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Sample records for abuse multivariate analysis

  1. Multivariate analysis with LISREL

    CERN Document Server

    Jöreskog, Karl G; Y Wallentin, Fan

    2016-01-01

    This book traces the theory and methodology of multivariate statistical analysis and shows how it can be conducted in practice using the LISREL computer program. It presents not only the typical uses of LISREL, such as confirmatory factor analysis and structural equation models, but also several other multivariate analysis topics, including regression (univariate, multivariate, censored, logistic, and probit), generalized linear models, multilevel analysis, and principal component analysis. It provides numerous examples from several disciplines and discusses and interprets the results, illustrated with sections of output from the LISREL program, in the context of the example. The book is intended for masters and PhD students and researchers in the social, behavioral, economic and many other sciences who require a basic understanding of multivariate statistical theory and methods for their analysis of multivariate data. It can also be used as a textbook on various topics of multivariate statistical analysis.

  2. Robust multivariate analysis

    CERN Document Server

    J Olive, David

    2017-01-01

    This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given.  The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory.   The robust techniques  are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis.  A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with...

  3. Methods of Multivariate Analysis

    CERN Document Server

    Rencher, Alvin C

    2012-01-01

    Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere."-IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life sit

  4. Applied multivariate statistical analysis

    CERN Document Server

    Härdle, Wolfgang Karl

    2015-01-01

    Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...

  5. Multivariate data analysis

    DEFF Research Database (Denmark)

    Hansen, Michael Adsetts Edberg

    Interest in statistical methodology is increasing so rapidly in the astronomical community that accessible introductory material in this area is long overdue. This book fills the gap by providing a presentation of the most useful techniques in multivariate statistics. A wide-ranging annotated set...

  6. Applied multivariate statistical analysis

    National Research Council Canada - National Science Library

    Johnson, Richard Arnold; Wichern, Dean W

    1988-01-01

    .... The authors hope that their discussions will meet the needs of experimental scientists, in a wide variety of subject matter areas, as a readable introduciton to the staistical analysis of multvariate observations...

  7. Multivariate analysis techniques

    Energy Technology Data Exchange (ETDEWEB)

    Bendavid, Josh [European Organization for Nuclear Research (CERN), Geneva (Switzerland); Fisher, Wade C. [Michigan State Univ., East Lansing, MI (United States); Junk, Thomas R. [Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)

    2016-01-01

    The end products of experimental data analysis are designed to be simple and easy to understand: hypothesis tests and measurements of parameters. But, the experimental data themselves are voluminous and complex. Furthermore, in modern collider experiments, many petabytes of data must be processed in search of rare new processes which occur together with much more copious background processes that are of less interest to the task at hand. The systematic uncertainties on the background may be larger than the expected signal in many cases. The statistical power of an analysis and its sensitivity to systematic uncertainty can therefore usually both be improved by separating signal events from background events with higher efficiency and purity.

  8. Practical multivariate analysis

    CERN Document Server

    Afifi, Abdelmonem; Clark, Virginia A

    2011-01-01

    ""First of all, it is very easy to read. … The authors manage to introduce and (at least partially) explain even quite complex concepts, e.g. eigenvalues, in an easy and pedagogical way that I suppose is attractive to readers without deeper statistical knowledge. The text is also sprinkled with references for those who want to probe deeper into a certain topic. Secondly, I personally find the book's emphasis on practical data handling very appealing. … Thirdly, the book gives very nice coverage of regression analysis. … this is a nicely written book that gives a good overview of a large number

  9. Essentials of multivariate data analysis

    CERN Document Server

    Spencer, Neil H

    2013-01-01

    ""… this text provides an overview at an introductory level of several methods in multivariate data analysis. It contains in-depth examples from one data set woven throughout the text, and a free [Excel] Add-In to perform the analyses in Excel, with step-by-step instructions provided for each technique. … could be used as a text (possibly supplemental) for courses in other fields where researchers wish to apply these methods without delving too deeply into the underlying statistics.""-The American Statistician, February 2015

  10. Multivariable modeling and multivariate analysis for the behavioral sciences

    CERN Document Server

    Everitt, Brian S

    2009-01-01

    Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues.The author begins by exploring the types and design of behavioral studies. He also explains how models are used in the analysis of data. After describing graphical methods, such as scatterplot matrices, the text covers simple linear regression, locally weighted regression, multip

  11. Multivariate Analysis in Nuclear Physics

    Science.gov (United States)

    Désesquelles, P.

    Nuclear physics deals more and more with experiments involving a large number of parameters. The analysis of such experiments requires well adapted statistical techniques. The multivariate analysis techniques consist in the representation of the experimental events as points in the multidimensional space of the physical variables. One aim will be to treat experimental information as a whole. This formalism permits the simultaneous studies of the structures of the event cloud and of the correlations between the variables. Principal Component Analysis is concerned with the determination of the so-called principal variables, linear combinations of the primary physical variables, which represent the maximum information. Correspondence Analysis visualises, on a 2D diagram, the correlations between the modalities of qualitative variables. The goal of the Discriminant Analysis is to discriminate different types of events, that is to affect them to a familly. The last part of the work is devoted to a global protocol, involving the PCA, for the comparison of experimental data with data generated by a simulation code. Lecture given at the Joliot-Curie summer school on nuclear physics (Maubuisson France, sept. 1994)

  12. Multivariate analysis of hydrophobic descriptors

    Directory of Open Access Journals (Sweden)

    Stefan Dove

    2014-04-01

    Full Text Available Multivariate approaches like principal component analysis (PCA are powerful tools to investigate hydrophobic descriptors and to discriminate between intrinsic hydrophobicity and polar contributions as hydrogen bonds and other electronic effects. PCA of log P values measured for 37 solutes in eight solvent-water systems and of hydrophobic octanol-water substituent constants p for 25 meta- and para-substituents from seven phenyl series were performed (re-analysis of previous work. In both cases, the descriptors are repro­duced within experimental errors by two principal components, an intrinsic hydrophobic component and a second component accounting for differences between the systems due to electronic interactions. Underlying effects were identified by multiple linear regression analysis. Log P values depend on the water solubility of the solvents and hydrogen bonding capabilities of both the solute and the solvents. Results indicate different impacts of hydrogen bonds in nonpolar and polar solvent-water systems on log P and their dependence on isotropic and hydrated surface areas. In case of the p-values, the second component (loadings and scores correlates with electronic substituent constants. More detailed analysis of the data as p-values of disubstituted benzenes XPhY has led to extended symmetric bilinear Hammett-type models relating interaction increments to cross products pX sY, pY sX and sX sY which are mainly due to mutual effects on hydrogen-bonds with octanol.

  13. Exploratory multivariate analysis by example using R

    CERN Document Server

    Husson, Francois; Pages, Jerome

    2010-01-01

    Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the prin

  14. Multivariate refined composite multiscale entropy analysis

    Energy Technology Data Exchange (ETDEWEB)

    Humeau-Heurtier, Anne, E-mail: anne.humeau@univ-angers.fr

    2016-04-01

    Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.

  15. Factor analysis of multivariate data

    Digital Repository Service at National Institute of Oceanography (India)

    Fernandes, A.A.; Mahadevan, R

    A brief introduction to factor analysis is presented. A FORTRAN program, which can perform the Q-mode and R-mode factor analysis and the singular value decomposition of a given data matrix is presented in Appendix B. This computer program, uses...

  16. Multivariate Analysis of Industrial Scale Fermentation Data

    DEFF Research Database (Denmark)

    Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.

    2015-01-01

    of multivariate modelling were carried out using different data pre-processing and scaling methods in order to extract the trends from the industrial data set, obtained from a production process operating in Novozymes A/S. This data set poses challenges for data analysis, combining both online and offline......, with an average prediction error of 7.6%. A methodology is proposed for applying multivariate analysis to industrial scale batch process data....

  17. Multivariate meta-analysis: Potential and promise

    Science.gov (United States)

    Jackson, Dan; Riley, Richard; White, Ian R

    2011-01-01

    The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

  18. Multivariate survival analysis and competing risks

    CERN Document Server

    Crowder, Martin J

    2012-01-01

    Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

  19. Multivariate Analysis of Ladle Vibration

    Science.gov (United States)

    Yenus, Jaefer; Brooks, Geoffrey; Dunn, Michelle

    2016-08-01

    The homogeneity of composition and uniformity of temperature of the steel melt before it is transferred to the tundish are crucial in making high-quality steel product. The homogenization process is performed by stirring the melt using inert gas in ladles. Continuous monitoring of this process is important to make sure the action of stirring is constant throughout the ladle. Currently, the stirring process is monitored by process operators who largely rely on visual and acoustic phenomena from the ladle. However, due to lack of measurable signals, the accuracy and suitability of this manual monitoring are problematic. The actual flow of argon gas to the ladle may not be same as the flow gage reading due to leakage along the gas line components. As a result, the actual degree of stirring may not be correctly known. Various researchers have used one-dimensional vibration, and sound and image signals measured from the ladle to predict the degree of stirring inside. They developed online sensors which are indeed to monitor the online stirring phenomena. In this investigation, triaxial vibration signals have been measured from a cold water model which is a model of an industrial ladle. Three flow rate ranges and varying bath heights were used to collect vibration signals. The Fast Fourier Transform was applied to the dataset before it has been analyzed using principal component analysis (PCA) and partial least squares (PLS). PCA was used to unveil the structure in the experimental data. PLS was mainly applied to predict the stirring from the vibration response. It was found that for each flow rate range considered in this study, the informative signals reside in different frequency ranges. The first latent variables in these frequency ranges explain more than 95 pct of the variation in the stirring process for the entire single layer and the double layer data collected from the cold model. PLS analysis in these identified frequency ranges demonstrated that the latent

  20. A MULTIVARIATE ANALYSIS OF CROATIAN COUNTIES ENTREPRENEURSHIP

    Directory of Open Access Journals (Sweden)

    Elza Jurun

    2012-12-01

    Full Text Available In the focus of this paper is a multivariate analysis of Croatian Counties entrepreneurship. Complete data base available by official statistic institutions at national and regional level is used. Modern econometric methodology starting from a comparative analysis via multiple regression to multivariate cluster analysis is carried out as well as the analysis of successful or inefficacious entrepreneurship measured by indicators of efficiency, profitability and productivity. Time horizons of the comparative analysis are in 2004 and 2010. Accelerators of socio-economic development - number of entrepreneur investors, investment in fixed assets and current assets ratio in multiple regression model are analytically filtered between twenty-six independent variables as variables of the dominant influence on GDP per capita in 2010 as dependent variable. Results of multivariate cluster analysis of twentyone Croatian Counties are interpreted also in the sense of three Croatian NUTS 2 regions according to European nomenclature of regional territorial division of Croatia.

  1. Multivariate analysis: A statistical approach for computations

    Science.gov (United States)

    Michu, Sachin; Kaushik, Vandana

    2014-10-01

    Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

  2. Multivariate data analysis of 2 DE data

    DEFF Research Database (Denmark)

    Wulff, Tune; Jokumsen, Alfred; Jessen, Flemming

    achieved by 2-DE. Protein spots, which individually or in combination with other spots varied according to hypoxia were found by multivariate data analysis (partial least squares regression) on group scaled data (normalised spot volumes) followed by selection of significant spots by jack-knifing. Tandem...

  3. Multivariate Analysis of Industrial Scale Fermentation Data

    DEFF Research Database (Denmark)

    Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.

    2015-01-01

    Multivariate analysis allows process understanding to be gained from the vast and complex datasets recorded from fermentation processes, however the application of such techniques to this field can be limited by the data pre-processing requirements and data handling. In this work many iterations...

  4. Power Estimation in Multivariate Analysis of Variance

    Directory of Open Access Journals (Sweden)

    Jean François Allaire

    2007-09-01

    Full Text Available Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio. Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed.

  5. Some developments in multivariate image analysis

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey

    Multivariate image analysis (MIA), one of the successful chemometric applications, now is used widely in different areas of science and industry. Introduced in late 80s it has became very popular with hyperspectral imaging, where MIA is one of the most efficient tools for exploratory analysis...... and classification. MIA considers all image pixels as objects and their color values (or spectrum in the case of hyperspectral images) as variables. So it gives data matrices with hundreds of thousands samples in the case of laboratory scale images and even more for aerial photos, where the number of pixels could...... for and analyzing patterns on these plots and the original image allow to do interactive analysis, to get some hidden information, build a supervised classification model, and much more. In the present work several alternative methods to original principal component analysis (PCA) for building the projection...

  6. Urban water quality evaluation using multivariate analysis

    Directory of Open Access Journals (Sweden)

    Petr Praus

    2007-06-01

    Full Text Available A data set, obtained for the sake of drinking water quality monitoring, was analysed by multivariate methods. Principal component analysis (PCA reduced the data dimensionality from 18 original physico-chemical and microbiological parameters determined in drinking water samples to 6 principal components explaining about 83 % of the data variability. These 6 components represented inorganic salts, nitrate/pH, iron, chlorine, nitrite/ammonium traces, and heterotrophic bacteria. Using the PCA scatter plot and the Ward's clustering of the samples characterized by the first and second principal components, three clusters were revealed. These clusters sorted drinking water samples according to their origin - ground and surface water. The PCA results were confirmed by the factor analysis and hierarchical clustering of the original data.

  7. Advanced event reweighting using multivariate analysis

    International Nuclear Information System (INIS)

    Martschei, D; Feindt, M; Honc, S; Wagner-Kuhr, J

    2012-01-01

    Multivariate analysis (MVA) methods, especially discrimination techniques such as neural networks, are key ingredients in modern data analysis and play an important role in high energy physics. They are usually trained on simulated Monte Carlo (MC) samples to discriminate so called 'signal' from 'background' events and are then applied to data to select real events of signal type. We here address procedures that improve this work flow. This will be the enhancement of data / MC agreement by reweighting MC samples on a per event basis. Then training MVAs on real data using the sPlot technique will be discussed. Finally we will address the construction of MVAs whose discriminator is independent of a certain control variable, i.e. cuts on this variable will not change the discriminator shape.

  8. Multivariate analysis of data in sensory science

    CERN Document Server

    Naes, T; Risvik, E

    1996-01-01

    The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis. The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques. Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.

  9. Multivariate analysis applied to tomato hybrid production.

    Science.gov (United States)

    Balasch, S; Nuez, F; Palomares, G; Cuartero, J

    1984-11-01

    Twenty characters were measured on 60 tomato varieties cultivated in the open-air and in polyethylene plastic-house. Data were analyzed by means of principal components, factorial discriminant methods, Mahalanobis D(2) distances and principal coordinate techniques. Factorial discriminant and Mahalanobis D(2) distances methods, both of which require collecting data plant by plant, lead to similar conclusions as the principal components method that only requires taking data by plots. Characters that make up the principal components in both environments studied are the same, although the relative importance of each one of them varies within the principal components. By combining information supplied by multivariate analysis with the inheritance mode of characters, crossings among cultivars can be experimented with that will produce heterotic hybrids showing characters within previously established limits.

  10. An Introduction to Applied Multivariate Analysis

    CERN Document Server

    Raykov, Tenko

    2008-01-01

    Focuses on the core multivariate statistics topics which are of fundamental relevance for its understanding. This book emphasis on the topics that are critical to those in the behavioral, social, and educational sciences.

  11. Multivariate complexity analysis of team management problems

    OpenAIRE

    Bredereck, Robert

    2015-01-01

    Zugleich gedruckt erschienen im Universitätsverlag der TU Berlin unter der ISBN 978-3-7983-2764-1; ISSN 2199-5249 In dieser Dissertation identifizieren und entwickeln wir einfache kombinatorische Modelle für vier natürliche Teamverwaltungsaufgaben und untersuchen bezüglich Berechnungskomplexität handhabbare und nicht handhabbare Fälle. Hierzu analysieren wir die multivariate Komplexität der zu Grunde liegenden Probleme und testen manche unserer Algorithmen auf synthetischen und empirischen...

  12. Multivariate analysis of 2-DE protein patterns - Practical approaches

    DEFF Research Database (Denmark)

    Jacobsen, Charlotte; Jacobsen, Susanne; Grove, H.

    2007-01-01

    Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two...... of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times...

  13. Introduction to multivariate analysis linear and nonlinear modeling

    CERN Document Server

    Konishi, Sadanori

    2014-01-01

    ""The presentation is always clear and several examples and figures facilitate an easy understanding of all the techniques. The book can be used as a textbook in advanced undergraduate courses in multivariate analysis, and can represent a valuable reference manual for biologists and engineers working with multivariate datasets.""-Fabio Rapallo, Zentralblatt MATH 1296

  14. Dynamic speckle analysis using multivariate techniques

    International Nuclear Information System (INIS)

    López-Alonso, José M; Alda, Javier; Rabal, Héctor; Grumel, Eduardo; Trivi, Marcelo

    2015-01-01

    In this work we use principal components analysis to characterize dynamic speckle patterns. This analysis quantitatively identifies different dynamics that could be associated to physical phenomena occurring in the sample. We also found the contribution explained by each principal component, or by a group of them. The method analyzes the paint drying process over a hidden topography. It can be used for fast screening and identification of different dynamics in biological or industrial samples by means of dynamic speckle interferometry. (paper)

  15. Multivariate Analysis and Machine Learning in Cerebral Palsy Research

    Directory of Open Access Journals (Sweden)

    Jing Zhang

    2017-12-01

    Full Text Available Cerebral palsy (CP, a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

  16. Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

    Science.gov (United States)

    Zhang, Jing

    2017-01-01

    Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

  17. Special section on modern multivariate analysis

    OpenAIRE

    Kafadar, Karen

    2012-01-01

    A critically challenging problem facing statisticians is the identification of a suitable framework which consolidates data of various types, from different sources, and across different time frames or scales (many of which can be missing), and from which appropriate analysis and subsequent inference can proceed.

  18. Search for the top quark using multivariate analysis techniques

    International Nuclear Information System (INIS)

    Bhat, P.C.

    1994-08-01

    The D0 collaboration is developing top search strategies using multivariate analysis techniques. We report here on applications of the H-matrix method to the eμ channel and neural networks to the e+jets channel

  19. Matrix-based introduction to multivariate data analysis

    CERN Document Server

    Adachi, Kohei

    2016-01-01

    This book enables readers who may not be familiar with matrices to understand a variety of multivariate analysis procedures in matrix forms. Another feature of the book is that it emphasizes what model underlies a procedure and what objective function is optimized for fitting the model to data. The author believes that the matrix-based learning of such models and objective functions is the fastest way to comprehend multivariate data analysis. The text is arranged so that readers can intuitively capture the purposes for which multivariate analysis procedures are utilized: plain explanations of the purposes with numerical examples precede mathematical descriptions in almost every chapter. This volume is appropriate for undergraduate students who already have studied introductory statistics. Graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis will also find the book useful, as it is based on modern matrix formulations with a special emphasis on ...

  20. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    Science.gov (United States)

    Glascock, M. D.; Neff, H.; Vaughn, K. J.

    2004-06-01

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  1. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    International Nuclear Information System (INIS)

    Glascock, M. D.; Neff, H.; Vaughn, K. J.

    2004-01-01

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  2. Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

    Energy Technology Data Exchange (ETDEWEB)

    Glascock, M. D.; Neff, H. [University of Missouri, Research Reactor Center (United States); Vaughn, K. J. [Pacific Lutheran University, Department of Anthropology (United States)

    2004-06-15

    The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

  3. EURO AREA FISCAL STRUCTURES. A MULTIVARIATE ANALYSIS

    Directory of Open Access Journals (Sweden)

    HURDUZEU Gheorghe

    2014-07-01

    taxes on income of corporations and taxes on income of individuals and households and other current taxes. Actual social contributions were also split into employer’s actual contributions, employee’s social contributions and social contributions of self- and non-employed persons. As the primary data analysis revealed many differences between Euro Area member states, but also similarities concerning various fiscal aggregates, we completed the analysis through multidimensional analysis, with the aims of classifying Euro Area member states into subgroups with similar fiscal structures. Taking into consideration the above mentioned variables, we used cluster analysis in order to determine which member states have similar fiscal structures and which are the main similarities that characterize Euro Area in this respect.

  4. EXPLORATORY DATA ANALYSIS AND MULTIVARIATE STRATEGIES FOR REVEALING MULTIVARIATE STRUCTURES IN CLIMATE DATA

    Directory of Open Access Journals (Sweden)

    2016-12-01

    Full Text Available This paper is on data analysis strategy in a complex, multidimensional, and dynamic domain. The focus is on the use of data mining techniques to explore the importance of multivariate structures; using climate variables which influences climate change. Techniques involved in data mining exercise vary according to the data structures. The multivariate analysis strategy considered here involved choosing an appropriate tool to analyze a process. Factor analysis is introduced into data mining technique in order to reveal the influencing impacts of factors involved as well as solving for multicolinearity effect among the variables. The temporal nature and multidimensionality of the target variables is revealed in the model using multidimensional regression estimates. The strategy of integrating the method of several statistical techniques, using climate variables in Nigeria was employed. R2 of 0.518 was obtained from the ordinary least square regression analysis carried out and the test was not significant at 5% level of significance. However, factor analysis regression strategy gave a good fit with R2 of 0.811 and the test was significant at 5% level of significance. Based on this study, model building should go beyond the usual confirmatory data analysis (CDA, rather it should be complemented with exploratory data analysis (EDA in order to achieve a desired result.

  5. Multivariate analysis for selecting apple mutants

    International Nuclear Information System (INIS)

    Faedi, W.; Bagnara, G.L.; Rosati, P.; Cecchini, M.

    1992-01-01

    The mutlivariate analysis of four year records on several vegetative and productive traits of twenty-one apple mutants (3 of 'Jonathan', 3 of 'Ozark Gold', 14 of 'Mollie's Delicious', 1 of 'Neipling's Early Stayman)' induced by gamma radiations showed that observation of some traits of one-year-old shoots is the most efficient way to reveal compact growing apple mutants. In particular, basal cross-section area, total length and leaf area resulted the most appropriate parameters, while internode length together with conopy height and width are less appropriate. The most interesting mutants we found are: one of 'Mollie's Delicious for the best balance among tree and fruit traits and for high skin color; one of 'Neipling's Early Stayman' with an earlier and more extensively red colored apple than the original clone. (author)

  6. Looking Back at the Gifi System of Nonlinear Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Peter G. M. van der Heijden

    2016-09-01

    Full Text Available Gifi was the nom de plume for a group of researchers led by Jan de Leeuw at the University of Leiden. Between 1970 and 1990 the group produced a stream of theoretical papers and computer programs in the area of nonlinear multivariate analysis that were very innovative. In an informal way this paper discusses the so-called Gifi system of nonlinear multivariate analysis, that entails homogeneity analysis (which is closely related to multiple correspondence analysis and generalizations. The history is discussed, giving attention to the scientific philosophy of this group, and links to machine learning are indicated.

  7. The syntaxonomic position of Santolina etrusca - multivariate analysis

    OpenAIRE

    Claudia Angiolini; Vincenzo de Dominicis

    2014-01-01

    The results of multivariate analysis of the syntaxonomic role of Santolina etrusca (Lacaita) Marchi et D'Amato, a species endemic to Tyrrhenian central Italy, are reported. Classification was performed by polythetic divisive analysis using two-way indicator species (TWINSPAN). Ordination analysis was performed by correspondence analysis (CA). Classification and ordination showed that although Santolina etrusca grows prevalently in communities of Rosmarinetalia Br.-Bl. ex Molinier 1934, it is ...

  8. Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion

    International Nuclear Information System (INIS)

    Garcia-Cabrejo, Oscar; Valocchi, Albert

    2014-01-01

    Many mathematical and computational models used in engineering produce multivariate output that shows some degree of correlation. However, conventional approaches to Global Sensitivity Analysis (GSA) assume that the output variable is scalar. These approaches are applied on each output variable leading to a large number of sensitivity indices that shows a high degree of redundancy making the interpretation of the results difficult. Two approaches have been proposed for GSA in the case of multivariate output: output decomposition approach [9] and covariance decomposition approach [14] but they are computationally intensive for most practical problems. In this paper, Polynomial Chaos Expansion (PCE) is used for an efficient GSA with multivariate output. The results indicate that PCE allows efficient estimation of the covariance matrix and GSA on the coefficients in the approach defined by Campbell et al. [9], and the development of analytical expressions for the multivariate sensitivity indices defined by Gamboa et al. [14]. - Highlights: • PCE increases computational efficiency in 2 approaches of GSA of multivariate output. • Efficient estimation of covariance matrix of output from coefficients of PCE. • Efficient GSA on coefficients of orthogonal decomposition of the output using PCE. • Analytical expressions of multivariate sensitivity indices from coefficients of PCE

  9. Multivariate concentration determination using principal component regression with residual analysis.

    Science.gov (United States)

    Keithley, Richard B; Heien, Michael L; Wightman, R Mark

    2009-10-01

    Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method.

  10. Etiology of Drug Abuse: A Narrative Analysis

    Directory of Open Access Journals (Sweden)

    Nadjme Jadidi

    2014-01-01

    Full Text Available Introduction and Aim. Further gains in the prevention of drug abuse disorders require in-depth and holistic understanding of the risk factors of addiction from different perspectives. Lay persons and experts have different concepts of risk which could complement each other. The purpose of this study was to elaborate drug abuse risk factors through the story of individuals who had become drug dependent. Design and Methods. In this qualitative research, 33 individuals attending treatment centres for drug abuse were interviewed about the story of their addiction in Kerman, Iran. Interview questions were around the story of the participants. Results. All participants were male and in the age range of 18–40 years. Narrative analysis identified five themes as the main risk factors: family factors, peer pressure, the effect of gateway drugs (especially waterpipe, individual characteristics, and the community factors. More emphasis was placed upon the role of family factors, peer influence, and gateway effect. Discussion and Conclusion. This study elicited information from drug dependent subjects regarding the risk factors of drug abuse. According to drug dependent individuals’ views, more attention should be devoted to family and peer influences by policy makers, in developing culture-based preventive strategies.

  11. Looking back at the gifi system of nonlinear multivariate analysis

    NARCIS (Netherlands)

    Heijden, P.G.M. van der; Buuren, S. van

    2016-01-01

    Gifi was the nom de plume for a group of researchers led by Jan de Leeuw at the University of Leiden. Between 1970 and 1990 the group produced a stream of theoretical papers and computer programs in the area of nonlinear multivariate analysis that were very innovative. In an informal way this paper

  12. Using multivariate statistical analysis to assess changes in water ...

    African Journals Online (AJOL)

    Multivariate statistical analysis was used to investigate changes in water chemistry at 5 river sites in the Vaal Dam catchment, draining the Highveld grasslands. These grasslands receive more than 8 kg sulphur (S) ha-1·year-1 and 6 kg nitrogen (N) ha-1·year-1 via atmospheric deposition. It was hypothesised that between ...

  13. Reflections on univariate and multivariate analysis of metabolomics data

    NARCIS (Netherlands)

    Saccenti, E.; Hoefsloot, H.C.J.; Smilde, A.K.; Westerhuis, J.A.; Hendriks, M.M.W.B.

    2014-01-01

    Metabolomics experiments usually result in a large quantity of data. Univariate and multivariate analysis techniques are routinely used to extract relevant information from the data with the aim of providing biological knowledge on the problem studied. Despite the fact that statistical tools like

  14. Multivariate statistical analysis of major and trace element data for ...

    African Journals Online (AJOL)

    Multivariate statistical analysis of major and trace element data for niobium exploration in the peralkaline granites of the anorogenic ring-complex province of Nigeria. PO Ogunleye, EC Ike, I Garba. Abstract. No Abstract Available Journal of Mining and Geology Vol.40(2) 2004: 107-117. Full Text: EMAIL FULL TEXT EMAIL ...

  15. Multivariate Meta-Analysis Using Individual Participant Data

    Science.gov (United States)

    Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

    2015-01-01

    When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

  16. Robust methods for multivariate data analysis A1

    DEFF Research Database (Denmark)

    Frosch, Stina; Von Frese, J.; Bro, Rasmus

    2005-01-01

    Outliers may hamper proper classical multivariate analysis, and lead to incorrect conclusions. To remedy the problem of outliers, robust methods are developed in statistics and chemometrics. Robust methods reduce or remove the effect of outlying data points and allow the ?good? data to primarily...

  17. Canonical correspondence analysis and related multivariate methods in aquatic ecology

    NARCIS (Netherlands)

    Braak, Ter Cajo J.F.; Verdonschot, Piet F.M.

    1995-01-01

    Canonical correspondence analysis (CCA) is a multivariate method to elucidate the relationships between biological assemblages of species and their environment. The method is designed to extract synthetic environmental gradients from ecological data-sets. The gradients are the basis for succinctly

  18. Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

    Science.gov (United States)

    Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

    2014-03-01

    Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

  19. Multivariate analysis of wheat varieties grown on halomorphic soil

    Directory of Open Access Journals (Sweden)

    Dimitrijević Miodrag

    2005-01-01

    Full Text Available Additive and multivariate variation effects for grain weight per plant of seven wheat varieties from Novi Sad in three vegetation seasons have been studied. The trial has been established on halomorphic soil in Banat solonetz type, having a control as a standard of comparison, and two levels of melioration using phosphogypsum. AMMI analysis revealed very complex nature of trial variation, as well, as genotype by environment interaction.

  20. A robust multivariate long run analysis of European electricity prices

    OpenAIRE

    Bruno Bosco; Lucia Parisio; Matteo Pelagatti; Fabio Baldi

    2007-01-01

    This paper analyses the interdependencies existing in wholesale electricity prices in six major European countries. The results of our robust multivariate long run dynamic analysis reveal the presence of four highly integrated central European markets (France, Germany, the Netherlands and Austria). The trend shared by these four electricity markets appears to be common also to gas prices, but not to oil prices. The existence of long term dynamics among electricity prices and between electrici...

  1. Multivariate analysis between air pollutants and meteorological variables in Seoul

    International Nuclear Information System (INIS)

    Kim, J.; Lim, J.

    2005-01-01

    Multivariate analysis was conducted to analyze the relationship between air pollutants and meteorological variables measured in Seoul from January 1 to December 31, 1999. The first principal component showed the contrast effect between O 3 and the other pollutants. The second principal component showed the contrast effect between CO, SO 2 , NO 2 , and O 3 , PM 10 , TSP. Based on the cluster analysis, three clusters represented different air pollution levels, seasonal characteristics of air pollutants, and meteorological conditions. Discriminant analysis with air environment index (AEI) was carried out to develop an air pollution index function. (orig.)

  2. Multivariate statistical analysis a high-dimensional approach

    CERN Document Server

    Serdobolskii, V

    2000-01-01

    In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

  3. Multivariate analysis of eigenvalues and eigenvectors in tensor based morphometry

    Science.gov (United States)

    Rajagopalan, Vidya; Schwartzman, Armin; Hua, Xue; Leow, Alex; Thompson, Paul; Lepore, Natasha

    2015-01-01

    We develop a new algorithm to compute voxel-wise shape differences in tensor-based morphometry (TBM). As in standard TBM, we non-linearly register brain T1-weighed MRI data from a patient and control group to a template, and compute the Jacobian of the deformation fields. In standard TBM, the determinants of the Jacobian matrix at each voxel are statistically compared between the two groups. More recently, a multivariate extension of the statistical analysis involving the deformation tensors derived from the Jacobian matrices has been shown to improve statistical detection power.7 However, multivariate methods comprising large numbers of variables are computationally intensive and may be subject to noise. In addition, the anatomical interpretation of results is sometimes difficult. Here instead, we analyze the eigenvalues and the eigenvectors of the Jacobian matrices. Our method is validated on brain MRI data from Alzheimer's patients and healthy elderly controls from the Alzheimer's Disease Neuro Imaging Database.

  4. Analysis of multivariate extreme intakes of food chemicals.

    Science.gov (United States)

    Paulo, M J; van der Voet, H; Wood, J C; Marion, G R; van Klaveren, J D

    2006-07-01

    A recently published multivariate Extreme Value Theory (EVT) model is applied to the estimation of population risks associated with dietary intake of pesticides. The objective is to quantify the acute risk of pesticide intake above a threshold and relate it to the consumption of specific primary food products. As an example daily intakes of a pesticide from three foods are considered. The method models and extrapolates simultaneous intakes of pesticide, and estimates probability of exceeding unobserved large intakes. Multivariate analysis was helpful in identifying whether the avoidance of certain food combinations would reduce the likelihood of exceeding a threshold. We argue that the presented method can be an important contribution to exposure assessment studies.

  5. Handbook of univariate and multivariate data analysis with IBM SPSS

    CERN Document Server

    Ho, Robert

    2013-01-01

    Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows.New to the Second EditionThree new chapters on multiple discriminant analysis, logistic regression, and canonical correlationNew section on how to deal with missing dataCoverage of te

  6. Abuse

    Science.gov (United States)

    ... make friends. Abuse is a significant cause of depression in young people. Some teens can only feel better by doing things that could hurt them like cutting or abusing drugs or alcohol. They might even attempt suicide. It's common for those who have been abused ...

  7. Application of Multivariate Analysis Tools to Industrial Scale Fermentation Data

    DEFF Research Database (Denmark)

    Mears, Lisa; Nørregård, Rasmus; Stocks, Stuart M.

    The analysis of batch process data can provide insight into the process operation, and there is a vast amount of historical data available for data mining. Empirical modelling utilising this data is desirable where there is a lack of understanding regarding the underlying process (Formenti et al....... application of multivariate methods to industrial scale process data to cover these considerations....... prediction error of 7.6%. The success of the final regression model was heavily dependent on the decisions made in the pre-processing stages, where the issues of different batch lengths, different measurement intervals, and variable scaling are considered. Therefore a methodology is presented for future...

  8. Multivariate time series analysis with R and financial applications

    CERN Document Server

    Tsay, Ruey S

    2013-01-01

    Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

  9. Acceleration of multivariate analysis techniques in TMVA using GPUs

    CERN Document Server

    Hoecker, A; Therhaag, J; Washbrook, A

    2012-01-01

    A feasibility study into the acceleration of multivariate analysis techniques using Graphics Processing Units (GPUs) will be presented. The MLP-based Artificial Neural Network method contained in the TMVA framework has been chosen as a focus for investigation. It was found that the network training time on a GPU was lower than for CPU execution as the complexity of the network was increased. In addition, multiple neural networks can be trained simultaneously on a GPU within the same time taken for single network training on a CPU. This could be potentially leveraged to provide a qualitative performance gain in data classification.

  10. Micro-Raman Imaging for Biology with Multivariate Spectral Analysis

    KAUST Repository

    Malvaso, Federica

    2015-05-05

    Raman spectroscopy is a noninvasive technique that can provide complex information on the vibrational state of the molecules. It defines the unique fingerprint that allow the identification of the various chemical components within a given sample. The aim of the following thesis work is to analyze Raman maps related to three pairs of different cells, highlighting differences and similarities through multivariate algorithms. The first pair of analyzed cells are human embryonic stem cells (hESCs), while the other two pairs are induced pluripotent stem cells (iPSCs) derived from T lymphocytes and keratinocytes, respectively. Although two different multivariate techniques were employed, ie Principal Component Analysis and Cluster Analysis, the same results were achieved: the iPSCs derived from T-lymphocytes show a higher content of genetic material both compared with the iPSCs derived from keratinocytes and the hESCs . On the other side, equally evident, was that iPS cells derived from keratinocytes assume a molecular distribution very similar to hESCs.

  11. Multivariate analysis of craniometric characters in Bulgarian chamois

    Directory of Open Access Journals (Sweden)

    Giovanna Massei

    1994-05-01

    Full Text Available Abstract A craniometrical study was carried out to examine the skull characteristics of the Bulgarian chamois (Rupicapra rupicapra balcanica (1 to assess whether any difference between sexes is detectable and (2 to compare the Bulgarian material with other already described chamois populations occurring in other European regions. Results of multivariate analyses run on seven craniometrical characters showed sexual dimorphism in the Bulgarian sample. Discriminant Analysis performed on individuals from different populations showed that the positions of the samples in discriminant space were approximately congruent with their geographical position. Principal Component Analysis revealed that the main factor of variation among groups is a size factor. The structure of loadings on PC-II and PC-III and the amount of total variability expressed by these two components suggested also shape differences. Results from multivariate analyses carried out on the means of the characters confirmed these patterns. A dimensional cline for the genus Rupicapra is suggested, the north-east chamois populations showing the largest skulls and the south-west populations having the smallest sizes. Riassunto Analisi multivariata dei caratteri craniometrici ne1 camoscio bulgaro - Uno studio dei caratteri cranici del camoscio bulgaro (Rupicapra rupicapra balcanica è stato effettuato a1 fine di 1 valutare il grado di dimorfismo sessuale; 2 confrontare il campione bulgaro con altre popolazioni di camoscio europeo già descritte in letteratura. I risultati delle analisi multivariate effettuate su sette caratteri craniometrici hanno mostrato l'esistenza del dimorfismo sessuale nel camoscio bulgaro. L'analisi discriminante effettuata su individui appartenenti a diverse popolazioni ha mostrato che la posizione dei campioni nello spazio discriminante è congruente con la loro posizione geografica. L

  12. Environmental Performance in Countries Worldwide: Determinant Factors and Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Isabel Gallego-Alvarez

    2014-11-01

    Full Text Available The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.

  13. Multivariate data analysis of enzyme production for hydrolysis purposes

    DEFF Research Database (Denmark)

    Schmidt, A.S.; Suhr, K.I.

    1999-01-01

    of the structure in the data - possibly combined with analysis of variance (ANOVA). Partial least squares regression (PLSR) showed a clear connection between the two differentdata matrices (the fermentation variables and the hydrolysis variables). Hence, PLSR was suitable for prediction purposes. The hydrolysis......Data from enzyme production experiments were analysed using different multivariate methods. The data set comprised of 12 objects (3 fungi (¤Aspergillus oryzae, Aspergillus fumigatur, Trichoderma reesei¤) grown on 4 substrates (lenzing and/or wet-oxidisedzylan)) and 12 variables (pH, biomass, 7...... enzyme activities (xylanase, zylosidase, arabinosidase, cellulase, acetyl zylan esterase, glucuronidase, feroyl esterase) and 3 hydrolysis efficiencies (reducing suggars at 3 different enzyme loadings)). Principalcomponent analysis (PCA) proved to be an efficient method to obtain an overview...

  14. Multivariate image analysis for quality inspection in fish feed production

    DEFF Research Database (Denmark)

    Ljungqvist, Martin Georg

    . The colour appearance of fish products is important for customers. Salmonid fish get their red colour from a natural pigment called astaxanthin. To ensure a similar red colour of fish in aquaculture astaxanthin is used as an additive coated on the feed pellets. Astaxanthin can either be of natural origin......, or synthesised chemically. Common for both types is that they are relatively expensive in comparison to the other feed ingredients. This thesis investigates multi-variate data collection for visual inspection and optimisation of industrial production in the fish feed industry. Quality parameters focused on here...... of the work demonstrate a high potential of image analysis and spectral imaging for assessing the product quality of fish feed pellets, astaxanthin and fish meat. We show how image analysis can be used to inspect the pellet size, and how spectral imaging can be used to inspect the surface quality...

  15. A multivariate analysis of Antarctic sea ice since 1979

    Energy Technology Data Exchange (ETDEWEB)

    Magalhaes Neto, Newton de; Evangelista, Heitor [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Tanizaki-Fonseca, Kenny [Universidade do Estado do Rio de Janeiro (Uerj), LARAMG - Laboratorio de Radioecologia e Mudancas Globais, Maracana, Rio de Janeiro, RJ (Brazil); Universidade Federal Fluminense (UFF), Dept. Analise Geoambiental, Inst. de Geociencias, Niteroi, RJ (Brazil); Penello Meirelles, Margareth Simoes [Universidade do Estado do Rio de Janeiro (UERJ)/Geomatica, Maracana, Rio de Janeiro, RJ (Brazil); Garcia, Carlos Eiras [Universidade Federal do Rio Grande (FURG), Laboratorio de Oceanografia Fisica, Rio Grande, RS (Brazil)

    2012-03-15

    Recent satellite observations have shown an increase in the total extent of Antarctic sea ice, during periods when the atmosphere and oceans tend to be warmer surrounding a significant part of the continent. Despite an increase in total sea ice, regional analyses depict negative trends in the Bellingshausen-Amundsen Sea and positive trends in the Ross Sea. Although several climate parameters are believed to drive the formation of Antarctic sea ice and the local atmosphere, a descriptive mechanism that could trigger such differences in trends are still unknown. In this study we employed a multivariate analysis in order to identify the response of the Antarctic sea ice with respect to commonly utilized climate forcings/parameters, as follows: (1) The global air surface temperature, (2) The global sea surface temperature, (3) The atmospheric CO{sub 2} concentration, (4) The South Annular Mode, (5) The Nino 3, (6) The Nino (3 + 4, 7) The Nino 4, (8) The Southern Oscillation Index, (9) The Multivariate ENSO Index, (10) the Total Solar Irradiance, (11) The maximum O{sub 3} depletion area, and (12) The minimum O{sub 3} concentration over Antarctica. Our results indicate that western Antarctic sea ice is simultaneously impacted by several parameters; and that the minimum, mean, and maximum sea ice extent may respond to a separate set of climatic/geochemical parameters. (orig.)

  16. Multivariate analysis of microarray data: differential expression and differential connection.

    Science.gov (United States)

    Kiiveri, Harri T

    2011-02-01

    Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes. The methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.

  17. Multivariate analysis of microarray data: differential expression and differential connection

    Directory of Open Access Journals (Sweden)

    Kiiveri Harri T

    2011-02-01

    Full Text Available Abstract Background Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically allows for correlation between genes. As a result we combine gene network ideas with linear models and differential expression. Results We use sparse inverse covariance matrices and their associated graphical representation to capture the notion of gene networks. An important issue in using these models is the identification of the pattern of zeroes in the inverse covariance matrix. The limitations of existing methods for doing this are discussed and we provide a workable solution for determining the zero pattern. We then consider a method for estimating the parameters in the inverse covariance matrix which is suitable for very high dimensional matrices. We also show how to construct multivariate tests of hypotheses. These overall multivariate tests can be broken down into two components, the first one being similar to tests for differential expression and the second involving the connections between genes. Conclusion The methods in this paper enable the extraction of a wealth of information concerning the relationships between genes which can be conveniently represented in graphical form. Differentially expressed genes can be placed in the context of the gene network and places in the gene network where unusual or interesting patterns have emerged can be identified, leading to the formulation of hypotheses for future experimentation.

  18. Anthropometric profile of combat athletes via multivariate analysis.

    Science.gov (United States)

    Burdukiewicz, Anna; Pietraszewska, Jadwiga; Stachoń, Aleksandra; Andrzejewska, Justyna

    2017-11-07

    Athletic success is a complex phenotype influenced by multiple factors, from sport-specific skills to anthropometric characteristics. Considering the latter, the literature has repeatedly indicated that athletes possess distinct physical characteristics depending on the practiced discipline. The aim of the present study was to apply univariate and multivariate methods to assess a wide range of morphometric and somatotypic characteristics in male combat athletes. Biometric data were obtained from 206 male university-level practitioners of judo, jiu-jitsu, karate, kickboxing, taekwondo, and wrestling. Measures included height- and length-based variables, breadths, circumferences, and skinfolds. Body proportions and somatotype, using Sheldon's method of somatotopy as modified by Heath and Carter, were then determined. Body fat percentage was assessed by bioelectrical impedance analysis using tetrapolar hand-to-foot electrodes. Data were subjected to a wide array of statistical analysis. The results show between-group differences in the magnitudes of the analyzed characteristics. While mesomorphy was the dominant component of each group somatotype, enhanced ectomorphy was observed in those disciplines that require a high level of agility. Principal component analysis reduced the multivariate dimensionality of the data to three components (characterizing body size, height-based measures, and the anthropometric structure of the upper extremities) that explained the majority of data variance. The development of a sport-specific anthropometric profile via height- and mass-based and morphometric and somatotypic variables can aid in the design of training protocols and the identification of athlete markers as well as serve as a diagnostic criterion in predicting combat athlete performance.

  19. Abuse

    Science.gov (United States)

    ... someone else Sexual abuse: touching, fondling or any sexual activity when the person is unable to understand, unwilling to consent, threatened or physically forced Willful deprivation: willfully denying ...

  20. Multivariate statistical pattern recognition system for reactor noise analysis

    International Nuclear Information System (INIS)

    Gonzalez, R.C.; Howington, L.C.; Sides, W.H. Jr.; Kryter, R.C.

    1975-01-01

    A multivariate statistical pattern recognition system for reactor noise analysis was developed. The basis of the system is a transformation for decoupling correlated variables and algorithms for inferring probability density functions. The system is adaptable to a variety of statistical properties of the data, and it has learning, tracking, and updating capabilities. System design emphasizes control of the false-alarm rate. The ability of the system to learn normal patterns of reactor behavior and to recognize deviations from these patterns was evaluated by experiments at the ORNL High-Flux Isotope Reactor (HFIR). Power perturbations of less than 0.1 percent of the mean value in selected frequency ranges were detected by the system. 19 references

  1. Motivation and Self-Regulated Learning: A Multivariate Multilevel Analysis

    Directory of Open Access Journals (Sweden)

    Wondimu Ahmed

    2017-09-01

    Full Text Available This study investigated the relationship between motivation and self-regulated learning (SRL in a nationally representative sample of 5245, 15-year-old students in the USA. A multivariate multilevel analysis was conducted to examine the role of three motivational variables (self-efficacy, intrinsic value & instrumental value in predicting three SRL strategies (memorization, elaboration & control. The results showed that compared to self-efficacy, intrinsic value and instrumental value of math were stronger predictors of memorization, elaboration and control strategies. None of the motivational variables had a stronger effect on one strategy than the other. The findings suggest that the development of self-regulatory skills in math can be greatly enhanced by helping students develop positive value of and realistic expectancy for success in math.

  2. FactoMineR: An R Package for Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Sébastien Lê

    2008-01-01

    Full Text Available In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical, different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals and finally supplementary information (supplementary individuals and variables. Moreover, the dimensions issued from the different exploratory data analyses can be automatically described by quantitative and/or categorical variables. Numerous graphics are also available with various options. Finally, a graphical user interface is implemented within the Rcmdr environment in order to propose an user friendly package.

  3. explorase: Multivariate Exploratory Analysis and Visualization for Systems Biology

    Directory of Open Access Journals (Sweden)

    Michael Lawrence

    2008-03-01

    Full Text Available The datasets being produced by high-throughput biological experiments, such as microarrays, have forced biologists to turn to sophisticated statistical analysis and visualization tools in order to understand their data. We address the particular need for an open-source exploratory data analysis tool that applies numerical methods in coordination with interactive graphics to the analysis of experimental data. The software package, known as explorase, provides a graphical user interface (GUI on top of the R platform for statistical computing and the GGobi software for multivariate interactive graphics. The GUI is designed for use by biologists, many of whom are unfamiliar with the R language. It displays metadata about experimental design and biological entities in tables that are sortable and filterable. There are menu shortcuts to the analysis methods implemented in R, including graphical interfaces to linear modeling tools. The GUI is linked to data plots in GGobi through a brush tool that simultaneously colors rows in the entity information table and points in the GGobi plots.

  4. Jury Selection in Child Sex Abuse Trials: A Case Analysis

    Science.gov (United States)

    Cramer, Robert J.; Adams, Desiree D.; Brodsky, Stanley L.

    2009-01-01

    Child sex abuse cases have been the target of considerable psycho-legal research. The present paper offers an analysis of psychological constructs for jury selection in child sex abuse cases from the defense perspective. The authors specifically delineate general and case-specific jury selection variables. General variables include…

  5. The Medical Analysis of Child Sexual Abuse Images

    Science.gov (United States)

    Cooper, Sharon W.

    2011-01-01

    Analysis of child sexual abuse images, commonly referred to as pornography, requires a familiarity with the sexual maturation rating of children and an understanding of growth and development parameters. This article explains barriers that exist in working in this area of child abuse, the differences between subjective and objective analyses,…

  6. Multivariate analysis for customer segmentation based on RFM

    Directory of Open Access Journals (Sweden)

    Álvaro Julio Cuadros López

    2018-02-01

    Full Text Available Context: To build a successful relationship management (CRM, companies must start with the identification of the true value of customers, as this provides basic information to implement more targeted and customized marketing strategies. The RFM methodology, a classic analysis tool that uses three evaluation parameters, allows companies to understand customer behavior, and to establish customer segments. The addition of a new parameter in the traditional technique is an opportunity to refine the possible outcomes of a customer segmentation since it not only provides a new element of evaluation to identify the most valuable customers, but it also makes it possible to differentiate and get to know customers even better. Method: The article presents a methodology that allows to establish customer segments using an extended RFM method with new variables, selected through multivariate analysis..  Results: The proposed implementation was applied in a company in which variables such as profit, profit percentage, and billing due date were tested. Therefore, it was possible to establish a more detailed customer segmentation than with the classic RFM. Conclusions: the RFM analysis is a method widely used in the industry for its easy understanding and applicability. However, it can be improved with the use of statistical procedures and new variables, which will allow companies to have deeper information about the behavior of the clients, and will facilitate the design of specific marketing strategies.

  7. Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis

    Directory of Open Access Journals (Sweden)

    Sheng Zhang

    2016-01-01

    Full Text Available Cocaine dependence is associated with deficits in cognitive control. Previous studies demonstrated that chronic cocaine use affects the activity and functional connectivity of the thalamus, a subcortical structure critical for cognitive functioning. However, the thalamus contains nuclei heterogeneous in functions, and it is not known how thalamic subregions contribute to cognitive dysfunctions in cocaine dependence. To address this issue, we used multivariate pattern analysis (MVPA to examine how functional connectivity of the thalamus distinguishes 100 cocaine-dependent participants (CD from 100 demographically matched healthy control individuals (HC. We characterized six task-related networks with independent component analysis of fMRI data of a stop signal task and employed MVPA to distinguish CD from HC on the basis of voxel-wise thalamic connectivity to the six independent components. In an unbiased model of distinct training and testing data, the analysis correctly classified 72% of subjects with leave-one-out cross-validation (p < 0.001, superior to comparison brain regions with similar voxel counts (p < 0.004, two-sample t test. Thalamic voxels that form the basis of classification aggregate in distinct subclusters, suggesting that connectivities of thalamic subnuclei distinguish CD from HC. Further, linear regressions provided suggestive evidence for a correlation of the thalamic connectivities with clinical variables and performance measures on the stop signal task. Together, these findings support thalamic circuit dysfunction in cognitive control as an important neural marker of cocaine dependence.

  8. Multivariate study and regression analysis of gluten-free granola

    Directory of Open Access Journals (Sweden)

    Lilian Maria Pagamunici

    2014-03-01

    Full Text Available This study developed a gluten-free granola and evaluated it during storage with the application of multivariate and regression analysis of the sensory and instrumental parameters. The physicochemical, sensory, and nutritional characteristics of a product containing quinoa, amaranth and linseed were evaluated. The crude protein and lipid contents ranged from 97.49 and 122.72 g kg-1 of food, respectively. The polyunsaturated/saturated, and n-6:n-3 fatty acid ratios ranged from 2.82 and 2.59:1, respectively. Granola had the best alpha-linolenic acid content, nutritional indices in the lipid fraction, and mineral content. There were good hygienic and sanitary conditions during storage; probably due to the low water activity of the formulation, which contributed to inhibit microbial growth. The sensory attributes ranged from 'like very much' to 'like slightly', and the regression models were highly fitted and correlated during the storage period. A reduction in the sensory attribute levels and in the product physical stabilisation was verified by principal component analysis. The use of the affective test acceptance and instrumental analysis combined with statistical methods allowed us to obtain promising results about the characteristics of gluten-free granola.

  9. SOFTWARE TOOLS FOR COMPUTING EXPERIMENT AIMED AT MULTIVARIATE ANALYSIS IMPLEMENTATION

    Directory of Open Access Journals (Sweden)

    A. V. Tyurin

    2015-09-01

    Full Text Available A concept for organization and planning of computational experiment aimed at implementation of multivariate analysis of complex multifactor models is proposed. It is based on the generation of calculations tree. The logical and structural schemes of the tree are given and software tools, as well, for the automation of work with it: calculation generation, carrying out calculations and analysis of the obtained results. Computer modeling systems and such special-purpose systems as RACS and PRADIS do not solve the problems connected with effective carrying out of computational experiment, consisting of its organization, planning, execution and analysis of the results. Calculation data storage for computational experiment organization is proposed in the form of input and output data tree. Each tree node has a reference to the calculation of model step performed earlier. The storage of calculations tree is realized in a specially organized directory structure. A software tool is proposed for creating and modifying design scheme that stores the structure of one branch of the calculation tree with the view of effective planning of multivariate calculations. A set of special-purpose software tools gives the possibility for the quick generation and modification of the tree, addition of calculations with step-by-step change in the model factors. To perform calculations, software environment in the form of a graphical user interface for creating and modifying calculation script has been developed. This environment makes it possible to traverse calculation tree in a certain order and to perform serial and parallel initiation of computational modules. To analyze the results, software tool has been developed, operating on the base of the tag tree. It is a special tree that stores input and output data of the calculations in the set of changes form of appropriate model factors. The tool enables to select the factors and responses of the model at various steps

  10. Classification of Malaysia aromatic rice using multivariate statistical analysis

    Energy Technology Data Exchange (ETDEWEB)

    Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A. [School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600 Arau, Perlis (Malaysia); Omar, O. [Malaysian Agriculture Research and Development Institute (MARDI), Persiaran MARDI-UPM, 43400 Serdang, Selangor (Malaysia)

    2015-05-15

    Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

  11. Analysis of multivariate extreme intakes of food chemicals

    NARCIS (Netherlands)

    Paulo, M.J.; Voet, van der H.; Wood, J.C.; Marion, G.R.; Klaveren, van J.D.

    2006-01-01

    A recently published multivariate Extreme Value Theory (EVT) model [Heffernan, J.E., Tawn, J.A., 2004. A conditional approach for multivariate extreme values (with discussion). Journal of the Royal Statistical Society Series B 66 (3), 497¿546] is applied to the estimation of population risks

  12. Multivariate analysis of gamma spectra to characterize used nuclear fuel

    Energy Technology Data Exchange (ETDEWEB)

    Coble, Jamie; Orton, Christopher; Schwantes, Jon

    2017-04-01

    Abstract—The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of reprocessing streams in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type. Locally weighted PLS models were fitted on-the-fly to estimate continuous fuel characteristics. Burn up was predicted within 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment within approximately 2% RMSPE. This automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters and material diversions.

  13. Bayesian multivariate meta-analysis of multiple factors.

    Science.gov (United States)

    Lin, Lifeng; Chu, Haitao

    2018-02-09

    In medical sciences, a disease condition is typically associated with multiple risk and protective factors. Although many studies report results of multiple factors, nearly all meta-analyses separately synthesize the association between each factor and the disease condition of interest. The collected studies usually report different subsets of factors, and the results from separate analyses on multiple factors may not be comparable because each analysis may use different subpopulation. This may impact on selecting most important factors to design a multifactor intervention program. This article proposes a new concept, multivariate meta-analysis of multiple factors (MVMA-MF), to synthesize all available factors simultaneously. By borrowing information across factors, MVMA-MF can improve statistical efficiency and reduce biases compared with separate analyses when factors were missing not at random. As within-study correlations between factors are commonly unavailable from published articles, we use a Bayesian hybrid model to perform MVMA-MF, which effectively accounts for both within- and between-study correlations. The performance of MVMA-MF and the conventional methods are compared using simulations and an application to a pterygium dataset consisting of 29 studies on 8 risk factors. Copyright © 2018 John Wiley & Sons, Ltd.

  14. Etiology of Drug Abuse: A Narrative Analysis

    OpenAIRE

    Jadidi, Nadjme; Nakhaee, Nouzar

    2014-01-01

    Introduction and Aim. Further gains in the prevention of drug abuse disorders require in-depth and holistic understanding of the risk factors of addiction from different perspectives. Lay persons and experts have different concepts of risk which could complement each other. The purpose of this study was to elaborate drug abuse risk factors through the story of individuals who had become drug dependent. Design and Methods. In this qualitative research, 33 individuals attending treatment centre...

  15. SAS/IML Macros for a Multivariate Analysis of Variance Based on Spatial Signs

    Directory of Open Access Journals (Sweden)

    Jaakko Nevalainen

    2006-05-01

    Full Text Available Recently, new nonparametric multivariate extensions of the univariate sign methods have been proposed. Randles (2000 introduced an affine invariant multivariate sign test for the multivariate location problem. Later on, Hettmansperger and Randles (2002 considered an affine equivariant multivariate median corresponding to this test. The new methods have promising efficiency and robustness properties. In this paper, we review these developments and compare them with the classical multivariate analysis of variance model. A new SAS/IML tool for performing a spatial sign based multivariate analysis of variance is introduced.

  16. The Inappropriate Symmetries of Multivariate Statistical Analysis in Geometric Morphometrics.

    Science.gov (United States)

    Bookstein, Fred L

    In today's geometric morphometrics the commonest multivariate statistical procedures, such as principal component analysis or regressions of Procrustes shape coordinates on Centroid Size, embody a tacit roster of symmetries -axioms concerning the homogeneity of the multiple spatial domains or descriptor vectors involved-that do not correspond to actual biological fact. These techniques are hence inappropriate for any application regarding which we have a-priori biological knowledge to the contrary (e.g., genetic/morphogenetic processes common to multiple landmarks, the range of normal in anatomy atlases, the consequences of growth or function for form). But nearly every morphometric investigation is motivated by prior insights of this sort. We therefore need new tools that explicitly incorporate these elements of knowledge, should they be quantitative, to break the symmetries of the classic morphometric approaches. Some of these are already available in our literature but deserve to be known more widely: deflated (spatially adaptive) reference distributions of Procrustes coordinates, Sewall Wright's century-old variant of factor analysis, the geometric algebra of importing explicit biomechanical formulas into Procrustes space. Other methods, not yet fully formulated, might involve parameterized models for strain in idealized forms under load, principled approaches to the separation of functional from Brownian aspects of shape variation over time, and, in general, a better understanding of how the formalism of landmarks interacts with the many other approaches to quantification of anatomy. To more powerfully organize inferences from the high-dimensional measurements that characterize so much of today's organismal biology, tomorrow's toolkit must rely neither on principal component analysis nor on the Procrustes distance formula, but instead on sound prior biological knowledge as expressed in formulas whose coefficients are not all the same. I describe the problems

  17. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2013-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented....... The discrete time models used are multivariate variants of the discrete relative risk models. These models allow for regular parametric likelihood-based inference by exploring a coincidence of their likelihood functions and the likelihood functions of suitably defined multivariate generalized linear mixed...

  18. Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

    Science.gov (United States)

    Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

    2012-07-01

    Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission ( P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

  19. Oil price and financial markets: Multivariate dynamic frequency analysis

    International Nuclear Information System (INIS)

    Creti, Anna; Ftiti, Zied; Guesmi, Khaled

    2014-01-01

    The aim of this paper is to study the degree of interdependence between oil price and stock market index into two groups of countries: oil-importers and oil-exporters. To this end, we propose a new empirical methodology allowing a time-varying dynamic correlation measure between the stock market index and the oil price series. We use the frequency approach proposed by Priestley and Tong (1973), that is the evolutionary co-spectral analysis. This method allows us to distinguish between short-run and medium-run dependence. In order to complete our study by analysing long-run dependence, we use the cointegration procedure developed by Engle and Granger (1987). We find that interdependence between the oil price and the stock market is stronger in exporters' markets than in the importers' ones. - Highlights: • A new time-varying measure for the stock markets and oil price relationship in different horizons. • We propose a new empirical methodology: multivariate frequency approach. • We propose a comparison between oil importing and exporting countries. • We show that oil is not always countercyclical with respect to stock markets. • When high oil prices originate from supply shocks, oil is countercyclical with stock markets

  20. Multivariate analysis of spatial-temporal scales in melanoma prevalence.

    Science.gov (United States)

    Valachovic, Edward; Zurbenko, Igor

    2017-07-01

    Melanoma is a particularly deadly form of skin cancer arising from diverse biological and physical origins, making the characterization and quantification of relationships with recognized risk factors very complex. Melanoma has known associations with ultraviolet light exposure. Natural variations in solar electromagnetic irradiation, length of exposure, and intensity operate on different and therefore uncorrelated time scale frequencies. It is necessary to separate and investigate the principal components, such as the annual and solar cycle components, free from confounding influences. Kolmogorov-Zurbenko spatial filters applied to melanoma prevalence and environmental factors affecting solar irradiation exposure are able to identify and separate the independent space and time scale components of melanoma. Multidimensional analysis in space and time produces significantly improved model fit of what is in effect a linear regression of maps, or motion picture, in different time scales between melanoma rates and prominent factors. The resulting multivariate model coefficients of influence for each unique spatial-temporal melanoma component help quantify the relationships and are valuable to future research and prevention.

  1. Multivariate analysis of marketing data - applications for bricolage market

    Directory of Open Access Journals (Sweden)

    FANARU Mihai

    2017-01-01

    Full Text Available By using concepts and analytical tools for computing, marketing is directly related to the quantitative methods of economic research and other areas where the efficiency of systems performances are studied. Any activity of the company must be programmed and carried out taking into account the consumer. Providing a complete success in business requires the entrepreneur to see the company and its products through the consumers eyes, to act as representative of its clients in order to acquire and satisfy their desires. Through its complex specific activities, marketing aims to provide goods and services the consumers require or right merchandise in the right quantity at the right price at the right time and place. An important consideration in capturing the link between marketing and multivariate statistical analysis is that it provides more powerful instruments that allow researchers to discover relationships between multiple configurations of the relationship between variables, configurations that would otherwise remain hidden or barely visible. In addition, most methods can do this with good accuracy, with the possibility of testing the statistical significance by calculating the level of confidence associated with the link validation to the entire population and not just the investigated sample.

  2. Some Simple Procedures for Handling Missing Data in Multivariate Analysis

    Science.gov (United States)

    Frane, James W.

    1976-01-01

    Several procedures are outlined for replacing missing values in multivariate analyses by regression values obtained in various ways, and for adjusting coefficients (such as factor score coefficients) when data are missing. None of the procedures are complex or expensive. (Author)

  3. Toxicological Analysis of Some Drugs of Abuse in Biological Samples

    Directory of Open Access Journals (Sweden)

    Anne Marie Ciobanu

    2015-10-01

    Full Text Available Consumption of drugs of abuse is a scourge of modern world. Abuse, drug addiction and their consequences are one of the major current problems of European society because of the significant repercussions in individual, family, social and economic level. In this context, toxicological analysis of the drugs of abuse in biological samples is a useful tool for: diagnosis of drug addiction, checking an auto-response, mandatory screening in some treatment programs, identification of a substance in the case of an overdose, determining compliance of the treatment. The present paper aims to address the needs of healthcare professionals involved in drugs addiction treatment through systematic presentation of information regarding their toxicological analysis. Basically, it is a tool that help you to select the suitable biological sample and the right collecting time, as well as the proper analysis technique, depending on the purpose of analysis, pharmacokinetic characteristics of the drugs of abuse, available equipment and staff expertise.

  4. Multivariate factor analysis of Girgentana goat milk composition

    Directory of Open Access Journals (Sweden)

    Pietro Giaccone

    2010-01-01

    Full Text Available The interpretation of the several variables that contribute to defining milk quality is difficult due to the high degree of  correlation among them. In this case, one of the best methods of statistical processing is factor analysis, which belongs  to the multivariate groups; for our study this particular statistical approach was employed.  A total of 1485 individual goat milk samples from 117 Girgentana goats, were collected fortnightly from January to July,  and analysed for physical and chemical composition, and clotting properties. Milk pH and tritable acidity were within the  normal range for fresh goat milk. Morning milk yield resulted 704 ± 323 g with 3.93 ± 1.23% and 3.48±0.38% for fat  and protein percentages, respectively. The milk urea content was 43.70 ± 8.28 mg/dl. The clotting ability of Girgentana  milk was quite good, with a renneting time equal to 16.96 ± 3.08 minutes, a rate of curd formation of 2.01 ± 1.63 min-  utes and a curd firmness of 25.08 ± 7.67 millimetres.  Factor analysis was performed by applying axis orthogonal rotation (rotation type VARIMAX; the analysis grouped the  milk components into three latent or common factors. The first, which explained 51.2% of the total covariance, was  defined as “slow milks”, because it was linked to r and pH. The second latent factor, which explained 36.2% of the total  covariance, was defined as “milk yield”, because it is positively correlated to the morning milk yield and to the urea con-  tent, whilst negatively correlated to the fat percentage. The third latent factor, which explained 12.6% of the total covari-  ance, was defined as “curd firmness,” because it is linked to protein percentage, a30 and titatrable acidity. With the aim  of evaluating the influence of environmental effects (stage of kidding, parity and type of kidding, factor scores were anal-  ysed with the mixed linear model. Results showed significant effects of the season of

  5. Multivariate analysis of factors Influencing reliability of teacher made tests

    Directory of Open Access Journals (Sweden)

    Z Meshkani

    2009-02-01

    Full Text Available Background: According to the measurements literature reliability of the test refers to the consistency of the test results and shows whether the obtained score is stable indication of the student’s performance in particular test Reliability can be measured by different statistics formula.Purpose: To determine the factors influenced the reliability of 392 MCQ examinations.Methods: The correlation of reliabilities of MCQ based examination and other characteristics of tests such as length difficult items, discrimination index, mean, standard deviation and time for answering was calculated based on the data available on examination center of Tehran University of Medical Sciences. Multivariate regression has been used for data analysis.Results: overall reliability of teacher made test is at satisfactory level in most cases. The mean value of reliability was 0.71 ±0.15. In comparing previous semester with last series of examination some improvement have been found during these years (P=0.000, for first semester, P=0.002 for second, P= 0.005 for third and P=0.005 for forth semester. Keeping other variable fixed the interaction of length of exam according to item difficulty showedl significant difference on value of test reliability. Comparing difficult and easy items question with moderate difficultyindex can increase reliability 8 times more than difficult and 13 times more than easy items P=0.000.Conclusion: Our study showed that with documentation of tests’ metric features an analysis and evaluation of tests are within reach of medical school .Key words: RELIABILITY , TEACHER MADE TEST, RELIABILITY MEASUREMENTS

  6. Multivariate analysis of flow cytometric data using decision trees.

    Science.gov (United States)

    Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

    2012-01-01

    Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

  7. Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods

    DEFF Research Database (Denmark)

    Arenas-Garcia, J.; Petersen, K.; Camps-Valls, G.

    2013-01-01

    Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data s...... applications involving audio processing for music genre prediction and hyperspectral satellite image processing for Earth and climate monitoring....

  8. Antioxidant activity of Costa Rican propolis: a multivariate analysis approach

    International Nuclear Information System (INIS)

    Umana Rojas, Eduardo; Solado, Godofredo; Tamayo-Castillo, Giselle

    2013-01-01

    Propolis is produced by Apis mellifera bees from resins of plants that are found around the apiary. The chemical composition is highly variable and Costa Rica has reported without studies of characterization to define the types of propolis in the country. 119 samples were collected from beekeeping areas of the country. The spectrum of 1 H-NMR and its antioxidant activity against DPPH radical were measured. The spectra have been divided into 243 blocks of 0,04 ppm and processed with the Minitab software for multivariate analysis. 99 of the samples collected were used for construction of models for the valuation of the predictive ability of the model have been used coefficients of determination (R 2 ) of prediction by the software and the remaining 20 samples. The existence of three types of propolis with chemically different metabolomes were determined by principal component analysis (PCA). A prediction model was constructed by analysis of partial least squares (PLS). The prediction model has allowed to classify a propolis according to the level of antioxidant activity (AAO), high (type I and II) or low (type III) from the spectrum of 1 H-NMR. The R 2 has been 0.88 and R 2 prediction of 0, 718 for new samples. The nconiferyl benzoate of group I and nemorosone of the group II as two discriminated antioxidants among the groups I and II were isolated and high concentration levels of these compounds have been differentiated with respect to type III. This has allowed the construction of a linear discriminant model with a success rate of 100% for the samples used for formulation and 92,9 for the prediction of different samples. The classification systems could be applied to the standardization of the quality of propolis from Costa Rica for future medicinal or cosmetic applications that take advantage of its antioxidant properties. Also, the methylated derivative has isolated and identified of the nconiferyl benzoate thereof propolis than was obtained his counterpart

  9. A Multivariate Examination of the Child-Abuse Potential of Parents with Children Aged 0-6

    Science.gov (United States)

    Cetin, Zeynep; Ozozen Danaci, Miray

    2016-01-01

    Problem Statement: Child abuse, defined by the World Health Organization as "intentional or unintentional behavior by adults, society, or a country with negative consequences for the health and physical development of the child," is a social problem frequently encountered in all cultures and societies. It is need to this study because of…

  10. A multivariate analysis of factors affecting adoption of improved ...

    African Journals Online (AJOL)

    This paper analyzes the synergies/tradeoffs involved in the adoption of improved varieties of multiple crops in the mixed crop-livestock production systems of the highlands of Ethiopia A multivariate probit (MVP) model involving a system of four equations for the adoption decision of improved varieties of barley, potatoes, ...

  11. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2014-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...

  12. Multivariate Time Series Analysis for Optimum Production Forecast ...

    African Journals Online (AJOL)

    ... by 0.002579KG/Month. Finally, this work adds to the growing body of literature on data-driven production and inventory management by utilizing historical data in the development of useful forecasting mathematical model. Keywords: production model, inventory management, multivariate time series, production forecast ...

  13. Multivariate Time Series Analysis for Optimum Production Forecast ...

    African Journals Online (AJOL)

    FIRST LADY

    on data-driven production and inventory management by utilizing historical data in the development of useful forecasting mathematical model. Keywords: production model, inventory management, multivariate time series, production forecast. Introduction. A large assortment of forecasting techniques has been developed ...

  14. Multivariate Stable Isotope Analysis to Determine Linkages between Benzocaine Seizures

    Science.gov (United States)

    Kemp, H. F.; Meier-Augenstein, W.; Collins, M.; Salouros, H.; Cunningham, A.; Harrison, M.

    2012-04-01

    In July 2010, a woman was jailed for nine years in the UK after the prosecution successfully argued that attempting to import a cutting agent was proof of involvement in a conspiracy to supply Cocaine. That landmark ruling provided law enforcement agencies with much greater scope to tackle those involved in this aspect of the drug trade, specifically targeting those importing the likes of benzocaine or lidocaine. Huge quantities of these compounds are imported into the UK and between May and August 2010, four shipments of Benzocaine amounting to more then 4 tons had been seized as part of Operation Kitley, a joint initiative between the UK Border Agency and the Serious Organised Crime Agency (SOCA). By diluting cocaine, traffickers can make it go a lot further for very little cost, leading to huge profits. In recent years, dealers have moved away from inert substances, like sugar and baby milk powder, in favour of active pharmaceutical ingredients (APIs), including anaesthetics like Benzocaine and Lidocaine. Both these mimic the numbing effect of cocaine, and resemble it closely in colour, texture and some chemical behaviours, making it easier to conceal the fact that the drug has been diluted. API cutting agents have helped traffickers to maintain steady supplies in the face of successful interdiction and even expand the market in the UK, particularly to young people aged from their mid teens to early twenties. From importation to street-level, the purity of the drug can be reduced up to a factor of 80 and street level cocaine can have a cocaine content as low as 1%. In view of the increasing use of Benzocaine as cutting agent for Cocaine, a study was carried out to investigate if 2H, 13C, 15N and 18O stable isotope signatures could be used in conjunction with multivariate chemometric data analysis to determine potential linkage between benzocaine exhibits seized from different locations or individuals to assist with investigation and prosecution of drug

  15. Multivariable analysis: a practical guide for clinicians and public health researchers

    National Research Council Canada - National Science Library

    Katz, Mitchell H

    2011-01-01

    .... It is the perfect introduction for all clinical researchers. It describes how to perform and interpret multivariable analysis, using plain language rather than complex derivations and mathematical formulae...

  16. Transport Traffic Analysis for Abusive Infrastructure Characterization

    Science.gov (United States)

    2012-09-01

    Then they extracted DNS information from each URL’s domain and used custom web crawlers to visit each URL to retrieve HTTP behavior and landing pages... web crawler . As the fetcher initiates each connection we record network flow information using tcpdump and save the information in a standard PCAP file...Alexa and 30,000 known-abusive web sites, we achieve a classification accuracy of 94 percent with a 3 percent false positive rate using only transport

  17. Microbiota analysis to reveal temperature abuse of fresh pork

    DEFF Research Database (Denmark)

    Buschhardt, Tasja; Bahl, Martin Iain; Hansen, Tina Beck

    monitored during aerobic chill-storage (4 °C and 7 °C) and temperature abuse (12 °C and 16 °C) for 96 hours, by culture-based methods and 16S rRNA gene sequencing. Bacterial genera that dominated during prolonged temperature abuse were Acinetobacter, Serratia and Pseudomonas, whereas chill-stored meat...... was dominated by Pseudomonas only. We also showed that the initial community affects subsequent changes during storage. The results suggest that principal coordinate analysis of beta diversity could be a useful tool to reveal temperature abused meat. Sequence data and culturing data revealed a strong positive......Violations of temperature regulations in the meat chain may affect meat safety. Methods are lacking to estimate whether meat has been subjected to temperature abuse. Exposure to too high temperatures may lead to systematic changes in the diverse bacterial communities of fresh meat. We investigated...

  18. Microbiota analysis to reveal temperature abuse of fresh pork

    DEFF Research Database (Denmark)

    Buschhardt, Tasja; Bahl, Martin Iain; Hansen, Tina Beck

    2017-01-01

    Violations of temperature regulations in the meat chain may affect meat safety. Methods are lacking to estimate whether meat has been subjected to temperature abuse. Exposure to too high temperatures may lead to systematic changes in the diverse bacterial communities of fresh meat. We investigated...... monitored during aerobic chill-storage (4 °C and 7 °C) and temperature abuse (12 °C and 16 °C) for 96 hours, by culture-based methods and 16S rRNA gene sequencing. Bacterial genera that dominated during prolonged temperature abuse were Acinetobacter, Serratia and Pseudomonas, whereas chill-stored meat...... was dominated by Pseudomonas only. We also showed that the initial community affects subsequent changes during storage. The results suggest that principal coordinate analysis of beta diversity could be a useful tool to reveal temperature abused meat. Sequence data and culturing data revealed a strong positive...

  19. Multivariate analysis of the population representativeness of related clinical studies.

    Science.gov (United States)

    He, Zhe; Ryan, Patrick; Hoxha, Julia; Wang, Shuang; Carini, Simona; Sim, Ida; Weng, Chunhua

    2016-04-01

    To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies. We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called "Multivariate Underrepresented Subgroup Identification" (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data. According to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment. mGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of

  20. Systematic wavelength selection for improved multivariate spectral analysis

    Science.gov (United States)

    Thomas, Edward V.; Robinson, Mark R.; Haaland, David M.

    1995-01-01

    Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=.function.(cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.

  1. [Forensic Analysis of 20 Dead Cases Related to Heroin Abuse].

    Science.gov (United States)

    Huang, W Q; Li, L H; Li, Z; Hong, S J

    2016-08-01

    To perform retrospective analysis on 20 dead cases related to heroin abuse, and to provide references for the forensic assessment of correlative cases. Among 20 dead cases related to heroin abuse, general situation, using method of drug, cause of death and result of forensic examination were analyzed by statistical analysis for summarizing the cause of death and pathologic changes. The dead were mostly young adults, with more male than female. The results of histopathological examinations showed non-specific pathological changes. There were four leading causes of death, including acute poisoning of heroin abuse or leakage (13 cases, 65%), concurrent diseases caused by heroin abuse (3 cases, 15%), inspiratory asphyxia caused by taking heroin (2 cases, 10%), and heroin withdrawal syndrome (2 cases, 10%). The forensic identification on dead related to heroin abuse must base on the comprehensive autopsy, and combine with the qualitative and quantitative analysis of heroin and its metabolites in death and the case information, as well as the scene investigation. Copyright© by the Editorial Department of Journal of Forensic Medicine

  2. Multivariable analysis: a practical guide for clinicians and public health researchers

    National Research Council Canada - National Science Library

    Katz, Mitchell H

    2011-01-01

    .... Numerous tables, graphs and tips help to demystify the process of performing multivariable analysis. The text is illustrated with many up-to-date examples from the medical literature on how to use multivariable analysis in clinical practice and in research"--Provided by publisher.

  3. A Multivariate Time Series Method for Monte Carlo Reactor Analysis

    International Nuclear Information System (INIS)

    Taro Ueki

    2008-01-01

    A robust multivariate time series method has been established for the Monte Carlo calculation of neutron multiplication problems. The method is termed Coarse Mesh Projection Method (CMPM) and can be implemented using the coarse statistical bins for acquisition of nuclear fission source data. A novel aspect of CMPM is the combination of the general technical principle of projection pursuit in the signal processing discipline and the neutron multiplication eigenvalue problem in the nuclear engineering discipline. CMPM enables reactor physicists to accurately evaluate major eigenvalue separations of nuclear reactors with continuous energy Monte Carlo calculation. CMPM was incorporated in the MCNP Monte Carlo particle transport code of Los Alamos National Laboratory. The great advantage of CMPM over the traditional Fission Matrix method is demonstrated for the three space-dimensional modeling of the initial core of a pressurized water reactor

  4. Multivariate longitudinal data analysis with mixed effects hidden Markov models.

    Science.gov (United States)

    Raffa, Jesse D; Dubin, Joel A

    2015-09-01

    Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies. © 2015, The International Biometric Society.

  5. The medical analysis of child sexual abuse images.

    Science.gov (United States)

    Cooper, Sharon W

    2011-11-01

    Analysis of child sexual abuse images, commonly referred to as pornography, requires a familiarity with the sexual maturation rating of children and an understanding of growth and development parameters. This article explains barriers that exist in working in this area of child abuse, the differences between subjective and objective analyses, methods used in working with this form of contraband, and recommendations that analysts document their findings in a format that allows for verbal descriptions of the images so that the content will be reflected in legal proceedings should there exist an aversion to visual review. Child sexual abuse images are a digital crime scene, and analysis requires a careful approach to assure that all victims may be identified.

  6. Changes in cod muscle proteins during frozen storage revealed by proteome analysis and multivariate data analysis

    DEFF Research Database (Denmark)

    Kjærsgård, Inger Vibeke Holst; Nørrelykke, M.R.; Jessen, Flemming

    2006-01-01

    Multivariate data analysis has been combined with proteomics to enhance the recovery of information from 2-DE of cod muscle proteins during different storage conditions. Proteins were extracted according to 11 different storage conditions and samples were resolved by 2-DE. Data generated by 2-DE...... was subjected to principal component analysis (PCA) and discriminant partial least squares regression (DPLSR). Applying PCA to 2-DE data revealed the samples to form groups according to frozen storage time, whereas differences due to different storage temperatures or chilled storage in modified atmosphere...... light chain 1, 2 and 3, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, aldolase A and two ?-actin fragments, and a nuclease diphosphate kinase B fragment to change in concentration, during frozen storage. Application of proteomics, multivariate data analysis and MS/MS to analyse...

  7. Using multivariate statistical analysis to assess changes in water ...

    African Journals Online (AJOL)

    2000; Evans et al., 2001; Kernan and Helliwell, 2001; Wright et al., 2001 .... Statistical analysis was used to examine the water quality at the five sites for ... An analysis of covariance. (ANCOVA) was used to test for site (spatial) differences in water quality. To assess for differences between sites, the ANCOVA compared the ...

  8. Multivariate Analysis Techniques for Optimal Vision System Design

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara

    The present thesis considers optimization of the spectral vision systems used for quality inspection of food items. The relationship between food quality, vision based techniques and spectral signature are described. The vision instruments for food analysis as well as datasets of the food items...... and simplifcation of the design of practical vision systems....... used in this thesis are described. The methodological strategies are outlined including sparse regression and pre-processing based on feature selection and extraction methods, supervised versus unsupervised analysis and linear versus non-linear approaches. One supervised feature selection algorithm...

  9. Helicopter Gas Turbine Engine Performance Analysis : A Multivariable Approach

    NARCIS (Netherlands)

    Arush, Ilan; Pavel, M.D.

    2017-01-01

    Helicopter performance relies heavily on the available output power of the engine(s) installed. A simplistic single-variable analysis approach is often used within the flight-testing community to reduce raw flight-test data in order to predict the available output power under different atmospheric

  10. A multivariate analysis of water quality in lake Naivasha, Kenya

    NARCIS (Netherlands)

    Ndungu, J.N.; Augustijn, Dionysius C.M.; Hulscher, Suzanne J.M.H.; Fulanda, B.; Kitaka, N.; Mathooko, J.M.

    2014-01-01

    Water quality information in aquatic ecosystems is crucial in setting up guidelines for resource management. This study explores the water quality status and pollution sources in Lake Naivasha, Kenya. Analysis of water quality parameters at seven sampling sites was carried out from water samples

  11. Multivariate analysis of germination ability and tolerance to salinity ...

    African Journals Online (AJOL)

    use

    2011-11-21

    Nov 21, 2011 ... among the traits studied. The first component included root length, plumule length, seedling length and seed vigor and accounted for 62.3% of the total variation among the traits. This component is entitled as the seed germination ability. Hierarchical cluster analysis classified the genotypes in three groups.

  12. Multivariate cluster analysis of some major and trace elements ...

    African Journals Online (AJOL)

    UFUOMA

    This study comprises soils formed on Paleoproterozoic Birimian Basement rocks (poorly graded silty sand, gravely sand and silty clays) from the unsaturated zone of the Densu River Basin, taken from a five meter depth. Elemental analysis of the soils samples were carried out by Energy Dispersive X-ray. Fluorescence ...

  13. Multivariate analysis of grassland in the Thee Rivers area, Natal ...

    African Journals Online (AJOL)

    Twenty grassland sites in the Three Rivers area, Natal, were sampled for presence of grass species in 20 8ft square quadrats placed in a restricted random manner at each site. The data were analysed using Wisconsin ordination principal components ordination and normal association analysis. Comparable results were ...

  14. Characterization of Nuclear Fuel using Multivariate Statistical Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Robel, M; Robel, M; Robel, M; Kristo, M J; Kristo, M J

    2007-11-27

    Various combinations of reactor type and fuel composition have been characterized using principle components analysis (PCA) of the concentrations of 9 U and Pu isotopes in the 10 fuel as a function of burnup. The use of PCA allows the reduction of the 9-dimensional data (isotopic concentrations) into a 3-dimensional approximation, giving a visual representation of the changes in nuclear fuel composition with burnup. Real-world variation in the concentrations of {sup 234}U and {sup 236}U in the fresh (unirradiated) fuel was accounted for. The effects of reprocessing were also simulated. The results suggest that, 15 even after reprocessing, Pu isotopes can be used to determine both the type of reactor and the initial fuel composition with good discrimination. Finally, partial least squares discriminant analysis (PSLDA) was investigated as a substitute for PCA. Our results suggest that PLSDA is a better tool for this application where separation between known classes is most important.

  15. Voxelwise multivariate analysis of multimodality magnetic resonance imaging

    OpenAIRE

    Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

    2013-01-01

    Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons amon...

  16. Multivariate analysis of groundwater quality in parts of Lagos - Nigeria

    African Journals Online (AJOL)

    The co-efficient of variation shows that all the groundwater parameters examined are highly variable except pH (10.92%).The factor analysis employed indicates that of the two Factors I and II, Factor I, which explains 62.73% of the total variance, has a strong positive loading on EC, TDS, TH, Na, Cl, Ca, K and SO4 . Factor II ...

  17. Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia

    Directory of Open Access Journals (Sweden)

    Eloísa Urrechaga

    2013-01-01

    Full Text Available Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA to the differential diagnosis of microcytic anemia. Methods. The training group was composed of 200 β-thalassemia carriers, 65 α-thalassemia carriers, 170 iron deficiency anemia (IDA, and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC, hemoglobin (Hb, mean cell volume (MCV, mean cell hemoglobin (MCH, and RBC distribution width (RDW. The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia. Results. For classifying patients into two groups (genetic anemia and acquired anemia, only one function was needed; 87.9% β-thalassemia carriers, and 83.3% α-thalassemia carriers, and 72.1% in the mixed group were correctly classified. Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.

  18. TMVA(Toolkit for Multivariate Analysis) new architectures design and implementation.

    CERN Document Server

    Zapata Mesa, Omar Andres

    2016-01-01

    Toolkit for Multivariate Analysis(TMVA) is a package in ROOT for machine learning algorithms for classification and regression of the events in the detectors. In TMVA, we are developing new high level algorithms to perform multivariate analysis as cross validation, hyper parameter optimization, variable importance etc... Almost all the algorithms are expensive and designed to process a huge amount of data. It is very important to implement the new technologies on parallel computing to reduce the processing times.

  19. A Case-Comparison Analysis of Elder Abuse and Neglect.

    Science.gov (United States)

    Godkin, Michael A.; And Others

    1989-01-01

    Compared 59 abused and 49 non-abused elders to identify factors contributing to elder abuse and neglect by caregivers in domestic setting. Found that members of abusive families often had emotional problems. Abused elders and caregivers had become increasingly interdependent because of loss of other family members, social isolation, and financial…

  20. A brief introduction to multivariate methods in grape and wine analysis

    Directory of Open Access Journals (Sweden)

    D Cozzolino

    2009-03-01

    Full Text Available D Cozzolino1, W U Cynkar1, N Shah1, R G Dambergs2, P A Smith11The Australian Wine Research Institute, Urrbrae, Glen Osmond, SA, Australia; 2The Australian Wine Research Institute, Tasmanian Institute of Agricultural Research, University of Tasmania, Hobart, Tasmania, AustraliaAbstract: Real-world systems are usually multivariate and hence usually cannot be adequately described by one selected variable without the risk of serious misrepresentation. Analyzing the effect of one variable at a time by analysis of variance techniques can give useful descriptive information, but this will not give specific information about relationships among variables and other important relationships in the entire matrix. Multivariate data analysis was developed in the late 1960s, and used by a number of research groups in analytical and physical organic chemistry due to the introduction of instrumentation giving multivariate responses for each sample analyzed. Development of such methods was also made possible by the availability of computers. Multivariate data analysis involves the use of mathematical and statistical techniques to extract information from complex data sets. The objective of this paper is to briefly describe and illustrate some multivariate data analysis methods used for grape and wine analysis.Keywords: multivariate analysis, data mining, wine, grape 

  1. Genetic Variation of Oriental Tobaccos Using Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    H Hatami Maleki

    2012-07-01

    Full Text Available Tobacco (Nicotiana tabaccum is one of the valuable agricultural and industrial crops that there is little information about its variation. For studying genetic variation on the basis of morphological characteristics, a number of 100 exotic and endemic oriental tobacco genotypes were obtained from the germplasm collection of the Urmia Tobacco Research Center, Urmia, Iran, using simple lattice design with 2 replications. Eight traits include: stem height and diameter, leaf number per plot, leaf length and width, fresh and dry leaf weight and day to 50% flowering were examined. Principal component analysis could reduce the studied morphological traits to 5 components having 96% accumulative variance. In the first component, all traits (except stem height showed positive significant correlations with. Cluster analysis using UPGMA method distinguished genotypes in 4 different groups. Maximum distance was between groups 1 and 4. Mean comparison revealed that genotypes (Trimph and Ohdaruma belong to group 4 had the maximum value of most examined traits, therefore, they could be utilized as parents of crosses in breeding programs.

  2. Safer approaches and landings: A multivariate analysis of critical factors

    Science.gov (United States)

    Heinrich, Durwood J.

    The approach-and-landing phases of flight represent 27% of mission time while resulting in 61 of the accidents and 39% of the fatalities. The landing phase itself represents only 1% of flight time but claims 45% of the accidents. Inadequate crew situation awareness (SA), crew resource management (CRM), and crew decision-making (DM) have been implicated in 51%, 63%, and 73% respectively of these accidents. The human factors constructs of SA, CRM, and DM were explored; a comprehensive definition of SA was proposed; and a "proactive defense" safety strategy was recommended. Data from a 1997 analysis of worldwide fatal accidents by the Flight Safety Foundation (FSF) Approach-and-Landing Accident Reduction (ALAR) Task Force was used to isolate crew- and weather-related causal factors that lead to approach-and-landing accidents (ALAs). Logistic regression and decision tree analysis were used on samplings of NASA's Aviation Safety Reporting System (ASRS) incident records ("near misses") and the National Transportation Safety Board's (NTSB) accident reports to examine hypotheses regarding factors and factor combinations that can dramatically increase the opportunity for accidents. An effective scale of risk factors was introduced for use by crews to proactively counter safety-related error-chain situations.

  3. Multivariate Statistical Methods as a Tool of Financial Analysis of Farm Business

    Czech Academy of Sciences Publication Activity Database

    Novák, J.; Sůvová, H.; Vondráček, Jiří

    2002-01-01

    Roč. 48, č. 1 (2002), s. 9-12 ISSN 0139-570X Institutional research plan: AV0Z1030915 Keywords : financial analysis * financial ratios * multivariate statistical methods * correlation analysis * discriminant analysis * cluster analysis Subject RIV: BB - Applied Statistics, Operational Research

  4. Application of Multivariable Statistical Techniques in Plant-wide WWTP Control Strategies Analysis

    DEFF Research Database (Denmark)

    Flores Alsina, Xavier; Comas, J.; Rodríguez-Roda, I.

    2007-01-01

    The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant...

  5. Early prediction of wheat quality: analysis during grain development using mass spectrometry and multivariate data analysis

    DEFF Research Database (Denmark)

    Ghirardo, A.; Sørensen, Helle Aagaard; Petersen, M.

    2005-01-01

    Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable fo...... data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later....

  6. Early prediction of wheat quality: analysis during grain development using mass spectrometry and multivariate data analysis

    DEFF Research Database (Denmark)

    Ghirardo, A.; Sørensen, Helle Aagaard; Petersen, M.

    2005-01-01

    Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable...... data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later....

  7. Multivariate analysis relating oil shale geochemical properties to NMR relaxometry

    Science.gov (United States)

    Birdwell, Justin E.; Washburn, Kathryn E.

    2015-01-01

    Low-field nuclear magnetic resonance (NMR) relaxometry has been used to provide insight into shale composition by separating relaxation responses from the various hydrogen-bearing phases present in shales in a noninvasive way. Previous low-field NMR work using solid-echo methods provided qualitative information on organic constituents associated with raw and pyrolyzed oil shale samples, but uncertainty in the interpretation of longitudinal-transverse (T1–T2) relaxometry correlation results indicated further study was required. Qualitative confirmation of peaks attributed to kerogen in oil shale was achieved by comparing T1–T2 correlation measurements made on oil shale samples to measurements made on kerogen isolated from those shales. Quantitative relationships between T1–T2 correlation data and organic geochemical properties of raw and pyrolyzed oil shales were determined using partial least-squares regression (PLSR). Relaxometry results were also compared to infrared spectra, and the results not only provided further confidence in the organic matter peak interpretations but also confirmed attribution of T1–T2 peaks to clay hydroxyls. In addition, PLSR analysis was applied to correlate relaxometry data to trace element concentrations with good success. The results of this work show that NMR relaxometry measurements using the solid-echo approach produce T1–T2 peak distributions that correlate well with geochemical properties of raw and pyrolyzed oil shales.

  8. A multivariate analysis of serum nutrient levels and lung function

    Directory of Open Access Journals (Sweden)

    Smit Henriette A

    2008-09-01

    Full Text Available Abstract Background There is mounting evidence that estimates of intakes of a range of dietary nutrients are related to both lung function level and rate of decline, but far less evidence on the relation between lung function and objective measures of serum levels of individual nutrients. The aim of this study was to conduct a comprehensive examination of the independent associations of a wide range of serum markers of nutritional status with lung function, measured as the one-second forced expiratory volume (FEV1. Methods Using data from the Third National Health and Nutrition Examination Survey, a US population-based cross-sectional study, we investigated the relation between 21 serum markers of potentially relevant nutrients and FEV1, with adjustment for potential confounding factors. Systematic approaches were used to guide the analysis. Results In a mutually adjusted model, higher serum levels of antioxidant vitamins (vitamin A, beta-cryptoxanthin, vitamin C, vitamin E, selenium, normalized calcium, chloride, and iron were independently associated with higher levels of FEV1. Higher concentrations of potassium and sodium were associated with lower FEV1. Conclusion Maintaining higher serum concentrations of dietary antioxidant vitamins and selenium is potentially beneficial to lung health. In addition other novel associations found in this study merit further investigation.

  9. The Pomatocalpa maculosum Complex (Orchidaceae Resolved by Multivariate Morphometric Analysis

    Directory of Open Access Journals (Sweden)

    Santi Watthana

    2006-03-01

    Full Text Available Principal components analysis (PCA was employed to analyse the morphological variation among 63 herbarium specimens tentatively identified as Pomatocalpa andamanicum (Hook.f. J. J. Sm., P. koordersii (Rolfe J. J. Sm., P. latifolium (Lindl. J. J. Sm., P. linearifolium Seidenf., P. maculosum (Lindl. J. J. Sm., P. marsupiale (Kraenzl. J. J. Sm., P. naevatum J. J. Sm., or P. siamense (Rolfe ex Downie Summerh. Thirty-seven quantitative and 5 binary characters were included in the analyses. Taxa were delimited according to the observed clustering of specimens in the PCA plots, diagnostic characters were identified, and the correct nomenclature was established through examination of type material. Four species could be recognized viz, P. diffusum Breda (syn. P. latifolium, P. fuscum (Lindl. J. J. Sm. (syn. P. latifolium, P. marsupiale (syn. P. koordersii and P. maculosum. For the latter species, two subspecies could be recognized, viz P. maculosum (Lindl. J. J. Sm. subsp. maculosum (syn. P. maculosum, P. naevatum p.p. and P. maculosum (Lindl. J. J. Sm. subsp. andamanicum (Hook.f. S. Watthana (syn. P. andamanicum, P. linearifolium, P. siamense, P. naevatum p.p.. An identification key and a taxonomic synopsis are provided.

  10. PRINCIPAL COMPONENT ANALYSIS AND CLUSTER ANALYSIS IN MULTIVARIATE ASSESSMENT OF WATER QUALITY

    Directory of Open Access Journals (Sweden)

    Elzbieta Radzka

    2017-03-01

    Full Text Available This paper deals with the use of multivariate methods in drinking water analysis. During a five-year project, from 2008 to 2012, selected chemical parameters in 11 water supply networks of the Siedlce County were studied. Throughout that period drinking water was of satisfactory quality, with only iron and manganese ions exceeding the limits (21 times and 12 times, respectively. In accordance with the results of cluster analysis, all water networks were put into three groups of different water quality. A high concentration of chlorides, sulphates, and manganese and a low concentration of copper and sodium was found in the water of Group 1 supply networks. The water in Group 2 had a high concentration of copper and sodium, and a low concentration of iron and sulphates. The water from Group 3 had a low concentration of chlorides and manganese, but a high concentration of fluorides. Using principal component analysis and cluster analysis, multivariate correlation between the studied parameters was determined, helping to put water supply networks into groups according to similar water quality.

  11. Multivariate techniques of analysis for ToF-E recoil spectrometry data

    Energy Technology Data Exchange (ETDEWEB)

    Whitlow, H.J.; Bouanani, M.E.; Persson, L.; Hult, M.; Jonsson, P.; Johnston, P.N. [Lund Institute of Technology, Solvegatan, (Sweden), Department of Nuclear Physics; Andersson, M. [Uppsala Univ. (Sweden). Dept. of Organic Chemistry; Ostling, M.; Zaring, C. [Royal institute of Technology, Electrum, Kista, (Sweden), Department of Electronics; Johnston, P.N.; Bubb, I.F.; Walker, B.R.; Stannard, W.B. [Royal Melbourne Inst. of Tech., VIC (Australia); Cohen, D.D.; Dytlewski, N. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

    1996-12-31

    Multivariate statistical methods are being developed by the Australian -Swedish Recoil Spectrometry Collaboration for quantitative analysis of the wealth of information in Time of Flight (ToF) and energy dispersive Recoil Spectrometry. An overview is presented of progress made in the use of multivariate techniques for energy calibration, separation of mass-overlapped signals and simulation of ToF-E data. 6 refs., 5 figs.

  12. Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

    Energy Technology Data Exchange (ETDEWEB)

    Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE

    2008-01-01

    Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.

  13. A General Framework for Multivariate Analysis with Optimal Scaling: The R Package aspect

    Directory of Open Access Journals (Sweden)

    Patrick Mair

    2009-11-01

    Full Text Available In a series of papers De Leeuw developed a general framework for multivariate analysis with optimal scaling. The basic idea of optimal scaling is to transform the observed variables (categories in terms of quantifications. In the approach presented here the multivariate data are collected into a multivariable. An aspect of a multivariable is a function that is used to measure how well the multivariable satisfies some criterion. Basically we can think of two different families of aspects which unify many well-known multivariate methods: Correlational aspects based on sums of correlations, eigenvalues and determinants which unify multiple regression, path analysis, correspondence analysis, nonlinear PCA, etc. Non-correlational aspects which linearize bivariate regressions and can be used for SEM preprocessing with categorical data. Additionally, other aspects can be established that do not correspond to classical techniques at all. By means of the R package aspect we provide a unified majorization-based implementation of this methodology. Using various data examples we will show the flexibility of this approach and how the optimally scaled results can be represented using graphical tools provided by the package.

  14. Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

    Science.gov (United States)

    Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

    2018-02-01

    It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

  15. Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data.

    Science.gov (United States)

    Beyene, Joseph; Tritchler, David; Bull, Shelley B; Cartier, Kevin C; Jonasdottir, Gudrun; Kraja, Aldi T; Li, Na; Nock, Nora L; Parkhomenko, Elena; Rao, J Sunil; Stein, Catherine M; Sutradhar, Rinku; Waaijenborg, Sandra; Wang, Ke-Sheng; Wang, Yuanjia; Wolkow, Pavel

    2007-01-01

    This paper summarizes contributions to group 12 of the 15th Genetic Analysis Workshop. The papers in this group focused on multivariate methods and applications for the analysis of molecular data including genotypic data as well as gene expression microarray measurements and clinical phenotypes. A range of multivariate techniques have been employed to extract signals from the multi-feature data sets that were provided by the workshop organizers. The methods included data reduction techniques such as principal component analysis and cluster analysis; latent variable models including structural equations and item response modeling; joint multivariate modeling techniques as well as multivariate visualization tools. This summary paper categorizes and discusses individual contributions with regard to multiple classifications of multivariate methods. Given the wide variety in the data considered, the objectives of the analysis and the methods applied, direct comparison of the results of the various papers is difficult. However, the group was able to make many interesting comparisons and parallels between the various approaches. In summary, there was a consensus among authors in group 12 that the genetic research community should continue to draw experiences from other fields such as statistics, econometrics, chemometrics, computer science and linear systems theory. (c) 2007 Wiley-Liss, Inc.

  16. A comparison between multivariate and bivariate analysis used in marketing research

    Directory of Open Access Journals (Sweden)

    Constantin, C.

    2012-01-01

    Full Text Available This paper is about an instrumental research conducted in order to compare the information given by two multivariate data analysis in comparison with the usual bivariate analysis. The outcomes of the research reveal that sometimes the multivariate methods use more information from a certain variable, but sometimes they use only a part of the information considered the most important for certain associations. For this reason, a researcher should use both categories of data analysis in order to obtain entirely useful information.

  17. A Retrospective Analysis of Shame, Dissociation, and Adult Victimization in Survivors of Childhood Sexual Abuse.

    Science.gov (United States)

    Kessler, Bonnie L.; Bieschke, Kathleen J.

    1999-01-01

    Examines relationship between childhood sexual abuse and victimization in adulthood using a sample of university women. Analysis shows that there is a significant association between women who were abused in childhood and adult victimization. Reports that the odds of revictimization in adulthood were greater for women who were abused in childhood…

  18. Chemical structure of wood charcoal by infrared spectroscopy and multivariate analysis

    Science.gov (United States)

    Nicole Labbe; David Harper; Timothy Rials; Thomas Elder

    2006-01-01

    In this work, the effect of temperature on charcoal structure and chemical composition is investigated for four tree species. Wood charcoal carbonized at various temperatures is analyzed by mid infrared spectroscopy coupled with multivariate analysis and by thermogravimetric analysis to characterize the chemical composition during the carbonization process. The...

  19. Exploratory Analysis of Multivariate Data (Unsupervised Image Segmentation and Data Driven Linear and Nonlinear Decomposition)

    DEFF Research Database (Denmark)

    Hilger, Klaus Baggesen

    2002-01-01

    This work describes different methods that are useful in the analysis of multivariate single and multiset data. The thesis covers selected aspects of relevant data analysis techniques in this context. Methods dedicated to handling data of a spatial nature are of primary interest with focus on dat...

  20. Explaining excess morbidity amongst homeless shelter users: A multivariate analysis for the Danish adult population.

    Science.gov (United States)

    Benjaminsen, Lars; Birkelund, Jesper Fels

    2018-03-01

    This article analyses excess morbidity amongst homeless shelter users compared to the general Danish population. The study provides an extensive control for confounding and investigates to what extent excess morbidity is explained by homelessness or other risk factors. Data set includes administrative micro-data for 4,068,926 Danes who were 23 years or older on 1 January 2007. Nationwide data on shelter use identified 14,730 individuals as shelter users from 2002 to 2006. Somatic diseases were measured from 2007 to 2011 through diagnosis data from hospital discharges. The risk of somatic diseases amongst shelter users was analysed through a multivariate model that decomposed the total effect into a direct effect and indirect effects mediated by other risk factors. The excess morbidity associated with shelter use is substantially lower than in studies that did not include an extensive control. Approximately 80% of excess morbidity amongst shelter users is attributed to other risk factors. A large part of the excess morbidity is explained by substance abuse problems and lack of employment, whilst mental illness, low income, low education, civil status and ethnic minority background explain only a limited part. However, when conducting an extensive control for confounding, a significantly higher morbidity was identified amongst shelter users for infectious diseases, lung, skin, blood and digestive diseases, injuries, and poisoning. Ill health amongst homeless shelter users is widely explained by substance abuse problems and other risk factors. Nonetheless, for many diseases homelessness poses an additional risk to the health.

  1. Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

    Science.gov (United States)

    Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

    2016-01-01

    As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

  2. Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure.

    Science.gov (United States)

    D'Amico, E J; Neilands, T B; Zambarano, R

    2001-11-01

    Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.

  3. Structure of rat behavior in hole-board: I) multivariate analysis of response to anxiety.

    Science.gov (United States)

    Casarrubea, Maurizio; Sorbera, Filippina; Crescimanno, Giuseppe

    2009-01-08

    Aim of present paper was to carry out an analysis of rat behavior in hole-board following different multivariate approaches. Thirty male Wistar rats were observed in a hole-board apparatus and their behavior recorded for 10 min through a digital videocamera for a following frame-by-frame analysis. Both descriptive and multivariate analyses were used. Descriptive analysis showed that roughly 85% of the whole behavioral structure encompassed six patterns appearing during the first minute of observation: walking, climbing, rearing, immobile-sniffing, edge-sniff and head-dip. As to multivariate approach, cluster analysis showed three main dyadic associations: [edge-sniff/head-dip], [walking/climbing], [face-grooming/body-grooming]. Path diagram, obtained on the basis of relative frequencies of transitions among behavioral patterns (stochastic analysis), emphasized cluster analysis results. Adjusted residuals confirmed, from a statistical point of view, the strong relationships among specific patterns. Results demonstrate that immobile-sniffing has a crucial role in rat behavioral organization and head-dip is closely related with edge-sniff which is suggested to be a specific sniffing activity of holes edge never properly considered. Present research shows that multivariate approaches, revealing patterning among different behavioral elements in hole-board, could improve test reliability, providing a more useful tool to investigate behavioral effects of anxiolytic drugs.

  4. Multivariate data analysis easily retrieves insight from a wealth of data

    NARCIS (Netherlands)

    Rubingh, C.; Thissen, U.; Voort Maarschalk, K. van de

    2010-01-01

    Statistics are often viewed as confusing and complicated, but multivariate data analysis (MVA) methods can be used to amass knowledge simply. MVA can increase the quality and yield of production processes by elucidating hidden knowledge and identifying opportunities for adaptation using historical

  5. Cardiovascular reactivity patterns and pathways to hypertension : a multivariate cluster analysis

    NARCIS (Netherlands)

    Brindle, R C; Ginty, A T; Jones, A; Phillips, A C; Roseboom, T J; Carroll, D; Painter, R C; de Rooij, S R

    2016-01-01

    Substantial evidence links exaggerated mental stress induced blood pressure reactivity to future hypertension, but the results for heart rate reactivity are less clear. For this reason multivariate cluster analysis was carried out to examine the relationship between heart rate and blood pressure

  6. Missing Data and Multiple Imputation in the Context of Multivariate Analysis of Variance

    Science.gov (United States)

    Finch, W. Holmes

    2016-01-01

    Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

  7. Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis

    NARCIS (Netherlands)

    Oosterhof, N.N.; Wiggett, AJ.; Cross, E.S.

    2014-01-01

    Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this

  8. Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis.

    Science.gov (United States)

    Oosterhof, Nikolaas N; Wiggett, Alison J; Cross, Emily S

    2014-04-01

    Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.

  9. Multivariate data analysis as a tool in advanced quality monitoring in the food production chain

    DEFF Research Database (Denmark)

    Bro, R.; van den Berg, F.; Thybo, A.

    2002-01-01

    This paper summarizes some recent advances in mathematical modeling of relevance in advanced quality monitoring in the food production chain. Using chemometrics-multivariate data analysis - it is illustrated how to tackle problems in food science more efficiently and, moreover, solve problems...

  10. Denial-of-service attack detection based on multivariate correlation analysis

    NARCIS (Netherlands)

    Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Lu, Bao-Liang; Zhang, Liqing; Kwok, James

    2011-01-01

    The reliability and availability of network services are being threatened by the growing number of Denial-of-Service (DoS) attacks. Effective mechanisms for DoS attack detection are demanded. Therefore, we propose a multivariate correlation analysis approach to investigate and extract second-order

  11. Decomposition of multivariate phenotypic means in multigroup genetic covarinace structure analysis

    NARCIS (Netherlands)

    Dolan, C.V.; Molenaar, P.C.M.; Boomsma, D.I.

    1992-01-01

    Uses D. Sorbom's (1974) method to study differences in latent means in multivariate twin data. By restricting the analysis to a comparison between groups, the results pertain only to the additive contributions of common genetic and environmental factors to the deviation of the group means from what

  12. Dissection of genomic correlation matrices of US Holsteins using multivariate factor analysis

    Science.gov (United States)

    Aim of the study was to compare correlation matrices between direct genomic predictions for 31 production, fitness and conformation traits both at genomic and chromosomal level in US Holstein bulls. Multivariate factor analysis was used to quantify basic features of correlation matrices. Factor extr...

  13. Cardiovascular reactivity patterns and pathways to hypertension: a multivariate cluster analysis

    NARCIS (Netherlands)

    Brindle, R. C.; Ginty, A. T.; Jones, A.; Phillips, A. C.; Roseboom, T. J.; Carroll, D.; Painter, R. C.; de Rooij, S. R.

    2016-01-01

    Substantial evidence links exaggerated mental stress induced blood pressure reactivity to future hypertension, but the results for heart rate reactivity are less clear. For this reason multivariate cluster analysis was carried out to examine the relationship between heart rate and blood pressure

  14. Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

    Science.gov (United States)

    Anderson, John R.

    2012-01-01

    Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

  15. Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

    Science.gov (United States)

    Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

    2017-01-01

    High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  16. Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology.

    Science.gov (United States)

    Hervé, Maxime R; Nicolè, Florence; Lê Cao, Kim-Anh

    2018-03-01

    Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.

  17. Child Sexual Abuse and Adolescent Prostitution: A Comparative Analysis.

    Science.gov (United States)

    Seng, Magnus J.

    1989-01-01

    Explored relationship between sexual abuse and adolescent prostitution by comparing 70 sexually abused children with 35 prostitution-involved children on 22 variables. Findings suggest that relationship is not direct, but involves runaway behavior as intervening variable. Concludes that it is not so much sexual abuse that leads to prostitution, as…

  18. The studies of post-medieval glass by multivariate and X-ray fluorescence analysis

    International Nuclear Information System (INIS)

    Kierzek, J.; Kunicki-Goldfinger, J.

    2002-01-01

    Multivariate statistical analysis of the results obtained by energy dispersive X-ray fluorescence analysis has been used in the study of baroque vessel glasses originated from central Europe. X-ray spectrometry can be applied as a completely non-destructive, non-sampling and multi-element method. It is very useful in the studies of valuable historical artefacts. For the last years, multivariate statistical analysis has been developed as an important tool for the archaeometric purposes. Cluster, principal component and discriminant analysis were applied for the classification of the examined objects. The obtained results show that these statistical tools are very useful and complementary in the studies of historical objects. (author)

  19. Application of multivariate analysis and vibrational spectroscopy in classification of biological systems

    Science.gov (United States)

    Salman, A.; Shufan, E.; Lapidot, I.; Tsror, L.; Zeiri, L.; Sahu, R. K.; Moreh, R.; Mordechai, S.; Huleihel, M.

    2015-12-01

    Fourier Transform Infrared (FTIR) and Raman spectroscopies have emerged as powerful tools for chemical analysis. This is due to their ability to provide detailed information about the spatial distribution of chemical composition at the molecular level. A biological sample, i.e. bacteria or fungi, has a typical spectrum. This spectral fingerprint, characterizes the sample and can therefore be used for differentiating between biology samples which belong to different groups, i.e., several different isolates of a given fungi. When the spectral differences between the groups are minute, multivariate analysis should be used to provide a good differentiation. We hereby review several results which demonstrate the differentiation success obtained by combining spectroscopy measurements and multivariate analysis.

  20. Maximizing information obtained from secondary ion mass spectra of organic thin films using multivariate analysis

    Science.gov (United States)

    Wagner, M. S.; Graham, D. J.; Ratner, B. D.; Castner, David G.

    2004-10-01

    Time-of-flight secondary ion mass spectrometry (ToF-SIMS) can give a detailed description of the surface chemistry and structure of organic materials. The high mass resolution and high mass range mass spectra obtainable from modern ToF-SIMS instruments offer the ability to rapidly obtain large amounts of data. Distillation of that data into usable information presents a significant problem in the analysis of ToF-SIMS data from organic materials. Multivariate data analysis techniques have become increasingly common for assisting with the interpretation of complex ToF-SIMS data sets. This study presents an overview of principal component analysis (PCA) and partial least squares regression (PLSR) for analyzing the ToF-SIMS spectra of alkanethiol self-assembled monolayers (SAMs) adsorbed onto gold substrates and polymer molecular depth profiles obtained using an SF5+ primary ion beam. The effect of data pretreatment on the information obtained from multivariate analysis of these data sets has been explored. Multivariate analysis is an important tool for maximizing the information obtained from the ToF-SIMS spectra of organic thin films.

  1. Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

    Science.gov (United States)

    Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

    2016-01-01

    Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms

  2. Sequential Structural and Fluid Dynamics Analysis of Balloon-Expandable Coronary Stents: A Multivariable Statistical Analysis.

    Science.gov (United States)

    Martin, David; Boyle, Fergal

    2015-09-01

    Several clinical studies have identified a strong correlation between neointimal hyperplasia following coronary stent deployment and both stent-induced arterial injury and altered vessel hemodynamics. As such, the sequential structural and fluid dynamics analysis of balloon-expandable stent deployment should provide a comprehensive indication of stent performance. Despite this observation, very few numerical studies of balloon-expandable coronary stents have considered both the mechanical and hemodynamic impact of stent deployment. Furthermore, in the few studies that have considered both phenomena, only a small number of stents have been considered. In this study, a sequential structural and fluid dynamics analysis methodology was employed to compare both the mechanical and hemodynamic impact of six balloon-expandable coronary stents. To investigate the relationship between stent design and performance, several common stent design properties were then identified and the dependence between these properties and both the mechanical and hemodynamic variables of interest was evaluated using statistical measures of correlation. Following the completion of the numerical analyses, stent strut thickness was identified as the only common design property that demonstrated a strong dependence with either the mean equivalent stress predicted in the artery wall or the mean relative residence time predicted on the luminal surface of the artery. These results corroborate the findings of the large-scale ISAR-STEREO clinical studies and highlight the crucial role of strut thickness in coronary stent design. The sequential structural and fluid dynamics analysis methodology and the multivariable statistical treatment of the results described in this study should prove useful in the design of future balloon-expandable coronary stents.

  3. A refined method for multivariate meta-analysis and meta-regression

    Science.gov (United States)

    Jackson, Daniel; Riley, Richard D

    2014-01-01

    Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

  4. MULTIVARIATE ANALYSIS OF THE PHYSICO MECHANICAL PARAMETERS VARIATION FOR HYDROPHOBIC TEXTILE

    Directory of Open Access Journals (Sweden)

    AILENI Raluca Maria

    2017-05-01

    Full Text Available This work presents a multivariate analyse regarding textile surfaces treated with fluorocarbon chemicals in order to obtain hydrophobic effect. The hydrophobic characteristics of the textile samples (cotton 100% were obtained after hydrophobization treatement in the laboratory, by using chemicals based on fluorocarbon and by process parameters variation (temperature, time. Experimental data were evaluated by means of laboratory tests and multivariate analysis in order to observe covariance and the connections between the process parameters and the final characteristics of the fabric hydrophobizated. For evaluating the hydrophobic effect, some investigations were performed by qualitative method Spraytest for determinating the resistance to surface wetting in accordance with the standard SR EN ISO 4920-2013, air permeability according to SR EN ISO 9237:1999 standard and contact angle computing by using the device VCA Optima for contact angle measuring, in accordance to the standard ASTM D7490-2008. In order to highlight the morphological changes that appear on the cotton fibers, samples were examined using scanning electron microscopy device (SEM with the magnitude of X2000 X4000, X8000. The purpose of multivariate analysis for parameters and influence factors for hydrophobization process, based on fluorocarbon, is to obtain information relating to the dependent variables and independent, which influence the process. We establish some dependence between parameters (contact angle, spray test resistance, air permeability by using covariance matrix analysis. This analysis shows that contact angle and the resistance to spray test are in direct dependence and in reverse dependence with the air permeability.

  5. Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions.

    Science.gov (United States)

    Bathke, Arne C; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

    2018-03-22

    To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer's disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved.

  6. Mapping informative clusters in a hierarchical [corrected] framework of FMRI multivariate analysis.

    Directory of Open Access Journals (Sweden)

    Rui Xu

    Full Text Available Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies.

  7. Multivariate data analysis modern support of the coal charge formation process

    Energy Technology Data Exchange (ETDEWEB)

    S. Czudek; J. Cieslar; M. Kaloc

    2002-07-01

    The complicated substance of the coal material requires the evaluation of a wide information spectrum for the following process of the charge formation. Practical experiences acquired through a long-time usage of certain coal types were used for the process mainly. A group of multivariate statistical methods, that are able to complete the above stated process suitably through their abilities, is developing intensively at present. The ability to reduce the multidimensional problems into two dimensions and by doing so to project the multivariate model into the two-dimensional space is a very significant ability of the methods. Multivariate statistical methods were applied in the area of the monitoring of coal supplies quality parameters, data analysis of the carbonizing tests in laboratory and industrial conditions. The results of the analyses approved the possible usage of new modern methods of the multivariate statistics in the areas. The stability monitoring of coal quality parameters. The monitoring of the scattering feature of the coal and coke quality parameters. The monitoring of the internal structure of the coal and coke quality parameters. 2 figs., 7 graphs.

  8. Multivariate data analysis for finding the relevant fatty acids contributing to the melting fractions of cream

    DEFF Research Database (Denmark)

    Buldo, Patrizia; Larsen, Mette Krogh; Wiking, Lars

    2013-01-01

    : Multivariate analysis of data from individual cows identified the most relevant fatty acids contributing to the melting point of the medium melting fraction of cream. The fatty acid composition of milk fat could differentiate cream from different feeding strategies; however, owing to individual cow variation......BACKGROUND: The melting behaviour and fatty acid composition of cream from a total of 33 cows from four farms were analysed. Multivariate data analysis was used to identify the fatty acids that contributed most to the melting points and to differentiate between creams from different practical......:0 and palmitoleic acid (C16:1) in milk fat, whereas it decreased the amount of stearic acid (C18:0) and C18:1 trans fatty acid. Average data on the melting behaviour of cream separated the farms into two groups where the main differences in feeding were the amounts of maize silage and rapeseed cake used. CONCLUSION...

  9. Local examination of skin diffusion using FTIR spectroscopic imaging and multivariate target factor analysis.

    Science.gov (United States)

    Tetteh, J; Mader, K T; Andanson, J-M; McAuley, W J; Lane, M E; Hadgraft, J; Kazarian, S G; Mitchell, J C

    2009-05-29

    In the context of trans-dermal drug delivery it is very important to have mechanistic insight into the barrier function of the skin's stratum corneum and the diffusion mechanisms of topically applied drugs. Currently spectroscopic imaging techniques are evolving which enable a spatial examination of various types of samples in a dynamic way. ATR-FTIR imaging opens up the possibility to monitor spatial diffusion profiles across the stratum corneum of a skin sample. Multivariate data analyses methods based on factor analysis are able to provide insight into the large amount of spectroscopically complex and highly overlapping signals generated. Multivariate target factor analysis was used for spectral resolution and local diffusion profiles with time through stratum corneum. A model drug, 4-cyanophenol in polyethylene glycol 600 and water was studied. Results indicate that the average diffusion profiles between spatially different locations show similar profiles despite the heterogeneous nature of the biological sample and the challenging experimental set-up.

  10. A Primer on Multivariate Analysis of Variance (MANOVA for Behavioral Scientists

    Directory of Open Access Journals (Sweden)

    Russell T. Warne

    2014-11-01

    Full Text Available Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012 show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA. However, MANOVA and its associated procedures are often not properly understood, as demonstrated by the fact that few of the MANOVAs published in the scientific literature were accompanied by the correct post hoc procedure, descriptive discriminant analysis (DDA. The purpose of this article is to explain the theory behind and meaning of MANOVA and DDA. I also provide an example of a simple MANOVA with real mental health data from 4,384 adolescents to show how to interpret MANOVA results.

  11. Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

    Science.gov (United States)

    Dinç, Erdal; Ozdemir, Abdil

    2005-01-01

    Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

  12. Demand for Indonesia, Singapore and Thailand Tourist to Malaysia:Seasonal Unit Root and Multivariate Analysis

    OpenAIRE

    Nanthakumar Loganathan; Ang Shy Han; Mori Kogid

    2013-01-01

    This is pioneer study conducted in Malaysia combining seasonal unit root and multivariate approach. Malaysia is a favorite destination for international tourist and the major tourism demand for Malaysia is from Indonesia, Singapore and Thailand. These three countries is neighboring country for Malaysia and tourists are more flexible to visit Malaysia using flights, rails and road transportations. This study attempts to investigate the seasonal and structural break analysis of tourist arrivals...

  13. Structure of rat behavior in hole-board: II) multivariate analysis of modifications induced by diazepam.

    Science.gov (United States)

    Casarrubea, Maurizio; Sorbera, Filippina; Crescimanno, Giuseppe

    2009-03-23

    In our previous study we suggested that multivariate analysis could improve hole-board test reliability providing a more useful tool to determine behavioral effects of anxiolytic drugs. To support this hypothesis, a multivariate analysis of rat behavior in hole-board, following administration of the reference anxiolytic drug diazepam, was carried out. Four groups, each composed of thirty male Wistar rats, were used: one saline and three diazepam injected (0.25, 0.5 and 2 mg/kg IP). Rat behavior was recorded for 10 min through a digital videocamera. Descriptive and multivariate analyses were carried out. In all groups, more than 80% of whole behavioral structure encompassed walking, climbing, rearing, immobile-sniffing, edge-sniff and head-dip. Moreover, modifications observed of a specific index, represented by edge-sniff/head-dip ratio, were correlated to diazepam-induced modifications of anxiety level. Cluster analysis showed that diazepam at 0.5 and 2 mg/kg induced important changes for [edge-sniff/head-dip] cluster. In addition, in all diazepam groups a [walking/climbing] cluster appeared. Path diagrams showed close relationships among different patterns both in saline and diazepam injected animals. Also, significant changes were detected following diazepam for transitions encompassing both general exploratory patterns (walking, climbing) and the specific ones (head-dip and edge-sniff). Adjusted residuals confirmed in all groups patterns relationships and, where present, significant behavioral associations. Results demonstrate that an anxiolytic activity can be revealed by head-dip/edge-sniff association weakening and by the addressing of behavioral structure toward general exploratory activity. Improvement of hole-board test reliability in behavioral study of anxiety, following multivariate analysis, is emphasized.

  14. Multivariate Analysis Approach to the Serum Peptide Profile of Morbidly Obese Patients

    Directory of Open Access Journals (Sweden)

    M. Agostini

    2013-01-01

    Full Text Available Background: Obesity is currently epidemic in many countries worldwide and is strongly related to diabetes and cardiovascular disease. Mass spectrometry, in particular matrix-assisted laser desorption/ionization time of flight (MALDI-TOF is currently used for detecting different pattern of expressed protein. This study investigated the differences in low molecular weight (LMW peptide profiles between obese and normal-weight subjects in combination with multivariate statistical analysis.

  15. Quantitative Soil Carbon Analysis with in Situ Laser-Induced Breakdown Spectroscopy by Multivariate Analysis

    Science.gov (United States)

    Harris, R. D.; Clegg, S. M.; Barefield, J. E.; Fessenden-Rahn, J. E.; Wiens, R. C.; Ebinger, M. H.

    2007-12-01

    The Earth's oceans, forests, agricultural lands and other natural areas absorb about half of the carbon dioxide emitted from anthropogenic sources. Terrestrial carbon sequestration strategies are immediately available to bridge the gap between current terrestrial sequestration capacity and high-capacity geologic sequestration projects available in 10 to 20 years. Terrestrial carbon sequestration strategies consist of implementing land management practices aimed at decreasing CO2 emitted into the atmosphere and developing advanced measurement tools to inventory and monitor carbon processes in soils and biota. Laser-Induced Breakdown Spectroscopy (LIBS) is one of the analytical tools used to determine the total soil carbon in samples within the Big Sky and Southwest Carbon Sequestration Regional Partnerships. LIBS involves focusing a Nd:YAG laser operating at 1064nm onto the surface of the sample. The laser ablates material from the surface, generating an expanding plasma containing electronically excited ions, atoms, and small molecules. As these electronically excited species relax back to the ground state, they emit light at wavelengths characteristic of the species present in the sample. Some of this emission is directed into one of three dispersive spectrometers. The experiments discussed in this paper were completed with a person portable LIBS instrument designed and built at Los Alamos National Laboratory that uses a Kigre Laser (25mJ/pulse) and an Ocean Optics HR2000 dispersive spectrometer. This instrument was used to probe samples collected from Illinois (no-till loam), Michigan (no-till clay), and North Dakota (reduced-till sand). A new multivariate analysis technique was employed to extract concentrations to 0.5%C with significantly greater statistical accuracy than conventional univariate techniques. These MVA techniques appear to completely compensate for these matrix effects because the analysis identifies the correlations between the spectra

  16. Multivariate statistical analysis for x-ray photoelectron spectroscopy spectral imaging: Effect of image acquisition time

    International Nuclear Information System (INIS)

    Peebles, D.E.; Ohlhausen, J.A.; Kotula, P.G.; Hutton, S.; Blomfield, C.

    2004-01-01

    The acquisition of spectral images for x-ray photoelectron spectroscopy (XPS) is a relatively new approach, although it has been used with other analytical spectroscopy tools for some time. This technique provides full spectral information at every pixel of an image, in order to provide a complete chemical mapping of the imaged surface area. Multivariate statistical analysis techniques applied to the spectral image data allow the determination of chemical component species, and their distribution and concentrations, with minimal data acquisition and processing times. Some of these statistical techniques have proven to be very robust and efficient methods for deriving physically realistic chemical components without input by the user other than the spectral matrix itself. The benefits of multivariate analysis of the spectral image data include significantly improved signal to noise, improved image contrast and intensity uniformity, and improved spatial resolution - which are achieved due to the effective statistical aggregation of the large number of often noisy data points in the image. This work demonstrates the improvements in chemical component determination and contrast, signal-to-noise level, and spatial resolution that can be obtained by the application of multivariate statistical analysis to XPS spectral images

  17. Resent state and multivariate analysis of a few juniper forests of baluchistan, pakistan

    International Nuclear Information System (INIS)

    Ahmed, M.; Siddiqui, M.F.

    2015-01-01

    Quantitative multivariate investigations were carried out to explore various forms of Juniper trees resulting human disturbances and natural phenomenon. Thirty stands were sampled by point centered quarter method and data were analysed using Wards cluster analysis and Bray-Curtis ordination. On the basis of multivariate analysis eight various forms i.e. healthy, unhealthy, over mature, disturbed, dieback, standing dead, logs and cut stem were recognized. Structural attributes were computed. Highest numbers (130-133 stem ha-1) of logs were recorded from Cautair and Khunk forests. Highest density ha-1 (229 ha-1) of healthy plants was estimated from Tangi Top area while lowest number (24 ha-1) of healthy plants was found from Saraghara area. Multivariate analysis showed five groups in cluster and ordination diagrams. These groups are characterized on the basis of healthy, over mature, disturbed and logged trees of Juniper. Higher number (115, 96, 84, 80 ha-1) of disturbed trees were distributed at Speena Sukher, Srag Kazi, Prang Shella and Tangi Top respectively. Overall density does not show any significant relation with basal area m2 ha-1, degree of slopes and the elevation of the sampling stands. Present study show that each and every Juniper stands are highly disturbed mostly due to human influence, therefore prompt conservational steps should be taken to safe these forests. (author)

  18. Estimation of failure criteria in multivariate sensory shelf life testing using survival analysis.

    Science.gov (United States)

    Giménez, Ana; Gagliardi, Andrés; Ares, Gastón

    2017-09-01

    For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a "yes" or "no" question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Multivariate methods for analysis of environmental reference materials using laser-induced breakdown spectroscopy

    Directory of Open Access Journals (Sweden)

    Shikha Awasthi

    2017-06-01

    Full Text Available Analysis of emission from laser-induced plasma has a unique capability for quantifying the major and minor elements present in any type of samples under optimal analysis conditions. Chemometric techniques are very effective and reliable tools for quantification of multiple components in complex matrices. The feasibility of laser-induced breakdown spectroscopy (LIBS in combination with multivariate analysis was investigated for the analysis of environmental reference materials (RMs. In the present work, different (Certified/Standard Reference Materials of soil and plant origin were analyzed using LIBS and the presence of Al, Ca, Mg, Fe, K, Mn and Si were identified in the LIBS spectra of these materials. Multivariate statistical methods (Partial Least Square Regression and Partial Least Square Discriminant Analysis were employed for quantitative analysis of the constituent elements using the LIBS spectral data. Calibration models were used to predict the concentrations of the different elements of test samples and subsequently, the concentrations were compared with certified concentrations to check the authenticity of models. The non-destructive analytical method namely Instrumental Neutron Activation Analysis (INAA using high flux reactor neutrons and high resolution gamma-ray spectrometry was also used for intercomparison of results of two RMs by LIBS.

  20. Multivariate data analysis approach to understand magnetic properties of perovskite manganese oxides

    International Nuclear Information System (INIS)

    Imamura, N.; Mizoguchi, T.; Yamauchi, H.; Karppinen, M.

    2008-01-01

    Here we apply statistical multivariate data analysis techniques to obtain some insights into the complex structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskite systems, AMnO 3 . The 131 samples included in the present analyses are described by 21 crystal-structure or crystal-chemical (CS/CC) parameters. Principal component analysis (PCA), carried out separately for the AFM and FM compounds, is used to model and evaluate the various relationships among the magnetic properties and the various CS/CC parameters. Moreover, for the AFM compounds, PLS (partial least squares projections to latent structures) analysis is performed so as to predict the magnitude of the Neel temperature on the bases of the CS/CC parameters. Finally, so-called PLS-DA (PLS discriminant analysis) method is employed to find out the most influential/characteristic CS/CC parameters that differentiate the two classes of compounds from each other. - Graphical abstract: Statistical multivariate data analysis techniques are applied to detect structure-property relations in antiferromagnetic (AFM) and ferromagnetic (FM) manganese perovskites. For AFM compounds, partial least squares projections to latent structures analysis predict the magnitude of the Neel temperature on the bases of structural parameters only. Moreover, AFM and FM compounds are well separated by means of so-called partial least squares discriminant analysis method

  1. Multivariate analysis of prognostic factors for idiopathic sudden sensorineural hearing loss in children.

    Science.gov (United States)

    Chung, Jae Ho; Cho, Seok Hyun; Jeong, Jin Hyeok; Park, Chul Won; Lee, Seung Hwan

    2015-09-01

    To evaluate clinical characteristics and possible associated factors of idiopathic sudden sensorineural hearing loss (ISSNHL) in children using univariate and multivariate analyses. A retrospective case series with comparisons. From January 2007 to December 2013, medical records of 37 pediatric ISSNHL patients were reviewed to assess hearing recovery rate and examine factors associated with prognosis (gender; side of hearing loss; opposite side hearing loss; treatment onset; presence of vertigo, tinnitus, and ear fullness; initial hearing threshold), using univariate and multivariate analysis, and compare them with 276 adult ISSNHL patients. Pediatric patients comprised only 6.6% of pediatric/adult cases of ISSNHL, and those below 10 years old were only 0.7%. The overall recovery rates (complete and partial) of the pediatric and adult patients were 57.4% and 47.2%, respectively. The complete recovery rate of the pediatric group (46.6%) was higher than that of the adult group (30.8%, P = .040). According to multivariate analysis, absence of tinnitus, later onset of treatment, and higher hearing threshold at initial presentation were associated with a poor prognosis in pediatric ISSNHL. The recovery rate of ISSNHL in pediatric patients is higher than in adults, and the presence of tinnitus and earlier treatment onset is associated with favorable outcomes. 4. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

  2. A Novel and Effective Multivariate Method for Compositional Analysis using Laser Induced Breakdown Spectroscopy

    International Nuclear Information System (INIS)

    Wang, W; Qi, H; Ayhan, B; Kwan, C; Vance, S

    2014-01-01

    Compositional analysis is important to interrogate spectral samples for direct analysis of materials in agriculture, environment and archaeology, etc. In this paper, multi-variate analysis (MVA) techniques are coupled with laser induced breakdown spectroscopy (LIBS) to estimate quantitative elemental compositions and determine the type of the sample. In particular, we present a new multivariate analysis method for composition analysis, referred to as s pectral unmixing . The LIBS spectrum of a testing sample is considered as a linear mixture with more than one constituent signatures that correspond to various chemical elements. The signature library is derived from regression analysis using training samples or is manually set up with the information from an elemental LIBS spectral database. A calibration step is used to make all the signatures in library to be homogeneous with the testing sample so as to avoid inhomogeneous signatures that might be caused by different sampling conditions. To demonstrate the feasibility of the proposed method, we compare it with the traditional partial least squares (PLS) method and the univariate method using a standard soil data set with elemental concentration measured a priori. The experimental results show that the proposed method holds great potential for reliable and effective elemental concentration estimation

  3. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies.

    Science.gov (United States)

    Kambeitz, Joseph; Kambeitz-Ilankovic, Lana; Leucht, Stefan; Wood, Stephen; Davatzikos, Christos; Malchow, Berend; Falkai, Peter; Koutsouleris, Nikolaos

    2015-06-01

    Multivariate pattern recognition approaches have recently facilitated the search for reliable neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking into account the multivariate nature of brain functional and structural changes as well as their distributed localization across the whole brain, they overcome drawbacks of traditional univariate approaches. To evaluate the overall reliability of neuroimaging-based biomarkers, we conducted a comprehensive literature search to identify all studies that used multivariate pattern recognition to identify patterns of brain alterations that differentiate patients with schizophrenia from healthy controls. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across studies as well as to assess the robustness to potentially confounding variables. In the total sample of n=38 studies (1602 patients and 1637 healthy controls), patients were differentiated from controls with a sensitivity of 80.3% (95% CI: 76.7-83.5%) and a specificity of 80.3% (95% CI: 76.9-83.3%). Analysis of neuroimaging modality indicated higher sensitivity (84.46%, 95% CI: 79.9-88.2%) and similar specificity (76.9%, 95% CI: 71.3-81.6%) of rsfMRI studies as compared with structural MRI studies (sensitivity: 76.4%, 95% CI: 71.9-80.4%, specificity of 79.0%, 95% CI: 74.6-82.8%). Moderator analysis identified significant effects of age (p=0.029), imaging modality (p=0.019), and disease stage (p=0.025) on sensitivity as well as of positive-to-negative symptom ratio (p=0.022) and antipsychotic medication (p=0.016) on specificity. Our results underline the utility of multivariate pattern recognition approaches for the identification of reliable neuroimaging-based biomarkers. Despite the clinical heterogeneity of the schizophrenia phenotype, brain functional and structural alterations differentiate schizophrenic patients from healthy controls with 80% sensitivity and specificity.

  4. Analysis of infantile subdural hematoma caused by abuse

    International Nuclear Information System (INIS)

    Park, Young-Soo; Nishio, Kenji; Fujimoto, Takatoshi; Nakase, Hiroyuki; Okuchi, Kazuo

    2010-01-01

    We report infantile subdural hematoma caused by abuse. Between January 2006 and December 2009, 10 cases of definite and highly suspicious abusive subdural hematoma in infants were treated at Nara Medical University Hospital. The mean age was 5.4 months. On CT examination, severe cerebral swelling was seen in 8 (80%) and wide spreading cerebral ischemia and atrophy in 9 (90%). Retinal hemorrhage was commonly seen in this series (90%). Subdural drainage and/or subdural-peritoneal shunt surgeries were performed in 6 cases, and intensive combined therapy of mild hypothermia and barbiturate was adapted in 7 cases. Favorable outcome was achieved in only 3 cases. In spite of aggressive treatment, clinical outcome are still bad. In our series, assailants were predominantly not father but mother. There were various and complex factors for child abuse. Cautious insight and suspicion are necessary to detect abusive injuries in infants. It is very important to endeavor to prevent recurrences of abusive injuries. (author)

  5. Types of abuse and risk factors associated with elder abuse.

    Science.gov (United States)

    Simone, Lacher; Wettstein, Albert; Senn, Oliver; Rosemann, Thomas; Hasler, Susann

    2016-01-01

    Detecting elder abuse is challenging because it is a taboo, and many cases remain unreported. This study aimed to identify types of elder abuse and to investigate its associated risk factors. Retrospective analyses of 903 dossiers created at an Independent Complaints Authority for Old Age in the Canton of Zurich, Switzerland, from January 1, 2008 to October 31, 2012. Characteristics of victims and perpetrators, types of abuse, and associated risk factors related to the victim or the perpetrator were assessed. Bi- and multivariate analysis were used to identify abuse and neglect determinants. A total of 150 cases reflected at least one form of elder abuse or neglect; 104 cases were categorised as abuse with at least one type of abuse (overall 135 mentions), 46 cases were categorised as neglect (active or passive). Psychological abuse was the most reported form (47%), followed by financial (35%), physical (30%) and anticonstitutional abuse (18%). In 81% of the 150 cases at least two risk factors existed. In 13% no associated risk factor could be identified. Compared with neglect, elders with abuse were less likely to be a nursing home resident than living at home (odds ratio [OR] 0.02, 95% confidence interval [CI] 0.00-0.19). In addition, they were more likely to be cohabiting with their perpetrators (OR 18.01, 95% CI 4.43-73.19). For the majority of the reported elder abuse cases at least two associated risk factors could be identified. Knowledge about these red flags and a multifaceted strategy are needed to identify and prevent elder abuse.

  6. Comparison of Early Maladaptive Schemas and Parenting Origins in Patients with Opioid Abuse and Non-Abusers

    Directory of Open Access Journals (Sweden)

    Ali Reza Kakavand

    2011-06-01

    Full Text Available "nObjective: The aim of this study was to examine the difference of early maladaptive schemas and parenting origins in opioid abusers and non-opioid abusers. "nMethod: The early maladaptive schemas and parenting origins were compared in 56 opioid abusers and 56 non-opioids abusers. Schemas were assessed by the Young Schema Questionnaire 3rd (short form; and parenting origins were assessed by the Young Parenting Inventory. "nResults: Data were analyzed by multivariate analysis of variance (MANOVA. The analysis showed that the means for schemas between opioid abusers and non-opioid abusers were different. Chi square test showed that parenting origins were significantly associated with their related schemas. "nConclusion:  The early maladaptive schemas and parenting origins in opioid abusers were more than non-opioid abusers ; and parenting origins were related to their Corresponding schemas.

  7. Childhood Abuse and Adolescent Sexual Re-Offending: A Meta-Analysis

    Science.gov (United States)

    Mallie, Adana L.; Viljoen, Jodi L.; Mordell, Sarah; Spice, Andrew; Roesch, Ronald

    2011-01-01

    Recent research indicates that adolescents who have sexually offended are more likely than other adolescents to have a history of sexual and physical abuse. However, it is unclear whether abuse predicts re-offending among these adolescents. To examine this relationship, a meta-analysis was conducted which included 29 effect sizes drawn from 11…

  8. Prevention of Child Sexual Abuse Victimization: A Meta Analysis of School Programs.

    Science.gov (United States)

    Rispens, Jan; Aleman, Andre; Goudena, Paul P.

    1997-01-01

    Meta-analysis of 16 evaluation studies of school programs aimed at the prevention of child sexual abuse victimization found significant and considerable mean postintervention and follow-up effect sizes, indicating that the programs were effective in teaching children sexual abuse concepts and self-protection skills. Program duration and content…

  9. A Predictive and Follow-Up Study of Abusive and Neglectful Families by Case Analysis.

    Science.gov (United States)

    Heap, Kari Killen

    1991-01-01

    A case analysis, predictive study, and follow-up study of 17 abused and/or neglected children found that the prognosis for abusive and/or neglectful parents is poorer when they are scored high on immaturity than when they are scored high on emotional problems. (BRM)

  10. Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Package

    Directory of Open Access Journals (Sweden)

    Nina Golyandina

    2015-10-01

    Full Text Available Implementation of multivariate and 2D extensions of singular spectrum analysis (SSA by means of the R package Rssa is considered. The extensions include MSSA for simultaneous analysis and forecasting of several time series and 2D-SSA for analysis of digital images. A new extension of 2D-SSA analysis called shaped 2D-SSA is introduced for analysis of images of arbitrary shape, not necessary rectangular. It is shown that implementation of shaped 2D-SSA can serve as a basis for implementation of MSSA and other generalizations. Efficient implementation of operations with Hankel and Hankel-block-Hankel matrices through the fast Fourier transform is suggested. Examples with code fragments in R, which explain the methodology and demonstrate the proper use of Rssa, are presented.

  11. Multivariate Classification of Original and Fake Perfumes by Ion Analysis and Ethanol Content.

    Science.gov (United States)

    Gomes, Clêrton L; de Lima, Ari Clecius A; Loiola, Adonay R; da Silva, Abel B R; Cândido, Manuela C L; Nascimento, Ronaldo F

    2016-07-01

    The increased marketing of fake perfumes has encouraged us to investigate how to identify such products by their chemical characteristics and multivariate analysis. The aim of this study was to present an alternative approach to distinguish original from fake perfumes by means of the investigation of sodium, potassium, chloride ions, and ethanol contents by chemometric tools. For this, 50 perfumes were used (25 original and 25 counterfeit) for the analysis of ions (ion chromatography) and ethanol (gas chromatography). The results demonstrated that the fake perfume had low levels of ethanol and high levels of chloride compared to the original product. The data were treated by chemometric tools such as principal component analysis and linear discriminant analysis. This study proved that the analysis of ethanol is an effective method of distinguishing original from the fake products, and it may potentially be used to assist legal authorities in such cases. © 2016 American Academy of Forensic Sciences.

  12. A multivariate analysis of youth violence and aggression: the influence of family, peers, depression, and media violence.

    Science.gov (United States)

    Ferguson, Christopher J; San Miguel, Claudia; Hartley, Richard D

    2009-12-01

    To examine the multivariate nature of risk factors for youth violence including delinquent peer associations, exposure to domestic violence in the home, family conflict, neighborhood stress, antisocial personality traits, depression level, and exposure to television and video game violence. A population of 603 predominantly Hispanic children (ages 10-14 years) and their parents or guardians responded to multiple behavioral measures. Outcomes included aggression and rule-breaking behavior on the Child Behavior Checklist (CBCL), as well as violent and nonviolent criminal activity and bullying behavior. Delinquent peer influences, antisocial personality traits, depression, and parents/guardians who use psychological abuse in intimate relationships were consistent risk factors for youth violence and aggression. Neighborhood quality, parental use of domestic violence in intimate relationships, and exposure to violent television or video games were not predictive of youth violence and aggression. Childhood depression, delinquent peer association, and parental use of psychological abuse may be particularly fruitful avenues for future prevention or intervention efforts.

  13. Stability of gene contributions and identification of outliers in multivariate analysis of microarray data

    Directory of Open Access Journals (Sweden)

    Schumacher Martin M

    2008-06-01

    Full Text Available Abstract Background Multivariate ordination methods are powerful tools for the exploration of complex data structures present in microarray data. These methods have several advantages compared to common gene-by-gene approaches. However, due to their exploratory nature, multivariate ordination methods do not allow direct statistical testing of the stability of genes. Results In this study, we developed a computationally efficient algorithm for: i the assessment of the significance of gene contributions and ii the identification of sample outliers in multivariate analysis of microarray data. The approach is based on the use of resampling methods including bootstrapping and jackknifing. A statistical package of R functions was developed. This package includes tools for both inferring the statistical significance of gene contributions and identifying outliers among samples. Conclusion The methodology was successfully applied to three published data sets with varying levels of signal intensities. Its relevance was compared with alternative methods. Overall, it proved to be particularly effective for the evaluation of the stability of microarray data.

  14. Combating substance abuse with the potential of geographic ...

    African Journals Online (AJOL)

    Substance abuse problems have been a growing concern for people from all over the world. The objective of this study is to demonstrate the usefulness of a combination between a geographic information system and a multivariate analysis in substance abuse research. However, due to the limited studies on a combination ...

  15. PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

    Science.gov (United States)

    Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

  16. Associations between depression and specific childhood experiences of abuse and neglect: A meta-analysis.

    Science.gov (United States)

    Infurna, Maria Rita; Reichl, Corinna; Parzer, Peter; Schimmenti, Adriano; Bifulco, Antonia; Kaess, Michael

    2016-01-15

    Research documents a strong relationship between childhood maltreatment and depression. However, only few studies have examined the specific effects of various types of childhood abuse/neglect on depression. This meta-analysis estimated the associations between depression and different types of childhood maltreatment (antipathy, neglect, physical abuse, sexual abuse, and psychological abuse) assessed with the same measure, the Childhood Experience of Care and Abuse (CECA) interview. A systematic search in scientific databases included use of CECA interview and strict clinical assessment for major depression as criteria. Our meta-analysis utilized Cohen's d and relied on a random-effects model. The literature search yielded 12 primary studies (reduced from 44), with a total of 4372 participants and 34 coefficients. Separate meta-analyses for each type of maltreatment revealed that psychological abuse and neglect were most strongly associated with the outcome of depression. Sexual abuse, although significant, was less strongly related. Furthermore, the effects of specific types of childhood maltreatment differed across adult and adolescent samples. Our strict criteria for selecting the primary studies resulted in a small numbers of available studies. It restricted the analyses for various potential moderators. This meta-analysis addressed the differential effects of type of childhood maltreatment on major depression, partially explaining between-study variance. The findings clearly highlight the potential impact of the more "silent" types of childhood maltreatment (other than physical and sexual abuse) on the development of depression. Copyright © 2015 Elsevier B.V. All rights reserved.

  17. Late rectal toxicity after conformal radiotherapy of prostate cancer (I): multivariate analysis and dose-response

    International Nuclear Information System (INIS)

    Skwarchuk, Mark W.; Jackson, Andrew; Zelefsky, Michael J.; Venkatraman, Ennapadam S.; Cowen, Didier M.; Levegruen, Sabine; Burman, Chandra M.; Fuks, Zvi; Leibel, Steven A.; Ling, C. Clifton

    2000-01-01

    Purpose: The purpose of this paper is to use the outcome of a dose escalation protocol for three-dimensional conformal radiation therapy (3D-CRT) of prostate cancer to study the dose-response for late rectal toxicity and to identify anatomic, dosimetric, and clinical factors that correlate with late rectal bleeding in multivariate analysis. Methods and Materials: Seven hundred forty-three patients with T1c-T3 prostate cancer were treated with 3D-CRT with prescribed doses of 64.8 to 81.0 Gy. The 5-year actuarial rate of late rectal toxicity was assessed using Kaplan-Meier statistics. A retrospective dosimetric analysis was performed for patients treated to 70.2 Gy (52 patients) or 75.6 Gy (119 patients) who either exhibited late rectal bleeding (RTOG Grade 2/3) within 30 months after treatment (i.e., 70.2 Gy--13 patients, 75.6 Gy--36 patients) or were nonbleeding for at least 30 months (i.e., 70.2 Gy--39 patients, 75.6 Gy--83 patients). Univariate and multivariate logistic regression was performed to correlate late rectal bleeding with several anatomic, dosimetric, and clinical variables. Results: A dose response for ≥ Grade 2 late rectal toxicity was observed. By multivariate analysis, the following factors were significantly correlated with ≥ Grade 2 late rectal bleeding for patients prescribed 70.2 Gy: 1) enclosure of the outer rectal contour by the 50% isodose on the isocenter slice (i.e., Iso50) (p max (p max

  18. Use of multivariate analysis to research career advancement of academic librarians

    Directory of Open Access Journals (Sweden)

    Filiberto Felipe Martínez Arellano

    2004-01-01

    Full Text Available Diverse variables dealing with credential factors, bureaucratiuc factors, organizational and disciplinary achievements, academic culture factors, social ascribed factors, and institutional factors were stated as explanatory elements of promotion, tenure status, and earnings. A survey was the research instrument for collecting data to test diverse variables dealing with academic librarians rewards and earnings. Since the study attempted to analyze variables in a multivariate context, variable interactions were tested using multiple regression analysis. Findings of this study contribute to a better understanding of those factors influencing career advancement of academic librarians. Likewise, research methodology of this study could be used in Library and Information Science(LIS research.

  19. Application of the techniques of Multivariate analysis in the characterization of germplasm of Quinua

    International Nuclear Information System (INIS)

    Garcia A, J.M.; Torres de la Cruz, E.

    2004-01-01

    Its were evaluated 20 lines of Chenopodium quinoa respect characters of agronomical interest finding that nine lines overcame the witness highlighting the lines: 20R1-41, 20R1-10, 20R2-27 that presented near yield to 1.5 ton/ha. The multivariate analysis of main components generated a dendrogram in that is appreciated that at an Euclidean distance of 0.75 its were formed seven groups according to its morphological characteristics and of yield, it highlights the formation of two big groups at a distance of 1.125, that they separate according to the radiation dose (200 and 250 Gy). (Author)

  20. Clinical patch test data evaluated by multivariate analysis. Danish Contact Dermatitis Group

    DEFF Research Database (Denmark)

    Christophersen, J; Menné, T; Tanghøj, P

    1989-01-01

    The aim of the present study was to evaluate the influence of individual explanatory factors, such as sex, age, atopy, test time and presence of diseased skin, on clinical patch test results, by application of multivariate statistical analysis. The study population was 2166 consecutive patients...... patch tested with the standard series of the International Contact Dermatitis Research Group (ICDRG) by members of the Danish Contact Dermatitis Group (DCDG) over a period of 6 months. For the 8 test allergens most often found positive (nickel, fragrance-mix, cobalt, chromate, balsam of Peru, carba...

  1. ANALYSIS OF MULTIVARIATE FAILURE TIME DATA USING MARGINAL PROPORTIONAL HAZARDS MODEL.

    Science.gov (United States)

    Chen, Ying; Chen, Kani; Ying, Zhiliang

    2010-01-01

    The marginal proportional hazards model is an important tool in the analysis of multivariate failure time data in the presence of censoring. We propose a method of estimation via the linear combinations of martingale residuals. The estimation and inference procedures are easy to implement numerically. The estimation is generally more accurate than the existing pseudo-likelihood approach: the size of efficiency gain can be considerable in some cases, and the maximum relative efficiency in theory is infinite. Consistency and asymptotic normality are established. Empirical evidence in support of the theoretical claims is shown in simulation studies.

  2. APPLICATION OF MULTIVARIATE ANALYSIS OF TRANSMISSION SPECTRA TO IDENTIFY WINES WITH PROTECTED GEOGRAPHICAL INDICATION (IGP

    Directory of Open Access Journals (Sweden)

    M. A. Khodasevich

    2016-01-01

    Full Text Available The simulation is carried out of physical and chemical characteristics of the unblended varietal young Moldovan wine harvested in 2014 by the projection to latent structures of the transmission spectra in the range of 220–2500 nm. The achieved accuracy of the regression determining the parameters is appropriate for practical application purposes (from 5 % for alcohol strength to 30 % for tartaric acid content in red wines. The possibility is shown of solving the problem of verification of the protected geographical indication of wines (IGP – Indication Géographique Protégée by the multivariate analysis of broadband transmission spectra. 

  3. Moors and Christians: an example of multivariate analysis applied to human blood-groups.

    Science.gov (United States)

    Reyment, R A

    1983-01-01

    Published data on the frequencies of the alleles of the ABO, MNS, and Rh systems for populations in the western Mediterranean region are analysed by the multivariate statistical methods of canonical variates, principal components, principal coordinates, correspondence analysis and discriminant functions. It is shown that there is a 'Moorish substrate' in the eastern and north-eastern parts of Spain and in southern Portugal. Serological effects, such as could derive from the assimilation of a large Jewish population, cannot be identified in the data available. The theory that most Hispano-Moslems and Spanish Jews were of indigenous origin is not gainsaid by the serological data available.

  4. Endogenous steroid profiling by gas chromatography-tandem mass spectrometry and multivariate statistics for the detection of natural hormone abuse in cattle

    NARCIS (Netherlands)

    Blokland, M.H.; Tricht, van E.F.; Rossum, van H.J.; Sterk, S.S.; Nielen, M.W.F.

    2012-01-01

    For years it has been suspected that natural hormones are illegally used as growth promoters in cattle in the European Union. Unfortunately there is a lack of methods and criteria that can be used to detect the abuse of natural hormones and distinguish treated from non-treated animals. Pattern

  5. Estimating the impact of environmental conditions on hatching results using multivariable analysis

    Directory of Open Access Journals (Sweden)

    IA Nääs

    2008-12-01

    Full Text Available Hatching results are directly related to environmental and biological surroundings. This research study aimed at evaluating the influence of incubation environmental conditions on hatchability and one-day-old chickling quality of five production flocks using multivariable analysis tool. The experiment was carried out in a commercial hatchery located in the state of São Paulo, Brazil. Environmental variables such as dry bulb temperature, relative humidity, carbon dioxide concentration, and number of colony forming units of fungi were recorded inside a broiler multi-stage setter, a hatcher after eggs transference, and a chick-processing room. The homogeneity of parameter distribution among quadrants inside the setter, the hatcher, and the chick room was tested using the non-parametric test of Kruskal-Wallis, and the fit analysis was applied. The multivariate analysis was applied using the Main Component Technique in order to identify possible correlations between environmental and production parameters. Three different groups were identified: the first group is represented by temperature, which was positively correlated both with good hatchability and good chick quality; the second group indicates that poor chick quality was positively correlated with air velocity and relative humidity increase. The third group, represented by carbon dioxide concentration and fungi colonies forming units, presented strong positive association with embryo mortality increase.

  6. Feature extraction techniques using multivariate analysis for identification of lung cancer volatile organic compounds

    Science.gov (United States)

    Thriumani, Reena; Zakaria, Ammar; Hashim, Yumi Zuhanis Has-Yun; Helmy, Khaled Mohamed; Omar, Mohammad Iqbal; Jeffree, Amanina; Adom, Abdul Hamid; Shakaff, Ali Yeon Md; Kamarudin, Latifah Munirah

    2017-03-01

    In this experiment, three different cell cultures (A549, WI38VA13 and MCF7) and blank medium (without cells) as a control were used. The electronic nose (E-Nose) was used to sniff the headspace of cultured cells and the data were recorded. After data pre-processing, two different features were extracted by taking into consideration of both steady state and the transient information. The extracted data are then being processed by multivariate analysis, Linear Discriminant Analysis (LDA) to provide visualization of the clustering vector information in multi-sensor space. The Probabilistic Neural Network (PNN) classifier was used to test the performance of the E-Nose on determining the volatile organic compounds (VOCs) of lung cancer cell line. The LDA data projection was able to differentiate between the lung cancer cell samples and other samples (breast cancer, normal cell and blank medium) effectively. The features extracted from the steady state response reached 100% of classification rate while the transient response with the aid of LDA dimension reduction methods produced 100% classification performance using PNN classifier with a spread value of 0.1. The results also show that E-Nose application is a promising technique to be applied to real patients in further work and the aid of Multivariate Analysis; it is able to be the alternative to the current lung cancer diagnostic methods.

  7. Multivariate Analysis of Transient State Infrared Images in Production Line Quality Control Systems

    Directory of Open Access Journals (Sweden)

    Cristina Cristalli

    2018-02-01

    Full Text Available Manufacturers would like to increase production volumes while preserving the high quality of their products. The long testing times can cause a bottleneck of production processes especially taking into account the observed tendency for testing all produced devices. The main aim of this work consists in the analysis of time changes of features extracted from thermal images using the multivariate approach. The paper shows that if the principal component analysis (PCA, belonging to multivariate methods, is applied for quality control based on infrared images, then the minimum testing times can be estimated. In order to draw the final conclusions regarding testing times and, what is also very important, which principal components should be selected for classification, a detailed temporal analysis for an exemplary production line has been carried out. The future impact of the results includes higher productivity and cost-effectiveness due to the determination of an optimal decision time in production line quality control systems using the proposed approach.

  8. Using sperm morphometry and multivariate analysis to differentiate species of gray Mazama

    Science.gov (United States)

    Duarte, José Maurício Barbanti

    2016-01-01

    There is genetic evidence that the two species of Brazilian gray Mazama, Mazama gouazoubira and Mazama nemorivaga, belong to different genera. This study identified significant differences that separated them into distinct groups, based on characteristics of the spermatozoa and ejaculate of both species. The characteristics that most clearly differentiated between the species were ejaculate colour, white for M. gouazoubira and reddish for M. nemorivaga, and sperm head dimensions. Multivariate analysis of sperm head dimension and format data accurately discriminated three groups for species with total percentage of misclassified of 0.71. The individual analysis, by animal, and the multivariate analysis have also discriminated correctly all five animals (total percentage of misclassified of 13.95%), and the canonical plot has shown three different clusters: Cluster 1, including individuals of M. nemorivaga; Cluster 2, including two individuals of M. gouazoubira; and Cluster 3, including a single individual of M. gouazoubira. The results obtained in this work corroborate the hypothesis of the formation of new genera and species for gray Mazama. Moreover, the easily applied method described herein can be used as an auxiliary tool to identify sibling species of other taxonomic groups. PMID:28018612

  9. Using sperm morphometry and multivariate analysis to differentiate species of grayMazama.

    Science.gov (United States)

    Cursino, Marina Suzuki; Duarte, José Maurício Barbanti

    2016-11-01

    There is genetic evidence that the two species of Brazilian gray Mazama , Mazama gouazoubira and Mazama nemorivaga , belong to different genera. This study identified significant differences that separated them into distinct groups, based on characteristics of the spermatozoa and ejaculate of both species. The characteristics that most clearly differentiated between the species were ejaculate colour, white for M. gouazoubira and reddish for M. nemorivaga , and sperm head dimensions. Multivariate analysis of sperm head dimension and format data accurately discriminated three groups for species with total percentage of misclassified of 0.71. The individual analysis, by animal, and the multivariate analysis have also discriminated correctly all five animals (total percentage of misclassified of 13.95%), and the canonical plot has shown three different clusters: Cluster 1, including individuals of M. nemorivaga ; Cluster 2, including two individuals of M. gouazoubira ; and Cluster 3, including a single individual of M. gouazoubira . The results obtained in this work corroborate the hypothesis of the formation of new genera and species for gray Mazama . Moreover, the easily applied method described herein can be used as an auxiliary tool to identify sibling species of other taxonomic groups.

  10. Oxidative stability of frozen mackerel batches ― A multivariate data analysis approach

    DEFF Research Database (Denmark)

    Helbo Ekgreen, M.; Frosch, Stina; Baron, Caroline Pascale

    2011-01-01

    deterioration and texture changes. The aim was to investigate the correlation between the raw material history and the quality loss observed during frozen storage using relevant multivariate data analysis such as Principal Component Analysis (PCA) and Partial Least Square Analysis (PLS). Preliminary results...... such as mackerel can lead to a significant loss in fish quality primarily due to oxidation of the long chain omega-3 fatty acids. These quality changes results in significant loss for the fish processing industries and in fish with poor eating quality. In order to investigate batch-to-batch variation due......Mackerel are usually caught in the autumn and often frozen either on board the fishing vessel or soon after landing. Due to the seasonality of the catching period, mackerel can be stored frozen for a long time period before entering the production chain. However, frozen storage of fatty fish...

  11. Reduction of the dimensionality and comparative analysis of multivariate radiological data

    Energy Technology Data Exchange (ETDEWEB)

    Seddeek, M.K. [Department of Physics, Faculty of Education, Suez Canal University, Al-Arish (Egypt); Kozae, A.M. [Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527 (Egypt); Sharshar, T. [Department of Physics and Chemistry, Faculty of Education, Kafr El-Shaikh University, Kafr El-Shaikh (Egypt); Physics Department, Faculty of Science, Taif University, Taif, 888 Hawiya (Saudi Arabia); Badran, H.M. [Department of Physics, Faculty of Science, Tanta University, Tanta 31527 (Egypt)], E-mail: Hussein_badran@hotmail.com

    2009-09-15

    Computational methods were used to reduce the dimensionality and to find clusters of multivariate data. The variables were the natural radioactivity contents and the texture characteristics of sand samples. The application of discriminate analysis revealed that samples with high negative values of the former score have the highest contamination with black sand. Principal component analysis (PCA) revealed that radioactivity concentrations alone are sufficient for the classification. Rough set analysis (RSA) showed that the concentration of {sup 238}U, {sup 226}Ra or {sup 232}Th, combined with the concentration of {sup 40}K, can specify the clusters and characteristics of the sand. Both PCA and RSA show that {sup 238}U, {sup 226}Ra and {sup 232}Th behave similarly. RSA revealed that one or two of them can be omitted without degrading predictions.

  12. Reduction of the dimensionality and comparative analysis of multivariate radiological data.

    Science.gov (United States)

    Seddeek, M K; Kozae, A M; Sharshar, T; Badran, H M

    2009-09-01

    Computational methods were used to reduce the dimensionality and to find clusters of multivariate data. The variables were the natural radioactivity contents and the texture characteristics of sand samples. The application of discriminate analysis revealed that samples with high negative values of the former score have the highest contamination with black sand. Principal component analysis (PCA) revealed that radioactivity concentrations alone are sufficient for the classification. Rough set analysis (RSA) showed that the concentration of (238)U, (226)Ra or (232)Th, combined with the concentration of (40)K, can specify the clusters and characteristics of the sand. Both PCA and RSA show that (238)U, (226)Ra and (232)Th behave similarly. RSA revealed that one or two of them can be omitted without degrading predictions.

  13. Multivariate analysis of the scattering profiles of healthy and pathological human breast tissues

    Energy Technology Data Exchange (ETDEWEB)

    Conceicao, A.L.C.; Antoniassi, M. [Departamento de Fisica e Matematica, FFCLRP, Universidade de Sao Paulo, Ribeirao Preto 14040-901, Sao Paulo (Brazil); Cunha, D.M. [Instituto de Fisica, Universidade Federal de Uberlandia, 38400-902, Uberlandia, Minas Gerais (Brazil); Ribeiro-Silva, A. [Departamento de Patologia, HCFMRP, Universidade de Sao Paulo, Ribeirao Preto 14040-901, Sao Paulo (Brazil); Poletti, M.E., E-mail: poletti@ffclrp.usp.br [Departamento de Fisica e Matematica, FFCLRP, Universidade de Sao Paulo, Ribeirao Preto 14040-901, Sao Paulo (Brazil)

    2011-10-01

    Scattering profiles of 106 healthy and pathological human breast samples were obtained using the angular dispersive X-ray scattering technique (AD-XRD) and synchrotron radiation covering the momentum transfer interval of 0.7 nm{sup -1}{<=}q(=4{pi} sin({theta}/2)/{lambda}){<=}70.5 nm{sup -1}. Multivariate analysis in the form of discriminant analysis was applied over the whole scattering profile curve of each sample in order to build a model for breast tissue classification. The classification results were validated and compared with histological sample classification obtained by microscopy analysis. Finally, the model allows classifying correctly 91.5% of the samples and presented values of 98.5%, 89.7% and 0.90 for sensitivity, specificity and Cohen's {kappa}, respectively, in correctly differentiating between healthy and pathological tissues.

  14. Multivariate diallel analysis allows multiple gains in segregating populations for agronomic traits in Jatropha.

    Science.gov (United States)

    Teodoro, P E; Rodrigues, E V; Peixoto, L A; Silva, L A; Laviola, B G; Bhering, L L

    2017-03-22

    Jatropha is research target worldwide aimed at large-scale oil production for biodiesel and bio-kerosene. Its production potential is among 1200 and 1500 kg/ha of oil after the 4th year. This study aimed to estimate combining ability of Jatropha genotypes by multivariate diallel analysis to select parents and crosses that allow gains in important agronomic traits. We performed crosses in diallel complete genetic design (3 x 3) arranged in blocks with five replications and three plants per plot. The following traits were evaluated: plant height, stem diameter, canopy projection between rows, canopy projection on the line, number of branches, mass of hundred grains, and grain yield. Data were submitted to univariate and multivariate diallel analysis. Genotypes 107 and 190 can be used in crosses for establishing a base population of Jatropha, since it has favorable alleles for increasing the mass of hundred grains and grain yield and reducing the plant height. The cross 190 x 107 is the most promising to perform the selection of superior genotypes for the simultaneous breeding of these traits.

  15. Beer fermentation: monitoring of process parameters by FT-NIR and multivariate data analysis.

    Science.gov (United States)

    Grassi, Silvia; Amigo, José Manuel; Lyndgaard, Christian Bøge; Foschino, Roberto; Casiraghi, Ernestina

    2014-07-15

    This work investigates the capability of Fourier-Transform near infrared (FT-NIR) spectroscopy to monitor and assess process parameters in beer fermentation at different operative conditions. For this purpose, the fermentation of wort with two different yeast strains and at different temperatures was monitored for nine days by FT-NIR. To correlate the collected spectra with °Brix, pH and biomass, different multivariate data methodologies were applied. Principal component analysis (PCA), partial least squares (PLS) and locally weighted regression (LWR) were used to assess the relationship between FT-NIR spectra and the abovementioned process parameters that define the beer fermentation. The accuracy and robustness of the obtained results clearly show the suitability of FT-NIR spectroscopy, combined with multivariate data analysis, to be used as a quality control tool in the beer fermentation process. FT-NIR spectroscopy, when combined with LWR, demonstrates to be a perfectly suitable quantitative method to be implemented in the production of beer. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Biodegradable blends of starch/polyvinyl alcohol/glycerol: multivariate analysis of the mechanical properties

    Directory of Open Access Journals (Sweden)

    Juliano Zanela

    Full Text Available Abstract The aim of the work was to study the mechanical properties of extruded starch/polyvinyl alcohol (PVA/glycerol biodegradable blends using multivariate analysis. The blends were produced as cylindrical strands by extrusion using PVAs with different hydrolysis degrees and viscosities, at two extrusion temperature profiles (90/170/170/170/170 °C and 90/170/200/200/200 °C and three conditioning relative humidities of the samples (33, 53, and 75%. The mechanical properties showed a great variability according to PVA type, as well as the extrusion temperature profile and the conditioning relative humidity; the tensile strength ranged from 0.42 to 5.40 MPa, elongation at break ranged from 10 to 404% and Young’s modulus ranged from 0.93 to 13.81 MPa. The multivariate analysis was a useful methodology to study the mechanical properties behavior of starch/PVA/glycerol blends, and it can be used as an exploratory technique to select of the more suitable PVA type and extrusion temperature to produce biodegradable materials.

  17. Multivariate analysis of attachment of biofouling organisms in response to material surface characteristics.

    Science.gov (United States)

    Gatley-Montross, Caitlyn M; Finlay, John A; Aldred, Nick; Cassady, Harrison; Destino, Joel F; Orihuela, Beatriz; Hickner, Michael A; Clare, Anthony S; Rittschof, Daniel; Holm, Eric R; Detty, Michael R

    2017-12-29

    Multivariate analyses were used to investigate the influence of selected surface properties (Owens-Wendt surface energy and its dispersive and polar components, static water contact angle, conceptual sign of the surface charge, zeta potentials) on the attachment patterns of five biofouling organisms (Amphibalanus amphitrite, Amphibalanus improvisus, Bugula neritina, Ulva linza, and Navicula incerta) to better understand what surface properties drive attachment across multiple fouling organisms. A library of ten xerogel coatings and a glass standard provided a range of values for the selected surface properties to compare to biofouling attachment patterns. Results from the surface characterization and biological assays were analyzed separately and in combination using multivariate statistical methods. Principal coordinate analysis of the surface property characterization and the biological assays resulted in different groupings of the xerogel coatings. In particular, the biofouling organisms were able to distinguish four coatings that were not distinguishable by the surface properties of this study. The authors used canonical analysis of principal coordinates (CAP) to identify surface properties governing attachment across all five biofouling species. The CAP pointed to surface energy and surface charge as important drivers of patterns in biological attachment, but also suggested that differentiation of the surfaces was influenced to a comparable or greater extent by the dispersive component of surface energy.

  18. Multivariate analysis of combining ability for soybean resistance to Cercospora sojina Hara

    Directory of Open Access Journals (Sweden)

    Geraldo de Amaral Gravina

    2004-01-01

    Full Text Available Seven soybean cultivars (Bossier, Cristalina, Davis, Kent, Lincoln, Paraná and Uberaba, with different levels of resistance to Cercospora sojina, race 04, were crossed according to a diallel design, with no reciprocals, to determine the general and the specific combining abilities for the resistance. The evaluations of the reaction to the disease were performed 20 days after the inoculation of the fungus on the most infected leaflet of the plant, in the parents and in the F1 hybrids. To quantify the resistance, the following characteristics were evaluated: infection degree (ID; number of lesions per leaflet (NLL; lesion mean diameter (LMD; lesioned leaf area (LLA; percentage of lesioned leaf area (PLLA; number of lesions per square centimeter (NLC and disease index (DI. The relative importance of each characteristic was evaluated by the canonical variables analysis and the LLA and NLL characteristics were eliminated from the multivariate function. With the remaining five characteristics, a multivariate index was created using the first canonical vector, which was submitted to the diallel analysis, according to Griffings fixed model, method 2. The most important characters to discriminate resistant from susceptible soybean plants to C. sojina were: ID, LMD, NLC, DI and PLLA. Cristalina, Davis and Uberaba cultivars are the best ones among those tested that can be recommended as parents in soybean breeding programs seeking resistance to Cercospora sojina. The additive, dominant and epistatic genetic effects were important for the expression of the resistance, although the additive genetic effect was the most important component.

  19. What makes a pattern? Matching decoding methods to data in multivariate pattern analysis

    Directory of Open Access Journals (Sweden)

    Philip A Kragel

    2012-11-01

    Full Text Available Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA of functional magnetic resonance imaging (fMRI data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique’s introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that nonlinear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.

  20. Use of multivariate analysis in mineral accumulation of rocket (Eruca sativa accessions

    Directory of Open Access Journals (Sweden)

    Bozokalfa Kadri M.

    2011-01-01

    Full Text Available The leafy vegetables contain high amount of mineral elements and health promoting compound. To solve nutritional problems in diet and reduced malnutrition among human population selection of specific cultivar among species would be help increasing elemental delivery in the human diet. While rocket plant observes several nutritional compounds no significant efforts have been made for genetic diversity for mineral composition of rocket plant accessions using multivariate analyses technique. The objective of this work was to evaluate variability for mineral accumulation of rocket accessions revealed by multivariate analysis to use further breeding program for achieve improving cultivar in targeting high nutrient concentration. A total twelve mineral element and twenty-three E. sativa accessions were investigated and considerable variation were observed in the most of concentration the principal component analysis explained that 77.67% of total variation accounted for four PC axis. Rocket accessions were classifies into three groups and present outcomes of experiments revealed that the first three principal components were highly valid to classify the examined accessions and separating mineral accumulations. Significant differences exhibited in mineral concentration among examined rocket accessions and the result could allow selecting those genotypes with higher elements.

  1. A method of signal transmission path analysis for multivariate random processes

    International Nuclear Information System (INIS)

    Oguma, Ritsuo

    1984-04-01

    A method for noise analysis called ''STP (signal transmission path) analysis'' is presentd as a tool to identify noise sources and their propagation paths in multivariate random proceses. Basic idea of the analysis is to identify, via time series analysis, effective network for the signal power transmission among variables in the system and to make use of its information to the noise analysis. In the present paper, we accomplish this through two steps of signal processings; first, we estimate, using noise power contribution analysis, variables which have large contribution to the power spectrum of interest, and then evaluate the STPs for each pair of variables to identify STPs which play significant role for the generated noise to transmit to the variable under evaluation. The latter part of the analysis is executed through comparison of partial coherence function and newly introduced partial noise power contribution function. This paper presents the procedure of the STP analysis and demonstrates, using simulation data as well as Borssele PWR noise data, its effectiveness for investigation of noise generation and propagation mechanisms. (author)

  2. Prevalence of Childhood Sexual Abuse in China: A Meta-Analysis.

    Science.gov (United States)

    Ma, Yidan

    2018-03-06

    The objective of the current study was to explore the estimated prevalence of childhood sexual abuse in China. We conducted a meta-analysis that used the data from 36 articles. A total of 125 independent samples and 131,734 participants were included. The results revealed no significant difference in the prevalence of childhood sexual abuse between Chinese men (9.1%) and women (8.9%). The prevalence of childhood sexual abuse in studies from mainland areas was significantly higher than that from Hong Kong/Taiwan. The estimated prevalence of childhood sexual abuse in China also differed according to the definition of child sexual abuse, data collection method, year of data collection, and the mean age of participants at the time of assessment.

  3. Reading Robert and beyond: Narrative analysis of the story of a sexually abused Catholic man

    Directory of Open Access Journals (Sweden)

    R. Ruard Ganzevoort

    2014-08-01

    Full Text Available This article seeks to contribute to the understanding of what is at stake in counselling religious male victims of sexual abuse. We analyse the narrative of �Robert�, a sexually abused Roman Catholic man who later committed suicide. We focus on issues that concern many sexually abused males, such as talking and relationships, agency and responsibility, emotions of guilt, shame and anger, sexual identity, God-talk and God-images. In terms of a triangulating case study, we then confront this narrative analysis with some biographical elements gathered from other sources, from which we complement and critique the analysis.

  4. Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

    Science.gov (United States)

    Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

    2015-05-01

    In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

  5. Application of bioreactor design principles and multivariate analysis for development of cell culture scale down models.

    Science.gov (United States)

    Tescione, Lia; Lambropoulos, James; Paranandi, Madhava Ram; Makagiansar, Helena; Ryll, Thomas

    2015-01-01

    A bench scale cell culture model representative of manufacturing scale (2,000 L) was developed based on oxygen mass transfer principles, for a CHO-based process producing a recombinant human protein. Cell culture performance differences across scales are characterized most often by sub-optimal performance in manufacturing scale bioreactors. By contrast in this study, reduced growth rates were observed at bench scale during the initial model development. Bioreactor models based on power per unit volume (P/V), volumetric mass transfer coefficient (kL a), and oxygen transfer rate (OTR) were evaluated to address this scale performance difference. Lower viable cell densities observed for the P/V model were attributed to higher sparge rates and reduced oxygen mass transfer efficiency (kL a) of the small scale hole spargers. Increasing the sparger kL a by decreasing the pore size resulted in a further decrease in growth at bench scale. Due to sensitivity of the cell line to gas sparge rate and bubble size that was revealed by the P/V and kL a models, an OTR model based on oxygen enrichment and increased P/V was selected that generated endpoint sparge rates representative of 2,000 L scale. This final bench scale model generated similar growth rates as manufacturing. In order to take into account other routinely monitored process parameters besides growth, a multivariate statistical approach was applied to demonstrate validity of the small scale model. After the model was selected based on univariate and multivariate analysis, product quality was generated and verified to fall within the 95% confidence limit of the multivariate model. © 2014 Wiley Periodicals, Inc.

  6. Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

    Energy Technology Data Exchange (ETDEWEB)

    Rodrigues, J.A. [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Barros, A.S. [QOPNA-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal); Carvalho, B.; Brandao, T. [UNICER, Bebidas de Portugal, Leca do Balio, 4466-955, S. Mamede de Infesta (Portugal); Gil, Ana M., E-mail: agil@ua.pt [CICECO-Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro (Portugal)

    2011-09-30

    Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: {center_dot} Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. {center_dot} Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. {center_dot} Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. {center_dot} 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known

  7. Probing beer aging chemistry by nuclear magnetic resonance and multivariate analysis

    International Nuclear Information System (INIS)

    Rodrigues, J.A.; Barros, A.S.; Carvalho, B.; Brandao, T.; Gil, Ana M.

    2011-01-01

    Graphical abstract: The use of nuclear magnetic resonance (NMR) metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging (at 45 deg. C for up to 18 days) is described. Both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and an aging trend was observed. Inspection of PLS-DA loadings and peak integration revealed the importance of well known markers (e.g. 5-HMF) as well as of other compounds: amino acids, higher alcohols, organic acids, dextrins and some still unassigned spin systems. 2D correlation analysis enabled relevant compound variations to be confirmed and inter-compound correlations to be assessed, thus offering improved insight into the chemical aspects of beer aging. Highlights: · Use of NMR metabonomics for monitoring the chemical changes occurring in beer exposed to forced aging. · Compositional variations evaluated by principal component analysis and partial least squares-discriminant analysis. · Results reveal importance of known markers and other compounds: amino and organic acids, higher alcohols, dextrins. · 2D correlation analysis reveals inter-compound relationships, offering insight into beer aging chemistry. - Abstract: This paper describes the use of nuclear magnetic resonance (NMR) spectroscopy, in tandem with multivariate analysis (MVA), for monitoring the chemical changes occurring in a lager beer exposed to forced aging (at 45 deg. C for up to 18 days). To evaluate the resulting compositional variations, both principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were applied to the NMR spectra of beer recorded as a function of aging and a clear aging trend was observed. Inspection of PLS-DA loadings and peak integration enabled the changing compounds to be identified, revealing the importance of well known markers such as 5-hydroxymethylfurfural (5

  8. Association between abuse history and adolescent pregnancy: a meta-analysis.

    Science.gov (United States)

    Madigan, Sheri; Wade, Mark; Tarabulsy, George; Jenkins, Jennifer M; Shouldice, Michelle

    2014-08-01

    Although a purported risk factor for early pregnancy is abuse history, the strength of this association has been inconsistent across studies and may vary as a function of abuse type. The purpose of this meta-analysis was to examine the extent to which sexual, physical, and emotional abuse, as well as neglect, increased the risk of adolescent pregnancy. A search of studies through MEDLINE, EMBASE, PsycINFO, Social Work Abstracts, and Web of Science was conducted. Studies were retained if they included (1) women who became pregnant before 20 years of age; (2) a comparison group of nonpregnant adolescents; and (3) abuse experience (pregnancy (odds ratio [OR], 2.06; 95% confidence interval [CI], 1.75-2.38 and OR, 1.48; CI, 1.24-1.76, respectively). The strongest effect was for the co-occurrence of sexual and physical abuse (OR, 3.83; CI, 2.96-4.97]). Nonsignificant effect sizes were found for emotional abuse (OR, 1.01; CI, .70-1.47) and neglect (OR, 1.29; CI, .77-2.17]), although these were moderated by journal impact factor, that is, greater effect sizes were reported in higher impact journals. The results of this meta-analysis reveal that the strength of the association between abuse and adolescent pregnancy varies as a function of abuse subtype. Sexual and physical abuse were associated with increased risk for adolescent pregnancy, whereas emotional abuse and neglect were not. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

  9. Investigation of the phase separation of PNIPAM using infrared spectroscopy together with multivariate data analysis

    DEFF Research Database (Denmark)

    Munk, Tommy; Baldursdottir, Stefania G.; Hietala, S.

    2013-01-01

    The use of vibrational spectroscopy to investigate complex structural changes in polymers yields chemically rich data, but interpretation can be challenging and subtle but meaningful spectral changes may be missed through visual inspection alone. Multivariate analysis is an efficient approach...... to gain an oversight of small but systematic spectral differences anywhere within the spectra, providing further insight into structural changes and associated transformation mechanisms. In this study, the novel analytical approach of infrared spectroscopy combined with principal component analysis...... a complex re-organization of the hydrogen bonds and change of the hydration layer. The changes agreed with existing results from other techniques, and new insights were gained into the effect of controlled tacticity on phase transformation behaviour. The study demonstrates that infrared spectroscopy...

  10. Implementation of multivariate linear mixed-effects models in the analysis of indoor climate performance experiments

    DEFF Research Database (Denmark)

    Jensen, Kasper Lynge; Spliid, Henrik; Toftum, Jørn

    2011-01-01

    The aim of the current study was to apply multivariate mixed-effects modeling to analyze experimental data on the relation between air quality and the performance of office work. The method estimates in one step the effect of the exposure on a multi-dimensional response variable, and yields...... important information on the correlation between the different dimensions of the response variable, which in this study was composed of both subjective perceptions and a two-dimensional performance task outcome. Such correlation is typically not included in the output from univariate analysis methods. Data....... The analysis seems superior to conventional univariate statistics and the information provided may be important for the design of performance experiments in general and for the conclusions that can be based on such studies....

  11. Multivariate genetic analysis of brain structure in an extended twin design

    DEFF Research Database (Denmark)

    Posthuma, D; de Geus, E.J.; Neale, M.C.

    2000-01-01

    . The remaining phenotypic variance of cerebellar volume is largely genetic (88%). These genetic factors partly overlap with the genetic factors that explain variance in intracranial space and body height. The applied method is presented as a general approach for the analysis of intermediate phenotypes in which......The hunt for genes influencing behavior may be aided by the study of intermediate phenotypes for several reasons. First, intermediate phenotypes may be influenced by only a few genes, which facilitates their detection. Second, many intermediate phenotypes can be measured on a continuous....... Intermediate phenotypes for discrete traits, such as psychiatric disorders, can be neurotransmitter levels, brain function, or structure. In this paper we conduct a multivariate analysis of data from 111 twin pairs and 34 additional siblings on cerebellar volume, intracranial space, and body height...

  12. imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel.

    Science.gov (United States)

    Grapov, Dmitry; Newman, John W

    2012-09-01

    Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).

  13. Screening of eight Eucalypt genotypes (Eucalyptus sp.) for water deficit tolerance using multivariate cluster analysis.

    Science.gov (United States)

    Cha-Um, S; Somsueb, S; Samphumphuang, T; Kirdmanee, C

    2014-06-01

    The present study evaluated eight genotypes of river red gum (Eucalyptus camaldulensis Dehnh.) and a hybrid (E. camaldulensis × E. urophylla) for mannitol-induced water deficit (WD) under photoautotrophic conditions using multivariate cluster analysis. Shoot height, plant dry weight, and chlorophyll a content in hybrid genotypes, 58H2 and 27A2, were maintained when exposed to 200 mM mannitol for 14 days. In addition, the diminution of photosynthetic abilities, i.e. maximum quantum yield of PSII, photon yield of PSII, photochemical quenching, and net photosynthetic rate, under WD was minimal in hybrid genotypes compared to that in selection clones of E. camaldulensis. Under WD condition, there was greater accumulation of proline in all genotypes. A positive relationship was observed between physiological and morphological attributes under WD stress. Using Ward's cluster analysis, hybrid genotypes-H4, 58H2, and 27A2-were classified as water deficit tolerant.

  14. Determination of geographic provenance of cotton fibres using multi-isotope profiles and multivariate statistical analysis

    Science.gov (United States)

    Daeid, N. Nic; Meier-Augenstein, W.; Kemp, H. F.

    2012-04-01

    The analysis of cotton fibres can be particularly challenging within a forensic science context where discrimination of one fibre from another is of importance. Normally cotton fibre analysis examines the morphological structure of the recovered material and compares this with that of a known fibre from a particular source of interest. However, the conventional microscopic and chemical analysis of fibres and any associated dyes is generally unsuccessful because of the similar morphology of the fibres. Analysis of the dyes which may have been applied to the cotton fibre can also be undertaken though this can be difficult and unproductive in terms of discriminating one fibre from another. In the study presented here we have explored the potential for Isotope Ratio Mass Spectrometry (IRMS) to be utilised as an additional tool for cotton fibre analysis in an attempt to reveal further discriminatory information. This work has concentrated on un-dyed cotton fibres of known origin in order to expose the potential of the analytical technique. We report the results of a pilot study aimed at testing the hypothesis that multi-element stable isotope analysis of cotton fibres in conjunction with multivariate statistical analysis of the resulting isotopic abundance data using well established chemometric techniques permits sample provenancing based on the determination of where the cotton was grown and as such will facilitate sample discrimination. To date there is no recorded literature of this type of application of IRMS to cotton samples, which may be of forensic science relevance.

  15. Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data

    Science.gov (United States)

    Ultsch, Alfred; Lötsch, Jörn

    2015-01-01

    Objective Multivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This paper proposes a novel method for calculating precise limits for the selection of parameter sets. Methods The algorithm is based on an ABC analysis and calculates these limits on the basis of the mathematical properties of the distribution of the analyzed items. The limits im-plement the aim of any ABC analysis, i.e., comparing the increase in yield to the required additional effort. In particular, the limit for set A, the “important few”, is optimized in a way that both, the effort and the yield for the other sets (B and C), are minimized and the additional gain is optimized. Results As a typical example from biomedical research, the feasibility of the ABC analysis as an objective replacement for classical subjective limits to select highly relevant variance components of pain thresholds is presented. The proposed method improved the biological inter-pretation of the results and increased the fraction of valid information that was obtained from the experimental data. Conclusions The method is applicable to many further biomedical problems in-cluding the creation of diagnostic complex biomarkers or short screening tests from comprehensive test batteries. Thus, the ABC analysis can be proposed as a mathematically valid replacement for traditional limits to maximize the information obtained from multivariate research data. PMID:26061064

  16. Multivariate analysis of prognostic factors for differentiated thyroid carcinoma in children

    Energy Technology Data Exchange (ETDEWEB)

    Jarzab, B.; Junak, D.H.; Kalemba, B.; Roskosz, J.; Kukulska, A.; Puch, Z. [Department of Nuclear Medicine and Endocrine Oncology, Centre of Oncology, Maria Sklodowska - Curie Memorial Institute, Gliwice (Poland); Wloch, J. [Department of Surgery, Centre of Oncology, Maria Sklodowska - Curie Memorial Institute, Gliwice (Poland)

    2000-07-01

    At most centres, the standard treatment for differentiated thyroid cancer (DTC) comprises total thyroidectomy, radioiodine treatment and thyroid-stimulating hormone (TSH) suppressive therapy. There is, however, considerable disagreement over the appropriate treatment for DTC in children. Some dispute the use of total thyroidectomy and/or question the routine application of iodine-131 therapy in children. The aim of this study was to perform a retrospective analysis of treatment results and prognostic factors for DTC in children treated at our centre. The study included 109 children with DTC (aged 6-17 years). The primary treatment comprised total thyroidectomy in 81 cases, radioiodine therapy in 85 cases and TSH suppressive therapy with l-thyroxine in all patients. Uni- and multivariate analysis of prognostic factors for disease-free survival was performed using the Cox regression method. The actuarial survival rate was 100%, and the 5- and 10-year actuarial disease-free survival rates were 80% and 61% respectively. Univariate analysis revealed that older age, total thyroidectomy and radioiodine treatment had a positive impact on disease-free survival whereas there were no statistical differences with regard to the child's sex, histological type of cancer or lymph node status. On multivariate analysis, radical surgery was estimated to be the most significant factor (P=0.007) for disease-free survival, while less than total thyroidectomy increased the relative risk of relapse by a factor of 10. Radioiodine treatment decreased the relative risk of relapse by a factor of 5, but with borderline significance (P=0.07). Permanent postoperative complications were observed in 17% of children: in 11 laryngeal palsy occurred, in six there was hypoparathyroidism, and one suffered from both. It is concluded that total thyroidectomy and radioiodine treatment significantly improve recurrence-free survival in children and should be routinely applied even in young children as

  17. Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

    Science.gov (United States)

    Sayemuzzaman, M.; Ye, M.

    2015-12-01

    The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface

  18. Multivariate analysis of prognostic factors for differentiated thyroid carcinoma in children

    International Nuclear Information System (INIS)

    Jarzab, B.; Junak, D.H.; Kalemba, B.; Roskosz, J.; Kukulska, A.; Puch, Z.; Wloch, J.

    2000-01-01

    At most centres, the standard treatment for differentiated thyroid cancer (DTC) comprises total thyroidectomy, radioiodine treatment and thyroid-stimulating hormone (TSH) suppressive therapy. There is, however, considerable disagreement over the appropriate treatment for DTC in children. Some dispute the use of total thyroidectomy and/or question the routine application of iodine-131 therapy in children. The aim of this study was to perform a retrospective analysis of treatment results and prognostic factors for DTC in children treated at our centre. The study included 109 children with DTC (aged 6-17 years). The primary treatment comprised total thyroidectomy in 81 cases, radioiodine therapy in 85 cases and TSH suppressive therapy with l-thyroxine in all patients. Uni- and multivariate analysis of prognostic factors for disease-free survival was performed using the Cox regression method. The actuarial survival rate was 100%, and the 5- and 10-year actuarial disease-free survival rates were 80% and 61% respectively. Univariate analysis revealed that older age, total thyroidectomy and radioiodine treatment had a positive impact on disease-free survival whereas there were no statistical differences with regard to the child's sex, histological type of cancer or lymph node status. On multivariate analysis, radical surgery was estimated to be the most significant factor (P=0.007) for disease-free survival, while less than total thyroidectomy increased the relative risk of relapse by a factor of 10. Radioiodine treatment decreased the relative risk of relapse by a factor of 5, but with borderline significance (P=0.07). Permanent postoperative complications were observed in 17% of children: in 11 laryngeal palsy occurred, in six there was hypoparathyroidism, and one suffered from both. It is concluded that total thyroidectomy and radioiodine treatment significantly improve recurrence-free survival in children and should be routinely applied even in young children as the

  19. A Meta-Analysis of Disparities in Childhood Sexual Abuse, Parental Physical Abuse, and Peer Victimization Among Sexual Minority and Sexual Nonminority Individuals

    Science.gov (United States)

    Marshal, Michael P.; Guadamuz, Thomas E.; Wei, Chongyi; Wong, Carolyn F.; Saewyc, Elizabeth; Stall, Ron

    2011-01-01

    Objectives. We compared the likelihood of childhood sexual abuse (under age 18), parental physical abuse, and peer victimization based on sexual orientation. Methods. We conducted a meta-analysis of adolescent school-based studies that compared the likelihood of childhood abuse among sexual minorities vs sexual nonminorities. Results. Sexual minority individuals were on average 3.8, 1.2, 1.7, and 2.4 times more likely to experience sexual abuse, parental physical abuse, or assault at school or to miss school through fear, respectively. Moderation analysis showed that disparities between sexual minority and sexual nonminority individuals were larger for (1) males than females for sexual abuse, (2) females than males for assault at school, and (3) bisexual than gay and lesbian for both parental physical abuse and missing school through fear. Disparities did not change between the 1990s and the 2000s. Conclusions. The higher rates of abuse experienced by sexual minority youths may be one of the driving mechanisms underlying higher rates of mental health problems, substance use, risky sexual behavior, and HIV reported by sexual minority adults. PMID:21680921

  20. Enhancing e-waste estimates: improving data quality by multivariate Input-Output Analysis.

    Science.gov (United States)

    Wang, Feng; Huisman, Jaco; Stevels, Ab; Baldé, Cornelis Peter

    2013-11-01

    Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input-Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

  1. Bone dimensional variations at implants placed in fresh extraction sockets: a multilevel multivariate analysis.

    Science.gov (United States)

    Tomasi, Cristiano; Sanz, Mariano; Cecchinato, Denis; Pjetursson, Bjarni; Ferrus, Jorge; Lang, Niklaus P; Lindhe, Jan

    2010-01-01

    To use multilevel, multivariate models to analyze factors that may affect bone alterations during healing after an implant immediately placed into an extraction socket. Data included in the current analysis were obtained from a clinical trial in which a series of measurements were performed to characterize the extraction site immediately after implant installation and at re-entry 4 months later. A regression multilevel, multivariate model was built to analyze factors affecting the following variables: (i) the distance between the implant surface and the outer bony crest (S-OC), (ii) the horizontal residual gap (S-IC), (iii) the vertical residual gap (R-D) and (iv) the vertical position of the bone crest opposite the implant (R-C). It was demonstrated that (i) the S-OC change was significantly affected by the thickness of the bone crest; (ii) the size of the residual gap was dependent of the size of the initial gap and the thickness of the bone crest; and (iii) the reduction of the buccal vertical gap was dependent on the age of the subject. Moreover, the position of the implant opposite the alveolar crest of the buccal ridge and its bucco-lingual implant position influenced the amount of buccal crest resorption. Clinicians must consider the thickness of the buccal bony wall in the extraction site and the vertical as well as the horizontal positioning of the implant in the socket, because these factors will influence hard tissue changes during healing.

  2. Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

    Science.gov (United States)

    Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

    2017-09-01

    In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  3. A Morphometric Survey among Three Iranian Horse Breeds with Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    M. Hosseini

    2016-12-01

    Full Text Available Three Iranian horse breeds, Turkoman, Caspian, and Kurdish, are the most important Iranian horse breeds which are well known in all around of the world because of their beauty, versatility, great stamina, and  intelligence. Phenotypic characterization was used to identify and document the diversity within and between distinct breeds, based on their observable attributes. Phenotypic characterization and body biometric in 23 traits were measured in 191 purebred horses belonging to three breeds, i.e. Turkoman (70 horses, Kurdish (77 horses, and Caspian (44 horses.  Caspian breed was  sampled from the Provinces of Alborz and Gilan. Kurdish breed was sampled from the Provinces of Kurdistan, Kermanshah, and Hamadan. Turkoman breed was sampled from the Provinces of Golestan, Markazi, and Isfahan. Multivariate analysis of variance (MANOVA was implemented. In addition, Canonical Discriminate Analysis (CDA, Principal Component Analysis (PCA, and Custer analysis were executed for assessing the relationship among the breeds. All statistical analysis was executed by SAS statistical program. The results of our investigation represented the breeds classification into 3 different classes (Caspian, Turkoman, and Kurdish based on different morphometrical traits. Caspian breed with smaller size in most variables was detached clearly from the others with more distance than Kurdish and Turkoman breeds. The result showed that the most variably trait for classification was Hind Hoof Length. Adaptation with different environments causes difference in morphology and difference among breeds. We can identify and classify domestic population using PCA, CDA, and cluster analysis.

  4. Qualitative and quantitative analysis of complex temperature-programmed desorption data by multivariate curve resolution

    Science.gov (United States)

    Rodríguez-Reyes, Juan Carlos F.; Teplyakov, Andrew V.; Brown, Steven D.

    2010-10-01

    The substantial amount of information carried in temperature-programmed desorption (TPD) experiments is often difficult to mine due to the occurrence of competing reaction pathways that produce compounds with similar mass spectrometric features. Multivariate curve resolution (MCR) is introduced as a tool capable of overcoming this problem by mathematically detecting spectral variations and correlations between several m/z traces, which is later translated into the extraction of the cracking pattern and the desorption profile for each desorbate. Different from the elegant (though complex) methods currently available to analyze TPD data, MCR analysis is applicable even when no information regarding the specific surface reaction/desorption process or the nature of the desorbing species is available. However, when available, any information can be used as constraints that guide the outcome, increasing the accuracy of the resolution. This approach is especially valuable when the compounds desorbing are different from what would be expected based on a chemical intuition, when the cracking pattern of the model test compound is difficult or impossible to obtain (because it could be unstable or very rare), and when knowing major components desorbing from the surface could in more traditional methods actually bias the quantification of minor components. The enhanced level of understanding of thermal processes achieved through MCR analysis is demonstrated by analyzing three phenomena: i) the cryogenic desorption of vinyltrimethylsilane from silicon, an introductory system where the known multilayer and monolayer components are resolved; ii) acrolein hydrogenation on a bimetallic Pt-Ni-Pt catalyst, where a rapid identification of hydrogenated products as well as other desorbing species is achieved, and iii) the thermal reaction of Ti[N(CH 3) 2] 4 on Si(100), where the products of surface decomposition are identified and an estimation of the surface composition after the

  5. Child sexual abuse prevention policy: an analysis of Erin's law.

    Science.gov (United States)

    Anderson, Gwendolyn D

    2014-01-01

    Child sexual abuse affects thousands of children in the United States and is vastly underreported. Tertiary prevention policies, primarily in the form of sex offender registries and community notification programs, have received the most attention and funding. Few policies have focused on school-based prevention. One recently passed law in Illinois mandates all K-5 public schools to implement sexual abuse prevention programs. The law was championed by a young social worker, Erin Merryn. Through the multiple streams framework, this article examines the unique set of political circumstances, united with Merryn's advocacy, which created the opportunity for the law to pass.

  6. Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Benoit Parmentier

    2014-12-01

    Full Text Available Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI, land surface temperature (LST and albedo (ALB. The analysis involves conducting a seasonal trend analysis (STA to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2 and using principal component analysis (PCA to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1 is characterized by a decrease in mean NDVI (Amplitude 0 with a concurrent increase in albedo (the mean and the annual amplitude and an increase in LST annual variability (Amplitude 1. These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series.

  7. Spatial and Temporal Assessment on Drug Addiction Using Multivariate Analysis and GIS

    International Nuclear Information System (INIS)

    Mohd Ekhwan Toriman; Mohd Ekhwan Toriman; Siti Nor Fazillah Abdullah; Izwan Arif Azizan; Mohd Khairul Amri Kamarudin; Roslan Umar; Nasir Mohamad

    2015-01-01

    There is a need for managing and displaying drug addiction phenomena and trend at both spatial and temporal scales. Spatial and temporal assessment on drug addiction in Terengganu was undertaken to understand the geographical area of district in the same cluster, in addition, identify the hot spot area of this problem and analysis the trend of drug addiction. Data used were topography map of Terengganu and number of drug addicted person in Terengganu by district within 10 years (2004-2013). Number of drug addicted person by district were mapped using Geographic Information system and analysed using a combination of multivariate analysis which is cluster analysis were applied to the database in order to validate the correlation between data in the same cluster. Result showed a cluster analysis for number of drug addiction by district generated three clusters which are Besut and Kuala Terengganu in cluster 1 named moderate drug addicted person (MDA), Dungun, Marang, Setiu and Hulu Terengganu in cluster 2 named lower drug addicted person (LDA) and Kemaman in cluster 3 named high drug addicted person(HDA). This analysis indicates that cluster 3 which is Kemaman is a hot spot area. These results were beneficial for stakeholder to monitor and manage this problem especially in the hot spot area which needs to be emphasized. (author)

  8. Multivariate analysis and extraction of parameters in resistive RAMs using the Quantum Point Contact model

    Science.gov (United States)

    Roldán, J. B.; Miranda, E.; González-Cordero, G.; García-Fernández, P.; Romero-Zaliz, R.; González-Rodelas, P.; Aguilera, A. M.; González, M. B.; Jiménez-Molinos, F.

    2018-01-01

    A multivariate analysis of the parameters that characterize the reset process in Resistive Random Access Memory (RRAM) has been performed. The different correlations obtained can help to shed light on the current components that contribute in the Low Resistance State (LRS) of the technology considered. In addition, a screening method for the Quantum Point Contact (QPC) current component is presented. For this purpose, the second derivative of the current has been obtained using a novel numerical method which allows determining the QPC model parameters. Once the procedure is completed, a whole Resistive Switching (RS) series of thousands of curves is studied by means of a genetic algorithm. The extracted QPC parameter distributions are characterized in depth to get information about the filamentary pathways associated with LRS in the low voltage conduction regime.

  9. Building a Reduced Reference Video Quality Metric with Very Low Overhead Using Multivariate Data Analysis

    Directory of Open Access Journals (Sweden)

    Tobias Oelbaum

    2008-10-01

    Full Text Available In this contribution a reduced reference video quality metric for AVC/H.264 is proposed that needs only a very low overhead (not more than two bytes per sequence. This reduced reference metric uses well established algorithms to measure objective features of the video such as 'blur' or 'blocking'. Those measurements are then combined into a single measurement for the overall video quality. The weights of the single features and the combination of those are determined using methods provided by multivariate data analysis. The proposed metric is verified using a data set of AVC/H.264 encoded videos and the corresponding results of a carefully designed and conducted subjective evaluation. Results show that the proposed reduced reference metric not only outperforms standard PSNR but also two well known full reference metrics.

  10. Physical analysis of multivariate measurements in the Atmospheric high-energy physics experiments within ADEI platform

    International Nuclear Information System (INIS)

    Avakyan, K.; Chilingarian, A.; Karapetyan, T.; Chilingaryan, S.

    2017-01-01

    To make transformational scientific progress in Space science and geophysics, the Sun, heliosphere, magnetosphere and different layers of the atmosphere must be studied as a coupled system. Presented paper describes how information on complicated physical processes on Sun, in the heliosphere, magnetosphere and atmosphere can be made immediately assessable for researchers via advanced multivariate visualization system with simple statistical analysis package. Research of the high-energy phenomena in the atmosphere and the atmospheric discharges is of special importance. The relationship between thundercloud electrification, lightning activity, wideband radio emission and particle fluxes have not been yet unambiguously established. One of most intriguing opportunities opening by observation of the high-energy processes in the atmosphere is their relation to lightning initiation. Investigations of the accelerated structures in the geospace plasmas can as well shed light on particle acceleration up to much higher energies in the similar structures of space plasmas in the distant objects of the Universe. (author)

  11. A multivariate discriminate analysis of behavioral measures in genetically nervous dogs.

    Science.gov (United States)

    Walls, R C; Murphree, O D; Angel, C; Newton, J E

    1976-01-01

    For some years we have studied a strain of genetically nervous dogs in the Neuropsychiatric Research Laboratory, Veterans Administration Hospital, North Little Rock, Arkansas. In the manner of Pavlov and Gantt and later Scott and Fuller we have characterized these dogs in such descriptive terms as timid, human aversive, and catatonic-like. Behavioral tests have been administered on nearly all dogs in this longitudinal study, and we are using these data to try to develop statistical procedures to maximize the discriminatory power of the behavioral assay and to more accurately characterize the behavioral deficit. A multivariate discriminate analysis of 13 variables on 91 healthy and 63 nervous dogs assayed at 3 months of age shows: (1) that much of our present behavioral testing procedures is redundant, and (2) that simple "friendliness to humans" in the dog is as effective for discriminating between the two groups as any of the 13 measures, taken either singly or collectively.

  12. Wavelet aided multivariate outlier analysis to enhance defect contrast in thermal images

    Science.gov (United States)

    Manohar, Arun; Lanza di Scalea, Francesco

    2011-04-01

    A novel two-stage signal reconstruction approach is proposed to analyze raw thermal image sequences for damage detection purposes by Infrared Thermographic NDE. The first stage involves low-pass filtering using Wavelets. In the second stage, a Multivariate Outlier Analysis is performed on filtered data using a set of signal features. The proposed approach significantly enhances the defective area contrast against the background in infrared thermography NDE. The two-stage approach has some advantages in comparison to the traditionally used methods, including automation in the defect detection process and better defective area isolation through increased contrast. The method does not require a reference area to function. The results are presented for the case of a composite plate with simulated delaminations, and a composite sandwich plate with skin-core disbonds.

  13. Prognostic factorsin inoperable adenocarcinoma of the lung: A multivariate regression analysis of 259 patiens

    DEFF Research Database (Denmark)

    Sørensen, Jens Benn; Badsberg, Jens Henrik; Olsen, Jens

    1989-01-01

    as an indicator for patients having minimal disease spread. Liver metastases were of limited clinical value as a prognostic factor because they were detected in only seven cases in this patient population. A new Cox analysis ignoring the influence of this variable revealed no other variables than those occurring...... status, stage IV disease, no prior nonradical resection, liver metastases, high values of white blood cell count, and lactate dehydrogenase, and low values of aspartate aminotransaminase. The nonradical resection may not be a prognostic factor because of the resection itself but may rather serve......The prognostic factors for survival in advanced adenocarcinoma of the lung were investigated in a consecutive series of 259 patients treated with chemotherapy. Twenty-eight pretreatment variables were investigated by use of Cox's multivariate regression model, including histological subtypes...

  14. Analysis of Regularly and Irregularly Sampled Spatial, Multivariate, and Multi-temporal Data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    1994-01-01

    localize areas where big changes occur. Use of MAFs of high order multiset Q-mode canonical variates seems successful. Due to lack of ground truth data it is very hard to determine empirically which of the five multiset methods described is best (if any). Because of their strong ability to isolate noise......This thesis describes different methods that are useful in the analysis of multivariate data. Some methods focus on spatial data (sampled regularly or irregularly), others focus on multitemporal data or data from multiple sources. The thesis covers selected and not all aspects of relevant data......-variograms are described. As a new way of setting up a well-balanced kriging support the Delaunay triangulation is suggested. Two case studies show the usefulness of 2-D semivariograms of geochemical data from areas in central Spain (with a geologist's comment) and South Greenland, and kriging/cokriging of an undersampled...

  15. Multivariate image analysis of laser-induced photothermal imaging used for detection of caries tooth

    Science.gov (United States)

    El-Sherif, Ashraf F.; Abdel Aziz, Wessam M.; El-Sharkawy, Yasser H.

    2010-08-01

    Time-resolved photothermal imaging has been investigated to characterize tooth for the purpose of discriminating between normal and caries areas of the hard tissue using thermal camera. Ultrasonic thermoelastic waves were generated in hard tissue by the absorption of fiber-coupled Q-switched Nd:YAG laser pulses operating at 1064 nm in conjunction with a laser-induced photothermal technique used to detect the thermal radiation waves for diagnosis of human tooth. The concepts behind the use of photo-thermal techniques for off-line detection of caries tooth features were presented by our group in earlier work. This paper illustrates the application of multivariate image analysis (MIA) techniques to detect the presence of caries tooth. MIA is used to rapidly detect the presence and quantity of common caries tooth features as they scanned by the high resolution color (RGB) thermal cameras. Multivariate principal component analysis is used to decompose the acquired three-channel tooth images into a two dimensional principal components (PC) space. Masking score point clusters in the score space and highlighting corresponding pixels in the image space of the two dominant PCs enables isolation of caries defect pixels based on contrast and color information. The technique provides a qualitative result that can be used for early stage caries tooth detection. The proposed technique can potentially be used on-line or real-time resolved to prescreen the existence of caries through vision based systems like real-time thermal camera. Experimental results on the large number of extracted teeth as well as one of the thermal image panoramas of the human teeth voltanteer are investigated and presented.

  16. Pain in diagnostic hysteroscopy: a multivariate analysis after a randomized, controlled trial.

    Science.gov (United States)

    Mazzon, Ivan; Favilli, Alessandro; Grasso, Mario; Horvath, Stefano; Bini, Vittorio; Di Renzo, Gian Carlo; Gerli, Sandro

    2014-11-01

    To study which variables are able to influence women's experience of pain during diagnostic hysteroscopy. Multivariate analysis (phase II) after a randomized, controlled trial (phase I). Endoscopic gynecologic center. In phase I, 392 patients were analyzed. Group A: 197 women with carbon dioxide (CO2); group B: 195 women with normal saline. In phase II, 392 patients were assigned to two different groups according to their pain experience as measured by a visual analogue scale (VAS): group VAS>3 (170 patients); group VAS≤3 (222 patients). Free-anesthesia diagnostic hysteroscopy performed using CO2 or normal saline as distension media. Procedure time, VAS score, image quality, and side effects during and after diagnostic hysteroscopy. In phase I the median pain score in group A was 2, whereas in group B it was 3. In phase II the duration of the procedure, nulliparity, and the use of normal saline were significantly correlated with VAS>3. A higher presence of cervical synechiae was observed in the group VAS>3. The multivariate analysis revealed an inverse correlation between parity and a VAS>3, whereas the use of normal saline, the presence of synechiae in the cervical canal, and the duration of the hysteroscopy were all directly correlated to a VAS score>3. Pain in hysteroscopy is significantly related to the presence of cervical synechiae, to the duration of the procedure, and to the use of normal saline; conversely, parity seems to have a protective role. NCT01873391. Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

  17. Pigmented nodular melanoma: the predictive value of dermoscopic features using multivariate analysis.

    Science.gov (United States)

    Pizzichetta, M A; Kittler, H; Stanganelli, I; Bono, R; Cavicchini, S; De Giorgi, V; Ghigliotti, G; Quaglino, P; Rubegni, P; Argenziano, G; Talamini, R

    2015-07-01

    Nodular melanoma (NM), representing 10-30% of all melanomas, plays a major role in global mortality related to melanoma. Nonetheless, the literature on dermoscopy of NM is scanty. To assess odds ratios (ORs) to quantify dermoscopic features of pigmented NM vs. pigmented superficial spreading melanoma (SSM), and pigmented nodular nonmelanocytic and benign melanocytic lesions. To assess the presence or absence of global patterns and dermoscopic criteria, digitized images of 457 pigmented skin lesions from patients with a histopathological diagnosis of NM (n = 75), SSM (n = 93), and nodular nonmelanocytic and benign melanocytic lesions (n = 289; namely, 39 basal cell carcinomas, 85 seborrhoeic keratoses, 81 blue naevi, and 84 compound/dermal naevi) were retrospectively collected and blindly evaluated by three observers. Multivariate analysis showed that ulceration (OR 4.07), homogeneous disorganized pattern (OR 10.76), and homogeneous blue pigmented structureless areas (OR 2.37) were significantly independent prognostic factors for NM vs. SSM. Multivariate analysis of dermoscopic features of NM vs. nonmelanocytic and benign melanocytic lesions showed that the positive correlating features leading to a significantly increased risk of NM were asymmetric pigmentation (OR 6.70), blue-black pigmented areas (OR 7.15), homogeneous disorganized pattern (OR 9.62), a combination of polymorphous vessels and milky-red globules/areas (OR 23.65), and polymorphous vessels combined with homogeneous red areas (OR 33.88). Dermoscopy may be helpful in improving the recognition of pigmented NM by revealing asymmetric pigmentation, blue-black pigmented areas, homogeneous disorganized pattern and abnormal vascular structures, including polymorphous vessels, milky-red globules/areas and homogeneous red areas. © 2015 British Association of Dermatologists.

  18. Factors related to the effectiveness of variable stiffness colonoscope: results of a multivariate analysis

    Directory of Open Access Journals (Sweden)

    Javier Sola-Vera

    2014-01-01

    Full Text Available Background: Various studies and two meta-analysis have shown that a variable stiffness colonoscope improves cecal intubation rate. However, there are few studies on how this colonoscope should be used. Objective: The aim of this study was to identify factors related to the advancement of the colonoscope when the variable stiffness function is activated. Methods: Prospective study enrolling consecutive patients referred for colonoscopy. The variable stiffness colonoscope (Olympus CF-H180DI/L® was used. We performed univariate and multivariate analyses of factors associated with the success of the variable stiffness function. Results: After the data inclusion period, 260 patients were analyzed. The variable stiffness function was used most in the proximal colon segments (ascending and transverse colon 85 %; descending/sigmoid colon 15.2 %. The body mass index was lower in patients in whom the endoscope advanced after activating the variable stiffness than those in which it could not be advanced (25.9 ± 4.8 vs. 28.3 ± 5.4 kg/m², p = 0.009. The endoscope advanced less frequently when the stiffness function was activated in the ascending colon versus activation in other segments of the colon (25 % vs. 64.5 % ascending colon vs. other segments; p < 0.05. In the multivariate analysis, only the colon segment in which the variable stiffness was activated was an independent predictor of advancement of the colonoscope. Conclusions: The variable stiffness function is effective, allowing the colonoscope advancement especially when applied in the transverse colon, descending colon and sigmoid. However, when used in the ascending colon it has a lower effectiveness.

  19. Network analysis of substance abuse and dependence symptoms

    NARCIS (Netherlands)

    Rhemtulla, M.; Fried, E.I.; Aggen, S.H.; Tuerlinckx, F.; Kendler, K.S.; Borsboom, D.

    Background: The DSM uses one set of abuse and dependence criteria to assess multiple substance use disorders (SUDs). Most SUD research aggregates across these symptoms to study the behavior of SUD as a static construct. We use an alternative approach that conceptualizes symptoms as directly

  20. Multivariate analysis of chromatographic retention data as a supplementary means for grouping structurally related compounds.

    Science.gov (United States)

    Fasoula, S; Zisi, Ch; Sampsonidis, I; Virgiliou, Ch; Theodoridis, G; Gika, H; Nikitas, P; Pappa-Louisi, A

    2015-03-27

    In the present study a series of 45 metabolite standards belonging to four chemically similar metabolite classes (sugars, amino acids, nucleosides and nucleobases, and amines) was subjected to LC analysis on three HILIC columns under 21 different gradient conditions with the aim to explore whether the retention properties of these analytes are determined from the chemical group they belong. Two multivariate techniques, principal component analysis (PCA) and discriminant analysis (DA), were used for statistical evaluation of the chromatographic data and extraction similarities between chemically related compounds. The total variance explained by the first two principal components of PCA was found to be about 98%, whereas both statistical analyses indicated that all analytes are successfully grouped in four clusters of chemical structure based on the retention obtained in four or at least three chromatographic runs, which, however should be performed on two different HILIC columns. Moreover, leave-one-out cross-validation of the above retention data set showed that the chemical group in which an analyte belongs can be 95.6% correctly predicted when the analyte is subjected to LC analysis under the same four or three experimental conditions as the all set of analytes was run beforehand. That, in turn, may assist with disambiguation of analyte identification in complex biological extracts. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. Geostatistical and multivariate statistical analysis of heavily and manifoldly contaminated soil samples.

    Science.gov (United States)

    Schaefer, Kristin; Einax, Jürgen W; Simeonov, Vasil; Tsakovski, Stefan

    2010-04-01

    The surroundings of the former Kremikovtzi steel mill near Sofia (Bulgaria) are influenced by various emissions from the factory. In addition to steel and alloys, they produce different products based on inorganic compounds in different smelters. Soil in this region is multiply contaminated. We collected 65 soil samples and analyzed 15 elements by different methods of atomic spectroscopy for a survey of this field site. Here we present a novel hybrid approach for environmental risk assessment of polluted soil combining geostatistical methods and source apportionment modeling. We could distinguish areas with heavily and slightly polluted soils in the vicinity of the iron smelter by applying unsupervised pattern recognition methods. This result was supported by geostatistical methods such as semivariogram analysis and kriging. The modes of action of the metals examined differ significantly in such a way that iron and lead account for the main pollutants of the iron smelter, whereas, e.g., arsenic shows a haphazard distribution. The application of factor analysis and source-apportionment modeling on absolute principal component scores revealed novel information about the composition of the emissions from the different stacks. It is possible to estimate the impact of every element examined on the pollution due to their emission source. This investigation allows an objective assessment of the different spatial distributions of the elements examined in the soil of the Kremikovtzi region. The geostatistical analysis illustrates this distribution and is supported by multivariate statistical analysis revealing relations between the elements.

  2. MULTIVARIATE AND MULTICRITERIAL FOREIGN DEBT ANALYSIS OF THE SELECTED TRANSITION ECONOMIES

    Directory of Open Access Journals (Sweden)

    Snježana Pivac

    2010-12-01

    Full Text Available There is a constant evidence of the growth of the foreign indebtedness in all the countries in transition, both EU member states and (preaccession countries. Status and trends of external debt are important indicators of potential macroeconomic problems, which determines that the management of foreign debt should be a task for all governments. Thus, methodology and measurement of foreign indebtedness is crucial for these countries. The aim of the paper is to classify, using multivariate cluster analysis, ten chosen countries in transition (Bosnia and Herzegovina, Croatia, Czech, Estonia, Hungary, Latvia, Latvia, Macedonia, Poland, Slovenia according to the key indicators of the state and trends of foreign indebtedness. In addition, ranking of those countries will be done in relation to the indebtedness indicators by the multicriteria analysis method. Comparative analysis of the results will be done. The advantage of these approaches is reflected in the fact that the analysis, classification and ranking can be done for all countries, based on all indicators of external indebtedness at the same time.

  3. Multivariate qualitative analysis of banned additives in food safety using surface enhanced Raman scattering spectroscopy.

    Science.gov (United States)

    He, Shixuan; Xie, Wanyi; Zhang, Wei; Zhang, Liqun; Wang, Yunxia; Liu, Xiaoling; Liu, Yulong; Du, Chunlei

    2015-02-25

    A novel strategy which combines iteratively cubic spline fitting baseline correction method with discriminant partial least squares qualitative analysis is employed to analyze the surface enhanced Raman scattering (SERS) spectroscopy of banned food additives, such as Sudan I dye and Rhodamine B in food, Malachite green residues in aquaculture fish. Multivariate qualitative analysis methods, using the combination of spectra preprocessing iteratively cubic spline fitting (ICSF) baseline correction with principal component analysis (PCA) and discriminant partial least squares (DPLS) classification respectively, are applied to investigate the effectiveness of SERS spectroscopy for predicting the class assignments of unknown banned food additives. PCA cannot be used to predict the class assignments of unknown samples. However, the DPLS classification can discriminate the class assignment of unknown banned additives using the information of differences in relative intensities. The results demonstrate that SERS spectroscopy combined with ICSF baseline correction method and exploratory analysis methodology DPLS classification can be potentially used for distinguishing the banned food additives in field of food safety. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. CoSMoMVPA: multi-modal multivariate pattern analysis of neuroimaging datain Matlab / GNU Octave

    Directory of Open Access Journals (Sweden)

    Nikolaas N Oosterhof

    2016-07-01

    Full Text Available Recent years have seen an increase in the popularity of multivariate pattern (MVP analysis of functional magnetic resonance (fMRI data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens.CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species.It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques.CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality

  5. Multivariate Analysis of the Factors Associated With Sexual Intercourse, Marriage, and Paternity of Hypospadias Patients.

    Science.gov (United States)

    Kanematsu, Akihiro; Higuchi, Yoshihide; Tanaka, Shiro; Hashimoto, Takahiko; Nojima, Michio; Yamamoto, Shingo

    2016-10-01

    employment (P = .020 and .026, respectively), and paternity was associated with the absence of additional surgery after completion of the initial repair (P = .013 by multivariate analysis). There was scant overlap of factors associated with the three events. The present findings provide reference information for surgeons and parents regarding future sexual and marriage experiences of children treated for hypospadias. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

  6. Rapid thyroid dysfunction screening based on serum surface-enhanced Raman scattering and multivariate statistical analysis

    Science.gov (United States)

    Tian, Dayong; Lü, Guodong; Zhai, Zhengang; Du, Guoli; Mo, Jiaqing; Lü, Xiaoyi

    2018-01-01

    In this paper, serum surface-enhanced Raman scattering and multivariate statistical analysis are used to investigate a rapid screening technique for thyroid function diseases. At present, the detection of thyroid function has become increasingly important, and it is urgently necessary to develop a rapid and portable method for the detection of thyroid function. Our experimental results show that, by using the Silmeco-based enhanced Raman signal, the signal strength greatly increases and the characteristic peak appears obviously. It is also observed that the Raman spectra of normal and anomalous thyroid function human serum are significantly different. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose thyroid dysfunction, and the diagnostic accuracy was 87.4%. The use of serum surface-enhanced Raman scattering technology combined with PCA-LDA shows good diagnostic performance for the rapid detection of thyroid function. By means of Raman technology, it is expected that a portable device for the rapid detection of thyroid function will be developed.

  7. Application of Multivariate Statistical Analysis to Biomarkers in Se-Turkey Crude Oils

    Science.gov (United States)

    Gürgey, K.; Canbolat, S.

    2017-11-01

    Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%), Stable Carbon Isotope, Gas Chromatography (GC), and Gas Chromatography-Mass Spectrometry (GC-MS) data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE) Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

  8. Leachate/domestic wastewater aerobic co-treatment: A pilot-scale study using multivariate analysis.

    Science.gov (United States)

    Ferraz, F M; Bruni, A T; Povinelli, J; Vieira, E M

    2016-01-15

    Multivariate analysis was used to identify the variables affecting the performance of pilot-scale activated sludge (AS) reactors treating old leachate from a landfill and from domestic wastewater. Raw leachate was pre-treated using air stripping to partially remove the total ammoniacal nitrogen (TAN). The control AS reactor (AS-0%) was loaded only with domestic wastewater, whereas the other reactor was loaded with mixtures containing leachate at volumetric ratios of 2 and 5%. The best removal efficiencies were obtained for a ratio of 2%, as follows: 70 ± 4% for total suspended solids (TSS), 70 ± 3% for soluble chemical oxygen demand (SCOD), 70 ± 4% for dissolved organic carbon (DOC), and 51 ± 9% for the leachate slowly biodegradable organic matter (SBOM). Fourier transform infrared (FTIR) spectroscopic analysis confirmed that most of the SBOM was removed by partial biodegradation rather than dilution or adsorption of organics in the sludge. Nitrification was approximately 80% in the AS-0% and AS-2% reactors. No significant accumulation of heavy metals was observed for any of the tested volumetric ratios. Principal component analysis (PCA) and partial least squares (PLS) indicated that the data dimension could be reduced and that TAN, SCOD, DOC and nitrification efficiency were the main variables that affected the performance of the AS reactors. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Metabolic fingerprinting of Tussilago farfara L. using ¹H-NMR spectroscopy and multivariate data analysis.

    Science.gov (United States)

    Zhi, Hai-Juan; Qin, Xue-Mei; Sun, Hai-Feng; Zhang, Li-Zeng; Guo, Xiao-Qing; Li, Zhen-Yu

    2012-01-01

    The flower bud of Tussilago farfara L. is widely used for the treatment of coughs, bronchitis and asthmatic disorders in traditional Chinese medicine. In Europe, the plant has been used as herbal remedies for virtually the same applications, but the leaves are preferred over flowers. To systematically evaluate the chemical profiles of Tusssilago farfara leaves and flowers along with the identification of the polar and non-polar metabolites. Metabolic profiling carried out by means of ¹H-NMR spectroscopy and multivariate data analysis was applied to crude extracts from flowers and leaves. Metabolites were identified directly from the crude extracts through one-dimensional and two-dimensional NMR spectra. A broad range of metabolites were detected without any chromatographic separation. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) of ¹H-NMR data provided a clear separation between the samples. The corresponding loadings plot indicated that higher levels of phenylpropanoids, amino acids, organic acids and fatty acids, as well as lower levels of sugars, terpenoids and sterols were present in the leaves, as compared with flowers. For the flowers, more phenylpropanoids were present in fully open flowers, while more sugars and fatty acids were present in flower buds. NMR spectra (one- and two-dimensional) are useful for identifying metabolites, especially for the overlapped signals. The NMR-based metabolomics approach has great potential for chemical comparison study of the metabolome of herbal drugs. Copyright © 2012 John Wiley & Sons, Ltd.

  10. Multivariate Analysis of Some Pine Forested Areas of Azad Kashmir-Pakistan

    International Nuclear Information System (INIS)

    Bokhari, T.Z.; Liu, Y.; Li, Q.; Malik, S.A.; Ahmed, M.; Siddiqui, M.F.; Khan, Z.U.

    2016-01-01

    Floristic composition and communities in Azad Kashmir area of Pakistan were studied by using multivariate analysis. Quantitative sampling from thirty one sites was carried out in different coniferous forests of Azad Kashmir in order to analyze the effects of past earthquakes and landslides on vegetation of these areas. Though coniferous forests were highly disturbed either naturally or anthropogenic activities, therefore sampling was preferred to those forests which were near fault line. Trees were sampled using Point Centered Quarter (PCQ) method. Results of cluster analysis (using Ward's method) yielded six groups dominated by different conifer species. Group I and V were dominated by Pinus wallichiana while this species was co-dominant in group III. Other groups showed the dominance of different conifer species i.e. Cedrus deodara, Pinus roxburghii, Picea smithiana and Abies pindrow. Both the cluster analysis and ordination techniques (by two dimensional non-metric multidimensional scaling) classify and ordinate the structure of various groups indicating interrelationship among different species. The groups of trees were readily be superimposed on NMS ordination axes; they were well classified and well separated out in ordination. The present research revealed that these forests had diverse and asymmetric structure due to natural anthropogenic disturbances and overgrazing, which were key factors in addition to natural disturbances. However, some of the forests showed considerably stable structure due to less human interference. (author)

  11. APPLICATION OF MULTIVARIATE STATISTICAL ANALYSIS TO BIOMARKERS IN SE-TURKEY CRUDE OILS

    Directory of Open Access Journals (Sweden)

    K. Gürgey

    2017-11-01

    Full Text Available Twenty-four crude oil samples were collected from the 24 oil fields distributed in different districts of SE-Turkey. API and Sulphur content (%, Stable Carbon Isotope, Gas Chromatography (GC, and Gas Chromatography-Mass Spectrometry (GC-MS data were used to construct a geochemical data matrix. The aim of this study is to examine the genetic grouping or correlations in the crude oil samples, hence the number of source rocks present in the SE-Turkey. To achieve these aims, two of the multivariate statistical analysis techniques (Principle Component Analysis [PCA] and Cluster Analysis were applied to data matrix of 24 samples and 8 source specific biomarker variables/parameters. The results showed that there are 3 genetically different oil groups: Batman-Nusaybin Oils, Adıyaman-Kozluk Oils and Diyarbakir Oils, in addition to a one mixed group. These groupings imply that at least, three different source rocks are present in South-Eastern (SE Turkey. Grouping of the crude oil samples appears to be consistent with the geographic locations of the oils fields, subsurface stratigraphy as well as geology of the area.

  12. Classification of emotions by multivariate analysis and individual differences of nuclear power plant operators' emotion

    International Nuclear Information System (INIS)

    Hasegawa, Naoko; Yoshimura, Seiichi

    1999-01-01

    The purpose of this study is the development of a simulation model which expresses operators' emotion under plant emergency. This report shows the classification of emotions by multivariate analysis and investigation results conducted to clarify individual differences of activated emotion influenced by personal traits. Although a former investigation was conducted to classify emotions into five basic emotions proposed by Johnson-Laird, the basic emotions was not based on real data. For the development of more realistic and accurate simulation model, it is necessary to recognize basic emotion and to classify emotions into them. As a result of analysis by qualification method 3 and cluster analysis, four basic clusters were clarified, i.e., Emotion expressed towards objects, Emotion affected by objects, Pleasant emotion, and Surprise. Moreover, 51 emotions were ranked in the order according to their similarities in each cluster. An investigation was conducted to clarify individual differences in emotion process using 87 plant operators. The results showed the differences of emotion depending on the existence of operators' foresight, cognitive style, experience in operation, and consciousness of attribution to an operating team. For example, operators with low self-efficacy, short experience or low consciousness of attribution to a team, feel more intensive emotion under plant emergency and more affected by severe plant conditions. The model which can express individual differences will be developed utilizing and converting these data hereafter. (author)

  13. Identification of Dactylopius cochineal species with high-performance liquid chromatography and multivariate data analysis.

    Science.gov (United States)

    Serrano, Ana; Sousa, Micaela; Hallett, Jessica; Simmonds, Monique S J; Nesbitt, Mark; Lopes, João A

    2013-10-21

    Identification of American cochineal species (Dactylopius genus) can provide important information for the study of historical works of art, entomology, cosmetics, pharmaceuticals and foods. In this study, validated species of Dactylopius, including the domesticated cochineal D. coccus, were analysed by high-performance liquid chromatography with a diode array detector (HPLC-DAD) and submitted to multivariate data analysis, in order to discriminate the species and hence construct a reference library for a wide range of applications. Principal components analysis (PCA) and partial least squares discriminant analysis (PLSDA) models successfully provided accurate species classifications. This library was then applied to the identification of 72 historical insect specimens of unidentified species, mostly dating from the 19th century, and belonging to the Economic Botany Collection, Royal Botanic Gardens, Kew, England. With this approach it was possible to identify anomalies in how insects were labelled historically, as several of them were revealed not to be cochineal. Nevertheless, more than 85% of the collection was determined to be species of Dactylopius and the majority of the specimens were identified as D. coccus. These results have shown that HPLC-DAD, in combination with suitable chemometric methods, is a powerful approach for discriminating related cochineal species.

  14. Classification of emotions by multivariate analysis and individual differences of nuclear power plant operators` emotion

    Energy Technology Data Exchange (ETDEWEB)

    Hasegawa, Naoko; Yoshimura, Seiichi [Central Research Inst. of Electric Power Industry, Tokyo (Japan)

    1999-03-01

    The purpose of this study is the development of a simulation model which expresses operators` emotion under plant emergency. This report shows the classification of emotions by multivariate analysis and investigation results conducted to clarify individual differences of activated emotion influenced by personal traits. Although a former investigation was conducted to classify emotions into five basic emotions proposed by Johnson-Laird, the basic emotions was not based on real data. For the development of more realistic and accurate simulation model, it is necessary to recognize basic emotion and to classify emotions into them. As a result of analysis by qualification method 3 and cluster analysis, four basic clusters were clarified, i.e., Emotion expressed towards objects, Emotion affected by objects, Pleasant emotion, and Surprise. Moreover, 51 emotions were ranked in the order according to their similarities in each cluster. An investigation was conducted to clarify individual differences in emotion process using 87 plant operators. The results showed the differences of emotion depending on the existence of operators` foresight, cognitive style, experience in operation, and consciousness of attribution to an operating team. For example, operators with low self-efficacy, short experience or low consciousness of attribution to a team, feel more intensive emotion under plant emergency and more affected by severe plant conditions. The model which can express individual differences will be developed utilizing and converting these data hereafter. (author)

  15. Newly Graduated Nurses' Competence and Individual and Organizational Factors: A Multivariate Analysis.

    Science.gov (United States)

    Numminen, Olivia; Leino-Kilpi, Helena; Isoaho, Hannu; Meretoja, Riitta

    2015-09-01

    To study the relationships between newly graduated nurses' (NGNs') perceptions of their professional competence, and individual and organizational work-related factors. A multivariate, quantitative, descriptive, correlation design was applied. Data collection took place in November 2012 with a national convenience sample of 318 NGNs representing all main healthcare settings in Finland. Five instruments measured NGNs' perceptions of their professional competence, occupational commitment, empowerment, practice environment, and its ethical climate, with additional questions on turnover intentions, job satisfaction, and demographics. Descriptive statistics summarized the demographic data, and inferential statistics multivariate path analysis modeling estimated the relationships between the variables. The strongest relationship was found between professional competence and empowerment, competence explaining 20% of the variance of empowerment. The explanatory power of competence regarding practice environment, ethical climate of the work unit, and occupational commitment, and competence's associations with turnover intentions, job satisfaction, and age, were statistically significant but considerably weaker. Higher competence and satisfaction with quality of care were associated with more positive perceptions of practice environment and its ethical climate as well as higher empowerment and occupational commitment. Apart from its association with empowerment, competence seems to be a rather independent factor in relation to the measured work-related factors. Further exploration would deepen the knowledge of this relationship, providing support for planning educational and developmental programs. Research on other individual and organizational factors is warranted to shed light on factors associated with professional competence in providing high-quality and safe care as well as retaining new nurses in the workforce. The study sheds light on the strength and direction of

  16. In-Process Control Assay of Pharmaceutical Microtablets Using Hyperspectral Imaging Coupled with Multivariate Analysis.

    Science.gov (United States)

    Kandpal, Lalit Mohan; Tewari, Jagdish; Gopinathan, Nishanth; Boulas, Pierre; Cho, Byoung-Kwan

    2016-11-15

    Monitoring the amount of active pharmaceutical ingredient (API) in finished dosage form is important to ensure the content uniformity of the product. In this report, we summarize the development and validation of a hyperspectral imaging (HSI) technique for rapid in-line prediction of the active pharmaceutical ingredient (API) in microtablets with concentrations varying from 60 to 130% API (w/w). The tablet spectra of different API concentrations were collected in-line using an HSI system within the visible/near-infrared (vis/NIR; 400-1000 nm) and short-wave infrared (SWIR; 1100-2500 nm) regions. The ability of the HSI technique to predict the API concentration in the tablet samples was validated against a reference high-performance liquid chromatography (HPLC) method. The prediction efficiency of two different types of multivariate data modeling methods, that is, partial least-squares regression (PLSR) and principle component regression (PCR), were compared. The prediction ability of the regression models was cross-validated against results generated with the reference HPLC method. The results obtained from the PLSR models showed reliable performance for predicting the API concentration in SWIR region. The highest coefficient of determination (R 2 p) was 0.96, associated with the lowest predicted error and bias of 4.45 and -0.35%, respectively. Furthermore, the concentration-mapped images of PLSR and PCR models were used to visually characterize the API concentration present on the tablet surface. Based on these results, we suggest that HSI measurement combined with multivariate data analysis and chemical imaging could be implemented in the production environment for rapid in-line determination of pharmaceutical product quality.

  17. Biological data analysis as an information theory problem: multivariable dependence measures and the shadows algorithm.

    Science.gov (United States)

    Sakhanenko, Nikita A; Galas, David J

    2015-11-01

    Information theory is valuable in multiple-variable analysis for being model-free and nonparametric, and for the modest sensitivity to undersampling. We previously introduced a general approach to finding multiple dependencies that provides accurate measures of levels of dependency for subsets of variables in a data set, which is significantly nonzero only if the subset of variables is collectively dependent. This is useful, however, only if we can avoid a combinatorial explosion of calculations for increasing numbers of variables.  The proposed dependence measure for a subset of variables, τ, differential interaction information, Δ(τ), has the property that for subsets of τ some of the factors of Δ(τ) are significantly nonzero, when the full dependence includes more variables. We use this property to suppress the combinatorial explosion by following the "shadows" of multivariable dependency on smaller subsets. Rather than calculating the marginal entropies of all subsets at each degree level, we need to consider only calculations for subsets of variables with appropriate "shadows." The number of calculations for n variables at a degree level of d grows therefore, at a much smaller rate than the binomial coefficient (n, d), but depends on the parameters of the "shadows" calculation. This approach, avoiding a combinatorial explosion, enables the use of our multivariable measures on very large data sets. We demonstrate this method on simulated data sets, and characterize the effects of noise and sample numbers. In addition, we analyze a data set of a few thousand mutant yeast strains interacting with a few thousand chemical compounds.

  18. Optical Spectroscopy and Multivariate Analysis for Biodosimetry and Monitoring of Radiation Injury to the Skin

    Energy Technology Data Exchange (ETDEWEB)

    Levitskaia, Tatiana G.; Bryan, Samuel A.; Creim, Jeffrey A.; Curry, Terry L.; Luders, Teresa; Thrall, Karla D.; Peterson, James M.

    2012-08-01

    In the event of an intentional or accidental release of ionizing radiation in a densely populated area, timely assessment and triage of the general population for the radiation exposure is critical. In particular, a significant number of the victims may sustain cutaneous radiation injury, which increases the mortality and worsens the overall prognosis of the victims suffered from combined thermal/mechanical and radiation trauma. Diagnosis of the cutaneous radiation injury is challenging, and established methods largely rely on visual manifestations, presence of the skin contamination, and a high degree of recall by the victim. Availability of a high throughput non-invasive in vivo biodosimetry tool for assessment of the radiation exposure of the skin is of particular importance for the timely diagnosis of the cutaneous injury. In the reported investigation, we have tested the potential of an optical reflectance spectroscopy for the evaluation of the radiation injury to the skin. This is technically attractive because optical spectroscopy relies on well-established and routinely used for various applications instrumentation, one example being pulse oximetry which uses selected wavelengths for the quantification of the blood oxygenation. Our method relies on a broad spectral region ranging from the locally absorbed, shallow-penetrating ultraviolet and visible (250 to 800 nm) to more deeply penetrating near-Infrared (800 – 1600 nm) light for the monitoring of multiple physiological changes in the skin upon irradiation. Chemometrics is a multivariate methodology that allows the information from entire spectral region to be used to generate predictive regression models. In this report we demonstrate that simple spectroscopic method, such as the optical reflectance spectroscopy, in combination with multivariate data analysis, offers the promise of rapid and non-invasive in vivo diagnosis and monitoring of the cutaneous radiation exposure, and is able accurately predict

  19. Multivariate Gradient Analysis for Evaluating and Visualizing a Learning System Platform for Computer Programming

    Directory of Open Access Journals (Sweden)

    Richard Mather

    2015-02-01

    Full Text Available This paper explores the application of canonical gradient analysis to evaluate and visualize student performance and acceptance of a learning system platform. The subject of evaluation is a first year BSc module for computer programming. This uses ‘Ceebot’, an animated and immersive game-like development environment. Multivariate ordination approaches are widely used in ecology to explore species distribution along environmental gradients. Environmental factors are represented here by three ‘assessment’ gradients; one for the overall module mark and two independent tests of programming knowledge and skill. Response data included Likert expressions for behavioral, acceptance and opinion traits. Behavioral characteristics (such as attendance, collaboration and independent study were regarded to be indicative of learning activity. Acceptance and opinion factors (such as perceived enjoyment and effectiveness of Ceebot were treated as expressions of motivation to engage with the learning environment. Ordination diagrams and summary statistics for canonical analyses suggested that logbook grades (the basis for module assessment and code understanding were weakly correlated. Thus strong module performance was not a reliable predictor of programming ability. The three assessment indices were correlated with behaviors of independent study and peer collaboration, but were only weakly associated with attendance. Results were useful for informing teaching practice and suggested: (1 realigning assessments to more fully capture code-level skills (important in the workplace; (2 re-evaluating attendance-based elements of module design; and (3 the overall merit of multivariate canonical gradient approaches for evaluating and visualizing the effectiveness of a learning system platform.

  20. Vertebral artery injury associated with blunt cervical spine trauma: a multivariate regression analysis.

    Science.gov (United States)

    Lebl, Darren R; Bono, Christopher M; Velmahos, George; Metkar, Umesh; Nguyen, Joseph; Harris, Mitchel B

    2013-07-15

    Retrospective analysis of prospective registry data. To determine the patient characteristics, risk factors, and fracture patterns associated with vertebral artery injury (VAI) in patients with blunt cervical spine injury. VAI associated with cervical spine trauma has the potential for catastrophical clinical sequelae. The patterns of cervical spine injury and patient characteristics associated with VAI remain to be determined. A retrospective review of prospectively collected data from the American College of Surgeons trauma registries at 3 level-1 trauma centers identified all patients with a cervical spine injury on multidetector computed tomographic scan during a 3-year period (January 1, 2007, to January 1, 2010). Fracture pattern and patient characteristics were recorded. Logistic multivariate regression analysis of independent predictors for VAI and subgroup analysis of neurological events related to VAI was performed. Twenty-one percent of 1204 patients with cervical injuries (n = 253) underwent screening for VAI by multidetector computed tomography angiogram. VAI was diagnosed in 17% (42 of 253), unilateral in 15% (38 of 253), and bilateral in 1.6% (4 of 253) and was associated with a lower Glasgow coma scale (P < 0.001), a higher injury severity score (P < 0.01), and a higher mortality (P < 0.001). VAI was associated with ankylosing spondylitis/diffuse idiopathic skeletal hyperosteosis (crude odds ratio [OR] = 8.04; 95% confidence interval [CI], 1.30-49.68; P = 0.034), and occipitocervical dissociation (P < 0.001) by univariate analysis and fracture displacement into the transverse foramen 1 mm or more (adjusted OR = 3.29; 95% CI, 1.15-9.41; P = 0.026), and basilar skull fracture (adjusted OR = 4.25; 95% CI, 1.25-14.47; P= 0.021), by multivariate regression model. Subgroup analyses of neurological events secondary to VAI occurred in 14% (6 of 42) and the stroke-related mortality rate was 4.8% (2 of 42). Neurological events were associated with male sex (P

  1. Analysis of multiple mass spectrometry images from different Phaseolus vulgaris samples by multivariate curve resolution.

    Science.gov (United States)

    Bedia, Carmen; Tauler, Romà; Jaumot, Joaquim

    2017-12-01

    A new procedure based on the simultaneous analysis of multiple mass spectrometry images using multivariate curve resolution is presented in this work. Advantages of the application of the proposed approach are shown for three cases of plant studies demonstrating its potential usefulness in metabolomics studies, particularly in lipidomics. In the first dataset, a three stage germination time course process of green bean seeds is presented. The second example is a dose-response study where the stem bases of a non-exposed plant are compared to those of plants exposed to increasing concentrations of the pesticide chlorpyrifos. Finally, the third study is the simultaneous analysis of several sequential transversal and longitudinal cuts of the same green bean plant stem segment. The analysis of these three examples required the comprehensive adaptation of different chemometric methodologies including data compression by selection of the regions of interest (ROI strategy), appropriate data normalization and baseline correction, all of them before MCR-ALS simultaneous image analysis of multiple samples and post processing of the achieved results. MCR-ALS resolved components provided spatial information about the changes in the spatial composition and distribution of the different lipids on the surface of the investigated samples. These results enabled the identification of single lipids and the clustering of those lipids that behaved similarly in the different images simultaneously analyzed. The proposed strategy for MSI analysis represents a step forward in the simultaneous analysis of multiple sets of images providing an improved recovery of both spatial and structural information in environmental and biomedical studies. Copyright © 2017 Elsevier B.V. All rights reserved.

  2. Multivariate Analysis of Hemicelluloses in Bleached Kraft Pulp Using Infrared Spectroscopy.

    Science.gov (United States)

    Chen, Zhiwen; Hu, Thomas Q; Jang, Ho Fan; Grant, Edward

    2016-12-01

    The hemicellulose composition of a pulp significantly affects its chemical and physical properties and thus represents an important process control variable. However, complicated steps of sample preparation make standard methods for the carbohydrate analysis of pulp samples, such as high performance liquid chromatography (HPLC), expensive and time-consuming. In contrast, pulp analysis by attenuated total internal reflection Fourier transform infrared spectroscopy (ATR FT-IR) requires little sample preparation. Here we show that ATR FT-IR with discrete wavelet transform (DWT) and standard normal variate (SNV) spectral preprocessing offers a convenient means for the qualitative and quantitative analysis of hemicelluloses in bleached kraft pulp and alkaline treated kraft pulp. The pulp samples investigated include bleached softwood kraft pulps, bleached hardwood kraft pulps, and their mixtures, as obtained from Canadian industry mills or blended in a lab, and bleached kraft pulp samples treated with 0-6% NaOH solutions. In the principal component analysis (PCA) of these spectra, we find the potential both to differentiate all pulps on the basis of hemicellulose compositions and to distinguish bleached hardwood pulps by species. Partial least squares (PLS) multivariate analysis gives a 0.442 wt% root mean square errors of prediction (RMSEP) for the prediction of xylan content and 0.233 wt% RMSEP for the prediction of mannan content. These data all support the idea that ATR FT-IR has a great potential to rapidly and accurately predict the content of xylan and mannan for bleached kraft pulps (softwood, hardwood, and their mixtures) in industry. However, the prediction of xylan and mannan concentrations presented a difficulty for pulp samples with modified cellulose crystalline structure. © The Author(s) 2016.

  3. Analysis of multi-species point patterns using multivariate log Gaussian Cox processes

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Guan, Yongtao; Jalilian, Abdollah

    Multivariate log Gaussian Cox processes are flexible models for multivariate point patterns. However, they have so far only been applied in bivariate cases. In this paper we move beyond the bivariate case in order to model multi-species point patterns of tree locations. In particular we address...

  4. Management of physical child abuse in South Africa: literature review and children's hospital data analysis.

    Science.gov (United States)

    Janssen, T L; van Dijk, M; Al Malki, I; van As, A B

    2013-11-01

    The reason for this review is the lack of data on the management of physical abused children in Africa. The primary goal of the first part is to outline the management of physical child abuse in (South) Africa and provide suggestions for other governments in Africa on which to base their management of physical child abuse, at both governmental and hospital management level. The main aim of the second part is to outline the extent of the problem as seen at the Red Cross Memorial Children's Hospital (RCH) in Cape Town. The National Library of Medicine's PubMed database was searched for articles specifically about the management of physical child abuse. Hospital data were analysed in two phases: one addressed various types of assault in order to assess the number of patients admitted to the trauma unit of RCH between 1991 and 2009, and the other to identify all children with suspected non-accidental injury (NAI) presenting to the trauma unit at RCH from January 2008 until December 2010. Information on physical abuse of children in Africa in the English scientific literature remains disappointing with only two articles focusing on its management. RCH data for the period 1991-2009 recorded a total number of 6415 children hospitalised with injuries following assault, who accounted for 4.2% of all trauma admissions. Types of abuse included assault with a blunt or sharp instrument, rape/sexual assault and human bite wounds. Over the last 2 decades, there has been a minor decline in the number of cases of severe abuse requiring admission; admissions for other injuries have remained stable. More detailed analysis of hospital data for 2008-2010, found that boys were far more commonly assaulted than girls (70.5% vs 29.5%). Physical abuse appeared to be the most common cause of abuse; 89.9% of all boys and 60.5% of all girls presented after physical abuse. In order to eradicate child abuse, awareness of it as to be promoted in the community at large. Because the types of child

  5. Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data

    Energy Technology Data Exchange (ETDEWEB)

    Mayer, B. P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Mew, D. A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); DeHope, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Spackman, P. E. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, A. M. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-09-24

    Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product - all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (GC-MS and LCMS/ MS-TOF) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

  6. Multivariate Analysis of Factors Affecting Presence and/or Agenesis of Third Molar Tooth

    Science.gov (United States)

    Alam, Mohammad Khursheed; Hamza, Muhammad Asyraf; Khafiz, Muhammad Aizuddin; Rahman, Shaifulizan Abdul; Shaari, Ramizu; Hassan, Akram

    2014-01-01

    To investigate the presence and/or agenesis of third molar (M3) tooth germs in orthodontics patients in Malaysian Malay and Chinese population and evaluate the relationship between presence and/or agenesis of M3 with different skeletal malocclusion patterns and sagittal maxillomandibular jaw dimensions. Pretreatment records of 300 orthodontic patients (140 males and 160 females, 219 Malaysian Malay and 81 Chinese, average age was 16.27±4.59) were used. Third-molar agenesis was calculated with respect to race, genders, number of missing teeth, jaws, skeletal malocclusion patterns and sagittal maxillomandibular jaw dimensions. The Pearson chi-square test and ANOVA was performed to determine potential differences. Associations between various factors and M3 presence/agenesis groups were assessed using logistic regression analysis. The percentages of subjects with 1 or more M3 agenesis were 30%, 33% and 31% in the Malaysian Malay, Chinese and total population, respectively. Overall prevalence of M3 agenesis in male and female was equal (P>0.05). The frequency of the agenesis of M3s is greater in maxilla as well in the right side (P>0.05). The prevalence of M3 agenesis in those with a Class III and Class II malocclusion was relatively higher in Malaysian Malay and Malaysian Chinese population respectively. Using stepwise regression analyses, significant associations were found between Mx (Pagenesis. This multivariate analysis suggested that Mx and ANB were significantly correlated with the M3 presence/agenesis. PMID:24967595

  7. Are Risk Attitudes and Individualism Predictors of Entrepreneurship? A Multivariate Analysis of Romanian Data

    Directory of Open Access Journals (Sweden)

    Adrian Hatos

    2015-02-01

    Full Text Available This paper emerges in the context of authors` previous investigations concerning the individual determinants of entrepreneurship. More specific, it focuses on elaborating and empirically testing hypotheses related to structural push and pull factors, e.g. age, gender, education, type of residence, and also to two kinds of psycho-attitudinal factors, i.e. risk aversion and individualist vs. etatist economic ideology. While the literature review gives credit to both hypotheses, especially for the influence of risk attitudes on starting a business, this paper focuses on the analysis of self-employment by using the block-model logistic regression on 2008 Romanian EVS (European Values Survey data. The results of multivariate analysis confirm the importance of risk aversion for entrepreneurship, as expected, but reject the hypothesis of a significant effect of individual’s option for individualist vs. collectivist (or statist continuum. It is important to notice that, contrary to expectations, two important push factors, i.e. age and education, do not correlate with self-employment and, on the other hand, risk attitude adds itself to the other effects without interacting with it. The theoretical consequences of the findings, the limits of the research and further developments are also discussed in the paper.

  8. Multivariate analysis in relation to breeding system in opium popy, Papaver somniferum L.

    Directory of Open Access Journals (Sweden)

    Singh S.P.

    2004-01-01

    Full Text Available The opium poppy (Papaver somniferum L. is an important medicinal plant of great pharmacopoel uses. 101 germplasm lines of different eco-geographical origin maintained at National Botanical Research Institute, Lucknow were evaluated to study the genetic divergence for seed yield/plant, opium yield/plant and its 8 component traits following multivariate and canonical analysis. The genotypes were grouped in 13 clusters and confirmed by canonical analysis. Sixty eight percent genotypes (69/101 were genetically close to each other and grouped in 6 clusters (II, III, IV, V, VIII, XII while apparent diversity was noticed for 32 percent (32/101 of the genotypes who diversed into rest 7 clusters (I, VI, VII, IX, X, XI, XIII. Inter cluster distance ranged from 47.28 to 234.55. The maximum was between IX and X followed by VII and IX (208.30 and IX and XI (205.53. The genotypes in cluster IX, X. XI, and XII had greater potential as breeding stock by virtue of high mean values of one or more component characters and high statistical distance among them. Based on findings of high cluster mean of component trait and inter-cluster distance among clusters, a breeding plan has been discussed.

  9. Assessment of the effect of silicon on antioxidant enzymes in cotton plants by multivariate analysis.

    Science.gov (United States)

    Alberto Moldes, Carlos; Fontão de Lima Filho, Oscar; Manuel Camiña, José; Gabriela Kiriachek, Soraya; Lia Molas, María; Mui Tsai, Siu

    2013-11-27

    Silicon has been extensively researched in relation to the response of plants to biotic and abiotic stress, as an element triggering defense mechanisms which activate the antioxidant system. Furthermore, in some species, adding silicon to unstressed plants modifies the activity of certain antioxidant enzymes participating in detoxifying processes. Thus, in this study, we analyzed the activity of antioxidant enzymes in leaves and roots of unstressed cotton plants fertilized with silicon (Si). Cotton plants were grown in hydroponic culture and added with increasing doses of potassium silicate; then, the enzymatic activity of catalase (CAT), guaiacol peroxidase (GPOX), ascorbate peroxidase (APX), and lipid peroxidation were determined. Using multivariate analysis, we found that silicon altered the activity of GPOX, APX, and CAT in roots and leaves of unstressed cotton plants, whereas lipid peroxidation was not affected. The analysis of these four variables in concert showed a clear differentiation among Si treatments. We observed that enzymatic activities in leaves and roots changed as silicon concentration increased, to stabilize at 100 and 200 mg Si L(-1) treatments in leaves and roots, respectively. Those alterations would allow a new biochemical status that could be partially responsible for the beneficial effects of silicon. This study might contribute to adjust the silicon application doses for optimal fertilization, preventing potential toxic effects and unnecessary cost.

  10. Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

    Science.gov (United States)

    Gaitan, S.; ten Veldhuis, J. A. E.

    2015-06-01

    Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.

  11. Market segmentation based on consumers’ susceptibility to reference group types of influence: Multivariance analysis

    Directory of Open Access Journals (Sweden)

    Mirela Mihić

    2006-12-01

    Full Text Available In this paper we begin with McGuire’s concept of influenceability, according to which individuals differ based on their susceptibility to social influence. The theoretical part explains three types of influence by reference groups and presents previous results relevant to the issue of this paper. The second part of the paper presents the methodology and research results. The aim of this research is to identify different types of reference group influence by using multivariance techniques, and determine whether they can serve as a basis for consumer market segmentation. The research was conducted on a sample of 250 respondents in the Split-Dalmatia County. Keeping in mind the issues and goals of the research, two hypotheses were set. Five factors – influence types were identified by using the factor analysis (normative influence, value-expressive or identificational influence, environment informative influence, salesperson’s informative influence, and comparison to environment and clothing conformity, and were then been used as basic segmentation variables. Cluster analysis singled out three segments: subject to identification or value-expressive influence, subject to information influence and non-subject to influence. To describe them better, demographic variables were employed, i.e. “relation-comparison and interaction with others” variables as well as personal indicators. The research results confirmed both starting hypotheses. The results attained suggest that consumers from particular segments require different communication strategies, based on which, each segment was supported by corresponding recommendations.

  12. Analysis of Surface Water Pollution in the Kinta River Using Multivariate Technique

    International Nuclear Information System (INIS)

    Hamza Ahmad Isiyaka; Hafizan Juahir

    2015-01-01

    This study aims to investigate the spatial variation in the characteristics of water quality monitoring sites, identify the most significant parameters and the major possible sources of pollution, and apportion the source category in the Kinta River. 31 parameters collected from eight monitoring sites for eight years (2006-2013) were employed. The eight monitoring stations were spatially grouped into three independent clusters in a dendrogram. A drastic reduction in the number of monitored parameters from 31 to eight and nine significant parameters (P<0.05) was achieved using the forward stepwise and backward stepwise discriminate analysis (DA). Principal component analysis (PCA) accounted for more than 76 % in the total variance and attributes the source of pollution to anthropogenic and natural processes. The source apportionment using a combined multiple linear regression and principal component scores indicates that 41 % of the total pollution load is from rock weathering and untreated waste water, 26 % from waste discharge, 24 % from surface runoff and 7 % from faecal waste. This study proposes a reduction in the number of monitoring stations and parameters for a cost effective and time management in the monitoring processes and multivariate technique can provide a simple representation of complex and dynamic water quality characteristics. (author)

  13. A Bayesian framework for cell-level protein network analysis for multivariate proteomics image data

    Science.gov (United States)

    Kovacheva, Violet N.; Sirinukunwattana, Korsuk; Rajpoot, Nasir M.

    2014-03-01

    The recent development of multivariate imaging techniques, such as the Toponome Imaging System (TIS), has facilitated the analysis of multiple co-localisation of proteins. This could hold the key to understanding complex phenomena such as protein-protein interaction in cancer. In this paper, we propose a Bayesian framework for cell level network analysis allowing the identification of several protein pairs having significantly higher co-expression levels in cancerous tissue samples when compared to normal colon tissue. It involves segmenting the DAPI-labeled image into cells and determining the cell phenotypes according to their protein-protein dependence profile. The cells are phenotyped using Gaussian Bayesian hierarchical clustering (GBHC) after feature selection is performed. The phenotypes are then analysed using Difference in Sums of Weighted cO-dependence Profiles (DiSWOP), which detects differences in the co-expression patterns of protein pairs. We demonstrate that the pairs highlighted by the proposed framework have high concordance with recent results using a different phenotyping method. This demonstrates that the results are independent of the clustering method used. In addition, the highlighted protein pairs are further analysed via protein interaction pathway databases and by considering the localization of high protein-protein dependence within individual samples. This suggests that the proposed approach could identify potentially functional protein complexes active in cancer progression and cell differentiation.

  14. imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel

    Science.gov (United States)

    Grapov, Dmitry; Newman, John W.

    2012-01-01

    Summary: Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Availability and implementation: Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010). Contact: John.Newman@ars.usda.gov Supplementary Information: Installation instructions, tutorials and users manual are available at http://sourceforge.net/projects/imdev/. PMID:22815358

  15. Identification of human sympathetic neurovascular control using multivariate wavelet decomposition analysis.

    Science.gov (United States)

    Saleem, Saqib; Teal, Paul D; Kleijn, W Bastiaan; Ainslie, Philip N; Tzeng, Yu-Chieh

    2016-09-01

    The dynamic regulation of cerebral blood flow (CBF) is thought to involve myogenic and chemoreflex mechanisms, but the extent to which the sympathetic nervous system also plays a role remains debated. Here we sought to identify the role of human sympathetic neurovascular control by examining cerebral pressure-flow relations using linear transfer function analysis and multivariate wavelet decomposition analysis that explicitly accounts for the confounding effects of dynamic end-tidal Pco2 (PetCO2 ) fluctuations. In 18 healthy participants randomly assigned to the α1-adrenergic blockade group (n = 9; oral Prazosin, 0.05 mg/kg) or the placebo group (n = 9), we recorded blood pressure, middle cerebral blood flow velocity, and breath-to-breath PetCO2 Analyses showed that the placebo administration did not alter wavelet phase synchronization index (PSI) values, whereas sympathetic blockade increased PSI for frequency components ≤0.03 Hz. Additionally, three-way interaction effects were found for PSI change scores, indicating that the treatment response varied as a function of frequency and whether PSI values were PetCO2 corrected. In contrast, sympathetic blockade did not affect any linear transfer function parameters. These data show that very-low-frequency CBF dynamics have a composite origin involving, not only nonlinear and nonstationary interactions between BP and PetCO2 , but also frequency-dependent interplay with the sympathetic nervous system. Copyright © 2016 the American Physiological Society.

  16. The potential of circulating extracellular small RNAs (smexRNA) in veterinary diagnostics-Identifying biomarker signatures by multivariate data analysis.

    Science.gov (United States)

    Melanie, Spornraft; Benedikt, Kirchner; Pfaffl, Michael W; Irmgard, Riedmaier

    2015-09-01

    Worldwide growth and performance-enhancing substances are used in cattle husbandry to increase productivity. In certain countries however e.g., in the EU, these practices are forbidden to prevent the consumers from potential health risks of substance residues in food. To maximize economic profit, 'black sheep' among farmers might circumvent the detection methods used in routine controls, which highlights the need for an innovative and reliable detection method. Transcriptomics is a promising new approach in the discovery of veterinary medicine biomarkers and also a missing puzzle piece, as up to date, metabolomics and proteomics are paramount. Due to increased stability and easy sampling, circulating extracellular small RNAs (smexRNAs) in bovine plasma were small RNA-sequenced and their potential to serve as biomarker candidates was evaluated using multivariate data analysis tools. After running the data evaluation pipeline, the proportion of miRNAs (microRNAs) and piRNAs (PIWI-interacting small non-coding RNAs) on the total sequenced reads was calculated. Additionally, top 10 signatures were compared which revealed that the readcount data sets were highly affected by the most abundant miRNA and piRNA profiles. To evaluate the discriminative power of multivariate data analyses to identify animals after veterinary drug application on the basis of smexRNAs, OPLS-DA was performed. In summary, the quality of miRNA models using all mapped reads for both treatment groups (animals treated with steroid hormones or the β-agonist clenbuterol) is predominant to those generated with combined data sets or piRNAs alone. Using multivariate projection methodologies like OPLS-DA have proven the best potential to generate discriminative miRNA models, supported by small RNA-Seq data. Based on the presented comparative OPLS-DA, miRNAs are the favorable smexRNA biomarker candidates in the research field of veterinary drug abuse.

  17. Investigation of rectal complication after RALS-therapy for uterine cervix cancer using multivariate analysis

    International Nuclear Information System (INIS)

    Inoue, Takehiro; Inoue, Toshihiko; Suzuki, Takaichiro

    1983-01-01

    Rectal injury is one of the major side effects after radiation therapy for carcinoma of the uterine cervix. According to our previous reports, the cases of rectal complication were mainly related to the measured rectal dose in half of patients, and the other causes were related to the following factors; such as diabetes mellitus, hemorrhagic tendency, syphilis and so on. Concerning to rectal complication, these factors were investigated by means of the discriminant analysis, one of the multivariate analyses, in this paper. Twenty-eight factors as to radiation dose, laboratory tests and physical condition of patients were analyzed. From August 1978 through January 1980, 52 cases of previously untreated carcinoma of the uterine cervix were treated using RALS, remotely controlled high dose rate intracavitary radiotherapy, at our department. The data from 49 out of 52 cases were available for the discriminant analysis. By m eans of this analysis, it was found that these factors, such as the dose of whole pelvic irradiation, Point A dose of RALS, measured rectal dose by RALS, WGC-Z and TPHA were important factors for occurence of rectal complication. According to the discriminant score, 46 out of 49 cases (94 %) could be correctly discriminated. There were two cases of false positive and one false negative. Form February 1980 through July 1980, 27 cases of previously untreated carcinoma of the uterine cervix were treated at our department. The obtained discriminant function was applied to these 27 cases, and 24 out of 27 cases (89 %) were correctly predicted. There were two cases of false positive, and one of false negative. Discriminant analysis is useful for the prediction of rectal complication after radiation therapy for carcinoma of the uterine cervix. (J.P.N.)

  18. Factors that impact the outcome of endoscopic correction of vesicoureteral reflux: a multivariate analysis.

    Science.gov (United States)

    Kajbafzadeh, Abdol-Mohammad; Tourchi, Ali; Aryan, Zahra

    2013-02-01

    To identify independent factors that may predict vesicoureteral reflux (VUR) resolution after endoscopic treatment using dextranomer/hyaluronic acid copolymer (Deflux) in children free of anatomical anomalies. A retrospective study was conducted in our pediatric referral center from 1998 to 2011 on children with primary VUR who underwent endoscopic injection of Deflux with or without concomitant autologous blood injection (called HABIT or HIT, respectively). Children with secondary VUR or incomplete records were excluded from the study. Potential factors were divided into three categories including preoperative, intraoperative and postoperative. Success was defined as no sign of VUR on postoperative voiding cystourethrogram. Univariate and multivariate logistic regression models were constructed to identify independent factors that may predict success. Odds ratio (OR) and 95 % confidence interval (95 % CI) for prediction of success were estimated for each factor. From 485 children received Deflux injection, a total of 372 with a mean age of 3.10 years (ranged from 6 months to 12 years) were included in the study and endoscopic management was successful in 322 (86.6 %) of them. Of the patients, 185 (49.7 %) underwent HIT and 187 (50.3 %) underwent HABIT technique. On univariate analysis, VUR grade from preoperative category (OR = 4.79, 95 % CI = 2.22-10.30, p = 0.000), operation technique (OR = 0.33, 95 % CI = 0.17-0.64, p = 0.001) and presence of mound on postoperative sonography (OR = 0.06, 95 % CI = 0.02-0.16, p = 0.000) were associated with success. On multivariate analysis, preoperative VUR grade (OR = 4.85, 95 % CI = 2.49-8.96, p = 0.000) and identification of mound on postoperative sonography (OR = 0.07, 95 % CI = 0.01-0.18, p = 0.000) remained as independent success predictors. Based on this study, successful VUR correction after the endoscopic injection of Deflux can be predicted with respect to preoperative VUR grade and presence of mound after operation.

  19. Association between spousal emotional abuse and reproductive outcomes of women in India: findings from cross-sectional analysis of the 2005-2006 National Family Health Survey.

    Science.gov (United States)

    Tiwari, Sucheta; Gray, Ron; Jenkinson, Crispin; Carson, Claire

    2018-03-09

    Spousal violence against women is a global public health problem. In India, approximately 40% of women report spousal violence. Like physical and sexual violence, emotional violence may be a determinant of women's health. This study explores the association between exposure to spousal emotional abuse and poor reproductive outcomes in Indian women. Data on 60,350 women, collected in the Third Indian National Family Health Survey were analysed to assess the impact of spousal emotional abuse on seven reproductive outcomes: age at first birth, number of children, terminated pregnancies, unwanted pregnancies, access to prenatal and skilled delivery care, and breastfeeding. Spousal emotional abuse was assessed using two overlapping constructs: emotional violence and controlling behaviour. Multivariable logistic regression was used for analysis. Spousal emotional violence and controlling behaviour was reported by 16 and 38% of the women, respectively. In unadjusted analyses, spousal emotional violence was associated with all adverse reproductive outcomes, except breastfeeding. Controlling for socio-demographic risk factors attenuated the association, and further adjustment for other forms of violence removed all significant associations. Spousal controlling behaviour was significantly associated with all outcomes, except breastfeeding. The effects remained statistically significant in multivariable regression. Women's experience of violence may be under-reported. When other forms of violence were adjusted for, emotional violence was not associated with adverse reproductive outcomes, whereas controlling behaviour remained associated with all but one adverse reproductive outcome. Therefore, spousal controlling behaviour requires further investigation as a determinant of reproductive health.

  20. Multivariate analysis on unilateral cleft lip and palate treatment outcome by EUROCRAN index: A retrospective study.

    Science.gov (United States)

    Yew, Ching Ching; Alam, Mohammad Khursheed; Rahman, Shaifulizan Abdul

    2016-10-01

    This study is to evaluate the dental arch relationship and palatal morphology of unilateral cleft lip and palate patients by using EUROCRAN index, and to assess the factors that affect them using multivariate statistical analysis. A total of one hundred and seven patients from age five to twelve years old with non-syndromic unilateral cleft lip and palate were included in the study. These patients have received cheiloplasty and one stage palatoplasty surgery but yet to receive alveolar bone grafting procedure. Five assessors trained in the use of the EUROCRAN index underwent calibration exercise and ranked the dental arch relationships and palatal morphology of the patients' study models. For intra-rater agreement, the examiners scored the models twice, with two weeks interval in between sessions. Variable factors of the patients were collected and they included gender, site, type and, family history of unilateral cleft lip and palate; absence of lateral incisor on cleft side, cheiloplasty and palatoplasty technique used. Associations between various factors and dental arch relationships were assessed using logistic regression analysis. Dental arch relationship among unilateral cleft lip and palate in local population had relatively worse scoring than other parts of the world. Crude logistics regression analysis did not demonstrate any significant associations among the various socio-demographic factors, cheiloplasty and palatoplasty techniques used with the dental arch relationship outcome. This study has limitations that might have affected the results, example: having multiple operators performing the surgeries and the inability to access the influence of underlying genetic predisposed cranio-facial variability. These may have substantial influence on the treatment outcome. The factors that can affect unilateral cleft lip and palate treatment outcome is multifactorial in nature and remained controversial in general. Copyright © 2016 Elsevier Ireland Ltd. All

  1. Multivariate Data Analysis on Tissue Diffuse Reflectance Spectra for Diagnostic Applications

    Science.gov (United States)

    Prince, Shanthi; Malarvizhi, S.

    2011-10-01

    Currently, clinical diagnosis of skin disease is generally accomplished by visual inspection under white light illumination. Aside from physical examination, the diagnosis of most of these lesions is invasive, time-consuming, and costly, often requiring surgical excision or biopsy followed by pathological investigations. Several approaches have been tried to improve dermatological diagnosis. Optical means of characterizing tissues have gained importance due to its noninvasive nature. Diffuse reflectance spectra are unique for normal and diseased tissues. Spectral characteristics of the tissue spectra provide useful information to identify various chromophores present in them, because different chromophores have different spectroscopic responses to electromagnetic waves of certain energy bands. An optical fiber spectrometer is set up for collection of diffuse reflectance data from different skin conditions. The method involves exposure of skin surface to white light produced by an incandescent source. These back scattered photons emerging from various layers of tissue are detected by spectrometer resulting in diffuse reflectance data. PCA can be considered as "the mother of all methods in multivariate data analysis". PCA is performed for data reduction and to obtain specific signature from the spectra to differentiate normal and the diseased skin. The proposed principal component analysis method is able to enhance the peculiar characteristics of the diseased diffuse reflectance spectra. Principal component analysis shows that the spectra from normal and diseased tissues are distinct from each other. PCA is recommended as an exploratory tool to uncover unknown trends in the data. A preliminary study, using PCA on the reparability of the spectra of normal and diseased tissue within each patient shows promise that this method is sensitive to changes in tissue brought upon by the onset of disease.

  2. Multivariate analysis of behavioural response experiments in humpback whales (Megaptera novaeangliae).

    Science.gov (United States)

    Dunlop, Rebecca A; Noad, Michael J; Cato, Douglas H; Kniest, Eric; Miller, Patrick J O; Smith, Joshua N; Stokes, M Dale

    2013-03-01

    The behavioural response study (BRS) is an experimental design used by field biologists to determine the function and/or behavioural effects of conspecific, heterospecific or anthropogenic stimuli. When carrying out these studies in marine mammals it is difficult to make basic observations and achieve sufficient samples sizes because of the high cost and logistical difficulties. Rarely are other factors such as social context or the physical environment considered in the analysis because of these difficulties. This paper presents results of a BRS carried out in humpback whales to test the response of groups to one recording of conspecific social sounds and an artificially generated tone stimulus. Experiments were carried out in September/October 2004 and 2008 during the humpback whale southward migration along the east coast of Australia. In total, 13 'tone' experiments, 15 'social sound' experiments (using one recording of social sounds) and three silent controls were carried out over two field seasons. The results (using a mixed model statistical analysis) suggested that humpback whales responded differently to the two stimuli, measured by changes in course travelled and dive behaviour. Although the response to 'tones' was consistent, in that groups moved offshore and surfaced more often (suggesting an aversion to the stimulus), the response to 'social sounds' was highly variable and dependent upon the composition of the social group. The change in course and dive behaviour in response to 'tones' was found to be related to proximity to the source, the received signal level and signal-to-noise ratio (SNR). This study demonstrates that the behavioural responses of marine mammals to acoustic stimuli are complex. In order to tease out such multifaceted interactions, the number of replicates and factors measured must be sufficient for multivariate analysis.

  3. Mixing of pharmaceutical solids. III: Multivariate statistical analysis of multicomponent mixing.

    Science.gov (United States)

    Chowhan, Z T; Chi, L H

    1981-03-01

    The multicomponent mixing for cohesive powders was evaluated by multivariate statistical methods. Tests were carried out for the sampling technique, completely random state and completely segregated state. Hotelling's statistics were not helpful in testing the practical sampling technique. Comparisons of the mixing indexes based on univariate and multivariate statistics indicated excellent consistency in optimizing mixing time. Neither mixing index approached unity because cohesive powders do not reach a completely random state. The multivariate mixing index was smaller than the univariate indexes largely due to interparticular forces among small cohesive particles.

  4. Substance abuse and psychosocial adaptation to physical disability: analysis of the literature and future directions.

    Science.gov (United States)

    Smedema, Susan Miller; Ebener, Deborah

    2010-01-01

    To analyse the current state of the literature with respect to substance abuse and psychosocial adjustment in persons with disabilities. The two primary databases containing the literature related to rehabilitation and disability issues (PsychINFO and MedLine) were searched to identify articles addressing the psychosocial impact of substance abuse in persons with disabilities. Eleven empirical articles specifically measuring the strength of the relationship between substance use and psychosocial outcomes in persons with disabilities were selected for analysis. Of the studies identified, five were related to spinal cord injury, three were related to traumatic brain injury, one was related to chronic back pain, one was related to HIV/AIDS, and one was related to persons with any type of disability. Each of the studies used different methodologies, measured substance abuse in different ways, and examined different psychosocial outcome variables. Examination of trends suggested that pre-injury substance abuse appears to be unrelated to acceptance of disability in persons with spinal cord injury and negatively associated with satisfaction in persons with traumatic brain injury. Recent substance abuse tends to have a detrimental effect on psychosocial outcomes across all disability groups. Future research, combined with appropriate pre-service and continuing education related to substance abuse and disability for rehabilitation practitioners, has the potential to lead to improved psychosocial outcomes in persons with disabilities.

  5. Retrospective analysis of necropsy reports suggestive of abuse in dogs and cats.

    Science.gov (United States)

    Almeida, Daniel C; Torres, Sheila M F; Wuenschmann, Arno

    2018-02-15

    OBJECTIVE To identify historical and necropsy findings suggestive of neglect or abuse of dogs and cats by retrospective analysis of necropsy reports from a veterinary diagnostic laboratory. DESIGN Retrospective cohort study. SAMPLE 119 necropsy reports of dogs and cats. PROCEDURES Necropsy reports from February 2001 to May 2012 were electronically searched to identify potential animal abuse or neglect cases. Cases were selected and categorized according to a previously proposed method for classification of animal abuse. Inclusion criteria included signs of neglect, nonaccidental injury (NAI; blunt-force or sharp-force trauma, gunshot, burns, drowning, asphyxiation, and suspicious intoxications), and sexual abuse. Poor preservation of cadavers, age abuse cases, determined on the basis of all necropsies performed in the study period, was 73 of 8,417 (0.87%) in dogs and 46 of 4,905 (0.94%) in cats. Neglect and NAI were commonly identified in cats; NAI was most commonly found in dogs. Gunshot and blunt-force trauma were the most common NAIs in dogs and cats, respectively. Pit bull-type dogs (29/73 [40%]) were overrepresented in several abuse categories. Most cats (29/46 [63%]) were domestic shorthair, but no breed association was found. Most (41/71 [58%]) affected animals with age data available were ≤ 2 years old. CONCLUSIONS AND CLINICAL RELEVANCE Approximately 1% of dogs and cats necropsied in the study period had signs suggestive of abuse. Medical findings alone are not necessarily indicative of abuse, but some findings can increase the index of suspicion.

  6. Breast tissue classification using x-ray scattering measurements and multivariate data analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ryan, Elaine A; Farquharson, Michael J [School of Allied Health Sciences, City University, Charterhouse Square, London EC1M 6PA (United Kingdom)

    2007-11-21

    This study utilized two radiation scatter interactions in order to differentiate malignant from non-malignant breast tissue. These two interactions were Compton scatter, used to measure the electron density of the tissues, and coherent scatter to obtain a measure of structure. Measurements of these parameters were made using a laboratory experimental set-up comprising an x-ray tube and HPGe detector. The breast tissue samples investigated comprise five different tissue classifications: adipose, malignancy, fibroadenoma, normal fibrous tissue and tissue that had undergone fibrocystic change. The coherent scatter spectra were analysed using a peak fitting routine, and a technique involving multivariate analysis was used to combine the peak fitted scatter profile spectra and the electron density values into a tissue classification model. The number of variables used in the model was refined by finding the sensitivity and specificity of each model and concentrating on differentiating between two tissues at a time. The best model that was formulated had a sensitivity of 54% and a specificity of 100%.

  7. Use of Selection Indices Based on Multivariate Analysis for Improving Grain Yield in Rice

    Directory of Open Access Journals (Sweden)

    Hossein SABOURI

    2008-12-01

    Full Text Available In order to study selection indices for improving rice grain yield, a cross was made between an Iranian traditional rice (Oryza sativa L. variety, Tarommahalli and an improved indica rice variety, Khazar in 2006. The traits of the parents (30 plants, F1 (30 plants and F2 generations (492 individuals were evaluated at the Rice Research Institute of Iran (RRII during 2007. Heritabilities of the number of panicles per plant, plant height, days to heading and panicle exsertion were greater than that of grain yield. The selection indices were developed using the results of multivariate analysis. To evaluate selection strategies to maximize grain yield, 14 selection indices were calculated based on two methods (optimum and base and combinations of 12 traits with various economic weights. Results of selection indices showed that selection for grain weight, number of panicles per plant and panicle length by using their phenotypic and/or genotypic direct effects (path coefficient as economic weights should serve as an effective selection criterion for using either the optimum or base index.

  8. Near and mid infrared spectroscopy and multivariate data analysis in studies of oxidation of edible oils.

    Science.gov (United States)

    Wójcicki, Krzysztof; Khmelinskii, Igor; Sikorski, Marek; Sikorska, Ewa

    2015-11-15

    Infrared spectroscopic techniques and chemometric methods were used to study oxidation of olive, sunflower and rapeseed oils. Accelerated oxidative degradation of oils at 60°C was monitored using peroxide values and FT-MIR ATR and FT-NIR transmittance spectroscopy. Principal component analysis (PCA) facilitated visualization and interpretation of spectral changes occurring during oxidation. Multivariate curve resolution (MCR) method found three spectral components in the NIR and MIR spectral matrix, corresponding to the oxidation products, and saturated and unsaturated structures. Good quantitative relation was found between peroxide value and contribution of oxidation products evaluated using MCR--based on NIR (R(2) = 0.890), MIR (R(2) = 0.707) and combined NIR and MIR (R(2) = 0.747) data. Calibration models for prediction peroxide value established using partial least squares (PLS) regression were characterized for MIR (R(2) = 0.701, RPD = 1.7), NIR (R(2) = 0.970, RPD = 5.3), and combined NIR and MIR data (R(2) = 0.954, RPD = 3.1). Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Multivariate classification of echellograms: a new perspective in Laser-Induced Breakdown Spectroscopy analysis.

    Science.gov (United States)

    Pořízka, Pavel; Klus, Jakub; Mašek, Jan; Rajnoha, Martin; Prochazka, David; Modlitbová, Pavlína; Novotný, Jan; Burget, Radim; Novotný, Karel; Kaiser, Jozef

    2017-06-09

    In this work, we proposed a new data acquisition approach that significantly improves the repetition rates of Laser-Induced Breakdown Spectroscopy (LIBS) experiments, where high-end echelle spectrometers and intensified detectors are commonly used. The moderate repetition rates of recent LIBS systems are caused by the utilization of intensified detectors and their slow full frame (i.e. echellogram) readout speeds with consequent necessity for echellogram-to-1D spectrum conversion (intensity vs. wavelength). Therefore, we investigated a new methodology where only the most effective pixels of the echellogram were selected and directly used in the LIBS experiments. Such data processing resulted in significant variable down-selection (more than four orders of magnitude). Samples of 50 sedimentary ores samples (distributed in 13 ore types) were analyzed by LIBS system and then classified by linear and non-linear Multivariate Data Analysis algorithms. The utilization of selected pixels from an echellogram yielded increased classification accuracy compared to the utilization of common 1D spectra.

  10. Correlations among behavior, performance and environment in broiler breeders using multivariate analysis

    Directory of Open Access Journals (Sweden)

    DF Pereira

    2007-12-01

    Full Text Available Animal welfare issues have received much attention not only to supply farmed animal requirements, but also to ethical and cultural public concerns. Daily collected information, as well as the systematic follow-up of production stages, produces important statistical data for production assessment and control, as well as for improvement possibilities. In this scenario, this research study analyzed behavioral, production, and environmental data using Main Component Multivariable Analysis, which correlated observed behaviors, recorded using video cameras and electronic identification, with performance parameters of female broiler breeders. The aim was to start building a system to support decision-making in broiler breeder housing, based on bird behavioral parameters. Birds were housed in an environmental chamber, with three pens with different controlled environments. Bird sensitivity to environmental conditions were indicated by their behaviors, stressing the importance of behavioral observations for modern poultry management. A strong association between performance parameters and the behavior "at the nest", suggesting that this behavior may be used to predict productivity. The behaviors of "ruffling feathers", "opening wings", "preening", and "at the drinker" were negatively correlated with environmental temperature, suggesting that the increase of in the frequency of these behaviors indicate improvement of thermal welfare.

  11. Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity.

    Science.gov (United States)

    Barigye, Stephen J; Freitas, Matheus P; Ausina, Priscila; Zancan, Patricia; Sola-Penna, Mauro; Castillo-Garit, Juan A

    2018-02-12

    We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.

  12. Multivariate Meta-Analysis of Brain-Mass Correlations in Eutherian Mammals

    Directory of Open Access Journals (Sweden)

    Charlene Steinhausen

    2016-09-01

    Full Text Available The general assumption that brain size differences are an adequate proxy for subtler differences in brain organization turned neurobiologists towards the question why some groups of mammals such as primates, elephants, and whales have such remarkably large brains. In this meta-analysis, an extensive sample of eutherian mammals (115 species distributed in 14 orders provided data about several different biological traits and measures of brain size such as absolute brain mass (AB, relative brain mass (RB; quotient from AB and body mass, and encephalization quotient (EQ. These data were analyzed by established multivariate statistics without taking specific phylogenetic information into account. Species with high AB tend to (1 feed on protein-rich nutrition, (2 have a long lifespan, (3 delay sexual maturity, and (4 have long and rare pregnancies with small litter sizes. Animals with high RB usually have (1 a short life span, (2 reach sexual maturity early, and (3 have short and frequent gestations. Moreover males of species with high RB also have few potential sexual partners. In contrast, animals with high EQs have (1 a high number of potential sexual partners, (2 delayed sexual maturity, and (3 rare gestations with small litter sizes. Based on these correlations, we conclude that Eutheria with either high AB or high EQ occupy high positions in the network of food chains (high trophic levels. Eutheria of low trophic levels can develop a high RB only if they have small body masses.

  13. Modeling Multi-Variate Gaussian Distributions and Analysis of Higgs Boson Couplings with the ATLAS Detector

    Science.gov (United States)

    Krohn, Olivia; Armbruster, Aaron; Gao, Yongsheng; Atlas Collaboration

    2017-01-01

    Software tools developed for the purpose of modeling CERN LHC pp collision data to aid in its interpretation are presented. Some measurements are not adequately described by a Gaussian distribution; thus an interpretation assuming Gaussian uncertainties will inevitably introduce bias, necessitating analytical tools to recreate and evaluate non-Gaussian features. One example is the measurements of Higgs boson production rates in different decay channels, and the interpretation of these measurements. The ratios of data to Standard Model expectations (μ) for five arbitrary signals were modeled by building five Poisson distributions with mixed signal contributions such that the measured values of μ are correlated. Algorithms were designed to recreate probability distribution functions of μ as multi-variate Gaussians, where the standard deviation (σ) and correlation coefficients (ρ) are parametrized. There was good success with modeling 1-D likelihood contours of μ, and the multi-dimensional distributions were well modeled within 1- σ but the model began to diverge after 2- σ due to unmerited assumptions in developing ρ. Future plans to improve the algorithms and develop a user-friendly analysis package will also be discussed. NSF International Research Experiences for Students

  14. Evaluation of herbicides photodegradation by photo-Fenton process using multivariate analysis

    Energy Technology Data Exchange (ETDEWEB)

    Paterlini, W.C.; Nogueira, R.F.P. [Inst. of Chemistry, Sao Paulo State Univ., R. Prof. Francisco Degni s/n, Araraquara, SP (Brazil)

    2003-07-01

    The photodegradation of herbicides in aqueous medium by photo-Fenton process using ferrioxalate complex (FeOx) as a source of Fe{sup 2+} was evaluated under blacklight irradiation. The commercial products of the herbicides tebuthiuron, 2,4-D and diuron were used. Multivariate analysis was used to evaluate the role of two variables in the photodegradation process, FeOx and hydrogen peroxide concentrations, and to define the concentration ranges that result in the most efficient photodegradation of the herbicides. The photodegradation of the herbicides was followed by monitoring the decrease of the original compounds concentration by HPLC, by the determination of remaining total organic carbon content (TOC), and by the chloride ion release. Under optimised conditions, 20 minutes irradiation was enough to remove 92.7% of TOC for 2,4 D and 89.5% for diuron. Complete dechlorination of these compounds was achieved after 10 minutes of irradiation. It was observed that the initial concentration of these compounds and tebuthiuron was reduced to less than 15% after only 1 minute of irradiation. (orig.)

  15. A multivariate analysis of biophysical parameters of tallgrass prairie among land management practices and years

    Science.gov (United States)

    Griffith, J.A.; Price, K.P.; Martinko, E.A.

    2001-01-01

    Six treatments of eastern Kansas tallgrass prairie - native prairie, hayed, mowed, grazed, burned and untreated - were studied to examine the biophysical effects of land management practices on grasslands. On each treatment, measurements of plant biomass, leaf area index, plant cover, leaf moisture and soil moisture were collected. In addition, measurements were taken of the Normalized Difference Vegetation Index (NDVI), which is derived from spectral reflectance measurements. Measurements were taken in mid-June, mid-July and late summer of 1990 and 1991. Multivariate analysis of variance was used to determine whether there were differences in the set of variables among treatments and years. Follow-up tests included univariate t-tests to determine which variables were contributing to any significant difference. Results showed a significant difference (p treatments in the composite of parameters during each of the months sampled. In most treatment types, there was a significant difference between years within each month. The univariate tests showed, however, that only some variables, primarily soil moisture, were contributing to this difference. We conclude that biomass and % plant cover show the best potential to serve as long-term indicators of grassland condition as they generally were sensitive to effects of different land management practices but not to yearly change in weather conditions. NDVI was insensitive to precipitation differences between years in July for most treatments, but was not in the native prairie. Choice of sampling time is important for these parameters to serve effectively as indicators.

  16. Multivariate logistic analysis of risk factors for stroke in Tilburg, The Netherlands.

    Science.gov (United States)

    Herman, B; Schmitz, P I; Leyten, A C; Van Luijk, J H; Frenken, C W; Op De Coul, A A; Schulte, B P

    1983-10-01

    By means of a case-control study conducted between October 1, 1978, and July 31, 1981, in Tilburg, The Netherlands, various characteristics and events, including personal data, health-related behavior, and medical history, were evaluated as risk factors for stroke. The study subjects included 132 stroke patients and 239 age- and sex-matched control patients interviewed at the two city hospitals. To assess joint effects and possible interactions, and to control for multiple confounding factors, a series of multivariate logistic models for matched data were studied. From this analysis, it appeared that hypertension, acute myocardial infarction, cardiac arrhythmias, transient cerebral ischemic attacks, obesity, physical activity during leisure time, education of head of household, and Rhesus factor were all significant stroke risk factors. These risk determinants demonstrated a multiplicative effect in general; however, the influence of some variables on stroke risk was not constant with age (hypertension, acute myocardial infarction, cardiac arrhythmias, obesity, and Rhesus factor) and sex (hypertension and education of head of household). The relationship of diabetes mellitus to stroke slightly decreased and became nonsignificant after adjustment for factors besides age and sex. Stroke risk was not associated with cigarette and alcohol use, family history of stroke and related disorders, marital status, and ABO blood typing.

  17. Safety and effectiveness of olanzapine in monotherapy: a multivariate analysis of a naturalistic study.

    Science.gov (United States)

    Ciudad, Antonio; Gutiérrez, Miguel; Cañas, Fernando; Gibert, Juan; Gascón, Josep; Carrasco, José-Luis; Bobes, Julio; Gómez, Juan-Carlos; Alvarez, Enrique

    2005-07-01

    This study investigated safety and effectiveness of olanzapine in monotherapy compared with conventional antipsychotics in treatment of acute inpatients with schizophrenia. This was a prospective, comparative, nonrandomized, open-label, multisite, observational study of Spanish inpatients with an acute episode of schizophrenia. Data included safety assessments with an extrapyramidal symptoms (EPS) questionnaire and the report of spontaneous adverse events, plus clinical assessments with the Brief Psychiatric Rating Scale (BPRS) and the Clinical Global Impressions-Severity of Illness (CGI-S). A multivariate methodology was used to more adequately determine which factors can influence safety and effectiveness of olanzapine in monotherapy. 339 patients treated with olanzapine in monotherapy (OGm) and 385 patients treated with conventional antipsychotics (CG) were included in the analysis. Treatment-emergent EPS were significantly higher in the CG (pOGm (p=0.005). Logistic regression analyses revealed that the only variable significantly correlated with treatment-emergent EPS and clinical response was treatment strategy, with patients in OGm having 1.5 times the probability of obtaining a clinical response and patients in CG having 5 times the risk of developing EPS. In this naturalistic study olanzapine in monotherapy was better-tolerated and at least as effective as conventional antipsychotics.

  18. Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates.

    Science.gov (United States)

    Kou, Peng Meng; Pallassana, Narayanan; Bowden, Rebeca; Cunningham, Barry; Joy, Abraham; Kohn, Joachim; Babensee, Julia E

    2012-02-01

    Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response toward the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R(prediction)(2) = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R(prediction)(2) = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection. Copyright © 2011 Elsevier Ltd. All rights reserved.

  19. Raman spectroscopy and multivariate analysis for the non invasive diagnosis of clinically inconclusive vulval lichen sclerosus.

    Science.gov (United States)

    Frost, Jonathan; Ludeman, Linmarie; Hillaby, Kathryn; Gornall, Robert; Lloyd, Gavin; Kendall, Catherine; Shore, Angela C; Stone, Nick

    2017-04-10

    Vulval lichen sclerosus (LS) is a common inflammatory condition associated with an increased risk of developing vulval carcinoma. Diagnosis is usually clinical although biopsy is necessary if the diagnosis is uncertain or if there is a failure to respond to adequate initial treatment. Raman spectroscopy has the potential to be applied in vivo for near real time objective non-invasive optical diagnosis, avoiding the need for invasive tissue biopsies. The aim of this study was to evaluate the diagnostic performance of Raman spectroscopy for differentiating LS from other vulval conditions in fresh vulval biopsies. Biopsies were analysed from 27 women with suspected LS in whom the attending gynaecologist could not establish the diagnosis on clinical presentation alone. Spectral variance was explored using principal component analysis and in conjunction with the histological diagnoses was used to develop and test a multivariate linear discriminant classification model. This model was validated with leave one sample out cross validation and the diagnostic performance of the technique assessed in comparison with the pathology gold standard. After cross validation the technique was able to correctly differentiate LS from other inflammatory vulval conditions with a sensitivity of 91% and specificity of 80%. This study demonstrates Raman spectroscopy has potential as a technique for in vivo non-invasive diagnosis of vulval skin conditions. Applied in the clinical setting this technique may reduce the need for invasive tissue biopsy. Further in vivo study is needed to assess the ability of Raman spectroscopy to diagnose other vulval conditions before clinical application.

  20. Automatic CTF correction for single particles based upon multivariate statistical analysis of individual power spectra.

    Science.gov (United States)

    Sander, B; Golas, M M; Stark, H

    2003-06-01

    Three-dimensional electron cryomicroscopy of randomly oriented single particles is a method that is suitable for the determination of three-dimensional structures of macromolecular complexes at molecular resolution. However, the electron-microscopical projection images are modulated by a contrast transfer function (CTF) that prevents the calculation of three-dimensional reconstructions of biological complexes at high resolution from uncorrected images. We describe here an automated method for the accurate determination and correction of the CTF parameters defocus, twofold astigmatism and amplitude-contrast proportion from single-particle images. At the same time, the method allows the frequency-dependent signal decrease (B factor) and the non-convoluted background signal to be estimated. The method involves the classification of the power spectra of single-particle images into groups with similar CTF parameters; this is done by multivariate statistical analysis (MSA) and hierarchically ascending classification (HAC). Averaging over several power spectra generates class averages with enhanced signal-to-noise ratios. The correct CTF parameters can be deduced from these class averages by applying an iterative correlation procedure with theoretical CTF functions; they are then used to correct the raw images. Furthermore, the method enables the tilt axis of the sample holder to be determined and allows the elimination of individual poor-quality images that show high drift or charging effects.

  1. A multivariate pattern analysis study of the HIV-related white matter anatomical structural connections alterations

    Science.gov (United States)

    Tang, Zhenchao; Liu, Zhenyu; Li, Ruili; Cui, Xinwei; Li, Hongjun; Dong, Enqing; Tian, Jie

    2017-03-01

    It's widely known that HIV infection would cause white matter integrity impairments. Nevertheless, it is still unclear that how the white matter anatomical structural connections are affected by HIV infection. In the current study, we employed a multivariate pattern analysis to explore the HIV-related white matter connections alterations. Forty antiretroviraltherapy- naïve HIV patients and thirty healthy controls were enrolled. Firstly, an Automatic Anatomical Label (AAL) atlas based white matter structural network, a 90 × 90 FA-weighted matrix, was constructed for each subject. Then, the white matter connections deprived from the structural network were entered into a lasso-logistic regression model to perform HIV-control group classification. Using leave one out cross validation, a classification accuracy (ACC) of 90% (P=0.002) and areas under the receiver operating characteristic curve (AUC) of 0.96 was obtained by the classification model. This result indicated that the white matter anatomical structural connections contributed greatly to HIV-control group classification, providing solid evidence that the white matter connections were affected by HIV infection. Specially, 11 white matter connections were selected in the classification model, mainly crossing the regions of frontal lobe, Cingulum, Hippocampus, and Thalamus, which were reported to be damaged in previous HIV studies. This might suggest that the white matter connections adjacent to the HIV-related impaired regions were prone to be damaged.

  2. Supervised multivariate analysis of sequence groups to identify specificity determining residues

    Directory of Open Access Journals (Sweden)

    Higgins Desmond G

    2007-04-01

    Full Text Available Abstract Background Proteins that evolve from a common ancestor can change functionality over time, and it is important to be able identify residues that cause this change. In this paper we show how a supervised multivariate statistical method, Between Group Analysis (BGA, can be used to identify these residues from families of proteins with different substrate specifities using multiple sequence alignments. Results We demonstrate the usefulness of this method on three different test cases. Two of these test cases, the Lactate/Malate dehydrogenase family and Nucleotidyl Cyclases, consist of two functional groups. The other family, Serine Proteases consists of three groups. BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids. Conclusion This overall combination of methods in this paper is powerful and flexible while being computationally very fast and simple. BGA is especially useful because it can be used to analyse any number of functional classes. In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.

  3. Quality assessment of pharmaceutical tablet samples using Fourier transform near infrared spectroscopy and multivariate analysis

    Science.gov (United States)

    Kandpal, Lalit Mohan; Tewari, Jagdish; Gopinathan, Nishanth; Stolee, Jessica; Strong, Rick; Boulas, Pierre; Cho, Byoung-Kwan

    2017-09-01

    Determination of the content uniformity, assessed by the amount of an active pharmaceutical ingredient (API), and hardness of pharmaceutical materials is important for achieving a high-quality formulation and to ensure the intended therapeutic effects of the end-product. In this work, Fourier transform near infrared (FT-NIR) spectroscopy was used to determine the content uniformity and hardness of a pharmaceutical mini-tablet and standard tablet samples. Tablet samples were scanned using an FT-NIR instrument and tablet spectra were collected at wavelengths of 1000-2500 nm. Furthermore, multivariate analysis was applied to extract the relationship between the FT-NIR spectra and the measured parameters. The results of FT-NIR spectroscopy for API and hardness prediction were as precise as the reference high-performance liquid chromatography and mechanical hardness tests. For the prediction of mini-tablet API content, the highest coefficient of determination for the prediction (R2p) was found to be 0.99 with a standard error of prediction (SEP) of 0.72 mg. Moreover, the standard tablet hardness measurement had a R2p value of 0.91 with an SEP of 0.25 kg. These results suggest that FT-NIR spectroscopy is an alternative and accurate nondestructive measurement tool for the detection of the chemical and physical properties of pharmaceutical samples.

  4. Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates

    Science.gov (United States)

    Kou, Peng Meng; Pallassana, Narayanan; Bowden, Rebeca; Cunningham, Barry; Joy, Abraham; Kohn, Joachim; Babensee, Julia E.

    2011-01-01

    Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response towards the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R2prediction = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R2prediction = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection. PMID:22136715

  5. Perirenal fat invasion on renal cell carcinoma: evaluation with multidetector computed tomography-multivariate analysis.

    Science.gov (United States)

    Tsili, Athina C; Goussia, Anna C; Baltogiannis, Dimitrios; Astrakas, Loukas; Sofikitis, Nikolaos; Malamou-Mitsi, Vasiliki; Argyropoulou, Maria I

    2013-01-01

    The objective of this study was to assess the accuracy of multidetector computed tomography (CT) in diagnosing perinephric (PN) and/or renal sinus (RS) fat invasion in patients with renal cell carcinoma (RCC), with reference to the CT findings predictive for the diagnosis of invasion. This was a retrospective study of 48 RCCs. Examinations were performed on a 16-row CT scanner, including unenhanced and 3-phase contrast-enhanced CT scanning. Unenhanced transverse images and multiplanar reformations of each contrast-enhanced CT phase were evaluated. The predictive value of CT findings in diagnosing PN and/or RS fat invasion was determined using multivariate logistic regression analysis. The CT findings that were most predictive for the diagnosis of PN fat invasion were the presence of contrast-enhancing nodules in the PN fat and tumoral margins. Invasion of the pelvicaliceal system was the most significant predictor in the diagnosis of RS fat invasion. Multidetector CT provides satisfactory results in detecting PN and/or RS fat invasion in RCC.

  6. Swallowing disturbances in Parkinson's disease: a multivariate analysis of contributing factors.

    Science.gov (United States)

    Cereda, Emanuele; Cilia, Roberto; Klersy, Catherine; Canesi, Margherita; Zecchinelli, Anna Lena; Mariani, Claudio Bruno; Tesei, Silvana; Sacilotto, Giorgio; Meucci, Nicoletta; Zini, Michela; Isaias, Ioannis Ugo; Cassani, Erica; Goldwurm, Stefano; Barichella, Michela; Pezzoli, Gianni

    2014-12-01

    Swallowing disturbances are an important issue in Parkinson's disease (PD) as several studies have shown that they are associated with increased risk of aspiration pneumonia and mortality. Information about factors related to swallowing disturbances, such as disease duration, age at assessment and concomitant dementia, is limited and would be useful for their management. All consecutive PD out-patients evaluated at a movement disorders clinic over a 7-year period (2007-2014), were included in the present retrospective study. Presence of symptomatic swallowing disturbances was assessed using the specific item of the Non Motor Symptom Questionnaire. In the whole PD population (N = 6462), prevalence of symptomatic swallowing disturbances was 11.7% (95%CI, 10.9-12.5). Multivariable logistic regression analysis (adjusted for education) disclosed a significant interaction between disease duration and gender (P = 0.009). In both gender strata, swallowing disturbances were significantly associated with longer disease duration and dementia (P disease duration (P disease duration (P disease duration and dementia all seem to contribute to the occurrence of swallowing disturbances independently. However, the role played by these factors in sub-groups of patients stratified by gender and concomitant dementia suggests that swallowing disturbances are likely related to different neuro-degenerative patterns within the brain. The underlying mechanisms deserve further investigation. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Current role and future perspectives of multivariate (chemometric) methods in NMR spectroscopic analysis of pharmaceutical products.

    Science.gov (United States)

    Monakhova, Yulia B; Holzgrabe, Ulrike; Diehl, Bernd W K

    2018-01-05

    Nuclear magnetic resonance (NMR) is a fast and accurate analytical method. Associated with chemometrics, it gradually becomes more important tool for the pharmaceutical industry. In this review studies dealing with the applications of multivariate analysis to NMR spectroscopic profiles were grouped and discussed according to the analytical problem solved. The following topics were covered: authenticity of medicines according to variety, seasonal and geographical differences of herbal plants; quantitative prediction of pharmacologically relevant parameters; production and batches approval; investigation of drug structure modifications; site-specific natural isotope fractionation (SNIF-NMR) fingerprinting for origin and manufacturer tracking and others. Special focus was put on the heparin authenticity by using 1D and 2D NMR measurements. Finally, further research directions have been outlined. Our review has shown that chemometrics plays an important role for the quality control and authenticity of pharmaceutical products and its role will definitely increase in the future. The discussed approaches are recommended to be implemented during development and production process of pharmaceuticals or in quality control laboratories. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Effect of training location and time period on racehorse performance in New Zealand. 2. Multivariable analysis.

    Science.gov (United States)

    Perkins, N R; Reid, S W J; Morris, R S

    2004-10-01

    To investigate training location (horses trained in Matamata vs those trained at all other venues in New Zealand), and time period (1996-1997 and 1998-1999), while controlling for other horse- and race- or trial-related factors, as a means of assessing the possible impact of construction of a new training surface at the Matamata Racing Club on indirect measures of racehorse performance (number of starts, and failure to race within 6 months of any start). Multivariable logistic regression and poisson analysis were used to analyse data derived using a retrospective cohort approach. Multivariable logistic regression was also used to analyse a case-control study. All data were derived from New Zealand Thoroughbred Racing (NZTR), records of race and trial results for racehorses trained in Matamata and other venues in New Zealand, covering two 19-month time periods (1996- 1997 and 1998-1999). Outcome variables included whether a horse started again in the 6 months following any start that occurred in the first 13 months of either time period, and a count of the total starts for every horse. Factors associated with increased risk of a start being followed by a 6-month no-race period included training location other than Matamata in comparison to horses trained in Matamata in the 1996-1997 time period, increasing age, 1998- 1999 over 1996-1997, starting in a trial rather than a race, placing fourth or worse in a start, softer track conditions, summer vs autumn, increasing cumulative exercise intensity in the 60 days prior to a start, and increasing race distance. Factors associated with an increase in the total number of starts included horses trained at Matamata in 1996-1997 compared with other time period-location combinations, younger age of horses at the time of a start, longer race distance, and an increasing proportion of starts in stakes races. Official race and trial results data provided a valuable resource for epidemiological studies of factors influencing

  9. Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flow.

    Science.gov (United States)

    Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Marwan, Norbert; Kurths, Jürgen

    2013-09-01

    Characterizing complex patterns arising from horizontal oil-water two-phase flows is a contemporary and challenging problem of paramount importance. We design a new multisector conductance sensor and systematically carry out horizontal oil-water two-phase flow experiments for measuring multivariate signals of different flow patterns. We then infer multivariate recurrence networks from these experimental data and investigate local cross-network properties for each constructed network. Our results demonstrate that a cross-clustering coefficient from a multivariate recurrence network is very sensitive to transitions among different flow patterns and recovers quantitative insights into the flow behavior underlying horizontal oil-water flows. These properties render multivariate recurrence networks particularly powerful for investigating a horizontal oil-water two-phase flow system and its complex interacting components from a network perspective.

  10. Correlation analysis of energy indicators for sustainable development using multivariate statistical techniques

    Energy Technology Data Exchange (ETDEWEB)

    Carneiro, Alvaro Luiz Guimaraes [Instituto de Pesquisas Energeticas e Nucleares (IPEN-CNEN/SP), Sao Paulo, SP (Brazil)], E-mail: carneiro@ipen.br; Santos, Francisco Carlos Barbosa dos [Fundacao Instituto de Pesquisas Economicas (FIPE/USP), Sao Paulo, SP (Brazil)], E-mail: fcarlos@fipe.org.br

    2007-07-01

    Energy is an essential input for social development and economic growth. The production and use of energy cause environmental degradation at all levels, being local, regional and global such as, combustion of fossil fuels causing air pollution; hydropower often causes environmental damage due to the submergence of large areas of land; and global climate change associated with the increasing concentration of greenhouse gases in the atmosphere. As mentioned in chapter 9 of Agenda 21, the Energy is essential to economic and social development and improved quality of life. Much of the world's energy, however, is currently produced and consumed in ways that could not be sustained if technologies were remain constant and if overall quantities were to increase substantially. All energy sources will need to be used in ways that respect the atmosphere, human health, and the environment as a whole. The energy in the context of sustainable development needs a set of quantifiable parameters, called indicators, to measure and monitor important changes and significant progress towards the achievement of the objectives of sustainable development policies. The indicators are divided into four dimensions: social, economic, environmental and institutional. This paper shows a methodology of analysis using Multivariate Statistical Technique that provide the ability to analyse complex sets of data. The main goal of this study is to explore the correlation analysis among the indicators. The data used on this research work, is an excerpt of IBGE (Instituto Brasileiro de Geografia e Estatistica) data census. The core indicators used in this study follows The IAEA (International Atomic Energy Agency) framework: Energy Indicators for Sustainable Development. (author)

  11. Correlation analysis of energy indicators for sustainable development using multivariate statistical techniques

    International Nuclear Information System (INIS)

    Carneiro, Alvaro Luiz Guimaraes; Santos, Francisco Carlos Barbosa dos

    2007-01-01

    Energy is an essential input for social development and economic growth. The production and use of energy cause environmental degradation at all levels, being local, regional and global such as, combustion of fossil fuels causing air pollution; hydropower often causes environmental damage due to the submergence of large areas of land; and global climate change associated with the increasing concentration of greenhouse gases in the atmosphere. As mentioned in chapter 9 of Agenda 21, the Energy is essential to economic and social development and improved quality of life. Much of the world's energy, however, is currently produced and consumed in ways that could not be sustained if technologies were remain constant and if overall quantities were to increase substantially. All energy sources will need to be used in ways that respect the atmosphere, human health, and the environment as a whole. The energy in the context of sustainable development needs a set of quantifiable parameters, called indicators, to measure and monitor important changes and significant progress towards the achievement of the objectives of sustainable development policies. The indicators are divided into four dimensions: social, economic, environmental and institutional. This paper shows a methodology of analysis using Multivariate Statistical Technique that provide the ability to analyse complex sets of data. The main goal of this study is to explore the correlation analysis among the indicators. The data used on this research work, is an excerpt of IBGE (Instituto Brasileiro de Geografia e Estatistica) data census. The core indicators used in this study follows The IAEA (International Atomic Energy Agency) framework: Energy Indicators for Sustainable Development. (author)

  12. Risk factors for pedicled flap necrosis in hand soft tissue reconstruction: a multivariate logistic regression analysis.

    Science.gov (United States)

    Gong, Xu; Cui, Jianli; Jiang, Ziping; Lu, Laijin; Li, Xiucun

    2018-03-01

    Few clinical retrospective studies have reported the risk factors of pedicled flap necrosis in hand soft tissue reconstruction. The aim of this study was to identify non-technical risk factors associated with pedicled flap perioperative necrosis in hand soft tissue reconstruction via a multivariate logistic regression analysis. For patients with hand soft tissue reconstruction, we carefully reviewed hospital records and identified 163 patients who met the inclusion criteria. The characteristics of these patients, flap transfer procedures and postoperative complications were recorded. Eleven predictors were identified. The correlations between pedicled flap necrosis and risk factors were analysed using a logistic regression model. Of 163 skin flaps, 125 flaps survived completely without any complications. The pedicled flap necrosis rate in hands was 11.04%, which included partial flap necrosis (7.36%) and total flap necrosis (3.68%). Soft tissue defects in fingers were noted in 68.10% of all cases. The logistic regression analysis indicated that the soft tissue defect site (P = 0.046, odds ratio (OR) = 0.079, confidence interval (CI) (0.006, 0.959)), flap size (P = 0.020, OR = 1.024, CI (1.004, 1.045)) and postoperative wound infection (P < 0.001, OR = 17.407, CI (3.821, 79.303)) were statistically significant risk factors for pedicled flap necrosis of the hand. Soft tissue defect site, flap size and postoperative wound infection were risk factors associated with pedicled flap necrosis in hand soft tissue defect reconstruction. © 2017 Royal Australasian College of Surgeons.

  13. Multivariate analysis of factors affecting presence and/or agenesis of third molar tooth.

    Directory of Open Access Journals (Sweden)

    Mohammad Khursheed Alam

    Full Text Available To investigate the presence and/or agenesis of third molar (M3 tooth germs in orthodontics patients in Malaysian Malay and Chinese population and evaluate the relationship between presence and/or agenesis of M3 with different skeletal malocclusion patterns and sagittal maxillomandibular jaw dimensions. Pretreatment records of 300 orthodontic patients (140 males and 160 females, 219 Malaysian Malay and 81 Chinese, average age was 16.27±4.59 were used. Third-molar agenesis was calculated with respect to race, genders, number of missing teeth, jaws, skeletal malocclusion patterns and sagittal maxillomandibular jaw dimensions. The Pearson chi-square test and ANOVA was performed to determine potential differences. Associations between various factors and M3 presence/agenesis groups were assessed using logistic regression analysis. The percentages of subjects with 1 or more M3 agenesis were 30%, 33% and 31% in the Malaysian Malay, Chinese and total population, respectively. Overall prevalence of M3 agenesis in male and female was equal (P>0.05. The frequency of the agenesis of M3s is greater in maxilla as well in the right side (P>0.05. The prevalence of M3 agenesis in those with a Class III and Class II malocclusion was relatively higher in Malaysian Malay and Malaysian Chinese population respectively. Using stepwise regression analyses, significant associations were found between Mx (P<0.05 and ANB (P<0.05 and M3 agenesis. This multivariate analysis suggested that Mx and ANB were significantly correlated with the M3 presence/agenesis.

  14. Multivariate analysis in the frequency mastery applied to the Laguna Verde Central

    International Nuclear Information System (INIS)

    Castillo D, R.; Ortiz V, J.; Calleros M, G.

    2006-01-01

    The noise analysis is an auxiliary tool in the detection of abnormal operation conditions of equipment, instruments or systems that affect to the dynamic behavior of the reactor. The spectral density of normalized power has usually been used (NPSD, by its initials in English), to watch over the behavior of some components of the reactor, for example, the jet pumps, the recirculation pumps, valves of flow control in the recirculation knots, etc. The behavior change is determined by individual analysis of the NPSD of the signals of the components in study. An alternative analysis that can allow to obtain major information on the component under surveillance is the multivariate autoregressive analysis (MAR, by its initials in English), which allows to know the relationship that exists among diverse signals of the reactor systems, in the time domain. In the space of the frequency, the relative contribution of power (RPC for their initials in English) it quantifies the influence of the variables of the systems on a variable of interest. The RPC allows, therefore that for a peak shown in the NPSD of a variable, it can be determine the influence from other variables to that frequency of interest. This facilitates, in principle, the pursuit of the important physical parameters during an event, and to study their interrelation. In this work, by way of example of the application of the RPC, two events happened in the Laguna Verde Central are analyzed: the rods blockade alarms by high scale in the monitors of average power, in which it was presents a power peak of 12% of width peak to peak, and the power oscillations event. The main obtained result of the analysis of the control rods blockade alarm event was that it was detected that the power peak observed in the signals of the average power monitors was caused by the movement of the valve of flow control of recirculation of the knot B. In the other oscillation event the results its show the mechanism of the oscillation of

  15. Multivariate data analysis to characterize gas chromatography columns for dioxin analysis.

    Science.gov (United States)

    Do, Lan; Geladi, Paul; Haglund, Peter

    2014-06-20

    Principal component analysis (PCA) was applied for evaluating the selectivity of 22 GC columns for which complete retention data were available for the 136 tetra- to octa-chlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). Because the hepta- and octa-homologues are easy to separate the PCA was focused on the 128 tetra- to hexa-CDD/Fs. The analysis showed that 21 of the 22 GC columns could be subdivided into four groups with different selectivity. Group I consists of columns with non-polar thermally stable phases (Restek 5Sil MS and Dioxin 2, SGE BPX-DXN, Supelco Equity-5, and Agilent DB-1, DB-5, DB-5ms, VF-5ms, VF-Xms and DB-XLB). Group II includes ionic liquid columns (Supelco SLB-IL61, SLB-IL111 and SLB-IL76) with very high polarity. Group III includes columns with high-percentage phenyl and cyanopropyl phases (Agilent DB-17 and DB-225, Quadrex CPS-1, Supelco SP-2331, and Agilent CP-Sil 88), and Group IV columns with shape selectivity (Dionex SB-Smectic and Restek LC-50, Supelco βDEXcst, Agilent VF-Xms and DB-XLB). Thus, two columns appeared in both Group I and IV (Agilent VF-Xms and DB-XLB). The selectivity of the other column, Agilent DB-210, differs from those of these four groups. Partial least squares (PLS) regression was used to correlate the retention times of the tetra- to hexa-CDD/Fs on the 22 stationary phases with a set of physicochemical and structural descriptors to identify parameters that significantly influence the solute-stationary phase interactions. The most influential physicochemical parameters for the interaction were associated with molecular size (as reflects in the total energy, electron energy, core-core repulsion and standard entropy), solubility (aqueous solubility and n-octanol/water partition coefficient), charge distribution (molecular polarizability and dipolar moment), and reactivity (relative Gibbs free energy); and the most influential structural descriptors were related to these parameters, in particular, size and

  16. Multivariate Analysis and Modeling of Sediment Pollution Using Neural Network Models and Geostatistics

    Science.gov (United States)

    Golay, Jean; Kanevski, Mikhaïl

    2013-04-01

    The present research deals with the exploration and modeling of a complex dataset of 200 measurement points of sediment pollution by heavy metals in Lake Geneva. The fundamental idea was to use multivariate Artificial Neural Networks (ANN) along with geostatistical models and tools in order to improve the accuracy and the interpretability of data modeling. The results obtained with ANN were compared to those of traditional geostatistical algorithms like ordinary (co)kriging and (co)kriging with an external drift. Exploratory data analysis highlighted a great variety of relationships (i.e. linear, non-linear, independence) between the 11 variables of the dataset (i.e. Cadmium, Mercury, Zinc, Copper, Titanium, Chromium, Vanadium and Nickel as well as the spatial coordinates of the measurement points and their depth). Then, exploratory spatial data analysis (i.e. anisotropic variography, local spatial correlations and moving window statistics) was carried out. It was shown that the different phenomena to be modeled were characterized by high spatial anisotropies, complex spatial correlation structures and heteroscedasticity. A feature selection procedure based on General Regression Neural Networks (GRNN) was also applied to create subsets of variables enabling to improve the predictions during the modeling phase. The basic modeling was conducted using a Multilayer Perceptron (MLP) which is a workhorse of ANN. MLP models are robust and highly flexible tools which can incorporate in a nonlinear manner different kind of high-dimensional information. In the present research, the input layer was made of either two (spatial coordinates) or three neurons (when depth as auxiliary information could possibly capture an underlying trend) and the output layer was composed of one (univariate MLP) to eight neurons corresponding to the heavy metals of the dataset (multivariate MLP). MLP models with three input neurons can be referred to as Artificial Neural Networks with EXternal

  17. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

    Science.gov (United States)

    Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

    2017-12-01

    Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis

  18. Multivariable analysis of a failure event of pressure regulator in a BWR; Analisis multivariable de un evento de falla del regulador de presion en un BWR

    Energy Technology Data Exchange (ETDEWEB)

    Castillo D, R.; Ortiz V, J. [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico); Calleros M, G. [Comision Federal de Electricidad, Central Nucleoelectrica Laguna Verde, Carretera Cardel-Nautla, Km. 43.5, Veracruz (Mexico)], e-mail: rogelio.castillo@inin.gob.mx

    2009-10-15

    The boiling water reactors can experiment three types of instabilities: one caused by the controllers failure of plant, another renowned instability by reactivity and the last knew as thermal hydraulics instability. An event of pressure regulator failure of electro-hydraulic control of Unit 1 of nuclear power plant of Laguna Verde was analyzed, which caused power oscillations that were increasing their magnitude in the time course. The event has been analyzed using the Fourier transformation in short time for time-frequency analysis and for the frequency domain be employment the power spectral density. Both techniques reported a resonance to oscillation frequency of 0.055 Hz in the power spectrum, this frequency is of observed order of magnitude when fail the reactor control systems. However, these analysis did not allow to study the interrelation of event signals. Of the previous studies, were obtained power spectral densities containing picks and valleys related with the dynamic behaviour of reactor, which includes the control systems performance. For a pick or present valley to a specific frequency in the power spectrum for one of previous variables, can determine the influence of other variables on the pick or valley by relative contribution of power. This method was established in a developed program of name Noise, which uses a multivariable autoregressive model to obtain the autoregressive coefficients, and starting from them the relative contribution of power is determined. Basically two important results were obtained, the first is related with the influence of feed water flow on the other variables to the frequency of 0.055 Hz, the second is related with the instability by reactivity and confirms that this way was not excited during the event. (Author)

  19. Multivariate analysis in the frequency mastery applied to the Laguna Verde Central; Analisis multivariable en el dominio de la frecuencia aplicado a la Central Laguna Verde

    Energy Technology Data Exchange (ETDEWEB)

    Castillo D, R.; Ortiz V, J. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico); Calleros M, G. [CFE, Central Nucleoelectrica de Laguna Verde, carretera Nautla-Cardel Km. 42.5, Alto Lucero, Veracruz (Mexico)]. e-mail: rcd@nuclear.inin.mx

    2006-07-01

    The noise analysis is an auxiliary tool in the detection of abnormal operation conditions of equipment, instruments or systems that affect to the dynamic behavior of the reactor. The spectral density of normalized power has usually been used (NPSD, by its initials in English), to watch over the behavior of some components of the reactor, for example, the jet pumps, the recirculation pumps, valves of flow control in the recirculation knots, etc. The behavior change is determined by individual analysis of the NPSD of the signals of the components in study. An alternative analysis that can allow to obtain major information on the component under surveillance is the multivariate autoregressive analysis (MAR, by its initials in English), which allows to know the relationship that exists among diverse signals of the reactor systems, in the time domain. In the space of the frequency, the relative contribution of power (RPC for their initials in English) it quantifies the influence of the variables of the systems on a variable of interest. The RPC allows, therefore that for a peak shown in the NPSD of a variable, it can be determine the influence from other variables to that frequency of interest. This facilitates, in principle, the pursuit of the important physical parameters during an event, and to study their interrelation. In this work, by way of example of the application of the RPC, two events happened in the Laguna Verde Central are analyzed: the rods blockade alarms by high scale in the monitors of average power, in which it was presents a power peak of 12% of width peak to peak, and the power oscillations event. The main obtained result of the analysis of the control rods blockade alarm event was that it was detected that the power peak observed in the signals of the average power monitors was caused by the movement of the valve of flow control of recirculation of the knot B. In the other oscillation event the results its show the mechanism of the oscillation of

  20. Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era.

    Science.gov (United States)

    Ferreira, Ana P; Tobyn, Mike

    2015-01-01

    In the pharmaceutical industry, chemometrics is rapidly establishing itself as a tool that can be used at every step of product development and beyond: from early development to commercialization. This set of multivariate analysis methods allows the extraction of information contained in large, complex data sets thus contributing to increase product and process understanding which is at the core of the Food and Drug Administration's Process Analytical Tools (PAT) Guidance for Industry and the International Conference on Harmonisation's Pharmaceutical Development guideline (Q8). This review is aimed at providing pharmaceutical industry professionals an introduction to multivariate analysis and how it is being adopted and implemented by companies in the transition from "quality-by-testing" to "quality-by-design". It starts with an introduction to multivariate analysis and the two methods most commonly used: principal component analysis and partial least squares regression, their advantages, common pitfalls and requirements for their effective use. That is followed with an overview of the diverse areas of application of multivariate analysis in the pharmaceutical industry: from the development of real-time analytical methods to definition of the design space and control strategy, from formulation optimization during development to the application of quality-by-design principles to improve manufacture of existing commercial products.

  1. Search for the Higgs Boson in the H→ ZZ(*)→4μ Channel in CMS Using a Multivariate Analysis

    International Nuclear Information System (INIS)

    Alonso Diaz, A.

    2007-01-01

    This note presents a Higgs boson search analysis in the CMS detector of the LHC accelerator (CERN, Geneva, Switzerland) in the H→ ZZ ( *)→4μ channel, using a multivariate method. This analysis, based in a Higgs boson mass dependent likelihood, constructed from discriminant variables, provides a significant improvement of the Higgs boson discovery potential in a wide mass range with respect to the official analysis published by CMS, based in orthogonal cuts independent of the Higgs boson mass. (Author) 8 refs

  2. Multivariate Analysis of Reproductive Risk Factors for Ovarian Cancer in Alexandria, Egypt

    International Nuclear Information System (INIS)

    El-Khwsky, F.S.; Maghraby, H.K.; Rostom, Y.A.; Abdel-Rahman, A.H.

    2006-01-01

    Background: Ovarian cancer is the eighth leading cancer in women, as it accounts for 4% of all malignant tumors in females. The incidence of ovarian cancer is up to 10 times higher in western countries than in rural Asian and Africa ones. Different reproductive characteristics, life styles and specific medical conditions are responsible for different pattern and incidence of ovarian cancer worldwide. Material and Methods: A case control study was conducted during the time period from 2000 to 2003 including 172 cases of epithelial ovarian cancer, recently diagnosed and confirmed by histopathology. The patients were accessed at the hospitals currently covered by Alexandria Cancer Registry. In addition, 441 control subjects, comparable by age and address, were randomly selected from patients admitted to the same hospitals for non gynecological, non endocrinal acute diseases. Both cases and controls were subjected to a specific predesigned questionnaire to cover menstrual, reproductive and lifestyle indicators. Univariate and multivariate analysis were conducted and 5% level of significance was adopted. Results: Significantly increased risks were reported with increased number of abortions and increased number of ovarian cycles (OR=1.8, 95% CI (1.7-2.8), and 2.8, 95% CI 2.8 (1.5-5.2), respectively. Similarly, high risks were also reported for increased number of pregnancies, OR= 1.6, 95% CI 1.1-2.4) for I to three pregnancies and 4.2,95% CI 1.2-15.9) for more than four pregnancies On the other hand, decreased risks were reported for those with increased parity compared to nulliparous. Conclusion: Although ovarian cancer is less frequent in our community, yet the significant positive and negative associations between risk factors and ovarian cancer were similar to the results of other studies, apart from the primary prevention program that should be outlined according to prevalence of significant risk factors in the studied local community

  3. Multivariate Analysis for Umbel per plant in Land races of Coriander (Coriandrum sativum L.

    Directory of Open Access Journals (Sweden)

    Hari Shankar Yadava

    2014-10-01

    Full Text Available Twenty five land races from Madhya Pradesh and ten germplasm of coriander were evaluated in four environments to assess umbel per plant using multivariate analysis. Mean sum of squares due to genotypes, environments and GEI were highly significant for umbels per plant. Variation in GEI was mainly due to heterogeneity. . PCA 1 and PCA 2 captures the 99.42% of interaction sum of squares hence, these two principal component axes were the best predictive. The potential environment the potential environments E3 (high fertility, 2009-10 bearing lowest interaction effect while, least potential environments E2 (low fertility, 2008-09 exhibited high PCA scores. The biplot of genotype, environment and IPCA 1 showed three groups. One group exhibited the similar main effects (mean umbels per plant to the grand mean. The second group showed high interaction effect varied in direction while third group bear the low interaction effect. AMMI Stability Values (ASV, ranging from from 7.444 to 31.099 was lowest in RVC 8 followed by RVC 4, RVC 11, RVC 21, RVC 9 and RVC 3 whereas, it was noted maximum in RVC 19 followed by Moroccan, CS 193, Simpo S 33 and G 5363. The genotypes exhibiting low IPCA scores and ASV namely, RVC 8, RVC 4, RVC 11, RVC 21, RVC 19 and RVC 25 showed wider adaptability for umbels per plant while, RVC 19, Moroccan, CS 193, Simpo S 33 and G 5363 exhibiting specific adaptability towards environmental conditions. These genotypes can be utilized in breeding programmes to transfer the adaptability genes for umbel per plant into high yielding genetic back ground in coriander.

  4. Accelerating policy decisions to adopt haemophilus influenzae type B vaccine: a global, multivariable analysis.

    Directory of Open Access Journals (Sweden)

    Jessica C Shearer

    2010-03-01

    Full Text Available Adoption of new and underutilized vaccines by national immunization programs is an essential step towards reducing child mortality. Policy decisions to adopt new vaccines in high mortality countries often lag behind decisions in high-income countries. Using the case of Haemophilus influenzae type b (Hib vaccine, this paper endeavors to explain these delays through the analysis of country-level economic, epidemiological, programmatic and policy-related factors, as well as the role of the Global Alliance for Vaccines and Immunisation (GAVI Alliance.Data for 147 countries from 1990 to 2007 were analyzed in accelerated failure time models to identify factors that are associated with the time to decision to adopt Hib vaccine. In multivariable models that control for Gross National Income, region, and burden of Hib disease, the receipt of GAVI support speeded the time to decision by a factor of 0.37 (95% CI 0.18-0.76, or 63%. The presence of two or more neighboring country adopters accelerated decisions to adopt by a factor of 0.50 (95% CI 0.33-0.75. For each 1% increase in vaccine price, decisions to adopt are delayed by a factor of 1.02 (95% CI 1.00-1.04. Global recommendations and local studies were not associated with time to decision.This study substantiates previous findings related to vaccine price and presents new evidence to suggest that GAVI eligibility is associated with accelerated decisions to adopt Hib vaccine. The influence of neighboring country decisions was also highly significant, suggesting that approaches to support the adoption of new vaccines should consider supply- and demand-side factors.

  5. Multivariate analysis of water quality and environmental variables in the Great Barrier Reef catchments

    Science.gov (United States)

    Ryu, D.; Liu, S.; Western, A. W.; Webb, J. A.; Lintern, A.; Leahy, P.; Wilson, P.; Watson, M.; Waters, D.; Bende-Michl, U.

    2016-12-01

    The Great Barrier Reef (GBR) lagoon has been experiencing significant water quality deterioration due in part to agricultural intensification and urban settlement in adjacent catchments. The degradation of water quality in rivers is caused by land-derived pollutants (i.e. sediment, nutrient and pesticide). A better understanding of dynamics of water quality is essential for land management to improve the GBR ecosystem. However, water quality is also greatly influenced by natural hydrological processes. To assess influencing factors and predict the water quality accurately, selection of the most important predictors of water quality is necessary. In this work, multivariate statistical techniques - cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) - are used to reduce the complexity derived from the multidimensional water quality monitoring data. Seventeen stations are selected across the GBR catchments, and the event-based measurements of 12 variables monitored during 9 years (2006 - 2014) were analysed by means of CA and PCA/FA. The key findings are: (1) 17 stations can be grouped into two clusters according to the hierarchical CA, and the spatial dissimilarity between these sites is characterised by the different climatic and land use in the GBR catchments. (2) PCA results indicate that the first 3 PCs explain 85% of the total variance, and FA on the entire data set shows that the varifactor (VF) loadings can be used to interpret the sources of spatial variation in water quality on the GBR catchments level. The impact of soil erosion and non-point source of pollutants from agriculture contribution to VF1 and the variability in hydrological conditions and biogeochemical processes can explain the loadings in VF2. (3) FA is also performed on two groups of sites identified in CA individually, to evaluate the underlying sources that are responsible for spatial variability in water quality in the two groups. For the Cluster 1 sites

  6. Batch-to-batch quality consistency evaluation of botanical drug products using multivariate statistical analysis of the chromatographic fingerprint.

    Science.gov (United States)

    Xiong, Haoshu; Yu, Lawrence X; Qu, Haibin

    2013-06-01

    Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many kinds of industrial products. In this paper, the combined use of multivariate statistical analysis and chromatographic fingerprinting is presented here to evaluate batch-to-batch quality consistency of botanical drug products. A typical botanical drug product in China, Shenmai injection, was selected as the example to demonstrate the feasibility of this approach. The high-performance liquid chromatographic fingerprint data of historical batches were collected from a traditional Chinese medicine manufacturing factory. Characteristic peaks were weighted by their variability among production batches. A principal component analysis model was established after outliers were modified or removed. Multivariate (Hotelling T(2) and DModX) control charts were finally successfully applied to evaluate the quality consistency. The results suggest useful applications for a combination of multivariate statistical analysis with chromatographic fingerprinting in batch-to-batch quality consistency evaluation for the manufacture of botanical drug products.

  7. Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

    Science.gov (United States)

    Joo, Soohyung; Kipp, Margaret E. I.

    2015-01-01

    Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

  8. Analysis of the stability and accuracy of the discrete least-squares approximation on multivariate polynomial spaces

    KAUST Repository

    Migliorati, Giovanni

    2016-01-05

    We review the main results achieved in the analysis of the stability and accuracy of the discrete leastsquares approximation on multivariate polynomial spaces, with noiseless evaluations at random points, noiseless evaluations at low-discrepancy point sets, and noisy evaluations at random points.

  9. Comparative urine analysis by liquid chromatography-mass spectrometry and multivariate statistics : Method development, evaluation, and application to proteinuria

    NARCIS (Netherlands)

    Kemperman, Ramses F. J.; Horvatovich, Peter L.; Hoekman, Berend; Reijmers, Theo H.; Muskiet, Frits A. J.; Bischoff, Rainer

    2007-01-01

    We describe a platform for the comparative profiling of urine using reversed-phase liquid chromatography-mass spectrometry (LC-MS) and multivariate statistical data analysis. Urinary compounds were separated by gradient elution and subsequently detected by electrospray Ion-Trap MS. The lower limit

  10. Factors of tidal volume variation during augmented spontaneous ventilation in patients on extracorporeal carbon dioxide removal. A multivariate analysis

    NARCIS (Netherlands)

    Bein, T.; Müller, T.; Graf, B. M.; Philipp, A.; Zeman, F.; Schultz, M. J.; Slutsky, A. S.; Weber-Carstens, S.

    2015-01-01

    Extracorporeal carbon dioxide removal (ECCO2-R) allows lung protective ventilation using lower tidal volumes (VT) in patients with acute respiratory failure. The dynamics of spontaneous ventilation under ECCO2-R has not been described previously. This retrospective multivariable analysis examines VT

  11. Traumatic brain injury and alcohol/substance abuse: A Bayesian meta-analysis comparing the outcomes of people with and without a history of abuse.

    Science.gov (United States)

    Unsworth, David J; Mathias, Jane L

    2017-08-01

    Alcohol and substance (drugs and/or alcohol) abuse are major risk factors for traumatic brain injury (TBI); however, it remains unclear whether outcomes differ for those with and without a history of preinjury abuse. A meta-analysis was performed to examine this issue. The PubMed, Embase, and PsycINFO databases were searched for research that compared the neuroradiological, cognitive, or psychological outcomes of adults with and without a documented history of alcohol and/or substance abuse who sustained nonpenetrating TBIs. Data from 22 studies were analyzed using a random-effects model: Hedges's g effect sizes measured the mean difference in outcomes of individuals with/without a history of preinjury abuse, and Bayes factors assessed the probability that the outcomes differed. Patients with a history of alcohol and/or substance abuse had poorer neuroradiological outcomes, including reduced hippocampal (g = -0.82) and gray matter volumes (g = -0.46 to -0.82), and enlarged cerebral ventricles (g = -0.73 to -0.80). There were limited differences in cognitive outcomes: Executive functioning (g = -0.51) and memory (g = -0.39 to -0.43) were moderately affected, but attention and reasoning were not. The findings for fine motor ability, construction, perception, general cognition, and language were inconclusive. Postinjury substance and alcohol use (g = -0.97 to -1.07) and emotional functioning (g = -0.29 to -0.44) were worse in those with a history of alcohol and/or substance abuse (psychological outcomes). This study highlighted the type and extent of post-TBI differences between persons with and without a history of alcohol or substance abuse, many of which may hamper recovery. However, variation in the criteria for premorbid abuse, limited information regarding the history of abuse, and an absence of preinjury baseline data prevented an assessment of whether the differences predated the TBI, occurred as a result of ongoing alcohol/substance abuse, or

  12. TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies.

    Directory of Open Access Journals (Sweden)

    Sophie van der Sluis

    Full Text Available To date, the genome-wide association study (GWAS is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype-phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure, inspired by the GATES procedure proposed by Li et al (2011. For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype-phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5-9 times higher than the power of univariate tests based on composite scores and 1.5-2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype-phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor.

  13. Characterization of Used Nuclear Fuel with Multivariate Analysis for Process Monitoring

    International Nuclear Information System (INIS)

    Dayman, Kenneth J.; Coble, Jamie B.; Orton, Christopher R.; Schwantes, Jon M.

    2014-01-01

    The Multi-Isotope Process (MIP) Monitor combines gamma spectroscopy and multivariate analysis to detect anomalies in various process streams in a nuclear fuel reprocessing system. Measured spectra are compared to models of nominal behavior at each measurement location to detect unexpected changes in system behavior. In order to improve the accuracy and specificity of process monitoring, fuel characterization may be used to more accurately train subsequent models in a full analysis scheme. This paper presents initial development of a reactor-type classifier that is used to select a reactor-specific partial least squares model to predict fuel burnup. Nuclide activities for prototypic used fuel samples were generated in ORIGEN-ARP and used to investigate techniques to characterize used nuclear fuel in terms of reactor type (pressurized or boiling water reactor) and burnup. A variety of reactor type classification algorithms, including k-nearest neighbors, linear and quadratic discriminant analyses, and support vector machines, were evaluated to differentiate used fuel from pressurized and boiling water reactors. Then, reactor type-specific partial least squares models were developed to predict the burnup of the fuel. Using these reactor type-specific models instead of a model trained for all light water reactors improved the accuracy of burnup predictions. The developed classification and prediction models were combined and applied to a large dataset that included eight fuel assembly designs, two of which were not used in training the models, and spanned the range of the initial 235U enrichment, cooling time, and burnup values expected of future commercial used fuel for reprocessing. Error rates were consistent across the range of considered enrichment, cooling time, and burnup values. Average absolute relative errors in burnup predictions for validation data both within and outside the training space were 0.0574% and 0.0597%, respectively. The errors seen in this

  14. Significant drivers of the virtual water trade evaluated with a multivariate regression analysis

    Science.gov (United States)

    Tamea, Stefania; Laio, Francesco; Ridolfi, Luca

    2014-05-01

    International trade of food is vital for the food security of many countries, which rely on trade to compensate for an agricultural production insufficient to feed the population. At the same time, food trade has implications on the distribution and use of water resources, because through the international trade of food commodities, countries virtually displace the water used for food production, known as "virtual water". Trade thus implies a network of virtual water fluxes from exporting to importing countries, which has been estimated to displace more than 2 billions of m3 of water per year, or about the 2% of the annual global precipitation above land. It is thus important to adequately identify the dynamics and the controlling factors of the virtual water trade in that it supports and enables the world food security. Using the FAOSTAT database of international trade and the virtual water content available from the Water Footprint Network, we reconstructed 25 years (1986-2010) of virtual water fluxes. We then analyzed the dependence of exchanged fluxes on a set of major relevant factors, that includes: population, gross domestic product, arable land, virtual water embedded in agricultural production and dietary consumption, and geographical distance between countries. Significant drivers have been identified by means of a multivariate regression analysis, applied separately to the export and import fluxes of each country; temporal trends are outlined and the relative importance of drivers is assessed by a commonality analysis. Results indicate that population, gross domestic product and geographical distance are the major drivers of virtual water fluxes, with a minor (but non-negligible) contribution given by the agricultural production of exporting countries. Such drivers have become relevant for an increasing number of countries throughout the years, with an increasing variance explained by the distance between countries and a decreasing role of the gross

  15. SEBAL-based Daily Actual Evapotranspiration Forecasting using Wavelets Decomposition Analysis and Multivariate Relevance Vector Machines

    Science.gov (United States)

    Torres, A. F.

    2011-12-01

    two excellent tools from the Learning Machine field know as the Wavelet Decomposition Analysis (WDA) and the Multivariate Relevance Vector Machine (MVRVM) to forecast the results obtained from the SEBAL algorithm using LandSat imagery and soil moisture maps. The predictive capability of this novel hybrid WDA-RVM actual evapotranspiration forecasting technique is tested by comparing the crop water requirements and delivered crop water in the Lower Sevier River Basin, Utah, for the period 2007-2011. This location was selected because of their success increasing the efficiency of water use and control along the entire irrigation system. Research is currently on going to assess the efficacy of the WDA-RVM technique along the irrigation season, which is required to enhance the water use efficiency and minimize climate change impact on the Sevier River Basin.

  16. Metabolomic analysis of Echinacea spp. by 1H nuclear magnetic resonance spectrometry and multivariate data analysis technique.

    Science.gov (United States)

    Frédérich, Michel; Jansen, Céline; de Tullio, Pascal; Tits, Monique; Demoulin, Vincent; Angenot, Luc

    2010-01-01

    The genus Echinacea (Asteraceae) comprises about 10 species originally distributed in North America. Three species are very well known as they are used worldwide as medicinal plants: Echinacea purpurea, E. pallida, E. angustifolia. To discriminate between these three Echinacea species and E. simulata by (1)H NMR-based metabolomics. (1)H NMR and multivariate analysis techniques were applied to diverse Echinacea plants including roots and aerial parts, authentic plants, commercial plants and commercial dry extracts. Using the (1)H NMR metabolomics, it was possible, without previous evaporation or separation steps, to obtain a metabolic fingerprint to distinguish between species. A clear distinction between the three pharmaceutical species was possible and some useful metabolites were identified. (c) 2009 John Wiley & Sons, Ltd.

  17. Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

    Science.gov (United States)

    Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

    2009-05-30

    An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

  18. Applying multivariate analysis as decision tool for evaluating sediment-specific remediation strategies

    DEFF Research Database (Denmark)

    Pedersen, Kristine B.; Lejon, Tore; Jensen, Pernille Erland

    2016-01-01

    and final concentrations of Cu and Pb were below background levels in large parts of the experimental domain when operating at low current densities (...Multivariate methodology was employed for finding optimum remediation conditions for electrodialytic remediation of harbour sediment from an Arctic location in Norway. The parts of the experimental domain in which both sediment- and technology-specific remediation objectives were met were...

  19. Clusterwise simultaneous component analysis for analyzing structural differences in multivariate multiblock data.

    NARCIS (Netherlands)

    De Roover, Kim; Ceulemans, Eva; Timmerman, Marieke E.; Vansteelandt, Kristof; Stouten, Jeroen; Onghena, Patrick

    Many studies yield multivariate multiblock data, that is, multiple data blocks that all involve the same set of variables (e.g., the scores of different groups of subjects on the same set of variables). The question then rises whether the same processes underlie the different data blocks. To explore

  20. A Multivariate Genetic Analysis of Specific Phobia, Separation Anxiety and Social Phobia in Early Childhood

    Science.gov (United States)

    Eley, Thalia C.; Rijsdijk, Fruhling V.; Perrin, Sean; O'Connor, Thomas G.; Bolton, Derek

    2008-01-01

    Background: Comorbidity amongst anxiety disorders is very common in children as in adults and leads to considerable distress and impairment, yet is poorly understood. Multivariate genetic analyses can shed light on the origins of this comorbidity by revealing whether genetic or environmental risks for one disorder also influence another. We…

  1. A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

    Science.gov (United States)

    Rey S. Ofren; Edward Harvey

    2000-01-01

    A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

  2. TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies

    NARCIS (Netherlands)

    van der Sluis, S.; Posthuma, D.; Dolan, C.V.

    2013-01-01

    To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in

  3. TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

    NARCIS (Netherlands)

    S. van der Sluis (Sophie); D. Posthuma (Danielle); C.V. Dolan (Conor)

    2013-01-01

    textabstractTo date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often

  4. Multivariate statistical treatment of PIXE analysis of some traditional Chinese medicines

    International Nuclear Information System (INIS)

    Xiaofeng Zhang; Jianguo Ma; Junfa Qin; Lun Xiao

    1991-01-01

    Elements in two kinds of 30 traditional Chinese medicines were analyzed by PIXE method, and the data were treated by multivariate statistical methods. The results show that these two kinds of traditional Chinese medicines are almost separable according to their elemental contents. The results are congruous with the traditional Chinese medicine practice. (author) 7 refs.; 2 figs.; 2 tabs

  5. Defining climate zones in México City using multivariate analysis

    NARCIS (Netherlands)

    Estrada, Feporrua; Martínez-Arroyo, A.; Fernández-Eguiarte, A.; Luyando, E.; Gay, C.

    2009-01-01

    Spatial variability in the climate of México City was studied using multivariate methods to analyze 30 years of meteorological data from 37 stations (from the Servicio Meteorológico Nacional) located within the city. Although it covers relatively small area, México City encompasses considerable

  6. Principal response curves technique for the analysis of multivariate biomonitoring time series

    NARCIS (Netherlands)

    Brink, van den P.J.; Besten, den P.J.; Vaate, bij de A.; Braak, ter C.J.F.

    2009-01-01

    Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been

  7. Meta-analysis of psychological treatments for posttraumatic stress disorder in adult survivors of childhood abuse

    NARCIS (Netherlands)

    Ehring, Thomas; Welboren, Renate; Morina, Nexhmedin; Wicherts, Jelte M.; Freitag, Janina; Emmelkamp, Paul M. G.

    2014-01-01

    Posttraumatic stress disorder (PTSD) is highly prevalent in adult survivors of childhood sexual and/or physical abuse. However, intervention studies focusing on this group of patients are underrepresented in earlier meta-analyses on the efficacy of PTSD treatments. The current meta-analysis

  8. Analysis of Caretaker Histories in Abuse: Comparing Initial Histories with Subsequent Confessions

    Science.gov (United States)

    Flaherty, Emalee G.

    2006-01-01

    Objective: We hypothesize that perpetrators of abuse include elements of truth in their initial history and that an analysis of perpetrator confessions can teach professionals how to identify these initial truths. Methods: The information from a consecutive sample of perpetrators' confessions concerning 41 children hospitalized because of injuries…

  9. Effects of Video Games and Online Chat on Mathematics Performance in High School: An Approach of Multivariate Data Analysis

    OpenAIRE

    Lina Wu; Wenyi Lu; Ye Li

    2016-01-01

    Regarding heavy video game players for boys and super online chat lovers for girls as a symbolic phrase in the current adolescent culture, this project of data analysis verifies the displacement effect on deteriorating mathematics performance. To evaluate correlation or regression coefficients between a factor of playing video games or chatting online and mathematics performance compared with other factors, we use multivariate analysis technique and take gender difference into account. We fin...

  10. Mini-DIAL system measurements coupled with multivariate data analysis to identify TIC and TIM simulants: preliminary absorption database analysis.

    Science.gov (United States)

    Gaudio, P.; Malizia, A.; Gelfusa, M.; Martinelli, E.; Di Natale, C.; Poggi, L. A.; Bellecci, C.

    2017-01-01

    Nowadays Toxic Industrial Components (TICs) and Toxic Industrial Materials (TIMs) are one of the most dangerous and diffuse vehicle of contamination in urban and industrial areas. The academic world together with the industrial and military one are working on innovative solutions to monitor the diffusion in atmosphere of such pollutants. In this phase the most common commercial sensors are based on “point detection” technology but it is clear that such instruments cannot satisfy the needs of the smart cities. The new challenge is developing stand-off systems to continuously monitor the atmosphere. Quantum Electronics and Plasma Physics (QEP) research group has a long experience in laser system development and has built two demonstrators based on DIAL (Differential Absorption of Light) technology could be able to identify chemical agents in atmosphere. In this work the authors will present one of those DIAL system, the miniaturized one, together with the preliminary results of an experimental campaign conducted on TICs and TIMs simulants in cell with aim of use the absorption database for the further atmospheric an analysis using the same DIAL system. The experimental results are analysed with standard multivariate data analysis technique as Principal Component Analysis (PCA) to develop a classification model aimed at identifying organic chemical compound in atmosphere. The preliminary results of absorption coefficients of some chemical compound are shown together pre PCA analysis.

  11. Mini-DIAL system measurements coupled with multivariate data analysis to identify TIC and TIM simulants: preliminary absorption database analysis

    International Nuclear Information System (INIS)

    Gaudio, P; Malizia, A; Gelfusa, M; Poggi, L.A.; Martinelli, E.; Di Natale, C.; Bellecci, C.

    2017-01-01

    Nowadays Toxic Industrial Components (TICs) and Toxic Industrial Materials (TIMs) are one of the most dangerous and diffuse vehicle of contamination in urban and industrial areas. The academic world together with the industrial and military one are working on innovative solutions to monitor the diffusion in atmosphere of such pollutants. In this phase the most common commercial sensors are based on “point detection” technology but it is clear that such instruments cannot satisfy the needs of the smart cities. The new challenge is developing stand-off systems to continuously monitor the atmosphere. Quantum Electronics and Plasma Physics (QEP) research group has a long experience in laser system development and has built two demonstrators based on DIAL (Differential Absorption of Light) technology could be able to identify chemical agents in atmosphere. In this work the authors will present one of those DIAL system, the miniaturized one, together with the preliminary results of an experimental campaign conducted on TICs and TIMs simulants in cell with aim of use the absorption database for the further atmospheric an analysis using the same DIAL system. The experimental results are analysed with standard multivariate data analysis technique as Principal Component Analysis (PCA) to develop a classification model aimed at identifying organic chemical compound in atmosphere. The preliminary results of absorption coefficients of some chemical compound are shown together pre PCA analysis. (paper)

  12. Multivariate analysis for groundwater chemistry near the Mizunami Underground Research Laboratory

    International Nuclear Information System (INIS)

    Todaka, Norifumi; Ajima, Shuji; Akasaka, Chitoshi; Nakanishi, Shigetaka

    2004-03-01

    One of the tasks in the program of the Mizunami Underground Research Laboratory is development of methodology for evaluating geochemical effects caused by construction of underground facilities. Using the M3 (Multivariate Mixing and Mass balance) analysis tool developed by SKB in Sweden and the reactive chemical transport simulator (TOUGHEREACT) developed by LBNL in USA, the analyses concerning geochemical evolution of groundwater in the Tono area was conducted to verify their modeling applicability and to construct a geochemical model for predicting perturbations caused by the shaft excavation in the next phase. M3 analysis is composed of 1) Principal Component Analysis, 2) mixing calculation from reference waters (end-members) and 3) mass balance calculation. 166 geochemical data are selected from 413 data of groundwaters, river waters, rain waters, hot spring and drilling waters, based on analyzed components and ion charge balance (within ±5%). Eight elements such as Na, K, Ca, Mg, Cl, HCO 3 , SO 4 and F were used for M3 analysis. Compiled waters were classified into some water types. Four reference waters ('Na-Cl type water (high salinity)', 'Na-Cl type water (low salinity)', 'Surface water', 'Na(Ca)-HCO 3 type water') were selected and mixing proportions of each reference water and deviations between measured and calculated compositions were estimated for all samples and visualized in the vertical section including the URL site. 'Surface water' (60-100%) is dominated in the NE part of the site including boreholes DH-10, 11 and 13, and 'Na-Cl type water (high salinity)' and 'Na-Cl type water (low salinity)' concentrate in the SW part including boreholes DH-2 and 12 located near the URL site. The overall M3 model uncertainty was ±10.7% for this data set. The mass balance calculation indicated reactions associated with organic decomposition, inorganic redox reactions, dissolution and precipitation of calcite and plagioclase, ion exchange and sulphate reduction

  13. Delineation of protein structure classes from multivariate analysis of protein Raman optical activity data.

    Science.gov (United States)

    Zhu, Fujiang; Tranter, George E; Isaacs, Neil W; Hecht, Lutz; Barron, Laurence D

    2006-10-13

    Vibrational Raman optical activity (ROA), measured as a small difference in the intensity of Raman scattering from chiral molecules in right and left-circularly polarized incident light, or as the intensity of a small circularly polarized component in the scattered light, is a powerful probe of the aqueous solution structure of proteins. On account of the large number of structure-sensitive bands in protein ROA spectra, multivariate analysis techniques such as non-linear mapping (NLM) are especially favourable for determining structural relationships between different proteins. Here NLM is used to map a dataset of 80 polypeptide, protein and virus ROA spectra, considered as points in a multidimensional space with axes representing the digitized wavenumbers, into readily visualizable two and three-dimensional spaces in which points close to or distant from each other, respectively, represent similar or dissimilar structures. Discrete clusters are observed which correspond to the seven structure classes all alpha, mainly alpha, alphabeta, mainly beta, all beta, mainly disordered/irregular and all disordered/irregular. The average standardised ROA spectra of the proteins falling within each structure class have distinct features characteristic of each class. A distinct cluster containing the wheat protein A-gliadin and the plant viruses potato virus X, narcissus mosaic virus, papaya mosaic virus and tobacco rattle virus, all of which appear in the mainly alpha cluster in the two-dimensional representation, becomes clearly separated in the direction of increasing disorder in the three-dimensional representation. This suggests that the corresponding five proteins, none of which to date has yielded high-resolution X-ray structures, consist mainly of alpha-helix and disordered structure with little or no beta-sheet. This combination of structural elements may have functional significance, such as facilitating disorder-to-order transitions (and vice versa) and suppressing

  14. Hydrochemical analysis of groundwater using multivariate statistical methods - The Volta region, Ghana

    Science.gov (United States)

    Banoeng-Yakubo, B.; Yidana, S.M.; Nti, E.

    2009-01-01

    Q and R-mode multivariate statistical analyses were applied to groundwater chemical data from boreholes and wells in the northern section of the Volta region Ghana. The objective was to determine the processes that affect the hydrochemistry and the variation of these processes in space among the three main geological terrains: the Buem formation, Voltaian System and the Togo series that underlie the area. The analyses revealed three zones in the groundwater flow system: recharge, intermediate and discharge regions. All three zones are clearly different with respect to all the major chemical parameters, with concentrations increasing from the perceived recharge areas through the intermediate regions to the discharge areas. R-mode HCA and factor analysis (using varimax rotation and Kaiser Criterion) were then applied to determine the significant sources of variation in the hydrochemistry. This study finds that groundwater hydrochemistry in the area is controlled by the weathering of silicate and carbonate minerals, as well as the chemistry of infiltrating precipitation. This study finds that the ??D and ??18O data from the area fall along the Global Meteoric Water Line (GMWL). An equation of regression derived for the relationship between ??D and ??18O bears very close semblance to the equation which describes the GMWL. On the basis of this, groundwater in the study area is probably meteoric and fresh. The apparently low salinities and sodicities of the groundwater seem to support this interpretation. The suitability of groundwater for domestic and irrigation purposes is related to its source, which determines its constitution. A plot of the sodium adsorption ratio (SAR) and salinity (EC) data on a semilog axis, suggests that groundwater serves good irrigation quality in the area. Sixty percent (60%), 20% and 20% of the 67 data points used in this study fall within the medium salinity - low sodicity (C2-S1), low salinity -low sodicity (C1-S1) and high salinity - low

  15. Advances in the analysis of energy commodities and of multivariate dependence structures

    Energy Technology Data Exchange (ETDEWEB)

    Schlueter, Stephan

    2011-01-27

    In the first chapter of the dissertation a new stochastic long-term/short-term model for short-term electricity prices is introduced and applied to four major European indices. Evidence is given that all time series contain certain periodic patterns, and it is shown how to use the wavelet transform for filtering purpose. The wavelet transform is also applied to separate the long-term trend from the short-term oscillation in the seasonal-adjusted log-prices. Moreover, dynamic volatility is found in all time series, which is incorporated by using a bivariate GARCH model with constant correlation. The residuals are modeled using the normal-inverse Gaussian distribution. In the second chapter an overview over different wavelet based time series forecasting methods is given. The methods are tested on four data sets, each with its own characteristics. Eventually, it can be seen that using wavelets does improve the forecasting quality, especially for longer time horizons than one day ahead. However, there is no single superior method; the performance depends on the data set and the forecasting time horizon. In the third chapter a new formula for extreme Student t quantiles is derived. The derivation is based on the proof for the Gaussian quantile and on the fact that the Student t distribution arises as the limit of a variance-mixture of normals. In the fourth chapter a theoretical framework and a solved example for valuing a European gas storage facility is presented. For modeling the gas price a mean reverting process with GARCH volatility is chosen. Based on this process dynamic programming methods are applied to derive partial differential equations for valuing the storage facility. As an example a storage site in Epe, Germany, is chosen. In this context the effects of multiple contract types for renting a storage site are investigated and a sensitivity analysis is performed. In the fifth chapter multivariate copula models are discussed. Using three different four

  16. The value of external beam radiation in pathologic node positive prostate cancer: a multivariate analysis

    International Nuclear Information System (INIS)

    Morris, Astrid D.; Zietman, Anthony L.; Althausen, Alex F.; Heney, Niall M.; Kaufman, Donald S.; Shipley, William U.

    1997-01-01

    Purpose: The goal of this study was to evaluate the effect of local/regional treatment, particularly external beam radiation alone versus radical prostatectomy and radiation therapy in patients with pathologic node positive prostate cancer on survival. The effect of delayed vs. immediate endocrine therapy on patients treated with radiation alone was also examined. Methods: Medical records of all 116 patients who received their initial treatment at the Massachusetts General Hospital between 1980 and 1996 for adenocarcinoma of the prostate with pathologic confirmed nodal metastasis and no distant disease were reviewed. The mean follow up was 5.5 years. Disease specific survival, time to PSA failure on endocrine therapy, and time to first intervention were evaluated. PSA failure was defined as two consecutive post-nadir rises following the first use of endocrine therapy. Intervention was defined as any surgical or radiotherapeutic procedure required for relief of symptoms related to local/regional recurrence. Survival comparisons were made between any local/regional treatment vs. none, radiation therapy alone vs. prostatectomy with radiation therapy, and immediate vs. delayed endocrine therapy. The effect of the different treatment options on survival were compared using multivariate Cox proportional hazard models to simultaneously adjust for patient and tumor characteristics (tumor stage, Gleason grade, number of positive nodes) that might influence survival. Results: The combined patient population had a 5 year disease specific survival of 74% and a 10 year disease specific survival of 48%. The comparison groups for local/regional treatment had the following adjusted outcomes. In a subgroup analysis of patients with clinical T1-T2 and clinical T3-T4 disease, local/regional treatment continued to confer a disease specific survival advantage over no local regional treatment in both subgroups (p=0.05 and p=0.02, respectively). PSA failure on endocrine therapy was

  17. Early life sexual abuse is associated with increased suicide attempts: An update meta-analysis.

    Science.gov (United States)

    Ng, Qin Xiang; Yong, Bob Zheng Jie; Ho, Collin Yin Xian; Lim, Donovan Yutong; Yeo, Wee-Song

    2018-04-01

    Suicide is an emerging, yet preventable global health issue associated with significant mortality. Identification of underlying risk factors and antecedents may inform preventive strategies and interventions. This study serves to provide an updated meta-analysis examining the extent of association of early life sexual abuse with suicide attempts. Using the keywords [early abuse OR childhood abuse OR sexual OR rape OR molest* OR violence OR trauma OR PTSD] AND [suicid* OR premature OR unnatural OR deceased OR died OR mortality], a preliminary search on the PubMed, Ovid, PsychINFO, Web of Science and Google Scholar databases yielded 12,874 papers published in English between 1-Jan-1988 and 1-June-2017. Of these, only 47 studies were included in the final meta-analysis. The 47 studies (25 cross-sectional, 14 cohort, 6 case-control and 2 twin studies) contained a total of 151,476 subjects. Random-effects meta-analysis found early life sexual abuse to be a significant risk factor for suicide attempts, compared to baseline population (pooled OR 1.89, 95% CI: 1.66 to 2.12, p < 0.001). Subgroup analyses of cross-sectional and longitudinal studies showed similar findings of increased risk as they yielded ORs of 1.98 (95% CI: 1.70 to 2.25, p < 0.001) and 1.65 (95% CI: 1.37 to 1.93, p < 0.001), respectively. In both cross-sectional and longitudinal studies, childhood sexual abuse was consistently associated with increased risk of suicide attempts. The findings of the present study provide strong grounds for funding public policy planning and interventions to prevent sexual abuse and support its victims. Areas for future research should include preventive and treatment strategies and factors promoting resilience following childhood sexual abuse. Future research on the subject should have more robust controls and explore the differential effects of gender and intra-versus extra-familial sexual abuse. Copyright © 2018 Elsevier Ltd. All rights reserved.

  18. Fourier Transform Infrared Spectroscopy (FTIR and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

    Directory of Open Access Journals (Sweden)

    Rosana de Cássia de Souza Schneider

    2013-03-01

    Full Text Available The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources—canola, cotton, corn, palm, sunflower and soybeans—were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR. For the multivariate analysis principal component analysis (PCA, hierarchical cluster analysis (HCA, interval principal component analysis (iPCA and soft independent modeling of class analogy (SIMCA were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples.

  19. Fourier transform infrared spectroscopy (FTIR) and multivariate analysis for identification of different vegetable oils used in biodiesel production.

    Science.gov (United States)

    Mueller, Daniela; Ferrão, Marco Flôres; Marder, Luciano; da Costa, Adilson Ben; Schneider, Rosana de Cássia de Souza

    2013-03-28

    The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources--canola, cotton, corn, palm, sunflower and soybeans--were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples.

  20. Multivariate analysis of factors influencing the effect of radiosynovectomy; Multivariate Analyse der Einflussfaktoren auf die Wirkung der Radiosynoviorthese bei entzuendlichen Gelenkerkrankungen

    Energy Technology Data Exchange (ETDEWEB)

    Farahati, J.; Schulz, G.; Koerber, C.; Geling, M.; Schmeider, P.; Reiners, Chr. [Wuerzburg Univ. (Germany). Klinik fuer Nuklearmedizin; Wendler, J. [Erlangen-Nuernberg Univ. (Germany). Klinik fuer Innere Medizin III; Kenn, W. [Wuerzburg Univ. (Germany). Inst. fuer Roentgendiagnostik; Reidemeister, C. [Wuerzburg Univ. (Germany). Klinik fuer Innere Medizin

    2002-04-01

    Objective: In this prospective study, the time to remission after radiosynovectomy (RSV) was analyzed and the influence of age, sex, underlying disease, type of joint, and duration of illness on the success rate of RSV was determined. Methods: A total number of 57 patients with rheumatoid arthritis (n = 33) and arthrosis (n = 21) with a total number of 130 treated joints (36 knee, 66 small and 28 medium-size joints) were monitored using visual analogue scales (VAS) from one week before RSV up to four to six months after RSV. The patients had to answer 3 times daily for pain intensity of the treated joint. The time until remission was determined according to the Kaplan-Meier survivorship function. The influence of the prognosis parameters on outcome of RSV was determined by multivariate discriminant analysis. Results: After six months, the probability of pain relief of more than 20% amounted to 78% and was significantly dependent on the age of the patient (p = 0.02) and the duration of illness (p = 0.05), however not on sex (p = 0.17), underlying disease (p = 0.23), and type of joint (p = 0.69). Conclusion: Irrespective of sex, type of joint and underlying disease, a measurable pain relief can be achieved with RSV in 78% of the patients with synovitis, whereby effectiveness is decreasing with increasing age and progress of illness. (orig.) [German] Ziel: In dieser prospektiven Studie wurde die Zeit bis zur Remission nach einer Radiosynoviorthese (RSO) untersucht. Ebenso wurde der Einfluss von Alter, Geschlecht, Grunderkrankung, Gelenktyp und Erkrankungsdauer auf die Erfolgsrate der RSO ermittelt. Methodik: Bei insgesamt 57 Patienten mit rheumatoider Arthritis (n = 33) und Arthritis bei aktivierter Arthrose (n = 24) wurden 130 Gelenke (36 Kniegelenke, 66 kleine und 28 mittelgrosse Gelenke) behandelt. Die Patienten wurden unter Verwendung so genannter visueller Analogskalen eine Woche vor RSO und vier bis sechs Monate danach 3-mal taeglich zur Schmerzintensitaet des

  1. Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

    Science.gov (United States)

    Maione, Camila; Barbosa, Rommel Melgaço

    2018-01-24

    Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

  2. Multivariate data analysis of two-dimensional gel electrophoresis protein patterns from few samples

    DEFF Research Database (Denmark)

    Jensen, Kristina Nedenskov; Jessen, Flemming; Jørgensen, Bo

    2008-01-01

    on single spot differences, but on the covariance structure between proteins. However, their outcome is dependent on data scaling, and they may fail in producing valid multivariate models due to the much higher number of "irrelevant" spots present in the gels. The case where only few gels are available......One application of 2D gel electrophoresis is to reveal differences in protein pattern between two or more groups of individuals, attributable to their group membership. Multivariate data analytical methods are useful in pinpointing the spots relevant for discrimination by focusing not only...... and where the aim is to find as many as possible of the group-dependent proteins seems particularly difficult to handle. The present paper investigates such a case regarding the effect of scaling and of prefiltering by univariate nonparametric statistics on the selection of spots. Besides, a modified...

  3. Renewable energy consumption and economic growth in Argentina. A multivariate co-integration analysis

    OpenAIRE

    Khobai, Hlalefang

    2018-01-01

    This paper applied the ARDL bounds test approach and the VECM test technique to examine the long run relationship and direction of causality between renewable energy consumption and economic growth in Argentina. Quarterly time series data was employed in this study covering a period between 1990 and 2014. Trade openness, capital and employment were included in the study to form a multivariate framework. The results established that there is a long run relationship between the variables. The V...

  4. Using sperm morphometry and multivariate analysis to differentiate species of gray Mazama

    OpenAIRE

    Cursino, Marina Suzuki; Duarte, Jos? Maur?cio Barbanti

    2016-01-01

    There is genetic evidence that the two species of Brazilian gray Mazama, Mazama gouazoubira and Mazama nemorivaga, belong to different genera. This study identified significant differences that separated them into distinct groups, based on characteristics of the spermatozoa and ejaculate of both species. The characteristics that most clearly differentiated between the species were ejaculate colour, white for M.?gouazoubira and reddish for M.?nemorivaga, and sperm head dimensions. Multivariate...

  5. Inheritance of Nitrogen Use Efficiency in Inbred Progenies of Tropical Maize Based on Multivariate Diallel Analysis

    Directory of Open Access Journals (Sweden)

    Fernando Lisboa Guedes

    2014-01-01

    Full Text Available The objective of our study was to characterize and determine the patterns of genetic control in relation to tolerance and efficiency of nitrogen use by means of a complete diallel cross involving contrasting inbred progenies of tropical maize based on a univariate approach within the perspective of a multivariate mixed model. Eleven progenies, previously classified regarding the tolerance and responsiveness to nitrogen, were crossed in a complete diallel cross. Fifty-five hybrids were obtained. The hybrids and the progenies were evaluated at two different nitrogen levels, in two locations. The grain yield was measured as well as its yield components. The heritability values between the higher and lower nitrogen input environment did not differ among themselves. It was observed that the general combining ability values were similar for both approaches univariate and multivariate, when it was analyzed within each location and nitrogen level. The estimate of variance of the specific combining ability was higher than general combining ability estimate and the ratio between them was 0.54. The univariate and multivariate approaches are equivalent in experiments with good precision and high heritability. The nonadditive genetic effects exhibit greater quantities than the additive genetic effects for the genetic control of nitrogen use efficiency.

  6. Inheritance of nitrogen use efficiency in inbred progenies of tropical maize based on multivariate diallel analysis.

    Science.gov (United States)

    Guedes, Fernando Lisboa; Diniz, Rafael Parreira; Balestre, Marcio; Ribeiro, Camila Bastos; Camargos, Renato Barbosa; Souza, João Cândido

    2014-01-01

    The objective of our study was to characterize and determine the patterns of genetic control in relation to tolerance and efficiency of nitrogen use by means of a complete diallel cross involving contrasting inbred progenies of tropical maize based on a univariate approach within the perspective of a multivariate mixed model. Eleven progenies, previously classified regarding the tolerance and responsiveness to nitrogen, were crossed in a complete diallel cross. Fifty-five hybrids were obtained. The hybrids and the progenies were evaluated at two different nitrogen levels, in two locations. The grain yield was measured as well as its yield components. The heritability values between the higher and lower nitrogen input environment did not differ among themselves. It was observed that the general combining ability values were similar for both approaches univariate and multivariate, when it was analyzed within each location and nitrogen level. The estimate of variance of the specific combining ability was higher than general combining ability estimate and the ratio between them was 0.54. The univariate and multivariate approaches are equivalent in experiments with good precision and high heritability. The nonadditive genetic effects exhibit greater quantities than the additive genetic effects for the genetic control of nitrogen use efficiency.

  7. Research Protocol for Systematic Review and Meta-Analysis of Elder Abuse Prevalence Studies.

    Science.gov (United States)

    Yon, Yongjie; Mikton, Christopher; Gassoumis, Zachary D; Wilber, Kathleen H

    2017-06-01

    Elder abuse is an important public health and human rights issue, yet its true extent is not well understood. To address this, we will conduct a systematic review and meta-analysis of elder abuse prevalence studies from around the world. This protocol describes the methodological approach to be adopted for conducting this systematic review and meta-analysis. In particular, the protocol describes the search strategies and eligibility criteria to be used to identify and select studies and how data from the selected studies will be extracted for analysis. The protocol also describes the analytical approach that will be used to calculate pooled prevalence estimates and discusses the use of meta-regression to assess how studies' characteristics influence the prevalence estimates. This protocol conforms to the Preferred Reporting Items for Systematic reviews and Meta-Analysis - or PRISMA - guidelines and has been registered with the PROSPERO International Prospective Register of systematic reviews.

  8. Assessing earthworm and sewage sludge impacts on microbiological and biochemical soil quality using multivariate analysis

    Directory of Open Access Journals (Sweden)

    Hanye Jafari Vafa

    2017-06-01

    with soil matrix.Heavy metals concentrations were found to be below the maximum permissible limits for municipal sewage sludge. After applying sewage sludge treatments, the pots were irrigated (70% soil field capacity for three months to achieve a relative equilibrium condition in the soil. Eight adult earthworms with fully-developed clitellum were added to each pot. In the pots with both earthworm species, 4 specimen of each earthworm species was added. At the end of the experiment (90 days, soil samples were collected from treatments and were separately air-dried for chemical analysis or kept fresh and stored (4oC for microbial analysis. Finally, data obtained from the study were analyzed using multivariate analysis. Results and Discussion: Factor analysis led to the selection of three factors with eigen value greater than 1. The first, second and third factors were accounted for 62, 17.7 and 9.2% of the variability in soil data, respectively. The three factors together explained 89% of the original variability (i.e., variance in the soil dataset. Consequently, three factors were retained to represent the original variability of the dataset. The first factor had 16 highly weighted variables with a negative loading for soil pH and positive loadings for other variables. The first factor, which included most soil indicators as input variables, clearly separated sewage sludge treatments. Most of the soil microbial characteristics were increased by sewage sludge application due to the high contents of organic matter and nutrients in sewage sludge, as well as low concentrations of heavy metals. Fungal respiration, bacterial respiration and microbial biomass carbon loaded heavily on the second factor with a negative loading for fungal respiration and positive loadings for bacterial respiration and microbial biomass carbon. The second factor, which included microbial biomass and community composition, noticeably discriminated earthworm treatments. In sewage sludge treatments

  9. Identification of Sexually Abused Female Adolescents at Risk for Suicidal Ideations: A Classification and Regression Tree Analysis

    Science.gov (United States)

    Brabant, Marie-Eve; Hebert, Martine; Chagnon, Francois

    2013-01-01

    This study explored the clinical profiles of 77 female teenager survivors of sexual abuse and examined the association of abuse-related and personal variables with suicidal ideations. Analyses revealed that 64% of participants experienced suicidal ideations. Findings from classification and regression tree analysis indicated that depression,…

  10. Analysis of natural red dyes (cochineal) in textiles of historical importance using HPLC and multivariate data analysis.

    Science.gov (United States)

    Serrano, Ana; Sousa, Micaela M; Hallett, Jessica; Lopes, João A; Oliveira, M Conceição

    2011-08-01

    A new analytical approach based on high-performance liquid chromatography with diode array detector (HPLC-DAD) and multivariate data analysis was applied and assessed for analyzing the red dye extracted from cochineal insects, used in precious historical textiles. The most widely used method of analysis involves quantification of specific minor compounds (markers), using HPLC-DAD. However, variation in the cochineal markers concentration, use of aggressive dye extraction methods and poor resolution of HPLC chromatograms can compromise the identification of the precise insect species used in the textiles. In this study, a soft extraction method combined with a new dye recovery treatment was developed, capable of yielding HPLC chromatograms with good resolution, for the first time, for historical cochineal-dyed textiles. After principal components analysis (PCA) and mass spectrometry (MS), it was possible to identify the cochineal species used in these textiles, in contrast to the accepted method of analysis. In order to compare both methodologies, 7 cochineal species and 63 historical cochineal insect specimens were analyzed using the two approaches, and then compared with the results for 15 historical textiles in order to assess their applicability to real complex samples. The methodology developed here was shown to provide more accurate and consistent information than the traditional method. Almost all of the historical textiles were dyed with Porphyrophora sp. insects. These results emphasize the importance of adopting the proposed methodology for future research on cochineal (and related red dyes). Mild extraction methods and HPLC-DAD/MS(n) analysis yield distinctive profiles, which, in combination with a PCA reference database, are a powerful tool for identifying red insect dyes.

  11. Auditory-perceptual analysis of voice in abused children and adolescents

    Directory of Open Access Journals (Sweden)

    Luciene Stivanin

    2015-02-01

    Full Text Available Introduction: Abused children and adolescents are exposed to factors that can trigger vocal changes. Objective: This study aimed to analyze the prevalence of vocal changes in abused children and adolescents, through auditory-perceptual analysis of voice and the study of the association between vocal changes, communication disorders, psychiatric disorders, and global functioning. Methods: This was an observational and transversal study of 136 children and adolescents (mean age 10.2 years, 78 male who were assessed by a multidisciplinary team specializing in abused populations. Speech evaluation was performed (involving the aspects of oral and written communication, as well as auditory-perceptual analysis of voice, through the GRBASI scale. Psychiatric diagnosis was performed in accordance with the DSM-IV diagnostic criteria and by applying the K-SADS; global functioning was evaluated by means of the C-GAS scale. Results: The prevalence of vocal change was 67.6%; of the patients with vocal changes, 92.3% had other communication disorders. Voice changes were associated with a loss of seven points in global functioning, and there was no association between vocal changes and psychiatric diagnosis. Conclusion: The prevalence of vocal change was greater than that observed in the general population, with significant associations with communication disorders and global functioning. The results demonstrate that the situations these children experience can intensify the triggering of abusive vocal behaviors and consequently, of vocal changes.

  12. Auditory-perceptual analysis of voice in abused children and adolescents.

    Science.gov (United States)

    Stivanin, Luciene; Santos, Fernanda Pontes dos; Oliveira, Christian César Cândido de; Santos, Bernardo dos; Ribeiro, Simone Tozzini; Scivoletto, Sandra

    2015-01-01

    Abused children and adolescents are exposed to factors that can trigger vocal changes. This study aimed to analyze the prevalence of vocal changes in abused children and adolescents, through auditory-perceptual analysis of voice and the study of the association between vocal changes, communication disorders, psychiatric disorders, and global functioning. This was an observational and transversal study of 136 children and adolescents (mean age 10.2 years, 78 male) who were assessed by a multidisciplinary team specializing in abused populations. Speech evaluation was performed (involving the aspects of oral and written communication, as well as auditory-perceptual analysis of voice, through the GRBASI scale). Psychiatric diagnosis was performed in accordance with the DSM-IV diagnostic criteria and by applying the K-SADS; global functioning was evaluated by means of the C-GAS scale. The prevalence of vocal change was 67.6%; of the patients with vocal changes, 92.3% had other communication disorders. Voice changes were associated with a loss of seven points in global functioning, and there was no association between vocal changes and psychiatric diagnosis. The prevalence of vocal change was greater than that observed in the general population, with significant associations with communication disorders and global functioning. The results demonstrate that the situations these children experience can intensify the triggering of abusive vocal behaviors and consequently, of vocal changes. Copyright © 2014 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial. Published by Elsevier Editora Ltda. All rights reserved.

  13. Multivariate Analysis As a Support for Diagnostic Flowcharts in Allergic Bronchopulmonary Aspergillosis: A Proof-of-Concept Study

    Directory of Open Access Journals (Sweden)

    Joana Vitte

    2017-08-01

    Full Text Available Molecular-based allergy diagnosis yields multiple biomarker datasets. The classical diagnostic score for allergic bronchopulmonary aspergillosis (ABPA, a severe disease usually occurring in asthmatic patients and people with cystic fibrosis, comprises succinct immunological criteria formulated in 1977: total IgE, anti-Aspergillus fumigatus (Af IgE, anti-Af “precipitins,” and anti-Af IgG. Progress achieved over the last four decades led to multiple IgE and IgG(4 Af biomarkers available with quantitative, standardized, molecular-level reports. These newly available biomarkers have not been included in the current diagnostic criteria, either individually or in algorithms, despite persistent underdiagnosis of ABPA. Large numbers of individual biomarkers may hinder their use in clinical practice. Conversely, multivariate analysis using new tools may bring about a better chance of less diagnostic mistakes. We report here a proof-of-concept work consisting of a three-step multivariate analysis of Af IgE, IgG, and IgG4 biomarkers through a combination of principal component analysis, hierarchical ascendant classification, and classification and regression tree multivariate analysis. The resulting diagnostic algorithms might show the way for novel criteria and improved diagnostic efficiency in Af-sensitized patients at risk for ABPA.

  14. Multivariate analysis for hydrochemical changes near the Mizunami Underground Research Laboratory

    International Nuclear Information System (INIS)

    Todaka, Norifumi; Ajima, Shuji; Nakanishi, Shigetaka; Tezuka, Shigeo

    2005-03-01

    One of the tasks in the program of the Mizunami Underground Research Laboratory is development of methodology for evaluating geochemical effects caused by construction of underground facilities. Using the Multivariate Mixing and Mass balance (M3) analysis tool developed by SKB in Sweden, PHREEQC developed by USGS in USA, and the reactive chemical transport simulator (TOUGHREACT) developed by LBNL in USA, the analyses concerning hydrochemical changes of groundwater at the Mizunami Underground Research Laboratory was conducted to verify their modeling applicability and to construct a hydrochemical model for predicting perturbations caused by the shaft excavation in the next phase. M3 analysis is composed of 1) Principal Component Analysis, 2) mixing calculation from reference waters (end-members) and 3) mass balance calculation. M3 analysis is executed using 180 geochemical data sets, selected from 441 data sets of groundwater, river waters, rain waters, hot springs and drilling waters, based on available components (Na, K, Ca, Mg, Cl, HCO 3 , SO 4 and F) and ion charge balance (within ±5 %). As the result of this analysis, groundwater is characterized by the mixing process of 4 types of reference waters, 'Na-Ca-Cl type water (high salinity)', 'Na-Ca-Cl type water (low salinity)', 'Surface water', and 'Na-(Ca)-HCO 3 type water' with water-rock interaction and cation exchange process. PHREEQC inverse modeling is carried out to build the confidence of the M3 model. The PHREEQC inverse model for Na-Ca-Cl type water (low and high salinity) matches well with the M3 model. It is considered that the M3 model is a useful tool for Na-Ca-Cl type water at the Mizunami URL site. On the other hands, the PHREEQC inverse model for Na-(Ca)-HCO 3 type water does not match well with the M3 model. It means that chemical reaction model rather than mixing model should be applied for Na-(Ca)-HCO 3 type water distributed in the northward of the site. One-dimensional reactive geochemical

  15. Multivariate analysis in the evaluation of the antinociceptive activity of irradiated essential oil of nutmeg

    International Nuclear Information System (INIS)

    Santos, Marcelo C.; Lima, Keila S.C.; Oliveira, Sergio E.M.; Lima, Antonio L.S.; Silva, Jose C.C.; Silva, Otniel F.

    2013-01-01

    saline solution, unirradiated oil and samples irradiated with 1.0, 3.0 and 5.0 kGy were compared. In this in vivo experiment the essential oil showed significant antinociceptive activity, with its results varying non-linearly with the radiation doses. The best result was achieved in the dose of 5.0 kGy, inhibiting 92,65% of the contortions. With the obtained results a multivariate analysis was performed, indicating which bioactive molecules of the essential oil were relevant in the antinociceptive activity. (author)

  16. Multivariate analysis of microarray data by principal component discriminant analysis: Prioritizing relevant transcripts linked to the degradation of different carbohydrates in Pseudomonas putida S12

    NARCIS (Netherlands)

    Werf, M.J. van der; Pieterse, B.; Luijk, N. van; Schuren, F.; Werff van der - Vat, B. van der; Overkamp, K.; Jellema, R.H.

    2006-01-01

    The value of the multivariate data analysis tools principal component analysis (PCA) and principal component discriminant analysis (PCDA) for prioritizing leads generated by microarrays was evaluated. To this end, Pseudomonas putida S12 was grown in independent triplicate fermentations on four

  17. The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum.

    Science.gov (United States)

    Goh, Choon Fu; Craig, Duncan Q M; Hadgraft, Jonathan; Lane, Majella E

    2017-02-01

    Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm -1 ) containing the carboxylate (COO - ) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO - asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Batch-to-Batch Quality Consistency Evaluation of Botanical Drug Products Using Multivariate Statistical Analysis of the Chromatographic Fingerprint

    OpenAIRE

    Xiong, Haoshu; Yu, Lawrence X.; Qu, Haibin

    2013-01-01

    Botanical drug products have batch-to-batch quality variability due to botanical raw materials and the current manufacturing process. The rational evaluation and control of product quality consistency are essential to ensure the efficacy and safety. Chromatographic fingerprinting is an important and widely used tool to characterize the chemical composition of botanical drug products. Multivariate statistical analysis has showed its efficacy and applicability in the quality evaluation of many ...

  19. The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum

    OpenAIRE

    Goh, C. F.; Craig, D. Q.; Hadgraft, J.; Lane, M. E.

    2017-01-01

    Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicl...

  20. The Long-Term Health Consequences of Child Physical Abuse, Emotional Abuse, and Neglect: A Systematic Review and Meta-Analysis

    Science.gov (United States)

    Norman, Rosana E.; Byambaa, Munkhtsetseg; De, Rumna; Butchart, Alexander; Scott, James; Vos, Theo

    2012-01-01

    Background Child sexual abuse is considered a modifiable risk factor for mental disorders across the life course. However the long-term consequences of other forms of child maltreatment have not yet been systematically examined. The aim of this study was to summarise the evidence relating to the possible relationship between child physical abuse, emotional abuse, and neglect, and subsequent mental and physical health outcomes. Methods and Findings A systematic review was conducted using the Medline, EMBASE, and PsycINFO electronic databases up to 26 June 2012. Published cohort, cross-sectional, and case-control studies that examined non-sexual child maltreatment as a risk factor for loss of health were included. All meta-analyses were based on quality-effects models. Out of 285 articles assessed for eligibility, 124 studies satisfied the pre-determined inclusion criteria for meta-analysis. Statistically significant associations were observed between physical abuse, emotional abuse, and neglect and depressive disorders (physical abuse [odds ratio (OR) = 1.54; 95% CI 1.16–2.04], emotional abuse [OR = 3.06; 95% CI 2.43–3.85], and neglect [OR = 2.11; 95% CI 1.61–2.77]); drug use (physical abuse [OR = 1.92; 95% CI 1.67–2.20], emotional abuse [OR = 1.41; 95% CI 1.11–1.79], and neglect [OR = 1.36; 95% CI 1.21–1.54]); suicide attempts (physical abuse [OR = 3.40; 95% CI 2.17–5.32], emotional abuse [OR = 3.37; 95% CI 2.44–4.67], and neglect [OR = 1.95; 95% CI 1.13–3.37]); and sexually transmitted infections and risky sexual behaviour (physical abuse [OR = 1.78; 95% CI 1.50–2.10], emotional abuse [OR = 1.75; 95% CI 1.49–2.04], and neglect [OR = 1.57; 95% CI 1.39–1.78]). Evidence for causality was assessed using Bradford Hill criteria. While suggestive evidence exists for a relationship between maltreatment and chronic diseases and lifestyle risk factors, more research is required to confirm these

  1. The long-term health consequences of child physical abuse, emotional abuse, and neglect: a systematic review and meta-analysis.

    Directory of Open Access Journals (Sweden)

    Rosana E Norman

    Full Text Available BACKGROUND: Child sexual abuse is considered a modifiable risk factor for mental disorders across the life course. However the long-term consequences of other forms of child maltreatment have not yet been systematically examined. The aim of this study was to summarise the evidence relating to the possible relationship between child physical abuse, emotional abuse, and neglect, and subsequent mental and physical health outcomes. METHODS AND FINDINGS: A systematic review was conducted using the Medline, EMBASE, and PsycINFO electronic databases up to 26 June 2012. Published cohort, cross-sectional, and case-control studies that examined non-sexual child maltreatment as a risk factor for loss of health were included. All meta-analyses were based on quality-effects models. Out of 285 articles assessed for eligibility, 124 studies satisfied the pre-determined inclusion criteria for meta-analysis. Statistically significant associations were observed between physical abuse, emotional abuse, and neglect and depressive disorders (physical abuse [odds ratio (OR = 1.54; 95% CI 1.16-2.04], emotional abuse [OR = 3.06; 95% CI 2.43-3.85], and neglect [OR = 2.11; 95% CI 1.61-2.77]; drug use (physical abuse [OR = 1.92; 95% CI 1.67-2.20], emotional abuse [OR = 1.41; 95% CI 1.11-1.79], and neglect [OR = 1.36; 95% CI 1.21-1.54]; suicide attempts (physical abuse [OR = 3.40; 95% CI 2.17-5.32], emotional abuse [OR = 3.37; 95% CI 2.44-4.67], and neglect [OR = 1.95; 95% CI 1.13-3.37]; and sexually transmitted infections and risky sexual behaviour (physical abuse [OR = 1.78; 95% CI 1.50-2.10], emotional abuse [OR = 1.75; 95% CI 1.49-2.04], and neglect [OR = 1.57; 95% CI 1.39-1.78]. Evidence for causality was assessed using Bradford Hill criteria. While suggestive evidence exists for a relationship between maltreatment and chronic diseases and lifestyle risk factors, more research is required to confirm these relationships. CONCLUSIONS: This overview of the evidence

  2. Integrated biomarker response in catfish Hypostomus ancistroides by multivariate analysis in the Pirapó River, southern Brazil.

    Science.gov (United States)

    Ghisi, Nédia C; Oliveira, Elton C; Mendonça Mota, Thais F; Vanzetto, Guilherme V; Roque, Aliciane A; Godinho, Jayson P; Bettim, Franciele Lima; Silva de Assis, Helena Cristina da; Prioli, Alberto J

    2016-10-01

    Aquatic pollutants produce multiple consequences in organisms, populations, communities and ecosystems, affecting the function of organs, reproductive state, population size, species survival and even biodiversity. In order to monitor the health of aquatic organisms, biomarkers have been used as effective tools in environmental risk assessment. The aim of this study is to evaluate, through a multivariate and integrative analysis, the response of the native species Hypostomus ancistroides over a pollution gradient in the main water supply body of northwestern Paraná state (Brazil). The condition factor, micronucleus test and erythrocyte nuclear abnormalities (ENA), comet assay, measurement of the cerebral and muscular enzyme acetylcholinesterase (AChE), and histopathological analysis of liver and gill were evaluated in fishes from three sites of the Pirapó River during the dry and rainy seasons. The multivariate general result showed that the interaction between the seasons and the sites was significant: there are variations in the rates of alterations in the biological parameters, depending on the time of year researched at each site. In general, the best results were observed for the site nearest the spring, and alterations in the parameters at the intermediate and downstream sites. In sum, the results of this study showed the necessity of a multivariate analysis, evaluating several biological parameters, to obtain an integrated response to the effects of the environmental pollutants on the organisms. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Evaluation of the microscopic distribution of florfenicol in feed pellets for salmon by Fourier Transform infrared imaging and multivariate analysis.

    Science.gov (United States)

    Bastidas, Camila Y; von Plessing, Carlos; Troncoso, José; Del P Castillo, Rosario

    2018-04-15

    Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.25 μm and 1.56 μm × 1.56 μm, in different zones of feed pellets. Since the concentration of the drug was 3.44 mg FF/g pellet, this is the first report showing the powerful ability of the used of spectroscopic techniques and multivariate analysis, especially the augmented MCR-ALS, to describe the FF distribution in both the surface and inner parts of feed pellets at low concentration, in a complex matrix and at the microscopic level. The results allow monitoring the incorporation of the drug into the feed pellets. Copyright © 2018 Elsevier B.V. All rights reserved.

  4. A Scheme for Initial Exploratory Data Analysis of Multivariate Image Data

    DEFF Research Database (Denmark)

    Hilger, Klaus Baggesen; Nielsen, Allan Aasbjerg; Larsen, Rasmus

    2001-01-01

    A new scheme is proposed for handling initial exploratory analyses of multivariate image data. The method is invariant to linear transformations of the original data and is useful for data fusion of multisource measurements. The scheme includes dimensionality reduction followed by unsupervised...... clustering of the data. A transformation is proposed which maximizes autocorrelation by projection onto subspaces with signal-to-noise ratio dependent variance. We apply the traditional fuzzy c-means algorithm and introduce two additional memberships enhancing the textural awareness of the algorithm. Cluster...

  5. Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

    Directory of Open Access Journals (Sweden)

    Hesse Morten

    2005-05-01

    Full Text Available Abstract Background Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. Methods The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133, and (b a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. Results It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. Conclusion The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

  6. Graft-versus-host disease after orthotopic liver transplantation: multivariate analysis of risk factors.

    Science.gov (United States)

    Elfeki, Mohamed A; Pungpapong, Surakit; Genco, Petrina V; Nakhleh, Raouf E; Nguyen, Justin H; Harnois, Denise M

    2015-12-01

    Graft-versus-host disease (GVHD) is a rare, fatal complication following orthotopic liver transplantation (OLT). To date, several risk factors have been proposed, but reports on these factors have been inconclusive. This is a retrospective, case-control study of prospectively collected data from 2775 OLTs performed at our institution. Eight cases of GVHD after OLT were diagnosed on the basis of the patient's clinical characteristics, and the findings were confirmed with skin and colonic biopsies. Each case was matched to three controls based on the diagnosis of liver disease, recipient's age, and blood group. Univariate and multivariate analyses were performed to identify risk factors associated with the development of GVHD after OLT. The univariate and multivariate analyses identified two main risk factors associated with development of GVHD in OLT recipients, a difference between recipient and donor age of >20 yr, and any human leukocyte antigen class I matches. Taking these two risk factors into consideration while matching prospective donors and recipients may reduce further incidence of GVHD in OLT patients. However, further studies are recommended to validate these findings. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  7. A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections

    Directory of Open Access Journals (Sweden)

    Evans Corey J

    2006-10-01

    Full Text Available Abstract Background Three-dimensional (3D multivariate Fourier Transform Infrared (FTIR image maps of tissue sections are presented. A villoglandular adenocarcinoma from a cervical biopsy with a number of interesting anatomical features was used as a model system to demonstrate the efficacy of the technique. Methods Four FTIR images recorded using a focal plane array detector of adjacent tissue sections were stitched together using a MATLAB® routine and placed in a single data matrix for multivariate analysis using Cytospec™. Unsupervised Hierarchical Cluster Analysis (UHCA was performed simultaneously on all 4 sections and 4 clusters plotted. The four UHCA maps were then stacked together and interpolated with a box function using SCIRun software. Results The resultant 3D-images can be rotated in three-dimensions, sliced and made semi-transparent to view the internal structure of the tissue block. A number of anatomical and histopathological features including connective tissue, red blood cells, inflammatory exudate and glandular cells could be identified in the cluster maps and correlated with Hematoxylin & Eosin stained sections. The mean extracted spectra from individual clusters provide macromolecular information on tissue components. Conclusion 3D-multivariate imaging provides a new avenue to study the shape and penetration of important anatomical and histopathological features based on the underlying macromolecular chemistry and therefore has clear potential in biology and medicine.

  8. A three-dimensional multivariate image processing technique for the analysis of FTIR spectroscopic images of multiple tissue sections.

    Science.gov (United States)

    Wood, Bayden R; Bambery, Keith R; Evans, Corey J; Quinn, Michael A; McNaughton, Don

    2006-10-03

    Three-dimensional (3D) multivariate Fourier Transform Infrared (FTIR) image maps of tissue sections are presented. A villoglandular adenocarcinoma from a cervical biopsy with a number of interesting anatomical features was used as a model system to demonstrate the efficacy of the technique. Four FTIR images recorded using a focal plane array detector of adjacent tissue sections were stitched together using a MATLAB routine and placed in a single data matrix for multivariate analysis using Cytospec. Unsupervised Hierarchical Cluster Analysis (UHCA) was performed simultaneously on all 4 sections and 4 clusters plotted. The four UHCA maps were then stacked together and interpolated with a box function using SCIRun software. The resultant 3D-images can be rotated in three-dimensions, sliced and made semi-transparent to view the internal structure of the tissue block. A number of anatomical and histopathological features including connective tissue, red blood cells, inflammatory exudate and glandular cells could be identified in the cluster maps and correlated with Hematoxylin & Eosin stained sections. The mean extracted spectra from individual clusters provide macromolecular information on tissue components. 3D-multivariate imaging provides a new avenue to study the shape and penetration of important anatomical and histopathological features based on the underlying macromolecular chemistry and therefore has clear potential in biology and medicine.

  9. Discrimination of cultivation ages and cultivars of ginseng leaves using Fourier transform infrared spectroscopy combined with multivariate analysis.

    Science.gov (United States)

    Kwon, Yong-Kook; Ahn, Myung Suk; Park, Jong Suk; Liu, Jang Ryol; In, Dong Su; Min, Byung Whan; Kim, Suk Weon

    2014-01-01

    To determine whether Fourier transform (FT)-IR spectral analysis combined with multivariate analysis of whole-cell extracts from ginseng leaves can be applied as a high-throughput discrimination system of cultivation ages and cultivars, a total of total 480 leaf samples belonging to 12 categories corresponding to four different cultivars (Yunpung, Kumpung, Chunpung, and an open-pollinated variety) and three different cultivation ages (1 yr, 2 yr, and 3 yr) were subjected to FT-IR. The spectral data were analyzed by principal component analysis and partial least squares-discriminant analysis. A dendrogram based on hierarchical clustering analysis of the FT-IR spectral data on ginseng leaves showed that leaf samples were initially segregated into three groups in a cultivation age-dependent manner. Then, within the same cultivation age group, leaf samples were clustered into four subgroups in a cultivar-dependent manner. The overall prediction accuracy for discrimination of cultivars and cultivation ages was 94.8% in a cross-validation test. These results clearly show that the FT-IR spectra combined with multivariate analysis from ginseng leaves can be applied as an alternative tool for discriminating of ginseng cultivars and cultivation ages. Therefore, we suggest that this result could be used as a rapid and reliable F1 hybrid seed-screening tool for accelerating the conventional breeding of ginseng.

  10. Recent trends in application of multivariate curve resolution approaches for improving gas chromatography-mass spectrometry analysis of essential oils.

    Science.gov (United States)

    Jalali-Heravi, Mehdi; Parastar, Hadi

    2011-08-15

    Essential oils (EOs) are valuable natural products that are popular nowadays in the world due to their effects on the health conditions of human beings and their role in preventing and curing diseases. In addition, EOs have a broad range of applications in foods, perfumes, cosmetics and human nutrition. Among different techniques for analysis of EOs, gas chromatography-mass spectrometry (GC-MS) is the most important one in recent years. However, there are some fundamental problems in GC-MS analysis including baseline drift, spectral background, noise, low S/N (signal to noise) ratio, changes in the peak shapes and co-elution. Multivariate curve resolution (MCR) approaches cope with ongoing challenges and are able to handle these problems. This review focuses on the application of MCR techniques for improving GC-MS analysis of EOs published between January 2000 and December 2010. In the first part, the importance of EOs in human life and their relevance in analytical chemistry is discussed. In the second part, an insight into some basics needed to understand prospects and limitations of the MCR techniques are given. In the third part, the significance of the combination of the MCR approaches with GC-MS analysis of EOs is highlighted. Furthermore, the commonly used algorithms for preprocessing, chemical rank determination, local rank analysis and multivariate resolution in the field of EOs analysis are reviewed. Copyright © 2011 Elsevier B.V. All rights reserved.

  11. Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes

    Science.gov (United States)

    Brizzi, S.; Sandri, L.; Funiciello, F.; Corbi, F.; Piromallo, C.; Heuret, A.

    2018-03-01

    The observed maximum magnitude of subduction megathrust earthquakes is highly variable worldwide. One key question is which conditions, if any, favor the occurrence of giant earthquakes (Mw ≥ 8.5). Here we carry out a multivariate statistical study in order to investigate the factors affecting the maximum magnitude of subduction megathrust earthquakes. We find that the trench-parallel extent of subduction zones and the thickness of trench sediments provide the largest discriminating capability between subduction zones that have experienced giant earthquakes and those having significantly lower maximum magnitude. Monte Carlo simulations show that the observed spatial distribution of giant earthquakes cannot be explained by pure chance to a statistically significant level. We suggest that the combination of a long subduction zone with thick trench sediments likely promotes a great lateral rupture propagation, characteristic of almost all giant earthquakes.

  12. Multivariate analysis methods to tag b quark events at LEP/SLC

    International Nuclear Information System (INIS)

    Brandl, B.; Falvard, A.; Guicheney, C.; Henrard, P.; Jousset, J.; Proriol, J.

    1992-01-01

    Multivariate analyses are applied to tag Z → bb-bar events at LEP/SLC. They are based on the specific b-event shape caused by the large b-quark mass. Discriminant analyses, classification trees and neural networks are presented and their performances are compared. It is shown that the neural network approach, due to its non-linearity, copes best with the complexity of the problem. As an example for an application of the developed methods the measurement of Γ(Z → bb-bar) is discussed. The usefulness of methods based on the global event shape is limited by the uncertainties introduced by the necessity of event simulation. As solution a double tag method is presented which can be applied to many tasks of LEP/SLC heavy flavour physics. (author) 29 refs.; 6 figs.; 1 tab

  13. Detecting a currency’s dominance using multivariate time series analysis

    Science.gov (United States)

    Syahidah Yusoff, Nur; Sharif, Shamshuritawati

    2017-09-01

    A currency exchange rate is the price of one country’s currency in terms of another country’s currency. There are four different prices; opening, closing, highest, and lowest can be achieved from daily trading activities. In the past, a lot of studies have been carried out by using closing price only. However, those four prices are interrelated to each other. Thus, the multivariate time series can provide more information than univariate time series. Therefore, the enthusiasm of this paper is to compare the results of two different approaches, which are mean vector and Escoufier’s RV coefficient in constructing similarity matrices of 20 world currencies. Consequently, both matrices are used to substitute the correlation matrix required by network topology. With the help of degree centrality measure, we can detect the currency’s dominance for both networks. The pros and cons for both approaches will be presented at the end of this paper.

  14. Analysis of fatty acid composition of sea cucumber Apostichopus japonicus using multivariate statistics

    Science.gov (United States)

    Xu, Qinzeng; Gao, Fei; Xu, Qiang; Yang, Hongsheng

    2014-11-01

    Fatty acids (FAs) provide energy and also can be used to trace trophic relationships among organisms. Sea cucumber Apostichopus japonicus goes into a state of aestivation during warm summer months. We examined fatty acid profiles in aestivated and non-aestivated A. japonicus using multivariate analyses (PERMANOVA, MDS, ANOSIM, and SIMPER). The results indicate that the fatty acid profiles of aestivated and non-aestivated sea cucumbers differed significantly. The FAs that were produced by bacteria and brown kelp contributed the most to the differences in the fatty acid composition of aestivated and nonaestivated sea cucumbers. Aestivated sea cucumbers may synthesize FAs from heterotrophic bacteria during early aestivation, and long chain FAs such as eicosapentaenoic (EPA) and docosahexaenoic acid (DHA) that produced from intestinal degradation, are digested during deep aestivation. Specific changes in the fatty acid composition of A. japonicus during aestivation needs more detailed study in the future.

  15. [The analysis of multivariate image and chemometrics in TLC fingerprinting of artificial cow-bezoar].

    Science.gov (United States)

    Yao, Ling-Wen; Shi, Yan; Sun, Dong-Mei; Cheng, Xian-Long; Wei, Feng; Ma, Shuang-Cheng

    2017-06-01

    A method of thin-layer fingerprinting chromatogram of artificial cow-bezoar was established with the developing solvent consisting of cyclohexane, ethyl acetate, acetic acid and methanol (2∶7∶1∶2), and 10% sulfuric acid ethanol solution sprayed as colour-developing agent. After heated at 105 ℃, TLC was recorded as an image in ultraviolet light at 366 nm which was converted into grayscale. By the gray value extracted from the grayscale, the multivariate data obtained from TLC of samples could be analyzed by chemometric method. The results indicated that samples from different manufacturers could be distinguished by this method and some specific bands were found out. All in one, this simple and practical method was suitable for the evaluation of quality difference. Copyright© by the Chinese Pharmaceutical Association.

  16. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

    Science.gov (United States)

    Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

    2016-07-01

    A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  17. Multivariate Analysis of Rangeland Vegetation and Soil Organic Carbon Describes Degradation, Informs Restoration and Conservation

    Directory of Open Access Journals (Sweden)

    Devan Allen McGranahan

    2013-07-01

    Full Text Available Agricultural expansion has eliminated a high proportion of native land cover and severely degraded remaining native vegetation. Managers must determine where degradation is severe enough to merit restoration action, and what action, if any, is necessary. We report on grassland degraded by multiple factors, including grazing, soil disturbance, and exotic plant species introduced in response to agriculture management. We use a multivariate method to categorize plant communities by degradation state based on floristic and biophysical degradation associated with historical land use. The variables we associate with degradation include abundance of the invasive cool-season grass, tall fescue (Schedonorus phoenix (Scop. Holub; soil organic carbon (SOC; and heavy livestock grazing. Using a series of multivariate analyses (ordination, hierarchical clustering, and multiple regression, we identify patterns in plant community composition and describe floristic degradation states. We found vegetation states to be described largely by vegetation composition associated primarily with tall fescue and secondarily by severe grazing, but not soil organic carbon. Categorizing grasslands by vegetation states helps managers efficiently apply restoration inputs that optimize ecosystem response, so we discuss potential restoration pathways in a state-and-transition model. Reducing stocking rate on grassland where grazing is actively practiced is an important first step that might be sufficient for restoring grassland with high native species richness and minimal degradation from invasive plants. More severe degradation likely requires multiple approaches to reverse degradation. Of these, we recommend restoration of ecological processes and disturbance regimes such as fire and grazing. We suggest old-field grasslands in North America, which are similar to European semi-natural grassland in composition and function, deserve more attention by conservation biologists.

  18. Substance Abuse Among Blacks Across the Diaspora.

    Science.gov (United States)

    Lacey, Krim K; Mouzon, Dawne M; Govia, Ishtar O; Matusko, Niki; Forsythe-Brown, Ivy; Abelson, Jamie M; Jackson, James S

    2016-07-28

    Lower rates of substance abuse are found among Black Americans compared to Whites, but little is known about differences in substance abuse across ethnic groups within the black population. We examined prevalence rates of substance abuse among Blacks across three geographic regions (US, Jamaica, Guyana). The study also sought to ascertain whether length of time, national context and major depressive episodes (MDE) were associated with substance abuse. We utilized three different data sources based upon probability samples collected in three different countries. The samples included 3,570 African Americans and 1,621 US Caribbean Black adults from the 2001-2003 National Survey of American Life (NSAL). An additional 1,142 Guyanese Blacks and 1,176 Jamaican Blacks living in the Caribbean region were included from the 2005 NSAL replication extension study, Family Connections Across Generations and Nations (FCGN). Mental disorders were based upon DSM-IV criteria. For the analysis, we used descriptive statistics, chi-square, and multivariate logistic regression analytic procedures. Prevalence of substance abuse varied by national context, with higher rates among Blacks within the United States compared to the Caribbean region. Rates of substance abuse were lower overall for women, but differ across cohorts by nativity and length of time in the United States, and in association with major depressive episode. The study highlights the need for further examination of how substance abuse disparities between US-based and Caribbean-based populations may become manifested.

  19. Toward a more comprehensive analysis of the role of organizational culture in child sexual abuse in institutional contexts.

    Science.gov (United States)

    Palmer, Donald; Feldman, Valerie

    2017-12-01

    This article draws on a report prepared for the Australian Royal Commission into Institutional Responses to Child Sexual Abuse (Palmer et al., 2016) to develop a more comprehensive analysis of the role that organizational culture plays in child sexual abuse in institutional contexts, where institutional contexts are taken to be formal organizations that include children among their members (referred to here as "youth-serving organizations"). We begin by integrating five strains of theory and research on organizational culture from organizational sociology and management theory into a unified framework for analysis. We then elaborate the main paths through which organizational culture can influence child sexual abuse in youth-serving organizations. We then use our unified analytic framework and our understanding of the main paths through which organizational culture can influence child sexual abuse in youth-serving organizations to analyze the role that organizational culture plays in the perpetration, detection, and response to child sexual abuse in youth-serving organizations. We selectively illustrate our analysis with case materials compiled by the Royal Commission into Institutional Responses to Child Sexual Abuse and reports of child sexual abuse published in a variety of other sources. We conclude with a brief discussion of the policy implications of our analysis. Copyright © 2017. Published by Elsevier Ltd.

  20. Characterization of ionizing radiation effects on bone using Fourier Transform Infrared Spectroscopy and multivariate analysis of spectra

    Energy Technology Data Exchange (ETDEWEB)

    Castro, Pedro Arthur Augusto de; Dias, Derly Augusto; Zezell, Denise Maria, E-mail: zezell@usp.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2017-11-01

    Ionizing radiation has been used as an important treatment and diagnostic method for several diseases. Optical techniques provides an efficient clinical diagnostic to support an accurate evaluation of the interaction of radiation with molecules. Fourier-transform infrared spectroscopy coupled with attenuated total reflectance (ATR-FTIR) is a label-free and nondestructive optical technique that can recognize functional groups in biological samples. In this work, 30 fragments of bone were collected from bovine femur diaphysis. Samples were cut and polished until 1 cm x 1 cm x 1 mm, which were then stored properly in the refrigerated environment. Samples irradiation was performed with a Cobalt-60 Gammacell Irradiator source at doses of 0.1 kGy, 1 kGy, whereas the fragments exposed to dose of 15 kGy was irradiated in a multipurpose irradiator of Cobalt-60. Spectral data was submitted to principal component analysis followed by linear discriminant analysis. Multivariate analysis was performed with Principal component analysis(PCA) followed by Linear Discriminant Analysis(LDA) using MATLAB R2015a software (The Mathworks Inc., Natick, MA, USA). We demonstrated the feasibility of using ATR-FTIR spectroscopy associated with PCA-LDA multivariate technique to evaluate the molecular changes in bone matrix caused by different doses: 0.1 kGy, 1 kGy and 15 kGy. These alterations between the groups are mainly reported in phosphate region. Our results open up new possibilities for protein monitoring relating to dose responses. (author)

  1. Application of UV/VIS spectrophotometry and multivariate analysis to characterization of the species of Solanum sect. Erythrotrichum CHILD.

    Science.gov (United States)

    Basílio, Ionaldo José L D; Moura, Renata K P; Bhattacharyya, Jnanabrata; de Fátima Agra, Maria

    2012-06-01

    The UV/VIS spectral characteristics of the standardized extracts of the leaves of 22 Solanum species of the Leptostemonum clade were investigated in the presence of shift reagents with the aid of multivariate analysis, to obtain data in support of the interspecific and subsectional delimitation proposed for Solanum sect. Erythrotrichum. Of these species, 20 belong to the section Erythrotrichum, S. paniculatum is assigned to the section Torva, and S. robustum is not attributed to a defined section. The results indicated characteristic λ(max) (absorbance maxima) for each species as well as the presence of phenolic compounds like flavonoids such as 5-hydroxy flavonols. Hierarchical cluster analysis (HCA) of the data obtained by UV/VIS analysis of the extracts or the extracts with the added shift reagents AlCl₃ and HCl showed a cophenetic correlation coefficient above 0.92 and the classification of the data into three groups. The principal-component analysis (PCA) revealed that the first three principal components accounted for over 98% of the total variance and showed results similar to those obtained by HCA. The present results supported the current proposal for interspecific delimitation of the studied species and partially supported the division of the section into two subsections. The UV/VIS spectral characteristics along with multivariate analysis appear to be a useful approach for distinguishing among species of the genus Solanum. Copyright © 2012 Verlag Helvetica Chimica Acta AG, Zürich.

  2. Characterization of ionizing radiation effects on bone using Fourier Transform Infrared Spectroscopy and multivariate analysis of spectra

    International Nuclear Information System (INIS)

    Castro, Pedro Arthur Augusto de; Dias, Derly Augusto; Zezell, Denise Maria

    2017-01-01

    Ionizing radiation has been used as an important treatment and diagnostic method for several diseases. Optical techniques provides an efficient clinical diagnostic to support an accurate evaluation of the interaction of radiation with molecules. Fourier-transform infrared spectroscopy coupled with attenuated total reflectance (ATR-FTIR) is a label-free and nondestructive optical technique that can recognize functional groups in biological samples. In this work, 30 fragments of bone were collected from bovine femur diaphysis. Samples were cut and polished until 1 cm x 1 cm x 1 mm, which were then stored properly in the refrigerated environment. Samples irradiation was performed with a Cobalt-60 Gammacell Irradiator source at doses of 0.1 kGy, 1 kGy, whereas the fragments exposed to dose of 15 kGy was irradiated in a multipurpose irradiator of Cobalt-60. Spectral data was submitted to principal component analysis followed by linear discriminant analysis. Multivariate analysis was performed with Principal component analysis(PCA) followed by Linear Discriminant Analysis(LDA) using MATLAB R2015a software (The Mathworks Inc., Natick, MA, USA). We demonstrated the feasibility of using ATR-FTIR spectroscopy associated with PCA-LDA multivariate technique to evaluate the molecular changes in bone matrix caused by different doses: 0.1 kGy, 1 kGy and 15 kGy. These alterations between the groups are mainly reported in phosphate region. Our results open up new possibilities for protein monitoring relating to dose responses. (author)

  3. The Effects of Sexual Abuse as a Child on the Risk of Mothers Physically Abusing Their Children: A Path Analysis Using Systems Theory

    Science.gov (United States)

    Mapp, Susan C.

    2006-01-01

    Objective: The potential path from sexual abuse as a child to the current risk of physical abuse by mothers was assessed. Ontogenic variables including the experience of the parent's sexual abuse as a child and current depression or substance abuse were expected to have a greater impact on the risk of child abuse than microsystem and exosystem…

  4. Multivariate Analysis of the Predictors of Survival for Patients with Hepatocellular Carcinoma Undergoing Transarterial Chemoembolization: Focusing on Superselective Chemoembolization

    International Nuclear Information System (INIS)

    Ji, Suk Kyeong; Cho, Yun Ku; Ahn, Yong Sik; Kim, Mi Young; Park, Yoon Ok; Kim, Jae Kyun; Kim, Wan Tae

    2008-01-01

    While the prognostic factors of survival for patients with hepatocellular carcinoma (HCC) who underwent transarterial chemoembolization (TACE) are well known, the clinical significance of performing selective TACE for HCC patients has not been clearly documented. We tried to analyze the potential factors of disease-free survival for these patients, including the performance of selective TACE. A total of 151 patients with HCC who underwent TACE were retrospectively analyzed for their disease-free survival (a median follow- up of 23 months, range: 1-88 months). Univariate and multivariate analyses were performed for 20 potential factors by using the Cox proportional hazard model, including 19 baseline factors and one procedure-related factor (conventional versus selective TACE). The parameters that proved to be significant on the univariate analysis were subsequently tested with the multivariate model. Conventional or selective TACE was performed for 40 and 111 patients, respectively. Univariate and multivariate analyses revealed that tumor multiplicity, venous tumor thrombosis and selective TACE were the only three independent significant prognostic factors of disease-free survival (p = 0.002, 0.015 and 0.019, respectively). In our study, selective TACE was a favorable prognostic factor for the disease-free survival of patients with HCC who underwent TACE

  5. A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians.

    Directory of Open Access Journals (Sweden)

    Heejung Shim

    Full Text Available We conducted a genome-wide association analysis of 7 subfractions of low density lipoproteins (LDLs and 3 subfractions of intermediate density lipoproteins (IDLs measured by gradient gel electrophoresis, and their response to statin treatment, in 1868 individuals of European ancestry from the Pharmacogenomics and Risk of Cardiovascular Disease study. Our analyses identified four previously-implicated loci (SORT1, APOE, LPA, and CETP as containing variants that are very strongly associated with lipoprotein subfractions (log(10Bayes Factor > 15. Subsequent conditional analyses suggest that three of these (APOE, LPA and CETP likely harbor multiple independently associated SNPs. Further, while different variants typically showed different characteristic patterns of association with combinations of subfractions, the two SNPs in CETP show strikingly similar patterns--both in our original data and in a replication cohort--consistent with a common underlying molecular mechanism. Notably, the CETP variants are very strongly associated with LDL subfractions, despite showing no association with total LDLs in our study, illustrating the potential value of the more detailed phenotypic measurements. In contrast with these strong subfraction associations, genetic association analysis of subfraction response to statins showed much weaker signals (none exceeding log(10Bayes Factor of 6. However, two SNPs (in APOE and LPA previously-reported to be associated with LDL statin response do show some modest evidence for association in our data, and the subfraction response proles at the LPA SNP are consistent with the LPA association, with response likely being due primarily to resistance of Lp(a particles to statin therapy. An additional important feature of our analysis is that, unlike most previous analyses of multiple related phenotypes, we analyzed the subfractions jointly, rather than one at a time. Comparisons of our multivariate analyses with standard

  6. Quantitative Analysis of Magnesium in Soil by Laser-Induced Breakdown Spectroscopy Coupled with Nonlinear Multivariate Calibration

    Science.gov (United States)

    Yongcheng, J.; Wen, S.; Baohua, Z.; Dong, L.

    2017-09-01

    Laser-induced breakdown spectroscopy (LIBS) coupled with the nonlinear multivariate regression method was applied to analyze magnesium (Mg) contents in soil. The plasma was generated using a 100 mJ Nd:YAG pulsed laser, and the spectra were acquired using a multi-channel spectrometer integrated with a CCD detector. The line at 383.8 nm was selected as the analysis line for Mg. The calibration model between the intensity of characteristic line and the concentration of Mg was constructed. The traditional calibration curve showed that the concentration of Mg was not only related to the line intensity of itself, but also to other elements in soil. The intensity of characteristic lines for Mg (Mg I 383.8 nm), manganese (Mn) (Mn I 403.1 nm), and iron (Fe) (Fe I 407.2 nm) were used as input data for nonlinear multivariate calculation. According to the results of nonlinear regression, the ternary nonlinear regression was the most appropriate of the studied models. A good agreement was observed between the actual concentration provided by inductively coupled plasma mass spectrometry (ICP-MS) and the predicted value obtained using the nonlinear multivariate regression model. The correlation coefficient between predicted concentration and the measured value was 0.987, while the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were reduced to 0.017% and 0.014%, respectively. The ratio of the standard deviation of the validation to the RMSEP increased to 8.79, and the relative error was below 1.21% for nine validation samples. This indicated that the multivariate model can obtain better predicted accuracy than the calibration curve. These results also suggest that the LIBS technique is a powerful tool for analyzing the micro-nutrient elements in soil by selecting calibration and validation samples with similar matrix composition.

  7. Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis.

    Science.gov (United States)

    Liu, Fei; Ye, Lanhan; Peng, Jiyu; Song, Kunlin; Shen, Tingting; Zhang, Chu; He, Yong

    2018-02-27

    Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

  8. A comparison of multivariate analysis techniques and variable selection strategies in a laser-induced breakdown spectroscopy bacterial classification

    Energy Technology Data Exchange (ETDEWEB)

    Putnam, Russell A., E-mail: putnamr@uwindsor.ca [Department of Physics, University of Windsor, Windsor, Ontario N9B 3P4 (Canada); Mohaidat, Qassem I., E-mail: q.muhaidat@yu.edu.jo [Department of Physics, Yarmouk University, Irbid 21163 (Jordan); Daabous, Andrew, E-mail: daabousa@uwindsor.ca [Department of Physics, University of Windsor, Windsor, Ontario N9B 3P4 (Canada); Rehse, Steven J., E-mail: rehse@uwindsor.ca [Department of Physics, University of Windsor, Windsor, Ontario N9B 3P4 (Canada)

    2013-09-01

    Laser-induced breakdown spectroscopy has been used to obtain spectral fingerprints from live bacterial specimens from thirteen distinct taxonomic bacterial classes representative of five bacterial genera. By taking sums, ratios, and complex ratios of measured atomic emission line intensities three unique sets of independent variables (models) were constructed to determine which choice of independent variables provided optimal genus-level classification of unknown specimens utilizing a discriminant function analysis. A model composed of 80 independent variables constructed from simple and complex ratios of the measured emission line intensities was found to provide the greatest sensitivity and specificity. This model was then used in a partial least squares discriminant analysis to compare the performance of this multivariate technique with a discriminant function analysis. The partial least squares discriminant analysis possessed a higher true positive rate, possessed a higher false positive rate, and was more effective at distinguishing between highly similar spectra from closely related bacterial genera. This suggests it may be the preferred multivariate technique in future species-level or strain-level classifications. - Highlights: • Laser-induced breakdown spectroscopy was used to classify bacteria by genus. • We examine three different independent variable down selection models. • A PLS-DA returned higher rates of true positives than a DFA. • A PLS-DA returned higher rates of false positives than a DFA. • A PLS-DA was better able to discriminate similar spectra compared to DFA.

  9. Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Fei Liu

    2018-02-01

    Full Text Available Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS, coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice. For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV. Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

  10. Useful multivariate kinetic analysis: Size determination based on cystein-induced aggregation of gold nanoparticles.

    Science.gov (United States)

    Rabbani, Faride; Hormozi Nezhad, Mohammad Reza; Abdollahi, Hamid

    2013-11-01

    This study describes spectrometric monitored kinetic processes to determine the size of citrate-capped Au nanoparticles (Au NPs) based on aggregation induced by l-cysteine (l-Cys) as a molecular linker. The Au NPs association process is thoroughly dependent on pH, concentration and size of nanoparticles. Size dependency of aggregation inspirits to determine the average diameters of Au NPs. For this aim the procedure is achieved in aqueous medium at pH 7 (phosphate buffer), and multivariate data including kinetic spectra of Au NPs are collected during aggregation process. Subsequently partial least squares (PLS) modeling is carried out analyzing the obtained data. The model is built on the basis of relation between the kinetics behavior of aggregation and different Au NPs sizes. Training the model was performed using latent variables (LVs) of the original data. The analytical performance of the model was characterized by relative standard error. The proposed method was applied to determination of size in unknown samples. The predicted sizes of unknown samples that obtained by the introduced method are interestingly in agreement with the sizes measured by Transmission Electron Microscopy (TEM) images and Dynamic Light Scattering (DLS) measurement. Copyright © 2013 Elsevier B.V. All rights reserved.

  11. Explaining public support for space exploration funding in America: A multivariate analysis

    Science.gov (United States)

    Nadeau, François

    2013-05-01

    Recent studies have identified the need to understand what shapes public attitudes toward space policy. I address this gap in the literature by developing a multivariate regression model explaining why many Americans support government spending on space exploration. Using pooled data from the 2006 and 2008 General Social Surveys, the study reveals that spending preferences on space exploration are largely apolitical and associated instead with knowledge and opinions about science. In particular, the odds of wanting to increase funding for space exploration are significantly higher for white, male Babyboomers with a higher socio-economic status, a fondness for organized science, and a post-secondary science education. As such, I argue that public support for NASA's spending epitomizes what Launius termed "Apollo Nostalgia" in American culture. That is, Americans benefitting most from the old social order of the 1960s developed a greater fondness for science that makes them more likely to lament the glory days of space exploration. The article concludes with suggestions for how to elaborate on these findings in future studies.

  12. Measuring Connectivity in Linear Multivariate Processes: Definitions, Interpretation, and Practical Analysis

    Directory of Open Access Journals (Sweden)

    Luca Faes

    2012-01-01

    Full Text Available This tutorial paper introduces a common framework for the evaluation of widely used frequency-domain measures of coupling (coherence, partial coherence and causality (directed coherence, partial directed coherence from the parametric representation of linear multivariate (MV processes. After providing a comprehensive time-domain definition of the various forms of connectivity observed in MV processes, we particularize them to MV autoregressive (MVAR processes and derive the corresponding frequency-domain measures. Then, we discuss the theoretical interpretation of these MVAR-based connectivity measures, showing that each of them reflects a specific time-domain connectivity definition and how this results in the description of peculiar aspects of the information transfer in MV processes. Furthermore, issues related to the practical utilization of these measures on real-time series are pointed out, including MVAR model estimation and significance assessment. Finally, limitations and pitfalls arising from model mis-specification are discussed, indicating possible solutions and providing practical recommendations for a safe computation of the connectivity measures. An example of estimation of the presented measures from multiple EEG signals recorded during a combined visuomotor task is also reported, showing how evaluation of coupling and causality in the frequency domain may help describing specific neurophysiological mechanisms.

  13. Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values.

    Directory of Open Access Journals (Sweden)

    Hairong Huang

    Full Text Available This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ values in clinical practice.We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement and T2 (before dental restoration. A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval.The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5. In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2. Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2.These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice.

  14. Reagent-free bacterial identification using multivariate analysis of transmission spectra

    Science.gov (United States)

    Smith, Jennifer M.; Huffman, Debra E.; Acosta, Dayanis; Serebrennikova, Yulia; García-Rubio, Luis; Leparc, German F.

    2012-10-01

    The identification of bacterial pathogens from culture is critical to the proper administration of antibiotics and patient treatment. Many of the tests currently used in the clinical microbiology laboratory for bacterial identification today can be highly sensitive and specific; however, they have the additional burdens of complexity, cost, and the need for specialized reagents. We present an innovative, reagent-free method for the identification of pathogens from culture. A clinical study has been initiated to evaluate the sensitivity and specificity of this approach. Multiwavelength transmission spectra were generated from a set of clinical isolates including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Spectra of an initial training set of these target organisms were used to create identification models representing the spectral variability of each species using multivariate statistical techniques. Next, the spectra of the blinded isolates of targeted species were identified using the model achieving >94% sensitivity and >98% specificity, with 100% accuracy for P. aeruginosa and S. aureus. The results from this on-going clinical study indicate this approach is a powerful and exciting technique for identification of pathogens. The menu of models is being expanded to include other bacterial genera and species of clinical significance.

  15. Multivariate statistical analysis of Raman spectra to distinguish normal, tumor, lymph nodes and mastitis in mouse mammary tissues

    Science.gov (United States)

    Dai, H.; Thakur, J. S.; Serhatkulu, G. K.; Pandya, A. K.; Auner, G. W.; Naik, R.; Freeman, D. C.; Naik, V. M.; Cao, A.; Klein, M. D.; Rabah, R.

    2006-03-01

    Raman spectra ( > 680) of normal mammary gland, malignant mammary gland tumors, and lymph node tissues from mice injected with 4T1 tumor cells have been recorded using 785 nm excitation laser. The state of the tissues was confirmed by standard pathological tests. The multivariate statistical analysis methods (principle component analysis and discriminant functional analysis) have been used to categorize the Raman spectra. The statistical algorithms based on the Raman spectral peak heights, clearly separated tissues into six distinct classes, including mastitis, which is clearly separated from normal and tumor. This study suggests that the Raman spectroscopy can possibly perform a real-time analysis of the human mammary tissues for the detection of cancer.

  16. Discrimination of wild Paris based on near infrared spectroscopy and high performance liquid chromatography combined with multivariate analysis.

    Directory of Open Access Journals (Sweden)

    Yanli Zhao

    Full Text Available Different geographical origins and species of Paris obtained from southwestern China were discriminated by near infrared (NIR spectroscopy and high performance liquid chromatography (HPLC combined with multivariate analysis. The NIR parameter settings were scanning (64 times, resolution (4 cm(-1, scanning range (10,000 cm(-1∼4000 cm(-1 and parallel collection (3 times. NIR spectrum was optimized by TQ 8.6 software, and the ranges 7455∼6852 cm(-1 and 5973∼4007 cm(-1 were selected according to the spectrum standard deviation. The contents of polyphyllin I, polyphyllin II, polyphyllin VI, and polyphyllin VII and total steroid saponins were detected by HPLC. The contents of chemical components data matrix and spectrum data matrix were integrated and analyzed by partial least squares discriminant analysis (PLS-DA. From the PLS-DA model of NIR spectrum, Paris samples were separated into three groups according to the different geographical origins. The R(2X and Q(2Y described accumulative contribution rates were 99.50% and 94.03% of the total variance, respectively. The PLS-DA model according to 12 species of Paris described 99.62% of the variation in X and predicted 95.23% in Y. The results of the contents of chemical components described differences among collections quantitatively. A multivariate statistical model of PLS-DA showed geographical origins of Paris had a much greater influence on Paris compared with species. NIR and HPLC combined with multivariate analysis could discriminate different geographical origins and different species. The quality of Paris showed regional dependence.

  17. Multivariate temporal pattern analysis applied to the study of rat behavior in the elevated plus maze: methodological and conceptual highlights.

    Science.gov (United States)

    Casarrubea, M; Magnusson, M S; Roy, V; Arabo, A; Sorbera, F; Santangelo, A; Faulisi, F; Crescimanno, G

    2014-08-30

    Aim of this article is to illustrate the application of a multivariate approach known as t-pattern analysis in the study of rat behavior in elevated plus maze. By means of this multivariate approach, significant relationships among behavioral events in the course of time can be described. Both quantitative and t-pattern analyses were utilized to analyze data obtained from fifteen male Wistar rats following a trial 1-trial 2 protocol. In trial 2, in comparison with the initial exposure, mean occurrences of behavioral elements performed in protected zones of the maze showed a significant increase counterbalanced by a significant decrease of mean occurrences of behavioral elements in unprotected zones. Multivariate t-pattern analysis, in trial 1, revealed the presence of 134 t-patterns of different composition. In trial 2, the temporal structure of behavior become more simple, being present only 32 different t-patterns. Behavioral strings and stripes (i.e. graphical representation of each t-pattern onset) of all t-patterns were presented both for trial 1 and trial 2 as well. Finally, percent distributions in the three zones of the maze show a clear-cut increase of t-patterns in closed arm and a significant reduction in the remaining zones. Results show that previous experience deeply modifies the temporal structure of rat behavior in the elevated plus maze. In addition, this article, by highlighting several conceptual, methodological and illustrative aspects on the utilization of t-pattern analysis, could represent a useful background to employ such a refined approach in the study of rat behavior in elevated plus maze. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences

    NARCIS (Netherlands)

    Timmerman, Mariek E.; Kiers, Henk A.L.

    A class of four simultaneous component models for the exploratory analysis of multivariate time series collected from more than one subject simultaneously is discussed. In each of the models, the multivariate time series of each subject is decomposed into a few series of component scores and a

  19. The iron bars from the ‘Gresham Ship’: employing multivariate statistics to further slag inclusion analysis of ferrous objects

    DEFF Research Database (Denmark)

    Birch, Thomas; Martinón-Torres, Marcos

    2015-01-01

    An assemblage of post-medieval iron bars was found with the Princes Channel wreck, salvaged from the Thames Estuary in 2003. They were recorded and studied, with a focus on metallography and slag inclusion analysis. The investigation provided an opportunity to explore the use of multivariate stat...... that the bars were formed from raw blooms, and all were made with iron produced by the direct process. The outward uniformity of the bars is at odds with the variable quality of iron displayed within and between bars....

  20. Prevalence of childhood abuse among people who are homeless in Western countries: a systematic review and meta-analysis.

    Science.gov (United States)

    Sundin, Eva C; Baguley, Thom

    2015-02-01

    This article systematically reviews studies of prevalence of childhood experience of physical and sexual abuse in adult people who are homeless in Western countries. Medline, PsychInfo, and the Cochrane Library were searched using the keywords: homeless*, child* abuse, child* trauma, and child* adversity and the bibliographies of identified articles were reviewed. Sources of heterogeneity in the prevalence rates were explored by meta-regression analysis. Twenty-four reports published between January 1990 and August 2013 in three countries provided estimates obtained from up to 9,730 adult individuals who were homeless. Prevalence of reported childhood physical abuse ranged from 6 to 94% with average prevalence of 37%, 95% CI [25, 51]. Reported sexual abuse ranged from 4 to 62%, with average prevalence estimated as 32%, 95% CI [23, 44] for female and 10% for male, 95% CI [6, 17]. Substantial heterogeneity was observed among the studies (I2 ≥ 98%). Including moderators greatly reduced but did not eliminate this heterogeneity. Moderator analyses suggested that reported physical abuse tended to be higher for predominately white samples and tended to be lower for younger samples. Sexual abuse was far more prevalent in predominately female samples and slightly higher in non-US samples and convenience samples. The findings of this study suggest that childhood physical and sexual abuse is more prevalent among the homeless in Western countries than in the global population. Physical abuse appears to be particularly prevalent in younger samples and sexual abuse rates are higher in predominately female samples. Further investigation is needed to advance our understanding of how trauma informed treatment and care for the homeless effectively can take into account the service user's experiences of childhood abuse.

  1. Multivariate analysis of traumatic brain injury: development of an assessment score

    Directory of Open Access Journals (Sweden)

    John E. Buonora

    2015-03-01

    Full Text Available Important challenges for the diagnosis and monitoring of mild traumatic brain injury (mTBI include the development of plasma biomarkers for assessing neurologic injury, monitoring pathogenesis and predicting vulnerability for the development of untoward neurologic outcomes. While several biomarker proteins have shown promise in this regard, used individually, these candidates lack adequate sensitivity and/or specificity for making a definitive diagnosis or identifying those at risk of subsequent pathology. The objective for this study was to evaluate a panel of six recognized and novel biomarker candidates for the assessment of TBI in adult patients. The biomarkers studied were selected on the basis of their relative brain-specificities and potentials to reflect distinct features of TBI mechanisms including: neuronal damage assessed by neuron-specific enolase (NSE and brain derived neurotrophic factor (BDNF; oxidative stress assessed by peroxiredoxin 6 (PRDX6; glial damage and gliosis assessed by glial fibrillary acidic protein (GFAP and S100 calcium binding protein beta (S100b; (4 immune activation assessed by monocyte chemoattractant protein 1/chemokine (C-C motif ligand 2 (MCP1/CCL2; and disruption of the intercellular adhesion apparatus assessed by intercellular adhesion protein-5 (ICAM-5. The combined fold changes in plasma levels of PRDX6, S100b, MCP1, NSE and BDNF resulted in the formulation of a TBI assessment score (TBIAS that identified mTBI with a receiver operator characteristic area under the curve of 0.97, when compared to healthy controls. This research demonstrates that a profile of biomarker responses can be used to formulate a diagnostic score that is sensitive for the detection of mTBI. Ideally, this multivariate assessment strategy will be refined with additional biomarkers that can effectively assess the spectrum of TBI and identify those at particular risk for developing neuropathologies as consequence of a mTBI event.

  2. Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity

    Directory of Open Access Journals (Sweden)

    Runa Bhaumik

    2017-01-01

    Full Text Available Understanding abnormal resting-state functional connectivity of distributed brain networks may aid in probing and targeting mechanisms involved in major depressive disorder (MDD. To date, few studies have used resting state functional magnetic resonance imaging (rs-fMRI to attempt to discriminate individuals with MDD from individuals without MDD, and to our knowledge no investigations have examined a remitted (r population. In this study, we examined the efficiency of support vector machine (SVM classifier to successfully discriminate rMDD individuals from healthy controls (HCs in a narrow early-adult age range. We empirically evaluated four feature selection methods including multivariate Least Absolute Shrinkage and Selection Operator (LASSO and Elastic Net feature selection algorithms. Our results showed that SVM classification with Elastic Net feature selection achieved the highest classification accuracy of 76.1% (sensitivity of 81.5% and specificity of 68.9% by leave-one-out cross-validation across subjects from a dataset consisting of 38 rMDD individuals and 29 healthy controls. The highest discriminating functional connections were between the left amygdala, left posterior cingulate cortex, bilateral dorso-lateral prefrontal cortex, and right ventral striatum. These appear to be key nodes in the etiopathophysiology of MDD, within and between default mode, salience and cognitive control networks. This technique demonstrates early promise for using rs-fMRI connectivity as a putative neurobiological marker capable of distinguishing between individuals with and without rMDD. These methods may be extended to periods of risk prior to illness onset, thereby allowing for earlier diagnosis, prevention, and intervention.

  3. Sexual Abuse History among Adult Sex Offenders and Non-Sex Offenders: A Meta-Analysis

    Science.gov (United States)

    Jespersen, Ashley F.; Lalumiere, Martin L.; Seto, Michael C.

    2009-01-01

    Objective: The sexually abused-sexual abuser hypothesis states there is a specific relationship between sexual abuse history and sexual offending, such that individuals who experience sexual abuse are significantly more likely to later engage in sexual offenses. Therefore, samples of adult sex offenders should contain a disproportionate number of…

  4. Does Parent-Child Interaction Therapy Reduce Future Physical Abuse? A Meta-Analysis

    Science.gov (United States)

    Kennedy, Stephanie C.; Kim, Johnny S.; Tripodi, Stephen J.; Brown, Samantha M.; Gowdy, Grace

    2016-01-01

    Objective: To use meta-analytic techniques to evaluating the effectiveness of parent-child interaction therapy (PCIT) at reducing future physical abuse among physically abusive families. Methods: A systematic search identified six eligible studies. Outcomes of interest were physical abuse recurrence, child abuse potential, and parenting stress.…

  5. Testing multivariate analysis in paleoenvironmental reconstructions using pollen records from Lagoa Salgada, NE Rio de Janeiro State, Brazil.

    Science.gov (United States)

    Toledo, Mauro B de; Barth, Ortrud M; Silva, Cleverson G; Barros, Marcia A

    2009-12-01

    Despite the indisputable significance of identification of modern analogs for Paleoecology research, relatively few studies attempted to integrate modern and fossil samples on paleoenvironmental reconstructions. In Palynology, this general pattern is not different from other fields of Paleoecology. This study demonstrates the practical application of modern pollen deposition data on paleoenvironmental reconstructions based on fossil pollen by using multivariate analysis. The main goal of this study was to use Detrended Correspondence Analysis (DCA) to compare pollen samples from two sediment cores collected at Lagoa Salgada, a coastal lagoon located at northeastern Rio de Janeiro State. Furthermore, modern surface samples were also statistically compared with samples from both cores, providing new paleoecological insights. DCA demonstrated that samples from both cores are more similar than previously expected, and that a strong pattern, related to a paleoenvironmental event, is present within the fossil data, clearly identifying in the scatter plot samples that represent pre- and post-environmental change. Additionally, it became apparent that modern vegetation and environmental conditions were established in this region 2500 years before present (BP). Multivariate Analysis allowed a more reliable integration of modern and fossil pollen data, proving to be a powerful tool in Paleoecology studies that should be employed more often on paleoclimate and paleoenvironmental reconstructions.

  6. SurvMicro: assessment of miRNA-based prognostic signatures for cancer clinical outcomes by multivariate survival analysis.

    Science.gov (United States)

    Aguirre-Gamboa, Raul; Trevino, Victor

    2014-06-01

    MicroRNAs (miRNAs) play a key role in post-transcriptional regulation of mRNA levels. Their function in cancer has been studied by high-throughput methods generating valuable sources of public information. Thus, miRNA signatures predicting cancer clinical outcomes are emerging. An important step to propose miRNA-based biomarkers before clinical validation is their evaluation in independent cohorts. Although it can be carried out using public data, such task is time-consuming and requires a specialized analysis. Therefore, to aid and simplify the evaluation of prognostic miRNA signatures in cancer, we developed SurvMicro, a free and easy-to-use web tool that assesses miRNA signatures from publicly available miRNA profiles using multivariate survival analysis. SurvMicro is composed of a wide and updated database of >40 cohorts in different tissues and a web tool where survival analysis can be done in minutes. We presented evaluations to portray the straightforward functionality of SurvMicro in liver and lung cancer. To our knowledge, SurvMicro is the only bioinformatic tool that aids the evaluation of multivariate prognostic miRNA signatures in cancer. SurvMicro and its tutorial are freely available at http://bioinformatica.mty.itesm.mx/SurvMicro. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  7. Novel Strategy for Non-Targeted Isotope-Assisted Metabolomics by Means of Metabolic Turnover and Multivariate Analysis

    Directory of Open Access Journals (Sweden)

    Yasumune Nakayama

    2014-08-01

    Full Text Available Isotope-labeling is a useful technique for understanding cellular metabolism. Recent advances in metabolomics have extended the capability of isotope-assisted studies to reveal global metabolism. For instance, isotope-assisted metabolomics technology has enabled the mapping of a global metabolic network, estimation of flux at branch points of metabolic pathways, and assignment of elemental formulas to unknown metabolites. Furthermore, some data processing tools have been developed to apply these techniques to a non-targeted approach, which plays an important role in revealing unknown or unexpected metabolism. However, data collection and integration strategies for non-targeted isotope-assisted metabolomics have not been established. Therefore, a systematic approach is proposed to elucidate metabolic dynamics without targeting pathways by means of time-resolved isotope tracking, i.e., “metabolic turnover analysis”, as well as multivariate analysis. We applied this approach to study the metabolic dynamics in amino acid perturbation of Saccharomyces cerevisiae. In metabolic turnover analysis, 69 peaks including 35 unidentified peaks were investigated. Multivariate analysis of metabolic turnover successfully detected a pathway known to be inhibited by amino acid perturbation. In addition, our strategy enabled identification of unknown peaks putatively related to the perturbation.

  8. NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review.

    Science.gov (United States)

    Xiao, Li; Wei, Hui; Himmel, Michael E; Jameel, Hasan; Kelley, Stephen S

    2014-01-01

    Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review

  9. ToF-SIMS studies of Bacillus using multivariate analysis with possible identification and taxonomic applications

    International Nuclear Information System (INIS)

    Thompson, C.E.; Ellis, J.; Fletcher, J.S.; Goodacre, R.; Henderson, A.; Lockyer, N.P.; Vickerman, J.C.

    2006-01-01

    In this paper we discuss the application of ToF-SIMS with an Au 3 + primary ion beam, combined with principal components analysis (PCA) and discriminant function analysis (DFA) for the identification of individual strains of two Bacillus species. The ToF-SIMS PC-DFA methodology is capable of distinguishing bacteria at the strain level based on analysis of surface chemical species. By classifying the data using hierarchical cluster analysis (HCA) we are able to show quantitative separation of species and of these strains. This has taxonomic implications in the areas of rapid identification of pathogenic microbes isolated from the clinic, food and environment

  10. Estimation of age in forensic medicine using multivariate approach to image analysis

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Belyaev, Ivan; Fominykh, Sergey

    2009-01-01

    A new method for victims' age estimation, based on the image processing and analysis of remains bones structure, is proposed. Digital images of lumbar vertebras cuts were used as a major information source. The age related properties were extracted from the images using classic texture analysis a...

  11. Estimation of age in forensic medicine using multivariate approach to image analysis

    DEFF Research Database (Denmark)

    Kucheryavskiy, Sergey V.; Belyaev, Ivan; Fominykh, Sergey

    2009-01-01

    A new method for victims' age estimation, based on the image processing and analysis of remains bones structure, is proposed. Digital images of lumbar vertebras cuts were used as a major information source. The age related properties were extracted from the images using classic texture analysis...

  12. Simultaneous analysis of climatic trends in multiple variables: an example of application of multivariate statistical methods

    Czech Academy of Sciences Publication Activity Database

    Huth, Radan; Pokorná, Lucie

    2005-01-01

    Roč. 25, - (2005), s. 469-484 ISSN 0899-8418 R&D Projects: GA AV ČR(CZ) IAA3017301 Institutional research plan: CEZ:AV0Z30420517 Keywords : climatic trends * trend consistency * principal component analysis * cluster analysis * Czech Republic Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.622, year: 2005

  13. Multivariate analysis of molecular and morphological diversity in fig (Ficus carica L.)

    Science.gov (United States)

    Genetic polymorphism across 15 microsatellite loci among 194 fig accessions including Common, Smyrna, San Pedro, and Caprifig were analyzed using a cluster analysis (CA) and the principal components analysis (PCA). The collection was moderately variable with observed number of alleles per locus rang...

  14. Hair analysis to monitor abuse of analgesic combinations containing butalbital and propyphenazone.

    Science.gov (United States)

    Ferrari, Anna; Tiraferri, Ilaria; Palazzoli, Federica; Verri, Patrizia; Vandelli, Daniele; Marchesi, Filippo; Ciccarese, Michela; Licata, Manuela

    2015-11-10

    Butalbital, a barbiturate, is present in analgesic combinations used by headache sufferers. Overuse/abuse of these combinations may cause dependence, chronic migraine, and medication-overuse headache (MOH). MOH is difficult to manage: it improves interrupting analgesic overuse, but requires monitoring, because relapses are frequent. A gas chromatography-mass spectrometry (GC-MS) method for hair analysis has been developed and validated to document abuse of an analgesic combination containing butalbital and propyphenazone by a patient with MOH. For over ten years the patient managed her headache using eight suppositories/day of an analgesic combination containing butalbital 150mg, caffeine 75mg, and propyphenazone 375mg per suppository. An outpatient detoxification treatment was carried out. After three weeks, the patient reduced the consumption to one suppository/day. At the first control visit, after three months from the beginning of detoxification, the patient increased the use of the combination to four suppositories/day and at the second control visit, after seven months from the beginning of detoxification, she was back to eight suppositories/day. At the two control visits, a hair sample was taken for determination of butalbital and propyphenazone. Moreover blood and urine samples for determination of butalbital were drawn at the beginning of detoxification treatment and at the two control visits. With the segmental analysis of two hair samples the medication history of ten months could be estimated. In the first hair sample, collected at the first control visit, in the distal segment, butalbital and propyphenazone concentrations were, respectively, 17.5ng/mg and 56.0ng/mg, confirming the prolonged abuse; in the proximal segment, concurrently with the detoxification treatment, butalbital and propyphenazone concentrations had reduced respectively to 5.45ng/mg and 11.1ng/mg. The second hair sample, collected at the second control visit, proved the fair course

  15. Multivariate Analysis of Profitability Indicators for Selected Companies of Croatian Market

    Directory of Open Access Journals (Sweden)

    Ana Perisa

    2017-12-01

    Full Text Available In this paper, the profitability indicators are analysed for the first hundred companies of the Croatian market, which are classified according to the net profit. The profitability indicators included in the analysis are the following: EBIT margin, EBITDA margin, net profit margin, return on assets (ROA, return on invested capital (ROI and return on capital employed (ROCE. By implementing the factor analysis, six chosen profitability indicators have been reduced to two factors, thus solving the multicollinearity problem, which is one of the prerequisites for the cluster analysis. For two extracted factors, the factor scores are calculated and used in the following cluster analysis. By implementing the cluster analysis, selected companies are grouped into clusters according to their similarity in accomplished results that are measured by profitability indicators. The hierarchical and non-hierarchical cluster analyses are conducted and resulted into two clusters where ten companies were in the first cluster, while the other ninety were in the second cluster

  16. Community Poverty and Child Abuse Fatalities in the United States.

    Science.gov (United States)

    Farrell, Caitlin A; Fleegler, Eric W; Monuteaux, Michael C; Wilson, Celeste R; Christian, Cindy W; Lee, Lois K

    2017-05-01

    Child maltreatment remains a problem in the United States, and individual poverty is a recognized risk factor for abuse. Children in impoverished communities are at risk for negative health outcomes, but the relationship of community poverty to child abuse fatalities is not known. Our objective was to evaluate the association between county poverty concentration and rates of fatal child abuse. This was a retrospective, cross-sectional analysis of child abuse fatalities in US children 0 to 4 years of age from 1999 to 2014 by using the Centers for Disease Control and Prevention Compressed Mortality Files. Population and poverty statistics were obtained from US Census data. National child abuse fatality rates were calculated for each category of community poverty concentration. Multivariate negative binomial regression modeling assessed the relationship between county poverty concentration and child abuse fatalities. From 1999 to 2014, 11 149 children 0 to 4 years old died of child abuse; 45% (5053) were poverty concentration had >3 times the rate of child abuse fatalities compared with counties with the lowest poverty concentration (adjusted incidence rate ratio, 3.03; 95% confidence interval, 2.4-3.79). Higher county poverty concentration is associated with increased rates of child abuse fatalities. This finding should inform public health officials in targeting high-risk areas for interventions and resources. Copyright © 2017 by the American Academy of Pediatrics.

  17. [An analysis of Spanish scientific productivity in substance abuse according to disciplinary collaboration].

    Science.gov (United States)

    Gonzalez Alcaide, Gregorio; Bolaños Pizarro, Maxima; Navarro Molina, Carolina; de Granda Orive, Jose Ignacio; Aleixandre Benavent, Rafael; Valderrama Zurian, Juan Carlos

    2008-01-01

    The analysis of productivity and disciplinary collaboration patterns for Spanish published scientific research in the field of Substance Abuse (2001-2005). From institutional affiliations we identified and quantified disciplinary participation in papers indexed in the IME/Indice Medico Español, ISOC/Indice Español de Ciencias Sociales y Humanidades, SCI-Expanded/Science Citation Index-Expanded and SSCI/Social Sciences Citation Index databases. A total of 31 disciplines and specialities were identified in ISOC, with 8.6% of documents in collaboration between them; 55 medical specialities were identified in IME, with 10.89% of documents in collaboration between them; and 62 specialities were identified in SCI-Expanded, with 41.68% of documents in collaboration between them. a) Substance Abuse, Psychology, Psychiatry, Epidemiology-Preventive Medicine and Public Health and Pharmacology are the disciplines and specialities that present the highest productivity. To these can be added, in papers published in foreign journals, specialities such as Biochemistry-Molecular Biology, Neurology and Neuroscience; b) Papers published in Spanish journals indexed in SCI-Expanded and in papers published in foreign journals present much higher collaboration indexes between disciplines and specialities; c) The main collaborations between specialities are those between Substance Abuse, Psychiatry and Psychology. To these can be added, in the case of journals indexed in SCI-Expanded, those between these specialities and Pharmacology, Neurology and Neuroscience.

  18. Modeling of Bacteria-Contaminated Particles Transfer in a Karst Aquifer by Means of Multivariate Analysis

    Science.gov (United States)

    Fournier, M.; Massei, N.; Dupont, J. P.; Berthe, T.; Petit, F.

    2017-12-01

    Karst aquifers are known to be highly vulnerable to bacterial contamination due to hydraulic connections between surface and ground water via karstic networks. In the environment, bacteria are mainly present into attached form on particles. So, it is particles contaminated by bacteria which constitute sanitary risk for drinking water and turbidity is used as a marker of sanitary hazard. But, relations between turbidity and bacteria-contaminated particles are complex. In this paper, the correlation between water turbidity and enumeration of bacteria was investigated by means of multivariate analyses to study the transport of particle-associated bacteria and to verify if turbidity is an accurate indicator of bacterial contamination. Turbidity, electrical conductivity, water discharge and sessile and planktonic bacteria concentrations of water at the infiltration point on a karst plateau and at the discharge point at a karstic spring were monitored during 2 rain events and 2 dry periods. During these events, particle transfer modalities (direct transfer of surface water to the karst spring, resuspension of intrakarstic sediments or deposition of suspended particulate matter) have been identified. Results show strong correlations, which allow the modeling of sessile and planktonic bacteria concentrations from turbidity datasets. This model accurately estimates bacteria concentrations during the periods of direct transfer of surface water to the spring, however during the resuspension and deposition periods bacteria concentrations are underestimated and overestimated, respectively. Additional lab experimentation has been realized in order to investigate the fate of E. coli (viable and culturable populations) according to the settling velocities of particles where they are attached. Results show that viable E. coli are present even when culturable E. coli are not detectable in drinking water supply. So, the karst aquifer is a permanent reservoir of viable E. coli which

  19. NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique : a review

    Directory of Open Access Journals (Sweden)

    Li eXiao

    2014-08-01

    Full Text Available Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR and pyrolysis-molecular beam mass spectrometry (Py-mbms are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis and for building regression models (partial least square regression between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated

  20. Prescription drug abuse communication: A qualitative analysis of prescriber and pharmacist perceptions and behaviors.

    Science.gov (United States)

    Hagemeier, Nicholas E; Tudiver, Fred; Brewster, Scott; Hagy, Elizabeth J; Hagaman, Angela; Pack, Robert P

    that prescription drug abuse communication is uncomfortable, variable, multifactorial, and often avoided. The themes that emerged from this analysis support the utility of communication science and health behavior theories to better understand and improve PDA communication behaviors of both prescribers and pharmacists, and thereby improve engagement in PDA prevention and treatment. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. Prescription Drug Abuse Communication: A Qualitative Analysis of Prescriber and Pharmacist Perceptions and Behaviors

    Science.gov (United States)

    Hagemeier, Nicholas E.; Tudiver, Fred; Brewster, Scott; Hagy, Elizabeth J.; Hagaman, Angela; Pack, Robert P.

    2016-01-01

    of engaging in PDA communication, HCPs reported that prescription drug abuse communication is uncomfortable, variable, multifactorial, and often avoided. The themes that emerged from this analysis support the utility of communication science and health behavior theories to better understand and improve PDA communication behaviors of both prescribers and pharmacists, and thereby improve engagement in PDA prevention and treatment. PMID:26806859

  2. Alcohol Use / Abuse in Medical Students

    OpenAIRE

    Sogi Uematzu, Cecilia; Perales Cabrera, Alberto

    2014-01-01

    OBJECTIVE: To study the frequency of alcohol use/abuse, its distribution by gender and age and associated risk factors in undergraduate medical students. MATERIAL AND METHODS: A mental health survey data base from 1115 medical students on a public university of Lima City was used. RESULTS: The frequency of CAGE positive, indicator of drinking problem, was 13,7%. The alcohol consumption onset mean age was earlier in the younger students, especially in women. Multivariate analysis showed signif...

  3. Chemotaxonomy of Hawaiian Anthurium cultivars based on multivariate analysis of phenolic metabolites.

    Science.gov (United States)

    Clark, Benjamin R; Bliss, Barbara J; Suzuki, Jon Y; Borris, Robert P

    2014-11-19

    Thirty-six anthurium varieties, sampled from species and commercial cultivars, were extracted and profiled by liquid-chromatography-mass spectrometry (HPLC-MS). Three hundred fifteen compounds, including anthocyanins, flavonoid glycosides, and other phenolics, were detected from these extracts and used in chemotaxonomic analysis of the specimens. Hierarchical cluster analysis (HCA) revealed close chemical similarities between all the commercial standard cultivars, while tulip-shaped cultivars and species displayed much greater chemical variation. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) supported the results from HCA and were used to identify key metabolites characteristic of standard and tulip cultivars and to identify chemical markers indicative of a particular ancestry. Discriminating metabolites included embinin, 4, which was characteristic of standard-shaped spathes and indicated ancestry from Anthurium andraeanum, while isocytisoside 7-glucoside, 7, was found in the majority of tulip-shaped cultivars and suggested that Anthurium amnicola or Anthurium antioquiense had contributed to their pedigree.

  4. [Study on the multivariate quantitative analysis method for steel alloy elements using LIBS].

    Science.gov (United States)

    Gu, Yan-hong; Li, Ying; Tian, Ye; Lu, Yuan

    2014-08-01

    Quantitative analysis of steel alloys was carried out using laser induced breakdown spectroscopy (LIBS) taking into account the complex matrix effects in steel alloy samples. The laser induced plasma was generated by a Q-switched Nd:YAG laser operating at 1064 nm with pulse width of 10 ns and repeated frequency of 10 Hz. The LIBS signal was coupled to the echelle spectrometer and recorded by a high sensitive ICCD detector. To get the best experimental conditions, some parameters, such as the detection delay, the CCDs integral gate width and the detecting position from the sample surface, were optimized. The experimental results showed that the optimum detection delay time was 1.5 micros, the optimal CCDs integral gate width was 2 micros and the best detecting position was 1.5 mm below the alloy sample's surface. The samples used in the experiments are ten standard steel alloy samples and two unknown steel alloy samples. The quantitative analysis was investigated with the optimum experimental parameters. Elements Cr and Ni in steel alloy samples were taken as the detection targets. The analysis was carried out with the methods based on conditional univariate quantitative analysis, multiple linear regression and partial least squares (PLS) respectively. It turned out that the correlation coefficients of calibration curves are not very high in the conditional univariate calibration method. The analysis results were obtained with the unsatisfied relative errors for the two predicted samples. So the con- ditional univariate quantitative analysis method can't effectively serve the quantitative analysis purpose for multi-components and complex matrix steel alloy samples. And with multiple linear regression method, the analysis accuracy was improved effectively. The method based on partial least squares (PLS) turned out to be the best method among all the three quantitative analysis methods applied. Based on PLS, the correlation coefficient of calibration curve for Cr is 0

  5. Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes

    Science.gov (United States)

    Åberg Lindell, M.; Andersson, P.; Grape, S.; Hellesen, C.; Håkansson, A.; Thulin, M.

    2018-03-01

    This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

  6. Evaluation of Extraction Protocols for Simultaneous Polar and Non-Polar Yeast Metabolite Analysis Using Multivariate Projection Methods

    Directory of Open Access Journals (Sweden)

    Nicolas P. Tambellini

    2013-07-01

    Full Text Available Metabolomic and lipidomic approaches aim to measure metabolites or lipids in the cell. Metabolite extraction is a key step in obtaining useful and reliable data for successful metabolite studies. Significant efforts have been made to identify the optimal extraction protocol for various platforms and biological systems, for both polar and non-polar metabolites. Here we report an approach utilizing chemoinformatics for systematic comparison of protocols to extract both from a single sample of the model yeast organism Saccharomyces cerevisiae. Three chloroform/methanol/water partitioning based extraction protocols found in literature were evaluated for their effectiveness at reproducibly extracting both polar and non-polar metabolites. Fatty acid methyl esters and methoxyamine/trimethylsilyl derivatized aqueous compounds were analyzed by gas chromatography mass spectrometry to evaluate non-polar or polar metabolite analysis. The comparative breadth and amount of recovered metabolites was evaluated using multivariate projection methods. This approach identified an optimal protocol consisting of 64 identified polar metabolites from 105 ion hits and 12 fatty acids recovered, and will potentially attenuate the error and variation associated with combining metabolite profiles from different samples for untargeted analysis with both polar and non-polar analytes. It also confirmed the value of using multivariate projection methods to compare established extraction protocols.

  7. Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo.

    Science.gov (United States)

    Sharif, K M; Rahman, M M; Azmir, J; Khatib, A; Sabina, E; Shamsudin, S H; Zaidul, I S M

    2015-12-01

    Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed. Copyright © 2015 John Wiley & Sons, Ltd.

  8. Financial abuse in elderly Korean immigrants: mixed analysis of the role of culture on perception and help-seeking intention.

    Science.gov (United States)

    Lee, Hee Yun; Eaton, Charissa K

    2009-07-01

    This study aims to evaluate how elderly Korean immigrants perceive and respond to a hypothetical incident of financial abuse on the basis of their cultural background. By using a quota sampling strategy, 124 elderly Korean immigrants were recruited. A mixed-method approach was employed to explore the role of culture on elderly immigrants' view of financial abuse and the construct of independent and interdependent self-construal was adopted to theoretically guide the study. Mixed-method analysis confirmed considerable influence of culture, particularly in responding to the abusive situation. Although the vast majority of the elders (92%) perceived financial abuse as elder mistreatment, only two-thirds (64%) intended to seek help. Five major themes for not seeking help were produced. These are: (a) issues related to family problems, (b) tolerance of the abuse, (c) shame, (d) victim blame, and (e) mistrust toward third party intervention. A series of binary logistic regressions revealed (a) a lower likelihood of seeking formal types of help with those who had higher level of adherence to traditional values and (b) the profile of vulnerable elderly Koreans who are at higher risk of being financially abused: male and less educated. This article also discusses implications for social work practice and elder mistreatment policy, particularly focusing on how to work with elderly Korean immigrants who are vulnerable to this problem and who tend to use collectivistic cultural values in responding to financial abuse.

  9. Physical vs photolithographic patterning of plasma polymers: an investigation by ToF-SSIMS and multivariate analysis.

    Science.gov (United States)

    Mishra, Gautam; Easton, Christopher D; McArthur, Sally L

    2010-03-02

    Physical and photolithographic techniques are commonly used to create chemical patterns for a range of technologies including cell culture studies, bioarrays and other biomedical applications. In this paper, we describe the fabrication of chemical micropatterns from commonly used plasma polymers. Atomic force microscopy (AFM) imaging, time-of-flight static secondary ion mass spectrometry (ToF-SSIMS) imaging, and multivariate analysis have been employed to visualize the chemical boundaries created by these patterning techniques and assess the spatial and chemical resolution of the patterns. ToF-SSIMS analysis demonstrated that well-defined chemical and spatial boundaries were obtained from photolithographic patterning, while the resolution of physical patterning via a transmission electron microscopy (TEM) grid varied depending on the properties of the plasma system including the substrate material. In general, physical masking allowed diffusion of the plasma species below the mask and bleeding of the surface chemistries. Multivariate analysis techniques including principal component analysis (PCA) and region of interest (ROI) assessment were used to investigate the ToF-SSIMS images of a range of different plasma polymer patterns. In the most challenging case, where two strongly reacting polymers, allylamine and acrylic acid were deposited, PCA confirmed the fabrication of micropatterns with defined spatial resolution. ROI analysis allowed for the identification of an interface between the two plasma polymers for patterns fabricated using the photolithographic technique which has been previously overlooked. This study clearly demonstrated the versatility of photolithographic patterning for the production of multichemistry plasma polymer arrays and highlighted the need for complementary characterization and analytical techniques during the fabrication plasma polymer micropatterns.

  10. Multivariate analysis and geostatistics of the fertility of a humic rhodic hapludox under coffee cultivation

    Directory of Open Access Journals (Sweden)

    Samuel de Assis Silva

    2012-04-01

    Full Text Available The spatial variability of soil and plant properties exerts great influence on the yeld of agricultural crops. This study analyzed the spatial variability of the fertility of a Humic Rhodic Hapludox with Arabic coffee, using principal component analysis, cluster analysis and geostatistics in combination. The experiment was carried out in an area under Coffea arabica L., variety Catucai 20/15 - 479. The soil was sampled at a depth 0.20 m, at 50 points of a sampling grid. The following chemical properties were determined: P, K+, Ca2+, Mg2+, Na+, S, Al3+, pH, H + Al, SB, t, T, V, m, OM, Na saturation index (SSI, remaining phosphorus (P-rem, and micronutrients (Zn, Fe, Mn, Cu and B. The data were analyzed with descriptive statistics, followed by principal component and cluster analyses. Geostatistics were used to check and quantify the degree of spatial dependence of properties, represented by principal components. The principal component analysis allowed a dimensional reduction of the problem, providing interpretable components, with little information loss. Despite the characteristic information loss of principal component analysis, the combination of this technique with geostatistical analysis was efficient for the quantification and determination of the structure of spatial dependence of soil fertility. In general, the availability of soil mineral nutrients was low and the levels of acidity and exchangeable Al were high.

  11. Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models

    International Nuclear Information System (INIS)

    Lamboni, Matieyendou; Monod, Herve; Makowski, David

    2011-01-01

    Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006 ) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.

  12. Lithology and mineralogy recognition from geochemical logging tool data using multivariate statistical analysis.

    Science.gov (United States)

    Konaté, Ahmed Amara; Ma, Huolin; Pan, Heping; Qin, Zhen; Ahmed, Hafizullah Abba; Dembele, N'dji Dit Jacques

    2017-10-01

    The availability of a deep well that penetrates deep into the Ultra High Pressure (UHP) metamorphic rocks is unusual and consequently offers a unique chance to study the metamorphic rocks. One such borehole is located in the southern part of Donghai County in the Sulu UHP metamorphic belt of Eastern China, from the Chinese Continental Scientific Drilling Main hole. This study reports the results obtained from the analysis of oxide log data. A geochemical logging tool provides in situ, gamma ray spectroscopy measurements of major and trace elements in the borehole. Dry weight percent oxide concentration logs obtained for this study were SiO 2 , K 2 O, TiO 2 , H 2 O, CO 2 , Na 2 O, Fe 2 O 3 , FeO, CaO, MnO, MgO, P 2 O 5 and Al 2 O 3 . Cross plot and Principal Component Analysis methods were applied for lithology characterization and mineralogy description respectively. Cross plot analysis allows lithological variations to be characterized. Principal Component Analysis shows that the oxide logs can be summarized by two components related to the feldspar and hydrous minerals. This study has shown that geochemical logging tool data is accurate and adequate to be tremendously useful in UHP metamorphic rocks analysis. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Multivariate analysis of attitudes on financial and other aspects of business ethics of future managers

    Directory of Open Access Journals (Sweden)

    Blaženka Knežević

    2017-01-01

    Full Text Available Business ethics is a set of rules by which individuals and institutions behave and conduct business in a responsible manner. It involves appropriate constraints on the pursuit of self-interest and profits, particularly when actions affect other stakeholders. Research on financial and other aspects of business ethics includes an examination of personal attitudes which give insight into ways in which people tend to behave as employees, managers, taxpayers and consumers. In this research, the standard ATBEQ questionnaire was extended with five variables covering corporate social responsibility and applied to a sample of business administration students in Croatia. The aim of the research was to identify groups of future managers based on an evaluation of their attitudes on business ethics and corporate social responsibility. The analysis was divided into two parts. In the first part, factor analysis was performed on 35 variables (attitudes relating to business ethics and corporate social responsibility. Six factors were extracted and factor scores were calculated. In the second part, hierarchical and non-hierarchical cluster analyses were conducted. Factor scores were used as input data for the cluster analysis. Firstly, the hierarchical cluster analysis was run on the calculated factor scores. According to the dendrogram, a three-cluster solution was chosen. The non-hierarchical cluster analysis was then used to improve the results of the hierarchical cluster solution. Finally, these clusters (groups of future managers were characterised according to their attitudes on financial and other aspects of business ethics and corporate social responsibility.

  14. Multivariate factor analysis of detailed milk fatty acid profile: Effects of dairy system, feeding, herd, parity, and stage of lactation.

    Science.gov (United States)

    Mele, M; Macciotta, N P P; Cecchinato, A; Conte, G; Schiavon, S; Bittante, G

    2016-12-01

    We investigated the potential of using multivariate factor analysis to extract metabolic information from data on the quantity and quality of milk produced under different management systems. We collected data from individual milk samples taken from 1,158 Brown Swiss cows farmed in 85 traditional or modern herds in Trento Province (Italy). Factor analysis was carried out on 47 individual fatty acids, milk yield, and 5 compositional milk traits (fat, protein, casein, and lactose contents, somatic cell score). According to a previous study on multivariate factor analysis, a variable was considered to be associated with a specific factor if the absolute value of its correlation with the factor was ≥0.60. The extracted factors were representative of the following 12 groups of fatty acids or functions: de novo fatty acids, branched fatty acid-milk yield, biohydrogenation, long-chain fatty acids, desaturation, short-chain fatty acids, milk protein and fat contents, odd fatty acids, conjugated linoleic acids, linoleic acid, udder health, and vaccelenic acid. Only 5 fatty acids showed small correlations with these groups. Factor analysis suggested the existence of differences in the metabolic pathways for de novo short- and medium-chain fatty acids and Δ 9 -desaturase products. An ANOVA of factor scores highlighted significant effects of the dairy farming system (traditional or modern), season, herd/date, parity, and days in milk. Factor behavior across levels of fixed factors was consistent with current knowledge. For example, compared with cows farmed in modern herds, those in traditional herds had higher scores for branched fatty acids, which were inversely associated with milk yield; primiparous cows had lower scores than older cows for de novo fatty acids, probably due to a larger contribution of lipids mobilized from body depots on milk fat yield. The statistical approach allowed us to reduce a large number of variables to a few latent factors with biological

  15. Adulteration and cultivation region identification of American ginseng using HPLC coupled with multivariate analysis

    Science.gov (United States)

    Yu, Chunhao; Wang, Chong-Zhi; Zhou, Chun-Jie; Wang, Bin; Han, Lide; Zhang, Chun-Feng; Wu, Xiao-Hui; Yuan, Chun-Su

    2014-01-01

    American ginseng (Panax quinquefolius) is originally grown in North America. Due to price difference and supply shortage, American ginseng recently has been cultivated in northern China. Further, in the market, some Asian ginsengs are labeled as American ginseng. In this study, forty-three American ginseng samples cultivated in the USA, Canada or China were collected and 14 ginseng saponins were determined using HPLC. HPLC coupled with hierarchical cluster analysis and principal component analysis was developed to identify the species. Subsequently, an HPLC-linear discriminant analysis was established to discriminate cultivation regions of American ginseng. This method was successfully applied to identify the sources of 6 commercial American ginseng samples. Two of them were identified as Asian ginseng, while 4 others were identified as American ginseng, which were cultivated in the USA (3) and China (1). Our newly developed method can be used to identify American ginseng with different cultivation regions. PMID:25044150

  16. Hydrate formation during wet granulation studied by spectroscopic methods and multivariate analysis

    DEFF Research Database (Denmark)

    Jørgensen, Anna; Rantanen, Jukka; Karjalainen, Milja

    2002-01-01

    PURPOSE: The aim was to follow hydrate formation of two structurally related drugs, theophylline and caffeine, during wet granulation using fast and nondestructive spectroscopic methods. METHODS: Anhydrous theophylline and caffeine were granulated with purified water. Charge-coupled device (CCD......) Raman spectroscopy was compared with near-infrared spectroscopy (NIR) in following hydrate formation of drugs during wet granulation (off-line). To perform an at-line process analysis, the effect of water addition was monitored by NIR spectroscopy and principal components analysis (PCA). The changes...

  17. Determination of wheat quality by mass spectrometry and multivariate data analysis

    DEFF Research Database (Denmark)

    Gottlieb, D.M.; Schultz, J.; Petersen, M.

    2002-01-01

    of storage proteins from the wheat gluten complex based on two-dimensional electrophoresis and analysis of the N-terminal sequence has revealed a protein homologous to gamma-gliadins, tentatively associated with quality and within the molecular weight range 27-35 kDa. Further examinations of gliadin data...... based on mass spectrometry revealed that quality among wheat varieties could be determined by means of principal component analysis. Further examinations by interval partial least squares made it possible to encircle an overall optimal molecular weight interval from 31.5 to 33.7 kDa. The use...

  18. Survival trees: an alternative non-parametric multivariate technique for life history analysis.

    Science.gov (United States)

    De Rose, A; Pallara, A

    1997-01-01

    "In this paper an extension of tree-structured methodology to cover censored survival analysis is discussed.... The tree-shaped diagram...can be used to draw meaningful patterns of behaviour throughout the individual life history.... The fundamentals of tree methodology are outlined; [then] an application of the technique to real data from a survey on the progression to marriage among adult women in Italy is illustrated; [and] some comments are presented on the main advantages and problems related to tree-structured methodology for censored survival analysis." (EXCERPT)

  19. Univariate and multivariate analysis on processing tomato quality under different mulches

    Directory of Open Access Journals (Sweden)

    Carmen Moreno

    2014-04-01

    Full Text Available The use of eco-friendly mulch materials as alternatives to the standard polyethylene (PE has become increasingly prevalent worldwide. Consequently, a comparison of mulch materials from different origins is necessary to evaluate their feasibility. Several researchers have compared the effects of mulch materials on each crop variable through univariate analysis (ANOVA. However, it is important to focus on the effect of these materials on fruit quality, because this factor decisively influences the acceptance of the final product by consumers and the industrial sector. This study aimed to analyze the information supplied by a randomized complete block experiment combined over two seasons, a principal component analysis (PCA and a cluster analysis (CA when studying the effects of mulch materials on the quality of processing tomato (Lycopersicon esculentum Mill.. The study focused on the variability in the quality measurements and on the determination of mulch materials with a similar response to them. A comparison of the results from both types of analysis yielded complementary information. ANOVA showed the similarity of certain materials. However, considering the totality of the variables analyzed, the final interpretation was slightly complicated. PCA indicated that the juice color, the fruit firmness and the soluble solid content were the most influential factors in the total variability of a set of 12 juice and fruit variables, and CA allowed us to establish four categories of treatment: plastics (polyethylene - PE, oxo- and biodegradable materials, papers, manual weeding and barley (Hordeum vulgare L. straw. Oxobiodegradable and PE were most closely related based on CA.

  20. Multivariate analysis of early and late nest sites of Abert's Towhees

    Science.gov (United States)

    Deborah M. Finch

    1985-01-01

    Seasonal variation in nest site selection by the Abert's towhee (Pipilo aberti) was studied in honey mesquite (Prosopis glandulosa) habitat along the lower Colorado River from March to July, 1981. Stepwise discriminant function analysis identified nest vegetation type, nest direction, and nest height as the three most important variables that characterized the...