
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems at 111 sampling sites around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis.</p> <p>Here we provide a high-resolution historical dataset that spans from 2002 to each sites’ sampling date and gives an overview of past climate variability and heatwaves experienced by corals sampled at each site. Ocean skin temperature (11 and 12 µm spectral bands longwave algorithm) was extracted from 1km resolution level-2 MODIS-Aqua and MODIS-Terra from 2002 to the sampling date and from level-2 VIIRS-SNPP from 2012 to the sampling date. Day and night overpasses were used to maximize data recovery. Following recommendations from NASA Ocean Color (OB.DAAC), only SST products of quality 0 and 1 were used. The 9 closest pixels to the sampling sites of each scene were extracted. All the extracted pixels from the 3 satellites were then averaged daily to obtain daily SST averages and standard deviations time series for each sampling site, from 2002 to the sampling date.</p> <p>Each time series was first averaged on a Julian day basis to provide a seasonal average. This yearly seasonal average was triplicated and concatenated into a 3-year seasonal cycle to apply a digital low pass filter on the middle year without generating artifacts. A digital low pass filter (filter order 3, pass band ripple 0.1; “filfilt” function in matlab) with 36 Julian days windows was applied to the concatenated time series to remove high frequency noise. The middle year was then extracted from the concatenated time series to recover the seasonal cycle. The sea surface temperature anomaly was calculated as the SST minus the seasonal cycle over the full time series. Considering the short periods of missing data (mean of the 95th percentile of the duration of consecutive days with missing data: 9.8 ± 4.1 days), the missing values in the SST and SST anomaly time series were linearly interpolated in order to calculate thermal stress indices. The SST anomaly frequency was calculated as the number of days over the past 52 weeks when the SST anomaly is greater than or equal to 1 °C. Thermal stress indices relevant to coral reef health were then calculated using methodology developed for the Coral Reef Temperature Anomaly Database (CoRTAD) data base (Saha et al. 2019). Events of cold temperature accumulation were also reported to cause bleaching and mortality (Lirman et al. 2011; González-Espinosa & Donner 2020), therefore, the same set of indices were calculated for cold stress adapting the CoRTAD method, but using the minimum weekly climatologies.</p> <p>A condensed table containing single values associated with each sampling site was created ('TaraPacific_SST_timeseries_mean_products') extracting the minimum, maximum, sum, averages, standard deviations, and value recorded at the sampling day of each of these indices (detailed in the readme file provided with the dataset 'README_TaraPacific_historical_SST.md'). Additional metrics of the last heating and cooling events as well as the time of recovery is also provided to represent the state of thermal stress at the day of sampling.</p>
Stress analysis and Q-factor of free-standing (La,Sr)MnO3 oxide resonators (Dataset)
<p>Datafiles of the article "Stress analysis and Q-factor of free-standing (La,Sr)MnO3 oxide resonators"</p>
Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse
<p>Datasets and R source code of the article Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Paulhac H, Gaillard J-M, Beltran-Bech S (2023) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <strong><em>Peer Community Journal</em></strong> 3:e7 http://dx.doi.org/<a href="https://doi.org/10.24072/pcjournal.228">10.24072/pcjournal.228</a></p> <p>This article previously appeared as preprint Depeux C, Branger A, Moulignier T, Moreau J, Lemaître J-F, Dechaume-Moncharmont F-X, Laverre T, Pauhlac H, Gaillard J-M, Beltran-Bech S (2022) Deleterious effects of thermal and water stresses on life history and physiology: a case study on woodlouse. <em><strong>bioRxiv</strong>, 2022.09.26.509512 </em> https://doi.org/10.1101/2022.09.26.509512</p> <p><em>Peer-reviewed and recommended by <strong>Peer Community in Ecology</strong>: </em> Belsare A (2022) An experimental approach for understanding how terrestrial isopods respond to environmental stressors. <em>Peer Community in Ecology, 100506. </em><a href="https://doi.org/10.24072/pci.ecology.100506"><strong>https://doi.org/10.24072/pci.ecology.100506</strong></a></p>
Shared genetic factors between stress-related disorders and cardiovascular disease
<p>The ultimate goal of this study is to advance our understanding of the biological mechanisms of stress-related disorders and CVD, through demonstrating pleiotropic genes and pathways underlying their comorbidity that are potentially testable as targets of future interventions in experimental investigations.</p>
Database of Residual Stress Measurements on Hot-rolled Wide Flange Steel Cross Sections
<p><a href="https://zenodo.org/deposit/7677600#:~:text=Delete-,Data_info.csv,-md5%3A98a0f787ce2ea1b81d42ac898f6bb110">Data_info.csv</a>: Database of 'Residual Stress Measurements on Hot-rolled Wide Flange Steel Cross Sections' including cross-sectional and material characteristics as well as information relevant to ploting the residual stress distributions.</p> <p><a href="https://zenodo.org/deposit/7677600#:~:text=7%20kB-,Distributions.zip,-md5%3Af7f66a9ad607f27edde3dc7438b82ad2">Distributions.zip</a>: Residual stress distributions for the web and the flanges. To be unziped and positioned at the same location with the 'Data_info.csv', 'Processor.m' and 'QP_Coefficients' folder.</p> <p><a href="https://zenodo.org/deposit/7677600#:~:text=197%20kB-,Processor.m,-md5%3A29935e24d40cbd4398d260124ec71fa8">Processor.m</a>: MATLAB code that plots the residual stress distributions of a selected research work.</p> <p><a href="https://zenodo.org/deposit/7677600#:~:text=16%20kB-,QP_Coefficients.zip,-md5%3Aafd1a8771cf39c9c6584331d10030d96">QP_Coefficients.zip</a>: Coefficients of a proposed optimization method to fit the measured residual stresses in the web and the flanges. To be unziped and positioned at the same location with the 'Data_info.csv', 'Processor.m' and 'Distributions' folder.</p>
ScienceDex guides
Understand access before you commit
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.