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136 results for “combined stress”
Data supporting: Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem
<p>Data used to obtain the results of the research paper entitled: "Combined stress of an insecticide and heatwaves or elevated temperature induce community and food web effects in a Mediterranean freshwater ecosystem", published in the journal "Water Research". The data derives from an outdoor (meso-) cosm experiment in Spain (Imdea Water, Alcala de Henares) in which the transportable temperature and heatwave control device (TENTACLE) was used to investigate the multiple stressors effects of two different climate change scenarios related to temperature (i.e., elevated temperature and reoccurring heatwaves) in combination with the neonicotinoid insecticide imidacloprid.</p>
Survival, wet weight and muscle cellular stress responses of Palaemon varians shrimps exposed to combined temperature and salinity variations
<p>Shrimps were exposed to a full factorial experiment combining different temperatures (20, 23 and 26 ºC) and salinities (20, 40). Cellular stress response biomarkers were assessed in the shrimps muscle at several time-points, namely the 7th, 14th, 21st and 28th days of exposure. Wet weight (as proxy for growth) and survival were also assessed during the experiment. These datasets refer to the publication of an article in STOTEN (<a href="https://doi.org/10.1016/j.scitotenv.2022.158732">https://doi.org/10.1016/j.scitotenv.2022.158732</a>).</p> <p>Note: the biomarker dataset contained 3.2% of missing values, which were replaced by group averages for the purpose of the statistical analyses in the article.</p>
Figure 4 in Gene expression changes in response to combination stresses in Phaseolus vulgaris L. (Fabaceae)
Figure 4. Relative gene expression of some genes (OS, PR3, LOX, PR4 and PAL) by real-time PCR in Tetranychus
Figure 3 in Gene expression changes in response to combination stresses in Phaseolus vulgaris L. (Fabaceae)
Figure 3. Relative gene expression of some genes (OS, PR3, LOX, PR4 and PAL) by real-time PCR in Tetranychus
Figure 2 in Gene expression changes in response to combination stresses in Phaseolus vulgaris L. (Fabaceae)
Figure 2. Relative gene expression of some genes (OS, PR3, LOX, PR4 and PAL) by real-time PCR in Tetranychus
Figure 1 in Gene expression changes in response to combination stresses in Phaseolus vulgaris L. (Fabaceae)
Figure 1. Relative gene expression of some genes (OS, PR3, LOX, PR4 and PAL) by real-time PCR in Tetranychus
Mechanical behavior and microstructure evolution of extruded AZ31 bar under combined stress states
<p>the raw data of mechanical behavior and pole figures of extruded AZ31 bar under combined stress states</p>
Raw data for Investigation of Combined Aging and Mullins Stress Softening of Rubber Nanocomposites
<p><strong>Specification of affiliations:</strong></p> <ul> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript Investigation of Combined Aging and Mullins Stress Softening of Rubber Nanocomposites.</p>
Data and coding files for: Within population plastic responses to combined thermal-nutritional stress differ from those in response to single stressors, and are genetically independent across traits in both males and females
Open the record for dataset details and reuse information.
Data from: Does local adaptation along a latitudinal cline shape plastic responses to combined thermal and nutritional stress?
<p>Thermal and nutritional stress are commonly experienced by animals. This will become increasingly so with climate change. Whether populations can plastically respond to such changes will determine their survival. Plasticity can vary among populations depending on the extent of environmental heterogeneity. However, theory conflicts as to whether environmental heterogeneity should increase or decrease plasticity. Using three locally-adapted populations of Drosophila melanogaster sampled from a latitudinal gradient, we investigated whether plastic responses to combinations of nutrition and temperature increase or decrease with latitude for four traits: egg-adult viability, egg-adult development time, and two body size traits. Employing nutritional geometry, we reared larvae on 25 diets varying in protein and carbohydrate content at two temperatures: 18ºC and 25ºC. Plasticity varied among traits and across the three populations. Viability was highly canalized in all three populations. The tropical population showed the least plasticity for development time, the sub-tropical showed the highest plasticity for wing area, and the temperate population showed the highest plasticity for femur length. We found no evidence of latitudinal plasticity gradients in either direction. Our data highlight that differences in thermal variation and resource predictability experienced by populations along a latitudinal cline are not sufficient to predict their plasticity. </p>
QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat
<p>The mapping population comprises of 180 RILs developed from a cross between GW322 and KAUZ.</p> <p><strong>Phenotypic data</strong><br>Phenotypic evaluation was conducted across two consecutive crop seasons (2021-22 and 2022-23) under late sown irrigation (LSIR) and late sown restricted irrigation (LSRI) conditions at ICAR-IARI, New Delhi. Various physiological and agronomic traits of importance were measured. The component traits of yield including days to heading (DH), normalized difference vegetation index (NDVI), SPAD chlorophyll content (SPAD), plant height (PH), spike length (SL), thousand-grain weight (TGW), grain weight per spike (GWPS), biomass (BM) and grain yield per plot (PY) were measured under heat and combined stress conditions.</p> <p><strong>Genotypic data</strong><br>DNA was isolated from 21-day-old seedlings using the CTAB method (Murray and Thompson, 1980). DNA quality check was done using 0.8% agarose gel electrophoresis. The Axiom Breeders' array containing 35K Single Nucleotide Polymorphism (SNP) was employed for the genotyping of RILs and parents.</p>
Vertical structural complexity of plant communities represents the combined effects of resource acquisition and environmental stress on the Tibetan Plateau
<p><span>Knowledge of vertical structural complexity (VSC) is important, because the resulting spatial partitioning is closely linked to resource utilization and environmental adaptation. However, the spatial pattern of VSC </span><span>on </span><span>large scales and its underlying mechanisms are poorly understood. Here, we systematically investigated 2,013 plant communities through grid sampling on </span><span>the </span><span>Tibetan Plateau (TP). </span><span>VSC was quantified </span><span>as </span><span>the maximum plant height within a plot (Height-max), coefficient of variation of plant height</span><span> (Height-var)</span><span>, and Shannon evenness of plant height (</span><span>Height-even</span><span>)</span><span>. Precipitation dominated the spatial variation in VSC in forests and shrublands, supporting the classic physiological tolerance hypothesis (PTH). In contrast, for alpine meadows, steppes, and desert grasslands in extreme environments, non-resource limiting factors (e.g., wide diurnal temperature ranges and strong winds) dominate VSC variation. Generally, with the shifting of climate from favorable to extreme, the effect of resource availability gradually decreases, but the effect of non-resource limiting factors gradually increase</span><span>s</span><span>, and that the PTH only applicable in "favorable conditions". With </span><span>the help of</span><span> machine learning models, maps of </span><span>VSC</span><span> at 1-km resolution were produced for the TP for the first time</span><span>. Our</span><span> new findings and maps of VSC provide new insights into macroecological studies, especially for adaptation mechanisms and model optimization.</span></p>
Screening of tomato lines under combined stress in generative stage
<ol> <li>Identification of lines with superior performance under combined stress</li> <li>same as (i)</li> <li>Three individual trials, 2018 - 2019</li> <li><em>S. lycopersicum; S. pennellii; S. lycopersicum x S. pennellii</em></li> <li>Greenhouse @ University of Bonn (50.62 N 6.99 E)</li> <li>Growing plants in rockwool slabs with drip irrigation (as commercial practise); stressed plants received modified nutrient solution and only 50 % water</li> <li>General drop in fruit yield and quality under stress conditions; Performance of tested lines (eg water use efficiency) differ significantly.</li> <li>(viii) in progress</li> </ol> <p>(ix) to be discussed</p>
Effects of treatment by synthetic strigolactone (SL) on WUE and NUE in two tomato varieties under normal and combined-stress conditions
<p>The present work aims to analyse the influence of strigolactones (SL) on WUE and NUE under combined stress (drought and phosphate deprivation) in two tomato TOMRES varieties. One variety was chosen because it was found to be less sensitive to nitrogen starvation (# 250) by UNA, while the other (# 270) is more or less as sensitive as the wild type used in previous experiments (M82).</p>
Performance of best ranking accessions of the TOMRES collection under combined stress in Northern European glasshouse conditions.
<ol> <li>to investigate the performance of three best ranking accessions as identified in previous STC work under combined water and nutrient stress tolerance and to compare it to performance of a commonly used commercial variety.</li> <li>to validate best performing TOMRES accessions under combined stresses.</li> <li>the trial took place in 2020.</li> <li><em>Solanum Lycopersicon</em> (syn. <em>Lycopersicon esculentum</em>) accessions TOMRES 27, TOMRES 43, TOMRES 271 and commercial variety ‘Piccolo’. </li> <li>Stockbridge Technology Centre, Stockbridge House, Cawood, YO8 3TZ.</li> <li>data was collected on four replicates of two treatments (UK commercial conditions and combined reduced water and nutrients) on a range of agronomic performance indicators and physiological plant responses as well as harvest assessments and post-harvest plant tissue analysis. </li> <li>data not yet analysed. </li> <li>complete.</li> <li>data not yet analysed.</li> </ol>
Screening of tomato lines under combined stress in generative stage
<ol> <li>Identification of lines with superior performance under combined stress</li> <li> same as (1)</li> <li>Three individual trials, 2018 - 2019</li> <li><em>S. lycopersicum; S. pennellii; S. lycopersicum x S. pennellii</em></li> <li>Greenhouse @ University of Bonn (50.62 N 6.99 E)</li> <li> Growing plants in rockwool slabs with drip irrigation (as commercial practise); stressed plants received modified nutrient solution and only 50 % water</li> <li> General drop in fruit yield and quality under stress conditions; Performance of tested lines (eg water use efficiency) differ significantly.</li> <li> in progress</li> <li>to be discussed</li> </ol>
Phase field modelling combined with data-driven approach to unravel the orientation influenced growth of interfacial Cu6Sn5 intermetallics under electric current stressing
<p><strong>Description:</strong></p> <p>The datasets are constituted by two folders, namely, (A) data_features_and_metric.zip and (B) grain_area_prediction.zip. </p> <p><strong>(A) data_features_and_metric.zip:</strong></p> <p>The following are the contents of this folder</p> <p>(i) <em>grainTheta.csv file</em> : The "grainTheta.csv" file consists the datasets generated from multiple phase field simulations. Name of the columns in the csv file are:</p> <p> <strong>gnid </strong>= grain id number "n", <strong>ntheta = </strong>orientation angle of n<sup>th</sup> grain (<sup>o</sup>); <strong>nltheta </strong>= orientation angle of grain to the left of n<sup>th</sup> grain (<sup>o</sup>); <strong>nrtheta </strong>= orientation angle of grain to the right of n<sup>th</sup> grain (<sup>o</sup>); <strong>j </strong>= current density (A/m<sup>2</sup>);<strong> t =</strong> time (s); <strong>area</strong> = area of n<sup>th</sup> grain (m<sup>2</sup>);<strong> tl</strong> = horizontal length of the top edge of grain "n" (m) ; <strong>bl </strong>= horizontal length of the bottom edge of grain "n" (m) </p> <p>The features gnid, ntheta, nltheta and nrtheta for a given observation are determined during the design of initial conditions of the corresponding phase field simulation. The value of "j" for the observation is determined via the boundary condition in the same numerical simulation. The result from the finite element method based phase field simulation has provided the numerical quantities for t, area, tl and bl attributes. The multiple observations in the data file have been obtained from multiple phase field simulations. </p> <p>(ii) <em>imc_theta.ipynb, imc_theta.py and imc_theta.html files</em>: These files contain the code to build the Pearson's Correlation Coefficient (PCC) heatmap analysis of the data contained in grainTheta.csv file. </p> <p>(iii) <em>comparison_mse.csv</em>: This data file includes the information about mean square error for training data (tmse) and mean square error for validation data (vmse) at Epoch = 199 resulting from 10 different artificial neural network (ANN) models distinguished by 10 different values of learning rates (lr) . Thus, the name of the columns in this csv file are <strong>modelno</strong>, <strong>lr</strong>, <strong>tmse</strong> and <strong>vmse</strong>. </p> <p>(iv) <em>mse_comparison.gnu</em>: This file consists the codes required to output a png image from the data provided in comparison_mse.csv<em>. </em></p> <p>(v) train_loss.csv and val_loss.csv: These files consist of the data of tmse and vmse at all points of Epochs for the ANN model with lr = 2.5E-4 . Thus, the first column in train_loss.csv file corresponds to tmse whereas the second column is Epochs number. Similarly, vmse and Epochs represent the two columns in val_loss.csv file. </p> <p> </p> <p>(vi) <em>mse_lr2p5e-4.gnu</em> : This file consists the codes required to output a png image from the data provided in train_loss.csv and val_loss.csv<em>. </em></p> <p><strong>(B) grain_area_prediction.zip:</strong></p> <p>Inside this folder, there is a folder named "prediction_of_grain_area" consisting of the following files:</p> <p><em>initial_area.csv file</em>: This file consists the value of the initial grain area of grain 4. It is a constant at all orientation angle.</p> <p><em>predicted_result_00_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_00_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 0<sup>o</sup> and 0<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e4.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+4 J/m<sup>2</sup> .</p> <p><em>predicted_result_9090_5e5.csv</em>: This file consists of the prediction result of grain 4 area (at different orientation angles and t = 1250 s) for grain 3 and grain 5 at orientation angles of 90<sup>o</sup> and 90<sup>o </sup>respectively, and for applied current density of 5.0E+5 J/m<sup>2</sup> .</p> <p><em>area_00_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_00_5e4.csv </em>and<em> predicted_result_00_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p><em>area_9090_adj.gnu</em> : This gnu file contains the code to produce the png image from the data contained in <em>predicted_result_9090_5e4.csv </em>and<em> predicted_result_9090_5e5.csv </em>. The information about the initial area of grain 4 is obtained from <em>initial_area.csv</em> file by the code.</p> <p> </p>
Brexpiprazole as Combination Therapy With Sertraline in the Treatment of Adults With Post-traumatic Stress Disorder
ClinicalTrials.gov study NCT04174170. IPD Sharing: YES. Countries: 1. Publications: 1.
A Study of Flexible Dose Brexpiprazole as Monotherapy or Combination Therapy in the Treatment of Adults With Post-traumatic Stress Disorder (PTSD)
ClinicalTrials.gov study NCT03033069. IPD Sharing: YES. Countries: 1. Publications: 1.
Combination Treatment for Posttraumatic Stress Disorder (PTSD) After the World Trade Center (WTC) Attack
ClinicalTrials.gov study NCT01130103. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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