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377 results for “Mass spectrometry”
Estimation of sulfuric acid concentrations using ambient ion composition and concentration data obtained by ion mass spectrometry measurements (APi-TOF)
<p>The dataset has been used to estimate the sulfuric acid concentration from APi-TOF data. Using the bisulfate ion, the sulfuric acid molecule clustered with the bisulfate ion (dimer) and the trimer, we were able to determine the daytime concentration of sulfuric acid with the concentration of ambient ions, including the condensation sink, ion-ion recombination and collision rate of HSO4- with H2SO4.</p>
Mass spectrometry proteomics data obtained from analysis of the secretome of Anisakis simplex (sensu stricto) L3 larvae.
<p>Mass spectrometry proteomics data obtained from analysis of the secretome of <em>Anisakis simplex</em> (sensu stricto) L3 larvae.</p>
Supplementary Movies: "Probing the dynamics of Streptococcus pyogenes Cas9 endonuclease bound to sgRNA complex using hydrogen-deuterium exchange mass spectrometry"
<p>Movies related to the article "Probing the dynamics of Streptococcus pyogenes Cas9 endonuclease bound to sgRNA complex using hydrogen-deuterium exchange mass spectrometry" in the International Journal of Molecular Science. "MD_Movie_Cas9_sgRNA_DNA" is the video of SpCas9-sgRNA-DNA complex behavior during 50 ns molecular dynamics simulation. In this movie, SpCas9 protein domains are shown in the following colors: REC lobe (green), CTD (blue), RuvC (pink), L-I-II (yellow), Arg (violet), and HNH (orange). RNA presented in cyan, and DNA -in dark blue.</p> <p>Movies "SpCas9_HeatMap" and "SpCas9-sgRNA_HeatMap" show the hydrogen exchange levels superimposed onto the protein structure obtained from MD trajectories. Relative uptake level presented at the time points of 10 s, 30 s, 1 min, 2 min, 5 min, 10 min, 30 min, 60 min, 120 min, 240 min, 360 min, and 480 min. The exchange scale is shown in a rainbow color scheme, where blue corresponds to the minimum uptake, while red corresponds to the highest observed uptake.</p>
Result files (ONLYSTEREO): "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"
<p>Result files associated with the publication: "<strong>Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data</strong>" by Bach et al.</p> <p>The following files are included in the archive:</p> <ul> <li>Raw max-marginal predictions using LC-MS²Struct for all LC-MS² experiments of the ONLYSTEREO setup</li> <li>Averaged max-marginals for the LC-MS²Struct over all SSVM models</li> <li>Ranks for the ground-truth structures predicted by Only MS² and LC-MS²Struct (molecule class analysis)</li> </ul> <p>Instructions:</p> <ul> <li>clone the repository containing the experimental scripts and analysis notebooks: <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp">https://github.com/aalto-ics-kepaco/lcms2struct_exp</a></li> <li>download the archive in this repository</li> <li>unpack the archive in the git-repository root directory</li> <li>follow the instructions given in the <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp/blob/main/README.md">README.md</a> of the git-repository to reproduce the figures, etc.</li> </ul>
Result files (ALLDATA): "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data with LC-MS²Struct"
<p>Result files associated with the publication: "<strong>Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data</strong>" by Bach et al.</p> <p>The following files are included in the archive:</p> <ul> <li>Raw max-marginal predictions using LC-MS²Struct for all LC-MS² experiments of the ALLDATA setup</li> <li>Averaged max-marginals for the LC-MS²Struct over all SSVM models</li> <li>Top-k accuracies for the comparison methods (MS²+RT, ...)</li> <li>Ranks for the ground-truth structures predicted by Only MS² and LC-MS²Struct (molecule class analysis)</li> </ul> <p>Instructions:</p> <ul> <li>clone the repository containing the experimental scripts and analysis notebooks: <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp">https://github.com/aalto-ics-kepaco/lcms2struct_exp</a></li> <li>download the archive in this repository</li> <li>unpack the archive in the git-repository root directory</li> <li>follow the instructions given in the <a href="https://github.com/aalto-ics-kepaco/lcms2struct_exp/blob/main/README.md">README.md</a> of the git-repository to reproduce the figures, etc.</li> </ul> <p>Version history:</p> <ul> <li><strong>Version 1</strong>: Experimental results for "Method comparison" and "Molecule classification analysis" where performed with <strong>2D fingerprints</strong> (<a href="https://www.biorxiv.org/content/10.1101/2022.02.11.480137v1">preprint v1</a>)</li> <li><strong>Version 2</strong> <em>(this version)</em>: Experimental results for "Method comparison" and "Molecule classification analysis" where performed with <strong>3D fingerprints</strong></li> </ul>
Dataset for the paper "Real-Time in Situ Monitoring of CO2 Electroreduction in the Liquid and Gas Phases by Coupled Mass Spectrometry and Localized Electrochemistry" DOI: 10.1021/acscatal.2c00609
<p>The data in the attached spreadsheet was used to produce the figures in the paper</p> <p>Authors:Guohui Zhang, Youxin Cui, Anthony Kucernak</p> <p>Title:Real-Time in Situ Monitoring of CO2 Electroreduction in the Liquid and Gas Phases by Coupled Mass Spectrometry and Localized Electrochemistry</p> <p>Journal:ACS Catalysis</p> <p>DOI:10.1021/acscatal.2c00609</p> <p>Please cite the above reference if you wish to use this data</p> <p>DOI of data:10.5281/zenodo.6526651</p>
Highly time-resolved chemical speciation and source apportionment of organic aerosol components in Delhi, India, using extractive electrospray ionization mass spectrometry
<p>In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution,<br> especially during winters. Comprehensive knowledge of the composition and sources of the organic aerosol<br> (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating<br> effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary<br> OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA<br> sources. We address the latter through the first field deployment of an extractive electrospray ionization timeof-<br> flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer<br> (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF)<br> was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded<br> three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning<br> OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LOOOA).<br> On average, 40%of the total OA mass was apportioned to the secondary factors. The SOA contribution to<br> total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %,<br> 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA<br> sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary<br> biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin:<br> aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in<br> the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the<br> total AMS SOA (i.e. MO-OOA C LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major<br> SOA component during the daytime, with a 55.2% contribution to total SOA mass (42.4% contribution to total<br> OA). Its contribution to total SOA, however, decreased to 25.4% (7.9% of total OA) during the nighttime. This<br> factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA<br> accounted for 18.4% of total SOA mass (14.2% of total OA) during the daytime and 36.1% of total SOA mass<br> (11.2% of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2%<br> and 11.0% of total SOA mass (11.7% and 8.5% of total OA mass), respectively, during the daytime and 15.4%<br> and 22.9% of total SOA mass (4.8% and 7.1% of total OA mass), respectively, during the nighttime. A simple<br> dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime<br> concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged<br> biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production,<br> likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was<br> related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study<br> show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass<br> burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction.<br> Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the<br> daytime suggests an increased OA toxicity and health-related consequences for the general public.</p> <p>This repository has excel file corresponding to each figures presented in main text published version.</p>
Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)
<p><strong>Proteomic Profiling Dataset for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)</strong></p>
Native mass spectrometry and structural studies reveal modulation of MsbA-nucleotide interactions by lipids
<p>The native MS data for paper <strong>"Native mass spectrometry and structural studies reveal modulation of MsbA-nucleotide interactions by lipids"</strong></p>
Data set for the figures in the manuscript "Real-Time Identification of Aerosol-Phase Carboxylic Acid Production Using Extractive Electrospray Ionization Mass Spectrometry"
Open the record for dataset details and reuse information.
Dataset for the optimization and validation of a gas chromatography-mass spectrometry method to analyze acetate, propionate and butyrate in the systemic circulation.
<p>This dataset contains data about the optimization and validation of a gas chromatography method to analyze acetate, propionate and butyrate in blood. Validation parameters include linearity, precision, accuracy and recovery. The method's applicability was demonstrated with the analysis of the short-chain fatty acids in human blood samples that were collected in a dietary intervention study.</p>
Anion exchange chromatography-mass spectrometry to characterize proteoforms of alpha-1-acid glycoprotein during and after pregnancy
<p><span>This data repository contains all previously unpublished raw data files for the manuscript "Anion exchange chromatography-mass spectrometry to characterize proteoforms of alpha-1-acid glycoprotein during and after pregnancy"</span></p>
Tandem Mass Spectrometry Dataset for Machine Learning in Metabolomics
<p>This dataset contains tandem mass spectrometry data cleaned and processed from the publicly available GNPS Spectral Library. We aim to continuously update this dataset with new data points as the spectral libraries expand.</p>
Systematic discovery of subcellular RNA patterns in the gut epithelium - Mass spectrometry data
<p>This collection contains the raw data created from the mass spectrometry experiment and the spectral counts that were used in the manuscript for protein abundance comparisons.</p>
Mass Spectrometry Data
<p>Proteomics and Phosphoproteomics data from <span>DOI: 10.1016/j.cell.2024.05.025</span></p>
Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation - ESI
<p>ESI data for the paper 'Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation'.</p>
Figure 1 in Quantitative phosphoproteomic analysis of chicken DF-1 cells infected with Eimeria tenella, using tandem mass tag (TMT) and parallel reaction monitoring (PRM) mass spectrometry
Figure 1. Proportion of serine, threonine, and tyrosine in phosphorylation sites.
USP5 Expression and Purification & Development of a Mass Spectrometry USP5 Catalytic Activity Assay
<p>Expression and purification of full length USP5<sup>1-835</sup> for optimization of a mass spectrometry (MS) based assay to detect polyubiquitin cleavage</p>
Mass spectrometry analysis of contaminating band in HTT samples 2019/10/31
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Mass spectrometry analysis of contaminating band in HTT samples</p> <p><strong>Date completed:­ </strong>2019/10/31</p> <p><strong>Rationale: </strong>To determine the identity of ~100 kDa band seen on SDS-PAGE in HTT preps – see <a href="https://zenodo.org/record/3555378">https://zenodo.org/record/3555378</a></p>
Purification of HTT Q23 and Q54 from EXPI293F for mass spectrometry analysis 2019/09/30
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Purification of Q23 and Q54 HTT from EXPI293F</p> <p><strong>Date completed:­ </strong>2019/09/30</p> <p><strong>Rationale: </strong>Purify HTT Q23 and Q54 for mass spectrometry analysis</p>
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OpenNeuro
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