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493 results for “LC”
Fingerprint Matrix Files for "Machine Learning-based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics"
<p>These files are the necessary dataset to reproduce the observed machine learning metrics in the paper "Machine Learning-based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics" in review at the Journal of Natural Products. </p> <ul> <li>Multiclassifier_23_Drug_Class_Train-Test_Fingerprint_Matrix.tsv is the accumulated positive training set for the 23 different classes demonstrated in the training and testing sets.</li> <li>Negative_Train-Test_Fingerprint_Matrix.tsv is the negatives training and testing examples derived from the RIKEN NP Depo which represent a diverse set of natural product compounds that serve as the counter points to the positive examples.</li> <li>GNPS_23_Drug_Class_Fingerprints_Matrix.tsv is the dataset of fingerprints generated from the publically available GNPS MSMS dataset. These training examples serve to confirm the ability of the machine learning model to generalize to experimental data. </li> <li> Negative_Train-Test_Fingerprint_Matrix.tsv is the dataset of negative training examples derived from the publically available spectra from the GNPS dataset. It is composed of nearly 2,800 random MSMS spectra to compose a diverse negative evaluation set. </li> <li>Random_GNPS_Fingerprints.tsv is the dataset of fingeprints of 9,443 random spectra from GNPS used to evaluate the false positive rate of each model.</li> </ul>
Data analysis of an LC-MS dataset from a human urine biofluid cohort study
<p>Supplementary dataset and tutorials for the "<strong>Statistical analysis in metabolic phenotyping"</strong></p> <p> </p> <p>This repository contains Jupyter Notebooks with two examplar metabolomic data analysis workflows, applied to a liquid chromatography mass spectrometry dataset (LC-MS). The LC-MS dataset used comes from a metabolic phenotyping investigation of human urine biofluid samples from a dementia cohort. In this sample set, baseline spot urine samples (first sample collected after recruitment to the study) were collected as part of the AddNeuroMed<sup>1</sup> and ART/DCR study consortia, with the aim of identifying biomarkers of neurocognitive decline and Alzheimer’s disease. These samples were analysed by LC-MS and <sup>1</sup>H NMR, using the methods described by Lewis <em>et al</em><sup>2</sup> and Dona <em>et al</em>. Detailed information about this cohort and other available phenotypic measurements can be found in Lovestone and the ANMERGE<sup>3</sup> repository, which can be accessed via the Sage BioNetworks portal (<a href="https://doi.org/10.7303/syn22252881">https://doi.org/10.7303/syn22252881</a>). Information about the metabolic profiling experiments can be found in the study's MetaboLights entry: <a href="https://www.ebi.ac.uk/metabolights/MTBLS719">https://www.ebi.ac.uk/metabolights/MTBLS719</a>.</p> <p> </p> <p>1. Lovestone, S. <em>et al.</em> AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. in <em>Annals of the New York Academy of Sciences</em> (2009). doi:10.1111/j.1749-6632.2009.05064.x</p> <p>2. Lewis, M. R. <em>et al.</em> Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. <em>Anal. Chem.</em> <strong>88</strong>, acs.analchem.6b01481 (2016).</p> <p>3. Birkenbihl, C. <em>et al.</em> ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset. <em>medRxiv</em> (2020). doi:10.1101/2020.08.04.20168229</p>
Proteomic data (LC-MS/MS) of human plasma samples
<p>LC-MS/MS analysis of 8 different samples of plasma: 4 samples correspond to the activated platelet-rich plasma (PRP) fractions from 4 different patients with infertility due to Asherman's syndrome and/or endometrial atrophy; 2 samples correspond to the activated and not-activated, respectively, PRP fractions from a control fertile patient; 2 samples correspond to the activated and not-activated, respectively, fractions from a commercial umbilical cord plasma.</p>
Text-fig. 1. Dental nomenclature. Peignecyon felinoides n. gen. et n. sp. a) TU 73916 upper right M1 in occlusal view; b) TU 7391147 lower right m1, b1) lingual view, b2) occlusal view. Abbreviations: bde, basal distobuccal expansion*; bc, buccal cingulum; Ec, ectoflexus; dc, distal cingulum; dlc, distolingual cristid; end, entoconid; hyd, hypoconid; lc, lingual cingulum; ME, metacone; MEC, metaconule; mc, mesial cingulum; med, metaconid; mlc, mesiolingual cristid; mpc, mesial protoconid cristid; MS, metastyle; PA, paracone; PAC, paraconule; pad, paraconid; PR, protocone; prd, protoconid; PS, parastyle. *Not developed in Peignecyon felinoides. in A New Thaumastocyoninae (Amphicyonidae, Carnivora) From The Early Miocene Of Tuchořice, The Czech Republic
Text-fig. 1. Dental nomenclature. Peignecyon felinoides n. gen. et n. sp. a) TU 73916 upper right M1 in occlusal view; b) TU 7391147 lower right m1, b1) lingual view, b2) occlusal view. Abbreviations: bde, basal distobuccal expansion*; bc, buccal cingulum; Ec, ectoflexus; dc, distal cingulum; dlc, distolingual cristid; end, entoconid; hyd, hypoconid; lc, lingual cingulum; ME, metacone; MEC, metaconule; mc, mesial cingulum; med, metaconid; mlc, mesiolingual cristid; mpc, mesial protoconid cristid; MS, metastyle; PA, paracone; PAC, paraconule; pad, paraconid; PR, protocone; prd, protoconid; PS, parastyle. *Not developed in Peignecyon felinoides.
LC-Evolution
<p>Code and data for the paper "Spatiotemporal graph-based analysis of land cover evolution using remote sensing time series data".</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.
ADCP data collected in the Southern California Bight by SWIFT drifters as part of the ONR "Langmuir Circulation Departmental Research Initiative (LC-DRI)"
<p>This is the public archive for ADCP data collected with SWIFT drifters during the 'Langmuir Circulation' Office of Naval Research Departmental Research Initiative (LC-DRI) field experiment, conducted between March 19th and April 6th, 2017 in the Southern California Bight 40 km west of Catalina Island. SWIFTs were deployed and recovered from the R/V R.G. Sproul during cruise SP1709 (Cruise DOI: 10.7284/907464). SWIFTs were deployed during storms with wind speeds up to 20 m/s and sampled strong diurnal warm layers during weaker wind periods.</p>
A global LC-MS2-based methodology to identify and quantify anionic phospholipids in plant samples
<p>This table contains peaks aera values from LC-MS used to develop a method for identification and quantification of anionic lipid in plant sample: Genva et al. 2023: “A global LC-MS2-based methodology to identify and quantify anionic phospholipids in plant samples”</p>
SWIFT data collected in the Southern California Bight by SWIFT drifters as part of the ONR Langmuir Circulation Departmental Research Initiative (LC-DRI)
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>
Machine learning refinement of in situ images acquired by low electron dose LC-TEM
<p>This is a dataset of images acquired in vacuum condition and corresponding images in a solution listed in the text file list_dataset.txt. </p><p> </p><p>The code is available at <a href="https://github.com/hiroyasukatsuno/Machine-learning-refinement-of-images-acquired-by-LC-TEM">this website</a>:</p><p>https://github.com/hiroyasukatsuno/Machine-learning-refinement-of-images-acquired-by-LC-TEM/</p><p><br>Equipment of TEM:</p><ul><li>field-emission gun (JEM-2100F, JEOL, Tokyo)</li><li>OneView IS (Gatan, Inc., Pleasanton, CA, USA)</li></ul><p> </p>
Data from Nicolle et al. LC-HRMS study of Streptomyces sp. AgN23 Culture Media Extract. Study of AgN23 exometabolome and analysis of Arabidopsis metabolomic responses to the bacteria
<p>This archive compiles several datasets related to studies of <i>Streptomyces</i> sp. AgN23 interaction with <i>Arabidopsis thaliana</i>. Ultra-high-performance liquid chromatography-high-resolution MS (UHPLC-HRMS) analyses were performed on a Q Exactive Plus quadrupole (Orbitrap) mass spectrometer, equipped with a heated electrospray probe (HESI II) coupled to a U-HPLC Ultimate 3000 RSLC system (Thermo Fisher Scientific, Hemel Hempstead, United Kigdom). For each biological sample, the RAW file obtained in ESI+ and ESI- mode were retrieved from the Xcalibur version 4.4 software and are deposited in separate sub-folders termed "RawPos" and "RawNeg". Each experimental cohort is grouped in a folder where the biological repeats can be retrieved, as well as QC (Quality Check, pool of all samples from the cohort), Blank samples and eventual alternative control such as Bennett, the mock control media of <i>Streptomyces</i> sp. AgN23. The details regarding samples preparation, analytic parameters and mass spectrometry, statistical treatment and visualization of the data will be made available in the publication relating to this archive. The folder " AgN23-WT_AgN23-pSC004" contains chromatograms related to metabolomic study of Wild-type and pSC004-1, pSC004-10, pSC004-16 and pSC004-22 mutants of <i>Streptomyces</i> sp. AgN23. The folder " Col-0_AgN23" contains chromatograms related to metabolomic study of <i>Arabidopsis thaliana</i> Col-0 responses to colonization by <i>Streptomyces</i> sp. AgN23-WT. The folder " Col-0_pad3-1_AgN23" contains chromatograms related to metabolomic study of <i>Arabidopsis thaliana</i> Col-0 and the <i>Arabidopsis</i> pad3-1 mutant responses to colonization by <i>Streptomyces</i> sp. AgN23-WT. The folder " Col-0_pSC004" contains chromatograms related to metabolomic study of <i>Arabidopsis thaliana</i> Col-0 responses to colonization by <i>Streptomyces</i> sp. AgN23-WT and the AgN23 mutants pSC004-10 and pSC004-22. It should be noted that in the publication associated with this archive, the pSC004-1, pSC004-10, pSC004-16 and pSC004-22 mutants are referred to as ΔgbnB-1, ΔgbnB-2, ΔgbnB-3 and ΔgbnB-4, respectively.</p>
Algoa Bay 2018/2019 LC-MS/MS DOM study
<p>This dataset contains the raw and processed data for a nontargeted tandem mass spectrometry study of dissolved organic matter (DOM) in surface water collected from the Algoa Bay system in the Eastern Cape of South Africa in 2018/2019</p>
lc_ms_method_params
Test upload of a dataset
Figure 6 in Colotis lais (Butler, 1876) and Colotis euippe omphale (Godart, [1819]) use Cadaba aphylla (Thunb.) Wild LC as a host-plant at Tswalu Kalahari, Northern Cape Province, South Africa)
Figure 6 – Location of Tswalu Kalahari in southern Africa.
Raw recordings for electrophysiological characterization of CNO-DREADD mediated responses in noradrenergic locus coeruleus (LC) neurons from lines RR1(P), RR2(P), and RC::FPDi (P).
<p>Whole-cell recordings were performed as described previously by the Jiang lab. Briefly, patch pipettes (2-7 MΩ) were filled with an internal solution containing 120 mM potassium gluconate, 10 mM HEPES, 4 mM KCl, 4 mM MgATP, 0.3 mM Na<sub>3</sub>GTP, 10 mM sodium phosphocreatine and 0.5% biocytin (pH 7.25). Whole-cell recordings from up to 8 LC neurons were performed using two Quadro EPC 10 amplifiers (HEKA Electronic, Germany). PatchMaster (HEKA) and custom-written Matlab-based programs (Mathworks) were used to operate the recording system and perform online and offline data analysis. In current-clamp recordings, neurons were first current clamped at ~-40pA to prevent spontaneous firing. Prior to investigating the effect of drugs, we calculated spike thresholds and recorded firing patterns in response to sustained depolarizing currents by injecting increasing current steps (+10pA). Continuous recordings were obtained from LC neurons current clamped at -40 to 0 pA during drug wash-on experiments. We also calculated other intrinsic electrophysiological parameters, such as the input resistance, membrane time constant, spike amplitude, after-hyperpolarization (AHP) etc.</p>
LC-HRMS files from wastewater samples collected in Portugal
<p>LC-HRMS files corresponding to wastewater samples associated to the paper:</p> <p><em><strong><a href="https://doi.org/10.1016/j.scitotenv.2021.152518">M.F.T. Sá et al. Tracking pollutants in a municipal sewage network impairing the operation of a wastewater treatment plant. Science of the Total Environment, 2022, 817, 152518. DOI: 10.1016/j.scitotenv.2021.152518</a></strong></em></p> <p>These files contain the LC-QTOF-HRMS data associated with the manuscript:</p> <p>Please note:</p> <p>Data was acquired with an Agilent 6550 QTOF in both:<br> - AutoMSMS (data-dependent acquisition, DDA) <br> - All Ions (data-independent acquisition, DIA)</p> <p>Data is provided in Agilent MassHunter B.10.00 format.</p> <p>In the case of AutoMSMS the iterative mode with two injections per sample was employed.<br> Further details on how samples were processed is presented in the manuscript.</p> <p>Data is therefore structure in two folders, one for DDA and one for DIA.<br> Each folder is then further divided between positive and negative mode.</p> <p>Files are named accordingly to the sample, using the same codes than in the manuscript.<br> The number after the code refers to the sample campaign.<br> Besides, there are "A" and "B" files in the DDA files, corresponding to the first and second iterative injection, respectively.</p> <p>As an example, the file:<br> "PVZ-2 2_B.d" in the AutoMSMS_DDA (either pos or neg) folder:<br> corresponds to the sample PVZ-2 from the second sampling campaign and second injection</p> <p><strong>If you reuse this data, please cite this dataset (<a href="https://doi.org/10.5281/zenodo.5830725">DOI: 10.5281/zenodo.5830725</a>) and the orginal article cited above (<a href="https://doi.org/10.1016/j.scitotenv.2021.152518">DOI: 10.1016/j.scitotenv.2021.152518</a>)</strong></p>
LC-MS raw data for proteomic elucidation of the targets and primary functions of picornavirus 2A protease
<p>This dataset contains LC-MS raw data for pulldowns from the project "Proteomic elucidation of the targets and primary functions of picornavirus 2A protease." The descriptions of the raw data files are in Summary_Table_MS_RawData.pdf.</p>
LC-IMPACT1.3
<p>LC-Impact data</p>
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>
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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.
Annotated Behaviour and Observability Dataset (ABODe)
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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.