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2,394 results for “ms”
Dataset: A New Approach to Scribal Abbreviation in the Bestiaire in Merton College Library, MS 249
<p>An accompanying dataset for analysis of scribal abbreviation in Oxford, Merton College Library, MS 249, ff. 1<sup>r</sup>-10<sup>v</sup>.</p>
Data analysis of LiP-MS data for high-throughput applications
<p>Proteins regulate biological processes by changing their structure or abundance to accomplish a specific function. In response to any perturbation or stimulus, protein structure may be altered by a variety of molecular events, such as post translational modifications, protein-protein interactions, aggregation, allostery, or binding to other molecules. The ability to probe these structural changes in thousands of proteins simultaneously in cells or tissues can provide valuable information about the functional state of a variety of biological processes and pathways. Here we present an updated protocol for LiP-MS, a proteomics technique combining limited proteolysis with mass spectrometry, to detect protein structural alterations in complex backgrounds and on a proteome-wide scale (Cappelletti et al., 2021; Piazza et al., 2020; Schopper et al., 2017). We describe advances in the throughput and robustness of the LiP-MS workflow and implementation of data-independent acquisition (DIA) based mass spectrometry, which together achieve high reproducibility and sensitivity, even on large sample sizes. In addition, we introduce MSstatsLiP, an R package dedicated to the analysis of LiP-MS data for the identification of structurally altered peptides and differentially abundant proteins. Altogether, the newly proposed improvements expand the adaptability of the method and allow for its wide use in systematic functional proteomic studies and translational applications. </p>
EMERGE 2016 Autochamber Sites LC-MS
METHODS:<br> Water soluble metabolites were extracted from peat by adding 7 mL of autoclaved milliQ water to 1g of peat in a sterile 15 mL Eppendorf tube. Tubes were vortexed twice for 30 seconds, and then the peat-water mixture was sonicated for 2 hours at 22˚C. Samples were then centrifuged to separate the supernatant, which served as the water extract. <br> <br> Water extracted metabolites were thawed at room temperature and centrifuged again to remove any potential particles that formed after thawing. Next, each sample was split into two 2ml glass tube vials (1 ml each), one for hydrophilic interaction liquid chromatography (HILIC) and the other for reverse-phase (RP) liquid chromatography. Samples in both vials were then dried down completely on a Vacufuge plus (Eppendorf, USA). Samples were resuspended in a solution of 50% Acetonitrile and 50% water for HILIC and a solution of 80% water and 20% HPLC grade methanol for RP.<br><br>A Thermo Scientific Vanquish Duo ultra-high performance liquid chromatography system (UHPLC) was used for the liquid chromatography step. Extracts were separated using a Waters ACQUITY HSS T3 C18 column for RP separation and a Waters ACQUITY BEH amide column for HILIC separation.<br><br>Samples were injected in a 1 μL volume on column and eluted as follows: for RP the gradient went from 99% mobile phase A (0.1% formic acid in H2O) to 95% mobile phase B (0.1% formic acid in methanol) over 16 minutes. For HILIC the gradient went from 99% mobile phase A (0.1% formic acid, 10 mM ammonium acetate, 90% acetonitrile, 10% H¬2O) to 95% mobile phase B (0.1% formic acid, 10 mM ammonium acetate, 50% acetonitrile, 50% H2O). Both columns were run at 45 °C with a flowrate of 300 μL/min.<br>A Thermo Scientific Orbitrap Exploris 480 was used for spectral data collection with a spray voltage of 3500 V for positive mode (for RP) and 2500 V for negative mode (for HILIC) using the H-ESI source. The ion transfer tube and vaporizer temperature were both 350 °C. Compounds were fragmented using data-dependent MS/MS with HCD collision energies of 20, 40, and 80.<br><br>The Compound Discoverer 3.2 software (Thermo Fisher Scientific) was used to analyze the data using the untargeted metabolomics workflow. Briefly, the spectra were first aligned followed by a peak picking step. Putative elemental compositions of unknown compounds were predicted using the exact mass, isotopic pattern, fine isotopic pattern, and MS/MS data using the built in HighChem Fragmentation Library of reference fragmentation mechanisms. Metabolite annotation was performed using spectral libraries and compound databases. First, fragmentation scans (MS2) searches in mzCloud were performed , which is a curated database of MSn spectra containing more than 9 million spectra and 20000 compounds.<br><br> Second, predicted compositions were obtained based on mass error, matched isotopes, missing number of matched fragments, spectral similarity score (calculated by matching theoretical and measured isotope pattern), matched intensity percentage of the theoretical pattern, the relevant portion of MS, and the MS/MS scan. The mass tolerance used for estimating predicted composition was 5 ppm. Finally, annotation was complemented by searching MS1 scans on different online databases with ChemSpider (using either the exact mass or the predicted formula). Based on the annotation results, metabolites were divided into three categories: 1) full match on the three methods used (mzCloud, predicted composition, and ChemSpider), 2) full match by two methods (Predicted composition and ChemSpider) and 3) annotated only by one method (ChemSpider).<br><br><br>COLUMN DEFINITIONS:<br>For both files: <br>Columns A-N : Annotation information<br>Columns O-P: KEGG pathway using molecular formula<br>Columns S-AW : Normalized peak areas per sample<br><br>FUNDING:<br>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0010580 and DE-SC0016440.
Data for manuscript, "An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples"
<p>Clinical BALF samples are rich in biomolecules, including proteins, and useful for molecular studies of lung health and disease. However, MS based proteomic analysis of BALF is impeded by the dynamic range of protein abundance, and potential for interfering contaminants. We have developed a workflow that eliminates these challenges. By combining high abundance protein depletion, protein trapping, clean-up, and in-situ tryptic digestion, our workflow is compatible with both qualitative and quantitative MS-based proteomic analysis. The workflow includes collection of endogenous peptides for peptidomic analysis of BALF, if desired, as well as amenability to offline semi-preparative or microscale fractionation of peptide mixtures prior to LC-MS/MS analysis, for increased depth of analysis. We show the effectiveness of this workflow on BALF samples from COPD patients. Overall, our workflow should allow MS-based proteomics to be applied to a wide variety of studies focused on BALF clinical samples. </p> <p>Note: Due to the nature of some of the files, file <em>wendt005_ostr0103_18260_20210831_BALF_FAIMS_MS2_TMT16.msf, wendt005_ostr0103_18976_20230202_quantReport.msf, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_1R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_2R.raw, cmsptc_higgi022_18988_20230203_18976DW_EnF_hcdlT_3R.raw and cmsptc_higgi022_18988_20230203_18976DW_Eclipse_noFAIMS_quantReport.msf</em> were zipped into compressed folders before uploading.</p>
Validation of highly sensitive method based on UHPLC-ESI-MS/MS for the quantification of progestogens and androgens in plant material.
<p>We prepared a highly sensitive method for the quantification of progestogens and androgens in plant materials. This method is based on UHPLC-ESI-MS/MS. We show here the data used for the method validation. This includes the determination of linearity, recovery, precision, limits of detection and limits of quantification. </p> <p>The general procedure can be found in the txt or pdf file. </p> <p>The resulting data are collected in the excel file and can be found in the csv files, additonally.</p>
MALDI-TOF-MS spectra of archaeological bone fragments from Klipdrift Shelter (South Africa) for ZooMS
<p>MALDI-TOF-MS spectra for archaeological bone fragments from Klipdrift Shelter (South Africa). All spectra are uploaded in .mzml format.</p>
Database for accurate EI-MS spectra for volatile compounds identified in e-liquids for e-cigarettes
<p>Database of accurate EI-MS spectra, recorded using a GC-EI-TOF-MS instrument, for volatile and semi-volatile compounds tentatively identified in diluted e-liquids for e-smoking. Tentative identifications were based on normalized spectral matches above 75 (0-100 scale) with the low resolution NIST17 database, linear retention index differences between 50 units compared to literature values for semipolar (HP5-type) and Carbowax columns, and mass errors lower than 50 ppm for a minimum of two relevant ions in the spectra of each compound.</p> <p>The database can be used in combination with Library Editor software, included in MassHunter software. Experimental spectra were obtained after spectral deconvolution of GC-MS records for 1:100 diluted commercially available e-liquids</p>
Raw SP-ICP-MS data for the publication "Vertical distribution of inorganic nanoparticles in a Norwegian fjord"
<p>The compressed archive contains SP-ICP-MS data to reproduce results for the paper "Vertical distribution of inorganic nanoparticles in a Norwegian fjord"". F4 in the sample name denotes the outer station, F2 denotes the inner station. The suffix integer (1-6) indicates the depth position, lowest numbers nearest bottom. The letter A denotes acidified sampling, N neutral sampling. The data consists of integer intensity versus time data in .csv-format.</p> <p>The remaining data to produce all other results, visualizations and statistics for the paper is included in the supplementary or GitHub. Associated code is deposited in a GitHub repository, github.com/arebruvold/fordefjorden_distribution. Note that these data may have different licenses:</p> <ul> <li>The excel file "total_metals.xlsx" contains the total metal data.</li> <li>"sjovann_partikler_noytral.xls" and "sjovann_minste_partikler_2.xls" contains quantitative SEM-EDS data.</li> <li>"204473_F4.txt" and "204473_F2.txt" contains CTD data for salinity and temperature.</li> <li>"dens_comps_large.csv" and "dens_comps_large.csv" contain the particle compositions used for particle size calculations.</li> </ul>
DBsimilarity of Natural Products to aid compound identification on MS and NMR pipelines, similarity networking and more
<p>DBsimilarity is proposed for organizing structure databases into Similarity Networks to assist researchers with analyzing and making sense of chemical data available. It converts SDF files into CSV files when needed, adds chemoinformatics data, constructs a MZMine custom database file and a NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities, and constructs CSV files for Similarity Networks using Cytoscape. DBsimilarity aims to bridge chemoinformatics to laboratory-focused natural products researchers and students. </p>
Data for ms. Dairy cattle welfare – the relative effect of legislation, industry standards and labelled niche production in five European countries
<p>Repository R 1: Scores on dimension values and weight on dimension from 38 international experts. </p> <p>Repository R2: Country Benchmark scores from 38 international experts.</p>
assessment of aldehydes to PTR-MS m/z 69 in indoor air measurements - data set
<ul> <li>contact: Lisa Ernle (lisa.ernle@mpic.de), Nijing Wang (nijing.wang@mpic.de), Jonathan Williams (jonathan.williams@mpic.de)</li> <li>instruments: fast GC-MS SOFIA (MPIC), PTR-ToF-MS 8000 (Ionicon)</li> <li>merged dataset</li> <li>calibrated with VOC standard gas mix (Apel-Riemer Environmental Inc., Colorado, USA)</li> <li>units (filename): <ul> <li>normalized counts per second [ncps] (20210426_p_ncps.txt, bar_mean.txt, bar_std.txt)</li> <li>parts per billion [ppb] (all_sub_20210426_ppb.txt)</li> </ul> </li> <li>for information concerning updated versions, please see ReadMe.txt</li> </ul>
Fluxes of the protonated masses from the soil samples collected from two temperate ecosystems detected by PTR-ToF-MS
<p>Volatile organic compounds (VOCs) are reactive gaseous compounds with significant impacts on air quality and the Earth's radiative balance. While natural ecosystems are known to be major sources of VOCs, primarily due to vegetation, soils, an important component of these ecosystems, have received relatively less attention as potential sources and sinks of VOCs.</p> <p>In this study, soil samples were collected from two temperate ecosystems: a beech forest and a heather heath, and then sieved, homogenized, and incubated under various controlled conditions such as different temperatures, oxic <em>vs</em>. anoxic conditions, and different ambient VOC levels. A dynamic flow-through system coupled to a proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) was used to measure production and/or uptake rates of selected VOCs, aiming to explore the processes and their controlling mechanisms.</p> <p>This dataset therefore is collected from these experiments. It includes the raw flux data and figure source data associated with a peer-reviewed publication in Soil Biology & Biochemistry at <a href="https://doi.org/10.1016/j.soilbio.2023.109153">https://doi.org/10.1016/j.soilbio.2023.109153</a>.</p> <p>Overall, our results showed that these soils were natural sources of a variety of VOCs, and the strength and profile of these emissions were influenced by soil biogeochemical properties (e.g. moisture, soil organic matter), oxic/anoxic conditions, and temperature. The soils also acted as sinks for most VOCs when VOC substrates at parts per billions levels (ranging between 0.18-68.65 ppb) were supplied to the headspace of the enclosed soils, and the size of the sink corresponded to the amount of VOCs available in the ambient air. Temperature-controlled incubations and glass bead simulations indicated that the uptake of VOCs by soils was likely driven by microbial metabolism, with a minor contribution from physical adsorption to soil particles. In conclusion, our study suggests that soil uptake of VOCs can mitigate the impact of other significant VOC sources in the near-surface environment and potentially regulate the net exchange of these trace gases in ecosystems.</p> <p>Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or Prof. Rinnan at riikkar@bio.ku.dk</p>
Full-scale MBR coupled with PAC for the removal of 232 OMPs: operating conditions, conventional pollutants, concentration of OMPs, UHPLC-QTOF-MS analysis
<p>The spreadsheet contains data regarding the operation and performance of a full-scale MBR coupled with powdered activated carbon (PAC) added inside the reactor. This hybrid system is chosen to evaluate the removal of 232 organic micropollutants (OMPs) and the potential enhancement in the removal efficiencies with the addition of PAC at a concentration of 0.1 g/L and 0.2 g/L</p><ul><li>First worksheet contains minimum, maximum, and average concentrations of conventional pollutants (COD, BOD, SST, SSV, DOC, UV254, nitrogen compounds, phosphorous, <i>E. coli</i>) in the influent and effluent of the WWTP. Methodologies adopted are also reported.</li><li>Second worksheet reports the operating conditions of the full-scale MBR as well as information about the tubular UF membranes installed. Characteristics of the PAC purchased to perform the experiments regarding the removal of OMPs are also described.</li><li>Third worksheet reports minimum, maximum and average concentration of 232 OMPs in the influent and effluent during:<ol><li>The monitoring period considering only the MBR</li><li>The experimental periods where PAC is added and maintained at a concentration of 0.1g/L and 0.2g/L inside the MBR</li></ol></li><li>Fourth worksheet contains metadata regarding the UHPLC–QTOF–MS analysis performed to evaluate the occurrence of OMPs in the influent and effluent of the WWTP. Sampling, storage and sample preparation is described, followed to LC-ESI-tandem MS analysis. LOD and LOQ are reported.</li></ul>
MS Study Evaluating Safety and Efficacy of Two Doses of Fingolimod Versus Copaxone
ClinicalTrials.gov study NCT01633112. IPD Sharing: YES. Countries: 7. Publications: 1.
Data from: Age estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease
Open the record for dataset details and reuse information.
Age estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: An exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM)
Open the record for dataset details and reuse information.
MS data set: Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data
<p>Data set consisting of raw LC-MS2 data, LC-MS1 peak data and a description</p> <p>For unreviewed publication preprint: <strong>Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS<sup>1</sup>) and <em>in silico </em>Peptide Mass Data</strong></p> <p>ABSTRACT</p> <p>Over the past decade, modern methods of mass spectrometry (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. While MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem mass spectrometry (LC-MS<sup>2</sup>) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be very time-consuming, especially if the experimental LC-MS<sup>2</sup> data are tested against sequence databases covering a broad panel of different microbiological taxa.</p> <p>In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. MS<sup>1</sup> data are then extracted and systematically tested against an in silico library of peptide mass data compiled in house. The library has been computed from the UniProt Knowledgebase Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from spectral distances between experimental and in silico peptide mass data and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient – less than two minutes per sample - and has been successfully tested by a set of 19 different microbial pathogens. The approach is rapid, accurate and automatable and holds great potential for future microbiological applications.</p> <p><em>For details see the following preprint: Lasch, P. Schneider, A. Blumenscheit, C. and Doellinger, J. “Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in silico Peptide Mass Data”. bioRxiv preprint, http://dx.doi.org/10.1101/870089</em></p> <p> </p>
UPLC-MS/MS Method for quantitation of the recently FDA approved combination of vaborbactam and meropenem in human plasma
A parenteral medical combination containing vaborbactam (VBR) and meropenem (MRP) is used mainly to treat complicated urinary tract infections. A novel ultra-performance liquid chromatography (UPLC)- tandem mass spectrometric (MS/MS) method was developed for the sensitive determination of both compounds in human plasma. Sample preparation was performed by precipitation technique. The chromatographic separation was accomplished using the Acquity C18-BEH column, 0.01 M ammonium formate: acetonitrile (47:53, v/v) as a mobile phase with a flow rate of 0.2 mL/min. Analytes were monitored by applying multiple reaction monitoring (MRM). The bioanalytical validation criteria were conducted following the FDA recommendations. The method was linear within range 0.5 to 50 µg/mL, for both drugs. The intra-day and inter-day precision, as coefficient variation (%CV) and the accuracy, as % bias not exceed than 15% for both drugs. The percentage recovery of targeted analytes was not less than 77 %, calculated at three QC levels. The proposed method showed a suitable LLOQ value of 0.5 µg/mL for both analytes, which is far lower than the expected Cmax, which permits the use of this method for pharmacokinetic studies. The proposed method proved to be useful for the evaluation of this combination in both human plasma and pharmaceutical formulation.
MALDI-TOF-MS archaeological spectra and for African bovid collagen for Zooarchaeology by Mass Spectrometry (ZooMS) from Zambia
<p>The MALDI data for archaeological samples from Zambia. The spectra are all in the folder in .mzml format. The samples are labeled the same as in the corresponding manuscript. The modern African bovid spectra that were used to determine markers can be found at Zenodo doi:10.5281/zenodo.3964709.</p>
Data from: Comparison of rapid biodiversity assessment of meiobenthos using MALDI-TOF MS and metabarcoding
<p>Nowadays, most biodiversity assessments involving meiofauna are mainly carried out using very time-consuming, specimen-wise morphological identifications, which demands comprehensive taxonomic knowledge. Animals have to be examined for minor differences of setae compositions, mouthpart morphology or number of segments for various extremities. DNA-based methods such as metabarcoding as well as recently emerged rapid analyses using MALDI-TOF mass spectrometry to identify specimens based on a proteome fingerprint could vastly accelerate the process of specimen identification in biodiversity assessments. However, these techniques depend on reference libraries to connect collected data to morphologically described species. In this study the success rate of both approaches have been tested based on reference libraries constructed using part of the samples from a new study area to identify unknown samples. Using MALDI-TOF MS we found, that species which do not exist in an incomplete mass spectra reference library only have minor impact on the results, when employing a post hoc test for Random Forest classifications. This test reveals specimens that demand morphological re-examination for the final species assignment. Metabarcoding however strongly demands a rich reference library to provide correct MOTU assessments in congruence with morphological determination. Nevertheless, with a complete library and a suitable data transformation [herein log(x + 1)], the number of reads per MOTU reflects relative species abundances in metabarcoding inference. The results of this study facilitate specimen identification by using MALDI-TOF MS, which is incomparably cheap for specimen-by specimen identification, but when it comes to sample-wise analyses, metabarcoding outperforms other techniques by far.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.