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184 results for “Organic Compounds”
Non-Targeted Screening of Organic Compounds in Environmental and Biological Matrices Related to Children's Environmental Exposure in South Florida, 2022-2024
This dataset provides a comprehensive list of chemicals relevant to children’s exposure from both dietary and non-dietary sources, across five environmental and biological matrices: drinking water (n = 206), food (n = 203), urine (n = 183), soil (n = 178), and household dust (n = 164). Samples were collected between May 2022 and June 2024 in Miami-Dade and Broward counties, Florida. A non-targeted screening approach using high-resolution mass spectrometry (HRMS) coupled with liquid chromatography was employed for analysis, with matrix-specific preparation methods: online solid-phase extraction (SPE) for water and urine, QuEChERS for food, and accelerated solvent extraction (ASE) for soil and dust. Analyses were conducted in full-scan mode under both positive and negative electrospray ionization to maximize compound detection coverage. Compound identification was performed using Compound Discoverer software, incorporating spectral and structural databases such as mzCloud, ChemSpider, ClassyFire, and MassList. Annotations were based on exact mass, mass error threshold (<5ppm), predicted molecular formula, retention time alignment, isotopic pattern fit, MS/MS spectral similarity, and match confidence levels derived from integrated spectral libraries and database scoring algorithms. Quality assurance was maintained through the use of quality control (QC) samples across all matrices and analytical batches. The integration of non-targeted analysis, matrix-optimized extraction, and rigorous QA/QC practices makes this dataset a valuable resource for environmental exposomics, chemical risk assessment, and evidence-based public health policy development.
Data for: Climate warming and drought effects on volatile organic compound emissions from Solidago altissima
Volatile organic compounds (VOCs) were collected from Solidago altissima in drought and warming treatments in the KBS-LTER Rain Exclusion Experiment (REX). This sampling took place in July 2022 when the plants had been experiencing warming (via open-top chambers) for 20 months, and drought (via rainout shelters) for 3 weeks. The data presented here are the final data files used for analysis, and contain VOC abundance values per plant across the four climate treatments: ambient, warmed, drought, and warmed + drought. Code is available at: https://github.com/dobsonk2/REX_VOCs (https://doi.org/10.5281/zenodo.15169943)
Dataset: Volatile organic compound fluxes in a subarctic peatland and lake
<p>Dataset used in the article "Volatile organic compound fluxes in a subarctic peatland and lake" published in the journal Atmospheric Chemistry and Physics 20: 13399–13416 (2020) <a href="https://doi.org/10.5194/acp-20-13399-2020">https://doi.org/10.5194/acp-20-13399-2020</a> .</p> <p>The tab-delimited file contains direct surface-atmosphere Volatile Organic Compound fluxes, measured by Eddy Covariance with a Proton Transfer Reaction -Time of Flight- Mass Spectrometer (PTR-ToF-MS) at a subarctic fen and a subarctic lake during 2018. It also contains PAR Photosynthetic Active Radiation, air temperature, and vegetation surface temperature.</p>
heat of hydrogenation for diverse organic compounds -- experimental and calculated data for 166 unique reactions
<h3>General remarks</h3> <p>The experimental data was drawn from reactions involving H2 that are available at <a href="https://webbook.nist.gov/cgi/cbook.cgi?Name=H2&Units=SI&cTR=on" target="_blank" rel="noopener">NIST</a> (accessed on 15/03/2024). Only reactions of type<strong><em> M + H2 => MH2</em></strong>, where M is a neutral, closed-shell organic molecule that accepts one equivalent of H2, were included in the collection. M corresponds to the oxidized form of the molecule ( => suffix '_ox'), MH2 to the reduced form (=> suffix '_red'). For reasons of clarity, the references to original publications were abbreviated in the main table (look up in separate table).</p> <p>For the molecules involved, Smiles were manually assigned. From those, 3D structures were generated and evaluated in order to match the thermodynamic properties as accurately as possible (for details on the procedure refer to the related work, see below).</p> <p>In addition to the experimental uncertainty, a significant scatter is seen for replicate measurements.</p> <p><strong>Please note</strong>: To compute the heat of hydrogenation from the calculated data for M/MH2 the contribution of H2 needs to be considered, take e.g. -1.164816 hartree (Energy at 298.15K, calculated at CCSD(T)=FULL/aug-cc-pVDZ) from <a href="https://cccbdb.nist.gov/energy3x.asp?method=63&basis=17&charge=0" target="_blank" rel="noopener">CCCBDB</a> (accessed on 15/03/2024).</p> <h3> </h3> <h3>Description of files</h3> <p>The file <strong>01_heat_of_hydrogenation_XP+QM.csv</strong> contains experimentally measured and calculated data.</p> <ul> <li>columns are separated by "|"</li> <li>column names and explanations: <ul> <li><strong>NIST_idx</strong> -- index of original reaction, mostly unique. In a few cases, data of the reverse reaction were subsumed under a different index</li> <li><strong>env</strong> -- if available, information about the environment a reported reaction took place in, e.g. gas phase, hexane, etc...</li> <li><strong>method</strong> -- if available, reference about the experimental technique, e.g. 'Eqk' = Heat of equilibrium, 'Cm' = Calorimetry, 'Chyd' = Calorimetry of hydrogenation</li> <li><strong>Temperature K</strong> -- if available, reported values </li> <li><strong>reference</strong> -- Abbreviation of reference to original publication</li> <li><strong>experimental heat of reaction kJ/mol</strong> -- measured value as reported by experimentalists</li> <li><strong>experimental uncertainty </strong>-- if available, uncertainty of measurement reported by experimentalists</li> <li><strong>comments</strong> -- notes relating to identification of compounds</li> <li><strong>SMILES_ox</strong> -- isomeric canonical SMILES for oxidized form M</li> <li><strong>InChI_ox</strong> -- InChI for oxidized form M </li> <li><strong>SMILES_red</strong> -- isomeric canonical SMILES for reduced form M</li> <li><strong>InChI_red </strong>-- InChI for reduced form M</li> <li><strong>reaction_index </strong>-- consequtively numbered for identical pairs (SMILES_ox, SMILES_red)<strong><br></strong></li> <li>the calculated properties are given for the oxidized and reduced form of the molecule (in hartree) <ul> <li><strong>E(B3LYP/6-31G(2df,p))</strong></li> <li><strong>E_thermal</strong></li> <li><strong>E(G4(MP2))@0K</strong></li> <li><strong>E(G4(MP2))@298K</strong></li> <li><strong>H(G4(MP2))</strong></li> <li><strong>heat_of_formation@0K</strong></li> <li><strong>heat_of_formation@298K</strong></li> </ul> </li> </ul> </li> </ul> <p><strong>02_molecules.sdf:</strong> provides for each molecule a low-energy geometry along with some descriptors and calculated energetic properties:</p> <blockquote> <ul> <li>coordinate block + bond information</li> <li>properties <ul> <li><strong>SMILES</strong> -- isomeric canonical smiles linking compound to reactions defined in 01_heat_of_hydrogenation_XP+QM.csv</li> <li><strong>radical_electrons</strong> -- number of unpaired electrons as determined by RDKit</li> <li><strong>empirical_formula</strong> -- elemental composition of molecule</li> <li><strong>molecular_weight</strong> -- as determined by RDKit in g/mol</li> <li><strong>TPSA </strong>-- topological polar surface area (<em>TPSA</em>) as determined by RDKit</li> <li><strong>logP </strong>-- octanol/water partition coefficient as predicted by RDKit</li> <li><strong>nof_heavy_atoms --</strong> number of non-hydrogen atoms in molecule</li> <li><strong>degree_of_unsaturation</strong> -- sum of multiplebonds and/or rings present in the compound</li> <li><strong>rings</strong> -- number of rings in the compound as determined by RDKit</li> <li><strong>multiplicity</strong> -- spin multiplicity for use as input for QM calculations</li> <li><strong>nof_multiple_bonds</strong> -- number of multiple bonds as determined by RDKit</li> <li><strong>Std_InChI</strong> -- standard InChi</li> <li><strong>FixedH_InChI</strong> -- variant of InChI to differentiate tautomers</li> <li><strong>tag </strong>-- dataset label</li> <li><strong>total_atoms </strong>-- total number of atoms (including H)</li> <li><strong>net_charge</strong> -- total charge of molecule in units of elementary charge</li> <li> <p>energetic properties (in hartree) </p> <ul> <li> <p><code>E(B3LYP/6-31G(2df,p))</code></p> </li> <li> <p><code>E</code><code>(HF/maug-cc-p(T+d)Z) </code></p> </li> <li> <p><code>E(HF/CBS)</code></p> </li> <li> <p><code>E(HF/maug-cc-p(Q+d)Z) </code></p> </li> <li> <p><code>E(MP2/6-31G(d))</code></p> </li> <li> <p><code>E(CCSD(T)/6-31G(d))</code></p> </li> <li> <p><code>E(HF/G3MP2LARGEXP) </code></p> </li> <li> <p><code>E(MP2/G3MP2LARGEXP)</code></p> </li> <li> <p><code>DE(MP2) hartreeDE(HF)</code></p> </li> <li> <p><code>ZPE(B3LYP) hartree</code></p> </li> <li> <p><code>ZPE_scale_factor hartree</code></p> </li> <li> <p><code>E(HLC) hartree</code></p> </li> <li> <p><code>E_thermal hartree</code></p> </li> <li> <p><code>H_thermal hartree</code></p> </li> <li> <p><code>E(G4(MP2))@0K hartree</code></p> </li> <li> <p><code>E(G4(MP2))@298K hartree</code></p> </li> <li> <p><code>H(G4(MP2)) hartree</code></p> </li> <li> <p><code>heat_of_formation@0K kcal/mol</code></p> </li> <li> <p><code>heat_of_formation@298K kcal/mol</code></p> </li> </ul> </li> </ul> </li> </ul> </blockquote> <p><strong>03_references.csv</strong> (separated by "|") lists abbreviations and corresponding full reference to original publication of individual data points.</p>
SONAR -- experimental redox potentials for organic compounds undergoing 2-electron/2-proton transfer reactions
<p>reference data for the demo-compounds used as input for predicting redox potentials by a trained model </p><p>The file</p><ul><li>lists redox potentials and oxidized/reduced form for organic molecules undergoing a two-electron/two-proton reduction reaction (M + 2 e- + 2 H+ --> MH2)</li><li>contains data for 25 organic compounds compiled from various sources in literature</li><li>uses "|" as a separator</li><li>column names and explanations<ol><li><strong>ID</strong>: abbreviated trivial names e.g. for labelling</li><li><strong>orig redox potential [V]:</strong> original values reported in respective reference</li><li><strong>solvent</strong>: total formula, water (H2O) throughout</li><li><strong>pH</strong>: pH value of electrolyte solution. If not reported, inferred from the concentration of supporting electrolyte</li><li><strong>supporting_electrolyte</strong>: if spefified: total formula, if available; concentration</li><li><strong>SMILES_ox</strong>: molecular structures encoded as (manually assigned) SMILES strings for the oxidized species (M)</li><li><strong>SMILES_red</strong>: molecular structures encoded as (manually assigned) SMILES strings for the reduced species (MH2)</li><li><strong>ref_electrode:</strong> reference electrode the originally reported half cell potential refers to. If not specified, RHE was used as default</li><li><strong>redox potential vs SHE [V]</strong>:<ul><li>In case of missing information, reversible hydrogen electrode (RHE at pH = 0) was assumed, which corresponds to SHE</li><li>In case of conflicting entries (SHE and pH != 0), we assumed the pH should be accounted for and replaced "RHE" as reference electrode instead of "SHE". "NHE" was treated like "RHE".</li><li>In case the reference electrode was other than SHE, NHE or RHE, a respective offset was added. This was the case once for Ag/AgCl (assuming saturated solution, offset = 0.210, see respective reference)</li><li>Finally, the potential values were transferred to SHE according to: E(SHE) = E(RHE) + 0.05913 * pH</li><li>CAVEAT: Lacking information about individual pKa values, no other correction was made.</li></ul></li><li><strong>reference</strong>: orginal source</li></ol></li></ul>
volatile organic compounds that were measred in various sites in Israel, available by the Israel Ministry of Environmental Protection
<p>The dataset comprises measurements of volatile organic compounds sampled at multiple sites across Israel from 2010 to 2024, encompassing urban, rural, and suburban locations.</p>
Strawberry volatile organic compounds metabolomic data and QTL study
<p>This dataset contains the supplementary materials of the publication "Multivariate QTL approach reveals a major regulator of terpenoid production and other volatiles in strawberry" of the same authors. In this study we extracted volatile organic compounds from several strawberry samples and analysed their identity and abundance. We used this volatile data to perform an extensive multivariate QTL study, the results of which can be found in this dataset.</p> <p>All analysis, results and figures can be reproduced using the folder included in the <strong>supplementary data 1</strong>. If you want to reproduce part or all of our analysis, only download sup data 1. </p> <p>If you only need one of our results or data table you can download them individually:</p> <ul> <li>Sup data 2: abundances of volatile organic compounds from a biparental and diverse panel (GWAS) population.</li> <li>Sup data 3 and 4: p-value tables for all metabolites as well as multivariate traits (see publication for more information).</li> <li>Sup table 1: Summary of metabolite abundances and heritabilities across both populations.</li> <li>Sup tables 2 and 3: significant QTL signals for each trait individually and summarised per QTL locus.</li> <li>Sup table 4: previously reported VOC QTLs in strawberry, with positions imputed in the Royal Royce genome.</li> <li>Sup table 5: metadata about all the identified compounds on this and previous studies.</li> <li>Sup table 6: SNP array positions imputed in the "Royal Royce" genome assembly.</li> <li>Sup table 7: Number of markers per chromosome in this analysis.</li> </ul> <h3>Update 2025</h3> <p>We updated the underlying code and datasets to reflect several revisions made to this work. Most notably, the QTL results have been reworked. They now do not include Blink or FarmCPU results (only mixed model results, obtained through statgenGWAS). Additionally, heritability estimations, QQ-plots and other figures have been added to the reproducible results code.</p>
Dimethylsulfoniopropionate-derived compound concentrations, volatile organic compound concentrations, and microorganism abundances around two corals and a seaweed in the reefs of Moorea (French Polynesia)
<p>These data belong in the paper: </p> <p>M. Masdeu-Navarro, J-F. Mangot, L. Xue, M. Cabrera-Brufau, S.G. Gardner, D.J. Kieber, J.M. González, R. Simó (2022). Spatial and diel patterns of volatile organic compounds, DMSP-derived compopunds and planktonic microorganisms around a tropical scleractinian coral colony. <em>Frontiers in Marine Science</em>.</p> <p>Concentrations of DMSP, acrylate, DMSO, DMS, DMDS, COS, CS2, isoprene, CH3I, CH2ClI, CH2Br2 and CHBr3 in seawater samples around colonies of the corals Acropora pulchra and Pocillopora sp., and the brown seaweed Turbinaria ornata. Abundances of high-DNA and low-DNA bacteria, Prochlorococcus, Synechococcus, picoeukaryotes and nanoeukaryotes in the same samples, as determined by flow cytometry. All samples were collected in April 2018 in the coral reefs of Mo'orea, French Polynesia. </p> <p>The upper set of data contains concentrations at the distance of 0.5 cm from the coral polyps on the branch tips or verrucae, as well as from the seaweed thalli (samples IN), and 2 m away, downcurrent (samples OUT). The second set of data corresponds to A. pulchra only, and contains seawater samples IN, OUT and AL, the latter being sampled at 0.5 cm from the base of the dead branches colonized by a turf alga. IN, OUT and AL samples were collected over an entire diel cycle, every 6 hours for a period of 30 hours.</p>
Emission of volatile organic compounds from residential biomass burning and their rapid chemical transformations.
<p>Volatile Organic Compounds (VOCs) were monitored during the Ioannina 2022/23 winter campaign, in north-east Greece. The campaign was carried out between December 6th, 2021, and January 10th, 2022. Nitrogen oxides, carbon monoxide, carbon dioxide, methane, PM10 and Black carbon were also monitored, as well as meteorological variables. Ioannina is nested within the Dinaric mountains and suffers from intense winter pollution events, due to the topology which traps the pollution over the city. The instruments deployed included a Proton Transfer Time-of-Flight Mass Spectrometry (PTR-ToF-MS 4000 – Ionicon GmbH, Austria), a greenhouse gas monitor (G2301 – Picarro Inc., USA), a suite of carbon monoxide, ozone, nitrogen oxide analyzers (APMA-360, APNA-360 and APOA-360 – Horiba Ltd., Japan), a PM10 monitor (F-701-20 – DURAG, Germany), an aethalometer (AE33 – Magee Scientific, USA) and a weather station. Radiation, historical temperature data from the University of Ioannina, and PMF analysis results are also submitted.</p>
DMS measurements dataset for manuscript "Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves"
<p>Differential mobility spectrometry dispersion plots collected for the manuscrit "Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves". The measurement files are located in the folders that represent certain week and day of measurement. The folders containing measurements are named with the following pattern: [chemical abbreviation]_[dilution rate]. For example "2PEtOH_1o10k" means that the folder contains measurement of 2-phenylethanol diluted with propylene glycol in volumetric proportion 1/10 000. Another example is "nBuOH_1o100" - n-Butanol diluted with propylene glycol in volumetric proportion 1/100. Please find the abbreviations in the article referred.</p> <p>For the first five weeks only 1/100 dilutions were measured. The last two weeks (weeks 6 and 7) 1/10 000 dilutions were measured. However, Carvone 1/100 was measured again on week 6 due to suspicion of faulty measurements during the previous weeks. The faulty measurements were not confirmed, and thus there 25 more samples of Carvone with dilution rate 1/100.</p>
Volatile organic compound analysis, a new tool in the quest for preterm birth prediction – an observational cohort study
<p>Vaginal swabs were taken in pregnancy in high risk asymptomatic women attending a preterm prevention clinic. Women in the study attended the clinical due to a history of preterm birth or midtrimester pregnancy loss, or due to a history of cervical surgery. Individualised management plans were made depending upon individual patient risk factors. During their attendance to the clinic vaginal swabs were taken for VOC analysis. Swabs were taken between 15 and 28 weeks gestation. Women consented to vaginal swabs at each of their visits to the clinic. The dataset contains GC-IMS VOC data from a G.A.S. GC-IMS and includes a Spreadsheet of demographics.</p>
Non-target screening of organic compounds in offshore produced water by GC×GC-MS (associated data)
<p>Associated data for the manuscript titled "<em>Non-target screening of organic compounds in offshore produced water by GC×GC-MS</em>"</p> <p>Preprint doi://10.26434/chemrxiv.13317938</p> <p> </p>
Non-methane volatile organic compound emissions over China estimated using TROPOMI HCHO retrievals
<p>We used the Regional multi-Air Pollutant Assimilation System (RAPAS) with the EnKF algorithm to optimize daily NMVOC emissions in China by assimilating TROPOMI HCHO retrievals. </p><p>airqual.qc.csv includes assimilated and verified surface NO2 observations.</p><p>HCHO.tar.gz includes assimilated TROPOMI HCHO retrievals.</p><p>posterior_emission_27km.nc and posterior_emission_mg_27km.nc includes inferred daily posterior anthropogenic and biogenic NMVOC emissions respectively for August 2022.</p>
Data archive for: Resting cells of Skeletonema marinoi assimilate organic compounds and respire by dissimilatory nitrate reduction to ammonium in dark, anoxic conditions
<p>Data archive for: “Resting cells of <em>Skeletonema marinoi</em> assimilate organic compounds and respire by dissimilatory nitrate reduction to ammonium in dark, anoxic conditions” <a href="https://doi.org/10.1111/1462-2920.16625">https://doi.org/10.1111/1462-2920.16625</a></p> <p> </p> <p>Dataset of single cell assimilation of organic/inorganic C/N by resting cells of the marine diatom <em>Skeletonema marinoi</em> captured using secondary ion mass spectrometry (SIMS) and stable isotopic tracers. The dataset also contains POC/PON changes over time during dormancy, DNRA (<sup>15</sup>N-NH<sub>4</sub><sup>+</sup> production), denitrification (<sup>15</sup>N-N<sub>2</sub> production) and a germination assay to determine survival rate, most probable number analysis (MPN). </p> <p>Two strains (GF04 and R05) were incubated in dark and anoxic conditions in two different incubation experiments.</p> <p>Incubation 1: Diatoms treated with antibiotics before entering dormancy compared to a control not treated with antibiotics then given <sup>15</sup>N-NO<sub>3</sub><sup>-</sup> in dark anoxic conditions.</p> <p>Incubation 2: Diatoms treated with antibiotics given, <sup>15</sup>N & <sup>13</sup>C urea, <sup>15</sup>N & <sup>13</sup>C urea + <sup>14</sup>N-NO<sub>3</sub><sup>-</sup>, <sup>13</sup>C-acetate, <sup>13</sup>C-acetate + <sup>15</sup>N-NO<sub>3</sub><sup>-</sup>, or <sup>15</sup>N-NO<sub>3</sub><sup>-</sup>.</p> <p>See the main manuscript for a extensive experimental setup.</p> <p> </p> <p><strong>Each file is uploaded as both a .CSV and .XLSX, so that you can choose which you prefer.</strong></p> <p><strong>DNRA_and_denitrification.csv/xlsx:</strong> DRNA and denitrification depending on volume (Incubation 1)</p> <p><strong>DNRA_per_cell.csv/xlsx:</strong> DNRA per cell (Incubation 1 & 2)</p> <p><strong>MPN_data.csv/xlsx:</strong> Most probable number analysis (Incubation 1 & 2)</p> <p><strong>POC_PON.csv/xlsx:</strong> POC and PON per cell and volume (Incubation 1 & 2)</p> <p><strong>SIMS_data.csv/xlsx:</strong> SIMS data (Incubation 1 & 2)</p> <p> </p> <p> </p>
Dataset for: On the Nature of Hydrophobic Organic Compound Adsorption to Smectite Minerals Using the Example of Hexachlorobenzene-Montmorillonite Interactions
<p><br>This dataset contains all data obtained from first principle DFT calculations at the PBE-D3 DFT level<br>by the program VASP for the paper published in the journal "Minerals". Please cite this article when using the dataset.<br><br>Title: "On the Nature of Hydrophobic Organic Compound Adsorption<br>to Smectite Minerals Using the Example of Hexachlorobenzene-Montmorillonite Interactions."</p> <p>Authors: Peter Grancic, Leonard Böhm, Martin H. Gerzabek, and Daniel Tunega <br>DOI10.3390/min13020280. </p> <p><br>The model systems are Ca-montmorillonite (Ca-Mt) models with varying layer charge from the Mg/Al substitutions.<br>Calculated are interaction energies of hexachlorobenzene (HCB) with Ca-Mt models in various configurations.<br>QE.dat file is file with collected energies of all models, Collect_QE.py is a selfmade python file for the collection of energies.<br>The structure of all directories is as following:<br>HCBCaxxx directories contain optimized HCB-CaMt complexes<br>HCBCaxxx_Clay directories contain pure CaMt models<br>HCBCaxxx_HOC directories contain only HOC molecule<br>Each directory contains:<br>-input geometry data (POSCAR file)<br>-optimized geometries (CONTCAR.norm file)<br>-input parameters for VASP (INCAR file)<br>-k-points (KPOINTS file)<br>-complete output files (OUTCAR.norm and vasprun.xml.norm files)<br>POTCAR file with pseudopotentials are not provided due to copyright rights.<br>Their type can be found in OUTCAR or vasprun.xml files.</p> <p>Funding: This work has been supported by German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), grant number 443637168, BO5388/1–1 and Austrian Science Fund (Fonds zur Förderung der Wissenschaftlichen Forschung, FWF), grant number I 4876–N in the bilateral project ”Clay minerals as sorbents for hydrophobic organic chemicals – ClayHOC”. The results<br>presented have been achieved using the Vienna Scientific Cluster (VSC), project number 70544.</p> <p>Terms of use: These data are provided "as is", without any warranty. This dataset is provided under the Creative Commons Attribution 4.0 International license.</p>
Data from: Effect of altitude on volatile organic and phenolic compounds of artemisia brevifolia wall ex Dc. from the Western Himalayas
<p>Adaptation to changing environmental conditions is a driver of plant diversification. Elevational gradients offer a unique opportunity for investigating adaptation to a range of climatic conditions. The use of specialized metabolites as volatile and phenolic compounds is a major adaptation in plants, affecting their reproductive success and survival by attracting pollinators and protecting themselves from herbivores and other stressors. The wormseed <em>Artemisia brevifolia</em> can be found across multiple elevations in the Western Himalayas, a region that is considered a biodiversity hotspot and is highly impacted by climate change. This study aims at understanding the volatile and phenolic compounds produced by <em>A. brevifolia </em>in the high elevation cold deserts of the Western Himalayas with the view to understanding the survival strategies employed by plants under harsh conditions. Across four sampling sites with different elevations, polydimethylsiloxane (PDMS) sampling and subsequent GCMS analyses showed that the total number of volatile compounds in the plant headspace increased with elevation and that this trend was largely driven by an increase in compounds with low volatility, which might improve the plant's resilience to abiotic stress. HPLC analyses showed no effect of elevation on the total number of phenolic compounds detected in both young and mature leaves. However, the concentration of the majority of phenolic compounds decreased with elevation. As the production of phenolic defense compounds is a costly trait, plants at higher elevations might face a trade-off between energy expenditure and protecting themselves from herbivores. This study can therefore help us understand how plants adjust secondary metabolite production to cope with harsh environments and reveal the climate adaptability of such species in highly threatened regions of our planet such as the Himalayas.</p>
Dataset: Consistent release of volatile organic compounds across an actively degrading permafrost peatland
<p>Here, we conducted in situ measurements of soil and pond VOC emissions across an actively degrading permafrost peatland in subarctic Norway. We used a permafrost thaw gradient that covered bare soil and vegetated palsa plateaus, underlain by intact permafrost, and increasingly degraded permafrost landscapes: thaw slumps, thaw ponds, and vegetated thaw ponds.</p> <p>This dataset includes two excel files: 1) the first one "Finnmark_source_data" is the source data for figures in the publication <a href="https://doi.org/10.1016/j.geoderma.2023.116355">https://doi.org/10.1016/j.geoderma.2023.116355</a>. ii) the second one "Rawdata_of_emission_rate" is the emission rate of the 210 VOC species identified in this study.</p> <p>Results showed that every peatland landscape type was an important and consistent source of atmospheric VOCs, with a large variety species, such as methanol, acetone, monoterpenes, sesquiterpenes, isoprene, hydrocarbons, oxygenated VOCs, etc. VOC composition varied considerably across the measurement period and across the permafrost thaw gradient. We observed enhanced terpenoid emissions following thaw slump degradation, highlighting the potential atmospheric impact of permafrost thaw, due to the high chemical reactivities of terpenoid compounds. Overall, our study demonstrates that VOCs are being emitted in significant quantities and with largely similar composition upon permafrost thawing, inundation, and subsequent vegetation development, despite major differences in microclimate, hydrological regime, vegetation, and permafrost occurrence.</p> <p>Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or the PI of this project Prof. Rinnan at riikkar@bio.ku.dk</p>
Data for: Schaub et al., Impact of Organic Compounds on the Stability of Influenza A Virus in deposited 1-µl droplets
<p><strong>Experimental data</strong></p> <p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>Figure 1: inactivation data after 0 and 60 min for 1-μl droplet experiments at various RH in PBS, SLF and nasal mucus (infectivity titer and genomic copy enumeration).</p> <p>Figure 3: inactivation data after 0 and 60 min for 1-μl droplet experiments at 60% RH in SLF derivatives (infectivity titer and genomic copy enumeration).</p> <p>Figure 4: inactivation data after 0 and 60 min for 1-μl droplet experiments at 60% RH in various albumin:NaCl mass ratios (infectivity titer and genomic copy enumeration).</p> <p>Figure 5: inactivation data after 0 and 60 min for 1-μl droplet experiments at 60% RH with various proteins (infectivity titer and genomic copy enumeration).</p> <p>Figure S1: Control in bulk for data from Figure 1 (infectivity titer).</p> <p>Figure S2: Recovery fraction for data from Figure 1 (GC/GC<sub>0</sub>). </p> <p> </p> <p><strong>Abbrevations used:</strong></p> <p>GC = Genomic Copies</p> <p>LoQ = Limit of Quantification</p> <p>PFU = Plaque Forming Unit</p> <p>ul = microliter</p>
Figure 3 in Chemotaxis of Caenorhabditis elegans Toward Volatile Organic Compounds from Stropharia rugosoannulata Induced by Amino Acids
Figure 3: Chemotaxis (percent attracted) and mortality (percent of attracted worms dead) in the groups supplemented with L-phenylalanine or L-tryptophan and the control without amino acids. The error bars indicate standard deviation. The statistical differences were analyzed using one-way ANOVA, *P <0.05, **P <0.01.
Figure 2 in Chemotaxis of Caenorhabditis elegans Toward Volatile Organic Compounds from Stropharia rugosoannulata Induced by Amino Acids
Figure 2: GC-MS total ion chromatography of different samples. A: L-phenylalanine alone, strain 1.202 alone and strain cultured on water agar plus L-phenylalanine, benzaldehyde was increased and 1-Octen-3-ol was newly produced from strain 1.2052 cultures added L-phenylalanine; B: strain 1.202 alone, L-tyrosine alone and strain cultured on water agar plus L-tyrosine, benzaldehyde was decreased and 1-Octen-3-ol and indole were newly produced were produced from strain 1.2052 cultures added L-tyrosine.
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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.
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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.