Relevant bibliographies by topics / Data Quantitation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data Quantitation'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "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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Dissertations / Theses on the topic "Data Quantitation"

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Lee, Wooram. "Protein Set for Normalization of Quantitative Mass Spectrometry Data." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/54554.

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Mass spectrometry has been recognized as a prominent analytical technique for peptide and protein identification and quantitation. With the advent of soft ionization methods, such as electrospray ionization and matrix assisted laser desorption/ionization, mass spectrometry has opened a new era for protein and proteome analysis. Due to its high-throughput and high-resolution character, along with the development of powerful data analysis software tools, mass spectrometry has become the most popular method for quantitative proteomics. Stable isotope labeling and label-free quantitation methods are widely used in quantitative mass spectrometry experiments. Proteins with stable expression level and key roles in basic cellular functions such as actin, tubulin and glyceraldehyde-3-phosphate dehydrogenase, are frequently utilized as internal controls in biological experiments. However, recent studies have shown that the expression level of such commonly used housekeeping proteins is dependent on cell type, cell cycle or disease status, and that it can change as a result of a biochemical stimulation. Such phenomena can, therefore, substantially compromise the use of these proteins for data validation. In this work, we propose a novel set of proteins for quantitative mass spectrometry that can be used either for data normalization or validation purposes. The protein set was generated from cell cycle experiments performed with MCF-7, an estrogen receptor positive breast cancer cell line, and MCF-10A, a non-tumorigenic immortalized breast cell line. The protein set was selected from a list of 3700 proteins identified in the different cellular sub-fractions and cell cycle stages of MCF-7/MCF-10A cells, based on the stability of spectral count data (CV<30 %) generated with an LTQ ion trap mass spectrometer. A total of 34 proteins qualified as endogenous standards for the nuclear, and 75 for the cytoplasmic cell fractions, respectively. The validation of these proteins was performed with a complementary, Her2+, SKBR-3 cell line. Based on the outcome of these experiments, it is anticipated that the proposed protein set will find applicability for data normalization/validation in a broader range of mechanistic biological studies that involve the use of cell lines.
Master of Science
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McQueen, Peter. "Alternative strategies for proteomic analysis and relative protein quantitation." Wiley-VCH, 2015. http://hdl.handle.net/1993/30850.

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The main approach to studying the proteome is a technique called data dependent acquisition (DDA). In DDA, peptides are analyzed by mass spectrometry to determine the protein composition of a biological isolate. However, DDA is limited in its ability to analyze the proteome, in that it only selects the most abundant ions for analysis, and different protein identifications can result even if the same sample is analyzed multiple times in succession. Data independent acquisition (DIA) is a newly developed method that should be able to solve these limitations and improve our ability to analyze the proteome. We used an implementation of DIA (SWATH) to perform relative protein quantitation in the model bacterial system, Clostridium stercorarium, using two different carbohydrate sources, and found that it was able to provide precise quantitation of proteins and was overall more consistent in its ability to identify components of the proteome than DDA. Relative quantitation of proteins is an important method that can determine which proteins are important to a biochemical process of interest. How we determine which proteins are differentially regulated between different conditions is an important question in proteomic analysis. We developed a new approach to analyzing differential protein expression using variation between biological replicates to determine which proteins are being differentially regulated between two conditions. This analysis showed that a large proportion of proteins identified by quantitative proteomic analysis can be differentially regulated and that these proteins are in fact related to biological processes. Analyzing changes in protein expression is a useful tool that can pinpoint many key processes in biological systems. However, these techniques fail to take into account that enzyme activity is regulated by other factors than controlling their level of expression. Activity based protein profiling (ABPP) is a method that can determine the activity state of an enzyme in whole cell proteomes. We found that enzyme activity can change in response to a number of different conditions and that these changes do not always correspond with compositional changes. Mass spectrometry techniques were also used to identify serine hydrolases and characterize their expression in this organism.
February 2016
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Wang, Xin. "A Novel Approach for Automatic Quantitation of 31P Magnetic Resonance Spectroscopy Data." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1236271757.

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Manso, Jalice Y. "Sensor fusion of IR, NIR, and Raman spectroscopic data for polymorph quantitation of an agrochemical compound." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 37 p, 2009. http://proquest.umi.com/pqdweb?did=1694432951&sid=2&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Lien, Tonje Gulbrandsen. "Statistical Analysis of Quantitative PCR Data." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13094.

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This thesis seeks to develop a better understanding of the analysis of gene expression to find the amount of transcript in a sample. The mainstream method used is called Polymerase Chain Reaction (PCR) and it exploits the DNA's ability to replicate. The comparative CT method estimate the starting fluorescence level f0 by assuming constant amplification in each PCR cycle, and it uses the fluorescence level which has risen above a certain threshold. We present a generalization of this method, where different threshold values can be used. The main aim of this thesis is to evaluate a new method called the Enzymological method. It estimates f0 by considering a cycle dependent amplification and uses a larger part of the fluorescence curves, than the two CT methods. All methods are tested on dilution series, where the dilution factors are known. In one of the datasets studied, the Clusterin dilution-dataset, we get better estimates from the Enzymological method compared to the two CT methods.
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Kafatos, George. "Statistical analysis of quantitative seroepidemiological data." Thesis, Open University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.539408.

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Martin, Anthony John Michael. "Quantitative data validation (automated visual evaluations)." Thesis, De Montfort University, 1999. http://hdl.handle.net/2086/6256.

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Historically, validation has been perfonned on a case study basis employing visual evaluations, gradually inspiring confidence through continual application. At present, the method of visual evaluation is the most prevalent form of data analysis, as the brain is the best pattern recognition device known. However, the human visual/perceptual system is a complicated mechanism, prone to many types of physical and psychological influences. Fatigue is a major source of inaccuracy within the results of subjects perfonning complex visual evaluation tasks. Whilst physical and experiential differences along with age have an enormous bearing on the visual evaluation results of different subjects. It is to this end that automated methods of validation must be developed to produce repeatable, quantitative and objective verification results. This thesis details the development of the Feature Selective Validation (FSV) method. The FSV method comprises two component measures based on amplitude differences and feature differences. These measures are combined employing a measured level of subjectivity to fonn an overall assessment of the comparison in question or global difference. The three measures within the FSV method are strengthened by statistical analysis in the form of confidence levels based on amplitude, feature or global discrepancies between compared signals. Highly detailed diagnostic infonnation on the location and magnitude of discrepancies is also made available through the employment of graphical (discrete) representations of the three measures. The FSV method also benefits from the ability to mirror human perception, whilst producing infonnation which directly relates human variability and the confidence associated with it. The FSV method builds on the common language of engineers and scientists alike, employing categories which relate to human interpretations of comparisons, namely: 'ideal', 'excellent', 'very good', 'good', 'fair', 'poor' and 'extremely poor' . Quantitative
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Babari, Parvaneh. "Quantitative Automata and Logic for Pictures and Data Words." Doctoral thesis, Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-221165.

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Mathematical logic and automata theory are two scientific disciplines with a close relationship that is not only fundamental for many theoretical results but also forms the basis of a coherent methodology for the verification and synthesis of computing systems. This connection goes back to a much longer history in the 1960s, through the fundamental work of Büchi-Elgot-Trakhtenbrot, which shows the expressive equivalence of automata and logical systems such as monadic second-order logic on finite and infinite words. This allowed the handling of specifications (where global system properties are stated), and implementations (which involve the definition of the local steps in order to satisfy the global goals laid out in the specifications) in a single framework. This connection has been extended to and well-investigated for many other structures such as trees, finite pictures, timed words and data words. For many computer science applications, however, quantitative phenomena need to be modelled, as well. Examples are vagueness and uncertainty of a statement, length of time periods, spatial information, and resource consumption. Weighted automata, introduced by Schützenberger, are prominent models for quantitative aspects of systems. The framework of weighted monadic second-order logic over words was first introduced by Droste and Gastin. They gave a characterization of quantitative behavior of weighted finite automata, as semantics of monadic second-order sentences within their logic. Meanwhile, the idea of weighted logics was also applied to devices recognizing more general structures such as weighted tree automata, weighted automata on infinite words or traces. The main goal of this thesis is to give logical characterizations for weighted automata models on pictures and data words as well as for Büchi-tiling systems in the spirit of the classical Büchi-Elgot theorem. As the second goal, we deal with synchronizing problem for data words. Below, we briefly summarize the contents of this thesis. Informally, a two-dimensional string is called a picture and is defined as a rectangular array of symbols taken from a finite alphabet. A two-dimensional language (or picture language) is a set of pictures. Picture languages have been intensively investigated by several research groups. In Chapter 1, we define weighted two-dimensional on-line tessellation automata (W2OTA) taking weights from a new weight structure called picture valuation monoid. This new weighted picture automaton model can be used to model several applications, e.g. the average density of a picture. Such aspects could not be modelled by semiring weighted picture automaton model. The behavior of this automaton model is a picture series mapping pictures over an alphabet to elements of a picture valuation monoid. As one of our main results, we prove a Nivat theorem for W2OTA. It shows that recognizable picture series can be obtained precisely as projections of particularly simple unambiguously recognizable series restricted to unambiguous recognizable picture languages. In addition, we introduce a weighted monadic second-order logic (WMSO) which can model average density of pictures. As the other main result, we show that W2OTA and a suitable fragment of our weighted MSO logic are expressively equivalent. In Chapter 2, we generalize the notion of finite pictures to +ω-pictures, i.e., pictures which have finite number of rows and infinite number of columns. We extend conventional tiling systems with a Büchi acceptance condition in order to define the class of Büchi-tiling recognizable +ω-picture languages. The class of recognizable +ω-picture languages is indeed, a natural generalization of ω-regular languages. We show that the class of all Büchi-tiling recognizable +ω-picture languages has the similar closure properties as the class of tiling recognizable languages of finite pictures: it is closed under projection, union, and intersection, but not under complementation. While for languages of finite pictures, tiling recognizability and EMSO-definability coincide, the situation is quite different for languages of +ω-pictures. In this setting, the notion of tiling recognizability does not even cover the language of all +ω -pictures over Σ = {a, b} in which the letter a occurs at least once – a picture language that can easily be defined in first-order logic. As a consequence, EMSO is too strong for being captured by the class of tiling recognizable +ω-picture languages. On the other hand, EMSO is too weak for being captured by the class of all Büchi-tiling recognizable +ω-picture languages. To obtain a logical characterization of this class, we introduce the logic EMSO∞, which extends EMSO with existential quantification of infinite sets. Additionally, using combinatorial arguments, we show that the Büchi characterization theorem for ω-regular languges does not carry over to the Büchi-tiling recognizable +ω-picture languages. In Chapter 3, we consider the connection between weighted register automata and weighted logic on data words. Data words are sequences of pairs where the first element is taken from a finite alphabet (as in classical words) and the second element is taken from an infinite data domain. Register automata, introduced by Francez and Kaminski, provide a widely studied model for reasoning on data words. These automata can be considered as classical nondeterministic finite automata equipped with a finite set of registers which are used to store data in order to compare them with some data in the future. In this chapter, for quantitative reasoning on data words, we introduce weighted register automata over commutative data semirings equipped with a collection of binary data functions in the spirit of the classical theory of weighted automata. Whereas in the models of register automata known from the literature data are usually compared with respect to equality or a linear order, here we allow data comparison by means of an arbitrary collection of binary data relations. This approach permits easily to incorporate timed automata and weighted timed automata into our framework. Motivated by the seminal Büchi-Elgot-Trakhtenbrot theorem about the expressive equivalence of finite automata and monadic second-order (MSO) logic and by the weighted MSO logic of Droste and Gastin, we introduce weighted MSO logic on data words and give a logical characterization of weighted register automata. In Chapter 4, we study the concept of synchronizing data words in register automata. The synchronizing problem for data words asks whether there exists a data word that sends all states of the register automaton to a single state. The class of register automata that we consider here has a decidable non-emptiness problem, and the subclass of nondeterministic register automata with a single register has a decidable non-universality problem. We provide the complexity bounds of the synchronizing problem in the family of deterministic register automata with k registers (k-DRA), and in the family of nondeterministic register automata with single register (1-NRA), and in general undecidability of the problem in the family of k-NRA. To this end, we prove that, for k-DRA, inputting data words with only 2k + 1 distinct data values, from the infinite data domain, is sufficient to synchronize. Then, we show that the synchronizing problem for k-DRA is in general PSPACE-complete, and it is in NLOGSPACE for 1-DRA. For nondeterministic register automata (NRA), we show that Ackermann(n) distinct data, where n is the number of states of the register automaton, might be necessary to synchronize. Then, by means of a construction, proving that the synchronizing problem and the non-universality problem in 1-NRA are interreducible, we show the Ackermann-completeness of the problem for 1-NRA. However, for k-NRA, in general, we prove that this problem is undecidable due to the unbounded length of synchronizing data words.
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王漣 and Lian Wang. "A study on quantitative association rules." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31223588.

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Wang, Lian. "A study on quantitative association rules /." Hong Kong : University of Hong Kong, 1999. http://sunzi.lib.hku.hk/hkuto/record.jsp?B2118561X.

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Books on the topic "Data Quantitation"

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P, Olson Wayne, ed. Automated microbial identification and quantitation: Technologies for the 2000s. Buffalo Grove, IL: Interpharm Press, 1996.

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Liu, Ray H. Quantitation and mass spectrometric data of drugs and isotopically labeled analogs. Boca Raton: CRC Press, 2010.

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Sheng-Meng, Wang, and Canfield Dennis V. 1943-, eds. Quantitation and mass spectrometric data of drugs and isotopically labeled analogs. Boca Raton: Taylor & Francis, 2010.

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Liu, Ray H. Quantitation and mass spectrometric data of drugs and isotopically labeled analogs. Boca Raton: Taylor & Francis, 2010.

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Liu, Ray H. Quantitation and mass spectrometric data of drugs and isotopically labeled analogs. Boca Raton: Taylor & Francis, 2010.

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Mertens, Willem, Amedeo Pugliese, and Jan Recker. Quantitative Data Analysis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42700-3.

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Interpreting quantitative data. London: SAGE, 2002.

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Preece, Peter. Basic quantitative data analysis. Exeter: Research Support Unit, School of Education, University of Exeter, 1994.

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Advanced quantitative data analysis. Philadelpha, PA: Open University Press, 2003.

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A, O'Connell Ann, and McCoach D. Betsy, eds. Multilevel modeling of educational data. Charlotte, NC: IAP, 2008.

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Book chapters on the topic "Data Quantitation"

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Heinrich, Kurt F. J. "Strategies of Electron Probe Data Reduction." In Electron Probe Quantitation, 9–18. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-2617-3_2.

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Graham, Michael M. "Quantitation of PET Data in Clinical Practice." In Clinical PET-CT in Radiology, 61–66. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-48902-5_6.

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Castiglioni, Isabella, Francesca Gallivanone, and Maria Carla Gilardi. "Quantitation and Data Analysis in Hybrid PET/MRI Systems." In PET-CT and PET-MRI in Neurology, 23–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31614-7_3.

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Borisov, Nicolas, Maxim Sorokin, Andrew Garazha, and Anton Buzdin. "Quantitation of Molecular Pathway Activation Using RNA Sequencing Data." In Methods in Molecular Biology, 189–206. New York, NY: Springer US, 2019. http://dx.doi.org/10.1007/978-1-0716-0138-9_15.

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Gupta, Shubham, and Hannes Röst. "Automated Workflow for Peptide-Level Quantitation from DIA/ Data." In Methods in Molecular Biology, 453–68. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1024-4_31.

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Nilse, Lars, Marc Sturm, David Trudgian, Mogjiborahman Salek, Paul F. G. Sims, Kathleen M. Carroll, and Simon J. Hubbard. "SILACAnalyzer - A Tool for Differential Quantitation of Stable Isotope Derived Data." In Computational Intelligence Methods for Bioinformatics and Biostatistics, 45–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14571-1_4.

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Eriksson, L., H. Herzog, and A. L. Nordström. "Quantitation Problems in Positron Emission Tomography (PET) as Applied to the Kinetic Analysis of the Striatum Dopamine Data." In Brain Dopaminergic Systems: Imaging with Positron Tomography, 53–64. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3528-3_5.

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Albert, Jim, and Maria Rizzo. "Quantitative Data." In R by Example, 43–78. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1365-3_2.

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Chen, Xinguang. "Data Quantity, Missing Data, and Imputing." In Quantitative Epidemiology, 275–300. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83852-2_9.

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Swift, Louise, and Sally Piff. "Categorical Data." In Quantitative Methods, 484–505. London: Macmillan Education UK, 2014. http://dx.doi.org/10.1007/978-1-137-33794-8_19.

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Conference papers on the topic "Data Quantitation"

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Bendriem, Bernard. "On the quantitation of PET data." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5761685.

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Bendriem. "On the Quantitation of PET Data." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.593774.

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Singh, M. Suheshkumar, and Phaneendra K. Yalavarthy. "Born-ratio type data normalization improves quantitation in photoacoustic tomography." In SPIE Medical Imaging, edited by Johan G. Bosch and Marvin M. Doyley. SPIE, 2014. http://dx.doi.org/10.1117/12.2035213.

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Maschhoff, B. L., K. R. Zavadil, K. W. Nebesny, J. W. Fordemwalt, and N. R. Armstrong. "Problems in Quantitation in X-Ray Photoelectron Spectroscopy (XPS): the Use of Data Reduction Techniques to Obtain Peak Areas." In 30th Annual Technical Symposium, edited by Thomas W. Rusch. SPIE, 1986. http://dx.doi.org/10.1117/12.936607.

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Holcapek, Michal. "Potential of lipid class separation- mass spectrometry approaches for high-throughput lipidomic quantitation." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/djkv2622.

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The coupling of liquid-phase separation techniques and mass spectrometry (MS) is the prevailing analytical strategy in lipidomic analysis [1]. Two basic chromatographic approaches are available, lipid class separation and lipid species separation. The lipid class separation is represented by hydrophilic liquid chromatography (HILIC) and ultrahigh-performance supercritical fluid chromatography (UHPSFC) and offers advantages for high-throughput quantitation due to the coelution of lipid class internal standards and analytes from the same class [2]. Multiple lipid classes from phospholipid, sphingolipid, glycerolipid, fatty acyl, and sterol categories can be quantified in biological samples, but the main issue in lipidomic quantitation is the long-term stability of measured data [3]. The development of the LipidQuant 1.0 tool for automated processing of lipidomic data [4] was essential for high-throughput analysis of large clinical cohorts. The comprehensive MS determination of a wide range of blood lipids reveals statistically significant differences between various types of cancer patients and healthy controls visualized by multivariate data analysis, such as nonsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). The most extensive results are obtained for pancreatic cancer [5] but similar patterns of dysregulation were observed for kidney, breast, and prostate cancers as well [6]. The most dysregulated lipids are very long chain monounsaturated sphingomyelins, ceramides, LPC 18:2, and some other phospholipids. The sensitivity and specificity to diagnose pancreatic cancer were more than 90%.References[1] Holčapek, M.; Liebisch, G.; Ekroos, K. Anal. Chem. 2018, 90, 4249-4257.[2] Wolrab, D.; Chocholoušková, M.; Jirásko, R.; Peterka, O.; Holčapek, M; Anal. Bioanal. Chem. 2020, 412, 2375.[3] Wolrab, D. et al., Anal. Chim. Acta 2020, 1137, 74-84.[4] Wolrab, D. et al., Bioinformatics 2021, 37, 4591-4592.[5] Wolrab, D. et al., Nat. Com. 2021, 13, 124.[6] Wolrab, D. et al., Sci. Rep. 2021, 11, 20322.
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Pearson, Mackenzie, Paul Norris, and Ryan Anderson. "Combining near-complete characterization with quantitation for lipid analysis in matrix using electron activated dissociation." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/atpd4997.

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EAD has become a valuable tool for comprehensively characterizing glycerophospholipids and neutral lipids—from lipid class to double bond configuration—on the ZenoTOF 7600 system. However, lipid mediators remain to be fully characterized using EAD analysis. This dissociation tool utilizes electron capture and excitation to provide richer molecular structure information compared to conventional CID. Limited information is often provided by CID, conversely the EAD cell produces diagnostic fragmentation that can be used to quantify, identify, and characterize lipid mediators. Lipid mediators are diverse in terms of the number of possible double bond and functional group positions and geometries; however, the number of distinct parent masses and fragment ion masses is relatively small due to these compounds being derived from a limited set of fatty acid precursors and having many shared structural features (e.g., conjugated dienes, trienes, and tetrataenes; usually 1-3 hydroxyl groups). As a result, liquid chromatography methods for lipid mediator profiling generally demands a longer run time on the order of 15 – 20 minutes (or longer) to achieve sufficient separation of the hundreds of distinct mediators and their numerous epimers and other isobaric species. IDA was initially used with an inclusion list to trigger and collect spectra for a library of lipid mediator standards. Then, using scheduled MRMhr analysis, diagnostic fragments were monitored for PGE2 and PGD2 to quantify these two isoelemental compounds at the same chromatographic retention time. This newly described methodology for the characterization and quantitation of lipid mediators uses EAD on a ZenoTOF 7600 System. The rich MS/MS data produced by EAD contains unique fragment ions with patterns and intensities that can be used for complete lipid structural  confirmation. Additionally, the scheduled MRMhr EAD experiment is sensitive, reproducible, and fast, enabling quantitation from diagnostic fragments from complex mixtures during rapid LC separation.
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Carter, Scott L., Gad Getz, and Matthew L. Meyerson. "Abstract 2131: Accurate quantitation of absolute allelic copy-numbers in primary solid tumor samples from SNP-array / WGS data reveals patterns of tumor progression." In Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/1538-7445.am10-2131.

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Chen, Lili, Wensheng Gan, Qi Lin, Jinbao Miao, and Chien-Ming Chen. "Mining On-shelf High-utility Quantitative Itemsets." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9672021.

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Gautier, S. "Restoring close discontinuities from ultrasonic data." In QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2002. http://dx.doi.org/10.1063/1.1472864.

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Crosby, Kevin J., and Glenn C. England. "An Emerging Technique for Low-Concentration Measurement of Particulate Emissions From Gas-Fired Gas Turbines." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-65061.

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Concentrations of particulate matter (PM, PM10, PM2.5) from gas-fired gas turbines typically are near or below the overall method detection and quantitation limits of traditional hot filter/cooled impinger methods used for PM/PM10/PM2.5 emissions measurements (e.g., USEPA reference methods 5, 201A, 202). The test results can be highly variable, and may significantly overstate emissions even with application of best practices optimized to minimize bias and variability. An emerging test protocol for modifying USEPA Conditional Test Method 39 (CTM 39), a dilution sampling method for determining PM10/PM2.5 emissions from stationary sources, was used to accurately measure much lower PM concentrations. The lower concentrations are believed to be more representative of actual PM emissions from gas turbines. The modifications include adding ambient air sample collection and analysis methods for improved method sensitivity. Data from several testing programs are presented to illustrate the limitations of traditional methods for determining PM emissions, including measurement challenges associated with post-combustion emission controls such as SCR. Results from the modified method are presented to demonstrate the potential improvement compared with traditional hot filter/cooled impinger methods.
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Reports on the topic "Data Quantitation"

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Ruediger, Dylan, Danielle Cooper, Angela Bardeen, Liesl Baum, Shmuel Ben-Gad, Shaun Bennett, Kathleen Berger, et al. Fostering Data Literacy: Teaching with Quantitative Data in the Social Sciences. Ithaka S+R, September 2022. http://dx.doi.org/10.18665/sr.317506.

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“Fostering Data Literacy: Teaching with Quantitative Data in the Social Sciences” explores why and how instructors teach with data, identifies the most important challenges they face, and describes how faculty and students utilize relevant campus and external resources. Full details and actionable recommendations for stakeholders are offered in the body of the report, which offers guidance to university libraries and other campus units, faculty, vendors, and others interested in improving institutional capacities to support data-intensive instruction in the social sciences.
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Nakhleh, C. W. Plausible inference and the interpretation of quantitative data. Office of Scientific and Technical Information (OSTI), February 1998. http://dx.doi.org/10.2172/573294.

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Baldwin, Robert. Inferring Relative Factor Price Changes from Quantitative Data. Cambridge, MA: National Bureau of Economic Research, March 1999. http://dx.doi.org/10.3386/w7019.

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Kirwan, Jr, Lipphardt A. D., and B. L. Jr. Quantitative Use of Lagrangian Data in Numerical Models. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada635230.

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Sharpton, V. L., and R. A. F. Grieve. A proposal for quantitative analysis of SeaMARC data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1985. http://dx.doi.org/10.4095/315240.

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Hogan, Michael, and Michael Gallaher. Quantitative Indicators for Country-Level Innovation Ecosystems. RTI Press, May 2018. http://dx.doi.org/10.3768/rtipress.2018.op.0051.1805.

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Innovation has been shown to be a key factor in determining a country’s competitiveness and economic growth potential. Through investments in education and research and development, many developing countries have tried to avoid the “middle income trap” of stagnation by working to create high-value employment opportunities. To better understand country-level readiness to innovate, we have compiled a set of publicly available data indicators and created a data tool to illustrate innovation capabilities and infrastructure by country. Our approach builds on and advances existing national innovation metrics by constructing transparent, publicly sourced indicators that emphasize changes over time and interrelationships between different indicators, as opposed to creating simple indices across groups of indicators. This occasional paper is targeted to an applied audience, explaining the methods used to assemble the data, an overview of the indicators, practical applications of the data, summary statistics, and data limitations. The data are not intended to be a tool for providing answers about innovation, but rather a starting point for future work including market landscaping, country-level diagnostics, and qualitative protocols for research.
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Sinha, A. K. An approach to quantitative interpretation of airborne VLF-EM data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/194125.

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Adamopoulos, Tasso, and Diego Restuccia. Land Reform and Productivity: A Quantitative Analysis with Micro Data. Cambridge, MA: National Bureau of Economic Research, April 2019. http://dx.doi.org/10.3386/w25780.

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Niederberger, Eva, and Ginger Johnson. Cholera Questions Bank: Quantitative Questions for Community Level Data Collection. Institute of Development Studies, August 2023. http://dx.doi.org/10.19088/sshap.2023.023.

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This questions bank resource is a menu of quantitative questions related to socio-behavioural factors and Cholera. This resource seeks to facilitate the collection of quality data on community’s capacity, behaviours, practices and perceptions in relation to cholera prevention and management. It can be used by field teams and / or local research teams working in communities with cholera transmission and those at risk adhering to safety and protection protocols. Gathering and using high quality data on social, behavioural and community dynamics in relation to cholera prevention and management is vital to: 1. Understand people’s ability, capacity and behaviour in relation to prevent and/or reduce cholera infection risks. 2. Support evidence-based decision-making on communication and engagement strategies that address people’s needs and priorities in an evolving context. 3. Provide data that decision-makers can use to adapt cholera response and preparedness strategies and activities. 4. Support public health promoters and outreach workers in engaging with local populations to strengthen community-led actions. 5. Enhance the knowledge of public health promoters and outreach workers and support them in clearly communicating on cholera prevention and control. 6. Inform the design and adaptation of information content / messages shared and discussed with the local population.
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Niederberger, Eva, and Ginger Johnson. Cholera Questions Bank: Quantitative Questions for Community Level Data Collection. Institute of Development Studies, March 2023. http://dx.doi.org/10.19088/sshap.2023.004.

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This questions bank resource is a menu of quantitative questions related to socio-behavioural factors and Cholera. This resource seeks to facilitate the collection of quality data on community’s capacity, behaviours, practices and perceptions in relation to cholera prevention and management. It can be used by field teams and / or local research teams working in communities with cholera transmission and those at risk adhering to safety and protection protocols. Gathering and using high quality data on social, behavioural and community dynamics in relation to cholera prevention and management is vital to: 1. Understand people’s ability, capacity and behaviour in relation to prevent and/or reduce cholera infection risks. 2. Support evidence-based decision-making on communication and engagement strategies that address people’s needs and priorities in an evolving context. 3. Provide data that decision-makers can use to adapt cholera response and preparedness strategies and activities. 4. Support public health promoters and outreach workers in engaging with local populations to strengthen community-led actions. 5. Enhance the knowledge of public health promoters and outreach workers and support them in clearly communicating on cholera prevention and control. 6. Inform the design and adaptation of information content / messages shared and discussed with the local population.
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