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2,394 results for “ms”
ms data positive mode CUSO 2024
<p>ms data positive mode CUSO 2024</p> <p>Metadata at https://github.com/digital-botanical-gardens-initiative/dbgi-metabolomics</p>
Relocation of the 2024 MS 7.1 Wushi, Xinjiang earthquake sequence and implications for seismogenic structure
<p>This file is the relocated data of the 2024 Wushi Ms 7.1 earthquake using Double-difference algorithm. It is only used for scientific research.</p>
MS data linked to manuscript: https://doi.org/10.1111/1751-7915.14215
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EC-MS dataset of electrocatalytic transformations of butane on Pt
<p>This distribution provides the code and reference data for the manuscript of "Understanding the interplay between electrocatalytic C(sp3)‒C(sp3) fragmentation and oxygenation reactions".</p> <p>The code can be excecuted using Python version 3.8. </p> <p><strong>Data</strong></p> <p>The distribution includes an <code>.xlsx</code> file with reference mass spectra data and experimental data in <code>.tsv</code> format.</p> <p> </p> <p><strong>Usage</strong></p> <ol> <li>Ensure Python 3.8 and Jupyter Notebook are installed.</li> <li>Execute each cell in the notebook <code>example.ipynb</code>. A pop-up window will prompt you to upload your experimental mass spectra data; select your file accordingly. The cells are organized as: <ol> <li><code>Load Data</code>: This step loads the reference spectra data.</li> <li><code>Preprocess Data</code>: This step removes background signals and smooths the signal.</li> <li><code>Optimization</code>: This step uses constrained least squares optimization to reconstruct spectra and predict flux.</li> <li><code>Plot</code>: This step displays the spectra reconstruction and flux prediction.</li> <li><code>File Output</code>: This step saves the predicted flux and reconstructed spectra to files.</li> </ol> </li> </ol>
Dataset on the characterization of the flavor of two red wine varieties using sensory descriptive analysis, volatile organic compounds quantitative analysis by GC-MS and odorant composition by GC-MS-O
<p>The dataset contains data that were collected on 2 sets of 8 French red wines from two grape varieties, Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Gas Chromatography–Mass Spectrometry (GC-MS) and (iii) odorant composition obtained by Gas Chromatography–Mass Spectrometry–Olfactometry (GC-MS-O).</p> <p> </p> <p>The dataset is a Microsoft Excel Worksheet containing 8 sheets.</p> <p>- Sheet 1: Information</p> <p>Gives information about the sheets contained in this .xlsx file</p> <p>- Sheet 2: Experimental_factors</p> <p>Each row represents a wine</p> <p>Each column corresponds to an experimental factors of the wines (Grape variety, Vintage and Protected Designation of Origin)</p> <p>- Sheet 3: List_sensory_descriptors</p> <p>Lists the 33 sensory descriptors used for the sensory descriptive analysis of the wines</p> <p>- Sheet 4: Sensory_descriptive_analysis</p> <p>Each row represents a wine</p> <p>Each column corresponds to a condition (2640 columns)</p> <p>Senso_(ortho or retro)_(Panelist1 to Panelist 16)_(1 to 33 Sensory descriptors)_(1 to 3 repetitions for ortho and 1 to 2 repetitions for retro)</p> <p>For the ortho (orthonasal) measurements, there is 16 panelists, 33 sensory descriptors and 3 repetitions = 1584 columns</p> <p>For the retro (retronasal) measurements, there is 16 panelists, 33 sensory descriptors and 2 repetitions = 1056 columns</p> <p>Each cell contains a sensory measurement for the corresponding condition in the corresponding wine</p> <p>- Sheet 5: List_VOC</p> <p>Lists the 45 VOC quantified in the wines with their corresponding CAS number</p> <p>VOC: Volatil Organic Compounds</p> <p>- Sheet 6: VOC_quantification</p> <p>Each row represents a wine</p> <p>Each column corresponds to a VOC (45 columns)</p> <p>Each cell contains the quantification of the corresponding VOC in the corresponding wine</p> <p>- Sheet 7: List_GC-MS-O</p> <p>Lists the 49 odor-active compounds identified with their corresponding CAS number and the 34 compounds identified by their apex indice</p> <p>- Sheet 8: GC-MS-O</p> <p>Each row represents a wine</p> <p>Each column corresponds to an odor-active compound identified by its CAS number or by its Apex indice if the compound was not identify (81 odor-active compounds) + the number of judges who smelled the compound and its description (by 8 judges) = 9 columns per odor-active compound for a total of 729 columns</p>
Compilation of LA-ICP-MS data from published literature of SMS and VMS deposits
<p>Compilation of LA-ICP-MS data from published literature of SMS and VMS deposits. Data was compiled as part of a PhD thesis by Hannah L. J. Grant (University of Kiel, 2019). References are contained within the file.</p>
GC-MS of 4 commercial perfumes
<p>GC-MS data from 4 commercial perfumes</p> <p>Four commercial perfumes were purchased and stored at 12 °C: Black opium from Yves Saint Laurent (France), Poison Girl from DIOR (France), 212 SEXY from Carolina Herrera (New york, USA) and 24 FAUBOURG from HERMES (France). The analysis of the four perfumes was performed using an Agilent 7890B GC, equipped with a Supelcowax 10 capillary column (30 m, 0.25 mm i.d., 0.25 µm film thickness) and coupled to a mass spectrometer 5977 Agilent Technologies. Helium was chosen as carrier gas at a flow rate of 1.3 mL/min. Column temperature was initially fixed at 40 °C for 2 min, then gradually increased to 240 °C at 3 °C/ min, and finally 240 °C for 5 min. For GC-MS detection an electron impact source was used with an ionization energy fixed at 70 eV. Data were acquired using full scan mode with a <em>m/z</em> range from 30 to 450. The perfumes were diluted 1:10 (v:v) with ethanol and 1.0 µL of the diluted samples was automatically injected in split mode (split ratio 100:1). Injector and detector temperatures were set at 250 and MS source at 230 °C, respectively. Agilent G1701EA MSD Productivity ChemStation Software Version E.02.02 was used to manage analysis.</p>
MS 21 مكتبة الرياض العامة السعودية
<p>MS 21 مكتبة الرياض العامة السعودية</p>
MS 21 مكتبة الرياض العامة السعودية
<p>MS 21 مكتبة الرياض العامة السعودية</p>
Antiox properties and mass fingerprint data (DLI-ESI and LTP MS) for elite maize (Zea mays) hybrids in different agroecologies
<p>Study of the nutraceutical value of elite maize (Zea mays) hybrids, using mass fingerprinting by direct liquid injection (DLI) electrospray ionization (ESI) and low-temperature plasma (LTP) ionization.</p>
GC-MS metabolite profile of Pseudocercospora fijiensis exposed to thiabendazole
Open the record for dataset details and reuse information.
MS Kathmandu NAK 5-333 (198-296)
<p>Images of a Sanskrit manuscript. Ff. 198-296</p>
Supplementary Table S1. Combined analysis of variance containing the degrees of freedom (DF), mean squares (MS), P value (P val.), mean, coefficient of experimental variation (CEV%) and selective accuracy (SA) for the traits of luminosity (L*), chromaticity a* (a*), chromaticity b* (b*), grain length (length, mm), grain width (width, mm), grain thickness (thickness, mm), mass of 100 grains (Mass, g), normal grains (Ng, %), water absorption (absorption, %), cooking time (Ct, min:s), and concentrations of potassium (K, g kg-1 dry matter - DM), phosphorus (P, g kg-1 DM), calcium (Ca, g kg-1 DM), magnesium (Mg, g kg-1 DM), iron (Fe, mg kg-1 DM), zinc (Zn, mg kg-1 DM), and copper (Cu, mg kg-1 DM) obtained in 25 common bean cultivars evaluated in four experiments carried out from 2019 to 2021
<p><strong><span>Table S1.</span></strong><span> Combined analysis of variance.</span></p> <p><strong><span>Indirect selection for multiple technological and nutritional traits in common bean cultivars under different degrees of multicollinearity</span></strong></p> <p><strong><span>Bragantia, 2024.</span></strong></p>
A machine-readable provisional transcription of the Piers Plowman text of Takamiya MS 23
<p>A machine-readable provisional transcription of the Middle English poem <em>Piers Plowman</em> as transmitted in New Haven, Beinecke Library, MS Takamiya 23. See the <a title="Technical Introduction" href="https://github.com/icornelius/s-takamiya-23/releases/latest/download/documentation.pdf">documentation</a>.</p>
GC-MS data corresponding to: Methanol-Based Esterification of Palm Oil Sludge – Preparation of Fatty Acids (Palmitic and Oleic) Ethyl Esters via Ethyl Acetate Transesterification by Javier Chaparro-Acosta and Juan-Manuel Urbina-González
<p>GC-MS data corresponding to:</p> <p>Methanol-Based Esterification of Palm Oil Sludge – Preparation of Fatty Acids (Palmitic and Oleic) Ethyl Esters via Ethyl Acetate Transesterification<br> by<br> Javier Chaparro-Acosta<sup>(1)</sup> and Juan-Manuel Urbina-González<sup>(2*)</sup></p> <p><sup>1</sup>Escuela de Ingeniería Química, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia<br> <sup>2</sup>Escuela de Química, Universidad Industrial de Santander, Bucaramanga, 680002, Colombia<br> * Correspondence: jurbina@uis.edu.co<br> <br> <strong>Abstract</strong><br> Acid catalyzed Fischer esterification of fatty acids using methanol (as reagent and solvent) allow the preparation of long chain alkyl methyl esters. Transesterification of palm oil in basic media using methanol is also a known path to monoalkyl methyl ethers derived of fatty acids. In this work we report the Fischer esterification using methanol of a sample of local palm oil sludge (a fraction rich in fatty acids) and how during the extraction with ethyl acetate a transesterification reaction occurred, allowing the preparation of ethyl esters of oleic and palmitic acids as main compounds.</p> <p> </p>
LA-ICP-MS line scan data and time-series analysis outputs for Baltic Sea sediment core F80
<p>The datafile contains two sheets: HTM and MCA, corresponding to geochemical data from the Holocene Thermal Maximum and Medieval Climate Anomaly intervals, respectively, of a sediment core from the Baltic Sea (site F80, 58°00.00N, 19°53.81E, water depth 191m, Fårö Deep, collected during the HYPER/COMBINE cruise of R/V Aranda, May/June 2009). In each sheet, columns A-J contain Laser Ablation (LA)-ICP-MS line scan data of element ratios in resin-embedded sediment (Mo/Al, Fe/Al and Br/P) presented in the time domain (Age in years BP). Dating of the sediment core is described in the accompanying manuscript and references therein. These profiles are presented in three forms: Raw= raw data resampled to 1 year resolution; Det= detrended and normalized to unit variance; Gau; Gaussian bandpass filter at a period of 20-100 years. Columns L-S contain time-series analysis results of the detrended, normalized elemental ratios in period domain, including power spectra of each ratio (Blackman-Tukey window, columns M-O) and cross-spectral analysis (Blackman-Tukey window, bandwidth 5 years) of Mo/Al vs Br/P (columns P-Q) and Mo/Al vs Fe/Al (columns R-S), respectively. All analyses were performed in Analyseries 1.1.1 (Paillard et al., 1996). Figures containing the data have been submitted as part of a manuscript to Geophysical Research Letters (Jilbert et al., forthcoming),</p> <p> </p> <p>Paillard, D., Labeyrie, L., & Yiou, P. (1996). Macintosh program performs time‐series analysis. <em>Eos, Transactions American Geophysical Union</em>,<em> 77</em>(39), 379-379. <a href="https://doi.org/10.1029/96EO00259">https://doi.org/10.1029/96EO00259</a></p> <p>Jilbert, T., Gustafsson, B.G., Veldhuijzen, S., Reed, D.C., van Helmond, N.A.G.M., Hermans, M., & Slomp, C.P (forthcoming). Iron-phosphorus feedbacks drive multidecadal oscillations in Baltic Sea hypoxia. Submitted to <em>Geophysical Research Letters</em></p>
Raw data from Incineration Ash-Inter Comparison Results for Metal Concentrations by ICP-mS method
<p>The dataset contains the raw data of all consortium member for metal concentrations on loaded filters.</p> <p>These data relate to the journal article <strong>Novel preparation of a candidate reference material of PM10 and PM2.5 airborne particulate filters loaded with incineration ash. Inter comparison results for metal c</strong>once<strong>ntrations</strong> by Alexandre Bescond, Caroline Oster, Paola Fisicaro, Sharon Goddard, Paul Quincey, Lamprini-Areti Tsakanika, Theopisti Lymperopoulou, Maria Ochsenkuehn-Petropoulou. <a href="https://doi.org/10.3390/atmos12010067">https://doi.org/10.3390/atmos12010067</a></p> <p>Theses datas was produced with ICP-MS devices for the determination of metal concentration on loaded filters. The filters was produced by LNE with an aerosol disperser.</p>
Multidimensional Statistical Technique for Interpreting the Spontaneous Breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study
<p>Simple Summary: Pain is one of the most common and debilitating symptoms in cancer patients. A clinical peculiarity of cancer pain is the breakthrough cancer pain (BTcP), which is defined as a temporary exacerbation of pain that “breaks through” a phase of adequate pain control by an opioid-based therapy. The NP-BTcP occurs in the absence of any specific activity. In this paper, we addressed the topic through a mathematical approach to provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management. Abstract: Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that “breaks through” a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting—Multicentric Survey (IOPS-MS). A correlation analysis was performed to characterize the NP-BTcP profile about its intensity, number of episodes per day, and type. The multiple correspondence analysis (MCA) determined the identification of four groups (phenotypes). A univariate analysis was performed to assess differences between the four phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (phenotype 1) to the worst (phenotype 4). The univariate analysis found a significant association between the onset time >10 min in the phenotype 1 (37.3%)’ vs. the onset > 10 min in phenotype 4 (25.8%) (p < 0.001). Phenotype 1 was characterized by the gastrointestinal type of cancer (26.4%) with respect to phenotype 4, where the most frequent cancer affected the lung (28.8%) (p < 0.001). Phenotype 4 was mainly managed with rapid-onset opioids, while in phenotype 1, many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p = 0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. Although requiring validation, this strategy can provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management.</p> <p> </p>
Novel Insights into the Bioactive Metabolites of Macrocybe gigantea (Massee) Pegler & Lodge, a Wild Edible Macrofungi Using Gas Chromatography Mass Spectrometry (GC-MS) Combined with Chemoinformatics Approaches
<p><em>Macrocybe gigantea </em>(MG) is an edible mushroom and has multiple pharmacological activities such as antibacterial, antioxidant, and antitumor activities. However, only a few reports were available on the bioactive compounds and bioactivity of this mushroom. In this concern, the present study was aimed to explore the unique chemical diversity from the fruiting body of MG<em>. </em>The species identification was done accurately with morphological and molecular methods followed by mycochemical extraction in different solvent systems. The ethanolic extract of the fruiting body gave maximum yield and its Gas Chromatography-Mass Spectrometry (GC-MS) analysis was performed along with antibacterial activity and cell viability by MTT assay. The GC-MS analysis revealed 50 metabolites and further chemoinformatics analysis of these metabolites revealed their possible biological activities. In addition, the mushrooms' physico-chemical and mineral element analysis revealed the quality and authenticity of the species. Altogether, the current investigation gives a comprehensive overview of the bioactive metabolites of MG.</p>
Pre-processed IgH receptor repertoire data from MS patients after aHSCT from BioProject PRJNA763367
<p><strong>Data Processing</strong></p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). Raw fastq files were filtered based on a quality score threshold of 20. Paired reads were joined if they had a minimum length of 10 nt, maximum error rate of 0.3 and a significance threshold of 0.0001. Reads with identical UMI were collapsed to a consensus sequence. Reads with identical full-length sequence and identical constant primer but differing UMI were further collapsed. Sequences were then submitted to IgBlast (3) for VDJ assignment and sequence annotation. Constant region sequences were mapped to germline using Stampy(4). The number and type of V gene mutations was calculated using the shazam R package.(2)</p> <p> </p> <p><strong>software_versions</strong> pRESTO:0.5.3,Change-O:0.3.4,IgBlast 1.6.1, stampy1.0.21. shazam0.1.8</p> <p><strong>quality_thresholds</strong> FilterSeq.py pRESTO Q>20</p> <p><strong>paired_reads_assembly</strong> AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p><strong>primer_match_cutoffs</strong> MaskPrimers.py pRESTO C primer & V primer maxerror 0.2</p> <p><strong>consensus_building</strong> BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p><strong>collapsing_method</strong> CollapseSeq.py pRESTO</p> <p><strong>germline_database </strong>IMGT</p> <p> </p> <p><strong>Format</strong></p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p> </p> <p><strong>ISOTYPE_SUBCLASS </strong>Isotype subclass</p> <p><strong>SEQUENCE_ID </strong>Sequence identifier</p> <p><strong>JUNCTION_LENGTH </strong>Junction length</p> <p><strong>CONSCOUNT </strong>Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT </strong>UMI count for the given unique sequence</p> <p><strong>ISOTYPE </strong>Constant region primer (isotype)</p> <p><strong>MUT_TOTAL </strong>Total number of mutations in V gene </p> <p><strong>SAMPLE </strong>Sample identifier, linking back to raw data</p> <p><strong>JUNCTION </strong>Junction nucleotide sequence</p> <p><strong>Protein_seq </strong>Amino acid sequence</p> <p><strong>CDR3_AA_GRAVY </strong>CDR3 hydrophobicity index</p> <p><strong>CDR3_AA_BULK </strong>CDR3 bulkiness</p> <p><strong>CDR3_AA_ALIPHATIC </strong>CDR3 aliphatic index</p> <p><strong>CDR3_AA_POLARITY </strong>CDR3 polarity</p> <p><strong>CDR3_AA_CHARGE </strong>CDR3 normalized net charge</p> <p><strong>CDR3_AA_BASIC </strong>CDR3 basic side chain residue content</p> <p><strong>CDR3_AA_ACIDIC </strong>CDR3 acidic side chain residue content</p> <p><strong>CDR3_AA_AROMATIC </strong>CDR3 aromatic side chain content</p> <p><strong>Subset </strong>Defined B cell subset </p> <p><strong>Repertoire </strong>Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR </strong>R/S ratio in CDR region</p> <p><strong>R_SFWR </strong>R/S ratio in FWR region</p> <p><strong>V_GENE </strong>V segment gene</p> <p><strong>D_GENE </strong>D segment gene</p> <p><strong>J_GENE </strong>J segment gene</p> <p><strong>V_FAM </strong>V family gene</p> <p><strong>Clust_REPRES </strong>Cluster representative</p> <p><strong>Clust_SIZE </strong>Cluster size</p> <p><strong>Sex </strong>Sex of the Subject</p> <p><strong>UNIQUE_ID </strong>Sample identifier </p> <p><strong>Bcellno </strong>Input B cell number</p> <p><strong>Days_posttx </strong>Sampling time point relative to transplantation</p> <p><strong>Age_at_tx </strong>Age of the subject (at aHSCT)</p> <p><strong>Disease </strong>MS subtype</p> <p><strong>Last_therapy </strong>Last therapy prior to aHSCT</p> <p><strong>Disease_duration </strong>Disease duration</p> <p><strong>CMV_reactivation </strong>Cytomegalovirus reactivation</p> <p><strong>Month_label </strong>Month post-aHSCT inverval bin</p> <p><strong>Patient_label </strong>Subject identifier</p> <pre> </pre> <p><strong>References</strong></p> <p>1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. <em>Bioinformatics</em>30: 1930–1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. <em>Bioinformatics</em>31: 3356–3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. <em>Nucleic Acids Res.</em>41.</p> <p>4. Lunter, G., and M. Goodson. 2011. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. <em>Genome Res.</em>21: 936–939.</p>
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Allen Brain Atlas
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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.