Journal articles: 'Data Quantitation'

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Palmisano, Giuseppe, and Tine E. Thingholm. "Strategies for quantitation of phosphoproteomic data." Expert Review of Proteomics 7, no. 3 (June 2010): 439–56. http://dx.doi.org/10.1586/epr.10.19.

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Held, Jason M., Birgit Schilling, Alexandria K. D'Souza, Tara Srinivasan, Jessica B. Behring, Dylan J. Sorensen, Christopher C. Benz, and Bradford W. Gibson. "Label-Free Quantitation and Mapping of the ErbB2 Tumor Receptor by Multiple Protease Digestion with Data-Dependent (MS1) and Data-Independent (MS2) Acquisitions." International Journal of Proteomics 2013 (April 4, 2013): 1–11. http://dx.doi.org/10.1155/2013/791985.

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The receptor tyrosine kinase ErbB2 is a breast cancer biomarker whose posttranslational modifications (PTMs) are a key indicator of its activation. Quantifying the expression and PTMs of biomarkers such as ErbB2 by selected reaction monitoring (SRM) mass spectrometry has several limitations, including minimal coverage and extensive assay development time. Therefore, we assessed the utility of two high resolution, full scan mass spectrometry approaches, MS1 Filtering and SWATH MS2, for targeted ErbB2 proteomics. Endogenous ErbB2 immunoprecipitated from SK-BR-3 cells was in-gel digested with trypsin, chymotrypsin, Asp-N, or trypsin plus Asp-N in triplicate. Data-dependent acquisition with an AB SCIEX TripleTOF 5600 and MS1 Filtering data processing was used to assess peptide and PTM coverage as well as the reproducibility of enzyme digestion. Data-independent acquisition (SWATH) was also performed for MS2 quantitation. MS1 Filtering and SWATH MS2 allow quantitation of all detected analytes after acquisition, enabling the use of multiple proteases for quantitative assessment of target proteins. Combining high resolution proteomics with multiprotease digestion enabled quantitative mapping of ErbB2 with excellent reproducibility, improved amino acid sequence and PTM coverage, and decreased assay development time compared to typical SRM assays. These results demonstrate that high resolution quantitative proteomic approaches are an effective tool for targeted biomarker quantitation.

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Alves, M. E., Y. P. Mascarenhas, D. H. French, and C. P. M. Vaz. "Rietveld-based mineralogical quantitation of deferrified oxisol clays." Soil Research 45, no. 3 (2007): 224. http://dx.doi.org/10.1071/sr06123.

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Although the mineralogical quantitative analysis of the soil clay fraction can provide useful information for the improvement of soil management practices, the quantitation of all clay components normally requires a combination of different analytical techniques, which makes this determination expensive and time-consuming. One alternative for more expeditious mineralogical quantitations consists of using the Rietveld method for the treatment of X-ray diffraction (XRD) data. In this study we evaluate the accuracy of the mineralogical quantitative analyses of oxisol deferrified clays carried out with the application of the Rietveld method to XRD data obtained for both non-spray- and spray-dried samples. Linear regression analyses were carried out for comparing the XRD-Rietveld results with those calculated from X-ray fluorescence spectroscopy (XRF) data. Correspondence was observed between the XRD-Rietveld and XRF-derived data, confirming the potential utility of the Rietveld method for soil clay mineralogical quantitative analysis. Although sample preparation by using the spray drying procedure tended to improve XRD mineralogical quantitation, accurate results can be also achieved when this procedure is not available in the XRD laboratory.

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Van Ormondt, D., R. De Beer, A. J. H. Mariën, J. A. Den Hollander, P. R. Luyten, and J. W. A. H. Vermeulen. "2D approach to quantitation of inversion-recovery data." Journal of Magnetic Resonance (1969) 88, no. 3 (July 1990): 652–59. http://dx.doi.org/10.1016/0022-2364(90)90298-n.

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Laudadio, T., N. Mastronardi, L. Vanhamme, P. Van Hecke, and S. Van Huffel. "Improved Lanczos Algorithms for Blackbox MRS Data Quantitation." Journal of Magnetic Resonance 157, no. 2 (August 2002): 292–97. http://dx.doi.org/10.1006/jmre.2002.2593.

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Parnandi, Avinash, Aakash Kaku, Anita Venkatesan, Natasha Pandit, Emily Fokas, Boyang Yu, Grace Kim, Dawn Nilsen, Carlos Fernandez-Granda, and Heidi Schambra. "Data-Driven Quantitation of Movement Abnormality after Stroke." Bioengineering 10, no. 6 (May 26, 2023): 648. http://dx.doi.org/10.3390/bioengineering10060648.

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Stroke commonly affects the ability of the upper extremities (UEs) to move normally. In clinical settings, identifying and measuring movement abnormality is challenging due to the imprecision and impracticality of available assessments. These challenges interfere with therapeutic tracking, communication, and treatment. We thus sought to develop an approach that blends precision and pragmatism, combining high-dimensional motion capture with out-of-distribution (OOD) detection. We used an array of wearable inertial measurement units to capture upper body motion in healthy and chronic stroke subjects performing a semi-structured, unconstrained 3D tabletop task. After data were labeled by human coders, we trained two deep learning models exclusively on healthy subject data to classify elemental movements (functional primitives). We tested these healthy subject-trained models on previously unseen healthy and stroke motion data. We found that model confidence, indexed by prediction probabilities, was generally high for healthy test data but significantly dropped when encountering OOD stroke data. Prediction probabilities worsened with more severe motor impairment categories and were directly correlated with individual impairment scores. Data inputs from the paretic UE, rather than trunk, most strongly influenced model confidence. We demonstrate for the first time that using OOD detection with high-dimensional motion data can reveal clinically meaningful movement abnormality in subjects with chronic stroke.

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McQueen, Peter, Vic Spicer, John Schellenberg, Oleg Krokhin, Richard Sparling, David Levin, and John A. Wilkins. "Whole cell, label free protein quantitation with data independent acquisition: Quantitation at the MS2 level." PROTEOMICS 15, no. 1 (December 10, 2014): 16–24. http://dx.doi.org/10.1002/pmic.201400188.

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Strehlow, David. "Software for Quantitation and Visualization of Expression Array Data." BioTechniques 29, no. 1 (July 2000): 118–21. http://dx.doi.org/10.2144/00291bc03.

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Toronen, P., G. Wong, and E. Castren. "Methods for Quantitation and Clustering of Gene Expression Data." Current Genomics 4, no. 5 (July 1, 2003): 445–63. http://dx.doi.org/10.2174/1389202033490303.

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Kim, Phillip D., Bhavinkumar B. Patel, and Anthony T. Yeung. "Isobaric Labeling and Data Normalization without Requiring Protein Quantitation." Journal of Biomolecular Techniques : JBT 23, no. 1 (April 2012): 11–23. http://dx.doi.org/10.7171/jbt.12-2301-002.

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Rosenqvist, G., M. Dahlbom, L. Eriksson, C. Bohm, and G. Blomqvist. "Quantitation of PET data with the EM reconstruction technique." IEEE Transactions on Nuclear Science 36, no. 1 (1989): 1113–16. http://dx.doi.org/10.1109/23.34614.

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Hartler, Jürgen, Martin Trötzmüller, Chandramohan Chitraju, Friedrich Spener, Harald C. Köfeler, and Gerhard G. Thallinger. "Lipid Data Analyzer: unattended identification and quantitation of lipids in LC-MS data." Bioinformatics 27, no. 4 (December 17, 2010): 572–77. http://dx.doi.org/10.1093/bioinformatics/btq699.

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Broxup, B., G. Losos, and G. Yipchuck. "Quantitation in Neuropathology." Journal of the American College of Toxicology 8, no. 1 (January 1989): 225–32. http://dx.doi.org/10.3109/10915818909009107.

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Qualitative neuropathologic evaluations are appropriate for initial screening of compounds for neurotoxicity. In the future, advances in tissue preparation and computer technology will make quantitative evaluation of some nervous tissue structures more routine. These evaluations will be used as part of tier II studies to determine dose-responses or no-effect levels. Quantitative methods can enable small but significant differences to be detected, and the data can be subjected to statistical analysis.

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Handler, David C. L., Flora Cheng, Abdulrahman M. Shathili, and Paul A. Haynes. "PeptideWitch–A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data." Proteomes 8, no. 3 (August 21, 2020): 21. http://dx.doi.org/10.3390/proteomes8030021.

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PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

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Laudadio, T., Y. Selén, L. Vanhamme, P. Stoica, P. Van Hecke, and S. Van Huffel. "Subspace-based MRS data quantitation of multiplets using prior knowledge." Journal of Magnetic Resonance 168, no. 1 (May 2004): 53–65. http://dx.doi.org/10.1016/j.jmr.2004.01.015.

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Carter, Robert J., Trevor Lukey, and Floyd F. Snyder. "Physiological amino acid data management: Quantitation, assessment, reporting and storage." Computers in Biology and Medicine 18, no. 6 (January 1988): 431–39. http://dx.doi.org/10.1016/0010-4825(88)90060-1.

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Jusko, William J. "Use of Pharmacokinetic Data Below Lower Limit of Quantitation Values." Pharmaceutical Research 29, no. 9 (June 23, 2012): 2628–31. http://dx.doi.org/10.1007/s11095-012-0805-6.

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Leung, Wing-Hung, William Sanders, and Edwin L. Alderman. "Coronary artery quantitation and data management system for paired cineangiograms." Catheterization and Cardiovascular Diagnosis 24, no. 2 (October 1991): 121–34. http://dx.doi.org/10.1002/ccd.1810240211.

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Matulis, C. E., and J. C. Taylor. "An Algorithm for Correction of Intensity Aberrations in Bragg-Brentano X-Ray Diffractometer Data; Its Importance in the Multiphase Full-Profile Rietveld Quantitation of a Montmorillonite Clay." Advances in X-ray Analysis 36 (1992): 301–7. http://dx.doi.org/10.1154/s0376030800018917.

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AbstractIntensity aberrations in Bragg-Brentano X-ray diffractometers reduce the measured intensities at low angles relative to higher angles, and the correction factor can be large at low 2θ angles. Aberration-free data is of great importance in full-profile Rietveld quantitative analysis or structure refinement. An algorithm is given which corrects the intensity aberrations for particular instrument dimensions and sample absorbance, with a fixed divergence slit. Variable divergence slits increase the Bragg-Brentano aberrations and skew the data. The importance of an aberration correction curve produced with the computer program BBCCURV is described in the multiphase Rietveld quantitation of a montmorillonite clay with the SIROQUANT quantitative analysis program system; good quantitation of the montmorillonite can only be obtained if the aberration corrections are applied.

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Jansen, Bas Cornelis, Lise Hafkenscheid, Albert Bondt, Richard Andrew Gardner, Jenifer Lynn Hendel, Manfred Wuhrer, and Daniel Ian Richard Spencer. "HappyTools: A software for high-throughput HPLC data processing and quantitation." PLOS ONE 13, no. 7 (July 6, 2018): e0200280. http://dx.doi.org/10.1371/journal.pone.0200280.

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21

Jones, Ron. "Quantitation of mixtures from two-dimensional data sets using orthonormal functions." Journal of Pharmaceutical and Biomedical Analysis 13, no. 12 (November 1995): 1437–40. http://dx.doi.org/10.1016/0731-7085(95)01587-6.

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Sjögren, Erik, David Dahlgren, Carl Roos, and Hans Lennernäs. "Humanin VivoRegional Intestinal Permeability: Quantitation Using Site-Specific Drug Absorption Data." Molecular Pharmaceutics 12, no. 6 (May 6, 2015): 2026–39. http://dx.doi.org/10.1021/mp500834v.

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Strohalm, Martin, Petr Novak, Petr Pompach, Petr Man, Daniel Kavan, Matthias Witt, Petr Dzubak, Marian Hajduch, and Vladimir Havlicek. "Utilization of high-accuracy FTICR-MS data in protein quantitation experiments." Journal of Mass Spectrometry 44, no. 11 (November 2009): 1565–70. http://dx.doi.org/10.1002/jms.1602.

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Gross, Michael L. "Quantitation and Mass Spectrometric Data for Drugs and Isotopically Labeled Analogs." Journal of The American Society for Mass Spectrometry 22, no. 4 (March 1, 2011): 795. http://dx.doi.org/10.1007/s13361-010-0062-z.

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Senem anl, Senem anl, Seyfi Sardogan Seyfi Sardogan, and Ay e. zdemir and Bar Atalay Ay e zdemir and Bar Atalay. "Determination of Pka Values of Antidiabetic Drugs from Mobility Data and Pharmaceutical Analysis by Capillary Electrophoresis." Journal of the chemical society of pakistan 44, no. 4 (2022): 366. http://dx.doi.org/10.52568/001068/jcsp/44.04.2022.

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In this study, the dissociation constants of six antidiabetic drugs (rosiglitazon maleate, pioglitazone, glimepride, glibenclamide, gliclazide and glipizide) have been calculated by capillary electrophoresis (CE) technique in water from the mobilities of compounds at several pH values. The dissociation constant values of antidiabetics had been checked with the ones formerly decided withinside the literature and additionally with the statistics expected through the SPARC on-line calculator and ACDLAB. Also, easy, precise, green, accurate and completely validated CE technique for the analysis of glibenclamide in pharmaceutical medicine has been fully developed. The CE technique allowed quantitation over the ranges of 1.00-12.00 μg mL-1. The detection and quantitation limits were determined as 0.036 μg mL-1 and 0.083 μg mL-1 respectively. Rosiglitazone was used as an internal standard and short analysis time (andlt; 3 min.) was observed. The developed capillary electrophoretic technique could be used for ordinary analysis of the glibenclamide and this method can also be used for pharmacokinetic studies.

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Feinberg, Max, Sophie Fernandez, Sylvanie Cassard, Chrystèle Charles-Delobel, Yves Bertheau, A. M. Balois, S. Cassard, et al. "Quantitation of 35S Promoter in Maize DNA Extracts from Genetically Modified Organisms Using Real-Time Polymerase Chain Reaction, Part 2: Interlaboratory Study." Journal of AOAC INTERNATIONAL 88, no. 2 (March 1, 2005): 558–73. http://dx.doi.org/10.1093/jaoac/88.2.558.

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Abstract The European Committee for Standardization (CEN) and the European Network of GMO Working Laboratories have proposed development of a modular strategy for stepwise validation of complex analytical techniques. When applied to the quantitation of genetically modified organisms (GMOs) in food products, the instrumental quantitation step of the technique is separately validated from the DNA extraction step to better control the sources of uncertainty and facilitate the validation of GMO-specific polymerase chain reaction (PCR) tests. This paper presents the results of an interlaboratory study on the quantitation step of the method standardized by CEN for the detection of a regulatory element commonly inserted in GMO maize-based foods. This is focused on the quantitation of P35S promoter through using the quantitative real-time PCR (QRT-PCR). Fifteen French laboratories participated in the interlaboratory study of the P35S quantitation operating procedure on DNA extract samples using either the thermal cycler ABI Prism® 7700 (Applied Biosystems, Foster City, CA) or Light Cycler® (Roche Diagnostics, Indianapolis, IN). Attention was focused on DNA extract samples used to calibrate the method and unknown extract samples. Data were processed according to the recommendations of ISO 5725 standard. Performance criteria, obtained using the robust algorithm, were compared to the classic data processing after rejection of outliers by the Cochran and Grubbs tests. Two laboratories were detected as outliers by the Grubbs test. The robust precision criteria gave values between the classical values estimated before and after rejection of the outliers. Using the robust method, the relative expanded uncertainty by the quantitation method is about 20% for a 1% Bt176 content, whereas it can reach 40% for a 0.1% Bt176. The performances of the quantitation assay are relevant to the application of the European regulation, which has an accepted tolerance interval of about ±50%. These data were fitted to a power model (r2 = 0.96). Thanks to this model, it is possible to propose an estimation of uncertainty of the QRT-PCR quantitation step and an uncertainty budget depending on the analytical conditions.

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Clarke, Lawrence R., Michael L. Wiederhold, and George A. Gates. "Quantitation of Pneumatic Otoscopy." Otolaryngology–Head and Neck Surgery 96, no. 2 (February 1987): 119–24. http://dx.doi.org/10.1177/019459988709600202.

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Pneumatic otoscopy has long been espoused as an easily and rapidly used, inexpensive method for routine evaluation of the tympanic membrane and middle ear. This widely used technique, however, has not been explored in a quantitative manner. Therefore, we measured the differential pressure and frequency of the pressure pulse used by a group of experienced otoscopists during routine pneumatic otoscopic examination. We found the pressure pulse varied from 170 to 520 mm H2O and the frequency varied from 2.2 to 4.9 pulses per second. We used a pneumatic pump to deliver a controlled pressure pulse—at a frequency of 4.0/sec—to the pneumatic otoscope. This device was used to measure the minimum pressure pulse necessary for the experienced otoscopist to merely detect tympanic membrane movement in normal ears. The threshold for visible movement ranged from 10 to 15 mm H2O. Thus, even the most gentle otoscopist used a pressure pulse an order of magnitude higher than the threshold for detection of visible movement in normal ears. The gap between the pressure pulse at threshold for visual detection of movement and that which is routinely used offers the possibility of increased sensitivity and specificity for this commonly used diagnostic tool. Preliminary data are presented from this study in which the pressure pulses required for visual detection of tympanic membrane movement in diseased ears were determined.

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Vuckovic, Dajana. "Improving metabolome coverage and data quality: advancing metabolomics and lipidomics for biomarker discovery." Chemical Communications 54, no. 50 (2018): 6728–49. http://dx.doi.org/10.1039/c8cc02592d.

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Trinh, Hung V., Jonas Grossmann, Peter Gehrig, Bernd Roschitzki, Ralph Schlapbach, Urs F. Greber, and Silvio Hemmi. "iTRAQ-Based and Label-Free Proteomics Approaches for Studies of Human Adenovirus Infections." International Journal of Proteomics 2013 (March 11, 2013): 1–16. http://dx.doi.org/10.1155/2013/581862.

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Both isobaric tags for relative and absolute quantitation (iTRAQ) and label-free methods are widely used for quantitative proteomics. Here, we provide a detailed evaluation of these proteomics approaches based on large datasets from biological samples. iTRAQ-label-based and label-free quantitations were compared using protein lysate samples from noninfected human lung epithelial A549 cells and from cells infected for 24 h with human adenovirus type 3 or type 5. Either iTRAQ-label-based or label-free methods were used, and the resulting samples were analyzed by liquid chromatography (LC) and tandem mass spectrometry (MS/MS). To reduce a possible bias from quantitation software, we applied several software packages for each procedure. ProteinPilot and Scaffold Q+ software were used for iTRAQ-labeled samples, while Progenesis LC-MS and ProgenesisF-T2PQ/T3PQ were employed for label-free analyses. R2 correlation coefficients correlated well between two software packages applied to the same datasets with values between 0.48 and 0.78 for iTRAQ-label-based quantitations and 0.5 and 0.86 for label-free quantitations. Analyses of label-free samples showed higher levels of protein up- or downregulation in comparison to iTRAQ-labeled samples. The concentration differences were further evaluated by Western blotting for four downregulated proteins. These data suggested that the label-free method was more accurate than the iTRAQ method.

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Lin, Hao, Yaxian Duan, Zhongxiu Man, Muhammad Zareef, Zhuo Wang, and Quansheng Chen. "Quantitation of volatile aldehydes using chemoselective response dyes combined with multivariable data analysis." Food Chemistry 353 (August 2021): 129485. http://dx.doi.org/10.1016/j.foodchem.2021.129485.

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Alger, Jeffry R., Sophia C. Symko, Alberto Bizzi, Stefan Posse, Daryl J. DesPres, and Mark R. Armstrong. "Absolute Quantitation of Short TE Brain 1H-MR Spectra and Spectroscopic Imaging Data." Journal of Computer Assisted Tomography 17, no. 2 (March 1993): 191–99. http://dx.doi.org/10.1097/00004728-199303000-00006.

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Bradley, David C., Garry M. Steil, and Richard N. Bergman. "OOPSEG: a data smoothing program for quantitation and isolation of random measurement error." Computer Methods and Programs in Biomedicine 46, no. 1 (January 1995): 67–77. http://dx.doi.org/10.1016/0169-2607(94)01600-k.

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Draper, William M., Donald Wijekoon, and Robert D. Stephens. "Speciation and quantitation of Aroclors in hazardous wastes based on PCB congener data." Chemosphere 22, no. 1-2 (January 1991): 147–63. http://dx.doi.org/10.1016/0045-6535(91)90272-f.

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Magnitsky, Sergey, Jinjin Zhang, Djaudat Idiyatullin, Geetha Mohan, Michael Garwood, Nancy E. Lane, and Sharmila Majumdar. "Positive contrast from cells labeled with iron oxide nanoparticles: Quantitation of imaging data." Magnetic Resonance in Medicine 78, no. 5 (January 17, 2017): 1900–1910. http://dx.doi.org/10.1002/mrm.26585.

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Stessl, Martina, Christian R. Noe, and Bodo Lachmann. "Influence of image-analysis software on quantitation of two-dimensional gel electrophoresis data." ELECTROPHORESIS 30, no. 2 (January 2009): 325–28. http://dx.doi.org/10.1002/elps.200800213.

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Campbell, J. Larry, and J. C. Yves Le Blanc. "Targeted Ion Parking for the Quantitation of Biotherapeutic Proteins: Concepts and Preliminary Data." Journal of the American Society for Mass Spectrometry 21, no. 12 (December 2010): 2011–22. http://dx.doi.org/10.1016/j.jasms.2010.08.015.

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Lebrón-Aguilar, R., A. C. Soria, and J. E. Quintanilla-López. "Comprehensive evaluation of direct injection mass spectrometry for the quantitative profiling of volatiles in food samples." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2079 (October 28, 2016): 20150375. http://dx.doi.org/10.1098/rsta.2015.0375.

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Although qualitative strategies based on direct injection mass spectrometry (DIMS) have recently emerged as an alternative for the rapid classification of food samples, the potential of these approaches in quantitative tasks has scarcely been addressed to date. In this paper, the applicability of different multivariate regression procedures to data collected by DIMS from simulated mixtures has been evaluated. The most relevant factors affecting quantitation, such as random noise, the number of calibration samples, type of validation, mixture complexity and similarity of mass spectra, were also considered and comprehensively discussed. Based on the conclusions drawn from simulated data, and as an example of application, experimental mass spectral fingerprints collected by direct thermal desorption coupled to mass spectrometry were used for the quantitation of major volatiles in Thymus zygis subsp. zygis chemotypes. The results obtained, validated with the direct thermal desorption coupled to gas chromatography–mass spectrometry method here used as a reference, show the potential of DIMS approaches for the fast and precise quantitative profiling of volatiles in foods. This article is part of the themed issue ‘Quantitative mass spectrometry’.

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Norwood, Daniel, Anna Michelson, Nicole Dunn, John Duett, Louis Fleck, and Gyorgy Vas. "Impact of the GC-MS Injection Solvent and the Analyte Concentration on Relative Responses for common Extractables." Reviews in Separation Sciences 4, no. 1 (March 22, 2022): e22002-e22002. http://dx.doi.org/10.17145/rss.22.002.

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Sample introduction in the GC-MS analysis is a relatively complex, multistep process, where usually a liquid sample is introduced to an injector, evaporated, and transferred to the GC column. To achieve reliable quantitation, the process must be well controlled. In the past decade or so, GC-MS based relative responses were evaluated for hundreds of extractables with the benefit of using those relative response factors for quantitation or to create databases to somewhat bridge the existing gap to estimate the amount of non-identified analytes. No data has yet been published indicating how relative responses are impacted by different injection solvents, or when the evaluation level is addressing the sub µg/mL levels. This paper presents data sets for 74 impurities (0.5-5 µg/mL range), with multiple internal standards, using 8 injection vehicles. Based on the presented data, the injection vehicle has a significant impact on the relative response factors, and therefore the quantitative assignment of the impurities.

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Carson, Richard E. "Precision and Accuracy Considerations of Physiological Quantitation in PET." Journal of Cerebral Blood Flow & Metabolism 11, no. 1_suppl (March 1991): A45—A50. http://dx.doi.org/10.1038/jcbfm.1991.36.

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The ability to differentiate regional patterns of flow and metabolism between various patient populations depends upon the signal-to-noise characteristics of the data. The approach chosen for producing quantitative data will affect the detection sensitivity of a method. Methods based on mathematical models can reduce intersubject variability by accounting for factors unrelated to the physiological measure of interest, in particular, differences in the input function. However, errors in the model and in the implementation of a model-based method can increase variability compared to simpler, empirical methods. Normalization of physiological measures can significantly reduce intersubject variation; however, interpretation of normalized results can be more complex. The advantages and disadvantages of various approaches for physiological quantitation are considered.

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Chua, Xien Yu, Theresa Mensah, Timothy Aballo, Samuel G. Mackintosh, Ricky D. Edmondson, and Arthur R. Salomon. "Tandem Mass Tag Approach Utilizing Pervanadate BOOST Channels Delivers Deeper Quantitative Characterization of the Tyrosine Phosphoproteome." Molecular & Cellular Proteomics 19, no. 4 (February 18, 2020): 730–43. http://dx.doi.org/10.1074/mcp.tir119.001865.

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Dynamic tyrosine phosphorylation is fundamental to a myriad of cellular processes. However, the inherently low abundance of tyrosine phosphorylation in the proteome and the inefficient enrichment of phosphotyrosine(pTyr)-containing peptides has led to poor pTyr peptide identification and quantitation, critically hindering researchers' ability to elucidate signaling pathways regulated by tyrosine phosphorylation in systems where cellular material is limited. The most popular approaches to wide-scale characterization of the tyrosine phosphoproteome use pTyr enrichment with pan-specific, anti-pTyr antibodies from a large amount of starting material. Methods that decrease the amount of starting material and increase the characterization depth of the tyrosine phosphoproteome while maintaining quantitative accuracy and precision would enable the discovery of tyrosine phosphorylation networks in rarer cell populations. To achieve these goals, the BOOST (Broad-spectrum Optimization Of Selective Triggering) method leveraging the multiplexing capability of tandem mass tags (TMT) and the use of pervanadate (PV) boost channels (cells treated with the broad-spectrum tyrosine phosphatase inhibitor PV) selectively increased the relative abundance of pTyr-containing peptides. After PV boost channels facilitated selective fragmentation of pTyr-containing peptides, TMT reporter ions delivered accurate quantitation of each peptide for the experimental samples while the quantitation from PV boost channels was ignored. This method yielded up to 6.3-fold boost in pTyr quantification depth of statistically significant data derived from contrived ratios, compared with TMT without PV boost channels or intensity-based label-free (LF) quantitation while maintaining quantitative accuracy and precision, allowing quantitation of over 2300 unique pTyr peptides from only 1 mg of T cell receptor-stimulated Jurkat T cells. The BOOST strategy can potentially be applied in analyses of other post-translational modifications where treatments that broadly elevate the levels of those modifications across the proteome are available.

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Mikolajczyk, K., M. Szabatin, P. Rudnicki, M. Grodzki, and C. Burger. "A JAVA environment for medical image data analysis: Initial application for brain PET quantitation." Medical Informatics 23, no. 3 (January 1998): 207–14. http://dx.doi.org/10.3109/14639239809001400.

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Sun, C., Y. Zhao, H. Li, Y. Dong, HJ MacIsaac, and A. Zhan. "Unreliable quantitation of species abundance based on high-throughput sequencing data of zooplankton communities." Aquatic Biology 24, no. 1 (May 20, 2015): 9–15. http://dx.doi.org/10.3354/ab00629.

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Qiu, Xi, Lijuan Kang, Martin Case, Naidong Weng, and Wenying Jian. "Quantitation of intact monoclonal antibody in biological samples: comparison of different data processing strategies." Bioanalysis 10, no. 13 (July 2018): 1055–67. http://dx.doi.org/10.4155/bio-2018-0016.

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Belén, Federico, Federico Danilo Vallese, Lisa G. T. Leist, Marco Flores Ferrão, Adriano de Araújo Gomes, and Marcelo Fabian Pistonesi. "Computer-vision based second-order (kinetic-color) data generation: arsenic quantitation in natural waters." Microchemical Journal 157 (September 2020): 104916. http://dx.doi.org/10.1016/j.microc.2020.104916.

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Li, Ka-Loh, and Alan Jackson. "New hybrid technique for accurate and reproducible quantitation of dynamic contrast-enhanced MRI data." Magnetic Resonance in Medicine 50, no. 6 (November 21, 2003): 1286–95. http://dx.doi.org/10.1002/mrm.10652.

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Fukuda, Akito, Sayaka Kondo, Kenichi Maruyama, Koji Suzuki, and Masafumi Hagiwara. "A Pseudo Data Generation Method and a Two-Stage Quantitation Method for Simultaneous Determination Sensor of Nucleotide Derivatives." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 7 (September 20, 2007): 751–58. http://dx.doi.org/10.20965/jaciii.2007.p0751.

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In this paper, we propose a pseudo data generation method and a two-stage quantitation method for simultaneous determination of nucleotide derivatives sensor that determines concentration of nucleotide derivatives based on sensor response. Conventional sensors are difficult to determine concentration of nucleotide derivatives simultaneously because they have similar structures and they influence each other, so the precision is low. In order to archive high precision and simultaneous determination sensor, this paper proposes a pseudo data generation method and a two-stage quantitation method. To analyze sensor response, we use GRNN (General Regression Neural Network). With this sensor, concentration of nucleotide derivatives is determined simultaneously, easily and fast. It was confirmed by the experiments that proposed methods are effective for determining concentration of nucleotide derivatives.

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Mudge, Elizabeth M., Paula N. Brown, Catherine A. Rimmer, and Melissa M. Phillips. "Determination of Curcuminoids in Turmeric Dietary Supplements by HPLC-DAD: Multi-laboratory Study Through the NIH-ODS/NIST Quality Assurance Program." Journal of AOAC INTERNATIONAL 103, no. 6 (May 19, 2020): 1625–32. http://dx.doi.org/10.1093/jaoacint/qsaa069.

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Abstract Background Turmeric is a medicinal herb containing curcuminoids, used as quality markers in dietary supplements. In 2016, an AOAC First Action Official MethodSM was adopted for quantitation of curcuminoids and requires multi-laboratory reproducibility data for Final Action status. Objective To collect reproducibility data for the quantitation of curcuminoids in dietary supplements through the National Institutes of Health Office of Dietary Supplements/National Institute of Standards and Technology Quality Assurance Program (QAP). Method Laboratories that participated in the QAP by following the Official Methods of AnalysisSM Method 2016.16, submitted data for ten turmeric products. The data were analyzed for mean, repeatability, and reproducibility standard deviations, repeatability, and reproducibility. Results The initial data collection resulted in insufficient replicates (five) for each test sample to determine reproducibility, therefore laboratories were provided additional materials resulting in an incremental data approach. For homogenous products, reproducibility for curcumin ranged from 3.4 to 10.3%, bisdemethoxycurcumin with reproducibility ranging from 6.4 to 14.8%, and demethoxycurcumin ranging from 5.6 to 9.9%. The method was unsuitable for the quantitation of curcuminoids in complex smoothie products, products containing microbeads, or tinctures based on interlaboratory variances. Recommendations were provided for future multi-laboratory studies performed through QAPs and incremental approaches. Conclusions Method 2016.16 is suitable for the quantitation of curcuminoids and should be adopted for Final Action status for single and multi-ingredient dietary supplements containing dried roots, dried powders/extracts in bulk material, capsules, and softgels. Highlights Reproducibility for Method 2016.16 was collected through a non-traditional incremental data multi-laboratory study. The method is suitable for quantitation of curcuminoids in most common dietary supplements.

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Zimmerman, Matthew, David Cooper, Marcin Cymborowski, Krzysztof Konina, Marek Grabowski, Elizabeth MacLean, and Wladek Minor. "Transforming biomedical and structural data into information and knowledge." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C492. http://dx.doi.org/10.1107/s2053273314095072.

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The LabDB laboratory information management system (LIMS) tracks, organizes and analyzes data from chemical and solution management, protein production, crystallization, diffraction, structure solution, and in vitro biochemical and biophysical experiments. The system is comprised of multiple modules specialized for different tasks, such as the Xtaldb system for crystallization or the hkldb module of the HKL-3000 suite for diffraction data collection and structure solution. The biochemical/biophysical experiments tracked by LabDB include spectrophotometric binding and kinetics, thermal shift binding, isothermal titration calorimetry (ITC) and protein quantitation. These tools associate functional and structural experiments, for example, for selecting likely substrates for co-crystallization and soaking experiments. Whenever possible, the system harvests data with no or minimal user intervention from laboratory hardware. Devices that may connect to or import data into LabDB include crystal observation (Rigaku Minstrel HT and Formulatrix Rock Imager), liquid handling (Formulatrix Rock Maker and Emerald Opti-Matrix Maker), chromatography (GE Healthcare AKTA), quantitation (Caliper LabChip GX II and Bio-Rad Gel Doc EZ), RT-PCR (Applied Biosystems 7900HT and Bio-Rad C1000/CFX96) and ITC (MicroCal iTC-200) systems. LabDB is used by two high-throughput PSI:Biology centers (MCSG and NYSGRC) as well as other major NIH consortia (the Center for Structural Genomics of Infectious Diseases and the Enzyme Function Initiative), and track millions of experiments on tens of thousands of targets.[1] The system also provides extensive data mining and analysis tools for translating raw experimental data into information and knowledge. We present examples of analyses generated by the system useful in designing new experiments.

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BUKHMAN, YURY V., MOYEZ DHARSEE, ROB EWING, PETER CHU, THODOROS TOPALOGLOU, THIERRY LE BIHAN, THEO GOH, et al. "DESIGN AND ANALYSIS OF QUANTITATIVE DIFFERENTIAL PROTEOMICS INVESTIGATIONS USING LC-MS TECHNOLOGY." Journal of Bioinformatics and Computational Biology 06, no. 01 (February 2008): 107–23. http://dx.doi.org/10.1142/s0219720008003321.

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Liquid chromatography–mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS–based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS–based differential quantitation. Supplementary material is available at .

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Keawbunsong, Pitak, Sarun Duangsuwan, Pichaya Supanakoon, and Sathaporn Promwong. "Quantitative Measurement of Path Loss Model Adaptation Using the Least Squares Method in an Urban DVB-T2 System." International Journal of Antennas and Propagation 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/7219618.

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The aim of this paper was to propose quantitative measurement of path loss model adaptation in urban radio propagation for a second-generation, terrestrial digital video broadcasting standard (DVB-T2) system. The measurement data was analyzed using data processing based on the least squares (LS) method to verify the probabilistic quantitation of realistic data measurement such as mean error (ME), root mean square error (RMSE), and standard deviation of error (SD), as well as relative error (RE). To distinguish the experimental evaluation, the researchers compared between the conventional Hata path loss model and the proposed model. The result showed that path loss based on the proposed model was more accurate in predicting the quantitative measurement of propagation data properly.

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Journal articles on the topic 'Data Quantitation'

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