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990 results for “quantification”
QUEST-MM, QUEST(Subtract), QUEST(Met+Back) and QUEST(Met+MM) quantification results of in-vivo and simulated MRS human and rat brain spectra
<p>QUEST-MM, QUEST(Subtract), QUEST(Met+Back) and QUEST(Met+MM) quantification results of rat and human brain <em>in-vivo</em> and simulated spectra alongside with the metabolite profiles and the simulated 9.4T rat and 4T and 3T human brain spectra.</p> <p>All files can be loaded in jMRUI software version 5 and later.</p>
The quantification of Open Scholarship - A mapping review
<p>We present the data set for the mapping review "The quantification of open scholarship"</p>
SPIT - Quantification files for tissue-dependent DTUs
<p>Quantification and phenotype files for GTEx samples used in the detection of 4 tissue-dependent DTU events. These events are provided as positive controls for the evaluation of SPIT. Corresponding sashimi plots with all transcripts for the given genes are included.</p>
18, 28, 42 DPI neuron and macrophage quantifications
<p>MHC-II (-/-) mice expressing HLA DRB1*15:01 were immunized with a-synuclein<sub>32-46</sub> peptide in complete Freund's adjuvant (CFA), CFA alone, or PBS alone. Enteric neuron density (ANNA1<sup>+</sup>), tyrosine hydroxylase (TH) neuron density, TH<sup>+</sup> area and mean fluorescence intensity, and macrophage density (IBA1<sup>+</sup>) were quantified from the submucosal plexus and myenteric plexus of mice at 18, 28, and 42 days post immunization. These values were normalized to the CFA-only condition. </p>
Enteric neuron and macrophage quantification following T cell depletion
<p>MHC-II (-/-) mice expressing HLA DRB1*15:01 were immunized with a-synuclein<sub>32-46</sub>. Data includes three conditions: isotype control, anti-CD4, and anti-CD8 T cell depleting antibodies.</p> <p>Enteric neuron (ANNA1<sup>+</sup>), macrophage density (IBA1<sup>+</sup>), tyrosine hydroxylase (TH<sup>+</sup>) neuron, TH<span><sup>+</sup> </span>area and mean fluorescence intensity in the myenteric and submucosal plexus of the ileum were quantified.</p>
Fig. 4 in Phenolic fingerprints of the Pacific seagrass Phyllospadix torreyi - Structural characterization and quantification of undescribed flavonoid sulfates
Fig. 4. Inter-annual variation in the amounts of phenolic compound in fresh (samples Phy1-F to Phy5-F) and detrital (sample Phy-3 D). Concentrations values on ordinate are given as mg g ¡1 dw of plant tissue, mean values SD (n 3). Products are given in order of elution: Caff: 1; Nep7,4': 2; OMeLu2S: 3; 6OHLu2S: 4; ± = Coum: 5; Lu2S: 6; Nep2S: 7; 5OMeLu7S: 8; 6OHLu7S: 9; RA: 10; L7S: 11; Nep7S: 12; Lu3′S: 13; Nep3′S: 14; Hispi7S: 15; Jaceo7S: 16. See Fig. 3 for formulae and Table 1 for full data.
Fig. 3 in Phenolic fingerprints of the Pacific seagrass Phyllospadix torreyi - Structural characterization and quantification of undescribed flavonoid sulfates
Fig. 3. Structural formulae of compounds 1–18 and a-e. Underlined names indicate the previously unreported products.
Fig. 1 in Phenolic fingerprints of the Pacific seagrass Phyllospadix torreyi - Structural characterization and quantification of undescribed flavonoid sulfates
Fig. 1. Schematic map showing the location of the sampling sites in La Jolla, San Diego County, California, USA. 1: site for fresh material. 2: site for detrital material.
Fig. 4 in The complexity of sound quantification of specialized metabolite biosynthesis: The stress related impact on the alkaloid content of Catharanthus roseus
Fig. 4. The stress-related increase of the concentration of natural products. In principle, two major effects are responsible for the stress-related increase, i. e., the decrease of the reference value (e.g. dry weight), and an enhancement of biosynthetic activity. The latter one is due either to stress-related up-regulation ("active increase" of enzymatic activity) or a "passive shift" cause by the stress-related overreduction due to stomatal closure. Decreases of factors are displayed in red, the related enhancements in blue. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in The complexity of sound quantification of specialized metabolite biosynthesis: The stress related impact on the alkaloid content of Catharanthus roseus
Fig. 3. Dry weight, alkaloid concentration, and alkaloid content in leaves of Catharanthus roseus plants under salt stress. (a): Dry weight of the entire leaves; (b): Concentration of alkaloids in old leaves; (c): Concentration of alkaloids in young leaves; (d): Total alkaloids content in all aerial plant parts. Differentiation between young and old leaves is mentioned in the Materials and methods section. Different lower-case letters on top of the columns for each period of time (10 and 20 days) indicate significant differences (P ≤ 0.05) as calculated using the least significant difference (LSD) test; n = 8. Every period has 5 bars, and these bars from left to right are control, 100 mM, 200 mM, 300 mM, 400 mM NaCl solution, respectively. The bars display the standard deviation.
Fig. 1 in The complexity of sound quantification of specialized metabolite biosynthesis: The stress related impact on the alkaloid content of Catharanthus roseus
Fig. 1. Evapotranspiration rate of drought-stressed Catharanthus roseus plants. The evapotranspiration rates for mild (20% watering) and severe drought stress (40% watering) were obtained by calculating the amount of water lost. Control plant results were used as a reference to normalize the rate. Arrows indicate the days of sampling.
Fig. 5 in A robust method for simultaneous quantification of eugenol, eugenyl acetate, and β-caryophyllene in clove essential oil by vibrational spectroscopy
Fig. 5. The score plots of the wavenumbers obtained from the best PLS models. a) Total eugenol, b) Eugenyl acetate, c) β-caryophyllene. PLS: Partial least square.
Fig. 4 in A robust method for simultaneous quantification of eugenol, eugenyl acetate, and β-caryophyllene in clove essential oil by vibrational spectroscopy
Fig. 4. The plots of the regression coefficients and the predicted versus reference values of the major compounds obtained from the best PLS models. a) Total eugenol, b) Eugenyl acetate, c) β-caryophyllene. PLS: Partial least square.
Fig. 3 in A robust method for simultaneous quantification of eugenol, eugenyl acetate, and β-caryophyllene in clove essential oil by vibrational spectroscopy
Fig. 3. ATR-FTIR spectra of the calibration sets. a) Total eugenol, b) Eugenyl acetate, c) β-caryophyllene. ATR-FTIR: Attenuated total reflectance- Fourier transform infrared.
Fig. 2 in A robust method for simultaneous quantification of eugenol, eugenyl acetate, and β-caryophyllene in clove essential oil by vibrational spectroscopy
Fig. 2. GC-MS chromatogram obtained with the analysis of CO13 sample. GC-MS: Gas chromatography-mass spectrometry; CO: Clove oil.
Fig. 1 in A robust method for simultaneous quantification of eugenol, eugenyl acetate, and β-caryophyllene in clove essential oil by vibrational spectroscopy
Fig. 1. The molecular structures of the major components of clove essential oil. a) Eugenol, b) Eugenyl acetate, c) β-caryophyllene.
Fig. 3 in Validation and uncertainty estimation of analytical method for quantification of phytochelatins in aquatic plants by UPLC-MS
Fig. 3. Contribution of the sources (%) to the total uncertainty for the quantification of GSH and PCs in the L. gibba.
Fig. 1. a in Validation and uncertainty estimation of analytical method for quantification of phytochelatins in aquatic plants by UPLC-MS
Fig. 1. a) Total ion chromatogram (TIC) for the L. gibba sample, b) Extracted ion chromatogram of GSH and PCs from the TIC of L. gibba, c) Extracted ion chromatogram of GSH and PCs from the standard solution at 10 μg mL 1.
Fig. 2 in Validation and uncertainty estimation of analytical method for quantification of phytochelatins in aquatic plants by UPLC-MS
Fig. 2. Ishikawa diagram representing the main sources of uncertainties for measuring GSH and PC concentration in aquatic plants.
Dataset for 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations'
<p> </p> <p>This dataset is associated with the research entitled: 'Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’. It contains raw data from six low-cost sensor loggers and one anemometer, collected during an experiment consisting of a series of controlled releases conducted in October 2019 at the TADI (TotalEnergies Anomaly Detection Initiative) platform.</p> <p><strong>Dataset Structure:</strong></p> <p>- `time`: Timestamp, marking the exact time the data was collected.<br> - `CH4`: Methane concentration measured by the reference instrument in parts per million (ppm).<br> - `2611C`: Voltage variation measured by the Figaro TGS 2611C-00 MOS sensor in volts (V).<br> - `2600`: Voltage variation measured by the Figaro TGS 2600 MOS sensor in volts (V).<br> - `2611E`: Voltage variation measured by the Figaro TGS 2611E-00 MOS sensor in volts (V).<br> - `RH_DHT22`: Relative humidity measured by the DHT22 sensor in percentage (%).<br> - `RH_SHT75`: Relative humidity measured by the SHT75 sensor in percentage (%).<br> - `T_DHT22`: Air temperature measured by the DHT22 sensor in degrees Celsius (°C).<br> - `T_SHT75`: Air temperature measured by the SHT75 sensor in degrees Celsius (°C).<br> - `T_BMP180`: Air temperature measured by the BMP180 sensor in degrees Celsius (°C).<br> - `T_BMP280`: Air temperature measured by the BMP280 sensor in degrees Celsius (°C).<br> - `P_BMP180`: Atmospheric pressure measured by the BMP180 sensor in pascals (Pa).<br> - `P_BMP280`: Atmospheric pressure measured by the BMP280 sensor in pascals (Pa).<br> - `Release`: Number of the controlled release.</p> <p><strong>Acknowledgment:</strong></p> <p>When using this dataset, please reference the original research paper titled ‘Using Metal Oxide Gas Sensors for the Estimate of Methane Controlled Releases: Reconstruction of the Methane Mole Fraction Time-Series and Quantification of the Release Rates and Locations’.</p> <p><strong>Contact Information:</strong></p> <p>Olivier Laurent (olivier.laurent@lsce.ipsl.fr)</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.