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1,923 results for “Compounds”
Primary producer biomarker profiles of bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA) and their fatty acid (FA) collected from the Beaufort Sea coastal lagoons,2021-2024
Within Stefansson Sound in Prudhoe Bay, AK various organic matter sources were collected to determine multiple biomarker baseline profiles (i.e., bulk carbon and nitrogen stable isotopes (SI), compound specific stable isotopes of carbon in essential amino acids (CSIA-EAA), fatty acids (FA)). Some organic matter sources were collected from Elson lagoon in Utqiaġvik, AK and Kaktovik and Jago lagoons in Kaktovik, AK to supplement low sample sizes in some organic matter source groups. Kelp, red algae, terrestrial plants, phytoplankton, and ice algae were collected in 2024 with some supplement samples collected in 2021 - 2023. Stable isotope values of δ13C and δ15N are reported as “del_13c” and “del_15n”, respectively. Individual fatty acids are reported as the percent relative to total fatty acids for 23 fatty acids: C11:0, C12:0, C14:0, C15:1, C15:0, C16:0, C16:1n7, C17:0, C17:1, C18:0, C18:1n9 trans, C18:2n6 cis, C18:1n7, C18:3n3, C20:0, C18:3n6, C20:4n6, C21:0, C22:0, C22:1n9, C23:0, C24:0, C22:6n3. Stable isotope values of δ13C are reported in the following essential amino acids: Valine (Val), Leucine (Leu), iLeu (isoleucine), Methionine (Met), Phenylalanine (Phe). Additionally, we used ice algal diatoms collected in the Arctic (landfast ice near Utqiaġvik, Alaska) and cultured in a laboratory setting at the University of Alaska Fairbanks to compare the CSIA-EAA fingerprints of field (composites) ice algal samples and isolate diatoms samples.
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.
InnoVine WP3: 105 phenolic compound quantification of 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated Vitis vinifera cultivars
<p>FP7/311775 InnoVine (Innovation in vineyard): Combining innovation in vineyard management and genetic diversity for a sustainable European viticulture</p> <p>WP3: Exploiting the genetic diversity in grapevine</p> <p>105 phenolic or related compounds, from 2014 and 2015 mature grape berries from a core-collection of 279 irrigated and non-irrigated <em>Vitis vinifera</em> cultivars, were quantified by UPLC-TQ-MRM Mass Spectrometry (Lambert M<em> et al., Molecules</em> <strong>2015</strong>, <em>20</em>(5), 7890-7914; doi:10.3390/molecules20057890 & Pinasseau L <em>et al.</em>, <em>Molecules</em> <strong>2016</strong>, <em>21</em>(10), 1409; doi:10.3390/molecules21101409).</p> <p>3 parameters were added:<br> - water/drought status (delta C13)<br> - sugar content (refractive index, brix degree)<br> - weight of 100 grape berries</p> <p>All plant material was collected at the Vassal repository: French National Grapevine Germplasm Collection, INRA Domaine de Vassal, 34340 Marseillan-Plage, France (Centre de Ressources Biologiques de la Vigne (CRB-Vigne) de Vassal-Montpellier).</p>
Bioactivity of small-molecule compounds against Haemonchus contortus
<div> <div> <div> <p>This dataset of small-molecule compounds and their effects on <em>H. contortus </em>was assembled based on the results obtained from screening two compound libraries (Medicines for Malaria Venture Pathogen Box, Compounds Australia Open Scaffolds set) to assess the effect of compounds on the motility of exsheathed third-stage larvae (xL3) of <em>H. contortus </em>(Preston et al., 2016, 2017). Additionally, select literature data were included to augment the in-house generated data.</p> </div> </div> </div>
Differential gene expression data of commercial compounds used to assess the performance of human TeraTox assay
<p>The dataset supplements the publication `Optimization of the <em>TeraTox</em> assay for preclinical teratogenicity assessment`. </p> <ul> <li>2022-02-18-TeraTox-commercial-logFC.gct: log2FC matrix of genes by compounds (in concentration ranges)</li> <li>2022-02-18-TeraTox-commercial-pScore.gct: p-scores (log 10 transformed p-values with the sign of logFC) of genes by compounds</li> <li>2022-02-18-TeraTox-commercial-featureData.txt: feature annotation in TSV format</li> <li>2022-02-18-TeraTox-commercial-phenoData.txt: sample annotation in TSV format</li> <li>2021-06-10-gcGeneFactorAnno-withPositiveCoefs.tsv: gene membership of germ-layer factors, with germ-layer annotation and average expression in copies per million (cpm).</li> </ul> <p>Citation: Jaklin, Manuela, Jitao David Zhang, Nicole Schäfer, Nicole Clemann, Paul Barrow, Erich Küng, Lisa Sach-Peltason, Claudia McGinnis, Marcel Leist, and Stefan Kustermann. “Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.” <em>Toxicological Sciences</em> 188, no. 1 (July 1, 2022): 17–33. <a href="https://doi.org/10.1093/toxsci/kfac046">https://doi.org/10.1093/toxsci/kfac046</a>.</p>
Chemical structures, Cell Painting and transcriptional profiles for compound bioactivity prediction.
<p>This is the related data, both input and produced for the paper <a href="https://doi.org/10.1101/2020.12.15.422887">"Predicting compound activity from phenotypic profiles and chemical structures"</a>.</p> <p>This data can be merged with <a href="https://github.com/CaicedoLab/2023_Moshkov_NatComm">paper's GitHub repository</a> for reproduction.</p> <p>Folders and files and are described below:</p> <pre><code>├── assay_data ├── assay_matrix_discrete_270_assays.csv Assay matrix with hits for assays (270) and compounds (16170). Note that this is the final file that we used to produce splits. ├── assay_metadata.csv Assay metadata ├── broad_ids.txt List of broad ids used in this study. That is an unfiltered list of compounds required by some analysis scripts. ├── smiles.txt Same as broad_ids.txt, but SMILES strings. ├── feature_data (for 16978 compounds, can be masked with ./misc/compounds16978to16170.npy) ├── cp.npz Classical chemical features ├── ge.npz Gene expression features ├── ge_scale.npz Gene expression scaled features ├── mo.npz Morphology features (not batch corrected) ├── mobc.npz Morphology features (batch corrected) ├── misc ├── compound_analysis.npz Compounds in the dataset identified as PAINS ├── compounds16978to16170.npy Used to filter features from the bigger set of compounds to the final one ├── fingerprints.npz Calculated fingerprints of compounds, those were then used to calculate similarity ├── similarity_fingerprints.npz Similarity matrix for compounds (16978) ├── population_normalized.csv.gz Well-level morphological profiles that were used for batch-correction ├── Table for PUMA Excel file with additional data and plots ├── predictions ├── scaffold_median(mean)_AUC.csv Aggregated median(mean) AUC scores over scaffold-based cross-validation splits. In the paper, median results were reported. ├── scaffold_median(mean)_EF.csv Aggregated median(mean) enrichment factor (EF) over scaffold-based cross-validation splits. In the paper, median results were reported. ├── toprank_chemical_cv{}_hitsnorm.csv Those files are needed to create enrichment plots and contain hit rate and top rank hit rate. ├── Each folder here stands for an experiment type, the number in the folder name is a number of the split. Inside each folder there are the following elements: ├── predictions Folder with predictions for each assay-compound pair for each modality ├── 2022_01_evaluation_all_data.csv File with AUC scores for each assay for the test set in the split ├── 2022_01_evaluation_all_data_EF.csv File with enrichment factor (EF) values for each assay for the test set in the split. Those files exist only for *chemical* folders. ├── assay_matrix_discrete_train(test)_old_scaff.csv Training and test subsets of data for the split. The first column contains broad_id. ├── assay_matrix_discrete_train(test)_old_scaff.csv Same, but SMILES strings in the first column. Those files are used as input to ChemProp! Experiments in this folder are the following: - chemical Scaffold-based 5-fold cross-validation splits, the main results in the paper are reported with this series of experiments. - chemical_bal Same splits as in chemical, but training were run with ChemProp built-in data balancing. - chemical_st Same splits as in chemical, but separate models were trained for each assay. - CV Random 5-fold cross-validation splits. - GE 5-fold cross-validation splits based on same-size clustering of gene expression features. - MOBC 5-fold cross-validation splits based on same-size clustering of batch-corrected morphology features. - random 10 random splits, ~80% of compounds in the training set and the rest in the test set. ├── splitting This folder contains numpy files which help to match compounds and features to create training and test sets for a split, which can be reused in the analysis notebook for data preparation. ├── scaffold_based_split.npz Splitting for scaffold-based splits. ├── random_split_{}.npz Random split indices of test set compounds (10 files). ├── cross_validation_indicies.npz Indices for random cross-validation splits ├── GE_clusters_size_constrained.npz Indicies of clusters of same-size clustering for gene-expression features. ├── MOBC_clusters_size_constrained.npz Indices of clusters of same-size clustering for batch-corrected morphology features.</code></pre> <p> </p>
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>
Bioactivity deep learning for structure-free compound-protein interaction
<p>CPI2M data for "<strong>Bioactivity deep learning for structure-free compound-protein interaction</strong>".</p> <p>CPI2M_main_Ki.csv: Bioactivity data with <strong>pKi </strong>activity type. Used for model training and internal validation.</p> <p>CPI2M_main_Kd.csv: Bioactivity data with <strong>pKd</strong> activity type. Used for model training and internal validation.</p> <p>CPI2M_main_EC50.csv: Bioactivity data with <strong>pEC50 </strong>activity type. Used for model training and internal validation.</p> <p>CPI2M_main_IC50.csv: Bioactivity data with <strong>pIC50 </strong>activity type. Used for model training and internal validation.</p> <p>CPI2M_few_Ki.csv: Bioactivity data with <strong>pKi </strong>activity type. Used for external validation.</p> <p>CPI2M_few_Kd.csv: Bioactivity data with <strong>pKd </strong>activity type. Used for external validation.</p> <p>CPI2M_few_EC50.csv: Bioactivity data with <strong>pEC50 </strong>activity type. Used for external validation.</p> <p>CPI2M_few_IC50.csv: Bioactivity data with <strong>pIC50 </strong>activity type. Used for external validation.</p> <p>potency.csv: BIoactivity data with <strong>pPotency </strong>activity type. Not used currently but can be potentially adopted as classification data for customized use.</p> <p>percentage.csv: BIoactivity data with <strong>Percentage Inhibition </strong>activity type. Not used currently but can be potentially adopted as classification data for customized use.</p> <p>Protein_pretrained_feat.zip: pre-calculated protein feature files with UniProt ID naming. <strong>Should be unzipped</strong> before start model training with CPI2M data.</p> <p> </p> <p>For each .csv data, columns include "<strong>smiles</strong>" (ligand SMILES), "<strong>exp_mean</strong>" (nM bioactivity), "<strong>y</strong>" (neg.log nM, final label), "<strong>cliff_mol</strong>" (whether activity cliff or not), "<strong>split</strong>" (splitting label by activity cliff), "<strong>Uniprot_id</strong>" (UniProt ID for protein), "<strong>Sequence</strong>" (wildtype sequence for protein), and "type_id" (bioactivity type token, pKi =0, pKd=1, pEC50=2, pIC50=3).</p> <p> </p> <p>Please find the project code at https://github.com/gu-yaowen/GGAP-CPI</p> <p> </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>
Compound hot and dry and wet and windy events in CMIP6 models
<p>NetCDF files containing maps of return periods (in years) for the joint occurrence of</p> <ol> <li>strong surface winds (sfcWind) and heavy rain (pr) and</li> <li>heatwaves (EHF) and drought (SPI) </li> </ol> <p>as realised by models participating in the Coupled Model Intercomparison Project Round 6 (CMIP6). Included is output from models that provided daily data for sfcWind, pr, tmax and tmin (to calculate EHF) and experiments historical, SSP126, SSP245, and SSP585 for ensemble member r1i1p1f1. The base period for the determination of hazard thresholds was 1980 – 2014 for all experiments. Time periods over which return periods were calculated were 1980 – 2014 for the 'historical' experiment and 2066 – 2100 for experiments 'SSP126', 'SSP245', and 'SSP585'.</p> <p>Values for return periods are determined following the method in Ridder et al. (2020a) doi: 10.1038/s41467-020-19639-3; Ridder et al. (2020b) doi: 10.1029/2020GL091152 and Ridder et al. (2021) doi: 10.1038/s41612-021-00224-4. </p> <p>Name convention:</p> <ul> <li> historical experiments: <br> <em>map_RP_${hazardX}_${hazardY}_${CMIP6model}_historical_r1i1p1f1_${model_grid}_19800101_20141231.nc</em></li> <li>ScenarioMIPs:<br> map_RP_<em>${hazardX}_${hazardY}_${CMIP6model}</em>_historic_threshold_${experiment}_r1i1p1f1_2066-2100.nc</li> </ul>
S19 | MZCLOUD | mzCloud Compounds
<p>This is the collection associated with list S19 MZCLOUD on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S19 MZCLOUD <strong>mzCloud Compounds</strong></p> <p>List of compounds on the <a href="https://www.mzcloud.org/">mzCloud</a> mass spectral database, provided by Robert Mistrik, update by Nikiforos Alygizakis.</p> <p>Update 17 July 2022: fix SMILES issues for PubChem deposition (removing <br> and spaces).</p>
Rapid structure determination of microcrystalline molecular compounds using electron diffraction (nanoArgovia Project A3EDPI)
<p>The are the data linked to the publication "Rapid structure determination of microcrystalline molecular compounds using electron diffraction", <a href="https://doi.org/10.1002/anie.201811318">10.1002/anie.201811318</a>. Electron Diffraction data collected with an EIGER X 1M detector (DECTRIS Ltd.).</p> <p>Each tar file contains the raw files in HDF5 format, together with the XDS.INP file used for data integration. Images of the respective crystals have '_img_' in their file names. The log files for recording the stage alpha angle are included with the same name and suffix .txt. See publication for details.</p> <p>NB: The meta-data in the HDF5 files have no meaning, please refer to the respective XDS.INP file for respective information.</p> <p>The crystallographic data (CIF-files) have been uploaded to the ICSD (High--throughput Structural Chemistry with Electron Diffraction) and CSD (https://www.ccdc.cam.ac.uk/) respectively:</p> <p>Paracetamol from Grippostad CCDC 1856579<br> electron structure of MBBF4 CCDC 1856580</p> <p>ZSM-5 x227 CSD 1856581</p> <p>ZSM-5 x331 CSD 1856582</p> <p>ZSM-5 x79 CSD 1856583<br> ZSM-5 x811 CSD 1856584</p> <p> </p>
S1 | MASSBANK | NORMAN Compounds in MassBank
<p>This is the collection associated with list S1 MASSBANK on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S1</p> <p>MASSBANK</p> <p><strong>NORMAN Compounds in MassBank </strong></p> <p><a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/MassBankEU_Cmpds_11042017_wMS_DTXSIDs_03102017.csv">CSV</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/031017Update/MassBankEU_Cmpds_11042017_wMS_DTXSIDs_03102017.xlsx">XLSX</a> with Fragments (3/10/2017) </p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/massbankref">MassBank EU Reference List</a></p> <p>CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/massbankeusp">MassBank EU Special Cases</a></p> <p>CompTox Fragment Download</p> <p><a href="http://www.norman-network.com/sites/default/files/files/suspectListExchange/MassBankEU_Compounds_InChIKeys_11042017.txt">MassBankEUInChIKeys</a> (11/04/2017) => Updated to full listing from 17/06/2019</p> <p>https://github.com/MassBank/MassBank-data/issues/77#issuecomment-502700313</p> <p><a href="http://massbank.eu/MassBank/">www.massbank.eu</a><br> Stravs <em>et al.</em> 2013.<br> DOI: <a href="http://onlinelibrary.wiley.com/doi/10.1002/jms.3131/full">10.1002/jms.3131</a></p>
S53 | UFZWANATARG | Target Compounds from UFZ WANA
<p>This is the collection associated with list S53 UFZWANATARG on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>List of target compounds (LC and GC) measured at WANA, UFZ (Leipzig, Germany), provided by Tobias Schulze and Martin Krauss.</p>
Compound I Formation and Reactivity in Dimeric Chlorite Dismutase: Impact of pH and the Dynamics of the Catalytic Arginine
<ul> <li><strong>Data type</strong>: spectroscopic measurements (UV-visible, ECD, EPR), enzyme activity measurements, mass spectrometry, spectroelectrochemistry and data analysis.</li> <li>Files are in<strong> .DTA, .DSC, .xlsx, .m, .mat, .BSW, .csv, .txt, .dsx, .pdf, .uds </strong>formats</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements in <strong>DTA </strong>and<strong> DSC</strong> formats</li> <li>EPR spectroscopic simulation and analyses in <strong>m </strong>and<strong> mat</strong> format</li> <li>Analysis of Rapid Freeze-Quench calibration curve is in <strong>xlsx</strong> format</li> <li>UV-vis spectroscopic measurements in <strong>csv, xlsx, txt, uds and dsx</strong> format</li> <li>ECD measurements in <strong>csv, xlsx and dsx</strong> format</li> <li>Enzyme activity data in <strong>csv and xlsx</strong> format</li> <li>Mass spectrometry data in <strong>pdf and xlsx</strong> format</li> <li>Spectroelectrochemistry data in <strong>BSW</strong> and <strong>xlsx</strong> format</li> </ul> </li> <li>The data are <strong>generated</strong> by: <ul> <li>UV−vis spectra were recorded using a Cary 60 UV–vis spectrophotometer (Agilent) and a U-3900 spectrophotometer (Hitachi, Mannheim, Germany).</li> <li>Electronic circular dichroism spectroscopy was performed using Chirascan (Applied Photophysics, Leatherhead, UK).</li> <li>Rapid Freeze-Quench of EPR sample was performed with the use of a device from BioLogic (Grenoble, France), consisting of an SFM-2000 stopped-flow unit and an MPS-70 controller unit, combined with a freeze-quench sample collector adapted for EPR tubes. The ejected volumes and flow rate were controlled by the BioLogic BIOKINE software, v. 4.72.</li> <li>X-Band CW-EPR experiments were performed on X-band ELEXSYS E580 spectrometer (Bruker BioSpin GmbH) operating at a microwave frequency of ∼9.4 GHz and equipped with a standard TE102 cavity and a liquid He cryostat (Oxford Inc.)</li> <li>Enzyme activity was measured polarographically following the release of O<sub>2</sub> by using a Clark-type oxygen electrode (Oxygraph Plus; Hansatech Instruments, Norfolk, UK).</li> <li>Stopped-flow spectroscopy measurements were performed with a SX-18MV or Pi-star from Applied Photophysics using either a diode array detector or a monochromator and photomultiplier detector.</li> <li>Mass spectrometry analysis was performed with an ion-trap mass spectrometer (amaZon speed ETD, Bruker) equipped with the standard ESI source in positive ion, DDA mode (i.e., switching to MSMS mode for eluting peaks).</li> <li>All spectroelectrochemistry experiments were conducted in a homemade OTTLE (optical transparent thin-layer spectroelectrochemical) cell. In detail, the three-electrode configuration consisted of a gold minigrid working electrode (Buckbee-Mears, Chicago, IL), a homemade Ag/AgCl/KCl<sub>sat</sub> microreference electrode separated from the working solution by a Vycor set, and a platinum wire as the counter electrode. UV−vis spectra were recorded using a Varian Cary C50 spectrophotometer.</li> </ul> </li> </ul> <p> </p> <p> </p> <ul> <li>If the dataset includes multiple files that relate to each other: <ul> <li>Files in <strong>PARACAT_WP3_20230302_01_EPR </strong>folder includes EPR spectroscopic measurements and computer simulations/analyses, original data are in <strong>DTA/DSC</strong> formats; files in <strong>m</strong> format were used to process the data.</li> <li>Files in <strong>PARACAT_WP3_20230302_02_UVVIS </strong>folder includes sequential and non-sequential stopped flow data, measured with detection by photodiode array or monochromator (time traces): original data is in <strong>dsx and csv </strong>format<strong>, xlsx </strong>format contains processed data; conventional photometric data is originally in <strong>txt and uds </strong>format, <strong>xlsx </strong>format contains processed data</li> <li>Files in <strong>PARACAT_WP3_20230302_03_ECD</strong> folder contain ECD spectra: original data is in <strong>dsx and csv </strong>format<strong>, xlsx </strong>format contains processed data</li> <li>Files in <strong>PARACAT_WP3_20230302_04_Enzyme activity </strong>folder contain polarographically detected changes in dioxygen concentration, due to enzyme activity: original data is in <strong>csv </strong>format<strong>, xlsx </strong>format contains processed data</li> <li>Files in <strong>PARACAT_WP3_20230302_05_MassSpec </strong>folder contain mass spectrometry data for the MNP assay: original data is in <strong>pdf </strong>format<strong>, xlsx </strong>format contains processed data</li> <li>Files in <strong>PARACAT_WP3_20230302_06_Spectroelectrochemistry </strong>folder includes spectroelectrochemistry measurements and computer analyses, original data are in <strong>BSW</strong> formats; files in <strong>xlsx</strong> format were used to process the data.</li> </ul> </li> </ul> <p> </p> <p>NB. See the “READ ME” text file in each subfolder for more detailed information on files organization.</p> <p> </p> <ul> <li>Information on: <ul> <li><strong>Abbreviations:</strong> <ul> <li>, chlorite dismutase; <strong><em>C</em>Cld</strong>, chlorite dismutase from Cyanothece sp. PCC7425; <strong>CcP</strong>, cytochrome c peroxidase;<strong> DaCld</strong>, chlorite dismutase from <em>Dechloromonas aromatica</em>; <strong>E°′</strong>, standard reduction potential; <strong>ECD</strong>, electronic circular dichroism; <strong>EPR</strong>, electron paramagnetic resonance; <strong>HRP</strong>, horseradish peroxidase; <strong>LPO</strong>, lactoperoxidase; <strong>MNP</strong>, 2-methyl-2-nitrosopropane; <strong>MPO</strong>, myeloperoxidase; <strong>PAA</strong>, peracetic acid; <strong>RFQ</strong>, rapid freeze-quench.</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Units of measurement</strong>: <ul> <li>Concentration: <strong>mM</strong> (millimolar), <strong>µM</strong> (micromolar), <strong>nM</strong> (nanomolar), <strong>mg/mL</strong> (milligrams per milliliter)</li> <li>Molecular mass: <strong>Da</strong> (Dalton)</li> <li>Absorptivity: <strong>M<sup>-1</sup></strong> <strong>cm<sup>-1</sup></strong></li> <li>Volume: <strong>mL</strong> (milliliters), <strong>µL </strong>(microliters), <strong>nL</strong> (nanoliters)</li> <li>Wavelength:<strong> nm</strong> (nanometers)</li> <li>Temperature:<strong> °C</strong> (Celsius degrees), <strong>K</strong> (Kelvin degrees)</li> <li>Time:<strong> ms</strong> (milliseconds), <strong>s</strong> (seconds), <strong>min</strong> (minutes), <strong>h</strong> (hours),</li> <li>Ellipticity: millidegrees</li> <li>Frequency: <strong>GHz</strong> (gigahertz), <strong>kHz</strong> (kilohertz)</li> <li>Power: <strong>mW</strong> (milliwatt)</li> <li>Magnetic field: <strong>mT</strong> (milliTesla)</li> <li>Reduction potential: <strong>mV </strong>(milliVolts)</li> </ul> </li> </ul>
Contamination pattern and risk assessment of polar compounds in snow melt: an integrative proxy of road runoffs
<p><strong>Abstract</strong></p> <p>To assess the contamination and potential risk of snow melt with polar compounds, road and background snow was sampled during a melting event at 23 sites at the city of Leipzig and screened for more than 500 chemicals using LC-HRMS. Additionally, six 24 h composite samples were taken from the influent and effluent of the Leipzig WWTP during the snow melt event. 207 compounds were at least detected once (concentrations between 0.80 ng/L and 75 µg/L). A toxic unit-based assessment was performed to investigate the risk of adverse environmental effects in the receiving water.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset contains the list of sampling points, the target compounds, the chemical findings, the results of the toxic unit assessment, the underlying ecotoxicity data, and the estimated compound removal rates in WWTP. The data is provided in xlsx and ods formats.</p>
Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis.
<p>This dataset supports the version 2 of the paper entitled <em>Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis :</em></p> <p><em>Hubas, Cédric; Gaubert-Boussarie, Julie; D’Hondt, An-Sofie; Jesus, Bruno; Lamy, Dominique; Meleder, Vona; Prins, Antoine; Rosa, Philippe; Stock, Willem; Sabbe, Koen. Identification of microbial exopolymer producers in sandy and muddy intertidal sediments by compound-specific isotope analysis. Peer Community Journal, Volume 3 (2023), article no. e104. doi : <a href="https://doi.org/10.24072/pcjournal.336">10.24072/pcjournal.336</a>. <a href="https://peercommunityjournal.org/articles/10.24072/pcjournal.336/">https://peercommunityjournal.org/articles/10.24072/pcjournal.336/</a></em></p>
S111 | PMTPFAS | Fluorine-containing Compounds in PMT Suspect Lists
<p>This is the collection associated with list S111 | PMTPFAS | Fluorine-containing Compounds in PMT Suspect Lists on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>PMTPFAS is a list of fluorine-containing compounds extracted from existing suspect lists for PMT (persistent, mobile, toxic) compounds, currently S36 UBAPMT, S82 EAWAGPMT and S84 UFZHSFPMT. All entries contain fluorine but are not necessarily PFAS. Two salt entries were replaced with the F-containing parts only.</p>
Differences in Chemo-signaling Compound-Evoked Brain Activity in Male and Female Young Adults: A Pilot Study in the Role of Sexual Dimorphism in Olfactory Chemo-Signaling
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