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82 results for “Untargeted”
Fig. 5 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 5. The hierarchical clustering of the differentially abundant metabolites based on GC-TOF-MS in the healthy flowers and petal blight flowers of R. agastum. The data were log2 transformed, and similarity assessment for clustering was based on the Euclidean distance coefficient and complete clustering algorithm. Columns and rows represent individual metabolites and different samples, respectively.
Fig. 2 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 2. Total ion current (TIC) chromatogram of healthy flowers (HF) and petal blight flowers (PBF) of R. agastum using GC-TOF-MS.
Fig. 3 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 3. The data analysis of the metabolites based on GC-TOF-MS in the healthy flowers and petal blight flowers of R. agastum. (A) Principal component analysis (PCA). (B) Orthogonal projections to latent structures discriminant analysis (OPLS-DA). The green and red circles display 95% confidence regions of petal blight and healthy flowers. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 1. The petal blight of R. agastum. (A) R. agastum grown in field habitat. (B) The healthy flower of R. agastum. (C) The petal blight flower of R. agastum (white arrow).
Fig. 4 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 4. The data analysis of the metabolites based on UHPLC-QTOF-MS/MS in the healthy flowers and petal blight flowers of R. agastum. (A) Principal component analysis (PCA) analysis in positive ion modes. (B) Principal component analysis (PCA) analysis in negative ion modes. (C) Orthogonal projections to latent structures discriminant (OPLS-DA) analysis in positive ion modes. (D) Orthogonal projections to latent structures discriminant (OPLS-DA) analysis in negative ion modes. The green and red circles display 95% confidence regions of petal blight and healthy flowers. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6 in Untargeted metabolite profiling of petal blight in field-grown Rhododendron agastum using GC-TOF-MS and UHPLC-QTOF-MS/MS
Fig. 6. The hierarchical clustering of the differentially abundant metabolites in the positive ion modes (A) and in the negative ion modes (B) based on UHPLC-QTOFMS/MS in the healthy flowers and petal blight flowers of R. agastum. The data were log2 transformed, and similarity assessment for clustering was based on the Euclidean distance coefficient and complete clustering algorithm. Columns and rows represent individual metabolites and different samples, respectively.
Fig. 2 in UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)
Fig. 2. (A) Molecular network of the molecular family of flavonoid glycosides and phenylethanoid extracted from the MN of the fruit extract of Crescentia cujete. (B) Proposed fragmentation pathway observed in the MS/MS spectrum naringin (33).
Fig. 1 in UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)
Fig. 1. UPLC-MS/MS based molecular networking in negative ionization mode of the fruit extract of Crescentia cujete. AG: alkyl glycosides, BC: benzoyl and cinnamoyl derivatives, FG1-3: flavonoid glucosides, PE: phenylethanoid derivatives, IG1-2: iridoids glycosides. Node text indicates the parent ion, node color shows the chemical group (green: n-alkyl sugars, sky blue: benzoyl derivatives, dark blue: cinnamoyl derivatives, red: flavonoids glycosides, purple: phenylpropanoids derivatives, and gold: iridoid glycosides). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in UPLC-MS/MS-based molecular networking and NMR structural determination for the untargeted phytochemical characterization of the fruit of Crescentia cujete (Bignoniaceae)
Fig. 3. Chemical structures of iridoid glycosides (11-14, 21 and 24) isolated from the fruit of Crescentia cujete.
G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics
<p>Benchmark datasets, manual annotation results, evaluation methods and results of the paper "G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics".</p>
Using Untargeted Metabolomics to Identify Urinary Biomarkers of Onion Intake
ClinicalTrials.gov study NCT05133986. IPD Sharing: NO. Countries: 1. Publications: 1.
Potential Harms of Untargeted Iron Supplementation in Cambodia Where Iron Deficiency is Not the Cause of Anemia
ClinicalTrials.gov study NCT04017598. IPD Sharing: NO. Countries: 1. Publications: 4.
Data from: Untargeted metabolic profiling reveals geography as the strongest predictor of metabolic phenotypes of a cosmopolitan weed
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Data from: Untargeted metabolomic profiling of urine from healthy dogs and dogs with chronic hepatic disease
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Untargeted LC–MS metabolomics reveals an adverse effect of high-fat diet on hepatic metabolism of Oreochromis niloticus
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Population genetics, trait mapping, and fungal pathogen surveillance using untargeted sequencing in timber rattlesnakes (<em>Crotalus horridus</em>)
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V28_1 Untargeted Metabolomics S.elongatus WT vs delta CutA
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3D-MSNet: A point cloud based deep learning model for untargeted feature detection and quantification in profile LC-HRMS data
<p>Supplementary data of 3D-MSNet</p>
Table S3 Total Protein detected by untargeted proteomics
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Table S2 Phosphoproteins detected by untargeted proteomics
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.